Efforts to explore optimal molecular methods for identifying plant mixtures, particularly pollen, are increasing. Pollen identification (ID) and quantification is important in many fields, including pollination ecology and agricultural sciences, but quantifying mixture proportions remains challenging. Traditional pollen ID using microscopy is time-consuming, requires expertise, and has limited accuracy and throughput. Molecular barcoding approaches being explored offer improved accuracy and throughput. The common approach, amplicon sequencing, employs PCR amplification to isolate DNA barcodes, but introduces significant bias, impairing downstream quantification. We apply a novel molecular hybridisation capture approach to artificial pollen mixtures, to improve upon current taxon ID and quantification methods. The method randomly fragments DNA, and uses RNA baits to capture DNA barcodes, which allows for PCR duplicate removal, reducing downstream quantification bias. Metabarcoding was tested using two reference libraries constructed from publicly available sequences; the matK plastid barcode, and RefSeq complete chloroplast references. Single barcode-based taxon ID did not consistently resolve to species or genus level. The RefSeq chloroplast database performed better qualitatively but had limited taxon coverage (relative to species used here) and introduced ID issues. At family level, both databases yielded comparable qualitative results, but the RefSeq database performed better quantitatively. A restricted matK database containing only mixture species yielded sequence proportions highly correlated with input pollen proportions, demonstrating that hybridization capture usefulness for metabarcoding and quantifying pollen mixtures. The choice of reference database remains one of the most important factors affecting qualitative and quantitative accuracy.

Globally, the diversity of arthropods and the plants upon which they rely are under increasing pressure due to a combination of biotic and abiotic anthropogenic stressors. Unfortunately, conventional survey methods used to monitor ecosystems are often challenging to conduct at large scales. Pan traps are a commonly used pollinator survey method and environmental DNA (eDNA) metabarcoding of pan-trap water may offer a high-throughput alternative to aid in the detection of both arthropods and the plant resources they rely on. Here, we examined if eDNA metabarcoding can be used to identify arthropod and plant species from pan-trap water, and invesitigated the effect of different DNA extraction methods. We then compared plant species identified by metabarcoding with observation-based floral surveys and also assessed the contribution of airborne plant DNA (plant DNA not carried by arthropods) using marble traps to reduce putative false positives in the pan trap dataset. Arthropod eDNA was only detected in 17% of pan trap samples and there was minimal overlap between the eDNA results and morphological identifications. In contrast, for plants, we detected 64 taxa, of which 53 were unique to the eDNA dataset, and no differences were identified between the two extraction kits. We were able to significantly reduce the contribution of airborne plant DNA to the final dataset using marble traps. This study demonstrates that eDNA metabarcoding of pan-trap water can detect plant resources used by arthropods and highlights the potential for eDNA metabarcoding to be applied to investigations of plant-animal interactions.

Anthropogenic activities are leading to changes in the environment at global scales, and understanding these changes requires rapid, high-throughput methods of assessment. Pollen DNA metabarcoding and related methods provide advantages in throughput and efficiency over traditional methods, such as microscopic identification of pollen and visual observation of plant-pollinator interactions. Pollen DNA metabarcoding is currently being applied to assessments of plant-pollinator interactions and their responses to land-use change such as increased agricultural intensity and urbanisation, surveillance of ecosystem change, and monitoring of spatiotemporal distribution of allergenic pollen. In combination with historical specimens, pollen DNA metabarcoding can compare contemporary and past ecosystems. Current technical challenges with pollen DNA metabarcoding include the need to understand the relationship between sequence read and species abundance, develop methods for determining confidence limits for detection and taxonomic classification, increase method standardisation, and improve of gaps in reference databases. Future research expanding the method to intraspecific identification, analysis of DNA in ancient pollen samples, and increased use of museum and herbarium specimens could open further avenues for research. Ongoing developments in sequencing technologies can accelerate progress towards these goals. Global ecological change is happening rapidly, and we anticipate that high-throughput methods such as pollen DNA metabarcoding are critical for assessing these changes and providing timely management recommendations to preserve biodiversity and the evolutionary and ecological processes that support it.