M1 - Ecology and Management of Natural Ecosystems EGEN

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

The ORPAM program begins in the first weeks of teaching. It begins with a 3-day field school (24h - integration internship) and continues with a mini-laboratory internship (24h). The course ends with the writing of a popular scientific article and an oral presentation of the results.

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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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