Polyommatus icarus butterflies in the Western and Eastern Palearctic exhibits a characteristic spectral difference (20 nm). We investigated the coloration and the de novo developed DNA microsatellites of 80 P. icarus specimens from Europe from four sampling locations, spanning a distance of 1621 km. Remarkably good concordance was found between the spectral properties of the blue sexual signaling color (coincident within 5 nm) and the population genetic structure as revealed by 10 microsatellites for the P. icarus species.The males of more than 80% of the Lycaenidae species belonging to the tribe Polyommatini exhibit structural coloration on their dorsal wing surfaces. These colors have a role in reinforcement in prezygotic reproductive isolation. The species-specific colors are produced by the cellular self-assembly of chitin/air nanocomposites. The spectral position of the reflectance maximum of such photonic nanoarchitectures depends on the nanoscale geometric dimensions of the elements building up the nanostructure. Previous work showed that the coloration of male  Many of these colors are efficiently used and optimized in sexual communication. These colors may be generated by selective light absorption on pigments, by selective light reflection on photonic nanoarchitectures, or by a combination of the two6. Structural colors started to evolve 200 million years ago7 and fossilized moth scales of 47 million years already exhibit structures resembling the scales of extant lepidopterans8. The structural colors are generated by the interaction of light with nanoarchitectures with typical dimensions in the wavelength range of the visible light9. Surprisingly, even the apparently colorless insect wings may generate structural color, so called “wing interference patterns”, for example in the transparent wings of small Hymenoptera and Diptera, patterns that have largely been overlooked by biologists10. Structural coloration is a striking component of sexual ornamentation and may function as a signal of mate quality11.The wings of butterflies exhibit a richness of colors and patterns unrivalled in the living world12—possess species-specific blue sexual signaling colors of structural origin14. The importance of male dorsal wing coloration in natural selection has been stressed by the role of coloration in prezygotic isolation mechanisms, such as reinforcement15. In a study on Polyommatus Latreille, 1804 subgenus Agrodiaetus Hübner, 1822 from 140 species belonging to the tribe Polyommatini (Polyommatinae), only 26 (18.5%) species were found with brown dorsal coloration of the males, all the other exhibiting structural coloration16.The males of many Gossamer-winged butterflies (Lepidoptera: Papilionoidea: Lycaenidae)—one of the most speciose butterfly families with 416 genera and 5201 species20. Although some intraspecific variation may be present in the optical properties of the nanoarchitectures—as in any biological structure—the natural variation in the spectral position of the reflectance maximum for a given population locally is surprisingly small, for example, in the species Polyommatus icarus , one of the most common blue butterflies of Europe21, it is of the order of only 10 nm22. Both the reflectance spectra and the geometric parameters of the photonic nanoarchitectures allow species identification with an accuracy of 96% and 91%, respectively14.Lepidopteran structural colors arise from species-specific, chitin based photonic nanoarchitectures of the dorsal cover scales27. The photonic nanoarchitectures are self-assembled from chitin during metamorphosis inside the living scale cell. Early stages of scale growth are observed as early as five hours after pupation28, while the final stage of scale formation can be observed for example for male Polyommatus icarus, regularly on the day prior to eclosion. This is indicated by the turning of the color seen inside the pupa of the males from light brown to the structural blue, see Fig. Lepidopteran wing scales and the intricate color generating photonic nanoarchitectures are produced by specialized cells30. The processes taking place inside the cells are governed by the DNA located in the cell nucleus. Although the nanometer scale processes are difficult to visualize in living cells, the micron scale processes revealed by confocal and multiphoton microscopy with fluorescent dye staining indicate that the actin filaments have a decisive role in shaping the micron scale system of chitin ridges of the butterfly scales, defining the architecture in this size range25. Inside cells, actin and various actin bundling proteins are responsible for building of the cytoskeleton, filopodia, microvilli, etc.33. Very likely, the mold in which the chitin of the species-specific photonic nanoarchitecture is shaped is also built from these proteins.The remarkable structural and spectral reproducibility in the macroscopic realm of the species-specific properties of the photonic nanoarchitectures inside the dorsal cover scales is achieved via self-assembly occurring at microscopic level, individually in each cover scale. The exact mechanism is still debatedPolyommatus icarus males in Europe and Asia34. In agreement with earlier allozyme based investigations, it was found that the males originating from the western part of Europe were not divided into distinct lineages35. Variations of the ventral wing patterns also delineated a western and an eastern group (based on 16 marker characters)36. However, investigations based on the mitochondrial barcoding region, a 650 base pair (bp) fragment of the mitochondrial cytochrome-oxidase I (COI) gene at its 3’ end, revealed five main European lineages: a large Palearctic lineage that prevailed the distribution area, with Iberia and Italy included; Sierra Nevada and Alicante-Provence38. Although COI could not separate lineages within the Palearctic lineage , clear differences in coloration were found between the European and Asian group of samples, with a transitional zone in the Anatolian region of Turkey34. The results of mitochondrial and nuclear DNA may show discordance40 and their phylogenetic resolution can also be markedly different41. The observed spectral similarity within Europe and the difference from the Asian samples, which must originate from difference in photonic nanoarchitectures, may be an indication of subtle divergence that can only be traced by fast-evolving DNA regions. The slightly different photonic nanoarchitectures being built in the scale cells of European an Asian P. icarus males are tentatively attributable to this divergence. As a first step in the investigation of this hypothesis, we investigated microsatellites—also called Single Sequence Repeats (SSRs)—in correlation with the optical characteristics of the dorsal wing scales of European male P. icarus butterflies. As SSRs are known to have exceptionally high mutation rate, these are good choice for tracing genetic variability of natural populations. A next level of analyses could be the application of genomic approaches that may provide new insights into the genetic architectures underlying color traits42.Recently, we investigated the biogeographic patterns in the structural blue coloration of 43. They have emerged as a very widely used choice for these studies in part because they have the potential to provide contemporary evaluation of migration and can estimate the relatedness of individuals. Many microsatellites have high-mutation rates (between 10–6 and 10–2 mutations per locus per generation), that generate the high levels of allelic diversity necessary for genetic studies of processes acting on ecological time scales44.Microsatellites are a popular and versatile marker type for ecological applicationsPolyommatus icarus specimens, from four different locations (20 per each site) were used to correlate the spectral properties of the coloration of males within local populations with the genetic characteristics as revealed by DNA microsatellites. Our specific question is if we can find concordance between wing coloration and the fast-evolving SSR variability on a large geographic range.In the present paper, 80 Polyommatus icarus  (English name: Common Blue) belongs to the family Gossamer-winged butterflies (Lycaenidae), and it is one of the most widespread Blue butterflies (Polyommatini) of the Eurasian landmass and is distributed from the Pacific to Atlantic coasts. It is not subjected to any restrictions: it is not threatened or endangered and collecting samples of this species is not disallowed. The specimens used in the present work (see Suppl. Table S1) were captured from wild populations in Taizé , two very close locations in the region of Érd  and Barațcoș . The distance between the westernmost and the easternmost sampling sites is of the order of 1600 km . The wing samples  were illuminated by an Avantes DH-S-BAL  UV–Vis light source  through the illumination port of an integrating sphere (Avantes AvaSphere-30). The light reflected by the wings under any angle was collected by the integrating sphere and transmitted to the spectrometer. All the measurements were carried out with respect to an Avantes diffuse tile (Avantes WS-2) as a Lambertian, white standard used to set the 100% reflectance. Evaluation of the spectral data was carried out using Origin 2018  software.Spectral measurements were carried out on the wings removed from the body, to allow precise measurements with an integrating sphere. This is necessary because the wings, due to the micro- and nanostructure of the individual scales, and to the 10–15 degrees deviation in the scale position from the wing plane, does not reflect light like a smooth, mirror-like surface. As a consequence, the measurements with the integrating sphere are more reliable than the data obtained in normal incidence45 by homogenizing the abdomen in 700 μl extraction buffer described by Gilbert et al. (2007)46 with 30 μl Proteinase K (20 mg/ml). The samples were incubated for 24 h at 55 °C followed by centrifugation at 14 000 rpm for 1 min. The DNA was precipitated by adding the mixture of 450 μl ammonium-acetate and storing the samples at − 20 °C for 30 min. The supernatant was washed twice with 600 μl of chloroform–isoamyl alcohol (24:1) to remove proteins. After centrifugation at 14,000 rpm for 5 min, 800 μl ice-cold ethanol was added to the supernatant and was stored at − 20 °C overnight. The precipitated DNA was pelletized by centrifugation at 14,000 rpm for 10 min at 4 °C. Following centrifugation, the supernatant was discarded, and the DNA pellet was washed twice with 96% ice-cold ethanol and stored at − 20 °C overnight. The pellet was air-dried for 1 h at 37 °C and was re-dissolved in 50 μl elution buffer .DNA was extracted following the procedure described in Bereczki et al. (2014)https://data.ccmb.res.in/msdb/) for potential microsatellite loci that can be used in our target species. As we could not find any described primers for amplifying SSR loci in Polyommatus icarus, we developed them de novo using a next-generation sequencing approach47. A high-quality DNA extract was provided to a commercially available service of GenoScreen Inc. , who sequenced a standard Nextera Library on MiSeq machine  for 300 bp long, paired-end reads. Paired reads were merged by USearch v.11.0.66748, and QDD v.3.1.249 was used to mine potential SSR loci in silico from the Illumina reads . Altogether, 50 potential microsatellite primer-pairs were tested in vivo for specific amplification in a polymerase chain reaction (PCR) using Polyommatus icarus DNA as template. The details of the PCR and selection of primers followed a general procedure of the Debrecen lab detailed elsewhere50, which resulted in selection of 10 primer pairs amplifying species-specific, apparently polymorphic microsatellite loci in our target species , to calculate population-level genetic diversity values and genetic differentiation (GST). We tested for the presence of null-alleles in the software Micro-Checker v.2.2.352. Individual level allelic frequency was used to visualize genetic distance between the samples genotyped in a Principal Component Analysis (PCA) as calculated in adegenet 2.1.253. The same software was used to test an isolation-by-distance (IBD) pattern in our dataset in a Mantel-test where individual genetic distances based on the Bruvo-distance were tested in 10 k permutations against geographic distance measured in a Euclidean way. Finally, we run a Bayesian analysis of population structure as implemented in Structure v.2.3.4.54. We run the analysis for K = 1 to 4 groups, each grouping was tested in five independent runs, with 100,000 MCMC iterations as “burn-in” followed by 500,000 MCMC steps. The best K value  was assessed by the method of Evanno55 as implemented in the on-line version of Clumpak56. This software was also used in averaging across the five runs at each K.Genotypic data were imported into MS Excel  and analyzed using GenAlEx v.6.5Polyommatus icarus  measured in the present study were used to generate the statistical presentation in Fig. The reflectance data for the four dorsal wing surfaces of all male Polyommatus icarus for ten, de novo developed species-specific microsatellite loci from four populations representing three geographically distinct groups . These loci do not show significant presence of null-alleles at the genotyped loci, and none of them showed significant deviation from the HWe. Therefore, we could use them to characterize the neutral genetic variance of our studied populations. Allelic richness, genetic diversity and levels of inbreeding were found to be remarkably high in all studied populations with signs of homozygote access  was found to be exceptionally low between the four populations analyzed  at the individual level.Population genetic differentiation . The a posteriori assignment of our individuals to these two clusters has, however, failed to find any structure in the dataset as all individuals were classified as belonging to either the first of the second cluster Fig. . Their o35, that using neutral genetic markers it was not possible to find local differences between Polyommatus icarus populations from the western part of their main range of distribution . In concordance with this finding, the blue structural color of the males, used for sexual signaling, was similar in all four sampled sites in Europe and markedly different from the structural color of the males from the eastern part of the distribution .Our results show, in good agreement with earlier worksPolyommatus icarus. This is indicated by the overlap of 95% population means in genetic space  in case of an extremely common butterfly species.Genetic data are in agreement with the measurement of physical color; we found almost no phylogeographic structure on the studied range of ace Fig. , the negace Fig. , the marace Fig. . As all ace Fig. , which m38, the structural blue coloration of male Polyommatus icarus specimens and their DNA microsatellites indicate that they constitute a single, large meta-population. This is in agreement with an earlier work, in which the allozymes of P. icarus were studied over a large part of Europe. The species had a rather high genetic diversity within populations, whereas differentiation between populations was very low35. Only a marginal trend of decline in genetic diversity from the south to the north was observed. We report here a similar pattern using microsatellites, although we could not find any cline in genetic diversity, probably due to low number of populations studied.Despite the existence of different lineages as indicated by the available mitochondrial DNA dataPolyommatus icarus butterflies34. In this work, we used museum samples. Because it was not allowed to use destructive sampling method for more than 300 specimens curated in museum collections, we used a setup specially designed for performing normal incidence, reproducible measurements on museum samples, the “spectroboard”57.The value of the maximal spectral difference between the mean values of the four sampled sites of 5.3 nm, is about half of the mean standard deviation (SD) value for any of the sampled sites Fig. A. This i34 are compared. The two data sets clearly show that the normal incidence measurements taken with the spectroboard on set museum exemplars , without detaching the wings, and the measurements taken with the integrating sphere on detached wings of freshly captured butterflies, are coincident within a few nanometers. The comparison of the data in Fig. Polyommatus icarus males have different coloration from the Asian ones.To facilitate the comparison of earlier results with those of the present work, in Fig. 58, even minor alterations—of the order of 10 nm (see Suppl. Table 1S in58)—in the characteristic dimensions of the photonic nanoarchitecture responsible for the blue color of male Polyommatus icarus butterflies cause a shift in the spectral position of the reflectance maximum. This can be seen by the naked eye and can be well measured on the whole wing, or on single scales by microspectrometric methods. The structural differences induced a shift in the position of the blue reflectance maximum of 28 nm. This value exceeds by five times the difference found between the four sampling sites. In other words, the only 5 nm maximal difference between the spectral position of the blue maximum of the four sampled sites in Europe and the very good overlap of the range of the histograms in Fig. 9. The alteration of the second set of parameters—of the geometric dimensions—makes possible the variety of colors being generated by essentially very similar nanostructures14. Recently, we showed for the blue dorsal coloration of the Polyommatus bellargus males that this alteration of the nanoscale geometry may cause a shift in the spectral position of the blue reflectance maximum of 30 nm59. The color change is induced by the thickening of the chitin rich layers in the scale nanoarchitecture from 67.5 nm to 97.5 nm, while the thickness of the air rich layers was almost unchanged. Moreover, recently we shoved the blue color generating nanoarchitecture of P. icarus males can be tuned over a spectral range of 160 nm. We demonstrated experimentally and by model calculations, that by the conformal thinning of the photonic nanoarchitecture (by oxygen plasma etching), it is possible to blue shift the spectral position of the reflectance maximum, and by thickening (by atomic layer deposition ALD), it is possible to red shift the reflectance maximum60. The findings above convincingly show that the coincidence within a few nanometers of the averaged spectral positions of the blue reflectance maxima 58. Moreover, a similar structural color can be induced by prolonged cooling of the freshly formed pupae, for the P. icarus, females, too61.On the other hand, the structural color of 62, the Lycaenidae scales in the lower layer, called ground scales, in general have a simpler structure constituted of the network of ridges, cross-ribs and trabeculae and they contain melanin, a broad band absorber in the UV–Vis range. Melanin is responsible for the brown coloration of these scales. The top layer—the cover scales—contains the photonic nanoarchitectures, or pigments, eventually both. They are responsible for the often complex visual appearance of the butterfly wings. The nanoscale processes shaping the photonic nanoarchitectures inside the cover scales are not yet fully elucidated, despite several decades of investigations29. The (dead) chitinous scales that are each the product of a single precursor cell, offer a biologic system where phenotypic diversity can be studied cell by cell, both within and between species. Those scales reveal complex ultrastructures in the sub-micrometer range that are often connected to a photonic function, including iridescent blues and greens, highly reflective whites, or light-trapping blacks33. The shaping of the body of the scale23, including the ridge structure33 is governed primarily by cytoskeletal proteins like actin and fascin. Polymerization-depolymerization and organizing of actin filaments into complex networks are under the control of many actin associated proteins63. The cellular extensions of insect epithelial cells: bristles, hairs and scales are regarded as homologous structures that differ in morphology and function. They contain actin bundles that dictate their cellular morphology64. While the organization, function, and identity of the major actin-bundling proteins in bristles and hairs are known, this information on scales is unknown. It was shown recently that although scale and bristle are thought to be homologous structures, actin bundles have a differential requirement in shaping mosquito scales compared to bristles65.As already mentioned in the Introduction, the scale formation takes place during the pupal stage and each scale develops individually from scale progenitor cellsPolyommatus icarus males collected in the four sampling sites indicate that despite the differences found between the mitochondrial DNA of the different European lineages established on the basis of COI data, the nuclear DNA governing the synthesis of the cytoskeletal proteins is highly similar. This is in good concordance with the result of the microsatellite investigation of the nuclear DNA of the butterflies collected in the four sampling sites. Both sets of data show the absence of major differences between the blue sexual signaling color of the male P. icarus butterflies collected in the four sampling sites and their nuclear DNA. Nevertheless, we need to emphasize that what have been studied so far is the neutral genetic variance, and it may not necessarily reflect traits that are under strong selection. Differences in structural color could also be coded epigenetically, and thus, only genomic or—more appropriately—transcriptomic investigations could unambiguously confirm the lack of differences. Despite this, the absence of differences in the optical properties exceeding 5 nm in the spectral position of the blue reflectance maximum, are a strong argument for the absence of such differences.As the protein synthesis is governed by the DNA in the cell nucleus, the remarkable coincidence in the spectral  properties of 15. Differences of male dorsal wing color are seen as the result of this reinforcement. As we showed earlier in the investigation of the wing coloration of nine lycaenid species inhabiting the same habitat in the environs of Budapest, the coloration of the males, and the photonic nanoarchitectures which generates their color, are both different enough that a neural network type software can efficiently identify the species with a better accuracy than 90%14. Polyommatus icarus is one of the nine investigated species and the males of all the nine species possess the same type of photonic nanoarchitecture: the so called “pepper-pot” type nanoarchitecture65. This is composed of perforated chitin layers stacked each over the other and separated by layers containing mostly air and a few pillars keeping the distance between the chitin layers . Subtle differences in the geometric properties of these nanocomposites are responsible for the species-specific coloration of the dorsal wing surface of the males. However, it has to be emphasized, that all nine nanoarchitectures have structures like imposed by very similar “molds”. These molds may be produced inside the scale producing cells due to species-specific interlinking proteins, which determine the actin mold in which chitin will be deposited. After the chitin deposition, this mold is decomposed, and the cell dies leaving behind the chitin scale. When this process is completed, and air penetrates into the voids of the photonic nanoarchitecture, the blue color can be observed in the pupae . It is argued that the chitin building up these species-specific photonic nanoarchitectures is deposited in protein-based molds, self-assembled in a process governed by DNA of the scale producing cell. This part of DNA is most probably under strong selection, thus, direct studying of this variation requires transcriptomic approaches.The blue sexual signaling coloration of the Supplementary Information.


Larval dispersal modelling along the southwest coast of India indicated a high dispersal rate. The two major subpopulations (Gulf of Oman and Indian) need to be managed regionally to ensure the preservation of genetic diversity, which is crucial for climatic resilience.The adaptive genetic variation in response to heterogeneous habitats of the Indian Ocean was investigated in the Indian oil sardine using ddRAD sequencing to understand the subpopulation structure, stock complexity, mechanisms of resilience, and vulnerability in the face of climate change. Samples were collected from different ecoregions of the Indian ocean and ddRAD sequencing was carried out. Population genetic analyses revealed that samples from the Gulf of Oman significantly diverged from other Indian Ocean samples. SNP allele-environment correlation revealed the presence of candidate loci correlated with the environmental variables like annual sea surface temperature, chlorophyll- In marine fishes, the dispersal of individuals at different life stages, mainly larvae, juveniles, and adults can contribute disproportionally to connectivity and gene flow. Even though marine pelagic fishes are considered to be highly dispersive with significant amounts of gene flow, recent evidence using advanced genomic tools indicates the presence of locally adapted populations with restricted gene flow2. Therefore, it is also important to understand the patterns of the partitioning of genetic variation within and among populations3 for identifying conservation units and developing fishery management plans, as well as understanding climatic impacts.The dynamics of marine connectivity is complex, operating over large geographical ranges across historical and contemporary periods and affected by environmental and oceanographic factors4. The low degree of genetic differentiation in marine fishes has been traditionally hypothesized to be the result of the low ecological heterogeneity (compared to freshwater), lack of dispersal barriers, and large effective population size4. However, recent investigations challenge these concepts as extensive genomic heterogeneity has been reported in marine fishes employing putative adaptive loci6 Both dispersal and local adaptation can be considered as strategies for coping with environmental change, and when dispersal rates are high compared to the evolutionary rescue by local adaptation, dispersal can reduce local adaptation. On the contrary, when local adaptation occurs faster than dispersal, evolutionary rescue dominates, causing monopolization effects by a species7. Yet, local adaptation and adaptive divergence are possible in spite of high gene flow, when adaptive alleles occur in tightly linked regions of the genome where recombination is not possible8.Marine fishes are considered to be less genetically structured than freshwater fishes9. Traditionally, these studies were limited to few genetic markers (often with insufficient genetic information) creating problems in interpreting the results  and making conclusions10.Partitioning of genetic variation within and among populations has a profound influence on species resilience, and several approaches have been employed to understand these patterns11. These genome-wide scans can sample loci from the so-called “genomic islands of divergence” or regions with low recombination where adaptive signals can be observed despite the level of gene flow across populations12. Using a population genomic approach with restriction site-associated DNA sequencing (RADseq), a method that sequences the DNA flanking specific restriction sites in the genome13, the detection of genome-wide variation like single nucleotide polymorphisms (SNPs) can be accomplished by sampling the same genomic region across individuals, thus generating a reduced representation of the genome14. The RADseq approach has been applied in many non-model organisms to develop thousands of SNPs15, linkage maps16, microsatellite markers17, undertake genome scans18, detect population differentiation19, and phylogeography20 using various protocols14. Examples of genomic investigations on fishes include studies on the three-spined stickleback, Gasterosteus aculeatus, which provided insights regarding the diversification of populations into three life forms 5, and on cichlids of African lakes21 providing information regarding their massive diversification that happened during the past 10 million years22. All these studies indicated the presence of adaptive diversity patterns not detected by neutral markers and revealed an additional layer of genetic diversity that needs conservation and management.Next-generation sequencing has enabled genomic approaches to sample large parts of the genome even in non-model organismsSardinella longiceps, is one of the very important pelagic fish of the Indian Ocean and supported the largest pelagic fishery of India with an average annual production of 0.572 million tons during the period 1950–201423. India has been the major contributor of oil sardine among the Indian ocean countries that harvest oil sardine as it contributes up to 80% of the global oil sardine catch 23. The annual harvest in India was 0.34 million tons in 2017 but a decline in abundance was recorded in 2019 with Indian oil sardine occupying the 9th position in landings (0.15 million tons). Ribbon fishes, lesser sardines and Indian mackerel formed the largest pelagic fishery in 2019, replacing Indian oil sardine24. Historically, sardine fishery has exhibited phases of decline followed by abundance in response to fluctuations in environmental parameters27. Availability of nitrogen by upwelling and other mixing processes, especially runoff from rivers28, also affects the productivity of oceanic habitats affecting the distribution and the abundance of sardines. Overfishing along with the alterations in the timing of the southwest monsoon, probably induced by climate change, have been presumed to be other reasons for the fishery decline29.Indian oil sardine, 30. They are distributed widely across tropical latitudes indicating the success of this species in colonizing varied habitats and subsequently making them excellent models for genomic investigations on adaptive evolution and divergence. Despite the possibility of high levels of dispersal, significant genetic structuring and locally adaptive variation have been indicated in sardines in previous studies32. The present authors have extensively investigated genetic structuring, phenotypic divergence and adaptation patterns using mitochondrial and microsatellite markers34. Mitochondrial markers revealed a lack of genetic differentiation in Indian oil sardines, whilst microsatellite markers detected significant genetic differentiation31. Comparative investigations using complete mitochondrial genomes indicated the presence of positive selection and local adaptation mainly along the southwest Indian Ocean, a region characterized by high variations in sea surface temperature, dissolved oxygen, and chlorophyll-a32. Phenotypic divergence has also been reported in Indian oil sardine indicating adaptive variation34. Wide variations in temperature, salinity, dissolved oxygen, and chlorophyll-a have been reported throughout the distribution of Indian oil sardine across the Indian Ocean32. These physical and biological barriers to dispersal, along with mismatches between phenotype and environment in addition to energetic costs of larval dispersal, could induce mortality of maladapted phenotypes and reduce connectivity substantially in the oceanic environment35.Indian oil sardine is considered a cheap source of protein for millions of people and is relished as a delicacy along the southwest coast of India. Being a forage fish, its importance in trophic ecology and the food web cannot be overemphasized. Sardine dwells in coastal waters with a depth up to 30 m and is characterized by relatively high levels of fecundity and a pelagic larval duration of around 40–50 days36. Fishery management in Indian waters occurs mainly through seasonal closures and mesh size regulations38, and species-specific management plans have not been implemented for the Indian oil sardine fishery despite the decline in landings. Additionally, knowledge regarding genomic patterns of divergence is crucial to understand and predict the response of Indian oil sardines to habitat variability and climate change in the Indian Ocean. We explored the spatial patterns of adaptive variation and genetic differentiation among sardine populations collected from the Northern Arabian Sea, southeast Arabian Sea, and Bay of Bengal by employing genome-wide genetic markers derived from ddRAD sequencing. The reduced representation genomic data was further analyzed to detect candidate SNP loci that may be indicative of local adaptation and loci associated with environmental gradients. We also investigated the larval transport patterns using a validated hydrodynamic model coupled with a particle tracking module along the southwest coast of India during summer and winter monsoons.Understanding genome-wide patterns of genetic diversity are crucial to devising conservation and management measures for this important species in Indian waters. Management measures should consider stock complexity which otherwise may lead to erosion of spawning components and depletion of the over-exploited populationsAll the sequenced samples passed the criteria of a minimum number of raw reads of < 1,000,000. After de novo processing using the 'denovo_map.pl' program in STACKS, 50,076 polymorphic RAD loci with 1SNP and two alleles were retained. Finally, 49,361 polymorphic loci conformed to Hardy Weinberg Equilibrium and read depth of < 10 per stack, representing 100 samples from five populations selected for population genomic analyses  in S. longiceps populations ranged from 0.0015 to 0.0028 and samples from the Oman Sea—OMAN (0.0020) and North Eastern Arabian Sea—NEAS (0.0015) had the lowest level of nucleotide diversity. The population of South-Eastern Arabian Sea—SEAS (0.0026), Southern Bay of Bengal—SBoB (0.0025), and Northern Bay of Bengal—NBoB (0.0028) had relatively high diversity when compared to OMAN and NEAS. All the five populations had unique (private) sites/alleles with the maximum in SEAS (970) and minimum in NEAS (387)  and average observed heterozygosity (Ob Het) of both fixed and variable sites from five populations ranged from 0.998 to 0.999 and 0.0017–0.0020, respectively. Whereas for the variable sites, the IS) of all restriction-site associated DNA (RAD sites) (variant and fixed) ranged from 0.0007 to 0.0025 but these were higher for variant sites only (0.0818 to − 0.2583). The allele frequency spectrum of major alleles across the loci was skewed towards 1.00 and varied slightly across the populations , in comparisons of pairwise genetic differentiation (FST and RST) . In all the other pairwise comparisons, the highest genetic differentiation was observed between the NEAS and NBoB followed by the SEAS and SBoB populations, but p-values were not significant. The Oman population was also highly separated along PC1 in PCA plots as compared to other populations while the majority of the individuals of NEAS were separated from others along PC2  Table  with hig40 in structure analysis indicated that K = 2 is the best fit model for the data and the plot of posterior probability confirmed these two groups  and western Indian Ocean (SEAS and NEAS) and no outlier SNPs could be identified for differentiating NBoB, SBoB and SEAS  and sea surface temperature (SST) indicated substantial variations along the coastal regions of OMAN, NEAS, SEAS, SBoB and NBoB. OMAN exhibited the lowest mean SST (25.4 °C) with the highest (24–27 °C) during the northeast monsoon season (October–March) and lowest (20–23 °C) during the southwest monsoon season (June–September)  during the southwest monsoon and lowest (1–3 mg/m3) during northeast monsoon season . By October, it recedes to a low (1–2 mg/m3) Chl-a concentration and continues up to May  during this period with the highest concentration in October and November (6–7 mg/m3)  and low SSS (28–33 ppt) were also observed throughout the year  is characterized by the lowest SST (24–26 °C) during November–April and the highest 27–30 °C) during May–October. The temperature in NBoB varied between 25 and 31 °C during November–April and May–October respectively  followed by SEAS associated with Malabar upwelling zone  contributed the largest share of Indian oil sardine fishery. Landings of Indian oil sardine exhibited large interannual variability with abundant (above average) and deficient years (below average) during the past 35 years  to September  during years with low sardine fishery, whereas it is below average during years with abundant fishery  flows towards the North Pole during winter and towards the South Pole during summer along the west coast of India. While East India Coastal Current (EICC) flows towards the North Pole during winter and towards the South Pole during summer45.Circulations in the Indian Ocean change their direction in the semi-annual scale due to the semi-annual reversal of monsoon over the Indian subcontinent. The major surface currents in the Indian Ocean are summarised in Supplementary Fig. 47. An anti-cyclonic eddy, the Great Whirl generated by the Somali current and the Socotra Gyre can be observed in the Oman Sea during the summer season. The horizontal diameter of the Great Whirl is 400–600 km and the surface current velocity is 1.5–2.0 m/s. Similar to the intense coastal upwelling along the Somali-Oman coast, an intense upwelling is also noticed along the southwest coast of India , during the southwest monsoon and becomes the second most productive region in the Indian ocean. But narrow continental shelf and immense freshwater influx overwhelm coastal upwelling in the Bay of Bengal region.One of the unique characteristics of the Oman Sea is the presence of strong coastal upwelling at the western ocean boundary induced by the Somali Current and Findlater jet. The Somali Current is a cold ocean boundary current that runs along the coast of Somalia and Oman and the Findlater jet is a narrow, atmospheric jet that blows diagonally across the Indian Ocean. This flow deflection creates open ocean upwelling and downwelling in its left and right side of the path, resulting in one of the most productive ecosystems in the ocean along the Somali Oman coasta and maximum dissolved oxygen—DO  confirmed correct distribution as recommended in the LFMM manual 48  using only the 4371 SNP loci exhibiting significant association with environmental gradients also indicated a similar population structure as observed in the analysis of all polymorphic loci  recorded significant similarity to the known genes in the SwissProt database and they were characterized into 320 groups involving molecular function, cellular component and biological process. The adaptive loci encoded genes are mostly involved in cellular energy metabolism, transcription, cell growth and signalling , the spawners with an average size of 19 cm appear in the coastal waters every year42 and spawning takes place at approximately 40–60 m depth51. Eggs of the oil sardine are pelagic, transparent and spherical with an average diameter of 1.4 mm42. The newly hatched larva generally floats passively on the surface of the water (oil globule and buoyancy of the yolk helps to floats over the water surface). Embryonic development of the oil sardine is completed within 24–48 h and larval duration is between 30 and 40 days30. The average length of the newly hatched larva is 2.75 mm and by the end of this first day, the oil globule in the yolk disappears42. The smallest size of oil sardine recruited to the fishery is ~ 6 cm size.Probable spawning grounds of Indian oil sardine and its larval dispersion along the Indian coast are not yet located. The spawning grounds were identified based on the occurrence of oozing spawners or planktonic eggs along the southwest coast of IndiaSimulation studies revealed the spatial variation of egg and larvae (passive particles) during July and January, which were shown in Fig. ST values (0.065–0.075), which indicated reduced connectivity between these populations. On the contrary, no significant genetic differentiation was observed between populations along the Indian coast when FST values were analyzed (FST ranging from 0.0001 to 0.075). Yet, a low degree of genetic differentiation was observed between NEAS vs other populations from the Indian coast mainly SEAS, SBoB and NBoB in both PCA and the least-squares estimate of ancestry proportions. The large intra-population variation observed in NEAS might represent cryptic diversity that needs to be investigated further. Larval transport modelling with particle tracking method indicated the major role of wind and associated coastal currents in the dispersal of the larvae in the water column along the southwest coast of India. Wide variations in the monthly Chl-a, SST and SSS were noticed between coastal regions of OMAN, NEAS, SEAS, SBoB and NBoB of the Indian Ocean, and its correlation with oil sardine abundance is noticed. The association of the sardine fishery with sea surface temperature and chlorophyll-a observed in the present study corroborate the findings that successful sardine fishery is associated with the optimum values of these parameters (lower temperature (26–28 °C) and higher food (Chl-a < 5 mg/m3) availability during May–July). The shift in the habitat characteristics may be acting as environmental barriers for successful larval dispersal, persistence and genetic introgression . Fluctuations in the environmental parameters may induce selective pressure in the oil sardine populations of different eco-regions (adaptive divergence). The SST, Chl-a, and DO were the predominant factors explaining the observed genetic variation between Oman and the other Indian Ocean populations. Signals of local adaptation were present in Indian oil sardine populations and a set of loci associated with SST, Chl-a and DO were also identified in the analyses.Genome-wide SNPs generated from ddRAD sequencing data indicated that Oman (OMAN) population has highly diverged from Indian populations  with significant F52. However, relatively higher levels of genetic diversity were observed in Indian oil sardine populations in the present study (π ranged from 0.0015 to 0.0028) than those observed in other marine fishes53. The average observed heterozygosity values using microsatellites were four times higher than those observed when using SNPs31. On the contrary, the genetic differentiation, quantified as pairwise FST, was similar in both types of datasets. Comparable results were obtained in STRUCTURE analysis using microsatellite markers as the runs converged at K = 2 with low admixture between populations31, when Oman vs Indian Ocean populations was analyzed. Analyzing the microsatellite data omitting Oman samples illustrated a low level of genetic structuring between NEAS vs SEAS and BoB31. In the present study also, the maximum number of outlier loci were detected in comparisons between Oman and other Indian Ocean samples. On the contrary, a lower level of outlier loci was detected in comparisons between NEAS and SEAS, potentially indicating a low level of adaptive divergence between them.Genetic differentiation is considered to be proportional to the number of migrants in each generation. In a pelagic fish like oil sardine with large effective population size, high fecundity, long planktonic larval phase and high dispersal due to oceanic circulation, the effect of genetic drift is considered to be negligible on generating signals of genetic differentiationS. longiceps indicated restricted gene flow between these two regions, mainly contributed by the oceanographic and environmental barriers. A high level of variation in monthly SST, Chl-a and SSS was noticed between coastal regions of OMAN and NEAS of the Indian Ocean, which can act as environmental barriers for successful larval dispersal and persistence. Oceanographic barriers like currents and eddies also may be preventing successful larval dispersal and genetic introgression. Oman Sea is characterized by strong coastal upwelling along the western ocean boundary induced by the Somali Current and Findlater jet, and the Oman sardine populations are associated with this highly productive ecosystem. The northward flowing Somali Current (SC) during Southwest monsoon seasons can disperse larvae from OMAN to NEAS. However, the absence or discontinuous low-temperature zone and Chl-a zones between OMAN to SEAS (Gulf of Oman) may prevent the survival of larvae dispersed by SC. Besides, the presence of eddies like the Great Whirl generated by the Somali current and the Socotra Gyre observed in the Oman sea during the summer season54, coincides with the spawning time of Indian oil sardine which may act as larval retention zones creating restricted mixing and subsequent genetic differentiation.Significant genetic differentiation between Oman and Indian populations of ST analyses whilst a low level of genetic differentiation between NEAS and other regions  was observed in both PCA and Least-squares estimates of ancestry proportions. The intra-population divergence observed in NEAS in PCA analysis might indicate cryptic diversity which needs to be investigated further.Along the Indian coastline, a lack of significant genetic differentiation was observed in FS. longiceps populations along the Indian coastline with significant genetic differentiation between SEAS and NEAS31. This low level of genetic differentiation may be due to locally adapted regions of the genome because the low genetic divergence can be maintained even in the presence of geographical connectivity and gene flow, as selection and adaptive divergence can operate over micro-spatial scales55. The presence of outlier loci and the high number of private alleles in these populations corroborate these conclusions. The homogenising effect of the high larval dispersal rate in the water column along the southwest coast of India by wind and associated coastal currents, as observed in the larval transport modelling in the present study may be the reason for observed poor genetic differentiation signals between SEAS, SBoB and NBoB. We could identify only a few FST outliers in comparisons between NEAS and other regions . This may be due to the stringent filtration method used by BAYEScan and the de novo method of analysis. Thus, to generate a complete picture of the selective forces acting on this species, a reference genome-based analysis  is needed. Higher density reference-based genome scans can reveal some additional candidate loci under strong or moderate selection in the S. longiceps populations that were not covered in the present study57. A zone of high temperature and low Chl-a is also existing between NEAS and SEAS till the end of southwest monsoon season (June–September) which also acts as an environmental barrier restricting larval dispersal and gene flow. Overall, the conclusions of the present study using RADseq agree with the findings regarding the genetic structuring of Indian oil sardine derived using mitochondrial and polymorphic microsatellite markers32. Similar levels of low but significant genetic differentiation have been reported in marine species like Marine snail58, European seabass6 and Amur ide56 using the ddRAD sequencing approach.A significant number of private alleles were also observed in each population along the Indian coastline. Consistent with this observation, the study using microsatellite markers has revealed the occurrence of intra-population divergence in the 60. The small pelagic fishery is associated with major upwelling regions which are the most productive in the world’s oceans and consequently their abundance is controlled by food availability61. The seasonal-annual fluctuations in the sardine fishery have been linked with fluctuations in productivity (Chl-a) of upwelling regions and changes in the environmental parameters such as temperature (SST)44. These two parameters have a significant role in the spawning of sardine and biological behaviour of early life stages  which has also been established from the present study. An optimum environmental window of temperature (lower temperature (26–28) during May—July), food , salinity and dissolved oxygen along with other physicochemical ocean characters such as wind events, upwelling37 and tidal variability  throughout early life stages is necessary for the survival of larvae and subsequent recruitment. Thus, shifts in habitat characteristics may not be acting strictly as a barrier to the migration of adults but may prevent successful spawning, larval dispersal and subsequent colonization62. The near optimum characteristics (lower temperature (26–28 °C) and higher food (Chl-a) availability during May–July) found in OMAN and SEAS make them the most productive regions. Fluctuations in the sardine landings from these regions were linked with deviations from the optimum habitat requirements (temperature and food).The observed genetic structuring in Indian oil sardine is due to the presence of barriers between eco-regions such as geographic distance, patchiness in the environment, local and global oceanic circulation patterns and environmental gradients which prevents gene flow to some extent46 provide adequate opportunities for successful larval dispersal and subsequent colonization. Continuous transport of larvae from SEAS to BoB by WICC and EICC is likely responsible for the observed genetic connectivity between the two oceans. The continuous low temperature and high Chl-a zones along the southwest and southeast coast of India during spawning time can support the survival of larvae and subsequent colonization by providing an optimum environmental window of temperature and food. This could be one of the survival mechanisms of oil sardine in its heterogeneous and changing habitat.The SEAS population is associated with the highly productive Malabar upwelling zone in the south-eastern boundary of the Arabian Sea, and a high level of connectivity and lack of genetic divergence has been observed between NEAS vs SBoB and NBoB. Oceanographic parameters along these regions may be providing a favourable condition for substantial gene flow and mixing between these regions. Seasonal reversals in the boundary currents mainly the West India Coastal Current (WICC) and the East India Coastal Current (EICC) along the coastal regions30, but no information exists regarding their dispersal potential. The spawning season of Indian oil sardine coincides with the period of West India Coastal Current (WICC) that flows towards the south along the west coast of India and the East India Coastal Current (EICC) that flows towards the north during the southwest monsoon season45. The Lagrangian particle tracking method was widely used in larval transport in the Gulf of Maine63 and pollutant transport studies in the Cochin estuary64. The study by Seena et al.65 using the FVCOM model revealed that numerous rivers debouching freshwater to the coastal areas create buoyant plumes and transport in accordance with coastal currents. Larval transport modelling with particle tracking method in the present study indicated the major role of wind and associated coastal currents (WICC) in the dispersal of the larvae in the water column along the southwest coast of India. Besides larval dispersal, localized migration by adult sardine shoals may also contribute to gene flow and connectivity to some extent as sardine shoals are reported to swim at a speed of 5 km/h37.Very little information is available regarding the dispersal of larvae and migratory potential or pattern of sardine post larvae. Sardine larvae are pelagic and planktonic with a larval duration of approximately 40 daysS. longiceps from the Indian ocean can be considered as two locally adapted populations; the OMAN population adapted to a habitat with low temperature (24–27 °C) and high food availability (Chl-a) and the Indian coastal population  adapted to high temperature (26–30 °C) and low food availability (Chl-a). The illustrated genetic divergence  and Indian coastal regions (two morphotypes mainly stout and slender)34. These morphotypes may be the result of divergent selection and adaptive variation which corroborates our findings.The genetically differentiated 66. These natal habitats will also be important for the aggregation of spawners from locally adapted populations which may induce natal philopatry and subsequent genetic differentiation. Spawning aggregations and natal philopatry have been reported in many fish species68. Local adaptation is expected in species with restricted gene flow69, but recent investigations have provided evidence for selection in high gene flow species72 like Indian oil sardine. Adaptation and selection can occur even in the presence of high gene flow as genomic regions associated with selection may be tightly linked where recombination does not take place as adaptive alleles may exhibit hitchhiking and consequent divergent selection8. An important benefit of high-density marker loci like SNPs generated by RADseq is the possibility of locating the genomic regions with high population structuring which may restrict gene flow73. The loci identified as FST outliers with FIS > 0 may be the representation of genomic regions of local adaptation, isolated genomic regions of divergence with gene flow, and genomic regions of speciation76 in S. longiceps. Thus, the signals of cryptic structuring or assortative mating can be used as a starting point for a more detailed study to identify the genomic region of genetic divergence in S. longiceps and Clupeoids. Reanalysis of the RADseq data with a reference genome-based method is necessary for identifying genome-wide distribution or chromosomal regions of genetic divergence. Locally adapted populations correlating with habitat heterogeneity may be providing resilience to environmental fluctuations as biological complexity and cryptic diversity acts as buffering mechanisms. Larval dispersal and population connectivity across homogeneous habitats like Indian coastal regions may provide additional benefits to an abundant population to survive and persist.Local adaptation may prevent successful colonization and persistence of recruits from non-matching environments due to the competitive advantage of adapted populations77. Knowledge regarding genetic structure at a spatial and temporal scale can be used to monitor climatic impacts as shifts in the stable structuring can happen due to climate change-induced localized extinctions and recolonizations78. Extirpation of local populations causes irreversible changes in the gene pool, impairing the potential for adaptive evolution79. Besides, when locally adapted populations or cryptic diversity decline, immigration cannot replenish or rehabilitate declining populations as non-matching habitats will induce negative selection79. Recent evidence regarding recruitment failure and decline in sardine populations due to global warming and overfishing, especially along the Malabar upwelling zone emphasizes the vital need for preserving the adaptive potential of exploited species. The present study is the first attempt in these lines and the findings of the present study are very relevant for devising management and conservation measures to augment the biodiversity of natural stocks of Indian oil sardine which otherwise may decline to the point of no recovery due to the threat of warming of oceans and overfishing. The two major locally adapted subpopulations need to be managed separately to ensure the preservation of biodiversity (spawning components), which is crucial for climatic resilience68. Range expansion of Indian oil sardines to northern latitudes has been reported80 which in due course of time may result in range shifts and localized extinctions especially along Malabar upwelling zones as oceans of the lower latitudes will be more prone to the perils of ocean warming. We suggest managing Indian oil sardine region wise by seasonal closures and mesh size regulations to prevent further deterioration of natural stocks. Management and conservation of this vital resource capable of providing a cheap source of protein for the millions in developing countries like India are very important for ensuring sustainability.Climate change is capable of altering the ecological interactions, inducing physiological stress, and bringing about changes in phenology and seasonal timingSardinella longiceps samples were collected from the five eco-regions of the Indian Ocean, mainly, Oman Sea (OMAN), North Eastern Arabian Sea (NEAS), South Eastern Arabian Sea (SEAS), Southern Bay of Bengal (SBoB), and Northern Bay of Bengal (NBoB) during 2014–2017 .A total of 100 017 Fig. . Tissue 14. The DNA of each sample was double digested completely with high-fidelity MspI and EcoRI restriction enzymes (New England Biolabs). The barcode with a unique 5-bp sequence and P1 adapter was ligated to EcoRI overhang and the P2 adapter was ligated to MspI overhang. The DNA fragments were selected on an automated size selection technology BluePippin (Sage Science) with a mean size of 300 bp on a 2% agarose cartridge. The fragments were then PCR amplified and purified with AMPure XP Beads. Libraries were prepared with approximately equal amounts of DNA from each sample. The barcoded ddRAD libraries were sequenced on an Illumina HiSeq 2500 platform with a 2 × 100 bp sequencing approach.ddRAD sequencing was carried out using the high-quality DNA extracted from each sample . The ddRAD libraries were prepared based on the protocol by Peterson et al.82. The quality of the demultiplexed reads was assessed using FastQC83, reads with low quality (Phred score < 30) and uncalled bases were discarded and the sequences were trimmed to 85 bp. Identification of SNPs and genotype calling were performed in STACKS using ‘denovo_map.pl’ program. ‘ustacks’  constructed stacks for each sample, ‘cstacks’  used all individuals from each population to construct a catalog of loci and 'sstacks' compared each sample against the catalog. No reference genome was available for this species during analysis. The number of SNPs was used to optimize the values of three main parameters -m, -M, -N of ‘ustacks’ and -n of 'csstacks’, as described by Paris et al.84. We used 50 samples with the greatest depth of coverage to optimize combinations of different parameter values by setting -N as M + 1 and increasing parameters  until it reached a plateau. The number of SNPs and percentage of polymorphic loci reached a plateau at a combination of -m = 4, -M = 5 and -n = 6 and it was selected as optimal for analyzing the entire data set. Then individuals with more than 20% missing data were removed and polymorphic RAD loci with 1SNP and 2alleles were retained. Finally, loci that deviated from the Hardy–Weinberg equilibrium (HWE) in a single population were removed.The raw reads were demultiplexed with a specific barcode index and filtered using the 'process_radtags’ program in STACKS v1.40ST and RST values in GENEPOP v4.085.We used different methods to analyze genetic diversity and patterns of genetic structuring within our dataset. Population genetic statistics  at the sites) were computed using 'population’ program in STACKS v1.40. We used one random SNP per locus, the minimum number of populations in which a locus must be present to process a locus (–min-populations) as 5, the minimum percentage of individuals in a population required to process a locus for that population (–min-samples-per-pop) as 90% and minimum minor allele frequency required to process a nucleotide site at a locus (–min_maf) as 2%. The global estimate for genetic differentiation was assessed using F86 with a model-based Bayesian MCMC clustering was used to determine the number of genetically distinct populations (K) with the highest posterior probability. K values ranging from 1 to 5 were simulated under the admixture model to determine the optimal K value. The program was run for a burn-in period of 100,000 followed by 500,000 MCMC steps. ∆K method40 was used to detect the optimal K value. To identify the presence of any hierarchical structure, the analysis was repeated after excluding the cluster identified in the first run.STRUCTURE v2.387 to visualize broad-scale population structure. A neighbour-joining method, clustering of the population as implemented in Neighbor (from Phylip programs)88 was used to generate a phylogenetic tree using average pairwise FST values as input. The tree was then visualized in FigTree89. A simple mantel test using FST and spatial distance matrix (shortest sea route between the sampling sites) was performed with zt90. A regression analysis with pairwise FST/(1 − FST) and log of the pairwise spatial distance between populations91 was also carried out.We performed a principal component analysis (PCA) of full data with R package Adegenet v2.1.292 to identify loci under divergent selection, based on the differences in the allele frequencies between populations. The allele frequencies of each locus in each population or input file were prepared by converting GENEPOP files to BAYESCAN input files using PGDSpider v2.1.1.593. The SNP loci with false discovery rate (FDR) < 0.05 were selected as outlier SNPs and all other optional parameters were set as default. The result was plotted on a graph with the R statistical package.We used BAYESCAN v2.1https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.highres.html) and chlorophyll-a (Chl-a mg/m3) L3 data from MODIS site (https://modis.gsfc.nasa.gov/data/dataprod/chlor_a.php), for a period of 18 years from 2002 to 2020. In addition to surface climatology data, we also analyzed water column data (2014–2017) such as SST, sea surface salinity (SSS ppt) and dissolved oxygen (DO μmol/kg) from World Ocean Database available at https://www.nodc.noaa.gov/OC5/woa18/woa18data.html and Chl-a (mg/m3) L3 data from MODIS site (https://modis.gsfc.nasa.gov/data/dataprod/chlor_a.php). The data sets were analyzed using open-source software Ferret and visualized in Ocean Data View (ODV 5.1.7). For each eco-region, the annual minimum, maximum and mean of each of these environmental factors were estimated and used to test the environmental variable that explains the genetic variation among the S. longiceps population samples, using the gradient forests R package94.We analyzed the long-term catch data of oil sardine with climatology data to understand the relationship between fluctuations in sardine abundance, distribution and climatology from the five eco-regions of the present study. Annual landing data of Indian oil sardine along the east and west coast of India was analyzed (CMFRI 1985–2019) for a period of 34 years from 1985 to 2019. Monthly sea surface temperature (SST °C) data were extracted from NOAA, Physical Science Laboratory  that best explained the genotypic data48. Five independent LFMM runs were conducted using 100,000 iterations with a burn-in of 10,000 and calculated the median Z-score  for each locus. Adjusted p-values (q) were calculated using the false discovery rate (FDR) method and the histogram of q inspected as recommended in the LFMM manual48. SNP loci with q < 0.05 (or FDR < 0.05) were classified as candidate loci. Each candidate or outlier SNP loci that contained putatively adaptive regions were subjected to a BLASTx search of all sequences in the SwissProt, RefSeq protein and NCBI . GO Annotator (http://xldb.di.fc.ul.pt/rebil/tools/goa/.) was used for assisting the GO annotation of loci that produced significant blast hits.The association between SNPs and climate gradients was tested using the latent factor mixed model (LFMM) in LEA95, a validated hydrodynamic model65 coupled with a particle tracking module64 during January 2014 (Winter monsoon) and July 2014 (Summer monsoon). GMSH software (https://gmsh.info) was used to generate an unstructured grid that consisted of 27,264 nodes and 48,089 elements with 21 vertical sigma layers. The model domain extended from 6°N (Kanyakumari) to 15°N (Goa) along the Southwest coast of India with a spatial resolution range of 10 m (near to the coast and islands) to 50 km (Open Ocean boundary). The open boundary of the model was forced with the tide (FES2014), salinity, temperature and sea surface height (HYCOM data). The daily river discharge data (26 rivers) during the year 2014 collected from the Central water commission of India was included in the model. The model was forced by the surface meteorological parameters such as atmospheric pressure, air temperature, wind, heat flux and precipitation taken from the European Centre for Medium Weather Forecast (ECMWF). Fifty thousand passive particles were introduced as neutrally buoyant at the surface layer in the entire model domain node and recoded its path at 1-h intervals during January and July Supplementary Information.


The urban agglomeration in the middle reaches of the Yangtze River, which is the second largest urban agglomeration in China, represents a typical land space range of ecological vulnerability in China. Large differences occur in economic development mode between resource- and non-resource-based cities in this basin area. Accurate identification of the evolution and regional differences in the production-living-ecological space (PLES) is very important in order to elucidate the development and utilization of land space in the region. At present, relevant research has largely focused on the classification and determination of PLES temporal and spatial patterns. Temporal and spatial pattern research has mainly considered a single scale of administrative division, whereas fewer studies have analyzed the temporal and spatial patterns and regional differences in the PLES in ecologically fragile natural watersheds. Therefore, based on PLES classification, the regional differences in the PLES between two types of cities in the basin are measured via the Theil index and exploratory spatial data analysis (ESDA). First, the ecological space (ES) of these two types of cities in the urban agglomeration in the middle reaches of the Yangtze River is compressed by the production space (PS) and living space (LS), in which the ES of resource-based cities is compressed for a longer period, and the phenomenon involving PS compression by the LS and ES mainly occurs in non-resource-based cities within the urban agglomeration in the middle reaches of the Yangtze River. Second, the PLES of these two types of cities exhibits the characteristics of spatial aggregation, and high- and low-density areas of the PLES remain relatively stable. Third, the regional differences in the PLES of the urban agglomeration in the middle reaches of the Yangtze River mainly originate from intraregional differences. The PLES of these two types of cities in the urban agglomeration in the middle reaches of the Yangtze River is more sensitive to changes in economic development than to those in the population distribution. Since the reform and opening up, rapid economic growth in China has led to tense human–Earth relations, and the phenomenon of production-living-ecological space (PLES) competition has increasingly occurred ,2. DisorThe PLES theory is based on the perspective of the element structure function in systematics . ElementMoreover, the evolution of national land and regional differences are the result of man–land interrelations ,26. The At present, research on the PLES focuses on three directions: type division, spatiotemporal pattern determination and spatial optimization of the PLES. Spatial classification of the PLES comprises the basis for research on PLES spatiotemporal patterns and spatial optimization, which has been reported in the existing literature ,31. StarScholars have examined PLES patterns at different scales. At the national scale, the reclassification assignment method has been applied in ArcGIS software to explore the spatial distribution pattern of the PLES. Research has indicated that PSs are mainly distributed in the main urban agglomeration and grain-producing areas on the eastern side of the Hu Huanyong line, LSs largely occur in the main cities and urban agglomerations in China, and ESs are mostly located on the western side of the Hu Huanyong line ,36. At tConsidering that the interactions between the PS, LS and ES are intensifying, with the transformation from functional zoning of a single element to territorial zoning covering an entire region, many scholars have examined the territorial space from the PLES perspective, carried out research on ecotourism areas  and coasThe researches on the spatial-temporal pattern and regional differences of the PLES of natural watersheds provide the basis for the land space control and natural watershed governance. Previous studies have important reference values for this paper, but most of them just studied the classification and temporal and spatial pattern of PLES. Furthermore, the research on the temporal and spatial pattern focused on the single scale of administrative division, such as national, provincial and urban scale. Few studies have analyzed the temporal and spatial pattern and regional differences of PLES in ecologically fragile natural watersheds. In this study, we aimed to reveal the temporal and spatial pattern and regional differences of the PLES of urban agglomerations in the middle reaches of the Yangtze River. Another aim was to make proposals for eco-environmental protection and high-quality development in the Yangtze River Basin.The urban agglomeration in the middle reaches of the Yangtze River is a giant urban agglomeration mainly comprising the Wuhan Urban Circle, Changsha Zhuzhou Xiangtan Urban Agglomeration and Poyang Lake Urban Agglomeration. Rapid urbanization of the urban agglomeration in the middle reaches of the Yangtze River imposes an increasingly prominent coercive effect on the ecosystem, which seriously threatens the sustainable development process of this urban agglomeration. Therefore, scientific measurement of the temporal and spatial evolution characteristics of the PLES of the above urban agglomeration in the middle reaches of the Yangtze River could be highly important for the formulation of ecosystem health protection policies for this urban agglomeration.At the first National Symposium on resource-based cities in 2018, relevant experts pointed out that according to existing relevant research, comparative analysis of resource- and non-resource-based cities and determination of the similarities and differences between these two types of cities are urgently required, which could better guide regional sustainable development. Based on PLES classification, this paper examines the evolution of the PLES of these two types of cities (resource- and non-resource-based cities) . in the http://www.resdc.cn/ accessed on 20 September 2021). Gridded Chinese population distribution and GDP data with a spatial resolution of 1 km for 1995, 2000, 2005, 2010 and 2015 are acquired from the Data Center of Resources and Environmental Science, Chinese Academy of Sciences (http://www.resdc.cn/ accessed on 20 September 2021), extracted with ArcGIS software. Both types of data include the effects of the land use type, night light brightness and residential area density, which ensures that the Theil index can more accurately reflect the evolution of PLES patterns and regional differences with the GDP and population as weights. Choosing the GDP and population distribution as weights considers the impacts of economic development and population distribution differences, respectively, on the PLES (The data in this paper include Chinese land use/land cover remote sensing monitoring data (land use/cover change (LUCC), 1-km accuracy) pertaining to 1995, 2000, 2005, 2010 and 2015. ArcGIS software is employed to extract PLES land use data of county-level cities in the urban agglomeration in the middle reaches of the Yangtze River, which are considered to analyze the spatial and temporal patterns and regional differences in the PLES among county-level cities in the Yangtze River Basin. The selected land use data are retrieved from the Resource and Environmental Science Data Center of the Chinese Academy of Sciences  and intraregional differences. Moreover, this approach can calculate the contribution rate of intraregional and interregional differences to the total differences. The Theil index is adopted to calculate the regional differences in the PLES in the urban agglomeration in the middle reaches of the Yangtze River, and the calculation equation of the Theil index of the PLES is expressed as:In Equation (1), The Theil index achieves a good decomposition effect. When the sample is divided into multiple groups, the Theil index can be divided into intergroup and intragroup gaps, which can measure the difference between regions. According to the basic concept of Theil index decomposition and drawing on the results of previous studies, the cities in the urban agglomeration in the middle reaches of the Yangtze River are divided into resource- and non-resource-based cities. The area and regional development interregional and intraregional differences are calculated based on the Theil index of the PLES. The total Theil index of the urban agglomeration in the middle reaches of the Yangtze River is divided into the intraregional and interregional Theil indices.In Equations (2) and (3), Based on Theil index decomposition, various contribution rates to the Theil index were measured to reflect the impact of the spatial differences between the resource- and non-resource-based cities on the differences in the PLES of the urban agglomeration in the middle reaches of the Yangtze River. According to Equation (6), dividing the left and right terms by In Equations (8)–(11), Exploratory spatial data analysis (ESDA) encompasses a collection of spatial data analysis methods. ESDA adopts the spatial relevance as the core, obtains the spatial agglomeration aspects and anomalies of objects through visual description of the spatial dependence and spatial heterogeneity in data, and explains the spatial relationship between regions through the definition of the spatial weight matrix. Moreover, ESDA reveals the spatial action mechanism and evolution rules between objects. (1) Global spatial autocorrelation, which can be expressed as follows:In Equation (12), n is the total number of all counties and cities in the urban agglomeration in the middle reaches of the Yangtze River, The value of global Moran’s I generally occurs in . (2) Local spatial autocorrelation is calculated as follows:Local Moran’s I > 0, this suggests that the high value of a given regional city in the Yangtze River urban agglomeration is surrounded by high values (high-high), or a low value is surrounded by low values (low-low). For Local Moran’s I < 0, this indicates that the high value of a regional city in the Yangtze River urban agglomeration is surrounded by low values (high-low), i.e., a hotspot area occurs, or a low value is surrounded by high values (low-high), i.e., a cold spot area occurs.For According to the PLES classification system , after rIn ArcGIS software, the global Moran I value of the PLES of all provinces and cities in the middle reaches of the Yangtze River was calculated, and the inverse distance method was adopted for spatial conceptualization. The inverse distance method adopted the Euclidian distance, and row standardization was used. The global Moran index revealed that the spatial area of county-level cities near the urban agglomeration in the middle reaches of the Yangtze River experienced a spatial agglomeration effect, but it did not reflect the specific structure of spatial agglomeration. This paper further applied the local Moran index to measure the spatial heterogeneity among the county-level cities in the urban agglomeration in the middle reaches of the Yangtze River. The measurement results are shown in We measured the degree of functional coordination spatially by local spatial auto-correlation analysis. The local spatial autocorrelation results of resource-based cities in the middle reaches of the Yangtze River show: high-high aggregation and low-low aggregation characteristics indicate that the functional coordination degree of a single county and surrounding counties is high. The functional coordination degree of the territorial space is in a relatively stable state .Using the Theil index and its decomposition formula, we analyze the grid data of urban agglomeration in the middle reaches of the Yangtze River from 1995 to 2015, obtain the Theil index with GDP and population as the weight of the urban agglomeration in the middle reaches of the Yangtze River, and calculate the regional differences between the two types of cities. Based on the GDP and population distribution as the weight , the TheAccording to the change trend, with the GDP as the weight a,b, the In terms of the numerical value, the interregional differences across the urban agglomeration in the middle reaches of the Yangtze River are very small with either the GDP or population distribution as the weight, namely, smaller than 0.2, and most differences do not exceed 0.01 , while tA notable fluctuation is observed. Since the overall difference in the interregional Theil index of all cities mainly originates from the difference in the intraregional Theil index between these two types of cities, this paper further observes the change trend of the Theil index across the region . With thNumerically, considering the two weights, the contribution rate of the non-resource-based cities to the regional differences in the PS, LS and ES is higher than that of the resource-based cities, namely, CTr-PS > CTg-PS, CTr-LS > CTg-LS, CTr-ES > CTg-ES . This inBased on the change trend, with the GDP as the weight, the contribution rates of the regional differences in the PLES of the resource-based cities exhibit an upward–downward–upward trend pattern. The contribution rate of the regional differences in the PS and ES of the non-resource-based cities indicate a downward–upward–downward trend pattern, while the contribution rate of the regional differences in the LS first decreases and then increases a,b. WithIn this study, we can conclude that in the urban agglomeration in the middle reaches of the Yangtze River, from 1995 to 2005, the synchronous increase of the PS and LS in resource-based cities squeezed the ES. From 2005 to 2010, the PS and LS of non-resource-based cities increased, squeezing the ES. From 2010 to 2015, the PS and LS of resource-based cities increased simultaneously, squeezing the ES. The LS and ES of the non-resource-based cities increased simultaneously, thus compressing the PS. The PLES of the two types of cities have the characteristics of spatial aggregation, and the density area is relatively stable. The regional differences in the PLES mainly originate from intraregional differences. Economic development can better disturb the changes of PLES.Cui et al., by analyzing the evolution characteristics of PLES pattern in Hubei Province, concluded that from 2009 to 2015, the PS and LS in Hubei Province expanded significantly, and the ES shrank significantly . The expCombined with the above analysis, the following policy suggestions are formulated to provide a relevant basis for land and space control, ecological environment protection and sustainable development of the urban agglomeration in the middle reaches of the Yangtze River.Against the strategic background of joint protection and the absence of large-scale development, we should strive to ensure prioritization of the ES, improve the occupation rate of the ES of these two types of cities, increase the ES through industrial migration or upgrading and population control, and reduce the frequency of human social and economic activities in the ES to alleviate the notable contradiction between human activities and the ecological vulnerability of the ES.Economic growth exerts a notable impact on the differences between the three types of spaces in the urban agglomeration in the middle reaches of the Yangtze River. In the future, in regard to control of the three types of spaces of these two types of cities, we should pay more attention to the control of the three types of spaces of those cities with faster economic growth between these two types of cities.Corresponding policies should be differentiated and formulated. According to the results of local Moran index, we can determine the density areas of various PLES in the two types of cities. Regarding PS and LS high-density areas, the improvement and transformation of the utilization of these two types of spaces should be strengthened. Regarding PD and LS low-density areas, long-term space utilization planning should be achieved to avoid space utilization waste. In terms of ES high-density areas, we should continue to maintain the stability of the ES across the region, and regarding ES low-density areas, we should improve the ES across the region.Via processing of spatiotemporal data related to the PLES of the resource- and non-resource-based cities in the urban agglomeration in the middle reaches of the Yangtze River from 1995 to 2015, this paper examines the spatial evolution and spatial aggregation characteristics of these two types of cities and dynamically assesses the regional differences in the PLES between these two types of cities under the influence of economic development and population distribution with the Theil index. The main conclusions are as follows:(1) From 1995 to 2015, the PLES of resource-based cities and non-resource-based cities in the middle reaches of the Yangtze River changed sharply. In different periods of this process, the following phenomena exist in both types of cities. The ES of was compressed by the PS and LS, and the ES of the resource-based cities was compressed for a longer period. That was not conducive to ecological environment protection of the urban agglomeration in the middle reaches of the Yangtze River. The LS and ES mainly compressed the PS. This phenomenon occurs for a longer time in non-resource-based cities, which did not facilitate economic production enhancement.(2) From 1995 to 2015, the spatial aggregation characteristics of the PLES of the county-level cities in the urban agglomeration in the middle reaches of the Yangtze River were obvious, exhibiting positive spatial autocorrelation characteristics. From 1995 to 2015, the high-density areas (high-high category) and low-density areas (low-low category) of various PLES in resource-based cities and non-resource-based cities remained stable.(3) The regional differences in the PLES of the urban agglomeration in the middle reaches of the Yangtze River mainly originated from the differences between urban areas. The PLES of the two types of cities in the urban agglomeration in the middle reaches of the Yangtze River was more sensitive to changes in economic development. The regional differences in the PLES of the non-resource-based cities are relatively large, while those in the PLES of the resource-based cities are relatively small. The possible main reason is that the economic development modes of the non-resource-based cities are diverse, while the development modes of the resource-based cities are relatively singular. The regional differences in the PS, LS and ES of the resource-based cities exert a major impact on the PLES of the urban agglomeration in the middle reaches of the Yangtze River.In the study, Theil index and ESDA are used to explore the regional differences of PLES between the two types of cities under the influence of economic development and population distribution. However, the pattern, structure, form and relationship of the micro scale of PLES have not been studied in depth. In addition, the specific reasons leading to the change of PLES have not been explored in depth. For the above deficiencies, further exploration and research are needed.


P. destructans), affecting hibernating bat species in North America. Emerging early in 2006 in New York State, infections of P. destructans have spread to 38 US States and seven Canadian Provinces. Since then, clonal isolates of P. destructans have accumulated genotypic and phenotypic variations in North America. Using microsatellite and single nucleotide polymorphism markers, we investigated the population structure and genetic relationships among P. destructans isolates from diverse regions in North America to understand its pattern of spread, and to test hypotheses about factors that contribute to transmission. We found limited support for genetic isolation of P. destructans populations by geographic distance, and instead identified evidence for gene flow among geographic regions. Interestingly, allelic association tests revealed evidence for recombination in the North American P. destructans population. Our landscape genetic analyses revealed that the population structure of P. destructans in North America was significantly influenced by anthropogenic impacts on the landscape. Our results have important implications for understanding the mechanism(s) of P. destructans spread.White-Nose Syndrome is an ongoing fungal epizootic caused by epidermal infections of the fungus, Pseudogymnoascus destructans ( Pseudogymnoascus destructans (P. destructans)  is caused by the fungal pathogen tructans . Within tructans . The N. t strain ,4. The Eortality ,6. Since species . Coupled species ,9. Despiifficult ,11.P. destructans populations showed results consistent with clonal spread of a single genotype in N. America [P. destructans and the geographic distance from the site of initial infection. However, recent studies have failed to identify large-scale geographic or temporal structure in the N. American P. destructans population [P. destructans isolate from the western-most Washington State was more closely related to the isolate from the epicenter in New York State than some other isolates from within New York State are to the earliest known isolate [P. destructans has been transmitted between bats and their hibernacula. Alternatively, the spread of P. destructans may not follow a strictly step-wise model of outward expansion from the epicenter, but may instead be a more complex processes, influenced by multiple modes of transmission, and both forward and backward migrations between infected regions. In addition, secondary introductions of P. destructans genotypes between hibernacula could also influence genetic variation and complicate population structure, making the patterns of genetic variations inconsistent with the step-wise model of clonal expansion. Indeed, the introduction of additional foreign genotypes into N. America could further complicate the population structure and genetic variation. This is especially true if additional introduced strain(s) from Eurasia have a complementary mating type (MAT1-2) to that within the N. American P. destructans population (MAT1-1), which could significantly contribute to generating genotypic and phenotypic diversity [Early investigations into genetic structure of  America ,13,14. Ipulation . For exa isolate . Howeveriversity ,16.P. destructans spread in N. America remains unknown. Broadly speaking, P. destructans could be spread by three methods: primarily via host bats, and secondarily through alternative hosts such as human activities or through passive mechanisms such as wind currents via the dispersal of airborne spores. The majority of WNS spread occurs via the dispersal of P. destructans propagules, which are transferred through contact events between hosts. Additional opportunities for transmission may incorporate the movement of bat ectoparasites [P. destructans infection in some species result in few surviving individuals within most hibernacula impacted by WNS. Over particularly long distances, the activity of secondary hosts may also contribute to the spread of P. destructans. Indeed, human-mediated transmission of P. destructans was the most likely cause for P. destructans spread from Europe to N. America [P. destructans between caves in N. America [P. destructans through the air are unlikely [P. destructans spores [P. destructans to temperature and radiation [Much about when and how arasites ,18, or tarasites , but thearasites . Indeed,arasites . However America , and it  America . Lastly, America ,25. Whilunlikely , aerosols spores . Ultimatadiation ,29,30 aradiation .P. destructans is an important component of assessing the risk to the spread of further infections. In this study, we employed microsatellite markers and genomic variants to assess the genetic relationships among P. destructans isolates and populations, and to identify the potential patterns of dispersal that may exist among the locations sampled across the WNS distribution range in North America. Specifically, we compare the potential influences of landscape features with the null model that geographic distance was the main contributor to genetic distance between isolates. Here, the landscape features that we analyzed include geographical, environmental, or anthropogenic factors. We consider patterns of wind conductance as an additional abiotic factor that may influence connectivity, not as a viable mechanism for the transmission of P. destructans in N. America. The relationships of these environmental variables with genotype distributions of P. destructans strains across the landscape can also help infer the presence/absence of barriers to P. destructans spread. Furthermore, any one of these mechanisms are not mutually exclusive, and may contribute to dispersal depending on local conditions of the hibernacula and/or the surrounding climate. Identifying the dominant influences on the connectivity between bat hibernacula could help formulate targeted approaches to WNS control and prevention within different regions.The host species impacted by WNS are distributed across a diverse landscape with highly variable climatic conditions. Understanding how landscape features influence the spread of The strains analyzed in this study came from two sources: (i) those from published literature that contained genome sequences and/or microsatellite genotype information and (ii) our own strains described in an earlier study . BrieflyGeneious (4 November 2018), based on the known fragment sizes of isolates in [For microsatellite genotyping of our own strains, we chose the nine most polymorphic microsatellite loci from the panel developed by . We genolates in  as referP. destructans populations, we analyzed single nucleotide polymorphisms from 41 published genome sequences from the Short Read Archive on NCBI, plus the genomes of three newly sequenced strains [bwa mem algorithm  at a minimum calling threshold of 20. Variants within repeat-rich regions were discarded after aligning back to the reference genome using NUCmer . We added annotations and prediction of effects of SNPs using SnpEff [To further understand the potential geographic patterns of genetic variation in  strains . The thr strains  using thV0.7.15; . We sort initially genotyped by [We calculated several population genetic statistics using our microsatellite and SNP datasets. All population genetic statistics were generated using the ge poppr . Our mictyped by . For thep-values below 0.05 indicates that the null hypothesis of random recombination should be rejected. To further reveal which pairs of loci might show evidence of recombination, we also measured linkage disequilibrium (LD) between all pairs of loci.The patterns of allelic relationships among loci were measured using the index of association tests standardized by the number of loci used (FGTpartitioner [SplitsTree4 (V4.13.1) [STRUCTURE (V2.3.4) and fastSTRUCTURE (V2.7) [The proportion of pairwise loci that were phylogenetically incompatible, also known as the four-gamete test (FGT) , was dettitioner . Lastly,V4.13.1) . PopulatE (V2.7)  for micrall_comb function in ResistanceGA [commuteDistance algorithm in gdistance [MLPE.lmm [To analyze how landscape features might be associated with genetic relationships among isolates, we first generated genetic distance metrics from our SNP and microsatellite datasets using the first two principal components based on individual-level allele usage . We follstanceGA . Resistadistance . OptimizMLPE.lmm . We consworldclim.org, variables “bio1-19”, accessed 18 March 2020) into a single surface using raster principal component analysis (PCA), availible in the R package RStoolbox [R package elevatr [rWind [To test the different models, we collected rasters of climate, elevation, anthropogenic impact, and wind conductance across regions overlapping the sampling locations for the isolates that were investigated in this study. The consensus of climatic differences were determined by the combining of 19 bio-climatic variables (Stoolbox  in order elevatr . We collr [rWind . All varEEMS) software package [EEMS creates effective migration surfaces across space without incorporating environmental variables. We conducted three independent simulations of 200 demes with 1 million burn-in Markov chain Monte Carlo steps over 2 million iterations. Estimated migration surfaces were created from averaging the results of all simulation replicates using the rEEMSplots package [Lastly, we estimated effective migration rates based on genetic distances using the Estimated Effective Migration Surfaces  . All SNPp = 0.22) and SNP genotypes (p = 0.23) failed to reject the null hypothesis. These tests of index of association were also performed in a hierarchical manner, to identify patterns of linkage between pairs of loci . However, phylogenetic incompatibility was not evenly distributed, as combinations of alleles between some loci  had higher proportions of phyllogenetically compatible alleles  compared to others . Whereas, with the inclusion of European strains in these tests, p < 0.01). FGT loci suggest the presence of four breakpoint events within the N. American P. destructans population, compared to the 402 FGT breakpoints estimated from EU loci in the P. destructans genome  and genotype clusters often include isolates from multiple locations . These results suggest that the anthropogenic factors, such as differences in land use and population density, may have prominent impacts on the connectivity between bat hibernacula, and in-turn the genetic similarity between P. destructans isolates.We examined the potential influence of multiple landscape factors on the patterns of P. destructans. However, the relationship between genetic and geographic distance is relatively weak . Furthermore, models suggesting isolation via human influence on the landscape consistently had the lowest average AIC and highest average marginal P. destructans strains and resistance to human influence across the landscape .Based on the SNP dataset, the MLPE model with the highest bootstrap support was IBD (51.3%), followed by IBR—Human influence (22.7%), and IBE 21%; . These rs tested . TogetheWe estimated effective migration surfaces across the landscape for an area roughly P. destructans, including the potential landscape factors influencing strain relationships and population structure. Using microsatellite markers and whole-genome sequence data, we found substantial allelic diversity, including novel alleles not reported in prior studies with these markers [P. destructans population is inconsistent with the expectations of strict clonality. We built upon similar recent studies by including a collection of isolates from regions not previously investigated [P. destructans isolates strongly influenced by climate and human impacts on landscapes in N. America. Below we discuss the factors influencing the genetic diversity of P. destructans across the landscape.This study investigated the patterns of genetic variation in the N. American population of  markers ,52. In astigated ,11. Our P. destructans population to harbor low genotype diversity [P. destructans population . A receP. destructans in Europe and Asia. However, only one mating type (MAT1-1) has been reported in the N. American P. destructans population  is relaxed (P. destructans.The breadth of sampling for WNS may be limited, as the understanding of where particular environmental sources and sinks exist could be further developed. Much of the least populated areas are less likely to be surveyed as those near city centers. As our analyses suggest higher rates of migration that span areas with lesser human influence, where resistance to migration (and connectivity of  relaxed , more thP. destructans population structure. Both types of genotype data showed deviations from the null expectations of strict clonality in N. America. In addition, we find that the N. American P. destructans population could not strictly be explained a stepping-stone model, involving IBD scenarios of dispersal. Instead, N. American population structure of P. destructans has been influenced by anthropogenic impacts across the landscape and IBD and IBE may play secondary roles.The increasing prevalence of fungal epidemics emphasizes the importance of understanding how fungal pathogens spread and invade new ecological niches . Rapid aP. destructans prevalence in environments, and measurement of fungal loads on free-flying bats are needed to better understand the population structure of P. destructans in N. America.   More complex models incorporating seasonal changes across the landscape, including more robust estimates of