Seasonal variation in coyote feeding behaviour and mortality of white-tailed deer and mule deer
Bibliographic record
Abstract
Young ungulates are considered especially susceptible to predation in the initial weeks following birth. However, the timing of mortality can vary depending on the availability of alternative prey and the type of predator, and could vary depending on antipredator defenses used by prey. I used coyote (Canis latrans) scats, observations of coyote hunting behaviour, and mortality data for deer to examine seasonal variation in coyote feeding behaviour and mortality of sympatric white-tailed deer (Odocoileus virginianus) and mule deer (Odocoileus hemionus) fawns. Coyotes captured the vast majority of deer they consumed, forming groups that hunted deer from June through March. Coyotes were observed hunting deer most often in winter when ground squirrels were not available, and an inverse correlation between the amount of deer and ground squirrel in coyote scat reflected this relationship (rs = 0.77, P = 0.004). Fawns of both species had poor survival rates in 1994 (1 of 10 tagged whitetails survived to 1 year, none of 22 mule deer survived), improved survival rates in 1995 (33% of 15 whitetails, 25% of 24 mule deer), and most mortality appeared to be due to coyote predation. The season in which fawns of each species were most vulnerable differed. Tagged whitetail fawns had similar mortality rates in early summer, when they were less than 8 weeks old, as they did in winter, when they were 5-9 months old (35 and 37%, respectively, in 1995). In contrast, mule deer fawns had low mortality rates in early summer (4% in 1994, 17% in 1995), but high mortality rates in winter (100% in 1994, 53% in 1995). Changes in fawn:doe ratios and the examination of carcasses similarly indicated that coyotes captured more whitetails in summer and more mule deer in winter. The seasonal variation in mortality rates of the two species cannot be explained by physical prey characteristics, their relative abundance, or extrinsic factors, and may be due instead to species differences in antipredator behaviour.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".