A total of 1000 ear-to-row C0 families was genotyped with 955,690 genotyping-by-sequencing SNP markers; their testcrosses were phenotyped at four optimal locations in Mexico to form the training population. Individuals from families with the best plant types, maturity, and grain yield were selected and intermated to form RCGS cycle 1 (C1). Predictions of the genotyped individuals forming cycle C1 were made, and the best predicted grain yielders were selected as parents of C2; this was repeated for more cycles , thereby achieving two cycles per year. Multi-environment trials of individuals from populations C0, C1, C2, C3, and C4, together with four benchmark checks were evaluated at two locations in Mexico. Results indicated that realized grain yield from C1 to C4 reached 0.225 ton ha−1 per cycle, which is equivalent to 0.100 ton ha−1 yr−1 over a 4.5-yr breeding period from the initial cross to the last cycle. Compared with the original 18 parents used to form cycle 0 (C0), genetic diversity narrowed only slightly during the last GS cycles (C3 and C4). Results indicate that, in tropical maize multi-parental breeding populations, RCGS can be an effective breeding strategy for simultaneously conserving genetic diversity and achieving high genetic gains in a short period of time.Genomic selection (GS) increases genetic gain by reducing the length of the selection cycle, as has been exemplified in maize using rapid cycling recombination of biparental populations. However, no results of GS applied to maize multi-parental populations have been reported so far. This study is the first to show realized genetic gains of rapid cycling genomic selection (RCGS) for four recombination cycles in a multi-parental tropical maize population. Eighteen elite tropical maize lines were intercrossed twice, and self-pollinated once, to form the cycle 0 (C In the last 20 yr, marker-assisted selection has been widely used in plant breeding where a few markers significantly associated with the phenotypic trait are employed to predict the genetic value of the candidates for selection , 2016. O−1 and that hybrids derived from cycle 3 produced 7.3% (0.176 ton ha−1) higher grain yield than those developed through the conventional pedigree breeding method. RCGS in biparental populations offered the advantage of significant time efficiency over conventional breeding methods, as up to three cycles of RCGS can be conducted within a year. Interestingly, et al. (2016) recently reported a study on realized genetic gains in grain yield using two biparental populations generated by crossing two elite Asian maize inbred lines with an African drought tolerant line. Cycle 1 (C1) was formed by recombining the top 10% of the F2:3 families. Cycle 2 (C2) was derived using two different methods: (1) phenotypic selection (C2-PS); and (2) GS (C2-GS). Results showed that C2-GS top-crosses produced 4–43% higher grain yield than C2-PS top-crosses.In tropical maize, For RCGS within biparental populations, prediction accuracy is achieved thanks to high linkage disequilibrium (LD), no pedigree, and no family substructure  is more valuable than LD information given by models that use marker effects. However, when markers were in strong LD with QTL with large effects, models based on marker effects were better predictors than models based on the genomic relationship between individuals.However, several theoretical complexities and challenges arise when attempting to perform GS in MPP. Plant breeders usually work with sets of full-sib families generated from crosses of inbred parents that vary in size so that extensive LD exists within each family; however, different LD patterns must exist across families. Therefore, because, in a typical MPP, the breeder is faced with LD across a large set of families, the level of LD as well as the marker density are important factors to be considered when applying GS to MPP. The reliability of GS is greatly influenced by the number of phenotypes; therefore, combining data sets from MPP should increase the reliability of GS by making it more efficient and attractive for use in breeding. However, when combining populations, allele frequencies, LD and segregating haplotypes are different in different populations. Thus, when the marker effects are different between the different combined populations, this can reduce the reliability of GS. 0) training population. A total of 1000 ear-to-row C0 families were genotyped with dense GBS markers, and their testcrosses were phenotyped at four locations in Mexico to develop genomic prediction models. One cycle of phenotypic selection (C0–C1) and three cycles of RCGS (C1–C4) were carried out. The main objectives of this study were: (1) to report the realized genetic gains of four cycles , plus the original training population (C0) in multi-environmental field trials of RCGS-assisted breeding evaluated together with four benchmark checks in two Mexican environments (locations), and (2) to investigate the genetic diversity of the families within each RCGS selection cycle to assess the level of genetic diversity after three cycles of rapid cycling GS.Despite the above theoretical and simulation results, which show that genomic-enabled prediction accuracy of MPPs is higher than the accuracy achieved within a single population, studies implementing RCGS in MPPs have not been reported. The research presented in this paper was initially conceived in 2009 and started in 2010. It included an initial MPP made up of 18 elite tropical maize lines intercrossed twice and self-pollinated once to form the cycle 0 (Chttp://masagro.mx).The RCGS experiment was designed in 2009 as part of the MasAgro project funded by Mexico’s Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food  through the Sustainable Modernization of Traditional Agriculture program .The steps in the breeding scheme used for RCGS are shown in 1 progenies  for developing genomic prediction models was formed with the best 1000 selected S2s. Testcrosses of all 1000 selected S2 were planted using a partial replicated design with 25% of replicated genotypes at four optimal Mexican locations. Phenotypic data were collected at all locations for >10 agronomic traits, including grain yield at 12.5% moisture content (GY), anthesis date (AD), silking date (SD), plant height (PH), ear height (EH), and moisture content (MOI). For each S2 family, DNA was extracted by bulking equal amounts of leaf tissue from 15 individual plants. Genotyping-by-sequencing was performed at Cornell University Biotechnology Resource Center as described by The training population  was formed by intermating the 50 selected S2 families. The 50 families were divided into two equal groups, and bulk pollen from the first group was used to pollinate all plants in the other group and vice versa. Based on visual evaluation of flowering time, plant type, plant/ear height, well-filled ears, and reaction to naturally occurring major diseases, along with among-family and within-family selection, 157 ears (1–6 ears from each selected family) were harvested and shelled individually to form C1.In Cr family . Cycle 11, 157 selected ears  were not genotyped, as this was the last rapid cycling recombination.In Cted ears  were plated ears , 2008 im0, which is a subset of the best 50 families selected from the training population. Another group of entries represents RCGS cycles C1 (47 entries), C2 (48 entries), C3 (43 entries), and C4 (43 entries), and the last group of entries comprises the four benchmark checks . All the entries were crossed with a single-cross tester (CML495/CML549). Their testcrosses were planted at two locations in Mexico  in a modified split-plot design where the selection cycles were the main plots and the entries within each selection cycle were the subplots. The experimental design within each selection cycle (main plot) was an alpha-lattice design with two replications per location. The four benchmark checks were repeated in each subplot and planted together with the entries belonging to the different GS cycle; for example, the four checks were planted in the two replicates of the subplot where entries from cycle C0 were planted.A total of 233 testcrosses was tested in the field to estimate the realized genetic gains; these entries belong to different groups. One group of entries (48) represents selection cycle C0, C1, C2, C3, and C4) within each location and combined across both locations. ANOVA for AD, SD, PH, EH, and MOI were performed for each location and combined across both locations.Phenotypic data were collected at the two locations for the main agronomic traits including GY, AD, SD, PH, EH, and MOI. A linear mixed model was fitted to the data considering the incomplete block within replicate as random effects, and locations, cycles (main plot), entry within cycle (subplot), cycle × location interaction, and entry within cycle × location interactions as fixed effects. Random errors are assumed to be identically and independently normally distributed with mean zero and homogeneous variance. ANOVA for GY were performed including the evaluated entries from the selection cycles and the checks. Genetic gain response was assessed by regressing mean GY values on the selection cycle means  to assess the genetic similarity of all the materials in each selection cycle.Multidimensional scaling (MDS) was performed with the TASSEL software (0) evaluated in four sites, the phenotypic and genotypic data for the evaluation of the entries from the different selection cycles , as well as a brief GUIDE can be found in the link http://hdl.handle.net/11529/10927. A marker information file and characteristics of the genetic materials are also included in the link.The phenotypic and genotypic data for the training population (cycle C0 (training population) for GY; the mean correlation between the predicted and observed values was 0.55.The combined GY heritability across both locations was 0.34, while GY heritability at individual locations was 0.48, and 0.19 in Agua Fria and Tlaltizapan, respectively. Low-to-intermediate GY heritability was observed in the individual location analysis and combined analysis, mainly because GY is a complex trait. To mimic future prediction problems we will face, we implemented a fivefold random cross-validation with 100 replicates using entries in C0, C1, C2, C3, and C4 plus four checks were used for field evaluation at two Mexican locations . Mean grain yield for each cycle and average gains per cycle are shown in 4 reaching 10.96 ton ha−1. At both individual locations, the average performance across all C4 entries surpassed the grain yield performance of the other cycles; average grain yield performance across all C4 entries was 7.13 ton ha−1, and 10.96 ton ha−1 for Agua Fria and Tlaltizapan, respectively. Also, the combined analyses of the two locations showed an increase in mean grain yield in C4 of 9.05 ton ha−1 over that achieved in C3 (8.92 ton ha−1) and over the other GS cycles.A total of five groups of entries from C1) had lower GY than entries representing the base selection cycle (C0); this was due to the fact that the parents of the C1 population . In C3, GY substantially increased in TL and combined across two locations with respect to previous selection cycles. For analyses in Agua Fria location, GY declined slightly to 6.88 ton ha−1 for C3  in Agua Fria and Tlaltizapan; however, the opposite occurred when combining cycles C1 and C3 in both locations as compared to the heritability in the base cycle (C0). In the combined analyses across both locations, broad-sense heritability (H2) showed no decrease. In general, results showed that at each location and combined, while there was a decrease in GY from C0 to C1 there were also important increases in realized genetic gains for trait GY at the two locations from C2 to C3 and from C3 to C4. The mean grain yield of the benchmark checks varied slightly for each cycle at Agua Fria but stayed fairly constant at Tlaltizapan and combined locations for all selection cycles. Mean performance of the checks in selection cycle C0 was consistently lower than the mean performance in the other selection cycles.All selection cycles had higher average grain yield than the corresponding mean of the checks for individual locations and combined. Concerning broad-sense heritability, there was a decline in cycles C−1 to 0.177 ton ha−1 when considering all cycles (C0–C4), and from 0.171 ton ha−1 to 0.276 ton ha−1 when considering only rapid cycling GS (C1–C4). For the combined location analyses, the realized genetic gains were 0.158 ton ha−1 and 0.225 ton ha−1 for cycles (C0–C4) and cycles (C1–C4), respectively. The realized genetic gains due to RCGS were highest in C4 at the two locations and combined across them.The average gains per cycle for each location and combined across the two locations ranged from 0.131 ton ha4 did not increase with respect to their averages in early GS cycles . They ranged from 56 d for anthesis and 57 d for silking for all genomic selection cycles, and showed good general synchrony between both flowering times. However, GS produced taller plants and ear insertions during cycles C3 and C4 than during cycles C1 and C2. Grain moisture content did not seem to have been greatly affected after the three cycles of RCGS.The effects of genomic selection on unselected flowering, moisture and height traits are shown in 3 and C4), and tended to decline with respect to the early cycles. Similarly, trends in broad-sense heritability are smaller in the last cycle (C4) than in early cycles (C0 and C1). The mean of the entries within each selection cycle, and the mean of the benchmark checks did not differ much for the different traits. For example, the check had ∼56 d to anthesis and 57 d to silking. In general, the checks had smaller plant height, taller ear height and similar grain moisture than the entries in the selection cycle.The plant and ear heights for the two latest GS cycles were ∼6–10 cm higher than the plant and ear heights of the maize plants for the two earlier GS cycles . The pheThe phenotypic correlation between the different traits and grain yield changed from different selection cycles (data not shown). Results indicated positive correlations between grain yield and days to silk ranging from 0.3 to 0.5 for the different selection cycles. Negligible and negative correlations between grain yield and plant height, anthesis days, and moisture content were found.0 families and the individuals selected as parents of C1 is displayed in 0 families. The original parents, the C1 families and the selected individuals (that form the parents of C2 selection based on genomic prediction) are depicted in 1 families are located between dimensions 1 and 3, close to four of the original parents located in this region of the figure.The diversity structure pattern including the 18 parents, the C2 families, and the parents selected to form the next selection cycle are shown in 2 families and the selected parents are located between dimensions 1 and 3, clearly heading in the direction of two of the original parents located in this region of the three-dimensional figure. Finally, 3 families and the selected parents that were intermated in RCGS to form C4. Clearly, the C3 families and the selected parents that form C4 are concentrated around the two original parents located in the upper region of the figure, between dimensions 1 and 3. However, a direct comparison between the genetic diversity of different populations (C0–C4) may be confounded by the differences in population size, and also by the different levels of inbreeding in the different selection cycles.The original 18 parents, the C0–C4. The C3 families and the selected parents that form C4 are located toward the upper left quadrant of the biplot in the same direction as one of the original parents.0–C4 entries, 0–C4, the selected parents from C0 to C4, along with all the entries. Results of Shannon’s Diversity Index and the heterozygosity for cycles C0–C4, along with the selected parents from C0 to C4 and all other entries, are displayed in the barplots in 3 entries, as well as for the parents selected to form C4. Note that entries from C4 were not included because they were not genotyped.Concerning the genetic diversity of the different C0, and the allele frequency of the C3 and C4 entries. Initially, allele frequency was calculated for the 18 parents, and for the entries of cycles C3 and C4; SNP markers with allele frequencies between 0.05 and 0.95 were considered polymorphic markers, whereas markers with allele frequencies <0.05 or >0.95 were considered monomorphic markers. Using these criteria, we found that from a total of 1120 SNP markers  that changed their polymorphic/monomorphic status, 123 markers swapped the major allele frequency, 968 markers became monomorphic in C3–C4 although they were polymorphic in the 18 parents and C0, and only 29 markers were polymorphic in cycles C3 and C4, although they were monomorphic in the 18 parents and cycle C0 . A total of 88 clusters (haplotypes) of different sizes were found with three or more SNPs that changed their frequency. The distribution and size of the clusters as well the type of change are shown in mosome 5 .0 to polymorphic in cycles C3 and C4 (blue color), and three clusters with three markers, four clusters with four markers, and two clusters with six markers that changed from polymorphic in the 18 parents and cycle C0 to monomorphic in cycles C3 and C4 (black color). As shown in et al. 2016). In this study, our results showed realized gains of RCGS in a multi-parental tropical maize population that originated from crosses of 18 CIMMYT elite tropical maize lines. From a practical breeding perspective, multi-parental populations might not be an attractive option because the mean of 18 parental lines might be lower than the mean of the best few lines that could be used in biparental crosses; however, as diversity becomes an important issue in GS, multi-parental populations offer the opportunity to maintain diversity, while still achieving rapid cycles with high realized grain yield genetic gains achieved in a shorter period of time, as found in this study. As for the decrease in genetic diversity, this is not of much concern in a short-term selection (four to five cycles), especially if the new developed lines from C4 are crossed with other lines for further breeding.Previous studies on temperate and tropical maize showed realized gains of RCGS in biparental populations (i×r×h)/I, where i is the selection intensity, r is the selection accuracy, h is the square root of narrow-sense heritability, and I is the time (in years) it takes to complete a selection cycle. In this study, the gains in GY in different selection cycles were not consistent, decreasing slightly from C0 to C2, while increasing significantly from C2 to C3, and from C3 to C4. As for analyses combining the two sites, the gains in grain yield were 6.2 and 7.7% from C0 to C4 and from C1 to C4, respectively. The combined realized genetic gains reached 0.158 ton ha−1 per cycle for C0–C4,  were used to represent selection cycle C0 in the genetic gain evaluation study. The GY mean of the best 50 selected families is much higher than that value of the 50 random selected families.The lower GY observed in CIn terms of the prediction models used to predict the genetic values of the entries to be selected in each genomic cycle, we used the direct genotyping-by-sequencing marker as biallelic. Since a multi-parental  population was used, haplotype rather than biallelic marker could have been used in order to attempt to capture the whole allelic diversity. However, the problem on how to define the length of the haplotype segment in each chromosome could impose a major drawback for using this approach; different haplotypes methods exist but none of them seems to give clear superiority in terms of genomic-enabled prediction accuracy.−1 yr−1), it is necessary to account for the number of cycles per year (two cycles per year in this study), and also for the time from the initial cross to the last cycle (4.5 yr from F1 development to harvesting the C4 in this study). Therefore, given that grain yield from C1 (8.40 ton ha−1) to C4 (9.05 ton ha−1) increased by 7.74%, the average genetic gain of 0.225 ton ha−1 per cycle /4.5] under optimal conditions.To compute the realized genetic gains per year  are at the same or higher level than those observed in other studies under phenotypic selection but with a shorter breeding cycle. However, the 0.070 ton ha−1 yr−1 achieved by In this study, results obtained from MPPs in optimal environments reinforce the usefulness of GS-assisted recombination for achieving high genetic gains in GY. Although only two cycles per year were completed in this study ; all the other chromosomes showed a small number of clusters with markers that became monomorphic  after cycles of RCGS. It is likely that some of those clusters of SNP markers with changes in their polymorphic status are related to less complex traits that were selected during C0 and C1, such as flowering time, and plant and ear height.Results of this study show that only a few SNPs changed their polymorphic status after three cycles of GS. This result indicates that marker interaction (epistasis) may play an important role in complex traits (such as grain yield), in which phenotype is the result of the sum of the small effects of many genes. Only chromosome 5 showed a total of 19 clusters, with 16 of them having markers that became monomorphic . Another important finding is that genetic diversity was well controlled up to C3 and then declined. Although other traits were correlated with GY, they did not show any important change after three cycles of RCGS for GY. In the end, 64.3% of the markers changed their allele frequencies but never became monomorphic, and 33.99% of the markers modified their allele frequency by <15%.Results described in this study are the first report of RCGS in MPPs. A realized genetic gain of ∼2% for GY with two rapid cycles per year saves time and produces efficient genetic gains overall. The decline in genetic gains from cycle CThe target of this study was to perform three rapid cycles per year; however, we only achieved two rapid cycles per year due to delays in the DNA preparation and genotyping turnaround time. Therefore, further studies in maize or other crops are required to confirm the promising RCGS results obtained in MPP using tropical maize lines described in this study, and examining if the length of selection cycle could be further reduced by implementing RCGS.


Mustela putorius furo) was domesticated from European polecats (M. putorius), transported to multiple continents, and has been artificially selected for several traits. The ferret is now a common pet, a laboratory model organism, and feral ferrets can impact native biodiversity. We hypothesized global ferret trade resulted in distinct international genetic clusters and that ferrets transported to other continents would have lower genetic diversity than ferrets from Europe because of extreme founder events and no hybridization with wild polecats or genetically diverse ferrets. To assess these hypotheses, we genotyped 765 ferrets at 31 microsatellites from 11 countries among the continents of North America, Europe, and Australia and estimated population structure and genetic diversity. Fifteen M. putorius were genotyped for comparison. Our study indicated ferrets exhibit geographically distinct clusters and highlights the low genetic variation in certain countries. Australian and North American clusters have the lowest genetic diversities and highest inbreeding metrics whereas the United Kingdom (UK) cluster exhibited intermediate genetic diversity. Non‐UK European ferrets had high genetic diversity, possibly a result of introgression with wild polecats. Notably, Hungarian ferrets had the highest genetic diversity and Hungary is the only country sampled with two wild polecat species. Our research has broad social, economic, and biomedical importance. Ferret owners and veterinarians should be made aware of potential inbreeding depression. Breeders in North America and Australia would benefit by incorporating genetically diverse ferrets from mainland Europe. Laboratories using ferrets as biomedical organisms should consider diversifying their genetic stock and incorporating genetic information into bioassays. These results also have forensic applications for conserving the genetics of wild polecat species and for identifying and managing sources of feral ferrets causing ecosystem damage.Domestication and breeding for human‐desired morphological traits can reduce population genetic diversity via founder events and artificial selection, resulting in inbreeding depression and genetic disorders. The ferret ( AdditioMustela putorius furo Linnaeus, 1758) was domesticated from the European polecat  primarily for hunting rabbits and rats  and 15 European polecats  via buccal swab and/or hair (>10 hairs per ferret) plucked from the base of the tail (Table Cells for DNA extraction were collected from 765 domestic ferrets (il Table . Samplesil Table . Any indil Table .2.2Forward primers for 31 microsatellite loci Table  were flu2.3FST) was calculated in GenAlEx 6.502 . Population divergence (K) is a parameter optimized by the model. We followed developer recommendations for determining K and individual population assignments . We ran 10 spatially conditional autoregressive admixture models for each K to the DIC plateau of nonadmixture models. All models included pairwise great circle geographic distances for weighting the Voronoi neighborhood, 100,000 iterations, and a 25% burn‐in period. We retained 20% of the models which contained the lowest DIC scores and used CLUMPP 1.1.2 to perform model averaging  cluster was not strongly differentiated from non‐UK European cluster , program STRUCTURE also indicated six clusters based on model probability (LnP(D); Figure Program GENELAND identified four genetic clusters Figure a, includThe DAPC showed similar patterns to the previous analyses and supports the genetic distinction between ferrets and polecats in the UK Figure . Austral3.2Based on genetic diversity estimates that are robust to sample size or account for sample size, ferrets from Australia, Canada, and the United States had the lowest genetic diversity Table . Austral4Mustela putorius furo) trade has resulted in geographically distinct genetic clusters, likely resulting from founder events and geographic isolation combined with inbreeding and genetic drift. Spatially explicit programs TESS and GENELAND showed comparable population structure, however, TESS identified additional substructure within North America and Hungary. Additionally, our nonspatial STRUCTURE results were highly consistent with those from TESS and GENELAND except that STRUCTURE identified the sampled polecats to be genetically distinct from domestic ferrets, indicating the algorithms from the spatial programs or unequal sample sizes may have overridden the genetic differences between polecats and ferrets in the UK. Within our sample, ferrets within any single country tended to assign to only one of the four genetic clusters, including the United Kingdom (UK), non‐UK Europe, North America, or the Australia cluster. Exceptions to this pattern were New Zealand and Norway. Ferrets in New Zealand primarily assigned to the UK cluster but also shared assignments with the Australian cluster, supporting the hypothesis that New Zealand ferrets originated from England and from Australia  and allelic richness (3.93 ± 0.13) among populations of European polecats from mainland Europe  and Steppe polecats    or allelic richness  approaching those of their wild counterparts (Moller et al., One of the primary ways to reverse inbreeding depression is through genetic restoration (Frankham, We do not see any particular utility for maintaining genetically distinct ferret clusters in most cases. Thus, for countries with inbred ferrets, we recommend that pet and laboratory breeding programs should incorporate ferrets from other countries. Although outbreeding depression could result from such crosses, the potential for inbreeding depression is much more likely (Rollinson et al., The United States and Canada allow ferret importation (Animal and Plant Health Inspection Service, Overall, our study identified international and intercontinental domestic ferret population structure and indicates Australian and North American ferrets have the lowest genetic diversities and are most highly diverged from the European polecat. Given current importation bans, New Zealand ferrets are essentially isolated and, in the future, may also exhibit patterns of low genetic diversity and inbreeding. Prevention or mitigation of inbreeding depression will require international cooperation among breeding programs and should include genetically diverse ferrets from mainland Europe. In some cases, political factors limit the ability of potential international breeding programs. This research highlights the need for politicians, coat‐oriented breeders, and laboratory‐stock breeders to reassess their policies. Given the close relatedness among domestic ferrets, European polecats, Steppe polecats, black‐footed ferrets, American mink, and European mink (Cabria et al., https://doi.org/10.5061/dryad.24c24Genotype data available from the Dryad Digital Repository:  Click here for additional data file.


Neoconocephalus melanorhinus belongs to a genus with high mobility. Unlike other members of the genus, N. melanorhinus is a salt marsh specialist restricted to a narrow corridor along the Atlantic and Gulf coasts. In addition, their range crosses at least one known biogeographic barrier and possesses biogeographic characteristics of the stepping-stone as well as the hierarchical island model of dispersal. Using AFLP markers we searched for areas that conform to the predictions of isolation by distance and for areas of non-uniform increases in genetic variance, indicative of isolation by barrier. We found significant genetic differentiation between all twelve sampled sites. Isolation by distance was the predominant pattern of variation across their range. In addition, we saw possible evidence of two biogeographic barriers to gene flow, one at the Atlantic-Gulf divide and the other along the Gulf coast. We also observed a change in body size across the range. Body size, as measured by male hind femur length, correlated closely with latitude, a possible indication of differential selection across the species range.Biogeography plays a significant role in species’ dispersal, and in turn population structure, across the landscape. The North American katydid  The phylogeography of organisms, i.e. the distribution of genetic lineages of a species across landscapes , is stroA number of models explain the genetic structure resulting from different dispersal limitations , 6 such In an alternative model, the hierarchical island model, barriers between certain neighboring sites reduce the rate of dispersal and gene flow. This produces a non-uniform increase in genetic differentiation between these sites, the signature of IBB . This diNeoconocephalus is a diverse group of New World katydids with variation in habitat preference and species distributions [N. bivocatus and N. robustus show a lack of genetic structure across more than 450 km of grassland habitat (G. Ney and J. Schul unpublished data).ibutions . All speibutions ). Most sibutions . For exaNeoconocephalus melanorhinus is a habitat specialist found only in North American salt marshes along the Atlantic and Gulf coasts [Neoconocephalus katydids, that is more than 2,000 km long, but no more than a few kilometers wide in most localities. This one-dimensional arrangement of populations, which is similar to those in a one-dimensional stepping-stone model, may limit dispersal to neighboring sites and reduce gene flow across the range. Neoconocephalus melanorhinus’ range also falls along several known biogeographic divides, including one dividing the range on either side of the Florida peninsula  J. roemerianus was less common and individuals were predominantly found calling from cord grass. We identified individuals as belonging to the target species through their call and morphological features, including cone pigmentation and body shape [All necessary permissions were obtained prior to the collection of specimens . We collected a total of 227 es sites , Fig 1 ady shape , 12.N. melanorhinus from all the Atlantic coast sites and the Gulf coast sites DH and CK . DNA quantification was performed on each sample by spectrophotometry . We used amplified fragment length polymorphism (AFLP) markers to quantify genetic variation.We removed one hind femur from each AFLP bands were generated using the method described in Snyder et al. . BrieflyWe tested the rate of genotyping errors between collection years to determine the viability of combining AFLP datasets. In order to test repeatability, eight 2014 samples were rerun in replicate with the 2015 samples. Fragment lengths were recalled for the original as well as both sets of rerun samples as described previously and the mismatch error rate was calculated within and between AFLP runs .The 2014 AFLP analysis produced 2170 distinct fragments from 188 individuals. The 2015 AFLP run produced 1175 fragments from fifty-seven individuals. Mismatch error rates averaged 6.1% for within-run peak calls and 14.0% for between-run peak calls. Because of the increase in mismatch error rates between AFLP runs we chose to not combine 2014 and 2015 datasets. By treating datasets independently we avoid added error incorporated by uncontrolled variations in fragment amplification or peak intensities between-runs.Neoconocephalus, the automated peak-calling scheme is preferable due to its sensitivity in detecting lower levels of genetic variation. Significant patterns of genetic structure found in this study would indicate that these levels of within-run noise do not obscure the overall genetic signal.Automated peak calling schemes produce a larger number of total fragments, but tend to produce larger error rates than manual peak calling schemes. Larger AFLP datasets, despite added noise, reveal greater genetic differentiation than smaller, less error prone, datasets . As geneA fifty percent rule Bayesian tree was constructed in MrBayes v.3.2 from individuals collected in 2014 . The MCMN. melanorhinus’ range. First, genetic differentiation among localities was calculated using a Bayesian non-uniform allele frequency distribution method for each year’s dataset independently [ST was estimated from 100,000 permutations (10 bootstrapped runs) of individuals among groups.We used four methods to quantify genetic differentiation across endently  as impleendently . ExpecteSecond, a Bayesian clustering analysis of individuals from 2014, as implemented in the program STRUCTURE v.2.3.4 , produceThird, multiple AMOVAs were performed using the 2014 and 2015 AFLP datasets. AMOVA is a method for testing hypotheses of population structure against genetic evidence , 27. SitST/(1-FST)), genetic structure, and geographic distance. Geographic distance was measured as the simplified shoreline distance, the distance along a path connecting neighboring sites by straight lines. This path is likely similar to the path N. melanorhinus must disperse through, not necessarily following all of the contours of the coast, but also not crossing large stretches of non-marsh habitat. In addition, we conducted a linear model analysis of variance in R v.3.2.0 [Fourth, standard and partial Mantel tests were conducted for populations sampled in 2014 using the program GenoDive v.2.0  to exami v.3.2.0  to test We produced three matrices representing the most well supported models of population structure formulated from the other analyses. Significance was calculated by 1,000 permutations of samples between groups. To directly take into account population structure, partial Mantel tests were performed measuring the relationship between genetic and geographic distance, while using the hypothesized genetic clustering as a covariate. In addition, partial Mantel tests examining the relationship between genetic differentiation and the three hypothesized population structures were run with geographic (simplified shoreline) distance as a covariate.Neoconocephalus, only the body measures from males were used in this analysis. In addition, as there may be an effect between years, we chose to compare only males collected in 2014. Hind femur length was correlated with latitude and the significance evaluated using Pearson’s correlation coefficient. In addition, the difference in average male hind femur length between sites was correlated with genetic differentiation and evaluated using a Mantel test.We measured body size using the length of the hind femur. After removing a single hind leg from each sampled individual, the length from the proximal end of the leg to the first joint was measured using a ruler to the nearest half-millimeter. As there is sexual dimorphism in A phylogenetic analysis of individuals collected from all sites in 2014 allowed us to determine a hypothesis of the ancestry among all individuals. Sites whose individuals share ancestry with individuals from other sites have likely seen significant gene flow in their past. Sites comprised of monophyletic clusters of individuals have likely remained isolated and have an ancestry less affected by gene flow. Individuals from South Atlantic sites did not form a monophyletic clade, but a large clade made up almost exclusively of North Atlantic (sites SU and CM) individuals was nested within the larger cluster of South Atlantic sites . This suAll but two individuals from the Gulf coast sites  formed a monophyletic clade , nested Our Bayesian clustering analysis, utilized to independently evaluate the grouping of individuals seen in the phylogeny, revealed a peak ΔK  for K = N. melanorhinus’ population structure, as well as an additional barrier along the Gulf. Genetic differentiation along the Atlantic appears to be clinal, with IBD alone shaping genetic structure.We observed a second cluster ) and geographic distance, often used as an indication of IBD, and found a significant correlation  among the 2014 sites , geographic distance described more of the variation in genetic differentiation between sites.We utilized Mantel tests to determine the significance of correlations between genetic distance, geographic distance, and several hypothesized population structures. We produced three hypothesized population structures based on the presence of one or both hypothesized barriers (Atlantic-Gulf divide and/or within Gulf barrier). We observed a significant correlation between genetic differentiation and the population structure model built with both barriers  row 2. TBecause of these significant correlations of genetic differentiation with both distance and hierarchical population structures described above, we performed several partial Mantel tests to control for the confounding effects between them. Partial Mantels allowed for the comparison of two variables, while controlling for the effects of a third. Genetic differentiation and geographic distance maintained a significant relationship when population structure was controlled for ) and geographic distance along both the Atlantic and Gulf coasts , where many ecological factors, especially those correlated with season length or temperature are likely to be more conserved. As such, geographic distance explained genetic differentiation (In the case of ntiation  better tCorrecting for relative effects of IBD and IBB).We found evidence for two barriers to gene flow in the phylogenetic pattern , the STRIncreased genetic differentiation in vertebrates, invertebrates, and plant species occurs along the Atlantic and Gulf coasts, specifically at the site of the Atlantic-Gulf divide . ThN. melanorhinus. Contemporary salt marsh habitats became established in North America in the past 3,000 to 4,000 years [N. melanorhinus’ single generation per year, the recent biogeography of the region is the most likely source of their population structure. Thus, we assume that contemporary biogeographic factors were sufficient to produce the patterns of IBB observed in N. melanorhinus.The contemporary lack of obligatory salt marsh habitat along most of the Florida peninsula seems a plausible barrier to dispersal for 00 years . Given NIn addition to the Atlantic-Gulf divide, we found evidence of a second barrier to dispersal along the Florida panhandle between localities IB and CR. This stretch of coast possesses few barrier islands and reduced salt marsh habitat. The salt marsh habitat that is present is primarily fragmented patches found upstream in estuaries. It is likely that the reduced connectivity of salt marsh habitat along the Gulf has resulted in the observed pattern of genetic differentiation.The ability to differentiate the affects of IBD and IBB is important for the proper interpretation of population structure. Some of the most commonly used tools (e.g. STRUCTURE) to investigate patterns of hierarchical genetic clustering may falsely detect hierarchical population structure in datasets influenced only by IBD , 38, 39.N. melanohrinus’ observed genetic variance. The results of the partial Mantel tests indicated that a pattern of IBD alone explained a significant proportion of the genetic variation. However the partial Mantel tests did not indicate an affect of IBB. A result that contradicted the findings of several of our previous analyses  that indicated hierarchical genetic structure explained a large proportion of the total genetic variance. We find it likely that the partial Mantel test failed to find the true signal of IBB where the other analyses did, as apposed to a type one error being present in all the other analyses. Multiple facets of this study’s sampling scheme may have made the detection of a signal of IBB difficult.Partial Mantel tests were used to separate the affects of IBD and IBB on N. melanorhinus’ genetic structure and may have obscured a signal of between-group variance [Inconsistences between our sampled data and our model of IBB may have led to type two errors in the partial Mantel tests. Within-group variance, like that observed across the Atlantic range, has a significant impact on variance , 40. TheNeoconocephalus melanorhinus’ strong signal of IBD is likely, at least in part, the result of its strict habitat specificity and the linear arrangement of its coastal habitat. Habitat specialists often show decreased dispersal across complex, heterogeneous landscapes because of the uneven distribution of their resources [esources . Populatesources , as the esources .N. melanorhinus could clarify the genetic contribution to size determination.We observed significant correlation between hind femur length and latitude. This variation in body size could be due to differences in either genetic or environmental effects. Ectotherms, reliant on a limited warm period for development, grow larger at lower latitudes where the summer seasons are longer . WhNeoconocephalus melanorhinus’ population structure showed characteristics in common with both the stepping-stone and hierarchical island (barrier) models. While IBD has a larger overall effect, biogeographic barriers have a measureable impact on genetic differentiation in N. melanorhinus. As the loss of North American salt marsh habitat continues, anthropogenic barriers to dispersal may become more common and of larger effect. Changes that reduce dispersal among salt marsh sites are likely to decrease genetic diversity within populations and increase the risk of local extinction [N. melanorhinus, which may be able to maintain dispersal across a more highly fragmented landscape. The significance of biogeographic changes on genetic variation should be of continued concern as our world is further shaped by anthropogenic habitat loss.tinction . HabitatS1 TableST values were significant (P < 0.05). Color gradient shows range of pairwise FST values in dataset, green the least divergent and red the most.All pairwise F(TIF)Click here for additional data file.S1 Text(TXT)Click here for additional data file.S2 Text(TXT)Click here for additional data file.S3 Text(TXT)Click here for additional data file.


Linckia laevigata is common on shallow water coral reefs of the Indo-West Pacific. Its large geographic distribution and comprehensive data from previous studies makes it suitable to examine genetic differentiation and connectivity over large geographical scales. Based on partial sequences of the mitochondrial cytochrome oxidase I (COI) gene this study investigates the genetic population structure and connectivity of L. laevigata in the Western Indian Ocean (WIO) and compares it to previous studies in the Indo-Malay-Philippines Archipelago (IMPA). A total of 138 samples were collected from nine locations in the WIO. AMOVA revealed a low but significant ΦST-value of 0.024 for the WIO populations. In the hierarchical AMOVA, the following grouping rejected the hypothesis of panmixia: (1) Kenya  and Tanzanian Island populations  and (2) the rest of the WIO sites . The genetic population structure was stronger and more significant (ΦST = 0.13) in the comparative analysis of WIO and IMPA populations. Three clades were identified in the haplotype network. The strong genetic differentiation  suggests that Indo-West Pacific populations of L. laevigata can be grouped into four biogeographic regions: (1) WIO (2) Eastern Indian Ocean (3) IMPA and (4) Western Pacific. The findings of this study support the existence of a genetic break in the Indo-West Pacific consistent with the effect of lowered sea level during the Pleistocene, which limited gene flow between the Pacific and Indian Ocean.The coral reef sea star  Amphiprion spp [Pterois spp [Chaetodon spp [Linckia spp [Eucidaris, Diadema, and Tripneustes [Echinolittorina reticulate [Thyca crystallina [The tropical Indo-West Pacific region hosts the world’s greatest diversity of marine shallow water species , with sprion spp , lionfisrois spp , butterfodon spp , and seackia spp  indicatepneustes –12, the ticulate  and Thycstallina  lack thistallina .Stylophora and Seriatopora, which have a shorter PLD [Haptosquilla pulchella and the tropical abalone Haliotis asinina contradict this pattern. Haptosquilla pulchella, with a long-distance dispersal capacity, shows strong genetic structure among Indo-West Pacific populations, while Haliotis asinina displays high levels of gene flow, despite having a restricted PLD [Most marine shallow water species are sedentary in nature, depending on their planktonic larval stage for long distance dispersal. Usually, the newly spawned larvae drift with the ocean currents, travelling 1000s of kilometres. This aspect makes larval dispersal in marine organisms a crucial factor, influencing population dynamics, population persistence, and range expansion. Genetic markers provide a tool that can determine the extent of larval dispersal. Generally, successfully dispersed larvae will leave a genetic trail of their movements, providing a proxy of estimating realised gene flow or connectivity . Speciesrter PLD . Althougcted PLD , 17. Thected PLD , topogracted PLD , and intcted PLD .Linckia laevigata is a common benthic organism on Indo-West Pacific coral reefs, with a wide distribution from the Western Indian Ocean (WIO) to southeastern Polynesia [Linckia laevigata, such as other asteroids, is a sedentary organism depending on its planktonic larval stage for long distance dispersal. Due to its wide distribution and comprehensive data from previous studies, it is a suitable species to examine and understand levels of connectivity in coral reef ecosystems in the Indo-West Pacific [L. laevigata [The blue sea star olynesia –23. It a Pacific . It repraevigata , suggestThe WIO is a constituent of the tropical Indo-West Pacific, representing an important biogeographic region of tropical seas . DespiteLinckia laevigata across the Indo-Pacific [Linckia laevigata, the present study investigates the genetic population structure, genetic diversity and historical demography of L. laevigata at nine sites in the WIO. Due to the long PLD of Linckia laevigata broad-scale genetic homogeneity in the WIO would be expected. Additionally, this study aims to assess connectivity of populations in the WIO to the IMPA. The comparative genetic analysis of L. laevigata populations from the WIO and IMPA complements previous research in testing the vicariance hypothesis [Linckia laevigata across the Indo-West Pacific. The sequence data of populations from the IMPA were obtained from two previous studies [Although several studies have been performed on -Pacific , 24, 33, studies , 34.Permission to collect samples was provided by Kenya Wildlife Services, Tanzania Commission for Science and Technology (COSTEC), Zanzibar Research Committee, the University of Tuléar (Madagascar), and Offices of the Municipal Agriculturist . Sampling in Indonesia was permitted under the governmental agreement between the German Federal Ministry of Education and Research (BMBF) and the Indonesian Ministry for Research and Technology (RISTEK), Indonesian Institute of Sciences (LIPI), Indonesian Ministry of Maritime Affairs and Fisheries (DKP), and Agency for the Assessment and Application of Technology (BPPT). It was carried out in co-operation with Hassanuddin University , Agricultural University Bogor , and Jenderal Soedirman University .L. laevigata were collected between February 2011 and February 2012 at nine sites located in the WIO  and HC02198 (5’-TAA ACT TCA GGG TGA CCA AAA AAT CA-3’). PCR was conducted in a 50 μl reaction volume containing 4 μl DNA template, 10 mM Tris-HCl (pH 9), 50 mM KCl, 0.2 mM dNTPs, 0.4 μl BSA (10 mg/ml), 0.2 μl Taq polymerase (5u/μl), 0.4 μM of each primer and 2.0–2.5 mM MgCl2. The following temperature profile was used to conduct PCR: 94°C for 5 min, followed by 35 cycles of 1 min at 94°C, 1.5 min at 45°C and 1 min at 72°C. Final extension was conducted at 72°C for 5 min [Extraction of genomic DNA was done with the Nucleospin extraction kit, following the manufacturer’s protocol. The amplification of the partial COI gene was through polymerase chain reaction (PCR), using the primers described by : LC01490The 138 sequences analysed for this study are available in GenBank under accession numbers KF527577-KF527714. In total, 396 sequences from the IMPA were utilised for the comparative analysis .http://cmpg.unibe.ch/software/arlequin35, version 3.5.1.3) [The software ChromasPro  was used for editing the WIO sequences. The correct species identity of each sequence was verified using the Basic Local Alignment Search Tool (BLAST) at GenBank. To ensure that only functional mitochondrial DNA was used and not nuclear pseudogenes, the sequences were translated into amino acids with the software Squint Alignment Editor (version 1.02), in order to verify that the DNA sequences code for a functional protein. The online service of FaBox (version 1.4) was used to collapse the sequences into haplotypes. A multiple sequence alignment was obtained with CLUSTAL W  as imple3.5.1.3) .D-test [Fs-test [D values indicate selective sweeps or population expansion after a recent bottleneck event [Tajima’s D-test  and Fu’F[Fs-test  were useck event . To analck event  of the sck event  and Harpck event . The misL. Laevigata was tested using analysis of molecular variance (AMOVA) [ST-values. The software Arlequin (version 3.5.1.3) [L. laevigata population was tested using a Mantel test [The significance of population structure in  (AMOVA)  and pair3.5.1.3)  was used3.5.1.3) . Clades tel test , as impltel test .ST) may be affected by unequal sample sizes, two datasets were analysed: (1) dataset excluding all sample sites with less than 10 individuals and (2) dataset with all sample sites. However, the genetic differentiation (ΦST) based on the two datasets was very similar and significant. Therefore, we present the results based on all samples sites, while the results based on the reduced dataset is provided as supplementary information  and 184 substitutions. High values of haplotype (h) and nucleotide (π) diversity were recorded, with the average haplotype diversity and nucleotide diversity being 0.865 (0.56–1.0) and 1.9% (0.7–5.4%) respectively . From thD test, while the results of Fu’s Fs test could not reject the null hypothesis for all the sites. On the other hand, the mismatch distribution analysis and Rogers’ test of sudden population expansion indicate population expansion for all sites, except Tuléar . After Bonferroni correction for the 36 pairwise ΦST-values, the significance level was adjusted from 0.05 to 0.0014. Correspondingly, the estimated pairwise ΦST-values were low and insignificant for all sites, with the exception of one site in Kenya (Diani), which was significantly differentiated from Angel reef, Tanzania . Nevertheless, a plot of pairwise ΦST-values against geographic distance among sample sites did not exhibit a positive correlation, confirming the lack of isolation-by-distance.The genetic population structure of Tanzania . Three hTanzania , but onlL. laevigata haplotypes found in the WIO and the IMPA are presented in the haplotype network . The hierarchical AMOVA was also conducted for two groupings based on geographical location of sites, both rejecting the hypothesis of panmixia: (1) WIO, (2) EIO (Andaman Sea and Kupang), (3) IMPA, and (4) WP (New Britain and Biak) , as well as (1) Indian Ocean (WIO and EIO), (2) IMPA, and (3) WP .AMOVA results for the 36 sample sites in the WIO and IMPA revealed a strong genetic structure  indicates low genetic diversity and little genetic divergence . The est monodon , fiddlernnulipes , mangrovguttatum , but simmeinerti . The higmeinerti . Indeed,ttleneck –45. Thisttleneck –56. Duriaevigata , 34, 57 llopisos , mangrovmeinerti , fiddlernnulipes , mud cra serrata , and gia monodon .L. laevigata that provides a mechanism for long distance dispersal, AMOVA results for the population from the WIO showed a weak, but significant population structure . This result is similar to the weak genetic structure in L. laevigata reported for populations in the IMPA, with a low fixation index (ΦST = 0.03) but significant structure [Scylla serata and the mangrove crab Neosarmatium meinerti along the East African coast, despite their long PLD of 3–4 weeks [L. laevigata populations might be caused by local ocean currents, life history traits, topographic features or success of immigrants in mating after settlement [L. laevigata is caused by local oceanographic features, such as local downwelling or eddies [Acropora tenuis, separating Kenyan and northern Tanzanian sites from those in the South [Previous studies have demonstrated that genetic divergence among marine populations can occur even in the absence of any apparent barriers to dispersal –61. Thistructure , 34. Sha–4 weeks , 59. Howttlement . Given tr eddies . A more he South . A plaushe South –65. ThisL. laevigata populations due to sea level fluctuations in the Pleistocene [L. laevigata, because their sampling efforts were concentrated in the IMPA. The present study complements these previous investigations by combining the sequence data from the WIO and IMPA. The AMOVA for the combined data sets revealed a strong genetic structure , comparable with other studies on L. laevigata in the Indo-West Pacific [Penaeus monodon [Acanthaster planci [Holothuria atra and Holothuria nobilis [L. laevigata is consistent with limited gene flow between the two ocean basins for tens of thousands of years [L. laevigata could have happened either in the past or present along the southeastern coast of Australia because the larvae of this tropical species would not survive the low temperate temperatures [Echinolittorina reticulata [Myripristis berndi [Xiphius gladius, and the mud crab Scylla serrata [Tridacna maxima and Tridacna squamosa [Amphiprion akallopisos [Acanthaster planci [Penaeus monodon [Numerous studies have shown a sharp genetic break between Indian and Pacific Ocean istocene , 33, 34. Pacific , 33. Mor monodon , 66, ther planci , and the nobilis –69. The of years . The Torof years . It is us berndi , the swo serrata . This lasquamosa , the anellopisos , the cror planci , and the monodon , 66. TheThe genetic break between the Indian and Pacific Ocean is also corroborated by the geographical distribution of the different clades . On the S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.


Posidonia oceanica, the foundation species of one of the most important ecosystems in the Mediterranean Sea. Yet, the location of cold climate refugia (persistence regions) for this species during the Last Glacial Maximum (LGM) is not clear, precluding the understanding of its biogeographical history. We used Ecological Niche Modelling together with existing phylogeographic data to locate Pleistocene refugia in the Mediterranean Sea and to develop a hypothetical past biogeographical distribution able to explain the genetic diversity presently found in P. oceanica meadows. To do that, we used an ensemble approach of six predictive algorithms and two Ocean General Circulation Models. The minimum SST in winter and the maximum SST in summer allowed us to hindcast the species range during the LGM. We found separate glacial refugia in each Mediterranean basin and in the Central region. Altogether, the results suggest that the Central region of the Mediterranean Sea was the most relevant cold climate refugium, supporting the hypothesis that long-term persistence there allowed the region to develop and retain its presently high proportion of the global genetic diversity of P. oceanica.Past environmental conditions in the Mediterranean Sea have been proposed as main drivers of the current patterns of distribution of genetic structure of the seagrass  Past changes in oceanographic conditions in the Mediterranean Sea have largely influenced the current patterns of biodiversity and genetic structure of its species due to changes in sea level, circulation, and climate forcing across time3. In addition, the genetic structure of Mediterranean species has also been shaped by persistent differences between the Western and Eastern basins of the Mediterranean Sea, with the later being more oligotrophic and warmer but less biodiverse than the western basin4. A genetic boundary between the Eastern and Western Mediterranean Sea has been described for various species including seagrasses5, fish6, sea cucumbers7, crabs8 and bivalves (e.g. ref. The Mediterranean Sea is a semi-enclosed basin considered a hotspot of biodiversityPosidonia oceanica (L.) Delile is one of the most important Mediterranean endemics, as its meadows form valuable ecosystems providing food, substrate and shelter for other species10. The beds of P. oceanica are threatened ecosystems, which have been declining mainly in the north-west Mediterranean Sea because of its sensitivity to human impacts, biological invasions and climate change13, resulting in a loss rate of P. oceanica density or biomass at −6.9% yr−1 in the Mediterranean14. A west-east separation has been found in the genetic structure of P. oceanica populations, leading to hypothesize an on-going vicariance process17. These studies found higher allelic richness in the central P. oceanica populations, around the Siculo-Tunisian Strait, and hypothesized that it might have acted as a genetic boundary during low sea level periods and as a secondary contact zone after the Last Glacial Maximum (LGM ~26000–19000 ya). Given that P. oceanica meadows grow very slowly an can persist over millenary time scales18, with genotypes possibly exceeding 10,000 years in maximum age19, Pleistocene glacial episodes are particularly likely to have affected the current genetic structure and distribution of P. oceanica. However, the location of refugia allowing persistence during the most recent cold maximum (the last Pleistocene ice age) remains unclear, leaving uncertainties in the biogeographical history of this foundational seagrass species.Posidonia oceanica by using ecological niche modelling (ENM) to infer the hypothetical effects of past climatic conditions on the distribution of genetic diversity of P. oceanica, and by comparing the model results with available information on genetic structure of the populations across the species distributional range. ENM combines the known distributional data for the species with predictors  to produce a statistical model transferable to the geographical space. Species distribution models together with phylogeographic data lead to inferences of biogeographical processes (e.g. ref. 21. These approaches (ENM coupled with genetic data) applied in the marine realm have revealed the key roles played by glacial refugia in harboring unique high diversity that was left behind as climate-driven range expansions proceeded25.In this study we address these biogeographic questions for P. oceanica to the conditions during the Last Glacial Maximum (LGM) to identify the putative past glacial refugia and assess their correspondence with the currently known spatial distribution of genetic diversity of the species throughout its range. We hypothesize that glacial refugia areas still harbour the highest genetic diversity across the species range, due to long-term persistence of stable populations. We further hypothesize that regions of reduced genetic diversity found in the extant range of P. oceanica correspond to areas affected by range contractions during colder periods in the Mediterranean Sea, as local extinctions and bottlenecks reduce genetic diversity. To test these hypotheses, we produce an ensemble of species distribution models for current conditions and its corresponding projections to the LGM under two different ocean general circulation models (CNRM-CM5 and CCSM4). Our results are then compared with available data on allelic richness and private alleles for P. oceanica across the Mediterranean Sea.In this study, we hindcast the distribution of Two uncorrelated variables were retained for the rest of analyses: the minimum SST in winter and the maximum SST in summer. Validation scores obtained for the ensemble for present conditions were: AUC = 0.866; TSS = 0.570; sensitivity = 91.504; specificity = 65.424; while mean validation results for all the models computed were: AUC = 0.813; TSS = 0.526; sensitivity = 87.303; specificity = 66.187  showed higher probability of presence during the LGM than those for the Western and Eastern Mediterranean Fig. . This isPosidonia oceanica meadows compared to those found at present. According to our LGM ensemble, SST seems to have been sufficiently warm in the Southern Mediterranean Sea during the LGM to allow persistence of this seagrass species in this region. There, three putative glacial refugia covered separate coastal areas in each of the Western, Central and Eastern Mediterranean Sea, in agreement with the three main genetically structured regions revealed by previous studies17. Model results suggest that the Central Mediterranean Sea may have constituted the most relevant region allowing the persistence for Posidonia oceanica meadows during the Last Glacial Maximum (LGM), as it is the region that showed both the highest probability of occurrence under LGM conditions and where suitable conditions have remained available through present times. The concurrence between our predictions and the higher genetic diversity  found in this Central region further confirms the relevance of the Siculo-Tunisian Strait as a secondary contact zone between western and eastern P. oceanica populations, as suggested by ref. The cold waters dominating the Mediterranean Sea during the Last Glacial Maximum probably resulted in poorer conditions for the development of P. oceanica . Thus, after the LGM, the Northern Mediterranean basin could have experienced a recolonization process by seagrass originating in the southern LGM refugia, from which most of the current meadows derive. In contrast, the maximum SST could not have been a factor impeding the presence of P. oceanica anywhere in the Mediterranean during the LGM, as all the area was under the thermal limit (29.21 °C) inferred from current presence patterns.The progressive warming of the Mediterranean waters from the LGM to the present time caused a northwards shift in the distribution of ica Fig. , which nica Fig. . The min17, which found in this Central region the highest allelic richness . However, together with the poorer conditions revealed by our study, there is evidence of meltwater discharges affecting this area after the LGM (from 9.2 ka to 6.5 ka) which affected cold-sensitive planktonic assemblages26. Thus, past meadows of P. oceanica could have been more scattered than nowadays and might have suffered eventual range contractions until its postglacial colonization was favoured by widespread warming of the Mediterranean basin throughout the Holocene.Although our findings support that during the LGM the Central Mediterranean region could have acted as an important refugial area, the main connection between the western and eastern basins (the Siculo-Tunisian Strait) is also an important boundary in the genetic structure of several Mediterranean species besides P. oceanica populations are remnants of past more widespread meadows. A northwestern to southeastern decreasing gradient of species richness and nutrient concentrations has been described in the Mediterranean Sea2. However, although the distributional range of P. oceanica appears to reflect a general trend of reduction towards the southeastern basin, similar to other Mediterranean species, unique distinct genetic diversity remains there, in apparently genetically isolated ancient populations, as also found for the seagrass Cymodocea nodosa5.The existence of a higher number of private alleles in the Eastern Mediterranean Fig.  suggests27. The minimum thermal limit found for the species occurrences renders the entire study area suitable in the winter (except the colder Black Sea). However, there is a scarcity of occurrences below 11 °C  in the eastern basin27. Our model, however, also predicts that excessive present summer temperatures already limits the distribution of P. oceanica at present in some areas of the Eastern Mediterranean, particularly in the SE region  and according to the known current distribution of the seagrasssee Fig. . OccurreCymodocea nodosa31. Another source of uncertainty could be attributed to the differences that exist between the LGM OGCMs available to produce the hindcasts, a challenge which we have tried to reduce by using an ensemble approach.The study is affected by uncertainties associated to the resolution of the input data used, characteristic of efforts to model coastal species. Satellite-derived data for large-scale oceanic variables are not usually available at a resolution higher than 9.2 km (about 0.083° × 0.083°) which could affect the capacity to resolve marine microhabitats. Besides, the availability of LGM oceanic variables is more limited than for current times, thus the set of predictors accessible for hindcasting is reduced in comparison to that available for terrestrial studies. In this study, the use of just two SST variables might also have produced some overprediction. This could have affected mainly the prediction for the North African coast where uncertainty is higher due to the lack of records for most of the countries in the region, a bias also noticed for P. oceanica is experiencing an important decline in meadows in the last 50 years due to local  and global impacts 27. This tendency towards regression affects the areal extent, shoot density and cover of the seagrass14, and possibly is already producing a loss in the genetic diversity shown here. The results of this research support the idea that past climate influenced the genetic structure and diversity of P. oceanica. But the western basin, which shows the higher probability of occurrence nowadays, is also showing a higher detected decline due to anthropogenic pressures combined with current warming14, which is forecasted to become more acute in the future32. The fragmentation and disappearance of meadows could produce irreversible genetic bottlenecks which could threaten the survival of key populations and of much of the species gene pool in case of a probable future climate warming, as the poleward migration of the biogeographical range generally displayed by marine species to cope with ocean warming33 is precluded, for the case of the endemic Mediterranean species, by the presence of the European continental mass35. P. oceanica has already been considered vulnerable to climate change and a possible extinction of the western P. oceanica meadows by 2050 due to increasing frequency and severity of heat waves with climate change has been suggested32. More research on forecasting the distribution of this long-lived seagrass could provide insight on the impact of climate warming on the future extent of the species, and help apportion the effects of ocean warming from those of local impacts.P. oceanica might have been restricted to the southern Mediterranean Sea as a consequence of the colder LGM northern Mediterranean Sea water. In the South, three glacial refugia have been detected in congruence with previously studied genetic regions. Among them, the most important might be the refugium located in the Siculo-Tunisian Strait, that was previously defined as a transition zone between western and eastern basins, and where populations of P. oceanica have been able to preserve their highest genetic diversity.This study has shown that past LGM distribution of P. oceanica were compiled from an extensive literature review, and were complemented with locations digitized from the seagrass atlas of Spain36, and ref. http://www.gebco.net/) to limit the study area to the photic zone (~40 m depth).Data on the presence of 37, while paleoclimate data were obtained from two LGM experiments from the Coupled Model Intercomparison Project (CMIP5): CNRM-CM5 (over 200 years) and CCSM4 (over 100 years). We inferred the LGM’s coastline by calculating a mean sea-level change of −116 m relative to present from bathymetric GEBCO data, consistently with the ice-sheet reconstruction provided by the Paleoclimate Modelling Intercomparison Project Phase III .We used environmental variables available to represent relevant present and LGM conditions: the minimum, mean and maximum SST in summer and winter, and the average salinity. Variables for current conditions were derived from OSTIA system38 in R. Six presence-absence algorithms were used: generalized additive model (GAM), flexible discriminant analysis (FDA), generalized boosting model (GBM), multiple adaptive regression splines (MARS), generalized linear model (GLM), and random forest (RF). Although “presence” records are unambiguous, the reliability of “absence” data are more difficult to ascertain. If no reliable absence data are available, the method used to obtain “pseudo-absences” strongly conditions the prediction. Here we use a random selection of pseudo-absences to obtain a constrained distribution39. Due to the relatively small study area , the same number of pseudo-absences as presence cells were extracted at random, but creating 3 pseudo-absence sets. We split data into a validation (30%) and a calibration set (70%) and performed 10 iterations for each set of pseudo-absences and algorithm. The 180 models obtained (6 modelling techniques x 3 pseudo-absence sets x 10 iterations) were evaluated by means of the area under the receiver operating characteristic (ROC) curve (AUC), ROC-derived specificity (absences correctly predicted) and sensitivity (presences correctly predicted) measures40, and the true skill statistic (TSS41). We used the threshold at which the sum of the sensitivity and specificity is highest38. A “committee averaging” ensemble for present conditions was computed by averaging binary predictions, we used this ensemble approach for its adequate performance on the prediction of other seagrass species31. Just those models achieving TSS ≥ 0.55 or AUC ≥ 0.8 were used to produce ensembles. Both ensembles  were projected to the LGM to produce separate hindcasts derived from the two LGM reconstructions (CCSM and CNRM). As ocean general circulation models (OGCMs) show variability among them, we produced an averaged ensemble computed from both OGCMs hindcasts and validation criteria. Moreover, we computed a clamping mask to identify uncertain locations in our hindcasts using LGM variables which minimum and maximum values were outside the training range.We first tested correlation among the seven initial variables discarding those showing a Pearson’s product-moment correlation (r) ≥ 0.7 (p < 0.001), to avoid the use of strongly-correlated variables. We implemented an ensemble approach using “biomod2” packageWe assessed the importance of each variable in the ensemble for present conditions by calculating iteratively the correlation between the complete model and the model without one variable see ref. . After pP. oceanica populations across the Mediterranean Sea from two previous studies17 and calculated the mean values of allelic richness and private alleles in each genetic cluster identified by them. Reference We compiled data of genetic diversity and structure in Supplementary InformationDataset 1


Alvinella pompejana. DNA sequences from one mitochondrial and eleven nuclear genes were examined in samples collected from ten vent localities that comprise the species’ known range from 23°N latitude on the East Pacific Rise to 38°S latitude on the Pacific Antarctic Ridge.The Equator and Easter Microplate regions of the eastern Pacific Ocean exhibit geomorphological and hydrological features that create barriers to dispersal for a number of animals associated with deep-sea hydrothermal vent habitats. This study examined effects of these boundaries on geographical subdivision of the vent polychaete Nm < 1). Estimates of effective population size decreased with southern latitudes: NEPR > SEPR > PAR.Multi-locus genotypes inferred from these sequences clustered the individual worms into three metapopulation segments — the northern East Pacific Rise (NEPR), southern East Pacific Rise (SEPR), and northeastern Pacific Antarctic Ridge (PAR) — separated by the Equator and Easter Microplate boundaries. Genetic diversity estimators were negatively correlated with tectonic spreading rates. Application of the isolation-with-migration (IMa2) model provided information about divergence times and demographic parameters. The PAR and NEPR metapopulation segments were estimated to have split roughly 4.20 million years ago (Mya) ), followed by splitting of the SEPR and NEPR segments about 0.79 Mya . Estimates of gene flow between the neighboring regions were mostly low  contains supplementary material, which is available to authorized users. The past 25 years of population genetic studies have revealed a number of physical and biological processes that shape the geographical structure, interpopulation connectivity and genetic diversity of deep-sea hydrothermal vent species (reviewed in ). ExtrinAlvinella pompejana Desbruyères and Laubier,  sequences (710 bp) in samples that ranged between 21°N to 32°S latitude identified distinct NEPR and SEPR metapopulation segments, but found no evidence for a distinct segment occupying the PAR axis [mtCOI, multi-locus allozymes and four nuclear genes in A. pompejana samples ranging between 13°N and 21°S confirmed the distinct NEPR and SEPR segments [A. pompejana, 23°N on the EPR to 38°S on the PAR, reported for the first time in this study.Previous studies of PAR axis . A subsesegments , 20. To Tevnia jerichonana exhibited a broad zone of intergradation between distinct metapopulation segments occupying the NEPR and PAR axes [mtCOI evidence alone reached different conclusions [Riftia pachyptila did not corroborate an earlier mtCOI study that concluded SEPR and PAR populations were partially isolated across the Easter Microplate boundary [Bathymodiolus thermophilus and B. antarcticus [Comprehensive geographical sampling and the application of multi-locus genetic markers have often improved or even contradicted previous inferences about genetic structure and the demographic history of vent species. For example, a multi-locus investigation revealed that SEPR populations of the siboglinid polychaete PAR axes . Inferenclusions , 18. Coyclusions  multi-loboundary , 24 thatmtCOI and 11 nuclear genes in geographical samples that extend the known range for A. pompejana northward to 23°N latitude in the Alarcón Basin and southward to 38°S on the Pacific Antarctic Ridge. The present study reports the most comprehensive geographical sampling and genetic analysis of this species, to date.Computer simulations revealed that inferences about population subdivision and isolation-by-distance are limited by the number of independent gene loci examined and the completeness of population sampling . SamplinAlvin  and the remotely operated vehicles (ROVs) Tiburon and Doc Ricketts . Upon recovery at the surface, the samples were briefly stored in cold (2 °C) filtered seawater prior to dissection and tissue removal. Tissue samples were frozen at −70 °C on board the vessels and subsequently stored at −80 °C in the land based laboratories. DNA sequencing was conducted with subsamples of individuals from each of the sample localities. Genomic DNA was extracted from muscle tissue with the Qiagen Blood and Tissue kit, following manufacturer’s protocols .Samples were obtained during oceanic expeditions that spanned 21 years  microsatellite repeats. Of these, we chose eight polymorphic loci, based on their PCR-efficacy, for further study: AP_NC1, AP_NC3, AP_NC8, AP_NC20, AP_NC22, AP_NC28, AP_NC32 and AP_NC43. The primer pairs used for nested PCRs are provided  extracted from one individual from 14°S was sent to the National Instrumentation Center for Environmental Management (NICEM) at Seoul National University, Seoul Korea, for pyrosequencing on a Roche/454 Life Sciences Genome sequencer GS FLX machine. We obtained 28,222 reads with a total length of 9,804,791 base pairs. The pyrosequencing sequence reads were partitioned into protein-coding genes vs. non-coding DNA fragments identified through BLAST. Primer pairs were then designed for a subset of non-coding fragments containing > 450 base pairs (bp) and no poly  for cloning and prepared the products for sequencing with the Hybrid-Q Plasmid rapidprep kit . All DNA sequences obtained in this study were deposited in GenBank .We used PHASE v. 2.1.1 , 28 to rIMa2), Tajima’s D, and Fu's FS test. Putatively recombinant fragments were excluded and the longest remaining fragment was used for subsequent analyses  and nuclear (π) diversity indices, Tajima’s D [FS [D and Fu’s Fs were corrected by Bonferroni method. Allelic richness and private allelic richness was estimated by rarefaction methods implemented in Hp-rare v. 1.0 [Genepop v. 4.2 [HapStar v. 0.7 [Arlequin v. 3.5 [We used SITES  program mmended, ). We usen v. 3.5  to estimjima’s D  and Fu’ss D [FS . Signifie v. 1.0 . Tests fp v. 4.2 . A haplor v. 0.7  based onn v. 3.5 .PGDSpider [Structure v. 2.3.4 [K) [K range from 1 to 10 and repeated the simulations at least five times for each value of K using admixture model with correlated allele frequencies among populations [6 MCMC (Markov chain Monte Carlo) generations after excluding the first 5 × 105 as ‘burn-in’. The most probable value of K was estimated by the delta K method of Evanno et al. [BayesAss v. 3 [7 iterations (−i) with random number seed (−s) and burn-in of 1 × 106 (−b), a sample interval of 100 (−n), allele frequencies of 0.3 (−a), and inbreeding coefficients of 0.4 for (−f).We used the GDSpider  data forv. 2.3.4  to estim.3.4 [K) , 39. We ulations . All simo et al. . We usedAss v. 3  to assesStructure: northern East Pacific Rise ; southern East Pacific Rise ; and Pacific Antarctic Ridge (PAR: 32S and 38S)  . Samplesn v. 3.5  with an GenoDive v. 2.0b23 [r) between pairwise genetic differentiation (FST) based on nuclear genes and mtCOI gene separately, and geographic distances . Because hierarchical subdivision can generate an apparent Isolation-by-Distance (IBD) pattern, we partitioned the samples into three regions, NEPR, SEPR, and PAR and applied a stratified Mantel test. The “Stratified” option in GenoDive randomly permutes the data within partitions to test for the residual correlations between the genetic and geographic matrices.We used . 2.0b23  to examiHd) for the twelve loci. Seafloor spreading rate was estimated under the NUVEL-1A model as implemented by the website < http://ofgs.aori.u-tokyo.ac.jp/~okino/platecalc_new.html>. Correlation analyses were implemented with SPSS v.21 (IBM Inc.).Finally, we examined correlations between degrees north latitude, tectonic spreading rates (mm/year) for the sampled localities, and average haplotype diversities (IMa2) method [IMa2 model to analyze neighboring pairs of population clusters (NEPR vs. SEPR and SEPR vs. PAR) previously identified with the Structure analysis. Because IMa2 assumes no recombination, we used non-recombinant segments of each of the 11 nuclear loci resulting from the SITES [mtCOI sequence data. We applied the Hasegawa-Kishino-Yano (HKY) [mtCOI  and the Infinite Sites (IS) model [I = 1.0). Each analysis included at least 1.0 × 107 MCMC steps, and the first 1.0 × 105 steps were discarded as burn-in, with 40 attempts of chain swapping per step, 40 chains with geometric heating, and h1 and h2 values of 0.975 and 0.75, respectively. The mutation scaled model parameters of IMa2 were transformed into corresponding demographic quantities  using a mutation rate of mtCOI. Due to the absence of confirmed mutation rates in Alvinella pompejana, we borrowed a substitution rate of 1.0–2.0 % per million years for mtCOI, as estimated for marine taxa partitioned across the Isthmus of Panama [We used the Isolation with Migration  method , 46 to ihe SITES  program no (HKY)  mutationS) model  for the f Panama , 50. Sigf Panama .mtCOI and nuclear markers as in the 2-population analyses. After 5.0 × 105 burn-in steps, at least 4.5 × 106 MCMC steps proceeded with 100 chains with geometric heating, and h1 and h2 values of 0.99 and 0.75, respectively, for 200 attempts of chain swapping per step. We conducted this process multiple times with the same options using random seeds, and genealogies were saved every 100 Markov chain steps (default). Finally, we combined 2.0 × 105 genealogies which were produced by multiple runs in L-mode, and tested significance with log-likelihood ratio test [The pairwise results were then used to analyze a three-population model  as described in , 46. We tio test . DemograP-value: 0.06–1.00). Noticeable regional differences existed in frequencies of the nuclear alleles and mitochondrial haplotypes. For example, the mtCOI haplotype network , but the NEPR variants tended to differ from predominant haplotypes by more mutations, leading to substantially greater nucleotide diversity in the NEPR samples  and variants that differed by only one or two mutations. AP_NC1 also had a single dominant allele, but some of NEPR variants (blue) differed by as many as 11 mutational steps. The remaining eight loci exhibited more diverse networks. In general, the NEPR samples had more singletons and rare alleles than SEPR and PAR samples. These regional differences were reflected in estimates of genetic diversity . Haplotype diversity (Hd) ranged from 0.44 (SAHH) to 0.87 (mtCOI) for the protein-coding markers, and from 0.41 (AC_NC1) to 0.87 (AC_NC22) for the non-coding markers.Haplotype networks for eleven nuclear loci did not exhibit comparable fixed differences between the NEPR and the southern (SEPR + PAR) groups Fig. . Insteadty Table . Aside fHd, r = −0.893, P < 0.001), rarefaction estimates of allelic richness , and private alleles  all decreased with southern latitudes. Estimates of allelic richness and of private alleles are correlated . Although Hd is expected to be more sensitive to the evenness of allelic frequencies, in this case Hd is almost perfectly correlated with richness . Tectonic spreading rates for the sampled localities ; consequently, spreading rates also were inversely correlated with genetic diversity Hd  and richness .Estimators of genetic diversity declined with southern latitudes Table . For exaStructure analysis identified three geographical clusters : NEPR ; SEPR ; and PAR (32S and 38S), north to south in order ; and the pink individual in SEPR (PP = 0.087).The ers Fig.  but contmtCOI, more of the variation (52.8 %) existed among regions, 0.6 % among samples within regions, and 46.6 % among individuals within samples. Mantel tests identified significant correlations between genetic and geographic distances . However, assignment of each sample to its respective geographical region (i.e. strata), and application of the stratified Mantel test revealed no significant residual correlation with geographical distance .AMOVA identified hierarchical partitioning of genetic variation for the 11 nuclear loci: 22.7 % among the NEPR, SEPR and PAR regions, 2.6 % among samples within regions, and 74.7 % among individuals within samples. For Nsms = 0.61) exceeded northward migration (2Nnmn = 0.15). A series of nested model tests on this observation resulted in statistically supports for the presence of gene flow , although unidirectional migration was not rejected . This northward direction was statistically supported by the log-likelihood ratio test. The estimated time of population splitting between the SEPR-PAR pair was much older, ~4.20 Mya (95 % HPD: 2.42–33.42 Mya)  can be ranked in the following order: NEPR > SEPR > PAR.Analysis of a three-population IMa2 model  and southward by 666 km to a northeastern segment of the Pacific Antarctic Ridge (38°S latitude). Multi-locus genotypes of individuals sampled from ten localities distributed across this range clustered into three metapopulation segments . Dispersal along contiguous segments of the three ridge axes appeared to be relatively unimpeded, despite high bottom-currents and distances of several hundred kilometers between active vents [A. pompejana’s capacity for long-distance dispersal [A. pompejana populations distributed along a ridge axis [Deep-sea expeditions conducted during 2005 and 2015 obtained samples that extend the known range of ve vents . Developispersal . Disconndge axis .FST values) increased significantly with geographical distances among the sample locations . The PAR and SEPR axes exhibit “superfast” spreading rates of 141–151 mm/yr [A. pompejana and the co-distributed siboglinid tubeworm, Riftia pachyptila, correspond with the rapid cycles of habitat extinction and rebirth in this region [A. pompejana reported evidence for recent demographic expansion within individual populations [D and Fu’s FS from a larger sample of genes did not corroborate these conclusions  was partitioned among three geographical regions. Maximum likelihood estimates of divergence times suggest that subdivision originated ~1 Mya across the Equator and 2.5–5.3 Mya across the Easter Microplate boundary. Nonetheless, low degrees of gene flow (2 Nm < 1) appeared to maintain some genetic continuity across both boundaries.The geographical distribution of genetic diversity of habitats , 60. Uni


The evolutionary trajectory of populations through time is influenced by the interplay of forces  acting on the standing genetic variation. We used microsatellite and mitochondrial loci to examine the influence of population declines, of varying severity, on genetic diversity within two Hawaiian endemic waterbirds, the Hawaiian coot and Hawaiian gallinule, by comparing historical (samples collected in the late 1800s and early 1900s) and modern (collected in 2012–2013) populations. Population declines simultaneously experienced by Hawaiian coots and Hawaiian gallinules differentially shaped the evolutionary trajectory of these two populations. Within Hawaiian coot, large reductions (between −38.4% and −51.4%) in mitochondrial diversity were observed, although minimal differences were observed in the distribution of allelic and haplotypic frequencies between sampled time periods. Conversely, for Hawaiian gallinule, allelic frequencies were strongly differentiated between time periods, signatures of a genetic bottleneck were detected, and biases in means of the effective population size were observed at microsatellite loci. The strength of the decline appears to have had a greater influence on genetic diversity within Hawaiian gallinule than Hawaiian coot, coincident with the reduction in census size. These species exhibit similar life history characteristics and generation times; therefore, we hypothesize that differences in behavior and colonization history are likely playing a large role in how allelic and haplotypic frequencies are being shaped through time. Furthermore, differences in patterns of genetic diversity within Hawaiian coot and Hawaiian gallinule highlight the influence of demographic and evolutionary processes in shaping how species respond genetically to ecological stressors. Standing genetic variation within populations is influenced by a variety of factors: biological characteristics of the species , human disturbance , and evolutionary forces acting although stochastic , deterministic , and adaptive  processes ; the only vertebrates to colonize it were highly vagile species (birds and bats), and its diverse avifauna can be traced to as few as 20 colonizing species James, . SimilarFulica alai) and Hawaiian gallinule  are endangered waterbirds endemic to the Hawaiian Archipelago  bones have only been found in deposits postdating human colonization . Although these two species share many life history traits, a few potentially influential demographic differences exist, such as severity of decline, effective population sizes predecline, dispersal propensity, and longevity  and Hawaiian gallinule (n = 23) collected between 1893 and 1939 from Oahu, Hawaii, were obtained through museum tissue loans  and Hawaiian gallinule (n = 29) were trapped on James Campbell National Wildlife Refuge, Oahu, Hawaii, from 2012 to 2013. These samples represent the modern time period. Blood samples were collected from the brachial vein and stored in preservation buffer .DNA from historical samples was extracted using a phenol–chloroform protocol and recovered with a Microcon centrifugal filter . Genomic DNA from contemporary samples was extracted using a “salting out” procedure described by Medrano, Aasen, and Sharrow . GenomicMF673896–MF673904).Hawaiian coot and Hawaiian gallinule individuals were sequenced at two mitochondrial DNA (mtDNA) loci: We amplified a 824 base pair (bp) and 826 bp fragment, respectively, of control region using primer pairs CR200L (5′‐TTCATGCATGCTTTAGGG‐3′) and CR1029H (5′‐CACCAATTTCCAGRAGTCC‐3′) as well as a 752 bp and 753 bp fragment, respectively, of NADH dehydrogenase (ND) two with primer pairs ND2_224L (5′‐CTMCTACTATTCTCCAGCAC‐3′) and ND2_1003H (5′‐GGGTGATAAGGGTAGGAG‐3′). Smaller fragments of mtDNA were amplified for the historical samples using primer pairs CR200L and CR343H (5′‐GTGGRGGRGTATATTCGTG‐3′) for control region resulting in a 140 bp and 141 bp fragments in Hawaiian coot and Hawaiian gallinule, respectively, and ND2 primer pairs ND2_464L (5′‐GCCATCCTCTCAGCWGCC‐3′) and ND2_720H (5′‐GCCTGCTAGGGAKAG‐3′) resulting in a 172 bp fragment. PCR amplifications, cycle‐sequencing protocols, and postsequencing processing followed Sonsthagen, Talbot, and McCracken . For qua2.3h) and nucleotide (π) diversity were calculated at mtDNA loci in ARLEQUIN version 2.0 , and linkage disequilibrium at the microsatellite loci in FSTAT version 2.9.3 Goudet, . Tests fr, S Fu,  and Taji Tajima,  were cals FS Fu, . An unros FS Fu,  using ths FS Fu, , to illu2.4FST and ΦST for microsatellite and sequence data, respectively, in ARLEQUIN, adjusting for multiple comparisons using Bonferroni correction (α = 0.05). Tamura–Nei nucleotide substitution model  ranging from one to five; the analysis was repeated 10 times to ensure consistency across runs. We followed the method of Evanno, Regnaut, and Goudet  was estimated with NeESTIMATOR v2  had evidence of null alleles. Reamplification of these loci yielded consistent genotypes, and all loci and populations were in HWE and linkage equilibrium. Therefore, we retained all loci in subsequent analyses. Indices of genetic diversity based on microsatellite loci were similar between time periods for both species (95% confidence limits overlapped), although fewer private alleles were observed within the contemporary Hawaiian gallinule  were observed; however, no structure was detected based on FST and RST  suggestive of population decline. The other timeframes were in mutation drift equilibrium.Fluctuations in population demography were observed within the contemporary Hawaiian gallinule timeframe; significant heterozygote excess was detected at all mutation models  provide anecdotal evidence that Hawaiian coot has occupied the Hawaiian Archipelago for a longer duration and therefore has had more time to overcome the initial reduction in genetic diversity in the wake of colonization. Among the same 48 microsatellite loci screened for variability, genetic diversity is ~48% less within Hawaiian gallinule  than Hawaiian coot  and have similar life history characteristics. Furthermore, because these species cooccur where their distributions overlap, management strategies were implemented to conserve these species simultaneously. Yet despite these similarities, allelic frequencies differed markedly pre‐ and postdecline within Hawaiian gallinule, and only minimal differences were observed within Hawaiian coot. These findings illustrate the importance of considering how demographic  and evolutionary processes  influence population recovery when planning and implementing conservation programs, especially for endemic species that likely harbor reduced levels of genetic variation relative to their mainland counterparts. In light of these results, Hawaiian gallinule may be at increased risk of genetic endangerment relative to its ecological equivalent, the Hawaiian coot. Although indices of genetic diversity are not reduced , allelic frequencies have changed indicating genetic drift is strongly influencing genetic variation within the Hawaiian gallinule on Oahu. Continued persistence in isolation, coupled with low genetic diversity, could result in the continued reduction of genetic diversity through genetic drift making the Hawaiian gallinule more vulnerable to stochastic processes and ultimately extirpation.All authors were involved with the concept and design; JGU collected the genetic samples; REW and SAS collected the genetic data and conducted the analyses; all authors were involved in interpretation of the results; SAS led the writing; and all authors were involved in the writing process. All authors approved of the final draft of the manuscript.None declared.