Pinus yunnanensis. We used genetic and ecological methods to investigate the patterns of genetic differentiation and ecological niche divergence across the distribution range of this species. We found both continuous genetic differentiation over the majority of its range, and discrete isolated local clusters. The discrete differentiation between two genetic groups in the west and east peripheries is consistent with niche divergence and geographical isolation of these groups. In the central area of the species’ range, population structure was shaped mainly by neutral processes and geography rather than by ecological selection. These results show that geographical and environmental factors together created stronger and more discrete genetic differentiation than isolation by distance alone, and illustrate the importance of ecological factors in forming or maintaining genetic divergence across a complex landscape. Our findings differ from other phylogenetic studies that identified the historical drainage system in the region as the primary factor shaping population structure, and highlight the heterogeneous contributions that geography and environment have made to genetic diversity among taxa in southwest China.Southwest China is a biodiversity hotspot characterized by complex topography, heterogeneous regional climates and rich flora. The processes and driving factors underlying this hotspot remain to be explicitly tested across taxa to gain a general understanding of the evolution of biodiversity and speciation in the region. In this study, we examined the role played by historically neutral processes, geography and environment in producing the current genetic diversity of the subtropical pine  Genetic differentiation is strongly influenced by neutral processes and ecological selection. Population divergence engendered by geographical isolation as a consequence of topographical change and recent climatic oscillations are well documented in phylogeographic studies Southwest (SW) China is a biodiversity hotspot characterized by complex topography, heterogeneous regional climates and rich flora Pinus yunnanensis is a subtropical pine endemic to SW China, which has a continuous distribution in the Yunnan-Guizhou region at elevations ranging from 700–3000 m above sea level across all the major river valleys Pinus tabuliformis, generating a homoploid hybrid, Pinus densataP. yunnanensis, of which 45% and 4%, respectively, resided between populations P. yunnanensis was used as a parental reference to characterize the hybrid nature of P. densata and therefore only relevant representative populations were sampled and analysed. The western and south-eastern marginal populations of P. yunnanensis, which have distinct ecological and morphological characters, were inadequately represented or not sampled at all in the previous mt- and cpDNA analyses. The species-wide pattern of genetic diversity in P. yunnanensis therefore remains unknown. Moreover, the ecological and phylogeographic processes responsible for the current population structure of P. yunnanensis have not been explicitly addressed. In this study, we sampled 16 populations throughout the range of P. yunnanensis to cover all ecological habitats, especially those on the western and south-eastern peripheries. Both mtDNA and cpDNA variations were analyzed, together with environmental data, in order to assess the influence of ecological and historical factors on genetic divergence in P. yunnanensis. We addressed the following questions: How is genetic diversity distributed geographically in P. yunnanensis, and does the observed genetic pattern reflect the modern or the historical geography? What is the extent of environmental heterogeneity within the species’ range, and could ecological factors have promoted genetic differentiation in this pine?P. yunnanensis populations used in our study did not require any specific permission from any authority as it is a dominant forest species in SW China. Thus this study does not involve endangered or protected species.Here we state that the sampling of P. yunnanensis. The distribution of these populations is illustrated in We sampled 255 individuals from 16 populations throughout the range of nad1 intron 2, nad4 intron 3 and nad5 intron 1) and five cpDNA microsatellite (cpSSR) loci  de novo for this study. For these additional populations, composite seed samples were collected from more than 100 mature trees in each stand, and then 16 bulked seeds per population were used to grow small seedlings for genotyping. Total DNA was extracted using a Plant Genomic DNA Kit  according to the manufacturer’s instructions. These new samples were characterized with the same set of mt and cp genetic markers used by Wang et al. Eleven of the 16 populations  have been characterized for mtDNA and cpDNA variation in a previous study nad1, nad4, nad5 and the combination of these three mtDNA sequences were identified among the sampled individuals. Their relationships were then established by median-joining networks using Network v. 4.6.1.0 P. tabuliformisP. yunnanensis and P. tabuliformis. Following the approach of Wang et al. Mitochondrial DNA sequences were aligned using Clustal X 1.81 For cpDNA data, size scores for the five cpSSR loci in each individual were combined into a 5-locus chloroplast haplotype (chlorotype). Relationships among the chlorotypes were reconstructed using the median-joining model implemented in Network. For simplicity, singletons were excluded from the network analysis.D) proposed by Jost http://chao.stat.nthu.edu.tw/softwareCE.html). Jost’s D provides a measure of actual differentiation of haplotypic frequencies among populations that is mathematically independent of within-population diversity All genetic diversity analyses were based on individual genotypes. For both mt- and cpDNA, the observed number of haplotypes and genetic diversity were calculated for each population and for the species. Genetic differentiation among populations and groups of populations were estimated by analysis of molecular variance (AMOVA) GST and NST) and cpSSR (GST and RST). GST is based solely on allele frequencies, while NST (or RST) takes into account similarities or relatedness among haplotypes. Thus, a significantly higher value for NST (or RST) than for GST implies that closely related haplotypes occur geographically closer to each other than distantly related haplotypes, indicating significant phylogeographic structure. The program Permut & CpSSR v. 2.0 GSTvs. NST or RST values using 10 000 random permutations.The population structure was analyzed by comparing two coefficients of population divergence for both mtDNA  that maximize the proportion of total divergence due to differences between groups of populations (FCT). For each mitotype and chlorotype dataset, K values ranging from 2 to 10 were tested to search for the K that gave the highest FCT. The significance of each FCT was tested by simulating the annealing process 1000 times.To further assess genetic structure in Finally, historical population expansion was assessed by mismatch distributions of both mt- and cpDNA data, using Arlequin. In this analysis, cpSSR data were coded in a binary format following the method described by Navascués et al. P. yunnanensis. These data consist of 148 geo-referenced occurrence records and 14 environmental variables . We then used these locations for inclusion in GIS environmental layers. The 14 environmental variables were first examined for pairwise correlations within the distribution of P. yunnanensis. Highly correlated variables could result in over-fitting of niche models and should thus be removed. After evaluation, we retained eight variables with pairwise Pearson correlation coefficients r<0.70 for subsequent analyses (2), and analyzed using the raster package (avaiable at http://raster.r-forge.r-project.org) in R and ArcGIS 9.2 .We extracted ecological data from Mao & Wang ariables . The 148analyses , to miniP. yunnanensis. This analysis estimates the number of ecotypic clusters within P. yunnanensis and their membership based on our 148 occurrence points and 8 environmental variables. Firstly, a sequential clustering approach was implemented to divide records into subclusters by constructing a modified cluster feature (CF) tree. The process scans records one by one and merges them into subclusters based on a distance defined by the log-likelihood decrease. Secondly, subclusters identified in step one were grouped into the desired number of clusters that maximize the Bayesian information criterion (BIC). For each ecotypic cluster pair, the relative contribution of environmental variables to their discrimination was evaluated by discriminant function analysis (DFA) using SPSS, and Wilks’ λ was used to test the null hypothesis that the two clusters have identical means for the specific variables.We performed a TwoStep clustering analysis in SPSS 13.0  to quantitatively assess environmental heterogeneity within P. yunnanensis. PCA was applied to scaled data for all eight environmental variables corresponding to 148 P. yunnanensis occurrence records, without a prior designation of ecotypic clusters. The relative contribution of each environmental parameter to the formation of niche spaces was then represented in a PCA distance biplot, and the magnitude and statistical significance of niche shifts among the occurrence clouds in the PCA graph were assessed using between-class inertia percentages and 99 Monte-Carlo randomization tests We also performed principal components analysis (PCA) to further investigate ecological differentiation within P. yunnanensis cluster. Model accuracy was evaluated by assessing the area under the curve (AUC) of the receiver-operating characteristic (ROC) plot We then followed the procedure and parameter settings described in Mao & Wang DIWe also performed a niche-identity test to examine the null hypothesis that each pair of the ecotypic clusters is distributed in identical environmental space. This test compares the similarity of an ecotypic cluster’s actual niches to a distribution of niche similarities, obtained from pairs of pseudoniches based on randomly reshuffled occurrence points of the two clusters. The niche-identity test was performed in ENMTools db) with observed niche divergence (dn) in the PCA-reduced axes, with the null hypothesis db = dndb<dn and the observed niche divergence itself (dn) is significant (according to a t-test), whereas niche conservatism is supported if db>dn. For each climatic cluster, the eight environmental variables, longitude, latitude, and altitude were extracted from the occurrence points and from 1000 random background points within the background region of each ecotype using the packages dismo (available at http://cran.r-project.org/web/packages/dismo) and raster in R. The eight variables were reduced by PCA of the correlation matrix with the ade4 package. Correlations between the reduced PCA axes and the geographical variables  were examined by a nonparametric correlation test implemented in perm dn and db were computed as the differences between the mean scores of 75% random samples of the occurrence points of the two niches being compared (dn) and of the 1000 background points of the two compared background habitats (db), in the reduced PCA axes. The distributions of db and dn were generated with 1000 resamplings, and the mean of dn was compared to the 95% confidence interval of db to determine its significance. The significance of the observed divergence between two compared niches was determined by a permutation t-test in perm.Finally, we applied a niche space-based multivariate test FST) vs. geographic distance and genetic distance (FST) vs. ecological distance, controlling for ecological distance and geographic distance, respectively. Because the sample sites of all 16 populations were included in the 148 occurrence points for the species used in PCA, we estimated ecological distance by calculating the Euclidean distance between population pairs in a principal components space defined by the first two PC axes. Partial Mantel tests were performed with Arlequin, and 10 000 permutations were used in significance testing.To determine whether ecological factors explain genetic differentiation above and beyond differentiation due simply to isolation by distance (IBD), we performed partial Mantel tests on distances between populations. We compared matrices of pairwise genetic distance (P. tabuliformis (M1–M10), which was used as an outgroup, and all neighboring mitotypes differed by only one mutational step  were found in the central, south-eastern and western regions of the P. yunnanensis distribution, respectively. The other mitotypes were restricted to local populations at low frequencies, except for M25 which was fixed in population no. 4. In the case of the cp genome, a total of 39 chlorotypes (including 17 singletons) were detected over the five concatenated cpSSR loci  were identified . All of nal step . A markeSSR loci . After eSSR loci . The fivSSR loci  dominateHT (0.804) across all populations was much higher than the average within-population diversity HS (0.371), resulting in strong between-population differentiation . In contrast, the HT value of 0.816 based on chlorotype variations was close to HS (0.728), and both GST (0.108) and D (0.209) values were much lower than those for mtDNA <0.01, P(SSD)>0.05). This analysis was not performed on group II because of its monomorphism. It was noticeable that the τ value of groups I and VII (3.0) was 2–3 times greater than that of the other groups (1.2–1.8). Based on the relationship τ = 2ut MtDNA-based mismatch distribution rejected a recent population expansion model at the species level . This division was in good agreement with that produced by the TwoStep clustering analysis  that collectively explained 60.5% of the observed variation in the 148 occurrence records, accounting for 31.7% and 28.8% of the total variation, respectively . The relanalysis . The PCAP<0.0001). The predicted distributions of the three clusters were generally consistent with the geographical ranges of their occurrence points, except that the projection for Py-eco3 extended beyond the observed distribution range of the species  was the lowest among the three niche pair comparisons , except in the comparisons of PC1 for Py-eco1 vs. Py-eco3 and Py-eco2 vs. Py-eco3  from the central distribution. Hierarchical AMOVA for mtDNA variation showed significant divergence between ecotypic clusters, since 34.41% of the variation occurred among ecotypes . In contrgen-geo = 0.22, P<0.05; controlling for ecological distance) and between genetic distance and ecological distance . For the same reason, this test could be performed for only one (group VI) of the seven mtDNA SAMOVA groups. Within group VI, the genetic distance correlated with neither geographic nor ecological distance . For cpDNA, the genetic distance correlated with geographic distance only at the species level , and within Py-eco2  are located to the west of the paleo-Honghe River, and well separated from the other groups. This group is dominated by M24, a mitotype also detected in three northern populations  at relatively high frequencies. In addition, this mitotype was found in the ancient hybrid zone between P. yunnanensis and P. tabuliformis located allopatrically north of the current P. yunnanensis range P. yunnanensis, are dominated by M23, a mitotype shared with three other southern populations (Nos. 8–10). Although the Pearl River separates group VII from the other populations, connectivity in this southern range is apparent, and M23 has drifted to a high frequency in the south-eastern periphery. It might be argued that the regional fixation of M24 and M23 could be due to introgression from neighboring species. This hypothesis seems unlikely because these two mitotypes were not detected in three other pines of the subgenus Pinus found in nearby regions, Pinus massoniana, Pinus kesiya and Pinus merkusiiAnalysis of mtDNA diversity revealed both discrete and continuous spatial structure in P. yunnanensis was in population equilibrium and had not undergone recent demographic expansion at the species level, but regional expansion was detected for all population groups. In addition, expansion in group I and VII seemed to have occurred earlier than that in other groups. This result suggests that groups I and VII were not established by recent colonization from the central area of the P. yunnanensis range. In this scenario, long-term isolation of group I and VII could help to reinforce population differentiation after colonization. Other factors, such as local adaptation during the period of isolation, may also have contributed to genetic divergence of groups I and VII; this issue will be discussed in the next section.Spatial expansion can favor the fixation of low frequency alleles by drift in newly colonized areas P. yunnanensis range  is characterized by the presence of the M19 mitotype at high frequencies. These populations are all located on the eastern side of the paleo-Honghe River but separated by the paleo-Jinsha, Yalong, and Dadu Rivers into parallel zones. This separation, however, does not seem to have impaired migration across the zones. After the reversal of the middle Jinsha River and the capture of its major tributaries by the East China Sea, the paleo-network became separated into disconnected northern and southern sections The central region of the P. densataP. densataP. densata, and further confirms that these two populations have been isolated from seed flow from nearby regions.Two populations (Nos. 4 and 5) in the north-west each had a unique mitotype composition and lacked the M19 mitotype which was found in the neighboring region. MtDNA SAMOVA identified each of these two populations as a distinct group . They were distributed along the paleo-Jinsha River, but separated from the other populations along this river by a sharp bend in the current river course. Geological analysis carried out by Clark et al. P. yunnanensis has been shaped by both the paleo-landscape and the formation of the modern regional topography. The connectivity between populations throughout the main range of the species reflects continuous distribution over a low relief paleo-landscape. This finding is similar to that of other phylogeographic studies of the region P. yunnanensis. Rather than showing that historical drainage systems played a major role in determining current intraspecific genetic structure, the observation of continuous genetic differentiation over the main range of P. yunnanensis, together with discrete isolated local clusters, suggests an ancient landscape that imposed little constraint on migration, but which was subsequently disrupted due to regional geo-movements. The discrete differentiation observed at the peripheries appears to reflect both geographic isolation and environment (see the following section).Taking all these results into consideration, we propose that the distribution of mtDNA variation in P. yunnanensis. This division is broadly congruent with that based on mtDNA SAMOVA. The two periphery groups I and VII each corresponded to Py-eco1 and Py-eco3, respectively, and all the other five groups (II–VI) in the central area belonged to Py-eco2. Multivariate analysis showed that environmental elements associated with availability of heat energy and water were the main factors that differentiated the three ecotypic clusters. Heat and water availability have strong impacts on the natural distribution of plant species, and are major determinants of plant productivity P. yunnanensis could have important consequences for local adaptation and represent an impediment to gene flow. The Py-eco1 region has a humid subtropical climate. It is warmer and wetter than those where the other ecotypes occur, and is similar to the Mio-Pliocene paleo-climate of SW China P. yunnanensis was present in a milder and moister climate during the Late Miocene than that of today P. yunnanensis adapted to drier climate in its central distribution area, while in the western periphery (Py-eco1 region) it survived in a warmer and more humid region P. yunnanensis. The Py-eco3 region represents a much drier climate than Py-eco1. Populations in this region have a distinct morphology characterized by thin and pendulous needles P. yunnanensis populations P. yunnanensis. The congruence between genetic and ecological divisions suggests that environmental adaptation could have contributed to the genetic divergence of groups I and VII. Py-eco2 covers the major range of the species, including population groups II–VI. In this region/environment, population differentiation is characterized by IBD as shown by a partial Mantel test. Thus, the genetic groups recognized in this region were shaped by historically neutral processes and local barriers to gene flow rather than by ecological selection.Three distinct ecotypic clusters  were identified in P. yunnanensis is distributed in a subtropical region that is recognized as having been a refuge during the last glaciations P. yunnanensis niches. The impact of immigrant inferiority (or inviability) is less visible in a genome that is dispersed through pollen (cpDNA in pines) than in the seeds that produce immigrant plant individuals P. yunnanensis. The selective pressure exerted by niche divergence upon fitness in this species remains to be explicitly tested.Neutral DNA markers are not expected to reflect the history of natural selection and adaptation. However, maternally inherited mtDNA, which is dispersed through seeds, is often used to track population establishment and migration history P. yunnanensis that were different from those seen in other case studies in SW China. In other taxa from the same region, the intraspecific genetic structure reflects the major role played by historic drainage systems. In contrast, in the case of P. yunnanensis our observation of continuous genetic differentiation over the majority of its range, together with discrete isolated local clusters, suggests a paleo-landscape that was generally well connected and imposed few migration constraints, but which was subsequently disrupted as a result of geomorphological movements in response to the uplift of the eastern Tibetan Plateau. The finding of discrete differentiation between two genetic groups in the peripheries is consistent with niche divergence and geographical isolation of these groups. In the central area of the species’ range, population structure was shaped mainly by neutral processes and local geography rather than by ecological selection. These results show that geographical and environmental factors acting in combination have created stronger and more discrete genetic differentiation than IBD alone, and illustrate the importance of ecological factors in promoting and maintaining genetic divergence across a complex landscape. Our study highlights the heterogeneous contributions made by historic neutral processes and environment to genetic variation among different taxa in SW China. Further research incorporating multiple approaches applied to additional taxa would permit a better understanding of the origin and maintenance of biological diversity in this geologically unique region.Integrating the results of genetic analysis and ecological niche modeling revealed the occurrence of ecological and phylogeographic processes in Figure S1SAMOVA analysis of mtDNA and cpDNA. X-axis shows different K values (number of groups) and Y-axis shows corresponding FCT values.(TIF)Click here for additional data file.Table S1148 occurrence records for Pinus yunnanensis, with the corresponding 14 environmental variables. The eight variables used in ecological niche modeling are indicated in bold.(PDF)Click here for additional data file.Table S2The frequencies of mitotypes and chlorotypes in the 16 sampled Pinus yunnanensis populations.(PDF)Click here for additional data file.


Myrtus nivellei, endemic in the Saharan mountains since at least the end of the last Green Sahara period, around 5.5 ka B.P. Multilocus genotypes (nuclear microsatellites and AFLP) were obtained from 215 individuals collected from 23 wadis (temporary rivers) in the three main mountain ranges in southern Algeria . Identical genotypes were found in several plants growing far apart within the same wadis, a pattern taken as evidence of clonality. Multivariate analyses and Bayesian clustering revealed that genetic diversity was mainly structured among the mountain ranges, while low isolation by distance was observed within each mountain range. The range contraction induced by the last episode of aridification has likely increased the genetic isolation of the populations of M. nivellei, without greatly affecting the genetic diversity of the species as a whole. The pattern of genetic diversity observed here suggests that high connectivity may have prevailed during humid periods, which is consistent with recent paleoenvironmental reconstructions.The identification of past glacial refugia has become a key topic for conservation under environmental change, since they contribute importantly to shaping current patterns of biodiversity. However, little attention has been paid so far to interglacial refugia despite their key role for the survival of relict species currently occurring in climate refugia. Here, we focus on the genetic consequences of range contraction on the relict populations of the evergreen shrub  Total DNA was extracted from silica gel-dried samples (30 mg of leaves), using the Doyle and Doyle procedure The fresh leaves of Myrcom1 to Myrcom11) developed for Myrtus communis by Albaladejo et al.2, 0.6 mM dNTPs, 0.2 µM of each primer, and 2.5 units of Taq polymerase  in a PTC-200 Gradient Thermal Cycler , using touchdown PCR protocols FAM, HEX, TAMRA or ATTO565 with a view to multiplexing the PCR products. The fluorescently-labelled PCR products were separated by capillary electrophoresis with a 500 bp size standard (LIZ500(−250)), using an ABI Prism® 3730xl  automatic sequencer. Alleles were sized using the GeneMapper version 4.1 software program (Applied Biosystems). The microsatellite dataset was deposited in Dryad (doi:10.5061/dryad.1vt02).Eleven nuclear microsatellite markers  and for 3 h at 65°C with 4 units of Tru9I (Eurofins MWG) in a total volume of 25 µL. Digestion products were then ligated for 3 h at room temperature by adding 2.5 pmol EcoRI and 25 pmol MseI adaptors, 0.5 units T4 DNA ligase and 10 mM ATP (Eurofins MWG). Five µL of 8-fold diluted ligation products were used as a template in the preamplification step, using 10 pmol of EcoRI (+A) and MseI (+C) primers, 0.16 mM dNTPs, 0.65 mM MgCl2 and 1.5 units of Taq DNA polymerase  in a final volume of 50 µL. The preamplification thermocycle profile was 94°C for 2 min, followed by 20 cycles at 94°C for 45 s, 56°C for 45 s, 72°C for 1 min and 72°C for 10 min. Selective amplification was performed using 5 pmol of EcoRI (+ANNN) and MseI (+CNNN) primers with 5 µL of 100-fold diluted preamplification product in a final volume of 20 µL. Each selective amplification reaction mixture contained 0.5 mM dNTPs, 0.65 mM MgCl2 and 1 unit of Taq DNA polymerase . The selective amplification thermocycle profile was: 94°C for 2 min, 10 touchdown cycles of 94°C for 30 s, 65°C for 30 s (step −0.7°C per cycle), 72°C for 1 min, followed by 20 cycles at 94°C for 30 s, 56°C for 30 s, 72°C for 1 min and 72°C for 5 min (thermocycler: MJ Research PTC-200). After screening selective primers, three primer combinations giving clear-cut profiles were chosen for further analysis .The Amplified Fragment Length Polymorphism reaction was performed as described by Vos ETROX900), using the enetic ProfilerG software program (Amersham Biosciences). Low quality profiles  were discarded and manual scoring was carefully performed in order to detect any markers prone to errors not giving reliable peaks. The reproducibility and reliability of the AFLP markers were checked by repeating the complete analysis from the DNA amplification to the AFLP screening on 20 samples for each pair of primers Polyacrylamide electrophoreses (0.4%) were performed on a 96-capillary Megabace 1000 automated sequencer . Markers were scored in view of the fluorescent peaks in the chromatograms after calibration with a standard size marker  2h, where ip is the frequency in the population of each allele present in the genotypes and h is the number of heterozygous loci. The genP value gives thus the probability that two different samples will have the same genotype by chance. The genP was calculated using the GenAlEx 6.4 software program To determine that identical genotypes were clones, the probability that a zygote would acquire a given diploid genotype I, the observed heterozygosity oH, the unbiased expected heterozygosity eUH and the Wright F-statistics (STF and ISF), using the GenAlEx 6.4 program are program AR) and the private allelic richness (PAR) after standardizing to the smallest population sample size (26).Genetic diversity at microsatellite markers was characterized by Shannon’s information index M. nivellei within and among mountain ranges, all analyses were performed in parallel on microsatellite and AFLP datasets. Hierarchical analyses of the molecular variance  was then conducted using AMOVA results to identify groups of individuals as priors. The DAPC multivariate method relies thus on data transformation using principal component analysis as a prior step to discriminant analysis, in order to explore genetic diversity without any explicit evolutionary models and underlying assumptions  M. nivellei individuals were also assigned to genetically homogeneous clusters via the model-based clustering algorithm provided in Structure 2.3.3 program K from 2 to 23 and 5 iterations for each K-value. The most likely number of clusters was determined using the log-probability of the data (lnPr(X/K); et al.In parallel, enAlEx 6.4 PTФ-statistics. In the thoroughly sampled Tarit wadi, the distances between genotypes were calculated as described in GenAlEx 6.4 Finally, to test for isolation by distance M. communisM. nivellei and were therefore used to estimate genetic diversity parameters. The total number of alleles ranged between 5 and 13 alleles, with a mean value of 7 alleles per locus  was extremely low . Clones detected by microsatellites were not rejected by AFLP data, since the small amount of observed variation from AFLP markers between individuals having identical microsatellite genotypes fell within the bounds of scoring errors for AFLPs (5.7%) or could be explained by somatic mutations in some of the clones as explained by Bakker et al.Among the eleven microsatellite loci developed for er locus . MicrosaAR = 3.49 and PAR = 1.65) and Tassili n’Ajjer samples (AR = 4.19 and PAR = 2.33)  appeared in the Hoggar , as compared with the Tassili n’Immidir ( = 2.33) . HoweverThe AFLP analysis yielded 119 scored fragments. Their length ranged from 66.3 to 453.1 bp in the three pairs of primers analyzed. Seventy-seven bands were counted in the Hoggar, 83 in the Tassili n’Immidir and 87 in the Tassili n’Ajjer. More private bands were counted in the samples from the Tassili n’Ajjer (14) than in those from Hoggar (8) and Tassili n’Immidir (9).PTФ values were obtained with both AFLPs and microsatellites  relationship was observed in the Mantel analysis between the genetic and geographic distances, but very low isolation-by-distance values were obtained. After removing the ATAT samples from the analysis because of their genetic and geographic isolation  and heterozygote deficiency particularly high (F = 0.274±0.066). Neither microsatellites nor AFLPs could detect any particular spatial genetic structure within the Tarit wadi. The Mantel test was not significant  and no clear-cut spatial clustering of genotypes was observed.Finally, genetic diversity at microsatellites was particularly low in the Tarit wadi  rather than a process of vegetative fragmentation. However, almost all the M. nivellei genets consisted of a very small number of ramets distributed in the same wadi only some tens of meters apart. It therefore seems likely that episodes of catastrophic floods resulting in large amounts of suddenly accumulated rainwater in the dendritic network of wadis may have dispersed the vegetative parts of the myrtle shrubs over large distances.There is a surprising high rate of occurrence of clones (30%) in Myrtus in the Sahara desert still remains to be established. Clonal growth, often combined with longevity M. nivellei , in sharp contrast with M. communis which produces many berries throughout its Mediterranean range. Asexual reproduction is not a rare occurrence in central Saharan tree populations. For example, in Olea europaea subsp. laperrinei, clones were observed in significant proportions, reaching 26% in the Tassili n’Ajjer, 14% in the Hoggar and between 13 and 40% in the Aïr range Cupressus dupreziana, clonality via paternal apomixis from the production of unreduced pollen germinating in the seed tissues of a surrogate mother The exact contribution of clonal reproduction processes to the persistence of M. nivellei (M. nivellei in a much larger range of distribution (up to the Chad Basin), and at lower altitudes (ca. 300 m a.s.l.) during several Pleistocene and Holocene periods . These patterns of genomic divergence are consistent with the hypothesis that M. nivellei survived several climatic changes from pluvial to interpluvial periods. The data obtained here using a multimarker approach provide therefore the phylogeographical framework required to examine the effects of fast, drastic climatic changes on genetic diversity and the role played by the central Saharan mountains as climate refugia for relict species Our molecular data are congruent with the fossil records. First, a previous study using cpDNA indicated the presence of Myrtus nivellei populations display a marked genetic isolation within each Saharan mountain range. A high level of genetic differentiation was found among the mountains in the study area  has been reported in the Laperrine’s olive and may maintain connectivity between distant patches, but to our knowledge no data is available regarding pollen dispersal in Myrtus.MyrtusM. communis lineages in each of the main central Saharan mountain ranges. Separate populations still inhabit the Hoggar, Tassili n’Ajjer and Tassili n’Immidir nowadays, as established here from multivariate and Bayesian clustering analyses results  and the presence of harsh desert barriers during arid periods have probably prevented, or at least greatly restricted, the occurrence of gene flow between mountain ranges, increasing genetic differentiation. By contrast, along the Mediterranean coast, M. communis has been less exposed to isolation and several periods of extensive expansion have been described M. nivellei grows . The central Saharan mountains can therefore be said to constitute terrestrial “mountain-islands” for Nivelle’s myrtle, since they play the role of true biogeographic islands After the founder events, the combined effects of the geographic distances between mountains , there is no support for the existence of clear genetic clusters within mountains among wadis. This low spatial genetic structure was also observed on the finest scale of analysis in the Tarit wadi, which was intensively sampled in the Tassili n’Immidir. To account for these results, which are in contrast with the high level of population fragmentation, it seems likely that a higher degree of landscape connectivity occurred during past humid periods. Myrtus nivellei probably persisted in situ during arid periods in local mountainous climate refugia and recolonized lowland areas during humid periods. In Saharan mesophilous species, range expansion is known to increase gene flow between populations connected by suitably moist habitats, e.g. via the huge network of rivers that existed during humid periods Although high to moderate levels of genetic differentiation were detected among mountain ranges . Blue squares represent samples from the Hoggar, red circle refer to samples from Tassili n’Immidir, green triangles indicate samples from Tassili n’Ajjer, and asterisks report samples from the ATAT population collected in the northern part of the Tassili n’Ajjer mountain range. DIVA-GIS software was used (http://www.diva-gis.org/).(TIF)Click here for additional data file.Figure S2Genetic structure of Myrtus nivelleitructure) for microsatellite ( based on Bayesian clustering (SK = 3) and AFLP markers (K = 4). Blue squares represent samples from the Hoggar, red circle refer to samples from Tassili n’Immidir, green triangles indicate samples from Tassili n’Ajjer, and asterisks report samples from the ATAT population collected in the northern part of the Tassili n’Ajjer mountain range.(TIF)Click here for additional data file.Table S1Genetic characteristics for each microsatellite locus (MYRCOM1 to MYRCOM11) examined for Myrtus nivellei. The total number of alleles, the allelic richness (AR), the observed heterozygosity (oH), the unbiased expected heterozygosity (eUH), and the Shannon's information index (I) were calculated for each microsatellite marker.(DOCX)Click here for additional data file.Table S2Probabilities of occurrence of a diploid genotype for each individual of Myrtus nivellei analyzed through microsatellite markers in the Hoggar, Tassili n’Immidir and Tassili n’Ajjer.(DOCX)Click here for additional data file.


Bubo bubo), we investigated the emergence and maintenance of traits that reveal individual identity by focusing on vocal features. We found that individuals inhabiting a high density population characterized by a relative lack of heterogeneity (in terms of prey availability and breeding success) among breeding sites might be selected for reducing the levels of identity. Two non-mutually exclusive hypotheses may explain the structural call patterns we detected: (1) similarity in calls may be principally a consequence of the particular characteristics of the population; and (2) high density may encourage individuals to mimic each other’s vocalizations in a cascade effect, leading to a widespread and unique communication network.Individual variability influences the demographic and evolutionary dynamics of spatially structured populations, and conversely ecological and evolutionary dynamics provide the context under which variations at the individual level occur. Therefore, it is essential to identify and characterize the importance of the different factors that may promote or hinder individual variability. Animal signaling is a prime example of a type of behavior that is largely dependent on both the features of individuals and the characteristics of the population to which they belong. After 10 years studying the dynamics of a population of a long-lived species, the eagle owl ( Over the last four decades, the field of ecology has shifted from a phenomenological-based discipline, in which the linkage between an observed pattern and a process in nature is only inferential, to one that structures explanations of population, community, and ecosystem phenomena in terms of underlying mechanisms i) the age of maturation ii) clutch size iii) reproductive success iv) resting metabolic rates v) dispersal strategies Individual variability can be generated by differences in environmental conditions or genetic background. Numerous examples of individual variability in life history traits are mentioned in the scientific literature, such as  with the aim of addressing an important question: is it possible that a given ecological scenario may reduce the levels of individual call identity? Two previous studies of the eagle owl, a long-lived species characterized by both strong territoriality and pair bonding 2; nearest neighbor distance: 250 m; Here, we study individual vocal identity in eagle owls . The capture and manipulation of breeding owls posed little risk to the birds given that we immediately removed them from the net, and they remain motionless when manipulated. After eight years of continuous radio-tracking, we have never detected a possible adverse effect that could be directly attributed to the backpacks placed on the birds.Below we describe an extensive array of methodological approaches used to characterize the eagle owl population (see i) two measures of productivity, i.e. the mean and the coefficient of variation (CV) of young fledged per breeding pair ii) an estimate of the quality of breeding sites via census methods of the main eagle owl prey species in the study area, the rabbit Oryctolagus cuniculus ; (iii) an analysis of the diet through the collection of a minimum of 100 pellets (and as much of prey remains as possible) for each nesting site  landscape characteristics by intersecting a digital layer representing the boundaries of the owls’ home ranges with a map of landcover elements . Following the studies of Aebischer et al. From 2002 to 2012 we studied an eagle owl population located in the Sierra Norte of Seville  and the lanyu scops owl (Otus elegans botelensis). We extracted DNA, following a Hotshot protocol 4)2 SO4, 2 mM of MgCl2, 0.2 mM of dNTPs, 0.1 ng/µl of bovine serum albumin , 0.5 µM of reverse and fluorescently-labeled universal M13 primer, 0.041 µM of forward primer and 0.5 units of Taq polymerase . Reaction conditions were as follows: an initial denaturation step of 2 min at 94°C, 30 s at 55°C annealing temperature decreasing 1°C/cycle for 15 cycles, and 30 s at 72°C, followed by 27 additional cycles with an annealing temperature of 40°C and a final step of 5 min at 72°C. Products were analyzed on an ABI PRISM® 3100 DNA Genetic Analyser (Applied Biosystems) and alleles were scored with GeneMapper 4.0 .Finally, we analyzed the genetic structure of the population by using a set of loci developed for eagle owls, the spotted owl (www.biotrack.co.uk). The mass of the backpack was less than 3% of the mass of the smallest adult male  in our population. This telemetry study allowed us to collect detailed information at the individual level concerning both the dispersal process We trapped and radio-tagged 34 breeding individuals  from 24 nests, as well as 96 juveniles  from 21 different nest sites  Recordings were always made at sunset for birds positioned on known call posts in close proximity to their nests We extracted the acoustic features of the 478 calls that were recorded on audiotape by performing a spectrographic analysis. For this analyses, we used Avisoft SASLab Pro software  and expected heterozygosity (EH), mean number of alleles per locus (aN), and the population inbreeding coefficient (ISF), was estimated for each locus for the population using FSTAT. Significance of ISF was determined by bootstrapping over loci to obtain a 95% confidence interval based on 10,000 replications. The same program was used to perform tests for Hardy-Weinberg equilibrium (HWE) using 10,000 permutations of alleles among individuals. Sequential Bonferroni corrections were applied to correct for multiple simultaneous comparisons. To analyze the genetic structure of the population we used two approaches. First, we used Structure v.2.2 software 6 MCMC steps. The number of populations was varied from1 to 5, and for each k 20 replicates were run under an admixture model with correlated gene frequencies. We assessed the support for k populations based on visual inspection of the plot of the algorithm of the posterior density (lnP (D)) as a function of k, and Δk, following Genetic diversity, i.e. observed . Finally, there was no effect of landscape structure, diet and resource abundance on either mean reproductive output or its CV . All these results provide evidence for a relatively homogeneous population, which is characterized by territories of similar quality showing rather similar annual variance in productivity.The occupancy rate was very high over the years, i.e. the only context in which we did not find any evidence of reproduction was when a pair disappeared (e.g. one or both members of a breeding pair died), and breeding pairs always reproduced successfully . After cEH = 0.675 (range 0.304–0.918). The inbreeding coefficient was high and significantly positive for two of the analyzed markers , suggesting the occurrence of null alleles at these loci. Overall inbreeding was high and significant when these loci were included , but low and not significant when they were removed . Genetic analysis in Structure supported one panmictic population with no significant genetic structure. That is, lnP (D) was highest for k = 1. Moreover, the intra-population analysis at the individual level detected no signal of spatial genetic autocorrelation in any of the distance classes analyzed, nor in the dispersal distance class (1500 m).All markers analyzed were polymorphic in the study population with the observed number of alleles ranging from two (Bb131) to 15 (Oe2-57), with an average of 6.1. Genetic diversity was moderate with an average observed heterozygosity of p<0.01), we conservatively decided to only select them for the following multivariate analysis. We entered these six acoustic variables into a DFA that correctly classified 95.8% of vocalization bouts to the marked individual from which they were recorded. The first 5 discriminant functions explained 69.8% of overall data variation and had eigenvalues = 11.2, Wilks’ Lambda = 0.002 and 2χ = 301.71 (p<0.001). The maximum value for acoustic (Euclidean) intra-individual distances of known birds was 130.0. However, when using this value as the acoustic threshold of similarity, nearly all of the Euclidean distances between the acoustic features of pairs of unknown birds fell below the intra-individual threshold, in both the same (91.85%) and different (92.46%) years. Therefore, even though such acoustic variables showed some inter-individual variation, they were not able to discriminate between the different eagle owls , as would be expected if communication were limited by distance or if birds matched their songs to local neighbors. The lack of significant differences observed between the calls of eagle owls in our study population did not allow us to discriminate individuals based on the information concerning their vocalizations.All the studied acoustic parameters appeared useful for individual identification, as they exhibited CVb/CVi ratios greater than 1  the natural vocal variation within individuals over time is high, leading to levels of ambiguity in the identification of an individual 2; minimum distance between two recording sites ca. 5 km; i) fecundity was relatively high and rather identical for the whole population and (ii) all pairs successfully bred every year. These two features are not typical attributes of eagle owl populations, which instead are usually characterized by their heterogeneity in quality and fecundity among breeding sites iii) genetic results showed that there is no genetic structure between individuals in this population: they seem to form one unique panmictic population with no substructure, and with no spatial genetic autocorrelation.Following the idea stressed by Tibbetts and Dale Similarity of eagle owl call types found across the whole range of nest distances might be consistent with a peculiar type of social synchronization of vocalizations between all individuals , matching each other and forming a wide communication network There are at least two biological benefits of the network we identified in this study. A first obvious benefit at the individual level is that possessing similar individual calls can be advantageous during interactions with neighbors. Several researchers have attempted to address the question of why, in numerous territorial species, males interact with neighbors by partially sharing or matching some portion of their song repertoire A second benefit of the observed communication network is that the reduced aggression toward neighbors may lead to the appearance of a high social stability, i.e. territory owners may decrease boundary disputes by having similar calls. This stability may prevent the attraction of floaters to the area Recently, Laiolo and Tella Finally, it is important to stress that evolutionary theory predicts that the amount of genetic variance together with the nature of environmental variability can promote or prevent the evolution of individual variability


When genetic structure is identified using mitochondrial DNA (mtDNA), but no structure is identified using biparentally-inherited nuclear DNA, the discordance is often attributed to differences in dispersal potential between the sexes.Perna perna in a South African bay and along the nearby open coast, and sequenced maternally-inherited mtDNA  and a biparentally-inherited marker. By treating males and females as different populations, we identified significant genetic structure on the basis of mtDNA data in the females only.We sampled the intertidal rocky shore mussel This is the first study to report sex-specific differences in genetic structure based on matrilineally-inherited mtDNA in a passively dispersing species that lacks social structure or sexual dimorphism. The observed pattern most likely stems from females being more vulnerable to selection in habitats from which they did not originate, which also manifests itself in a male-biased sex ratio. Our results have three important implications for the interpretation of population genetic data. First, even when mtDNA is inherited exclusively in the female line, it also contains information about males. For that reason, using it to identify sex-specific differences in genetic structure by contrasting it with biparentally-inherited markers is problematic. Second, the fact that sex-specific differences were found in a passively dispersing species in which sex-biased dispersal is unlikely highlights the fact that significant genetic structure is not necessarily a function of low dispersal potential or physical barriers. Third, even though mtDNA is typically used to study historical demographic processes, it also contains information about contemporary processes. Higher survival rates of males in non-native habitats can erase the genetic structure present in their mothers within a single generation. Genetic markers with sex-specific inheritance have long been used to study differences in genetic structure between males and females. Numerous studies have used maternally-inherited mitochondrial DNA (mtDNA) in conjunction with biparentally-inherited nuclear DNA markers -7, and dMytilus edulis, because this should have resulted in two copies of COI amplifying with the universal primers used here. Maternally- and paternally-inherited mtDNA genomes are usually highly distinct [P. perna does not exhibit DUI  and so Although more ITS-2 alleles were recovered for males than for females, no genetic structure was found with this genetic marker for either sex. Possible reasons for this include less genetic variation and the larger effective population size of the nuclear genome .Our results have important implications for interpreting genetic structure and highlight the value of analysing genetic data from males and females separately. Surprisingly, in many studies on species with breeding behaviours that differ between the sexes, genetic data from males and females have been combined -4. GendeP. perna  could be explained by sex-specific differences in larval behaviour, including differences in the way this behaviour influences their position in the water column [P. perna has not been investigated, this seems unlikely as larvae of this species disperse as passive particles [P. perna is male-biased , so the stronger male bias on the open coast, where wave action is stronger, could reflect the negative consequences of weakened attachment strength due to greater reproductive effort by the female mussels [Male-biased gene flow in r column , and difarticles , and we articles , it is pP. perna, bay individuals having certain haplotypes may be particularly vulnerable to strong wave action on the open coast. We hypothesise that females having these haplotypes expend more energy on reproduction rather than attachment, which results in an overall greater gamete output in mussels that reside in bays [P. perna may therefore stem not from differential dispersal of the sexes, but from sex- and habitat-specific differences in reproductive effort and the effects of differential selection pressures between bays and the open coast.Females of bay populations tend to have a larger number of private haplotypes than those of coastal populations . Hence,  in bays . Sex-speThe fact that female-only mtDNA structure can be present in a species in which sex-biased dispersal is highly unlikely indicates that female philopatry and male roving -22 do noMitochondrial DNA is by far the most frequently used locus in phylogeographic studies , but itsPerna perna was sampled at four sites in Algoa Bay  and four sites along the adjacent open coast .Mytilus, the gonads of males can become orange in response to environmental stress [P. perna, we considered it necessary to confirm that gonad colour can be used to distinguish between males and females of this species. We selected 20 individuals with white gonads and 20 individuals with orange gonads, dehydrated the tissues and embedded them in paraffin wax, and sectioned them to a thickness of approximately 5 µm. Histological sections were then stained with haematoxylin-eosin and examined under a compound microscope, and the sex was determined by the presence of ova or spermatids. We found that all individuals with white gonads were male and all individuals with orange gonads female, and thus conclude that gonad colour is a reliable indicator of gender in our samples of P. perna.Individuals with white gonads were considered to be male and those with orange gonads to be female. We excluded smaller individuals lacking gonads and those in which the difference was not obvious, which can be a consequence of infection by bucephalid trematodes . It has l stress . AlthougP. perna were not usable because they contained long sections with multiple electropherogram peaks that were difficult to interpret and may have resulted from a combination of secondary structure problems, slip-strand mispairing when sequencing through a long AT-array, and length differences between alleles in the case of individuals that were heterozygous at this locus. For that reason, we decided to use only a relatively short segment (216 bp) that started on the 3’ side of the AT-array and that could be unequivocally scored in a total of 108 individuals. In these sequences, a particular nucleotide position was considered to be heterozygous when the intensity of a secondary peak in the chromatograms was at least 25% of that of the primary peak. While this threshold is slightly above that of 20% typically used in the literature [e.g. [DNA was extracted from gonad tissue using a modified CTAB protocol . The COIre [e.g. ,38, the re [e.g. ,40 implere [e.g. . We specFST[ST[GST[D[CT, which in this case represents genetic structure between a group including all coastal sites vs. a second group that includes all sites within the bay, and on ΦST, which represents genetic structure between all male and all female individuals (COI sequences only). Ninety-five percent confidence intervals were obtained by generating 1000 bootstrap replications over variable positions.Genetic distances among sequences were calculated in MEGA v5.05 . HaplotyFST and ΦST[FST[ST have beeST[ST[GST and D[49[ST[GST[D. We baseThe author(s) declare that they have no competing interests.CDM and NPB conceived the study. PRT and IP collected samples and generated DNA sequence data. PRT analysed the genetic data. CDM investigated sex-ratios and gonad-based indentification of gender. PRT, IP and CDM wrote the paper. All authors read and approved the final version.COI sequences.Click here for fileITS-2 sequences.Click here for file


Ne) of wild and hatchery stocks. Our goals were to evaluate how genetic metrics responded to changes in wild fish density and whether they corresponded to the number and levels of diversity of hatchery-reared repatriates. Genetic diversity and all measures of Ne in the wild population did not correlate with wild fish density until hatchery supplementation began in earnest. Estimates of variance and inbreeding effective size were not correlated. Our results suggest source–sink dynamics where captive stocks form a genetically diverse source and the wild population behaves as a sink. Nevertheless, overall genetic diversity of silvery minnow has been maintained over the last decade, and we attribute this to a well-designed and executed propagation management plan. When multiple factors like environmental fluctuation and hatchery supplementation act simultaneously on a population, interpretation of genetic monitoring data may be equally complex and require considerable ecological data.The endangered Rio Grande silvery minnow persists as a remnant population in a highly fragmented and regulated arid-land river system. The species is subject to dramatic annual fluctuations in density. Since 2003, the wild population has been supplemented by hatchery-reared fish. We report on a 12-year (1999–2010) monitoring study of genetic diversity and effective population size ( Ne), across a contemporary time series  to demographic monitoring of key population parameters that assess conservation status, including abundance . Genetice series . These mNe, and census size, Nc, has important implications for conservation and management and has thus received considerable attention  or heterozygosity (NeI) over time as the real population (Ne estimates are inbreeding (NeI) and variance (NeV) effective population size. These measures of Ne track different facets of genetic change and do not estimate effective size in exactly the same generation  that may impinge on abundance and genetic diversity of the target species and considered relatively few time points . Historically, the species was distributed widely in the Rio Grande from northern New Mexico to the Gulf of Mexico, and in the Pecos River from above Sumner Reservoir (New Mexico) to the confluence of the Rio Grande in Texas  from throughout the remaining native range of the species, in addition to genotyping of repatriated individuals collected since the inception of the supportive breeding and supplementation program in 2002. Using these data and existing knowledge of life history and recent population trends in Rio Grande silvery minnow, we tested several simple predictions based on premises underpinning genetic monitoring. In regard to life history, the species is characterized by a type III survivorship curve, a 1:1 sex ratio, and a generation time that is roughly 1 year  and Arkansas River shiner (Notropis girardi) were documented to contain 52 and 51 haplotypes among 1361 and 366 samples, respectively  wild fish density, genetic diversity metrics, and all measures of effective size are positively correlated; (ii) supplementation provides a buffer against loss of diversity; hence, genetic variability has been maintained despite population declines in the wild; (iii) effects of population supplementation on genetic effective size depends on the source repatriated fishes ; and (iv) captive fish broodstock composition (number of breeding pairs) and a point estimate of effective size, Rio Grande silvery minnow were sampled from the Rio Grande annually from 1999 to 2010 (between December and April – adult fishes sampled prior to reproduction). In addition, 43 individuals obtained from the Museum of Southwestern Biology Division of Genomic Resources , 0.40–0.50 μm each primer, 0.375 units Thermus aquaticus (TAQ) polymerase], Lco3 and Lco8  and Ppro 118/Ppro126 . Ca8 was amplified alone . PCR cycling conditions for most loci were as follows: one denaturation cycle of 92°C for 2 mins followed by 30 cycles of 90°C for 20 s, 50°C for 20 s, 72°C for 30 s. For Ppro 118/Ppro126, cycling conditions were one denaturation cycle of 92°C for 2 mins followed by 30 cycles of 90°C for 20 s, 60°C for 20 s, 72°C for 30 s. Ppro118 is a complex repeat microsatellite with alleles encompassing a broad size range. For this reason, a subset of samples that appeared homozygous at locus Ppro118 were amplified again to verify allele designations and to minimize the likelihood of large allele dropout. Primer concentrations in multiplex reactions were adjusted to facilitate equal amplification of both loci. Prior to electrophoresis, 1 μL of PCR product was mixed with 10 μL of formamide and 0.3 μL of HD400 size standard and denatured at 93°C for 5 min prior to loading. PCR products were run on an ABI 3100 automated capillary sequencer. Genotype data were obtained using Genemapper version 4.0 .Total nucleic acids, including genomic DNA and mitochondrial DNA (mtDNA), were extracted from air-dried fin clips, using proteinase-K digestion and phenol/chloroform extractions . Individo7, Lco8 , Ca6 and and Ca8  and Ppro Ppro126 . The folm MgCl2, 125 μm dNTPs, 0.5 μm forward (5′-GAC CGT CTG CAA AAC CTT AA-3′) and reverse (5′-GGG GAT GAG AGT GGC TTC AA-3′) primers, and 0.375 U TAQ. PCR conditions were 90°C initial denaturation for 2 mins followed by 30 cycles of 90°C for 30 s, 50°C for 30 s, and 72°C for 30 s. Nucleotide sequence variation among individual fragments was visualized with single-strand conformational polymorphism analysis . Genbank Accession numbers are provided in A 295-base pair (bp) fragment of the mtDNA ND4 gene from each individual was amplified in a 10-μL reaction containing 1 μL template DNA, 1 μL 10× reaction buffer, 2 manalysis , and rephttp://animalgenomics.ucd.ie/sdepark/ms-toolkit/. The computer program Micro-Checker (fstat vers. 2.9.3.1 (FIS) were obtained using fstat. Estimates of unbiased gene diversity (h) were obtained using Arlequin vers. 3.11 (ontrib vers. 1.02 (available at http://www.pierroton.inra.fr/genetics/labo/Software/Contrib/), which uses a rarefaction approach to correct for unequal sample sizes.Microsatellite data were checked for errors, using Microsatellite Toolkit  were drawn without replacement from each temporal sample. Diversity and 95% CIs were calculated for each locus across subsamples and a mean was obtained across loci for each statistic . This analysis was conducted in the http://www.r-project.org; R script available on request). Standard diversity measures  for microsatellites are provided as Supporting information (n = 15 (2004 Isleta) and the smallest sample n = 6 (2004 San Acacia) was excluded. Corrected measures of diversity were compared between river reaches using a nonparametric Kruskal–Wallis one-way anova on ranks implemented in SigmaPlot vers. 11.0 .In some cases, sample sizes differed between collections particularly between some samples collected early in the study and those collected more recently. As the number of alleles and expected heterozygosity are dependent on sample size, we used resampling to examine the effect of sample size on diversity measures. For microsatellites, 1000 random subsamples (iversity  (Hec), hormation . To faciF-statistics (microsatellites) and Φ-statistics (mtDNA) were calculated in Arlequin vers. 3.11 (amova) was conducted to test whether a significant proportion of genetic variance was partitioned into components attributable to differences among wild, captive-spawned, and captive-reared stocks  , among samples within these three groups  and among all samples . P-values for all statistics were generated using bootstrapping (1000 permutations), as implemented in Arlequin.Weir and Cockerham’s (1984)rs. 3.11 . HierarcNeV) and 95% CIs were estimated from temporal changes in microsatellite allele frequencies across annual samples, using the temporal method , was used to estimate NeV. Rio Grande silvery minnows were sampled under Plan I  for all methods; therefore, calculations of NeV via TempoFs required an estimate of census size (Nc). No reliable, long-term data  were available for Nc, so each pairwise comparison in TempoFs was run under the following two Nc scenarios: a ‘crashed’ (Nc = 10 000) and a ‘large’  population. The former value is lower than any census size estimate to date, and the latter is within the order of magnitude for which larger Nc have been recorded  are reported herein; NeV and confidence intervals calculated under both Nc = 10 000 and 1 000 000 can be obtained by request. Jackknife estimation over all loci was used to calculate NeV and associated 95% CI.Variance genetic effective size . However, to account for small but known deviations from the discrete generation model (G = 1.27), we corrected consecutive estimates of Ne and Nef for overlapping generations , using the program ldne (Ne methods (such as those provided by ldne) yield an estimate of the effective number of parents that produced the progeny from which the sample is drawn, and most closely approximate inbreeding effective size, NeI  was estimated with temporal (mlne) methods. TempoFs was not used for mtDNA data because it assumes diploidy  fall recruitment [given by mean October catch per unit effort (CPUE) provided by American Southwest Ichthyological Researchers], (ii) spring runoff, and (iii) number of fish stocked as part of the supportive breeding program for reference purposes . Spring Ne estimates were paired for the generation that was affected by the conditions, such that CPUE and runoff in year t were matched with Ne estimates in year t − 1. For example, a temporal estimate calculated from adult samples collected prior to breeding in 2000 (gen t0) and 2001 (gen t) provides an estimate of effective size in generation t0 ; for a detailed explanation, see ldne) and temporal measures of effective size do not refer to exactly the same generation .To test the first prediction , CPUE data were log-transformed according to neration . SpecifiU-tests . Likewise, corrected diversity statistics for the San Acacia reach were also compared before and after the commencement of supplementation. This analysis was not performed for the Isleta or Angostura reaches because of the small number or absence of samples for the presupplementation years.To test the second prediction (population supplementation acts as a buffer against loss of diversity), sample-size-corrected measures of diversity were compared between years prior to population supplementation (1999–2003) and years after supplementation commenced (2004–2010) by Mann–Whitney U-test. Estimates of NeD were also compared among these samples. Data were examined qualitatively for perceptible changes in genetic effective size that could be attributed to supplementation strategy .To examine the third prediction [effects of population supplementation are dependent on broodstock source (wild-caught eggs or captive-spawned)], we compared diversity metrics (obtained using resampling for microsatellites) among fishes reared from wild-caught eggs to those produced with captive spawning using a Mann–Whitney NeD) will be positively associated was tested using ordinary least-squares linear regression of broodstock effective size  and estimates of NeD.The final prediction that captive fish broodstock composition and point estimates of effective size  conformed to HWE in all or nearly all comparisons. Micro-Checker suggested that null alleles probably caused departures from HWE. We adjusted allele frequencies in Microchecker to account for null alleles and reran a subset of analyses and did not obtain different results . Metrics of genetic diversity did not differ significantly by river reach   . In all hc) was highest in the 1987 samples (hc = 0.743) and lowest in the 2000 sample (h = 0.364) (P = 0.033) differed significantly by river reach whilst HR did not (P = 0.066)  which was monomorphic for haplotype D . Three h= 0.364) , Table 3= 0.066) .FCT = 0.00052, P = 0.007). Samples within each of these groups also differed significantly from one another , and a significant portion of variance was also explained by differences among samples irrespective of group affinity .A small but significant portion of variance was explained by differences among wild, captive-spawned, and captive-reared samples  but not among groups .A significant portion of variance was explained by differences among samples within groups  and among samples irrespective of groups . TempoFs indicated relatively low effective size (NeV = 64–152) for pairwise, consecutive year comparisons between 1999 and 2003 (NeV = 433) in 2003–2004, but another reduction in Ne occurred from 2006 to 2008. The same pattern of fluctuation in Ne was detected with moments-based and mlne estimators. TempoFs estimates of NeV were stable and above 200 from 2008 to 2010; similarly, moments and mlne estimates for this period were above 200 and 400, respectively. Overall, TempoFs estimates had wider upper confidence limits than moments and mlne estimates. Mean values of NeV from the moments estimators were greater than from TempoFs but were still small for the 1999–2000 (NeV = 115) and 2006–2007 (NeV = 160) comparisons. In accordance with mlne estimates were higher than TempoFs and moments estimates.Multi-locus temporal estimates of and 2003 . EffectiNeD estimates of infinity were obtained for the 1987, 1999, 2000, 2007, and 2010 samples , with estimates rebounding in the years between 2005 and 2010 (2562-infinity). NeD was also estimated without Lco8  and mlne (Nef < 100)  and 565 (moments) for the 2009–2010 comparison.Female effective size based on temporal variance of mtDNA haplotype frequencies also fluctuated dramatically over the period of study. Between 2000 and 2003, f < 100) . Both esA positive relationship between density, diversity, and effective size is expectedNe estimates calculated using temporal or linkage disequilibrium methods were not correlated over the entire time series. However, after supplementation began, NeV moments estimates were positively correlated (Spearman’s rank order) with mlne estimates and TempoFs estimates , as expected. Furthermore, Ne estimates were not inter-correlated, except for a significant negative correlation between mlne and NeD .CPUE, diversity metrics (microsatellite and mtDNA), and Supportive breeding and population supplementation buffers against loss of diversityHec and Nac were significantly greater  in wild samples collected after (2004–2010) than prior to (1999–2003) the commencement of the supplementation program as was HR (P = 0.048). Two measures of diversity (Hec and Nac) were significantly higher  for fish collected from the San Acacia reach after commencement of supplementation, whilst Hoc was significantly less (P = 0.009) in these fish. There were no significant differences in haplotype diversity (P = 0.662) or haplotype richness (P = 0.126) in samples collected from the San Acacia reach before and after supplementation commenced.Overall values of The source of brood stock influences genetic effects of supplementation on the wild population.Hec: P = 0.691; Hoc: P = 0.663, Nac: P = 0.937; h: P = 0.383; HR: P = 0.052). Hec and Nac differed significantly between wild and captive-spawned (from hatchery broodstock) samples  and between the captive-spawned and wild-caught eggs , with wild and wild-caught eggs having greater genetic variation than captive-spawned samples. Haplotype richness was significantly lower in captive-spawned fish when compared with wild samples (P = 0.05) and wild-caught eggs (P = 0.009). Use of adults reared from wild-caught eggs for population supplementation was predicted to be associated with an increase in NeV. This was observed for the 2003–2004 TempoFs comparison. mlne and moments NeV estimates did not change appreciably from the previous temporal comparison. Female NeV (mlne) increased for the same time period, whilst the moments estimate increased for this comparison and the next temporal estimate.Diversity measures did not differ significantly between wild samples and samples derived from wild-caught eggs  and two were overestimates. The slope from least-squares regression was ∼0.5, indicating that NeD was approximately one-half of Ne explained by the number of brood fish and sex ratio variation among captive lots.As predicted, there was a positive linear relationship between the effective number of broodstock and estimates of NeV are consistently observed in the wild population, suggesting that underlying causes of genetic decline continue to operate despite supplementation.Identification of relationships between demography and genetic variation is a crucial but difficult task in conservation and management of exploited or endangered organisms, especially those subject to hatchery-based supplementation, and/or those that experience highly variable population sizes and fluctuating environmental conditions. Here, we evaluated the ability of genetic monitoring to recover such potentially complex interactions in a species that has been subject to all three of these effects in recent times. Our study used long-term, empirical data from genetic  and population monitoring (1993–2010) in an attempt to synthesize this information with temporal records of environmental conditions, captive breeding, and wild population supplementation. A notable increase in mean values of key diversity metrics and a coincident decrease in inter-annual variability in these metrics indicated that population supplementation has been the single most important factor that has influenced trajectories of genetic diversity over the last decade in Rio Grande silvery minnow. Large-scale repatriation of hatchery-reared fishes into the Rio Grande has potentially obscured (and ameliorated) most genetic effects of density fluctuations and severe environmental conditions in the wild population. One important exception is that very low values of 2, and during this time, the threat of extinction in the wild was acute. A key premise of genetic monitoring is that population declines will be accompanied by erosion of genetic diversity and reduction in genetic effective size. However, in Rio Grande silvery minnow, strong positive relationships between population density, genetic metrics, and effective size were not observed over the 12-year time frame of the study.Extensive demographic surveys show that the wild population of Rio Grande silvery minnow has experienced multiple changes in density that exceed an order of magnitude over the past two decades . From 20It is apparent for both microsatellites and mtDNA that there is considerable inter-annual variability in gene diversity metrics and effective size estimates from 1987 and 1999–2004 . HoweverNeV (derived from TempoFs) revealed an effective size of <100 , and five (TempoFs) and three (moments) comparisons were <200 during periods of low wild fish density (<1 to ∼6 fish per 100 m2) and no supplementation. From 2004 to 2010, estimates of NeV remained low  but less variable  than in the 1987–2003 period . For more recent comparisons (2008–2010), estimates of NeV from moments and TempoFs methods converged. Although mlne estimates were typically larger than those based on TempoFs or moments, they nevertheless indicate a decline in mean NeV from (1987–2004 = 842) comparisons to recent estimates (2004–2010 = 422). One estimate based on TempoFs is larger (NeV = 433) than other estimates obtained using this method and was recorded over a time frame (2003–2004) where some of the lowest densities of Rio Grande silvery minnow occurred. This result is somewhat counterintuitive, as this increase pre-dated a large boost in wild population densities in 2005 that resulted from favorable flow conditions in spring of that year and absence of river intermittency during the summer.Temporal method estimates of genetic effective size remained very low over the entire study but increased slightly in more recent samples. Three estimates of Ne estimates (discussed below).There are at least two reasons for the apparent disconnection of population density, genetic diversity, and effective size in Rio Grande silvery minnow. First, it may take several generations of very small population sizes for diversity to be depleted enough to be detected by genetic monitoring. It is well established that heterozygosity is a relatively insensitive indicator of population bottlenecks , even onHec and Nac) and mitochondrial diversity (HR) than those prior to supplementation, which supports the prediction that supplementation buffers the population against loss of genetic diversity following bottlenecks in Rio Grande silvery minnow. This may be because the supportive breeding program for Rio Grande silvery minnow differs in some respects from traditional hatchery programs that spawn a small portion of the wild population in captivity. In Rio Grande silvery minnow, the preferred source of fish for population supplementation are eggs collected from natural spawning events. These egg collections should represent reproductive effort of a large segment of the wild population and therefore have the potential to capture a representative sample of its genetic variability. For example in 2002, more than 900 000 eggs were collected from the wild, and 230 000 of them were repatriated to the Rio Grande between 2003 and 2004. Some were retained by conservation hatcheries in New Mexico for use as the founding captive broodstock. Subsequent wild-breeding of captive-released fish likely made a significant genetic contribution to the wild population, and collection of eggs prior (2001–2003) to the population collapse that occurred from 2002 to 2004 may have helped to preserve diversity that would otherwise have been lost during these severe population contractions. Similarly, maintenance of diversity through periods of population supplementation has been demonstrated in both Chinook and Chum salmon  to around one fish per 100 m2 (2006 and 2010). Over one million captive-spawned fish were released compared to only around 26 000 progeny of wild-caught and captive-raised eggs during the same time period. These fish were progeny of a captive broodstock derived from eggs collected in 2002. In years when recruitment of wild fish was poor , progeny (produced in the wild) of captive-bred fish potentially comprised a disproportionate fraction of the subsequent generation. Despite efforts to maximize diversity of captive stocks, observed depression of Ne could reflect a Ryman–Laikre effect  is counterbalanced by reduced competition for mates or spawning sites when Nc is small, reducing among-spawner differences in offspring survivorship. It is not possible to distinguish between these two effects (compensation versus supplementation) with the current data set. However, stabilization of genetic diversity metrics and NeV coincides with the onset of supplementation and occurs despite dramatic fluctuations in the wild. Thus, supplementation appears to be the most plausible explanation for the results.There are alternative hypotheses that could explain the apparent discrepancy between  from Nc . CompensNeV estimates obtained using different methods should be positively inter-correlated, but this was not observed for values obtained over the entire 12-year time series. However, after the commencement of supplementation, all three commonly used estimators of NeV produced positively correlated values. Lack of correlation overall may be due to known biases of methods used to estimate NeV  when allele frequencies are close to zero or one. Estimators of quencies  and can quencies . Both Waquencies  tended tNeV estimates suggest values of Ne that are smaller than the minimum benchmark of Ne = 500 in seven of eight (mlne) and all (moments) of the most recent temporal comparisons.Several benchmarks for ascertaining extinction risk have been suggested for interpreting effective size in threatened species. The most conservative targets deemed necessary to maintain long-term genetic security of a species range from an effective size of 500–5000 . RegardlNeD, which provide a measure of the inbreeding effective size, were uniformly higher than and not positively correlated with estimates of NeV. The underlying principle of the LD method is that as Ne decreases, genetic drift increases nonrandom association among alleles at different loci  to account for this potential source of bias, so this is unlikely to explain the lack of correlation between NeV and NeD estimates.Values of ent loci . As erosis drawn . EstimatNeD and NeV estimators to (i) detect a population decline, (ii) correctly identify a bottleneck with low bias and high precision, and (iii) evaluate whether the methods were subject to a high rate of false positives . They found that temporal-method estimates of NeV were very close to the bottlenecked population size in the first generation, whereas NeD always overestimated Ne with relatively low precision. Moreover, in the first generation following a decline, values of NeD were much closer to the prebottleneck population size. This result is consistent with the idea that estimates of NeD include information on the effective size of previous generations  and NeV. These two measures should be similar in stable populations but show predictable differences in declining (or growing) populations. NeV is reduced more rapidly than NeI, and, as a consequence, NeV will be smaller than NeI until equilibrium is reached. Conversely, NeV and NeI in Rio Grande silvery minnow is precisely the signature expected for a declining population. In fact, the only significant correlation we found between NeV (mlne) and NeD was negative . At present, Rio Grande silvery minnow are subject to source–sink dynamics. Specifically, captive stocks (source) contribute breeders to the wild (sink) each year, where reproductive success and recruitment are highly variable. Under these circumstances, we would expect discrepancy between NeV and NeD. Such dynamics are also likely to occur in other endangered but highly fecund species, especially fishes.From a management perspective, there are a number of theoretical and practical distinctions between NeV and NeI in a declining population, theoretical and empirical treatments of this issue are too limited to provide firm guidance with respect to Rio Grande silvery minnow. For example, a theoretical evaluation by Nc < 50); therefore, it is unclear how effects on severely bottlenecked populations relate to supplemented populations in which thousands to millions of individuals potentially contribute to annual reproduction, which is the case for Rio Grande silvery minnow. However, NeI after supplementation is whether the population maintains a large size; this finding is consistent with observations of larger NeI than NeV in the hatchery supplemented Rio Grande silvery minnow. A simulation study is currently underway  to determine relationships between NeI, NeV, and supplementation in this species.Although supplementation could confound relationships between NeD dropped substantially in 2004 (NeD = 595) from estimates of ∼2000 in prior years. This coincided with very poor wild recruitment into the 2002 and 2003 year-classes. Subsequent increases in NeD ranged from 2000 to 4400 and coincided with an increase in wild fish densities (2005), the input of large numbers of captive-spawned fish (∼106 between 2005 and 2010), and somewhat more favorable environmental conditions .The value of Ne varied across methods, but all estimators indicated a genetic effective size that is one or more orders of magnitude smaller than the census size , all but three NeV estimates  were lower than 500 and three of the unbiased estimates from TempoFs were <100. We can conclude, therefore, that the genetic effective size of the wild Rio Grande silvery minnow population is smaller than expected from census size. Relatively large values of NeD reflect a population in decline rather than evidence of robust genetic resistance to extinction.Estimates of NeV in wild Rio Grande silvery minnow is up to three orders of magnitude lower than adult census size  among captive spawners.As predicted, a significant and positive correlation was observed between The preponderance of evidence suggests that the trajectory of genetic change in Rio Grande silvery minnow was primarily determined by supplementation from captively reared stocks, and not by fluctuations of population density of wild fishes. Standing levels of genetic diversity  observed prior to supplementation were maintained or increased slightly over the study. These results suggest that inbreeding genetic effective size is large enough to preclude significant losses of genetic diversity in the near term. However, variance effective size remained lower than inbreeding effective size and substantially lower than population density, suggesting that the interaction between early life history and river fragmentation is still exerting downward pressure on this metric, despite supplementation. In the absence of supplementation, we predict convergence of inbreeding and variance effective sizes and substantive losses of genetic diversity each generation thereafter.In more general terms, however, some of the genetic signals of population decline  in the wild may not be detectable using genetic monitoring when the population is being heavily supplemented from captive stocks. In order to fully assess the effects of population supplementation (or any other management action aimed at maintaining genetic diversity), it is necessary to assay baseline samples obtained prior to supplementation. Such samples are typically not available but see  because