Robotics-based study provides insight into predator-prey interactions
WASHINGTON, D.C., July 19, 2017 — Researchers have recently gained advanced understanding of a variety of processes through the information-theoretic concept of transfer entropy. Today, scientists are able to explain coupled dynamical systems like functional connective patterns in the brain and climate patterns all around the globe. Researchers from New York University's department of mechanical and aerospace engineering in Brooklyn found this method to hold great promise for advancing our understanding of animal behavior, particularly related to predator-prey interactions.
A research team led by New York University professor Maurizio Porfiri put forth a robotics-based study to control information flow in predator-prey interactions, as well as test the validity of transfer entropy when attempting to understand causal influences of the system. They report their findings this week in the journal Chaos, from AIP Publishing.
Specifically, the team studied the behavioral response of a zebrafish subjected to a fear-evoking robotic stimulus, modeled after the morpho-physiology of a red tiger oscar fish. They programmed the robotic threats to actuate along specific trajectories establishing a controlled, one-directional information flow. The predator motion in this interaction was independent of the response of the prey.
"Something which is really important from our community point of view is to be able to merge robotics and dynamical systems to address questions in animal behavior," Porfiri said.
As expected by the researchers, transfer entropy was able to isolate the causal relationship underlying experimental observations, and they were able to show a one-directional informational flow from the stimulus to the zebrafish.
Expanding on their validation of transfer entropy in the controlled robotics-based setup, the research team studied interactions between a zebrafish and a live red tiger oscar fish (whose response to the zebrafish could not be controlled). Unlike the robotics-based interaction, transfer entropy did not overly identify a direction of information flow in the presence of a live predator. So not only was the zebrafish influenced by the predator, but also the predator reacted to the zebrafish, in a two-directional interaction.
"We are able, from raw data, to understand that both the predator and the prey modify their behavior once one is in the presence of each other," Porfiri said.
To provide some biological basis for the observed difference in information flow, Porfiri and his group studied the specific reactions of the predator in response to the presence of the prey. Although their experimental setup could not fully replicate the habitat of the red tiger oscar fish, they observed basic behavioral reactions observed in the wild, verifying the fish's natural hunting instincts still played a role in their reactions.
Although there is still more to understand regarding the behavior of prey and predators, the researchers demonstrated the validity of transfer entropy to discover a cause-and-effect process, which has important implications in science and engineering. This is especially interesting from the perspective of the many potential ways robotics can help us understand how species share and use information.
The article, "Information theory and robotics meet to study predator-prey interactions," is authored by Daniele Neri, Tommaso Ruberto, Gabrielle Cord-Cruz and Maurizio Porfiri. The article appeared in Chaos July 18, 2017 [DOI: 10.1063/1.4990051] and can be accessed at http://aip.scitation.org/doi/full/10.1063/1.4990051.
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Chaos is devoted to increasing the understanding of nonlinear phenomena in all disciplines and describing their manifestations in a manner comprehensible to researchers from a broad spectrum of disciplines. See http://chaos.aip.org.