Behavioural diversification in brook charr: adaptive responses to local conditions
Bibliographic record
Abstract
Summary Recently emerged brook charr (Salvelinus fontinalis Mitchill) foraging in still‐water pools along the sides of streams exhibit conspicuous variation in foraging behaviour. Some charr are sedentary and eat crustaceans from the lower portion of the water column. Others are mobile and eat insects from the upper portion of the water column. This study examines whether this behavioural diversification represents potentially adaptive responses made by individuals to local environmental conditions. Four models were constructed to predict how the ratio of mobile to sedentary charr was expected to change with pool morphometry, and how the location and orientation of mobile and sedentary charr were expected to differ within pools. The models assumed the charr forage competitively, but each model differed in assumptions regarding which features of pool morphometry determine the availability of the two main prey types. A spatial constraint hypothesis also was considered. Findings from this study support the hypothesis that the divergent foraging tactics represent adaptive adjustments made by individual charr in response to competition for spatially separated food sources. Variation in the numbers of sedentary and mobile charr across pools, and spatial distributions of the charr and their crustacean and insect prey within pools, were most consistent with a model assuming that the availability of insect prey, and hence the number of mobile charr, was determined by the pool surface area while the availability of crustacean prey, and hence the number of sedentary charr, was determined by the pool perimeter. Parallels between this pool system and polymorphic fish populations in larger lake systems identifies a potential link between the adaptive decision making of individuals and the spatial and phenotypic divergence of populations in response to spatial heterogeneity in food resources.
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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 | 0.001 | 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".