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Series: Elements of Paleontology
Phylogenetic Comparative Methods: A User's Guide for Paleontologists
Published online by Cambridge University Press: 21 April 2021
Summary
Recent advances in statistical approaches called phylogenetic comparative methods (PCMs) have provided paleontologists with a powerful set of analytical tools for investigating evolutionary tempo and mode in fossil lineages. However, attempts to integrate PCMs with fossil data often present workers with practical challenges or unfamiliar literature. This Element presents guides to the theory behind and the application of PCMs with fossil taxa. Based on an empirical dataset of Paleozoic crinoids, example analyses are presented to illustrate common applications of PCMs to fossil data, including investigating patterns of correlated trait evolution and macroevolutionary models of morphological change. The authors emphasize the importance of accounting for sources of uncertainty and discuss how to evaluate model fit and adequacy. Finally, the authors discuss several promising methods for modeling heterogeneous evolutionary dynamics with fossil phylogenies. Integrating phylogeny-based approaches with the fossil record provides a rigorous, quantitative perspective on understanding key patterns in the history of life.
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- Series: Elements of PaleontologyOnline ISBN: 9781108894142Publisher: Cambridge University PressPrint publication: 27 May 2021
References
Ackerly, D. 2009. Conservatism and diversification of plant functional traits: Evolutionary rates versus phylogenetic signal. Proc. Natl. Acad. Sci. 106: 19699–19706.Google Scholar
Adams, D. C. 2014. Quantifying and comparing phylogenetic evolutionary rates for shape and other high-dimensional phenotypic data. Syst. Biol. 63: 166–177.Google Scholar
Anderson, P. S. L., Friedman, M., Ruta, M. 2013. Late to the table: Diversification of tetrapod mandibular biomechanics lagged behind the evolution of terrestriality. Integr. Comp. Biol. 53: 197–208.Google Scholar
Ausich, W. I., Wright, D. F., Cole, S. R., Sevastopulo, G. D. 2020. Homology of posterior interray plates in crinoids: A review and new perspectives from phylogenetics, the fossil record and development. Palaeontology. 63: 525–545.Google Scholar
Bapst, D. W. 2012. Paleotree: An R package for paleontological and phylogenetic analyses of evolution. Methods Ecol. Evol. 3: 803–807.Google Scholar
Bapst, D. W. 2013a. A stochastic rate-calibrated method for time-scaling phylogenies of fossil taxa. Methods Ecol. Evol. 4: 724–733.Google Scholar
Bapst, D. W. 2013b. When can clades be potentially resolved with morphology? PLoS One. 8: e62312.Google Scholar
Bapst, D. W. 2014a. Assessing the effect of time-scaling methods on phylogeny-based analyses in the fossil record. Paleobiology. 40: 331–351.Google Scholar
Bapst, D. W. 2014b. Preparing palaeontological datasets for phylogenetic comparative methods. In: Garamszegi, L. Z., editor. Modern phylogenetic comparative methods and their application in evolutionary biology. Berlin, Heidelberg: Springer-Verlag. pp. 515–544.Google Scholar
Bapst, D. W., Hopkins, M. J. 2017. Comparing cal3 and other a posteriori time-scaling approaches in a case study with the pterocephaliid trilobites. Paleobiology. 43: 49–67.Google Scholar
Barido-Sottani, J., Pett, W., O’Reilly, J. E., Warnock, R. C. M. 2019. FossilSim: An R package for simulating fossil occurrence data under mechanistic models of preservation and recovery. Methods Ecol. Evol. 10: 835–840.Google Scholar
Barido-Sottani, J., Saupe, E., Smiley, T. M., Soul, L. C., Wright, A. M., Warnock, R. C. M. 2020. Seven rules for simulations in paleobiology. Paleobiology. 46(4): 435–444.Google Scholar
Barido-Sottani, J., Tiel, N. van, Hopkins, M. J., Wright, D. F., Stadler, T., Warnock, R. C. M. 2020. Ignoring fossil age uncertainty leads to inaccurate topology and divergence times in time calibrated tree inference. Frontiers in Ecology and Evolution, 8: 183Google Scholar
Baum, D. A., Smith, S. D. 2013. Tree thinking: An introduction to phylogenetic biology. Greenwood Village, CO: Roberts.Google Scholar
Benson, R. B. J., Choiniere, J. N. 2013. Rates of dinosaur limb evolution provide evidence for exceptional radiation in Mesozoic birds. Proc. R. Soc. B Biol. Sci. 280: 20131780.Google Scholar
Blomberg, S. P., Garland, T., Ives, A. R. 2003. Testing for phylogenetic signal in comparative data: Behavioral traits are more labile. Evolution. 57: 717–745.Google Scholar
Blomberg, S. P., Rathnayake, S. I., Moreau, C. M. 2020. Beyond Brownian motion and the Ornstein-Uhlenbeck process: Stochastic diffusion models for the evolution of quantitative characters. Am. Nat. 195: 145–165.Google Scholar
Blomberg, S. P., Lefevre, J. G., Wells, J. A., Waterhouse, M. 2012. Independent contrasts and PGLS regression estimators are equivalent. Syst. Biol. 61: 382–391.Google Scholar
Boettiger, C., Coop, G., Ralph, P. 2012. Is your phylogeny informative? Measuring the power of comparative methods. Evolution. 66: 2240–2251.Google Scholar
Boucher, F. C., Démery, V., Conti, E., Harmon, L. J., Uyeda, J. 2018. A general model for estimating macroevolutionary landscapes. Syst. Biol. 67: 304–319.Google Scholar
Brocklehurst, N., Brink, K. S. 2017. Selection towards larger body size in both herbivorous and carnivorous synapsids during the Carboniferous. Facets. 2: 68–84.Google Scholar
Butler, M. A., King, A. A. 2004. Phylogenetic comparative analysis: A modeling approach for adaptive evolution. Am. Nat. 164: 683–695.Google Scholar
Button, D. J., Barrett, P. M., Rayfield, E. J. 2017. Craniodental functional evolution in sauropodomorph dinosaurs. Paleobiology. 43: 435–462.Google Scholar
Clarke, J. T., Lloyd, G. T., Friedman, M. 2016. Little evidence for enhanced phenotypic evolution in early teleosts relative to their living fossil sister group. Proc. Natl. Acad. Sci. 113: 11531–11536.Google Scholar
Close, R. A., Friedman, M., Lloyd, G. T., Benson, R. B. J. 2015. Evidence for a mid-Jurassic adaptive radiation in mammals. Curr. Biol. 25: 2137–2142.Google Scholar
Cole, S. R., Wright, D. F., Ausich, W. I. 2019. Phylogenetic community paleoecology of one of the earliest complex crinoid faunas (Brechin Lagerstätte, Ordovician). Palaeogeogr. Palaeoclimatol. Palaeoecol. 521: 82–98.Google Scholar
Cooper, N., Thomas, G. H., FitzJohn, R. G. 2016. Shedding light on the “dark side” of phylogenetic comparative methods. Methods Ecol. Evol. 7: 693–699.Google Scholar
Darwin, C. R. 1859. On the origin of species by means of natural selection. London: John Murray.Google Scholar
Diniz-Filho, J. A. F., Alves, D. M. C. C., Villalobos, F., Sakamoto, M., Brusatte, S. L., Bini, L. M. 2015. Phylogenetic eigenvectors and nonstationarity in the evolution of theropod dinosaur skulls. J. Evol. Biol. 28: 1410–1416.Google Scholar
Drury, J., Clavel, J., Manceau, M., Morlon, H. 2016. Estimating the effect of competition on trait evolution using maximum likelihood inference. Syst. Biol. 65: 700–710.Google Scholar
Eastman, J. M., Alfaro, M. E., Joyce, P., Hipp, A. L., Harmon, L. J. 2011. A novel comparative method for identifying shifts in the rate of character evolution on trees. Evolution. 65: 3578–3589.Google Scholar
Erwin, D. H. 2007. Disparity: Morphological pattern and developmental context. Palaeontology. 50: 57–73.Google Scholar
Finarelli, J. A., Flynn, J. J. 2006. Ancestral state reconstruction of body size in the Caniformia (Carnivora, Mammalia): The effects of incorporating data from the fossil record. Syst. Biol. 55: 301–313.Google Scholar
Foote, M. 1996. On the probability of ancestors in the fossil record. Paleobiology. 22: 141–151.Google Scholar
Freckleton, R. P. 2009. The seven deadly sins of comparative analysis. J. Evol. Biol. 22: 1367–1375.Google Scholar
Garamszegi, L. Z. 2014. Modern phylogenetic comparative methods and their application in evolutionary biology. Berlin, Heidelberg: Springer-Verlag.Google Scholar
Garland, T., Ives, A. R. 2000. Using the past to predict the present: Confidence intervals for regression equations in phylogenetic comparative methods. Am. Nat. 155: 346–364.Google Scholar
Gascuel, O., Steel, M. 2014. Predicting the ancestral character changes in a tree is typically easier than predicting the root state. Syst. Biol. 63: 421–435.Google Scholar
Gavryushkina, A., Welch, D., Stadler, T., Drummond, A. 2014. Bayesian inference of sampled ancestor trees for epidemiology and fossil calibration. PLoS Comput. Biol. 10: e1003919.Google Scholar
Gavryushkina, A., Heath, T. A., Ksepka, D. T., Stadler, T., Welch, D., Drummond, A. J. 2017. Bayesian total-evidence dating reveals the recent crown radiation of penguins. Syst. Biol. 66: 57–73.Google Scholar
Gearty, W., Payne, J. L. 2020. Physiological constraints on body size distributions in Crocodyliformes. Evolution. 74: 245–255.Google Scholar
Halliday, T. J. D., Goswami, A. 2016. The impact of phylogenetic dating method on interpreting trait evolution: A case study of Cretaceous-Palaeogene eutherian body-size evolution. Biol. Lett. 12: 6–12.Google Scholar
Hansen, T. F. 1997. Stabilising selection and the comparative analysis of adaptation. Evolution. 51: 1342–1351.Google Scholar
Hansen, T. F., Martins, E. P. 1996. Translating between microevolutionary process and macroevolutionary patterns: The correlation structure of interspecific data. Evolution. 50: 1404–1417.Google Scholar
Harmon, Luke. 2019. “Phylogenetic Comparative Methods: Learning from Trees.” EcoEvoRxiv. May 20. doi:10.32942/osf.io/e3xnr.Google Scholar
Harmon, L. J., Weir, J. T., Brock, C. D., Glor, R. E., Challenger, W. 2008. GEIGER: Investigating evolutionary radiations. Bioinformatics. 24: 129–131.Google Scholar
Harmon, L. J., Losos, J. B., Davies, T. J., Gillespie, R. G., Gittleman, J. L., Jennings, B. W., Kozak, K. H., McPeek, M. A., Moreno-Roark, F., Near, T. J., Purvis, A., Ricklefs, R. E., Schluter, D., Schulte, II J. A., Seehausen, O., Sidlauskas, B. L., Torres-Carvajal, O., Weir, J. T., Mooers, A. Ø. 2010. Early bursts of body size and shape evolution are rare in comparative data. Evolution. 64: 2385–2396.Google Scholar
Harrison, L. B., Larsson, H. C. E. 2015. Among-character rate variation distributions in phylogenetic analysis of discrete morphological characters. Syst. Biol. 64: 307–324.Google Scholar
Harvey, P. H., Read, A. F., Nee, S. 1995. Further remarks on the role of phylogeny in comparative ecology. J. Ecol. 83: 733.Google Scholar
Heath, T. A., Huelsenbeck, J. P., Stadler, T. 2014. The fossilized birth–death process for coherent calibration of divergence-time estimates. Proc. Natl. Acad. Sci. 111: E2957–E2966.Google Scholar
Hedman, M. M. 2010. Constraints on clade ages from fossil outgroups. Paleobiology. 36: 16–31.Google Scholar
Ho, L. S. T., Ané, C. 2014a. A linear-time algorithm for Gaussian and non-Gaussian trait evolution models. Syst. Biol. 63: 397–408.Google Scholar
Ho, L. S. T., Ané, C. 2014b. Intrinsic inference difficulties for trait evolution with Ornstein-Uhlenbeck models. Methods Ecol. Evol. 5: 1133–1146.Google Scholar
Hopkins, M. J., Smith, A. B. 2015. Dynamic evolutionary change in post-Paleozoic echinoids and the importance of scale when interpreting changes in rates of evolution. Proc. Natl. Acad. Sci. U.S.A. 112: 3758–3763.Google Scholar
Hunt, G. 2012. Measuring rates of phenotypic evolution and the inseparability of tempo and mode. Paleobiology. 38: 351–373.Google Scholar
Hunt, G. 2013. Testing the link between phenotypic evolution and speciation: An integrated palaeontological and phylogenetic analysis. Methods Ecol. Evol. 4: 714–723.Google Scholar
Hunt, G., Carrano, M. T. 2010. Models and methods for analyzing phenotypic evolution in lineages and clades. Paleontol. Soc. Pap. 16: 245–269.Google Scholar
Hunt, G., Slater, G. 2016. Integrating paleontological and phylogenetic approaches to macroevolution. Annu. Rev. Ecol. Evol. Syst. 47: 189–213.Google Scholar
Beaulieu, Jeremy M. and O’Meara, Brian (2020). OUwie: Analysis of Evolutionary Rates in an OU Framework. R package version 2.5. https://CRAN.R-project.org/package=OUwie.Google Scholar
Kammer, T. W. 2008. Paedomorphosis as an adaptive response in pinnulate cladid crinoids from the Burlington limestone (Mississippian, Oseadean) of the Mississippi Valley. In: Webster, G. D., Maples, C. D., editors. Echinoderm paleobiology. Bloomington, IN: University of Indiana Press. pp. 177–195.Google Scholar
Lande, R. 1976. Natural selection and random genetic drift in phenotypic evolution. Evolution. 30: 314.Google Scholar
Landis, M. J. 2017. Biogeographic dating of speciation times using paleogeographically informed processes. Syst. Biol. 64: 307–324.Google Scholar
Landis, M., Schraiber, J. G. 2017. Pulsed evolution shaped modern vertebrate diversity. Proc. Natl. Acad. Sci. U.S.A. 114: 13224–13229.Google Scholar
Lewis, P. O. 2001. A likelihood approach to estimating phylogeny from discrete morphological character data. Syst. Biol. 50: 913–925.Google Scholar
Lloyd, G. T. 2016. Estimating morphological diversity and tempo with discrete character-taxon matrices: Implementation, challenges, progress, and future directions. Biol. J. Linn. Soc. 118: 131–151.Google Scholar
Lloyd, G. T., Wang, S. C., Brusatte, S. L. 2012. Identifying heterogeneity in rates of morphological evolution: Discrete character change in the evolution of lungfish (Sarcopterygii; Dipnoi). Evolution. 66: 330–348.Google Scholar
Manceau, M., Lambert, A., Morlon, H. 2017. A unifying comparative phylogenetic framework including traits coevolving across interacting lineages. Syst. Biol. 66: 551–568.Google Scholar
Matzke, N. J., Wright, A. 2016. Inferring node dates from tip dates in fossil Canidae: The importance of tree priors. Biol. Lett. 12: 1–4.Google Scholar
Mitov, V., Bartoszek, K., Stadler, T. 2019. Automatic generation of evolutionary hypotheses using mixed Gaussian phylogenetic models. Proc. Natl. Acad. Sci. 116: 16921–16926.Google Scholar
Morlon, H., Lewitus, E., Condamine, F. L., Manceau, M., Clavel, J., Drury, J. 2016. RPANDA: An R package for macroevolutionary analyses on phylogenetic trees. Methods Ecol. Evol. 7: 589–597.Google Scholar
Nunn, C. L. 2011. The comparative approach in evolutionary anthropology and biology. Chicago: University of Chicago Press.Google Scholar
Nunn, C. L., Barton, R. A. 2001. Comparative methods for studying primate adaptation and allometry. Evol. Anthropol. 10: 81–98.Google Scholar
O’Meara, B. C., Ané, C., Sanderson, M. J., Wainwright, P. C. 2006. Testing for different rates of continuous trait evolution using likelihood. Evolution. 60: 922–933.Google Scholar
O’Reilly, J. E., Puttick, M. N., Parry, L., Tanner, A. R., Tarver, J. E., Fleming, J., Pisani, D., Donoghue, P. C. J. 2016. Bayesian methods outperform parsimony but at the expense of precision in the estimation of phylogeny from discrete morphological data. Biol. Lett. 12: 20160081.Google Scholar
Paradis, E., Claude, J., Strimmer, K. 2004. APE: Analyses of phylogenetics and evolution in R language. Bioinformatics. 20: 289–290.Google Scholar
Parins-Fukuchi, C. 2020. Detecting mosaic patterns in macroevolutionary disparity. Am. Nat. 195: 129–144.Google Scholar
Pennell, M. W., Fitzjohn, R. G., Cornwell, W. K., Harmon, L. J. 2015. Model adequacy and the macroevolution of angiosperm functional traits. Am. Nat. 186: E33–E50.Google Scholar
Pennell, M. W., Eastman, J. M., Slater, G. J., Brown, J. W., Uyeda, J. C., FitzJohn, R. G., Alfaro, M. E., Harmon, L. J. 2014. Geiger V2.0: An expanded suite of methods for fitting macroevolutionary models to phylogenetic trees. Bioinformatics. 30: 2216–2218.Google Scholar
Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. 2019. nlme: Linear and nonlinear mixed effects models. R package version 3: 1–140.Google Scholar
Polly, P. D. 2019. Spatial processes and evolutionary models: A critical review. Palaeontology. 62: 175–195.Google Scholar
Price, S. A. 2019. State-dependent diversification of traits. http://treethinkers.org/tutorials/state-dependent-diversification-of-traits.Google Scholar
Price, S. A., Friedman, S. T., Wainwright, P. C. 2015. How predation shaped fish: The impact of fin spines on body form evolution across teleosts. Proc. R. Soc. B Biol. Sci. 282. https://doi.org/10.1098/rspb.2015.1428.Google Scholar
Puttick, M. N. 2016. Partially incorrect fossil data augment analyses of discrete trait evolution in living species. Biol. Lett. 12: 20160392.Google Scholar
Puttick, M. N., Ingram, T., Clarke, M., Thomas, G. H. 2020. MOTMOT: Models of trait macroevolution on trees (an update). Methods Ecol. Evol. 11: 464–471.Google Scholar
Puttick, M. N., O’Reilly, J. E., Pisani, D., Donoghue, P. C. J. 2019. Probabilistic methods outperform parsimony in the phylogenetic analysis of data simulated without a probabilistic model. Palaeontology. 62: 1–17.Google Scholar
Revell, L.J. (2010), Phylogenetic signal and linear regression on species data. Methods in Ecology and Evolution, 1: 319–329.Google Scholar
Revell, L. J. 2012. phytools: An R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3: 217–223.Google Scholar
Revell, L. J. 2013. Two new graphical methods for mapping trait evolution on phylogenies. Methods Ecol. Evol. 4: 754–759.Google Scholar
Revell, L. J. 2014. Ancestral character estimation under the threshold model from quantitative genetics. Evolution. 68: 743–759.Google Scholar
Revell, L. J., Schliep, K., Valderrama, E., Richardson, J. E. 2018. Graphs in phylogenetic comparative analysis: Anscombe’s quartet revisited. Methods Ecol. Evol. 9: 2145–2154.Google Scholar
Ruta, M., Krieger, J., Angielczyk, K. D., Wills, M. A. 2019. The evolution of the tetrapod humerus: Morphometrics, disparity, and evolutionary rates. Earth Environ. Sci. Trans. R. Soc. Edinburgh. 109: 351–369.Google Scholar
Sallan, L., Friedman, M., Sansom, R. S., Bird, C. M., Sansom, I. J. 2018. The nearshore cradle of early vertebrate diversification. Science. 464: 460–464.Google Scholar
Silvestro, D., Kostikova, A., Litsios, G., Pearman, P. B., Salamin, N. 2015. Measurement errors should always be incorporated in phylogenetic comparative analysis. Methods Ecol. Evol. 6: 340–346.Google Scholar
Simpson, G. G. 1944. Tempo and mode in evolution. New York: Columbia University Press.Google Scholar
Slater, G. J. 2013. Phylogenetic evidence for a shift in the mode of mammalian body size evolution at the Cretaceous-Palaeogene boundary. Methods Ecol. Evol. 4: 734–744.Google Scholar
Slater, G. J. 2014. Correction to “Phylogenetic evidence for a shift in the mode of mammalian body size evolution at the Cretaceous-Palaeogene boundary,” and a note on fitting macroevolutionary models to comparative paleontological data sets. Methods Ecol. Evol. 5: 714–718.Google Scholar
Slater, G. J., Pennell, M. W. 2014. Robust regression and posterior predictive simulation increase power to detect early bursts of trait evolution. Syst. Biol. 63: 293–308.Google Scholar
Slater, G. J., Harmon, L. J., Alfaro, M. E. 2012. Integrating fossils with molecular phylogenies improves inference of trait evolution. Evolution. 66: 3931–3944.Google Scholar
Soul, L. C., Benson, R. B. J. 2017. Developmental mechanisms of macroevolutionary change in the tetrapod axis: A case study of Sauropterygia. Evolution. 71: 1164–1177.Google Scholar
Soul, L. C., Friedman, M. 2015. Taxonomy and phylogeny can yield comparable results in comparative palaeontological analyses. Syst. Biol. 64: 608–620.Google Scholar
Soul, L. C., Friedman, M. 2017. Bias in phylogenetic measurements of extinction and a case study of end-Permian tetrapods. Palaeontology. 60: 169–185.Google Scholar
Speed, M. P., Arbuckle, K. 2017. Quantification provides a conceptual basis for convergent evolution. Biol. Rev. 92: 815–829.Google Scholar
Stadler, T. 2010. Sampling-through-time in birth-death trees. J. Theor. Biol. 267: 396–404.Google Scholar
Stadler, T., Gavryushkina, A., Warnock, R. C. M., Drummond, A. J., Heath, T. A. 2018. The fossilized birth-death model for the analysis of stratigraphic range data under different speciation modes. J. Theor. Biol. 447: 41–55.Google Scholar
Stayton, C. T. 2015. The definition, recognition, and interpretation of convergent evolution, and two new measures for quantifying and assessing the significance of convergence. Evolution. 69: 2140–2153.Google Scholar
Thomas, G. H., Freckleton, R. P. 2012. MOTMOT: Models of trait macroevolution on trees. Methods Ecol. Evol. 3: 145–151.Google Scholar
Uyeda, J. C., Zenil-Ferguson, R., Pennell, M. W. 2018. Rethinking phylogenetic comparative methods. Syst. Biol. 67: 1091–1109.Google Scholar
Voje, K. L., Starrfelt, J., Liow, L. H. 2018. Model adequacy and microevolutionary explanations for stasis in the fossil record. Am. Nat. 191: 509–523.Google Scholar
Wagner, P. J. 2012. Modelling rate distributions using character compatibility: implications for morphological evolution among fossil invertebrates. Biol. Lett. 8: 143–146.Google Scholar
Wagner, P. J., Marcot, J. D. 2010. Probabilistic phylogenetic inference in the fossil record: current and future applications. Paleontol. Soc. Pap. 16: 189–211.Google Scholar
Wagner, P.J. and Marcot, J.D., 2013. Modelling distributions of fossil sampling rates over time, space and taxa: assessment and implications for macroevolutionary studies. Methods in Ecology and Evolution, 4(8), pp.703–713.Google Scholar
Warnock, R. C. M., Wright, A. M. 2020. Understanding the tripartite approach to Bayesian divergence time estimation. EcoEvoRxiv. https://doi.org/10.32942/osf.io/4vazh.Google Scholar
Wesley-Hunt, G. D. 2005. The morphological diversification of carnivores in North America. Paleobiology. 31: 35–55.Google Scholar
Westoby, M., Leishman, M., Lord, J. 2016. Further remarks on phylogenetic correction. J. Ecol. 83: 727–729.Google Scholar
Wiley, E. O., Lieberman, B. S. 2011. Phylogenetics: Theory and practice of phylogenetic systematics. New York: John Wiley & Sons.Google Scholar
Wright, A. M. 2019. A systematist’s guide to estimating Bayesian phylogenies from morphological data. Insect Syst. Divers. 3: 2.Google Scholar
Wright, A. M., Hillis, D. M. 2014. Bayesian analysis using a simple likelihood model outperforms parsimony for estimation of phylogeny from discrete morphological data. PLoS One. 9: e109210.Google Scholar
Wright, A. M., Lloyd, G. T., Hillis, D. M. 2016. Modeling character change heterogeneity in phylogenetic analyses of morphology through the use of priors. Syst. Biol. 65: 602–611.Google Scholar
Wright, A. M., Wagner, P. J., Wright, D. F. 2020. Testing character evolution models in phylogenetic paleobiology: A case study with Cambrian echinoderms. EcoEvoRxiv. https://doi.org/10.32942/osf.io/ykzg5.Google Scholar
Wright, D. F. 2015. Fossils, homology, and phylogenetic paleo-ontogeny: A reassessment of primary posterior plate homologies among fossil and living crinoids with insights from developmental biology. Paleobiology. 41: 570–591.Google Scholar
Wright, D. F. 2017a. Bayesian estimation of fossil phylogenies and the evolution of early to middle Paleozoic crinoids (Echinodermata). J. Paleontol. 91: 799–814.Google Scholar
Wright, D. F. 2017b. Phenotypic innovation and adaptive constraints in the evolutionary radiation of palaeozoic crinoids. Sci. Rep. 7: 1–10.Google Scholar
Wright, D. F., Toom, U. 2017. New crinoids from the Baltic region (Estonia): Fossil tip‐dating phylogenetics constrains the origin and Ordovician–Silurian diversification of the Flexibilia (Echinodermata). Palaeontology. 60: 893–910.Google Scholar
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