Black-tailed deer resource selection reveals some mechanisms behind the ‘luxury effect’ in urban wildlife
Bibliographic record
Abstract
The global urban population is expected to increase by 2.5 billion people over the next 30 years. Yet the doubling of urban landscapes in the last decades have already led to habitat loss and concomitant impacts to biodiversity. Nonetheless urban landscapes remain important for wildlife, and global syntheses have revealed that wealthy urban areas house more biodiversity, a ‘luxury effect’. We researched some of the mechanisms for the luxury effect for urban black-tailed deer, a species of increasing concern in urban landscapes across the northwestern Nearctic. We satellite collared twenty deer in an urban landscape in British Columbia, Canada, with high-resolution fix rates. We used generalized models in an information-theoretic framework to weigh evidence for competing hypotheses about the role of tree cover, productivity, public green spaces, and wealth in explaining deer selection. Wealth, manifesting as housing lot size, emerged as the dominant predictor of deer space-use, which is highly concentrated into very small home-ranges. Other landscape elements stemming from affluence, including golf courses and parklands, were also strongly selected by deer. We show post-colonization landscape conversion from dry semi-arid savannah to well-watered high-productivity landscapes is supporting deer, with ramifications for the rest of the biotic community. With urban landscapes becoming an increasingly important for biodiversity conservation, understanding these mechanisms can help to promote wildlife-human coexistence.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".