Analog‐based fire regime and vegetation shifts in mountainous regions of the western US
Bibliographic record
Abstract
Climate change is expected to result in substantial ecological impacts across the globe. These impacts are uncertain but there is strong consensus that they will almost certainly affect fire regimes and vegetation. In this study, we evaluated how climate change may influence fire frequency, fire severity, and broad classes of vegetation in mountainous ecoregions of the contiguous western US for early, middle, and late 21st century (2025, 2055, and 2085, respectively). To do so, we employed the concept of a climate analog, whereby specific locations with the best climatic match between one time period and a different time period are identified. For each location (i.e. 1‐km 2 pixel), we evaluated potential changes by comparing the reference period fire regime and vegetation to that of the fire regime and vegetation of the nearest pixels representative of its future climate. For the mountainous regions we investigated, we found no universal increase or decrease in fire frequency or severity. Instead, potential changes depend on the bioclimatic domain. Specifically, wet and cold regions (i.e. mesic and cold forest) generally exhibited increased fire frequency but decreased fire severity, whereas drier, moisture‐limited regions (i.e. shrubland/grassland) displayed the opposite trend. Results also indicate the potential for substantial changes in the amount and distribution of some vegetation types, highlighting important interactions and feedbacks among climate, fire, and vegetation. Our findings also shed light on a potential threshold or tipping point at intermediate moisture conditions that suggest shifts in vegetation from forest to shrubland/grassland are possible as the climate becomes warmer and drier. However, our study assumes that fire and vegetation are in a state of equilibrium with climate, and, consequently, natural and human‐induced disequilibrium dynamics should be considered when interpreting our findings.
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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 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".