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1. Handling Logical Character Dependency in Phylogenetic Inference: Extensive Performance Testing of Assumptions and Solutions Using Simulated and Empirical Data

   https://doi.org/10.1093/sysbio/syad006  

   Simões, TR; Vernygora, OV; and de Medeiros, BAS (April 2023, Systematic biology)

   Liliana Davalos (Ed.)

   Logical character dependency is a major conceptual and methodological problem in phylogenetic inference of morphological data sets, as it violates the assumption of character independence that is common to all phylogenetic methods. It is more frequently observed in higher-level phylogenies or in data sets characterizing major evolutionary transitions, as these represent parts of the tree of life where (primary) anatomical characters either originate or disappear entirely. As a result, secondary traits related to these primary characters become “inapplicable” across all sampled taxa in which that character is absent. Various solutions have been explored over the last three decades to handle character dependency, such as alternative character coding schemes and, more recently, new algorithmic implementations. However, the accuracy of the proposed solutions, or the impact of character dependency across distinct optimality criteria, has never been directly tested using standard performance measures. Here, we utilize simple and complex simulated morphological data sets analyzed under different maximum parsimony optimization procedures and Bayesian inference to test the accuracy of various coding and algorithmic solutions to character dependency. This is complemented by empirical analyses using a recoded data set on palaeognathid birds. We find that in small, simulated data sets, absent coding performs better than other popular coding strategies available (contingent and multistate), whereas in more complex simulations (larger data sets controlled for different tree structure and character distribution models) contingent coding is favored more frequently. Under contingent coding, a recently proposed weighting algorithm produces the most accurate results for maximum parsimony. However, Bayesian inference outperforms all parsimony-based solutions to handle character dependency due to fundamental differences in their optimization procedures—a simple alternative that has been long overlooked. Yet, we show that the more primary characters bearing secondary (dependent) traits there are in a data set, the harder it is to estimate the true phylogenetic tree, regardless of the optimality criterion, owing to a considerable expansion of the tree parameter space. 

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2. Incorporating Hierarchical Characters into Phylogenetic Analysis

   https://doi.org/10.1093/sysbio/syab005  

   Hopkins, Melanie J; St. John, Katherine (February 2021, Systematic Biology)

   Wright, April (Ed.)

   Abstract Popular optimality criteria for phylogenetic trees focus on sequences of characters that are applicable to all the taxa. As studies grow in breadth, it can be the case that some characters are applicable for a portion of the taxa and inapplicable for others. Past work has explored the limitations of treating inapplicable characters as missing data, noting that this strategy may favor trees where internal nodes are assigned impossible states, where the arrangement of taxa within subclades is unduly influenced by variation in distant parts of the tree, and/or where taxa that otherwise share most primary characters are grouped distantly. Approaches that avoid the first two problems have recently been proposed. Here, we propose an alternative approach which avoids all three problems. We focus on data matrices that use reductive coding of traits, that is, explicitly incorporate the innate hierarchy induced by inapplicability, and as such our approach extend to hierarchical characters, in general. In the spirit of maximum parsimony, the proposed criterion seeks the phylogenetic tree with the minimal changes across any tree branch, but where changes are defined in terms of dissimilarity metrics that weigh the effects of inapplicable characters. The approach can accommodate binary, multistate, ordered, unordered, and polymorphic characters. We give a polynomial-time algorithm, inspired by Fitch’s algorithm, to score trees under a family of dissimilarity metrics, and prove its correctness. We show that the resulting optimality criteria is computationally hard, by reduction to the NP-hardness of the maximum parsimony optimality criteria. We demonstrate our approach using synthetic and empirical data sets and compare the results with other recently proposed methods for choosing optimal phylogenetic trees when the data includes hierarchical characters. [Character optimization, dissimilarity metrics, hierarchical characters, inapplicable data, phylogenetic tree search.] 

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3. Testing the phylogeny of Periptychidae and "archaic" Paleocene mammals uner different optimality criteria

   Puschel, H. P.; Shelley, S. L.; Williamson, T. E.; Wible, J. R.; Brusatte, S. L. (October 2019, Journal of Vertebrate Paleontology, Abstracts and Program)

   It is now well established that the end-Cretaceous mass extinction had enormous repercussions for mammalian evolution. Following the extinction, during the Paleocene, mammals started to radiate, occupying new and diverse ecological niches. However, the phylogenetic relationships between the socalled “archaic” mammals of this time, and their position within Placentalia, remain contentious. The Periptychidae are a clade of distinctive “archaic” ungulates, composed of ~17 genera of small to large bodied, highly bunodont, terrestrial herbivores that were among the first placental mammals to appear after the end-Cretaceous mass extinction. Although the Periptychidae has been historically considered a distinctive “condylarth” subgroup, their higherlevel relationships have been rarely tested. Here, we present an inclusive cladistic analysis to determine and test the phylogenetic affinities of Periptychidae and other key Paleocene groups within Placentalia under different cladistic optimality criteria. We scored 140 taxa for 503 dental, cranial and postcranial characters, incorporating new morphological and taxonomic data. The data were then subject to parsimony and Bayesian tree of morphological evolution, running 5000000 generations with samples every 200 generations and discarding 25% of the samples as burn-in. Stationarity was achieved and a 50 percent majority rule consensus tree from the sampled trees was obtained. The parsimony analysis recovered 48 most parsimonious trees. The two consensus trees derived from the different analyses are largely congruent and recover a monophyletic Periptychidae, although the parsimony consensus tree is better resolved. These results are consistent with simulation studies showing that parsimony tends to be more precise (more nodes reconstructed) than Bayesian analyses, although less accurate. The main topological differences between the results relate to the position of poorly known Puercan (earliest Paleocene) species. Our results affirm the monophyly of Periptychidae and its nesting within a group of “condylarths” positioned at the base of Laurasiatheria and closely related to Artiodactyla. Within Periptychidae we found support for the three major subfamilial divisions in both analyses. These results highlight the importance of using different optimality criteria when resolving a phylogeny and provide a new insight into how placental mammals were evolving after the end-Cretaceous extinction. Grant Information: CONICYT PFCHA/DOCTORADO BECAS CHILE/2018, European Research Council Starting Grant (ERC StG 2017, 756226, PalM), National Science Foundation (NSF EAR 1654952, DEB 1654949) 

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4. Assessing the Adequacy of Morphological Models Using Posterior Predictive Simulations

   https://doi.org/10.1093/sysbio/syae055  

   Mulvey, Laura P_A; May, Michael R; Brown, Jeremy M; Höhna, Sebastian; Wright, April M; Warnock, Rachel C_M (October 2024, Systematic Biology)

   Klopfstein, Seraina (Ed.)

   Abstract Reconstructing the evolutionary history of different groups of organisms provides insight into how life originated and diversified on Earth. Phylogenetic trees are commonly used to estimate this evolutionary history. Within Bayesian phylogenetics a major step in estimating a tree is in choosing an appropriate model of character evolution. While the most common character data used is molecular sequence data, morphological data remains a vital source of information. The use of morphological characters allows for the incorporation fossil taxa, and despite advances in molecular sequencing, continues to play a significant role in neontology. Moreover, it is the main data source that allows us to unite extinct and extant taxa directly under the same generating process. We therefore require suitable models of morphological character evolution, the most common being the Mk Lewis model. While it is frequently used in both palaeobiology and neontology, it is not known whether the simple Mk substitution model, or any extensions to it, provide a sufficiently good description of the process of morphological evolution. In this study we investigate the impact of different morphological models on empirical tetrapod datasets. Specifically, we compare unpartitioned Mk models with those where characters are partitioned by the number of observed states, both with and without allowing for rate variation across sites and accounting for ascertainment bias. We show that the choice of substitution model has an impact on both topology and branch lengths, highlighting the importance of model choice. Through simulations, we validate the use of the model adequacy approach, posterior predictive simulations, for choosing an appropriate model. Additionally, we compare the performance of model adequacy with Bayesian model selection. We demonstrate how model selection approaches based on marginal likelihoods are not appropriate for choosing between models with partition schemes that vary in character state space (i.e., that vary in Q-matrix state size). Using posterior predictive simulations, we found that current variations of the Mk model are often performing adequately in capturing the evolutionary dynamics that generated our data. We do not find any preference for a particular model extension across multiple datasets, indicating that there is no “one size fits all” when it comes to morphological data and that careful consideration should be given to choosing models of discrete character evolution. By using suitable models of character evolution, we can increase our confidence in our phylogenetic estimates, which should in turn allow us to gain more accurate insights into the evolutionary history of both extinct and extant taxa. 

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5. Maximum Parsimony Inference of Phylogenetic Networks in the Presence of Polyploid Complexes

   https://doi.org/10.1093/sysbio/syab081  

   Yan, Zhi; Cao, Zhen; Liu, Yushu; Ogilvie, Huw A; Nakhleh, Luay (October 2021, Systematic Biology)

   Holder, Mark (Ed.)

   Abstract Phylogenetic networks provide a powerful framework for modeling and analyzing reticulate evolutionary histories. While polyploidy has been shown to be prevalent not only in plants but also in other groups of eukaryotic species, most work done thus far on phylogenetic network inference assumes diploid hybridization. These inference methods have been applied, with varying degrees of success, to data sets with polyploid species, even though polyploidy violates the mathematical assumptions underlying these methods. Statistical methods were developed recently for handling specific types of polyploids and so were parsimony methods that could handle polyploidy more generally yet while excluding processes such as incomplete lineage sorting. In this article, we introduce a new method for inferring most parsimonious phylogenetic networks on data that include polyploid species. Taking gene tree topologies as input, the method seeks a phylogenetic network that minimizes deep coalescences while accounting for polyploidy. We demonstrate the performance of the method on both simulated and biological data. The inference method as well as a method for evaluating evolutionary hypotheses in the form of phylogenetic networks are implemented and publicly available in the PhyloNet software package. [Incomplete lineage sorting; minimizing deep coalescences; multilabeled trees; multispecies network coalescent; phylogenetic networks; polyploidy.] 

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