Dietary breadth in kangaroos facilitated resilience to Quaternary climatic variations
Data files
Jan 02, 2025 version files 3.36 MB
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README.md
7.82 KB
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S1_data.xlsx
987.46 KB
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S2_data.xlsx
2.37 MB
Abstract
Identifying what drove the late Pleistocene megafaunal extinctions on the continents remains one of the most contested topics in historical science. This is especially so in Australia, which lost 90% of its large species by 40,000 years ago, more than half of them kangaroos. Determining causation has been obstructed by a poor understanding of their ecology. Using Dental Microwear Texture Analysis, we show that most members of Australia’s richest Pleistocene kangaroo assemblage had diets that were much more generalized than their craniodental anatomy implies. Mixed feeding across most kangaroos pinpoints dietary flexibility as a key behavioral adaptation to climate-driven fluctuations in vegetation structure, dispelling the likelihood that late Pleistocene climatic variation was the sole or primary driver of their disappearance.
README: Dietary breadth in kangaroos facilitated resilience to Quaternary climatic variations
https://doi.org/10.5061/dryad.4j0zpc8nf
The dataset includes two Excel (.xlsx) spreadsheets to accompany the manuscript, and seven R scripts to replicate the results.
File: S1_data.xlsx
The Data S1 spreadsheet contains all of the data collected and more detailed results of the various analyses included in the manuscript. In all worksheets, significant (p<0.05) results are highlighted in yellow. The contents of each worksheet is:
Worksheet | Description |
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Raw data LME | All data used in LME analysis. For explanation of factors included see Table S2, and variables in Table S3. Empty Cells for some intraspecific factors, particularly Depth and Ecoregion are where that information was not available. 'NULL' and 'NA' are not used for empty cells as some R functions would not work if included. |
Raw data ANOVA | All data used in the ANOVA analysis (limited to one scan per individual). Includes ‘VFC depth layer’ indicating unit for depth analysis. Empty Cells for some intraspecific factors, particularly Depth and Ecoregion are where that information was not available. 'NULL' and 'NA' are not used for empty cells as some R functions would not work if included. |
How Normalised | Transformation used for normalization, lambda (where applicable) and Pearson’s P / DF (see Table S4) |
ANOVA summary interspecific | Summary of the interspecific ANOVA models. |
ANOVA PHC interspecific | Tukey’s post-hoc comparisons of the interspecific ANOVA models. Lower left of each intraspecific table is the species-only ANOVA; upper left is intraspecific comparisons where models include intraspecific factors (based on LME models). Lowest table is a count of significant (p<0.05) interactions. Post-hoc comparisons for intraspecific factors are on the right of interspecific tables. |
ANOVA summary depth analysis | Summary of the depth analysis ANOVA. |
ANOVA PHC depth | Tukey’s post-hoc comparisons of the depth analysis ANOVA. Lowest table of each column is a count of significant (p<0.05) interactions. |
File: S2_data.xlsx
Data S2 presents the outputs of the model comparisons undertaken in the LME modelling. See Table S4 for definitions of the various model comparison terms. Models with >2 singularities (which were removed from consideration) highlighted in red. The ten best performing model for each parameter used to refine model selection are highlighted in green. Final model (Table S3) selected is in bold. Each worksheet is named by the dependent DMTA variable being modelled (see table S3 for full variable names).
File: LME process for macropodid microwear
This file is the R script used to run the LME analysis. The file is marked down with instructions describing how to replicate the results. Users will need to save the 'Raw Data LME' worksheet from Data S1 in an appropriate directory which they can use as the working directory to carry out the analysis. This analysis is somewhat complicated and takes several hours to process for each variable. A more detailed explanation of the method used can be found in Arman et al. (2019) https://royalsocietypublishing.org/doi/full/10.1098/rsif.2018.0957. The R script also contains instructions for exporting the models for comparison (replicating Data S2), as well as calculating the final modelled DMTA variables for each taxon, and producing the plots to replicate figures S4-6.
File: ANOVA interspecific.R
This file is the R script used to run the interspecific ANOVA comparisons, including Tukey's post-hoc comparisons. The file is marked down with instructions describing how to replicate the results. Users will need to save the 'Raw Data ANOVA' worksheet from Data S1 in an appropriate directory which they can use as the working directory to carry out the analysis.
File: Depth_Analysis_individual_species.R
This file is the R script used to run the ANOVA comparisons between depth layers for the four most abundant species in VFC: Macropus giganteus, Notamacropus rufogriseus, Procoptodon browneorum *and *P. gilli. The file is marked down with instructions describing how to replicate the results. The user will need to filter the 'Raw Data ANOVA' worksheet in S1 Data by the 'species' column to create the datasets for analysis.
File: Depth_Analysis_subfamily.R
This file is the R script used to run the ANOVA comparisons between depth layers for the subfamilies Macropodinae and Sthenurinae. The file is marked down with instructions describing how to replicate the results. The user will need to filter the 'Raw Data ANOVA' worksheet in S1 Data by the 'Subfamily' column to create the datasets for analysis.
File: Depth_Analysis_full_no_taxon.R
This file is the R script used to run the ANOVA comparisons between depth layers independant of taxonomy. The file is marked down with instructions describing how to replicate the results. The user will need to use the 'Raw Data ANOVA' worksheet in S1 Data to undertake the analysis.
File: PCA.R
This file is the R script used to run the Principal Components Analysis features in Figures 3 and S7. The file is marked down with instructions describing how to replicate the results, including calculating the means for each taxon for each variable. The user will need to use the 'Raw Data ANOVA' worksheet in S1 Data to undertake the analysis.