Downscaling global reference points to assess the sustainability of local fisheries
Bibliographic record
Abstract
Multispecies coral reef fisheries are typically managed by local communities who often lack research and monitoring capacity, which prevents estimation of well-defined sustainable reference points to perform locally relevant fishery assessments. Recent research modeling coral reef fisheries globally has estimated multispecies sustainable reference points (i.e., the maximum reef fish yields that can be harvested sustainably and the corresponding reef fish standing biomass at which those are expected to be achieved) based on environmental indicators. These global reference points are a promising tool for assessing data-poor reef fisheries but need to be downscaled to be relevant to resource practitioners. Using a small-scale multispecies reef fishery in Papua New Guinea, we estimated sustainable reference points and assessed the sustainability of the fishery by integrating global-scale analyses with local-scale environmental conditions (i.e., coral cover, sea surface temperature, ocean productivity, and whether the reef is an atoll), reef area, fish catch and standing biomass estimates, and fishers' perceptions. Local-scale relevant data were obtained from a combination of remote sensing products, underwater visual censuses, catch surveys, and household structured social surveys. Our sustainability assessment based on downscaled estimated sustainable reference points was consistent with local fishers' perceptions. Specifically, our downscaled results suggested that the fishing community was overfishing their reef fish stocks and stocks were below biomass levels that maximize production, making the overall reef fishery unsustainable. These results were consistent with fisher perceptions that reef fish stocks were declining in abundance and mean fish length and that fishers had to spend more time finding fish. Our downscaled site-level assessment revealed severe local resource exploitation, the dynamics of which were masked in national-scale assessments, emphasizing the importance of matching assessments to the scale of management. Overall, we show how global reference points can be applied locally when long-term data are not available, providing baseline assessments for sustainably managing previously unassessed multispecies reef fisheries around the globe.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".