A Field‐based Bioenergetics Model for Estimating Time‐Varying Food Consumption and Growth
Bibliographic record
Abstract
Abstract Bioenergetics models are often used to describe the implications of changes in growth and consumption of specific wild populations, and yet most parameters are derived from a variety of laboratory studies on other populations or species, leading to questions regarding the validity of predictions. A novel bioenergetics approach was recently developed where many parameters are estimated from the population being modeled, but growth and consumption are assumed invariant over time, which would not hold true when manipulations to the system are known or suspected. In the present paper, a bioenergetics model with many key parameters estimated from field data are presented where temporal deviations in growth rates were directly estimated. A series of rainbow trout Oncorhynchus mykiss and northern pikeminnow Ptychocheilus oregonensis populations, which have undergone various population manipulations, were used to evaluate the model. Further, the model was fit to a series of rainbow trout size‐classes stocked into each of the study lakes to compare with their wild counterparts and evaluate intercohort differences in growth and consumption. We found the model with time‐varying consumption was more parsimonious compared with models where growth and consumption were assumed to be constant over time. Our field data demonstrated how the model can detect different patterns in growth and consumption across populations and species. The model detected highly variable growth and consumption in rainbow trout over time and between populations but did not seem to be particularly influenced by past population manipulations. By contrast, northern pikeminnow demonstrated differences between lakes, but showed little temporal variation in growth and consumption. Stocked rainbow trout demonstrated similar growth rates to their wild counterparts, helping to validate growth estimates. Our bioenergetics model moves beyond existing ones by allowing measurement and process errors to be explicitly represented, while also permitting growth and consumption to vary over time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".