Drought legacy effects in radial tree growth are rarely significant under heightened statistical scrutiny
Bibliographic record
Abstract
Drought legacy effects in radial tree growth have been extensively studied over the last decade and are found to critically influence carbon sequestration in woody biomass. Typically quantified as a deviation from “normal” growth, drought legacy magnitude and statistical significance depend on the definition of expected vs. unexpected growth variability under average conditions – a definition that has received insufficient theoretical validation. Here, we revisit popular legacy effect analyses using the International Tree-Ring Data Bank (ITRDB) and employ a synthetic data simulation to disentangle four key variables influencing the magnitude of legacy effects. We show that legacy effects i) are mainly influenced by the overall auto-correlation of radial growth time series, ii) depend on climate-growth cross-correlation, iii) are directly proportional to the year-to-year variability of the growth time series, and iv) scale with the chosen extreme event threshold. Our analysis revealed that legacy effects are a direct outcome of the omnipresent biological memory. We further found that the interpretation of legacy effects following individual drought events at specific sites is challenged by high stochasticity, and show that the commonly perceived stronger legacy effects for conifers are the result of higher auto-correlation compared to deciduous broadleaves. Given that the existing literature has not sufficiently addressed biological memory, we present two pathways to improve future assessment and interpretation of legacy effects. First, we provide a simulation algorithm to a posteriori account for auto-correlated residuals of the initial regression model between growth and climate, i.e. a corrected Null model to determine statistical significance, thereby retrospectively adjusting expectations for “normal” growth variability. The second pathway is to a priori include lagged climate parameters in the regression model. This substantially reduces the magnitude of observed legacy effects and thus challenges us to revisit estimates of drought-induced growth deviations by considering the full spectrum of expected growth behavior.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".