Lakhasly

Rhododendron species from the eastern Himalayan region of North Sikkim, India, with R. arboreum also collected from Himachal Pradesh. Specimens of 11 Indian Rhododendron species were sequenced, with 9 species analyzed for the first time. The study also included 87 species of Rhododendron and outgroup species from Washington, USA. Existing sequence information was used to understand the phylogenetic positions of the Indian species in a larger context.

Molecular data :
Total genomic DNA was extracted using Qiagen's DNeasy Plant Mini Kit (Qiagen GmbH, Hilden, Germany) from young leaves collected from natural populations as silica-dried samples. Ltd) for portions of the RPB2-I gene following Goetsch et al. (2005).

The PCR protocol (modified after Goetsch et al. 2005) used was:
Initial denaturation at 94 °C for 4 min, then a 35 cycle step with denaturation at 94 °C for 45 s, annealing temperature gradient from 54 to 59 °C for 45 s, and extension at 72 °C for 1 min 15 s followed by a final extension of 7 min at 72 °C; for some species, a Hot Start activation (Sambrook and Russell 2001) of 5 min was used to minimize non-specific amplification. Overlapping regions from both the sense and the antisense primer sequences were used to build contigs (DNA Baser or Codon Code Aligner) after trimming the poor reads at the ends; in some cases single pass sequences was also used.

2013. (see data matrix: Online Resource 4). The newly obtained 11 sequences were combined with a dataset of 88 sequences retrieved from TreeBASE: M2277 (Goetsch et al. We partitioned the data into regions of introns and exons (Online Resource 3).

Therefore, hooked and straight states were merged in binary coding (stamen, style flexion) and three-state coding (style flexion). Morphological data
Morphological variations in corolla, androecium and gynoe-cium of Rhododendron were analysed based on observations in the field as well as from published and online sources (Online Resource 2). Our identification of characters and character states for morphological coding of floral symmetry in Rhododendron is mostly based on Cullen (1980) with additions and modifications based on our own observations (Online Resource 2).

Genetic trees were obtained using Maximum Probability Analysis (ML) using RAxML (Stamatakis et al. 2008) and the Bayese analysis using MrBayes 3.2.5 (Huelsenbeck and Ronquist 2001; Ronquist and Huelsenbeck 2003). I used the HKY+I+G (external regions) and HKY+G (internal regions) models as defined by JModelTest (Posada 2008). Four operations of the Marcov Monte Carlo (MCMC) series were conducted for 8.6 x 10 generations, with samples taken every 1000 generations. Virtualisation precursors were used in MrBayes 3.2.5. The convergence of operations was evaluated using Tracer 1.5 (Rambaut and Drummond 2004). Two criteria were used to assess the convergence of operations: the average standard deviation of division frequencies below 0.01 for division frequencies, and a possible scale reduction factor (PSRF) of 1. The first 25% of the trees were excluded, and the remaining trees were used to build a consensus tree and reliable groups of trees at 99%. ML analyses (1000 bis) were performed on a subset of the dataset using the GTR+GAMMA model (without HKY) with the RAxML-HPC2 8.2.6 application via the CIPRES Science Gateway (Miller et al. 2010). Trees obtained from Bayesi and ML estimates were scrutinised using FigTree 1.4.2 (Rambaut 2014)

Tests of correlation
The morphological data were tested for statistical correlation between pigment patterns and other floral traits.Correlations among the four symmetry traits were also tested (in BayesTraits V2) similar to the above ML method described.The log BF (Bayes Factor) was used to determine whether the difference in the marginal likelihood of the two models was significant [log BF > 2 was taken to favour the dependent model (Pagel and Meade 2014, Bayes-Traits V2)].We performed phylogenetic tests of correlation using ML and Bayesian methods as implemented in BayesTraits V2
(Meade and Pagel 2014).For ML analy-ses, we used the likelihood ratio test (LRT) to determine whether the difference in the likelihood scores between the independent and dependent model was significant; the model showing a significantly higher likelihood was accepted.The number of ML attempts per tree was 10.

Reconstruction of ancestral states
We used maximum parsimony (MP: Mesquite 3.04, Maddison and Maddison 2015), maximum likelihood and Bayesian (ML: BayesTraits V2, Meade and Pagel 2014) methods to reconstruct ancestral states.For MP analyses we obtained a strict consensus of the 99% most credible trees, randomly resolved this tree to get 100 resolved trees and estimated branch-lengths using ML (PAUP* 4.0a147: Swofford 2002).We explored the use of different methods in order to uncover the range of possible directions of evolution because MP and statistical methods are known to differ in their estimates (Cunningham et al. 1998).These nodes belong to the two major clades A and B (Goe-tsch et al. 2005) and include the two major subgenera Hyme-nanthes and Rhododendron.In ML analyses on BayesTraits, we used the entire set of most credible trees to generate likelihood values for character states at particular nodes and calculated the mean likelihood value.We used 1.01 x 10.