Evolutionary potential but not extinction risk of Lahontan cutthroat trout (<i>Oncorhynchus clarkii henshawi</i>) is associated with stream characteristics
Bibliographic record
Abstract
Habitat fragmentation represents a major extinction threat for species of all taxa. Isolated populations have a higher risk of local extinction because of environmental variability and demographic processes associated with small populations. Here we examine the relationships among isolation, habitat size, habitat characteristics and variability, and genetic effective population size with extinction risk for 10 isolated and three interconnected populations of Lahontan cutthroat trout ( Oncorhynchus clarkii henshawi ) sampled from throughout their range. Contrary to expectations, we did not find a relationship between most habitat variables and extinction risk. However, we did find strong relationships between habitat variables and genetic effective population size, including a significant negative correlation between pool density and effective population size. Small effective population sizes can result in reduced genetic variation and losses of evolutionary potential and adaptability to changing environments. The absence of strong habitat correlates with extinction risk — despite an observed relationship with effective population size — highlights the need to consider habitat diversity at multiple spatial scales when considering management scenarios to both promote population persistence and maintain evolutionary relevance.
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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 | 0.000 | 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".