Opportunities for climate‐risk reduction through effective fisheries management
Bibliographic record
Abstract
Risk of impact of marine fishes to fishing and climate change (including ocean acidification) depend on the species' ecological and biological characteristics, as well as their exposure to over-exploitation and climate hazards. These human-induced hazards should be considered concurrently in conservation risk assessment. In this study, we aim to examine the combined contributions of climate change and fishing to the risk of impacts of exploited fishes, and the scope for climate-risk reduction from fisheries management. We combine fuzzy logic expert system with species distribution modeling to assess the extinction risks of climate and fishing impacts of 825 exploited marine fish species across the global ocean. We compare our calculated risk index with extinction risk of marine species assessed by the International Union for Conservation of Nature (IUCN). Our results show that 60% (499 species) of the assessed species are projected to experience very high risk from both overfishing and climate change under a "business-as-usual" scenario (RCP 8.5 with current status of fisheries) by 2050. The risk index is significantly and positively related to level of IUCN extinction risk (ordinal logistic regression, p < 0.0001). Furthermore, the regression model predicts species with very high risk index would have at least one in five (>20%) chance of having high extinction risk in the next few decades (equivalent to the IUCN categories of vulnerable, endangered or critically endangered). Areas with more at-risk species to climate change are in tropical and subtropical oceans, while those that are at risk to fishing are distributed more broadly, with higher concentration of at-risk species in North Atlantic and South Pacific Ocean. The number of species with high extinction risk would decrease by 63% under the sustainable fisheries-low emission scenario relative to the "business-as-usual" scenario. This study highlights the substantial opportunities for climate-risk reduction through effective fisheries management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".