Habitat alteration or climate: What drives the densities of an invading ungulate?
Bibliographic record
Abstract
Abstract Anthropogenic habitat alteration and climate change are two well‐known contributors to biodiversity loss through changes to species distribution and abundance; yet, disentangling the effects of these two factors is often hindered by their inherent confound across both space and time. We leveraged a contrast in habitat alteration associated with the jurisdictional boundary between two Canadian provinces to evaluate the relative effects of spatial variation in habitat alteration and climate on white‐tailed deer (Odocoileus virginianus) densities. White‐tailed deer are an invading ungulate across much of North America, whose expansion into Canada's boreal forest is implicated in the decline of boreal caribou (Rangifer tarandus caribou), a species listed as Threatened in Canada. We estimated white‐tailed deer densities using 300 remote cameras across 12 replicated 50 km2 landscapes over 5 years. White‐tailed deer densities were significantly lower in areas where winter severity was higher. For example, predicted deer densities declined from 1.83 to 0.35 deer/km2 when winter severity increased from the lowest value to the median value. There was a tendency for densities to increase with increasing habitat alteration; however, the magnitude of this effect was approximately half that of climate. Our findings suggest that climate is the primary driver of white‐tailed deer populations; however, understanding the mechanisms underpinning this relationship requires further study of over‐winter survival and fecundity. Long‐term monitoring at the invasion front is needed to evaluate the drivers of abundance over time, particularly given the unpredictability of climate change and increasing prevalence of extreme weather events.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".