Fire sparks upslope range shifts of <scp>N</scp>orth <scp>C</scp>ascades plant species
Bibliographic record
Abstract
As ongoing climate change drives suitable habitats to higher elevations, species ranges are predicted to follow. However, observed range shifts have been surprisingly variable, with most species differing in rates of upward shift and others failing to shift at all. Disturbances such as fires could play an important role in accelerating range shifts by facilitating recruitment in newly suitable habitats (leading edges) and removing adults from areas no longer suited for regeneration (trailing edges). To date, empirical evidence that fires interact with climate change to mediate elevational range shifts is scarce. Resurveying historical plots in areas that experienced climate change and fire disturbance between surveys provides an exciting opportunity to fill this gap. To investigate whether species have tended to shift upslope and if shifts depend on fires, we resurveyed historical vegetation plots in North Cascades National Park, Washington, USA, an area that has experienced warming, drying, and multiple fires since the original surveys in 1983. We quantified range shifts by synthesizing across two lines of evidence: (1) displacement at range edges and the median elevation of species occurrences, and (2) support for the inclusion of interactions among time, fire and elevation in models of species presence with elevation. Among species that experienced fire since the original survey, a plurality expanded into new habitats at their upper edge. In contrast, a plurality of species not experiencing fire showed no evidence of shifts, with the remainder exhibiting responses that were variable in magnitude and direction. Our results suggest that fires can facilitate recruitment at leading edges, while species in areas free of disturbance are more likely to experience stasis.
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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 | 0.000 | 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".