DOI: 10.5061/dryad.fxpnvx0q6
title: Data from: Palaeontology meets metacommunity ecology: The Maastricthian dinosaur fossil record of North America as a case study

description: Documenting the patterns and potential associated processes of ancient
 biotas has always been a central challenge in palaeontology. Over the last
 decades, intense debate has focused on the organisation of
 dinosaur–dominated communities, yet no general consensus has been reached
 on how these communities were organised in a spatial context and if
 primarily affected by abiotic or biotic agents. Here, we used analytical
 routines typically applied in metacommunity ecology to provide novel
 insights into dinosaurian distributions across the latest Cretaceous of
 North America. To do this, we combined fossil occurrences with functional,
 phylogenetic and palaeoenvironmental modelling, and adopted the
 perspective that more reasonable conclusions on palaeoecological
 reconstructions can be gained from studies that consider the organisation
 of biotas along ecological gradients at multiple spatial scales. Our
 results showed that dinosaurs were restricted in range to different parts
 of the Hell Creek Formation, prompting the recognition of discrete and
 compartmentalised faunal areas during the Maastrichtian at fine-grained
 scales, whereas taxa ranges formed quasi–nested groups when combining data
 from various geological formations across the Western Interior of North
 America. Although groups of dinosaurs had coincident range boundaries,
 their communities responded to multiple ecologically–important gradients
 when compensating for differences in sampling effort. Metacommunity
 structures of both ornithischians and theropods were correlated with
 climatic barriers and potential trophic relationships between herbivores
 and carnivores, thereby suggesting that dinosaurian faunas were shaped by
 physiological constraints and a combination of bottom-up and top-down
 forces across multiple spatial grains and extents.
description: Dinosaur occurrences for
 the Maastrichtian of North America were retrieved from the Palaeobiology
 Database &lt;PaleoDB.org&gt; on May 2020, using the taxon name
 ‘<i>Dinosauria</i>’ and a time span of 72.1 – 66.0 Ma.
 Critically, although studies on modern community associations are limited
 to relatively brief periods of sampling time, fossil assemblages are
 windows on the faunas of ancient worlds occurring within particular
 chronostratigraphic units (Benson <i>et al.</i> 2018).
 Although this coarse temporal resolution will undoubtedly confound the
 data (which is addressed in detail below), it would be problematic to
 subdivide the time bins further, not least because only a handful of
 fossil assemblages are sufficiently informative to provide confident
 community-level estimates so far (Vavrek & Larsson 2010).
 Additionally, due to an insufficient amount of comparative data within
 high–resolution time bins (Dean <i>et al.</i> 2020) and the
 inherent errors in radiometric dating (Gates <i>et al.</i>
 2010), the creation of a more tightly constrained correlative window is
 presently impractical. Here, we only retained occurrences belonging to
 Ornithischia and Theropoda since these two clades were the most diverse
 and abundant non–avian dinosaur groups in the latest Cretaceous of North
 America (Brusatte <i>et al.</i> 2015). Generic–level
 identifications were used in our study, and all avian taxa were excluded
 when delineating community types to keep our data more comparable to
 previous works (e.g. Vavrek & Larsson 2010; Dean <i>et
 al.</i> 2020). While birds are phylogenetically part of the
 dinosaurian clade, the different habits and habitats of latest Cretaceous
 Avialae (either diving or volant taxa) separates these faunas enough from
 ground-dwelling dinosaurs to justify their functional distinction in the
 context of the communities modelled here (see Heino <i>et
 al.</i> 2015b for an example on present-day biotas). Although the
 value of generic taxonomic ranks in community analyses has been debated,
 palaeontologists have used generic–level clades to investigate
 distributional patterns and variation in community composition of fossil
 taxa (e.g. Vavrek & Larsson 2010; Chiarenza <i>et
 al.</i> 2019; Dean <i>et al.</i> 2020). Indeed,
 generic–level identifications are preferred over species taxonomic ranks
 in dinosaur palaeobiology studies as most dinosaur genera
 (<i>c.</i> 87%) are easily diagnosed and monospecific
 (Weishampel <i>et al.</i> 2004; Mannion <i>et
 al.</i> 2012). Moreover, genus-level and species–level diversity
 patterns generally appear to track each other for Mesozoic tetrapods
 (Barrett <i>et al.</i> 2009), and genera are more
 taxonomically stable than species for many groups (Robeck <i>et
 al.</i> 2000). Here, however, taxa with unclear genus identification
 were discarded (i.e. we did not incorporate ‘cryptic’ diversity
 represented by taxonomically undiagnostic fossil remains that potentially
 represent distinct taxa, nor we did infer ghost lineages based on
 phylogenetic diversity estimates; Barrett <i>et al.</i> 2009;
 Mannion <i>et al.</i> 2011), and so were collections lacking
 formational assignment. If questionable ages appeared (e.g. ages notably
 deviating from ages of other collections from the same formation), they
 were either revised or excluded. These data are an up–to–date record of
 North American dinosaur faunas and therefore incorporate new Late
 Cretaceous fossils discovered over the past few years. Overall, our pruned
 dataset comprised 43 dinosaur genera, and consisted of 11 formations
 across the WIB of North America and 17 well–sampled locations across the
 Hell Creek landscape.   <i>Palaeoclimatic general
 circulation model. </i>In this study, we used palaeoclimatic model
 outputs (here, near-surface [1.5 m] mean annual temperature
 (<i>TempMean</i>), near-surface [1.5 m] annual temperature
 standard deviation (<i>TempSDann</i>), annual average
 precipitation (<i>PrecMean</i>) and annual precipitation
 standard deviation (<i>PrecSDann</i>)) from the fully coupled
 atmosphere-ocean GCM HadCM3L v. 4.5 Atmospheric–Ocean General Circulation
 Model (Valdes <i>et al.</i> 2017). More specifically, we
 followed the nomenclature of Valdes <i>et al.</i> (2017) and
 applied the HadCM3BL–M2.1aE version of the model. The conditions of the
 model simulations for the Maastrichtian consist of an atmospheric
 CO<sub>2</sub> concentration of 1120 ppmv, which is within the
 range of uncertainty provided by the recent proxy
 pCO<sub>2</sub> reconstructions of Foster <i>et
 al.</i> (2017). The model simulations were run for a total of 1422
 years, and the climate variables used in our analyses were an annual
 average of the last 30 years of these simulations. HadCM3L has contributed
 to the Coupled Mode Intercomparison Project experiments demonstrating
 skill when it comes to reproducing present-day climates (Collins
 <i>et al. </i>2001; Valdes <i>et al.</i> 2017) and
 has also been used for an array of different palaeoclimate evaluations
 during the Eocene (Lunt <i>et al.</i> 2012), the Oligocene (Li
 <i>et al.</i> 2018) and the Miocene (Bradshaw <i>et
 al.</i> 2012). Detailed information on this palaeoclimatic model,
 including large–scale circulation (and associated energy and momentum
 fluxes) and temporal fluctuations, as well as the impacts of fine-scale
 orographic features on climate signals, are available elsewhere (e.g. Lunt
 <i>et al.</i> 2016; Chiarenza <i>et al.</i>
 2019). <i>Palaeogeographical
 digital elevation models (DEMs). </i>The Maastrichtian
 palaeogeography for this study is that of Scotese & Wright (2018),
 which has been compiled as a palaeo-digital elevation model to facilitate
 grid-based analyses. In brief, these maps were created from publicly
 available stratigraphic literature, supplemented by fieldwork, including
 lithology, palaeoenvironmental information and broad-scale facies
 identification. For large–scale analyses, these palaeogeographies were
 upscaled to the palaeoclimatic model resolution (3.75° x 2.5°). This means
 that topographic and bathymetric information was broadly conserved, as it
 was resolved at a lower resolution (see Chiarenza <i>et
 al.</i> 2019 for a similar approach). <i>Functional features.
 </i>Each dinosaur taxon was classified into several functional
 guilds based on body mass (very small, small, medium, large and very
 large), locomotor mode (bipeds, facultative bipeds –capable of both
 quadrupedal and bipedal motion– and quadrupeds) and trophic habits
 (carnivores, omnivores and herbivores, and for the latter, low and high
 browsers). Body
 mass is perhaps the single most important and meaningful functional trait
 for animals, as it ultimately affects many aspects of their biology
 including metabolic rates, mechanical constraints, ecological performance
 and lifestyle strategies related to feeding, locomotion and reproduction
 (Loeuille & Loreau 2006; Iossa <i>et al.</i> 2008).
 Here, we used body mass estimates (<i>very small</i> ≤ 10 kg;
 10 kg &lt; <i>small</i> <i>≤</i> 100 kg; 100
 kg &lt; <i>medium</i> <i>≤</i> 1000 kg; 1000
 kg &lt; <i>large</i> <i>≤</i> 10000 kg;
 <i>very large</i> &gt; 10000 kg; Noto & Grossman
 2010) based on adult representatives from the comprehensive dataset of
 Benson <i>et al.</i> (2014), which provides a wide list of
 dinosaur taxa using the scaling relationship of limb bone robustness
 (stylopodial circumference; Campione & Evans 2012). To obtain a
 more comprehensive understanding of body mass distributions in our data,
 we further applied an inflection point criterion based on the Barry
 & Hartigan (1993) product partition model with Markov chain Monte
 Carlo (MCMC). More specifically, this algorithm used the posterior
 probability of changes over 10000 MCMC iterations, excluding the first
 1000 as burn in, to distinguish among different body mass categories in
 the latest Cretaceous dinosaurs of North America. Interestingly, this
 Bayesian analysis roughly identified most of the original body mass
 categories used in our study, with each category broadly representing an
 order of magnitude (García<i>–</i>Girón <i>et
 al.</i> 2020b, appendix S1, fig. S1). Trophic habits refer to the food
 processing strategies and diet of an animal, and it generally includes
 three primary categories, i.e. carnivores, herbivores and omnivores.
 Further subdivisions depend on the biological knowledge of the morphology
 (e.g. teeth morphology and skull) and behaviour of the study organismal
 group. Here, we assigned herbivores to categories of browse height rather
 than plant type due to the virtually unknown nature of plant preferences
 in dinosaurs. More specifically, we roughly assigned a simple maximum
 browsing limit (<i>low</i> <i>≤</i> 2 m; <i>high</i> &gt; 2 m) based on characters such as limb length and neck posture using Noto & Grossman (2010) and Mallon <i>et al.</i> (2013). We further divided locomotor mode into two major categories: quadrupeds and bipeds. For those taxa with intermediate axial and limb morphologies in proportions between those of bipeds and obligate quadrupeds (e.g. Hadrosauridae), we included an additional locomotor division, i.e. facultative bipeds (see Noto & Grossman, 2010 for a similar approach). For the following analyses, we applied the mixed–variables coefficient of distance (i.e. a generalisation of Gower’s distance; Pavoine <i>et al.</i> 2009) to extract a functional distance matrix, which described the functional differences between all taxon pairs based on body mass, trophic habits and locomotor mode (e.g. Heino & Tolonen 2017). Thereafter, the pairwise output values for the functional distance matrix were synthesised into separate axes using principal coordinate analysis (PCO) and following Duarte <i>et al.</i> (2012). See the main text for References.
description: <strong>Additional Supporting files</strong> include
 the following Appendices: <em>Appendix S1.</em> Body mass distributions based on product partition models with Markov sampling computations.             <em>Appendix S2.</em> Functional and phylogenetic features for each spatial scale and study clade.             <em>Appendix S3.</em> R packages and statistical routines.             <em>Appendix S4. </em>Elements of metacommunity structure for the conservative fixed–fixed null model.             <em>Appendix S5.</em> Results for the forward selection of explanatory variables.             <em>Appendix S6.</em> Results for ordinary least squares (OLS) regression models.             <em>Appendix S7.</em> Results for commonality analysis (CA) for each spatial scale and study clade.            <em> Appendix S8.</em> Measuring the spatial autocorrelation of OLS model residuals. The Excel file includes occurrence data, palaeoenvironmental reconstructions, and functional features: <strong>Sheets 1 and 2 </strong>contain raw information on each study site for the Hell Creek and other North American geological formations, respectively. <strong>Sheet 1 </strong>includes palaeoenvironmental information for the Hell Creek Formation (i.e. lithofacies -C, channel; FP, floodplain- and palaeotopography -m.a.s.l. after log-transformation). Raw PalaeoDEM data (Scotese and Wright, 2018) are also available here: https://www.earthbyte.org/paleodem-resource-scotese-and-wright-2018/ <strong>Sheet 2</strong> contains raw information on the log-transformed palaeoenvironmental reconstructions for the Maastrichtian of North America (<em>Palaeotopography</em> -m.a.s.l., <em>TempMean</em> and <em>TempSDann </em>in K; <em>Prec</em> and <em>PrecSDann</em> in kgm-2). Raw palaeoclimate GCMs (Valdés et al., 2017) can also be obtained here: https://www.paleo.bristol.ac.uk/ummodel/scripts/papers/ <strong>Sheet 3 </strong>includes a taxon-specific classification into several functional guilds (see the main text for details): These files may be opened and edited in Excel. <br> For details or further queries, please contact Jorge García-Girón (jogarg@unileon.es).