Hélène Morlon

The vast majority of plants form mycorrhizal symbioses. The ecological importance of these mutualistic interactions has sparked interest in their long evolutionary history. By examining interaction networks and phylogenetic trees, several studies have suggested that plants codiversify with their associated mycorrhizal fungi. However, recent research has demonstrated that phylogenetic congruence (interpreted as codiversification when divergence times match) is often conflated with another pattern called cophylogenetic signal (ie closely related plants interacting with closely related fungi and vice versa), which may arise from different biological processes. We performed cophylogenetic analyses on 29 diverse mycorrhizal networks to reevaluate the evidence for codiversification in mycorrhizal symbioses. We found a significant cophylogenetic signal but no phylogenetic congruence: Closely related plants often interact with closely related fungi, but their phylogenies do not match. Instead of codiversification, this finding is consistent with trait matching between plants and mycorrhizal fungi, where compatible, evolutionarily conserved plant and fungal traits govern their interactions. Our work highlights the importance of appropriately interpreting the cophylogenetic methods used to study the macroevolution of plants and their mycorrhizal fungi. It suggests that previous evidence of codiversification in mycorrhizal symbioses actually detected cophylogenetic signal, since, across the multiple and diverse networks analyzed here, there is no evidence that codiversification occurred during the evolution of mycorrhizal symbioses.

Molecular clock and preservation of early microfossil assemblages suggest that eukaryotes were present and already diverse more than ~1600 million years ago (Ma). Yet, the earliest identifiable eukaryotic crown group fossil only appeared around 1050 Ma, leaving a ~600 My gap in the Mesoproterozoic during which the evolution and diversification of early eukaryotes remain poorly understood. Here, we infer a timeline of eukaryote evolution using molecular clock and birth–death diversification models, including the large diversity of environmental sequences to take into account the uncultured majority of microbial life. Our analyses, based on a unique dataset of 75,975 nonredundant Operational Taxonomic Units and 77 well-supported fossil calibrations, indicate a steady diversification of crown group eukaryotes during the Proterozoic after the Last Eukaryotic Common Ancestor. We show that Archaeplastida was one of the earliest diversifying supergroups and the most diverse throughout the Proterozoic, suggesting that the first successful plastid endosymbiosis gave Archaeplastida a measurable evolutionary advantage. Discoba, Amoebozoa, and Rhizaria followed Archaeplastida in phylogenetic diversity throughout the Proterozoic, indicating that crown eukaryotes were already thriving in this Era. These results contrast with the common view that the geologically stable Proterozoic Era experienced little eukaryotic evolutionary innovation and indicate that all current supergroups were already established in the Mesoproterozoic.

Multivariate phylogenetic comparative methods for modeling high-dimensional traits such as 3D shapes or gene expression profiles have been recently developed. However, these approaches are impractical and almost impossible to use when the number of traits exceeds a few thousands, as they become computationally prohibitive. We overcome these limitations by proposing a new maximum likelihood approach based on the Empirical Bayes framework. This approach takes into account the information of the complete covariances (among species and traits) to infer parameters and compare trait evolution models for high-dimensional data sets. Through simulations, we demonstrate that the proposed approach can accurately estimate parameters of various trait evolution models, even when the number of traits is 10 times larger than the number of lineages; it requires less memory and is at least 10 times faster than currently available approaches. This efficient framework enabled us to extend the high-dimensional multivariate phylogenetic comparative toolkit by including an Ornstein–Uhlenbeck process with multiple optima to study adaptation to various selective regimes. Applying our approach to the evolution of jaw morphology in relation to dietary adaptation in mammals, we demonstrate morphological convergence in carnivorous and herbivorous lineages. The proposed Empirical Bayes approach, implemented in the R package mvMORPH, enables phylogenetic comparative methods to efficiently handle high-dimensional data sets and complex models of trait evolution.

Given the ever-increasing availability of highly detailed phenotypes, modelling trait evolution in a multivariate framework is becoming a challenging task. Current phylogenetic comparative methods often struggle with high-dimensional datasets because they suffer computational limitations and interpretability. Here, we propose a maximum likelihood-based approach called Probabilistic and Phylogenetic Principal Components Analysis (P3CA) to circumvent current limitations. This approach is based on a continuous latent variable model, whereby observed traits are explained by a smaller number of unobserved variables that evolve according to a given evolutionary model. We implement the approach under Pagel’s lambda model using an Expectation-Maximisation algorithm that makes it computationally efficient and allows missing values. Using simulations, we demonstrate that evolutionary parameters are accurately estimated, regardless of phylogenetic signal, the number of traits or the proportion of missing values. The reconstruction of the reduced space is more accurate than the one obtained using other dimensionality reduction approaches, such as phylogenetic and conventional PCA. Likewise, the estimated values for missing data are more accurate than those obtained using current phylogenetic data imputation approaches. We illustrate the approach on a 3D geometric morphometric dataset describing Crocodyliformes skull shapes and containing around 4% of missing data. Our P3CA method unlocks the possibility to analyse and more easily interpret the large-scale multivariate datasets generated in recent decades within a phylogenetic comparative framework.

Multivariate phylogenetic comparative methods for modelling high-dimensional traits such as 3D shapes or gene expression proBiles have been recently developed. However, these approaches are impractical and almost impossible to use when the number of traits exceeds a few thousands, as they become computationally prohibitive. We overcome these limitations by proposing a new maximum likelihood approach based on the Empirical Bayes framework. This approach takes into account the information of the complete covariances (among species and traits) to infer parameters and compare models of trait evolution for high-dimensional datasets. Through simulations, we demonstrate that the proposed approach can accurately estimate parameters of various trait evolution models, even when the number of traits is ten times larger than the number of lineages; it requires less memory and is at least 10 times faster than currently available approaches. This fast, efBicient framework enabled us to extend the high-dimensional multivariate phylogenetic comparative toolkit by including an Ornstein-Uhlenbeck process with multiple optima to study adaptation to various selective regimes. Applying our approach to the evolution of jaw morphology in relation to dietary adaptation in mammals, we demonstrate morphological convergence in carnivorous and herbivorous lineages. The proposed Empirical Bayes framework, implemented in the R package mvMORPH, enables phylogenetic comparative methods to efBiciently handle high-dimensional datasets and complex models of trait evolution.

Speciation – the process by which two lineages become reproductively isolated – plays a key role in the emergence and maintenance of biodiversity. Yet, our understanding of the time it takes for speciation to occur, and of the microevolutionary processes that influence this tempo, remains limited. Here, we thoroughly characterize how population size, mutation rate, local adaptation and migration are expected to influence the duration of speciation, as well as the shape of the “grey zone” of speciation. We show that the relationship between population size and speciation time is indicative of the speciation mode, as faster speciation in smaller populations only occurs in the case of non-ecological speciation. Leveraging genomic estimates of population size and speciation duration across 196 pairs of plant species, we uncover a positive association between population size and speciation duration. Taken together, these results challenge the view that ecological speciation is the source of much of species diversity. Significance statement: How new species arise, and how quickly they do so, plays a key role in shaping Earth’s biodiversity. Yet, the links between how speciation occurs, how fast it proceeds, and basic properties of species such as population size, remain debated. We clarify these relationships using an integrative model of speciation. We show, in particular, that faster speciation in smaller populations is a signature of non-ecological speciation. Analyses of genomic data from plants reveal precisely this pattern, challenging the prevailing view that speciation in nature is predominantly ecological.

Birth-death models are widely used to describe the diversification process which leads to the observed species and phylogenies. When integrated into Bayesian phylogenetic inference, birth-death models allow the joint inference of the phylogeny and the diversification parameters from molecular information. Two major classes of extensions of the birth-death process are considered in this article. The first extends the phylogenetic tree to include fossil samples alongside extant species, allowing the inference to integrate information about the past diversity. This type of inference uses either morphological or taxonomic information to place fossils in the phylogeny. The second extension models diversification rates which can vary between lineages, and is thus able to infer patterns of variation in speciation or extinction rates. In this work, we combine these two types of extension into a multi-type fossilized birth-death (MTFBD) process, which can perform the joint inference of a phylogeny including extinct and extant samples, and lineage-specific diversification and fossil sampling rates in a Bayesian framework. The MTFBD model is implemented as part of the phylogenetic inference framework BEAST2. Using simulated and empirical datasets, we demonstrate the performance and accuracy of the new model compared to a model with rate heterogeneity but using only extant samples, and compared to a model without rate variation including fossils. We demonstrate that including fossils improves the accuracy of the phylogeny and diversification rates, especially extinction rates, provided that the inference includes detailed morphological information to accurately place the fossil samples. When this information is not available however, MTFBD estimates are strongly driven by the priors and are thus no better or even worse than estimates obtained only with extant samples. With informative fossil characters, the MTFBD model provides accurate phylogenies, and precisely characterizes how speciation, extinction and fossil sampling rates vary as diversification proceeds.

Molecular clock and preservation of early microfossil assemblages suggest that eukaryotes were present and already diverse more than ~1600 million years ago (Ma). Yet, the earliest identifiable eukaryotic crown group fossil only appeared around 1050 Ma, leaving a ~600 million years gap in the Mesoproterozoic during which the evolution and diversification of early eukaryotes remain poorly understood. Here, we inferred a timeline of eukaryote evolution using molecular clock and birth-death diversification models, including the great diversity of environmental sequences to take into account the uncultured majority of microbial life. Our analyses, based on a unique dataset of 75,975 non-redundant Operational Taxonomic Units and 77 well-supported fossil calibrations, indicate a steady diversification of crown group eukaryotes during the Proterozoic after the Last Eukaryotic Common Ancestor (LECA). We show that Archaeplastida was one of the earliest diversifying supergroups and the most diverse throughout the Proterozoic, suggesting that the first successful plastid endosymbiosis gave Archaeplastida a measurable evolutionary advantage. Discoba, Amoebozoa and Rhizaria followed Archaeplastida in phylogenetic diversity throughout the Proterozoic, suggesting that crown eukaryotes were already thriving in this Era. These results contrast with the common view that the geologically stable Proterozoic Era experienced little eukaryotic evolutionary innovation, and indicate that it was already at the dawn of eukaryote evolution that all current supergroups were established.

How and why species diversity varies over geological timescales remains disputed. Debate revolves around the existence of equilibrium dynamics, the predominance of adaptive radiations and the relative importance of speciation and extinction in driving diversity trajectories. We analyse the evolutionary history of 27 radiations of plants, arthropods and vertebrates, with phylogenetic information incorporating extinct and extant species under a new ‘fossilized birth–death diffusion’ model that provides a detailed characterization of past diversification and resulting diversity dynamics. Here, lineages undergo speciation and extinction rates that diffuse continuously in time and generate fossils with rates that can vary with stratigraphy. Clade diversity trajectories follow rise and decline dynamics, with fast accumulation following recurrent speciation while slowdowns and losses are modulated primarily by changes in extinction. Diversity dynamics do not appear to be governed by clade-level processes expected from adaptive radiations or diversity dependence. Rather, these patterns emerge from dynamics at the species level, where lineages tend to become increasingly vulnerable to extinction and less likely to speciate with time. Those species that counteract this trend create and maintain biodiversity through deep time. The rise and fall of clades results from species-level fates.

A bstract: Phylogenetic diversification analyses often overlook the wealth of information contained in fossils that cannot be directly integrated into phylogenetic trees (called fossil occurrences), as well as fine-grained variations in speciation and extinction rates across lineages. Addressing both of these shortcomings, we develop the Occurrence Birth-Death Diffusion (OBDD) process. This process incorporates the sampling of fossil occurrences by extending the recently developed Fossilized Birth-Death Diffusion (FBDD) process, thereby taking advantage of a clade’s entire fossil record to infer its evolutionary history. To mitigate the computational challenges associated with the OBDD process, we employ Bayesian data augmentation methods. We illustrate our approach with an empirical analysis of cetacean diversification. Our analyses show that the integration of fossils is essential to recover diversification rates and richness trajectories. More specifically, all types of fossils are highly informative about past diversification dynamics, but fossil occurrences do not provide significant additional information when the phylogeny already contains many fossils. We make the OBDD available in the Julia package Tapestree.