Inferring the intrinsic and extrinsic drivers of species diversification and phenotypic disparity across the Tree of Life is a major challenge in evolutionary biology. In green plants, polyploidy (or whole-genome duplication, WGD) is known to play a major role in microevolution and speciation 1 , but the extent to which WGD has shaped macroevolutionary patterns of diversification and phenotypic innovation across plant phylogeny remains an open question.Here we examine the relationship of various facets of genomic evolution-including gene and genome duplication, genome size, and chromosome number-with macroevolutionary patterns of phenotypic innovation, species diversification, and climatic occupancy in gymnosperms. We show that genomic changes, such as WGD and genome-size shifts, underlie the origins of most major extant gymnosperm clades, and notably our results support an ancestral WGD in the gymnosperm lineage. Spikes of gene duplication typically coincide with major spikes of phenotypic innovation, while increased rates of phenotypic evolution are typically found at nodes with high gene-tree conflict, representing historic population-level dynamics during speciation. Most shifts in gymnosperm diversification since the rise of angiosperms are decoupled from putative WGDs and instead are associated with increased rates of climatic occupancy evolution, particularly in cooler and/or more arid climatic conditions, suggesting that ecological opportunity, especially in the later Cenozoic, and environmental heterogeneity have driven a resurgence of gymnosperm diversification. Our study provides critical insight on the processes underlying diversification and phenotypic evolution in gymnosperms, with important broader implications for the major drivers of both micro-and macroevolution in plants..

The recent surge in enthusiasm for simultaneously inferring relationships from extinct and extant species has reinvigorated interest in statistical approaches for modeling morphological evolution. Current statistical methods use the Mk model to describe substitutions between discrete character states. Although representing a significant step forward, the Mk model presents challenges in biological interpretation, and its adequacy in modeling morphological evolution has not been well explored. Another major hurdle in morphological phylogenetics concerns the process of character coding of discrete characters. The often subjective nature of discrete character coding can generate discordant results that are rooted in individual researchers' subjective interpretations. Employing continuous measurements to infer phylogenies may alleviate some of these issues. Although not widely used in the inference of topology, models describing the evolution of continuous characters have been well examined, and their statistical behavior is well understood. Also, continuous measurements avoid the substantial ambiguity often associated with the assignment of discrete characters to states. I present a set of simulations to determine whether use of continuous characters is a feasible alternative or supplement to discrete characters for inferring phylogeny. I compare relative reconstruction accuracy by inferring phylogenies from simulated continuous and discrete characters. These tests demonstrate significant promise for continuous traits by demonstrating their higher overall accuracy as compared to reconstruction from discrete characters under Mk when simulated under unbounded Brownian motion, and equal performance when simulated under an Ornstein-Uhlenbeck model. Continuous characters also perform reasonably well in the presence of covariance between sites. I argue that inferring phylogenies directly from continuous traits may be benefit efforts to maximize phylogenetic information in morphological data sets by preserving larger variation in state space compared to many discretization schemes. I also suggest that the use of continuous trait models in phylogenetic reconstruction may alleviate potential concerns of discrete character model adequacy, while identifying areas that require further study in this area. This study provides an initial controlled demonstration of the efficacy of continuous characters in phylogenetic inference.

Jointly developing a comprehensive tree of life from living and fossil taxa has long been a fundamental goal in evolutionary biology. One major challenge has stemmed from difficulties in merging evidence from extant and extinct organisms. While these efforts have resulted in varying stages of synthesis, they have been hindered by their dependence on qualitative descriptions of morphology. Though rarely applied to phylogenetic inference, traditional and geometric morphometric data can improve these issues by generating more rigorous ways to quantify variation in morphological structures. They may also facilitate the rapid and objective aggregation of large morphological datasets. I describe a new Bayesian method that leverages quantitative trait data to reconstruct the positions of fossil taxa on fixed reference trees composed of extant taxa. Unlike most formulations of phylogenetic Brownian motion models, this method expresses branch lengths in units of morphological disparity, suggesting a new framework through which to construct Bayesian node calibration priors for molecular dating and explore comparative patterns in morphological disparity. I am hopeful that the approach described here will help to facilitate a deeper integration of neo- and paleontological data to move morphological phylogenetics further into the genomic era.

Evolutionary biologists have long been fascinated with the episodes of rapid phenotypic innovation that underlie the emergence of major lineages. Although our understanding of the environmental and ecological contexts of such episodes has steadily increased, it has remained unclear how population processes contribute to emergent macroevolutionary patterns. One insight gleaned from phylogenomics is that gene-tree conflict, frequently caused by population-level processes, is often rampant during the origin of major lineages. With the understanding that phylogenomic conflict is often driven by complex population processes, we hypothesized that there may be a direct correspondence between instances of high conflict and elevated rates of phenotypic innovation if both patterns result from the same processes. We evaluated this hypothesis in six clades spanning vertebrates and plants. We found that the most conflict-rich regions of these six clades also tended to experience the highest rates of phenotypic innovation, suggesting that population processes shaping both phenotypic and genomic evolution may leave signatures at deep timescales. Closer examination of the biological significance of phylogenomic conflict may yield improved connections between micro- and macroevolution and increase our understanding of the processes that shape the origin of major lineages across the Tree of Life.

Puttick (2017, 20162290 (doi:10.1098/rspb.2016.2290)) performed a simulation study to compare accuracy among methods of inferring phylogeny from discrete morphological characters. They report that a Bayesian implementation of the Mk model (Lewis 2001 , 913-925 (doi:10.1080/106351501753462876)) was most accurate (but with low resolution), while a maximum-likelihood (ML) implementation of the same model was least accurate. They conclude by strongly advocating that Bayesian implementations of the Mk model should be the default method of analysis for such data. While we appreciate the authors' attempt to investigate the accuracy of alternative methods of analysis, their conclusion is based on an inappropriate comparison of the ML point estimate, which does not consider confidence, with the Bayesian consensus, which incorporates estimation credibility into the summary tree. Using simulation, we demonstrate that ML and Bayesian estimates are concordant when confidence and credibility are comparably reflected in summary trees, a result expected from statistical theory. We therefore disagree with the conclusions of Puttick and consider their prescription of any default method to be poorly founded. Instead, we recommend caution and thoughtful consideration of the model or method being applied to a morphological dataset.