Responses of ecological indicators to fishing pressure under environmental change: exploring non-linearity and thresholds
Bibliographic record
Abstract
Abstract Marine ecosystems are influenced by multiple stressors in both linear and non-linear ways. Using generalized additive models (GAMs) fitted to outputs from a multi-ecosystem, multi-model simulation experiment, we investigated 14 major ecological indicators across ten marine ecosystems about their responses to fishing pressure under: (i) three different fishing strategies (focusing on low-, high-, or all-trophic-level taxa); and (ii) four different scenarios of directional or random primary productivity change, a proxy for environmental change. From this work, we draw four major conclusions: (i) responses of indicators to fishing mortality in shapes, directions, and thresholds depend on the fishing strategies considered; (ii) most of the indicators demonstrate decreasing trends with increasing fishing mortality, with a few exceptions depending on the type of fishing strategy; (iii) most of the indicators respond to fishing mortality in a linear way, particularly for community and biomass-based indicators; and (iv) occurrence of threshold for non-linear-mixed type (i.e. non-linear with inflection points) is not prevalent within the fishing mortality rates explored. The conclusions drawn from the present study provide a knowledge base in indicators’ dynamics under different fishing and primary productivity levels, thereby facilitating the application of ecosystem-based fisheries management worldwide.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".