Bat Noise Scrubber Bias Adjustment Using the Rogan-Gladen Estimator and Bayesian Inference

Abstract

The number of bat call files recorded in acoustic surveys may be used to assess comparative bat activity levels over time and among habitats. Acoustic signal processing can segregate bat files from noise files and thus quickly provide an estimate of the number of bat files in a large sample of recordings. However, false positive and false negative classifications may result in a biased estimate requiring adjustment, as inaccurate bat numbers may impact bat conservation decisions. Previous research has ranked software classification accuracy in comparison to the visual classification of spectrograms. Small classification errors can result in considerable bias in software-derived estimates of the number of bat call files in a sample. Estimation bias may not have a linear relationship to the percentage of files containing bats, requiring unique correction coefficients. The focus of this note is to 1) illustrate patterns of bias that may result from noise scrubbing and 2) to illustrate the application of two methods of bias adjustment, the Rogan-Gladen estimator and Bayesian inference. The expected bias of four noise scrubbing tools from the literature, each of different measured accuracy, was plotted over a simulated range of true bat file prevalence while holding constant the accuracy of each scrubber. Rogan-Gladen bias adjustment was accurate for all four noise scrubbers. Bayesian bias adjustment showed low overall error, with some inflation at very low bat file prevalence. Caveats in the use of both bias adjustment methods are discussed.