ABI Innovation: Quantifying, simulating, and visualizing the tree growth and its antecedent endogenous and climatic predictors

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Past environmental conditions are likely to be important for predicting current processes such as ecosystem productivity and tree growth. Moreover, past environmental conditions may interact with past tree growth patterns to affect current tree growth. Understanding the role of past conditions for predicting current and future growth responses of trees is expected to be important for predicting how trees, forests, and the terrestrial biosphere in general, may be impacted by future environmental changes, such as those anticipated to occur in tandem with climate change. However, methods for inferring the importance of past conditions for current processes (e.g., tree growth) are not well developed. Thus, this study develops statistical and computing methods for quantifying the past (antecedent) factors governing tree growth, with specific focus on identifying the time scales over which past climate conditions and past tree growth patterns affect current tree growth. This study draws upon large amounts of existing data on annual tree growth ("tree rings") available on-line for multiple tree species across the southwestern US, and it will contribute additional data via a focused field study that will sample several species in this region. The project will train at least three graduate students from diverse disciplines (ecology/biology, modeling/statistics, computing/software development) and several undergraduate students. Results and data generated by this study will be used to develop education modules related to new undergraduate coursework and short courses aimed at training early career scientists. Environmental conditions averaged over past days, weeks, months, seasons, or years are important predictors of plant and ecosystem productivity. For example, tree-ring studies indicate that tree growth is affected by antecedent exogenous (e.g., past climate) and endogenous (e.g., past ring widths) factors, but existing analysis methods do not explicitly evaluate the role of each factor.To address this need, this study will (1) develop a stochastic antecedent model (SAM) for quantifying antecedent climatic and endogenous conditions and their influence on tree growth, (2) apply the SAM to estimate the time-scales over which antecedent factors affect tree growth for multiple species across multiple sites in the Southwest, (3) identify potential physiological mechanisms underlying the antecedent effects on tree growth, and (4) develop software for more general applications of SAM and for simulating and visualizing tree growth. This study combines large datasets, field studies, literature data, Bayesian synthesis, and an individual-based model (IBM) of tree growth and physiology. This study is expected to lend insight into the role trees play as integrators of past environments and physiological states, and the SAM approach should improve our ability to forecast plant and ecosystem responses to climate change, disturbances, or other perturbations. Importantly, the general SAM framework for quantifying the temporal properties of the antecedent conditions and their effects on the process of interest will be broadly applicable to a wide range of fields. To address the aforementioned research objectives, this project will provide interdisciplinary training for a post-doc, 3 graduate students (ecology, statistics, computer science), and multiple undergraduates. Products from this study will be incorporated into a new undergraduate seminar at ASU, a Bayesian modeling workshop organized for annual ecology meetings (ESA), and a 2-week summer course in Bayesian methods aimed at early career and senior ecological scientists. Two software (R) packages will be developed, one for SAM and one for the IBM. Data generated from this study will be made publically and globally available via data repositories. Results from the project can be found at: www.ogle.lab.asu.edu.