Practical guidelines for Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC).

Phylogenetic estimation is, and has always been, a complex endeavor. Estimating a phylogenetic tree involves evaluating many possible solutions and possible evolutionary histories that could explain a set of observed data, typically by using a model of evolution. Values for all model parameters need to be evaluated as well. Modern statistical methods involve not just the estimation of a tree, but also solutions to more complex models involving fossil record information and other data sources. Markov chain Monte Carlo (MCMC) is a leading method for approximating the posterior distribution of parameters in a mathematical model. It is deployed in all Bayesian phylogenetic tree estimation software. While many researchers use MCMC in phylogenetic analyses, interpreting results and diagnosing problems with MCMC remain vexing issues to many biologists. In this manuscript, we will offer an overview of how MCMC is used in Bayesian phylogenetic inference, with a particular emphasis on complex hierarchical models, such as the fossilized birth-death (FBD) model. We will discuss strategies to diagnose common MCMC problems and troubleshoot difficult analyses, in particular convergence issues. We will show how the study design, the choice of models and priors, but also technical features of the inference tools themselves can all be adjusted to obtain the best results. Finally, we will also discuss the unique challenges created by the incorporation of fossil information in phylogenetic inference, and present tips to address them.

Pollen sterols are associated with phylogeny and environment but not with pollinator guilds.

Phytosterols are primary plant metabolites that have fundamental structural and regulatory functions. They are also essential nutrients for phytophagous insects, including pollinators, that cannot synthesize sterols. Despite the well-described composition and diversity in vegetative plant tissues, few studies have examined phytosterol diversity in pollen. We quantified 25 pollen phytosterols in 122 plant species (105 genera, 51 families) to determine their composition and diversity across plant taxa. We searched literature and databases for plant phylogeny, environmental conditions, and pollinator guilds of the species to examine the relationships with pollen sterols. 24-methylenecholesterol, sitosterol and isofucosterol were the most common and abundant pollen sterols. We found phylogenetic clustering of twelve individual sterols, total sterol content and sterol diversity, and of sterol groupings that reflect their underlying biosynthesis pathway (C-24 alkylation, ring B desaturation). Plants originating in tropical-like climates (higher mean annual temperature, lower temperature seasonality, higher precipitation in wettest quarter) were more likely to record higher pollen sterol content. However, pollen sterol composition and content showed no clear relationship with pollinator guilds. Our study is the first to show that pollen sterol diversity is phylogenetically clustered and that pollen sterol content may adapt to environmental conditions.

On the complexity of triggering evolutionary radiations.

Recent developments in phylogenetic methods have made it possible to reconstruct evolutionary radiations from extant taxa, but identifying the triggers of radiations is still problematic. Here, we propose a conceptual framework to explore the role of variables that may impact radiations. We classify the variables into extrinsic conditions vs intrinsic traits, whether they provide background conditions, trigger the radiation, or modulate the radiation. We used three clades representing angiosperm phylogenetic and structural diversity (Ericaceae, Fagales and Poales) as test groups. We located radiation events, selected variables potentially associated with diversification, and inferred the temporal sequences of evolution. We found 13 shifts in diversification regimes in the three clades. We classified the associated variables, and determined whether they originated before the relevant radiation (backgrounds), originated simultaneously with the radiations (triggers), or evolved later (modulators). By applying this conceptual framework, we establish that radiations require both extrinsic conditions and intrinsic traits, but that the sequence of these is not important. We also show that diversification drivers can be detected by being more variable within a radiation than conserved traits that only allow occupation of a new habitat. This framework facilitates exploration of the causative factors of evolutionary radiations.

As old as the mountains: the radiations of the Ericaceae.

Mountains are often more species-rich than lowlands. This could be the result of migration from lowlands to mountains, of a greater survival rate in mountains, or of a higher diversification rate in mountains. We investigated this question in the globally distributed family Ericaceae, which includes c. 4426 species ranging from sea level to > 5000 m. We predict that the interaction of low specific leaf area (SLA) and montane habitats is correlated with increased diversification rates. A molecular phylogeny of Ericaceae based on rbcL and matK sequence data was built and dated with 18 fossil calibrations and divergence time estimates. We identified radiations using bamm and correlates of diversification rate changes using binary-state speciation and extinction (BiSSE) and multiple-state speciation and extinction (MuSSE) analyses. Analyses revealed six largely montane radiations. Lineages in mountains diversified faster than nonmountain lineages (higher speciation rate, but no difference in extinction rate), and lineages with low SLA diversified faster than high-SLA lineages. Further, habitat and trait had a positive interactive effect on diversification. Our results suggest that the species richness in mountains is the result of increased speciation rather than reduced extinction or increased immigration. Increased speciation in Ericaceae was facilitated by low SLA.