Data analysis
The F and R sequences were assembled in Geneious Prime. Sequences ofOstreobium obtained from NCBI were included in the comparation and analyses (Table 1). The alignment was performed in the MEGA X program using the MuscleG algorithm, considering identical sequences when performing the alignment were combined into a representative one. The result obtained was the basis for making the selection of evolutionary models in the jModelTest 2.1.2 software (Darriba et al. 2012), where the result was the GTR model with a Gamma distribution of rates, according to the Bayesian Information criterion. The phylogenetic relationships among the individuals were inferred using a Bayesian inference (BI) method (Rambaut et al. 2018) and maximum likelihood (ML) (Hoang et al. 2018). The outgroups were Codium fragile(AB103021), Caulerpa taxifolia (KF419327) and Caulerpa webbiana (KY062946); in both cases the selection was based on papers that showed the nearness of the lineage (Jackson et al. 2018; Verbruggen et al. 2009) and authors like Gonzalez-Zapata (2018) and Gutner-Hotch (2011) uses as base of their analyses. The ML tree topology was estimated in IQ-Tree v.2.0 (Minh et al., 2020). Subsequently, the Bayesian tree was useful as support to estimate divergence times. To complain this, use BEASt v. 2.0, the substitution rate parameter assumed a normal prior distribution whit mean= 0.00056 , SD=0.0001 (Villarreal y Renner 2014) substitutions/site/lineage/million years. The molecular clock model for the dataset was selected based on the likelihood radio test in MEGA 6.0 (Tamura et al. 2013) and the result implies the use of a uncorrelated relaxed clock, set the number of generations for the MCMC (Markov chain Monte Carlo) analysis at 50 million and sampling every 1000 generations. Finally we used tracer to verify the quality of the analysis and obtain parameters with an effective sample size, ESS >200 (Rambaut et al. 2018). The substitution model corresponded to GTR together with a gamma distribution of 4 rate categories and the maximum credibility tree was computed in TREEANNOTATOR (Drummond et al. 2012) removing 10% of the trees as burn-in. The nodes indicate the posterior probability of the Bayesian analysis, accepting a suitable grouping at probabilities ≥ 0.95. Data on the origin of Ostreobium were based on prior studies (Del Cortona et al. 2020; Jackson et al. 2018; Marcelino y Verbruggen 2016) with a range of between 470 Ma and 500 Ma for its origin. To point ideas about the grouping of the species, the GMYC (General Model Yule Coalescent) and mPTP (multi-rate Poisson Tree Process) analyses were employed. In this case the setting of GMYC analysis was 100000000 in MCMC, sampling every 1000 steps and discarded 10% as burn-in. The lineages that were congruent in both methods were considered as potential species.
The last analysis included a biogeographical history hypothesis ofOstreobium inferred using parsimony-based statistical dispersal vicariance (S-DIVA), Bayesian binary MCMC analysis (BBM), and dispersal–extinction–cladogenesis analysis (DEC). These three models were implemented in RASP v. 3.0 (Yu et al. 2015). The initial parameter was setting as default, based on the minimum result for AIC number was made selection of analysis DEC+J, and the maximum clade credibility (MCC) tree produced in BEAST analysis was used as the input tree. To evaluate the degree to which Ostreobium and its hosts were co-evolving, ML analyses of a Markov k-state parameter performed in Mesquite v. 2.75 (Maddison and Maddison 2011) were used. Parsimony analysis weights the contribution of each character state to a node equally, while ML uses the information provided by the lengths of the branches to estimate the probability that a given character state exists at each node in the tree (Cunningham, Zhu, y Hillis 1998). The ancestral patterns of change in the hosts were compared, paying special attention to the lineages that showed passage between more than two hosts and the genera that belonged to different geographic regions.
Table 1. NCBI accession codes for the sequences of rbcLgene of Ostreobium spp. present in different substrates along locations in the world. The number of sequences was obtained after filtering the repeat sequences and the unidentified host. The average length was obtained after alignment.