Spooky Interaction at a Distance in Cave and Surface Dwelling Electric Fishes

Fortune, Eric S.; Andanar, Nicole; Madhav, Manu S.; Jayakumar, Ravikrishnan P.; Cowan, Noah J.; Bichuette, Maria Elina; Soares, Daphne

doi:10.3389/fnint.2020.561524

Cited by 14 publications

(24 citation statements)

References 60 publications

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“…Since tracked EOD traces allow insights into both movement and communication behaviors, a reliable tracking algorithm is key to many behavioral studies, both in the laboratory and in the field, that have not been possible before. From such big-data behavioral studies we expect many novel insights into the sensory ecology and into social and communication behaviors of these fascinating fishes (Henninger et al, 2018;Raab et al, 2019Raab et al, , 2021Fortune et al, 2020), that also impact the way we study sensory processing.…”

Section: Discussionmentioning

confidence: 99%

“…Accordingly, such natural observations yield the unique opportunity to discover novel and unexpected behavioral traits and associated causalities. For example, Fortune et al (2020) described behavioral and physiological adaptations of Eigenmannia vicentespelea, another gymnotiform wave-type electric fish, in response to living in a constantly dark cave.…”

Section: Applications Of the Developed Algorithmmentioning

confidence: 99%

“…By combining, refining, and extending previous approaches, our algorithm is capable of tracking EODs of individual fish with unprecedented accuracy, i.e., tracking errors occur less often in complex tracking scenarios (e.g., when EOD frequency traces cross each other, Figure 5) which tremendously reduces required post-processing time to manually correct flawed connections. Since both movement behaviors (Madhav et al, 2018;Henninger et al, 2020) and communication (Smith, 2013;Henninger et al, 2018;Fortune et al, 2020) can be analyzed based on EOD recordings, our algorithm is a fundamental advancement for a wide range of behavioral studies on freely moving and interacting electric fish (Raab et al, 2019(Raab et al, , 2021. Finally, we demonstrate the performance of our tracking algorithm on recordings of Apteronotus leptorhynchus taken with an array of 64 electrodes in a stream in the Llanos in Colombia.…”

Section: Introductionmentioning

confidence: 98%

“…Alternatively, behaving animals can be tracked by means of remote sensing devices (Kühl and Burghardt, 2013;Theriault et al, 2014;Henninger et al, 2018Henninger et al, , 2020Hughey et al, 2018;Torney et al, 2018;Raab et al, 2019;Aspillaga et al, 2021). In this approach, recorded signals can originate from small micro-transmitters that get affixed to animals (e.g., acoustic telemetry system for fish: Aspillaga et al, 2021) or from the animals themselves (photography, video recordings: Sherley et al, 2010;Lahiri et al, 2011;Theriault et al, 2014;Nourizonoz et al, 2020;ultrasound vocalizations: Surlykke and Kalko, 2008;Seibert et al, 2013;Hügel et al, 2017;electric signals: Henninger et al, 2018;Raab et al, 2019;Fortune et al, 2020). These methods benefit from minimal interference with the animals themselves.…”

Section: Introductionmentioning

confidence: 99%

“…Electric fish are particularly well-suited for being tracked in the laboratory and in their natural habitats based on remote sensing (Jun et al, 2013;Henninger et al, 2018;Madhav et al, 2018;Raab et al, 2019;Fortune et al, 2020). These fish are capable of producing an electric field through discharges of an electric organ (EOD, Turner et al, 2007) used for electrolocation (Fotowat et al, 2013) and communication (Albert and Crampton, 2005;Smith, 2013;Benda, 2020).…”

Section: Introductionmentioning

confidence: 99%

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

Raab

Madhav²,

Jayakumar³

et al. 2022

Front. Integr. Neurosci.

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Applications Of the Developed Algorithmmentioning

confidence: 99%