Adaptive landscapes and density‐dependent selection in declining salmonid populations: going beyond numerical responses to human disturbance
Bibliographic record
Abstract
Theory suggests an important role for population density in shaping adaptive landscapes through density-dependent selection. Here, we identify five methodological approaches for studying such selection, review the existing empirical evidence for it, and ask whether current declines in abundance can be expected to trigger evolutionary responses in salmonid fishes. Across taxa we find substantial amounts of evidence for population density influencing the location of adaptive peaks for a range of traits, and, in the presence of frequency dependence, changing the shape of selection (stabilizing versus disruptive). For salmonids, biological and theoretical considerations suggest that the optimal value of a number of traits associated with juvenile competitive ability (e.g. egg size, timing of emergence from nests, dominance ability), may depend on population density. For adults, more direct experimental and comparative evidence suggest that secondary sexual traits can be subject to density-dependent selection. There is also evidence that density affects the frequency-dependent selection likely responsible for the expression of alternative male reproductive phenotypes in salmon. Less is known however about the role of density in maintaining genetic variation among juveniles. Further efforts are required to elucidate the indirect evolutionary effects of declining population abundances, both in salmonids and in other anthropogenically challenged organisms.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| 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".