Human impacts on the world’s raptors
Bibliographic record
Abstract
Raptors are emblematic of the global biodiversity crisis because one out of five species are threatened with extinction and over half have declining populations due to human threats. Yet our understanding of where these “threats” impact raptor species is limited across terrestrial Earth. This is concerning because raptors, as apex predators, are critically positioned in ecological food webs, and their declining populations can undermine important ecosystem services ranging from pest control to disease regulation. Here, we map the distribution of 15 threats within the known ranges of 172 threatened and near threatened raptor species globally as declared by the International Union for the Conservation of Nature. We analyze the proportion of each raptor range that is exposed to threats, identify global hotspots of impacted raptor richness, and investigate how human impacts on raptors vary based on several intrinsic (species traits) and extrinsic factors. We find that humans are potentially negatively affecting at least one threatened raptor species across three quarters of Earth’s terrestrial area (78%; 113 million km 2 ). Our results also show that raptors have 66% of their range potentially impacted by threats on average (range 2.7–100%). Alarmingly, critically endangered species have 90% of their range impacted by threats on average. We also highlight 57 species (33%) of particular concern that have > 90% of their ranges potentially impacted. Without immediate conservation intervention, these 57 species, including the most heavily impacted Forest Owlet ( Athene blewitti ), the Madagascar Serpent-eagle ( Eutriorchis astur ), and the Rufous Fishing-owl ( Scotopelia ussheri ), will likely face extinction in the near future. Global “hotspots” of impacted raptor richness are ubiquitous, with core areas of threat in parts of the Sahel and East Africa where 92% of the assessed raptors are potentially impacted per grid cell (10 species on average), and in Northern India where nearly 100% of raptors are potentially impacted per grid cell (11 species). Additionally, “coolspots” of unimpacted richness that represent refuges from threats occur in Greenland and Canada, where 98 and 58% of raptors are potentially unimpacted per grid cell, respectively (nearly one species on average), Saharan Africa, where 21% of raptors are potentially unimpacted per grid cell (one species on average), and parts of the Amazon, where 12% of raptors are potentially unimpacted per grid cell (0.6 species on average). The results provide essential information to guide conservation planning and action for the world’s imperiled raptors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 teacher head, 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".