Cryptic multispecies genetic structure reflects ocean climate and is associated with response to climate change.

Individual assignment and genetic mixture analysis are commonly utilized in contemporary wildlife and fisheries management. Although microsatellite loci provide unparalleled numbers of alleles per locus, their use in assignment applications is increasingly limited. However, next‐generation sequencing, in conjunction with novel bioinformatic tools, allows large numbers of microsatellite loci to be simultaneously genotyped, presenting new opportunities for individual assignment and genetic mixture analysis. Here, we scanned the published Atlantic salmon genome to identify 706 microsatellite loci, from which we developed a final panel of 101 microsatellites distributed across the genome (average 3.4 loci per chromosome). Using samples from 35 Atlantic salmon populations (n = 1,485 individuals) from coastal Labrador, Canada, a region characterized by low levels of differentiation in this species, this panel identified 844 alleles (average of 8.4 alleles per locus). Simulation‐based evaluations of assignment and mixture identification accuracy revealed unprecedented resolution, clearly identifying 26 rivers or groups of rivers spanning 500 km of coastline. This baseline was used to examine the stock composition of 696 individuals harvested in the Labrador Atlantic salmon fishery and revealed that coastal fisheries largely targeted regional groups (<300 km). This work suggests that the development and application of large sequenced microsatellite panels presents great potential for stock resolution in Atlantic salmon and more broadly in other exploited anadromous and marine species.

BackgroundSomatic growth is a complex process that involves the action and interaction of genes and environment. A number of quantitative trait loci (QTL) previously identified for body weight and condition factor in rainbow trout (Oncorhynchus mykiss), and two other salmonid species, were used to further investigate the genetic architecture of growth-influencing genes in this species. Relationships among previously mapped candidate genes for growth and their co-localization to identified QTL regions are reported. Furthermore, using a comparative genomic analysis of syntenic rainbow trout linkage group clusters to their homologous regions within model teleost species such as zebrafish, stickleback and medaka, inferences were made regarding additional possible candidate genes underlying identified QTL regions.ResultsBody weight (BW) QTL were detected on the majority of rainbow trout linkage groups across 10 parents from 3 strains. However, only 10 linkage groups (i.e., RT-3, -6, -8, -9, -10, -12, -13, -22, -24, -27) possessed QTL regions with chromosome-wide or genome-wide effects across multiple parents. Fewer QTL for condition factor (K) were identified and only six instances of co-localization across families were detected (i.e. RT-9, -15, -16, -23, -27, -31 and RT-2/9 homeologs). Of note, both BW and K QTL co-localize on RT-9 and RT-27. The incidence of epistatic interaction across genomic regions within different female backgrounds was also examined, and although evidence for interaction effects within certain QTL regions were evident, these interactions were few in number and statistically weak. Of interest, however, was the fact that these predominantly occurred within K QTL regions. Currently mapped growth candidate genes are largely congruent with the identified QTL regions. More QTL were detected in male, compared to female parents, with the greatest number evident in an F1 male parent derived from an intercross between domesticated and wild strain of rainbow trout which differed strongly in growth rate.ConclusionsStrain background influences the degree to which QTL effects are evident for growth-related genes. The process of domestication (which primarily selects faster growing fish) may largely reduce the genetic influences on growth-specific phenotypic variation. Although heritabilities have been reported to be relatively high for both BW and K growth traits, the genetic architecture of K phenotypic variation appears less defined (i.e., fewer major contributing QTL regions were identified compared with BW QTL regions).

Domestication is rife with episodes of interbreeding between cultured and wild populations, potentially challenging adaptive variation in the wild. In Atlantic salmon, Salmo salar, the number of domesticated individuals far exceeds wild individuals, and escape events occur regularly, yet evidence of the magnitude and geographic scale of interbreeding resulting from individual escape events is lacking. We screened juvenile Atlantic salmon using 95 single nucleotide polymorphisms following a single, large aquaculture escape in the Northwest Atlantic and report the landscape-scale detection of hybrid and feral salmon (27.1%, 17/18 rivers). Hybrids were reproductively viable, and observed at higher frequency in smaller wild populations. Repeated annual sampling of this cohort revealed decreases in the presence of hybrid and feral offspring over time. These results link previous observations of escaped salmon in rivers with reports of population genetic change, and demonstrate the potential negative consequences of escapes from net-pen aquaculture on wild populations.

The ability to detect and characterize hybridization in nature has long been of interest to many fields of biology and often has direct implications for wildlife management and conservation. The capacity to identify the presence of hybridization, and quantify the numbers of individuals belonging to different hybrid classes, permits inference on the magnitude of, and timescale over which, hybridization has been or is occurring. Here, we present an r package and associated workflow developed for the detection, with estimates of efficiency and accuracy, of multigenerational hybrid individuals using genetic or genomic data in conjunction with the program newhybrids. This package includes functions for the identification and testing of diagnostic panels of markers, the simulation of multigenerational hybrids, and the quantification and visualization of the efficiency and accuracy with which hybrids can be detected. Overall, this package delivers a streamlined hybrid analysis platform, providing improvements in speed, ease of use and repeatability over current ad hoc approaches. The latest version of the package and associated documentation are available on GitHub (https://github.com/bwringe/hybriddetective).

Hybridization among populations and species is a central theme in many areas of biology, and the study of hybridization has direct applicability to testing hypotheses about evolution, speciation and genetic recombination, as well as having conservation, legal and regulatory implications. Yet, despite being a topic of considerable interest, the identification of hybrid individuals, and quantification of the (un)certainty surrounding the identifications, remains difficult. Unlike other programs that exist to identify hybrids based on genotypic information, newhybrids is able to assign individuals to specific hybrid classes (e.g. F , F ) because it makes use of patterns of gene inheritance within each locus, rather than just the proportions of gene inheritance within each individual. For each comparison and set of markers, multiple independent runs of each data set should be used to develop an estimate of the hybrid class assignment accuracy. The necessity of analysing multiple simulated data sets, constructed from large genomewide data sets, presents significant computational challenges. To address these challenges, we present parallelnewhybrid, an r package designed to decrease user burden when undertaking multiple newhybrids analyses. parallelnewhybrid does so by taking advantage of the parallel computational capabilities inherent in modern computers to efficiently and automatically execute separate newhybrids runs in parallel. We show that parallelization of analyses using this package affords users several-fold reductions in time over a traditional serial analysis. parallelnewhybrid consists of an example data set, a readme and three operating system-specific functions to execute parallel newhybrids analyses on each of a computer's c cores. parallelnewhybrid is freely available on the long-term software hosting site github (www.github.com/bwringe/parallelnewhybrid).

Invasive species have been associated with significant negative impacts in their introduced range often outcompeting native species, yet the long-term evolutionary dynamics of biological invasions are not well understood. Hybridization, either among waves of invasion or between native and introduced populations, could alter the ecological and evolutionary impacts of invasions yet has rarely been studied in marine invasive species. The European green crab (Carcinus maenas) invaded eastern North America twice from northern and southern locations in its native range. Here we examine the frequency of hybridization among these two distinct invasions at locations from New Jersey, USA to Newfoundland, Canada using restriction-site-associated DNA sequencing (RAD-seq), microsatellite loci and cytochrome c oxidase subunit I mitochondrial DNA (mtDNA) sequences. We used Bayesian clustering and hybrid assignment analyses to investigate hybridization between the northern and southern populations. Of the samples analyzed, six locations contained at least one hybrid individual, while two locations were characterized by extensive hybridization, with 95% of individuals collected from Placentia Bay, Newfoundland being hybrids (mostly F) and 90% of individuals from Kejimkujik, Nova Scotia being classified as hybrids, mostly backcrosses to the northern ecotype. The presence of both F hybrids and backcrossed individuals suggests that these hybrids are viable and introgression is occurring between invasions. Our results provide insight into the demographic and evolutionary consequences of hybridization between independent invasions, and will inform the management of green crabs in eastern North America.

Advances in genetic sequencing technologies and techniques have made large, genome-wide data sets comprised of hundreds or even thousands of individuals and loci the norm rather than the exception even for nonmodel organisms. While such data present new opportunities for evaluating population structure and demographic processes, the large size of these genomic data sets brings new computational challenges for researchers needing to parse, convert and manipulate data often into a variety of software-specific formats required of genomic analyses. We developed genepopedit as a flexible tool for the manipulation of multilocus molecular data sets. Functionality can be divided among diagnostic-, manipulation-, sampling-, simulation-, and transformation-based tools. Metadata from large genomic data sets can be efficiently extracted, without the need to view data in a text-editing program. genepopedit provides tools to manipulate loci, individual samples and populations included in genomic data sets, in addition to the ability to convert directly to a variety of software formats. Functions are compiled as an R package, which can integrate into existing analysis workflows. Importantly, genepopedit provides a simple yet robust code-based tool for repeatable genomic data manipulation, which has been proven to be stable for data sets in excess of 200 000 SNPs. The latest version of the package and associated documentation are available on Github (github.com/rystanley/genepopedit).