Uneven spatial sampling distorts reconstructions of Phanerozoic seawater temperature

Paleotemperature proxy records are widely used to reconstruct the global climate throughout the Phanerozoic and to test macroevolutionary hypotheses. However, the spatial distribution of these records varies through time. This is problematic because heat is unevenly distributed across Earth's surface. Consequently, heterogeneous spatial sampling of proxy data has the potential to bias reconstructed temperature curves. We evaluated the spatiotemporal evolution of sampling using a compilation of Phanerozoic δ18O data. We tested the influence of variable spatial coverage on global estimates of paleotemperature by sampling a steep “modern-type” latitudinal temperature gradient and a flattened “Eocene-type” gradient, based on the spatial distribution of δ18O samples. We show that global paleotemperature is overestimated in ∼70% of Phanerozoic stages. Perceived climatic trends for some intervals might be artifactually induced by shifts in paleolatitudinal sampling, with equatorward shifts in sampling concurring with warming trends, and poleward shifts concurring with cooling trends. Yet, the magnitude of some climatic perturbations might also be underestimated. For example, the observed Ordovician cooling trend may be underestimated due to an equatorward shift in sampling. Our findings suggest that while proxy records are vital for reconstructing Earth's paleotemperature in deep time, consideration of the spatial nature of these data is crucial to improving these reconstructions

palaeoverse: A community‐driven R package to support palaeobiological analysis

1. 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. 2. 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. 3. 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. 4. 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

palaeoverse: A community‐driven R package to support palaeobiological analysis

Abstract 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