Figure 5: Location of the sampling areas in P. oceanica meadows off Lacco Ameno and ...... (Malone et al., 1983; Lévy et al., 2003; van Haren et al., 2004).
Spatio-temporal Variability of Plant Features and Motile Invertebrates in Posidonia oceanica Seagrass Meadows
Claudio Vasapollo
The Open University Milton Keynes (UK)
Affiliated Research Centre Stazione Zoologica “Anton Dohrn” Naples, (Italy)
Thesis submitted for the degree of Doctor of Philosophy September 2009
This thesis was made under the supervision of:
Dr. Maria Cristina Gambi (Director of Studies) Functional and Evolutionary Ecology Laboratory Stazione Zoologica Anton Dohrn (Naples, Italy)
Dr. Valerio Zupo (Supervisor) Functional and Evolutionary Ecology Laboratory Stazione Zoologica Anton Dohrn (Naples, Italy)
Dr. Gordon Paterson (Supervisor) Department of Zoology Natural History Museum (London, UK)
Table of Contents Table of Contents ............................................................................................ 1 List of Figures .................................................................................................. 4 List of Tables ................................................................................................. 13 Aknowledgement ........................................................................................... 21 Abstract .......................................................................................................... 22 1. Introduction ............................................................................................ 23 1.1
The Scaling Issue in Ecology ................................................................................23
1.2
The Scaling Principles ...........................................................................................24
1.3
Seagrass Ecosystem: a Landscape Approach ........................................................27
1.4
Posidonia oceanica Ecosystem ..............................................................................29
1.4.1
Posidonia oceanica Associated Assemblages ................................................32
1.4.2
Patterns of Spatial Variability in Posidonia oceanica Ecosystem ..................37
2. Objectives ................................................................................................ 42 3. Material & Methods ............................................................................... 45 3.1
Study Area .............................................................................................................45
3.1.1
Lacco Ameno Meadow ..................................................................................46
3.1.2
Scarrupata Meadow........................................................................................47
3.2
Measure of Temperature........................................................................................48
3.3
Sampling Strategy .................................................................................................48
3.3.1
Plant morphometric features ..........................................................................51
3.2.2
Motile Macro-invertebrate Assemblages .......................................................53 1
3.4
Statistical Analysis ................................................................................................ 56
3.3.1
Univariate Analysis ....................................................................................... 57
3.4.2
Multivariate Analysis..................................................................................... 61
3.4.3
Diversity Indices for Motile Fauna ................................................................ 64
3.4.4
Relationships Between Plant and Faunal Features ........................................ 65
4. Results ...................................................................................................... 68 4.1
Temperature Trends .............................................................................................. 68
4.2
Morphometric Parametres ..................................................................................... 69
4.2.1
Univariate Results .......................................................................................... 69
4.2.2
Multivariate Results ....................................................................................... 94
4.2.3
Summary ........................................................................................................ 95
4.3
4.3.1
Univariate Analysis ....................................................................................... 96
4.3.2
Multivriate Analysis .................................................................................... 105
4.3.3
Summary ...................................................................................................... 106
4.4
2
Borer Polychaetes ................................................................................................. 96
Motile Macro-invertebrates Abundances and Distribution ................................. 107
4.4.1
Higher Taxa ................................................................................................. 107
4.4.2
Higher Taxa: Summary................................................................................ 114
4.4.3
Gastropoda ................................................................................................... 115
4.4.4
Gastropoda: Summary ................................................................................. 120
4.4.5
Polychaeta .................................................................................................... 121
4.4.6
Polychaeta: Summary .................................................................................. 126
4.4.7
Amphipoda .................................................................................................. 127
4.4.8
Amphipoda: Summary ................................................................................. 132
4.5
Correlations between Plant and Faunal Features.................................................133
4.5.1
Canonical Analysis of Principal Coordinates (CAP) ...................................133
4.5.2
Spearman’s Correlations ..............................................................................140
4.5.3
Summary ......................................................................................................142
5. Discussion and Conclusions................................................................. 144 5.1
Shoot Density and Morphometric Plant Features ................................................144
5.2
Borer polychaetes ................................................................................................149
5.3
Macro-invertebrate Assemblages ........................................................................152
5.3.1 Higher Taxa .........................................................................................................153 5.3.2 Gastropods ...........................................................................................................157 5.3.3 Polychaetes ..........................................................................................................158 5.3.4 Amphipods ..........................................................................................................159 5.4
Final Considerations and Conclusions ................................................................161
5.4.1 Posidonia oceanica Features ................................................................................163 5.4.2 Borer Polychaetes ................................................................................................164 5.4.3 Macro-Invertebrate Assemblages ........................................................................165 5.5
General Conclusions............................................................................................166
Appendix 1 ................................................................................................... 168 Appendix 2 ................................................................................................... 176 Appendix 3 ................................................................................................... 186 Appendix 4 ................................................................................................... 195 Appendix 5 ................................................................................................... 205 7. References ............................................................................................. 208 3
List of Figures Figure 1: A generalized 3 levels hierarchical system. Thick arrows indicate strong interactions; broken arrows, weak interactions (from Urban et al., 1987 modified). .......... 25 Figure 2: Hierarchical patch structure and the grain-extent ranges for four types of organisms (A-D). For each organism, grain is restricted to scales greater than perception limits. For organisms A and B the scale of perception limits corresponds to grain. For organism C and D, the grain is set not by perception limits but by behavioral response (from Kotliar & Wiens, 1990 modified). ............................................................................. 26 Figure 3: Posidonia oceanica representation (from the website http://takocito.overblog.com modified).............................................................................................................. 30 Figure 4: a) example of two sheaths with borer polychaetes inside tunnels; b) Lysidice collaris; c) Lysidice ninetta; d) Nematonereis unicornis. .................................................... 35 Figure 5: Location of the sampling areas in P. oceanica meadows off Lacco Ameno and Scarrupata (Gulf of Naples, Italy). LA: Lacco Ameno meadow; Sc: Scarrupata meadow. 45 Figure 6: Hierarchical sampling design used in this study. 2 quadrats nested in each 3 plot, nested within each of two stations, nested within each of 3 sites, nested within each of 2 meadows (locations). Depth of sampling is 15 m and sampling design was repeated in two different dates per season per meadow (see text for further explanations). ........................ 49 Figure 7: Water column temperature recorded in 2007 and 2008 in the fixed stations of the Ischia Island and at “Marechiara”: a) surface (3 m) patterns of temperature (Ischia Island), b) depth profile of temperature during 2007 (“Marechiara”), c) depth profile of temperature during 2008 (“Marechiara”). ............................................................................................... 68
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Figure 8: Shoot densities in the two meadows along the three main spatial scales and the two seasons: a) plot (a, b, c); b) station (1, 2); c) site (A, B, C). Red colors represent Summer season and blue colors Winter season. In each graph are represented both the date of sampling 1 and the date of sampling 2. Bars represent standard errors...........................70 Figure 9: Leaf Area Index (LAI) in the two meadows along the three main spatial scales and the two seasons: a) plot (a, b, c); b) station (1, 2); c) site (A, B, C). Red colors represent Summer season and blue colors Winter season. In each graph are represented both the date of sampling 1 and the date of sampling 2. Bars represent standard errors. ....72 Figure 10: Leaf Standing Crop (LSC) in the two meadows along the three main spatial scales and the two seasons: a) plot (a, b, c); b) station (1, 2); c) site (A, B, C). Red colors represent Summer season and blue colors Winter season. In each graph are represented both the date of sampling 1 and the date of sampling 2. Bars represent standard errors. ....73 Figure 11: Number of leaves per shoot in the two meadows along the three main spatial scales and the two seasons: a) plot (a, b, c); b) station (1, 2); c) site (A, B, C). Red colors represent Summer season and blue colors Winter season. In each graph are represented both the date of sampling 1 and the date of sampling 2. Bars represent standard errors. ....75 Figure 12: Number of Adult leaves per shoot in the two meadows along the three main spatial scales and the two seasons: a) plot (a, b, c); b) station (1, 2); c) site (A, B, C). Red colors represent Summer season and blue colors Winter season. In each graph are represented both the date of sampling 1 and the date of sampling 2. Bars represent standard errors. ...................................................................................................................................76 Figure 13: Number of Intermediate leaves per shoot in the two meadows along the three main spatial scales and the two seasons: a) plot (a, b, c); b) station (1, 2); c) site (A, B, C). Red colors represent Summer season and blue colors Winter season. In each graph are 5
represented both the date of sampling 1 and the date of sampling 2. Bars represent standard errors. ................................................................................................................................... 78 Figure 14: Number of Juvenile leaves per shoot in the two meadows along the three main spatial scales and the two seasons: a) plot (a, b, c); b) station (1, 2); c) site (A, B, C). Red colors represent Summer season and blue colors Winter season. In each graph are represented both the date of sampling 1 and the date of sampling 2. Bars represent standard errors. ................................................................................................................................... 80 Figure 15: Average leaf length (cm) per shoot in the two meadows along the three main spatial scales and the two seasons: a) plot (a, b, c); b) station (1, 2); c) site (A, B, C). Red colors represent Summer season and blue colors Winter season. In each graph are represented both the date of sampling 1 and the date of sampling 2. Bars represent standard errors. ................................................................................................................................... 83 Figure 16: Average leaf width (cm) per shoot in the two meadows along the three main spatial scales and the two seasons: a) plot (a, b, c); b) station (1, 2); c) site (A, B, C). Red colors represent Summer season and blue colors Winter season. In each graph are represented both the date of sampling 1 and the date of sampling 2. Bars represent standard errors. ................................................................................................................................... 85 Figure 17: Leaf surface (cm2) per shoot in the two meadows along the three main spatial scales and the two seasons: a) plot (a, b, c); b) station (1, 2); c) site (A, B, C). Red colors represent Summer season and blue colors Winter season. In each graph are represented both the date of sampling 1 and the date of sampling 2. Bars represent standard errors. ... 87 Figure 18: Leaf biomass (grams of dry weight) per shoot in the two meadows along the three main spatial scales and the two seasons: a) plot (a, b, c); b) station (1, 2); c) site (A, B, C). Red colors represent Summer season and blue colors Winter season. In each graph 6
are represented both the date of sampling 1 and the date of sampling 2. Bars represent standard errors. .....................................................................................................................89 Figure 19: Sheaths length (cm) per shoot in the two meadows along the three main spatial scales and the two seasons: a) plot (a, b, c); b) station (1, 2); c) site (A, B, C). Red colors represent Summer season and blue colors Winter season. In each graph are represented both the date of sampling 1 and the date of sampling 2. Bars represent standard errors. ....91 Figure 20: Sheath biomass (grams of dry weight) per shoot in the two meadows along the three main spatial scales and the two seasons: a) plot (a, b, c); b) station (1, 2); c) site (A, B, C). Red colors represent Summer season and blue colors Winter season. In each graph are represented both the date of sampling 1 and the date of sampling 2. Bars represent standard errors. .....................................................................................................................93 Figure 21: nMDS of centroids calculated by means of Principal Coordinates of morphometric features. LA (Lacco Ameno site A), LB (Lacco Ameno site B), LC (Lacco Ameno site C); SA (Scarrupata site A), SB (Scarrupata site B), SC (Scarrupata site C). Sum represents summer season, Win is winter. 1 is for date 1 and 2 is for date 2. .............94 Figure 22: Relative percentage of abundances for each single borer polycheate species (L. collaris, L. ninetta and N. unicornis) in both meadows and both seasons. ..........................97 Figure 23: Index of Borers (IB) calculated for all the polychaete borer species together in the two meadows along the three main spatial scales and the two seasons: a) plot (a, b, c); b) station (1, 2); c) site (A, B, C). Red colors represent Summer season and blue colors Winter season. In each graph are represented both the date of sampling 1 and the date of sampling 2. Bars represent standard errors. .......................................................................100 Figure 24: Index of Borers (IB) calculated only for L. collaris in the two meadows along the three main spatial scales and the two seasons: a) plot (a, b, c); b) station (1, 2); c) site 7
(A, B, C). Red colors represent Summer season and blue colors Winter season. In each graph are represented both the date of sampling 1 and the date of sampling 2. Bars represent standard errors. ................................................................................................... 102 Figure 25: Index of Borers (IB) calculated only for L. ninetta in the two meadows along the three main spatial scales and the two seasons: a) plot (a, b, c); b) station (1, 2); c) site (A, B, C). Red colors represent Summer season and blue colors Winter season. In each graph are represented both the date of sampling 1 and the date of sampling 2. Bars represent standard errors. ................................................................................................... 104 Figure 26: nMDS of centroids calculated by means of Principal Coordinates of IB of single species. LA (Lacco Ameno site A), LB (Lacco Ameno site B), LC (Lacco Ameno site C); SA (Scarrupata site A), SB (Scarrupata site B), SC (Scarrupata site C). Sum represents summer season, Win is winter. 1 is for date 1 and 2 is for date 2. .................................... 106 Figure 27: Equitability (J’) of higher taxa at the three spatial scales. LA is Lacco Ameno and Sc is Scarrupata. Bars represent standard deviation. .................................................. 109 Figure 28: Shannon – Wiener diversity index (H’) of higher taxa at the three spatial scales. LA - Lacco Ameno and Sc - Scarrupata. Bars represent standard deviation. ................... 109 Figure 29: Community structures (percentage of single groups on the total of individuals) at location scale for each season. The group Others includes flat worms, sipunculids, polyplacophores, nudibranches, sea-spiders, chetognaths, echinoderms and tanaids. ...... 110 Figure 30: Community structures (percentage of a single group s on the total of individuals) at site scales for each season. ‘Others’ includes flat worms, sipunculids, polyplacophores, nudibranches, sea-spiders, chetognaths, echinoderms and tanaids. ...... 111
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Figure 31: Community structures (percentage of a single group on the total of individuals) at station scale for each season. ‘Others’ includes flat worms, sipunculids, polyplacophores, nudibranches, sea-spiders, chetognaths, echinoderms and tanaids........111 Figure 32: nMDS of centroids calculated by means of Principal Coordinates of higher taxa on the basis of Bray-Curtis dissimilarity matrix after fourth root transformation. LA (Lacco Ameno site A), LB (Lacco Ameno site B), LC (Lacco Ameno site C); SA (Scarrupata site A), SB (Scarrupata site B), SC (Scarrupata site C). Sum represents summer season, Win is winter. ................................................................................................................................113 Figure 33: Shannon – Wiener diversity index (H’) of gastropods at the three spatial scales. LA is Lacco Ameno and Sc is Scarrupata. Bars represent standard deviation. .................117 Figure 34: Equitability (J’) of gastropods at the three spatial scales. LA is Lacco Ameno and Sc is Scarrupata. Bars represent standard deviation. ...................................................118 Figure 35: nMDS of centroids calculated by means of Principal Coordinates of gastropods on the basis of Bray-Curtis dissimilarity matrix after fourth root transformation. LA (Lacco Ameno site A), LB (Lacco Ameno site B), LC (Lacco Ameno site C); SA (Scarrupata site A), SB (Scarrupata site B), SC (Scarrupata site C). Sum represents summer season, Win is winter. ................................................................................................................................119 Figure 36: Shannon – Wiener diversity index (H’) of polychaetes at the three spatial scales. LA is Lacco Ameno and Sc is Scarrupata. Bars represent standard deviation. .................123 Figure 37: Equitability (J’) of polychaetes at the three spatial scales. LA is Lacco Ameno and Sc is Scarrupata. Bars represent standard deviation. ...................................................124 Figure 38: nMDS of centroids calculated by means of Principal Coordinates of polychaetes families on the basis of Bray-Curtis dissimilarity matrix after fourth root transformation. LA (Lacco Ameno site A), LB (Lacco Ameno site B), LC (Lacco Ameno site C); SA 9
(Scarrupata site A), SB (Scarrupata site B), SC (Scarrupata site C). Sum represents summer season, Win is winter. ........................................................................................................ 125 Figure 39: Shannon – Wiener diversity index (H’) of amphipods genera at the three spatial scales. LA is Lacco Ameno and Sc is Scarrupata. Bars represent standard deviation. ..... 129 Figure 40: Equitability (J’) of amphipod genera at the three spatial scales. LA is Lacco Ameno and Sc is Scarrupata. Bars represent standard deviation....................................... 130 Figure 41: nMDS of centroids calculated by means of Principal Coordinates of amphipods genera on the basis of Bray-Curtis dissimilarity matrix after fourth root transformation. LA (Lacco Ameno site A), LB (Lacco Ameno site B), LC (Lacco Ameno site C); SA (Scarrupata site A), SB (Scarrupata site B), SC (Scarrupata site C). Sum represents summer season, Win is winter. ........................................................................................................ 131 Figure 42: CAP analysis between plant features and borer polychaetes frequencies. The first canonical axis explains 9.4% of the variability among variables. NL : number of leaves; SL : sheaths length; LL : leaves length; LW : leaves width; LS : leaf surface; SD : :shoot density; LB : leaves biomass; SB : sheath biomass. ................................... 134 Figure 43: CAP analysis between plant features and the most frequent and abundant higher taxa and diversity indices. The first canonical axis explains 65.0% of the variability among variables. Area highlighted in pink corresponds to a correlation < ± 0.40. NL : number of leaves; SL : sheaths length; LL : leaves length; LW : leaves width; LS : leaf surface; SD : :shoot density; LB : leaves biomass; SB : sheath biomass; Pol : Polychaeta; Gas : Gastropoda; Biv : Bivalvia; Dec : Decapoda; Mys : Mysidacea; Iso : Isopoda; Tan : Tanaidacea; Cum : Cumacea; Amp : Amphipoda; Ech : Echinodermata; S : number of taxa per quadrat; N : Number of individuals per quadrat; J’ : Equitability; H’ : Shannon – Wiener diversity index. ................................................................................................... 135 10
Figure 44: CAP analysis between plant features and the most frequent and abundant gastropod species and diversity indices. The first canonical axis explains 63.2% of the variability among variables. Area highlighted in pink corresponds to a correlation < ± 0.40. NL : number of leaves; SL : sheaths length; LL : leaves length; LW : leaves width; LS : leaf surface; SD : :shoot density; LB : leaves biomass; SB : sheath biomass; S : number of taxa per quadrat; N : Number of individuals per quadrat; J’ : Equitability; H’ : Shannon – Wiener diversity index. ...........................................................................137 Figure 45: CAP analysis between plant features and the most frequent and abundant polychaete families and diversity indices. The first canonical axis explains 61.8% of the variability among variables. Area highlighted in pink corresponds to a correlation < ± 0.40. NL : number of leaves; SL : sheaths length; LL : leaves length; LW : leaves width; LS : leaf surface; SD : :shoot density; LB : leaves biomass; SB : sheath biomass; Chr : Chrysopetalidae; Cir : Cirratulidae; Eun : Eunicidae; Eup : Euphrosinidae; Fla : Flabelligeridae; Hes : Hesionidae; Ner : Nereididae; Oph : Ophelidae; Par : Paraonidae; Pol : Polynoidae; Sab : Sabellidae; Spi : Spionidae; Syl : Syllidae; Ter : Terebellidae; S : number of taxa per quadrat; N : Number of individuals per quadrat; J’ : Equitability; H’ : Shannon – Wiener diversity index. ..............................................138 Figure 46: CAP analysis between plant features and the most frequent and abundant amphipod genera and diversity indices. The first canonical axis explains 64.8% of the variability among variables. Area highlighted in pink corresponds to a correlation < ± 0.40. NL : number of leaves; SL : sheaths length; LL : leaves length; LW : leaves width; LS : leaf surface; SD : :shoot density; LB : leaves biomass; SB : sheath biomass; Apo : Apolochus; Git : Gitana; Aor : Aora; Aor gen.sp. : Aoridae gen.sp; Dex : Dexamine; Aph : Apherusa; Iph : Iphimedia; Gam : Gammaropsis; Isc gen.sp. : Ischyroceridae gen.sp.; Lil : Liljeborgia; Lys : Lysianassa; Orc : Orchomene; Che : Cheirocratus; Gamm : Gammarella; Per : Perioculodes; Pho : Phoxocephalus; Cap : Caprella; 11
Pht : Phtisica; S : number of taxa per quadrat; N : Number of individuals per quadrat; J’ : Equitability; H’ : Shannon – Wiener diversity index. .............................................. 139
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List of Tables Table 1: Geographical coordinates of the sampling sites in Lacco Ameno and Scarrupata meadows...............................................................................................................................50 Table 2: Model of ANOVA and PERMANOVA (a), Expected Mean Square (b), denominators used in the calculation of F – ratio in ANOVA and PERMANOVA............59 Table 3: ANOVA and Cochran’s C tests for shoot density, Leaf Area Index (LAI) and Leaf Standing Crop (LSC) at the different spatial and temporal scales: L = Location; S = Season; D = Date of sampling; Si = Site; St= Station; Pl = Plot. (* = p < 0.05; ** = p < 0.01; *** = p < 0.001; ns = not significant; F = Fixed factor; R = Random factor). ...........69 Table 4: ANOVA and Cochran’s C tests for number of leaves per shoot, number of juveniles, intermediate and adult leaves at the different spatial and temporal scales: L = Location; S = Season; D = Date of sampling; Si = Site; St= Station; Pl = Plot, Qu = Quadrat. (* = p < 0.05; ** = p < 0.01; *** = p < 0.001; ns = not significant; F = Fixed factor; R = Random factor). .................................................................................................74 Table 5: ANOVA and Cochran’s C tests for leaf length, leaf width, leaf surface and sheaths length at the different spatial and temporal scales: L = Location; S = Season; D = Date of sampling; Si = Site; St= Station; Pl = Plot, Qu = Quadrat. (* = p < 0.05; ** = p < 0.01; *** = p < 0.001; ns = not significant; F = Fixed factor; R = Random factor). ...........81 Table 6: ANOVA and Cochran’s C tests for leaf biomass and sheaths biomass at the different spatial and temporal scales: L = Location; S = Season; D = Date of sampling; Si = Site; St= Station; Pl = Plot, Qu = Quadrat. (* = p < 0.05; ** = p < 0.01; *** = p < 0.001; ns = not significant; F = Fixed factor; R = Random factor). ....................................................88
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Table 7: PERMANOVA results of the main morphometric and biomass plant parameters. P(perm) are values calculated by permutations; P(MC) are values calculated by Monte Carlo simulations. Significant values are represented in bold. L = Location; S = Season; D = Date of sampling; Si = Site; St= Station; Pl = Plot .......................................................... 95 Table 8: ANOVA and Cochran’s C test results for total IB and single species IB (L. collaris, L. ninetta and N. unicornis) at the different spatial and temporal scales: L = Location; S = Season; D = Date of sampling; Si = Site; St= Station; Pl = Plot. In bold are signed significant values. (F = Fixed factor; R = Random factor). ..................................... 97 Table 9: ANOVA results obtained by testing for each meadow at a time for total IB and single species IB (L. collaris, L. ninetta) at the different spatial and temporal scales: S = Season; D = Date of sampling; Si = Site; St= Station; Pl = Plot. (* = p < 0.05; ** = p < 0.01; *** = p < 0.001; ns = not significant) ........................................................................ 98 Table 10: PERMANOVA results of single species IB. P(perm) are values calculated by permutations; P(MC) are values calculated by Monte Carlo simulations. In bold significant values. ................................................................................................................................ 105 Table 11: a) ANOVA of number of taxa (S), number of individuals (N), evenness (J’) and Shannon – Wiener diversity index (H’); b) ANOVA of higher taxa at different spatial scales. Bold characters indicate significant values. ........................................................... 108 Table 12: PERMANOVA results of higher taxa. P(perm) are values calculated by permutations; P(MC) are values calculated by Monte Carlo simulations. Bold numbers are significant values. .............................................................................................................. 114 Table 13: ANOVA of gastropods diversity indices at different spatial scales. Bold characters indicate significant values. Abbreviations- see Table 9. .................................. 116
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Table 14: PERMANOVA results for gastropods. P(perm) are values calculated by permutations; P(MC) are values calculated by Monte Carlo simulations. Bold numbers are significant values................................................................................................................119 Table 15: ANOVA of polychaetes families diversity indices at different spatial scales. Bold characters indicate significant values. Abbreviations see Table 9. ...........................124 Table 16: PERMANOVA results for polychaete families. P(perm) are values calculated by permutations; P(MC) are values calculated by Monte Carlo simulations. Bold numbers are significant values................................................................................................................125 Table 17: ANOVA of amphipod genera diversity indices at different spatial scales. Bold characters indicate significant values. ................................................................................130 Table 18: PERMANOVA results for amphipods genera. P(perm) are values calculated by permutations; P(MC) are values calculated by Monte Carlo simulations. Bold numbers are significant values................................................................................................................132 Table 19: Average number of higher taxa and individuals along Lacco Ameno and Scarrupata meadows in the two seasons (mean ± SD). ......................................................168 Table 20: Average similarity percentages of higher taxonomic groups: a) Lacco Ameno similarity in summer and winter; b) Scarrupata similarity in summer and winter; c) dissimilarity pairwises summer vs winter for both meadows. The contribution percentage for each taxon is reported. ..................................................................................................169 Table 21: Average similarity percentages of sites in each meadow and season. The contribution percentage for each taxon is reported. ...........................................................170 Table 22: Average dissimilarity percentages between sites in each meadow and season. The contribution percentage for each taxon is reported. ....................................................170 15
Table 23: Average similarity percentages of stations in each meadow and season. The contribution percentage for each taxon is reported............................................................ 171 Table 24: Average dissimilarity percentages between stations in Lacco Ameno meadow in summer. The contribution percentage for each taxon is reported. .................................... 172 Table 25: Average dissimilarity percentages between stations in Lacco Ameno meadow in winter. The contribution percentage for each taxon is reported. ....................................... 173 Table 26: Average dissimilarity percentages between stations in Scarrupata meadow in summer. The contribution percentage for each taxon is reported. .................................... 174 Table 27: Average dissimilarity percentages between stations in Scarrupata meadow in winter. The contribution percentage for each taxon is reported. ....................................... 175 Table 28: Average number of species and individuals along Lacco Ameno and Scarrupata meadows in the two seasons (mean ± SD). ....................................................................... 177 Table 29: Average similarity percentages of gastropod species: a) Lacco Ameno similarity in summer and winter; b) Scarrupata similarity in summer and winter; c) dissimilarity pairwises summer vs winter for both meadows. The contribution percentage for each taxon is reported. ......................................................................................................................... 178 Table 30: Average similarity percentages of sites in each meadow and season. The contribution percentage for each species is reported. ........................................................ 179 Table 31: Average dissimilarity percentages between sites in each meadow and season. The contribution percentage for each species is reported. ................................................. 180 Table 32: Average similarity percentages of stations in each meadow and season. The contribution percentage for each species is reported. ........................................................ 181
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Table 33: Average dissimilarity percentages between stations in Lacco Ameno meadow in summer. The contribution percentage for each species is reported. ..................................182 Table 34: Average dissimilarity percentages between stations in Lacco Ameno meadow in winter. The contribution percentage for each species is reported. .....................................183 Table 35: Average dissimilarity percentages between stations in Scarrupata meadow in summer. The contribution percentage for each species is reported. ..................................184 Table 36: Average dissimilarity percentages between stations in Scarrupata meadow in winter. The contribution percentage for each species is reported. .....................................185 Table 37: Average number of families and individuals along Lacco Ameno and Scarrupata meadows in the two seasons (mean ± SD). ........................................................................187 Table 38: Average similarity percentages of polychaete families: a) Lacco Ameno similarity in summer and winter; b) Scarrupata similarity in summer and winter; c) dissimilarity pairwises summer vs winter for both meadows. The contribution percentage for each taxon is reported. ..................................................................................................188 Table 39: Average similarity percentages of sites in each meadow and season. The contribution percentage for each family is reported. .........................................................189 Table 40: Average dissimilarity percentages between sites in each meadow and season. The contribution percentage for each family is reported. ..................................................189 Table 41: Average similarity percentages of stations in each meadow and season. The contribution percentage for each family is reported. .........................................................190 Table 42: Average dissimilarity percentages between stations in Lacco Ameno meadow in summer. The contribution percentage for each family is reported. ...................................191
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Table 43: Average dissimilarity percentages between stations in Lacco Ameno meadow in winter. The contribution percentage for each family is reported....................................... 192 Table 44: Average dissimilarity percentages between stations in Scarrupata meadow in summer. The contribution percentage for each family is reported. ................................... 193 Table 45: Average dissimilarity percentages between stations in Scarrupata meadow in winter. The contribution percentage for each family is reported....................................... 194 Table 46: Average number of species and individuals along Lacco Ameno and Scarrupata meadows in the two seasons (mean ± SD). ....................................................................... 196 Table 47: Average similarity percentages of amphipod genera: a) Lacco Ameno similarity in summer and winter; b) Scarrupata similarity in summer and winter; c) dissimilarity pairwises summer vs winter for both meadows. The contribution percentage for each taxon is reported. ......................................................................................................................... 197 Table 48: Average similarity percentages of sites in each meadow and season. The contribution percentage for each species is reported. ........................................................ 198 Table 49: Pairwise dissimilarity percentages between sites in Lacco Ameno meadow and seasons. The contribution percentage for each species is reported. .................................. 198 Table 50: Pairwise dissimilarity percentages between sites in Scarrupata meadow and seasons. The contribution percentage for each species is reported. .................................. 199 Table 51: Average similarity percentages of stations in Lacco Ameno meadow and seasons. The contribution percentage for each species is reported. .................................. 199 Table 52: Average similarity percentages of stations in Scarrupata meadow and seasons. The contribution percentage for each species is reported. ................................................. 200
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Table 53: Average dissimilarity percentages between stations in Lacco Ameno meadow in summer. The contribution percentage for each species is reported. ..................................201 Table 54: Average dissimilarity percentages between stations in Lacco Ameno meadow in winter. The contribution percentage for each species is reported. .....................................202 Table 55: Average dissimilarity percentages between stations in Scarrupata meadow in summer. The contribution percentage for each species is reported. ..................................203 Table 56: Average dissimilarity percentages between stations in Scarrupata meadow in winter. The contribution percentage for each species is reported. .....................................204 Table 57: Correlations between plant features and borer polychaete frequencies. NL: Number of Leaf; SL: Sheaths Length; LL: Leaf Length; LW: Leaf Width; LS: Leaf Surface; ShD: Shoot Density; LB: Leaf Biomass; SB: Sheaths Biomass. Signficant values in bold. ...............................................................................................................................205 Table 58: Correlations between plant features and most abundant higher taxa. NL: Number of Leaf; SL: Sheaths Length; LL: Leaf Length; LW: Leaf Width; LS: Leaf Surface; ShD: Shoot Density; LB: Leaf Biomass; SB: Sheaths Biomass. Pol: Polychaetes; Gas: Gastropods; Biv: Bivalvs; Dec: Decapods; Mys: Mysids; Iso: Isopods; Tan: Tanaids; Cum: Cumaceans; Amp: Amphipods; Echi: Echinoderms. S: Number of Taxa per quadrat; N: Number of Individuals per quadrat; J’: Pielou Index; ES(100); Rarefaction; H’: ShannonWiener diversity Index. Signficant values in bold. ............................................................205 Table 59: Correlations between plant features and most abundant gastropod species. NL: Number of Leaf; SL: Sheaths Length; LL: Leaf Length; LW: Leaf Width; LS: Leaf Surface; ShD: Shoot Density; LB: Leaf Biomass; SB: Sheaths Biomass. S: Number of species per quadrat; N: Number of Individuals per quadrat; J’: Pielou Index; ES(20); Rarefaction; H’: Shannon-Wiener diversity Index. Signficant values in bold...................206 19
Table 60: Correlations between plant features and most abundant gastropod species. NL: Number of Leaf; SL: Sheaths Length; LL: Leaf Length; LW: Leaf Width; LS: Leaf Surface; ShD: Shoot Density; LB: Leaf Biomass; SB: Sheaths Biomass. Chr: Chrysopetalidae;
Cir:
Cirratulidae;
Eun:
Eunicidae;
Eup:
Euphrosinidae;
Fla:
Flabelligeridae; Hes: Hesionidae; Ner: Nereididae; Oph: Ophelidae; Par: Paraonidae; Pol: Polynoidae; Sab: Sabellidae; Spi: Spionidae; Syl: Syllidae; Ter: Terebellidae. S: Number of families per quadrat; N: Number of Individuals per quadrat; J’: Pielou Index; ES(50); Rarefaction; H’: Shannon-Wiener diversity Index. Signficant values in bold. ................. 206 Table 61: Correlations between plant features and most abundant gastropod species. NL: Number of Leaf; SL: Sheaths Length; LL: Leaf Length; LW: Leaf Width; LS: Leaf Surface; ShD: Shoot Density; LB: Leaf Biomass; SB: Sheaths Biomass. Apo: Apolochus; Git: Gitana; Aor: Aora; Aor gen.sp.: Aoridae gen.sp; Dex: Dexamine; Aph: Apherusa; Iph: Iphimedia; Gam: Gammaropsis; Isc gen.sp.: Ischyroceridae gen.sp.; Lil: Liljeborgia; Lys: Lysianassa; Orc: Orchomene; Che: Cheirocratus; Gamm: Gammarella; Per: Perioculodes; Pho: Phoxocephalus; Cap: Caprella; Pht: Phtisica. S: Number of species per quadrat; N: Number of Individuals per quadrat; J’: Pielou Index; ES(20); Rarefaction; H’: ShannonWiener diversity Index. Signficant values in bold............................................................. 207
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Aknowledgement I would like to thank first of all my supervisors: Dr. Maria Cristina Gambi, Dr. Valerio Zupo and Dr. Gordon Paterson for their constant help and advice. Their professionality and their preparation were of huge help for this project from every point of view. MCG has been my mentor both underwater and outside the sea. She trusted in me when she had to select a PhD candidate and I will be forever grateful to her for this important opportunity. VZ has been of great support with his simpaty and humor during my long days spent in the laboratory scraping Posidonia leaves. GP has been my principal advicer both for scientific and for “language” deals. I am very proud to have understood all what he said during a call!! Obviously, I want to thank to all the Laboratory of Benthos of Ischia staff, starting from Rosanna Messina, my second mum and my principal source of spettacular food!! Dr. Francesco Patti was my computer grandmaster and confessor, and without him I probably had not improved my knowledge in “renting” softwares from the web. Maurizio Lorenti and our statistical afternoon were of great inspiration for me. Moreover he was of big help in sampling at the sea. Dr. Maria Cristina Buia has been of big support concerning the “plant stuffs” thanks to her expertise and help in sampling. Cpt. Vincenzo Rando and the diving officer Bruno Iacono have been the principal protagonists of the sampling because of their expertises. Finally, a big THANK YOU to all my friend of the lab: Michela, Patrizia, Rosanna “Sanny”, Francesco, Alessandra, Lucia, Adriana, Myriam, Monia, Stefania, Alessio, Emanuela, Carmen, Luca, Gianmaria, Ines, Belen. I would thank my “real” family for their support during these years. Finally, I would thank my girlfriend Monica. She was of great support, but above all, she showed a huge patience when I had to write this thesis…I am in debt with her of one Summer!! 21
Abstract Posidonia oceanica is a seagrass species endemic of the Mediterranean Sea that forms a complex ecosystem providing a suitable habitat for hundreds of animal and algal species. The aim of this work was to evaluate the spatial and temporal variations of plant features and of associated faunal assemblages, and the relationships between them. Samples were collected following a hierarchical sampling design, in two meadows off the island of Ischia (Italy), in summer and winter (when maximum and minimum plant growth is observed). Most of the plant features showed multiple spatial scales (100s to few m) of variation, but only shoot density varied at the largest scale (few km). Seasonal differences were recorded for most of the plant features with lower values in winter than in summer. It was clear that a variety of environmental factors independently influenced the patterns of variability of the Posidonia features. Multi-scale variations are characteristic of natural processes, and it is likely that human activities (e.g., boat anchoring) may emphasize the effects of natural factors. On the contrary, the main taxonomic groups (gastropods, polychaetes and amphipods) showed significant variability between meadows (few km). The spatial and temporal distribution of the fauna seemed to be affected more by processes linked to their ecology than by association with the plant. However, habitat complexity may influence to different degrees the associated fauna. Ambiguous results were recorded for gastropod species, negatively influenced by leaf features (length, leaf, surface), however, it is likely that their distribution was linked to epiphyte distribution. Polychaete families were negatively related mainly to leaf and sheath biomass, probably because many of the families were typical of soft-bottom habitats. Amphipods were influenced by shoot density, with higher shoot density values providing more suitable conditions. Thus different plant features influenced in different ways different taxonomic groups.
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1. Introduction 1.1
The Scaling Issue in Ecology
The problem of understanding patterns depending on different scales has become central issues in ecology (Levin, 1992). As reported by Wiens (1989), scientists in other disciplines recognized the importance of the scales (both spatial and temporal). For example, the fundamental basis of atmospheric and earth sciences is scaling because of the many physical processes that create local and global patterns linked in a hierarchy of effects (Schumm & Lichty, 1965; Clark, 1985; Dagan, 1986; Anhert, 1987). Physical and biological oceanographers often concentrate on spectra of physical processes from circulation patterns in oceanic basins or large gyres to the small-scale eddies or rips (Malone et al., 1983; Lévy et al., 2003; van Haren et al., 2004). Even physicists and mathematicians address scaling as a primary issue of their investigations studying fractal geometry, percolation theory and chaos (Nittman et al., 1985; Orbach, 1986; Grebogi et al., 1987). In terrestrial ecology, plant ecologists first recognized the importance of sampling scale in the description of species distributions (Greig-Smith, 1952), but many ecologists were insensitive to the scaling issue. Simberloff (1988) was one of the first to be conscious of the fact that many controversies and disagreements among conservation biologists over the optimal design of nature reserves were partly due to a failure in appreciating scaling differences among organisms. However, in the last decades, ecologists started to be more interested about scaling effects and their awareness about the influence of scale-dependent processes on communities led to the production of a huge amount of literature (Dayton & Tegner, 1984; Wiens et al., 1986; Meetenmeyer & Box, 1987; Frost et al., 1988; Kotliar & Wiens, 1990; Levin, 1992; Fraschetti et al., 2005; Boström et al., 2006a). As Wiens (1989) noticed, “scale” has rapidly become a new ecological “buzzword”, a new fashion word that led to the development of new statistical instruments to investigate communities and 23
species distribution (e.g. Underwood, 1992; Anderson, 2001). The answer to the question “why have ecologists been so slow to recognize the importance of scaling”, is maybe that many familiar phenomena occur on a variety of spatial and temporal scales and there is often a mismatch in the design of sampling and scaling aspects of the phenomena being studied. Wiens (1989) suggested that it happened because of a “somewhat tradition, using quadrats or plots of a particular size simply because previous workers did”.
1.2
The Scaling Principles
Theoretical ecology tries to relate processes occurring at different spatial, time and even organizational complexity scales. The analysis of the patterns produced by such processes are the keys to develop the principles for environmental management. Furthermore, the problem of scale has also a practical importance. Global and regional changes in biological diversity, in the distribution of greenhouse gases and pollutants, and in climate, all have origins in and consequences for fine-scale phenomena (Levin, 1992). The concepts of scale and pattern are interlinked, as Hutchinson (1953) showed. In fact, the description of pattern is the description of variation, and the quantification of variation requires the determination of scales. At very fine spatial and temporal scales, stochastic phenomena may make the systems of interest unpredictable and pattern explanation difficult, thus the attention focuses on larger spatial regions and longer time scales, for which macroscopic statistical behaviors are more regular, thus increasing predictability. So the rule is that, by moving to increasingly larger scales of description, the variability decreases and becomes regular enough to allow generalizations to be made. But so doing, one has to trade off the loss of details, or heterogeneity, in the system under examination (Levin, 1992). The key to understand in what way and how much information is transferred across scales is to determine what information is preserved and what information is lost as one moves from 24
one scale to the other. For example, studies on interactions among species could be sensitive to scaling. In fact, the population dynamics of predators and of their prey may be influenced by factors operating at different scales and when one tries to link these dynamics directly, without recognizing the scale differences, this could generate confusion instead of clarification. Thus, it is clear that a scale-dependency exists in ecological systems and every change in a certain scale brings with it changes in patterns and processes (Wiens, 1989). This consideration is at the basis of the landscape ecology that has as hallmark the understanding of patterns generated by processes at various scales (Urban et al., 1987). A landscape is a mosaic of patches and all ecosystems exhibit heterogeneity and patchiness on a broad range of scales. Patchiness in the distribution of resources is fundamental to the way organisms exploit their environment (Mangel & Clark, 1986; Pulliam, 1989). Environmental heterogeneity provides a diversity of resources leading to coexistence among competitors, that could not coexist in homogeneous environments (Horn & McArthur, 1972). What emerges is a landscape formed by a complex network of patches, both in space and time. This leads to a hierarchical structure organized into levels according to functional scale (Figure 1). Events at a given level have a characteristic natural frequency and a corresponding spatial scale.
Level 3
Level 2
Level 1 v
Figure 1: A generalized 3 levels hierarchical system. Thick arrows indicate strong interactions; broken arrows, weak interactions (from Urban et al., 1987 modified).
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Low-level events are smaller and faster than higher level events, that are instead larger and slower. Nevertheless, lower level units interact to generate higher level behaviors and units, which control those of lower levels, and such a mechanism permits the prediction of how external factors will alter an ecosystem (Urban et al., 1987; Levin, 1992). In the same way, a patch at a given scale has an internal structure that is the reflection of patchiness at finer scales, and the mosaic containing that patch has a structure that is determined by patchiness at broader scales (Kotliar & Wiens, 1990) and the responses of organisms to this hierarchy is not limited to a single level. This leads to the conclusion that there is not a single correct scale at which to view ecosystems (Levin, 1992) or understand the responses of organisms, above all because our ability to detect patterns is a function of both the extent and the grain (sensu O’Neill et al., 1986). The grain is the smallest scale beyond which an organism perceives its environment as homogenous; the extent is the largest scale of heterogeneity to which an organism responds (Figure 2; Kotliar & Wiens, 1990).
0.1 Perception limits
A
B
Grain
Grain
Scale
1
C
Extent
10
Grain
100
D Extent
Patch Structure
Extent
Grain
Range of Grain -Extent
Figure 2: Hierarchical patch structure and the grain-extent ranges for four types of organisms (A-D). For each organism, grain is restricted to scales greater than perception limits. For organisms A and B the scale of perception limits corresponds to grain. For organism C and D, the grain is set not by perception limits but by behavioral response (from Kotliar & Wiens, 1990 modified).
Grain and extent vary according to the type of organisms studied. Therefore, extent and grain define the upper and lower limits of resolution of a study (e.g. extent could be the 26
overall area of a study and the grain could be the size of the quadrat used by a field ecologist; Wiens, 1989) and any inferences beyond these two limits are not able to clarify the real patterns and processes leading to incorrect interpretations of the ecosystem functioning.
1.3 Seagrass Ecosystem: a Landscape Approach Seagrasses form unique, productive and highly diverse ecosystems worldwide (Hemminga & Duarte, 2000; Duffy, 2006; Orth et al., 2006), but unlike other taxonomic groups distributed across the globe, they exhibit low taxonomic diversity (~ 60 species). Despite this low species diversity, seagrasses successfully colonized all but the most polar seas (Hemminga & Duarte, 2000; den Hartog & Kuo, 2006). Globally, seagrasses are defined as “ecosystem engineers” (Bouma et al., 2009; Brun et al., 2009) since their patches provide physical structures with important consequences for ecological functions such as providing food, shelter and nursery for invertebrates and fish (Heck et al., 2003), and also supporting a high epiphytic biodiversity (Borowitzka et al., 2006). Seagrass meadows are important sources of detrital carbon, providing supply of organic matter also in food-limited environments (e.g. deep sea; Suchanek et al., 1985), exporting to other adjacent systems such as mangroves, salt marshes and coral reefs in tropical regions (Beck et al., 2001; Romero et al., 2006; Heck et al., 2008); in addition, the excess of the carbon produced may be buried within seagrass sediment, forming hotspots for carbon sequestration in the biosphere (Duarte et al., 2005). In this way, seagrass meadows provide worldwide services of high value, compared to other marine and terrestrial habitats (Costanza et al., 1997). While, as shown above, the landscape ecology principles have been applied for several decades to terrestrial ecology, only recently has the same principles been applied to seagrasses (Robbins & Bell, 1994; Bell et al., 2006). However, Kendrick et al. (2005) 27
showed that, even then, landscape dynamics have been generally described by mapping historical changes in seagrass distributions, but these changes were not associated with processes and rarely was an effort made to correlate spatial changes in seagrass landscape with environmental factors (Kendrick et al., 2005 and references therein). Kendrick et al. (2005) concluded that the descriptions of landscape modifications were made without studying the processes that drove those changes, even if many modifications could be due to human impacts. Thus, to better apply the landscape ecology principles, an understanding of the spatial heterogeneity and patch dynamics of seagrass is first required. Clonality (asexual reproduction) has been considered one of the main ways of seagrass to grow and expand, limiting the importance of seed production (Marbà & Duarte, 1998; Kendrick et al., 2005; Vermaat, 2009). In addition to these internal regulatory factors, abiotic factors such as nutrients (Lee et al., 2007), sedimentation, currents and waves (Fonseca & Bell, 1998; Bell et al., 1999; Frederiksen et al., 2004, Infantes et al., 2009) may also contribute to the spatial arrangement of the seagrass landscapes. At present, seagrasses have also to face human-induced changes in landscape patterns. In fact, many human activities such as boat anchoring (Francour, 1999; Milazzo et al., 2004; Montefalcone et al., 2006), dredging and other destructive fishing methods (Sanchez-Jerez et al., 1999), and eutrophication (Burkholder et al., 2007) may artificially create patches or gaps in the meadows, altering the natural formation and evolution of the landscape. Both the natural and the humaninduced changes in the physical structure of seagrasses affect the animal communities associated to seagrasses. Many studies examined how spatial arrangement and structural features of seagrasses affect the fauna living within, on, and above seagrass vegetation (Boström et al., 2006a for a review). What emerges from these studies, as pointed out by Boström et al. (2006a) and by Bell et al. (2006), is that variations in seagrass landscapes produce equal proportions of significant and non-significant effects (both positive and negative), suggesting that patchy seagrass meadows are not necessarily detrimental for 28
associated animals. For example, many authors reported that fragmented seagrass meadows support more decapods than unfragmented ones (Egglestone et al., 1998; Hovel & Lipcius, 2001, 2002) while others (Irlandi, 1994, 1995) reported higher densities of clams in continuous meadows. Even when a multi-scale approach is considered, patterns of distributions are not clearly evident. For example, Turner et al. (1999) found that infauna variations in the distribution of seagrass beds was better explained by the landscape analysis than by the patch-scale analysis, showing that landscape level effects on abundance were not necessarily related to effects observed at a smaller scale (Turner et al., 1999). The above evidences clearly show the need for a better understanding of the processes, operating at the various scales, to manage the services provided by seagrass ecosystems, even if they are complex, difficult to predict and relatively under-studied (Boström et al., 2006a).
1.4 Posidonia oceanica Ecosystem Of the 5 species of seagrasses occurring in the Mediterranean Sea, Posidonia oceanica (L.) Delile (Figure 3) is the only endemic species forming one of the most common and widespread meadows in the Mediterranean basin with a total extension estimated between 2.5 and 5.5 millions of hectares (Procaccini et al. 2003).
29
Leaves
Shoot
Vertical rhizome Sheaths Roots Horizontal rhizome
Figure 3: Posidonia oceanica representation (from the website http://takocito.over-blog.com modified).
It represents one of the most productive and complex shallow coastal ecosystem (Mazzella et al., 1992; Pergent et al., 1994; Buia et al., 2000), colonizing mostly soft bottoms, but even rocky substrata, with the exception of estuaries, where the input of fresh water and fine sediments is too high (Hemminga & Duarte, 2000; Zupo et al., 2006a; Giovannetti et al., 2008), along depth ranges from 1 to 40 m (when sufficient light is available) forming wide continuous meadows and supporting many associated species (both algal and animal) serving as nursery and feeding grounds (Mazzella et al., 1992). The importance of Posidonia systems is shown in its autotrophic production (150-3000 gDWm-2y-1), acting as a carbon sink (due also to deposition of carbon from the seston and retained in the sediment because of the reduced resuspension due to the canopy; Gacia et al., 2002), and oxygen release (Ott, 1980; Pergent, 1990; Pergent-Martini et al., 1994; Romero et al., 1994; Hemminga & Duarte, 2000; Cebrian & Duarte, 2001). It has been calculated that 1020% of this production (as leaf biomass) is exported from the system and transferred by waves and currents into adjacent systems (Zupo, 1993; Mateo & Romero, 1997; Cardona et al., 2007). On the contrary, belowground detritus (i.e. dead rhizome and roots) is not 30
exported and is accumulated within the meadow accounting for 25-35% of refractory material (Romero et al., 1994; Mateo & Romero, 1997; Cebrian & Duarte, 2001). Posidonia oceanica is characterized by long persistence (it is considered a “climax” species; Molinier & Picard, 1952; den Hartog, 1977), slow vegetative growth and sporadic sexual reproduction (Buia & Mazzella, 1991; Buia et al., 1992, Balestri & Vallerini, 2003; Ballesteros et al., 2005). The sexual reproduction is guaranteed by hermaphroditic inflorescences, and the formation of fruits and seeds (Mazzella et al., 1983). Sexual reproduction generally occurs at the end of autumn (from December to February, depending on the depth of the meadow stands), with fruit formation in March – April (Mazzella et al., 1983). Asexual reproduction is more frequent and a common colonization strategy. P. oceanica presents dimorphic rhizomes, consisting of two differentiated types: horizontal (plagiotropic) and vertical (orthotropic) rhizomes. Horizontal growth allows the colonization of new areas, expanding the meadow or increasing its density, while vertical growth allows the plant to avoid burial and reach the light. The leaves act as sediment traps, storing inorganic and organic particulate matter (Terrados & Duarte, 2000; Gacia & Duarte, 2001; Gobert et al., 2006). The progressive silting and the two types of rhizomes produce a typical structure forming terraces called “matte” consisting of the intertwining of various strata of rhizomes, roots, and sediment. Posidonia oceanica bed morphology is influenced by natural events and environmental processes such as storms, currents and sedimentation (Marbà & Duarte, 1997), geomorphology and nutrient distribution (Zupo et al., 2006; Giovannetti et al., 2008); depth (Duarte, 1991), and by anthropogenic activities such as trawling (Sanchez-Lizaso et al., 1990), coastal development (Leriche et al., 2006), boat anchoring (Francour et al., 1999; Milazzo et al., 2004; Montefalcone et al., 2006, 2008b), and pollution (PergentMartini & Pergent, 1995). Resulting from these disturbances, P. oceanica meadows can be patchy (i.e. a cluster of isolated patches, each completely surrounded by a different habitat 31
type), reticulate (non continuous beds intermixed with a different habitat type, such as bare sands) or continuous (Colantoni et al., 1982; Buia et al., 2000; Borg et al., 2005).
1.4.1
Posidonia oceanica Associated Assemblages
The high productivity of P. oceanica maintains a high biodiversity of organisms. It represents one of the most complex habitats of the Mediterranean, both from a functional and a taxonomical point of view (Mazzella et al., 1992). The structure of the plant creates a high, multi-dimensional habitat for organisms that exploit the plant itself for feeding, nursery for juveniles and shelter. The system could be divided into three main compartments: the leaf canopy, the rhizome layer and the sediment – matte layers. Each compartment requires specific adaptations of the associated benthic organisms, that are reflected in the complex food webs of the system (Mazzella et al., 1992; Zupo, 1993; Scipione et al., 1996; Zupo & Mazzocchi, 1998; Buia et al., 2000). Epiphyte Assemblages The leaves offer a suitable settlement surface for many species of epiphytes (both algal and animal), and a large literature describing these communities is available (Battiato et al., 1982; Cinelli et al., 1984; Mazzella & Ott, 1984; Casola et al., 1987; Mazzella et al., 1989; Fradà-Orestano et al., 1993; Cebrián et al., 1999; Zupo et al., 2001; Alcoverro et al., 2004; Templado & Luque, 2004). It is even possible to recognize a spatio–temporal succession of the leaf epiphytes according to the vertical age-gradient of blade tissues and time of exposure, regardless of season and depth. In basal portions of the leaf (about one week old), diatom coverage is dominant; in the middle portion of the leaf (up to about 100 days), an encrusting layer of brown and red macroalgae is observed; toward the distal portion (which represents the oldest part of the leaf, reaching in selected leaves and in some seasons an age of 300 days), an upright macroalgal layer is observed, growing on the 32
earlier assemblages (Wittmann et al., 1981; Mazzella et al., 1992; Buia et al.,1989, 2000; Cebrián et al., 1999; Alcoverro et al., 2004). Ballesteros (1987) recognized two different phases in the structural changes of these assemblages: a settling phase, after the leaf fall of the oldest (external) leaves in October, where different shoots have a rather different species composition, and a colonization phase, where there is a progressive similarity in the quali- quantitative composition of epiphytes among shoots. The epiphytic algal flora, because of the limited life span of the leaves, in some seasons is mainly composed of ephemeral algae. The highest cover is due to encrusting Corallinaceae (Pneophyllum and Hydrolithon spp.), the most obiquitous epiphytes of seagrasses, and to the brown alga Myrionema orbicularea. In addition to the epiphytes on the leaves, there is a flora associated with the rhizome, for which a conspicuous algal assemblage has been observed and considered numerically more abundant than the leaf assemblages (Boudouresque, 1968; Boudouresque et al., 1979). While leaf communities have been widely studied and exhibit unique characteristics, rhizome communities received little attention since a first accurate description (Boudouresque, 1968, 1974; Boudouresque et al., 1979, 1981; Piazzi et al., 2002) and they showed a structure similar to those of other sciaphilous subtidal Mediterranean phytocoenoses (Panayotidis, 1980) since canopy reduces the light penetration and the hydrodynamic forces. Rhizomes are long-lived structures that allow for colonization by perennial species without any characteristic species and mainly of the genus Peyssonnelia and Ceramium (Rhodophyta), Halopteris and Dictyopteris (Fucophyceae), Flabellia and Cladophora (Chlorophyta). Faunal Assemblages High animal richness has been demonstrated in Posidonia oceanica meadows with respect to bare sediments, but even to shallow hard-substratum biotopes (Mazzella et al., 1989, 1992; Sarda, 1991; Gambi et al., 1992; Sanchez-Jerez et al., 1999). Again, each compartment of the plant shows various adaptations of associated organisms. Some sessile 33
species appear to be preferentially and almost exclusively recorded from P. oceanica leaves, for example the bryozoan Electra posidoniae and the hydroid Sertularia perpusilla. Vagile fauna of the leaf layer have been the most intensively studied in P. oceanica (Mazzella et al., 1989; Gambi et al., 1992; Scipione & Fresi, 1984; Scipione et al., 1996; Sanchez-Jerez et al., 1999; Barberá-Cebrián et al., 2002) and it is evident that molluscs, polychaetes and crustaceans (mainly amphipods and decapods) are the most abundant and diverse taxa. It is evident, from those studies, that a depth gradient exists, for which shallow (1 – 5 m depth) and deep (15 – 30 m depth) communities are recognizable, the latter assemblage comprised of a mix of species including those from neighbouring biotopes (Mazzella et al., 1989). A temporal succession in the distribution of the major taxa is also evident. In fact, the dominant taxa (mainly herbivores depending on leaf epiphytes) change according to the season in the shallower meadows. Mazzella et al., (1989) found different dominant species in spring and in autumn (the mollusc Columbella rustica, the isopod Munna petiti, the amphipod Amphithoe helleri and the echinoderm Psammechinus microtuberculatus in spring, while in autumn the genus Gibbula with the three main species and the amphipod Hyale shmidti). Such a pattern was attributed to strong hydrodynamic movements of this shallow sector of the meadows that selects a community characterized by a low number of species and individuals (Mazzella et al., 1989, Gambi et al., 1992). The intermediate communities (5 – 10 m) seem to be more homogenous in the two seasons, better structured (stable communities) and persistent in time. As pointed out by Stoner & Lewis (1985), both the low variability and the high density (but also the lower hydrodynamic forces) of the meadow can influence the structure of the motile community. The deeper communities are again variable in the two seasons with molluscs (Bittium sp., Rissoa sp. and Jujubinus sp.) and the amphipod Gitana sarsi dominant in spring, while in autumn the dominant taxa are amphipods (Ischyrocerus inexpectatus, Phtisica marina and Lembos rubomaculatus), molluscs (Turboella radiata 34
and Lissopecten hyalinus) and the echinoderm Asterina pancerii. This deep community is characterized by a high number of species but it is probably enriched by many species from the surrounding soft bottoms (Ledoyer, 1968), since the P. oceanica meadow at that depth has a very low shoot density. The rhizome and the matte-sediment layers are relatively poorly studied although they are characterized by an even higher degree of diversity and abundance (Harmelin, 1964; Novak, 1982, 1989; Giangrande, 1985; Pansini & Pronzato, 1985; Chimenz et al., 1989; Danovaro et al., 2002; Borg et al., 2006; Covazzi Harriague et al., 2006). Rhizome epifauna is exposed to a more stable environment than those of the leaves (Pansini & Pronzato, 1985; Mazzella et al., 1992). Echinoderms are important organisms because of the sedimentary nature of this compartment; holothurians play, in fact, an important role in the reworking of surface sediments (Zupi & Fresi, 1984). A new ecological group of organisms has been described as strictly associated to rhizomes, boring inside the sheaths (bases of dead leaves persisting along the rhizome; Figure 4a) that represent the major component of the rhizomes.
b)
a)
d)
c)
Figure 4: a) example of two sheaths with borer polychaetes inside tunnels; b) Lysidice collaris; c) Lysidice ninetta; d) Nematonereis unicornis.
Sheaths overlap along the rhizome enhancing the colonizing surface available for the organisms and favoring the accumulation of sediment (Pronzato & Belloni, 1981; Guidetti, 2000). The organisms exploiting this resource are a limnorid isopod (Limnoria mazzellae; 35
Cookson & Lorenti, 2001), a family of isopods previously known only from Australian (Brearly & Walker, 1993, 1995, 1996) and Caribbean seagrass species (van Tussenbroek & Brearly, 1998), and three species of eunicid polychaetes (Lysidice collaris Grube, 1870 Figure 4b, L. ninetta Audin and Milne Edwards, 1833 Figure 4c and Nematonereis unicornis Grube, 1840 Figure 4d; Guidetti et al., 1997; Gambi, 2000, 2002; Guidetti, 2000; Gambi & Cafiero, 2001; Gambi et al., 2005) described as borers for the first time in P. oceanica but also reported by Gambi et al. (2003) and Vasapollo et al. (2008) for Thalassia testudinum (Banks ex König) off the Mexican and Belizian coasts, and recently recorded also on Thalassia hemprichii of the Eastern coast of Africa (Gambi & Lorenti, 2009). Lysidice ninetta is widespread in warm and temperate waters (George & HartmanSchroeder, 1985; Cantone, 1993; Castriota et al., 2003) while L. collaris is considered a lessepsian migrant in the Mediterranean (Ben-Eliahu, 1972; Martin, 1987). Both species of Lysidice were already well known as borers in shallow and deep calcareous algae (Martin, 1987; Cantone, 1993). Nematonereis unicornis is a cosmopolitan species showing a wider ecological distribution, occurring also in soft bottom assemblages (George & HartmanSchroeder, 1985; Cantone, 1993). The three species burrow sinuous galleries by removing the plant tissue partly utilized as food (Gambi et al., 2000; Guidetti, 2000; Gambi, 2002). This capability is due to both a quite complex maxillary apparatus (Fauchald & Jumars, 1979) and the presence of cellulose enzyme activity which may suggest that these species are potentially capable of digesting sheath detritus tissues (Cigliano et al., 2003). These polychaetes represent a particular guild of detritivores exploiting a low palatable and refractory resource, favoring the microbial dissolution of the dead sheaths tissue and creating a link between detritus and higher levels in the food web. Micro and meiofauna are poorly known although they are considered important in terms of both biomass and as trophic links between epiphytic organisms and macrofaunal predators (Harmelin, 1964; Novak, 1982, 1989; Danovaro et al., 2002; Covazzi Arriague et al., 36
2006). Both the living and dead matte support high species diversity and abundances. Borg et al. (2005) showed that the physical complexity of dead mattes plays an important role in promoting diversity and they found that many of these species are generally rare, thus increasing the conservation role of this particular habitat. This could be possible thanks to the fact that dead matte has a spongy texture, and a less compact structure that permits many burrowing organisms to form galleries, ventilating the inner part of the matte and creating an environment which is rich in organic compounds and oxygen. This in turn favours bacterial growth and they in turn convert the refractory detritus into a more palatable and calorific resource for detritivores organisms that, in fact, are the predominant organisms of the matte (Borg et al., 2006).
1.4.2
Patterns of Spatial Variability in Posidonia oceanica
Ecosystem The application of new technical and statistical tools in the analysis of spatial patterns of populations over different spatial scales, has helped the investigation of a wide range of natural phenomena (Perry et al., 2002; Underwood & Chapman, 1998a,b; Underwood et al., 2000; Benedetti-Cecchi, 2001; Fraschetti et al., 2005). In coastal systems, many authors have highlighted the importance from small- (10s to 100s cm) to middle-scale (10s to 100s m) variation in the distribution and abundance of populations across a wide range of habitats and organisms; the finding that small-scale processes are as important as largescale processes in creating patterns of variation lead to the reconsideration of many phenomena only partially or even not at all understood in the past (Underwood & Chapman, 1996,1998; Thrush et al., 1994, 1997a,b; Underwood et al., 2000; BenedettiCecchi, 2001; Fraschetti et al., 2005; Vaselli et al., 2008).
37
A recent review by Fraschetti et al. (2005) gives the idea of how the problem of scaling up local processes to generate large-scale patterns has been investigated so far in marine habitats. They found that most papers focused on rocky intertidal habitats and that nearly all population and assemblages showed a patchy distribution at the small spatial scale and patterns common to most organisms emerged. Seagrasses, an important component of the coastal system, and their associated assemblages have been rarely investigated so far from the point of view of the horizontal spatial distribution compared to the rocky substrates (Robbins & Bell, 1994; Balestri et al., 2003; Piazzi et al., 2004; Bell et al., 2006; Boström et al., 2006a; Pardi et al., 2006; Zupo et al., 2006a,b; Montefalcone et al., 2008a). The importance of understanding the processes that link local to regional scales in Posidonia oceanica meadows is of great importance when protection measures must be taken. However, there is great confusion surrounding this aspect of P. oceanica. In fact, investigations on the health status of Posidonia oceanica meadows along Mediterranean Marine Protected Areas (MPAs) found no widespread P. oceanica decline (GonzálezCorrea et al., 2007), suggesting that the regressive status of many meadows was the result of numerous local anthropogenic impacts. So the establishment of many MPAs could mitigate the regression of meadows recorded in the last decades. But as pointed out by Montefalcone et al. (2009) the establishment of only local MPAs could not reduce the impact at the regional scale because the risks for P. oceanica originate mainly from outside the MPAs boundaries. These two main papers clearly show how the possible protection of the P. oceanica pass through the understanding of the principal patterns governing the functions of the system along several spatial and temporal scale processes arguing strongly in favor of a more complete understanding of the structural and functional aspects of seagrass biodiversity (Boström et al., 2006b). Therefore, since effective management of seagrass habitats depends on knowledge of the processes that govern the spatial and temporal properties of seagrass coverage (Fonseca et al., 2000; Borg et al., 2005), there is 38
an urgent need to study the ecological consequences of the physical alteration of seagrass habitats even at small local scales, to attempt to recognize patterns that could scale up to generate large scale patterns. But given the lack of data on the consequences of fragmentation of seagrass habitat (mainly in P. oceanica), a useful starting point to address this issue could be the examination of the variability of between-/within-bed architectural characteristics (e.g. number of leaves, leaf length and width, plant biomass, shoot density) of different natural bed types compared with impacted ones. Moreover, the spatial structures of P. oceanica beds, and the scales at which they are studied, are crucial elements to evaluate both the expansion/regression of the meadows and the associated communities (Gambi et al., 2006). Most investigations have focused on variations on a geographical scale or within meadows across depth (e.g. shallow vs. deep stands) and few studies have examined variations within a meadow at a given depth (Balestri et al., 2003; Zupo et al, 2006a,b; Montefalcone et al., 2008a), thus, the knowledge about horizontal scales of variability in spatial distribution of plant (and associated communities) features is scarce. This topic has remained quite unexplored in the Mediterranean Sea, except for a few studies on epiphytes of the Posidonia leaves (Piazzi et al., 2004; Pardi et al., 2006; Balata et al., 2007), despite the fact that the ecological consequences of physical alteration of P. oceanica beds are of major interest for conservation and management strategies (Borg et al., 2005; Vega Fernandez et al., 2005). These studies reported high variability of epiphyte assemblages at smaller scales (10 – 100s cm), or, when present, also at larger scale of 1000s of m (Piazzi et al., 2004; Pardi et al., 2006; Balata et al., 2007). The large variability at smaller spatial scales suggests that P. oceanica meadows, despite their apparent homogeneity at wider scales, are systems characterized by a patchy distribution of epiphytic organisms. This variability could be explained by differences in shoot density and in characteristics of the canopy affecting light intensity and water movements (Gambi et al., 1989). 39
Furthermore, recent findings demonstrate that P. oceanica beds are characterized by high patchiness at medium and small spatial scales (Balestri et al., 2003; Kendrick et al., 2005; Zupo et al., 2006a) so there is the risk that previous studies on large spatial scales had generalized the variability resulting from small scale patchiness. It has been demonstrated, in fact, that the variability at small scale is higher than at larger scale even for shoot density and other plant characteristics, and thus an inadequate sampling procedure may underestimate such variability and connected ecological inferences (Zupo et al., 2006a). This variation has been correlated to differences in bottom morphology that lead to the formation of onion-like structures represented by radial increases or decreases in shoot density, mainly due to lowering or elevation of the seafloor, respectively (Zupo et al., 2006a,b). Variation in bed structure, in shoot density and in plant morphology, both in space and time, could alter the structure and function of associated communities. Few studies focused on the relationships between P. oceanica features and faunal distribution and abundance (while a huge amount of works is available, e.g., for seagrass species of the genus Zostera spp.; Woods & Schiel, 1997; Attrill et al.,2000; Hovel & Lipcius, 2001, 2002; Bowden et al., 2001; Lee et al., 2001; Healy & Hovel, 2004; Hovel & Fonseca, 2005), although a large literature is available on the distribution of vagile fauna in P. oceanica in the Mediterranean Sea at different (vertical) spatial and temporal scales (Scipione & Fresi, 1984; Mazzella et al., 1989; Gambi et al., 1992; Scipione et al., 1996; Buia et al., 2000), or in comparing assemblages in adjacent unvegetated sediments (Sanchez-Jerez et al., 1999; Barberá-Cebrián et al., 2002). While it has been demonstrated that seagrass beds contain greater faunal diversity and abundance than surrounding bare sediments, and that there are correlations between increased faunal abundance and diversity and some measures of seagrass structural complexity (Bowden et al., 2001; but see also Barberá-Cebrián et al., 2002 for a contradictory result on mysids), these phenomena have not been related to the potentially confounding effects of patch size. For 40
this purpose, P. oceanica seems to represent the ideal candidate (Borg et al., 2005) because, unlike other seagrasses, it does not undergo large seasonal changes in spatial coverage or shoot density. Therefore, the confounding effects of temporal changes in seagrass coverage and shoot density are not included in comparison studies of different bed types of P. oceanica. So, data on the architectural characteristics of different bed types collected over appropriate spatial scales will contribute to our understanding of how seagrass patchiness (heterogeneity) may affect the diversity and the distribution of the associated biotic assemblages. A deep knowledge of spatial variability patterns in macrobenthic assemblages is relevant to characterize properly one of the major sources of biotic diversity in natural environments such as Posidonia oceanica meadows. Moreover, this information is extremely useful for developing strategies of management and conservation, as well as to advise suitable guidelines for periodic monitoring programs (Underwood, 1997), since the description of the distributional patterns at multiple spatial scales and the identification of the most relevant ones are needed to formulate possible explanations about ecological processes, or anthropic impacts on ecosystem structures (Underwood & Chapman, 1996, 1998; Underwood et al., 2000).
41
2. Objectives The aims of this study were to investigate the spatial variability at different hierarchical scales of Posidonia shoot density and some plant features (morphometric variables), as well as of some associated animal components, such as borer organisms and motile invertebrate fauna in two Posidonia meadows off the coast of the island of Ischia (Gulf of Naples, Tyrrhenian Sea, Italy), submitted to different environmental conditions and human impact and disturbances. We also intended to test whether the scales of spatial variability were similar in time, considering the two main vegetative growth seasons of the plant: late spring-summer (maximum growth and canopy development) and late autumn- winter (minimum growth and canopy development). Main questions addressed were: Was the spatial scale of variability the same among the various plant features examined (density and morphometric variables), and the borer organisms (as borers are more strictly associated to single shoots)? Was the spatial scale of variability of the motile invertebrates, related to that of the plant features at similar spatial hierarchical levels? And, therefore, were the scales studied appropriate to highlight plant-animal relationships? Was the spatial scale of variability of the plant features the same comparing the two main vegetative growth phases of the plants (late spring-summer vs late autumn-winter) Was the spatial scale of variability of the associated animal communities (motile invertebrates and borer organisms) changing in time, in relation to changes in the vegetative features of the plant? Were the spatial and temporal scales of variability the same in meadows submitted to different environmental conditions and human impact?
42
Was the observed scale of variability, both for space and time, suitable for the use of some selected plant features and animal organisms as biological indicators of the health status of the meadows and of the water quality? Our hypotheses were: -
Shoot density and plant features showing different temporal dynamics have different scales of spatial variability: higher dynamics (or shorter life-span) = less spatial variability, higher temporal differences.
-
Associated animal organisms show spatial and temporal variability patterns similar and comparable to that of shoot density and other plant features, at least considering the same hierarchical scales.
-
Spatial variability also depends on general ecological conditions of the meadows, and we expect that more fragmented and impacted beds should show higher patchiness, and therefore higher small-scale variability, both for shoot density and plant features as well as for the associated faunal components, than continuous and spatially more homogeneous beds, also thriving in more pristine and stable environmental conditions.
-
Suitable biological descriptors of the meadows’ ecological conditions and health status (water quality) should show higher differences between locations than those recorded at smaller spatial scales, and that could be related to actual differences in pristine vs impacted conditions.
-
Differences in the various descriptors between the two studied beds could be highlighted both as actual and absolute values (a variable can be consistently and significantly higher or lower in pristine vs impacted beds) or as a different pattern of spatial and temporal variability (e.g., a variable can show higher or lower patchiness and smaller scale variability (in space and time) in pristine vs impacted beds). 43
The null hypothesis tested was, therefore, that there were no differences in variability patterns at any of the spatial and temporal scales investigated in Posidonia oceanica shoot density, morphometric features and biomass. Similarly, there were no differences in abundances and diversity of macro-invertebrate assemblages at any of the spatial and temporal scales studied.
44
3. Material & Methods 3.1
Study Area
The fieldwork for this project was conducted around the Island of Ischia (Tyrrhenian Sea, Gulf of Naples, Italy; Figure 5).
N
N
Punta Vico
Lacco Ameno Casamicciola
LA
LA
N
500 m
N
Sc
Ischia Sc 2000 m
Capo Grosso
Punta San Pancrazio 500 m
Figure 5: Location of the sampling areas in P. oceanica meadows off Lacco Ameno and Scarrupata (Gulf of Naples, Italy). LA: Lacco Ameno meadow; Sc: Scarrupata meadow.
The Posidonia oceanica meadows around this island have been widely studied since the first works by Funk (1927) and Parenzan (1956). Following these studies, more detailed investigations of the meadows have been produced, particularly since 1975 carried out by the Laboratory of Benthic Ecology (today Functional and Evolutionary Ecology Laboratory – Research Group of Benthic Ecology, Stazione Zoologica “A. Dorhn”) (see the synthesis in Gambi & Buia, 2003). The distribution of the Posidonia meadows around the island has been mapped by Colantoni et al. (1982). The meadows are almost continuous around the island settling most on sand and more rarely on rocks, and from a depths of 0.5 m to 39 m, covering an area of ca. 17 Km2 (Colantoni et al., 1982). Different hydrodynamic conditions characterize the upper limits of the meadows. In
northern
meadows around Lacco Ameno and Castello Aragonese, which are less exposed to wave action, the upper limit almost reach the surface about 0.5 m, while in the southern and 45
eastern parts (e.g. Cava dell’Isola and Scarrupata), more exposed to the main currents and wave action, the upper limits are around 8 to 15 m depth. I decided to sample in two different meadows (see below the description of the two meadows) to study the horizontal spatial variations and the relationships between plant features and associated motile macro-invertebrates.
3.1.1
Lacco Ameno Meadow
The Lacco Ameno meadow is situated between the sheltered bay of Monte Vico continuing to the town of Lacco Ameno and after a small canyon on until the town of Casamicciola. Previous studies on this meadow have been limited to the “pocket-beach” called “Spiaggia delle monache” between the Monte Vico and Lacco Ameno. The area is protected from the main currents and wave action, and due to this reason the meadow starts from 0.5 m reaching 28 – 32 m depth. The granulometry is variable along the depth gradient: from 1 to 6 m depth the dominance of medium (0.250 mm) and fine sand (0.125 mm) is evident, the latter becoming dominant at 10 m depth; at around 15 and 25 m the silty fractions increase (0.063 mm and 5 cm without sheath; and adult, of length > 5 cm with sheath. For both meadows considered here, the biometric characteristics have been measured on 10 vertical shoots per each quadrat, with a total of 2,880 shoots examined (two meadows sampled in two seasons). The main measurements made for this work were: the number of leaves per shoot; their length; their width; sheaths length; number of each leaf category; the leaf surface per shoot; leaf biomass; sheaths biomass; and the two derived indices Leaf Area Index per square metre and Leaf Standing Crop per square metre (Buia et al., 2004; Pergent-Martini et al., 2005). Measurements of leaves were made using a ruler. The length of the leaves was measured from the apex to the attachment of the rhizome, except for adult leaves where the measurement was made from the apex to the attachment of the sheath, and the sheaths measured separately. The width was measured in the central part of the blade for all the leaf types. From these “synthetic descriptors” it was possible to calculate some derived descriptors. The Leaf Surface per shoot (cm2 shoot-1) was 52
calculated as the sum of the single leaf area of only one side per leaf (juvenile leaves were excluded from the count). The Leaf Area Index (L.A.I. m2 m-2) was calculated as the leaf surface per shoot multiplied by the shoot density per square metre. The number of leaves in each age class was also counted (i.e., number of juveniles, intermediates and adults). The biomass of the leaves per shoot was measured as the sum of the biomass of all the leaves and then referred to grams of dry weight (gDWshoot-1). In the same way, the biomass of the sheaths per shoot was calculated. To obtain the dry weight, the leaves and the sheaths were put in the oven at 60 °C for 48 h and then weigthed. From these data the Leaf Standing Crop was calculated as leaf biomass per shoot multiplied by the shoot density per square metre (gDWm-2). As mentioned above, the single shoot represented the functional unit to describe a meadow, and this lead to the consideration that the single shoot could be used as replicate for a quadrat. This means that the minimum spatial scale we can evaluate for plant morphometry is that of the few centimetres. By contrast, the derived parametres (LAI and LSC) should be treated as the shoot density, since they refer to the whole quadrat (i.e., representing the LAI or LSC of a single shoot a nonsense). The average for each single spatial scale has been calculated as that of the shoot density mentioned above.
3.2.2
Motile Macro-invertebrate Assemblages
Borer Polychaetes The borer polychaetes are represented mainly by three species of family Eunicidae: Lysidice collaris, L. ninetta and Nematonereis unicornis. The study of this particular guild of organism living inside the sheaths, is made by examining the orthotopic (vertical) shoots collected and then searching for the animals inside the individual sheaths. In other works, the search for these animals has been carried out by examining between 20 to 80 shoots 53
(Gambi, 2000, 2002), but given the complexity of the sampling design and the huge quantity of data collected, I decided to sample the minimum number of 20 shoots per quadrat (for a total of 5,760 shoots examined considering both meadows and seasons). For each shoot, the presence of the species found and the number of specimens were noted. The presence of polychaete borers inside the sheaths can be expressed by means of an index quantifying their frequency (Gambi, 2000): the Index of Borers (IB), namely the percentage of rhizomes containing living borers with respect to the total number of rhizome examined (in this case 20 per quadrat). The IB has been calculated for all three species altogether (i.e., without discriminating between the three species) and for each single species alone. Other Taxa of Motile Invertebrates The sampling of motile fauna associated to seagrasses in general, and in P. oceanica in particular, is quite complicated, and this is mainly due to operational difficulties of sampling through an intricate canopy and on a definite portion of the substratum, managing to capture a representative number of species. The main instruments to sample in Posidonia meadows are the hand-towed net, the air-lift (a suction device) and, less used, the corer (an enclosure device). The hand-towed net is mainly used to collect motile fauna of the leaf layer, but since it is a semi-quantitative instrument, used on large areas (about 21 m2 sampled with 60 strokes; see Vinci & Russo, 1991), it was not appropriate for the sampling design applied in this study. The suction device is a quantitative instrument useful for collecting samples both from the rhizome and leaf layer. Finally, the corer gives the opportunity to collect both the aboveground component of the plant (leaf and rhizome layer) and the belowground part (rhizome/root covered by sediment), but due to its destructive nature, and sine the sorting of samples obtained by means of this device is highly time consuming, it was decided not to use this sampling device in this study.
54
The suction device is mainly used for soft bottom sampling, but very often it was also used for Posidonia (e.g. Scipione et al., 1984; Giangrande, 1985; Gambi et al., 1995; Russo & Terlizzi, 1998). The technical specification of this instrument is not reported here but is available in Russo et al.(1986) even if for our study we used a modified gear described in Giangrande et al.(1986) and Buia et al. (2004). While Borg et al. (2002) criticized this technique, arguing on the difficulty in sampling the deeper layer of the P. oceanica root/rhizome matrix, and that the fauna collected was usually damaged because of the turbulence generated by air and water in the collection bag, it is important to understand that this sampling technique is the only quantitative sampler with no or very little impact on the meadow. One of the drawbacks of the suction device is that it is less efficient than the hand-towed net for sampling the leaf layer fauna and may undersample the small swimming macroinvertebrates. This is because previous studies have used 1 x 1 m quadrats. These large quadrat sizes enabled amphipods and decapods to escape from the action of the air-lift and from the quadrat. To avoid this problem, I used a smaller quadrat of 40 x 40 cm to relate the shoot density (as a measure of the complexity of the system) to the assemblages composition and abundance of the fauna. Moreover, to avoid swimming taxa escaping, a net (1 mm mesh) was attached around the quadrat edge at the centre of which there was the hole where the air-lift opening was placed. In the field, the air-lift was used in the following way: once the quadrat was placed randomly on the bottom, the instrument was activated and it started to suck in the samples and collected in a bag (400 μm mesh) linked at the opposite side of the air-lift. The operator made periodic rhythmic upward movements of the sampler to keep the mouth of the device free from the leaves. Collecting time was standardised to two minutes in each quadrat this being a compromise on the sampling time and the amount of air available in the associated air tank. Two minutes at each quadrat enabled me to sample three quadrats per station during a single dive. 55
The macro-invertebrate air-lift samples examined here correspond to only one date for each season per meadow (i.e., May and November-December, for Lacco Ameno; and June and December-January, for Scarrupata) for a total of 48 samples (for both meadows in both seasons). The samples collected were brought to the laboratory and fixed with 4% buffered seawater formalin. When necessary, Rose Bengal solution, diluted in 4% buffered formalin (0.5 g L1
) was added to the samples, particularly those with a large amount of leaf and biogenic
detritus in the collection bag. Before sorting, each sample was collected in a 315 μm sieve and washed with fresh water to eliminate the formalin and the excess of Rose Bengal. Under the stereomicroscope the individuals were sorted at the level of high taxonomical grouping (Phyla, Classes and Orders). Only some selected taxa of invertebrates were classified at lower taxonomical levels (gastropods at species, polychaetes at family and amphipods at genus level). The rest of the animals were not classified further than general group.
3.4
Statistical Analysis
Hierarchical, nested designs are useful tools to estimate the proportion of variability associated with each examined scale and to identify the most relevant spatial scale to describe a particular pattern or distribution (Underwood, 1997). In these designs smallscale sampling units are nested within larger-scaled ones, allowing unconfounded statistical comparisons among each spatial scale. Basically, the factor B is nested in A if each level of B is present in a single level of A. Nested designs enable analysis of patterns and processes at multiple spatial and temporal scales simultaneously because they allow hypothesis testing in the form: nesting factor A has an effect over and above that of nested
56
factor B, offering a solution to problems of spatial and temporal confounding in hypothesis-testing. Nested designs have been successfully used to investigate populations and assemblages across a wide range of marine habitats and organisms. Most studies have been focused on intertidal and subtidal rocky shores (e.g., Underwood & Chapman, 1996; BenedettiCecchi, 2001; Fraschetti et al., 2001, 2005; Chapman & Underwood, 2008). There have been a few studies on Posidonia oceanica following similar designs (e.g., Balestri et al., 2003; Borg et al., 2005; Montefalcone et al., 2008a). One of the main problems in sampling design is to obtain the correct replication both in space and in time. Furthermore, the sampling design must be appropriate to extrapolate most possible information about samples whose natural variation is large (Underwood, 1992), paying particular attention to spatial variation (different results of the same ecological process in different areas), temporal variation (different patterns of the biological process in different seasons), and complex interactions between both space and time. Without a correct spatial replication one could reach wrong conclusions that does not correspond to the natural variation. Thus, I tried to impose a series of factors to avoid the problem of pseudoreplication or confused replication to obtain data with respect to the null hypothesis (Hurlbert, 1984; Underwood, 1997). To cope with the problem of spatial and temporal variability, I decided to use both univariate and multivariate analysis as follows.
3.3.1
Univariate Analysis
For univariate analysis of data the Analysis of Variance (ANOVA) has been used. This analysis permits the analysis of complex experiments that include several factors and their interactions. In particular, I used mixed models of ANOVA with fixed and random factors. 57
Whether a factor is fixed or random depends on how the levels of the factors are chosen for the experiment, and this is specified by the hypothesis (Benedetti-Cecchi, 2004). Thus, a factor is fixed when all the levels that are relevant for a test of the hypothesis are included in the experiment and are fixed by the researcher. A factor is random if the levels included in the experiment are a random sample of a theoretically infinite number of possible levels. In this study, the fixed factors are locations and seasons, while random factors are sites, stations, plots, quadrates and dates because they represent only a part of the entire meadow or a temporal interval of a specific season. Beside these substantial differences in the definition of factors, there are marked differences in the information that the two classes of factors provide to the researcher that could lead to different conclusions. In fact, in the case of fixed factors, the effects of the treatment are measured without error and this is a consequence of having all the levels of the factor included in the experiment. In the case of random factors, effects are measured with error and this happens because in the experiment are included only some samples of possible levels, so that there is a variance associated with the effect of the treatment. Interpretation of results from random factors can be generalized to entire distribution from which the experimental levels have been chosen, and this is an important property of random factors (Underwood, 1997; Benedetti-Cecchi, 2004). The model used for this work is represented in the following Table 2a:
58
Table 2: Model of ANOVA and PERMANOVA (a), Expected Mean Square (b), denominators used in the calculation of F – ratio in ANOVA and PERMANOVA. a) Location Season Location x Season Date Site Station Plot Quadrates (*)
(fixed, 2 levels) (fixed, 2 levels) (interaction between the previuos two) (random and nested in LS, 2 levels) (random and nested in Date (LS), 3 levels) (random and nested in Si(D(LS)), 2 levels) (random and nested in St(Si(D(LS))), 3 levels) (random and nested in Pl(St(Si(D(LS)))), 6 levels)
=L =S = LS = D(LS) = Si(D(LS)) = St(Si(D(LS))) = Pl(St(Si(D(LS)))) = Qu(Pl(St(Si(D(LS)))))
(*) considered only in univariate analyses for plant parameters
b) Source L S LS D(LS) Si(D(LS)) St(Si(D(LS))) Pl(St(Si(D(LS)))) Residual
= = = = = = = =
1 2 12 3(12) 4(123) 5(1234) 6(12345) R
R R R R R R R R
+ + + + + + +
Terms included in the EMS 6(12345) + 5(1234) + 4(123) 6(12345) + 5(1234) + 4(123) 6(12345) + 5(1234) + 4(123) 6(12345) + 5(1234) + 4(123) 6(12345) + 5(1234) + 4(123) 6(12345) + 5(1234) 6(12345)
+ + + +
3(12) + 1 3(12) + 2 3(12) + 12 3(12)
c) Source denom. MS in F-ratio L D(LS) S D(LS) LS D(LS) D(LS) Si(D(LS)) Si(D(LS)) St(Si(D(LS))) St(Si(D(LS))) Pl(St(Si(D(LS)))) Pl(St(Si(D(LS)))) R
Table 2b shows the Expected Mean Square (EMS) corresponding to the progressive sum of variances from the lower to the higher factor (i.e. from plot (or quadrat) to location) used to evaluate the mean square of each factor. Table 2c shows the denominator used in the calculation of the F-ratio. The F-ratio is obtained by the formula:
F
MS A MS B ( A)
where A and B (nested in A) are hypothetical factors of a hypothetical design. It represents a statistic test used to determine whether the variances in two independent samples are equal. For the null hypothesis when the two mean squares are equal (i.e. there are not differences between samples) the ratio is 1, otherwise, for any alternative to the null hypothesis, the ratio is greater than 1 (see Underwood, 1997). To use the analysis of variance in the correct way, some important assumptions must be given: 59
a) Data independence. This is an important problem in the sampling designs with relevant consequences in the interpretation of the results (Hurlbert, 1984; Underwood, 1997). It is difficult to evaluate whether or not data are independent, but the best way to avoid this problem is to plan carefully the sampling design and its execution. In this work, I randomly collected samples to avoid the dependence of data and any possible statistical correlation. b) Homogeneity of Variance. This is perhaps the most important assumption when using ANOVA. Starting from the null hypothesis, the variables should not have any variability in their means both within and between samples. To test for homogeneity of variance as a priori evaluation, the Cochran’s C test is the most used and it is represented by the following formula (Underwood, 1997): C
major ( si2 ) a
s i 1
2 i
where si2 are the samples estimates of variances of the a populations sampled. When Cochran test is significant there is evidence of a serious potential problem for any ensuing analysis of variance. Heterogeneous variance lead to excessive Type I error (reject the null hypothesis when it is true, creating a “false positive”) in analysis of balanced samples, so non-significant results are perfectly acceptable. To avoid this problem, it is possible to transform the data to homogenize the variance. Where heterogeneity of variance for a certain variable was found, data were transformed using ln(x+1) transformation (for plant features), arcsin√(x/100) (for IB) and either √, or √√, or ln(x+1) (for faunal abundance) (Underwood, 1997). When none of these transformations was able to homogenize the variance, no transformations were possible according to Underwood (1997), because in large experiments analysis of variance is robust to departures from the assumptions; in other words, the validity of the analysis is not affected much by violation of the 60
assumptions. But to be sure to avoid the Type I error, even if our samples are large and balanced, we decided to consider a more stringent criterion of α = 0.01 to reject the null hypothesis (Underwood, 1997). c) Normality of data in each distribution. Although it is necessary for the construction of the statistical test, it is not so important because ANOVA is already quite robust to non-normality, in other words, its outcome and interpretation are not affected by the data being non-normal (Underwood, 1997). This is the case when experiments are large and/or samples of each treatment are large and balanced.
3.4.2
Multivariate Analysis
Ecologist often need to test hypothesis concerning the effects of experimental factors on whole assemblages of species at once (Anderson, 2001a). Permutational Multivariate Analysis of Variance (PERMANOVA; Anderson, 2001a, 2005) was preferred to the classical Analysis of Similarities (ANOSIM; Clarke 1988) because this latter is not suitable for sampling designs more complex than one- or two-factors. This present work has more than 6 levels and different interactions between the levels (Anderson, 2001a; Benedetti-Cecchi, 2004). Both analysis are based on the comparison of groups of samples using Bray-Curtis (or any other similarity/dissimilarity index) and test of hypothesis are based on permutations. This is because of the nature of multivariate analysis, the univariate tabled p-values cannot be used because the variables are not independent, and even the F-statistic of classical ANOVA is not distributed like Fisher’s Fratio under the null hypothesis (for a detailed explanation see Anderson, 2001a). In this case the p-value is calculated by means of permutations of the observations (i.e., the shuffling of the observations among the different groups repeated for all possible reorderings of the observations). In other words, if the null hypothesis of no variance is true, 61
then one can shuffle some values of different groups (i.e. they are exchangeable). So the precision of the p-value will increase with increasing numbers of permutations. Manly (1997) recommended that at least 999 permutations should be used for tests at an α-level of 0.05, while at least 4999 permutations should be used for tests at an α-level of 0.01. PERMANOVA was performed (following the same model of ANOVA; Table 2a) to test the hypothesis that parametres showed different patterns of variation in relation to the spatial scales and seasons in the two meadows. The analysis were based on Euclidean distance matrix calculated after normalization of data for plant features and on Bray-Curtis dissimilarities matrix on untransformed data for borer frequency (IB index) and fourth root transformation for faunal data. Each term in the analysis was tested using 4999 permutations (Manly, 1997; Anderson, 2001a,b). Monte Carlo procedures were used to calculate probability when possible permutations were not enough to get a reasonable test (Anderson, 2001b). To graphically represent the data, a Non-metric Multidimensional Scaling (nMDS) was performed (Shepard, 1962; Kruskal, 1964). nMDS has been demonstrated as a particularly robust and useful ordination procedure for ecology (Field et al., 1982; Kenkel & Orlóci, 1986; Minchin, 1987) The nMDS is based on similarity or dissimilarity matrix among samples and its purpose is to construct a “map” or configuration of the samples, in a specified number of dimensions (usually 2 or 3 dimensions), according to the distances between pairs of samples. The goodness-of-fit for the nMDS for a certain number of dimensions is given by the stress value. The stress represent the difference between the distances of the samples in the original matrix and the correspondent distances between the samples in the final ordination. The stress value increases not only with reducing dimensionality of the ordination but also by increasing quantity of data. To correctly interpret the nMDS, in order it has been observed that the stress must not be greater than 0.2. If stress is < 0.05 the ordination gives an excellent representation; if it is < 0.1 it 62
corresponds to a good ordination with no prospect of misleading interpretations; if stress is < 0.2 it still gives a potentially useful 2-dimensional picture; if it is > 0.3 this indicates that the points are close to being arbitrarily placed in the in the 2-dimensional ordination space. Thus, values between 0.2 and 0.3 and upper must be treated with skepticism and discarded especially for a small to moderate number of points (say < 50) (Clarke & Warwick, 2001). As noticed above, the stress value increases with increasing quantity of data. Since this work is characterized by a large quantity of samples, and therefore of data, I had the problem to fit them in the nMDS ordination, maintaining a low value of stress and, most of all, representing graphically broad patterns across the entire data cloud of the parametres considered (Anderson & Wills, 2003). Thus, the “centroids” of the replicates were plotted to limit the number of observations point in the graph highlighting the patterns in distribution of the variables (Anderson, 2001a; McArdle & Anderson, 2001). This is a relatively new tendency in the nMDS analysis. Briefly, in the case of an analysis based on Euclidean distances, the average for each variable across the observations within a group constitutes the measure of central location for the group in Euclidean space. This central location is called “centroid” (Anderson, 2001a). Thus, to reduce the number of points in the nMDS it is necessary to calculate the mean values of the replicates, calculate the Euclidean distances and obtain the ordination. This procedure was carried out for plant parametres. In this case, Euclidean distance matrices fit better than others because after a normalization (z-score standardization) of the data due to their different measure units, we obtained some negative values that are not accepted by, e.g. Bray-Curtis similarity. On the other hand, when we cope with non-Euclidean distance measures, the calculation of a central location may be problematic. For example, when considering Bray-Curtis measures, a simple average across replicates does not correspond to the “central location” in multivariate Bray-Curtis space, and an appropriate measure of centroid could not be calculated easily directly from the data. Thus, to solve this problem, distances among 63
centroids were obtained using principal coordinates from the Bray-Curtis dissimilarity matrix (Anderson, 2001a, 2003). Principal coordinates analysis (PCO) is a technique proposed by Gower (1966) to analyze a set of multivariate data in terms of the distances between the rows (generally the objects) of the data matrix. The purpose of the technique is to represent the objects in a low-dimensional representation space (namely Euclidean space), in which the distances between the objects should resemble the distances derived from the scores of the objects on the variables as closely as possible. As mentioned above, because the Bray-Curtis is a semi-metric index (Legendre & Legendre, 1998; Legendre & Anderson, 1999), centroids cannot be obtained simply as arithmetic averages of these dissimilarities, but the principal coordinates calculated from the Bray-Curtis dissimilarity matrix place the observations into an Euclidean space without altering the Bray-Curtis measure (i.e. the distance between any pair of observations based on principal coordinates is equivalent to the dissimilarity between those observations obtained from the original variables; McArdle & Anderson, 2001). This is what I did for IB, IT and the abundances of motile macro-invertebrates for which we calculated the Bray-Curtis dissimilarity matrices.
3.4.3
Diversity Indices for Motile Fauna
The main aim in applying diversity indices is to reduce the multivariate (i.e., the multispecies) complexity of assemblage data into a single index (or small number of indices) evaluated for each sample, which can then be handled statistically by univariate analysis (Clarke & Warwick, 2001). The diversity indices tend to exploit some combination of just two features of the sample information: the species richness, that represents the total number of the species present in a certain area and depends on sample size; and equitability (also called evenness), that expresses how evenly the individuals are distributed among the different species and is represented by the formula: 64
J '
H' H' H ' max log S
where H’max is the maximum possible value of Shannon-Wiener diversity, i.e. that which would be achieved if all species were equally abundant (namely log S). The Shannon-Wiener diversity index is the most used diversity measure and is represented by the formula: H ' i pi ln pi
where pi is the proportion of the total count arising from the ith species.
3.4.4
Relationships Between Plant and Faunal Features
CAP Analysis In many ecological studies, investigators search for simultaneous response of many species by reference to some specified hypothesis. For example, it may be hypothesized that a whole set of species will change in response to some experimental treatments, or in response to some changes in environmental or other predictor variables. In these situations, multivariate ordination methods are required to visualize patterns in multivariate data (Anderson & Willis, 2003). Many ordination methods exist such as Principal Component Analysis (PCA), Correspondence Analysis (CA), metric and non-metric Multidimensional Scaling (MDS). These methods are defined as unconstrained ordination procedures and do not use a priori hypothesis in any way, but reduce dimensions on the basis of some general criterion, such as minimizing residual variance (PCA) or minimizing a stress function (MDS as explained above). However, certain patterns of overall dispersion can sometimes mask real patterns of differences in multivariate location among groups in an unconstrained ordination (Anderson & Willis, 2003). On the other hand, methods called constrained ordinations, use a priori hypothesis in some manner to produce the plot. These methods include Canonical Discriminant Analysis (CDA), in which ordination axes are 65
designed so as to maximize differences among groups (James & Wilkinson, 1971), and Canonical Correlation Analysis (CCorA), in which axes are designed so as to maximize their correlation with linear combinations of some quantitative predictor variables (Gittings, 1985). Unfortunately, existing constrained ordination procedures are limited to being based implicitly on some particular distance matrix (Euclidean distances, Mahalanobis distances, Chi-square distances etc.). Furthermore constrained ordinations may be problematic also because they have unrealistic assumptions about the distribution of the response variables (e.g., multivariate normality). To avoid the problem of flexibility in using any distance or dissimilarity measure as the basis for analysis, here I used Canonical Analysis of Principal Coordinates (CAP), a flexible and particularly useful constrained ordination procedure for ecologist (Anderson & Willis, 2003; Anderson, 2004). CAP has the advantage of allowing any distance or dissimilarity measure to be used. A summary for the CAP approach is as follow: 1) Do a Principal Coordinate Analysis (PCO) on the fauna data matrices Y (N observations x p variables) using a dissimilarity measure of choice, yielding orthonormal axes Q; 2) Choose an appropriate number of axes m as a subset of Q (matrix Qm) to use for the ensuing canonical analysis; 3) Do a traditional canonical analysis (say CCorA if environmental data matrices X contains quantitative variables) on the first m axes of Q. These three steps are sufficient to produce an ordination. To test the hypothesis of no significant relationships with quantitative environmental variables, one can use trace statistic (i.e. sum of canonical eigenvalues = sum of squared canonical correlations) and obtain a p value by permutations (Anderson, 2001b). An extremely important point is to determine how many PCO axes (m) should be retained (Anderson & Robinson, 2003; Anderson & Willis, 2003). If m is too small, then some important ecological information will not be included in the canonical analysis. On the other hand, if m is too large, then a misleading canonical plot could result. As a general 66
rule of thumb, Anderson & Willis (2003) suggest that the choice of m should be made such that the percentage of the total variance in the dissimilarity matrix explained by the first m PCO axes should exceed 60%, but should not exceed 100%. The power of correlation is given by the δ2 (squared canonical correlation), representing the square of eigenvalues from PCO, i.e. the strength of the correlations between the variables. The goodness of fitness of the method is the so called “leave-one-out” procedure providing a statistical estimate of misclassification error. The basis of the method is to take out a single observation and do the CAP analysis on the remaining observations without it. This can be done for every single observation in the data set and then calculate the proportion of the observations that were incorrectly classified. This is the misclassification error, which equals one minus the proportion of correct classifications (Anderson & Willis, 2003). CAP was performed to evaluate any possible correlation between plant parametres (namely “environmental” variables) and IB (both total and for single species) and fauna abundances. Spearman’s correlations Spearman’s rank correlation was performed in order to compare works of the past, using different kind of sampling strategies, with the present one. Spearman’s rank correlation coefficient (ρ) is a non-parametric measure of correlation, i.e. it assesses how well an arbitrary monotonic function could describe the relationship between two variables, without making any other assumptions about the particular nature of the relationship between the variables. This is in contrast to other measure of correlation based on possible relationships of a parametreized form, such as a linear relationship. Basically, the raw scores are converted to ranks, and the differences between ranks of each observation on the two variables are calculated. Spearman’s coefficients were calculated to find relationships between the plant features and the animal abundances and diversity and IB. 67
4. Results 4.1
Temperature Trends
The profiles of the water column temperatures during the sampling periods are given in Figure 7 (V. Saggiomo and M. Lorenti courtesy). The Figure 7 a) represents the mean temperature recorded by means of a HOBO device placed in a fixed station in the island of Ischia at 3 m depth. The Figures 7 b) and c) represent the mean temperature recorded in the fixed station “Marechiara” located 2 nm off the city of Naples (Ribera d’Alcalà et al., 2004). In summer, all samplings were conducted under the thermocline. The mean surface temperature during summer (from May to September) was 23.5 ± 2.2 °C (mean ± SD), while in winter the mean temperature was 15.4 ± 1.6 °C. 28.5 27.0
2007
2008
25.5
a)
24.0
°C
22.5 21.0 19.5 18.0 16.5 15.0 13.5
5 may 25 May 15 Jun 5 Jul 25 Jul 15 Aug 5 Sep 25 Sep 15 Oct 5 Nov 25 Nov 15 Dec 5 Jan 25 Jan 15 Feb 5 Mar 25 Mar 15 Apr 5 May 25 May 15 Jun 5 Jul 25 Jul 15 Aug 5 Sep 25 Sep
12.0
b)
c)
Figure 7: Water column temperature recorded in 2007 and 2008 in the fixed stations of the Ischia Island and at “Marechiara”: a) surface (3 m) patterns of temperature (Ischia Island), b) depth profile of temperature during 2007 (“Marechiara”), c) depth profile of temperature during 2008 (“Marechiara”).
68
4.2
Morphometric Parametres
4.2.1
Univariate Results
All P. oceanica variables analyzed showed significant differences in their mean values for at least one of the spatial scales and commonly at multi-spatial scales for most of the variables. All the variables showed significant differences for at least one of the two temporal scales (namely, dates of sampling and seasons), with the exception of the shoot density. Among the structural variables, the shoot density (Figure 8) varied only on the large scale of a few km (Location) and on the intermediate scale of tens of m (Station) (Table 3). The mean values of shoot density were always higher in Scarrupata than in Lacco Ameno in both dates of sampling and seasons. In Scarrupata the shoot density values ranged between 150 and 875 shoot/m2, and in Lacco Ameno between 75 and 419 shoot/m2, with total mean values of 413.8 ± 10.5 shoot/m2 (mean ± SE) in Scarrupata and 244.1 ± 5.6 shoot/m2 (mean ± SE) in Lacco Ameno. These differences were probably due to the mechanical (i.e., the boat anchoring) and the chemical human impacts (reducing both the light intensity and the transparency of the water column) affecting the Lacco Ameno bed. Differences in shoot density along the temporal scales were not evident, and this is not surprising since P. oceanica, unlike other seagrasses, does not undergo large seasonal changes in spatial coverage and shoot density. Table 3: ANOVA and Cochran’s C tests for shoot density, Leaf Area Index (LAI) and Leaf Standing Crop (LSC) at the different spatial and temporal scales: L = Location; S = Season; D = Date of sampling; Si = Site; St= Station; Pl = Plot. (* = p < 0.05; ** = p < 0.01; *** = p < 0.001; ns = not significant; F = Fixed factor; R = Random factor).
L
Effect df F 1
S LS D(LS)
F F R
Si(D(LS)
R
St(Si(D(LS))) R Pl(St(Si(D(LS)))) R Error Cochran C Tranfsormation
1 1 4
Shoot Density MS F p 2076764 90.68 *** 44222 20104 22902
1.93 0.88 0.72
ns ns ns
LAI MS F p 32.21 28.22 ** 10.01 8.77 * 1.80 1.58 ns 1.14 3.27 *
16
31665
1.72
ns
0.35
2.39
24 96 144
18394 7458 7708
2.47 0.97
**
0.15 0.08 0.08
1.79 1.07
ns
0.08 ns none
* * ns
0.11 *** log(x+1)
LSC MS F p 55.22 34.78 ** 20.46 12.89 * 3.59 2.26 ns 1.59 2.86 ns 0.55
2.75
0.20 0.12 0.12
1.64 * 1.01 ns
*
0.15 *** log(x+1)
69
a)
Date 1
900 800
Shoot density/m2
Date 1
Date 2
900
Lacco Ameno Summer
800
700
700
600
600
500
500
400
400
300
300
200
200
100
100 0
0
Shoot density/m2
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
900 800 700 600 500 400 300 200 100 0
900 800 700 600 500 400 300 200 100 0
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
Shoot density/m2
700
Scarrupata Winter
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
Lacco Ameno Winter
600
500
500
400
400
300
300
200
200
100
100 0 A1A2
B1B2
C1C2
700
Shoot density/m2
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
700 Lacco Ameno Summer
600
0 A1A2
B1B2
C1C2
A1A2
B1B2
C1C2
700
Scarrupata Summer
600
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Scarrupata Winter
0 A1A2
B1B2
C1C2
A1A2
B1B2
C1C2
c) 700
Shoot density/m2
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
Scarrupata Summer
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
b)
A1A2
B1B2
C1C2
700
Lacco Ameno Summer
600
600
500
500
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300
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200
100
100
0
A1A2
Lacco Ameno Winter
0 A
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700
Shoot density/m2
Date 2 Lacco Ameno Winter
A
B
C
700 600
500
500
400
400
300
300
200
200
100
100
0
B
C
A
B
C
Scarrupata Winter
Scarrupata Summer
600
A
0 A
B
C
A
B
C
A
B
C
Figure 8: Shoot densities in the two meadows along the three main spatial scales and the two seasons: a) plot (a, b, c); b) station (1, 2); c) site (A, B, C). Red colors represent Summer season and blue colors Winter season. In each graph are represented both the date of sampling 1 and the date of sampling 2. Bars represent standard errors.
70
Other structural variables are the Leaf Area Index (LAI) and the Leaf Standing Crop (LSC). LAI and LSC (Figures 9 and 10) varied at the scales of few km (Location), 100s of m (Site) and 10s of m (Station) (Table 3). As for the shoot density, the LAI and LSC values also were always higher in Scarrupata than in Lacco Ameno. In Scarrupata, LAI reached values ranging from 2.16 to 18.94 m2/m2, and in Lacco Ameno they varied between 0.72 and 11.52 m2/m2, with total mean values of 8.34 ± 0.30 and 3.85 ± 0.17 m2/m2 in Scarrupata and Lacco Ameno, respectively. LSC, in Scarrupata ranged between 90.71 and 1244.94 g/m2, and in Lacco Ameno between 26.94 and 460.56 g/m2, with total mean values of 377.80 ± 14.13 and 167.01 ± 7.81 g/m2 in Scarrupata and Lacco Ameno, respectively. LAI and LSC showed differences between seasons (with higher values recorded in Summer than in Winter), but only LAI varied with date of sampling (mainly due to the differences between the first and the second date of sampling in the summer season of Scarrupata). From these results, it seems clear that for both of these two derivative parameters their differences at Location scale are influenced more by shoot density, than by real differences between meadows (cfr. the results obtained for leaf surface and total leaf biomass, below). In the same way, the seasonal differences shown by both parameters are clearly due to differences in the leaf traits from which the two parameters derivate, as presented below.
71
a)
LAI (m2/m2)
Date 1
LAI (m2/m2)
abc A1
abc A2
abc B1
abc A2
abc B1
abc C1
abc B2
abc abc abc C2 A1 A2 Scarrupata Summer
abc C1
abc C2
abc A1
abc A2
22.0 20.0 18.0 16.0 14.0 12.0 10.0 8.0 6.0 4.0 2.0 0.0 abc B1
abc B2
abc C1
abc C2
22.0 20.0 18.0 16.0 14.0 12.0 10.0 8.0 6.0 4.0 2.0 0.0 abc B1
abc B2
abc C1
abc C2
Lacco Ameno Summer
16.0
LAI (m2/m2)
abc B2
22.0 20.0 18.0 16.0 14.0 12.0 10.0 8.0 6.0 4.0 2.0 0.0 abc A1
b)
Date 1
Date 2 Lacco Ameno Summer
22.0 20.0 18.0 16.0 14.0 12.0 10.0 8.0 6.0 4.0 2.0 0.0
14.0
12.0
12.0
10.0
10.0
8.0
8.0
6.0
6.0
4.0
4.0
2.0
2.0 0.0 A1A2
B1B2
C1C2
LAI (m2/m2)
A1A2
B1B2
B1B2
C1C2
14.0
12.0
12.0
10.0
10.0
8.0
8.0
6.0
6.0
4.0
4.0
2.0
2.0
A1A2
B1B2
C1C2
A1A2
B1B2
C1C2
A
B
C
A
B
C
Scarrupata Winter
16.0
14.0
0.0
0.0 A1A2
B1B2
18.0
LAI (m2/m2)
A1A2
C1C2
Scarrupata Summer
16.0
C1C2
A1A2
B1B2
A1A2
C1C2
B1B2
18.0
Lacco Ameno Summer
16.0
16.0
14.0
14.0
12.0
12.0
10.0
10.0
8.0
8.0
6.0
6.0
4.0
4.0
2.0
2.0
C1C2 Lacco Ameno Winter
0.0
0.0 A
B
C
A
B
A
C
B
C Scarrupata Winter
Scarrupata Summer
LAI (m2/m2)
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
Lacco Amen Winter
16.0
0.0
c)
abc abc abc abc abc abc abc abc abc abc abc abc A1 A2 B1 B2 C1 C2 A1 A2 B1 B2 C1 C2 Scarrupata Winter
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
14.0
Date 2
Lacco Ameno Winter
18.0
18.0
16.0
16.0
14.0
14.0
12.0
12.0
10.0
10.0
8.0
8.0
6.0
6.0
4.0
4.0
2.0
2.0 0.0
0.0 A
B
C
A
B
C
A
B
C
Figure 9: Leaf Area Index (LAI) in the two meadows along the three main spatial scales and the two seasons: a) plot (a, b, c); b) station (1, 2); c) site (A, B, C). Red colors represent Summer season and blue colors Winter season. In each graph are represented both the date of sampling 1 and the date of sampling 2. Bars represent standard errors.
72
a) Date 1
LSC (g/m2)
1400
Lacco Ameno Summer
Date 2
1200
1000
1000
800
800
600
600
400
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200
0 abc A2
abc B1
abc B2
abc C1
abc C2
1400
abc A1
abc A2
abc B1
abc B2
abc C1
abc C2
Scarrupata Summer
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
1400 1200
1000
1000
LSC (g/m2)
1200
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600
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0 abc A2
abc B1
abc B2
abc C1
abc C2
abc A1
abc A2
abc B1
abc B2
abc C1
abc C2
LSC (g/m2)
Scarrupata Winter
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
Lacco Ameno Winter
900
Lacco Ameno Summer
900 800
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C1C2
1000 900 800 700 600 500 400 300 200 100 0
LSC (g/m2)
A1A2
A1A2
C1C2
Scarrupata Summer
900
LSC (g/m2)
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
1000
1000
A1A2
B1B2
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C1C2
900
Lacco Ameno Summer
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Scarrupata Winter
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A1A2
Lacco Ameno Winter
0
0 A
B
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900
LSC (g/m2)
Date 2
0 abc A1
c)
Lacco Ameno Winter
0 abc A1
b)
Date 1
1400
1200
A
B
A
C
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C
900
Scarrupata Summer
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Scarrupata Winter
0
0 A
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A
B
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A
B
C
Figure 10: Leaf Standing Crop (LSC) in the two meadows along the three main spatial scales and the two seasons: a) plot (a, b, c); b) station (1, 2); c) site (A, B, C). Red colors represent Summer season and blue colors Winter season. In each graph are represented both the date of sampling 1 and the date of sampling 2. Bars represent standard errors.
73
Patterns of differences on various scales were recorded for each individual leaf trait. All of these variables differed on the smallest spatial scale of a few cm (Quadrat scale) (Table 4), and most of them, with the only exception of the sheaths length, showed significant differences at least for one of the temporal scales. Table 4: ANOVA and Cochran’s C tests for number of leaves per shoot, number of juveniles, intermediate and adult leaves at the different spatial and temporal scales: L = Location; S = Season; D = Date of sampling; Si = Site; St= Station; Pl = Plot, Qu = Quadrat. (* = p < 0.05; ** = p < 0.01; *** = p < 0.001; ns = not significant; F = Fixed factor; R = Random factor).
L S LS D(LS) Si(D(LS)) St(Si(D(LS)))
Effect F F F R R R
Pl(St(Si(D(LS))))
R
Qu(Pl(St(Si(D(LS))))) R Error Cochran C Tranfsormation
No. Leaves No. Juveniles No. Intermediate No. Adult MS F p MS F p MS F p MS F p 1 89.9 0.33 ns 0.0126 0.00 ns 0.00 0.00 ns 86.95 1.45 ns 1 120.5 0.44 ns 5.4037 0.06 ns 1407.78 28.17 * 583.68 9.75 ns 1 87.7 0.32 ns 60.2415 0.62 ns 10.23 0.20 ns 23.19 0.39 ns 49.97 18.94 *** 59.89 17.10 *** 4 271.1 41.85 *** 97.8995 99.45 *** 16 6.5 1.40 ns 0.9844 1.35 ns 2.64 2.03 ns 3.50 1.32 ns 24 4.6 1.47 ns 0.7294 1.91 * 1.30 1.59 ns 2.65 1.49 ns df
96
3.1
1.16
144 2592
2.7 1.1
2.53 ***
ns
0.01 *** none
0.3819
1.31
0.2909 0.1813
1.60 ***
ns
0.01 ns none
0.82
1.38
0.59 0.35
1.71 ***
*
0.02 ns none
1.77
1.17
1.51 0.56
2.67 ***
ns
0.01 *** none
The mean number of leaves per shoot (Figure 11), and similarly the mean number of adult leaves per shoot (Figure 12), varied only at the Quadrat scale (Table 4). The range values of the number of leaves per shoot were identical for both meadows and were between 1 and 10 leaves per shoot. The mean values for both meadows were 6.3 ± 0.03 and 5.9 ± 0.03 leaves per shoot for Scarrupata and Lacco Ameno, respectively, and the differences were not significant. In the same way, the range of the number of adult leaves per shoot was the same for both meadows with values between 0 and 6 adult leaves per shoot. The differences between the mean values of the number of adult leaves per shoot for both meadows were negligible, and they accounted for 3.2 ± 0.03 and 2.9 ± 0.03 for Scarrupata and Lacco Ameno, respectively. Both the above parameters varied with the date of sampling (Table 4), the mean values for both sites were always higher in the second date of sampling in summer for both meadows, while, on the contrary, in winter the higher values were recorded on the first sampling date. 74
a) No. of leaves/shoot
Date 2
Date 1
9 8
8
7
7
6
6
5
5
4
4
3
3
2
2
1
1
0 abc A2
abc B1
abc B2
abc C1
No. of leaves/shoot
9
abc abc C2 A1 Scarrupat Summer
abc A2
abc B1
abc B2
abc C1
abc C2
9
8
8
7
7
6
6
5
5
4
4
3
3
2
2
1
1 abc A1
abc A2
abc B1
abc B2
abc C1
No. of leaves/shoot
abc C2
abc A1
abc A2
abc B1
abc B2
abc C1
8
7
7
6
6
5
5
4
4
3
3
2
2
1
1
abc B1
abc B2
abc C1
abc C2
abc A1
abc A2
abc B1
abc B2
abc C1
abc C2
Lacco Ameno Winter
0 A1A2
B1B2
C1C2
9
No. of leaves/shoot
abc A2
9
8
0 A1A2
B1B2
A1A2
C1C2
B1B2
C1C2
A1A2
B1B2
C1C2
C1C2 A1A2 Lacco Ameno Winter
B1B2
C1C2
A
B
C
A
B
C
9
Scarrupata Summer
8
8
7
7
6
6
5
5
4
4
3
3
2
2
1
1
0
Scarrupata Winter
0 A1A2
B1B2
8
No. of leaves/shoot
abc A1
abc C2
Lacco Ameno Summer
9
C1C2
A1A2
B1B2
C1C2
A1A2
B1B2
A
B
8
Lacco Ameno Summer
7
7
6
6
5
5
4
4
3
3
2
2
1
1
0
0 A
B
C
A
B
C
Scarrupata Summer
8
No. of leaves/shoot
abc abc abc abc abc abc abc abc abc abc abc abc A1 A2 B1 B2 C1 C2 A1 A2 B1 B2 C1 C2 Scarrupata Winter
0
0
c)
Date 2
Lacco Ameno Winter
0 abc A1
b)
Date 1
9
Lacco Ameno Summer
C Scarrupata Winter
8
7
7
6
6
5
5
4
4
3
3
2
2
1
1 0
0 A
B
C
A
B
C
A
B
C
Figure 11: Number of leaves per shoot in the two meadows along the three main spatial scales and the two seasons: a) plot (a, b, c); b) station (1, 2); c) site (A, B, C). Red colors represent Summer season and blue colors Winter season. In each graph are represented both the date of sampling 1 and the date of sampling 2. Bars represent standard errors.
75
a) No. Adult Leaves/shoot No. Adult Leaves/shoot
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
No. Adult Leaves/shoot
5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0
No. Adult Leaves/shoot
B1B2
5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0
C1C2
B1B2
C1C2
A1A2
B1B2
No. Adult Leaves/shoot
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
C1C2
B1B2
A1A2
C1C2
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
Lacco Ameno Winter
B1B2
C1C2
5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 A1A2
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
Scarrupata Winter
5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0
5.0
A1A2
B1B2
C1C2
B1B2
C1C2
A
B
C
A
B
C
Scarrupata Winter
A1A2
B1B2
C1C2
A1A2
5.0 Lacco Ameno Summer
4.5
Lacco Ameno Winter
4.5
4.0
4.0
3.5
3.5
3.0
3.0
2.5
2.5
2.0
2.0
1.5
1.5
1.0
1.0
0.5
0.5 0.0
0.0 A
B
C
5.0
No. Adult Leaves/shoot
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
Scarrupata Summer
A1A2
Lacco Ameno Winter
5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0
Lacco Ameno Summer
A1A2
c)
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
Scarrupata Summer
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
b)
5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0
Lacco Ameno Summer
5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0
Date 2
Date 1
Date 2
Date 1 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0
A
B
A
C
B
C
5.0
Scarrupata Summer
4.5
4.5
4.0
4.0
3.5
3.5
3.0
3.0
2.5
2.5
2.0
2.0
1.5
1.5
1.0
1.0
0.5
0.5
0.0
Scarrupata Winter
0.0 A
B
C
A
B
C
A
B
C
Figure 12: Number of Adult leaves per shoot in the two meadows along the three main spatial scales and the two seasons: a) plot (a, b, c); b) station (1, 2); c) site (A, B, C). Red colors represent Summer season and blue colors Winter season. In each graph are represented both the date of sampling 1 and the date of sampling 2. Bars represent standard errors.
76
The mean number of intermediate leaves per shoot (Figure 13) varied at the smallest spatial scales of Quadrat and Plot (Table 4). The range values for this leaf category were from 0 and 5 leaves per shoot for both meadows, while the same mean values, accounting for 2.49 ± 0.03 intermediate leaves per shoot, were found in both meadows. The intermediate leaves reached their maxima values in winter for both meadows, but showed differences according to the date of sampling. In Scarrupata the second date of sampling showed higher values than the first, while in winter the opposite was recorded. An inverse trend was evident in Lacco Ameno where, in summer, the highest number of intermediate leaves was counted on the first date of sampling, while in winter it was on the second.
77
No. Int. Leaves/shoot
a)
Date 1
4.5
Date 2
4.0
4.0
3.5
3.5
3.0
3.0
2.5
2.5
2.0
2.0
1.5
1.5
1.0
1.0
0.5
0.5
No. Int. Leaves/shoot
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
4.5
4.5
Scarrupata Summer
4.0
4.0
3.5
3.5
3.0
3.0
2.5
2.5
2.0
2.0
1.5
1.5
1.0
1.0
0.5
0.5
No. Int. Leaves/shoot
4.0
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
abc A2
abc abc abc B1 B2 C1
4.0
Lacco Ameno Summer
3.5
3.0
3.0
2.5
2.5
2.0
2.0
1.5
1.5
1.0
1.0
0.5
0.5
abc C2
abc abc A1 A2
abc B1
abc B2
abc abc C1 C2
Lacco Ameno Winter
0.0 A1A2
No. Int. Leaves/shoot
Scarrupata Winter
abc A1
3.5
0.0 B1B2
4.0
C1C2 A1A2 Scarrupata Summer
B1B2
C1C2
A1A2
B1B2
C1C2 A1A2 Scarrupata Winter
B1B2
C1C2
A1A2
B1B2
C1C2
B1B2
C1C2
B
C
4.0
3.5
3.5
3.0
3.0
2.5
2.5
2.0
2.0
1.5
1.5
1.0
1.0
0.5
0.5
0.0
0.0 A1A2
B1B2
4.0
No. Int. Laeves/shoot
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
0.0 abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
C1C2
A1A2
B1B2
C1C2
4.0
Lacco Ameno Summer
3.5
3.5
3.0
3.0
2.5
2.5
2.0
2.0
1.5
1.5
1.0
1.0
0.5
0.5
0.0
A1A2
Lacco Ameno Winter
0.0 A
B
C
A
B
C
Scarrupata Summer
4.0
No. Int. Laeves/shoot
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
0.0
c)
Date 2 Lacco Ameno Winter
0.0
0.0
b)
Date 1
4.5
Lacco Ameno Summer
A
B
C
3.5
3.5
3.0
3.0
2.5
2.5
2.0
2.0
1.5
1.5
1.0
1.0
0.5
0.5
0.0
A
Scarrupata Winter
4.0
0.0 A
B
C
A
B
C
A
B
C
A
B
C
Figure 13: Number of Intermediate leaves per shoot in the two meadows along the three main spatial scales and the two seasons: a) plot (a, b, c); b) station (1, 2); c) site (A, B, C). Red colors represent Summer season and blue colors Winter season. In each graph are represented both the date of sampling 1 and the date of sampling 2. Bars represent standard errors.
78
The mean number of juvenile leaves per shoot (Figure 14) varied only at the smallest spatial scale of Quadrat (Table 4). The range values for this leaf category were the same for both meadows, i.e. from 0 and 3 juvenile leaves per shoot. Also the mean values found were the same for both meadows, i.e. 0.55 ± 0.02 juv/shoot. There was temporal variability regarding the date of sampling (Table 4). For both meadows, the highest values were recorded on the second sampling date of summer, while, on the contrary, during winter it was the first date that showed the highest values. During summer, in Scarrupata the larger number of juvenile leaves were recorded compared with Lacco Ameno (0.75 ± 0.02 vs 0.45 ± 0.02 juv/shoot, respectively), while the opposite was evident in winter, when the higher number of juveniles was recorded in Lacco Ameno (0.37 ± 0.02 vs 0.65 ± 0.02 juv/shoot in Scarrupata and Lacco Ameno, respectively). These different recruitment patterns (i.e., the number of the juvenile and intermediate leaves) are probably the consequence of the human impacts affecting the Lacco Ameno bed that possibly act on the biological cycle of the plant causing a delay in the juvenile and intermediate leaves formation.
79
a) No. Juv. Leaves/shoot
Date 1
Date 1
2.0 1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0
No. Juv. Leaves/shoot
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
2.0 1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0
Scarrupata Summer
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
b)
1.6
No. Juv. Leaves/shoot
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
1.4
1.2
1.2
1.0
1.0
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
No. Juv. Leaves/shoot
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
Lacco Ameno Winter
0.0 A1A2
B1B2
C1C2
A1A2
B1B2
C1C2
1.6
Scarrupata Summer
1.4
1.4
1.2
1.2
1.0
1.0
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
A1A2
B1B2
C1C2
A1A2
B1B2
C1C2
A1A2 Scarrupata Winter
B1B2
C1C2
0.0
0.0 A1A2
B1B2
1.4
No. Juv. Leaves/shoot
abc abc abc abc abc abc abc abc abc abc abc abc A1 A2 B1 B2 C1 C2 A1 A2 B1 B2 C1 C2 Scarrupata Winter
1.6
Lacco Ameno Summer
1.4
1.6
C1C2
A1A2
B1B2
C1C2
1.4
Lacco Ameno Summer
1.2
1.2
1.0
1.0
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
A1A2
B1B2
C1C2
A
B
C
A
B
C
Lacco Ameno Winter
0.0
0.0 A
B
1.4
No. Juv. Leaves/shoot
Lacco Ameno Winter
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
0.0
c)
2.0 1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0
Date 1
Date 1
2.0 1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0
Lacco Ameno Summer
C
A
B
A
C
B
1.4
Scarrupata Summer
1.2
1.2
1.0
1.0
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
C
Scarrupata Winter
0.0
0.0 A
B
C
A
B
C
A
B
C
Figure 14: Number of Juvenile leaves per shoot in the two meadows along the three main spatial scales and the two seasons: a) plot (a, b, c); b) station (1, 2); c) site (A, B, C). Red colors represent Summer season and blue colors Winter season. In each graph are represented both the date of sampling 1 and the date of sampling 2. Bars represent standard errors.
80
The mean leaf length (Figure 15) significantly varied at the intermediate spatial scale of Site (100s of m) and at the smallest scale of Quadrat (a few cm), and there were only significant differences between the dates of sampling (Table 5). Table 5: ANOVA and Cochran’s C tests for leaf length, leaf width, leaf surface and sheaths length at the different spatial and temporal scales: L = Location; S = Season; D = Date of sampling; Si = Site; St= Station; Pl = Plot, Qu = Quadrat. (* = p < 0.05; ** = p < 0.01; *** = p < 0.001; ns = not significant; F = Fixed factor; R = Random factor).
L S LS D(LS)
Effect F F F R
Si(D(LS))
R
St(Si(D(LS))) R Pl(St(Si(D(LS)))) R Qu(Pl(St(Si(D(LS))))) R Error Cochran C Tranfsormation
df 1 1 1 4 16 24 96 144 2592
Leaf Length MS F p 29049 0.96 ns 381063 12.54 ns 17768 0.58 ns 30385 30.29 *** 1003 5.12 *** 196 303 325 52
0.65 ns 0.93 ns 6.25 ***
0.01 *** none
Leaf Width Leaf Surface MS F p MS F p 0.132 5.66 ns 1414439 3.62 ns 1.853 79.37 *** 5802693 14.86 ns 0.051 2.19 ns 322005 0.82 ns 0.023 0.25 ns 390474 14.95 *** 0.092 2.24 ns 26116 2.27 ns 0.041 0.016 0.018 0.004
2.55 *** 0.90 ns 4.61 ***
0.03 *** none
11522 14722 13812 2985
0.78 ns 1.07 ns 4.63 ***
0.03 *** none
Sheaths Length MS F p 80.44 2.06 ns 84.72 2.17 ns 32.25 0.83 ns 38.99 4.52 ns 8.62 2.10 2.03 1.59 0.35
4.10 ** 1.04 ns 1.28 ns 4.52 ***
0.03 *** none
The leaf length was always longer in Scarrupata than in Lacco Ameno, with the exception of the second date in summer where higher values were recorded in Lacco Ameno. This difference was possibly due to the fact that the second date of sampling in Scarrupata corresponded at the end of the summer when the first sea-storms tend to tear the brown tissue from the apex of the leaves. The length varied between 10.13 cm and 90.75 cm in Scarrupata, while in Lacco Ameno it ranged between 6.07 cm and 80.68 cm. The mean values observed were not that different between the two meadows: 39.32 ± 0.41 cm vs 32.97 ± 0.44 cm in Scarrupata and Lacco Ameno, respectively. No seasonal variability was found between the two meadows, although differences in the mean leaf length values were evident for each single meadow: in Scarrupata the mean value in summer was 48.34 ± 0.57 cm, while in winter it was 30.30 ± 0.33 cm (although the pairwise test showed that this difference was not significant); in Lacco Ameno in summer the mean value was 46.95 ± 0.43 cm, while in winter it was 18.98 ± 0.24 cm. Both meadows showed differences according to the dates of sampling (Table 5). In Scarrupata, the first date showed mean values higher than the second one, while the opposite occurred in winter. In Lacco Ameno, 81
during summer it was the second date that showed the highest mean value, while in winter there was the same pattern as at Scarrupata.
82
a)
Date 1
Avg. Leaf length (cm)
80.0
Date 2
70.0
70.0
60.0
60.0
50.0
50.0
40.0
40.0
30.0
30.0
20.0
20.0
10.0
10.0
0.0
Avg. Leaf length (cm)
80.0
70.0
60.0
60.0
50.0
50.0
40.0
40.0
30.0
30.0
20.0
20.0
10.0
10.0
Avg. Leaf length (cm)
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
Lacco Ameno Summer
60.0
60.0
50.0
50.0
40.0
40.0
30.0
30.0
20.0
20.0
10.0
10.0
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
Lacco Ameno Winter
0.0 A1A2
B1B2
70.0
Avg. Leaf length (cm)
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
70.0
0.0 C1C2 A1A2 Scarrupata Summer
B1B2
C1C2
A1A2
B1B2
C1C2 A1A2 Scarrupata Winter
B1B2
C1C2
A1A2
B1B2
C1C2
B1B2
C1C2
A
B
C
A
B
C
70.0
60.0
60.0
50.0
50.0
40.0
40.0
30.0
30.0
20.0
20.0
10.0
10.0 0.0
0.0 A1A2
B1B2
70.0
Avg. Leaf lenght (cm)
abc abc abc abc abc abc abc abc abc abc abc abc A1 A2 B1 B2 C1 C2 A1 A2 B1 B2 C1 C2 Scarrupata Winter
0.0
70.0
C1C2
A1A2
B1B2
C1C2
70.0
Lacco Ameno Summer
60.0
60.0
50.0
50.0
40.0
40.0
30.0
30.0
20.0
20.0
10.0
10.0
0.0
A1A2
Lacco Ameno Winter
0.0 A
B
C
A
B
C
Scarrupata Summer
70.0
Avg. Leaf lenght (cm)
80.0
70.0
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
c)
Date 2 Lacco Ameno Winter
0.0 abc abc abc abc abc abc abc abc abc abc abc abc A1 A2 B1 B2 C1 C2 A1 A2 B1 B2 C1 C2 Scarrupata Summer
0.0
b)
Date 1
80.0
Lacco Ameno Summer
A
B
60.0
60.0
50.0
50.0
40.0
40.0
30.0
30.0
20.0
20.0
10.0
10.0
C Scarrupata Winter
70.0
0.0
0.0 A
B
C
A
B
C
A
B
C
Figure 15: Average leaf length (cm) per shoot in the two meadows along the three main spatial scales and the two seasons: a) plot (a, b, c); b) station (1, 2); c) site (A, B, C). Red colors represent Summer season and blue colors Winter season. In each graph are represented both the date of sampling 1 and the date of sampling 2. Bars represent standard errors.
83
The leaf width (Figure 16) showed significant differences at Station (10s of m) and Quadrat (a few cm) scales, while, temporally, significant differences were recorded only at the seasonal scale (Table 5). No differences were found either in the range values or in the mean values between the two meadows. Although slight, the winter season showed differences with the highest width values for both meadows: in Scarrupata the mean value in summer was 0.901 ± 0.002 cm, while in winter it was 0.943 ± 0.003 cm; in Lacco Ameno the mean values were 0.879 ± 0.002 cm in summer and 0.939 ± 0.003 cm in winter.
84
a)
Date 1 1.2
Avg. Leaf width (cm)
Date 2
1.0
1.0
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0.0
Avg. Leaf width (cm)
1.2
1.0
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
Avg. Leaf width (cm)
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
abc abc abc abc abc A1 A2 B1 B2 C1
1.2
Lacco Ameno Summer
1.0
1.0
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
A1A2
B1B2
C1C2
1.2
Avg. Leaf width (cm)
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2 Lacco Ameno Winter
0.0
0.0 A1A2
B1B2
A1A2
C1C2
B1B2
C1C2
1.2
Scarrupata Summer
1.0
1.0
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0.0
A1A2
B1B2
C1C2
B1B2
C1C2
A
B
C
A
B
C
Scarrupata Winter
0.0 A1A2
B1B2
1.2
Avg. Leaf width (cm)
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
Scarrupata Winter
0.0
1.2
C1C2
A1A2
B1B2
C1C2
A1A2
B1B2
1.2
Lacco Ameno Summer
1.0
1.0
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
C1C2
A1A2
Lacco Ameno Winter
0.0
0.0
Avg. Leaf width (cm)
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
1.2
1.0
abc abc abc abc abc A1 A2 B1 B2 C1
c)
Date 2 Lacco Ameno Winter
0.0 abc abc abc abc abc abc abc abc abc abc abc abc A1 A2 B1 B2 C1 C2 A1 A2 B1 B2 C1 C2 Scarrupata Summer
0.0
b)
Date 1
1.2
Lacco Ameno Summer
A
B
1.2
C
A
B
A
C
B
C
1.2
Scarrupata Summer
1.0
1.0
0.8
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0.0
Scarrupata Winter
0.0 A
B
C
A
B
C
A
B
C
Figure 16: Average leaf width (cm) per shoot in the two meadows along the three main spatial scales and the two seasons: a) plot (a, b, c); b) station (1, 2); c) site (A, B, C). Red colors represent Summer season and blue colors Winter season. In each graph are represented both the date of sampling 1 and the date of sampling 2. Bars represent standard errors.
85
The leaf surface (Figure 17) was calculated on the basis of the leaf length and width, but apparently it did not follow the same pattern of spatial variability neither of the former nor of the latter, as one might expect. In fact, the leaf surface showed significant differences only at Quadrat scale (Table 5). The leaf surface was always wider in Scarrupata than in Lacco Ameno, with the only exception of the second date of sampling in summer where the opposite was found as a result of the shorter length of the leaves in Scarrupata for the reasons explained above. In Scarrupata this parameter showed a range between 17.6 and 521.5 cm2/shoot, while in Lacco Ameno it was between 7.7 and 408.2 cm2/shoot. The mean values recorded for Scarrupata was 202.4 ± 2.1 cm2/shoot while in Lacco Ameno it was 158.1 ± 2.2 cm2/shoot. By contrast, temporal differences seemed to follow the same pattern as leaf length with significant differences between the dates of sampling (Table 5): the first date in summer always showed higher values in respect to the second one, while the contrary was recorded in winter. As for the leaf length, when the two meadows are considered separately, differences in the mean values of the leaf surface was evident, with the lower values recorded in winter for both meadows: in Scarrupata the mean values accounted for 236.7 ± 3.2 cm2/shoot and 168.1 ± 2.0 cm2/shoot in summer and winter, respectively (although the pairwise test did not show significant differences); in Lacco Ameno the mean values were 213.6 ± 3.0 cm2/shoot and 106.6 ± 1.7 cm2/shoot in summer and winter, respectively.
86
a)
Date 2
Date 1
Leaf Surface (cm2/shoot)
400
Date 1 400
Lacco Ameno Summer
350
350
300
300
250
250
200
200
150
150
100
100
50
50 0
Leaf Surface (cm2/shoot)
0 400
abc abc abc abc abc abc abc abc abc abc abc abc A1 A2 B1 B2 C1 C2 A1 A2 B1 B2 C1 C2 Scarrupata Summer
350
300
300
250
250
200
200
150
150
100
100
50
50 abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
Leaf Surface (cm2/shoot)
350
350
Lacco Ameno Summer
300
300
250
250
200
200
150
150
100
100
50
50
Leaf Surface (cm2/shoot)
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
Lacco Ameno Winter
0 A1A2
B1B2
C1C2
350
A1A2
B1B2
C1C2
A1A2
B1B2
C1C2
350
Scarrupata Summer
300
300
250
250
200
200
150
150
100
100
50
50
A1A2
B1B2
C1C2
B1B2
C1C2
Scarrupata Winter
0
0 A1A2
Leaf Surface (cm2/shoot)
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
0
B1B2
350
C1C2
A1A2
B1B2
A1A2
C1C2
B1B2
350
Lacco Ameno Summer
300
300
250
250
200
200
150
150
100
100
50
50
C1C2
A1A2
Lacco Ameno Winter
0
0 A
B
C
350
Leaf Surface (cm2/shoot)
abc abc abc abc abc abc abc abc abc abc abc abc A1 A2 B1 B2 C1 C2 A1 A2 B1 B2 C1 C2 Scarrupata Winter
0
0
c)
400
350
b)
Date 2 Lacco Ameno Winter
A
B
A
C
B
C
350
Scarrupata Summer
300
300
250
250
200
200
150
150
100
100
50
50
A
B
C
A
B
C
Scarrupata Winter
0
0 A
B
C
A
B
C
A
B
C
Figure 17: Leaf surface (cm2) per shoot in the two meadows along the three main spatial scales and the two seasons: a) plot (a, b, c); b) station (1, 2); c) site (A, B, C). Red colors represent Summer season and blue colors Winter season. In each graph are represented both the date of sampling 1 and the date of sampling 2. Bars represent standard errors.
87
The leaf biomass (Figure 18) varied significantly at the spatial scales of Site (100s of m) and Quadrat (a few cm) (Table 6), and, on the temporal scale, it showed significant differences both with seasons and date of sampling (Table 6). Table 6: ANOVA and Cochran’s C tests for leaf biomass and sheaths biomass at the different spatial and temporal scales: L = Location; S = Season; D = Date of sampling; Si = Site; St= Station; Pl = Plot, Qu = Quadrat. (* = p < 0.05; ** = p < 0.01; *** = p < 0.001; ns = not significant; F = Fixed factor; R = Random factor).
L
Effect df F 1
S LS D(LS)
F F R
Si(D(LS)) St(Si(D(LS))) Pl(St(Si(D(LS)))) Qu(Pl(St(Si(D(LS))))) Error
R R R R
Cochran C Tranfsormation
Leaf Biomass MS F p 3.94E+07 6.41 ns
Sheaths Biomass MS F p 1568642 12.76 *
1 1 4
1.42E+08 23.08 ** 6109538 49.69 ** 4.25E+06 0.69 ns 277257 2.25 ns 6.14E+06 8.23 *** 122954 3.29 ns 37369 2.64 ns 16 7.46E+05 3.96 ** 24 1.88E+05 0.62 ns 14182 1.34 ns 96 3.05E+05 0.94 ns 10591 0.85 ns 12403 3.33 *** 144 3.23E+05 4.71 *** 2592 6.86E+04 3723 0.03 *** none
0.16 *** none
The range of values recorded was between 0.76 and 3.53 gDW/shoot in Scarrupata, while in Lacco Ameno it was between 0.34 and 1.99 gDW/shoot. The leaf biomass values were always higher in Scarruapta than in Lacco Ameno, and the mean values recorded accounted for 0.91 ± 0.10 gDW/shoot and 0.7 ± 0.10 gDW/shoot for Scarrupata and Lacco Ameno, respectively. The mean values recorded were higher in summer than in winter, and this is not surprising since Posidonia oceanica produces less tissue during winter, increasing the production in spring and summer. In the same way, the differences recorded between dates of sampling could be due to the delays in production in the first dates than in the second ones, as clearly evident, above all, in winter, where the second dates in both meadows correspond to an increase in production because of the near ending of the cold season.
88
Leaf biomass (gDW/shoot)
a)
Date 2
Date 1
2.00
1.75
1.50
1.50
1.25
1.25
1.00
1.00
0.75
0.75
0.50
0.50
0.25
0.25
Leaf biomass (gDW/shoot)
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
2.00
2.00
Scarrupata Summer
1.75
1.50
1.50
1.25
1.25
1.00
1.00
0.75
0.75
0.50
0.50
0.25
0.25 abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
Leaf biomass (gDW/shoot)
1.75
1.75
Lacco Ameno Summer
1.50
1.50
1.25
1.25
1.00
1.00
0.75
0.75
0.50
0.50
0.25
0.25
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
Lacco Ameno Winter
0.00 A1A2
Leaf biomass (gDW/shoot)
Scarrupata Winter
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
0.00 B1B2
C1C2
1.75
A1A2
B1B2
A1A2
C1C2
B1B2
C1C2
1.75
Scarrupata Summer
1.50
1.50
1.25
1.25
1.00
1.00
0.75
0.75
0.50
0.50
0.25
0.25
A1A2
B1B2
C1C2
Scarrupata Winter
0.00
0.00 A1A2
Leaf biomass (gDW/shoot)
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
0.00
0.00
B1B2
1.50
C1C2
A1A2
B1B2
A1A2
C1C2
B1B2
1.50
Lacco Ameno Summer
1.25
1.25
1.00
1.00
0.75
0.75
0.50
0.50
0.25
0.25
C1C2
A1A2
B1B2
C1C2
A
B
C
A
B
C
Lacco Ameno Winter
0.00
0.00 A
Leaf biomass (gDW/shoot)
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
1.75
c)
Lacco Ameno Winter
0.00
0.00
b)
Date 2
Date 1
2.00
Lacco Ameno Summer
1.75
B
1.50
C Scarrupata Summer
A
B
A
C
B
C Scarrupata Winter
1.50
1.25
1.25
1.00
1.00
0.75
0.75
0.50
0.50
0.25
0.25 0.00
0.00 A
B
C
A
B
C
A
B
C
Figure 18: Leaf biomass (grams of dry weight) per shoot in the two meadows along the three main spatial scales and the two seasons: a) plot (a, b, c); b) station (1, 2); c) site (A, B, C). Red colors represent Summer season and blue colors Winter season. In each graph are represented both the date of sampling 1 and the date of sampling 2. Bars represent standard errors.
89
Another leaf trait considered, strictly connected to the adult leaves, was the sheath length (Figure 19). The sheath length did not vary temporally, while, spatially, it showed significant differences at Site (100s of m) and Quadrat (a few cm) scales (Table 5). Both meadows showed the same range of values, i.e., between 0 (corresponding to no sheaths for some shoots) and 6 cm, and similar mean values: 3.73 ± 0.02 cm and 3.40 ± 0.02 cm for Scarrupata and Lacco Ameno, respectively.
90
Sheaths length (cm)/shoot
a)
Date 1
5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0
Date 2
Sheaths length (cm)/shoot
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0
Sheaths length (cm)/shoot
5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0
Sheaths length (cm)/shoot
b)
5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0
Sheats length (cm)/shoot
5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0
Sheats length (cm)/shoot
c)
5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0
5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
A1A2
B1B2
C1C2 A1A2 Scarrupata Summer
B1B2
C1C2
A1A2
B1B2
C1C2
B1B2
C1C2
A1A2
C
A
B
A
B
C
A1A2
B1B2
C1C2 A1A2 Scarrupata Winter
A1A2
B1B2
C1C2
B
5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 A
B
C
A1A2
B1B2
B1B2
C1C2
C1C2
Lacco Ameno Winter
A
C
Scarrupata Summer
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
Lacco Ameno Winter
5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0
Lacco Ameno Summer
B
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
Scarrupata Winter
5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0
Lacco Ameno Summer
A
Date 2 Lacco Ameno Winter
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
Scarrupata Summer
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
Date 1
5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0
Lacco Ameno Summer
C
A
B
A
B
C
Scarrupata Winter
A
B
C
C
Figure 19: Sheaths length (cm) per shoot in the two meadows along the three main spatial scales and the two seasons: a) plot (a, b, c); b) station (1, 2); c) site (A, B, C). Red colors represent Summer season and blue colors Winter season. In each graph are represented both the date of sampling 1 and the date of sampling 2. Bars represent standard errors.
91
Significant differences were recorded in sheath biomass (Figure 20) between seasons, while the only spatial scale showing differences was that of Quadrat (a few cm) (Table 6). The same arguments about the seasonal productivity used for the leaf biomass could be advocated for the sheath biomass, i.e., a higher production during spring and summer than the late autumn – winter. In order, Scarrupata showed the highest biomass values in both seasons, although the differences between the two meadows were not significant. The range values observed for Scarrupata were between 0 and 1.5 gDW/shoot and for Lacco Ameno between 0 and 1.3 gDW/shoot, while the mean value for Scarrupata was 1.5 ± 0.03 gDW/shoot and for Lacco Ameno was 0.10 ± 0.02 gDW/shoot.
92
Sheaths biomass (gDW/shoot)
Sheaths biomass (gDW/shoot)
a)
Date 1
0.40
0.30
0.30
0.25
0.25
0.20
0.20
0.15
0.15
0.10
0.10
0.05
0.05 abc abc abc abc abc abc abc abc abc abc abc abc A1 A2 B1 B2 C1 C2 A1 A2 B1 B2 C1 C2 Scarrupata Summer
0.35
0.30
0.30
0.25
0.25
0.20
0.20
0.15
0.15
0.10
0.10
0.05
0.05
0.30
0.30
Lacco Ameno Summer
0.25
0.25
0.20
0.20
0.15
0.15
0.10
0.10
0.05
0.05
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
Lacco Ameno Winter
0.00 A1A2
Sheaths biomass (gDW/shoot)
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
0.00 B1B2
0.30
C1C2
A1A2
B1B2
C1C2
A1A2
B1B2
C1C2
0.30
Scarrupata Summer
0.25
0.25
0.20
0.20
0.15
0.15
0.10
0.10
0.05
0.05
A1A2
B1B2
C1C2
A1A2
B1B2
C1C2
Scarrupata Winter
0.00
0.00 A1A2
Sheaths biomass (gDW/shoot)
abc abc abc abc abc abc abc abc abc abc abc abc A1 A2 B1 B2 C1 C2 A1 A2 B1 B2 C1 C2 Scarrupata Winter
0.00
0.00 abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
B1B2
0.25
C1C2
A1A2
B1B2
A1A2
C1C2
B1B2
0.25
Lacco Ameno Summer
0.20
0.20
0.15
0.15
0.10
0.10
0.05
0.05
C1C2
Lacco Ameno Winter
0.00
0.00 A
Sheaths biomass (gDW/shoot)
0.40
0.35
c)
Date 2 Lacco Ameno Winter
0.00
0.00
0.40
Date 1
0.40 0.35
b) Sheaths biomass (gDW/shoot)
Date 2
Lacco Ameno Summer
0.35
B
C
A
B
Scarrupata Summer
0.25
A
C
B
0.20
0.20
0.15
0.15
0.10
0.10
0.05
0.05
0.00
C
A
B
C
A
B
C
Scarrupata Winter
0.25
0.00 A
B
C
A
B
C
A
B
C
Figure 20: Sheath biomass (grams of dry weight) per shoot in the two meadows along the three main spatial scales and the two seasons: a) plot (a, b, c); b) station (1, 2); c) site (A, B, C). Red colors represent Summer season and blue colors Winter season. In each graph are represented both the date of sampling 1 and the date of sampling 2. Bars represent standard errors.
93
4.2.2
Multivariate Results
When approaching the multivariate analyses, I decided to exclude from the calculation LAI, LSC and the leaf categories to reduce the “noise” these variables could give. This decision was born from the consideration that LAI and LSC are strongly dependent on shoot density and some of the leaf traits, thus, their use could result in a co-variation of data distribution. The same is true of the number of leaf categories that are co-variables of the total number of leaves per shoot. The general pattern recorded with the univariate analyses were consistent with the multivariate results. In the nMDS plot (Figure 21), the general patterns of distribution of the meadow features were evident. The two meadows were well separated both in terms of location and in terms of season. In Lacco Ameno, the two summer sampling dates were closer than those of the Scarrupata meadow which were well separated. This difference was probably driven by those life traits (e.g., leaf length) that in Scarrupata were influenced by the approaching of the first sea-storms in the second summer date of sampling (September). In winter, both meadows were separated and within each meadow both dates of sampling were also well separated.
Stress: 0.09
LA LB LC SA SB SC
Sum2
Sum1 Sum1 Sum2
Win2 Win2 Win2
Sum2 Sum1 Sum1
Win1
Win2 Win2
Win1 Win1 Win1 Win1
LocationSites
Sum1 Sum1
Win2
Sum2 Sum2 Win1
Sum2
Figure 21: nMDS of centroids calculated by means of Principal Coordinates of morphometric features. LA (Lacco Ameno site A), LB (Lacco Ameno site B), LC (Lacco Ameno site C); SA (Scarrupata site A), SB (Scarrupata site B), SC (Scarrupata site C). Sum represents summer season, Win is winter. 1 is for date 1 and 2 is for date 2.
94
The results of the PERMANOVA (Table 7) were consistent with the ordination of the nMDS. The results indicated that there were significant differences between the two meadows (say Locations), at the smaller scales of Site and Station (although this latter was not represented in the plot), but also significant differences between the seasons. Moreover, the pairwise test, showed that, even if the two summer dates of sampling of Lacco Ameno appeared closer than those of Scarrupata, they are nevertheless significantly different. Table 7: PERMANOVA results of the main morphometric and biomass plant parameters. P(perm) are values calculated by permutations; P(MC) are values calculated by Monte Carlo simulations. Significant values are represented in bold. L = Location; S = Season; D = Date of sampling; Si = Site; St= Station; Pl = Plot
Source df L 1 S 1 LS 1 D(LS) 4 Si(D(LS)) 16 St(Si(D(LS))) 24 Pl(St(Si(D(LS)))) 96 Residual 144 Total 287
Morphomometrics and Biomass parameters* SS MS F P(perm) P(MC) 276.52 276.52 3.53 0.0868 0.0390 677.55 677.55 8.65 0.0032 0.0024 48.85 48.85 0.62 0.5818 0.6286 313.41 78.35 7.24 0.0002 0.0002 173.10 10.82 2.42 0.0006 0.0006 107.10 4.46 1.51 0.0150 0.0100 283.16 2.95 1.02 0.4200 0.4248 416.31 2.89 2296.00 *without LAI, LSC and leaf categories
4.2.3
Summary
All the plant variables showed significant differences in their mean values for at least one spatial scale, and commonly at multi-spatial scales. In order, Scarrupata showed almost always higher values than Lacco Ameno for all the parameters. All three structural variables (shoot density, LAI and LSC) showed significant differences at Location and Station scales, but only LAI and LSC also showed significant differences at Site scale. All the leaf traits varied, at least, at the smallest scale of Quadrat. Only the sheaths biomass varied at Location scale. The number of leaves, the number of adult leaves and the leaf 95
surface showed differences in only one of the spatial scales (namely, only Quadrat). The number of juvenile leaves and the leaf width also varied according to the Station scale, while leaf length, sheaths length and leaf biomass showed significant differences at Site scale, and only the number of intermediate leaves showed significant differences at Plot scale. Temporally, differences in the mean values of the variables were significant for at least one of the two temporal scales (except shoot density). Both LAI and LSC showed significant differences with the Seasons, but only LAI also with date of sampling. All the leaf traits showed significant differences with the date of sampling (except leaf width, sheaths length and sheaths biomass), while the number of intermediate leaves, leaf width, leaf biomass and sheaths biomass, showed variability according to seasons. The multivariate analysis results were consistent with the univariate ones, clearly separating the two meadows, and recognizing several scales of variability from Location to Station.
4.3
Borer Polychaetes
4.3.1
Univariate Analysis
All the three main borer polychaete species (Lysidice collaris, L. ninetta and Nematonereis unicornis) were found with a total of 1,252 individuals. In particular, L. collaris accounted for 790 individuals, L. ninetta for 337 and N. unicornis for 125. The percentages of the relative abundance for each single species (Figure 22), showed that L. collaris was the most abundant species in both meadows and both seasons with values ranging from 52.5% in Lacco Ameno in winter and 70.8% in Scarrupata in winter, while L. ninetta represented the second abundant species in both meadows and both seasons with values ranging from 23.6% in Scarrupata in winter and 30.1% in Scarrupata in summer. N. unicornis was 96
always the less abundant species with values ranging from 5.7% in Scarrupata in winter and 17.5% in Lacco Ameno in winter.
Summer
Winter
Lacco Ameno
9.3%
17.5% 24.4% 66.3%
L. collaris
L. ninetta
N. unicornis
5.7%
10.2%
Scarrupata
52.5%
30.0%
23.6%
30.1% 59.7%
70.8%
Figure 22: Relative percentage of abundances for each single borer polycheate species (L. collaris, L. ninetta and N. unicornis) in both meadows and both seasons.
Borer polychaetes presented an elevated variability both between meadows and within each meadow. Total IB (all species together; Figure 23) varied at several spatial scales (from Location to Site and Station) (Table 8). Table 8: ANOVA and Cochran’s C test results for total IB and single species IB (L. collaris, L. ninetta and N. unicornis) at the different spatial and temporal scales: L = Location; S = Season; D = Date of sampling; Si = Site; St= Station; Pl = Plot. In bold are signed significant values. (F = Fixed factor; R = Random factor).
Effect L F S F LS F D(LS) R Si(D(LS)) R St(Si(D(LS))) R Pl(St(Si(D(LS)))) R Error Cochran C Tranfsormation
Total IB df MS F 1 3068.04 15.94 1 901.25 4.68 1 429.67 2.23 4 192.49 0.30 16 633.86 4.14 24 152.98 1.64 96 93.14 0.78 144 118.84 0.06
p 0.016 0.096 0.209 0.871 0.001 0.048 0.900
IB L. collaris MS F p 2199.75 45.85 0.002 542.02 11.30 0.028 1289.35 26.88 0.007 47.97 0.08 0.987 592.97 5.17 0.000 114.67 1.71 0.035 66.94 0.62 0.993 107.58
IB L. ninetta MS F p 302.25 2.23 0.210 190.82 1.41 0.301 23.13 0.17 0.701 135.57 1.00 0.436 135.47 1.94 0.068 69.65 2.07 0.007 33.63 1.10 0.295 30.49
0.99 none
0.07 0.99 none
0.10 1.00 none
IB N. unicornis MS F p 3.44 0.22 0.666 12.33 0.78 0.428 56.36 3.54 0.133 15.91 0.62 0.653 25.56 1.68 0.121 15.19 1.41 0.124 10.79 0.84 0.812 12.77 0.10
1.00 none
Generally, the frequency of the borer polychaetes was always higher in Scarrupata than in Lacco Ameno, although in the latter some higher peaks were reached (e.g., in Site C, 97
Stations 1 and 2 during Summer season). The borer polychaetes frequencies varied in a range between 0 and 64% in Scarrupata and in Lacco Ameno between 0 and 60%. In the whole, the mean number of IB recorded in both meadows accounted for 22.8 ± 1.0% in Scarrupata and for 16.3 ± 1.0% in Lacco Ameno. Concerning the temporal scales, no significant differences were recorded when both meadows were considered together, but different trends were evident for the two meadows. In fact, while in Scarrupata the mean IB values were formally the same (23.4 ± 1.4% in Summer and 22.3 ± 1.5% in Winter), in Lacco Ameno the mean IB values recorded accounted for 19.3 ± 1.8% in Summer and 13.3 ± 0.9% in Winter, difference that the pairwise test showed as significant. It seemed that the human impact acted as a source of variability, decreasing the frequency of borer polychaetes, apparently by the reduction of shoots available for settling of these species, which was confirmed by the ANOVA made for one meadow at a time (Table 9), which showed that in Lacco Ameno the IB varied at several spatial scales, while in Scarrupata it did not vary at any scale. In other words, it seemed that pristine conditions (i.e. Scarrupata) were not characterized by differences in the distribution of the borer polychaetes, while the disturbances altered their spatial variability in Lacco Ameno. Table 9: ANOVA results obtained by testing for each meadow at a time for total IB and single species IB (L. collaris, L. ninetta) at the different spatial and temporal scales: S = Season; D = Date of sampling; Si = Site; St= Station; Pl = Plot. (* = p < 0.05; ** = p < 0.01; *** = p < 0.001; ns = not significant) Lacco Ameno
S D(S) Si(D(S) St(Si(D(S))) Pl(St(Si(D(S))))
IB * ns ** * ns
IB L. IB L. collaris ninetta ns * ns ns ns *** ns *** ns ns
Scarrupata IB ns ns ns ns ns
IB L. IB L. collaris ninetta ns ns ns ns ns ns ns ns ns ns
Moreover, it was not negligible the significant difference along the seasons in Lacco Ameno for the total IB. In fact, since the main human impacts (namely, the boat anchoring and the chemical pollution) increased during Summer season, one may expect the 98
frequency of the borer polychaetes to be lower during that season, while instead the contrary was true, and, apparently the impacts had no effects on the borer frequencies. Thus other reasons should be investigated to obtain an alternative explanation, such as the influence of the bare sediment assemblages contaminating the Lacco Ameno meadow’s one, or the reproductive strategies of these polychaetes.
99
a)
Date 2
Date 1
IB%
70.0
Date 1 70.0
Lacco Ameno Summer
60.0
60.0
50.0
50.0
40.0
40.0
30.0
30.0
20.0
20.0
10.0
10.0 0.0
0.0 abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
IB%
70.0
70.0
Scarrupata Summer
60.0
50.0
50.0
40.0
40.0
30.0
30.0
20.0
20.0
10.0
10.0
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
IB%
50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0
IB%
50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0
B1B2
50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0
C1C2
A1A2
B1B2
C1C2
A1A2
B1B2
C1C2
A1A2
B1B2
A1A2
C1C2
Lacco Ameno Summer
B1B2
C1C2
A1A2
35.0
30.0
30.0
25.0
25.0
20.0
20.0
15.0
15.0
10.0
10.0
5.0
5.0
A1A2
B1B2
C1C2
B1B2
C1C2
Scarrupata Winter
B1B2
40.0
35.0
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
Lacco Ameno Winter
50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0
Scarrupata Summer
40.0
IB%
ScarrupataWinter
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
Lacco Ameno Summer
A1A2
c)
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
0.0
0.0
C1C2
A1A2
Lacco Ameno Winter
0.0
0.0 A
B
C
40.0
IB%
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
60.0
b)
Date 2 Lacco Ameno Winter
A
B
A
C
B
C
40.0
ScarrupataSummer
35.0
35.0
30.0
30.0
25.0
25.0
20.0
20.0
15.0
15.0
10.0
10.0
5.0
5.0
0.0
A
B
A
B
C
Scarrupata Winter
0.0 A
B
C
A
B
C
A
B
C
C
Figure 23: Index of Borers (IB) calculated for all the polychaete borer species together in the two meadows along the three main spatial scales and the two seasons: a) plot (a, b, c); b) station (1, 2); c) site (A, B, C). Red colors represent Summer season and blue colors Winter season. In each graph are represented both the date of sampling 1 and the date of sampling 2. Bars represent standard errors.
100
When considered individually, the IB of the single borer polychaete species presented different patterns of variability. The most abundant species, L. collaris (Figure 24), showed significant differences at almost all the spatial scales considered except the Plot scale (Table 8). It showed differences along the seasons as well. In the whole, L. collaris showed a more patchy distribution in Lacco Ameno than in Scarrupata, and this was more evident, above all, during Summer, while in winter its frequency distribution appeared more homogeneous. In order, the higher L. collaris IB values were recorded in Scarrupata, although two main peaks were observed in Lacco Ameno during summer at station 1 site C in the first date, and at station 1 site C in the second date of sampling. The range values reported for IB of L. collaris for both meadows were almost the same (from 0 to 52.9% and from 0 to 50% in Scarrupata and Lacco Ameno, respectively), but the mean numbers were significantly different: 16.4 ± 1.0% in Scarrupata and 10.9 ± 0.9% in Lacco Ameno. The ANOVA made for each meadow (Table 9) clearly showed the dependency of the total IB variability on the L. collaris IB, because the main spatial and temporal differences were in common between them (Season and Site, but also Location).
101
a)
IB% L. collaris
Date 2
Date 1
60.0 50.0
50.0
40.0
40.0
30.0
30.0
20.0
20.0
10.0
10.0
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
IB% L. collaris
60.0
60.0
Scarrupata Summer
50.0
40.0
40.0
30.0
30.0
20.0
20.0
10.0
10.0
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
IB% L. collaris
40.0
40.0
Lacco Ameno Summer
35.0
35.0
30.0
30.0
25.0
25.0
20.0
20.0
15.0
15.0
10.0
10.0
5.0
5.0
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
Lacco Ameno Winter
0.0 A1A2
B1B2
C1C2
40.0
IB% L. collaris
Scarrupata Winter
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
0.0 A1A2
B1B2
A1A2
C1C2
B1B2
C1C2
40.0
Scarrupata Summer
35.0
35.0
30.0
30.0
25.0
25.0
20.0
20.0
15.0
15.0
10.0
10.0
5.0
5.0
A1A2
B1B2
C1C2
Scarrupata Winter
0.0
0.0 A1A2
B1B2
35.0
IB% L. collaris
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
0.0
0.0
C1C2
A1A2
B1B2
A1A2
C1C2
B1B2
35.0
Lacco Ameno Summer
30.0
30.0
25.0
25.0
20.0
20.0
15.0
15.0
10.0
10.0
5.0
5.0
0.0
C1C2
A1A2
B1B2
C1C2
B
C
Lacco Ameno Winter
0.0 A
B
35.0
IB% L. collaris
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
50.0
c)
Lacco Ameno Winter
0.0
0.0
b)
Date 2
Date 1
60.0
Lacco Ameno Summer
C
A
B
C
A
B
35.0
ScarrupataSummer
30.0
30.0
25.0
25.0
20.0
20.0
15.0
15.0
10.0
10.0
5.0
5.0
0.0
C
A
ScarrupataWinter
0.0 A
B
C
A
B
C
A
B
C
A
B
C
Figure 24: Index of Borers (IB) calculated only for L. collaris in the two meadows along the three main spatial scales and the two seasons: a) plot (a, b, c); b) station (1, 2); c) site (A, B, C). Red colors represent Summer season and blue colors Winter season. In each graph are represented both the date of sampling 1 and the date of sampling 2. Bars represent standard errors.
102
Also the second most abundant borer polychaete species IB, L. ninetta (Figure 25), showed high patchiness in Summer in the Lacco Ameno bed, while, during Winter, its distribution pattern was almost homogeneous. L. ninetta showed significant differences only at the Station scale (Table 8). The Scarrupata bed showed the highest frequency of this polychaete species, in both seasons, although a high peak was evident in Lacco Ameno at the station 1 of the site C in the first date of sampling in Summer. The IB of L. ninetta varied between 0 and 45% in Scarrupata, and between 0 and 25% in Lacco Ameno, with mean values of 6.8 ± 0.6% in Scarrupata, and 4.8 ± 0.5% in Lacco Ameno. No substantial differences were recorded with season with mean values in Scarrupata accounting for 7.9 ± 0.9% and 5.7 ± 0.8% in Summer and Winter, respectively, while in Lacco Ameno mean values were 5.3 ± 0.8% and 4.2 ± 0.5% in Summer and Winter, respectively. The ANOVA results obtained for each single meadow (Table 9), showed that in Lacco Ameno the IB of L. ninetta varied at the Station scale, while in Scarrupata, once again, no differences were recorded. The significant variability at the Station scale recorded in Lacco Ameno, moreover, is strong enough to drive the Station scale variation when both meadows are compared together, and, furthermore, it influenced the total IB Station variability.
103
IB% L. ninetta
a)
Date 1
50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0
IB% L. ninetta
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0
IB% L. ninetta
25.0
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
25.0
Lacco Ameno Summer
15.0
15.0
10.0
10.0
5.0
5.0
B1B2
25.0
C1C2
A1A2
B1B2
C1C2
A1A2
20.0
15.0
15.0
10.0
10.0
5.0
5.0
0.0
B1B2
C1C2
A1A2
B1B2
C1C2
B1B2
C1C2
A
B
C
A
B
C
Scarrupata Winter
0.0 A1A2
IB% L. ninetta
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
Lacco Ameno Winter
25.0
Scarruppata Summer
20.0
IB% L. ninetta
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
0.0 A1A2
20.0 18.0 16.0 14.0 12.0 10.0 8.0 6.0 4.0 2.0 0.0
Date 2
Scarrupata Winter
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
0.0
IB% L. ninetta
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
20.0
20.0 18.0 16.0 14.0 12.0 10.0 8.0 6.0 4.0 2.0 0.0
Lacco Ameno Winter
50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0
20.0
c)
Date 1
50.0 45.0 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0
Scarrupata Summer
abc abc abc abc abc abc A1 A2 B1 B2 C1 C2
b)
Date 2
Lacco Ameno Summer
B1B2
C1C2
A1A2
B1B2
C1C2
Lacco Ameno Summer
A
B
C
A
B
C
B
C
B1B2
C1C2
A
B
C
A1A2
Lacco Ameno Winter
A
B
C
20.0 18.0 16.0 14.0 12.0 10.0 8.0 6.0 4.0 2.0 0.0
Scarrupata Summer
A
A1A2
20.0 18.0 16.0 14.0 12.0 10.0 8.0 6.0 4.0 2.0 0.0
ScarrupataWinter
A
B
C
Figure 25: Index of Borers (IB) calculated only for L. ninetta in the two meadows along the three main spatial scales and the two seasons: a) plot (a, b, c); b) station (1, 2); c) site (A, B, C). Red colors represent Summer season and blue colors Winter season. In each graph are represented both the date of sampling 1 and the date of sampling 2. Bars represent standard errors.
104
Index of borers of the less abundant species N. unicornis is not represented since this species did not show any particular pattern of distribution with no significant differences at any scale or with season (Table 8).
4.3.2
Multivriate Analysis
The multivariate analyses were made only for the single species IB to avoid the covariation between the total IB with the former ones. The PERMANOVA results (Table 10) clearly showed the spatial variability between the two meadows (i.e., Location, Site and Station scales), but it also showed that the single species IB presented a variability in the interaction Location x Site. This means that a certain variability due to season existed for each single meadow, which was consistent with the results obtained by the ANOVA made for each single meadow (Table 9) that clearly showed that L. collaris varied along the seasons in the Lacco Ameno bed, and, since that was the most abundant species, it drove the significant differences with season in the PERMANOVA too. Table 10: PERMANOVA results of single species IB. P(perm) are values calculated by permutations; P(MC) are values calculated by Monte Carlo simulations. In bold significant values.
Source df SS MS L 1 17299.34 17299.34 S 1 5114.12 5114.12 LS 1 8355.89 8355.89 D(LS) 4 10350.02 2587.51 Si(D(LS)) 16 71771.00 4485.69 St(Si(D(LS))) 24 63262.25 2635.93 Pl(St(Si(D(LS)))) 96 150580.53 1568.55 Residual 144 242674.24 1685.24 Total 287 569407.39
Borer Frequencies F P(perm) 6.69 0.0028 1.98 0.1612 3.23 0.0416 0.58 0.8422 1.70 0.0244 1.68 0.0008 0.93 0.7620
P(MC) 0.0042 0.1506 0.0498 0.8744 0.0142 0.0002 0.7552
The ordination obtained by plotting the centroids (Figure 26), showed (although not very clearly) the separation of the two meadows, and also between sites and stations, although the separation between summer and winter in Lacco Ameno was not so evident. The 105
nMDS appeared to highlight the fact that at more stable conditions of the meadows there were more homogeneous distributions and frequencies of the three species of borer polychaetes (i.e., less scattered).
Stress: 0.1
Win1 Sum1
Win1
Win2 Win1
Sum1
Sum1
Win2 Win2
Sum2
Win2
Sum1 Sum2 Win2
Win1
Sum2
Win1 Sum2 Sum2 Sum1 Win1
LocationSites LA LB LC SA SB SC
Sum1
Win2
Sum2
Figure 26: nMDS of centroids calculated by means of Principal Coordinates of IB of single species. LA (Lacco Ameno site A), LB (Lacco Ameno site B), LC (Lacco Ameno site C); SA (Scarrupata site A), SB (Scarrupata site B), SC (Scarrupata site C). Sum represents summer season, Win is winter. 1 is for date 1 and 2 is for date 2.
4.3.3
Summary
All the three borer polychaete species were found. L. collaris was the most abundant species, followed by L. ninetta and more rare N. unicornis. The frequency of the borer polychaetes was always higher in Scarrupata than in Lacco Ameno. Several spatial scales of variation were found: the total IB varied at Location, Site and Station scales; L. collaris IB varied at Location, Site and Station scales; L. ninetta varied only at Station scale; while N. unicornis did not show any pattern of variability. The only temporal variation was recorded for L. collaris that showed significant differences with seasons, showing higher values in summer than in winter, more evident in Lacco Ameno than in Scarrupata. The univariate tests made for each meadow showed that
106
a high variability occurred only in Lacco Ameno, suggesting that probably the human disturbances made the distribution of these animals more disperse and fragmented. The multivariate analysis made on each of the three species showed a similar pattern to that of the univariate tests, that is, a multi-spatial scale of variability represented by a quite scattered nMDS plot.
4.4
Motile Macro-invertebrates Abundances and Distribution
4.4.1
Higher Taxa
On the whole, 18 taxonomic groups were found, represented by a total of 26,678 individuals. In Lacco Ameno, 15,384 individuals from 18 taxa were found, while in Scarrupata there were 17 taxa and 10,973 individuals in total. In Lacco Ameno, the number of taxa found in summer was 18 accounting for 5,630 individuals, while in winter the number of taxa was 16 for 9,754 individuals. In Scarrupata, the number of taxa found in summer was 15 with 4,651 individuals, while in winter the number of taxa was 17 with 6,322 individuals. Data on number of taxa and individuals per quadrat are reported in Appendix 1 Table 19. An average of 12.7 ± 2.4 taxa per quadrat (mean ± SD) were found in Lacco Ameno in summer and 12.6 ± 1.5 taxa per quadrat in winter. In Scarrupata, a total of 11.8 ± 1.3 taxa per quadrat were found in summer and 13.6 ± 1.1 taxa per quadrat in winter. There was not any significant differences between meadows, as shown by the ANOVA results (Table 11 a), but differences with Seasons, and in the interaction Location x Season were recorded. Differences between Summer and Winter were found in Scarrupata but not in Lacco Ameno. On the contrary, the number of individuals showed significant differences between Locations and with Seasons (Table 11 a). The average number of individuals per quadrat varied within a wide range, in Lacco Ameno 330.6 ± 173.6 and 541.9 ± 181.2 individuals per quadrat were recorded in summer and in winter, 107
respectively, while in Scarrupata the average number of individuals varied between 258.4 ± 84.4 and 351.2 ± 132.6 individuals per qudrat in summer and in winter, respectively. The two diversity indices, Evenness (J’) and Shannon – Wiener (H’) (Figures 27 and 28), showed significant differences only for the interaction Location x Season (Table 11 a). This means that the distribution of the individuals, among the taxa, depended on a temporal variation of the environmental features (such as the plant features), or even on differences of predatory pressure decreasing during Winter. Moreover, the pairwise test showed that these differences depended by a significant variability between Summer and Winter in Scarrupata (i.e., the pristine meadow). Table 11: a) ANOVA of number of taxa (S), number of individuals (N), evenness (J’) and Shannon – Wiener diversity index (H’); b) ANOVA of higher taxa at different spatial scales. Bold characters indicate significant values. a) Effect L F S F LS F Si(LS) R St(Si(LS)) R Error
df 1 1 1 8 12 48
b) Effect df L S LS Si(LS) St(Si(LS)) Error
108
F F F R R
1 1 1 8 12 48
Effect L F S F LS F Si(LS) R St(Si(LS)) R Error
df 1 1 1 8 12 48
Effect L F S F LS F Si(LS) R St(Si(LS)) R Error
df 1 1 1 8 12 48
Effect L F S F LS F Si(LS) R St(Si(LS)) R Error
df 1 1 1 8 12 48
Higher taxa diversity indices S N F p F p F 0.0 0.8718 12.5 0.0077 0.2 6.2 0.0369 16.7 0.0035 0.9 8.0 0.0220 2.5 0.1505 13.4 1.1 0.4304 2.2 0.1115 1.4 0.6 0.8244 0.5 0.9147 0.9
Plathelminthes F p 15.3 0.2 25.2 0.3 0.9
0.0044 0.6750 0.0010 0.9615 0.5366
Polychaeta F p 15.0 2.3 0.1 1.8 1.4
J' p 0.7021 0.3670 0.0063 0.3077 0.5380
Sipunculida F p
0.0047 8.7 0.0183 0.1706 17.5 0.0031 0.8246 6.1 0.0392 0.1748 1.3 0.3206 0.2102 0.4 0.9580
H' F 0.4 4.7 24.2 1.0 0.8
p 0.5369 0.0619 0.0012 0.5127 0.6010
Gastropoda F p 1.2 30.2 0.4 0.8 2.4
0.3066 0.0006 0.5564 0.6208 0.0140
Mysidacea Isopoda Tanaidacea Cumacea F p F p F p F p 11.8 0.0090 95.5 0.0000 16.2 0.0038 28.1 0.0007 2.6 0.1450 70.8 0.0000 5.4 0.0484 0.2 0.6702 5.9 0.0410 38.6 0.0003 1.3 0.2901 17.0 0.0033 9.4 0.0004 0.8 0.6448 2.9 0.0461 1.8 0.1696 0.6 0.8479 0.3 0.9782 0.4 0.9347 0.3 0.9750
Amphipoda F p 29.4 0.0006 0.9 0.3652 0.5 0.5138 2.3 0.0912 0.4 0.9448
Pantopoda Chaetognatha Echinodermata F p F p F p 1.7 0.2329 1.5 0.2556 9.5 0.0152 0.6 0.4738 0.3 0.5966 34.4 0.0004 5.2 0.0518 3.2 0.1137 2.4 0.1594 7.1 0.0015 1.3 0.3445 1.1 0.4229 0.8 0.6108 0.8 0.6635 0.5 0.8991
Bivalvia Polyplacophora Opisthobranchia Decapoda F p F p F p F p 31.1 0.0005 1.2 0.3089 7.7 0.0242 190.4 0.0000 0.4 0.5526 0.5 0.4895 7.7 0.0242 63.5 0.0000 4.9 0.0581 0.1 0.7266 11.6 0.0092 45.8 0.0001 0.6 0.7676 1.7 0.1978 2.9 0.0456 4.0 0.0157 1.1 0.3549 1.4 0.1812 0.3 0.9899 0.1 0.9998
J'
J'
0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 LA Summer
LA Winter
Sc Summer
J'
J'
0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 A
B
C
A
B
C
A
B
C
A
B
Sc Summer
0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
C
Sc Winter
0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 A1 A2 B1 B2 C1 C2
A1 A2 B1 B2 C1 C2
LA Summer
Sc Winter
0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
LA Winter
J'
J'
LA Summer
A1 A2 B1 B2 C1 C2
0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0
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Figure 27: Equitability (J’) of higher taxa at the three spatial scales. LA is Lacco Ameno and Sc is Scarrupata. Bars represent standard deviation.
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Figure 28: Shannon – Wiener diversity index (H’) of higher taxa at the three spatial scales. LA - Lacco Ameno and Sc - Scarrupata. Bars represent standard deviation.
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The community structures (as percentage of single groups of the total of individuals) (Figures 29, 30 and 31) showed major differences in the relative abundances of higher taxonomical groups, above all for dominant taxa. On the whole, in Lacco Ameno in both seasons the most abundant taxa were decapods (36.6%), followed by polychaetes (29.0%) and by amphipods (19.5%), less abundant were gastropods (5.1%), bivalves (4.5%) and cumaceans (2.4%). In Scarrupata in both seasons the most abundant taxa were amphipods (49.4%), polychaetes (13.9%), mysids (8.3%), decapods (7.3%), isopods (7.1%), gastropods (5.8%), cumaceans (5.7%) and bivalvia (5.8%). 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Summer Lacco Ameno Polychaeta
Gastropoda
Winter Lacco Ameno Bivalvia
Decapoda
Summer Scarrupata Mysidacea
Isopoda
Cumacea
Winter Scarrupata Amphipoda
Others
Figure 29: Community structures (percentage of single groups on the total of individuals) at location scale for each season. The group Others includes flat worms, sipunculids, polyplacophores, nudibranches, sea-spiders, chetognaths, echinoderms and tanaids.
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Figure 30: Community structures (percentage of a single group s on the total of individuals) at site scales for each season. ‘Others’ includes flat worms, sipunculids, polyplacophores, nudibranches, seaspiders, chetognaths, echinoderms and tanaids. 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% A1
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Biva lvia
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Figure 31: Community structures (percentage of a single group on the total of individuals) at station scale for each season. ‘Others’ includes flat worms, sipunculids, polyplacophores, nudibranches, seaspiders, chetognaths, echinoderms and tanaids.
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The community structure recorded in Scarrupata, was consistent with the assemblages found in previous works in other pristine meadows in the Mediterranean Sea, and, furthermore, the relative percentages of the higher taxonomical groups found in the present study clearly showed the strong dependence of these taxa on the P. oceanica features and its status of health. It is worth noting that, notwithstanding the net dominance of the amphipods in both seasons, in Scarrupata an extremely more diversified community was found, with a similar number of individuals well distributed in each of the “minor” taxa. When pristine conditions were not present anymore (reduction of the shoot density, lower canopy, etc.), as in the Lacco Ameno bed, the sensible amphipod assemblages decreased in number and other taxonomical groups, such as polychaetes and decapods, that better fitted with lower plant covering conditions and lower shoot density, and thus, of soft sediments increased. Furthermore, it was not to be excluded the contamination due to the enrichment by surrounding, soft-sediment species, that increased the number of individuals of sediment-dwelling species in Lacco Ameno. The spatial distribution and seasonal variations of each single taxonomic group were evaluated by means of univariate analysis of variance (Table 11 b). ANOVA showed a significant variation at the meadow scale for polychaetes and amphipods (p < 0.01 and p < 0.001, respectively) but no significant variations were found between Seasons. On the other hand, decapods showed a significant variability both at Location and Site scales (p < 0.001 and p < 0.05) and also between Seasons (p < 0.001), presenting higher abundances in winter than in summer, and in the interaction Location x Season (p < 0.001) for which the pairwise test gave a significant variability between summer and winter in Lacco Ameno (p < 0.001) but no significant variability for Scarrupata. Gastropods showed a significant variability between Seasons (p < 0.001), presenting a higher abundance in winter than in summer; significant variation in distribution at the Station scale was also detected (p < 0.05) indicating a patchy distribution at lower scales. Isopods showed a significant 112
variability at Location scale (p < 0.001). They significantly varied also according to Seasons (p < 0.001) with higher abundances in winter than in summer, but also the interaction Location x Season was significantly variable (p < 0.001) and the pairwise test reported a significant difference between summer and winter in Scarrupata (p < 0.001). The Figure 32 shows the nMDS for higher taxonomical groups. It was evident that the two meadows were completely separated, demonstrating that the communities living in Scarrupata and Lacco Ameno, were different in compositions as well as in relative abundances. Furthermore, even the temporal differences of the two seasons were evident, with winter and summer clearly separated in both meadows. Apparently, the impacted conditions of the Lacco Ameno bed determined the smoothing of the differences in assemblage compositions as compared to the Scarrupata one, modifying the natural temporal variability of the characteristic fauna assemblages. In the latter meadow, differences in the community structures between seasons were probably due to different predatory conditions that increased the number of individuals of a few taxa (e.g., isopods, decapods, gastropods, polychaetes, etc.). Win
Stress: 0.06 LocationSites LA LB LC SA SB SC
Win Win WinWin Win
Sum Sum Sum
Sum
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Sum
Figure 32: nMDS of centroids calculated by means of Principal Coordinates of higher taxa on the basis of Bray-Curtis dissimilarity matrix after fourth root transformation. LA (Lacco Ameno site A), LB (Lacco Ameno site B), LC (Lacco Ameno site C); SA (Scarrupata site A), SB (Scarrupata site B), SC (Scarrupata site C). Sum represents summer season, Win is winter.
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Consistently with the picture of the nMDS, PERMANOVA results (Table 12) indicated, with the only exception of the Station scale, that all the spatial scales, from Location to Site, were significantly different. This finding showed that at different meadow conditions and health status, corresponded different assemblages and community structures. Table 12: PERMANOVA results of higher taxa. P(perm) are values calculated by permutations; P(MC) are values calculated by Monte Carlo simulations. Bold numbers are significant values.
Source L S LS Si(LS) St(Si(LS) Residual Total
df 1 1 1 8 12 48 71
SS 3618.66 1718.26 1262.90 2264.02 1334.72 7383.87 17582.44
Higher Taxa MS F P(perm) P(MC) 3618.66 12.79 0.0002 0.0002 1718.26 6.07 0.0026 0.0008 1262.90 4.46 0.0100 0.0058 283.00 2.54 0.0008 0.0012 111.23 0.72 0.9348 0.9196 153.83
The percentages of similarity (SIMPER) due to each taxonomical group in the community structures was evaluated and summarized in Appendix 1 Tables from 20 to 27. Data are reported for meadows, sites and stations per seasons. Pairwise values between sites and stations are reported too. The dissimilarity between Summer and Winter in Lacco Ameno accounted for 41.8% and the three main taxa contributing to this dissimilarity were decapods (43.8%), polychaetes (22.2%) and amphipods (12.7%). The dissimilarity between Summer and Winter in Scarrupata accounted for 36.0% and the main taxa contributing to this dissimilarity were amphipods (29.3%), polychaetes (17.9%), mysids (12.4%) and isopods (10.4%). Thus, also the SIMPER confirmed the weight of the main taxa in differentiating the two meadows.
4.4.2
Higher Taxa: Summary
The number of individuals was always significantly higher in Lacco Ameno than in Scarrupata, while the number of taxa did not show differences at the Location scale. Both number of individuals and number of taxa were significantly different between the seasons.
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In Lacco Ameno the community was dominated by decapods, polychaetes and amphipods, but with different percentages, while in Scarrupata amphipods were extremely more abundant than the other taxa, accounting for almost half of the total individuals found. The great difference in the structure of the two communities, might be due to the contamination in Lacco Ameno by organisms (above all decapods and polychaetes) from the surrounding bare sediments after lowering of the number of shoots and the canopy covering, conditions that had particularly negative effects on the amphipods. Different patterns of variation were observed for each single taxonomical group. Polychaetes and amphipods varied at Location scale; Decapods showed significant differences both at Location and Site scales, and with Seasons. Gastropods showed seasonal patterns of variability, and also small scale variability (Station) as was the case also for the minor taxonomical groups. The ordination of the multivariate data clearly showed the differences between the two meadows, both spatially and temporally. PERMANOVA showed significant differences at all spatial scales (except Station) and with Seasons, suggesting that different meadow conditions and health status resulted in different community structures.
4.4.3
Gastropoda
On the whole, 35 species of gastropods were found represented by 1,389 individuals (for a list of the species found see Appendix 2). Both species numbers and individual densities were always higher in Lacco Ameno than in Scarrupata. The number of gastropod species, showed significant differences only at the largest spatial scale of Location (few km), and between Seasons (Table 13). Concerning the number of individuals, the spatial variability was evident only at the smallest spatial scale of Station (10s of m), and between Seasons.
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Table 13: ANOVA of gastropods diversity indices at different spatial scales. Bold characters indicate significant values. Abbreviations- see Table 9. Gastropoda N p F p 0.0124 1.0 0.3426 0.0004 31.5 0.0005 0.6126 0.2 0.6737 0.7216 0.8 0.6262 0.0543 2.7 0.0073 S
Effect L F S F LS F Si(LS) R St(Si(LS)) R Error
df F 1 10.3 1 33.6 1 0.3 8 0.7 12 1.9 48
J' F 0.9 4.7 0.0 2.6 0.6
p 0.3770 0.0617 0.8577 0.0663 0.8636
H' F 6.2 19.9 0.4 1.3 1.4
p 0.0377 0.0021 0.5356 0.3270 0.1790
In Lacco Ameno in both seasons 758 individuals from 33 species were found and in Scarrupata 631 individuals from 19 species in both seasons. In Lacco Ameno, in Summer, 216 individuals from 23 species were counted, while in winter the number of species was 23 accounting for 542 individuals. In Scarrupata, the number of species found in summer was 10 from 125 individuals, while in winter the number of species was 18 from 506 individuals. Only 7 species were always present both in Lacco Ameno and Scarrupata in both seasons: Melanella alba, Melanella polita, Gibberula philippii, Scissurella costata, Caecum glabrum, Tricolia tenuis and Payraudeautia intricata, while the other species were alternatively present in winter or in summer, or even lacking in one of the two meadows. The average number of species and individuals of gastropods per quadrat found along the two meadows and seasons are reported in Appendix 2 Table 28. An average of 4.7 ± 3.2 and 7.8 ± 2.3 species per quadrat (mean ± SD) were recorded in Lacco Ameno in summer and winter, respectively, while in Scarrupata there were 2.6 ± 2.0 and 6.2 ± 1.8 species per quadrat in summer and winter, respectively. The average number of individuals recorded in Lacco Ameno was 12.0 ± 7.6 and 30.1 ± 13.7 per quadrat in summer and winter, respectively, while in Scarrupata the average number of individuals was 6.9 ± 5.6 and 28.1 ± 17.9 per quadrat in summer and winter, respectively. In Figure 33 is shown the Shannon – Wiener diversity index (H’) at the three spatial scales. In order, during winter higher values of gastropod diversity were recorded in both meadows, with highest values in Lacco 116
Ameno. Apparently, the disturbances due to human impact increased the diversity, and, as mentioned above, the increase in diversity was possibly caused by the contamination from the surrounding bare sediment species that found suitable conditions after the shoot uprooting due to, e.g., boat anchoring. In winter, H’ reached values of 1.1 ± 0.7 and in summer 1.7 ± 0.4 in Lacco Ameno. In
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Figure 33: Shannon – Wiener diversity index (H’) of gastropods at the three spatial scales. LA is Lacco Ameno and Sc is Scarrupata. Bars represent standard deviation.
ANOVA results (Table 13) for H’ showed significant differences at Location scale and between Seasons. Figure 34 represents the equitability (or evenness J’) at the three spatial scales. In Lacco Ameno, in the two seasons, J’ was 0.7 ± 0.3 and 0.9 ± 0.1 in summer and winter, respectively, while in Scarrupata it was 0.6 ± 0.4 and 0.8 ± 0.1 in summer and winter, respectively.
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Figure 34: Equitability (J’) of gastropods at the three spatial scales. LA is Lacco Ameno and Sc is Scarrupata. Bars represent standard deviation.
Although the evenness was more homogeneous in winter than in summer for both meadows, the mean values in summer and in winter were almost the same, even between meadows, as reported also by the ANOVA (Table 13). These results showed that, although Lacco Ameno was more diversified, apparently there was not a single species dominating respect to the other. The multivariate analysis confirmed, to some degree, the results of the univariate analyses. Figure 35 shows the nMDS for gastropods.
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Sum
WinWin Win
Stress: 0.03
Sum Sum
Win Win Win
LocationSites LA LB LC SA SB SC
Sum Sum
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Figure 35: nMDS of centroids calculated by means of Principal Coordinates of gastropods on the basis of Bray-Curtis dissimilarity matrix after fourth root transformation. LA (Lacco Ameno site A), LB (Lacco Ameno site B), LC (Lacco Ameno site C); SA (Scarrupata site A), SB (Scarrupata site B), SC (Scarrupata site C). Sum represents summer season, Win is winter.
A clear separation between seasons, as well as between Locations, was evident. These differences were mainly due to different number of species and abundances found between Lacco Ameno and Scarrupata, and, for each meadow, in different seasons. The two communities of gastropods were well differentiated between the two seasons, and no clear influences were evident due to the human impacts affecting Lacco Ameno. PERMANOVA results (Table 14) statistically confirmed these evidences, highlighting the significant differences between meadows and between seasons. Table 14: PERMANOVA results for gastropods. P(perm) are values calculated by permutations; P(MC) are values calculated by Monte Carlo simulations. Bold numbers are significant values. Gastropoda Source df SS MS F P(perm) L 1 17626.4 17626.4 7.4 0.0036 S 1 45611.7 45611.7 19.1 0.0002 LS 1 4965.8 4965.8 2.1 0.1418 Si(LS) 8 19100.6 2387.6 1.5 0.1342 St(Si(LS)) 12 19273.4 1606.1 1.1 0.2944 Residual 48 69123.7 1440.1 Total 71 175701.6
P(MC) 0.0006 0.0002 0.0922 0.1030 0.2808
The results of SIMPER for gastropods are summarized in Appendix 2 Tables from 29 to 36. Data are reported for meadows, sites, stations and seasons. Pairwise values between sites and stations were reported too. The average dissimilarity between summer and winter in Lacco Ameno was 84.6% and the main species contributing to this dissimilarity were 119
Melanella alba (17.1%), Gibberula philippii (16.5%), Smaragdia viridis (12.4%) and Caecum glabrum (12.1%). The average dissimilarity between summer and winter in Scarrupata accounted for 84.2% and the main species contributing to this dissimilarity were Scissurella costata (31.2%), C. glabrum (18.1%), S. viridis (12.4%) and M. alba (8.4%).
4.4.4
Gastropoda: Summary
The number of gastropod species was significantly different at Location scale, while the number of individuals was significantly different at Station scale. Both number of species and number of individuals significantly varied with Seasons. Of the 35 species found, only 7 were in common between the two meadows, while the other species were alternatively present in Summer or in Winter, or even completely lacking in one of the meadows. In Lacco Ameno were recorded the highest values of species diversity, suggesting that the disturbancess due to human impacts (e.g., boat anchoring) increased the diversity likely because of the contamination by the surrounding bare sediments species. Moreover, the highest values of species diversity were recorded during Winter, and this could be due to the decrease of the natural predators of mollusks that migrate during Winter. Despite the major diversification of species in Lacco Ameno, the gastropod communities did not show any particular dominant species, as shown by the evenness index. Multivariate analysis results were consistent with those of the univariate analysis. A clear separation between meadows and Seasons were evident in the nMDS plot, and PERMANOVA highlighted both the differences between meadows and between seasons.
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4.4.5
Polychaeta
On the whole, 36 families of polychaetes were found (for a list of the families found see Appendix 3) with 4,550 individuals. Both number of families and individuals were significantly different at Location scale only, suggesting the possible influence of the human-induced disturbances in structuring the polychaete communities. A contamination of organisms from bare sediments around the Lacco Ameno meadow could explain the higher values of both number of families and individuals, caused by the reduction of shoot density due to, e.g., the mechanical uprooting of the shoots (boat anchoring). In Lacco Ameno, in fact, 35 families were recorded from 3,265 individuals, while in Scarrupata 31 families were accounted for from 1,285 individuals. In Lacco Ameno the number of families found in summer was 31 from 630 individuals, while in winter the number of families was 29 from 2,004 individuals. In Scarrupata the number of families found in summer was 21 from 303 individuals, while in winter the number of families was 28 from 982 individuals. On the whole, 20 families were in common between Lacco Ameno and Scarrupata for both seasons: Chrysopetalidae, Cirratulidae, Dorvilleidae, Eunicidae, Euphrosinidae,
Glyceridae,
Hesionidae,
Lumbrineridae,
Maldanidae,
Nereididae,
Ophelidae, Paraonidae, Phyllodocidae, Polynoidae, Sabellidae, Sigalionidae, Spionidae, Syllidae, Terebellidae and Trychobranchidae. In Appendix 3 Table 37 the number of families and individuals found along the two meadows and seasons are reported. In Lacco Ameno, the average number of families found was 14.2 ± 5.0 per quadrat in summer and 13.8 ± 4.3 per quadrat in winter, while in Scarrupata the averages were 6.2 ± 3.2 per quadrat in summer and 10.4 ± 3.0 per quadrat in winter. Although no significant variability was found for the interaction Location x Season,
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pairwise test for Scarrupata showed a significant difference between summer and winter (p < 0.05). In Lacco Ameno during summer the average number of individuals per quadrat recorded was 70.1 ± 41.8 and in winter 111.3 ± 70.1. In Scarrupata, instead, an average number of individuals per quadrat of 16.8 ± 11.5 was recorded during summer and 54.6 ± 23.5in winter. ANOVA results (Table 15) showed a significant difference between Seasons (p < 0.05). Figure 36 presents the Shannon – Wiener diversity index (H’) results. The values of H’ were always higher in Lacco Ameno than in Scarrupata showing that, as for the number of families, the influence of the human impacts tend to increase the diversity by allowing other polychaete families to colonize some empty niches. In Lacco Ameno in summer H’ measured 2.0 ± 0.3 while in winter 1.8 ± 0.3. In Scarrupata in summer it measured 1.4 ± 0.4 while in winter 1.8 ± 0.2. The ANOVA results (Table 15) showed that H’ vary significantly at Location scale (p < 0.01) and also vary significantly for the interaction Location x Season (p < 0.01), and the pairwise test confirmed that there was a significant difference between summer and winter in Scarrupata (p < 0.01).
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Figure 36: Shannon – Wiener diversity index (H’) of polychaetes at the three spatial scales. LA is Lacco Ameno and Sc is Scarrupata. Bars represent standard deviation.
Figure 37 shows the equitability (J’) results. Although slightly, the values of J’ were always higher in Scarrupata than in Lacco Ameno, i.e., the opposite trend of H’, showing that, even if Lacco Ameno is more diversified, only a few families were dominant while in Scarrupata the individuals were more regularly distributed among the polychaete families. In Lacco Ameno in summer J’ measured 0.8 ± 0.1 while in winter 0.7 ± 0.1. In Scarrupata in summer it measured 0.9 ± 0.1 while in winter 0.8 ± 0.1. The ANOVA results (Table 15) showed a significant difference between Locations (p < 0.05) and a significant variability was also observed between Seasons (p < 0.05). Although no significant variation was recorded for the interaction Location x Season, the pairwise test reported a significant difference between summer and winter in Scarrupata (p < 0.05).
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Figure 37: Equitability (J’) of polychaetes at the three spatial scales. LA is Lacco Ameno and Sc is Scarrupata. Bars represent standard deviation.
Table 15: ANOVA of polychaetes families diversity indices at different spatial scales. Bold characters indicate significant values. Abbreviations see Table 9.
Effect L F S F LS F Si(LS) R St(Si(LS)) R Error
df 1 1 1 8 12 48
Polychaete Families diversity indices S N F p F p F 20.4 0.0020 13.4 0.0064 7.8 2.4 0.1597 6.9 0.0302 10.4 3.5 0.0999 0.0 0.9087 0.0 1.6 0.2252 2.2 0.1101 1.8 1.4 0.2215 1.3 0.2552 0.9
J'
H' p F p 0.0232 21.5 0.0017 0.0121 1.1 0.3151 0.8663 14.3 0.0054 0.1743 0.7 0.7188 0.5804 1.4 0.1803
The multivariate analysis was consistent with the univariate one in describing the possible effects of the human disturbances on the polychaete communities. In figure 38 is shown the nMDS of polychaete families based on Bray – Curtis dissimilarity matrix calculated after the fourth root transformation of the data. The separation between the two meadows and the distinction of the two seasons was evident, but while the differences between summer and winter in Lacco Ameno were not so high, major differences did occur between seasons in Scarrupata. It seemed that, as observed for the high taxonomical groups, the impacted conditions tended to smooth the differences in community structure 124
between summer and winter in Lacco Ameno, while, in pristine conditions (Scarrupata), the communities were influenced, to some degree, by seasons.
Stress: 0.07 Win
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LocationSites LA LB LC SA SB SC
Sum Win
Sum Sum
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Sum Sum
Figure 38: nMDS of centroids calculated by means of Principal Coordinates of polychaetes families on the basis of Bray-Curtis dissimilarity matrix after fourth root transformation. LA (Lacco Ameno site A), LB (Lacco Ameno site B), LC (Lacco Ameno site C); SA (Scarrupata site A), SB (Scarrupata site B), SC (Scarrupata site C). Sum represents summer season, Win is winter.
PERMANOVA test (Table 16) supported these differences reporting a significant variability at Location scale (pperm < 0.001) and a significant difference also between Seasons (pperm < 0.001). A significant difference was also recorded for the interaction Location x Season (pperm < 0.001) and the pairwise test showed a significant variability in summer and winter for both meadows (pmc < 0.05 and pmc < 0.01, for Lacco Ameno and Scarrupata, respectively). Table 16: PERMANOVA results for polychaete families. P(perm) are values calculated by permutations; P(MC) are values calculated by Monte Carlo simulations. Bold numbers are significant values.
Source L S LS Si(LS) St(Si(LS)) Residual Total
df 1 1 1 8 12 48 71
SS 14944.62 12080.23 10421.77 11578.19 12748.01 42483.99 104256.8
Polychaete Families MS F P(perm) P(MC) 14944.62 10.33 0.0002 0.0002 12080.23 8.35 0.0002 0.0002 10421.77 7.20 0.0004 0.0002 1447.274 1.36 0.1586 0.1332 1062.334 1.20 0.1596 0.1522 885.0831
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The results of SIMPER are summarized in Appendix 3 Tables from 38 to 45 for meadows, sites, stations and seasons. Pairwise values between sites and stations are also reported. SIMPER indicated high dissimilarity between seasons in both meadows, with higher values in Scarrupata suggesting that in more pristine meadows a significant difference occurred between summer and winter that could be masked in impacted and low shoot density meadows as the result of several factors acting at large scale. The average dissimilarity between summer and winter in Lacco Ameno was 60.9% and the main families contributing to this dissimilarity were the Hesionidae (31.9%), Syllidae (19.3%), Chrysopetalidae (6.7%) and Nereididae (4.2%). The average dissimilarity between summer and winter in Scarrupata was 72.0% and the main families contributing to this dissimilarity were the Syllidae (25.8%), Hesionidae (22.9%), Sabellidae (8.5%) and Ophelidae (7.5%).
4.4.6
Polychaeta: Summary
The number of polychaete families found, as well as the number of individuals, was significantly higher in Lacco Ameno than in Scarrupata,. These differences were likely due to the reduction in shoot density in Lacco Ameno that increased the effects of the “contamination” of families from the surrounding bare sediments. The number of individuals varied also with the Seasons, increasing in winter in both meadows, suggesting that a possible effect of the predatory pressure affected the polychaete assemblages. The analysis of the evenness index, showed that, although Lacco Ameno was more diversified, only a few families dominated the community, while in Scarrupata no family was particularly dominant. The multivariate analysis results were consistent with the univariate results. The nMDS plot clearly showed the separation between the two meadows. What was also evident in the 126
nMDS graph was that the human-induced disturbances tended to smooth the differences in community structures between summer and winter in Lacco Ameno, while in pristine conditions (Scarrupata), the community structure was influenced by seasons. PERMANOVA statistically supported these differences reporting a significant variability at Location scale and with Seasons, as well as for the interaction Location x Seasons.
4.4.7
Amphipoda
A total of 55 genera of amphipods was found in both meadows and both seasons (for a list of the genera found see Appendix 4) represented by 7,521 individuals. The number of genera and individuals found were always higher in Scarrupata than in Lacco Ameno. In particular, in Lacco Ameno in both seasons, 41 genera and 2,702 individuals were found , while in Scarrupata 51 genera and 4,819 individuals were recorded. In Lacco Ameno the number of genera found in summer season was 40 accounting for 1,167 individuals, while in winter the number of genera was 31 from 1,522 individuals. In Scarrupata the number of genera found in summer was 45 from 2,041 individuals, while in winter the number of genera was 48 from 2,471 individuals. Only 25 genera were found in common between Lacco Ameno and Scarrupata for both seasons: Ampelisca, Apolochus, Gitana, Aora, Leptocheirus, Aoridae gen. sp., Peltocoxa, Atylus, Dexamine, Apherusa, Ericthonius, Ischyrocidae gen. sp., Liljeborgia, Lysianassa, Normanion, Orchomene, Cheirocratus, Gammarella,
Perioculoides,
Synchelidium,
Phoxocephalus,
Urothoe,
Caprella,
Pseudoprotella and Phtisica. The number of genera and individuals found in the two meadows and seasons are given in Appendix 4 Table 46. In Lacco Ameno the average number of genera found was 16.1 ± 4.3 per quadrat in summer and 15.8 ± 2.9 per quadrat in winter, while in Scarrupata there were 21.7 ± 4.2 genera per quadrat in summer and 25.3 ± 4.6 per quadrat in winter. ANOVA 127
results (Table 17) showed that the number of genera found was significantly different at Location scale (p < 0.001) and also significant variability was recorded for the interaction Location x Season (p < 0.05), and the pairwise test for Scarrupata showed a significant difference between summer and winter (p < 0.05), indicating different amphipod community structures between seasons probably due to a decrease in predator abundances during winter. In Lacco Ameno during summer the average number of amphipods individuals per quadrat was 65.5 ± 41.5 and in winter 86.8 ± 24.5. In Scarrupata, during summer an average number of individuals per quadrat of 131.3 ± 48.8 was recorded while in winter 136.4 ± 59.9 individuals were found . ANOVA results (Table 17) showed a significant difference only at the Location scale (p < 0.001). The differences both in the number of genera and individuals between the two meadows, put in evidence the negative responses that this class of crustacean had with respect to the alteration of their habitat due to the humaninduced disturbances. The reduction of the shoot density and the lower canopy, may have caused a decrease in amphipod diversity and abundances in Lacco Ameno, also smoothing the natural variability due to seasons, as observed in the Scarrupata meadow. The Shannon – Wiener diversity index (H’) results are given in Figure 39. In Lacco Ameno in summer H’ was 2.3 ± 0.3 while in winter 2.3 ± 0.2. In Scarrupata in summer H’ measured 2.4 ± 0.3 while in winter 2.7 ± 0.2. The ANOVA results (Table 17) showed that H’ varied significantly at Location scale (p < 0.01), between Seasons (p < 0.05) and also for the interaction Location x Season (p < 0.05). The pairwise test confirmed that there was a significant difference between summer and winter in Scarrupata (p < 0.01).
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A1 A2 B1 B2 C1 C2 LA Winter
A1 A2 B1 B2 C1 C2 Sc Summer
A1 A2 B1 B2 C1 C2 Sc Winter
Figure 39: Shannon – Wiener diversity index (H’) of amphipods genera at the three spatial scales. LA is Lacco Ameno and Sc is Scarrupata. Bars represent standard deviation.
Equitability (J’) results are given in Figure 40. In Lacco Ameno in summer J’ measured 0.9 ± 0.1 and in winter 0.8 ± 0.0. In Scarrupata in summer J’ measured 0.8 ± 0.1 and in winter 0.9 ± 0.1. The ANOVA results (Table 17) showed a significant variability only at Station scale (p < 0.05), and this probably meant that, although less diversified, in Lacco Ameno the individuals were almost equally distributed among the species, as for Scarrupata. The fact that the equitability changed at the scale of stations, indicated that small scale phenomena acted on the distribution of the amphipod species, with micro-hotspots where few species dominated, probably linked to the massive presence of certain epiphytes that some amphipod species in particular fed on.
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Figure 40: Equitability (J’) of amphipod genera at the three spatial scales. LA is Lacco Ameno and Sc is Scarrupata. Bars represent standard deviation.
Table 17: ANOVA of amphipod genera diversity indices at different spatial scales. Bold characters indicate significant values. Amphipoda S Effect L F S F LS F Si(LS) R St(Si(LS)) R Error
df 1 1 1 8 12 48
F 80.9 3.2 5.9 1.1 0.7
N
p F p 0.0000 27.6 0.0008 0.1119 1.2 0.3144 0.4 0.5514 0.0408 0.4263 2.3 0.0921 0.7666 0.4 0.9442
J' F 1.2 1.2 3.6 2.4 2.1
H' p F p 0.3000 18.5 0.0026 0.2965 6.4 0.0358 0.0955 11.3 0.0100 0.0860 0.9 0.5219 0.0345 1.1 0.3732
The nMDS of amphipod genera is given in Figure 41 based on the Bray – Curtis dissimilarity matrix calculated after the fourth root transformation of the data. The net separation between the two meadows was evident. The homogeneity in amphipod community structures between seasons in Scarrupata was also evident, suggested by the lower scattering compared to the points representing Lacco Ameno. A clear separation between seasons in Scarrupata was evident, while in Lacco Ameno, due to the scattering, the separation although present was less evident (see for example Site B summer). The 130
differences recorded in Scarruapata between summer and winter, were probably due to the decrease of predators during winter that allowed for an increase both in the number of individuals and in the number of genera. The same explanation could be advocated for Lacco Ameno, although in for this meadow a decrease in the number of genera during winter but an increase in the number of individuals were evident. Stress: 0.04 Sum
LocationSite LA LB
Sum
LC
Win
SA
Win
SB Win
Win
Win
Sum Sum Sum
SC
Sum
Win
Figure 41: nMDS of centroids calculated by means of Principal Coordinates of amphipods genera on the basis of Bray-Curtis dissimilarity matrix after fourth root transformation. LA (Lacco Ameno site A), LB (Lacco Ameno site B), LC (Lacco Ameno site C); SA (Scarrupata site A), SB (Scarrupata site B), SC (Scarrupata site C). Sum represents summer season, Win is winter.
PERMANOVA test (Table 18) was consistent with what was observed in the nMDS and showed a significant variability at Location (pperm < 0.001) and at Site scales (p < 0.01) and also between Seasons (pperm < 0.05), confirming the differences of the amphipod communities in Lacco Ameno and Scarrupata. The significant differences at Site scale were probably due to the large variability in Lacco Ameno (see the scattering in the nMDS), indicating a high patchiness likely driven by the fragmentation of the habitat.
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Table 18: PERMANOVA results for amphipods genera. P(perm) are values calculated by permutations; P(MC) are values calculated by Monte Carlo simulations. Bold numbers are significant values.
Source L S LS Si(LS) St(Si(LS)) Residual Total
The results of SIMPER are
df 1 1 1 8 12 48 71
SS 23547.22 5343.99 3874.86 13582.03 8442.23 24773.63 79563.96
Amphipoda MS 23547.22 5343.99 3874.86 1697.75 703.52 516.12
F P(perm) 13.87 0.0002 3.15 0.0398 2.28 0.0840 2.41 0.0030 1.36 0.0502
P(MC) 0.0002 0.0154 0.0564 0.0012 0.0520
summarized in Appendix 4 Tables from 47 to 56) for
meadows, sites, stations and seasons. Pairwise values between sites and stations are reported too. The average dissimilarity between summer and winter in Lacco Ameno was 61.2% and the main genera contributing to this dissimilarity were Apherusa (13.5%), Aora (13.1%), Dexamine (8.6%) and Gammarella (8.2%). The average dissimilarity between summer and winter in Scarrupata is 50.8% and the main genera contributing to this dissimilarity were Apolochus (19.4%), Gammarella (8.7%), Gitana (5.2%) and Apherusa (5.1%).
4.4.8
Amphipoda: Summary
Differently from the other taxa considered, the number of genera and individuals of amphipods were more abundant in Scarrupata than in Lacco Ameno. The highest number of genera and of individuals were recorded in winter in Scarrupata, likely due to the migration of the amphipod natural predators. The differences both in the number of genera and of individuals between Lacco Ameno and Scarrupata, put in evidence the negative effects of the human disturbances and alteration of the habitat for amphipods, furthermore smoothing the natural variability due to seasons (as shown in Scarrupata). This conclusion was also supported by the nMDS plot that clearly showed the separation between meadows, and the clear separation between 132
seasons in Scarrupata. PERMANOVA statistically supported the results obtained by the univariate results of differences between Locations and between seasons.
4.5
Correlations between Plant and Faunal Features
4.5.1
Canonical Analysis of Principal Coordinates (CAP)
CAP analysis between plant features and borer polychaetes frequencies are shown in figure 42. A maximum of m = 3 dimensions resulted in a correct allocation of data in the plot explaining 82.9% of the variability among the two data sets. The squared canonical correlations (δ12 and δ22) accounted for 0.09 and 0.03, respectively, showing very low correlation among variables. All the values lay on the CAP correlation axis 2 indicating no correlations between borer frequencies and plant features, suggesting that these polychaete species, although depending to some degree from the plant (e.g., for shelter), are not influenced either by shoot density or by any other plant feature. This was not surprising since these animals do not live specifically in the Posidonia oceanica sheaths, but they are found mainly in the bare sediments and calcareous algae.
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Figure 42: CAP analysis between plant features and borer polychaetes frequencies. The first canonical axis explains 9.4% of the variability among variables. NL : number of leaves; SL : sheaths length; LL : leaves length; LW : leaves width; LS : leaf surface; SD : :shoot density; LB : leaves biomass; SB : sheath biomass.
CAP analysis between plant features, the most frequent higher taxa abundances and diversity indices are represented in figure 43. A maximum of m = 2 dimensions resulted in a correct allocation of data in the plot and they explained 72.7% of the variability among the two data sets. The squared canonical correlations (δ12 and δ22) accounted for 0.65 and 0.33, respectively, showing a good correlation among variables. Four taxa were negatively correlated with plant features (decapods, bivalves, polychaetes and tanaids) while three were positively correlated (mysids, amphipods and cumaceans). The main plant features explaining the relations were shoot density, sheath biomass, leaf biomass, leaf length and leaf surface. None of the diversity indices were correlated with plant features. It was interesting to observe how the two main groups of Lacco Ameno (decapods and polychaetes), as well as bivalves, were negatively related to the main plant features, while 134
the dominant group in Scarrupata (namely, amphipods), but also mysids and cumaceans, were positively related to the plant features. In the first case, and as explained in the previous paragraphs, the contamination from the bare sediments around the Lacco Ameno meadow could be the main reason that led to major abundances of those taxa in this low shoot density meadow. The “contaminants” were mainly scavengers (decapods) or detritivores (polychaetes and bivalves) (although the feeding habits were not considered here). On the other hand, the more structured habitat in Scarrupata seemed favourable to amphipods, a taxon strictly associated to Posidonia, and that are major grazers of the epiphytes on the Posidonia leaves.
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Figure 43: CAP analysis between plant features and the most frequent and abundant higher taxa and diversity indices. The first canonical axis explains 65.0% of the variability among variables. Area highlighted in pink corresponds to a correlation < ± 0.40. NL : number of leaves; SL : sheaths length; LL : leaves length; LW : leaves width; LS : leaf surface; SD : :shoot density; LB : leaves biomass; SB : sheath biomass; Pol : Polychaeta; Gas : Gastropoda; Biv : Bivalvia; Dec : Decapoda; Mys : Mysidacea; Iso : Isopoda; Tan : Tanaidacea; Cum : Cumacea; Amp : Amphipoda; Ech : Echinodermata; S : number of taxa per quadrat; N : Number of individuals per quadrat; J’ : Equitability; H’ : Shannon – Wiener diversity index.
135
CAP analysis between plant features and gastropods abundances was represented in figure 44. A maximum of m = 2 dimensions resulted in a correct allocation of data in the plot and explained 72.9% of the variability among the two data sets. The squared canonical correlations (δ12 and δ22) accounted for 0.63 and 0.29, respectively, showing high correlations among variables. Most of the taxa were negatively correlated with a number of plant features. The main species S. viridis, G. philippii, M. alba, T. speciosa, T. tenuis, T. pullus and S. costata were negatively correlated with sheath length, sheath biomass, leaf surface, leaf biomass and leaves length. The same species were positively related with the number of leaves per shoot. On the other hand, only C. glabrum seemed to be positively related with the main plant features. The diversity indices H’ and J’, the number of individuals and the number of species per quadrat, as for most of the species, were negatively correlated with several plant features and positively correlated with number of leaves per shoot. Most of the species represented in the plot were grazers of the epiphytes of the leaves. It is well known that in eutrophic areas, the epiphytes are more abundant than in meadows without or with little eutrophic conditions. Thus, the presence of more epibionts on the leaves of Posidonia in the Lacco Ameno meadow might justify the presence of more individuals of those species than in Scarrupata, and a clue for that was the negative correlations with the plant features that in Scarrupata reached their maxima.
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Figure 44: CAP analysis between plant features and the most frequent and abundant gastropod species and diversity indices. The first canonical axis explains 63.2% of the variability among variables. Area highlighted in pink corresponds to a correlation < ± 0.40. NL : number of leaves; SL : sheaths length; LL : leaves length; LW : leaves width; LS : leaf surface; SD : :shoot density; LB : leaves biomass; SB : sheath biomass; S : number of taxa per quadrat; N : Number of individuals per quadrat; J’ : Equitability; H’ : Shannon – Wiener diversity index.
CAP analysis between plant features and polychaete families abundances and diversity were represented in figure 45. A maximum of m = 2 dimensions resulted in a correct allocation of data in the plot and they explained 69.3% of the variability among the two data sets. The squared canonical correlations (δ12 and δ22) accounted for 0.62 and 0.28, respectively, showing high correlations among variables. All the most frequent and abundant families (except Sabellidae), and the diversity indices (except J’), were inversely correlated with plant features except for the number of leaves per shoot. The pattern put in evidence in the following plot was clearly due to the contamination from bare sediments. In fact, most of the main polychaete families were burrowers, carnivores and deposit feeders (only three families of the most abundant ones were herbivores, i.e., Eunicidae,
137
Nereididae and Syllidae) that in Lacco Ameno found the best conditions for their feeding habits since the sedimentation was higher because of the lesser shoot density.
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Figure 45: CAP analysis between plant features and the most frequent and abundant polychaete families and diversity indices. The first canonical axis explains 61.8% of the variability among variables. Area highlighted in pink corresponds to a correlation < ± 0.40. NL : number of leaves; SL : sheaths length; LL : leaves length; LW : leaves width; LS : leaf surface; SD : :shoot density; LB : leaves biomass; SB : sheath biomass; Chr : Chrysopetalidae; Cir : Cirratulidae; Eun : Eunicidae; Eup : Euphrosinidae; Fla : Flabelligeridae; Hes : Hesionidae; Ner : Nereididae; Oph : Ophelidae; Par : Paraonidae; Pol : Polynoidae; Sab : Sabellidae; Spi : Spionidae; Syl : Syllidae; Ter : Terebellidae; S : number of taxa per quadrat; N : Number of individuals per quadrat; J’ : Equitability; H’ : Shannon – Wiener diversity index.
CAP analysis between plant features and amphipod genera abundances and diversity are represented in figure 46. A maximum of m = 2 dimensions resulted in a correct allocation of data in the plot and they explained 66.8% of the variability among the two data sets. The squared canonical correlations (δ12 and δ22) accounted for 0.64 and 0.32, respectively, showing high correlations among variables and a significant variability was recorded both for the trace statistic (0.98) and the first squared canonical correlation (δ12) (p < 0.001 for both). Eight of the most frequent and abundant genera (Apolochus, Aoridae gen.sp., 138
Gammaropsis, Iphimedia, Gitana, Ischyroderidae gen.sp., Liljeborgia and Orchomene), the number of species per quadrat (S) and the number of individuals per quadrat (N), were directly and strongly correlated with shoot density and less with sheath biomass and leaf biomass. This pattern could be explained by the fact that amphipods are the most characteristic taxon of Posidonia oceanica meadows, where they find shelter and food. These correlations confirm, to some extent, what was found by the univariate and multivariate analysis in the previous paragraphs, that is, that this taxon prefers stable conditions of its habitat where it might build a well structured and stable community.
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Figure 46: CAP analysis between plant features and the most frequent and abundant amphipod genera and diversity indices. The first canonical axis explains 64.8% of the variability among variables. Area highlighted in pink corresponds to a correlation < ± 0.40. NL : number of leaves; SL : sheaths length; LL : leaves length; LW : leaves width; LS : leaf surface; SD : :shoot density; LB : leaves biomass; SB : sheath biomass; Apo : Apolochus; Git : Gitana; Aor : Aora; Aor gen.sp. : Aoridae gen.sp; Dex : Dexamine; Aph : Apherusa; Iph : Iphimedia; Gam : Gammaropsis; Isc gen.sp. : Ischyroceridae gen.sp.; Lil : Liljeborgia; Lys : Lysianassa; Orc : Orchomene; Che : Cheirocratus; Gamm : Gammarella; Per : Perioculodes; Pho : Phoxocephalus; Cap : Caprella; Pht : Phtisica; S : number of taxa per quadrat; N : Number of individuals per quadrat; J’ : Equitability; H’ : Shannon – Wiener diversity index.
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4.5.2
Spearman’s Correlations
Spearman’s rank correlations between plant features and borer polychaete frequencies are reported in Appendix 5 Table 57. Consistently with the results of the CAP analysis, Spearman’s correlations were always negligible (ρ < 0.40) for all the parameters, indicating that the borer frequency was not influenced by the plant but possibly by other factors such as reproductive and feeding habits, or even shelter from predators. Spearman’s correlations between plant features and selected higher taxa abundances and diversity indices are given in Appendix 5 Table 58, where only correlations > ±0.40 have been highlighted. The most relevant correlations (that is, those that reach the highest correlation values, both negative and positive), are reported for gastropods, decapods, amphipods and echinoderms. All these taxa (except amphipods) were predominantly correlated with the leaf traits of the plant: gastropods (number of leaves per shoot, leaf length, leaf surface and leaf biomass); decapods (leaf length, leaf surface and leaf biomass); echinoderms (number of leaves per shoot, leaf length, leaf surface, leaf biomass and sheath biomass). Amphipods were instead strongly correlated with the shoot density. From these data, the complexity of the system, more than other factors, seemed to drive (both directly and indirectly) the distribution of the organisms, but with different trends. In fact, only amphipods responded positively to the increase in complexity (shoot density), while other organisms responded negatively to the increased complexity of the system (leaf traits). The negative responses were likely due to both a decrease of those populations peculiar of the Posidonia habitat and an increase in abundance of those species living in the surrounding bare sediments. That is what happened, for example, for decapods and gastropods, that in the previous paragraphs were shown to be contaminated by species coming from sediments around the meadow in Lacco Ameno.
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Spearman’s correlations between plant features and gastropod abundances and diversity indices are given in Appendix 5 Table 59, where only correlations > ±0.40 have been highlighted. The main species showing high correlation values (both negative and positive) with the leaf traits where: S. viridis (number of leaf per shoot, leaf surface, leaf length and leaf biomass), G. philippii (leaf length, leaf surface and leaf biomass) and T. speciosa (leaf length, leaf surface and leaf biomass). All these three main species are characteristics of the Posidonia habitat, where they feed on the epiphytes of the leaves. The fact that all the signs of the correlation coefficients were negative (and remember that even the CAP analysis showed the same negative trends), was likely related to the major abundance of the epiphytes in the disturbed meadow of Lacco Ameno. Thus, it appears that , the distribution of these main species was driven rather by resource availability than by the characteristics of the habitat. Spearman’s correlations between plant features and polychaete abundances and diversity indices are given in Appendix 5 Table 60, and only correlations > ±0.40 have been highlighted. The most relevant correlations, namely those that reach the highest correlation values (both negative and positive), are reported for: Hesionidae (number of leaves per shoot, leaf length, leaf surface, leaf biomass and sheath biomass); Nereididae (leaf biomass and sheath biomass); Polynoidae (number of leaves per shoot). Also for these families (except for Polynoidae) the correlation coefficients were negative, indicating an opposite trend between the abundance of these animals and the main leaf traits. It was interesting to notice that two of these three main families (Hesionidae and Polynoidae) are substantially carnivores, while the Nereididae are herbivores. It is likely that the major abundance of predators in the disturbed meadow of Lacco Ameno was favoured by the indirect effects of the major abundance of epiphytes (both of the leaves and of the rhizomes), increasing the number of herbivores, and, at the same time, it should be favoured by the occurrence of
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many other families of detritivores from the surrounding bare sediments, that were potential prays for the predators. Spearman’s correlations between plant features and amphipod abundances and diversity indices are given in Appendix 5 Table 61, and only correlations > ±0.40 have been highlighted. The most relevant correlations, namely those that reach the highest correlation values (both negative and positive), are reported for: Apolochus (shoot density); Gitana (shoot density); Aoridae gen.sp. (shoot density); Dexamine (sheath length and leaf length); Gammaropsis (shoot density); Liljeborgia (shoot density); Gammarella (number of leaves per shoot and leaf length); Perioculodes (leaf length). In this case, the strong positive correlations between these genera particularly with the shoot density indicated that their abundances where driven by the complexity of the habitat, measured as number of shoots. Amphipods may prefer “engineered” habitats likely for three main reasons: refuges from predators, as sites for preferential foraging, and as protection from harsh environmental conditions. A more structured habitat, due to a major number of shoots, can effectively reduce predation by predator fishes, providing a refuge for herbivorous amphipods.
4.5.3
Summary
The two correlation analysis performed here (CAP and Spearman’s Rank correlation) gave some different results (more significant relationships between animals and plant features in the CAP analysis than in Spearman’s), as one may expect since they are based on different assumptions. Even so, they showed similar trends and relationships between taxa and plant features. Moreover, these analyses showed how different organisms respond to different environmental conditions by increasing or decreasing their abundances in relation to the complexity of the habitat. Surprisingly, gastropods responded negatively to the increase in complexity (mainly due to leaf traits), when one may have expected the contrary because, 142
e.g., an increase in leaf surface could increase the surface of grazing. Instead, we found that in eutrophic conditions like those in Lacco Ameno, there was a major abundance of epiphytes that possibly attract more grazers. Less complexity inducted a major presence of detritivorous organisms that tended to increase generalist polychaete carnivores abundances. Finally, more stable conditions and structured habitat represented an ideal home for amphipods that are possibly the dominant taxa in a pristine Posidonia meadow.
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5. Discussion and Conclusions 5.1
Shoot Density and Morphometric Plant Features
The importance of using more than one spatial scale when studying the distribution patterns of animals, or plants, in complex habitats has been widely discussed by many authors (e.g.: Underwood, 1981,1991,1996; Wiens et al., 1986; Levin, 1992; Fraschetti et al., 2005). Among complex habitats, seagrasses, in general, and Posidonia oceanica in particular, could be considered one of the most heterogeneous habitats in the Mediterranean Sea. The problem when approaching a study on the ecology of a habitat engineer species, or the distribution of the communities associated to it in such an habitat is to generalize the use of the features of the habitat itself (used as descriptors of particular conditions) to highlight differences between, e.g., seagrass meadows in different areas. This problem could be avoided by using an appropriate spatial sampling design that considers the natural variability of the biotic descriptors (Underwood, 1981,1991,1996; Wiens et al., 1986; Levin, 1992; Morrisey et al., 1992; Underwood & Petraitis, 1993; Hewitt et al., 1998; Kendall & Widdicombe, 1999; Balestri et al., 2003; Fraschetti et al., 2005). In fact, the failure to consider the appropriate spatial scale would result in an inadequate and unrepresentative description of the state of a particular meadow. Furthermore, an erroneous selection of the spatial and temporal scales could prevent any comparison between meadows because each recorded difference could be confused with the simple stochastic natural variation of a portion of the seagrass bed with respect to another in the same meadow (Balestri et al., 2003). Thus, the starting hypothesis for the present work, and in this supported by previous papers (Balestri et al. 2003; Borg et al., 2005, 2006; Zupo et al., 2006a,b; Montefalcone et al., 2008a), was that, effectively, there is a spatial (and temporal) variability at different scales (from quadrats to meadows) in the two meadows under investigation. So the first step to test this hypothesis was to understand 144
whether and how the plant features varied within and between the two meadows, i.e. how they changed at several spatial scales. Few authors have investigated (using hierarchical sampling designs) multi-spatial scale variability of the descriptors in Posidonia oceanica meadows (Balestri et al. 2003; Borg et al., 2005, 2006; Montefalcone et al., 2008a) and some authors focused on epiphytes associated with Posidonia leaves and rhizomes (Piazzi et al., 2004; Pardi et al., 2006; Balata et al., 2007). The hierarchical sampling design adopted in the present study provides evidence of the great variability of most of the plant parameters considered in the two meadows: Lacco Ameno and Scarrupata. Significant differences in basic structural (shoot density), morphological (e.g. leaf length, leaf width, leaf surface) and biomass characteristics (leaf and sheath biomass), used as indicators of the status health of P. oceanica meadows, occurred at a variety of spatial scales, within individual meadows and between the two meadows, along the same depth. Shoot density (as a covering measure of the bottom by the plant) has been widely used by many authors as a descriptor of the ecological status of a meadow (e.g.: Buia et al., 2004; Pergent et al., 1995; Pergent-Martini et al., 2005) and the present work confirmed this measure as a good indicator of the health conditions of the meadows because it clearly differentiated Scarrupata from Lacco Ameno, highlighting how the shoot density was significantly lower in the latter meadow. The variability between meadows could be explained by the anthropogenic impact (with a resident population of 4,693 inhabitants in Lacco Ameno and 8,272 inhabitants in Casamicciola, that triplicate during spring and summer; www.demo.istat.it, Bertini, 2003; the inappropriate waste waters treatment, Zucco, 2003; discharge of massive terrigenous material near the shoreline, Guidetti & Fabiano, 2000) affecting the Lacco Ameno meadow (particularly the effects of boat anchoring and discharge of waste waters) and, in fact, the progressive decrease of shoot 145
density in this meadow has been reported by several authors and associated with increased turbidity due to the reasons above (Mazzella et al., 1989; Buia et al., 2005; Zupo et al., 2006a,b). On the other hand, the differences in shoot density obtained at Station scale (10s of metres) could be explained by the presence of within-meadow patches due, for example, to different local factors, such as bottom morphology and topographic complexity, or to nutrient availability (Balestri et al., 2003; Zupo et al. 2006b). Balestri et al. (2003) found significant shoot density variability at the scale of 100s of m using the same approach adopted in the current study. Unfortunately, neither physical nor nutrient data of the bottom were available for the present study, however, our results suggest that these factors may explain the observed variability in shoot density, as also supported by previous studies (Zupo et al., 2006a,b; Giovannetti et al., 2008). The presence of these medium and small spatial scales of variation, was revealed only thanks to the particular sampling design adopted. The importance of small scale/within-bed variability has not been well studied in the past because of the major importance researchers have given to depth differences in shoot density (as well as of other plant features and of associated organisms). The fact that our sampling strategy resulted in the collection of a much greater quantity of samples and higher sampling strength than those used in past studies may also have been a contributory factor. Thus, although shoot density is considered a good parameter to indicate the health status of meadows at different degrees of disturbance, it is important not to under-estimate the small-scale-variability that potentially could affect this parameter if wrong sampling designs are used without the appropriate consideration of its spatial variability. Shoot density did not present any variability due to season or date of sampling, but this is not surprising since P. oceanica, as compared to other seagrass species, does not undergo
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large seasonal changes in spatial coverage or shoot density (except in rare events of heavy storms). None of the other morphometric features measured in this work presented any variability at Location scale (except sheath biomass), but were all highly variable at the smallest scale of Quadrat (10s of cm). This is an important finding, because many of these features have been used in several monitoring programmes to evaluate the health status of meadows (Pergent-Martini et al., 2005). Moreover, most of these parameters presented multi-spatial scales of variation from Sites (100s of m) to Quadrats (10s of cm). Such an elevated variability demonstrates, once again, how local factors play important roles in influencing patterns of distribution of the plant, and highlights the problems and risks in generalizing patterns at larger scales in complex habitat. In similar studies on P. oceanica, Balestri et al. (2003), Borg et al. (2005) and Montefalcone et al. (2008a) found the same high variability at the smallest scales, comparable with the findings of the present work. According to Balestri et al. (2003), these studies do not indicate the factors determining these patterns, nor does the simple application of hierarchical sampling design indicate why these spatial patterns exist (Underwood, 1997), but several hypotheses could be formulated by observing patterns of morphology of Posidonia oceanica. Variations at the largest scale (between meadows, several km apart) might reflect differences in the ecological setting at the localities, such as wave exposure, substratum type, sediment characteristics and even grazing pressure and anthropogenic impact. Other factors, such as physical disturbance, topographic complexity and nutrient availability (Zupo et al., 2006a,b), might operate on smaller scales to modify morphological variables. Variations observed at the smallest scales (cm to m) are more difficult to explain, but they could be attributable to undefined nested components of variation (Underwood, 1997) and/or differences in the microhabitat (Balestri et al., 2003, Zupo et al., 2006a,b). The results obtained in the present study confirm the high plasticity of P. oceanica that respond to a wide range of environmental 147
conditions with biological adaptations and this leads to the need of a more rigorous approach when studying ecological problems at large scale or long-term monitoring in complex systems like Posidonia meadows. Different considerations must be made for morphometric and biomass features such as LAI and LSC. These two parameters present variability also at Location scale, but this is not surprising since they both derive from shoot density. Thus, this variability at the largest scale is substantially due to the differences in shoot densities between Lacco Ameno and Scarrupata meadows and not to a difference in leaf surface or leaf biomass per se. The plant features, i.e. leaf width, number of intermediate leaves, leaf biomass and sheath biomass, varied with season and above all with date of sampling (almost all parameters). The causes for these differences are well known, since they are related to the biology of the plant (e.g.: Pirc, 1985; Buia et al., 1992) and of physical conditions that, for a few of parameters, are crucial. For example, many differences observed from one date of sampling to another (e.g., for the leaf length) depended on the strength of water movements that acted as a “grass-cutter” tearing away the brown tissue from the leaves. This is evident in graph 9 of the Results Chapter that shows the second date in summer and the first date in winter having the lower values, as they were preceded by the first autumn storms. Coupled with this physical reason, there is also the fact that the plant decreases the leaf production, which explains why there are lower values of leaf length. The same explanations are valid also for the other parameters that followed the same pattern of variability as the leaf length. For example, the number of intermediate leaves depends on the season, but also on the date of sampling as the other two leaf categories. This was not trivial because, as Pirc (1985) showed, the juvenile leaves are the most important sink of nitrogen produced by the plant and, in fact, their nitrogen content is proportionately higher than in intermediate and adult leaves in which, by contrast, the carbon concentration increases. In the first date in winter, there were more young leaves than in the second date 148
because in the former the plant is exploiting its nitrogen reserves while in the latter these reserves finished. In the second date in winter, the young leaves have already grown into intermediate ones and the number of adult leaves (by this time nearly totally brown) has decreased, probably because the late winter storms have detached them.
5.2
Borer polychaetes
The second purpose of this work was to evaluate the spatial (and temporal) variation of macro-invertebrates associated to Posidonia oceanica in the two meadows. So the second step was to understand whether and how the plant associated animal assemblages varied in space and time. Once having dealt with this main question, it was interesting to evaluate if the observed patterns of distribution of the plant may drive, to some degree, the distribution of the associated fauna. To date, only two studies on the spatial variability of fauna associated with Posidonia oceanica are available and these are focused on the guild of polychaete borers (Gambi et al., 2005, 2006). The mean values of IB obtained in this work for the two meadows (16.3% in Lacco Ameno and 22.8% in Scarrupata) were lower than those obtained in previous works conducted in Lacco Ameno. Gambi (2000) reported a mean IB value of 30.5% at a depth of 22 m and with a shoot density of 131 shoots/m2, while Gambi et al. (2006) found mean values of 20% and 46% at depth of 13 and 15.5 m, respectively, with shoot densities of 190.6 and 331.2 shoots/ m2. The differences from the results of Gambi (2000) may be explained by the greater depth and the lower shoot density that, given parity of polychaete individuals, tend to increase the percentage of the indices (Gambi et al., 2005). On the other hand, the differences observed between Gambi et al. (2006) and the present work at comparable depths, may be the result of different sampling approaches. Differences in the spatial scale 149
factors and the number of replicates, could lead to an assessment of the spatial distribution that does not consider the random variability of the borers. When all borer species were considered together, the IB varied at several spatial scales (from Location to Site and Station), and no seasonal pattern was evident. When considered individually, IB of single species presented different patterns. L. collaris (the most abundant species with more than 60% of borer individuals) showed significant variation at almost all the scales considered (except for Plot) and also for Season and the interaction of Location x Season. In the Lacco Ameno meadow, IB of L. collaris showed significant differences between summer and winter values, with summer IB values higher than those in winter. IB of L. ninetta by contrast, showed high variability only at Station scale, but no variability with season. N. unicornis, probably because of its low frequency, did not show any particular pattern in spatial and seasonal variability. From these results it is clear that total IB derived from L. collaris frequency, since they varied almost in the same way. Moreover, the fact that the total IB index varied at Location scale supports the hypotheses of Di Maida et al. (2003) and Gambi et al. (2005) that IB could be used as descriptor of the general environmental condition and health of the meadows. Nevertheless, the use of IB as indicator of the condition of a meadow should be considered with caution because of the high IB variability also at smaller scales, suggesting that, as in the variability of plant features noted above, local factors could be more relevant at smaller scales than differences between meadows at larger scales. Thus, an appropriate spatial sampling design, above all in cases like this, is required to assess the variability at several spatial scales. When the two meadows were analyzed one by one, it was evident that most if not all variance associated to the IB indices (both all species and single species) was determined by the Lacco Ameno meadow. In fact, as shown in table 7 of the Result Chapter, no variability at all was associated to Scarrupata, while Lacco Ameno showed the same significant patterns obtained when the two meadows were considered together. This was an 150
important finding that showed how, in pristine conditions, the borer polychaetes did not show any particular spatial distribution, while unnatural conditions such as those registered in Lacco Ameno (anthropogenic impacts and disturbance), influenced these organisms creating patches of distribution. One may expect that the patchy distribution of borers in Lacco Ameno was due to the reduction in habitat (say Posidonia oceanica features) as a result of human activities such as boat anchoring that created gaps in the distribution of the plant, and thus, the distribution of the borer polychaetes may be directly associated to the plant characteristics. Unfortunately, the correlation tests made between the plant features and the IB of total and single species did not support this conclusion. The results of Spearman’s rank correlations and of CAP analysis found no correlations between the IB and any of the plant features. Thus, other reasons must be advocated to explain the higher IB variabilities in Lacco Ameno, such as an elevated predatory pressure where the meadow was not highly dense, or some life-history traits of the borer polychaete that did not permit an homogeneous distribution of the individuals or of the larvae, or even other unknown reasons. Unfortunately, these aspects where not considered in the present work so they represent mere speculations. It is worth noting that several studies have pointed out that the frequency of borers was related to increasing shoot density (Guidetti et al., 1997; Guidetti, 2000; Gambi & Cafiero, 2001), sometimes by applying spatial autocorrelation techniques (Kriging method; Gambi et al., 2006). However, these patterns were not always so clear, particularly if the species are considered individually (Gambi et al., 2005). The results of the correlation analysis in the present study seem to suggest the opposite from the previous studies, probably because of the different sampling designs applied and the different number of samples collected. Finally, it was evident that in natural conditions, the borer polychaetes (L. collaris and L. ninetta, the two main species) had not a clear spatial and temporal distribution pattern; in a highly dense and pristine meadow (Scarrupata) they probably tend to occupy all the 151
available space. On the contrary, when external factors led to a disturbance of their habitat (the Lacco Ameno meadow) inevitably they were affected too, with possible modifications of the capacity of distribution within the meadow, or even with a lower capacity of larval distribution. Further studies are needed to investigate these particular aspects of the biology of the borer polychaetes in areas of human induced disturbances.
5.3
Macro-invertebrate Assemblages
The high morphometric heterogeneity recorded in seagrass meadows has an influence on the associated fauna, as already demonstrated in several seagrass systems although a clear relationship is not always easily recognizable (Irlandi, 1994, 1995; Woods & Schiel, 1997; Egglestone et al., 1998; Attrill et al.,2000; Hovel & Lipcius, 2001, 2002; Bowden et al., 2001; Lee et al., 2001; Healy & Hovel, 2004; Hovel & Fonseca, 2005; Boström et al., 2006; Bell et al., 2006). For Posidonia oceanica, there are fewer studies on the relationships between plant features and associated assemblages (Idato et al., 1983; Scipione & Fresi, 1984; Mazzella et al., 1989; Gambi et al., 1992, 1995; Scipione et al., 1996; Sanchez-Jerez et al., 1999, 2000; Barberá-Cebrián et al., 2002; Dimech et al., 2002; Covazzi Harriague et al., 2006), while many studies have investigated differences between vegetated and un-vegetated habitat, or along depth gradients, or comparing particular habitats such as dead vs alive matte (Borg et al., 2006). Nevertheless, some relationships between plant features and associated assemblages have been found; for example, Sanchez-Jerez et al. (2000) found a slight positive correlation between amphipods and plant features (but not for decapods), Scipione et al. (1996), on the contrary, found high negative correlations between number of decapod individuals and LAI, Mazzella et al. (1989) found that most of the mollusc and amphipod species were closely related to the Posidonia leaf stratum and increasing shoot density. Thus, a clear relationship between 152
plant features and associated animals has not yet been clearly defined. The main questions, in order to try to reduce the confusion of the previous works, are: a) do the Posidonia oceanica associated organisms have any particular spatial scale of variation? b) Does the spatial distribution of the plant drive the spatial distribution of the organisms and is it possible to recognize some relationships between plant features and animals?
5.3.1
Higher Taxa
As discussed in the previous paragraphs, the Lacco Ameno meadow showed lower values of, for example, the shoot density, leaf length, or IB, as compared to Scarrupata. Notably, opposite trends were found for higher taxonomical groups. In fact, higher values were observed in Lacco Ameno (i.e., the more impacted meadow) than in Scarrupata. This was an important difference and might be due to the lower shoot density in the meadow of Lacco Ameno which may result in the inclusion of soft-sediment species in the samples (Ledoyer, 1983; Mazzella et al., 1989). On the other hand, the mean number of taxa was comparable between the two meadows, so enrichment due to sediment-dwelling animals apparently affected only the number of individuals in Lacco Ameno. In Scarrupata, amphipods dominated in both seasons, although in winter they decreased in number. Such decrease could be mainly due to changes in the morphological structure of the plant. In fact, as seen in the paragraph 5.1 of the present chapter, in winter almost all the parameters of the plant reached their minimum values. The shorter plant canopy does not give adequate shelter for these animals during the winter season when the effects of waves are strongest. Another possibility is the decrease of food resources such as epiphytes which are associated with the reduction of the canopy. The high positive correlations between amphipods and shoot density recorded by both Spearman’s and CAP analyses support the shelter hypothesis. In fact, a denser meadow could create a more suitable refuge for these 153
animals even if the leaf canopy is shorter, as already reported by many authors (Scipione et al., 1996; Sanchez-Jerez et al., 1999, 2000). To some degree, the shelter hypothesis was confirmed by comparing the community structures in Lacco Ameno and in Scarrupata. In the former, amphipods were less abundant and were not the dominant taxa. Shoot density in the Lacco Ameno meadow was lower than in Scarrupata confirming that amphipods were largely dependent from shoot density. Significant differences in the abundance of amphipods were found between Lacco Ameno and Scarrupata, and the ANOVA test (Results Chapter Table 9) showed that variability in amphipod distribution depends on large-scale factors. Once again, this could be best explained by the high variability in shoot density. Thus, factors affecting the spatial variability in shoot density could indirectly affect amphipods which are the free-living taxa most closely associated with the Posidonia shoots (Scipione, 1999). On the other hand, the abundance of the other main group, Decapoda, was the opposite to that of the Amphipoda. Decapods were the dominant taxon in Lacco Ameno (reaching 40% in terms of number of individuals of the entire faunal community), while in Scarrupata they never reached 10%. It seems that the more complex the habitat, the less suitable it is for these animals. The issue of habitat complexity was, to some degree, also reported by Scipione et al. (1996) that found negative correlations between decapod numbers of individuals and LAI. The results obtained in the present work was also supported by the correlation analysis, with high negative values between decapod numbers and the main plant descriptors (as a measure of complexity: leaf length, leaf surface, shoot density and leaf biomass). Another reason for the pattern observed here could be that decapods are a mixture of seagrass specialist and species commonly found in bare sediments. In Lacco Ameno, due to the lower shoot density, the habitat was characterized by a large number of sandy patches within seagrass meadow that allowed for the addition of many soft-bottom species to the total community. A similar situation was described by 154
Mazzella et al. (1989) for deeper and low-shoot density meadows. The higher number of individuals found was in agreement with the findings of Egglestone (1998) and Hovel & Lipcius (2001, 2002) who reported that Zostera spp. beds with fragmented, small patches and low-shoot density supported more decapod individuals than those with a continuous coverage. The difference in abundances with season could be explained by the presence in summer of several predators of decapods (e.g., fishes) (Bell & Harmelin-Vivien, 1983) that in winter migrate into deeper water in the same way as observed for grazer fishes by Peirano et al. (2001). There is another reason which could explain the differences in decapod abundances between Lacco Ameno and Scarrupata. As noted by Sanchez-Jerez et al. (2000), many decapods in their post-larval stage are strong swimmers and are capable of swimming appreciable distances. At the same time, the currents flowing along the coast have a potential role in the dispersal of both decapods adults and larvae (Shanks, 1995). The combination of all of these processes, active and passive transportation, could explain the lack of clear patterns of decapod distributions. For example, studies on Zostera, Posidonia, as well as Amphibolis and Halophila, and associated decapod assemblages, showed confusing results, indicating that shoot density was of little importance in determining the abundance of decapods over the large spatial scale (Edgar, 1990; Worthington et al., 1992, Edgar & Robertson, 1992; Unsworth et al., 2007). A model to account for variations in the distribution and abundance of decapods settling to seagrass from plankton, pointed out that the abundance of animals among separate beds reflected the variation in the supply of larvae from one site to another (Bell & Westoby, 1986). The results of the ANOVA analysis made on the individual number (shown in the Results Chapter table 9) in the present study seem to confirm the variation in the supply of larvae since the decapod group was widely variable in spatial distribution showing significant variability both at Location and Site scales. Therefore, both large and medium scales factors could contribute to the distribution of this group. 155
Concerning polychaetes, they were the second largest group in Lacco Ameno but they had lower abundance in winter than in summer. Opposite seasonal trends were evident in Scarrupata compared to Lacco Ameno, in fact, polychaetes also represented the second dominant group, but abundance increased from summer to winter. These different seasonal trends are difficult to explain. As reported by Gambi et al. (1992), vagile polychaetes do not find the leaf substratum a favourable habitat and Gambi et al. (1995) affirm that leaf substratum polychaetes are probably impoverished assemblages of the below strata community being selected by the more stressed and less complex environment. This hypothesis is supported in the present study by the negative, albeit slight, correlations found between polychaetes and leaf length, leaf surface and leaf biomass as well as shoot density. The hypothesis of an impoverished assemblage of the below strata community fit well with the results obtained in Scarrupata, where, in fact, the abundances increased with the decrease in morphometric features, but it was not valid for Lacco Ameno, where the opposite was observed. However, considering that for the Lacco Ameno meadow there might be an enrichment of species from the surrounding soft-bottom areas, as occurred for decapods, the “natural” seasonal trend followed in Scarrupata could be masked in Lacco Ameno by the higher number of individuals coming from patches of bare sediments. Similarly to the distribution of the two groups of crustacean discussed above, also the group of polychaetes varied at the Location scale (ANOVA, see Results Chapter table 9). Thus, large-scale phenomena may influence their spatial distribution, and it is likely that the main phenomenon leading to the observed trends by these three main groups, is the modification of the habitat due to the human induced disturbances. The human impact on the Lacco Ameno meadow tends to reduce the habitat formed by Posidonia oceanica in favour of the bare soft sediments, thus modifying the associated faunal communities.
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5.3.2
Gastropods
The number of gastropod species found as well as the number of individuals for each single species was very low compared to other studies in which the investigators used a hand-towed net (Idato et al., 1983; Russo et al., 1985, 1986; Chessa et al., 1989; Mazzella et al., 1989; Gambi et al., 1992; Scipione et al., 1996; Russo & Terlizzi, 1998). However, our numbers were higher than those found by Covazzi Harriague et al. (2006) who also used a suction device, similar to the one used in the present study. Covazzi Harriague and collegues sampled an area of 0.1 m2 area, but when scaled up to 0.16 m2 (the area used in the present work) their abundances were still lower than the ones reported in this study. The lower number of gastropod individuals found here was likely due to the low efficiency of the sampling device in collecting this group, although comparisons between hand-towed net and suction devices (Russo et al., 1986; Russo & Terlizzi, 1998) showed that both methods give comparable results in describing mollusc community structures. Apart from the considerations on the sampling method, it was, nonetheless, interesting to notice how the gastropod communities varied between the two meadows. Gastropod diversity was always higher in Lacco Ameno compared to Scarrupata. Apparently, the disturbances due to human impact increased the diversity, and, again, the increase in diversity may be explained by the contamination from the surrounding bare sediment thus adding species that found suitable conditions because of the larger sand patches formed after the shoot uprooting due to, e.g., boat anchoring. Besides the high variability in the total number of species and individuals, a huge difference existed between sampling periods in each meadow. However, also the temporal differences might be due to different efficiencies of the sampling device in the two sampling seasons. In fact, in winter the Posidonia canopy was lower and it is possible that sampling with the air-lift was more efficient and the gastropods, attached to the leaves,
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detached more easily than in summer, when the canopy was higher and the leaves might obstruct the Sorbona mouth reducing the power of the suction. Most of the gastropod species were negatively correlated with most of the plant features, even those species that are meso-grazers of the epiphytes of the plant leaves. This is in contrast with what might be expected. In fact, longer leaves host more epiphytes and should thus be more exploited by gastropod grazers, but our correlation analyses showed the opposite. The reason for the negative correlations between gastropods and plant features are hard to explain, in particular the ecological meaning. One hypothesis could be due to the sampling method, as already explained above for the seasonal differences. In a dense canopy with long leaves, the suction device may be less efficient. But besides this sampling device inefficiency hypothesis, the negative correlations may, to some extent, be related to the complexity of the habitat in Lacco Ameno. On one hand the most important plant features for the gastropods (say shoot density, leaf length, leaf surface) were always lower than those reported for Scarrupata, and, on the other, the number of both species and individuals recorded in Lacco Ameno included species from the bare sediments (namely detritivorous species not strictly associated to Posidonia) which together drove the correlations toward negative values.
5.3.3
Polychaetes
On the whole, the number of polychaete families found here were higher than those found by other authors (Mazzella et al., 1989; Gambi et al., 1992; Scipione et al., 1996). Also in this case, the differences could be due to the sampling device used. In fact, since polychaetes are more abundant in the rhizome layer (Giangrande, 1985; Gambi et al., 1995), the hand-towed net is less efficient than the suction device in sampling those animals living in the lower rhizome layer. 158
The higher number of families recorded in Lacco Ameno compared to Scarrupata, could be explained, once again, by the presence of families characteristic of soft sediments (Mazzella et al., 1989). But according to Gambi et al. (1995) another reason to explain the higher number of families in Lacco Ameno could be the result of a decrease in the effect of environmental factors, such as hydrodynamics. In fact, the Lacco Ameno meadow is less exposed to wave action and currents, while Scarrupata is in one of the more hydrodynamically exposed sites off the island of Ischia. The different abundance values recorded during the seasons are more difficult to explain, although polychaete abundance in Lacco Ameno could be enhanced by an increase in individuals from the adjacent bare sediments. On hard bottoms, the peak of abundances was generally recorded in summer (Giangrande, 1988), while in Posidonia beds, the peak was often recorded in winter (Mazzella, et al., 1989; Gambi et al., 1992, 1995). According to these authors, also the present data confirm this contrasting pattern, and a possible reason could be the intense predation occurring during summer (Mazzella et al., 1989) when predator concentrations are high. As reported in Gambi et al. (1995) an intense predation acts, in fact, mainly on abundances of species and individuals. Both correlation analyses seem to confirm the negative influence of the increasing habitat complexity on polychaete diversity and abundances. But the differences in life histories and reproductive and larval settlement periods between species may be important drivers of the seasonal differences (Giangrande et al., 1994).
5.3.4
Amphipods
On the whole, the number of amphipod genera found in the present study was higher than that reported in other works (Scipione & Fresi, 1984; Mazzella et al., 1989; Sanchez-Jerez et al., 1999, 2000; Zakhama-Sraieb et al., 2006) but comparable to those found by Gambi 159
et al. (1992) and by Scipione et al. (1996). The discrepancy could be explained by the sampling device used, which was more efficient in capturing these animals, as recently reported by Michel et al. (2010) who made a comparative study on three common amphipod sampling methods: hand-towed net, air-lift and light trap. The air-lift was the best method for quantitative estimates and for biodiversity surveys (Michel et al., 2010). Moreover, in the present study, the net placed around the quadrat used during the suction improved this efficiency by preventing the amphipod from escaping. In the past, hand nets were used to sample the communities but this method probably permitted amphipods to escape from the mouth of the net. In any case, what emerged from the previous studies (Scipione & Fresi, 1984, Mazzella et al., 1989, 1992; Scipione, 1992; Scipione et al., 1996) is that some amphipods species are strictly associated with Posidonia oceanica and have a certain habitat fidelity. For example, species of the genera Lysianassa, Ampelisca and Siphonocetes are related to soft bottoms and are commonly associated with P. oceanica meadows, while those of the genus Dexamine are associated with the leaf stratum (Ruffo, 1982, 1989, 1993; Scipione, 1992; Scipione et al., 1996). The dependence of amphipod communities to the habitat created by seagrass blades, or shoot density, suggests the importance of complex habitats to these animals. In fact, in the present study, and as reported in many other studies, the most abundant and diversified communities were found in Scarrupata, where P. oceanica meadows reach their maximum in complexity. This is supported also by the high Spearman’s correlation values found between amphipods and the main plant features, and by the great affinity between the most abundant species and the shoot density emerging from the CAP analysis. The analysis of nMDS showed an homogeneity in the distribution of amphipods when the habitat was stable and homogeneous (in Scarrupata) while where the habitat was fragmented and less complex (with lower shoot densities as in Lacco Ameno) the animals were less abundant and patchily distributed. Moreover, the abundance found in Lacco Ameno could be augmented 160
by the occurrence of soft bottom species. In fact, Scipione & Fresi (1984) recognized that the deeper amphipod communities (30 m depth) could be affected by the “contamination” from the surrounding bare soft bottoms. In Lacco Ameno, at a depth of 15 m (the sampling depth in the present study) the meadow had relatively low shoot densities, comparable to those found in deeper more pristine meadows, with shoots scattered among sediment. Since amphipods seem to be strongly related to shoot density, it is not surprising that the same large-scale factors influencing shoot density may also influence the amphipod assemblages (as shown by the ANOVA results in the previous chapter). The results from this study clearly show that amphipods, because of their high sensibility to changing environmental conditions due to human impact (e.g., low shoot density due to the boat anchoring), constitute a useful tool to assess the health of P. oceanica meadows, as also reported by Conradi et al. (1997) and by Zakhama-Sraieb et al. (2006).
5.4
Final Considerations and Conclusions
Positive relationships between faunal abundance and structural components of seagrass beds such as shoot density and biomass are common (Heck & Orth, 1980; Lewis & Stoner, 1983; Bell & Westoby, 1986; Attrill et al., 2000). Moreover, local and landscape scale seagrass habitat structure are strongly related to hydrodynamic regime, and this covariation makes it difficult to understand which processes are responsible for shaping faunal communities (Hovel et al., 2002). Spatial patterning of marine habitats at the landscape scale often has strong effects on processes such as predation that influence faunal abundance. Understanding how seagrass landscape patterns relate to ecological processes (e.g., recruitment, competition, predation) that generate the observed patterns of benthic community composition, and how these processes vary spatially, is fundamental to developing an understanding of the relationships between seagrass habitats and benthic 161
community composition and dynamics at landscape scale (Turner et al., 1999). What emerged from the present study was that the influence of the Posidonia features was the main driving force of the spatial distribution of the associated invertebrate assemblages. In fact, when the Posidonia meadow suffered the negative impact due to human activities (say in Lacco Ameno) the communities modified their relative abundances. Organisms typically dominant in a Posidonia meadow, such as the amphipods, that have a strong affinity with the plant, reduced their abundances because of the degradation of the habitat formed by the leaves and the rhizomes of Posidonia. Unfortunately, from the present study also emerged the difficulty to compare different spatial scales between faunal organisms and plant features. The few spatial scales considered for the invertebrates associated to the plant, mainly due to technical limitations of the sampling method used, did not permit to investigate the closer affinities between the small-scale variability recorded by the plant (that was the most important and common variability scale among all the variables considered) and the animal themselves. Thus, un-answered questions remained: a) what happens to the distribution of animals at smaller scales? b) what are the relationships between plant and animals at smaller scales? c) which is the role of other environmental characteristics, (unfortunately) not considered here? Further studies might be addressed to answer these questions, and even more intensive sampling designs should be made to investigate the smaller spatial scales of distribution of the Posidonia associated communities. Seagrass structural complexity is not the only parameter that potentially influences community structures at any spatial scale considered, and research so far strongly discriminated between effects due to complexity and those due to large-scale processes, e.g., hydrodynamic regimes that have been described as the major factor influencing patterns in seagrass beds (Turner et al., 1999; Hovel et al., 2002). On the other hand, temporal variability represents a greater amount of the variation in the species abundance, 162
and in the present work it was significant in both meadows. This may be a reflection of seasonal and annual trends in both the number of individuals and species present in the communities. The temporal structure of the species abundance data may thus be a descriptor of unmeasured underlying processes that were influencing community composition (e.g. recruitment dynamics), and which were unrelated to the measured environmental variables, as suggested by Turner et al. (1999). As a whole, the results obtained in the present work suggest that:
5.4.1
Posidonia oceanica Features
The two meadows studied presented relevant differences and variability, both spatially and temporally, for the main structural, morphometric and biomass Posidonia oceanica parameters considered. Thus the null hypothesis of no spatial and temporal variability of the parameters was rejected and the main aim of finding some differences in the plant distribution between and in the two meadows was reached. The shoot density varied at Location (few kms) and at the Station (10s of m) spatial scales, indicating an influence of different factors acting at these two spatial scales. On the other hand, no seasonal influence was recorded. Thus, this result confirms the importance of analyzing shoot density for discriminating the ecological status of meadows at different degrees of impact, but it is important not to under-value the small variability that potentially could affect this parameter. Most morphometric features showed multiple spatial scales of variability (at least two scales). None of these parameters varied significantly at the Location scale, but all of them showed a high variation at the smallest scale of Quadrat (10s of cm). This is not negligible when studies for monitoring the health status of Posidonia 163
meadows are conducted, because there could be the risk of generalizing patterns at larger scales and variabilities at small spatial scales could lead to confusion, or even to overestimation of differences between meadows. Most morphometric parameters presented variations with seasons, but the reasons for this are well known, as they relate directly to the biology of Posidonia. It is, therefore, important to factor the date of sampling for each season into the sampling program. The degree of development of many morphometric, as well as biomass, features strongly depends on the particular month of sampling. Thus, differences recorded between meadows which were sampled in different months should be viewed with caution because there could be the risk of reaching wrong conclusions about the productivity or the health status of the meadows.
5.4.2
Borer Polychaetes
The three species of borer polychaetes were found in most of the plots examined and, as a whole, a high variability at almost all the spatial scales examined was observed. The mean values of total IB recorded in the present work were lower than those recorded in previous studies in the same meadows, even at comparable depth. This result apparently seems to confirm that different sampling strategies and different number of samples taken could lead to different results and interpretations of data. Total IB varied at several spatial scales (Locations, Sites and Stations). IB of L. collaris varied at all spatial scales but Plot scale. IB of L. ninetta varied only at Station scale and IB of N. unicornis did not vary at any spatial scale.
164
The potential role of total IB as an indicator of general environmental condition and health of the meadows, as hypothesized by several authors, seems to be confirmed in the present work. However total IB must be used with caution because of the high variability of this index at smaller scales, suggesting that factors acting at small scales could be of relevance in borer distributions.
5.4.3
Macro-Invertebrate Assemblages
The community structures in the two meadows presented major differences in the relative abundances of higher taxa. In Scarrupata, amphipods dominated the community in both seasons, while in Lacco Ameno the dominant taxon was represented by decapods. Polychaetes represented the second most important group in terms of dominance in both meadows, with marked differences between seasons. The diversity of gastropods presented significant differences at Location scale and among seasons, with higher values in winter in both meadows. By contrast, the number of individuals showed variability at the Station scale and between seasons, with higher values in winter. Gastropods seem to be an ideal candidate for indicators of status and health of meadows able to discriminate between impacted and pristine meadow conditions. Negative correlations were observed between the main Posidonia features (i.e., system complexity) and gastropod species diversity and abundance. This result could explain the higher number of both species and individuals in the less complex meadow of Lacco Ameno. Polychaete families were significantly different both in abundances and diversity between meadows, with higher values recorded in Lacco Ameno than in 165
Scarrupata. Differences in abundance and diversity during seasons could be explained by the intense predation occurring during summer, as suggested by several authors. Negative correlations between polychaetes and main Posidonia features seems to suggest the relationship with higher patchiness and occurrence of bare softsediment in Lacco Ameno (less structured) that favor the presence and abundance of specific taxa (e.g. interstitial forms). Amphipod genera diversity was considerably higher in the Scarrupata, meadow considered as pristine. This huge variability at Location scale seems to confirm the habitat fidelity these organisms have with respect to Posidonia meadows. The complexity of the habitat (measured as shoot density) seems to be the key for the distribution and diversity of this group, and, in fact, they are positively correlated with shoot density. Amphipods are strong indicators of the ecological status and sensitive to seagrass degradation showing rapid response to variations in plant covering. The results emerged from this study clearly show that amphipods constitute a useful tool to assess the health of P. oceanica meadows, confirming the results of other studies at Location scale.
5.5
General Conclusions
It is clear that a variety of environmental factors may independently influence the patterns of variability both of Posidonia features and of associated assemblages. Variations at several spatial scales may be characteristics of specific natural processes affecting meadows, and the impact due to human activities may emphasize, in many cases, the 166
effects of such natural factors. The spatial and temporal distribution of fauna seems to be affected more by processes linked to their biology and ecology than by association with the plant features. However, habitat complexity appears to influence to different degrees the associated fauna. Borer polychaetes did not seem to be influenced by any of the plant features. Ambiguous results were recorded for gastropod species that were negatively influenced mainly by leaf features (leaf length, leaf width, leaf surface, etc). However, as many gastropod species are grazers, it is likely that their distribution is linked more to epiphyte distribution and diversity but that this in turn may be influenced by other factors including the plant itself. Polychaete families were negatively related mainly by leaves and sheaths biomass, probably because many of the families are typical of soft-bottom. Amphipods were mainly influenced by shoot density as suggested by high positive correlation values, where they probably found more suitable conditions. Thus different plant features (both morphometrical and structural) influence in different ways different taxonomic groups. More studies are needed to examine independent effects of co-varying factors on fauna in seagrass habitats to determine whether their influences can be generalized or are primarily species- and context-specific. To date, we can only speculate that differences in faunal densities and abundances between seasons and between meadows (and for each meadow between adjacent areas) are determined more by pre-settlement factors (e.g., larval abundance and current regimes) or by environmental factors not examined in the present work than by the variables included here. Various processes operating at a variety of scales may make relationships between fauna and environmental variables complex and difficult to predict and the present results indicate that different species or taxa may respond to different aspects of seagrass habitat.
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Appendix 1 Number of taxa and individuals Table 19: Average number of higher taxa and individuals along Lacco Ameno and Scarrupata meadows in the two seasons (mean ± SD). Location and season Lacco Ameno Summer Lacco Ameno Winter
Sites
No. Taxa 12.7 ± 2.4 12.6 ± 1.5
No. Individuals 330.6 ± 173.6 541.9 ± 181.2
Lacco Ameno Summer
A B C
13.0 ± 1.8 13.3 ± 3.1 11.8 ± 2.1
326.8 ± 148.5 362.0 ± 154.5 303.0 ± 233.3
Lacco Ameno Winter
A B C
12.7 ± 2.0 13.3 ± 1.0 11.8 ± 1.0
648.7 ± 267.2 471.7 ± 86.8 505.3 ± 107.3
Lacco Ameno Summer
A
A1 A2 B1 B2 C1 C2
13.0 13.0 15.0 11.7 11.3 12.3
± 1.0 ± 2.6 ± 3.5 ± 2.1 ± 2.5 ± 2.1
325.0 328.7 375.3 348.7 338.3 267.7
± 143.0 ± 186.3 ± 228.9 ± 82.0 ± 340.5 ± 128.1
A1 A2 B1 B2 C1 C2
12.7 12.7 13.0 13.7 11.7 12.0
± 3.1 ± 0.6 ± 1.0 ± 1.2 ± 1.5 ± 0.0
760.3 537.0 492.7 450.7 536.7 474.0
± 130.0 ± 352.4 ± 116.7 ± 62.3 ± 157.6 ± 31.8
B C
Lacco Ameno Winter
A B C
Stations
Scarrupata Summer Scarrupata Winter
258.4 ± 84.4 351.2 ± 132.6
Scarrupata Summer
A B C
11.8 ± 1.5 12.0 ± 0.9 11.7 ± 1.6
284.8 ± 124.8 267.2 ± 16.2 223.2 ± 77.0
Scarrupata Winter
A B C
13.5 ± 1.0 13.3 ± 1.6 14.0 ± 0.6
431.8 ± 149.0 278.5 ± 133.3 343.3 ± 74.7
Scarrupata Summer
A
A1 A2 B1 B2 C1 C2
11.7 12.0 12.3 11.7 11.7 11.7
± 1.5 ± 1.7 ± 1.2 ± 0.6 ± 0.6 ± 2.5
249.0 320.7 265.3 269.0 235.7 210.7
± 56.2 ± 178.7 ± 25.1 ± 4.0 ± 41.6 ± 112.3
A1 A2 B1 B2 C1 C2
14.0 13.0 13.7 13.0 14.0 14.0
± 1.0 ± 1.0 ± 2.3 ± 1.0 ± 0.0 ± 1.0
511.0 352.7 270.7 286.3 333.7 353.0
± 161.7 ± 102.9 ± 131.4 ± 164.2 ± 112.3 ± 32.4
B C
Scarrupata Winter
A B C
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11.8 ± 1.3 13.6 ± 1.1
SIMPER Table 20: Average similarity percentages of higher taxonomic groups: a) Lacco Ameno similarity in summer and winter; b) Scarrupata similarity in summer and winter; c) dissimilarity pairwises summer vs winter for both meadows. The contribution percentage for each taxon is reported. a)
Lacco Ameno Summer Average similarity: 62.94 Taxa Contrib% Policheti 31.33 Decapodi 28.57 Anfipodi 22.2 Bivalvi 6.01 Gasteropodi 3.97
b)
c)
Winter Average similarity: 73.62 Taxa Contrib% Decapodi 44.36 Policheti 19.79 Anfipodi 19.01 Gasteropodi 6.03 Bivalvi 2.47 Scarrupata
Summer Average similarity: 73.22 Taxa Contrib% Anfipodi 58.89 Misidacei 11.76 Policheti 9.42 Decapodi 6.55 Cumacei 4.98
Winter Average similarity: 69.00 Taxa Contrib% Anfipodi 44.66 Policheti 17.39 Isopodi 9.34 Gasteropodi 7.54 Decapodi 7.32 Cumacei 6.11
Lacco Ameno Summer vs Winter Average dissimilarity = 41.76 Taxa Contrib% Decapodi 43.8 Policheti 22.24 Anfipodi 12.74 Gasteropodi 6.06 Tanaidacei 3.36 Bivalvi 3.28
Scarrupata Summer vs Winter Average dissimilarity = 36.00 Taxa Contrib% Anfipodi 29.33 Policheti 17.88 Misidacei 12.03 Isopodi 10.41 Gasteropodi 9.98 Decapodi 7.34 Cumacei 4.84
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Table 21: Average similarity percentages of sites in each meadow and season. The contribution percentage for each taxon is reported. Lacco Ameno Summer Site A Site B Average similarity 65.3% Average similarity 66.0% Taxa Contrib% Taxa Contrib% Polychaeta 36.2 Polychaeta 31.9 Decapoda 28.8 Decapoda 29.5 Amphipoda 13.3 Amphipoda 20.8 Bivalvia 6.6 Bivalvia 5.1 Gastropoda 3.7 Cumacea 4.1 Tanaidacea 3.1
Site C Average similarity 59.2% Taxa Contrib% Amphipoda 31.3 Polychaeta 25.7 Decapoda 24.4 Bivalvia 6.0 Gastropoda 5.2
Site A Average similarity 62.1% Taxa Contrib% Decapoda 40.4 Polychaeta 26.2 Amphipoda 15.9 Gastropoda 7.1 Echinodermata 2.3
Winter Site B Site C Average similarity 83.6% Average similarity 79.1% Taxa Contrib% Taxa Contrib% Decapoda 47.9 Decapoda 42.4 Polychaeta 18.4 Amphipoda 21.4 Amphipoda 17.5 Polychaeta 18.3 Gastropoda 5.3 Gastropoda 5.2 Bivalvia 2.2 Tanaidacea 3.5
Scarrupata Summer Site A Site B Average similarity 69.3% Average similarity 78.2% Taxa Contrib% Taxa Contrib% Amphipoda 51.2 Amphipoda 56.4 Polychaeta 13.4 Mysidacea 14.1 Mysidacea 12.6 Polychaeta 8.1 Cumacea 6.9 Decapoda 5.3 Decapoda 6.2 Cumacea 4.4 Isopoda 4.1
Site C Average similarity 70.9% Taxa Contrib% Amphipoda 63.2 Mysidacea 9.9 Polychaeta 8.1 Decapoda 7.4 Cumacea 3.6
Site A Average similarity 73.8% Taxa Contrib% Amphipoda 37.8 Polychaeta 21.4 Gastropoda 7.9 Isopoda 6.2 Mysidacea 6.2 Cumacea 4.8 Decapoda 4.3 Pantopoda 3.8
Winter Site B Site C Average similarity 61.4% Average similarity 73.9% Taxa Contrib% Taxa Contrib% Amphipoda 43.8 Amphipoda 45.8 Polychaeta 14.0 Polychaeta 15.0 Isopoda 10.3 Isopoda 10.3 Decapoda 9.5 Decapoda 7.5 Gastropoda 7.1 Gastropoda 7.2 Cumacea 6.1 Cumacea 6.3
Table 22: Average dissimilarity percentages between sites in each meadow and season. The contribution percentage for each taxon is reported. Lacco Ameno Summer A vs B A vs C Average dissimilarity = 33.79 Average dissimilarity = 41.60 Taxa Contrib% Taxa Contrib% Polychaeta 29.11 Polychaeta 31.72 Decapoda 22.87 Amphipoda 20.32 Amphipoda 17.64 Decapoda 20.21 Bivalvia 4.92 Bivalvia 5.82 Tanaidacea 4.28 Tanaidacea 4.39 Gastropoda 4.24 Cumacea 4.03 Pantopoda 3.2 Gastropoda 3.27 Cumacea 2.77 Echinodermata 2.37 Isopoda 2.69
B vs C Average dissimilarity = 40.92 Taxa Contrib% Polychaeta 30.17 Decapoda 23.69 Amphipoda 18.6 Bivalvia 4.73 Cumacea 4.19 Gastropoda 4.1 Pantopoda 2.72 Echinodermata 2.28
Winter A vs B A vs C Average dissimilarity = 32.23 Average dissimilarity = 31.98 Taxa Contrib% Taxa Contrib% Decapoda 36.37 Decapoda 38.14 Polychaeta 34.05 Polychaeta 33.34 Amphipoda 8.31 Amphipoda 8.34 Gastropoda 5.00 Gastropoda 4.56 Tanaidacea 3.57 Tanaidacea 3.9 Bivalvia 3.37 Bivalvia 3.39
B vs C Average dissimilarity = 18.07 Taxa Contrib% Decapoda 35.5 Polychaeta 19.58 Amphipoda 13.7 Gastropoda 6.81 Tanaidacea 5.27 Bivalvia 4.5 Echinodermata 3.46 Mysidacea 2.9
Scarrupata Summer A vs B A vs C Average dissimilarity = 26.48 Average dissimilarity = 30.77 Taxa Contrib% Taxa Contrib% Amphipoda 37.96 Amphipoda 37.5 Mysidacea 15.03 Polychaeta 14.5 Polychaeta 13.29 Mysidacea 11.8 Isopoda 6.19 Cumacea 6.99 Cumacea 5.35 Isopoda 6.74 Decapoda 4.76 Decapoda 4.51 Chaetognatha 4.09 Gastropoda 4.12 Bivalvia 3.89 Chaetognatha 3.74 Bivalvia 3.68
170
B vs C Average dissimilarity = 26.87 Taxa Contrib% Amphipoda 39.93 Mysidacea 20.63 Polychaeta 6.29 Isopoda 5.49 Cumacea 5.41 Decapoda 5.19 Bivalvia 4.81 Chaetognatha 3.11
Winter A vs B A vs C Average dissimilarity = 35.95 Average dissimilarity = 29.17 Taxa Contrib% Taxa Contrib% Amphipoda 27.62 Amphipoda 28.85 Polychaeta 16.92 Polychaeta 14.55 Gastropoda 10.38 Gastropoda 10.23 Mysidacea 8.84 Mysidacea 9.71 Decapoda 8.8 Isopoda 8.75 Isopoda 8.09 Decapoda 7.26 Bivalvia 4.45 Bivalvia 4.23 Pantopoda 4.03 Pantopoda 4.2 Cumacea 3.98 Cumacea 3.71
B vs C Average dissimilarity = 32.54 Taxa Contrib% Amphipoda 37.59 Polychaeta 17.21 Decapoda 9.1 Isopoda 8.47 Gastropoda 7.03 Cumacea 5.24 Bivalvia 3.25 Echinodermata 2.86
Table 23: Average similarity percentages of stations in each meadow and season. The contribution percentage for each taxon is reported. Lacco Ameno Summer B1 B2 Average similarity: 52.55 Average similarity: 79.18 Taxa Contrib% Taxa Contrib% Polychaeta 27.75 Polychaeta 38.02 Decapoda 22.73 Decapoda 32.89 Amphipoda 22.46 Amphipoda 17.79 Bivalvia 9.02 Cumacea 3.41 Gastropoda 4.66 Cumacea 4.38
C1 Average similarity: 43.48 Taxa Contrib% Polychaeta 31.4 Decapoda 25.24 Amphipoda 25.24 Bivalvia 7.67 Gastropoda 5.49
C2 Average similarity: 67.03 Taxa Contrib% Amphipoda 35.9 Polychaeta 23.03 Decapoda 22.53 Bivalvia 5.24 Gastropoda 5.03
A2 Average similarity: 49.28 Taxa Contrib% Decapoda 35.42 Polychaeta 19.95 Amphipoda 19.6 Gastropoda 13.98 Tanaidacea 2.69
Lacco Ameno Winter B1 B2 Average similarity: 80.55 Average similarity: 87.47 Taxa Contrib% Taxa Contrib% Decapoda 43.53 Decapoda 51.49 Polychaeta 21.81 Amphipoda 17.65 Amphipoda 16.92 Polychaeta 15.66 Gastropoda 4.33 Gastropoda 6.33 Bivalvia 2.75 Tanaidacea 2.51
C1 Average similarity: 71.66 Taxa Contrib% Decapoda 31.92 Amphipoda 28.58 Polychaeta 23.33 Bivalvia 4.56 Tanaidacea 3.35
C2 Average similarity: 92.97 Taxa Contrib% Decapoda 48.78 Amphipoda 17.09 Polychaeta 14.99 Gastropoda 6.96 Tanaidacea 3.25
A1 Average similarity: 77.28 Taxa Contrib% Amphipoda 59.67 Mysidacea 13.58 Polychaeta 7.98 Cumacea 5.77 Decapoda 4.36
A2 Average similarity: 60.52 Taxa Contrib% Amphipoda 41.4 Polychaeta 21.3 Mysidacea 10.12 Decapoda 8.61 Cumacea 7.79 Isopoda 3.83
Scarrupata Summer B1 B2 Average similarity: 66.95 Average similarity: 87.74 Taxa Contrib% Taxa Contrib% Amphipoda 53.9 Amphipoda 57.9 Polychaeta 9.24 Mysidacea 17.81 Mysidacea 8.66 Polychaeta 7.2 Decapoda 7.43 Isopoda 4.38 Cumacea 5.3 Decapoda 4.24 Isopoda 3.78 Gastropoda 3.58
C1 Average similarity: 74.56 Taxa Contrib% Amphipoda 68.17 Mysidacea 9.5 Polychaeta 6.66 Decapoda 6.4
C2 Average similarity: 63.82 Taxa Contrib% Amphipoda 61.2 Mysidacea 10.07 Polychaeta 9.29 Decapoda 8.3 Cumacea 4.62
A1 Average similarity: 75.43 Taxa Contrib% Amphipoda 32.67 Polychaeta 19.19 Gastropoda 13.75 Decapoda 8.54 Mysidacea 6.65 Cumacea 4.39 Isopoda 4.31 Bivalvia 3.52
A2 Average similarity: 74.61 Taxa Contrib% Amphipoda 40.02 Polychaeta 24.55 Isopoda 6.89 Mysidacea 5.35 Gastropoda 4.93 Cumacea 4.67 Pantopoda 4.47
C1 Average similarity: 65.77 Taxa Contrib% Amphipoda 40.87 Polychaeta 19.94 Isopoda 10.37 Decapoda 7.81 Gastropoda 7.34 Cumacea 7.04
C2 Average similarity: 78.33 Taxa Contrib% Amphipoda 50.1 Polychaeta 11.05 Isopoda 10.01 Gastropoda 7.36 Decapoda 7.14 Cumacea 5.74
A1 Average similarity: 69.29 Taxa Contrib% Polychaeta 39.29 Decapoda 33.28 Bivalvia 7.42 Amphipoda 7.05 Gastropoda 3.51
A2 Average similarity: 58.41 Taxa Contrib% Polychaeta 29.1 Amphipoda 23.99 Decapoda 23.26 Bivalvia 5.32 Gastropoda 4.4 Cumacea 3.96
A1 Average similarity: 72.66 Taxa Contrib% Decapoda 40.91 Polychaeta 31.69 Amphipoda 14.15 Gastropoda 3.25
Scarrupata Winter B1 B2 Average similarity: 57.51 Average similarity: 55.49 Taxa Contrib% Taxa Contrib% Amphipoda 45.31 Amphipoda 42.65 Decapoda 11.5 Polychaeta 18.14 Gastropoda 9.98 Isopoda 11.45 Isopoda 9.83 Decapoda 7.29 Polychaeta 8.71 Cumacea 6.26 Cumacea 5.5 Gastropoda 4.42
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Table 24: Average dissimilarity percentages between stations in Lacco Ameno meadow in summer. The contribution percentage for each taxon is reported. A1 vs A2 Average dissimilarity = 33.81 Taxa Contrib% Polychaeta 34.53 Decapoda 21.53 Amphipoda 15.08 Bivalvia 6.08 Tanaidacea 4.76 Gastropoda 3.9 Cumacea 3.18 Isopoda 3.06
Lacco Ameno Summer A2 vs B1 A1 vs B1 Average dissimilarity = 37.34 Average dissimilarity = 38.38 Taxa Contrib% Taxa Contrib% Polychaeta 30.48 Polychaeta 32.26 Decapoda 22.83 Amphipoda 20.27 Amphipoda 17.18 Decapoda 19.63 Tanaidacea 4.75 Gastropoda 4.32 Gastropoda 4.7 Bivalvia 3.95 Pantopoda 4.12 Pantopoda 3.84 Bivalvia 3.7 Tanaidacea 3.07 Cumacea 2.49 Cumacea 2.84
A2 vs B2 Average dissimilarity = 29.79 Taxa Contrib% Decapoda 30.03 Polychaeta 26.27 Amphipoda 12.25 Tanaidacea 5.81 Bivalvia 5.61 Isopoda 3.18 Echinodermata 2.87 Gastropoda 2.86 Cumacea 2.53
A1 vs B2 Average dissimilarity = 29.66 Taxa Contrib% Polychaeta 26.18 Amphipoda 20.25 Decapoda 19.92 Bivalvia 7.02 Gastropoda 4.94 Tanaidacea 3.72 Isopoda 3.31 Cumacea 3.29 Mysidacea 2.59
A2 vs C1 Average dissimilarity = 41.02 Taxa Contrib% Polychaeta 31.9 Decapoda 20.67 Amphipoda 16.3 Bivalvia 6.32 Tanaidacea 5.2 Cumacea 4.23 Echinodermata 3.59 Gastropoda 3.01
A1 vs C1 Average dissimilarity = 44.50 Taxa Contrib% Polychaeta 34.72 Decapoda 20.19 Amphipoda 17.1 Bivalvia 6.67 Gastropoda 3.89 Tanaidacea 3.59 Cumacea 3.49 Echinodermata 2.82
B1 vs B2 Average dissimilarity = 34.69 Taxa Contrib% Polychaeta 28.85 Decapoda 25.24 Amphipoda 18.08 Gastropoda 5.2 Bivalvia 4.4 Pantopoda 3.79 Tanaidacea 2.58 Isopoda 2.49
B2 vs C1 Average dissimilarity = 43.10 Taxa Contrib% Polychaeta 30 Decapoda 27.49 Amphipoda 15.88 Bivalvia 5.48 Cumacea 4.23 Gastropoda 3.58 Isopoda 2.51 Echinodermata 2.42
B1 vs C1 Average dissimilarity = 44.49 Taxa Contrib% Polychaeta 31.72 Amphipoda 19.29 Decapoda 18.39 Bivalvia 5.49 Gastropoda 4.51 Cumacea 4 Pantopoda 3.45 Tanaidacea 2.85 Echinodermata 2.56
A1 vs C2 Average dissimilarity = 44.40 Taxa Contrib% Polychaeta 32.73 Amphipoda 26.06 Decapoda 17.24 Bivalvia 5.6 Cumacea 3.81 Tanaidacea 3.24 Gastropoda 3.15
A2 vs C2 Average dissimilarity = 36.48 Taxa Contrib% Polychaeta 26.61 Decapoda 23.32 Amphipoda 21.76 Tanaidacea 5.86 Cumacea 4.72 Bivalvia 4.48 Gastropoda 2.98 Isopoda 2.65
B1 vs C2 Average dissimilarity = 40.49 Taxa Contrib% Polychaeta 29.04 Decapoda 21.63 Amphipoda 21.51 Gastropoda 4.57 Cumacea 4.01 Pantopoda 3.91 Bivalvia 3.83 Tanaidacea 2.49
B2 vs C2 Average dissimilarity = 35.61 Taxa Contrib% Polychaeta 29.71 Decapoda 28.04 Amphipoda 17.73 Cumacea 4.6 Bivalvia 3.9 Gastropoda 3.67 Isopoda 2.44
C1 vs C2 Average dissimilarity = 38.25 Taxa Contrib% Amphipoda 27.95 Polychaeta 23.61 Decapoda 19.15 Cumacea 5.23 Bivalvia 4.72 Gastropoda 4.53 Echinodermata 3.58 Tanaidacea 2.39
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Table 25: Average dissimilarity percentages between stations in Lacco Ameno meadow in winter. The contribution percentage for each taxon is reported. A1 vs A2 Average dissimilarity = 37.16 Taxa Contrib% Decapoda 36.32 Polychaeta 35.08 Amphipoda 7.8 Gastropoda 4.23 Tanaidacea 4.09 Bivalvia 4.04
Lacco Ameno Winter A2 vs B1 A1 vs B1 Average dissimilarity = 35.12 Average dissimilarity = 26.95 Taxa Contrib% Taxa Contrib% Decapoda 46.43 Polychaeta 42.93 Polychaeta 24.26 Decapoda 26.27 Amphipoda 7.27 Amphipoda 6.86 Gastropoda 6.42 Tanaidacea 4.72 Bivalvia 3.51 Gastropoda 4.7 Tanaidacea 2.63 Bivalvia 3.75 Echinodermata 2.98
A2 vs B2 Average dissimilarity = 36.42 Taxa Contrib% Decapoda 47.09 Polychaeta 24.77 Amphipoda 11.6 Gastropoda 4.46 Tanaidacea 2.65
A1 vs B2 Average dissimilarity = 30.44 Taxa Contrib% Polychaeta 48.58 Decapoda 20.87 Amphipoda 6.86 Tanaidacea 4.73 Bivalvia 4.37 Gastropoda 4.28 Echinodermata 3.09
B1 vs B2 Average dissimilarity = 16.66 Taxa Contrib% Polychaeta 24.38 Decapoda 23.08 Amphipoda 17.93 Gastropoda 8.19 Bivalvia 5.76 Echinodermata 4.67 Mysidacea 4.1 Chaetognatha 2.74
A1 vs C1 Average dissimilarity = 28.46 Taxa Contrib% Polychaeta 38.12 Decapoda 33.9 Amphipoda 5.06 Tanaidacea 4.64 Gastropoda 4.54 Echinodermata 2.86 Isopoda 2.81
A2 vs C1 Average dissimilarity = 37.46 Taxa Contrib% Decapoda 44.87 Polychaeta 22.11 Amphipoda 13.79 Gastropoda 6.2 Bivalvia 3.92
B1 vs C1 Average dissimilarity = 22.52 Taxa Contrib% Decapoda 44.76 Polychaeta 16.56 Amphipoda 14.67 Gastropoda 5.14 Bivalvia 3.4 Isopoda 2.99 Echinodermata 2.84
B2 vs C1 Average dissimilarity = 23.10 Taxa Contrib% Decapoda 40.99 Polychaeta 21.03 Amphipoda 10.22 Gastropoda 7.11 Bivalvia 5.29 Tanaidacea 3.79 Echinodermata 2.73
A1 vs C2 Average dissimilarity = 28.17 Taxa Contrib% Polychaeta 52.33 Decapoda 20.51 Amphipoda 5.85 Tanaidacea 5.23 Bivalvia 4.19 Gastropoda 3.28
A2 vs C2 Average dissimilarity = 33.85 Taxa Contrib% Decapoda 48.91 Polychaeta 25.93 Amphipoda 7.15 Tanaidacea 3.85 Gastropoda 3.81 Bivalvia 2.7
B1 vs C2 Average dissimilarity = 15.11 Taxa Contrib% Decapoda 28.66 Polychaeta 26.53 Amphipoda 8.69 Gastropoda 7.51 Tanaidacea 6.43 Bivalvia 5.71 Chaetognatha 3.73 Mysidacea 3.71
B2 vs C2 Average dissimilarity = 11.56 Taxa Contrib% Amphipoda 25.32 Decapoda 15.44 Polychaeta 13.45 Tanaidacea 12.41 Gastropoda 8.58 Echinodermata 6.09 Mysidacea 4.99 Bivalvia 3.52 Isopoda 2.92
C1 vs C2 Average dissimilarity = 23.10 Taxa Contrib% Decapoda 41.69 Polychaeta 20.69 Amphipoda 13.17 Gastropoda 5.37 Bivalvia 4.32 Isopoda 3.46 Tanaidacea 3.14
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Table 26: Average dissimilarity percentages between stations in Scarrupata meadow in summer. The contribution percentage for each taxon is reported. A1 vs A2 Average dissimilarity = 30.39 Taxa Contrib% Amphipoda 37.04 Polychaeta 14.59 Mysidacea 11.54 Isopoda 8.98 Cumacea 5.31 Decapoda 4.85 Gastropoda 4.53 Chaetognatha 4.48
Scarrupata Summer A1 vs B1 A2 vs B1 Average dissimilarity = 23.99 Average dissimilarity = 32.18 Taxa Contrib% Taxa Contrib% Amphipoda 36.88 Amphipoda 41.4 Mysidacea 17.45 Mysidacea 13.1 Polychaeta 14.12 Polychaeta 10.58 Decapoda 7.1 Isopoda 8.14 Cumacea 5.38 Cumacea 4.87 Bivalvia 5.23 Chaetognatha 4.62 Chaetognatha 3.85 Gastropoda 4.1 Decapoda 3.54
A1 vs B2 Average dissimilarity = 17.78 Taxa Contrib% Amphipoda 29.38 Mysidacea 20.11 Polychaeta 16.53 Cumacea 5.97 Decapoda 5.61 Bivalvia 5.05 Isopoda 4.61 Chaetognatha 3.84
A2 vs B2 Average dissimilarity = 31.96 Taxa Contrib% Amphipoda 40.07 Polychaeta 13.58 Mysidacea 12.34 Isopoda 7.91 Cumacea 5.48 Gastropoda 4.25 Chaetognatha 3.87 Decapoda 3.74
B1 vs B2 Average dissimilarity = 21.30 Taxa Contrib% Amphipoda 40.24 Mysidacea 20.49 Decapoda 7.62 Polychaeta 6.37 Bivalvia 5.27 Cumacea 4.49 Chaetognatha 4.05 Isopoda 3.6
A1 vs C1 Average dissimilarity = 22.58 Taxa Contrib% Amphipoda 35.79 Polychaeta 15.66 Mysidacea 14.21 Cumacea 8.76 Decapoda 5.68 Bivalvia 4.8 Gastropoda 3.54 Chaetognatha 3.53
A2 vs C1 Average dissimilarity = 34.99 Taxa Contrib% Amphipoda 41.05 Polychaeta 14.45 Mysidacea 9.52 Isopoda 8.11 Cumacea 6.57 Gastropoda 4.58 Chaetognatha 3.86 Decapoda 3.03
B1 vs C1 Average dissimilarity = 25.75 Taxa Contrib% Amphipoda 42.26 Mysidacea 17.11 Polychaeta 7.98 Cumacea 6.5 Decapoda 6.05 Bivalvia 5.02 Chaetognatha 3.44 Gastropoda 3.17
B2 vs C1 Average dissimilarity = 20.84 Taxa Contrib% Amphipoda 35.42 Mysidacea 27.37 Isopoda 6.04 Cumacea 5.94 Polychaeta 5.43 Bivalvia 4.92 Decapoda 3.96 Chaetognatha 2.89
A1 vs C2 Average dissimilarity = 29.10 Taxa Contrib% Amphipoda 35.76 Mysidacea 14.25 Polychaeta 13.55 Cumacea 6.68 Isopoda 6.24 Decapoda 5.91 Bivalvia 4.24 Gastropoda 3.5
A2 vs C2 Average dissimilarity = 36.40 Taxa Contrib% Amphipoda 36.53 Polychaeta 14.59 Mysidacea 10.52 Isopoda 8.5 Cumacea 6.54 Gastropoda 4.52 Chaetognatha 4.12 Decapoda 4.08 Bivalvia 3.33
B1 vs C2 Average dissimilarity = 30.99 Taxa Contrib% Amphipoda 39.65 Mysidacea 16.37 Polychaeta 7.31 Decapoda 5.99 Isopoda 5.94 Cumacea 5.39 Bivalvia 5.08 Chaetognatha 4.19 Gastropoda 3.33
B2 vs C2 Average dissimilarity = 29.90 Taxa Contrib% Amphipoda 41.37 Mysidacea 23.4 Isopoda 7 Decapoda 4.47 Polychaeta 4.37 Bivalvia 4.28 Cumacea 4.1 Gastropoda 2.7
C1 vs C2 Average dissimilarity = 27.95 Taxa Contrib% Amphipoda 53.04 Cumacea 6.39 Isopoda 6.23 Polychaeta 5.92 Mysidacea 5.81 Decapoda 5.26 Bivalvia 5.06 Gastropoda 3.12
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Table 27: Average dissimilarity percentages between stations in Scarrupata meadow in winter. The contribution percentage for each taxon is reported. A1 vs A2 Average dissimilarity = 27.06 Taxa Contrib% Amphipoda 28.04 Gastropoda 16.81 Decapoda 12.81 Polychaeta 11.04 Isopoda 9.63 Mysidacea 5.71 Cumacea 3.88 Bivalvia 2.92
Scarrupata Winter A1 vs B1 A2 vs B1 Average dissimilarity = 38.68 Average dissimilarity = 35.93 Taxa Contrib% Taxa Contrib% Amphipoda 25.45 Amphipoda 26.84 Polychaeta 20.2 Polychaeta 18.04 Gastropoda 12.43 Decapoda 11.64 Mysidacea 9.12 Isopoda 9.05 Isopoda 8.41 Mysidacea 8.38 Decapoda 7.17 Bivalvia 5.74 Bivalvia 4.23 Gastropoda 5.69 Cumacea 3.72 Pantopoda 4.28 Cumacea 3.91
A1 vs B2 Average dissimilarity = 37.46 Taxa Contrib% Amphipoda 28.52 Polychaeta 14.9 Gastropoda 14.52 Mysidacea 8.98 Decapoda 8.72 Isopoda 7.18 Cumacea 3.73 Pantopoda 3.64
A2 vs B2 Average dissimilarity = 31.73 Taxa Contrib% Amphipoda 30.08 Polychaeta 14.03 Mysidacea 8.85 Gastropoda 8.29 Decapoda 7.7 Isopoda 7.69 Bivalvia 5.14 Pantopoda 4.73 Cumacea 4.66
B1 vs B2 Average dissimilarity = 35.32 Taxa Contrib% Amphipoda 27.35 Polychaeta 19.59 Decapoda 12.1 Isopoda 9.41 Gastropoda 9.27 Cumacea 5.27 Bivalvia 4.63 Tanaidacea 2.55
A1 vs C1 Average dissimilarity = 32.46 Taxa Contrib% Amphipoda 28.16 Gastropoda 14.05 Polychaeta 14.04 Mysidacea 9.68 Isopoda 8.74 Decapoda 6.37 Pantopoda 4.33 Bivalvia 3.59 Cumacea 2.77
A2 vs C1 Average dissimilarity = 28.33 Taxa Contrib% Amphipoda 33.03 Polychaeta 11.47 Isopoda 9.12 Mysidacea 9.1 Decapoda 7.6 Gastropoda 5.88 Bivalvia 5.68 Pantopoda 5.4 Cumacea 3.26
B1 vs C1 Average dissimilarity = 33.07 Taxa Contrib% Amphipoda 35.45 Polychaeta 20.05 Decapoda 10.16 Isopoda 9.94 Gastropoda 6.24 Cumacea 4.13 Bivalvia 2.75 Echinodermata 2.57
B2 vs C1 Average dissimilarity = 32.55 Taxa Contrib% Amphipoda 34.81 Polychaeta 17.96 Isopoda 8.7 Gastropoda 8.48 Decapoda 7.74 Cumacea 4.5 Bivalvia 4.32 Echinodermata 3.25 Pantopoda 2.53
A1 vs C2 Average dissimilarity = 29.97 Taxa Contrib% Amphipoda 23.06 Polychaeta 19.25 Gastropoda 14.67 Mysidacea 10.27 Isopoda 8.51 Decapoda 6.48 Cumacea 3.55 Bivalvia 3.29 Pantopoda 3.25
A2 vs C2 Average dissimilarity = 25.91 Taxa Contrib% Amphipoda 31.85 Polychaeta 13.13 Mysidacea 9.74 Decapoda 8.91 Isopoda 8.64 Cumacea 5.58 Gastropoda 5.07 Bivalvia 4.52 Pantopoda 3.82
B1 vs C2 Average dissimilarity = 30.98 Taxa Contrib% Amphipoda 39.91 Polychaeta 15.11 Decapoda 10.59 Isopoda 8.18 Cumacea 6.63 Gastropoda 5.32 Echinodermata 3.13 Bivalvia 2.32
B2 vs C2 Average dissimilarity = 33.54 Taxa Contrib% Amphipoda 40.24 Polychaeta 15.62 Decapoda 7.99 Gastropoda 7.97 Isopoda 7.06 Cumacea 5.75 Bivalvia 3.58 Pantopoda 2.72
C1 vs C2 Average dissimilarity = 24.82 Taxa Contrib% Amphipoda 43.84 Polychaeta 14.15 Isopoda 7.45 Decapoda 6.23 Cumacea 5.34 Gastropoda 4.6 Echinodermata 4.11 Bivalvia 3.34 Pantopoda 2.58
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Appendix 2 List of gastropods species found in both meadows Melanella alba Melanella polita Smaragdia viridis Gibberula philippii Gibberula miliaria Scissurella costata Caecum glabrum Tricolia speciosa Tricolia tenuis Tricolia pullus Bolma rugosa Cerithium vulgatum Bittium sp. Bulla striata Payraudeautia intricata Natica millepunctata Euspira sp. Haliotis sp. Haliotis tubercolata lamellosa Emarginula sp. Rissoa variabilis Rissoa violacea Rissoa auriscalpium Cylichna cylindracea Gibbula umbilicaris Jujubinus sp. Engina leucozona Mitra cornicula Williamia gussoni Raphitoma linearis Acmea sp. Marshallora sp. Eulimella ventricosa Turbonilla sp. Chrysallida sp.
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Number of gastropod species and individuals Table 28: Average number of species and individuals along Lacco Ameno and Scarrupata meadows in the two seasons (mean ± SD). Location and season Lacco Ameno Summer Lacco Ameno Winter
Sites
No. Species 4.7 ± 3.2 7.8 ± 2.3
No. Individuals 12.0 ± 7.6 30.1 ± 13.7
Lacco Ameno Summer
A B C
6.3 ± 2.3 4.8 ± 4.3 3.0 ± 2.0
11.7 ± 7.3 12.5 ± 10.6 11.8 ± 5.3
Lacco Ameno Winter
A B C
7.3 ± 2.4 8.5 ± 1.5 7.5 ± 3.0
36.2 ± 17.5 28.0 ± 11.3 26.2 ± 11.6
Lacco Ameno Summer
A
A1 A2 B1 B2 C1 C2
6.7 6.0 7.7 2.0 2.7 3.3
± 3.2 ± 1.7 ± 4.5 ± 1.0 ± 2.9 ± 1.2
15.0 8.3 18.0 7.0 11.0 12.7
± 10.0 ± 0.6 ± 12.2 ± 6.6 ± 6.0 ± 5.8
A1 A2 B1 B2 C1 C2
6.3 8.3 8.7 8.3 5.7 9.3
± 3.1 ± 1.5 ± 1.2 ± 2.1 ± 3.2 ± 1.5
27.0 45.3 22.7 33.3 20.0 32.3
± 18.2 ± 13.6 ± 8.0 ± 13.1 ± 14.7 ± 2.5
B C
Lacco Ameno Winter
A B C
Stations
Scarrupata Summer Scarrupata Winter
2.6 ± 2.0 6.2 ± 1.8
6.9 ± 5.6 28.1 ± 17.9
Scarrupata Summer
A B C
3.2 ± 2.8 2.2 ± 0.4 2.3 ± 2.3
9.7 ± 8.4 6.3 ± 1.6 4.8 ± 4.3
Scarrupata Winter
A B C
7.0 ± 1.3 6.2 ± 2.2 5.5 ± 1.9
40.3 ± 23.0 21.8 ± 15.8 22.2 ± 6.1
Scarrupata Summer
A
A1 A2 B1 B2 C1 C2
1.7 4.7 2.3 2.0 1.3 3.3
± 0.6 ± 3.5 ± 0.6 ± 0.0 ± 0.6 ± 3.2
6.3 13.0 7.3 5.3 3.3 6.3
± 4.2 ± 11.1 ± 1.5 ± 1.2 ± 2.1 ± 5.9
A1 A2 B1 B2 C1 C2
8.0 6.0 7.3 5.0 6.0 5.0
± 0.0 ± 1.0 ± 2.3 ± 1.7 ± 2.6 ± 1.0
59.0 21.7 24.3 19.3 22.7 21.7
± 10.1 ± 13.2 ± 14.6 ± 19.9 ± 9.5 ± 2.1
B C
Scarrupata Winter
A B C
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SIMPER Table 39: Average similarity percentages of gastropod species: a) Lacco Ameno similarity in summer and winter; b) Scarrupata similarity in summer and winter; c) dissimilarity pairwises summer vs winter for both meadows. The contribution percentage for each taxon is reported. a)
Lacco Ameno Summer Average similarity: 27.99 Species Caecum glabrum Melanella alba Gibberula miliaria Melanella polita
b)
Scarrupata Summer Average similarity: 35.35 Species Caecum glabrum Tricolia tenuis
c)
178
Contrib% 63.76 16.27 8.26 3.5
Winter Average similarity: 45.09 Species Contrib% Melanella alba 29.44 Smaragdia viridis 20.62 Gibberula philippii 20.4 Scissurella costata 8.1 Gibberula miliaria 6.92 Tricolia tenuis 6.3
Contrib% 81.34 9.24
Lacco Ameno Summer vs Winter Average dissimilarity = 84.58 Species Contrib% Melanella alba 17.08 Gibberula philippii 16.45 Smaragdia viridis 12.36 Caecum glabrum 12.07 Scissurella costata 7.04 Tricolia tenuis 6.11 Gibberula miliaria 5.67 Tricolia speciosa 4.23 Melanella polita 3.57 Euspira sp. 3.49 Cerithium vulgatum 1.42 Payraudeautia intricata 1.32
Winter Average similarity: 36.83 Species Contrib% Scissurella costata 47.08 Smaragdia viridis 19.1 Tricolia tenuis 7.82 Melanella polita 7.28 Melanella alba 6.65 Caecum glabrum 5.62 Scarrupata Summer vs Winter Average dissimilarity = 84.17 Species Contrib% Scissurella costata 31.23 Caecum glabrum 18.14 Smaragdia viridis 12.38 Melanella alba 8.44 Melanella polita 8.1 Tricolia tenuis 6.74 Tricolia pullus 4.68 Natica millepunctata 1.95
Table 30: Average similarity percentages of sites in each meadow and season. The contribution percentage for each species is reported. Lacco Ameno Summer Site A Site B Site C Average similarity: 36.5% Average similarity: 29.9% Average similarity: 26.8% Species Contrib% Species Contrib% Species Contrib% Melanella alba 31.2 Caecum glabrum 70.1 Caecum glabrum 79.9 Gibberula miliaria 28.3 Melanella polita 12.8 Euspira sp. 14.0 Caecum glabrum 23.5 Gibberula philippii 7.5 Payraudeautia intricata 6.4 Acmea sp. 3.5
Site A Average similarity: 43.5% Species Contrib% Gibberula philippii 40.2 Melanella alba 31.3 Smaragdia viridis 12.1 Gibberula miliaria 6.3 Scissurella costata 3.9
Winter Site B Average similarity: 49.6% Species Contrib% Melanella alba 25.2 Smaragdia viridis 20.8 Gibberula philippii 20.0 Tricolia tenuis 11.1 Tricolia speciosa 7.7 Scissurella costata 5.7
Site C Average similarity: 46.9% Species Contrib% Melanella alba 27.0 Smaragdia viridis 25.4 Scissurella costata 15.4 Gibberula philippii 10.7 Gibberula miliaria 10.4 Tricolia tenuis 6.6
Scarrupata Summer Site A Average similarity: 40.3% Species Caecum glabrum Scissurella costata
Site B Average similarity: 38.5% Contrib% Species 88.9 Caecum glabrum 6.3 Tricolia tenuis
Site C Average similarity: 27.0% Contrib% Species Contrib% 72.0 Caecum glabrum 94.2 28.0
Site A Average similarity: 40.7% Species Caecum glabrum Melanella polita Scissurella costata Melanella alba Tricolia tenuis
Winter Site B Average similarity: 38.5% Contrib% Species 46.3 Scissurella costata 16.7 Smaragdia viridis 12.0 Melanella polita 8.8 Tricolia tenuis 7.3 Melanella alba
Site C Average similarity: 53.8% Contrib% Species Contrib% 47.7 Scissurella costata 65.4 28.0 Smaragdia viridis 19.4 7.1 Tricolia tenuis 6.5 6.5 5.9
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Table 31: Average dissimilarity percentages between sites in each meadow and season. The contribution percentage for each species is reported. Lacco Ameno Summer A vs C A vs B Average dissimilarity = 72.8% Average dissimilarity = 74.7% Species Contrib% Species Contrib% Caecum glabrum 26.7 Caecum glabrum 26.0 Melanella alba 13.4 Euspira sp. 18.6 Gibberula miliaria 12.5 Gibberula miliaria 10.7 Melanella polita 6.3 Melanella alba 8.7 Gibberula philippii 6.3 Melanella polita 7.4 Payraudeautia intricata 3.9 Scissurella costata 4.1 Acmea sp. 3.6 Payraudeautia intricata 3.3 Bittium sp. 3.2 Acmea sp. 3.2 Jujubinus sp. 2.6 Bittium sp. 3.0 Raphitoma linearis 2.6 Gibberula philippii 2.6 Rissoa violacea 2.5 Cylichna cylindracea 2.2 Turbonilla sp. 2.5 Cerithium vulgatum 2.1 Cylichna cylindracea 2.4 Smaragdia viridis 2.3
A vs B Average dissimilarity = 55.7% Species Contrib% Gibberula philippii 27.7 Melanella alba 17.9 Smaragdia viridis 10.2 Tricolia tenuis 7.7 Gibberula miliaria 7.6 Scissurella costata 6.5 Caecum glabrum 4.3 Melanella polita 4.1 Tricolia speciosa 3.4 Cerithium vulgatum 2.8
Winter A vs C Average dissimilarity = 58.3% Species Contrib% Gibberula philippii 30.4 Melanella alba 17.7 Scissurella costata 9.9 Gibberula miliaria 8.0 Tricolia tenuis 6.8 Smaragdia viridis 5.3 Tricolia speciosa 4.6 Caecum glabrum 3.7 Tricolia pullus 1.9 Payraudeautia intricata 1.9
B vs C Average dissimilarity = 72.4% Species Contrib% Caecum glabrum 29.1 Euspira sp. 20.0 Melanella polita 11.8 Melanella alba 10.7 Gibberula philippii 5.5 Gibberula miliaria 4.5 Scissurella costata 3.5 Turbonilla sp. 3.2 Chrysallida sp. 2.6
B vs C Average dissimilarity = 52.7% Species Contrib% Melanella alba 19.3 Gibberula philippii 14.3 Smaragdia viridis 11.5 Scissurella costata 10.6 Tricolia tenuis 10.0 Gibberula miliaria 6.3 Tricolia speciosa 5.7 Melanella polita 3.5 Caecum glabrum 3.4 Cerithium vulgatum 2.7 Cylichna cylindracea 2.7 Rissoa violacea 2.2
Scarrupata Summer A vs B A vs C B vs C Average dissimilarity = 60.4% Average dissimilarity = 64.6% Average dissimilarity = 68.8% Species Contrib% Species Contrib% Species Contrib% Caecum glabrum 35.1 Caecum glabrum 34.8 Caecum glabrum 34.9 Scissurella costata 23.2 Scissurella costata 19.0 Tricolia tenuis 18.4 Tricolia tenuis 16.0 Melanella alba 16.3 Melanella alba 17.7 Melanella alba 11.7 Tricolia tenuis 7.8 Scissurella costata 15.6 Melanella polita 6.6 Melanella polita 7.6 Rissoa auriscalpium 8.4 Rissoa auriscalpium 6.2 Winter A vs B A vs C B vs C Average dissimilarity = 73.1% Average dissimilarity = 73.3% Average dissimilarity = 52.5% Species Contrib% Species Contrib% Species Contrib% Caecum glabrum 28.0 Caecum glabrum 26.8 Scissurella costata 42.1 Scissurella costata 22.1 Scissurella costata 24.7 Smaragdia viridis 9.1 Melanella alba 10.9 Melanella polita 10.5 Melanella alba 8.6 Melanella polita 10.5 Melanella alba 9.2 Tricolia pullus 8.1 Smaragdia viridis 8.9 Smaragdia viridis 9.1 Tricolia tenuis 7.0 Tricolia tenuis 6.9 Tricolia tenuis 6.6 Melanella polita 6.5 Tricolia pullus 3.7 Tricolia pullus 4.6 Tricolia speciosa 3.4 Acmea sp. 3.0 Payraudeautia intric 2.7
180
Table 32: Average similarity percentages of stations in each meadow and season. The contribution percentage for each species is reported. A1 Average similarity: 48.06 Species Contrib% Melanella alba 31.79 Gibberula miliaria 31.79 Caecum glabrum 19.08 Smaragdia viridis 3.47 Cerithium vulgatum 3.47 Raphitoma linearis 3.47
Lacco Ameno Summer A2 Average similarity: 27.94 Species Contrib% Melanella alba 42.98 Caecum glabrum 14.91 Gibberula philippii 14.04 Gibberula miliaria 14.04 Payraudeautia intricata 14.04
B1 Average similarity: 37.08 Species Contrib% Caecum glabrum 42.81 Melanella alba 26.8 Melanella polita 16.01 Gibberula philippii 7.19
B2 C1 C2 Average similarity: 29.92 Average similarity: 20.63 Average similarity: 26.24 Species Contrib% Species Contrib% Species Contrib% Caecum glabrum 100 Caecum glabrum 100 Euspira sp. 62.66 Caecum glabrum 30.38
A1 Average similarity: 32.22 Species Contrib% Smaragdia viridis 36.82 Gibberula philippii 28.83 Melanella alba 16.55 Tricolia speciosa 12.27
B2 Average similarity: 57.40 Species Contrib% Melanella alba 37.17 Smaragdia viridis 28.4 Gibberula philippii 14.43 Tricolia tenuis 6.7 Scissurella costata 5.38
Lacco Ameno Winter A2 Average similarity: 61.93 Species Contrib% Gibberula philippii 37.47 Melanella alba 35.02 Gibberula miliaria 18.06
C1 Average similarity: 34.24 Species Contrib% Smaragdia viridis 51.33 Melanella alba 13.91 Scissurella costata 13.91 Gibberula miliaria 10.43 Gibberula philippii 6.95
B1 Average similarity: 46.51 Species Contrib% Gibberula philippii 22.09 Melanella alba 19.39 Tricolia speciosa 12.81 Smaragdia viridis 12.4 Tricolia tenuis 11.62 Gibberula miliaria 9.69 Caecum glabrum 5.41 C2 Average similarity: 59.65 Species Contrib% Melanella alba 31.08 Gibberula philippii 15.57 Scissurella costata 13.72 Smaragdia viridis 12.1 Tricolia speciosa 10.38 8.63 Gibberula miliaria
Scarrupata Summer A1 A2 B1 Average similarity: 38.69 Average similarity: 38.10 Average similarity: 67.74 Species Contrib% Species Contrib% Species Contrib% Caecum glabrum 100 Caecum glabrum 70.83 Caecum glabrum 73.15 Scissurella costata 14.58 Tricolia tenuis 26.85 Melanella polita 9.72 B2 C1 C2 Average similarity: 12.22 Average similarity: 13.33 Average similarity: 34.51 Species Contrib% Species Contrib% Species Contrib% Caecum glabrum 54.55 Caecum glabrum 100 Caecum glabrum 88.64 Tricolia tenuis 45.45 Scissurella costata 11.36
A1 Average similarity: 48.11 Species Contrib% Caecum glabrum 49.56 Tricolia tenuis 17.7 Melanella polita 15.4 Smaragdia viridis 5.97 Melanella alba 5.69
Scarrupata Winter A2 B1 Average similarity: 34.36 Average similarity: 56.75 Species Contrib% Species Contrib% Caecum glabrum 46.51 Scissurella costata 50.77 Scissurella costata 22.87 Smaragdia viridis 23.39 Melanella alba 14.44 Melanella alba 9.79 Melanella polita 12.65 Melanella polita 7.8
B2 C1 Average similarity: 17.62 Average similarity: 48.33 Species Contrib% Species Contrib% Smaragdia viridis 46.89 Scissurella costata 68.93 Scissurella costata 45.97 Smaragdia viridis 18.76 Melanella alba 4.93
C2 Average similarity: 49.21 Species Contrib% Scissurella costata 65.44 Smaragdia viridis 21.8 Tricolia pullus 6.61
181
Table 33: Average dissimilarity percentages between stations in Lacco Ameno meadow in summer. The contribution percentage for each species is reported. A1 vs A2 Average dissimilarity = 64.53 Species Contrib% Caecum glabrum 29.56 Gibberula miliaria 11.17 Melanella alba 10.3 Acmea sp. 5.32 Cerithium vulgatum 5.31 Raphitoma linearis 5.31 Bittium sp. 5.19 Gibberula philippii 5.05 Smaragdia viridis 4.99 Payraudeautia intricata 4.18 Cylichna cylindracea 3.38 Melanella polita 2.59
Lacco Ameno Summer A1 vs B1 A2 vs B1 Average dissimilarity = 69.39 Average dissimilarity = 68.45 Species Contrib% Species Contrib% Caecum glabrum 21.93 Caecum glabrum 19.64 Gibberula miliaria 12.16 Melanella alba 13.3 Melanella alba 12 Gibberula miliaria 8.49 Melanella polita 6.97 Gibberula philippii 6.51 Gibberula philippii 5.02 Melanella polita 6.05 Jujubinus sp. 5.02 Jujubinus sp. 5.85 Turbonilla sp. 4.88 Turbonilla sp. 5.67 Cerithium vulgatum 4.03 Bittium sp. 5.14 Acmea sp. 4.03 Payraudeautia intricata 4.54 Raphitoma linearis 3.84 Rissoa violacea 4.34 Smaragdia viridis 3.67 Chrysallida sp. 4.26 Chrysallida sp. 3.66 Eulimella ventricosa 2.34 Rissoa violacea 2.58 Scissurella costata 2.2 Payraudeautia intricata 2.01 Cylichna cylindracea 2.2
A1 vs B2 Average dissimilarity = 77.76 Species Contrib% Caecum glabrum 32.01 Melanella alba 18.16 Gibberula miliaria 15.32 Smaragdia viridis 5.47 Melanella polita 4.88 Gibberula philippii 4.88 Cerithium vulgatum 4.82 Raphitoma linearis 4.82
A2 vs B2 Average dissimilarity = 75.67 Species Contrib% Caecum glabrum 31.85 Gibberula miliaria 13.36 Melanella alba 9.78 Gibberula philippii 8.53 Melanella polita 7.4 Bittium sp. 6.7 Payraudeautia intricata 6.43 Rissoa violacea 3.35 Eulimella ventricosa 3.35
B1 vs B2 Average dissimilarity = 69.36 Species Contrib% Caecum glabrum 25.69 Melanella alba 22.85 Melanella polita 9.31 Gibberula philippii 7.32 Jujubinus sp. 6.18 Turbonilla sp. 5.98 Gibberula miliaria 5.83 Chrysallida sp. 4.48 Rissoa violacea 3.19
A1 vs C1 Average dissimilarity = 72.68 Species Contrib% Caecum glabrum 31.31 Gibberula miliaria 14.83 Melanella polita 12.29 Melanella alba 11.75 Scissurella costata 6.15 Cerithium vulgatum 4.43 Raphitoma linearis 4.43 Acmea sp. 4.43 Smaragdia viridis 4.09
A2 vs C1 Average dissimilarity = 73.09 Species Contrib% Caecum glabrum 34.29 Melanella polita 15.77 Gibberula miliaria 9.87 Scissurella costata 8.47 Melanella alba 7.59 Bittium sp. 5.16 Gibberula philippii 4.42 Payraudeautia intricata 4.42 Rissoa violacea 2.58
B1 vs C1 Average dissimilarity = 68.23 Species Contrib% Caecum glabrum 25.5 Melanella alba 17.46 Melanella polita 16.38 Scissurella costata 6.21 Jujubinus sp. 5.54 Gibberula philippii 5.54 Turbonilla sp. 5.38 Chrysallida sp. 4.03 Rissoa violacea 2.85 Payraudeautia intricata 1.55
B2 vs C1 Average dissimilarity = 66.35 Species Contrib% Caecum glabrum 49.97 Melanella polita 21.15 Scissurella costata 8.79 Gibberula philippii 7.21 Gibberula miliaria 6.27
A1 vs C2 Average dissimilarity = 75.70 Species Contrib% Euspira sp. 33.67 Caecum glabrum 22.55 Gibberula miliaria 10.64 Melanella alba 8.9 Cerithium vulgatum 4.02 Raphitoma linearis 4.02 Acmea sp. 4.02 Smaragdia viridis 3.56
A2 vs C2 Average dissimilarity = 77.30 Species Contrib% Euspira sp. 39 Caecum glabrum 16.61 Gibberula miliaria 7.59 Melanella alba 6.56 Bittium sp. 5.02 Gibberula philippii 4.33 Payraudeautia intricata 4.33 Melanella polita 2.22 Rissoa violacea 2.22 Eulimella ventricosa 2.22
B1 vs C2 Average dissimilarity = 74.06 Species Contrib% Euspira sp. 32.61 Caecum glabrum 14.38 Melanella alba 13.56 Melanella polita 6.68 Turbonilla sp. 5.5 Gibberula philippii 4.87 Jujubinus sp. 4.87 Chrysallida sp. 4.32 Gibberula miliaria 2.71 Rissoa violacea 2.5
B2 vs C2 Average dissimilarity = 80.91 Species Contrib% Euspira sp. 41.58 Caecum glabrum 28.4 Melanella alba 7.56 Gibberula miliaria 7.23 Melanella polita 4.79 Gibberula philippii 4.79
C1 vs C2 Average dissimilarity = 74.06 Species Contrib% Euspira sp. 37.94 Caecum glabrum 24.83 Melanella polita 12.49 Melanella alba 7.29 Scissurella costata 6.25 Gibberula miliaria 2.9
182
Table 34: Average dissimilarity percentages between stations in Lacco Ameno meadow in winter. The contribution percentage for each species is reported. A1 vs A2 Average dissimilarity = 58.92 Species Contrib% Gibberula philippii 30.96 Melanella alba 18.76 Gibberula miliaria 13.62 Scissurella costata 7.32 Smaragdia viridis 5.36 Tricolia tenuis 4.35 Caecum glabrum 3.99 Tricolia speciosa 3.02 Payraudeautia intricata 2.93
Lacco Ameno Winter A1 vs B1 A2 vs B1 Average dissimilarity = 58.56 Average dissimilarity = 60.34 Species Contrib% Species Contrib% Gibberula philippii 24.67 Gibberula philippii 32.92 Melanella alba 17.42 Melanella alba 21.92 Scissurella costata 9.69 Gibberula miliaria 10.74 Tricolia tenuis 8.42 Smaragdia viridis 5.06 Melanella polita 7.62 Melanella polita 4.76 Smaragdia viridis 7.6 Tricolia tenuis 4.66 Caecum glabrum 7.15 Tricolia speciosa 3.52 Cerithium vulgatum 5.01 Scissurella costata 3.19 Gibberula miliaria 4.67 Cerithium vulgatum 3.09 Caecum glabrum 2.93
A1 vs B2 Average dissimilarity = 53.61 Species Contrib% Melanella alba 21.7 Gibberula philippii 20.28 Smaragdia viridis 13.33 Tricolia tenuis 11.08 Scissurella costata 9.08 Caecum glabrum 5.56 Tricolia speciosa 4.44 Gibberula miliaria 2.86 Melanella polita 2.71
A2 vs B2 Average dissimilarity = 50.46 Species Contrib% Gibberula philippii 32.72 Smaragdia viridis 15.87 Gibberula miliaria 12.3 9.72 Melanella alba Tricolia tenuis 6.98 Scissurella costata 4.12 Tricolia speciosa 3.98 Cerithium vulgatum 2.68 Payraudeautia intricata 2.65
B1 vs B2 Average dissimilarity = 52.02 Species Contrib% Melanella alba 22.98 Smaragdia viridis 17.19 Gibberula philippii 16.39 Tricolia tenuis 7.97 Melanella polita 6.13 Cerithium vulgatum 4.62 Scissurella costata 4.47 Tricolia speciosa 4.37 Caecum glabrum 3.9 Gibberula miliaria 2.88
A1 vs C1 Average dissimilarity = 62.84 Species Contrib% Gibberula philippii 24.99 Melanella alba 19.94 Scissurella costata 12.46 Tricolia speciosa 7.6 Gibberula miliaria 6.33 Caecum glabrum 5.41 Smaragdia viridis 5.37 Tricolia tenuis 4.42 Cylichna cylindracea 3.82
A2 vs C1 Average dissimilarity = 66.17 Species Contrib% Gibberula philippii 39.26 Melanella alba 19.58 Gibberula miliaria 9.74 Smaragdia viridis 7.3 Scissurella costata 5.88 Tricolia tenuis 4.26 Cylichna cylindracea 2.63 Cerithium vulgatum 2.56
B1 vs C1 Average dissimilarity = 63.60 Species Contrib% Gibberula philippii 16.52 Melanella alba 16.43 Smaragdia viridis 10.28 Scissurella costata 9.83 Tricolia tenuis 7.38 Tricolia speciosa 7.06 Melanella polita 6.57 Gibberula miliaria 6.44 Cerithium vulgatum 5.23 Caecum glabrum 4.7
B2 vs C1 Average dissimilarity = 55.08 Species Contrib% Melanella alba 25.16 Gibberula philippii 15.69 Smaragdia viridis 13.52 Tricolia tenuis 10.52 Scissurella costata 8.56 Tricolia speciosa 5.82 Gibberula miliaria 5.79 Cylichna cylindracea 3.74 Bolma rugosa 2.47
A1 vs C2 Average dissimilarity = 54.23 Species Contrib% Gibberula philippii 19.99 Melanella alba 19.81 Scissurella costata 13.46 Tricolia tenuis 11.45 Gibberula miliaria 8.14 Caecum glabrum 5.96 4.52 Rissoa violacea Tricolia speciosa 4.11 Smaragdia viridis 3.4
A2 vs C2 Average dissimilarity = 49.83 Species Contrib% Gibberula philippii 36.85 Melanella alba 10.12 Tricolia tenuis 8.16 Scissurella costata 8.13 Gibberula miliaria 7.78 Tricolia speciosa 6.35 Smaragdia viridis 4.49 Rissoa violacea 3.54 Cerithium vulgatum 2.7 Payraudeautia intricata 2.58
B1 vs C2 Average dissimilarity = 50.25 Species Contrib% Melanella alba 20.74 Scissurella costata 12.77 Gibberula philippii 11.39 Tricolia tenuis 10.9 Melanella polita 6.29 Smaragdia viridis 6.28 Gibberula miliaria 5.7 Rissoa violacea 4.92 Cerithium vulgatum 4.72 Tricolia speciosa 3.78 Caecum glabrum 3.68
B2 vs C2 Average dissimilarity = 41.74 Species Contrib% Smaragdia viridis 16.81 Melanella alba 14.25 Gibberula philippii 12.79 Tricolia tenuis 12.32 Scissurella costata 11.8 Gibberula miliaria 7.25 Tricolia speciosa 5.88 Rissoa violacea 5.01 Caecum glabrum 2.95 Tricolia pullus 2.59
C1 vs C2 Average dissimilarity = 53.12 Species Contrib% Melanella alba 22.81 Scissurella costata 12.39 Tricolia tenuis 11.95 Tricolia speciosa 10.34 Gibberula philippii 8.64 Gibberula miliaria 6.78 Smaragdia viridis 6.13 Rissoa violacea 5.13 Cylichna cylindracea 3.85 Caecum glabrum 3.42
183
Table 35: Average dissimilarity percentages between stations in Scarrupata meadow in summer. The contribution percentage for each species is reported. A1 vs A2 Average dissimilarity = 58.46 Species Contrib% Scissurella costata 29.76 Caecum glabrum 28.77 Melanella polita 13.91 Melanella alba 9.43 Tricolia tenuis 7.09 Gibberula philippii 3.68
Scarrupata Summer A1 vs B1 A2 vs B1 Average dissimilarity = 53.65 Average dissimilarity = 53.43 Species Contrib% Species Contrib% Caecum glabrum 45.61 Scissurella costata 29.58 Tricolia tenuis 32.71 Tricolia tenuis 19.85 Melanella alba 10.18 Caecum glabrum 17.24 Scissurella costata 6.1 Melanella polita 14.12 Rissoa auriscalpium 4.2 Gibberula philippii 3.86 Natica millepunctata 3.86
A1 vs B2 Average dissimilarity = 67.93 Species Contrib% Caecum glabrum 49.75 Scissurella costata 23.67 Melanella alba 18.05
A2 vs B2 Average dissimilarity = 66.63 Species Contrib% Scissurella costata 31.32 Caecum glabrum 26.15 Melanella alba 13.06 Melanella polita 12.52 Tricolia tenuis 7.04
B1 vs B2 Average dissimilarity = 62.45 Species Contrib% Caecum glabrum 40.04 Tricolia tenuis 20.73 Scissurella costata 20.19 Melanella alba 14.3
A1 vs C1 Average dissimilarity = 70.19 Species Contrib% Caecum glabrum 53.27 Melanella alba 28.38 Tricolia tenuis 10.15
A2 vs C1 Average dissimilarity = 76.49 Species Contrib% Caecum glabrum 27.06 Scissurella costata 24.7 Melanella alba 17.31 Melanella polita 12.35 Tricolia tenuis 9.32
B1 vs C1 Average dissimilarity = 70.04 Species Contrib% Caecum glabrum 49.73 Tricolia tenuis 25.48 Melanella alba 19.49
B2 vs C1 Average dissimilarity = 73.31 Species Contrib% Melanella alba 31.93 Caecum glabrum 28.87 Scissurella costata 25.29 Tricolia tenuis 13.91
A1 vs C2 Average dissimilarity = 55.03 Species Contrib% Caecum glabrum 42.43 Rissoa auriscalpium 16.13 Scissurella costata 12.82 Melanella alba 12.5 Natica millepunctata 6.45
A2 vs C2 Average dissimilarity = 56.83 Species Contrib% Scissurella costata 30.59 Caecum glabrum 15.07 Melanella polita 14.58 Rissoa auriscalpium 12.75 Tricolia tenuis 7.34 Natica millepunctata 6.87 Gibberula philippii 3.83
B1 vs C2 Average dissimilarity = 59.86 Species Contrib% Caecum glabrum 33.52 Tricolia tenuis 28.07 Rissoa auriscalpium 16.94 Scissurella costata 10.47 Natica millepunctata 5.5
B2 vs C2 Average dissimilarity = 71.92 Species Contrib% Caecum glabrum 27.88 Scissurella costata 25.2 Melanella alba 16.24 Rissoa auriscalpium 12.68 Tricolia tenuis 7.86 Natica millepunctata 5.07
C1 vs C2 Average dissimilarity = 70.98 Species Contrib% Caecum glabrum 24.92 Melanella alba 24.23 Rissoa auriscalpium 14.54 Scissurella costata 12.64 Tricolia tenuis 12.04 Natica millepunctata 5.82
184
Table 36: Average dissimilarity percentages between stations in Scarrupata meadow in winter. The contribution percentage for each species is reported. A1 vs A2 Average dissimilarity = 59.60 Species Contrib% Caecum glabrum 22.97 Scissurella costata 19.18 Melanella alba 16.89 Tricolia tenuis 11.68 Melanella polita 9.52 Smaragdia viridis 8.99 Tricolia pullus 3.77
Scarrupata Winter A1 vs B1 A2 vs B1 Average dissimilarity = 69.55 Average dissimilarity = 67.56 Species Contrib% Species Contrib% Caecum glabrum 31.21 Scissurella costata 29.86 Scissurella costata 20.89 Caecum glabrum 24.13 Melanella alba 13.93 Smaragdia viridis 12.55 Tricolia tenuis 8.95 Melanella polita 11.04 Melanella polita 8.83 Tricolia pullus 5.15 Smaragdia viridis 5.94 Tricolia tenuis 4.76 Tricolia pullus 2.85 Melanella alba 3.19
A1 vs B2 Average dissimilarity = 78.11 Species Contrib% Caecum glabrum 30.37 Scissurella costata 18.82 Melanella alba 15.79 Melanella polita 10.01 Tricolia tenuis 9.16 Smaragdia viridis 6.4
A2 vs B2 Average dissimilarity = 77.32 Species Contrib% Caecum glabrum 26.1 Scissurella costata 19.87 Melanella polita 11.9 Smaragdia viridis 10.79 Melanella alba 9.89 Tricolia tenuis 4.56 Tricolia pullus 4.53 Acmea sp. 4.41
B1 vs B2 Average dissimilarity = 60.60 Species Contrib% Scissurella costata 45.16 Melanella alba 12.89 Smaragdia viridis 8.43 Melanella polita 6.85 Acmea sp. 5.15 Tricolia pullus 4.78 Tricolia tenuis 4.71 Tricolia speciosa 4.26
A1 vs C1 Average dissimilarity = 73.31 Species Contrib% Caecum glabrum 30.39 Scissurella costata 20.24 Melanella alba 14.12 Melanella polita 10.72 Tricolia tenuis 7.53 Smaragdia viridis 6.14 Tricolia pullus 3.1
A2 vs C1 Average dissimilarity = 75.77 Species Contrib% Scissurella costata 29.29 Caecum glabrum 22.51 Melanella polita 10.88 Smaragdia viridis 9.89 Tricolia tenuis 5.64 Tricolia pullus 5.34 Melanella alba 3.91 Williamia gussoni 2.89
B1 vs C1 Average dissimilarity = 38.43 Species Contrib% Scissurella costata 37.19 Tricolia tenuis 9.4 Tricolia pullus 9.4 Melanella polita 8.44 Smaragdia viridis 7.01 Melanella alba 6.89 Williamia gussoni 5.54 Payraudeautia intricata 4.66 Gibberula miliaria 3.62
B2 vs C1 Average dissimilarity = 64.80 Species Contrib% Scissurella costata 45.8 Melanella alba 10.14 Tricolia tenuis 6.92 Tricolia pullus 6.05 Smaragdia viridis 5.58 Acmea sp. 4.88 Melanella polita 4.27 Williamia gussoni 4.14 Tricolia speciosa 3.56
A1 vs C2 Average dissimilarity = 71.74 Species Contrib% Caecum glabrum 31.15 Scissurella costata 20.33 Melanella alba 15.24 Melanella polita 9.78 Tricolia tenuis 8.10 Smaragdia viridis 6.27
A2 vs C2 Average dissimilarity = 72.43 Species Contrib% Scissurella costata 28.88 Caecum glabrum 23.24 Smaragdia viridis 13.92 Melanella polita 10.42 Tricolia pullus 6.52 Tricolia tenuis 5.31 Melanella alba 3.84
B1 vs C2 Average dissimilarity = 42.07 Species Contrib% Scissurella costata 37.36 Smaragdia viridis 11.84 Melanella polita 9.44 Tricolia pullus 9.21 Melanella alba 8.17 Tricolia tenuis 6.88 Natica millepunctata 3.72 Tricolia speciosa 3.49
B2 vs C2 Average dissimilarity = 64.54 Species Contrib% Scissurella costata 44.46 Smaragdia viridis 12.05 Tricolia pullus 8.74 Melanella alba 8.23 Tricolia tenuis 5.80 Melanella polita 5.66 Acmea sp. 4.76 Natica millepunctata 3.46
C1 vs C2 Average dissimilarity = 42.88 Species Contrib% Scissurella costata 35.03 Smaragdia viridis 12.81 Tricolia pullus 10.53 Tricolia tenuis 9.13 Melanella polita 6.06 Melanella alba 6.00 Williamia gussoni 4.63 Payraudeautia intricata 3.95 Natica millepunctata 3.69
185
Appendix 3 List of polychaete families found in both meadows Ampharetidae Aphroditidae Capitellidae Chrysopetalidae Cirratulidae Dorvilleidae Eunicidae Euphrosinidae Flabelligeridae Glyceridae Goniadidae Hesionidae Lacydonidae Lumbrineridae Maldanidae Nereididae Nephthyidae Oenonidae Onuphidae Ophelidae Orbiniidae Paraonidae Pectinaridae Phyllodocidae Pilargidae Pisionidae Polynoidae Sabellaridae Sabellidae Serpulidae Sigalionidae Spionidae Spirorbidae Syllidae Terebellidae Trychobranchidae
186
Number of polychaete families and individuals Table 37: Average number of families and individuals along Lacco Ameno and Scarrupata meadows in the two seasons (mean ± SD). Location and season Lacco Ameno Summer Lacco Ameno Winter
Sites
Lacco Ameno Summer
Polychaeta Stations
No. Families 14.2 ± 5.0 13.8 ± 4.3
No. Individuals 70.1 ± 41.8 111.3 ± 70.1
A B C
18.0 ± 6.1 13.8 ± 3.8 10.7 ± 4.5
95.3 ± 50.4 69.2 ± 30.1 45.7 ± 31.7
Lacco Ameno Winter
A B C
14.0 ± 5.9 13.2 ± 3.1 14.2 ± 4.2
161.8 ± 99.7 83.5 ± 27.6 88.7 ± 37.3
Lacco Ameno Summer
A
A1 A2 B1 B2 C1 C2
20.7 15.3 12.0 15.7 10.7 10.7
± 4.5 ± 7.1 ± 4.0 ± 3.1 ± 1.5 ± 1.5
110.0 80.7 69.3 69.0 55.7 35.7
± 47.0 ± 59.1 ± 46.4 ± 10.4 ± 46.8 ± 5.5
A1 A2 B1 B2 C1 C2
17.3 10.7 15.0 11.3 16.3 12.0
± 4.2 ± 6.1 ± 2.6 ± 2.5 ± 4.0 ± 3.6
211.0 112.7 100.7 66.3 111.3 66.0
± 101.0 ± 86.0 ± 28.1 ± 15.0 ± 43.5 ± 6.0
B C
Lacco Ameno Winter
A B C
Scarrupata Summer Scarrupata Winter
6.2 ± 3.2 10.4 ± 3.0
16.8 ± 11.5 54.6 ± 23.5
Scarrupata Summer
A B C
8.5 ± 3.7 5.5 ± 2.9 4.5 ± 1.6
27.0 ± 14.0 12.5 ± 6.3 11.0 ± 5.1
Scarrupata Winter
A B C
11.2 ± 0.8 10.7 ± 4.2 9.5 ± 3.3
65.5 ± 19.0 50.5 ± 28.6 47.7 ± 21.9
Scarrupata Summer
A B C
Scarrupata Winter
A B C
A1 A2 B1 B2 C1 C2
7.3 9.7 5.7 5.3 3.3 5.7
± 4.0 ± 3.8 ± 4.0 ± 2.1 ± 0.6 ± 1.5
23.7 30.3 14.3 10.7 9.0 13.0
± 18.9 ± 10.0 ± 9.3 ± 1.5 ± 2.0 ± 6.9
A1 A2 B1 B2 C1 C2
11.0 11.3 12.0 9.3 10.7 8.3
± 1.0 ± 0.6 ± 5.0 ± 3.8 ± 3.8 ± 2.9
73.0 58.0 54.3 46.7 58.7 36.7
± 27.1 ± 1.0 ± 30.7 ± 32.6 ± 25.5 ± 13.6
187
SIMPER Table 38: Average similarity percentages of polychaete families: a) Lacco Ameno similarity in summer and winter; b) Scarrupata similarity in summer and winter; c) dissimilarity pairwises summer vs winter for both meadows. The contribution percentage for each taxon is reported.
188
a)
Lacco Ameno Summer Winter Average similarity: 46.31 Average similarity: 56.03 Families Contrib% Families Contrib% Syllidae 34.17 Hesionidae 52.3 Hesionidae 26.25 Syllidae 13.2 Chrysopetalidae 10.02 Chrysopetalidae 12.06 Spionidae 6.31 Nereididae 5.63 Sabellidae 4.15 Spionidae 4.51 Paraonidae 3.05 Polynoidae 2.57 Nereididae 2.57 Cirratulidae 2.54 Goniadidae 1.62
b)
Scarrupata Summer Winter Average similarity: 46.73 Average similarity: 54.53 Families Contrib% Families Contrib% Syllidae 58.77 Syllidae 39.96 Spirorbidae 19.84 Hesionidae 29.03 Chrysopetalidae 8.2 Sabellidae 7.65 Hesionidae 6.05 Ophelidae 7.13 Nereididae 5.07 Chrysopetalidae 4.44
c)
Lacco Ameno Summer vs Winter Average dissimilarity = 60.92 Families Contrib% Hesionidae 31.94 Syllidae 19.31 Chrysopetalidae 6.66 Nereididae 4.16 Spionidae 3.8 Cirratulidae 3.16 Polynoidae 2.93 Terebellidae 2.76 Trychobranchidae 2.68 Paraonidae 2.6 Flabelligeridae 2.51 Euphrosinidae 1.89 Sabellidae 1.82 Eunicidae 1.79 Goniadidae 1.63 Ophelidae 1.1
Scarrupata Summer vs Winter Average dissimilarity = 72.08 Families Contrib% Syllidae 25.81 Hesionidae 22.85 Sabellidae 8.51 Ophelidae 7.53 Spirorbidae 5.25 Nereididae 5.22 Chrysopetalidae 5.14 Polynoidae 3.42 Spionidae 2.26 Euphrosinidae 1.94 Phyllodocidae 1.57 Eunicidae 1.18
Table 49: Average similarity percentages of sites in each meadow and season. The contribution percentage for each family is reported. Lacco Ameno Summer Site A Site B Average similarity: 54.79 Average similarity: 45.50 Families Contrib% Families Contrib% Syllidae 32 Syllidae 39.55 Hesionidae 23.89 Hesionidae 16.27 Paraonidae 9.37 Chrysopetalidae 14.09 Chrysopetalidae 7.26 Sabellidae 6.57 Spionidae 5.68 Spionidae 6.28 Cirratulidae 4.62 Trychobranchidae 2.89 Nereididae 2.49 Nereididae 2.23 Sabellidae 2.26 Paraonidae 1.91 Terebellidae 2.02 Goniadidae 1.79 Goniadidae 1.75
Site C Average similarity: 42.67 Families Contrib% Hesionidae 37.81 Syllidae 30.66 Chrysopetalidae 8.38 Spionidae 5.93 Polynoidae 4.65 Sabellidae 3.93
Site A Average similarity: 48.69 Families Contrib% Hesionidae 55.22 Syllidae 18.05 Chrysopetalidae 10.05 Flabelligeridae 2.97 Spionidae 2.72 Polynoidae 2.53
Winter Site B Average similarity: 67.51 Families Contrib% Hesionidae 45.66 Syllidae 15.65 Chrysopetalidae 12.96 Nereididae 8.22 Spionidae 5.18 Euphrosinidae 2.45
Site C Average similarity: 63.15 Families Contrib% Hesionidae 57.58 Chrysopetalidae 12.59 Nereididae 7.33 Syllidae 6.86 Spionidae 4.98 Polynoidae 2.5
Winter Site B Average similarity: 48.06 Families Contrib% Hesionidae 38.45 Syllidae 33.26 Ophelidae 7.91 Nereididae 6.98 Polynoidae 6.28
Site C Average similarity: 54.58 Families Contrib% Syllidae 35.31 Hesionidae 24.61 Sabellidae 16.77 Chrysopetalidae 9.66 Nereididae 4.59
Scarrupata Summer Site A Site B Average similarity: 50.51 Average similarity: 50.20 Families Contrib% Families Contrib% Syllidae 60.66 Syllidae 69.51 Chrysopetalidae 10.84 Spirorbidae 16.6 Hesionidae 9.86 Hesionidae 5.84 Spirorbidae 6.12 Sabellidae 5.09
Site C Average similarity: 53.03 Families Contrib% Syllidae 51.42 Spirorbidae 35.43 Chrysopetalidae 12.32
Site A Average similarity: 64.63 Families Contrib% Syllidae 47.99 Hesionidae 23.85 Ophelidae 10.15 Sabellidae 4.84 Chrysopetalidae 4.67
Table 50: Average dissimilarity percentages between sites in each meadow and season. The contribution percentage for each family is reported. Lacco Ameno Summer A vs B A vs C Average dissimilarity = 49.31 Average dissimilarity = 56.72 Families Contrib% Families Contrib% Syllidae 31.61 Syllidae 32.61 Hesionidae 8.88 Hesionidae 9.56 Cirratulidae 7.04 Paraonidae 7.29 Chrysopetalidae 5.69 Cirratulidae 6.81 Paraonidae 5.51 Chrysopetalidae 4.76 Goniadidae 3.8 Spionidae 4.45 Terebellidae 3.58 Terebellidae 3.6 Pilargidae 3.29 Pilargidae 3.3 Polynoidae 3.13 Nereididae 2.94 Spionidae 3.11 Goniadidae 2.89 Nereididae 2.94 Sabellidae 2.36 Trychobranchidae 2.24 Ophelidae 1.92 Eunicidae 2 Polynoidae 1.86 Sigalionidae 1.93 Sigalionidae 1.67 Sabellidae 1.87 Dorvilleidae 1.59 Pisionidae 1.71 Eunicidae 1.56 Dorvilleidae 1.65 Ampharetidae 1.31 Ophelidae 1.63
B vs C Average dissimilarity = 56.72 Families Contrib% Syllidae 33.39 Hesionidae 10.49 Chrysopetalidae 7.75 Cirratulidae 5.02 Spionidae 4.45 Polynoidae 4.28 Nereididae 3.37 Goniadidae 3.25 Sabellidae 3.19 Paraonidae 3.1 Trychobranchidae 2.8 Eunicidae 2.25 Ophelidae 2.12 Terebellidae 1.78 Dorvilleidae 1.48 Sigalionidae 1.28 Lumbrineridae 1.26
A vs B Average dissimilarity = 51.64 Families Contrib% Hesionidae 34.41 Syllidae 16.06 Chrysopetalidae 8.36 Spionidae 5.14 Terebellidae 4.29 Trychobranchidae 4.28 Nereididae 4.16 Polynoidae 3.7 Flabelligeridae 3.69 Eunicidae 2.41 Euphrosinidae 2.04 Cirratulidae 1.58
Winter A vs C Average dissimilarity = 51.60 Families Contrib% Hesionidae 32.45 Syllidae 17.62 Chrysopetalidae 8.25 Spionidae 5.27 Nereididae 4.73 Terebellidae 4.47 Polynoidae 3.94 Flabelligeridae 3.58 Eunicidae 2.46 Trychobranchidae 1.85 Paraonidae 1.5 Cirratulidae 1.49 Euphrosinidae 1.45 Phyllodocidae 1.37
B vs C Average dissimilarity = 33.37 Families Contrib% Hesionidae 25 Syllidae 11.72 Trychobranchidae 7.72 Chrysopetalidae 6.37 Nereididae 6.15 Spionidae 5.43 Flabelligeridae 4.42 Terebellidae 4.22 Euphrosinidae 3.92 Polynoidae 3.5 Eunicidae 2.62 Sabellidae 2.18 Glyceridae 2.15 Dorvilleidae 1.83 Sigalionidae 1.8 Phyllodocidae 1.68
Scarrupata Summer A vs B A vs C Average dissimilarity = 57.58 Average dissimilarity = 59.33 Families Contrib% Families Contrib% Syllidae 31.15 Syllidae 34.45 Spirorbidae 11.01 Spirorbidae 11.65 Chrysopetalidae 9.81 Hesionidae 11.44 Hesionidae 8.4 Chrysopetalidae 8.68 Sabellidae 7.8 Sabellidae 7.6 Spionidae 5.22 Spionidae 4.17 Polynoidae 3.5 Cirratulidae 2.35 Nereididae 2.88 Polynoidae 2.29 Cirratulidae 2.6 Paraonidae 2.21 Paraonidae 2.53 Nereididae 2.15 Maldanidae 1.78 Euphrosinidae 1.72 Capitellidae 1.65 Capitellidae 1.64 Ophelidae 1.52 Euphrosinidae 1.45
B vs C Average dissimilarity = 48.57 Families Contrib% Chrysopetalidae 15.2 Spirorbidae 15.07 Syllidae 14.64 Hesionidae 10.62 Spionidae 7.5 Sabellidae 7.25 Nereididae 4.35 Sigalionidae 3.42 Maldanidae 2.9 Polynoidae 2.9 Cirratulidae 2.45 Terebellidae 1.93 Eunicidae 1.68 Sabellaridae 1.68
A vs B Average dissimilarity = 44.79 Families Contrib% Syllidae 25 Hesionidae 19.89 Chrysopetalidae 7.62 Ophelidae 6.84 Sabellidae 6.5 Nereididae 5.11 Polynoidae 3.87 Spionidae 3.82 Euphrosinidae 3.26 Eunicidae 2.53 Phyllodocidae 2.29 Flabelligeridae 1.42 Terebellidae 1.38 Cirratulidae 1.38
Winter A vs C Average dissimilarity = 42.82 Families Contrib% Syllidae 25.9 Hesionidae 18.72 Sabellidae 10.55 Ophelidae 9.41 Chrysopetalidae 6.09 Nereididae 5.11 Polynoidae 3.94 Spionidae 3.42 Phyllodocidae 2.63 Euphrosinidae 2.57 Terebellidae 1.91
B vs C Average dissimilarity = 50.36 Families Contrib% Syllidae 26.39 Hesionidae 16.53 Sabellidae 13.53 Ophelidae 7.28 Chrysopetalidae 5.55 Nereididae 5.02 Polynoidae 4.83 Eunicidae 2.22 Euphrosinidae 2.19 Flabelligeridae 1.95 Phyllodocidae 1.81 Terebellidae 1.71 Spionidae 1.55
189
Table 41: Average similarity percentages of stations in each meadow and season. The contribution percentage for each family is reported. A1 Average similarity: 65.87 Families Contrib% Syllidae 40.91 Hesionidae 15.38 Chrysopetalidae 7.02 Paraonidae 5.89 Cirratulidae 4.93 Goniadidae 4.84 Spionidae 4.84 Nereididae 3.42 Sigalionidae 1.99 Ophelidae 1.8
A2 Average similarity: 45.44 Families Contrib% Hesionidae 34.1 Syllidae 15.32 Paraonidae 14.88 Terebellidae 12.15 Chrysopetalidae 6.93 Spionidae 5.77 Sabellidae 3.72
A1 Average similarity: 58.95 Families Contrib% Hesionidae 52.64 Syllidae 20.13 Chrysopetalidae 6.16 Flabelligeridae 5.27 Nereididae 2.76 Phyllodocidae 2.51 Polynoidae 1.94
A2 Average similarity: 36.19 Families Contrib% Hesionidae 54.08 Syllidae 20.22 Chrysopetalidae 13.9 Spionidae 4.56
A1 Average similarity: 39.40 Families Contrib% Syllidae 60.4 Hesionidae 15.5 Chrysopetalidae 12.37 Spirorbidae 5.46
A1 Average similarity: 61.05 Families Contrib% Syllidae 49.27 Hesionidae 19.21 Ophelidae 8.14 Chrysopetalidae 6.89 Sabellidae 5.5 Euphrosinidae 4.59
190
Lacco Ameno Summer B1 B2 Average similarity: 36.02 Average similarity: 53.92 Families Contrib% Families Contrib% Syllidae 40.9 Syllidae 39.34 Hesionidae 28.53 Chrysopetalidae 15.35 Chrysopetalidae 11.44 Sabellidae 8.09 Sabellidae 5.99 Spionidae 7.04 Spionidae 5.87 Hesionidae 6.59 Nereididae 5.53 Trychobranchidae 4.35 Euphrosinidae 2.7 Paraonidae 2.7
Lacco Ameno Winter B1 B2 Average similarity: 60.95 Average similarity: 77.65 Families Contrib% Families Contrib% Hesionidae 38.16 Hesionidae 48.78 Syllidae 16.52 Chrysopetalidae 14.84 Chrysopetalidae 9.87 Syllidae 14.39 Euphrosinidae 6.61 Nereididae 9.79 Nereididae 6.39 Spionidae 4.49 Spionidae 5.92 Flabelligeridae 3.94 Trychobranchidae 3.82
C1 Average similarity: 35.97 Families Contrib% Hesionidae 43.85 Syllidae 21.3 Spionidae 10.83 Chrysopetalidae 7.33 Polynoidae 5.86 Dorvilleidae 3.14
C2 Average similarity: 49.47 Families Contrib% Syllidae 37.14 Hesionidae 31.87 Chrysopetalidae 8.98 Nereididae 7.51 Polynoidae 3.64 Sabellidae 3.59
C1 Average similarity: 59.99 Families Contrib% Hesionidae 44.45 Syllidae 14.4 Chrysopetalidae 12.3 Nereididae 8.25 Spionidae 6.09 Terebellidae 4.32 Dorvilleidae 2.03
C2 Average similarity: 76.23 Families Contrib% Hesionidae 67.77 Chrysopetalidae 10.61 Nereididae 5.88 Spionidae 3.98 Syllidae 3.32
Scarrupata Summer A2 B1 B2 C1 Average similarity: 58.04 Average similarity: 51.38 Average similarity: 46.32 Average similarity: 74.72 Families Contrib% Families Contrib% Families Contrib% Families Contrib% Syllidae 65.15 Syllidae 59.39 Syllidae 81.23 Syllidae 49.44 Chrysopetalidae 8.97 Spirorbidae 36.9 Hesionidae 6.26 Spirorbidae 44.98 Sabellidae 6.38 Nereididae 6.26 Hesionidae 5.78 Spionidae 5.78
A2 Average similarity: 65.52 Families Contrib% Syllidae 47.37 Hesionidae 27.19 Ophelidae 12.28 Sabellidae 3.5
Scarrupata Winter B1 B2 Average similarity: 48.54 Average similarity: 37.34 Families Contrib% Families Contrib% Syllidae 36.38 Hesionidae 43.71 Hesionidae 34.46 Syllidae 29.99 Ophelidae 6.86 Ophelidae 8.88 Nereididae 6.76 Polynoidae 7.52 Polynoidae 4.94 Sabellidae 4.94
C1 Average similarity: 58.63 Families Contrib% Syllidae 30.64 Hesionidae 22.81 Sabellidae 15.46 Ophelidae 10.4 Nereididae 7.78 Chrysopetalidae 6.91
C2 Average similarity: 33.33 Families Contrib% Syllidae 48.89 Chrysopetalidae 24.44 Spirorbidae 20
C2 Average similarity: 48.53 Families Contrib% Syllidae 35.17 Hesionidae 24.86 Sabellidae 17.9 Chrysopetalidae 13.11
Table 42: Average dissimilarity percentages between stations in Lacco Ameno meadow in summer. The contribution percentage for each family is reported. A1 vs A2 Average dissimilarity = 45.78 Families Contrib% Syllidae 36.34 Cirratulidae 7.54 Terebellidae 5.76 Hesionidae 4.96 Chrysopetalidae 4.94 Goniadidae 4.86 Pilargidae 3.97 Nereididae 3.03 Spionidae 2.93 Paraonidae 2.78 Sigalionidae 2.51 Pisionidae 2.31 Dorvilleidae 2.06 Phyllodocidae 2.05 Ampharetidae 1.91 Ophelidae 1.56 Trychobranchidae 1.47
Lacco Ameno Summer A1 vs B1 A2 vs B1 Average dissimilarity = 46.60 Average dissimilarity = 55.09 Families Contrib% Families Contrib% Syllidae 36.75 Syllidae 34.16 Cirratulidae 7.98 Hesionidae 9.14 Hesionidae 6.26 Paraonidae 7.85 Chrysopetalidae 5.16 Cirratulidae 7.25 Paraonidae 4.58 Terebellidae 6.85 Goniadidae 4.29 Chrysopetalidae 4.41 Pilargidae 3.7 Spionidae 3.61 Nereididae 3.57 Polynoidae 3.49 Spionidae 3.33 Pilargidae 2.97 Polynoidae 2.94 Eunicidae 2.84 Eunicidae 2.83 Nereididae 2.28 Sigalionidae 2.77 Sigalionidae 1.7 Pisionidae 2.44 Goniadidae 1.58 Phyllodocidae 2.05 Ampharetidae 1.47 Ophelidae 2.01 Trychobranchidae 1.37
A1 vs B2 Average dissimilarity = 41.84 Families Contrib% Syllidae 30.74 Hesionidae 9.28 Cirratulidae 7.61 Chrysopetalidae 5.67 Goniadidae 5.28 Pilargidae 4.03 Paraonidae 3.38 Sabellidae 3.32 Polynoidae 3.19 Trychobranchidae 2.89 Pisionidae 2.7 Sigalionidae 2.63 Nereididae 2.45 Spionidae 2.18 Ophelidae 1.99 Dorvilleidae 1.95 Euphrosinidae 1.86
A2 vs B2 Average dissimilarity = 53.70 Families Contrib% Syllidae 25.22 Hesionidae 10.57 Chrysopetalidae 7.48 Terebellidae 6.14 Paraonidae 5.56 Cirratulidae 5.56 Goniadidae 4.51 Trychobranchidae 3.64 Nereididae 3.46 Spionidae 3.14 Polynoidae 2.88 Pilargidae 2.67 Sabellidae 2.55 Pisionidae 2.04 Dorvilleidae 1.93 Ampharetidae 1.76 Phyllodocidae 1.53
B1 vs B2 Average dissimilarity = 54.15 Families Contrib% Syllidae 34.37 Hesionidae 8.35 Chrysopetalidae 7.59 Cirratulidae 5.63 Polynoidae 4.65 Goniadidae 4.55 Nereididae 4.48 Paraonidae 3.73 Trychobranchidae 3.15 Spionidae 2.72 Eunicidae 2.67 Sabellidae 2.4 Pisionidae 2.2 Euphrosinidae 1.95 Sigalionidae 1.75
A1 vs C1 Average dissimilarity = 55.78 Families Contrib% Syllidae 37.51 Hesionidae 8.74 Cirratulidae 6.14 Chrysopetalidae 5.71 Goniadidae 4.68 Paraonidae 4.24 Spionidae 4.06 Nereididae 3.88 Pilargidae 3.35 Sigalionidae 2.66 Pisionidae 2.23 Phyllodocidae 2.18 Polynoidae 1.51 Sabellidae 1.51 Eunicidae 1.49 Ophelidae 1.46
A2 vs C1 Average dissimilarity = 53.93 Families Contrib% Syllidae 30.08 Hesionidae 12.78 Paraonidae 8.91 Cirratulidae 7.14 Terebellidae 6.65 Spionidae 5.65 Pilargidae 3.18 Chrysopetalidae 3.08 Dorvilleidae 2.59 Nereididae 2.44 Sabellidae 1.98 Ophelidae 1.9 Eunicidae 1.86 Ampharetidae 1.6 Trychobranchidae 1.2
B1 vs C1 Average dissimilarity = 56.62 Families Contrib% Syllidae 39.42 Hesionidae 12.23 Cirratulidae 7.35 Spionidae 6.04 Chrysopetalidae 5.33 Polynoidae 4.6 Eunicidae 3.34 Paraonidae 2.87 Sabellidae 2.26 Dorvilleidae 2.18 Ophelidae 2.06 Sigalionidae 1.6 Goniadidae 1.58
B2 vs C1 Average dissimilarity = 58.88 Families Contrib% Syllidae 28.06 Hesionidae 13.85 Chrysopetalidae 9.65 Nereididae 5.06 Spionidae 4.78 Goniadidae 4.75 Cirratulidae 3.86 Trychobranchidae 3.64 Sabellidae 3.05 Paraonidae 3.02 Polynoidae 2.91 Pisionidae 2.23 Dorvilleidae 2.05 Phyllodocidae 1.96 Ophelidae 1.78
A1 vs C2 Average dissimilarity = 59.56 Families Contrib% Syllidae 36.67 Cirratulidae 7.8 Hesionidae 6.26 Paraonidae 5.46 Chrysopetalidae 5.01 Goniadidae 4.36 Spionidae 3.51 Pilargidae 3.38 Sabellidae 2.51 Pisionidae 2.28 Sigalionidae 2.27 Polynoidae 2.04 Ophelidae 1.98 Nereididae 1.94 Phyllodocidae 1.82 Terebellidae 1.67 Dorvilleidae 1.52
A2 vs C2 Average dissimilarity = 57.62 Families Contrib% Syllidae 26.03 Hesionidae 10.75 Paraonidae 10.63 Cirratulidae 6.14 Terebellidae 5.39 Chrysopetalidae 5.14 Spionidae 4.68 Nereididae 3.52 Sabellidae 3.37 Pilargidae 3.26 Polynoidae 2.62 Ampharetidae 2.36 Ophelidae 2.31 Spirorbidae 1.85 Eunicidae 1.49 Goniadidae 1.4
B1 vs C2 Average dissimilarity = 59.74 Families Contrib% Syllidae 41.69 Hesionidae 6.8 Chrysopetalidae 6.39 Cirratulidae 5.66 Polynoidae 5.2 Nereididae 3.99 Sabellidae 3.49 Eunicidae 3.13 Terebellidae 2.63 Spionidae 2.47 Ophelidae 2.29 Paraonidae 2.06 Sigalionidae 2.04 Spirorbidae 2 Trychobranchidae 1.53
B2 vs C2 Average dissimilarity = 51.65 Families Contrib% Syllidae 23.27 Chrysopetalidae 9.83 Hesionidae 9 Goniadidae 5.71 Trychobranchidae 5.36 Paraonidae 4.66 Spionidae 4.63 Polynoidae 4.41 Sabellidae 4.01 Nereididae 3.5 Cirratulidae 3.06 Pisionidae 2.79 Terebellidae 2.52 Ophelidae 2.36 Spirorbidae 1.91 Euphrosinidae 1.87 Phyllodocidae 1.86
C1 vs C2 Average dissimilarity = 57.36 Families Contrib% Syllidae 27.18 Hesionidae 16.03 Spionidae 7.84 Chrysopetalidae 5.89 Cirratulidae 5.37 Nereididae 5.31 Sabellidae 4.68 Terebellidae 3.18 Ophelidae 3.07 Polynoidae 2.81 Dorvilleidae 2.61 Spirorbidae 2.34 Paraonidae 1.93 Eunicidae 1.78
191
Table 43: Average dissimilarity percentages between stations in Lacco Ameno meadow in winter. The contribution percentage for each family is reported. A1 vs A2 Average dissimilarity = 50.57 Families Contrib% Hesionidae 27.16 Syllidae 23.1 Chrysopetalidae 6.64 Terebellidae 6.47 Spionidae 4.95 Flabelligeridae 4.31 Polynoidae 4 Nereididae 2.89 Eunicidae 2.35 Paraonidae 2.31 Phyllodocidae 2.18 Cirratulidae 2.07 Capitellidae 1.92
192
Lacco Ameno Winter A1 vs B1 A2 vs B1 Average dissimilarity = 49.19 Average dissimilarity = 51.59 Families Contrib% Families Contrib% Hesionidae 26.02 Hesionidae 36.23 Syllidae 23.18 Chrysopetalidae 9.6 Terebellidae 6.71 Trychobranchidae 8.23 Chrysopetalidae 5.93 Syllidae 7.45 Trychobranchidae 5.58 Nereididae 5.89 Spionidae 4.94 Spionidae 5.39 Flabelligeridae 3.01 Flabelligeridae 4.21 Polynoidae 2.84 Polynoidae 4.17 Nereididae 2.55 Euphrosinidae 3.57 Capitellidae 2.19 Eunicidae 2.77 Euphrosinidae 2.05 Terebellidae 2.51 Cirratulidae 2.01 Paraonidae 1.98 Phyllodocidae 1.92
A1 vs B2 Average dissimilarity = 53.53 Families Contrib% Hesionidae 30.37 Syllidae 24.73 Terebellidae 6.78 Chrysopetalidae 6.03 Flabelligeridae 5.2 Spionidae 4.46 Polynoidae 3.07 Paraonidae 2.34 Capitellidae 2.1 Cirratulidae 2.08 Eunicidae 1.92 Nereididae 1.83
A2 vs B2 Average dissimilarity = 52.26 Families Contrib% Hesionidae 44.64 Chrysopetalidae 11.81 Syllidae 8.96 Nereididae 6.35 Spionidae 5.77 Polynoidae 4.68 Eunicidae 3.06 Trychobranchidae 2.49 Flabelligeridae 2.25
B1 vs B2 Average dissimilarity = 33.68 Families Contrib% Hesionidae 27.07 Trychobranchidae 13.89 Flabelligeridae 6.85 Euphrosinidae 6.67 Syllidae 5.92 Spionidae 5.81 Nereididae 4.94 Chrysopetalidae 4.43 Terebellidae 3.54 Eunicidae 3.25 Sabellidae 2.63 Polynoidae 2.57 Glyceridae 2.02 Sigalionidae 1.92
A1 vs C1 Average dissimilarity = 44.94 Families Contrib% Syllidae 23.94 Hesionidae 23.41 Terebellidae 6.98 Chrysopetalidae 6.1 Spionidae 5.6 Nereididae 4.18 Flabelligeridae 3.85 Polynoidae 3.56 Paraonidae 2.5 Eunicidae 2.17 Cirratulidae 2.15 Capitellidae 2.11 Phyllodocidae 2.07 Aphroditidae 1.34 Glyceridae 1.3
A2 vs C1 Average dissimilarity = 50.02 Families Contrib% Hesionidae 35.99 Chrysopetalidae 9.63 Nereididae 8.46 Syllidae 8.28 Spionidae 5.96 Polynoidae 4.24 Terebellidae 3.44 Flabelligeridae 3.21 Trychobranchidae 2.74 Eunicidae 2.6 Euphrosinidae 2.14 Dorvilleidae 2.1 Lumbrineridae 1.46
B1 vs C1 Average dissimilarity = 34.53 Families Contrib% Hesionidae 26.63 Trychobranchidae 10.89 Syllidae 7.23 Nereididae 7.18 Spionidae 5.7 Chrysopetalidae 5.53 Terebellidae 5.24 Flabelligeridae 4.29 Euphrosinidae 3.39 Polynoidae 3.12 Dorvilleidae 2.29 Eunicidae 2.11 Glyceridae 1.83 Lumbrineridae 1.78 Paraonidae 1.73 Sabellidae 1.64
B2 vs C1 Average dissimilarity = 35.37 Families Contrib% Hesionidae 28.66 Syllidae 9.42 Nereididae 7.45 Spionidae 6.79 Terebellidae 6.52 Chrysopetalidae 5.67 Flabelligeridae 4.29 Polynoidae 3.38 Dorvilleidae 3.22 Euphrosinidae 3.22 Trychobranchidae 2.83 Eunicidae 2.09 Lumbrineridae 2.07 Maldanidae 1.95 Sigalionidae 1.83 Phyllodocidae 1.61
A1 vs C2 Average dissimilarity = 55.74 Families Contrib% Syllidae 28.45 Hesionidae 26 Terebellidae 6.5 Chrysopetalidae 6.06 Flabelligeridae 5.21 Spionidae 4.3 Polynoidae 2.87 Paraonidae 2.47 Eunicidae 2.32 Cirratulidae 2.01 Capitellidae 1.89 Phyllodocidae 1.87 Nereididae 1.79
A2 vs C2 Average dissimilarity = 55.72 Families Contrib% Hesionidae 43.03 Chrysopetalidae 10.92 Syllidae 10.06 Spionidae 5.36 Polynoidae 5.03 Nereididae 4.78 Trychobranchidae 2.78 Eunicidae 2.7 Flabelligeridae 2.06 Sabellidae 1.89 Glyceridae 1.76
B1 vs C2 Average dissimilarity = 38.38 Families Contrib% Hesionidae 21.52 Syllidae 12.53 Trychobranchidae 12.19 Flabelligeridae 6.48 Euphrosinidae 5.88 Spionidae 5.55 Chrysopetalidae 5.45 Nereididae 5.11 Eunicidae 3.92 Terebellidae 3.33 Polynoidae 2.72 Glyceridae 2.71 Sabellidae 1.89 Aphroditidae 1.59
B2 vs C2 Average dissimilarity = 25.20 Families Contrib% Hesionidae 22.95 Syllidae 19.9 Chrysopetalidae 9.88 Polynoidae 5.39 Sabellidae 4.91 Nereididae 4.49 Glyceridae 4 Trychobranchidae 3.46 Sigalionidae 3.08 Aphroditidae 3.03 Phyllodocidae 2.97 Spionidae 2.96 Euphrosinidae 2.65 Eunicidae 2.07
C1 vs C2 Average dissimilarity = 40.16 Families Contrib% Hesionidae 24.27 Syllidae 15.34 Nereididae 7.64 Chrysopetalidae 7.1 Spionidae 6.39 Terebellidae 5.25 Flabelligeridae 3.93 Polynoidae 3.41 Euphrosinidae 2.84 Trychobranchidae 2.44 Dorvilleidae 2.36 Eunicidae 2.19 Sabellidae 2.09 Lumbrineridae 1.98 Aphroditidae 1.75 Maldanidae 1.71
Table 44: Average dissimilarity percentages between stations in Scarrupata meadow in summer. The contribution percentage for each family is reported. A1 vs A2 Average dissimilarity = 48.29 Families Contrib% Syllidae 31.21 Spirorbidae 10.14 Hesionidae 9.91 Sabellidae 9.02 Chrysopetalidae 7.62 Paraonidae 4.38 Cirratulidae 3.83 Polynoidae 3.16 Ophelidae 2.78 Spionidae 2.69 Capitellidae 2.55 Nereididae 2.55 Euphrosinidae 2.03
Scarrupata Summer A1 vs B1 A2 vs B1 Average dissimilarity = 53.36 Average dissimilarity = 61.21 Families Contrib% Families Contrib% Syllidae 26.39 Syllidae 33.98 Spirorbidae 16.54 Chrysopetalidae 9.1 Chrysopetalidae 10.59 Hesionidae 8.93 Sabellidae 8.75 Sabellidae 8.12 Hesionidae 7.52 Spirorbidae 6.63 Polynoidae 5.32 Polynoidae 4.1 Maldanidae 4.22 Paraonidae 4.02 Spionidae 3.3 Spionidae 3.57 Ophelidae 3.21 Cirratulidae 3.46 Nereididae 2.95 Maldanidae 3.03 Terebellidae 2.81 Terebellidae 2.02 Euphrosinidae 1.71 Trychobranchidae 1.71
A1 vs B2 Average dissimilarity = 53.57 Families Contrib% Syllidae 28 Spirorbidae 16.21 Chrysopetalidae 10.29 Spionidae 8.22 Hesionidae 7.08 Sabellidae 5.23 Nereididae 4.98 Ophelidae 3.35 Cirratulidae 2.33 Polynoidae 2.25 Capitellidae 2.24
A2 vs B2 Average dissimilarity = 62.18 Families Contrib% Syllidae 35.18 Hesionidae 9.78 Chrysopetalidae 9.43 Sabellidae 8.88 Spirorbidae 6.09 Spionidae 5.89 Cirratulidae 4.21 Paraonidae 3.58 Polynoidae 2.4 Nereididae 2.31 Euphrosinidae 1.81 Trychobranchidae 1.81
B1 vs B2 Average dissimilarity = 48.90 Families Contrib% Spirorbidae 21.31 Sabellidae 10.86 Spionidae 10.52 Chrysopetalidae 8.85 Hesionidae 7.11 Maldanidae 5.74 Polynoidae 5.74 Nereididae 5.12 Terebellidae 3.83 Syllidae 3.78 Sigalionidae 3.31 Cirratulidae 3.2 Goniadidae 2.91
A1 vs C1 Average dissimilarity = 52.31 Families Contrib% Syllidae 32.39 Spirorbidae 19.96 Hesionidae 13.14 Chrysopetalidae 6.92 Sabellidae 6.36 Spionidae 4.75 Ophelidae 3.54 Polynoidae 2.46 Capitellidae 2.36
A2 vs C1 Average dissimilarity = 64.65 Families Contrib% Syllidae 35.67 Hesionidae 11.83 Sabellidae 9.04 Chrysopetalidae 8.1 Spirorbidae 7.75 Spionidae 5.24 Paraonidae 4.25 Cirratulidae 3.6 Polynoidae 2.4 Euphrosinidae 1.85 Trychobranchidae 1.85
B1 vs C1 Average dissimilarity = 35.45 Families Contrib% Syllidae 13.97 Chrysopetalidae 13.84 Sabellidae 13.66 Spirorbidae 9.07 Maldanidae 8.32 Polynoidae 8.32 Hesionidae 7.76 Terebellidae 5.54 Sigalionidae 4.99 Spionidae 4.99
B2 vs C1 Average dissimilarity = 47.57 Families Contrib% Spirorbidae 25.61 Syllidae 12.07 Spionidae 11.78 Hesionidae 10.41 Chrysopetalidae 9.6 Nereididae 6.88 Sabellidae 5.9 Cirratulidae 3.93 Goniadidae 3.53 Onuphidae 3.53
A1 vs C2 Average dissimilarity = 57.34 Families Contrib% Syllidae 32.4 Spirorbidae 14.77 Chrysopetalidae 10.39 Hesionidae 10.33 Sabellidae 5.83 Nereididae 3.49 Spionidae 3.12 Ophelidae 3.03 Sigalionidae 2.9 Eunicidae 2.18 Sabellaridae 2.18
A2 vs C2 Average dissimilarity = 63.02 Families Contrib% Syllidae 36.79 Hesionidae 10.61 Chrysopetalidae 9.17 Sabellidae 8.76 Spirorbidae 5.92 Paraonidae 3.98 Cirratulidae 3.84 Spionidae 3.52 Polynoidae 2.28 Sigalionidae 2.12 Euphrosinidae 1.88 Nereididae 1.74
B1 vs C2 Average dissimilarity = 54.16 Families Contrib% Chrysopetalidae 18.36 Syllidae 15.64 Spirorbidae 12.24 Hesionidae 10.86 Sabellidae 6.61 Sigalionidae 5.13 Maldanidae 4.96 Polynoidae 4.96 Spionidae 3.55 Nereididae 3.34 Terebellidae 3.31 Eunicidae 2.89
B2 vs C2 Average dissimilarity = 57.09 Families Contrib% Chrysopetalidae 17.71 Syllidae 16.27 Spirorbidae 12.71 Hesionidae 12.35 Spionidae 9.25 Sabellidae 5 Nereididae 4.18 Sigalionidae 3.69 Cirratulidae 3.36 Eunicidae 2.98 Sabellaridae 2.98
C1 vs C2 Average dissimilarity = 47.63 Families Contrib% Syllidae 20.99 Chrysopetalidae 17.1 Hesionidae 15.62 Spirorbidae 14.82 Sigalionidae 4.68 Eunicidae 3.92 Nereididae 3.92 Sabellaridae 3.92 Spionidae 3.92 Sabellidae 3.37
193
Table 45: Average dissimilarity percentages between stations in Scarrupata meadow in winter. The contribution percentage for each family is reported. A1 vs A2 Average dissimilarity = 34.47 Families Contrib% Hesionidae 24.53 Syllidae 14.71 Chrysopetalidae 10.25 Spionidae 6.28 Sabellidae 6.27 Ophelidae 6.2 Nereididae 6.09 Polynoidae 5.33 Phyllodocidae 3.25 Euphrosinidae 3.21 Terebellidae 2.28 Cirratulidae 2.08
194
Scarrupata Winter A1 vs B1 A2 vs B1 Average dissimilarity = 41.79 Average dissimilarity = 39.24 Families Contrib% Families Contrib% Syllidae 23.66 Syllidae 21.79 Hesionidae 22.98 Hesionidae 15.58 Chrysopetalidae 8.6 Ophelidae 6.71 Ophelidae 7.51 Spionidae 6.59 Sabellidae 6.36 Nereididae 6.53 Nereididae 5.12 Sabellidae 6.04 Polynoidae 4.36 Chrysopetalidae 5.3 Euphrosinidae 3.76 Polynoidae 4.79 Flabelligeridae 2.33 Phyllodocidae 2.77 Phyllodocidae 2.12 Flabelligeridae 2.71 Eunicidae 1.84 Euphrosinidae 2.41 Glyceridae 1.67 Cirratulidae 2.41 Terebellidae 2.4 Glyceridae 1.94 Ampharetidae 1.86 Eunicidae 1.73
A1 vs B2 Average dissimilarity = 50.59 Families Contrib% Syllidae 26.77 Hesionidae 22.95 Chrysopetalidae 10.25 Sabellidae 7.27 Ophelidae 6.61 Euphrosinidae 4.5 Nereididae 3.66 Polynoidae 3.4 Eunicidae 3.12 Dorvilleidae 1.66
A2 vs B2 Average dissimilarity = 47.53 Families Contrib% Syllidae 26.93 Hesionidae 17.5 Ophelidae 6.62 Spionidae 6.5 Sabellidae 6.18 Chrysopetalidae 5.86 Nereididae 5.48 Eunicidae 3.19 Polynoidae 3.16 Phyllodocidae 2.73 Euphrosinidae 2.2 Terebellidae 2.17 Cirratulidae 2
B1 vs B2 Average dissimilarity = 48.53 Families Contrib% Syllidae 29.5 Hesionidae 17.88 Sabellidae 7.75 Nereididae 5.77 Ophelidae 5.4 Chrysopetalidae 3.8 Eunicidae 3.65 Polynoidae 3.13 Flabelligeridae 2.51 Spionidae 2.26 Glyceridae 1.82 Paraonidae 1.81 Dorvilleidae 1.8 Ampharetidae 1.73 Euphrosinidae 1.33
A1 vs C1 Average dissimilarity = 39.82 Families Contrib% Syllidae 23.49 Hesionidae 21.07 Sabellidae 13.6 Ophelidae 7.33 Chrysopetalidae 7.05 Nereididae 4.95 Polynoidae 4.59 Euphrosinidae 4.34 Phyllodocidae 3.13 Flabelligeridae 1.59
A2 vs C1 Average dissimilarity = 38.00 Families Contrib% Syllidae 20.77 Sabellidae 17.92 Hesionidae 13.19 Ophelidae 7.03 Chrysopetalidae 6.34 Spionidae 6.34 Nereididae 5.83 Phyllodocidae 2.8 Cirratulidae 2.33 Terebellidae 2.31 Sigalionidae 2.18 Euphrosinidae 2.13 Flabelligeridae 1.8
B1 vs C1 Average dissimilarity = 41.54 Families Contrib% Syllidae 21.46 Sabellidae 18.39 Hesionidae 13.62 Ophelidae 9.08 Nereididae 5.35 Chrysopetalidae 5.17 Polynoidae 4.73 Flabelligeridae 2.99 Phyllodocidae 2.53 Sigalionidae 2.3 Ampharetidae 2.05 Glyceridae 1.86 Lacydonidae 1.77
B2 vs C1 Average dissimilarity = 53.84 Families Contrib% Syllidae 24.96 Sabellidae 18.12 Hesionidae 15.15 Ophelidae 8.5 Chrysopetalidae 7.23 Nereididae 4.25 Polynoidae 3.01 Eunicidae 2.78 Phyllodocidae 2.73 Spionidae 1.81 Flabelligeridae 1.69
A1 vs C2 Average dissimilarity = 46.07 Families Contrib% Syllidae 30.05 Hesionidae 23.71 Ophelidae 11.23 Chrysopetalidae 6.16 Sabellidae 5.92 Polynoidae 5.23 Nereididae 3.85 Euphrosinidae 2.21 Terebellidae 2.1
A2 vs C2 Average dissimilarity = 47.37 Families Contrib% Syllidae 28 Hesionidae 16.34 Ophelidae 11.3 Sabellidae 6.59 Spionidae 5.96 Nereididae 5.88 Chrysopetalidae 5 Polynoidae 4.03 Terebellidae 3.01 Phyllodocidae 2.8 Cirratulidae 2.27
B1 vs C2 Average dissimilarity = 46.74 Families Contrib% Syllidae 26.73 Hesionidae 18.11 Sabellidae 8.79 Ophelidae 7.93 Nereididae 6.77 Polynoidae 6.35 Euphrosinidae 3.15 Terebellidae 3.12 Chrysopetalidae 3.04 Flabelligeridae 2.72 Glyceridae 1.96 Ampharetidae 1.87
B2 vs C2 Average dissimilarity = 59.31 Families Contrib% Syllidae 30.87 Hesionidae 18.58 Sabellidae 9.69 Chrysopetalidae 6.27 Polynoidae 5.36 Ophelidae 4.41 Nereididae 4.09 Euphrosinidae 3.41 Eunicidae 2.8 Spionidae 2.42 Terebellidae 2.4
C1 vs C2 Average dissimilarity = 44.75 Families Contrib% Syllidae 23.98 Sabellidae 16.26 Hesionidae 13.99 Ophelidae 13.91 Nereididae 6.89 Polynoidae 4.55 Chrysopetalidae 3.57 Phyllodocidae 3.3 Euphrosinidae 2.84 Terebellidae 2.49
Appendix 4 List of amphipod genera found in both meadows Ampelisca Apolochus Gitana Ampithoe Aora Leptocheirus Microdeutopus Aoridae gen. sp. Cressa Colomastix Peltocoxa Atylus Dexamine Guernea Tritaeta Apherusa Eusiroides Hyale Iphimedia Gammaropsis Megamphopus Ericthonius Jassa Ischyroceridae gen.sp. Leucothoe Idunella Liljeborgia Acidostoma Lepidepecreum Lysianassa Nannonyx Normanion Orchomene Socarnes Tmetonyx Cheirocratus Elasmopus Gammarella Maera Melita Deflexilodes Monoculodes Perioculodes Pontocrates Synchelidium Harpinia Metaphoxus Phoxocephalus Podocerus Stenothoe Urothoe Caprella Liropus Pseudoprotella Phtisica
195
Number of amphipod species and individuals Table 46: Average number of species and individuals along Lacco Ameno and Scarrupata meadows in the two seasons (mean ± SD). Location and season Lacco Ameno Summer Lacco Ameno Winter
Sites
Lacco Ameno Summer
Stations
No. Genera 16.1 ± 4.3 15.8 ± 2.9
No. Individuals 65.6 ± 41.5 86.8 ± 24.5
A B C
14.2 ± 4.7 16.7 ± 4.1 17.5 ± 4.1
39.9 ± 23.1 72.8 ± 38.1 84.0 ± 50.9
Lacco Ameno Winter
A B C
14.7 ± 2.0 15.0 ± 2.8 17.0 ± 2.8
75.8 ± 24.7 83.3 ± 22.8 94.5 ± 16.7
Lacco Ameno Summer
A
A1 A2 B1 B2 C1 C2
13.3 15.0 18.0 15.3 18.0 17.0
± 6.4 ± 3.6 ± 5.6 ± 2.5 ± 5.3 ± 3.6
23.5 56.3 82.0 63.7 75.3 92.7
± 15.5 ± 17.0 ± 53.0 ± 24.0 ± 65.4 ± 44.5
A1 A2 B1 B2 C1 C2
16.0 13.3 13.7 16.3 19.3 14.7
± 0.0 ± 2.1 ± 3.1 ± 2.1 ± 1.5 ± 1.2
88.3 63.3 77.3 89.3 109.3 79.7
± 24.7 ± 21.2 ± 15.2 ± 31.0 ± 2.5 ± 5.9
B C
Lacco Ameno Winter
A B C
Scarrupata Summer Scarrupata Winter
131.3 ± 48.8 136.4 ± 59.9
Scarrupata Summer
A B C
21.5 ± 5.2 21.2 ± 2.2 22.5 ± 5.3
127.8 ± 62.7 138.3 ± 38.8 127.8 ± 50.8
Scarrupata Winter
A B C
23.8 ± 4.2 24.7 ± 5.6 27.5 ± 3.8
152.8 ± 63.6 102.7 ± 47.2 153.7 ± 62.4
Scarrupata Summer
A
A1 A2 B1 B2 C1 C2
21.7 21.3 22.0 20.3 21.0 24.0
± 6.0 ± 5.5 ± 2.0 ± 2.5 ± 1.0 ± 7.9
116.7 139.0 132.7 144.0 146.3 109.3
± 13.9 ± 96.3 ± 59.0 ± 13.7 ± 50.3 ± 53.8
A1 A2 B1 B2 C1 C2
26.7 21.0 25.7 23.7 27.0 28.0
± 2.9 ± 3.5 ± 6.8 ± 5.5 ± 5.6 ± 2.0
171.3 134.3 103.0 102.3 137.3 170.0
± 80.2 ± 51.5 ± 48.4 ± 56.8 ± 74.5 ± 58.1
B C
Scarrupata Winter
A B C
196
21.7 ± 4.2 25.3 ± 4.6
SIMPER Table 47: Average similarity percentages of amphipod genera: a) Lacco Ameno similarity in summer and winter; b) Scarrupata similarity in summer and winter; c) dissimilarity pairwises summer vs winter for both meadows. The contribution percentage for each taxon is reported. Lacco Ameno Summer Average similarity: 35.88 Genera Contrib% Apherusa 36.22 Perioculodes 6.51 Cheirocratus 6.21 Caprella 5.87 Dexamine 5.09 Phtisica 4.21 Liljeborgia 4.11 Gammarella 3.7 Aora 2.94 Lysianassa 2.88 Phoxocephalus 2.81 Urothoe 2.59 Synchelidium 2.37 Iphimedia 2.32 Hyale 2.14 Apolochus 1.9
Winter Average similarity: 57.79 Genera Contrib% Apherusa 28.12 Aora 16.96 Dexamine 14.78 Perioculodes 7.38 Phtisica 6.11 Gammarella 6.05 Caprella 5.31 Gitana 2.9 Phoxocephalus 2.81
Scarrupata Summer Winter Average similarity: 58.91 Average similarity: 52.45 Genera Contrib% Genera Contrib% Apolochus 38.62 Apolochus 26.32 Apherusa 11.6 Apherusa 10.01 Phtisica 7.44 Gammarella 7.18 Aoridae gen. sp. 6.33 Aoridae gen. sp. 6.51 Iphimedia 4.52 Gitana 6.04 Gitana 4.51 Aora 5.78 Gammaropsis 4.38 Liljeborgia 5.59 Aora 3.88 Gammaropsis 4.09 Ischyroceridae gen.sp. 3.68 Iphimedia 3.16 Liljeborgia 3.35 Dexamine 2.47 Podocerus 2.34 Peltocoxa 2.23 Lysianassa 2.13 Phtisica 2.1 Cressa 1.76 Phoxocephalus 1.64 Tmetonyx 1.57 Cheirocratus 1.55 Lacco Ameno Summer vs Winter Average dissimilarity = 61.16 Genera Contrib% Apherusa 13.5 Aora 13.16 Dexamine 8.6 Gammarella 8.2 Cheirocratus 5.24 Perioculodes 5.15 Caprella 4.42 Phtisica 4.3 Apolochus 3.58 Phoxocephalus 3.39 Liljeborgia 2.99 Gitana 2.68 Lysianassa 2.47 Hyale 2.3 Aoridae gen. sp. 2.27 Urothoe 2.2 Ampithoe 1.97 Orchomene 1.88 Synchelidium 1.7 Harpinia 1.55
Scarrupata Summer vs Winter Average dissimilarity = 50.83 Genera Contrib% Apolochus 19.38 Gammarella 8.74 Gitana 5.16 Apherusa 5.12 Phtisica 4.4 Aoridae gen. sp. 4.06 Gammaropsis 3.54 Aora 3.27 Liljeborgia 3.14 Ischyroceridae ge 2.87 Iphimedia 2.82 Lysianassa 2.73 Dexamine 2.55 Podocerus 2.54 Tmetonyx 2.1 Cheirocratus 2.06 Cressa 2 Perioculodes 1.99 Eusiroides 1.92 Orchomene 1.89 Phoxocephalus 1.81 Atylus 1.68 Peltocoxa 1.61 Normanion 1.18 Synchelidium 0.93 Leucothoe 0.88
197
Table 48: Average similarity percentages of sites in each meadow and season. The contribution percentage for each species is reported. Lacco Ameno Summer A B C Average similarity: 46.65 Average similarity: 49.13 Average similarity: 42.66 Genera Contrib% Genera Contrib% Genera Contrib% Apherusa 18.59 Apherusa 35.36 Apherusa 44.16 Cheirocratus 16.46 Apolochus 11.32 Hyale 8.36 Caprella 12.6 Dexamine 8.64 Dexamine 8.08 Harpinia 9.61 Phoxocephalus 7.63 Cheirocratus 6.92 Gammarella 8.04 Perioculodes 7.02 Caprella 4.79 Perioculodes 5.73 Aora 5.54 Gammarella 4.61 Liljeborgia 4.39 Liljeborgia 4.13 Phtisica 4.43 Phtisica 4.29 Lysianassa 3.59 Lysianassa 3.01 Iphimedia 4.15 Iphimedia 3.39 Perioculodes 2.51 Aora 3.91 Urothoe 3.34 Guernea 2.24 Metaphoxus 2.75 Synchelidium 2.47 Orchomene 1.68 Scarrupata Summer A B C Average similarity: 58.09 Average similarity: 60.69 Average similarity: 56.82 Species Contrib% Species Contrib% Species Contrib% Apolochus 32.95 Apolochus 41.41 Apolochus 39.91 Apherusa 13.82 Apherusa 10.09 Apherusa 10.55 Phtisica 6.71 Phtisica 6.69 Phtisica 8.3 Iphimedia 6.36 Aora 5.88 Aoridae gen. sp. 8.05 Gammaropsis 6.3 Iphimedia 5.81 Liljeborgia 6.85 Ischyroceridae gen.sp. 6.01 Aoridae gen. sp. 5 Aora 3.91 Aoridae gen. sp. 5.87 Gammaropsis 4.8 Gitana 3.76 Podocerus 4.46 Gitana 4.37 Ischyroceridae gen.sp. 2.86 Gitana 4.26 Ischyroceridae gen.sp. 2.74 Iphimedia 2.64 Aora 2.46 Liljeborgia 2.57 Orchomene 2.55 Dexamine 2.04 Podocerus 2.38 Eusiroides 2.36
Winter A B C Average similarity: 58.64 Average similarity: 64.87 Average similarity: 63.64 Genera Contrib% Genera Contrib% Genera Contrib% Apherusa 24.41 Aora 29.76 Apherusa 26.29 Dexamine 17.33 Apherusa 29.42 Gammarella 19.95 Perioculodes 13.69 Dexamine 11.07 Aora 15.18 Phtisica 9.01 Phtisica 5.19 Dexamine 12.33 Aora 8.52 Caprella 4.17 Perioculodes 5.34 Caprella 6.3 Perioculodes 3.73 Caprella 4.13 Gitana 4.31 Phoxocephalus 3.43 Phtisica 3.32 Orchomene 3.16 Gitana 3.39 Aoridae gen. sp. 2.65 Gammarella 2.61 Liljeborgia 2.35 Ampithoe 2.6
Winter A B C Average similarity: 53.36 Average similarity: 51.30 Average similarity: 58.60 Species Contrib% Species Contrib% Species Contrib% Apolochus 27.73 Apolochus 25.7 Apolochus 24.89 Gammarella 13.79 Apherusa 8.38 Apherusa 12.33 Aoridae gen. sp. 9.37 Aora 7.55 Aoridae gen. sp. 6.15 Apherusa 8.27 Gitana 6.79 Gammaropsis 5.64 Liljeborgia 6.75 Aoridae gen. sp. 5.6 Liljeborgia 5.28 Gitana 6.2 Gammarella 4.49 Gammarella 4.9 Aora 5.82 Liljeborgia 4.16 Gitana 4.64 Gammaropsis 2.58 Dexamine 4.04 Iphimedia 4.56 Cheirocratus 2.13 Peltocoxa 4 Phoxocephalus 4.01 Iphimedia 2.12 Lysianassa 3.64 Aora 3.94 Phtisica 2.04 Gammaropsis 3.57 Dexamine 2.62 Orchomene 1.99 Cressa 2.98 Phtisica 2.12 Peltocoxa 1.62 Iphimedia 2.58 Tmetonyx 2.03 Atylus 2.08 Orchomene 1.94 Perioculodes 1.84 Cressa 1.84 Tmetonyx 1.56 Perioculodes 1.83 Cheirocratus 1.46 Lysianassa 1.54
Table 69: Pairwise dissimilarity percentages between sites in Lacco Ameno meadow and seasons. The contribution percentage for each species is reported. Lacco Ameno Winter Summer A vs C B vs C A vs B A vs C B vs C A vs B Average dissimilarity = 72.09 Average dissimilarity = 68.32 Average dissimilarity = 64.79 Average dissimilarity = 45.03 Average dissimilarity = 44.80 Average dissimilarity = 42.56 Genera Contrib% Genera Contrib% Genera Contrib% Genera Contrib% Genera Contrib% Genera Contrib% Apherusa 15.27 Apherusa 23.84 Apherusa 15.47 Aora 20.1 Gammarella 15.49 Gammarella 17.99 Apolochus 11.45 Cheirocratus 8.99 Apolochus 9.7 Apherusa 11.56 Apherusa 13.94 Apherusa 12.53 Cheirocratus 8.27 Dexamine 7.5 Cheirocratus 6.79 Perioculodes 8.78 Aora 9.73 Aora 11.91 Phoxocephalus 5.52 Caprella 6.7 Hyale 5.63 Dexamine 7.83 Perioculodes 6.77 Dexamine 6.2 Dexamine 5.46 Hyale 6.16 Dexamine 5.36 Gammarella 7.48 Dexamine 6.38 Cheirocratus 4.66 Caprella 5.11 Gammarella 5.69 Caprella 5.17 Cheirocratus 5.04 Caprella 4.28 Perioculodes 4.54 Liljeborgia 4.74 Phtisica 5.31 Phtisica 4.89 Caprella 4.48 Gitana 4.02 Phoxocephalus 4.08 Perioculodes 3.94 Lysianassa 3.59 Gammarella 4.78 Phoxocephalus 4.02 Aoridae gen. sp. 3.96 Aoridae gen. sp. 3.81 Phtisica 3.85 Harpinia 3.26 Phoxocephalus 4.09 Phtisica 3.93 Phoxocephalus 3.93 Urothoe 3.61 Aora 3.8 Liljeborgia 2.73 Aora 3.94 Liljeborgia 3.7 Phtisica 3.6 Ampithoe 3.24 Gammarella 3.68 Perioculodes 2.71 Liljeborgia 3.88 Orchomene 3.4 Urothoe 3.54 Phtisica 3.12 Lysianassa 3.6 Aoridae gen. sp. 1.95 Lysianassa 3.58 Gitana 3.23 Liljeborgia 2.79 Gitana 2.94 Harpinia 3.29 Guernea 1.93 Perioculodes 3.32 Aoridae gen. sp. 3.16 Orchomene 2.72 Caprella 2.72 Urothoe 3.26 Synchelidium 1.89 Urothoe 2.87 Metaphoxus 2.49 Lysianassa 2.57 Lysianassa 2.58 Iphimedia 2.75 Phoxocephalus 1.86 Iphimedia 2.8 Ampithoe 2.39 Ampithoe 2.54 Liljeborgia 2.52 Synchelidium 2.39 Aora 1.76 Synchelidium 2.23 Metaphoxus 2.29 Orchomene 2.2 Ischyroceridae gen.sp. 1.83 Urothoe 1.73 Aoridae gen. sp. 1.75 Synchelidium 2.11 Synchelidium 2.05 Gitana 1.38 Orchomene 1.65 Guernea 1.72 Metaphoxus 1.26 Iphimedia 1.26 Ischyroceridae gen.sp. 1.56 Orchomene 1.2
198
Table 70: Pairwise dissimilarity percentages between sites in Scarrupata meadow and seasons. The contribution percentage for each species is reported. Scarrupata Summer Winter A vs B A vs C B vs C A vs B A vs C B vs C Average dissimilarity = 39.82 Average dissimilarity = 42.94 Average dissimilarity = 40.04 Average dissimilarity = 51.78 Average dissimilarity = 45.06 Average dissimilarity = 48.28 Genera Contrib% Genera Contrib% Genera Contrib% Genera Contrib% Genera Contrib% Genera Contrib% Apolochus 26.78 Apolochus 23.59 Apolochus 27.48 Gammarella 15.52 Apolochus 13.9 Apolochus 14.09 Apherusa 7.01 Apherusa 7.41 Gitana 6.29 Apolochus 13.78 Gammarella 13.57 Apherusa 6.44 Gitana 6.66 Gitana 5.61 Apherusa 6.06 Gitana 6.52 Gitana 6.74 Aoridae gen. sp. 6.36 Aora 5.75 Gammaropsis 5.56 Gammaropsis 5.51 Apherusa 4.18 Aoridae gen. sp. 5.05 Gammarella 4.67 Gammaropsis 5.23 Aoridae gen. sp. 5.21 Aoridae gen. sp. 5.28 Aoridae gen. sp. 4.08 Apherusa 4.24 Lysianassa 4.49 Phtisica 4.24 Iphimedia 4.66 Aora 4.87 Liljeborgia 3.91 Aora 3.72 Gitana 4.04 Iphimedia 3.71 Phtisica 4.57 Phtisica 4.57 Lysianassa 3.54 Tmetonyx 3.39 Liljeborgia 3.79 Eusiroides 3.5 Liljeborgia 4.35 Iphimedia 4.1 Aora 3.51 Liljeborgia 3.25 Phoxocephalus 3.41 Ischyroceridae gen.sp. 3.46 Podocerus 4.22 Liljeborgia 3.98 Dexamine 3.04 Phoxocephalus 3.09 Perioculodes 3.29 Podocerus 3.42 Ischyroceridae gen.sp. 3.5 Dexamine 2.72 Cheirocratus 2.9 Ischyroceridae gen.sp. 2.78 Dexamine 3.28 Aoridae gen. sp. 3.34 Eusiroides 3.3 Podocerus 2.71 Tmetonyx 2.67 Gammaropsis 2.76 Iphimedia 3.12 Dexamine 3.22 Aora 2.47 Orchomene 2.29 Perioculodes 2.67 Dexamine 2.61 Orchomene 2.98 Liljeborgia 2.69 Lysianassa 2.36 Atylus 2.12 Gammaropsis 2.57 Lysianassa 2.49 Cheirocratus 2.96 Orchomene 2.13 Dexamine 2.15 Eusiroides 2.08 Phtisica 2.57 Iphimedia 2.41 Aora 2.93 Cheirocratus 1.7 Atylus 2.03 Lysianassa 2.07 Cressa 2.27 Phtisica 2.33 Gammaropsis 2.9 Ericthonius 1.52 Orchomene 2 Cheirocratus 1.86 Atylus 2.15 Orchomene 2.21 Ischyroceridae gen.sp. 2.72 Lysianassa 1.51 Hyale 1.13 Ischyroceridae gen.sp. 1.84 Peltocoxa 2.12 Cressa 2.18 Cressa 2.66 Atylus 1.17 Stenothoe 1.08 Ericthonius 1.25 Ischyroceridae gen.sp. 1.88 Perioculodes 2.07 Phtisica 2.49 Hyale 1.1 Perioculodes 1.07 Caprella 1.2 Iphimedia 1.8 Normanion 1.94 Tmetonyx 2.4 Stenothoe 0.94 Peltocoxa 1.03 Ampelisca 1.02 Urothoe 1.71 Leucothoe 1.85 Leucothoe 1.92 Peltocoxa 0.92 Cheirocratus 1.01 Synchelidium 1 Normanion 1.5 Peltocoxa 1.83 Atylus 1.85 Cressa 0.98 Orchomene 1.48 Cheirocratus 1.83 Urothoe 1.85 Caprella 0.95 Guernea 1.4 Atylus 1.75 Normanion 1.71 Phoxocephalus 1.16 Guernea 1.15 Peltocoxa 1.42 Caprella 1.03 Melita 0.99 Eusiroides 1.14 Melita 0.95 Maera 1.1
Table 51: Average similarity percentages of stations in Lacco Ameno meadow and seasons. The contribution percentage for each species is reported. A1 Average similarity: 53.35 Genera Contrib% Apherusa Harpinia Cheirocratus Aora Iphimedia Gammarella Liljeborgia Caprella Gitana Hyale
A2 Average similarity: 63.32 Genera Contrib% 18.25 Apherusa 18.25 Cheirocratus 11.63 Caprella 10.63 Gammarella 8.63 Phtisica 8.63 Perioculodes 5 Metaphoxus 5 2 2
A1 Average similarity: 67.17 Genera Contrib% Dexamine Apherusa Perioculodes Phtisica Aora Gitana Orchomene Ampithoe Aoridae gen. sp.
A2 Average similarity: 59.18 Genera Contrib% 22.68 Apherusa 15.94 Dexamine 12.93 Perioculodes 9.51 Gammarella 8.53 Phtisica 6.1 Caprella 5.69 Aora 5.12 4.7
Lacco Ameno Summer B1 B2 Average similarity: 38.32 Average similarity: 58.14 Genera Contrib% Genera Contrib% 22.3 Apherusa 26.95 Apherusa 21.85 Perioculodes 12.7 Phoxocephalus 15.02 Apolochus 11.22 Apolochus 9.66 Urothoe 7.92 Dexamine 9.52 Dexamine 7.44 Lysianassa 8.53 Aora 6.64 Iphimedia 3.84 Liljeborgia 4.81 Perioculodes Synchelidium 4.49 Aora Iphimedia 4.23 Phtisica 4.01 Lacco Ameno Winter B1 B2 Average similarity: 70.59 Average similarity: 61.08 Genera Contrib% Genera Contrib% 30.04 Aora 28.49 Apherusa 14.54 Apherusa 28.3 Aora 12.78 Dexamine 13.08 Cheirocratus 10.18 Phoxocephalus 5.9 Dexamine 8.89 Gitana 5.53 Caprella 8.14 Ampithoe 4.93 Phtisica 6.45 Phtisica 4.79 Gammarella Perioculodes
C1 Average similarity: 34.70 Genera Contrib% 40.02 Apherusa 14.71 Gammarella 11.1 Hyale 8.99 Cheirocratus 5.55 Lysianassa 3.95 Caprella 3.61 Leptocheirus 3.57 Perioculodes
C2 Average similarity: 53.13 Genera Contrib% 24.81 Apherusa 22.58 Dexamine 12.75 Phtisica 9.28 Hyale 6.37 Cheirocratus 5.44 Caprella 4.52 Guernea 4.52 Liljeborgia
C1 Average similarity: 65.88 Genera Contrib% 28.53 Apherusa 27.51 Dexamine 9.34 Gammarella 7.98 Aora 4.75 Perioculodes 4.75 Urothoe 4.5 Orchomene 4.39 Aoridae gen. sp. Synchelidium
C2 Average similarity: 75.28 Genera Contrib% 30.16 Aora 15.27 Apherusa 15.23 Gammarella 8.79 Dexamine 8.33 Caprella 4.2 Phoxocephalus 3.24 Phtisica 3.24 2.3
55.16 13.96 7.02 3.75 3.75 3.27 3 2.66
25.6 20.42 20.05 10.58 5.57 5.01 4.47
199
Table 52: Average similarity percentages of stations in Scarrupata meadow and seasons. The contribution percentage for each species is reported. A1 Average similarity: 60.90 Genera Contrib% Apolochus Apherusa Gammaropsis Phtisica Aoridae gen. sp. Ischyroceridae gen.sp. Podocerus Gitana Iphimedia Lysianassa Aora
A2 Average similarity: 49.53 Genera Contrib% 28.47 Apolochus 13.14 Apherusa 7.98 Iphimedia 7.98 Ischyroceridae gen.sp. 6.98 Aoridae gen. sp. 6.09 Phtisica 6.02 Gammaropsis 5.5 Podocerus 3.72 Gitana 2.82 Aora 2.41
A1 Average similarity: 57.82 Genera Contrib% Apolochus Apherusa Aora Aoridae gen. sp. Gammarella Liljeborgia Gammaropsis Tmetonyx Dexamine Gitana Normanion Cheirocratus Peltocoxa Phoxocephalus
A2 Average similarity: 51.57 Genera Contrib% 28.46 Apolochus 11.59 Gammarella 10.09 Gitana 8.29 Aoridae gen. sp. 6.9 Liljeborgia 4.69 Apherusa 3.77 Aora 3.33 Iphimedia 3.03 Atylus 2.49 Phtisica 2.41 Orchomene 2.11 1.63 1.63
200
Scarrupata Summer B1 B2 Average similarity: 52.95 Average similarity: 70.97 Genera Contrib% Genera Contrib% 35.09 Apolochus 31.31 Apolochus 17.68 Apherusa 20.36 Aora 9.88 Gammaropsis 9.38 Phtisica 5.55 Phtisica 5.63 Iphimedia 5.18 Aora 5.19 Apherusa 4.72 Iphimedia 4.89 Aoridae gen. sp. 4.33 Aoridae gen. sp. 4.8 Gitana 3.2 Liljeborgia 3.42 Ischyroceridae gen.sp. 2.72 Gitana 3.41 Gammaropsis 2.35 Atylus 2.08 Podocerus Orchomene
C1 Average similarity: 65.00 Genera Contrib% 46.45 Apolochus 7.24 Apherusa 6.56 Phtisica 6.5 Aora 5.88 Aoridae gen. sp. 4.49 Eusiroides 3.93 Liljeborgia 3.28 Iphimedia 2.94 Ischyroceridae gen.sp. 2.58 1.96
C2 Average similarity: 59.72 Genera Contrib% 54.09 Apolochus 9.2 Aoridae gen. sp. 7.46 Liljeborgia 4.63 Apherusa 3.83 Phtisica 3.56 Gitana 3.56 Orchomene 2.76 Aora 2.49 Ischyroceridae gen.sp. Dexamine Iphimedia Gammaropsis
Scarrupata Winter B1 B2 Average similarity: 51.37 Average similarity: 45.64 Genera Contrib% Genera Contrib% 24.29 Apolochus 24.95 Apolochus 23 Apherusa 11.3 Aora 9.88 Gitana 6.86 Gammarella 9.39 Aora 5.86 Peltocoxa 7.79 Atylus 5.86 Gitana 5.43 Aoridae gen. sp. 4.9 Liljeborgia 2.97 Dexamine 4.44 Aoridae gen. sp. 2.47 Cressa 3.94 Apherusa 2.28 Lysianassa 3.94 Dexamine 1.99 Iphimedia 3.91 Gammaropsis 1.98 Liljeborgia 2.72 Lysianassa Gammaropsis 2.65 Urothoe Peltocoxa 2.45 Cressa Ischyroceridae gen. 2.45 Cheirocratus 1.99 Leptocheirus 1.95
C1 Average similarity: 52.25 Genera Contrib% 26.62 Apolochus 9.82 Apherusa 9.63 Gammarella 6.8 Gammaropsis 6.23 Iphimedia 6.23 Liljeborgia 5.66 Tmetonyx 5.66 Phoxocephalus 3.65 Lysianassa 3.4 Ischyroceridae gen.sp. 2.83 Dexamine 2.08 Gitana 1.7 Leucothoe Aora Phtisica Ampelisca
C2 Average similarity: 64.31 Genera Contrib% 22.31 Apolochus 16.67 Aoridae gen. sp. 9.24 Apherusa 6.76 Aora 6.67 Gitana 6.34 Phoxocephalus 3.38 Gammaropsis 2.81 Liljeborgia 2.62 Iphimedia 2.29 Dexamine 2.24 Cressa 2.13 Perioculodes 1.9 Orchomene 1.71 Phtisica 1.53 Gammarella 1.48
35.34 11.48 8.95 8.9 6.7 5.69 3.44 2.48 2.48 2.07 2.07 1.74
25.79 13.09 9.02 6.54 6.07 4.95 4.3 4.02 3.08 2.99 2.97 2.63 2.15 2.05 1.65
Table 53: Average dissimilarity percentages between stations in Lacco Ameno meadow in summer. The contribution percentage for each species is reported. Lacco Ameno Summer A1 vs A2 A1 vs B1 A2 vs B1 A1 vs B2 Average dissimilarity = 61.14 Average dissimilarity = 74.45 Average dissimilarity = 63.14 Average dissimilarity = 79.92 Genera Contrib% Genera Contrib% Genera Contrib% Genera Contrib% Cheirocratus 18 Apherusa 17.78 Cheirocratus 14.04 Apherusa 22.76 Apherusa 14.38 Apolochus 13.7 Apolochus 12.89 Apolochus 10.6 Caprella 9.29 Perioculodes 6.77 Apherusa 8.59 Phoxocephalus 9.44 Gammarella 8.96 Liljeborgia 5.6 Caprella 6.51 Dexamine 7.23 Phtisica 8.37 Phtisica 5.33 Gammarella 6.3 Lysianassa 5.18 Perioculodes 7.26 Dexamine 5.2 Liljeborgia 4.99 Iphimedia 4.86 Liljeborgia 4.25 Urothoe 4.48 Phtisica 4.9 Liljeborgia 4.78 A2 vs C1 A2 vs B2 B1 vs B2 A1 vs C1 Average dissimilarity = 70.84 Average dissimilarity = 50.27 Average dissimilarity = 72.42 Average dissimilarity = 57.25 Genera Contrib% Genera Contrib% Genera Contrib% Genera Contrib% Cheirocratus 13.25 Apolochus 15.37 Apherusa 18.62 Apherusa 18.06 Apherusa 10.14 Apherusa 13.08 Gammarella 11.1 Cheirocratus 11.61 Apolochus 8.77 Liljeborgia 6.53 Cheirocratus 8.93 Caprella 7.61 Caprella 7.49 Phoxocephalus 6.47 Hyale 6.58 Hyale 6.76 Phoxocephalus 6.7 Phtisica 5.81 Caprella 5.25 Gammarella 5.63 Gammarella 6.2 Perioculodes 5.35 Lysianassa 5.05 Lysianassa 5.03 Phtisica 4.92 Aora 5.07 Dexamine 4.72 Dexamine 4.68 Dexamine 4.87 Lysianassa 4.97 Harpinia 4.31 Perioculodes 4.67 Lysianassa 4.26 Urothoe 4.78 Liljeborgia 4.07 Phtisica 4.36 B1 vs C1 B2 vs C1 A1 vs C2 A2 vs C2 Average dissimilarity = 70.30 Average dissimilarity = 67.93 Average dissimilarity = 81.29 Average dissimilarity = 62.30 Genera Contrib% Genera Contrib% Genera Contrib% Genera Contrib% Apherusa 14.59 Apherusa 15.05 Apherusa 32.4 Apherusa 24.06 Apolochus 11.17 Gammarella 8.91 Dexamine 10.53 Cheirocratus 10.39 Gammarella 7.99 Apolochus 8.56 Phtisica 7.26 Dexamine 9.37 Cheirocratus 5.85 Phoxocephalus 6.47 Caprella 6.87 Caprella 7.31 Hyale 4.99 Cheirocratus 6.41 Cheirocratus 6.12 Phtisica 6.45 Dexamine 4.58 Dexamine 6.16 Hyale 5.5 Hyale 5.97 Perioculodes 4.57 Hyale 5.8 Harpinia 2.95 Gammarella 5.31 B1 vs C2 B2 vs C2 C1 vs C2 Average dissimilarity = 62.74 Average dissimilarity = 58.20 Average dissimilarity = 58.18 Genera Contrib% Genera Contrib% Genera Contrib% Apherusa 17.61 Apherusa 14.73 Apherusa 21.37 Apolochus 10.85 Cheirocratus 8.06 Cheirocratus 9.04 Cheirocratus 7.07 Apolochus 7.99 Dexamine 8.99 Caprella 6.52 Phtisica 7.91 Gammarella 8.23 Phtisica 6.09 Caprella 7.66 Caprella 7.74 Dexamine 5.56 Hyale 6.5 Phtisica 7.37 Hyale 5.37 Phoxocephalus 5.3 Hyale 5.79 Perioculodes 4.53 Dexamine 5.17 Lysianassa 4.29
201
Table 54: Average dissimilarity percentages between stations in Lacco Ameno meadow in winter. The contribution percentage for each species is reported. Lacco Ameno Winter A1 vs A2 A1 vs B1 A2 vs B1 A1 vs B2 Average dissimilarity = 44.38 Average dissimilarity = 41.06 Average dissimilarity = 43.79 Average dissimilarity = 49.32 Genera Contrib% Genera Contrib% Genera Contrib% Genera Contrib% Dexamine 13.24 Aora 18.64 Aora 25.16 Aora 14.87 Gammarella 12.61 Perioculodes 12.53 Gammarella 14.25 Apherusa 12.34 Perioculodes 9.5 Apherusa 10.73 Apherusa 9.36 Dexamine 10.21 Aoridae gen. sp. 7.24 Dexamine 8.31 Dexamine 8.11 Perioculodes 9.6 Aora 6.69 Aoridae gen. sp. 6.58 Perioculodes 8.07 Cheirocratus 7.72 Caprella 6.07 Orchomene 6.12 Phoxocephalus 5.2 Aoridae gen. sp. 5.58 Gitana 5.44 Phoxocephalus 5.65 Caprella 4.65 Phtisica 4.55 Phtisica 5.3 Caprella 5.45 Ampithoe 4.07 Gammarella 4.51 Apherusa 5.08 Phtisica 5.28 Liljeborgia 3.32 Gitana 4.47 A2 vs B2 B1 vs B2 A1 vs C1 A2 vs C1 Average dissimilarity = 45.94 Average dissimilarity = 35.77 Average dissimilarity = 46.76 Average dissimilarity = 45.07 Genera Contrib% Genera Contrib% Genera Contrib% Genera Contrib% Aora 22.18 Apherusa 13.53 Apherusa 19.3 Apherusa 20.97 Apherusa 13.55 Aora 12.53 Gammarella 17.73 Gammarella 13.2 Gammarella 10.38 Cheirocratus 10.22 Perioculodes 6.63 Dexamine 7.87 Cheirocratus 9.7 Dexamine 9 Phtisica 5.61 Urothoe 6.07 Perioculodes 5.22 Gammarella 7.29 Urothoe 5.45 Aoridae gen. sp. 5.19 Liljeborgia 5.09 Perioculodes 6.17 Caprella 4.25 Lysianassa 4.37 Dexamine 4.59 Phoxocephalus 5.77 Gitana 4.07 Aora 4.21 B1 vs C1 B2 vs C1 A1 vs C2 A2 vs C2 Average dissimilarity = 48.63 Average dissimilarity = 47.38 Average dissimilarity = 50.63 Average dissimilarity = 36.74 Genera Contrib% Genera Contrib% Genera Contrib% Genera Contrib% Gammarella 18.36 Aora 13.48 Gammarella 16.63 Aora 22.77 Aora 13.71 Gammarella 13.44 Aora 10.81 Gammarella 13.87 Apherusa 13.67 Apherusa 13 Dexamine 10.21 Apherusa 8.96 Urothoe 5.59 Dexamine 6.91 Perioculodes 10.04 Perioculodes 7.4 Perioculodes 5.41 Cheirocratus 6.72 Apherusa 6.36 Phoxocephalus 6.91 Phoxocephalus 4.64 Urothoe 5.05 Gitana 5.62 Liljeborgia 4.51 Dexamine 4.6 Aoridae gen. sp. 4.32 Phoxocephalus 4.66 Caprella 4.49 B1 vs C2 B2 vs C2 C1 vs C2 Average dissimilarity = 36.57 Average dissimilarity = 37.68 Average dissimilarity = 40.99 Genera Contrib% Genera Contrib% Genera Contrib% Gammarella 25.11 Gammarella 16.3 Apherusa 18.32 Apherusa 8.68 Apherusa 14.2 Aora 11.68 Dexamine 8.58 Aora 11.02 Gammarella 8.73 Aora 8.4 Cheirocratus 10.5 Dexamine 7.73 Perioculodes 6.49 Phoxocephalus 5.1 Urothoe 5.93 Ampithoe 5.88 Dexamine 5.05 Phoxocephalus 5.56 Phoxocephalus 5.42 Phtisica 4.66 Perioculodes 4.77 Gitana 5.15 Liljeborgia 4.23 Lysianassa 4.04
202
Table 55: Average dissimilarity percentages between stations in Scarrupata meadow in summer. The contribution percentage for each species is reported. A1 vs A2 Average dissimilarity = 40.00 Genera Contrib% Apolochus 20.64 Apherusa 10.23 Gitana 7.19 Gammaropsis 5.87 Eusiroides 5.37 Iphimedia 4.96 Podocerus 4.65
Scarrupata Summer A1 vs B1 A2 vs B1 Average dissimilarity = 40.98 Average dissimilarity = 41.42 Genera Contrib% Genera Contrib% Apolochus 27.26 Apolochus 23.76 Gitana 7.56 Apherusa 9.19 Gammaropsis 5.41 Gitana 7.91 Eusiroides 4.83 Gammaropsis 6.57 Apherusa 4.61 Phtisica 5.23 Podocerus 4.6 Iphimedia 4.33 Ischyroceridae gen.sp. 4.31 Ischyroceridae gen.sp. 4.1
A1 vs B2 Average dissimilarity = 36.16 Genera Contrib% Apolochus 29.62 Aora 10.27 Gammaropsis 5.39 Eusiroides 5.29 Gitana 4.6 Iphimedia 4.01 Podocerus 3.81
A2 vs B2 Average dissimilarity = 40.73 Genera Contrib% Apolochus 26.86 Apherusa 10.6 Aora 8.47 Gitana 6.29 Phtisica 4.68
B1 vs B2 Average dissimilarity = 40.15 Genera Contrib% Apolochus 29.41 Aora 7.05 Apherusa 6.96 Gitana 6.87 Gammaropsis 6.27
A1 vs C1 Average dissimilarity = 40.93 Genera Contrib% Apolochus 30.05 Gitana 6.23 Gammaropsis 6.19 Podocerus 5.35 Aoridae gen. sp. 5.24
A2 vs C1 Average dissimilarity = 43.02 Genera Contrib% Apolochus 29.14 Apherusa 8.49 Gitana 6.17 Aoridae gen. sp. 5.19 Gammaropsis 4.8
B1 vs C1 Average dissimilarity = 38.60 Genera Contrib% Apolochus 32.88 Gitana 7.99 Gammaropsis 7.72 Apherusa 5.98 Aoridae gen. sp. 5.81 Phtisica 3.91
B2 vs C1 Average dissimilarity = 31.35 Genera Contrib% Apolochus 21.24 Aora 8.25 Gitana 7.54 Aoridae gen. sp. 6.64 Apherusa 6.5 Iphimedia 5.67
A1 vs C2 Average dissimilarity = 43.71 Genera Contrib% Apolochus 20.55 Gammaropsis 6.09 Apherusa 5.79 Liljeborgia 5.59 Phtisica 5.56 Podocerus 5.44
A2 vs C2 Average dissimilarity = 44.09 Genera Contrib% Apolochus 15.2 Apherusa 11.1 Iphimedia 6.74 Phtisica 6.22 Gitana 6.21 Aoridae gen. sp. 5.97
B1 vs C2 Average dissimilarity = 44.35 Genera Contrib% Apolochus 22.43 Apherusa 7.84 Gammaropsis 7.19 Gitana 6.98 Phtisica 5.65 Aoridae gen. sp. 5.27 Liljeborgia 4.33
B2 vs C2 Average dissimilarity = 45.86 Genera Contrib% Apolochus 32.07 Aora 7.81 Iphimedia 5.65 Phtisica 4.72 Liljeborgia 4.4 Apherusa 4.1 Aoridae gen. sp. 3.91
C1 vs C2 Average dissimilarity = 46.87 Genera Contrib% Apolochus 35.17 Aoridae gen. sp. 6.16 Apherusa 6.09 Gitana 5.37 Phtisica 4.31 Gammaropsis 4.03 Atylus 3.85
203
Table 56: Average dissimilarity percentages between stations in Scarrupata meadow in winter. The contribution percentage for each species is reported. A1 vs A2 Average dissimilarity = 47.53 Genera Contrib% Gammarella 18.23 Apolochus 15.59 Gitana 9.38 Aora 5.23 Tmetonyx 4.93
Scarrupata Winter A1 vs B1 A2 vs B1 Average dissimilarity = 50.87 Average dissimilarity = 53.74 Genera Contrib% Genera Contrib% Apolochus 19.12 Gammarella 24.65 Gitana 8.42 Apolochus 8.07 Gammarella 7.37 Lysianassa 5.14 Aora 5.49 Apherusa 5.05 Lysianassa 4.55 Dexamine 5
A1 vs B2 Average dissimilarity = 50.09 Genera Contrib% Apolochus 19.31 Gitana 8.67 Gammarella 6.44 Apherusa 4.91 Aoridae gen. sp. 4.59
A2 vs B2 Average dissimilarity = 52.41 Genera Contrib% Gammarella 22.74 Apolochus 9.18 Gitana 4.84 Aoridae gen. sp. 3.8 Perioculodes 3.66 Liljeborgia 3.56 Cheirocratus 3.27
B1 vs B2 Average dissimilarity = 46.84 Genera Contrib% Gammarella 7.67 Apherusa 7.39 Apolochus 7 Lysianassa 6.66 Dexamine 5.34 Perioculodes 5.08 Cheirocratus 4.6
A1 vs C1 Average dissimilarity = 42.43 Genera Contrib% Apolochus 19.73 Gitana 10 Aoridae gen. sp. 6.74 Aora 6.56 Gammarella 4.92 Tmetonyx 4.09 Lysianassa 3.8
A2 vs C1 Average dissimilarity = 51.86 Genera Contrib% Gammarella 19.56 Apolochus 12.53 Aoridae gen. sp. 5.48 Apherusa 4.86 Gitana 4.57 Lysianassa 3.71 Gammaropsis 3.41
B1 vs C1 Average dissimilarity = 47.08 Genera Contrib% Apolochus 14.36 Lysianassa 6.38 Apherusa 5.39 Gammarella 5.22 Aoridae gen. sp. 4.98 Dexamine 4.55 Liljeborgia 4.08
B2 vs C1 Average dissimilarity = 49.63 Genera Contrib% Apolochus 14.64 Apherusa 7.07 Gammarella 4.94 Iphimedia 4.69 Aoridae gen. sp. 4.6 Lysianassa 4.55 Gitana 3.6
A1 vs C2 Average dissimilarity = 38.86 Genera Contrib% Apolochus 14.28 Gitana 10.42 Gammarella 6.32 Tmetonyx 4.68 Apherusa 4.55 Liljeborgia 3.93 Perioculodes 3.91
A2 vs C2 Average dissimilarity = 47.10 Genera Contrib% Gammarella 20.76 Apolochus 9.85 Apherusa 6.09 Phoxocephalus 4.65 Dexamine 4.4 Aoridae gen. sp. 4.39 Cressa 3.62
B1 vs C2 Average dissimilarity = 46.95 Genera Contrib% Apolochus 14.25 Aoridae gen. sp. 7.97 Apherusa 5.9 Lysianassa 5.1 Phoxocephalus 4.76 Gitana 4.42 Liljeborgia 4.18 Dexamine 4.14
B2 vs C2 Average dissimilarity = 49.48 Genera Contrib% Apolochus 13.15 Aoridae gen. sp. 7.9 Apherusa 7.33 Gammarella 5.14 Gitana 4.53 Perioculodes 4.27 Phoxocephalus 3.77 Orchomene 3.57
C1 vs C2 Average dissimilarity = 41.19 Genera Contrib% Apolochus 15.46 Aoridae gen. sp. 8.62 Gitana 5.26 Aora 4.83 Apherusa 4.46 Lysianassa 4.12 Liljeborgia 3.83 Orchomene 3.56
204
Appendix 5 Spearman’s Rank Correlations Table 57: Correlations between plant features and borer polychaete frequencies. NL: Number of Leaf; SL: Sheaths Length; LL: Leaf Length; LW: Leaf Width; LS: Leaf Surface; ShD: Shoot Density; LB: Leaf Biomass; SB: Sheaths Biomass. Signficant values in bold. NL SL LL LW LS ShD LB SB Total IB% IB% L. collaris IB% L. ninetta IB% N. unicornis
NL 1.000 -0.145 -0.458 0.264 -0.184 0.029 -0.140 0.167 0.086 0.010 0.071 0.091
SL
LL
LW
LS
ShD
LB
SB
1.000 0.597 0.187 0.677 0.297 0.612 0.495 -0.011 -0.022 -0.050 0.016
1.000 -0.248 0.917 0.126 0.895 0.705 0.097 0.140 0.020 -0.061
1.000 -0.018 0.172 -0.061 -0.083 -0.058 -0.068 -0.032 0.017
1.000 0.162 0.967 0.798 0.116 0.142 0.049 -0.060
1.000 0.180 0.139 0.116 0.138 0.013 -0.109
1.000 0.858 0.182 0.205 0.092 -0.069
1.000 0.206 0.177 0.131 0.006
Total IB% IB% L. collaris IB% L. ninetta IB% N. unicornis
1.000 0.850 0.578 0.125
1.000 0.275 -0.119
1.000 -0.080
1.000
Table 58: Correlations between plant features and most abundant higher taxa. NL: Number of Leaf; SL: Sheaths Length; LL: Leaf Length; LW: Leaf Width; LS: Leaf Surface; ShD: Shoot Density; LB: Leaf Biomass; SB: Sheaths Biomass. Pol: Polychaetes; Gas: Gastropods; Biv: Bivalvs; Dec: Decapods; Mys: Mysids; Iso: Isopods; Tan: Tanaids; Cum: Cumaceans; Amp: Amphipods; Echi: Echinoderms. S: Number of Taxa per quadrat; N: Number of Individuals per quadrat; J’: Pielou Index; ES(100); Rarefaction; H’: Shannon-Wiener diversity Index. Signficant values in bold. NL SL LL LW LS ShD LB SB Pol Gas Biv Dec Mys Iso Tan Cum Amp Echi S N J' ES(100) H'
NL
SL
LL
LW
LS
ShD
LB
SB
Pol
Gas
Biv
Dec
Mys
Iso
Tan
1.000 -0.323 -0.720 0.440 -0.598 0.018 -0.619 -0.375 0.375 0.625 0.201 0.382 -0.125 0.336 0.420 -0.078 0.039 0.500 0.202 0.479 0.064 0.036 0.155
1.000 0.648 0.182 0.682 0.112 0.664 0.612 -0.147 -0.270 0.069 -0.321 -0.076 -0.200 -0.184 0.090 0.049 -0.396 -0.058 -0.263 -0.086 0.003 -0.091
1.000 -0.138 0.961 0.121 0.967 0.805 -0.460 -0.646 -0.267 -0.583 0.186 -0.243 -0.482 0.241 0.110 -0.608 -0.230 -0.531 -0.104 -0.040 -0.185
1.000 -0.023 0.278 -0.058 0.062 -0.029 0.179 -0.039 -0.074 -0.005 0.232 0.036 0.064 0.267 0.119 0.105 0.159 -0.223 -0.057 -0.127
1.000 0.089 0.988 0.873 -0.428 -0.598 -0.233 -0.543 0.113 -0.238 -0.436 0.248 0.102 -0.603 -0.203 -0.491 -0.117 -0.036 -0.179
1.000 0.090 0.058 -0.428 0.026 -0.275 -0.551 0.434 0.423 -0.260 0.380 0.609 -0.046 0.163 -0.111 0.049 0.240 0.107
1.000 0.875 -0.451 -0.617 -0.272 -0.572 0.154 -0.244 -0.455 0.249 0.117 -0.606 -0.248 -0.518 -0.091 -0.054 -0.178
1.000 -0.392 -0.445 -0.233 -0.459 0.250 -0.271 -0.366 0.174 0.169 -0.520 -0.259 -0.376 -0.144 -0.146 -0.217
1.000 0.490 0.737 0.747 -0.364 0.069 0.715 -0.011 -0.235 0.564 0.336 0.764 0.068 0.064 0.244
1.000 0.306 0.532 -0.127 0.443 0.612 0.183 0.197 0.684 0.562 0.688 0.252 0.310 0.446
1.000 0.532 -0.302 -0.045 0.551 0.018 -0.211 0.485 0.317 0.544 0.207 0.196 0.337
1.000 -0.416 -0.112 0.696 -0.297 -0.281 0.527 0.201 0.725 -0.155 -0.177 -0.020
1.000 0.226 -0.210 0.231 0.588 -0.050 -0.087 -0.007 -0.001 0.036 -0.005
1.000 0.155 0.581 0.543 0.383 0.559 0.347 0.245 0.525 0.433
1.000 0.003 -0.043 0.714 0.387 0.755 0.081 0.129 0.268
Cum Amp
Echi
S
1.000 0.525 0.134 0.479 0.131 0.417 0.601 0.566
1.000 0.446 0.723 0.172 0.269 0.364
1.000 0.465 0.226 0.785 0.606
1.000 0.174 0.309 0.295 -0.113 0.277 0.051
N
J'
ES(100) H'
1.000 -0.139 1.000 0.083 0.586 1.000 0.126 0.889 0.824 1.000
205
Table 59: Correlations between plant features and most abundant gastropod species. NL: Number of Leaf; SL: Sheaths Length; LL: Leaf Length; LW: Leaf Width; LS: Leaf Surface; ShD: Shoot Density; LB: Leaf Biomass; SB: Sheaths Biomass. S: Number of species per quadrat; N: Number of Individuals per quadrat; J’: Pielou Index; ES(20); Rarefaction; H’: Shannon-Wiener diversity Index. Signficant values in bold. NL SL LL LW LS ShD LB SB M. alba M. polita S. viridis G. philippii G. miliaria S. costata C. glabrum T. speciosa T. tenuis T. pullus P. intricata S N J' ES(20) H'
NL
SL
LL
LW
LS
ShD
LB
1.000 -0.323 -0.720 0.440 -0.598 0.018 -0.619 -0.375 0.515 0.192 0.755 0.473 0.307 0.505 -0.327 0.461 0.446 0.273 0.034 0.520 0.634 0.014 0.450 0.440
SB
1.000 0.648 0.182 0.682 0.112 0.664 0.612 -0.126 0.043 -0.329 -0.410 -0.230 -0.280 0.321 -0.361 -0.262 -0.099 -0.077 -0.274 -0.274 -0.192 -0.323 -0.295
1.000 -0.138 0.961 0.121 0.967 0.805 -0.518 -0.077 -0.743 -0.599 -0.382 -0.461 0.377 -0.587 -0.367 -0.227 -0.149 -0.579 -0.647 -0.176 -0.562 -0.547
1.000 -0.023 0.278 -0.058 0.062 0.182 -0.065 0.470 0.036 0.074 0.301 -0.288 0.103 0.172 0.232 -0.061 0.135 0.203 -0.046 0.115 0.122
1.000 0.089 0.988 0.873 -0.482 -0.065 -0.677 -0.574 -0.355 -0.450 0.337 -0.584 -0.363 -0.188 -0.140 -0.553 -0.604 -0.174 -0.536 -0.526
1.000 0.090 0.058 -0.184 0.243 0.138 -0.370 -0.460 0.366 0.121 -0.128 0.302 0.386 -0.097 -0.057 0.033 0.090 -0.087 -0.071
1.000 0.875 -0.503 -0.045 -0.693 -0.588 -0.378 -0.458 0.354 -0.583 -0.359 -0.223 -0.149 -0.577 -0.625 -0.187 -0.559 -0.552
M. alba M. polita S. viridis G. philippii G. miliaria S. costata C. glabrum T. speciosa T. tenuis T. pullus P. intricata
1.000 -0.362 1.000 -0.013 0.167 -0.502 0.493 -0.400 0.590 -0.319 0.530 -0.415 0.282 0.384 -0.101 -0.504 0.364 -0.231 0.258 -0.199 0.189 -0.216 0.224 -0.485 0.709 -0.453 0.729 -0.228 0.118 -0.498 0.642 -0.484 0.644
1.000 0.166 0.086 -0.117 0.273 0.119 -0.036 0.168 0.328 0.051 0.376 0.335 0.228 0.341 0.349
1.000 0.388 0.239 0.536 -0.401 0.446 0.467 0.393 0.082 0.541 0.618 0.136 0.482 0.500
1.000 0.556 0.166 -0.205 0.549 0.243 -0.026 0.216 0.642 0.560 0.213 0.616 0.615
1.000 0.052 -0.147 0.228 0.142 -0.133 0.368 0.578 0.430 0.180 0.551 0.572
1.000 -0.316 0.250 0.480 0.519 0.296 0.490 0.584 0.049 0.487 0.452
1.000 -0.315 -0.065 -0.083 -0.057 -0.075 0.003 -0.122 -0.129 -0.122
1.000 0.208 0.149 -0.016 0.418 0.376 0.186 0.391 0.408
1.000 0.217 0.118 0.470 0.541 0.164 0.430 0.428
1.000 0.026 0.298 0.421 -0.003 0.317 0.288
1.000 0.437 0.313 0.014 0.428 0.391
S
N
1.000 0.825 0.297 0.952 0.934
J'
ES(20)
H'
1.000 -0.009 1.000 0.727 0.411 1.000 0.694 0.531 0.971 1.000
Table 60: Correlations between plant features and most abundant gastropod species. NL: Number of Leaf; SL: Sheaths Length; LL: Leaf Length; LW: Leaf Width; LS: Leaf Surface; ShD: Shoot Density; LB: Leaf Biomass; SB: Sheaths Biomass. Chr: Chrysopetalidae; Cir: Cirratulidae; Eun: Eunicidae; Eup: Euphrosinidae; Fla: Flabelligeridae; Hes: Hesionidae; Ner: Nereididae; Oph: Ophelidae; Par: Paraonidae; Pol: Polynoidae; Sab: Sabellidae; Spi: Spionidae; Syl: Syllidae; Ter: Terebellidae. S: Number of families per quadrat; N: Number of Individuals per quadrat; J’: Pielou Index; ES(50); Rarefaction; H’: Shannon-Wiener diversity Index. Signficant values in bold. NL SL LL LW LS ShD LB SB Chr Cir Eun Eup Fla Hes Ner Oph Par Pol Sab Spi Syl Ter S N J' ES(50) H'
NL
SL
LL
LW
LS
ShD
LB
SB
Chr
Cir
Eun
Eup
Fla
Hes
Ner
Oph
Par
Pol
Sab
Spi
Syl
Ter
S
1.000 -0.323 -0.720 0.440 -0.598 0.018 -0.619 -0.375 0.257 -0.027 0.308 0.398 0.375 0.638 0.390 0.267 -0.074 0.552 -0.013 0.152 0.159 0.289 0.286 0.454 -0.413 0.191 0.025
1.000 0.648 0.182 0.682 0.112 0.664 0.612 -0.270 0.201 -0.173 -0.255 -0.293 -0.329 -0.251 0.081 0.234 -0.282 0.001 -0.155 0.047 -0.241 -0.124 -0.197 0.169 -0.117 -0.064
1.000 -0.138 0.961 0.121 0.967 0.805 -0.511 0.021 -0.317 -0.516 -0.464 -0.750 -0.536 -0.133 0.048 -0.488 -0.023 -0.261 -0.197 -0.295 -0.369 -0.559 0.495 -0.294 -0.161
1.000 -0.023 0.278 -0.058 0.062 -0.020 -0.172 -0.028 0.135 0.155 0.077 0.078 -0.025 -0.127 0.133 -0.034 -0.147 -0.065 0.001 -0.056 -0.002 -0.040 -0.092 -0.171
1.000 0.089 0.988 0.873 -0.485 0.026 -0.325 -0.538 -0.448 -0.704 -0.531 -0.106 0.048 -0.406 0.001 -0.253 -0.192 -0.239 -0.345 -0.520 0.483 -0.284 -0.153
1.000 0.090 0.058 -0.405 -0.383 -0.365 -0.156 -0.282 -0.258 -0.143 0.227 -0.430 -0.194 0.099 -0.493 -0.066 -0.291 -0.454 -0.334 0.125 -0.440 -0.420
1.000 0.875 -0.507 0.015 -0.352 -0.516 -0.478 -0.709 -0.544 -0.105 0.041 -0.406 -0.005 -0.265 -0.224 -0.233 -0.359 -0.544 0.510 -0.288 -0.151
1.000 -0.370 -0.013 -0.303 -0.387 -0.431 -0.602 -0.572 -0.210 -0.012 -0.234 -0.146 -0.253 -0.292 -0.168 -0.362 -0.497 0.430 -0.333 -0.216
1.000 0.368 0.435 0.484 0.509 0.616 0.443 -0.048 0.253 0.347 0.057 0.468 0.441 0.259 0.641 0.730 -0.521 0.507 0.410
1.000 0.437 0.152 0.327 0.351 0.201 0.165 0.709 0.087 0.023 0.521 0.527 0.201 0.634 0.549 -0.322 0.554 0.484
1.000 0.423 0.686 0.556 0.352 0.048 0.391 0.297 -0.028 0.657 0.422 0.342 0.648 0.639 -0.295 0.567 0.523
1.000 0.515 0.535 0.352 0.023 0.137 0.328 0.082 0.314 0.332 0.315 0.493 0.538 -0.383 0.457 0.301
1.000 0.628 0.512 -0.031 0.334 0.356 -0.102 0.445 0.496 0.254 0.670 0.716 -0.468 0.545 0.400
1.000 0.567 0.192 0.336 0.547 -0.010 0.561 0.468 0.386 0.671 0.862 -0.703 0.522 0.339
1.000 0.265 0.168 0.379 0.101 0.372 0.377 0.271 0.595 0.617 -0.340 0.570 0.489
1.000 0.149 0.061 0.384 -0.011 0.499 0.138 0.321 0.310 -0.122 0.322 0.331
1.000 0.053 -0.056 0.446 0.468 0.206 0.599 0.494 -0.311 0.539 0.455
1.000 0.123 0.262 0.193 0.308 0.458 0.503 -0.333 0.398 0.327
1.000 -0.081 0.342 -0.052 0.242 0.184 0.015 0.334 0.373
1.000 0.318 0.436 0.624 0.610 -0.317 0.553 0.499
1.000 0.208 0.693 0.763 -0.603 0.581 0.428
1.000 0.415 0.368 -0.123 0.376 0.416
1.000 0.879 -0.503 0.945 0.808
206
N
J'
ES(50)
H'
1.000 -0.719 1.000 0.724 -0.327 1.000 0.548 0.020 0.893 1.000
Table 61: Correlations between plant features and most abundant gastropod species. NL: Number of Leaf; SL: Sheaths Length; LL: Leaf Length; LW: Leaf Width; LS: Leaf Surface; ShD: Shoot Density; LB: Leaf Biomass; SB: Sheaths Biomass. Apo: Apolochus; Git: Gitana; Aor: Aora; Aor gen.sp.: Aoridae gen.sp; Dex: Dexamine; Aph: Apherusa; Iph: Iphimedia; Gam: Gammaropsis; Isc gen.sp.: Ischyroceridae gen.sp.; Lil: Liljeborgia; Lys: Lysianassa; Orc: Orchomene; Che: Cheirocratus; Gamm: Gammarella; Per: Perioculodes; Pho: Phoxocephalus; Cap: Caprella; Pht: Phtisica. S: Number of species per quadrat; N: Number of Individuals per quadrat; J’: Pielou Index; ES(20); Rarefaction; H’: Shannon-Wiener diversity Index. Signficant values in bold. NL NL SL LL LW LS ShD LB SB Apo Git Aor Aor gen. sp. Dex Aph Iph Gam Isc gen.sp. Lil Lys Orc Che Gamm Per Pho Cap Pht S N J' ES(100) H'
SL
1.000 -0.323 1.000 -0.720 0.648 0.440 0.182 -0.598 0.682 0.018 0.112 -0.619 0.664 -0.375 0.612 -0.236 0.256 0.113 -0.021 0.476 -0.371 -0.033 0.073 0.410 -0.516 0.014 -0.356 -0.187 0.136 -0.189 0.053 -0.298 0.098 0.009 0.181 0.009 -0.030 0.004 -0.029 -0.067 0.172 0.557 -0.137 0.417 -0.309 0.338 -0.166 0.147 -0.207 -0.124 -0.050 0.027 0.009 0.056 0.042 0.228 -0.073 0.082 -0.071 0.204 -0.148
LL
LW
LS
1.000 -0.138 1.000 0.961 -0.023 1.000 0.121 0.278 0.089 0.967 -0.058 0.988 0.805 0.062 0.873 0.475 0.190 0.445 0.060 0.280 0.044 -0.476 0.298 -0.472 0.195 0.138 0.181 -0.508 -0.002 -0.491 -0.244 -0.118 -0.245 0.398 0.170 0.395 0.333 0.119 0.315 0.380 -0.033 0.348 0.138 0.307 0.159 0.073 -0.131 0.127 0.082 0.223 0.041 0.053 -0.075 0.060 -0.532 0.147 -0.475 -0.516 0.120 -0.454 -0.344 0.233 -0.281 -0.322 -0.096 -0.313 0.177 0.034 0.145 0.155 0.181 0.169 0.087 0.272 0.083 -0.210 -0.004 -0.144 0.068 0.162 0.091 -0.104 0.107 -0.063
ShD LB
1.000 0.090 0.058 0.709 0.595 0.178 0.633 -0.191 -0.055 0.492 0.661 0.424 0.518 0.297 0.367 0.025 0.159 -0.381 -0.047 -0.461 0.111 0.629 0.603 -0.204 0.570 0.346
1.000 0.875 0.448 0.051 -0.487 0.196 -0.488 -0.234 0.389 0.320 0.358 0.126 0.117 0.054 0.095 -0.465 -0.483 -0.310 -0.307 0.135 0.165 0.098 -0.183 0.084 -0.093
SB Apo Git Aor Aor gen. sp. Dex Aph Iph Gam Isc gen.sp. Lil
1.000 0.411 1.000 0.104 0.600 1.000 -0.254 0.104 0.314 1.000 0.194 0.684 0.623 0.086 -0.378 -0.322 -0.030 0.355 -0.154 -0.098 -0.092 0.205 0.325 0.755 0.419 0.104 0.288 0.833 0.590 0.104 0.317 0.657 0.406 -0.018 0.103 0.543 0.382 0.214 0.035 0.267 0.088 -0.144 0.133 0.509 0.395 0.233 0.067 -0.020 -0.148 -0.207 -0.420 -0.184 -0.074 0.116 -0.431 -0.507 -0.182 0.176 -0.270 -0.136 -0.125 0.283 -0.222 -0.624 -0.345 0.131 0.206 0.320 0.295 0.182 0.156 0.723 0.490 0.109 0.153 0.722 0.619 0.328 -0.231 -0.419 -0.162 -0.108 0.086 0.629 0.399 0.102 -0.113 0.227 0.263 0.086
1.000 -0.042 -0.101 0.501 0.636 0.465 0.435 0.281 0.538 0.062 0.083 -0.261 -0.083 -0.357 0.302 0.700 0.730 -0.164 0.617 0.414
1.000 0.512 1.000 -0.333 -0.088 1.000 -0.306 -0.080 0.740 1.000 -0.086 0.152 0.572 0.587 -0.145 -0.002 0.443 0.503 0.048 0.059 0.323 0.283 0.191 0.171 0.343 0.349 -0.118 0.037 0.013 -0.018 0.052 0.045 -0.154 -0.133 0.502 0.232 -0.325 -0.506 0.442 0.309 -0.149 -0.091 0.418 0.278 -0.505 -0.558 0.163 0.053 0.236 0.214 -0.062 0.058 0.635 0.710 0.121 0.265 0.564 0.630 0.039 -0.392 -0.171 -0.171 -0.017 0.043 0.573 0.637 0.094 -0.097 0.334 0.394
1.000 0.329 0.203 0.386 -0.068 -0.268 -0.380 -0.258 -0.260 0.491 0.489 0.591 -0.342 0.409 0.142
Lys Orc Che Gamm Per Pho Cap Pht
S
N
J' ES(100) H'
1.000 0.320 1.000 0.441 0.092 1.000 0.071 0.049 -0.032 1.000 0.155 0.179 -0.064 0.279 1.000 -0.125 -0.094 -0.068 0.067 0.266 1.000 0.168 0.118 -0.072 -0.060 0.257 0.407 1.000 -0.191 -0.218 -0.147 0.197 0.177 0.380 0.045 1.000 0.237 -0.124 0.389 0.069 -0.272 -0.031 -0.109 0.158 1.000 0.626 0.466 0.476 0.293 0.174 -0.130 0.046 -0.322 0.288 1.000 0.601 0.220 0.599 0.202 0.140 -0.105 0.092 -0.183 0.446 0.775 1.000 -0.048 0.146 -0.253 0.045 0.240 0.385 0.181 0.228 -0.150 -0.058 -0.367 1.000 0.582 0.504 0.418 0.302 0.196 -0.065 0.069 -0.277 0.262 0.970 0.665 0.031 1.000 0.488 0.498 0.227 0.244 0.310 0.193 0.218 -0.011 0.128 0.718 0.342 0.570 0.773 1.000
207
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