CHOICE1

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1. Bayesian inference: Motivation and simple example. 

2. The likelihood. 

3. A detour to explore priors. 

4. Markov chain Monte Carlo methods (MCMC)

5. Bayesian analyses in R with the Jags software.  

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

7. Heterogeneity and multilevel models (also known as mixed models). 

See the full page

The overall objective is to present human evolutionary biology, proposing to use the tools of evolutionary biology to better understand human behavior and that observed in non-human primates in the context of their evolutionary history. Whether it be health, sociality, culture, local adaptations, language, morality, reproduction, or sexual preferences, the topics are addressed within the theoretical framework of evolutionary biology and ecology. Summary of course content: Anthropology, human sciences, and evolutionary biology / Evolution of cooperation / Cultural evolution / Evolution of diet / Evolution of sociality in primates / Family ecology / Medicine, public health, and evolution / Evolution of language / Evolutionary demography / The origins of equity.

See the full page

1. Bayesian inference: Motivation and simple example. 

2. The likelihood. 

3. A detour to explore priors. 

4. Markov chain Monte Carlo methods (MCMC)

5. Bayesian analyses in R with the Jags software.  

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

7. Heterogeneity and multilevel models (also known as mixed models). 

See the full page

The courses present four 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 what role does science play in BC? 
2. Species conservation: Which species are priorities? How can species be conserved? How can we tell if a species is "well conserved"? 
3. Conserving spaces: Which spaces are priorities? How can spaces be conserved? 
4. Does conservation work?The importance of social acceptability and political commitment. The need for biodiversity indicators and measuring the impact of conservation. 

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

See the full page

The objectives of this EU are to explore key concepts related to climate change, illustrate important notions in ecology and evolution in light of climate change in many different ecosystems, and summarize the various scientific and societal issues and challenges posed by CC.

See the full page

Quantitative genetics is a discipline that emerged in the early 20th century to understand the inheritance 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 topical than ever at the beginning of the 21st century, with the emergence of genomics (a factor of scientific progress, provided that not all evolutionary problems are reduced to the fiction of a few Mendelian alleles with strong effects) and the resurgence of alternative models of heredity (epigenetics) that go beyond the sequence-centered vision inherited from classical molecular biology.

The aim of the module is to provide sufficient knowledge of quantitative genetics to (i) understand the classical foundations of the discipline, manipulate key quantities (genetic variances, heritabilities, genetic correlations) and the statistical techniques used to estimate 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 relates to the classical Mendelian view.

See the full page

The overall objective is to present human evolutionary biology, proposing to use the tools of evolutionary biology to better understand human behavior and that observed in non-human primates in the context of their evolutionary history. Whether it be health, sociality, culture, local adaptations, language, morality, reproduction, or sexual preferences, the topics are addressed within the theoretical framework of evolutionary biology and ecology. Summary of course content: Anthropology, human sciences, and evolutionary biology / Evolution of cooperation / Cultural evolution / Evolution of diet / Evolution of sociality in primates / Family ecology / Medicine, public health, and evolution / Evolution of language / Evolutionary demography / The origins of equity.

See the full page

Behavioral ecology approaches the study of behavior from an evolutionary perspective in order to examine its mechanisms, function, and contribution to evolutionary and ecological processes. Research conducted in behavioral ecology helps us understand other phenomena observed in other disciplines of biology, as all animals, from single-celled organisms to the most complex vertebrates, exhibit behavior.

The module exposes students to various basic concepts and the multitude of tools that can be used (observations and experiments in natural populations or on captive individuals, comparative analyses, use of modeling tools, ecophysiology, molecular biology, biochemistry, embedded electronics, etc.). Part of the training is based on specific discussions about the research approaches that can be used, the tools employed, and the limits of the inferences that can be made. Students will be asked to participate actively at these different levels, particularly through critical discussions of articles.

The topics covered range from exploring food supply strategies, partner selection, habitat choice, and investment in reproduction, to the study of animal communication and the reasons for living in groups. The historical dimension of the discipline is addressed in the introduction, but also according to the sensitivity of the speakers and the topics covered (meaning and relationships between 'Animal Behavior', 'Ethology', Behavioral Ecology, etc.).

See the full page

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

- Theme 1: Genetic burden and evolution of reproductive systems: recombination, sexual/asexual reproduction, self/cross-fertilization

- Theme 2: Kinship structures and their evolutionary consequences: kin 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 the full page

The main objective of this course is to provide students with all the skills necessary to understand and use the concepts and methods underlying the quantitative study of population phenomena. The main methods of analysis and modeling of these phenomena will be addressed from both a theoretical (formal calculations) and practical (statistics, simulations) perspective, using examples exploring different phylogenetic scales (microbial dynamics, invasive species, human demography), spatial (from local to global) and temporal (transient and permanent regimes, eco-evolutionary coupling) scales, with particular attention paid to heterogeneity (spatial, genetic or phenotypic) and randomness (stochasticity, uncertainties) characteristic of populations or inherent in their study.

See the full page

The objective of this EU is to demonstrate that biological diversity is functional:

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

2) at different organizational scales (from organisms to ecosystems). The lessons aim to explain how to approach this functional aspect of diversity for the more than 10 million organisms present on the planet's surface, using examples from highly and minimally anthropized environments.

See the full page

Phylogeny is a quest for evolutionary clues. The aim of this module is to highlight the existence of gene phylogenies within species phylogenies, the methods used to represent evolutionary histories in the form of trees, and the challenge of positional molecular homology through sequence alignment. The principles of phylogenetic inference methods are at the heart of this course unit. Distance methods highlight the difficulties of separating homology and homoplasy, and the need to construct models of character evolution. The cladistic approach with maximum parsimony illustrates, on the one hand, the use of bootstrapping to estimate the robustness of phylogeny nodes and, on the other hand, the impact of taxonomic sampling on the detection of multiple substitutions.

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

See the 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, thus forming an important bridge between biology and paleontology.

During the module, we will discuss several evolutionary issues relevant to Evo-Devo approaches based on articles: the question of homology, the establishment and evolution of repeated structures, the genetic basis of development, and the links between genome evolution and form evolution. We will illustrate these concepts using examples from metazoans and the green lineage, and apply them to both large modern groups and populations.

See the full page

See the full page

1. Bayesian inference: Motivation and simple example. 

2. The likelihood. 

3. A detour to explore priors. 

4. Markov chain Monte Carlo methods (MCMC)

5. Bayesian analyses in R with the Jags software.  

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

7. Heterogeneity and multilevel models (also known as mixed models). 

See the full page

The courses present four 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 what role does science play in BC? 
2. Species conservation: Which species are priorities? How can species be conserved? How can we tell if a species is "well conserved"? 
3. Conserving spaces: Which spaces are priorities? How can spaces be conserved? 
4. Does conservation work?The importance of social acceptability and political commitment. The need for biodiversity indicators and measuring the impact of conservation. 

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

See the full page

The objectives of this EU are to explore key concepts related to climate change, illustrate important notions in ecology and evolution in light of climate change in many different ecosystems, and summarize the various scientific and societal issues and challenges posed by CC.

See the full page

Quantitative genetics is a discipline that emerged in the early 20th century to understand the inheritance 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 topical than ever at the beginning of the 21st century, with the emergence of genomics (a factor of scientific progress, provided that not all evolutionary problems are reduced to the fiction of a few Mendelian alleles with strong effects) and the resurgence of alternative models of heredity (epigenetics) that go beyond the sequence-centered vision inherited from classical molecular biology.

The aim of the module is to provide sufficient knowledge of quantitative genetics to (i) understand the classical foundations of the discipline, manipulate key quantities (genetic variances, heritabilities, genetic correlations) and the statistical techniques used to estimate 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 relates to the classical Mendelian view.

See the full page

The overall objective is to present human evolutionary biology, proposing to use the tools of evolutionary biology to better understand human behavior and that observed in non-human primates in the context of their evolutionary history. Whether it be health, sociality, culture, local adaptations, language, morality, reproduction, or sexual preferences, the topics are addressed within the theoretical framework of evolutionary biology and ecology. Summary of course content: Anthropology, human sciences, and evolutionary biology / Evolution of cooperation / Cultural evolution / Evolution of diet / Evolution of sociality in primates / Family ecology / Medicine, public health, and evolution / Evolution of language / Evolutionary demography / The origins of equity.

See the full page

Behavioral ecology approaches the study of behavior from an evolutionary perspective in order to examine its mechanisms, function, and contribution to evolutionary and ecological processes. Research conducted in behavioral ecology helps us understand other phenomena observed in other disciplines of biology, as all animals, from single-celled organisms to the most complex vertebrates, exhibit behavior.

The module exposes students to various basic concepts and the multitude of tools that can be used (observations and experiments in natural populations or on captive individuals, comparative analyses, use of modeling tools, ecophysiology, molecular biology, biochemistry, embedded electronics, etc.). Part of the training is based on specific discussions about the research approaches that can be used, the tools employed, and the limits of the inferences that can be made. Students will be asked to participate actively at these different levels, particularly through critical discussions of articles.

The topics covered range from exploring food supply strategies, partner selection, habitat choice, and investment in reproduction, to the study of animal communication and the reasons for living in groups. The historical dimension of the discipline is addressed in the introduction, but also according to the sensitivity of the speakers and the topics covered (meaning and relationships between 'Animal Behavior', 'Ethology', Behavioral Ecology, etc.).

See the full page

See the full page

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

- Theme 1: Genetic burden and evolution of reproductive systems: recombination, sexual/asexual reproduction, self/cross-fertilization

- Theme 2: Kinship structures and their evolutionary consequences: kin 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 the full page

The main objective of this course is to provide students with all the skills necessary to understand and use the concepts and methods underlying the quantitative study of population phenomena. The main methods of analysis and modeling of these phenomena will be addressed from both a theoretical (formal calculations) and practical (statistics, simulations) perspective, using examples exploring different phylogenetic scales (microbial dynamics, invasive species, human demography), spatial (from local to global) and temporal (transient and permanent regimes, eco-evolutionary coupling) scales, with particular attention paid to heterogeneity (spatial, genetic or phenotypic) and randomness (stochasticity, uncertainties) characteristic of populations or inherent in their study.

See the full page

The objective of this EU is to demonstrate that biological diversity is functional:

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

2) at different organizational scales (from organisms to ecosystems). The lessons aim to explain how to approach this functional aspect of diversity for the more than 10 million organisms present on the planet's surface, using examples from highly and minimally anthropized environments.

See the full page

Phylogeny is a quest for evolutionary clues. The aim of this module is to highlight the existence of gene phylogenies within species phylogenies, the methods used to represent evolutionary histories in the form of trees, and the challenge of positional molecular homology through sequence alignment. The principles of phylogenetic inference methods are at the heart of this course unit. Distance methods highlight the difficulties of separating homology and homoplasy, and the need to construct models of character evolution. The cladistic approach with maximum parsimony illustrates, on the one hand, the use of bootstrapping to estimate the robustness of phylogeny nodes and, on the other hand, the impact of taxonomic sampling on the detection of multiple substitutions.

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

See the 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, thus forming an important bridge between biology and paleontology.

During the module, we will discuss several evolutionary issues relevant to Evo-Devo approaches based on articles: the question of homology, the establishment and evolution of repeated structures, the genetic basis of development, and the links between genome evolution and form evolution. We will illustrate these concepts using examples from metazoans and the green lineage, and apply them to both large modern groups and populations.

See the full page

See the full page

1. Bayesian inference: Motivation and simple example. 

2. The likelihood. 

3. A detour to explore priors. 

4. Markov chain Monte Carlo methods (MCMC)

5. Bayesian analyses in R with the Jags software.  

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

7. Heterogeneity and multilevel models (also known as mixed models). 

See the full page

The courses present four 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 what role does science play in BC? 
2. Species conservation: Which species are priorities? How can species be conserved? How can we tell if a species is "well conserved"? 
3. Conserving spaces: Which spaces are priorities? How can spaces be conserved? 
4. Does conservation work?The importance of social acceptability and political commitment. The need for biodiversity indicators and measuring the impact of conservation. 

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

See the full page

The objectives of this EU are to explore key concepts related to climate change, illustrate important notions in ecology and evolution in light of climate change in many different ecosystems, and summarize the various scientific and societal issues and challenges posed by CC.

See the full page

Quantitative genetics is a discipline that emerged in the early 20th century to understand the inheritance 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 topical than ever at the beginning of the 21st century, with the emergence of genomics (a factor of scientific progress, provided that not all evolutionary problems are reduced to the fiction of a few Mendelian alleles with strong effects) and the resurgence of alternative models of heredity (epigenetics) that go beyond the sequence-centered vision inherited from classical molecular biology.

The aim of the module is to provide sufficient knowledge of quantitative genetics to (i) understand the classical foundations of the discipline, manipulate key quantities (genetic variances, heritabilities, genetic correlations) and the statistical techniques used to estimate 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 relates to the classical Mendelian view.

See the full page

The overall objective is to present human evolutionary biology, proposing to use the tools of evolutionary biology to better understand human behavior and that observed in non-human primates in the context of their evolutionary history. Whether it be health, sociality, culture, local adaptations, language, morality, reproduction, or sexual preferences, the topics are addressed within the theoretical framework of evolutionary biology and ecology. Summary of course content: Anthropology, human sciences, and evolutionary biology / Evolution of cooperation / Cultural evolution / Evolution of diet / Evolution of sociality in primates / Family ecology / Medicine, public health, and evolution / Evolution of language / Evolutionary demography / The origins of equity.

See the full page

Behavioral ecology approaches the study of behavior from an evolutionary perspective in order to examine its mechanisms, function, and contribution to evolutionary and ecological processes. Research conducted in behavioral ecology helps us understand other phenomena observed in other disciplines of biology, as all animals, from single-celled organisms to the most complex vertebrates, exhibit behavior.

The module exposes students to various basic concepts and the multitude of tools that can be used (observations and experiments in natural populations or on captive individuals, comparative analyses, use of modeling tools, ecophysiology, molecular biology, biochemistry, embedded electronics, etc.). Part of the training is based on specific discussions about the research approaches that can be used, the tools employed, and the limits of the inferences that can be made. Students will be asked to participate actively at these different levels, particularly through critical discussions of articles.

The topics covered range from exploring food supply strategies, partner selection, habitat choice, and investment in reproduction, to the study of animal communication and the reasons for living in groups. The historical dimension of the discipline is addressed in the introduction, but also according to the sensitivity of the speakers and the topics covered (meaning and relationships between 'Animal Behavior', 'Ethology', Behavioral Ecology, etc.).

See the full page

See the full page

See the full page

1. Bayesian inference: Motivation and simple example. 

2. The likelihood. 

3. A detour to explore priors. 

4. Markov chain Monte Carlo methods (MCMC)

5. Bayesian analyses in R with the Jags software.  

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

7. Heterogeneity and multilevel models (also known as mixed models). 

See the full page

The overall objective is to present human evolutionary biology, proposing to use the tools of evolutionary biology to better understand human behavior and that observed in non-human primates in the context of their evolutionary history. Whether it be health, sociality, culture, local adaptations, language, morality, reproduction, or sexual preferences, the topics are addressed within the theoretical framework of evolutionary biology and ecology. Summary of course content: Anthropology, human sciences, and evolutionary biology / Evolution of cooperation / Cultural evolution / Evolution of diet / Evolution of sociality in primates / Family ecology / Medicine, public health, and evolution / Evolution of language / Evolutionary demography / The origins of equity.

See the full page

1. Bayesian inference: Motivation and simple example. 

2. The likelihood. 

3. A detour to explore priors. 

4. Markov chain Monte Carlo methods (MCMC)

5. Bayesian analyses in R with the Jags software.  

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

7. Heterogeneity and multilevel models (also known as mixed models). 

See the full page

The courses present four 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 what role does science play in BC? 
2. Species conservation: Which species are priorities? How can species be conserved? How can we tell if a species is "well conserved"? 
3. Conserving spaces: Which spaces are priorities? How can spaces be conserved? 
4. Does conservation work?The importance of social acceptability and political commitment. The need for biodiversity indicators and measuring the impact of conservation. 

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

See the full page

The objectives of this EU are to explore key concepts related to climate change, illustrate important notions in ecology and evolution in light of climate change in many different ecosystems, and summarize the various scientific and societal issues and challenges posed by CC.

See the full page

Quantitative genetics is a discipline that emerged in the early 20th century to understand the inheritance 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 topical than ever at the beginning of the 21st century, with the emergence of genomics (a factor of scientific progress, provided that not all evolutionary problems are reduced to the fiction of a few Mendelian alleles with strong effects) and the resurgence of alternative models of heredity (epigenetics) that go beyond the sequence-centered vision inherited from classical molecular biology.

The aim of the module is to provide sufficient knowledge of quantitative genetics to (i) understand the classical foundations of the discipline, manipulate key quantities (genetic variances, heritabilities, genetic correlations) and the statistical techniques used to estimate 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 relates to the classical Mendelian view.

See the full page

The overall objective is to present human evolutionary biology, proposing to use the tools of evolutionary biology to better understand human behavior and that observed in non-human primates in the context of their evolutionary history. Whether it be health, sociality, culture, local adaptations, language, morality, reproduction, or sexual preferences, the topics are addressed within the theoretical framework of evolutionary biology and ecology. Summary of course content: Anthropology, human sciences, and evolutionary biology / Evolution of cooperation / Cultural evolution / Evolution of diet / Evolution of sociality in primates / Family ecology / Medicine, public health, and evolution / Evolution of language / Evolutionary demography / The origins of equity.

See the full page

Behavioral ecology approaches the study of behavior from an evolutionary perspective in order to examine its mechanisms, function, and contribution to evolutionary and ecological processes. Research conducted in behavioral ecology helps us understand other phenomena observed in other disciplines of biology, as all animals, from single-celled organisms to the most complex vertebrates, exhibit behavior.

The module exposes students to various basic concepts and the multitude of tools that can be used (observations and experiments in natural populations or on captive individuals, comparative analyses, use of modeling tools, ecophysiology, molecular biology, biochemistry, embedded electronics, etc.). Part of the training is based on specific discussions about the research approaches that can be used, the tools employed, and the limits of the inferences that can be made. Students will be asked to participate actively at these different levels, particularly through critical discussions of articles.

The topics covered range from exploring food supply strategies, partner selection, habitat choice, and investment in reproduction, to the study of animal communication and the reasons for living in groups. The historical dimension of the discipline is addressed in the introduction, but also according to the sensitivity of the speakers and the topics covered (meaning and relationships between 'Animal Behavior', 'Ethology', Behavioral Ecology, etc.).

See the full page

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

- Theme 1: Genetic burden and evolution of reproductive systems: recombination, sexual/asexual reproduction, self/cross-fertilization

- Theme 2: Kinship structures and their evolutionary consequences: kin 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 the full page

The main objective of this course is to provide students with all the skills necessary to understand and use the concepts and methods underlying the quantitative study of population phenomena. The main methods of analysis and modeling of these phenomena will be addressed from both a theoretical (formal calculations) and practical (statistics, simulations) perspective, using examples exploring different phylogenetic scales (microbial dynamics, invasive species, human demography), spatial (from local to global) and temporal (transient and permanent regimes, eco-evolutionary coupling) scales, with particular attention paid to heterogeneity (spatial, genetic or phenotypic) and randomness (stochasticity, uncertainties) characteristic of populations or inherent in their study.

See the full page

The objective of this EU is to demonstrate that biological diversity is functional:

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

2) at different organizational scales (from organisms to ecosystems). The lessons aim to explain how to approach this functional aspect of diversity for the more than 10 million organisms present on the planet's surface, using examples from highly and minimally anthropized environments.

See the full page

Phylogeny is a quest for evolutionary clues. The aim of this module is to highlight the existence of gene phylogenies within species phylogenies, the methods used to represent evolutionary histories in the form of trees, and the challenge of positional molecular homology through sequence alignment. The principles of phylogenetic inference methods are at the heart of this course unit. Distance methods highlight the difficulties of separating homology and homoplasy, and the need to construct models of character evolution. The cladistic approach with maximum parsimony illustrates, on the one hand, the use of bootstrapping to estimate the robustness of phylogeny nodes and, on the other hand, the impact of taxonomic sampling on the detection of multiple substitutions.

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

See the 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, thus forming an important bridge between biology and paleontology.

During the module, we will discuss several evolutionary issues relevant to Evo-Devo approaches based on articles: the question of homology, the establishment and evolution of repeated structures, the genetic basis of development, and the links between genome evolution and form evolution. We will illustrate these concepts using examples from metazoans and the green lineage, and apply them to both large modern groups and populations.

See the full page

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

- Theme 1: Genetic burden and evolution of reproductive systems: recombination, sexual/asexual reproduction, self/cross-fertilization

- Theme 2: Kinship structures and their evolutionary consequences: kin 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 the full page

The main objective of this course is to provide students with all the skills necessary to understand and use the concepts and methods underlying the quantitative study of population phenomena. The main methods of analysis and modeling of these phenomena will be addressed from both a theoretical (formal calculations) and practical (statistics, simulations) perspective, using examples exploring different phylogenetic scales (microbial dynamics, invasive species, human demography), spatial (from local to global) and temporal (transient and permanent regimes, eco-evolutionary coupling) scales, with particular attention paid to heterogeneity (spatial, genetic or phenotypic) and randomness (stochasticity, uncertainties) characteristic of populations or inherent in their study.

See the full page

The objective of this EU is to demonstrate that biological diversity is functional:

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

2) at different organizational scales (from organisms to ecosystems). The lessons aim to explain how to approach this functional aspect of diversity for the more than 10 million organisms present on the planet's surface, using examples from highly and minimally anthropized environments.

See the full page

Phylogeny is a quest for evolutionary clues. The aim of this module is to highlight the existence of gene phylogenies within species phylogenies, the methods used to represent evolutionary histories in the form of trees, and the challenge of positional molecular homology through sequence alignment. The principles of phylogenetic inference methods are at the heart of this course unit. Distance methods highlight the difficulties of separating homology and homoplasy, and the need to construct models of character evolution. The cladistic approach with maximum parsimony illustrates, on the one hand, the use of bootstrapping to estimate the robustness of phylogeny nodes and, on the other hand, the impact of taxonomic sampling on the detection of multiple substitutions.

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

See the 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, thus forming an important bridge between biology and paleontology.

During the module, we will discuss several evolutionary issues relevant to Evo-Devo approaches based on articles: the question of homology, the establishment and evolution of repeated structures, the genetic basis of development, and the links between genome evolution and form evolution. We will illustrate these concepts using examples from metazoans and the green lineage, and apply them to both large modern groups and populations.

See the full page

See the full page

See the full page

1. Bayesian inference: Motivation and simple example. 

2. The likelihood. 

3. A detour to explore priors. 

4. Markov chain Monte Carlo methods (MCMC)

5. Bayesian analyses in R with the Jags software.  

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

7. Heterogeneity and multilevel models (also known as mixed models). 

See the full page

The overall objective is to present human evolutionary biology, proposing to use the tools of evolutionary biology to better understand human behavior and that observed in non-human primates in the context of their evolutionary history. Whether it be health, sociality, culture, local adaptations, language, morality, reproduction, or sexual preferences, the topics are addressed within the theoretical framework of evolutionary biology and ecology. Summary of course content: Anthropology, human sciences, and evolutionary biology / Evolution of cooperation / Cultural evolution / Evolution of diet / Evolution of sociality in primates / Family ecology / Medicine, public health, and evolution / Evolution of language / Evolutionary demography / The origins of equity.

See the full page

1. Bayesian inference: Motivation and simple example. 

2. The likelihood. 

3. A detour to explore priors. 

4. Markov chain Monte Carlo methods (MCMC)

5. Bayesian analyses in R with the Jags software.  

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

7. Heterogeneity and multilevel models (also known as mixed models). 

See the full page

The courses present four 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 what role does science play in BC? 
2. Species conservation: Which species are priorities? How can species be conserved? How can we tell if a species is "well conserved"? 
3. Conserving spaces: Which spaces are priorities? How can spaces be conserved? 
4. Does conservation work?The importance of social acceptability and political commitment. The need for biodiversity indicators and measuring the impact of conservation. 

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

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The objectives of this EU are to explore key concepts related to climate change, illustrate important notions in ecology and evolution in light of climate change in many different ecosystems, and summarize the various scientific and societal issues and challenges posed by CC.

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Quantitative genetics is a discipline that emerged in the early 20th century to understand the inheritance 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 topical than ever at the beginning of the 21st century, with the emergence of genomics (a factor of scientific progress, provided that not all evolutionary problems are reduced to the fiction of a few Mendelian alleles with strong effects) and the resurgence of alternative models of heredity (epigenetics) that go beyond the sequence-centered vision inherited from classical molecular biology.

The aim of the module is to provide sufficient knowledge of quantitative genetics to (i) understand the classical foundations of the discipline, manipulate key quantities (genetic variances, heritabilities, genetic correlations) and the statistical techniques used to estimate 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 relates to the classical Mendelian view.

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The overall objective is to present human evolutionary biology, proposing to use the tools of evolutionary biology to better understand human behavior and that observed in non-human primates in the context of their evolutionary history. Whether it be health, sociality, culture, local adaptations, language, morality, reproduction, or sexual preferences, the topics are addressed within the theoretical framework of evolutionary biology and ecology. Summary of course content: Anthropology, human sciences, and evolutionary biology / Evolution of cooperation / Cultural evolution / Evolution of diet / 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 approaches the study of behavior from an evolutionary perspective in order to examine its mechanisms, function, and contribution to evolutionary and ecological processes. Research conducted in behavioral ecology helps us understand other phenomena observed in other disciplines of biology, as all animals, from single-celled organisms to the most complex vertebrates, exhibit behavior.

The module exposes students to various basic concepts and the multitude of tools that can be used (observations and experiments in natural populations or on captive individuals, comparative analyses, use of modeling tools, ecophysiology, molecular biology, biochemistry, embedded electronics, etc.). Part of the training is based on specific discussions about the research approaches that can be used, the tools employed, and the limits of the inferences that can be made. Students will be asked to participate actively at these different levels, particularly through critical discussions of articles.

The topics covered range from exploring food supply strategies, partner selection, habitat choice, and investment in reproduction, to the study of animal communication and the reasons for living in groups. The historical dimension of the discipline is addressed in the introduction, but also according to the sensitivity of the speakers and the topics covered (meaning and relationships between 'Animal Behavior', 'Ethology', Behavioral Ecology, etc.).

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

- Theme 1: Genetic burden and evolution of reproductive systems: recombination, sexual/asexual reproduction, self/cross-fertilization

- Theme 2: Kinship structures and their evolutionary consequences: kin 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 objective of this course is to provide students with all the skills necessary to understand and use the concepts and methods underlying the quantitative study of population phenomena. The main methods of analysis and modeling of these phenomena will be addressed from both a theoretical (formal calculations) and practical (statistics, simulations) perspective, using examples exploring different phylogenetic scales (microbial dynamics, invasive species, human demography), spatial (from local to global) and temporal (transient and permanent regimes, eco-evolutionary coupling) scales, with particular attention paid to heterogeneity (spatial, genetic or phenotypic) and randomness (stochasticity, uncertainties) characteristic of populations or inherent in their study.

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The objective of this EU is to demonstrate that biological diversity is functional:

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

2) at different organizational scales (from organisms to ecosystems). The lessons aim to explain how to approach this functional aspect of diversity for the more than 10 million organisms present on the planet's surface, using examples from highly and minimally anthropized environments.

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Phylogeny is a quest for evolutionary clues. The aim of this module is to highlight the existence of gene phylogenies within species phylogenies, the methods used to represent evolutionary histories in the form of trees, and the challenge of positional molecular homology through sequence alignment. The principles of phylogenetic inference methods are at the heart of this course unit. Distance methods highlight the difficulties of separating homology and homoplasy, and the need to construct models of character evolution. The cladistic approach with maximum parsimony illustrates, on the one hand, the use of bootstrapping to estimate the robustness of phylogeny nodes and, on the other hand, the impact of taxonomic sampling on the detection of multiple substitutions.

Probabilistic approaches are presented and explored in depth. The artifact of attraction to long branches leads to the introduction of probabilistic reasoning. The maximum likelihood method allows us to address likelihood calculation, model parameter estimation by optimality, the construction of different character evolution models, and model comparison. Bayesian inference introduces the distinction between density-based and optimality-based approaches. It then shows the a priori use of probability densities, the estimation of the posterior distributions of model parameters based on the data, their approximation by Markov chains with Monte Carlo techniques and Metropolis coupling (MCMCMC), the ignition and convergence phases, and the calculation and interpretation of the posterior probabilities of trees and clades. 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, thus forming an important bridge between biology and paleontology.

During the module, we will discuss several evolutionary issues relevant to Evo-Devo approaches based on articles: the question of homology, the establishment and evolution of repeated structures, the genetic basis of development, and the links between genome evolution and form evolution. We will illustrate these concepts using examples from metazoans and the green lineage, and apply them to both large modern groups and populations.

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

- Theme 1: Genetic burden and evolution of reproductive systems: recombination, sexual/asexual reproduction, self/cross-fertilization

- Theme 2: Kinship structures and their evolutionary consequences: kin 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 the full page

The main objective of this course is to provide students with all the skills necessary to understand and use the concepts and methods underlying the quantitative study of population phenomena. The main methods of analysis and modeling of these phenomena will be addressed from both a theoretical (formal calculations) and practical (statistics, simulations) perspective, using examples exploring different phylogenetic scales (microbial dynamics, invasive species, human demography), spatial (from local to global) and temporal (transient and permanent regimes, eco-evolutionary coupling) scales, with particular attention paid to heterogeneity (spatial, genetic or phenotypic) and randomness (stochasticity, uncertainties) characteristic of populations or inherent in their study.

See the full page

The objective of this EU is to demonstrate that biological diversity is functional:

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

2) at different organizational scales (from organisms to ecosystems). The lessons aim to explain how to approach this functional aspect of diversity for the more than 10 million organisms present on the planet's surface, using examples from highly and minimally anthropized environments.

See the full page

Phylogeny is a quest for evolutionary clues. The aim of this module is to highlight the existence of gene phylogenies within species phylogenies, the methods used to represent evolutionary histories in the form of trees, and the challenge of positional molecular homology through sequence alignment. The principles of phylogenetic inference methods are at the heart of this course unit. Distance methods highlight the difficulties of separating homology and homoplasy, and the need to construct models of character evolution. The cladistic approach with maximum parsimony illustrates, on the one hand, the use of bootstrapping to estimate the robustness of phylogeny nodes and, on the other hand, the impact of taxonomic sampling on the detection of multiple substitutions.

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

See the 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, thus forming an important bridge between biology and paleontology.

During the module, we will discuss several evolutionary issues relevant to Evo-Devo approaches based on articles: the question of homology, the establishment and evolution of repeated structures, the genetic basis of development, and the links between genome evolution and form evolution. We will illustrate these concepts using examples from metazoans and the green lineage, and apply them to both large modern groups and populations.

See the full page

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1. Bayesian inference: Motivation and simple example. 

2. The likelihood. 

3. A detour to explore priors. 

4. Markov chain Monte Carlo methods (MCMC)

5. Bayesian analyses in R with the Jags software.  

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

7. Heterogeneity and multilevel models (also known as mixed models). 

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The courses present four 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 what role does science play in BC? 
2. Species conservation: Which species are priorities? How can species be conserved? How can we tell if a species is "well conserved"? 
3. Conserving spaces: Which spaces are priorities? How can spaces be conserved? 
4. Does conservation work?The importance of social acceptability and political commitment. The need for biodiversity indicators and measuring the impact of conservation. 

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

See the full page

The objectives of this EU are to explore key concepts related to climate change, illustrate important notions in ecology and evolution in light of climate change in many different ecosystems, and summarize the various scientific and societal issues and challenges posed by CC.

See the full page

Quantitative genetics is a discipline that emerged in the early 20th century to understand the inheritance 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 topical than ever at the beginning of the 21st century, with the emergence of genomics (a factor of scientific progress, provided that not all evolutionary problems are reduced to the fiction of a few Mendelian alleles with strong effects) and the resurgence of alternative models of heredity (epigenetics) that go beyond the sequence-centered vision inherited from classical molecular biology.

The aim of the module is to provide sufficient knowledge of quantitative genetics to (i) understand the classical foundations of the discipline, manipulate key quantities (genetic variances, heritabilities, genetic correlations) and the statistical techniques used to estimate 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 relates to the classical Mendelian view.

See the full page

The overall objective is to present human evolutionary biology, proposing to use the tools of evolutionary biology to better understand human behavior and that observed in non-human primates in the context of their evolutionary history. Whether it be health, sociality, culture, local adaptations, language, morality, reproduction, or sexual preferences, the topics are addressed within the theoretical framework of evolutionary biology and ecology. Summary of course content: Anthropology, human sciences, and evolutionary biology / Evolution of cooperation / Cultural evolution / Evolution of diet / Evolution of sociality in primates / Family ecology / Medicine, public health, and evolution / Evolution of language / Evolutionary demography / The origins of equity.

See the full page

Behavioral ecology approaches the study of behavior from an evolutionary perspective in order to examine its mechanisms, function, and contribution to evolutionary and ecological processes. Research conducted in behavioral ecology helps us understand other phenomena observed in other disciplines of biology, as all animals, from single-celled organisms to the most complex vertebrates, exhibit behavior.

The module exposes students to various basic concepts and the multitude of tools that can be used (observations and experiments in natural populations or on captive individuals, comparative analyses, use of modeling tools, ecophysiology, molecular biology, biochemistry, embedded electronics, etc.). Part of the training is based on specific discussions about the research approaches that can be used, the tools employed, and the limits of the inferences that can be made. Students will be asked to participate actively at these different levels, particularly through critical discussions of articles.

The topics covered range from exploring food supply strategies, partner selection, habitat choice, and investment in reproduction, to the study of animal communication and the reasons for living in groups. The historical dimension of the discipline is addressed in the introduction, but also according to the sensitivity of the speakers and the topics covered (meaning and relationships between 'Animal Behavior', 'Ethology', Behavioral Ecology, etc.).

See the full page