Not only hedgerows, but also flower fields can enhance bat activity in intensively used agricultural landscapes

Agri-environment schemes (AES) have become important tools for farmland biodiversity conservation, providing suitable habitats, resources, and connectivity within agricultural landscapes. Bats are rarely studied in relation to AES effectiveness in contrast to birds, even though their presence and activity as biological control agents on insects, especially pest species, can be important for agricultural crops. While the role of hedgerows for bat occurrence and activity, as well as for their prey's diversity and abundance has been widely studied, the role of other AESs such as flower fields remains unclear. We monitored the activity of the main functional groups (edge, narrow and open space foragers) using ultrasound recorders, as well as potential prey abundances using light traps, across 35 study sites representing different AES in Central Germany from late July to September 2018. The sampled AES consisted of annual flower fields, mixed flower fields (with annual and biennial vegetation), perennial flower fields (sown every 5 years), hedgerows (surrounded by meadows and agricultural fields), and were compared to winter wheat (control) in a balanced design. Bat activity over hedgerows increased threefold for edge space foragers and sevenfold for narrow space foragers compared to wheat fields. Compared to wheat fields, narrow space forager activity increased fourfold over perennial flower fields, threefold over annual and twofold over mixed flower fields. This group's activity over hedgerows also increased almost threefold compared to mixed flower fields. However, the number of feeding buzzes and prey abundance did not differ significantly between AES. We detected foraging group-specific differences in bat activity between the studied AES. Thus, to promote bats in agricultural landscapes and to ensure their biological control services, it is important to establish more AES, such as hedgerows and flower fields, to increase their diversity and connectivity in intensively used agricultural landscapes.The datasets are in .csv format, which open in the R Software (R Development Core Team, 2019).Funding provided by: Nemzeti Kutatási Fejlesztési és Innovációs HivatalCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100011019Award Number: KKP 133839Bats were recorded with ultrasound recorders (Song Meter SM2Bat + model, Wildlife Acoustics, Inc.) Potential insect prey was collected using light traps, each lamp possessed four small LED lights: green, blue and two UV (ELEKON AG, 2018), with wavelengths of λmax ∼530 nm, 465 nm, 340 nm, and 348 nm, and a total illuminance of 5 lx, powered by two AA NiMH batteries (Villarroya-Villalba et al., 2021). We used QGIS 3.6 (Development Team, 2019) and information obtained from land use maps (SLA, 2018) to digitise agricultural fields and landscape elements based on satellite images (Map data ©2019 Google based on GoogleSatellite plugin). All statistical analyses were conducted in R 3.6.0 (R Development Core Team, 2019)