Long‐term benefits of burns for large mammal habitat undermined by large, severe fires in the American West
Bibliographic record
Abstract
Escalating wildfire frequency and severity are altering wildland habitats worldwide. Yet investigations into fire impacts on wildlife habitat rarely extend to the macroecological scales relevant to species conservation and global change processes. We evaluate the effects of wildfire on habitat quality and selection by large mammals spanning three trophic levels in the Western United States. We analyze 12 years of GPS telemetry data for 2966 mule deer Odocoileus hemionus , 52 black bears Ursus americanus , and 74 cougars Puma concolor across Utah and Nevada, USA. Over 800 areas burned between 1990–2022 overlapped with the home ranges of 1892 animals, resulting in almost 23 000 km 2 of burned habitat and representing 12.8% of the total home range area for animals in our sample. Habitat suitability models for 664 mule deer, 14 black bears and 11 cougars indicated that burns improved summer home range quality for mule deer and black bears by 7% and 14%, respectively, highlighting the benefits of fires for nutrient cycling, understory herbaceous growth, and resultant caloric value for animal nutrition. When making fine‐scale movement decisions, however, mule deer avoided burned habitats, and all three species generally avoided high‐severity burns for up to 30 years post‐fire. Thus, the effects of burns on wildlife habitat selection appear to be dependent on spatial scale. Given projected increases in large, severe fires, our results suggest potential reductions in beneficial habitat for wildlife in the long term. However, our results also suggest that prescribed burns, because of their smaller spatial footprints and lower severity relative to wildfires, can benefit wildlife habitat quality through improvements in forage, cover, and other vegetation characteristics. Therefore, managing for low‐severity burns and limiting large, severe wildfires, e.g. via prescribed burns or fire control policies, could positively impact the habitat quality of these three common species and, therefore, the economic and ecosystem services they provide.
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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 | 0.001 |
| 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".