Optimizing passive acoustic sampling of bats in forests

Abstract

Passive acoustic methods are increasingly used in biodiversity research and monitoring programs because they are cost-effective and permit the collection of large datasets. However, the accuracy of the results depends on the bioacoustic characteristics of the focal taxa and their habitat use. In particular, this applies to bats which exhibit distinct activity patterns in three-dimensionally structured habitats such as forests. We assessed the performance of 21 acoustic sampling schemes with three temporal sampling patterns and seven sampling designs. Acoustic sampling was performed in 32 forest plots, each containing three microhabitats: forest ground, canopy, and forest gap. We compared bat activity, species richness, and sampling effort using species accumulation curves fitted with the clench equation. In addition, we estimated the sampling costs to undertake the best sampling schemes. We recorded a total of 145,433 echolocation call sequences of 16 bat species. Our results indicated that to generate the best outcome, it was necessary to sample all three microhabitats of a given forest location simultaneously throughout the entire night. Sampling only the forest gaps and the forest ground simultaneously was the second best choice and proved to be a viable alternative when the number of available detectors is limited. When assessing bat species richness at the 1-km2 scale, the implementation of these sampling schemes at three to four forest locations yielded highest labor cost-benefit ratios but increasing equipment costs. Our study illustrates that multiple passive acoustic sampling schemes require testing based on the target taxa and habitat complexity and should be performed with reference to cost-benefit ratios. Choosing a standardized and replicated sampling scheme is particularly important to optimize the level of precision in inventories, especially when rare or elusive species are expected.

Insectivorous bats respond to vegetation complexity in urban green spaces

“…Although acoustic sampling may misrepresent bats that use echolocation calls of lower intensity (e.g., Plecotus spec.) or be influenced by habitat features (Patriquin, Hogherg, Chruszcz, & Barclay, 2003;Schnitzler & Kalko, 2001), we are confident that, by sampling the entire night and using a standardized and replicated sampling scheme (Froidevaux, Zellweger, Bollmann, & Obrist, 2014;Skalak, Sherwin, & Brigham, 2012), we registered the majority of species and most of the activity in the green spaces.…”

Accounting for automated identification errors in acoustic surveys

“…The reduced costs of acoustic recorders and the huge increase in storage capacity have resulted in an exponential increase in the use of PAM on a very wide range of species groups within a few years (e.g. Froidevaux, Zellweger, Bollmann, & Obrist, 2014;Frommolt, 2017;Jeliazkov et al, 2016;Kalan et al, 2015;Nowacek, Christiansen, Bejder, Goldbogen, & Friedlaender, 2016;Stahlschmidt & Brühl, 2012). Such approaches are already widely used by researchers as well as by people working for environmental consultancies and government agencies for various biodiversity evaluations (Adams, Jantzen, Hamilton, & Fenton, 2012).…”

Emerging opportunities and challenges for passive acoustics in ecological assessment and monitoring

“…Although previously often overlooked in the PAM literature, there are now increasing efforts to systematically quantify sources of bias and improve survey standardisation. These include sensor calibration guidelines (Merchant et al., ), metadata standards (Roch et al., ), assessing the efficacy of sampling designs (Braun de Torrez et al., ; Froidevaux, Zellweger, Bollmann, & Obrist, ; Van Parijs et al., ), quantifying sensitivity differences between sensor models and over time due to environmental degradation (Adams et al., ; Merchant et al., ), and quantifying effects of sensor proximity to habitat features (e.g., vegetation, water surface, topography) on sound detection (Darras, Pütz, Fahrurrozi, Rembold, & Tscharntke, ; Farcas et al., ). Ultimately, these efforts should facilitate more robust, data‐driven approaches to analysing large, multisensor acoustic datasets, which currently tend to assume constant species detectability over space and time (e.g., Davis et al., ; Newson et al., ).…”

“…Braun de Torrez et al, 2017;Froidevaux, Zellweger, Bollmann, & Obrist, 2014;Van Parijs et al, 2009), quantifying sensitivity differences between sensor models and over time due to environmental degradation(Adams et al, 2012;Merchant et al, 2015), and quantifying effects of sensor proximity to habitat features (e.g., vegetation, water surface, topography) on sound detection(Darras, Pütz, Fahrurrozi, Rembold, & Tscharntke, 2016;Farcas et al, 2016).…”