Kryptolebias marmoratus is a unique mixed-mating vertebrate inhabiting mangrove swamps under highly variable tidal regimes and environmental conditions. We hypothesized that geographical isolation and ecological pressures influence outcrossing rates and genetic diversity, and ultimately determine the local population structuring of K. marmoratus. By comparing genetic variation at 32 microsatellites, diel fluctuations of environmental parameters, and parasite loads among four locations with different degrees of isolation, we found significant differences in genetic diversity and genotypic composition but little evidence of isolation by distance. Locations also differed in environmental diel fluctuation and parasite composition. Our results suggest that mating system, influenced by environmental instability and parasites, underpins local population structuring of K. marmoratus. More generally, we discuss how the conservation of selfing species inhabiting mangroves and other biodiversity hotspots may benefit from knowledge of mating strategies and population structuring at small spatial scales.Genetic variation within populations depends on population size, spatial structuring, and environmental variation, but is also influenced by mating system. Mangroves are some of the most productive and threatened ecosystems on earth and harbor a large proportion of species with mixed-mating (self-fertilization and outcrossing). Understanding population structuring in mixed-mating species is critical for conserving and managing these complex ecosystems.  Mangroves, together with coral reefs, are some of the most productive habitats of the planet and act as important nursery grounds and breeding sites for fish, crustaceans, and shellfish but also for birds, reptiles, and mammals of closely associated habitats . Around In species that undergo partial self-fertilization (selfing), genetic differentiation between subpopulations can be more pronounced than in fully outcrossing species . This isThe asymmetric distribution of selfing rates in animals, with high outcrossing rates being more frequently represented than high selfing rates, suggests that not only genetic factors  where it inhabits mangrove swamps under highly variable conditions of temperature, oxygen, salinity, and ammonia, mainly within the burrows of the land crabs, Ucides cordatus or Cardisoma guanhumi  and listed as a “species of concern” (http://www.nmfs.noaa.gov/pr/species/concern) in North America due to mangrove habitat degradation.The mangrove rivulus  more geographically isolated locations would display higher rates of genetic differentiation and lower values of genetic diversity and (2) ecological differences among locations  may influence genetic diversity and rates of selfing/outcrossing. In particular, we expected that individuals from sites with more environmental fluctuation and higher parasite loads would display lower rates of selfing and more genetic diversity.We investigated the patterns of genetic diversity, migration rates, and population structuring of C. guanhumi) by using cup traps and wire minnow traps . A total of 118 fish  and temperature were measured by placing a dual electrode  into the crab burrow approximately 6 cm below the water surface. A water sample (50–60 mL) was collected from the same location by carefully inserting a tube (6 cm) connected to a disposable syringe (60 mL) and withdrawing a sample for later determination of ammonia, pH, and salinity. Water samples were transferred to conical tubes (60 mL) and tubes were sealed immediately.Water pH was determined immediately following return to the lab (∼30 min) with a pH electrode  and meter . Salinity was determined with a portable refractometer . Water was filtered (Whatman no. 1 filter paper) and frozen for later determination of ammonia levels (within 24–48 h). Total ammonia was measured on the water samples with the salicylate method . AbsorbaTm], and 90 s at 72°C), then 25 cycles at 56°C and finishing with 10 min at 72°C. PCR products were resolved on an ABI3130xl sequencer with GeneScan-500 LIZ size standard (Applied Biosystems CA) and analyzed using the software programme GeneMapper v 4.0.Total genomic DNA was extracted from muscle tissue using the Wizard® SV 96 DNA Purification Kit . DNA was quantified using the NanoDrop1000 v.3.7 Spectrophotometer (©2008 Thermo Fisher Scientific). Thirty-two of the 36 microsatellite loci described in HL). This measure weighs contribution of each locus to overall homozygosity depending on its allelic variability. HL correlates better than uncorrected homozygosity  with genome-wide homozygosity and inbreeding coefficients in open populations  was used to test for differences in mean homozygosity, allelic richness, and relatedness among sites after the data were tested for normality. Homozygosity was arcsine transformed. Where overall significant differences were found, Tukey's post-hoc tests were used to identify significant pairwise comparisons.r  was nonrandom. The autocorrelation coefficient r  was calcr . The sigFST) in FSTAT and the probability of genetic differences being significant was calculated based on 1000 permutations. The association between genetic distance (FST) and the natural logarithm of geographic distance was assessed using a Mantel test  based on their microsatellite genotypes. The most likely number of genetic groups was inferred from the largest rate of change in the likelihood function with respect to k (Δk) as in STRUCTURE (k (1–15) with a burn-in of 10,000 replicates followed by 50,000 replicates to collect estimated parameters and likelihoods. The outputs of the repeated runs at each k were aligned using CLUMPP 1.1.1  individuals from those originating from recent outcrossing events (hybrids of different selfing lines) as in The Bayesian clustering method in INSTRUCT  was usedTRUCTURE . INSTRUCPP 1.1.1  and reprPP 1.1.1 . Posterilation k . Hybridsclusters . A thresJ′) (q) derived from INSTRUCT. This index will range from 0 (if the genetic ancestry corresponds to a single group) to 1 . Differences in genetic diversity and admixture among lines were compared using ANOVA to test for differences in heterozygosity (HL), relatedness, and genetic admixture (J′).The extent of admixture in the genetic ancestry of each individual was estimated using a modification of Pielou's evenness index (J′)  based onq-value greater than 0.5 and individuals with q-values lower than 0.5 for all lines were deemed as “unclassified.” A G-test of independence was used to assess whether the distribution of lines among sampling sites was nonrandom  for each genetic line.One-way ANOVA and Tukey's post-hoc pairwise comparisons were used to compare individual parasite loads among sampling sites. In order to assess the distribution of parasites among genetic lines, we used one-way ANOVAs followed by Tukey's post-hoc pairwise tests to compare individual parasite loads from  of pure-ANOVAs were also used to assess differences in environmental parameters  among sites. Environmental diel fluctuation at each sampling location was estimated as the variance for each variable. Significant differences in variances were tested using the Fligner–Killeen test of homogeneity of variances and 95% confidence intervals were calculated via bootstrapping .In order to assess the influence of both parasites and environmental diel variation in the variation of selfing rates, we first carried out a correlational PCA to analyze site differences in the three parasite infections and the three environmental variation factors analyzed. Regression analysis was then carried out to assess the association of selfing rate with the first two components of the PCA. This method allowed us to overcome potential problems derived from collinearity of some of the variables. All statistical analyses were carried out using R version 2.12.1.FIS oscillated between 0.55 and 0.83. Levels of homozygosity and selfing rates estimates indicated relatively frequent outcrossing events  and mean relatedness . Site 2 had significantly lower homozygosity (HL = 0.68 ± 0.24) than site 3 . Site 2 also had significantly lower relatedness (−0.001 ± 0.038) than sites 3 (0.013 ± 0.062) and 4 (0.007 ± 0.040) while site 1 showed the highest average relatedness  to account for the potential effect of the small sample size; the significance or direction of the results did not change with the exclusion of site 1.All loci deviated from Hardy–Weinberg equilibrium due to an excess of homozygotes  and linkg events . Sites dP = 0.001) and significant negative correlations were found at 200–600 m (P < 0.001) and 800–1000 m (P = 0.002). However, we did not find any significant spatial autocorrelation among the burrows within site 2 (P = 0.002), there was a significant positive correlation among genotypes  and geographical distance indicated that there was no isolation by distance at the scale measured . Mantel tests within sites 2 and 4 were also nonsignificant .Pairwise comparison of Focations . Resultsk) was 5 (q > 0.9), the first male from site 4 was from line E (q = 0.8), and the second male from site 4 was classified as a hybrid (q < 0.5). Significant differences were found in the mean individual admixture index (J′) among sites . The largest difference in admixture was found between site 2, with the highest value (J′ = 0.46 ± 0.25), and site 3 with the lowest (J′ = 0.25 ± 0.19) (The Bayesian clustering in INSTRUCT found that the most likely number of selfing lines (k) was 5  and 4A,  ± 0.19) .HL) were found among selfing lines  (HL (0.60) and line A had the highest (0.88). All lines had a mean relatedness value higher than the population average (range 0.0082–0.0538) and they significantly differed in relatedness , line A being significantly more related than all other lines (J′) . The post-hoc Tukey's test revealed that line D had a significantly higher J′ (0.49 ± 0.26) than line E (0.31 ± 0.19) . Differences were mainly due to site 2 having a significantly lower proportion of individuals assigned to line E than expected whereas site 4 had a significant excess of individuals of line E (n = 8) to account for the potential effect of the small sample size and the significance or direction of the results did not change .Significant differences in mean heterozygosity (= 0.004) . Line C er lines . Lines a ± 0.19) . Proportf line E . The anaF3,44 = 10.96, P < 0.001)   . There were no significant differences in dissolved oxygen  and ammonia  among sites  than all the other sites  (< 0.001) . Sites 1< 0.001) . Site 1 = 0.020) . Site 1 ng sites .2 = 8.02, P = 0.046), ammonia , and depth . Site 1 had significantly greater variation in water temperature (1.94 ± 1.06) than all other sites  , pH , and salinity . Taken together, these results indicated that site 1 had the highest diel variation of the four sampled locations in the environmental parameters measured.We used the variance of the physical measurements taken at four times covering a period of 24 h (diel variation) as a proxy for environmental instability and found significant differences among sites in the variation of three of the parameters measured, water temperature (χ ± 0.24) . Variati ± 0.24) . Site 1 F3,90 = 10.69, P < 0.001; F3,90 = 11.64, P < 0.001). No significant differences were found in trichodinid numbers among sites . No significant differences in acanthocephalan and trichodinid infections were found among genetic lines . However, differences in bacterial gill cysts among genetic lines were found to be significant  and line E had the highest occurrence of bacterial gill cysts, significantly higher than lines C and D.The three most common infections in the fish analyzed were gastro-intestinal acanthocephalans, trichodinid protozoans, and bacterial gill cysts . Sites dF2,91 = 5.09, P = 0.026) but not for component 2 .The first two components of the PCA of all parasite and environmental variation factors explained 76.6%  of the variation among sites A. The hiK. marmoratus colonizing ability, could help explaining the maintenance of mixed-mating on this species  in the population structuring of a mixed-mating species. A mating system of selfing and occasional outcrossing may result in highly distinct genetic composition of populations even at very fine spatial scales. The uncertainty and harshness of the mangrove habitat, together with  species . On thisFST analyses identified significant genetic differences among all sampling locations and the composition of genetic lines varied significantly among sites. The autocorrelation analyses indicated that there was nonrandom association of genotypes at small spatial scales (200 m) but a negative correlation at greater distances and within sampling sites. The two sites with the highest differences in relatedness and homozygosity (sites 2 and 3) were the closest geographically and we did not find evidence of isolation by distance , suggesting that differentiation could not be solely attributed to spatial isolation, as it has been often observed at localized scales , separated between 150 m and 1 km. In addition, d scales . Thus, od scales .K. marmoratus at small scales. Similar patterns have been observed in Daphnia magna and its parasite Octosporea bayeri, where parasites appear to be specialized on the local host clone community and immigrants have the advantage of being rare (Potamopyrgus antipodarum (Spatial and temporal instability in environmental parameters can influence the mating system and help maintain intermediate rates of selfing in mixed-mating populations . Outcrosing rare , or in tipodarum , suggestipodarum .K. marmoratus, that display an array of selfing rates from extensive selfing to extensive outcrossing (Caenorhabditis elegans, populations with the highest frequency of males and outcrossing are characterized by being genetically diverse following exposure to novel environmental conditions (In mixed-mating populations, such as crossing , outcroscrossing . In labonditions . Moreovenditions . Variablnditions .K. marmoratus. For example, low oxygen induces K. marmoratus to emerse (K. marmoratus.Habitat fragmentation and obstruction of tidal flows within mangrove swamps can lead to sediment accumulation, lower water depths, extreme salinity, and low dissolved oxygen . Changeso emerse  what in o emerse , nitrogeo emerse , gene exo emerse , and gilo emerse . In addio emerse . Loss ofo emerse . Our reso emerse . AlthougReductions in population density influence selfing rates  and the K. marmoratus at surprisingly small spatial scales, which in turn influence genetic structure. Given the abundance of plant and animal mixed-mating species in mangrove ecosystems (Reduction in connectivity between marine and inland ecosystems due to habitat fragmentation represents a pervasive threat to tropical mangrove estuaries, and the consequences will likely become further exacerbated by climate change and predicted rises in sea level . Island-osystems , underst


Microbotryum species causing anther smut disease on the plants Silene latifolia and Silene dioica. Clustering analyses revealed the existence of genetically distinct groups for the pathogen on S. latifolia, providing a clear-cut example of European phylogeography reflecting recolonization from southern refugia after glaciation. The pathogen genetic structure was congruent with the genetic structure of its host species S. latifolia, suggesting dependence of the migration pathway of the anther smut fungus on its host. The fungus, however, appeared to have persisted in more numerous and smaller refugia than its host and to have experienced fewer events of large-scale dispersal. The anther smut pathogen on S. dioica also showed a strong phylogeographic structure that might be related to more northern glacial refugia. Differences in host ecology probably played a role in these differences in the pathogen population structure. Very high selfing rates were inferred in both fungal species, explaining the low levels of admixture between the genetic clusters. The systems studied here indicate that migration patterns caused by climate change can be expected to include pathogen invasions that follow the redistribution of their host species at continental scales, but also that the recolonization by pathogens is not simply a mirror of their hosts, even for obligate biotrophs, and that the ecology of hosts and pathogen mating systems likely affects recolonization patterns.Climate warming is predicted to increase the frequency of invasions by pathogens and to cause the large-scale redistribution of native host species, with dramatic consequences on the health of domesticated and wild populations of plants and animals. The study of historic range shifts in response to climate change, such as during interglacial cycles, can help in the prediction of the routes and dynamics of infectious diseases during the impending ecosystem changes. Here we studied the population structure in Europe of two  st century ecologists and epidemiologists. Because past climatic events have shaped the current distribution of species, the study of migration of pathogens during glaciation cycles provides insights into the constraints upon pathogen spread. We studied the structure of European populations of two pathogenic fungal species infecting two species of wild plants. The pathogen with the host's broadest ecological tolerance has followed the postglacial colonisation pathway of its host from the major refugia in southern Europe, although the pathogen likely persisted in more fragmented refugia. The other pathogen showed less clear-cut genetic patterns and some evidence of possible northern refugia, in agreement with the host's more fragmented distribution and ecological preference. These results indicate that pathogen invasions are likely to follow large-scale migration events of their host species in response to climate change, but also that the recolonization by pathogens is not simply a mirror of their hosts, and that the correlation between the colonization patterns of hosts and pathogens depends on host ecology.Global change is expected to cause the large-scale redistribution of species, including pathogens that threaten the health of domestic populations and natural ecosystems. Predicting the dynamics of invasive pathogens is therefore a major challenge for the 21 Understanding the dynamics of emerging infectious diseases and their routes to colonize new geographic regions is a major challenge for ecologists in an effort to prevent negative impacts upon human, domestic, and natural populations. Because pathogens causing emerging diseases have not coevolved with the host or the ecosystem in which they emerged, they may be more likely to pose a threat to biodiversity through biomass loss and extinction of host species than those responsible for endemic diseases One way to predict the invasion routes and dynamics of emerging infectious diseases in response to current climate warming is to study the past migrations of pathogens and their hosts during historic periods of climate changes. During the last glacial maximum, the Arctic ice sheet extended into a large part of Europe and limited the survival of most organisms into Southern Mediterranean refugia  and S. dioica (red campion). These two closely related host-pathogen systems are interesting models for studying the combined demographic histories of pathogens and their hosts because (i) populations of this pathogen are more differentiated than those of its hosts Microbotryum species are highly host specific in the field  SiS. latifolia, analyses of chloroplast DNA (cpDNA) polymorphism showed clearly structured haplotype variation in Europe, with haplotypes from Eastern and Western Europe forming divergent groups descended from haplotypes currently distributed in southern Europe, and in particular from the Iberian and Balkan Peninsulas S. dioica in Europe has been less well studied, although the pattern of cpDNA polymorphism was also suggestive of post-glacial recolonisation from multiple refugia Microbotryum species parasitizing S. latifolia and S. dioica showed similar patterns of post-glacial history. We also investigated whether life history differences between the two host species constrained the distributions of the pathogens within the host migration pathways. S. latifolia and S. dioica differ with respect to their ecologies, which might strongly impact the genetic structure and diversity of their specialized pathogens. S. latifolia has an extensive range and occurs in most of Europe, as well as in Middle Asia and the Steppe area of south Siberia S. dioica covers mainly Central, Northern, and Western Europe S. latifolia, and experiencing more stable population dynamics S. latifolia than in the fungus parasitizing S. dioicaRecent studies on the phylogeographic history of the two host plants showed genetic evidence of post-glacial recolonization from Mediterranean refugia. In Microbotryum species infecting S. latifolia and S. dioica using microsatellite markers in order to address the following specific questions: (i) Do the phylogeographical structures of the Microbotryum species show signatures of post-glacial recolonisation of Europe, and in particular from Southern refugia? (ii) do the population structures of the two pathogens differ from each other, and if so, are these differences consistent with expectations based upon known ecological differences of their plant hosts? (iii) are the phylogeographic patterns of Microbotryum species comparable at a continental scale to those of their respective hosts?We therefore determined the population structures of the Microbotryum samples collected on S. latifolia and on S. doica across Europe  increased from K = 1 to 10 , indicatα) between clusters was low, as shown by the mean α  = 0.033±0.000 over all the runs between K = 2 to 15 (10 replicates for each K) in the STRUCTURE analysis. This indicates that most of the genotypes are drawn from a single cluster, with little admixture among clusters  and the other more to the south . At K = 5, the Eastern group splitted into two clusters, one bordering the Balkan peninsula  and one spreading toward Eastern Europe and Russia . When increasing K, further clusters were identified, without evidence of admixture, and corresponding to more local geographical regions: for instance the UK became isolated, and then the most eastern part of Europe  on the microsatellite allele frequencies, which is a multivariate approach that does not rely on any model assumptions. It instead transforms a number of possibly correlated variables into a smaller number of uncorrelated components, the first principal components accounting for as much variability in the data as possible. The PCA fully recovered the population structure inferred by the three Bayesian clustering methods, as shown by the first four PCs, which explained 35% of the total variance in allelic frequencies  and S6.ST = 0.38, 95% CI: [0.29–0.46], P<0.001 for all pairs of clusters). The Nwestern and Swestern clusters showed the lowest differentiation (FST = 0.24) while the Balkan and Swestern clusters were the most different (FST = 0.47). The clusters also differed significantly with respect to their genetic diversities  than the Balkan, Eastern and Nwestern clusters (Wilcoxon Signed Rank (WSR) tests, P = 0.008, P = 0.033, and P = 0.026, respectively, Balkan cluster exhibited a significantly lower allelic richness  than the Eastern and Italian clusters , while the others had intermediate values.The clusters displayed large and significant differences in allelic frequencies  was detected in the clusters , indicatclusters . The levclusters .IS values within clusters , in agreement with previous studies and with the high Fclusters .A European spatial map of genetic diversity was generated by aggregating geographically close samples together on a grid, considering only grid cells where the sample size was higher than 4. The interpolated values of allelic richness showed that genetic diversity increased in the southward direction, with the highest value observed in the Italian and Iberian peninsulas and the lowest values in northern Europe . Such a A distance, shared allele distance DSA, Chord's distance and Goldstein's (δµ)2 distance. As the different trees provided similar topologies, only the tree based on Nei's DA distance is presented . The successive PCs accounted for less than 11% of the total variance in allelic frequencies each and revealed the same clusters of genotypes as those observed in Bayesian clustering analyses  all indicated that DIC decreased and LnP(X|K) increased with increasing K . Again, analyses .ST values between sites where the sampling was higher or equal to 10 samples showed that a very high level of genetic differentiation between clusters was observed, even in the regions where populations from different clusters were intermingled . The two clusters showed a spatial genetic structure of a similar level to that observed in MvSl, with Sp values of 0.06 and 0.15 for the clusters 1 and 2, respectively , consistent with the high FS values . The twoS values , and didS values , althougMicrobotryum species (s = 0.91 for MvSl and s = 0.93 for MvSd), in agreement with the high deficits in heterozygotes  between demes  and the other from Germany eastward. This pattern is consistent with colonization from distinct refugia located in the Balkans and further East in Eurasia, following a similar scenario as those reported in some animals, such as the bear The two clusters identified in Western Europe had more diffuse geographic distributions, with a slight longitudinal partitioning. One of the clusters was distributed more towards the West of France while the second was more present in east-central France and in Eastern UK. Such a pattern may be due to the pathogen having survived in distinct regional refugia in Western Europe, from which they would have expanded their range over France and UK. Such a hypothesis is consistent with recent findings that the main glacial refugia in Europe were probably not composed of a single population, but instead could have been structured into several local refugia more or less isolated from one another . On the other hand, given the more northern current distribution of the plant S. dioica compared to S. latifolia, one can alternatively speculate that its tolerance to cold temperatures The anther smut pathogen MvSd also exhibited a strong genetic structure, albeit with biogeographic patterns more difficult to interpret. The two main clusters had largely intermingled distributions, with an estimated time of divergence of the same order of magnitude as observed for MvSl. The distinct clusters in MvSd could correspond to genetic groups having diverged in distinct southern refugia during the glaciations, similar to MvSl, although the locations of the putative refugia are more difficult to identify. This may be due to the restricted distribution of MvSd, constrained by ecological specificities of the host and disease: the plant S. dioica based on cpDNA RFLP similarly indicated the existence of genetically distinct groups, more or less longitudinally separated, albeit with large overlap in their ranges S. dioica across Europe from multiple southern refugia S. dioica and S. latifolia was consistent with a history of hybridization and introgression events, making it difficult to assess whether the present distribution of these haplotypes resulted from the recolonization history of S. dioica or S. latifoliaThe phylogeography of the host plant Microbotryum species was the low level of admixture among genetic clusters (≤3%), suggesting almost complete lack of gene flow, despite the existence of contact zones. Such low levels of gene flow among clusters are likely influenced by the very high selfing rates in Microbotryum. High selfing rates have been invoked to explain reproductive isolation between sympatric Microbotryum species A striking pattern observed in both Microbotryum species, highly dependent on their hosts for survival and using the same dispersal vectors, we interestingly found that the pathogens likely subsisted during glaciations in a more fragmented distribution, with their genetic diversity divided among a higher number of smaller refugia. Moreover, the extent of large-scale dispersal across Europe after recolonization was less for the pathogen than for its host: in particular, the clusters were much more clumped in MvSl than in S. latifolia, and footprints of refugia appeared in MvSl that were absent in S. latifolia, such as the Italian peninsula. Our findings thus indicate that vector-borne, obligate pathogens may colonize new areas following climate warming with some delay compared to their hosts, and to a lesser extent. The invasive potential of pathogens following climate change is therefore likely to depend on the obligate nature of the interactions with their host and on the dispersal modes, as could be expected.Large-scale congruence between the pathogens' phylogeographic patterns and those of their respective hosts indicates that their glacial refugia and migration pathways during recolonization have been similar. While this may be expected for obligate pathogens like S. latifolia, and has remained in a much more restricted geographic area S. vulgaris, that is otherwise free of anther smut disease in the continent S. dioica and S. latifolia and of hybridization between MvSl and MvSd that occurred after secondary contact Microbotryum, where co-phylogenetic analyses showed that speciation events were most often associated with host shifts However, once the original host and its fungal pathogen invade a geographic region, the pathogen poses a risk of emerging as an infectious disease on new host species found in that area. For instance, MvSl was introduced in the United States some time after its host plant S. latifolia and S. dioica, which may be attributed to differences in the hosts' ecology, is also relevant for predicting the fate of infectious diseases following global warming. The redistribution of pathogens under warmer climatic conditions should indeed be highly dependent on the hosts' ecological preferences and adaptive potentials, in particular regarding the temperature and competition in new ecological communities The substantive contrast in phylogeography for the anther smut fungi on S. latifolia and its anther smut pathogen have kept clear genetic footprints of postglacial colonization from the major southern European refugia. The pathogens showed striking evidence for more numerous and localized refugia than their hosts. On the other hand, the ecological preference of the plant S. dioica for wetter and colder habitats In this study, we showed that high selfing rates and metapopulation dynamics in two plant pathogenic fungi had strong impact on their genetic diversity and structure. At the scale of the species' distribution ranges, the population structures in the two fungal species were quite different, likely due to differences in the ecological preferences of the two host-pathogen systems. The broadly distributed Beyond the interest of our study for understanding the dynamics of diseases under climate warming and the impact of host life histories on the genetic structure of pathogens, our study illustrates several important points to take into account when performing clustering genetic analyses, which are still often poorly recognized. First, several K values are often interesting to consider in clustering analyses, and it may be non-heuristic to search for a “single optimal” number of clusters. As long as increasing K does not lead to admixed clusters, the new clusters revealed by increasing K probably reveal a genuine genetic structure that may be interesting to investigate. This appears especially true in selfing species, for which the smallest panmictic cluster may be the individual.Microbotryum analyzed in this study were collected as diploid teliospores from 187 localities on S. latifolia (n = 701) and 68 localities on S. dioica (n = 342) across Europe , the observed and unbiased expected heterozygosities (oH and eH), and the fixation index (ISF). These statistics were calculated using FSTAT 2.9.3.2 The within-species genetic polymorphism at each locus was quantified using the allelic richness (2r) calculated in Genetix 4.05 2r within sampled localities that contained at least 10 genotypes, i.e. 15 out of 187 localities for MvSl .Linkage disequilibrium (LD) among loci was quantified using the correlation coefficient , in order to identify potential distinct modes among the results of independent replicate runs for each K value and to average individual assignment probabilities (q) over replicated runs showing a similar mode. Barplots were generated by the DISTRUCT program For both STRUCTURE and TESS algorithms, we used a haploid setting because both species of mozygous . Run conq) to each cluster InStruct run conditions were as follows: we used diploid data to jointly estimate the selfing rate within each clusters and the individual assignment probability . For Kmax = 5, we performed 50 additional longer runs , and we averaged the estimated admixture coefficients (Q matrix) over the 5 runs with the smallest values of the DIC . To account for label switching among runs, we used the software CLUMPP version 1.1, whose greedy algorithm computed a symmetric similarity coefficient equal to 0.85 .TESS run conditions were as follows: we used the conditional auto-regressive (CAR) Gaussian model of admixture with linear trend surface Finally, we applied a centred-normed Principal Component Analysis (PCA) on the microsatellite allele frequencies for each species to corroborate the clustering solution inferred by the three Bayesian clustering algorithms using a multivariate approach that does not rely on any model assumption. We conducted the analyses using ADEgenet AD distance SADδµ)2 distance, the latter one assuming that microsatellite evolve following a Stepwise Mutation Model (SMM) model PowerMarkera distance of Nei et al. We represented the relationships among clusters using neighbour-joining population trees, respectively based on Nei's Allelic richness and private allelic richness of each genetic cluster or combination of clusters identified in the Bayesian clustering analysis were calculating using ADZE 1.0 ijF)  to test for isolation by distance, as recommended for highly selfing species ijF between individuals belonging to a first distance interval that includes all pairs of neighbours . Under some assumptions, the Sp statistic is equal to the inverse of 4πDσ2 , which matches Wright's neighbourhood size, under the hypothesis of Gaussian dispersal functions. Therefore, high values of the Sp statistic are indicative of low population density or limited dispersal ability.We used pairwise kinship coefficients between individuals  and M. silenes-dioicae .Map of sampled localities for (0.68 MB TIF)Click here for additional data file.Figure S2Microbotryum lychnidis-dioicae (MvSl) from STRUCTURE (a and b), InStruct (c) and TESS (d) analyses. STRUCTURE analyses: (a) mean (± SD) probabilities of the data [LnPr(X|K)] over 10 Structure replicated runs plotted as a function of the putative number of clusters (K). (b) Mean variations of probabilities of the data (Δ(LnPr(X|K)) between successive K considered in STRUCTURE analyses. Deviation Index Criterion (DIC) for InStruct (c) and TESS (d) analyses.Estimated number of populations in (0.16 MB TIF)Click here for additional data file.Figure S3Estimated population structure from the STRUCTURE, InStruct and TESS analyses assuming 5 clusters. Each individual is represented by a thin horizontal line divided into K coloured segments that represent the individual's estimated membership fractions in K clusters. Black lines separate individuals from different geographic areas labelled on the right. Each plot is based on the dominant clustering solution identified at that value of K.(0.46 MB TIF)Click here for additional data file.Figure S4Population structure inferred from InStruct analyses assuming 2 to 8 clusters. Each individual is represented by a thin vertical line divided into K coloured segments that represent the individual's estimated membership fractions in K clusters. Black lines separate individuals from different geographic areas. Several barplots are shown for each K and represent the distinct modal solutions observed.(5.37 MB TIF)Click here for additional data file.Figure S5Microbotryum lychnidis-dioicae (MvSl). Scatter plots for the first four principal components are shown using a colour labelling of genotypes defined according to the membership probability to belong to the 5 identified clusters using Bayesian clustering analyses. Each genotype that received a probability above 0.7 was coloured according to the colour pattern used in Bayesian clustering, otherwise it was considered as admixed and coloured in orange.Principal component analysis on microsatellite allelic frequencies of (0.33 MB TIF)Click here for additional data file.Figure S6Microbotryum lychnidis-dioicae (MvSl). Maps for the first four principal components scores (PC1 to 4) are shown.Principal component analysis on microsatellite allelic frequencies of (0.97 MB TIF)Click here for additional data file.Figure S7Microbotryum silenes-dioicae (MvSd).Same as (0.15 MB TIF)Click here for additional data file.Figure S8Microbotryum silenes-dioicae (MvSd).Same as (0.76 MB TIF)Click here for additional data file.Figure S9Microbotryum silenes-dioicae (MvSd) for K = 2, 3 and 5. Scatter plots for the first four principal components are shown using a colour labelling of genotypes defined according to the membership probability to belong to the K identified clusters using Bayesian clustering analyses. Each genotype that received a probability above 0.7 was coloured according to the colour pattern used in Bayesian clustering, otherwise it was considered as admixed and coloured in orange.Principal component analysis on microsatellite allelic frequencies of (0.21 MB TIF)Click here for additional data file.Figure S10Microbotryum silenes-dioicae (MvSd).Level of inter-sites differentiation, expressed a FST value, as a fonction of their geographic distance in (0.05 MB TIF)Click here for additional data file.Figure S11Microbotryum lychnidis-dioicae (MvSl) population where the sample size was at least of 10 teliospores. The statistics shown include the sample size per locations (N), the mean allelic richness (Ar) per population, the mean observed and expected heterozygosity (Ho and He), the average number of fixed loci per populations (Nb. fixed loci), the overall loci inbreeding coefficient (FIS) per population, and the linkage disequilibrium across loci estimated using the correlation coefficient (2r) averaged across loci. We used the function quilt.plot in the R “Fields” package to plot the values on a map according to a grid system of 50×33 pixels. Locations that fall into the same grid box will have their values averaged.Mapped values overall loci for several descriptive statistics in (0.75 MB TIF)Click here for additional data file.Figure S12Microbotryum silenes-dioicae (MvSd). The locus SL19 was excluded in the calculation of Ho, He, FIS, to avoid bias related to its almost complete fixation in a heterozygous state (see the text).Same as (0.58 MB TIF)Click here for additional data file.Text S1Microbotryum lychnidis-dioicae (MvSl) and M. silenes-dioicae (MvSd).Description of genetic polymorphism in (0.07 MB PDF)Click here for additional data file.Table S1ST value, among samples of M. lychnidis-dioicae where n was higher than 10 strains. All pairwise values were significantly different at p<0.0001.Level of pairwise differentiation, estimated as F(0.05 MB PDF)Click here for additional data file.Table S2Microbotryum silenes-dioicae (MvSd) populations. All pairwise values but one (*) were significantly different at p<0.0001.Same as (0.05 MB PDF)Click here for additional data file.


Amphiprion mccullochi, restricted to three MPA offshore reefs in subtropical East Australia. We infer high levels of gene flow and genetic diversity among locations over evolutionary time, but limited contemporary gene flow amongst locations and high levels of self-replenishment (68 to 84%) within locations over contemporary time. While long distance dispersal explained the species’ integrity in the past, high levels of self-replenishment suggest locations are predominantly maintained by local replenishment. Should local extinction occur, contemporary rescue effects through large scale connectivity are unlikely. For isolated islands with large numbers of endemic species, and high local replenishment, there is a high premium on local species-specific management actions.Marine protected areas (MPAs) are increasingly being advocated and implemented to protect biodiversity on coral reefs. Networks of appropriately sized and spaced reserves can capture a high proportion of species diversity, with gene flow among reserves presumed to promote long term resilience of populations to spatially variable threats. However, numerically rare small range species distributed among isolated locations appear to be at particular risk of extinction and the likely benefits of MPA networks are uncertain. Here we use mitochondrial and microsatellite data to infer evolutionary and contemporary gene flow among isolated locations as well as levels of self-replenishment within locations of the endemic anemonefish  This resulted in a comprehensive understanding of gene flow in this study system and together these molecular tools provided a complete view of different parts of the dispersal kernel Amphiprion mccullochi) as a model organism. Since this species is rare at two locations (Middleton and Elizabeth Reefs) and all three locations are either World (LHI) or National Heritage  listed, sacrificing individual fish (particularly new recruits) at the ideal scale required for parentage based analyses , is not feasible. Thus a of118 A. mccullochi fin clips were taken from four locations, MR (n = 30) The main aim of this study was to determine the patterns and levels of gene flow between isolated locations, using the endemic McCulloch’s anemonefish , Middleton Reef (MR) and Lord Howe Island (LHI).The three sites have extensive shallow reefs (<30 m depth) enclosed within MPAs which are separated from each other by deep ocean (>2000 m depth).A. mccullochi from the four locations.The four most commonly used phylogenetic analyses were performed on the aligned mtDNA (D Loop) sequence data as described in A. mccullochi were estimated between or within each of the four locations using MIGRATE-n 2.4.3 (http://popgen.sc.fsu.edu/Migrate-n.Html) A. mccullochi and A. akindynosEvolutionary migration rates and effective population sizes of To establish spatial population partitioning in msatDNA, we used three molecular analytical tools: (i) discriminant analysis of principal components (DAPC) A. mccullochi were estimated between each of the four locations using MIGRATE-n 2.4.3 as above. However, we tested a combination of various: migration priors (FST and OWN: isolation-by-distance) and custom-migration models ; all with a constant mutation rate over all loci. A Log Maximum-Likelihood analysis (Ln ML) comparing all possible combinations selected: migration prior (FST), custom-migration model (migration model with variable Theta) and constant mutation rate over all loci.We set the datatype to Microsatellite (a simple electrophoretic ladder model with stepwise mutation) and the migration rate parameters for msatDNA (θ and M were both set to a maximum of 100) to conduct Bayesian analysis using one long chain that sampled every 100th of 100 k sampled trees and applied a 20 k iteration burn-in. All parameters converged and fell within the 90% CI yielding values for θ and M  for each locus per location.Contemporary migration rates and effective population sizes of st≥0.05); however, when the assumptions are violated, accurate estimates are obtained only when migration rates are very low (m = 0.01) and genetic differentiation is high (Fst≥0.10) This study did not sample new anemonefish recruits in order to determine self-recruitment as in π) and for msatDNA - genetic diversity (gd), were estimated in ARLEQUIN 3.5 h) and nucleotide diversities (π) of the data were interpreted as either low with specified cut-off values of h and π (%) were <0.5 or high if values of h and π (%) were >0.5 Molecular diversity indices for mtDNA - haplotype diversity (h); nucleotide diversity . Allelic diversity was lowest at LHI-L and highest at LHI, whilst FIS did not differ significantly across the three regions surveyed  consisting of a total of five MU with each location being represented in all MU ; MU 3 was under-represented by ER individuals ; MU 4 was ER dominated  and MU 5 was LHIL dominated . This indicates that three of the MU  are overrepresented by three specific locations − LHI, ER and LHIL, respectively. In contrast, MR individuals were relatively evenly distributed across all five MU. showed tst = −0.017  and all of the genetic variation (101.74%) was within locations, Φp = 0.8, , howeverBayesian analysis, informed by the phylogenetic structure, was performed using MIGRATE-n, because analyses based on spatial structure failed. High levels of evolutionary gene flow were indicated within - but less between groups: between Groups (i.e. Group 1 - Group 2) M ranged from 19 to 42 . These vA. mccullochi from the four locations in the latter three of the four analyses. Low levels of contemporary gene flow were detected between the four locations, consistent with the patterns of contemporary gene flow and with the high levels of inferred self-replenishment evident at all four locations (next section). This is in stark contrast to the patterns and levels of evolutionary gene flow.msatDNA allele frequencies, genotypic distributions in space, genotypic assignments and genotypic posterior probability distributions suggested significant spatial partitions between st = −0.49 to 0.056, p<0.05, st = 0.007, p = 0.015, st comparisons also identified significant genetic partitioning between all locations . Similarly, four geographically partitioned populations were identified by STRUCTURE analyses, as the likelihood of the marginal posterior probability distribution was highest when K = 4.The statistically rigorous AMOVA found significant structure in the locus by locus msatDNA  between locations, both DAPC and STRUCTURE partitioned the data into 4 distinct clusters. Used together, these programs are likely to be better than Fst values m = 26%), LHI to/from ER  and MR to LHI , nucleotide diversity (%π) and genotypic diversity (gd): h = 0.846 to 0.939,%π = 5.03 to 7.16, gd = 0.690 to 0.736  sufficient gene flow between locations resulting in a lack of geographic partitioning over evolutionary time scales; (ii) genetically differentiated populations at all four sampled locations, due to low levels of contemporary gene flow between locations, despite the evolutionary homogenisation; (iii) demographic dependence between LHI and LHIL, LHI and ER and MR and LHI,yet high levels of inferred self-replenishment at all four locations and; (iv) high genetic diversity at all locations, despite high levels of inferred self replenishment. This is consistent with inter-location gene flow at evolutionary time scales.Isolated islands are global hotspots of endemicity for a range of coral reef organisms A. mccullochi mtDNA. Despite this, the relative percentage of each location within MU suggests geographic structure. The occurrence of two lineages within a species has also been found for coral reef fishes on the Great Barrier Reef (GBR). Both Plectropomus maculatus and Lutjanus carponotatus show a lack of geographic partitioning along the GBR, yet display two distinct lineages, suggesting admixtures of differentiated lineages rather than stable populations Chaetodon multicinctus, Chaetodon miliaris, Chaetodon frembliiHalichoeres ornatissimusS. frenatusC. sordidusLethrinus miniatusPseudochromis fuscusPlectropomus leopardusThe identification of discrete phylogenetic lineages or management units (MU) is critical for developing effective management strategies A. mccullochi showed high evolutionary gene flow between MU within lineages and to a far lesser extent, between lineages. Higher gene flow from Group 2 into Group 1 is clear, suggesting introgression of mtDNA  Gasterosteus aculeatu) have revealed natural hybridisation and break down of a species pair into a hybrid swarm Larabicus quadrilineatus, Chromis viridis and Pseudanthias squamipinnisA. mccullochi, is likely due to high levels of evolutionary gene flow, which is sufficient for all locations to be connected on evolutionary time scales, thereby maintaining genetic homogeneity.A. mccullochi showed strong contemporary genetic differentiation between locations, consistent with other coral reef fish such as the Hawaiian endemic surgeonfish Ctenochaetus strigosusA. mccullochi are a direct result of different spatial and temporal time scales. Discrepancies in gene flow, between time scales, has also been shown for Lutjanus synagrisPlectropomus maculatus and Lutjanus carponotatusA. mccullochi showed very low levels of gene flow at contemporary timescales which is consistent with model prediction. The low levels of contemporary gene flow in this system most likely result from the short pelagic larval duration of A. mccullochi and the geographical isolation between locations enhanced by predominant east to west oceanographic currents limiting north-south gene flow between locations As previously highlighted, only a few individuals are needed over evolutionary time scales to ensure homogeneity across a species entire geographical range A. mccullochi, compared to the above studies, results from the complete sampling of all locations leaving no 'ghost ' populations un-sampled. However, further investigation using direct methods  and keeping the natural genetically distinct sub-populations (MU) of endemic fish intact, should be a priority of management plans. In addition, isolated locations that are predominantly dependent on self-recruitment are unlikely to be sustained by long distance transport over hundreds of kilometres Conserving endemic species such as . LHI, like other isolated islands, is facing an escalation of threats  Climate change offers an additional suite of threats and concernsA. mccullochi may be representative of other endemic species.The present study highlights the importance of estimating both evolutionary and contemporary levels of gene flow (connectivity) due to the different spatial and temporal scales at which these processes operate. While populations are primarily being maintained by self replenishment, exchange among islands over evolutionary time is critical to understanding patterns of genetic diversity and differentiation. Locations with high levels of self-replenishment  each require protection as they receive few dispersing larvae from each other. Locations with lower levels of self-replenishment (e.g. LHIL) are just as important to protect as they provide a dual benefit because they are a source for their own and other populations, aiding in rescue effects of depleted/extinct populations and enhancing genetic diversity. Thus both predominantly self-replenishing and predominantly dispersing locations should ideally be protected, from activities such as aquarium collecting, to maximise biodiversity conservation in low abundance endemics living on isolated reefs and islands. Although this study focused on a single coral reef species at four locations in the South-West Pacific Ocean, the region harbours 16 other species of endemic marine fishes, as well as numerous other endemic marine species that have similar geographic distributions as our study species. Thus patterns of gene flow and self-replenishment in Table S1Summary statistics for 17 microsatellite loci Am1–24.(DOC)Click here for additional data file.Table S2AMOVA analysis for a) mtDNA sequences from Amphiprion mccullochi structured into geographic regions and b) global AMOVA weighted across all seventeen microsatellite loci.(DOC)Click here for additional data file.Table S3st) for AMOVA fixation indices Click here for additional data file.Table S4Pairwise population Fst values for four populations of Amphiprion mccullochi using both d loop (mtDNA) and microsatellite (msat). Pairwise population structures (Fst) for four populations of A. mccullochi, using both d loop (mtDNA) and microsatellite (msat) loci showing raw and corrected Fst for null allele frequencies.(DOC)Click here for additional data file.Table S5Sample sizes for D loop .(DOC)Click here for additional data file.


Mephitis mephitis), which is a frequent vector of rabies, a lethal zoonosis of great concern for public health. Our main objective was to identify landscape elements shaping the genetic structure of this species in Southern Québec, Canada, in an area where the raccoon rabies variant has been detected. We hypothesised that geographic distance and landscape barriers, such as highways and major rivers, would modulate genetic structure. We genotyped a total of 289 individuals sampled across a large area  at nice microsatellite loci. Genetic structure analyses identified a single genetic cluster in the study area. Major rivers and highways, however, influenced the genetic relatedness among sampled individuals. Sex-specific analyses revealed that rivers significantly limited dispersal only for females while highways only had marginal effects. Rivers and highways did not significantly affect male dispersal. These results support the contention that female skunks are more philopatric than males. Overall, our results suggest that the effects of major rivers and highways on dispersal are sex-specific and rather weak and are thus unlikely to prevent the spread of rabies within and among striped skunk populations.Evaluating the permeability of potential barriers to movement, dispersal and gene exchanges can help describe spreading patterns of wildlife diseases. Here, we used landscape genetics methods to assess the genetic structure of the striped skunk ( Studies investigating patterns of gene flow and dispersal among and within populations can help understand evolution on ecological time scales  in an area where cases of the raccoon rabies variant were found in skunks and raccoons 2Procyon lotor) variant of the rabies virus The main objective of this study was to assess the extent of genetic structuring in striped skunks  are drivers of genetic structure in our study area. The purpose of our study is to determine if some landscape barriers can be targeted to improve efficiency of ORV zones. We hypothesized that isolation by distance (IBD) in the population would be detected, because daily movement rates of skunks are thought to be small  Québec government agency and its partners in a disease management perspective in areas of Québec where rabies was detected. Animal trapping and handling methods complied with the Agreement on International Humane Trapping Standards . Biopsy sampling protocol was approved by the Canadian Council on Animal Care (protocol numbers are CPA-FAUNE 2009–12 and CPA-FAUNE 2010–29). Captured live animals were anesthetised by trained professionals under the supervision of a veterinarian.2 (2 zones), a random subset of individuals  was selected for each year to generate a balanced sample over the study area , in an area of approximately 22 000 km2 . Tissue udy area .rcGIS 9.2 . Major rivers were determined based on their mean water flow (m/sec) rcGIS 9).Major rivers and highways were mapped using aerial photographs of Southern Québec provided by the MRNF  with ArceneMapper 4.0 (Applied Biosystems).DNA extraction was conducted using a salting out protocol as described in Chambers and Garant 2, 2.5 µg of bovine serum albumin, 1x PCR buffer , 1 unit of Taq polymerase (Life Technologies) and 25 ng of DNA. PCR products were visualized after a 10 minutes electrophoresis migration at 300 volts on 1% agarose gels in a sodium borate buffer (10 mM NaOH pH 8.5 adjusted with H3BO3) with ethidium bromide. A 100-bp DNA ladder (Life technologies) was used each time to standardize the migration.Molecular sexing was conducted using a protocol modified from Shaw et al. Despite the high reliability of the method, sex could not be determined for 19% of the selected samples, possibly due to degraded DNA . Consequently, we had a smaller sample size of known-sex individuals .Fis) for each locus using the software Genepop 4.0 stat 2.9.3.2 ervus 3.0 We tested all loci for departure from Hardy-Weinberg equilibrium and calculated inbreeding coefficient  estimator of Wang pagedi V1.3 xy to generate a pairwise genetic distance metric, as in Côté et al. t test.To estimate the effects of landscape features on pairwise genetic relatedness among individuals, we used Multiple Regressions on distance Matrices A total of 289 skunks were genotyped at nine microsatellites. Number of alleles identified per locus varied between 7.0 and 19.0 (mean = 11.9) and expected heterozygosity varied between 0.615 and 0.899 , suggesting that the genetic structure within each sex was similar to that detected using the whole sample.The results obtained with Sas K = 1 , suggestr = 0.705 with number of rivers and r = 0.451 with number of highways). No significant effect of the year of sampling was detected . Using a pairwise genetic relatedness estimator at the individual level, however, we found that landscape features, such as highways and major rivers, influenced the structure of genetic relatedness between individuals and especially females. Our results thus support the contention that males disperse more than females in this species and would likely be more active vectors of rabies propagation.The goal of our study was to investigate the population genetic structure of the striped skunk in a heterogeneous landscape in Southern Québec, Canada. We hypothesised that geographic distance and landscape barriers, such as highways and major rivers, would create genetic structure for this species in our study area, and that structure would be greater for females than for males. Our results suggest the presence of a single genetic cluster at the spatial scale studied . However, when major rivers and highways were taken into account in multivariate analyses, the IBD signal was no longer detected. In cases where past or current barriers to gene flow are present in a study area, a relationship between geographic distance and genetic distance might potentially reflect the effect of barriers to gene flow rather than the simple effect of geographic distance e.g., .We also found that major rivers and, to some extent, highways affected genetic relatedness of striped skunk individuals sampled in Southern Québec and that this effect was detected in females but not in males. More specifically, female individuals located on opposite sides of these landscape elements were generally less related than individuals located on the same side. A similar effect was detected for raccoons separated by the Richelieu River in this area , repetition pattern and lengths of alleles for the nine microsatellite loci used in this study in Southern Québec, Canada, in 2009 and 2010 ((DOC)Click here for additional data file.Table S2Polymerase chain reaction reagent volume and concentrations  for the nine microsatellite loci used in this study in Southern Québec, Canada, in 2009 and 2010 Click here for additional data file.Table S3Amplification conditions for the nine microsatellite loci used in this study in Southern Québec, Canada, in 2009 and 2010 Click here for additional data file.


Cytochrome Oxidase I and 16S rRNA genes as genetic markers, this study aimed to elucidate patterns of genetic connectivity among populations of two common benthic invertebrates with contrasting life history strategies. Populations of the squat lobster Munida gracilis and the polychaete Hyalinoecia longibranchiata were sampled from continental slope, seamount, and offshore rise habitats on the Chatham Rise, Hikurangi Margin, and Challenger Plateau. For the polychaete, significant population structure was detected among distinct populations on the Chatham Rise, the Hikurangi Margin, and the Challenger Plateau. Significant genetic differences existed between slope and seamount populations on the Hikurangi Margin, as did evidence of population differentiation between the northeast and southwest parts of the Chatham Rise. In contrast, no significant population structure was detected across the study area for the squat lobster. Patterns of genetic connectivity in Hyalinoecia longibranchiata are likely influenced by a number of factors including current regimes that operate on varying spatial and temporal scales to produce potential barriers to dispersal. The striking difference in population structure between species can be attributed to differences in life history strategies. The results of this study are discussed in the context of existing conservation areas that are intended to manage anthropogenic threats to deep-sea benthic communities in the New Zealand region.Patterns of genetic connectivity are increasingly considered in the design of marine protected areas (MPAs) in both shallow and deep water. In the New Zealand Exclusive Economic Zone (EEZ), deep-sea communities at upper bathyal depths (<2000 m) are vulnerable to anthropogenic disturbance from fishing and potential mining operations. Currently, patterns of genetic connectivity among deep-sea populations throughout New Zealand’s EEZ are not well understood. Using the mitochondrial  Current anthropogenic pressures on the marine environment, including the deep sea, are unprecedented Genetic connectivity has recently come to the fore as a major scientific component of sound MPA design in both shallow and deep-sea environments e.g., . GeneticRanking as the sixth largest globally, the New Zealand Exclusive Economic Zone (EEZ) is one of the most topographically diverse seafloor environments in the world Currently, there is no legislation that allows for the creation of marine reserves  in the New Zealand EEZ , limiting the tools available for management of human activities in New Zealand’s deep sea. There are areas closed to bottom trawling that include specific seamounts et al. (2004) Cytochrome Oxidase I (COI) data, Kojima et al. (2006) Lamellibrachia juni tubeworms at Brothers seamount in the Kermadec Volcanic Arc contains two distinct genetic groups, one of which was phylogenetically related to samples from the TOTO caldera in the Mariana Volcanic Arc. These studies show how complex patterns can exist over various spatial scales. Similarly, genetic investigation of the Internal Transcribed Spacer regions 1 and 2, COI and 16S rRNA genes in populations of the coral Desmophyllum dianthus from Chile, New Zealand, and Australia revealed greater variation between populations at different depths within a region than between populations at the same depth in two different regions 16S rRNA and a non-coding mitochondrial marker also found no genetic structure in the New Zealand region–which may present an accurate evolutionary pattern or may be the result of using evolutionarily conserved genetic markers that can be slow to change over time Most population genetic studies in the New Zealand region have been carried out in coastal waters e.g., , with reThe present study aims to elucidate patterns of genetic connectivity among populations of benthic invertebrates found at three different deep-sea regions–a prominent rise, an adjacent slope margin, and a nearby plateau–and to consider the implications of the observed patterns for management decisions. The three study regions were the Chatham Rise, the Hikurangi Margin, and the Challenger Plateau . The ChaMunida gracilisHyalinoecia longibranchiataM. gracilis, as is typical for many Munida species Hyalinoecia species having incubated embryos We focus on two benthic invertebrates–the squat lobster Our study of the population genetics of these two species aimed to address fundamental questions regarding connectivity of the deep benthos among some of the prominent geomorphic features in the New Zealand EEZ: (1) Is there regional genetic structure across the study area and if so, can this structure be explained by factors known to affect genetic connectivity ?; (2) Is there significant genetic structure within the three regions? For example, is there a difference among populations that are found in different habitats but are geographically close together? Is there significant genetic structure between populations on the north and south flanks of the Chatham Rise, potentially influenced by the presence of the Subtropical Front?; (3) Do the inferred life history strategies correlate with the observed patterns of genetic connectivity?; and (4) What implications do the patterns of genetic population connectivity between species and among sample sites, habitats, and regions have for Marine Protected Area design and the efficacy of the current Benthic Protection Areas?Hyalinoecia longibranchiata and Munida gracilis were collected during four research cruises onboard the R/V Tangaroa: TAN0705 , TAN0707 , TAN0905 , and TAN1004 . All necessary permits were obtained for the described field studies. The specimens used in this study were taken from samples collected or obtained by New Zealand’s National Institute of Water and Atmospheric Research (NIWA) under a “Special Permit (421)” issued by the New Zealand Ministry of Fisheries for the taking of fish, aquatic life, and seaweed for the purposes of education and investigative research. Samples were collected using NIWA’s epibenthic “seamount sled” , a hyperbenthic “Brenke” sled, and a beam trawl. Upon collection, M. gracilis and H. longibranchiata specimens were preserved in ethanol, except for 29 H. longibranchiata individuals that were frozen upon collection. All specimens are stored in the NIWA Invertebrate Collection (NIC).Populations of H. longibranchiata were sampled from 478 m to 746 m and M. gracilis from 421 m to 634 m depth. To explore the role of geomorphological habitat types as a factor for structuring connectivity, we identified sites that spanned habitat types in the three regions . When possible, sites with samples for both species were used.Due to limitations in the number of sampled individuals and resources available to this study, not all sample sites for which there were specimens in the NIC could be used in this study. In order to avoid confounding geographic site comparisons with depth variability, populations  from sites were selected from a restricted depth range (400–800 m). Sites did not straddle a previously identified depth disjunction in population structure between populations at <600 and >1000 m H. longibranchiata and “C100” for M. gracilis. Two sites from the Hikurangi Margin were used for H. longibranchiata: “14a,” a slope habitat site and “3B,” a seamount site. On the Chatham Rise, five sites were used for H. longibranchiata: “7A07,” “9D28,” and “1B15” on the southwest part of the rise, and “6C63” and “3CX2” on the northeast part of the rise. Six sites on the Chatham Rise were identified for M. gracilis: “7A07” in the southwest, “6A06” centrally located on the northern flank of the rise, “9D11” and “9D09” located in the south-central region of the rise, and “Iceberg Seamount” and “Diamondhead Seamount” of the Andes Seamount cluster at the eastern end of the rise.The study sites  are presented in H. longibranchiata and leg tissue from M. gracilis were sub-sampled for genomic DNA (gDNA) extraction. To increase gDNA yield, many ethanol-preserved samples (n = 61) were soaked for 24 hours in a buffer containing 500 mM Tris-HCL (pH8), 20 mM EDTA, and 10 mM NaCl before extraction 2O , depending on the condition of the original tissue sample. For samples with poor tissue quality due to disintegration in ethanol, elution occurred in smaller volumes of water in order to achieve a higher concentration of gDNA. Genomic DNA was quantified using Quant-iT PicoGreen DNA quantification kit according to the manufacturer’s instructions , and working stocks of DNA (approximately 10 ng/µl) were stored at 4°C for up to six months prior to use.Mid-section muscular tissue from COI gene was amplified using universal primers COI gene was amplified using iProof High-Fidelity DNA Polymerase Master Mix , using 1–5 µl of gDNA and primer concentrations of 0.2 mM each. A subset of reactions was trialed with HOT FIREPol Master Mix with 1.5 mM MgCl2 (Solis BioDyne) in an unsuccessful attempt to increase PCR yield. A “touch-up” PCR profile was used to eliminate non-specific binding. The profile used for COI consisted of denaturing at 98°C for 2 minutes followed by 10 cycles of denaturing at 98°C for 10 seconds, annealing at 49°C incrementally raising to 54°C for 30 seconds, and extension at 72°C for 30 seconds; followed by twenty cycles of denaturing at 98°C for 10 seconds, annealing at 54°C for 30 seconds, and extension at 72°C for 30 seconds; with a final extension step of 72°C for 7 minutes in a 2700 Applied Biosystems PCR machine. For some samples with low gDNA concentrations, an extra ten cycles  were added in the final PCR profile.For both target species, a fragment of the mitochondrial H. longibranchiata 16S gene amplification were from Zanol et al. (2010) 16S gene was amplified using a “touch-down” method as described in Zanol et al. (2010) 16S was sequenced for a small subset of M. gracilis samples (n = 7); however, the portion of the genetic marker that we were able to sequence exhibited no variation among the sequenced individuals. The same was true for a small set of Internal Transcribed Spacer Region sequences (n = 12) generated for H. longibranchiata.Primers used for PCR amplification was assessed using gel electrophoresis and Quant-iT PicoGreen DNA quantification kit according to the manufacturer’s instructions . PCR products of the correct size were purified using either a Zymogenetics PCR purification kit (Zymogentics D4013) or a Qiagen PCR purification kit  and eluted in DNA/RNAase-free water. Purified PCR reactions of approximately 10 ng were shipped to Macrogen, Inc. for sequencing.H. longibranchiata individual from 7A07 for which only one direction was usable for the16S sequence. Final datasets consisted of 680 basepairs of 16S for H. longibranchiata, 524 basepairs of COI for H. longibranchiata, and 526 basepairs of COI for M. gracilis. DNA sequences have been deposited in GenBank . Sequences (and subsequent species identity) were compared to the Genbank database (http://www.ncbi.nlm.nih.gov/) and alignments of COI were translated into amino acid sequences and checked for stop codons to assess whether or not the amplified fragments could be considered pseudo-genes. The genetic distances among individuals in each dataset were compared to genetic distances among species within the same families and/or genera to correlate genetic divergence and morphological species boundaries . Genetic distances were calculated (using Kimura 2 Parameter model in PAUP) among the COI sequences from seven Munida species: Munida spilota, Munida stia, Munida notata, Munida tyche, Munida zebra, Munida taenia, and Munida thoeCOI sequences from seven species of onuphid worms: Diopatra cf. ornate, Diopatra dentate, Diopatra dentate, Hyalinoecia sp., Onuphis elegans, Onuphis cf. iridescens, and Paradiopatra quadricuspisDNA sequences were edited and aligned (using CLUSTAL-W) in Geneious Pro 5.3.4 Genetic diversity indices  for each population and gene were calculated in DnaSP, version 5.10.01 H. longibranchiata given that initial assessments indicated there was no genetic structure for M. gracilis.In order to assess population genetic structure among and within populations, Analysis of Molecular Variance (AMOVA) was conducted in Arlequin version 3.11 by grouping sites into the three regions: Chatham Rise, Hikurangi Margin, and Challenger Plateau and running a standard AMOVA with default program settings. To address the question of spatial variation along the Chatham Rise, an AMOVA was conducted to separate the sites into two groups: a northeast subset that consisted of 6C63 and 3CX2 and a southwest subset that consisted of 7A07, 9D28 and 1B15. AMOVA tests were run only on COI sequences for various Munida species (listed in the Methods section) ranged from 8.2% to 15.4%, while the genetic distances of our COI dataset for Munida gracilis ranged from 0% to 1.9%. The genetic distances among COI sequences for various onuphid worms ranged from 4.6% to 28.5%. Genetic divergences in the COI dataset for Hyalinoecia longibranchiata ranged from 0% to 2.9%. These results indicate that the individuals we used from each respective morphospecies are within the genetic divergence diagnostic of their respective species, implying that there are no cryptic species in our samples.Prior to any population genetic analyses, the assessment and confirmation of phylogenetic species was conducted across all sequences for each of the target taxa. The genetic distances among COI and 61 16S partial sequences from H. longibranchiata were obtained. There were eight H. longibranchiata specimens for which we did not obtain COI sequences, but did obtain 16S sequences. There were four H. longibranchiata specimens for which we did not obtain 16S sequences but we did obtain COI sequences. A total of 52 COI sequences from M. gracilis were obtained. All sequence reads were unambiguous except for one gene sequence for H. longibranchiata, which had a single undetermined base. The lack of complete overlap between sequenced individuals for each gene prevented joint analysis of the two genetic datasets. The per-site number of sequences and genetic diversity indices for each gene in both species is presented in A total of 58 M. gracilis (n = 52), had high haplotype diversity for the COI gene  consisted of 10 haplotypes shared by more than one individual and 10 unique haplotypes for a total of 20 haplotypes (16S sequence dataset for H. longibranchiata (n = 61) consisted of nine haplotypes shared by more than one individual and 10 unique haplotypes, for a total of 19 haplotypes  than all other populations (Fst >0.564), as were both populations from the Hikurangi Margin sites (Fst >0.484), which were also different from one another (Fst  = 0.47989). Differences between populations within the Chatham Rise were small (<0.17647) and only the difference between the two northeast populations at 6C63 and 3CX2 was statistically significant  (The 0.31089) . SpecifiHyalinoecia longibranchiata AMOVA results .Our study is one of few that have examined genetic connectivity of deep-sea invertebrate populations in the New Zealand EEZ. Using mitochondrial H. longibranchiata provides evidence of little to no historic gene flow between the Challenger Plateau, the Hikurangi Margin, and the Chatham Rise. In contrast, the sampled M. gracilis population data demonstrated high haplotype diversity for COI and no population structure at the geographic scale examined in this study. A number of factors are known to affect genetic connectivity including large and small-scale current regimes, topography, settlement habitat, depth, dispersal strategies, adult mobility and reproductive success, etc. The population structure on a regional scale for H. longibranchiata populations between the three regions can be explained partly by geographic distribution and partly by currents. Large geographic distance between sites can limit connectivity, especially for species with low dispersal capability. The Hikurangi Eddy, located to the East of Cook Strait could create an isolated water mass around the study sites at the southern end of the Hikurangi Margin The observed large-scale genetic differences in M. gracilis throughout the study area. Based on the large proportion of unique haplotypes, we conclude that the genetic diversity of COI in the M. gracilis population has not been fully ascertained. Considering the high levels of diversity in the mitochondrial genes we sampled, it is difficult to draw conclusions about the M. gracilis population except to say that the presence of certain haplotypes across the study area indicates that there is likely a single population with high levels of mixing not impeded by geographic distance or current patterns.In contrast to the quill worm, we found unstructured yet genetically diverse populations of the squat lobster in M. gracilis, it is likely that more individuals would provide more informative results. Despite the limited H. longibranchiata data set, the data provide insight into the population structure of this species, and the results are supported by both genetic markers (16S and COI). From our initial genetic survey and for future studies of these species, the COI gene can be considered a useful marker for resolving genetic structure in H. longibranchiata, and somewhat less in M. gracilis.As with all population genetic studies, the numbers of individuals, loci, and sites have to be considered in the interpretation of the data. The available sample sizes at various sites in the study area (which we have termed populations) are not considered large, nor are they consistent across sites. However, this is not atypical for deep-sea population genetic studies in which collecting large sample sizes yielding highly robust estimates of genetic diversity is considerably difficult given the inaccessibility and expense of obtaining these populations. Comparing haplotype diversity between sites with highly variable sample sizes could lead to inappropriate assumptions about spatial patterns of haplotype diversity, including the performance of unbiased estimators and rarefaction methods H. longibranchiata was observed between the seamount and slope sites on the Hikurangi Margin and potential differentiation was detected between populations on the northeast and southwest sites on the Chatham Rise. No genetic structure between sites of varying habitat types–specifically between seamount and slope–was observed for M. gracilis in any region.In addition to the larger scale patterns discussed above, population structure for Addressing questions of genetic connectivity is especially complex in the deep sea given that suitable habitats can be patchy over large spatial scales (hundreds to thousands of km). For example, several thousand kilometers may separate hydrothermal vent fields or seamounts and yet gene flow may occur between the geographically distant sites of the same habitat type e.g., . The oppAn added layer of complexity to deep-sea connectivity is the potential for inter-habitat connectivity when different habitats may be found in a small geographic area. For example, in the Norfolk Ridge seamount system, populations on the seamounts have been shown to be genetically connected to populations on the island slope H. longibrachiata at the Hikurangi Margin sites were significantly different from the Challenger Plateau sites and the Chatham Rise sites, they were also significantly different from each other. These sites–one a seamount, the other a slope–are only ∼38 km apart, but are separated by a small canyon. It is possible that there is a local current regime on this margin that limits the connectivity of these two populations or perhaps some habitat preference that results in the observed difference in genetic structure. The seamount in question may likely be considered too small for localized isolating hydrographic features. So alternatively it could be the predominance of down slope currents associated with canyons rather than along slope currents on this margin that are responsible for the limited connectivity between the populations of quill worms at the seamount and slope sites. For M. gracilis, the two seamount sites in the Andes Seamount cluster–Diamondhead Seamount and Iceberg Seamount–shared haplotypes with populations at sites elsewhere on the Chatham Rise, indicating that for this species in the New Zealand region, habitat type may not play a strong role in genetic connectivity.Because of the constrained sample availability, the sites from which we were able to obtain samples and the sample sizes did not provide a robust enough dataset to fully understand the extent of inter-habitat genetic connectivity. Nevertheless, our data provide some interesting indications about inter-habitat connectivity. While populations of H. longibranchiata did not allow us to separate strict north-south effects from possible east-west effects, we were able to test for northeast to southwest variation in the genetic make-up of populations. COI, but not 16S, data provide some support for this hypothesis for H. longibrachiata because populations at sites on the northeast of the rise were sufficiently different from those on the southwest of the rise.Results suggest that there is variation in the level of connectivity across the Rise. Other studies have found marked differences in benthic community structure between the northern and southern flanks of the rise, which have been attributed to the different environmental and biological conditions imposed by the location of the STF et al. (2003) Depth has been shown to play a major role in connectivity within and between deep-sea ocean basins and slopes e.g.,  and seamet al. (2006) The results of our study indicate that the genetic connectivity patterns of the two study species are different. Differences in life history strategy and inferred pelagic larval duration likely explain the difference between observed patterns in the two species. Larval dispersal and adult mobility contribute to making the squat lobster a better potential disperser than the quill worm and correlate directly with the differences in inferred patterns of connectivity. The relationship between life history and population structure across seamounts is well documented in Samadi In 2007, a fishing industry-driven initiative resulted in the creation of seventeen areas within the New Zealand EEZ that were designated Benthic Protection Areas (BPAs). These areas comprise roughly 30% of the EEZ and are closed to bottom trawling, but not to other uses such as mining. Still, they are considered by some A deep-water MPA process is scheduled to commence in 2013 M. gracilis), indicate that populations in different closed areas have historically been well connected, and one can reasonably presume are currently connected. However, for common species with limited dispersal capabilities (like H. longibranchiata), our study findings provide a framework with which one can analyze the efficacy of future MPA design. At the broadest level, our main finding is that a species with direct development has pronounced population structure across the Challenger Plateau, Hikurangi Margin, and the Chatham Rise (The difference in genetic population structure between the squat lobster and the quill worm confirms that, in terms of connectivity, MPA design should consider the implications of protecting assemblages of species with different life history strategies ham Rise . If the Our results suggest that on smaller spatial scales within regions, local topography and current regimes may have a profound impact on gene flow, leading to the differentiation of populations at different habitats. Populations at slope and seamount habitat in close proximity on the Hikurangi Margin were shown to host genetically different populations. There are some protected areas in the study area that have been specifically closed to trawling in order to protect the communities on seamounts, in part because of the then perceived isolated nature of seamount fauna The results of our within region comparison suggest that the location of BPAs on the Chatham Rise may require revision. The central BPA on the Chatham Rise is located in the middle of the crest of the rise at depths of 300–450 m and the BPA at the eastern end of the rise extends over a depth range of 300–900 m. We do not have any population data from these specific locations but we have shown the potential for genetic variation across the Rise. Given this finding and our understanding about differences in benthic communities on the north and south flank of the Rise e.g., , and theH. longibranchiata. For example, what is the true geographic extent of populations found in the three regions–Challenger Plateau, Hikurangi Margin, and Chatham Rise? Are the Hikurangi Margin haplotypes found along the margin to the south or north? Similarly, are quill worm populations on the central north part of the Chatham Rise unique to these sites or will they resemble populations at the northeastern sites? What about populations at other sites to the west of New Zealand? Is the population of quill worms at the Challenger Plateau site different from the sites at the other study regions simply because it is on the western side of New Zealand or is the plateau in some way isolated? Such questions apply to other invertebrate species with potentially limited dispersal capabilities. Future genetic studies of population connectivity should include a greater range of study species in order to generate information useful for the design of protected areas in the deep sea.Our assessment of the genetic connectivity of two abundant benthic invertebrates found throughout a range of deep-sea habitats in the New Zealand EEZ represents a step towards understanding the spatial structure of benthic communities in the New Zealand region, and informing the future design of deep-water MPAs in the region. However, the study has raised a number of questions about the populations of


Pulleniatina obliquiloculata in the Indo-Pacific Warm Pool (IPWP) by using partial small subunit ribosomal DNA (SSU rDNA) sequences. We found longitudinal clines in the frequencies of three distinct genetic types in the IPWP area. These frequencies were correlated with environmental factors that are characteristic of three water masses in the IPWP. Noteworthy, populations inhabiting longitudinally distant water masses at the Pacific and Indian sides of the IPWP were genetically different, despite transportation of individuals via oceanic currents. These results demonstrate that populations of pelagic plankton have diverged genetically among different water masses within a single climate zone. Changes of the oceanic circulation system could have impacted the geographic patterns of dispersal and divergence of pelagic plankton.Evolutionary processes in marine plankton have been assumed to be dependent on the oceanic circulation system, which transports plankton between populations in marine surface waters. Gene flow facilitated by oceanic currents along longitudinal gradients may efficiently impede genetic differentiation of pelagic populations in the absence of confounding marine environmental effects. However, how responsible oceanic currents are for the geographic distribution and dispersal of plankton is poorly understood. We examined the phylogeography of the planktic foraminifer  The phylogeography of pelagic organisms on a global scale is essential for understanding the history of biological responses to oceanic environmental changes. Environmental variables and their geological history govern biogeographic patterns . These pOceanic currents also function as conduits between water masses. Accordingly, some studies have demonstrated that oceanic currents disperse marine organisms with high conveyance and determine connectivity between habitats . In partSimplistically the oceanic currents in the Indo-Pacific Warm Pool (IPWP) in the tropical climate zone flow in a longitudinal direction. The IPWP is defined as warm surface water that accumulates around the Indonesian Archipelago after being driven across the Pacific Ocean by the westward equatorial current system . Warm waDespite this interocean transport system, several studies have shown regional genetic differentiation of marine organisms in the IPWP  has been the genetic marker used in most cases to classify genetic types of planktic foraminifera e.g., . MultiplPulleniatina obliquiloculata based on a phylogeographic analysis of partial SSU rDNA sequence data. This species is distributed from the tropics to the subtropics, particularly in the IPWP area  Pacific and Indian Oceans, we used the CEREGE MultiNet Midi , which was equipped with an opening–closing 100-μm mesh net. In the Northwest (NW) and Central Pacific, the Motoda (MTD) closing net system  with 63-μm mesh was towed horizontally. We obtained 775 specimens with these systems. We obtained the other 117 specimens from samples collected with vertical tows in the upper 150–200 m of the water column at each location.We isolated and cleaned each specimen in filtered seawater under a microscope. Each individual specimen was separately preserved with the GITC* (guanidine-base) buffer at –20°C in an onboard freezer. Genomic DNA was extracted by the GITC* protocol .PCR amplifications of the terminal 3′ end of the SSU rDNA was performed as described by P. obliquiloculata  analysis by using the enzyme PsiI , which cuts at the palindromic sequence TTA/TAA. We detected two distinct patterns for types I + IIa and IIb after migration on 1% agarose gel. Second, we directly sequenced both first and last variable regions (∼200 bp) in the 3′ end of SSU rDNA of 747 specimens to classify types I and IIa. We amplified the first variable region with the primers S14F1 and S15r (5′-GAA CTA AGA ACG GCC ATG CAA-3′) and the last one with the primers S19rf (5′-CTA GGA ATG CCT (CT)GT ACG GG-3′) and SBf.We used Spearman's rank correlation analysis to test the relationships of genetic-type frequency with geographic (longitude and latitude) and environmental (SST and SSS) factors. We used sampling sites where at least 10 individuals were collected to reduce sampling errors in frequency estimation. We confirmed that the results of these tests did not change in terms of statistical significance even when we used sites where at least 20 individuals were collected.Our SSU rDNA phylogeny detected the presence of two clades (clades I and II) supported with 1.00 posterior probability (PP) and a 100% bootstrap value (BV) A. Clade Foraminiferal SSU rDNA sequences have shown relatively high diversity among eukaryotes due to their rapid evolution and long-fragment insertions . In the Because the restriction enzyme PsiI that we used for the RFLP method cut the examined sequences in the variable regions II and III, the RFLP patterns distinguished types I + IIa and IIb B. DirectDepth-specific samplings revealed the vertical distributions of the three genetic types in the Indian, and SW and central Equatorial Pacific oceans . Most spWe found type I specimens in all of the areas we studied. In contrast, type IIa was exclusively found in the equatorial Pacific area, and type IIb mainly in the Pacific including the Indonesian Archipelago ; Fig. 5.r = –0.772, P < 0.001), whereas those of type IIa displayed a positive correlation with longitude . Only type IIb did not show a significant correlation with longitude. In contrast, the relationship between the frequencies of three types and latitude was not significant.In the Indo-Pacific Ocean, the frequency of each genetic type at each sampling site was significantly correlated with longitude A. The frr = –0.796, P < 0.001) . The frequencies of types IIa and IIb were significantly correlated with SSS , whereas type IIa showed a positive correlation .The frequencies of types I and IIa were correlated with SST. Type I was negatively correlated with SST (< 0.001) B. In conwith SSS C. Type IP. obliquiloculata. At each sampling site, two or three genetic types were distributed vertically at the same depth in the water column  separation of water masses, each of which is inhabited by a population, and (2) transport of individuals among water masses.The present results on spatial distribution of planktic foraminiferal genetic types provide new insights into their dispersal abilities at the population level. The most striking result is that populations of No type IIa specimens were obtained from the Indian Ocean, in spite of our thorough surveys across the IPWP . The geoG. ruber were distributed at each of two basins that are adjacently located under the same current in the Mediterranean Sea (G. ruber genetic types were found only in either one of the two populations, which were attributed to niche partitioning. However, this interpretation remains questionable without knowing their ecological or physiological characteristics that may allow differential adaptation to environmental conditions to be examined. Although the frequency of P. obliquiloculata type IIb was here correlated with SSS, there is no sufficient evidence to argue for evolutionary adaptation of the present populations to different salinities or other environmental conditions.The longitudinal distribution pattern of type IIb across the Indonesian Archipelago suggests that this type is dispersed to the Indian Ocean through the ITF. We detected type IIb only at the easternmost site on the Indian Ocean side of the IPWP, though all specimens were identified as type I further west in the Indian Ocean. Our surveys were performed during the Asian summer monsoon that provides strong westerly flow from the Pacific to Indian Oceans via the ITF . Despitenean Sea . Two of The present study examined the hypothesis that gene flow among populations is enhanced by passive transport through oceanic currents . We show