We would like to thank the National Science Foundation, Population and Community Ecology Program for supporting this course (Award DEB 2042028 to Colorado State University). Some of the materials in this course were developed with support from the National Socio-Environmental Synthesis Center (SESYNC) (NSF awards DBI-1052875 and DBI-1639145 to the University of Maryland) as well as award DEB-1145200 from the Population and Community Ecology Program of the National Science Foundation to Colorado State University.
Progress in ecological research depends on models that can deal with complexity and are honest about uncertainty. Bayesian hierarchical models provide a powerful approach to analysis of ecological data.
Past participants of this short course have worked on research questions including the use of network analyses to understand measurement uncertainly in the context of extreme weather events, responses of elk to movements of wolves on the landscapes of Yellowstone, the study of governance effectiveness and fisheries biomass, the effect of changing climate on population dynamics of polar bears, and the efficacy of marine reserves.
The goals of the course are to:
Provide a principles-based understanding of Bayesian methods needed to train students, evaluate papers and proposals, and solve research problems.
Communicate the statistical concepts and vocabulary needed to foster collaboration between ecologists and statisticians.
Provide the conceptual foundations and quantitative confidence needed for self-teaching modern analytic methods.
The course will include lectures and laboratory exercises. Labs will emphasize problem solving requiring programming in R and JAGS. The course will enable participants to:
Explain key principles of Bayesian statistics, including the concepts of joint, conditional, and marginal probabilities; posterior and prior distributions; likelihood; conjugacy; and the relationship between Bayesian and maximum likelihood approaches to inference.
Use basic statistical distributions (e.g., binomial, Poisson, normal, log normal, multinomial, beta, Dirichlet, gamma, multivariate normal) to write joint and conditional posterior distributions for hierarchical Bayesian models that couple models of socio-ecological processes, models of data, and random effects.
Explain how Markov chain Monte Carlo (MCMC) methods can be used to estimate the posterior distributions of parameters.
Write algorithms and computer code in R implementing MCMC methods to estimate parameters in simple models.
Use JAGS software to implement MCMC methods for estimating posterior distributions of parameters, latent states, and derived quantities.
Evaluate model convergence and assess goodness of fit of models to data.
Develop and implement hierarchical models that explicitly partition uncertainties.
Understand the basis for statistical inference from single and multiple Bayesian models.
Use Bayesian methods to synthesize results from multiple scientific studies.
Understand Bayesian methods for modeling spatially structured data.
Dr. Tom Hobbs is a Professor Emeritus in the Department of Ecosystem Science and Sustainability and a Senior Research Scientist at the Natural Resource Ecology Laboratory at Colorado State University. He has taught a semester long course on ecological modeling for 20 years. He has also taught short courses for the National Socioenviromental Synthesis Center, the U.S. Geological Survey, Conservation Science Partners, the Woods Hole Research Center, the Grimsö Wildlife Research Institute, and the Department of Ecology, Swedish Agricultural University. He is the author, with Mevin Hooten, of Bayesian Models: A Statistical Primer for Ecologists from Princeton University Press. Dr. Hobbs takes special pride in making challenging, quantitative concepts clear and accessible to students who never considered themselves to be particularly adept with mathematics and statistics. Dr. Hobbs primary research interests focus on population and community ecology of large mammals.
Dr. Becky Tang is an Assistant Professor of Statistics in the Department of Mathematics and Statistics at Middlebury College. Her research focuses on developing Bayesian hierarchical models for ecological applications. From a pedagogical perspective, Tang enjoys developing statistics courses that help students build intuition for and confidence in grappling with difficult quantitative concepts. She serves on the board of the International Society for Bayesian Analysis’s Section on Bayesian Education Research and Practice and aims to facilitate the teaching and use of Bayesian statistics across various disciplines.
Dr. Mevin Hooten is a Professor in the Department of Statistics and Data Sciences at The University of Texas at Austin. He is an elected Fellow of the American Statistical Association (ASA) and winner of the distinguished achievement award by the ASA Section on Statistics and the Environment. He has taught statistical methods for 25 years and has coauthored three textbooks and four additional monographs that demystify the use and application of statistical modeling for ecological and environmental problems. Hooten has developed and taught shortcourses and workshops on a number of statistical topics including: Bayesian methods and computing, spatial and spatio-temporal statistics, Bayesian decision theory and model selection, and animal movement modeling. He serves as Associate Editor for three statistical journals (Biometrics, Environmetrics, and Journal of Agricultural, Biological, and Environmental Statistics).
Dr. Alison Ketz is an ecological data scientist at a climate-tech startup called Funga, where she is working at the forefront of using statistics, machine learning, and mycorrhizal fungi to address climate change. She previously worked for five years as a research scientist at the University of Wisconsin, Madison, developing and deploying Bayesian methods for population and disease ecology. She brings an extensive depth of practical experience in applying Bayesian statistics to a wide breadth of ecological applications. Her research experience has focused on developing non-parametric Bayesian survival and time-to-event models. Dr. Ketz has a unique background as a career-changer–she was once a successful professional photographer. Her background facilitates communication and helps her teach difficult quantitative concepts to diverse students.
Please email tom.hobbs@colostate.edu or mevin.hooten@austin.utexas.edu