CHOIX1

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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

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

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? 

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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.

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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.

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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.

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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.).

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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 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

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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. 

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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