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 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.
Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher.
Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?
Some links on this page may take you to non-federal websites. Their policies may differ from this site.
-
more » « less
Abstract 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. -
more » « less
Abstract Identifying genetic conservation units (CUs) in threatened species is critical for the preservation of adaptive capacity and evolutionary potential in the face of climate change. However, delineating CUs in highly mobile species remains a challenge due to high rates of gene flow and genetic signatures of isolation by distance. Even when CUs are delineated in highly mobile species, the CUs often lack key biological information about what populations have the most conservation need to guide management decisions. Here we implement a framework for CU identification in the Canada Warbler (
Cardellina canadensis ), a migratory bird species of conservation concern, and then integrate demographic modelling and genomic offset to guide conservation decisions. We find that patterns of whole genome genetic variation in this highly mobile species are primarily driven by putative adaptive variation. Identification of CUs across the breeding range revealed that Canada Warblers fall into two evolutionarily significant units (ESU), and three putative adaptive units (AUs) in the South, East, and Northwest. Quantification of genomic offset, a metric of genetic changes necessary to maintain current gene–environment relationships, revealed significant spatial variation in climate vulnerability, with the Northwestern AU being identified as the most vulnerable to future climate change. Alternatively, quantification of past population trends within each AU revealed the steepest population declines have occurred within the Eastern AU. Overall, we illustrate that genomics‐informed CUs provide a strong foundation for identifying current and future regional threats that can be used to inform management strategies for a highly mobile species in a rapidly changing world. -
more » « less
Abstract Understanding the geographic linkages among populations across the annual cycle is an essential component for understanding the ecology and evolution of migratory species and for facilitating their effective conservation. While genetic markers have been widely applied to describe migratory connections, the rapid development of new sequencing methods, such as low‐coverage whole genome sequencing (lcWGS), provides new opportunities for improved estimates of migratory connectivity. Here, we use lcWGS to identify fine‐scale population structure in a widespread songbird, the American Redstart (
Setophaga ruticilla ), and accurately assign individuals to genetically distinct breeding populations. Assignment of individuals from the nonbreeding range reveals population‐specific patterns of varying migratory connectivity. By combining migratory connectivity results with demographic analysis of population abundance and trends, we consider full annual cycle conservation strategies for preserving numbers of individuals and genetic diversity. Notably, we highlight the importance of the Northern Temperate‐Greater Antilles migratory population as containing the largest proportion of individuals in the species. Finally, we highlight valuable considerations for other population assignment studies aimed at using lcWGS. Our results have broad implications for improving our understanding of the ecology and evolution of migratory species through conservation genomics approaches.
