Effects of Variation on Harvest Limits for Nontimber Forest Species in Mexico
Bibliographic record
Abstract
Abstract: Successful conservation of nontimber products and their rainforest habitats requires the identification of optimal harvest regimes, the accurate estimation of maximum harvest limits, and the implementation of those limits by local harvesters. We used a combination of participatory research techniques and demographic modeling to determine maximum sustainable harvest rates of the bromeliad Aechmea magdalenae in the buffer zone of the Los Tuxtlas Biosphere Reserve in Mexico. We examined the effects of three types of variation on maximum harvest rates: variation between forest types, between harvest regimes, and over time. We also tested the accuracy of estimating maximum harvest limits using matrix‐model projections of unharvested populations. Maximum harvest rates of ramets from secondary forest populations were much higher than from primary‐forest populations. Likewise, variation in local harvest regimes had a large effect on maximum harvest rates. Populations concurrently harvested for leaves and ramets had higher maximum sustainable levels of ramet harvest than those harvested for ramets only. Simulations using harvested and unharvested populations yielded significantly different estimates of maximum sustainable harvest limits, indicating that methods for calculating maximum harvest limits that assume linear responses to harvest may lead to erroneous conclusions. Active participation in the research process enabled local harvesters to accept as valid the harvest limits determined in this study, to switch to using a more sustainable harvest regime, and to pass a local law prohibiting the destruction of their remaining primary forest because of its potential as A. magdalenae habitat.
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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".