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Special Topics in Ecology and Evolutionary Biology

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BIOEE 4940
Special Topics in Ecology and Evolutionary Biology 2022


Spring 2022


BIOEE 4940 001 - Ecology of Infectious Diseases
4 credits. Student grading option. Co-meets with ENTOM 4940. Prerequisites: This course assumes familiarity with general ecology and biology, and past coursework in calculus, linear algebra or statistics. Instructors: Megan Greischar and Courtney Murdock.

This course introduces students to the field of infectious disease ecology, an area of study that has developed rapidly over the past three decades and addresses some of the most significant challenges to human health and conservation. Students will learn about the incredible diversity of parasitic organisms, arguably the most abundant life forms on the planet, and examine how pathogens invade and spread through host populations. Throughout the course, an emphasis will be placed on understanding of infectious diseases dynamics at the population level, and on quantitative approaches for studying pathogen spread and impacts. Specific topics include types of pathogens and their ecological properties, epidemiology and impacts on host populations, types of transmission, evolution of resistance and virulence, drivers of the emergence of new diseases, parasites in the context of ecological communities, strategies for controlling outbreaks, and the role of parasites in biodiversity and conservation.


BIOEE 4940 002 – Quantitative Ecology: An Introduction Under Big-Data Era
3 credits. Student option grading. Prerequisite: calculus, BIOEE 1610 or equivalent, or permission of instructor. Instructor: Xiangtao Xu.

Ecology is running into a 'big data' era, with unprecedented data availability thanks to effective data sharing, crowd-sourcing, remote sensing, and other technological advances. It almost becomes a necessity for ecologists to know how to access these usually heterogeneous data and to conduct robust and informative quantitative analyses, which can sometimes seem daunting due to the involvement of mathematical, statistical, and computational techniques. 

This course aims to cover common quantitative methods in ecological studies. Complementary to the existing modeling courses in EEB, topics will focus on descriptive and inferential statistics, data-driven statistics (largely overlapping with machine-learning in a lot of cases), and linking data with empirical and mechanistic models (e.g. model parameterization, optimization, and introductory Bayesian statistics). The course will introduce fundamentals of different quantitative methods, substantiated with classical and current usages in ecology. Students will participate in numerical labs (examples and assignments) using public ecological data sets from individual to ecosystem and landscape scales. Course materials will mainly use R and Python but other programming languages are accepted for assignments. Overall students will gain skills related with handling data, statistical analysis, modeling, programming and data visualization through hands-on experience of quantitative analysis.