William Gearty

The open-source programming language ‘R’ has become a standard tool in the palaeobiologist’s toolkit. Its popularity within the palaeobiological community continues to grow, with published articles increasingly citing the usage of R and R packages. However, there are currently a lack of agreed standards for data preparation and available frameworks to support the implementation of such standards. Consequently, data preparation workflows are often unclear and not reproducible, even when code is provided. Moreover, due to a lack of code accessibility and documentation, palaeobiologists are often forced to ‘reinvent the wheel’ to find solutions to issues already solved by other members of the community. Here, we introduce palaeoverse, a community-driven R package to aid data preparation and exploration for quantitative palaeobiological research. The package is freely available and has three core principles: (1) streamline data preparation and analyses; (2) enhance code readability; and (3) improve reproducibility of results. To develop these aims, we assessed the analytical needs of the broader palaeobiological community using an online survey, in addition to incorporating our own experiences. In this work, we first report the findings of the survey, which shaped the development of the package. Subsequently, we describe and demonstrate the functionality available in palaeoverse and provide usage examples. Finally, we discuss the resources we have made available for the community and our future plans for the broader Palaeoverse project. palaeoverse is a community-driven R package for palaeobiology, developed with the intention of bringing palaeobiologists together to establish agreed standards for high-quality quantitative research. The package provides a user-friendly platform for preparing data for analysis with well-documented open-source code to enhance transparency. The functionality available in palaeoverse improves code reproducibility and accessibility, which is beneficial for both the review process and future research.

Effective data visualisation is vital for data exploration, analysis and communication in research. In ecology and evolutionary biology, data are often associated with various taxonomic entities. Graphics of organisms associated with these taxa are valuable for framing results within a broader biological context. However, acquiring and using such resources can be challenging due to availability and licensing constraints. The PhyloPic database solves many of these issues by making organism silhouettes freely available. Tools that integrate this database with existing research workflows are needed to remove hurdles associated with data visualisation in the biological sciences. Here, we introduce rphylopic, an R package for fetching, transforming and visualising silhouettes of organisms from the PhyloPic database. In addition to making over 8000 organism silhouettes available within the R programming language, rphylopic empowers users to modify the appearance of these silhouettes for ultimate customisability when coding production–quality visualisations in both base R and ggplot2 workflows. In this work, we provide details about how the package can be installed, its implementation and potential use cases. For the latter, we showcase three examples across the ecology and evolutionary biology spectrum. Our hope is that rphylopic will make it easier for biologists to develop more accessible and engaging data visualisations by making external resources readily accessible, customisable and usable within R. In turn, by integrating into existing workflows, rphylopic helps to ensure that science is reproducible and accessible.

The species that compose local communities possess unique sets of functional and ecological traits that can be used as indicators of biotic and abiotic variation across space and time. Body size is a particularly relevant trait because species with different body sizes typically have different life history strategies and occupy distinct niches. Here we used the body sizes of non-volant (i.e., non-flying) terrestrial mammals to quantify and compare the body size disparity of mammal communities across the globe. We used IUCN range maps of 3982 terrestrial mammals to identify 1876 communities. We then combined diet data with data on climate, elevation and anthropogenic pressures to evaluate these variables’ relative importance on the observed body size dispersion of these communities and its deviation from a null model. Dispersion for these communities is significantly greater than expected in 54% of communities and significantly less than expected in 30% of communities. The number of very large species, continent, range sizes, diet disparity and annual temperature collectively explain >50% of the variation in observed dispersion, whereas continent, the number of very large species, and precipitation collectively explain >30% of the deviation from the null model. Climate and elevation have minimal predictive power, suggesting that biotic factors may be more important for explaining community body size distributions. However, continent is consistently a strong predictor of dispersion, likely due to it capturing the combined effects of climate, size-selective human-induced extinctions and more. Overall, our results are consistent with several plausible explanations, including, but not limited to, competitive exclusion, unequal distribution of resources, within-community environmental heterogeneity, habitat filtering and ecosystem engineering. Further work focusing on other confounding variables, at finer spatial scales and/or within more causal frameworks is required to better understand the driver(s) of these patterns.