Genomic Underpinnings of Population Persistence in Isle Royale Moose

Island ecosystems provide natural laboratories to assess the impacts of isolation on population persistence. However, most studies of persistence have focused on a single species, without comparisons to other organisms they interact with in the ecosystem. The case study of moose and gray wolves on Isle Royale allows for a direct contrast of genetic variation in isolated populations that have experienced dramatically differing population trajectories over the past decade. Whereas the Isle Royale wolf population recently declined nearly to extinction due to severe inbreeding depression, the moose population has thrived and continues to persist, despite having low genetic diversity and being isolated for ∼120 years. Here, we examine the patterns of genomic variation underlying the continued persistence of the Isle Royale moose population. We document high levels of inbreeding in the population, roughly as high as the wolf population at the time of its decline. However, inbreeding in the moose population manifests in the form of intermediate-length runs of homozygosity suggestive of historical inbreeding and purging, contrasting with the long runs of homozygosity observed in the smaller wolf population. Using simulations, we confirm that substantial purging has likely occurred in the moose population. However, we also document notable increases in genetic load, which could eventually threaten population viability over the long term. Overall, our results demonstrate a complex relationship between inbreeding, genetic diversity, and population viability that highlights the use of genomic datasets and computational simulation tools for understanding the factors enabling persistence in isolated populations

A community-maintained standard library of population genetic models

The explosion in population genomic data demands ever more complex modes of analysis, and increasingly, these analyses depend on sophisticated simulations. Recent advances in population genetic simulation have made it possible to simulate large and complex models, but specifying such models for a particular simulation engine remains a difficult and error-prone task. Computational genetics researchers currently re-implement simulation models independently, leading to inconsistency and duplication of effort. This situation presents a major barrier to empirical researchers seeking to use simulations for power analyses of upcoming studies or sanity checks on existing genomic data. Population genetics, as a field, also lacks standard benchmarks by which new tools for inference might be measured. Here, we describe a new resource, stdpopsim, that attempts to rectify this situation. Stdpopsim is a community-driven open source project, which provides easy access to a growing catalog of published simulation models from a range of organisms and supports multiple simulation engine backends. This resource is available as a well-documented python library with a simple command-line interface. We share some examples demonstrating how stdpopsim can be used to systematically compare demographic inference methods, and we encourage a broader community of developers to contribute to this growing resource.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Understanding the impact of deleterious genetic variation on extinction risk in small populations

Deleterious genetic variation is abundant in wild populations and can contribute to extinction when populations become small and isolated. For example, elevated levels of inbreeding in small populations can expose recessive deleterious mutations as homozygous and depress population fitness. Additionally, increased genetic drift in small populations can result in relaxed selection against weakly deleterious mutations, leading to an accumulation of such mutations that can also contribute to fitness declines. Genomic sequencing tools have enabled a proliferation of studies on the threat of deleterious genetic variation in small populations of conservation concern. However, how to best leverage such data to predict extinction risk in these populations remains unclear. My dissertation aims to provide clarity to this issue by leveraging computational genetic simulations in concert with genomic data to better understand the threat that deleterious genetic variation poses to extinction risk. In my first chapter, I used eco-evolutionary simulations to explore the effects of deleterious genetic variation on extinction risk under a variety of demographic scenarios. These results implicate recessive strongly deleterious mutations as the key drivers of extinction in small populations, as the exposure of such mutations via inbreeding can lead to extinction much faster than the more gradual impacts of weakly deleterious variation. In my second chapter, I applied a similar simulation framework to explore the threat of deleterious genetic variation to extinction risk in the critically endangered vaquita porpoise. My results suggest that the species is genetically well-equipped to recover from a severe bottleneck due to its small historical population size, which implies a low load of recessive strongly deleterious variation that can contribute to future inbreeding depression. In my third chapter, I examined the genomic factors enabling persistence in an isolated population of moose on Isle Royale. My results suggest a role for ‘purging’ of recessive deleterious mutations during a severe founder event for the population as a key factor resulting in the continued health of the population. Finally, in my fourth chapter, I reviewed simulation-based approaches for quantifying genetic load and predicting extinction risk. Here, I aim to encourage other researchers to also employ simulations in studies of deleterious variation in small populations, providing an overview of the components of a simulation of deleterious genetic variation and the relevant model parameters. Altogether, this dissertation provides novel perspectives and approaches for understanding the risks of extinction due to deleterious genetic variation in wild populations.

Deleterious Variation in Natural Populations and Implications for Conservation Genetics.

Deleterious mutations decrease reproductive fitness and are ubiquitous in genomes. Given that many organisms face ongoing threats of extinction, there is interest in elucidating the impact of deleterious variation on extinction risk and optimizing management strategies accounting for such mutations. Quantifying deleterious variation and understanding the effects of population history on deleterious variation are complex endeavors because we do not know the strength of selection acting on each mutation. Further, the effect of demographic history on deleterious mutations depends on the strength of selection against the mutation and the degree of dominance. Here we clarify how deleterious variation can be quantified and studied in natural populations. We then discuss how different demographic factors, such as small population size, nonequilibrium population size changes, inbreeding, and gene flow, affect deleterious variation. Lastly, we provide guidance on studying deleterious variation in nonmodel populations of conservation concern

The critically endangered vaquita is not doomed to extinction by inbreeding depression.

In cases of severe wildlife population decline, a key question is whether recovery efforts will be impeded by genetic factors, such as inbreeding depression. Decades of excess mortality from gillnet fishing have driven Mexico's vaquita porpoise (Phocoena sinus) to ~10 remaining individuals. We analyzed whole-genome sequences from 20 vaquitas and integrated genomic and demographic information into stochastic, individual-based simulations to quantify the species' recovery potential. Our analysis suggests that the vaquita's historical rarity has resulted in a low burden of segregating deleterious variation, reducing the risk of inbreeding depression. Similarly, genome-informed simulations suggest that the vaquita can recover if bycatch mortality is immediately halted. This study provides hope for vaquitas and other naturally rare endangered species and highlights the utility of genomics in predicting extinction risk

The critically endangered vaquita is not doomed to extinction by inbreeding depression

International audienceIn cases of severe wildlife population decline, a key question is whether recovery efforts will be impeded by genetic factors, such as inbreeding depression. Decades of excess mortality from gillnet fishing have driven Mexico’s vaquita porpoise ( Phocoena sinus ) to ~10 remaining individuals. We analyzed whole-genome sequences from 20 vaquitas and integrated genomic and demographic information into stochastic, individual-based simulations to quantify the species’ recovery potential. Our analysis suggests that the vaquita’s historical rarity has resulted in a low burden of segregating deleterious variation, reducing the risk of inbreeding depression. Similarly, genome-informed simulations suggest that the vaquita can recover if bycatch mortality is immediately halted. This study provides hope for vaquitas and other naturally rare endangered species and highlights the utility of genomics in predicting extinction risk