Testing models of increasing complexity to develop ecosystem‐informed fisheries advice
Bibliographic record
Abstract
Abstract Despite continued calls for the application of ecosystem‐based fisheries management, tactical fisheries management continues to be heavily reliant on single‐species stock assessments. These stock assessments rarely quantitatively integrate the effects of ecosystem processes on fish stock productivity. This lack of integration is ultimately driven by the complexity of interactions between populations, ecosystems and fisheries, which produces uncertainty when defining which processes to include and how to include them. Models developed using a structured hypothesis testing framework would allow formalizing uncertainties while underscoring the importance of incorporating different population and ecosystem processes to explain non‐stationary stock productivity. Here, we develop a conceptual framework for extending and comparing population dynamics models of increasing complexity. We illustrate the utility of the framework by investigating the population and ecosystem processes that most likely affected the differential recovery of two flatfish populations (American plaice and yellowtail flounder) on the Newfoundland Grand Banks over the past three decades. We found that yellowtail flounder population dynamics were primarily driven by recruitment variability, which was negatively affected by warmer climatological conditions, as indicated by an integrated regional climate index. Meanwhile, American plaice population dynamics were affected by a combination of temporal variability in recruitment and natural mortality, where natural mortality increased during colder than average conditions. By exploring hypotheses about the effects of population and ecosystem processes on population dynamics, this modelling framework will improve understanding about the drivers of shifts in population productivity while serving as a transparent and robust approach to support ecosystem‐based fisheries management.
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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 | 0.005 | 0.023 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".