Bushmeat Exploitation in Tropical Forests: an Intercontinental Comparison
Bibliographic record
Abstract
Abstract: We calculated extraction and production rates of bushmeat species in two main tropical, moist‐forest regions, the Amazon and Congo basins. Extraction was estimated from the average number of animals consumed per person per year from anthropological studies that reported animal kills brought into settlements in the regions. We calculated extraction rates ( kg / km 2 /year) for 57 and 31 mammalian taxa in the Congo and Amazon, respectively. We then examined the sustainability of these extraction rates by basin and by taxa, using extraction‐to‐production ( E:P ) mass‐balance equations. Production (tonnes/year) was calculated as the product of r max (the intrinsic rate of natural increase), mammal biomass, and total area of forest in each region. Species exploitation rates at specific body masses were significantly greater in the Congo than in the Amazon. The E :P ratio for the Congo was 2.4, 30 times the Amazon's ratio of 0.081. Thus, Congo Basin mammals must annually produce approximately 93% of their body mass to balance current extraction rates, whereas Amazonian mammals must produce only 4% of their body mass. We calculated sustainability levels derived from Robinson and Redford's harvest model for each taxa. On a basin‐wide level, 60% and none of the mammal taxa in the Congo and Amazon basins, respectively, were exploited unsustainably. To evaluate the effect of error on the estimates of E :P , we conducted a sensitivity analysis, which suggests that the mass‐balance was most sensitive to error in standing stock but that our results are robust. We estimated that over 5 million tons of wild mammal meat feed millions in Neotropical (0.15 million) and Afrotropical (4.9 million) forests annually. Our Congo basin estimates are four times higher than those calculated for the region by other workers, and we conclude that the current situation of bushmeat extraction in African rain forests is more precarious than previously thought.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".