Fall 2017:

ANTHRBIO 461 / ENVIRON 461: Primate Conservation Biology

This seminar is intended to foster critical consideration of a range of issues within primate conservation biology. Weekly discussions will be based on a number of broad topics. We will begin by considering alternative conceptual approaches commonly employed in conservation biology, surveying the role of models in conservation, assessing the present-day conservation status of primate populations and habitats, and discussing the major threats facing wild primate populations. Next, we will consider the relationship between the discipline of conservation biology and the practice of conservation on the ground. We will progress to a discussion of conservation priority setting, strategies and tactics, local human communities as both potential allies and threats to conservation of wild primate populations, and whether or not primates deserve protection and conservation more than other taxa. Over the course of the semester seminar participants will identify topics of particular interest that will be pursued in depth and developed through peer review and discussion, culminating in a term paper and formal presentation.

ANTHRBIO 463 / ENVIRON 473: Statistical modeling and data visualization in R

This course is a boot camp in statistical modeling and data visualization using the R computer language. Topics include basic R programming, data exploration, statistical modeling, formal model comparison, parameter estimation and interpretation, and the visual display of quantitative information. Students will learn how to use the R statistical environment to process, analyze, and visualize data. The instructor will provide R code to execute all example analyses used in class; assignments will entail modifying this code to solve similar problems. Statistical topics will focus primarily on various types of generalized linear models (GLMs and GLMMs) and formal model comparison using information criteria. Classes on data visualization will help students to learn principled, effective ways to visually depict data using R. This is not an introductory statistics course. Participants are expected to begin the course with a solid understanding of basic statistical methods. No formal modeling experience, programming ability, or knowledge of advanced mathematics is required. Some prior experience with R is advisable, but not required. This course fulfills the university’s Quantitative Reasoning Requirement (QR1).

I will be on teaching leave Winter 2018.