Final published version
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Research output: Contribution to Journal/Magazine › Review article › peer-review
Research output: Contribution to Journal/Magazine › Review article › peer-review
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TY - JOUR
T1 - State‐space models for ecological time‐series data
T2 - Practical model‐fitting
AU - Newman, Ken
AU - King, Ruth
AU - Elvira, Víctor
AU - Valpine, Perry
AU - McCrea, Rachel S.
AU - Morgan, Byron J. T.
PY - 2023/1/31
Y1 - 2023/1/31
N2 - State-space models are an increasingly common and important tool in the quantitative ecologists’ armoury, particularly for the analysis of time-series data. This is due to both their flexibility and intuitive structure, describing the different individual processes of a complex system, thus simplifying the model specification step.State-space models are composed of two processes (a) the system (or state) process that describes the dynamics of the true underlying state of the system over time; and (b) the observation process that links the observed data with the current true state of the system at that time. Specification of the general model structure consists of considering each distinct ecological process within the system and observation processes, which are then automatically combined within the state-space structure.There is typically a trade-off between the complexity of the model and the associated model-fitting process. Simpler model specifications permit the application of simpler model-fitting tools; whereas more complex model specifications, with nonlinear dynamics and/or non-Gaussian stochasticity often require more sophisticated model-fitting algorithms to be applied.We provide a brief overview of general state-space models before focusing on the different model-fitting tools available. In particular for different general state-space model structures we discuss established model-fitting tools that are available. We also offer practical guidance for choosing a specific fitting procedure.
AB - State-space models are an increasingly common and important tool in the quantitative ecologists’ armoury, particularly for the analysis of time-series data. This is due to both their flexibility and intuitive structure, describing the different individual processes of a complex system, thus simplifying the model specification step.State-space models are composed of two processes (a) the system (or state) process that describes the dynamics of the true underlying state of the system over time; and (b) the observation process that links the observed data with the current true state of the system at that time. Specification of the general model structure consists of considering each distinct ecological process within the system and observation processes, which are then automatically combined within the state-space structure.There is typically a trade-off between the complexity of the model and the associated model-fitting process. Simpler model specifications permit the application of simpler model-fitting tools; whereas more complex model specifications, with nonlinear dynamics and/or non-Gaussian stochasticity often require more sophisticated model-fitting algorithms to be applied.We provide a brief overview of general state-space models before focusing on the different model-fitting tools available. In particular for different general state-space model structures we discuss established model-fitting tools that are available. We also offer practical guidance for choosing a specific fitting procedure.
KW - hidden Markov model
KW - Kalman filter
KW - Laplace approximation
KW - likelihood-free methods
KW - Markov chain Monte Carlo
KW - sampling-based methods
KW - sequential Monte Carlo
U2 - 10.1111/2041-210X.13833
DO - 10.1111/2041-210X.13833
M3 - Review article
VL - 14
SP - 26
EP - 42
JO - Methods in Ecology and Evolution
JF - Methods in Ecology and Evolution
SN - 2041-210X
IS - 1
ER -