Challenges and directions in analytical paleobiology

Abstract

Over the last 50 years, access to new data and analytical tools has expanded the study of analytical paleobiology, contributing to innovative analyses of biodiversity dynamics over Earth's history. Despite-or even spurred by-this growing availability of resources, analytical paleobiology faces deep-rooted obstacles that stem from the need for more equitable access to data and best practices to guide analyses of the fossil record. Recent progress has been accelerated by a collective push toward more collaborative, interdisciplinary, and open science, especially by early-career researchers. Here, we survey four challenges facing analytical paleobiology from an early-career perspective: (1) accounting for biases when interpreting the fossil record; (2) integrating fossil and modern biodiversity data; (3) building data science skills; and (4) increasing data accessibility and equity. We discuss recent efforts to address each challenge, highlight persisting barriers, and identify tools that have advanced analytical work. Given the inherent linkages between these challenges, we encourage discourse across disciplines to find common solutions. We also affirm the need for systemic changes that reevaluate how we conduct and share paleobiological research.

Figure 1

A, the interpretation and integration of different data types pose two major challenges in analytical paleobiology given their contrasting properties and scales. Moving from fine to coarse: A1, real-time monitoring data—indicated here by elephants—often having a very fine temporal (days, months), spatial (localities, sites), and taxonomic (populations, species) resolution; A2, microfossil data—often recovered from marine sediment cores and represented here by a Globigerina foraminifer fossil—having a fine temporal (thousands of years), spatial (basins), and taxonomic (species, genera) resolution; and A3, macrofossil data—indicated here by fossil remains from mammoth and Deinotherium—having a coarser temporal (millions of years), spatial (continents, worldwide), and taxonomic (genera, families) resolution. Microfossil, pollen, and geological data can also produce interpolated paleoenvironmental maps with low temporal (stages, periods) and spatial (km²) resolution (B5). B, to overcome these challenges, paleobiologists are developing quantitative approaches that use computer programming languages, software, and online databases. The scope of these analyses is vast, including but not limited to: B1, reconstructing phylogenetic relationships; B2, visualizing morphological differences among taxa; B3, quantifying biotic interactions (e.g., using ecological networks); B4, calculating diversity dynamics; and B5, pairing paleo-environmental patterns with taxon occurrences to model ecological niches through time.

Figure 2

We identify four main barriers that hinder data accessibility and equity in analytical paleobiology: institutional (relating to museums, universities, and other research institutions), socioeconomic, technological, and financial. The arrows show relationships between these barriers and highlight where solutions are being applied.