Postfire deciduous canopies drive patterns in snowshoe hare herbivory of regenerating black spruce
Bibliographic record
Abstract
The future of boreal forests in Alaska, United States, will likely consist of more deciduous-dominant stands because larger and more severe fires facilitate the establishment of deciduous species such as trembling aspen (Populus tremuloides Michx.) and Alaska birch (Betula neoalaskana Sarg.). Whether stands transition to a deciduous-dominant system or mixed-wood forest or return to being dominated by black spruce (Picea mariana (Mill.) Britton, Sterns & Poggenb.) depends on the capacity of regenerating black spruce to grow and produce seed before the next fire. We hypothesized that winter herbivory by snowshoe hares (Lepus americanus Erxleben, 1777) can suppress black spruce under deciduous canopies. We addressed this question by measuring changes in spruce height and herbivory across 54 plots in Interior Alaska that burned 8–88 years ago and related these data to plot-level data collected by the Bonanza Creek Long-Term Ecological Research program. Spruce were more likely browsed at deciduous-dominant sites with dense canopies, and this browsing likely reduced their height growth. Although we found more subtle effects of browsing on height at the individual level, browsing was an important variable in a confirmatory path analysis at the plot level. These observations affirm our broader hypothesis of the selectivity of hare browsing, in that snowshoe hares prefer to browse spruce that are taller and faster growing, effectively “leveling” regenerating seedlings and saplings so that browsed and unbrowsed individuals within a site are the same height.
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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 | 0.000 | 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) | 0.002 | 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".