Investigating climate anomalies associated with the sudden mortality of balsam fir trees in eastern Canada
Bibliographic record
Abstract
The identification of climate anomalies associated with large-scale stand disturbances can help inform climate-focused forest management. In the spring and summer of 2018, an unusual spike in balsam fir ( Abies balsamea ) mortality was reported in multiple areas across its southern distribution range limit, from Wisconsin, United States, to New Brunswick, Canada. Such an event was previously reported in 1986 in the study region and referred to as Stillwell’s Syndrome. To identify the role of climate anomalies in the 2018 event, we used monthly climate anomalies as explanatory variables in a Random Forest model predicting the presence of mortality from air and field-based surveys across the Maritimes region of eastern Canada. Results were validated by (1) comparing common climate predictors of mortality from this model and a separate Random Forest model fitted on the 1986 event, and (2) using the 2018 model to predict areas of mortality in 1986. Both the 1986 and 2018 models identified multiple common climate anomalies. Areas with unusually high water deficit and temperatures in the previous growing season, followed by thick April snowpack and high May temperatures the same year, were associated with balsam fir mortality. Such climate anomalies have been previously associated with water stress and desiccation in trees. When using the 2018 model to map the occurrence of mortality in 1986 using historical climate data, we report a 95% accuracy in prediction (kappa = 0.88). The approach used here in conjunction with mapped records of past stand disturbances could help with understanding the impacts of future climate anomalies and adaptive management strategies to deal with these events.
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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".