Assessing the Behavioural Responses of Small Cetaceans to Unmanned Aerial Vehicles

Underwater sound of three unoccupied aerial vehicles at varying altitudes and horizontal distances

Unoccupied aerial vehicles (UAVs), or "drones," are increasingly used as a tool for cetacean research, but knowledge about how these tools contribute to underwater sound is lacking. In this study, underwater sound levels of three commonly used UAV models (Mavic Pro Platinum, Phantom 4 Pro v2.0, Inspire 1 Pro) were recorded. For each model, three replicate flights were conducted at 36 positions at standardized horizontal (0-30 m) and vertical (2-40 m) distances from a hydrophone (1 m depth). Median broadband received levels of the Inspire were highest at 96.5 dBrms 141-17 783 Hz re 1 μPa2, followed by the Phantom (92.4 dBrms 141-17 783 Hz re 1 μPa2) and Mavic, which was quietest (85.9 dBrms 141-17 783 Hz re 1 μPa2). Median ambient sound levels in the absence of an UAV were 82.7 dBrms 141-17 783 Hz re 1 μPa2. Significant increases in ambient sound levels associated with UAV flights occurred at higher altitudes than previously reported, and received levels decreased more with increasing horizontal distance of the UAV than with altitude. To minimize potential noise impacts on sensitive marine animal subjects, we recommend increasing horizontal distance to the animal, rather than altitude, and choosing the quietest UAV feasible.

First Unmanned Aerial Vehicle Observation of Epimeletic Behavior in Indo-Pacific Humpback Dolphins

Simple Summary

In this study, we reported a case of epimeletic behavior in Indo-Pacific humpback dolphin. Using a combination of drone and conventional photography, we were able to comprehensively document individual body condition, swimming pattern, and group behavior during the epimeletic event. Our research highlighted the application of drones in wildlife research to provide important insights into the health, behavior, and ecology of free-ranging populations.

Abstract

Epimeletic behavior has been reported in various species of cetaceans and sometimes in wild populations during vessel-based surveys. Epimeletic behavior in cetaceans involves complex social interactions which have been described using observational and acoustic studies. The recent advances in unmanned aerial vehicle (UAV) technology allowed its application in wildlife research and frequently in cetaceans in conjunction with vessel-based surveys. This article is the first report of intraspecific epimeletic behavior of Indo-Pacific humpback dolphins (Sousa chinensis) in Hong Kong waters using a combination of UAV- and vessel-based photography. Using both techniques, we were able to observe and qualitative analyze the individual body condition, group behavior, and swimming pattern during the epimeletic event. This study highlighted that UAVs can be used to observe the complex social behaviors and interactions of cetaceans from the aerial angle while keeping a minimal level of disturbance to the animals. Aerial footage can also be quantitatively analyzed to provide further insights on the group behaviors of cetaceans. The application allows efficient assessment of health, behavior, and ecology of wild animals, offering valuable opportunities for researchers working on free-ranging populations.

Application of Feature Point Matching Technology to Identify Images of Free-Swimming Tuna Schools in a Purse Seine Fishery

Tuna fish school detection provides information on the fishing decisions of purse seine fleets. Here, we present a recognition system that included fish shoal image acquisition, point extraction, point matching, and data storage. Points are a crucial characteristic for images of free-swimming tuna schools, and point algorithm analysis and point matching were studied for their applications in fish shoal recognition. The feature points were obtained by using one of the best point algorithms (scale invariant feature transform, speeded up robust features, oriented fast and rotated brief). The k-nearest neighbors (KNN) algorithm uses ‘feature similarity’ to predict the values of new points, which means that new data points will be assigned a value based on how closely they match the points that exist in the database. Finally, we tested the model, and the experimental results show that the proposed method can accurately and effectively recognize tuna free-swimming schools.