A length-based multispecies model for evaluating community responses to fishing
Bibliographic record
Abstract
Quantitative ecosystem indicators are needed to fulfill the mandate for ecosystem-based fisheries management. A variety of community metrics could potentially be used, but before reference levels for such indices can be established the sensitivity of candidate indices to fishing and other disturbances must be determined. One approach for obtaining such information is to test candidate indicators with models that mimic real ecosystems and can be manipulated experimentally. Here we construct a size-based multispecies model of a community of fish species that interact by predation. The model was parameterized for 21 fish species to obtain a predation-regulated community. Following an analysis of the sensitivity of the model to parameter uncertainty, we tested the sensitivity of community-level indicators to increasing levels of fishing mortality (F). Abundance and biomass spectra were sensitive to fishing mortality, with the slope decreasing with increasing F. Species diversity size spectra were also very sensitive to F, with diversity in the largest size classes declining rapidly. In contrast, k-dominance curves were less sensitive to fishing pressure. Importantly, however, although most community-level metrics showed clear trends in response to fishing, single-species declines in spawning stock biomass were the most sensitive indicators of fishing effects.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".