The monitoring of bats across the world is mostly conducted using invasive mist-netting, whereby vertical nets are placed to capture bats mid-flight. Many studies have demonstrated how this approach causes sampling bias, is labor-intensive and increases the risk of white-nose syndrome fungus, Pseudogymnoascus destructans, transmission among bats. Increasingly, acoustic devices are being employed to collect data on bat activity and richness. Community-based monitoring is an important data collection source for bat monitoring programs in countries such as the UK (National Bat Monitoring Program), whereby walking bat transects are conducted using bat detectors. Since the launch of smartphone devices to record and auto-identify bat echolocation calls, the quality of data collection that community members can collect has increased significantly, however, this approach is seldom used to generate data in scientific studies. In our study, we have showcased how our study design paired with state of the art acoustic monitoring devices, can be applied to community-based monitoring of bats across the world. Through employing smartphone acoustic devices, we have determined how primary and secondary vegetation cover are predictors of bat species occurrence and identified the importance of riverine and deciduous swamp habitats for rare bat species in southwestern Ontario.