Community-based management expands ecosystem protection footprint in Amazonian forests
Bibliographic record
Abstract
Community-based conservation has gained traction in the Brazilian Amazon because of its potential in combining territorial protection, local well-being and biodiversity conservation. We assessed the footprint of effective protection, areas actively monitored and defended through community-led surveillance, where illegal activities such as poaching, fishing and logging are successfully prevented, within the largest community-based fisheries conservation arrangement in the Amazon. While the arrangement focused specifically on 13 lakes which were on average 47 ha in size, the effectively protected floodplain area was approximately eightfold larger than the extent of direct protection, defined as the immediate focal area sustaining financial returns through co-management. The additional protection of this ‘functional area’ was on average 11,188 ha, or 36-fold larger than the directly protected area. Although the average cost of effective protection was low (US$0.95 ha−1 yr−1), this was entirely incurred by low-income local communities. Our study underscores the remarkable effort leveraged by Amazonian rural communities in protecting natural ecosystems and the imperative need to develop compensation mechanisms to financially reward them, which are currently lacking. Community-based conservation efforts for ecosystems can have manifold effects beyond the direct areas being managed. This study finds that, deep in the Amazon, communities effectively protected an area 36 times larger than the area they were guarding, but also had to bear all of the costs.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".