M1 - Evolutionary Biology and Ecology (DARWIN)

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)

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The overall objective is to consolidate the foundations in ecology acquired by students and to give them the tools they need to apply them in an integrated way to interpret the functioning of ecological systems. The course includes: 1) lectures on ecological concepts from the population scale to the macroecological scale, using examples of applications that place the discipline in the current ecological and societal context; 2) practical and supervised work focused on tools (sampling strategies, modeling, data analysis); 3) field teaching, during which students are encouraged to ask relevant scientific questions based on their observations in the field and to use their knowledge to answer them in a reasoned manner.

Summary of EU content:

- CM: History of the emergence of concepts in ecology; Population dynamics/metapopulations; Biotic interactions and food webs; Community ecology, metacommunities; Ecosystem ecology/functional ecology; Concepts of macroecology/biogeography; Global change and ecosystem functioning;

- Field: Integrative analysis of ecosystem functioning in situ;

- TD/TP: sampling and experimentation strategies in ecology; modeling in population dynamics/metapopulations, community ecology/metacommunities, food webs; biodiversity measures (alpha, beta, etc.).

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The overall objective is to consolidate students' foundations in evolutionary biology by addressing both (i) macroevolutionary phenomena and the general methods used to analyze them, and (ii) microevolutionary processes with an emphasis on the population genetics approach. This course unit aims to provide a solid foundation of knowledge in evolutionary biology and to illustrate the applications of the discipline to students' future areas of specialization. The course includes: 1) lectures on the concepts of evolution; 2) practical work in two main forms: 2a. sessions focused on the use of tools (phylogeny) and the mathematical formalization of evolutionary processes (population genetics); and 2b: sessions built around group work, allowing students, depending on their background and professional goals, to explore a particular topic in depth (fundamental question or application of evolutionary biology).

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English tutorial courses aimed at developing professional autonomy in the English language.

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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].

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Generalized linear mixed models + methodology and experimental protocols to account for biological reality: non-normal distribution and pseudo-replication

Protocol optimization, power, and uncontrolled type I risk: variable transformation, polynomial regression, link function, likelihood, model selection

Deviance and goodness-of-fit analysis

Incorporation of blocks, repeated measurements over time, consideration of spatial and temporal correlation, over-dispersion

Graphical representation of predictions.

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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.

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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].

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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.

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The individual M1 internship lasts approximately three months and, depending on the program, must be completed in a research laboratory or a non-academic organization. It allows students to gain professional experience in the field of biodiversity, evolution, or ecology. It can be carried out in a local, national, or international organization, on a topic approved by the teaching team so as to fit in with the objectives specific to the program followed by the student.

Assessment: Preparation for the internship is assessed on the basis of a written document and a presentation of the internship project. The internship work is assessed during a public presentation before a panel, during which the content of the dissertation and the quality of the responses to the panel's questions are evaluated. The student's behavior and enthusiasm during the internship are assessed by the internship supervisor.

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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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"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

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How is biodiversity distributed across the Earth? What ecological, evolutionary, and historical factors determine these patterns of biodiversity distribution? What changes have human activities brought about in the global distribution of biodiversity? In this course, we will study the role of spatial and temporal variations in the environment on a global scale on the dynamics of biodiversity. In particular, we will examine the influence of long-term climate cycles on the past and present diversity of organisms. We will also address the impact of human activities and global changes on biodiversity on a planetary scale.

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