A roadmap for survey designs in terrestrial acoustic monitoring

Abstract

Passive acoustic monitoring (PAM) is increasingly popular in ecological research and conservation programs, with high-volume and long-term data collection provided by automatized acoustic sensors offering unprecedented opportunities for faunal and ecosystem surveys. Practitioners and newcomers interested in PAM can easily find technical specifications for acoustic sensors and microphones, but guidelines on how to plan survey designs are largely scattered over the literature. Here, we (i) review spatial and temporal sampling designs used in passive acoustic monitoring, (ii) provide a synthesis of the crucial aspects of PAM survey design and (iii) propose a workflow to optimize recording autonomy and recording schedules. From 1992 to 2018, most of the 460 studies applying PAM in terrestrial environments have used a single recorder per site, covered broad spatial scales and rotated recorders between sites to optimize sampling effort. Continuous recording of specific diel periods was the main recording procedure used. When recording schedules were applied, a larger number of recordings per hour was generally associated with a smaller recording length. For PAM survey design, we proposed to (i) estimate memory/battery autonomy and associated costs, (ii) assess signal detectability to optimize recording schedules in order to recover maximum biological information and (iii) evaluate cost-benefit scenarios between sampling effort and budget to address potential biases from a given PAM survey design. Establishing standards for PAM data collection will improve the quality of inferences over the broad scope of PAM research and promote essential standardization for cross-scale research to understand long-term biodiversity trends in a changing world.

Testing a New Passive Acoustic Recording Unit to Monitor Wolves

“…Our detectability findings are directly relevant to occupancy studies in distinguishing between nondetection and absence of wild wolves (i.e., probability of detection). For additional details regarding survey designs for terrestrial acoustic monitoring, we refer readers to Sugai et al (2019). We suggest additional research be conducted in areas of different environmental variables (e.g., wind, temperature, habitat, topography; Wiley and Richards 1978) to determine the AudioMoth's performance under varying conditions and also when fitted with a parabolic dish.…”

SAFE Acoustics: An open‐source, real‐time eco‐acoustic monitoring network in the tropical rainforests of Borneo

“…Every ecological monitoring effort has its own unique challenges and goals, and developing a one‐size‐fits‐all solution is not possible (Sugai, Desjonquères, Silva, & Llusia, 2020). Therefore, we have open sourced all the technology developed during our study, including the hardware design and firmware, and complete source code for the web server and public‐facing website.…”

Precision as a metric for acoustic survey design using occupancy or spatial capture-recapture

“…In their most basic usage, the recorders act as a replacement for field observers in order to collect survey data at a lower cost (Lemckert et al, 2005;Williams et al, 2018), but they have great potential, opening up entirely new monitoring possibilities, such as analysing the ultrasound range for bats (Sugai et al, 2018), localising the calling animals (Collier et al, 2010), or using automated sound detectors to speed up processing (Potamitis et al, 2014). This has sparked various developments in acoustic survey methodology: studies have investigated the optimal number and placement of recorders (e.g., Pérez-Granados et al (2018); see also the review in Sugai et al (2019)), compared hardware (Rempel et al, 2013;Pérez-Granados et al, 2019), and optimised recording time and duration (Cook and Hartley, 2018;Hagens et al, 2018;Pérez-Granados et al, 2018). The large amounts of data generated by ARUs has also prompted interest in algorithms to perform call denoising, detection and recognition (Acevedo et al, 2009;Priyadarshani et al, 2018b;Stowell et al, 2018).…”