Growth synchrony in white spruce across Canada and Alaska: climate or distance?
Bibliographic record
Abstract
Abstract Boreal forests, which serve as major terrestrial carbon sinks, are experiencing rapid warming across much of their range. Spatial synchrony in tree growth is crucial for the stability and persistence of these forests. Despite its importance, the geographic patterns and drivers of tree growth synchrony in boreal forests remain underexplored. This study aims to address these gaps by investigating growth synchrony of white spruce ( Picea glauca ), a widespread boreal species of significant ecological and economic value. Using tree-ring data from 187 sites, we quantified growth synchrony with the synchronous growth change coefficient, a non-parametric index capturing consistency in year-to-year variations. We then analyzed its spatial pattern and drivers using complex network analysis and multiple regression on distance matrices (MRM). We found that white spruce growth synchrony follows a clear biogeographical pattern, decreasing from northwest to southeast. The relationship between growth synchrony and geographic distance was non-linear, deviating from the typical distance-decay pattern described by Tobler’s First Law of Geography. Specifically, synchrony increased as geographic distance decreased at shorter distances, but reversed at longer distances, where more distant sites showed relatively stronger synchrony. MRM analysis showed that climate factors explained 55% of the variance in growth synchrony, with geographic proximity contributing minimally after accounting for climate (increasing to 56%). These results suggest that synchronization of climate, particularly temperature, was the primary driver of spatial synchrony in white spruce growth, while spatial proximity-related mechanisms played a limited role. Given that high synchrony can reduce population stability, we recommend prioritizing management efforts that promote asynchronous growth, especially in regions exhibiting strong synchrony (e.g. northern Northwest Territories and Yukon). These findings provide new insights into boreal forest dynamics and inform adaptive management and conservation strategies in the face of ongoing climate change.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| 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 | 0.000 |
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".