Genetic rescue leads to higher fitness as a result of increased heterozygosity across animal taxa
Bibliographic record
Abstract
Biodiversity loss has reached critical levels due in part to anthropogenic habitat loss and degradation. These landscape changes are particularly damaging as they can result in fragmenting species distributions into small and isolated populations, resulting in limited gene flow, population declines and reduced adaptive potential. Genetic rescue, the translocation of individuals for the purpose of restoring gene flow, has been shown to produce promising results for fragmented populations but remains relatively under-used due to a lack of long-term data and monitoring of genetic rescue attempts. To promote a better understanding of genetic rescue and its potential risks and benefits over the short-term, we reviewed and analyzed all genetic rescue attempts to date to identify whether genetic diversity increases following rescue, and if this change is associated with increased fitness. Our review identified only 19 genetic rescue studies, that included experimental, natural, and conservation motivated, with the majority of studies being on mammals. We used a Bayesian meta-analytical approach to examine the relationship between fitness and genetic diversity. We found that genetic diversity, as represented by heterozygosity, was a positive predictor of population fitness, and this relationship extended to the third-generation post-rescue. These data suggest a single introduction can have lasting fitness benefits, supporting translocation as another tool to ensure conservation success. Given the limited number of studies with long-term data, we echo the need for genetic monitoring of translocations to ascertain whether genetic rescue may also limit the loss of adaptive potential in the long-term.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".