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

过去的环境条件可能是重要的预测当前的过程，如生态系统的生产力和树木的生长。此外，过去的环境条件可能与过去的树木生长模式相互作用，影响当前的树木生长。了解过去的条件对预测树木当前和未来的生长反应的作用，对于预测树木、森林和一般陆地生物圈如何受到未来环境变化的影响，例如预计将与气候变化同时发生的环境变化，预计是重要的。然而，用于推断过去条件对当前过程的重要性的方法（例如，树木的生长并不发达。因此，本研究开发的统计和计算方法，用于量化过去（先行）的树木生长的因素，具体重点是确定过去的气候条件和过去的树木生长模式影响当前的树木生长的时间尺度。这项研究利用了大量现有的数据，每年树木生长（“树轮”）在线提供多个树种在美国西南部，它将通过一个重点领域的研究，将在该地区的几个物种的样本提供额外的数据。该项目将培训至少三名来自不同学科（生态学/生物学，建模/统计学，计算/软件开发）的研究生和几名本科生。本研究产生的结果和数据将用于开发与新的本科课程和旨在培训早期职业科学家的短期课程相关的教育模块。过去几天、几周、几个月、几个季节或几年的平均环境条件是植物和生态系统生产力的重要预测因子。例如，树木年轮研究表明，树木的生长受到前因外源（例如，过去的气候）和内源性（例如，为了解决这一问题，本研究将（1）建立一个随机先行模型（SAM），用于量化先行气候和内源条件及其对树木生长的影响，（2）应用SAM来估计西南部多个地点的多个物种的前因因素影响树木生长的时间尺度，（3）确定潜在的生理机制，这些机制是树木生长的前因效应的基础;（4）开发软件，用于SAM的更一般的应用以及模拟和可视化树木生长。这项研究结合了大型数据集，实地研究，文献数据，贝叶斯综合，以及基于个体的树木生长和生理模型（IBM）。这项研究有望深入了解树木作为过去环境和生理状态的整合者所发挥的作用，SAM方法应提高我们预测植物和生态系统对气候变化、干扰或其他扰动的反应的能力。重要的是，用于量化前因条件的时间属性及其对感兴趣过程的影响的通用SAM框架将广泛适用于广泛的领域。为了实现上述研究目标，本项目将为一名博士后，3名研究生（生态学，统计学，计算机科学）和多名本科生提供跨学科培训。这项研究的产品将被纳入亚利桑那州立大学的一个新的本科生研讨会，为年度生态会议（ESA）组织的贝叶斯建模研讨会，以及针对早期职业和高级生态科学家的为期2周的贝叶斯方法夏季课程。将开发两个软件包，一个用于SAM，一个用于IBM。本研究生成的数据将通过数据存储库在全球范围内提供。该项目的成果可在以下网址查阅：www.ogle.lab.asu.edu。

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.