Menu

Volume 53, No. 2

Search by author or title:

Quantifying the effect of time on geolocation accuracy in seabirds.


Authors

SOPHIE BENNETT1*, RICHARD A. PHILLIPS2, & JONATHAN A. GREEN3
1BTO Scotland, Stirling University Innovation Park, Stirling, FK9 4NF, UK *(sophie.bennett@bto.org)
2British Antarctic Survey, Natural Environment Research Council, Cambridge, CB3 0ET, UK
3School of Environmental Sciences, University of Liverpool, Liverpool, L69 3GP, UK

Citation

Bennett, S., Phillips, R. A., & Green, J. A. (2025). Quantifying the effect of time on geolocation accuracy in seabirds. Marine Ornithology 53(2), 331-335
http://doi.org/10.5038/2074-1235.53.2.1654

Received 16 December 2024, accepted 24 April 2025

Date Published: 2025/10/15
Date Online: 2025/10/07
Key words: biologging; location estimation; geolocation error; seabirds; space use

Abstract

Light-based geolocation can be an effective tool for understanding movements and distributions of free-ranging seabirds, particularly during migrations and long-distance foraging trips. The light levels recorded by geolocators (global location sensors; GLS loggers) are used to infer latitude and longitude of individuals from day length and time of local midday relative to UTC (Coordinated Universal Time), respectively. However, there is an associated inaccuracy in these location estimates, associated with both systematic and random error. Previous studies have quantified this error by calculating distances to locations over the same time period derived from more accurate devices such as satellite transmitters (platform terminal transmitters; PTTs) or GPS devices. These approaches to quantifying error have focussed on twice daily or daily locations, whereas the aims of many studies using geolocation can be achieved by identifying areas used over long time periods, typically during the non-breeding season. We reanalysed data from a previous study where 12 Black-browed Albatrosses Thalassarche melanophris were tracked simultaneously with GLS loggers and PTTs. Rather than assessing location error over individual half-days or days, we took advantage of the principle of central tendency and calculated the distances between centroids obtained from GLS loggers and PTTs over different time periods. Our results show that overall geolocation error decreases with an increase in the number of days of data (Δ centroid distance: 76.5 ± 3.9 km [mean ± standard error] when using 30 d of location data), most likely due to a reduction in random error. Centroids are still subject to residual error, but researchers can have confidence that their accuracy is sufficient to answer many research questions for seabirds.

References


Atkins, K., Bearhop, S., Bodey, T. W., Grecian, W. J., Hamer, K., Pereira, J. M., Meinertzhagen, H., Mitchell, C., Morgan, G., Morgan, L., Newton, J., Sherley, R. B., & Votier, S. C. (2023). Geolocator-tracking seabird migration and moult reveal large-scale, temperature-driven isoscapes in the NE Atlantic. Rapid Communications in Mass Spectrometry, 37(9), Article e9489. https://doi.org/10.1002/rcm.9489

Bennett, S., Daunt, F., Searle, K. R., Harris, M. P., Buckingham, L., Duckworth, J., Dunn, R. E., Wanless, S., Newell, M. A., & Green, J. A. (2024). Distribution and time budgets limit occupancy of breeding sites in the nonbreeding season in a colonial seabird. Animal Behaviour, 216, 213-233. https://doi.org/10.1016/j.anbehav.2024.07.023

Egevang, C., Stenhouse, I. J., Phillips, R. A., Petersen, A., Fox, J. W., & Silk, J. R. D. (2010). Tracking of Arctic terns Sterna paradisaea reveals longest animal migration. Proceedings of the National Academy of Sciences, 107(5), 2078-2081. https://doi.org/10.1073/pnas.0909493107

Franklin, K. A., Norris, K., Gill, J. A., Ratcliffe, N., Bonnet-Lebrun, A.-S., Butler, S. J., Cole, N. C., Jones, C. G., Lisovski, S., Ruhomaun, K., Tatayah, V., & Nicoll, M. A. C. (2022). Individual consistency in migration strategies of a tropical seabird, the Round Island petrel. Movement Ecology, 10(1), Article 13. https://doi.org/10.1186/s40462-022-00311-y

Guilford, T., Meade, J., Willis, J., Phillips, R. A., Boyle, D., Roberts, S., Collett, M., Freeman, R., & Perrins, C. M. (2009). Migration and stopover in a small pelagic seabird, the Manx shearwater Puffinus puffinus: Insights from machine learning. Proceedings of the Royal Society B, 276(1660), 1215-1223. https://doi.org/10.1098/rspb.2008.1577

Halpin, L. R., Ross, J. D., Ramos, R., Mott, R., Carlile, N., Golding, N., Reyes-González, J. M., Militão, T., De Felipe, F., Zajková, Z., Cruz-Flores, M., Saldanha, S., Morera-Pujol, V., Navarro-Herrero, L., Zango, L., González-Solís, J., & Clarke, R. H. (2021). Double-tagging scores of seabirds reveals that light-level geolocator accuracy is limited by species idiosyncrasies and equatorial solar profiles. Methods in Ecology and Evolution, 12(11), 2243-2255. https://doi.org/10.1111/2041-210X.13698

Hill, R. D. (1994). Theory of geolocation by light levels. In B. J. Le Beouf & R. M. Laws (Eds.), Elephant seals: Population ecology, behavior, and physiology (pp. 227-236). University of California Press.

Lisovski, S., Bauer, S., Briedis, M., Davidson, S. C., Dhanjal‐Adams, K. L., Hallworth, M. T., Karagicheva, J., Meier, C. M., Merkel, B., Ouwehand, J., Pedersen, L., Rakhimberdiev, E., Roberto‐Charron, A., Seavy, N. E., Sumner, M. D., Taylor, C. M., Wotherspoon, S. J., & Bridge, E. S. (2020). Light-level geolocator analyses: A user's guide. Journal of Animal Ecology, 89(1), 221-236. https://doi.org/10.1111/1365-2656.13036

Lisovski, S., & Hahn, S. (2012). GeoLight - Processing and analysing light-based geolocator data in R. Methods in Ecology and Evolution, 3(6), 1055-1059. https://doi.org/10.1111/j.2041-210X.2012.00248.x

Lisovski, S., Hewson, C. M., Klaassen, R. H. G., Korner-Nievergelt, F., Kristensen, M. W., & Hahn, S. (2012). Geolocation by light: Accuracy and precision affected by environmental factors. Methods in Ecology and Evolution, 3(3), 603-612. https://doi.org/10.1111/j.2041-210X.2012.00185.x

Merkel, B., Phillips, R. A., Descamps, S., Yoccoz, N. G., Moe, B., & Strøm, H. (2016). A probabilistic algorithm to process geolocation data. Movement Ecology, 4(1), Article 26. https://doi.org/10.1186/s40462-016-0091-8

Militão, T., Sanz-Aguilar, A., Rotger, A., & Ramos, R. (2022). Non-breeding distribution and at-sea activity patterns of the smallest European seabird, the European Storm Petrel (Hydrobates pelagicus). Ibis, 164(4), 1160-1179. https://doi.org/10.1111/ibi.13068

Pebesma, E., & Bivand, R. (2023). Spatial data science: With applications in R (1st ed.). Chapman and Hall/CRC. https://doi.org/doi:10.1201/9780429459016

Pelletier, D., Seyer, Y., Garthe, S., Bonnefoi, S., Phillips, R. A., & Guillemette, M. (2020). So far, so good… Similar fitness consequences and overall energetic costs for short and long-distance migrants in a seabird. PLOS One, 15(3), Article e0230262. https://doi.org/10.1371/journal.pone.0230262

Phillips, R. A. (2002). Black-browed albatross, Bird Island, 2001-02 [Data set]. BirdLife International Seabird Tracking Database. https://data.seabirdtracking.org/dataset/457

Phillips, R. A., Silk, J. R. D., Croxall, J. P., Afanasyev, V., & Bennett, V. J. (2005). Summer distribution and migration of nonbreeding albatrosses: Individual consistencies and implications for conservation. Ecology, 86(9), 2386-2396. https://doi.org/10.1890/04-1885

Phillips, R. A., Silk, J. R. D., Croxall, J. P., Afanasyev, V., & Briggs, D. R. (2004). Accuracy of geolocation estimates for flying seabirds. Marine Ecology Progress Series, 266, 265-272. https://doi.org/10.3354/meps266265

Porter, R., & Smith, P. A. (2013). Techniques to improve the accuracy of location estimation using light-level geolocation to track shorebirds. Wader Study Group Bulletin, 120(3), 147-158.

R Core Team. (2023). R: A language and environment for statistical computing (Version 4.3.1) [Computer software]. R Foundation for Statistical Computing. https://www.R-project.org/

Rakhimberdiev, E., Saveliev, A., Piersma, T., & Karagicheva, J. (2017). FLightR: An R package for reconstructing animal paths from solar geolocation loggers. Methods in Ecology and Evolution, 8(11), 1482-1487. https://doi.org/10.1111/2041-210X.12765

Shaffer, S. A., Tremblay, Y., Awkerman, J. A., Henry, R. W., Teo, S. L. H., Anderson, D. J., Croll, D. A., Block, B. A., & Costa, D. P. (2005). Comparison of light- and SST-based geolocation with satellite telemetry in free-ranging albatrosses. Marine Biology, 147(4), 833-843. https://doi.org/10.1007/s00227-005-1631-8

Swindells, M. (2019). Non-breeding movements of Black-legged Kittiwakes Rissa tridactyla from a North Sea urban colony. Seabird, 32, 33-45. https://doi.org/10.61350/sbj.32.33

Tranquilla, L. A. M., Montevecchi, W. A., Hedd, A., Fifield, D. A., Burke, C. M., Smith, P. A., Regular, P. M., Robertson, G. J., Gaston, A. J., & Phillips, R. A. (2013). Multiple-colony winter habitat use by murres Uria spp. in the Northwest Atlantic Ocean: Implications for marine risk assessment. Marine Ecology Progress Series, 472, 287-303. https://doi.org/10.3354/meps10053

Wickham, H. (2016). ggplot2: Elegant graphics for data analysis. Springer-Verlag. https://ggplot2.tidyverse.org

Zajková, Z., Militão, T., & González-Solís, J. (2017). Year-round movements of a small seabird and oceanic isotopic gradient in the tropical Atlantic. Marine Ecology Progress Series, 579, 169-183. https://doi.org/10.3354/meps12269

Zuur, A. F., Saveliev, A. A., & Ieno, E. N. (2014). A beginner's guide to generalised additive mixed models with R. Highland Statistics Ltd.

Search by author or title:

Browse previous volumes: