Integration of aerial surveys and resource selection analysis indicates human land use supports boreal deer expansion
Bibliographic record
Abstract
Landscape change is a driver of global biodiversity loss. In the western Nearctic, petroleum exploration and extraction is a major contributor to landscape change, with concomitant effects on large mammal populations. One of those effects is the continued expansion of invasive white-tailed deer populations into the boreal forest, with ramifications for the whole ecosystem. We explored deer resource selection within the oil sands region of the boreal forest using a novel application of aerial ungulate survey (AUS) data. Deer locations from AUS were "used" points and together with randomly allocated "available" points informed deer resource selection in relation to landscape variables in the boreal forest. We created a candidate set of generalized linear models representing competing hypotheses about the role of natural landscape features, forest harvesting, cultivation, roads, and petroleum features. We ranked these in an information-theoretic framework. A combination of natural and anthropogenic landscape features best explained deer resource selection. Deer strongly selected seismic lines and other linear features associated with petroleum exploration and extraction, likely as movement corridors and resource subsidies. Forest harvesting and cultivation, important contributors to expansion in other parts of the white-tailed deer range, were not as important here. Stemming deer expansion to conserve native ungulates and maintain key predator-prey processes will likely require landscape management to restore the widespread linear features crossing the vast oil sands region.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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 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".