Linking remote sensing data to the estimation of pollination services in agroecosystems. Issue 5 (19th May 2022)

• Record Type: Journal Article
• Authors: Ariza, Daniel; Meeus, Ivan; Eeraerts, Maxime; Pisman, Matti; Smagghe, Guy
• Is Part Of: Ecological applications. Volume 32:Issue 5(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-05-19
• Subjects: crop pollination -- landscape characteristics -- landscape ecology -- nesting resources -- remote sensing -- spatial modeling -- wild bees; Ecology -- Periodicals; Environmental protection -- Periodicals; Biology, Economic -- Periodicals; 577.05
• DOI: 10.1002/eap.2605
• Languages: English
• ISSNs: 1051-0761

Abstract

Abstract: 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.