Infectious and parasitic eco-epidemiology - apprenticeship (Eco-EPI App)

"General linear models with 1 or more random explanatory variables: from the translation of 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 seen through regression and 1-factor ANOVA (R2, F, ddl, least squares, likelihood, diagnosis, validation, goodness of fit, interpretation of effect sizes); nested and cross-factor ANOVA, multiple regression (notion of parameter and effects, and interaction)

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

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The aim of this course is to provide the statistical foundations needed to follow all the more advanced modules in the curriculum, so it's a general refresher. Descriptive statistics are reviewed (quantile, cumulative frequency polygon, sample estimators), simple tests are introduced, essential graphs for univariate and multivariate data are presented, the general principle of a statistical test, hypothesis design, the notion of p-value, first and second species risk are presented. In practical exercises, students are also brought up to speed in the R environment.

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This course is taught entirely in English. Its aim is twofold: 

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

2. Enable students to enrich their vocabulary in the field of ecology of particular interest to them (e.g. marine ecology, terrestrial ecology).  

Practical exercises: oral presentations by students, videos in English with English subtitles, quizzes, games.   

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"The phylogenetic tree is a central concept in biology for students in the "Biodiversity, Ecology & Evolution", "Biology Agrosciences" and "Eco-epidemiology" majors. To tackle phylogeny, this UE is divided into two successive parts of 22.5h each: "Phylogeny and Evolution (Basics)" (HAB708B) and "Phylogeny and Evolution (Advanced)" (HAB714B).

The following skills will be taught:

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

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

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

(iv) Distance-based phylogenetic inference methods [Advanced].

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

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

(vii) Measures of phylogeny robustness (bootstrap, topology comparison, multigene corroboration, gene and species trees) [Advanced].

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

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This UE has three objectives:

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

2) Offer an overview of research themes in evolutionary genomics in the form of educational seminars: molecular evolution, evolutionary genomics of endosymbioses, chromosome evolution and molecular evolution.

3) Finally, the EU proposes a project for the bioanalysis of an empirical dataset to understand the analysis of evolutionary genomics and get to grips with the bioinformatics aspects increasingly developed in the discipline.

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"The aim of this course is to complement the first semester's teaching by developing the issues involved in the evolution of phenotypes and the main associated methodological approaches. Lessons 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 game-theoretic formalization, adaptive dynamics, quantitative genetic approaches and the work of confronting theoretical predictions with empirical data. Teaching includes:

1) lectures on the main concepts of evolutionary ecology;

2) tutorials focusing on document studies and exercises".

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"This module introduces table management and the link between multivariate and univariate: matrix manipulation and common operations; notion of projection and distance; translation of descriptive and univariate statistics with multiple regression/ACP/AFD as an example; indices of (dis)similarity, distance; correlation".

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The aim of this course is to provide the statistical foundations needed to follow all the more advanced modules in the curriculum, so it's a general refresher. Descriptive statistics are reviewed (quantile, cumulative frequency polygon, sample estimators), simple tests are introduced, essential graphs for univariate and multivariate data are presented, the general principle of a statistical test, hypothesis design, the notion of p-value, first and second species risk are presented. In practical exercises, students are also brought up to speed in the R environment.

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