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

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

Biological Conservation 142 (2009) 1560–1569 Contents lists available at ScienceDirect Biological Conservation journal homepage: www.elsevier.com/lo...

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Biological Conservation 142 (2009) 1560–1569

Contents lists available at ScienceDirect

Biological Conservation journal homepage: www.elsevier.com/locate/biocon

Habitat fragmentation reduces genetic diversity and connectivity among toad populations 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, Brazil Department of Genetics and Evolutionary Biology, University of São Paulo. Rua do Matão, 277, CP 11461, CEP 05422-970 São Paulo, SP, Brazil c Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, 14853-2701, USA b

a r t i c l e

i n f o

Article history: Received 21 June 2008 Received in revised form 18 November 2008 Accepted 24 November 2008 Available online 8 January 2009 Keywords: Rhinella ornata Anuran Spatial analysis Gene flow Anthropogenic Conservation

a b s t r a c t Tropical rainforests are becoming increasingly fragmented and understanding the genetic consequences of fragmentation is crucial for conservation of their flora and fauna. We examined populations of the toad Rhinella ornata, a species endemic to Atlantic Coastal Forest in Brazil, and compared genetic diversity among small and medium forest fragments that were either isolated or connected to large forest areas by corridors. Genetic differentiation, as measured by FST, was not related to geographic distance among study sites and the size of the fragments did not significantly alter patterns of genetic connectivity. However, population genetic diversity was positively related to fragment size, thus haplotype diversity was lowest in the smallest fragments, likely due to decreases in population sizes. Spatial analyses of genetic discontinuities among groups of populations showed a higher proportion of barriers to gene flow among small and medium fragments than between populations in continuous forest. Our results underscore that even species with relatively high dispersal capacities may, over time, suffer the negative genetic effects of fragmentation, possibly leading to reduced fitness of population and cases of localized extinction. Ó 2008 Elsevier Ltd. All rights reserved.

1. Introduction Fragmentation of natural habitats is a major challenge in conservation biology and one of the top threats to biodiversity (Hanski, 1999; Fahrig, 2003; Henle et al., 2004). The negative effects of fragmentation result from the decrease in overall habitat availability and changes in spatial configuration and habitat quality of fragments (Fahrig, 2003; Ezard and Travis, 2006). Both theoretical and empirical studies show that habitat fragmentation can erode neutral and adaptive genetic diversity of populations due to decreases in effective population size and inter-population connectivity (Johansson et al., 2007). After fragmentation, small populations, and lower genetic diversity lead to genetic drift, higher risks of inbreeding, lower evolutionary potential, and consequently, higher risk of extinction (Avise et al., 1987; Young et al., 1996; Saccheri et al., 1998; Reed and Frankham, 2003). The maintenance of semi-natural levels of habitat connectivity via habitat corridors has been proposed as a means of reducing the negative effects of fragmentation (Harris, 1984; Bennett, 1990; Saunders et al., 1991). Corridors, if adequate in arrangement and number, can in theory offset the negative consequences of population isola-

* Corresponding author. Tel.: +55 11 30917603; fax: +55 11 30918096. E-mail addresses: [email protected] (M. Dixo), [email protected] (J.P. Metzger), [email protected] (J.S. Morgante), [email protected] (K.R. Zamudio). 0006-3207/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.biocon.2008.11.016

tion in fragments (Harris 1984; Mech and Hallett, 2001) and reduce demographic stochasticity (Merriam, 1991). One of the difficulties in managing anthropogenically altered landscapes is that susceptibility to fragmentation is highly species-specific, depending in part on historical population sizes, dispersal 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 typically have lower population sizes are often less genetically diverse (Frankham, 1996), and therefore should be more susceptible to genetic erosion resulting from habitat fragmentation, but this effect can be mitigated in species with sustained levels of dispersal between fragments (Galbusera et al., 2004; Cushman, 2006). In contrast, some empirical studies show that surprisingly, habitat fragmentation can have similar effects on common species (Dixo, 2005; Peakall and Lindenmayer, 2006), including those that are habitat generalists and have high rates of gene flow (Williams et al., 2003). Thus, interactions between species characteristics and those of the altered landscape are complex, precluding generalizations 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 anthropogenic habitat modification (Dodd and Smith, 2003; Cushman 2006). Amphibians are the vertebrate lineage with highest number of species threatened with extinction (Stuart et al., 2004; Beebee

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and Griffiths, 2005) and their vulnerability is at least partially explained by their often narrow environmental tolerances (Findlay and Houlahan, 1997; Bridges and Semlitsch, 2000; Pounds et al., 2006) and generally low dispersal capacities (Gibbs, 1998; deMaynedier and Hunter, 2000), both characteristics that exacerbate the negative effects of habitat degradation and loss of population connectivity. In this study we used population genetic analyses to test for the predicted effects of fragmentation in populations of a toad endemic to the Brazilian Atlantic Rainforest, one of the top 10 biodiversity hot spots (Mittermeier et al., 1999; Myers et al., 2000) and one of the most fragmented biomes in the world, with only 12% of its original range remaining (SOS Mata Atlântica, 2008). Our study focused on Rhinella ornata, a habitat-generalist that is common in forested as well as edges of disturbed areas (Baldissera et al., 2004). We studied this species in forests on the Ibiúna Plateau, a highly developed region in southwestern Brazil that still harbors both large continuous tracts as well as forest fragments (Dixo, 2005; Pardini et al., 2005). R. ornata is able to move through the agricultural matrix that isolates fragments and is abundant in fragments of all sizes; nonetheless, populations in small and mediumsized fragments show significant annual variation in abundance, suggesting possible demographic instability of populations in fragments (Dixo, 2005). We compared genetic diversity and structure of R. ornata populations in fragments and continuous tracts of Atlantic Rainforest to test the hypotheses that (i) this generalist species is vulnerable to genetic erosion, (ii) larger fragment sizes and corridors mitigate fragmentation and maintain genetic diversity in populations, and (iii) fragmentation negatively impacts gene flow among remaining patches of habitat, despite this species’ ability to survive in deforested areas. We might expect that this generalist species would not be highly susceptible to the negative impacts of habitat fragmentation; therefore, any evidence of genetic erosion in this generalist and relatively tolerant species, implies that less common and more specialized species may suffer even more severe effects as a result of 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 Paranapiacaba Mountain Range, approximately 40 km northwest of the city of São Paulo; the landscape on the plateau includes both continuous forest and a highly fragmented landscape (Fig. 1). The original forest in the region is classified as Lower Montane rainforest (Oliveira-Filho and Fontes, 2000) with elements of semi-deciduous and Araucaria mixed forests (Catharino et al., 2006). The study area is part of the Atlantic Forest biome, which formerly extended along the entire Brazilian coast. Although large undisturbed tracts still exist (Fonseca, 1985), the remaining smaller forest fragments are sparse and isolated, sometimes with less than 100 ha and highly disturbed vegetation (Fonseca, 1985; Turner and Corlett, 1996). The approximately 9400 hectares of continuous forest on the Ibiúna Plateau are protected in the Morro Grande Forest Reserve (Fig. 1). The fragmented landscape is located southwest of the reserve and includes secondary forests (31% of the landscape), developed areas (rural installations, and urban areas, 15%), and open areas (agriculture and pasture, including fallow lands and those in early successional stages, 39.3%). These open areas form most of the landscape matrix surrounding forest fragments. Agricultural lands (57% of the matrix) are predominantly used for horticultural crops that supply the 40 million inhabitants of São Paulo State (IBGE, 2007).

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We collected R. ornata from 18 sites on the Ibiúna plateau: three control-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. Fragments connected to other larger fragments (>60 ha) by corridors of native vegetation measuring 25–100 m in width were classified as ‘‘connected” fragments. Straight line distances between collecting sites ranged from 557 to 17,158 m. Tissue samples (toe clippings) were collected at each site from adult toads captured in pitfall traps. We collected on average 14.55 samples per locality (range 12–16). Tissues were stored individually in absolute ethanol, and the toads released at the sites of capture. 2.2. Laboratory protocols We isolated genomic DNA from toe clips using a standard phenol–chloroform extraction method (Sambrook and Russell, 2001). We amplified a fragment of the mtDNA control region using primers Control Wrev-L and Control P-H (Goebel et al., 1999). Polymerase chain reactions (PCRs) consisted of an initial denaturation step of 94 °C for 5 min, followed by 35 cycles of denaturation at 94 °C for 40 s, annealing at 58 °C for 40 s, and extension at 72 °C for 40 s, followed by a final extension at 72 °C for 7 °min. Amplified fragments were sequenced with Big Dye Terminator components (Applied Biosystems) according to manufacturer’s protocols. Sequencing reaction products were electrophoresed on an ABI PRISM 310 DNA sequencer (Applied Biosystems). Sequence electropherograms were edited manually and aligned using the program Sequence Navigator version 1.01. All sequences have been deposited in GenBank (Accession no. FJ558516–FJ558542). 2.3. Population genetic analyses We used the program Arlequin v. 3.0 (Excoffier et al., 2005) to calculate three population indices of genetic diversity: number of haplotype, nucleotide diversity, and haplotype diversity. We used an ordinary least square regression to test for possible biases in the number of haplotypes at each site due to uneven sample sizes. Because we found no significant association between sample size and haplotype diversity, we used uncorrected haplotype numbers in all subsequent statistical analyses; nonetheless, we also report the number of haplotypes adjusted for sample size (h/N), for comparison. Pairwise FST values were estimated in the program Arlequin v. 3.0 (Excoffier et al., 2005) and their significance tested using 10,000 permutations of haplotypes between populations as a null distribution. We used ordinary least square regression between the logarithm of fragment areas and within-population indices of genetic diversity to test for possible genetic effects of forest area at our sampling sites. To test the hypothesis that corridors contribute to maintenance of genetic diversity in fragments, we used a two-factor ANOVA including fragment size (small, medium) and connectivity (connected or isolated). Prior to all statistical analyses, we tested our data for homogeneity of variance using the Bartlett test (Zar, 1996); only haplotype diversity required transformation and the data were rank-transformed for statistical analyses. All statistical tests were performed using Statistica v. 6.1 (StatSoft, 2001). We created a network based on all haplotypes in the program TCS, v.1.21 (Clement et al., 2000). This method organizes haplotypes into networks according to genetic distance (Templeton et al., 1992). Haplotypes are connected when the number of differences between them does not exceed a 95% parsimony probability threshold (Templeton et al., 1992). Haplotype networks better illustrate genetic divergence at the intra-specific level, especially in cases of multiple haplotypes that are derived from a single ancestral sequence (Templeton et al., 1992).

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S 23° 38´ W 46 ° 53´

Brazil

Continuous forest

Fragments

CT1

CT2

São Paulo CT3

O

S 23° 50´ W 47 ° 13´

5

10

Kilometers S 23°40´49” W 47 °02´10”

MC1

MC3

SC2

SI3

MC2 MI4

MI3

SC3

MC4 MI1 MI2 SC4 SC1 SI1

SI2

S 23° 47´10” W 47 ° 07´24” O

1

2

Kilometers

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) and medium (M) fragments of forest are classified as isolated (I) or connected (C) depending on the presence of corridors to larger tracts of forest.

2.4. Spatial analyses We used three approaches to characterize the patterns of the spatial distribution of genetic variation in our samples. First we tested for a pattern of isolation by distance (IBD) across all sites by correlating a matrix of pairwise genetic distances (FST/1 + FST) among all sites with geographic distances using a Mantel matrix correspondence test. We implemented this test in the program IBD v. 1.52 (Bohonak, 2002); the significance of the association was measured using 1000 randomizations. We also used a reduced major axis regression (RMA) implemented in the software RMA (Bohonak, 2004) to test whether fragments of different sizes might exhibit different patterns of spatial genetic distribution. We used RMA models because both axes are equally subject to error (Sokal and Rohlf 1995; McArdle 1988). To assess whether IBD might depend on fragmentation status, we performed an analysis of all sites combined, as well as two independent analyses of pairwise comparisons that included only fragments and comparisons that included at least one sample from a continuous forest site. Because

both reduced gene flow and high drift in small populations can obscure historical patterns of IBD, we use the comparison of all three IBD analyses to examine whether the sampled populations might show genetic changes due to fragmentation. If patterns of IBD are evident among all populations, but not among fragments, we infer that the population genetic consequences of fragmentation have obscured the historical pattern. In contrast, if neither the overall comparisons nor the fragments show a pattern of IBD, we infer that the populations sampled are members of a single panmictic genetic deme, and that although fragmentation may already cause demographic changes in populations (Dixo, 2005), the changes in population size and connectivity have not yet affected the distribution of genetic diversity on this landscape. Second, we used a spatial analysis of molecular variance implemented in the program SAMOVA v. 1.0 (Dupanloup et al., 2002) to define partitions of local populations that are maximally differentiated from each other based on our sequence data. The method is based on a simulated annealing procedure that maximizes the proportion of genetic variance that can be explained by differences

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0.0183 0.0417 0.0365 0.0038 0.0594 0.0095 0.0523

0.1134 0.0223 0.0550 0.0338 0.0497 0.0476 0.0039 0.0245 0.0025 0.0073 0.0066

0.0160 0.0427 0.0434 0.0498 0.0349 0.0390 0.0545 0.0324 0.0674 0.0230 0.0537 0.0351 0.0268 0.0448 0.0262

0.0372 0.0376 0.0253 0.0267 0.0062 0.0414 0.0496 0.0645 0.0459 0.0222 0.0430 0.0292 0.0528 0.0056 0.0384 0.0357 0.0031 0.0115 0.0013

0.0668 0.0398 0.0793 0.0718 0.0861 0.1602 0.0404 0.0140 0.1193 0.0533 0.0884 0.0906 0.0414 0.0176 0.0435 0.0334 0.0005 0.0118 0.0553 0.0190 0.0091 0.0109 0.0385

0.0476 0.1554 0.0809 0.0427 0.0881 0.0146 0.0516 0.0183 0.1173 0.0195 0.0326 0.0365 0.0101 0.0188 0.0075 0.1127 0.0183 0.0008 0.0317 0.0397 0.0862 0.0319 0.0885 0.0298 0.0040 0.0109 0.0252

0.0498 0.0579 0.0387 0.1240 0.0297 0.0125 0.0362 0.0319 0.1183 0.0265 0.1143 0.0289 0.0098 0.0045 0.0052 0.0504 0.0 304 0.0471 0.0061 0.1121 0.0325 0.0507 0.0057 0.0152 0.0142 0.0267 0.0521 0.0052 0.0555 0.0575 0.0481

0.0249 0.0159 0.0060 0.0131 0.0369 0.0280 0.0151 0.0210 0.0194 0.0404 0.0090 0.0108 0.0029 0.0402 0.0072 0.0067 0.0172

CT3 CT2

13,535 11,661 12,568 6903 13,002 9557 11,861 5757 11,791 4078 11,702 6989 12,968 8821 12,636 11,044 17,158 12,835 17,324 12,242 12,196 7103 14,506 10,704 15,866 12,097 15,246 12,996 12,942 10,083 4571 12,232 0.0560 9285 0.0531 0.0573

CT1

12,093 13,178 12,444 12,835 13,529 12,063 12,761 11,235 16,392 16,923 12,617 13,843 14,985 13,708 12,107 1760 4031 799 4924 7126 3263 1789 1671 4308 4862 3430 1749 2943 2918

MC2 MC3

1712 6652 3477 7672 9879 6095 4366 2610 3574 4818 6157 2841 2422 3188 5278 2917 6412 8520 5149 3334 3879 1424 2433 4994 1455

MI2 MI1

2732 3987 1505 5089 7252 3726 1896 3131 2653 3114 3621 5187 709 2710 1516 3725 557 1797 4902 5798 5447

MI4 MC4

5555 5412 4483 6524 8301 5817 4356 6145 1411 4587 5932 4156 7079 9046 6060 4351 5300

MI3 MC1

900 5558 2459 6328 8480 4622 3421 3527 2303 997 3309 5513 1833

SI3 SC3

4993 1214 2617 1707 3876 8867 3264 6431 2210

SC1 SC4

6674 1149 4222 2531 3275

SI1 SI2

We obtained 262 individual sequences, each 585 base pairs in length. We found 27 distinct haplotypes that varied significantly in frequencies over all populations (Table S1). Eight haplotypes were unique to single population (Table S1). The number of haplotypes per population (adjusted for sample size) varied from 0.47 in small and medium isolated fragments (SI1, SI2, and MI4), to 0.71 in one of the control areas (CT2). The absolute number of haplotypes per 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) in the small isolated fragment SI3, to 0.945 (±0.045) in the control area CT2 (Table S1), whereas nucleotide diversity by site (p) varied from 0.018 (±0.010) in the medium connected fragment MC4, to 0.009 (±0.005) in the small connected fragment SC1 (Table S1). The number of polymorphic nucleotide sites at each sampling locality varied from 18 in MI1 to 30 in CT1 (Table S1). Pairwise FST values (a measure of connectivity between pairs of sites) ranged from 0.0005 to 0.1602, with the highest values found between fragments SI3 and M12 (Table 1). Although most pairwise

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3.1. Population genetic diversity in fragments and continuous forest

SC2

3. Results

SC2 SI2 SI1 SC4 SC1 SC3 SI3 MC1 MI3 MC4 MI4 MI1 MI2 MC3 MC2 CT1 CT2 CT3

between groups of populations (the FCT coefficient of the AMOVA, Excoffier et al., 1992). Our analyses were based on 100 simulated annealing steps, and a prior definition of the number of groups, K, ranging from 2 to 17. For each analysis with increasing K, we examined the proportion of genetic variance due to differences between groups, FCT, and found the range of K for which FCT was largest and statistically significant. In a third analysis we applied Monmonier’s maximum difference algorithm (Monmonier, 1973), a procedure to identify regions of sharp genetic change on a landscape (Manel et al., 2003). This method, implemented in the program Alleles In Space (Miller, 2005), is based on a spatially explicit Delauney network for all sampling localities. Genetic distances are computed among all pairs of populations connected in the network, and barriers to gene flow are inferred by tracing lines along network segments with highest associated genetic distances. If iterated, this procedure results in a series of ‘‘ranked” barriers from highest to lowest genetic discontinuity (Miller, 2005). Variances in geographic distances among localities can bias spatial analyses (Miller et al., 2006), and our IBD showed that on average, geographic divergences among populations in continuous forests are further from other populations than are fragments. These differences result from the process of fragmentation itself, because the continuous sampling sites are contained within large tracts of forests, they are necessarily more distant from other sites (Fig. 1). To correct for this potential bias, we used residual genetic distances derived from the linear regression of pairwise genetic and geographic distances to minimize biases in the estimates of barriers on the Delauney network (Manni et al., 2004). We iterated the Monmonier algorithm ten times, and examined the location of the predicted breaks and the proportion of barriers inferred between fragmented and continuous sampling sites. A simulation-based comparison of the performance of SAMOVA and Monmonier methods for detecting barriers (Dupanloup et al., 2002) indicates that they both perform relatively well and find maximally differentiated groups, especially in the absence of isolation by distance. The two methods differ in their ability to detect genetic discontinuities when differentiation among populations is low, and they also differ in whether they are limited to clustering adjacent localities. Given these differences, we applied both methods to our data, and interpret the results with the limitations of each method in mind (Dupanloup et al., 2002).

Table 1 Pairwise FST estimates among all populations of Rhinella ornata included in this study (lower matrix). Significant pairwise comparisons (p < 0.05) are indicated in bold. Pairwise geographical distances among sampling sites are in the upper matrix (m). Locality names are the same as those in Fig. 1.

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comparisons indicated relatively low differentiation among populations, 12 of the pairwise comparisons were significantly different than zero (Table 1). In most cases the significant FST comparisons included populations from small fragments: five of these occurred between two small fragments, four between small and medium fragments, two between small fragments and continuous forest, and one occurred between two medium fragments (Table 1). Although not statistically significant, the number of haplotypes at each site showed a positive trend with greater diversity in larger control areas compared to fragments (Fig. 2). Haplotype diversity was significantly and positively related to the logarithm of forest

area (Fig. 2). In contrast, nucleotide diversity did not vary significantly with forest area (Fig. 2). The ANOVA testing for the effect of fragment size and connectivity showed no significant differences for any of the indices of genetic diversity. The number of haplotypes, haplotype diversity, and nucleotide diversity did not vary significantly between isolated fragments and those connected by corridors to larger fragments (Fig. 3), despite a positive trend for higher nucleotide diversity in medium-sized fragments. TCS grouped haplotypes into two independent networks (Fig. 4). The networks are not connected at 95% level and six additional steps would be necessary to join them. These haplotype groups do not appear to be related to fragmentation, be-

Number of haplotypes Number of haplotypes

11

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

10

10 9 8

8 7 r2 =0.11; p=0.096

6

0

1

4

2 3 Area (log)

5

Small Medium

6 Isolated

Haplotype diversity

Haplotype diversity

15

15

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

10

10

5

5

0 0

Connected

r2 =0.23; p=0.024

1

4

2 3 Area (log)

Small Medium

0 5

Isolated

Connected

Nucleotidic diversity

Nucleotidic diversity 0.018

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

0.018 0.015 0.015 0.012 0.012 0.009 0

Small Medium

r2 =0.00; p=0.370

1

2 3 Area (log)

4

5

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

0.009 Isolated

Connected

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

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h10

h8

h25

h24

h1

h5

h11

h9

h2

h4

h26

h23

h13 h19

h12

h18

h22

h27 h14

h6

h21

h15

h7 h16

h3

h17 h20

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 represent haplotypes found in continuous forest sites and fragments, respectively. Gray circles represents haplotypes found in both continuous and fragmented forests.

cause each network contains haplotypes from continuous forest areas as well as fragments (Table S1; Fig. 4). The two groups are also unrelated to the geographical distribution of the fragments; both networks include haplotypes found at 17 of the 18 study sites. 3.2. Spatial analyses of barriers to gene flow We found no evidence of isolation by distance in the Mantel test including all samples (Mantel test r = 0.06, p = 0.596). Because our control sites are geographically distant from most of our sampled fragments, we tested for any potential bias due to the spatial distribution of our continuous forest and fragment plots, by comparing IBD patterns calculated only among fragments to those including at least one of the three continuous forest sites (Fig. 5). We found no significance for patterns of genetic IBD when considering all sampling sites, only fragments, or comparisons that in-

clude at least one control area. In all analyses, reduced major axis regression showed that geographic distance explained only a small 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 differentiated groups in our sample. The sequential analyses with values of K ranging 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) show the highest values of FCT (differentiation among groups), the parameter that is maximized in this procedure. The FCT estimated at higher values of K are still significant, but increase incrementally as differentiation within each group decreases (Dupanloup et al., 2002). This interplay between FCT and FST is expected and is one of the difficulties of using FCT to define the ‘‘real” number of differentiated groups (Dupanloup et al., 2002). Therefore, we compare the inferred population structure in the range

Genetic distance (FST/1-FST)

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0.20

Fragments Continuous

0.10

0.00

-0.10

0

5000

10000

15000

20000

Geographical distance (m) Fig. 5. Scatter plot of pairwise genetic distance versus geographical distance for all 18 sites included in this study (n = 153). We found no evidence of isolation by distance among population of Rhinella ornata.

of K that shows the highest significant values for FCT (Table 2). Our results show a hierarchical grouping arrangement as K increases from 2 to 5 (Table 2). In every case, the population samples that form independent groups are populations from fragments. We did not find any evidence that fragment size or connectivity influence this assignment, as fragments in the successively higher grouping arrangements included both categories of size and connectivity. Our final spatial analysis of possible barriers to gene flow corroborates the patterns observed in the SAMOVA analyses. Iterations of the Monmonier algorithm resulted in 10 barriers among populations that reflect genetic discontinuities and possible barriers to gene flow. Of those ten, only one is a barrier isolating a continuous site (barrier 5, Fig. 6). The remaining nine inferred barriers isolate fragments. As in the SAMOVA analyses, we found no pattern associated with the connectedness and size of the fragments, suggesting that both medium and small fragments are equally likely to become disconnected from remaining populations in the landscape. The Monmonier algorithm incrementally adds barriers to a set of populations based on the interpolation of genetic differences

Table 2 Results of the spatial analysis of molecular variance (SAMOVA) to identify barriers to gene flow between maximally differentiated sites. The variance components from the SAMOVA using each of the groupings are reported with their p-values (significant components are in bold). The groupings with highest FCT values ranged between 2 and 5 groups; in each case variation among groups is statistically significant and explains between 6.24 and 8.62% of genetic variance in the sample. All significant subdivisions involve the separation of small or medium fragments from remaining populations. Groups

F values

p-value

% Var

2 Groups 1. SI3 and MC3 2. Remaining populations

FCT = 0.0862 FSC = 0.0118 FST = 0.0754

0.0068 0.2933 0.2776

8.62 1.08 92.46

3 Groups 1. SI3 2. MC3 3. Remaining populations

FCT = 0.0767 FSC = 0.0100 FST = 0.0674

0.0078 0.5455 0.2776

7.67 0.93 93.26

4 Groups 1. SI3 2. MC3 3. MI1 4. Remaining populations

FCT = 0.0681 FSC = 0.0144 FST = 0.0547

0.0020 0.7097 0.2766

6.81 1.34 94.53

5 Groups 1. SI3 2. MC3 3. MI1 4. SC1 5. Remaining populations

FCT = 0.0624 FSC = 0.0188 FST = 0.0448

0.0010 0.2991 0.2776

6.24 1.76 95.52

on the network, whereas SAMOVA maximizes differentiation among the inferred groups; therefore, we would not necessarily expect the inferences from both methods to be identical (Dupanloup et al. 2002). Nonetheless, the two populations that were considered most isolated in SAMOVA (MC3 and SI3) are also isolated in the Monmonier analysis (Fig. 6).

4. Discussion Populations of R. ornata sampled in this fragmented Atlantic Coastal Forest landscape have undergone slight but detectable ge-

Fig. 6. Spatial analysis of inferred barriers to gene flow from the Monmonier analysis. The Delauney network uniting all sampled populations is overlayed on a map of forest distribution. The ten largest spatial genetic discontinuities in our data are highlighted with heavy black lines and numbered on the network (b1–b10). Nine of the 10 barriers to gene flow isolate populations in fragments from other fragments and from the area of continuous forest.

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netic changes in response to fragmentation, drift, and/or barriers to gene flow. We found a significant decrease in haplotype diversity in fragments with smaller forest areas (Fig. 2), likely due to genetic drift and random loss of haplotypes in smaller fragments with reduced effective population sizes. We also found a trend (although not statistically significant) of fewer haplotypes overall in fragments of smaller size (Fig. 2). Despite these within-populations differences, we observed only weak differentiation among populations in FST (Table 1), and no significant pattern of isolation by distance among fragments, continuous forest sites, or both population categories (Fig. 5). Nonetheless, our spatially explicit analysis of genetic discontinuity among sampling sites identified barriers to dispersal primarily among fragmented areas (Fig. 6). These discordant patterns could be due to the rate at which intraand inter-population signatures of fragmentation become evident (Peakall and Lindenmayer, 2006) or due to lower power of interpopulation comparisons that stem from our experimental design and relatively small sample sizes. The limited effects of habitat fragmentation on genetic diversity in some species have led some authors to emphasize the difficulty of 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 and historical signatures of interrupted gene flow (Cunningham and Moritz, 1998; Sumner et al., 2004). This problem can be compounded if habitat fragmentation is very recent, and may not yet have left a population genetic signature. Historical effects appear to be more severe at larger geographical scales (Lindenmayer and Peakall, 2000) and may be accentuated in studies using molecular markers with conservative rates of mutation, such as allozymes and mtDNA. Therefore, it may seem surprising that we showed reduced genetic diversity and the presence of ‘barriers’ among fragments in our system. The two divergent mtDNA haplotype groups we found among our samples may be indicative of historical interruption to gene flow; however, any signal of anthropogenic fragmentation should not be confounded by historical differentiation because haplotypes from both groups are found across almost all sites with no geographic pattern in their distribution (Fig. 4). Reduction in genetic diversity, or changes in haplotypic diversity due to genetic drift can occur rapidly if migration is curtailed and population sizes are small (Lacy, 1987; Peakall and Lindenmayer, 2006). The fragmentation of the forests on the Ibiúna Plateau intensified in the 1900s, with logging and clearing for agriculture (Seabra, 1971); thus most fragments have been isolated at least for 75 years (Teixeira et al., 2008). Assuming that our focal species has a generation time of approximately 3 years, the toads living in these fragments have been isolated for only 25 generations. The fact that we can detect differences attributable to this recent fragmentation suggests that drift and restriction to gene flow must be significant in the fragments (Lacy, 1987). Studies in this same fragmented landscape show the toad population sizes in small fragment are highly variable over time (Dixo, 2005); it is possible that the processes leading to this demographic instability also contribute to decreased survival or reproductive success of migrants in fragments. Past studies of fragmentation have emphasized the importance of species-typical characteristics, such as habitat specialization, vagility, and ecological tolerance, for determining the susceptibility of individual species to habitat fragmentation (Sumner et al., 2004; Luoy et al., 2007). Both dispersal ability and habitat availability will determine the genetic consequences of fragmentation (Peakall and Lindenmayer, 2006). Species with high dispersal ability and with a high number of habitat patches have a better chance of maintaining gene flow and panmixia. Lower dispersal capacity and high numbers of accessible fragments results in a pattern of isolation by distance. Finally, species with low dispersal capacity

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and low habitat availability will become isolated, gene flow will cease, and drift will act independently in each population (Luoy et al., 2007). However, gene flow and fragment availability alone do not explain genetic variation among fragmented populations, because certain fragments may be too small to sustain adequate effective population sizes and maintain genetic diversity. It is the interplay between migration and effective population size that determines the retention of genetic diversity in fragmented landscapes. R. ornata has the ability to traverse the matrix of this fragmented landscape (Dixo, 2005). Although this landscape is highly fragmented, there are corridors of appropriate habitat joining fragments as well as several habitat patches that we did not sample (Fig. 1). Nonetheless, we found no significant correlation between genetic and geographic distance, suggesting that dispersal limitation alone does not underlie the distribution of genetic diversity for this species in this landscape. A number of studies have reported changes in genetic diversity associated with habitat fragmentation in reptiles and amphibians (Hitchings and Beebee, 1997, 1998; Andersen et al., 2004; Wahbe et 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 in fragmented habitats suggest that populations could be at risk from the factors associated with decreased genetic diversity, but often the genetic signals of fragmentation are subtle, due to the recency of the disturbance, and the lack of migration-drift equilibrium. Our data show that genetic diversity of R. ornata in many of the remaining patches of habitat is reduced, therefore, any reduction in connectivity among these habitat patches will compound the rate at which genetic variation will be lost within populations due to inbreeding. A study of Cunningham’s skinks (Egernia cunninhami) in fragmented habitats showed limited dispersal in fragmented habitats, but adult skinks avoided excessive inbreeding through the choice of more unrelated mates (Stow and Sunnucks, 2004). Whether R. ornata also has inbreeding avoidance strategies is unknown; however, the reproductive mode of this species makes the evolution of these strategies unlikely. R. ornata is an explosively breeding species and restricted to pools and ponds that are patchily distributed; both characteristics that decrease the opportunity for mate choice (Wells, 1977). Short and explosive breeding seasons also contribute to high reproductive skew (and a small number of individuals successfully mating), thus further reducing effective population sizes and potential inbreeding in isolated populations. Finally, our results emphasize the importance of fragment area for the maintenance of genetic diversity in anthropogenically modified landscapes. The current distribution of fragmented remnants in the landscape may not completely isolate toad populations, nonetheless, we observed a significant, and positive correlation between genetic diversity and fragment size. This relationship, combined with the pattern of lowered genetic diversity and large temporal variation in population abundance in isolated fragments (Dixo, 2005) suggest that even very common species may be susceptible to local stochastic events and may suffer local extinctions. Amphibians often depend on different, geographically disjunct habitat types to complete their life cycle. Fragmentation can cause a separation between these two habitats critical for reproduction, a phenomenon has been termed habitat split (Becker et al., 2007). Habitat split has a larger affect on amphibian species with aquatic larval development, and can significantly exacerbate the negative effects of fragmentation. R. ornata is an aquatic breeder and therefore will be particularly susceptible to fragmentation that isolates breeding wetlands from upland forests that are the preferred habitats of adults. Our results underscore that even species with relatively high dispersal capacities and high tolerance to disturbed environments may, over time, suffer the negative genetic effects of fragmentation (Andersen et al., 2004; Peakall and Lindenmayer, 2006).

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