The influence of environmental parameters on the performance and detection range of acoustic receivers

Abstract

Summary Acoustic telemetry is being increasingly used to study the ecology of many aquatic organisms. This widespread use has been advanced by national and international tracking programs that coordinate deployment of passive acoustic telemetry networks on a regional and continental scale to detect tagged animals. While it is well‐known that environmental conditions can affect the performance of acoustic receivers, these effects are rarely quantified despite the profound implications for tag detection and hence the ecological inferences. Here, we deployed eight receivers at different depths within the water column and at different orientations (hydrophone up or down) and 12 tags 200–800 m from the receivers for 234 days to investigate how the tag detection range of acoustic receivers varied through time and under different meteorologic and oceanographic conditions. The study showed that receiver depth and orientation, and time since deployment had the largest effect on the detection range. Thermocline gradient and depth, and wind speed were the environmental factors most affecting detection range, while wind direction, precipitation and atmospheric pressure had negligible or no effect. Comparison of results to a proposed general acoustic theory model and previous studies showed that findings from specific habitat types cannot be generalised and applied across other habitats or environments. A good understanding of the acoustic coverage and temporal variations in relation to environmental conditions are crucial to accurate interpretation of results, and ensuing management recommendations. We recommend that each study include stationary reference tags to measure changes in detection probability with time, help refine detection range, and be used to improve confidence in the reporting and interpretation of the data.

Environmental factors influence the detection probability in acoustic telemetry in a marine environment: results from a new setup

“…The present study demonstrates that there is a good detection probability up to 200 m, but it quickly reduces beyond this distance. This detection range is in the range of previous reports, which encompass both higher (Hobday & Pincock, 2011;Huveneers et al, 2016) and similar range values (Welsh et al, 2012;Cagua et al, 2013, Stocks et al, 2014. Some other publications have reported a broad range of distances within the same study (How & de Lestang, 2012;Cagua et al, 2013;Gjelland & Hedger, 2013).…”

“…It was mainly influenced by noise and wind speed in relation to distance, the interaction between tilt and azimuth, and current speed, which is in agreement with Gjelland & Hedger (2013) and Huveneers et al (2016). In contrast, Stocks et al (2014) listed wave height as the principal factor affecting detection range.…”

“…Both anthropogenic and natural sound sources may mask the transmission signal (De Jong et al, 2011;Huveneers et al, 2016;Gjelland & Hedger, 2017), and it is difficult to attribute the impact to a specific sound source. As the sound sources, and thus the SNR, strongly vary in both spatial and temporal context, the influence of noise on the detection probability may strongly differ between receiver stations in the Belgian network.…”

“…temperature, salinity, substrate type, vegetation, suspended particulate matter) (Jensen et al, 1994;How & de Lestang, 2012;Kessel et al, 2014;Gjelland & Hedger, 2017). In addition, both anthropogenic and natural sound sources may mask the signal as the signal-to-noise ratio becomes too low (De Jong et al, 2011;Huveneers et al, 2016). The BPNS, for instance, is a shallow ocean basin with sandy sediments and strong tidal currents and winds (Baeye et al, 2011;Fettweis et al, 2012).…”

Habitat use and preference of adult perch (Perca fluviatilis L.) in a deep reservoir: variations with seasons, water levels and individuals

“…Hence, we brought a correction to smooth this artificial spatial variability by using the probabilities of positioning calculated by Roy et al (2014) in winter when the mixing of water was complete. In addition, the performance of a system can vary in time due to the modifications of the thermocline gradient and thermocline depth (Huveneers et al, 2016). …”