New Guinea has the world's richest island flora.

New Guinea is the world's largest tropical island and has fascinated naturalists for centuries1,2. Home to some of the best-preserved ecosystems on the planet3 and to intact ecological gradients-from mangroves to tropical alpine grasslands-that are unmatched in the Asia-Pacific region4,5, it is a globally recognized centre of biological and cultural diversity6,7. So far, however, there has been no attempt to critically catalogue the entire vascular plant diversity of New Guinea. Here we present the first, to our knowledge, expert-verified checklist of the vascular plants of mainland New Guinea and surrounding islands. Our publicly available checklist includes 13,634 species (68% endemic), 1,742 genera and 264 families-suggesting that New Guinea is the most floristically diverse island in the world. Expert knowledge is essential for building checklists in the digital era: reliance on online taxonomic resources alone would have inflated species counts by 22%. Species discovery shows no sign of levelling off, and we discuss steps to accelerate botanical research in the 'Last Unknown'8.

Phylogenetic Biodiversity Metrics Should Account for Both Accumulation and Attrition of Evolutionary Heritage.

Phylogenetic metrics are essential tools used in the study of ecology, evolution and conservation. Phylogenetic diversity (PD) in particular is one of the most prominent measures of biodiversity and is based on the idea that biological features accumulate along the edges of phylogenetic trees that are summed. We argue that PD and many other phylogenetic biodiversity metrics fail to capture an essential process that we term attrition. Attrition is the gradual loss of features through causes other than extinction. Here we introduce "EvoHeritage", a generalization of PD that is founded on the joint processes of accumulation and attrition of features. We argue that while PD measures evolutionary history, EvoHeritage is required to capture a more pertinent subset of evolutionary history including only components that have survived attrition. We show that EvoHeritage is not the same as PD on a tree with scaled edges; instead, accumulation and attrition interact in a more complex non-monophyletic way that cannot be captured by edge lengths alone. This leads us to speculate that the one-dimensional edge lengths of classic trees may be insufficiently flexible to capture the nuances of evolutionary processes. We derive a measure of EvoHeritage and show that it elegantly reproduces species richness and PD at opposite ends of a continuum based on the intensity of attrition. We demonstrate the utility of EvoHeritage in ecology as a predictor of community productivity compared with species richness and PD. We also show how EvoHeritage can quantify living fossils and resolve their associated controversy. We suggest how the existing calculus of PD-based metrics and other phylogenetic biodiversity metrics can and should be recast in terms of EvoHeritage accumulation and attrition.

Detection of Ghost Introgression Requires Exploiting Topological and Branch Length Information.

In recent years, the study of hybridization and introgression has made significant progress, with ghost introgression-the transfer of genetic material from extinct or unsampled lineages to extant species-emerging as a key area for research. Accurately identifying ghost introgression, however, presents a challenge. To address this issue, we focused on simple cases involving 3 species with a known phylogenetic tree. Using mathematical analyses and simulations, we evaluated the performance of popular phylogenetic methods, including HyDe and PhyloNet/MPL, and the full-likelihood method, Bayesian Phylogenetics and Phylogeography (BPP), in detecting ghost introgression. Our findings suggest that heuristic approaches relying on site-pattern counts or gene-tree topologies struggle to differentiate ghost introgression from introgression between sampled non-sister species, frequently leading to incorrect identification of donor and recipient species. The full-likelihood method BPP uses multilocus sequence alignments directly-hence taking into account both gene-tree topologies and branch lengths, by contrast, is capable of detecting ghost introgression in phylogenomic datasets. We analyzed a real-world phylogenomic dataset of 14 species of Jaltomata (Solanaceae) to showcase the potential of full-likelihood methods for accurate inference of introgression.

The Limits of the Constant-rate Birth-Death Prior for Phylogenetic Tree Topology Inference.

Birth-death models are stochastic processes describing speciation and extinction through time and across taxa and are widely used in biology for inference of evolutionary timescales. Previous research has highlighted how the expected trees under the constant-rate birth-death (crBD) model tend to differ from empirical trees, for example, with respect to the amount of phylogenetic imbalance. However, our understanding of how trees differ between the crBD model and the signal in empirical data remains incomplete. In this Point of View, we aim to expose the degree to which the crBD model differs from empirically inferred phylogenies and test the limits of the model in practice. Using a wide range of topology indices to compare crBD expectations against a comprehensive dataset of 1189 empirically estimated trees, we confirm that crBD model trees frequently differ topologically compared with empirical trees. To place this in the context of standard practice in the field, we conducted a meta-analysis for a subset of the empirical studies. When comparing studies that used Bayesian methods and crBD priors with those that used other non-crBD priors and non-Bayesian methods (i.e., maximum likelihood methods), we do not find any significant differences in tree topology inferences. To scrutinize this finding for the case of highly imbalanced trees, we selected the 100 trees with the greatest imbalance from our dataset, simulated sequence data for these tree topologies under various evolutionary rates, and re-inferred the trees under maximum likelihood and using the crBD model in a Bayesian setting. We find that when the substitution rate is low, the crBD prior results in overly balanced trees, but the tendency is negligible when substitution rates are sufficiently high. Overall, our findings demonstrate the general robustness of crBD priors across a broad range of phylogenetic inference scenarios but also highlight that empirically observed phylogenetic imbalance is highly improbable under the crBD model, leading to systematic bias in data sets with limited information content.

Deep Learning and Likelihood Approaches for Viral Phylogeography Converge on the Same Answers Whether the Inference Model Is Right or Wrong.

Analysis of phylogenetic trees has become an essential tool in epidemiology. Likelihood-based methods fit models to phylogenies to draw inferences about the phylodynamics and history of viral transmission. However, these methods are often computationally expensive, which limits the complexity and realism of phylodynamic models and makes them ill-suited for informing policy decisions in real-time during rapidly developing outbreaks. Likelihood-free methods using deep learning are pushing the boundaries of inference beyond these constraints. In this paper, we extend, compare, and contrast a recently developed deep learning method for likelihood-free inference from trees. We trained multiple deep neural networks using phylogenies from simulated outbreaks that spread among 5 locations and found they achieve close to the same levels of accuracy as Bayesian inference under the true simulation model. We compared robustness to model misspecification of a trained neural network to that of a Bayesian method. We found that both models had comparable performance, converging on similar biases. We also implemented a method of uncertainty quantification called conformalized quantile regression that we demonstrate has similar patterns of sensitivity to model misspecification as Bayesian highest posterior density (HPD) and greatly overlap with HPDs, but have lower precision (more conservative). Finally, we trained and tested a neural network against phylogeographic data from a recent study of the SARS-Cov-2 pandemic in Europe and obtained similar estimates of region-specific epidemiological parameters and the location of the common ancestor in Europe. Along with being as accurate and robust as likelihood-based methods, our trained neural networks are on average over 3 orders of magnitude faster after training. Our results support the notion that neural networks can be trained with simulated data to accurately mimic the good and bad statistical properties of the likelihood functions of generative phylogenetic models.

Tree Visualization By One Table (tvBOT): a web application for visualizing, modifying and annotating phylogenetic trees.

tvBOT is a user-friendly and efficient web application for visualizing, modifying, and annotating phylogenetic trees. It is highly efficient in data preparation without requiring redundant style and syntax data. Tree annotations are powered by a data-driven engine that only requires practical data organized in uniform formats and saved as one table file. A layer manager is developed to manage annotation dataset layers, allowing the addition of a specific layer by selecting the columns of a corresponding annotation data file. Furthermore, tvBOT renders style adjustments in real-time and diversified ways. All style adjustments can be made on a highly interactive user interface and are available for mobile devices. The display engine allows the changes to be updated and rendered in real-time. In addition, tvBOT supports the combination display of 26 annotation dataset types to achieve multiple formats for tree annotations with reusable phylogenetic data. Besides several publication-ready graphics formats, JSON format can be exported to save the final drawing state and all related data, which can be shared with other users, uploaded to restore the final drawing state for re-editing or used as a style template for quickly retouching a new tree file. tvBOT is freely available at: https://www.chiplot.online/tvbot.html.

Using pose estimation to identify regions and points on natural history specimens.

A key challenge in mobilising growing numbers of digitised biological specimens for scientific research is finding high-throughput methods to extract phenotypic measurements on these datasets. In this paper, we test a pose estimation approach based on Deep Learning capable of accurately placing point labels to identify key locations on specimen images. We then apply the approach to two distinct challenges that each requires identification of key features in a 2D image: (i) identifying body region-specific plumage colouration on avian specimens and (ii) measuring morphometric shape variation in Littorina snail shells. For the avian dataset, 95% of images are correctly labelled and colour measurements derived from these predicted points are highly correlated with human-based measurements. For the Littorina dataset, more than 95% of landmarks were accurately placed relative to expert-labelled landmarks and predicted landmarks reliably captured shape variation between two distinct shell ecotypes ('crab' vs 'wave'). Overall, our study shows that pose estimation based on Deep Learning can generate high-quality and high-throughput point-based measurements for digitised image-based biodiversity datasets and could mark a step change in the mobilisation of such data. We also provide general guidelines for using pose estimation methods on large-scale biological datasets.

TRAPID 2.0: a web application for taxonomic and functional analysis of de novo transcriptomes.

Advances in high-throughput sequencing have resulted in a massive increase of RNA-Seq transcriptome data. However, the promise of rapid gene expression profiling in a specific tissue, condition, unicellular organism or microbial community comes with new computational challenges. Owing to the limited availability of well-resolved reference genomes, de novo assembled (meta)transcriptomes have emerged as popular tools for investigating the gene repertoire of previously uncharacterized organisms. Yet, despite their potential, these datasets often contain fragmented or contaminant sequences, and their analysis remains difficult. To alleviate some of these challenges, we developed TRAPID 2.0, a web application for the fast and efficient processing of assembled transcriptome data. The initial processing phase performs a global characterization of the input data, providing each transcript with several layers of annotation, comprising structural, functional, and taxonomic information. The exploratory phase enables downstream analyses from the web application. Available analyses include the assessment of gene space completeness, the functional analysis and comparison of transcript subsets, and the study of transcripts in an evolutionary context. A comparison with similar tools highlights TRAPID's unique features. Finally, analyses performed within TRAPID 2.0 are complemented by interactive data visualizations, facilitating the extraction of new biological insights, as demonstrated with diatom community metatranscriptomes.

DNA barcodes for the pipefish genus Corythoichthys (Actinopterygii: Syngnathiformes) from the Indian Ocean provide insights into cryptic diversity.

The syngnathiform genus Corythoichthys comprises a group of taxonomically complex, tail-brooding (Syngnathinae) pipefishes widely distributed in the Indo-Pacific region. Due to the presence of overlapping interspecific morphological characters, reliable taxonomic information on Corythoichthys is still lacking. Using 52 CO1 sequences, including seven newly generated, a phylogenetic analysis was carried out to understand the genetic diversity, distribution and 'species groups' within the genus Corythoichthys. Species delimitation using Automatic Barcode Gap Discovery (ABGD) analysis confirmed the presence of 13 species which include 'species-complexes' previously considered as a single taxon. Our results revealed the presence of three species groups, 'C. amplexus', 'C. conspicillatus' and 'C. haematopterus' and four unidentified/undescribed species in the wider Indo-Pacific realm. Interestingly, 60 sequences and a mitogenome identified as Corythoichthys in GenBank are misidentified at the genus level. Based on our findings, we suggest that the taxonomy and systematics of Corythoichthys need to be re-examined and validated using integrative methods, and care should be taken while selecting specimens for genetic studies.

Digital Single-operator Cholangioscopy (DSOC) Improves Interobserver Agreement (IOA) and Accuracy for Evaluation of Indeterminate Biliary Strictures: The Monaco Classification.

BACKGROUND: Visual characteristics seen during digital single-operator cholangioscopy (DSOC) have not been validated. The aim of this 2-phase study was to define terminology by consensus for the visual diagnosis of biliary lesions to develop a model for optimization of the diagnostic performance of DSOC. MATERIALS AND METHODS: In phase 1 (criteria identification), video-cholangioscopy clips were reviewed by 12 expert biliary endoscopists, who were blinded to the final diagnosis. Visual criteria were consolidated into the following categories: (1) stricture, (2) lesion, (3) mucosal features, (4) papillary projections, (5) ulceration, (6) abnormal vessels, (7) scarring, (8) pronounced pit pattern.During the second phase (validation), 14 expert endoscopists reviewed DSOC (SpyGlass DS, Boston Scientific) clips using the 8 criteria to assess interobserver agreement (IOA) rate. RESULTS: In phase 1, consensus for visual findings were categorized into 8 criteria titled the "Monaco Classification." The frequency of criteria were: (1) presence of stricture-75%, (2) presence of lesion type-55%, (3) mucosal features-55%, (4) papillary projections-45%, (5) ulceration-42.5%, (6) abnormal vessels-10%, (7) scarring-40%, and (8) pronounced pit pattern-10%. The accuracy on final diagnosis based on visual impression alone was 70%.In phase 2, the IOA rate using Monaco Classification criteria ranged from slight to fair. The presumptive diagnosis IOA was fair (κ=0.31, SE=0.02), and overall diagnostic accuracy was 70%. CONCLUSIONS: The Monaco classification identifies 8 visual criteria for biliary lesions on single-operator digital cholangioscopy. Using the criteria, the IOA and diagnostic accuracy rate of DSOC is improved compared with prior studies.

Do we have infraspecific taxa of Salvia macrosiphon Boiss. (Lamiaceae) in Iran?

BACKGROUND: Salvia macrosiphon is an aromatic perennial species of Lamiaceae family that grows naturally in different parts of Iran. This herb is widely used in folk and modern medicine. Although in Flora Iranica and Flora of Iran, no infraspecific taxonomic rank has been detected for S. macrosiphon, some infraspecific taxonomic ranks have been reported. In the current study, we evaluated the genetic diversity and structure of 11 populations of this species to detect inter and intrapopulation genetic diversity and to survey the possibility of infraspecific taxonomic ranks in this species. METHODS AND RESULTS: We utilized the modified C-TAB protocol for DNA extraction and amplified the genomes using several SCoT molecular markers. We calculated of genetic diversity and polymorphism parameters using GenAlex 6.4, Geno-Dive ver.2, PopGene, PopART and Structure 2.3.4. The parameters of genetic polymorphism differed between the populations. Moreover, a low rate of gene flow supported a moderate level of population's genetic diversity and differentiation. According to haplotypes network (TCS) analysis, a high level of genetic mutation has occurred among the individuals of some populations leading to high intrapopulation diversity. On the basis of structure analysis and Nei's genetic distance, the examined populations were classified into four genotypes. CONCLUSIONS: The clustering pattern of the populations in each group was not related to geographical distance or phytogeography. It seems that the wide geographic distribution, a small gene flow rate and the occurrence of a high level of genetic mutation lead to infraspecific genetic differentiation in the species and we suppose some infraspecific ranks exist for it.

Comparing Published Gut Microbiome Taxonomic Data Across Multinational Studies.

BACKGROUND: Nurse researchers are well poised to study the connection of the microbiome to health and disease. Evaluating published microbiome results can assist with study design and hypothesis generation. OBJECTIVES: This article aims to present and define important analysis considerations in microbiome study planning and to identify genera shared across studies despite methodological differences. This methods article will highlight a workflow that the nurse scientist can use to combine and evaluate taxonomy tables for microbiome study or research proposal planning. METHODS: We compiled taxonomy tables from 13 published gut microbiome studies that had used Ion Torrent sequencing technology. We searched for studies that had amplified multiple hypervariable (V) regions of the 16S rRNA gene when sequencing the bacteria from healthy gut samples. RESULTS: We obtained 15 taxonomy tables from the 13 studies, comprised of samples from four continents and eight V regions. Methodology among studies was highly variable, including differences in V regions amplified, geographic location, and population demographics. Nevertheless, of the 354 total genera identified from the 15 data sets, 25 were shared in all V regions and the four continents. When relative abundance differences across the V regions were compared, Dorea and Roseburia were statistically different. Taxonomy tables from Asian subjects had increased average abundances of Prevotella and lowered abundances of Bacteroides compared with the European, North American, and South American study subjects. DISCUSSION: Evaluating taxonomy tables from previously published literature is essential for study planning. The genera found from different V regions and continents highlight geography and V region as important variables to consider in microbiome study design. The 25 shared genera across the various studies may represent genera commonly found in healthy gut microbiomes. Understanding the factors that may affect the results from a variety of microbiome studies will allow nurse scientists to plan research proposals in an informed manner. This work presents a valuable framework for future cross-study comparisons conducted across the globe.

External validation of the EULAR/ACR idiopathic inflammatory myopathies classification criteria with a Japanese paediatric cohort.

OBJECTIVES: To assess the performance of the EULAR/ACR idiopathic inflammatory myopathies (IIMs) classification criteria to classify juvenile IIMs (JIIMs) in an Asian paediatric population. METHODS: Sixty-eight JIIM patients and 49 non-JIIM patients diagnosed at seven major paediatric rheumatology centres in Japan between 2008 and 2015 were enrolled. Retrospective data were collected, and each patient's data form was submitted. The expert group reviewed the forms and re-examined the diagnoses. The EULAR/ACR criteria were then applied and the probability of having JIIM was determined for each case. The sensitivity and specificity of the EULAR/ACR criteria were compared with those of other existing criteria. RESULTS: The sensitivity/specificity of the EULAR/ACR classification criteria were 92.1/100% with muscle biopsy data (n = 38); 86.7/100% without muscle biopsy data (n = 30) and 89.7/100% in our total cohort (n = 68). The sensitivity of Bohan and Peter's criteria and Tanimoto's criteria were 80.9 and 64.7% in our total cohort, respectively. Among 68 physician-diagnosed JIIM patients, seven cases (three JDM and four overlap myositis) were not classified as JIIM because the probability did not reach the cut-off point (55%). The three JDM patients all presented with only one of the three skin manifestations that are listed in the criteria: Gottron's sign. CONCLUSION: Our validation study with Japanese JIIM cases indicates that the EULAR/ACR classification criteria for IIM generally perform better than existing diagnostic criteria for myositis.

The Effect of Fossil Sampling on the Estimation of Divergence Times with the Fossilized Birth-Death Process.

Timescales are of fundamental importance to evolutionary biology as they facilitate hypothesis tests of historical evolutionary processes. Through the incorporation of fossil occurrence data, the fossilized birth-death (FBD) process provides a framework for estimating divergence times using more paleontological data than traditional node calibration approaches have allowed. The inclusion of more data can refine evolutionary timescale estimates, but for many taxonomic groups it is computationally infeasible to include all available fossil occurrence data. Here, we utilize both empirical data and a simulation framework to identify approaches to subsampling fossil occurrence data that result in the most accurate estimates of divergence times. To achieve this we assess the performance of the FBD-Skyline model when implementing multiple approaches to incorporating subsampled fossil occurrence data. Our results demonstrate that it is necessary to account for all available fossil occurrence data to achieve the most accurate estimates of clade age. We show that this can be achieved if an empirical Bayes approach, accounting for fossil sampling through time, is applied to the FBD process. Random subsampling of occurrence data can lead to estimates of clade age that are incompatible with fossil evidence if no control over the affinities of fossil occurrences is enforced. Our results call into question the accuracy of previous divergence time studies incorporating the FBD process that have used only a subsample of all available fossil occurrence data.

Interdependent Phenotypic and Biogeographic Evolution Driven by Biotic Interactions.

Biotic interactions are hypothesized to be one of the main processes shaping trait and biogeographic evolution during lineage diversification. Theoretical and empirical evidence suggests that species with similar ecological requirements either spatially exclude each other, by preventing the colonization of competitors or by driving coexisting populations to extinction, or show niche divergence when in sympatry. However, the extent and generality of the effect of interspecific competition in trait and biogeographic evolution has been limited by a dearth of appropriate process-generating models to directly test the effect of biotic interactions. Here, we formulate a phylogenetic parametric model that allows interdependence between trait and biogeographic evolution, thus enabling a direct test of central hypotheses on how biotic interactions shape these evolutionary processes. We adopt a Bayesian data augmentation approach to estimate the joint posterior distribution of trait histories, range histories, and coevolutionary process parameters under this analytically intractable model. Through simulations, we show that our model is capable of distinguishing alternative scenarios of biotic interactions. We apply our model to the radiation of Darwin's finches-a classic example of adaptive divergence-and find limited support for in situ trait divergence in beak size, but stronger evidence for convergence in traits such as beak shape and tarsus length and for competitive exclusion throughout their evolutionary history. These findings are more consistent with presympatric, rather than postsympatric, niche divergence. Our modeling framework opens new possibilities for testing more complex hypotheses about the processes underlying lineage diversification. More generally, it provides a robust probabilistic methodology to model correlated evolution of continuous and discrete characters. [Bayesian; biotic interactions; competition; data augmentation; historical biogeography; trait evolution.].

Model-Based Species Delimitation: Are Coalescent Species Reproductively Isolated?

A large and growing fraction of systematists define species as independently evolving lineages that may be recognized by analyzing the population genetic history of alleles sampled from individuals belonging to those species. This has motivated the development of increasingly sophisticated statistical models rooted in the multispecies coalescent process. Specifically, these models allow for simultaneous estimation of the number of species present in a sample of individuals and the phylogenetic history of those species using only DNA sequence data from independent loci. These methods hold extraordinary promise for increasing the efficiency of species discovery but require extensive validation to ensure that they are accurate and precise. Whether the species identified by these methods correspond to the species that would be recognized by alternative species recognition criteria (such as measurements of reproductive isolation) is currently an open question and a subject of vigorous debate. Here, we perform an empirical test of these methods by making use of a classic model system in the history of speciation research, flies of the genus Drosophila. Specifically, we use the uniquely comprehensive data on reproductive isolation that is available for this system, along with DNA sequence data, to ask whether Drosophila species inferred under the multispecies coalescent model correspond to those recognized by many decades of speciation research. We found that coalescent based and reproductive isolation-based methods of inferring species boundaries are concordant for 77% of the species pairs. We explore and discuss potential explanations for these discrepancies. We also found that the amount of prezygotic isolation between two species is a strong predictor of the posterior probability of species boundaries based on DNA sequence data, regardless of whether the species pairs are sympatrically or allopatrically distributed. [BPP; Drosophila speciation; genetic distance; multispecies coalescent.].

Adaptive Estimation for Epidemic Renewal and Phylogenetic Skyline Models.

Estimating temporal changes in a target population from phylogenetic or count data is an important problem in ecology and epidemiology. Reliable estimates can provide key insights into the climatic and biological drivers influencing the diversity or structure of that population and evidence hypotheses concerning its future growth or decline. In infectious disease applications, the individuals infected across an epidemic form the target population. The renewal model estimates the effective reproduction number, R, of the epidemic from counts of observed incident cases. The skyline model infers the effective population size, N, underlying a phylogeny of sequences sampled from that epidemic. Practically, R measures ongoing epidemic growth while N informs on historical caseload. While both models solve distinct problems, the reliability of their estimates depends on p-dimensional piecewise-constant functions. If p is misspecified, the model might underfit significant changes or overfit noise and promote a spurious understanding of the epidemic, which might misguide intervention policies or misinform forecasts. Surprisingly, no transparent yet principled approach for optimizing p exists. Usually, p is heuristically set, or obscurely controlled via complex algorithms. We present a computable and interpretable p-selection method based on the minimum description length (MDL) formalism of information theory. Unlike many standard model selection techniques, MDL accounts for the additional statistical complexity induced by how parameters interact. As a result, our method optimizes p so that R and N estimates properly and meaningfully adapt to available data. It also outperforms comparable Akaike and Bayesian information criteria on several classification problems, given minimal knowledge of the parameter space, and exposes statistical similarities among renewal, skyline, and other models in biology. Rigorous and interpretable model selection is necessary if trustworthy and justifiable conclusions are to be drawn from piecewise models. [Coalescent processes; epidemiology; information theory; model selection; phylodynamics; renewal models; skyline plots].

Evaluating the Performance of Probabilistic Algorithms for Phylogenetic Analysis of Big Morphological Datasets: A Simulation Study.

Reconstructing the tree of life is an essential task in evolutionary biology. It demands accurate phylogenetic inference for both extant and extinct organisms, the latter being almost entirely dependent on morphological data. While parsimony methods have traditionally dominated the field of morphological phylogenetics, a rapidly growing number of studies are now employing probabilistic methods (maximum likelihood and Bayesian inference). The present-day toolkit of probabilistic methods offers varied software with distinct algorithms and assumptions for reaching global optimality. However, benchmark performance assessments of different software packages for the analyses of morphological data, particularly in the era of big data, are still lacking. Here, we test the performance of four major probabilistic software under variable taxonomic sampling and missing data conditions: the Bayesian inference-based programs MrBayes and RevBayes, and the maximum likelihood-based IQ-TREE and RAxML. We evaluated software performance by calculating the distance between inferred and true trees using a variety of metrics, including Robinson-Foulds (RF), Matching Splits (MS), and Kuhner-Felsenstein (KF) distances. Our results show that increased taxonomic sampling improves accuracy, precision, and resolution of reconstructed topologies across all tested probabilistic software applications and all levels of missing data. Under the RF metric, Bayesian inference applications were the most consistent, accurate, and robust to variation in taxonomic sampling in all tested conditions, especially at high levels of missing data, with little difference in performance between the two tested programs. The MS metric favored more resolved topologies that were generally produced by IQ-TREE. Adding more taxa dramatically reduced performance disparities between programs. Importantly, our results suggest that the RF metric penalizes incorrectly resolved nodes (false positives) more severely than the MS metric, which instead tends to penalize polytomies. If false positives are to be avoided in systematics, Bayesian inference should be preferred over maximum likelihood for the analysis of morphological data.