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Feeding behavior of fish

Feeding is how fish acquire energy for growth and reproduction1. Therefore, feeding behavior has been studied extensively. Fish feeding behavior depends on various factors such as flow, prey density, prey size, light, and predators2-11.



Among these factors, flow conditions have the greatest impact on fish foraging. Some reef fish optimize their behavior in response to changes in flow speed by adjusting their searching range, by changing their proportions of fin type usage, and by adopting sheltering behavior5,12,13.

However, these studies have focused on swimming fish in coral reefs or rivers, taking advantage of coral shelter that decreases the flow speed experienced by fish by more than 60%13-15. Garden eels, however, live in fringe areas of coral reefs and don't have shelter other than their burrow, which is why they are expected to have a unique feeding behavior depending on the flow.

Feeding behavior of garden eels

Based on the above motivation, we are studying effects of flow speed on the garden eel's feeding behavior. The relationships between flows and the feeding behavior of garden eels (Gorgasia sillneri) was studied for the first time by Khrizman et al (2018) in the Red Sea16. In their research, they found that the eels maintained their feeding rate even under strong flow speeds by decreasing the drag force through bending their posture.

However, because the research was done in the field, it was hard to exclude effects of other environmental factors. For this reason, additional research in the controlled lab condition is needed.

Research overview

The tank in the picture below is called a “flume” in which you can freely control the flow speed. Using the flume, we are studying the detailed effects of flow speed on the feeding behavior of spotted garden eels (Heteroconger hassi) under controlled conditions.

This research is being conducted at the Okinawa Institute of Science Technology (OIST), where researchers from various background work together. This is an interdisciplinary research project studying effects of flows, as a physical factor, on garden eels biological behavior.



We are investigating the feeding rate (the number of plankton captured by garden eels in a specific time) and the detailed feeding behavior by reconstructing their behavior in three dimension.

3D reconstruction of feeding behavior

3D reconstruction of feeding movement consist of tracking body parts and reconstructing 2D videos into 3D. In behavioral studies, animals are usually marked with something distinguishable such as beads and tracked with softwares. However, it is difficult to put markers on garden eels and even if it were possible, you would need to consider the effects of markers on their behavior. In our study, we need to track body features frame by frame which is an endless task if done manually; So we have automated the task using Python package, DeepLabCut, which tracks unlabeled points using deep learning (this package is free!)17,18.



Two of eyes enable us to convert visual information into three dimensions. In 3D reconstruction, multiple cameras are calibrated to track points while recording their relative position to one another. For this purpose, we are using dltdv package in Matlab19.



Even iPhones are suitable for this research!



3D reconstructions of behavior enable us to investigate various parameters in detail, such as time, distance, speed, angle, and the trajectory of a garden eel capturing a prey item. With this detailed behavioral analysis, we are trying to reveal how garden eels differ from other fish and what behavioral advantage to flows they have.

The figure below shows the movement of a garden eel's face under different flow speeds. Points indicate the location of the face in each frame. You can see that the movement flexibility gets smaller as the flow speed increases.



Future research

Previous research has mostly focused on effects of mean flow speeds as a simplification, although more complex flow conditions may happen in the field. Thus, we plan to look into effects of turbulence on reef fish including garden eels. We will apply the same methods for the behavioral analysis and use particle image velocimetry (PIV) for assessing complex flows. PIV enables us to measure turbulent parameters from flow velocities visualized by shining a laser sheet on the fluid filled with particles. How will garden eels react to the turbulent flows? We hope you are looking forward to results!




Kota Ishikawa

I'm mainly working on the garden eel project. With an interest in animal behavior, I started the interdisciplinary research investigating effects of flows as a physical factor primarily on garden eels, initiated by Prof. Amatzia Genin. As a first author of the project, I'm conducting not only lab experiments and analysis, but also fieldwork for surveying flow conditions in the habitats. I launched this website to convey interesting facts about garden eels.
Personal website


Heng Wu

Heng is a postdoc in Marine biophysics unit at OIST. She specializes in experimental hydrodynamics with a focus on turbulent boundary layer flows over a rough or porous bed and their impact on biological processes and sediment transport. She takes care of the engineering aspect of the garden eel project, including the fluid dynamics and some image/video processing.



Satoshi Mitarai

The primary objective of Professor Mitarai’s research is to understand the role of ocean turbulence in regulating biological processes and its consequences for population structure and dynamics of marine ecosystems, through international collaborations. These studies include investigations of larval dispersal via coastal eddies and the role of dispersal in structuring marine populations, understanding biological responses of corals to turbulent flows and their integrated effects on biogeochemical cycling, and impacts of tropical cyclones on particle aggregation and biological pumps. Using his skills and experience as a fluid dynamicist, Professor Mitarai contributes to a new interdisciplinary field in the marine sciences.


Amatzia Genin

Prof. (emeritus) Amatzia Genin is a marine ecologist and biological oceanographer at the Hebrew University of Jerusalem and the Interuniversity Institute for Marine Sciences of Eilat, Israel. His major interest is in the coupling between physical and biological processes in the marine environment, focusing on the effects of water motion on fundamental ecological processes, including predator-prey relationships, competition, symbiosis, mass transfer, and behavior. Since 2020, he is co-supervising Kota Ishikawa for his thesis research.






1. Stoner, A. W. Effects of environmental variables on fish feeding ecology: Implications for the performance of baited fishing gear and stock assessment. J. Fish Biol. 65, 1445–1471 (2004).

2. Clarke, R. D., Finelli, C. M. & Buskey, E. J. Water flow controls distribution and feeding behavior of two co-occurring coral reef fishes: II. Laboratory experiments. Coral Reefs 28, 475–488 (2009).

3. Finelli, C. M., Clarke, R. D., Robinson, H. E. & Buskey, E. J. Water flow controls distribution and feeding behavior of two co-occurring coral reef fishes: I. Field measurements. Coral Reefs 28, 461–473 (2009).

4. Fulton, C. J., Bellwood, D. R. & Wainwright, P. C. Wave energy and swimming performance shape coral reef fish assemblages. Proc. R. Soc. B Biol. Sci. 272, 827–832 (2005).

5. Kiflawi, M. & Genin, A. Prey flux manipulation and the feeding rates of reef-dwelling planktivorous fish. Ecology 78, 1062–1077 (1997).

6. Noda, M., Kawabata, K., Gushima, K. & Kakuda, S. Importance of zooplankton patches in foraging ecology of the planktivorous reef fish Chromis chrysurus (Pomacentridae) at Kuchinoerabu Island, Japan. Mar. Ecol. Prog. Ser. 87, 251–263 (1992).

7. Hill, J. & Grossman, G. D. An Energetic Model of Microhabitat Use for Rainbow Trout and Rosyside Dace. Ecology 74, 685–698 (1993).

8. Manatunge, J. & Asaeda, T. Optimal foraging as the criteria of prey selection by two centrarchid fishes. Hydrobiologia 391, 223–240 (1998).

9. Howard, E. W. & Bori, L. O. Behavior of Marine Animals: Current Perspectives in Research. Plenum Press New York-London vol. 2 (Plenum Press, New York, 1972).

10. Rickel, S. & Genin, A. Twilight transitions in coral reef fish: The input of light-induced changes in foraging behaviour. Anim. Behav. 70, 133–144 (2005).

11. Morgan, M. J. The influence of hunger, shoal size and predator presence on foraging in bluntnose minnows. Anim. Behav. 36, 1317–1322 (1988).

12. Heatwole, S. J. & Fulton, C. J. Behavioural flexibility in reef fishes responding to a rapidly changing wave environment. Mar. Biol. 160, 677–689 (2013).

13. Johansen, J., Bellwood, D. & Fulton, C. Coral reef fishes exploit flow refuges in high-flow habitats. Mar. Ecol. Prog. Ser. 360, 219–226 (2008).

14. Johansen, J. L., Fulton, C. J. & Bellwood, D. R. Avoiding the flow: Refuges expand the swimming potential of coral reef fishes. Coral Reefs 26, 577–583 (2007).

15. Taguchi, M. & Liao, J. C. Rainbow trout consume less oxygen in turbulence: the energetics of swimming behaviors at different speeds. J. Exp. Biol. 214, 1428–1436 (2011).

16. Khrizman, A., Ribak, G., Churilov, D., Kolesnikov, I. & Genin, A. Life in the flow: unique adaptations for feeding on drifting zooplankton in garden eels. J. Exp. Biol. 221, (2018).

17. Mathis, A. et al. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat. Neurosci. 21, 1281–1289 (2018).

18. Nath, T. et al. Using DeepLabCut for 3D markerless pose estimation across species and behaviors. Nat. Protoc. 14, 2152–2176 (2019).

19. Hedrick, T. L. Software techniques for two- and three-dimensional kinematic measurements of biological and biomimetic systems. Bioinspiration and Biomimetics 3, (2008).