This demonstrates that clustering methods may spuriously reveal cryptic genetic structure. Based upon these data, we find no evidence to support the existence of spatial or cryptic population genetic structure of minke whales within the NE Atlantic. However, in order to conclusively evaluate population structure within this highly mobile species, more markers will be required.Inferring the number of genetically distinct populations and their levels of connectivity is of key importance for the sustainable management and conservation of wildlife. This represents an extra challenge in the marine environment where there are few physical barriers to gene-flow, and populations may overlap in time and space. Several studies have investigated the population genetic structure within the North Atlantic minke whale with contrasting results. In order to address this issue, we analyzed ten microsatellite loci and 331 bp of the mitochondrial D-loop on 2990 whales sampled in the North East Atlantic in the period 2004 and 2007–2011. The primary findings were: (1) No spatial or temporal genetic differentiations were observed for either class of genetic marker. (2) mtDNA identified three distinct mitochondrial lineages without any underlying geographical pattern. (3) Nuclear markers showed evidence of a single panmictic population in the NE Atlantic according STRUCTURE's highest average likelihood found at K = 1. (4) When K = 2 was accepted, based on the Evanno's test, whales were divided into two more or less equally sized groups that showed significant genetic differentiation between them but without any sign of underlying geographic pattern. However, mtDNA for these individuals did not corroborate the differentiation. (5) In order to further evaluate the potential for cryptic structuring, a set of 100  Stevick et al. e.g. matrilineal based groups), the resource specialization and the great capacity for learning Anthropogenic activities are key factors affecting wildlife populations, including altering population structure and distribution patterns Balaenoptera acutorostrata, Lacepede, 1804) and the Antarctic minke whale , which is confined to the Southern hemisphere with the exceptions of rare inter-oceans migration events B.a. acutorostrata), the North Pacific (B.a. scammoni), and the dwarf common minke whale (B.a. unnamed sub-species), which is thought to be restricted to the Southern hemisphere.Minke whales, the second smallest baleen whales (about 10 m in length), are currently considered as two species B.a. acutorostrata occurs in the entire North Atlantic during the Northern hemisphere summer months, limited in the northern range by the ice B.a. scammoni breeding populations on either side of Japan are known to mix on feeding grounds in the Okhotsk Sea B.a. acutorostrata in the North Atlantic is regulated under the Revised Management Procedure (RMP) developed by the Scientific Committee of the International Whaling Commission which also regularly reviews the species status through Implementation Reviews, the last one completed in 2009 i.e. “IWC Small Areas” The minke whale is still harvested in significant numbers, and the management of 137Cs et al. Distinct breeding populations have not been identified for North Atlantic minke whales. Hence, the assessment of genetic structuring of minke whales within the North Atlantic has been based upon samples collected in the feeding grounds and stranded individuals. The question of population genetic structure within this species remains unresolved with partially conflicting results. There seems to be a general agreement regarding the absence of any clear spatial genetic structuring at mitochondrial level B.a. acutorostrata in the Northeast Atlantic, and each year, approximately 500 whales are captured across five IWC Management Areas  and MT3(M13Rev) modified from Árnason et al. et al.et al.The D-loop region of mtDNA was amplified by performing forward sequencing of one DNA isolate and reverse sequencing of the second one. The first PCR reaction yielded a 1066 bp amplification product, which was forward strand sequenced. The second PCR reaction entailed the amplification of a 331 bp product that was sequenced in the reverse direction. PCR conditions for the two directions were identical, thus containing 0.5 units Go Taq polymerase (Promega), 1.5 mM MgClIS per population and per year were calculated with MSA O) and unbiased expected heterozygosity (UHE) were computed with GenAlEx Total number of alleles, allelic richness and the inbreeding coefficient FSTST analyses ST is an analogue of Wright's FSTWe used several methods to estimate population structure, including STRUCTURE ST between populations from year class 2004 to 2011.Both genetic differentiation among Management Areas per year class, and the level of temporal population genetic differentiation were tested using the Analysis of Molecular Variance (AMOVA) implemented in ARLEQUIN v.3.5.1.2 Both STRUCTURE et al. ad hoc summary statistic ΔK, which is based on the rate of change of the ‘estimated likelihood’ between successive K values. The usual scenario where this approach is appropriate are those cases where once the real K is reached, L(K) at larger Ks plateaus or continues increasing slightly and the variance between runs increases. Hence, the estimated ‘log probability of data’ does not provide a correct estimation of the number of clusters and instead, ΔK accurately detects the uppermost hierarchical level of structure We used the Bayesian model-based clustering algorithms implemented in STRUCTURE v. 2.3.4 to identify genetic clusters under a model assuming admixture and correlated allele frequencies without using population information. Ten runs with a burn-in period consisting of 100000 replications and a run length of 1000000 Markov chain Monte Carlo (MCMC) iterations were performed for a number of clusters ranging from K = 1 to K = 5. If applicable, we then used STRUCTURE Harvester Secondly, we used BAPS 6.0 Estimates of genetic diversity were calculated with DnaSP SP-values were generated using 10000 simulations.Demographic changes were examined using three different approaches: Tajima's D We used Tajima's D and Fu's Fs to test for shift in the allele frequency spectrum compared to a neutral Wright-Fisher model consistent with population expansion under neutral evolution. The neutrality test Fs The evolutionary relationships between haplotypes were examined with the software Network BAPS clustering was used to validate Network results and thus the program was run 100 times for the number of clusters reported by the median-joining tree.et al. B.a. scammoni from the Sea of Japan as an outgroup. We added this approach to our former STRUCTURE analyses using two different outgroups: firstly, 95 individuals of the subspecies Pacific minke whale (B. a. scammoni); secondly, 93 individuals of the Antarctic minke whale (B. bonaerensis) and, thirdly, both outgroups simultaneously. STRUCTURE and BAPS analyses together with the assessment of genetic differentiation between groups of individuals after clustering procedures are exhaustively detailed in the Anderwald in silico generated panmictic populations based on the allele frequencies observed in our samples. Hence, at each of the ten loci, the allelic values  were put in a pool, and then randomly re-assigned to individuals, thereby preserving the original allele frequencies. The resulting in silico simulated panmictic populations were analysed automatizing STRUCTURE with the program ParallelStructure under a model assuming admixture and correlated allele frequencies without using population information. Ten runs per K ranging from 1 to 5, a burn-in period of 100000 replications, and a run length of 1000000 MCMC iterations were followed by Evanno's test. For the sake of the comparison, ten of the populations yielding K = 2 after Evanno's test were averaged with CLUMPP and pairwise FST between resulting clusters was performed as above. In addition, BAPS analyses were also conducted for K ranging from 1 to 5.In addition to the above analyses, and to test the alternative hypothesis of minke whales constituting a panmictic population, we created a set of 100 et al. IS, FST, Ho, Hs (the within group gene diversity), the mean corrected assignment index (mAIc) and the variance around the assignment index (vAIc) The potential for sex-biased dispersal was investigated using the microsatellite data with the methods described by Goudet The sex distribution per Management Area across years was biased towards females in 73% of the cases . Namely,The microsatellite data set contained no missing data and both the number of alleles (116–124), and allelic richness (11.5–12.3) were stable across the six year classes analysed . ObserveST in any case (ST = 0.024 (P = 0.046) but not after Bonferroni correction (P = 0.0017).The analysis of geographic genetic structuring among Management Areas revealed no differentiation over time. Thus, AMOVA performed separately for each of the year classes reported no significant Fany case . LikewisST between years 2007–2008 ; which was no longer significant after Bonferroni correction.Similar to results of spatial genetic structure above, AMOVA reported high temporal genetic stability, and the pairwise comparison between the different year classes  yielded ST that exhibited 0.003 as the highest value. Likewise, the pairwise comparisons between years within each area were also non-significant with the exception of area EN. The pairwise matrix for EN reached significance in three out of the total fifteen cases corresponding to the comparisons between year 2008 and years 2004, 2007 and 2009 respectively.The individual analysis of every Management Area also showed temporal genetic stability as a general picture. Hence, the AMOVA performed separately in each of the five IWC zones yielded a non-significant FSTRUCTURE showed that the highest average likelihood was found to be K = 1 in all year classes together with a decreasing trend of LnP(D) across consecutive values of K , 25 of them unique . Six of the haplotypes were shared by 4–9% of the individuals whereas the most abundant one was present in 806 whales (27%). The number of haplotypes found per year class ranged mostly between 36 and 38 (H = 62). Both haplotype (HD) and nucleotide diversity (π) showed high and stable values across the years  in 2004. Similarly, high temporal stability , although not significant after Bonferroni correction.The distribution of the most common haplotypes was even across Management Areas and AMOVA revealed that no differentiation was observed among them in any of the sampling years , with thtability  was repoi.e. 2004 and 2007–2011 excluding singletons) given the temporal stability detected across year classes. A central ancestral haplotype was reported in 27% of the individuals, whereas none of the remaining ones exceeded a frequency of 9%. This ancestral haplotype did not show any phylogeographic structure, i.e. it was not linked to any of the Management Areas as it was present in relatively even proportions in each of them. The MJ-tree suggests the existence of three lineages: a central one that evolved through two episodes of expansion, with haplotypes connected between them via single mutational steps in the vast majority of cases. Two of the lineages gathered 85% of the haplotypes in a quite even distribution whereas the third one accounted for the 15% remaining. The haplotype composition per lineages revealed by Network perfectly matched BAPS clustering for K = 3, with 100% coincidence. The same individuals that showed significant differentiation in three lineages at mtDNA yielded FST = 0.00018 (P = 0.8966) when analysed for microsatellites.A median-joining tree  was builDifferent insights  invoke pB.a. acutorostrata) constituted a compact cluster whereas the outgroups (i.e B.a. scammoni or/and B. bonaerensis) constituted a second compact one . Thus, when using outgroups, NE Atlantic minke whales (STRUCTURE without outgroup (K = 2).- Although individuals showed very narrow ranges of membership  to clusters  and Factorial Correspondence Analyses . The same individuals genotyped at mtDNA did not produce any significant FST in any sampling year .B.a. scammoni) as an outgroup . The same individuals genotyped at mtDNA did not show any genetic differentiation between clusters  as an outgroup . Again a weak but significant differentiation between groups per year class was shown by pairwise FST  and Factorial Correspondence Analyses . Once more, the same individuals genotyped at mtDNA did not show any evidence of genetic differentiation  and Factorial Correspondence Analyses . The same individuals genotyped at mtDNA only produced significant FST for year classes 2007 and 2008 based on Tamura-Nei distance and haplotype frequencies, respectively  the highest average likelihood was detected at K = 1, and b) a decreasing trend of LnP(D) across consecutive values of K was found in all the cases. As formerly reported, even if Evanno's test is not applicable in this situation, we wanted to test its outputs and thus we found that the most likely number of clusters showed different values: K = 2 in 58% of the cases, K = 3 in 33% and K = 4 in the remaining 9%. Similarly, low values of ΔK (ranging from 1 to 15) were also reported for the 100 panmictic populations. CLUMPP was performed on a set of ten randomly chosen simulated populations that showed K = 2 after Evanno's method, and individuals were distributed into clusters after overcoming a threshold of 0.50. In all cases, both clusters showed similar size (ratio 1–1.3) and 7–19% of individuals were left unassigned (Table M in ST between groups exhibited low (0.012–0.020) but significant values (P<0.0001). Importantly, these values were equal in magnitude to the observations based upon the real data reported above. BAPS analyses showed that, in spite of dealing with panmictic populations, in no case the most likely K was found to be 1. Instead, the number of putative populations took the following values: 3 (4% of the cases), 4 (38%) and 5 (58%) respectively.The analyses of the 100 vs. 0.020) and vAIc was higher (2.90 vs. 2.35) Fig. G in IS, FST, Ho and Hs) took almost identical values in both sexes. However, the Mann–Whitney U-test proved to be non-significant (P>0.5) therefore we were unable to detect sex-biased dispersal in North Atlantic minke whales. Furthermore, when performing a two-tailed U-test we found a non-significant result (P = 0.953) that would not support a higher female dispersal either.According to the expectations that dispersal should be biased towards males, as in most of mammals, mAIc was lower in males than in females  consisted of 28% males and 72% females; proportions that exactly coincide with Anderwald B. brydeiB. physalusB. borealisB. mysticetusEschrichtius robustusB.a. acutorostrata within the same geographic area B. bonaerensis) is higher (0.0159) and this was interpreted as the Antarctic species having larger long-term effective population size than B.a. acutorostrataMicrosatellite loci used here exhibited a range of variation of genetic diversity comparable to what has been formerly reported for the same species The mismatch distribution analyses were consistent with exponential population expansion suggesting that populations of North Atlantic minke whale are not at equilibrium, something that had already been reported in the literature for this species in the same geographic area ST = 0.024, P = 0.046) and EB-ES for mtDNA in 2004 . This translates to 1% of pairwise tests showing some spatial and temporal divergence, and neither were significant following Bonferroni correction for multiple testing. This lack of spatial genetic differentiation was the case when analyzing each of the six year classes separately, and also for all the specimens combined in the same AMOVA analysis, both for mitochondrial  and nuclear  DNA. Likewise, none of the resulting pairwise comparisons between areas were significant. The analyses using the joint data set seems legitimate given the temporal genetic stability found at both markers, stability that had already been reported within similar time frames The present molecular markers (ten microsatellite loci and 331 bp of mtDNA D-loop) studied on 2990 individuals congruently failed to reveal any genetic differentiation among Management Areas during the period 2004 and 2007–2011. Only two weak and marginally significant pairwise comparisons were recorded: EB-EW for microsatellites in 2010  and indicated that this could be the result of the profound ecological differences between feeding areas  posing different selective pressures, coupled with a strong affiliation between mother and calf to the feeding site. Seasonal site fidelity that had been already reported for minke whales The lack of geographic genetic differentiation as revealed in the present study is in agreement with some former studies of minke whales in the N Atlantic that were based on nuclear ST obtained for females would reflect the maximum spatial genetic differentiation of the species. Through the population structure observed in the maternally inherited mtDNA, Baker et al. ST = 0.00009, P = 0.326) or mtDNA . This result disagrees, again, with Andersen et al. ST at both markers for females.We also tested Tiedemann's In conclusion, our data set of ten microsatellites and 331 bp of mtDNA control region failed to reveal any spatial genetic variation across 2990 individual whales harvested in the five management areas in the NE Atlantic for the years 2004 and 2007–2011.et al. et al. et al. The division of North Atlantic minke whale into two mtDNA lineages had already been reported et al. ad hoc statistic ΔK based on the rate of change in the log probability of data between successive K values obtained through Evanno's test can, unfortunately, never validate K = 1 in silico generated panmictic populations revealed K = 2 in a majority of cases (58%) whereas the remaining ones were distributed between K = 3 (33%) and K = 4 (9%). Hence, both LnP(D) pointed at 1, together with the fact that the highest ΔK was found mainly at K = 2 in the real and simulated panmictic populations, supports that North Atlantic minke whale constitutes one single panmictic population.Nuclear markers provided no evidence to reject the hypothesis that North Atlantic minke whales constitute a single panmictic population. This is in spite of certain insights from STRUCTURE analyses conducted both in this study and in Anderwald i.e. when the number of clusters is unequivocally two, ΔK exhibits significantly higher values. Thus, for instance, when we conducted STRUCTURE analyses for Atlantic minke whales including Antarctic or Pacific whales as an outgroup  can still be found , there are multiple features that strongly suggest a false result. The first hint to be considered is the low values of ΔK, which is an indication that the strength of the signal detected by STRUCTURE is weak B.a. scammoni), the averaged inferred membership was 0.89 but when the outgroup was the Antarctic species (B. bonaerensis), this value was even higher (average 0.95) and the percentage of non-assigned individuals was slightly lower. This higher inferred membership to cluster could be expected to result in a higher genetic differentiation. However, the resulting FST values between these clusters were virtually identical in the following cases: real data without outgroups, real data with outgroups, simulated panmictic populations without outgroups, one randomly chosen simulated panmictic population without outgroups  95 individuals of the subspecies Pacific minke whale (B. a. scammoni); b) 93 individuals of the Antarctic minke whale (B. bonaerensis), and c) both former outgroups together. The number of clusters that best fitted the data was K = 2 after Evanno's Fig. C. Bayesian clustering of North East Atlantic minke whale with outgroups in each year class. In the column to the left, the outgroup are 95 individuals of the subspecies Pacific minke whale (B. a. scammoni) whereas in the column to the right, the outgroup are 93 individuals of the Antarctic minke whale (B. bonaerensis). The number of clusters that best fitted the data was distinctively K = 2 after Evanno's Fig. D. Geographic distribution of individuals after different clustering methods: BAPS and STRUCTURE for microsatellites. Pie charts represent the percentage of individuals belonging to clusters 1 (dark grey) and 2 (light grey) per Management Area taking year class 2008 as an example  for 2156 females (light grey bars above axis) and 834 males (dark grey bars below axis). AIc values differed among sexes, males having on average negative values (−0.051) and higher variance (2.90) and females positive values (0.020) with lower variance (2.35). However, Mann–Whitney U-test proved sex-biased dispersal to be non-significant (P>0.5). Tables. Table A. Summary result of STRUCTURE without outgroups: a) Data set of 8 microsatellites. b) Data set of 10 microsatellites. Table B. STRUCTURE without outgroups: Clustering of individuals per year class after Evanno's test : Number of individuals per cluster and range of inferred membership to each of them (in brackets). Summary of the results of the AMOVA  conducted with Arlequin with 10000 permutations. Analyses were performed for the same sets of individuals genotyped at mtDNA. Statistically significant values were highlighted in boldface type. Negative FST values found at mtDNA were transformed into 0. a) Data set of 8 microsatellites. b) Data set of 10 microsatellites. Table C. Summary statistics after STRUCTURE clustering showing total number of alleles, number of private alleles, observed heterozygosity (average ± SE), unbiased expected heterozygosity (average ± SE), and inbreeding coefficient (FIS) (average ± SD). We show in italics the distribution of the individuals for K = 2 for the two year classes that showed the highest Evanno's ΔK at K = 3. a) Data set of 8 microsatellites. b) Data set of 10 microsatellites. Table D. STRUCTURE with the Pacific minke whale subspecies (B. a. scammoni) as an outgroup. Clustering of individuals per year class and one randomly chosen simulated panmictic population after Evanno's test and CLUMPP averaging: Number of individuals per cluster and range of inferred membership to each of them (in brackets). Summary of the results of the AMOVA  conducted with Arlequin with 10000 permutations. Analyses were performed for the same sets of individuals genotyped at mtDNA. Statistically significant values were highlighted in boldface type. Negative FST values found at mtDNA were transformed into 0. Table E. STRUCTURE with Antarctic minke whale species (B. bonaerensis) as an outgroup. Clustering of individuals per year class and one randomly chosen simulated panmictic population after Evanno's test and CLUMPP averaging: Number of individuals per cluster and range of inferred membership to each of them (in brackets). Summary of the results of the AMOVA  conducted with Arlequin with 10000 permutations. Analyses were performed for the same sets of individuals genotyped at mtDNA. Statistically significant values were highlighted in boldface type. Negative FST values found at mtDNA were transformed into 0. Table F. STRUCTURE consensus clustering of individuals (i.e. agreement between Antarctic and Pacific outgroup clustering) into two groups per year class. Summary of the results of the AMOVA  conducted with Arlequin with 10000 permutations. Analyses were performed for the same sets of individuals at mtDNA. Statistically significant values are highlighted in boldface type. Table G. Summary statistics after STRUCTURE consensus clustering (i.e. consensus between Antarctic and Pacific outgroup clustering) showing total number of alleles, allelic richness (minimum sample size), number of private alleles, observed heterozygosity (average ± SE), unbiased expected heterozygosity (average ± SE), and inbreeding coefficient (FIS) (average ± SD). Table H. BAPS clustering of individuals genotyped with microsatellites into two groups per year class. Summary of the results of the AMOVA  conducted with ARLEQUIN with 10000 permutations. Analyses were performed for the same sets of individuals at mtDNA. Statistically significant values were highlighted in boldface type. Table I. Summary statistics after BAPS clustering showing total number of alleles, allelic richness (minimium sample size), number of private alleles, observed heterozygosity (average ± SE), unbiased expected heterozygosity (average ± SE), and inbreeding coefficient (FIS) (average ± SD). Table J. GeneClass self-assignment: Percentage of individuals genotyped at microsatellites that were correctly assignment after clustering procedures. Table K. Number of individuals genotyped at microsatellites per Management Areas after clustering with BAPS and STRUCTURE (with and without outgroup). ND = No data. Table L. Matrix of numbers and percentage of coincident individuals when comparing the three clustering methods: BAPS, STRUCTURE without outgroup (STR), and STRUCTURE with outgroup (STR consensus). The percentage of coincident individuals was calculated by dividing the number of by the lowest number of individuals in the corresponding cluster. STRUCTURE analyses were performed with 8 microsatellites. Table M. STRUCTURE clustering of individuals in the 10 randomly selected simulated panmictic populations showing K = 2 after Evanno's test. Number of individuals per cluster and range of inferred membership to each of them (in brackets); number of non-assigned individuals . Summary of the results ofthe AMOVA  conducted with Arlequin with 10000 permutations. Statistically significant values were highlighted in boldface type.(DOCX)Click here for additional data file.


Poecile atricapillus), along 10 different river systems in Southern Alberta. Using a landscape genetic approach, landscape features  were found to have a significant effect on patterns of genetic differentiation. Populations are genetically structured as a result of natural breaks in continuous habitat at small spatial scales, but the artificial barriers we tested do not appear to restrict gene flow. Dispersal between rivers is impeded by grasslands, evident from isolation of nearby populations (~ 50 km apart), but also within river systems by large treeless canyons (>100 km). Significant population genetic differentiation within some rivers corresponded with zones of different cottonwood (riparian poplar) tree species and their hybrids. This study illustrates the importance of considering the impacts of habitat fragmentation at small spatial scales as well as other ecological processes to gain a better understanding of how organisms respond to their environmental connectivity. Here, even in a common and widespread songbird with high dispersal potential, small breaks in continuous habitats strongly influenced the spatial patterns of genetic variation.Habitat loss and fragmentation can affect the persistence of populations by reducing connectivity and restricting the ability of individuals to disperse across landscapes. Dispersal corridors promote population connectivity and therefore play important roles in maintaining gene flow in natural populations inhabiting fragmented landscapes. In the prairies, forests are restricted to riparian areas along river systems which act as important dispersal corridors for forest dependent species across large expanses of unsuitable grassland habitat. However, natural and anthropogenic barriers within riparian systems have fragmented these forested habitats. In this study, we used microsatellite markers to assess the fine-scale genetic structure of a forest-dependent species, the black-capped chickadee ( Dispersal and gene flow play an important role in maintaining the genetic and functional connectivity of populations; a process necessary for species’ persistence. However, it has long been recognised that variation within the landscape matrix separating habitat patches can influence the transition stage of individuals across a landscape . ConsequSome landscapes that are spatially heterogeneous can affect movement of organisms among resource patches as well as the pattern of dispersal, and in turn influence gene flow and population dynamics . In suchPopulus spp.) [One example of a heterogeneous landscape is the Great Plains in North America, a broad area of flat land found east of the Rocky Mountains and west of the Missouri River. The landscape is dominated by prairie, steppe, and grassland; with forested areas restricted to riparian zones. These zones are situated adjacent to streams, rivers, lakes and wetlands are among the most valuable, productive and structurally diverse landscapes –9. This us spp.) ,12 whereus spp.) . More imus spp.) ,15. HoweRiver management can have long-lasting, negative impacts on riparian species. Urbanisation and increasing demand for water for agriculture, industrial and domestic use have resulted in 82% of large rivers (> 1000 km) across North America being dammed and/ or diverted . ChangesNot only is there concern over riparian forest decline, these riparian habitats can also provide unique zones of ecological transitions. Within river systems, the distributions of riparian poplars can overlap resulting in hybrid zones. These hybrid poplar zones can dramatically impact riparian biodiversity and habitat complexity ,21. As aRiparian woodlands are important areas for breeding, wintering and migrating birds providing corridors through areas of unsuitable habitat . Loss of riparian woodland could have a negative impact on organisms throughout large portions of their range. A number of studies have documented the distribution, density and diversity of riparian bird species ,29 and tPoecile atricapillus), a common songbird to North America [With growing concern over global anthropogenic change, it is important to understand the influence of landscape features on dispersal, gene flow and population connectivity across heterogeneous landscapes. The Great Plains offers a unique and valuable study area for testing the effects of spatial heterogeneity on gene flow of the black-capped chickadee, a forest-dependent species. The black-capped chickadee  at high elevations, would restrict dispersal resulting in increased genetic differentiation in the west. Alternatively, the more densely forested areas in the foothills of the Rocky Mountains may facilitate dispersal. Finally, we predicted reduced dispersal in hybrid poplar zones within riparian corridors. These zones harbour diverse insect communities which are prey for black-capped chickadees, and may attract chickadees in large numbers; reducing dispersal.The aim of this study was to assess the genetic diversity, population structure and genetic differentiation of the black-capped chickadee across multiple river drainages in a small region of the Great Plains. Genetically distinct populations have previously been identified in this species on a large geographical scale –52, so wPopulus angustifolia), balsam poplar  and the closely related black cottonwood (P. tricocarpa), and prairie or plains cottonwood (P. deltoides). These four species hybridize to provide globally-unique hybrid zones [Southern Alberta, located within the northern Great Plains, is a highly heterogeneous landscape characterised by densely forested montane habitat in the west (Rocky Mountains), transitioning to a narrow zone of aspen parkland and then quickly to prairies, dominated by temperate grasslands. Within the prairies, forested areas are restricted to riparian ecosystems within river systems which flow throughout the landscape. Both naturally treeless river canyons and artificial reservoirs exist along the river systems, resulting in a patchy woodland corridor . In our id zones ,22  were taken from the brachial vein . Using aSampling was conducted and approved under the University of Lethbridge Animal Welfare Protocol No. 1028 in accordance with the Canadian Council on Animal Care Regulations.DNA extraction, amplification and genotyping were performed on all individuals following the procedures described in . Twelve Errors within the data  were assessed in MICRO-CHECKER v2.2 . To asseFST implemented in GenAlEx v6.5 to assess the level of population genetic differentiation. To complement the conventional F-statistic, we calculated an additional pairwise estimate of genetic differentiation (DEST) in SMOGD v1.2.5 [F’ST in GenAlEx v6.5 and significance was tested by the FDR correction method. To further assess population genetic structure we carried out a hierarchical analysis of molecular variance (AMOVA) in ARLEQUIN v3.5 [Populations with ≤ 5 individuals were removed from population based analyses. Genetic structure was quantified for all pairwise combinations of populations using D v1.2.5 ,61 and sUIN v3.5 . A basicUIN v3.5 , was theUIN v3.5  with 10,K) by assigning individuals to groups while maximising HWE and minimising LD. All individuals were included as assignments are based on individual multilocus genotypes and STRUCTURE was run with the admixture model, correlated allele frequencies [K (1–10) to infer the optimal number of clusters (K). Results were averaged and the true K was determined using STRUCTURE HARVESTER v0.6.6 [K (ΔK) [K value.To validate pairwise estimates of genetic differentiation, we explored the number of genetic groups within the study system using an individual based Bayesian clustering method, STRUCTURE v2.3.4 , as wellquencies  and locaR v0.6.6  from bot [K (ΔK) , and meaK with 50,000 burn in, 200,000 McMC) for each pair of populations separated by a “barrier”. This method also removes noise present from additional data and allows the determination of population structuring at very small spatial scales.In addition to assessing population genetic structure across the whole study area, we were also interested in determining whether populations located on either side of a potential barrier were genetically distinct. If so, this would provide an indication of restricted gene flow. Populations of interest include those separated by an extensive break in riparian woodland  and thoa priori assumption on groupings. Unlike STRUCTURE, DAPC does not assume Hardy-Weinberg or linkage equilibrium. For DAPC analysis (function dapc), first a principal component analysis (PCA) is performed on predefined populations  where the genotypic data are transformed into principal components. The PCA variables are then used in the discriminant analysis (DA). This initial PCA step ensures that no correlated variables are input into the DA and that a weighted and reduced number of variables are included; 50 principal components (PCs) were retained corresponding to > 85% of the variance. DAPC defines groups by minimising within group variation and maximising among group variation.DAPC is a multivariate method implemented in the program R v 3.1.3  using thwww.geobase.ca) were then reclassified to represent three separate hypothesised resistance maps; land cover, elevation and land-elevation, with the latter accounting for the influence of both land cover and elevation combined. Finally, to determine if hybrid poplar zones influence chickadee dispersal and gene flow, we created an additional hypothesised resistance map, ‘hybrid’  which assumes spatial homogeneity  and isol‘hybrid’ . For hybFST and DEST) using simple and partial Mantel tests in IBDWS v3.2.3 [To test for an effect of pairwise landscape resistance distances on gene flow, each resistance distance matrix was compared with linearized pairwise genetic distance matrices , they are assumed to have relatively low statistical power –79. To oFST and DEST). The same set of models were used as the Mantel tests to allow a direct comparison of model fit. First, models were tested for all 15 populations, followed by a separate model set after including the ‘hybrid’ parameter for 12 populations that were in or near hybrid zones. For each fitted MLPE model, the explanatory variables represented the fixed effect whereas the random effect, which remained constant, represented the dependency between pairwise distances . The ‘lmer’ function was modified so that the random factor would account for multiple memberships [R2 statistic developed by [A strict model selection was conducted in two steps to test for the effects of different landscape (or explanatory) variables on pairwise genetic distances  followed by SB1 (PA = 5) (P ≤ 0.001) within RD2 and between loci PAT MP 2–14 and Titgata39 (P ≤ 0.001) within populations SSK and LE (P ≤ 0.001).A total of 343 individuals from 28 locations were successfully genotyped for 12 variable microsatellite loci . The num(PA = 5) . Overall(PA = 5)  respecti(PA = 5) . After cFST and DEST values showed low to moderate genetic differentiation among population comparisons ranging from 0.007–0.049 (FST) and 0.000–0.089 (DEST). Population wide F’ST was 0.060. After corrections for multiple tests, 50 (DEST) and 52 (FST) of the 105 tests were significant  were significantly differentiated from all other populations; two of which (LE and DR) are situated within a poplar hybrid zone. In addition, BO was significantly differentiated from all populations south of the Bow River. Significant pairwise DEST values confirm these patterns. A standard AMOVA with no hierarchy generated an FST of 0.020 and 2.04% of the variance was among populations and 97.96% within populations (P ≤ 0.001). Using a hierarchical AMOVA, a number of groups were tested to identify the grouping that explains the largest among group variance  or DEST .Pairwise nificant . For FSvariance . The higK (ΔK) and mean log likelihood (LnPr(X|R)) for the initial STRUCTURE runs involving all 343 individuals showed two (K = 2) and three (K = 3) groups respectively  and mean log likelihood (K = 3) over the true K  suggesting that splitting of BO and NSK was an overestimation and so we took K = 2 as the true value . In addition, STRUCTURE identified BO as genetically distinct from all southern populations  and ΔK) confirming significant FST values. DAPC analysis confirms population genetic differentiation in the black-capped chickadee. We see separation of SSK and LE on the x axis with some overlap  and land-elevation  for FST, but only land cover  for DEST. There was no significant effect of isolation by distance, elevation or hybrid variables on either estimate of genetic distance. However, controlling for isolation by distance slightly increased the association between all five variables and FST in the partial Mantel tests, and between elevation and DEST. Controlling for land cover or elevation produced similar results.All statistically significant Mantel correlations were positive  and inclFST and DEST [We found no significant effect of landscape resistance distances on pairwise mean individual GD (results not shown) nor when populations with ≤ 20 individuals were removed. As GD are more sensitive to picking up the effects of more recent fragmentation in comparison to nd DEST ,86, and FST (R2 (mar) = 0.456) and DEST (R2 (mar) = 0.130). In both cases, land cover had a positive effect on genetic distance  (FST (R2 (mar) = 0.451) and DEST (R2 (mar) = 0.247) . The variable land-elevation ranked second and, with one exception, there was no effect of the remaining variables. The hybrid variable was ranked third for both genetic distances and showed a positive effect for FST  . Land-el= 0.247)  where a for FST .R2 values. Although the effect of land-elevation was positive when included in the model and Mantel tests, the lack of effect of elevation as a single parameter suggests that this positive effect stems from the influence of land cover alone. While no correlation between hybrid zones and genetic distance was found in the Mantel tests, there appears to some effect of hybrid poplar zones on FST.Overall, the MLPE models found a positive influence of land cover (for both model sets) and hybrid zones (for the 12 population model set) on population genetic differentiation, but no effect of isolation by distance or elevation. Models were ranked in the same order for both model sets . The effIn this study, we examined how riparian woodlands influence patterns of population structure and genetic differentiation in black-capped chickadees. These forested woodlands are expected to act as corridors to allow organisms to disperse across large areas of unsuitable prairie grassland habitat and to maintain population and genetic connectivity in heterogeneous landscapes. Despite the small spatial scale of the study area, as well as the dispersal potential of this generalist species, we found that black-capped chickadee populations were genetically differentiated. The most concordant groups are SSK; LE, StM and WO; and all remaining populations. Additional analyses also identified DR and BO to be significantly differentiated from other populations . These rA significant effect of landscape resistance distances on genetic distance revealed that variation in landscape features influences population differentiation in chickadees. Both Mantel correlations and MLPE model selection indicated a significant association between land cover and population genetic differentiation, with little or no support for the effect of geographical distance or elevation. Given the fragmented nature of the study area  and the dependence of birds on riparian woodland for movement, this result supported our predictions. Considering all possible landscape factors influencing dispersal is essential in studies such as this as even small gaps  in continuous habitat have a significant impact on population differentiation in a generalist and widespread species.FST and DEST supports this observation. The distribution of riparian woodland is influenced by survival, establishment and regeneration of riparian poplars and natural disturbance regimes  [Malurus coronatus), where functional isolation of populations from natural stretches of treeless river (~ 140 km) contributed to patterns of genetic differentiation [Patterns of genetic differentiation within river systems, particularly in the east , suggest that gaps in riparian corridors restrict dispersal and gene flow, and the positive effects of land cover on climate)  which cantiation . Therefoet al. [r whereas for FST, it was ten times longer. With the oldest reservoir built in 1951 (St. Mary River), and the average lifespan of chickadees being 1.5–3 years , it is possible that genetic differentiation is not yet detectable using FST. Alternatively, the smaller samples sizes from these populations may have impeded the resolution [P. balsamifera and P. angustifolia are found in the Rocky Mountains and foothills in the west, whereas the sparsely distributed P. deltoides is found in semi-arid grasslands of the east [P. deltoides sections of the river, whereas the genetic cluster containing LE, StM and WO coincides with the distribution of P. angustifolia  [Rivers that cross the plains are confined to coulees  of varying depth, but coulees themselves are separated by large expanses of grassland and low shrubby vegetation with scattered depressions  where patches of forest sometimes exist. In this study area, black-capped chickadees would therefore need to disperse approximately 100 km across unsuitable habitat between river systems which, given their low dispersal potential, is highly unlikely. While some populations on different rivers systems showed signs of differentiation in the east  . The RocTurdus migratorius), brown thrasher (Toxostoma rufum) and loggerhead shrike (Lanius ludovicianus) have been shown to preferentially cross agricultural landscapes through connecting woodland corridors [Agricultural practices and long term and intensive grazing along river valleys are becoming a serious concern for the health of riparian woodlands, as well as abundance and diversity of riparian bird communities . These porridors , highligFST and DEST) are poplar hybrid zone-associated chickadee populations . Forty of the 52 significant pairwise FST, and 37 of the 50 significant pairwise DEST comparisons, included populations associated with a di- or tri-specific poplar hybrid zone (P. deltoides) may be influencing population genetic differences in chickadees. It is possible their movement decisions are due to their ecologically rich and diverse community favourable for insectivorous, cavity nesting birds; however, this will require a more rigorous sampling design to test this hypothesis.It has been widely recognised that hybridisation is important for plant speciation , but therid zone . In addiThis study demonstrated the importance of assessing patterns of population genetic differentiation at small spatial scales as additional substructure may go undetected. Determining the effects of landscape features on microevolutionary processes can provide additional insights into the way organisms interact with their environment. Here, we found significant genetic structuring of a common, resident riparian species which was not observed at the rangewide scale, as well as a significant effect of landscape variables on patterns of genetic differentiation.Overall, large expanses of prairie grassland and breaks within the riparian corridor are important factors influencing population genetic differentiation. Despite the assumption that forested corridors can facilitate dispersal among river systems in highly fragmented landscapes, spatial heterogeneity within these corridors can lead to genetic isolation. Other genetic differences that could not be explained by gaps in woodland, coincide with poplar species distributions or hybrid zones. As such, these hybrid zones may have important conservation implications by promoting population divergence in poplar-dependent organisms and therefore requires further investigation. This study has demonstrated the need to explore genetic and environmental relationships at small geographical scales as understanding the role of landscape features on the genetic diversity of populations is crucial in helping to maintain genetic mixing and biodiversity.S1 FigK over K for STRUCTURE runs as shown in K is determined by the highest estimated log probability of the data and delta K infers the correct number of clusters from the difference of LnPr (X|K).(a) Log likelihood plots (LnPr (X|K)) and (b) Δ(DOC)Click here for additional data file.S1 TableSample ID’s in grey were removed from analyses.(XLS)Click here for additional data file.S2 TableNa) and MgCl2 concentration for each microsatellite locus used to genotype black-capped chickadee individuals.Repeat type (if known), primer sequence, allele size range (bp), number of alleles ((XLS)Click here for additional data file.S3 TableHe) and observed (Ho) heterozygosities and the total number of alleles (Na) for 15 populations of black-capped chickadees at 12 microsatellite loci. Overall population and loci means are also provided. See Table includes expected ((XLS)Click here for additional data file.S4 TableP ≤ 0.001)Table shows the percentage of variation for each of the three levels  and across different group combinations. Click here for additional data file.S5 TableResults from STRUCTURE analysis of individuals from populations separated by artificial and/ or natural barriers to gene flow within river systems.(XLS)Click here for additional data file.


Eunicella cavolini, with samples from different depths and by means of a common garden experiment. This species does not host photosynthetic Symbiodinium, enabling us to focus on the cnidarian response. We compared the thermotolerance of individuals from 20 m and 40 m depths from the same site and with replicates from the same colony. On the basis of an innovative statistical analysis of necrosis kinetics and risk, we demonstrated the occurrence of a very different response between depths at this local scale, with lower thermotolerance of deep individuals. Strongly thermotolerant individuals were observed at 20 m with necrosis appearing at higher temperatures than observed in situ. On the basis of nine microsatellite loci, we showed that these marked thermotolerance differences occur within a single population. This suggests the importance of acclimatization processes in adaptation to these different depths. In addition, differences between replicates demonstrated the occurrence of a variability of response between fragments from the same colony with the possibility of an interaction with a tank effect. Our results provide a basis for studying adaptation and acclimatization in Mediterranean octocorals in a heterogeneous environment.Climate change has a strong impact on marine ecosystems, including temperate species. Analysing the diversity of thermotolerance levels within species along with their genetic structure enables a better understanding of their potential response to climate change. We performed this integrative study on the Mediterranean octocoral  This also provides an opportunity for a real-time investigation of biological adaptation from ecological and evolutionary points of view. The response to climate change will depend on the type and rate of adaptation to a fluctuating environment. The nature of the adaptive response, either through genetic adaptation (evolutionary response) or acclimatization (phenotypic buffering u Reusch ), is cruu Reusch –5. One mu Reusch ,3. An alu Reusch .In heterogeneous landscapes, the evolution of genetic adaptation will depend on the relative strength of local selection and gene flow. High levels of gene flow compared to the scale of environmental heterogeneity can lead to migration load  or to thCorallium rubrum, shallow populations (from more variable and stressful conditions) appeared to be more thermotolerant than deeper ones [Eunicella singularis, shallow populations exhibited more stress signals than deeper ones [Symbiodinium in the adaptive response. The levels of genetic differentiation between depths were also different in these two cases, with a significant differentiation for C. rubrum [E. singularis [The effects of climate change are already clearly visible in the marine realm through acidification and temperature changes  and the per ones ,19. In tper ones ,21 but wper ones . This ra. rubrum ,23,24 congularis .Symbiodinium, we studied the thermotolerance of the non-symbiotic gorgonian Eunicella cavolini. Eunicella cavolini has been affected by mortality events [E. cavolini from two depths and between two replicate batches from the same colonies. Considering the genetic structure of octocoral species in this area, this provides a basis for discussion of the adaptive abilities of these species in a heterogeneous environment and in the face of seawater warming.In order to better understand the evolution of adaptive differences under high gene flow conditions, and independently of the potential effect of y events  and prel3.3.1E. cavolini  is an octocoral endemic from the Mediterranean Sea, but more common in the western than in the eastern Mediterranean [The yellow gorgonian erranean . It can erranean . Populat3.2http://t-mednet.org/index.php). The minimum summer temperatures were similar between depths (13.9°C at 20 m and 13.7°C at 40 m in July 2012) and the mean temperature between June and September 2012 was 17.9°C at 20 m and 15.5°C at 40 m. From the end of autumn and during winter, the temperature is homogeneous between depths, with a minimum of 12.7°C in February 2012. For the common garden experiment, 20 individuals were sampled at each depth in March 2012, with a 13.2°C temperature at both depths. For the population genetic analyses, 30 additional individuals were sampled at each depth.Individuals were sampled at two depths  along the same wall at Riou Island . These two depths are characterized by different thermal regimes with higher and more variable summer temperatures at 20 m than 40 m. During 379 days of survey from June 2011 to July 2012, the maximum temperature reached 25.4°C at 20 m and 22.8°C at 40 m . Stress and control conditions were separated in two tank systems and the replicates were placed randomly in three 24 l tanks for the control and four 18 l tanks for the stress conditions. The tanks corresponding to the same experimental condition (control or stress) were connected to the same water filtration system in order to focus on the variability linked to the different colonies or fragments. For each depth, replicates were separated in different tanks (two tanks for each batch of replicates) and replicates from different depths were mixed inside tanks. At the beginning of the experiment, the fragments were fixed on plastic supports and kept in open seawater at 16°C during 50 days for acclimation . This temperature is intermediate between the mean summer temperatures at each depth. After that step, the stress and control tanks were kept in a closed system with natural seawater and filtration, in order to accurately control the temperature conditions inside the tanks. The colonies were not fed during the whole experiment and air pumps provided oxygenation of the water. The control fragments were kept at 16°C during the following experiment. The thermal stress protocol was chosen after a preliminary experiment allowing determination of necrosis thresholds and based on the observed maximum temperature in summer . The final stress protocol comprised a two-stage temperature increase: from 16 to 23°C and from 23 to 27.5°C. The colonies were then kept at 27.5°C until the end of the experiment . The whole experiment lasted 90 days between 28 March and 25 June. Temperatures were recorded every 15 min with TidbiT data loggers  and each day necrosis levels were estimated as the percentage of necrosed tissue. Two types of necrosis were recorded: partial necrosis when living tissues presented a colour alteration or were partly detached from the colony; or full necrosis was recorded when the skeletal axis was visible . The percentages of partial and full necrosis for each fragment were estimated by eye with a ruler. The comparison of these measurements between three observers confirmed the repeatability of this approach. For the statistical analyses, we focused on the appearance of necrosis 3.4S(t) which is defined as: S(t)=Pr(T>t), the probability that the necrosis time T (i.e. the time of first necrosis) is greater than some specified time t. The survivor function is related to the lifetime cumulated distribution function FT(t) with the relationship FT(t)=Pr(T≤t)=1−S(t).In order to study the kinetics of necrosis, we focused on the proportion of fragments which did not present any necrosis  in relation to time. We used this kinetics as a basis for understanding whether multiple causes of death or failure should be taken into account and how the probability of survival changed during the experimental time. The necrosis dynamics was processed through survival data analysis. Consider the survivor function fT(t) of T as, by definition S(t) is a decreasing function of time, starting from 1  and going to 0  as t goes to infinity. The shape of S(t) is strongly dependent on the shape of a density fT(t). Without assumption upon the form of fT(t), we fitted the observed data with a Weibull distribution:θ, and can be used to model various life behaviours [θ,λ) have been obtained by nonlinear regression  by minigure S4; ).HT(t)=(1/θλ)tλ. This function is a measure of risk and increases with time: the greater the value of HT(t), the greater the risk of necrosis by time t. This allowed comparison of the different groups (depths and replicates).We then considered the cumulative hazard function, which in the case of a Weibull distribution is given by 3.5enclone v. 2.0 [p-sex of obtaining identical MLGs through sexual reproduction. We observed one and four repeated MLGs at 20 and 40 m, respectively, with p-sex≤0.0001. They corresponded to one MLG present both in the experiment and in the additional genetic sampling: this most probably corresponded to the repeated sampling of the same colonies and we discarded the corresponding individuals from the additional genetic sampling. Linkage disequilibrium between loci was tested with the log-likelihood ratio statistic implemented in Genepop v. 4.2 [enepop as 1-Qintra and 1-Qinter, respectively. The estimators of F statistics FIS and FST [enepop. We tested the null hypothesis of panmixia with a global Hardy–Weinberg test implemented in Genepop by considering the alternative hypothesis of heterozygote deficit. The genetic differentiation between samples was tested by an exact test procedure [enepop with default parameters. An analysis of molecular variance  and FCT which compares groups of samples (here different depths). The AMOVA was repeated by excluding Ever009. As a complementary analysis of genetic structure, a Bayesian clustering analysis has been performed .The 20 individuals used in the experiment and the 30 additional individuals from each depth were genotyped with nine microsatellite loci . Gp v. 4.2 . Allelic and FST  were comrocedure  implemen  was per 3.5.1.3 . Two fix4.4.1During the whole experiment, almost no sign of necrosis was observed in the control condition at 16°C in specimens from either depth. Only one 40 m fragment appeared necrosed at its base, with 13% of necrosis from the 5th day of the experiment, and this level did not change until the end of the experiment . In the The fit of a Weibull distribution allowed a good description of the observed data . This inp=0.022 for the closest comparison between 20 m and 40 m; p=0.013). An analysis of survivor curves per tank and per depth indicated that fragments from 20 m in the C5 tank showed necrosis sooner and faster than fragments from the same depth in other tanks . This was not observed for the fragments from 40 m in this C5 tank when compared with other fragments from the same depth.Cumulative hazard functions indicated that the risk of necrosis increased with time . A clear4.2in situ samples) with Hexp=0.56 at 20 m and 0.55 at 40 m . The number of alleles per locus and per depth ranged from 2 to 8, with a mean value over the nine loci of 5.2 at 20 m and 5.4 at 40 m . Tests of adequation to Hardy–Weinberg proportion were not significant in most cases, apart from locus EC24 at both depths and S14 at 20 m for which deficits of heterozygotes were observed. Significant heterozygote deficits were also observed for the multilocus test at each depth. Four alleles were private to one depth: two at 20 m and two at 40 m with a maximum frequency of these private alleles reaching 0.04 (data not shown). The test of linkage disequilibrium between loci revealed one significant association for both depths, between loci C21 and EC32 .Similar levels of genetic diversity were observed for the 20 m and 40 m samples . The differentiation between depths, by grouping experimental and in situ samples for each depth, was not significant . Single locus genic differentiation tests between depths were non-significant apart from locus Ever009, for which a significant differentiation was observed . The AMOVA indicated a significant differentiation between samples inside each depth with FST like analysis  but not with RST . The differentiation between depths was not significant with the AMOVA . If locus Ever009 was omitted from AMOVA, no differentiation was significant, neither between depths nor between samples inside depths. The Bayesian clustering analysis did not reveal any genetic structure, which agrees with the lack of differentiation .The differentiation test between experimental and additional 5.5.1in situ thermotolerance levels. Under natural conditions in 2003, mortality events were observed for three octocoral species, including E. cavolini, with maximum summer temperatures around 22°C at 20 m [in situ mortality events often took place at the end of summer, after the repetition of several thermal stress episodes of different intensity and duration. The date of the experiment may be important as well. Gene expression studies revealed different responses to thermal stress depending on the month for the Mediterranean red coral C. rubrum, with a potentially more efficient response in March compared with June [Paramuricea clavata [Eunicella verrucosa, diseased colonies also show modifications of bacterial communities [in situ observations of mortality events following episodes of stable warm temperatures. We demonstrated that this risk is higher and increases more quickly for 40 m than 20 m individuals.Our results underline the thermotolerance abilities of the yellow gorgonian in the northwestern Mediterranean Sea. For the 20 m individuals, after 28 days of exposure at 27.5°C, 15–20% of the fragments (depending on the replicate) displayed less than 5% necrosis levels. This demonstrates the existence of highly thermotolerant individuals despite the fact that this temperature is rarely or never encountered by the populations tested here. This could suggest that shallow populations may live far from their thermotolerance limits but such experimental results can differ from  at 20 m . Such di at 20 m ,20,36. Tith June . Our expith June . It shou clavata . For themunities . The necmunities . Here, t5.2Eunicella species. First, at the interspecific level, the necrosis threshold observed here for E. cavolini is similar to that observed for E. singularis, in the same area and depth range [E. cavolini compared with deep ones, in E. singularis deep populations appeared more thermotolerant than shallow ones for a similar depth range [E. singularis [E. singularis colonies were fed [Symbiodinium symbionts in E. singularis, in contrast to E. cavolini, is probably a major factor explaining these different results. As suggested by Ferrier-Pagès et al. [E. singularis. Photosynthetic symbionts might be advantageous as a way of overcoming the low food summer season [Symbiodinium may not always be mutualistic [E. singularis, E. cavolini and E. verrucosa [Symbiodinium: therefore, this symbiosis may be relatively recent, which raises the question of its stability and its adaptive consequences.The comparison of the experimental thermotolerance results obtained in this article and in previous studies provides a basis for discussion of the differences in adaptation to local thermal regime and depth for different th range . Neverthth range ,21. Nevengularis . The difwere fed ,22 whichs et al. , shallowr season  but theyualistic . The cloerrucosa , do not 5.3E. cavolini. Such differential phenotypic buffering is in sharp contrast with the low and non-significant genetic differentiation observed between depths (FST=0.001). A low genetic structure was also observed for E. singularis, with five microsatellite loci . Two otctively; ,43). Coric pools ,19,44. OE. cavolini, the low observed differentiation implies regular gene flow between depths. In the case of the genetic hypothesis, gene flow would induce a migration load at each generation and this could limit the action of local selection [E. cavolini, the adaptation to the different thermal regimes could also correspond to acclimatization or to a combination of the genetic and non-genetic effects as for the tropical coral Acropora hyacinthus [C. rubrum along the same depths as the ones studied here. At higher distances, contrasted responses have been demonstrated between Catalonian and Balearic individuals for E. singularis [Two hypotheses should be considered to explain the observed differential response to thermal stress in this context. The genetic hypothesis states that these differences are genetically based and locally selected (i.e. genetic adaptation). According to the acclimatization hypothesis, such differences could be environmentally induced during individual life history and maintained for example through epigenetic processes. Concerning election . Preciseelection . For E. acinthus . Other Mngularis : here, tngularis ) make geAdditional studies, both experimental and genetics, are required to test these hypotheses. Our observation of a wide range in necrosis time  indicatein situ and their position in the colony will be required to go further with this line of inquiry. They should be performed in common tanks. Our results indeed underline the interest of replicates from the same individuals for experimental studies on colonial organisms.The observed differences between replicates suggest either a tank effect or thermotolerance differences within colonies. The marginal distributions of the shape parameter of the Weibull hazard function showed no difference between replicates at 40 m but significant differences between replicates at 20 m . The sha5.4E. cavolini with potentially both acclimatization ability and adaptive genetic diversity, as in the tropical coral A. hyacinthus [E. cavolini, acclimatization should partly buffer the expected climate changes. Acclimatization through time has also been suggested for tropical corals but its long-term stability remains to be studied [E. cavolini, connectivity levels between contrasted environments would be favourable to the spread of alleles allowing genetic adaptation [As a consequence of global warming, Mediterranean octocorals will face more stressful thermal regimes. For these species, a northward range shift is difficult because of the geography of the Mediterranean Sea. Their persistence will rely either on deeper populations acting as refugia  or on pacinthus . For E.  studied . Non-gen studied  but temp studied . With re studied ). Generaaptation . Neverthaptation .6.The diversity of response to thermal stress in temperate octocorals underlines the need to take into account local diversity to study the potential response to climate change. Acclimatization and genetic diversity can induce very different thermotolerance levels along small vertical distances. The modular organization of these organisms provides an additional level of adaptation, with differences between parts of the same colony. These results open the way for transcriptomic and genomic studies of adaptation in temperate octocorals which are impacted by climate change. Non-symbiotic species are good alternative models to study adaptive processes in corals by focusing on the host (cnidarian) response. It is also important to take into account a dynamic view of the thermotolerance of species, populations and individuals for a better understanding of the potential fate of coral communities in the face of climate change. From a management point of view, our results demonstrate the need to protect shallow octocoral populations which are both the most thermotolerant, but also exposed to the harshest thermal conditions and human pressures.


Clupea harengus, is one of the ecologically and commercially most important species in European northern seas, where two distinct ecotypes have been described based on spawning time; spring and autumn. To date, it is unknown if these spring and autumn spawning herring constitute genetically distinct units. We assessed levels of genetic divergence between spring and autumn spawning herring in the Baltic Sea using two types of DNA markers, microsatellites and Single Nucleotide Polymorphisms, and compared the results with data for autumn spawning North Sea herring. Temporally replicated analyses reveal clear genetic differences between ecotypes and hence support reproductive isolation. Loci showing non-neutral behaviour, so-called outlier loci, show convergence between autumn spawning herring from demographically disjoint populations, potentially reflecting selective processes associated with autumn spawning ecotypes. The abundance and exploitation of the two ecotypes have varied strongly over space and time in the Baltic Sea, where autumn spawners have faced strong depression for decades. The results therefore have practical implications by highlighting the need for specific management of these co-occurring ecotypes to meet requirements for sustainable exploitation and ensure optimal livelihood for coastal communities.Herring,  Intra-specific variation is an important component of biodiversity and it is a central issue to determine evolutionary divergent population units in conservation and management. For instance, ecosystem and hereunder fisheries management hinges on the ability to estimate population- (or ‘stock’-) specific dynamics and rates of exchange of individuals among local demes. Population genetic tools have been successfully applied to describe and monitor population structure across a range of marine taxa . HoweverClupea harengus, is a classic example of a widely distributed marine fish exhibiting very large and fluctuating population sizes ) per se  and the per se .In the present study we used both neutral and ‘selective outlier’ marker information generated for temporally replicated samples from three spawning locations within the Gulf of Riga in conjunction with previously published data to test the hypotheses that autumn and spring spawning herring in the gulf constitute a single genetic population and whether ecotypes co-vary with outliers at broader geographic scales. We compared inference from neutral markers that allow insights into demographic parameters following neutral model expectations, with outlier markers to assess levels of ecotype differentiation and demographic connectivity. We show that outlier markers, but not neutral markers, support a link between ecotype and genotype. Also, even disjoint autumn spawning populations display convergent allele frequencies at specific loci. Our results contribute both towards a general understanding of genetic processes in marine fish populations, and specifically point to the importance of incorporating ecotype variation in fisheries management.Spring spawning herring (here abbreviated SS) were collected from commercial trapnet catches in Pärnu Bay in the Gulf of Riga (abbreviated GoR): Samples were expected to represent mostly local GoR SS but may have included SS transient migrants from elsewhere. Autumn spawning herring (abbreviated AS) were obtained from gillnets operating in two spawning areas: near Kihnu Island, and at the southern coast of the Island Saaremaa . The AS DNA was extracted from all 288 fish using E.Z.N.A. Tissue DNA kit . A NanoDrop Spectrophotometer (Thermo Fisher Scientific Inc.) was used to ensure adequate quality and quantity of DNA prior to genotyping.All individuals were genotyped for Single Nucleotide Polymorphism (SNP) and microsatellite marker variation as follows. Variation was screened for 96 SNP loci selected from  to maximFor SNPs, PCR amplification and genotyping were performed in 96.96 Dynamic Arrays using the Fluidigm IFC thermal cycler and BioMark instruments with SNPtypeTM chemistry. Genotypes were scored using the BioMark Genotyping Analysis software . SNP genotypes obtained using a different genotyping platform from  were valhierfstat [http://www.cmpg.unibe.ch/software/bayescan/), adopting the same settings as [Conformance with Hardy-Weinberg proportions (HWE) and gametic phase equilibrium (LD) were examined for all markers and collections using Genepop . Observeierfstat . Differeierfstat , and staierfstat . To asseierfstat , we perftings as . A log10tings as .adegenet [For exploration of the genetic structure reflected in the SNP data within and among SS and AS collections from the Baltic and the North Sea, we applied discriminant analysis of principal components, DAPC , implemeadegenet . We firsadegenet to perform a clustering analysis where these fish were excluded from the initial clustering analysis and then tested the clustering (‘assignment’) of these hold-out fish against a model fitted to spawning fish only.Finally, to assess potential bias of including non-spawning individuals, we used Her114, exhibited heterozygote deficiency and statistically significant deviation from HWE proportions in three collections (following correction for multiple testing). This locus was therefore excluded from further analysis. For SNPs, one locus (Cha_8386.6_423) suffered technical error (lack of clustering) in genotyping and was excluded from further analysis. Generally, levels of He, Ho and allelic richness were similar across all collections  Baltic AS, 2) Baltic SS and 3) North Sea AS. Examination of posterior membership probabilities showed that the genetic classification of an individual into the three clusters overall corresponded with its collection location. Thus, across the ten collections, 363 of 400 individuals (91%) were correctly classified to their spawning-type cluster. Inspection of PCs showed that PC1 (explaining 54% variation) differentiated Baltic Sea from North Sea collections, but with closer relationships between North Sea AS and Baltic Sea AS populations than North Sea AS and Baltic Sea SS populations . PC2 ex. One locpost hoc PCA using only 15 loci, 14 outlier loci from [post hoc analyses of neutral sample differentiation only returned five statistically significant pairwise comparisons, three of which involved Bothnian Bay SS versus GoR AS samples and two which involved Northern North Sea AS versus GoR SS (not shown).A Repeating the DAPC analysis where information was initially excluded for 19 non-spawning fish distributed across collections and then ‘assigning’ non-spawners to population clusters, showed that in most cases non-spawning fish clustered closest with the population they were caught with. Thus, 12 of 14 fish collected in autumn assigned to the Baltic Sea AS clusters; one showed closest grouping with North Sea AS, and one showed closest grouping with Baltic Sea SS. All five non-spawning fish collected in spring assigned to Baltic Sea SS . TherefoWe show that herring spawning in spring and autumn in one distinct sub-system of the Baltic Sea, the Gulf of Riga, are genetically highly differentiated. Differentiation was observed for three SNPs and for one transcriptome derived microsatellite marker, but clear population separation was only detectable with SNP markers, and neutral marker frequencies exhibited statistically non-significant differentiation among collections. Thus, the two ecotypes can overall be correctly classified based on 15 SNP markers, of which previously identified outlier loci exhibit strong explanatory power. In contrast to  only thrCha_15360.2–279) is annotated to a skeletal muscle troponin-coding gene, and another outlier SNP exhibiting strong divergence among clusters (Cha_1025.1–149) annotates to a ribosomal protein. However, whether these specific gene sequence variations have any causative effects remains to be determined. We hypothesise that Baltic autumn spawning herring may harbour one or more ‘genomic islands’ linked to ecotype divergence . Although the mechanisms underlying outlier differentiation may vary (reviewed in ) and arece (c.f. ), in thiOur results show that Baltic autumn-spawners exhibit the closest genetic relationships with Baltic spring spawning populations. This suggests that all herring sampled in the Gulf of Riga, spring and autumn spawners, from both gulf and open sea, originate from the same post-glacial colonisation event. We did not examine whether samples represented one or more of the three phylogenetic clades represented in all Baltic collections examined to date .Gasterosteus aculeatus [Gadus morhua [Strikingly, for a handful of outlier SNP loci Gulf of Riga autumn-spawners showed closer genetic similarity with autumn spawning populations from the North Sea, which are both geographically distant  and demographically isolated . Our resculeatus  and Atlas morhua , 45. Hows morhua ). Lamichs morhua  found SNs morhua ,16), ours morhua ), reprodThe adaptive value of adopting a specific spawning time is believed to be related to trade-offs between energy spent on migration and spawning, as well as optimising environmental conditions experienced by developing offspring . In herrS1 Fig(DOCX)Click here for additional data file.S2 Fig(DOCX)Click here for additional data file.S3 Fig(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.


Lymnaea stagnalis. We utilized the mixed mating system of this species by producing full-sib families in which each parental snail had produced offspring as both a dam and as a sire, and tested whether genetic background (family) and non-genetic maternal effects (dam nested within family) explain trait variation. Immune defense traits and growth were affected solely by individuals’ genetic background. Survival of snails did not show family-level variation. Additionally, some snails were produced through self-fertilization. They showed reduced growth and survival suggesting recessive load or overdominance. Immune defense traits did not respond to inbreeding. Our results suggest that the variation in snail immune function and growth was due to genetic differences. Since immune traits did not respond to inbreeding, this variation is most likely due to additive or epistatic genetic variance.Evolution of host defenses such as immune function requires heritable genetic variation in them. However, also non-genetic maternal effects can contribute to phenotypic variation, thus being an alternative target for natural selection. We investigated the role of individuals’ genetic background and maternal effects in determining immune defense traits , as well as in survival and growth, in the simultaneously hermaphroditic snail  Parasites are a ubiquitous selective force  against Other possibly important factors determining host responses to selection by parasites are non-genetic maternal effects. They occur when the phenotype of the mother affects the phenotype of her offspring in addition to the maternally inherited genes , 13. SinEstimating the relative importance of direct genetic effects and non-genetic maternal effects in determining phenotypic variation among individuals typically requires complicated breeding designs. A paternal full-sib/half-sib breeding design  is most Lymnaea stagnalis. This species is commonly infected with trematode parasites , as well as in survival and growth, of the simultaneously hermaphroditic freshwater snail arasites , 24 and arasites –28. Thatarasites , 29 and arasites  more comariation .This study was carried out in accordance with the laws governing animal experimentation in Switzerland, where work with snails does not require permissions. The study did not involve endangered or protected species. No specific permits were required for the field operations as the used water body is not private property or a nature reserve.L. stagnalis is able to store allosperm from previous matings and use it for cross-fertilization for over two months after the last copulation   . We did S was included as a covariate for the specific growth rate). Second, we tested whether self-fertilized snails differed from the snails produced by mating pairs in which all the individuals were produced through outcrossing using similar models as above. In the latter analyses, we pooled the individuals from both dams in mating pairs in which all the snails were produced through outcrossing since those snails represented the same genetic background (i.e. family) and did not differ between dams , we further tested whether self-fertilized snails differed from outcrossed snails in the examined traits due to inbreeding depression. When self-fertilization occurred it always took place in only one parental snail of a mating pair. Thus, we tested whether self-fertilized snails differed from outcrossed individuals in two separate analyses. First, we analyzed the variation in the examined traits in the snails produced by those parental pairs in which both mating types occurred using models with mating type  and family (nested within mating type) as factors  software.To investigate if self-fertilization modified the family-level variation in the examined traits, we calculated variances of the family means for each trait in self-fertilized snails, outcrossed snails in the mating pairs in which both mating types occurred, and in snails produced by mating pairs in which both parents reproduced through outcrossing . We then tested whether these variances differed between self-fertilized and outcrossed snails using χ2 = 8.692, df = 6, p = 0.192; dam(family): Wald χ2 = 6.612, df = 7, p = 0.470; Microsatellite genotyping revealed that in eight mating pairs all genotyped offspring were produced through outcrossing and could be considered as full-sib families. In these snails, family (i.e. genetic background) was a significant predictor for immune defense traits as well as for growth –3a, and F1,206/479 ≥ 6.507, p ≤ 0.011 for both, χ2 ≥ 15.772, df = 1, p < 0.001 for both, F1,477 = 12.397, p < 0.001, F1,206 = 3.887, p = 0.050). The tendency towards increased PO activity was, however, probably due to one self-fertilized family that showed exceptionally high immune activity. Mating type did not affect antibacterial activity of snail hemolymph .In five mating pairs, one dam reproduced through self-fertilization. Self-fertilized snails showed slower growth rate and lower survival when compared to outcrossed snails both within the mating pairs in which both mating types occurred as well as when compared to the snails in mating pairs in which all individuals were produced through outcrossing  could have revealed the possible importance of maternal effects in this trait. It is also important to note that the parental snails were not exposed to any specific pathogens to activate their immune function. Instead, they were maintained under standard laboratory conditions under which they are exposed to opportunistic micro-organisms that invade the snails , survival [survival of experimental snails to the end of the study ], growth , ln_size , PO_activity , and antibacterial_activity .(TXT)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.


Eretmochelys imbricata) nesting colonies in the EP (Eastern Pacific), the species was considered virtually absent from the region. Research since that time has yielded new insights into EP hawksbills, salient among them being the use of mangrove estuaries for nesting. These recent revelations have raised interest in the genetic characterization of hawksbills in the EP, studies of which have remained lacking to date. Between 2008 and 2014, we collected tissue samples from 269 nesting hawksbills at nine rookeries across the EP and used mitochondrial DNA sequences (766 bp) to generate the first genetic characterization of rookeries in the region. Our results inform genetic diversity, population differentiation, and phylogeography of the species. Hawksbills in the EP demonstrate low genetic diversity: We identified a total of only seven haplotypes across the region, including five new and two previously identified nesting haplotypes , the former only evident in Central American rookeries. Despite low genetic diversity, we found strong stock structure between the four principal rookeries, suggesting the existence of multiple populations and warranting their recognition as distinct management units. Furthermore, haplotypes EiIP106 and EiIP108 are unique to hawksbills that nest in mangrove estuaries, a behavior found only in hawksbills along Pacific Central America. The detected genetic differentiation supports the existence of a novel mangrove estuary “reproductive ecotype” that may warrant additional conservation attention. From a phylogeographic perspective, our research indicates hawksbills colonized the EP via the Indo‐Pacific, and do not represent relict populations isolated from the Atlantic by the rising of the Panama Isthmus. Low overall genetic diversity in the EP is likely the combined result of few rookeries, extremely small reproductive populations and evolutionarily recent colonization events. Additional research with larger sample sizes and variable markers will help further genetic understanding of hawksbill turtles in the EP.Prior to 2008 and the discovery of several important hawksbill turtle ( Resolving ecological uncertainties about small and highly endangered wildlife populations is fundamental to their management and recovery Lande . A multiEretmochelys imbricata, Fig. Until 2008 hawksbill turtles  continues to be a preferred genetic marker for evaluating population structure and phylogeography of marine turtles. Insights from mtDNA studies have informed management of hawksbills in the Atlantic, particularly the Caribbean, and Indo‐Pacific oceans  understand overall genetic diversity in the region, (2) evaluate stock structure and connectivity of hawksbill rookeries, (3) assess what geographic scales of management are appropriate, and (4) examine evolutionary history of the species. This study represents the first genetic characterization of hawksbill rookeries in the data deficient EP Ocean region.Tissue samples were collected at nine hawksbill rookeries spanning the EP Rim Fig.  between Monitoring patrols to locate hawksbills were conducted at the project sites and when a female turtle was encountered nesting, tissue samples were collected from the shoulder area Dutton . In ordeμL reaction containing reagents  using the following incubation profile: initial denaturation at 94°C for 2 min, followed by 30 cycles of denaturing at 94°C for 50 sec, primer annealing at 56°C for 50 sec, and primer extension at 72°C for 1 min, followed by a final primer extension for 5 min at 72°C. PCR products were analyzed for quality and quantity on agarose gels and then purified prior to sequencing using ExoSap. Cycle sequencing reactions were conducted with Big Dye fluorescent dye terminator (Applied Biosystems), and the fragments were analyzed using Sanger sequencing on an automated sequencer (Applied Biosystems Inc. model 3730). Sequences were evaluated for both forward and reverse reactions, and all potential new sequences were resequenced to ensure correct identification. We assigned haplotypes by comparing aligned sequences against a local reference library of ~760 bp using Geneious v. R8 (Biomatters Inc.) as well as searching the database on GenBank (http://www.ncbi.nlm.nih.gov) for sequences within our reading frame. New sequences were deposited in GenBank under the following Accession Numbers: KR012503, KR012504, KT003685, KR012505.DNA was extracted from hawksbill tissue and prepared for PCR (polymerase chain reaction)  and nucleotide (π) diversities using Arlequin v 3.5.1.2  comparisons were also calculated within Arlequin to test for population structure among these rookeries and the results of these methods were compared. We used the program IBD , and the latter including samples from Los Cóbanos, Punta Amapala, Southern Rivas, Osa Peninsula, and Azuero Peninsula (pooled n = 19).We calculated haplotype , we estimated time to most recent common ancestry (TMRCA) between EP haplotypes and selected hawksbill haplotypes from the Indo‐Pacific  and Caribbean . Effective sample sizes (ESS > 2000) and 95% highest posterior densities (HPDs) were observed for all parameters in TRACER v1.3 , are also found in the Indo‐Pacific, the former being widespread and encountered at numerous rookeries across the region and the latter only found in the Solomon Islands . Haplotype EiIP33 was the most common and widespread haplotype we encountered , followed by EiIP106 (33.1%), EiIP74 (5.2%), EiIP23 (2.6%), EiIP108 (2.2%), EiIP115 (0.7%), and EiIP107 (0.4%). Haplotype EiIP107 was found in a single turtle nesting in Southern Rivas, Nicaragua. Five of the sequence variable sites were transitions and one was a transversion. Haplotype diversities within the four main rookeries ranged from h = 0.0667 to 0.5111, with an overall value of h = 0.5778  structure among the four main rookeries included in our interpopulation analysis . Haplotypes EiIP106 and EiIP108 were only found in hawksbills nesting in mangrove estuaries.is Table . Becausersa Fig. . The hapAs has been previously documented, phylogenetic analysis revealed strong node support for a deep bifurcation between lineages in the Atlantic and the Pacific  are lower than the average values found for rookeries in the Indo‐Pacific . While previous satellite telemetry has tracked postnesting females from both of these rookeries to foraging grounds in Bahía de Jiquilisco , as well as the potential discovery of new rookeries, may reveal that additional haplotypes do exist. Nonetheless, considering that our sampling sites represent the primary known rookeries in the EP and that we attained samples from approximately 50% of the nesting females estimated to remain in the region , olive ridley (Lepidochelys olivacea), and green turtles (Chelonia mydas), which has found populations of each species in the EP to be more closely related to conspecifics in the Indo‐Pacific than to those in the Atlantic   from five rookeries, our preliminary results suggesting there may be mtDNA genetic differences between hawksbill nesting in these two habitats are compelling.We also found potential evidence for fine scale genetic segregation between hawksbills nesting in mangrove estuaries and along open‐coast beaches in Central America: The haplotypes found in turtles nesting in mangrove estuaries (at Bahía de Jiquilisco and Estero Padre Ramos) were rarely present in open‐coast nesters  and vice versa Fig. . While sRegardless of population differences, considering that haplotypes EiIP106 and EiIP108 are unique to hawksbills that nest in mangrove estuaries and that this behavior appears exclusive to hawksbills along Pacific Central America   represent prime examples of collaboration and information sharing on marine turtle genetics. Efforts to expand and/or reproduce these models for other species and/or regions would be prudent.With the escalating number of individuals and institutions embarking on marine turtle (including hawksbill) genetic studies, particularly those implementing mtDNA markers, the need to regiment sequence identification and nomenclature is heightened. Increased communication, collaboration, and data sharing among researchers are among the most effective mechanisms to ensure the integrity of genetic studies. In this respect, the Atlantic‐Mediterranean loggerhead genetics working group  markers  are needed to verify and further evaluate the genetic characterization of hawksbills in the EP as the data from mtDNA markers only reflect variation among female lineages (Carreras et al. None declared.