The explosion in population genomic data demands ever more complex modes of analysis, and increasingly these analyses depend on sophisticated simulations. Re-cent 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.
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.
10Human-driven habitat fragmentation and loss has led to a proliferation of small and isolated 11 plant and animal populations that may be threatened with extinction by genetic factors. The 12 prevailing approach for managing these populations is to maintain high genetic diversity, which 13 is often equated with fitness. Increasingly, this is being done using genetic rescue, where 14 individuals from populations with high genetic diversity are translocated to small populations 15 with high levels of inbreeding. However, the potentially negative consequences of this 16 approach have recently been highlighted by the demise of the gray wolf population on Isle 17Royale, which only briefly recovered after genetic rescue by a migrant from the large mainland 18 wolf population and then declined to the brink of extinction. Here, we use ecologically-19 motivated population genetic simulations to show that extinction risk in small populations is 20 often increased by maximizing genetic diversity but is consistently decreased by minimizing 21 deleterious variation. Surprisingly, we find that small populations that are founded or rescued 22
Anthropogenic habitat loss and climate change are reducing species’ geographic ranges, increasing extinction risk and losses of species’ genetic diversity. Although preserving genetic diversity is key to maintaining species’ adaptability, we lack predictive tools and global estimates of genetic diversity loss across ecosystems. We introduce a mathematical framework that bridges biodiversity theory and population genetics to understand the loss of naturally occurring DNA mutations with decreasing habitat. By analyzing genomic variation of 10,095 georeferenced individuals from 20 plant and animal species, we show that genome-wide diversity follows a mutations-area relationship power law with geographic area, which can predict genetic diversity loss from local population extinctions. We estimate that more than 10% of genetic diversity may already be lost for many threatened and nonthreatened species, surpassing the United Nations’ post-2020 targets for genetic preservation.
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