Local habitats matter most: scale- and species-dependent drivers of butterfly communities in agroecosystems

Abstract Context Biodiversity responses to land-use and climatic change are shaped by processes operating across spatial and temporal scales. However, community-level patterns may mask interspecific variation in responses to environmental drivers in agricultural landscapes. Objectives We assessed the relative importance of environmental variables measured at local and landscape scales in explaining butterfly occurrence and abundance. Specifically, we asked whether (1) the importance of drivers varies among species and through time, and (2) species with similar life-history traits show consistent scale-dependent responses. Methods We analysed a butterfly monitoring dataset from agricultural landscapes, combined with environmental covariates including local habitat characteristics, landscape composition, climate and weather variables. Using hierarchical modelling of species communities, we quantified species-specific responses and evaluated changes in driver importance across four years spanning two decades. Results Overall, local habitat variables, including nectar plant availability, were the most important predictors of occurrence and abundance for most species. However, species differed markedly in both the direction and magnitude of their responses. In addition, temporal context across butterfly communities and functional context within the communities provided insights on species responses to environmental drivers. The relative importance of drivers changed over time: land-use variables increased in importance, whereas climate and weather variables importance decreased. Functional groupings revealed some environment scale-dependent patterns for overwintering stage and voltinism. Conclusions Our results demonstrate that the studied butterfly community dynamics are structured primarily by local habitat conditions, but in species-specific and temporally dynamic ways. Accounting for cross-scale and interspecific variation is essential for landscape management and biodiversity conservation planning.