Linking alternative food sources to winter habitat selection of herbivores in overbrowsed landscapes
Bibliographic record
Abstract
Abstract During winter, ungulates in boreal forests must cope with high energetic costs related to locomotion in deep snow and reduced forage abundance and quality. At high density, ungulates face additional constraints, because heavy browsing reduces availability of woody browse, the main source of forage during winter. Under these severe conditions, large herbivores might forage on alternative food sources likely independent of browsing pressure, such as litterfall or windblown trees. We investigated the influence of alternative food sources on winter habitat selection, by studying female white‐tailed deer ( Odocoileus virginianus ) living in 2 landscapes with contrasted browse abundance, recently logged and regenerated landscapes, in a population at high density and on a large island free of predators. We fitted 21 female white‐tailed deer with Global Positioning System (GPS) collars and delineated winter home ranges and core areas. We measured snow conditions in different habitat categories and sampled vegetation in the core areas and in the rest of the home ranges to determine how forage abundance, protective cover, and snow conditions influenced habitat selection within the home range. In both landscapes, deer were less likely to use open habitat categories as snow accumulated on the ground. At a finer scale, deer inhabiting the regenerated landscape intensively used areas where balsam fir cover was intermediate with greater balsam fir browse density than in the rest of the home range. In the recently logged landscape, deer were more likely to be found near edges between clear‐cuts and balsam fir stands and in areas where windblown balsam fir trees were present; the latter being the most influential variable. Although balsam fir browse was sparse and mainly out of reach in this landscape, deer increased the use of areas where it was present. Our results offer novel insights into the resource selection processes of northern ungulates, as we showed that access to winter forage, such as woody browse and alternative food sources, depends on climatic conditions and stochastic events, such as abundant compacted snow or windthrows. To compensate for these scarce and unpredictable food supplies, deer selected habitat categories, but mostly areas within those habitat categories, where the likelihood of finding browse, litterfall, and windblown trees was greatest. © 2011 The Wildlife Society.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".