High genetic load without purging in a diverse species-at-risk
Bibliographic record
Abstract
SUMMARY High intra-specific genetic diversity is associated with adaptive potential which is key for resilience to global change. However, high variation may also support deleterious alleles through genetic load, unless purged, thereby increasing the risk of inbreeding depression if population sizes decrease rapidly. Purging of deleterious variation has now been demonstrated in some threatened species. However, less is known about the costs of population declines and inbreeding in species with large population sizes and high genetic diversity even though this encompasses many species globally that have or are expected to undergo rapid population declines. Caribou is a species of ecological and cultural significance in North America with a continental-wide distribution supporting extensive phenotypic variation, but with some populations undergoing significant declines resulting in their at-risk status in Canada. We assessed intra-specific genetic variation, adaptive divergence, inbreeding, and genetic load across populations with different demographic histories using an annotated chromosome-scale reference genome and 66 whole genome sequences. We found high genetic diversity and nine phylogenomic lineages across the continent with adaptive diversification of genes, but also high genetic load among lineages. We also found highly divergent levels of inbreeding across individuals including the loss of alleles by drift (genetic erosion) but not purging, likely due to rapid population declines not allowing time for purging of deleterious alleles. As a result, further inbreeding may need to be mitigated through conservation efforts. Our results highlight the ‘double-edged sword’ of genetic diversity that may be representative of other species-at-risk affected by anthropogenic activities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".