TY - UNPB
T1 - An introduction to state-space modeling of ecological time series
AU - Auger-Methe, Marie
AU - Newman, Ken
AU - Cole, Diana
AU - Empacher, Fanny
AU - Gryba, Rowena
AU - King, Aaron
AU - Leos-Barajas, Vianey
AU - Mills-Fleming, Joanna
AU - Nielsen, Anders
AU - Petris, Giovanni
AU - Thomas, Len
PY - 2020/2/5
Y1 - 2020/2/5
N2 - State-space models (SSMs) are an important modeling framework for analyzing eco-logical time series. These hierarchical models are commonly used to model population dynamics and animal movement, and are now increasingly being used to model othere cological processes. SSMs are popular because they are flexible and they model the natural variation in ecological processes separately from observation error. Their flexibility allows ecologists to model continuous, count, binary, and categorical data with linear or nonlinear processes that evolve in discrete or continuous time. Modeling the two sources of stochasticity separately allows researchers to differentiate between biological stochasticity (e.g., in birth processes) and imprecision in the sampling methodology, and generally provides better estimates of the ecological quantities of interest than if only one source of stochasticity is directly modeled. Since the introduction of SSMs, a broad range of fitting procedures have been proposed. However, the variety and complexity of these procedures can limit the ability of ecologists to formulate and fit their own SSMs. In addition, many SSM users are unaware of the potential estimation problems they could encounter, and of the model selection and validation toolsthat can help them assess how well their models fit their data. In this paper, we present a review of SSMs that will provide a strong foundation to ecologists interested in learning about SSMs, introduce new tools to veteran SSM users, and highlight promising research directions for statisticians interested in ecological applications. The review is accompanied by an in-depth tutorial that demonstrates how SSMs models can be fitted and validated in R. Together, the review and tutorial present an introduction to SSMs that will help ecologists to formulate, fit, and validate their models.
AB - State-space models (SSMs) are an important modeling framework for analyzing eco-logical time series. These hierarchical models are commonly used to model population dynamics and animal movement, and are now increasingly being used to model othere cological processes. SSMs are popular because they are flexible and they model the natural variation in ecological processes separately from observation error. Their flexibility allows ecologists to model continuous, count, binary, and categorical data with linear or nonlinear processes that evolve in discrete or continuous time. Modeling the two sources of stochasticity separately allows researchers to differentiate between biological stochasticity (e.g., in birth processes) and imprecision in the sampling methodology, and generally provides better estimates of the ecological quantities of interest than if only one source of stochasticity is directly modeled. Since the introduction of SSMs, a broad range of fitting procedures have been proposed. However, the variety and complexity of these procedures can limit the ability of ecologists to formulate and fit their own SSMs. In addition, many SSM users are unaware of the potential estimation problems they could encounter, and of the model selection and validation toolsthat can help them assess how well their models fit their data. In this paper, we present a review of SSMs that will provide a strong foundation to ecologists interested in learning about SSMs, introduce new tools to veteran SSM users, and highlight promising research directions for statisticians interested in ecological applications. The review is accompanied by an in-depth tutorial that demonstrates how SSMs models can be fitted and validated in R. Together, the review and tutorial present an introduction to SSMs that will help ecologists to formulate, fit, and validate their models.
KW - state-space models
KW - ecology
M3 - Working paper
BT - An introduction to state-space modeling of ecological time series
PB - ArXiv
ER -