Testing efficacy of distance and tree-based methods for DNA barcoding of grasses (Poaceae tribe Poeae) in Australia

Abstract

In Australia, Poaceae tribe Poeae are represented by 19 genera and 99 species, including economically and environmentally important native and introduced pasture grasses [e.g. Poa (Tussock-grasses) and Lolium (Ryegrasses)]. We used this tribe, which are well characterised in regards to morphological diversity and evolutionary relationships, to test the efficacy of DNA barcoding methods. A reference library was generated that included 93.9% of species in Australia (408 individuals, [Formula: see text] = 3.7 individuals per species). Molecular data were generated for official plant barcoding markers (rbcL, matK) and the nuclear ribosomal internal transcribed spacer (ITS) region. We investigated accuracy of specimen identifications using distance- (nearest neighbour, best-close match, and threshold identification) and tree-based (maximum likelihood, Bayesian inference) methods and applied species discovery methods (automatic barcode gap discovery, Poisson tree processes) based on molecular data to assess congruence with recognised species. Across all methods, success rate for specimen identification of genera was high (87.5-99.5%) and of species was low (25.6-44.6%). Distance- and tree-based methods were equally ineffective in providing accurate identifications for specimens to species rank (26.1-44.6% and 25.6-31.3%, respectively). The ITS marker achieved the highest success rate for specimen identification at both generic and species ranks across the majority of methods. For distance-based analyses the best-close match method provided the greatest accuracy for identification of individuals with a high percentage of "correct" (97.6%) and a low percentage of "incorrect" (0.3%) generic identifications, based on the ITS marker. For tribe Poeae, and likely for other grass lineages, sequence data in the standard DNA barcode markers are not variable enough for accurate identification of specimens to species rank. For recently diverged grass species similar challenges are encountered in the application of genetic and morphological data to species delimitations, with taxonomic signal limited by extensive infra-specific variation and shared polymorphisms among species in both data types.

Fig 1. Intra- and inter-specific pairwise genetic distances (K80 model) for the Internal Transcribed Spacer (ITS) marker.

Data for all Australian tribe Poeae genera with more than two species represented in this study are shown. Inter, Inter-specific; Intra, Intra-specific.

Fig 2. Success rates (percentages) for specimen identification of tribe Poeae taxa using distance-based methods.

Success rates were calculated for all genera (A.) and species (B.) collectively and for species within single genus datasets (C.–E.) using nearest neighbour (A., B., C.), best close match (A., B., D.), and threshold ID (A., B., E.) methods based on individual (rbcL, matK, ITS) and concatenated (rbcL+matK, rbcL+matK+ITS) DNA barcode markers. BCM, Best close match; Chlor, Chloroplast dataset (rbcL+matK); Comb, Combined dataset (rbcL+matK+ITS); ITS, Internal transcribed spacer; NN, Nearest neighbour; TID, Threshold Identification.

Fig 3. The Bayesian inference of phylogenetic relationships among Australian tribe Poeae based on the ITS marker.

Support values are provided above the branches including bootstrap (maximum likelihood) and posterior probabilities (Bayesian inference) before and after the forward slash, respectively.

Fig 4. Molecular entities distinguished based on genetic sequence data from the ITS marker estimated by ABGD and PTP methods.

Analyses were completed for Australian tribe Poeae genera with more than two native species. The maximum likelihood phylogeny for Australian tribe Poeae based on the ITS dataset is inset in the top left corner with genera analysed indicated with uppercase letters and shown in detail. Genetic entities are indicated by a black bar to the right of the individuals contained within that entity for ABGD (left) and PTP (right) analyses. ABGD, Automated barcode gap discovery; ITS, Internal transcribed spacer; PTP, Poisson tree processes.