The article discusses state-space models (SSMs) for analyzing individual animal movement. SSMs integrate observation and process models to account for uncertainties in movement data, enabling more accurate inferences about animal behavior, physiology, and environmental influences. Movement data are inherently stochastic and subject to observation errors, making SSMs a powerful tool for movement ecology. SSMs allow for the estimation of movement parameters, behavioral modes, and prediction of future movements, while also incorporating environmental and physiological factors. The article reviews the importance of understanding individual movement in ecology, the challenges of analyzing movement data, and the potential of SSMs to improve ecological inference. It also outlines the application of SSMs in various ecological contexts, such as tracking marine species, understanding habitat use, and predicting population dynamics. The article highlights the need for further research to refine SSMs, including incorporating more biological details, improving prediction of behavior from physiological states, and integrating environmental variables into observation models. SSMs are seen as a promising approach for advancing movement ecology by providing a flexible and robust framework for analyzing animal movement data.The article discusses state-space models (SSMs) for analyzing individual animal movement. SSMs integrate observation and process models to account for uncertainties in movement data, enabling more accurate inferences about animal behavior, physiology, and environmental influences. Movement data are inherently stochastic and subject to observation errors, making SSMs a powerful tool for movement ecology. SSMs allow for the estimation of movement parameters, behavioral modes, and prediction of future movements, while also incorporating environmental and physiological factors. The article reviews the importance of understanding individual movement in ecology, the challenges of analyzing movement data, and the potential of SSMs to improve ecological inference. It also outlines the application of SSMs in various ecological contexts, such as tracking marine species, understanding habitat use, and predicting population dynamics. The article highlights the need for further research to refine SSMs, including incorporating more biological details, improving prediction of behavior from physiological states, and integrating environmental variables into observation models. SSMs are seen as a promising approach for advancing movement ecology by providing a flexible and robust framework for analyzing animal movement data.