Lagrangian model of copepod dynamics: clustering by escape jumps in turbulence

H. Ardeshiri, I. Benkeddad, F.G. Schmitt, S. Souissi, F. Toschi, E. Calzavarini

Research output: Contribution to journalArticleAcademicpeer-review

7 Citations (Scopus)
146 Downloads (Pure)

Abstract

Planktonic copepods are small crustaceans that have the ability to swim by quick powerful jumps. Such an aptness is used to escape from high shear regions, which may be caused either by flow perturbations, produced by a large predator (i.e., fish larvae), or by the inherent highly turbulent dynamics of the ocean. Through a combined experimental and numerical study, we investigate the impact of jumping behavior on the small-scale patchiness of copepods in a turbulent environment. Recorded velocity tracks of copepods displaying escape response jumps in still water are here used to define and tune a Lagrangian copepod (LC) model. The model is further employed to simulate the behavior of thousands of copepods in a fully developed hydrodynamic turbulent flow obtained by direct numerical simulation of the Navier-Stokes equations. First, we show that the LC velocity statistics is in qualitative agreement with available experimental observations of copepods in turbulence. Second, we quantify the clustering of LC, via the fractal dimension D2. We show that D2 can be as low as ∼2.3 and that it critically depends on the shear-rate sensitivity of the proposed LC model, in particular it exhibits a minimum in a narrow range of shear-rate values. We further investigate the effect of jump intensity, jump orientation, and geometrical aspect ratio of the copepods on the small-scale spatial distribution. At last, possible ecological implications of the observed clustering on encounter rates and mating success are discussed.

Original languageEnglish
Article number043117
Pages (from-to)1-11
JournalPhysical Review E
Volume93
Issue number4
DOIs
Publication statusPublished - 18 Apr 2016

Fingerprint Dive into the research topics of 'Lagrangian model of copepod dynamics: clustering by escape jumps in turbulence'. Together they form a unique fingerprint.

Cite this