Transitional climate mortality: slower warming may result in increased climate‐induced mortality in some systems
Bibliographic record
Abstract
Abstract The potential for climate change to cause mass tree mortality in forested systems by pushing environmental conditions past physiological tolerance thresholds is well documented. Less well studied is damage and mortality associated with climatic transitions, where mortality is less on either side of the transition; the shift from freezing winter conditions to thawed winter conditions in temperate and high latitudes is a clear example. “Transitional climate mortality” is sporadic, but widespread, associated with exposure to mortality during these climatic transitions, and triggered by proximal weather events like a hard freeze after a period of above freezing temperatures. Interestingly, this suggests that slower warming could result in more intensive mortality because of extended exposure to potential mortality events. The concept is tested using a well‐studied species ( Callitropsis nootkatensis ) on the US /Canadian Pacific coast. To identify the transitional mortality zone, statistical modeling combined with a current mortality map and bioclimatic variables was used. This process identified the −5° to 0°C zone (mean temperature of the coldest month) as particularly associated with transitional mortality. Weather station data from multiple locations were used to validate observations. Four GCM s (General Circulation Model) and two future warming scenarios (representative concentration pathway 2.6 and 8.5) were used to estimate time and spatial extent of exposure at broad scales; slower warming results in more intensive cumulative exposure than faster warming. Finally, by combining the observed mortality zone with weather station data a generalizable binomial model was tested as a means to estimate potential future mortality. This model is applicable to any system where the transitional mortality zone (e.g., temperature range) and the frequency of proximal mortality triggers are known. This type of mortality phenomena has been understudied but may be a large driver of future forest change, given the frequency of mortality events and the ubiquity of the freeze–thaw transition across temperate systems globally.
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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 | 0.001 | 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) | 0.001 | 0.001 |
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".