Bibliographic record
Abstract
Matching habitat choice is a habitat preference mechanism based on self‐assessment of local performance, such that individuals settle in the habitats that are best suited to their phenotypes, promoting local adaptation. Despite the important evolutionary implications of matching habitat choice, examples from natural populations are rare. One possible reason for this apparent rarity is that phenotype‐matching habitat choice might be manifest only in those population segments for which the cost of a phenotype–environment mismatch is high, although this hypothesis remains to be tested. Here, we test for matching habitat choice in a breeding population of sockeye salmon Oncorhynchus nerka where the strength of performance tradeoffs across environments, and therefore the costs of mischoosing, can be evaluated in meaningful discrete groups (e.g. male versus females, and ocean‐age 2 versus ocean‐age 3). Consistent with matching habitat choice, salmon of similar ocean‐age and size tended to cluster together in sites of similar water depth. However, matching habitat choice was only favored (longer life span) in 3‐ocean females – the segment of the population most vulnerable to bear predation. Our findings support the hypothesis that matching habitat choice is more likely to be evident in those segments of a population that suffer a major cost of mischoosing, leading to ‘partial matching habitat choice’.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".