FST outliers, and extent and pattern of linkage disequilibrium in five populations of Keteleeria davidiana var. formosana, which is listed as a critically endangered species by the Council of Agriculture, Taiwan. Twelve amplified fragment length polymorphism primer pairs generated a total of 465 markers, of which 83.74% on average were polymorphic across populations, with a mean Nei’s genetic diversity of 0.233 and a low level of genetic differentiation (approximately 6%) based on the total dataset. Linkage disequilibrium and HICKORY analyses suggested recent population bottlenecks and inbreeding in K. davidiana var. formosana. Both STRUCTURE and BAPS observed extensive admixture of individual genotypes among populations based on the total dataset in various clustering scenarios, which probably resulted from incomplete lineage sorting of ancestral variation rather than a high rate of recent gene flow. Our results based on outlier analysis revealed generally high levels of genetic differentiation and suggest that divergent selection arising from environmental variation has been driven by differences in temperature, precipitation, and humidity. Identification of ecologically associated outliers among environmentally disparate populations further support divergent selection and potential local adaptation.The present study investigated the genetic diversity, population structure,  Natural environments are spatially and temporally heterogeneous. Divergent selection across environments may induce adaptive divergence, resulting in local adaptation FST-based population genomic methods can be employed to search for adaptive loci by scanning a large number of markers including dominant markers such as amplified fragment length polymorphisms (AFLPs) FST values under a classic symmetrical island model FST deviations from neutral estimates. However, DFDIST is prone to generating false positives when gene flow is asymmetrical across populations, and/or when a population has experienced a bottleneck. To address this problem, the BAYESCAN approach estimates population-specific FST values by considering different demographic histories and different amounts of genetic drift between populations Geographically isolated populations of a species can serve as natural laboratories for studying adaptive processes An alternative approach to detecting adaptive variation is to correlate allelic frequencies with environmental gradients, in order to determine which ecological factors may have played a role in natural selection Keteleeria davidiana (Franchet) Beissner var. formosana) is an endemic coniferous evergreen tree species. It is presently disjunctly distributed on rocky ridges of northern Taiwan at elevations of 300–600 m and of southern Taiwan at elevations of 500–900 m K. davidiana (Bertrand) Beissner that occurs in China and K. davidiana var. formosana was proposed by Farjon Keteleeria began to appear at the Plio-Pleistocene boundary and continued through the early Pleistocene Praetiglian in central Taiwan at an elevation of 550 m, but decreased to a minor proportion afterwards Keteleeria was also observed in very minor proportions or rarely during the late Pleistocene until the early phase of the Holocene around 10,000 years ago in central Taiwan Machilus and CastanopsisK. davidiana var. formosana may have been a disadvantage in competing with rapidly growing subtropical forest species. Moreover, habitat destruction due to severe logging in the early 20th century caused substantial reductions in population sizes of this species in northern and southern Taiwan K. davidiana var. formosana in Taiwan were historically larger and more common in low-elevation forests than at present. Population bottlenecks might have occurred in this species and resulted in the loss of genetic variability, therefore we suspect that populations of this species may exhibit relatively high frequencies of linkage disequilibrium (LD). Moreover, present K. davidiana var. formosana populations occupy significantly different environmental niches and also vary in their floristic compositions K. davidiana var. formosana populations in Taiwan may be ideal for the study of early characteristics of divergent selection evoked by environmental differences, leading to local adaptation Taiwan cow-tail fir . We then applied logistic regression methods including spatial analysis method (SAM) In this study, we first determined the level of genetic diversity and partitioning of genetic variation among natural populations of n = 20), Gupoliao , and Shihtsao , and from southern Taiwan including sites at Dawu 30  and Dawu 41  . Digested DNA products were added to a 10-µl reaction mixture containing 0.5 µM of the EcoRI adaptor, 5 µM of the MseI adaptor, and 30 U T4 DNA ligase (Yeastern Biotech) at 22°C for 1 h. Digested samples were diluted (1∶9 dilution with water), and 1 µl of each diluted sample was used as a template to perform pre-selective amplification in a 10-µl volume containing 1× PCR buffer , 100 nM each of the EcoRI (E00: 5′-GACTGCGTACCAATTC-3′) and MseI (M00: 5′-GATGAGTCCTGAGTAA-3′) primers, 0.25 mM dNTPs, and 1 U Taq DNA polymerase . Conditions of the pre-selective amplification were initial holding at 72°C for 2 min and pre-denaturation at 94°C for 3 min, followed by 25 cycles of 30 s at 94°C, 30 s at 56°C, and 1 min at 72°C, with a final 5-min holding at 72°C. Selective amplification was performed using 12 EcoRI-MseI selective primer combinations with sequences of the three bases additional to the E00 and M00 primers (EcoRI selective primer was labeled with fluorescent dye (6-FAM or HEX). Conditions of selective amplification were a 10-µl volume containing 1× PCR buffer, 100 nM of the EcoRI selective primer, 100 nM of the MseI selective primer, 0.25 mM dNTPs, 0.75 U Taq DNA polymerase, and 1 µl diluted pre-selective amplified product (1∶9 dilution with water). The selective PCR amplification was performed with initial holding at 94°C for 3 min, followed by 13 cycles of 30 s at 94°C, 30 s at 65°C with 0.7°C touchdown per cycle, and 1 min at 72°C, then 23 cycles of 30 s at 94°C, 30 s at 56°C, and 1 min at 72°C, with a final 5-min holding at 72°C.We collected 163 individuals from northern Taiwan including sites at Jingualiao  . All pla primers , in whicet al.http://dx.doi.org/10.5061/dryad.7 cm6b.Amplified fragments were separated by electrophoresis on an ABI PRISM 3100 sequencer . Electropherograms were automatically analyzed for fragment presence/absence in each individual in the range of 50–500 bp with a GeneMapper v3.7 setting of a fluorescent signal detection threshold of 100 units to avoid background noise. All loci of all individuals were rechecked to remove loci with low peaks and loci separated by less than one nucleotide. The genotyping error rate per locus was calculated as the ratio of mismatches in 30 replicates to the total number of replicated markers. The error rate for 465 markers (21–61 per primer combination) was estimated to be 3.8% , which w30), days of minimum temperature <10°C (D10), and wet days . Values of the 11 environmental variables at localities of K. davidiana var. formosana investigated were obtained by interpolation of nearby meteorological observations, resulting in a 1 km2 grid resolution using a universal spherical model and the Kriging method in ArcGIS We obtained data for 11 environmental variables recorded from 1990–2011 at 390 meteorological stations from the Central Weather Bureau, Taiwan. Environmental variables included mean temperature (Tmean), maximum temperature (Tmax), minimum temperature (Tmin), mean wind speed (WSmean), precipitation (PRE), relative humidity (RH), cloud cover (CLO), time of sunshine (SunH), days of maximum temperature >30°C (DHE) HE calculations.AFLP profiles of band presence (1) and absence (0) were scored and used to calculate unbiased Nei’s genetic diversity (UFST) between populations was calculated using ARLEQUIN v3.5 K ( = 1–5) with 106 iterations and 105 burn-in steps. The mean log probability, (LnP(D)) L(K) K) was set to 2–4 according to results obtained from the mean log probability analyzed by STRUCTURE. Each run was replicated three times, and the results were averaged according to the resultant likelihood scores.Total AFLP, outlier, and neutral locus datasets were used to estimate genetic differentiation via several approaches. First, pairwise genetic differentiation  from dominant markers, and is unaffected by Hardy-Weinberg and inbreeding coefficient (f) assumptions f and θB; (ii) the f = 0 model that assumes no inbreeding and calculates θB; (iii) the θB = 0 model that implies no differentiation between populations and calculates f; and (iv) the f-free model that chooses f at random from a prior distribution and calculates θB separately during a Monte Carlo Markov chain (MCMC) run. In the full and θB = 0 models, a uniform prior distribution of f was assumed. Separate analyses of the population structure, including within-population, among-population within regions, and between-region components, were performed in HICKORY. The Deviance Information Criterion (DIC) was used to choose a best fitting model. Caution must be taken if the difference in DIC between models is smaller than six units The Bayesian program, HICKORY v1.1 FST conditioned on heterozygosity was generated using 105 iterations of coalescent simulations in a symmetrical island migration model at migration-drift equilibrium. To estimate the empirical multilocus FST, the highest and lowest 30% of the initial FST calculated for all AFLP loci were removed when calculating the mean FST. A locus with an unusually high FST value conditioned on heterozygosity was considered an outlier potentially under positive selection. Significant P values of outlier loci were evaluated at the 95% confidence level by applying a false discovery rate (FDR) of 5% 6 iterations (with 105 burn-in) were used to identify loci potentially under selection. In this study, a locus with log10(BF) >0.5 was considered evidence for selection applying Jeffreys’ scale of evidence We performed pairwise population comparisons for signatures of selection using the genome scan methods DFDIST and BAYESCAN. In DFDIST, allele frequencies are estimated from the proportion of recessive phenotypes in the sample based on the Bayesian approach by Zhivotovsky P values of Wald tests with Bonferroni adjustment (α = 0.05) for multiple comparisons were used to evaluate the significance of the associations. However, Joost et al. P values of Wald tests with Bonferroni correction (α = 0.05) were used to compare the null model (the PC effect restricted to 0) with the model which included one predictor variable (PC) to evaluate the significance of adding that individual predictor to the null model.To search for ecologically associated outliers, direct associations between allelic frequencies of all AFLP loci and values of environmental variables were tested using a univariate logistic regression method implemented in SAM K. davidiana var. formosana populations, indicative of a possibility of inbreeding. We calculated the association of alleles at different loci within each population for indices of association IArd, independent of number of loci IA and rd values was tested against 1000 permuted datasets, randomly reshuffled alleles across individuals among populations that were independently compared for each locus, in which sexual recombination was imposed. We further tested for LD based on the distribution of allelic mismatches between pairs of genotypes over all loci using an exact test implemented in ARLEQUIN.Multilocus v1.3 HE averaged 0.233 and ranged from 0.197 (ST) to 0.312 (DW30) (HE was higher for southern populations (mean UHE = 0.259) than northern populations (mean UHE = 0.216), but the difference was not significant . Populations JGL, GPL, ST, and DW41 had UHE values of 32.7%, 22.9%, 37.0%, and 34.2% lower than the UHE of population DW30.Twelve primers generated a total of 465 AFLP loci in the entire sample with an overall repeatability of 96.2% . The pro2 (DW30) . The meaK. davidiana var. formosana based on the Pillai-Bartlett statistic . Moreover, significant differences in the four environmental variables of RH, CLO, SunH, and RainD among populations were found using the Welch ANOVA . Overall respective pairwise FST values were 0.0607 and 0.1997 for the total and outlier datasets. Pairwise FST was significantly greater than zero when comparing populations of the north to the south  and also significantly greater than zero when comparing the two southern populations  after applying the Bonferroni correction at P<0.05 . The majority of genetic variation was partitioned within-population for one- or two-regional groups in the AMOVA based on the total  and outlier datasets  , but was significant using the outlier dataset . However, the AMOVA showed significant genetic differentiation between the two southern populations for both the total and outlier datasets . Genetic variation partitioned among populations was also significant respectively for the total and outlier datasets in both the one-  and two-regional groups . However, the level of genetic differentiation between the northern and southern regional groups was not significant according to the AMOVA result .Because similar levels of genetic differentiation, as analyzed either by pairwise t P<0.05 . Pairwisctively) . The AMOK. davidiana var. formosana. The full model had the best fit to the data based on DIC value . It is known that estimates of f derived from dominant marker data can be unrealistically large f-free model was implemented to deal with unreliable f estimates by selecting f values from a uniform prior distribution without generating a posterior distribution of f, and gave an estimate of f around 0.5 for all hierarchical population groups : 0.051–0.063) was determined to be an unbiased Bayesian estimate of genetic differentiation among all populations. Between regions, the Bayesian θB estimate was 0.0454 (95% CI: 0.038–0.053). Bayesian θB estimates were 0.0045 (95% CI: 0.001–0.009) and 0.0997 (95% CI: 0.083–0.118) for genetic differentiation among populations respectively within the northern and southern regions, under the full model. The θB = 0 model had a substantially larger DIC than that of the full model, indicating good evidence for genetic differentiation among populations in all hierarchical population groups. However, only a very small amount of genetic variation was partitioned among populations within the northern region, similar to the pairwise FST and AMOVA results.Using the total dataset, Bayesian HICKORY analyses were used to evaluate the contemporary reproductive mode of IC value  [36]. Aln groups . This reK value occurred at K = 2  were detected as outliers by both DFDIST and BAYESCAN when comparisons were made between the two southern populations . Among t30, but decreased values of PRE, RH, CLO, RainD, and D10. Increasing the value of PC2 increased value of WSmean. GEE method found independent sets of 54 and 34 AFLP loci significantly associated with the PC1 and PC2, respectively   of the total variation. Increasing the value of PC1 increased values of Tmean, Tmax, Tmin, SunH, and De <10−6) . Three lIA and rd indices, and it was found to be statistically greater than expected under a null distribution in all populations  at the total population level. The proportion of significant pairwise LD averaged 6.10% and varied from 2.75% (DW30) to 9.75% (GPL). Moreover, patterns of LD between ecologically and non-ecologically associated outliers substantially varied among K. davidiana var. formosana populations  than the average UHE ( = 0.184) value of a temperate coniferous species Cunninghamia konishii also endemic to Taiwan based on an AFLP analysis HE was also higher in K. davidiana var. formosana compared to the average UHE of other Pinaceae species such as Cedrus atlantica (average UHE = 0.129) endemic to Morocco and Algeria Abies ziyuanensis (average UHE = 0.136) that occurs in China HE of K. davidiana var. formosana was lower than the average UHE ( = 0.283) of a widespread Pinaceae species Pinus contorta that occurs in North America K. davidiana var. formosana was close to the average value for 13 plant species in general (average HE = 0.230) based on AFLPs, and also close to the average values for 37 long-lived perennials (average HE = 0.250) and seven wind and/or water-dispersed species (average HE = 0.270) based on random amplified polymorphic DNAs K. davidiana var. formosana, although distributed in restricted areas of northern and southern Taiwan, appear to still harbor a large proportion of variation from ancestral populations and can likely provide substantial amounts of genetic variation for evolutionary dynamics under natural selection The average UIA, rd, and the proportion of significant LD between AFLP loci, although LD is known to decay rapidly over time at the genome-wide and within-gene levels in Pinaceae K. davidiana var. formosana that probably have not had sufficient time to significantly decay. However, other factors such as the ages of the mutations, natural selection, and recombination rates can also influence the degree of LD HE compared to the DW30 population.Significant LD was detected in all populations according to FST and AMOVA results, similar patterns of genetic differentiation analyzed in all hierarchical population groups using both the total and outlier datasets were found. In light of comparisons of our findings with other studies, we focus discussion on results obtained from the total dataset if not indicated otherwise. The level of genetic differentiation of K. davidiana var. formosana was much lower based on AFLPs, according to pairwise FST, AMOVA, and HICKORY results, than the level of genetic differentiation of another endemic coniferous species Cun. konishii (ΦST = 0.246) that occurs in Taiwan K. davidiana var. formosana was also much lower than that in other Pinaceae species such as the disjunctly distributed Ced. atlantica (FST = 0.25) A. ziyuanensis  P. contorta  K. davidiana var. formosana based on AFLPs was in accordance with low levels of genetic differentiation generally found for coniferous species using allozymes and microsatellites In the pairwise et al. FST around 0.03, which was smaller than the overall pairwise FST of K. davidiana var. formosana. However, both STRUCTURE and BAPS observed extensive admixture of individual genotypes across populations, especially at K = 2 and 3, within regions and between the two geographically isolated regions of K. davidiana var. formosana. The admixture of individual genotypes among populations might have resulted from incomplete lineage sorting of ancestral variation or recent extensive hybridization. If recent extensive hybridization has occurred among populations because of effective gene flow commonly observed in wind-pollinated conifers, then the genetic data would fit the f = 0 better than the full model in HICKORY, reaching migration-drift equilibrium and displaying limited LD. Moreover, the recalcitrant seed storage behavior K. davidiana var. formosana may have substantially restricted seed dispersal. Therefore, recent extensive hybridization due to high levels of gene flow is unlikely, suggesting that the retention of historically more-widespread ancestral polymorphisms may have been the reason for the current admixture of individual genotypes among populations, especially between the disjunctly distributed northern and southern populations. However, rare long-distance dispersals especially via pollen flow cannot completely be discounted In a simulation study by Latch K. davidiana var. formosana populations as suggested by the significant associations between alleles within populations according to significant IA and rd values.High levels of genetic differentiation in all hierarchical population groups based on the outlier dataset are potentially results from recent bottleneck inflicted by human disturbances. The bottleneck may have caused inbreeding and genetic isolation in K = 4 revealed a genetic subdivision within the DW41 population based on the total dataset in this study. However, with the outlier dataset, no genetic substructuring within the DW41 population was found. The underlying cause of the observed subdivision within the DW41 population based on the total dataset remains unclear; however, the finding of no subdivision based on the outlier dataset suggests that previous colonization of different ancestral lineages and later uniform, directional selection increased the fixation rate of a set of beneficial alleles which differed from those of other populations. The inference of directional selection can also be applied to the DW30 population, as analyzed using the outlier dataset by both STRUCTURE and BAPS at K = 4. The genetic distinction between the disjunctly distributed northern and southern populations and between the two southern populations revealed by Bayesian clustering in both STRUCTURE and BAPS at K = 4, based on the outlier dataset, suggest that divergent selection regimes might have been effectively acting on environmentally disparate populations for a long period of time BAPS is known to be more efficient at identifying hidden population structures 10(BF) of slightly larger than 0.5 in one population pair comparison can have a log10(BF) value of >1 in other population pair comparisons, and can be considered to be a “strong” or even “decisive” signature of selection according to Jeffreys’ scale of evidence 10(BF) >0.5 in BAYESCAN. It is possible that several factors can account for discrepant log10(BF) values obtained for the same specific outlier locus in different population pair comparisons, such as (i) different selection regimes acting between populations; (ii) different allele frequencies of genetic variation between populations; or (iii) different patterns of divergence hitchhiking between populations. Anonymous AFLP loci detected as outliers may represent genomic regions rather than specific variation under selection themselves One locus with a logIt is not unexpected that no outliers were found in pairwise population comparisons of the three northern populations using both DFDIST and BAYESCAN, because of very low levels of genetic differentiation, their geographic proximity, and environmental homogeneity. However, divergent selection can be further inferred between the two southern populations and the three northern populations, because DFDIST and BAYESCAN identified a common outlier (locus 4) specific to all comparisons between the southern DW30 and the three northern populations, and one common outlier (locus 6) specific to all comparisons between the southern DW41 and the three northern populations.K. davidiana var. formosanaThe AFLP loci are not likely the loci that are under selection. More likely, they are linked to candidate genes. Three AFLP loci  can be considered strong candidates of selection, because they were identified by both the DFDIST and BAYESCAN tests under different assumptions, they are also potentially associated to ecological variables in the SAM and GEE analyses. Outliers that were not associated with environmental variables may simply be the results of divergence hitchhiking, because they are closely linked to loci directly selected by natural selection K. davidiana var. formosana populations, and because common ecologically associated outliers were identified when the two southern populations were independently compared to the northern ST population (loci 1 and 5). It is worth noting that ecologically associated AFLP loci 1 and 5 potentially exhibiting strong selection acted to segregate local adaptation at these outliers even while populations were more widespread, suggesting strong selection acting on the same genetic variation in closely related populations. This result of repeated selection of the same ecologically associated outliers suggests that common selection regimes in distinct environments have played important roles in local adaptation.Local adaptation could be inferred because of identification of three ecologically associated outliers among environmentally disparate K. davidiana var. formosana, despite its population size decline due to deforestation. However, K. davidiana var. formosana seems to be genetically endangered because of population size reductions that may have generated significant within population LD and inbreeding. The high level of genetic differentiation based on outlier loci suggests a divergence and selection process, resulting in local adaptation of K. davidiana var. formosana populations. The two southern populations should perhaps be used as seed sources for future population reestablishment. Information obtained in this study is essential to guide policy of management and conservation action for this critically endangered species. More studies can be performed to test for environmentally associated adaptive divergence using high-throughput sequencing technologies at the level of whole genome The AFLP data found a relatively high level of genetic diversity on average within Table S1Primer combinations and sequences of the three bases additional to the EcoRI (5′GACTGCGTACCAATTC3′) or MseI (5′GATGAGTCCTGAGTAA3′) adaptor used for AFLP analysis.(DOC)Click here for additional data file.Table S2Assessment of differences in environmental variables among populations of Keteleeria davidiana var. formosana using a multivariate analysis of variance.(DOC)Click here for additional data file.Table S3Detection of outliers using DFDIST, BAYESCAN, both DFDIST and BAYESCAN in a variety of population pairwise comparisons, and SAM and GEE in total population.(DOC)Click here for additional data file.


Anolis carolinensis is an emerging model species and the sole member of its genus native to the United States. Considerable morphological and physiological variation has been described in the species, and the recent sequencing of its genome makes it an attractive system for studies of genome variation. To inform future studies of molecular and phenotypic variation within A. carolinensis, a rigorous account of intraspecific population structure and relatedness is needed. Here, we present the most extensive phylogeographic study of this species to date. Phylogenetic analyses of mitochondrial DNA sequence data support the previous hypothesis of a western Cuban origin of the species. We found five well-supported, geographically distinct mitochondrial haplotype clades throughout the southeastern United States. Most Florida populations fall into one of three divergent clades, whereas the vast majority of populations outside Florida belong to a single, shallowly diverged clade. Genetic boundaries do not correspond to major rivers, but may reflect effects of Pleistocene glaciation events and the Appalachian Mountains on migration and expansion of the species. Phylogeographic signal should be examined using nuclear loci to complement these findings. Phylogeographic studies examine the spatial distribution of genetic lineages within species and are critical to understanding organismal variation Avise . By reveAnolis lizards have revealed high levels of population divergence within widespread species. On islands of the Greater Antilles, deep mitochondrial divergence has been reported for widespread species from Puerto Rico  representing 37 sites across the species' range and 19 populations of its Cuban progenitor Anolis porcatus. Using mitochondrial DNA (mtDNA) sequences we (1) examine phylogenetic relatedness of populations across the species' range and (2) identify geographic factors and historical events that potentially influenced the evolutionary history of the species.To better inform studies of phenotypic and genomic variation within this species, a comprehensive account of evolutionary history and population structure is needed. Toward this aim, we report the most geographically extensive phylogeographic analysis of A. carolinensis from each of 17 populations throughout the southeastern United States. Collection sites included both natural and human-modified habitats, but did not contain artificial water sources. Due to low population densities, collections were restricted to 10 and 17 lizards from Brownsville, TX and Naples, FL, respectively. We also sampled 3–8 individuals from 10 populations throughout Florida, and obtained 24 samples from museum collections, one individual from North Carolina, and 23 individuals from nine populations in Florida  were obtained from GenBank.During May–June of 2006 and 2007, we collected 29–42 male and female Trp, and tRNAAla. This entire region was amplified using the primers L4437 . The purified PCR products were used as template for Big Dye® Terminator v.3.1 sequencing reactions , which were cleaned with Sephadex®  and visualized on an ABI 3730 .We extracted genomic DNA from liver, tail, or toe tissue using a salt extraction protocol  as in previous studies of reptiles using the ND2, tRNAMaximum Likelihood (ML) analyses were performed using the program RAxML version 7.3.0  was conducted using SMARTPCA  formed a paraphyletic group with respect to the southwestern (Fort Myers) population. These two regions were separated from Central Florida Clade II, North Carolina, and Gulf Coast/Inland populations by ∼5.1–10.0 MYA. Populations across the majority of the species range from Texas to the central Atlantic coast formed a strongly supported, but shallowly divergent clade with a substantial degree of paraphyly among populations. This geographically widespread clade diverged from its most closely related populations  ∼2.5–6.1 MYA. North Carolina samples formed a well-supported, monophyletic clade that was most closely related to populations from inland Florida . These populations were estimated to have separated ∼1.4–4.1 MYA.Within the The PCA showed five distinct population clusters A with thP = 0.04) and in southeast Florida , and was also lower than in Central Florida Clade I and southwest Florida, but not significantly so . When populations from the Central Florida and Southern Florida Clades were combined, each group displayed significantly higher levels of variation than the Gulf Coast/Inland Clade . Diversity in North Carolina did not differ from other clades; however, sampling in this region was limited. For a full table of diversity statistics see Nucleotide diversity estimates within Florida clades were on average higher than in populations from other parts of the species' range. The diversity in the Gulf Coast/Inland Clade was significantly lower than in the Central Florida Clade II , whereas the highest level of genetic differentiation was found between Central Florida Clade I and Southwest Florida (FST = 0.67). Across the entire range of A. carolinensis, North Carolina showed the highest levels of genetic differentiation as compared with other populations . A similar pattern was found for the Gulf Coast/Inland Clade with a relatively high level of differentiation from all Florida regions (FST = 0.74–0.81), but lower differentiation with Central Florida Clade II (FST = 0.46). Within the Gulf Coast/Inland Clade there was no significant difference in genetic diversity among populations. For a full list of FST values, see ions see , with FSA. carolinensis populations showed a slightly higher proportion of rare alleles than expected under a model of constant population size , providing weak evidence for population expansion when considering all populations. When analyzed separately, the Gulf Coast/Inland Clade populations showed a greater shift in the spectrum toward rare alleles , supporting population expansion within this clade. In contrast, the frequency spectrum for populations within Florida displayed an overrepresentation of intermediate frequency alleles when compared with the expected distribution .The observed distribution of the frequency spectrum for all ion size A. Furthe alleles B and Tajribution C, suggesA. carolinensis similar to those observed in many other species of widespread anoles. There are five highly differentiated clades within the species; three of these clades occur within Florida, one in North Carolina, and one encompasses populations across the Gulf Coast, Atlantic Coast, and inland southeastern United States.  based on our estimates, the divergence event that separates the clade containing A. brunneus, A. longiceps, and A. maynardi is estimated at ∼9.3–32.6 MYA, perhaps the early Oligocene. However, because our dataset only represents a portion of the mitochondrial region used by Macey et al. , indicating a possible recent range expansion across this region. This hypothesis is further supported by the observed shift in the site frequency spectrum toward rare alleles and a significantly negative Tajima's A. carolinensis from Florida, Louisiana, and Texas, reporting a high degree of genetic uniformity among these populations using allozymes, with Florida and Louisiana populations more closely related to each other than to Texas. These results contrast with the findings of this study, which show that Florida populations are highly diverged from Louisiana and Texas. The Silver Springs Florida population used by Webster et al. (Webster et al.  comparedr et al.  is near A. carolinensis and western Cuba populations of A. porcatus. They found small genetic distances separated Texas and Georgia, a result consistent with our findings.Buth et al.  used eleWade et al.  found thA recent expansion of populations within the Gulf Coast/Inland Clade explains the small genetic distances reported in earlier studies. The Lower Mississippi Valley, a hypothesized refugial area Davis  lies in A. carolinensis show signatures of population expansion by the species. However, the drastic shift seen in the Gulf Coast/Inland populations suggests that the species-level signature of a shift toward rare alleles is being driven by this clade. In the absence of this clade, A. carolinensis populations display shifts, although statistically nonsignificant, in site frequency spectra expected from a bottleneck event. Given the topology of the tree, these data may highlight molecular signatures of both the bottleneck associated with the initial colonization of the species from Cuba into mainland Florida and the recent expansion of populations out of Florida into its current range. To better understand the genetic effects of this demographic history, a multilocus study including nuclear markers is needed.The observed shifts in the site frequency spectrum of the ND2 region of Clustering patterns identified in the PCA largely mirror the topological structure recovered in the Bayesian and maximum likelihood trees. The tight clustering pattern of most major clades reflects a high degree of genetic structure. Conversely, the lack of clustering of populations within the Gulf Coast/Inland Clade further supports the hypothesis of low genetic differentiation between populations occupying most of the species' range, likely due to a recent expansion event.Anolis lizards and many other species, and has been proposed as a major factor for Caribbean Anolis diversification (Glor et al. A. carolinensis range. Molecular analyses suggest there are at least five highly divergent lineages within A. carolinensis. However, despite the Miocene-Pliocene arrival of the species to the southeastern United States, three of these haplogroups represent limited geographic distributions within Florida, the likely region of colonization. It is not until the periodic warming cycles of the Pleistocene that significant changes in the geographic distribution occur, leading to migrations north along the Atlantic coast, west across the Gulf coast and inland southeastern United States, and establishment of populations in newly habitable areas.Pleistocene glaciation has influenced the current distribution and population relatedness within and between species of A. carolinensis. Future studies of the nuclear genome of this species will elucidate the importance of demographic history in shaping patterns of variation and adaptation. We are aware of the limitations of phylogeographic studies based solely on mitochondrial loci (Edwards and Bensch Our work sets the stage for future studies of phenotypic and genetic variation, demographic history, and adaptation within


Astyanax mexicanus, represents a particularly strong model for both developmental and genetic analyses as these fish can be raised in the laboratory and hybridized with conspecific surface form counterparts to produce large F2 pedigrees. As studies have begun to illuminate the genetic bases for trait evolution in these cavefish, it has become increasingly important to understand these phenotypic changes within the context of cavefish origins. Understanding these origins is a challenge. For instance, widespread convergence on similar features renders morphological characters less informative. In addition, current and past gene flow between surface and cave forms have complicated the delineation of particular cave populations.The loss of phenotypic characters is a common feature of evolution. Cave organisms provide excellent models for investigating the underlying patterns and processes governing the evolutionary loss of phenotypic traits. The blind Mexican cavefish, Astyanax cavefish evolution. In recent years, thanks to the combined use of morphological and genetic data, a much clearer picture has emerged regarding the origins of Astyanax cavefish.Past population-level analyses have sought to: 1) estimate at what time in the geological past cave forms became isolated from surface-dwelling ancestors, 2) define the extent to which cave form populations originated from a common invasion (single origin hypothesis) or several invasions (multiple origin hypothesis), and 3) clarify the role of geological and climatic events in Astyanax cavefish origins will help investigators draw conclusions regarding the evolution of phenotypic traits through parallelism versus convergence. Additionally, it will help us understand how the presence of cave-associated traits in old versus young cave populations may be influenced by the time since isolation in the cave environment. This will, in turn, help to inform our broader understanding of the forces that govern the evolution of phenotypic loss.The consensus view, based on several recent studies, is that cave forms originated from at least two distinct ancestral surface-dwelling stocks over the past several million years. In addition, each stock gave rise to multiple invasions of the subterranean biotope. The older stock is believed to have invaded the El Abra caves at least three times while the new stock separately invaded the northern Guatemala and western Micos caves. This renewed picture of  Astyanax mexicanus, as a powerful model for genomic and evolutionary research. This is a credit to the tractability of Astyanax as a laboratory animal and the availability of both surface and cave morphotypes. Until recently, our ability to draw conclusions on fundamental questions pertinent to the evolution of Astyanax has remained limited by an incomplete understanding of the origin of these fascinating creatures.The mechanism governing the evolution of regressive features remains unknown. Regressive features are highly conspicuous in cave-limited organisms, arise frequently across the animal kingdom, and are probably more common than adaptive traits . In receFor instance, the origin of a trait through convergent versus parallel evolution rests on a clear understanding of the inter-relationships of distinct cave populations . HybridiAstyanax cavefish have centered on several topics. One of the earliest questions relates to the number of times the El Abra limestone caves of northeastern Mexico were colonized by ancestral surface-dwelling forms . A p. A p5]. ee below). The autee below).Astyanax populations in Central America is the Trans-Mexican Volcanic Belt (TMVB), which harbored intense volcanic activity 3–12 MYa, through the present day . Th. Thin seAstyanax cave forms from multiple invasions and colonization events is now well accepted. Supporting evidence from population genetic studies is presented below.Phylogenetically “old” versus “young” categories were based on the notion that the accumulation of neutral mutations is the principle mechanism driving regressive evolution, and therefore a longer time since isolation is predicted to lead to the accumulation of more phenotypic regression . HoweverRecent population level analyses have provided clarity to early classifications of “phylogenetically old” versus “young” cave populations. These studies estimate the age of different cave populations utilizing genetic rather than strictly morphological characteristics. This is particularly important given that widespread convergence on the cave phenotype, through diverse genetic mechanisms, renders morphology a less powerful tool in defining relationships among different cave forms .NADH dehydrogenase 2 (ND2) locus [Astyanax is both high, and comparable to other freshwater fish species [The first analysis of mitochondrial DNA (mtDNA) sequence variation was carried out using the 2) locus . This st species ; see als species . Combini species .Astyanax aeneus) than they are to each other. The authors found that surface fish harbor more genetic variation compared to cave forms. However, more than one haplotype was observed in both the Tinaja and Subterráneo caves. This may be due to the fact that Tinaja represents the largest cave, and Subterráneo harbors an opening through which epigean forms may enter, and therefore could be explained by introgression with the surface fish. The authors concluded that the divergent ND2 haplotypes observed between lineages is consistent with multiple origins, followed by convergence on the eye loss phenotype [The majority of individuals in Lineage B  harbor 11 ribs . These phenotype .A subsequent analysis of genetic variation within cave forms was carried out using nuclear (microsatellite) loci as well as mtDNA . This stThese results, largely congruent with the findings of Dowling et al. (2002), implied a common origin for the Pachón, Sabinos and Tinaja caves. Pachón cavefish, which harbor very low levels of genetic diversity, may have been isolated from ancestral surface forms for a much longer period of time compared to the other cavefish populations . The ChicytB. This extensive study, which sampled 174 individuals comprising 9 cave and 26 surface fish populations, identified seven major clades. The observed clade distribution is likely due to episodes of invasion followed by extinction of surface forms, as reported also by Dowling et al., 2002. Four cave localities  were categorized as “strongly eye- and pigment-reduced populations” (SEP) that fail to cluster with surface fish near surrounding regions  clade based on mtDNA. This clade, similar to Lineage A , clustercytB sequence variation , to est1.5%/MY; . Followi1.5%/MY; . The SEP1.5%/MY;  in refugAstyanax genus in Mesoamerica by examining DNA sequences of three mitochondrial genes (cytBCOI16 S) and a nuclear gene (rag1) using 208 individuals from 147 localities [Astyanax cave forms fell into Group I, subdivided into four clades. Of the four lineages within Clade I, the more ‘recent’ troglobitic forms are in lineage Ia , Pachón, Yerbaniz, Japonés). More ancient populations are clustered within clade II , including cave populations from the Sabinos cluster, comprising Curva, Tinaja, Sabinos, and Piedras  independent colonizations of epigean forms into upper Mesoamerica. The first colonization of epigean forms was predicted to be quite ancient  is a coers see ,47,48). ,48. Astyd bridge .Astyanax, further characterized the reported discordance between nuclear genotypes and the mtDNA clades previously defined from other studies. Hausdorf et al., (2011) surveyed variation at six microsatellite loci in 25 populations comprised of 4 cave and 21 surface-dwelling localities. The results of their mtDNA analysis were largely congruent with prior findings [A more recent study, evaluating both mtDNA and microsatellite data in cave and surface populations of t al., 20 surveyedfindings , howeverAstyanax cavefish , that have evolved independently in multiple cave populations may have resulted in pre-mating isolation, representing an initial step towards parallel speciation [Admixture analyses indicated that there has been recent gene flow between distinct mitochondrial clades, most notably from the Pachón cave locality . The auteciation .Astyanax cavefish represent an instance of parallel evolution, Strecker et al. (2012) performed a population-level analysis of seven cave and seven surface populations. These authors reported significant population differentiation between Astyanax cave and surface forms through analyses of both nuclear (microsatellite) DNA and mtDNA. Their mtDNA analysis also agrees with findings of other groups indicating epigean Astyanax invaded northeastern Mexico, and the caves, at least twice [To determine if st twice ,11,21,23Astyanax surface fish. Two nuclear genotypic clusters were distinguished from these old and new invasions [Astyanax cave populations  implying mitochondrial “capture” may be a frequent phenomenon in Astyanax cavefish [In this study, the old invasion was distinguished on the basis of mtDNA (clade G = old) , whereasnvasions ,23. As onvasions ,23, furtnvasions ,23. Intecavefish .Using a STRUCTURE analysis of microsatellite data , the autIn contrast, a very recent study by Bradic et al., 2012, analyzing 26 different loci (microsatellites) in 11 cave and 10 surface populations, argues that measurable gene flow has occurred between both cave and surface and from cave to cave populations. This population analysis agrees with prior reports suggesting cave populations originated from two (‘old’ and ‘new’) waves of ancestral surface-dwelling forms -11,21,23This study found cavefish populations are more genetically differentiated than their surface form counterparts, despite surface fish being more geographically separated. This was supported by the finding of lower allelic diversity in cave versus surface populations. The authors also argue that the observed lower genetic diversity, which correlates with smaller effective population sizes, likely arose as a consequence of limited food and space in the subterranean environment and/or periodic bottleneck events ,23.Astyanax cavefish populations were colonized by an old and new stock of surface-dwelling ancestors. The older population invaded the El Abra caves at least three times and then went extinct , the entire geographical extent of northeastern Mexico harboring blind 2, the enAstyanax cave populations originated from two waves of ancestral epigean forms from the south. The older wave gave rise to the El Abra populations, while the younger wave gave rise to the Guatemala and the Micos populations  concluded that Chica cavefish probably did not arise from the same invasion as the older Sabinos, Tinaja and Pachón caves since most of the fish at this locality grouped with nuclear genotypic cluster I. It should be noted however, that ~14% of the fish from Chica did group with the older caves. These authors argue that Chica was colonized by a younger cave population that later introgressed with an older cave population ,23.ST analysis demonstrated that this population is significantly different from the other cave and surface populations surveyed in their study. Moreover, the authors suggest that – due to its southern geographic position – Chica may have been one of the first Astyanax cave populations seeded from the older wave. This may be due to the fact that Chica harbors a large bat roost that provides a significant nutrient base. This higher nutrient base may have allowed periodic surface migrants to survive in the otherwise nutrient-poor environment of the cave [In contrast, Bradic et al. (2012) concluded that Chica was seeded by the old stock of epigean fish. This was based on the finding that the genetic distance for Chica cavefish was equidistant between the older and newer populations . The resthe cave .Astyanax researchers. Generally, cave organisms are assumed to harbor low levels of genetic variability [Beyond the Chica cave, the level of genetic variability within and between other cavefish populations has long been of interest to iability . Classiciability ; revieweiability . On the iability . Poulsoniability .Astyanax, Avise and Selander (1972) first set out to determine if cave forms harbored reduced heterozygosity compared to surface form fish. They discovered that cave forms demonstrated reduced levels of heterozygosity  compared to surface forms. The common assumption that all Astyanax cavefish populations harbor low levels of genetic diversity, however, may need to be revised. Based on an analysis of RAPDs and 19 microsatellite markers, Panaram et al. (2005) demonstrated a low, but not absent, level of genetic diversity in cave populations. After testing the effects of a variety of causes, only the presence of eyed fish within the cave population was associated with increased genetic diversity. The authors concluded that this intra-cave diversity likely reflects recent gene flow between surface and cavefish. Microsatellite analyses provided further support for the notion of measurable gene flow between cave and surface forms [To assess the validity of these competing theories in ander 197 first seA fascinating case study of intra-cave phenotypic variability occurs at the Caballo Moro locality that harbors both eyed and eyeless fish populations. This cave has a karst “window”, originating from a collapsed roof that illuminates a cave lake during certain times of day . EspinasIn a more recent study, Strecker et al., 2012 observed that all of the fish at the Caballo Moro locality grouped with a nuclear genotypic cluster (IV) that is more genetically similar to surface fish populations south of the TVMB than neighboring surface populations in northern Mexico. The authors categorized these fish as a VEP (variable eye and pigmentation) population. Based on their microsatellite data analysis, these authors concluded that Caballo Moro cavefish arose from a relatively recent colonization across the TMVB into the cave environment. Thus, Caballo Moro is likely a young cave population that has undergone divergent selection within the cave that has led to the first stages of genetic separation, representing an example of incipient speciation .Astyanax cavefish? Hausdorf et al. (2011) argue that, despite flooding events that bring hundreds of surface fish into the western Micos cave and the Yerbaniz caves, there is no appreciable level of hybridization. Indeed, in Yerbaniz, hundreds of fish are reportedly swept into the caves following spring flooding, however only two specimens have been reported that bear intermediate phenotypes [In other caves, intra-cave variation could be explained, in principle, by differences in trophic diversity or hybridization with surface form fish . The conenotypes ,9. The aenotypes .brown, respectively) from the Pachón cave populations were present in the geographically distant Japonés and Yerbaniz caves [Past expeditions to the Pachón cave have similarly reported changes in population-level phenotypes  suggesting that contact with surface fish is a random event that occurs unevenly over time . Recent iz caves ,56. ThesStrecker et al. 2012) reported no gene flow between the strongly eye- and pigment-reduced (SEP) cave populations  and surrounding surface-dwelling forms. Gene flow was not observed even in cases where surface fish were present within the same cave . The authors also reported little gene flow, if any, between populations demonstrating variability in eye size and pigmentation  reported a significant level of gene flow among  surface . The Mic surface . This fi surface .Astyanax cave form origins has emerged from the literature. There are important challenges associated with generating a conclusive phylogeny of cave forms since widespread convergence among cave forms renders identification of precise lineages problematic [Astyanax cavefish populations. Several independent invasions from each “stock” of these epigean ancestors have seeded multiple caves. For instance, both the Pachón and Sabinos cave populations are derived from the same “old” stock [In recent years, through numerous genetic and molecular analyses, a clearer picture of blematic . The cond” stock , but theOca2 and Mc1r in geographically distant caves .Astyanax cavefish, as a model for understanding regressive phenotypic evolution, underscores the importance of a higher resolution phylogeny for interpreting experimental studies. In addition, studies investigating the genetic basis for trait evolution in cavefish can be useful for informing the extent to which alleles are shared between geographically distinct cave forms.The growing interest in The author declares no competing interests.JBG wrote the manuscript.


Oncorhynchus nerka) populations from the Kvichak River, Alaska, we assessed the relative roles of geography (discrete boundaries or continuous distance) and ecology (spawning habitat and timing) driving genetic divergence in this species at varying spatial scales within the drainage. We also evaluated two outlier detection methods to characterize candidate SNPs responding to environmental selection, emphasizing which mechanism(s) may maintain the genetic variation of outlier loci.Disentangling the roles of geography and ecology driving population divergence and distinguishing adaptive from neutral evolution at the molecular level have been common goals among evolutionary and conservation biologists. Using single nucleotide polymorphism (SNP) multilocus genotypes for 31 sockeye salmon  class II that appeared robust to violations of demographic assumptions from an initial pool of eight candidates for selection.First, our results suggest that geography and ecology have influenced genetic divergence between Alaskan sockeye salmon populations in a hierarchical manner depending on the spatial scale. Second, we found consistent evidence for diversifying selection in two loci located in the MHC class II by means of outlier detection methods; yet, alternative scenarios for the evolution of these loci were also evaluated. Both conclusions argue that historical contingency and contemporary adaptation have likely driven differentiation between Kvichak River sockeye salmon populations, as revealed by a suite of SNPs. Our findings highlight the need for conservation of complex population structure, because it provides resilience in the face of environmental change, both natural and anthropogenic. Disentangling the roles of geography and ecology driving population divergence and speciation has been a common goal among evolutionary biologists . SeveralOncorhynchus nerka, Walbaum 1792) spawn among tributaries, outlet rivers, beaches, and even glacial habitats [Research on salmonid systems has greatly enhanced our understanding of hierarchical divergence; in fact, genetic variance is normally larger between salmon populations inhabiting different basins than between salmon populations from the same basin that differ in ecological attributes ,3,13,14.habitats ,19. A cohabitats ,21). In habitats -24.Molecular tools have become instrumental for quantifying population divergence and reproductive isolation in applied evolutionary biology studies. Single nucleotide polymorphisms (SNPs) are now the marker of choice among many geneticists for addressing evolutionary questions -28. The O. nerka populations because (i) they were likely recolonized at varying times through different founding events as ice sheets sequentially retreated at the end of the Late Wisconsin glacial maximum  may maintain the genetic variation of outlier loci. These analyses were conducted and reported first (see below) in order to avoid estimates of  divergence that may be biased by selection. Emphasis was placed on violations of the demographic assumptions of outlier detection models, including potential bottlenecks and hierarchical structure between populations ,51. We sOne_HpaI-99: Figure Results: Genetic diversity and differentiation), possibly indicating presence of bottlenecks. Marked allele frequency differences between subdrainages (~0.5) characterized other outliers, such as One_GPH-414 , suggesting that gene flow was more predominant within subdrainages than between subdrainages. Simulations in Arlequin 3.5 [One_GPH-414 and One_STC-410 were no longer outliers assuming hierarchical structure between subdrainages  class II which corresponded to a nonsynonymous substitution (aspartic acid to tyrosine) in the putative antigen recognition site [One_MHC2-251 located within an intervening intron [One_MHC2-190 and One_MHC2-251 for some populations  departures from Hardy-Weinberg equilibrium were observed in any population or locus following exclusion of three mitochondrial SNPs : One_MHC2-190 (I = 0.433) and One_MHC2-251 (I = 0.465); One_Tf_ex10-7 (I = 0.632) and One_Tf_ex3-18 (I = 0.084); One_GPDH (I = 0.668) and One_GPDH2 (I = 0.082). Based on the information content per locus, I (Shannon-Weaver's index: in parentheses above), both One_MHC2-190 and One_MHC2-251 were kept for subsequent analyses, while One_Tf_ex3-18 and One_GPDH2 were excluded from further analyses. Exclusion of these loci decreased the pool of nuclear SNPs from 42 to 40.No significant (Ps Table . HoweverOne_MHC2-190 and One_MHC2-251), 'Outlier 1' (39 SNPs: including One_MHC2-190), and 'Outlier 2' (39 SNPs including One_MHC2-251). Differences between sets were emphasized if present; otherwise 'No Outliers' should be considered the default set.Estimates of SNP diversity and differentiation were obtained for three distinct nuclear sets of markers to account for possible effects of selection: 'No Outliers' or the putatively neutral set (38 SNPs: excluding AR) ranged between 1.785 (3-Battle Lake River) and 1.979 (26-Nick Creek) and expected heterozygosity (HE) varied between 0.207 (13-Upper Tlikakila) and 0.264 . We also detected differences in AR  and HE between lakes . The lowest diversities were found within Clark and Battle, whereas the highest were in Sixmile and Iliamna .Allelic richness  and 'Outlier 2'  than for the 'No Outliers' set . Pairwise FST for the entire drainage were significantly correlated between SNP sets based on simple Mantel tests and Pearson correlation coefficient, r . Global differentiation among mitochondrial SNPs was also highly significant  but lower than estimates found for nuclear SNPs.Estimates of global divergence were slightly higher for 'Outlier 1'  from pairwise s Figure . Island R2 = 0.06; 'Outlier 1' R2 = 0.07; 'Outlier 2' R2 = 0.15). These values underwent a two- to five-fold increase once we conducted separate isolation-by-distance analyses for different spawning habitats  were prominent as well, especially after accounting for the differentiation of Lake Clark.HE and AR were also noticeable between the Alagnak and Kvichak subdrainages, which can be attributed to differences in both number and size of populations. For instance, sockeye salmon have historically been much more abundant in the Kvichak than the Alagnak systems [Large-scale divergence between sockeye salmon populations in the Kvichak River may have initially evolved from historical contingency, followed by contemporary adaptation. Recolonization of lakes and subdrainages was likely sequential as ice sheets retreated after the Late Wisconsin glacial maximum . The bes systems ,58, thou systems .Contemporary adaptation to distinct limnetic environments may also help explain the high fidelity of returning adults. Reproductive isolation between nursery lakes is typical of lake-type sockeye salmon, which spend half of their lives rearing in freshwater before migrating to sea ,44,60-62The role of ecology (spawn timing and habitat) was more prominent at fine scales or for intralake patterns of divergence, with geographic distance having a significant but secondary role. This conclusion was drawn from analyses conducted within Iliamna Lake, a large and population-rich lake harbouring three different spawning habitats. Mantel tests for one SNP set ('Outlier 2') strongly suggested that variable timing of spawning, particularly among populations of the three spawning-habitat types  than for the other two sets: 'No Outliers'  suggested mild separation, whereas 'Outlier 1'  indicated no clear separation. Assuming MHC introns have shorter coalescent times than exons , populations from mainland beaches may have diversified only recently from tributaries, and different SNP sets reflect different times since divergence. Consistent with this hypothesis, mainland beach populations appeared undifferentiated from 16-Chinkelyes Creek, a tributary of the Iliamna River, implying that habitat-driven isolation is incomplete. It is possible that populations from 16-Chinkelyes Creek and adjacent tributaries colonized 20-Finger Beach given that only ca. 5 km separates it from the mouth of Iliamna River, where 16-Chinkelyes Creek drains. Interestingly, 23-Iliamna River also clustered more closely to mainland beaches than the majority of tributary spawners. We speculate that the four populations from the east of Iliamna Lake  shared a common ancestor. Likely, divergent attributes of their breeding sites as well as discrete patterns of spawn timing have driven them apart over time.Differences between mainland beaches and tributaries were more obvious for 'Outlier 2'  and one intron (One_MHC2-251) within the MHC class II locus appeared robust to violations of demographic assumptions  and consistently appeared as outliers during genome scans. MHC class II genes are translated on the surface of antigen-presenting B cells and macrophages and play a key role in the successful mounting of the immune response of vertebrates [et al. [One_MHC2-190 located in the antigen recognition site. Yet, evidence for diversifying selection or neutrality of alleles could not be ruled out for some populations [Two SNPs found in one exon  in comparison with putatively neutral loci are consistent with signatures of diversifying selection; such force is expected to drive adaptive mutations and tightly linked sites to fixation by positive selection, hence increasing differentiation between populations [We hypothesize that large ulations . Kvichakulations . Three oulations -77.Salmo salar) from Central Québec (Canada), and concluded that between-river differentiation was highly correlated between alleles of different marker types, whereas within-river differentiation was not. For Kvichak River sockeye salmon, it is feasible that neutral evolution has played a more prominent role than selection influencing large-scale divergence. Moreover, recent studies argue that the evolution of MHC variation may proceed in neutral fashion: estimates of population divergence from MHC genes appear no different from estimates using neutrally evolving microsatellite DNA markers [Even though the argument for diversifying selection seems compelling, it is based on a genome scan involving only 42 nuclear loci, a limitation of most genetic surveys in nonmodel organisms ,32. Eval markers -81.Could differences between SNP sets found at fine spatial scales be also attributed to selection at the molecular level? Within Iliamna Lake only, differences were evident among 'No Outliers', 'Outlier 1', and 'Outlier 2' sets, including results from PCO and Mantel tests relating spawn timing and genetic distances. These differences were expected if diversifying selection affects those sets containing MHC outliers  but not the putatively neutral set ('No Outliers'). The observed dichotomy between sets containing the MHC exon ('Outlier 1') and MHC intron ('Outlier 2') was however unexpected based on the premise that diversifying selection should fix variation in tightly linked sites around the adaptive mutation; here, we have assumed that the MHC exon may have a selective advantage, whereas the MHC intron is subjected to hitchhiking selection . We propIn summary, diversifying selection--as suggested by outlier analyses--acting on two SNPs found in the MHC class II locus complex remains one possible hypothesis with support from other three studies in salmonids. However, neutral evolution of these polymorphisms and impacts of balancing selection represent alternatives that may operate at varying spatial scales. Resolving between these hypotheses is beyond the scope of this investigation; we encourage further studies in sockeye salmon that address mate choice  as well Two main conclusions emerge from this study. First, we have demonstrated that geography and ecology have hierarchically influenced genetic divergence between Kvichak River sockeye salmon populations depending on the spatial scale. Contrasts between lakes, subdrainages, and geographic distance dominated large-scale differentiation, whereas differences in the timing of spawning linked to discrete spawning habitat dominated fine-scale  differentiation. Second, we determined signatures of selection in two SNPs located in the MHC class II that appeared robust to violations of demographic assumptions. We propose that one possible mechanism that has driven the evolution of these SNPs is diversifying selection in response to local pathogens; however, neutral evolution of these polymorphisms at large spatial scales, as well as effects of balancing selection at fine spatial scales, cannot be ruled out at this stage. Both conclusions imply that historical contingency and contemporary adaptation have driven differentiation between Kvichak River sockeye salmon populations, as revealed by a suite of SNPs. Our findings highlight the need for conservation of complex population structure, because it provides resilience in the face of environmental change, both natural and anthropogenic.et al. [Adult sockeye salmon n = 3,945) were taken from a larger collection of reference populations used for identification of juvenile mixtures in the high seas of the northern Pacific Ocean . Briefly were taket al.  were alset al. [Uniplex and array-based genotyping followed Seeb et al. . All indet al. -41. QualWe employed BAYESCAN to identify outliers or those SNPs characterized by higher or lower levels of population divergence than strictly neutral loci, suggestive of diversifying or balancing selection, respectively . BAYESCAWe also used Arlequin 3.5  to detecet al. [-5 was found.Outlier loci were classified as exonic or intronic using BLASTX or TBLASTX  to gauge the scope of selection acting directly on these markers. We followed Smith et al.  guidelink tests [I) per locus supplied by GENALEX 6.3 [et al. [We tested for deviations from linkage equilibrium between markers in each population using GENEPOP 4.0 . Exact pk tests . For SNPALEX 6.3 . Linked  [et al.  and analDeviations from Hardy-Weinberg equilibrium among nuclear SNPs were estimated in GENALEX with probabilities for locus-specific χ2 tests. Multilocus HWE goodness-of-fit probabilities were calculated by summing across loci (degrees of freedom = number of loci).R coefficient in SPSS 17.0. For mitochondrial SNPs, we calculated haplotype frequencies in GENALEX via the AMOVA option for haploid data. Differences in genetic diversity between spatial groups  were evaluated using nonparametric Kruskall-Wallis χ2 tests or Mann-Whitney U-tests for multiple and pairwise comparisons, respectively, using SPSS 17.0.Estimation of allele frequencies and heterozygosities (observed and expected) for nuclear SNPs was done in GENALEX. Allelic richness or the number of alleles corrected for sample size was additionally estimated using a rarefaction method implemented in FSTAT 2.9.3 ,91. CorrFST plus confidence intervals (95% CI) were obtained from FSTAT, whereas pairwise FST values were calculated in GENALEX along with population differentiation tests based on 1000 permutations. Global differentiation tests for FST were calculated in GENEPOP using Fisher's method. For mitochondrial SNP haplotypes we quantified global differentiation using ΦPT [For nuclear SNPs, global sing ΦPT  in GENALWe employed the AMOVA option in GENALEX to partition the total genetic variance within and between regions. Hierarchical regions corresponded to either subdrainages or lakes. AMOVA first included populations from the entire drainage; variance components were recalculated after excluding highly differentiated populations to account for an uneven distribution of the total genetic variance. Our objective was to define which grouping explained the highest proportion of the variance. Permutations (1000 times) of elements between and within regions were carried out using Φ-statistics  to enablR2-value that explains how well the branch lengths of a bifurcating tree captures the variation of the distance matrix. Kalinowski [R2 >0.90 reliably reflect the underlying spatial genetic structure of a distance matrix.Using PHYLIP  we builtlinowski  suggestsFST were preferred at fine scales instead of Cavalli-Sforza & Edwards [We used a principal coordinate analysis (PCO) in GENALEX to summarize multidimensional genetic data between populations within Iliamna Lake. Pairwise  Edwards  chord di® 6.0 software (Alagnak subdrainage) or were taken from a spatial analysis of historical abundances among populations of the Kvichak subdrainage [r) and their p-values after 10000 randomizations. Independence of geographic distances and differences in spawn timing was verified at large scales  and fine scales .Geographic distances (km) corresponded to direct waterway distances calculated in the DeLorme Topo USAdrainage . The difdrainage  using sidrainage . A partiFST and geographic distance for tributary-to-tributary, tributary-to-island-beach, and tributary-to-mainland-beach population comparisons. Linearized  or stanExperimental research reported in this manuscript consisted of genetic analyses of animal tissues collected from wild populations. Collection was done in compliance with protocols from the University of Washington Institutional Animal Care and Use Committee and permits from the Alaska Department of Fish and Game.DGU conducted the majority of statistical analyses and was the primary responsible for writing the manuscript. JES conceived and designed the study, helped collect many of the samples and interpret the results, and contributed to drafting and editing the manuscript. MJS performed exploratory spatial and genetic analyses and commented on the manuscript. CH participated in the study design, collected and analyzed genetic data, helped interpreting the results, and commented on earlier drafts. TPQ provided ecological expertise on the system, helped collect many of the samples, and contributed to the writing of the manuscript. LWS conceived and designed the study, helped interpret the results, and contributed to drafting and editing the manuscript. All authors read and approved the final manuscript.FST values and tests of differentiation between Kvichak River sockeye salmon populationsPairwise Click here for fileHierarchical analysis of molecular variance (AMOVA) between sockeye salmon populationsClick here for fileHabitat and life-history attributes of three spawning ecotypes of sockeye salmonClick here for file


Rhinolophus capensis) across a gradient of increasingly cluttered habitats.Across heterogeneous environments selection and gene flow interact to influence the rate and extent of adaptive trait evolution. This complex relationship is further influenced by the rarely considered role of phenotypic plasticity in the evolution of adaptive population variation. Plasticity can be adaptive if it promotes colonization and survival in novel environments and in doing so may increase the potential for future population differentiation via selection. Gene flow between selectively divergent environments may favour the evolution of phenotypic plasticity or conversely, plasticity itself may promote gene flow, leading to a pattern of trait differentiation in the presence of gene flow. Variation in sensory traits is particularly informative in testing the role of environment in trait and population differentiation. Here we test the hypothesis of ‘adaptive differentiation with minimal gene flow’ in resting echolocation frequencies (RF) of Cape horseshoe bats  illuminate the subtle interplay between selection, plasticity and gene flow in the evolution of adaptive traits and ii) demonstrate that evolutionary processes may act simultaneously and that their relative influence may vary across different environments.Our study reveals significant sensory trait differentiation in the presence of historical gene flow and suggests roles for both selection and plasticity in the evolution of echolocation variation in  In populations inhabiting heterogeneous environments adaptive trait divergence in the absence of gene flow is both predicted by theoretical models -3 and suUndoubtedly there is a complex and dynamic relationship between selection, fitness and gene flow, but the interaction among these factors together with the role of trait plasticity is seldom considered in studies of adaptive population variation ,19. PhenGeographic variation in sensory traits i.e. traits used by organisms to perceive and respond to information about their environment  is well Rhinolophus capensis. We assess echolocation variation within a neutral evolutionary framework to (i) quantify the extent to which population genetic structure and gene flow contribute to variation in echolocation frequencies, and (ii) using a model-based approach explore the roles of habitat structure  and humRhinolophus capensis. Eleven populations were sampled across several major biomes in the region  microphone connected to a laptop running Avisoft SasLab Pro software (sampling rate 500 kHz). Resting peak frequency  was recHd) and nucleotide diversity (π) [We also collected a tissue sample for DNA extraction and sequencing from most individuals for which we had echolocation recordings. Biopsy punches (3mm) were taken from the wing or tail membrane following . Membransity (π)  were calsity (π) . All uniWe conducted a Kolmogorov-Smirnov test for normality, Levene’s test for homogeneity of variances and regressed means against standard deviations for all grouping levels to ensure the data met the assumptions of subsequent analyses.To quantify variation in RF within and between populations we calculated the mean ± standard deviation (SD) and the coefficient of variation (CV = SD/mean × 100) of each population.R. capensis. Mixed models are robust to unbalanced designs and thus data from single-sex populations could be included [Rhinolophus damarensis  and extrinsic (e.g. habitat characteristics) factors can influence the evolution of geographic variation in acoustic signals across a species range. To tease apart the effects of these factors on RF divergence we first conducted a general linear model (GLM) with RF as the dependent variable and population, sex and their interaction as categorical predictors. We then used SPSS  to conduct linear mixed models (LMM’s) performed with type III sums of squares to determine the ecological factors which best predict RF in included . LMM’s eincluded . Our GLMincluded . Fixed f overlap ), only ome model . Lastly,92.2 kHz   were calculated to determine the likelihood that a given model was the best model relative to other candidate models [i value of zero indicates the best fit model; values up to two indicate models with substantial empirical support; values between four and seven indicate less support and models with values > 10 have essentially no support [wi) to calculate the probability that a given model is the best among a candidate set of models. Thus, the best model has the lowest Δi and highest wi[R. capensis using the restricted maximum likelihood method. Then, to determine the importance of each variable included in the best model, we calculated the summed Akaike weight (w+) for all models containing that particular variable. The variable with the largest w+ is likely the most important variable in the model [We employed a model selection approach based on Akaike’s information criterion (AIC) to determine which model, out of a range of candidate models, best explained RF divergence in  capensis,98. The he model .Our model selection results led us to explore the influence of habitat structure  and body size on RF variation in greater detail. The Normalized Difference Vegetation Index (NDVI) was used as a measure of vegetation cover. NDVI is a suitable measure because it provides an estimate of above ground primary productivity  and it hWe also investigated whether there was a negative relationship between relative humidity and mean RF as predicted by the Humidity Hypothesis . RelativR. capensis forage in or near highly cluttered habitats [R. capensis at De Hoop; Jacobs and Parsons, unpublished data) and the auditory threshold of the bat . Wehoe bats  were derhoe bats  and inclmm-19 mm ). Mean mR. capensis and (ii) quantify levels of historic gene flow among populations. We first explored the evolutionary relationships among mtDNA haplotypes to determine whether observed relationships reflected either the geographic sampling of populations or specific biome discontinuities across the range of the species by constructing a Neighbour-net network in SplitsTree v4.12.6 [p’ distances. Network analysis allows reticulations among branches and is highly suitable for analysing evolutionary relationships among populations [Geographic variation in phenotypic traits may be a consequence of neutral evolutionary processes, particularly when dispersal distances result in a pattern of predominantly nearest-neighbour gene exchange ,113. To  v4.12.6  using unulations .ΦST  werNeμ, together with pairwise migration rates summarised as M = m/μ, where m is the effective immigration rate per generation between populations. Banding data from European horseshoe bats reveal generally small home ranges where maximum dispersal distances rarely exceed 100km over the course of an individual’s life time [R. capensis likely occurs over relatively short distances. Thus we estimated Θ and M using a custom designed migration matrix where migration was only allowed between neighbouring populations. Populations separated by large geographic distances were not directly connected except in cases where unique haplotypes were shared. Starting values for all parameter estimates were initially obtained using FST[To obtain estimates of historic gene flow among populations we used a maximum likelihood method based on the coalescent implemented in MIGRATE-N v3.3.2 ; MIGRATEife time . If simiusing FST and the ΦST[Genetic divergence among populations is dependent on both the degree of physical isolation and levels of connectivity between them . To testΦST and RF dR. capensis ranging from 75.7 kHz (LS) in the west, to 86.5 kHz (BAV) in the east  with the exception of KNY which used similar frequencies to HDH and DHC , suggesting that the degree of sexual dimorphism in RF was identical across populations. The CV of call frequencies for each population was low and at most 1%  and body mass  of which the former was the most important variable in the model   but it only explained 12% of the variation in RF , with NDVI accounting for 80% of the variation in RF  after controlling for the effect of body size  but we did discover significant differences in detection distances for large prey and vegetation edge  rather than between populations (ΦST = 0.33) or biomes (ΦST = 0.13) (P’s < 0.005). Bayesian clustering identified four spatially explicit genetic clusters in our data  revealed generally asymmetrical patterns of gene flow between populations situated in different biomes . Despite the significance of the correlation, genetic distance only explained a small proportion of the variation in echolocation frequencies (7.5%).Pairwise differences in both RF and genetic distance were characterised by significantly positive relationships with geographic distance Figure A, B. RF R. capensis despite substantial historical gene flow across the distribution of this species. Body size, genetic distance and geographic distance play minor roles in the evolution of geographic variation in RF in R. capensis. Further, despite a lack of strong geographic structure in mitochondrial lineages, biome was identified as the best predictor of RF. In support of this we found a significant relationship between RF and increasing vegetation clutter from west to east across the range of this species. Our results suggest that the evolution of geographic variation in RF in the face of homogenizing gene flow in R. capensis was probably influenced by selection for lower echolocation frequencies in less cluttered habitats. However, the relationship we found between RF and habitat type is solely correlative, and therefore a thorough comparison of foraging behaviours of bats between different habitats is required. Thus, alternative evolutionary processes e.g. selection for discrete frequency bands or phenotypic plasticity cannot be excluded at this stage.Our results reveal significant geographic variation in the resting echolocation frequencies (RF) of R. pumilus: [R. pearsoni: [R. sinicus: [R. yunanensis to R. pearsoni: [R. capensis to reflect mtDNA structure given that (i) the fine tuning of the echolocation frequency of young horseshoe bats are partly learned from their mothers[R. capensis populations. A Bayesian clustering analysis identified four dominant genetic lineages which broadly reflect estimated patterns of regional gene flow that are not limited by biomes; notably only LS and TF (populations at the opposite extremes of the distribution of the species) are identified as unique genetic clusters  or histearsoni: ; R. sinisinicus: ; or nuclearsoni: ) betweenearsoni: ), few stearsoni: ,69. In or mothers, (ii) fer mothers,133, andr mothers. Our ress Figure . A numbeR. capensis. For example, we detected considerable gene flow between populations at SKK, ZPK and DHL, but no direct gene flow between these populations and the population at DHC, even though they were all assigned to the same genetic cluster . As we  rouxii: ,138. In  rouxii: ,29. If p rouxii: .R. capensis with larger bats generally using lower RF’s. However, body size only explained a minor proportion of the variation in RF because concomitant changes in body size and RF were not evident across all populations. For example, although BAV bats are not the smallest, they use the highest echolocation frequencies. In contrast, individuals in populations at both ends of the distribution of this species (LS in the west and TF in the east) were similar in size but echolocated at the lowest and second highest frequencies respectively .R. ferrumequinum) use a variety of habitats with differing degrees of clutter and use significantly lower echolocation frequencies in relatively open habitats than in cluttered habitats [R. capensis uses both aerial hawking and perch hunting styles to different degrees in the different habitats perhaps also altering its call frequency to deal with different degrees of clutter. The distinctly lower call frequency at LS could possibly be explained by the pronounced clutter gradient between LS and the other habitats occupied by R. capensis. However, our detection distance calculations must be interpreted with caution because different populations may have different echolocation call intensities, which could greatly influence the calculation of maximum detection distance [The positive correlation we found between RF and NDVI suggests that variation in the degree of habitat clutter might explain variation in RF. Across our sampling sites, the mean detection distance for large prey and background vegetation edge was significantly different between populations; bats occupying more open habitats have lower RF’s and thus longer detection distances than those in more cluttered habitats, allowing them to detect larger prey or background targets at greater distances. While this result was statistically significant, LS bats only had a 10 cm and 50 cm greater detection distance than their nearest neighbours for large prey and vegetation edge, respectively  is sympatric with R. capensis in the extremely arid area of LS but uses frequencies , and maintain effective intraspecific communication. However, the closest known roosts of these two species are 40 km apart and it is not known if the foraging areas of these two species overlap, i.e. if they are syntopic, as required by the Acoustic Communication Hypothesis.Although differences in habitat structure provide a compelling explanation for different RF’s in –89 kHz; ) as high capensis. Of courz Figure . InsteadPseudacris feriarum, diverged to maximize differences from the heterospecific assemblage present, ultimately promoting reproductive isolation between conspecific populations via sexual selection. Ecologically adaptive traits can also promote divergence if divergence has a pleiotropic effect on reproductive isolation via assortative mating; so called ‘magic traits’ [R. philippinensis assortative mating has evolved between size morphs as a by-product of selection for different frequencies used to exploit different prey sizes [R. capensis  and emballonurid bats (Saccopteryx bilineata) are indeed able to recognise the sex of conspecifics based on their echolocation calls [R. capensis as potential mates. Divergence in RF may have under-appreciated consequences for the evolution of reproductive isolation via female preference for male RFs in different populations of horseshoe bats. Evaluating female preference in LS bats for local versus allopatric RFs may provide intriguing insights into the causes and consequences of sexual selection in horseshoe bats. Finally, because R. damarensis only occurs in sympatry with R. capensis at a single site (LS), we were not able to test whether habitat or the presence of R. damarensis offered a better explanation for RF differentiation in R. capensis. An experimental approach is required to test whether R. capensis has shifted its RF to avoid acoustic overlap with R. damarensis. Such an approach would evaluate whether R. capensis from LS responds differentially to the echolocation calls of heterospecifics and acoustically divergent conspecifics using playback experiments.Nevertheless, character displacement, mediated by some form of resource partitioning or local adaptation, in sonar parameters can contribute to the initial stages of lineage divergence by causing populations in sympatry with heterospecifics to diverge from their respective conspecific populations in allopatry. The result would be reduced gene flow between populations found in different selective regimes or heterospecific assemblages . For exa traits’ . In R. pey sizes . The sigon calls ,156. ThuR. capensis, and the evolution of sensory divergence in the presence of this gene flow may also reflect a degree of adaptive phenotypic plasticity in RF. Despite the tight coupling between RF and the acoustic fovea in high duty cycle bats [R. capensis use RF’s within the best hearing range of the acoustic fovea of their nearest neighbours, possibly facilitating gene flow and promoting relatively flexible RF’s in these populations. While small shifts in the acoustic fovea and its corresponding reference frequency are possible in high duty cycle bats [R. capensis revealed that RF is best predicted by nasal chamber length [R. capensis certainly merit further attention.At a ‘local to regional’ scale geographic distance is not a significant barrier to gene flow in cle bats , empiriccle bats ) and difcle bats ). Such scle bats ,81, we dcle bats ,158. Selcle bats . Previour length , but a tThis study reveals significant sensory trait variation in the Cape horseshoe bat despite substantial historical gene flow. While genetic and geographic distances do influence sensory variation to some extent, our results suggest that differences in habitat complexity across the range of the Cape horseshoe bat may be the dominant driver of sensory differentiation in this system. Classical divergent selection together with some degree of phenotypic plasticity may be responsible for RF variation in the presence of gene flow. However, an investigation of the variation in foraging behaviour within and between populations of the Cape horseshoe bat is required to support our results. Nonetheless, our findings highlight the importance of population level analyses to elucidate the complex interactions among selection, plasticity and gene flow in the evolution of adaptive trait variation and reveal a number of interesting avenues for future research into the evolution of this remarkable sensory system.All authors have declared that no competing interests exist.LJO, DSJ and JMB conceived of and designed the study and carried out the fieldwork. LJO generated the molecular data, and conducted all genetic and ecological analyses with input from JMB and DSJ. All authors contributed to the writing of the manuscript and read and approved the final manuscript.Rhinolophus capensis sampled in this study Biomes are from Rutherford et al. [Map of the biomes of South Africa together with the geographic locations of the 11 populations of d et al.  and lineClick here for filehttp://134.2.91.93/~peter/calculator/range.php). The size range of prey taken by R. capensis at De Hoop is 2-19 mm [Climatic variables for each population and the mean detection distances for prey and background vertical targets (leafy vegetation edge). Detection ranges calculated from the method of Stilz and Schnitzler  , number of haplotypes, nucleotide diversity (π) and number of polymorphic sites are shown.Genetic variability in 11 populations of Click here for filep-corrected distances of the 39 unique haplotypes isolated in this study. Each circle represents a unique haplotype coloured according to the biome/s in which it occurs. Pie graphs indicate where haplotypes are shared across several biomes.Neighbour-net network based on Click here for fileM, the number of immigrants per generation scaled by mutation rate, calculated in Migrate-N (Beerli [Lower and upper profile likelihood percentiles of  (Beerli ).Click here for file