Assessing Ecosystem State Space Models: Identifiability and Estimation

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dc.contributor.authorSmith, John W.en
dc.contributor.authorJohnson, Leah R.en
dc.contributor.authorThomas, R. Quinnen
dc.date.accessioned2023-10-16T18:56:19Zen
dc.date.available2023-10-16T18:56:19Zen
dc.date.issued2023-03en
dc.description.abstractHierarchical probability models are being used more often than non-hierarchical deterministic process models in environmental prediction and forecasting, and Bayesian approaches to fitting such models are becoming increasingly popular. In particular, models describing ecosystem dynamics with multiple states that are autoregressive at each step in time can be treated as statistical state space models (SSMs). In this paper, we examine this subset of ecosystem models, embed a process-based ecosystem model into an SSM, and give closed form Gibbs sampling updates for latent states and process precision parameters when process and observation errors are normally distributed. Here, we use simulated data from an example model (DALECev) and study the effects changing the temporal resolution of observations on the states (observation data gaps), the temporal resolution of the state process (model time step), and the level of aggregation of observations on fluxes (measurements of transfer rates on the state process). We show that parameter estimates become unreliable as temporal gaps between observed state data increase. To improve parameter estimates, we introduce a method of tuning the time resolution of the latent states while still using higher-frequency driver information and show that this helps to improve estimates. Further, we show that data cloning is a suitable method for assessing parameter identifiability in this class of models. Overall, our study helps inform the application of state space models to ecological forecasting applications where (1) data are not available for all states and transfers at the operational time step for the ecosystem model and (2) process uncertainty estimation is desired.en
dc.description.notesThis work was supported by the National Science Foundation Grants DBI # 2016264, DMS/DEB #1750113, and DEB # 1926388en
dc.description.sponsorshipNational Science Foundation [2016264, 1750113, 1926388]; Direct For Biological Sciences; Div Of Biological Infrastructure [2016264] Funding Source: National Science Foundation; Division Of Environmental Biology; Direct For Biological Sciences [1926388] Funding Source: National Science Foundation; Division Of Mathematical Sciences; Direct For Mathematical & Physical Scien [1750113] Funding Source: National Science Foundationen
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1007/s13253-023-00531-8en
dc.identifier.eissn1537-2693en
dc.identifier.issn1085-7117en
dc.identifier.urihttp://hdl.handle.net/10919/116480en
dc.language.isoenen
dc.publisherSpringeren
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectBayesian analysisen
dc.subjectData cloningen
dc.subjectEcological forecastingen
dc.subjectMCMCen
dc.titleAssessing Ecosystem State Space Models: Identifiability and Estimationen
dc.title.serialJournal of Agricultural Biological and Environmental Statisticsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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