Environmental and spatial factors influencing patterns in stink bug communities in soybean

Globally, stink bugs are economically important soybean pests. Understanding the factors influencing stink bug community patterns may help predict and manage outbreaks. Using a large-scale survey dataset, we analyzed the beta-diversity patterns in soybean stink bug communities (6 species, 89 fields spread over 6838 km² in western Maryland, West Virginia & Virginia) and the spatial and environmental factors (topographic, climatic and land use) influencing the patterns.

Redundancy analysis (RDA) showed that environmental variables significantly explained variation in stink bug communities (R²adj=0.25, p=0.01). The first RDA axis (96% of the total explained variance) was correlated with elevation (r = - 0.85), forest cover proportion at 5km and 2.5km radii (r = - 0.74 each), and maximum temperature (r = 0.53). The invasive Halyomorpha halys received positive scores along Axis 1. Halyomorpha halys was negatively associated with elevation and forest cover proportion while being positively associated with daily maximum temperature and developed areas proportion. The native Acrosternum hilare received negative scores along Axis 1 and was positively associated with elevation and forest cover proportion.

Variation partitioning using Moran's Eigenvector Maps as spatial predictors identified significant fractions of pure broad-scale spatial (R²adj=0.10, p=0.005), pure environment (R²adj = 0.08, p = 0.01), broad-scale structured environment (R²adj=0.4, p=0.015), fine-scale structured environment (R²adj=0.02, p=0.03). These results indicate prominent effects of both environmental drivers (altitude, forest cover, and temperature) and large-scale spatial processes on the beta-diversity patterns of soybean stink bug communities, and have implications for landscape scale management of stink bugs as soybean pests.