Local knowledge enhances the sustainability of interconnected fisheries
Bibliographic record
Abstract
Abstract Local knowledge (LK) refers to the ancestral understanding that Indigenous Peoples and local communities have developed over centuries through trial‐and‐error and hands‐on management of natural resources. LK may provide valuable insights for biodiversity conservation and human well‐being. However, its effectiveness remains under‐explored at large scales, especially where multiple communities manage ecosystems. One example is fisheries, which form complex, interconnected networks where fish move across spatial boundaries between managed areas. Fisheries are critical for food security and income, yet face threats from overharvesting. Fisheries Co‐Management (FCM)—a partnership between local communities and governments—leverages LK. However, the value of LK in designing protection strategies remains unclear. Using a process‐based dynamical model parameterized with empirical data, we evaluated FCM strategies for pirarucu ( Arapaima gigas ) fisheries, which form a metapopulation network of protected and unprotected lakes in the Brazilian Amazon. We combined our metapopulation model with LK, fish biology and network theory to assess how lake protection, fishing quotas and illegal fishing impact pirarucu population abundance at the riverscape scale. By analysing 13 FCM‐protected lakes and 18 unprotected lakes, we contrasted six hypothesis‐driven management strategies against the current one, which is based on LK. In all strategies, protected lakes support higher pirarucu populations and buffer against increased fishing pressure, while unprotected lakes face population collapse due to the lack of fishing regulations. While a strategy that provides the best outcomes in terms of metapopulation persistence was based on pirarucu carrying capacity, the currently applied FCM strategy closely matched its efficacy. Synthesis and applications . Our modelling approach allows managers to compare alternative conservation strategies under different socio‐ecological scenarios, highlighting trade‐offs and guiding investment of effort and resources. While immediately valuable for pirarucu management in the Middle Juruá, the framework scales across tiers of applicability, each requiring progressively greater model adaptation: from supporting FCM in other Amazonian regions (with minimal adjustment), to adaptation for other riverine fisheries and ultimately to broader socio‐ecological systems. In this way, we provide both system‐specific insights and a flexible tool for advancing sustainable management of natural resources across contexts.
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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 | 0.001 | 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 | 0.002 |
| 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".