Forage stoichiometry predicts the home range size of a small terrestrial herbivore
Bibliographic record
Abstract
Abstract Consumers make space use decisions based on resource quality. Most studies that investigate the influence of resource quality on the spatial ecology of consumers use diverse proxies for quality including measures based on habitat classification, forage species diversity and abundance, and nutritional indicators, e.g., protein. Ecological stoichiometry measures resource quality in terms of elemental ratios, e.g., carbon (C):nitrogen (N) ratio, but rarely have these currencies been used to study consumer space use decisions. Yet, elemental ratios provide a uniquely quantitative way to assess resource quality. Consequently, ecological stoichiometry allows for investigation of how consumers respond to spatial heterogeneity in resource quality by changing their space use, e.g. their home range size, and how this may influence ecosystem dynamics and trophic interactions. Here, we test whether the home range size of a keystone boreal herbivore, the snowshoe hare ( Lepus americanus ), varies with differences in the C:N, C:phosphorus (P), and N:P ratios of two preferred forage species, lowland blueberry ( Vaccinium angustifolium ) and red maple ( Acer rubrum ). We consider forage resources with higher C content relative to N and P to be lower quality than resources with lower relative C content. We use a novel approach, combining elemental distribution models with herbivore home range size estimates to test our hypothesis that hare home range size will be smaller in areas with access to high, homogeneous resource quality compared to areas with access to low, heterogeneous resource quality during summer months. Our results support our prediction for lowland blueberry, but not for red maple. Herbivore home range size decreased with increasing blueberry foliage quality, but also with decreasing spatial heterogeneity in blueberry foliage quality, i.e. N or P content. Herbivores in the boreal forest face strong nutritional constraints due to the paucity of N and P. Access to areas of high, homogeneous resource quality is paramount to meeting their dietary requirements with low effort. In turn, this may influence community (e.g., trophic interactions) and ecosystem (e.g., nutrient cycling) processes. Paradoxically, our study shows that taking a reductionist approach of viewing resources through a biochemical lens can lead to holistic insights of consumer spatial ecology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".