Journal of HeredityGenomicsPeer-reviewed journal article
Genomic erosion in the assessment of species extinction risk and recovery potential
Cock van OosterhoutiD, Samuel A. SpeakiD, Thomas BirleyiD, Lewis W G Hitchings, Chiara BortoluzziiD, Lawrence Percival‐AlwyniD, Lara UrbaniD, Jim J. GroombridgeiD, Gernot SegelbacheriD, Hernán E. MoralesiD
Abstract
Many species are undergoing rapid population declines and environmental deterioration, leading to genomic erosion, the loss of genetic diversity, accumulation of deleterious mutations, maladaptation, and introgression, that undermines fitness and long-term viability. Critically, this process continues even after demographic recovery due to a time-lagged genetic burden known as drift debt. Current conservation assessments, such as the IUCN Red List, focus on short-term extinction risk and do not capture the long-term consequences of genomic erosion. Likewise, the longer-term assessments of the IUCN Green Status may overestimate population recovery by failing to account for the lasting effects of genomic erosion. As genome sequencing becomes increasingly accessible, there is a growing opportunity to quantify genomic erosion and integrate it into conservation planning. Here, we use genomic simulations to illustrate how how different genomic metrics are sensitive to the drift debt. We test how ancestral effective population size (Ne) and bottleneck history influence the tempo and severity of genomic erosion, and demonstrate how these dynamics shape genetic load and additive genetic variation, key indicators of long-term evolutionary potential. Finally, we present a proof-of-concept for a Genomic Green Status framework that aligns genomic metrics with conservation impact assessments, laying the foundation for genomics-informed strategies to support species recovery.
- assay: whole-genome sequencing
- research focus: benchmarking
- research focus: database / resource
- research focus: software / tool
- approach: statistical model
- Citations
- 11
- FWCI
- 43.19
- Field percentile
- top 1%
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