ORI GIN AL PA PER

Biogeographic patterns of spiders in coastal dunesalong a gradient of mediterraneity

Jose C. Carvalho • Pedro Cardoso • Luıs C. Crespo • Sergio Henriques •

Rui Carvalho • Pedro Gomes

Received: 29 November 2010 / Accepted: 27 January 2011 / Published online: 8 February 2011� Springer Science+Business Media B.V. 2011

Abstract The Iberian Peninsula is recognized for its high levels of species richness,

rarity and endemicity. Our main aim was to study biogeographic patterns of spiders in

coastal dunes along a gradient of mediterraneity. Distance-decay of similarity, nestedness

and co-occurrence metrics were used to explore spider’ distribution patterns. A similarity

analysis was performed in order to obtain a hierarchical classification of sites. Indicator

species analysis was conducted to identify indicator species for the various clustering

levels of the site typology. The differentiation among assemblages was further explored by

non-metric multidimensional scaling (NMDS) and analysis of similarities (ANOSIM).

Assemblages’ similarity among sites decreased with climatic/geographic distance. The

observed values of nestedness metrics (T and NODF) were not significant, while the

co-occurrence metrics (C-score and Checkerboard units) were higher than expected by

chance. Cluster analysis showed that spider’ assemblages were structured along a gradient

from North to South, forming four geographically distinct clusters. ANOSIM tests and

NMDS supported the biogeographic patterns identified by cluster analysis. Several indi-

cator species were found for the different levels of the hierarchic site typology. Spider

assemblages revealed a high degree of biogeographical structure along the Atlantic coast of

the Iberian Peninsula. The coast is a ‘‘biogeographic crossroad’’, encompassing faunistic

elements of different origins. The hierarchic typology of sites is generally consistent with

the major biogeographic regions and the thermoclimatic belts recognized for the region.

J. C. Carvalho (&) � P. GomesCBMA – Molecular and Environmental Centre, Department of Biology, University of Minho,Gualtar Campus, 4710-057 Braga, Portugale-mail: josecarvalho@bio.uminho.pt

P. CardosoSmithsonian Institution, National Museum of Natural History, Washington, DC, USA

P. Cardoso � L. C. CrespoAzorean Biodiversity Group – CITA-A, University of Azores, Angra do Heroısmo, Portugal

S. Henriques � R. CarvalhoDepartment of Biology, University of Evora, Evora, Portugal

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Biodivers Conserv (2011) 20:873–894DOI 10.1007/s10531-011-0001-8

Our results indicate that the climatic gradient and historic factors played a key role in the

current spiders’ biogeographic patterns.

Keywords Biogeography � Mediterranean � Iberian Peninsula � Coastal dunes �Indicator species � Spiders

Introduction

The Iberian Peninsula has long been recognized for its high levels of species richness,

rarity and endemicity, being considered as one of the most important areas for conservation

included in the mediterranean biodiversity ‘‘hotspot’’ (Myers et al. 2000). Several factors

have been invoked to explain the high biodiversity of the Iberian Peninsula, such as the

particular biogeography, high environmental heterogeneity and the occurrence of impor-

tant historic events. This region comprises two distinct biogeographic regions, Eurosibe-

rian (Northern Iberia) and Mediterranean (Centre and Southern Iberia). Hence, the Iberian

Peninsula represents the southern limit of the distribution for many Eurosiberian taxa,

and the northern limit for many Mediterranean taxa (Gonzalez et al. 2009). The Iberian

Peninsula is characterized by high environmental heterogeneity due to climatic and

physiographic complexity, which are important determinants of the composition and

structure of communities (Stefanescu et al. 2004; Jimenez-Valverde and Lobo 2007).

Historic events have played also an important role in shaping current biogeographical

patterns. The Messinian Salinity Crisis (*5.5 Ma) caused the desiccation of the Medi-

terranean Sea, allowing the exchange of terrestrial animals between the Iberian Peninsula

and Northern Africa (e.g. Cabrero-Sanudo and Lobo 2006). Later, the Iberian Peninsula

played an important role as refugia during glacial-interglacial cycles of the Pleistocene,

resulting in the differentiation and speciation of many taxa (Gomez and Lunt 2007;

Jimenez-Moreno et al. 2010).

The dunes of the Atlantic coast of the Iberian Peninsula have been recognized as

a priority area for conservation due to the large number of rare or endemic species (Lomba

et al. 2008; Maltez-Mouro et al. 2010). Moreover, coastal dunes are one of the most

threatened systems due to human disturbance, such as urbanization and tourism, and

climatic changes (Meulen and Salman 1996; Heslenfeld et al. 2004). The reduction of the

current levels of biodiversity loss depends on our ability to understand the biogeographic

patterns in order to develop efficient conservation programs in the context of human usage

(Whittaker et al. 2005). This knowledge is also crucial for predicting and monitoring the

potential impacts of climate change. Invertebrate communities of coastal dunes are par-

ticularly sensitive to the potential effects of human disturbance and climatic changes, due

to the harshness of the environmental conditions (wind exposure, salt spray, high solar

radiation, extreme temperatures) where they live (Comor et al. 2008; Bonte and Maes

2008). Furthermore, being a linear and usually fragmented habitat, costal dunes pose a

problem for the dispersal of specialized species dependent on it (Bonte et al. 2004). This

linearity and fragmentation influences the source-sink dynamics of metapopulations, fur-

ther increasing the vulnerability of species (Bonte et al. 2003a).

In this work we study the biogeographic patterns of spider assemblages in coastal dunes

along the western Atlantic coast of the Iberian Peninsula. Spiders were chosen as a model

group because they are speciose, abundant, easy to sample and play an important role in

shaping terrestrial arthropod communities (Wise 1993). Spiders have been pointed as good

candidates for bioindication studies since they respond sensitively to environmental

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changes and anthropogenic impacts (Marc et al. 1999; Entling et al. 2007; Finch et al.

2008; Cardoso et al. 2010). Despite their functional role in ecosystems, spiders have been

one of the most neglected groups in conservation biology and biogeography. This lack of

knowledge might have negative consequences to ensure the representativeness and com-

plementarity principles of conservation.

By using spiders as a model we intend to obtain more quantitative knowledge on the

biogeographic patterns of coastal species distribution necessary to develop efficient con-

servation strategies. Due to the strong climate gradient characteristic of the Atlantic coast

of the Iberian Peninsula and its complex geological history, we hypothesize that climate

and historic events are potential important determinants of biogeographic patterns of spider

assemblages. In this line of research, we established the following aims: (1) to identify

distribution patterns of spiders; (2) to analyse biogeographic affinities among the coastal

dunes using spiders as a model and compare these to previously described biogeographic

regions and climatic conditions; (3) to identify characteristic species for delimited regions;

(4) to relate the delimited regions with the distribution range of spider species.

Materials and methods

Study area and sampling sites

The western Atlantic coast of the Iberian Peninsula is dominated by a gradient of medi-

terraneity from North to South. The Northern coast is included in the Eurosiberian bio-

geographical region, while the Centre and South are included in the Mediterranean

biogeographical region (Costa et al. 1998; Rivas-Martınez et al. 2004a). Three thermo-

climatic belts have been recognised by the Worldwide Bioclimatic Classification System:

Temperate, Mesomediterranean and Thermomediterranean (Rivas-Martınez et al. 2004b).

Sampling was conducted in the grey dunes area of each site, i.e. interior fixed dunes

with herbaceous vegetation according to the CORINE biotope classification. Grey dunes

demand special conservation concern, being considered a priority habitat according to the

European Union Habitats Directive. Sampling took place in 18 sites, along a total distance

of 445 km of the Portuguese Atlantic coast, in 2008 and 2009 (Fig. 1). One of the most

striking geographic features of the coastline is its alignment with the latitude axis, which

results in a latitudinal climatic gradient of mediterraneity, characterized by a decrease of

precipitation and an increase of temperature and the period of summer drought, from North

to South (see Appendix 1 for the location and climatic characteristics of the studied sites).

Biological sampling design

We used a standardized and optimized semi-quantitative sampling protocol developed for

Mediterranean spiders (Cardoso et al. 2008a, b, 2009a; Cardoso 2009). Field procedures

were carried out during May and June, when spider species richness and abundance are

highest in Mediterranean ecosystems (Cardoso et al. 2007). This protocol uses different

collecting methods (e.g. pit-fall, sweeping, beating and hand collecting), however we used

only pitfall trapping due to the poor structuring of vegetation of the sampled dunes. By

adopting this methodology, we assume that our sampling target is the epigeal spiders guild

and not the entire community of spiders. Therefore, occasional catches (only singletons

and doubletons) of web-building spiders (Linyphiidae, Theridiidae and Araneidae) were

discarded. Pitfall trapping was used in previous studies, allowing a clear assessment of the

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regional and local variations of spider assemblages composition of grey dunes (Bonte et al.

2003b).

At each site, a square sampling plot (100 9 100 m2) was delimited. At each plot, 48

traps were distributed in 12 groups, forming small squares (5 m wide) of 4 traps. The

groups were placed in a 4 9 3 arrangement parallel to the sea. The distance between

adjacent groups was 20 meters. Plastic cups (8 cm diameter 9 12 cm depth) were used as

pitfall traps. Cups were buried in the sand and covered with a cork plate placed 2 cm above

ground. For preservation, a solution of ethylene glycol diluted in water (1/1, v/v), with a

few drops of detergent to break surface tension, was used. The biological material was

collected after 15 days in individual bags and transported to the laboratory for triage and

identification.

By concentrating a high trap density in a short period, we avoided the so called spatial

edge effects (vagrants from other habitats types in the vicinity of the sampling place) and

phenological edge effects (mature individuals characteristic of other seasons outside the

sampled period), a common source of error in ecological studies (see Longino et al. (2002),

Scharff et al. (2003)).

Only adult spiders were identified to species level, juveniles were discarded from the

analyses. Nomenclature followed Platnick (2010).

Data analysis

For the purpose of estimating the completeness of the inventory, each group of four traps

was considered as a sample (n = 12 per sampling plot). The inventory completeness was

assessed by calculating the ratio of observed species richness to six commonly used

Mediterranean sea

Por

tuga

l Spain

North of Africa0 100

Km

N

AMOCARBELESPAPUMIN

FURTORSJAMIRQUILEIOBA

STAABRSANSTOALM

Fig. 1 Location of the sampling sites along the western Atlantic coast of the Iberian Peninsula

876 Biodivers Conserv (2011) 20:873–894

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estimators: Chao1, Chao2, Jack1, Jack2, Boot and MMMeans. Estimator curves for each

site were obtained, based on 1000 randomizations of the species 9 12 samples data matrix,

using the EstimateS 8.2 package (Colwell 2009).

Distance-decay of similarity, nestedness and co-occurrence patterns

The distance decay of similarity was studied by regressing the similarity in species

composition between sites against the geographical distance between them (Nekola and

White 1999). Similarity in species composition was quantified with the complement of

the Williams b-3 index (i.e. 1 - b-3). We used this measure because it is particularly

robust to undersampling and is also insensitive to differences of species richness

between communities (Cardoso et al. 2009b). Distance decay regressions were fit in

log–log space to linearize the relationship between similarity and distance. A Mantel

test (Legendre and Legendre 1998) was applied to assess the significance of the dis-

tance decay of similarity.

We performed a nestedness analysis on the sites x species presence–absence matrix, in

order to verify if smaller species assemblages were a non-random subset of larger species

assemblages. The binary matrix nestedness temperature calculator (BINMATNEST) was

used to quantify the level of nestedness. The algorithm maximizes the matrix nestedness by

permuting rows and columns and a temperature T ranging from 0 (complete order) to 100

(complete disorder) is calculated (for details, see Rodrıguez-Girones and Santamarıa

(2006)). Since most nestedness metrics tend to measure some degree of nestedness, even in

matrices with a completely random pattern, we also used the Nestedness based on Overlap

and Decreasing Fill (NODF) metric that identifies these cases (Almeida-Neto et al. 2007).

NODF varies between 0 (complete disorder) and 100 (complete order).

In order to describe patterns of co-occurrence of species among sites, we calculated the

number of unique species pairs that never co-occur (CU—checkerboard units) and the

C-score (see Stone and Roberts (1990)). A simple score is computed for each pair of

species as (Ri - S)(Rj - S) where Ri and Rj are the number of sites where species i and j

occur, and S is the number of sites in which both species co-occur. The C-score corre-

sponds to the mean of the values calculated for all possible pairs of species in the matrix. If

the CU and the C-score were higher then expected by chance, then we may conclude that

species tended not to occur in the same sites.

To determine the statistical significance of the estimated nestedness and co-occurrence

metrics (T, NODF, CU and C-score), we compared the observed values with the 95%

confidence intervals produced by an appropriate random null model (1000 simulations).

Although several null models exist, we used the null model in which row and columns

sums are fixed as recommended by Ulrich et al. (2009). The analyses were performed in

the R statistical language (R Development Core Team 2009) using the package ‘‘vegan’’

(Oksanen et al. 2010).

Similarity analysis

We performed a similarity analysis in order to obtain a hierarchical classification of sites.

A distance matrix was constructed using the Bray-Curtis index as a measure of similarity.

Hierarchical agglomerative clustering was conducted on the square-root transformed dis-

tance matrix, using the Ward method as the linkage rule and the AGNES algorithm

(Kaufman and Rousseeuw 1990) implemented in the R-package ‘‘cluster’’ (Maechler

2010). AGNES calculates a coefficient, called Agglomerative Coefficient—AC, which

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measures the clustering structure of the data set. AC is a dimensionless quantity, varying

between 0 (the algorithm has not found a natural structure) and 1 (a clear structure has been

found). Following Kelley et al. (1996), we used a penalty function (KGS) in order to

identify consistent and distinct clusters of sites. This method maximizes differences

between groups and cohesiveness within groups (using the species pairwise distance

matrix). The minimum of the KGS function corresponds to the optimal number of clusters.

We used the ‘kgs’ function implemented in R-package ‘maptree’ (White 2009).

Differences among the identified clusters were tested by the ANOSIM test (Clarke and

Warwick 1994; Legendre and Legendre 1998). This non-parametric technique produces an

R statistic that measures the difference of mean ranks of distance between and within

clusters. The significance of the R statistic was assessed by a permutation approach (1000

permutations).

Indicator species

Indicator species analysis (Dufrene and Legendre 1997) was performed to identify indi-

cator species for the various clustering levels of the site typology obtained in the similarity

analysis. This method provides an indicator value (IndVal) for each species, combining the

degree of specificity (abundance and distribution within clusters) and fidelity (frequency of

occurrence within a cluster). The significance of the IndVal was assessed by permutation

(1000 permutations). Species with significant (P \ 0.05) IndVals higher than 50% in a

cluster were then regarded as ‘‘indicator species’’ of that particular cluster. The analysis

was performed in the R statistical language (R Development Core Team 2009) using the

package ‘‘labdsv’’ (Roberts 2010).

Biogeographic range

A specific geographic distribution category was attributed to each species: wide-range

(included cosmopolitan, Holarctic and Palearctic species), Eurosiberian, Mediterranean

and Iberian. Mediterranean species were further divided in Northern Mediterranean

(occurring in the Iberian Peninsula and European Mediterranean countries only), Southern

Mediterranean (occurring in the Iberian Peninsula and Northern Africa) and Pan-medi-

terranean (occurring in the Iberian Peninsula, European Mediterranean countries and

Northern Africa).

Data on the distribution of each species was obtained form the World Spider catalogue

(Platnick 2010), the Fauna Europaea database (Helsdingen 2009) and the Iberian Spider

checklist (Cardoso and Moreno 2010). We also obtained information from taxonomic

specialists to complement our database (Robert Bosmans, Laboratorium voor Ecologie,

Gent, Belgium and Hisham El-Hennawy, Serket and Arachnid Collection of Egypt),

namely to identify Mediterranean species that occur in Northern Africa.

We used non-metric multidimensional scaling (NMDS) to further analyse differenti-

ation among assemblages and regional patterns of spider species. The analysis was

carried out on a Bray-Curtis distance matrix. Species scores were computed and plotted

in the ordination space to aid in the interpretation of results. Species were coded

according to their biogeographic category. The analysis was performed in the R statis-

tical language (R Development Core Team 2009) using the package ‘‘vegan’’ (Oksanen

et al. 2010).

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Results

Species richness, abundance and inventory completeness

We identified 7379 adult individuals, belonging to 101 species (Appendix 2). A latitudinal

pattern of decreasing species richness along the coast was detected (r = -0.5361,

P \ 0.05). Average species richness was 19.4 across all sites (SD = 5.3). Total abundance

of individuals per site varied greatly among sites, tending to be lower in Southern sites.

However, no significant latitudinal pattern was detected (r = -0.4248; P = 0.079). The

degree of inventory completeness varied among sites, but values were in general high,

therefore, we considered it as fairly complete (Table 1).

Distance-decay of similarity, nestedness and co-occurrence patterns

Assemblages’ similarity among sites decreased with climatic/geographic distance (Mantel

r = -0.5228; P \ 0.05), revealing the strong influence of the spatial gradient of

mediterraneity on the assemblage structure of spiders (Fig. 2). The observed values of

nestedness metrics (T and NODF) did not statistically differ from the null models, indi-

cating that poorer species assemblages were not subsets of larger ones (Table 2). The co-

occurrence analyses showed that the observed values for both metrics (C-score and CU)

significantly differed from the null models, indicating that some species co-occurred less

than expected by chance (Table 2).

Table 1 Number of species,abundance and inventory com-pleteness per sampling site

Completeness was calculated as[(observed/estimated) 9 100] foreach sampling location. Sixestimators (Chao1, Chao2, Jack1,Jack2, Boot and MMMeans)were used

Site Sobs Individuals Completeness(min–max)

AMO 24 356 64–86

CAR 19 430 77–91

BEL 20 434 69–89

ESP 27 552 73–93

APU 21 427 76–96

MIN 28 444 83–96

FUR 25 1208 68–87

TOR 24 1469 68–86

SJA 20 794 42–85

MIR 23 213 62–85

QUI 12 74 82–98

LEI 8 32 50–81

OBA 16 62 69–87

STA 16 188 70–90

ABR 17 175 57–86

SAN 18 448 74–89

STO 14 36 49–80

ALM 17 37 51–82

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Similarity analyses

Cluster analysis showed that spider assemblages were structured along a gradient from

North to South, suggesting that there was a strong spatial biogeographical structure in the

data set (Fig. 3). The KGS penalty function suggests that the optimal number of clusters in

the resulting hierarchy was four (Fig. 4). The agglomerative coefficient was 0.71 indicating

that a clear structuring has been found (Kaufman and Rousseeuw 1990). The resulting

hierarchic typology is generally consistent with the major biogeographic regions and the

thermoclimatical belts recognized in the Atlantic coast of Portugal. The first division

separates the sites included in the Eurosiberian region (AMO, CAR, BEL, ESP and APU)

from the Mediterranean region (remaining sites). The second division splits the Mediter-

ranean region in two thermoclimatic areas, the Mesomediterranean belt (MIN, FUR, TOR,

SJA, MIR and QUI) and the Thermomediterranean belt (STA, ABR, SAN, STO and

ALM). At this point, the two most northern sites in the Thermomediterranean region (LEI

and OBA) are clustered with the Mesomediterranean sites, reflecting their position in the

transition area. The third division separates the Mesomediterranean area in two additional

clusters, a Northern clade (MIN, FUR, TOR and SJA) and a Southern clade (MIR, QUI and

the two already mentioned sites LEI and OBA). The four delimited faunistic regions differ

1 2 3 4 5 6

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

log (distance)

log

(sim

ilarit

y)

Fig. 2 Distance decay ofsimilarity of spider’ assemblagesalong the western Atlantic coastof the Iberian Peninsula.Similarity in species compositionwas quantified with the Williamsb-3 index

Table 2 Summary of nestedness and co-occurrence analysis

Metric Observed z 95% CI simulated P

T 36.38 -0.314 34.07–39.18 NS

NODF 26.95 -0.004 26.32–27.50 NS

C-score 5.34 4.457 5.17–5.27 \0.01

CU 26963 5.144 26054–26556 \0.01

NS not significant

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AMO

CAR

BEL

ESP

APU

MIN

FUR

TOR

SJA

MIR

QUI

LEI

OBA

STA

SAN

ABR

STO

ALM

0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6

Height0

Med

iterr

anea

nE

uros

iber

i an

Mes

otem

pera

t eM

esom

edite

rran

ean

The

rmom

edite

rran

ean

10

15

15 16 17 18

BG TB

Fig. 3 Cluster analyses performed in a square-root transformed matrix, using the Bray-Curtis index as ameasure of resemblance and the Ward’s method as the linkage rule. The biogeographic regions (BG),thermoclimatic belts (TB) and the mean annual temperature (T) of sampling sites are also shown. Thebiogeographic regions and thermoclimatic belts were extracted from Rivas-Martınez et al. (2004a, b)

5 10 15

810

1214

1618

number of clusters

KG

S p

enal

ty fu

nctio

n

Fig. 4 Relationship between thenumber of clusters and the KGSpenalty function. The minimumof the KGS function indicates theoptimum number of clusters(n = 4)

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significantly in their composition (global ANOSIM R = 0.8502, P \ 0.001). Pairwise

analyses of similarity also revealed significant differences (0.469 \ R \ 0.975; P \ 0.05

in all cases).

Indicator species

Among the 101 species analyzed, 29 had a significant IndVal index above 50% (Fig. 5).

Only one species reached its maximum Indval at the root level (grouping all sites). At the

first level, 11 species had their maximum value in the Eurosiberian clade and two species

in the Mediterranean clade (Mesomediterranean and Thermomediterranean included). At

the second level of the hierarchy, seven species were indicator for the Thermomediterra-

nean area, while two species were indicator of the Mesomediterranean region (Northern

and Southern clades included). At the third level of the hierarchic typology, six species had

significant IndVal indexes for the Northern Mesomediterranean clade. For the Southern

Mesomediterranean clade we didn’t find any species with significant IndVal index. Only

three species showed an IndVal = 50% (P [ 0.05).

Biogeographic range

Spider’ assemblages were dominated by Mediterranean (36.6%) and Wide-range elements

in almost equal proportions (35.5%). Iberian species represented 18.3% and Eurosiberian

species comprised only 9.7%. The Mediterranean species recorded (n = 34) were further

Euophrys gambosa (100)

Group IVAelurillus luctuosus (100)Nemesia uncinata (80)Heliophanus agricola (80)Thanatus atratus (80)Callilepis concolor (60)Macaroeris moebi (60)Pardosa proxima (52)

Group IIIBerlandina plumalis (50) n.s.Titanoeca monticola (50) n.s.Phaecedus braccatus (50) n.s.

Malthonica lusitanica (88)Dysdera lusitanica (63)

Group IIAlopecosa simoni(96)Haplodrassus dalmatensis (90)Hapodrassus signifer (75)Zodarion viduum (75)Zelotes civicus (75)Zora manicata (70)

Group IZelotes manius (100)Zodarion machadoi (100)Zelotes fulvopilosus (97)Xysticus cristatus (89)Alopecosa albimana (83)Phlegra bresnieri (78)Drassodes pubescens (65)Malthonica oceanica (60)Arctosa personata (60)Euophrys semiglabrata (60)Xysticus bliteus (58)

Yllenus squamifer (80)Zelotes lagrecai (77)

Fig. 5 Indicator species for the different levels of the hierarchic site typology. Species are only mentionedat the level where they have their maximum, significant indicator value (indicator value betweenparentheses). No species had its maximum significant Indval for group III. In this case, species that havetheir maximum in this cluster, although not significant (n.s.), were presented. Group I AMO, CAR, BEL,ESP and APU; Group II MIN, FUR, TOR and SJA; Group III MIR, QUI, LEI and OBA; Group IV STA,ABR, SAN, STO and ALM

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dissected into three geographic distributional types: Northern (38.2%), Southern (17.7%)

and Pan-mediterranean (44.1%). The significant proportion of Southern Mediterranean

elements in Portugal is illustrative of the past historical relationship between the Iberian

Peninsula and the North of Africa.

Ordination of the data through NMDS (stress = 0.1345) supported the existence of the

four main biogeographic groups previously identified by cluster analysis (Fig. 6). The

dimension 1 showed a significant positive correlation with latitude (r = 0.9418; P \ 0.05).

The ordination plot showed the complex mixture of elements from different origins that

characterized the four main groups of sites. The Eurosiberian species were restricted to the

Northern part of the Atlantic coast that represented the southernmost limit of their

geographic distribution. Wide-range and Mediterranean species were distributed along the

entire coast, but each region had its own sub-set of species. The Iberian elements (en-

demics and sub-endemics) showed a structured biogeographic pattern. The three main

concentration areas of Iberian species corresponded mainly to the three thermoclimatic

belts.

Discussion

The Iberian Peninsula is an interesting place for the study of biogeographic patterns. Its

climatic complexity and the occurrence of historic events have been pointed as major

-1.0 -0.5 0.0 0.5 1.0 1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

Dimension 1

Dim

ensi

on 2

LEI

QUI

MIR

OBA

SJA

TOR

FUR

MIN

ESP APU

AMO

CARBEL

SANSTA

ABR

ALM

STO

Iberian

Northern Mediterranean

Eurosiberian

Wide-range

Legend

Pan- Mediterranean

Southern Mediterranean

Fig. 6 Non-metric multidimensional scaling performed in a square-root transformed matrix, using theBray–Curtis index as a measure of similarity. Species were identified according to their biogeographiccategory. The hierarchic cluster of sites was overlayed into the ordination space using the ordiclusterfunction from the vegan package (Oksanen et al. 2010). The function connects cluster centroids to eachother with line segments (dashed lines)

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drivers of the emergent biogeographic patterns and endemism origins (Garcia-Barros et al.

2002; Filipe et al. 2009; Romo and Garcıa-Barros 2010). In this study, we investigated the

biogeographic patterns of spider assemblages along the western Atlantic coast of the

Iberian Peninsula, characterized by a strong gradient of mediterraneity.

Distribution patterns of species

Climate is one of the most important drivers of the distribution of spiders at broader scales

(Finch et al. 2008). Our results provided evidence that the distribution of spider species

along the coast is strongly influenced by the latitudinal climatic gradient of mediterraneity.

A significant latitudinal pattern of decreasing species richness with increasing mediterra-

neity was detected, contradicting the well-known latitudinal diversity gradient that predicts

an increase of species richness from North to South in the Holartic (Willig et al. 2003).

This reverse latitudinal richness gradient was also verified in butterflies (Martın and Gurrea

1990) and birds (Tellerıa and Santos 1993) in the Iberian Peninsula. This pattern seems to

be caused by the decrease of the Eurosiberian spider species, which are restricted to the

North of the coast. Two main mechanisms may be responsible for the decline of the

Eurosiberian species from North to South, the so-called ‘‘peninsular effect’’ (Simpson

1964) and environmental filtering effects. The ‘‘peninsular effect’’ predicts a decrease of

species richness, as a result of a decrease in the ratio immigration/colonization, with

distance from the base to the tip of a peninsula. The hypothesis of environmental filtering

effects acting on Eurosiberian species is also likely. In this case, the decline of Eurosi-

berian species may be attributed to their inability to survive in Mediterranean areas due to

climatic requirements. Both mechanisms seem to be possible, however, based in our results

we cannot determine the relative contribution of each one to the observed pattern.

Non-random species loss gives rise to the formation of smaller species assemblages that

are nested subsets of larger ones (Ulrich et al. 2009). In our study, no significant nestedness

pattern was detected, indicating that smaller assemblages are not sub-sets of larger ones. In

contrast, significant co-occurrence patterns were detected. This means that some pairs of

species occur less often than expected by chance (Stone and Roberts 1990). Several

possible mechanisms might be responsible for the emergence of reduced co-occurrence of

species, such as competition, differential habitat requirements (habitat checkerboards) and

biogeographical and evolutionary influences (historical checkerboards) (Gotelli and

McCabe 2002). In our case study, a combination of all of these mechanisms seems to be

plausible. For example, Malthonica lusitanica and Malthonica oceanica are two closely

related species, with similar body size and habitat requirements. They exhibit a checker-

board pattern alternating their presence/absence among sites, suggesting the possibility of a

competition interaction between them. The presumable inability of Eurosiberian species to

colonize southern locations due to climatic requirements generates ‘habitat checkerboards’.

Some species may also exhibit ‘historical checkerboards’ and co-occur infrequently

because of allopatric speciation. This seems to be the case of the species of the genus

Zodarion. All of the six recorded species are endemic to the Iberian Peninsula.

Biogeographic affinities

In spite of a strong continuous climatic/geographic gradient, cluster analysis and NMDS

identified a clear hierarchical typology of sites. The consistency of this typology is rein-

forced by the identification of indicator species for the different levels of the hierarchy. The

resulting typology is consistent with the main biogeographic regions (Eurosiberian and

884 Biodivers Conserv (2011) 20:873–894

123

Mediterranean) and the thermoclimatic belts recognized for the Atlantic coast of the

Iberian Peninsula (Rivas-Martınez et al. 2004a, b). This strong biogeographic structure

may be explained on the basis of (1) a high turnover of species caused by the steep gradient

of mediterraneity, (2) the restriction of the Eurosiberian species to the North of the country,

(3) the high geographic structure of the Iberian species (endemics and sub-endemics) and

(4) the colonization of the Iberian Peninsula by Northern African species.

Biogeographic range of species

The analysis of the geographic range of spider species indicates that the studied region is a

‘‘biogeographic crossroad’’, encompassing faunistic elements of different origins (Wide-

range, Eurosiberian, Mediterranean and Iberian). The mixture of organisms of different

origins is one of the most striking aspects of the biogeography of the Iberian Peninsula

(Cabrero-Sanudo and Lobo 2006; Gonzalez et al. 2009) and of the Mediterranean Basin

(Blondel et al. 2010). Nevertheless, the region has a predominance of Mediterranean

species, which highlights its importance as a significant reservoir of the mediterranean-type

fauna. By decomposing further the biogeographic range of Mediterranean species into

three categories (Northern, Southern and Pan-Mediterranean) we found the signature of

historic processes that shaped the current biogeographic patterns. The presence of species

only found in the Iberian Peninsula and Northern Africa is most likely a consequence of the

historic connection between the two regions (e.g. Messinian Salinity Crisis, *5.5 Ma),

which allowed the interchange of species (Jimenez-Moreno et al. 2010). One striking

feature of the spider fauna inhabiting the coast of the Iberian Peninsula is the relatively

high proportion of Iberian species (*20% of endemics and sub-endemics). This pattern

could be related to the Pleistocene climate cycles that have repeatedly increased and

decreased the areas of coastal zones, favouring the differentiation of species as repeated

fragmentation of initial areas of distribution took place over time (Blondel et al. 2010). The

geographic structure of the Iberian species distribution is congruent with the thermocli-

matic belts, suggesting an interaction between ecological factors and historic events.

However, this issue could only be clarified with more specific biogeographic studies and

incorporating phylogenetic information.

Conservation implications

Understanding the spatial patterns of biodiversity and their underlying processes is a

fundamental task for conservation biology (Whittaker et al. 2005). Delimiting distinct

biotic regions provides an important framework for setting conservation priorities. This

requires knowledge of different taxa. However, despite their functioning role in ecosys-

tems, some groups, such as spiders, are rarely taking in account in conservation policies.

From the point of view of conservation biogeography, four aspects deserve special

attention in the Western coast of the Iberian Peninsula: (1) a high number of species are

endemic or sub-endemic; (2) the coast is a biogeographic crossroad, encompassing species

from different origins, thus providing opportunities to meet the representativeness and

complementarity goals of conservation; (3) many species show their distribution limits in

this region and are most probably historically isolated lineages with evolutionary potential;

(4) many species present a small-ranged distribution along the coast and tended to be

concentrated in specific areas (indicator species), thus increasing their vulnerability to

human impacts. Although this study only focuses on the distribution patterns of spiders,

these features are probably true for other groups of invertebrates. This work provides

Biodivers Conserv (2011) 20:873–894 885

123

important baseline information that might be incorporated in future conservation and

research programmes in the Atlantic coast of Portugal.

Acknowledgments We are acknowledged to Jose Antonio Barrientos, Carmen Urones and Stano Pekar fortheir help with the taxonomic identification of some spider specimens and to Robert Bosmans and HishamEl-Hennawy for their contribution to identify Mediterranean species that occur in Northern Africa. J.C.C.express his gratitude to the Luso-American Foundation for its support (FLAD CandT Links 2010 grant).P.C. is supported by Fundacao para a Ciencia e Tecnologia (SFRH/BPD/40688/2007).

Appendix 1

See Table 3.

Appendix 2

See Table 4.

Table 3 Location and climatic characteristics of the studied sites: mean annual precipitation (P), precip-itation of the least rainy month (Pmin), precipitation of the most rainy month (Pmax), mean annual tem-perature (T), mean temperature of the coldest month (Tmin), mean temperature of the hottest month (Tmax)and summer drought (number of months where Pmonth \ 2Tmonth)

Sit Code Latitude(N)

Longitude(W)

P(mm)

Pmin

(mm)Pmax (mm) T

(8C)Tmin

(8C)Tmax

(8C)Summerdrought

Amorosa AMO 4183902400 884902600 1355 21.6 206.7 15 9.2 22 2

Carruagem CAR 41�3601400 8�4802200 1341 21.1 203.8 15 9.3 22 2

Belinho BEL 4183501700 884801400 1338 21.3 202.4 15 9.2 22 2

Esposende ESP 4183205700 884702400 1312 20.3 199 15 9.3 22.1 2

Apulia APU 4182803500 884602800 1296 19.9 194.8 15 9.3 22.1 2

Mindelo MIN 4181900900 884401500 1236 18.7 184.9 15 9.1 22.1 2

Furadouro FUR 4085104000 884003800 1064 14.1 157.4 16 9.4 22.4 3

Torreira TOR 40�4701200 8�4201000 1037 13.4 153 16 9.5 21.9 3

Sao Jacinto SJA 40�4304500 8�4301800 1003 13 147.5 15 9.7 20.6 3

Mira MIR 40�2601700 8�4802400 976 11 144.5 15 9.7 21.3 3

Quiaios QUI 4082203100 884904000 946 10.5 139.1 16 9.7 22 3

Leirosa LEI 4080305200 885300800 820 9.2 120.2 16 9.9 21.6 4

Osso da Baleia OBA 4080000400 885405100 849 9.4 123.6 16 9.8 22.5 4

Santo Andre STA 38�0503700 8�4801800 649 4 101.4 17 10.8 25.1 4

AreiasBrancas

ABR 38�0303300 8�4900900 654 4.3 102.5 17 10.7 25 4

Lagoa daSancha

SAN 3880103600 885000100 640 3.5 101.7 17 10.8 25.2 4

Sao Torpes STO 37�5404300 8�4705100 658 3.6 104.4 18 10.4 25.2 4

Almograve ALM 37�3802900 8�4801500 729 5.2 114.9 18 11 25.2 4

886 Biodivers Conserv (2011) 20:873–894

123

Tab

le4

Lis

to

fsp

ecie

sfo

rea

chsi

te(n

om

encl

atu

reac

cord

ing

toP

latn

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20

10)

Sp

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sA

MO

CA

RB

EL

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PA

PU

MIN

FU

RT

OR

SJA

MIR

QU

IL

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AS

TA

AB

RS

AN

ST

OA

LM

Ag

elen

idae

Ma

lth

onic

alu

sita

nic

a0

00

00

15

81

01

15

80

24

00

00

0

Ma

lth

onic

ao

cea

nic

a1

20

52

00

00

06

11

00

00

0

Ma

lth

onic

ara

mb

lae

00

02

14

00

00

00

00

00

00

Teg

ena

ria

mo

nti

gen

a0

00

03

20

00

00

90

10

00

00

0

Teg

ena

ria

sp.

12

11

16

52

00

00

00

00

00

00

00

Clu

bio

nid

ae

Clu

bio

na

vege

ta0

00

00

00

00

00

10

20

20

0

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rin

nid

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Lio

phr

uri

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avi

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00

00

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90

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00

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Ph

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01

00

00

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0

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10

05

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11

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00

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01

00

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ta0

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02

00

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00

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Ha

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00

50

00

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00

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20

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00

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0

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sidae

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ari

10

00

00

00

00

00

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Biodivers Conserv (2011) 20:873–894 887

123

Tab

le4

con

tin

ued

Sp

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sA

MO

CA

RB

EL

ES

PA

PU

MIN

FU

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ST

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LM

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lan

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ap

lum

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00

00

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88

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82

93

56

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32

50

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50

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44

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11

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70

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00

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Mic

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00

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Mic

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00

00

00

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00

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No

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73

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82

46

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21

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51

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25

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ote

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70

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00

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0

Zel

ote

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43

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Dra

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11

00

00

00

00

00

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00

0

Hah

nii

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Ha

hn

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nd

ida

10

00

03

10

00

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00

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00

Lio

cran

idae

Ag

roec

ain

op

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00

01

00

00

00

00

00

00

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888 Biodivers Conserv (2011) 20:873–894

123

Tab

le4

con

tin

ued

Sp

ecie

sA

MO

CA

RB

EL

ES

PA

PU

MIN

FU

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OR

SJA

MIR

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OB

AS

TA

AB

RS

AN

ST

OA

LM

Sco

tin

ace

lans

00

02

00

00

00

00

00

00

00

Ly

cosi

dae

Arc

tosa

vari

ana

00

01

01

40

00

00

00

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00

0

Arc

tosa

per

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00

00

04

00

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01

00

Arc

tosa

per

sona

ta0

48

12

00

00

00

00

00

00

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Tra

bea

pa

rad

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00

00

00

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00

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00

Pir

ata

pir

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00

00

00

00

00

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00

Alo

pec

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acc

entu

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00

00

00

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00

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Alo

pec

osa

alb

ofa

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ta4

76

41

62

73

12

14

31

00

10

00

00

00

Alo

pec

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pu

lver

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11

00

00

00

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Alo

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00

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05

38

10

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00

Pa

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s0

01

01

02

00

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00

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00

0

Pa

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21

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04

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01

25

20

Mit

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Chei

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Nem

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00

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Ox

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00

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Ph

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Th

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00

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03

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an

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tipes

00

00

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00

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00

00

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10

Biodivers Conserv (2011) 20:873–894 889

123

Tab

le4

con

tin

ued

Sp

ecie

sA

MO

CA

RB

EL

ES

PA

PU

MIN

FU

RT

OR

SJA

MIR

QU

IL

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OB

AS

TA

AB

RS

AN

ST

OA

LM

Th

an

atu

svu

lga

ris

01

12

00

00

00

00

00

00

00

00

Tib

ellu

so

blo

ng

us

00

00

00

00

10

00

00

00

00

Pis

auri

dae

Pis

au

ram

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21

10

00

21

00

00

00

00

10

Sal

tici

dae

Nea

eth

ace

russ

ata

00

00

00

00

00

00

03

00

00

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uri

llus

v-in

sig

nit

us

00

00

07

00

00

00

00

00

00

Ael

uri

llus

luct

uosu

s0

00

00

00

00

00

00

21

33

3

Eu

op

hry

sfr

on

tali

s0

00

40

00

00

00

00

00

00

0

Eu

op

hry

sg

am

bo

sa1

71

45

17

39

13

82

51

16

18

11

01

56

16

18

88

41

21

19

8

Eu

op

hry

sse

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labr

ata

02

03

10

00

00

00

00

00

00

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rico

la0

00

00

00

00

00

00

11

60

1

Ma

caro

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mo

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00

00

00

00

00

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01

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12

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00

00

10

00

00

00

00

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0

Neo

nle

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00

01

03

00

00

00

10

00

00

Neo

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bu

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00

00

00

02

00

00

00

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00

Pel

len

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len

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00

01

00

10

00

40

00

0

Ph

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61

31

88

14

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pro

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quu

s0

01

00

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00

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ticu

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lta

tor

02

11

01

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Syn

age

les

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aru

lus

00

00

00

00

10

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00

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71

51

60

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12

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00

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00

10

00

00

00

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890 Biodivers Conserv (2011) 20:873–894

123

Tab

le4

con

tin

ued

Sp

ecie

sA

MO

CA

RB

EL

ES

PA

PU

MIN

FU

RT

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MIR

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IL

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OB

AS

TA

AB

RS

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ST

OA

LM

Th

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pti

lapauxi

lla

00

15

20

06

00

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00

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00

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1

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laum

bra

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20

00

00

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00

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00

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00

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ticu

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lite

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22

11

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43

55

92

41

10

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00

00

01

00

00

00

00

00

00

Xys

ticu

sn

ub

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s0

00

00

00

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00

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0

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idae

Tit

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am

on

tico

la0

00

00

00

00

12

30

00

00

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Zo

dar

iidae

Zo

da

rio

na

tla

nti

cum

00

00

01

90

00

00

00

00

00

0

Zo

da

rio

nm

ach

ado

i1

67

13

82

23

12

19

60

00

00

00

00

00

00

Zodari

on

alg

arv

ense

00

00

00

00

00

00

00

21

60

0

Zo

da

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du

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00

00

00

93

81

20

55

38

12

20

00

00

00

Zo

da

rio

nsp

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00

00

00

00

00

07

17

00

00

0

Zo

da

rio

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00

00

00

00

00

00

00

81

50

2

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ae

Zo

ram

an

ica

ta0

00

00

01

91

00

00

00

01

0

Biodivers Conserv (2011) 20:873–894 891

123

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