Articles | Volume 17, issue 5
https://doi.org/10.5194/os-17-1341-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-17-1341-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Drifting dynamics of the bluebottle (Physalia physalis)
Coastal and Regional Oceanography Lab, School of Mathematics and Statistics, UNSW Australia, Sydney, New South Wales, Australia
Centre for Marine Science and Innovation, UNSW Australia, Sydney, New South Wales, Australia
Amandine Schaeffer
Coastal and Regional Oceanography Lab, School of Mathematics and Statistics, UNSW Australia, Sydney, New South Wales, Australia
Centre for Marine Science and Innovation, UNSW Australia, Sydney, New South Wales, Australia
Sjoerd Groeskamp
NIOZ Royal Netherlands Institute for Sea Research, Texel, the Netherlands
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Cited articles
Abbott, I., Doenhoff, A., and Stivers, L.: Summary of Airfoil
Data, National Advisory Committee for Aeronautics, USA, Report 824, 1945. a
Breivik, O., Allen, A., Maisondieu, C., and Roth, J.: Wind-induced drift of
objects at sea: The leeway field method, Appl. Ocean Res., 33, 100–109,
https://doi.org/10.1016/j.apor.2011.01.005, 2011. a
Clarke, A. and Vander, S.: The Relationship of Near-Surface Flow, Stokes Drift
and the Wind Stress, J. Geophys. Res.-Oceans, 123, 4680–4692,
https://doi.org/10.1029/2018JC014102, 2018. a, b, c
Daw, S., Lawes, J., Cooney, N., Ellis, A., and Strasiotto, L.: National Coastal
Safety Report, Tech. rep., Surf Life Saving Australia, Sydney, 2020. a
Ferrer, L. and González, M.: Relationship between dimorphism and drift in the
Portuguese man-of-war, Cont. Shelf Res., 212, 104269,
https://doi.org/10.1016/j.csr.2020.104269, 2020. a, b, c
Ferrer, L., Zaldua-Mendizabal, N., del Campo, A., Franco, J., Mader, J.,
Cotano, U., Fraile, I., Rubio, A., Uriarte, A., and Caballero, A.:
Operational protocol for the sighting and tracking of Portuguese man-of-war
in the southeastern Bay of Biscay: Observations and modeling, Cont.
Shelf Res., 95, 39–53, https://doi.org/10.1016/j.csr.2014.12.011, 2014. a, b
Hackett, B., Breivik, O., and Wettre, C.: Forecasting the Drift of Objects and
Substances in the Ocean, Springer Netherlands, 507–523,
https://doi.org/10.1007/1-4020-4028-8_23, 2006. a
Headlam, J. L., Lyons, K., Kenny, J., Lenihan, E. S., Quigley, D. T., Helps,
W., Dugon, M. M., and Doyle, T. K.: Insights on the origin and drift
trajectories of Portuguese man of war (Physalia physalis) over the Celtic Sea
shelf area, Estuarine, Coast. Shelf Sci., 246, 107033, https://doi.org/10.1016/j.ecss.2020.107033, 2020. a
Munro, C., Vue, Z., Behringer, R., and Dunn, C.: Morphology and development of
the Portuguese man of war, Physalia physalis, Sci. Rep.-UK, 9, 15522,
https://doi.org/10.1101/645465, 2019. a, b
Ni, Z., Qiu, Z., and Su, T.: On predicting boat drift for search and rescue,
Ocean Eng., 37, 1169–1179, https://doi.org/10.1016/j.oceaneng.2010.05.009,
2010. a
Prieto, L., Macías, D., Peliz, A., and Ruiz, J.: Portuguese Man-of-War
(Physalia physalis) in the Mediterranean: A permanent invasion or a casual
appearance?, Sci. Rep.-UK, 5, 11545, https://doi.org/10.1038/srep11545, 2015. a, b, c, d
Shannon, P. and Chapman, L.: Incidence of Physalia on beaches in the
south-western Cape Province during January 1983, S. Afr. J.
Sci., 79, 454, 1983. a
Szelangiewicz, T. and Żelazny, K.: Mathematical Model for Predicting the Ship
Speed in the Actual Weather Conditions on the Planned Ocean Route, New Trends
in Production Engineering, 1, 105–112, https://doi.org/10.2478/ntpe-2018-0013, 2018. a, b
Totton, A. and Mackie, G.: Diphormism in the Portuguese-Man-of-War, Nature,
177, 290, https://doi.org/10.1038/177290b0, 1956. a, b
Wang, S.-Z., Nie, H.-B., and Shi, C.-J.: A drifting trajectory prediction model
based on object shape and stochastic motion features, J.
Hydrodynam., 26, 951–959, https://doi.org/10.1016/S1001-6058(14)60104-9, 2015. a
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Short summary
The bluebottle (Physalia physalis), or Portuguese man o' war, is well known for the painful stings caused by its tentacles. Its drifting dynamics have not been widely explored, with previous studies using simple assumptions to calculate its drift. Considering similarities with a sailboat, we present a new theoretical model for the drifting speed and course of the bluebottle in different wind and ocean conditions, providing new insights into the parameterization of its complex drifting dynamics.
The bluebottle (Physalia physalis), or Portuguese man o' war, is well known for the painful...