Depredation, the partial or complete removal of hooked fish (prey) by a nontarget predator species, is a cryptic interaction that negatively affects predators, prey, and fishing industries. However, these interactions are rarely observed, rendering positive identification of the predator nearly impossible. We therefore tested a genetic method for predator identification. Depredated remains from sharks and bony fish were sampled with buccal swabs. Genetic material was isolated from the swabs, which we hypothesized contained oral cells from the predator. A portion of the cytochrome‐c oxidase subunit I locus was amplified using prey‐specific blocking primers and sequenced in high depth using a metagenetics approach. We sequenced haplotypes from the remains of four sharks, where the predator was visually confirmed, and four bony fish, where the predator was unknown. For all interactions with known predators, our technique suggested the correct predator species. For all interactions where the predators were unknown, our technique suggested species previously confirmed as perpetrators in depredation events. Our findings provide a basis for the development of a genetic technique for predator identification, while highlighting challenges to be overcome before predator identification can be applied to large‐scale fisheries.

The autonomous navigation of robots in unknown environments is a challenge since it needs the integration of a several subsystems to implement different functionality. It needs drawing a map of the environment, robot map localization, motion planning or path following, implementing the path in real-world, and many others; all have to be implemented simultaneously. Thus, the development of autonomous robot navigation (ARN) problem is essential for the growth of the robotics field of research. In this paper, we present a simulation of a swarm intelligence method is known as Particle Swarm Optimization (PSO) to develop an ARN system that can navigate in an unknown environment, reaching a pre-defined goal and become collision-free. The proposed system is built such that each subsystem manipulates a specific task which integrated to achieve the robot mission. PSO is used to optimize the robot path by providing several waypoints that minimize the robot traveling distance. The Gazebo simulator was used to test the response of the system under various envirvector representing a solution to the optimization problem.onmental conditions. The proposed ARN system maintained robust navigation and avoided the obstacles in different unknown environments. vector representing a solution to the optimization problem.

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