M1 - Evolutionary biology and ecology (DARWIN)

"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 general aim is to consolidate the ecological foundations acquired by students, and to give them the tools to mobilize them in an integrative way to interpret the functioning of ecological systems. The course includes: 1) lectures covering the concepts of ecology from population to macro-ecological scales, with examples of applications that place the discipline in the current ecological and societal context; 2) practical work and tutorials focusing on tools (sampling strategies, modelling, data analysis); 3) field courses in which students are invited to ask themselves relevant scientific questions based on observation in a given situation, and to mobilize their knowledge to answer them in a reasoned way.

Summary content of the EU :

- CM: History of the emergence of concepts in ecology; Population dynamics / metapopulations; Biotic interactions and food webs; Ecology of communities, meta-communities; Ecology of ecosystems / functional ecology; Notions of macroecology / biogeography; Global change and ecosystem functioning;

- Field: Integrative analysis of ecosystem functioning in real-life situations ;

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

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"The overall aim is to consolidate students' evolutionary biology foundations, covering both (i) macro-evolutionary phenomena, and the general methods used to analyze them, and (ii) micro-evolutionary processes, with an emphasis on the population genetics approach. The aim of this course is both to provide a common foundation of solid knowledge in evolutionary biology, and to illustrate the applications of the discipline to students' future fields of specialization. Teaching includes: 1) lectures on evolutionary concepts; 2) practical work in two main forms: 2a. sessions focusing on the use of tools (phylogeny) and on the mathematical formalization of evolutionary processes (population genetics), and 2b: sessions built around group work, enabling students, depending on their career path and professional objectives, to delve deeper into a particular theme (fundamental question or application of evolutionary biology)."

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English TD courses aimed at professional autonomy in the English language.

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

Protocol optimization, power and uncontrolled 1st order risk: variable transformation, polynomial regression, link function, likelihood, model selection

Deviance analysis and goodness of fit

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

Graphical representation of predictions.

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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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"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 individual M1 internship lasts around three months, and must be carried out in a research laboratory or a non-academic structure, depending on the course concerned. It enables students to gain professional experience in the field of biodiversity, evolution or ecology. It can be carried out in a local, national or international structure, on a subject validated by the teaching staff to fit in with the objectives of the course followed by the student.

Evaluation : The preparation of the internship is a graded exercise based on a written document and a presentation of the internship project. The internship work is assessed at a public presentation before a jury, during which the content of the dissertation and the quality of the answers to the jury's questions are evaluated. The student's behavior and dynamism during the internship are assessed by the internship supervisor.

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"The aim of this course is to consolidate students' grounding in ecology and/or evolution by inviting them to define a research topic and question(s), by defining relevant hypotheses in a well-argued manner, and by justifying a strategy for acquiring and analyzing the data needed to test them.

Synthetic content of the EU:

- Independent tutored work: identification of a relevant scientific question; bibliographical synthesis 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.

Type of subject:

The topics can be based on any question identified by the students (in groups of 3/4), and validated by the teaching team, and draw on different approaches to suit the expectations of the different courses. For example, students may propose a field or experimental sampling 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 the students in the materials and methods requested in M1S2, with a provisional timetable for the project's progress and identification of the tasks that each student will carry out within each group as part of the project's implementation in M2S3. Projects must also be financially realistic, with a provisional budget, and must be able to be finalized within the time available in M2S3.

Assessment of knowledge:

Teaching is based on a problem-based learning approach, and students are assessed on how they progress in constructing their approach (40% of CC), as well as on their ability to present and defend their project at a final oral (60% of the overall mark)."

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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 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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How is biodiversity distributed on 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 spatio-temporal variations in the global environment on biodiversity dynamics. In particular, we will examine the influence of long-term climatic cycles on the past and present diversity of organisms. We will also look at the impact of human activities and global change on biodiversity on a planetary scale.

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