Advances in non-invasive tracking of wave-type electric fish in natural and laboratory settings

Recent technological advances greatly improved the possibility to study freely behaving animals in natural conditions. However, many systems still rely on animal-mounted devices, which can already bias behavioral observations. Alternatively, animal behaviors can be detected and tracked in recordings of stationary sensors, e.g., video cameras. While these approaches circumvent the influence of animal-mounted devices, identification of individuals is much more challenging. We take advantage of the individual-specific electric fields electric fish generate by discharging their electric organ (EOD) to record and track their movement and communication behaviors without interfering with the animals themselves. EODs of complete groups of fish can be recorded with electrode arrays submerged in the water and then be tracked for individual fish. Here, we present an improved algorithm for tracking electric signals of wave-type electric fish. Our algorithm benefits from combining and refining previous approaches of tracking individual specific EOD frequencies and spatial electric field properties. In this process, the similarity of signal pairs in extended data windows determines their tracking order, making the algorithm more robust against detection losses and intersections. We quantify the performance of the algorithm and show its application for a data set recorded with an array of 64 electrodes distributed over a 12 m(2) section of a stream in the Llanos, Colombia, where we managed, for the first time, to track Apteronotus leptorhynchus over many days. These technological advances make electric fish a unique model system for a detailed analysis of social and communication behaviors, with strong implications for our research on sensory coding.

Automated summary

Recent technological advances have allowed for the study of freely behaving animals in natural conditions, but many existing systems still rely on animal-mounted devices, which can bias behavioral observations. This study presents a method for recording and tracking the movement and communication behaviors of electric fish using their individual-specific electric fields. An improved algorithm is introduced to track the electric signals of wave-type electric fish, making the process more robust and reliable. These technological advances are important for studying social and communication behaviors in electric fish and have implications for sensory coding research.