Large carnivore habitat suitability modelling for Romania and associated predictions for protected areas
Bibliographic record
Abstract
Habitat characteristics associated with species occurrences represent important baseline information for wildlife management and conservation, but have rarely been assessed for countries recently joining the EU. We used footprint tracking data and landscape characteristics in Romania to investigate the occurrence of brown bear ( Ursus arctos ), gray wolf ( Canis lupus ) and Eurasian lynx ( Lynx lynx ) and to compare model predictions between Natura 2000 and national-level protected areas (gap analysis). Wolves were more likely to occur where rugged terrain was present. Increasing proportion of forest was positively associated with occurrence of all large carnivores, but forest type (broadleaf, mixed, or conifer) generally varied with carnivore species. Areas where cultivated lands were extensive had little suitable habitat for lynx, whereas bear occurrence probability decreased with increasing proportion of built areas. Pastures were positively associated with wolf and lynx occurrence. Brown bears occurred primarily where national roads with high traffic volumes were at low density, while bears and lynx occurred at medium-high densities of communal roads that had lower traffic volumes. Based on predictions of carnivore distributions, natural areas protected in national parks were most suitable for carnivores, nature parks were less suitable, whereas EU-legislated Natura 2000 sites had the lowest probability of carnivore presence. Our spatially explicit carnivore habitat suitability predictions can be used by managers to amend borders of existing sites, delineate new protected areas, and establish corridors for ecological connectivity. To assist recovery and recolonization, management could also focus on habitat predicted to be suitable but where carnivores were not tracked.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".