Experimental influence of population density and vegetation biomass on the movements and activity budget of a large herbivore
Bibliographic record
Abstract
Population density could influence herbivore foraging decisions as it affects the availability of preferred plant species and intraspecific competition. We tested the effect of density on white-tailed deer (Odocoileus virginianus) movements and activity budgets at controlled densities of 7.5 and 15 deer/km2. We also measured the activity budget of deer and plant biomass in an unfenced area at >20 deer/km2. Deer in the unfenced area spent less time active than those at controlled densities, possibly because of the greater time required to process a low quality diet. Biomass of preferred plant species significantly increased through years but did not differ between controlled densities. Adults were less active than yearlings at 7.5 but not at 15 deer/km2 but, otherwise, movements and activity budgets were similar between densities. Deer at controlled densities responded to the increase of plant biomass by increasing the number of activity bouts and shortening their duration. When vegetation was less abundant, adults at 7.5 deer/km2 spent more time active. Augmentation of population density and, thus, of intraspecific competition, can have direct effects on deer foraging behavior. Increases in plant biomass, however, revealed that plant biomass appears to have a stronger influence on deer foraging behavior than population density.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".