Amazilia beryllina, Amazilia cyanura and Amazilia saucerottei, show evidence of historical or current introgression as their plumage colour variation might suggest. We also analysed the role of past and present climatic events in promoting genetic introgression and species diversification. We collected mitochondrial DNA (mtDNA) sequence data and microsatellite loci scores for populations throughout the range of the three Amazilia species, as well as morphological and ecological data. Haplotype network, Bayesian phylogenetic and divergence time inference, historical demography, palaeodistribution modelling, and niche divergence tests were used to reconstruct the evolutionary history of this Amazilia species complex. An isolation-with-migration coalescent model and Bayesian assignment analysis were assessed to determine historical introgression and current genetic admixture. mtDNA haplotypes were geographically unstructured, with haplotypes from disparate areas interdispersed on a shallow tree and an unresolved haplotype network. Assignment analysis of the nuclear genome (nuDNA) supported three genetic groups with signs of genetic admixture, corresponding to: (1) A. beryllina populations located west of the Isthmus of Tehuantepec; (2) A. cyanura populations between the Isthmus of Tehuantepec and the Nicaraguan Depression ; and (3) A. saucerottei populations southeast of the Nicaraguan Depression. Gene flow and divergence time estimates, and demographic and palaeodistribution patterns suggest an evolutionary history of introgression mediated by Quaternary climatic fluctuations. High levels of gene flow were indicated by mtDNA and asymmetrical isolation-with-migration, whereas the microsatellite analyses found evidence for three genetic clusters with distributions corresponding to isolation by the Isthmus of Tehuantepec and the Nicaraguan Depression and signs of admixture. Historical levels of migration between genetically distinct groups estimated using microsatellites were higher than contemporary levels of migration. These results support the scenario of secondary contact and range contact during the glacial periods of the Pleistocene and strongly imply that the high levels of structure currently observed are a consequence of the limited dispersal of these hummingbirds across the isthmus and depression barriers.The influence of geologic and Pleistocene glacial cycles might result in morphological and genetic complex scenarios in the biota of the Mesoamerican region. We tested whether berylline, blue-tailed and steely-blue hummingbirds,  Current studies of molecular biogeography employ several genetic markers in both the nuclear and mitochondrial genome, with broad taxon sampling encompassing a broad geographic scope. Within species, the resulting patterns of phylogeographic breaks that arise between mtDNA clades generally align with those observed in the nuclear genome . HoweverMost of the taxa that display patterns of biogeographical mito-nuclear discordance are groups of closely related species that were isolated for long periods of time and are either currently in secondary contact, or have experienced range contact at some point in their past. Upon secondary contact, these groups formed hybrid zones, interbreeding to varying extents, and the mito-nuclear discordance was promoted by divergent patterns of gene flow between the two genomes . PotentiMesoamerica, a region located from Mexico to northern Costa Rica, is among the richest biodiversity hotspots in the world . MesoameAmazilia is exceptionally rich (c. 29 species) with species distributed from the southern USA to southern Brazil , A. cyanura Gould, A. saucerottei (DeLattre and Boucier), that show some overlap in their distributions, have extensive plumage colour variation that has not been fully explained, and there are reports of hybridization. The taxonomic history of the species complex is mired in problems. Amazilia beryllina populations occur in montane habitats from north-western Mexico (rarely seen in the south-western US) to central Honduras where five subspecies  have been described based on slight differences in morphology and distribution; Amazilia cyanura ; and Amazilia saucerottei populations occur at low elevation habitats from western Nicaragua to Costa Rica, Colombia and Venezuela, where four subspecies  are currently recognized based on geography . However, species delimitation remained unresolved because the one sample of cyanura is nested within a clade of several beryllina individuals , A. viridigaster  and A. edward (Panama). Amazilia cyanura and A. saucerottei, sympatric in Nicaragua, differ solely in the rufous wing patch in cyanura  is more closely related to beryllina and cyanura than to its two conspecifics from South America , which are more closely related to other Amazilia species as suggested by A. saucerottei hoffmanni resemble those saucerottei from South America  based on notorious behavioural and vocal differences. However, the unresolved relationships within the group of A. beryllina, A. cyanura and Central American A. saucerottei hoffmanni syn. A. sophiae , which could play a role in assortative mating and interspecific introgression, have not been quantified.Recent molecular work based on mtDNA sequences suggests monophyly of ividuals . These tividuals , or thateryllina . These s cyanura , and tho America , the moles e.g.,  supports sophiae  suggest  sophiae . In spitberyllina, cyanura and saucerottei hummingbird species to infer the role of Mesoamerican geographic barriers and recent glacial dynamics in driving the historical isolation and secondary contact of these taxa. We used mtDNA sequence data and microsatellite loci and extensive population-level sampling across the full range of these three Amazilia species, jointly analysed with morphological, ecological, and distributional data, to assess the following species divergence scenarios: (1) deep splits between species, in which the Amazilia species as taxonomically defined represent fully isolated clades defined by unique mtDNA haplotypes and representative microsatellite allele frequencies (mito-nuclear concordance); (2) the process of speciation is recent and occurred in the presence of gene flow, in which the following biogeographic mito-nuclear discordance patterns ; or (d) a pattern of nuclear genetic divergence between lineages separated by the isthmus and depression barriers, with genetic signatures of secondary contact or range contact matching historic glacial events.Here we examine the molecular biogeography of patterns  are expeOur fieldwork was conducted with the permission of Mexico’s Secretaría de Medio Ambiente y Recursos Naturales, Subsecretaría de Gestión para la Protección Ambiental, Dirección General de Vida Silvestre  and the Consejo Nacional de Áreas Protegidas (CONAP) in Guatemala (permit number 14815). We thank the staff and owners of private reserves , Municipalidad de Todos Santos Cuchumatán, and the Asociación de Reservas Naturales Privadas de Guatemala for allowing us to access these areas.beryllina, cyanura and saucerottei , and ATPase 6 and ATPase 8 (750 bp)—were amplified by polymerase chain reactions (PCR) and sequenced to infer phylogenetic relationships among haplotypes. Amplification of ND2 was conducted with primers L5215 and H5578 , following the protocol recommended by the manufacturer. Three mtDNA genes—350 base pairs (bp) of NADH nicotinamide dehydrogenase subunit 2 . All unique sequences used in this study are deposited in GenBank  and then manually aligned with SE-AL v. 2.0a11 (H), segregating sites (S), haplotype diversity (h) and nucleotide diversity (π) per species. A statistical haplotype network was generated with the median-joining algorithm in NETWORK v. 4.6.1.1 (http://www.fluxus-technology.com) using the combined mtDNA matrix  and each codon position within the coding genes , as suggested by Bayes Factor analysis (A Bayesian inference (BI) phylogenetic tree was constructed using MRBAYES v. 3.1.2  with datanalysis . The subanalysis  and the analysis .A. saucerottei from South America, A. edward, A. tobaci, A. viridigaster, A. cyanocephala, A. violiceps, A. viridifrons, and Campylopterus curvipennis based on A. saucerottei, EU042523 and GU167205; A. viridigaster, EU042526).Seven species of the emerald hummingbird group were included as outgroups, including conspecifics http://tree.bio.ed.ac.uk/software/figtree/).The BI analysis employed partition-specific DNA evolution models for each gene with two parallel Markov chain Monte Carlo (MCMC) analyses executed simultaneously; each was run for 20 million generations, sampling every 1000 generations. A majority consensus tree was calculated, showing nodes with a posterior probability (PP) of 0.5 or more. Bayesian PP values were calculated from the sampled trees remaining after 5000 burn-in samples were discarded  to only beryllina-cyanura-saucerottei was estimated using BEAST v. 1.5.4 (ND2 and ATPase 6–8) and those of the outgroup hummingbird species. The best-fit model of evolution, HKY+G for each partition, empirical base frequencies, and an uncorrelated lognormal relaxed model as the clock model were used. A coalescent model assuming constant population size was used to model the tree prior. The coalescent tree prior used in this analysis appears to be a better fit when mixed datasets are predominantly intraspecific data (Amazilia sequences and constrained them to be monophyletic (The time of the most recent common ancestor (TMRCA) of the clade v. 1.5.4 . We usedfic data . We grouphyletic .ND2 and the geometric mean of 0.026 and 0.019 for the ATPase 6–8  obtained for Hawaiian honeycreepers (ND2 and the geometric mean of 0.0059 and 0.0047 (0.00265 s/s/l/My) for the ATPase 6–8 obtained for major bird orders  for the creepers , or the d orders .C. curvipennis and all the Amazilia hummingbirds  divergence time for the basal split between ingbirds , followeWe performed one run of 30 million generations with random starting trees, sampling every 1000 steps, and discarding the first 10% of trees as burn-in. Results were viewed in TRACER v. 1.5  to ensurAmazilia species was inferred by means of neutrality tests and mismatch distributions with ARLEQUIN v. 3.5  and the two descendant populations (q1 and q2), effective number of migrants per generation in both directions (m1-to-2 and m2-to-1), and time since divergence (t) at which the ancestral population gave rise to the descendant populations.The Isolation-with-Migration model implemented in IMa , 2007 waThree independent runs of 50 million generations were performed for each group comparison under the HKY model of evolution using the parameter values empirically determined in the preliminary runs to verify convergence of the different independent analyses. Each run used identical conditions, but different starting seed values, and a burn-in period of 30 million steps.ND2 and ATPase 6–8 described above (Ne) of each genetic group. The mutation rate was converted to per locus rate by multiplying the fragment length in base pairs for conversion to demographic units ) ) , where at, we divided the time parameter (B) by the mutation rate per year (U) converted to the per locus rate by multiplying by the fragment length in base pairs and, calculated this for the low and high rates  equilibrium within populations and loci were calculated using GENEPOP v. 4.2  with BonK, from K = 1 to K = 8. The length of the burn-in was 100,000 and the number of MCMC iterations after burn-in was 500,000 and convergence was achieved. The most likely number of populations was determined estimating the DeltaK (ΔK) and the log likelihood of K, Ln P(K) = L(K) between successive K values  on the microsatellite individual-genotype matrix was conducted using the R package ADEgenet , whereas MIGRATE uses the coalescent approach to jointly estimate the relative effective population size θNe (4Neμ) and asymmetrical gene flow M (m/μ) between pairs of populations over much longer periods of time, approximately thousands of years , migration rate (M), and inbreeding (F). Subsequent runs incorporated different delta values to ensure that the acceptance rate for proposed changes in parameters were between 20–40% for each parameter. Adjusted final delta values used were ΔA = 0.3 (35% acceptance rate), ΔM = 0.2 (27%), and ΔF = 0.3 (34%), respectively. To ensure convergence, we performed five independent runs  each with a different seed number, comparing the posterior mean parameter estimates for concordance. We also analysed the trace file of each run with TRACER to ensure an appropriate mixing of parameters and burn-in number. For this best fit run, we then ran the analysis increasing the run length to 30 million iterations with a 3-million burn-in.To compare migration rates over contemporary and historical timescales, we followed M) among groups of populations. We used a Brownian-motion model with a constant mutation rate and FST to estimate θ. We performed several short runs to look for the appropriate priors. After finding suitable priors, MIGRATE was run three times to confirm convergence. These final runs consisted of one long chain, 1,000,000 sampled trees, 10,000 recorded, with a burn-in of 10,000 with 30 replicates and each run with a different seed number. We set the minimum and maximum boundaries for theta (θ) and migration (M) as 0.0 and 100.0, with a delta value of 10. A four-chain heating at temperatures of 1, 1.5, 3 and 10,000 was implemented to increase the efficiency of the MCMC  generated by MIGRATE by dividing all M values by an estimated mutation rate of 5 × 10−4 for microsatellites  against longitude coordinates to examine how morphology varies with geographical distribution.Plumage colour variation was assessed for 145 museum specimens  and S5 aberyllina, cyanura and saucerottei resided during the Last Glacial Maximum  and the Last Interglacial . Georeferenced records from museum specimens and from our own collection efforts were used to construct ecological niche models (ENM) based on current climate data using the maximum entropy algorithm implemented in MAXENT v. 3.3.3k . Localities were distributed in three groups that correspond to the three genetic groups obtained in the microsatellite analyses. We obtained a total of 158 different records for beryllina, 98 for cyanura and hummingbirds with intermediate phenotypes, and 32 for saucerottei. Present climate layers were drawn from WorldClim (http://www.worldclim.org), and MAXENT was set to randomly use 75% of the values for training and 25% of the values for testing the model. After extracting climatic data for each location from 19 bioclimate layers (c. 1 km2 resolution), we tested for correlation among these variables for each of the three groups and chose uncorrelated variables (< 0.7 Pearson correlation coefficient) for further analysis. For each pair of correlated variables, we selected the variable that was most temporally inclusive , seven for cyanura  and eight for saucerottei .A complementary species distribution modelling approach was used to frame the information derived from the genetic and morphological analyses within an explicit palaeoecological context. We constructed a species distribution model SDM;  to predi. 3.3.3k . Data frnclusive . Six var−5) and 500 iterations  and nucleotide diversity (π) values were high, ranging from 0.87 in saucerottei to 0.94 in beryllina and from 0.005 in beryllina to 0.007 in saucerottei, respectively  were observed at their range edges, from localities close to the Isthmus of Tehuantepec, including Cerro Baúl , Nueva Colombia  and Huehuetenango . Likewise, cyanura hummingbirds that shared haplotypes with saucerottei (H48–H51) come from Jinotega , near the Nicaraguan Depression.The haplotype network was more informative about the relationships among haplotypes than the BI gene tree was –1C. The beryllina phenotype had the same haplotypes as hummingbirds with the cyanura or an intermediate phenotype between these two species. Again, the beryllina from Jalisco and Michoacán formed a haplogroup (H35–H40) that was more closely connected to cyanura and saucerottei haplotypes than to other beryllina haplotypes , hummingbirds with different phenotypes shared the same haplotypes. For instance, those with the  H1–H14; , and oneP-value = 0.00024) at loci Cacu4-8 and HumB1 from Suchitepequez  and HumB1 from Jinotega (Nicaragua). Three genetic groups were detected by STRUCTURE  cyanura from the NCA region, and (iii) saucerottei from the south of the Nicaraguan Depression. Using STRUCTURE, the estimated logarithm of probability of the data, Ln P(K), increased linearly from K = 1 to K = 3 and then plateaued with increased variability  and cyanura (4 out of 6). Similarly, one population  from the northern side of the Nicaraguan Depression  has individuals grouped with both cyanura (5 out of 12) and saucerottei (4 out of 12), as well as individuals with signs of admixture among the three groups (3 out of 12) , one from Nueva Colombia (Chiapas) and one from Jinotega (Nicaragua), and one cyanura from Jinotega (Nicaragua) was assigned with migrant ancestry.At dmixture . Populatt of 12)  and S2C. cyanura –1E. Inteberyllina (west of the Isthmus of Tehuantepec), cyanura (NCA), and saucerottei (southern side of the Nicaraguan Depression).Congruent with the Bayesian assignment analysis, the principal component analysis of the microsatellite data  and S2D beryllina, cyanura and saucerottei were not significant using the combined mtDNA data set  when using high substitution rates  for the on rates  and 2.6 on rates . The moron rates  are more species .beryllina and saucerottei occurred c. 300,000 years before present  had 95% confidence intervals that approached zero, indicating little to no recent migration between genetic groups (M) calculated using MIGRATE revealed asymmetrical migration among groups over the long term, with estimates of M ranging from 2 to 13 and in general decreasing from north to south (emN) ranged from 0.58 to 2.18. Results from the Mantel test were not significant  indicating that the two matrices of contemporary and historical migration values are not correlated with each other, which implies that the rate and intensity of migration between groups decreased from past to present.Final runs of BAYESASS yielded consistently low estimates of contemporary gene flow (<1%) between the genetic groups of c groups . Estimatto south . The numberyllina and saucerottei at the north/southern limits of the combined range of the species complex are morphologically homogenous. In contrast, cyanura samples in the central portion of the range (NCA) are the morphologically intermediate of the other two species, with different phenotypes occurring at the same locality according to the plumage colouration index  yielded a good fit for the current geographic distribution of barriers . These mcyanura and saucerottei whereas for beryllina the CCSM and MIROC models differed, with the expansion of suitable habitat in the southern, central and northwest portions of Mexico according to the projections of the CCSM and MIROC models, respectively. Interestingly, predictions under LIG conditions revealed that areas of suitable habitat for beryllina populations extended southward compared to predicted distributions under current or LGM conditions to include those of cyanura and saucerottei. In contrast, the predicted areas of suitable habitat for cyanura and saucerottei contracted during the LIG, with conditions of suitable habitat for saucerottei almost disappearing from the region , where beryllina inhabits higher elevations and saucerottei, lower elevations .Tests of niche conservatism and divergence on four PC axes showed evidence for niche conservatism on niche axis 1  and southern (saucerottei) limits of the combined range. Despite the potential for spatial autocorrelation, the distribution models give evidence of niche conservatism and potential range expansions and contractions promoted by Pleistocene climatic oscillations that may have contributed to historical isolation and secondary contact. These results indicate that the three genetic clusters are very young and their evolutionary history has occurred in the presence of gene flow, despite the difficulty in distinguishing the processes of incomplete lineage sorting from complete introgression using the currently available data.Two general patterns of genetic divergence were found among The phylogeographic pattern of mtDNA haplotypes and microsatellites were discordant, with haplotype sharing of mtDNA between clades showing no discernible geographic pattern in the distribution of non-monophyletic mtDNA species clades, and microsatellite analyses showing a pattern of differentiation between three groups isolated by topographic barriers, the Isthmus of Tehuantepec and the Nicaraguan Depression with some admixed individuals. These particular geographic lowlands recurrently have imposed impassable or nearly impassable barriers to gene flow for several montane plant e.g.,  and birdberyllina mainly from Jalisco and Michoacán, are more closely related to cyanura and saucerottei than to other beryllina, and one saucerottei from Costa Rica was more closely connected to cyanura and beryllina than to saucerottei south of the Nicaraguan Depression . However, these same hummingbirds were assigned to the beryllina or saucerottei clusters in the microsatellite analyses, suggesting that species have recently stopped exchanging genes  yet still possess haplotypes very closely related to those of geographically distant populations  suggest that these closely related species have retained their ecological niche characteristics over evolutionary time  were high and different in magnitude than contemporary levels of migration. High levels of historical migration rates are not surprising because extant populations might have experienced secondary contact and range contact during the glacial periods of the Pleistocene, as suggested by species distribution modelling, making dispersal across the isthmus and depression barriers likely. More surprising is that contemporary migration rates are low because assignment analyses identified genetic admixture both at the contact zones and scattered throughout the sampled ranges. These results strongly imply that the high levels of structure currently observed are a consequence of the limited dispersal of these hummingbirds across the isthmus and depression barriers, and that the admixed individuals scattered throughout the STRUCTURE plot were likely pure, incorrectly assigned individuals at these levels of differentiation  are two different taxa with considerable genetic divergence. Thus, our results and those of Amazilia species, and support the proposal of A. saucerottei hoffmanni is a different species (A. sophiae) based on notorious behavioural and vocal differences. Further multilocus analysis of samples from Colombia and Venezuela is necessary to assess divergence patterns between South American populations of saucerottei and other Amazilia species. Second, our results reveal three genetic clusters that have distributions consistent with known geographic barriers, with some hummingbirds showing signs of admixture and intermediate phenotypes in the central portion of the range: (1) A. beryllina located west of the Isthmus of Tehuantepec, (2) A. cyanura between the Isthmus of Tehuantepec and the Nicaraguan Depression , and (3) A. saucerottei hoffmanni syn. A sophiae located between the Nicaraguan Depression and the Isthmus of Panama. Increased genomic resources and behavioural data will likely confirm that they constitute separate taxonomic species. These findings are in line with several examples of recent speciation events reported in hummingbird species, some of which show congruent genetic and phenotypic divergence  Species distribution models generated with MaxEnt v. 3.3.3k for beryllina, cyanura and saucerottei for the present, Last Glacial Maximum , Last Glacial Maximum , and Last Interglacial (LIG). Darker shading indicates the most probable predicted distribution. (B) Principal components analysis showing occurrence points (closed symbols) and 1000 random points from the background (open symbols) where each of the three species is found; beryllina (red squares), cyanura (blue diamonds), and saucerottei (green triangles).Figure 4: Present and past distribution models and environmental space for Click here for additional data file.


Canis lupus L., 1758) dietary niche and parasite prevalence data indicate strong ecological divergence between marine-oriented wolves inhabiting islands and individuals on the coastal mainland that interact primarily with terrestrial prey. Local holders of traditional ecological knowledge, who distinguish between mainland and island wolf forms, also informed our hypothesis that genetic differentiation might occur between wolves from these adjacent environments.Emerging evidence suggests that ecological heterogeneity across space can influence the genetic structure of populations, including that of long-distance dispersers such as large carnivores. On the central coast of British Columbia, Canada, wolf  might contribute to genetic differentiation. Accordingly, this working hypothesis presents an exciting avenue for future research where marine resources or other components of ecological heterogeneity are present. Clupea harengus L., 1758; André et al. ). F). FClupei et al.  showed si et al. , Nosil ei et al. , Edelaari et al. ), which,Ursus acrtos horribilis Ord, 1815), which compete with wolves for marine resources  and mountain goats  that are absent or rare on coastal islands. Consequently, these major prey items are commonly detected in wolf diet in mainland areas and only very rarely on islands , the environments have striking geological and ecological differences. The mainland is topographically rugged, contains less shoreline for a given area and is relatively species-rich. In contrast, the neighbouring islands are less mountainous, have more complex shorelines, and host fewer species; notably absent are grizzly bears, . Addt et al. ). Moreovn et al. ).2, with a generally east–west mainland-island axis of <30 km) to test the hypothesis that ecological heterogeneity can drive population genetic structure of a highly mobile animal within a small area. We note that this prediction was also informed by holders of traditional ecological knowledge (TEK) in the Heiltsuk First Nation area, who distinguish between mainland “timber wolf” and island “coastal wolf” forms. Given these scholarly- and TEK-informed hypotheses and the sharp environmental gradients on the BC coast, we expected mainland-island genetic differentiation that mirrors ecological differences among neighbouring social groups.Our objective was to examine genetic data from wolves of coastal BC over a limited geographic area (~2000 km2) centered on Bella Bella  served as the location for this study  and five main islands ranging in size from 150–250 km2. Distances from island to mainland range from 250 m to 1450 m. Observational and genetic data  industrial anthropogenic disturbance. The area is characterized by a wet and temperate climate, and annual precipitation typically exceeds 350 cm (Darimont and Paquet ). A coret et al. ; Navid [t et al. ) suggestt et al. ; Darimont et al. ; McAllishttp://www.qiagen.com/literature/). We performed DNA extractions in a room physically separated from amplified PCR products and used exclusively for this study to reduce the risk of contamination. Final purified extracts were refrigerated at +4°C until use.One thousand and seventy-four (1074) wolf faecal samples were collected between winter 2003 and winter 2004. We collected the following number of samples per season: spring: n = 416 summer: n = 297 fall: n = 292 winter: n = 69. Sampling areas included wildlife trails, logging roads, and electrical power rights-of-way. We preserved each sample in a 50-ml Falcon tube with 95% ethanol. We selected samples for genetic analysis based on characteristics of the samples and collection sites that best predicted amplification success . We extFH2001, FH2010, FH2017, FH2054, FH2088, FH2096, FH2422 . These were n et al. ), FH3313n et al. ), PEZ06,f et al. ), and tht et al. ). We genWe accepted for further analyses samples that amplified at least 9/14 loci, and used the Excel Microsatellite Toolkit (Park ) to testMS41B . We used MICRO-CHECKER 2.2.3  were identified as males, and we observed six alleles for marker t et al. ) to asseIS according to Weir and Cockerham [adegenet-package  per locockerham , for maiockerham ). The reockerham ). Subseq(Jombart ) in R 2.(Jombart ). The PC(Jombart ). We rep(Jombart ), which  to examIS results were positive for all loci with a mean value of 0.264. For island wolves, expected heterozygosity was 0.690 and seven loci showed departures from Hardy-Weinberg equilibrium (four of these were consistent between mainland and island wolves). FIS results for island wolves were positive for all except two loci, with a mean value of 0.211. We identified possible null alleles and stutter peaks for the overall sample, but dropout of large alleles was not detected  and coastal islands in Arctic Canada (0.181) suggest a higher degree of mating among relatives in mainland (0.264) and island (0.211) wolves from coastal BC. However, Carmichael et al. [IS values on Victoria Island  and on islands in the High Arctic . Based on the findings from Alaska wolves, continental wolves appear to have higher genetic diversity. We would also expect a similar situation for our study area, as mainland wolves have a wider surrounding area from which to receive immigrants. However, there are known wolf groups on neighbouring islands not included in this study and we cannot exclude the possibility that immigration from these areas may have augmented the diversity in our sample of island wolves.Allelic diversity and expected heterozygosity for island wolves  and mainland wolves  were relatively high and comparable to values reported for island populations of wolves on the Pacific Coast in southeast Alaska  and coal et al.  also obsIS values. Our results could also have been influenced by the presence of null alleles. When most of the loci indicate null alleles, however, the MICRO-CHECKER program warns there may not be random mating in the population (panmixia). The PCA and sPCA findings of island-mainland differentiation suggest absence of panmixia in our study area. We therefore believe that island-mainland structure contributed to the frequent reports of null alleles. The study area is difficult to access, and many samples may have been several weeks old and thus affected by exposure to the humid climate  could, i et al. ; Navid [i et al. ). Our rei et al. ) would bThe partial Mantel test showed no significant relationship between genetic distance and island-mainland habitat type when accounting for geographic distance. However, there was a significant correlation between geographic distance and island-mainland habitat type, suggesting that the two matrices are collinear and their effects cannot be differentiated. The spatial autocorrelation indicated negative autocorrelation from approximately 17–45 km. These results appear to contrast with those of Muñoz-Fuentes et al.  who repounpublished data). Immigrants from outside the study area could also influence the observed east–west gradient in genetic profiles. The differentiation seen in YC profiles, for example, may be explained by gene flow from unsampled wolves on the outer islands father west. Similarly, profiles from the UR group, which showed considerable overlap with island wolves, might result from immigration by one or more island wolves with high reproductive success. Furthermore, the presence of intermediate profiles in the north and south of our study area implies an island-mainland gradient. A strict island-mainland dichotomy may thus be simplistic and should be evaluated on a broader geographic scale. Without genetic data from a larger spatial extent, however, we cannot evaluate these hypotheses.Water barriers between the mainland and islands might restrict dispersal and gene flow. For example, captive wolves released on Coronation Island in Alaska did not swim 900 m to nearby habitat with abundant food (Klein ). In ourMS41B likely reduced our ability to identify male wolves. A possible higher prevalence of females in the sample might nevertheless exacerbate genetic structuring in species where males are more likely to disperse. However, male wolf profiles we assessed showed a similar island-mainland gradient. Observational and tracking data suggest that wolf group size in the study area was ≤ 10 individuals , and itDarimont ).Despite the above-mentioned uncertainties, we offer the working hypothesis that the sharp ecological gradient between island and mainland locations, as revealed by the landscape characteristics and the dietary and parasitic data from wolves in our study area, can influence population genetic structure. Although our study must be interpreted with caution, and should be repeated with genetic profiles of higher quality, the results appear consistent with an increasing body of literature reporting genetic differentiation in wolves and other highly mobile species (see Introduction) influenced by ecological and environmental factors. Dispersal rates and gene flow might differ substantially between island and mainland sub-populations, and the extent to which populations are demographically independent could help define management units  along tAssociated morphological or other characteristics observed over time might have allowed TEK knowledge holders to recognize these dissimilar wolf forms. Such intra-specific nomenclature is common among indigenous knowledge holders . IndeedU. maritimus Phipps, 1774) are thought to have evolved from grizzly bears in peripheral areas where marine resources were abundant . Moreovs et al. ). IndiviOur results indicate the presence of a genetic cline between island and mainland wolves. Although overlap was extensive, the results suggest an east–west gradient in profiles across < 30 km. We hypothesize that adaptive responses to heterogeneity in food resources can influence genetic differentiation. Accordingly, this line of inquiry presents an exciting avenue for future research where marine resources or other components of ecological heterogeneity are present.The authors declare that they have no competing interests.ELN carried out laboratory work, and contributed to fieldwork, statistical analyses, manuscript writing and revisions. AVS performed statistical analyses and manuscript writing. MSQ supervised the study and helped revise the manuscript. CTD participated in study design, fieldwork, and manuscript writing. PCP contributed to study design, field work and manuscript revisions. HMB created the maps and helped revise the manuscript. All authors read and approved the final manuscript.Map of the study area on the central coast of British Columbia, Canada. Shown are estimated home ranges of five wolf (Canis lupus) social groups.Click here for fileMultiplex combinations of 14 microsatellite markers for genetic analyses of wolves from the central coast of British Columbia, Canada.Click here for fileCalculation of amplification and error rates and assessment of null alleles, large allele dropout, and stutter peaks for wolf samples (n = 116) from the central coast of British Columbia, Canada.Click here for fileColour plot of wolf profiles from the central coast of British Columbia, Canada. a) Individual profiles (n = 116) based on ≥ 10 microsatellite loci. The first axis represents 6.1% of the variation, the second axis 5.1%. b) A subsample of individual profiles (n = 18) based on ≥ 5 duplicated loci. Genetic diversity is represented by distance and colour; individuals further apart and/or labelled with more dissimilar colours have more divergent genotypes. The first axis represents 18.8% of the variation, the second axis 13.9%.Click here for filePrincipal component analysis (PCA) of male wolves from the central coast of British Columbia, Canada, showing island  and mainland  individuals. The first axis represents 11.9% of the variation, the second axis 9.7%. PCA is based on the 10 loci retained for final analyses  on 10 microsatellite loci from 116 wolves from the central coast of British Columbia, Canada. Positive values (left side) represent global structures and negative values (right side) show local patterns. Tests for local and global structure revealed the presence of one global structure, which was subsequently interpreted.Click here for fileSpatial autocorrelation analysis of wolf samples (n = 116) from the central coast of British Columbia, Canada, using 5 km distance classes. The Y axis shows the kinship coefficient (r), and U and L are the upper and lower limits for the 95% confidence interval of no spatial structure occurring in the data set after permutation (n = 999). Error bars show the 95% confidence interval around r as determined by bootstrap resampling (n = 999).Click here for file


Nothofagus dombeyi (Mirb.) Oerst. (Nothofagaceae) is an endemic tree species occurring both in Chile and in Argentina temperate forests. Postglacial history has been studied with chloroplast DNA and evolutionary forces shaping genetic variation patterns have been analysed with isozymes but fine-scale genetic diversity studies are needed. The study of demographic and selection histories in Nothofagus dombeyi requires more informative markers such as single nucleotide polymorphisms (SNP). Genotyping-by-Sequencing tools now allow studying thousands of SNP markers at reasonable prices in nonmodel species. We investigated more than 10 K SNP loci for signatures of local adaptation and showed that interrogation of genomic resources can identify shifts in genetic diversity and putative adaptive signals in this nonmodel woody species.Within a woody plant species, environmental heterogeneity has the potential to influence the distribution of genetic variation among populations through several evolutionary processes. In some species, a relationship between environmental characteristics and the distribution of genotypes can be detected, showing the importance of natural selection as the main source of differentiation. In population genetics and conservation the big question is genetic drift instead of natural selection. Definitely both processes determine evolution, but genetic drift operates randomly and depends on effective population size while natural selection proceeds nonrandomly and relies on environmental variables. The evolution towards hereditary adaptations to the current environment is determined by natural selection and has a direction; genetic drift instead is governed solely by chance. Consequently, drift acts on alleles, which generally have no phenotypic effect; instead selection favours certain alleles that increase fitness, reduce the unfavourable alleles frequencies, and ignore neutral alleles . KnowingApproaches to addressing adaptive variation have been incorporated into the definition of evolutionary significant units , 4. New Studies in nonmodel organisms have shown relatively broad candidate genomic regions that are under selection, but it remains difficult to identify the genes (or the mutations) that are affected by selection. Increasing the density of markers in genome scans is paramount to overcome this problem, and validating signals of selection from particular genes using multiple methods should also help . One of Nothofagus dombeyi (Mirb.) Oerst. (Nothofagaceae) is an endemic tree species occurring both in Chile and in Argentina temperate forests with a remarkably broad altitudinal and latitudinal distribution, across many different ecological gradients in the former [ N. dombeyi is a pioneer species and constitutes an important element in the dynamics of South American forest. Its postglacial history has been studied with chloroplast DNA and evolutionary forces shaping genetic variation patterns have been analysed with isozymes [e former . The eveisozymes , 13. How N. dombeyi to develop high quantity of informative markers such as single nucleotide polymorphisms (SNP). Our aim is to determine the contribution of selection and molecular adaptation to shaping genome-wide variation. We expect higher genetic population differentiation  for adaptive SNP than neutral SNP if natural selection is the principal source of differentiation. Alternatively, if other sources of differentiation  are relevant, they will equally affect both types of SNP. Knowing the contribution of selection effect to shaping genome variation patterns will have many applications for biodiversity conservation, especially in endangered species, because neutral and adaptive genetic diversity will likely have different impacts on long-term survival. In fact, in most cases, only adaptive diversity will allow a population to adapt to changing environmental conditions.In this work, we assess GBS in the nonmodel woody species Nothofagus dombeyi in Chile using BIOMOD [To consider the remarkably broad altitudinal and latitudinal distribution of this species, across many different ecological gradients, we used the method proposed by Alarcón and Cavieres  to nicheg BIOMOD . Eight vg BIOMOD  correspoF statistic as in ANOVA, was used to assess the best number of strata identified by K-means partitioning [Relatively homogeneous units in ecological terms (strata) were defined from natural populations of the species associated with the geographical areas projected in its ecological niche modelling. The Calinski-Harabasz criterion, which is a pseudo-itioning . N. dombeyi in Chile. One to four sites were assigned to each stratum according to its superficies and 2 to 9 samples per site were taken (http://dx.doi.org/10.1155/2016/3654093).Adult trees with a diameter higher than 50 cm were sampled during the growing season of 2013-2014 from twenty-one sites covering almost the entire range of the speciesμL (instead of 100 μL) to increase the final DNA concentration in the eluate (>100 ng/μL). The integrity of genomic DNA was evaluated by agarose gel and quantified using a Qubit fluorometer .DNA extraction was performed using a Qiagen DNeasy Plant kit . Lyophilized leaf tissue (20 mg) was ground in a Precellys®24  homogenizer with two 1/4′′ ceramic spheres  and AP1 buffer. The objective is grinding tissues and lysing cells prior to DNA extraction. DNA extraction protocol was done following the manufacturer instructions but elution was done with 30 Library preparation and high-throughput sequencing were performed at University of Wisconsin Biotechnology Center (DNA Sequencing Facility). The GBS genomic library preparation was done following the protocol detailed by Elshire et al.  with the N. dombeyi. SNP discovery is performed directly within pairs of matched sequence tags (unique sequence representing a group of reads) and filtered through network analysis. The network filter trimmed reads to 64 bp to reduce the effects of error sequencing and enabled efficient storage of data in bit format. SNP were assigned with default settings. Briefly, tags differing by a single nucleotide were retained as SNP and those with a minor allele frequency 0.05 were removed to minimize the impact of sequencing errors [Sequence results were analysed and SNP genotypes were assigned using the UNEAK  GBS pipeline , which ig errors . We usedN” values . These Ns represent individuals where the allele cannot be called from the sequence reads. This is because either no read is available at this site  or the sequence quality is too low to be called.Given that we are trying to find SNP for population genetic analysis, we applied some filters to remove loci and individuals that contain very low levels of information prior to further analysis. We applied two functions of TASSEL 5.2 that removed all SNP (rows) and then samples (columns) containing 90% or more “k-nearest neighbour genotype imputation method, LD-kNNi [In order to cope with missing data, genotype imputation was used to fill in the missing data and improve the power of downstream analyses. We used LinkImpute implemented in TASSEL 5.2, a software package based on a  LD-kNNi . This imTo identify adaptive SNP (putative loci under selection), we used LOSITAN . LOSITANLOSITAN analyses were complemented with BayeScan 2.1  for estiβada v0.5.1 [ N. dombeyi in Chile. Samβada uses logistic regressions to model the probability of presence of an allelic variant in a polymorphic marker given the environmental conditions of the sampling locations. Eight environmental variables previously described in G) tests [G score distribution.To confirm the adaptive SNP detected by previous methods, the spatial analysis method (SAM) implemented in the program Sama v0.5.1  was usedG) tests  selectinWe made all estimations in parallel with neutral SNP  and adaptive SNP.All the results were obtained using the adegenet , 27 and To describe the genetic biodiversity of a species, more important than diversity among individuals is the diversity between groups of individuals. First we analysed individual data to identify populations, or more large genetic clusters, and then we described these clusters by adegenet R package. To get a simplified picture of the genetic diversity observed among individuals or populations we used Principal Component Analysis (PCA). Discriminant Analysis of Principal Components (DAPC) function was used to describe the relationships between these clusters. The main results of DAPC were DAPC scatterplots.K) at individual level.For each pair of strata, we computed pairwise Fst values with hierFstat R package. Principal Coordinates Analysis (PCoA) on Fst values was performed to detect major genetic clusters  under dispersal constraints is 72,928 km2. Although it is not qualified with a status of threatened species, we estimated a decrease of almost 20% of its habitat area in a relatively short time, particularly in the northern populations, associated with Mediterranean-influenced climate, which is at least worrisome considering we used the most conservative future climate scenario. Fuss et al. [K-means analyses, applied to four best least correlated variables of WorldClim , identified seven strata groups of N. dombeyi in Chile at very broad spatial scales  sequence reads per sample were obtained (range from 278 to 8.2 millions) . After removal of SNP with a minor allele frequency 0.05 and removing samples and SNP with more than 90% “NN” (unassigned) genotypes, the dataset consisted of 73 individuals with 10,109 binary SNP from seven strata. This dataset was subjected to the impute step and finally total numbers of “NN” genotypes were 19,067 (0.03%) missing data.We obtained more than 17 Gb out of 172,171,356 reads, from which 85.82% keep a score  N. dombeyi individuals and 10,109 loci), which may be considered as strongly differentiated.Using the LOSITAN approach, 124 adaptive SNP (1.2%) were identified  at the false discovery rate (FDR) threshold of 0.1  and only 3% correspond to heterozygous genotypes. From the 124 outlier SNP identified by LOSITAN, genotypes in 121 SNP were identified as associated with environmental variables by Samβada (see Supplementary Table 1). Genetic differentiation associated with both temperature and precipitation gradients was detected.Using Sam N. dombeyi in Chile show low to medium genetic diversity level and low level of strata differentiation , different basic statistics of genetic variation ofntiation . The lows gender , 34. Wit N. dombeyi in Chile  tree . Again higher resolution is achieved with adaptive SNP. As expected, both approaches give congruent results, but both are complementary; NJ shows bunches of related individuals, but the cline of genetic differentiation is much clearer in PCA.Overall average pairwise Fst values among populations, calculated over the set of the 10,109 SNP, show medium population structuring across the distribution ofin Chile . The avein Chile . The Pri c. 43°S found within subgenus Nothofagus in southern South America, including N. dombeyi [Visualisation of broad-scale population structure using a DAPC  with 124 dombeyi . N. dombeyi population differentiation at neutral versus adaptive SNP, we have detected several distinct differences. Primarily stochastic processes drive differentiation of neutral SNP, whereas both selective and stochastic processes drive that of adaptive SNP, for example, [ Nothofagus pumilio [Clearly, when examining patterns ofexample, –39. This pumilio , 41. Ide pumilio . N. dombeyi , thus confirming its contribution to population structure. Adaptive SNP ID 4, 14, 42, 50, and 91 are five of the most discriminating loci and their allele frequencies over seven strata show a high or even dominant allele frequency in stratum 3  show a high identity with a heat shock protein or adaptive SNP ID 44 (TP32296) with histone acetyltransferase enzyme (data not shown). The first is fundamental for heat or other environmental stresses and the second can be involved in drought sensing or another response to environmental factors. We therefore suggest that these two SNP may be interesting candidates for future functional studies that might facilitate other studies, for example, [ Nothofagus species. However, improvements in study design and analyses of replicated studies will be needed before this very promising approach can be brought to application for managing genetic resources [Over 24% of the adaptive SNP detected by LOSITAN had a high loading (here defined as ≥0.02) in one or two of the three PC in a PCA with more than 10 K loci of the Chilean population oftratum 3 . This is N. dombeyi using GBS made evaluation of genomic diversity and fine-scale population structure possible for the first time in this species in Chile. Results showed that genome-wide patterns of genetic diversity and differentiation varied widely across the genome. As such, we identified numerous genomic regions exhibiting signatures of divergent selection. We have also provided strong evidence of substantial genetic differentiation associated with both temperature and precipitation gradients, suggesting local adaptation.Development of 10,109 genome-wide SNP forThe supplementary material contain information of distribution of sampling locations , and the read number by sample after demultiplexing step in GBS process . The supplementary information also contains the complete list of outlier SNP as putative candidates for adaptation and visualization of genetic relationships among samples by PCA  and neighbour-joining (NJ) tree . Lastly, the loadings of outliers SNPs showing the most contributing loci to PC1, PC2 y PC3 are given in Figure S5, S6 and S7, respectively.


Lupinus angustifolius) exhibit large phenotypic and genetic diversity. An association between root traits and DArT markers demonstrates potential for a marker-assisted selection programme for this species.Roots in narrow-leafed lupin ( Lupinus angustifolius L.) is the predominant grain legume crop in southern Australia, contributing half of the total grain legume production of Australia. Its yield in Australia is hampered by a range of subsoil constraints. The adaptation of lupin genotypes to subsoil constraints may be improved by selecting for optimal root traits from new and exotic germplasm sources. We assessed root trait diversity and genetic diversity of a core collection of narrow-leafed lupin (111 accessions) using 191 Diversity Arrays Technology (DArT) markers. The genetic relationship among accessions was determined using the admixture model in STRUCTURE. Thirty-eight root-associated traits were characterized, with 21 having coefficient of variation values >0.5. Principal coordinate analysis and cluster analysis of the DArT markers revealed broad diversity among the accessions. An ad hoc statistics calculation resulted in 10 distinct populations with significant differences among and within them (P < 0.001). The mixed linear model test in TASSEL showed a significant association between all root traits and some DArT markers, with the numbers of markers associated with an individual trait ranging from 2 to 13. The percentage of phenotypic variation explained by any one marker ranged from 6.4 to 21.8%, with 15 associations explaining >10% of phenotypic variation. The genetic variation values ranged from 0 to 7994, with 23 associations having values >240. Root traits such as deeper roots and lateral root proliferation at depth would be useful for this species for improved adaptation to drier soil conditions. This study offers opportunities for discovering useful root traits that can be used to increase the yield of Australian cultivars across variable environmental conditions.Narrow-leafed lupin ( Lupinus angustifolius L.) has been a predominant grain legume crop and an important component of sustainable farming systems in southern Australia since its domestication was completed in Western Australia in the 1960s and 1970s (Narrow-leafed lupin (nd 1970s . As a leTriticum aestivum), barley (Hordeum vulgare), and other dominant crops in Mediterranean regions, current commercial cultivars of narrow-leafed lupin incorporate only a small fraction of their genetic diversity because of the short and fragmented domestication history  from diverse climatic and geographic locations, provides a broad genetic basis to improve crop breeding in this species  across a substantial subsample of the world collection of narrow-leafed lupin was characterized in a recent study (L. angustifolius), consisting of 108 wild genotypes, one landrace, and two cultivars from 13 countries and four regions (Supplementary Table S1), was evaluated for phenotypic diversity in RSA traits  were used to determine the general relationship between root trait pairs (P ≤ 0.05) and to generate an agglomerative hierarchical clustering (AHC) dendrogram tree. Variability in root traits across genotypes was determined by principal component analysis  was used to analyse root trait data for genotype main effects with a general linear model (GLM) multivariate analysis after identifying non-significant differences between bins and harvesting times  value indicates the informativeness of a marker locus or marker system. PIC was determined as follows:Pi is the frequency of the ith allele in the examined genotypes , band absence (0), and missing data (?). Jaccard’s coefficient was used for the clustering analysis with the neighbour-joining (NJ) method.where K (number of populations) of up to 15 on the entire dataset (111 genotypes). The software provides the likelihood (the posterior probability) of the data for a given number of assumed populations K, and the value of K with the highest likelihood can be interpreted to correspond to an estimate for the underlying number of clusters. An ad hoc quantity (ΔK) based on the rate of the log probability of data between successive K values was used to determine the best K (Members (accessions/genotypes) in subgroups were identified using a model-based approach for dominant DArT markers implemented in the STRUCTURE software , with a L(K)=LnP(D), the log likelihood (estimated probability) for each K; L'(K)=L(K)n−L(K)n−1;> L''(K)=L'(K)n−L'(K)n−1; and  S[L(K)] is the SD.where K estimate approximates identity by descent via adjusting the probability of identity by state between two individuals using the average probability of identity by state between random individuals , which was used to construct a phylogenetic tree topology according to an NJ method (d=1−s) to identify significant differences among populations and within populations (Marker-based relative kinship estimates have proven useful for quantitative inheritance studies in different populations . This K J method  in MEGA (H'), variance, and SD were calculated to measure the diversity of populations in the core collection  method  values were calculated as the proportion of population variance due to among-population variation [i.e. ∅ST=VaVa+Vb] , whereas a value of 1 implies that all genetic variation is explained by the population structure (the two populations do not share any genetic diversity).where VaVa+Vb] . The totQ) and relative kinship (Kr) matrices was performed using the TASSEL (v. 2.1) software package (P ≤ 0.05) and then compared the significance of these markers (P ≤ 0.05) in the permutation-based GLM and SA association tests.A mixed linear model (MLM) association test of root traits incorporating population structure . The number of root traits allocated to an individual PC varied from 1 to 14, with PC4 containing only root growth rate at 0–2 weeks (RGR_2wk), and PC1 having 14 root traits including branch length (BL), branch number (BN), root length (RL), and root mass (RM) . Among these, the first three components  . The scrSupplementary Table S1).An AHC similarity dendrogram constructed with the Pearson correlation coefficients of root trait data showed a large diversity in root architecture traits among the core collection . Six genA total of 191 DArT markers were polymorphic among the 111 accessions. The present set of DArT markers contained between 0 and 28% missing observations. The PIC values of these markers varied from 0.086 to 0.375, with an average PIC value of 0.330 . Marker The genetic diversity of the 111 genotypes was assessed by PCoA using 191 DArT markers. PCoA identified 65 principal coordinates with positive eigenvalues, including 28 with values >1, indicating large diversity in the collection. The generated Jaccard similarity matrix was used to construct principal coordinate plots deciphering the genetic relationships among the genotypes. The first two principal coordinates derived from the scores jointly explained 23.3% of the total variance . NJ treead hoc statistics (ΔK) calculation resulted in 10 distinct populations (P < 0.001) . The average distance between individuals within each population ranged from 0.172 (Pop10) to 0.301 (Pop06)  was determined using the admixture model in STRUCTURE and an ulations . Differe< 0.001) . The gen< 0.001) , and wer (Pop06) . The comH′) and the associated variance and SD  except for Pop10. Pop03 was significantly different from Pop01 and Pop06 (P < 0.01) and Pop10 (P ≤ 0 .05), but did not statistically differ from the other six populations. Population–population distances based on the Bayesian method ranged from 0.077 (Pop03 versus Pop05) to 0.146 (Pop02 versus Pop 03), indicating varied genetic relationships among populations (Supplementary Table S2). The genetic structure between identified populations was further assessed using pairwise Fst analysis. The Fst values estimated for population pairs ranged from 0.129 to 0.398, confirming pronounced genetic differentiation among populations (Supplementary Table S3).T-test analyses on population data exhibited significant differences between most population pairs (P ≤ 0.05) associations with DArT markers, while the number of markers associated with an individual trait ranged from 2 to 13 (α = 0.01). Among them, 16 were associated with multiple traits (two to eight), whereas each of the remaining 14 was associated with a single trait. Marker IPb–333104 had the highest association with root traits, including branch density (BD), branch number (BN), subsoil branch length (BL_sub), and root length in diameter class <0.75mm (DCL_thin) (R2) ranged from 6.4  to 21.8 , with 15 associations having Marker R2 values >10%. Genetic variation values ranged from 0 to 7994, with 23 associations having values >240. A wide range of values was observed for residual variation (0−17897).The MLM association test of root traits revealed associations between root traits and DArT markers. All 38 root traits showed significant  . The perL. angustifolius) comprising 108 wild types from around the world (P < 0.01) between 15 root traits  and a number of above-ground traits (e.g. leaflet number and shoot dry weight)   ; a similL. angustifolius evaluated in this study exhibited large phenotypic and genetic diversity in a range of root traits  have been identified   method (FAMD).Table S3. Estimates of pairwise Fst values for populations based on random allelic permutation testing of the DArT dataset (P < 0.01).Figure S1. Scree plot of the PCA of all 38 root traits across 111 genotypes of L. angustifolius showing the total variance explained for each component (PC).


AFLP) data to assess genetic divergence and test for signatures of evolutionary change driven by long‐term simulated climate change applied to natural grassland at Buxton Climate Change Impacts Laboratory (BCCIL). Experimental climate treatments were applied to grassland plots for 15 years using a replicated and spatially blocked design and included warming, drought and precipitation treatments. We detected significant genetic differentiation between climate change treatments and control plots in two coexisting perennial plant study species (Festuca ovina and Plantago lanceolata). Outlier analyses revealed a consistent signature of selection associated with experimental climate treatments at individual AFLP loci in P. lanceolata, but not in F. ovina. Average background differentiation at putatively neutral AFLP loci was close to zero, and genomewide genetic structure was associated neither with species abundance changes (demography) nor with plant community‐level responses to long‐term climate treatments. Our results demonstrate genetic divergence in response to a suite of climatic environments in reproductively mature populations of two perennial plant species and are consistent with an evolutionary response to climatic selection in P. lanceolata. These genetic changes have occurred in parallel with impacts on plant community structure and may have contributed to the persistence of individual species through 15 years of simulated climate change at BCCIL.Genetic diversity may play an important role in allowing individual species to resist climate change, by permitting evolutionary responses. Our understanding of the potential for such responses to climate change remains limited, and very few experimental tests have been carried out within intact ecosystems. Here, we use amplified fragment length polymorphism ( Thus, we know very little regarding either the extent to which climatic selection can drive adaptive divergence within plant populations occupying intact ecosystems, or about adaptive responses in the established (reproductively mature) phase of perennial plant species.Although previous studies suggest adaptive responses to environmental selection, there have been very few direct experimental tests of genetic responses to climate change in plant species within intact ecosystems  in northern England, intact species‐rich limestone grassland has been subjected to experimentally manipulated climate treatments since 1993  markers to test for genetic differentiation in coexisting populations of established individuals of et al.  near Harpur Hill, Buxton, UK. The experiment now running at BCCIL was set up as a series of 3 × 3 m plots within this pasture, each containing intact species‐rich limestone grassland vegetation. These plots have been exposed to annual climatic treatments since 1993 using a replicated, spatially blocked design. Treatments include (i) winter warming, where soil surface is maintained at 3 °C above ambient (nonmanipulated) temperature from November to April; (ii) drought treatments, where rainfall in July and August is intercepted by automated rain shelters; (iii) water addition (watered), where water is added to experimental plots from June to September at a rate of 20% above the long‐term average; (iv) warming and drought (heated/drought); (v) warming and increased precipitation (heated/watered); and (vi) control. Experimental plots (3 × 3 m) are replicated five times in a randomized block design  and Plantago lanceolata L.  are perennial (polycarpic) plants with wide geographical distributions; both species are self‐incompatible (obligate outcrossers) and wind pollinated. Both study species are common in all experimental plots at BCCIL, and both have a limited capacity for clonal growth: P. lanceolata via lateral rosette formation and F. ovina through tillering, typically leading to the formation of loose tussocks. Festuca ovina was both the most frequent of all the species (occurring in 223 of 240 permanent 10 × 10 cm quadrats distributed throughout the grassland at BCCIL) and the species with greatest cover in the plots at BCCIL , after 15 years of experimental treatments (further details are given in Text S2). We sampled 12 individuals of each species from each plot, excluding a 0.3‐m boundary zone at the edge of each plot. We used eight previously established permanent 10 × 10 cm quadrats , and AFLP phenotypes were scored using aflpscore  were used to calculate gene diversity (an unbiased estimate of the expected heterozygosity) for climatic and soil‐depth subpopulations at BCCIL for each study species . Significance of the regression slope was tested using 2000 permutations of AFLP genotypes among sampling locations. The extent of SGS was estimated by quantifying Wright's neighbourhood size, which measures the effective population size of locally panmictic units within the total population, and σ , which defines the physical size of these panmictic units . These analyses assume that populations have reached drift–dispersal equilibrium and that dispersal is isotropic and also require an estimate of effective population density (methods described in Text S2). Populations were assumed to be in Hardy–Weinberg equilibrium (i.e. FIS = 0) on the basis of previous isozyme studies carried out on our study species  within each species by regressing pairwise kinship coefficients on the distance separating plant individuals (implemented using the software β estimator of FST (R‐functions given as Supporting Information). The significance of FST was determined by creating a null distribution for FST via 5000 permutations of individuals among climatic subpopulations (incorporating the observed FST as a datum). We used permutational analysis of variance to determine whether genetic distances among individuals could be explained by climate treatment, soil depth, treatment × soil depth interaction or block, using function ‘adonis’ in r package vegan 1.17‐4 . Second, we performed additional analyses that paired each climate treatment with control. In these permutational anova, we fitted a treatment × soil depth interaction and incorporated blocks as strata. All of these analyses used 1999 permutations of the genetic distance matrix to compute significance tests on the parameter estimates.Genomewide (multilocus) differentiation between climatic environments was assessed using Cockerham & Weir's  β estimaet al.  against plot‐level harmonic mean subpopulation abundance over the 15 years of monitoring at BCCIL and against current subpopulation density. These former abundance data are point quadrat records drawn from Grime et al. ; Fig. S1et al. . Thus, aet al. . These abayescan v. 2.1 . The prior odds for the neutral model were set to 10. We set a beta prior for FIS with mean 0.05 and standard deviation 0.1 to incorporate information regarding the mating system of our study species  were insufficient to support separate, robust outlier analyses for each climate treatment within each block. We also carried out a bayescan analysis that used experimental blocks as subpopulations, to test for signatures of selection resulting from spatial variation in the environment at BCCIL. The second outlier analysis used logistic regression to investigate associations between the presence–absence phenotypes at individual AFLP loci and climate treatment and soil depth . Logistic regressions were fitted separately for each locus, using the R function bayesglm, with a logit link , using a simple mantel test.We carried out two analyses in order to determine whether responses to simulated climate change at BCCIL at the community level were coupled with multilocus genetic responses within species. First, we performed correlations of plot‐level species diversity (species richness and Shannon diversity index) with multilocus gene diversity [species abundance data were taken from Fridley et al. ]. SecondF. ovina: F5,295 = 4.3, P = 0.046; P. lanceolata: F5,213 = 18.8, P < 0.001; Fig. F. ovina, decrease in P. lanceolata; Fig. P < 0.05 in each case). Plot‐level estimates of population density were associated positively with plot‐level abundance (cover) within the permanent quadrats .Population density varied significantly with climate change treatment for both study species  based on polymorphic AFLP loci were 0.281 and 0.332 for F. ovina and P. lanceolata, respectively. All individuals possessed a unique multilocus AFLP genotype, indicating that all sampled plants represented distinct genetic individuals. Subpopulations of F. ovina that had been exposed for 15 years to different simulated climatic environments possessed closely similar gene diversities , and watered and heated‐drought subpopulations were the most genetically diverse . Neighbourhood sizes  were 320 and 140, and neighbourhood areas were 9.29 and 9.97 m2 for F. ovina and P. lanceolata, respectively (assuming an NE/N ratio of 50%). These neighbourhood areas are approximately the size of individual plots at BCCIL (9 m2). Neighbourhood sizes and areas were substantially greater for F. ovina when we assumed an NE/N ratio of 10% . The procedure used to estimate dispersal parameters did not converge using an assumed NE/N ratio of 10% when applied to P. lanceolata.Both study species showed evidence of spatial genetic structure . Permutational anova analysis supported the presence of climate‐associated differentiation  and P. lanceolata  between individual climate treatments and control plots occurred for the watered treatment . There was no clear correspondence between the occurrence of control–climate treatment genetic differentiation  from control subpopulations  nor current population density .To determine whether the data supported a role for drift in shaping genetic structure, we regressed plot‐level gene diversity on ‘long‐term’ (15‐year) population abundance estimates and on current population density. Gene diversity was associated with neither population abundance . Two of the AFLP loci typed in P. lanceolata showed evidence of genetic divergence in response to climatic selection . These loci were identified in the analysis involving all climate treatment subpopulations.Outlier analyses using F. ovina AFLP loci (X306.3_TCT_CGG_Fo and X327_TCC_CGC_Fo) with significant variation in fragment frequency between climate change treatments . Similar analyses identified eighteen P. lanceolata loci as showing outlier patterns of genetic differentiation among climate change treatments, which included the two previously identified outlier loci for this species (range in FST = 0.021–0.218). Soil depth and the soil depth × treatment interaction were not significant predictors of AFLP fragment frequency for any locus in either study species. Genetic differentiation among climate treatments at putatively neutral (nonoutlier) loci was low for both species .Logistic regression identified two bayescan identified eight P. lanceolata loci that showed significant divergence among experimental blocks . The same eight loci were present in the sets of candidate outliers identified by analysis of the block environment using logistic regression. In contrast, analysis of the block environment using bayescan did not detect any F. ovina AFLP locus with an outlier pattern of genetic differentiation .Outlier analysis using rP ¦ < 0.1; P > 0.05). Similarly, associations between species‐level ecological dissimilarity and genetic differentiation within species were weak and nonsignificant .Correlations between species‐level and genetic diversity were all small and nonsignificant for both study species  and single locus‐oriented outlier analyses. This differentiation has occurred within contiguous grassland over a very small spatial extent (thirty 9‐m2 plots spread over approximately 0.2 ha grassland), and happened rapidly, during 15 years of climate change manipulations. Similarly, rapid genetic change occurring in response to a natural drought has been documented in the annual species Brassica rapa , there was no obvious link between the extent or presence of genetic differentiation and treatment‐mediated changes in population density. Hence, neither genetic diversity nor genetic differentiation was associated consistently with sustained changes in population size at BCCIL.Our survey data suggested that 15 years of experimental climate change had altered population density within some of the experimental treatments . Cross‐fertilization between plant populations can occur at appreciable rates at scales of 100–1000 m  are also consistent with a scenario of effective gene flow. FST can be used to calculate a crude measure of the effective number of migrants per generation (Nem) exchanged between populations  greatly exceeding the average background genetic differentiation at putatively neutral loci in this species (median FST = 0.003). Genome regions associated with the two outlier loci identified consistently in P. lanceolata represent candidate targets for climatic selection and may control phenotypes that are adaptive under the modified climates at BCCIL. The observed genetic differentiation at the putatively selected AFLP loci occurred against a background of low genomewide differentiation. As we argue above, low levels of background differentiation are likely to have been maintained by gene exchange among grassland plots. Thus, implying that climatic selection has had to work against gene flow to establish divergence at the genome regions putatively responding to selection.Outlier analyses detected a consistent signature of selection in response to simulated climate change at individual AFLP loci in F. ovina was equivocal; outlier loci associated with climate change treatment were only detected using logistic regression and were weakly differentiated . Failure to detect a stronger response to climatic selection in F. ovina may simply represent the absence of a response, but it may also reflect a genomic marker density insufficient to detect such an effect. In other words, linkage disequilibrium (LD), which may create associations between loci responding to climatic selection and neutral loci, may decay too rapidly for our genotyping strategy to be useful. For example, in maize, LD can decay within 10 kb  was observed for the watered treatment, yet this was the treatment with the lowest community‐level compositional change relative to control plots  phase of perennial plant species in an intact ecosystem in response to a suite of simulated climate change environments. As the climate treatments are replicated in a spatially blocked experimental design : DRYAD entries.Figure S1. Changes in abundance of Festuca ovina and Plantago lanceolata during the first 15 years of climatic manipulations at BCCIL.Text S2. Detailed methods for field survey and sample collection, AFLP genotyping and population genetics analysis.Table S3. Selective primer combinations used in the AFLP analysis and results for AFLP phenotype scoring.Table S4. Diversity statistics for climatic sub‐populations of F. ovina and P lanceolata at BCCIL.Table S5. Genetic differentiation among climatic and edaphic sub‐populations of F. ovina and P. lanceolata at BCCIL.Table S6. Pairwise genetic differentiation between climate treatment sub‐populations of F. ovina and P. lanceolata at BCCIL.Text S7. structure analysis.Figure S8. Graphical output from structure harvester.Figure S9. structure plots.Click here for additional data file.File S10. R‐script containing functions for population genetics analyses.Click here for additional data file.File S11. Readme file for R functions.Click here for additional data file.


Gazella gazelle), Dorcas gazelle (Gazella Dorcas) and acacia gazelle (Gazella arabica acacia) were historically abundant in the southern Levant, and more specifically in Israel. Anthropogenic and natural changes have caused a rapid decline in gazelle populations, raising concerns about their conservation status and future survival. The genetic profile of 111 wild gazelles from Israel was determined based on three regions of mitochondrial DNA  and nine nuclear microsatellite markers. Genetic analysis of the mountain gazelle population, the largest known population of this rare species, revealed adequate diversity levels and gene flow between subpopulations. Nevertheless, ongoing habitat degradation and other human effects, such as poaching, suggest the need for drastic measures to prevent species extinction. Dorcas gazelles in Israel displayed inbreeding within subpopulations while still maintaining considerable genetic diversity overall. This stable population, represented by a distinctive genetic profile, is fragmented and isolated from its relatives in neighboring localities. Based on the genetic profile of a newly sampled subpopulation in Israel, we provide an alternative hypothesis for the historic dispersal of Dorcas gazelle, from the Southern Levant to northern Africa. The small acacia gazelle population was closest to gazelles from the Farasan Islands of Saudi Arabia, based on mitochondrial markers. The two populations did not share haplotypes, suggesting that these two populations may be the last remnant wild gazelles of this species worldwide. Only a dozen acacia gazelles survive in Israel, and urgent steps are needed to ensure the survival of this genetically distinctive lineage. The genetic assessments of our study recognize new conservation priorities for each gazelle species in the Southern Levant.The mountain gazelle ( Gazella  is distributed widely across Africa, the Middle East and Asia  and then years) . Followin years) .o) and expected (He) heterozygosity and fixation index (F)) and measures of genetic distance between populations (Fst and Dest) were calculated using GenAlEx 6.5b3 [Genotypes were scored by eye with GeneMapper (Applied Biosystems). Genotyping data was screened with MICROCHECKER version 2.2.3 to detect null alleles and heterozygote deficiency. Arlequin 3.5 software was used to test for Hardy-Weinberg equilibrium (HWE) and linkage disequilibrium (LD) in the microsatellite data. Genetic diversity measures  and each run was repeated five times. We used a burn-in period of 10,000 followed by 2,000,000 Markov chain Monte Carlo (MCMC) steps for each run, and the admixture model with the option of correlated allele frequencies was chosen. The same analyses were conducted without a priori definition of populations and with sampling locations as prior information (LOCPRIOR option). The analyses were carried out for the complete dataset and also separately for the mountain gazelles and for the Dorcas gazelles, to detect more subtle genetic structures. To verify that the results were not affected by null alleles we also utilized the recessive alleles option. To test for hybrids between the different species we conducted an additional analysis with the no admixture model for the complete dataset. Structure Harvester [The nuclear genetic partitioning was examined using STRUCTURE 2.3.4  by inferarvester  was usedarvester  for the All 111 samples were successfully Sanger sequenced for all three mitochondrial regions . Quality control excluded 17 sequences from further analysis due to poor sequencing chromatogram quality. In addition, 99 nuclear microsatellite profiles were determined . Out of the 10 microsatellite loci, one locus (HAUT27) exhibited inconsistent amplifications and thus was removed from the analyses. For each species MICROCHECKER detected no signs of allele dropout but identified heterozygote deficiency suggesting that null alleles may be present at several loci which may lower the power of several of the analyses. Significant deviations from HWE (α = 0.05) were detected in each species, however, the results of both MICROCHECKER and HWE were not consistent across subpopulations for any loci, therefore all loci were included in the final analyses. Significant LD was found only for one combination of loci out of 36 combinations in Dorcas gazelles and acacia gazelle populations after Bonferonni correction.We created three mitochondrial sequence alignments to be used for analyses: alignment I: a concatenated 1286 bp mtDNA fragment , a total of 94 sequences from Israel obtained in this study ; alignment II: 607 bp of the Cytb gene region consisting of 158 mtDNA sequences (105 this study and 53 published ); alignmst values ranging from 0.75 to 0.78  exhibited strong support (P>0.95) for separation into three lineages, with a closer relationship between the mountain gazelles and acacia gazelles . Similar levels of differentiation were found between the Farasan Islands Arabian gazelles and Arabian gazelles from the mainland of the Arabian Peninsula (0.39). Comparison of the acacia gazelles from Israel to mainland Arabian gazelles indicates significant genetic differentiation  indicating that they form distinct populations and potentially could be classified as different species.Analysis of the CR between the acacia gazelles from Israel and Arabian gazelles from the Farasan Islands showed high FAll microsatellite loci were polymorphic in both Dorcas and mountain gazelles with a mean of eight alleles per locus . Given tst (0.15 to 0.3) and Dest (0.34 to 0.53) values were all high and significant , with only one significant result supporting moderate differentiation (Fst = 0.10) between the WNegev subpopulation and the Northern subpopulation, the two geographically most distant subpopulations  placed the majority of mountain gazelle specimens into two supported clades that did not correspond to geographic origin  only between two neighboring subpopulations—the WNegev and Coastal (Fst = 0.10). The observed and expected heterozygosity values were similar in most subpopulations with fixation index values implying random mating among the subpopulations (F = 0.006–0.088)  using two different settings   . AFS revst value (0.133) was estimated between the two Dorcas geographic subpopulations  supporting a moderate differentiation.Based on mtDNA analyses two highly supported clades were identified among the Dorcas gazelle in Israel  and 0.21 (Cytb))  and nucleotide diversity (π) values . For the (Cytb)) .Microsatellites diversity: Unlike the mtDNA markers, the geographic division of the two Dorcas gazelle subpopulations was less apparent in the microsatellite analyses, which revealed low differentiation between the Negev and the Arava subpopulations (Fst = 0.02). The observed heterozygosity (Ho) was lower than the expected heterozygosity (He) values by at least 0.1, which may indicate sex-biased dispersal or low gene flow between the subpopulations . In addist values    . STRUCTUt = 0.3)  and PCoAt = 0.3)  analysesOur study of wild gazelles from the Southern Levant (n = 111) revealed their uniqueness compared to other populations of the same species worldwide. In all three species, the populations in Israel harbored distinguishing haplotypes , which wGazella sp. can be found in the Negev as early as the Early Miocene [Environmental forces during the Pleistocene and Holocene have had an impact on the distribution of the gazelles in the Southern Levant . All thr Miocene . This st Miocene ,51, hypoGazella [The changes in the environment in the Levant during glacial cycles caused adaptation and speciation of local gazelles, creating a cradle province for Gazella . The mouGazella  where itGazella ,54. The Gazella ,55. In 2Gazella  reportedIn the past, the Arabian gazelle’s distribution encompassed the Levant and the Arabian Peninsula, co-existing with Dorcas and mountain gazelles. Recent climatic and anthropogenic changes have limited its distribution to isolated populations, one of which is a relict population in Israel, the acacia gazelle in the Arava. We found that within the Arabian gazelle lineage, based on the mitochondrial markers, all of the studied acacia gazelles formed a cluster together with gazelles from the Farasan Islands and a few captive specimens. This cluster (Farasan Islands and acacia gazelles) differed significantly from the other Arabian gazelles (mainland), with no shared haplotypes between the two groups . Previoua priori species designation , no incongruent results between the mtDNA and nDNA markers were found and pairwise comparisons revealed significant differentiation between the two species in this habitat—no hybrids were present in our samples , however at the edge of their distribution, in the Western Negev, both species live sympatrically. Only one report (based on sightings) was found in the literature in which the authors claimed the offspring of such cross-breeding to be unfertile . All thees Figs.  & 3.Acacia gazelles share their protective enclosure with Dorcas gazelles. The genetic analyses applied in this study found a clear separation of the two populations based on both the nuclear and mitochondrial clustering, for all but one individual. Specimen Gd13 was identified as a product of a possible ancient hybridization event: its nuclear DNA profile was found to be ca. 99% of Dorcas descent yet its mitochondrial sequence clustered with acacia haplotypes, though its lineage diverged approximately 500 kya from the other acacia mitochondrial sequences . Based oEach species of gazelle in Israel demonstrated different genetic characteristics. These characteristics suggest different conservation management programs for each species:Using both mtDNA and nDNA markers we found low genetic diversity within the mountain gazelle subpopulations with no genetic differences between them, demonstrating an effectively homogeneous population. As noted, we believe the genetic profile is due to the recent population expansion, but also points to ongoing gene flow between the subpopulations. The evidence for gene flow is provided by both the mtDNA markers and the bi-parental microsatellites, which show lack of biogeographic structure and may be a result of natural migration of individuals or occasional translocations as part of species management by INPA . AdditioThe new species status given to the mountain gazelles ,9, whichWithin the Dorcas population in Israel we found genetic evidence for subpopulation fragmentation, even though the habitat is a wide, relatively open, desert with no known geographic barriers. While their habitat in southern Israel is less populated by humans (in comparison to the mountain gazelle habitat), the type of human activity in this area has greater impact on the Dorcas gazelles. Quarrying, agriculture and military activities in the Negev and Arava cause natural habitat disturbance and fragmentation. To test this hypothesis further would require increasing the sample size with samples collected from the borders of both localities. Management programs should promote ecological corridors and control predation pressure to permit migration of individuals between subpopulations (Negev and Arava).The genetic diversity measures estimated for the Dorcas gazelles from Israel, including haplotype diversity and nucleotide diversity, were comparable to values reported for other Dorcas gazelle populations from different localities ,50, indiGazella arabica, not affected by genetic introgression. If this is the case, acacia gazelles share the maternal origins of this unique genetic diversity.In this study we compared the genetic profile of this unique population in Israel to different Arabian gazelle populations available in the literature. Our results, based on the mitochondrial control region, revealed acacia gazelles are most closely related to Arabian gazelles from the Farasan Islands, and did not cluster with Arabian gazelles from mainland Saudi Arabia. The same clustering was observed also in a study by Wronski et al (2010). However, in a recent study of nuclear markers acacia gazelle specimens from Israel grouped with mainland gazelles, and Farasan gazelles formed a distinct group by themselves (Lerp 2014). Due to the nature of the markers, we could not compare the nuclear profiles. As mentioned above, most of the mainland gazelle sequences originated from captive specimen with potentially questionable taxonomic status. The distinctive Farasan lineage may represent the original wild haplotype of Consequently, conservation measures should treat the acacia gazelle population as its own management unit. Should the need for captive breeding program arises, animals from the mainland or the Farasan islands can be included, however due to the different profiles uncovered in these populations their genetic makeup must be examined thoroughly. The acacia gazelle population in Israel is the only representative of a third gazelle species in Israel and may be the last breeding wild population of acacia gazelles in the world. Additionally, to determine the level, if any, of genetic variation that remains in this single population, we recommend all extant acacia gazelle individuals to be genetically characterized using Next Generation sequencing approach. The information from such a research will help promote the conservation of the extremely rare and genetically unique acacia gazelle population, before it becomes extinct.Gazella species, integrating the genetic data into the species’ conservation and management plans must be considered an indispensable step in promoting their survival. To maintain the current levels of genetic diversity, the enforcement of the wildlife protection laws must be enhanced in order to reduce poaching, ecological corridors must be maintained to provide migration between subpopulations, and predation pressure must be controlled to minimize its affect on gazelle recruitment.For all S1 File(DOCX)Click here for additional data file.S2 File(PDF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.