Dr. Christen H. Fleming (UCF Biology) will speak on "Scaling individual-level movement models up to the population level" at this week's seminar.
Abstract: Animal tracking data have transformed our ability to quantify movement processes at the level of individual organisms, yet their ability to inform population-level processes remains limited. In this talk, I will give a brief introduction to continuous-time stochastic processes models of animal movement and present multiple approaches to generalizing this framework to population-level modeling. In particular, I will cover quantifying the population-level variation in individual movement/behavioral characteristics, estimating population spatial distributions or ‘population ranges’, and integrating individual-level movement data into species distribution models of habitat suitability.
Short Bio: Dr. Christen H. Fleming is an Assistant Professor in the Department of Biology with a highly interdisciplinary background. He completed his Bachelor of Science at the University of South Alabama, majoring in Physics, Mathematics and Statistics, and obtained his Ph.D. in Physics at the University of Maryland. Dr. Fleming then became a postdoctoral researcher, fellow, and, finally, research associate at the Smithsonian Conservation Biology Institute, where he developed and continues to lead the “continuous-time movement modeling” (ctmm) R software package, along with ctmm associated labs at the Center for Advanced Systems Understanding, the University of Maryland, the University of British Columbia, and the University of Arizona. ctmm is an award-winning statistical analysis package for animal tracking data, wherein tracked animal locations are modeled as a continuous-time stochastic process that is discretely sampled in time. Dr. Fleming currently heads the Ecoinformatics Lab, which works on the development and application of analytic methods for ecological, environmental, and evolutionary data, with a strong focus on addressing conservation needs. The general aim of the Ecoinformatics Lab is to resolve how abundant, yet complex data can be used to inform conservation and management, through the combination of mathematical, statistical, and computational approaches.
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