Infectious and Parasitic Eco-Epidemiology - Learning (Eco-EPI App)

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

General properties viewed through regression and one-factor ANOVA (R2, F, ddl, least squares, likelihood, diagnosis, validation, goodness of fit, interpretation of effect sizes); nested and crossed factor ANOVA, multiple regression (concept of parameters and effects, and interaction)

incorporation of the dependence of explanatory random variables, confounding effects (quantitative for multiple regression, and unbalanced designs for ANOVAs)

See the full page

See the full page

The objective of this course unit is to provide the necessary statistical foundations for following the more advanced modules in the curriculum; it is therefore a general refresher course. Descriptive statistics are reviewed (quantiles, cumulative frequency polygons, sample estimators), simple tests are presented, essential graphs for univariate and multivariate data are presented, and the general principle of a statistical test, hypothesis testing, the concept of p-value, and Type I and Type II errors are presented. In practical work, students are also brought up to speed in the R environment.

See the full page

See the full page

See the full page

This EU is conducted entirely in English. Its objective is twofold: 

1. Enable students to improve their oral expression and comprehension in English. 

2. Enable students to enrich their vocabulary in the area of ecology that interests them most (e.g., marine ecology, terrestrial ecology).  

TP: oral presentations by students, listening to videos in English with English subtitles, quizzes, games.   

See the full page

See the full page

The phylogenetic tree is a central concept in biology for students studying "Biodiversity, Ecology & Evolution," "Agricultural Biology," and "Eco-epidemiology." To address phylogeny, this course is divided into two successive parts, each lasting 22.5 hours: "Phylogeny and Evolution (Basics)" (HAB708B) and "Phylogeny and Evolution (Advanced)" (HAB714B).

The following subjects will be taught:

(i) History of the concept of evolution [Basics].

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

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

(iv) Phylogenetic inference methods 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 (bootstrap, topology comparison, multigenic corroboration, gene and species trees) [Advanced].

(viii) Applications to the phylogeny of some major taxonomic groups (mammals, eukaryotes) [Advanced].

See the full page

See the full page

See the full page

See the full page

See the full page

See the full page

See the full page

See the full page

See the full page

See the full page

See the full page

See the full page

See the full page

See the full page

This EU has three objectives:

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

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

3) Finally, the EU is proposing a bioanalysis project using an empirical dataset to understand evolutionary genomics analysis and tackle the increasingly sophisticated bioinformatics aspects of the discipline.

See the full page

"The objective of this course is to complement the teachings of the first semester by developing issues related to the evolution of phenotypes and the main methodological approaches associated with them. The teachings will address the evolution of different types of traits (life history traits, traits involved in reproductive strategies, traits involved in biotic interactions, quantitative traits). The main approaches covered include the formalization of game theory, adaptive dynamics, quantitative genetics approaches, 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 studies and exercises

See the full page

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.

See the full page

See the full page

See the full page

See the full page

See the full page

See the full page

See the full page

See the full page

See the full page

See the full page

The objective of this course unit is to provide the necessary statistical foundations for following the more advanced modules in the curriculum; it is therefore a general refresher course. Descriptive statistics are reviewed (quantiles, cumulative frequency polygons, sample estimators), simple tests are presented, essential graphs for univariate and multivariate data are presented, and the general principle of a statistical test, hypothesis testing, the concept of p-value, and Type I and Type II errors are presented. In practical work, students are also brought up to speed in the R environment.

See the full page

See the full page

See the full page

See the full page

See the full page

See the full page

See the full page

See the full page

See the full page