Key Features and Context‐Dependence of Fishery‐Induced Trophic Cascades
Bibliographic record
Abstract
Trophic cascades triggered by fishing have profound implications for marine ecosystems and the socioeconomic systems that depend on them. With the number of reported cases quickly growing, key features and commonalities have emerged. Fishery-induced trophic cascades often display differential response times and nonlinear trajectories among trophic levels and can be accompanied by shifts in alternative states. Furthermore, their magnitude appears to be context dependent, varying as a function of species diversity, regional oceanography, local physical disturbance, habitat complexity, and the nature of the fishery itself. To conserve and manage exploited marine ecosystems, there is a pressing need for an improved understanding of the conditions that promote or inhibit the cascading consequences of fishing. Future research should investigate how the trophic effects of fishing interact with other human disturbances, identify strongly interacting species and ecosystem features that confer resilience to exploitation, determine ranges of predator depletion that elicit trophic cascades, pinpoint antecedents that signal ecosystem state shifts, and quantify variation in trophic rates across oceanographic conditions. This information will advance predictive models designed to forecast the trophic effects of fishing and will allow managers to better anticipate and avoid fishery-induced trophic cascades.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".