The Zoʻé perspective on what scientists call “forest management” and its implications for floristic diversity and biocultural conservation
Bibliographic record
Abstract
Indigenous perspectives on forest management are grounded in traditional ecological knowledge (TEK), so that socioculture influences the ways Indigenous Peoples transform their landscapes. However, how socioculture structures Indigenous perspectives on forest management is unclear. Moreover, little is known about the influence of Indigenous landscape transformations on forest succession and floristic diversity. Here, we test hypotheses from biocultural and ecological theories suggesting that: (i) key social-ecological relationships with specific taxa structure Indigenous perspectives on forest management; (ii) such relationships guide sustainable management that generates resilient forest regrowth; and (iii) this management promotes floristic diversity by acting as an intermediate disturbance. We collected information about cosmology, occupation history and management among the Zoʻé, in Brazilian Amazonia. We also carried out floristic inventories in old-growth forests and in old Zoʻé swidden-fallow areas to analyze forest structure and alpha- and beta-diversity along a gradient of forest successional stages. We show that the Zoʻé perspective on forest management is structured by an ethical principle involving a social-ecological relationship with different beings, especially the spider monkey (Ateles sp.). This relationship generates mobility among the Zoʻé that allows forest regrowth in their fallow areas, so that in 28 years, forest basal area may equal that of old-growth forests. Also, Zoʻé forest management has increased alpha- and beta-diversity by increasing species richness and diversity in intermediate secondary forests and promoting floristic turnover at the landscape-level. These results show that some aspects of Zoʻé cosmology influence forest disturbance regimes that generate a sustainable social-ecological system, therefore being key for Zoʻé well-being and local biodiversity conservation. We believe that Indigenous perspectives about forest management should be included in forest conservation efforts aimed at protecting Amazonian biocultural diversity, thus valuing TEK and engendering sustainable social-ecological systems.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.034 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".