Integral Projection Models: Construction, Analysis and Interpretation
Start: January 26th, 2015
End: January 31st, 2015 (half a day)
Location: Max Planck Institute for Demographic Research (MPIDR), Rostock, Germany.
Rob Salguero-Gómez (The University of Queensland, Australia; MPIDR, Germany)
Eelke Jongejans (Radboud University, The Netherlands)
Maria Paniw (Universidad de Cadiz, Spain)
Johan Dahlgren (University of Southern Denmark, Denmark)
Jessica Metcalf (Princeton University, USA) - remotely
Sean McMahon (Smithsonian Institution, USA) - remotely
Cory Merow (University of Connecticut, USA) - remotely
Dylan Childs (Sheffield University, UK) – remotely
Population modeling provides insights on ecological and evolutionary processes as diverse as the probability of a local extinction, the evolution of cellular maintenance, or the influence of clonality on senescence. Biologists have long worked with a variety of tools that model patterns of change in populations and explore the underlying causes of these dynamics. In recent decades, structured population models have served as an important link between these theoretical pursuits and applied analyses of observational and experimental data on populations. With advances in computing power and theoretical developments, population models have evolved rapidly and new approaches such as Integral Projection Models (IPMs) have emerged. IPMs represent the next generation of stage-classified demographic models by offering all the advantages of discrete matrix models in a more general framework. In the simplest form of an IPM, individuals of a population are classified along a continuous state variable (e.g., volume, height, weight, degree of oxidative stress, etc), and the vital rates involved in the life cycle of the species (e.g., survival, growth, reproduction, clonality, etc) are modeled through a series of simple, biologically intuitive regressions. More complex IPMs can include age × size × habitat interactions, stochastic modeling, coupling of genetic information on population dynamics, etc.
This course will teach basic and advanced applications of IPMs. By the end of the workshop, attendees will be able to construct IPMs with their own data, and estimate a number of important population rates, including: passage time, age-specific survival, age-specific reproductive curves, deterministic and stochastic population growth rates, sensitivities, elasticities, and life table response experiment analyses (i.e. variance decomposition techniques).
The course will alternate between instructor lectures, computer exercises, and student presentations. The computer labs will use the R programming language and the R package IPMpack. The course will culminate in personal projects using student-collected or publicly available demographic data.
Although there are no strict pre-requisites, we expect students to be familiar with the R working environment, and to have worked through some of the tutorials for the IPMpack. Attendees will be provided with a reading package to come up to speed with the basics of structured population models prior to the workshop.
Students are encouraged to visit the IPMpack website and work through its examples prior to attending the workshop: http://ipmpack.r-forge.r-project.org/
Students will be evaluated based on participation in class discussion, presentations, and final project completion.
Recruitment of Students
Applicants should either be enrolled in a MSc or PhD program (more advanced students will be favored) or have recently received their PhD.
A maximum of 25 students will be admitted.
The selection will be made by the MPIDR based on the applicants’ scientific qualifications, experience with population modeling, and interest in using IPM models in future research.
How to apply
Applications should be sent by email to the MPIDR. Please write in the subject line of your email “Application for course IDEM 184 – Integral Projection Models”. You also need to attach the following items integrated in **a single pdf file**: (1) a max two-page curriculum vitae, including a list of your scholarly publications, (2) a one page research-statement clearly indicating how your current and future research will benefit from attending this workshop, and (3) a one-page letter of recommendation from your supervisor or member of your MSc/PhD committee at your home institution. In your research statement document, please include a brief description of your experience with demographic models and programming (matlab, R, etc) as well as statistical skills. At the very end of your research statement, in a separate paragraph, please add “I request financial support from the MPIDR” if you would like to be considered for accommodation support provided by the MPIDR.
Send your email to Heiner Maier (email@example.com) no later than October 10th, 2014. Acceptance will be notified by October 31, 2014.