Beyond the defaults: functional response parameter space and ecosystem-level fishing thresholds in dynamic food web model simulations
Bibliographic record
Abstract
Dynamic food web models are increasingly used to investigate the ecosystem effects of fishing; however, key unknown functional response parameters describing predator-prey interactions strongly influence model behavior. We explored functional response parameter uncertainty and its effect on fishing simulation results using a dynamic food web model of the Gulf of Alaska (GOA) with14 fishing fleets, 104 consumer groups, four primary producer groups, and five detritus pools. After generating millions of potential ecosystems with randomly selected functional response parameters, we assigned groups of these randomly parameterized systems to one of five increasingly intense ecosystem-wide fishing treatments. For each fishing treatment, we counted and compared resulting ecosystems with no extinctions. Surprisingly, the model GOA ecosystems were robust to a wide range of functional response parameters. However, we found an abrupt threshold effect between moderate and heavy exploitation rates, beyond which a much lower proportion of model ecosystems persisted. Beyond this fishing threshold, extinction was more likely, and system attributes differed greatly from moderately fished model ecosystems. Fishing thresholds were not found with default functional response parameters, implying that model simulations should include a wide range of parameterizations to reflect ecological uncertainty and to support sustainable ecosystem-based fishery 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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".