Tree Branch Lengths Research Articles

Reliable estimation of tree branch lengths using deep neural networks.

A phylogenetic tree represents hypothesized evolutionary history for a set of taxa. Besides the branching patterns (i.e., tree topology), phylogenies contain information about the evolutionary distances (i.e. branch lengths) between all taxa in the tree, which include extant taxa (external nodes) and their last common ancestors (internal nodes). During phylogenetic tree inference, the branch lengths are typically co-estimated along with other phylogenetic parameters during tree topology space exploration. There are well-known regions of the branch length parameter space where accurate estimation of phylogenetic trees is especially difficult. Several novel studies have recently demonstrated that machine learning approaches have the potential to help solve phylogenetic problems with greater accuracy and computational efficiency. In this study, as a proof of concept, we sought to explore the possibility of machine learning models to predict branch lengths. To that end, we designed several deep learning frameworks to estimate branch lengths on fixed tree topologies from multiple sequence alignments or its representations. Our results show that deep learning methods can exhibit superior performance in some difficult regions of branch length parameter space. For example, in contrast to maximum likelihood inference, which is typically used for estimating branch lengths, deep learning methods are more efficient and accurate. In general, we find that our neural networks achieve similar accuracy to a Bayesian approach and are the best-performing methods when inferring long branches that are associated with distantly related taxa. Together, our findings represent a next step toward accurate, fast, and reliable phylogenetic inference with machine learning approaches.

Morphological estimation of primary branch length of individual apple trees during the deciduous period in modern orchard based on PointNet++

Primary branch length is an important morphological trait of individual apple tree phenotypes. This study presents a novel method for estimating the primary branch lengths of individual apple trees during the deciduous period by distinguishing their instances, i.e., merging those belonging to the same primary branch based on part segmentation outputs of PointNet++. Firstly, colored and colorless 3D-datasets were prepared for training PointNet++ models. The model with higher overall accuracy (OA), class average accuracy (CAA), and mean intersection-over-union (mIoU) was employed to segment the point cloud of a tree into primary branches (PB), trunk (TK), and end-points of primary branches (EPB) of individual apple trees. Skeletonization was applied to the outputs of the three parts of individual apple trees. Subsequently, each primary branch instance was distinguished by determining its corresponding path and retaining the longest path of the same primary branch only. Finally, the primary branch length was estimated by calculating the sum of Euclidean distances between adjacent points on the corresponding path. Results indicated that adding color to point clouds did not improve segmentation accuracy of PointNet++ on segmenting PB, TK, and EPB with similar color features. The PointNet++ model that was trained without color achieved an OA, CAA, and mIoU of 0.84, 0.83, and 0.70, respectively. The proportion of estimated and ground-truth values of the number of primary branches was 93.64 %. The mean absolute percentage error of estimating primary branch lengths was 12.00 %. These findings demonstrate that the proposed method is promising for high-throughput phenotyping of apple trees.

Applications of machine learning in phylogenetics

Machine learning has increasingly been applied to a wide range of questions in phylogenetic inference. Supervised machine learning approaches that rely on simulated training data have been used to infer tree topologies and branch lengths, to select substitution models, and to perform downstream inferences of introgression and diversification. Here, we review how researchers have used several promising machine learning approaches to make phylogenetic inferences. Despite the promise of these methods, several barriers prevent supervised machine learning from reaching its full potential in phylogenetics. We discuss these barriers and potential paths forward. In the future, we expect that the application of careful network designs and data encodings will allow supervised machine learning to accommodate the complex processes that continue to confound traditional phylogenetic methods.

Nonlinear social evolution and the emergence of collective action.

Organisms from microbes to humans engage in a variety of social behaviors, which affect fitness in complex, often nonlinear ways. The question of how these behaviors evolve has consequences ranging from antibiotic resistance to human origins. However, evolution with nonlinear social interactions is challenging to model mathematically, especially in combination with spatial, group, and/or kin assortment. We derive a mathematical condition for natural selection with synergistic interactions among any number of individuals. This result applies to populations with arbitrary (but fixed) spatial or network structure, group subdivision, and/or mating patterns. In this condition, nonlinear fitness effects are ascribed to collectives, and weighted by a new measure of collective relatedness. For weak selection, this condition can be systematically evaluated by computing branch lengths of ancestral trees. We apply this condition to pairwise games between diploid relatives, and to dilemmas of collective help or harm among siblings and on spatial networks. Our work provides a rigorous basis for extending the notion of "actor", in the study of social evolution, from individuals to collectives.

Abstract IA016: Bayesian phylodynamics to quantify cancer growth across space and time

Abstract Phylogenetics has traditionally inferred shared genetic histories of species over macroevolutionary time. With the expansion of cancer multi-region and single-cell sequencing, phylogenetic approaches have found new uses mapping the evolutionary histories of tumors as well. Recently developed “phylodynamic” methods that integrate phylogenetic and population dynamic modeling offer further promise - uncovering cancer growth dynamics from the topologies and branch lengths of tumor trees. Here, I discuss two recent efforts in this area: 1) Heterogeneous growth rates across a tumor become encoded in the branching patterns of phylogenetic trees built from multi-region sequencing. We introduce a cancer-specific extension to a class of multi-state birth-death models to infer these growth rate differences across space. We apply our model (SDevo) to a sample of hepatocellular carcinomas and infer that these tumor peripheries grow 2-6 times faster than their interiors. 2) Tumors experience differential growth rates over time as they acquire new driver mutations and invade their surrounding tissues, and these growth dynamics similarly leave characteristic signatures in their phylogenies. We employ Bayesian skyline models to dissect tumor growth dynamics from a dataset of diverse cancers and reveal variation in cancer growth rates over time. As cancer sequencing continues to proliferate, phylodynamic approaches are poised to reveal new quantitative insights into cancer growth dynamics across time and space. Citation Format: Maya Lewinsohn, Trevor Bedford, Nicola F. Müller, Alison Feder. Bayesian phylodynamics to quantify cancer growth across space and time [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr IA016.

Assessing the Allometric Scaling of Vectorized Branch Lengths of Trees with Terrestrial Laser Scanning and Quantitative Structure Modeling: A Case Study in Guyana

Allometric scaling is closely related to the morphology, function and behavior of trees, which are of great significance to the study of ecology. However, most of the traditional allometric scaling studies used the scalar attributes of trees, without considering the 3D vector mode of tree growth. In order to investigate the allometric relationships between branch lengths in 3D vector mode, in this study, an accurate and detailed quantitative structure model was used to reconstruct tree architectures from 3D point cloud data collected by terrestrial laser scanning and extract the structural parameters of each branch (length, branching level and zenith angle). The standardized major axis was used to establish and analyze the scalar and vector allometric relationships between branch lengths. Our results show that at the same branching level and using the same allometric model, the scaling exponents between the lengths of branches and the lengths of cumulative child (descendant) branches (no matter whether the lengths are in scalar or vector form) are similar among trees, and there is no significant difference between the scaling exponents of most trees. And the scaling exponents between the lengths of the horizontal components of branches and the cumulative lengths of the horizontal components of the child (descendant) branches are much larger than those between the lengths of the vertical components of branches and the cumulative lengths of vertical components of the child (descendant) branches. At different branching levels, the scaling exponents between the lengths of branches and the cumulative lengths of descendant branches tend to decrease with the increase in the branching level. The allometric models in terms of the cumulative lengths of horizontal components of the child (descendant) branches and the allometric models in terms of the cumulative lengths of child (descendant) branches have similar model accuracy and scaling exponents. The study results of allometric relationships between tree branch lengths in 3D mode are of great importance for understanding the crown morphology and the branching rule, which is helpful to further understand the growth strategies and adaptation mechanisms of trees and explain the growth and development mechanisms of trees from a physiological perspective.

The Lateral Growth of Branches into Small Canopy Gaps: Implications for Competition between Canopy Trees

Much research in forest ecology has been devoted to examining the effect of gap formation on regeneration dynamics. However, comparatively little research has examined the process of gap closure, in which larger trees bordering the gap grow laterally to exploit available light. Thus, it remains uncertain whether disturbance disrupts or reinforces the competitive hierarchy established among different species and sizes classes. We quantified the lateral growth of three hardwood tree species with differing autecologies both before and after the formation of small gaps created by single-tree selection. Linear mixed-effect models were employed to link lateral growth to species and stem diameter to examine whether gap formation favors intolerant species and small trees in the canopy. Additional models were also developed to examine the relationship of lateral growth with branch length and tree height. Before gap formation, the mid-tolerant yellow birch grew considerably faster than the tolerant sugar maple and American beech. However, yellow birch was less responsive to gap formation (~16%) than sugar maple or beech, whose lateral growth increased by 42% and 39%, respectively. This suggests that gap formation reinforces the competitive dominance of tolerant species. In contrast, gap formation disrupts the competitive dominance of large trees in the canopy, since the lateral growth of small trees increased five times that of large trees. Thus, small silvicultural gaps bordered by small trees may close too quickly to permit the regeneration of mid-tolerant species. Following the release, small trees also grew faster than their larger counterparts, suggesting that lateral growth declines as the cost of reproduction increases with tree size. However, lateral growth did not vary with tree height or branch length, suggesting that lateral growth does not decline due to increasing support costs or hydraulic limitation.

Phylogenomic branch length estimation using quartets.

Branch lengths and topology of a species tree are essential in most downstream analyses, including estimation of diversification dates, characterization of selection, understanding adaptation, and comparative genomics. Modern phylogenomic analyses often use methods that account for the heterogeneity of evolutionary histories across the genome due to processes such as incomplete lineage sorting. However, these methods typically do not generate branch lengths in units that are usable by downstream applications, forcing phylogenomic analyses to resort to alternative shortcuts such as estimating branch lengths by concatenating gene alignments into a supermatrix. Yet, concatenation and other available approaches for estimating branch lengths fail to address heterogeneity across the genome. In this article, we derive expected values of gene tree branch lengths in substitution units under an extension of the multispecies coalescent (MSC) model that allows substitutions with varying rates across the species tree. We present CASTLES, a new technique for estimating branch lengths on the species tree from estimated gene trees that uses these expected values, and our study shows that CASTLES improves on the most accurate prior methods with respect to both speed and accuracy. CASTLES is available at https://github.com/ytabatabaee/CASTLES.

Rainwater infiltration and nutrient recharge system significantly enhance the growth of degraded artificial forest in semi-arid areas with shallowly buried soft bedrock

The improvement of deep soil water and nutrient pools by maximizing in situ rainwater resource utilization and soil nutrient recharge is an important approach to promote slope vegetation restoration in the Chinese Loess Plateau, especially in areas with shallowly buried soft bedrock (Pisha Sandstone). However, the effects of integrated slope engineering measures (fish scale pits (a semicircle-shaped pit), infiltration holes, organic fertilizer) on runoff utilization, nutrient restoration and vegetation growth are not well known. This study combined rainwater infiltration and nutrient recharge (RINR), and evaluated the effect of RINR on soil water, organic carbon, total nitrogen storage and vegetation growth in a field experiment in slope artificial forestland (David peach, Prunus davidiana Franch) one year later (from 2018 to 2019). Four treatments: (1) rainwater infiltration and nutrient recharge (RINR) system (fish scale pit + infiltration hole + hole filling with 30% organic fertilizer and 70% surface loess, PHF treatment), (2) fish scale pit + infiltration hole + filling surface loess (PH treatment), (3) pure fish scale pit (P treatment) and (4) natural slope land (control, SL treatment, CK). The results showed that, compared with the control (162 mm), PHF, PH and P treatments increased the soil water storage of 0–100 cm soil layer by 37 mm, 76 mm and 30 mm, respectively. The RINR system (PHF treatment) had a significantly higher restoration effect of SOC and STN value than other treatments in all soil profiles (0–100 cm), especially at 40 cm. Compared with the control (2.42 kg/m2, 0.28 kg/m2), the RINR system 0–40 cm soil layer soil organic carbon storage increased by 320%, and total nitrogen storage increased by 129%. Meanwhile, changes in height of tree (David peach), crown width and annual branch length under RINR system increased by 0.18 m, 0.30 m and 0.33 m, respectively. Overall, the RINR system is an effective approach for enhancing the growth of degraded revegetation. This system is conducive to mitigating the water and nutrient scarcity and maintaining the persistence of vegetation restoration in arid and semi-arid areas with shallowly buried soft bedrock.

Modelling internal tree attributes for breeding applications in Douglas-fir progeny trials using RPAS-ALS

Coastal Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) is one of the most commercially important softwood species in North America. In British Columbia, Canada, breeding has increased volume gains between 20 and 30%, while 97% of seedlings come from improved seed sources. Branching traits in particular, have a strong influence on strength and stiffness of Douglas-fir wood; however, they are rarely measured. Remotely Piloted Aerial Systems and Airborne Laser Scanning Systems (RPAS-LS) produce high-density three-dimensional point clouds that can be used for the creation of internal geometric features describing individual tree branching structures. We analyzed a Coastal Douglas-fir progeny test trial located in British Columbia, Canada, and developed a new method to estimate branch attributes from RPAS-LS data for inclusion as selection criteria in tree improvement programs. Branch length, angle, width, and volume were estimated for each tree. Narrow-sense heritability (the proportion of variation due to genetics) and genetic correlations were also estimated. The method extracted branch length with a correlation (r) of 0.93 compared to manual measurements. Using these branch attributes, results then show that branch angle had the highest heritability (0.277), while tree height and branch length had the highest genetic correlation (0.668). These findings are encouraging for forest managers as they indicate that branch level metrics should be considered when selecting trees in breeding programs.

Data integration in Bayesian phylogenetics.

Researchers studying the evolution of viral pathogens and other organisms increasingly encounter and use large and complex data sets from multiple different sources. Statistical research in Bayesian phylogenetics has risen to this challenge. Researchers use phylogenetics not only to reconstruct the evolutionary history of a group of organisms, but also to understand the processes that guide its evolution and spread through space and time. To this end, it is now the norm to integrate numerous sources of data. For example, epidemiologists studying the spread of a virus through a region incorporate data including genetic sequences (e.g. DNA), time, location (both continuous and discrete) and environmental covariates (e.g. social connectivity between regions) into a coherent statistical model. Evolutionary biologists routinely do the same with genetic sequences, location, time, fossil and modern phenotypes, and ecological covariates. These complex, hierarchical models readily accommodate both discrete and continuous data and have enormous combined discrete/continuous parameter spaces including, at a minimum, phylogenetic tree topologies and branch lengths. The increased size and complexity of these statistical models have spurred advances in computational methods to make them tractable. We discuss both the modeling and computational advances below, as well as unsolved problems and areas of active research.

Analyzing Phylogenetic Trees with a Tree Lattice Coordinate System and a Graph Polynomial.

Phylogenetic trees are a central tool in many areas of life science and medicine. They demonstrate evolutionary patterns among species, genes, and patterns of ancestry among sets of individuals. The tree shapes and branch lengths of phylogenetic trees encode evolutionary and epidemiological information. To extract information from tree shapes and branch lengths, representation and comparison methods for phylogenetic trees are needed. Representing and comparing tree shapes and branch lengths of phylogenetic trees are challenging, for a tree shape is unlabeled and can be displayed in numerous different forms, and branch lengths of a tree shape are specific to edges whose positions vary with respect to the displayed forms of the tree shape. In this article, we introduce representation and comparison methods for rooted unlabeled phylogenetic trees based on a tree lattice that serves as a coordinate system for rooted binary trees with branch lengths and a graph polynomial that fully characterizes tree shapes. We show that the introduced tree representations and metrics provide distance-based likelihood-free methods for tree clustering, parameter estimation, and model selection and apply the methods to analyze phylogenies reconstructed from virus sequences. [Graph polynomial; likelihood-free inference; phylogenetics; tree lattice; tree metrics.].

SpeciesRax: A Tool for Maximum Likelihood Species Tree Inference from Gene Family Trees under Duplication, Transfer, and Loss.

Species tree inference from gene family trees is becoming increasingly popular because it can account for discordance between the species tree and the corresponding gene family trees. In particular, methods that can account for multiple-copy gene families exhibit potential to leverage paralogy as informative signal. At present, there does not exist any widely adopted inference method for this purpose. Here, we present SpeciesRax, the first maximum likelihood method that can infer a rooted species tree from a set of gene family trees and can account for gene duplication, loss, and transfer events. By explicitly modeling events by which gene trees can depart from the species tree, SpeciesRax leverages the phylogenetic rooting signal in gene trees. SpeciesRax infers species tree branch lengths in units of expected substitutions per site and branch support values via paralogy-aware quartets extracted from the gene family trees. Using both empirical and simulated data sets we show that SpeciesRax is at least as accurate as the best competing methods while being one order of magnitude faster on large data sets at the same time. We used SpeciesRax to infer a biologically plausible rooted phylogeny of the vertebrates comprising 188 species from 31,612 gene families in 1 h using 40 cores. SpeciesRax is available under GNU GPL at https://github.com/BenoitMorel/GeneRax and on BioConda.

1397. Modern Lineages of Mycobacterium tuberculosis Were Recently Introduced in Western India and Demonstrate Increased Transmissibility

Background Mycobacterium tuberculosis (Mtb) transmissibility may vary between lineages (or variants) and this may contribute to the slow decline of tuberculosis incidence. The objective of our study was to compare transmissibility across four major lineages (L1-4) of Mtb in Pune, India.MethodsWe performed whole-genome sequencing (WGS) of Mtb isolated from sputum culture of adult patients with pulmonary TB. We performed genotypic susceptibility testing for both first- and second-line drugs using a previously validated random forest predictor. We identified single nucleotide polymorphisms and generated a multiple sequence alignment excluding drug resistance conferring mutations to avoid skewing the phylogeny due to convergent evolution in these regions. We used Bayesian molecular dating to generate phylogenies and compared tree characteristics using a two-sample Kolmogorov-Smirnov (KS) test.ResultsOf the 642 isolates from distinct study participants that underwent WGS, 612 met quality criteria. The median age of participants was 31 years (range 18-74), the majority were male (64.7%) and sputum smear-positive (83.3%), and 6.7% had co-infection with HIV (Table 1). There was no significant difference in baseline characteristics between lineages. The majority of isolates belonged to L3 (44.6%). The majority (61.1%) of multidrug-resistant (MDR, resistant to isoniazid and rifampin) isolates belonged to L2. In phylogenetic analysis, we found evidence of higher transmissibility of L2 as indicated by shorter branch lengths (i.e., less time had elapsed between transmission and sampling) and more genetic similarity (smaller pairwise single nucleotide polymorphism [SNP] distances) among L2 isolates as compared to other lineages (Figure 1). Branching times for L2 and L4 were smaller than L1 and L3 indicating recent introduction into the region (p < 0.001 [KS test]). Figure 1: Lineage-wise distribution of A) phylogenetic tree branch lengths (log) and B) pairwise single nucleotide polymorphism (SNP) distance, using 612 tuberculosis isolates from Pune, India. P values calculated using two-sample Kolmogorov-Smirnov test. Table 1: Demographic characteristics of study participants included in the study, by lineage.ConclusionModern Mtb lineages (L2 and L4) were relatively recently introduced in western India, as compared to older lineages (L1 and L3), with the more drug-resistant L2 showing higher transmissibility. These findings highlight the need for early detection and treatment initiation to interrupt transmission with important implications for antimicrobial stewardship and heightened surveillance of TB resistance rates.Disclosures All Authors: No reported disclosures

New Tools for Mechanical Thinning of Apricot Fruitlets

In this study, the thinner machine with yellow rod equipment was tested in relation to tree branch length and orientation in April 2019, in a narrow-canopied apricot orchard of Emilia Romagna Region, Italy. The trees were mechanically thinned with manual finishing, and comparative tests were carried out simultaneously with the ordinary hand thinning (control). Three groups of two plants were identified as replication for a total of six plants per row. Three rows were checked, considering field uniformity average. The branches were grouped into four classes according to their length: &lt;30 cm, 30–60 cm, 60–90 cm and &gt;90 cm. Branch inclination on the plant, radial or longitudinal with respect to the row, was evaluated. Fruit number before the thinning, after the first and the second machine intervention, after three days of the mechanical thinning and after the hand finishing was recorded. This experience showed satisfactory results in terms of thinning efficiency and reduced damage to both fruits and branches, as a function of the class length and insertion point in the main branch of the plant. Thinning efficiency was always kept above 37% of the left load after hand finishing, and on average between the treatments close to 44%. Fruit damages always remained below the economic thresholds to marketable production or to the plant.

Phylogenetic sampling affects evolutionary patterns of morphological disparity

AbstractCladistic character matrices are routinely repurposed in analyses of morphological disparity. Unfortunately, the sampling of taxa and characters within such datasets reflects their intended application (to resolve phylogeny, rather than distinguish between phenotypes) resulting in tree shapes that often misrepresent broader taxonomic and morphological diversity. Here we use tree shape as a proxy to explore how sampling can affect perceptions of evolving morphological disparity. Through analyses of simulated and empirical data, we demonstrate that sampling can introduce biases in morphospace occupation between clades that are predicted by differences in tree symmetry and branch length distribution. Symmetrical trees with relatively long internal branches predict more expansive patterns of morphospace occupation. Conversely, asymmetrical trees with relatively short internal branches predict more compact distributions. Additionally, we find that long external branches predict greater phenotypic divergence by peripheral morphotypes. Taken together, our results caution against the uncritical repurposing of cladistic datasets in disparity analyses. However, they also demonstrate that when morphological diversity is proportionately sampled, differences in tree shape between clades can speak to genuine differences in morphospace occupation. While cladistic datasets may serve as a useful starting point, disparity datasets must attempt to achieve uniformity in lineage sampling across time and topology. Only when all potential sources of bias are accounted for can evolutionary phenomena be distinguished from artefactual signals. It must be accepted that the non‐uniformity of the fossil record may preclude representative sampling and, therefore, a faithful characterization of the evolution of morphological disparity.

Exploring the Potential to Improve the Estimation of Boreal Tree Structural Attributes with Simple Height- and Distance-Based Competition Index

In many cases, the traditional ground-based estimates of competition between trees are not directly applicable with modern aerial inventories, due to incompatible measurements. Moreover, many former studies of competition consider extreme stand densities, hence the effect of competition under the density range in managed stands remains less explored. Here we explored the utility of a simple tree height- and distance-based competition index that provides compatibility with data produced by modern inventory methods. The index was used for the prediction of structural tree attributes in three boreal tree species growing in low to moderate densities within mixed stands. In silver birch, allometric models predicting tree diameter, crown height, and branch length all showed improvement when the effect of between-tree competition was included. A similar but non-significant trend was also present in a proxy for branch biomass. In Siberian larch, only the prediction of branch length was affected. In Scots pine, there was no improvement. The results suggest that quantification of competitive interactions based on individual tree heights and locations alone has potential to improve the prediction of tree attributes, although the outcomes can be species-specific.

ESTIMATION OF CELL LINEAGE TREES BY MAXIMUM-LIKELIHOOD PHYLOGENETICS.

CRISPR technology has enabled cell lineage tracing for complex multicellular organisms through insertion-deletion mutations of synthetic genomic barcodes during organismal development. To reconstruct the cell lineage tree from the mutated barcodes, current approaches apply general-purpose computational tools that are agnostic to the mutation process and are unable to take full advantage of the data's structure. We propose a statistical model for the CRISPR mutation process and develop a procedure to estimate the resulting tree topology, branch lengths, and mutation parameters by iteratively applying penalized maximum likelihood estimation. By assuming the barcode evolves according to a molecular clock, our method infers relative ordering across parallel lineages, whereas existing techniques only infer ordering for nodes along the same lineage. When analyzing transgenic zebrafish data from McKenna, Findlay and Gagnon et al. (2016), we find that our method recapitulates known aspects of zebrafish development and the results are consistent across samples.

Gene Tree Estimation Error with Ultraconserved Elements: An Empirical Study on Pseudapis Bees.

Summarizing individual gene trees to species phylogenies using two-step coalescent methods is now a standard strategy in the field of phylogenomics. However, practical implementations of summary methods suffer from gene tree estimation error, which is caused by various biological and analytical factors. Greatly understudied is the choice of gene tree inference method and downstream effects on species tree estimation for empirical data sets. To better understand the impact of this method choice on gene and species tree accuracy, we compare gene trees estimated through four widely used programs under different model-selection criteria: PhyloBayes, MrBayes, IQ-Tree, and RAxML. We study their performance in the phylogenomic framework of $>$800 ultraconserved elements from the bee subfamily Nomiinae (Halictidae). Our taxon sampling focuses on the genus Pseudapis, a distinct lineage with diverse morphological features, but contentious morphology-based taxonomic classifications and no molecular phylogenetic guidance. We approximate topological accuracy of gene trees by assessing their ability to recover two uncontroversial, monophyletic groups, and compare branch lengths of individual trees using the stemminess metric (the relative length of internal branches). We further examine different strategies of removing uninformative loci and the collapsing of weakly supported nodes into polytomies. We then summarize gene trees with ASTRAL and compare resulting species phylogenies, including comparisons to concatenation-based estimates. Gene trees obtained with the reversible jump model search in MrBayes were most concordant on average and all Bayesian methods yielded gene trees with better stemminess values. The only gene tree estimation approach whose ASTRAL summary trees consistently produced the most likely correct topology, however, was IQ-Tree with automated model designation (ModelFinder program). We discuss these findings and provide practical advice on gene tree estimation for summary methods. Lastly, we establish the first phylogeny-informed classification for Pseudapis s. l. and map the distribution of distinct morphological features of the group. [ASTRAL; Bees; concordance; gene tree estimation error; IQ-Tree; MrBayes, Nomiinae; PhyloBayes; RAxML; phylogenomics; stemminess].

A Major Change in Rate of Climate Niche Envelope Evolution during Hominid History.

SummaryHomo sapiens is the only species alive able to take advantage of its cognitive abilities to inhabit almost all environments on Earth. Humans are able to culturally construct, rather than biologically inherit, their occupied climatic niche to a degree unparalleled within the animal kingdom. Precisely, when hominins acquired such an ability remains unknown, and scholars disagree on the extent to which our ancestors shared this same ability. Here, we settle this issue using fine-grained paleoclimatic data, extensive archaeological data, and phylogenetic comparative methods. Our results indicate that whereas early hominins were forced to live under physiologically suitable climatic conditions, with the emergence of H. heidelbergensis, the Homo climatic niche expanded beyond its natural limits, despite progressive harshening in global climates. This indicates that technological innovations providing effective exploitation of cold and seasonal habitats predated the emergence of Homo sapiens.