DOI: 10.5061/dryad.p8cz8w9rq
title: Online Appendix and Cetacean Datasets for: The Occurrence Birth-Death Process for combined-evidence analysis in macroevolution and epidemiology

description: Phylodynamic models generally aim at jointly inferring phylogenetic
 relationships, model parameters, and more recently, the number of lineages
 through time, based on molecular sequence data. In the fields of
 epidemiology and macroevolution these models can be used to estimate,
 respectively, the past number of infected individuals (prevalence) or the
 past number of species (paleodiversity) through time. Recent years have
 seen the development of “total-evidence” analyses, which combine molecular
 and morphological data from extant and past sampled individuals in a
 unified Bayesian inference framework. Even sampled individuals
 characterized only by their sampling time, i.e. lacking morphological and
 molecular data, which we call occurrences, provide invaluable
 information to reconstruct the past number of lineages. Here, we present
 new methodological developments around the Fossilized Birth-Death Process
 enabling us to (i) incorporate occurrence data in the likelihood function;
 (ii) consider piecewise-constant birth, death and sampling rates; and
 (iii) reconstruct the past number of lineages, with or without knowledge
 of the underlying tree. We implement our method in the RevBayes
 software environment, enabling its use along with a large set of models of
 molecular and morphological evolution, and validate the inference workflow
 using simulations under a wide range of conditions. We finally illustrate
 our new implementation using two empirical datasets stemming from
 the fields of epidemiology and macroevolution. In epidemiology, we infer
 the prevalence of the COVID-19 outbreak on the Diamond Princess ship, by
 taking into account jointly the case count record (occurrences) along with
 viral sequences for a fraction of infected individuals. In macroevolution,
 we infer the diversity trajectory of cetaceans using molecular and
 morphological data from extant taxa, morphological data from fossils, as
 well as numerous fossil occurrences. The joint modeling of occurrences and
 trees holds the promise to further bridge the gap between between
 traditional epidemiology and pathogen genomics, as well as paleontology
 and molecular phylogenetics.
description: Online Appendix : available in the `Related
 Works` section Cetacean Datasets
 : [copied from the subsection *Material and methods* &gt; *Cetacean
 data analysis* &gt; *Molecular, morphological and occurrence datasets*
 of the main paper] The data can be subdivided in three
 parts: molecular, morphological, and occurrences. Datasets were collected
 and analysed separately and are stored on the Open Science Framework
 (https://osf.io) ([dataset] Aguirre-Fern´andez et al., 2020). Molecular
 data comes from Steeman et al. (2009), and comprises 6 mitochondrial and 9
 nuclear genes, for 87 of the 89 accepted extant cetacean species.
 Morphological data was obtained from Churchill et al. (2018), the most
 recent version of a widely-used dataset first produced by Geisler and
 Sanders (2003). After merging 2 taxa that are now considered synonyms on
 the Paleobiology Database (PBDB) and removing 3 outgroups that would have
 violated our model’s assumptions, it now contains 327 variable
 morphological characters for 27 extant and 90 fossil taxa (mostly
 identified at the species level but 21 remain undescribed). In order to
 speed up the analysis we further excluded the undescribed specimens and
 reduced this dataset to the generic level by selecting the most complete
 specimen in each genera. Indeed, the computing cost increases
 quadratically with the maximum number of hidden lineages N, to the point
 of becoming the bottleneck in our MCMC when N &gt; 100. Given that a
 mid-Miocene peak diversity between 100 and 220 species is expected
 (Quental and Marshall, 2010), with less than 100 observed lineages in our
 inferred tree at that time, N should therefore be about 150. Inferring
 instead the tree of cetacean genera allows us to reduce N to 70
 hidden lineages. The final dataset thus contains 41 extant and 62 extinct
 genera. Occurrences come from the PBDB (data archive 9,
 M. D. Uhen) on May 11, 2020. The dataset initially consisted of all 4678
 cetacean occurrences, but the cetacean fossil record is known to be
 subject to several biases (Uhen and Pyenson, 2007; Marx et al., 2016;
 Dominici et al., 2020). A detailed exploration (see Online Appendix E) of
 this occurrence dataset revealed several notable biases. First, an
 artefactual cluster of occurrences in very recent times, combined with
 other expected Pleistocene biases (Dominici et al., 2020), led us to
 remove all Late Pleistocene and Holocene occurrences. Second, we detected
 substantial variations in fossil recovery per time unit across lineages
 (see Fig. S10) resulting from oversampling of some species and localities,
 295 possibly due to greater abundance or spatio-temporal biases (Dominici
 et al., 2020). This observation violates our assumption of identical
 fossil sampling rates among taxa during a given interval. In order to
 reduce this bias, we retained occurrences identified at the genus level
 and further aggregated all occurrences belonging to an identical genus
 found at the same geological formation. In the case of occurrences for
 which the geological formation was not specified, we used geoplate data
 combined with stratigraphic interval as a proxy for geological formation.
 This resulted in a total of 968 occurrences retained for the
 analysis.
description: Online Appendix : This
 online Appendix presents the detailed derivation of the model used by
 Andréoletti, Zwaans et al., as well as supplementary results and figures.
 We extend results of Gupta et al. (2020) and Manceau et al. (2021) to
 piecewise-constant parameters, describe our implementation in the RevBayes
 software, and give detailed information on all priors used for simulation
 or inference in our analyses. Cetacean
 molecular, morphological and occurrence datasets :
 The initial raw files are included, but the modified files that
 were effectively used in the analysis are the following :<br> - Taxa
 : Cetacea_genera.csv<br> - Nuclear sequences
 : M4358_nuclear_simplified_newNames_genera_removeOutgroups.nex<br> -
 Mitochondrial sequences
 : M4376_mt_simplified_newNames_genera_removeOutgroups.nex<br> -
 Morphological characters
 : morpho_simplified_newNames_genera_removeOutgroupsUndescribedInvariants.nex<br> - Fossil occurrences : Cetacea_occurrences_min_max_age_species_corrected.csv All modifications are described in the methods section and/or below. Molecular dataset :<br> - "newNames" = updated names from the PBDB in May 2020 (physeter catodon -&gt; Physeter macrocephalus) <br> - "genera" = keep only the most complete specimen in each genus has been kept (present in the morphological dataset then longest nuclear sequence) for genus-level analyses<br>   // Removed species : Balaenoptera acutorostrata, Balaenoptera bonaerensis, Balaenoptera borealis, Balaenoptera brydei, Balaenoptera edeni, Balaenoptera musculus, Balaenoptera omurai, Berardius arnuxii, Cephalorhynchus commersonii, Cephalorhynchus eutropia, Cephalorhynchus hectori, Delphinus capensis, Delphinus tropicalis, Eubalaena australis, Eubalaena japonica, Globicephala melas, Hyperoodon planifrons, Kogia simus, Lagenorhynchus acutus, Lagenorhynchus obliquidens, Lagenorhynchus australis, Lagenorhynchus cruciger, Lagenorhynchus obscurus, Lissodelphis peronii, Mesoplodon bidens, Mesoplodon bowdoini, Mesoplodon carlhubbsi, Mesoplodon densirostris, Mesoplodon stejnegeri, Mesoplodon ginkgodens, Mesoplodon grayi, Mesoplodon hectori, Mesoplodon layardii, Mesoplodon mirus, Mesoplodon perrini, Mesoplodon peruvianus, Mesoplodon traversii, Phocoena dioptrica, Phocoena sinus, Phocoena spinipinnis, Platanista minor, Sotalia guianensis, Stenella attenuata, Stenella clymene, Stenella frontalis, Stenella longirostris, Tursiops aduncus<br> - remove outgroups (Bos taurus, Sus scrofa, Hippopotamus amphibius) Morphological dataset :<br> - morpho_conservative.nex : initial dataset<br> - "newNames" = updated names from the PBDB in May 2020<br> - "simplified" = simpler NEXUS files for RevBayes<br> - "genera" = keep only the most complete specimen in each genus has been kept (lowest missing proportion then higher number of unambiguous states) for genus-level analyses<br>   // Removed species : Atocetus nasalis, Brachydelphis jahuayensis, Haborophocoena minutus, Lophocetus repenningi, Odobenocetops peruvianus, Otekaikea huata, Parapontoporia wilsoni<br> - remove outgroups (Bos taurus, Sus scrofa, Hippopotamus amphibius)<br> - remove undescribed taxa (CCNHM 1078, CCNHM 208, CCNHM 210, CCNHM 567, CCNHM Schizodelphis, ChM PV2758, ChM PV2761, ChM PV2764, ChM PV4178, ChM PV4745, ChM PV4746, ChM PV4755, ChM PV4802, ChM PV4834, ChM PV4961, ChM PV5711, ChM PV5720, ChM PV5852, ChM PV7679, Schizodelphis morckhoviensis, Xenorophus sp.)<br> - remove invariant characters<br> - remove uncertainty-polymorphism (viewed as missing) Occurrence dataset :<br> - downloaded from the Paleobiology Database (PBDB) on May 11th 2020 Scripts : Validation 2 : simulation-based calibration<br>- Validation_2_simulations.py : python script to simulate 1000 trees from prior distributions<br>- OBDP_validation_2.Rev : RevLanguage script to infer posterior distributions of the OBDP parameters for each of the simulated trees Cetacean diversification analysis :<br>- mcmc_OBDP_Cetaceans_genera_constrained.Rev : RevLanguage script to estimate the cetacean tree and posterior distributions of the OBDP parameters<br>- inferKt_Cetaceans.Rev : RevLanguage script to infer the probability distribution of the number of cetacean genera through time Diamond Princess SARS-CovV-2 transmission analysis :<br>- mcmc_DiamondPrincess.Rev : RevLanguage script to estimate the transmission tree and posterior distributions of the OBDP parameters<br>- inferKt_DiamondPrincess.Rev : RevLanguage script to infer the probability distribution of the number of infected individuals through time - Plot_Kt_density.R : R script using new RevGadgets functions to plot the results from the inferKt scripts