Latitudinal trends in Phanerozoic reefs
In palaeoverse: Prepare and Explore Data for Palaeobiological Analyses

Authors: The Palaeoverse Development Team

Last updated: 2024-10-14

# Introduction `palaeoverse` is an R package developed by palaeobiologists, for palaeobiologists. The aim of the package is to generate a community-driven toolkit of generic functions for the palaeobiological community. The package does not provide implementations of statistical analyses, rather it provides auxiliary functions to help streamline analyses and improve code readability and reproducibility. This vignette (or tutorial if you prefer) is provided to guide you through the installation process and some of the functionality available within `palaeoverse`. To do so, we will focus on a usage example looking at latitudinal trends in Phanerozoic reefs. ## Installation The `palaeoverse` package can be installed via CRAN or its dedicated [GitHub repository](https://github.com/palaeoverse/palaeoverse) if the development version is preferred. To install via the CRAN, simply use: wzxhzdk:0 To install the development version, first install the `devtools` package, and then use `install_github` to install `palaeoverse` directly from GitHub. wzxhzdk:1 You can now load `palaeoverse` using the default `library` function: wzxhzdk:2 **Before we get onto the good stuff, the development team has a small request**. If you use `palaeoverse` in your research, please cite the associated publication. This will help us to continue our work in supporting you to do yours. You can access the appropriate citation via: wzxhzdk:3 ## Using the package Functions in `palaeoverse` are designed around the assumption that most users' data are stored in an occurrence `data.frame`. As such, this is the expected input data format for most functions. We provide two example occurrence datasets (`tetrapods` and `reefs`) within the package from different data sources. In this vignette, we will focus on the `reefs` dataset which is a compilation of Phanerozoic reef occurrences (*n* = 4,363) from the [PaleoReefs Database](https://www.paleo-reefs.pal.uni-erlangen.de) (Kiessling & Krause, 2022). wzxhzdk:4 wzxhzdk:5 *Note the structure of the dataset. Each row contains a unique occurrence and each column contains a unique variable.* # Let's begin! Reefs have long been considered good tracers of past tropical and subtropical conditions (Kiessling, 2001; Perrin & Kiessling, 2010). More specifically, their distribution has historically been used to infer the latitudinal extent of tropical and subtropical climatic conditions in deep time. This is of course an oversimplification given all we know about modern reefs systems and their engineers. However, for the purpose of this example, let's not get weighed down by the finer details. In this example, we will use the `reefs` dataset to examine latitudinal trends in Phanerozoic reefs and demonstrate some of the functionality available in `palaeoverse`. Let's start by exploring the dataset. Before conducting any analyses, it is always a good idea to explore your data and understand what you are working with. Details related to the example datasets can be accessed via the usual documentation call: wzxhzdk:6 Let's start by exploring the data a little: wzxhzdk:7 wzxhzdk:8 Not bad (*n* = 4363), that's quite a bit of data to play with! Remember that these are reefs (whole ecosystems), not individual occurrences of fossil organisms like in `tetrapods`. But how much does this vary across time? We can make use of the `group_apply` wrapper function to run a function across subsets of data. wzxhzdk:9 wzxhzdk:10 Oh dear, we've just hit a common issue... our reef data does not conform to a common time scale! Some reefs are resolved to stage level, while others are resolved to a coarser or finer temporal resolution. In some instances, regional names are also used, while international names are used in others. This makes it challenging to make comparisons through time... one solution could be to use age values to bin the reefs into time bins, but no age range data is provided in the dataset (i.e. "max_ma" and "min_ma"). We must therefore use the names of each interval to link to a common time scale, the '[International Geological Stages](https://stratigraphy.org/chart)' established by the International Commission on Stratigraphy (ICS). Fortunately, we can make use of the `look_up` function to do so. This function can be used to assign international geological stages and numeric ages to occurrences, or use a user-defined interval key to link to a common time scale. Here, we will use the example `interval_key` available in `palaeoverse` to assign international geological stage names and numeric ages. wzxhzdk:11 wzxhzdk:12 Great! We now have a common time scale and numeric ages. However, some of our reefs seem to range through several stages and we can't count just by the early or late stage. Our data still need to be binned. Let's check out the `time_bins` and `bin_time` functions. wzxhzdk:13 wzxhzdk:14 wzxhzdk:15 Note that the number of rows of `reefs` have now increased. This is because the "all" method in `bin_time` duplicates an occurrence for every bin the age range overlaps with. Let's check those reef numbers through time again using our binned data: wzxhzdk:16 wzxhzdk:17 That's more like it! But, let's not forget it's always useful to visualise our data. Why don't we plot the number of reefs through time? We can even make use of the `axis_geo` function to add the Geological Time Scale to our plot. wzxhzdk:18

plot of chunk n-reef

You would (and should) of course want to explore your data a little more than this. However, for the sake of brevity, let's focus on our research theme: latitudinal trends in Phanerozoic reefs. When studying modern latitudinal trends (e.g. in biodiversity), researchers can use the geographic coordinates of their samples to conduct analyses. However, as the continents shift through time, palaeobiologists must reconstruct the palaeogeographic coordinates of fossil localities. To do so, plate rotation models are used (e.g. the PALEOMAP model) to rotate the modern coordinates of fossil localities to their location at time of deposition. The `palaeorotate` function allows the user to do so using either the 'point' or 'grid' method. The first approach makes use of the GPlates Web Service and allows point data to be rotated to specific ages using the available models (see the [GPlates Web Service documentation](https://gwsdoc.gplates.org)). The second approach uses reconstruction files of pre-generated palaeocoordinates to spatiotemporally link occurrences' modern coordinates and age estimates with their respective palaeocoordinates. Let's give it a shot: wzxhzdk:19 wzxhzdk:20 wzxhzdk:21 Now let's plot that palaeolatitudinal distribution! wzxhzdk:22

plot of chunk reefs-dist

Neat! But it's hard to draw any trends from this data (there is a lot of it!). We can do better... let's summarise this information... if we assume that reefs are broadly limited to tropical and subtropical climatic conditions, the most poleward reef in each time bin should give us an estimate of the palaeolatitudinal extent of these climatic conditions. wzxhzdk:23

plot of chunk reefs-max

That's definitely much clearer! Should we stop there? Well... how about one last thing... let's consider how plate rotation model choice might impact these results using three different models: GOLONKA, PALEOMAP, and MERDITH2021 (Wright et al. 2013; Scotese & Wright, 2018; Merdith et al. 2021). wzxhzdk:24 wzxhzdk:25 wzxhzdk:26 Now we've palaeorotated our data, let's repeat what we did earlier and calculate the most poleward reef occurrence for each model. wzxhzdk:27

plot of chunk reefs-max-multi

We will leave you to make your own conclusions... Hopefully this vignette has shown you the potential uses for `palaeoverse` functions and helped provide a workflow for your own analyses. If you have any questions about the package or its functionality, please feel free to join our [Palaeoverse Google group](https://groups.google.com/g/palaeoverse) and leave a question; we'll aim to answer it as soon as possible! # References Kiessling, W. (2001). Paleoclimatic significance of Phanerozoic reefs. Geology 29, 751–754. Perrin, C. & Kiessling, W. (2010) Latitudinal trends in Cenozoic reef patterns and their relationship to climate. Carbonate Syst. Oligocene–Miocene Clim. Transit. 17–33. Kiessling, W. & Krause, M. C. (2022). PaleoReefs Database (PARED) (1.0) Data set. DOI: 10.5281/zenodo.6037852. Merdith, A., Williams, S.E., Collins, A.S., Tetley, M.G., Mulder, J.A., Blades, M.L., Young, A., Armistead, S.E., Cannon, J., Zahirovic, S., Müller. R.D. (2021). Extending full-plate tectonic models into deep time: Linking the Neoproterozoic and the Phanerozoic. Earth-Science Reviews, 214(103477). DOI: 10.1016/j.earscirev.2020.103477. Scotese, C., & Wright, N. M. (2018). PALEOMAP Paleodigital Elevation Models (PaleoDEMs) for the Phanerozoic. [PALEOMAP Project](https://www.earthbyte.org/paleodem-resource-scotese-and-wright-2018/). Wright, N., Zahirovic, S., Müller, R. D., & Seton, M. (2013). Towards community-driven paleogeographic reconstructions: integrating open-access paleogeographic and paleobiology data with plate tectonics. Biogeosciences, 10(3), 1529-1541. DOI: 10.5194/bg-10-1529-2013.