Biological Conservation 142 (2009) 1560–1569

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Biological Conservation

journal homepage: www.elsevier .com/locate /b iocon

Habitat fragmentation reduces genetic diversity and connectivity among toadpopulations in the Brazilian Atlantic Coastal Forest

Marianna Dixo a,b,*, Jean Paul Metzger a, João S. Morgante b, Kelly R. Zamudio c

a Department of Ecology, University of São Paulo. Rua do Matão, Travessa 14, 321, Caixa Postal 11461, CEP 05508-090 São Paulo, SP, Brazilb Department of Genetics and Evolutionary Biology, University of São Paulo. Rua do Matão, 277, CP 11461, CEP 05422-970 São Paulo, SP, Brazilc Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, 14853-2701, USA

a r t i c l e i n f o a b s t r a c t

Article history:Received 21 June 2008Received in revised form 18 November 2008Accepted 24 November 2008Available online 8 January 2009

Keywords:Rhinella ornataAnuranSpatial analysisGene flowAnthropogenicConservation

0006-3207/$ - see front matter � 2008 Elsevier Ltd. Adoi:10.1016/j.biocon.2008.11.016

* Corresponding author. Tel.: +55 11 30917603; faxE-mail addresses: mariannadixo@yahoo.com.br (

Metzger), jvmorgan@ib.usp.br (J.S. Morgante), kellZamudio).

Tropical rainforests are becoming increasingly fragmented and understanding the genetic consequencesof fragmentation is crucial for conservation of their flora and fauna. We examined populations of the toadRhinella ornata, a species endemic to Atlantic Coastal Forest in Brazil, and compared genetic diversityamong small and medium forest fragments that were either isolated or connected to large forest areasby corridors. Genetic differentiation, as measured by FST, was not related to geographic distance amongstudy sites and the size of the fragments did not significantly alter patterns of genetic connectivity. How-ever, population genetic diversity was positively related to fragment size, thus haplotype diversity waslowest in the smallest fragments, likely due to decreases in population sizes. Spatial analyses of geneticdiscontinuities among groups of populations showed a higher proportion of barriers to gene flow amongsmall and medium fragments than between populations in continuous forest. Our results underscore thateven species with relatively high dispersal capacities may, over time, suffer the negative genetic effects offragmentation, possibly leading to reduced fitness of population and cases of localized extinction.

� 2008 Elsevier Ltd. All rights reserved.

1. Introduction

Fragmentation of natural habitats is a major challenge in con-servation biology and one of the top threats to biodiversity (Han-ski, 1999; Fahrig, 2003; Henle et al., 2004). The negative effectsof fragmentation result from the decrease in overall habitat avail-ability and changes in spatial configuration and habitat quality offragments (Fahrig, 2003; Ezard and Travis, 2006). Both theoreticaland empirical studies show that habitat fragmentation can erodeneutral and adaptive genetic diversity of populations due to de-creases in effective population size and inter-population connec-tivity (Johansson et al., 2007). After fragmentation, smallpopulations, and lower genetic diversity lead to genetic drift, high-er risks of inbreeding, lower evolutionary potential, and conse-quently, higher risk of extinction (Avise et al., 1987; Young et al.,1996; Saccheri et al., 1998; Reed and Frankham, 2003). The main-tenance of semi-natural levels of habitat connectivity via habitatcorridors has been proposed as a means of reducing the negativeeffects of fragmentation (Harris, 1984; Bennett, 1990; Saunderset al., 1991). Corridors, if adequate in arrangement and number,can in theory offset the negative consequences of population isola-

ll rights reserved.

: +55 11 30918096.M. Dixo), jpm@b.usp.br (J.P.y.zamudio@cornell.edu (K.R.

tion in fragments (Harris 1984; Mech and Hallett, 2001) and re-duce demographic stochasticity (Merriam, 1991).

One of the difficulties in managing anthropogenically alteredlandscapes is that susceptibility to fragmentation is highly spe-cies-specific, depending in part on historical population sizes, dis-persal capacity, and historical population structure (Williams et al.,2003; Galbusera et al., 2004; Cushman, 2006; Luoy et al., 2007).Comparative analyses suggest that, in general, species that typi-cally have lower population sizes are often less genetically diverse(Frankham, 1996), and therefore should be more susceptible to ge-netic erosion resulting from habitat fragmentation, but this effectcan be mitigated in species with sustained levels of dispersal be-tween fragments (Galbusera et al., 2004; Cushman, 2006). In con-trast, some empirical studies show that surprisingly, habitatfragmentation can have similar effects on common species (Dixo,2005; Peakall and Lindenmayer, 2006), including those that arehabitat generalists and have high rates of gene flow (Williamset al., 2003). Thus, interactions between species characteristicsand those of the altered landscape are complex, precluding gener-alizations of the effect of habitat alteration on entire lineages(Cushman, 2006).

Amphibians are declining worldwide at an unprecedented rate(Stuart et al., 2004) and are particularly threatened by anthropo-genic habitat modification (Dodd and Smith, 2003; Cushman2006). Amphibians are the vertebrate lineage with highest numberof species threatened with extinction (Stuart et al., 2004; Beebee

M. Dixo et al. / Biological Conservation 142 (2009) 1560–1569 1561

and Griffiths, 2005) and their vulnerability is at least partially ex-plained by their often narrow environmental tolerances (Findlayand Houlahan, 1997; Bridges and Semlitsch, 2000; Pounds et al.,2006) and generally low dispersal capacities (Gibbs, 1998; deMa-ynedier and Hunter, 2000), both characteristics that exacerbatethe negative effects of habitat degradation and loss of populationconnectivity.

In this study we used population genetic analyses to test for thepredicted effects of fragmentation in populations of a toad endemicto the Brazilian Atlantic Rainforest, one of the top 10 biodiversityhot spots (Mittermeier et al., 1999; Myers et al., 2000) and one ofthe most fragmented biomes in the world, with only 12% of its ori-ginal range remaining (SOS Mata Atlântica, 2008). Our study fo-cused on Rhinella ornata, a habitat-generalist that is common inforested as well as edges of disturbed areas (Baldissera et al.,2004). We studied this species in forests on the Ibiúna Plateau, ahighly developed region in southwestern Brazil that still harborsboth large continuous tracts as well as forest fragments (Dixo,2005; Pardini et al., 2005). R. ornata is able to move through theagricultural matrix that isolates fragments and is abundant in frag-ments of all sizes; nonetheless, populations in small and medium-sized fragments show significant annual variation in abundance,suggesting possible demographic instability of populations in frag-ments (Dixo, 2005).

We compared genetic diversity and structure of R. ornata popu-lations in fragments and continuous tracts of Atlantic Rainforest totest the hypotheses that (i) this generalist species is vulnerable togenetic erosion, (ii) larger fragment sizes and corridors mitigatefragmentation and maintain genetic diversity in populations, and(iii) fragmentation negatively impacts gene flow among remainingpatches of habitat, despite this species’ ability to survive in defor-ested areas. We might expect that this generalist species would notbe highly susceptible to the negative impacts of habitat fragmenta-tion; therefore, any evidence of genetic erosion in this generalistand relatively tolerant species, implies that less common and morespecialized species may suffer even more severe effects as a resultof forest fragmentation and isolation in this threatened landscape.

2. Methods

2.1. Study area and population sampling

Our study was carried out on the Ibiúna Plateau of the Parana-piacaba Mountain Range, approximately 40 km northwest of thecity of São Paulo; the landscape on the plateau includes both con-tinuous forest and a highly fragmented landscape (Fig. 1). The ori-ginal forest in the region is classified as Lower Montane rainforest(Oliveira-Filho and Fontes, 2000) with elements of semi-deciduousand Araucaria mixed forests (Catharino et al., 2006). The study areais part of the Atlantic Forest biome, which formerly extended alongthe entire Brazilian coast. Although large undisturbed tracts stillexist (Fonseca, 1985), the remaining smaller forest fragments aresparse and isolated, sometimes with less than 100 ha and highlydisturbed vegetation (Fonseca, 1985; Turner and Corlett, 1996).The approximately 9400 hectares of continuous forest on the Ibiú-na Plateau are protected in the Morro Grande Forest Reserve(Fig. 1). The fragmented landscape is located southwest of the re-serve and includes secondary forests (31% of the landscape), devel-oped areas (rural installations, and urban areas, 15%), and openareas (agriculture and pasture, including fallow lands and thosein early successional stages, 39.3%). These open areas form mostof the landscape matrix surrounding forest fragments. Agriculturallands (57% of the matrix) are predominantly used for horticulturalcrops that supply the 40 million inhabitants of São Paulo State(IBGE, 2007).

We collected R. ornata from 18 sites on the Ibiúna plateau: threecontrol-areas in a continuous forest in the Morro Grande Reserve(CT1, CT2, and CT3, Fig. 1) and fifteen populations in small (S, 1–5.5 ha) or medium-sized (M, 10–50 ha) forest fragments. Frag-ments connected to other larger fragments (>60 ha) by corridorsof native vegetation measuring 25–100 m in width were classifiedas ‘‘connected” fragments. Straight line distances between collect-ing sites ranged from 557 to 17,158 m. Tissue samples (toe clip-pings) were collected at each site from adult toads captured inpitfall traps. We collected on average 14.55 samples per locality(range 12–16). Tissues were stored individually in absolute etha-nol, and the toads released at the sites of capture.

2.2. Laboratory protocols

We isolated genomic DNA from toe clips using a standard phe-nol–chloroform extraction method (Sambrook and Russell, 2001).We amplified a fragment of the mtDNA control region using prim-ers Control Wrev-L and Control P-H (Goebel et al., 1999). Polymer-ase chain reactions (PCRs) consisted of an initial denaturation stepof 94 �C for 5 min, followed by 35 cycles of denaturation at 94 �Cfor 40 s, annealing at 58 �C for 40 s, and extension at 72 �C for40 s, followed by a final extension at 72 �C for 7 �min. Amplifiedfragments were sequenced with Big Dye Terminator components(Applied Biosystems) according to manufacturer’s protocols.Sequencing reaction products were electrophoresed on an ABIPRISM 310 DNA sequencer (Applied Biosystems). Sequence electro-pherograms were edited manually and aligned using the programSequence Navigator version 1.01. All sequences have been depos-ited in GenBank (Accession no. FJ558516–FJ558542).

2.3. Population genetic analyses

We used the program Arlequin v. 3.0 (Excoffier et al., 2005) tocalculate three population indices of genetic diversity: number ofhaplotype, nucleotide diversity, and haplotype diversity. We usedan ordinary least square regression to test for possible biases inthe number of haplotypes at each site due to uneven sample sizes.Because we found no significant association between sample sizeand haplotype diversity, we used uncorrected haplotype numbersin all subsequent statistical analyses; nonetheless, we also reportthe number of haplotypes adjusted for sample size (h/N), for com-parison. Pairwise FST values were estimated in the program Arle-quin v. 3.0 (Excoffier et al., 2005) and their significance testedusing 10,000 permutations of haplotypes between populations asa null distribution.

We used ordinary least square regression between the loga-rithm of fragment areas and within-population indices of geneticdiversity to test for possible genetic effects of forest area at oursampling sites. To test the hypothesis that corridors contribute tomaintenance of genetic diversity in fragments, we used a two-fac-tor ANOVA including fragment size (small, medium) and connec-tivity (connected or isolated). Prior to all statistical analyses, wetested our data for homogeneity of variance using the Bartlett test(Zar, 1996); only haplotype diversity required transformation andthe data were rank-transformed for statistical analyses. All statisti-cal tests were performed using Statistica v. 6.1 (StatSoft, 2001).

We created a network based on all haplotypes in the programTCS, v.1.21 (Clement et al., 2000). This method organizes haplo-types into networks according to genetic distance (Templetonet al., 1992). Haplotypes are connected when the number of differ-ences between them does not exceed a 95% parsimony probabilitythreshold (Templeton et al., 1992). Haplotype networks betterillustrate genetic divergence at the intra-specific level, especiallyin cases of multiple haplotypes that are derived from a singleancestral sequence (Templeton et al., 1992).

W 46° 53´

W 47° 13´

W 47°02´10”

O 1 2 Kilometers

S 23° 47´ 10” W 47° 07´24”

S 23°40´49”

O 5 10 Kilometers

S 23° 38´

S 23° 50´

São Paulo

Continuous forest

Brazil

MC3 MC1

SC2

SI3

MI4

MC2

SC3 MI3

MC4

MI2SC4

SC1

SI1

SI2

MI1

CT1

CT2

CT3

Fragments

Fig. 1. Location and distribution of forest fragments and continuous forest habitat of the Morro Grande Reserve on the Ibiúna Plateau, São Paulo state, Brazil. Small (S) andmedium (M) fragments of forest are classified as isolated (I) or connected (C) depending on the presence of corridors to larger tracts of forest.

1562 M. Dixo et al. / Biological Conservation 142 (2009) 1560–1569

2.4. Spatial analyses

We used three approaches to characterize the patterns of thespatial distribution of genetic variation in our samples. First wetested for a pattern of isolation by distance (IBD) across all sitesby correlating a matrix of pairwise genetic distances (FST/1 + FST)among all sites with geographic distances using a Mantel matrixcorrespondence test. We implemented this test in the programIBD v. 1.52 (Bohonak, 2002); the significance of the associationwas measured using 1000 randomizations. We also used a reducedmajor axis regression (RMA) implemented in the software RMA(Bohonak, 2004) to test whether fragments of different sizes mightexhibit different patterns of spatial genetic distribution. We usedRMA models because both axes are equally subject to error (Sokaland Rohlf 1995; McArdle 1988). To assess whether IBD might de-pend on fragmentation status, we performed an analysis of all sitescombined, as well as two independent analyses of pairwise com-parisons that included only fragments and comparisons that in-cluded at least one sample from a continuous forest site. Because

both reduced gene flow and high drift in small populations can ob-scure historical patterns of IBD, we use the comparison of all threeIBD analyses to examine whether the sampled populations mightshow genetic changes due to fragmentation. If patterns of IBDare evident among all populations, but not among fragments, weinfer that the population genetic consequences of fragmentationhave obscured the historical pattern. In contrast, if neither theoverall comparisons nor the fragments show a pattern of IBD, weinfer that the populations sampled are members of a single pan-mictic genetic deme, and that although fragmentation may alreadycause demographic changes in populations (Dixo, 2005), thechanges in population size and connectivity have not yet affectedthe distribution of genetic diversity on this landscape.

Second, we used a spatial analysis of molecular variance imple-mented in the program SAMOVA v. 1.0 (Dupanloup et al., 2002) todefine partitions of local populations that are maximally differen-tiated from each other based on our sequence data. The methodis based on a simulated annealing procedure that maximizes theproportion of genetic variance that can be explained by differences

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M. Dixo et al. / Biological Conservation 142 (2009) 1560–1569 1563

between groups of populations (the FCT coefficient of the AMOVA,Excoffier et al., 1992). Our analyses were based on 100 simulatedannealing steps, and a prior definition of the number of groups,K, ranging from 2 to 17. For each analysis with increasing K, weexamined the proportion of genetic variance due to differences be-tween groups, FCT, and found the range of K for which FCT was larg-est and statistically significant.

In a third analysis we applied Monmonier’s maximum differencealgorithm (Monmonier, 1973), a procedure to identify regions ofsharp genetic change on a landscape (Manel et al., 2003). This meth-od, implemented in the program Alleles In Space (Miller, 2005), isbased on a spatially explicit Delauney network for all samplinglocalities. Genetic distances are computed among all pairs of popu-lations connected in the network, and barriers to gene flow are in-ferred by tracing lines along network segments with highestassociated genetic distances. If iterated, this procedure results in aseries of ‘‘ranked” barriers from highest to lowest genetic disconti-nuity (Miller, 2005). Variances in geographic distances among local-ities can bias spatial analyses (Miller et al., 2006), and our IBDshowed that on average, geographic divergences among popula-tions in continuous forests are further from other populations thanare fragments. These differences result from the process of fragmen-tation itself, because the continuous sampling sites are containedwithin large tracts of forests, they are necessarily more distant fromother sites (Fig. 1). To correct for this potential bias, we used residualgenetic distances derived from the linear regression of pairwise ge-netic and geographic distances to minimize biases in the estimatesof barriers on the Delauney network (Manni et al., 2004). We iter-ated the Monmonier algorithm ten times, and examined the loca-tion of the predicted breaks and the proportion of barriersinferred between fragmented and continuous sampling sites.

A simulation-based comparison of the performance of SAMOVAand Monmonier methods for detecting barriers (Dupanloup et al.,2002) indicates that they both perform relatively well and findmaximally differentiated groups, especially in the absence of isola-tion by distance. The two methods differ in their ability to detectgenetic discontinuities when differentiation among populations islow, and they also differ in whether they are limited to clusteringadjacent localities. Given these differences, we applied both meth-ods to our data, and interpret the results with the limitations ofeach method in mind (Dupanloup et al., 2002).

Tabl

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3. Results

3.1. Population genetic diversity in fragments and continuous forest

We obtained 262 individual sequences, each 585 base pairs inlength. We found 27 distinct haplotypes that varied significantlyin frequencies over all populations (Table S1). Eight haplotypeswere unique to single population (Table S1). The number of haplo-types per population (adjusted for sample size) varied from 0.47 insmall and medium isolated fragments (SI1, SI2, and MI4), to 0.71 inone of the control areas (CT2). The absolute number of haplotypesper population varied from seven, in five fragments (SI1, SI2, MC4,MI2, and MI4) to 10 in four populations (CT1, CT2, MI4, and SI1)(Table S1). Haplotype diversity (h) varied from 0.658 (±0.138) inthe small isolated fragment SI3, to 0.945 (±0.045) in the controlarea CT2 (Table S1), whereas nucleotide diversity by site (p) variedfrom 0.018 (±0.010) in the medium connected fragment MC4, to0.009 (±0.005) in the small connected fragment SC1 (Table S1).The number of polymorphic nucleotide sites at each samplinglocality varied from 18 in MI1 to 30 in CT1 (Table S1).

Pairwise FST values (a measure of connectivity between pairs ofsites) ranged from 0.0005 to 0.1602, with the highest values foundbetween fragments SI3 and M12 (Table 1). Although most pairwise

1564 M. Dixo et al. / Biological Conservation 142 (2009) 1560–1569

comparisons indicated relatively low differentiation among popu-lations, 12 of the pairwise comparisons were significantly differentthan zero (Table 1). In most cases the significant FST comparisonsincluded populations from small fragments: five of these occurredbetween two small fragments, four between small and mediumfragments, two between small fragments and continuous forest,and one occurred between two medium fragments (Table 1).

Although not statistically significant, the number of haplotypesat each site showed a positive trend with greater diversity in largercontrol areas compared to fragments (Fig. 2). Haplotype diversitywas significantly and positively related to the logarithm of forest

0Area (log)

6

7

8

9

10

11

r2 =0.11; p=0.096

Haplotype diversity

Number of haplotypes

Area (log)

0

5

10

15

r2 =0.23; p=0.024

Nucleotidic diversity

Area (log)

0.009

0.012

0.015

0.018

r2 =0.00; p=0.370

1 2 3 4 5

0 1 2 3 4 5

0 1 2 3 4 5

Fig. 2. Variation in the number of haplotype, haplotype diversity and nucleotidediversity estimated from mtDNA control region sequences of Rhinella ornata inrelation to the logarithm of the area of the sample sites.

area (Fig. 2). In contrast, nucleotide diversity did not vary signifi-cantly with forest area (Fig. 2).

The ANOVA testing for the effect of fragment size and connec-tivity showed no significant differences for any of the indices of ge-netic diversity. The number of haplotypes, haplotype diversity, andnucleotide diversity did not vary significantly between isolatedfragments and those connected by corridors to larger fragments(Fig. 3), despite a positive trend for higher nucleotide diversity inmedium-sized fragments.

TCS grouped haplotypes into two independent networks(Fig. 4). The networks are not connected at 95% level and sixadditional steps would be necessary to join them. These haplo-type groups do not appear to be related to fragmentation, be-

Number of haplotypes

Isolated Connected6

8

10

Small Medium

F(1, 11)=0.590, p=0.460

Haplotype diversity

Isolated Connected

0

5

10

15

Small Medium

F(1, 11)=0.13, p=0.730

Nucleotidic diversity

Isolated Connected0.009

0.012

0.015

0.018

Small Medium

F(1, 11)=3.58, p=0.085

Fig. 3. Variation in the number of haplotype, haplotype diversity and nucleotidediversity estimated from mtDNA control region sequences of Rhinella ornataconsidering the connectivity (connected vs. isolated) and size and of the fragments(small vs. medium).

h6

h20

h26

h12

h2 h5 h1

h8

h18

h13

h10

h11 h9

h4

h19

h14 h21

h15

h3 h16 h17

h24

h23

h25

h27

h22

h7

Fig. 4. Haplotype network based on 585 base pairs of Rhinella ornata mtDNA control region. Lines uniting haplotypes indicate a single base pair difference between them;transverse bars represent multiple mutational steps. The size of haplotype circles is proportional to their relative frequency in the sample. Black and white circles representhaplotypes found in continuous forest sites and fragments, respectively. Gray circles represents haplotypes found in both continuous and fragmented forests.

M. Dixo et al. / Biological Conservation 142 (2009) 1560–1569 1565

cause each network contains haplotypes from continuous forestareas as well as fragments (Table S1; Fig. 4). The two groupsare also unrelated to the geographical distribution of the frag-ments; both networks include haplotypes found at 17 of the18 study sites.

3.2. Spatial analyses of barriers to gene flow

We found no evidence of isolation by distance in the Mantel testincluding all samples (Mantel test r = �0.06, p = 0.596). Becauseour control sites are geographically distant from most of our sam-pled fragments, we tested for any potential bias due to the spatialdistribution of our continuous forest and fragment plots, by com-paring IBD patterns calculated only among fragments to thoseincluding at least one of the three continuous forest sites (Fig. 5).We found no significance for patterns of genetic IBD when consid-ering all sampling sites, only fragments, or comparisons that in-

clude at least one control area. In all analyses, reduced majoraxis regression showed that geographic distance explained only asmall proportion of the variation in genetic diversity (all sites:r = 0.0364; fragments only: r = 0.0043; at least one continuous site:r = 0.0223).

The SAMOVA analysis identified maximally differentiatedgroups in our sample. The sequential analyses with values of Kranging from 2 to 17 were all significant at the p < 0.05 level.However, the smaller values of K in this range (K = 2–5) showthe highest values of FCT (differentiation among groups), theparameter that is maximized in this procedure. The FCT estimatedat higher values of K are still significant, but increase incremen-tally as differentiation within each group decreases (Dupanloupet al., 2002). This interplay between FCT and FST is expectedand is one of the difficulties of using FCT to define the ‘‘real”number of differentiated groups (Dupanloup et al., 2002). There-fore, we compare the inferred population structure in the range

-0.10

0.00

0.10

0.20

0 5000 10000 15000 20000Geographical distance (m)

Gen

etic

dis

tanc

e (F

ST/1

-FST

)

Fragments Continuous

Fig. 5. Scatter plot of pairwise genetic distance versus geographical distance for all18 sites included in this study (n = 153). We found no evidence of isolation bydistance among population of Rhinella ornata.

Table 2Results of the spatial analysis of molecular variance (SAMOVA) to identify barriers togene flow between maximally differentiated sites. The variance components from theSAMOVA using each of the groupings are reported with their p-values (significantcomponents are in bold). The groupings with highest FCT values ranged between 2 and5 groups; in each case variation among groups is statistically significant and explainsbetween 6.24 and 8.62% of genetic variance in the sample. All significant subdivisionsinvolve the separation of small or medium fragments from remaining populations.

Groups F values p-value % Var

2 Groups FCT = 0.0862 0.0068 8.621. SI3 and MC3 FSC = 0.0118 0.2933 �1.082. Remaining populations FST = 0.0754 0.2776 92.46

3 Groups FCT = 0.0767 0.0078 7.671. SI3 FSC = 0.0100 0.5455 �0.932. MC3 FST = 0.0674 0.2776 93.263. Remaining populations

4 Groups FCT = 0.0681 0.0020 6.811. SI3 FSC = 0.0144 0.7097 �1.342. MC3 FST = 0.0547 0.2766 94.533. MI14. Remaining populations

5 Groups FCT = 0.0624 0.0010 6.241. SI3 FSC = �0.0188 0.2991 �1.762. MC3 FST = 0.0448 0.2776 95.523. MI14. SC15. Remaining populations

1566 M. Dixo et al. / Biological Conservation 142 (2009) 1560–1569

of K that shows the highest significant values for FCT (Table 2).Our results show a hierarchical grouping arrangement as K in-creases from 2 to 5 (Table 2). In every case, the population sam-ples that form independent groups are populations fromfragments. We did not find any evidence that fragment size orconnectivity influence this assignment, as fragments in the suc-cessively higher grouping arrangements included both categoriesof size and connectivity.

Our final spatial analysis of possible barriers to gene flow cor-roborates the patterns observed in the SAMOVA analyses. Itera-tions of the Monmonier algorithm resulted in 10 barriers amongpopulations that reflect genetic discontinuities and possible barri-ers to gene flow. Of those ten, only one is a barrier isolating a con-tinuous site (barrier 5, Fig. 6). The remaining nine inferred barriersisolate fragments. As in the SAMOVA analyses, we found no patternassociated with the connectedness and size of the fragments, sug-gesting that both medium and small fragments are equally likely tobecome disconnected from remaining populations in the land-scape. The Monmonier algorithm incrementally adds barriers to aset of populations based on the interpolation of genetic differences

Fig. 6. Spatial analysis of inferred barriers to gene flow from the Monmonier analysis. Thdistribution. The ten largest spatial genetic discontinuities in our data are highlighted wito gene flow isolate populations in fragments from other fragments and from the area o

on the network, whereas SAMOVA maximizes differentiationamong the inferred groups; therefore, we would not necessarilyexpect the inferences from both methods to be identical (Dupan-loup et al. 2002). Nonetheless, the two populations that were con-sidered most isolated in SAMOVA (MC3 and SI3) are also isolated inthe Monmonier analysis (Fig. 6).

4. Discussion

Populations of R. ornata sampled in this fragmented AtlanticCoastal Forest landscape have undergone slight but detectable ge-

e Delauney network uniting all sampled populations is overlayed on a map of forestth heavy black lines and numbered on the network (b1–b10). Nine of the 10 barriersf continuous forest.

M. Dixo et al. / Biological Conservation 142 (2009) 1560–1569 1567

netic changes in response to fragmentation, drift, and/or barriers togene flow. We found a significant decrease in haplotype diversityin fragments with smaller forest areas (Fig. 2), likely due to geneticdrift and random loss of haplotypes in smaller fragments with re-duced effective population sizes. We also found a trend (althoughnot statistically significant) of fewer haplotypes overall in frag-ments of smaller size (Fig. 2). Despite these within-populations dif-ferences, we observed only weak differentiation amongpopulations in FST (Table 1), and no significant pattern of isolationby distance among fragments, continuous forest sites, or both pop-ulation categories (Fig. 5). Nonetheless, our spatially explicit anal-ysis of genetic discontinuity among sampling sites identifiedbarriers to dispersal primarily among fragmented areas (Fig. 6).These discordant patterns could be due to the rate at which intra-and inter-population signatures of fragmentation become evident(Peakall and Lindenmayer, 2006) or due to lower power of inter-population comparisons that stem from our experimental designand relatively small sample sizes.

The limited effects of habitat fragmentation on genetic diversityin some species have led some authors to emphasize the difficultyof studying recent fragmentation using molecular techniques(Sumner et al., 2004; Banks et al., 2005; Richmond et al., in press).The difficulty arises in trying to distinguish between recent andhistorical signatures of interrupted gene flow (Cunningham andMoritz, 1998; Sumner et al., 2004). This problem can be com-pounded if habitat fragmentation is very recent, and may not yethave left a population genetic signature. Historical effects appearto be more severe at larger geographical scales (Lindenmayer andPeakall, 2000) and may be accentuated in studies using molecularmarkers with conservative rates of mutation, such as allozymesand mtDNA. Therefore, it may seem surprising that we showed re-duced genetic diversity and the presence of ‘barriers’ among frag-ments in our system. The two divergent mtDNA haplotype groupswe found among our samples may be indicative of historical inter-ruption to gene flow; however, any signal of anthropogenic frag-mentation should not be confounded by historical differentiationbecause haplotypes from both groups are found across almost allsites with no geographic pattern in their distribution (Fig. 4).Reduction in genetic diversity, or changes in haplotypic diversitydue to genetic drift can occur rapidly if migration is curtailedand population sizes are small (Lacy, 1987; Peakall and Lindenma-yer, 2006). The fragmentation of the forests on the Ibiúna Plateauintensified in the 1900s, with logging and clearing for agriculture(Seabra, 1971); thus most fragments have been isolated at leastfor 75 years (Teixeira et al., 2008). Assuming that our focal specieshas a generation time of approximately 3 years, the toads living inthese fragments have been isolated for only 25 generations. Thefact that we can detect differences attributable to this recent frag-mentation suggests that drift and restriction to gene flow must besignificant in the fragments (Lacy, 1987). Studies in this same frag-mented landscape show the toad population sizes in small frag-ment are highly variable over time (Dixo, 2005); it is possiblethat the processes leading to this demographic instability also con-tribute to decreased survival or reproductive success of migrants infragments.

Past studies of fragmentation have emphasized the importanceof species-typical characteristics, such as habitat specialization,vagility, and ecological tolerance, for determining the susceptibil-ity of individual species to habitat fragmentation (Sumner et al.,2004; Luoy et al., 2007). Both dispersal ability and habitat avail-ability will determine the genetic consequences of fragmentation(Peakall and Lindenmayer, 2006). Species with high dispersal abil-ity and with a high number of habitat patches have a better chanceof maintaining gene flow and panmixia. Lower dispersal capacityand high numbers of accessible fragments results in a pattern ofisolation by distance. Finally, species with low dispersal capacity

and low habitat availability will become isolated, gene flow willcease, and drift will act independently in each population (Luoyet al., 2007). However, gene flow and fragment availability alonedo not explain genetic variation among fragmented populations,because certain fragments may be too small to sustain adequateeffective population sizes and maintain genetic diversity. It is theinterplay between migration and effective population size thatdetermines the retention of genetic diversity in fragmented land-scapes. R. ornata has the ability to traverse the matrix of this frag-mented landscape (Dixo, 2005). Although this landscape is highlyfragmented, there are corridors of appropriate habitat joining frag-ments as well as several habitat patches that we did not sample(Fig. 1). Nonetheless, we found no significant correlation betweengenetic and geographic distance, suggesting that dispersal limita-tion alone does not underlie the distribution of genetic diversityfor this species in this landscape.

A number of studies have reported changes in genetic diversityassociated with habitat fragmentation in reptiles and amphibians(Hitchings and Beebee, 1997, 1998; Andersen et al., 2004; Wahbeet al., 2005; Arens et al., 2007; Noël et al., 2007; Telles et al.,2007). As in our study, in most of these cases genetic profiles infragmented habitats suggest that populations could be at risk fromthe factors associated with decreased genetic diversity, but oftenthe genetic signals of fragmentation are subtle, due to the recencyof the disturbance, and the lack of migration-drift equilibrium.Our data show that genetic diversity of R. ornata in many of theremaining patches of habitat is reduced, therefore, any reductionin connectivity among these habitat patches will compound therate at which genetic variation will be lost within populationsdue to inbreeding. A study of Cunningham’s skinks (Egernia cun-ninhami) in fragmented habitats showed limited dispersal in frag-mented habitats, but adult skinks avoided excessive inbreedingthrough the choice of more unrelated mates (Stow and Sunnucks,2004). Whether R. ornata also has inbreeding avoidance strategiesis unknown; however, the reproductive mode of this species makesthe evolution of these strategies unlikely. R. ornata is an explosivelybreeding species and restricted to pools and ponds that are patchilydistributed; both characteristics that decrease the opportunity formate choice (Wells, 1977). Short and explosive breeding seasonsalso contribute to high reproductive skew (and a small number ofindividuals successfully mating), thus further reducing effectivepopulation sizes and potential inbreeding in isolated populations.

Finally, our results emphasize the importance of fragment areafor the maintenance of genetic diversity in anthropogenically mod-ified landscapes. The current distribution of fragmented remnantsin the landscape may not completely isolate toad populations,nonetheless, we observed a significant, and positive correlation be-tween genetic diversity and fragment size. This relationship, com-bined with the pattern of lowered genetic diversity and largetemporal variation in population abundance in isolated fragments(Dixo, 2005) suggest that even very common species may be sus-ceptible to local stochastic events and may suffer local extinctions.Amphibians often depend on different, geographically disjuncthabitat types to complete their life cycle. Fragmentation can causea separation between these two habitats critical for reproduction, aphenomenon has been termed habitat split (Becker et al., 2007).Habitat split has a larger affect on amphibian species with aquaticlarval development, and can significantly exacerbate the negativeeffects of fragmentation. R. ornata is an aquatic breeder and there-fore will be particularly susceptible to fragmentation that isolatesbreeding wetlands from upland forests that are the preferred hab-itats of adults. Our results underscore that even species with rela-tively high dispersal capacities and high tolerance to disturbedenvironments may, over time, suffer the negative genetic effectsof fragmentation (Andersen et al., 2004; Peakall and Lindenmayer,2006).

1568 M. Dixo et al. / Biological Conservation 142 (2009) 1560–1569

Acknowledgements

This study would not have been possible without the generoushelp of colleagues; we are especially grateful to Jose Mario B.Ghellere, Ricardo Braga-Neto and Sergio Marques de Sousa for helpin the field, Cristina Peruzin, Nadia Moraes, Patrícia Farias and Sil-via Geurgas for the help in the laboratory, and Leandro Tambosi forpreparing Fig. 6. We thank José Roberto Nali, SABESP, for facilitat-ing our research at the Morro Grande Reserve and many landown-ers for permission to work on their properties. This work wasapproved by a permit (13/2002) from the Instituto Brasileiro doMeio Ambiente e Recursos Naturais Renováveis (IBAMA). Fundingwas provided by the Fundação de Amparo a Pesquisa do Estadode São Paulo (FAPESP), Fundação O Boticário de Proteção à Nature-za, and the National Science Foundation.

Appendix A. Supplementary material

Supplementary data associated with this article can be found, inthe online version, at doi:10.1016/j.biocon.2008.11.016.

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