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

auto-correlation; biological memory; lag effect; recovery; resilience; superposed epoch analysis; synthetic data; tree rings; Ecology, Evolution, Behavior and Systematics; Ecology; Plant Science

Abstract :

[en] 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. 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.

Disciplines :

Environmental sciences & ecology

Author, co-author :

Klesse, Stefan ; Forest Dynamics, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmendorf, Switzerland

Babst, Flurin ; Laboratory of Tree Ring Research, University of Arizona, Tucson, United States ; School of Natural Resources and the Environment, University of Arizona, Tucson, United States

Evans, Margaret E. K. ; Laboratory of Tree Ring Research, University of Arizona, Tucson, United States

Hurley, Alexander ; Climate Dynamics and Landscape Evolution, GFZ German Research Centre for Geosciences, Potsdam, Germany

Pappas, Christoforos ; Department of Civil Engineering, University of Patras, Rio Patras, Greece

Peters, Richard ; Université de Liège - ULiège > TERRA Research Centre > Gestion durable des bio-agresseurs ; Forest Dynamics, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmendorf, Switzerland ; Department of Environmental Sciences, University of Basel, Basel, Switzerland

Language :

English

Title :

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

Publication date :

2022

Journal title :

Journal of Ecology

ISSN :

0022-0477

eISSN :

1365-2745

Publisher :

John Wiley and Sons Inc

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://onlinelibrary.wiley.com/doi/pdf/10.1111/1365-2745.14045

Funders :

SNF - Schweizerische Nationalfonds zur Förderung der wissenschaftlichen Forschung
BAFU - Bundesamt für Umwelt

Funding text :

Stefan Klesse was supported by the SwissForestLab (Research Grants SFL‐17 P3 and SFL‐20 P5), and by the Federal Office for the Environment FOEN. Richard L. Peters acknowledges the support of the Swiss National Science Foundation (SNSF), Grant P2BSP3_184475.

Available on ORBi :

since 25 December 2022

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