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

Marie Auger-Méthé, Christoffer Moesgaard Albertsen, Ian D. Jonsen, Andrew E. Derocher, Damian C. Lidgard, Katharine R. Studholme, W. Don Bowen, Glenn T. Crossin, Joanna Mills Flemming

Research output: Contribution to journalJournal articleResearchpeer-review

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
Original languageEnglish
JournalMarine Ecology - Progress Series
Volume565
Pages (from-to)237-249
ISSN0171-8630
DOIs
Publication statusPublished - 2017

Cite this

Auger-Méthé, M., Albertsen, C. M., Jonsen, I. D., Derocher, A. E., Lidgard, D. C., Studholme, K. R., ... Flemming, J. M. (2017). Spatiotemporal modelling of marine movement data using Template Model Builder (TMB). Marine Ecology - Progress Series, 565, 237-249. https://doi.org/10.3354/meps12019
Auger-Méthé, Marie ; Albertsen, Christoffer Moesgaard ; Jonsen, Ian D. ; Derocher, Andrew E. ; Lidgard, Damian C. ; Studholme, Katharine R. ; Bowen, W. Don ; Crossin, Glenn T. ; Flemming, Joanna Mills. / Spatiotemporal modelling of marine movement data using Template Model Builder (TMB). In: Marine Ecology - Progress Series. 2017 ; Vol. 565. pp. 237-249.
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title = "Spatiotemporal modelling of marine movement data using Template Model Builder (TMB)",
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 samebears. 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 potentialreaches beyond marine movement studies",
author = "Marie Auger-M{\'e}th{\'e} and Albertsen, {Christoffer Moesgaard} and Jonsen, {Ian D.} and Derocher, {Andrew E.} and Lidgard, {Damian C.} and Studholme, {Katharine R.} and Bowen, {W. Don} and Crossin, {Glenn T.} and Flemming, {Joanna Mills}",
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Auger-Méthé, M, Albertsen, CM, Jonsen, ID, Derocher, AE, Lidgard, DC, Studholme, KR, Bowen, WD, Crossin, GT & Flemming, JM 2017, 'Spatiotemporal modelling of marine movement data using Template Model Builder (TMB)', Marine Ecology - Progress Series, vol. 565, pp. 237-249. https://doi.org/10.3354/meps12019

Spatiotemporal modelling of marine movement data using Template Model Builder (TMB). / Auger-Méthé, Marie; Albertsen, Christoffer Moesgaard; Jonsen, Ian D.; Derocher, Andrew E.; Lidgard, Damian C.; Studholme, Katharine R.; Bowen, W. Don; Crossin, Glenn T.; Flemming, Joanna Mills.

In: Marine Ecology - Progress Series, Vol. 565, 2017, p. 237-249.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Spatiotemporal modelling of marine movement data using Template Model Builder (TMB)

AU - Auger-Méthé, Marie

AU - Albertsen, Christoffer Moesgaard

AU - Jonsen, Ian D.

AU - Derocher, Andrew E.

AU - Lidgard, Damian C.

AU - Studholme, Katharine R.

AU - Bowen, W. Don

AU - Crossin, Glenn T.

AU - Flemming, Joanna Mills

PY - 2017

Y1 - 2017

N2 - 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 samebears. 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 potentialreaches beyond marine movement studies

AB - 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 samebears. 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 potentialreaches beyond marine movement studies

U2 - 10.3354/meps12019

DO - 10.3354/meps12019

M3 - Journal article

VL - 565

SP - 237

EP - 249

JO - Marine Ecology - Progress Series

JF - Marine Ecology - Progress Series

SN - 0171-8630

ER -