Assessing the performance of DNA barcoding using posterior predictive simulations

Abstract

Accurate estimates of biodiversity are required for research in a broad array of biological subdisciplines including ecology, evolution, systematics, conservation and biodiversity science. The use of statistical models and genetic data, particularly DNA barcoding, has been suggested as an important tool for remedying the large gaps in our current understanding of biodiversity. However, the reliability of biodiversity estimates obtained using these approaches depends on how well the statistical models that are used describe the evolutionary process underlying the genetic data. In this study, we utilize data from the Barcode of Life Database and posterior predictive simulations to assess the performance of DNA barcoding under commonly used substitution models. We demonstrate that the success of DNA barcoding varies widely across DNA substitution models and that model choice has a substantial impact on the number of operational taxonomic units identified (changing results by ~4-31%). Additionally, we demonstrate that the widely followed practice of a priori assuming the Kimura 2-parameter model for DNA barcoding is statistically unjustified and should be avoided. Using both data-based and inference-based test statistics, we detect variation in model performance across taxonomic groups, clustering algorithms, genetic divergence thresholds and substitution models. Taken together, these results illustrate the importance of considering both model selection and model adequacy in studies quantifying biodiversity.

Highlighting Astyanax Species Diversity through DNA Barcoding

“…fasciatus complex) the number of clusters identified by the GMYC analyses was almost twice higher than NJ, suggesting a better capacity of this method in species separation. Some studies suggest that in complex groups the K2P model may underestimated the total number of clusters [79, 80]. Thus, we can suggest that herein GMYC, in general, was the better method for species separation.…”

Molecular operational taxonomic units reveal restricted geographic ranges and regional endemism in the Indo‐Pacific octocoral family Xeniidae

“…Pairwise genetic distances (Kimura 2‐parameter) among concatenated sequences were calculated using the DNADist program in phylip 3.69 (Felsenstein, ). Although use of the Kimura 2‐parameter model of evolution for genetic barcoding studies has been criticized (Srivathsan & Meier, ), simulations have suggested that it performs adequately when genetic divergence among sequences is low, as it is here (Barley & Thomson, ). mothur 1.29 (Schloss et al., ) was used to delimit MOTUs based on genetic distance thresholds of 0.1%–0.5% (McFadden, Brown, et al., ; McFadden, Reynolds, et al., ).…”

Coalescing molecular evolution andDNAbarcoding

“…To evaluate substitution model adequacy, Barley & Thomson () used posterior predictive simulations (Fig. , see Barley & Thomson for more details).…”

“…To evaluate substitution model adequacy, Barley & Thomson () used posterior predictive simulations (Fig. , see Barley & Thomson for more details). The analysis of posterior predictive distributions is widely used in Bayesian statistics to assess model plausibility, but remains underused in phylogenetics or in ecology (but see e.g.…”

“…This issue has been the focus of active research in phylogenetics/phylogenomics (Philippe et al 2011a), but has been comparatively seldom considered by the barcoding community (Collins & Cruickshank 2012). In this issue of Molecular Ecology, Barley & Thomson (2016) bridge this gap by assessing substitution model adequacy in describing genetic variation and estimating species richness from barcoding data. The work is mainly motivated by the wide use of the simplistic Kimura 2parameter model (K2P, Kimura 1980) for generating distance matrices, although its underlying assumptions are most likely violated by barcoding data (Galtier et al 2009).…”