Collaborative Research: Integral Projection Models for Populations in Varying Environments: Construction and Analysis

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All environments are variable and uncertain: some years are hotter, some wetter; predators wax and wane in abundance. How organisms buffer themselves against this variability, and exploit it, are major challenges for ecology. This project will combine new statistical and mathematical theory with long-term data sets on semi-arid plant communities to address two challenges in understanding how variable environments affect populations. The first goal will be to identify which environmental variables most strongly affect plant demographic rates such as survival, growth, and fecundity. This work will draw upon long-term studies on natural communities that provide observations on thousands of individual plants over multiple decades, along with numerous environmental variables that are measured at high spatial and temporal resolution. With so many potential explanatory variables, standard statistical methods for variable selection are unstable and unreliable. This project will combine data mining methods from machine learning techniques with traditional statistical theory to identify important environmental drivers, and build more reliable demographic models. Ecological questions that will be addressed include whether demographic rates respond primarily to resource availability (e.g., soil moisture) versus non-resource variables such as temperature; how environment and competition interact; and how strongly past conditions affect current performance. The empirical data sets will be used to answer these questions and look for generalities across multiple communities. A second goal is to understand the individual mechanisms that underlie effects of environmental variation. For example, if a plant population's growth is most affected by rainfall, is it because many plants die immediately, or because of long-term effects such as decreased life expectancy and smaller size throughout life? To address such questions, statistical methods from discrete-state random process theory will be extended to continuous states (e.g., individual plant size) and varying environments, and applied to the fitted plant demographic models. An expected transformative outcome from this project will be new methods for demographic modeling and analysis, and much of its broader significance will be the applications of those tools in ecology, conservation biology and invasive species management. Statistical products from this work will be disseminated freely as R code modules that users can adapt to their own study systems. The project will also support the research training and mentoring of a doctoral student in statistics, and a postdoctoral student in quantitative ecology.