Habitat selection and occupancy of feral horses in comparison to cattle and elk in the Rocky Mountain Foothills of Canada
Bibliographic record
Abstract
Understanding species occupancy and habitat selection is fundamental to ecology and provides critical information for management. In the Rocky Mountain Foothills of western Canada, feral horses (Equus ferus caballus) are now sympatric with many native species and free-ranging cattle. From 2018–2020 we assessed the seasonal habitat selection of GPS-tracked feral horses in Alberta’s Sundre Equine Management Zone; and compared summer probability of occupancy of horses, domestic cattle (Bos taurus; not present in winter), and elk (Cervus elaphus) using a 120-unit array of trail-camera data. GPS-tracked horses varied in selection for vegetation type and counter to expectations horses tended to avoid native rangeland in summer compared to greater selection for forestry cutblocks. In winter, horses were closer to native rangelands and selected areas closer to roads, areas of lower terrain ruggedness, and areas of higher solar radiation farther from forests, suggesting that forage, habitat accessibility, and thermoregulation are important drivers of winter habitat use. GPS-tracking results were supported by trail-camera occupancy analyses that pointed to the presence of cattle as a potential modulator of horse habitat use. Summer probability of occupancy for horses was highest with increasing coverage of cutblocks in contrast to cattle where occupancy probability decreased strongly with the latter. Cattle occupancy was also negatively influenced by terrain, though positively influenced by the presence of linear features and reduced distance to roads. Elk summer occupancy increased with decreasing distance to conifer forest and increasing native rangeland, though spatial coverage of elk was low compared to cattle and horses. Our results suggest that human-caused landscape changes are important drivers of feral horse occupancy in this northern ecosystem. While cattle may displace horses from native rangeland in summer, horses appear to seasonally adjust their foraging strategy to focus on forestry cutblocks and clearings that are less used by cattle, until cattle are removed from the system for winter. Horse populations can be expected to respond favorably to increasing access to cutblocks in this ecosystem, and their presence can be predicted by increasing anthropogenic activity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".