Asset protection in juvenile salmon: how adding biological realism changes a dynamic foraging model
Bibliographic record
Abstract
The “asset-protection principle” created by Clark is based on a dynamic programming model and states that individuals should (1) become more averse to predation risk as they accumulate fitness assets but (2) generally be more willing to accept predation risk later in the foraging season. To test whether these predictions hold under biologically meaningful foraging parameters, I constructed a dynamic model of the optimal trade-off between foraging and predator avoidance in juvenile salmon. The model incorporates temperature and body-size dependent bio-energetic constraints typical for juvenile fish, which grow by orders of magnitude over a season. In its simplest form using seasonally constant growth potential and a linear over-winter survival function, my results equal those of Clark's model. Adding a fitness function and environmental data from field studies accentuates the asset-protection effect and fundamentally changes the seasonal pattern of optimal effort. Simulation of typical poor feeding conditions in mid-summer yields the prediction of increased foraging in the spring in anticipation of worsening conditions. Increasing overall predation risk results in smaller fish at the end of the season with a trade-off between summer and winter survival. The model generates testable predictions for juvenile salmon and provides insights for other organisms (particularly poikilotherms) that are subject to size-dependent or seasonally changing foraging dynamics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".