Conserv Genet (2014) 15:49–59 DOI 10.1007/s10592-013-0520-9
RESEARCH ARTICLE
Genetic diversity and structure of an endemic and critically endangered stream river salamander (Caudata: Ambystoma leorae) in Mexico Armando Sunny • Octavio Monroy-Vilchis Victor Fajardo • Ulises Aguilera-Reyes
•
Received: 23 February 2013 / Accepted: 24 July 2013 / Published online: 8 August 2013 Ó Springer Science+Business Media Dordrecht 2013
Abstract Small or isolated populations are highly susceptible to stochastic events. They are prone and vulnerable to random demographic or environmental fluctuations that could lead to extinction due to the loss of alleles through genetic drift and increased inbreeding. We studied Ambystoma leorae an endemic and critically threatened species. We analyzed the genetic diversity and structure, effective population size, presence of bottlenecks and inbreeding coefficient of 96 individuals based on nine microsatellite loci. We found high levels of genetic diversity expressed as heterozygosity (Ho = 0.804, He = 0.613, He* = 0.626 and HNei = 0.622). The population presents few alleles (4–9 per locus) and genotypes (3–14 per locus) compared with other mole salamanders species. We identified three genetically differentiated subpopulations with a significant level of genetic structure (FST = 0.021, RST = 0.044 y Dest = 0.010, 95 % CI). We also detected a reduction signal in population size and evidence of a genetic bottleneck (M = 0.367). The effective population size is small (Ne = 45.2), but similar to another mole salamanders with restricted distributions or with recently fragmented habitat. The inbreeding coefficient levels detected are low (FIS = -0.619–0.102) as is gene flow. Despite, high levels of genetic diversity A.
Electronic supplementary material The online version of this article (doi:10.1007/s10592-013-0520-9) contains supplementary material, which is available to authorized users. A. Sunny O. Monroy-Vilchis (&) V. Fajardo U. Aguilera-Reyes Estacio´n Biolo´gica Sierra Nanchititla, Facultad de Ciencias, Universidad Auto´noma del Estado de Me´xico, Instituto literario # 100, Colonia Centro, CP 50000 Toluca, Estado de Me´xico, Mexico e-mail:
[email protected];
[email protected]
leorae is critically endangered because it is a small isolated population. Keywords Microsatellites Endemic species Endangered species Conservation Mexico
Introduction Small or isolated populations represent ‘‘islands’’ in terms of gene flow and genetic diversity (Kim et al. 1998). Such populations are very susceptible to stochastic events (Gibbs 1998; Hicks and Pearson 2003), resulting in fluctuations of effective population size and other demographic and environmental parameters that can lead to extinction (Gibbs 1998). Small populations also tend to have less genetic diversity than larger ones due to the loss of alleles through genetic drift and the increased chance of inbreeding (Van Treuren et al. 1991; Templeton and Read 1994; Garner et al. 2005; Frankham et al. 2005). Reduced genetic diversity impairs the population’s ability to cope with environmental variation and can translate to lower fitness for the individuals in the population, increasing the extinction risk (Newman and Tallmon 2001; Johansson et al. 2006). Establishing the genetic structure of small and isolated populations may be critically important for management and conservation decisions (Palsbøll et al. 2007). Although, genetic variability is often less critical for population persistence than factors like habitat loss or fragmentation (Lande 1988), it has a decisive role in a long term because it enables populations to adapt and persist in a changing environment. Ambystoma leorae (Taylor 1943), is a micro-endemic salamander from the ‘‘Sierra Nevada’’, Central Mexico. The mole salamander was historically restricted to six locations
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within the protected area Iztaccihuatl-Popocatepetl National Park (Fig. 1, IPNP). Recent research identified a new and relict locality of A. leorae, possibly the last one (Monroy Vilchis et al. 2012). No data exist on their ecology and basic biology, and some experts considered this species extinct (Parra-Olea, pers. comm.). The maximum number of organisms reported for the only known population studied was 55 (Lemos-Espinal et al. 1999). The population is next to the world’s most human populous region. This lack of knowledge, the limited distribution and the demand for resources (deforestation, pollution, water) by humans threaten this species with extinction (SEMARNAT 2010; Shaffer et al. 2004). Ecological and genetic studies are important in order to provide information on the status of the species. In the study area, the threats include the introduction of cattle, the use of water for human consumption and the direct collection of specimens for food. Our goals are to understand the genetic diversity and structure, the effective population size, the presence of bottlenecks and to determine the percentage of inbreeding of the last remaining population of A. leorae in order to develop conservation strategies.
Materials and methods
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religiosa). The vertebrate community consists of 25 species of amphibians and reptiles, 46 species of birds, and 38 species of mammals (Monroy Vilchis et al. 2012). Given its proximity to Mexico City, the area is subject to constant stress by land use changes mainly by human settlements and the exploitation of its forest resources. Population sampling and molecular analyses We sampled monthly for 1 year. The search for specimens was intensive and all were caught by hand, between 9:00 am and 5 pm. Sampling was done in two rivers separated by 0.56 km2. The rivers join after 0.68 km2. The rivers flow over plains and gentle slopes, with pools approximately 5 m from each other. There are three basic substrates (stones, sand and mud) and the mole salamanders where captured every time in the same place. We obtained 96 tissue samples (tail tip) and place immediately in 90 % ethanol, and then frozen at -20 °C until processed. We performed DNA extraction with a commercial kit Dneasy Blood and Tissue Kit (Qiagen), following the manufacturer’s instructions. We assessed DNA quantity and quality with a biophotometer (Eppendorf, Hauppauge, New York) and in 1.5 % agarose gel stained with 0.5 lg/ml ethidium bromide and observed with UV light.
Study area Microsatellite typing Iztaccihuatl-Popocatepetl National Park (IPNP) is located on the border of the State of Mexico with Puebla. Geographical coordinates of the study site are 19°210 0900 N, 98°400 1100 W and 19°350 25’’N, 98°660 9700 W, at an altitude of 4,130 masl (Fig. 1). We sampled individuals from a 1 km stream confined to a small alpine grassland (Muhlenbergia sp.) surrounded by forest (Pinus hartwegii and Abies Fig. 1 Map of Mexico a showing Iztaccı´huatlPopocate´petl-Zoquiapan National Park. b The points marked from 2 to 7 are historic populations considered extinct and the number 1 is the study population at Volcano Tla´loc. c Volcano Tla´loc showing the sample sites
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Genomic DNA was used as template for amplification of nine loci following published protocols (Parra-Olea et al. 2007), with an Apollo PCR Touchscreen ATC201. We multiplexed amplified products on an ABI Prism3730xl and sized with a Rox-500 standard in GENEMAPPER v. 4.0 (Applied Biosystems, Foster City, CA, USA). Multiple
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samples were sized at least twice to assure reproducibility and correct readings. Statistical analyses Identification of identical genotypes for recapture and potential scoring errors We performed an analysis using the software Gimlet 1.3.2 Valie`re (2002) in order to identify recaptured individuals and reduce error in the interpretation of the results (Kohn et al. 1999). The presence of null alleles and other typing errors was determined using MICROCHECKER (Van Oosterhout et al. 2004). These alleles may affect estimations of population differentiation, by reducing genetic diversity within populations (Chapuis and Estoup 2007). Genetic structure We used the software Geneland 3.2.2 (Guillot et al. 2005), this software uses a Bayesian algorithm implemented in a Markov Chain Monte Carlo scheme considering genetic data and geographical coordinates. We choose the most suitable combination of models for our data: we assumed a correlated allelic frequencies model and a true spatial model (Guillot et al. 2005), with a coordinate uncertainty value of 100 m. We performed 10 independent runs with 1,000,000 iterations, thinning = 100 and burnin = 100, using K = 10, K = 5, K = 3, K = 2 and K = 1 in each trial. Once we got the maximum number of possible populations (in this case K = 3), we proceeded with the assigning of individuals, using 20 independent runs with 1,000,000 iterations (thinning = 100, burnin = 1,000). In addition, we did an assignment analysis with the software GeneClass version 2.0 (Piry et al. 2004), in which each individual’s genotype is analyzed to determine to which population each individual belongs, based on the genotypic frequencies present in the populations (Paetkau et al. 1995). In order to estimate their degree of genetic structure, we used several approaches. Given that microsatellites are closer to the stepwise mutation model than the infinite allele model, we calculated RST (Slatkin 1995; Michalakis and Excoffier 1996). However, because the sample size is low and few loci, the FST values give a more conservative assessment (Gaggiotti et al. 1999). We calculated FST based on Weir and Cockerham (1984), using Arlequin version 3.5.1.2 (Excoffier and Lischer 2010). We also calculated Jost’s (2008) D, using Software for the Measurement of Genetic Diversity (SMOGD; Crawford 2010) with 1,000 replicates in the bootstrapped parameters. To analyze the distribution of the genetic variance between and within populations, we used an analysis of molecular variance (AMOVA) (Excoffier et al. 1992)
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based on FST and RST. We calculate the significance using a Wilcoxon test with 30,000 permutations of genotypes among populations. In order to detect the degree of similarity of the sampling sites based on the species genotypes, we did a factorial correspondence analysis of the microsatellite data using Genetix 4.05 (Belkhir et al. 2004). This test graphically projects the individuals in the factor space defined by the similarity of their allelic states. Genetic diversity We assessed genetic variability in the population by estimating the observed (no) and effective (ne) number of alleles, observed (Ho) and expected (He) heterozygosity, (He*). Expected heterozygosity corrected for small sample sizes and Nei’s unbiased expected heterozygosity (HNei— Nei 1973), using the software POPGENE version 1.31 (Yeh et al. 1997) and Genetic studio (Dyer 2009), for each population-by-locus combination and then averaged over all loci to produce population estimates. Linkage disequilibrium between pairs of microsatellite loci and departures from Hardy–Weinberg equilibrium (HWE) within each sample and locus were evaluated with a Markov chain approximation (10,000 dememorizations, 1,000 batches, and 10,000 iterations per batch) of the Fisher’s exact test performed and calculated the unbiased P value with a Markov chain algorithm in Genepop 4.0 (Raymond and Rousset 1995). In order to identify non-neutral loci, we used the software LOSITAN (Antao et al. 2008) based on an F-outlier method and coalescent simulations to produce a null distribution of FST values based on an infinite island model for the populations and an infinite allele model or a stepwise mutation model for polymorphism. Loci with high FST are under directional selection, and loci with low FST are under stabilizing selection. We ran 100,000 iterations with a significance of P B 0.05. Isolation by distance, historic demography and genetic bottlenecks In order to test the correlation between the pairwise genetic and geographical distance, we performed a Mantel test (Mantel 1967) in GenAlEx v. 6 (Peakall and Smouse 2006) with 10,000 randomizations. With the software, Populations 1.2.32 (Langella 2002) was constructed NeighbourJoining tree (NJ) with the Nei0 s standardized genetic distance (Nei 1972) with 1,000 bootstraps. We calculated the effective population size (Ne) by a method that estimates Ne from linkage disequilibrium (LD), using a Jackknife method with LD values between pairs of loci and a random mating system, with the software LDNE v.1.31 (Waples 2006; Waples and Do 2008).
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We used the software BOTTLENECK version 5.1.26 (Cournet and Luikart 1996; Piry et al. 1999) to test for a genetic signature of recent historical reduction in the effective population size (i.e., a bottleneck), based on the two-phase model, which is an intermediate model of evolution considered more appropriate for microsatellites (Cournet and Luikart 1996). Accordingly, we estimated the observed and expected heterozygosity under the two-phase model, with settings of 90 % stepwise mutation model, 10 % infinite allele model, and 10 % variance; and used default values (70 % stepwise mutation model, 30 % infinite allele model, and 10 % variance). Both settings were run with 10,000 replicates. Excess heterozygosity was tested using a Wilcoxon test. We estimated another measure of population size reduction, the Garza–Williamson index (M, the ratio of number of alleles to range in allele size) and the critical value (Mc), with the software CriticalM. M-values lower than the critical number are indicative of historical population declines. The latter was done based on 10,000 simulations and parameters from the two-phase mutation model, as described in Garza and Williamson (2001). Inbreeding and relatedness Finally, we evaluated the relatedness among individuals within each population, and between populations in order to test the levels of endogamy, with the software MLRELATE (Kalinowski et al. 2006), which has the advantages of being designed for microsatellites, is based on maximum likelihood tests, and considers null alleles. We also estimated the within-population coefficient of genetic relatedness, r (Queller and Goodnigh 1989) in GenAlEx v. 6 (Peakall and Smouse 2006). We bootstrapped allelic data within populations 999 times to derive 95 % confidence intervals for r estimates; populations with non-overlapping bootstrap intervals are statistically distinct. We also permuted genotypes from all populations 999 times and derived upper and lower 95 % confidence intervals (CIs) for r based on all populations. These intervals represent the range of r expected if random mating occurs across all populations. Population r-values that fall above the upper bound of the 95 % CI indicate that reproductive skew, inbreeding or drift are increasing relatedness, despite potential gene flow among localities.
Results Identification of same genotypes and potential scoring errors Do not exist same genotypes and null alleles in any loci.
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Genetic population structure Three subpopulations (subpop1 N = 31, subpop2 N = 22 and subpop3 N = 44) were defined with GENELAND based on 20 runs {[Ln Pr (K = 3) = -3,581.49]} the individuals were systematically distributed in the same groups. We performed data analysis considering the subpopulations together (population) and the three other ones independently (subpop1, subpop2 and subpop3). The assignment analyses assigned 56 % of the individuals to the three subpopulations found with Geneland (Table A1) correctly. Another result regarding genetic differentiation showed significant values (FST = 0.021, RST = 0.044 and Dest = 0.010, 95 % CI. Table 1), in agreement with the AMOVA results that revealed the majority of genetic variation resided within populations (98 %; P = 0.01), followed by that among populations (2 %; P = 0.01, Table A8). Genetic structuring was low, three subpopulations can be clearly distinguished (Fig. A3), and the same topology was obtained with the principal components analysis (Fig. A4). The NJ tree found that the ancestral subpopulation is the subpop2 with a bootsrap of 69 %. The Nei0 s value between the subpop1 and subpop2 was 0.081, between subpop2 and subpop3 0.045, and subpop1 and subpop3 are sister groups with a bootstrap of 100 % and a Nei0 s value of 0.041 (Fig. A2). Genetic diversity We found 43 alleles across the nine loci, with a range of 4–9 (average 5.4) alleles per locus for the population: subpop 1 had 3–8 (mean = 4.3), subpop2 had 3–6 (mean = 3.9) and subpop3 had 3–8 (mean = 5) (Table 2 and Table A2). Atig52.143, Atig52.115 and At52.2 were the most variable loci (Fig. A6). We found 67 genotypes for the population (Table A3). The smaller number of genotypes per locus was three to At52.20, At52.10 and At52.34, however the largest was 14 (Atig52.143). Atig52.143 had the highest number of heterozygous genotypes (10), while the lowest was at At52.10 At52.20 had 2. Loci that had the lowest and highest number of homozygous genotypes were At52.34, with 0 and At52.1 with 5. The subpopulations with the highest number of genotypes was subpop1 and subpop3 with 48, and the lower was subpop2 with 39. Expected and observed heterozygosities in the population showed high values (Ho = 0.804, He = 0.613, He* = 0.626 and HNei = 0.622), while subpop1 had slightly higher (Ho = 0.849, He = 0.616, He* = 0.626 and HNei = 0.616), subpop2 (Ho = 0.762, He = 0.627, He* = 0.641 and HNei = 0.627) and subpop3 (Ho = 0.803, He = 0.596, He* = 0.602 and HNei = 0.596) (Table 2). False discovery rate correction tests found departures from HWE in one locus in the population and in the subpop2 and subpop3 due to heterozygote
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Table 1 Differentiation indices for the population and for each locus Locus
Population RIS
RIT
RST
GST_est
G’ST_est
DST
D
Dest
0.003
-0.097
-0.103
-0.005
0
0
1.018
0.026
0
0.002
0.112
0.122
0.011
0.003
0.012
1.029
0.042
0.009
-0.253
-0.268
-0.012
0
0
1.005
0.008
0
0.032
-0.107
-0.005
0.091
0.018
0.057
1.044
0.063
0.040
0.049
-0.106
-0.082
0.022
0.029
0.079
1.048
0.069
0.051
-0.189
-0.005
0.199
0.194
-0.006
0
0
1.008
0.011
0
-0.619 -0.619
-0.595 -0.595
0.043 0.043
-0.128 -0.128
0.023 0.023
0.134 0.134
0.028 0.028
0.091 0.091
1.062 1.062
0.087 0.087
0.064 0.064
At52.34
-0.382
-0.371
0.022
0.629
0.640
0.028
0.013
0.067
1.068
0.095
Average
-0.294
-0.284
0.021
0.013
0.060
0.044
0.011
0.038
1.038
0.054
0.054 N˜ = 0.010
FIS
FIT
Atig52.143
-0.251
-0.249
Atig52.115
0.102
0.103
At60.3
-0.606
-0.606
At52.1
-0.105
-0.092
At52.2
0.019
0.036
A52.6
-0.187
At52.20 At52.10
FST
-0.0
FIS, FST and FIT fixation indices estimated according to Weir and Cockerham (1984), RIS, RST and RIT fixation indices estimated according to Slatkin (1995), GST_est genetic differentiation, G’ST_est genetic differentiation according to Hedrick (2005), DS component of the genetic diversity and effective number of distinct genetic groups according to Jost (2008), D current differentiation by Jost (2008), Dest estimated current genetic differentiation according to Jost (2008) with a correction for small sample sizes, N˜ harmonic mean
deficiency, but at different loci: At52.1 loci found departures from HWE in the subpop1 due to heterozygote deficiency (Table 1). We found linkage disequilibrium between the loci At52.1-At52.34 in the subpop1, At52.20-At52.10 in the subpop2 and At52.20-At52.10 in the subpop3. Most loci showed private alleles in each subpopulation (subpop1 = 32, subpop2 = 35 and subpop3 = 45; Table A2 and Fig. A6). The loci At60.3 and A52.6 under both models (IAM and SMM) were found under balancing selection FST = -0.012 and -0.009, p = 0. The remaining seven loci were under neutral selection (Fig. A1 and Table A6). Isolation by distance, historic demography and genetic bottlenecks The Mantel test for isolation-by-distance found a positive but not significant relationship between geographic and population genetic distance, (r2 = 0.011, P = 0.09) (Fig. A5). Evidence of a recent genetic bottleneck associated with a heterozygote excess (BOTTLENECK results) was observed for the three subpopulations (subpop1, subpop2 and subpop3) when analyzed independently and when combined, under the IAM model (P = 0.01). They also showed a significant heterozygote excess with the TPM model (P = 0.01) in the subpop1 and subpop2 and under the SMM model none showed a significant heterozygote excess (Table A10). The results from the Garza–Williamson test showed empirical M values (population = 0.367, subpop1 = 0.414, subpop2 = 0.433 and subpop3 = 0.377) significantly lower than Mc for all the subpopulations (population = 0.9444, subpop1 = 0.9444, subpop2 = 0.9407 and subpop3 = 0.9444), indicative of a historical bottleneck
(Table A11 and A12). The effective population size (Ne) estimated from linkage disequilibrium was Ne = 45.2 (26.7–45.2, 95 % CI) for the population, Ne = 16.7 (13.6–17.1) for subpop1, Ne = 22.3 (21.3–22.3) for subpop2 and Ne = 27.1 (19.6–32.9) for subpop3 (Table A9). Inbreeding and relatedness Finally, the values of the inbreeding coefficient (FIS; from -0.619 to 0.102) indicates that there is no inbreeding in the locus Atig52.143, Atig52.115, At52.100, At52.200 and A52.600; and there is moderate inbreeding at locus At60.300, At52.100 and At52.200 (Table 1 and Table A4). The proportion of individual relatedness within each population was similar (Table A13). Most individuals were unrelated (population = 68.90 %, subpop1 = 62.36 %, subpop2 = 74.45 % and subpop3 = 66.66 %), followed by siblings (population = 26.42 %, subpop1 = 34.40 %, subpop2 = 23.37 % and subpop3 = 30.56 %), half-siblings (population = 3.96 %, subpop1 = 2.58 %, subpop2 = 3.03 % and subpop3 = 2.54 %), and parent/offspring (population = 0.70 %, subpop1 = 0.64 %, subpop2 = 0 % and subpop3 = 0.22 %). Average pairwise relatedness (rqg) was low for all subpopulations, subpop1 = 0.049, subpop2 = 0.010 and subpop3 = 0.072. (Fig. 2).
Discussion This is the first study on genetic variability of A. leorae using nine microsatellite loci from the last known population of the species. High genetic diversity was found for the population and for the three subpopulations obtained by
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Table 2 Genetic diversity values per locus for the population and for each subpopulation Locus Atig52.143
Atig52.115
At60.3
At52.1
At52.2
A52.6
At52.20
At52.10
At52.34
Average
Ho
0.816
0.605
0.992
0.642
0.515
0.713
0.981
0.981
1
0.804
He
0.651
0.662
0.610
0.583
0.520
0.605
0.586
0.586
0.716
0.613
He*
0.662
0.673
0.619
0.592
0.552
0.615
0.595
0.595
0.728
0.626
HNEI
0.655
0.681
0.614
0.600
0.518
0.602
0.603
0.603
0.721
0.622
na
9
6
4
6
6
4
4
4
6
5.44
ne A
2.890 7.177
3.130 3.999
2.590 3.898
2.500 3.919
2.070 4.683
2.510 3
2.520 2.919
2.520 2.919
3.580 3.997
2.700 4.057
Population
Subpop1 Ho
0.838
0.709
1
0.612
0.741
0.806
0.967
0.967
1
0.849
He
0.704
0.697
0.573
0.654
0.595
0.623
0.530
0.530
0.635
0.616
He*
0.715
0.708
0.583
0.664
0.605
0.634
0.539
0.539
0.645
0.626
HNEI
0.704
0.697
0.573
0.654
0.595
0.623
0.530
0.530
0.635
0.616
na
8
4
4
4
5
3
3
3
4
4.220
ne
3.370
3.300
2.340
2.890
2.470
2.650
2.130
2.130
2.740
2.670
A
3
4
4
3
5
4
3
3
6
3.889
Subpop2 Ho
0.772
0.500
1
0.545
0.409
0.636
1
1
1
0.762
He
0.631
0.598
0.615
0.542
0.584
0.615
0.624
0.624
0.807
0.627
He*
0.645
0.612
0.630
0.555
0.598
0.630
0.638
0.638
0.826
0.641
HNEI
0.631
0.598
0.615
0.542
0.584
0.615
0.624
0.624
0.807
0.627
na ne
3 2.710
4 2.480
4 2.600
3 2.180
5 2.400
4 2.600
3 2.650
3 2.650
6 5.200
3.880 2.830
A
5.809
5.501
3.947
4.265
4.040
3
3.763
3.763
5.885
4.441
Subpop3 Ho
0.837
0.604
0.976
0.767
0.395
0.697
0.976
0.976
1
0.803
He
0.617
0.691
0.639
0.551
0.379
0.574
0.602
0.602
0.705
0.596
He*
0.625
0.699
0.646
0.557
0.383
0.580
0.609
0.609
0.713
0.602
HNEI
0.617
0.691
0.639
0.551
0.379
0.574
0.602
0.602
0.705
0.596
na
8
6
4
5
5
3
4
4
6
5
ne
2.610
3.240
2.770
2.220
1.610
2.340
2.510
2.510
3.390
2.580
A
5.643
5.050
3.874
4.043
4.790
3.407
3.647
3.647
5.870
4.441
Ho observed heterozygosity, He expected heterozygosity, He* heterozygosity expected with a correction for small samples (Dyer 2009), HNEI expected heterozygosity, na observed number of alleles, ne effective number of alleles, A allelic richness (estimated for 22 individuals, the minimum number of samples in a population)
Geneland, in contrast with what would be expected for small (\200 individuals) and isolated populations (Kilpatrick 1981; Frankham 1998). Genetic diversity There is significant deviation of the Hardy–Weinberg (HW) proportions due to a heterozygote deficiency. This is a common result for threatened species with fragmented populations (Degne et al. 2007; Spear and Storfer 2010; Castan˜eda-Rico et al. 2011; Va´zquez-Domı´nguez et al.
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2012). The explanation for the observed Hardy–Weinberg disequilibrium could be genetic drift (Hedrick 2005). In addition, other ecological or behavioral factors such as social structure, could be affecting movement or reproduction. The genetic diversity found was higher than expected, the values obtained were similar to species that have been demographically stable (Allendorf and Luikart 2007; Dlugosh and Parker 2008; Ho & 0.85). These levels of genetic diversity are similar (Goprenko et al. 2007, Greenwald et al. 2009) or slightly higher than those reported in other studies with mole salamanders and
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Fig. 2 Mean within-population pairwise relatedness values (rqg) between subpopulations of A. leorae. The red bars are the above confidence intervals and the green bars are the lower confidence intervals with 95 % confidence with a null distribution generated with 999 permutations, the blue bars are the observed kinship mean conducted with 999 bootstraps
amphibian species (Myers and Zamudio 2004; Steinfartz et al. 2006; Marsh et al. 2007; Zamudio and Wieczorek 2007; Rhoads 2011). Moreover, the He average is 0.519 with a range from 0.140 to 0.937 regardless of the size of the population (Curtis and Taylor 2003; Myers and Zamudio 2004; Adams et al. 2005; Jehle et al. 2005); similar results to those obtained in the present study (He = 0.520–0.716 Table 2). However, when the average number of alleles is considered, the population and the subpopulations of A. leorae have fewer alleles than in most other mole salamander and amphibian studies (na = 5.44; Table 2). This is important to consider because the genetic diversity of A. leorae could be starting to decline because due to habitat fragmentation, anthropogenic activities and isolation. The high levels of heterozygosity in this population could be caused by high founder size, high effective population size in the past, multiple paternity or, maybe, overlapping generations. Genetic structure and isolation by distance We have 3 subpopulations (LnPr (k = 3) = -3,581.49) which coincides with the three substrates present in the sampled rivers (stones, sand and mud). However, only 56 % of the individuals was correctly assigning to their original population with Bayesian assignment method (Table A1). We also found weak population structure (FST = 0.021, RST = 0.044 and Dest = 0.010; Table 1), small percentage of variation (2 %) generated by the AMOVA method (Table A8), and low genetic differentiation with a correlation factor analysis and principal components analysis (Figs. A3 and A4). With these results, we conclude that the structure observed is weak; a common trend found in amphibian populations in small geographical scales with limited distributions (Rowe et al. 2000; Newman and Squire 2001; Palo et al. 2003; Funk et al. 2005; Jehle et al. 2005; Spear et al. 2005; Johansson et al. 2006; Giordano et al. 2007; Noe¨l et al. 2007; Zamudio and
Wieczorek 2007; Purrenhage et al. 2009). These results suggest that our genetic groups behave as a metapopulation, rather than isolated populations (Jehle et al. 2005; Kinkead et al. 2006). No positive correlation exists between genetic and geographic distances; this is consistent with field data because the individuals are collected and recaptured in the same place. This pattern generally occurs in amphibians with highly philopatric tendencies (Funk et al. 2005; Spear et al. 2005; Savage and Zamudio 2005; Vences and Wake 2007; Gamble et al. 2007; Calhoun and deMaynadier 2008; Semlitsch 2008; Wang et al. 2009; Wang and Summers 2010) showing that there is little gene flow between substrate types. This may also be due to poor dispersal ability (Trenham and Shaffer 2005; Gamble et al. 2006; Gamble et al. 2007; Searcy and Shaffer 2008; Summitt 2009). It is also known that the main environmental factors that influence the locomotion capacity are low temperatures (Johnson et al. 2010) and habitats with low vegetation cover (Naughton et al. 2000; Wang et al. 2009). In the Tla´loc volcano, the weather is very cold; water temperature ranges from 6 to 10 °C and air temperature ranges from 4 to 8 °C. Outside the area beside the rivers there is no continuous forest allowing mole salamander dispersion (Wang et al. 2009). The results suggest that the limited dispersive capacity and the highly philopatric tendencies of this species mimics the pattern of isolation-by-distance (Fig. A5) making the populations subdivided or little structured (Jehle et al. 2005; Kinkead et al. 2006). Moreover, no evidence of isolation by distance pattern implies a balance between drift and gene flow, due to an ancestral allelic variation. This means the population could be a relict of a larger ancestral population; showing that this allelic variation is not due gene flow. Effective population size The low values of effective population size (Table A9) are similar to other mole salamanders (Funk et al. 1999; Jehle
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and Arntzen 2002; Davis and Verrell 2005; Savage et al. 2010), with low effective population sizes Ne = 3.2–37.8 and no more than 100 individuals per population. These estimates are low but some studies have shown that effective population sizes are lower than thought (Frankham 2009). Small effective population sizes can occur for a variety of factors including bottlenecks, genetic isolation, asymmetry in the proportions of males and females and difference in reproductive success between individuals (Tennessen and Zamudio 2003; Myers and Zamudio 2004; Wang 2009). Historical demography There are no recent bottlenecks (Table A10), but we detect ancestral bottlenecks (Tables A11 and A12) which can be associated with two factors (1) the founder effect suffered when this population was separated from a larger ancestral population and (2) because local people ate mole salamanders, and stopped because the mole salamander populations drastically decreased. Inbreeding and relatedness Despite the small sample size and the characteristics of this species (endemic, restricted, isolated) no inbreeding was detected in the population and in the subpopulations (Fig. 2, Table A13) coinciding with other mole salamanders and amphibians studies located in small areas (Noe¨l and Lapointe 2010). We found many siblings, possibly because we collected most of the individuals from March to June (19, 38 and 20, Table 3 and Table A13), and most of them were gilled larva. A. leorae populations may have some mechanism to avoid inbreeding like P. cinereus, through chemical recognition (Waldman, 1988; Walls and Roudebush 1991; Pfennig et al. 1994; Masters and Forester 1995; Cabe et al. 2007). Conservation implications Although A. leorae is critically endangered in the world (Shaffer et al. 2004) and endangered in Mexico (SEMARNAT 2010), and all historical populations of this
Table 3 Number of samples per month collected at Volcano Tla´loc
January February
Number of samples per month 12 7
March
19
May
38
June
20
123
species have become extinct by pollution and over exploitation of water resources, no conservation action is currently under consideration because of limited information on population size and current distribution. This last population probably is an evolutionarily significant unit (ESU) in terms of conservation (Moritz 1994; Hedrick et al. 2001). The purpose of defining this unit is to ensure that the evolutionary heritage will be recognized and protected (Moritz 1994, 1995). We consider that the best conservation strategy is the creation of a protected natural area to preserve the habitat of A. leorae and the creation of an ecotourism park in order to allow people to be made aware of the importance of this species. In conclusion, A. leorae is a micro-endemic and endangered species with high levels of genetic diversity. However, it has few alleles and genotypes compared with other mole salamanders species. The population and the specie is critically endangered because it is an isolated small population. Therefore, it is urgent to undertake conservation strategies to avoid extinction, especially due to the deterioration and loss of habitat. Acknowledgments We deeply thank to Dr. Carlos Aguilar Ortigoza for borrowed a thermalcycler. We thank Brenda Cole and Carl Mitchell for valuable comments and English editing. We thank all the students who helped in field. We thank two anonymous reviewers for their comments that helped improve the manuscript. AS is grateful to the graduate program Maestrı´a en Ciencias Agropecuarias y Recursos Naturales to Universidad Auto´noma del Estado de Me´xico for the scholarship granted and also the scholarships received from CONACYT and COMECYT.
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