Bioclimatic and Land Use/Land Cover Factors as Determinants of Crabronidae (Hymenoptera) Community Structure in...

Crabronid wasps (Hymenoptera: Crabronidae) are ecologically important predators that provide various ecological services by regulating the arthropod populations, enhancing soil processes through nesting, serving as sensitive indicators of habitat condition, and providing pollen transfer for plants. However, as other invertebrates face biodiversity threats, these wasps might be under threat from environmental changes, and we need to assess the biodiversity patterns of these wasps in Yunnan Province. Unfortunately, no information is currently available about the pattern and factors responsible for the assemblages of these wasps within our study region. This study provides the first province-level assessment of habitat suitability, species richness, assemblage structure, and environmental determinants for Crabronidae in Yunnan by integrating species distribution modeling (SDM), multivariate clustering, and ordination analyses. More than 50 species were studied to assess habitat suitability in Yunnan using MaxEnt. Model performance was robust (AUC > 0.7). Suitability patterns varied distinctly among regions. Species richness peaked in southern Yunnan, particularly in the counties of Jinghong, Mengla, Menghai, and Jiangcheng Hani & Yi. Land use/land cover (LULC) variables were the dominant predictors for 90% of species, whereas precipitation-related variables contributed most strongly to the remaining 10%. Ward's hierarchical clustering grouped the 125 counties into three community assemblage zones, with Zone III comprising the most significant area. A unique species composition was found within a particular zone, and clear separation among zones based on environmental variation was supported by Principal Component Analysis (PCA), which explained more than 70% variability among zones. Furthermore, Canonical Correspondence Analysis (CCA) indicated that both LULC and climatic factors shaped community structure assemblages, with axes 1 and 2 explaining 70% of variance (p = 0.001). The most relevant key factors in each zone were precipitation variables (bio12, bio14, bio17), which were dominant in Zone I; for Zone II, temperature and vegetation variables were most important; and urban, wetland, and water variables were most important in Zone III.