Acute drought desiccates highly used habitat and drives herbivores into irrigated croplands
Bibliographic record
Abstract
In arid and semiarid regions, extreme, extended droughts are becoming more frequent due to climate change. Drought is driving wildlife to seek out food or water resources where they are not as limited, such as in irrigated croplands. We collected GPS locations from 41 mule deer, a generalist herbivore reliant on primary productivity, within three study areas in Utah, USA, during a summer without drought conditions and a summer with extreme drought. This natural experiment provided an opportunity to assess how mule deer shifted their habitat selection, specifically whether drought increased mule deer's use of anthropogenic resources. We integrated remotely sensed estimates from ECOSTRESS, an instrument mounted on the International Space Station that measures evapotranspiration, to characterize a shift in resource use. Mule deer resource use was strongly influenced by the amount of evapotranspiration. In the drought year, shrub habitats lost succulence and mule deer avoided them (57.0% shrub habitat use in baseline vs. 44.6% during drought) and sought out agricultural croplands (increase from 6.2% to 11.8% from baseline to drought). Critically, this behavioral switch from shrub to crop was mediated by the rate of evapotranspiration and we identify the shift when evapotranspiration was >1.03 mm/day. We estimated that the proportion of shrub habitat in the study area with evapotranspiration >1.03 mm/day dropped from 68.8% to 27.2% between the baseline and the acute drought year. Evapotranspiration measured by ECOSTRESS provides complementary information to normalized difference vegetation index (NDVI), a commonly used metric of vegetative greenness, and offers a mechanistic understanding of ungulate resource use that increases the performance of habitat selection models for herbivores. As the impacts of climate change become more acute, wildlife will be drawn from natural areas to locations with anthropogenic resources, elevating the risk of human-wildlife conflict and mortality. Our study points to the need for the use of new data streams, like data derived from ECOSTRESS, into adaptive wildlife management and climate change adaptation planning to minimize human-wildlife risk and damages to humans.
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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) | 0.000 | 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 teacher head, 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".