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Learning from the Multiple Fossil Records Bridges Research and Teaching Practices and Promotes Systems Thinking in Paleontology Education

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25 August 2026

Posted:

26 August 2026

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Abstract
Large-scale efforts to catalog the fossil record and digitize museum collections have driven the recognition of two distinct forms of fossil records: the physical record, consisting of specimens, and the abstracted record, consisting of data derived from those specimens. This framework provides a conceptual basis for distinguishing between specimen-based and data-driven resources for teaching and learning paleontology in formal educational settings. In this position paper, we show that state-of-the-art paleontological research increasingly conceptualizes the so-called abstracted fossil record not merely as digital data or archives (e.g., databases, digital models, and imagery), but primarily as complex representations of the spatiotemporally resolved and high-dimensional geohistorical data. Building on this perspective, we highlight a major gap between modern paleontological research and teaching practices in higher education, where paleontology courses often rely on the specimen-data dichotomy and ignore that abstracted representations of the fossil record, such as network models, encode assumptions about its spatiotemporal structure and dynamics that shape the patterns inferred from fossil data. Because a physical fossil record can give rise to multiple abstracted representations, each capturing different aspects of the underlying data, we introduce the concept of multiple fossil records within a complex network framework. We argue that leveraging network-based tools can bridge paleontological research and teaching practices while fostering systems thinking in the paleontology classroom, consistent with the UNESCO Education 2030 Framework for Action.
Keywords: 
paleontology
;  
network science
;  
systems thinking
;  
higher education
Subject: 
Environmental and Earth Sciences  -   Paleontology

Introduction

The educational value of the physical fossil record, comprising primarily shells, bones, wood fragments, and fossil traces, for teaching and learning geological and paleontological concepts in formal educational environments is well stablished (Dilcher, 1967; Macdonald et al., 2005; Kelley and Visaggi, 2012; Pimiento, 2015; Grant et al., 2016; Martinez and Serpa, 2022). However, the growth of quantitative paleobiology (Figure 1), driven by extensive compilations of fossil occurrence data (e.g., Alroy et al., 2008; J. J. Sepkoski, 1981), and large-scale digitalization of museum collections (Blagoderov et al., 2012; Marshall et al., 2018; Nelson and Paul, 2019), has stimulated a growing interest in fossil databases (Lockwood et al., 2018; Price, 2026) and digital models (MacFadden, 2019; Ziegler et al., 2020) as complementary educational resources. First-generation compilations of paleontological data such as the Paleobiology Database (Peter & McClennen, 2016), have being used to simulate research experiences into the classroom and to develop inquiry-based teaching modules and instructional materials (Lockwood et al., 2018). In the era of quantitative paleobiology, the distinction between specimens and their associated data has shaped both research and teaching practices. Recent breakthroughs in macroevolutionary studies (Rojas et al., 2017, 2021; Muscente et al., 2018; Kocsis et al., 2018) challenge this approach, which does not account for abstracted representations of the high-dimensional and time-resolved geohistorical data, such as the so-called complex network (Lambiotte et al. 2019). These abstracted representation connect the structure, dynamics and function of the biosphere over deep time by encoding different types of dependencies in the underlying data (Rojas et al. 2021). Overall, network representations are more than the data from which they are built because they reveal emergent system-level organization and dynamics that are not apparent from individual observations alone.
In this position paper, we demonstrate that state-of-the-art paleontological research increasingly conceptualizes the so-called abstracted fossil record (sensu Allmon et al. 2018) as network-based representations of the high-dimensional and time-resolved geohistorical data. We show how this conceptual shift influences fundamental areas of paleontological research, including bioregionalization, macroevolution, and biostratigraphy, and identify a gap between state-of-the-art research practices and higher education approaches in paleontology, where those abstract representations of the fossil record are often neglected. Finally, we discuss how leveraging network-based tools in the paleontology classroom would bridge this gap while fostering data-driven learning and systems thinking, consistent with the UNESCO Education 2030 Framework for Action (UNESCO, 2015).

The Physical Fossil Record and Its Abstracted Representations

The Two Fossil Records in Quantitative Paleobiology

The global assemblage of physical objects, including specimens observed in the field, specimens housed in collections, and specimens that remain undiscovered in nature, constitute the physical fossil record that serves as the primary source of data in paleontology (Allmon et al., 2018). Specimen-based taxonomic and systematic studies remain an important component of paleontological research in the twenty-first century (Maidment and Butler, 2025; Smith et al., 2025). Studies focus on fossil morphology, including anatomical description and comparative studies among specimens, represent the core of fossil-driven paleontological research (Yu et al., 2024). Furthermore, paleontological collections are inherently structured around physical specimens (Da Silva Mano et al., 2024). Since the mid-twentieth century, large-scale datasets derived from the physical fossil record, together with quantitative approaches, have increasingly become central in paleontological research (Sepkoski, 2013). This major transformation in the history of paleontology has been described as the paleobiological revolution (Sepkoski and Ruse, 2009), with some researchers arguing that the discipline has shifted from a primarily specimen-based approach toward a data-driven field (Dillon et al. 2023). Regardless of whether such a shift has occurred, the application of new technologies to paleontological remains is driving an unprecedented expansion in the quantity and type of data that can be recovered from the physical fossil record, including three-dimensional digital morphological data, protein sequence data, and geochemical data (Davies et al., 2017; Martin et al., 2017; Schweitzer, 2023; Lueders-Dumont et al., 2026).
The concept of the abstracted fossil record was recently introduced to describe the extensive body of data stored in large paleontological databases (Allmon et al., 2018). Examples include geographically and temporally explicit, taxonomically identified fossil occurrences in the Paleobiology Database (Peters and McClennen 2016), and three-dimensional (3-D) ‘virtual specimen’ data in the MorphoSource repository (Boyer et al., 2016). Although specimen-based research relies upon abstracted information derived from fossil specimens (e.g., specimens’ dimensions and morphological descriptions), the distinction between the physical fossil record, consisting of the specimens themself, and the abstracted fossil record consisting of data derived from those specimens, emphasizes the extent to which information in the abstracted fossil record has become disconnected from its underlying physical basis. The description of modern paleontology as a data-driven science, reflects the transition toward viewing the fossil record as a record of data (Sepkoski, 2013; Dunne et al., 2025).

The Multiple Fossil Records in Network-Based Paleobiology

Advances in computational power, databases, and modeling have contributed to innovative analyses in state-of-the-art paleontological research (Dillon et al. 2023). In particular, network science (Battiston et al., 2020) is transforming paleontological research by enhancing modeling capacities and helping researchers to reveal the complexity of the global biosphere over deep-time (Rojas et al. 2021). Network science provides analytical, statistical and computational methods to describe the behavior of complex systems (Lambiotte et al. 2019), beyond the standard representations and approaches used to describe species interactions in food webs (Roopnarine, 2010). Intuitively, high-dimensional and time-resolved geohistorical data can be represented as networks, where nodes represent, for instance, taxa (e.g., species, genera, families), sampling units (e.g., collections, localities, grid cells), stratigraphic units (e.g., beds, members, formations, stages), or geological units (e.g., basins, tectonic plates), connected through links that indicate their relationships. In the last decade, network models based on pairwise or direct interactions between individual components have been applied to almost every area of paleontological research, including biostratigraphy (Muscente et al., 2019; Viglietti et al., 2022), bioregionalization (Rojas et al., 2017; Kocsis et al., 2018; Du et al., 2026), paleoecology (Roopnarine 2010, Rojas et al. 2022; Guo et al. 2023), and macroevolution (Muscente et al., 2018; Du et al., 2026). Despite conceptual issues and methodological inconsistencies (see Rojas et al. 2022), the growing body of network-based paleontological studies is shifting thinking from a data-mining approach towards a system perspective in paleontology, concurrent with . Network representations of geohistorical data enable the analysis of structural properties, multiscale organization, and dynamics arising from interactions among components those are not captured by examining individual entities alone. By adding relational context, networks can reveal patterns and dynamics that are not apparent from the underlying data alone (Muscente et al., 2018; Kröger and Rojas, 2026).
The complexity of data retrieved from the diverse geohistorical records, including stratigraphic sections, sediments, fossil collections, and ice cores (National Research Council 2005), raises questions about the limitations of the network models based on pairwise interactions. For instance, such models cannot account for the law of faunal succession, the inherent asymmetry of temporal processes, or other higher-order interactions within the Earth-life system. To overcome these limitations, researchers have modeled Phanerozoic occurrence data from the Paleobiology Database (PaleoDB) as multilayer networks (Rojas et al. 2021), in which layers represent ordered geological stages and nodes represent taxa and geographic cells, and as hypergraphs (Eriksson et al. 2021, 2022), in which geological stages are represented as hyperedges linking taxa. We argue that such networks can be considered abstracted fossil records (Figure 2), based on two observations: First, multilayer, hypergraph, and other higher-order network models representing well-preserved benthic marine taxa (i.e., brachiopods, bivalves, gastropods, bryozoans, echinoderms, anthozoans, decapods, and trilobites), as well as standard unipartite networks (Kocsis et al. 2018; Muscente et al. 2018), are constructed from the same underlying data but capture different aspects of the system. Second, when these data are incomplete, whether because of sampling biases or limitations inherent to the physical fossil record (Nanglu and Cullen, 2023), network-based methods can be used to predict missing connections or links (see Lindström et al. 2025), therefore improving the abstracted fossil record without requiring additional information from the physical record.

The Multiple Fossil Records in Paleontology Education

The educational value of the physical fossil record for teaching and learning paleontology has long been recognized (Dilcher, 1967). Bringing the physical fossil record into the paleontology classroom, including shells, carapaces, bones, wood fragments, and trace fossils, aligns with the central role of the practical experience in science education (Barberà and Valdés, 1996). Fossil materials have been used to engage students with concepts in biology, ecology, and geology through hands-on activities (Macdonald et al., 2005; Pimiento, 2015; Grant et al., 2016; Martinez and Serpa, 2022). Fossils have also been used to implement and evaluate alternative methodological approaches for teaching and learning paleontology (Calonge García et al., 2003). Fossil-based activities can connect paleontological concepts with observable evidence, and provide students with opportunities to interpret and evaluate such evidence in accordance with contemporary practices in paleontology (Achiam et al., 2016; Lockwood et al., 2018). Despite its educational value, the physical fossil record represents only one of the multiple records available for paleontology education.
The rise of paleontological databases has not only transformed paleontological research but has also influenced teaching practices in the Earth sciences. Resources such as the Paleobiology Database (Peters and McClennen 2016) have been increasingly incorporated into higher education environments to simulate authentic research experiences and support the development of teaching modules and instructional materials, including tutorials and learning activities for physical geology, historical geology, paleontology, sedimentology, and stratigraphy courses. These resources have also been incorporated into distance-learning initiatives as alternatives to traditional laboratory exercises (see Lockwood et al. 2018). From a broader perspective, the transition from a primarily specimen-based toward a data-driven field, linked to the rise of paleontological databases (Dillon et al. 2023), has expanded the potential of paleontology for science education (Estrup and Achiam, 2019), including opportunities to introduce students to database technologies (Nelson and Fatimazahra, 2010). Despite the increasing use of databases in paleontology education, abstracted fossil records, such as network, which have driven transformative breakthroughs in modern paleobiological research, remain largely unexplored as educational resources. This observation highlights a major gap between modern paleontological research and teaching practices in higher education.
Over the last decade, network scientists and educators have worked together to address the need for curricula, resources, and tools that introduce students to the concept of networks. These efforts to promote network literacy have been motivated by the recognition of networks as a crucial framework for understanding complex systems (Sayama et al., 2017; Cramer et al., 2018) and the potential of network science to foster skills relevant for an increasingly interconnected world (Harrington et al., 2013). Training students to develop a holistic understanding of complex systems is a well-established goal across the Earth sciences, natural sciences, and social sciences (see Schuler et al., 2018; Marcos-Sánchez et al., 2022). It is consistent with the UNESCO Education 2030 Framework for Action (UNESCO, 2015), in which systems thinking, a set of synergistic analytic skills used to improve the capability of identifying and understanding complex systems (Arnold and Wade, 2015), is identified as a key competency for sustainable development. However, as observed in other fields (see Sayama et al., 2017), current practices in paleontology teaching and learning are largely based on reductionist approaches that isolate specific components and processes (Lockwood et al., 2018). Building on the systems perspective underlying network theory and the success of the Earth-Life System (Rojas et al., 2021) in integrating taxonomy, biostratigraphy, biogeography, macroevolution and macroecology (Kröger and Rojas, 2026), we argue that integrating network-based representations of the fossil record into paleontology curricula provides a straightforward opportunity to reconsider learning goals and competencies, develop relevant skills that foster systems thinking, and ultimately enable students to reason about the Phanerozoic biosphere as a complex system.

Acknowledgments

AR, AM, YB and MG thank to the many students of the biology program at the Universidad Pedagógica Nacional de Colombia, whose interest in paleontology inspired this contribution. AR was supported by the Johannes Kepler University Linz, Linz Institute of Transformative Change (LIFT_C).

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Figure 1. An alluvial diagram describing the major topics addressed in the literature on paleontology teaching and education research. It depicts the modular structure of a weighted co-occurrence network of authors keywords in filtered documents from the Web of Science core collection database (WoS). Data: 107 documents retrieved from WoS under the search: (PALEONTOLOGY OR PALEOBIOLOGY) AND (TEACHING OR EDUCATION). The network was built using VOSviewer (Van Eck and Waltman, 2010). After removing names (e.g., personal names, cities, countries, localities) and disconnected words, the input network comprises 191 keywords linked through 586 weighted edges. Because the optimized network partition (A) does not show a hierarchical structure, it was partitioned using varying Markov times models (A = 1 and B = 2) with the Map Equation framework (Edler et al., 2017) to describe its modular organization at different resolutions.
Figure 1. An alluvial diagram describing the major topics addressed in the literature on paleontology teaching and education research. It depicts the modular structure of a weighted co-occurrence network of authors keywords in filtered documents from the Web of Science core collection database (WoS). Data: 107 documents retrieved from WoS under the search: (PALEONTOLOGY OR PALEOBIOLOGY) AND (TEACHING OR EDUCATION). The network was built using VOSviewer (Van Eck and Waltman, 2010). After removing names (e.g., personal names, cities, countries, localities) and disconnected words, the input network comprises 191 keywords linked through 586 weighted edges. Because the optimized network partition (A) does not show a hierarchical structure, it was partitioned using varying Markov times models (A = 1 and B = 2) with the Map Equation framework (Edler et al., 2017) to describe its modular organization at different resolutions.
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Figure 2. The multiple fossil records of the Phanerozoic benthic marine faunas. (left) Selection of fossils representing the major physical components of the fossil record; (middle) graphical representation of the extensive data stored in the Paleobiology Database (PaleoDB); (right) alternative network models representing the data in the PaleoDB. The foreground model is a multilayer network with a four-tier modular structure (Rojas et al., 2021). Adapted from (Rojas et al. 2022).
Figure 2. The multiple fossil records of the Phanerozoic benthic marine faunas. (left) Selection of fossils representing the major physical components of the fossil record; (middle) graphical representation of the extensive data stored in the Paleobiology Database (PaleoDB); (right) alternative network models representing the data in the PaleoDB. The foreground model is a multilayer network with a four-tier modular structure (Rojas et al., 2021). Adapted from (Rojas et al. 2022).
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