CHOICE2

The objective of this course is to consolidate students' foundations in ecology and/or evolution by inviting them to define a research topic and question(s), formulate relevant hypotheses with supporting arguments, and justify a data acquisition and analysis strategy for testing them.

Summary of EU content:

- Independent work under supervision: identification of a relevant scientific question; bibliographic review to establish the state of the art and justify scientific hypotheses; proposal and justification of a methodological approach (materials and methods) to test the proposed hypotheses.

Types of topics:

Topics may cover any issue identified by students (in groups of 3/4) and approved by the teaching team, and may be based on different approaches to suit the requirements of different courses. For example, students may propose a field sampling or experimentation strategy, a meta-analysis of literature data, an analysis of sequences retrieved from GenBank, an analysis of occurrence data retrieved from GBIF, etc.

In all cases, projects must involve a genuine data acquisition strategy, identified, justified, and described by students in the materials and methods required for M1S2, with a provisional schedule for the project and identification of the tasks that each student will carry out within each group as part of the implementation of the project in M2S3. Projects must also be financially realistic and include a provisional budget, and must be able to be completed within the time available in M2S3.

Assessment methods:

Teaching is based on a problem-based learning approach. Students are assessed on how they progress in developing their approach (40% of the final grade), as well as on their ability to present and defend their project in a final oral exam (60% of the final grade).

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This module presents table management and the link between multivariate and univariate analysis: matrix manipulation and common operations; the concepts of projection and distance; translation of descriptive and univariate statistics using multiple regression/ACP/AFD as examples; indices of (dis)similarity, distance; correlation.

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