Bayesian Inference for Environmental Models (BIO/ENV 665, spring 2022)

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 – 1:45 PM

prerequisite: one semester each of stats, calculus

Schedule and links for 2022:scheduleENV665

 previous years:

data files from lab manual

Environmental Change in the Big Data Era (ENV 89S, spring 2022)

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

Recent presentations

Global seed production and implications for 21st-century forest

Make Our Planet Great Again, 6 December 2021

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