Systematic Biology最新文献

Genomes are composed of a mosaic of segments inherited from different ancestors, each separated by past recombination events. Consequently, genealogical relationships among multiple genomes vary spatially across different genomic regions. Genealogical variation among unlinked (uncorrelated) genomic regions is well described for either a single population (coalescent) or multiple structured populations (multispecies coalescent). However, the expected similarity among genealogies at linked regions of a genome is less well characterized. Recently, an analytical solution was derived for the distribution of the waiting distance for a change in the genealogical tree spatially across a genome for a single population with constant effective population size. Here we describe a generalization of this result, in terms of the distribution of waiting distances between changes in genealogical trees and topologies for multiple structured populations with branch-specific effective population sizes (i.e., under the multispecies coalescent). We implemented our model in the Python package ipcoal and validated its accuracy against stochastic coalescent simulations. Using a novel likelihood framework we show that tree and topology-change waiting distances in an ARG can be used to fit species tree model parameters, demonstrating an application of our model for developing new methods for phylogenetic inference. The Multi-Species Sequentially Markov Coalescent (MS-SMC) model presented here represents a major advance for linking local ancestry inference to hierarchical demographic models.

For many questions in ecology and evolution, the most relevant data to consider are attributes of lineage pairs. Comparative tests for causal relationships among traits like 'diet niche overlap', 'divergence time', and 'strength of reproductive isolation (RI)' - measured for pairwise combinations of related species or populations - have led to several groundbreaking insights, but the correct statistical approach for these analyses has never been clear. Lineage-pair traits are non-independent, but unlike the expected covariance among species' traits, which is captured by a phylogenetic covariance matrix arising from a given model, the expected covariance among lineage-pair traits has not been explicitly formulated. Analyses of pairwise-defined data have thus employed untested workarounds for non-independence rather than direct models of lineage-pair covariance, with consequences that are unexplored. Here, we consider how evolutionary relatedness among taxa translates into non-independence among taxonomic pairs. We develop models by which phylogenetic signal in an underlying character generates covariance among pairs in a lineage-pair trait. We incorporate the resulting lineage-pair covariance matrices into modified versions of phylogenetic generalized least squares and a new phylogenetic beta regression for bounded response variables. Both outperform previous approaches in simulation tests. We find that a common heuristic method, node averaging, imparts a greater cost to model performance than does the non-independence it was designed to correct. We re-analyze two empirical datasets to find dramatic improvements in model fit and, in the case of avian hybridization data, an even stronger relationship between pair age and RI than is revealed from uncorrected analysis. We finally present a new tool, the R package phylopairs, that allows empiricists to test relationships among pairwise-defined variables in a way that is statistically robust and more straightforward to implement.

Species diversification is characterized by speciation and extinction, the rates of which can, under some assumptions, be estimated from time-calibrated phylogenies. However, maximum likelihood estimation methods (MLE) for inferring rates are limited to simpler models and can show bias, particularly in small phylogenies. Likelihood-free methods to estimate parameters of diversification models using deep learning have started to emerge, but how robust neural network methods are at handling the intricate nature of phylogenetic data remains an open question. Here we present a new ensemble neural network approach to estimate diversification parameters from phylogenetic trees that leverages different classes of neural networks (dense neural network, graph neural network, and long short-term memory recurrent network) and simultaneously learns from graph representations of phylogenies, their branching times and their summary statistics. Our best-performing ensemble neural network (which adjusts the graph neural network result using a recurrent neural network) delivers estimates faster than MLE and shows less sensitivity to tree size for constant-rate and diversity-dependent speciation scenarios. It performs well compared to an existing convolutional network approach. However, like MLE, our approach still fails to recover parameters precisely under a protracted birth-death process. Our analysis suggests that the primary limitation to accurate parameter estimation is the amount of information contained within a phylogeny, as indicated by its size and the strength of effects shaping it. In cases where MLE is unavailable, our neural network method provides a promising alternative for estimating phylogenetic tree parameters. If detectable phylogenetic signals are present, our approach delivers results that are comparable to MLE but without inherent biases.

The Tree of Life is central to evolutionary biology, yet resolving deep, recalcitrant phylogenetic relationships remains challenging due to complex processes such as incomplete lineage sorting (ILS), hybridization, and polyploidization. Although previous phylogenetic studies have advanced our understanding of Leguminosae (Fabaceae), a species-rich and ecologically diverse family, many deep relationships at the tribal and higher levels remain unresolved. Incorporating newly generated genome skimming data for 231 species with previously issued plastid genomic, mitochondrial genomic and transcriptomic data, we reconstructed a phylogeny of the family using whole plastomes, 39 mitochondrial genes, and 1559 low-copy nuclear genes, achieving dense taxonomic sampling across almost all recognized tribes and major unplaced lineages. Our results supported the monophyly of the six subfamilies and 49 recognized tribes, identified ten clades worthy of recognition as new tribes in subfamily Papilionoideae, and clarified many contentious relationships. However, nuclear-nuclear and cytonuclear conflicts persist at multiple nodes among trees inferred from different datasets and analytical methods. We proposed the most probable resolution for 22 contentious nodes by applying nuclear gene-tree quartet analysis with corroboration from support of nuclear Maximum Likelihood (ML) and ASTRAL trees. Our results indicate ILS significantly contributes to observed phylogenetic conflicts, while gene flow represents an additional and previously underappreciated factor that mainly contributes to cytonuclear conflicts, particularly along the branches of the Angylocalyceae + Dipterygeae + Amburaneae (ADA) clade and Wisterieae. These processes likely underlie recalcitrant phylogenetic relationships, such as those within the 50-kb inversion clade of Papilionoideae. Our study uses multiple data partitions and analytical methods to resolve contentious phylogenetic relationships in Leguminosae, resulting in a robust phylogenomic framework to guide further investigations in this economically important and exceptionally diverse family.

One puzzling feature of avian life histories is that individuals in many different lineages delay reproduction for several years after they finish growing. Intraspecific field studies suggest that various complex social environments-such as cooperative breeding groups, nesting colonies, and display leks-result in delayed reproduction because they require forms of sociosexual development that extend beyond physical maturation. Here, we formally propose this hypothesis and use a full suite of phylogenetic comparative methods to test it, analyzing the evolution of age at first reproduction (AFR) in females and males across 963 species of birds. Phylogenetic regressions support increased AFR in colonial females and males, cooperatively breeding males, and lekking males. Continuous Ornstein-Uhlenbeck models support distinct evolutionary regimes with increased AFR for all of cooperative, colonial, and lekking lineages. Discrete hidden state Markov models suggest a net increase in delayed reproduction for social lineages, even when accounting for hidden state heterogeneity and the potential reverse influence of AFR on sociality. Our results support the hypothesis that the evolution of sociality reshapes the dynamics of life history evolution in birds. Comparative analyses of even the most broadly generalizable characters, such as AFR, must reckon with unique, heterogeneous, historical events in the evolution of individual lineages.