Predicting coordinated group movements of sharks with limited observations using AUVs

Abstract

This paper presents a method for modeling and then tracking the 2D planar size, location, orientation, and number of individuals of an animal aggregation using Autonomous Underwater Vehicles (AUVs). It is assumed that the AUVs are equipped with sensors that can measure the position states of a subset of individuals from within the aggregation being tracked. A new aggregation model based on provably stable Markov Process Matrices is shown as a viable model for representing aggregations. Then, a multi-stage state estimation architecture based on Particle Filters is presented that can estimate the time-varying model parameters in real-time using sensor measurements obtained by AUVs. To validate the approach, a historical data set is used consisting of >100 shark trajectories from a leopard shark aggregation observed in the La Jolla, CA coast area. The method is generalizable to any stable group movement model constructed using a Markov Matrix. Simulation results show that, when at least 40% of sharks are tagged, the estimated number of sharks in the aggregation has an error of 6%. This error increased to 27% when the system was tested with real data.

Operational Protocols for the Use of Drones in Marine Animal Research

“…Published research on sharks using drone-based methods has become commonplace in the scientific literature over the last five years. These studies have largely focused on detection and abundance estimates [19,29], and tracking and behavioural observations [25,[30][31][32][33][34][35]. Drones typically survey comparatively smaller spatial scales compared to manned aircraft [36,37], and while sharks may be rare in abundance [38], they are often easy to detect from drones if conditions are ideal [39,40].…”

The Drone Revolution of Shark Science: A Review

“…Documentation of the use of shallow water as a refuge was only possible because of the aerial view provided by the drone. Although other studies have examined social behaviour of sharks with drones [4,8,16,18], Doan and Kajiura's [11] study is the first to document predator avoidance behaviour by large adult sharks. The regular predictable occurrence of large numbers of sharks in a nearshore environment with clear water provides a rare opportunity to use drones to observe and study natural predation in the wild.…”

“…The goal is to deploy a robust and accurate software tool to assist beach managers in confidently identifying potentially dangerous sharks in real time. Further enhancements will aim to mitigate against surface distortions, e.g., [140] and apply photogrammetric methods, e.g., [141] to measure shark size, orientation and swim-parameters [8].…”

Adult blacktip sharks (Carcharhinus limbatus) use shallow water as a refuge from great hammerheads (Sphyrna mokarran)

“…The use of UAVs permits unobtrusive observation and allows natural behaviours to be documented in the wild, providing insight into seldom‐seen predator–prey interactions (Lea et al ., 2019; Raoult et al ., 2018). Other studies have also used aerial drone footage to document various behaviours exhibited by different shark species in the wild, including feeding, social and aggregative behaviour (Gore et al ., 2019; Ho et al ., 2017; Rieucau et al ., 2018). The footage presented here can be analysed in depth to quantify swimming alignment, nearest‐neighbour distances, velocity and tail beat frequency to provide a more comprehensive analysis of these parameters for both predator and prey.…”