Linking moose habitat selection to limiting factors
Bibliographic record
Abstract
It has been suggested that patterns of habitat selection of animals across spatial scales should reflect the factors limiting individual fitness in a hierarchical fashion. Animals should thus select habitats that permit avoidance of the most important limiting factor at large spatial scales while the influence of less important factors should only be evident at fine scales. We tested this hypothesis by investigating moose Alces alces habitat selection using GPS telemetry in an area where the main factors limiting moose numbers were likely (in order of decreasing importance) predation risk, food availability and snow. At the landscape scale, we predicted that moose would prefer areas where the likelihood of encountering wolves was low or areas where habitats providing protection from predation were dominant. At the home‐range scale, we predicted that moose selection would be driven by food availability and snow depth. Wolf territories were delineated using telemetry locations and the study area was divided into 3 sectors that differed in terms of annual snowfall. Vegetation surveys yielded 6 habitat categories that differed with respect to food availability, and shelter from predation or snow. Our results broadly supported the hypothesis because moose reacted to several factors at each scale. At the landscape scale, moose were spatially segregated from wolves by avoiding areas receiving the lowest snowfall, but they also preferentially established their home range in areas where shelter from snow bordered habitat types providing abundant food. At the home‐range scale, moose also traded off food availability with avoidance of deep snow and predation risk. During winter, moose increased use of stands providing shelter from snow along edges with stands providing abundant food. Habitat selection patterns of females with calves differed from that of solitary moose, the former being associated primarily with habitats providing protection from predation. Animals should attempt to minimize detrimental effects of the main limiting factors when possible at the large scale. However, when the risk associated with several potential limiting factors varies with scale, we should expect animals to make trade‐offs among these.
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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 | 0.000 | 0.000 |
| 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".