Climate‐induced yellow‐cedar decline on the island archipelago of Haida Gwaii
Bibliographic record
Abstract
Abstract The global rise in temperature and associated changes in climate have led to decline of forests around the globe, across multiple species and ecosystems. Yellow‐cedar ( Callitropsis nootkatensis ) decline is one of the most severe in North America. We found abundant evidence of tree decline and mortality on Haida Gwaii across multiple watersheds and over a range of elevations. This decline on Haida Gwaii parallels the broader yellow‐cedar decline in terms of spatial distribution, symptoms, magnitude, and timing. However, Haida Gwaii has a more temperate climate and ephemeral snowpack than declining yellow‐cedar forests in Alaska where the link to climate was first uncovered. Given these important differences, we investigated several possible drivers both at the local and at the regional scale, using population demography, dendrochronology, and daily weather data. We explored stand dynamics as a driver and tested the known link to climate. Our results are inconsistent with stand dynamics as a driver of elevated decline and mortality. Neither increased competition nor aging of a cohort explains the decline. Alternatively, the magnitude and timing of the decline are consistent with well‐documented long‐term directional trends in regional climate. Onset of basal area increment decline and mortality have been accumulating over time, with increased rates since the 1980s. Our sites were located at the edge of the expected range of mortality, and we found only four thaw–freeze events over the past ~80 yrs. However, superposed epoch analysis using daily weather data revealed that mortality and onset of decline events were associated with warmer winter conditions, consistent with the drivers from Alaska. Rather than isolated extreme thaw–freeze events, warmer winter temperatures on Haida Gwaii may mean less cold hardening throughout the winter, which exposes yellow‐cedar’s fine roots to varying degrees of freezing damage over multiple winter thaw–freeze cycles, causing physiological stress, tree decline, and eventual death.
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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) | 0.015 | 0.002 |
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; both teacher heads agree on what is shown here.
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".