Using surface environmental DNA to assess arthropod biodiversity within a forested ecosystem

Abstract

Terrestrial arthropods are a diverse taxonomic group of significant ecological and economic importance. Our ability to understand the diversity that comprises this group is hampered by the variety of sampling techniques and high level of taxonomic expertise required to identify individual species. DNA metabarcoding approaches have potential to overcome these challenges but have been mainly limited to studies where DNA is directly extracted from trapped individuals. We posit that collection of environmental DNA (eDNA) deposited on vegetation surfaces could provide an alternative method of conducting metabarcoding‐based arthropod inventories. In this study, we illustrate the promise of characterizing arthropod biodiversity based on eDNA collected from terrestrial plant surfaces. We collected 40 paired samples using two novel eDNA sampling techniques—tree bark and foliage sampling—in a New Jersey, USA, pine barrens forest. Metabarcoding using two primer sets revealed significantly higher taxonomic richness for the 16S versus COI primer set (1077 vs. 650 molecular operational taxonomic units; MOTUs), as well as higher richness and diversity in foliage versus bark samples. Accumulation curves suggest that our samples captured about half of the available MOTU‐level diversity. Matching to reference databases revealed 28 arthropod orders, 181 families, 353 genera, and 292 species. Despite having lower MOTU‐level richness, the COI primer set revealed more taxa that were identified to species (197 vs. 115) and genus (227 vs. 173) thanks to a more complete reference database. The two primer sets and sampling substrates showed distinct community compositions that differed in important ecological traits (feeding guild, body size), demonstrating the utility of a multi‐faceted sampling and analytical approach. Our study highlights the value of exploiting eDNA left on plant surfaces via metabarcoding for contributing to rapid arthropod inventories, and thus realizing a range of ecological research and management goals.

Metabarcoding advances agricultural invertebrate biomonitoring by enhancing resolution, increasing throughput, and facilitating network inference

“…Aside from capturing whole specimens, methods are being developed to apply metabarcoding for alternative sources of eDNA that avoid disturbing invertebrate populations altogether; for example, detecting species from airborne eDNA (Pumkaeo, Takahashi and Iwahashi, 2021;Clare et al, 2022;Roger et al, 2022), or eDNA in rain and on foliage (Valentin et al, 2020;Yoneya, Ushio and Miki, 2022;Allen et al, 2023;Weber et al, 2023). This overcomes the need to kill specimens during conventional entomological trapping, which occurs for morphological identification and bulk sample metabarcoding, and alleviates the problem of biomonitoring where rare species or sensitive populations are known to occur (Chua et al, 2023).…”

Plant‐derived environmental DNA complements diversity estimates from traditional arthropod monitoring methods but outperforms them detecting plant–arthropod interactions

“…eDNA is typically recovered from plant surfaces by washing it off with water and filtering the wash-off (Allen et al, 2022;Macher et al, 2023;Valentin et al, 2020). However, washes are probably limited to taxa from the surface of the plant.…”

Development of novel LAMP and qPCR assays for rapid and specific identification of Bronze birch borer (Agrilus anxius)

“…However, surveys using environmental DNA (eDNA) detection tools offer a solution to aid in finding target populations at low or rare abundance. These methods have advanced aquatic sampling research for decades by reliably increasing detection probabilities through their ability to pick up DNA signatures left behind by target organisms without ever having to see them (Darling & Blum, 2007;Jerde et al, 2011) and have also gained traction in invasive species detection (Allen et al, 2021;Valentin et al, 2018;Valentin, Fonseca, et al, 2020; and biodiversity monitoring of terrestrial landscapes more recently (Allen et al, 2023).…”