Linking remote sensing data to the estimation of pollination services in agroecosystems

Bioacoustic IoT Sensors as Next-Generation Tools for Monitoring: Counting Flying Insects through Buzz

The global loss of biodiversity is an urgent concern requiring the implementation of effective monitoring. Flying insects, such as pollinators, are vital for ecosystems, and establishing their population dynamics has become essential in conservation biology. Traditional monitoring methods are labour-intensive and show time constraints. In this work, we explore the use of bioacoustic sensors for monitoring flying insects. Data collected at four Italian farms using traditional monitoring methods, such as hand netting and pan traps, and bioacoustic sensors were compared. The results showed a positive correlation between the average number of buzzes per hour and insect abundance measured by traditional methods, primarily by pan traps. Intraday and long-term analysis performed on buzzes revealed temperature-related patterns of insect activity. Passive acoustic monitoring proved to be effective in estimating flying insect abundance, while further development of the algorithm is required to correctly identify insect taxa. Overall, innovative technologies, such as bioacoustic sensors, do not replace the expertise and data quality provided by professionals, but they offer unprecedented opportunities to ease insect monitoring to support conservation biodiversity efforts.

Linking remote sensing data to the estimation of pollination services in agroecosystems

Wild bees are key providers of pollination services in agroecosystems. The abundance of these pollinators and the ecosystem services they provide rely on supporting resources in the landscape. Spatially explicit models that quantify wild bee abundance and pollination services in food crops are built on the foundations of foraging and nesting resources. This dependence limits model implementation as land-cover maps and pollination experts capable of evaluating habitat resource quality are scarce. This study presents a novel approach to assessing crop pollination services using remote sensing data (RSD) as an alternative to the more conventional use of land-cover data and local expertise on spatially explicit models. We used landscape characteristics derived from remote sensors to qualify nesting resources in the landscape and to evaluate the delivery of pollination services by mining bees (Andrena spp.) in 30 fruit orchards located in the Flemish region of Belgium. For this study, we selected mining bees for their importance as local pollinators and underground nesting behavior. We compared the estimated pollination services derived from RSD with those derived from the conventional qualification of nesting resources. We did not observe significant differences (p = 0.68) in the variation in mining bee activity predicted by the two spatial models. Estimated pollination services derived from RSD and conventional characterizations explained 69% and 72% of the total variation, respectively. These results confirmed that RSD can deliver nesting suitability characterizations sufficient for estimating pollination services. This research also illustrates the importance of nesting resources and landscape characteristics when estimating pollination services delivered by insects like mining bees. Our results support the development of holistic agroenvironmental policies that rely on modern tools like remote sensors and promote pollinators by considering nesting resources.