Trophodynamic indicators for an ecosystem approach to fisheries
Bibliographic record
Abstract
Acknowledging ecological interactions, such as predation, is key to an ecosystem approach to fisheries. Trophodynamic indicators are needed to measure the strength of the interactions between the different living components, and of structural ecosystem changes resulting from exploitation. We review trophodynamic indicators derived from models, as well as from emergent patterns such as trophic cascades and regime shifts. From 46 indicators identified in the literature, six (catch or biomass ratios, primary production required to support catch, production or consumption ratios and predation mortality, trophic level of the catch, fishing-in-balance, and mixed trophic impact) were selected because of their ability to reveal ecosystem-level patterns, and because they match published criteria. This suite of indicators is applied to the northern and southern Benguela ecosystems, and their performance is evaluated to depict drastic and contrasted ecosystem changes. A few complementary indicators are suggested as needed to detect the trophodynamic impacts of fisheries and ecosystem changes. Trends in indicators are sensitive to the choice of trophic level made for certain species. Trophodynamic indicators appear to be conservative, because they respond slowly to large structural changes in an ecosystem. Application of the selected indicators to other marine ecosystems is encouraged so as to evaluate fully their usefulness to an ecosystem approach to fisheries, and to establish international comparability.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".