Sequential droughts: A silent trigger of boreal forest mortality
Bibliographic record
Abstract
Despite great concern for drought-driven forest mortality, the effects of frequent low-intensity droughts have been largely overlooked in the boreal forest because of their negligible impacts over the short term. In this study, we used data from 6876 permanent plots distributed across most of the Canadian boreal zone to assess the effects of repeated low-intensity droughts on forest mortality. Specifically, we compared the relative impact of sequential years under low-intensity dry conditions with the effects of variables related to the intensity of dry conditions, stand characteristics, and local climate. Then, we searched for thresholds in forest mortality as a function of the number of years between two forest surveys affected by dry conditions of any intensity. Our results showed that, in general, frequent low-intensity dry conditions had stronger effects on forest mortality than the intensity of the driest conditions in the plot. Frequent low-intensity dry conditions acted as an inciting factor of forest mortality exacerbated by stand characteristics and environmental conditions. Overall, the mortality of forests dominated by shade-tolerant conifers was significantly and positively related to frequent low-intensity dry conditions, supporting, in some cases, the existence of thresholds delimiting contrasting responses to drought. In mixtures with broadleaf species, however, sequential dry conditions had a negligible impact. The effects of frequent dry conditions on shade-intolerant forests mainly depended on local climate, inciting or mitigating the mortality of forests located in wet places and dominated by broadleaf species or jack pine, respectively. Our results highlight the importance of assessing not only climate-driven extreme events but also repeated disturbances of low intensity. In the long term, the smooth response of forests to dry conditions might abruptly change leading to disproportional mortality triggered by accumulated stress conditions. Forest and wildlife managers should consider the cumulative effects of climate change on mortality to avoid shortfalls in timber and habitat.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".