M1 - Environmental and Biodiversity Management

"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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This learning unit is conducted entirely in English. It has two objectives: 

1. To help students improve their speaking and listening skills in English 

2. To enable students to expand their vocabulary in the field of ecology that interests them most (e.g., marine ecology, terrestrial ecology).  

Practical sessions: oral presentations by students, watching videos in English with English subtitles, quizzes, and games.   

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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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The M1 internship lasts a minimum of 4.5 months and a maximum of 6 months on a full-time basis (from mid-March to the end of August at the latest).
Students are responsible for proactively seeking out their own internship opportunities.
The internship is approved by the academic advisor based on its alignment with the student’s career goals.
Each student is supervised during the internship by an academic advisor.
It may take place in France or abroad.
The internship takes place within a professional organization and, in principle, is governed by an internship agreement issued by the UM.

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By the end of this course, students will have acquired the essential basic knowledge needed to properly prepare and carry out a science communication initiative tailored to a specific target audience, both orally and in writing.

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This course is entirely based on hands-on projects focused on the creation and management of web pages, as well as the understanding, use, and management of social media for science communication.

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A course designed to link theoretical ecology, its practical application, and regional challenges as perceived by stakeholders in society. Built around a format that combines theoretical lectures covering the fundamentals necessary for understanding on-the-ground issues (ecosystem dynamics, anthropogenic impacts, socio-ecosystem resilience, in situ conservation, etc.), this course unit includes several field components (each consisting of a preparatory tutorial and an “active” field trip). Visits to these areas will provide opportunities to meet stakeholders (managers, elected officials, nonprofit organizations, shepherds, etc.) whose perspectives help students understand how ecological issues shape their actions and, in turn, how those actions impact biodiversity, its dynamics, and its distribution.

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-Defining the broad outlines of an integrated coastal zone management project and the methodological approach to be adopted.

-Description of the partners (government agencies, professional fishing organizations, etc.) and how they operate

-Introduction to and use of project management and planning tools

-Practical application using a real-world case.

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The EU’s general introduction lays out the conceptual foundations for ecological rehabilitation and restoration, as well as the resilience of ecosystems and aquatic environments.
The concepts of ecological and hydrological continuity, river morphology, and hydrogeomorphology are explained and illustrated.
A field trip provides an opportunity to analyze the challenges, formulate hypotheses about how systems function, and assess impacts.
The course’s practical exercises are preferably directly linked to fieldwork.
The main tools and techniques for species reintroduction are presented and accompanied by illustrative practical exercises:
- Animal model practical exercise: reintroduction of a vulture population
- Plant model practical exercise: transplantation.
- Riparian forest practical exercise: and the concept of the “turquoise grid”
The main impacts of soil sealing and interventions on aquatic environments (maintenance and development) and methods for assessing these impacts, accompanied by illustrative practical exercises:
- Practical exercise on the Water Act: assessing the impacts of soil sealing and hydrological compensation.
- Hydrogeomorphological practical exercise: analysis of historical and current hydraulic connectivity.
Finally, the course offers students the opportunity to integrate ecological and hydrological connectivity and to explore options for optimized restoration.

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"This module introduces array management and the relationship between multivariate and univariate analysis: matrix manipulation and common operations; the concepts of projection and distance; interpretation of descriptive and univariate statistics, using multiple regression, PCA, and AFD as examples; (dis)similarity indices, distance; and correlation."

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This course is entirely based on hands-on projects in the fields of computer graphics (designing and creating diagrams using vector graphics software) and layout design (designing and creating a poster, an article, or a booklet using desktop publishing software).

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