Traits, threats, and popularity explain extinction risk of birds globally
Bibliographic record
Abstract
As the biodiversity crisis deepens, understanding extinction risk is essential for conserving at-risk species and triaging those potentially overlooked. Extinction risk is often estimated with traits (e.g. larger species are more vulnerable) without considering the context of threats or human bias in the listing process (e.g. more popular species are more or less likely to be listed). On the other hand, current global assessments of threats do not include the context of the biological variation of species (e.g. hunting mainly impacts large species). Here, we show that biological traits, threats, and popularity all interact to influence extinction risk for birds globally. We find particularly strong interactions between body mass and hunting (large body mass increases extinction risk for species threatened by hunting), habitat breadth and agriculture (narrow habitat breadth increases extinction risk for species threatened by agriculture), body mass and agriculture (small bodied species have increased extinction risk when threatened by agriculture) and range size and agriculture (for mid range-sized species, agriculture increases extinction risk). Further, we find that extinction risk increases with popularity, likely reflecting the increased chance of popular species having been listed given the same traits and threats. Overall, our results show the importance and necessity of including both biological and human biases, as well as human-posed threats when estimating extinction risk and identifying regions harbouring disproportionally high numbers of vulnerable species.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".