What drives fine‐scale movements of large herbivores? A case study using moose
Bibliographic record
Abstract
Understanding animal movements across heterogeneous landscapes is of great interest because it helps explain the dynamic processes influencing the distribution of individuals in space. Research on how animals move relative to short‐range environmental characteristics are scarce. Our objective was to determine the variables influencing movement of a large ungulate, the moose Alces alces , ranging across a boreal landscape, and to link movement behaviour with limiting factors at a fine scale. We assessed 7 candidate models composed of vegetation, solar energy, and topography variables using step selection functions (SSF) for male and female moose across daily and annual periods. We selected and weighted models using the Bayesian Information Criterion. Variables influencing small‐scale movements of moose differed among periods and between sexes, likely in response to corresponding changes in the importance of limiting factors. Best models often combined many types of variables, although simpler models composed of only vegetation or topography variables explained male's movements during rut and early winter. Moose steps were observed in good feeding stands from summer to early winter for females and from spring to early winter for males, supporting other studies of moose habitat selection. From summer to early winter, females alternatively selected and avoided cover stands during day and night, respectively. Solar energy reaching the ground was important, particularly during late winter and spring, likely due to its effect on snow cover, air temperature, or plant phenology. Moose generally moved in gentle slopes and variable elevation, which may have increased their chances of finding high quality forage, or improved their search of suitable calving sites or mates. Our study revealed the great complexity and dynamic aspects of animal movements in a heterogeneous landscape. Analysis of animal movement provides complementary information to more static habitat selection analyses and helps understanding the spatial variations in the distribution of individuals through time.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".