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Adaptive dynamics Modeling

Word - 47.5 ko
M2 E11 DYAD planning-2016_v1
Coordination:

Régis Ferriere, ENS

Format:

Hours: 30 h (15 h lectures, 15 h tutorials)
Credits: 3 ECTS
Semester: 1st semester
Number of students: 15 max.

Presentation:
Aims:

Understanding the adaptive dynamics approach to model the interaction between ecological and evolutionary processes. Mastering the concepts, mathematics, and computational tools in so as to be able to construct and analyse models and apply these models to answer biological questions.

Themes:

Adaptive dynamics modeling has become the dominant theoretical framework for ‘Darwinian ecology’, i.e. the investigation of how evolution shapes the structure of ecological interactions and influences ecological processes within and between biological populations. The course will present the key concepts that are underlying the adaptive dynamics approach : environmental feedback loop, invasion fitness, evolutionary singularity, evolutionary stability, evolutionary branching, evolutionary suicide, pairwise invasibility plots and canonical equations. The general framework will be applied to study the eco-evolutionary dynamics of populations competing for resources, predator-prey interactions, and mutualistic systems. Hands-on tutorial sessions will make use of the software ZEN for simulations of the adaptive dynamics of specific examples.
Enrolled students are expected to have some experience with population and community modeling. There are no prerequisites in populations genetics.

Organization:

• This is a one-week intensive course, mornings and afternoons included.
• Lecture-style presentations will be complemented by computer-based tutorials.
• Three 3-hour sessions will be devoted to individual projects (under the instructors’ supervision) involving the design and numerical implementation of simple models.
• Evaluation will be made on the basis of the presentation of the individual projects at the end of the week.

Teaching team:

Régis Ferrière (ENS Paris)
David Claessen (ENS Paris)
Stéphane Legendre (CNRS – ENS Paris)