DOI: 10.5061/dryad.5dv41nscc
title: Data from: Bayesian total-evidence dating revisits sloth phylogeny and biogeography: a cautionary tale on morphological clock analyses

description: Combining morphological and molecular characters through Bayesian
 total-evidence dating allows inferring the phylogenetic and timescale
 framework of both extant and fossil taxa, while accounting for the
 stochasticity and incompleteness of the fossil record. Such an integrative
 approach is particularly needed when dealing with clades such as sloths
 (Mammalia: Folivora), for which developmental and biomechanical studies
 have shown high levels of morphological convergence whereas molecular data
 can only account for a limited percentage of their total species richness.
 Here, we propose an alternative hypothesis of sloth evolution that
 emphasizes the pervasiveness of morphological convergence and the
 importance of considering the fossil record and an adequate taxon sampling
 in both phylogenetic and biogeographic inferences. Regardless of different
 clock models and morphological datasets, the extant sloth Bradypus is
 consistently recovered as a megatherioid, and Choloepus as a mylodontoid,
 in agreement with molecular-only analyses. The recently extinct Caribbean
 sloths (Megalocnoidea) are found to be a monophyletic sister-clade of
 Megatherioidea, in contrast to previous phylogenetic hypotheses. Our
 results contradict previous morphological analyses and further support the
 polyphyly of “Megalonychidae”, whose members were found in five different
 clades. Regardless of taxon sampling and clock models, the Caribbean
 colonization of sloths is compatible with the exhumation of islands along
 Aves Ridge and its geological time frame. Overall, our total-evidence
 analysis illustrates the difficulty of positioning highly incomplete
 fossils, although a robust phylogenetic framework was recovered by an a
 posteriori removal of taxa with high percentages of missing characters.
 Elimination of these taxa improved topological resolution by reducing
 polytomies and increasing node support. However, it introduced a
 systematic and geographic bias because most of these incomplete specimens
 are from northern South America. This is evident in biogeographic
 reconstructions, which suggest Patagonia as the area of origin of many
 clades when taxa are underrepresented, but Amazonia and/or Central and
 Southern Andes when all taxa are included. More generally, our analyses
 demonstrate the instability of topology and divergence time estimates when
 using different morphological datasets and clock models, and thus caution
 against making macroevolutionary inferences when node support is weak or
 when uncertainties in the fossil record are not considered.
description: # Bayesian total-evidence dating revisits sloth phylogeny and
 biogeography: a cautionary tale on morphological clock analyses Summary of
 methods conducted: Phylogeny and divergence time estimations: Bayesian
 total-evidence dating approach with the fossilized birth-death model (FBD,
 Heath et al. 2014; Zhang et al. 2016; Gavryushkina et al. 2017), performed
 with MrBayes 3.2.7a Molecular partitions with their own evolutionary
 model: three for protein-coding genes (one per codon position) and one for
 the ribosomal RNAs 12S and 16S RNAs Models of sequence evolution were
 sampled with the reversible-jump Markov Chain Monte Carlo (MCMC) with
 gamma-distributed site rates and the default parameter of four gamma rate
 categories for rate heterogeneity (Huelsenbeck et al. 2004). Additionally,
 we performed analyses with an eight-category lognormal rate distribution.
 One partition for all the morphological characters. Morphological
 evolution computed under the Markov one-parameter (Mk1) model (Lewis 2001)
 with a gamma rate variation across characters. MrBayes analysis consisted
 of two runs with eight incrementally heated MCMC starting from a random
 tree. MCMC were run for 20 million generations with trees and associated
 model parameters sampled every 2,000 generations. Analyses conducted using
 two types of relaxed clock models: (1) an uncorrelated independent gamma
 rate model (IGR model, Lepage et al. 2007), in which tree branches have
 their own evolutionary rates that follow a gamma distribution, and (2) an
 autocorrelated lognormal rate model (TK02 model; Thorne and Kishino 2002)
 in which branch rates follow a lognormal distribution and the evolutionary
 rate in a descendant branch is influenced by that of its ancestral branch.
 Sampling strategy of taxa was set to diversity (prset
 samplestrat=diversity) Fossilization prior set with a beta distribution:
 prset fossilizationpr=beta(1, 1). An exponential prior and a beta prior
 were used for the net speciation rate and the relative extinction rate,
 respectively: prset speciationpr=exp(10); prset extinctionpr=beta(1, 1)
 Node age prior was set to ‘calibrated’ (prset nodeagepr=calibrated).
 Topological constraints were set for Pilosa (excluding Pseudoglyptodon),
 and for Bradypus spp. Although results without the constraint on Pilosa
 are also presented Dates of fossil occurrence (fixed, for
 radiometrically-dated Quaternary sloths; uniform intervals for all
 pre-Quaternary sloths) Root age prior: 72-51 Ma (prset treeagepr=unif(51,
 72)) based on previous studies (Meredith et al. 2011; Gibb et al. 2016;
 Delsuc et al. 2019) and the most ancient xenarthran fossil
 (†Riostegotherium yanei Oliveira and Bergqvist 1998, from the Itaboraí
 Formation [50-53 Ma]). For the reduced matrix of 64 taxa, an age prior was
 also set on the Mylodontoidea node (45-33.2 Ma) based on the record from
 Antoine et al (2021), but total-evidence dating analyses without this
 internal calibration were also performed Convergence diagnostics were
 checked for each analysis with average standard deviation of split
 frequencies (ASDF) &lt;0.01, potential scale reduction factor (PSRF) close
 to 1.0, and effective sample size (ESS) &gt;200 in Tracer 1.7.1 (Rambaut
 et al. 2018). Biogeographic estimations: Ancestral ranges on the
 total-evidence time-calibrated phylogenies were done using the
 Dispersal-Extinction-Cladogenesis (DEC) model (Ree and Smith 2008) as
 implemented in DECX (Beeravolu and Condamine 2016). ## Description of the
 data and file structure -Data file is available in the form of an Excel
 file which includes the distribution matrix used for the biogeographic
 reconstructions, as described in main text and SI -Abbreviations of column
 header of the "Distribution_matrix" Excel file refer to: AM:
 Amazonia, AF: Atlantic Forest, DD: Dry Diagonal, SSA: Southern South
 America, NAD: Northern Andes, SAD: Southern Andes, CNA: Central and North
 America, GrA: Greater Antilles, LeA_N: Northern Lesser Antilles, LeA_S:
 Southern Lesser Antilles, PR: Puerto Rico, Aves: Aves Ridge ##
 Sharing/Access information -All data sources are available in SI file in
 DRYAD