Density and distribution of a brown bear (Ursus arctos) population within the Caucasus biodiversity hotspot
Bibliographic record
Abstract
Population declines and extirpations of large mammalian carnivores are major concerns for global biodiversity conservation. Many large carnivores are vulnerable to conflict with humans and attract conservation attention for their flagship appeal and ecological importance. Coexisting with carnivores requires an understanding of carnivore distribution and abundance relative to human activities and disturbances. Such knowledge is often hindered by the rare and elusive nature of carnivores and the lack of systematic ecological surveys in biodiverse regions facing high levels of threat. The Caucasus Ecoregion is one such biodiversity hotspot harboring several threatened mammal species for which there is a paucity of reliable data, including brown bears (Ursus arctos). Caucasus brown bear populations have declined significantly from historical times and may be isolated and vulnerable to disturbance from development activities such as mining, as well as increasing hunting pressure. To inform land-use planning and bear conservation in the Caucasus Ecoregion, we conducted systematic surveys in May-October 2015 in the foothills of the Caucasus Mountains within the Vayots Dzor region of Armenia. We used noninvasive genetic sampling, camera trapping, and statistical models that account for imperfect detection to estimate density and distribution of the bear population in the 1,000-km 2 study area. Across 34 sampling sites, we obtained 3,163 camera-trap photos of brown bears and genotyped 28 individual bears (7 males and 21 females). Spatially explicit capture-recapture models revealed an unexpectedly high density of bears (59.4/1,000 km 2 ; females = 44.6, 95% confidence interval, CI = 25.4-78.4; males = 14.8, 95% CI = 6.6-34.0), and multi-method occupancy models indicated that bears were distributed across most of the study area ( = 0.85; SE = 0.07). These results provide robust evidence that a significant population of brown bears persists in Armenia's Vayots Dzor region, despite a history of hunting and habitat loss that have driven declines in brown bear populations throughout much of the Caucasus Ecoregion. Continued persistence of this flagship species may be threatened by mining, poaching, and other anthropogenic pressures in the region, underscoring the urgent need for strategic conservation planning, impact mitigation, and expanded ecological monitoring within this biodiversity hotspot.
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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 | 0.001 | 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 | 0.000 | 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) | 0.000 | 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".