Fine-scale disturbances shape space-use patterns of a boreal forest herbivore
Bibliographic record
Abstract
Natural disturbance is a key determinant of ecosystem structure and function. Disturbances can create novel resource patches and modify habitat structure, thereby inducing spatial heterogeneity in the trade-off between food acquisition and predator avoidance by prey. We evaluated how canopy gap dynamics in eastern Canadian old-growth boreal forest alter the spatial distribution of food and cover for snowshoe hares (Lepus americanus) and how hares responded to these spatial patterns. We 1st compared browse availability within canopy gaps and the surrounding forest. We then examined fine-scale habitat selection, movement patterns, and foraging decisions by hares during winter. Perception of risk within canopy gaps was assessed using foraging experiments. We found that browse availability was 4 times higher within gaps than under forest cover. Although hares acquired most of their browse from gaps, their use of space during winter was influenced by a greater perception of predation risk within gaps. Hares selectively used areas of higher canopy closure suggesting that they restricted their use of gaps to foraging activities. Furthermore, hares biased their movements away from gaps or increased their speed of travel in areas of relatively low cover. Hares consumed experimental browse stems more intensively under forest cover than in canopy gaps, indicating a trade-off between food and safety. When foraging within canopy gaps, hares also were less likely to use both experimental and natural food patches located far away from cover. Our study demonstrates how gap dynamics in old-growth stands can structure the fine-scale spatial organization of a key prey species of the boreal forest by creating spatial heterogeneity in their landscapes of fear and food. Spatial variation in browse use in response to predation risk may in turn influence patterns of sapling growth and survival within canopy gaps. Gap dynamics therefore may be a fundamental process structuring predator-prey interactions in old-growth boreal forests.
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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 | 0.000 |
| 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) | 0.001 | 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".