Dipartimento di Biologia, University di Padova, via Trieste 75, 1-35121 Padova, Italy ... density and biovolume data were employed; the Bray & Curtis index, ...
49
Hydrobiologia 337 : 49-68, 1996 . © 1996 Kluwer Academic Publishers . Printed in Belgium .
Seasonal variation in the composition and rate of change of the phytoplankton community in a deep subalpine lake (Lake Garda, Northern Italy) . An application of nonmetric multidimensional scaling and cluster analysis Nico Salmaso Dipartimento di Biologia, University di Padova, via Trieste 75, 1-35121 Padova, Italy Received 10 October 1995 ; in revised form 30 April 1996 ; accepted 4 June 1996
Key words : phytoplankton, seasonality, community change rate, nonmetric multidimensional scaling, cluster analysis, Lake Garda
Abstract Seasonal variations and spatial homogeneity of the phytoplankton community were followed, from spring 1991 to spring 1992, in four pelagic stations of a large deep subalpine lake (Lake Garda, Northern Italy) . Both cluster analysis and nonmetric multidimensional scaling (NMDS) applied to Bray & Curtis' dissimilarity matrices computed on density and biovolume data were employed ; the Bray & Curtis index, calculated between pairs of chronologically contiguous samples, was also used as a measure of the community change rate over the temporal succession . In the tree diagrams obtained both from density and biovolume data, six groups of different size have been identified, with ordered sequences of samples within them. Superimposition of the results of cluster analysis on the NMDS configurations has allowed interpretations of the trajectories of the samples as a chronological and cyclical succession of compositionally homogeneous groups . A clear relationship between the community change rate and stability of the water column has been assessed . The specific composition of the six groups has been discussed in relation to environmental variables and in terms of adaptive strategies . During winter (winter-spring group) the turbulence of the water and the availability of nutrients favour the development of colonial Bacillariophyceae (Fragilaria crotonensis Kitton, Tabellaria fenestrata (Lyngb .) Kiitz ., Aulacoseira spp .), Cyanophyceae (Planktothrix agardhii (Gom .) Anagn. et Kom. and Planktolyngbya limnetica (Lemm .) Kom .-Legn . et Cronb.) and Cryptophyceae . In late spring group, with the stabilisation of the water column and silicon depletion, the diatoms give way to small, opportunistic species (Ankyra judayi (G . M . Smith) Fott, Cyclotella spp ., Chroomonas acuta Utermhol) and larger species (Ceratium hirundinella (0 . F. Mull .) Dujardin) . The three summer groups are characterised by a development of Chlorophyceae (chiefly Chlorococcales), Cyanophyceae (mainly Chroococcales), Cyclotella spp . and Dinophyceae. With the autumn destratification the summer community undergoes a rearrangement principally in favour of Cyanophyceae and Cryptophyceae .
Introduction Over the year phytoplankton communities undergo changes in both their quantity and specific composition . Such changes may be repeated on a more or less regular basis from one year to another (annual cycles), showing comparable features according to the type of lake . The first observations on this matter date from the early 20th century and have been summarised by
Hutchinson (1967) . In more recent years there have been many examples proving the existence of such regular patterns of change (e .g . Lund, 1954 ; Round, 1971 ; Bartell et al ., 1978 ; Bailey-Watts, 1982 ; Sommer, 1987), making it also possible to formulate some general models of development concerning lakes in different trophic states and/or geographical location (Reynolds, 1984 ; Rott, 1984 ; Harris, 1986 ; Sommer et al ., 1986) .
50 There are few examples of temporal development of phytoplankton, with identification of typical seasonal groups, regarding the deep lakes south of the Alps . The only lake for which a historical series exists is Lake Maggiore (Ruggiu, 1989) . In the case of Lake Garda a regular series of studies comprising phytoplankton has only begun recently . This study aims to further knowledge of phytoplankton development in the biggest of the Italian lakes (Lake Garda) and in particular (i) to point out seasonal development of the community with respect to environmental factors and (ii) to identify the dominant species typical of compositionally homogeneous periods . Analysis of the phytoplankton data are carried out using both classical cluster analysis and techniques that are not generally employed in phytoplankton studies (nonmetric multidimensional scaling) . Thus a further important aim of the study is (iii) to evaluate the usefulness and effectiveness of this method of ordination in describing seasonal changes in the specific composition of phytoplankton expressed in terms of both density and biovolume . The strategy of analysis and numerical methods adopted basically follow those proposed by Field et al . (1982) ; a recent updating of this strategy has been carried out by Clarke (1993) . The biological data are analysed first and the results are discussed and interpreted on the basis of environmental variables . This approach, which contrasts with direct gradient analysis, has a long history in the field of ecology (e.g . Williams & Lambert, 1959) and is also applied with multivariate methods (for a general discussion see Gauch, 1982) .
General characteristics of Lake Garda Lake Garda is the largest (49 x 10 9 m3 ) and most extensive (368 km 2 ) of the Italian lakes . Along with lakes Maggiore, Lugano, Como and Iseo it forms part of the group of deep lakes situated south of the Alps in what is known as the subalpine lake district . On the basis of bathymetric values Lake Garda may be clearly divided into two basins - northwestern and southeastern-divided by an underwater ridge connecting the Sirmione peninsula with Punta S . Vigilio (Figure 1) . The northwestern basin is the larger and deeper of the two ; in its northern part the shores descend at sharp slopes and the bottom spreads over 20 km at depths from 300 to 350 m (maximum depth) . In the southeastern basin, on the other hand, the maximum
t4 1
1r4_ J
Figure 1 . Bathymetric map of Lake Garda (from Barbanti, 1974,
modified) and location of the sampling stations .
depth is around 81 m and the shape is nearly conical ; despite its extent, this basin represents only a small portion of the overall volume of the lake (less than 7%) . The low ratio between the surface of the catchment area and the surface of the lake (6.1) explains the long theoretical water renewal time (about 27 years) in Lake Garda with respect to other deep lakes in the district . The results of the most recent limnological studies carried out on the lake after the work by IRSA (1974) are reported in Salmaso & Cordella (1994), Cordella & Salmaso (1993) and Chiaudani & Premazzi (1990) . These studies show that, on the basis of the usual trophic parameters (OECD, 1982 : transparency, phosphorus and chlorophyll-a concentrations), the lake appears to be in an oligo-mesotrophic condition . However, recent episodes of cyanophyte algal bloom reported in recent years (Salmaso et al ., 1994) would seem to indicate a worsening of the lake's trophic condition .
51 Table 1 . Sampling dates
1
12 Mar 91
10
17 Sep 91
1 3 4
26 Mar 91 16 Apr 91 18 Jun 91
11 12 13
08 Oct 91 05 Nov 91 26 Nov 91
5 6
09 Jul 91 23 Jul 91
14 15
17 Dec 91 10 Feb 92
7 8
06 Aug 91 20 Aug 91
16 17
25 Feb 92 17 Mar 92
9
03 Sep 91
18
07 Apr 92
Materials and methods Methods in the field and laboratory The data considered in this study refer to samples collected between March 1991 and April 1992 in four stations, two of which are situated in the northwestern basin (Brenzone and Tom) and two in the southeastern one (Bardolino and Lazise) (Figure 1) . Sampling was carried out every two weeks between July and September and at intervals of 14-28 days during the rest of the year. It was not possible to collect samples in May 1991, nor in January 1992 due to dense fog . In all, 18 expeditions were undertaken (Table 1) . On the water column measurements of temperature, pH, conductivity and dissolved oxygen were carried out using a multiparameter probe . Transparency was estimated, with the aid of a bathyscope, using a 20 cmdiameter Secchi disk . The SURFER 4 programme was used to draw the isopleths of temperature . The phytoplankton counts and analyses of nutrients were carried out on integrated samples collected along surface strips of about 100 m using a membrane hand pump working at a depth of 10-15 cm from the lake's surface . The phytoplankton samples, totalling 72, were preserved in acetic Lugol's solution (Saraceni & Ruggiu, 1974) and the algal cells were counted on a Zeiss IM35 invertoscope adopting the methodology described by Lund et al . (1958) . In all the samples, at least 100 individuals (corresponding to an error of approximately ±20%, Lund et al ., 1958) of the most common phytoplankton species were counted ; less abundant species were counted on the entire or half sedimentation chamber. The count, including the colonial forms, was carried out by enumerating single cells . Biovolume values were calculated from recorded abundance and specific biovolumes approximated
to simple geometrical solids (Rott, 1981) . Counts were made of both determinable and non-determinable fractions : the latter are almost exclusively represented by ultraplankton (small spherical and flagellate cells of maximum linear dimension less than or around 4 ,um) . The adopted taxonomic classification was that proposed by Bold and Winne (1985) . Species identification was carried out following the manuals of the series Siifiwasserffora von Mitteleuropa founded by A . Pascher and Bourrelly (1972) . Some groups were identified using other specific keys : Huber-Pestalozzi (1955) for the Euglenophyceae and Huber-Pestalozzi & Fott (1968) for the Cryptophyceae . The taxonomy of Oscillatoriales has been updated following Anagnostidis & Komarek (1988) and Komarkova-Legnerova & Cronberg (1992) and that of Chlorococcales following Komarek & Fott (1983) . Chemical analyses for determination of the nutrients are only available for the Brenzone, Torri and Bardolino stations; they were carried out by the PMPULSS 25 of Verona (Castellani et al ., 1994) following the methods described in APHA (1989) . Analyses of almost all the samples are only available for silica (Si02-Si) and nitrate (N03-N), while analyses of phosphorus (orthophosphate and total P) were carried out less frequently . Methods of numerical analysis According to Feoli & Orloci (1991) ordination and classification should always be applied jointly to obtain more reliable structure evaluations . Therefore both nonmetric multidimensional scaling (NMDS) and cluster analysis (average linkage) were carried out on the samples on the basis of their phytoplanktonic composition . Both these techniques require, at the outset, a symmetrical matrix of similarity (or dissimilarity) expressing the level of identity among all the possible pairs of samples analysed . Many coefficients may be used for quantitative study of the compositional differences among pairs of samples (cf. Sneath & Sokal, 1973 ; Orloci, 1978 ; Legendre & Legendre, 1984), but not all of them are useful in analysing the count tables examined in this context . One of the striking features of the phytoplankton counts is the fact that many species are represented in just one or a few samples, so that many of the data in the tables (reporting values of density or biovolume of s species with respect to n samples) consist of zeros . The measures taking account of the presence of double zeros in calculations of similarity between
52 two samples, including the euclidean distance or the correlation coefficient based on deviations from the mean, are not sufficiently robust to be applied to these types of matrices (Orl6ci, 1978 ; Field et al ., 1982 ; Clarke & Ainsworth, 1993) . Orloci (1978 : 46), for example, presents an extreme case in which, according to the euclidean distance, two samples with no species in common appear to be more similar than two samples having the same specific composition . One of the measures of dissimilarity exempt from the abovementioned defects is the Bray-Curtis index (Bray & Curtis, 1957) : s
IYhj - YhkI h=1 Sjk = s
(Yhj +Yhk) h=1
where Yhj and Yhk are the `scores' of h species in samples j and k respectively and 8jk is the dissimilarity between samples j and k on the basis of all the species s . This index not only has a robust monotonic relationship with ecological distance, but also a robust linear (proportional) relationship until ecological distances became large (Faith et al ., 1987) . The values of the Bray-Curtis index vary between 0 (two identical samples) and 1 (no species in common) and are more determined by comparisons among numerically more abundant species . The use of untransformed data therefore appears to lead to definition of dissimilarity matrices expressing the contribution of a few species, which are not necessarily the most informative . The transformations able to provide differentiation among samples, also on the basis of differences among the rarer species, include logarithmic transformation, Yhj = log(Xhj + 1), and root-root transformation, Yhj = X,~ 4 . The results obtained from application of these two transformations are rarely distinguishable (Clarke & Green, 1988) . Root-root transformation was used in this study as it has the advantage that the dissimilarity, when calculated with the BrayCurtis index, is invariant to scale changes (Field et al., 1982), that is the results do not change if, for example, cells ml -1 or cells 1 -1 are used as expressions of phytoplankton density (for a general discussion of these two types of transformation see Clarke & Green, 1988 ; for applications see Gray et al ., 1988 and Kroncke & Rachor, 1992) . The two modes in which algal abundance is expressed - density and biovolume - underline different aspects and properties of the phytoplankton com-
munity ; the contribution of the organisms to the entire community may differ greatly depending on the type of variable utilised (cf. Ruggiu & Saraceni, 1977) . Algal cells sizes in this study varied by over four orders of magnitudes (from a few µm 3 to over 80000 µm3 ) . Cells at either end of the range can certainly not be considered physiological and/or ecological equivalents (Taylor & Wetzel, 1988) . Thus, in order to characterise the phytoplankton dynamic taking account of the two expressions of its abundance, calculation of the Bray-Curtis index of all the possible comparisons n(n - 1)/2 among n = 72 samples was carried out on both the density and biovolume data relative to identifiable algae (ultraplankton were excluded), thus obtaining two dissimilarity matrices, Ad and Ob respectively . Species found on at least two occasions were used in the calculations (55 taxa on a total of 71) ; the rarest taxa, found on one occasion only, are considered found by chance and so contribute little to explaining the community's evolution . Starting from the two dissimilarity matrices, cluster analysis (average linkage, Orloci, 1978) and nonmetric multidimensional scaling (NMDS, Kruskal & Wish, 1978) were undertaken on the n samples . The NMDS has proven advantages in analysing ecological communities with respect to other classical ordination methods (for a comparison of the various ordination methods - metric and nonmetric - see Prentice, 1977 ; Minchin, 1987 ; Kenkel & Orl6ci, 1986). Nevertheless, NMDS has only rarely been applied in studies of phytoplankton communities (some applications can be found, for example, in Harris & Piccinin, 1980 and Tafas & Economou-Amilli, 1991) . In the context of sample ordination on the basis of specific composition, the primary objective of NMDS is to construct a configuration of points in a specified number of dimensions p < s, such that the rank order agreement between the inter-point distances djk in the configuration (measured as euclidean distances) and the dissimilarity values bjk is maximised . The greater the resemblance between two samples on the basis of the index used, the closer they will be in the configuration . The solution is determined through an iterative process starting from an initial configuration and its departure from the optimal solution (perfect monotonic relationship between dissimilarities and distances) is evaluated using a measure called "stress" (Kruskal, 1964) . The choice of the number of dimensions of the configuration was made, on the basis of the decrease in stress with the increase in dimensionality, according to the criteria suggested by Kruskal & Wish (1978) .
53 It is important to note, in interpreting the configurations obtained in this manner, that the resulting axes are completely arbitrary and are subject to rotation and reflection . Such transformations are possible as they have no influence on the relative positions of the n samples in the configurations . The Bray-Curtis index, calculated between pairs of chronologically contiguous samples, was also used as a measure of the community change rate over the temporal succession . This is equivalent to taking account of the comparisons bj k alone in the dissimilarity matrices Ad and Ab, so that, for each station, k = j + 1 ; j = l . .n - 1 . This method of estimating community turnover is not new in ecological studies ; other indexes were used in this estimation, for example based on the complement of Whittaker's index of association (Tokeshi, 1990) ; similarly, stability indexes were also used, calculated, among others, according to Stander's similarity index, SIMI (Elber & Schanz, 1989) . Calculation of the triangular matrices Ad and Ab of the Bray-Curtis index and the community change rates was undertaken using a collection of programmes written by the author in Turbo Pascal . In NMDS and cluster analysis the respective modules in the SYSTAT (Wilkinson, 1990) package were used . In this package the initial configurations for NMDS analysis are obtained starting from metric multidimensional scaling and calculation of stress according to the method reported by Kruskal (1964) ; lastly, the final configuration is rotated to its principal axis . The results of the final configurations in two dimensions were confirmed using other initial configurations (for example, the first two axes of the three-dimensional configurations, cf. Field et al ., 1982) .
Results Phytoplankton development Figure 2 depicts the seasonal fluctuations of phytoplankton which are based on average values of density and biovolume recorded in the four sampled stations . Figure 2a reports total density and biovolume values (ultraplankton included) whereas Figures 2b and 2c show density and biovolume values subdivided by classes . Total density values reach over 20000 cells ml -1 in August and September; biovolume values, on the other hand, present various peaks (1500-3000 mm 3 m -3 ) in the spring of the two years and in August (Figure 2a) .
The ultraplanktonic component contributes an important fraction of overall phytoplankton density (up to 12000 cells ml -1 on September 3) ; in terms of biovolume, however, this component is less important (with m-3) values lower than 110 mm 3 due to its modest size (cells around 10-15 µm 3 ) . The largest number of organisms identified refer to Chlorophyceae (30), Cyanophyceae (16) and Bacillariophyceae (13) . The remaining groups contribute between 2 and 4 species each . On a lakewide basis, the most frequent taxa, found in all the sampling dates, are represented by Ceratium hirundinella (O .F. Mull.) Dujardin, Chroomonas acuta Utermhol, Closterium aciculare T. West, Closterium pronum Breb., Fragilaria crotonensis Kitton, Mougeotia sp ., Planktolyngbya limnetica (Lemm .) Kom .-Legn . et Cronb., Planktothrix agardhii (Gom .) Anagn . et Kom . and Staurastrum cf. planctonicum Teiling . Apart from the two Closterium species, all these taxa contribute, in different and for more or less long periods of the year (as is shown in subsequent sections, cf . Table 2), to a significant proportion of the total density or biovolume . The least abundant classes are Euglenophyceae and Chrysophyceae ; all the other groups became important in at least one sampling date (Figures 2b and 2c) . Cyanophyceae and Chlorophyceae contribute to a great portion of the high summer density values ; the high spring and summer biovolume values are due to Bacillariophyceae, Cryptophyceae and Dinphyceae. Taxa developing with average density values (calculated, for each date ; on the basis of the four stations) greater than 2500 cells ml - ' (cf . also Figure 2b) are represented - in chronological order - by Sphaerocystis schroeteri Chod . (6/8/91, 4950 cells ml - ') and Coelastrum spp . (including C. reticulatum (Dang .) Senn . and C. polychordum (Kors .) Hind ., 6/8/91, 3970 cells ml-1 ), undetermined Tetrasporales (August, 60506580 cells ml -1 ), Cyclotella spp . (20/8/91, 8635 cells ml -1 ), P limnetica (September, 4670-5660 cells ml - ' ; November, 5240-7020 cells ml -1 ) and P agardhii (5/11/91, 2500 cells ml -1 ) . Taxa developing with average biovolume values greater than 250 mm3 m-3 (cf. also Figure 2c) include - in chronological order - F crotonensis (26/3/91, m-3 ; 400 mm3 16/4/91, 390 mm 3 m -3 ), Tabellaria fenestrata (Lyngb.) Kiitz. (26/3/91, 480 mm 3 m' 3 ; 16/4/91, 450 mm 3 m -3 ), Cryptomonas spp . (26/3/91, 540 mm 3 m -3 ; 17/3/92, 635 mm3 m -3 ), C. hirundinella (18/6/91, 1865 mm 3 m -3 ), Cyclotella spp . (18/6/91, 700 mm3 m-3 ; 20/8/91, 1600 rum3 m -3 ), S. schroeteri (6/8/91, 440 mm 3 m -3 ), Coelastrum spp . (6/8/91,
•
0 O O I
O
1
Biovolume (mm3 m -3) (X 1000)
12/3 26/3 16/4 18/6 9/7 23/7 6/8 20/8 3/9 17/9 8/10 5/I1 26/11 17/12 10/2 25/2 17/3 7/4 MM'
I
O
n
O 12/3 uu ; 26/3 16/4 18/6 11 9/7 II1 23/7 6/8 20/8 3/9 17/9 8/10 5/11 26/11 i 17/12 10/2 25/2 11 17/3 7/4 11 i
v~ • I
v,
1
o
~.
oo0o -
o
I
o
El V., (DUE] 0
I
o
Density (cells ml -1) (X 1000)
d
I
26/11 17/12 10/2 25/2 17/3 7/4 O 0
5111
O 12/3 26/3 16/4 1816 9/7 23/7 6/8 20/8 3/9 17/9 8/10
O
N O
0
W O
•
c~
• + -
0
N
--N W 0 0 0 Biovolume (mm3 m -3 ) X1000
U
Density (cells ml -1 ) X1000 W
55
Bray-Curtis index 0 .00 LA 05 BA_ 05 TO 05 BR 05 BR04 T0704 BA 04 LA 04 LA 03 BA 03 TO 03 BR 03 BR 14 BR 13 LA 13 TO 13 T07 14 BR 02 LA 14 BA 14 BR 16 BR 15 BR 18 LA 15 BA 15 BA 16 T0716 TO _15 LA 16 BR 17 LA_18 T0_18 BA 18 BA 17 TO 17 LA 17 LA 02 BA 02 TO02 To 01 LA 01 BA 01 BR_01 BA 13 LA 12 T6 12 BA 12 BR 12 LA 11 BA 11 TO 11 SR 11 BR 10 TO lU BA 1u LA 1u LA 09 BA 08 TO OB BR 09 TO 09 BA 09 BR 08 LA 08 LA O7 TO O7 BR 07 BA 07 BR 06 TO_ 06 BA 06 LA 06
0 .50
0 .75
~~
I
~~
j
Figure 3a. For legend see p.56 .
56 Bray-Curtis index 0 .00 18 15 16 16 15 15 16 TO15 LA- 16 BR 17 LA 18 TO 18 BA 18 BA 17 TO_ 17 LA 17 BR 02 BR 03 BR 13 BR 14 TO 14 T07- 13 LA 14 BA 14 LA 12 LA 13 BR12 TO 12 BA 12 BA 13 3 LA 03 BA 03 LA 0 1 TO-0 I TO 02 LA 02 BA 02 BA 01 BR 01 TO_ 11 BR 11 BA 11 LA 11 M-1 0 T071 0 BR 10 LA 08 TO 09 BR 09 BA 09 BR 08 To 08 BA 08 LA 10 LA 09 LA 07 TO 07 BR 07 BA 07 BR 06 TO 06 BA 06 LA 06 LA 05 BA 05 TO 05 BR 05 BR 04 T07 04 BA 04 LA 04
0 .50
0 .75
BR BR BR BA BA LA TO
Too
J
Figure 3b Figure 3. Tree diagrams showing the results of the classification of samples from Lake Garda on the basis of phytoplankton density (a) and biovolume (b). Single samples were marked with a code indicating the station (BR : Brenzone; TO : Tom; BA: Bardolino; LA : Lazise) and the sample series (1-18 : cf. Table 1) . The groups were numbered taking account of the chronological or seasonal sequence ; the numbers correspond to the groups : winter-spring (0), late spring (I), summer 1(2), summer 11(3), summer 111(4) and autumn (5) . Regarding the single sample Y, see explanation in the text .
57 380 mm3 m-3 )and Mougeotia sp . (17/3/92,1290 mm 3 m -3 ) . Further information on the phytoplankton basic data is reported in Salmaso & Cordella (1994) . Cluster analysis The results of cluster analysis are reported in Figures 3a and 3b . In the tree diagram obtained from the density data (Figure 3a) three main groups corresponding to the samples collected in winter-spring, late springsummer and late summer-autumn may be identified at dissimilarity levels between 0 .5 and 0 .6 . At a lower hierarchical level the structure is further characterised ; at dissimilarity values around 0 .4, 6 groups of different sizes may be easily identified, with ordered sequences of samples within the groups . In such groups the 4 stations are always interlinked with respect to single collection dates, with the exception of two samples discussed below . Moreover, the collection dates within the groups are always in chronological sequence . The groups were numbered pointing out chronological or seasonal order. The most numerous group is group 0 (winter-spring) ; in this group the sampling dates, as well as being in chronological sequence, also include dates from comparable seasons though over two years . This group in fact includes 9 sampling dates from spring 1991 (from 12/3 to 16/4) and winter-spring 1991-1992 (from 26/11 to 7/4) . A distinction between the periods of spring development in the two different years can only be noted at a level of dissimilarity lower than that used in defining the major groups . Group 0 does not include the sample taken on 26/11 from Bardolino station and forming part of the autumn group . Considering the seasonal sequence, this large group is followed by two others - group 1, late spring and group 2, summer I - each of which consists of a single sampling date (respectively 18/6 and 9/7) . Group 3 (summer II) comprises two series of samples (23/7 and 6/8) ; in this group the sample from Lazise on 6/8 appears to present peculiar characteristics, being associated with the group at a slightly higher level of dissimilarity with respect to the level chosen beforehand . Group 4 (summer III) includes three series of samples from late summer (from 20/8 to 17/9), while group 5 (autumn) comprises two series of autumn samples (8/10 and 5/11) . The number and composition of the groups identified in Figure 3a can also be observed in the tree diagram obtained from the biovolume data (Figure 3b), with the exception that group 0 is now defined at a slightly higher dissimilarity value ; moreover, it also
includes the samples from 5/11 previously included in group 5 (autumn) which now consists of a single date . With respect to the previous tree diagram, a greater distinction now emerges in group 0 between the 1991 and 1992 samples . The size of the groups identified in the two cluster analyses and their chronological development may be effectively pointed out by considering the ordination of the sample points in the two NMDS configurations . Nonmetric multidimensional scaling The results of NMDS are reported in Figures 4a and 4b ; the final solutions have been reached after 15 and 11 iterations respectively regarding density and biovolume . The configurations obtained on the basis of density and biovolume appear to be directly comparable between one another . The large decrease in stress values in the transition of the configurations from one to two dimensions (from 0 .25 to 0 .13 and from 0 .26 to 0 .15, respectively regarding density and biovolume), with respect to the lower decrease in the transition from two to three dimensions (with stress values respectively of 0 .10 and 0 .11), suggest that the relationships between the sample points are adequately represented in a two-dimensional map . The adequacy of the two-dimensional configurations is confirmed by the near perfect correspondence with the first two axes obtained from the three-dimensional one (not reported here) . In Figures 4a and 4b the points corresponding to chronologically contiguous samples were united by lines in order to point out the temporal sequence of development over the four sampling stations . Starting from the month of March 1991 the sampling dates follow a cycle which returns almost exactly, with the samples of April 1992, to the starting point . Rotation of the configuration along the principal axis in both cases determined a meaningful direction of the first and second axes . Along the first axis the winter-spring samples are distinguished from those of late summer, while along the second axis there is a distinction between the samples of June-early July and those of the autumn . The trajectories delineated by the chronological succession of the samples are more extensive for the southern stations with respect to the northern ones . The differences, however, are limited to the period covering the June and July samples (series 4-6) ; in the other months the trajectories do not show substantial differ-
58 (a)
(b)
5-autumn (11-12
5-autumn (11 0 wint -spring (1-3; 13-18
4-sug
r
0-winter-spring (1-3; 12-18
Y
YO AM
3-s r
r 1-late spring (4) 2-summer 1(5)
ummer Aids I
: Torri o -A-Bardohno O. .. Iazise
Aids I
Figure 4 . Ordination of samples in the two-dimensional NMDS configurations obtained on the basis of density (a) and biovolume (b) values . The
points corresponding to the four stations were united by lines in order to point out the cyclical trend. The two ordinations point out the groups of samples (indicated by group number, name and sample series, but for further details see text) identified in the tree diagrams in Figures 3a and 3b ; the continuous and dotted lines include samples that in Figures 3 have been grouped by a level of dissimilarity respectively of around 0.4 and 0 .5 .
ences between the two basins, so that on the whole they can be superimposed . In the two NMDS graphs the distance between two successive dates is directly proportional to the differences in phytoplankton composition and shows different values over the course of the year . The different degree of aggregation of the points in the two configurations suggests the presence of zones - which may be more or less easily identified - with a greater level of stability and homogeneous internal composition . By overlapping the results of cluster analysis in the previous section with the two configurations it is possible to distinguish objectively the zones of greater stability from those of greater compositional replacement . An estimate of the community change rate among pairs of samples in the temporal succession is reported in Figures 5a and 5b. The results obtained from the two estimates of algal abundance are equivalent. In terms of its basic features the trend is repeated in all four stations ; the most evident discrepancies can be observed from the 13th sampling date onwards (late November), but after this date the index shows values that are rather limited, between 0 .10 and 0.35 . The trend may be better illustrated by considering the average values of the comparisons between one date and the following one calculated on the basis of the four values available. Two periods of greater change may be identified (with the Bray-Curtis index > 0 .4, from April 1991 to early August and between 17/9 and
8/10) alternating with two periods of relative stability (< 0 .3, from late August to mid-September and from November to April 1992) . The remaining dates show an intermediate rate of change (between 0 .3 and 0 .4) and include March 1991 and the transition from early to late August . The average dissimilarities between successive samples appear to be directly comparable with the corresponding average distances calculated in the two configurations (Figure 5c) . Dominant species in the seasonal groups The dominant species, with percentages of density and/or biovolume equal to at least 5% with respect to the totals of the different groups defined by the cluster analyses, are reported in Table 2 . As far as group 0 (winter-spring) and group 5 (autumn) are concerned, the results reported in Table 2 are almost indistinguishable, independently of the fact that the calculations of the biovolume percentages were carried out on the basis of the groups defined by the tree diagram in Figure 3a (density-based) or Figure 3b (biovolume-based) . The only point worth noting is the fact that, in the case of the groups defined by the tree diagram in Figure 3b (on the basis of which the biovolume calculations were made), when the samples of November 5 (series 12) are incorporated in group 0, F crotonensis becomes less important in group 5 (autumn) .
59 Table 2 . Average values of density (cells ml - ') and biovolume (mm3 M-3) of the identified component (ultraplankton excluded) calculated separately for single groups defined on the basis of the tree diagrams in Figures 3a and 3b and taxa found with percentages of density and/or biovolume equal to at least 5% of the totals of the different groups. The symbols +, * and ® indicate the dominant taxa, respectively in terms of density, biovolume or both. Abbreviations of taxa: Ankyr juda, Ankyra judayi (G . M . Smith) Fott; Apha sp ., cf. Aphanothece sp . ; Aulac spp ., Aulacoseira spp . (A. islandica (0 . Mull .) Simonsen, A . granulata (Ehc) Simonsen); Cerat hiru, Ceratium hirundinella (0. F. Midi .) Dujardin ; Chroo acut, Chroomonas acuta Utermhol ; Coela spp., Coelastrum spp . (C. reticulatum (Dang.) Senn, C. polychordum (Kors .) Hind .) ; Coelo kuet, Coelosphaerium kuetzingianum Nag . ; Crypt spp ., Cryptomonas spp . (chiefly C . ovata Ehr.) ; Cyclo spp., Cyclotella spp . ; Dinob dive, Dinobryon divergens Imhof; Dinob soci st, Dinobryon sociale Ehr. var. stipitatum (Stein) Lemm. ; Fragi crot, Fragilaria crotonensis Kitton ; Gleno sp., Glenodinium sp., Mouge sp., Mougeotia sp. ; Plank agar, Planktothrix agardhii (Gom .) Anagn . et Kom. ; Plank limn, Planktolyngbya limnetica (Lemm.) Kom .-Legn et Cronb . ; Perid sp ., Peridinium sp . ; Sphae schr, Sphaerocystis schroeteri Chod . ; Staur plan, Staurastrum cf . planctonicum Teiling; Tabel fene, Tabellaria fenestrata (Lyngb.) Kutz . ; Tetra ind, Tetrasporales 0 winter-
Average dens . Average biov.
2 summer I
3 summer
4 summer
spring
1 late spring
5 autumn
II
III
2830 950
3615 2925
3440 625
11595 1240
15635 1395
7575 305
*
*
*
Fragi crot Tabelfene Aulac spp
*
Chroo acut Mouge sp
® ED
Plank limn Plank agar
+ ®
Crypt spp Ankyrjuda
+
+
Cerat hire Cyclo spp Staur plan Dinob soci st Aphan sp Sphae schr Perid sp Tetra ind Coela spp Coelo kuet Gleno sp Dinob dive
From Table 2 the cyclic character in the progressive replacement of the dominant species with the chronological succession of the groups appears distinctly . In the following descriptions, density and biovolume values reported for the single taxa refer to average values calculated within the single groups defined respectively in Figures 3a and 3b .
*
The stability of the large group 0 (winter-spring) appears to be due to the constant presence of colonial Bacillariophyceae (F crotonensis, 200 cells ml -1 , 145 mm3 m -3 ; TT fenestrata, 35 cells m1 -1 , 125 mm3 m _3 ; Aulacoseira spp ., 55 cells ml -1 , 45 mm3 m -3 ) and Cryptophyceae (Cryptomonas spp ., 225 cells m1 -1 , 190 mm3 m -3 ; C. acuta, 445 cells m1 -1 , 60 mm 3
60
0 .6 0 .5
0 .3 0.2 0.1
44
cis
0.2 .NM`eN%Dt-oo Nen'T n rN0? ~Or --wry-_4l1 i ' NA4y) ~b l-L - -~NM~ v)~CI~COckd,-~ Figure 5 . Community change rate estimated by the Bray-Curtis index (6, k ) calculated, for each of the four stations (dotted lines), on couples of chronologically contiguous samples in terms of density (a) and biovolume (b) data; axis x shows the compared sample series (cf. Table 1); the continuous line joins the average values calculated for each single date . (c) shows the average distances (obtained starting from single values of dik for the four stations) calculated in the two NMDS configurations among groups of successive dates .
m -3 ) . Among the Chlorophyceae there is a prevalence of Mougeotia sp . (145 cells ml -1 , 180 mm3 my 3 ), while among the Cyanophyceae, P. limnetica (760 cells m1 -1 , 15 mm 3 m -3 ) and P. agardhii (665 cells ml-1 , 55 mm3 m -3 ) also become prevalent, particularly in terms of density . Though present in a smaller quantity (c . 3% of total biovolume), Asterionella formosa (40 cells m1 -1 , 30 mm 3 m-3) may also be mentioned among the colonial Bacillariophyceae in this group . With regard to biovolume alone, the greater differentiation between the 1991 samples and those of 1992 in group 0 (Figure 3b) is not due to differences in the community's specific composition, but above all to a different distribution of the prevalence of the larger species in the two years . With equal composition and in terms of biovolume, in the 1991 samples there was a greater prevalence of F. crotonensis, T fenestrata and A . formosa, while in 1992 Aulacoseira spp . and Mougeotia sp. were more important . The following two groups, both of which comprise a single series of samples, are represented by a community undergoing rapid change. In group 1 (late spring) the populations of colonial Bacillariophyceae and Cyanophyceae give way to small (50300 µm3 ) species (Cyclotella spp ., 2290 cells ml -1 , 700 mm3 m -3 ; C. acuta, 255 cells ml -1 , 50 mm 3 M -3 ; Ankyra judayi, 240 cells ml - 1, 10 MM 3 M-3) and large (around 50000 µm 3 ) ones (C. hirundinella, 35 cells ml -1 , 1865 mm 3 m -3 ) . In group 2 (summer I) there is a continuing presence of Cyclotella spp. (580 cells ml -1 , 115 mm3 m -3) and C. hirundinella (0.7 cells ml-1 , 35 mm3 m -3) while an increase in Cyanophyceae can be observed (especially with colonial forms attributable to the Aphanothece genus, 1640 cells ml -1 , 2 mm 3 m-3) ; an important contribution to the biovolume and density values is made by Mougeotia sp . (165 cells ml -1 , 245 mm3 m -3 ) . The remaining two summer groups (3 and 4 : summer II and Ill) are characterised by notable development, particularly in terms of density (cf. Figure 2b), of small size species (cells < 300 µm 3 ) belonging mainly to Chlorophyceae, Cyanophyceae and Bacillariophyceae . This development, as we shall see, has had serious repercussions on the chemical and physical characteristics of the waters . On the basis of both density and biovolume values the summer II group is characterised by the presence of colonial Chlorophyceae (S. schroeteri, 3390 cells m1-1 , 375 mm3 m -3 ; undetermined Tetrasporales, 2705 cells ml -1 , 100 mm3 M -3 ; Coelastrum spp ., 1645 cells ml -1 , 200 mm3 M -3 ) ; in terms of density values the Cyanophyceae
61 also take on importance (cf . Aphanothece sp ., 1200 cells m1-1 , Coelosphaerium kuetzingianum, 615 cells m1 -1 ), while the high biovolume values are due especially to Dinophyceae with Peridinium sp . (120 mm3 M -3 ) . The samM -3 ) and C. hirundinella (95 mm3 ple from Lazise on August 6 (indicated as 3' in Figures 3a and 3b, not considered in Table 2) appears to be disconnected from this group due to the lower presence, in terms of density, of S. schroeteri, cf. Aphanothece sp . and C. kuetzingianum in favour of colonial Cyanophyceae attributable to the Microcystis genus ; Carteria sp. and Cosmarium sp . also contribute to this sample in terms of biovolume . In the summer III group there is a prevalence, in density -1 , and biovolume, of Cyclotella spp. (4010 cells ml 665 mm, 3 m' 3 ) and undetermined Tetrasporales (2830 cells ml -1 , 85 mm 3 m -3 ) . P. limnetica (3970 cells ml -1 ), C. kuetzingianum, (1720 cells ml-1 ), Coelastrum spp. (875 cells ml -1 ) and P. agardhii (745 cells ml -1 ), also contribute to the density values . Among the Dinophyceae, as well as C. hirundinella (130 mm3 m -3 ) and Glenodinium sp. (70 mm3 m-3 ) also contribute to the biovolume values . In group 5 (autumn) the density values show a drastic reduction in Chlorophyceae and Bacillariophyceae and the community return gradually, with the dominance of Cyanophyceae and Cryptophyceae, to a composition more similar to that of the winter-spring group . As well as these two classes, C. hirundinella (35 mm3 ), M -3 Mougeotia sp. (15 mm3 m -3 ) and Dinobryon divergens (15 mm.3 m -3 ) also contribute to the biovolume values . Environmental variables In Figure 6 the values of some of the environmental variables were superimposed on the ordination of samples obtained from NMDS . For the sake of brevity, and for the obvious reason of equivalent results, the values were superimposed on one configuration alone (the one obtained from the density data, cf . Figure 4a) . In Table 3 the same variables were reported as average values calculated within the single groups . In Figure 6 and Table 3 the values of phosphorus compounds were not taken into account as they are nearly always found at concentrations lower than the detectable limit (in the case of P04-P) or because they are present with incomplete determinations (in the case of total phosphorus ; average annual epilimnetic concentrations of this component are in any case around 11 Mg TP 1 -1 , Salmaso & Cordella, 1994) .
There are important correlations between all the environmental variables and the chronological evolution of the samples in the NMDS configuration . The temperature values (Figure 6a) clearly separate the samples from the autumn and winter-spring groups from those of the warmer months ; the distinction is further confirmed by the lower values of pH and dissolved 02 and the higher transparency levels measured in the first two groups (Figures 6b, 6c, 6g ; Table 3) . The lowest values of conductivity are to be found in the warmest period (Figure 6d) . Regarding this decrease, it is reasonable to hypothesise a link with the biogenic precipitation of calcite (cf . Ostuki & Wetzel, 1974) . Chemical analyses currently being collected (including determinations of Cat + and HC03) may clarify better the degree of influence of these processes on the observed trends . The concentrations of Si02-Si (Figure 6e) show high values (over 500 pg Si 1 -1 ) only in the winterspring group while concentrations of N03-N (Figure 6f) show greater variations over the seasonal succession of the groups, even though, on average, they present low concentrations during the warmer period, particularly in late summer (Table 3) . Further information on the environmental variables is reported in Salmaso & Cordella (1994) .
Discussion Nonmetric multidimensional scaling appears to be effective for summarising and defining the most important features of temporal evolution of phytoplankton . In smaller and shallower lakes this approach had facilitated identification of the deviations in chronological succession (Salmaso et al ., 1995), as well as providing direct comparison of the community's dynamics in following years . The characteristics of the configurations obtained in this way may be considered one of the attributes of the phytoplankton communities, on a par with its member organisms . Figures 4a and 4b demonstrate a strong seasonal component in the changes in the community's composition . The varying rate of change is, in both configurations, at the basis of discrete regions of movement (sensu Allen et al ., 1977) constituted by samples separated by low values of di k . Application of cluster analysis allows one to delimit these regions objectively, pointing out the presence of chronologically contiguous samples within them . The arrangement of the groups at the higher hierarchical levels of the tree diagrams
62 Table 3. Average values of the major environmental variables calculated separately for single groups defined on the basis of the tree diagrams in Figures 3a and 3b . In the winter-spring and autumn groups the values in brackets refer to average values calculated on the basis of the groups defined by the tree diagram in Figure 3b (biovolume-based) . Regarding N03-N and SiO2-Si the averages do not include the values for Lazise as chemical analyses of nutrients are not available for this station 0
1
winter spring
late
2 summer
3 summer
4 summer
spring
I
II
III
Temperature (° C)
9 .9 (10 .4)
20.0 8 .75
24.0 8.68
16 .3 (19 .3)
8 .37 (8 .37)
23 .1 8 .64
24.0
pH Oxygen (%)
116 209
118 209
119 207
8 .42 (8 .43) 91(87)
Cond. (pS cm -1 20°C) _1 ) Si02-Si (µg Si 1
98(97) 214 (212) 548(518)
8 .69 113
302 (291) 10 .5 (10.3)
31 450
140 98
193 37 77
200 (200) 234 (109)
N03-N (µg N 1-1 ) Secchi disk (m)
39 143 6 .1
6 .6
6.2
4 .9
and superimposition of the results of cluster analysis on the NMDS configurations allows one to interpret the trajectories of the samples as a chronological succession of homogeneous groups representing more or less long periods . In the sequence of these groups the differences in composition found between the various sampling dates mask the differences in composition that may be found among the various survey stations, particularly between the northern (Brenzone and Tom) and southern (Bardolino and Lazise) stations . The size of the groups identified, their temporal succession and their influence on the chemical and physical characteristics of the waters appear to be directly related to thermal stratification (Figure 7) . It is well known how changes in the composition of the species tend to reflect critical changes in the environment, particularly changes connected with the stability of the water column (e.g . Harris & Piccinin, 1980 ; Viner, 1985 ; Harris, 1986) . The large winter-spring group develops in correspondence with the lake's period of water mixing and homogeneity (Figure 7) . Over this time span surface temperatures range from 8 to 13°C . The three successive groups (late spring, summer I and summer II), forming part of a period of a high rate of change in the phytoplankton community, develop in correspondence with heating and stabilisation of the water column . Surface temperatures rise from 17-18°C to 24°C and the thermal structure is characterised by sharp temperature gradients . The summer III group develops in correspondence with a well defined epilimnion . Separation between this group and the following one (autumn) is clearly marked by rapid destratification of the water column .
5 autumn
156(71) 8 .5 (8.2)
The influence of the water column's stability on the phytoplankton community may be pointed out by considering the relationship between the community change rate (measured with the Bray-Curtis index between successive samples, cf. Figures 5a and 5b) and the average thermal gradient, calculated between 0 and 10 m, for the two dates compared (Figure 8) . Owing to the considerable dispersion of values in correspondence with the homeothernvic situation the relationships are not truly linear ; however the significant association between the two parameters is confirmed both by correlation coefficients (r equals 0 .60 and 0 .52 for relationships based respectively on density and biovolume ; P < 0.01) and Spearman correlation coefficients (r8 equal to 0 .62 for both relationships ; P < 0 .01) . With stabilisation of the physical environment the changes observed in the phytoplankton community are more strongly controlled by biotic interactions among organisms (e .g. Reynolds, 1988a; Sommer, 1991) . Complete mixing of the water during the cold months, along with decreased metabolic activity, attenuates and dilutes in depth the effects of algal activity . With greater stability of the water column the organisms can determine, on the other hand, progressive modification of the environment, making the transition possible from a community basically dominated by external perturbations (group 0) to a community controlled also by processes of internal regulation (groups 1-5) . There is evidence that release from physical control brings about an acceleration in the degree of change in the phytoplankton community, a change which is regulated by competition for resources segregrated along vertical gradients . The intensity of the observed modifications is also connected with greater metabolic activity in
63
(b)
(a) r
0
0
,
c ° e
a
°
° O
0
~~
00
a
A
ad
0
O 0 (c) ++
o0
Temperature t
+
(d)
;
pH
° X
X
+~
++
I
t
t
+
x
x
x
X
Xj
x
-----------------
+ +++
+
x xl` XX
++ + +
X
++
oxygen
(%)
XX X
i
Figure 6. NMDS ordination of samples on the basis of density values (cf . Figure 4a) with superimposed symbols of linear dimension proportional
to the values of the selected variables . The smaller and larger symbols represent the minimum and maximum values of the different variables . Temperature: min. - 8.0, max. - 26.3'C; pH: min. - 8.10, max . - 8 .84; oxygen %: min. - 80, max. - 128%; conductivity (20 0 C): min . - 190, max . - 222 µS cm-1 ; N03-N: min. - 23, max. - 472µg 1 -1 ; SiO2-Si: min. - 23, max. - 865µ.g 1-1 ; Secchi disk: min . - 3.0, max . - 20.1 m . In Figures (e) and (f) the symbols relating to the samples from Lazise are lacking as chemical analyses of nutrients are not available for this station; moreover, in Figure (f) the N03-N data for series 6 (July 23) are lacking .
64 (a)
0 1-,
-5 -10
1-1
4-15 P--20 N
A -25 -30
Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr
0
u
1( 1 1 1 I
(b)
I
-5 f. -10 -15 -20 -25 to
-30
Figure 7 .
to
t~ A ~ ~ Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr I
I
I
Isopleths of temperatures surveyed, from 0 to 30 m, in the stations of Torri (a) and Bardolino (b),
presence (at superficial water strata) of higher temperatures and a favourable light regime and hence greater reactivity of the phytoplankton to variations in environmental stimuli. The increased influence of the phytoplankton's activity during the summer, in the presence of a stable, isolated superficial layer, appears evident considering its effects on the variables that are more closely linked with abundance and photosynthetic activity (pH and 02, Figures 6b and 6c), transparency (Figure 6g) and depletion of nutrients (Figures 6e and 6f) . Levels of pH and the 02 saturation show the highest values during the warmest period and are closely related to phytoplankton abundance . Regressions of the pH values and the 02 saturation on values of total density (normalised by logarithmic transformation) appear to be highly significant (r 2 equal to 0 .37 and 0 .28 respectively ; P < 0 .01) . The common effect of photosynthetic processes on the two variables is expressed by a high correlation coefficient between the two (r = 0 .74; P < 0.01) . Phytoplankton particles are also fundamental in controlling water transparency; regression between the Secchi disk values and total phytoplankton density (both normalised through log-
arithmic transformation) is in fact highly significant (r2 =0 .43 ;P
