Energy allocation strategies of young temperate fish: an eco-genetic modeling approach
Bibliographic record
Abstract
We use an individual-based eco-genetic model to explore the relative selective pressures of size-dependent predation, overwinter mortality, and density-dependent energy acquisition in structuring plastic and adaptive energy allocation during the first year of life of a temperate fish population. While several patterns emerging from a suite of eco-genetic model simulations were consistent with past theoretical models and empirical evaluations of energy allocation by young fishes, results also highlight the utility of eco-genetic models for simultaneous consideration of plastic and adaptive processes. Across simulations, variation in genetic control of energy allocation was limited during very early ontogeny when size-dependent predation pressure was particularly high. While this stabilizing selection on energy allocation diminished later in the growing season, predation, overwinter mortality, and density-dependent processes simultaneously structured energy partitioning later in ontogeny through the interactive influence of plastic and adaptive processes. Specifically, high risk of overwinter mortality and low predation selected for high prioritization of energy storage. We suggest that simulations demonstrate the utility of eco-genetic models for generating null predictions of how selective pressures may structure expression of life history traits, such as early life energy allocation.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".