Why does animal home range size decrease with population density?
Bibliographic record
Abstract
Spatial confinement to a home range is theorized to be a more energetically efficient method of acquiring resources than random searching due to spatial memory. Intraspecific studies that have compared home range size at different population densities have found that home ranges shrink as population density increases. This negative trend could be due to increased conspecific competition via population density increase or due to correlations between resource density and population density. We use the 10-year population cycle of snowshoe hares (Lepus americanus) and individual-level food-add experiments as a case study to assess whether the mechanism of the relationship between home range size and population density is related to competition from increased conspecific density or confounds between population density and resource density. Over six winters (1 December-31 March) and a 50-fold change in population density, we estimated weekly home range sizes (n = 464; 90% minimum convex polygons) of 88 radio-collared hares, of which 26 were food-supplemented. We found a negative relationship between home range size and population density in controls; home ranges decreased by 2.5 ha as hare density increased from 0.24 to 1.2 hare/ha. Food-supplemented hares showed a more negative response to population density than controls (4.0 ± 0.56 ha decrease per 1 hare/ha increase). Our results suggest that the negative trend between home range size and population density is not due to confounds between population and resource density. Likely, there is a trade-off between resource acquisition and some other density-driven constraint when foraging at high densities, which we suggest is a reduction in resource sharing to minimize competition and maintain resource familiarity at high densities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".