Behavior and survival of white-tailed deer neonates in two suburban forest preserves
Bibliographic record
Abstract
Neonatal survival influences growth of unhunted populations of suburban white-tailed deer ( Odocoileus virginianus (Zimmerman, 1780)). Understanding the interaction of habitat and survival may inform conservation efforts and studies of life history of cervids at high density. We chose two forest preserves representative of forests in suburban Chicago. We radio-marked 56 neonates (1999–2001) to investigate mortality and habitat use. Through 1 July, 21 of 29 (72%) neonates and 6 of 22 (27%) died mostly because of predation by coyotes ( Canis latrans Say, 1823). Akaike’s information criterion suggested that optimal mark–recapture models of survival contained covariates reflecting differences by preserve and timing chosen to coincide with behavioral change from hiding to accompanying the doe. Survival was lower during early parturition (0.26–0.78) relative to the latter part (0.90–0.96). Early fawns (hiders) at one site had lower survival (0.26–0.29) than fawns at the other (0.78). Lower survival associated with larger home ranges, greater movement, and reduced understory cover, suggesting that hiding cover may mediate fawn survival in the presence of predators. Our study demonstrates spatial heterogeneity in population biology of suburban deer and suggests that site-specific differences may influence neonate survival in the face of coyote predation.
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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 | 0.000 | 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) | 0.000 | 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".