The ecology of human–carnivore coexistence
Bibliographic record
Abstract
With a shrinking supply of wilderness and growing recognition that top predators can have a profound influence on ecosystems, the persistence of large carnivores in human-dominated landscapes has emerged as one of the greatest conservation challenges of our time. Carnivores fascinate society, yet these animals pose threats to people living near them, resulting in high rates of carnivore death near human settlements. We used 41 y of demographic data for more than 2,500 brown bears-one of the world's most widely distributed and conflict-prone carnivores-to understand the behavioral and demographic mechanisms promoting carnivore coexistence in human-dominated landscapes. Bear mortality was high and unsustainable near people, but a human-induced shift to nocturnality facilitated lower risks of bear mortality and rates of conflict with people. Despite these behavioral shifts, projected population growth rates for bears in human-dominated areas revealed a source-sink dynamic. Despite some female bears successfully reproducing in the sink areas, bear persistence was reliant on a supply of immigrants from areas with minimal human influence (i.e., wilderness). Such mechanisms of coexistence reveal a striking paradox: Connectivity to wilderness areas supplies bears that likely will die from people, but these bears are essential to avert local extirpation. These insights suggest carnivores contribute to human-carnivore coexistence through behavioral and demographic mechanisms, and that connected wilderness is critical to sustain coexistence landscapes.
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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 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".