A simulation study of acoustic-assisted tracking of whales for mark-recapture surveys.
Collecting enough data to obtain reasonable abundance estimates of whales is often difficult, particularly when studying rare species. Passive acoustics can be used to detect whale sounds and are increasingly used to estimate whale abundance. Much of the existing effort centres on the use of acoustics to estimate abundance directly, e.g. analysing detections in a distance sampling framework. Here, we focus on acoustics as a tool incorporated within mark-recapture surveys. In this context, acoustic tools are used to detect and track whales, which are then photographed or biopsied to provide data for mark-recapture analyses. The purpose of incorporating acoustics is to increase the encounter rate beyond using visual searching only. While this general approach is not new, its utility is rarely quantified. This paper predicts the "acoustically-assisted" encounter rate using a discrete-time individual-based simulation of whales and survey vessel. We validate the simulation framework using existing data from studies of sperm whales. We then use the framework to predict potential encounter rates in a study of Antarctic blue whales. We also investigate the effects of a number of the key parameters on encounter rate. Mean encounter rates from the simulation of sperm whales matched well with empirical data. Variance of encounter rate, however, was underestimated. The simulation of Antarctic blue whales found that passive acoustics should provide a 1.7-3.0 fold increase in encounter rate over visual-only methods. Encounter rate was most sensitive to acoustic detection range, followed by vocalisation rate. During survey planning and design, some indication of the relationship between expected sample size and effort is paramount; this simulation framework can be used to predict encounter rates and establish this relationship. For a case in point, the simulation framework indicates unequivocally that real-time acoustic tracking should be considered for quantifying the abundance of Antarctic blue whales via mark-recapture methods.
CSIRO Computational Informatics/Wealth from Oceans National Research Flagship, Castray Esplanade, Hobart, Tasmania, Australia; Australian Marine Mammal Centre, Australian Antarctic Division, Department of the Environment, Channel Highway, Kingston, Australia.
David
D
Peel
Australian Marine Mammal Centre, Australian Antarctic Division, Department of the Environment, Channel Highway, Kingston, Australia.
Brian S
BS
Miller
CSIRO Computational Informatics/Wealth from Oceans National Research Flagship, Castray Esplanade, Hobart, Tasmania, Australia; Australian Marine Mammal Centre, Australian Antarctic Division, Department of the Environment, Channel Highway, Kingston, Australia.
Natalie
N
Kelly
University of Otago, Dunedin, New Zealand.
Steve
S
Dawson
University of Otago, Dunedin, New Zealand.
Elisabeth
E
Slooten
Australian Marine Mammal Centre, Australian Antarctic Division, Department of the Environment, Channel Highway, Kingston, Australia.
Michael C
MC
Double
D000162:Acoustics / Q000295:instrumentation; D000818:Animals; D000864:Antarctic Regions; D050616:Balaenoptera / Q000502:physiology*; D003198:Computer Simulation; D003247:Conservation of Natural Resources; D003625:Data Collection; D008297:Male; D009520:New Zealand; D011157:Population Dynamics; D012815:Signal Processing, Computer-Assisted; D013016:Sound; D013018:Sound Spectrography / Q000295:instrumentation; D013997:Time Factors; D014828:Vocalization, Animal / Q000502:physiology*