A new framework for analysing automated acoustic species detection data: Occupancy estimation and optimization of recordings post‐processing

Abstract

The development and use of automated species detection technologies, such as acoustic recorders, for monitoring wildlife are rapidly expanding. Automated classification algorithms provide cost‐ and time‐effective means to process information‐rich data, but often at the cost of additional detection errors. Appropriate methods are necessary to analyse such data while dealing with the different types of detection errors. We developed a hierarchical modelling framework for estimating species occupancy from automated species detection data. We explore design and optimization of data post‐processing procedures to account for detection errors and generate accurate estimates. Our proposed method accounts for both imperfect detection and false‐positive errors and utilizes information about both occurrence and abundance of detections to improve estimation. Using simulations, we show that our method provides much more accurate estimates than models ignoring the abundance of detections. The same findings are reached when we apply the methods to two real datasets on North American frogs surveyed with acoustic recorders. When false positives occur, estimator accuracy can be improved when a subset of detections produced by the classification algorithm is post‐validated by a human observer. We use simulations to investigate the relationship between accuracy and effort spent on post‐validation, and found that very accurate occupancy estimates can be obtained with as little as 1% of data being validated. Automated monitoring of wildlife provides opportunity and challenges. Our methods for analysing automated species detection data help to meet key challenges unique to these data and will prove useful for many wildlife monitoring programmes.

Automated identification of avian vocalizations with deep convolutional neural networks

“…Reviewing all apparent detections is onerous and unnecessary for most applications; Chambert et al. () concluded that review of as little as 1% of apparent detections could yield unbiased and reasonably precise estimates of site occupancy, provided that researchers employ a modeling approach that explicitly accounts for false detections.…”

The recent past and promising future for data integration methods to estimate species’ distributions

“…An extreme example of over‐dispersion occurs when counts are not actual individuals but signals (e.g. scat samples or bird calls) clumped around the locations of individuals (Chambert, Waddle, Miller, Walls, & Nichols, ).…”

Spatio‐temporal scaling of biodiversity in acoustic tropical bird communities

“…Due to constraints on signal-to-noise ratio (see also Darras et al 2016, Chambert et al 2018, automated methods are most likely to observe species from the foreground, rather than the background of audio recordings. Due to constraints on signal-to-noise ratio (see also Darras et al 2016, Chambert et al 2018, automated methods are most likely to observe species from the foreground, rather than the background of audio recordings.…”

“…This is because different species are differently detectable, as influenced by their behavior and vocal characteristics (Alldredge et al 2007, Pacifici et al 2008, Luther 2009, Robert et al 2012. the observation process (Darras et al 2016, Chambert et al 2018. We stress that this is a question different from that of physical presence/absence of a potentially Table 1.…”