Individual asynchrony promotes population‐level tree growth stability
Bibliographic record
Abstract
Abstract Community‐level stability is widely considered to increase with species richness and asynchrony. However, it is not well understood to what extent population‐level stability may be regulated by population size and within‐population asynchrony among individuals. Using a large set of global tree‐ring data, we quantified the effects of population size and within‐population tree growth asynchrony on the temporal stability of population‐level tree growth rate. We also examined the relationship between the global distributions of within‐population tree growth asynchrony and population‐level tree growth stability. The results showed that population‐level tree growth stability asymptotically increased with population size and quickly levelled off at an average population size of 26. After population size was controlled, population‐level tree growth stability increased with within‐population tree growth asynchrony ( R 2 = 0.54). Globally, population‐level tree growth stability was 52% higher than individual‐level tree growth stability on average. This percentage varied considerably across climate zones and was highest in the Tropical zone (84%) due to its highest within‐population asynchrony, while lowest in the Dry zone (34%) due to its lowest asynchrony. Synthesis . Our results indicate that individual asynchrony plays a primary role in stabilizing population‐level tree growth rate, followed by population size. This finding highlights the importance of individual‐level differences in alleviating environmental stresses on forest growth.
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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 | 0.000 | 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) | 0.002 | 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".