A Quantitative Investigation of the Effects of Landscape Composition and Spatial Configuration on Epigaeic Arthropods

In recent years, the homogenization and fragmentation of agricultural landscapes have intensified, leading to a decline in epigaeic arthropods. Landscape heterogeneity is a core factor regulating biodiversity, encompassing two key dimensions: composition heterogeneity and spatial configuration heterogeneity. Both landscape composition and spatial configuration heterogeneity influence the distribution of epigaeic arthropods through independent and joint effects. However, quantitative evidence addressing their relative and combined influences remains limited. This study was conducted across 30 independent landscape units (1 km × 1 km) in Changtu County. Pitfall traps were deployed across different habitat types, with three traps per habitat. The proportion of semi-natural habitats was used as an indicator of landscape compositional heterogeneity, while multiple landscape metrics were used to characterize spatial configuration heterogeneity. The effects of landscape heterogeneity on epigaeic arthropods were evaluated using two response variables: activity density (mean number of individuals captured per trap) and diversity (effective number of species). Variance partitioning analysis (VPA) and Bioenv analysis were applied to explore their individual and joint effects on epigaeic arthropods. The results showed that higher landscape composition heterogeneity was associated with greater activity density of epigaeic arthropods, but no significant correlation was found with arthropod diversity. In terms of landscape spatial configuration, patch density (PD) and landscape division index (DIVISION) constituted the optimal model explaining the activity density of epigaeic arthropods, highlighting the importance of patch structure within landscapes. Furthermore, spatial configurational heterogeneity showed a stronger independent contribution than compositional heterogeneity, although their joint effect accounted for the largest proportion of explained variation. These findings provide a theoretical basis for landscape optimization and biodiversity conservation in intensive agricultural regions of Northeast China.