Genetic variation in westslope cutthroat trout reveals that widespread genetic rescue is warranted
Bibliographic record
Abstract
Although human fragmentation of freshwater habitats is ubiquitous, the genetic consequences of isolation and a roadmap to address them are poorly documented for most fishes. This is unfortunate, because translocation for genetic rescue could help mitigate problems. We used genetic data (32 SNPs) from 203 populations of westslope cutthroat trout (Oncorhynchus clarkii lewisi) to (1) document the effect of fragmentation on genetic variation and population structure, (2) identify candidate populations for genetic rescue, and (3) quantify the potential benefits of strategic translocation efforts. Human-isolated populations had substantially lower genetic variation and elevated genetic differentiation, indicating that many populations are strongly influenced by random genetic drift. Based on simple criteria, 23 populations were candidates for genetic rescue, which represented a majority (51%) of suitable populations in one major region (Missouri drainage). Population genetic theory suggests that translocation of a small number of individuals (∼5 adults) from nearby populations could dramatically increase heterozygosity by up to 58% (average across populations). This effort provides a clear template for future conservation of westslope cutthroat trout, while simultaneously highlighting the potential need for similar efforts in many freshwater species.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".