Biodiversity varies predictably with environmental energy around the globe, but the underlaying mechanisms remain incompletely understood. The evolutionary speed hypothesis predicts that environmental kinetic energy shapes variation in speciation rates through temperature- or life history-dependent rates of evolution. To test whether variation in evolutionary speed can explain the relationship between energy and biodiversity in birds, mammals, amphibians, and reptiles, we simulated diversification over 65 million years of geological and climatic change with a spatially explicit eco-evolutionary simulation model. We modelled four distinct evolutionary scenarios in which speciation-completion rates were dependent on temperature (M1), life history (M2), temperature and life history (M3), or were independent of temperature and life-history (M0). To assess the agreement between simulated and empirical data, we performed model selection by fitting supervised machine learning models to multidimensional biodiversity patterns. We show that a model with temperature-dependent rates of speciation (M1) consistently had the strongest support. In contrast to statistical inferences, which showed no general relationships between temperature and speciation rates in tetrapods, we demonstrate how process-based modelling can disentangle the causes behind empirical biodiversity patterns. Our study highlights how environmental energy has played a fundamental role in the evolution of biodiversity over deep time.

The species studied in any evolutionary investigation generally constitute a small proportion of all the species currently existing or that have gone extinct. It is therefore likely that introgression, which is widespread across the tree of life, involves "ghosts," i.e., unsampled, unknown, or extinct lineages. However, the impact of ghost introgression on estimations of species trees has rarely been studied and is poorly understood. Here, we use mathematical analysis and simulations to examine the robustness of species tree methods based on the multispecies coalescent model to introgression from a ghost or extant lineage. We found that many results originally obtained for introgression between extant species can easily be extended to ghost introgression, such as the strongly interactive effects of incomplete lineage sorting (ILS) and introgression on the occurrence of anomalous gene trees (AGTs). The relative performance of the summary species tree method (ASTRAL) and the full-likelihood method (*BEAST) varies under different introgression scenarios, with the former being more robust to gene flow between non-sister species whereas the latter performing better under certain conditions of ghost introgression. When an outgroup ghost (defined as a lineage that diverged before the most basal species under investigation) acts as the donor of the introgressed genes, the time of root divergence among the investigated species generally was overestimated, whereas ingroup introgression, as commonly perceived, can only lead to underestimation. In many cases of ingroup introgression that may or may not involve ghost lineages, the stronger the ILS, the higher the accuracy achieved in estimating the time of root divergence, although the topology of the species tree is more prone to be biased by the effect of introgression.

The loss of information accompanying assessment of absolute fit of substitution models to phylogenetic data negatively affects the discriminatory power of previous methods and can make them insensitive to lineage-specific changes in the substitution process. As an alternative, I propose evaluating absolute fit of substitution models based on a novel statistic which describes the observed data without information loss and which is unlikely to become zero-inflated with increasing numbers of taxa. This method can accommodate gaps and is sensitive to lineage-specific shifts in the substitution process. In simulation experiments, it exhibits greater discriminatory power than previous methods. The method can be implemented in both Bayesian and Maximum Likelihood phylogenetic analyses, and used to screen any set of models. Recently, it has been suggested that model selection may be an unnecessary step in phylogenetic inference. However, results presented here emphasize the importance of model fit assessment for reliable phylogenetic inference.

Organismal traits can evolve in a coordinated way, with correlated patterns of gains and losses reflecting important evolutionary associations. Discovering these associations can reveal important information about the functional and ecological linkages among traits. Phylogenetic profiles treat individual genes as traits distributed across sets of genomes and can provide a fine-grained view of the genetic underpinnings of evolutionary processes in a set of genomes. Phylogenetic profiling has been used to identify genes that are functionally linked, and to identify common patterns of lateral gene transfer in microorganisms. However, comparative analysis of phylogenetic profiles and other trait distributions should take into account the phylogenetic relationships among the organisms under consideration. Here we propose the Community Coevolution Model (CCM), a new coevolutionary model to analyze the evolutionary associations among traits, with a focus on phylogenetic profiles. In the CCM, traits are considered to evolve as a community with interactions, and the transition rate for each trait depends on the current states of other traits. Surpassing other comparative methods for pairwise trait analysis, CCM has the additional advantage of being able to examine multiple traits as a community to reveal more dependency relationships. We also develop a simulation procedure to generate phylogenetic profiles with correlated evolutionary patterns that can be used as benchmark data for evaluation purposes. A simulation study demonstrates that CCM is more accurate than other methods including the Jaccard Index and three tree-aware methods. The parameterization of CCM makes the interpretation of the relations between genes more direct, which leads to Darwin's scenario being identified easily based on the estimated parameters. We show that CCM is more efficient and fits real data better than other methods resulting in higher likelihood scores with fewer parameters. An examination of 3786 phylogenetic profiles across a set of 659 bacterial genomes highlights linkages between genes with common functions, including many patterns that would not have been identified under a non-phylogenetic model of common distribution. We also applied the CCM to 44 proteins in the well-studied Mitochondrial Respiratory Complex I and recovered associations that mapped well onto the structural associations that exist in the complex.

The supergroup Holomycota, composed of Fungi and several related lineages of unicellular organisms (Nucleariida, Rozellida, Microsporidia, and Aphelida), represents one of the major branches in the phylogeny of eukaryotes. Nevertheless, except for the well-established position of Nucleariida as the first holomycotan branch to diverge, the relationships among the other lineages have so far remained unresolved largely owing to the lack of molecular data for some groups. This was notably the case aphelids, a poorly known group of endobiotic phagotrophic protists that feed on algae with cellulose walls. The first molecular phylogenies including aphelids supported their sister relationship with Rozellida and Microsporidia which, collectively, formed a new group called Opisthosporidia (the 'Opisthosporidia hypothesis'). However, recent phylogenomic analyses including massive sequence data from two aphelid genera, Paraphelidium and Amoeboaphelidium, suggested that the aphelids are sister to fungi (the 'Aphelida+Fungi hypothesis'). Should this position be confirmed, aphelids would be key to understanding the early evolution of Holomycota and the origin of Fungi. Here, we carry out phylogenomic analyses with an expanded taxonomic sampling for aphelids after sequencing the transcriptomes of two species of the genus Aphelidium (A. insulamus and A. tribonematis) in order to test these competing hypotheses. Our new phylogenomic analyses including species from the three known aphelid genera strongly rejected the Opisthosporidia hypothesis. Furthermore, comparative genomic analyses further supported the Aphelida+Fungi hypothesis via the identification of 19 orthologous genes exclusively shared by these two lineages. Seven of them originated from ancient horizontal gene transfer events predating the aphelid-fungal split and the remaining 12 likely evolved de novo, constituting additional molecular synapomorphies for this clade. Ancestral trait reconstruction based on our well-resolved phylogeny of Holomycota suggests that the progenitor of both fungi and rozellids, was aphelid-like, having an amoeboflagellate state and likely preying endobiotically on cellulose-containing, cell-walled organisms. Two lineages, which we propose to call Phytophagea and Opisthophagea, evolved from this ancestor. Phytophagea, grouping aphelids and classical fungi, mainly specialized in endobiotic predation of algal cells. Fungi emerged from this lineage after losing phagotrophy in favour of osmotrophy. Opisthophagea, grouping rozellids and Microsporidia, became parasites, mostly of chitin-containing hosts. This lineage entered a progressive reductive process that resulted in a unique lifestyle, especially in the highly derived Microsporidia.

African cichlids (subfamily: Pseudocrenilabrinae) are among the most diverse vertebrates, and their propensity for repeated rapid radiation has made them a celebrated model system in evolutionary research. Nonetheless, despite numerous studies, phylogenetic uncertainty persists, and riverine lineages remain comparatively underrepresented in higher-level phylogenetic studies. Heterogeneous gene histories resulting from incomplete lineage sorting (ILS) and hybridization are likely sources of uncertainty, especially during episodes of rapid speciation. We investigate relationships of Pseudocrenilabrinae and its close relatives while accounting for multiple sources of genetic discordance using species tree and hybrid network analyses with hundreds of single-copy exons. We improve sequence recovery for distant relatives, thereby extending the taxonomic reach of our probes, with a hybrid reference guided/de novo assembly approach. Our analyses provide robust hypotheses for most higher-level relationships and reveal widespread gene heterogeneity, including in riverine taxa. ILS and past hybridization are identified as sources of genetic discordance in different lineages. Sampling of various Blenniiformes (formerly Ovalentaria) adds strong phylogenomic support for convict blennies (Pholidichthyidae) as sister to Cichlidae, and points to other potentially useful protein-coding markers across the order. A reliable phylogeny with representatives from diverse environments will support ongoing taxonomic and comparative evolutionary research in the cichlid model system.

Modern phylogenetic methods allow inference of ancestral molecular sequences given an alignment and phylogeny relating present day sequences. This provides insight into the evolutionary history of molecules, helping to understand gene function and to study biological processes such as adaptation and convergent evolution across a variety of applications. Here we propose a dynamic programming algorithm for fast joint likelihood-based reconstruction of ancestral sequences under the Poisson Indel Process (PIP). Unlike previous approaches, our method, named ARPIP, enables the reconstruction with insertions and deletions based on an explicit indel model. Consequently, inferred indel events have an explicit biological interpretation. Likelihood computation is achieved in linear time with respect to the number of sequences. Our method consists of two steps, namely finding the most probable indel points and reconstructing ancestral sequences. First, we find the most likely indel points and prune the phylogeny to reflect the insertion and deletion events per site. Second, we infer the ancestral states on the pruned subtree in a manner similar to FastML. We applied ARPIP on simulated datasets and on real data from the Betacoronavirus genus. ARPIP reconstructs both the indel events and substitutions with a high degree of accuracy. Our method fares well when compared to established state-of-the-art methods such as FastML and PAML. Moreover, the method can be extended to explore both optimal and suboptimal reconstructions, include rate heterogeneity through time and more. We believe it will expand the range of novel applications of ancestral sequence reconstruction.

The fossilized birth-death (FBD) model is a naturally appealing way of directly incorporating fossil information when estimating diversification rates. However, an important yet often overlooked property of the original FBD derivation is that it distinguishes between two types of sampled lineages. Here we first discuss and demonstrate the impact of severely undersampling, and even not including fossils that represent samples of lineages that also had sampled descendants. We then explore the benefits of including fossils, generally, by implementing and then testing two-types of FBD models, including one that converts a fossil set into stratigraphic ranges, in more complex likelihood-based models that assume multiple rate classes across the tree. Under various simulation scenarios, including a scenario that exists far outside the set of models we evaluated, including fossils rarely outperforms analyses that exclude them altogether. At best, the inclusion of fossils improves precision but does not influence bias. Similarly, we found that converting the fossil set to stratigraphic ranges, which is one way to remedy the effects of undercounting the number of k-type fossils, results in turnover rates and extinction fraction estimates that are generally underestimated. While fossils remain essential for understanding diversification through time, in the specific case of understanding diversification given an existing, largely modern tree, they are not especially beneficial.