Ecological and Animal-Level Drivers of Habitat Selection in Beef Cattle Grazing Aspen Parkland Rangelands
Bibliographic record
Abstract
This study explores patterns of habitat selection by beef cattle and their production implications while grazing heterogeneous Aspen Parkland rangelands in western Canada. Using GNSS-based geospatial data, high-resolution vegetation maps derived from remote sensing, local weather records, and individual animal traits including breed composition, residual feed intake (RFI), and growth performance, the study examines how cattle respond to spatial and climatic variability across seasons. Cattle exhibited seasonal shifts in habitat preferences, with strong selection for grasslands and wetlands in summer and increased use of open shrublands in fall, likely to maintain forage access as biomass and quality declined. Habitat selection also varied with forage depletion over time, diurnally, and in response to heat and cold stress. Building on these findings, further investigation demonstrated that selection patterns were shaped by the interaction between breed composition and RFI, particularly in summer. Efficient cattle with higher Continental ancestry more often selected forests, potentially benefiting from thermal buffering and diverse forage structure, while efficient animals with greater British influence favored open shrublands. These behavioral differences translated into performance outcomes, as grassland and wetland use were positively associated with weight gain in both cows and calves, especially during periods of high nutritional demand. In contrast, use of shrubland and forest habitats, particularly in fall, was linked to lower productivity, likely due to reduced forage quality and more challenging foraging conditions. Overall, these findings highlight the importance of accounting for both animal traits and landscape heterogeneity in commercial cattle management. Aligning cattle genetics with habitat features and monitoring spatial behavior can enhance efficiency and productivity in variable rangeland environments.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".