M1 - Eco-Epidemiology

"General linear models with one or more explanatory random variables: translating the figure that answers the biological question into a statistical model—that is, taking into account numerous effects and knowing how to interpret them"

general properties examined through regression and one-way ANOVA (R², F, ddl, least squares, likelihood, diagnostics, validation, goodness of fit, interpretation of effect sizes); ANOVA with nested and crossed factors, multiple regression (concepts of parameters, effects, and interactions)

"accounting for the dependence of explanatory random variables, confounding effects (quantitative for multiple regression, and unbalanced designs for ANOVA)"

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The objective of this course unit is to provide the necessary foundation in statistics to follow the more advanced modules in the curriculum; it is therefore a general refresher course. Descriptive statistics are reviewed (quantiles, cumulative frequency polygons, sample-based estimators); simple tests are introduced; essential graphs for univariate and multivariate data are presented; and the general principles of statistical testing, hypothesis testing, the concept of the p-value, and Type I and Type II errors are covered. In lab sessions, students also receive training in the R environment.

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This learning unit is conducted entirely in English. It has two objectives: 

1. To help students improve their speaking and listening skills in English 

2. To enable students to expand their vocabulary in the field of ecology that interests them most (e.g., marine ecology, terrestrial ecology).  

Practical sessions: oral presentations by students, watching videos in English with English subtitles, quizzes, and games.   

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"The phylogenetic tree is a central concept in biology for students in the majors "Biodiversity, Ecology & Evolution," "Agricultural Biology," and "Eco-epidemiology." To introduce students to phylogeny, this course unit is divided into two consecutive parts, each lasting 22.5 hours: “Phylogeny and Evolution (Fundamentals)” (HAB708B) and “Phylogeny and Evolution (Advanced)” (HAB714B).

The following subjects will be taught there:

(i) History of the Concept of Evolution [Basics].

(ii) Phylogenetic systematics (characters, rules of taxonomy, molecular barcoding, genomics, alignment, homology and homoplasy, orthology and paralogy) [half in the Basics course; half in the Advanced course].

(iii) Phylogenetic representation (networks, trees, roots, dendrograms, topology, branch lengths) [Basics].

(iv) Methods of phylogenetic inference based on distances [Advanced].

(v) The cladistic approach and the principle of maximum parsimony [Basics].

(vi) The probabilistic approach, the maximum likelihood principle, and sequence evolution models [Advanced].

(vii) Measures of phylogenetic robustness (bootstrapping, topology comparison, multigene corroboration, gene and species trees) [Advances].

(viii) Applications to the phylogeny of several major taxonomic groups (Mammals, Eukaryotes) [Progress]."

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This learning unit has three objectives:

1) To deepen understanding of concepts in evolutionary genetics and genomics, such as linkage disequilibrium, selection, coalescence theory, the detection of natural selection, and the evolutionary forces acting on genome evolution and the process of genomic speciation.

2) Provide an overview of research topics in evolutionary genomics through educational seminars: molecular evolution, evolutionary genomics of endosymbiosis, chromosomal evolution, and molecular evolution.

3) Finally, the EU is proposing a bioanalysis project based on an empirical dataset to gain an understanding of evolutionary genomics and to tackle the increasingly sophisticated bioinformatics aspects of the discipline.

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"The objective of this course unit is to build upon the material covered in the first semester by exploring issues related to phenotypic evolution and the main methodological approaches associated with it. The course will address the evolution of various types of traits (life history traits, traits involved in reproductive strategies, traits involved in biotic interactions, and quantitative traits). The main approaches covered include game theory formalization, adaptive dynamics, quantitative genetics, and the comparison of theoretical predictions with empirical data. The course includes:

1) lectures on the main concepts of evolutionary ecology;

2) tutorials focused on document analysis and exercises"

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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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