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bioRxiv · 10.1101/2022.01.24.477478

Assessing flower-visiting arthropod diversity in apple orchards through environmental DNA flower metabarcoding and visual census

Gamonal, N.·Hedegaard Sorensen, D.·Chua, P. Y. S.·Sigsgaard, L.
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Abstract

Arthropods are essential to maintaining healthy and productive agricultural systems. Apples are cultivated worldwide and rely on pollination. Honey bees are used for pollination but wild bees and other arthropods also contribute to pollination. Flower visitors can also be natural enemies or herbivores. In some cases, such as Syrphids, a group can have more than one role, adults being pollinators and the larvae being natural enemies of pests. In the present study, we assessed the biodiversity of arthropod flower visitors in four Danish apple orchards and compared the use of molecular and non-molecular techniques to study arthropod communities in agricultural ecosystems. Arthropod DNA collected from apple flowers was analysed by metabarcoding and pollinators were recorded through visual assessment in the orchards. These techniques resulted in two complementary lists of arthropods detected. Non-bee arthropods constituted a big part of the community of apple flower visitors by both methods. Metabarcoding detected 14 taxa and had 72% species resolution while visual census identified 7 different taxa with 14% species resolution. This study showed the importance of using different sampling methodologies to obtain a more accurate picture of fauna present. It also revealed the high presence of non-bee arthropods visiting flowers in apple orchards. The outcome of our study provides information regarding the effects of management practices on arthropod biodiversity, which can contribute to informing on suitable management practices to increase crop yield and maintain healthy agricultural systems.

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Gamonal, N., Hedegaard Sorensen, D., Chua, P. Y. S., Sigsgaard, L.. 2022-01-24. Assessing flower-visiting arthropod diversity in apple orchards through environmental DNA flower metabarcoding and visual census. https://doi.org/10.1101/2022.01.24.477478

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