期刊
MARINE ECOLOGY PROGRESS SERIES
卷 565, 期 -, 页码 237-249出版社
INTER-RESEARCH
DOI: 10.3354/meps12019
关键词
TMB; State-space model; Telemetry; Argos; GLS; FastLoc GPS
资金
- Aquarium du Quebec
- ArcticNet
- DFO
- Hauser Bears
- Environment Canada
- Natural Sciences and Engineering Research Council of Canada (NSERC)
- Pittsburgh Zoo
- Polar Continental Shelf Program
- Polar Bears International and Quark Expeditions
- NSERC [NETGP 375118-08]
- Canadian Foundation [13011]
- Macquarie University
- Bureau of Ocean Energy Management
- Canada's Accredited Zoos and Aquariums
- Canadian Wildlife Federation
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.
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