A four-credit course offered in the second semester of the 2nd year. 

Objectives:

1. Recognize the usefulness of molecular ecology for ecological studies

2. Understand and critically evaluate studies that use molecular tools to describe ecological processes

3. Identify the main processes related to genetic diversity and microevolutionary processes

4. Identify molecular methods to address different ecological and evolutionary questions

A four-credit course offered in the second semester of the 3rd year. 

Objectives:

1. Identify the interactions between microorganisms and biotic components

2. Identify factors that determine the richness, abundance, and distribution of microorganisms in different habitats

3. Recognize the main techniques for sampling microbial communities

4. Contextualize studies of microorganisms within ecological theories

5. Select interesting topics and develop articles on microbial ecology targeted to the non-scientific community

A two-credit course (30h) offered in the 4th year. 

Objectives:

1. Work the principles of the scientific method

2. Formulate scientific questions, hypothesis and predictions

3. Recognize the different types and styles of research projects and proposals

4. Understand the basics of experimental design 

5. Write-up a scientific research proposal

6. Practice oral and written presentations of the research ideas

Two-credit course (30 h) 

*Not offered in 2025

Objectives:

Landscape genetics is a relatively new and emerging scientific field in Brazil, which consolidates concepts and methods from landscape ecology and population genetics. It presents high practical and basic interest for the scientific community. Its application has increased due to the great advance of genetic methods, development of statistical models, and computational power. At the end of the course, the student should have a comprehensive view of the usefulness and application of landscape genetics, the types of genetic, ecological, and spatial data, how to set up an experimental design, and about possible tools for data analysis. In addition, the student will be able to develop exploratory and specific analyses of genetic spatial data using freely available software such as R.