Survival and cause-specific mortality of coyotes in Wisconsin
Bibliographic record
Abstract
Abstract Understanding the drivers of population dynamics informs management actions and assures the public that harvest activities are not detrimental to the long-term stability of wildlife populations. We examined the survival and cause-specific mortality of 66 adult coyotes (34 males, 32 females) using GPS radiotelemetry in southwestern Wisconsin during October 2016 to March 2020. We paired our study with a literature review of coyote survival and mortality across the United States and Canada, focusing on the geographical distribution of studies, demographic aspects of survival, and the level of exploitation by humans on coyote populations. In Wisconsin, annual survival did not differ between sexes or across years but did vary among seasons and social statuses. The relative risk for a coyote dying was higher during the winter compared to the summer. A transient coyote had a higher relative risk of mortality compared to a resident coyote. Mean annual survival probability (sexes combined) was higher for a year-long resident compared to a year-long transient. The predominant sources of known mortality (n = 37) were harvest (83.8%) and vehicle collisions (13.5%). For our literature review, we identified 56 studies estimating coyote survival or mortality from 1971 to 2021 spanning the geographic range of coyotes. We found no distinct temporal or regional patterns in survival probability or the proportion of human-induced mortality, although fewer studies originated from the northeast region of the United States. Additionally, we detected weak correlation between survival probability and proportion of human-induced mortality, suggesting coyote harvest may be compensatory. Although our findings indicate that the Wisconsin coyote population had relatively higher human-induced mortality than populations in other regions, these mortality rates appear to be sustainable for this population under current landscape and habitat conditions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".