Monitoring migratory birds in stopover habitat: assessing the value of extended duration audio recording

ABSTRACT Because birds are frequently detected by sound, autonomous audio recorders (called automated recording units or ARUs) are now an established tool in addition to in-person observations for monitoring the status and trends of bird populations. ARUs have been evaluated and applied during breeding seasons, and to monitor the nocturnal flight calls of migrating birds. However, birds behave differently during migration stopover than during the breeding season. Here we present a method for using ARUs to monitor land birds in migration stopover habitat.We conducted in-person point counts next to continuously recording ARUs, and compared estimates of the number of species detected and focal species relative abundance from point counts and ARUs. We used a desk-based audio bird survey method for processing audio recordings, which does not require automated species identification algorithms. We tested two methods of using extended duration ARU recording: surveying consecutive minutes, and surveying randomly selected minutes.Desk-based surveys using randomly selected minutes from extended duration ARU recordings performed similarly to point counts, and better than desk-based surveys using consecutive minutes from ARU recordings. Surveying randomly selected minutes from ARUs provided estimates of relative abundance that were strongly correlated with estimates from point counts, and successfully showed the increase in abundance associated with migration timing. Randomly selected minutes also provided estimates of the number of species present that were comparable to estimates from point counts.ARUs are an effective way to track migration timing and intensity in remote or seasonally inaccessible migration stopover habitats. We recommend that desk-based surveys use randomly sampled minutes from extended duration ARU recordings, rather than using consecutive minutes from recordings. Our methods can be immediately applied by researchers with the skills to conduct point counts, with no additional expertise necessary in automated species identification algorithms.