State‐space models for ecological time‐series data: Practical model‐fitting

Newman, Ken; King, Ruth; Elvira, Vı́ctor; Valpine, Perry de; McCrea, Rachel S.; Morgan, Byron J. T.

doi:10.1111/2041-210x.13833

Cited by 28 publications

(18 citation statements)

References 135 publications

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“…Ecological time-series data of species abundance are a very common input within IPMs, as they provide direct information on abundance as well as the (indirect) demographic processes. State-space models provide a structured way of describing such time series data and can be viewed as a special case of a wider class of models known as hidden process models [18,[36][37][38][39]. These models can be described via two separate processes: (i) the state process that describes the evolution of the true underlying (unobserved) state-vector corresponding to true abundance over time; and (ii) the observation equation that links the elements of the state vector to the observed data at each time point.…”

Section: Integrated Population Modelsmentioning

confidence: 99%

“…A closedform likelihood for state-space models is only available when specifying either (i) a linear and Gaussian model, or (ii) where the state vector is discrete-valued, leading to a hidden Markov model (HMM). See [39] for further discussion. The information contained within the temporal abundance data alone may be relatively weak, in terms of the demographic parameters, which may be strongly correlated and/or even confounded.…”

Section: Integrated Population Modelsmentioning

confidence: 99%

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Integrated Population Models: Achieving Their Potential

Frost

McCrea

King

et al. 2022

J Stat Theory Pract

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Precise and accurate estimates of abundance and demographic rates are primary quantities of interest within wildlife conservation and management. Such quantities provide insight into population trends over time and the associated underlying ecological drivers of the systems. This information is fundamental in managing ecosystems, assessing species conservation status and developing and implementing effective conservation policy. Observational monitoring data are typically collected on wildlife populations using an array of different survey protocols, dependent on the primary questions of interest. For each of these survey designs, a range of advanced statistical techniques have been developed which are typically well understood. However, often multiple types of data may exist for the same population under study. Analyzing each data set separately implicitly discards the common information contained in the other data sets. An alternative approach that aims to optimize the shared information contained within multiple data sets is to use a “model-based data integration” approach, or more commonly referred to as an “integrated model.” This integrated modeling approach simultaneously analyzes all the available data within a single, and robust, statistical framework. This paper provides a statistical overview of ecological integrated models, with a focus on integrated population models (IPMs) which include abundance and demographic rates as quantities of interest. Four main challenges within this area are discussed, namely model specification, computational aspects, model assessment and forecasting. This should encourage researchers to explore further and develop new practical tools to ensure that full utility can be made of IPMs for future studies.

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Section: Integrated Population Modelsmentioning

confidence: 99%

Section: Integrated Population Modelsmentioning

confidence: 99%

Integrated Population Models: Achieving Their Potential

Frost

McCrea

King

et al. 2022

J Stat Theory Pract

Self Cite

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show abstract

“…Approaches for dealing with this issue include, for example, approximating the likelihood using Gaussian or linear approximations (Julier and Uhlmann, 1997;Wan and Van Der Merwe, 2000), numerical approximation using Laplace or HMM approximations (Bucy and Senne, 1971;Koyama et al, 2010;Thygesen et al, 2017), the use of sequential Monte Carlo to obtain an unbiased approximation to the likelihood (Andrieu and Roberts, 2009), and data augmentation using sequential Monte Carlo, numerical approximation, and/or MCMC approaches (Frühwirth-Schnatter, 1994;Lindsten et al, 2014;Fearnhead and Meligkotsidou, 2016). A review of these methods, and others, is given in Newman et al (2022).…”

Section: Introductionmentioning

confidence: 99%

A Point Mass Proposal Method for Bayesian State-Space Model Fitting

Llewellyn¹,

King²,

Elvira³

et al. 2022

Preprint

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State-space models (SSMs) are often used to model time series data where the observations depend on an unobserved latent process. However, inference on the process parameters of an SSM can be challenging, especially when the likelihood of the data given the parameters is not available in closed form. We focus on the problem of model fitting within a Bayesian framework, to which a variety of approaches have been applied, including MCMC using Bayesian data augmentation, sequential Monte Carlo (SMC) approximation, and particle MCMC algorithms, which combine SMC approximations and MCMC steps. However, these different methods can be inefficient because of sample impoverishment in the sequential Monte Carlo approximations and/or poor mixing in the MCMC steps. In this article, we propose an approach that borrows ideas from discrete hidden Markov models (HMMs) to provide an efficient MCMC with data augmentation approach, imputing the latent states within the algorithm. Our approach deterministically approximates the SSM by a discrete HMM, which is subsequently used as an MCMC proposal distribution for the latent states. We demonstrate that the algorithm provides an efficient alternative approach via two different case studies.

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“…These states can be either geographical sites or categorical variables like reproductive status or size class (Lebreton and Cefe, 2002;Arnason and Cam, 2004) Multistate models permit estimation of state-specific survival rates, transition probabilities and observation probabilities (for example, it might only be possible to observe animals at a certain site or in their breeding state). State-space models are hierarchical models with two components; a process model that describes the natural stochasticity in ecological processes, and an observation model that describes the error associated with sampling or observing animals, given their current state (de Valpine and Hastings, 2002;Auger Méthe et al, 2021;Newman et al, 2022). In a state-space framework, multistate mark-recapture models allow for probabilistic transition of individuals between a set of latent states (Gimenez et al, 2007;Kéry and Schaub, 2012).…”

Section: Introductionmentioning

confidence: 99%

Informing Management of Atlantic Bluefin Tuna Using Telemetry Data

et al. 2022

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Sustainable management of exploited marine fish and wildlife populations requires knowledge about their productivity. Survival from natural causes of mortality is a key component of population productivity, but is notoriously difficult to estimate. We evaluate the potential for acoustic telemetry as a long-term monitoring tool to estimate rates of natural mortality. We present a Bayesian multistate mark-recapture model for telemetry data collected over a decade from 188 Atlantic bluefin tuna (Thunnus thynnus) and apply it to estimate the rate of natural mortality using only acoustic tag detections for all animals, or using acoustic tag detections for 96 single-tagged tuna plus acoustic tag detections combined with estimated positions from pop-up satellite archival tags for 92 double-tagged animals. We support the model for bluefin tuna with a simulation study to quantify bias in estimates of population dynamics parameters and investigate the effect of auxiliary information from satellite tagging on mortality rate estimates for different acoustic tag detection probability scenarios.We obtained posterior estimates of the instantaneous annual natural mortality (survival) rate across a decade of tagging for Atlantic bluefin tuna of 0.17 yr-1 (0.84 yr-1) both using only acoustic tagging data, and using a combination of acoustic and satellite tagging data. Use of a prior implying a higher rate of fishing mortality yielded an instantaneous annual natural mortality (survival) estimate of 0.10 yr-1 (0.90 yr-1), with combined acoustic and satellite tag data. Results from the simulation study indicate that the use of satellite tags can improve the precision and accuracy of estimates of detection probabilities, area-specific movement probabilities and mortality rates, where the extent of the improvement depends on true underlying acoustic tag detection probabilities. Our work demonstrates that long-term acoustic tagging data sets have strong potential for monitoring of highly migratory marine fish and wildlife populations, providing information on a number of key parameters, including survival and movement rates. However, improved information on tag reporting rates or fishing mortality is needed to better separate natural and fisheries mortality for Atlantic bluefin tuna.

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State‐space models for ecological time‐series data: Practical model‐fitting

Cited by 28 publications

References 135 publications

Integrated Population Models: Achieving Their Potential

Integrated Population Models: Achieving Their Potential

A Point Mass Proposal Method for Bayesian State-Space Model Fitting

Informing Management of Atlantic Bluefin Tuna Using Telemetry Data

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