CAREER: Advancing Macroecology Using Informatics and Entropy Maximization

Utah State University, Logan UT

Investigators

Abstract

Climate change, invasive species and other important factors impacting ecological systems operate at continental to global scales. At these scales, conducting experiments can be difficult, if not impossible. Therefore, ecologists increasingly rely on analyses of large scale observational data to predict how these systems will respond to increasing changes in climate and habitat. Progress in this area of research has been slowed by the large number of patterns used to characterize ecological structure, and by the fact that most research focuses on a single group of species thus limiting the generality of the results. This project will increase the speed at which knowledge of ecological systems is acquired by characterizing the relationships among ecological patterns and focusing research on the small number of key patterns that need to be studied to understand the behavior of ecological systems. This will be accomplished using advanced methods from physics that characterize the most likely form of an ecological pattern given a small number of constraints on the system. This research will test the performance of this approach using data on wide variety of species. This method will then be combined with established ecological models to predict a suite of major ecological patterns using only a small number of environmental variables. This project will improve how ecologists test and establish the generality of theories by educating ecologists in advanced computational methods through online and university courses, by providing web-based resources on the collections of data that are available and how to utilize them, and by automating complicated database tasks using computer programs that download, configure, and install optimized versions of ecological databases, thus allowing the rapid incorporation of available data into research projects. This combined research and education program has the potential to substantially improve the rate at which the field of ecology advances by focusing the research effort on a smaller number of patterns and processes, and by allowing ecologists to rapidly determine if a given pattern or hypothesis is general and if not how it varies among ecological systems.