Quantifying ecological memory in plant and ecosystem processes |
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| Authors: | Kiona Ogle Jarrett J. Barber Greg A. Barron‐Gafford Lisa Patrick Bentley Jessica M. Young Travis E. Huxman Michael E. Loik David T. Tissue |
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| Affiliation: | 1. School of Life Sciences, Arizona State University, Tempe, AZ, USA;2. School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, USA;3. School of Geography and Development & B2 Earthscience, University of Arizona, Tucson, AZ, USA;4. Environmental Change Institute, Oxford University Centre for the Environment, University of Oxford, Oxford, UK;5. International Arctic Research Center, University of Alaska, Fairbanks, AK, USA;6. Ecology and Evolutionary Biology & Center for Environmental Biology, University of California, Irvine, CA, USA;7. Department of Environmental Studies, University of California, Santa Cruz, CA, USA;8. Hawkesbury Institute for the Environment, University of Western Sydney, Richmond, NSW, Australia |
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| 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. |
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| Keywords: | Antecedent conditions hierarchical Bayesian model lag effects legacy effects net primary production soil respiration stomatal conductance time‐series tree growth tree rings |
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