High Habitat Potential but Limited Connectivity for Brown Bears Throughout Europe
Bibliographic record
Abstract
ABSTRACT Aim Large carnivores worldwide have experienced substantial range contractions due to human activities, though several species are recolonising parts of their historical range. We aimed to assess current and potential European brown bear ( Ursus arctos arctos ) habitat as well as habitat connectivity on a continental scale. Location The extended biogeographical regions of Europe, spanning from Portugal to central Russia, longitudinally, and from Norway to Türkiye, latitudinally. Excluding inland seas; this area covers 11,151,636 km 2 . Methods We assessed habitat suitability throughout the study area using an ensemble species distribution model with nine submodels, using data from 10 European bear populations and Türkiye. We used the resulting habitat suitability maps to conduct a least‐cost path connectivity analysis and an omnidirectional circuit connectivity analysis. Main Conclusions Habitat suitability was strongly associated with low percentages of agricultural cover, low percentages of human development, and proximity to forest. Of our entire study area, 37% (4.09 million km 2 ) is occupied or potentially suitable for bears. Connectivity analyses identified corridors that could facilitate movement among southern European bear populations, though agricultural land and human development limit connectivity between northern and southern European bear populations. Previous research estimated bears occupied 0.5 million km 2 across the European Union, while our results estimate 1.82 million km 2 of this part of our study area is potentially suitable for bears, though connectivity is limited. Our results inform conservation strategies and policy development for the future of brown bears in Europe, emphasising the need for transboundary conservation efforts.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".