Focus: Biodiversity and Nature Conservation
This course is an introduction to molecular ecology, and population genetics modeling and inference in R. You will learn how to use genomics and bioinformatics to test hypotheses in ecology and evolution. The course will include an introduction to the R statistical platform, basic population genetics statistics, the Wright-Fisher model of genetic drift, the coalescent model. We will also cover simulation-based inferences, such as approximate Bayesian computation and supervised machine learning. The course is heavily based on computer and programming activities, attendees must bring a laptop to every meeting (alternative arrangements can be made for those who don’t own one). Basic understanding of probability is essential, however a strong math background is not required.
Learning Objectives: My objective with this course is to expand your understanding of ecology and evolution. By the end of this course I hope students will:
(1) have a basic understanding of R;
(2) understand evolutionary models such as the coalescent model, the wright-fisher model and the infinite site model;
(3) understand the influence of demographic processes on genetic diversity;
(4) know how to test demographic hypothesis at the level of population and community using simulation-based inferences;
(5) have a basic understanding of approximate Bayesian computation;
(6) have a basic understanding of supervised machine-learning and artificial neural networks.
Language: English
Host: Roberto Júnio Pedroso Dias
Lecturer: Marcelo Gehara (Rutgers University-USA)
Mode of instruction: Rectory Auditorium
Courseload: 12h
Date&Time: Jul 31-Aug 1, from 9am to 4pm
Target audience: undergraduate and graduate students
Spots available: 30
Sustainable Development Goals (SDG):