Multiple divergent patterns in yellow-cedar growth driven by anthropogenic climate change
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. A particularly severe example of this is yellow-cedar ( Callitropsis nootkatensis ) decline along the coast of British Columbia and Alaska, where anthropogenic climate change has led to reduced insulating snowpack, leaving yellow-cedar roots vulnerable to thaw-freeze events, resulting in freezing damage to fine roots and water stress during the subsequent growing season. This includes abundant evidence of tree decline and mortality on the islands of Haida Gwaii. Yellow-cedar decline is complex, with the potential for freezing injury over multiple years and damage that can accumulate over time. We found trees in various stages of decline, from long dead to currently declining, and multiple growth patterns at each study site. We conducted a principal component analysis and identified patterns of divergent growth and divergent response to climate among yellow-cedars within the same stands, across all sites, including three distinct periods of an onset of growth decline (1960s, 1990s, 2000s). Yellow-cedars affected by decline were decreasing in growth and negatively associated with warmer drier winter conditions, whereas yellow-cedars not affected by decline were increasing in growth and positively associated with warmer growing season temperatures. The limiting factors for declining trees, warm dry winter conditions, are consistent with the hypothesis from the mainland that climate warming has led to root freezing. Our research highlights the need to consider multiple signals within a site that would be masked by a single site-level chronology. This is especially relevant within the context of forest decline, where stressors may have differing effects on individual trees. Graphical abstract
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".