Quantifying ecological memory in plant and ecosystem processes

TLDR

Ecology has long focused on concurrent abiotic conditions, but recent work increasingly considers how antecedent conditions influence current ecological dynamics. This study aims to evaluate the length, temporal pattern, and strength of ecological memory by assessing how past conditions affect present processes. The authors developed the stochastic antecedent modelling (SAM) framework, a flexible analytic tool that decomposes exogenous and endogenous components of memory and was applied to four ecological examples. Models incorporating antecedent effects explained an additional 18–28 % of response variation versus models without such effects, and SAM identified underlying memory mechanisms, revealing temporal properties invisible to traditional time‑series analyses and enabling new hypothesis generation.

Abstract

The role of time in ecology has a long history of investigation, but ecologists have largely restricted their attention to the influence of concurrent abiotic conditions on rates and magnitudes of important ecological processes. Recently, however, ecologists have improved their understanding of ecological processes by explicitly considering the effects of antecedent conditions. To broadly help in studying the role of time, we evaluate the length, temporal pattern, and strength of memory with respect to the influence of antecedent conditions on current ecological dynamics. We developed the stochastic antecedent modelling (SAM) framework as a flexible analytic approach for evaluating exogenous and endogenous process components of memory in a system of interest. We designed SAM to be useful in revealing novel insights promoting further study, illustrated in four examples with different degrees of complexity and varying time scales: stomatal conductance, soil respiration, ecosystem productivity, and tree growth. Models with antecedent effects explained an additional 18-28% of response variation compared to models without antecedent effects. Moreover, SAM also enabled identification of potential mechanisms that underlie components of memory, thus revealing temporal properties that are not apparent from traditional treatments of ecological time-series data and facilitating new hypothesis generation and additional research.