A Biased Random Walk Model for the Trajectories of Swimming Micro-organisms

Hill, Nicholas M.; Häder, Donat-Peter

doi:10.1006/jtbi.1997.0421

Cited by 192 publications

(207 citation statements)

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“…This modification refutes the notion that parameter is insensitive and therefore should be considered within future applications of the model. For the BCRW model inheriting orientation towards a specific target, Codling et al (2004) presented a turning frequency of 0.5 Hz (equal to a reorientation time of between 4 and 20 s), based on the experiments of Hill and Häder (1997). Our study proposes that much lower reorientation times (equal to higher values) are inevitable in terms of modelling rheoreaction.…”

Section: Discussionmentioning

confidence: 78%

“…The concentration parameter of the von Mises distribution, dependent on the modified Bessel functions I 0 ( ) and denoted as "orientating ability", determines randomness of movement direction. Mean turn angle d is based on linear reorientation (Hill and Häder 1997):…”

Section: Bcrw Model (Codling Et Al 2004)mentioning

confidence: 99%

“…Experimental studies on microorganisms have shown that, typically, swimming speeds are either exponentially (e.g., Codling and Hill 2005;Hill and Häder 1997) or normally distributed (Bearon and Grunbaum 2008). Codling et al (2010) found that a variable swimming speed can significantly change the spatial distribution of microorganisms when implemented in CRW models.…”

Section: Introductionmentioning

confidence: 99%

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Modelling the dispersal of riverine fish larvae: from a raster-based analysis of movement patterns within a racetrack flume to a rheoreaction-based correlated random walk (RCRW) model approach

Glas

Tritthart

Zens

et al. 2017

Can. J. Fish. Aquat. Sci.

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Recruitment of Chondrostoma nasus and similar fish species in rivers is related to spatiotemporal linkages between larval hatching and nursery habitats. Active swimming behaviour contradicts the assumption that passive particle tracing models can serve as a proxy for larval dispersal models. A racetrack flume with an inshore area of near-natural slope was created to observe individual larval trajectories. A new three-step, raster-based analysis was developed to distinguish four types of movement patterns: active upstream, active downstream, active-passive, and passive. Both larval developmental stage-specific and release site-specific occurrences of these movement patterns were experimentally found for nine flow velocity classes (≤0.225 m·s −1 ). These currentinduced movement patterns, and evaluated durations within them, were used to develop a biased and correlated random walk model that includes rheoreaction -a key behavioural response of fish to flow within rivers. The study introduces the concept and application of a rheoreaction-based correlated random walk model, which coupled with a 3D hydrodynamic model, allows prediction of the spatiotemporal effects of various river discharges, morphologies, and restoration scenarios on larval fish dispersal.Résumé : Le recrutement de Chondrostoma nasus et d'espèces de poissons semblables dans les rivières est relié à des liens spatiotemporels entre l'éclosion des larves et les habitats d'alevinage. Le comportement de nage active contredit l'hypothèse voulant que les modèles de suivi de particules passives puissent se substituer aux modèles de dispersion des larves. Un canal de nage ovale avec une zone côtière de pente quasi naturelle a été créé pour observer les trajectoires de larves individuelles. Une nouvelle analyse de mappage en trois étapes a été mise au point pour distinguer quatre types de déplacements, à savoir : actif vers l'amont, actif vers l'aval, actif-passif et passif. Des cas de ces types de déplacements tant à l'étape du développement des larves que pour des sites de lâcher précis ont été observés expérimentalement pour neuf catégories de vitesses d'écoulement (≤0,225 m·s -1 ). Ces motifs de déplacement induits par le courant et leurs durées évaluées ont été utilisés pour développer un modèle de parcours aléatoire biaisé et corrélé qui comprend la rhéoréaction, une réaction comportementale clé des poissons à l'écoulement dans les rivières. L'étude introduit le concept et l'application d'un modèle de parcours aléatoire corrélé basé sur la rhéoréaction qui, jumelé à un modèle hydrodynamique en 3D, permet de prédire les effets spatiotemporels de différents débits, morphologies et scénarios de restauration de rivière sur la dispersion des larves de poisson. [Traduit par la Rédaction]

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Section: Discussionmentioning

confidence: 78%

Section: Bcrw Model (Codling Et Al 2004)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modelling the dispersal of riverine fish larvae: from a raster-based analysis of movement patterns within a racetrack flume to a rheoreaction-based correlated random walk (RCRW) model approach

Glas

Tritthart

Zens

et al. 2017

Can. J. Fish. Aquat. Sci.

View full text Add to dashboard Cite

show abstract

“…The reorientation timescale, while known only for a handful of species 16,20,21,[27][28][29] , generally spans the range BB1-10 s, which, for typical turbulent dissipation rates (e ¼ 10 À 8 -10 À 6 m 2 s À 3 ), corresponds to CB1. Phytoplankton swimming speeds 30,31 , V C B100-1,000 mm s À 1 , are often comparable to or larger than the Kolmogorov velocities, V K B300-1,000 mm s À 1 , associated with these dissipation rates, suggesting F can often be of order unity.…”

Section: Resultsmentioning

confidence: 99%

Turbulence drives microscale patches of motile phytoplankton

et al. 2013

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Patchiness plays a fundamental role in phytoplankton ecology by dictating the rate at which individual cells encounter each other and their predators. The distribution of motile phytoplankton species is often considerably more patchy than that of non-motile species at submetre length scales, yet the mechanism generating this patchiness has remained unknown. Here we show that strong patchiness at small scales occurs when motile phytoplankton are exposed to turbulent flow. We demonstrate experimentally that Heterosigma akashiwo forms striking patches within individual vortices and prove with a mathematical model that this patchiness results from the coupling between motility and shear. When implemented within a direct numerical simulation of turbulence, the model reveals that cell motility can prevail over turbulent dispersion to create strong fractal patchiness, where local phytoplankton concentrations are increased more than 10-fold. This 'unmixing' mechanism likely enhances ecological interactions in the plankton and offers mechanistic insights into how turbulence intensity impacts ecosystem productivity.

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“…Regarding the swimming characteristics of motile phytoplankton, there have been various theoretical, numerical and experimental studies (Durham et al 2009(Durham et al , 2013Pedley et al 1988;Pedley and Kessler 1990;Son et al 2015), with focus on the swimming behaviors of Chlamydomonas reinhardtii (Martin et al 2016;Sineshchekov et al 2000), Chlamydomonas nivalis (Hill and Häder 1997), Heterosigma akashiwo (Durham et al 2009(Durham et al , 2013 and Volvox (Drescher et al 2009;Goldstein 2015). Two types of models, i.e.…”

Section: Introductionmentioning

confidence: 99%

Swimming characteristics of gyrotactic microorganisms in low-Reynolds-number flow: Chlamydomonas reinhardtii

Chen

Zeng

et al. 2017

Energ. Ecol. Environ.

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The swimming characteristics of gyrotactic microorganisms are significant to understand the ecological activities in lakes, rivers and oceans. The swimming velocity of a typical motile microorganism, Chlamydomonas reinhardtii, was measured for both still water and low-Reynolds-number flow, based on a microfluidic system. Results show that the swimming speed is subject to Gaussian distribution for the still water, and corresponding mean swimming speed is 41 lm/s. The streamwise mean swimming velocity, 35 lm/s, in the moving water is slightly less than that in the still water. It is also shown that the swimming direction in the horizontal plane is dominated by cell randomness for the still water, and 80% of the cells are aligned with the ambient flow when the flow velocity exceeds 333 lm/s. The standard deviation of swimming direction and the percent of swimming direction in the streamwise direction can reach a stable status with the increase of the flow velocity.

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A Biased Random Walk Model for the Trajectories of Swimming Micro-organisms

Cited by 192 publications

References 0 publications

Modelling the dispersal of riverine fish larvae: from a raster-based analysis of movement patterns within a racetrack flume to a rheoreaction-based correlated random walk (RCRW) model approach

Modelling the dispersal of riverine fish larvae: from a raster-based analysis of movement patterns within a racetrack flume to a rheoreaction-based correlated random walk (RCRW) model approach

Turbulence drives microscale patches of motile phytoplankton

Swimming characteristics of gyrotactic microorganisms in low-Reynolds-number flow: Chlamydomonas reinhardtii

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