Hostname: page-component-78c5997874-g7gxr Total loading time: 0 Render date: 2024-11-12T20:05:29.829Z Has data issue: false hasContentIssue false

Challenges and directions in analytical paleobiology

Published online by Cambridge University Press:  27 February 2023

Erin M. Dillon*
Affiliation:
Department of Ecology, Evolution, and Marine Biology, University of California, Santa Barbara, California 93106, U.S.A.; Smithsonian Tropical Research Institute, Balboa, Republic of Panama. E-mail: [email protected]
Emma M. Dunne
Affiliation:
GeoZentrum Nordbayern, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91054 Erlangen, Germany; School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom. E-mail: [email protected]
Tom M. Womack
Affiliation:
School of Geography, Environment and Earth Sciences, Victoria University of Wellington, P.O. Box 600, Wellington, New Zealand. E-mail: [email protected]
Miranta Kouvari
Affiliation:
Department of Earth Sciences, University College London, Gower Street, London WC1E 6BT, United Kingdom; Life Sciences Department, Natural History Museum, Cromwell Road, London SW7 5BD, United Kingdom. E-mail: [email protected]
Ekaterina Larina
Affiliation:
Jackson School of Geosciences, University of Texas, Austin, Texas 78712, U.S.A. E-mail: [email protected]
Jordan Ray Claytor
Affiliation:
Department of Biology, University of Washington, Seattle, Washington 98195, U.S.A; Burke Museum of Natural History and Culture, Seattle, Washington 98195, U.S.A. E-mail: [email protected]
Angelina Ivkić
Affiliation:
Department of Palaeontology, University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria. E-mail: [email protected]
Mark Juhn
Affiliation:
Department of Ecology and Evolutionary Biology, University of California Los Angeles, Los Angeles, California 90095, U.S.A. E-mail: [email protected]
Pablo S. Milla Carmona
Affiliation:
Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Ciencias Geológicas, Buenos Aires C1428EGA, Argentina; Instituto de Estudios Andinos “Don Pablo Groeber” (IDEAN, UBA-CONICET), Buenos Aires C1428EGA, Argentina. E-mail: [email protected]
Selina Viktor Robson
Affiliation:
Department of Biological Sciences, University of Calgary, Calgary, Alberta T2N 1N4, Canada. E-mail: [email protected]
Anwesha Saha
Affiliation:
Institute of Palaeobiology, Polish Academy of Sciences, ul. Twarda 51/55, 00-818 Warsaw, Poland; Laboratory of Paleogenetics and Conservation Genetics, Centre of New Technologies (CeNT), University of Warsaw, S. Banacha 2c, 02-097 Warsaw, Poland. E-mail: [email protected]
Jaime A. Villafaña
Affiliation:
Department of Palaeontology, University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria; Centro de Investigación en Recursos Naturales y Sustentabilidad, Universidad Bernardo O'Higgins, Santiago 8370993, Chile. E-mail: [email protected]
Michelle E. Zill
Affiliation:
Department of Earth and Planetary Sciences, University of California Riverside, Riverside, California 92521, U.S.A. E-mail: [email protected]
*
*Corresponding author.

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.

Type
On The Record
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press on behalf of The Paleontological Society

Introduction

Paleobiological research practices are evolving. Advances in computational power, modeling, and databases have equipped paleobiologists with new tools to analyze the fossil record. These advances have given rise to analytical paleobiology as a research topic within paleontology. Analytical paleobiology comprises paleobiological research that uses analytical (primarily quantitative) methods, including database-driven analyses, meta-analyses, and primary data analyses (Signor and Gilinsky Reference Signor and Gilinsky1991). Although analytical methods have long been used in paleontology, analytical paleobiology crystallized in the 1970s and 1980s following pivotal computational work that examined past biodiversity dynamics (e.g., Valentine Reference Valentine1969; Raup Reference Raup1972; Raup et al. Reference Raup, Gould, Schopf and Simberloff1973; Sepkoski et al. Reference Sepkoski, Bambach, Raup and Valentine1981; Raup and Sepkoski Reference Raup and Sepkoski1982). Since then, it has matured both by adapting methods from other disciplines and by developing new methods specific to analyzing the fossil record (Raup Reference Raup1991; Liow and Nichols Reference Liow and Nichols2010; Silvestro et al. Reference Silvestro, Salamin and Schnitzler2014; Alroy Reference Alroy2020; Warnock et al. Reference Warnock, Heath and Stadler2020). Analytical paleobiology has now grown to touch most subfields within paleontology. For example, analytical tools have been used to document macroevolutionary patterns, evaluate the causes and consequences of ecosystem change, and predict biotic responses to the current biodiversity and climate crises (Condamine et al. Reference Condamine, Rolland and Morlon2013; Finnegan et al. Reference Finnegan, Anderson, Harnik, Simpson, Tittensor, Byrnes, Finkel, Lindberg, Liow, Lockwood, Lotze, McClain, McGuire, O'Dea and Pandolfi2015; Muscente et al. Reference Muscente, Prabhu, Zhong, Eleish, Meyer, Fox, Hazen and Knoll2018; Yasuhara et al. Reference Yasuhara, Huang, Hull, Rillo, Condamine, Tittensor, Kučera, Costello, Finnegan, O'Dea, Hong, Bonebrake, McKenzie, Doi, Wei, Kubota and Saupe2020). The demand for workshops on these topics, such as the Analytical Paleobiology Workshop (https://www.cnidaria.nat.uni-erlangen.de/shortcourse/index.html) and Paleontological Society Short Courses at the Geological Society of America annual meeting (https://www.paleosoc.org/short-courses), indicates that this research frontier is set to grow.

Although analytical paleobiology has been firmly established as a research topic, it continues to face challenges related to data analysis, synthesis, and accessibility. Some of these challenges are long-standing (Seddon et al. Reference Seddon, Mackay, Baker, Birks, Breman, Buck, Ellis, Froyd, Gill, Gillson, Johnson, Jones, Juggins, Macias-Fauria, Mills, Morris, Nogués-Bravo, Punyasena, Roland, Tanentzap, Willis, Aberhan, van Asperen, Austin, Battarbee, Bhagwat, Belanger, Bennett, Birks, Bronk Ramsey, Brooks, de Bruyn, Butler, Chambers, Clarke, Davies, Dearing, Ezard, Feurdean, Flower, Gell, Hausmann, Hogan, Hopkins, Jeffers, Korhola, Marchant, Kiefer, Lamentowicz, Larocque-Tobler, López-Merino, Liow, McGowan, Miller, Montoya, Morton, Nogué, Onoufriou, Boush, Rodriguez-Sanchez, Rose, Sayer, Shaw, Payne, Simpson, Sohar, Whitehouse, Williams and Witkowski2014), while others have been recently illuminated or even amplified by analytical advances (Raja et al. Reference Raja, Dunne, Matiwane, Khan, Nätscher, Ghilardi and Chattopadhyay2022). In response, many paleobiologists—particularly early-career researchers—have advocated for more collaborative, interdisciplinary, and open science. Their willingness to embrace new research practices has already begun to permeate the broader paleontological community. However, the guidelines and community buy-in that are needed to standardize these practices are still developing. As both the challenges that face analytical paleobiology and our capacity to tackle them evolve, it can be productive to monitor progress and reflect on how this research topic might continue to mature.

As one of the most recent cohorts to graduate from the Analytical Paleobiology Workshop (2019), we present this synthetic survey to signpost obstacles in analytical paleobiology from an early-career perspective and map them onto emerging solutions. We outline four interconnected challenges (Table 1), highlight recent progress, and collate a list of tools that have pushed analytical paleobiology in new directions (Supplementary Tables 1, 2). By surveying a wide range of topics, we aim to link disparate advances and provide readers with entry points for engagement with each challenge, while directing them to comprehensive discourse on each. We also echo calls for more consistent and equitable approaches to data production, synthesis, and sharing within analytical paleobiology.

Table 1. Summary of four challenges facing analytical paleobiology. Key advances are highlighted under each challenge.

Challenge 1: Measuring Biodiversity across Space and Time

The fossil record provides an invaluable but imperfect time capsule to explore how and why biodiversity has changed over Earth's history. Early studies of deep-time biodiversity interpreted the fossil record at face value, but these interpretations are now widely documented to be confounded by a combination of geological, taphonomic, and sampling biases (Raup Reference Raup1972, Reference Raup1976; Sepkoski et al. Reference Sepkoski, Bambach, Raup and Valentine1981; Benton Reference Benton1995; Smith and McGowan Reference Smith and McGowan2011; Walker et al. Reference Walker, Dunhill and Benton2020). These biases can distort biodiversity estimates and hinder meaningful comparisons of fossil assemblages across space and time (Close et al. Reference Close, Benson, Alroy, Carrano, Cleary, Dunne, Mannion, Uhen and Butler2020a; Benson et al. Reference Benson, Butler, Close, Saupe and Rabosky2021). In recent years, quantitative methods have accrued to alleviate some of these limitations, improving our ability to quantify true biodiversity patterns (Supplementary Table 2). However, researchers now face the challenge of creating transparent, reproducible workflows to navigate this landscape of resources as they prepare their raw data for analysis (Fig. 1). Here, we focus on four aspects of this workflow: taxonomic resolution, sampling standardization, spatial standardization, and time series analysis.

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 paleoenvironmental patterns with taxon occurrences to model ecological niches through time.

Estimates of taxonomic diversity are influenced by the resolution at which specimens are identified. Deep-time biodiversity patterns have long been quantified using counts of higher taxa, such as families (Sepkoski Reference Sepkoski1981; Labandeira and Sepkoski Reference Labandeira and Sepkoski1993) or genera (Sepkoski Reference Sepkoski1997; Alroy et al. Reference Alroy, Aberhan, Bottjer, Foote, Fürsich, Harries, Hendy, Holland, Ivany, Kiessling, Kosnik, Marshall, McGowan, Miller, Olszewski, Patzkowsky, Peters, Villier, Wagner, Bonuso, Borkow, Brenneis, Clapham, Fall, Ferguson, Hanson, Krug, Layou, Leckey, Nürnberg, Powers, Sessa, Simpson, Tomašových and Visaggi2008; Cleary et al. Reference Cleary, Benson, Evans and Barrett2018). Genera are often preferred, because they are typically easier to identify, more robust to stratigraphic binning, and more taxonomically stable than fossil species (Allmon Reference Allmon1992; Foote Reference Foote2000), such that they are considered to be a good substitute for biodiversity (Jablonski and Finarelli Reference Jablonski and Finarelli2009). However, genera are not perfect proxies for species, which are more directly shaped by evolutionary and ecological processes (Hendricks et al. Reference Hendricks, Saupe, Myers, Hermsen and Allmon2014). Nor are they immediately comparable with ecological data, which are often collected at the species level and are increasingly delineated using genetics (Pinzón et al. Reference Pinzón, Sampayo, Cox, Chauka, Chen, Voolstra and LaJeunesse2013; Zamani et al. Reference Zamani, Fric, Gante, Hopkins, Orfinger, Scherz, Bartoňová and Pos2022) (Fig. 1A). Authors have therefore called for greater transparency when analyzing genus-level patterns (e.g., justifying the use of genera as well as reporting species-to-genus ratios) and discussing their implications for species (Hendricks et al. Reference Hendricks, Saupe, Myers, Hermsen and Allmon2014). At the same time, the taxonomic work that underpins specimen identification remains chronically undervalued (Zeppelini et al. Reference Zeppelini, Dal Molin, Lamas, Sarmiento, Rheims, Fernandes, Lima, Silva, Carvalho-Filho, Kováč, Montoya-Lerma, Moldovan, Souza-Dias, Demite, Feitosa, Boyer, Weiner and Rodrigues2021; Gorneau et al. Reference Gorneau, Ausich, Bertolino, Bik, Daly, Demissew, Donoso, Folk, Freire-Fierro, Ghazanfar, Grace, Hu, Kulkarni, Lichter-Marck, Lohmann, Malumbres-Olarte, Muasya, Pérez-González, Singh, Siniscalchi, Specht, Stigall, Tank, Walker, Wright, Zamani and Esposito2022; although see Costello et al. Reference Costello, Wilson and Houlding2013). To preserve taxonomic knowledge, efforts could be made to invest in taxonomy courses (e.g., Smithsonian Training in Tropical Taxonomy), grants that fund curation and systematics (e.g., Paleontological Society Arthur James Boucot Research Grants), and taxonomy databases (Costello et al. Reference Costello, Wilson and Houlding2013; Fawcett et al. Reference Fawcett, Agosti, Cole and Wright2022; Grenié et al. Reference Grenié, Berti, Carvajal‐Quintero, Dädlow, Sagouis and Winter2023). Investments in systematics might, in turn, encourage stronger connections between genus- and species-level analyses when studying biodiversity through time.

Biodiversity estimates are also sensitive to sampling. In the last two decades, numerous quantitative methods have been developed to compare numbers of taxa (taxonomic richness) among assemblages while accounting for variation in sampling. Yet there is still no one-size-fits-all approach, leaving researchers to weigh the trade-offs between different methods (Close et al. Reference Close, Evers, Alroy and Butler2018; Alroy Reference Alroy2020; Roswell et al. Reference Roswell, Dushoff and Winfree2021) or use multiple complementary methods (e.g., Allen et al. Reference Allen, Wignall, Hill, Saupe and Dunhill2020). Richness estimators are a popular sampling standardization method (Alroy Reference Alroy2020). One example is shareholder quorum subsampling (Alroy et al. Reference Alroy, Aberhan, Bottjer, Foote, Fürsich, Harries, Hendy, Holland, Ivany, Kiessling, Kosnik, Marshall, McGowan, Miller, Olszewski, Patzkowsky, Peters, Villier, Wagner, Bonuso, Borkow, Brenneis, Clapham, Fall, Ferguson, Hanson, Krug, Layou, Leckey, Nürnberg, Powers, Sessa, Simpson, Tomašových and Visaggi2008; Alroy Reference Alroy2010a,Reference Alroyb,Reference Alroyc), which standardizes samples based on a measure of sample completeness, or coverage. This approach is mathematically similar to coverage-based rarefaction, which is commonly used in ecology to standardize samples when measuring species diversity (Chao and Jost Reference Chao and Jost2012; Chao et al. Reference Chao, Kubota, Zelený, Chiu, Li, Kusumoto, Yasuhara, Thorn, Wei, Costello and Colwell2020, Reference Chao, Henderson, Chiu, Moyes, Hu, Dornelas and Magurran2021; Roswell et al. Reference Roswell, Dushoff and Winfree2021). Other popular methods focus on macroevolutionary rates (e.g., origination and extinction). These range from relatively straightforward equations (Kocsis et al. Reference Kocsis, Reddin, Alroy and Kiessling2019) to more complex Bayesian frameworks (PyRate; Silvestro et al. Reference Silvestro, Salamin and Schnitzler2014) and models that incorporate phylogenetic information (fossilized birth–death process; Heath et al. Reference Heath, Huelsenbeck and Stadler2014; Warnock et al. Reference Warnock, Heath and Stadler2020). Ecological methods, such as capture–mark–recapture (Liow and Nichols Reference Liow and Nichols2010), can also be used to infer biodiversity dynamics from incomplete samples but have not been as widely applied in paleobiology. The diversity of available methods underscores the complexity of measuring biodiversity but also presents an opportunity to establish best practices that fine-tune their usage. As consensus forms, paleobiologists and ecologists could collaborate to consolidate sampling standardization methods across disciplines (Challenge 2).

Although sampling standardization corrects for differences in sample completeness, it does not consider the geographic distribution of samples. Biodiversity patterns in the fossil record have traditionally been interpreted at global scales, yet these inferences are affected by the fossil record's spatial structure (Bush and Bambach Reference Bush and Bambach2004; Vilhena and Smith Reference Vilhena and Smith2013; Close et al. Reference Close, Benson, Saupe, Clapham and Butler2020b). If spatial variation in sampling is not addressed, apparent changes in biodiversity might reflect heterogeneity in depositional, environmental, or climatic conditions rather than genuine patterns (Shaw et al. Reference Shaw, Briggs and Hull2020; Benson et al. Reference Benson, Butler, Close, Saupe and Rabosky2021). Additionally, global analyses can mask local- or regional-scale variation in biodiversity (Benson et al. Reference Benson, Butler, Close, Saupe and Rabosky2021). Researchers are increasingly using spatially explicit approaches to track biodiversity changes at nested spatial scales (Cantalapiedra et al. Reference Cantalapiedra, Domingo and Domingo2018; Womack et al. Reference Womack, Crampton and Hannah2021). A variety of procedures have been developed in recent years to account for the spatial distribution of samples. Some are relatively simple metrics, such as the convex-hull area (Close et al. Reference Close, Benson, Upchurch and Butler2017) and number of occupied equal-area grid cells (Womack et al. Reference Womack, Crampton and Hannah2021). Others are more complex, such as kernel density estimators (Chiarenza et al. Reference Chiarenza, Mannion, Lunt, Farnsworth, Jones, Kelland and Allison2019), summed minimum spanning tree length (Jones et al. Reference Jones, Dean, Mannion, Farnsworth and Allison2021; Womack et al. Reference Womack, Crampton and Hannah2021), and spatial subsampling procedures (Antell et al. Reference Antell, Kiessling, Aberhan and Saupe2020; Close et al. Reference Close, Benson, Saupe, Clapham and Butler2020b; Flannery-Sutherland et al. Reference Flannery-Sutherland, Silvestro and Benton2022). Some of the newer statistical approaches have been released with reproducible code or as R packages to allow updates from community members, providing an example of how methods in analytical paleobiology might mature (Challenge 3). Next steps could include efforts to establish incentive structures for contributing to this codebase, guidelines that compare methods, and workflows that link these packages.

Many paleobiological studies aim to quantify biodiversity through time, yet such analyses are complicated by variation in the fossil record's temporal resolution and quality (Fig. 1A). Because stratigraphic sequences are irregularly arranged in time and variably time-averaged, many common approaches to time series analysis (such as autoregressive integrated moving average, or ARIMA, models) cannot be readily applied (Kidwell and Holland Reference Kidwell and Holland2002; Yasuhara et al. Reference Yasuhara, Tittensor, Hillebrand and Worm2017; Simpson Reference Simpson2018; Fraser et al. Reference Fraser, Soul, Tóth, Balk, Eronen, Pineda-Munoz, Shupinski, Villaseñor, Barr, Behrensmeyer, Du, Faith, Gotelli, Graves, Jukar, Looy, Miller, Potts and Lyons2021). Additionally, biodiversity dynamics can be scale dependent (Levin Reference Levin1992; McKinney and Drake Reference McKinney and Drake2001; Lewandowska et al. Reference Lewandowska, Jonkers, Auel, Freund, Hagen, Kucera and Hillebrand2020; Yasuhara et al. Reference Yasuhara, Huang, Hull, Rillo, Condamine, Tittensor, Kučera, Costello, Finnegan, O'Dea, Hong, Bonebrake, McKenzie, Doi, Wei, Kubota and Saupe2020) or can interact over different scales to yield emergent patterns (Mathes et al. Reference Mathes, van Dijk, Kiessling and Steinbauer2021). Recent efforts to analyze biodiversity trends have been aided by advances in geochronology and age–depth modeling that provide more robust age control as well as models of depositional processes (Tomašových and Kidwell Reference Tomašových and Kidwell2010; Kidwell Reference Kidwell2015; Tomašových et al. Reference Tomašových, Kidwell and Barber2016; Hohmann Reference Hohmann2021; McKay et al. Reference McKay, Emile-Geay and Khider2021). Progress has also been made by implementing analyses that can accommodate observations from different types of stratigraphic sequences while accounting for age-model uncertainty. In particular, generalized additive models (Simpson Reference Simpson2018), causal analyses like convergent cross mapping (Hannisdal and Liow Reference Hannisdal and Liow2018; Runge et al. Reference Runge, Bathiany, Bollt, Camps-Valls, Coumou, Deyle, Glymour, Kretschmer, Mahecha, Muñoz-Marí, van Nes, Peters, Quax, Reichstein, Scheffer, Schölkopf, Spirtes, Sugihara, Sun, Zhang and Zscheischler2019; Doi et al. Reference Doi, Yasuhara and Ushio2021), multivariate rate-of-change analyses (Mottl et al. Reference Mottl, Grytnes, Seddon, Steinbauer, Bhatta, Felde, Flantua and Birks2021), and machine learning methods (Karpatne et al. Reference Karpatne, Ebert-Uphoff, Ravela, Babaie and Kumar2019) are changing research norms from describing temporal change to estimating statistical trends and making causal inferences among paleobiological time series. These approaches are still gaining momentum but will likely become more mainstream as they are incorporated into stratigraphic paleobiology and paleoecology training programs (Birks et al. Reference Birks, Lotter, Juggins and Smol2012; Patzkowsky and Holland Reference Patzkowsky and Holland2012; Holland and Loughney Reference Holland and Loughney2021).

As we highlighted earlier, paleobiological data often require extensive cleaning and standardization before they can be meaningfully analyzed. Open-source tools are being developed to streamline this workflow (e.g., Jones et al. Reference Jones, Gearty, Allen, Eichenseer, Dean, Galván, Kouvari, Godoy, Nicholl, Buffan, Dillon, Flannery-Sutherland and Chiarenza2022), typically in the R programming environment (Supplementary Table 2). Moving forward, this ecosystem of tools might encourage more reproducible data processing workflows within analytical paleobiology (Challenge 3). Nevertheless, quantitative methods cannot mitigate all biases, particularly those influencing the extent of the sampled fossil record. For example, variation in the preservational potential or environmental types represented by samples elude simple statistical corrections (Purnell et al. Reference Purnell, Donoghue, Gabbott, McNamara, Murdock and Sansom2018; Walker et al. Reference Walker, Dunhill and Benton2020; Benson et al. Reference Benson, Butler, Close, Saupe and Rabosky2021; de Celis et al. Reference de Celis, Narváez, Arcucci and Ortega2021). Socioeconomic disparities can also exacerbate taphonomic or geological biases by fueling differences in sampling effort across countries (Amano and Sutherland Reference Amano and Sutherland2013; Guerra et al. Reference Guerra, Heintz-Buschart, Sikorski, Chatzinotas, Guerrero-Ramírez, Cesarz, Beaumelle, Rillig, Maestre, Delgado-Baquerizo, Buscot, Overmann, Patoine, Phillips, Winter, Wubet, Küsel, Bardgett, Cameron, Cowan, Grebenc, Marín, Orgiazzi, Singh, Wall and Eisenhauer2020; Moudrý and Devillers Reference Moudrý and Devillers2020; Raja et al. Reference Raja, Dunne, Matiwane, Khan, Nätscher, Ghilardi and Chattopadhyay2022) (Challenge 4). Although quantitative methods can help illuminate the potential severity of these biases, they cannot fill sampling gaps. As such, understanding the context in which samples were collected and communicating how they were interpreted will remain critical aspects of analytical paleobiology.

Challenge 2: Integrating Fossil and Modern Biodiversity Data

Studies that link data from ancient and modern ecosystems offer holistic insight into processes spanning long timescales. For example, time series of taxon occurrences and environmental conditions in the fossil record can complement real-time monitoring to disentangle drivers of community assembly (Lyons et al. Reference Lyons, Amatangelo, Behrensmeyer, Bercovici, Blois, Davis, DiMichele, Du, Eronen, Tyler Faith, Graves, Jud, Labandeira, Looy, McGill, Miller, Patterson, Pineda-Munoz, Potts, Riddle, Terry, Tóth, Ulrich, Villaseñor, Wing, Anderson, Anderson, Waller and Gotelli2016), assess extinction risk (Raja et al. Reference Raja, Lauchstedt, Pandolfi, Kim, Budd and Kiessling2021), evaluate how ecosystems respond to disturbances (Buma et al. Reference Buma, Harvey, Gavin, Kelly, Loboda, McNeil, Marlon, Meddens, Morris, Raffa, Shuman, Smithwick and McLauchlan2019; Tomašových et al. Reference Tomašových, Albano, Fuksi, Gallmetzer, Haselmair, Kowalewski, Nawrot, Nerlović, Scarponi and Zuschin2020; Dillon et al. Reference Dillon, McCauley, Morales-Saldaña, Leonard, Zhao and O'Dea2021), and inform conservation decisions (Dietl et al. Reference Dietl, Kidwell, Brenner, Burney, Flessa, Jackson and Koch2015; Kiessling et al. Reference Kiessling, Raja, Roden, Turvey and Saupe2019). However, despite becoming more intertwined over the last decade, paleontology and ecology continue to progress as separate disciplines (Willis and Birks Reference Willis and Birks2006; Goodenough and Webb Reference Goodenough and Webb2022). Here, we outline four obstacles that impede the synthesis of paleobiological and ecological data, although these extend to other multiproxy work.

A first obstacle is data acquisition. Recent years have seen advances in data archiving as well as funding for projects that aggregate fossil and modern biodiversity data. Databases and museum collections, especially when digitized (Allmon et al. Reference Allmon, Dietl, Hendricks, Ross, Rosenberg and Clary2018), have promoted data discovery (Supplementary Table 1). In turn, application programming interfaces and web interfaces have facilitated data downloads. Examples include the paleobioDB R package, which extracts data from the Paleobiology Database (Varela et al. Reference Varela, González-Hernández, Sgarbi, Marshall, Uhen, Peters and McClennen2015), and the EarthLife Consortium (https://earthlifeconsortium.org), which queries the Paleobiology Database, Neotoma Paleoecology Database, and Strategic Environmental Archaeology Database (Uhen et al. Reference Uhen, Buckland, Goring, Jenkins and Williams2021). As these tools have gained traction, there have been calls to standardize archiving and formatting protocols to increase database interoperability (Guralnick et al. Reference Guralnick, Hill and Lane2007; Morrison et al. Reference Morrison, Sillett, Funk, Ghalambor and Rick2017; König et al. Reference König, Weigelt, Schrader, Taylor, Kattge and Kreft2019; Wüest et al. Reference Wüest, Zimmermann, Zurell, Alexander, Fritz, Hof, Kreft, Normand, Cabral, Szekely, Thuiller, Wikelski and Karger2020; Heberling et al. Reference Heberling, Miller, Noesgaard, Weingart and Schigel2021; Nieto-Lugilde et al. Reference Nieto-Lugilde, Blois, Bonet-García, Giesecke, Gil-Romera and Seddon2021; Huang et al. Reference Huang, Yasuhara, Horne, Perrier, Smith and Brandão2022) as well as maintain interdisciplinary funding structures (e.g., Past Global Changes, https://pastglobalchanges.org) to ensure their future accessibility (Challenge 4).

A second obstacle stems from the practical aspects of integrating paleobiological and ecological data. Integrative analyses involve combining datasets with different units, scales, resolutions, biases, and uncertainties (e.g., paleoclimate proxies aligned with taxon occurrences; Fig. 1). These disparate data properties can hinder their inclusion in statistical models, which typically require consistent inputs that meet certain conditions (Yasuhara et al. Reference Yasuhara, Tittensor, Hillebrand and Worm2017; Su and Croft Reference Su, Croft, Croft, Su and Simpson2018). In recent years, data synthesis has been streamlined by efforts to: (1) develop analyses that can accommodate heterogeneous datasets (Challenge 3); (2) calibrate complementary methods (Vellend et al. Reference Vellend, Brown, Kharouba, McCune and Myers-Smith2013; Buma et al. Reference Buma, Harvey, Gavin, Kelly, Loboda, McNeil, Marlon, Meddens, Morris, Raffa, Shuman, Smithwick and McLauchlan2019); (3) standardize data harmonization protocols (König et al. Reference König, Weigelt, Schrader, Taylor, Kattge and Kreft2019; Rapacciuolo and Blois Reference Rapacciuolo and Blois2019; Nieto-Lugilde et al. Reference Nieto-Lugilde, Blois, Bonet-García, Giesecke, Gil-Romera and Seddon2021); and (4) support interdisciplinary work (Ferretti et al. Reference Ferretti, Crowder, Micheli, Blight, Kittinger, McClenachan, Gedan and Blight2014). As integrative analyses become more common, best practices could be formalized to describe data properties, processing workflows, and boundaries of inference (e.g., Bennington et al. Reference Bennington, Dimichele, Badgley, Bambach, Barrett, Behrensmeyer, Bobe, Burnham, Daeschler, Dam, Eronen, Erwin, Finnegan, Holland, Hunt, Jablonski, Jackson, Jacobs, Kidwell, Koch, Kowalewski, Labandeira, Looy, Lyons, Novack-Gottshall, Potts, Roopnarine, Stromberg, Sues, Wagner, Wilf and Wing2009; McClenachan et al. Reference McClenachan, Cooper, McKenzie and Drew2015; Wilke et al. Reference Wilke, Wagner, Van Bocxlaer, Albrecht, Ariztegui, Delicado, Francke, Harzhauser, Hauffe, Holtvoeth, Just, Leng, Levkov, Penkman, Sadori, Skinner, Stelbrink, Vogel, Wesselingh and Wonik2016; Lendemer and Coyle Reference Lendemer and Coyle2021). One potential path forward is through frameworks that guide the practice of integration and provide conceptual scaffolding for new analytical techniques (Price and Schmitz Reference Price and Schmitz2016; Kliskey et al. Reference Kliskey, Alessa, Wandersee, Williams, Trammell, Powell, Grunblatt and Wipfli2017; Rapacciuolo and Blois Reference Rapacciuolo and Blois2019; Napier and Chipman Reference Napier and Chipman2022).

Conceptual barriers to data integration pose a third obstacle. These barriers often arise from differences between discipline histories, research goals, or methods (Szabó and Hédl Reference Szabó and Hédl2011; Sievanen et al. Reference Sievanen, Campbell and Leslie2012; Yasuhara et al. Reference Yasuhara, Tittensor, Hillebrand and Worm2017). Process-, function-, or trait-based metrics offer a potential workaround. These metrics can help align datasets over multiple scales and identify common currencies that are grounded in ecological or evolutionary theory (Eronen et al. Reference Eronen, Polly, Fred, Damuth, Frank, Mosbrugger, Scheidegger, Stenseth and Fortelius2010; Ezard et al. Reference Ezard, Aze, Pearson and Purvis2011; Mouillot et al. Reference Mouillot, Graham, Villéger, Mason and Bellwood2013; Wolkovich et al. Reference Wolkovich, Cook, McLauchlan and Davies2014; Yasuhara et al. Reference Yasuhara, Doi, Wei, Danovaro and Myhre2016; Pimiento et al. Reference Pimiento, Griffin, Clements, Silvestro, Varela, Uhen and Jaramillo2017, Reference Pimiento, Leprieur, Silvestro, Lefcheck, Albouy, Rasher, Davis, Svenning and Griffin2020; Spalding and Hull Reference Spalding and Hull2021). This paradigm moves away from conventional attempts to explore an ecological or evolutionary process within the bounds of a single discipline, instead encouraging interaction among researchers who approach the same process from different angles. For example, resilience concepts from the ecological literature are already being applied to the fossil record (Davies et al. Reference Davies, Streeter, Lawson, Roucoux and Hiles2018; Scarponi et al. Reference Scarponi, Nawrot, Azzarone, Pellegrini, Gamberi, Trincardi and Kowalewski2022). Moving forward, we echo existing calls to improve interdisciplinary communication (Benda et al. Reference Benda, Poff, Tague, Palmer, Pizzuto, Cooper, Stanley and Moglen2002; Boulton et al. Reference Boulton, Panizzon and Prior2005; Eigenbrode et al. Reference Eigenbrode, O'Rourke, Wulfhorst, Althoff, Goldberg, Merrill, Morse, Nielsen-Pincus, Stephens, Winowiecki and Bosque-Pérez2007), which could help design meaningful metrics that are comparable between fossil and modern datasets.

Finally, the paleontological and ecological communities remain siloed despite their complementarity. They ask similar questions but use different terminology and tools over different timescales (Rull Reference Rull2010). Interdisciplinary networks, conferences, departments, journals, and training programs can facilitate cross talk between these disciplines. Many examples already exist that provide blueprints for future partnerships. These include the Oceans Past Initiative (https://oceanspast.org), Conservation Paleobiology Network (https://conservationpaleorcn.org), Crossing the Palaeontological-Ecological Gap meeting (https://www.cpegberlin.com) and journal issue (Dunhill and Liow Reference Dunhill and Liow2018), and the PaleoSynthesis Project (https://www.paleosynthesis.nat.fau.de). Collectively, such efforts could increase institutional support for interdisciplinary research and gradually change the culture of interdisciplinarity (Ferretti et al. Reference Ferretti, Crowder, Micheli, Blight, Kittinger, McClenachan, Gedan and Blight2014; Price and Schmitz Reference Price and Schmitz2016; Yasuhara et al. Reference Yasuhara, Tittensor, Hillebrand and Worm2017). We could also learn from other interdisciplinary work such as social-ecological systems research, which links insights across the natural and social sciences (Schoon and van der Leeuw Reference Schoon and van der Leeuw2015). Ultimately, the high buy-in from early-career researchers in these initiatives bodes well for their longevity and impact.

Challenge 3: Building Data Science Skills to Analyze the Fossil Record

Paleobiology is embracing “big data.” Not only are there more ways to collect high-resolution data (Olsen and Westneat Reference Olsen and Westneat2015; del Carmen Gomez Cabrera et al. Reference del Carmen Gomez Cabrera, Young, Roff, Staples, Ortiz, Pandolfi and Cooper2019; Goswami et al. Reference Goswami, Watanabe, Felice, Bardua, Fabre and Polly2019) and automate analyses using machine learning (Peters et al. Reference Peters, Zhang, Livny and Ré2014; Hsiang et al. Reference Hsiang, Nelson, Elder, Sibert, Kahanamoku, Burke, Kelly, Liu and Hull2018, Reference Hsiang, Brombacher, Rillo, Mleneck-Vautravers, Conn, Lordsmith, Jentzen, Henehan, Metcalfe, Fenton, Wade, Fox, Meilland, Davis, Baranowski, Groeneveld, Edgar, Movellan, Aze, Dowsett, Miller, Rios and Hull2019; Kopperud et al. Reference Kopperud, Lidgard and Liow2019; Muñoz and Price Reference Muñoz and Price2019; Beaufort et al. Reference Beaufort, Bolton, Sarr, Suchéras-Marx, Rosenthal, Donnadieu, Barbarin, Bova, Cornuault, Gally, Gray, Mazur and Tetard2022) but also new opportunities to tap into online databases (Alroy Reference Alroy2003; Brewer et al. Reference Brewer, Jackson and Williams2012) (Fig. 1B). These advances have contributed to the volume, velocity, and variety of datasets that characterize big data (LaDeau et al. Reference LaDeau, Han, Rosi-Marshall and Weathers2017). However, with this accumulating information (Supplementary Table 1) comes the need for more awareness of quantitative tools (Supplementary Table 2) and best practices for data analysis. Data science training programs paired with proactive efforts to collaborate with environmental data scientists could aid the transition toward more quantitative research.

There is a growing need for paleobiologists to learn statistical and coding skills. These skills are needed to analyze large heterogeneous datasets, implement reproducible coding practices (Nosek et al. Reference Nosek, Alter, Banks, Borsboom, Bowman, Breckler, Buck, Chambers, Chin, Christensen, Contestabile, Dafoe, Eich, Freese, Glennerster, Goroff, Green, Hesse, Humphreys, Ishiyama, Karlan, Kraut, Lupia, Mabry, Madon, Malhotra, Mayo-Wilson, McNutt, Miguel, Paluck, Simonsohn, Soderberg, Spellman, Turitto, VandenBos, Vazire, Wagenmakers, Wilson and Yarkoni2015; Lowndes et al. Reference Lowndes, Best, Scarborough, Afflerbach, Frazier, O'Hara, Jiang and Halpern2017), and streamline analytical workflows (Wilson et al. Reference Wilson, Bryan, Cranston, Kitzes, Nederbragt and Teal2017; Bryan Reference Bryan2018) (Challenges 1 and 2). Training could take the form of community-based discussions (Lowndes et al. Reference Lowndes, Froehlich, Horst, Jayasundara, Pinsky, Stier, Therkildsen and Wood2019) and meetups (e.g., TidyTuesday), formal courses (e.g., Software Carpentry, https://software-carpentry.org), or independent instruction through coding tutorials (e.g., Coding Club, https://ourcodingclub.github.io/course.html). Additionally, data science topics could continue to be incorporated into paleobiology degree programs or taught as stand-alone analytical paleobiology courses. These training opportunities would provide a foundation for paleobiologists to use existing quantitative methods and create new software to analyze the fossil record.

As more paleobiologists run analyses in R, Python, and other coding languages, they could benefit from engagement with data scientists as well as with other disciplines that interface with data science, such as ecology and environmental science. Building computational skills might seem daunting, but there is no need to reinvent the wheel. Tools and infrastructure already exist (Sandve et al. Reference Sandve, Nekrutenko, Taylor and Hovig2013; Michener Reference Michener2015; Hart et al. Reference Hart, Barmby, LeBauer, Michonneau, Mount, Mulrooney, Poisot, Woo, Zimmerman and Hollister2016; Lowndes et al. Reference Lowndes, Best, Scarborough, Afflerbach, Frazier, O'Hara, Jiang and Halpern2017; Wilson et al. Reference Wilson, Bryan, Cranston, Kitzes, Nederbragt and Teal2017; Filazzola and Lortie Reference Filazzola and Lortie2022) that can be adapted to paleobiology (e.g., Barido-Sottani et al. Reference Barido-Sottani, Saupe, Smiley, Soul, Wright and Warnock2020). Working groups at synthesis centers such as the National Center for Ecological Analysis and Synthesis (which produced the Paleobiology Database) and online communities like LinkedEarth (https://linked.earth) have already begun to foster data-driven collaborations in paleontology, foreshadowing how quantitative research agendas might progress.

Challenge 4: Increasing Data Accessibility and Equity

Paleobiological data and computing resources are more accessible now than ever, but access to them is not equitable among researchers. Many financial, technological, institutional, and socioeconomic factors determine who participates in research as well as how paleobiological data are collected, interpreted, and shared (Núñez et al. Reference Núñez, Rivera and Hallmark2020; Valenzuela-Toro and Viglino Reference Valenzuela-Toro and Viglino2021) (Fig. 2). Advancing equity in the context of analytical paleobiology entails acknowledging that access to analytical resources is unequal and allocating them in relation to researchers’ needs to achieve fairer outcomes (CSSP 2019). Here, we discuss barriers pertaining to the access of paleobiological data and resources. These are by no means exhaustive but represent several broadscale challenges for which solutions have been proposed.

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.

Fossil specimens and their associated morphological, geographic, and stratigraphic information underpin research in analytical paleobiology. Data collection often involves visiting museums or gathering digital data from publications and repositories. However, these data are not always accessible. Visiting museums to study specimens can be logistically, financially, or politically infeasible—or even impossible. Travel grants (e.g., John W. Wells Grants-in-Aid of Research Program at the Paleontological Research Institution) can help offset transportation costs, but they cannot alleviate visa issues or other travel restrictions. Likewise, data underlying publications might be buried in supplementary files or locked behind paywalls or might lack consistent metadata or formatting—if they are even made available. As such, emphasis could be placed on finding alternative ways to make paleobiological data more open, particularly for researchers who historically have had less access.

One major step forward is digitization. For example, many museums have committed to digitizing their collections (Nelson and Ellis Reference Nelson and Ellis2019; Bakker et al. Reference Bakker, Antonelli, Clarke, Cook, Edwards, Ericson, Faurby, Ferrand, Gelang, Gillespie, Irestedt, Lundin, Larsson, Matos-Maraví, Müller, von Proschwitz, Roderick, Schliep, Wahlberg, Wiedenhoeft and Källersjö2020; Hedrick et al. Reference Hedrick, Heberling, Meineke, Turner, Grassa, Park, Kennedy, Clarke, Cook, Blackburn, Edwards and Davis2020; Sandramo et al. Reference Sandramo, Nicosia, Cianciullo, Muatinte and Guissamulo2021). However, only a fraction of these “dark data” have been mobilized given the substantial time, money, and effort required (Nelson et al. Reference Nelson, Paul, Riccardi and Mast2012; Paterson et al. Reference Paterson, Albuquerque, Blagoderov, Brooks, Cafferty, Cane, Carter, Chainey, Crowther, Douglas, Durant, Duffell, Hine, Honey, Huertas, Howard, Huxley, Kitching, Ledger, McLaughlin, Martin, Mazzetta, Penn, Perera, Sadka, Scialabba, Self, Siebert, Sleep, Toloni and Wing2016; Marshall et al. Reference Marshall, Finnegan, Clites, Holroyd, Bonuso, Cortez, Davis, Dietl, Druckenmiller, Eng, Garcia, Estes-Smargiassi, Hendy, Hollis, Little, Nesbitt, Roopnarine, Skibinski, Vendetti and White2018). If paleobiology continues to value digital data, financial and logistical support could be expanded for online databases and museum digitization efforts as well as resources for researchers to access those data.

Open-data practices do not end with digitization, however, as digital assets must also be maintained. In 2016, the FAIR Guiding Principles (Findability, Accessibility, Interoperability, and Reusability) for scientific data management and stewardship were published to enhance data discovery and reuse (Wilkinson et al. Reference Wilkinson, Dumontier, Aalbersberg, Appleton, Axton, Baak, Blomberg, Boiten, da Silva Santos, Bourne, Bouwman, Brookes, Clark, Crosas, Dillo, Dumon, Edmunds, Evelo, Finkers, Gonzalez-Beltran, Gray, Groth, Goble, Grethe, Heringa, ’t Hoen, Hooft, Kuhn, Kok, Kok, Lusher, Martone, Mons, Packer, Persson, Rocca-Serra, Roos, van Schaik, Sansone, Schultes, Sengstag, Slater, Strawn, Swertz, Thompson, van der Lei, van Mulligen, Velterop, Waagmeester, Wittenburg, Wolstencroft, Zhao and Mons2016). Additionally, the TRUST Principles (Transparency, Responsibility, User focus, Sustainability and Technology) were developed to demonstrate the trustworthiness of digital repositories (Lin et al. Reference Lin, Crabtree, Dillo, Downs, Edmunds, Giaretta, De Giusti, L'Hours, Hugo, Jenkyns, Khodiyar, Martone, Mokrane, Navale, Petters, Sierman, Sokolova, Stockhause and Westbrook2020). Although the biological sciences have embraced these principles, paleontology still lags behind (Stuart et al. Reference Stuart, Baynes, Hrynaszkiewicz, Allin, Penny, Lucraft and Astell2018; Kinkade and Shepherd Reference Kinkade and Shepherd2021). To encourage better data management practices, paleontological journals could require authors to archive their data, metadata, and code in centralized online repositories instead of only in supplementary files (Kaufman and PAGES 2k Special-Issue Editorial Team Reference Kaufman2018). Unique dataset identifiers could, in turn, be adopted to track data reuse and credit the authors (Pierce et al. Reference Pierce, Dev, Statham and Bierer2019). Normalizing these practices begins with data stewardship training to highlight resources (e.g., https://fairsharing.org) and community standards (e.g., Biodiversity Information Standards, https://www.tdwg.org) when managing paleobiological data (Koch et al. Reference Koch, Glover, Zambri, Thomas, Benito and Yang2018; Seltmann et al. Reference Seltmann, Lafia, Paul, James, Bloom, Rios, Ellis, Farrell, Utrup, Yost, Davis, Emery, Motz, Kimmig, Shirey, Sandall, Park, Tyrrell, Thackurdeen, Collins, O'Leary, Prestridge, Evelyn and Nyberg2018; Stall et al. Reference Stall, Yarmey, Boehm, Cousijn, Cruse, Cutcher-Gershenfeld, Dasler, de Waard, Duerr, Elger, Fenner, Glaves, Hanson, Hausman, Heber, Hills, Hoebelheinrich, Hou, Kinkade, Koskela, Martin, Lehnert, Murphy, Nosek, Parsons, Petters, Plante, Robinson, Samors, Servilla, Ulrich, Witt and Wyborn2018; Krimmel et al. Reference Krimmel, Karim, Little, Walker, Burkhalter, Byrd, Millhouse and Utrup2021).

As analytical paleobiology moves toward a future of open data, concerns regarding data ownership, representation, and control have been rekindled, particularly in relation to Indigenous communities and lands (Kukutai and Taylor Reference Kukutai, Taylor, Kukutai and Taylor2016; Jennings et al. Reference Jennings, David-Chavez, Martinez, Lone Bear Rodriguez and Rainie2018; Rainie et al. Reference Rainie, Kukutai, Walter, Figueroa-Rodriguez, Walker, Axelsson, Davies, Walker, Rubinstein and Perini2019; McCartney et al. Reference McCartney, Anderson, Liggins, Hudson, Anderson, TeAika, Geary, Cook-Deegan, Patel and Phillippy2022). In response, the CARE Principles of Indigenous Data Governance (Collective Benefit, Authority to Control, Responsibility, and Ethics) were created to complement the FAIR Guiding Principles and promote the ethical use and reuse of Indigenous data (Carroll et al. Reference Carroll, Garba, Figueroa-Rodríguez, Holbrook, Lovett, Materechera, Parsons, Raseroka, Rodriguez-Lonebear, Rowe, Sara, Walker, Anderson and Hudson2020, Reference Carroll, Herczog, Hudson, Russell and Stall2021). Methods for implementing the FAIR Guiding Principles and CARE Principles in tandem (Rainie et al. Reference Rainie, Kukutai, Walter, Figueroa-Rodriguez, Walker, Axelsson, Davies, Walker, Rubinstein and Perini2019; Carroll et al. Reference Carroll, Garba, Figueroa-Rodríguez, Holbrook, Lovett, Materechera, Parsons, Raseroka, Rodriguez-Lonebear, Rowe, Sara, Walker, Anderson and Hudson2020, Reference Carroll, Herczog, Hudson, Russell and Stall2021) should be incorporated into analytical paleobiology courses to train researchers how to work with Indigenous data and partners without perpetuating entrenched power imbalances (Liboiron Reference Liboiron2021; Monarrez et al. Reference Monarrez, Zimmt, Clement, Gearty, Jacisin, Jenkins, Kusnerik, Poust, Robson, Sclafani, Stilson, Tennakoon and Thompson2021).

Another dimension of access pertains to the language used to communicate information. Studies in analytical paleobiology rely heavily on information published in English (Raja et al. Reference Raja, Dunne, Matiwane, Khan, Nätscher, Ghilardi and Chattopadhyay2022). Although having a shared language of science can facilitate global collaboration, it also selectively excludes voices (Tardy Reference Tardy2004). For example, non-English publications are frequently omitted from data compilations, which might bias results from literature reviews (Amano et al. Reference Amano, González-Varo and Sutherland2016, Reference Amano, Rios Rojas, Boum II, Calvo and Misra2021; Nuñez and Amano Reference Nuñez and Amano2021; Raja et al. Reference Raja, Dunne, Matiwane, Khan, Nätscher, Ghilardi and Chattopadhyay2022) and meta-analyses (Konno et al. Reference Konno, Akasaka, Koshida, Katayama, Osada, Spake and Amano2020). To help alleviate language biases, researchers could conduct literature searches and disseminate their findings in multiple languages, advocate for translation or English proofing services at journals, and be considerate of non-native English speakers (Márquez and Porras Reference Márquez and Porras2020; Ramírez-Castañeda Reference Ramírez-Castañeda2020; Amano et al. Reference Amano, Rios Rojas, Boum II, Calvo and Misra2021; Gaynor et al. Reference Gaynor, Azevedo, Boyajian, Brun, Budden, Cole, Csik, DeCesaro, Do-Linh, Dudney, Galaz García, Leonard, Lyon, Marks, Parish, Phillips, Scarborough, Smith, Thompson, Vargas Poulsen and Fong2022; Steigerwald et al. Reference Steigerwald, Ramírez-Castañeda, Brandt, Báldi, Shapiro, Bowker and Tarvin2022). Creating space for multilingual collaborations in analytical paleobiology would welcome knowledge, perspectives, and skills that might otherwise be overlooked due to language barriers.

Paleontology's history has left an indelible imprint on how research in the field is conducted today, contextualizing the challenges we highlight throughout this article. Knowledge production in analytical paleobiology, like other natural sciences, depends in part on socioeconomic factors such as wealth, education, and political stability, as well as colonial legacy (Boakes et al. Reference Boakes, McGowan, Fuller, Chang-qing, Clark, O'Connor and Mace2010; Amano and Sutherland Reference Amano and Sutherland2013; Hughes et al. Reference Hughes, Orr, Ma, Costello, Waller, Provoost, Yang, Zhu and Qiao2021; Monarrez et al. Reference Monarrez, Zimmt, Clement, Gearty, Jacisin, Jenkins, Kusnerik, Poust, Robson, Sclafani, Stilson, Tennakoon and Thompson2021; Trisos et al. Reference Trisos, Auerbach and Katti2021; Raja et al. Reference Raja, Dunne, Matiwane, Khan, Nätscher, Ghilardi and Chattopadhyay2022). Consequently, sampling effort is not equally distributed across the world. For example, 97% of fossil occurrence data recorded in the Paleobiology Database over the last 30 years was generated by higher-income countries, particularly those in western Europe and North America (Raja et al. Reference Raja, Dunne, Matiwane, Khan, Nätscher, Ghilardi and Chattopadhyay2022). These socioeconomic factors intensify other geographic biases in the fossil record and warp biodiversity estimates (Challenge 1). As such, efforts to obtain a representative view of biodiversity across space and time are not disconnected from efforts to advance equity, inclusion, and ethics in analytical paleobiology. Recent publications have spotlighted actions that individuals and institutions should take to change research norms, urging our community to not only reflect on its past but forge a new path forward (Cronin et al. Reference Cronin, Alonzo, Adamczak, Baker, Beltran, Borker, Favilla, Gatins, Goetz, Hack, Harenčár, Howard, Kustra, Maguiña, Martinez-Estevez, Mehta, Parker, Reid, Roberts, Shirazi, Tatom-Naecker, Voss, Willis-Norton, Vadakan, Valenzuela-Toro and Zavaleta2021; Liboiron Reference Liboiron2021; Theodor et al. Reference Theodor, Lewis E and Rayfield J2021; Cisneros et al. Reference Cisneros, Raja, Ghilardi, Dunne, Pinheiro, Regalado Fernández, Sales, Rodríguez-de la Rosa, Miranda-Martínez, González-Mora, Bantim, de Lima and Pardo2022; Dunne et al. Reference Dunne, Raja, Stewens and Zaw2022; Mohammed et al. Reference Mohammed, Turner, Fowler, Pateman, Nieves-Colón, Fanovich, Cooke, Dávalos, Fitzpatrick, Giovas, Stokowski, Wrean, Kemp, LeFebvre and Mychajliw2022; Raja et al. Reference Raja, Dunne, Matiwane, Khan, Nätscher, Ghilardi and Chattopadhyay2022).

Conclusion

Analytical paleobiology has grown in available data, computational power, and community interest over the last half century. Notably, progress in quantitative methods, conceptual frameworks, interdisciplinary partnerships, and data stewardship has contributed to more open and reproducible paleobiological research. These advances have expanded our ability to account for biases in the fossil record, accommodate different data types in models, integrate insights across disciplines, and pursue innovative research questions. Early-career researchers in particular, despite being precarious in terms of employment and career prospects, are embracing these evolving research practices. However, there is still a need to increase their acceptance among the broader paleontological community, establish best practices, and dismantle systemic inequities in how paleobiological data have historically been generated, shared, and accessed. Fortunately, we are not alone in facing these issues, and we can learn a great deal from solutions proposed by other disciplines. Great opportunity lies in both individual and institutional action to transform the future of how we study the past.

Acknowledgments

We thank the Analytical Paleobiology Workshop organizing committee, who indirectly catalyzed this paper by bringing us together as the Class of 2019. We also thank our wonderful instructors, whose teaching and insight shaped our perspectives on the four challenges we present. We thank G. Mathes, N. Raja, and Á. Kocsis for their invaluable feedback, and K. Anderson for their insight into museum collections. We also thank W. Kiessling, M. Yasuhara, and an anonymous reviewer whose detailed comments greatly improved the article. Finally, we thank the University of California for covering the publication fees. E. M. Dillon was supported by a University of California Santa Barbara Chancellor's Fellowship. E. M. Dunne was supported by a Leverhulme Research Project Grant (RPG-2019-365). A.I. was supported by the Austrian Science Fund (FWF; P31592-B25). M.K. was supported by a Royal Society of Science Grant (RGF\EA\180318). S.V.R. was supported by the University of Calgary Faculty of Graduate Studies Eyes High Doctoral Recruitment Scholarship. This paper was composed during the COVID-19 pandemic, and the authors wish to acknowledge the widespread and profound political, economic, and personal effects that this event has had, and continues to have, on the early-career researcher community.

Declaration of Competing Interest

The authors declare no competing interest.

Data Availability Statement

Supplementary Tables are available from the Zenodo Digital Repository: https://doi.org/10.5281/zenodo.7340036.

Footnotes

These authors contributed equally.

References

Literature Cited

Allen, B. J., Wignall, P. B., Hill, D. J., Saupe, E. E., and Dunhill, A. M.. 2020. The latitudinal diversity gradient of tetrapods across the Permo–Triassic mass extinction and recovery interval. Proceedings of the Royal Society of London B 287:20201125.Google ScholarPubMed
Allmon, W. D. 1992. Genera in paleontology: definition and significance. Historical Biology 6:149158.CrossRefGoogle Scholar
Allmon, W. D., Dietl, G. P., Hendricks, J. R., and Ross, R. M.. 2018. Bridging the two fossil records: paleontology's “big data” future resides in museum collections. In Rosenberg, G. D. and Clary, R. M., eds. Museums at the forefront of the history and philosophy of geology: history made, history in the making. Geological Society of America Special Paper 535:3544.Google Scholar
Alroy, J. 2003. Global databases will yield reliable measures of global biodiversity. Paleobiology 29:2629.2.0.CO;2>CrossRefGoogle Scholar
Alroy, J. 2010a. Fair sampling of taxonomic richness and unbiased estimation of origination and extinction rates. Paleontological Society Papers 16:5580.CrossRefGoogle Scholar
Alroy, J. 2010b. Geographical, environmental and intrinsic biotic controls on Phanerozoic marine diversification. Palaeontology 53:12111235.CrossRefGoogle Scholar
Alroy, J. 2010c. The shifting balance of diversity among major marine animal groups. Science 329:11911194.CrossRefGoogle ScholarPubMed
Alroy, J. 2020. On four measures of taxonomic richness. Paleobiology 46:158175.CrossRefGoogle Scholar
Alroy, J., Aberhan, M., Bottjer, D. J., Foote, M., Fürsich, F. T., Harries, P. J., Hendy, A. J. W., Holland, S. M., Ivany, L. C., Kiessling, W., Kosnik, M. A., Marshall, C. R., McGowan, A. J., Miller, A. I., Olszewski, T. D., Patzkowsky, M. E., Peters, S. E., Villier, L., Wagner, P. J., Bonuso, N., Borkow, P. S., Brenneis, B., Clapham, M. E., Fall, L. M., Ferguson, C. A., Hanson, V. L., Krug, A. Z., Layou, K. M., Leckey, E. H., Nürnberg, S., Powers, C. M., Sessa, J. A., Simpson, C., Tomašových, A., and Visaggi, C. C.. 2008. Phanerozoic trends in the global diversity of marine invertebrates. Science 321:97100.CrossRefGoogle ScholarPubMed
Amano, T., and Sutherland, W. J.. 2013. Four barriers to the global understanding of biodiversity conservation: wealth, language, geographical location and security. Proceedings of the Royal Society of London B 280:20122649.Google Scholar
Amano, T., González-Varo, J. P., and Sutherland, W. J.. 2016. Languages are still a major barrier to global science. PLoS Biology 14:e2000933.CrossRefGoogle Scholar
Amano, T., Rios Rojas, C., Boum II, Y., Calvo, M., and Misra, B. B.. 2021. Ten tips for overcoming language barriers in science. Nature Human Behaviour 5:11191122.CrossRefGoogle ScholarPubMed
Antell, G. S., Kiessling, W., Aberhan, M., and Saupe, E. E.. 2020. Marine biodiversity and geographic distributions are independent on large scales. Current Biology 30:115121.CrossRefGoogle ScholarPubMed
Bakker, F. T., Antonelli, A., Clarke, J. A., Cook, J. A., Edwards, S. V., Ericson, P. G. P., Faurby, S., Ferrand, N., Gelang, M., Gillespie, R. G., Irestedt, M., Lundin, K., Larsson, E., Matos-Maraví, P., Müller, J., von Proschwitz, T., Roderick, G. K., Schliep, A., Wahlberg, N., Wiedenhoeft, J., and Källersjö, M.. 2020. The Global Museum: natural history collections and the future of evolutionary science and public education. PeerJ 8:e8225.CrossRefGoogle ScholarPubMed
Barido-Sottani, J., Saupe, E. E., Smiley, T. M., Soul, L. C., Wright, A. M., and Warnock, R. C. M.. 2020. Seven rules for simulations in paleobiology. Paleobiology 46:435444.CrossRefGoogle Scholar
Beaufort, L., Bolton, C. T., Sarr, A.-C., Suchéras-Marx, B., Rosenthal, Y., Donnadieu, Y., Barbarin, N., Bova, S., Cornuault, P., Gally, Y., Gray, E., Mazur, J.-C., and Tetard, M.. 2022. Cyclic evolution of phytoplankton forced by changes in tropical seasonality. Nature 601:7984.CrossRefGoogle ScholarPubMed
Benda, L. E., Poff, L. N., Tague, C., Palmer, M. A., Pizzuto, J., Cooper, S., Stanley, E., and Moglen, G.. 2002. How to avoid train wrecks when using science in environmental problem solving. BioScience 52:11271136.CrossRefGoogle Scholar
Bennington, J. B., Dimichele, W. A., Badgley, C., Bambach, R. K., Barrett, P. M., Behrensmeyer, A. K., Bobe, R., Burnham, R. J., Daeschler, E. B., Dam, J. V., Eronen, J. T., Erwin, D. H., Finnegan, S., Holland, S. M., Hunt, G., Jablonski, D., Jackson, S. T., Jacobs, B. F., Kidwell, S. M., Koch, P. L., Kowalewski, M. J., Labandeira, C. C., Looy, C. V., Lyons, S. K., Novack-Gottshall, P. M., Potts, R., Roopnarine, P. D., Stromberg, C. A. E., Sues, H.-D., Wagner, P. J., Wilf, P., and Wing, S. L.. 2009. Critical issues of scale in paleoecology. Palaios 24:14.CrossRefGoogle Scholar
Benson, R. B. J., Butler, R., Close, R. A., Saupe, E., and Rabosky, D. L.. 2021. Biodiversity across space and time in the fossil record. Current Biology 31:R1225R1236.CrossRefGoogle ScholarPubMed
Benton, M. J. 1995. Diversification and extinction in the history of life. Science 268:5258.CrossRefGoogle ScholarPubMed
Birks, H. J. B., Lotter, A. F., Juggins, S., and Smol, J. P., eds. 2012. Tracking environmental change using lake sediments: data handling and numerical techniques, Vol. 5. Springer Science & Business Media, Dordrecht, Netherlands.CrossRefGoogle Scholar
Boakes, E. H., McGowan, P. J. K., Fuller, R. A., Chang-qing, D., Clark, N. E., O'Connor, K., and Mace, G. M.. 2010. Distorted views of biodiversity: spatial and temporal bias in species occurrence data. PLoS Biology 8:e1000385.CrossRefGoogle ScholarPubMed
Boulton, A. J., Panizzon, D., and Prior, J.. 2005. Explicit knowledge structures as a tool for overcoming obstacles to interdisciplinary research. Conservation Biology 19:20262029.CrossRefGoogle Scholar
Brewer, S., Jackson, S. T., and Williams, J. W.. 2012. Paleoecoinformatics: applying geohistorical data to ecological questions. Trends in Ecology and Evolution 27:104112.CrossRefGoogle ScholarPubMed
Bryan, J. 2018. Excuse me, do you have a moment to talk about version control? American Statistician 72:2027.CrossRefGoogle Scholar
Buma, B., Harvey, B. J., Gavin, D. G., Kelly, R., Loboda, T., McNeil, B. E., Marlon, J. R., Meddens, A. J. H., Morris, J. L., Raffa, K. F., Shuman, B., Smithwick, E. A. H., and McLauchlan, K. K.. 2019. The value of linking paleoecological and neoecological perspectives to understand spatially-explicit ecosystem resilience. Landscape Ecology 34:1733.CrossRefGoogle Scholar
Bush, A. M., and Bambach, R. K.. 2004. Did alpha diversity increase during the Phanerozoic? Lifting the veils of taphonomic, latitudinal, and environmental biases. Journal of Geology 112:625642.CrossRefGoogle Scholar
Cantalapiedra, J. L., Domingo, M. S., and Domingo, L.. 2018. Multi-scale interplays of biotic and abiotic drivers shape mammalian sub-continental diversity over millions of years. Scientific Reports 8:13413.CrossRefGoogle ScholarPubMed
Carroll, S. R., Garba, I., Figueroa-Rodríguez, O. L., Holbrook, J., Lovett, R., Materechera, S., Parsons, M., Raseroka, K., Rodriguez-Lonebear, D., Rowe, R., Sara, R., Walker, J. D., Anderson, J., and Hudson, M.. 2020. The CARE Principles for Indigenous data governance. Data Science Journal 19:43.CrossRefGoogle Scholar
Carroll, S. R., Herczog, E., Hudson, M., Russell, K., and Stall, S.. 2021. Operationalizing the CARE and FAIR Principles for Indigenous data futures. Scientific Data 8:16.CrossRefGoogle ScholarPubMed
[CSSP] Center for the Study of Social Policy. 2019. Key equity terms and concepts: a glossary for shared understanding. CSSP, Washington, D.C. https://cssp.org/resource/key-equity-terms-and-concepts-a-glossary-for-shared-understanding, accessed 5 September 2022.Google Scholar
Chao, A., and Jost, L.. 2012. Coverage-based rarefaction and extrapolation: standardizing samples by completeness rather than size. Ecology 93:25332547.CrossRefGoogle ScholarPubMed
Chao, A., Kubota, Y., Zelený, D., Chiu, C.-H., Li, C.-F., Kusumoto, B., Yasuhara, M., Thorn, S., Wei, C.-L., Costello, M. J., and Colwell, R. K.. 2020. Quantifying sample completeness and comparing diversities among assemblages. Ecological Research 35:292314.CrossRefGoogle Scholar
Chao, A., Henderson, P. A., Chiu, C.-H., Moyes, F., Hu, K.-H., Dornelas, M., and Magurran, A. E.. 2021. Measuring temporal change in alpha diversity: a framework integrating taxonomic, phylogenetic and functional diversity and the iNEXT.3D standardization. Methods in Ecology and Evolution 12:19261940.CrossRefGoogle Scholar
Chiarenza, A. A., Mannion, P. D., Lunt, D. J., Farnsworth, A., Jones, L. A., Kelland, S.-J., and Allison, P. A.. 2019. Ecological niche modelling does not support climatically-driven dinosaur diversity decline before the Cretaceous/Paleogene mass extinction. Nature Communications 10:1091.CrossRefGoogle Scholar
Cisneros, J. C., Raja, N. B., Ghilardi, A. M., Dunne, E. M., Pinheiro, F. L., Regalado Fernández, O. R., Sales, M. A. F., Rodríguez-de la Rosa, R. A., Miranda-Martínez, A. Y., González-Mora, S., Bantim, R. A. M., de Lima, F. J., and Pardo, J. D.. 2022. Digging deeper into colonial palaeontological practices in modern day Mexico and Brazil. Royal Society Open Science 9:210898.CrossRefGoogle ScholarPubMed
Cleary, T. J., Benson, R. B. J., Evans, S. E., and Barrett, P. M.. 2018. Lepidosaurian diversity in the Mesozoic–Palaeogene: the potential roles of sampling biases and environmental drivers. Royal Society Open Science 5:171830.CrossRefGoogle ScholarPubMed
Close, R. A., Benson, R. B. J., Upchurch, P., and Butler, R. J.. 2017. Controlling for the species-area effect supports constrained long-term Mesozoic terrestrial vertebrate diversification. Nature Communications 8:15381.CrossRefGoogle ScholarPubMed
Close, R. A., Evers, S. W., Alroy, J., and Butler, R. J.. 2018. How should we estimate diversity in the fossil record? Testing richness estimators using sampling-standardised discovery curves. Methods in Ecology and Evolution 9:13861400.CrossRefGoogle Scholar
Close, R. A., Benson, R. B. J., Alroy, J., Carrano, M. T., Cleary, T. J., Dunne, E. M., Mannion, P. D., Uhen, M. D., and Butler, R. J.. 2020a. The apparent exponential radiation of Phanerozoic land vertebrates is an artefact of spatial sampling biases. Proceedings of the Royal Society of London B 287:20200372.Google ScholarPubMed
Close, R. A., Benson, R. B. J., Saupe, E. E., Clapham, M. E., and Butler, R. J.. 2020b. The spatial structure of Phanerozoic marine animal diversity. Science 368:420424.CrossRefGoogle ScholarPubMed
Condamine, F. L., Rolland, J., and Morlon, H.. 2013. Macroevolutionary perspectives to environmental change. Ecology Letters 16:7285.CrossRefGoogle ScholarPubMed
Costello, M. J., Wilson, S., and Houlding, B.. 2013. More taxonomists describing significantly fewer species per unit effort may indicate that most species have been discovered. Systematic Biology 62:616624.CrossRefGoogle ScholarPubMed
Cronin, M. R., Alonzo, S. H., Adamczak, S. K., Baker, D. N., Beltran, R. S., Borker, A. L., Favilla, A. B., Gatins, R., Goetz, L. C., Hack, N., Harenčár, J. G., Howard, E. A., Kustra, M. C., Maguiña, R., Martinez-Estevez, L., Mehta, R. S., Parker, I. M., Reid, K., Roberts, M. B., Shirazi, S. B., Tatom-Naecker, T.-A. M., Voss, K. M., Willis-Norton, E., Vadakan, B., Valenzuela-Toro, A. M., and Zavaleta, E. S.. 2021. Anti-racist interventions to transform ecology, evolution and conservation biology departments. Nature Ecology and Evolution 5:12131223.CrossRefGoogle ScholarPubMed
Davies, A. L., Streeter, R., Lawson, I. T., Roucoux, K. H., and Hiles, W.. 2018. The application of resilience concepts in palaeoecology. The Holocene 28:15231534.CrossRefGoogle Scholar
de Celis, A., Narváez, I., Arcucci, A., and Ortega, F.. 2021. Lagerstätte effect drives notosuchian palaeodiversity (Crocodyliformes, Notosuchia). Historical Biology 33:30313040.CrossRefGoogle Scholar
del Carmen Gomez Cabrera, M., Young, J. M., Roff, G., Staples, T., Ortiz, J. C., Pandolfi, J. M., and Cooper, A.. 2019. Broadening the taxonomic scope of coral reef palaeoecological studies using ancient DNA. Molecular Ecology 28:26362652.CrossRefGoogle ScholarPubMed
Dietl, G. P., Kidwell, S. M., Brenner, M., Burney, D. A., Flessa, K. W., Jackson, S. T., and Koch, P. L.. 2015. Conservation paleobiology: leveraging knowledge of the past to inform conservation and restoration. Annual Review of Earth and Planetary Sciences 43:79103.CrossRefGoogle Scholar
Dillon, E. M., McCauley, D. J., Morales-Saldaña, J. M., Leonard, N. D., Zhao, J. X., and O'Dea, A.. 2021. Fossil dermal denticles reveal the preexploitation baseline of a Caribbean coral reef shark community. Proceedings of the National Academy of Sciences USA 118:e2017735118.CrossRefGoogle ScholarPubMed
Doi, H., Yasuhara, M., and Ushio, M.. 2021. Causal analysis of the temperature impact on deep-sea biodiversity. Biology Letters 17:20200666.CrossRefGoogle ScholarPubMed
Dunhill, A. M., and Liow, L. H.. 2018. Crossing the palaeontological-ecological gap virtual issue. Methods in Ecology and Evolution. https://besjournals.onlinelibrary.wiley.com/doi/toc/10.1111/(ISSN)2041-210x.PaleoecologyMEE32018.Google Scholar
Dunne, E. M., Raja, N. B., Stewens, P. P., Zin-Maung-Manug-Thein, and Zaw, K.. 2022. Ethics, law, and politics in palaeontological research: the case of Myanmar amber. Communications Biology 5:1023.CrossRefGoogle ScholarPubMed
Eigenbrode, S. D., O'Rourke, M., Wulfhorst, J. D., Althoff, D. M., Goldberg, C. S., Merrill, K., Morse, W., Nielsen-Pincus, M., Stephens, J., Winowiecki, L., and Bosque-Pérez, N. A.. 2007. Employing philosophical dialogue in collaborative science. BioScience 57:5564.CrossRefGoogle Scholar
Eronen, J. T., Polly, P. D., Fred, M., Damuth, J., Frank, D. C., Mosbrugger, V., Scheidegger, C., Stenseth, N. Chr., and Fortelius, M.. 2010. Ecometrics: the traits that bind the past and present together. Integrative Zoology 5:88101.CrossRefGoogle Scholar
Ezard, T. H. G., Aze, T., Pearson, P. N., and Purvis, A.. 2011. Interplay between changing climate and species’ ecology drives macroevolutionary dynamics. Science 332:349351.CrossRefGoogle ScholarPubMed
Fawcett, S., Agosti, D., Cole, S. R., and Wright, D. F.. 2022. Digital accessible knowledge: mobilizing legacy data and the future of taxonomic publishing. Bulletin of the Society of Systematic Biologists 1:1.CrossRefGoogle Scholar
Ferretti, F., Crowder, L. B., Micheli, F., and Blight, L. K.. 2014. Using disparate datasets to reconstruct historical baselines of animal populations. Pp. 6386 in Kittinger, J. N., McClenachan, L., Gedan, K. B., and Blight, L. K., eds. Marine historical ecology in conservation: applying the past to manage for the future. University of California Press, Oakland, Calif.CrossRefGoogle Scholar
Filazzola, A., and Lortie, C. J.. 2022. A call for clean code to effectively communicate science. Methods in Ecology and Evolution 13:21192128.CrossRefGoogle Scholar
Finnegan, S., Anderson, S. C., Harnik, P. G., Simpson, C., Tittensor, D. P., Byrnes, J. E., Finkel, Z. V., Lindberg, D. R., Liow, L. H., Lockwood, R., Lotze, H. K., McClain, C. R., McGuire, J. L., O'Dea, A., and Pandolfi, J. M.. 2015. Paleontological baselines for evaluating extinction risk in the modern oceans. Science 348:567570.CrossRefGoogle ScholarPubMed
Flannery-Sutherland, J. T., Silvestro, D., and Benton, M. J.. 2022. Global diversity dynamics in the fossil record are regionally heterogeneous. Nature Communications 13:2751.CrossRefGoogle ScholarPubMed
Foote, M. 2000. Origination and extinction components of taxonomic diversity: general problems. Paleobiology 26:74102.CrossRefGoogle Scholar
Fraser, D., Soul, L. C., Tóth, A. B., Balk, M. A., Eronen, J. T., Pineda-Munoz, S., Shupinski, A. B., Villaseñor, A., Barr, W. A., Behrensmeyer, A. K., Du, A., Faith, J. T., Gotelli, N. J., Graves, G. R., Jukar, A. M., Looy, C. V., Miller, J. H., Potts, R., and Lyons, S. K.. 2021. Investigating biotic interactions in deep time. Trends in Ecology and Evolution 36:6175.CrossRefGoogle ScholarPubMed
Gaynor, K. M., Azevedo, T., Boyajian, C., Brun, J., Budden, A. E., Cole, A., Csik, S., DeCesaro, J., Do-Linh, H., Dudney, J., Galaz García, C., Leonard, S., Lyon, N. J., Marks, A., Parish, J., Phillips, A. A., Scarborough, C., Smith, J., Thompson, M., Vargas Poulsen, C., and Fong, C. R.. 2022. Ten simple rules to cultivate belonging in collaborative data science research teams. PLoS Computational Biology 18:1010567.CrossRefGoogle ScholarPubMed
Goodenough, A. E., and Webb, J. C.. 2022. Learning from the past: opportunities for advancing ecological research and practice using palaeoecological data. Oecologia 199:275287.CrossRefGoogle ScholarPubMed
Gorneau, J. A., Ausich, W. I., Bertolino, S., Bik, H., Daly, M., Demissew, S., Donoso, D. A., Folk, R., Freire-Fierro, A., Ghazanfar, S. A., Grace, O. M., Hu, A.-Q., Kulkarni, S., Lichter-Marck, I. H., Lohmann, L. G., Malumbres-Olarte, J., Muasya, A. M., Pérez-González, A., Singh, Y., Siniscalchi, C. M., Specht, C. D., Stigall, A. L., Tank, D. C., Walker, L. A., Wright, D. F., Zamani, A., and Esposito, L. A.. 2022. Framing the future for taxonomic monography: improving recognition, support, and access. Bulletin of the Society of Systematic Biologists 1(1). doi: 10.18061/bssb.v1i1.8328.CrossRefGoogle Scholar
Goswami, A., Watanabe, A., Felice, R. N., Bardua, C., Fabre, A.-C., and Polly, P. D.. 2019. High-density morphometric analysis of shape and integration: the good, the bad, and the not-really-a-problem. Integrative and Comparative Biology 59:669683.CrossRefGoogle ScholarPubMed
Grenié, M., Berti, E., Carvajal‐Quintero, J., Dädlow, G. M. L., Sagouis, A., and Winter, M.. 2023. Harmonizing taxon names in biodiversity data: a review of tools, databases and best practices. Methods in Ecology and Evolution 14:1225.CrossRefGoogle Scholar
Guerra, C. A., Heintz-Buschart, A., Sikorski, J., Chatzinotas, A., Guerrero-Ramírez, N., Cesarz, S., Beaumelle, L., Rillig, M. C., Maestre, F. T., Delgado-Baquerizo, M., Buscot, F., Overmann, J., Patoine, G., Phillips, H. R. P., Winter, M., Wubet, T., Küsel, K., Bardgett, R. D., Cameron, E. K., Cowan, D., Grebenc, T., Marín, C., Orgiazzi, A., Singh, B. K., Wall, D. H., and Eisenhauer, N.. 2020. Blind spots in global soil biodiversity and ecosystem function research. Nature Communications 11:3870.CrossRefGoogle ScholarPubMed
Guralnick, R. P., Hill, A. W., and Lane, M.. 2007. Towards a collaborative, global infrastructure for biodiversity assessment. Ecology Letters 10:663672.CrossRefGoogle ScholarPubMed
Hannisdal, B., and Liow, L. H.. 2018. Causality from palaeontological time series. Palaeontology 61:495509.CrossRefGoogle Scholar
Hart, E. M., Barmby, P., LeBauer, D., Michonneau, F., Mount, S., Mulrooney, P., Poisot, T., Woo, K. H., Zimmerman, N. B., and Hollister, J. W.. 2016. Ten simple rules for digital data storage. PLoS Computational Biology 12:e1005097.CrossRefGoogle ScholarPubMed
Heath, T. A., Huelsenbeck, J. P., and Stadler, T.. 2014. The fossilized birth–death process for coherent calibration of divergence-time estimates. Proceedings of the National Academy of Sciences USA 111:E2957E2966.CrossRefGoogle ScholarPubMed
Heberling, J. M., Miller, J. T., Noesgaard, D., Weingart, S. B., and Schigel, D.. 2021. Data integration enables global biodiversity synthesis. Proceedings of the National Academy of Sciences USA 118:e2018093118.CrossRefGoogle ScholarPubMed
Hedrick, B. P., Heberling, J. M., Meineke, E. K., Turner, K. G., Grassa, C. J., Park, D. S., Kennedy, J., Clarke, J. A., Cook, J. A., Blackburn, D. C., Edwards, S. V., and Davis, C. C.. 2020. Digitization and the future of natural history collections. BioScience 70:243251.CrossRefGoogle Scholar
Hendricks, J. R., Saupe, E. E., Myers, C. E., Hermsen, E. J., and Allmon, W. D.. 2014. The generification of the fossil record. Paleobiology 40:511528.CrossRefGoogle Scholar
Hohmann, N. 2021. Incorporating information on varying sedimentation rates into paleontological analyses. Palaios 36:5367.CrossRefGoogle Scholar
Holland, S., and Loughney, K. M.. 2021. The stratigraphic paleobiology of nonmarine systems. Cambridge University Press, Cambridge.CrossRefGoogle Scholar
Hsiang, A. Y., Nelson, K., Elder, L. E., Sibert, E. C., Kahanamoku, S. S., Burke, J. E., Kelly, A., Liu, Y., and Hull, P. M.. 2018. AutoMorph: accelerating morphometrics with automated 2D and 3D image processing and shape extraction. Methods in Ecology and Evolution 9:605612.CrossRefGoogle Scholar
Hsiang, A. Y., Brombacher, A., Rillo, M. C., Mleneck-Vautravers, M. J., Conn, S., Lordsmith, S., Jentzen, A., Henehan, M. J., Metcalfe, B., Fenton, I. S., Wade, B. S., Fox, L., Meilland, J., Davis, C. V., Baranowski, U., Groeneveld, J., Edgar, K. M., Movellan, A., Aze, T., Dowsett, H. J., Miller, C. G., Rios, N., and Hull, P. M.. 2019. Endless forams: >34,000 modern planktonic foraminiferal images for taxonomic training and automated species recognition using convolutional neural networks. Paleoceanography and Paleoclimatology 34:11571177.CrossRefGoogle Scholar
Huang, H.-H. M., Yasuhara, M., Horne, D. J., Perrier, V., Smith, A. J., and Brandão, S. N.. 2022. Ostracods in databases: state of the art, mobilization and future applications. Marine Micropaleontology 174:102094.CrossRefGoogle Scholar
Hughes, A. C., Orr, M. C., Ma, K., Costello, M. J., Waller, J., Provoost, P., Yang, Q., Zhu, C., and Qiao, H.. 2021. Sampling biases shape our view of the natural world. Ecography 44:12591269.CrossRefGoogle Scholar
Jablonski, D., and Finarelli, J. A.. 2009. Congruence of morphologically-defined genera with molecular phylogenies. Proceedings of the National Academy of Sciences USA 106:82628266.CrossRefGoogle ScholarPubMed
Jennings, L. L., David-Chavez, D. M., Martinez, A., Lone Bear Rodriguez, D., and Rainie, S.. 2018. Indigenous data sovereignty: How scientists and researchers can empower Indigenous data governance. American Geophysical Union, Fall Meeting 2018, abstract PA43C-1376.Google Scholar
Jones, L. A., Dean, C. D., Mannion, P. D., Farnsworth, A., and Allison, P. A.. 2021. Spatial sampling heterogeneity limits the detectability of deep time latitudinal biodiversity gradients. Proceedings of the Royal Society of London B 288:20202762.Google ScholarPubMed
Jones, L. A., Gearty, W., Allen, B. J., Eichenseer, K., Dean, C. D., Galván, S., Kouvari, M., Godoy, P. L., Nicholl, C., Buffan, L., Dillon, E. M., Flannery-Sutherland, J. T., and Chiarenza, A. A.. 2022. Palaeoverse: a community-driven R package to support palaeobiological analyses. EarthArXiv. doi:10.31223/X5Z94Q, accessed 31 October 2022.Google Scholar
Karpatne, A., Ebert-Uphoff, I., Ravela, S., Babaie, H. A., and Kumar, V.. 2019. Machine learning for the geosciences: challenges and opportunities. IEEE Transactions on Knowledge and Data Engineering 31:15441554.CrossRefGoogle Scholar
Kaufman, D. S., and PAGES 2k Special-Issue Editorial Team. 2018. Technical note: open-paleo-data implementation pilot—the PAGES 2k special issue. Climate of the Past 14:593600.CrossRefGoogle Scholar
Kidwell, S. M. 2015. Biology in the Anthropocene: challenges and insights from young fossil records. Proceedings of the National Academy of Sciences USA 112:49224929.CrossRefGoogle ScholarPubMed
Kidwell, S. M., and Holland, S. M.. 2002. The quality of the fossil record: implications for evolutionary analyses. Annual Review of Ecology and Systematics 33:561588.CrossRefGoogle Scholar
Kiessling, W., Raja, N. B., Roden, V. J., Turvey, S. T., and Saupe, E. E.. 2019. Addressing priority questions of conservation science with palaeontological data. Philosophical Transactions of the Royal Society of London B 374:20190222.CrossRefGoogle ScholarPubMed
Kinkade, D., and Shepherd, A.. 2021. Geoscience data publication: practices and perspectives on enabling the FAIR guiding principles. Geoscience Data Journal 9:177186.CrossRefGoogle Scholar
Kliskey, A., Alessa, L., Wandersee, S., Williams, P., Trammell, J., Powell, J., Grunblatt, J., and Wipfli, M.. 2017. A science of integration: frameworks, processes, and products in a place-based, integrative study. Sustainability Science 12:293303.CrossRefGoogle Scholar
Koch, A., Glover, K. C., Zambri, B., Thomas, E. K., Benito, X., and Yang, J. Z.. 2018. Open-data practices and challenges among early-career paleo-researchers. Past Global Change Magazine 26:5455.CrossRefGoogle Scholar
Kocsis, Á. T., Reddin, C. J., Alroy, J., and Kiessling, W.. 2019. The r package divDyn for quantifying diversity dynamics using fossil sampling data. Methods in Ecology and Evolution 10:735743.CrossRefGoogle Scholar
König, C., Weigelt, P., Schrader, J., Taylor, A., Kattge, J., and Kreft, H.. 2019. Biodiversity data integration—the significance of data resolution and domain. PLoS Biology 17:e3000183.CrossRefGoogle ScholarPubMed
Konno, K., Akasaka, M., Koshida, C., Katayama, N., Osada, N., Spake, R., and Amano, T.. 2020. Ignoring non-English-language studies may bias ecological meta-analyses. Ecology and Evolution 10:63736384.CrossRefGoogle ScholarPubMed
Kopperud, B. T., Lidgard, S., and Liow, L. H.. 2019. Text-mined fossil biodiversity dynamics using machine learning. Proceedings of the Royal Society of London B 286:20190022.Google ScholarPubMed
Krimmel, E., Karim, T., Little, H., Walker, L., Burkhalter, R., Byrd, C., Millhouse, A., and Utrup, J.. 2021. The Paleo Data Working Group: a model for developing and sustaining a community of practice. Biodiversity Information Science and Standards 5:e74370.CrossRefGoogle Scholar
Kukutai, T., and Taylor, J.. 2016. Data sovereignty for Indigenous peoples: current practice and future needs. Pp. 122 in Kukutai, T. and Taylor, J., eds. Indigenous data sovereignty, Vol. 38. ANU Press, Canberra, Australia.CrossRefGoogle Scholar
Labandeira, C. C., and Sepkoski, J. J.. 1993. Insect diversity in the fossil record. Science 261:310315.CrossRefGoogle ScholarPubMed
LaDeau, S. L., Han, B. A., Rosi-Marshall, E. J., and Weathers, K. C.. 2017. The next decade of big data in ecosystem science. Ecosystems 20:274283.CrossRefGoogle Scholar
Lendemer, J. C., and Coyle, J. R.. 2021. Dissimilar biodiversity data sets yield congruent patterns and inference in lichens. Botany 99:5567.CrossRefGoogle Scholar
Levin, S. A. 1992. The problem of pattern and scale in ecology: the Robert H. MacArthur award lecture. Ecology 73:19431967.Google Scholar
Lewandowska, A. M., Jonkers, L., Auel, H., Freund, J. A., Hagen, W., Kucera, M., and Hillebrand, H.. 2020. Scale dependence of temporal biodiversity change in modern and fossil marine plankton. Global Ecology and Biogeography 29:10081019.CrossRefGoogle Scholar
Liboiron, M. 2021. Decolonizing geoscience requires more than equity and inclusion. Nature Geoscience 14:876877.CrossRefGoogle Scholar
Lin, D., Crabtree, J., Dillo, I., Downs, R. R., Edmunds, R., Giaretta, D., De Giusti, M., L'Hours, H., Hugo, W., Jenkyns, R., Khodiyar, V., Martone, M. E., Mokrane, M., Navale, V., Petters, J., Sierman, B., Sokolova, D. V., Stockhause, M., and Westbrook, J.. 2020. The TRUST Principles for digital repositories. Scientific Data 7:144.CrossRefGoogle ScholarPubMed
Liow, L. H., and Nichols, J. D.. 2010. Estimating rates and probabilities of origination and extinction using taxonomic occurrence data: capture-mark-recapture (CMR) approaches. Paleontological Society Papers 16:8194.CrossRefGoogle Scholar
Lowndes, J. S. S., Best, B. D., Scarborough, C., Afflerbach, J. C., Frazier, M. R., O'Hara, C. C., Jiang, N., and Halpern, B. S.. 2017. Our path to better science in less time using open data science tools. Nature Ecology and Evolution 1:0160.CrossRefGoogle ScholarPubMed
Lowndes, J. S. S., Froehlich, H. E., Horst, A., Jayasundara, N., Pinsky, M. L., Stier, A. C., Therkildsen, N. O., and Wood, C. L.. 2019. Supercharge your research: a ten-week plan for open data science. Nature. doi:10.1038/d41586-019-03335-4CrossRefGoogle ScholarPubMed
Lyons, K. S., Amatangelo, K. L., Behrensmeyer, A. K., Bercovici, A., Blois, J. L., Davis, M., DiMichele, W. A., Du, A., Eronen, J. T., Tyler Faith, J., Graves, G. R., Jud, N., Labandeira, C., Looy, C. V., McGill, B., Miller, J. H., Patterson, D., Pineda-Munoz, S., Potts, R., Riddle, B., Terry, R., Tóth, A., Ulrich, W., Villaseñor, A., Wing, S., Anderson, H., Anderson, J., Waller, D., and Gotelli, N. J.. 2016. Holocene shifts in the assembly of plant and animal communities implicate human impacts. Nature 529:8083.CrossRefGoogle ScholarPubMed
Márquez, M. C., and Porras, A. M.. 2020. Science communication in multiple languages is critical to its effectiveness. Frontiers in Communication 5:31.CrossRefGoogle Scholar
Marshall, C. R., Finnegan, S., Clites, E. C., Holroyd, P. A., Bonuso, N., Cortez, C., Davis, E., Dietl, G. P., Druckenmiller, P. S., Eng, R. C., Garcia, C., Estes-Smargiassi, K., Hendy, A., Hollis, K. A., Little, H., Nesbitt, E. A., Roopnarine, P., Skibinski, L., Vendetti, J., and White, L. D.. 2018. Quantifying the dark data in museum fossil collections as palaeontology undergoes a second digital revolution. Biology Letters 14:20180431.CrossRefGoogle ScholarPubMed
Mathes, G. H., van Dijk, J., Kiessling, W., and Steinbauer, M. J.. 2021. Extinction risk controlled by interaction of long-term and short-term climate change. Nature Ecology and Evolution 5:304310.CrossRefGoogle ScholarPubMed
McCartney, A. M., Anderson, J., Liggins, L., Hudson, M. L., Anderson, M. Z., TeAika, B., Geary, J., Cook-Deegan, R., Patel, H. R., and Phillippy, A. M.. 2022. Balancing openness with Indigenous data sovereignty: an opportunity to leave no one behind in the journey to sequence all of life. Proceedings of the National Academy of Sciences USA 119:e2115860119.CrossRefGoogle Scholar
McClenachan, L., Cooper, A. B., McKenzie, M. G., and Drew, J. A.. 2015. The importance of surprising results and best practices in historical ecology. BioScience 65:932939.CrossRefGoogle Scholar
McKay, N. P., Emile-Geay, J., and Khider, D.. 2021. geoChronR—an R package to model, analyze, and visualize age-uncertain data. Geochronology 3:149169.CrossRefGoogle Scholar
McKinney, M. L., and Drake, J. A., eds. 2001. Biodiversity dynamics: turnover of populations, taxa, and communities. Columbia University Press, New York.CrossRefGoogle Scholar
Michener, W. K. 2015. Ten simple rules for creating a good data management plan. PLoS Computational Biology 11:e1004525.CrossRefGoogle ScholarPubMed
Mohammed, R. S., Turner, G., Fowler, K., Pateman, M., Nieves-Colón, M. A., Fanovich, L., Cooke, S. B., Dávalos, L. M., Fitzpatrick, S. M., Giovas, C. M., Stokowski, M., Wrean, A. A., Kemp, M., LeFebvre, M. J., and Mychajliw, A. M.. 2022. Colonial legacies influence biodiversity lessons: how past trade routes and power dynamics shape present-day scientific research and professional opportunities for Caribbean scientists. American Naturalist 200:140155.CrossRefGoogle ScholarPubMed
Monarrez, P. M., Zimmt, J. B., Clement, A. M., Gearty, W., Jacisin, J. J., Jenkins, K. M., Kusnerik, K. M., Poust, A. W., Robson, S. V., Sclafani, J. A., Stilson, K. T., Tennakoon, S. D., and Thompson, C. M.. 2021. Our past creates our present: a brief overview of racism and colonialism in Western paleontology. Paleobiology 48:173185.CrossRefGoogle Scholar
Morrison, S. A., Sillett, T. S., Funk, W. C., Ghalambor, C. K., and Rick, T. C.. 2017. Equipping the 22nd-century historical ecologist. Trends in Ecology and Evolution 32:578588.CrossRefGoogle ScholarPubMed
Mottl, O., Grytnes, J.-A., Seddon, A. W. R., Steinbauer, M. J., Bhatta, K. P., Felde, V. A., Flantua, S. G. A., and Birks, H. J. B.. 2021. Rate-of-change analysis in paleoecology revisited: a new approach. Review of Palaeobotany and Palynology 293:104483.CrossRefGoogle Scholar
Moudrý, V., and Devillers, R.. 2020. Quality and usability challenges of global marine biodiversity databases: an example for marine mammal data. Ecological Informatics 56:101051.CrossRefGoogle Scholar
Mouillot, D., Graham, N. A. J., Villéger, S., Mason, N. W. H., and Bellwood, D. R.. 2013. A functional approach reveals community responses to disturbances. Trends in Ecology and Evolution 28:167177.CrossRefGoogle ScholarPubMed
Muñoz, M. M., and Price, S. A.. 2019. The future is bright for evolutionary morphology and biomechanics in the era of big data. Integrative and Comparative Biology 59:599603.CrossRefGoogle Scholar
Muscente, A. D., Prabhu, A., Zhong, H., Eleish, A., Meyer, M. B., Fox, P., Hazen, R. M., and Knoll, A. H.. 2018. Quantifying ecological impacts of mass extinctions with network analysis of fossil communities. Proceedings of the National Academy of Sciences USA 115:52175222.CrossRefGoogle ScholarPubMed
Napier, J. D., and Chipman, M. L.. 2022. Emerging palaeoecological frameworks for elucidating plant dynamics in response to fire and other disturbance. Global Ecology and Biogeography 31:138154.CrossRefGoogle Scholar
Nelson, G., and Ellis, S.. 2019. The history and impact of digitization and digital data mobilization on biodiversity research. Philosophical Transactions of the Royal Society of London B 374:20170391.CrossRefGoogle Scholar
Nelson, G., Paul, D., Riccardi, G., and Mast, A.. 2012. Five task clusters that enable efficient and effective digitization of biological collections. ZooKeys 209:1945.CrossRefGoogle Scholar
Nieto-Lugilde, D., Blois, J. L., Bonet-García, F. J., Giesecke, T., Gil-Romera, G., and Seddon, A.. 2021. Time to better integrate paleoecological research infrastructures with neoecology to improve understanding of biodiversity long-term dynamics and to inform future conservation. Environmental Research Letters 16:095005.CrossRefGoogle Scholar
Nosek, B. A., Alter, G., Banks, G. C., Borsboom, D., Bowman, S. D., Breckler, S. J., Buck, S., Chambers, C. D., Chin, G., Christensen, G., Contestabile, M., Dafoe, A., Eich, E., Freese, J., Glennerster, R., Goroff, D., Green, D. P., Hesse, B., Humphreys, M., Ishiyama, J., Karlan, D., Kraut, A., Lupia, A., Mabry, P., Madon, T., Malhotra, N., Mayo-Wilson, E., McNutt, M., Miguel, E., Paluck, E. L., Simonsohn, U., Soderberg, C., Spellman, B. A., Turitto, J., VandenBos, G., Vazire, S., Wagenmakers, E. J., Wilson, R., and Yarkoni, T.. 2015. Promoting an open research culture. Science 348:14221425.CrossRefGoogle ScholarPubMed
Núñez, A.-M., Rivera, J., and Hallmark, T.. 2020. Applying an intersectionality lens to expand equity in the geosciences. Journal of Geoscience Education 68:97114.CrossRefGoogle Scholar
Nuñez, M. A., and Amano, T.. 2021. Monolingual searches can limit and bias results in global literature reviews. Nature Ecology and Evolution 5:264264.CrossRefGoogle ScholarPubMed
Olsen, A. M., and Westneat, M. W.. 2015. StereoMorph: an R package for the collection of 3D landmarks and curves using a stereo camera set-up. Methods in Ecology and Evolution 6:351356.CrossRefGoogle Scholar
Paterson, G., Albuquerque, S., Blagoderov, V., Brooks, S., Cafferty, S., Cane, E., Carter, V., Chainey, J., Crowther, R., Douglas, L., Durant, J., Duffell, L., Hine, A., Honey, M., Huertas, B., Howard, T., Huxley, R., Kitching, I., Ledger, S., McLaughlin, C., Martin, G., Mazzetta, G., Penn, M., Perera, J., Sadka, M., Scialabba, E., Self, A., Siebert, D. J., Sleep, C., Toloni, F., and Wing, P.. 2016. iCollections—digitising the British and Irish butterflies in the Natural History Museum, London. Biodiversity Data Journal 4:e9559.CrossRefGoogle Scholar
Patzkowsky, M. E., and Holland, S. M.. 2012. Stratigraphic paleobiology: understanding the distribution of fossil taxa in time and space. University of Chicago Press, Chicago.CrossRefGoogle Scholar
Peters, S. E., Zhang, C., Livny, M., and , C.. 2014. A machine reading system for assembling synthetic paleontological databases. PLoS ONE 9:e113523.CrossRefGoogle ScholarPubMed
Pierce, H. H., Dev, A., Statham, E., and Bierer, B. E.. 2019. Credit data generators for data reuse. Nature 570:3032.CrossRefGoogle ScholarPubMed
Pimiento, C., Griffin, J. N., Clements, C. F., Silvestro, D., Varela, S., Uhen, M. D., and Jaramillo, C.. 2017. The Pliocene marine megafauna extinction and its impact on functional diversity. Nature Ecology and Evolution 1:11001106.CrossRefGoogle ScholarPubMed
Pimiento, C., Leprieur, F., Silvestro, D., Lefcheck, J. S., Albouy, C., Rasher, D. B., Davis, M., Svenning, J.-C., and Griffin, J. N.. 2020. Functional diversity of marine megafauna in the Anthropocene. Science Advances 6:eaay7650.CrossRefGoogle ScholarPubMed
Pinzón, J. H., Sampayo, E., Cox, E., Chauka, L. J., Chen, C. A., Voolstra, C. R., and LaJeunesse, T. C.. 2013. Blind to morphology: genetics identifies several widespread ecologically common species and few endemics among Indo-Pacific cauliflower corals (Pocillopora, Scleractinia). Journal of Biogeography 40:15951608.CrossRefGoogle Scholar
Price, S. A., and Schmitz, L.. 2016. A promising future for integrative biodiversity research: an increased role of scale-dependency and functional biology. Philosophical Transactions of the Royal Society of London B 371:20150228.CrossRefGoogle ScholarPubMed
Purnell, M. A., Donoghue, P. J. C., Gabbott, S. E., McNamara, M. E., Murdock, D. J. E., and Sansom, R. S.. 2018. Experimental analysis of soft-tissue fossilization: opening the black box. Palaeontology 61:317323.CrossRefGoogle Scholar
Rainie, S., Kukutai, T., Walter, M., Figueroa-Rodriguez, O., Walker, J., and Axelsson, P.. 2019. Indigenous data sovereignty. Pp. 300319 in Davies, T., Walker, S., Rubinstein, M., and Perini, F., eds. The state of open data: histories and horizons. African Minds and International Development Research Centre, Cape Town and Ottawa.Google Scholar
Raja, N. B., Lauchstedt, A., Pandolfi, J. M., Kim, S. W., Budd, A. F., and Kiessling, W.. 2021. Morphological traits of reef corals predict extinction risk but not conservation status. Global Ecology and Biogeography 30:15971608.CrossRefGoogle Scholar
Raja, N. B., Dunne, E. M., Matiwane, A., Khan, T. M., Nätscher, P. S., Ghilardi, A. M., and Chattopadhyay, D.. 2022. Colonial history and global economics distort our understanding of deep-time biodiversity. Nature Ecology and Evolution 6:145154.CrossRefGoogle ScholarPubMed
Ramírez-Castañeda, V. 2020. Disadvantages in preparing and publishing scientific papers caused by the dominance of the English language in science: the case of Colombian researchers in biological sciences. PLoS ONE 15:e0238372.CrossRefGoogle ScholarPubMed
Rapacciuolo, G., and Blois, J. L.. 2019. Understanding ecological change across large spatial, temporal and taxonomic scales: integrating data and methods in light of theory. Ecography 42:12471266.Google Scholar
Raup, D. M. 1972. Taxonomic diversity during the Phanerozoic. Science 177:10651071CrossRefGoogle ScholarPubMed
Raup, D. M. 1976. Species diversity in the Phanerozoic: an interpretation. Paleobiology 2:289297.CrossRefGoogle Scholar
Raup, D. M. 1991. The future of analytical paleobiology. Short Courses in Paleontology 4:207216.CrossRefGoogle Scholar
Raup, D. M., and Sepkoski, J. J.. 1982. Mass extinctions in the marine fossil record. Science 215:15011503.CrossRefGoogle ScholarPubMed
Raup, D. M., Gould, S. J., Schopf, T. J. M., and Simberloff, D. S.. 1973. Stochastic models of phylogeny and the evolution of diversity. Journal of Geology 81:525542.CrossRefGoogle Scholar
Roswell, M., Dushoff, J., and Winfree, R.. 2021. A conceptual guide to measuring species diversity. Oikos 130:321338.CrossRefGoogle Scholar
Rull, V. 2010. Ecology and palaeoecology: two approaches, one objective. Open Ecology Journal 3:15.CrossRefGoogle Scholar
Runge, J., Bathiany, S., Bollt, E., Camps-Valls, G., Coumou, D., Deyle, E., Glymour, C., Kretschmer, M., Mahecha, M. D., Muñoz-Marí, J., van Nes, E. H., Peters, J., Quax, R., Reichstein, M., Scheffer, M., Schölkopf, B., Spirtes, P., Sugihara, G., Sun, J., Zhang, K., and Zscheischler, J.. 2019. Inferring causation from time series in Earth system sciences. Nature Communications 10:2553.CrossRefGoogle ScholarPubMed
Sandramo, D., Nicosia, E., Cianciullo, S., Muatinte, B., and Guissamulo, A.. 2021. Unlocking the entomological collection of the natural history museum of Maputo, Mozambique. Biodiversity Data Journal 9:e64461.CrossRefGoogle ScholarPubMed
Sandve, G. K., Nekrutenko, A., Taylor, J., and Hovig, E.. 2013. Ten simple rules for reproducible computational research. PLoS Computational Biology 9:e1003285.CrossRefGoogle ScholarPubMed
Scarponi, D., Nawrot, R., Azzarone, M., Pellegrini, C., Gamberi, F., Trincardi, F., and Kowalewski, M.. 2022. Resilient biotic response to long-term climate change in the Adriatic Sea. Global Change Biology 28:40414053.CrossRefGoogle ScholarPubMed
Schoon, M., and van der Leeuw, S.. 2015. The shift toward social-ecological systems perspectives: insights into the human-nature relationship. Natures Sciences Sociétés 23:166174.CrossRefGoogle Scholar
Seddon, A. W. R., Mackay, A. W., Baker, A. G., Birks, H. J. B., Breman, E., Buck, C. E., Ellis, E. C., Froyd, C. A., Gill, J. L., Gillson, L., Johnson, E. A., Jones, V. J., Juggins, S., Macias-Fauria, M., Mills, K., Morris, J. L., Nogués-Bravo, D., Punyasena, S. W., Roland, T. P., Tanentzap, A. J., Willis, K. J., Aberhan, M., van Asperen, E. N., Austin, W. E. N., Battarbee, R. W., Bhagwat, S., Belanger, C. L., Bennett, K. D., Birks, H. H., Bronk Ramsey, C., Brooks, S. J., de Bruyn, M., Butler, P. G., Chambers, F. M., Clarke, S. J., Davies, A. L., Dearing, J. A., Ezard, T. H. G., Feurdean, A., Flower, R. J., Gell, P., Hausmann, S., Hogan, E. J., Hopkins, M. J., Jeffers, E. S., Korhola, A. A., Marchant, R., Kiefer, T., Lamentowicz, M., Larocque-Tobler, I., López-Merino, L., Liow, L. H., McGowan, S., Miller, J. H., Montoya, E., Morton, O., Nogué, S., Onoufriou, C., Boush, L. P., Rodriguez-Sanchez, F., Rose, N. L., Sayer, C. D., Shaw, H. E., Payne, R., Simpson, G., Sohar, K., Whitehouse, N. J., Williams, J. W., and Witkowski, A.. 2014. Looking forward through the past: identification of 50 priority research questions in palaeoecology. Journal of Ecology 102:256267.CrossRefGoogle Scholar
Seltmann, K., Lafia, S., Paul, D., James, S., Bloom, D., Rios, N., Ellis, S., Farrell, U., Utrup, J., Yost, M., Davis, E., Emery, R., Motz, G., Kimmig, J., Shirey, V., Sandall, E., Park, D., Tyrrell, C., Thackurdeen, R. S., Collins, M., O'Leary, V., Prestridge, H., Evelyn, C., and Nyberg, B.. 2018. Georeferencing for Research Use (GRU): an integrated geospatial training paradigm for biocollections researchers and data providers. Research Ideas and Outcomes 4:e32449.CrossRefGoogle Scholar
Sepkoski, J. J. 1981. A factor analytic description of the Phanerozoic marine fossil record. Paleobiology 7:3653.CrossRefGoogle Scholar
Sepkoski, J. J. 1997. Biodiversity: past, present, and future. Journal of Paleontology 71:533539.CrossRefGoogle ScholarPubMed
Sepkoski, J. J., Bambach, R. K., Raup, D. M., and Valentine, J. W.. 1981. Phanerozoic marine diversity and the fossil record. Nature 293:435437.CrossRefGoogle Scholar
Shaw, J. O., Briggs, D. E. G., and Hull, P. M.. 2020. Fossilization potential of marine assemblages and environments. Geology 49:258262.CrossRefGoogle Scholar
Sievanen, L., Campbell, L. M., and Leslie, H. M.. 2012. Challenges to interdisciplinary research in ecosystem-based management. Conservation Biology 26:315323.CrossRefGoogle ScholarPubMed
Signor, P. W., and Gilinsky, N. L.. 1991. Introduction to analytical paleobiology. Short Courses in Paleontology 4:13.CrossRefGoogle Scholar
Silvestro, D., Salamin, N., and Schnitzler, J.. 2014. PyRate: a new program to estimate speciation and extinction rates from incomplete fossil data. Methods in Ecology and Evolution 5:11261131.10.1111/2041-210X.12263CrossRefGoogle Scholar
Simpson, G. L. 2018. Modelling palaeoecological time series using generalised additive models. Frontiers in Ecology and Evolution 6:149.CrossRefGoogle Scholar
Smith, A. B., and McGowan, A. J.. 2011. The ties linking rock and fossil records and why they are important for palaeobiodiversity studies. Geological Society of London Special Publication 358:17CrossRefGoogle Scholar
Spalding, C., and Hull, P. M.. 2021. Towards quantifying the mass extinction debt of the Anthropocene. Proceedings of the Royal Society of London B 288:20202332.Google ScholarPubMed
Stall, S., Yarmey, L. R., Boehm, R., Cousijn, H., Cruse, P., Cutcher-Gershenfeld, J., Dasler, R., de Waard, A., Duerr, R., Elger, K., Fenner, M., Glaves, H., Hanson, Brooks, Hausman, J., Heber, J., Hills, D. J., Hoebelheinrich, N., Hou, S., Kinkade, D., Koskela, R., Martin, R., Lehnert, K., Murphy, F., Nosek, B., Parsons, Mark A., Petters, J., Plante, R., Robinson, E., Samors, R., Servilla, M., Ulrich, R., Witt, M., and Wyborn, L.. 2018. Advancing FAIR data in earth, space, and environmental science. Eos 99. doi: 10.1029/2018EO109301.CrossRefGoogle Scholar
Steigerwald, E., Ramírez-Castañeda, V., Brandt, D. Y. C., Báldi, A., Shapiro, J. T., Bowker, L., and Tarvin, R. D.. 2022. Overcoming language barriers in academia: machine translation tools and a vision for a multilingual future. BioScience 72:988998.CrossRefGoogle Scholar
Stuart, D., Baynes, G., Hrynaszkiewicz, I., Allin, K., Penny, D., Lucraft, M., and Astell, M.. 2018. Practical challenges for researchers in data sharing. Springernature.com white paper. https://doi.org/10.6084/m9.figshare.5975011.v1.Google Scholar
Su, D. F., and Croft, D. A.. 2018. Making sense of the evidence: synthesizing paleoecological data. Pp. 395404 in Croft, D. A., Su, D. F., and Simpson, S. W., eds. Methods in paleoecology: reconstructing Cenozoic terrestrial environments and ecological communities. Springer International, Cham, Switzerland.CrossRefGoogle Scholar
Szabó, P., and Hédl, R.. 2011. Advancing the integration of history and ecology for conservation. Conservation Biology 25:680687.CrossRefGoogle ScholarPubMed
Tardy, C. 2004. The role of English in scientific communication: lingua franca or Tyrannosaurus rex? Journal of English for Academic Purposes 3:247269.CrossRefGoogle Scholar
Theodor, J.M., Lewis E, M.., and Rayfield J, E.. 2021. Amber specimens acquired from Myanmar following military coup. Society of Vertebrate Paleontology. https://vertpaleo.org/wp-content/uploads/2021/06/SVP-Letter-to-paleontological-community-on-Myanmar-Amber_FINAL.pdf, accessed 18 April 2022.Google Scholar
Tomašových, A., and Kidwell, S. M.. 2010. Predicting the effects of increasing temporal scale on species composition, diversity, and rank-abundance distributions. Paleobiology 36:672695.CrossRefGoogle Scholar
Tomašových, A., Kidwell, S. M., and Barber, R. F.. 2016. Inferring skeletal production from time-averaged assemblages: skeletal loss pulls the timing of production pulses towards the modern period. Paleobiology 42:5476.CrossRefGoogle Scholar
Tomašových, A., Albano, P. G., Fuksi, T., Gallmetzer, I., Haselmair, A., Kowalewski, M., Nawrot, R., Nerlović, V., Scarponi, D., and Zuschin, M.. 2020. Ecological regime shift preserved in the Anthropocene stratigraphic record. Proceedings of the Royal Society of London B 287:20200695.Google ScholarPubMed
Trisos, C. H., Auerbach, J., and Katti, M.. 2021. Decoloniality and anti-oppressive practices for a more ethical ecology. Nature Ecology and Evolution 5:12051212.CrossRefGoogle ScholarPubMed
Uhen, M. D., Buckland, P. I., Goring, S. J., Jenkins, J. P., and Williams, J. W.. 2021. The EarthLife Consortium API: an extensible, open-source service for accessing fossil data and taxonomies from multiple community paleodata resources. Frontiers of Biogeography 13:e50711.CrossRefGoogle Scholar
Valentine, J. W. 1969. Patterns of taxonomic and ecological structure of the shelf benthos during Phanerozoic time. Palaeontology 12:684709.Google Scholar
Valenzuela-Toro, A. M., and Viglino, M.. 2021. How Latin American researchers suffer in science. Nature 598:374375.CrossRefGoogle Scholar
Varela, S., González-Hernández, J., Sgarbi, L. F., Marshall, C., Uhen, M. D., Peters, S., and McClennen, M.. 2015. paleobioDB: an R package for downloading, visualizing and processing data from the Paleobiology Database. Ecography 38:419425.CrossRefGoogle Scholar
Vellend, M., Brown, C. D., Kharouba, H. M., McCune, J. L., and Myers-Smith, I. H.. 2013. Historical ecology: using unconventional data sources to test for effects of global environmental change. American Journal of Botany 100:12941305.CrossRefGoogle ScholarPubMed
Vilhena, D. A., and Smith, A. B.. 2013. Spatial bias in the marine fossil record. PLoS ONE 8:e74470.CrossRefGoogle ScholarPubMed
Walker, F. M., Dunhill, A. M., and Benton, M. J.. 2020. Variable preservation potential and richness in the fossil record of vertebrates. Palaeontology 63:313329.CrossRefGoogle Scholar
Warnock, R. C. M., Heath, T. A., and Stadler, T.. 2020. Assessing the impact of incomplete species sampling on estimates of speciation and extinction rates. Paleobiology 46:137157.CrossRefGoogle Scholar
Wilke, T., Wagner, B., Van Bocxlaer, B., Albrecht, C., Ariztegui, D., Delicado, D., Francke, A., Harzhauser, M., Hauffe, T., Holtvoeth, J., Just, J., Leng, M. J., Levkov, Z., Penkman, K., Sadori, L., Skinner, A., Stelbrink, B., Vogel, H., Wesselingh, F., and Wonik, T.. 2016. Scientific drilling projects in ancient lakes: integrating geological and biological histories. Global and Planetary Change 143:118151.CrossRefGoogle Scholar
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., Gonzalez-Beltran, A., Gray, A. J. G., Groth, P., Goble, C., Grethe, J. S., Heringa, J., ’t Hoen, P. A. C., Hooft, R., Kuhn, T., Kok, R., Kok, J., Lusher, S. J., Martone, M. E., Mons, A., Packer, A. L., Persson, B., Rocca-Serra, P., Roos, M., van Schaik, R., Sansone, S.-A., Schultes, E., Sengstag, T., Slater, T., Strawn, G., Swertz, M. A., Thompson, M., van der Lei, J., van Mulligen, E., Velterop, J., Waagmeester, A., Wittenburg, P., Wolstencroft, K., Zhao, J., and Mons, B.. 2016. The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data 3:160018.CrossRefGoogle ScholarPubMed
Willis, K. J., and Birks, H. J. B.. 2006. What is natural? The need for a long-term perspective in biodiversity conservation. Science 314:12611265.CrossRefGoogle ScholarPubMed
Wilson, G., Bryan, J., Cranston, K., Kitzes, J., Nederbragt, L., and Teal, T. K.. 2017. Good enough practices in scientific computing. PLoS Computational Biology 13:e1005510.CrossRefGoogle ScholarPubMed
Wolkovich, E. M., Cook, B. I., McLauchlan, K. K., and Davies, T. J.. 2014. Temporal ecology in the Anthropocene. Ecology Letters 17:13651379.CrossRefGoogle ScholarPubMed
Womack, T. M., Crampton, J. S., and Hannah, M. J.. 2021. Spatial scaling of beta diversity in the shallow-marine fossil record. Paleobiology 47:3953.CrossRefGoogle Scholar
Wüest, R. O., Zimmermann, N. E., Zurell, D., Alexander, J. M., Fritz, S. A., Hof, C., Kreft, H., Normand, S., Cabral, J. S., Szekely, E., Thuiller, W., Wikelski, M., and Karger, D. N.. 2020. Macroecology in the age of big data—where to go from here? Journal of Biogeography 47:112.CrossRefGoogle Scholar
Yasuhara, M., Doi, H., Wei, C.-L., Danovaro, R., and Myhre, S. E.. 2016. Biodiversity–ecosystem functioning relationships in long-term time series and palaeoecological records: deep sea as a test bed. Philosophical Transactions of the Royal Society of London B 371:20150282.CrossRefGoogle ScholarPubMed
Yasuhara, M., Tittensor, D. P., Hillebrand, H., and Worm, B.. 2017. Combining marine macroecology and palaeoecology in understanding biodiversity: microfossils as a model. Biological Reviews 92:199215.CrossRefGoogle ScholarPubMed
Yasuhara, M., Huang, H.-H., Hull, P., Rillo, M., Condamine, F., Tittensor, D., Kučera, M., Costello, M., Finnegan, S., O'Dea, A., Hong, Y., Bonebrake, T., McKenzie, R., Doi, H., Wei, C.-L., Kubota, Y., and Saupe, E.. 2020. Time machine biology: cross-timescale integration of ecology, evolution, and oceanography. Oceanography 33:1628.CrossRefGoogle Scholar
Zamani, A., Fric, Z. F., Gante, H. F., Hopkins, T., Orfinger, A. B., Scherz, M. D., Bartoňová, A. S., and Pos, D. D.. 2022. DNA barcodes on their own are not enough to describe a species. Systematic Entomology 47:385389CrossRefGoogle Scholar
Zeppelini, D., Dal Molin, A., Lamas, C. J. E., Sarmiento, C., Rheims, C. A., Fernandes, D. R. R., Lima, E. F. B., Silva, E. N., Carvalho-Filho, F., Kováč, Ľ., Montoya-Lerma, J., Moldovan, O. T., Souza-Dias, P. G. B., Demite, P. R., Feitosa, R. M., Boyer, S. L., Weiner, W. M., and Rodrigues, W. C.. 2021. The dilemma of self-citation in taxonomy. Nature Ecology and Evolution 5:2.CrossRefGoogle ScholarPubMed
Figure 0

Table 1. Summary of four challenges facing analytical paleobiology. Key advances are highlighted under each challenge.

Figure 1

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 paleoenvironmental patterns with taxon occurrences to model ecological niches through time.

Figure 2

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.