MEPS 565:237-249 (2017)  -  DOI: https://doi.org/10.3354/meps12019

Spatiotemporal modelling of marine movement data using Template Model Builder (TMB)

Marie Auger-Méthé1,*, Christoffer M. Albertsen2, Ian D. Jonsen3, Andrew E. Derocher4, Damian C. Lidgard5, Katharine R. Studholme5, W. Don Bowen6, Glenn T. Crossin5, Joanna Mills Flemming

1Department of Mathematics and Statistics, Dalhousie University, Halifax, NS B3H 4R2, Canada
2National Institute of Aquatic Resources, Technical University of Denmark, 2920 Charlottenlund, Denmark
3Department of Biological Sciences, Macquarie University, Sydney, NSW 2109, Australia
4Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2G1, Canada
5Department of Biology, Dalhousie University, Halifax, NS B3H 4R2, Canada
6Bedford Institute of Oceanography, Dartmouth, NS B2Y 4A2, Canada
*Corresponding author:

ABSTRACT: Tracking of marine animals has increased exponentially in the past decade, and the resulting data could lead to an in-depth understanding of the causes and consequences of movement in the ocean. However, most common marine tracking systems are associated with large measurement errors. Accounting for these errors requires the use of hierarchical models, which are often difficult to fit to data. Using 3 case studies, we demonstrate that Template Model Builder (TMB), a new R package, is an accurate, efficient and flexible framework for modelling movement data. First, to demonstrate that TMB is as accurate but 30 times faster than bsam, a popular R package used to apply state-space models to Argos data, we modelled polar bear Ursus maritimus Argos data and compared the locations estimated by the models to GPS locations of these same bears. Second, to demonstrate how TMB’s gain in efficiency and frequentist framework facilitate model comparison, we developed models with different error structures and compared them to find the most effective model for light-based geolocations of rhinoceros auklets Cerorhinca monocerata. Finally, to maximize efficiency through TMB’s use of the Laplace approximation of the marginal likelihood, we modelled behavioural changes with continuous rather than discrete states. This new model directly accounts for the irregular sampling intervals characteristic of Fastloc-GPS data of grey seals Halichoerus grypus. Using real and simulated data, we show that TMB is a fast and powerful tool for modelling marine movement data. We discuss how TMB’s potential reaches beyond marine movement studies.


KEY WORDS: TMB · State-space model · Telemetry · Argos · GLS · FastLoc GPS


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Cite this article as: Auger-Méthé M, Albertsen CM, Jonsen ID, Derocher AE and others (2017) Spatiotemporal modelling of marine movement data using Template Model Builder (TMB). Mar Ecol Prog Ser 565:237-249. https://doi.org/10.3354/meps12019

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