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Larval brooding correlated with high early origination rates in cheilostome Bryozoa

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NIAID Data Ecosystem2026-05-02 收录
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http://datadryad.org/dataset/doi%253A10.5061%252Fdryad.1vhhmgr44
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Life-history traits such as dispersal affect population attributes like gene flow, which can have consequences for speciation and extinction rates over macroevolutionary timescales. Here we use the Cheilostomatida, a monophyletic order of marine bryozoans, to test whether a life-history trait, larval brooding, affected the origination and extinction rates of genera throughout their fossil record. Cheilostome lineages that brood their larvae have shorter larval dispersal distances than non-brooding lineages, which has led to the hypothesis that the evolution of larval brooding decreased gene flow, increased origination, and drove their Cretaceous diversification. Brooding cheilostomes are far more diverse than non-brooding cheilostomes today, but it remains to be shown that brooding lineages have a higher origination rate than non-brooders. We fit time-varying Pradel Seniority capture-mark-recapture models to look at the effect of brooding on origination and extinction rates during the Cretaceous cheilostome diversification, the Cretaceous-Paleogene mass extinction and recovery, and through the Cenozoic. Our results support the hypothesis that brooding affects origination rate, but only in the Cenomanian to Campanian. Extinction rates do not differ between brooding and non-brooding genera, and there is no regime shift specific to the Cretaceous-Paleogene mass extinction. Our work illustrates the importance of using fossil occurrences and time-varying models, which can detect interval-specific diversification differentials. Methods This dataset is derived from the fossil occurrence data compiled by Lidgard et al. 2021 (DOI: 10.1098/rspb.2021.1632 with additional fossil occurrences from Håkansson et al. 2024 (DOI: 10.18261/9788215072081-2024). This combined dataset is processed in our RStudio code by synonymizing taxa using taxonomy from WoRMS and removing occurrences not constrained to a single geologic stage. Then, the dataset is transformed into an RMark data frame for our Pradel models and our POPAN model.
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