Construction, validation, and application of nocturnal pollen transport networks in an agro-ecosystem: a comparison using light microscopy and DNA metabarcoding

1. Moths are globally relevant as pollinators but nocturnal pollination remains poorly understood. Plant–pollinator interaction networks are traditionally constructed using either flower‐visitor observations or pollen‐transport detection using microscopy. Recent studies have shown the potential of DNA metabarcoding for detecting and identifying pollen‐transport interactions. However, no study has directly compared the realised observations of pollen‐transport networks between DNA metabarcoding and conventional light microscopy. 2. Using matched samples of nocturnal moths, we constructed pollen‐transport networks using two methods: light microscopy and DNA metabarcoding. Focussing on the feeding mouthparts of moths, we developed and provide reproducible methods for merging DNA metabarcoding and ecological network analysis to better understand species interactions. 3. DNA metabarcoding detected pollen on more individual moths, and detected multiple pollen types on more individuals than microscopy, although the average number of pollen types per individual was unchanged. However, after aggregating individuals of each species, metabarcoding detected more interactions per moth species. Pollen‐transport network metrics differed between methods because of variation in the ability of each to detect multiple pollen types per moth and to separate morphologically similar or related pollen. We detected unexpected but plausible moth–plant interactions with metabarcoding, revealing new detail about nocturnal pollination systems. 4. The nocturnal pollination networks observed using metabarcoding and microscopy were similar yet distinct, with implications for network ecologists. Comparisons between networks constructed using metabarcoding and traditional methods should therefore be treated with caution. Nevertheless, the potential applications of metabarcoding for studying plant–pollinator interaction networks are encouraging, especially when investigating understudied pollinators such as moths