Rapid escalation and release of risks to forest ecosystems triggered by warming: Insights from tree growth synchrony in temperate forests
Bibliographic record
Abstract
Tree growth synchrony serves as a valuable ecological indicator of forest resilience to climate stress and disturbances. However, our understanding of how increasing temperature affects tree growth synchrony during rapidly and slowly warming periods in ecosystems with varying climatic conditions remains limited. By using tree-ring data from temperate broadleaf ( Fraxinus mandshurica , Phellodendron amurense , Quercus mongolica , and Juglans mandshurica ) and Korean pine ( Pinus koraiensis ) mixed forests in northeast China, we investigated the effects of climate change, particularly warming, on the growth synchrony of five dominant temperate tree species across contrasting warm-dry and cool-wet climate conditions. Results show that temperature over water availability was the primary factor driving the growth and growth synchrony of the five species. Growth synchrony was significantly higher in warm-dry than in cool-wet areas, primarily due to more uniform climate conditions and higher climate sensitivity in the former. Rapid warming from the 1960s to the 1990s significantly enhanced tree growth synchrony in both areas, followed by a marked reversal as temperatures exceeded a certain threshold or warming slowed down, particularly in the warm-dry area. The growth synchrony variation patterns of the five species were highly consistent over time, although broadleaves exhibited higher synchrony than conifers, suggesting potential risks to forest resilience and stability under future climate change scenarios. Growing season temperatures and non-growing season temperatures and precipitation had a stronger positive effect on tree growth in the cool-wet area compared to the warm-dry area. High relative humidity hindered growth in the cool-wet area but enhanced it in the warm-dry area. Overall, our study highlights that the diversity and sensitivity of climate-growth relationships directly determine spatiotemporal growth synchrony. Temperature, along with water availability, shape long-term forest dynamics by affecting tree growth and synchrony. These results provide crucial insights for forest management practice to enhance structural diversity and resilience capacity against climate change-induced synchrony shifts.
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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 | 0.001 |
| 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) | 0.001 | 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".