Summary1. Species identification via DNA barcodes has recently become an important and routine task in many biodiversity projects using DNA sequence data. 2. Here, we present BarcodingR, an integrated software package that provides a comprehensive implementation of species identification methods, including artificial intelligence, fuzzy-set, Bayesian and kmer-based methods, that are not readily available in other packages. 3. BarcodingR additionally provides new functions for barcode evaluation, barcoding gap analysis, delimitation comparison analysis, species membership analysis and consensus identification. 4. Comparison with other barcoding methods using 11 empirical data sets indicates that on average, FZKMER (implemented in BarcodingR) and one extant barcoding method BRONX outperform all other methods examined in this study. Two other methods, BP and FZ (both implemented in BarcodingR), present similar performance as SVM and BLOG, respectively, and all display better performance than that of Jrip. 5. The software of BarcodingR is open source under GNU General Public License and freely available for all major operating systems.

Understanding diversity patterns requires accounting for the roles of both historical and contemporary factors in the assembly of communities. Here, we compared diversity patterns of two moth assemblages sampled from Taihang and Yanshan mountains in Northern China and performed ancestral range reconstructions using the Multi‐State Speciation and Extinction model, to track the origins of these patterns. Further, we estimated diversification rates of the two moth assemblages and explored the effects of contemporary ecological factors. From 7,788 specimens we identified 835 species belonging to 23 families, using both DNA barcode analysis and morphology. Moths in Yanshan mountains showed higher species diversity than in Taihang mountains. Ancestral range analysis indicated Yanshan as the origin, with significant historical dispersals from Yanshan to Taihang. Asymmetrical diversification, population expansion, along with frequent and considerable gene flow were detected between communities. Moreover, dispersal limitation or the joint effect of environment filtering and dispersal limitation were inferred as main driving forces shaping current diversity patterns. In summary, we demonstrate that a multiscale (community, population and species level) analysis incorporating both historical and contemporary factors can be useful in delineating factors contributing to community assembly and patterning in diversity.

Species identification through DNA barcoding or metabarcoding has become a key approach for biodiversity evaluation and ecological studies. However, the rapid accumulation of barcoding data has created some difficulties: for instance, global enquiries to a large reference library can take a very long time. We here devise a two-step searching strategy to speed identification procedures of such queries. This firstly uses a Hidden Markov Model (HMM) algorithm to narrow the searching scope to genus level and then determines the corresponding species using minimum genetic distance. Moreover, using a fuzzy membership function, our approach also estimates the credibility of assignment results for each query. To perform this task, we developed a new software pipeline, FuzzyID2, using Python and C++. Performance of the new method was assessed using eight empirical data sets ranging from 70 to 234,535 barcodes. Five data sets (four animal, one plant) deployed the conventional barcode approach, one used metabarcodes, and two were eDNA-based. The results showed mean accuracies of generic and species identification of 98.60% (with a minimum of 95.00% and a maximum of 100.00%) and 94.17% (with a range of 84.40%-100.00%), respectively. Tests with simulated NGS sequences based on realistic eDNA and metabarcode data demonstrated that FuzzyID2 achieved a significantly higher identification success rate than the commonly used Blast method, and the TIPP method tends to find many fewer species than either FuzztID2 or Blast. Furthermore, data sets with tens of thousands of barcodes need only a few seconds for each query assignment using FuzzyID2. Our approach provides an efficient and accurate species identification protocol for biodiversity-related projects with large DNA sequence data sets.

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To explore the feasibility of assessing species diversity using DNA barcoding, we investigated this approach by focusing on moths species (Lepidoptera) in Suqian, China. The study evaluated community species richness and rank-abundance curves using the DNA barcoding method, and compared it with the traditional morphology method. Results indicated that there was no significant difference between the DNA barcode-based approach and the morphology-based approach. All DNA barcode-based rank-abundance curves gave similar and clear patterns when compared with morphology-based curves (Kolmogorov-Smirnov two sample test, P > 0.05). Our results indicate that the DNA barcode-based approach is able to be used to estimate species richness.