Evaluating the influence of predator–prey interactions on stock assessment and management reference points for a large lake ecosystem
Bibliographic record
Abstract
Ecosystem models are thought to offer advantages over single-species models in terms of management policy analysis. This hypothesis has proven difficult to test because of underlying system complexities, coupled with short time series and minimal contrast in environmental conditions or management policies. This paper presents a Bayesian statistical catch-at-age model to compare ecosystem models and test hypotheses about the management of a recreational fishery based on a predator–prey system using a relatively simple and data-rich ecosystem in a large lake, Kootenay Lake, British Columbia, where kokanee (Oncorhynchus nerka) are the prey and piscivorous rainbow trout (Oncorhynchus mykiss) are the predator. A model that explicitly incorporates the predator–prey interaction explained long-term data of field and fishery surveys much better than single-species models without any interactions. Minimally realistic multispecies models that treated predation identically but differed in their representation of the effects of prey abundance on predator mortality produced quite different results. Management reference points, for example, differed considerably between the models. Our study thus emphasizes that the choice of a management approach for this type of fishery will depend strongly on the model form and should take into consideration results from empirically based models that include species interactions.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".