Legacy effects in radial tree growth are rarely significant after accounting for biological memory

Abstract

1. Drought legacies in radial tree growth are an important feature of variability in biomass accumulation and are widely used to characterize forest resilience to climate change. Defined as a deviation from normal growth, the statistical significance of legacy effects depends on the definition of "normal"-expected growth under average conditions-which has not received sufficient scrutiny.2. We re-examined legacy effect analyses using the International Tree-Ring Data Bank (ITRDB) and then produced synthetic tree-ring data to disentangle four key variables influencing the magnitude of legacy effects. We hypothesized that legacy effects (i) are mainly influenced by the auto-correlation of the radial growth time series (phi), (ii) depend on climate-growth cross-correlation (rho), (iii) are directly proportional to the inherent variability of the growth time series (standard deviation, SD), and (iv) scale with the chosen extreme event threshold.3. Using a data simulation approach, we were able to reproduce observed lag patterns, demonstrating that legacy effects are a direct outcome of ubiquitous biological memory. We found that stronger legacy effects for conifers compared to angiosperms is a consequence of their higher auto-correlation, and that the detectability of legacy effects following rare drought events at individual sites is compromised by strong background stochasticity. Synthesis.We propose two pathways forward to improve the assessment and interpretation of legacy effects: First, we highlight the need to account for auto-correlated residuals of climate-growth regression models a posteriori, thereby retrospectively adjusting expectations for "normal" growth variability.Alternatively, we recommend including lagged climate variables in regression models a priori. By doing so, the magnitude of detected legacy effects is greatly reduced and biological memory is directly attributed to antecedent climatic drivers. We argue that future analyses should focus on understanding the functional | 1189

Still recovering or just remembering? To understand drought legacies, modelling choices matter

“…The Klesse et al (2022) study provides a detailed analysis, using both observed and simulated data, that highlights properties of tree‐ring time series that can impact conclusions about drought legacies. Inspired by this study, we see four major outcomes deserving wider attention in the community: (1) autocorrelation in tree‐ring time series; (2) treatment of pre‐ and postdrought conditions; (3) individual tree idiosyncrasy; and (4) differentiating climate memory and legacies.…”

“…Second, treatment of pre-and postdrought climate is essential, where our definition of 'normal' has implications for how 'abnormal' postdrought growth appears. While not discussed in Klesse et al (2022), we argue that analyses should account for the role of postdrought climate in recovery. This is because wetter years may lead to faster recovery, while drier years (perhaps more common in the western United States) may lead to prolonged impairment.…”

“…Our reading of the literature suggests points (1)-( 4) are accounted for inconsistently. While some of these issues are highlighted in Klesse et al (2022), particularly (1), ( 3) and (4), we expand discussion of these themes to suggest a more nuanced treatment of tree-ring modelling, and highlight implications for our understanding of tree growth responses to drought.…”

“…Finally, the results of the Klesse et al (2022) suggest a broader need for research into climate memory of tree growth. When the authors relate ring widths to climate at increasing lags, they found that accounting for the effects of past climate reduces the strength or duration of estimated drought legacies.…”

“…The Klesse et al (2022) study provides a detailed analysis, using both observed and simulated data, that highlights properties of treering time series that can impact conclusions about drought legacies.…”