![]() We compared diversity and compositional differences between forests and plantations (rubber and tea) for all MOTUs and for eight arthropod groups. Quality filtered reads were clustered into 3,624 MOTUs at ≥97% similarity and the taxonomy of each MOTU was predicted. A new primer combination and the MiSeq platform were used to amplify and sequence a wide variety of litter arthropods using simulated and real-world communities. We collected litter arthropods from 35 matched forest-plantation sites across Xishuangbanna, southwestern China. We investigated its utility in studying patterns of litter arthropod diversity and composition in the tropics. Metabarcoding potentially offers a rapid and cheap method of monitoring biodiversity, but real-world applications are few. We propose that metabarcoded leaf-litter and soil samples be widely tested as a candidate method for rapid environmental monitoring in terrestrial ecosystems. ![]() In fact, leaf-litter samples appear to be as or more powerful than Malaise-trap and canopy-fogging samples at detecting habitat differences. Here we show that while the taxonomic compositions of soil and leaf-litter samples are very different from aboveground samples, both types of samples provide similar ecological information, in terms of ranking sites by species richness and differentiating sites by beta diversity. A soil or leaf-litter sample can be collected in minutes, whereas an above- ground sample, such as from Malaise traps or canopy fogging, can require days to set up and run, during which time they are subject to theft, damage, and deliberate contamination. Now, sampling has become a rate-limiting step in biodiversity measurement, and in an effort to reduce turnaround time, we use arthropod samples from southern China and Vietnam to ask whether soil, leaf litter, and aboveground samples provide similar ecological information. Metabarcoding helps to remove the taxonomic impediment, which refers to the great logistical difficulties of describing and identifying species, and thus promises to improve our ability to detect and respond to changes in the natural environment. The targeted sequencing of taxonomically informative genetic markers, sometimes known as metabarcoding, allows eukaryote biodiversity to be measured rapidly, cheaply, comprehensively, repeatedly, and verifiably. These advantages increase the efficiency of large-scale surveillance of biodiversity trends and can alleviate many of the hidden-information problems that beset biodiversity-offset, environmental certification, and payment-for-environmental-service (PES) instruments. Compared with standard biodiversity datasets, which require individual identification of large numbers of specimens, metabarcoded samples are taxonomically more comprehensive, many times quicker to produce, lower-cost, less reliant on scarce taxonomic expertise, and auditable by third parties, which is essential for dispute resolution. We demonstrate that the metabarcode and standard datasets exhibit statistically correlated alpha- and beta-diversities, and we show that metabarcode and standard datasets produce similar policy conclusions for two conservation applications: restoration ecology and systematic conservation planning. Here we validate metabarcoding as a means of biodiversity monitoring by testing it against three high-quality standard datasets collected in Malaysia (tropical), China (subtropical), and the UK (temperate), that comprised 55,813 arthropod and bird specimens identified to species level with the expenditure of 2505 person-hours of taxonomic expertise. Metabarcoding technology can characterize the species compositions of mass samples of eukaryotes or environmental DNA. However, the great difficulty of quantifying biodiversity poses a serious roadblock to the development of effective policies to protect it. 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.
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