Estimating animal population density using passive acoustics

Abstract

Reliable estimation of the size or density of wild animal populations is very important for effective wildlife management, conservation and ecology. Currently, the most widely used methods for obtaining such estimates involve either sighting animals from transect lines or some form of capture-recapture on marked or uniquely identifiable individuals. However, many species are difficult to sight, and cannot be easily marked or recaptured. Some of these species produce readily identifiable sounds, providing an opportunity to use passive acoustic data to estimate animal density. In addition, even for species for which other visually based methods are feasible, passive acoustic methods offer the potential for greater detection ranges in some environments (e.g. underwater or in dense forest), and hence potentially better precision. Automated data collection means that surveys can take place at times and in places where it would be too expensive or dangerous to send human observers.Here, we present an overview of animal density estimation using passive acoustic data, a relatively new and fast-developing field. We review the types of data and methodological approaches currently available to researchers and we provide a framework for acoustics-based density estimation, illustrated with examples from real-world case studies. We mention moving sensor platforms (e.g. towed acoustics), but then focus on methods involving sensors at fixed locations, particularly hydrophones to survey marine mammals, as acoustic-based density estimation research to date has been concentrated in this area. Primary among these are methods based on distance sampling and spatially explicit capture-recapture. The methods are also applicable to other aquatic and terrestrial sound-producing taxa.We conclude that, despite being in its infancy, density estimation based on passive acoustic data likely will become an important method for surveying a number of diverse taxa, such as sea mammals, fish, birds, amphibians, and insects, especially in situations where inferences are required over long periods of time. There is considerable work ahead, with several potentially fruitful research areas, including the development of (i) hardware and software for data acquisition, (ii) efficient, calibrated, automated detection and classification systems, and (iii) statistical approaches optimized for this application. Further, survey design will need to be developed, and research is needed on the acoustic behaviour of target species. Fundamental research on vocalization rates and group sizes, and the relation between these and other factors such as season or behaviour state, is critical. Evaluation of the methods under known density scenarios will be important for empirically validating the approaches presented here.

Year-round acoustic detection of bearded seals (Erignathus barbatus) in the Beaufort Sea relative to changing environmental conditions, 2008–2010

“…With the increased loss of sea ice in the Arctic, there is greater interest in obtaining abundance estimations of bearded seals as well as other ice-obligate species. The recent development of methods that utilize passive acoustic detections to provide estimates of density or abundance could be applied to bearded seals (Marques et al 2012). By combining call rates of these animals (e.g., Van Parijs et al 2001) with density estimation techniques (Marques et al 2012), it may be possible to obtain estimates of relative abundance and density of this typically solitary and widespread pan-Arctic species.…”

Autonomous recording units in avian ecological research: current use and future applications

“…Many bird species vocalize reliably, and thus, acoustic surveys can be used to estimate abundance, density, or occupancy , Marques et al 2013, Lambert and McDonald 2014, Sovern et al 2014, Drake et al 2016. Surveys repeated on an annual basis are useful for longterm monitoring (e.g., Furnas and Callas 2015).…”

A spatially explicit capture–recapture estimator for single‐catch traps

“…1993; Marques et al. 2013) and there is often interest in understanding how and why density varies in space (Gaston 2003). Spatially explicit capture–recapture (SECR) models provide a tool for investigating this as they incorporate spatial information on where captures are made (Efford et al.…”