A guide to state–space modeling of ecological time series

State–space models (SSMs) are an important modeling framework for analyzing ecological time series. These hierarchical commonly used to model population dynamics, animal movement, and capture–recapture data, now increasingly being other processes. SSMs popular because they flexible the natural variation in processes separately from observation error. Their flexibility allows ecologists continuous, count, binary, categorical data with linear or nonlinear that evolve discrete continuous time. Modeling two sources of stochasticity researchers differentiate between biological imprecision sampling methodology, generally provides better estimates quantities interest than if only one source is directly modeled. Since introduction SSMs, a broad range fitting procedures have been proposed. However, variety complexity these can limit ability formulate fit their own SSMs. We provide knowledge create robust common, often hidden, estimation problems, selection validation tools help them assess how well data. present review will strong foundation interested learning about introduce new veteran SSM users, highlight promising research directions statisticians applications. The accompanied by in-depth tutorial demonstrates be fitted validated R. Together, formulate, fit, validate models.