Idiosyncratic responses of grizzly bear habitat to climate change based on projected food resource changes
Bibliographic record
Abstract
Climate change vulnerability assessments for species of conservation concern often use species distribution and ecological niche modeling to project changes in habitat. One of many assumptions of these approaches is that food web dependencies are consistent in time and environmental space. Species at higher trophic levels that rely on the availability of species at lower trophic levels as food may be sensitive to extinction cascades initiated by changes in the habitat of key food resources. Here we assess climate change vulnerability for Ursus arctos (grizzly bears) in the southern Canadian Rocky Mountains using projected changes to 17 of the most commonly consumed plant food items. We used presence-absence information from 7088 field plots to estimate ecological niches and to project changes in future distributions of each species. Model projections indicated idiosyncratic responses among food items. Many food items persisted or even increased, although several species were found to be vulnerable based on declines or geographic shifts in suitable habitat. These included Hedysarum alpinum (alpine sweet vetch), a critical spring and autumn root-digging resource when little else is available. Potential habitat loss was also identified for three fruiting species of lower importance to bears: Empetrum nigrum (crowberry), Vaccinium scoparium (grouseberry), and Fragaria virginiana (strawberry). A general trend towards uphill migration of bear foods may result in higher vulnerability to bear populations at low elevations, which are also those that are most likely to have human-bear conflict problems. Regardless, a wide diet breadth of grizzly bears, as well as wide environmental niches of most food items, make climate change a much lower threat to grizzly bears than other bear species such as polar bears and panda bears. We cannot exclude, however, future alterations in human behavior and land use resulting from climate change that may reduce survival rates.
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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) | 0.024 | 0.005 |
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".