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Lagrangian Particle Tracker

A Lagrangian Particle Tracker (LPT) model can be coupled to SLIM to simulate the dispersal of discrete Lagrangian particles in complex environmental flows. Based on previously simulated hydrodynamics of the region of interest, the model predicts the fate of released particles. The model is object-based, i.e. each individual has its own behaviour, which offers a flexibility to model a wide range of different « particles ». For example, the LPT has been resorted to in order to study the fate of plastic debris, the connectivity between coral reefs and the recruitment of snappers. Current collaborative studies using our LPT focus on the dispersal of turtle juveniles, the spread of a disease in the Florida Reef Tract, and the connectivity between seagrass meadows.

The methodology allows us to easily include specific behaviours, such as landing on beach, settling on the sea floor, resuspension, degradation, etc. Each individual has its own characteristics: mass, age, origin, …. The particles are located inside the mesh where various fields, such as the water current, are interpolated. The particle displacement, based on a velocity field which may depend on other variables than the water currents, is solved using a 4th order Runge Kutta scheme.

To learn more…

Frys, C., Saint-Amand, A., Le Hénaff, M., Figueiredo, J., Kuba, A., Walker, B., … Hanert, E. (2020). Fine-Scale Coral Connectivity Pathways in the Florida Reef Tract: Implications for Conservation and Restoration. Frontiers in Marine Science, 7. https://doi.org/10.3389/fmars.2020.00312
Schlaefer, J. A., Wolanski, E., Lambrechts, J., & Kingsford, M. J. (2018). Wind Conditions on the Great Barrier Reef Influenced the Recruitment of Snapper (Lutjanus carponotatus). Frontiers in Marine Science, 5. https://doi.org/10.3389/fmars.2018.00193
Li, Y., Wolanski, E., Dai, Z., Lambrechts, J., Tang, C., & Zhang, H. (2018). Trapping of plastics in semi-enclosed seas: Insights from the Bohai Sea, China. Marine Pollution Bulletin, 137, 509–517. https://doi.org/10.1016/j.marpolbul.2018.10.038
Grech, A., Hanert, E., McKenzie, L., Rasheed, M., Thomas, C., Tol, S., … Coles, R. (2018). Predicting the cumulative effect of multiple disturbances on seagrass connectivity. Global Change Biology, 24(7), 3093–3104. https://doi.org/10.1111/gcb.14127
Grech, A., Wolter, J., Coles, R., McKenzie, L., Rasheed, M., Thomas, C., … Hanert, E. (2016). Spatial patterns of seagrass dispersal and settlement. Diversity and Distributions, 22(11), 1150–1162. https://doi.org/10.1111/ddi.12479
Critchell, K., & Lambrechts, J. (2016). Modelling accumulation of marine plastics in the coastal zone; what are the dominant physical processes? Estuarine, Coastal and Shelf Science, 171, 111–122. https://doi.org/10.1016/j.ecss.2016.01.036
Thomas, C. J., Bridge, T. C. L., Figueiredo, J., Deleersnijder, E., & Hanert, E. (2015). Connectivity between submerged and near-sea-surface coral reefs: Can submerged reef populations act as refuges? Diversity and Distributions, 21(10), 1254–1266. https://doi.org/10.1111/ddi.12360
Thomas, C. J., Lambrechts, J., Wolanski, E., Traag, V. A., Blondel, V. D., Deleersnijder, E., & Hanert, E. (2014). Numerical modelling and graph theory tools to study ecological connectivity in the Great Barrier Reef. Ecological Modelling, 272, 160–174. https://doi.org/10.1016/j.ecolmodel.2013.10.002
Hamann, M., Grech, A., Wolanski, E., & Lambrechts, J. (2011). Modelling the fate of marine turtle hatchlings. Ecological Modelling, 222(8), 1515–1521. https://doi.org/10.1016/j.ecolmodel.2011.02.003