CHOICE 2

"The objective of this course is to strengthen students' foundations in ecology and/or evolution by encouraging them to identify a research topic and question(s), formulate well-reasoned and relevant hypotheses, and justify a strategy for collecting and analyzing data to test those hypotheses.

Summary of the learning unit:

- Supervised independent work: identifying a relevant scientific question; conducting a literature review to assess the state of the art and justify scientific hypotheses; proposing and justifying a methodological approach (materials and methods) to test the proposed hypotheses.

Topic types:

Topics may address any issue identified by the students (in groups of 3 or 4) and approved by the teaching staff, and may draw on various approaches to align with the learning objectives of the different programs. For example, students may propose a field sampling or experimental strategy, a meta-analysis of data from the literature, an analysis of sequences retrieved from GenBank, an analysis of occurrence data retrieved from GBIF, etc.

In all cases, projects must include a concrete data collection strategy—identified, justified, and described by the students in the “Materials and Methods” section required for the M1S2 course—along with a projected timeline for the project’s progress and a breakdown of the tasks each student will perform within each group as part of the project’s implementation during the M2S3 course. Projects must also be financially realistic and include a projected budget, and they must be capable of being completed within the time available during the M2S3 semester.

Assessment Procedures:

"The course is based on a problem-based learning approach; students are evaluated on their progress in developing their methodology (40% of the grade), as well as on their ability to present and defend their project during a final oral presentation (60% of the overall grade)."

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"This module introduces array management and the relationship between multivariate and univariate analysis: matrix manipulation and common operations; the concepts of projection and distance; interpretation of descriptive and univariate statistics, using multiple regression, PCA, and AFD as examples; (dis)similarity indices, distance; and correlation."

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