Abstract
All ecosystems face ecological challenges in this century. Therefore, it is becoming increasingly important to understand the ecology and degree of local adaptation of functionally important Arctic-alpine biomes by looking at the most diverse taxon of metazoans: the Arthropoda. This is the first study to utilize metabarcoding in the Alpine tundra, providing insights into the effects of micro-environmental parameters on alpha- and beta-diversity of arthropods in such unique environments. To characterize arthropod diversity, pitfall traps were set at three middle-alpine sampling sites in the Scandinavian mountain range in Norway during the snow-free season in 2015. A metabarcoding approach was then used to determine the small-scale biodiversity patterns of arthropods in the Alpine tundra. All DNA was extracted directly from the preservative EtOH from 27 pitfall traps. In order to identify the controlling environmental conditions, all sampling locations were equipped with automatic data loggers for permanent measurement of the microenvironmental conditions. The variables measured were: air temperature [°C] at 15 cm height, soil temperature [°C] at 15 cm depth, and soil moisture [vol.%] at 15 cm depth. A total of 233 Arthropoda OTUs were identified. The number of unique OTUs found per sampling location (ridge, south-facing slope, and depression) was generally higher than the OTUs shared between the sampling locations, demonstrating that niche features greatly impact arthropod community structure. Our findings emphasize the fine-scale heterogeneity of arctic-alpine ecosystems and provide evidence for trait-based and niche-driven adaptation. The spatial and temporal differences in arthropod diversity were best explained by soil moisture and soil temperature at the respective locations. Furthermore, our results show that arthropod diversity is underestimated in alpine-tundra ecosystems using classical approaches and highlight the importance of integrating long-term functional environmental data and modern taxonomic techniques into biodiversity research to expand our ecological understanding of fine- and meso-scale biogeographical patterns.