Annual Pronghorn Survival of a Partially Migratory Population
Bibliographic record
Abstract
ABSTRACT The importance of conserving migratory populations is recognized across a variety of ungulate taxa, yet the demographic benefits of migration remain uncertain for ungulate populations that exhibit partial migration. We hypothesized that migratory pronghorn (Antilocapra americana) would experience greater survival compared to residents by moving longer distances to avoid severe winter weather and access higher quality forage. We used a Bayesian time‐to‐event approach to analyze the fates of 175 radio‐collared adult female pronghorn monitored over 8 biological years (2004–2011) in the Northern Sagebrush Steppe ecosystem. Annual survivorship of migratory pronghorn was 7% higher on average compared to residents but not statistically different. Migratory pronghorn had higher survivorship in summer and winter compared to residents, and few mortalities were observed during the short autumn and spring migration periods. Mortality risk for both movement tactics intensified under more severe winter weather; winter weather severity alone best explained annual pronghorn mortality risk. The top model predicted survival rates to decline on average by 56% over the range of observed winter climatic conditions. To minimize human impacts to pronghorn during extreme climatic events, we recommend working with transportation departments and land managers to enhance pronghorn crossings of roads and railroads, and landholders to modify fences to wildlife‐friendly standards. © 2020 The Authors. Journal of Wildlife Management published by Wiley Periodicals, LLC on behalf of 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 | 0.000 | 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) | 0.001 | 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".