Mapping and quantifying habitat fragmentation in small ... - Springer Link

Environ Monit Assess (2012) 184:693–713 DOI 10.1007/s10661-011-1995-9

Mapping and quantifying habitat fragmentation in small coastal areas: a case study of three protected wetlands in Apulia (Italy) Valeria Tomaselli · Patrizia Tenerelli · Saverio Sciandrello

Received: 6 September 2010 / Accepted: 23 February 2011 / Published online: 15 April 2011 © Springer Science+Business Media B.V. 2011

Abstract In the Mediterranean Region, habitat loss and fragmentation severely affect coastal wetlands, due to the rapid expansion of anthropogenic activities that has occurred in the last decades. Landscape metrics are commonly used to define landscape patterns and to evaluate fragmentation processes. This investigation focuses on the performance of a set of landscape pattern indices within landscapes characterized by coastal environments and extent below 1,000 ha. The aim is to assess the degree of habitat fragmentation for the monitoring of protected areas and to learn whether values of landscape metrics can characterize fine-resolution landscape patterns. The study areas are three coastal wetlands belonging to the Natura 2000 network and sited on the Adriatic side of Apulia (Southern Italy). The Habitat Maps were derived from the Vegeta-

V. Tomaselli (B) C.N.R.—Institute of Plant Genetics, via G. Amendola 165/A, 70126 Bari, Italy e-mail: [email protected] P. Tenerelli European Commission, Joint Research Centre, Institute for the Protection and Security of the Citizen, T.P. 268, via E. Fermi 2749, 21027 Ispra, VA, Italy S. Sciandrello Department of Botany, University of Catania, via A. Longo 19, 95125 Catania, Italy

tion Maps generated integrating phytosociological relevés and Earth Observation data. In the three sites, a total of 16 habitat types were detected. A selected set of landscape metrics was applied in order to investigate their performance in assessing fragmentation and spatial patterns of habitats. The final results showed that the most significant landscape patterns are related to highly specialized habitat types closely linked to coastal environments. In interpreting the landscape patterns of these highly specialized habitats, some specific ecological factors were taken into account. The shape indices were the most useful in assessing the degree of fragmentation of habitat types that usually have elongated morphology along the shoreline or the coastal lagoons. In all the cases, to be meaningful, data obtained from the application of the selected indices were jointly assessed, especially at the class level. Keywords Habitat fragmentation · Landscape metrics · Habitat directive · Natura 2000 network · Habitat mapping · Coastal wetlands · Protected areas

Introduction Habitat fragmentation is a process deriving from both natural processes and human land use activities and affecting both habitat structure and

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function (McGarigal and McComb 1999; McGarigal et al. 2002; Neel et al. 2004). Habitat fragmentation is the main trend of landscape change in several bioregions of the world, mainly due to the expansion of built-up areas into natural environments and the increase of population pressure on natural resources. This is causing a dramatic reduction in biodiversity (Brooks et al. 2002; Fahring 2002, 2003; Hoffmeister et al. 2005) and strong alteration in plant richness and composition (Bascompte and Rodríguez 2001; Chust et al. 2006). Coastal wetlands are one of the most threatened environments, both in the Mediterranean region and worldwide (Gray 1997; Gibbs 2000). Wetlands of the Mediterranean Region are undergoing rapid anthropogenic development. The increasing of human pressures, such as land reclamation for farming and building up and exploitation of water resources, is causing a decreasing surface area of coastal wetlands and the degradation, fragmentation, and isolation of habitats (Valdemoro et al. 2007; Levin et al. 2009). A detailed monitoring of the status of coastal ecosystems is fundamental for supporting dynamic habitat management (Pethicj 1996). Several studies have been carried out to evaluate habitat fragmentation through the application of landscape metrics (Collinge 1996; Hargis et al. 1998; McGarigal and McComb 1999; Plieninger 2006; Aparicio 2008; Geri et al. 2010). The analysis of landscape patterns is tightly related to the observation scale (Forman and Godron 1986; Turner et al. 1989; O’Neill et al. 1991; Wu 2004). Patches may be distinguishable at some scales but not at others (Turner et al. 1989; Rutledge 2003; Corry 2004). Scale may also critically influence the behavior of the landscape metrics, and this “scaledependence” effect may be more or less emphasized depending on the considered metric type (Hargis et al. 1998). In highly disturbed landscapes, biodiversity is often concentrated in small and scattered patches, which provide two important landscape functions: the capture and concentration of scarce resources and the conservation of a high diversity of organisms (Schwartz and van Mantgem 1997; Ludwig

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1999; Laguna 2001; Fischer and Lindenmayer 2002). It is demonstrated that small wetlands as well as large ones are important in maintaining biodiversity (Gibbs 1993; Scheffer et al. 2006). In fragmented contexts, fine-resolution spatial data represent the key element to characterizing landscape heterogeneity. There is a lack of documented investigations demonstrating the application of landscape metrics to fine-resolution data. Generally, when metrics were applied to highly fragmented landscapes, data resolution was so coarse that common fine-scale patterns were undetected (Cook 2002; Corry 2004). The EC Habitats Directive (Council of the European Union 2007) is the main European Union legal instrument concerning biodiversity and nature conservation of natural habitats (Ladoux et al. 2000; Levantis and Kaltsa 2002; Mehtälä and Vuorisalo 2001; Bunce et al. 2008; Mücher et al. 2009). The Habitat Directive, like all the other main European habitat classifications (Paleartic Classification, CORINE Biotopes, EUNIS-Hab), is a physiognomy-based vegetation classification system. The recording of vegetation types in Habitat Maps is the starting point for the understanding of the ecosystems and their conservation status (Haines Young et al. 2000; Kutiel 2001; Berberoglu et al. 2004; Dimopoulos et al. 2005). Nevertheless, some widely recognized habitats are not directly linked to vegetation associations (Rodwell et al. 2002). At the same time, there are several gaps in protection of plant syntaxa by the Habitat Directive (Petermann and Ssymank 2007). The goal of this study is to map the habitat types according to the Habitat Directive classification by integrating high-resolution Earth Observation and ground truth data and to test the behavior of landscape pattern indices in three small protected coastal wetlands characterized by a highly fragmented landscape. This paper aims at identifying a set of metrics useful for the quantification of habitat fragmentation within landscapes with limited extent and characterized by coastal environments and to learn whether landscape pattern indices can characterize fine-resolution landscape patterns.

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Material and methods Study area The areas selected for this study are three wetlands of small extent, sited on the Adriatic side of Southern Apulia (Fig. 1). Torre Guaceto (TG) is a Ramsar site, a Marine Protected Area, and a National Natural Reserve covering an area of about 1,200 ha. According to the EU Habitat and Bird directives, this site is a Special Protection Area (SPA, IT9140008) and a proposed Site of Community Importance (pSCI, IT9140005) of respectively, 548 and 251 ha. The coastal strip is characterized by both rocky and sandy shores. Coastal lagoons occupy only a small surface area, due to the drainage and partial silting that operated in the past century to create spaces for agriculture. These areas are now colonized mainly by reeds. Saline di Punta della Contessa (LS) is a Regional Natural Park covering an area of 1,960 ha. It is an SPA (IT9140003) and a pSCI (IT9140003) of 213 ha. A large part of the protected area is occupied by farmlands. The natural area, consisting

Fig. 1 Study areas

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of a system of coastal lagoons and salt marshes, is bounded between the cultivated areas and the sandy coastline. Le Cesine (LC) is a Ramsar site and a National Natural Reserve covering about 350 ha. It is also a SPA (IT9150014) and a pSCI (IT9150032) of respectively, 647 and 897 ha. The coastal strip consists almost entirely of sandy shores. The wetland is one of the most important of the Apulia Region and is formed by a system of swamps. In addition to the two larger water pools, there are also various channels, marshes, and humid grasslands. In the landscape analysis and mapping, we considered the areas within the SCI boundaries in order to facilitate comparison between the three sites. Vegetation and habitat mapping The studied areas were first delimited on topographic maps at a 1:5,000 scale. Vegetation units were then preliminarily digitized over highresolution orthophotos at a 1:2,000 scale, which allowed the studied landscapes to be represented with 5 m resolution.

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The choice of such a detailed scale is justified by the following considerations: 1. The high fragmentation and heterogeneity of natural and semi-natural environments; 2. The small extent of the studied areas; 3. The absence of pre-existing high-resolution vegetation or land-cover maps. In a second phase, a field survey was conducted in order to validate the Vegetation Map. A vegetation analysis was carried out following the phytosociological method, in accordance with the Zurich–Montpellier school (Braun-Blanquet 1964). More than 300 phytosociological relevés were performed in plots randomly positioned on the field and stratified in order to cover all habitat types occurring in the study areas. According to the Braun-Blanquet method, in each plot the complete list of vascular plant species was recorded and for each species the cover value (percentage of covered soil surface) was assessed and classified according to the ranks reported in Table 1. The plot sizes vary from 5 m2 (annual ephemeral communities) to a maximum of 200 m2 (forest vegetation), depending on vegetation type and microtopography. The relevés were then ranked according to the phytosociological classification (Rivas-Martínez et al. 2001) and related to plant associations. The plant associations were finally used to characterize the vegetation units (syntaxa). The field data were geocoded using a Global Positioning System and integrated in a geodatabase using ArcGis 9.2. The Vegetation Map generated through preliminary photo interpretation was thus ground-truthed on the basis of the vegetation units derived by the phytosociological relevés. The Habitat Map was finally derived from

Table 1 Cover scale according to Braun-Blanquet (1964) Class of cover value

Range of soil cover (%)

r + 1 2 3 4 5

Isolated individuals 75

the Vegetation Map. Each vegetation patch was classified according to Annex I of the Habitat Directive and boundaries between adjacent patches of the same habitat type were dissolved. The adopted habitat classification system can be referred to a hierarchical model with three different information levels: the relevè, the vegetation unit, and the habitat. The three layers are integrated in a geodatabase which allows the periodical updating of the vegetation status and the habitat qualification. Landscape pattern analysis Several spatial metrics were selected in order to assess the landscape structure of the protected areas on the basis of the Habitat Maps. The landscape metrics were computed using the public domain software for spatial pattern analysis FRAGSTATS (ver. 3.3, McGarigal et al. 2002). This software works in a raster environment and allows a series of parameters and output statistics to be set. In this work, we adopted the eightneighborhood pixels criteria for the computation of the metrics and we set the output statistics at two levels, the class (habitat type) and the landscape as a whole. In order to use this tool, the Habitat Map was converted from vector to raster format with a pixel size of 1 m, which is less than the size of the minimum mapping unit and less than the minimum distance between patches. In selecting the most appropriate landscape metrics for the fragmentation assessment, we referred to the following literature. Hargis et al. (1998) evaluated a set of landscape metrics commonly used in the study of habitat fragmentation and analyzed their sensitivity to the patch size, patch shape and spatial arrangement. Botequilha Leitão and Ahren (2002) proposed a core set of Landscape Composition and Configuration metrics as the most useful and relevant for landscape planning. Munroe et al. (2007) quantified meaningful aspects of landscape fragmentation employing three well-known landscape metrics (Patch size, Shape index, and Nearest neighbor distance). Cushman et al. (2008) identified independent components of class and landscapelevel structure, using three measures (Universality, Strength, and Consistency) to evaluate the

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importance of each component and to identify a suite of structure components that together account for the major independent dimensions of landscape structure. Soverel et al. (2010), in evaluating and comparing the forest fragmentation process in 26 of Canada’s National Parks, used a set of effective landscape metrics. Peng et al. (2010) evaluated the behavior of 23 widely used landscape metrics at the landscape level and the results showed that all the metrics were effective in quantifying a certain component of landscape pattern and that they quantify not just a single component but the complexity of several components of spatial pattern. The selected metrics with relative references are reported in Tables 2 and 3. Here follows a brief description. A thorough explanation of these metrics is given by McGarigal and Marks (1995) and Turner et al. (2001). Class level metrics (Table 2) Class area (CA) is the sum of the area of all patches of the corresponding class (habitat type), and is useful when comparing different study areas with the same extent. The percentage of landscape (PLAND) is a more appropriate measure than CA for class area comparison among landscapes of varying sizes. Number of patches (NP) has limited interpretive value by itself; however, it can provide useful information when compared with other metrics such as Patch density (PD) or Mean patch size (MPS). PD has the same basic utility as NP except that it facilitates comparisons among landscapes of varying sizes. Largest patch index (LPI) is the percentage of the landscape comprised by the largest patch; it is a synthetic measure of habitat type dominance. Edge density (ED) is the total length of the patch edge per unit area within each landscape. ED increases with habitat fragmentation, it is a direct measure of habitat fragmentation. This measure is sensitive to grain resolution; it is therefore useful only for comparison between landscapes with a common grain size. ED is also influenced by the patch shape, so landscapes with small patches or irregular shapes will have higher ED than landscapes with large patches or simple shapes (Hargis

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et al. 1998). ED is not sensitive to the spatial distribution of patches. Mean patch size (MPS) is another measure of habitat fragmentation. Patch types with smaller MPS might be considered more fragmented (McGarigal and Marks 1995). MPS is best interpreted in conjunction with PLAND, PD, and patch size variability (Patch size standard deviation, PSSD). A given MPS value can refer either to patches of the same size or to patches of very different sizes. Hence it is very useful to associate MPS to PSSD. PSSD is a measure of patch size absolute variation. Shape index (SHAPE) is a measure of shape complexity which allows the size dependency problem of other shape indices such as the perimeter–area ratio (PARA) to be minimized (McGarigal et al. 2002). SHAPE equals 1 when the patch is maximally compact and increases without limit as patch shape becomes more irregular. Related circumscribing circle (CIRCLE) is an elongation index that attains a minimum value for circular patches and increases for more elongated and linear patches. This index may be particularly useful for distinguishing patches that are both linear and elongated, such as sandy dunes and other coastal environments. Mean proximity index (PROX) is a measure of patches aggregation. It measures the isolation of a patch within a system of patches; it increases as the neighborhood is increasingly occupied by patches of the same type and as those patches become closer and more contiguously distributed. Mean nearest neighbor distance (ENN) defines the average edge-to-edge distance (m) between a patch and its nearest neighbor. ENN has been extensively used to quantify habitats isolation. This index is limited in applicability because it requires landscapes of similar extents and grains for comparative studies. Landscape-level metrics (Table 3) LPI, MPS, and PSSD were used also at the landscape level. Patch richness density (PRD) is a measure of diversity and facilitates comparison among landscapes of different size.



MPS =

PSSD =

Patch size standard

deviation

j=1

ni

 ⎛ n ⎡ ⎞⎤2    aij ⎟⎥  ⎜ ⎢  n ⎜ j=1 ⎟⎥ ⎢  ⎢aij − ⎜ ⎟⎥  ⎝ ni ⎠⎦  j=1 ⎣  

ni

j=1

(100) A E ED = (10, 000) A E = total length (m) of edge in landscape n  aij

LPI =

Mean patch size

Edge density

Largest patch index

Patch density

Number of patches

PL AN D = Pi =

Landscape percentage

(100) A Pi = proportion of the landscape occupied by patch type (class) i; aij = area (m2 ) of patch j of class i; A = total landscape area (m2 ) N P = ni ni = number of patches in the landscape of patch type (class) i ni PD = (10, 000) (100) A n   max aij

CA =

Class area

j=1

 1 10, 000 j=1 aij = area (m2 ) of patch j of class i n  aij aij

Area/density/edge metrics n 

Formula

Index

Table 2 Class level metrics

1 10, 000

 (8)

(7)

(6)

(5)

(4)

(3)

(2)

(1)

Range

PSSD ≥ 0, without limit

MPS ≥ 0, without limit

Soverel et al. (2010)

Soverel et al. (2010)

Botequilha Leitão and Ahren (2002),

Hargis et al. (1998)

ED ≥ 0, without limit

Botequilha Leitão and Ahren (2002)

PD > 0

Munroe et al. (2007)

Botequilha Leitão and Ahren (2002), Soverel et al. (2010)

NP ≥ 1, without limit

0 < LPI ≤ 100

Botequilha Leitão and Ahren (2002)

–

0 < PLAND ≤ 100

CA > 0, without limit

References

698 Environ Monit Assess (2012) 184:693–713

distance

Mean nearest neighbor

Mean proximity

Isolation/proximity metrics

Circle

Shape index

Shape metrics

i=1 g=1

2 hijg

j=1

ni ni = number of patches in the landscape of patch type (class) i that have nearest neighbor

EN N =

(m2 )

ni of patch ijg within specified aijg = area neighborhood of patch ij; hijg = distance (m) between patch ijg and patch ij, based on patch edge-to-edge distance, computed from cell center to cell center n  hij

PROX =

n  n aijg 

pi min pi p j = total perimeter of class i; min pi = minimum  perimeter   of class i n  aij 1− aijs i=1 CI RCLE = ni aijs = area (m2 ) of smallest circumscribing circle around patch j of class i

SH APE =

(13)

(12)

(11)

(10)

ENN > 0, without limit

limit of PROX is affected by the search radius and the minimum distance between patches

PROX ≥ 0 The upper

0 ≤ CIRCLE < 1

without limit

SHAPE ≥ 1,

Botequilha Leitão and Ahren (2002), Munroe et al. (2007)

Cushman et al. (2008), Hargis et al. (1998),

Cushman et al. (2008), Hargis et al. (1998)

Bailey et al. (2007)

Saura and Carballal (2004),

Botequilha Leitão and Ahren (2002), Munroe et al. (2007)

Cushman et al. (2008),

Environ Monit Assess (2012) 184:693–713 699

index

Shannon evenness

index

Shannon diversity

deviation Diversity metrics

Patch size standard

Mean patch size

Largest patch index

Patch richness density

Patch density

Area/density/edge metrics

Index

j=1

i=1

m 

(Pi ln Pi )

Pi = proportion of the landscape occupied by patch type (class) i m (P ln P )  i i SH EI = ln m i=1 m = number of patch types (classes) present in the landscape, excluding the landscape border if present

SH DI =

100 A   A 1 MPS = N  10, 000   2  n A  m   aij −   i=1 j=1 N 1 PSSD = N 10, 000

LPI =

N (10, 000) (100) A N = total number of patches in the landscape m PRD = (10, 000) (100) A m =number of patch types (classes) present in the landscape, excluding the landscape border if present n  max aij

PD =

Formula

Table 3 Landscape-level metrics

(16)

(15)

(19)

(18)

(17)

Botequilha Leitão and Ahren (2002),

PRD > 0, without limit

(14)

Peng et al. (2010)

PSSD ≥ 0, without limit

0 ≤ SHEI ≤ 1

Nagendra (2002), Peng et al. (2010)

Nagendra (2002), Peng et al. (2010)

Peng et al. (2010)

MPS > 0, without limit

SHDI ≥ 0, without limit

Peng et al. (2010)

0 < LPI ≤ 100

Peng et al. (2010)

Botequilha Leitão and Ahren (2002)

PD > 0

References

(4)

Range

700 Environ Monit Assess (2012) 184:693–713

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Shannon diversity index (SHDI) is a common measure of diversity in community ecology. In this work it was applied as a measure of the equitability of the patch type number and of the proportional distribution of area among patch types (McGarigal et al. 2002; Nagendra 2002). SHDI increases as the number of different patch types (habitat richness) increases and/or the proportional distribution of area among patch types become more equitable. Shannon evenness index (SHEI) is another common diversity measure borrowed from community ecology, indicating the evenness of the distribution of area among the different patch types (Nagendra 2002). As SHEI approaches 1, the observed diversity approaches perfect evenness.

Results and discussion Habitat classification Table 4 lists the detected vegetation units (syntaxa) and habitat types with CA and PLAND values, for each site. A crucial issue was to find the correlation between the vegetation units and the habitats reported in Annex I of the EC Habitat Directive. Many vegetation types noticed in the studied areas are not reported in Annex I. All vegetation types belonging to Phragmito-Magnocaricetea Klika in Klika and Novák (1941) class, such as Bolboscenetum compacti or Phragmitetum communis, are not reported in Annex I, and therefore such environmental units were omitted from the Habitats Map. Many of these types are important habitats for birds’ reproduction in coastal wetlands and host a number of rare species at regional scale. In the case of Cladium mariscus communities, we referred to the priority habitat 7210. Petermann and Ssymank (2007) presented a list of the syntaxa which are covered by Annex I within Natura 2000 sites in Germany and also the gaps, intended as the syntaxa, which are threatened or remarkable for protection, not covered by Annex I habitats. The communities belonging to “eutraphent reed and sedge beds”, including the three alliances Phragmition, Magnocaricion,

701

and Glycerio-Sparganion, were found to be almost not covered, while most of the grasslands of coastal environments are included in Annex I in “coastal and halophytic habitats” and “coastal sand dunes and inland dunes” (e.g., 1210, 1310, 1320, 1410, 2110, 2120, 2230, 2240). Carranza et al. (2008) noted that all dune land elements could potentially hold at least one EU habitat of interest, while not all the wetland units contain EU habitats. These syntaxa and the associated habitat types need to be addressed in nature conservation activities at national as well as regional level. This would also contribute to enhancing the coherence of the Natura 2000 network as these syntaxa are often functionally linked to habitats already listed in Annex I (Petermann and Ssymank 2007). Another crucial issue was the correct interpretation of the Habitat Directive for finding the correct correspondence between vegetation units and habitat types. The main tools are the “Interpretation Manual of European Union Habitats” (European Commission 2007) and the “Technical Reports for the Management of Natura 2000 Habitats” (European Commission 2008). Recently the “Interpretation Manual of European Union Habitats present in Italy” has been published (http://vnr.unipg.it/habitat/). However, when adhering strictly to these documents, some types of vegetation which are certainly worthy of preservation cannot be regarded as habitat communities. This is the case of Myrto-Pistacietum lentisci, a typical syntaxon of consolidated dunes, which was formerly included in habitats 5330 or 9320. The Italian manual excludes this solution because of the absence of diagnostic species or structural characteristics. This is also the case of the Cisto-Micromerietea garrigues, which often host some rare or endemic species and were formerly included in habitat 5420 but lack the main guide species Sarcopoterium spinosum. However, pending future amendments of Annex I, these syntaxa need to be addressed by nature conservation activities. So, aiming at testing the behavior of landscape metrics for different vegetation types, here we report Myrto-Pistacietum lentisci for habitat 5330 and garrigues for habitat 5420. The final Habitats maps are reported, for each protected area, in Fig. 2a, b, c.

Annual vegetation of drift lines

Vegetated sea cliffs of the Mediterranean coasts with endemic Limonium spp. Salicornia and other annuals colonizing mud and sand

Mediterranean salt meadow (Juncetalia maritimi)

Mediterranean and thermoAtlantic halophilous scrubs (Sarcocornetea fruticosi) Embryonic shifting dunes

1210

1240

1410

1420

2120

2110

Shifting dunes along the shoreline with Ammophila arenaria (white dunes)

Coastal lagoons

1150a

1310

Habitat

Natura 2000 code POTAMETEA Klika in Klika and Novak (1941); RUPPIETEA J.Tx. 1960 CAKILETEA MARITIMAE R.Tx. and Preising in Braun-Blanquet and Tuxen (1952) Salsolo-Cakiletum maritimae CRITHMO-LIMONIETEA Br.-Bl. in Br- Bl., Braun-Blanquet et al. (1952) Crithmo-Limonietum apuli SAGINETEA MARITIMAE Westhoff et al. (1962) THERO-SUADETEA Rivas-Martínez (1972) JUNCETEA MARITIMI Br.Bl. in Br.-Bl., Braun-Blanquet et al. (1952) Schoeno-Plantaginetum crassifoliae Juncetum gerardi SARCOCORNIETEA FRUTICOSAE Br.-Bl. & R.Tx. ex de Bolòs and de Bolòs (1950) Sarcocornia perennis communities AMMOPHILETEA Br.-Bl. & R.Tx. ex Westhoff et al. (1946) AGROPYRENION FARCTI Rivas-Martínez et al. (1980) Cypero capitati–Agropyretum juncei AMMOPHILETEA Br.-Bl. & R.Tx. ex Westhoff et al. (1946) AMMOPHILION AUSTRALIS Br.-Bl.1921 em. Gèhu, Rivas-Martínez and R.Tx. in Rivas-Martínez et al. (1980) Medicagini marinae–Ammophiletum arenariae

Syntaxon

Table 4 Natura 2000 habitat for each study area with corresponding syntaxa, PLAND and CA values

0.58

1.2

0.3

2.3

0.36

3.4

2.7

0.90

TG PLAND

1.43

2.96

0.73

5.58

0.82

8.52

6.87

2.25

CA

0.36

1.43

14.5

7.14

4.2

–

3.7

12

LS PLAND

0.34

3.28

29.5

15.2

10.9

–

5.53

22.9

CA

–

1.24

0.02

2.06

0.17

–

1.64

12.53

LC PLAND

CA

–

10.5

0.05

18

1.67

–

12.8

112

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Brachypodietalia dune grasslands with annuals Coastal dunes with Juniperus spp.

2240

Sarcopoterium spinosum phryganas

Calcareous fens with Cladium mariscus and species of the Carcion davallianae Quercus ilex and Quercus rotundifolia forests

5420

7210a

a Priority

habitat

SCI (ha) TOT PLAND

9340

5330

Mediterranean temporary ponds Thermo-Mediterranean and pre-desert scrub

3170a

2250a

Malcolmietalia dune grasslands

2230

MALCOLMIETALIA Rivas Goday (1958) Alkanna tinctoria and Plantago albicans communities TUBERARIETALIA GUTTATAE Br.-Bl. in Br.-Bl. and Braun-Blanquet et al. (1940) JUNIPERION TURBINATAE Rivas-Martínez (1975) 1987 Pistacio-Juniperetum macrocarpae ISÖETO-NANOJUNCETEA Br.-Bl. and R.Tx. ex Westhoff et al. (1946) PISTACIO-RHAMNETALIA ALATERNI Rivas Martinéz (1975) Myrto-Pistacietum lentisci Pistacia lentiscus communities CISTO-MICROMERIETEA Oberdorfer (1954) Saturejo-Ericetum manipuliflorae Thymus capitatus communities PHRAGMITO-MAGNOCARICETEA Klika in Klika and Novak (1941) Soncho-Cladietum marisci QUERCETALIA ILICIS Br. Bl. ex de Bolòs and de Bolòs (1950) Pistacio-Quercetum ilicis 251 34

5.6

0.2

1.04

11.6

–

3.1

–

0.8

13.8

0.43

2.56

28.6

–

7.67

–

1.94

213 46.5

–

–

2.3

1.8

–

–

–

–

–

4.95

4.04

–

–

–

–

897 46.7

–

11.9

7.18

9.47

0.15

0.3

0.06

–

–

107

63.8

84.2

1.3

2.5

0.9

–

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a

Fig. 2 Habitat maps for the three study areas: TG (a), LS (b), LC (c)

Spatial pattern analysis Class metrics Area/density/edge metrics The variance of PLAND values per habitat type among different sites (Table 4) can be related to different ecological conditions. Habitat types 1310, 1410, and 1420 are related to meaningful salt rates of water and soil; they have high habitat cover values (PLAND) in LS, revealing an advanced stage of salinization in LS compared to the other sites. Conversely, habitat 7210, linked to oligohaline waters, shows high PLAND values in LC and a scarce presence in TG, due to the low extent of water bodies, while it is absent in LS. The overall distribution of these habitats is equally fragmented in the three sites

and therefore where CA (and PLAND) is higher, PD is also higher. By associating PD values (Fig. 3a) to PLAND and MPS (Fig. 3b), we obtained a better interpretation of the landscape pattern. For example, habitat 1150 in LC has the lower PD value and the higher PLAND and MPS values: this indicates a wider and more continuous habitat distribution compared to the other two sites. MPS values are better understood if compared to patch size variability (PSSD; Fig. 3e), highlighting the degree of uniformity of the landscape pattern. The values of LPI (Fig. 3c) provide a relevant indicator of habitat composition and ecological processes, emphasizing what was already highlighted from the analysis of PLAND: the

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b

Fig. 2 (continued)

halophytic plant communities (habitats 1420 and 1410) have the largest extent in LS, while phytocoenoses of oligohaline waters (habitat 7210) have the largest extent in LC. This phenomenon is related to the higher water salt rates in LS. In the site of TG, these three habitats show a low representativeness as a result of the small extent of the water bodies. ED values, on the whole, are high when PD values are high, indicating a general habitat fragmentation. The high ED values of the sandy shore habitats in TG are due to the alternation of sandy and rocky shores along the coast and the resulting natural fragmentation, while the high ED values of these same habitats in LS are due to a fragmentation process deriving from anthropogenic pressure (especially farming practice; Fig. 3d).

Shape metrics SHAPE shows the lowest value for the habitat type 3170 (Fig. 4a), since Mediterranean temporary ponds tend to assume a nearly circular shape, while the highest values of SHAPE are reached in the habitat types 1210 and 2110, due to the fact that sandbanks have a natural arrangement in linear belts (except in TG, where sandbanks alternate with rocky shores). As a whole, when considering coastal sandy dunes habitats, low values of SHAPE indicate fragmented habitats, as from continuous belts they split into short segments. This characteristic of coastal environments is well emphasized by the CIRCLE index (Fig. 4b). Coastal habitats are normally arranged along the coastline forming sequences of parallel belts, and hence CIRCLE assumes high values when

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c

Fig. 2 (continued)

applied to sandy dune habitats and also to environments related to coastal wetlands. When analyzing these habitat types, characterized in nature by linear and elongated shapes, low values of CIRCLE can be referred to fragmentation due to anthropogenic or natural disturbance. Isolation/proximity metrics Generally, landscape metrics quantify the structure of the landscape within the designated landscape boundary only. Consequently, the interpretation of these metrics and their ecological significance requires an acute awareness of the landscape context and of the openness of the landscape (McGarigal and Marks 1995). This is particularly true for proximity and isolation indices. PROX values must be related to the resolution and the extent of the analyzed

landscape map (spatial scale). In this study, habitat type 9340 (TG) shows the highest PROX value (Fig. 5a); however, this value is highly influenced by the presence of only two patches, which are tightly adjacent. At this small scale, PROX values have no, or scarce, meaning when applied to forest habitat types. Therefore, PROX values of forest or shrub habitat types as 5330 and 5420 are not meaningful if not applied at larger scales. In this study, at the given scale, PROX values have a discrete significance when applied to coastal habitat types, sensu strictu. In fact, these habitat types have a very localized distribution (often falling within the boundaries of pSCI, SPA, or protected areas) and to better assess their conservation status it is necessary to analyze their patterns at a local scale.

Environ Monit Assess (2012) 184:693–713 Fig. 3 Class area/density/edge metrics distribution per habitat type and study area: a density; b, c, e area; d edge

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a

b

c

d

e

High PROX values should correspond to low ENN values; however, PROX expresses general distribution in the landscape, while ENN expresses the distance between the nearest patches. The highest ENN values are shown by the most

isolated habitat types (Fig. 5b). ENN values have only an indicative and relative meaning for forest habitats, while they have a meaning in the case of habitats such as coastal wetland environments which are limited to a few sites at the regional

708 Fig. 4 Class shape metrics distribution per habitat type and study area: a SHAPE; b CIRCLE

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a

scale and have no neighbors within a radius of several kilometers. Landscape metrics Area/density/edge metrics The highest values of PD at the level of the landscape as a whole were found in TG (Table 5). Considering that TG also has the largest habitat richness (PRD), this site was found to be the one with the major degree of landscape fragmentation. As shown in Table 4, MPS increases from TG to LC, showing a trend towards an increasingly largegrained landscape. The highest value of PSSD was found for LC, showing a high degree of patch size variation in the landscape.

Fig. 5 Class isolation/proximity metrics distribution per habitat type and study area: a isolation; b proximity

a

b

Diversity metrics The highest SHDI value appears in TG, in accordance with the highest PD and PRD values. The highest value of SHEI was found for LS, in accordance with the lowest number of habitat types and the homogeneous distribution of patch size. Landscape fragmentation analysis In order to analyze the landscape fragmentation we relied on the class level indices. The data obtained from the application of the selected indices were jointly assessed. ED is a metric commonly used in the study of habitat fragmentation. A primary outcome of habitat fragmentation is an increase in habitat

b

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709

Table 5 Landscape metrics per study area SITE

TG

LS

LC

Total SIC area Total area covered by habitat types SIC area covered by habitat types (%) PRD NP PD LPI MPS PSSD SHDI SHEI

251 84.38

213 96.77

897 415.94

32.78

45.43

46.37

14 143 56.97 40.63 0.59 1.38 2.09 0.79

9 70 32.86 24.96 1.38 2.22 1.82 0.82

12 188 20.95 46.75 2.21 8 1.74 0.7

edges, which is effectively quantified by ED values (Hargis et al. 1998). However, to be meaningful, ED values have to be related to NP, PLAND, and SHAPE. ED seems to be particularly influenced by the patch shape. Landscapes with small patches or irregular shapes have higher ED values than large landscapes with simple shapes (Hargis et al. 1998). As a whole, when high ED and low SHAPE values appear, a highly fragmented landscape can be defined; when high ED values are observed in conjunction with high SHAPE, a meaningful shape complexity characterizes the landscape. In this study, despite the high PLAND values of the priority habitat 1150, both in LS and in LC, the ED index behaves differently, showing a higher value in LS; together with a high PD value, this indicates a higher habitat fragmentation in LS. MPS can also serve as a habitat fragmentation index, although it has to be associated to PLAND, PSSD and PD (especially if the studied areas do not have the same extent). For example, considering two landscapes with the same PD and MPS but with very different levels of variation in patch size, a greater PSSD variability indicates less uniformity of pattern. In our case, MPS and PSSD values indicate uniformity in patch size in LS and a dominance of a few large patches in LC. A comparative assessment of these four indices can be useful to compare the fragmentation degree of a habitat either between different areas or in the same area at different times. CIRCLE can highlight the degree of elongation of the habitat type and, joined to SHAPE, gives an idea of the actual morphology of the patches. High

values of SHAPE jointly with high values of CIRCLE indicate an elongated morphology, while high SHAPE values with medium/low CIRCLE values refer to a shape complexity of the patches, probably due to external pressure. SHAPE and CIRCLE have to be assessed differently when considering coastal habitats such as sandy dunes. These habitats have high values of SHAPE and CIRCLE in normal conditions and, as a whole, low SHAPE values indicate a high fragmentation, as from continuous belts they split into shorter segments. PROX is a good measure of patch isolation (often deriving from fragmentation), but it is affected by patch size and the spatial distribution of patches. Landscapes characterized by large patches have lower values and greater PROX variance than small patched (and/or more fragmented) landscapes. Landscapes with aggregated patches have higher PROX values than landscapes with dispersed patches. PROX values are best interpreted in conjunction with PLAND, PD, and MPS values. However PROX values have scarce meaning, or may even be misleading, when applied to forest habitat types at this fine scale and small extent. PROX values of forest habitats are meaningful only when applied at the regional scale, as those habitats are generally distributed across larger extents and interact over longer distances. High PROX values are also affected by the “clumpiness” effect (Hargis et al. 1998). ENN is another measure of patch isolation, and isolation is a consequence of habitat fragmentation. Nevertheless, ENN does not adequately describe the spatial distribution of patches. A landscape with clumped patches can produce the same value as a landscape with widely dispersed pairs of patches (Hargis et al. 1998). ENN values decrease with increasing disturbance when patches are uniformly dispersed, but are low when patches are aggregated. Therefore, ENN is affected by the “clumpiness” effect.

Conclusions The analysis of the landscape patterns at fine spatial resolution and in areas of small extent is a crucial item in biodiversity assessment in the

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Mediterranean Region, where the landscape is highly fragmented and the main biodiversity is harboured in small patches, often nested in a matrix of agricultural land or marginal areas (Benton et al. 2003; Plieninger 2006; Concepción et al. 2008). Many protected areas and a large part of SPA and SCI belonging to the Natura 2000 network have small extents (a few hundred hectares). During the last century, in Central and Southern Italy, wetlands have been lost and degraded due to the conversion to arable land and the impact of the of agricultural systems intensification on ecosystem dynamics. This has also caused fragmentation and isolation of habitats. The maintenance of the biodiversity in the Mediterranean coastal environments requires appropriate tools for monitoring and land management, but also adequate land use policies. Management strategies for conserving biodiversity in wetlands should not only prioritize large water bodies, but also emphasize the preservation of ecological integrity of smaller wetlands. In addition, when referring to small wetlands, land use policies implemented in the surrounding landscapes can strongly affect the ecosystem integrity. Possible measures aiming to reverse present trends in habitat fragmentation/destruction could be the inclusion of buffer zones in the protected areas, the application of less intensive agricultural practices in the surrounding arable lands, the regulation of the underground water usage. Future efforts should therefore address the monitoring and evaluation of the impact of various land management strategies and policies on wetland functions. Institutions involved in nature conservation require tools for rapid and accurate assessment of habitat patterns which can be used for monitoring the habitat status. The analysis of landscape metrics appears to be an effective and standardized method for this purpose. The performance of landscape metrics for fine-scale data is largely unknown or generalized from coarse resolution applications. In spite of the extensive applicability of the landscape pattern metrics, few studies focused on the application to highly fragmented patterns at fine scale in small areas. In this study we tested whether landscape pattern indexes computed starting from high-resolution habitat maps

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comply with intuitive characterizations of highly fragmented landscapes at fine scale. Among the issues highlighted in this work, we found that: (a) the most relevant results are those related to the specialized habitat types, closely linked to coastal lagoons environments; (b) in analyzing the coverage and distribution in the landscape of these highly specialized habitats, we must take into account some ecological factors which have great significance in these environments, such as water and soil salinity (e.g. habitat types 1310, 1410, 1420) or, in other cases, the soil texture (e.g. habitat types 1210, 2110, 2120, 1240); (c) sensitive habitats, such as 1310, 1410, and 1420, showed the highest degree of fragmentation in the LS site, where there is a higher human pressure (agricultural practices, presence of industrial activities and infrastructure, lack of surveillance); (d) CIRCLE and SHAPE are very useful in assessing the degree of fragmentation of habitat types that usually have elongated morphology on the shoreline or the coastal lagoons (e.g. 1210, 2110, 2120, 1310, 1410, 1420); e) forest habitat types, like 2250 and 5330, show a higher fragmentation in TG, also because in TG these habitat types cover a greater extent of the landscape; however these data should be compared with analyses performed on larger areas and at larger scale; (f) the results are strongly influenced by the spatial resolution of the Habitat Maps, but overall by the extent of the whole analyzed landscape (scale of analysis). It has been widely recognised that spatial pattern is scale-dependent (Turner et al. 1989; O’Neill et al. 1991; Gardner et al. 1989) and that spatial scale has strong effects on the quantification of many landscape pattern metrics (Wu 2004; Saura and Pascual-Hortal 2007). We expect that, when analyzing habitats with widespread distribution, such as forest habitats, on a regional scale, the results will be more reliable and that, in general, the wider the study area is, the more reliable the results are. Scale has a relevant influence depending not only on the type of observed habitat, but also on the process under observation. More generally, there is no single correct scale for studying landscapes, and we can select different scales of interest, depending on the object or the process under investigation (Forman and Godron 1986; Turner et al. 2001; Stephens

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et al. 2003). However, and this is the point at issue, when considering some specialized habitats that are normally scattered at a regional scale, such as vegetation types of coastal wetlands, an analysis at small scale and high spatial resolution could be significant, especially when carrying out comparisons in time or space. When analyzing landscape inventories of the same region, they can be characterized by different extent, spatial resolution and accuracy level. The multi-scale and multi-resolution approach can overcome both the issues of finding the correct scale of observation and of comparing data deriving from different sources. Therefore, there is ample scope for improvement in the interpretation of habitat spatial pattern and ecological processes at different scales, the influence of the observation scale on different landscape metrics could be also better understood. In this work, the computed metrics were interpreted to facilitate comparison between different study areas, with particular focus on landscape fragmentation, and to better understand the links between landscape patterns and ecological factors. The proposed methodology can be also applied to monitor future habitat conditions and landscape processes. The detection of landscape pattern change over time would allow the analysis of different trends and scenarios evaluations and would support the choice of various land management systems and nature conservation practices. Acknowledgements This study is part of the INTERREG Project (European Program Interreg III-A Greece– Italy 2000–2006, Misure 3.1) entitled “INFO-NAT: Integrated software development for monitoring and management in NATURA 2000 protected areas in Greece and Italy—Pilot implementation in natural ecosystems of Greece and Italy”.

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