Rethinking Trade-Driven Extinction Risk in Marine and Terrestrial Megafauna
Bibliographic record
Abstract
Large animals hunted for the high value of their parts (e.g., elephant ivory and shark fins) are at risk of extinction due to both intensive international trade pressure and intrinsic biological sensitivity. However, the relative role of trade, particularly in non-perishable products, and biological factors in driving extinction risk is not well understood [1-4]. Here we identify a taxonomically diverse group of >100 marine and terrestrial megafauna targeted for international luxury markets; estimate their value across three points of sale; test relationships among extinction risk, high value, and body size; and quantify the effects of two mitigating factors: poaching fines and geographic range size. We find that body size is the principal driver of risk for lower value species, but that this biological pattern is eliminated above a value threshold, meaning that the most valuable species face a high extinction risk regardless of size. For example, once mean product values exceed US$12,557 kg(-1), body size no longer drives risk. Total value scales with size for marine animals more strongly than for terrestrial animals, incentivizing the hunting of large marine individuals and species. Poaching fines currently have little effect on extinction risk; fines would need to be increased 10- to 100-fold to be effective. Large geographic ranges reduce risk for terrestrial, but not marine, species, whose ranges are ten times greater. Our results underscore both the evolutionary and ecosystem consequences of targeting large marine animals and the need to geographically scale up and prioritize conservation of high-value marine species to avoid extinction.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".