A practical sampling design for acoustic surveys of bats

Abstract

Acoustic surveys are widely used for describing bat occurrence and activity patterns and are increasingly important for addressing concerns for habitat management, wind energy, and disease on bat populations. Designing these surveys presents unique challenges, particularly when a probabilistic sample is required for drawing inference to unsampled areas. Sampling frame errors and other logistical constraints often require survey sites to be dropped from the sample and new sites added. Maintaining spatial balance and representativeness of the sample when these changes are made can be problematic. Spatially balanced sampling designs recently developed to support aquatic surveys along rivers provide solutions to a number of practical challenges faced by bat researchers and allow for sample site additions and deletions, support unequal‐probability selection of sites, and provide an approximately unbiased local neighborhood‐weighted variance estimator that is efficient for spatially structured populations such as is typical for bats. We implemented a spatially balanced design to survey canyon bat (Parastrellus hesperus) activity along a stream network. The spatially balanced design accommodated typical logistical challenges and yielded a 25% smaller estimated standard error for the mean activity level than the usual simple random sampling estimator. Spatially balanced designs have broad application to bat research and monitoring programs and will improve studies relying on model‐based inference (e.g., occupancy models) by providing flexibility and protection against violations of the independence assumption, even if design‐based estimators are not used. Our approach is scalable and can be used for pre‐ and post‐construction surveys along wind turbine arrays and for regional monitoring programs. © 2011 The Wildlife Society.

The effect of call libraries and acoustic filters on the identification of bat echolocation

“…; Rodhouse et al. ). In addition, the USFWS is currently evaluating the call analysis software, BCID (Bat Call Identification, Inc., Kansas City, MO) for possible inclusion in the revised summer survey guidelines for Indiana bats (U.S.…”

Disease and community structure: white‐nose syndrome alters spatial and temporal niche partitioning in sympatric bat species

“…Although spatially balanced, stratified random sampling designs are ideal for making inference to a larger area (e.g. Rodhouse et al ., ), these designs are difficult to implement over a large area and often need to be balanced with low probability of detection of many bat species in certain habitat types within heterogeneous landscapes (Johnson et al ., ; Coleman, ). Accordingly, to maximize detection of multiple species in our study area, we prioritized selection of study sites within or near (<100 m) riparian areas based on: (1) findings from previous studies suggesting summer bat activity is generally positively correlated with riparian habitat (Vaughan et al ., ; Ford et al ., ; Menzel et al ., ; Rogers et al ., ) and (2) placement of acoustical devices in riparian areas has been shown to maximize detection probabilities for a variety of bat species present within Fort Drum (Owen et al ., ; Menzel et al ., ).…”

Evidence of region‐wide bat population decline from long‐term monitoring and Bayesian occupancy models with empirically informed priors

“…In 2016-2018 (Period 2), acoustic surveys were conducted in 190 grid cells, informed by a statistical power analysis (Banner, Irvine, Rodhouse, Donner, & Litt, 2019; Figure 1). During Period 2, grid cells were selected using the NABat spatially balanced (via the Generalized Random Tessellation Stratified design; Rodhouse et al, 2012;Rodhouse, Vierling, & Irvine, 2011;Stevens & Olsen, 2004) randomized master sample (Larsen, Olsen, & Stevens, 2008;Loeb et al, 2015). During Period 2, grid cells were selected using the NABat spatially balanced (via the Generalized Random Tessellation Stratified design; Rodhouse et al, 2012;Rodhouse, Vierling, & Irvine, 2011;Stevens & Olsen, 2004) randomized master sample (Larsen, Olsen, & Stevens, 2008;Loeb et al, 2015).…”