State-space models' dirty little secrets: even simple linear Gaussian models can have estimation problems

Marie Auger-Méthé*, Chris Field, Christoffer M. Albertsen, Andrew E. Derocher, Mark A. Lewis, Ian D. Jonsen, Joanna Mills Flemming

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    81 Citations (Scopus)
    83 Downloads (Pure)


    State-space models (SSMs) are increasingly used in ecology to model time-series such as animal movement paths and population dynamics. This type of hierarchical model is often structured to account for two levels of variability: biological stochasticity and measurement error. SSMs are flexible. They can model linear and nonlinear processes using a variety of statistical distributions. Recent ecological SSMs are often complex, with a large number of parameters to estimate. Through a simulation study, we show that even simple linear Gaussian SSMs can suffer from parameter- and state-estimation problems. We demonstrate that these problems occur primarily when measurement error is larger than biological stochasticity, the condition that often drives ecologists to use SSMs. Using an animal movement example, we show how these estimation problems can affect ecological inference. Biased parameter estimates of a SSM describing the movement of polar bears (Ursus maritimus) result in overestimating their energy expenditure. We suggest potential solutions, but show that it often remains difficult to estimate parameters. While SSMs are powerful tools, they can give misleading results and we urge ecologists to assess whether the parameters can be estimated accurately before drawing ecological conclusions from their results.

    Original languageEnglish
    Article number26677
    Pages (from-to)1-10
    Number of pages10
    JournalScientific Reports
    Publication statusPublished - 25 May 2016

    Bibliographical note

    Copyright the Author(s) 2016. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


    Dive into the research topics of 'State-space models' dirty little secrets: even simple linear Gaussian models can have estimation problems'. Together they form a unique fingerprint.

    Cite this