Computational protocol: A New Species of Microhyla (Anura: Microhylidae) from Nilphamari, Bangladesh

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Protocol publication

[…] Measurements were taken with digital calipers to the nearest 0.02 mm. Characters were measured following the definitions of Islam et al. [], including the following: SVL (snout-vent length); HL (head length); HW (head width); MN (distance from back of mandible to nostril); SL (snout length); MFE (distance from back of mandible to front of the eye); MBE (distance from back of mandible to back of the eye); IN (internarial distance); IOD (interorbital distance); EN (distance from front of eyes to the nostril); NS (nostril–snout length); EL (eye length); UEW (maximum width of upper eyelid); HAL (hand length); FAL (forearm length); THIGHL (thigh length); TL (tibia length); TFOL (length of tarsus and foot); FOL (foot length); IMTL (inner metatarsal tubercle length). The trait definitions are depicted graphically in . Webbing formula follows that of Glaw and Vences [].Morphological comparisons were done using ratios, as well as by using multivariate statistical methods. The ratios were used to allow comparisons to other Southern Asian Microhyla species, as well to provide diagnostic criteria for field-identification. The formal multivariate analyses were used to test for morphological differences among the newly described species and its closest morphological and phylogenetic congeners (M. ornata and M. rubra). Multivariate analysis of variance (MANOVA) was used to test if species centroids were significantly different, followed by discriminant function analysis (DFA). A principal component analysis (on correlations) and simple bivariate scatterplots (of diagnostic characters) were used to further explore and illustrate morphometric differences among the species. One-way ANOVAs followed by Tukey’s HSD tests were used test if the PC scores differed significantly among species. All statistical analyses were performed using JMP Pro 10.0.2 software (SAS Institute Inc. USA) [...] Whole genomic DNA was extracted from muscle tissue (N = 7) using a silica-based method [] and stored at -20°C. PCR amplification and sequencing of the 16S rRNA gene was done with primers F51 (5'-CCCGCCTGTTTACCAAAAACAT-3') and R51 (5'-GGTCTGAACTCAGATCACGTA-3'); Sumida et al. []. PCR conditions for amplification consisted of 5.72 μl of dH2O, 2 μl of 5 × buffer, 0.08 μl of dNTP, 0.2 μl of Phire enzyme (Thermo Fisher), 0.5 μl of each primer and 1 μl of template DNA, in a total reaction volume of 10 μl. The PCR program was comprised of a preliminary denaturation step at 98°C for 30s, followed by 34 cycles of 98°C for 10s, 55°C for 10s, 72°C for 30s, and ended with final extension at 72°C for 1 min. PCR products were purified by using ExoSap IT (USB Corporation, Cleveland, OH, USA) and sequenced at the Institute for Molecular Medicine Finland (FIMM). Sequence ambiguities were edited by aligning forward and reverse reads using the Geneious 5.6.5 program []. Final sequences were deposited in GenBank and their accession numbers are provided in .The nucleotide sequences of the 16S gene were aligned with sequences for other Microhyla species available from GenBank (N = 21, ), with ClustalW built into BIOEDIT [, ] using the default parameters. The final sequence length used for further phylogenetic analyses was 446 bp. Sequence divergences (uncorrected p-values) were calculated using Mega v 5.5.6 [], excluding the sites with indels. The phylogenetic analyses were performed using Maximum likelihood (ML) and Bayesian inference methods. The GTR + I + G substitution model was selected as the optimal nucleotide substitution model for both methods. For the ML analysis, branch support was evaluated by using 1000 bootstrap replicates [] as implemented in Mega v 5.5.6 []. For the Bayesian analysis, one million generations were run (Markov chain Monte Carlo method) with a sampling frequency of 100, as implemented in MrBayes 3.1.2 []. Convergence of the runs was assessed by the average split frequency of standard deviations (<0.01) and by checking the potential scale reduction factors (~ 1.0) for all model parameters. 25% of the trees were discarded as burn-in and the remaining trees were used to generate the 50% majority rule consensus tree and to estimate the Bayesian posterior probabilities.We estimated the divergence time between the Microhyla species by generating a time tree using the program BEAST 1.8.1 []. Our time tree was calibrated by using two nodal constraints that correspond to: (1) M. fissipes separated from M. mymensinghensis before 10.53 (5.48–16.95) mya [] and (2) 1.7 million year old fossil series from the genus Gastrophryne (Family: Microhylidae) [, ]. In this case, a normal distribution with standard deviation of 0.5 was used to constrain the node leading to G. olivacea and G. mazatlanensis as having occurred between 0.72 and 2.68 mya. This calibration point was used as many fossils of G. olivacea and G. mazatlanensis have been reported from Pleistocene deposits ranging from 0.24 to 1.8 mya []. The divergence time and node ages were estimated using a lognormal relaxed molecular clock in a Bayesian framework. Markov chain Monte Carlo analyses were run for ten million generations, sampled every 1000 generations. We used Tracer 1.5 [] to view the BEAST 1.8.1 output and to verify that all parameters were adequately sampled (effective sample sizes > 200). A burn-in of 1000 was used before summarizing the time trees. […]

Pipeline specifications

Software tools JMP Pro, Geneious, Clustal W, BioEdit, MEGA-V, MrBayes
Databases FIMM
Applications Miscellaneous, Phylogenetics, GWAS
Organisms Microhyla ornata