Invasive alien species threaten natural ecosystems worldwide, prey on native species, and deplete their food sources. Mosquitofish is one of the most invasive freshwater fish worldwide and its negative impacts on the native fauna are alarming. Despite the urgency of contrasting the mosquitofish invasion, we have access to very few methods to combat them. Even when successful, these methods can be excessively labor-intensive or dangerous to native species. Robotic predators may constitute a promising tool in combating mosquitofish. Our group has recently proposed the use of a robotic predator that can perform targeted attacks against mosquitofish. The robotic predator consists of three operational parts: a two-dimensional robotic platform, a magnetically connected replica of a native mosquitofish predator, and an in-house developed live tracking software. The robotic replica was programmed to swim along a predetermined trajectory and randomly target mosquitofish in real time through a dedicated tracking software. Building on available experimental results, we put forward a comprehensive mathematical toolbox based on symbolic dynamics, recurrence quantification, and information theory to detail the behavioral interaction between the robotic predator and mosquitofish.
Zebrafish is extensively used in behavioral, pharmacological, and neurological studies due to a number of method- ological and practical advantages, including genetic and neurobiological homologies with humans and a fully se- quenced genome. Critical to a biologically-based understanding of zebrafish behavior is the ability to reconstruct their complex behavioral repertoire in three-dimensions. Toward this aim, several efforts have been made to score their ethogram in three-dimensions, but most of these studies are constrained by a single-view imaging. A promising line of approach to extract refined information about the mechanosensory and perceptual systems of zebrafish is point cloud reconstruction. Here, we provide an initial review of the state of knowledge in zebrafish tracking and we propose a potential methodology that can capture the dynamic three-dimensional geometry of fish swimming. We utilize a stereo vision camera, calibrated with a pinhole camera model with refraction cor- rection to allow for multi-medium imaging. The corrected pinhole camera model accounts for refraction through multiple mediums and allows for more accurate point cloud reconstruction from two cameras. From the point cloud data, we could recreate the three-dimensional geometric model of the fish and analyze its swimming be- havior in three dimensions. The extracted dynamic fish geometry should allow for an improved understanding of mechanosensation and perception, which are critical to elucidate how zebrafish process visual cues and perceive flow structures.