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

Abstract

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 m2 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.

Supervised learning algorithm for analysis of communication signals in the weakly electric fish Apteronotus leptorhynchus

“…“ Calculation of EOD frequency and amplitude ”, works only for time–voltage data that were generated by a single EOD source, for the analysis of multiple (either synthetic or recorded from fish) simultaneously recorded EOD signals, one can employ a different method (e.g., Raab et al. 2022 ) to extract individual time–frequency–amplitude signals.…”

A perspective on neuroethology: what the past teaches us about the future of neuroethology

“…Altogether, each tracking method must be carefully evaluated before moving from the lab into the field. Fortunately, technological progress is rapid (Daniel Kissling et al 2014;Nourizonoz et al 2020;Vo-Doan and Straw 2020;Lioy et al 2021;Shearwood et al 2021;Walter et al 2021;Gaidica and Dantzer 2022;Raab et al 2022;Nagy et al 2023), which is reflected by ICN contributions working on tracking techniques. These contributions have increased since the ICN in 2014 from 0.45% to almost 3% (Fig.…”

Tracking spatial patterns and nocturnal arousal in an undisturbed natural population of the pulse-type weakly electric fishGymnotus omarorum

“…It remains challenging to identify the proper site of remote sensing to get reliable data, or to monitor small animals that cannot carry individual loggers, or to track animals that live in inaccessible or complex environments for video recordings. On top of that, tracking requires individual identification and therefore a reliable individual-specific trait to act as a biometric cue, which is an additional challenge for the resolution of the available recording devices [3][4][5] .…”

“…It remains challenging to identify the proper site of remote sensing to get reliable data, or to monitor small animals that cannot carry individual loggers, or to track animals that live in inaccessible or complex environments for video recordings. On top of that, tracking requires individual identification and therefore a reliable individual-specific trait to act as a biometric cue, which is an additional challenge for the resolution of the available recording devices [3][4][5] .Freshwater weakly electric fish, that have evolved independently in South America and Africa, generate weak electric fields to locate and identify nearby objects (electrolocation) and to communicate with each other (electrocommunication) 6,7 . Electrocytes densely packed into the peripheral electric organ fire in synchrony, driven by spinal electromotor neurons that receive input from a hindbrain command circuit, to generate an external electric field 8,9 .…”