Over‐harvesting driven by consumer demand leads to population decline: big‐leaf mahogany in South America
Bibliographic record
Abstract
Abstract Consumer demand for the premier neotropical luxury timber, big‐leaf mahogany ( Swietenia macrophylla ), has driven boom‐and‐bust logging cycles for centuries, depleting local and regional supplies from Mexico to Bolivia. We revise the standard historic range map for mahogany in South America and estimate the extent to which commercial stocks have been depleted using satellite data, expert surveys, and sawmill processing center data from Brazil. We estimate an historic range of 278 million hectares spanning Venezuela to Bolivia, 57% of this in Brazil. Approximately 58 million hectares (21%) of mahogany's historic range had been lost to forest conversion by 2001. Commercial populations had been logged from at least 125 million more hectares, reducing the commercial range to 94 million hectares (34% of historic). Surviving stocks are extremely low‐density populations in remote regions representing a smaller fraction of historic stocks than expected based on estimated current commercial range. Our method could advance international policy debates such as listing proposals for CITES Appendices by clarifying the commercial and conservation status of high‐value timber species similar to mahogany about which little information is available. The fate of remaining mahogany stocks in South America will depend on transforming current forest management practices into sustainable production 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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".