Synergistic Population Density and Environmental Effects on Deer Body Condition
Bibliographic record
Abstract
ABSTRACT The ability to predict energy and protein allocation to different body condition parameters according to environmental constraints is a key component in understanding the processes underlying population dynamics. We investigated the influence of a proxy of population density and environmental factors on individual body condition parameters of female white‐tailed deer (Odocoileus virginianus) based on long‐term monitoring (2002–2013) of autumn harvest on Anticosti Island, Québec, Canada. We used dressed body mass, peroneus muscle mass, and rump fat thickness to evaluate the nutritional status of 3,123 adult females. Density index and winter precipitation negatively affected fat reserves in autumn. We detected the negative effect of winter precipitation on fat reserves only at low density likely because individuals at high density were already in bad condition. High normalized difference vegetation index (NDVI) in spring (May–Jun) reduced body mass, and this influence was more pronounced under high population density, probably because individuals at high densities were less likely to be buffered against environmental fluctuations when resources were scarcer than resources at low population density. Using different body condition parameters, our results provide additional insights on how northern ungulates influenced by food limitation may respond to future environmental changes. We recommend managers to collect long‐term data on multiple physiological indicators of body condition. These data could be used as an index of ecological changes and provide a quantitative basis to help setting harvest objectives or supporting adaptive management. © 2020 The Wildlife Society.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".