Uncovering Hidden Processes
State-space models were used to land humans on the moon for the Apollo missions by course-correcting the capsule so that it went to the moon. Since then they have played an increasingly important role in characterising dynamic ecological and environmental processes based on imperfect time series of observations.
Climate change and biodiversity loss are challenges that BioSS is helping to address by developing quantitative tools to characterise dynamic ecological and environmental processes.
State-space models (SSMs) are a means of using times series of observations that are imperfect or incomplete measurements of underlying dynamic processes to uncover these processes. SSMs fit within a much larger general framework of Data Fusion and Hierarchical Models, a means of incorporating different kinds of data from different surveys and sources and embedding them in a single mathematical structure.
Publications:
- Newman, K., et al. 2022. State-space models for ecological time-series data: Practical model-fitting. https://doi.org/10.1111/2041-210X.13833
- Polansky, L., Newman, K., and Mitchell, L. 2022. Improving inference for nonlinear state-space models of animal population dynamics given biased sequential life stage data. https://doi.org/10.1111/biom.13267
- Auger-Methe, M., Newman, K., et al. 2021. A guide to state–space modeling of ecological time series. https://doi.org/10.1002/ecm.1470
- Newman, K. et al. 2014. Modelling population dynamics: Model Formulation, Fitting and Assessment. https://link.springer.com/book/10.1007/978-1-4939-0977-3
Acknowledgements:
RESAS, University of Edinburgh, US Fish and Wildlife Service.
