Bats provide a number of ecosystem services in agricultural areas, including the predation of night-flying insects, for which they are estimated to save agricultural industries billions of dollars per year. Intensive agriculture has many negative effects on biodiversity, and it is important to understand how wildlife exploit available habitats to allow persistence in these human-modified landscapes. To better evaluate the effectiveness of bats' pest-controlling services, and to increase understanding of bat foraging behavior in these historically open grassland landscapes, we estimated bat activity and insect abundance in and around crop fields in southeast Nebraska, USA. Specifically, we used a novel acoustic grid sampling approach to document and visualize spatiotemporal activity patterns by different bat species over agricultural fields and forested habitat along crop field edges. Bat activity was highest in areas with the most forested edge habitat, and sites with more trees and water typically had more species present. Bat species and activity was low in isolated forest fragments and sites with minimal habitat edges, but overall insect volume did not decline away from field edges, suggesting that ecosystem services provided by bats likely diminish not because of a decline in resource availability, but because of the lack of structure. Woodland interfaces are important habitats for bats, and the invasion of grasslands by woody species in the Great Plains has increased available bat habitat, and therefore services provided by bats, but with a cost to grasslands and the ecological services they provide. However, although bats are clearly important insect predators that benefit agricultural activities, our ability to quantify the ecosystem services they provide will be greatly improved with a more nuanced understanding of how their activity varies relative to habitat structure and scale within the landscapes where these services are required.
Bats play crucial ecological roles and provide valuable ecosystem services, yet many populations face serious threats from various ecological disturbances. The North American Bat Monitoring Program (NABat) aims to use its technology infrastructure to assess status and trends of bat populations, while developing innovative and community‐driven conservation solutions. Here, we present NABat ML, an automated machine‐learning algorithm that improves the scalability and scientific transparency of NABat acoustic monitoring. This model combines signal processing techniques and convolutional neural networks (CNNs) to detect and classify recorded bat echolocation calls. We developed our CNN model with internet‐based computing resources (‘cloud environment’), and trained it on >600,000 spectrogram images. We also incorporated species range maps to improve the robustness and accuracy of the model for future ‘unseen’ data. We evaluated model performance using a comprehensive, independent, holdout dataset. NABat ML successfully distinguished 31 classes (30 species and a noise class) with overall weighted‐average accuracy and precision rates of 92%, and ≥90% classification accuracy for 19 of the bat species. Using a single cloud‐environment computing instance, the entire model training process took <16 h. Synthesis and applications. Our convolutional neural network (CNN)‐based model, NABat ML, classifies 30 North American bat species using their recorded echolocation calls with an overall accuracy of 92%. In addition to providing highly accurate species‐level classification, NABat ML and its outputs are compatible with Bayesian and other statistical techniques for measuring uncertainty in classification. Our model is open‐source and reproducible, enabling future implementations as software on end‐user devices and cloud‐based web applications. These qualities make NABat ML highly suitable for applications ranging from grassroots community science initiatives to big‐data methods developed and implemented by researchers and professional practitioners. We believe the transparency and accessibility of NABat ML will encourage broad‐scale participation in bat monitoring, and enable development of innovative solutions needed to conserve North American bat species.
Roadsides can be vectors for tree invasion within rangelands by bisecting landscapes and facilitating propagule spread to interior habitat. Current invasive tree management in North America’s Great Plains focuses on reducing on-site (i.e., interior habitat) vulnerability through on-site prevention and eradication, but invasive tree management of surrounding areas known to serve as invasion vectors, such as roadsides and public rights-of-ways, is sporadic. We surveyed roadsides for invasive tree propagule sources in a central Great Plains grassland landscape to determine how much of the surrounding landscape is potentially vulnerable to roadside invasion, and by which species, and thereby provide insights into the locations and forms of future landcover change. Invasive tree species were widespread in roadsides. Given modest seed dispersal distances of 100–200 m, our results show that roadsides have potential to serve as major sources of rangeland exposure to tree invasion, compromising up to 44% of rangelands in the study area. Under these dispersal distances, funds spent removing trees on rangeland properties may have little impact on the landscape’s overall vulnerability, due to exposure driven by roadside propagule sources. A key implication from this study is that roadsides, while often neglected from management, represent an important component of integrated management strategies for reducing rangeland vulnerability to tree invasion.
Concentrated PhotoVoltaic (CPV) systems have the potential to minimize system cost as compared with traditional PV systems by replacing a portion of the solar cells with less-expensive reflective material. In this project, a CPV prototype was designed, built, constructed, and tested that utilized far Infrared (IR) transmissive mirror film for the reflective material. The film was adhered to a series of panels which articulated to track the sun throughout the day and concentrated 2 to 3 times the sunlight onto a set of stationary PV arrays. The system was designed such that it could be installed on a residential rooftop. When compared to a PV array on a similarly sloped roof without concentration, the system produced 51% more energy output over the course of several days. BACKGROUND AND NEED
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