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Harnessing Environmental DNA to Explore Frugivorous Interactions: A Case Study in Papaya (Carica papaya) and Pineapple (Ananas comosus)

Pritam Banerjee,J. Maity,Nalonda Chatterjee,Sven Weber,Gobinda Dey,Raju Kumar Sharma,Chien-Yen Chen

Published 2025 in Environmental DNA

ABSTRACT

Plant–animal interactions (PAIs) are critical in natural and agricultural ecosystems, mediating energy flow with both positive and negative interactions. Traditional methods of tracking PAIs, such as morphological identification and camera trapping, are limited in speed and scalability, posing challenges for comprehensive biodiversity monitoring. Recently, environmental DNA (eDNA) metabarcoding has emerged as a promising technique for detecting species interactions non‐destructively. This pilot study explores the application of eDNA metabarcoding to investigate frugivorous interactions involving 18 partially consumed and three intact fruits of each Carica papaya and Ananas comosus. Metabarcoding of mitochondrial COI gene fragments generated 796,234 paired‐end reads representing 117 ASVs spanning diverse taxonomic groups, including Metazoans, Protozoans, Algae, Fungi, and Bacteria. After filtering for animal taxa, 41 ASVs were retained, dominated by Arthropoda (~97%). Major frugivores included Drosophila, Zaprionus, and Bactrocera species. Additional detections included beetles, ants, parasitoid wasps, and vertebrates such as Acridotheres javanicus, Callosciurus erythraeus, and Bandicota indica. Moreover, consumed fruits showed high insect (~90%–95%) and mammal (~4%–5%) DNA, while intact fruits were dominated by rotifers (~75%–80%). Distinct communities were found between pineapple and papaya, with 15 and 11 unique ASVs, respectively, and only one ASV was unique to intact fruits. Alpha and beta diversity analyses confirmed the differences in community structure between fruit types. Despite the limited sample size, our findings demonstrate the potential of fruit‐surface eDNA to monitor frugivory and species interactions. Future studies should scale this approach across seasons and crop types to assess its potential in long‐term biodiversity and pest management monitoring.

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