Fish life history dynamics: shifts in prey size structure evoke shifts in predator maturation traits
Bibliographic record
Abstract
A bioenergetic framework is developed to predict optimal life history responses to environmentally driven changes in the rate of energy production by a predator. This framework is used to predict the responses of age at maturation, size at maturation, and asymptotic size to changes in the predator–prey size ratio. Predators feeding on relatively smaller prey (i.e., having larger predator–prey size ratios) have lower growth efficiency and are predicted as a consequence to mature earlier, at smaller sizes, and reach smaller asymptotic sizes. This prediction was tested using a 78-year time series (1936–2013) of data from a natural population of lake trout (Salvelinus namaycush) in Lake Opeongo, Algonquin Park, Ontario, Canada. A large decrease in the predator–prey size ratio for this population occurred over the period 1950–1965 when a preferred prey (cisco, Coregonus artedii) was introduced to the lake. This decrease was followed by ∼20 years of constancy in the size ratio and then 25 years of progressive increase. Lake trout life history responded plastically during both periods and consistently with our predictions. Extensive analysis of available data provided little empirical support for alternative explanations for the observed changes in lake trout size and maturity (e.g., changes in cisco and (or) lake trout density and harvest rates). The framework developed here derives plastic life history changes from fixed developmental thresholds that are based on the scaling of net production with body size and can be used to predict the shape of maturation reaction norms for the major shifts in community structure that are compactly summarized by changes in size spectrum parameters.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".