Land-use change is having a negative effect on pollinator communities, and these changes in community structure may have unexpected impacts on the functional composition of those communities. Such changes in functional composition may impact the capacity of these assemblages to deliver pollination services, affecting the reproduction of native and wild plants. However, elucidating those relationships requires studies in multiple spatial scales because effects and consequences are different considering biological groups and interactions. In that sense, by using a multi-trait approach, we evaluated whether the landscape structure and/or local environmental characteristics could explain the functional richness, divergence, and dispersion of bee communities in agroecosystems. In addition, we investigated to what extent this approach helps to predict effects on pollination services. This study was conducted in an agroecosystem situated in the Chapada Diamantina region, State of Bahia, Brazil. Bees were collected using two complementary techniques in 27 sample units. They were classified according to their response traits (e.g., body size, nesting location) and effect traits (e.g., means of pollen transportation, specialty in obtaining resources). The Akaike information criterion was used to select the best models created through the additive combination of landscape descriptors (landscape diversity, mean patch shape, and local vegetation structure) at the local, proximal, and broad landscape levels. Our results indicate that both landscape heterogeneity and configuration matter in explaining the three properties of bee functional diversity. We indicate that functional diversity is positively correlated with compositional and configurational heterogeneity. These results suggest that landscape and local scale management to promote functional diversity in pollinator communities may be an effective mechanism for supporting increased pollination services.
Management‐dependent effects of pollinator functional diversity on apple pollination services: a response‐effect trait approach
