Time-series data are commonly collected as part of ecological studies. Variability enters such data through process error (stochasticity in the underlying dynamics) and through observation error. In many ecological studies, the variability due to observation error cannot be independently estimated because it arises from some complex (often unknown) interaction with the environment. State-space modeling provides an established and straight-forward method for analyzing time-series data with both process and observation error. Additionally, state-space models provide a framework for dealing with multiple observation time series and missing observations. In these types of models, process variability, observation error, or demographic parameters may be hierarchically structured with single or multiple levels of variation. Models with multiple levels allow us to compare variation across different spatial and social levels. This workshop will give students an overview of state-space time-series modeling and give hands-on practice analyzing time-series data. The morning lectures will introduce autoregressive and state-space modeling. The afternoon will consist of three hands-on computer labs using R: analysis of animal movement data, estimation of forecasting models using a multivariate time series of observations, and analysis of the spatial structure with a population using multi-site data. The material will be presented at an introductory statistical level, but students need to have a basic understanding of likelihood inference (e.g. familiarity with the terms likelihood function, likelihood surface, and probability density function). The computer labs will be done in R, but knowledge of R is not necessary. Students must provide laptops.