Data from: Standardising fossil disparity metrics using sample coverage
Data files
Oct 10, 2024 version files 78.62 KB
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Jones_Close_Data.zip
73.33 KB
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README.md
5.29 KB
Abstract
Estimating past biodiversity using the fossil record is a central goal of palaeobiology. Because raw estimates of biodiversity are biased by variation in sampling intensity across time, space, environments, and taxonomic groups, sampling standardisation is routinely applied when estimating taxonomic diversity (e.g., species richness). However, sampling standardisation is less commonly used when estimating alternative currencies of biological diversity, such as morphological disparity. Here, we show the effects of standardising fossil time series of morphological disparity to equal sample completeness, or “coverage” of the underlying taxon-frequency distribution. We apply coverage-based standardisation to three published datasets of discrete morphological characters (echinoderms, ichthyosaurs, and ornithischian dinosaurs), and quantify disparity using two metrics: weighted mean pairwise dissimilarity (WMPD) and the sum of variance (SOV). We also compare the effects of coverage-based and sample-size-based standardisation. Our results show that coverage standardisation can yield estimates of disparity through time that dramatically deviate from raw estimates, both in magnitude and direction of changes. These findings demonstrate that future studies of morphological disparity should control for variation in sampling intensity to make more reliable inferences.
https://doi.org/10.5061/dryad.wpzgmsbxt
Description of the data and file structure
Files and variables
File: Nordenetal2018.xlsx
Description:
This file contains one dataset used in our study: discrete morphological character data for 194 ornithischian species.
File: SupportingInformation
Description:
This file contains Figs. S1-S7 that are not contained in the main manuscript.
File: Jones_Close_Data.zip
Description:
We have deposited the input data files and scripts for the analyses described in our article, ‘Standardising fossil disparity metrics using sample coverage’. This article outlines a novel coverage-based rarefaction approach for standardising discrete morphological character data, as opposed to traditional size-based rarefaction. Our approach is trialled on three published datasets for ichthyosaurs, echinoderms, and ornithischian dinosaurs.
Folder: inputs
This folder contains one of the three published datasets used in our study.
- novackgottshall2022/EchinoTree_Morph.nex - Discrete morphological character data for 366 echinoderm species.
Folder: scripts
This folder contains the R scripts to perform the analyses in our study.
- 00-format-tibble.R
- 01-disparity-timeseries.R
- 02-multi-quorum.R
- 03-coverage-analysis.R
- 04-rarefaction-analysis.R
- master-script.R
- packages-functions.R
- run-coverage-based-rarefaction-analysis-in-rscript.R
- run-master-script.R
- run-rarefaction-script.R
- run-size-based-rarefaction-analysis-in-rscript.R
Subfolder: scripts/plotting:
- generate-coverage-plots.R
- generate-disparity-plots.R
- generate-multiQ-plots.R
- generate-rarefaction-plots.R
Folder: plots
This is an empty folder where plots saved throughout the analysis will automatically be saved.
Folder: saved-workspaces
This is an empty folder where workspace images saved throughout the analysis will automatically be saved.
Code/software
All computational work was conducted in R v4.3.1 (R Core Team 2023) via RStudio (RStudio Team 2023). The R scripts used to perform the analyses are contained within the ‘scripts’ folder and are outlined below.
Folder: scripts
- 00-format-tibble.R - Script to load the datasets and create the basic data structure for later analyses.
- 01-disparity-timeseries.R - Calculates raw, size-standardised, and coverage-standardised (‘SQS”) disparity estimates through time for the three datasets.
- 02-multi-quorum.R - Generates SQS disparity time series for a range of specified quorum values.
- 03-coverage-analysis.R - Calculates sampling and spatial coverage through time.
- 04-rarefaction-analysis.R - Sources the scripts for performing size-based and SQS rarefaction analyses.
- master-script.R - This ‘master’ script contains the individual components of the analysis which are run without the script. It generates the necessary workspace images that can be later loaded and used to generate plots.
- packages-functions.R - Loads the necessary packages and defines the custom functions used to perform our analyses.
- run-coverage-based-rarefaction-analysis-in-rscript.R - Called from within 04-rarefaction-analysis.R to run the SQS rarefaction analysis
- run-master-script.R - Run this script to source and work through the master-script.R
- run-rarefaction-script.R - Calls the 04-rarefaction-analysis.R script for performing the size-based and SQS disparity analysis.
- run-size-based-rarefaction-analysis-in-rscript.R - Called from within 04-rarefaction-analysis.R to run the size-basd rarefaction analysis
Subfolder: scripts/plotting:
- generate-coverage-plots.R - produces plots of sampling and spatial coverage through time (Fig. 5)
- generate-disparity-plots.R - produces disparity time series (Figs. 2, S1)
- generate-multiQ-plots.R - produces SQS disparity time series for a range of quorum values (Fig. 6)
- generate-rarefaction-plots.R - produces size- and coverage-based rarefaction plots for the WMPD and SOV disparity metrics. Bins are plotted together (Figs. 3, 4) and individually (Figs. S2-S7)
Access information
Data was derived from the following sources:
Paleobiology Database (CC0 1.0 license)
Harvard Dataverse (CC0 1.0 license)
FigShare (CC by 4.0 license). Note that this data is not included in this Dryad dataset due to conflicting non-CC0 licensing. The link is provided for access to the dataset.
Dryad (CC0 1.0 license)