Range Expansion of Moose in Arctic Alaska Linked to Warming and Increased Shrub Habitat
Bibliographic record
Abstract
Twentieth century warming has increased vegetation productivity and shrub cover across northern tundra and treeline regions, but effects on terrestrial wildlife have not been demonstrated on a comparable scale. During this period, Alaskan moose (Alces alces gigas) extended their range from the boreal forest into tundra riparian shrub habitat; similar extensions have been observed in Canada (A. a. andersoni) and Eurasia (A. a. alces). Northern moose distribution is thought to be limited by forage availability above the snow in late winter, so the observed increase in shrub habitat could be causing the northward moose establishment, but a previous hypothesis suggested that hunting cessation triggered moose establishment. Here, we use recent changes in shrub cover and empirical relationships between shrub height and growing season temperature to estimate available moose habitat in Arctic Alaska c. 1860. We estimate that riparian shrubs were approximately 1.1 m tall c. 1860, greatly reducing the available forage above the snowpack, compared to 2 m tall in 2009. We believe that increases in riparian shrub habitat after 1860 allowed moose to colonize tundra regions of Alaska hundreds of kilometers north and west of previous distribution limits. The northern shift in the distribution of moose, like that of snowshoe hares, has been in response to the spread of their shrub habitat in the Arctic, but at the same time, herbivores have likely had pronounced impacts on the structure and function of these shrub communities. These northward range shifts are a bellwether for other boreal species and their associated predators.
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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 | 0.000 |
| 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 | 0.000 | 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".