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Ecology, Evolution, and Behavior Graduate Courses

EEB 601 PRINCIPLES AND PROCESSES IN ECOLOGY, EVOLUTION AND BEHAVIOR 1 (3-3-4)(F). Discusses principal ecological processes and interactions, both biotic and abiotic, that organisms rely on and perform to acquire the necessary energy, water, carbon, and nutrients for growth, metabolism, and reproduction. Mechanisms driving evolutionary responses at the species and population levels are discussed in the context of how evolutionary processes influence ecosystem level responses to a variety of factors, including changing climate, anthropogenic use patterns, species invasions, and nutrient cycles.

EEB 603 REPRODUCIBLE SCIENCE (3-0-3)(F). Examines the reproducibility crisis in the scientific community. Focuses on evidence and causes supporting this crisis and highlights factors that can boost reproducibility in the sciences. Provides a framework for gathering, storing, sharing, preparing and analyzing data and communicating results to the scientific community. Introduction to open source research software may include R, RStudio, RMarkdown (incl. knitr) and GitHub.

EEB 604 SCIENCE AND COMMUNICATION II (3-0-3)(S). Continues the focus, skills development and practice begun in EEB 603. PREREQ: EEB 603

EEB 605 CURRENT RESEARCH IN EEB (2-0-2)(F/S). Invited and contributed presentations on current topics in ecology, evolution, and behavior. Examines presentation style and effective techniques. Examination of literature on current topics, contributing to speaker scheduling and hosting. May be repeated for credit.

EEB 622 STATISTICAL APPROACHES IN ECOLOGY (3-0-3)(F/S). Examines statistical models for ecological data. Includes probability distributions, generalized linear models. PREREQ: Graduate Standing, and BIOL 601 or PERM/INST.

EEB 697 SPECIAL TOPICS (Variable Credit). Instruction on a topic that is not included in the catalog of regular graduate courses. Either graded or pass/fail.

Special Topic Offerings

Hierarchical Models Ecolog Data

Offered: Spring 2018
Instructor: Timothy Caughlin
Description: (3 credits) An advanced course designed to bring graduate students to the forefront of statistical models for ecological data. The first unit of the course will begin with probability distributions as a foundational concept in statistics, develop proficiency with maximum likelihood estimation, and end with generalized linear models. The second unit of the course will address hierarchical models, including mixed effect models to account for pseudoreplication, and will end with Bayesian approaches as a flexible and powerful technique for hierarchical data. The third unit of the course will delve into hierarchical Bayesian modeling as a unifying framework for a variety of problems in ecology, including occupancy, time series, and apatial autocorrelation.