Larval brooding correlated with high early origination rates in cheilostome Bryozoa
Data files
Dec 06, 2024 version files 8.26 MB
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alldata.csv
646.73 KB
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brood_data.csv
130.96 KB
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labels_categorical.csv
127 B
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labels_categorical.posthoc.csv
90 B
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labels_foote.csv
52 B
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labels.csv
75 B
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Lidgard_wormsmatch.csv
113.26 KB
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mark_out_post.data
17.16 KB
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mark_out_sensitivity1.data
3.09 MB
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mark_out_sensitivity2.data
445.62 KB
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mark_out1.data
3.06 MB
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mark_out2.data
440.74 KB
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mark_out3.data
49.92 KB
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mark_popan.data
255.20 KB
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README.md
14.95 KB
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timescale_2023.csv
766 B
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timescale_plot_categorical.csv
27 B
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timescale_plot_categorical.posthoc.csv
24 B
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timescale_plot_foote.csv
150 B
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timescale_plot.csv
203 B
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WA_Maast_occurrences.csv
1.40 KB
Abstract
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.
README: Larval brooding correlated with high early origination rates in cheilostome Bryozoa
https://doi.org/10.5061/dryad.1vhhmgr44
Description of the data and file structure
Supplementary File | Description |
---|---|
alldata.csv | A spreadsheet view of the occurrence dataset created by the R script, including our brooding classification |
brood_data.csv | Novel compiled dataset of brooding traits for 621 Cheilostome genera from 270 references, used to identify inferred brooders for the sensitivity analysis |
CheiloDiv_master_SLMSF.Rmd | R script for all analyses |
labels_categorical.csv | input file needed for R script figures |
labels_categorical.posthoc.csv | input file needed for R script figures |
labels_foote.csv | input file needed for R script figures |
labels.csv | input file needed for R script figures |
Lidgard_wormsmatch.csv | input file matching genera to families using WORMS (cited in the main text) |
mark_out_post.data | RMark output for post-hoc model (Supplementary Figure 3) |
mark_out_sensitivity1.data | Same as mark_out1.data but with data from sensitivity analysis |
mark_out_sensitivity2.data | Same as mark_out2.data but with data from sensitivity analysis |
mark_out1.data | RMark output for stage-level time-varying CMR models (Supplementary Figure 2) |
mark_out2.data | RMark output for time-binned CMR models (main text analysis and figures) |
mark_out3.data | Rmark output for CMR model comparison of origination-brooding interaction versus diversity-dependence |
mark_popan.data | RMark output for POPAN model (Figure 2) |
SupplementaryFigure1.pdf | Supplementary Figure 1 of Foote's per-capita rate results |
SupplementaryFigure2.pdf | Supplementary Figure 2 of stage-level time-varying CMR results |
SupplementaryFigure3.pdf | Supplementary Figure 3 of post-hoc model |
supplement.docx | Supplementary text |
timescale_2023.csv | input file needed for R script |
timescale_plot_categorical.csv | input file needed for R script figures |
timescale_plot_categorical.posthoc.csv | input file needed for R script figures |
timescale_plot_foote.csv | input file needed for R script figures |
timescale_plot.csv | input file needed for R script figures |
WA_Maast_occurrences.csv | Occurrence data from Håkansson, E., Gordon, D. P. & Taylor, P. D. 2024: Bryozoa from the Maastrichtian Korojon Formation, Western Australia. Fossils and Strata. 70, 1–155. https://doi.org/10.18261/9788215072081-2024 |
SupplementaryTable1_SLMSF.pdf | Model comparison for linear regressions of per-capita extinction rates |
SupplementaryTable2_SLMSF.pdf | Model comparison for linear regressions of per-capita origination rates |
SupplementaryTable3_SLMSF.pdf | Model comparison for the CMR models from the sensitivity analysis |
Files and variables
File: brood_data.csv
Description:
Novel compiled dataset of brooding traits for 621 Cheilostome genera from 270 references, was used to identify inferred brooders for the sensitivity analysis
Variables
- genus: genus names
- Family: family names
- brooding (genus-level): TRUE (brooding present) or FALSE (brooding absent). NA values correspond to taxa for which information was unavailable.
- references: source for genus-level brooding status. NA values correspond to taxa for which information was unavailable.
- Brooding (family-level): TRUE (brooding present) or FALSE (brooding absent). NA values correspond to taxa for which information was unavailable.
File: labels_categorical.csv
Description:
input file needed for R script figures
Variables
- tpx: text displayed in timescale
- epochs: order of timescale labels in plot
File: labels.csv
Description:
input file needed for R script figures
Variables
- tpx: text displayed in timescale
- epochs: order of timescale labels in plot
File: timescale_plot_categorical.csv
Description:
input file needed for R script figures
Variables
- stage.age: order of categories to plot
File: labels_foote.csv
Description:
input file needed for R script figures
Variables
- tpx: text displayed in timescale
- epochs: order of timescale labels in plot
File: timescale_plot_foote.csv
Description:
input file needed for R script figures
Variables
- stage.age: start and end ages of each geologic epoch used in plotting
File: labels_categorical.posthoc.csv
Description:
input file needed for R script figures
Variables
- tpx: text to display on labels
- epochs: order of timescale labels in plot
File: timescale_2023.csv
Description:
Variables
- stage_name: names of geologic stages
- max_ma: lower boundary of each stage (Myr)
- min_ma: upper boundary of each stage (Myr)
- stage_num: chronological number of stages from 1 (oldest) to 30 (youngest)
File: WA_Maast_occurrences.csv
Description:
Occurrence data from Håkansson, E., Gordon, D. P. & Taylor, P. D. 2024: Bryozoa from the Maastrichtian Korojon Formation, Western Australia. Fossils and Strata. 70, 1–155. https://doi.org/10.18261/9788215072081-2024
Variables
- genus: genus names
- stage: stage names
- max_ma: lower boundary of each stage (Myr)
- min_ma: upper boundary of each stage (Myr)
- stage_num: chronological numbering of stages used in our analysis from 1 (oldest) to 30 (youngest)
- brooding: brooding status: b (brooding) or nb (non-brooding)
File: timescale_plot_categorical.posthoc.csv
Description:
input file needed for R script figures
Variables
- stage.age: order of categories to plot
File: timescale_plot.csv
Description:
input file needed for R script figures
Variables
- stage.age: start and end ages of each stage (Myr)
File: mark_out_post.data
Description:
RMark output for post-hoc model (Supplementary Figure 3)
File: mark_out_sensitivity1.data
Description:
Same as mark_out1.data but with data from sensitivity analysis
File: mark_out3.data
Description:
Rmark output for CMR model comparison of origination-brooding interaction versus diversity-dependence
File: mark_out_sensitivity2.data
Description:
Same as mark_out2.data but with data from sensitivity analysis
File: mark_out2.data
Description:
RMark output for time-binned CMR models (main text analysis and figures)
File: mark_popan.data
Description:
RMark output for POPAN model (Figure 2)
File: mark_out1.data
Description:
RMark output for stage-level time-varying CMR models (Supplementary Figure 2)
File: alldata.csv
Description:
A spreadsheet view of the occurrence dataset created by the R script, including our brooding classification
Variables
- Occ_num: occurrence number in list (internal ID)
- genus: genus of fossil
- stage: stage of occurrence
- max_ma: maximum age estimate of stage (Myr)
- min_ma: minimum age estimate of stage (Myr)
- stage_num: chronological numbering of stages used in our analysis from 1 (oldest) to 30 (youngest)
- brooding: brooding status for each genus occurrence: b (brooding) or nb (non-brooding)
File: Lidgard_wormsmatch.csv
Description:
input file matching genera to families using WORMS (cited in the main text)
Variables
- ScientificName: genus name, phylum name
- AphiaID: ID in WoRMS system
- Match type: match type from WoRMS (exact or ambiguous)
- Kingdom: taxonomic kingdom
- Phylum: taxonomic phylum
- class: taxonomic class
- order: taxonomic order
- family: taxonomic family
- genus: taxonomic genus
- Subgenus: taxonomic subgenus
- Species: taxonomic species
- Subspecies: taxonomic subspecies
Code/software
R (version 3.6.0), RStudio (version 2024.09.1+394), RMark (version 3.0.0)
Access information
Data was derived from the following sources:
- Lidgard et al. 2021 (DOI: https://doi.org/10.1098/rspb.2021.1632)
- Håkansson et al. 2024 (DOI: https://doi.org/10.18261/9788215072081-2024-01)
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.