Fishing triggers trophic cascade in terms of variation, not abundance, in an allometric trophic network model
Bibliographic record
Abstract
Trophic cascade studies often rely on linear food chains instead of complex food webs and are typically measured as biomass averages, not as biomass variation. We study trophic cascades propagating across a complex food web including a measure of biomass variation in addition to biomass average. We examined whether different fishing strategies induce trophic cascades and whether the cascades differ from each other. We utilized an allometric trophic network (ATN) model to mechanistically study fishing-induced changes in food web dynamics. Different fishing strategies did not trigger traditional, reciprocal trophic cascades, as measured in biomass averages. Instead, fishing triggered a variation cascade that propagated across the food web, including fish, zooplankton and phytoplankton species. In fisheries that removed a large amount of top-predatory and cannibalistic fish, the biomass oscillations started to decrease after fishing was started. In fisheries that mainly targeted large planktivorous fish, the biomass oscillations did not dampen but slightly increased over time. Removing species with specific ecological functions might alter the food web dynamics and potentially affect the ecological resilience of aquatic ecosystems.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".