William Gearty

I am a Postdoctoral Researcher in the Open Source Program Office at Syracuse University, where I promote, teach, and practice open science and open-source software development practices. I develop open-source R packages revolving around data acquisition, cleaning, and visualization. Outside of this I also conduct research in computational paleobiology. I'm specifically interested in the biotic and abiotic contraints and drivers of taxonomic and functional diversity across space and time. To accomplish this, I integrate paleontological and neontological data with advanced computational and statistical tools to investigate the evolution of various biological systems and test hypotheses regarding the constraints and drivers of that evolution.

Right now, I'm interested in:

• Developing tools for standardizing evolutionary and paleontological research
• Teaching others about extinctions, R, and phylogenetic comparative methods
• The functional morphology and diversification of secondarily marine tetrapods
• How humans have impacted the community dynamics of mammals
• The role of energetics in diversity dynamics across space and time

1. deeptime: an R package that facilitates highly customizable and reproducible visualizations of data over geological time intervals

   William Gearty

   Big Earth Data

   Abs HTML PDF Code

   Data visualization is a key component of any scientific data analysis workflow and is vital for the summarization and dissemination of complex ideas and results. One common hurdle across the Earth Sciences and other scientific fields remains the reproducibility of many types of visualizations of data over long time intervals (>10,000 years). This paper introduces the R package deeptime, which provides easy-to-use functions to facilitate a wide array of fully reproducible visualizations of geological data. The package facilitates streamlined access to geological reference data, such as geological timescales and lithostratigraphic patterns, and includes novel functionality to incorporate these data into a wide range of existing visualizations. By leveraging the existing framework of the ggplot2 R package, deeptime allows for these visualizations to be highly customizable. The open-source and constantly evolving package is accompanied by exhaustive documentation about the myriad options available to users and several tutorials demonstrating the available functionality. It is anticipated that deeptime will reduce the amount of time and experience needed to make reproducible and professional data visualizations, giving scientists more time to ensure that these visualizations are more accessible and engaging.
2. Investigating the Biotic and Abiotic Drivers of Body Size Disparity in Communities of Non-Volant Terrestrial Mammals

   William Gearty, Lawrence H. Uricchio, and S. Kathleen Lyons

   Global Ecology and Biogeography, 33(12), e13913

   Abs HTML PDF Code

   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.
3. rphylopic: An R package for fetching, transforming, and visualising PhyloPic silhouettes

   William Gearty and Lewis A. Jones

   Methods in Ecology and Evolution, 14(11), 2700-2708

   Abs HTML PDF Code

   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.