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

The M1 individual internship lasts approximately three months and, depending on the student’s program track, must be completed in a research laboratory or a non-academic organization. It allows students to gain professional experience in the fields of biodiversity, evolution, or ecology. It may be carried out at a local, national, or international organization, on a topic approved by the faculty to ensure it aligns with the specific objectives of the student’s program.

Assessment: Preparation for the internship is a graded assignment based on a written document and a presentation of the internship proposal. The internship work is assessed during a public presentation before a panel, during which the content of the report and the quality of the student’s responses to the panel’s questions are evaluated. The student’s conduct and initiative during the internship are assessed by the internship supervisor.

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The goal of this course unit is to understand the adaptive biology of organisms by examining individual and population responses to environmental variations. Concrete examples from the field of evolutionary animal 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, and pollutants) will be examined, along with their interactive effects. The course will demonstrate the role of physiological mechanisms in ecology, ranging from phenotypic and cognitive processes at the intra-individual level to functional variations 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 with examples of analyses of phenotypic traits (including behavior) within populations. Links to genetic and epigenetic markers will also be discussed. Various approaches (-omics vs. gene/protein targets), several experimental designs, and diverse organizational scales of living organisms will be considered (molecule, gene, phenotype, individual, population, species).

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The courses in this module provide an introduction to ethnobotany and ethnoecology, with the aim of understanding 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 systems of nomenclature and classification, perceptions and representations of nature, resource use and management practices, and biocultural, ecological, and evolutionary interactions. Ethnobotany and ethnoecology are disciplines at the intersection of anthropology, botany, and ecology, and may also draw on tools and concepts from linguistics, archaeology, geography, and agronomy. This module complements the “Ethnoecology and Sustainable Development” module (Master’s 2) by providing the theoretical and methodological foundations of ethnobotany.

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"The objective of this course unit is to build upon the material covered in the first semester by exploring issues related to phenotypic evolution and the main methodological approaches associated with it. The course will address the evolution of various types of traits (life history traits, traits involved in reproductive strategies, traits involved in biotic interactions, and quantitative traits). The main approaches covered include game theory formalization, adaptive dynamics, quantitative genetics, 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 analysis and exercises"

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"The objective of this course is to strengthen students' foundations in ecology and/or evolution by encouraging them to identify a research topic and question(s), formulate well-reasoned and relevant hypotheses, and justify a strategy for collecting and analyzing data to test those hypotheses.

Summary of the learning unit:

- Supervised independent work: identifying a relevant scientific question; conducting a literature review to assess the state of the art and justify scientific hypotheses; proposing and justifying a methodological approach (materials and methods) to test the proposed hypotheses.

Topic types:

Topics may address any issue identified by the students (in groups of 3 or 4) and approved by the teaching staff, and may draw on various approaches to align with the learning objectives of the different programs. For example, students may propose a field sampling or experimental strategy, a meta-analysis of data from the literature, an analysis of sequences retrieved from GenBank, an analysis of occurrence data retrieved from GBIF, etc.

In all cases, projects must include a concrete data collection strategy—identified, justified, and described by the students in the “Materials and Methods” section required for the M1S2 course—along with a projected timeline for the project’s progress and a breakdown of the tasks each student will perform within each group as part of the project’s implementation during the M2S3 course. Projects must also be financially realistic and include a projected budget, and they must be capable of being completed within the time available during the M2S3 semester.

Assessment Procedures:

"The course is based on a problem-based learning approach; students are evaluated on their progress in developing their methodology (40% of the grade), as well as on their ability to present and defend their project during a final oral presentation (60% of the overall grade)."

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

general properties examined through regression and one-way ANOVA (R², F, ddl, least squares, likelihood, diagnostics, validation, goodness of fit, interpretation of effect sizes); ANOVA with nested and crossed factors, multiple regression (concepts of parameters, effects, and interactions)

"accounting for the dependence of explanatory random variables, confounding effects (quantitative for multiple regression, and unbalanced designs for ANOVA)"

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The overall objective is to reinforce the students’ foundational knowledge of ecology and to provide them with the tools to apply this knowledge in an integrated manner to interpret the functioning of ecological systems. The curriculum includes: 1) lectures covering ecological concepts ranging from the population level to macroecological scales, supported by practical examples that place the discipline within the current ecological and societal context; 2) practical and guided exercises focused on tools (sampling strategies, modeling, data analysis); 3) fieldwork sessions during which students are encouraged to ask relevant scientific questions based on in-situ observations and to apply their knowledge to answer these questions in a well-reasoned manner.

Summary of the learning unit:

- 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;

- Fieldwork: Integrative analysis of ecosystem functioning in real-world settings;

- Tutorials/Lab Sessions: sampling and experimental strategies in ecology; modeling of population dynamics/metapopulations, community/metacommunity ecology, and food webs; measures of biodiversity (alpha, beta, etc.)."

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"The overall objective is to strengthen students’ foundational knowledge of 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 both to provide a solid common foundation of knowledge in evolutionary biology and to illustrate the discipline’s applications to the students’ future fields of specialization. The course includes: 1) lectures on evolutionary concepts; 2) laboratory sessions in two main formats: 2a. sessions focused on the use of analytical tools (phylogenetics) and on the mathematical formalization of evolutionary processes (population genetics), as well as 2b: sessions structured around group work, allowing students—based on their academic background and career goals—to explore a specific topic in greater depth (a fundamental question or an application of evolutionary biology)."

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English tutorial courses designed to foster professional proficiency in English.

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ORPAL is a course offered as a combined program (1/3 fieldwork and 2/3 laboratory practicals). The work, carried out in pairs or groups of three under the supervision of an instructor, covers the entire research process, from defining the research question and collecting samples in the field, data collection, through interpretation, the writing of a scientific article (see https://biologie-ecologie.com/exemples-travaux/), and the oral presentation of the results.

'ORPAM takes place during the first few weeks of the semester. This course unit begins with a 3-day field training session (24 hours—orientation workshop) and continues with a short laboratory workshop (24 hours). The course unit concludes 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 Distributions and Pseudo-Replication

Protocol Optimization, Power, and Uncontrolled Type I 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, and overdispersion

Graphical representation of the predictions.

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The objective of this course unit is to provide the necessary foundation in statistics to follow the more advanced modules in the curriculum; it is therefore a general refresher course. Descriptive statistics are reviewed (quantiles, cumulative frequency polygons, sample-based estimators); simple tests are introduced; essential graphs for univariate and multivariate data are presented; and the general principles of statistical testing, hypothesis testing, the concept of the p-value, and Type I and Type II errors are covered. In lab sessions, students also receive training in the R environment.

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