Skip to main content
Dryad logo

Data from: Using full-length metabarcoding and DNA barcoding to infer community assembly for speciose taxonomic groups: a case study

Citation

Hao, Mengdi et al. (2020), Data from: Using full-length metabarcoding and DNA barcoding to infer community assembly for speciose taxonomic groups: a case study, Dryad, Dataset, https://doi.org/10.5061/dryad.tqjq2bvvm

Abstract

How insect communities are assembled in nature remains largely unknown. In particular, whether habitat filtering or competition serves as the main mechanism in forming insect communities is rarely subject to an in-depth investigation. One bottleneck lies in the difficulty of species identification when dealing with a large number of diverse insects. However, High-Throughput Sequencing (HTS) technology coupled with classic DNA barcoding offers a great opportunity to infer community assembly for this speciose group. In this study, using 13,909 full-length barcodes obtained by Sanger sequencing or the SOAPBarcode metabarcoding method, we showed that competition was the main assembly mechanism for the moth communities studied in temperate forests of China. The two sequencing methods showed highly consistent results with regards to both diversity composition and community assembly mechanism. Significant phylogenetic signals and structure suggested that the focal moth communities were the result of the non-neutral assembly process, which was further confirmed by results of neutral assembly test that accounted for immigration and speciation rates. In conclusion, HTS coupled with a well-curated DNA barcode library can facilitate community assembly inferences, especially for speciose taxonomic groups.

Methods

Please see the published article and contact the author if unavailable.

Usage Notes

Please see README files for specific information.

Funding

Natural Science Foundation of China, Award: 3,177,250,131,272,340,000,000,000,000,000,000,000,000

China National Funds for Distinguished Young Scientists, Award: 31425023

Chinese Universities Scientific Fund, Award: 2017QC114

Program for Changjiang Scholars and Innovative Research Team in University, Award: IRT_17R75

Academy for Multidisciplinary Studies, Capital Normal University

Support Project of High-level Teachers in Beijing Municipal Universities, Award: IDHT20180518