Risky business: The combined effects of fishing and changes in primary productivity on fish communities
Bibliographic record
Abstract
There is an increasing need to understand ecosystem responses to multiple stressors in that such complex responses depend not only on species-level responses, but also on species interactions and ecosystem structure. In this study, we used a multi-model ecosystem simulation approach to explore the combined effects of fishing and primary productivity on different components of the food-web across a suite of ecosystems and a range of model types. Simulations were carried out under different levels of primary productivity and various fishing scenarios. In addition to exploring synergistic, additive or antagonistic combined effects of multiple stressors, we included a fourth category “dampened”, which refers to less negative or less positive impacts compared to additive ones, and in contrast to previous studies, we explicitly considered the direction (positive or negative) of the combined effects. We focused on two specific combined effects (negative synergism and positive dampened) associated with the risk of resultant lower fish biomass than expected under additive effects. Through a meta-analysis of the multi-models' simulation results, we found that (i) the risk of negative synergism was generally higher for low-trophic-level (LTL) taxa, implying that following an increase of fishing pressure on a given LTL stock, the subsequent decrease of biomass under low primary productivity would be larger than expected under additive effects and (ii) the risk of positive dampened effects was generally higher for high-trophic-level (HTL) taxa, implying that given a management measure aimed at reducing the impact of fishing on HTL stocks, the subsequent rebuilding of these stocks would be slower than expected. Our approach to categorizing and exploring cumulative effects can be applied to evaluate other community properties, and provide guidance for 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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".