Spatiotemporal habitat use by a multitrophic Alaska alpine mammal community
Bibliographic record
Abstract
Evaluating sympatric habitat use of a mammal community can help determine intra- and inter-guild interactions and identify important habitats, potentially improving the management of these communities with a changing climate. Increasingly variable climatic patterns in Alaska, USA, are raising concerns of mismatched phenologies and altered ecosystem structures. We studied the occupancy of 10 mammal species over 15 months, via camera traps, occupying alpine areas of the Alaska Range in interior Alaska, from 2013 to 2014. We tested hypotheses about how habitat use of these species within and between groups varied by spatial and temporal covariates. Furthermore, we modeled two-species occupancy of brown bears (Ursus arctos Linnaeus, 1758) and gray wolves (Canis lupus Linnaeus, 1758) against different potential prey species. Our results suggest that medium-sized and large herbivore use was positively correlated with fine-scale covariates including rock, forb, and graminoid coverage. Large herbivore habitat use was also correlated with abiotic landscape covariates. Detection probabilities of predators and Dall’s sheep (Ovis dalli dalli Nelson, 1884) was improved by camera traps on wildlife trails. Two-species models suggested co-occurrence of habitat use between brown bear – caribou (Rangifer tarandus (Linnaeus, 1758)) and gray wolf – caribou. Results demonstrate the sympatric habitat use by multiple groups of mammals within Alaskan alpine ecosystems and the importance of incorporating multiple groups and spatial scales when making management decisions.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".