bioRxiv preprint doi: https://doi.org/10.1101/866186; this version posted June 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. A multiscale view of the Phanerozoic fossil record reveals the three major biotic transitions Alexis Rojasa,1, Joaquin Calatayuda, Michal Kowalewskib, Magnus Neumana, and Martin Rosvalla aIntegrated Science Lab, Department of Physics, Umeå University,SE-901 87 Umeå, Sweden; bFlorida Museum of Natural History, Division of Invertebrate Paleontology, University of Florida, Gainesville, FL 32611, USA This manuscript was compiled on June 2, 2020 1 The hypothesis of the Great Evolutionary Faunas is a foundational questions: How can we identify global-scale mega-assemblage 27 2 concept of macroevolutionary research postulating that three global shifts without relying on critical methodological decisions? 28 3 mega-assemblages have dominated Phanerozoic oceans following And given the underlying Earth-Life System, how should we 29 4 abrupt biotic transitions. Empirical estimates of this large-scale pat- represent the paleontological input data to accurately cap- 30 5 tern depend on several methodological decisions and are based on ture complex interdependencies? These limitations result in 31 6 approaches unable to capture multiscale dynamics of the underlying methodologically volatile and often inconsistent estimates of 32 7 Earth-Life System. Combining a multilayer network representation large-scale macroevolutionary structures, thus obscuring the 33 8 of fossil data with a multilevel clustering that eliminates the subjec- causative drivers that underlie biotic transitions between suc- 34 9 tivity inherent to distance-based approaches, we demonstrate that cessive global mega-assemblages. As a result, whether abrupt 35 10 Phanerozoic oceans sequentially harbored four global benthic mega- global perturbations, such as large bolide impacts and massive 36 11 assemblages. Shifts in dominance patterns among these global volcanic eruptions (12, 13), and long-term ecological changes 37 12 marine mega-assemblages are abrupt (end-Cambrian 494 Ma; end- (14) both operate at the higher levels of the macroevolutionary 38 13 Permian 252 Ma) or protracted (mid-Cretaceous 129 Ma), and rep- hierarchy remains unclear (14, 15). 39 14 resent the three major biotic transitions in Earth’s history. This find- Our understanding of the macroevolutionary dynamics of 40 15 ing suggests that the mid-Cretaceous radiation of the so-called Mod- Phanerozoic life is being transformed by network-based ap- 41 16 ern evolutionary Fauna, concurrent with gradual ecological changes proaches (6, 16–18). Because the input network can capture 42 17 associated with the Mesozoic Marine Revolution, triggered a biotic the complexity inherent to the underlying system, network 43 18 transition comparably to the transition following the largest extinc- analysis has become an increasingly popular alternative to the 44 19 tion event in the Phanerozoic. Overall, our study supports the notion typical procedures used in almost every area of paleontological 45 20 that both long-term ecological changes and major geological events research (19–22). However, as might be expected of an emer- 46 21 have played crucial roles in shaping mega-assemblages that domi- gent interdisciplinary field, methodological inconsistencies and 47 22 nated Phanerozoic oceans. conceptual issues in the body of network paleobiology research 48 make it difficult to compare outcomes across studies. Also, 49 Phanerozoic | Biotic transitions | marine faunas | multilayer networks the rapid development of the broader field of network science 50 demands a major effort from paleobiologists working across 51 1 epkoski’s hypothesis of the Three Great Evolutionary disciplinary boundaries. Moreover, current network paleobi- 52 2 SFaunas that sequentially dominated Phanerozoic oceans ology studies use standard network representations based on 53 3 represents a foundational concept of macroevolutionary re- pairwise statistics and clustering limited to a single scale of 54 4 search. This hypothesis postulates that the major groups of analysis (6, 18, 19, 21). That is, they use only the connection 55 5 marine animals archived in the Phanerozoic fossil record were strength between nodes of geographic areas and taxa in the 56 6 non-randomly distributed through time and can be grouped paleontological data and apply standard network clustering 57 7 into Cambrian, Paleozoic, and Modern evolutionary faunas of nodes into communities, which does not capture temporal 58 8 (1). Sepkoski formulated this three-phase model based on a interactions between components or multiscale dynamics of 59 9 factor analysis of family-level diversity (2), which became a the underlying Earth-Life System (23). 60 10 framework-setting assumption in studies on the evolution of We employed a multilayer framework that integrates the 61 11 marine faunas and ecosystems (3–6), changing our view of the higher-order relationships over time in the underlying paleonto- 62 12 Phanerozoic history of life. However, because Sepkoski’s study logical data (23, 24). Specifically, our input network takes into 63 13 predicts unusual volatility in the Modern evolutionary fauna account the temporal arrangement of fossils in the geological 64 14 starting during the mid-Cretaceous, a three-phase model fails record, combined with multilevel hierarchical clustering (25) to 65 15 to capture the overall diversity dynamics during long portions test for major biotic transitions in the Phanerozoic fossil record 66 16 of the Mesozoic (7). Whether such mid-Cretaceous radiation of the benthic marine faunas (11). This multilayer network 67 17 (8) represents an intra-faunal dynamic or a biotic transition approach is transforming research on higher-order structures 68 18 from Sepkoski’s Modern evolutionary fauna towards a ne- in both natural and social systems (26), and can help us to un- 69 19 glected mid-Cretaceous-Cenozoic fauna remains unexplored. derstand the structure and dynamics of the macroevolutionary 70 20 Despite recognition that Phanerozoic marine diversity is 21 highly structured (9), empirical estimates of the macroevolu- A.R. conceived the project. A.R., and M.R. designed the experiments. A.R. performed the network analysis. J.C., A.R., and M.N. performed the robustness assessment. A.R., M.K., and M.R. wrote 22 tionary pattern depend on several methodological decisions, in- the manuscript with input from all authors. All authors discussed the results and commented on 23 cluding background assumptions, statistical threshold, hierar- the manuscript. 24 chical level (1, 6, 7), and the choice of input data: for example, Authors declare no competing interests. 25 Sepkoski’s compendia or benthic taxa from the Paleobiology 2 26 Database (1, 10, 11). These limitations raise two fundamental To whom correspondence should be addressed. E-mail: [email protected] 1–6 bioRxiv preprint doi: https://doi.org/10.1101/866186; this version posted June 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. A. Bipartite B. Unipartite C. Multilayer In our network representation, we account for this higher- 113 layer order dynamic to reveal the multiscale organization of the 114 115 t1 Earth-Life System, which can be translated into a macroevo- lutionary hierarchy (27). We analyze multilayer relationships 116 state node (23, 24) in the paleontological data (11) using a multilayer 117 t0 network framework (25, 29). Network layers represent or- 118 119 physical node dered geological stages (30), and physical nodes depicting the taxa are split into state nodes (24), with one state node per 120 areas taxa each geological stage in which a given taxon occurs (Fig. 1C; 121 Data S1). This higher-order network representation captures 122 Fig. 1. Network models used in macroevolution. A-B. Standard first-order network both the geographical and temporal aspects of the underlying 123 representations. A. Bipartite occurrence network. This representation comprises Earth-Life System simultaneously. 124 two sets of nodes that represent geographic areas and taxa (21). B. Unipartite co- We use the map equation multilayer framework (31), which 125 occurrence networks (6, 18). These representations are weighted projections of the bipartite network onto each set of nodes. C. Higher-order multilayer representation. operates directly on the assembled multilayer network and 126 In this network, nodes are organized into layers representing a series of time intervals thereby preserves the higher-order interdependencies when 127 (t0, t1). The physical nodes resenting taxa are split into state nodes, with one state identifying dynamical modular patterns in the data. The map 128 node per layer in which a given taxon occurs (24). equation framework consists of an objective function that mea- 129 sures the quality of a given network partition (32), and an 130 efficient search algorithm that optimizes this function over 131 71 hierarchy. different solutions (24). This algorithm provides the optimal 132 72 We demonstrate that Phanerozoic oceans sequentially har- multilevel solution for the input network, eliminating