Journal
SCIENTIFIC REPORTS
Volume 6, Issue -, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/srep26677
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Funding
- Natural Sciences and Engineering Research Council of Canada [NETGP 375118-08]
- Canada Foundation for Innovation
- Aquarium du Quebec
- ArcticNet
- US Department of Interior Bureau of Ocean Energy Management
- Canadian Association of Zoos and Aquariums
- Canadian Wildlife Federation
- Circumpolar/Boreal Alberta Research
- Environment Canada
- Hauser Bears
- Northern Scientific Training Program
- Polar Continental Shelf Program
- Polar Bears International
- Quark Expeditions
- World Wildlife Fund (Canada International)
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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.
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