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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. Expected final online publication date for the

More species than ever before are at risk of extinction due to anthropogenic habitat loss and climate change. But even species that are not threatened have seen reductions in their populations and geographic ranges, likely impacting their genetic diversity. Although preserving genetic diversity is key to maintaining adaptability of species, we lack predictive tools and global estimates of genetic diversity loss across ecosystems. By bridging theories of biodiversity and population genetics, we introduce a mathematical framework to understand the loss of naturally occurring DNA mutations within decreasing habitat within a species. Analysing genome-wide variation data of 10,095 geo-referenced individuals from 20 plant and animal species, we show that genome-wide diversity follows a power law with geographic area (the mutations-area relationship), which can predict genetic diversity loss in spatial computer simulations of local population extinctions. Given pre-21st century values of ecosystem transformations, we estimate that over 10% of genetic diversity may already be lost, surpassing the United Nations targets for genetic preservation. These estimated losses could rapidly accelerate with advancing climate change and habitat destruction, highlighting the need for forecasting tools that facilitate implementation of policies to protect genetic resources globally.

The genetic consequences of species-wide declines are rarely quantified because the timing and extent of the decline varies across the species' range. The sea otter (Enhydra lutris) is a unique model in this regard. Their dramatic decline from thousands to fewer than 100 individuals per population occurred range-wide and nearly simultaneously due to the 18th-19th century fur trade. Consequently, each sea otter population represents an independent natural experiment of recovery after extreme population decline. We designed sequence capture probes for 50 Mb of sea otter exonic and neutral genomic regions. We sequenced 107 sea otters from five populations that span the species range to high coverage (18-76×) and three historical Californian samples from ~1500 and ~200 years ago to low coverage (1.5-3.5×). We observe distinct population structure and find that sea otters in California are the last survivors of a divergent lineage isolated for thousands of years and therefore warrant special conservation concern. We detect signals of extreme population decline in every surviving sea otter population and use this demographic history to design forward-in-time simulations of coding sequence. Our simulations indicate that this decline could lower the fitness of recovering populations for generations. However, the simulations also demonstrate how historically low effective population sizes prior to the fur trade may have mitigated the effects of population decline on genetic health. Our comprehensive approach shows how demographic inference from genomic data, coupled with simulations, allows assessment of extinction risk and different models of recovery.

Island ecosystems provide models 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 simple predator-prey system of moose and gray wolves on Isle Royale provides allows a direct contrast of genetic variation in a prey species with their natural predator. Wolves on Isle Royale exhibited signs of severe inbreeding depression, which nearly drove the population to extinction in 2019. In the relative absence of wolves, the moose population has thrived and exhibits no obvious signs of inbreeding depression despite being isolated for ∼120 years and having low genetic diversity. Here, we examine the genomic underpinnings of population persistence in 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 indicative of gradual inbreeding, contrasting with the severe recent inbreeding observed in the wolf population. Using simulations, we demonstrate that this more gradual inbreeding in the moose population has resulted in an estimated 50% purging of the inbreeding load, helping to explain the continued persistence of the population. However, we also document notable increases in genetic load, which could eventually threaten population viability over the long term. Finally, we document low diversity in mainland North American moose populations due to a severe founder event occurring near the end of the Holocene. Overall, our results demonstrate a complex relationship between inbreeding, genetic diversity, and population viability that highlights the importance of maintaining isolated populations at moderate size to avert extinction from genetic factors.Significance statementIsolated wildlife populations face a high risk of extinction due in part to the deleterious consequences of inbreeding. Whether purifying natural selection can overcome these negative impacts by “purging” harmful recessive mutations is a topic of active debate. We characterized the extent of purging in an isolated moose population. Our results demonstrate signatures of gradual inbreeding in the population, ideal circumstances to facilitate purging. Using simulations, we demonstrate substantial potential for purging in the population, though we also show that fitness is reduced by small population size and inbreeding. Our findings provide insight into the mechanisms enabling persistence in isolated populations, with implications for conserving the growing number of isolated populations worldwide.

Small and isolated wildlife populations face numerous threats to extinction, among which is the deterioration of fitness due to an accumulation of deleterious genetic variation. Genomic tools are increasingly used to quantify the impacts of deleterious variation in small populations; however, these approaches remain limited by an inability to accurately predict the selective and dominance effects of individual mutations. Computational simulations of deleterious genetic variation offer an alternative and complementary tool that can help overcome these limitations, though such approaches have yet to be widely employed. In this Perspective, we aim to encourage conservation genomics researchers to adopt greater use of computational simulations to aid in quantifying and predicting the threat that deleterious genetic variation poses to extinction. We first provide an overview of the components of a simulation of deleterious genetic variation, describing the key parameters involved in such models. Next, we clarify several misconceptions about an essential simulation parameter, the distribution of fitness effects (DFE) of new mutations, and review recent debates over what the most appropriate DFE parameters are. We conclude by comparing modern simulation tools to those that have long been employed in population viability analysis, weighing the pros and cons of a ‘genomics-informed’ simulation approach, and discussing key areas for future research. Our aim is that this Perspective will facilitate broader use of computational simulations in conservation genomics, enabling a deeper understanding of the threat that deleterious genetic variation poses to biodiversity.

Human-driven habitat fragmentation and loss have led to a proliferation of small and isolated plant and animal populations with high risk of extinction. One of the main threats to extinction in these populations is inbreeding depression, which is primarily caused by recessive deleterious mutations becoming homozygous due to inbreeding. The typical approach for managing these populations is to maintain high genetic diversity, increasingly by translocating individuals from large populations to initiate a “genetic rescue.” However, the limitations of this approach have recently been highlighted by the demise of the gray wolf population on Isle Royale, which declined to the brink of extinction soon after the arrival of a migrant from the large mainland wolf population. Here, we use a novel population genetic simulation framework to investigate the role of genetic diversity, deleterious variation, and demographic history in mediating extinction risk due to inbreeding depression in small populations. We show that, under realistic models of dominance, large populations harbor high levels of recessive strongly deleterious variation due to these mutations being hidden from selection in the heterozygous state. As a result, when large populations contract, they experience a substantially elevated risk of extinction after these strongly deleterious mutations are exposed by inbreeding. Moreover, we demonstrate that, although genetic rescue is broadly effective as a means to reduce extinction risk, its effectiveness can be greatly increased by drawing migrants from small or moderate-sized source populations rather than large source populations due to smaller populations harboring lower levels of recessive strongly deleterious variation. Our findings challenge the traditional conservation paradigm that focuses on maximizing genetic diversity in small populations in favor of a view that emphasizes minimizing strongly deleterious variation. These insights have important implications for managing small and isolated populations in the increasingly fragmented landscape of the Anthropocene.

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 presence and impact of recessive lethal mutations has been widely documented in diploid outcrossing species. However, precise estimates in different species of the proportion of mutations that are recessive lethal remain limited. Here, we attempt to quantify the fraction of new mutations that are recessive lethal using Fit∂a∂i, a commonly-used method for inferring the distribution of fitness effects (DFE) using the site frequency spectrum. Using simulations, we demonstrate that Fit∂a∂i cannot accurately estimate the fraction of recessive lethal mutations, as expected given that Fit∂a∂i assumes that all mutations are additive by default. Consistent with the idea that mis-specification of the dominance model can explain this performance, we find that Fit∂a∂i can accurately infer the fraction of additive lethal mutations. Moreover, we demonstrate that in both additive and recessive cases, inference of the deleterious non-lethal portion of the DFE is minimally impacted by a small proportion (Drosophila melanogaster. In both species, we find that the segregating recessive lethal load can be explained by a very small fraction (

More species than ever before are at risk of extinction due to anthropogenic habitat loss and climate change. But even species that are not threatened have seen reductions in their populations and geographic ranges, likely impacting their genetic diversity. Although preserving genetic diversity is key to maintaining adaptability of species, we lack predictive tools and global estimates of genetic diversity loss across ecosystems. By bridging theories of biodiversity and population genetics, we introduce a mathematical framework to understand the loss of naturally occurring DNA mutations within decreasing habitat within a species. Analysing genome-wide variation data of 10,095 geo-referenced individuals from 20 plant and animal species, we show that genome-wide diversity follows a power law with geographic area (the mutations-area relationship), which can predict genetic diversity loss in spatial computer simulations of local population extinctions. Given pre-21st century values of ecosystem transformations, we estimate that over 10% of genetic diversity may already be lost, surpassing the United Nations targets for genetic preservation. These estimated losses could rapidly accelerate with advancing climate change and habitat destruction, highlighting the need for forecasting tools that facilitate implementation of policies to protect genetic resources globally.

Molecular phylogenetic approaches have greatly improved our knowledge of the pattern and process of biological diversification across the globe; however, many regions remain poorly documented, even for well-studied vertebrate taxa. The Philippine archipelago, one of the least-studied ‘biodiversity hotspots’, is an ideal natural laboratory for investigating the factors driving diversification in an insular and geologically dynamic setting. We investigated the history and geography of diversification of the Philippine populations of a widespread montane bird, the White-browed Shortwing (Brachypteryx montana). Leveraging dense archipelago-wide sampling, we generated a multi-locus genetic dataset (one nuclear and two mtDNA markers), which we analyzed using phylogenetic, population genetic, and coalescent-based methods. Our results demonstrate that Philippine shortwings (1) likely colonized the Philippines from the Sunda Shelf to Mindanao in the late Miocene or Pliocene, (2) diversified across inter-island barriers into three divergent lineages during the Pliocene and early Pleistocene, (3) have not diversified within the largest island, Luzon, contrary to patterns observed in other montane taxa, and (4) colonized Palawan from the oceanic Philippines rather than from Borneo, challenging the assumption of Palawan functioning exclusively as a biogeographic extension of the Sunda Shelf. Additionally, our finding that divergent (c. 4.0 mya) lineages are coexisting in secondary sympatry on Mindanao without apparent gene flow suggests that the speciation process is likely complete for these shortwing lineages. Overall, these investigations provide insight into how topography and island boundaries influence diversification within remote oceanic archipelagos and echo the results of many other studies in demonstrating that taxonomic diversity continues to be underestimated in the Philippines.

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.Stdpopsimis 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 howstdpopsimcan be used to systematically compare demographic inference methods, and we encourage a broader community of developers to contribute to this growing resource.

Despite its status as a global biodiversity hotspot there is still much to be discovered about the birds of Madagascar, including a full accounting of species-level diversity and the avifauna’s origins. The Bernieridae is a Malagasy endemic family that went unrecognized by science for decades and unnamed until 2010. This cryptic family has long represented a missing piece of the puzzle of the avian tree of life. We present the first comprehensive phylogeny of Bernieridae in order to examine its diversification history on Madagascar and its place within Passeriformes. In light of recent discoveries of cryptic species-level diversity in Madagascar’s vertebrate fauna, we used broad geographic sampling and integrative taxonomic methods to investigate the potential for cryptic lineages within every known species of the Bernieridae. Our approach combines phylogenomics using ∼4500 loci of ultraconserved elements (UCEs), genetic clustering of thousands of single nucleotide polymorphisms (SNPs), and statistical analysis of morphological variation. These methods yielded the discovery of two unrecognized species in the previously monotypic genus Bernieria, along with new insights into patterns of fine-scale endemism in Madagascar’s humid forests. Our phylogenomic analyses provide conclusive support for Donacobiidae and Bernieridae as sister families, a biogeographically intriguing result given that the former is restricted to the Neotropics. We found a significant decline in the rate of speciation over time on Madagascar, consistent with a model of adaptive radiation. Bernieridae therefore joins the Vangidae as a second avian adaptive radiation on the island of Madagascar. These insights into the evolution of Bernieridae represent a step forward in understanding the origins and diversity of Madagascar’s endemic avifauna.

Human-driven habitat fragmentation and loss have led to a proliferation of small and isolated plant and animal populations with high risk of extinction. One of the main threats to extinction in these populations is inbreeding depression, which is primarily caused by the exposure of recessive deleterious mutations as homozygous by inbreeding. The typical approach for managing these populations is to maintain high genetic diversity, often by translocating individuals from large populations to initiate a ‘genetic rescue.’ However, the limitations of this approach have recently been highlighted by the demise of the gray wolf population on Isle Royale, which was driven to the brink of extinction soon after the arrival of a migrant from the large mainland wolf population. Here, we use a novel population genetic simulation framework to investigate the role of genetic diversity, deleterious variation, and demographic history in mediating extinction risk due to inbreeding depression in small populations. We show that, under realistic models of dominance, large populations harbor high levels of recessive strongly deleterious variation due to these mutations being hidden from selection in the heterozygous state. As a result, when large populations contract, they experience a substantially elevated risk of extinction after these strongly deleterious mutations are exposed by inbreeding. Moreover, we demonstrate that although translocating individuals to small populations is broadly effective as a means to reduce extinction risk, using small or moderate-sized source populations rather than large source populations can greatly increase the effectiveness of genetic rescue due to greater purging in these smaller populations. Our findings challenge the traditional conservation paradigm that focuses on maximizing genetic diversity to reduce extinction risk in favor of a view that emphasizes minimizing strongly deleterious variation. These insights have important implications for managing small and isolated populations in the increasingly fragmented landscape of the Anthropocene.Impact SummaryNumerous threats to extinction exist for small populations, including the detrimental effects of inbreeding. Although much of the focus in reducing these harmful effects in small populations has been on maintaining high genetic diversity, here we use simulations to demonstrate that emphasis should instead be placed on minimizing strongly deleterious variation. More specifically, we show that historically-large populations with high levels of genetic diversity also harbor elevated levels of recessive strongly deleterious mutations hidden in the heterozygous state. Thus, when these populations contract, inbreeding can expose these strongly deleterious mutations as homozygous and lead to severe inbreeding depression and rapid extinction. Moreover, we demonstrate that, although translocating individuals to these small populations to perform a ‘genetic rescue’ is broadly beneficial, the effectiveness of this strategy can be greatly increased by targeting historically-smaller source populations where recessive strongly deleterious mutations have been purged. These results challenge long-standing views on how to best conserve small and isolated populations facing the threat of inbreeding depression, and have immediate implications for preserving biodiversity in the increasingly fragmented landscape of the Anthropocene.