Fishers' ecological knowledge points to fishing‐induced changes in the <scp>P</scp>eruvian <scp>A</scp>mazon
Bibliographic record
Abstract
Scientists increasingly draw on fishers' ecological knowledge (FEK) to gain a better understanding of fish biology and ecology, and inform options for fisheries management. We report on a study of FEK among fishers along the Lower Ucayali River in Peru, a region of exceptional productivity and diversity, which is also a major supplier of fish to the largest city in the Peruvian Amazon. Given a lack of available scientific information on stock status, we sought to identify temporal changes in the composition and size of exploited species by interviewing fishers from 18 communities who vary in years of fishing experience since the mid-1950s. We develop four FEK-based indicators to assess changes in the fish assemblage and compare findings with landings data. We find an intensification of fishing gear deployed over time and spatiotemporal shifts in the fish assemblage and reported declines in species weight, which point to a fishing-down process with declines across multiple species. This finding is reflected in a shifting baseline among our participants, whereby younger generations of fishers have different expectations regarding the distribution and size of species. Our study points to the importance of spillover effects from the nearby Pacaya-Samira National Reserve and community initiatives to support the regional fishery. Reference to fishers' knowledge also suggests that species decline is likely underreported in aggregated landings data. Despite the dynamism and diversity of Amazonian floodplain fisheries, simple FEK-based indicators can provide useful information for understanding fishing-induced changes in the fish assemblage. Fishers hold valuable knowledge for fishery management and conservation initiatives in the region.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".