Anthropogenic Landscape in Southeastern Amazonia: Contemporary Impacts of Low-Intensity Harvesting and Dispersal of Brazil Nuts by the Kayapó Indigenous People
Bibliographic record
Abstract
Brazil nut, the Bertholletia excelsa seed, is one of the most important non-timber forest products in the Amazon Forest and the livelihoods of thousands of traditional Amazonian families depend on its commercialization. B. excelsa has been frequently cited as an indicator of anthropogenic forests and there is strong evidence that past human management has significantly contributed to its present distribution across the Amazon, suggesting that low levels of harvesting may play a positive role in B. excelsa recruitment. Here, we evaluate the effects of Brazil nut harvesting by the Kayapó Indigenous people of southeastern Amazonia on seedling recruitment in 20 B. excelsa groves subjected to different harvesting intensities, and investigated if management by harvesters influences patterns of B. excelsa distribution. The number of years of low-intensity Brazil nut harvesting by the Kayapó over the past two decades was positively related to B. excelsa seedling density in groves. One of the mechanisms behind the higher seedling density in harvested sites seems to be seed dispersal by harvesters along trails. The Kayapó also intentionally plant B. excelsa seeds and seedlings across their territories. Our results show not only that low-intensity Brazil nut harvesting by the Kayapó people does not reduce recruitment of seedlings, but that harvesting and/or associated activities conducted by traditional harvesters may benefit B. excelsa beyond grove borders. Our study supports the hypothesis that B. excelsa dispersal throughout the Amazon was, at least in part, influenced by indigenous groups, and strongly suggests that current human management contributes to the maintenance and formation of B. excelsa groves. We suggest that changes in Brazil nut management practices by traditional people to prevent harvesting impacts may be unnecessary and even counterproductive in many areas, and should be carefully evaluated before implementation.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".