Legacy effects in radial tree growth are rarely significant after accounting for biological memory
Evans, Margaret E. K.
Peters, Richard L.
AffiliationLaboratory of Tree Ring Research, University of Arizona
School of Natural Resources and the Environment, University of Arizona
superposed epoch analysis
MetadataShow full item record
CitationKlesse, S., Babst, F., Evans, M. E. K., Hurley, A., Pappas, C., & Peters, R. L. (2022). Legacy effects in radial tree growth are rarely significant after accounting for biological memory. Journal of Ecology.
JournalJohn Wiley and Sons Inc
Rights© 2022 The Authors. Journal of Ecology © 2022 British Ecological Society.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at firstname.lastname@example.org.
AbstractDrought 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. 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. 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 reasons for how and why key statistical parameters describing this biological memory differ across species and sites. These two pathways should also stimulate improved process-based representation of vegetation carbon dynamics in mechanistic models.
Note12 month embargo; first published: 23 November 2022
VersionFinal accepted manuscript
SponsorsSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung