Megalaspis cordyla (Osteichthyes: Carangidae) in the area surrounding Taiwan during 2000–2001. Genotyping was performed on M. cordyla collected seasonally around Taiwan as well as specimens collected from Singapore  and Indonesia (Banda Sea). Gonadosomatic indices (GSI) revealed that M. cordyla does not spawn near Taiwan. Data related to the mitochondrial control region revealed that the samples from Singapore and Indonesia represented two distinct genetic cohorts. Genotyping revealed that during the summer (June–August 2000), the Indonesian variant was dominant in eastern Taiwan (presumably following the Kuroshio Current) and in the Penghu region (following the Kuroshio Branch Current). During the same period, the Singapore genotype was dominant along the western coast of Taiwan (presumably following the South China Sea Current); however, the number dropped during the winter (December–February 2001) under the effects of the China Coast Current. Divergence time estimates indicate that the two genetic cohorts split during the last glacial maximum. Despite the fact that these results are based on sampling from a single year, they demonstrate the importance of seasonal sampling in unravelling the genetic diversity in pelagic ecosystems.Many fisheries management and conservation plans are based on the genetic structure of organisms in pelagic ecosystems; however, these structures tend to vary over time, particularly in cyclic ocean currents. We performed genetic analyses on the populations of the pelagic fish,  The fact that the genetic structures observed in many pelagic fish are associated with oceanographic dynamics5 means that the conclusions drawn from genetic diversity and heterogeneity among populations are often attributed to connectivity among populations. This approach is well suited to deriving detailed descriptions of spatial genetic structures and identifying sources of genetic variation; however, the oceanographic dynamics associated with pelagic fish populations tend to be cyclic 8. It is crucial therefore to base plans for the conservation of pelagic fish stocks on the dynamics of genetic structures within the context of oceanographic cycles. Unfortunately, many studies on genetic differentiation among populations of highly migratory marine organisms rely heavily on one-time sampling at each sampling site. As a result, the cyclic dynamics of genetic diversity are often obscured. In this study, we sampled the pelagic fish stocks of torpedo scad, Megalaspis cordyla  (Perciform: Carangidae) in the offshore ocean currents surrounding Taiwan. Note that sampling was performed at various time-points throughout the year to enable the analysis of seasonal variations in genotype diversity. Despite the fact that these results are based on sampling from a single year, our findings clearly illustrate the importance of using serial sampling to identify changes in cryptic genetic structures associated with oceanographic dynamics as well as the important implications this can have for conservation.Management plans for fisheries and the conservation of biodiversity in pelagic ecosystems are commonly based on geospatial data related to a target speciesM. cordyla) is an important commercial species characterized by large scutes along the lateral line and a large black spot on opercle. This species is broadly distributed throughout equatorial areas of the West Pacific and Western Indian Oceans9. As a migratory pelagic fish, the torpedo scad is frequently found in areas ranging from southern Japan to the Indo-west-Pacific including Australia10. Trawling for M. cordyla in offshore regions around Taiwan peaks between April and November11. The strong swimming ability of this schooling species allows it to feed primarily on other fish. The body size is generally in the range of 30–40 cm, with exceptionally large specimens reaching 80 cm in total length (TL) and 3–4 kg in weight.The torpedo scad 13. The KC is a northward deflection of the horizontal North Equatorial Current . The KC is a strong ocean current that runs constantly northward along the east of Taiwan to Ryukyu, reaching ~ 30°N off the coast of Japan15. The KC carries warm water (> 25 °C) from equatorial regions. In the Strait of Luzon, it branches toward the west of Taiwan, but this Kuroshio Branch Current (KBC) weakens near Penghu between April and November, such that none of the warm water extends north of Penghu between December and March16. The SCSC passes through the Bashi Channel and South China Sea moving north into the Taiwan Strait in the summer, whereas the CCC moves toward the south during winter16. The migratory M. cordyla originates in tropical regions; therefore, we predicted that the KC carries the Indonesian sourced individuals toward Taiwan via the Philippine Sea, whereas the SCSC carries the Indian Ocean sourced individuals via the South China Sea (only during the summer). No previous research has indicated whether there is any mixing of the discrete populations of M. cordyla. Furthermore, the time-course dynamics of the genetic structure of this species has yet to be elucidated.The main ocean currents around Taiwan are the Kuroshio Current (KC), the South China Sea Current (SCSC), and the China Coastal Current (CCC)22. A high degree of homogeneity has also been observed in the genetic makeup of other widely distributed oceanic organisms , which drift along ocean currents during the larval stage. Some of the pelagic migratory species with life histories similar to that of M. cordyla present discrete genetic structures, despite the fact that they are widely dispersed throughout the West Pacific and East Asian oceans. There is considerable genetic heterogeneity among populations of scad mackerel (Decapterus macarellus) from the Molucca Sea and Banda Sea27. Populations of grey mullet  in the East China Sea present distinct differences at two loci, GPI 100 and GPI 13528. Other nonmigratory but widely distributed fishes, such as damselfish29 (Dascyllus carneus and D. aruanus), also present genetic differences according to whether they appear in the Indian Ocean or the West Pacific Ocean. There are two general hypotheses pertaining to the evolutionary processes underlying the genetic differentiation of highly migratory marine organisms. The first hypothesis posits that discrete populations are isolated by existing oceanographic processes , and thus became non-panmictic. The second hypothesis attributes genetic differentiation to historical events  resulting in genetically differentiated cohorts that are unable to interbreed31. Note however that many studies on migratory marine species rely on one-time sampling per site; i.e., they fail to consider the influence of oceanographic cycles on population divergence. The M. cordyla in the Indo-Pacific oceans and the amalgamation of the cohorts from different source populations in the offshore waters around Taiwan provide a rare opportunity to test the hypothesis that seasonal dynamics in the genetic structures of highly migratory pelagic organisms are associated with cyclic ocean currents. Our objectives in the current study were as follows: (a) to examine seasonal variations in the reproductive status of torpedo scad in the offshore waters of Taiwan; (b) to test whether different ocean currents on the eastern and western sides of Taiwan carry different genetic populations of torpedo scad; (c) to examine the correlation between seasonal ocean current dynamics and seasonal changes in the genetic structure of torpedo scad populations; (d) to estimate the time of population divergence and demographic changes in populations. Our results clearly demonstrate the importance of performing sampling at multiple timepoints in order to capture time-sensitive fluctuations in the genetic structure of migratory pelagic organisms and thereby elucidate the corresponding cryptic genetic diversity.Most of the mtDNA polymorphisms in pelagic migratory fishes present low intraspecific genetic divergence due to strong gene flow among populations32. The results of ANOVA and Duncan’s multiple range test revealed that the GSI peaked in the summer on the east coast (KC) as well as the west coast (KBC) of Taiwan, and was significantly higher at this time than during other seasons (p < 0.05)  , Penghu 0.41–2.20%), and Kaohsiung (0.39–5.41%)  Fig. . The bodThe mtDNA control region of samples collected from the offshore waters of Taiwan, Singapore, and Indonesia Fig.  indicate. cordyla samples collected from the eight locations in this study, we identified 150 haplotypes, corresponding to an average haplotype diversity of Hd = 0.991 and average nucleotide diversity of π = 0.035 . The divergence date of M. cordyla populations calibrated using the time of the last glacial maximum (LGM)35 was 0.012 ± 0.055 Mya. The divergence date of M. cordyla populations calibrated using the Pleistocene event was 1.96 ± 0.010 Mya. BEAST analysis revealed that divergence estimates based on Pleistocene events were not realistic. Rather, it appears that the divergence date between the two genotypes was linked to a glacial episode   captured throughout the study period (2000–2001). While flowing toward the north, the KC carries a fish population from the Banda Sea near Indonesia. Depending on the strength of the SCSC, another population from the South China Sea near Singapore is carried north mainly toward the west of Taiwan  present a homogeneous genetic structure. By contrast, Decapterus macrosoma exhibits significantly different genetic structures between populations in some adjacent regions27  as well as notable genetic homogeneity over extensive regions from the South China Sea to Sulawesi Sea via the Java Sea and Makassar Strait. The geographical isolation of the D. macrosoma population in the Sunda Strait coincides with the presence of a sharp transition zone between the Indian Ocean and the Sunda Shelf. Researchers have also obtained evidence indicating that differences in the genetic structure of discrete populations may also be attributed to differences in salinity in different ocean currents27.Taiwan is an island affected by the KC, KBC, SCSC and CCC currents. The seasonal patterns in these ocean currents no doubt also affect the migration and commercial landings of other marine species (migratory and non-migratory)M. cordyla in this study presented at least two well-structured genetic populations (Singapore and Indonesia), based on structures identified in the mtDNA control region. From the spawning areas near the equator, the Singapore population is carried northward by the SCSC whereas the Indonesian population moves with the KC12. The pronounced seasonal changes in the Kaohsiung and Dashi populations (affected by the KC) were not observed in the Penghu population. Nucleotide diversities in the Kaohsiung samples were higher in the spring (π = 0.028) and in the summer (π = 0.023) when the SCSC shifted northward, and subsequently decreased in fall (π = 0.019) and winter (π = 0.018) when the CCC reversed southwardly. We argued that the high nucleotide diversity pattern near Kaohsiung can be attributed to the multi-directions of the ocean current trajectories near Kaohsiung  caused by the vortex of the KC and SCSC with the mixture of two genotypes. We also observed seasonal fluctuations in the nucleotide diversity of the Dashi sample; however, the lowest nucleotide diversity  occurred when the direction of ocean currents was constant. We infer that the lowest nucleotide diversity in the summer could be attributed to the strengthen of KC during summer thus brings the Indonesian genotypes to Dashi offshores while SCSC has less effect thus no Singapore genotypes arriving to the northern Taiwan sampling site. Nucleotide diversity in the Penghu sample was high throughout the year (π = 0.033–0.041), which could be attributed to the vortex of KBC and CCC especially during the winter near Penghu. Overall, changes in the genetic structure of M. cordyla around Taiwan (within and among populations) were strongly influenced by KC and less so by the CCC.The 22. The M. cordyla in the current study is also widely dispersed; however, there is clear genetic differentiation between populations. The genetic differentiation of other pelagic organisms in the West Pacific region have been attributed to glacial events31. Our divergence time estimates based on time-calibrated phylogenetic analysis indicate that the Singapore and Indonesia genotypes can probably be attributed to the glacial periods short before the LGM event associated with the formation of a land bridge separating the South China Sea and South Pacific Ocean. Subsequent interglacial periods no doubt expanded the range and the population size of both genetic cohorts, resulting in the distributions observed today. However, whether these two genetic cohorts have gone through speciation event, which is difficult to test using mitochondrial data that we currently have, need to be further tested using multiple nuclear makers or genomic data.Some widely dispersed marine organisms present a highly homogeneous genetic structure resulting from the gene flow among geographic regionsM. cordyla in the offshore waters of Taiwan demonstrated the importance of time-series sampling of migratory pelagic fish, which are often assumed to be genetically homogeneous. Our results are not representative of long-term fluctuations in the genetic structure of M. cordyla; however, our results clearly indicate the need to invest in multi-year monitoring for similar pelagic marine organisms. A failure to account for genetic variations over extended timespans could greatly curtail the formulation of plans for the conservation of fish stocks. To do the long-term and large sample size monitoring, the application of the cost-effective genotyping methods, e.g., genomic methods, need to be considered. Our findings may be applicable to other pelagic fish species in these area as well as other oceanic organisms existing under similar oceanographic systems.This population genetics study of Megalaspis cordyla samples around Taiwan were collected monthly during June 2000 to May 2001 from the offshores near the east of Taiwan, i.e., Dashi, where KC runs to the north year-round; from the offshores of Kaohsiung, where KBC reaches year-round; and from the offshores of Penghu, where KBC does not arrive in winter  to compare seasonal changes of the mean GSI values. We did not calculate the GSI of the samples from Singapore and Indonesia because we only sampled the muscle tissues for molecular work. Furthermore, we also measured the body length of the collected samples from the offshores in Taiwan.The study was approved by the Institutional Animal Care and Utilization Committee of Academia Sinica, Taiwan. All methods were performed in accordance with the applicable guidelines for the care and use of animals. We measured the gonadosomatic index using the equation (gonad weight (g)/body weight (g)) × 100% of the collected samples in each month from the offshores of Taiwan. We analyzed the seasonal reproductive status using the mean GSI values of 2; 0.1% Triton X-100), 0.4 μM each of the primers, 0.2 μM each of dNTP, 50–100 ng crude DNA, and 1 unit Taq DNA polymerase . We used two primers, namely P1 and PB, to amplify ~ 1.2 kb partial fragment of the control region, tRNAphe, and partial 12S RNA regimes. The following two internal primers for M. cordyla mitochondrial control region, corF and corB , were designed for sequencing. The thermocycler conditions were initial denaturation for 2 min at 94 °C, 35 cycles of denaturation for 1 min, primer annealing for 1 min at 50 °C, and extension for 1.5 min at 72 °C, followed by final extension for 10 min at 72 °C. We conducted the sequencing using the automatic sequencer, Biosystems 377A . Chromatograms and contiguous alignments were edited using Seqman version 4.00 . All partial sequences of the control region were aligned using the Clustal W program39 in MegAlign (DNAstar), and then BioEdit version 4. 7. 840.We extracted the genomic DNA from muscle tissues using a Puregene DNA Isolation Kit . The nucleic acid pellet was re-suspended in deionized water and frozen at − 20 °C until we conducted PCR. We conducted PCRs in a mixture of 50 μl reaction solution containing 5 μl of 10X reaction buffer  as the distal outgroup. We set up three competing scenarios for time-calibration in the BEAST analyses, which are (1) the time-calibration based on the evolutionary rate, 3% to 10% per million years (My), of control region34; (2) the divergence of M. cordyla populations occurred in the last glacial maximum (LGM) in ~ 250,000 years ago35; (3) the divergence of M. cordyla populations occurred in the Pleistocene . In the time-calibrated BEAST analyses, we applied an uncorrelated relaxed clock for independent branch rates, Yule model for speciation, and GTR + I + G model for nucleotide substation for all three BEAST analyses. We used a 100,000,000 chain length with 10,000 sampling frequency to collect at least 200 effective sample sizes (ESS). In the molecular clock analysis, we used ucld.mean = 0.065 and ucld.stdv = 0.01 in real space to constraint the molecular clock within 3–10% per My under the lognormal distribution. We dated the age of the ingroup  in 0.025 My with arbitrary standard deviation 0.1 My for the LGM hypothesis. We dated the age of the ingroup in 2.0 My with arbitrary standard deviation 0.1 My for the Pleistocene hypothesis. We then compared the posterior distributions of the estimated divergence time of M. cordyla in the samples that we collected. Genealogical relationship network of haplotypes was prepared with TCS version 1.243 and visualized in tcsBU44. Haplotype diversities and nucleotide diversities of the control region sequences from each subpopulation and season were calculated using DnaSp 6.12.0345. We also conducted the mismatch distribution analyses for testing the population size changes in the lineages that we detected in the phylogenetic analyses using DnaSp 6.12.03. All sequence data are deposited at GenBank (accessions MN849043-MN849132); the tree files are deposited at TreeBASE (submission ID 25561).We reconstructed the time-calibrated phylogenetic tree using BEAST v1.10.4Supplementary Information.


Hydrobates castro), a long-lived philopatric seabird, suffered massive population declines resulting in its listing under the Endangered Species Act in 2016 as federally Endangered. We used high-throughput sequencing to assess patterns of genetic diversity and potential for inbreeding in remaining populations in the Hawaiian Islands. We compared a total of 24 individuals, including both historical and modern samples, collected from breeding colonies or downed individuals found on the islands of Kaua‘i, O‘ahu, Maui, and the Big Island of Hawai‘i. Genetic analyses revealed little differentiation between breeding colonies on Kaua‘i and the Big Island colonies. Although small sample sizes limit inferences regarding other island colonies, downed individuals from O‘ahu and Maui did not assign to known breeding colonies, suggesting the existence of an additional distinct breeding population. The maintenance of genetic diversity in future generations is an important consideration for conservation management. This study provides a baseline of population structure for the remaining nesting colonies that could inform potential translocations of the Endangered H. castro.Seabirds in the order of Procellariiformes have one of the highest proportions of threatened species of any avian order. Species undergoing recovery may be predicted to have a genetic signature of a bottleneck, low genetic diversity, or higher rates of inbreeding. The Hawaiian Band-rumped Storm Petrel (‘Akē‘akē;  Many seabird species in the order Procellariiformes have experienced historical bottlenecks due to vulnerability to anthropogenic disturbances . CurrentHydrobates castro or formerly Oceanodroma castro—see H. castro occurs at 5 years in Galápagos and 7 years in Salvages , a Hawai‘i Department of Land and Natural Resources Division of Forestry and Wildlife project, collected blood samples from the metatarsal vein from individuals on Kaua‘i island between 2014 and 2017 from birds captured using conspecific playback and mist-netting techniques. KESRP stored blood samples on filter paper from one individual in Waimea Canyon, Kaua‘i, five individuals on Honopu Ridge, Kaua‘i, and one from a downed fledgling found in Poipu, Kaua‘i. The Pōhakuloa Training Area (PTA) Natural Resources Office collected samples from individuals at the newly discovered breeding colony on the Big Island between 2015 and 2017, using dog and personnel searches . PTA col®  and we prepared fragments between 150 and 350 bp in length for sequencing on the Illumina®HiSeq using the Kapa Biosystems  Hyper Prep kit with Illumina TruSeq index adapters. We conducted laboratory work at the Hawai‘i Institute of Marine Biology (HIMB) in Kāne‘ohe Bay, O‘ahu, Hawai‘i. We sent libraries to Vincent J. Coates Genomics Sequencing Laboratory at the University of California, Berkeley where they sequenced them on the Ilumina®HiSeq 4000 platform with paired-end 2 × 150 bp read length. The raw DNA sequences for each individual were deposited in NCBI Sequence Read Archive (BioProject accession number PRJNA559669).We individually extracted DNA from the blood and feather samples using the DNeasy Blood and Tissue Kit  according to the manufacturer’s protocol. We quantified the extracted DNA with the AccuClear™ Ultra High Sensitivity dsDNA Quantitation Kit . Due to low DNA yield, we performed whole genome amplification on individual samples with the REPLI-g UltraFast Mini-kit  which effectively and accurately increases yields of high-fidelity DNA . We prepH. castro mitochondrial genome (Fst) for both nDNA and mtDNA by using the K), with 10 iterations for each K at a burn-in period of 10,000 steps and 10,000 steps after burn-in and 10 iterations at a burn-in period of 100,000 steps and 100,000 steps after burn-in. We performed multiple runs to obtain better estimates of the posterior probability of each K value. We input results from both STRUCTURE runs into the program STRUCTURE HARVESTER (K) suggested by Watterson estimator of diversity (θ), and Tajima’s D (DT). For population statistics, individuals were grouped by island from which they were sourced from.We used the dDocent pipeline  to asseml genome  using bwl genome , samtooll genome , and vcfl genome . We calcARVESTER , to calcNe). The Bayesian Skyline Plot framework takes into account genealogy, demographic history, and substitution-model parameters in a single analysis  mtDNA using only Hydrobates spp.  was negative for all island colonies (FIS) ranged from −0.354 to 0.097 across islands.From our 24 individuals  we obtained a total of 650,048,040 sequences. We calculated genetic diversity statistics using 13,708 genomic SNPs shared among all individuals. The average nucleotide diversity (π) was similar across individuals found on Kaua‘i (0.159), O‘ahu (0.197), Maui (0.172), and Big Island (0.176). The Watterson estimator of expected genetic diversity at equilibrium (θ) was higher for Maui (0.376) and O‘ahu (0.333) with the caveat of low sample size, but lower for Kaua‘i (0.193) and the Big Island (0.225) where the sample size was higher. Tajima’s colonies . The inbFst values between island-scale groupings of individuals using both nDNA and mtDNA. We identified 13,641 nuclear and 67 mitochondrial shared SNPs among all individuals. Based on nDNA among islands, the highest differentiation was found between two groupings of islands , and therefore we performed the same analyses combining the Maui and O‘ahu individuals into a single population, which did not produce qualitatively different results or alter conclusions  ranged from 166 to 2327 individuals with a mean of 414. As the Bayesian skyline plot assumes that there is panmixia within the population tested, the four individuals found on Maui and O‘ahu were removed from this analysis as they may belong to a different genetic group. Furthermore, results from this analysis should be interpreted with caution as the SNP data may differ from the overall mutation rate for mtDNA, as the more slowly evolving sites within mtDNA are likely not fully represented.Reconstruction of the population size history by means of the coalescent Bayesian skyline plot using mtDNA data suggests a likely continuous population decline over the last 500 years for 25 years . AlthougH. castro in the Hawaiian Islands had relatively low inbreeding estimates and high genetic diversity, despite a relatively small population size and an assumed high degree of philopatry based on taxonomic order , low rates of inbreeding (mean FIS = −0.164 ± 0.221), and high nucleotide diversity (mean π = 0.176 ± 0.016). Although the species has undergone a decline in population size, there is no evidence of inbreeding, potentially due to promiscuous mating behavior or sex-biased dispersal . Although a lack of detection at the suspected Maui Nui breeding colony precludes direct testing, this island may host a breeding colony distinct from the others  and FST from nDNA (dark gray) for pairwise comparisons between Supplementary Material S2: (A) Fst estimates (B) structure plot, and (C) PCA plot, using nDNA and assigning the Maui and O‘ahu sourced individuals together as all belonging to the Maui island population.Hydrobates castro for STRUCTURE runs with the number of clusters (K) set between 1 and 4. Delta K statistic takes into account the change in the log probability of the data as K increases, but it cannot be calculated for K = 1 which is also a potential answer. The highlighted row is the chosen K with the highest support from STRUCTURE HARVESTER.Supplementary Material S3: K statistics  using (AHydrobates castro based on the 67 mitochondrial SNPs from 20 samples collected in Kaua‘i and Big Island. The black line represents the median of the parameter Ne, proportional to the effective population size, while the purple shading is the 95% CI.Supplementary Material S4: Bayesian Skyline Plot of Click here for additional data file.10.7717/peerj.8463/supp-2Supplemental Information 2Oceanodroma castro in the Hawaiian Islands.The SNP dataset includes VCF files of the filtered nuclear and mitochondrial SNPs shared between Click here for additional data file.


Pyura chilensis, a species that exhibits a low larval transport potential but high anthropogenic dispersal. A recent study in this species recorded significant genetic differentiation across a transition zone around 30°S in putatively adaptive SNPs, but not in neutral ones, suggesting an important role of environmental heterogeneity in driving genetic structure. Here, we aim to understand genomic-oceanographic associations in P. chilensis along the Southeastern Pacific coast using two combined seascape genomic approaches. Using 149 individuals from five locations along the HCS, a total of 2,902 SNPs were obtained by Genotyping-By-Sequencing, of which 29–585 were putatively adaptive loci, depending on the method used for detection. In adaptive loci, spatial genetic structure was better correlated with environmental differences along the study area  than to the geographic distance between sites. Additionally, results consistently showed the presence of two groups, located north and south of 30°S, which suggest that local adaptation processes seem to allow the maintenance of genomic differentiation and the spatial genomic structure of the species across the 30°S biogeographic transition zone of the Humboldt Current System, overriding the homogenizing effects of gene flow.The highly heterogeneous Humboldt Current System (HCS) and the 30°S transition zone on the southeast Pacific coast, represent an ideal scenario to test the influence of the environment on the spatial genomic structure in marine near-shore benthic organisms. In this study, we used seascape genomic tools to evaluate the genetic structure of the commercially important ascidian  Dispersal has been often assumed to be an effective mechanism of maintaining connectivity between local populations5. Nonetheless, many species display inconsistent patterns of genetic structure with those predicted from their developmental modes9. For instance, several direct developers are broadly distributed over thousands of kilometers, probably through long-distance dispersal mediated by rafting13, while many broadcast-spawners exhibit high levels of genetic structure at narrower geographical scales14. Currently, it is widely accepted that evolutionary forces, despite dispersal, can shape the genetic structure of a species across its distribution. Such factors include natural selection, phenotype-environmental mismatch and local adaptation, which may override the genetic signatures expected solely on the basis of gene flow17.Most marine taxa, and especially near-shore invertebrates, have free-living larval stages allowing dispersal across heterogeneous environments that lack evident barriers to gene flow23. Many relevant environmental variables are heterogeneous across a species’ distributional range and generate differential local selective pressures that may lead to major differences in global patterns of genetic differentiation. Thus, local adaptation can lead to genetic structure across a species’ range even in the presence of high levels of gene flow25.Understanding how environmental factors influence the contemporary spatial distribution of genomic diversity has become a main goal in evolutionary ecology and biogeography18. A main focus is the study of adaptive genetic structure associated with environmental data, which has been increasingly applied using Next Generation Sequencing (NGS) techniques through the identification of thousands of polymorphic loci scattered across the genomes27. However, very few studies on marine benthic invertebrates have attempted to identify loci that are putatively under selection in association with seascape; most studies have use traditional DNA markers33, and seascape genetic studies have evaluated neutral processes associated with genetic connectivity, niche modeling, as well as the role of physical factors33. Genomic-based studies considering the potential effects of environmental factors in neutral and adaptive genetic variation in marine invertebrates are still scarce37.Landscape genomics combines population genomics and landscape ecology to further understand how biotic and abiotic factors affect the processes associated with local adaptation and population structure38. Such patterns of genetic structure have mainly been associated with a widely reported biogeographical/phylogeographic transition zone at ~ 30°S40. This transition zone has been attributed to historical factors because only species with low dispersal capacity exhibit signals of marked genetic differentiation3. According to Lara et al.41, patterns of differentiation in poorly dispersing taxa are probably maintained through environmental differences north and south of 30°S. Therefore, this environmental discontinuity may also have contemporary influence on the genetic structure in species with low dispersal potential along the HCS. Conversely, in species with high dispersive potential, the signals of genetic differentiation across this transition zone have been completely erased by neutral processes probably associated with the homogenizing effect of gene flow3.The Humboldt Current System (HCS) is an environmentally heterogeneous system and accordingly, a very interesting area to evaluate adaptive genetic structure in marine organisms. This system does not have evident geographic or physical barriers to gene flow along its ~ 2,600 km of linear coastline. Nevertheless, several studies have recorded significant genetic differences between local populations in the areaPyura chilensis Molina 1782 is a benthic intertidal tunicate that is endemic to the southeastern Pacific coast, and represents one of the most conspicuous inhabitants of the HCS42. Like many other tunicates, P. chilensis is considered a poor disperser because it has a very short free-living lecithotrophic larval stage (12–24 h), after which it settles on hard substrate or on the matrix of conspecifics45. Phylogeographic studies using allozymes46, and nucleic acids8 suggest low or even a complete lack of genetic structure across the 30°S transition zone of the HCS. The pattern of genetic homogeneity in P. chilensis contrasts with those recorded in other near-shore species with low dispersive potential along the study area40. The absence of genetic differentiation in P. chilensis has been explained by anthropogenic transport promoting high levels of connectivity47, as recorded in other ascidians worldwide49. Even though the mechanism associated with the anthropogenic dispersal of P. chilensis has not been explicitly evaluated, this tunicate has repeatedly been reported on artisanal boat hulls, buoys and marine installations as an important component of the biofouling communities51. Hence, it is likely that human activities may play an important role in maintaining genetic homogeneity recorded in the species across the HCS. More recently Segovia et al.47 using Single Nucleotide Polymorphisms generated with Genotyping-By-Sequencing (SNP-GBS) evaluated neutral and putatively adaptive genetic structure in the species and recorded contrasting results to those previously detected, particularly across the 30°S transition zone. Consistent with traditional markers46, neutral SNPs loci47 showed absence of genetic structure across the 30° zone. In this neutral loci, 30°S structure appear only forcing a sub-optimal clustering. In contrast, the analyses of putatively selective SNP loci showed strong evidence of genomic structure across the transition zone47. Such results indicate that marine environmental stressors have a selective role shaping patterns of genomic structure along the HCS and override the effect of gene flow that is most likely mediated by anthropogenic transport47.The ascidian P. chilensis across the 30°S transition zone of the HCS through the combined use of two seascape genomic-oceanographic approaches. Through this, it will be possible gain a better understanding of the role of environmental differences in the spatial genomic structure in a species with low intrinsic dispersion potential but high physical transport along the study area.The aim of this study was to characterize both, neutral and putatively adaptive genomic structure in tags were successfully retained yielding, before filtering, 102,816 SNPs in the variant call process using TASSEL. Using restrictive bioinformatic filters described by Segovia et al.46 for P. chilensis , a total of 2,902 SNPs were genotyped. Among these, 67 candidate loci for positive selection and 2,521 as putatively neutral loci were identified using Bayescan analysis. A total of 51 loci (76%) showed strong or very strong evidence of positive selection following the Bayes Factor. Similarly, Out-flank and PCAdapt detected 31 and 97 candidate loci for positive selection, respectively. In consensus, the three different methods detected in common a total of 29 candidate loci for positive selection  across the HCS transition zone. At the same time, we considered those SNPs explicitly defined as putatively neutral loci  by Bayescan. Neutral data were used in two ways. First, to analyze neutral spatial genetic structure and correlate it with environmental variables (RDA) and compare with adaptive genetic structure. Second, to incorporate the neutral genetic signal in the detection of local adaptation candidate loci based on genotype-oceanographic associations.A total of 344,964,130 reads with a mean quality score (QF) of 34.34 were obtained from 149 individuals collected from five localities along the southern Chilean coast from two consecutive years  than neutral loci  and Caleta Pajonales (CP), which was non-significant , we determined the relative contribution of geographical position and environmental variables to the genetic structure of the outlier and neutral genotypes. For this, using the 29 consensus outlier loci, we carried on a multivariate correlation with environmental variables with a Partial Redundancy Analysis . Optimal models were estimated incorporating oceanographic variables as fixed factors and controlling by spatial variables (dbMEMs), which resulted in a highly significant model . The first two axes of the outlier loci model explained 98.4% of the total variance, and the distribution of the loci showed two groups, north and south of 30°S, that were significantly correlated to Sea Surface Temperature (SST), pH, fluorescence, and non-significant for silicate , but with a much lower correlation value (Radj2 = 0.036). This model included SST, Nitrate, Silicate and Chl-a, being statistically significant only in the first three variables , geographic distance, and the Mahalanovic environmental distance  as a co-variate resulted in a highly positive correlation for neutral loci . Interestingly, the same model for outlier loci was not significant for geographic distance .In summary, the RDA analyses showed dissimilar signals for the influence of environmental variables of putatively adaptive and neutral loci, except for the influence of SST. Although the neutral model was significant, the correlation coefficient was low, suggesting that neutral genotypes could be more associated with geographic distance than with environmental differences. Interpretation of these results was validated by the fact that a Partial Mantel Test using standardized matrices of genetic distance (find.clusters function) using the Bayesian Information Criterion (BIC), and to determine the neutral genetic structure. With this, DAPC identified an optimal K of two genetic groups; one north ) and another group south , Ancud (AC)) of 30° transition zone. Taking into the account that GEA analyses could be corrected with neutral structure, autocorrelation and geographic distance, this clustering was used in subsequent analyses to control for neutral effect in the determination of local adaptation candidate loci to avoid false positives.With neutral loci, we performed a Discriminant Analysis of Principal Components (DAPC) to estimate the number of genetic groups , to detect candidate loci for local adaptation we performed genotype environment association (GEA) analyses which correlate each environmental variable with individual genotypes. With the independent application of three different methods , we detected a range of 315 – 666 putatively adaptive loci that exhibited significant correlation with each of the seven environmental variables. This result was substantially greater than those obtained with outlier detection approaches , while Chl-a and silicate were correlated with fewer loci . A total of 94 local adaptation candidate lociwere identified by all three methods , with 68% of the loci significantly associated with more than one environmental variable. In this context, the most relevant variables were pH (28), phosphate (26) and fluorescence (16), which detected most loci identified by the different methodologies, followed by SST (11) and Nitrate (6). In contrast, two and zero consensus loci, respectively, were detected for Chl-a and Silicate , which represent the genetic variability linked to the spatial structure among sites. The linear regressions performed with the multi-locus clines and the spatial variables (dbMEMs), indicated that the variables that best fit the adaptive genetic structure were SST . From sPCA’s, we extracted the FST, ordination) or based on genotype-environment association (GEA), were suitable and consistent for the search of relevant environmental factors in the adaptive structure of P. chilensis in the study area. The second type of methods (GEA), however, was more specific and allowed identification of a large number of putatively adaptive loci associated with each environmental variable independently.Both types of analysis, i.e. based on outlier detection  showed significant differences in allele frequencies associated with latitude Fig. A–E. The P. chilensis was more influenced by geographic distance between sites than by environmental differences. Conversely, the genomic spatial structure of putatively adaptive loci was more influenced by environmental features. Specifically, adaptive genetic structure of P. chilensis across the HCS was better explained by the contrasting environmental conditions found north and south of the 30°S transition zone than by geographic distance.This study represents the first evaluation of the role of environmental factors in the adaptive processes of benthic species across a biogeographic transition zone (30°S) in the Humboldt Current System (HCS). A main conclusion is that the spatial genetic structure of neutral loci in 47 was the first to suggest an active role of the environment in the maintenance of the 30° transition zone in the HCS at the genomic level. Likely, environmental differences north and south of 30°S, as reported by several studies54, could be shaping the genomic structure of the species. This transition zone has been considered as a historical biogeographical break3 , because only species with low capacity of larval dispersal have a coincident genetic differentiation at the 30°S . In addition to the historic origin of the break, there are ongoing processes that are likely shaping adaptive genetic variation. Moreover, genetic differentiation of neutral markers does not reflect a genetic break at 30°S neither with mtDNA, nuclear traditional markers8 or neutral SNPs in P. chilensis46. This is consistent with several studies in were SNPs markers show finer genetic structure respect to traditional markers, but the general patterns tend to be in touch59. The fact that both, traditional markers  and neutral SNPs (contemporary gene-flow) showed absence of genetic structure across the 30°S transition zone, suggests that anthropogenic transport is an effective agent of long-distance dispersal in the species47.The study of Segovia et al.P. chilensis at 30°S in putatively adaptive loci were Sea Surface Temperature (SST), pH, upwelling-related variables , and photosynthetic activity measured by fluorescence (Flu), chlorophyll a (Chl-a). Association of these variables to genotypes tended to generate two consistent groups –north and south of 30°S – instead of multi-locus gradient clines. In spite of slight latitudinal variations, multi-locus clines showed an important intra-group (north and south of 30°S) degree of cohesion and accordingly, the most plausible scenario is the presence of two independent groups. Environmental differences north and south of the 30°S transition zone41 may constitute key factors in maintaining the genetic differentiation in spite of transport of individuals between local populations47. In the context of our study model, natural selection seems to override the homogenizing effect of gene flow across the 30°S transition zone of the HCS in putatively selected loci and accordingly lead to local adaptation processes.The main environmental factors that explain genetic structure in P. chilensis could be due to the presence of two areas with contrasting differences in terms of upwelling intensity. The first one, between 30°S and 18°S in northern Chile, includes upwelling-favorable winds across the year. The second one, between 30°S and 41°S, exhibits a marked seasonality in upwelling, especially in the austral spring and summer60, which also produces cold water near the coast from 30° southward52. The influence of environmental variables associated with temperature and productivity on the ecological structuring in near-shore marine benthic organisms has been widely reported both in the northeast and southeast Pacific coasts63. These studies suggest that upwelling regimes have an influence on the communities in terms of recruitment rates, biomass, and individual growth of the organisms64. More specifically, the concentration of ecological suppliers of primary productivity (i.e. nutrients) tend to show a variation in their growth rate along with upwelling and that the greatest response occurs when upwelling is intermittent65.In addition to the commonly reported effects of temperature on the genetic structure, the relevance of environmental variables associated with upwelling in maintaining adaptive genetic differentiation across the transition zone of the HCS in P. chilensis, eco-physiological differences have been reported across the study area68 which together with the limited capacity of the species to colonize new areas, despite being a dominant substrate competitor69, suggest an active role of local adaptation. The weak colonizing capacity of P. chilensis may be a consequence of non-selective larval settlement that could be influenced by water flow, coastal configurations, ecological interactions45, and other selective environmental variables. For example, differences in SST have a significant influence on the seasonal and spatial reproductive activity of P. chilensis43. Despite this, it has also been reported that the species may have a wide range of physiological tolerance to temperature70, which could explain its wide geographic distribution range (10°–44°S)42. It is possible that the wide physiological tolerance range may be counteracted by adaptive genetic differentiation along the species distribution. Local adaptation could lead to genotypic differences between adjacent populations inhabiting contrasting environmental conditions. In fact, recently Giles et al.71 and Morales-González et al.72 showed that P. chilensis is significantly structured over fine spatial scales (20–40 km). Consistent with this finding, the genetic diversity of candidate loci for local adaptation varied along the study area. Both, major and minor alleles, were in some cases present as heterozygotes along the study area but, particularly the minor allele, tended to be geographically restricted to the region north of 30°S. This suggests that transport of individuals between regions by anthropogenic vectors could allow the recruitment of individuals in areas with sub-optimal conditions for genetic migrants, as proposed by Haye & Muñoz-Herrera8 and Segovia et al.47.For the particular case of 75. Taking this caveat into account, one locus was consistently and significantly correlated with four environmental variables  and its sequence matched that of a gene that appears to be involved in cilia-mediated processes, including filtration and ventilation. Interestingly, temperature and phytoplankton concentration appear to be important environmental variables that control fundamental processes associated with ciliate-mediated filtration in ascidians77. A positive relationship between filtration rate (cilia activity) and temperature has been described in the ascidian Ciona intestinalis78. Cilia activity also varies with water productivity, with a negative relationship between cilia activity and phytoplankton biomass78. Thus, both changes in productivity and temperature could have a significant effect on the rate of cilia mediated filtration and ventilation in P. chilensis, which could be a key factor in the maintenance of contemporary genetic structure and restriction of certain alleles north and south of 30°S.From all candidate SNPs, several potential genes with known function were significantly associated with one or more environmental variables. However, since GBS loci are random short loci distributed across the genome, gene mapping could be highly influenced by chance, or by hitchhikingP. chilensis may indicate the influence of continental waters, especially river discharges79. The influence of continental waters is also variable in the study zone; river discharges have little influence north of 32°S while between 36°S and 42°S there is a considerable influence of continental waters, particularly during autumn and winter80. The multilocus clines associated with putatively adaptive loci showed similar values in the two sites south of 30°S, which suggests that the allele frequencies varied less than expected on the basis on considerable distance between them (~ 580 km). Such genomic homogeneity in the putatively adaptive genetic structure south of 30°S may be due to the influence of continental waters (glaciers and river discharges) occurring mainly in that area79 combined with the effect of anthropogenic transport that allows dispersion of individuals47.Finally, in addition to the role of upwelling, the influence of the pH, nutrients such as phosphate and photosynthetic activity in the genomic structure of 19. Remote sensing of oceanographic variables can be quantified across relevant spatial scales, and including genomic diversity analyses, allows to better understand how seascape attributes can shape the spatial genetic structure in near-shore marine organisms81. This study represents the first attempt to evaluate the association between environmental factors and the maintenance of a genetic discontinuity across the HCS in a species for which previous studies showed genetic homogeneity at a large spatial scale, suggesting active gene flow along the southeastern Pacific coast. The main results suggest that at the recorded transition zone (30°S), local adaptation processes could allow the maintenance of contemporary genomic differentiation and spatial genetic structure in P. chilensis. This species is an example of how seascape heterogeneity across a biogeographic transition zone may override the effects of gene flow and lead to local adaptative processes. Even in the presence of high gene flow, probably mediated by anthropogenic transport between and within regions, P. chilensis appears to be sensitive to environmental heterogeneity, especially for those variables that could influence fundamental processes like feeding, filtration, recruitment and growth. Accordingly, putatively adaptive loci showed strong genetic structure associated mainly with environmental differences north and south of 30°S biogeographic transition zone.Signatures of selection and local adaptation can now be evaluated in populations across entire genomes or genome sampling using population differentiation approaches (i.e. outliers) or in association with environmental variables to test the influence of biotic and abiotic factors in the spatial genomic structure. These evaluations give insights into contemporary processes, and may explain how environmental factors influence selective and neutral genomic diversity within and among populationsThe environmental variables used in this study may be a first step in identifying the selective factors that maintain the current genetic structure north and south of 30°S in other benthic marine species from the southeastern Pacific coast.47 were re-analyzed adding 89 individuals obtained from the same sampling sites, the year after the original sampling (2014–2015). From the original dataset, to avoid bias in the candidate loci detection, the location of Los Molinos (~ 39°S) was excluded due to the presence of a highly differentiated lineage47. In total, thirty individuals of Pyura chilensis were collected from each of five localities between 26°S and 42°S from two consecutive years (2014–2015) in the Southeast Pacific Coast  following the manufacturer’s instructions. The DNA was sequenced using the restriction site-associated DNA sequencing method Genotyping-by-Sequencing82 using the ApeKI restriction enzyme. This enzyme was used following the optimization of Segovia et al.47 for the species, which was chosen due to the wide genome distribution of flanking region and avoiding of repetitive zones83. Libraries were prepared in a HiSeq2000  platform and the resulted reads (100pb) were visualized and analyzed in FastQC version 0.10.1 for quality checks. Demultiplexing and SNP calling filters were done following Segovia et al.47 using the Universal Network-Enabled Analysis Kit pipeline (UNEAK84) from the TASSEL platform (www.maizegenetics.net) using a minimum call rate (mnC) of 85% and a minor allele frequency (mAF) of 0.04 excluding also those loci in Hardy–Weinberg disequilibrium in at least 60% of the sampled sites after a False Discovery Rate (FDR) with a q-value of 0.05.Raw data from Segovia et al.mpling 204–2015. FP. chilensis using seven relevant oceanographic variables for each locality  and from the Bio-Oracle database , and 2) Genotype-environmental (oceanographic) association analysis.We analyzed the effect of environmental factors on the genetic structure of ty Table . Environ87 analyses were performed with a pr_odds of 1,000 with 100,000 iterations and a burn-in of 10,000 steps. Results were corrected using a FDR of 0.05 with the logarithm of the q values. The candidate loci for positive selection considered were those with strong or very strong evidence of selection according to Jeffrey’s criterion88 based on the values of the Bayes Factor (bf > 10).For outlier detection, three independent methods were used: 1) Bayescan 2.17789 analyses were performed using a neutral FST distribution, eliminating loci with extreme FST values in both tails, and using a LeftTrimFraction and RightTrimFraction of 0.05, with a minimum heterozygosity of 0.1. The outliers were identified in the 5 sampling sites using an interval of q values of 0.05. Finally, 3) PCAdapt90 were done by first using a PCA with the number of groups (K) equal to the number of populations studied (5) to define the optimal value of K. Distribution of p values was corrected using a FDR with q-values of 0.05, finally obtaining a list of outlier loci that were candidates for selected loci (See Appendix 2 and Table S2 for detailed methods). Multiple approximations were used to reduce the probability of false positives in the final dataset following Lotterhos & Whitlock91 and Rellstab et al.18.2) Out-flankUsing the information of the data obtained with outlier detection using population differentiation approaches , we determined the relative contribution of geographical position and each considered environmental variable to the genetic structure of outlier and neutral genotypes.P. chilensis was estimated with the geographic coordinates of the sampling sites using distance vectors based on distance-based Moran’s eigenvector maps, dbMEMs93. Following this, dbMEMs were analyzed in Redundancy Analyses (RDA) in the R package vegan94 using standardized environmental variables and the genotypes associated with the neutral and positive selection candidate loci as response variables. Prior to the analysis, genotype data were standardized by removing the broad scale trend using the decostand function with the Hellinger’s method in vegan. With this, we carried out a Partial RDA controlling for the effect of space (dbMEMs). Optimal model in vegan were determined using ordistep and ordiR2step functions and the significances were evaluated for each fixed factor through a marginal ANOVA test . Finally, we calculated the pairwise FST matrices for both datasets using Arlequin 3.5.2.295 using 10,000 permutations. With this, we estimated the relation between genetic, geographic and environmental distances using standardized values with a Partial Mantel test in ecodist package in R. This test was carried on using genetic distance (i.e. FST) and geographical distance between localities, using an environmental distance matrix as covariate. The environmental distance matrix was calculated using Mahalanovic distances of the standardized oceanographic variables  in the R package StatMatch. Final matrix was constructed with the vegdist function of vegan in R, using a Euclidean method.The spatial genomic structure of 18 we used both, multiple approximations and the signal of previously determined neutral loci as control97. For this, the find.clusters function of a Discriminant Analysis of Principal Components (DAPC) of putatively neutral SNPs was performed first in adegenet98 package of R 3.22. This was done to determine the number of genetic groups/clusters using neutral loci with a Bayesian Information Criteria (BIC). Once the neutral structure was determined, we used three approximations: (1) LFMM v 1.4 (Latent Factors of Mixed Models)99, which were executed in the LEA package in R 3.22 using the number of latent factors based on the results of the DAPC . 200,000 iterations were done after a burn-in of the first 20,000 steps. Five independent runs were done for each analysis to estimate the different parameters, as recommended by Frichot et al99, and the mean of the z-scores was used for the final results. The significance of the values associated with each SNP was calculated using α = 0.01 corrected with a FDR.In parallel, we also carried out GEA approaches, that explicitly incorporated the environmental variables to determine candidate loci for local adaptation. In order to avoid false positives due to neutral processes and/or spatial autocorrelation100 we evaluate the correlation between SNPs and environmental factors testing whether the model that includes the environmental variable  fits the data better than a null model . To estimate this matrix, we performed five independent runs using 100,000 iterations. Then the entire set of SNPs was tested individually with the environmental standardized variables using 80,000 MCMC iterations. To identify candidate SNPs with evidence of positive selection for each environmental variable we used Bayes factor > 10 according to Jeffrey’s criterion88.(2) With Bayenv2101 we tested for Genotype-Environment associations considering neutral structure based both, on the number of groups present (optimum K determined by DAPC) and the spatial autocorrelation using the information of the geographic coordinates of the studied sites. We used a multiple logistic regression in which the p values were calculated from regressions of all possible genetic-environmental associations according to a Wald Score Test. The results were compared to a χ2 distribution with one degree of freedom. The loci with q-values less than 0.05, corrected with an FDR, were considered to have significant associations with a specific environmental variable.Finally, with (3) SAMβADAlagged scores associated with the first two principal components for each site. The lagged scores were then used to transform the genetic variation of the candidate SNPs into multilocus geographic clines. To evaluate if the variation of the candidate SNPs correlated best to environmental or spatial variables, the multi-locus clines were used to perform linear regressions with both groups of variables using dbMEM vectors. For statistical reasons, only those sets including more than five loci were retained.Once all consensus candidate SNP loci for local adaptation were obtained, i.e. for each environmental variable and each of the three methods , spatial structure of loci was analyzed independently using spatial Principal Components Analyses (sPCA), in adegenet. These analyses were performed to infer if the variation in the candidate SNPs was significantly associated with each environmental variable above that expected based only on the geographic proximity of sites. For this, we extracted the tags of detected putatively adaptive loci for each environmental variable were extracted to identify potential genes with known functions available in the SWISS-PROT database102 and in GenBank (https://www.ncbi.nlm.nih.gov/genbank/). In the case of Sea Surface Temperature (SST), we used tag sequences of those loci that were significantly correlated in at least two of the three genotype-oceanographic association approaches. SNPs were filtered by e-values preserving only matches greater than 1 × 10−4 and a minimum of 75% homology. The tag sequences associated with candidate loci were used as query sequences for BLAST searches executed in the database of annotated genes from SWISS-PROT.As an exploratory analysis, Using positive matches for Gene Ontology, we used a General Lineal Model (GLM) with binomial error to correlate the genotypes of each candidate locus SNP and the latitude using R environment.https://bitbucket.org/NicoSegoviaC/seascape_pyurachilensis/src/master/.Raw SNP-GBS data (.vcf file) for each sample and environmental variable were deposited in the Bitbucket database: Supplementary file1


Uncertainty hampers innovative mixed‐fisheries management by the scales at which connectivity dynamics are relevant to management objectives. The spatial scale of sustainable stock management is species‐specific and depends on ecology, life history and population connectivity. One valuable approach to understand these spatial scales is to determine to what extent population genetic structure correlates with the oceanographic environment. Here, we compare the level of genetic connectivity in three codistributed and commercially exploited demersal flatfish species living in the North East Atlantic Ocean. Population genetic structure was analysed based on 14, 14 and 10 neutral DNA microsatellite markers for turbot, brill and sole, respectively. We then used redundancy analysis (RDA) to attribute the genetic variation to spatial , temporal (sampling year) and oceanographic (water column characteristics) components. The genetic structure of turbot was composed of three clusters and correlated with variation in the depth of the pycnocline, in addition to spatial factors. The genetic structure of brill was homogenous, but correlated with average annual stratification and spatial factors. In sole, the genetic structure was composed of three clusters, but was only linked to a temporal factor. We explored whether the management of data poor commercial fisheries, such as in brill and turbot, might benefit from population‐specific information. We conclude that the management of fish stocks has to consider species‐specific genetic structures and may benefit from the documentation of the genetic seascape and life‐history traits. Stocks www.cbd.int) signed in 1992. The holistic approach of EBM has gained considerable momentum in the fisheries sector. Ecosystem‐based fisheries management (EBFM) aims at sustaining healthy marine ecosystems and the fisheries they support  includes key principles such as ecosystem connections, appropriate spatial and temporal scales, use of scientific knowledge, ecological integrity and biodiversity, and sustainability , brill (S. rhombus) and sole (Solea solea) live on the continental shelves of the North East Atlantic Ocean from Norway to the Iberian Peninsula—brill and sole to North Africa, the Mediterranean Sea and Black Sea. Turbot also breeds in the brackish Baltic Sea. These species represent two families  of temperate marine flatfishes, each with distinct life‐history traits (LHT), but sharing the same seascape. Turbot matures at a determinate moment and spawns during a short spawning moment . Brill matures over a protracted period and has an intermittent release of eggs, which is characteristic for an income spawner. Both spawn offshore; eggs and larvae have pelagic phases of short to intermediate duration  and the same type of molecular markers (microsatellites). We hypothesize that populations of codistributed and ecologically similar flatfishes are structured at comparable environmental and spatio‐temporal scales. We discuss the observed subtle genetic structure of each species and the interaction between hydrodynamics and larval dispersal, and the role of adult dispersal. We explore the results in the context of an integrated framework for fish stock management.22.1N = 748), 23 samples from 15 sites for brill (N = 879) and 13 samples from 11 sites for sole  were considered , two EST‐derived markers described in Bouza et al. (SmaUSC‐E32 and SmaUSC‐E41) and 10 novel markers developed through gDNA pyrosequencing  . Brill samples were genotyped at 14 microsatellite loci, including two markers described in Iyengar et al.   using a randomization test  and expected (HE) heterozygosity. Genetic structure among populations within each species was investigated using two methods. First, population structure in space and time was investigated using global and pairwise FST between all samples , 10 independent replicates were run. For each replicate, 104 iterations were used as burn‐in, followed by 105 Markov Chain Monte Carlo (MCMC) iterations. At completion of the STRUCTURE runs, the most likely number of clusters was selected by choosing K with the largest log‐likelihood , sampling year (TIME) and water column dynamics (ENV) in explaining genetic connectivity patterns. To do so, the genotype matrix of each species was first converted into allele counts, where each row is an individual and each column indicates the count  per allele. Redundancy analysis (RDA) identified the spatio‐temporal and environmental features explaining genetic  variation among the individuals of each species. RDA is a canonical extension of principal component analysis (PCA) in which the principal components produced are constrained to linear combinations of a set of predictor variables  and net primary production  were included. Primary production was included as proxy for food availability for early life stages. Hydrodynamic parameters are included as our main aim was to investigate the potential effect of the Frisian Front in the Central North Sea. Frontal influences are represented by the depth of pycnocline  and a density‐based stratification index  which describe the seasonal changes of the water column dynamics. Bottom shear stress  represents the shearing force by the current. A detailed description of the water column dynamic parameters is available in the Supporting information Geographical variables (SPACE) were represented by Moran's Eigenvector Maps (MEMs), along with longitude and latitude. The MEMs were calculated for each individual species, based on a distance based matrix of the shortest geographical waterway distance between sampling locations Table . TemporaFor each of the nine water column parameters, the monthly and yearly average were available for the period 1980–2004. The yearly standard deviation of each variable across the 12 months was calculated to capture the intra‐annual variation. Seascape genetic analyses were conducted with two sets of environmental variables  > .8 were inspected to identify strong correlations between selected and nonselected environmental variables  was significant in 6 out of 23 samples for turbot, in 13 out of 23 samples for brill and in 5 out of 13 samples for sole , brill  and sole . Pairwise estimates of population differentiation ranged from zero to a significant maximum of 0.027 in turbot  for turbot  are observed for sole. One cluster covers Skagerrak, Kattegat and German Bight . A second cluster occurred throughout the distribution range of sole, but was more pronounced in the Bay of Biscay (BOB). The third cluster had a wider distribution across the Eastern English Channel, Irish Sea and North Sea. In contrast to turbot and sole, Bayesian clustering did not subdivide brill into genetically distinct subgroups , followed by brill (R2adj = .010) and sole (R2adj = .001). In turbot, SPACE accounted for the largest significant single fraction of explained variation (R2adj = .013). The effect of TIME (R2adj = .003) was also significant, and both effects remained after control for ENV . SPACE also accounted for the largest significant single fraction of explained variation in brill (R2adj = .007). The effect of ENV was also significant (R2adj = .004), and both effects remained after control for TIME . In sole, the only significant single fraction of explained variation was TIME (R2adj = .001). This effect remained after control for ENV (TIME|ENV: R2adj = .001).The genetic variation among individuals of each species was partitioned with RDA into variation attributable to geographical location (SPACE), water column variables (ENV) and the effect of sampling across different years (TIME) Table . We firsIn a second step, forward selection was applied to reduce the predictor variables to only those variables that significantly correlated with the observed genetic variation among individuals. In turbot, this analysis revealed a significant effect of the variation in the depth of the pycnocline (PYC_SD), in addition to effects of five species‐specific MEMs , provided quantitatively similar results to the previous analysis Table . In turb4Our comparative interspecific seascape analysis reveals unique and shared characteristics among three codistributed and commercially exploited flatfishes in the North East Atlantic region. While focusing on the same geographical area and using the same type and comparable number of molecular markers, we found no genetic structure in brill and confirmed the weak genetic differentiation of sole and turbot with traditional population genetic analyses. Seascape analysis pointed to the contribution of environmental factors such as stratification (brill) and the variation in pycnocline depth (turbot) to the genetic patterns. These variables highlight the impact of hydrodynamic features on gene flow in two of three investigated flatfishes. Below, we discuss the subtle genetic structure and seascape of each of the three species and explore the integrative management of their fishery.4.1Our observations on spatial differentiation are in agreement with previous population genetic studies. We confirm that sole populations group in a Baltic, northern and southern Atlantic population, the Atlantic populations showing a pattern of isolation by distance  in the depth of the pycnocline, and the sea surface temperature and sea bottom salinity in September. In a data set of genotypes extended with the Baltic Sea, Vandamme et al. , followed by water column variables (ENV). A single key environmental variable, stratification, was identified based on forward selection. The identification of spatial and environmental determinants of genetic structure in this species based on RDA is remarkable given its extremely shallow population structure revealed by other methods. There was no contribution of sampling location (SPACE) or water column variables to the genetic variation of sole. Instead, genetic variation was attributed to variation between years (TIME). A previous study in sole highlighted winter sea temperature, food availability and coastal currents as the major determinants of genetic structure , followed by sampling year (TIME). The global contribution of water column variables (ENV) was weak, but a few key environmental variables associated with genetic variability emerged via forward selection: the variability , where we opted for species‐specific seasonal variation in reproduction time (start and peak spawning time). Spring and early summer are of crucial importance for the connectivity and survival of the larval stage of all three species across the northern range of their distribution. September was chosen in all three species because of the importance of maternal effects on gonad development in adults and hence egg quality in fall adult stage, in the life cycle. Larvae are advected over considerable distances during the planktonic phase  on brill is difficult to explain. Unlike turbot, brill spawns over a longer period but similar to turbot and sole settles in coastal nurseries. The spring‐spawned larvae experience a tidally and wind‐mixed environment. The homogeneous genetic structure does not match with the modelled larval dispersal, where the North Sea populations are characterized by a high level of self‐recruitment  considers population genetic insights an important aspect of biodiversity conservation .All data and codes used in this article are publicly available on Dryad (None declared.Supplementary MaterialClick here for additional data file.


AbstractLGM), drastic environmental changes modified the topology of the Japanese Archipelago, impacting species distributions. An example is Faguscrenata, which has a present continuous distribution throughout Japan. However, by the end of the LGM it was restricted to southern refugia. Similarly, Dasyscyphellalongistipitata  occurs strictly on cupules of F.crenata, sharing currently an identical distribution. As the effects of the LGM remain poorly understood for saprobiotic microfungal species, herein we identified past structuring forces that shaped the current genetic diversity within D.longistipitata in relation to its host using a phylogeographic approach. We inferred present and past potential distributions through species distribution modeling, identifying environmental suitability areas in mid-southern Japan from which subsequent colonizations occurred. Our findings suggest that current high genetic diversity and lack of genetic structure within D.longistipitata are the result of recent multiple re-colonization events after the LGM.During the Last Glacial Maximum ( LGM), the advent of a strong cold dry period led to a significant sea level lowering that exposed the entire Seto Sea area  along the shoreline in southern areas of Japan, and then expanded northwards via two migration routes through the western and eastern shore of Japan  overcomes the above-mentioned impediments. This culturable ascomycete is highly specific to F.crenata, sharing an identical wide geographical distribution throughout Japan  in the northeast to Mt. Shibi  in the southwest  as voucher specimens. Single ascospore isolates were obtained from one of the apothecia occurring on each cupule using a Skerman’s micromanipulator  sites (S), number of haplotypes (h), haplotype diversity (Hd), and nucleotide diversity (Pi). The Tajima’s D neutrality test  was compAscomycota species: Colletotrichumscovillei Damm, P.F. Cannon and Crous, Aspergillusfumigatus Fresen., Ceratocystismontium (Rumbold) J. Hunt, and Clavicepspurpurea (Fr.) Tul. We concatenated ITS and beta-tubulin sequences, and calculated nucleotide diversity (Pi) using DNAsp v6.12.03  at the individual level (isolates), and a Principal Correspondence Analysis (PCoA) using the localities as a grouping factor as implemented in the R-package adegenet v2.1.1 (IBD) using a Mantel test of an Edwards’ distance-matrix against a geographic-distance matrix using R. Furthermore, a haplotype network based on statistical parsimony  Skyride Plots (AIC) using jModelTest2  substitution model with invariable-site proportion and gamma distribution for D.longistipitata; and Hasegawa-Kishino-Yano (HKY)+I+G for F.crenata. Both analyses run for 500 million generations, and the MCMC convergence was checked using tracer v1.7.1  to evaluate the geographical areas of conserved environmental suitability using the BioClim environmental layers . To reduce bias due to autocorrelation caused by a local overrepresentation of records, we divided the sampling area for each species into cells of 1/10 degrees and selected one point at random from each cell using the R-package raster.We implemented the analysis of species distribution modeling , random forest  by means of ensemble models . Six starest RF; , multivaAreas of conserved environmental suitability across time were delimited as follows: the ensemble layers were transformed to presence/absence considering a threshold of 90% of the distribution of suitability values, and the resulting layers were merged. The area of the resulting layer was classified as highly conserved (overlapping of four layers), medium (at least three overlapping layers), and low (two overlapping layers).Dasyscyphellalongistipitata and Faguscrenata showing colonization trends are available upon request.Finally, to estimate the ancestral locations and the spatial dynamics of the two species, we performed a continuous-space phylogeographic analysis using BEAST software. Runs were performed using the molecular clock rates, and substitution models above mentioned. The statistics for spatial traits  were generated using the Cauchy RRW model. Also, to avoid noise due to duplicate location traits, a jitter window size of 0.01 was used. Posterior to convergence checking, the consensus discrete trait-annotated trees were obtained by means of treeAnnotator using the “Common Ancestor Heights” option and discarding the first 10% trees. Then, this information was used as an input in SPREAD3 v1.0.7  to geogrWe analyzed a total of 270 isolates (12–24 per site). Overall, 85 ITS-based haplotypes were identified. The majority was represented by H12 (40.7%), followed by H28 (14.4 %), and H10 (10.7 %), and the 27.8 % were singletons. Both H12 and H28 were found in all the 14 sites, while H10 was found from 12 sites.In contrast, the beta-tubulin sequences were remarkably diverse, with 224 recognized haplotype patterns. The most frequently observed haplotypes were B4 (3.7 %), B27 (2.2 %), and B75 1.9 %), and the majority (74.8 %) was occupied by singletons. The ITS and beta-tubulin concatenated sequences ranged from 980–1022 bp, resulting in 255 haplotypes with 270 segregating sites  yielded to limited areas of conserved environmental suitability , Obora (site 5), Ashiu (site 10), and Suganuma regions  are critical to the dynamics and resilience of ecosystems . Howeverribution . Field eive host  have beeae e.g., , overallD.longistipitata . Considering that demographic history represents a determinant of genetic diversity, governing effective population size , which is reasonable, as C.purpurea comprises three divergent lineages , agreeing with the canonical 1–3%  Herink throughout ~200 km. In this case, biological traits such as host infection ability and reproductive cycle shaped the genetic structure. Furthermore, Melampsorapopulnea (Pers.) P. Karst. within continental populations in a similar geographic range to the present study. The authors noted positive isolation by distance associated with dispersal constraints, and host preference. In contrast, Corollosporamaritima Werderm., perhaps as a result of high dispersal potential, or meteorological phenomena like hurricanes.Several factors influence the genetic structure in fungal populations, due in part to the diverse ecological roles of these osmotrophs. For instance, D.longistipitata is a consequence of the homogeneous distribution of the genetic diversity, instead of a similar genetic composition among localities. Even though dispersion mechanisms remain unknown for D.longistipitata, based on its host specificity and field observations, its occurrence as an endophyte is feasible. Under this assumption, a joint seed-vectored dispersion may be possible, opening the opportunity for a broader animal-mediated dispersal , our rough calculations on the estimates for population growth agree with the hypothesis of post-glacial demographic expansion. Furthermore, the demographic increase trend in D.longistipitata may be related to a higher mutation rate and shorter generation times. So, growth trends for D.longistipitata in the Skyride plots could correspond to a fraction of the time scale in F.crenata. In this sense, the mast seeding  in F.crenata , and the influence of past environmental changes on suitable areas for its distribution.Modifications in species distribution ranges result from the interaction between ecological and evolutionary processes . Our datDasyscyphellalongistipitata has been recognized as a fine woody debris decomposer, in particular of cupule litter, an essential component of litterfall in beech forests ( forests . Hence i


Dibothriocephalus dendriticus is one of the causative agents of the fish-borne zoonosis diphyllobothriosis. Polymorphic microsatellite markers were originally developed for future genetic studies using microsatellite library screening and next-generation sequencing (NGS). Out of 128 microsatellite candidates selected after NGS analysis, 126 yielded PCR products of the expected size. A declared repetitive motif was confirmed in 92 loci by Sanger sequencing. The level of polymorphism was tested by fragment analysis. Statistical tests for observed and expected heterozygosities and deviations from Hardy–Weinberg equilibrium revealed 14 polymorphic microsatellite loci suitable for studies on the finer genetic structure of global populations of D. dendriticus.  Diphyllobothrium, Adenocephalus and Dibothriocephalus (Cestoda: Diphyllobothriidea) . The probability of self-fertilization in D. latus and cross-fertilization in the studied D. dendriticus populations is also supported by their basic parasitological characteristics, such as the intensity of infection and prevalence. Although the primary reason for STR design for D. latus (the European perch Perca fluviatilis and Northern pike Esox lucius) are mainly infected by a single plerocercoid in the epaxial musculature [D. latus in Europe [D. latus. Other definitive hosts, mainly fish-eating Canidae  and Felidae (domestic cat Felis catus) acquire infection by the ingestion of fish infected with a D. latus plerocercoid. It is highly probable that the majority of definitive hosts are infected by the random consumption of a plerocercoid from one infected fish. The rarity of such infections suggests that mostly single plerocercoids develop into adult tapeworms in the definitive hosts. This is supported by the fact that D. latus has been found as a single worm in wolves [The principal second intermediate fish hosts of culature . In addin Europe . Humans,n wolves  and human wolves . D. dendriticus are present within the visceral organs or body cavity in fish in high numbers [D. dendriticus tapeworms [D. dendriticus adults, respectively [D. dendriticus in different species of gulls , in which the greatest number of adult specimens in a single bird was 11 and of immature tapeworms as many as 51.Contrary to this, the encapsulated larval stages of  numbers . In our  numbers . Thus, fapeworms , and twoectively . Hickey ectively  studied D. dendriticus show a high level of polymorphism and are suitable for the detection of deeper and more detailed genetic structure of allopatric populations of this zoonotic tapeworm. The effectivity of their further application depends on a broad sample set of parasites coming from different geographic regions. Future analyses of STR loci polymorphism of world-wide populations could reveal if D. dendriticus is native to Northern Europe or if it is a result of transcontinental introduction from North America.The currently designed and validated microsatellite markers in


Peromyscus leucopus) and 380 big brown bats (Eptesicus fuscus) across an urban‐to‐rural gradient within the Providence, Rhode Island (U.S.A.) metropolitan area . We found that mice and bats exhibit clear differences in their spatial genetic structure that are consistent with their dispersal abilities, with urbanization having a stronger effect on Peromyscus mice. There were sharp breaks in the genetic structure of mice within the Providence urban core, as well as reduced rates of migration and an increase in inbreeding with more urbanization. In contrast, bats showed very weak genetic structuring across the entire study area, suggesting a near‐panmictic gene pool likely due to the ability to disperse by flight. Genetic diversity remained stable for both species across the study region. Mice also exhibited a stronger reduction in gene flow between island and mainland populations than bats. This study represents one of the first to directly compare multiple species within the same urban‐to‐rural landscape gradient, an important gap to fill for urban ecology and evolution. Moreover, here we document the impacts of dispersal capacity on connectivity for native species that have persisted as the urban landscape matrix expands.As the rate of urbanization continues to increase globally, a growing body of research is emerging that investigates how urbanization shapes the movement—and consequent gene flow—of species in cities. Of particular interest are native species that persist in cities, either as small relict populations or as larger populations of synanthropic species that thrive alongside humans in new urban environments. In this study, we used genomic sequence data (SNPs) and spatially explicit individual‐based analyses to directly compare the genetic structure and patterns of gene flow in two small mammals with different dispersal abilities that occupy the same urbanized landscape to evaluate how mobility impacts genetic connectivity. We collected 215 white‐footed mice ( Both species are found within large forested, field and edge habitats, which are their native habitats. But they are also common within the entire range of human‐developed landscapes, including the urban core. While bats can roost in nearly any vertical structure within a city , urban white‐footed mice are more likely to be found in patches of secondary forest with canopy cover, as well as areas with thick vegetative ground cover  should be tightly associated with levels of gene flow and genetic structuring over space Bohonak, , 1951. TResearch into the evolutionary impacts of urban environments is still relatively new, but there is a rapidly growing body of literature to draw predictions from. Reviews of the literature have found that urbanization can impact gene flow, drift, and natural selection pressures, all potentially impacting genetic structure  and big brown bats (E. fuscus), two native synanthropic species with very different modes of dispersal. To generate hypotheses about how urbanization will impact each species' movement and connectivity, we use the three models described earlier—which we will refer to as fragmentation, facilitation, and neutral hereafter. Consistent with the fragmentation model, we predicted that the lower dispersal capacity of white‐footed mice  and big brown bats (E. fuscus) between 2014 and 2019 across the Providence metropolitan area. This area encompasses all of Rhode Island, parts of southeastern Massachusetts, and is inhabited by approximately 1,600,000 people. This focal landscape includes ~3,500 km2 and is roughly bisected by the city of Providence  and the ~400 km2 Narragansett Bay  Rabies Surveillance Program. Nuisance bats found inside human‐occupied buildings were sent to the RI DOH testing facility, and we obtained tongue and ear tissue from 380 rabies‐negative specimens. Because this is a state monitoring program, these samples are only from the state of Rhode Island, and do not cross into Massachusetts Figure .2.2DNA was extracted from mouse tail and bat tongue tissue using standard kit‐based extraction protocols (Qiagen DNeasy Blood and Tissue Kit) and included an RNase treatment step to maximize sequencing of DNA over RNA. For mice, we performed double‐digest restriction site‐associated DNA sequencing (ddRADseq) library preparation following the Peterson et al.  protocolFor bats, we performed quaddRAD library preparation following the protocol from Franchini et al.  with mod2.3P. maniculatus, we used the process_radtags script from Stacks , with a minor allele frequency of at least 5% (min_maf = 0.05), and a heterozygosity upper bound of 0.8 (max_het = 0.8) in order to limit the effects of duplication within the genome. We also only retained one single nucleotide polymorphism (SNP) per RADtag (‐‐write_single_snp), to meet the assumptions of linkage equilibrium in subsequent analyses . For both species, we also created a dataset of 5,000 random SNPs using the whitelist function in populations within Stacks. This random subset of 5,000 SNPs was used in MEMGENE, while a bed2diffs file was generated for other analyses . To investigate potentially sex‐biased dispersal patterns, we also ran populations with grouping samples by sex (using a popmap file) for both species.For mice, which have a reference genome available from the congener clone filter and process_radtags modules in the Stacks 2.5 pipeline , rescue barcodes (‐r), and discard reads with low‐quality scores (‐q) were applied with default values. Sequence quality assessment was performed using the software FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Then, we removed 13 samples (out of 388) with a low number of reads . Next, we employed the denovo_map pipeline in Stacks. Initially, we tested the assembly parameters and genotyping error by running the pipeline on a subset of samples including replicates. Values for the three main parameters −m , −M , and −n  were chosen following the optimization procedure described by Rochette and Catchen , on SNP genotypes in the R package SNPrelate  analysis in the R package MEMGENE  and observed heterozygosity (Hobs) for an expected genetic neighborhood surrounding each sample, with a radius of 10 km. We then visualized the output values using QGIS to plot on a map of our focal landscape.We also used the sGD package in R to understand the distribution of genetic diversity across the study area , denoting samples from the mainland versus islands to visually inspect for any differences in samples from these contiguous or insular landscape contexts. Lastly, we also performed this same analysis between urbanization and observed heterozygosity, a measure of genetic diversity.We conducted a linear regression between this metric of urbanization and the inbreeding coefficient produced in sGD (described above) using the "lm" function in base R. We then used the package 2.6glPCA implemented in the R package adegenet 2.1.2 , to assess the number of genetic clusters (K) without any prior information regarding population identity or geographic location for both species. The upper K limit was set to K = 20 and the chooseK.py function was used in order to select the appropriate number of model components that explain structure in each dataset.The genetic clustering in mice and bats across our study region was assessed with a principal component analysis (PCA) using the function Wickham, . We alsoWickham, , with de33.1We obtained 206,139 and 26,736 SNPs for 215 mice and 367 bats, respectively, postfiltering Table . We iden3.2Moran's eigenvector mapping (MEM) analysis, which looks at genetic variation after accounting for patterns of spatial autocorrelation, found a sharp shift in the genetic signature of mice along a south‐to‐north axis of the study region Figure , with a ISF) in mice were highest on the three islands, and in the urbanized core of Providence and surrounding developed landscape  also showed a starkly different pattern between the mice and bats. The mice showed restricted migration through the Providence urban core, as well as in the urbanized areas of Woonsocket/Cumberland/Lincoln  and between the island and mainland mice Figure . The bat3.3ISF, p < .0001, r2 = 0.102), and island mice exhibited uniformly higher ISF levels . Observed heterozygosity—a metric of genetic diversity—was not associated with urban land cover for either species .The percent urban land cover within 500 m of each sampled animal ranged from zero to 100% within the study region. For mice, there was a significant positive relationship between urbanization and inbreeding  than in the bats . PCA provided no clear signal of land cover; however, female bats showed more structuring  than males , the dispersal, life history, and social structure of E. fuscus likely means that their gene pool is well mixed enough at our studies' spatial scale to prevent substructuring over generations. The range of ISF values was much narrower for bats across the study region, with a less clear signal of spatial variation relative to the mice  generally have higher genetic diversity and less genetic differentiation than the mammal species evaluated , a species that is inefficient with long‐distance flight . Previous studies have observed long‐distance dispersal in bats, particularly for males, which minimized genetic structure in those populations  in Germany found some mtDNA, but little nuclear DNA structuring in this highly insular, colonial species  that roost and hibernate individually or in small groups within our study area, typically in hollow trees, attics, barns, and other structures. ISF increasing as the urban land cover around each sample increased  are spatially explicit, meaning they account for spatial locations, autocorrelation, and potential genetic neighborhoods to provide a more sophisticated picture of fine‐scale genetic structure  and evolutionary biology (drift–mutation–selection dynamics), particularly investigating multiple species in the same landscape. In addition, urbanization is a form of habitat fragmentation for many native species, and the residual patches surrounded by less suitable urban habitat can sometimes operate like true islands (Olejniczak et al., We found that gene flow in big brown bats is less impacted by urbanization than that of white‐footed mice along the same urban‐to‐rural gradient. This supports the theoretical expectation that species with greater dispersal capacity will maintain higher levels of connectivity, even as their habitat becomes fragmented during landscape development. More work in other cities will need to be done before we know how representative our data are for other locations. But the Providence metropolitan area certainly represents a large urban‐to‐rural landscape matrix, with 1,600,000 people total, and a population density of 3,736 people per kmPeromyscus mice in urbanizing landscapes likely depends on the maintenance or creation of patches of secondary urban forests and canopy cover. Native big brown bats, on the other hand, showed minimal evidence of restricted gene flow. This suggests that E. fuscus across the region are well connected via flight, maintaining levels of genetic variation and perhaps able to supplement local populations if they decline in the future. Big brown bats are no doubt aided in their synanthropy by their rapid colonization and adaptable use of urban infrastructure, such as buildings and bridges (Neubaum et al., E. fuscus is restricted within the city of Providence, or even the entire ~3,100 km2 area of Rhode Island, tree‐roosting bats require specific microhabitats with roosting sites and prey resources (Grindal & Brigham, White‐footed mice in our study exhibited higher levels of inbreeding within the urbanized landscape around Providence. Our trapping efforts indicate that capture probabilities were much higher within or adjacent to patches of trees, often near parks and cemeteries. Therefore, the movement of mice likely relies on some level of connectivity between vegetated areas, making dispersal harder into and out of these urbanized islands of green space (Munshi‐South, None declared.Supplementary MaterialClick here for additional data file.