Computational protocol: Adetogramma (Polypodiaceae), a new monotypic fern genus segregated from Polypodium

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[…] Fifty species from thirty-two genera (sensu ) of Polypodiaceae were included in our phylogenetic analyses (Appendix). Davallia solida (G.Forst.) Sw. (Davalliaceae) and Oleandra pistillaris (Sw.) C.Chr. (Oleandraceae) were used as outgroups, following . All vouchers and GenBank accessions are listed in the Appendix. Aligned data matrix was deposited in TreeBASE ( [...] Sequence electropherograms were edited using the STADEN package software (). Edited sequences were submitted to automated alignment with MUSCLE () and the resulting alignment was checked manually using MEGA 7 ().Datasets were analyzed using maximum likelihood (ML) and Bayesian inference (BI). Maximum likelihood was performed using IQ-TREE () with partitioned matrix (), automatic selection of the best-fit substitution model (using Bayesian Information Criterion, ), and branch support assessed with 10,000 ultrafast bootstrap replicates (). Best-fit models according to BIC were TIM2e+G4 for rbcL, K3Pu+G4 for rps4 gene and TVM+G4 for rps4-trnS IGS. For BI, a model-based phylogenetic analysis using Markov chain Monte Carlo-based was performed using MrBayes v3.2.2 (), treating each DNA region (rbcL, rps4 gene and rps4-trnS IGS) as separate partitions. An evolutionary model for each DNA region was selected in jModelTest 2 (; ), using the Bayesian Information Criterion (, Table ). For the rbcL dataset, the SYM+I+G was selected, and for the rps4 gene and rps4-trnS datasets the GTR+G model was selected. Each analysis consisted of two independent runs with four simultaneous Markov Chains run for 5,000,000 metropolis-coupled generations, starting with random initial trees and sampling one tree every 1000 generations. To improve swapping of chains the temperature parameter for heating the chains was lowered to 0.05. To check the convergence of the runs, ESS (effective sample size) and PSRF (potential scale reduction factor) were examined () using Tracer v.1.6 (). Based on the sampled parameter values examined, 2000 trees, including the ones generated during the burn-in phase, were discarded. Remaining trees were used to assess topology and posterior probabilities (PP) in a majority-rule consensus tree. Because PP in Bayesian analysis are not equivalent to bootstrap (BP) (), we used criteria similar to a standard statistical test, considering groups with PP > 95% as strongly supported, PP 90–95% as moderately supported and PP < 90% as weakly supported. Results were summarized on a majority rule consensus tree. […]

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