AMMOD-Plant metabarcoding of volumetric air samplers

The development of high-throughput and quality techniques to monitor plant biodiversity and plant-insect interactions has become critical with the documented decline in biodiversity and abundance of both plants and insects. Metabarcoding has the potential to outperform conventional methods for large-scale biomonitoring due to lower cost, potential for automation, and lack of technician bias. Here we present the optimization of plant trace monitoring within the AMMOD stations via metabarcoding, with two different collection methods, airborne pollen from Hirst traps and plant traces from the preservative ethanol of Malaise traps. A new wind pollen trap was developed that allows autonomous operations in the field over a longer period than previous models, and offers the more options for sampling intervals. Recommendations for laboratory processing are made, and an optimized data analysis workflow are described.