Traits, Threats, and Popularity Explain Extinction Risk of Birds Globally
Bibliographic record
Abstract
ABSTRACT Aim This study aims to understand how biological traits, human‐induced threats, and species popularity interact to influence the extinction risk of bird species globally. We seek to improve the accuracy of extinction risk assessments and inform conservation strategies. Location Global. Time Period Current. Major Taxa Studied Birds. Methods We predict extinction risk for bird species globally based on relevant biological traits, threats (based on the IUCN Red List), and species popularity estimated from Google search frequency using a Bayesian hierarchical phylogenetic model. Results We find that biological traits, human‐induced threats, and species popularity all explain extinction risk. Some interactions are important (e.g., larger species are more likely threatened by hunting and small‐ranged and migratory species are more threatened by agriculture). We also find that more popular species are more likely to be listed as at risk than unpopular species with similar traits and threats. Main Conclusions Our study highlights the need for incorporating biological traits, anthropogenic threats and human bias into extinction risk assessments. These factors interact in complex ways, influencing the vulnerability of bird species. By accounting for these interactions, conservation efforts can be more effectively targeted to protect species with the highest risk.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".