Planning for coexistence : assessing predictors of human-carnivore conflict on Southern Vancouver Island
Bibliographic record
Abstract
The urban-wildland interface is growing as human development expands, potentially increasing human-wildlife conflict. Conflicts include animals accessing garbage, damaging agricultural crops, or depredating livestock. For mammalian carnivores this often leads to lethal mitigation. Mortality from conflict represents a major threat to carnivores who miscalculate the risk of human-dominated areas. By contrast, carnivores that adapt to these novel anthropogenic environments may facilitate human-wildlife coexistence. \nHuman-carnivore conflict is an increasing issue on Vancouver Island, British Columbia, due to rapidly expanding development and high concentrations of black bears (Ursus americanus) and cougars (Puma concolor). To reduce these conflicts and promote coexistence, it is critical to target proactive mitigations using reliable evidence to distinguish where conflict is probable from where carnivores are adapting to coexist. \nI modelled relative conflict probability using seven years of reported conflicts and GIS data to investigate which anthropogenic and environmental predictors best explained the spatial and temporal distribution of conflict in Victoria’s Capital Regional District. I found that the probability of conflict for both species increased along the urban-wildland interface, where human disturbance adjoined natural habitat. Black bear conflict also increased in rural areas in autumn before winter denning. \nI subsequently used a camera trap survey to see when and where bears were active across a gradient of human disturbance and compared bear habitat use to the previously estimated probabilities of conflict. For much of the year, bears used areas of low to medium conflict, such as forests near urban areas, avoided areas of higher human density, and were more nocturnal in urban and rural areas compared to wild. However, in autumn, bears were more active in areas of high conflict probability, specifically rural lands with ripe crops. This suggests that bear behaviour may allow for coexistence in most seasons by spatially and temporally avoiding humans, except in autumn when hyperphagia and peak anthropogenic crop availability increase the risk of human-bear conflict. \nOverall, I recommend proactive conflict mitigation to secure anthropogenic attractants against multiple carnivore species, and a particular focus on mitigations during seasonal peaks in attractive human food resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".