State Space Model for Light Based Tracking of Marine Animals: Validation on Swimming and Diving Creatures

  • Anders Nielsen
  • John R. Sibert
  • Suzanne Kohin
  • Michael K. Musyl
Part of the Reviews: Methods and Technologies in Fish Biology and Fisheries book series (REME, volume 9)


Determining light-based geolocations of electronically tagged marine animals by using irradiance measurements in an integrated random walk movement model is presented for improving the precision of the reconstructed geographical tracks. The model avoids making any irradiance threshold assumptions, or constraining the movement of the tag between dawn and dusk, which has limited the efficacy of previous models. Unlike previous models which relied on using previously calculated raw geolocations from outside sources, the new model uses irradiance data to produce the most probable track. The model generates two estimates of geographic positions per day (at dawn and dusk). Prior to this study, the model has been successfully evaluated for tags mounted on moorings, drifters, and in simulated cases. This paper documents the application of the model to wild caught swimming and diving mako sharks and blue marlin. The reconstructed tracks via the new model are compared to tracks adjusted using sea-surface temperature and to very accurate satellite-based tracks. The model performs well for the tested tags, which is surprising as these are pop-off satellite archival tags (PSATs), from which only a small fraction of the light record around approximately dusk and dawn is stored. The integrated model was vastly superior to classical purely light-based methods in all cases.


Light based geolocation Geolocation errors Unscented Kalman filter Archival tags pop-up satellite archival tags 


  1. Arnold G, Dewar H (2001) Electronic tags in marine fisheries research: a 30-year perspective. In: Sibert JR and Nielsen J (eds) Electronic Tagging and Tracking in Marine Fisheries Kluwer Academic Publishers, The Netherlands, pp. 7–64.Google Scholar
  2. Ekstrom PA (2004) An advance in geolocation by light. Mem Natl Inst Polar Res, 58:210–226.Google Scholar
  3. Gordon NJ, Salmond DJ, Smith AFM (1993) Novel approach to non-linear/non-Gaussian Bayesian state estimation. IEE-Proceedings-F, 140:107–113.Google Scholar
  4. Hill RD, Braun MJ (2001) Geolocation by Light Level - The Next Step: Latitude. In: Sibert JR and Nielsen J (eds) Electronic Tagging and Tracking in Marine Fisheries Kluwer Academic Publishers, The Netherlands, pp. 315–330.Google Scholar
  5. Julier SJ, Uhlmann JK, Durrant-Whyte HF (2000) A new method for the nonlinear transformation of means and covariances in filters and estimators. IEEE Trans Autom Control 45:477–482.CrossRefGoogle Scholar
  6. Meeus J (1998) Astronomical Algorithms. 2nd ed. Willman-Bell, Richmond, Virginia, USA.Google Scholar
  7. Musyl MK, Brill RW, et al. (2001) Ability of archival tags to provide estimates of geographical position based on light intensity. In: Sibert JR and Nielsen J (eds) Electronic Tagging and Tracking in Marine Fisheries Kluwer Academic Publishers, The Netherlands, pp. 343–368.Google Scholar
  8. Musyl MK, Brill RW, et al. (2003) Vertical movements of bigeye tuna (Thunnus obesus) associated with islands, buoys, and seamounts near the main Hawaiian Islands from archival tagging data. Fish Oceanogr, 12:152–169.CrossRefGoogle Scholar
  9. Nielsen A, Bigelow KA, et al. (2006) Improving light-based geolocation by including sea surface temperature. Fish Oceanogr, 15:314–325.CrossRefGoogle Scholar
  10. Nielsen A, Sibert JR (2007) State-space model for light-based tracking of marine animals. Can J Fish Aquat Sci, 64:1055–1068.CrossRefGoogle Scholar
  11. Pedersen MW (2007) Hidden Markov Models for Geolocation of Fish. Master thesis, Technical University of Denmark, Available at:
  12. Sibert JR, Musyl MK, et al. (2003) Horizontal movements of bigeye tuna (Thunnus obesus) near Hawaii determined by Kalman filter analysis of archival tagging data. Fish Oceanogr, 12:141–152.CrossRefGoogle Scholar
  13. Smith P, Goodman D (1986) Determining fish movement from an “archival” tag: precision of geographical positions made from a time series of swimming temperature and depth. NOAA Technical Memorandum, NOAA-TM-NMFS-SWFC- 60 13pp.Google Scholar
  14. Teo SLH, Boustany A, et al. (2004) Validation of geolocation estimates based on light level and sea surface temperature from electronic tags. Mar Ecol Prog Ser, 283:81–98.CrossRefGoogle Scholar
  15. Welch DW, Eveson JP (1999) An assessment of light-based geoposition estimates from archival tags. Can J Fish Aquat Sci 56:1317–1327.CrossRefGoogle Scholar
  16. Wilson SG, Lutcavage ME, et al. (2005) Movements of bluefin tuna (Thunnus thynnus) in the northwestern Atlantic Ocean recorded by pop-up satellite archival tags. Mar Biol 146:409–423.CrossRefGoogle Scholar
  17. Wilson SG, Stewart BS, et al. (2007) Accuracy and precision of archival tag data: a multiple-tagging study conducted on a whale shark (Rhincodon typus) in the Indian Ocean. Fish Oceanog, 16:547–554.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  • Anders Nielsen
    • 1
  • John R. Sibert
    • 1
  • Suzanne Kohin
    • 2
  • Michael K. Musyl
    • 1
  1. 1.Pelagic Fisheries Research ProgramJoint Institute for Marine and Atmospheric Research, University of Hawai’i at MānoaHonoluluUSA
  2. 2.NOAA Fisheries Southwest Fisheries Science CenterLa JollaUSA

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