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Low‐coverage whole‐genome sequencing (WGS) is increasingly used for the study of evolution and ecology in both model and non‐model organisms; however, effective application of low‐coverage WGS data requires the implementation of probabilistic frameworks to account for the uncertainties in genotype likelihoods. Here, we present a probabilistic framework for using genotype likelihoods for standard population assignment applications. Additionally, we derive the Fisher information for allele frequency from genotype likelihoods and use that to describe a novel metric, the effective sample size, which figures heavily in assignment accuracy. We make these developments available for application through WGSassign, an open‐source software package that is computationally efficient for working with whole‐genome data. Using simulated and empirical data sets, we demonstrate the behaviour of our assignment method across a range of population structures, sample sizes and read depths. Through these results, we show that WGSassign can provide highly accurate assignment, even for samples with low average read depths (<0.01X) and among weakly differentiated populations. Our simulation results highlight the importance of equalizing the effective sample sizes among source populations in order to achieve accurate population assignment with low‐coverage WGS data. We further provide study design recommendations for population assignment studies and discuss the broad utility of effective sample size for studies using low‐coverage WGS data.

Population genetic patterns and changes in allele frequency within blow flies can provide valuable insight into population structure, diversification, dispersal, gene flow, and population assignments. The population genetic structure of the oriental latrine blow fly, Chrysomya megacephala (Fabricius), was investigated using amplified fragment length polymorphisms (AFLP). The goal was to validate the use of this technique to create genetic profile data and determine it is feasibility for inferring postmortem relocation of corpses. The AFLP technique generated 590 polymorphic loci for C. megacephala . Analysis of molecular variation (AMOVA) found significantly high genetic variation within individuals of all fly populations, with little variation among populations from different geographic locations. STRUCTURE and principal coordinate analyses (PCoA) revealed no population structure based on geography, with weak correlation between genetic and geographic distances, and moderate temporal differentiation was noted among C. megacephala samples. Across the entire data set, the mean relative relatedness coefficients were positive, suggesting that flies arriving at the same bait (carcass) share nonrandom proportions of alleles and comprise of closely related individuals. Genetic assignment of C. megacephala flies to a putative source population resulted in a 90.81% success rate, indicating the possibility of using these flies to connect sites between which a corpse had been moved even in the absence of overall geographic population structure.

Long-distance dispersal (LDD) is consequential to metapopulation ecology and evolution. In systems where dispersal is undertaken by small propagules, such as larvae in the ocean, documenting LDD is especially challenging. Genetic parentage analysis has gained traction as a method for measuring larval dispersal, but such studies are generally spatially limited, leaving LDD understudied in marine species. We addressed this knowledge gap by uncovering LDD with population assignment tests in the coral reef fish Elacatinus lori, a species whose short-distance dispersal has been well-characterized by parentage analysis. When adults (n = 931) collected throughout the species’ range were categorized into three source populations, assignment accuracy exceeded 99%, demonstrating low rates of gene flow between populations in the adult generation. After establishing high assignment confidence, we assigned settlers (n = 3,828) to source populations. Within the settler cohort, <0.1% of individuals were identified as long-distance dispersers from other populations. These results demonstrate an exceptionally low level of connectivity between E. lori populations, despite the potential for ocean currents to facilitate LDD. More broadly, these findings illustrate the value of combining genetic parentage analysis and population assignment tests to uncover shortand long-distance dispersal, respectively.

The spongy moth, Lymantria dispar, is an irruptive forest pest native to Eurasia where its range extends from coast to coast and overspills into northern Africa. Accidentally introduced from Europe in Massachusetts in 1868–1869, it is now established in North America where it is considered a highly destructive invasive pest. A fine‐scale characterization of its population genetic structure would facilitate identification of source populations for specimens intercepted during ship inspections in North America and would enable mapping of introduction pathways to help prevent future incursions into novel environments. In addition, detailed knowledge of L. dispar's global population structure would provide new insight into the adequacy of its current subspecies classification system and its phylogeographic history. To address these issues, we generated >2000 genotyping‐by‐sequencing‐derived SNPs from 1445 contemporary specimens sampled at 65 locations in 25 countries/3 continents. Using multiple analytical approaches, we identified eight subpopulations that could be further partitioned into 28 groups, achieving unprecedented resolution for this species' population structure. Although reconciliation between these groupings and the three currently recognized subspecies proved to be challenging, our genetic data confirmed circumscription of the japonica subspecies to Japan. However, the genetic cline observed across continental Eurasia, from L. dispar asiatica in East Asia to L. d. dispar in Western Europe, points to the absence of a sharp geographical boundary (e.g., the Ural Mountains) between these two subspecies, as suggested earlier. Importantly, moths from North America and the Caucasus/Middle East displayed high enough genetic distances from other populations to warrant their consideration as separate subspecies of L. dispar. Finally, in contrast with earlier mtDNA‐based investigations that identified the Caucasus as L. dispar's place of origin, our analyses suggest continental East Asia as its evolutionary cradle, from where it spread to Central Asia and Europe, and to Japan through Korea.

Abstract Human‐assisted movement has allowed the Asian longhorned beetle (ALB, Anoplophora glabripennis (Motschulsky)) to spread beyond its native range and become a globally regulated invasive pest. Within its native range of China and the Korean peninsula, human‐mediated dispersal has also caused cryptic translocation of insects, resulting in population structure complexity. Previous studies used genetic methods to detangle this complexity but were unable to clearly delimit native populations which is needed to develop downstream biosurveillance tools. We used genome‐wide markers to define historical population structure in native ALB populations and contemporary movement between regions. We used genotyping‐by‐sequencing to generate 6102 single‐nucleotide polymorphisms (SNPs) and amplicon sequencing to genotype 53 microsatellites. In total, we genotyped 712 individuals from ALB’s native distribution. We observed six distinct population clusters among native ALB populations, with a clear delineation between northern and southern groups. Most of the individuals from South Korea were distinct from populations in China. Our results also indicate historical divergence among populations and suggest limited large‐scale admixture, but we did identify a restricted number of cases of contemporary movement between regions. We identified SNPs under selection and describe a clinal allele frequency pattern in a missense variant associated with glycerol kinase, an important enzyme in the utilization of an insect cryoprotectant. We further demonstrate that small numbers of SNPs can assign individuals to geographic regions with high probability, paving the way for novel ALB biosurveillance tools.

Genetic methods have become an essential component of ecological investigation and conservation planning for fish and wildlife. Among these methods is the use of genetic marker data to identify individuals to populations, or stocks, of origin. More recently, methods that involve genetic pedigree reconstruction to identify relationships between individuals within populations have also become common. We present here a novel set of multiallelic microhaplotype genetic markers for Chinook salmon, which provide excellent resolution for population discrimination and relationship identification from a rapidly and economically assayed panel of markers. We show how this set of genetic markers assayed by sequencing 204 amplicons, in tandem with a reference dataset of 1636 individual samples from 17 populations, provides definitive power to identify all known lineages of Chinook salmon in California. The inclusion of genetic loci that have known associations with phenotype and that were identified as outliers in examination of whole‐genome sequence data allows resolution of stocks that are not highly genetically differentiated but are phenotypically distinct and managed as such. This same set of multiallelic genetic markers has ample variation to accurately identify parent‐offspring and full‐sibling pairs in all California populations, including the genetically depauperate winter‐run lineage. Validation of this marker panel in coastal salmon populations not previously studied with modern genetic methods also reveals novel biological insights, including the presence of a single copy of a haplotype for a phenotype that has not been documented in that part of the species range, and a clear signal of mixed ancestry for a salmon population that is on the geographic margins of the primary evolutionary lineages present in California.

Background The advent and democratization of next generation sequencing and genotyping technologies lead to a huge amount of data for the characterization of population genetic diversity in model and non model-species. However, efficient storage, management, cross-analyzing and exploration of such dense genotyping datasets remain challenging. This is particularly true for the bovine species where many SNP datasets have been generated in various cattle populations with different genotyping tools. Description We developed WIDDE, a Web-Interfaced Next Generation Database that stands as a generic tool applicable to a wide range of species and marker types ( http://widde.toulouse.inra.fr ). As a first illustration, we hereby describe its first version dedicated to cattle biodiversity, which includes a large and evolving cattle genotyping dataset for over 750,000 SNPs available on 129 (89 public) different cattle populations representative of the world-wide bovine genetic diversity and on 7 outgroup bovid species. This version proposes an optional marker and individual filtering step, an export of genotyping data in different popular formats, and an exploration of genetic diversity through a principal component analysis. Users can also explore their own genotyping data together with data from WIDDE, assign their samples to WIDDE populations based on distance assignment method and supervised clustering, and estimate their ancestry composition relative to the populations represented in the database. Conclusion The cattle version of WIDDE represents to our knowledge the first database dedicated to cattle biodiversity and SNP genotyping data that will be very useful for researchers interested in this field. As a generic tool applicable to a wide range of marker types, WIDDE is overall intended to the genetic diversity exploration of any species and will be extended to other species shortly. The structure makes it easy to include additional output formats and new tools dedicated to genetic diversity exploration.

The leatherback turtle (Dermochelys coriacea) undertakes extensive migrations between nesting and foraging areas, where it is exposed to threats such as fisheries bycatch, coastal development, and pollution. Although classified globally as Vulnerable by the IUCN, the Southwest Atlantic subpopulation is considered Critically Endangered, with nesting restricted to Brazil. While satellite telemetry and previous mitochondrial DNA (mtDNA) Genetic Mixed Stock Analysis (MSA) studies have indicated that leatherbacks from West African rookeries migrate to foraging grounds off Argentina and Uruguay, the potential for connectivity with rookeries from other regions remains an open question. Genetic Stock Identification (GSI) using 15 nuclear DNA (nDNA) microsatellite markers was conducted on 78 stranded or incidentally caught leatherbacks from feeding grounds off the coasts of Argentina and Uruguay. Assignment analysis results demonstrated that 92% of the foraging leatherbacks originated from Ghana and Gabon in the Southeast Atlantic (SEA), with lesser contributions from the rookeries in the Northwest Atlantic (NEA; 6%) and the Southwest Indian Ocean (SWI; 1%) rookery in South Africa, all with assignment probabilities (AP) exceeding 95%. Our findings corroborate and extend previous mtDNA studies by enhancing the precision of GSI for individuals possessing common haplotypes and by clarifying the unknown origin of individuals with ‘orphan’ mtDNA haplotypes, such as Dc7.1, which were assigned to the SEA rookeries (AP = 99%). Furthermore, we directly assigned one individual, previously of uncertain mtDNA origin (Dc9.1), to the South Africa rookery (AP = 97%), highlighting the need to consider the extension of the SWI Regional Management Unit (RMU) boundaries to Southwest Atlantic waters in future assessments. The absence of detected connectivity with Brazilian nesting populations underscores the necessity for increased sample sizes and the application of advanced molecular markers. These results advance the understanding of population connectivity across oceanic scales and emphasize the crucial role of international collaboration in conservation endeavors. Genetic Stock Identification (GSI) of 78 stranded or incidentally caught leatherbacks from feeding grounds off the coasts of Argentina and Uruguay was genotyped with 15 microsatellite markers. Assignment analysis results indicated that 92% of leatherbacks in this region originated from Ghana and Gabon, with smaller contributions from the Northwest Atlantic (6%) and Southwest Indian Ocean (1%), all with assignment probabilities > 95%. By improving knowledge of source populations and their links to foraging areas, this study contributes informative data to future refinement of RMU boundaries and establishes a baseline for monitoring fisheries bycatch and other anthropogenic threats, contributing to the protection of critically endangered leatherback populations.

Sustainable fisheries management is important for the continued harvest of the world's marine resources, especially as they are increasingly challenged by a range of climatic and anthropogenic factors. One of the pillars of sustainable fisheries management is the accurate identification of the biological units, i.e., populations. Here, we developed and implemented a genetic baseline for Atlantic herring harvested in the Norwegian offshore fisheries to investigate the validity of the current management boundaries. This was achieved by genotyping > 15,000 herring from the northern European seas, including samples of all the known populations in the region, with a panel of population-informative SNPs mined from existing genomic resources. The final genetic baseline consisted of ~1000 herring from 12 genetically distinct populations. We thereafter used the baseline to investigate mixed catches from the North and Norwegian Seas, revealing that each management area consisted of multiple populations, as previously suspected. However, substantial numbers (up to 50% or more within a sample) of herring were found outside of their expected management areas, e.g., North Sea autumn-spawning herring north of 62° N (average = 19.2%), Norwegian spring-spawning herring south of 62° N (average = 13.5%), and western Baltic spring-spawning herring outside their assumed distribution area in the North Sea (average = 20.0%). Based upon these extensive observations, we conclude that the assessment and management areas currently in place for herring in this region need adjustments to reflect the populations present. Furthermore, we suggest that for migratory species, such as herring, a paradigm shift from using static geographic stock boundaries towards spatial dynamic boundaries is needed to meet the requirements of future sustainable management regimes.

The use of high‐throughput, low‐density sequencing approaches has dramatically increased in recent years in studies of eco‐evolutionary processes in wild populations and domestication in commercial aquaculture. Most of these studies focus on identifying panels of SNP loci for a single downstream application, whereas there have been few studies examining the trade‐offs for selecting panels of markers for use in multiple applications. Here, we detail the use of a bioinformatic workflow for the development of a dual‐purpose SNP panel for parentage and population assignment, which included identifying putative SNP loci, filtering for the most informative loci for the two tasks, designing effective multiplex PCR primers, optimizing the SNP panel for performance, and performing quality control steps for downstream applications. We applied this workflow to two adjacent Alaskan Sockeye Salmon populations and identified a GTseq panel of 142 SNP loci for parentage and 35 SNP loci for population assignment. Only 50–75 panel loci were necessary for >95% accurate parentage, whereas population assignment success, with all 172 panel loci, ranged from 93.9% to 96.2%. Finally, we discuss the trade‐offs and complexities of the decision‐making process that drives SNP panel development, optimization, and testing. The use of high‐throughput, low‐density sequencing approaches has dramatically increased in recent years in studies of eco‐evolutionary processes in wild populations and domestication in commercial aquaculture. Here, we detail the use of a bioinformatic workflow for the development of a dual‐purpose SNP panel for parentage and population assignment in Alaskan Sockeye Salmon. We discuss the trade‐offs and complexities of the decision‐making process that drives SNP panel development, optimization, and testing.