Applied Times Series for Ecology is a PhD level course offered for the first time at the Department of Ecology, Environment and Plant Sciences, Stockholm University in March 2014.
This course provides an introduction to the analysis of ecological time series data. We will address questions such as: What are the trends in abundance and are they shared among species or populations? Which members of a community are strong (or weak) interactors? How stable is a particular community? How do effects of the environment on species change over time? And many more.
The first part of the course will provide an overview of time series characteristics (trends, autocorrelation, etc.) and the state-space modeling framework. The primary emphasis of this course will be to extend this diverse class of models to analyze various forms of time series data via multivariate autoregressive state-space (MARSS) models. Students will apply these models (using the MARSS package for R) to a variety of time series questions including estimating species interactions, population structure and trends, and identifying shared trends among time series. The course will include interactive lectures and computer labs. Example datasets from limnology, fisheries, and ecology will be used to explore the characteristics of time-series data and to practice implementing R code for MARSS analyses, diagnostics, and model selection. Instructors will be available to discuss datasets students use in their research and these datasets may be used during the course.
Eli is a population ecologist focusing on the analysis of noisy data from stochastic populations and communities. Mark studies how natural processes and anthropogenic factors interact to drive ecological dynamics of Pacific salmon populations and their aquatic ecosystems.
Supervisor: Monika Winder; Course coordinator: Jennifer Griffiths
Course location: Stockholm University (Frescati Backe), Stockholm, Sweden
Course dates / times: 24 – 28 March 2014, 9am – 5pm
Course credits: 3
Course requirements: Solid statistical background; familiarity with R programming language; personal laptop
Language: The course will be taught in English
The course has been taught for 2014. Feel free to check out the materials from the course and the additional resources.
Download course announcement here