Abstract
The study of animal behavior has been revolutionized by sophisticated methodologies that identify and track individuals in video recordings. Video recording of behavior, however, is challenging for many species and habitats including fishes that live in turbid water. Here we present a methodology for identifying and localizing weakly electric fishes on the centimeter scale with subsecond temporal resolution based solely on the electric signals generated by each individual. These signals are recorded with a grid of electrodes and analyzed using a two-part algorithm that identifies the signals from each individual fish and then estimates the position and orientation of each fish using Bayesian inference. Interestingly, because this system involves eavesdropping on electrocommunication signals, it permits monitoring of complex social and physical interactions in the wild. This approach has potential for large-scale non-invasive monitoring of aquatic habitats in the Amazon basin and other tropical freshwater systems.
Highlights
The study of animal behavior often requires identification and localization of individuals as they move through the environment
Our approach relies on recordings made with an electrode grid system (Fig. 1) and a two-step algorithm to extract the identities and positions of individuals in a laboratory tank in which the positions of the fish were tracked via video recordings
The tracking system involves three steps: (1) capturing the data using an array of electrodes placed in the habitat of the animals, (2) extracting the parameters of the electric signals of each individual fish, and (3) tracking the spatial position and orientation of each fish with respect to the grid geometry
Summary
The study of animal behavior often requires identification and localization of individuals as they move through the environment. We developed a sensor array and analytic tools for measuring the positions and electrical behaviors of weakly electric fishes. These fish species are widespread throughout the Amazon basin and in certain river systems in Africa. We have developed a recording system and computational approach to track multiple wave-type electric fish that relies solely on their EOD signals. Using this system, we tracked Eigenmannia virescens, a species of Gymnotiform fish, in both laboratory and field settings. We believe the inverse problem is best addressed using statistical estimation techniques
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