Testing the effectiveness of rbcLa DNA-barcoding for species discrimination in tropical montane cloud forest vascular...

DNA-barcoding is a species identification tool that uses a short section of the genome that provides a genetic signature of the species. The main advantage of this novel technique is that it requires a small sample of tissue from the tested organism. In most animal groups, this technique is very effective. However, in plants, the recommended standard markers, such as rbcLa, may not always work, and their efficacy remains to be tested in many plant groups, particularly from the Neotropical region. We examined the discriminating power of rbcLa in 55 tropical cloud forest vascular plant species from 38 families (Oaxaca, Mexico). We followed the CBOL criteria using BLASTn, genetic distance, and monophyly tree-based analyses (neighbor-joining, NJ, maximum likelihood, ML, and Bayesian inference, BI). rbcLa universal primers amplified 69.0% of the samples and yielded 91.3% bi-directional sequences. Sixty-three new rbcLa sequences were established. BLAST discriminates 80.8% of the genus but only 15.4% of the species. There was nil minimum interspecific genetic distances in Quercus, Oreopanax, and Daphnopsis. Contrastingly, Ericaceae (5.6%), Euphorbiaceae (4.6%), and Asteraceae (3.3%) species displayed the highest within-family genetic distances. According to the most recent angiosperm classification, NJ and ML trees successfully resolved (100%) monophyletic species. ML trees showed the highest mean branch support value (87.3%). Only NJ and ML trees could successfully discriminate Quercus species belonging to different subsections: Quercus martinezii (white oaks) from Q. callophylla and Q. laurina (red oaks). The ML topology could distinguish species in the Solanaceae clade with similar BLAST matches. Also, the BI topology showed a polytomy in this clade, and the NJ tree displayed low-support values. We do not recommend genetic-distance approaches for species discrimination. Severe shortages of rbcLa sequences in public databases of neotropical species hindered effective BLAST comparisons. Instead, ML tree-based analysis displays the highest species discrimination among the tree-based analyses. With the ML topology in selected genera, rbcLa helped distinguish infra-generic taxonomic categories, such as subsections, grouping affine species within the same genus, and discriminating species. Since the ML phylogenetic tree could discriminate 48 species out of our 55 studied species, we recommend this approach to resolve tropical montane cloud forest species using rbcLa, as an initial step and improve DNA amplification methods.