William Gearty

Phanerozoic marine biodiversity dynamics have been shaped by continuous environmental change and biotic interactions. Although ecosystem engineers – animals whose behaviours modify resource availability – have established impacts on modern community ecology and diversity, their impacts on ancient ecosystems over geologic time have remained quantitatively underexplored. Here, we investigate how biodiversity was impacted by reef-building or bioturbating ecosystem engineers at each stage of the Phanerozoic, including their ability to maintain biodiversity over mass extinctions, and identify extrinsic conditions that modulate their impacts. We analyse fossil communities with and without ecosystem engineers to calculate effect sizes, thereby quantifying the effects of the presence of ecosystem engineers on Shannon’s Diversity ( H ) at each stage in the Phanerozoic. We show that ecosystem engineers are significantly associated with increased biodiversity during the majority of the Phanerozoic. We also show that, over the last ~250 million years, the effectiveness of modern-type reef-builders is more sensitive to climate-driven stress than for modern-style bioturbators. Finally, our results reveal that climate may modulate ecosystem engineer impacts, with both bioturbators and reef-builders exhibiting the strongest effect sizes within an optimal moderate temperature range. These results underscore ecosystem engineers’ crucial role in contributing to fluctuating biodiversity dynamics over the Phanerozoic.

1. Data- and code-archiving are important components of open science, as both make research more transparent, reproducible, accountable, and credible, allowing future researchers to identify errors and build on previous work. Despite progress in implementing data- and code-archiving policies in journals publishing ecology and evolution research, issues remain. To be more useful to future researchers, archived data and code must not only be archived, but also meet good practice standards. 2. We collected data from 1,861 papers published between 2017 and 2024 in the seven British Ecological Society (BES) journals, during a hackathon event. We systematically checked associated data and/or code, metadata, help files and annotations to assess archiving practices. We determined if and where data and code files were archived, whether they could be located, downloaded, and opened, and whether they had associated READMEs, digital object identifiers (DOI) and licenses. We also recorded the file extensions used to save data/code files, and which programming languages code was written in. 3. 93% of the 1,861 papers we examined used data and ~90% used code. While 97% of the 1,735 papers that used data also archived it, only 35% of the 1,670 papers that used code also archived code. Over 85% of archived data and code could be located, downloaded, and opened. Reusability, however, was more limited; around a third of papers did not have a README or similar to explain their data/code files, and the quality of READMEs varied substantially. 4. We recommend that researchers archive their code, and that archived code be explicitly mentioned in the Data (or Code) Availability statement. We also encourage researchers to provide more accessible and informative READMEs for data and code. To help achieve these recommendations, we advocate that journals employ Data/Code editors to review data and code quality, research institutions deliver more training in open science practices, and funding bodies set clear expectations on open data and code practices.

Palaeoverse, a grassroots organization that develops R packages for paleontology, has been awarded a Large Grant from the Software Sustainability Institute, via the Research Software Maintenance Fund, for the project 'Converting Users to Contributors: Enabling Sustainable Maintenance and Development of Palaeoverse'.This funding will support efforts to improve the sustainability and maintainability of several key R packages used in

Palaeoverse, a grassroots organization that develops R packages for paleontology, has been awarded a Large Grant from the Software Sustainability Institute, via the Research Software Maintenance Fund, for the project 'Converting Users to Contributors: Enabling Sustainable Maintenance and Development of Palaeoverse'.This funding will support efforts to improve the sustainability and maintainability of several key R packages used in

Aims: Today, we are experiencing rapid biodiversity loss due to climate change and human impacts. Such biodiversity loss is not only harmful to the environment but can also alter the composition of communities and the interactions of their members. The late Pleistocene experienced a drastic loss of large‐bodied mammals which resulted in significant changes in community structure due to changes in body size, diet, and species associations. However, the effect of climate change on species interactions and community structure across the Pleistocene–Holocene transition remains poorly understood. Location: Edwards Plateau, Texas. Time Period: Late Pleistocene–Holocene. Major Taxa Studied: Terrestrial Mammals. Methods: Using a robust data set on mammal species composition, stable isotopes, and body size, we constructed ecological networks for 16 time intervals across the last 22,000 years on the Edwards Plateau, Texas. We compared the structure and shifts in the food web over time using modularity and an index of node overlap and segregation. Results: We found that node overlap and connectance increased while modularity decreased over time. Spearman‐Rank correlation analyses indicate that changes in all network metrics were not driven by changes in species richness across time, nor were they driven by climate change. The degree of node overlap and connectance also shifted dramatically across the Pleistocene–Holocene transition and was significantly different from null model expectations in the Holocene but not in the Pleistocene. Main Conclusions: These results suggest that the transition from a diverse and compartmentalised network to a network of less complexity with an overlap of interacting species may have been driven by other factors that altered the food web. This implies that the change in mammal food web structure of the Edwards Plateau was mainly a consequence of the megafauna extinctions and not coupled with climate change.

Large datasets of fossil occurrences, often downloaded from online community‐maintained databases, are a vital resource for understanding broad‐scale evolutionary patterns, such as how biodiversity has changed through time and space. Such datasets, however, are not infallible and must be ‘cleaned’ of inaccurate, incomplete, or duplicate data prior to analysis. Researchers must decide upon the extent, feasibility, and value of data cleaning steps to perform, but while guides are available for working with neontological occurrences, there is currently no clear procedure for palaeobiological data despite its unique attributes. Here, we outline ten rules that aim to aid the process of cleaning fossil occurrence data for downstream analysis. These rules cover the major steps involved in processing data prior to analysis, including project setup, data exploration and cleaning, and finalizing and reporting work. We provide accompanying examples and a vignette covering the entire data cleaning process to demonstrate the application of each rule. We believe that these rules will serve as a useful guideline to support data cleaning and foster new standards for the palaeobiological community.

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 Geosciences and other scientific fields remains the reproducibility of many types of visualizations of data over long time intervals (104+ years). Here I introduce the R package deeptime, which provides easy-to-use functions to facilitate fully reproducible visualizations of geological data. The package includes functionality to add various geological timescales to many different types of plots, use standardized stratigraphical patterns within figures, visualize continuous and discrete temporal data, and more. 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. My hope is 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.

We analysed fossil mammal assemblages from over 350 Late Pleistocene and Holocene sites worldwide to test whether human activities, such as agriculture, domestication and intensified land use, restructured global patterns of mammal co-occurrence. Using presence–absence data, we contrasted a novel iterative ‘chase clustering’ method, which is compositionally driven, against a traditional spatially constrained Ward’s clustering approach. Both methods recovered continental-scale groupings in the Pleistocene, consistent with known biogeographic boundaries. Holocene land use and domestication reconfigured these historical patterns, creating novel assemblages independent of previous biogeographic constraints. Faunal turnover at the local scale varied substantially across regions, being especially pronounced in the Americas, whereas other areas showed relative stability. Even moderate expansion of domesticates altered how communities grouped, highlighting their disproportionate ecological influence. Our findings demonstrate that human-driven niche modification, beyond earlier megafaunal extinctions, profoundly reshaped mammal communities on a global scale. Recognizing these anthropogenic legacies provides essential context for anticipating how current and future human pressures might further transform biodiversity.

Large datasets of fossil occurrences, often downloaded from online community-maintained databases, are a vital resource for understanding broad-scale evolutionary patterns, such as how biodiversity has changed through time and space. Such datasets, however, are not infallible and must be ‘cleaned’ of inaccurate, incomplete, or duplicate data prior to analysis. Researchers must decide upon the extent, feasibility, and value of data cleaning steps to perform, but while guides are available for working with neontological occurrences, there is currently no clear procedure for palaeobiological data despite its unique attributes. Here, we outline ten rules that aim to aid the process of cleaning fossil occurrence data for downstream analysis. These rules cover the major steps involved in processing data prior to analysis, including project setup, data exploration and cleaning, and finalising and reporting work. We provide accompanying examples and a vignette covering the entire data cleaning process to demonstrate the application of each rule. We believe that these rules will serve as a useful guideline to support data cleaning and foster new standards for the palaeobiological community.