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.
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White, EP; Ernest, SKM; Adler, PB; Hurlbert, AH; Lyons, SK. "Integrating spatial and temporal approaches to understanding species richness," PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, v.365, 2010, p. 3633.
Thibualt, K.M., S.R. Supp, M. Giffen, E.P. White, and S.K.M. Ernest. "Species composition and abundance of mammalian communities," Ecology, v.92, 2011, p. 2316.
Supp, S. R.; Xiao, X.; Ernest, S. K. M.; White, E. P.. "An experimental test of the response of macroecological patterns to altered species interactions," ECOLOGY, v.93, 2012, p. 2505-2511.
White, Ethan P.; Ernest, S. K. Morgan; Adler, Peter B.; Hurlbert, Allen H.; Lyons, S. Kathleen. "Integrating spatial and temporal approaches to understanding species richness," PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, v.365, 2010, p. 3633-3643.
White, Ethan P.; Thibault, Katherine M.; Xiao, Xiao. "Characterizing species abundance distributions across taxa and ecosystems using a simple maximum entropy model," ECOLOGY, v.93, 2012, p. 1772-1778.
Thibault, Katherine M.; Supp, Sarah R.; Giffen, Mikaelle; White, Ethan P.; Ernest, S. K. Morgan. "Species composition and abundance of mammalian communities," Ecology, v.92, 2011, p. 2316-2316.
Coyle, Jessica R.; Hurlbert, Allen H.; White, E.P.. "Opposing mechanisms drive diversity patterns of core and occasional bird species," American Naturalist, v.181, 2013, p. E83-E90.
Wilson, G., D.A. Aruliah, C.T. Brown, N.P. Chue Hong, M. Davis, R.T. Guy, S.H.D. Haddock, K. Huff, I. Mitchell, M. Plumbley, B. Waugh, E.P. White, and P. Wilson. "Best Practices for Scientific Computing," PLOS Biology, v.12, 2014, p. e1001745.
McGlinn, D.J., X. Xiao, and E.P. White. "An empirical comparison of four variants of a universal species-area relationship," PeerJ, v.1, 2013, p. e212.
Locey, K.J. and E.P. White. "How species richness and total abundance constrain the distribution of abundance," Ecology Letters, v.16, 2013, p. 1177.
White, E.P., E. Baldridge, Z.T. Brym, K.J. Locey, D.J. McGlinn, S.R. Supp. "Nine simple ways to make it easier to (re)use your data," Ideas in Ecology and Evolution, v.6, 2013, p. 1.
Desjardins-Proulx P., E.P. White, J.J. Adamson, K. Ram, T. Poisot, and D. Gravel. "The case for open preprints in biology," PLOS Biology, v.11, 2013, p. e1001563.
Morris, B.D. and E.P. White. "The EcoData Retriever: improving access to existing ecological data," PLOS One, v.8, 2013, p. e65848.
Coyle, J.R., A.H. Hurlbert, and E.P. White. "Opposing mechanisms drive diversity patterns of core and occasional bird species," American Naturalist, v.181, 2013, p. E83.
Locey, K.J., and D.J. McGlinn. "Efficient algorithms for sampling feasible sets of abundance distributions," PeerJ Preprints, 2014.
Xiao, X., D.J. McGlinn, and E.P. White. "A strong test of the Maximum Entropy Theory of Ecology," American Naturalist, v.185, 2015, p. E70.
McGlinn, D.J., X. Xiao, and E.P. White. "Exploring spatially explicit predictions of the Maximum Entropy Theory of Ecology," Global Ecology and Biogeography, v.24, 2015, p. 675.
Mislan, KAS and Heer, Jeffrey M and White, Ethan P. "Elevating the status of code in ecology," Trends in ecology \& evolution, v.31, 2016, p. 4--7.
Teal, Tracy K and Cranston, Karen A and Lapp, Hilmar and White, Ethan and Wilson, Greg and Ram, Karthik and Pawlik, Aleksandra. "Data carpentry: workshops to increase data literacy for researchers," International Journal of Digital Curation, v.10, 2015, p. 135--143.
White, Ethan P. "Some thoughts on best publishing practices for scientific software," Ideas in Ecology and Evolution, v.8, 2015.
Xiao, Xiao and Locey, Kenneth J and White, Ethan P. "A process-independent explanation for the general form of Taylor?s Law," The American Naturalist, v.186, 2015, p. E51--E60.
Xiao, Xiao and McGlinn, Daniel J and White, Ethan P. "A strong test of the maximum entropy theory of ecology," The American Naturalist, v.185, 2015, p. E70--E80.
Xiao, Xiao and O'Dwyer, James P and White, Ethan P. "Comparing process-based and constraint-based approaches for modeling macroecological patterns," Ecology, v.97, 2016, p. 1228--123.
