Recent demographic histories and genetic diversity across pinnipeds are shaped by anthropogenic interactions and mediated by ecology and life-history
Bibliographic record
Abstract
Abstract A central paradigm in conservation biology is that population bottlenecks reduce genetic diversity and negatively impact population viability and adaptive potential. In an era of unprecedented biodiversity loss and climate change, understanding both the determinants and consequences of bottlenecks in wild populations is therefore an increasingly important challenge. However, as most studies have focused on single species, the multitude of potential drivers and the consequences of bottlenecks remain elusive. Here, we used a comparative approach by integrating genetic data from over 11,000 individuals of 30 pinniped species with demographic, ecological and life history data to elucidate the consequences of large-scale commercial exploitation by 18 th and 19 th century sealers. We show that around one third of these species exhibit strong genetic signatures of recent population declines, with estimated bottleneck effective population sizes reflecting just a few tens of surviving individuals in the most extreme cases. Bottleneck strength was strongly associated with both breeding habitat and mating system variation, and together with global abundance explained a large proportion of the variation in genetic diversity across species. Overall, there was no relationship between bottleneck intensity and IUCN status, although three of the four most heavily bottlenecked species are currently endangered. Our study reveals an unforeseen interplay between anthropogenic exploitation, ecology, life history and demographic declines, sheds new light on the determinants of genetic diversity, and is consistent with the notion that both genetic and demographic factors influence population viability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".