Extreme events and subtle ecological effects: lessons from a long‐term sugar maple–American beech comparison
Bibliographic record
Abstract
Abstract Increasing extreme events that are related to global change are expected to affect the dynamics of forest ecosystems. If disruptive stressors (e.g., insects, drought) affect tree vigor without causing mortality, the ecological effects may be subtle, making subsequent ecosystem dynamics more difficult to predict than in the case of disturbances causing death. Based on the literature and our personal observations, we expected that such a subtle change could have occurred in the dynamics between sugar maple and American beech. We implemented a targeted paired‐sampling design (1) to verify whether a change occurred (gradual or abrupt, recovered or not) in the growth dynamics between the two species over a 57‐yr period, (2) to identify the likely causes of this change, and (3) to investigate whether such changes could trigger other long‐time ecological consequences. We found that sugar maple growth was negatively affected by an extreme event (or a few events) between 1986 and 1989, while American beech was not affected. Twenty years after the 1986–1989 abrupt growth decrease, sugar maple (1) had a slower growth than American beech, although it was previously similar, (2) did not respond to monthly climatic variations as it did prior to the abrupt growth decrease, and (3) had lower resilience when faced with a new stress event. Overall, our study, besides showing that extreme events with subtle effects may change the dynamics of an ecosystem, also illustrates that these events may accelerate ecosystem misadaptation to climate. Fine‐scale targeted monitoring is essential to complement broad‐scale monitoring to detect such misadaptations in a global change context.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".