Temporal Study of Environmental DNA and Acoustic Data Reveals Coexistence of Sympatric Bat Species in a North American Ecosystem

ABSTRACT Bats are a species‐rich mammalian order that provide a host of ecosystem services, but presently face threats from habitat loss, disease, climate change, and insect declines. Bat species often co‐occur with other ecologically similar bats, making them a suitable group in which to study niche overlap and partitioning. This study aimed to compare different non‐invasive sources of data on wildlife populations, while examining dietary, temporal, and spatial partitioning patterns among sympatric bat species. We used two different methods to assess niche partitioning among insectivorous bats at a site in the San Francisco Bay Area, California: (1) eDNA sequencing of bat feces that were collected weekly from a bat roost, and (2) nightly acoustic recordings of ultrasonic bat calls from recorders at multiple sites. Both the eDNA and acoustic data were collected over the course of an entire roosting season in 2020. We hypothesized that the insectivorous bats at this site would rely on one or more niche partitioning mechanisms to promote interspecific coexistence and limit competition. We found evidence of fine‐scale spatial partitioning of the broad community of bat species in our study area based on acoustic data, as well as temporal differences in activity of different species. The two species using the roosting site, Tadarida brasiliensis and Eptesicus fuscus , displayed some differences in the identities and relative abundances of prey species consumed, but both ultimately exhibited a strong reliance on dipterans and aquatic‐dependent insects. We demonstrate differences between the acoustic data and eDNA data, which has implications for how such datasets may be interpreted in future research. The study finds evidence of some types of niche partitioning in this community and characterizes baseline interactions between species, providing a foundation for future efforts to non‐invasively monitor for unexpected biological change in local ecosystems.