M1 - Ecology and Management of Natural Ecosystems (EGEN)

"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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