Genetic rescue often leads to higher fitness as a result of increased heterozygosity across animal taxa
Bibliographic record
Abstract
Biodiversity loss has reached critical levels partly due to anthropogenic habitat loss and degradation. These landscape changes are damaging as they can fragment species distributions into small, isolated populations, resulting in limited gene flow, population declines and reduced adaptive potential. Genetic rescue, the translocation of individuals to increase genetic diversity and ultimately fitness, has produced promising results for fragmented populations but remains underutilized due to a lack of long-term data and monitoring. To promote a better understanding of genetic rescue and its potential risks and benefits over the short-term, we reviewed and analysed published genetic rescue attempts to identify whether genetic diversity increases following translocation, and if this change is associated with increased fitness. Our review identified 19 studies that provided genetic and fitness data from before and after the translocation; the majority of these were on mammals, and included experimental, natural and conservation-motivated translocations. Using a Bayesian meta-analytical approach, we found that on average, genetic diversity and fitness increased in populations post translocations, although there were some exceptions to this trend. Overall, genetic diversity was a positive predictor of population fitness, and in some cases this relationship extended three generations post-rescue. These data suggest a single translocation can have lasting fitness benefits, and support translocation as another tool to facilitate conservation success. Given the limited number of studies with long-term data, we echo the need for genetic monitoring of populations post-translocation to understand whether genetic rescue can also limit the loss of adaptive potential in the long-term.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".