Using Flower eDNA Metabarcoding to Identify the Effects of Forest Structure and Microclimate on Flower-Visiting Arthropods

Abstract

Recording flower-associated taxa can be challenging in contexts where plant–arthropod interactions are limited, thereby constraining the assessment of their ecological responses. For example, forests typically provide fewer floral resources for pollinators than other ecosystems, such as grasslands, while understory microclimates influence the spatiotemporal dynamics of insect activity, further complicating their detection. In this study, we use environmental DNA (eDNA) to address these challenges and investigate the influence of forest microclimate, density, and tree composition on the diversity of flower-associated arthropods in a Swedish forest. We used two flowering plant species, Fragaria vesca and Trifolium pratense, as sentinel plants, translocating them to a mixed forest across 40 plots spanning a gradient of forest density and broadleaf tree dominance. The metabarcoding of flower eDNA documented a high diversity of arthropods with very specific communities in different forest plots. This high species turnover suggests either short eDNA persistence on flowers or unmeasured ecological factors structuring these communities. We found that forest structure, particularly light availability in broadleaf-dominated open plots, positively influenced species richness of arthropods detectable in flowers, while microclimate had a small impact. These effects varied between plant species, likely due to differences in flower visitor communities. Our study also offers significant methodological insights into using flower eDNA for detecting flower-associated taxa. We also emphasize the need for optimized sampling and DNA extraction processes to enhance the likelihood of successful amplification. We show that the number of flowers pooled in the same DNA extraction positively influences the number of taxa detected. By improving methods in flower eDNA sampling and analysis, future studies can more accurately assess the ecological interactions and conservation needs of forest environments and other ecosystems.