Reliable, verifiable and efficient monitoring of biodiversity via metabarcoding

TLDR

Biodiversity conservation requires knowledge of losses, locations, causes, and effective remedies, and metabarcoding can rapidly characterize species composition from bulk or environmental DNA samples. The study aims to validate metabarcoding by comparing it with three high‑quality standard datasets from tropical, subtropical, and temperate regions comprising 55,813 arthropod and bird specimens identified to species level. The authors compared metabarcoding results with three standard datasets from Malaysia, China, and the UK, each containing 55,813 arthropod and bird specimens identified to species level through 2,505 person‑hours of taxonomic expertise. Metabarcoding data correlated with standard datasets in alpha and beta diversity, yielding comparable policy conclusions for restoration ecology and systematic conservation planning, while offering greater taxonomic coverage, faster production, reduced reliance on taxonomic expertise, and third‑party auditability.

Abstract

Abstract To manage and conserve biodiversity, one must know what is being lost, where, and why, as well as which remedies are likely to be most effective. Metabarcoding technology can characterise the species compositions of mass samples of eukaryotes or of environmental DNA . Here, we validate metabarcoding by testing it against three high‐quality standard data sets that were collected in Malaysia (tropical), China (subtropical) and the United Kingdom (temperate) and that comprised 55,813 arthropod and bird specimens identified to species level with the expenditure of 2,505 person‐hours of taxonomic expertise. The metabarcode and standard data sets exhibit statistically correlated alpha‐ and beta‐diversities, and the two data sets produce similar policy conclusions for two conservation applications: restoration ecology and systematic conservation planning. Compared with standard biodiversity data sets, metabarcoded samples are taxonomically more comprehensive, many times quicker to produce, less reliant on taxonomic expertise and auditable by third parties, which is essential for dispute resolution.