BATS: Bat-aggregated time series—A python-based toolkit for landscape-level monitoring of free-tailed bats via weather radar

Abstract

The US operates a system of 160 S-band Doppler weather radars known as NEXRAD (NEXt generation weather RADar) that continuously monitors the airspace around the majority of the United States and outlying territories. These radars detect and track birds, insects, and bats. Free-tailed bats (Genus Tadarida) provide considerable ecosystem services through their voracious insect consumption; but their movements and ecosystem service provision have historically been difficult to track/study in space and time. We introduce ‘BATS’, a Python toolkit that streamlines the process of downloading, classifying, and aggregating time series of free-tailed bats across large landscapes. BATS retrieves data from NOAA's weather radar data repositories and classifies the processed radar data using a pre-trained ML trained to detect and classify radar echoes associated with free-tailed bats. We trained various machine learning approaches at classifying pixels containing free-tailed bats and compared the effectiveness across approaches. With an AUC of 0.963, the neural network approach is highly effective in identifying free-tailed bats in NEXRAD data over our study sites in California and Texas. Furthermore, BATS is capable of quickly distilling 6 months of radar data from a single tower (3.5 Tb) into a single 15 Mb-sized map of bat occurrence, contingent on available computing resources. BATS will help scientists and stakeholders identify areas of high bat occupancy at the landscape level over long periods of time. This ability has the potential to increase our understanding of the economic and agricultural value of these species.