Spatiotemporal risk factors predict landscape‐scale survivorship for a northern ungulate
Bibliographic record
Abstract
Abstract Effective wildlife conservation requires decomposing the drivers of population dynamics for species affected by anthropogenic habitat alterations. Ungulates are often the focus of management actions to restore habitat and maintain connectivity as they are sensitive to landscape disturbances. We used Bayesian proportional hazards models to assess anthropogenic risk factors that could potentially predict landscape‐scale survivorship for pronghorn ( Antilocapra americana ) in the Northern Sagebrush Steppe ecosystem, where extensive habitat alterations occurred from the conversion of native sagebrush grasslands to agricultural lands. Using 170 adult female pronghorn monitored from 2003 to 2011, we tested the importance of linear features (road and fence densities) and forage productivity (maximum decadal normalized difference vegetation index [NDVI]) for spatiotemporal pronghorn mortality risk, while accounting for a seasonally varying proxy of snow depth. We found moderate support for the effects of linear features on mortality risk as coefficient estimates translated to predicted declines in survivorship of 27.1% over the observed range of road densities (0–1.4 km/km 2 ) and 11.8% over the range of fence densities (0–6.1 km/km 2 ) encountered by pronghorn. Our results also suggested that agricultural areas could act as ecological traps for pronghorn, based on mortality risk increasing by a factor of 14.3% with every 0.1 increase in maximum decadal NDVI in summer (range = 0.38–0.73). Like our previous findings, we found considerable support for the effects of average depth (in centimeters) of snow water equivalent (SWE; SWE depth = snow depth × snow density/water density) within pronghorn seasonal ranges, with mortality risk increasing by 45.7% with every 1 cm increase in SWE depth (range = 0–5.37 cm). We then developed the first broadscale, spatially explicit map of predicted annual pronghorn survivorship based on anthropogenic features and environmental gradients to identify areas for conservation and habitat restoration efforts. These efforts to highlight anthropogenic risk factors on the landscape will hopefully support conservation and habitat restoration for pronghorn populations at the northern periphery of their range.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".