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Data from: Diversification dynamics of Cheilostome Bryozoa based on a Bayesian analysis of the fossil record

Citation

Moharrek, Farideh et al. (2021), Data from: Diversification dynamics of Cheilostome Bryozoa based on a Bayesian analysis of the fossil record, Dryad, Dataset, https://doi.org/10.5061/dryad.4xgxd257n

Abstract

Cheilostomata is the most diverse and ecologically dominant order of bryozoans living today. We apply a Bayesian framework to estimate macroevolutionary rates of cheilostomes since the Late Jurassic across four datasets: I) manually curated genus ranges, II) published text-mined genus ranges, III) non-revised Paleobiology Database (PBDB) records, IV) revised and augmented PBDB records. All datasets revealed increased origination rates in the Albian, and a twin K-Pg and Danian extinction rate peak. High origination rates in the late Selandian-Ypresian in Dataset I indicate the onset of an ascophoran-grade radiation. Lineage-through-time plots confirm the macroevolutionary lag preceding the radiation of cheilostomes in the mid-Cretaceous, and their renewed diversification in the late Paleocene and Eocene. A multivariate birth-death model indicates that origination rates are shaped by diversity-dependent dynamics coupled with a positive correlation with sea surface temperature, while extinction rates negatively correlate with sea level. Text-mined data provide broadly similar rate dynamics as manually curated data, although discrepancies could be attributed to the omission of key literature in Dataset II, and the inclusion of new published and unpublished data, and revised ranges in Dataset I. Revision and augmentation of PBDB occurrences were necessary to generate rate profiles akin to those of Datasets I and II and highlight the risks of using unedited occurrence data. Our results support the widely held assumption that diversification dynamics are controlled by both biotic and abiotic factors and pave the way for integrating fossils with molecular phylogenies to study these processes in more detail.

Methods

Four datasets were assembled as follows: (I) Stage-level cheilostome fossil genus ranges were collated manually from the literature, unpublished records (PDT) and from the Bryozoa homepage (www.bryozoa.net; accessed in December 2019). (II) This dataset was obtained from Kopperud et al. (2019), who extracted stage-level cheilostome fossil genus ranges from digital bibliographic resources in the English language using machine learning. (III) All cheilostome fossil occurrences identified to the genus level were downloaded from PBDB on 11th November 2019. (IV) PBDB data from Dataset III were cleaned of synonyms, outdated combinations, nomina dubia and other erroneous and doubtful records. Additional occurrences were added from the literature and unpublished records (PDT) for underrepresented time intervals (notably, the Late Cretaceous) relative to the known diversities of genera from those time intervals. Furthermore, missing genera that were present in Dataset I were added by transforming their stage-level genus ranges into occurrence records as the minimum and maximum ages for those stages. Temporal dynamics of origination and extinction rates were modelled using the PyRate software. Furthermore, we tested for correlations between origination and extinction rates and time-continuous abiotic and biotic variables using the multivariate birth-death model (MBD) using the fixed and estimated times of origination and extinction of Datasets I and IV, respectively.

Usage Notes

In Appendices S3 - S6 and S8, no 95% HPD Interval long-term means are given. Their absence is indicate by 'n/a'. Furthermore, in Appendix S8, Dataset IV did not include any occurrences and therefore no preservation rate estimates for the Bathonian, Callovian and Oxfordian time bins. This absence is indicated by 'n/a'. Furthermore, in Dataset III, for most occurrences only early interval bounds were given; missing late interval bounds are indicated by 'n/a'.

Funding

Leverhulme Trust, Award: RPG-2016-429

Swiss National Science Foundation, Award: PCEFP3_187012

Swedish Research Council, Award: 2019-04739

Swiss National Science Foundation, Award: FN-1749

Swiss National Science Foundation, Award: PCEFP3_187012

Swedish Research Council, Award: 2019-04739