M2 - Evolutionary biology and ecology (DARWIN)

- Firstly, to provide students with a solid foundation of computer knowledge and skills, enabling them to learn and use the bioinformatics tools used more specifically in evolution and ecology.

- Secondly, to make them aware of the need to produce reproducible results, and to introduce them to the key concepts and tools for doing so.

- Thirdly, to get students to work on concrete examples that can be used during their Master's internship and future professional life.

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"The Darwin Field School takes place over one week with the following objectives:

- Create a group dynamic and integration within the Master 2 DARWIN-BEE class.

- Analyze ecological issues in their technical, scientific and social dimensions (e.g. reintroduction operations).

- Addressing biodiversity management issues in a humanized protected area.

- Oral presentation and comparison of results to an audience; assessment of the course.

Activities in Florac :

- Discover the landscapes of the Causse Méjean.

- Understand the specific missions of the Cévennes National Park and its scientific knowledge acquisition policy.

- Study the example of vultures in the Causses, the capercaillie and the beaver in the CNP, and the chamois in the Gorges du Tarn.

- Work in 3 sub-groups on different scientific and ethical themes.

Activities around Montpellier :

- Study bird migration and Mediterranean coastal ecosystems.

- Practicing urban ecology.

- Discover the fauna of the Mediterranean scrublands, with daytime hikes along the Buèges (entomofauna discovery) and evening hikes (bat and/or moth and nocturnal orthopteran evenings)".

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The module covers the following fundamental themes in evolutionary biology: Micro evolution -Macro evolution, Fitness, Natural and sexual selves, Speciation. Other topics (mutation, epigenetics, evo-devo ...) are presented by the students themselves.

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The aim of this course is to help students build their career plans and find internships, while beginning to prepare for their integration into professional life through a comprehensive and personal vision of possible career paths.

In concrete terms, a series of meetings with various participants introduces the doctoral thesis (presentation of the GAIA doctoral school, presentations by thesis students) and the professional environment targeted by the different career paths (research careers and the non-academic sector). Activities specific to each pathway then enable students to better target the scientific fields most closely aligned with their career plans. Finally, TD sessions are designed to prepare students to write scientific articles in English.

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The aim of this course is to design a research project on one of the major themes in ecology, such as the ecological niche, biogeography, networks of ecological interactions or functional diversity. A short review of the major theories and concepts in these major ecology themes is presented by specialists in the field. This is followed by example(s) illustrating the conceptual bases for formulating a relevant and new research question, and how to answer it using different methodologies, especially experimental ones, derived from the speakers' own research. Having chosen one of these major themes, each student develops an original research project (the size of an M2 internship), conducting bibliographical research and proposing a coherent experimental plan to test the hypotheses. This project is presented to the lecturers and other students.

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The aim of this EU is to show that biological diversity is functional:

1) for different groups of organisms: plants, insects, aquatic organisms, vertebrates, and

2) at different scales of organization (from organisms to ecosystems). The aim of the lessons is to explain how to approach this functional facet of diversity for the 10+ million organisms present on the planet's surface, taking examples from both highly and less anthropized environments.

See full page

The module addresses the theoretical and empirical advances of recent research in evolutionary genetics through a number of major issues:

- theme 1: genetic burden and evolution of reproductive systems: recombination, sex/asex, auto/allofecundation

- theme 2: kinship structures and their evolutionary consequences: kinship selection, group selection, evolution of cooperation, sex ratios

- theme 3: sustainable interactions between species: parasitism, mutualism, coevolution

- theme 4: traces of evolutionary history in genomes, genomics of adaptation.

See full page

The main aim of this course is to provide the skills needed to understand and use the concepts and methods on which the quantitative study of population phenomena is based. The main methods for analyzing and modeling these phenomena will be approached from both a theoretical (formal calculations) and practical (statistics, simulations) point of view, using examples exploring different phylogenetic scales (microbial dynamics, invasive species, human demography), spatial (from local to global) and temporal (transient and steady-state regimes, eco-evolutionary coupling), with particular attention to the heterogeneity (spatial, genetic or phenotypic) and randomness (stochasticity, uncertainties) characteristic of populations or inherent to their study.

See full page

Evo-devo is an evolutionary approach to developmental genetics. This discipline seeks to shed light on the changes in developmental mechanisms that explain current and past morphological diversity, and thus opens up an important bridge between biology and paleontology.

In the course of the module, we will use articles to discuss a number of evolutionary issues relevant to Evo-Devo approaches: the question of homology, the establishment and evolution of repeated structures, the genetic bases of development and the links between genome evolution and the evolution of form. We will illustrate these concepts using examples from metazoans and the green lineage, and apply them to the scale of today's major groups and populations.

See full page

Phylogeny is a quest for evolutionary clues. The aim of this module is to recall the existence of gene phylogenies within species phylogenies, the ways in which evolutionary histories can be represented in tree form, and the challenge of positional molecular homology through sequence alignment. The principles of phylogenetic inference methods are at the heart of this course. Distance methods highlight the difficulties of separating homology and homoplasy, and the need to build models of character evolution. The maximum parsimony cladistic approach illustrates the use of bootstrapping to estimate the strength of phylogeny nodes, and the impact of taxonomic sampling in detecting multiple substitutions.

Probabilistic approaches are presented and explored in greater depth. The attraction artifact of long branches leads to an introduction to probabilistic reasoning. The maximum likelihood method is used to calculate likelihood, to estimate model parameters by optimality, to construct different character evolution models, and to compare models. Bayesian inference introduces the distinction between density-based and optimality-based approaches. It then shows the a priori use of probability densities, the data-driven estimation of a posteriori distributions of model parameters, their approximation by Markov chains with Monte Carlo techniques and Metropolis coupling (MCMCMC), the ignition and convergence phases, and the calculation and interpretation of tree and clade posterior probabilities. The importance of DNA, RNA and protein sequence evolution models and their improvement is emphasized.

See full page

See full page

See full page

1. Bayesian inference: Motivation and simple example. 

2. The likelihood. 

3. A detour to explore priors. 

4. Markov chains Monte Carlo methods (MCMC)

5. Bayesian analyses in R with the Jags software.  

6. Contrast scientific hypotheses with model selection (WAIC).

7. Heterogeneity and multilevel models (aka mixed models. 

See full page

The general objective is to present human evolutionary biology, proposing to mobilize the tools of evolutionary biology to better understand human behaviors and those observed in non-human primates in the context of their evolutionary history. Themes such as health, sociality, culture, local adaptations, language, morality, reproduction and sexual preferences are addressed within the theoretical framework of evolutionary biology and ecology. EU contents: Anthropology, human sciences and evolutionary biology / Evolution of cooperation / Cultural evolution / Evolution of nutrition / Evolution of sociality in primates / Family ecology / Medicine, public health and evolution / Evolution of language / Evolutionary demography / The origins of equity.

See full page

1. Bayesian inference: Motivation and simple example. 

2. The likelihood. 

3. A detour to explore priors. 

4. Markov chains Monte Carlo methods (MCMC)

5. Bayesian analyses in R with the Jags software.  

6. Contrast scientific hypotheses with model selection (WAIC).

7. Heterogeneity and multilevel models (aka mixed models. 

See full page

The courses present 4 aspects of Conservation Biology, based on current scientific research in this discipline:  

1. Introduction to Biodiversity Conservation(BC): definition of Conservation Biology. Why conserve biodiversity? Who are the main players in BC and the role of science in BC. 
2. Species conservation: What are the priority species? How can species be conserved? How do you know if a species is "well conserved"? 
3. Space conservation: What are the priority spaces? How to conserve spaces? 
4. Theimportance of social acceptability and political commitment. Need for biodiversity indicators and to measure the impact of conservation. 

Students also carry out group work in which they present a BC project, based on the questions: why, what, where, how, how much does it cost and how do we know if it's effective? 

See full page

The aim of this course is to deepen understanding of key concepts relating to climate change, to illustrate important concepts in ecology and evolution in the light of climate change, in many different ecosystems, and to produce a synthesis of the various scientific and societal questions and issues raised by CC.

See full page

Quantitative genetics is a discipline that emerged in the early 20th century to understand the heredity of continuous traits, i.e. the majority of traits of agronomic interest (yield, etc.) or evolutionary interest (life-history traits, morphology). It is therefore an essential tool for understanding, modeling and predicting natural or artificial selection, and the evolution of natural systems or cultivated plants/animals. Its relevance is more relevant than ever at the start of the 21st century, with the advent of genomics (a factor of scientific progress, provided we don't reduce every evolutionary problem to the fiction of a few Mendelian alleles with a strong effect), and the return in force of alternative models of heredity (epigenetics) going beyond the sequence-centric vision inherited from classical molecular biology.

The aim of the module is to provide a culture of quantitative genetics sufficient to (i) understand the classical foundations of the discipline, manipulate the key quantities (genetic variances, heritabilities, genetic correlations) and the statistical techniques for estimating these parameters (ii) understand the power of this technique for posing and understanding fundamental or applied evolutionary problems (agronomic improvement) (iii) understand how this formalization of heredity fits in with the classical Mendelian vision.

See full page

The general objective is to present human evolutionary biology, proposing to mobilize the tools of evolutionary biology to better understand human behaviors and those observed in non-human primates in the context of their evolutionary history. Themes such as health, sociality, culture, local adaptations, language, morality, reproduction and sexual preferences are addressed within the theoretical framework of evolutionary biology and ecology. EU contents: Anthropology, human sciences and evolutionary biology / Evolution of cooperation / Cultural evolution / Evolution of nutrition / Evolution of sociality in primates / Family ecology / Medicine, public health and evolution / Evolution of language / Evolutionary demography / The origins of equity.

See full page

Behavioral Ecology takes an evolutionary approach to the study of behavior, investigating its mechanisms, function and contribution to evolutionary and ecological processes. The work carried out in Behavioral Ecology helps us to understand other phenomena observed in other disciplines of life biology, because all animals, from unicellulars to the most complex vertebrae, exhibit behaviors.

The module exposes students to the various basic concepts, as well as to the multitude of tools likely to be used (observations and experiments on natural populations or captive individuals, comparative analyses, use of modeling tools, ecophysiology, molecular biology, biochemistry, on-board electronics, etc.). Part of the training is based on specific discussions of the research approaches likely to be employed, the tools used and the limits of the inferences that can be made. Students will be expected to play an active role at all these levels, in particular through critical discussions of articles.

Topics range from the exploration of strategies for food provisioning, mate choice, habitat selection and investment in reproduction, to the study of animal communication and the reasons for group living. The historical dimension of the discipline is addressed in the introduction, but also according to the sensibilities of the contributors and the themes addressed (meaning and relationships between 'Animal Behaviour', 'Ethology', Behavioral Ecology etc.).

See full page

The module addresses the theoretical and empirical advances of recent research in evolutionary genetics through a number of major issues:

- theme 1: genetic burden and evolution of reproductive systems: recombination, sex/asex, auto/allofecundation

- theme 2: kinship structures and their evolutionary consequences: kinship selection, group selection, evolution of cooperation, sex ratios

- theme 3: sustainable interactions between species: parasitism, mutualism, coevolution

- theme 4: traces of evolutionary history in genomes, genomics of adaptation.

See full page

The main aim of this course is to provide the skills needed to understand and use the concepts and methods on which the quantitative study of population phenomena is based. The main methods for analyzing and modeling these phenomena will be approached from both a theoretical (formal calculations) and practical (statistics, simulations) point of view, using examples exploring different phylogenetic scales (microbial dynamics, invasive species, human demography), spatial (from local to global) and temporal (transient and steady-state regimes, eco-evolutionary coupling), with particular attention to the heterogeneity (spatial, genetic or phenotypic) and randomness (stochasticity, uncertainties) characteristic of populations or inherent to their study.

See full page

The aim of this EU is to show that biological diversity is functional:

1) for different groups of organisms: plants, insects, aquatic organisms, vertebrates, and

2) at different scales of organization (from organisms to ecosystems). The aim of the lessons is to explain how to approach this functional facet of diversity for the 10+ million organisms present on the planet's surface, taking examples from both highly and less anthropized environments.

See full page

Phylogeny is a quest for evolutionary clues. The aim of this module is to recall the existence of gene phylogenies within species phylogenies, the ways in which evolutionary histories can be represented in tree form, and the challenge of positional molecular homology through sequence alignment. The principles of phylogenetic inference methods are at the heart of this course. Distance methods highlight the difficulties of separating homology and homoplasy, and the need to build models of character evolution. The maximum parsimony cladistic approach illustrates the use of bootstrapping to estimate the strength of phylogeny nodes, and the impact of taxonomic sampling in detecting multiple substitutions.

Probabilistic approaches are presented and explored in greater depth. The attraction artifact of long branches leads to an introduction to probabilistic reasoning. The maximum likelihood method is used to calculate likelihood, to estimate model parameters by optimality, to construct different character evolution models, and to compare models. Bayesian inference introduces the distinction between density-based and optimality-based approaches. It then shows the a priori use of probability densities, the data-driven estimation of a posteriori distributions of model parameters, their approximation by Markov chains with Monte Carlo techniques and Metropolis coupling (MCMCMC), the ignition and convergence phases, and the calculation and interpretation of tree and clade posterior probabilities. The importance of DNA, RNA and protein sequence evolution models and their improvement is emphasized.

See full page

Evo-devo is an evolutionary approach to developmental genetics. This discipline seeks to shed light on the changes in developmental mechanisms that explain current and past morphological diversity, and thus opens up an important bridge between biology and paleontology.

In the course of the module, we will use articles to discuss a number of evolutionary issues relevant to Evo-Devo approaches: the question of homology, the establishment and evolution of repeated structures, the genetic bases of development and the links between genome evolution and the evolution of form. We will illustrate these concepts using examples from metazoans and the green lineage, and apply them to the scale of today's major groups and populations.

See full page

See full page

1. Bayesian inference: Motivation and simple example. 

2. The likelihood. 

3. A detour to explore priors. 

4. Markov chains Monte Carlo methods (MCMC)

5. Bayesian analyses in R with the Jags software.  

6. Contrast scientific hypotheses with model selection (WAIC).

7. Heterogeneity and multilevel models (aka mixed models. 

See full page

The courses present 4 aspects of Conservation Biology, based on current scientific research in this discipline:  

1. Introduction to Biodiversity Conservation(BC): definition of Conservation Biology. Why conserve biodiversity? Who are the main players in BC and the role of science in BC. 
2. Species conservation: What are the priority species? How can species be conserved? How do you know if a species is "well conserved"? 
3. Space conservation: What are the priority spaces? How to conserve spaces? 
4. Theimportance of social acceptability and political commitment. Need for biodiversity indicators and to measure the impact of conservation. 

Students also carry out group work in which they present a BC project, based on the questions: why, what, where, how, how much does it cost and how do we know if it's effective? 

See full page

The aim of this course is to deepen understanding of key concepts relating to climate change, to illustrate important concepts in ecology and evolution in the light of climate change, in many different ecosystems, and to produce a synthesis of the various scientific and societal questions and issues raised by CC.

See full page

Quantitative genetics is a discipline that emerged in the early 20th century to understand the heredity of continuous traits, i.e. the majority of traits of agronomic interest (yield, etc.) or evolutionary interest (life-history traits, morphology). It is therefore an essential tool for understanding, modeling and predicting natural or artificial selection, and the evolution of natural systems or cultivated plants/animals. Its relevance is more relevant than ever at the start of the 21st century, with the advent of genomics (a factor of scientific progress, provided we don't reduce every evolutionary problem to the fiction of a few Mendelian alleles with a strong effect), and the return in force of alternative models of heredity (epigenetics) going beyond the sequence-centric vision inherited from classical molecular biology.

The aim of the module is to provide a culture of quantitative genetics sufficient to (i) understand the classical foundations of the discipline, manipulate the key quantities (genetic variances, heritabilities, genetic correlations) and the statistical techniques for estimating these parameters (ii) understand the power of this technique for posing and understanding fundamental or applied evolutionary problems (agronomic improvement) (iii) understand how this formalization of heredity fits in with the classical Mendelian vision.

See full page

The general objective is to present human evolutionary biology, proposing to mobilize the tools of evolutionary biology to better understand human behaviors and those observed in non-human primates in the context of their evolutionary history. Themes such as health, sociality, culture, local adaptations, language, morality, reproduction and sexual preferences are addressed within the theoretical framework of evolutionary biology and ecology. EU contents: Anthropology, human sciences and evolutionary biology / Evolution of cooperation / Cultural evolution / Evolution of nutrition / Evolution of sociality in primates / Family ecology / Medicine, public health and evolution / Evolution of language / Evolutionary demography / The origins of equity.

See full page

Behavioral Ecology takes an evolutionary approach to the study of behavior, investigating its mechanisms, function and contribution to evolutionary and ecological processes. The work carried out in Behavioral Ecology helps us to understand other phenomena observed in other disciplines of life biology, because all animals, from unicellulars to the most complex vertebrae, exhibit behaviors.

The module exposes students to the various basic concepts, as well as to the multitude of tools likely to be used (observations and experiments on natural populations or captive individuals, comparative analyses, use of modeling tools, ecophysiology, molecular biology, biochemistry, on-board electronics, etc.). Part of the training is based on specific discussions of the research approaches likely to be employed, the tools used and the limits of the inferences that can be made. Students will be expected to play an active role at all these levels, in particular through critical discussions of articles.

Topics range from the exploration of strategies for food provisioning, mate choice, habitat selection and investment in reproduction, to the study of animal communication and the reasons for group living. The historical dimension of the discipline is addressed in the introduction, but also according to the sensibilities of the contributors and the themes addressed (meaning and relationships between 'Animal Behaviour', 'Ethology', Behavioral Ecology etc.).

See full page

See full page

See full page

1. Bayesian inference: Motivation and simple example. 

2. The likelihood. 

3. A detour to explore priors. 

4. Markov chains Monte Carlo methods (MCMC)

5. Bayesian analyses in R with the Jags software.  

6. Contrast scientific hypotheses with model selection (WAIC).

7. Heterogeneity and multilevel models (aka mixed models. 

See full page

The general objective is to present human evolutionary biology, proposing to mobilize the tools of evolutionary biology to better understand human behaviors and those observed in non-human primates in the context of their evolutionary history. Themes such as health, sociality, culture, local adaptations, language, morality, reproduction and sexual preferences are addressed within the theoretical framework of evolutionary biology and ecology. EU contents: Anthropology, human sciences and evolutionary biology / Evolution of cooperation / Cultural evolution / Evolution of nutrition / Evolution of sociality in primates / Family ecology / Medicine, public health and evolution / Evolution of language / Evolutionary demography / The origins of equity.

See full page

1. Bayesian inference: Motivation and simple example. 

2. The likelihood. 

3. A detour to explore priors. 

4. Markov chains Monte Carlo methods (MCMC)

5. Bayesian analyses in R with the Jags software.  

6. Contrast scientific hypotheses with model selection (WAIC).

7. Heterogeneity and multilevel models (aka mixed models. 

See full page

The courses present 4 aspects of Conservation Biology, based on current scientific research in this discipline:  

1. Introduction to Biodiversity Conservation(BC): definition of Conservation Biology. Why conserve biodiversity? Who are the main players in BC and the role of science in BC. 
2. Species conservation: What are the priority species? How can species be conserved? How do you know if a species is "well conserved"? 
3. Space conservation: What are the priority spaces? How to conserve spaces? 
4. Theimportance of social acceptability and political commitment. Need for biodiversity indicators and to measure the impact of conservation. 

Students also carry out group work in which they present a BC project, based on the questions: why, what, where, how, how much does it cost and how do we know if it's effective? 

See full page

The aim of this course is to deepen understanding of key concepts relating to climate change, to illustrate important concepts in ecology and evolution in the light of climate change, in many different ecosystems, and to produce a synthesis of the various scientific and societal questions and issues raised by CC.

See full page

Quantitative genetics is a discipline that emerged in the early 20th century to understand the heredity of continuous traits, i.e. the majority of traits of agronomic interest (yield, etc.) or evolutionary interest (life-history traits, morphology). It is therefore an essential tool for understanding, modeling and predicting natural or artificial selection, and the evolution of natural systems or cultivated plants/animals. Its relevance is more relevant than ever at the start of the 21st century, with the advent of genomics (a factor of scientific progress, provided we don't reduce every evolutionary problem to the fiction of a few Mendelian alleles with a strong effect), and the return in force of alternative models of heredity (epigenetics) going beyond the sequence-centric vision inherited from classical molecular biology.

The aim of the module is to provide a culture of quantitative genetics sufficient to (i) understand the classical foundations of the discipline, manipulate the key quantities (genetic variances, heritabilities, genetic correlations) and the statistical techniques for estimating these parameters (ii) understand the power of this technique for posing and understanding fundamental or applied evolutionary problems (agronomic improvement) (iii) understand how this formalization of heredity fits in with the classical Mendelian vision.

See full page

The general objective is to present human evolutionary biology, proposing to mobilize the tools of evolutionary biology to better understand human behaviors and those observed in non-human primates in the context of their evolutionary history. Themes such as health, sociality, culture, local adaptations, language, morality, reproduction and sexual preferences are addressed within the theoretical framework of evolutionary biology and ecology. EU contents: Anthropology, human sciences and evolutionary biology / Evolution of cooperation / Cultural evolution / Evolution of nutrition / Evolution of sociality in primates / Family ecology / Medicine, public health and evolution / Evolution of language / Evolutionary demography / The origins of equity.

See full page

Behavioral Ecology takes an evolutionary approach to the study of behavior, investigating its mechanisms, function and contribution to evolutionary and ecological processes. The work carried out in Behavioral Ecology helps us to understand other phenomena observed in other disciplines of life biology, because all animals, from unicellulars to the most complex vertebrae, exhibit behaviors.

The module exposes students to the various basic concepts, as well as to the multitude of tools likely to be used (observations and experiments on natural populations or captive individuals, comparative analyses, use of modeling tools, ecophysiology, molecular biology, biochemistry, on-board electronics, etc.). Part of the training is based on specific discussions of the research approaches likely to be employed, the tools used and the limits of the inferences that can be made. Students will be expected to play an active role at all these levels, in particular through critical discussions of articles.

Topics range from the exploration of strategies for food provisioning, mate choice, habitat selection and investment in reproduction, to the study of animal communication and the reasons for group living. The historical dimension of the discipline is addressed in the introduction, but also according to the sensibilities of the contributors and the themes addressed (meaning and relationships between 'Animal Behaviour', 'Ethology', Behavioral Ecology etc.).

See full page

The module addresses the theoretical and empirical advances of recent research in evolutionary genetics through a number of major issues:

- theme 1: genetic burden and evolution of reproductive systems: recombination, sex/asex, auto/allofecundation

- theme 2: kinship structures and their evolutionary consequences: kinship selection, group selection, evolution of cooperation, sex ratios

- theme 3: sustainable interactions between species: parasitism, mutualism, coevolution

- theme 4: traces of evolutionary history in genomes, genomics of adaptation.

See full page

The main aim of this course is to provide the skills needed to understand and use the concepts and methods on which the quantitative study of population phenomena is based. The main methods for analyzing and modeling these phenomena will be approached from both a theoretical (formal calculations) and practical (statistics, simulations) point of view, using examples exploring different phylogenetic scales (microbial dynamics, invasive species, human demography), spatial (from local to global) and temporal (transient and steady-state regimes, eco-evolutionary coupling), with particular attention to the heterogeneity (spatial, genetic or phenotypic) and randomness (stochasticity, uncertainties) characteristic of populations or inherent to their study.

See full page

The aim of this EU is to show that biological diversity is functional:

1) for different groups of organisms: plants, insects, aquatic organisms, vertebrates, and

2) at different scales of organization (from organisms to ecosystems). The aim of the lessons is to explain how to approach this functional facet of diversity for the 10+ million organisms present on the planet's surface, taking examples from both highly and less anthropized environments.

See full page

Phylogeny is a quest for evolutionary clues. The aim of this module is to recall the existence of gene phylogenies within species phylogenies, the ways in which evolutionary histories can be represented in tree form, and the challenge of positional molecular homology through sequence alignment. The principles of phylogenetic inference methods are at the heart of this course. Distance methods highlight the difficulties of separating homology and homoplasy, and the need to build models of character evolution. The maximum parsimony cladistic approach illustrates the use of bootstrapping to estimate the strength of phylogeny nodes, and the impact of taxonomic sampling in detecting multiple substitutions.

Probabilistic approaches are presented and explored in greater depth. The attraction artifact of long branches leads to an introduction to probabilistic reasoning. The maximum likelihood method is used to calculate likelihood, to estimate model parameters by optimality, to construct different character evolution models, and to compare models. Bayesian inference introduces the distinction between density-based and optimality-based approaches. It then shows the a priori use of probability densities, the data-driven estimation of a posteriori distributions of model parameters, their approximation by Markov chains with Monte Carlo techniques and Metropolis coupling (MCMCMC), the ignition and convergence phases, and the calculation and interpretation of tree and clade posterior probabilities. The importance of DNA, RNA and protein sequence evolution models and their improvement is emphasized.

See full page

Evo-devo is an evolutionary approach to developmental genetics. This discipline seeks to shed light on the changes in developmental mechanisms that explain current and past morphological diversity, and thus opens up an important bridge between biology and paleontology.

In the course of the module, we will use articles to discuss a number of evolutionary issues relevant to Evo-Devo approaches: the question of homology, the establishment and evolution of repeated structures, the genetic bases of development and the links between genome evolution and the evolution of form. We will illustrate these concepts using examples from metazoans and the green lineage, and apply them to the scale of today's major groups and populations.

See full page

See full page

See full page

1. Bayesian inference: Motivation and simple example. 

2. The likelihood. 

3. A detour to explore priors. 

4. Markov chains Monte Carlo methods (MCMC)

5. Bayesian analyses in R with the Jags software.  

6. Contrast scientific hypotheses with model selection (WAIC).

7. Heterogeneity and multilevel models (aka mixed models. 

See full page

The general objective is to present human evolutionary biology, proposing to mobilize the tools of evolutionary biology to better understand human behaviors and those observed in non-human primates in the context of their evolutionary history. Themes such as health, sociality, culture, local adaptations, language, morality, reproduction and sexual preferences are addressed within the theoretical framework of evolutionary biology and ecology. EU contents: Anthropology, human sciences and evolutionary biology / Evolution of cooperation / Cultural evolution / Evolution of nutrition / Evolution of sociality in primates / Family ecology / Medicine, public health and evolution / Evolution of language / Evolutionary demography / The origins of equity.

See full page

1. Bayesian inference: Motivation and simple example. 

2. The likelihood. 

3. A detour to explore priors. 

4. Markov chains Monte Carlo methods (MCMC)

5. Bayesian analyses in R with the Jags software.  

6. Contrast scientific hypotheses with model selection (WAIC).

7. Heterogeneity and multilevel models (aka mixed models. 

See full page

The courses present 4 aspects of Conservation Biology, based on current scientific research in this discipline:  

1. Introduction to Biodiversity Conservation(BC): definition of Conservation Biology. Why conserve biodiversity? Who are the main players in BC and the role of science in BC. 
2. Species conservation: What are the priority species? How can species be conserved? How do you know if a species is "well conserved"? 
3. Space conservation: What are the priority spaces? How to conserve spaces? 
4. Theimportance of social acceptability and political commitment. Need for biodiversity indicators and to measure the impact of conservation. 

Students also carry out group work in which they present a BC project, based on the questions: why, what, where, how, how much does it cost and how do we know if it's effective? 

See full page

The aim of this course is to deepen understanding of key concepts relating to climate change, to illustrate important concepts in ecology and evolution in the light of climate change, in many different ecosystems, and to produce a synthesis of the various scientific and societal questions and issues raised by CC.

See full page

Quantitative genetics is a discipline that emerged in the early 20th century to understand the heredity of continuous traits, i.e. the majority of traits of agronomic interest (yield, etc.) or evolutionary interest (life-history traits, morphology). It is therefore an essential tool for understanding, modeling and predicting natural or artificial selection, and the evolution of natural systems or cultivated plants/animals. Its relevance is more relevant than ever at the start of the 21st century, with the advent of genomics (a factor of scientific progress, provided we don't reduce every evolutionary problem to the fiction of a few Mendelian alleles with a strong effect), and the return in force of alternative models of heredity (epigenetics) going beyond the sequence-centric vision inherited from classical molecular biology.

The aim of the module is to provide a culture of quantitative genetics sufficient to (i) understand the classical foundations of the discipline, manipulate the key quantities (genetic variances, heritabilities, genetic correlations) and the statistical techniques for estimating these parameters (ii) understand the power of this technique for posing and understanding fundamental or applied evolutionary problems (agronomic improvement) (iii) understand how this formalization of heredity fits in with the classical Mendelian vision.

See full page

The general objective is to present human evolutionary biology, proposing to mobilize the tools of evolutionary biology to better understand human behaviors and those observed in non-human primates in the context of their evolutionary history. Themes such as health, sociality, culture, local adaptations, language, morality, reproduction and sexual preferences are addressed within the theoretical framework of evolutionary biology and ecology. EU contents: Anthropology, human sciences and evolutionary biology / Evolution of cooperation / Cultural evolution / Evolution of nutrition / Evolution of sociality in primates / Family ecology / Medicine, public health and evolution / Evolution of language / Evolutionary demography / The origins of equity.

See full page

Behavioral Ecology takes an evolutionary approach to the study of behavior, investigating its mechanisms, function and contribution to evolutionary and ecological processes. The work carried out in Behavioral Ecology helps us to understand other phenomena observed in other disciplines of life biology, because all animals, from unicellulars to the most complex vertebrae, exhibit behaviors.

The module exposes students to the various basic concepts, as well as to the multitude of tools likely to be used (observations and experiments on natural populations or captive individuals, comparative analyses, use of modeling tools, ecophysiology, molecular biology, biochemistry, on-board electronics, etc.). Part of the training is based on specific discussions of the research approaches likely to be employed, the tools used and the limits of the inferences that can be made. Students will be expected to play an active role at all these levels, in particular through critical discussions of articles.

Topics range from the exploration of strategies for food provisioning, mate choice, habitat selection and investment in reproduction, to the study of animal communication and the reasons for group living. The historical dimension of the discipline is addressed in the introduction, but also according to the sensibilities of the contributors and the themes addressed (meaning and relationships between 'Animal Behaviour', 'Ethology', Behavioral Ecology etc.).

See full page

The module addresses the theoretical and empirical advances of recent research in evolutionary genetics through a number of major issues:

- theme 1: genetic burden and evolution of reproductive systems: recombination, sex/asex, auto/allofecundation

- theme 2: kinship structures and their evolutionary consequences: kinship selection, group selection, evolution of cooperation, sex ratios

- theme 3: sustainable interactions between species: parasitism, mutualism, coevolution

- theme 4: traces of evolutionary history in genomes, genomics of adaptation.

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The main aim of this course is to provide the skills needed to understand and use the concepts and methods on which the quantitative study of population phenomena is based. The main methods for analyzing and modeling these phenomena will be approached from both a theoretical (formal calculations) and practical (statistics, simulations) point of view, using examples exploring different phylogenetic scales (microbial dynamics, invasive species, human demography), spatial (from local to global) and temporal (transient and steady-state regimes, eco-evolutionary coupling), with particular attention to the heterogeneity (spatial, genetic or phenotypic) and randomness (stochasticity, uncertainties) characteristic of populations or inherent to their study.

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The aim of this EU is to show that biological diversity is functional:

1) for different groups of organisms: plants, insects, aquatic organisms, vertebrates, and

2) at different scales of organization (from organisms to ecosystems). The aim of the lessons is to explain how to approach this functional facet of diversity for the 10+ million organisms present on the planet's surface, taking examples from both highly and less anthropized environments.

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Phylogeny is a quest for evolutionary clues. The aim of this module is to recall the existence of gene phylogenies within species phylogenies, the ways in which evolutionary histories can be represented in tree form, and the challenge of positional molecular homology through sequence alignment. The principles of phylogenetic inference methods are at the heart of this course. Distance methods highlight the difficulties of separating homology and homoplasy, and the need to build models of character evolution. The maximum parsimony cladistic approach illustrates the use of bootstrapping to estimate the strength of phylogeny nodes, and the impact of taxonomic sampling in detecting multiple substitutions.

Probabilistic approaches are presented and explored in greater depth. The attraction artifact of long branches leads to an introduction to probabilistic reasoning. The maximum likelihood method is used to calculate likelihood, to estimate model parameters by optimality, to construct different character evolution models, and to compare models. Bayesian inference introduces the distinction between density-based and optimality-based approaches. It then shows the a priori use of probability densities, the data-driven estimation of a posteriori distributions of model parameters, their approximation by Markov chains with Monte Carlo techniques and Metropolis coupling (MCMCMC), the ignition and convergence phases, and the calculation and interpretation of tree and clade posterior probabilities. The importance of DNA, RNA and protein sequence evolution models and their improvement is emphasized.

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Evo-devo is an evolutionary approach to developmental genetics. This discipline seeks to shed light on the changes in developmental mechanisms that explain current and past morphological diversity, and thus opens up an important bridge between biology and paleontology.

In the course of the module, we will use articles to discuss a number of evolutionary issues relevant to Evo-Devo approaches: the question of homology, the establishment and evolution of repeated structures, the genetic bases of development and the links between genome evolution and the evolution of form. We will illustrate these concepts using examples from metazoans and the green lineage, and apply them to the scale of today's major groups and populations.

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The aim of this course is to help students finalize their professional projects and prepare for the post-master's period.

The UE is organized on a pathway-wide basis, with regular discussion sessions between the teaching team and students.

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The individual M2 internship lasts approximately 5 to 6 months, and must be carried out in a research laboratory or a non-academic structure, depending on the course. It enables students to gain in-depth professional experience in the field of biodiversity, evolution or ecology. It can be carried out in a local, national or international structure, on a subject validated by the teaching team to fit in with the objectives of the course followed by the student.

Evaluation: The internship is evaluated at a public presentation before a jury, during which the content of the thesis and the quality of the answers to the jury's questions are assessed. The student's behavior and dynamism during the internship are evaluated by the internship supervisor.

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