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Quantitative Ecology and Evolution EBIO 5460

Fall 2017

Course description
This is a graduate level course. The course is all about how to construct models based on biology (as opposed to purely statistical models) and how to use this as a framework to test hypotheses in ecology and evolution by fitting the models to data and comparing the support of the models. This is neither a pure statistics course nor a pure modeling course. As you will see, statistics and modeling are best integrated. You will learn the fundamentals of each but most importantly, how to integrate them. This is fast becoming the most desirable approach to understanding biological processes.

Model based inference in ecology and evolution. The relationship between models, hypotheses, experiments, and data. How to make a model. How to test models against data, including likelihood and Bayesian approaches. How to design biological experiments to get the most from a model. Models for genes, individuals, populations, communities, and ecosystems. We will learn some math, some statistics, and some computer programming. We will learn R, a powerful environment for scientific computing.

To be able to comprehend the exploding literature in biology where the science is based on models and model fitting.
To be proficient at writing R programs to analyze models and data.
To become confident at using customized quantitative approaches in your own research.

Learning format
Quantitative ecology and evolution are best learned by doing. As much as possible, we will use active learning. This means that I will often give a short lecture of 15-20 minutes at the beginning of class, with the rest of the class devoted to hands-on implementation of the idea in the R computing environment. Hands on work will often be done in small groups - collaboration is encouraged.

General Biology EBIO 1220

Spring 2016.

I teach the second half (from about March) of General Biology, and cover two topics 1) plant biodiversity 2) ecology.