M1 - Functional Ecology and Conceptualization of Terrestrial and Aquatic Ecosystems (EcoSystems)

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 aim of this EU is to understand the adaptive biology of organisms by considering individual and population responses to environmental variations. Concrete examples in animal evolutionary ecophysiology will be discussed in the context of global change. The responses of organisms and populations to abiotic parameters (such as temperature, salinity, oxygen availability, pollutants) will be considered, as well as their interactive effects. The course unit will demonstrate the involvement of physiological mechanisms in ecology, from phenotypic and cognitive processes at the intra-individual level to functional variants between individuals and between species. The concepts of intraspecific variability, phenotypic plasticity, and transgenerational effects will also be addressed. This course unit will be illustrated by examples of phenotypic trait analysis (including behavior) within populations. Links with genetic and epigenetic markers will also be discussed. Different approaches (-omics vs. gene/protein target), several experimental designs, and various scales of biological organization will be considered (molecule, gene, phenotype, individual, population, species).

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This module provides an introduction to ethnobotany and ethnoecology in order to understand the material and immaterial dimensions of the relationships between humans and their environment, with a particular focus on the plant world. We will focus in particular on local naming and classification systems, perceptions and representations of nature, resource management practices and uses, and biocultural, ecological, and evolutionary interactions. Ethnobotany and ethnoecology are disciplines at the interface of anthropology, botany, and ecology, which may also borrow tools and concepts from linguistics, archaeology, geography, and agronomy. This module complements the "Ethnoecology and Sustainable Development" module (Master 2) by providing the theoretical and methodological foundations of ethnobotany.

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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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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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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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ORPAL is a course unit in APP (1/3 fieldwork and 2/3 practical work in the laboratory). The work, carried out in pairs or groups of three under the supervision of a mentor, covers the entire research process, from defining the problem, sampling in the field, data acquisition, to interpretation, writing a scientific article (see https://biologie-ecologie.com/exemples-travaux/), and oral presentation of the results.

ORPAM takes place during the first weeks of teaching. This course begins with a three-day field school (24 hours - orientation course) and continues with a mini laboratory course (24 hours). The course ends with the writing of a popular science article and an oral presentation of the results.

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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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