A time-lagged effect of conspecific density on habitat selection by snowshoe hare
Bibliographic record
Abstract
Ideal free distribution theory predicts that increased conspecific density redistributes individuals to low-density, suboptimal habitat. However, possible lags in response to population density remain poorly documented. Snowshoe hares (Lepus americanus) may exhibit density-dependent habitat selection due to its marked variation in population density. Based on 11 years (2004-2014) of snow tracking in Quebec (Canada), we investigated snowshoe hares' short-term and delayed habitat selection responses to population density. We predicted that at high densities, hare distribution expands into low-density habitat, thus weakening the association between hares and high-density habitat. We surveyed hare tracks along 95 km of transects on average each year and georeferenced 14,240 tracks. We used Generalized Estimating Equations for track count per 100 m transect segment as a function of the proportion of different forest age classes (0-20 y, 20-40 y and 40-80 y) within 50 m of the segments. We used model coefficients for each age class as a measure of habitat preference, and modeled those coefficients as a function of a population density index in current and previous winters. Coefficients for 20- to 40-y-old forests were positive each year, indicating that this habitat was preferred. The association between track counts and 20- to 40-y-old forest significantly declined with density during the previous winter, suggesting that hare spread from preferred forest with a lagged response to density. To our knowledge, no previous empirical studies have documented a lagged habitat selection response to population density. Time lags offer possible explanation for documented deviations from ideal free distribution models.
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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 | 0.003 |
| 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) | 0.002 | 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".