Estimating bird density using passive acoustic monitoring: a review of methods and suggestions for further research

Passive acoustic monitoring is a non‐invasive tool for automated wildlife monitoring. This technique has several advantages and addresses many of the biases related to traditional field surveys. However, locating animal sounds using autonomous recording units (ARUs) can be technically challenging and therefore ARUs have traditionally been little employed to estimate animal density. Nonetheless, several approaches have been proposed in recent years to carry out acoustic‐based bird density estimations. We conducted a literature review of studies that used ARUs for estimating bird densities or bird abundances in order to describe the applications and improve future monitoring programmes. We detected a growing interest in the use of ARUs for estimating bird density in the last 6 years (2014–19), with a total of 31 articles assessing the topic. The most common approach was to estimate the relationship between the number of vocalizations per recording time with bird density or bird abundance estimated in the field (61%). In 26 studies (79%), bird estimates obtained by human surveyors agreed with those obtained using ARUs. Some approaches have proven able to reduce biases in acoustic surveys, such as considering imperfect detection (spatially explicit capture–recapture, using microphone arrays), applying paired acoustic sampling to control for different sampling radius between humans and ARUs, or including relative sound level measurements that allow researchers to estimate bird distance to recorder. However, several studies did not include any covariates to reduce existing biases and some did not estimate the sampling radius of the recorder, which may hamper future comparisons between human and ARU surveys. Future studies should include a measurement of the sampling radius of the recorder employed to be able to obtain density estimations using ARUs. Finally, we provide some guidelines to improve the applicability of ARUs to infer bird population estimates in future studies.