Assessing the Adequacy of Morphological Models Using Posterior Predictive Simulations - Mulvey, Laura; May, Michael R.; Brown, Jeremy M.; Höhna, Sebastian; Wright, April M.; Warnock, Rachel (Systematic Biology, 2025)
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.
CRIS-Team Scopus Importer:2025-02-28
Keywords: Bayesian phylogenetic analysis; model adequacy; model selection; morphological data; morphological models; palaeobiology

From fossils to phylogenies: exploring the integration of paleontological data into Bayesian phylogenetic inference - Mulvey, Laura; Nikolic, Mark C.; Allen, Bethany J.; Heath, Tracy A.; Warnock, Rachel (Paleobiology, 2025)
Incorporating paleontological data into phylogenetic inference can greatly enrich our understanding of evolutionary relationships by providing insights into the diversity and morphological evolution of a clade over geological timescales. Phylogenetic analysis of fossil data has been significantly aided by the introduction of the fossilized birth-death (FBD) process, a model that accounts for fossil sampling through time. A decade on from the first implementation of the FBD model, we explore its use in more than 170 empirical studies, summarizing insights gained through its application. We identify a number of challenges in applying the model in practice: it requires a working knowledge of paleontological data and their complex properties, Bayesian phylogenetics, and the mechanics of evolutionary models. To address some of these difficulties, we provide an introduction to the Bayesian phylogenetic framework, discuss important aspects of paleontological data, and finally describe the assumptions of the models used in paleobiology. We also present a number of exemplar empirical studies that have used the FBD model in different ways. Through this review, we aim to provide clarity on how paleontological data can best be used in phylogenetic inference. We hope to encourage communication between model developers and empirical researchers, with the ultimate goal of developing models that better reflect the data we have and the processes that generated them.
CRIS-Team Scopus Importer:2025-05-09

Increasing the equitability of data citation in paleontology: capacity building for the big data future - Smith, Jansen A.; Raja, Nussaïbah Begum; Clements, Thomas; Dimitrijevic, Danijela; Dowding, Elizabeth; Dunne, Emma; Gee, Bryan M.; Godoy, Pedro L.; Lombardi, Elizabeth M.; Mulvey, Laura; Nätscher, Paulina; Reddin, Carl J.; et al. (Paleobiology, 2023)
Data compilations expand the scope of research; however, data citation practice lags behind advances in data use. It remains uncommon for data users to credit data producers in professionally meaningful ways. In paleontology, databases like the Paleobiology Database (PBDB) enable assessment of patterns and processes spanning millions of years, up to global scale. The status quo for data citation creates an imbalance wherein publications drawing data from the PBDB receive significantly more citations (median: 4.3 ± 3.5 citations/year) than the publications producing the data (1.4 ± 1.3 citations/year). By accounting for data reuse where citations were neglected, the projected citation rate for data-provisioning publications approached parity (4.2 ± 2.2 citations/year) and the impact factor of paleontological journals (n = 55) increased by an average of 13.4% (maximum increase = 57.8%) in 2019. Without rebalancing the distribution of scientific credit, emerging “big data” research in paleontology—and science in general—is at risk of undercutting itself through a systematic devaluation of the work that is foundational to the discipline.<br
Keywords: Biodiversity; Open science; Paleobiology Database; Specimen-based; Taxonomy

Where traditional extinction estimates fall flat: using novel cophylogenetic methods to estimate extinction risk in platyhelminths - Mulvey, Laura; Warnock, Rachel; De Baets, Kenneth (Proceedings of the Royal Society of London, Series B: Biological Sciences, 2022)
Today parasites comprise a huge proportion of living biodiversity and play a major role in shaping community structure. Given their ecological significance, parasite extinctions could result in massive cascading effects across ecosystems. It is therefore crucial that we have a way of estimating their extinction risk. Attempts to do this have often relied on information about host extinction risk, without explicitly incorporating information about the parasites. However, assuming an identical risk may be misleading. Here, we apply a novel metric to estimate the cophylogenetic extinction rate, Ec, of parasites with their hosts. This metric incorporates information about the evolutionary history of parasites and hosts that can be estimated using event-based cophylogenetic methods. To explore this metric, we investigated the use of different cophylogenetic methods to inform the Ec rate, based on the analysis of polystome parasites and their anuran hosts. We show using both parsimony- and model-based approaches that different methods can have a large effect on extinction risk estimation. Further, we demonstrate that model-based approaches offer greater potential to provide insights into cophylogenetic history and extinction risk.
CRIS-Team Scopus Importer:2022-09-09
Keywords: conservation; cophylogenetic extinction risk; cophylogenetics; host–parasite interactions; parasites; platyhelminths