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https://doi.org/10.1038/s41467-019-08837-3 OPEN Ediacaran biozones identified with network analysis provide evidence for pulsed extinctions of early complex life

A.D. Muscente1,11, Natalia Bykova 2, Thomas H. Boag3, Luis A. Buatois4, M. Gabriela Mángano4, Ahmed Eleish5, Anirudh Prabhu5, Feifei Pan5, Michael B. Meyer6, James D. Schiffbauer 7,8, Peter Fox5, Robert M. Hazen9 & Andrew H. Knoll1,10 1234567890():,; Rocks of Ediacaran age (~635–541 Ma) contain the oldest fossils of large, complex organisms and their behaviors. These fossils document developmental and ecological innovations, and suggest that extinctions helped to shape the trajectory of early animal evolution. Conventional methods divide Ediacaran macrofossil localities into taxonomically distinct clusters, which may represent evolutionary, environmental, or preservational variation. Here, we investigate these possibilities with network analysis of body and trace fossil occurrences. By partitioning multipartite networks of taxa, paleoenvironments, and geologic formations into community units, we distinguish between biostratigraphic zones and paleoenvir- onmentally restricted biotopes, and provide empirically robust and statistically significant evidence for a global, cosmopolitan assemblage unique to terminal Ediacaran strata. The assemblage is taxonomically depauperate but includes fossils of recognizable eumetazoans, which lived between two episodes of biotic turnover. These turnover events were the first major extinctions of complex life and paved the way for the Cambrian radiation of animals.

1 Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA 02138, USA. 2 Trofimuk Institute of Petroleum Geology and Geophysics, Siberian Branch Russian Academy of Sciences, Novosibirsk 630090, Russia. 3 Department of Geological Sciences, Stanford University, Stanford, CA 94305, USA. 4 Department of Geological Sciences, University of Saskatchewan, Saskatoon, SK S7n 5E2, Canada. 5 Department of Earth and Environmental Sciences, Rensselaer Polytechnic Institute, Jonsson-Rowland Science Center, 1W19, 110 8th Street, Troy, NY 12180, USA. 6 Earth and Environmental Science Program, Harrisburg University of Science and Technology, Harrisburg, PA 17101, USA. 7 Department of Geological Sciences, University of Missouri, Columbia, MO 65211, USA. 8 X-ray Microanalysis Core Facility, University of Missouri, Columbia, MO 65211, USA. 9 Geophysical Laboratory, Carnegie Institution for Science, 5251 Broad Branch Road, Washington, D.C 20015, USA. 10 Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA. 11Present address: Department of Geological Sciences, Jackson School of Geoscience, University of Texas at Austin, Austin, TX 78712, USA. Correspondence and requests for materials should be addressed to A.D.M. (email: [email protected])

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acroscopic fossils in Ediacaran rocks document a biota itself. The same is true of the Shuram excursion29. Shelly diverse array of morphologically complex, multicellular fossils, in contrast, occur in sedimentary successions containing M 1–6 organisms that lived between 600 and 541 million non-skeletal macrofossils in China, Namibia, and the western US, years ago (Ma), before the ~541 Ma Cambrian radiation of among other places9. Indeed, the first appearance of the biomi- animals7. These fossils include carbonaceous compressions neralized fossil Cloudina, known from numerous localities of seaweeds and possible metazoans5,8, secondarily (authigeni- around the world, has received attention as a potential marker of cally and diagenetically) mineralized remains of tubicolous terminal Ediacaran strata9,29. Even so, shelly fossil taxa are gen- organisms interpreted as eumetazoans6, skeletons produced by erally preserved in carbonate rocks, limiting their application to early biomineralizing9,10 and agglutinating11 metazoans, and siliciclastic-dominated successions. Given these and other issues, traces12–14 left by motile animals. The most distinctive Ediacaran the International Subcommission on Ediacaran Stratigraphy has remains, however, are casts, molds, and impressions of soft- highlighted the need for innovative approaches to correlating the bodied organisms preserved in siliciclastic and, less commonly, system29. carbonate rocks4. These Ediacara-type fossils are rarely found in Our work approaches the issue by seeking to identify bios- Phanerozoic rocks but occur in Ediacaran strata around the tratigraphic zones (biozones) based on fossil assemblages (asso- world, recording an enigmatic group of organisms, colloquially ciations). An assemblage biozone, in essence, is a set of lithologic known as the ‘Ediacara biota.’ Although the phylogenetic packages representing roughly age-equivalent strata and the affinities of these organisms are unresolved, the Ediacara association of taxa preserved therein (Supplementary Table 1). biota probably includes stem- and early crown-group animals, Several prior investigations explored for assemblages in Ediacaran along with possible representatives of extinct non-metazoan rocks31–35. Parsimony analysis32, non-metric multidimensional clades7,15,16. Regardless of their precise affinities, Ediacaran fossils scaling (NMDS)31, and hierarchical clustering31 all show that document the advent of ecological tiering3,17, metazoan Ediacaran fossil localities can be sorted into three taxonomically locomotion12,14,18,19, skeletal biomineralization9,10, macroscopic distinct sets:1,26 the Avalon, White Sea, and Nama clusters. These predation13,20, and other innovations that ultimately set the stage clusters are usually called ‘assemblages,’ but this terminology is for Cambrian animal diversification3. problematic as it has multiple implications (e.g., life, death, and Few Ediacaran genera occur in rocks of Cambrian or younger biozone assemblages). We consider the clusters to be paleo- age1. Some works attribute this observation to changes in communities—recurrent and distinguishable associations of preservational environments across the Ediacaran–Cambrian taxa36—and herein, restrict our usage of ‘assemblage’ to the dis- transition2,21. However, due to emerging research22,23, such cussion of biozones and their characteristic fossil associations. explanations have fallen out of favor in lieu of others citing biotic Avalon localities include deposits in Newfoundland, northwestern turnover. A large magnitude negative carbon-isotopic excursion Canada, and the United Kingdom, where relatively deep (mar- near the Ediacaran–Cambrian boundary suggests that the ginal slope and basinal) turbiditic successions contain Ediacara- extinction of Ediacaran taxa may have been driven, at least in type fossils, including arboreomorph and rangeomorph fronds17. part, by global environmental disturbance24,25. Additionally, White Sea localities primarily occur in South Australia and the rise of eumetazoans may have contributed to one or Russia, where offshore and shoreface facies host diverse associa- more earlier episodes of ecological reorganization1,2,6,23,26,27. tions of Ediacara-type fossils, including many dickinsoniomorph, Detailed studies of deposits containing the Ediacaran–Cambrian bilateralomorph, and kimberellomorph taxa3,35. Nama localities, boundary23,25,28 provide empirical evidence that terminal Edia- in contrast, stand out for their taxonomically depauperate caran strata are taxonomically depauperate relative to older deposits with regard to Ediacara-type fossils, and correspond to Ediacaran and younger Cambrian rocks. If this observation shoreface and offshore facies with skeletal, trace, carbonaceous, represents a real signal, the Ediacara biota experienced an and Ediacara-type fossils as well as conspicuous tubicolous extinction prior to the end of the period, perhaps the first mass elements6. They are located in Namibia, South China, and the extinction of complex life in Earth history2,23,27. The evidence western US, amongst other places. Some analyses31 support for this extinction, however, is based on analyses of individual the existence of a fourth cluster, herein referred to as the sections and localities23,25,28, which may not provide repre- Miaohe cluster, named after shales with abundant macrofossils sentative samples of the ancient biosphere1. In this context, found near Miaohe, South China5. Localities in this cluster hypothesis testing in Ediacaran paleobiology necessitates a robust generally contain macroscopic carbonaceous compressions in biostratigraphic framework, allowing for discovery of global fine-grained siliciclastic beds5. In any case, because communities scale patterns in diversity over geologic time. vary through both time and space36, the clusters remain a subject The chronology of evolutionary events at the dawn of animal of debate1,31. Some authors have treated them as assemblage life remains unclear due to uncertainties in the correlation of biozones1,27,32,34, whereas others have argued that they represent Ediacaran rocks. Unlike the younger systems of the geologic biotopes—associations of taxa that inhabited environments with record, the Ediacaran has not yet been formally subdivided into distinct boundaries, limited spatial overlap, and few shared chronostratigraphic units (i.e., series or stages). Many strati- species1,31,33,35. These interpretations are not mutually exclusive, graphic boundaries in the Phanerozoic Eon are defined by the and the variation may additionally reflect biases in fossil first appearances of index fossils—abundantly preserved, mor- preservation22. phologically distinct, and easily recognizable species with cos- To contribute to Ediacaran biostratigraphy and to test the mopolitan distributions. However, most Ediacaran macrofossils hypothesis that macroscopic life experienced two extinctions represent soft-bodied organisms, and owing to environmental during the Ediacaran–Cambrian transition, we herein explore restrictions, taphonomic biases, and limited occurrences, their data on occurrences of body and trace fossils in Ediacaran and first appearances may vary among localities29. Consequently, lower Cambrian (Fortunian) rocks around the world (Methods). efforts at correlation have largely concentrated on radiometric The dataset also includes information on morphogroups dating, microfossils30, shelly fossils9, and carbon-isotopic excur- (see Supplementary Discussion), preservational modes (Supple- sions, such as the potentially global Shuram anomaly29. Where mentary Table 2), ichnofossil architectural designs (Supplemen- both occur in the same section, morphologically complex tary Table 3), and paleoenvironments (Supplementary Table 4). microfossils generally occur below beds containing the Ediacara We studied the data using conventional methods of paleoecolo- biota5,30, and so, cannot be used to discriminate ages within the gical analysis31, including hierarchical clustering and NMDS, as

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well as an innovative network-based methodology. Network 98 Dabis (Namibia) Nama cluster 94 Nudaus (Namibia) analysis encompasses an array of mathematical and visualization - Erniettomorphs Blueflower (Canada) methods for examining complex systems37. Such methods have Nama - Rangeomorphs 93 Floyd Church (USA) - Vendotaeniids 38 39 95 been employed to great effect in social science , biochemistry , 87 - Tubicoulous fauna Urusis (Namibia) - Biomineralized & proteomics40, mineralogy41, evolution42, ecology43, paleogeo- Raiga (Russia) agglutinated 44 36 89 Wood Canyon (USA) skeletal fossils with graphy , and paleoecology . Our work applies network analysis 95 predation marks Zaris (Namibia) 98 - Metazoans in reefs to biostratigraphy and the study of biofacies. Using network- - Burrows 99 Deep Spring (USA) - Bioturbation based methods, we integrate data on fossil occurrences with Dengying (China) metadata on facies and stratigraphy, and thereby, distinguish 100 98 Nagoryany (Moldova & Ukraine) Miaohe cluster Studenitsa (Moldova & Ukraine) between Ediacaran biotopes and biozones. Through this work, we 96 Carbonaceous fi Miaohe Doushantuo (China) compressions of provide empirically robust and statistically signi cant evidence Lyamtsa (Russia) macroscopic algae 99 97 (prokaryotes and for a global, cosmopolitan assemblage unique to terminal Edia- 100 Khatyspyt (Russia) eukaryotes) caran strata. This assemblage, which allows for the correlation of Perevalok (Russia) & possible animals “Ust’-Pinega” (Russia): taxa of uncertain position White seaSea terminal Ediacaran rocks around the world, lived between two 95 Basa (Russia) 97 cluster episodes of biotic turnover. These turnover events may represent 92 Yaryshev (Ukraine) - Dickinsoniomorphs 95 the earliest mass extinctions of complex life. Regardless, the 95 Kauriyala (India) - Bilateralomorphs White Sea Mogilev (Moldova & Ukraine) - Kimberellomorphs results corroborate the hypothesis that two extinctions helped to Rawnsley (Australia) - Tribrachiomorphs 99 95 - Triradialomorphs shape the trajectory of early animal evolution. 87 89 Chernyi Kamen (Russia) - Pentaradialomorphs Verkhovka (Russia) - Bilaterian trails 99 - Scratch marks 98 Erga (Russia) Results Zimniegory (Russia) Conventional methods 87 Bradgate (UK) Avalon cluster . Hierarchical clustering provides evi- Fermeuse (Canada) fi Avalon Frondose dence for four groups of Ediacaran formations with ve or more 100 Briscal (Canada) arboreomorphs & rangeomorphs – Drook (Canada) genera and ichnogenera (Fig. 1, Supplementary Figs. 1 4). These 85 Nadaleen (Canada) groups include the Avalon, White Sea, and Nama clusters as well 89 91 100 Trepassey (Canada) as a fourth (Miaohe) set of formations. The Lantian Formation of Mistaken Pt (Canada) China, which contains the early Ediacaran Lantian biota8, falls Lantian (China) outside of these clusters, regardless of the taxonomic similarity index used in clustering. All clusters identified with the 1.0 0.8 0.6 0.4 0.2 0.0 Kulczynski-2 index are supported by approximately unbiased Taxonomic (Kulczynski-2) dissimilarity (AU) P-values > 95% (Fig. 1), indicating that those clusters are Fig. 1 Hierarchical clustering. Dendrogram of 34 geologic formations fi strongly supported by the data. Clusters identi ed with the Jac- containing five or more macrofossil genera and/or ichnogenera of card index are also supported by AU P-values > 95% (Supple- Ediacaran age. Formations were clustered based on their taxonomic mentary Fig. 2), except the Nama cluster, which is supported at a (Kulczynski-2) dissimilarities (x-axis) using the average-linkage method. fi lower but non-negligible (90%) con dence level. The NMDS The high cophenetic correlation score of the dendrogram (0.8506276)— fi fi analyses con rm that the clusters identi ed with hierarchical the correlation between observed taxonomic dissimilarities and those clustering are taxonomically distinct (Supplementary Figs. 3 and estimated via hierarchical clustering—indicates that the dendrogram 4). Formations were ordinated, based on taxonomic similarity, in faithfully preserves the pairwise distances between the original data points. three-dimensional ordination space. A stress value, measuring the Red numbers are approximately unbiased (AU) P-values calculated for degree that ordination deviates from observed distances, was nodes from 1000 multiscale bootstrap resamples. From bottom to top calculated for each NMDS analysis. These values were generally above the Lantian Formation, the labeled Avalon (blue), White Sea (green), low (~0.10) for three-axis NMDS plots, indicating that such plots Miaohe (orange), and Nama (red) clusters have AU P-values ≥ 0.95 and ’ provide good representations of the formations rank orders in are supported by the data. Inset boxes are lists of the clusters’ dominant reduced dimensions (Supplementary Fig. 3 and Supplementary fossils. The “Ust’-Pinega” formation encompasses occurrences of taxa that Table 5). The plots show that the four clusters are distinct in belong to the Lyamtsa, Verkhovka, and Zimniegory formations but have fi ordination space, and the 95% con dence intervals around their uncertain stratigraphic positions. See Supplementary Information for centroids (mean scores) do not overlap, so the differences are Jaccard dissimilarity results (Supplementary Fig. 2). Source data are fi statistically signi cant. provided as a Source Data file

Network analysis. In general, network theory deals with the study of independent but interconnected entities37. A network consists This procedure involves extrapolating direct links between nodes of two basic elements: nodes (entities) and links (connections)37. of one set from their indirect connections across nodes of The nodes in this study are taxa (body and trace fossil genera), another. In all networks, nodes and links make up community paleoenvironments, and geologic formations, and the links among structures37. These structures consist of units (modules), which them represent connections supported by fossil occurrence data appear in network graphs as clusters of densely-connected nodes. (e.g., links between taxa and their geologic formations and facies Networks may contain multiple levels of community structure of occurrence). Network theory supports a diverse array of (e.g., clusters of communities), and the modules themselves may methods for building networked data structures and investigating represent non-overlapping (mutually exclusive) groups or over- their community units. This study includes unipartite and bipar- lapping groups that share nodes. tite networks (Table 1, Supplementary Table 1)37. A unipartite Network analysis has several advantages over conventional network includes a single set of nodes, in which any pair of nodes methodologies used in paleoecology and biostratigraphy. First, it may be connected. Each bipartite network, on the other hand, allows for the application of community-detection algorithms, includes two sets of nodes, and a node may only be connected to which can be used to partition fossil networks into community- another of a different set, so that nodes of the same set are never like modules36,44,45 (Supplementary Table 1). Second, network directly linked. This bipartite structure can be reduced to two theory accommodates an assortment of metrics for describing unipartite network-like projections, one for each set of entities. local (node specific) and global (whole network) properties and

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Table 1 List of networks in this study

Format Node type 1 Node type 2 (if present) Description of links Figure Unipartite Genera – Genera are connected to other taxa preserved Fig. 2a at their collection points Bipartite Environments Genera Taxa are connected to their habitats and Fig. 3a preservational environments Bipartite Environments Genera & ichnogenera Taxa are connected to their habitats and Supplementary preservational environments Fig. 10a Bipartite Ediacaran formations Genera & ichnogenera Taxa are connected to formations where their Fig. 4a fossils are preserved Bipartite Ediacaran formations Genera & ichnogenera Taxa are connected to formations where their Supplementary (shallow biotope) fossils are preserved Fig. 11a Bipartite Ediacaran formations Genera (shallow biotope) Taxa are connected to formations where their Supplementary fossils are preserved Fig. 12a Bipartite Ediacaran-Fortunian Genera & ichnogenera Taxa are connected to formations where their Supplementary formations fossils are preserved Fig. 13a

Each row is a network consisting of nodes and links. The nodes in this study are body macrofossil genera, trace fossil ichnogenera, paleoenvironments, and geologic formations. All valid taxa, which might serve as index fossils of ecological biotopes and geological biozones, were included in the networks (see Methods). See Supplementary Information for additional figures (Suppl. Figs.)

the variables underlying their community structures. Lastly, such as the preservational modes22 (Supplementary Table 2) and network theory supports analyses of integrated data structures morphogroups7,48 (see Supplementary Discussion and Supple- that leverage multiple types of information. Partitioning of a mentary Table 3) of the taxa and the locations of the formations. bipartite network, for example, can lead to the discovery of Finally, centrality represents a local property related to the community units based on the union of two sets of entities. relative importance of a node. A centrality score may, for Altogether, such methods indicate that network analysis can be example, equal a node’s degree (number of links) or its used to discover, identify, and characterize ecologically and betweenness, the number of shortest paths that pass through it geologically meaningful associations of Ediacaran macrofossils. (i.e., how often it serves as a bridge between other nodes). In this study, we analyze one unipartite network and six Application of community-detection algorithms to the net- bipartite networks derived from fossil occurrence data (Table 1). works resulted in the discovery of numerous modules (Figs. 2–4; All of the networks contain taxa nodes (see Methods). In the Supplementary Figs. 5–12). The community overlap propagation unipartite network, two genera are connected if they have been algorithm (COPRA) generally returned non-overlapping com- reported together at one or more fossil collection points (e.g., munity structures with the fewest modules and greatest Q scores beds, localities, sections, outcrops, etc.) anywhere in the world36. (Supplementary Figs. 5–7 and Supplementary Table 6). It also This structure represents the most basic expression of our dataset, returned overlapping modules, when procedures were formulated and its modules generally represent paleocommunities36. The to allow taxa to be assigned to multiple community units. The bipartite networks integrate occurrence data with metadata on overlapping community structures generally resemble their non- facies (rock types corresponding to specific environments) and overlapping counterparts, except where nodes are assigned to geologic units. In bipartite networks containing paleoenviron- multiple modules. Sensitivity analysis shows that the network ments, taxa are connected to their habitats and preservational partitioning results do not significantly change, even when at environments33,35 (Supplementary Table 4), and in bipartite least 10% of connections (30–100 links) and five weakly networks containing formations, taxa are connected to geologic connected (i.e., uncommon) taxa are omitted from each network units where their fossils are preserved. In this context, the (Supplementary Fig. 8). Moreover, data randomization indicates bipartite networks support investigation of Ediacaran biotopes that it is unlikely that any the community structures reported and biozones, as their modules reflect unions of taxa with in this study arose due to chance49 (Supplementary Fig. 9). Thus, environments and geologic units. All other differences among the overall, the community structures are robust. networks are related to raw data (Table 1). Application of the COPRA method to the unipartite network We applied 14 community-detection algorithms to the net- of Ediacaran genera (Fig. 2a) resulted in detection of four works to explore their community structures and identify overlapping modules (Fig. 2b). The largest and most central paleocommunities, biotopes, and biozones (see Supplementary cluster—the White Sea module—predominantly consists of Discussion). Additionally, we calculated a number of metrics: bilateralomorph, dickinsoniomorph, and kimberellomorph gen- modularity, homophily, and centrality (Supplementary Table 1). era, along with other Ediacara-type taxa (Fig. 2c, d). This cluster Modularity (Q) is a global property describing community overlaps with the Avalon and Nama modules. Whereas the structure. In its simplest form46,47, the modularity of a Avalon module primarily consists of rangeomorphs, the Nama community structure equals the fraction of links that connect cluster includes a mixture of taxa known from skeletal fossils, nodes of the same communities minus the corresponding fraction secondarily mineralized tubes, Ediacara-type fossils, and carbo- expected in an equivalent network with a random distribution naceous compressions. The remaining cluster, the Miaohe of connections. Homophily, another global property, measures module, overlaps with the Nama module but is dominated by the tendency of nodes to connect to others possessing similar filament-, strap-, and ribbon-shaped taxa typically preserved as nominal or continuous properties, and is measured with carbonaceous compressions. Assortativity coefficients indicate assortativity coefficients, which are similar to Pearson correlation the best predictor of association in this network is preservational coefficients. We calculated assortativity coefficients for the mode (Fig. 2c; Supplementary Fig. 7 and Supplementary Table 8). various network projections to assess the relative extents that The relationship between morphology (form or morphogroup) their topologies reflect the underlying properties of their nodes, and linkage is weak.

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a Namapoikia Piyuania Xiuningella Orbisiana Namacalathus Platysolenites Anhuiphyton Qianchuania Soldadotubulus Spirosolenites Redkinia Kalusina Lantianella Cambrotubulus Nama Jiangchuania Miaohe Huangshanophyton Waltheria Anabarites Sabellidites Overlap Aggregatosphaera Titanotheca Rugosilina Globusphyton Archaeichnium Siphonophycus Doushantuophyton Ningqiangella Miaohephyton Helanoichnus Cucullus Flabellophyton Cloudina Affinovendia Konglingiphyton Wenghuiia Onuphionella Longfengshania Enteromorphites Gesinella ProtoconitesAnomalophyton Fusosquamula Eoholynia Baculiphyca Wenghuiphyton Protolagena Saarina GlomulusSinospongia Sinocylindra Conotubus Kanilovia Zhongbaodaophyton Jiangkouphyton Harlaniella Calyptrina Corumbella Liulingjitaenia Vendotaenia Tyrasotaenia Jiuqunaoella Longifuniculum Gaojiashania Shaanxilithes Grypania Swartpuntia Vendoconularia Horodyskia Mezenia Ventogyrus Eoandromeda Elainabella Yarnemia Paleolina Annulatubus Protoarenicola Sinotubulites Ernietta Overlap Wutubus Atakia Podolimirus Palaeopascichnus Ausia Paracharnia Kimberella Lomosovis Triforillonia Petalostroma Yangtziramulus Miaohe Valdainia Somatohelix Pteridinium Dickinsonia Funisia 40 (0.25) Yorgia Fedomia Rangea Spriggina SolzaConomedusites Plexus Burykhia Tamga Aulozoon 3 White Sea Praecambridium Vaveliksia (0.02) Palaeoplatoda Onega Vendia Tribrachidium Nama Armillifera Parvancorina Phyllozoon 23 (0.15) Archangelia Cyanorus Lossinia Rugoconites Kelleria Orthogonium Pilitela Andiva Charnia Albumares Temnoxa Marywadea Ivovicia Paravendia Archaeaspinus Charniodiscus Arkarua Kharakhtia Platypholinia 14 Nasepia Pomoria (0.09) Anfesta Zolotytsia Namalia Vladimissa Gehlingia b Ramellina Overlap Propaleolina Beothukis White Sea Pectinifrons 56 (0.35) Fractofusus Frondophyllas Primocandelabrum Vinlandia Hadrynichorde Hadryniscala Trepassia Hapsidophyllas Avalofractus Thectardis Haootia Gladius Miettia Number of genera Dumbbell Bradgatia 5 0 1020304050607080 Avalon (0.03) Plumeropriscum Miaohe Carbonaceous c compressions Skeletal fossils Avalon Nama Ediacara-type fossils Other or unknown 18 (0.11) Skeletal Carbonaceous compressions Trace fossils White Sea Ediacara-type fossils Ediacara-type fossils & carbonaceous compressions Secondarily (authigenically & diagenetically) mineralized fossils Avalon Preservation Carbonaceous compressions & secondarily mineralized fossils Rangeomorpha & Ivesheadiomorpha Pentaradialomorpha, Tetraradialomorpha, & Triradialomorpha Miaohe d Erniettomorpha Porifera (putative) Anhuiphytomorpha Horodyskiomorpha Nama Vendotaeniomorpha Chuariomorpha, Moraniomorpha, & Tawuiomorpha Cambrian metazoa Arboreomorpha Bilateralomorpha, Dickinsoniomorpha, & Kimberellomorpha Ichnotaxa White Sea Problematica Sabelliditomorpha, Shaanxilithomorpha, & Sinosabelliditomorpha Anabaritidae, Cloudinomorpha, Namacalathomorpha, Platysolenitomorpha, & Protolagenomorpha Avalon Morphogroup Eoholyniomorpha, Glomulomorpha, Grypaniomorpha, Longfengshaniomorpha, & Mezeniomorpha

Fig. 2 Unipartite network of Ediacaran macrofossil genera. a Network graph. Two genera are linked if those taxa cooccur at any fossil collection point in the dataset. Colors indicate modules identified using the COPRA community-detection algorithm (v = 5). According to randomization testing, this community structure is statistically significant (Supplementary Fig. 9; Q = 0.82, P < 0.01, Z = 7.700). All genera fall into four modules: the Avalon (blue), White Sea (green), Miaohe (orange), and Nama (red) clusters (Fig. 1; Supplementary Figs. 2–4). b Venn diagram illustrating module overlap. Areas of circles correspond to their relative numbers of genera, numbers are counts of taxa, and values in parentheses are proportions. c, d Stacked bar graphs showing numbers of genera in the modules and their preservational modes (c) and morphogroups (d). Source data are provided as a Source Data file

The COPRA method facilitated the detection of three modules module—the intermediate biotope (intermediate with regard to in the bipartite network of paleoenvironments and Ediacaran water depth)—is comprised of White Sea, Nama, and Miaohe genera (Fig. 3a, b). Each module includes one or more taxa (Figs. 2a, 3c). These genera occur in clastic and carbonate paleoenvironments and many genera, and therefore, resembles facies representing offshore shelf and ramp paleoenvironments a biotope. The smallest module—the deep biotope—consists of located below fair-weather wave base and characterized by low- Avalon taxa (Figs. 2a, 3c) known exclusively from turbiditic facies energy conditions (Supplementary Table 4). This module also representing deep-water paleoenvironments (Supplementary includes the carbonate slope and basin paleoenvironment, which Table 4). The next smallest module—the shallow biotope— shares various taxa with the offshore shelf, offshore shelf predominately consists of White Sea and Nama genera, which transition, and outer ramp paleoenvironments. Virtually all occur in clastic and carbonate facies reflecting shallow water Miaohe genera (Fig. 2a) belong to this intermediate biotope paleoenvironments regularly influenced by wave action and tidal (Fig. 3c). All three clusters share genera with each other, but the processes (Supplementary Table 4). Conversely, the largest greatest overlap occurs between the shallow and intermediate

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a Valdainia Lomosovis Vladimissa Helanoichnus Pomoria Paravendia Kelleria Intertidal zone Shallow Kalusina Platypholinia Atakia Vaveliksia (Clastic) Elainabella Namapoikia Propaleolina Ivovicia Spicodiscus Pilitela Miettia Zolotytsia Ausia Protolagena Platysolenites Namacalathus Paracharnia Avalofractus Conotubus Reef Spirosolenites Vinlandia (Carbonate) Yangtziramulus Hadryniscala Lower shoreface Overlap Wutubus (Clastic) Ernietta Ningqiangella Dumbbell Pectinifrons Upper shoreface (Clastic) Inner ramp Primocandelabrum Namalia Harlaniella Vendia (Carbonate) Jiangchuania Trepassia Cyanorus Rangea Pteridinium NasepiaOnega Bradgatia Swartpuntia Corumbella Sinotubulites Frondophyllas Andiva Charniodiscus Middle ramp Lossinia Yorgia Gladius Offshore shelf Gaojiashania (Carbonate) (Clastic) Kimberella Slope & basin Armillifera Parvancorina Dickinsonia Thectardis Podolimirus Chambalia Hapsidophyllas (Clastic) Rugoconites Tyrasotaenia Sabellidites Aulozoon Haootia Tamga Funisia Temnoxa Conomedusites Cambrotubulus Archaeaspinus Fractofusus Archaeichnium Paleolina Cloudina Anabarites Triforillonia Albumares Eoholynia Intertidal zone Beothukis Fusosquamula (Carbonate) Plumeropriscum Charnia Palaeopascichnus Overlap Shaanxilithes Hadrynichorde Vendotaenia Yarnemia Orbisiana Deep Archangelia Tribrachidium Offshore shelf transition Kanilovia Longfengshania Anfesta (Clastic) Outer ramp Horodyskia Protoarenicola (Carbonate) Phyllozoon Overlap Soldadotubulus Eoandromeda Grypania Windermeria Favosiphycus Ventogyrus Redkinia Somatohelix Affinovendia Arkarua Mezenia Palaeoplatoda Gandvikia Burykhia Solza Praecambridium Plexus Annulatubus Paleoenvironment b Onuphionella Taxon Key Titanotheca Glomulus Waltheria Skinnera Archyfasma Fedomia Jiuqunaoella Liulingjitaenia Marywadea Vendoconularia Gehlingia Kharakhtia Longifuniculum Globusphyton Shallow Ljadlovites Huangshanophyton 44 (0.26) Sekwitubulus Sinospongia Slope & basin Chinggiskhaania Calyptrina (Carbonate) Zhongbaodaophyton Shallow & Spriggina 2 intermediate Sinocylindra Wenghuiia (0.01) 17 (0.10) Xiuningella Piyuania Deep 2 Intermediate 15 (0.09) (0.01) Subtidal lagoon Jiangkouphyton Intermediate (Carbonate) Siphonophycus 88 (0.53) Cucullus Anhuiphyton Lantianella Rugosilina Wenghuiphyton Phascolites Protoconites Doushantuophyton Gesinella Qianchuania Lanceoforma EnteromorphitesKonglingiphyton Aggregatosphaera Anomalophyton Miaohephyton Baculiphyca Flabellophyton c deZuunartsphyton Skeletal Shallow Nama Ediacara-type Carbonaceous Intermediate White Sea Miaohe ? fossils compressions Deep Paleocommunity Preservation Morphogroup Avalon

0 20406080100 0 20406080100 0 20 40 60 80 100 Number of genera Number of genera Number of genera

Fig. 3 Bipartite network of paleoenvironments and Ediacaran macrofossil genera. a Network graph. A genus and paleoenvironment are linked if fossils of the taxon have been reported from matching facies (Supplementary Table 4). Colors indicate modules identified using the COPRA community-detection algorithm (v = 2). According to randomization testing, this community structure is statistically significant (Supplementary Fig. 9, paleoenvironments projection, Q = 0.39, P = 0.05, Z = 1.46; taxa projection, Q = 0.51, P = 0.05, Z = 1.27). All paleoenvironments and genera fall into three modules—the deep (blue), shallow (red), and intermediate (green) clusters—named for the relative water depths of their centroids along a model shallow-to-deep-water transect. b Venn diagram illustrating taxonomic overlap of modules. Areas of circles correspond to their relative numbers of genera, numbers are counts of taxa, and values in parentheses are proportions. c Stacked bar graph showing numbers of genera belonging to the various Ediacaran paleocommunities in each module (colors are those used in Fig. 2a, b). d, e Stacked bar graphs showing numbers of genera in the modules and their preservational modes (d) and morphogroups (e). See Fig. 2 for color keys and Supplementary Fig. 10 for related analyses. Source data are provided as a Source Data file biotopes. Assortativity coefficients show that none of the nominal not significantly alter its community structure or the module properties of the genera (e.g., preservational mode or mor- assignments of taxa (Supplementary Fig. 10). phogroup) represent strong predictors of association (Supple- Partitioning of the bipartite network of Ediacaran formations and mentary Fig. 7). Addition of ichnogenera to this network does taxa (genera and ichnogenera) with COPRA again resulted in the

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a Tas−Yuryakh Membrillar Olistostrome Reed Dolomite Loma Negra Turkut Anabarites Overlap Byng Birba Stirling Quartzite Ediacaran/Cambrian Cambrotubulus Ust'−Yudoma KotodzhaTagatiya Guazu Soldadotubulus 2 (0.01) Oppokun Namapoikia Waltheria Archaeichnium Spirosolenites Syhargalakh Yerbal Titanotheca Ediacaran/Cambrian Onuphionella Platysolenites Elainabella Terminal La Cienega Corumbella Ediacaran Namacalathus Cloudina b 32 (0.17) Terminal Ediacaran biozone Formation (‘Nama assemblage’) Multina Conichnus Tamengo Key Zaris Breivik Taxon Raiga overlap Brzegi Shale Sete Lagoas Ingta Wood Canyon Deep Spring Sinotubulites 27 (0.15) Kanilov Okunets Elkera Wyman Deschilling Conotubus Wutubus Protolagena Khmel'nitsk Savinan Oichnus Ganabos Puerto Blanco Ningqiangella Paracharnia Ernietta Jiangchuania Circulichnis Treptichnus Urusis Gaojiashania Yangtziramulus Hoedberg Cid Tyrasotaenia Helminthoidichnites Sabellidites Ediacara biota Fowlers Gap Stelkuz Sokolets 114 (0.62) Arondegas Chapel Island Stappogiedde Feropont'evskPetalostroma Serra dos Lanceiros Aloformation Palaeophycus Nasepia Kaushan Kotlin Torrowangea Cijara Ausia Floyd Church Dengying Maneting Platonovskaya Pusa ShaleOrthogonium Estenilla Swartpuntia Vendotaenia Helminthopsis Dabis Gordia Overlap Windermeria Nudaus Lungkarta Fusosquamula Sekwitubulus Spicodiscus Kalusina Kurtun overlap Studenitsa Aim Danilov Nagoryany Shaliyan Blueflower Harlaniella Zharnovka 3 (0.02) Jodhpur Rugoconites Paleolina PteridiniumRangea Guaicurus Taozichong Basa Shaanxilithes Lantian Somatohelix 6 (0.03) Gehlingia Krushanov Zhoujieshan Marywadea Archaeonassa Longfengshania Funisia Phyllozoon Annulatubus Pilitela Plexus Rawnsley Quartzite Epibaion Namalia Kanilovia Arkarua Aulozoon Jarashi Archaeaspinus Eoholynia Glomulus Helanoichnus Praecambridium Tamga Zimniegory Paravendia Kimberichnus Yorgia Andiva Ventogyrus Lossinia Chernyi Kamen Zhengmuguan Moshakov Vendoconularia Verkhovka Kimberella Erga Pomoria Ediacara biota biozone Armillifera Vendia (Avalon, White Sea, & Miaohe) Fedomia Bergaueria Onega Palaeopascichnus Khatyspyt Archangelia Spriggina CyanorusDickinsonia Eoandromeda Affinovendia Palaeoplatoda Charniodiscus Nenoxites Vladimissa Burykhia Parvancorina Enteromorphites Kharakhtia Tribrachidium Temnoxa Ivovicia Solza Zigan Vaveliksia Calyptrina Liulingjitaenia Wenghuiphyton Sirbu Shale Albumares Conomedusites Jiuqunaoella Sinocylindra Yarnemia Contra Fogo Horodyskia Doushantuo Podolimirus Mogilev Lyamtsa LiuchapoLongifuniculum Konglingiphyton Jiangkouphyton Lanceoforma Campo Alegre Kauriyala Mezenia Arumbera Nadaleen Wonoka Wenghuiia Platypholinia PerevalokSinospongia Rugosilina Phascolites Beothukis Anfesta Yaryshev Gesinella Baculiphyca Miaohephyton Charnia Grypania Flabellophyton Anomalophyton Globusphyton Doushantuophyton Cucullus Pectinifrons Drook Fermeuse "Ust'−Pinega" Protoconites Aggregatosphaera Atakia Orbisiana Lakheri Overlap Dumbbell Bradgate Primocandelabrum Valdainia Archyfasma Zhongbaodaophyton Gladius Trepassey Lomosovis Favosiphycus Siphonophycus Mistaken Point Fractofusus Kelleria Gandvikia Chambalia Thectardis Briscal Zolotytsia Protoarenicola Huangshanophyton Hadrynichorde Saarina Lantian Piyuania Trepassia Ramellina Redkinia Propaleolina Bradgatia Vinlandia Ljadlovites Qianchuania Plumeropriscum Lantian Lantianella Hadryniscala Haootia Estfordi Stauridinia Hapsidophyllas Avalofractus Anhuiphyton Triforillonia c de Frondophyllas Xiuningella Ediacaran/ Cambrian Terminal Paleocommunity Preservation Biotope Ediacaran Nama Skeletal fossils Shallow Ediacara Carbonaceous biota White SeaMiaohe ? compressions Ediacara-type fossils Intermediate

Lantian Avalon Trace fossils Deep

0 20 40 60 80 100 120 0 20 40 60 80 100 120 0 20 40 60 80 100 120 Number of taxa (genera & ichnogenera) Number of taxa (genera & ichnogenera) Number of taxa (genera & ichnogenera)

Fig. 4 Bipartite network of Ediacaran formations and macrofossil taxa. a Network graph. A taxon (genus or ichnogenus) and geologic formation are linked if fossils of the taxon have been reported from that geologic unit. Colors indicate modules identified using the COPRA community-detection algorithm (v = 6). According to randomization testing, this community structure is statistically significant (Supplementary Fig. 9; formations projection, Q = 0.63, P < 0.01, Z = 12.17; taxa projection, Q = 0.51, P < 0.01, Z = 6.57). All formations and taxa fall into four modules: the Lantian biota (teal), Ediacara biota biozone (blue), Terminal Ediacaran biozone (red), and Ediacaran/Cambrian taxa (green) clusters. b Venn diagram illustrating taxonomic overlap of modules. Areas of circles correspond to their relative numbers of genera and ichnogenera, numbers are counts of taxa, and values in parentheses are proportions. c Stacked bar graph showing the numbers of taxa belonging to the various Ediacaran macrofossil paleocommunities (colors are those used in Fig. 2a, b) and representing traces in the modules. d Stacked bar graph showing numbers of taxa in the modules and their preservational modes (see Fig. 2 for color key). e Stacked bar graph showing numbers of taxa in the modules and their biotope assignments (colors are those used in Fig. 3a, b). See Supplementary Figs. 11 and 12 for related analyses. Source data are provided as a Source Data file discovery of four overlapping modules (Fig. 4a, b). These modules Collectively, the taxa of the EBB represent all biotopes, with the resemble assemblage-based biozones (Fig. 5), in that they are majority belonging to the module of intermediate water depth defined by various taxa and strata (see Supplementary Discussion). (Figs. 3a, 4e; Supplementary Fig. 10a). In contrast, the second Two modules contain the majority of nodes (Fig. 4b) as well as all largest module—the Terminal Ediacaran biozone (TEB)—consists nodes with high centrality scores (Fig. 6). The largest module—the of formations dominated by Nama genera (Fig. 4c), including Ediacara biota biozone (EBB)—is comprised of formations contain- skeletal taxa and forms known from Ediacara-type fossils and ing Avalon, White Sea, and Miaohe genera (Fig. 4c) known from carbonaceous compressions (Fig. 4d). These fossils are preserved in Ediacara-type fossils and carbonaceous compressions (Fig. 4d). both nearshore and offshore settings (Figs. 3a, 4e; Supplementary

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Yukon) Australia A (California)A (Nevada) United KingdomCanada (Newfoundland)South Russia (WhiteRussia Sea) (CentralRussia Urals) (SouthUkraine Urals) &Russia Moldova (OlenekSouth ChinaUplift)Canada (USA (Carolina)US US Brazil Namibia Oman a b Athel

540 1 Nomtsas ? ? ? 1 5

Ediacaran/Cambrian Kessyusa 8 3 2 13

Studenitsa Floyd Church Syhargalakh Wood Canyon Wood Urusis boundary Zolotitsa Zigan 7 Risky Krushanov 7 5 3 2 545 1 1 Nudaus

Kukkarauk Zharnov Birba

3 Cid 14 Zaris 2

7 Reed Deep Spring Danilov2 1 Stirling Dengying 28 Basa Tamengo 5

Nagoryany Turkut 3 5 Gibbett Erga 21 1 Dabis 550 Uryuk Blueflower Ingta Buah Hill Ediacara30 ‘upper’ 14

Zimniegory Wyman 18 10 Tillery Rawnsley Quartzite 13 Shuram Johnnie Bocaina Guaicurus Renews 5 Chernyi Yaryshev 555 Chace Rocks Head Kamen Hanging Verkhovka Mogilev 11 Terminal Ediacaran biota (Nama) Active predation 11 36 Bradgate 7 macrofossil biozone Biomineralization 8 Gametrail Khatyspyt 560 Fermeuse Bonney Formations of uncertain age 1 Bioturbation Trepassey Ediacara biota (Avalon, White Sea, & Miaohe) 6 13 Ust’-Pinega macrofossil biozone

Lyamtsa 29

14 Perevalok Geologic age (Ma)

565 Bilaterian traces Mistaken Pt Wonoka 11

Beacon Hill Briscal Key 6 Number of index fossil taxa 6 Staropechny 2 570 (taxa used in analyses) Doushantuo Bunyeroo Radiometric age & its Locomotion

Reservoir associated uncertainty 575 Blackbrook Drook Nadaleen (’June Beds’) Formations bracketed by Common Ives radiometric ages Head Key

Formations with limited Epifaunal tiering 580 age constraints Gaskiers Rare Formations containing Mall Bay Shuram anomaly Metazoan heterotrophy Very rare or absent 585

Fig. 5 Ediacaran stratigraphy and chronology of evolutionary events. a Stratigraphic distribution of Ediacaran biozones identified with network analysis. White formations contain no potential index fossils. b Ediacaran histories3 of epifaunal tiering17, metazoan heterotrophy14, locomotion14,69, bilaterian traces12,55, bioturbation18,19,70, biomineralization9,10, and predation13,20. Source data are provided as a Source Data file

Fig. 10a), and taxa of the deep biotope are notably missing. diversity can be rejected—the observed difference in diversity Together, the TEB and EBB modules dwarf the small remaining does not reflect unequal sampling of fossil collection points clusters (Fig. 4b). One of these small modules contains the Lantian (Fig. 7a) or geologic formations (Fig. 7b). Extrapolations of genus Formation and the genera of the Lantian Biota8. This module shares richness suggest that continued sampling may result in the dis- several genera (i.e., Doushantuophyton, Flabellophyton,andOrbisi- covery of additional rare taxa. Nonetheless, the projections pre- ana)withtheEBB(Fig.4a). The smallest module, the Ediacaran/ dict that, when the modules’ sampling levels become high and Cambrian cluster, overlaps with the TEB, and is comprised of their taxa accumulation rates are low, the EBB will represent a various Siberian formations, where Cambrotubulus and Anabarites total diversity two to three times greater than the TEB (Fig. 7a). (Cambrian faunal elements) are preserved in rocks of putative Estimates of actual genus richness, based on non-parametric Ediacaran age10,50,51, in some places with Cloudina10,51.Assorta- (sample-based) richness estimators52, also suggest that the EBB is tivity coefficients indicate that neither the geographic locations of two to three times more diverse than the TEB (Supplementary the formations nor the preservational modes or morphogroups of Fig. 14). Analysis of an expanded dataset—one that includes the taxa represent strong predictors of association (Supplementary occurrences of simple disc-shaped genera and taxa that may be Fig. 7). based on taphomorphs, pseudofossils, or microbial structures— Units resembling the EBB, TEB, and Ediacaran/Cambrian produced identical results (Fig. 7c, d; Supplementary Fig. 15). modules were detected in bipartite networks of Ediacaran The EBB and TEB modules also differ with respect to the formations and taxa, even after genera belonging exclusively to taxonomic diversity levels of their White Sea and Nama genera the intermediate and deep modules (Fig. 3a; Supplementary (Fig. 2a). Rarefaction curves demonstrate that, at the present Fig. 10) were removed from the data (Supplementary Fig. 11). sampling level, the null hypothesis that the two modules are equal The results did not significantly change if ichnogenera were with respect to diversity of these taxa can be rejected (Fig. 7e, f). excluded from the data along with the deep-water genera Estimates of actual genus richness suggest that this pattern should (Supplementary Fig. 12). In all cases, White Sea genera are hold up with continued sampling of fossils, predicting that the clustered together in the EBB, and Nama taxa are concentrated in EBB will yield twice as many White Sea and Nama genera as the the TEB (Supplementary Figs. 11 and 12). TEB (Supplementary Fig. 16). With regard to Ediacara-type genera, the analyses also indicate that the EBB is significantly more diverse than the TEB (Fig. 7g, h). Indeed, the EBB may Genus richness. Sample-based rarefaction and extrapolation ultimately produce three to four times more Ediacara-type genera curves (Fig. 7a, b), which provide estimates of taxonomic diversity than the TEB (Supplementary Fig. 17). vs. sampling intensity, show that the TEB contains significantly Analyses of Fortunian data (Supplementary Fig. 13) highlight fewer genera than the EBB, regardless of sample size (Fig. 4). the difference in diversity observed between the EBB and TEB. Unconditional 95% confidence intervals bracketing the curves The EBB and Fortunian are approximately equal with respect to illustrate that, at the present sampling level, the null hypothesis genus richness (Fig. 7a, b; Supplementary Fig. 14), unless the that the two modules are equal with respect to taxonomic estimates include disc-shaped genera and taxa that may be based

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10,000

Cloudina Charniodiscus Palaeopascichnus Helminthoidichnites Charnia 1000 Orbisiana Vendotaenia Kanilovia Pteridinium Calyptrina Rangea Longfengshania Eoandromeda Swartpuntia Tribrachidium Grypania Glomulus Kimberella Eoholynia Dickinsonia Beothukis Parvancorina Anabarites Flabellophyton Spriggina DoushantuophytonGlobusphyton Bergaueria Shaanxilithes Jiuqunaoella Palaeophycus Ediacaran/Cambrian taxa Gaojiashania Longifuniculum Nenoxites 100 Tyrasotaenia Conomedusites Archaeonassa Ernietta Sinospongia Terminal Ediacaran biozone Vaveliksia Yorgia Conotubus Liulingjitaenia Helminthopsis Pilitela Terminal Ediacaran & Namacalathus Torrowangea Ediacara biota biozones Namalia Platypholinia Anfesta Kimberichnus Zolotytsia Annulatubus Gordia Ediacara biota biozone Sabellidites Podolimirus Archaeaspinus Mezenia Gesinella EpibaionVendia Armillifera Ediacara biota biozone Primocandelabrum Temnoxa Andiva BradgatiaNasepia Rugoconites Onega Lossinia (also in Lantian Formation) Atakia Hadrynichorde Cyanorus Treptichnus Fractofusus Paleolina Lantian biota Wutubus Color key 10 Sinotubulites

Betweeness centrality (no. of shortest paths through taxon) (no. centrality Betweeness Harlaniella Fusosquamula Pectinifrons Horodyskia Haootia Thectardis Vinlandia ParavendiaTamga 51015 20 1 Platysolenites Corumbella Size key (number of formations) 0 0 10 20 30 40 50 60 70 80 90 100 110 120 Degree centrality (no. of direct links/edges to other taxa)

Fig. 6 Bubble plot showing network centrality scores for stratigraphic index fossils. Each bubble represents an Ediacaran macrofossil genus or ichnogenus in the bipartite network of Ediacaran formations and taxa (Fig. 4). Their colors correspond to biozones in that network (Fig. 4a, b). The diameter of each bubble equals the number of formations that contain fossils of the taxon. The x- and y-axes are node centrality scores. A node’s centrality score indicates its relative importance within a network. These scores were calculated from the taxa projection of the bipartite network based on two different definitions of centrality. The x-axis is degree centrality (or simply degree), which is the number of unique links between the taxon and any other. The scores in this plot do not reflect self-loops (i.e., connections between nodes and themselves). Whereas taxa with high degree centrality cooccur with a multitude of others, those with low scores cooccur with relatively few. The y-axis is betweenness centrality, which equals the number of shortest paths that pass through the node in its network. Whereas taxa with low betweenness centrality scores tend to be located at the beginning and end of paths, those with high scores commonly act as bridges for the flow of information. With regard to this plot, the best index fossils are red or blue (i.e., closely associated with one biozone) and have large bubbles and high centrality scores because they are geologically widespread and relatively common. Notable Terminal Ediacaran index fossils have black borders. Source data are provided as a Source Data file on taphomorphs, pseudofossils, or microbial structures, in which radiation, when this fauna, in turn, disappeared and skeletal case, the EBB overshadows the Fortunian (Fig. 7c, d; Supple- animals began their protracted diversification. Three non- mentary Fig. 15). Regardless, the analyses show that the exclusive1 hypotheses have been proposed to explain the dis- Fortunian is characterized by greater diversity than the TEB, appearance of the Ediacara Biota2, including (1) a competitive even when differences in sampling intensity are taken into biotic replacement model wherein eumetazoans out-competed consideration and the data include all body fossils (Fig. 7a–d; and marginalized Ediacara-type organisms;6,23 (2) a catastrophic, Supplementary Figs. 14 and 15). abiotically driven event;24 and (3) a taphonomic artefact brought about by the loss of environments conducive to Ediacara-type preservation21. In any case, attempts to test these hypotheses by Discussion measuring global changes in Ediacaran biodiversity have been Various authors have argued that Ediacaran fossils might docu- hampered by issues in biostratigraphy. Some studies have treated ment two major episodes of extinction and radiation, including the Ediacaran paleocommunities (Figs. 1, 2) as fossil assemblages ecological reorganization in terminal Ediacaran times and a sec- found in biozones of different ages1,34. However, the clusters may ond event coincident with the Ediacaran–Cambrian alternatively represent ecological-environmental biotopes and/or transition1,2,6,23–25,27,28. According to this hypothesis, Ediacara- associations of fossils with shared modes of preservation31,33,35. type organisms declined in middle-late Ediacaran times2, leaving Most efforts to address these concerns have relied on qualitative behind a depauperate Ediacara biota23 and ushering in a fauna studies of one or several exemplary regions33–35 rather than with recognizable sessile eumetazoans (a ‘Wormworld’ fauna)6 statistical analyses of global data. A few quantitative tests that dominated open seas until the onset of the Cambrian have been conducted31,32, but they have generally focused on

NATURE COMMUNICATIONS | (2019) 10:911 | https://doi.org/10.1038/s41467-019-08837-3 | www.nature.com/naturecommunications 9 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-08837-3 abEdiacara-type fossil localities and measurements of their taxo- 250 250 nomic similarity1. Until now, there has been little integration of Ediacara biota Ediacara biota 200 biozone 200 fossil occurrence data with metadata on stratigraphy and facies. n = 169 biozone n = 56 Our network analysis achieves such data integration by Fortunian 150 Fortunian 150 clustering Ediacaran taxa with paleoenvironments and geologic stage stage packages, thereby identifying associations with meaningful eco- 100 n = 297 100 Expected n = 43 logical and stratigraphic boundaries. In this context, our work diversity CI Terminal 50 Terminal ediacaran 50 Ediacaran allows for the formulation of a global biostratigraphic framework, n = 178 biozone n = 48 biozone grounded by empirical data and quantitative analysis, for testing 0 0 0 250 500 750 1000 0 50 100 150 200 the various hypotheses concerning biotic turnover across the Number of collection points Number of geologic formations Ediacaran–Cambrian transition. Overall, our networks are based on fossil occurrence data cd500 500 (Table 1). Each link signifies one or more actual cases of specimen sampling and reporting. Consequently, the networks’ topo- 400 400 Ediacara biota fl biozone graphies re ect patterns related to geologic age, preservational Ediacara biota pathway, and stratigraphic and geographic location, as all of these 300 n = 402 biozone 300 n = 56 Fortunian variables influence the likelihood of fossil occurrence36. The n = 173 Fortunian stage 200 stage 200 Ediacaran fossil occurrence dataset in this study is larger than its predecessors (Supplementary Fig. 1)1,22,31,32 and includes com- 100 Terminal 100 Terminal Ediacaran n = 49 Ediacaran paratively detailed data on occurrences of carbonaceous com- n = 212 biozone n = 44 biozone 0 0 pressions and skeletal fossils, in addition to the Ediacara-type 0 250 500 750 1000 0 50 100 150 200 casts and molds that have generally received most attention. Number of collection points Number of geologic formations Moreover, the data include trace fossils53,54, which were omitted 1,22,31,32 ef150 150 from the previous investigations , even though they occur with Ediacaran body fossils around the world3,12,14,18,55. Ediacara biota Ediacara biota 120 biozone 120 biozone We explored the networks for community units by applying n = 207 n = 47 fourteen algorithms to the data and comparing their results 90 90 (Supplementary Fig. 5). The COPRA method performed better fi 60 60 than other algorithms. It consistently identi ed the non- Terminal overlapping community structures with the highest Q values Terminal Ediacaran Number of genera 30 n = 172 Ediacaran 30 n = 48 biozone (Supplementary Fig. 5 and Supplementary Table 6), affirming biozone

(White Sea/Nama, all modes) Number of genera Number of index genera that it performed best at identifying nonrandom associations of 0 0 0 250 500 750 1000 0 50 100 150 200 nodes. It also typically returned the fewest communities, making Number of collection points Number of geologic formations it one of the most conservative approaches for partitioning the dataset. For this reason, we can interpret the modules as macro- gh 125 125 level community units, representing the largest and most sig- fi Ediacara biota Ediacara biota ni cant associations of nodes. Although the density of connec- 100 biozone 100 biozone tions within each module is high, the associations are separated 36 75 75 n = 30 by relatively sparse regions of connections that can be inter- preted as consequences of biotic turnover of Ediacaran taxa n = 150 Terminal 50 Terminal 50 n = 54 Ediacaran across space and time. For all practical purposes, the unipartite Ediacaran biozone network modules represent paleocommunities, (i.e., associations Number of genera biozone n = 15 25 25 of taxa that lived and were preserved together at various localities around the world). Along these same lines, the modules con- (White Sea/Nama, Ediacara-type) 0 0 0 250 500 750 1000 0 25 50 75 100 sisting of paleoenvironments and taxa constitute biotopes— Number of collection points Number of geologic formations environments with unique communities of taxa and specific Fig. 7 Sample-based rarefaction and extrapolation of biozone diversity. ranges of substrates, hydrodynamic conditions, and light avail- The sample-based rarefaction and extrapolation curves show generic ability. Finally, the modules consisting of formations and taxa richness vs. sampling intensity for various subsets of the data (see signify assemblage biozones, given that they consist of lithologic Methods). For a given sampling intensity level and biozone, diversity is packages that can be correlated based on associations of taxa. the number of macrofossil body genera expected when that number of Altogether, the results provide an empirical framework for samples is drawn at random without replacement from the biozone. The exploring how Ediacaran communities were distributed across mean values are bracketed by 95% confidence interval (CI) envelopes68 space and time. Analysis of the unipartite network (Fig. 2a, b) (see Methods). Dots indicate current sampling levels, i.e., the total corroborates the results of hierarchical clustering and NMDS numbers of samples in the dataset. a, b Data include only stratigraphic (Fig. 1; Supplementary Figs. 2–4), as well as the findings of other index fossil genera (taxa in Fig. 4; Supplementary Fig. 13). c, d Data studies31,32, which show that Ediacaran localities and formations include all genera, including discs, possible synonyms, and putative can be divided among four clusters based on taxonomic simi- taphomorphs and microbial structures. e, f Data include only White Sea larity. To a degree, the relative ages of these paleocommunities are and Nama genera (Fig. 2a). These paleocommunities occupied similar unknown, as their sequential appearance through stratigraphy shelf environments (Fig. 3c), unlike the Miaohe and Avalon clusters. generally varies from region to region (Supplementary Fig. 4). g, h Data include only White Sea and Nama genera known from Ediacara- Notably, the multipartite networks do not contain modules that type fossils. a, c, e, g Samples are body fossil collection points. are analogous to these paleocommunities. Assortativity coeffi- Collection points were assigned to biozones based on the community cients indicate that the topologies of the multipartite networks do assignments of their formations (Fig. 4; Supplementary Fig. 13). not strongly reflect the preservational modes and/or mor- b, d, f, h Samples are geologic formations containing body fossils. phogroups of the taxa or the geographic locations of the geologic Source data are provided as a Source Data file units. Therefore, the bipartite modules do not represent artifacts

10 NATURE COMMUNICATIONS | (2019) 10:911 | https://doi.org/10.1038/s41467-019-08837-3 | www.nature.com/naturecommunications NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-08837-3 ARTICLE of taphonomy or paleobiogeography, and instead most likely exact age of the Lantian biota is unclear8, most correlations sug- represent variation in taxa across facies and stratigraphy. gest that it pre-dates all other Ediacaran macrofossils29. Thus, the Consistent with previous interpretations31, network analysis network exhibits a notable polarity in its structure. Such polarity shows that the paleocommunities inhabited environments of develops when the stratigraphic position (geologic age) exerts a varying depth. The Avalon paleocommunity represents a deep- strong control on network topology36,44,45. In this context, we water (slope and basin) biotope31 that shared only a few taxa with interpret the EBB and TEB modules as assemblage biozones. In shelf environments (Figs. 2, 3a–c). Shelf environments were char- general, the TEB assemblage corresponds to the Nama paleo- acterized by two biotopes that shared many genera (Fig. 3b), with community, and the Avalon, White Sea, and Miaohe paleo- the boundary between them located around wave base. The White communities together constitute the EBB assemblage (Fig. 4a–c). Sea and Nama paleocommunities (Fig. 2a) occurred in both bio- The TEB includes numerous formations located just below the topes, and occupied similar habitats across shelf settings (Fig. 3c), Ediacaran–Cambrian boundary and various units that span the particularly in shoreface and offshore transition environments transition (Fig. 5). Where absolute age constraints are available where their fossils were commonly preserved (Fig. 3a)31,33–35,56.In (Source Data), TEB strata are younger than those of the EEB contrast, the Miaohe paleocommunity primarily lived and was (Fig. 5). The EBB demonstrably occurs below the TEB in South preserved in offshore and slope environments (Fig. 3c)5,57. Because China, Siberia (Olenek Uplift), and Canada (Yukon & Northwest few Avalon and Miaohe genera belonged to multiple biotopes, we Territories) and below formations of mixed (EBB and TEB) interpret those associations as environmentally, ecologically, and character in Moldova and Ukraine. Additionally, formations of taphonomically restricted paleocommunities. In conjunction with mixed character occur below the TEB in Namibia and the eastern the bipartite network of Ediacaran formations and taxa (Fig. 4a), the US. Radiometric and other absolute ages suggest that the age of results indicate that the Avalon and Miaohe paleocommunities this boundary is approximately 550 Ma (Fig. 5, Source Data). overlapped in age with the White Sea cluster. Centrality scores provide a means of comparing potential index The least ancient (Ediacaran/Cambrian) and most ancient fossils with respect to importance in Ediacaran stratigraphy (Lantian) taxa cluster on opposite sides of the bipartite network of (Fig. 6, Table 2). Centrality scores indicate that, in addition to the Ediacaran formations and taxa (Fig. 4a). Anabarites and Cam- possible coenocytic protist Palaeopascichnus11, the best- brotubulus are common in the Cambrian10,51, and although the connected taxa of the EBB are Ediacara-type fossil genera:

Table 2 Ediacaran stage-level biozones and their diagnostic taxa

Biozone Carbonaceous Ediacara-type Trace fossils Multiple or other compressions fossils types of fossils Terminal Ediacaran biozone (551 Ma – 541 Ma) Sabellidites Ernietta Helminthoidichnites Conotubus Tyrasotaenia Wutubus Palaeophycus Gaojiashania Torrowangea Cloudina Namacalathus Sinotubulites Ediacara biota biozone (571–551 Ma) Calyptrina Andiva Bergaueria Charnia Globusphyton Archaeaspinus Epibaion Charniodiscus Jiuqunaoella Armillifera Kimberichnus Gesinella Mezenia Atakia Nenoxites Liulingjitaenia Sinospongia Beothukis Longifuniculum Bradgatia Palaeopascichnus Conomedusites Cyanorus Dickinsonia Fractofusus Hadrynichorde Kimberella Lossinia Onega Parvancorina Platypholinia Podolimirus Primocandelabrum Spriggina Temnoxa Tribrachidium Vaveliksia Vendia Yorgia Zolotytsia Taxa of uncertain age and/or found in Eoholynia Namalia Archaeonassa Shaanxilithes both biozones Glomulus Nasepia Gordia Longfengshania Pteridinium Helminthopsis Paleolina Rangea Pilitela Rugoconites Vendotaenia Swartpuntia

Taxa are listed alphabetically by preservational mode. The table includes taxa that have high degree and betweenness centrality scores (both greater than 10) in the bipartite network of Ediacaran formations and taxa (Figs. 4, 6). Source data are provided as a Source Data file.

NATURE COMMUNICATIONS | (2019) 10:911 | https://doi.org/10.1038/s41467-019-08837-3 | www.nature.com/naturecommunications 11 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-08837-3

Charniodiscus, Charnia, Tribrachidium, Kimberella, Dickinsonia, Methods and Parvancorina (Fig. 6). None of the most important elements Data. Data on the formations, facies, architectural designs, and nomenclature of of the EBB are ichnogenera, and in most cases, the trace fossils of trace fossils in the Ediacaran and Cambrian systems were compiled from pre- viously published datasets53,54, and data on collections, formations, and taxonomy this biozone (e.g., Kimberichnus and Epibaion) were produced by of Cambrian (Fortunian) body fossils were accessed from the Paleobiology Data- Ediacara-type organisms14,58. In contrast, the most common and base (PBDB) on February 13, 2018 (https://paleobiodb.org/#/). The original dataset best-connected taxa in the TEB are biomineralized and non- on occurrences of Ediacaran macroscopic body fossils (n = 1829) was developed biomineralized tubes (i.e., Cloudina, Conotobus, and Gaojia- through revision of previously published datasets1,22,31 and incorporation of additional information from primary and secondary literature sources following shania) and metazoan trace fossils (i.e., Helminthoidichnites, search protocols for collection-level sampling set forth by the PBDB (https:// Palaeophycus, and Torrowangea), not Ediacara-type fossils paleobiodb.org/data1.1/), wherein a collection represents a set of fossil occurrences (Table 2). Not surprisingly, Cloudina represents the best index co-located at a unique point in geographic and stratigraphic space (Source Data fossil of terminal Ediacaran strata9,29. Our results show that other and references therein). We made every effort to exhaust all means of growing the dataset prior to conducting network analysis, and we made no attempts to revise taxa, which have received attention as potential index fossils of taxonomic work, alter designations of taxa in reports, or assign names to any 34,59–61 those strata (e.g., Shaanxilithes and Vendotaenia) , do not material of uncertain affinities (i.e., Swartpuntia-like fossils and fossils reminiscent by themselves represent good markers. of Swartpuntia were not entered as Swartpuntia). Occurrences were assigned to The network-based biozones illustrate that terminal Ediacaran 634 stratigraphically and geographically distinct fossil collection points. The dataset includes the geographic location (lat/long), lithostratigraphic unit, country, region formations do contain a depauperate Ediacara Biota and unique (continent), tectonic plate (geoplate in GPlates), and preservational mode of each fauna, represented by skeletons, tubes, and locomotion and point along with nearby radiometric ages (Source Data). The paleogeographic feeding traces of bilaterians, in addition to Ediacara-type fossils locations of the points at 550 Ma were estimated, based on their present coordi- (Figs. 4–6). Everything being equal, these formations contain nates, using the global plate motion model for the Phanerozoic produced by 62 significantly fewer genera than those of the older EBB and the Wright et al. in GPlates. This occurrence data is accompanied by a compre- hensive list of Ediacaran and Cambrian macroscopic body fossil genera (n = 416) younger Fortunian stage (Fig. 7). Thus, our results provide sta- and ichnogenera (n = 60). Each taxon was assigned to one of eight taphonomic tistically significant evidence of a global, cosmopolitan assem- modes (Supplementary Table 2)—recurrent styles of fossil preservation with blage unique to terminal Ediacaran strata. This outcome unique defining features—and any number of seventeen paleoenvironments supports the hypothesis that, in late but not latest Ediacaran (Supplementary Table 4) based on descriptions of fossil facies and preservation in – 6,27,28 the literature and PBDB. Additionally, each Ediacaran genus was assigned to one times (ca. 550 541 Ma) , ecological change involved the of 33 morphogroups7,48,54 (see Supplementary Discussion), each ichnogenus was initial decline (but not total extinction) of Ediacara-type organ- assigned to one of 24 trace architectural groups63 (Supplementary Table 3), and the isms and the rise of sessile eumetazoans that may have competed phyla of the Cambrian animals were compiled from the PBDB. These various with them for space and food6 (Fig. 5b). The TEB formations groups, in turn, were lumped into more inclusive form and trace categories (n = 22) that are more convenient for visualizing data and provide a secondary basis for also contain traces of various metazoan behaviors, including comparing assortativity coefficients. sediment-mixing18,19 and macroscopic predation13,20. Such behaviors by motile animals may have altered sediment sub- 31 strates, thereby marginalizing Ediacara-type organisms and Taxa counted in analyses. Like other studies , every effort was made to include 1 all valid taxa that might serve as index fossils of ecological biotopes and geological reducing their preservational potential . Regardless, the sessile biozones in the analyses. Each valid taxon fulfilled the following four criteria. First, eumetazoans specific to the TEB did not persist into the Cam- the taxon represents a body or behavior of an ancient organism, rather than a brian, so network analysis also provides evidence for a pulse of microbially induced sedimentary structure (MISS) or any sort of pseudofossil. extinction at the Ediacaran–Cambrian boundary. It remains Second, the taxon is morphologically or architecturally distinct from all others and, in all likelihood, does not represent a junior synonym of another taxon. Tapho- contentious whether, at that transition, emerging cnidarians and morphs, or fossils that have their own taxonomic designations but may represent bilaterians drove the final ecological collapse of the Ediacara morphological variants of other taxa64, were excluded from the data. Third, the biota, or unlike the latter, they simply survived an environmental taxon has been reported from collection points and formations containing other perturbation24. taxa and, therefore, could be linked to others by network connections. Lastly, the taxon can be recognized and identified with a reasonable amount of confidence. In summary, we demonstrate the application of network ana- Simple disc-shaped taxa were excluded from the network data. Fossils of these taxa lysis to biostratigraphy and the study of biofacies, and highlight likely represent a mix of discoidal bodies (e.g., medusae), microbial structures and its potential for addressing inter-related paleoecological and MISSs, and holdfasts of fronds that were not preserved in place. Additionally, a stratigraphic problems36. Using network analysis, we show that number of the taxa (e.g., Ediacaria, Cyclomedusa, and Tirasiana) may be junior synonyms of Aspidella65, which itself may be the form taxon of a frond holdfast66. the Ediacaran System contains a number of environmental- In any case, most disc-shaped fossils cannot be reliably ascribed to specific taxa. We ecological biotopes and assemblage biozones. In particular, included data on trace fossils in some of the networks because, unlike genera, our work supports a global framework, grounded by empirical ichnogenera generally do not disappear over time, and most trace architectures that data and quantitative analysis, for subdividing the upper originated in the Ediacaran persisted into the Phanerozoic. Therefore, trace fossils bridge the Ediacaran–Cambrian transition and offer information about the evo- Ediacaran System and correlating its terminal strata. Notably, lution of behaviors (mobility and predation) to accompany the record of mor- this framework provides a basis for testing hypotheses con- phological change. cerning the evolutionary and ecological changes that led to the Ediacaran–Cambrian transition. Although some variation among fl Hierarchical clustering. Hierarchical clustering analyses were performed using Ediacaran paleocommunities re ects environmental and tapho- functions of the vegan and pvclust packages in RStudio. The taxonomic dissim- nomic gradients, when these effects are accounted for, the data ilarities of Ediacaran geologic formations were calculated using the Jaccard and indicate that the evolution of early complex eukaryotes was Kulczynski-2 indices, which are commonly used in paleoecology31. To ensure that analyses were based on meaningful measurements of similarity, only formations affected by two biologic turnover events, perhaps the earliest mass fi 23,27 fi containing ve or more taxa (body fossil genera and trace fossil ichnogenera) were extinctions of complex life . Whereas the rst episode of included in the data. Formations were hierarchically clustered into pairs and ecological reorganization led to the establishment of a cosmo- groups based on their average taxonomic dissimilarities (i.e., the average-linkage politan metazoan assemblage in the terminal Ediacaran, the method). A multiscale bootstrap resampling with 1000 runs was performed with second caused the disappearance of the Ediacara biota2 and the each analysis using the pvclust function (one-sided statistical test) to calculate ‘ ’6 approximately unbiased P values for assessing the uncertainty of the results. decline of the Wormworld , paving the way for the Cambrian Clusters with P values larger than 95% are strongly supported by the data (Fig. 1, radiation of animal life. Future work should aim to determine Supplementary Fig. 2). the durations and drivers of these events. In the meantime, given available information on the chronology of eukaryotic evolution, Non-metric multidimensional scaling. Analyses were performed to corroborate hypotheses involving biotic replacement and environmental cat- the results of hierarchical clustering31. The formations analyzed with hierarchical astrophe merit serious consideration. clustering were ordinated in multidimensional space based on their Kulczynski-2

12 NATURE COMMUNICATIONS | (2019) 10:911 | https://doi.org/10.1038/s41467-019-08837-3 | www.nature.com/naturecommunications NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-08837-3 ARTICLE and Jaccard dissimilarities using the metaMDS function of the vegan package and networks (Supplementary Fig. 8). In this analysis, links connect taxa to collections, its default settings in RStudio. The NMDS scores (Supplementary Table 5), ellip- paleoenvironments, and formations. For each network, links were randomly sub- soid hulls, and centroid (mean) confidence intervals were plotted in three sampled from the data in order to identify a subnetwork. Next, the number of dimensions (Supplementary Fig. 3) using the ordirgl and orglellipse functions of nodes omitted from the subnetwork as a consequence of the subsampling proce- the vegan3D package in RStudio. Lack of overlap of two confidence intervals dure was determined. Then, the COPRA algorithm (v = 1) was applied 100,000 indicates the difference in mean NMDS scores between two clusters is statistically times to the subnetwork, and the community structure with the highest modularity significant. score was identified. Finally, the best subnetwork partition was compared to the best network partition (i.e. the reported community structure). This final step involved calculating a normalized mutual information (NMI) score with igraph Network metrics 47 . Extended (overlap) modularity scores (Supplementary package function in RStudio. In network analysis, NMI is a common measure of Tables 6 and 7) were computed for the overlapping and non-overlapping 49 67 similarity (linear and nonlinear dependence) for two clusterings . These scores are communities in the network projections using the COPRA software written in similar to Pearson correlation coefficients with values between 0 (no dependence) theJAVAlanguagebyS.Gregory(http://gregory.org/research/networks/ 67 and 1 (identical clusterings). Our NMI calculations assume that each omitted node software/copra.html) . The degree centrality and betweenness centrality scores represents its own module in a subnetwork. Overall, these steps were repeated one of nodes (Fig. 6) in the taxa projection of the bipartite network (Fig. 4)were hundred times at various sampling levels, each corresponding to a percentage of determined using functions of the igraphpackageandtheirdefaultsettingsin links. Using this procedure, we compiled distributions of NMI scores and omitted RStudio (Source Data). Measures of whole-network properties were also com- node counts vs. sampling level. High NMI scores, particularly those paired with puted using functions of the igraph package for the various networks and net- high omitted node counts, indicate results robust to variation in the data. To work projections in this study (Supplementary Fig. 7 and Supplementary fi fi produce a null model for testing the statistical signi cance of the NMI scores, we Table 8). Homophily was measured with assortativity coef cients, which are repeated the procedure, except each NMI score was calculated for a pair of net- similar to Pearson correlation coefficients, for various continuous and nominal fi works that were randomly produced with properties (size and degree distribution) properties of nodes. Assortativity coef cients measuring homophily with respect based on the network and subnetwork. Unipartite and bipartite null models were to degree were determined for all network projections (Supplementary Table 8). fi generated in RStudio using functions of the igraph (sample_degseq) and bipartite Additionally, assortativity coef cients were calculated for taxa projections from (vaznull) packages, respectively. In this case, the null hypothesis is that the network data on the preservational modes, morphogroups, and form categories of the fi and its subnetworks do not have comparable community structures at a given genera and ichnogenera. Lastly, assortativity coef cients were calculated for sampling level (i.e., the observed NMI scores reflect random similarities). If the formation projections from data on the G-Plate geoplates, regions (continents), majority (95%) of the observed NMI scores are greater than those of the random and countries of the geologic units. networks (one-sided statistical test), then the null hypothesis can be rejected.

Partitioning networks into non-overlapping modules. Prior to selecting the 67 Randomization testing. A number of methods have been proposed for deter- COPRA method , we partitioned the networks in this study into non-overlapping fi modules with fourteen community-detection algorithms (see Supplementary Dis- mining whether a community structure is statistically signi cant or, conversely, if it could have arisen due to chance49. Typically, a high modularity score is a cussion) and then compared the outputs in terms of their numbers of communities 46 and extended modularity scores47 (Supplementary Fig. 5 and Supplementary good indicator of community structure , but not all networks with high Table 6). Weighted and non-weighted versions of the unipartite network were modularity have strong community structure. To assess if the community partitioned with the leading eigenvector, Louvain, fast greedy, infomap, walktrap, structures reported in this study arose due to chance (Figs. 2a, 3a, 4a; Supple- and edge-betweenness algorithms in the igraph package of RStudio, in addition to mentary Figs. 10a, 11a, and 12a), we performed a randomization test (Supple- the COPRA method of the COPRA software67. Non-weighted versions of the mentary Fig. 9). For a unipartite network, the null hypothesis of this test is that bipartite networks were partitioned with the COPRA method; QuanBiMo, LPAwb, its observed modularity score equals the value of a random network of matching and DIRTLPAwb algorithms of the bipartite package in R; LP-BRIM algorithm of size and degree distribution, i.e. a network that has the same numbers of nodes the lpbrim package produced by T. Poisot and D. B. Stouffer (http://poisotlab.io/ with various degrees (numbers of connections). The null hypothesis is essentially software/) for R; simulated annealing algorithm of the rnetcarto package produced the same for bipartite networks. However, each projection in a bipartite network by G. Doulcier, R. Guimera, and D.B. Stouffer for R; leading eigenvector and has its own modularity score, so the null hypothesis states that one or both Adaptive BRIM algorithms of the BiMat package in MATLAB; and biSBM algo- scores are equal to those of random networks. To test this hypothesis for each ++ network, the links among nodes were randomized using functions of the igraph rithm of the C code made available by D. Larremore (http://danlarremore.com/ ’ bipartiteSBM/). Some of the algorithms do not output a single best fit community and BiRewire packages in RStudio. Nonetheless, the nodes degree distribution structure. The methods lacking output determinism include infomap, walktrap, was preserved for all projections in the random network. The randomized COPRA, LPAwb, DIRTLPAwb, LPBRIM, Adaptive BRIM, biSBM, and QuanBiMo network was then partitioned using the COPRA method and v parameter that methods. These algorithms start fromrandom starting states, and therefore, may was applied to the original network, and the modularity of the community produce multiple outputs from a single network. With the exception of the info- structure was recorded. These steps were repeated 100 times for each network, map, walktrap, and biSBM algorithms, which did not produce greatly varying producing one distribution of modularity scores per unipartite network and two results from one run to the next, these methods lacking output determinism were distributions of modularity scores per bipartite network (one for each projec- repeatedly applied to each network, and the outputs with the best modularity tion). These distributions were used to calculate P-values and Z scores to test the scores were saved. For each algorithm, the number of runs was determined so the null hypothesis (i.e., a one-sided statistical test). The P-value is the probability of analysis could finish in approximately two hours. The QuanBiMo algorithm was discovering a community structure with a higher modularity score if the con- run 100 times; the Adaptive BRIM algorithm was run 1000 times; the LPBRIM nections among nodes were randomly distributed (i.e., there is no meaningful algorithm was run 10,000 times; and the COPRA, LPAwb, and DIRTLPAwb community structure). If the P-value of a unipartite network or both P-values of a bipartite network are less than alpha (α) at the 90% (0.10), 95% (0.05), and/or algorithms were run 100,000 times. For the COPRA analyses in this comparative fi work, the v parameter (i.e., the maximum number of communities per vertex) was 99% (0.01) con dence levels, the null hypothesis can be rejected, and its com- 67 munity structure is considered statistically significant. A Z score greater than 1 set to 1, and following the recommendation of the software developer , the fi 49 solutions were extra-simplified throughout the partitioning process to remove also suggests that an observed community structure is signi cant . communities contained within others. Network visualization. Networks were visualized in RStudio using functions in Partitioning networks into overlapping modules. The networks were partitioned the following packages: igraph, GGally, ggplot2, ggnetwork, and intergraph. Static into overlapping modules with the COPRA method of the COPRA software67.To network graphs (Figs. 2a, 3a, 4a; Supplementary Figs. 10a, 11a, and 12a) were find the best solutions, we executed COPRA 100,000 times on each network. Again, generated using the ggnet2 function of ggplot2 and its default parameters, and the solutions were extra-simplified. For this work, we devised and implemented a nodes of equal size were placed without self-loops according to the Fruchterman- jackknife resampling and network partitioning procedure to identify the v para- Reingold force-directed algorithm. meter of each network in this study (Figs. 2a, 3a, 4a; Supplementary Figs. 10a, 11a, and 12a). For each network, a single node was removed from the data, the network Generic richness data was partitioned using the COPRA method (v = 1), and the number of non-over- . The taxonomic diversities of the biozones in this study lapping, non-singleton communities (n) was recorded. Then, the node was rein- were estimated from sample-based incidence (i.e., presence/absence) data with rarefaction, extrapolation, and non-parametric richness estimators (Fig. 7; Sup- serted into the network, and the steps were systematically repeated, so that every – node in the network was omitted once and a distribution of n values was produced plementary Figs. 14 17). In this work, samples are fossiliferous formations and (Supplementary Fig. 6), where the total number of n values equals the size of the fossil collection points, which vary in number among the biozones. Biozone network (i.e., the number of nodes). Following this procedure, the v parameter is assignments of Ediacaran formations were taken directly from network analysis equal to the maximum n value in the distribution. results (Fig. 4). Collection points, on the other hand, were assigned to biozones based on their formations. Four subsets of samples were analyzed. The first subset is comprised of all samples of body fossils. Genera in this subset include simple Sensitivity analysis. To assess the sensitivity of the network partitioning results to discs (e.g., Aspidella) and possible junior synonyms as well as taxa that may be the level of sampling, we analyzed the effects of omitting links and nodes from the based on taphomorphs, pseudofossils, and microbial structures. The second subset

NATURE COMMUNICATIONS | (2019) 10:911 | https://doi.org/10.1038/s41467-019-08837-3 | www.nature.com/naturecommunications 13 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-019-08837-3 consists of all samples of index fossil genera (i.e., the morphologically distinct taxa References that define the biozones, Fig. 4; Supplementary Fig. 13). In contrast, the third and 1. Muscente, A. D., Boag, T. H., Bykova, N. & Schiffbauer, J. D. Environmental fourth subsets exclude all samples associated with the deep biotope (i.e., the disturbance, resource availability, and biologic turnover at the dawn of animal Bradgate, Briscal, Drook, Fermeuse, Mistaken Point, Nadaleen, and Trepassey life. Earth Sci. Rev. 177, 248–264 (2018). samples) as well as all genera assigned to the environmentally restricted Avalon and 2. Laflamme, M., Darroch, S. A. F., Tweedt, S. M., Peterson, K. J. & Erwin, D. H. Miaohe paleocommunities (Figs. 2a, 3a–c). Whereas the third subset consists of all The end of the Ediacara biota: Extinction, biotic replacement, or Cheshire Cat? remaining samples of White Sea and Nama taxa, the final subset is comprised of Gondwana Res. 23, 558–573 (2013). samples of taxa that are known from Ediacara-type fossils and assigned to those 3. Droser, M. L. & Gehling, J. G. The advent of animals: The view from the paleocommunities (Fig. 2). Ichnogenera occurrences were not included in any of Ediacaran. Proc. Natl Acad. Sci. USA. 112, 4865–4870 (2015). the subsets. 4. Narbonne, G. M. The Ediacara Biota: Neoproterozoic origin of animals and their ecosystems. Ann. Rev. Earth Planet Sci. 33, 421–442 (2005). Sample-based rarefaction and extrapolation. A number of sample-based rar- 5. Xiao, S., Yuan, X., Steiner, M. & Knoll, A. H. Macroscopic carbonaceous efaction analyses were performed using the EstimateS software68 to compare compressions in a terminal Proterozoic shale: a systematic reassessment of the the three (Ediacara biota, Terminal Ediacaran, and Fortunian) stage-level Miaohe biota, South China. J. Paleontol. 76, 347–376 (2002). biozones (Supplementary Fig. 13) in terms of taxonomic diversity (genus 6. Schiffbauer, J. D. et al. The latest Ediacaran wormworld fauna: setting the richness) estimates and sampling intensity (Fig. 7). For sampling intensity 1:n ecological stage for the Cambrian explosion. GSA Today 26,4–11 (where n equals the number of samples within each biozone), the expected (2016). number of taxa and unconditional 95% confidence interval was calculated for 7. Erwin, D. H. et al. The Cambrian conundrum: early divergence and later 1000 runs using established analytical methods68, which duplicate the results ecological success in the early history of animals. Science 334, 1091–1097 of conventional subsampling techniques (Source Data). Additionally, non- (2011). parametric methods for extrapolation68 were used to estimate the expected 8. Yuan, X., Chen, Z., Xiao, S., Zhou, C. & Hua, H. An early Ediacaran numbers of taxa that would be found in augmented samplings with greater assemblage of macroscopic and morphologically differentiated eukaryotes. numbers of samples. These exact analytical methods were also used to calculate Nature 470, 390–393 (2011). fi unconditional 95% con dence intervals for the extrapolated values. The 9. Grant, S. W. F. Shell structure and distribution of Cloudina, a potential index fi unconditional 95% con dence intervals in this rarefaction and extrapolation fossil for the terminal Proterozoic. Am. J. Sci. 290-A, 261–294 (1990). work can be used for hypothesis testing. The null hypothesis is that two 10. Wood, R. A., Zhuravlev, A. Y., Sukhov, S. S., Zhu, M. & Zhao, F. Demise of assemblages (i.e., biozones) are equal with respect to their taxonomic diversity. Ediacaran dolomitic seas marks widespread biomineralization on the Siberian fi If the con dence intervals of two biozones do not overlap at the current Platform. Geology 45,27–30 (2017). sampling level of the biozone with fewer samples, the null hypothesis can be 11. Kolesnikov, A. V. et al. The oldest skeletal macroscopic organism rejected, and the observed difference in generic diversity is considered statis- Palaeopascichnus linearis. Precambrian Res. 316,24–37 (2018). tically significant. Values extrapolated beyond the current sampling level may 12. Chen, Z. et al. Trace fossil evidence for Ediacaran bilaterian animals with provide evidence to the contrary, particularly if the rate of taxa accumulation is complex behaviors. Precambrian Res. 224, 690–701 (2013). significantly greater in one assemblage than the other. The amount of variance, 13. Bengtson, S. & Yue, Z. 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Palaeoecol. 396, number of samples within each biozone), samples were randomly selected without 62–74 (2014). replacement from each biozone, and the number of genera was determined using 19. Buatois, L. A., Almond, J., Mángano, M. G., Jensen, S. & Germs, G. J. B. each estimation method. This subsampling was repeated 1000 times for each Sediment disturbance by Ediacaran bulldozers and the roots of the Cambrian biozone, and the mean number of genera was calculated for each sampling explosion. Sci. Rep. 8,1–9 (2018). intensity level. The distributions of iterated bootstrap, second-order jackknife, and ICE mean values were used to calculate conditional variance values and 95% 20. Becker-Kerber, B. et al. Ecological interactions in Cloudina from the Ediacaran confidence intervals for these estimators. Conversely, exact analytical methods68 of Brazil: implications for the rise of animal biomineralization. Sci. Rep. 7, were used to derive unconditional variance values and 95% confidence intervals for 5482 (2017). the mean second-order jackknife and Chao-2 estimators. Unlike the conditional 21. Gehling, J. G. Microbial mats in terminal Proterozoic siliciclastics: Ediacaran – confidence intervals, the unconditional intervals do not converge to zero at the death masks. Palaios 14,40 57 (1999). maximum sampling intensity level. If the unconditional 95% CIs of two assem- 22. Muscente, A. D. et al. Exceptionally preserved fossil assemblages through – blages do not overlap for a given estimator at this level, the data indicate that the geologic time and space. Gondwana Res. 48, 164 188 (2017). two assemblages are statistical different68. On the other hand, if two conditional 23. Darroch, S. A. F. et al. Biotic replacement and mass extinction of the Ediacara CIs do not overlap, the results simply suggest that the smaller reference sample was biota. Proc. R. Soc. Lond. B. Biol. Sci. 282,1–10 (2015). not drawn from the larger one. 24. Amthor, J. E. et al. Extinction of Cloudina and Namacalathus at the Precambrian-Cambrian boundary in Oman. Geology 31, 431–434 (2003). 25. Smith, E. F. et al. The end of the Ediacaran: Two exceptionally preserved body Code availability. The authors declare that the study does not include results fossil assemblages from Mount Dunfee, Nevada, USA. Geology 44, 911–914 produced using custom software or mathematical algorithms. All codes are avail- (2016). able from the corresponding author upon reasonable request. 26. Tarhan, L. G., Droser, M. L., Cole, D. B. & Gehling, J. G. Ecological expansion and extinction in the Late Ediacaran: Weighing the evidence for Reporting summary. Further information on experimental design is available in environmental and biotic drivers. Integr. Comp. Biol. 58, 688–702 (2018). the Nature Research Reporting Summary linked to this article. 27. Darroch, S. A. F., Smith, E. F., Laflamme, M. & Erwin, D. H. Ediacaran Extinction and Cambrian Explosion. Trends Ecol. Evol. 33, 653–663 (2018). Data availability 28. Smith, E. F., Nelson, L. L., Tweedt, S. M., Zeng, H. & Workman, J. B. A The authors declare that the main data supporting the findings of this study are available cosmopolitan late Ediacaran biotic assemblage: new fossils from Nevada and fi Namibia support a global biostratigraphic link. Proc. R. Soc. Lond. B. Biol. Sci. within the article and its Supplementary Information les. The source data underlying all – the figures and tables are provided as a Source Data file. 284,1 10 (2017). 29. Xiao, S. et al. Toward an Ediacaran time scale: problems, protocols, and prospects. Episodes 39, 540–555 (2016). Received: 20 September 2018 Accepted: 28 January 2019 30. Xiao, S., Zhou, C., Liu, P., Wang, D. & Yuan, X. Phosphatized acanthomorphic acritarchs and related microfossils from the Ediacaran Doushantuo Formation

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NATURE COMMUNICATIONS | (2019) 10:911 | https://doi.org/10.1038/s41467-019-08837-3 | www.nature.com/naturecommunications 15 Supplementary Information

Ediacaran biozones identified with network analysis provide evidence for pulsed extinctions of early complex life

Muscente et al.

1

SUPPLEMENTARY DISCUSSION

Supplementary Discussion of Community Detection Algorithms We applied a number of community detection algorithms to the networks in order to explore their community structures and identify paleocommunities, biotopes, and biozones. Network partitioning algorithms differ with regard to the definition of community (See Supplementary Table 1), and the effectiveness of any one algorithm may fluctuate with certain global (whole-network) properties, like network size (number of nodes) or mixing (i.e. the numbers of links between nodes belonging to different communities). Additionally, whereas some algorithms identify non-overlapping (mutually exclusive) modules, others can detect overlapping community units. As a result, two algorithms may return somewhat different community structures for a given network. For a number of reasons, we focus on the results returned by the community overlap propagation algorithm (COPRA)1 (See Supplementary Table 1). First, unlike other algorithms, the method could be applied to both unipartite and bipartite networks1. Second, because COPRA exhibits near-linear time complexity for sparse datasets1, its run times were relatively low. Third, every module identified in a bipartite network with COPRA included one or more nodes from each of its projections1, meaning that the method leveraged and integrated data. Fourth, the COPRA method allowed for detection of overlapping community units (e.g. paleocommunities, biotopes, and biozones)1, which are expected to share entities (i.e. taxa). Lastly, the COPRA method performed better than other community detection algorithms. It consistently identified the non-overlapping community structures with the highest Q values (See Supplementary Figure 5 and Supplementary Table 6), affirming that it performed best at identifying nonrandom associations of nodes. It also typically returned the fewest communities, making it one of the most conservative approaches for partitioning the dataset. For this reason, we can interpret the modules as macro-level community units, representing the largest and most significant associations of nodes. COPRA1 represents one of many algorithms that involve an iterative, non-deterministic process called label propagation2 (See Supplementary Table 1). In general, label propagation entails assigning a unique label to each node and then repeatedly replacing the label of each node with the most common label among its neighbors. If two or more labels occur with equal frequency, a node is randomly assigned one of those labels. After a number of iterations, all members of a module will ultimately possess the same label, and the algorithm stops when it achieves a solution with some termination criterion (e.g. every node attains the label used by the maximum number of neighbors). Notably, because label propagation involves randomization, COPRA is a non-deterministic algorithm, and may produce different solutions across multiple runs on a network. To find the best community structure, we ran COPRA 100,000 times on each network, and recorded the solution with the highest modularity. For bipartite networks, we selected the solutions with the highest modularity scores for paleoenvironment and formation projections. We use these projections because environments and formations are larger than organisms and taxa. The COPRA method innovates on the traditional process of label propagation in two ways. First, the method features a special integrative process for analyzing bipartite networks. Essentially, in bipartite network analysis, node labels are passed from the first projection to the second, and vice versa, back and forth over many iterations. As a result, community units propagate from one projection to the next, until the algorithm identifies the best solution for the

2 union of data. Second, it incorporates a belonging coefficient in the update step, which allows for nodes to accept multiple labels, and therefore, to belong to several modules. The amount of overlap depends on a user-defined parameter (v), the permissible (maximum) number of communities per node. Since the actual numbers of communities in our networks are unknown, we selected v parameters by jackknife resampling and partitioning networks. Our work assumes that a node could, in theory, be assigned to every module in its network, even if those communities did not otherwise overlap. This assumption accommodates taxa, which could belong to numerous modules if, for example, they existed over long intervals of geologic time, inhabited various environments, and/or were preserved under diverse conditions.

Supplemental Discussion of Modules Identified with COPRA Although each paleoenvironment was assigned to exactly one biotope, some taxa and formations were assigned to multiple modules. Cases of overlap indicate that nodes could not be assigned to individual modules based on the available data. In bipartite networks of formations and taxa, common taxa (e.g. Pteridinium, Rangea, and Vendotaenia) may be assigned to multiple modules because their fossils are stratigraphically wide-ranging and span multiple biozones. Rare and uncommon taxa, conversely, may be assigned to multiple biozones because they occur in deposits where no meaningful index fossils are preserved (Fig. 6). Along these same lines, formations may be assigned to multiple biozones because they contain ambiguous assemblages of mixed character or, alternatively, they lack index fossils of sufficient biostratigraphic value.

Hierarchical List of Body Fossil Form Categories and Morphogroups

Prokaryotic and Eukaryotic Algae

Form Category/Morphogroup: Horodyskiomorpha

Modular forms consisting of numerous spherical, ellipsoidal, cylindrical, ring- shaped, or (non-nested) cup-shaped units of roughly equal measure, which are generally regularly spaced and do not distort each other. Structures may be connected by strings.

Form category: Chuariomorpha, Moraniomorpha, & Tawuiomorpha

Morphologically simple spherical, discoidal, rectilinear, and simple curvilinear structures lacking ornamentation, possibly containing numerous substructures of equal measure.

Morphogroup: Chuariomorpha

Spherical or discoidal structures, potentially containing numerous sub- structures, but generally unornamented and lacking evidence of association with other similar spheres or discs.

Morphogroup: Moraniomorpha

3

Irregularly rounded spherical or discoidal structures lacking ornamentation, generally without wrinkles.

Morphogroup: Tawuiomorpha

Rectilinear to simple curvilinear sausage-shaped forms with broad, rounded termini, usually not twisted and lacking transverse elements.

Form category: Eoholyniomorpha, Glomulomorpha, Grypaniomorpha, Longfengshaniomorpha, & Mezeniomorpha

Filamentous, branching, and flat clavate thalli.

Morphogroup: Eoholyniomorpha

Slender (often bush-shaped) thalli that regularly branch. In comparison to Vendotaeniomorphs, these forms are delicate, commonly branch, and show no evidence of twisting.

Morphogroup: Glomulomorpha

Dense aggregates of fasciculate filaments, which may branch; aggregates do not resemble coherent blade-, vesicle, or tube-shaped thalli. In Longifuniculum, the aggregate is a fan- or dumbbell-shaped thallus, consisting of a bundle of twisted filaments that flare toward one or both ends.

Morphogroup: Grypaniomorpha

Slender, curvilinear filaments with pronounced coiling tendency and kinked filaments when drawn out.

Morphogroup: Longfengshaniomorpha

Clavate forms with ellipsoidal, ovoidal, or panduroidal (fiddle-shaped vesicles), possibly made up of filaments or fibers, subtended by stalks.

Morphogroup: Mezeniomorpha

Clavate non-branching forms, generally with massive (thick and wide) coherent blades, possibly made up of filaments or fibers, presumably with prominent globose or rhizoidal holdfast (as observed in Baculiphyca).

Complex eukaryotes & possible animals

Form Category / Morphogroup: Anhuiphytomorpha

4

Clavate forms with coherent cone-, fan-, or spindle-shaped thalli, which taper on one or both sides and are sometimes composed of longitudinal fibers or filaments, sometimes with septation. If thalli comprised of filaments, filaments may rarely branch.

Form Category / Morphogroup: Vendotaeniomorpha

Long tube- and ribbon-shaped forms, smooth or patterned, in some cases twisted, possibly comprised of longitudinal fibers or filaments, rarely branching, lacking annulations or segmentation.

Form Category: Sabelliditomorpha, Shaanxilithomorpha, & Sinosabelliditomorpha

Annulated and/or segmented tube-, cylinder-, and ribbon-shaped forms

Morphogroup: Sabelliditomorpha

Long, slender tube- and ribbon-shaped forms with regularly spaced, narrow transverse annulations or segmentation.

Morphogroup: Shaanxilithomorpha Tubes or cylinders, annulated in appearance, comprised of serially arranged but weakly articulated modules. Disarticulation of these modules results in discoidal, imbricated, meniscate, and lensoidal fossils.

Morphogroup: Sinosabelliditomorpha

Long, slender tube- and ribbon-shaped tomaculate forms with regularly spaced, narrow transverse annulations or segmentation and rounded terminations.

Putative metazoans

Form Category: Anabaritidae, Cloudinomorpha, Namacalathomorpha, Platysolenitomorpha, & Protolagenomorpha

Skeletal forms.

Morphogroup: Anabaritidae

Taxa in the family Anabaritidae, characterized by tubular biomineralized skeletons with trifold symmetry.

Morphogroup: Cloudinomorpha

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Tubular and conotubular forms with the appearance of nested (perhaps funnel-shaped) cylinders.

Morphogroup: Namacalathomorpha

Unbranched tubes, each tapering toward one (presumably apical) end and opening up to a cup at the other.

Morphogroup: Platysolenitomorpha

Agglutinated skeletons shaped like tubes.

Morphogroup: Protolagenomorpha

Agglutinated skeletons shaped like vases.

Form Category/Morphogroup: Porifera (putative)

Forms resembling sponges, and encompassing a wide array of morphologies, ranging from discs (e.g. Coronacollina and Palaeophragmodictya) and encrusters (e.g. Namapoikia) to filaments (e.g. Cucullus) and cones (e.g. Thectardis). In many cases, interpretations of these forms as sponges have been questioned3,4.

Ediacara-type elements that include possible crown- and stem-group animals

Form Category: Bilateralomorpha, Dickinsoniomorpha, & Kimberellomorpha

Forms with bilateral (or pseudo-bilateral) symmetry and anterior-posterior differentiation.

Morphogroup: Bilateralomorpha

Segmented forms with distinct bilateral symmetries along their lengths, which may be original features or a consequence of their taphonomy. Anterior-posterior differentiation is conspicuous, as these forms have distinct headshield-like anterior regions and repeatedly segmented posterior regions. Segments typically taper in size toward the posterior end. These segments may have been unattached or attached to each other and may have been surround by exterior membranes. Segments may have alternated, rather than opposed each other, across the midline. If so, such forms may not have been bilaterally symmetrical.

Morphogroup: Dickinsoniomorpha

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Oval-shaped forms composed of smooth, featureless tubes radiating from a midline that connects the anterior and posterior ends.

Morphogroup: Kimberellomorpha

Oval-shaped, bilaterally symmetrical fossils with several morphologically distinct and concentrically-arranges zones, including an outer smooth and crenulated zone and an inner zone bordered by thin transverse wrinkles and containing a deep longitudinal invagination. The oval shape narrows near one end, thought to represent the anterior end of the organism.

Form Category: Pentaradialomorpha, Tetraradialomorpha, & Triradialomorpha

Forms characterized by three or more lines of symmetry, often in the form of arm- like structures at their centers.

Morphogroup: Pentaradialomorpha

Forms with pentaradial symmetry, such as, circular discs with star-shaped structures of arms at their centers (e.g. Arkarura).

Morphogroup: Tetraradialomorpha

Forms with tetraradial symmetry, such as four-lobed radial bodies (e.g. Conomedusites).

Morphogroup: Triradialomorpha

Forms with triradial symmetry (e.g. Anfesta, Triforillonia, and Tribrachidium).

Form Category/Morphogroup: Arboreomorpha

Frondose forms with prominent central stalks and bifoliate petaloids. The forms generally consist of parallel primary branches that diverge from the central stalk at acute to right angles (45°–90°) and terminate at an outer margin. The branches are connected, possibly attached to a dorsal sheet. Primary branches consist of teardrop-shaped secondary branches, which are positioned at right angles to the primary branches.

Form Category/Morphogroup: Rangeomorpha & "Ivesheadiomorpha"

Modular, frondose forms composed of leaf-like structures (“frondlets”). The frondlets are repeatedly branched in self-similar patterns that are identical through three or more orders of branching. “Ivesheadiomorpha” is a problematic group of

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taxa that often cooccur with rangeomorphs. Given the poor preservation of their fossils, it is possible (if not likely) that ivesheadiomorphs are taphonomically degraded rangeomorphs, in which case, those taxa should be treated as taphomorphs (i.e. junior synonyms or unnamed rangeomorphs).

Form Category/Morphogroup: Erniettomorpha

Diverse modular forms that are smooth, unbranched, and cylindrical in shape. These forms include multi-foliate taxa, each consisting of three or more identical leaf-like petaloids composed of modular tubes arranged around a central axis. The tubes typically alternate along the central midline, indicating that they are not bilaterally symmetrical.

Form Category: Aspidellomorpha & Beltanelliformomorpha

Ornamented discs and discs occurring in groups.

Morphogroup: Aspidellomorpha

Discoidal forms, which are sometimes attached to stalks and often possess ridges as well as concentric and radiating structures, including external tentacle- and rhizoid-like offshoots, but never show evidence spheroidal or discoidal sub-structures.

Morphogroup: Beltanelliformomorpha

Discoidal or spherical forms found in accumulations of densely spaced individuals of equal or varying sizes, frequently distorting each other.

Problematica

Form Category/Morphogroup: Problematica

All other forms, which due to their characters or lack sufficient diagnoses, do not fall within the other morphogroups or form categories.

Crown-group Metazoa

Form Category: Cambrian Metazoa

Phylum: Arthropoda

Phylum: Brachiopoda

Phylum: Chordata

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Phylum: Cnidaria

Phylum: Hyolitha

Phylum: Metazoa (Incertae sedis)

Small shelly fossils

Phylum: Mollusca

Phylum: Porifera

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Supplementary Figure 1 | Global maps of Ediacaran fossil collection points included in the original dataset of this study. (a) Present locations. (b) Locations of sampling points during Ediacaran times around the transition between the Ediacara biota and Terminal Ediacaran biozones. Maps were produced with GPlates. Source data are provided as a Source Data file.

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Supplementary Figure 2 | Hierarchical clustering using Jaccard dissimilarities. Dendrogram of 34 geologic formations containing five or more macrofossil genera and/or ichnogenera of Ediacaran age. Formations were clustered based on their taxonomic (Jaccard) dissimilarities5 (x- axis) using the average-linkage method6. The high cophenetic correlation score of the dendrogram (0.8992476)—the correlation between observed taxonomic dissimilarities and those estimated via hierarchical clustering—indicates that the dendrogram faithfully preserves the pairwise distances between the original data points. Red numbers are approximately unbiased (AU) P values calculated for nodes from 1000 multiscale bootstrap resamples. From left to right after the Lantian Formation, the labeled Avalon (blue), White Sea (green), Miaohe (orange), and Nama (red) clusters have AU P values ≥ 0.90 and are supported by the data. The “Ust’-Pinega” formation encompasses occurrences of taxa that belong to the Lyamtsa, Verkhovka, and Zimniegory formations but have uncertain stratigraphic position. Source data are provided as a Source Data file.

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Supplementary Figure 3 | NMDS results. Ordination plots of 34 geologic formations containing five or more macrofossil genera and/or ichnogenera of Ediacaran age. (a, b) Kulczynski-2 dissimilarity-based distance results. (c, d) Jaccard dissimilarity-based distance results. The k values (numbers of axes) were selected based on the associated stress values (a: 0.121614; b: 0.1190979), which in general, indicate that the plots provide good representations of rank orders in reduced dimensions. In comparison, the k=2 stress values (a: 0.1782327; b: 0.1806682) indicate that two-axis plots would provide relatively poor representations of the data. (a, c) Ellipsoid hulls enclose the Avalon, White Sea, Miaohe, and Nama clusters (Fig. 1; See Supplementary Figure 2). (b, d) Ellipsoid hulls enclose the 95% confidence intervals around the centroid (means) values of clusters. The centroid ellipses do not overlap, indicating that taxonomic dissimilarities among the clusters are statistically significant. Source data are provided as a Source Data file.

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Supplementary Figure 4 | Stratigraphic distribution of Ediacaran macrofossil clusters identified with hierarchical clustering and NMDS (Fig. 1; See Supplementary Figures 2 and 3). White formations contain no potential index fossils. Source data are provided as a Source Data file.

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Supplementary Figure 5 | Results of various network partitioning methods. Plot shows results from fourteen community detection algorithms applied to the networks (See Supplementary Table 6). Weighted and non-weighted versions of the unipartite network were partitioned with the leading eigenvector, Louvain, fast greedy, infomap, walktrap, and edge- betweenness algorithms in the igraph package of R7, in addition to the COPRA method (v=1) of the COPRA software1. Non-weighted versions of the bipartite networks were partitioned with the COPRA method (v=1) 1; QuanBiMo, LPAwb, and DIRTLPAwb algorithms of the bipartite package in R8; LP-BRIM algorithm of the lpbrim package produced by T. Poisot and D. B. Stouffer (http://poisotlab.io/software/) for R; simulated annealing algorithm of the rnetcarto package produced by G. Doulcier, R. Guimera, and D. B. Stouffer for R; leading eigenvector and Adaptive BRIM algorithms of the BiMat package in MATLAB9; and biSBM algorithm of the C++ code made available by D. Larremore (http://danlarremore.com/bipartiteSBM/)10. Each algorithm outputs a community structure by dividing the nodes of a given network among modules of number (n). The algorithms, in essence, differ with regard to the definition of community, and sometimes partition networks into different numbers of modules. Each algorithm outputs a single best fit community structure, except for the COPRA, LPAwb, DIRTLPAwb, LPBRIM, Adaptive BRIM, biSBM, and QuanBiMo methods, which involve randomization processes and may produce any number of outputs for a given network. With the exception of biSBM, which produced results that did not greatly vary run to run, these methods lacking output determinism were repeatedly applied to each network, and the outputs with the best modularity scores were saved. The QuanBiMo algorithm was run 100 times; the Adaptive BRIM algorithm was run 1000 times; the LPBRIM algorithm was run 10,000 times; and the COPRA, LPAwb, and DIRTLPAwb algorithms were run 100,000 times. The x-axis is the number of non-overlapping modules, and the y-axis is the extended modularity score (Q)11 of the community structure of the best output. Modularity is a measure of the strength of the division of a community structure. For a given set of communities, Q is the fraction of links that connect nodes of the same communities minus the corresponding fraction expected in an equivalent network with a random distribution of connections. An ellipse has been drawn around the values produced by the COPRA algorithm to illustrate its results in comparison to those of other algorithms. Source data are provided as a Source Data file.

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Supplementary Figure 6 | Jackknife network partitioning results. The COPRA method allows for detection of overlapping community structure, in which, nodes may belong to multiple modules. The amount of overlap depends on a user-defined parameter (v), which equals the maximum number of communities per node. To determine v parameters, the following jackknife resampling procedure was applied to each network. A single node was removed, the network was partitioned using the COPRA method (v=1), and the number of non- overlapping communities (n) was recorded. The node was then reinserted into the network, and the steps were systematically repeated, so every node in the network was omitted once and a distribution of n values was produced. The number of n values in this distribution equals the number of nodes in the network. The distribution is presented as the n value (x- axis) versus the number of jackknife networks produced via resampling (y-axis). The raw numbers of jackknife networks are shown along with corrected counts, which exclude singleton and pair singleton communities. In unipartite networks (a), singleton communities are solitary nodes, and in bipartite networks (b–f), pair singleton communities are modules where one or both projections are represented by singletons. (a) Unipartite network of Ediacaran macrofossil genera (Fig. 2). (b) Bipartite network of paleoenvironments and Ediacaran genera (Fig. 3). (c) Bipartite network of paleoenvironments and Ediacaran genera and ichnogenera (See Supplementary Figure 10). (d) Bipartite network of Ediacaran formations and genera and ichnogenera (Fig. 4). (e) Bipartite network of Ediacaran formations and shallow genera/ichnogenera (See Supplementary Figure 11). (f) Bipartite network of Ediacaran formations and shallow genera (See Supplementary Figure 12). For a given network, the v parameter equals the maximum value of n in the distribution. Source data are provided as a Source Data file.

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Supplementary Figure 7 | Parallel coordinates plot of network metrics. From left to right, plot shows the values of diameter (maximum degree of separation); modularity (strength of division of network into a particular set of communities); transitivity (triadic closure, i.e. the probability that neighbors of a node are connected); edge density (the ratio between the numbers of actual connections and possible links within a given network); and homophily (the tendency of nodes to associate with others possessing similar properties). The homophily metric (assortativity coefficient) is provided for various nominal variables, including preservational mode (See Supplementary Table 2), morphogroup or phylum (See Supplementary Discussion), form category (See Supplementary Discussion), degree (number of links to other taxa), region (continent), geoplate (tectonic plate), and country. Values (See Supplementary Tables 7 and 8) are provided for the unipartite Ediacaran genera network (Fig. 2), the bipartite Ediacaran formations/genera and ichnogenera network (Fig. 4), the bipartite Ediacaran formations/shallow marine genera and ichnogenera network (See Supplementary Figure 11), the bipartite Ediacaran and Cambrian (formations/genera and ichnogenera) network (See Supplementary Figure 13), and the taxa projection of the bipartite paleoenvironment/Ediacaran genera network (Fig. 3). Source data are provided as a Source Data file.

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Supplementary Figure 8 | Sensitivity analysis. An analysis was conducted to assess the sensitivity of the network partitioning results to the level of sampling of links and nodes. (a, b) Unipartite network of Ediacaran genera (Fig. 2a). (c, d) Bipartite network of paleoenvironments and Ediacaran genera (Fig. 3a). (e, f) Bipartite network of Ediacaran formations and taxa (Fig. 4a). (a, c, e) Box-and-whisker plots of normalized mutual information (NMI) scores versus sampling level. Sampling level is the percentage of links that were randomly sampled from the network in order to specify a subnetwork. Each box-and-whisker plot illustrates a distribution of one hundred NMI scores. Each score compares the community structure of the network to the community structure of a subnetwork. Where NMI scores are high, networks and subnetworks have similar community structures and results are relatively robust and insensitive to variation in the data. The shaded areas illustrate the results of hypothesis testing with a null model. In this work, the null hypothesis is that a network and its subnetworks do not have similar community structures. Where the majority (95%) of scores calculated from the data are greater than those of the null model, the null hypothesis can be rejected. (b, d, f) Box-and-whisker plots of omitted node counts versus sampling intensity. Each box-and-whisker plot represents a distribution of 100 values, and each value is the number of nodes omitted from a subnetwork as a consequence of the subsampling procedure used to generate the NMI scores in (a, c, e). For each box-and- whisker plot, the box encompasses the first and third quartiles, the middle bar depicts the median, and the whiskers depict the true minimum and maximum values except where outliers (dots) greater than 1.5 times the interquartile range are present. Source data are provided as a Source Data file.

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Supplementary Figure 9 | Randomization testing. To determine if the community structures identified with the COPRA community detection algorithm are statistically significant, data randomization tests were conducted. In this work, for unipartite networks, the null hypothesis is that the observed modularity score (Q) of a network (See Supplementary Table 7) equals the score of a random network of matching size and degree distribution (the degree of a node equals the number of unique links incident upon it and excludes self-loops and multiple edges). For bipartite networks, the null hypothesis is that the Q values of one or both of its projections (See Supplementary Table 7) equal the corresponding score(s) of a random network of matching size and degree distribution. For each network, the links among nodes were randomized, but the nodes’ degree distribution was preserved. The randomized network was then partitioned using the COPRA method that was applied to the original network, and the Q value of the community structure was recorded. These steps were repeated 100 times for each network, producing one distribution of modularity scores per unipartite network and two distributions of modularity scores (one for each projection) per bipartite network. The distribution of Q for each network projection in this study is presented as a box and whisker plot (a). The box encompasses the first and third quartiles, the bar in the box depicts the median, and the whiskers show the true minimum and maximum values, except where outliers greater or less than 1.5 times the interquartile range were identified. The distributions were used to calculate P values (b) and Z scores (c). The P value indicates the probability that a random network projection would have a greater Q value. Similarly, its Z score indicates how many standard deviations the observed Q is above a random network. If the P value of a unipartite network or both P values of a bipartite network are less than alpha (α) at the 90%, 95%, and/or 99% confidence levels, the null hypothesis can be rejected, and its community structure is considered statistically significant. A Z score greater than 1 also indicates that an observed community structure is significant12. Source data are provided as a Source Data file.

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Supplementary Figure 10 | Bipartite network of paleoenvironments and Ediacaran macrofossil taxa. (a) Network graph. A taxon (genus or ichnogenus) and paleoenvironment are linked if fossils of the taxon have been reported from matching facies of Ediacaran age. Colors indicate modules identified using the COPRA community-detection algorithm (v=2). According to randomization testing, the community structure is not statistically significant (See Supplementary Figure 9; paleoenvironments projection, Q=0.38, P=0.12, Z=0.73; taxa projection, Q=0.50, P=0.04, Z=1.07) because ichnogenera tie together all modules. All paleoenvironments and taxa fall into three modules—the deep (blue), shallow (red), and intermediate (green) clusters—named for the relative water depths of their centroids along a model shallow-to-deep water transect. (b) Venn diagram illustrating taxonomic overlap of modules. Areas of circles correspond to their relative numbers of taxa, numbers are counts of taxa, and values in parentheses are proportions. (c) Stacked bar graph showing numbers of genera belonging to the various Ediacaran paleocommunities in each module (colors are those used in Fig. 2a, b). (d, e) Stacked bar graphs showing numbers of genera in the modules and their preservational modes (d) and morphogroups (e). See Fig. 2 for color keys and main text (Fig. 3) for a comparable network that excludes ichnogenera but has a statistically significant community structure. Source data are provided as a Source Data file.

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Supplementary Figure 11 | Bipartite network of shallow water macrofossil genera and ichnogenera in Ediacaran formations. (a) Network graph. A taxon (genus or ichnogenus) and formation are linked if fossils of the taxon have been reported from that geologic unit. Colors indicate modules identified using the COPRA community-detection algorithm (v=4). According to randomization testing, this community structure is statistically significant (See Supplementary Figure 9; formations projection, Q=0.69, P<0.01, Z=3.98; taxa projection, Q=0.65, P<0.01, Z=3.06). All formations and taxa fall into three modules: the Ediacara biota biozone (blue), the Terminal Ediacaran biozone (red), and the Ediacaran/Cambrian taxa (green) clusters. (b) Venn diagram illustrating taxonomic overlap of modules. Areas of circles correspond to their relative numbers of genera and ichnogenera, numbers are counts of taxa, and values in parentheses are proportions. (c) Stacked bar graph showing the numbers of taxa belonging to the various Ediacaran macrofossil paleocommunities (colors are those used in Fig. 2a, b) and representing traces in the modules. (d, e) Stacked bar graphs showing numbers of taxa in the modules and their preservational modes (d) and morphogroups (e). See Fig. 2 for color keys. Source data are provided as a Source Data file.

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Supplementary Figure 12 | Bipartite network of shallow water macrofossil genera in Ediacaran formations. (a) Network graph. A genus and formation are linked if fossils of the taxon have been reported from that geologic unit. Colors indicate modules identified using the COPRA community-detection algorithm (v=5). According to randomization testing, this community structure is statistically significant (See Supplementary Figure 9; formations projection, Q=0.74, P<0.01, Z=6.67; taxa projection, Q=0.66, P<0.01, Z=6.51). The majority of formations and genera (98%) fall into three modules: the Ediacara biota biozone (blue), the Terminal Ediacaran biozone (red), and the Ediacaran/Cambrian taxa (green) clusters. (b) Venn diagram illustrating taxonomic overlap of modules. Areas of circles correspond to their relative numbers of genera, numbers are counts of taxa, and values in parentheses are proportions. (c) Stacked bar graph showing the numbers of genera belonging to the various Ediacaran macrofossil paleocommunities in the modules (colors are those used in Fig. 2a, b). (d, e) Stacked bar graphs showing numbers of taxa in the modules and their preservational modes (d) and morphogroups (e). See Fig. 2 for color keys. Source data are provided as a Source Data file.

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Supplementary Figure 13 | Bipartite network of macrofossil taxa and geolologic formations in the Ediacaran System and Fortunian (basal Cambrian) Stage. (a) Network graph. The network is identical to the bipartite network of Ediacaran formations and taxa (Fig. 4), except it has been expanded and revised through incorporation of data (see methods) on occurrences of fossils in the Fortunian stage (~541-529 Ma). (b) Venn diagram illustrating taxonomic overlap of modules (Ediacaran biozone and Fortunian stage). Areas of circles correspond to their relative numbers of genera and ichnogenera, numbers are counts of taxa, and values in parentheses are proportions. (c, d) Stacked bar graphs showing numbers of genera in the modules and their preservational modes (c) and morphogroups (d). See Fig. 2 for color keys. Source data are provided as a Source Data file.

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Supplementary Figure 14 | Generic diversity of biozones estimated using non-parametric richness estimators (stratigraphic index fossil genera). Data includes only stratigraphic index fossils (Fig. 4a). Plots show generic diversity (number of genera) versus sampling intensity for the stage-level biozones identified in this study. For a given sampling intensity level, plotted values of diversity are means of 1,000 randomizations of sample order and are bracketed by 95% confidence interval (CI) envelopes. The values were calculated using five different richness estimators13. (a, b) Bootstrap estimator. (c, d) First-order Jackknife (Jackknife-1) estimator. (e, f) Second-order Jackknife (Jackknife-2) estimator. (g, h) Incidence coverage-based estimator (ICE). (i, j) Chao (1984, 1987) estimator (Chao-2). The estimates were calculated from samples defined as geologic formations (a, c, e, g, i) and collection points (b, d, f, h, j) with relevant body fossils. Collection points were assigned to biozones based on the community assignments of their formations (Fig. 4; See Supplementary Figure 13). Whereas the CIs in (a, b, e–h) were calculated from conditional standard deviations determined via stochastic resampling, the CIs in (c, d, i, j) were calculated from unconditional standard deviations, which were derived using exact analytical methods13. For this reason, the unconditional confidence intervals in (c, d, i, j) do not converge to zero variance at the reference sample, like the conditional confidence intervals in (a, b, e–h). In general, non-overlap of 95% CIs constructed from unconditional variance estimators (Jackknife-1 and Chao-2) can be used as a simple but conservative criterion of statistical difference13. On the other hand, non-overlap of 95% CIs constructed from conditional variance estimators (Bootstrap, Jackknife-2 and ICE) can be used as a criterion that a smaller reference sample was not drawn from a larger one. Source data are provided as a Source Data file.

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Supplementary Figure 15 | Generic diversity of biozones estimated using non-parametric richness estimators (all genera). Data includes all body fossil taxa, including discs and possible synonyms, taphomorphs, and microbially induced sedimentary structures. Plots show generic diversity (number of genera) versus sampling intensity for the stage-level biozones identified in this study. For a given sampling intensity level, plotted values of diversity are means of 1,000 randomizations of sample order and are bracketed by 95% confidence interval (CI) envelopes. The values were calculated using five different richness estimators13. (a, b) Bootstrap estimator. (c, d) First-order Jackknife (Jackknife-1) estimator. (e, f) Second-order Jackknife (Jackknife-2) estimator. (g, h) Incidence coverage-based estimator (ICE). (i, j) Chao (1984, 1987) estimator (Chao-2). The estimates were calculated from samples defined as geologic formations (a, c, e, g, i) and collection points (b, d, f, h, j) with relevant body fossils. Collection points were assigned to biozones based on the community assignments of their formations (Fig. 4; See Supplementary Figure 13). Whereas the CIs in (a, b, e–h) were calculated from conditional standard deviations determined via stochastic resampling, the CIs in (c, d, i, j) were calculated from unconditional standard deviations, which were derived using exact analytical methods13. For this reason, the unconditional confidence intervals in (c, d, i, j) do not converge to zero variance at the reference sample, like the conditional confidence intervals in (a, b, e–h). In general, non-overlap of 95% CIs constructed from unconditional variance estimators (Jackknife-1 and Chao-2) can be used as a simple but conservative criterion of statistical difference13. On the other hand, non-overlap of 95% CIs constructed from conditional variance estimators (Bootstrap, Jackknife-2 and ICE) can be used as a criterion that a smaller reference sample was not drawn from a larger one. Source data are provided as a Source Data file.

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Supplementary Figure 16 | Generic diversity of biozones estimated using non-parametric richness estimators (all White Sea and Nama genera). Data includes all body fossil taxa assigned to the White Sea and/or Nama (WS/N) paleocommunities (Fig. 2a). Plots show generic diversity (number of genera) versus sampling intensity for the stage-level biozones identified in this study. For a given sampling intensity level, plotted values of diversity are means of 1,000 randomizations of sample order and are bracketed by 95% confidence interval (CI) envelopes. The values were calculated using five different richness estimators13. (a, b) Bootstrap estimator. (c, d) First-order Jackknife (Jackknife-1) estimator. (e, f) Second-order Jackknife (Jackknife-2) estimator. (g, h) Incidence coverage-based estimator (ICE). (i, j) Chao (1984, 1987) estimator (Chao-2). The estimates were calculated from samples defined as geologic formations (a, c, e, g, i) and collection points (b, d, f, h, j) with relevant body fossils. Collection points were assigned to biozones based on the community assignments of their formations (Fig. 4; See Supplementary Figure 13). Whereas the CIs in (a, b, e–h) were calculated from conditional standard deviations determined via stochastic resampling, the CIs in (c, d, i, j) were calculated from unconditional standard deviations, which were derived using exact analytical methods13. For this reason, the unconditional confidence intervals in (c, d, i, j) do not converge to zero variance at the reference sample, like the conditional confidence intervals in (a, b, e–h). In general, non-overlap of 95% CIs constructed from unconditional variance estimators (Jackknife-1 and Chao-2) can be used as a simple but conservative criterion of statistical difference13. On the other hand, non-overlap of 95% CIs constructed from conditional variance estimators (Bootstrap, Jackknife-2 and ICE) can be used as a criterion that a smaller reference sample was not drawn from a larger one. Source data are provided as a Source Data file.

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Supplementary Figure 17 | Generic diversity of biozones estimated using non-parametric richness estimators (White Sea and Nama Ediacara-type fossil taxa). Data includes all genera known from Ediacara-type fossils and assigned to the White Sea and/or Nama (WS/N) paleocommunities (Fig. 2a). Plots show generic diversity (number of genera) versus sampling intensity for the stage-level biozones identified in this study. For a given sampling intensity level, plotted values of diversity are means of 1,000 randomizations of sample order and are bracketed by 95% confidence interval (CI) envelopes. The values were calculated using five different richness estimators13. (a, b) Bootstrap estimator. (c, d) First-order Jackknife (Jackknife- 1) estimator. (e, f) Second-order Jackknife (Jackknife-2) estimator. (g, h) Incidence coverage- based estimator (ICE). (i, j) Chao (1984, 1987) estimator (Chao-2). The estimates were calculated from samples defined as geologic formations (a, c, e, g, i) and collection points (b, d, f, h, j) with relevant body fossils. Collection points were assigned to biozones based on the community assignments of their formations (Fig. 4; See Supplementary Figure 13). Whereas the CIs in (a, b, e–h) were calculated from conditional standard deviations determined via stochastic resampling, the CIs in (c, d, i, j) were calculated from unconditional standard deviations, which were derived using exact analytical methods13. For this reason, the unconditional confidence intervals in (c, d, i, j) do not converge to zero variance at the reference sample, like the conditional confidence intervals in (a, b, e–h). In general, non-overlap of 95% CIs constructed from unconditional variance estimators (Jackknife-1 and Chao-2) can be used as a simple but conservative criterion of statistical difference13. On the other hand, non-overlap of 95% CIs constructed from conditional variance estimators (Bootstrap, Jackknife-2 and ICE) can be used as a criterion that a smaller reference sample was not drawn from a larger one. Source data are provided as a Source Data file.

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Supplementary Table 1 | Glossary of network analysis terms.

A term for any associated group of specimens, localities, or samples. For instance, it may describe the animal fossils found in a given geologic stratum, either locally at a Assemblage collection point or globally within a biozone; localities that are taxonomically similar to one another; or samples thought to represent a single population. Assortativity A score of homophily (see also: homophily). The mean degree score calculated from the nodes in one or more assemblages Average degree (communities, modules, biotopes, or biozones) in a network (see also: degree). Betweenness centrality The number of shortest paths that pass through a node in a network. A distinctive association of interacting taxa and their physical environment, which occupy a continuous geographical area that can be delimited by convenient Biotope (network-based) boundaries. Defining environmental features generally include a substrate, hydrodynamic conditions, and light. In network analysis, it is defined as an association (community or module) made of taxa and environment nodes. Biostratigraphic zone. An interval of geological strata, which is defined and can be recognized interregionally through stratigraphic correlation on the basis of its fossil content (i.e. taxa). In networks analysis, it is defined as an association (community or Biozone (network-based) module) made of taxa and geologic unit nodes. This association most closely resembles an assemblage biozone, defined by a unique association of three or more taxa, which distinguish it from adjacent strata. A network containing two types of nodes. Nodes of the first type are always Bipartite network connected to nodes of the second, and vice versa, but nodes of the same type are never connected to each other (see also: projection). The importance, however defined, of a node within a network. Centrality scores typically measure (1) the relative roles that nodes play in the flow of information Centrality through their networks (e.g. how often they serve as a bridge or how well connected they are to each other) and (2) the closeness of the nodes to the others. A subset of nodes/vertices, in which, every node in the subset is connected to every Clique other. The most basic level of organization in an undirected multi-modal structure. This term is used to describe the outputs of a number of statistical and analytical methods, including hierarchical clustering and network partitioning. In general, it Cluster refers to any group of units (e.g. individuals, taxa, or samples), which are more closely associated (related or similar) to each other than the others in the data. Equivalent to a network module (see also: module). A cluster of nodes within a Community network, usually identified via statistical and analytical means, representing closely (network-based) associated entities. Depending on the type(s) of its nodes, it may or may not represent an ecological/biological community or paleocommunity. Community structure The output of a community algorithm, consisting of multiple (network partition) communities/modules/clusters of nodes. A function that identifies the community structure of a network on the basis of some Community-detection definition of community. The computational method generally involves partitioning a algorithm network by assigning its nodes to overlapping or non-overlapping modules/communities of number (n). A label propagation method of network partitioning for identifying overlapping and COPRA algorithm non-overlapping communities within unipartite and bipartite networks. The number of connections incident on a node/vertex within a given network. The Degree number of links between a node and any other, including itself, if self-loops are (degree centrality) allowed (see also: degree centrality and self-loops). Degree distribution The distribution of degree/degree centrality values in a network. The number of connections/links/edges separating the most distant vertices in a Diameter network. A network, in which connections/links/edges have orientations and directionality Directed network (e.g. a connection goes from node A to node B, but not from node B to node A).

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Supplementary Table 1 (cont.) | Glossary of network analysis terms.

A connection between a pair of nodes/vertexes in a network, representing an Edge interaction between those entities (see also: link). The ratio between the number of actual connections/links/edges and the number of Edge Density possible connections/links/edges within a network. A function for community detection that seeks to identify community structure in a Edge-betweenness network by partitioning communities at the connections/links/edges that are most algorithm commonly found in the shortest paths between nodes A function for network partitioning and community detection that seeks to identify Fast greedy algorithm the best community structure of a network by optimizing its modularity. Fruchterman-Reingold A force-directed algorithm for defining relative node positions and connection force-directed algorithm lengths in a network graph. An emergent attribute of a whole network, which can usually be described with a Global network property single value or score. Homophily The tendency of entities to associate with others possessing similar properties. A function for network partitioning and community detection that seeks to identify Infomap algorithm community structure in a network by minimizing the expected description length of a random walker trajectory. A network produced by jackknife resampling of the nodes and links in a parent network, such that, each node is left out of exactly one jackknife network and a total number of jackknife networks is produced that is equal to the size of the parent Jackknife network network. These jackknife networks are used for estimating the v parameter used in network partitioning with the COPRA algorithm (see also: jackknife resampling, v parameter, and COPRA algorithm). Any community detection algorithm/network partitioning method that involves randomly assigning unique labels to the nodes in a network in an initial step and repeatedly revising those labels, so that over multiple iterations, the nodes take on the most common labels among their direct neighbors, leading to discovery of densely Label propagation connected groups of nodes (i.e. communities). Because this approach involves method randomly assigning labels to nodes at the start, it may produce any number of outputs, rather than a single optimal community structure. For this reason, label propagation methods must be repeated numerous times to ensure that the best community structure has been found (see also: COPRA algorithm). Leading eigenvector A function for network partitioning and community detection that seeks to identify algorithm the best community structure of a network by optimizing its modularity. A connection between a pair of nodes/vertexes in a network, representing an Link interaction between those entities (see also: edge) An attribute of a specific node/vertex within a network, which can usually be Local network property described with a single value or score. Louvain (multi-level) A function for network partitioning and community detection that seeks to identify algorithm the best community structure of a network by optimizing its modularity. The highest degree score in one or more assemblages (e.g. communities, modules, Maximum degree biotopes, or biozones) in a network (see also: degree). Mixing parameter An attribute of a community that specifies its level of connection with others A measure of the strength of division that ranges between 0 and 1 and provides an Modularity (Q) indication of community structure within a network (see also: main text). Equivalent to a network community. A cluster of nodes within a network, usually Module identified via statistical and analytical means, representing closely associated entities (see also: community).

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Supplementary Table 1 (cont.) | Glossary of network analysis terms.

Two or more connections that are incident upon the same two nodes/vertices in a Multiple edges network (see also: edges and links). These types of connections are not present in (multiple links) simple networks and graphs. A network containing two or more types of nodes/vertices, and two or more Multipartite network projections. A bipartite network, which contains two types of node/vertices and two projections, is an example of a multipartite network. Multi-modal data A pseudo-hierarchical, nested data structure with levels of organization: microscale structure cliques, mesoscale communities, and macroscale clusters of communities. Network A group of independent but associated entities (see also community) Network size The total number of nodes/vertices within a network. Node An entity within a network (see also: vertex) Non-overlapping Communities that do not share any element (nodes) in common. Two or more communities modules that are mutually exclusive with respect to one another. Non-weighted A network containing connections/links/edges of uniform strength. In these networks, (unweighted) network there are no differences in the magnitudes of connection among pairs of nodes. Overlapping Communities that share any number of elements (nodes) in common. Two or more communities modules that are not mutually exclusive with respect to one another. Paired singleton A community/module in a bipartite network, such that, one of the projections in the community community/module is represented by a single node A subset of nodes in a network, which typically represent a common type. In bipartite networks, there are two projections, one for each type of node. These projections can Projection be derived from bipartite networks by compressing the data and wiring nodes to neighbors of their neighbors. Accordingly, the projections can be visualized and analyzed as separate and independent unipartite networks. Self-loop (or loop) A connection between a node/vertex and itself. Not present in simple networks. Simple network A network or graph without multiple edges or self-loops. Singleton community A community in a unipartite network that contains exactly one node. A coefficient measuring the probability that adjacent vertices of a node are connected Transitivity to each other. A network, in which connections/links/edges do not have orientations or Undirected network directionality (e.g. a connection goes from node A to node B, and vice versa), A network with one type of node. Any pair of nodes may be connected, and the data Unipartite network does not support additional projections. The maximum number of communities/modules that a node may belong in a network v parameter with overlapping community structure. A parameter in the COPRA algorithm for network partitioning and community detection. Vertex An entity within a network (see also: node) A function for network partitioning that seeks to identify the best community Walktrap algorithm structure of a network with short random walks that tend to stay within their own communities. A network containing connections/links/edges of unequal strength. In these networks, Weighted network nodes in some pairs are more strongly connected than nodes in other pairs.

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Supplementary Table 2 | Preservational modes of Ediacaran macrofossils and taxa.

Mode Description Taxa known exclusively from carbonaceous compressions, which exhibit minor Carbonaceous compressions or no (non-replacive and non-pervasive) mineralization. Taxa known exclusively from casts, moulds, and impressions in siliciclastic Ediacara-type fossils rocks and/or limestone; fossils in limestone may additionally reflect mineralization via calcification. Taxa known exclusively from mineralized fossils (i.e. fossils with evidence of Mineralized fossils pervasive pyritization, silicification, phosphatization, and/or aluminosilicification). Taxa known from fossils of agglutinated and/or biomineralized skeletons, Skeletal fossils which may be preserved as steinkerns, external moulds, or secondary authigenic/diagenetic minerals. Ichnotaxa known from siliciclastic and carbonate rocks. This category includes Trace fossils predation traces. Carbonaceous compressions & Taxa known exclusively from carbonaceous compressions, mineralized fossils, mineralized fossils and fossils preserved with both organic and authigenic/diagenetic minerals. Taxa known from Ediacara-type fossils and carbonaceous compressions. Ediacara-type fossils & Examples include Eoandromeda, Gesinella, Flabelophyton, Liulingjitaenia, and carbonaceous compressions Longifuniculum, which are preserved as carbonaceous compressions in the Doushantuo Formation but Ediacara-type fossils in the Rawnsely Quartzite. Taxa do not match the criteria of any of the other categories, or there is insufficient data for categorization. These taxa include Kelleria (known from a Other or unknown modes of single specimen, which is now missing) as well as Horodyskia, preservation Palaeopaschichnus, and Yelovichnus, which are potentially known from Ediacara-type fossils, mineralized fossils, and carbonaceous compressions.

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Supplementary Table 3 | Trace architecture designs and architectural form categories.

General descriptor Trace-fossil architectural design Interpretation (Form category) (Morphogroup)

Scratch marks Scratch mark Feeding trace of mat grazers and detritus feeder Skeletal fossil Circular holes (borings) in skeletal Feeding trace (predation mark) of an active borings fossil predator Oval-shaped impression Resting trace of mat digester

Bilaterally symmetrical short, shallow Resting trace of deposit and detritus feeder Impressions to deep scratched impressions Pentameral-shaped impression or Resting trace of passive predators or detritus burrow feeder Grazing trail of deposit and detritus feeder Simple horizontal trail (including mat grazers) Actively filled (complex meniscate) Feeding structure of deposit feeder horizontal burrow

Trackways Circular trail Feeding structure of deposit feeder

Bilobate trail with paired grooves Locomotion trace of deposit and detritus feeder Trilobate flattened trail Locomotion trace of an active predator Trackway Locomotion trace of deposit and detritus feeder

Feeding structure of deposit feeder Horizontal branched burrow system (possibly an undermat miner) Actively filled (massive) horizontal Feeding structure of deposit feeder burrow Horizontal to oblique branching Feeding structure of deposit and detritus feeder burrow (possibly undermat miner) Dwelling burrow of suspension feeder and/or Passively filled horizontal burrow active predator Dwelling burrow of suspension feeder and/or Burrows Plug-shaped burrow passive predator Vertical helicoidal burrow Feeding burrow of deposit feeder

Surface-coverage branching burrow Feeding structure of undermat miner Radial branching ichnological Feeding structure of deposit feeder structure Burrow with vertical spreiten Feeding structure of deposit feeder

Burrow with horizontal spreiten Feeding structure of deposit feeder Mazes and boxworks (i.e. galleries) Feeding structure of deposit feeder Radial graphoglyptid Farming trace Graphoglyptids Regular network graphoglyptid Farming trace

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Supplementary Table 4 | Paleoenvironments used in this study.

Paleoenvironment Definition General description of facies Channelized, cross-stratified, medium- to Fluvial setting A riverine environment. coarse-grained sandstone and conglomerate with evidence of unidirectional flow Subtidal lagoon A restricted setting (e.g. intrashelf basin or Structureless, thinly laminated shale, setting cove) with quiet hydrodynamic conditions. mudstone, siltstone, and/or wackestone. The area from the fair-weather high tide Well-sorted and well-rounded, fine- to Supratidal zone / mark to the extreme limit of a beach, medium-grained sandstone with large-scale backshore (Clastic) which is inundated during storms. low-angle cross-bedding. Sandstone with primary current lineation (i.e. Intertidal zone / The area under water at high tide and seaward-dipping planar cross-bedded foreshore (Clastic) above water at low tide laminae), rill marks, and adhesion ripples. The area between the low tide line, and Upper shoreface / wave break marks, where sediments are Fine- to medium-grained sandstone with Proximal delta subjected to multidirectional current flows asymmetrical ripples and dunes and planar front (“mouth bar”) in the build-up and surf zones (plus fluvial laminated and trough cross-stratified beds (Clastic) discharge in the case of delta front). The area below the wave break mark and Contorted and hummocky cross-stratified, Lower shoreface / immediately above the fair-weather wave erosive-based, fine- to very fine-grained Distal delta front base, where sediments are dominantly sandstone. Symmetrical ripples and parallel (Clastic) affected by wave action (plus fluvial lamination are present. discharge in the case of delta front). The area between fair-weather and storm Offshore shelf Fine- to very fine-grained sandstone, wave bases, where sediments are transition / Prodelta siltstone, and mudstone. Ripples and sometimes affected by wave action (plus (Clastic) hummocky cross-stratified beds are present. fluvial discharge in the case of prodelta). Structureless mudstone and siltstone with The area below storm wave base on a shelf thin, laminated, graded beds of fine-grained Offshore shelf dominated by suspension fallout sandstone. Thin- to medium-bedded storm- (Clastic) deposition. generated turbidites are present as sole- marked sandstones with parallel lamination. Slope & basin The area off the shelf break dominated by Shale and mudstone containing sandstone (Clastic) gravity flow and hemipelagic deposition. turbidites, slumps, and exotic blocks. Supratidal zone / The area from the fair-weather high tide Dolostone with small-scale cross beds and backshore mark to the extreme limit of a beach, desiccation structures. (Carbonate) which is inundated during storms. Intertidal zone / Algal laminated limestone and dolostone The area under water at high tide and foreshore with desiccation structures, tidal channels, above water at low tide. (Carbonate) and small-scale cross beds. A biologically mediated carbonate build- Limestone and dolostone with grainstone, Reef (Carbonate) up or bioherm of metazoans or microbes. packstone, stromatolites, thrombolites. Grainstone and packstone with oolitic ridges Inner ramp The area below the low tide mark and interrupted by clastic intervals. Carbonates (Carbonate) above fairweather wave base. and clastics are segregated. Middle ramp The area below fairweather wave base and Wackestone and mudstone. Carbonates and (Carbonate) storm wave base. clastics (shale and siltstone) are well mixed. Micritic limestone with calcisiltite, shale, Outer ramp The area on a carbonate shelf below storm siltstone, and sandstone. Carbonates and (Carbonate) wave base. clastics are well mixed. The area on a carbonate shelf between a Oolitic lime sandstone or dolostone with Shelf edge barrier lagoon/basin and the slope/basin, where grainstone and evidence of exposure. (Carbonate) sediment occurs relatively shallow depths. Carbonates and clastics are well mixed. Slope & basin The area off a ramp or shelf dominated by Shale and mudstone with slump features. (Carbonate deposition of carbonate rock. Carbonates and clastics are segregated.

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Supplementary Table 5 | Non-metric multidimensional scaling scores. Scores were calculated from Kulczynski-2 and Jaccard indexes of taxonomic dissimilarity for 34 geologic formations of Ediacaran age containing 5 or more macrofossil genera or ichnogenera. Three scores were calculated for each formation and index.

Kulczynski-2 index Jaccard index Geologic formation NMDS1 NMDS2 NMDS3 NMDS1 NMDS2 NMDS3 "Ust-Pinega formation" (Russia) -1.26 -0.16 -0.80 -1.26 -0.16 -0.63 Basa Formation (Russia) 0.03 0.43 0.52 0.03 0.51 0.47 Blueflower Formation (Canada) 0.39 0.55 -0.19 0.39 0.44 -0.21 Bradgate Formation (UK) -0.91 0.95 0.35 -0.79 0.85 0.18 Briscal Formation (Canada) -1.37 0.78 0.16 -1.28 0.77 0.20 Chernyi Kamen Formation (Russia) -0.48 -0.32 -0.03 -0.58 -0.19 0.04 Dabis Formation (Namibia) 0.93 -0.15 -0.20 0.79 -0.13 -0.19 Deep Spring Formation (USA) 1.13 0.90 0.05 1.32 0.64 -0.21 Dengying Formation (China) 0.74 0.46 0.12 0.74 0.41 0.05 Doushantuo Formation (China) 0.13 -1.62 0.46 0.02 -1.46 0.53 Drook Formation (Canada) -0.90 0.91 -0.13 -0.81 0.89 -0.17 Erga Formation (Russia) -0.18 -0.54 -0.37 -0.14 -0.53 -0.45 Fermeuse Formation (Canada) -0.91 0.52 0.34 -0.74 0.59 0.27 Floyd Church Formation (USA) 0.74 0.39 -0.93 0.86 0.29 -0.93 Kauriyala Formation (India) -0.21 0.21 -0.76 -0.21 0.31 -0.75 Khatyspyt Formation (Russia) -0.18 -0.44 0.94 -0.17 -0.28 0.86 Lantian Formation (China) -0.26 -2.47 -0.77 -0.64 -2.25 -0.74 Lyamsta Formation (Russia) -0.55 -0.97 0.53 -0.55 -0.75 0.60 Mistaken Point Formation (Canada) -0.97 1.00 0.12 -0.84 0.94 0.00 Mogilev Formation (Ukraine) -0.34 -0.42 -0.89 -0.40 -0.34 -0.63 Nadaleen Formation (Canada) -0.88 0.83 0.02 -0.76 0.85 -0.10 Nagoryany Formation (Ukraine & Moldova) 1.01 -0.58 1.09 0.84 -0.58 1.07 Nudaus Formation (Namibia) 0.70 -0.07 -0.71 0.80 -0.10 -0.83 Perevalok Formation (Russia) -0.40 -0.95 0.93 -0.54 -0.57 1.02 Raiga Formation (Russia) 1.59 0.07 0.64 1.44 -0.14 0.66 Rawnsley Quartzite Formation (Australia) -0.12 0.00 -0.52 -0.06 -0.14 -0.57 Studenitsa Formation (Ukraine & Moldova) 0.33 -0.15 1.01 0.23 0.00 0.91 Trepassey Formation (Canada) -0.97 0.95 0.14 -0.85 0.86 0.04 Urusis Formation (Namibia) 1.21 0.21 -0.12 1.10 0.08 -0.07 Verhovka Formation (Russia) -0.28 -0.46 -0.48 -0.17 -0.68 -0.38 Wood Canyon Formation (USA) 1.78 0.49 -0.31 1.56 0.51 0.17 Yaryshev Formation (Ukraine) -0.85 -0.32 0.51 -0.72 -0.14 0.49 Zaris Formation (Namibia) 1.43 0.73 -0.20 1.62 0.22 -0.27 Zimnie Gory Formation (Russia) -0.15 -0.76 -0.52 -0.23 -0.74 -0.44

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Network Algorithm Nodes n Q Nodes n Q COPRA 4 0.84 4 0.81 Leading eigenvector 4 0.83 7 0.83

Louvain 6 0.81 5 0.78 Fast Greedy 5 0.74 4 0.80 links) Results genera Infomap Genera 9 0.81 Genera 9 0.76 Unipartite Ediacaran network of weighted weighted nonweighted -

( 9 0.81 16 0.75 5). Walktrap Edge-betweenness 23 0.80 (weighted links) 6 0.82 COPRA 3 0.39 3 0.52

DIRTLPAwb+ 7 0.14 7 0.35

LPAwb+ 13 0 13 0.23

LPBRIM 7 0.16 7 0.36 QuanBiMo 2 0.14 2 0.04 genera Simulated annealing 3 0.29 Genera 11 0.59 Adaptive BRIM 4 0.23 4 0.49 Environments and Ediacaran

paleoenvironments Leading eigenvector 6 0.14 6 0.41 Bipartite network of biSBM 4 0.21 4 0.24 COPRA 2 0.42 2 0.47

DIRTLPAwb+ 6 0.21 6 0.39 See Supplementary Figure Figure Supplementary See LPAwb+ 13 0.00 13 0.21 s ( LPBRIM 6 0.19 6 0.39 QuanBiMo 2 0.15 2 0.09 Simulated annealing 3 0.34 11 0.57 genera & Genera &

ichnogenera Adaptive BRIM 4 0.21 ichnogenera 4 0.46 Environments and Ediacaran

paleoenvironments Leading eigenvector 6 0.12 6 0.40 Bipartite network of biSBM 4 0.16 4 0.21 COPRA 6 0.64 6 0.55 DIRTLPAwb+ 6 0.44 6 0.52

LPAwb+ 55 0.10 55 0.25 LPBRIM 17 0.39 17 0.52 QuanBiMo 6 0.14 6 0.17 Simulated annealing 11 0.56 13 0.63 genera & Genera & Ediacaran Formations ichnogenera Adaptive BRIM 6 0.44 ichnogenera 6 0.60 formations and Leading eigenvector 8 0.45 8 0.55 Bipartite network of biSBM 4 0.32 4 0.27 COPRA 4 0.78 4 0.67

DIRTLPAwb+ 9 0.38 9 0.47 LPAwb+ 33 0.10 33 0.23

LPBRIM 11 0.41 11 0.47 QuanBiMo 11 0.30 11 0.39 he unipartite network of Ediacaran genera: one with weighted and one genera: one one and with non with weighted network of Ediacaran unipartite he

Simulated annealing 9 0.72 Genera 8 0.67 overlapping communities (n) and the associated extended modularity score (Q). Results are Results are score (Q). modularity the and extended communitiesassociated (n) overlapping Ediacaran - Adaptive BRIM Formations 6 0.47 6 0.52 shallow genera formations and 6 | Results of various network of partitioning6 | Results method Leading eigenvector 9 0.38 9 0.52 Bipartite network of biSBM 4 0.36 2 0.27 COPRA 3 0.73 3 0.65 DIRTLPAwb+ 8 0.39 8 0.39

LPAwb+ 41 0.11 41 0.24 LPBRIM 11 0.38 11 0.47 QuanBiMo 11 0.33 11 0.37 Simulated annealing 13 0.67 9 0.62 Genera & Ediacaran Formations ichnogenera Adaptive BRIM 6 0.35 ichnogenera 6 0.53 formations and

shallow genera & Leading eigenvector 9 0.29 9 0.49 Bipartite network of Supplementary Table Supplementary non of the number include two versions of t for included in this bipartite study. provided unipartiteof the networks of the all projections are for links. Values biSBM 4 0.33 2 0.27

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Supplementary Table 7 | Whole-network properties. Each row is a taxa projection, paleoenvironments projection (Environs.), or formations projection (Fms.) of a network in this study. Diameter (D), transitivity (T), modularity (Q), and edge-density (E-D) metrics were calculated for each projection. Diameter is the maximum degree of separation; transitivity is a measure of triadic closure (i.e. the probability that neighbors of a node are connected); and edge density is the ratio between the numbers of actual connections and possible links within a given network.

Network Projection D Q T E-D Figure

Unipartite (genera) Taxa 6 0.82 0.59 0.09 Fig. 2a Bipartite Environs. 2 0.39 0.83 0.78 (paleoenvironments/ Fig. 3a Ediacaran genera) Taxa 3 0.51 0.79 0.41 Bipartite Environs. 2 0.38 0.85 0.82 Supplementary Fig. (paleoenvironments/Ediacaran 10a genera & ichnogenera) Taxa 3 0.50 0.80 0.44

Bipartite Fms. 4 0.63 0.58 0.21 (Ediacaran formations/ Fig. 4a genera & ichnogenera) Taxa 5 0.51 0.62 0.16

Bipartite Fms. 5 0.69 0.62 0.22 Supplementary Fig. (Ediacaran formations/shallow 11a biotope genera & ichnogenera) Taxa 5 0.65 0.69 0.30

Bipartite Fms. 6 0.74 0.72 0.23 Supplementary Fig. (Ediacaran formations/ 12a shallow biotope genera) Taxa 6 0.66 0.73 0.28

Bipartite Fms. 5 0.64 0.60 0.15 (Ediacaran-Fortunian Supplementary Fig. formations/ Taxa 6 0.77 0.62 0.10 13a genera & ichnogenera)

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Supplementary Table 8 | Assortativity coefficients. Each row is a taxa projection or formations projection (Fms.) of a network in this study. Assortativity coefficients measure homophily (mixing) of nodes with respect to nominal and continuous properties. Assortativity coefficients, measuring mixing with respect to node degrees (D), were determined for all projections. Additionally, assortativity coefficients were calculated for taxa projections from data on the preservational modes (P), morphogroups (M), and form categories (F) of the genera and ichnogenera. Lastly, assortativity coefficients were calculated for formation projections from data on the geoplates (G), regions/continents (R), and countries (C) of the geologic units.

Network Projection D P M F G R C Figure

Unipartite (genera) Taxa -0.05 0.49 0.11 0.24 N/A N/A N/A Fig. 2a Bipartite (paleoenvironments/ Taxa 0.09 0.15 0.02 0.04 N/A N/A N/A Fig. 3a Ediacaran genera) Bipartite Suppl. (paleoenvironments/Ediacaran Taxa 0.10 0.11 0.01 0.03 N/A N/A N/A Fig. 10a genera & ichnogenera)

Bipartite Fms. -0.14 N/A N/A N/A 0.03 0.08 0.07 (Ediacaran formations/ Fig. 4a genera & ichnogenera) Taxa 0.00 0.29 0.06 0.12 N/A N/A N/A

Bipartite Fms. -0.14 N/A N/A N/A 0.03 0.04 0.06 Suppl. (Ediacaran formations/shallow Fig. 11a biotope genera & ichnogenera) Taxa -0.07 0.12 0.03 0.09 N/A N/A N/A

Bipartite Fms. -0.05 N/A N/A N/A 0.03 0.04 0.08 Suppl. (Ediacaran formations/ Fig. 12a shallow biotope genera) Taxa 0.01 0.17 0.04 0.12 N/A N/A N/A

Bipartite Fms. 0.01 N/A N/A N/A 0.06 0.10 0.09 (Ediacaran-Fortunian Suppl. formations/ Fig. 13a Taxa 0.01 0.62 0.12 0.40 N/A N/A N/A genera & ichnogenera)

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