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A novel mechanism for mechanosensory-based rheotaxis in larval zebrafish

Nature volume 547, pages 445448 (27 July 2017) | Download Citation

  • An Erratum to this article was published on 13 September 2017


When flying or swimming, animals must adjust their own movement to compensate for displacements induced by the flow of the surrounding air or water1. These flow-induced displacements can most easily be detected as visual whole-field motion with respect to the animal’s frame of reference2. Despite this, many aquatic animals consistently orient and swim against oncoming flows (a behaviour known as rheotaxis) even in the absence of visual cues3,4. How animals achieve this task, and its underlying sensory basis, is still unknown. Here we show that, in the absence of visual information, larval zebrafish (Danio rerio) perform rheotaxis by using flow velocity gradients as navigational cues. We present behavioural data that support a novel algorithm based on such local velocity gradients that fish use to avoid getting dragged by flowing water. Specifically, we show that fish use their mechanosensory lateral line to first sense the curl (or vorticity) of the local velocity vector field to detect the presence of flow and, second, to measure its temporal change after swim bouts to deduce flow direction. These results reveal an elegant navigational strategy based on the sensing of flow velocity gradients and provide a comprehensive behavioural algorithm, also applicable for robotic design, that generalizes to a wide range of animal behaviours in moving fluids.

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We are grateful to E. Soucy and J. Greenwood for technical support, and B. Jordan for discussions. We thank M. Baldwin, M. Häsemeyer, and T. Dunn for reading the manuscript, and M. McHenry for advice on the behavioural rig. We also thank M. Grünthal and R. Hellmiss for contributions to figure design, and V. Stih for sharing unpublished data. This work was supported by a Pew Latin-American Fellowship to P.O., BRAIN National Institutes of Health (NIH) grant U01NS090449, NIH Pioneer award, and DP1 NS082121 to F.E., Simons Foundations grant SCGB 325207 and Human Frontier Science Program grant RGP0033/2014 to F.E., and an Office of Naval Research grant N00014-09-1-0352 to G.L., monitored by T. McKenna. R.P. was funded by the Max-Planck-Gesellschaft during part of this work.

Author information

Author notes

    • Pablo Oteiza
    •  & Iris Odstrcil

    These authors contributed equally to this work.

    • Ruben Portugues
    •  & Florian Engert

    These authors jointly supervised this work.


  1. Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts 02138, USA

    • Pablo Oteiza
    • , Iris Odstrcil
    •  & Florian Engert
  2. Center for Brain Science, Harvard University, Cambridge, Massachusetts 02138, USA

    • Pablo Oteiza
    • , Iris Odstrcil
    •  & Florian Engert
  3. Max Planck Institute of Neurobiology, Sensorimotor Control Research Group, Martinsried 82152, Germany

    • Pablo Oteiza
    • , Ruben Portugues
    •  & Florian Engert
  4. Museum of Comparative Zoology, Harvard University, Cambridge, Massachusetts 02138, USA

    • George Lauder


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P.O. conceived the project, built the rig, performed the experiments, and analysed the data. P.O, I.O., and F.E. designed the experiments. I.O. performed the chemical and two-photon lateral line ablations. P.O. and R.P. designed the software for the behavioural rig. P.O. and G.L. performed the PIV experiments. R.P., F.E., and I.O. contributed to the mathematical and theoretical considerations. I.O. and F.E. wrote the model. P.O., I.O., and F.E. wrote the manuscript with the assistance of R.P. and G.L. All authors discussed the data and the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Pablo Oteiza or Ruben Portugues or Florian Engert.

Reviewer Information Nature thanks E. Brainerd, J. Dabiri and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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  1. 1.

    Supplementary Information

    This file contains mathematical considerations points 1-7.


  1. 1.

    Rheotaxis in larval zebrafish

    Time-lapse of a 6-day old zebrafish larvae performing rheotaxis in the absence of visual cues. Side (upper image) and top (lower image) views are shown. Light blue arrow indicates leftward flow stimulation. Video was acquired at 200fps and played back at 400fps.

  2. 2.

    Effect of flow velocity gradients on the larval zebrafish body

    Time-lapse of a paralyzed 6-day old zebrafish larvae immersed in water flow. Side (upper image) and top (lower image) views are shown. Light blue arrow indicates leftward flow displacement. Video was acquired at 200fps and played back at 400fps.

  3. 3.

    A demonstration of the labview simulation software (rheotaxis interactive.llb)

    The video shows a particle (model fish) that moves randomly in a virtual flow field which is matched to our experimental conditions. Specifically, turn angles are drawn from the distributions shown in the upper left corner – red for random walking, white for rheotaxis. Turbulence is introduced in the second half of the movie by superimposing rotational flow-fields in sub-areas as indicated. It becomes clear that under all of these conditions the rheotactic algorithm elicited rheotaxis in different gradient regimes with remarkable robustness. Voice over by Florian Engert.

  4. 4.

    The lateral line system is required for rheotaxis in larval zebrafish

    Timelapse of a neuromast-ablated 7-day old zebrafish larvae presented with water flow in the absence of visual cues. Side (upper image) and Top (lower image) views are shown. Light blue arrow indicates leftward flow stimulation. Video was acquired at 200fps and played back at 400fps.

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