Courses/presentations
Bayesian Inference for Environmental Models (BIO/ENV 665, spring 2021)
Application of environmental models and applications to data using Bayesian analysis. Provides the basic distribution theory needed for model building and algorithm development. Computation is done with the language R. Applications include physiology, population growth, species interactions, disturbance, and ecosystem dynamics. Discussions focus on classical and current primary literature.
Meetings: TTh – 12:00 PM
prerequisite: one semester each of stats, calculus
Schedule and links for 2021: scheduleENV665
- biodiversity sampling demos on youtube
Environmental Change in the Big Data Era (ENV 89S, spring 2021)
What are the changes happening now and where are they leading us? This course combines key topics in climate change, biodiversity, and big data, examining scientific issues, their importance for the public at large, and how well we understand them. 89S courses focus on student discussions. In this case, discussions consider a combination of scientific literature, contemporary media, and analysis of data.
Meetings: TTh – 10:15 AM
- schedule and links for 2021: scheduleENV89S
Recent presentations
Biodiversity confronts climate change in the big-data era: promise and pitfalls for understanding and anticipating change
Dean’s Lecture Series, 5 March, 2021
Continent-wide forest change driven by indirect climate effects on fecundity
International Forum on Advanced Environmental Sciences and Technology (iFAST), 25 Nov 2020
Past workshops
Generalized Joint Attribute Modeling (GJAM) at NEON
University of Tennessee, 25 Jan 2018
Uncertainty quantification for NEON and other biodiversity network data with hierarchical Bayes
text and code: gjamNeon
Data synthesis workshop: ILTER 1st Open Sci Meeting, Uncertainty Quantification for NEON,
Kruger National Park, 11 Oct 2016
Uncertainty quantification for NEON and other biodiversity network data with hierarchical Bayes
text and code: ilter2016