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INTRODUCTION The Anthropocene is marked by an accelerated loss of biodiversity, widespread population declines, and a global conservation crisis. Given limited resources for conservation intervention, an approach is needed to identify threatened species from among the thousands lacking adequate information for status assessments. Such prioritization for intervention could come from genome sequence data, as genomes contain information about demography, diversity, fitness, and adaptive potential. However, the relevance of genomic data for identifying at-risk species is uncertain, in part because genetic variation may reflect past events and life histories better than contemporary conservation status. RATIONALE The Zoonomia multispecies alignment presents an opportunity to systematically compare neutral and functional genomic diversity and their relationships to contemporary extinction risk across a large sample of diverse mammalian taxa. We surveyed 240 species spanning from the “Least Concern” to “Critically Endangered” categories, as published in the International Union for Conservation of Nature’s Red List of Threatened Species. Using a single genome for each species, we estimated historical effective population sizes ( N e ) and distributions of genome-wide heterozygosity. To estimate genetic load, we identified substitutions relative to reconstructed ancestral sequences, assuming that mutations at evolutionarily conserved sites and in protein-coding sequences, especially in genes essential for viability in mice, are predominantly deleterious. We examined relationships between the conservation status of species and metrics of heterozygosity, demography, and genetic load and used these data to train and test models to distinguish threatened from nonthreatened species. RESULTS Species with smaller historical N e are more likely to be categorized as at risk of extinction, suggesting that demography, even from periods more than 10,000 years in the past, may be informative of contemporary resilience. Species with smaller historical N e also carry proportionally higher burdens of weakly and moderately deleterious alleles, consistent with theoretical expectations of the long-term accumulation and fixation of genetic load under strong genetic drift. We found weak support for a causative link between fixed drift load and extinction risk; however, other types of genetic load not captured in our data, such as rare, highly deleterious alleles, may also play a role. Although ecological (e.g., physiological, life-history, and behavioral) variables were the best predictors of extinction risk, genomic variables nonrandomly distinguished threatened from nonthreatened species in regression and machine learning models. These results suggest that information encoded within even a single genome can provide a risk assessment in the absence of adequate ecological or population census data. CONCLUSION Our analysis highlights the potential for genomic data to rapidly and inexpensively gauge extinction risk by leveraging relationships between contemporary conservation status and genetic variation shaped by the long-term demographic history of species. As more resequencing data and additional reference genomes become available, estimates of genetic load, estimates of recent demographic history, and accuracy of predictive models will improve. We therefore echo calls for including genomic information in assessments of the conservation status of species. Genomic information can help predict extinction risk in diverse mammalian species. Across 240 mammals, species with smaller historical N e had lower genetic diversity, higher genetic load, and were more likely to be threatened with extinction. Genomic data were used to train models that predict whether a species is threatened, which can be valuable for assessing extinction risk in species lacking ecological or census data. [Animal silhouettes are from PhyloPic]
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Accurate predictive modeling of conservation status in animal species using supervised learning
Abstract Conservation management to mitigate extinction of wildlife becomes more crucial than ever as global impacts due to anthropogenic activities and climate change continue to create devastation for species around the globe. Despite ongoing efforts to understand species constantly changing population dynamics due to anthropogenic stressors, there is a strong disconnect between conservation research and conservation policy, what is known as the “Conservation Gap”. The International Union of Conservation of Nature, the IUCN, is a globally recognized organization that works to sustain biodiversity by maintaining a ranking of species known as their Red List. However, the IUCN does not currently utilize genetic information to assess species conservation status despite the availability of molecular data. Here we use over 7300 studies collated from the MacroPopGen database, and over 450 published articles from the public repository DataDryad, focused on conservation and population genetics, sampling across a variety of invertebrate and vertebrate taxa, and using IUCN classifications to predict species endangerment using machine learning. Our models were able to accurately predict species threat level classified by the IUCN using both measures of genetic diversity and differentiation with IUCN assessment criteria. Our goal is to use these models to help determine and communicate conservation status to practitioners that takes into consideration all available species-specific information.
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- Award ID(s):
- 2147812
- PAR ID:
- 10582832
- Publisher / Repository:
- Research Square
- Date Published:
- Format(s):
- Medium: X
- Institution:
- Research Square
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Posted Content:
- https://doi.org/10.21203/rs.3.rs-3471388/v1
