Conservation Reserve Program is a key element for managing white-tailed deer populations at multiple spatial scales
Bibliographic record
Abstract
Understanding the underlying mechanisms driving population demographics such as species-habitat relationships and the spatial scale in which these relationships occur is essential for developing optimal management strategies. Here we evaluated how landscape characteristics and winter severity measured at three spatial scales (1 km2, 9 km2, and hunting unit) influenced white-tailed deer occurrence and abundance across North Dakota by using 10 years of winter aerial survey data and generalized linear mixed effects models. In general, forest, wetland, and Conservation Reserve Program (CRP) lands were the main drivers of deer occurrence and abundance in most of the spatial scales analyzed. However, the effects of habitat features vary between the home-range scale (9 km2) and the finer spatial scale (1 km2; i.e., within home ranges). While escape cover was the main factor driving white-tailed deer occurrence and abundance at broad spatial scales, at a fine spatial scale deer also selected for food (mainly residual winter cropland). With CRP appearing in nearly all top models, here we had strong evidence that this type of program will be fundamental to sustaining populations of white-tailed deer that can meet recreational demands. In addition, land managers should focus on ways to protect other escape covers (e.g., forest and wetland) on a broad spatial scale while encouraging landowners to supply winter resources at finer spatial scales. We therefore suggest a spatial multi-scale approach that involves partnerships among landowners and government agencies for effectively managing white-tailed deer.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".