Importance and spatial patterns of invisible fisheries in Amazonian clear‐water rivers as revealed by fisher knowledge and collaboration
Bibliographic record
Abstract
The Brazilian Amazon contains the world's most diverse fish assemblages. These assemblages can be affected by freshwater fisheries, which provide food and income for riverine people, and by accelerating environmental change. We collaborated with local fishers to provide a comprehensive assessment of the spatial patterns of fish use in 3 clear-water rivers in the Brazilian Amazon: the Tapajos, Trombetas, and Tocantins. We interviewed 638 fishers in 39 communities about fish use for domestic consumption or sale, daily catches per fisher, and catch per unit effort (CPUE). We then assessed the influence of river identity, protected areas (PAs), forest cover, and landscape complexity (independent variables) on catches and CPUE estimated from interviews (response variables) through linear models. We also analyzed data from participatory catch monitoring in 21 communities along the Tapajos River (5668 fish landings). Twenty-one fish species were the most harvested and cited by interview respondents, 16 of which were migratory fishes, accounting for 82% of catches in the Tapajos River. According to fishers, daily fish catches per fisher were higher outside PAs (effect size 0.33) than inside, whereas CPUE was higher inside PAs than outside (-0.27). Catches were negatively associated with forest cover (-0.20), whereas river landscape complexity was positively associated with fish catch (0.96) and CPUE (0.66). These results can support management strategies, from regional to large scales, by reinforcing the relevance of PAs in clear-water rivers and showing the influence of landscape on fish catches. Our collaboration with fishers provided robust baseline data that can be used to inform inclusive, precautionary, and adaptive policies for conservation of threatened rivers.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".