University of Birmingham Quantifying the Completeness of the Bat Fossil

University of Birmingham

Quantifying the completeness of the bat fossil record Brown, Emily; Cashmore, Daniel; Simmons, Nancy; Butler, Richard

DOI: 10.1111/pala.12426 License: Other (please specify with Rights Statement)

Document Version Peer reviewed version Citation for published version (Harvard): Brown, E, Cashmore, D, Simmons, N & Butler, R 2019, 'Quantifying the completeness of the bat fossil record', Palaeontology, vol. 62, no. 5, pp. 757-776. https://doi.org/10.1111/pala.12426

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This is the peer reviewed version of the following article: Brown, E. E., Cashmore, D. D., Simmons, N. B. and Butler, R. J. (2019), Quantifying the completeness of the bat fossil record. Palaeontology. doi:10.1111/pala.12426, which has been published in final form at https://doi.org/10.1111/pala.12426. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.

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1 1 2 3 1 QUANTIFYING THE COMPLETENESS OF THE BAT FOSSIL RECORD 4 5 6 2 EMILY E. BROWN1, DANIEL D. CASHMORE1, NANCY B. SIMMONS2, RICHARD J. 7 8 1 9 3 BUTLER 10 11 1 12 4 School of Geography, Earth and Environmental Sciences, University of Birmingham, 13 14 5 Edgbaston, Birmingham, B15 2TT, UK 15 16 17 6 2Department of Mammalogy, Division of Vertebrate Zoology, American Museum of Natural 18 19 7 History, New York, New York, 10024, USA 20 21 22 8 23 24 25 9 Corresponding authors: 26 27 28 10 Emily E. Brown ([email protected]), Daniel D. Cashmore 29 30 11 ([email protected]) 31 32 33 12 34 35 36 13 37 38 39 14 40 41 42 15 43 44 45 16 46 47 48 17 49 50 51 18 52 53 54 19 55 56 57 20 58 59 60

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2 1 2 3 21 Abstract: Bats (Chiroptera) are one of the most successful extant mammalian orders, 4 5 6 22 uniquely capable of powered flight and laryngeal echolocation. The timing and evidence for 7 8 23 evolution of their novel adaptations have been difficult to ascertain from the fossil record due 9 10 24 to chronological gaps and the fragmentary nature of most fossil bat material. Here, we 11 12 quantify the quality of the bat fossil record using skeletal and character completeness metrics, 13 25 14 15 26 which respectively document for each taxon what proportion of a complete skeleton is 16 17 27 preserved, and the proportion of phylogenetic characters that can be scored. Completeness 18 19 28 scores were collected for 441 valid fossil bat species in 167 genera from the Eocene to 20 21 22 29 Pleistocene. All metrics record similar temporal patterns: peak completeness in the Lutetian 23 24 30 stage reflects the presence of Lagerstätten, while subsequent stages have very low 25 26 31 completeness, excepting an Aquitanian high and a Pleistocene peak in skeletal completeness. 27 28 29 32 Bat completeness is not correlated with intensity of sampling through geological time but has 30 31 33 a weak negative correlation with publication date. There is no correlation between taxonomic 32 33 34 richness and completeness, as the bat record predominately consists of diagnostic but isolated 34 35 teeth. Consequently, bat skeletal completeness is the lowest of any previously assessed 36 35 37 38 36 tetrapod group, but character completeness is similar to parareptiles and birds. Bats have 39 40 37 significantly higher character completeness in the northern hemisphere, likely due to 41 42 38 heightened historical interest and presence of Lagerstätten. Taxa derived from caves are more 43 44 45 39 complete than those from fluviolacustrine and marine deposits, but do not preserve highly 46 47 40 complete specimens. 48 49 50 41 51 52 53 42 54 55 56 43 KEYWORDS: Chiroptera, completeness metrics, Cenozoic, caves, Lagerstätten, sampling 57 58 44 bias 59 60

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3 1 2 3 45 4 5 6 46 INTRODUCTION 7 8 9 47 Bats (Chiroptera) are one of the most successful and diverse orders of extant mammals. They 10 11 12 48 have a nearly global distribution, a varied range of body sizes, are known to have 13 14 49 insectivorous, carnivorous, frugivorous, nectarivorous and hematophagous diets, and with 15 16 50 nearly 1400 species, bats account for ~20% of all extant mammalian species (Wilson and 17 18 51 Reeder 2005; Shi and Rabosky 2015; Tsang et al. 2015; Simmons and Cirranello 2018). Bats 19 20 21 52 are the only living mammals, and one of only three vertebrate groups (along with pterosaurs 22 23 53 and birds), that are capable of true self-powered flight (Padian 1985; Norberg 1990; Templin 24 25 54 2000; Dudley et al. 2007; Swartz et al. 2012). The extreme elongation of their forelimb digits 26 27 28 55 plus development of a thin interconnected membrane (patagium) gives bats a unique body 29 30 56 plan (Swartz et al. 1992; Swartz and Middleton 2008). They are also the only mammals 31 32 57 capable of sophisticated laryngeal echolocation, in which they emit high-frequency sounds 33 34 35 58 and interpret returning echoes to perceive their environment, enabling ‘visualisation’ in 36 37 59 complete darkness (Arita and Fenton 1997; Teeling et al. 2012). Echolocation is the 38 39 60 dominant mode of sensory perception in all bat families, with the exception of many of the 40 41 61 fruit bats (Pteropodidae), which instead rely primarily on vision and olfaction (Arita and 42 43 44 62 Fenton 1997; Boonman et al. 2013). Understanding the timing of and mechanisms 45 46 63 underpinning the evolution of the unique adaptations of bats is an important goal of 47 48 64 evolutionary biologists. 49 50 51 65 Monophyly of Chiroptera is consistently strongly supported by morphological and 52 53 54 66 molecular analyses (e.g. Springer et al. 2001; Teeling et al. 2005; Song et al. 2012; Meredith 55 56 67 et al. 2011; O’Leary et al. 2013; Tsagkogeorga et al. 2013; Lei and Dong 2016). The relative 57 58 68 position of Chiroptera within the placental mammal tree has been the subject of debate, but it 59 60

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4 1 2 3 69 is now uniformly placed within Laurasiatheria (Springer et al. 2001; Teeling et al. 2002; 4 5 6 70 Song et al. 2012; Meredith et al. 2013; O’Leary et al. 2013; Tsagkogeorga et al. 2013; 7 8 71 Halliday et al. 2017). Chiroptera is divided into two suborders, Yinpterochiroptera and 9 10 72 Yangochiroptera, based on morphological, molecular, and genomic evidence (Teeling et al. 11 12 2002; Meredith et al. 2013; Tsagkogeorga et al. 2013; Lei and Dong 2016). These suborders 13 73 14 15 74 are not distinguished by echolocation ability (both clades contain laryngeal echolocators), and 16 17 75 differ from the traditional division of bats into Megachiroptera and Microchiroptera (Teeling 18 19 76 et al. 2005; O’Leary et al. 2013). Time-calibrated molecular and genomic phylogenies 20 21 22 77 indicate that bats evolved shortly after the Cretaceous-Palaeogene (K-Pg) boundary, with the 23 24 78 split of Yinpterochiroptera and Yangochiroptera occurring around 63 million years ago (Ma) 25 26 79 (Teeling et al. 2005; Lei and Dong 2016). Analyses combining morphological and molecular 27 28 29 80 data similarly indicate an early origin around the K-Pg boundary, but suggest that the bat 30 31 81 crown group diversified later, perhaps 57 Ma (O’Leary et al. 2013). Fossils could potentially 32 33 82 provide the critical direct evidence of these relationships and divergence dates. However, the 34 35 fossil record does not shed light on earliest history of bat evolution as the first unambiguous 36 83 37 38 84 bats occur only in the early Eocene, leaving a 10-million-year gap between the apparent 39 40 85 origin of the lineage and the first recognizable bat fossils (Gunnell and Simmons 2005). 41 42 43 86 Despite existence of several well-known complete skeletons, the fossil record of bats 44 45 87 has generally been regarded as being exceptionally poor (Gunnell and Simmons 2005; 46 47 48 88 Teeling et al. 2005; Eiting and Gunnell 2009). There are large chronological gaps, and most 49 50 89 taxa are known from isolated teeth, jaw fragments, or postcranial elements. Only a small 51 52 90 number of Eocene taxa and one Miocene taxon are known from Lagerstätten. Consequently, 53 54 55 91 our understanding of the evolution of early bats, including their biology and functional 56 57 92 morphology, comes predominantly from a few complete or near complete fossils recovered 58 59 93 from Lagerstätten. The fragmentary nature of the bat record has almost exclusively been 60

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5 1 2 3 94 proposed to result from the fragility of their skeletons (Gunnell and Simmons 2005). It is very 4 5 6 95 important to take into account the limitations of a fossil record when making inferences about 7 8 96 the evolution of a group. The quality of the bat fossil record has been previously quantified 9 10 97 twice. Teeling et al. (2005) concluded that 61% of the bat fossil record is missing based on 11 12 the extent to which the fossil record underestimates first appearances derived from molecular 13 98 14 15 99 phylogenies. Eiting and Gunnell (2009) calculated that only 12% of bat fossil occurrence 16 17 100 ranges represent ‘true’ temporal ranges, based on the phylogenetically independent extinction 18 19 101 rates and preservation potential (Foote and Raup 1996). Both of these assessments analysed 20 21 22 102 the absolute completeness of the fossil record - out of all bats that have ever existed, the 23 24 103 relative percentage that are actually represented in the fossil record. 25 26 27 104 Completeness of the fossil record, and the impact(s) that missing data may have on 28 29 105 interpretations of modes and patterns of evolution, have been a source of discussion for 30 31 32 106 several decades (e.g. Dingus 1984; Foote et al. 1999; Benton et al. 2000; Smith 2001; Cooper 33 34 107 et al. 2006). Recently, numerous studies have assessed completeness of the fossil record 35 36 108 based on specimen-level data (Benton et al. 2004; Fountaine et al. 2005; Smith 2007; Dyke et 37 38 109 al. 2009; Benton 2010; Mannion and Upchurch 2010; Brocklehurst et al 2012; Walther and 39 40 41 110 Fröbisch 2013; Brocklehurst and Fröbisch 2014; Cleary et al. 2015; Dean et al. 2016; 42 43 111 Verrière et al. 2016; Davies et al. 2017). These studies aim to assess variation in information 44 45 112 content of fossil taxa and the entire record of a group, rather than trying to quantify temporal 46 47 48 113 gaps in the record or stratigraphic fit. A high-quality record defined using this approach 49 50 114 would be one that contains many highly complete taxa. Meaningful comparisons can 51 52 115 subsequently be drawn to various sampling biases that can influence the macroevolutionary 53 54 55 116 understanding of a group. Specimen quality of a group or time bin can theoretically be 56 57 117 influenced by environmental and geological parameters, such as the types of depositional 58 59 118 regimes or the number of localities preserved in the record (Dingus 1984; Retallack 1984). A 60

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6 1 2 3 119 high number of localities from depositional settings with higher quality preservation could 4 5 6 120 lead to increased specimen completeness. Specimen quality can be associated with body size 7 8 121 or the robustness of skeletons (Cooper et al. 2006; Brown et al. 2013). The anthropogenic 9 10 122 sampling of a group or time bin can also potentially influence the quality of the fossil record, 11 12 as variation in historical or geographical sampling by palaeontologists could reduce or 13 123 14 15 124 increase taxon completeness. Incomplete specimens may also be hard to identify 16 17 125 taxonomically, either reducing estimates of diversity for a group or time bin or, conversely, 18 19 126 increasing diversity as a result of taxonomic oversplitting (Brocklehurst and Fröbisch 2014) 20 21 22 127 based on partial skeletons. 23 24 25 128 A number of completeness metrics have been conceived to accurately quantify the 26 27 129 specimen completeness of different groups (Mannion and Upchurch 2010). Previous studies 28 29 130 have found varying correlations between these completeness metrics and changes in diversity 30 31 32 131 and fossil record sampling metrics through time (Mannion and Upchurch 2010; Brocklehurst 33 34 132 et al 2012; Walther and Fröbisch 2013; Brocklehurst and Fröbisch 2014; Cleary et al. 2015; 35 36 133 Dean et al. 2016; Verrière et al. 2016; Davies et al. 2017), thus highlighting the various 37 38 134 biases acting upon different fossil records. 39 40 41 135 Here, we quantitatively assess the fossil record of bats using several completeness 42 43 44 136 metrics originally developed by Mannion and Upchurch (2010) and Beardmore et al. (2012). 45 46 137 We statistically compare the relationships between completeness and temporal and 47 48 138 geographical distributions, depositional environments, rock record and taxonomic diversity 49 50 51 139 changes through geological time, and human research effort through historical time. We 52 53 140 aimed to understand the extent of the environmental and geological controls acting on the bat 54 55 141 fossil record, and to compare bat completeness to other tetrapod groups. 56 57 58 142 59 60

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7 1 2 3 143 MATERIALS AND METHODS 4 5 6 144 7 8 9 145 Dataset 10 11 12 146 Detailed records of the preserved osteological elements from over 4000 specimens of fossil 13 14 bat were compiled from photographs, illustrations, and descriptions from published literature 15 147 16 17 148 and museum catalogues. A list of genera from Eiting and Gunnell (2009) was used as the 18 19 149 basis for taxon inclusions, but any genera named since Eiting and Gunnell (2009) were also 20 21 150 collected from the literature. The Paleobiology Database (PaleoDB, www. paleobiodb.org) 22 23 24 151 was used to determine the validity of each taxon as of July 2018. We gathered completeness 25 26 152 scores for most specimens of each species and also for material indeterminate at species level 27 28 153 (e.g. Archaeonycteris sp.). Specimens taxonomically assigned using the cf. connotation were 29 30 31 154 treated as true representatives of those taxa. To avoid duplication of effort, for each 32 33 155 individual species additional referred specimens were only added to the dataset if the skeletal 34 35 156 elements represented were not already present in the material scored for that species (for 36 37 example, if the first specimen scored for a species included a complete right mandible, further 38 157 39 40 158 specimens of right mandibles for that species were not added to the dataset as they do not add 41 42 159 to the scores for the completeness metrics used). Only specimens from intervals prior to the 43 44 160 Holocene were recorded due to the vast number of specimens in the most recent deposits. 45 46 47 161 “Quaternary” or “sub-Recent” deposits were therefore only included if explicitly cited as 48 49 162 Pleistocene or dated older than 11,700 years old. In addition to preservation quality, 50 51 163 information on depositional environment (either ‘fluvio-marine’, defined as fluviolacustrine 52 53 54 164 and marine sedimentary regimes, or ‘caves’, here encompassing depositional settings defined 55 56 165 as caves, karstic fissure fills and sinkholes in the literature; Jass and George 2010) and 57 58 166 geographic locality were also recorded. Specimen records that were indeterminate at the 59 60

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8 1 2 3 167 species-level were excluded from species-level analyses, but were included when assessing 4 5 6 168 generic-level completeness. The final dataset consists of skeletal and character completeness 7 8 169 data for 441 fossil species in 167 genera and is up to date as of July 2018 (see Brown et al. 9 10 170 2019). 11 12 13 171 14 15 16 172 Completeness metrics 17 18 19 173 Fossil record quality has been examined previously using a wide range of methods. 20 21 174 Individual taxon completeness has been increasingly used to assess variation in fossil record 22 23 24 175 quality over the last decade, and applied to a variety of taxonomic groups. Initial methods for 25 26 176 quantifying taxon completeness were relatively crude and somewhat subjective. Benton et al. 27 28 177 (2004), Fountaine et al. (2005) and Benton (2008) scored the completeness of known fossil 29 30 31 178 material for a species by splitting preservation quality into between four and five categories 32 33 179 (e.g. ‘one bone’, ‘> one bone’, ‘one specimen’, ‘more than one specimen’; Fountaine et al. 34 35 180 2005). Similarly, Beardmore et al. (2012) assessed species completeness using five categories 36 37 (0-4) of preservation quality, but calculated the preservation quality separately for six skeletal 38 181 39 40 182 regions. 41 42 43 183 Two new completeness metrics, the skeletal completeness metric (SCM) and the 44 45 184 character completeness metric (CCM), were devised by Mannion and Upchurch (2010) in an 46 47 185 attempt to quantify specimen completeness more objectively and in more detail. SCM is a 48 49 50 186 measure of the absolute proportion of the skeleton that is preserved for a species, whereas 51 52 187 CCM measures the proportion of phylogenetically informative characters preserved. Each has 53 54 188 two implementations, respectively designated as SCM1/SCM2 and CCM1/CCM2. The first 55 56 57 189 implementation (SCM1 and CCM1) determines completeness for a species based on its most 58 59 190 completely known specimen, while the second uses all known specimens of a species in 60

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9 1 2 3 191 calculating completeness. The second implementation has been generally favoured in 4 5 6 192 previous studies (Brocklehurst et al. 2012; Dean et al. 2016; Verrière et al. 2016) and is also 7 8 193 preferred here. These metrics have been widely used in recent completeness studies on a 9 10 194 variety of fossil groups (Brocklehurst et al. 2012; Walther and Fröbisch 2013; Brocklehurst 11 12 and Fröbisch 2014; Cleary et al. 2015; Dean et al. 2016; Verrière et al. 2016; Davies et al. 13 195 14 15 196 2017; Tutin and Butler 2017). 16 17 18 197 For the SCM, relative proportions of skeletal elements need to be determined. 19 20 198 Different methods for calculating the relative proportions of skeletal regions have been 21 22 199 developed: Brocklehurst and Fröbisch (2014) modelled different pelycosaur-grade synapsid 23 24 25 200 bones as simple 3-dimensional shapes (e.g. cones, cylinders, etc.) to work out the relative 26 27 201 volumes of skeletal regions. Verrière et al. (2016) revised this method to increase accuracy 28 29 202 for use with parareptiles by modelling elements with truncated shapes to work out the surface 30 31 32 203 area (instead of the volume) of different skeletal regions. Here the skeletal proportions were 33 34 204 determined from: (1) the surface area of bones based on a 2D scientifically-informed skeletal 35 36 205 reconstruction (Fig. 1A-B), and (2) from bone volume of a CT-scanned 3D skeleton (Fig. 37 38 206 1C). These were used to generate “SCM2 2D” and “SCM2 3D” scores. Lateral view skeletal 39 40 41 207 reconstructions of Icaronycteris index (an Eocene fossil bat) and Myotis myotis (an extant 42 43 208 vespertilionid bat) were redrawn from Wimsatt (2012) in Adobe Illustrator (CS5, 44 45 209 www.adobe.com). The surface area of all skeletal elements was calculated for these two 46 47 48 210 species using a free Illustrator plug-in, Patharea Filter 49 50 211 (http://telegraphics.com.au/sw/product/patharea). The relative weighting of different skeletal 51 52 212 regions was determined respectively in both species. The relative skeletal proportions of 53 54 55 213 Icaronycteris index were attributed to all species belonging to extinct genera, while the 56 57 214 proportions of Myotis myotis were used for members of extant genera. The use of both 58 59 215 Ypresian and extant taxa – species that bracket the majority of the phylogenetic tree of bats 60

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10 1 2 3 216 (Simmons and Giesler 1998; Simmons et al. 2008) – accounts for some of the major 4 5 6 217 morphological changes in bat evolution; however, there are limitations as many families or 7 8 218 even genera exhibit large morphological disparity (Freeman 2000; Shi et al. 2018) not 9 10 219 captured by simple segregations of taxa as either extinct or extant. 11 12 13 220 The 3D method utilised a CT scan of a preserved extant specimen of Myotis 14 15 221 daubentonii (AMNH 218932). AMNH 218932 was scanned by NBS at The University of 16 17 18 222 Texas High-Resolution X-ray CT Facility as 1595 coronal slices (1024 x 1024 pixels, 0.036 19 20 223 voxels). Skeletal elements were virtually segmented using the ‘Masks’ and ‘Curves’ tools in 21 22 224 SPIERSedit (2.20, www.spiers-software.org). The ‘Measure volumes’ option provided the 23 24 25 225 volume (in voxels) of each element which was used to determine the relative weighting of 26 27 226 skeletal regions. This 3D approach may be the most accurate representation of relative 28 29 227 proportions used yet in the literature. The calcar was removed from the 3D skeletal body 30 31 32 228 proportions for extinct taxa as the 2D proportions for extinct taxa derived from analysis of 33 34 229 Icaronycteris index lacked the element. 2D SCM2 and 3D SCM2 scores of extant taxa were 35 36 230 statistically compared to directly test the validity of using 2D scientifically informative 37 38 231 diagrams to estimate relative skeletal proportions. 39 40 41 232 In addition to the methods described above, we also used a modified version of 42 43 44 233 SCM2. Traditional SCM2 assesses completeness of both the left and right sides of the 45 46 234 skeleton separately even though this is likely not necessary as tetrapods are bilaterally 47 48 235 symmetrical and only one whole side of the skeleton is potentially needed to understand the 49 50 51 236 complete anatomy of a taxon. For this reason, we also calculated the proportional 52 53 237 completeness of each taxon in relation to only one bilateral side of a full skeleton, but 54 55 238 utilising the composite material available from both sides. Cleary et al. (2015) first employed 56 57 58 239 a similar method because of the compressed nature of most ichthyosaur fossil specimens, but 59 60 240 it is used here to test whether traditional SCM significantly underestimates our understanding

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11 1 2 3 241 of fossil bats. This new metric does not provide an absolute measure of the preserved 4 5 6 242 skeleton like the standard SCM2, but instead more closely assesses the proportion of 7 8 243 preserved anatomical information, bridging the gap between SCM and CCM. This new 9 10 244 approach was used to calculate two additional versions of SCM2, CSCM2 2D and CSCM2 11 12 3D (‘Composite’ SCM2). 13 245 14 15 246 Beardmore’s Skeletal Completeness Metric (BSCM) was initially developed to assess 16 17 18 247 the geologically localised taphonomy of marine crocodylomorphs (Beardmore et al. 2012), 19 20 248 but has also since been modified and applied to assess the global fossil record of ichthyosaurs 21 22 249 (Cleary et al. 2015). The BSCM is a simpler metric that involves assigning a value between 0 23 24 25 250 (absent) and 4 (mostly/fully complete) different regions of the skeleton (in this case six 26 27 251 regions, combining neck, dorsal and lumbar vertebral sections). The values from these 28 29 252 regions are then totalled and transformed into a skeletal completeness percentage. Here 30 31 32 253 BSCM is incorporated to assess whether a less refined but more easily implemented metric 33 34 254 might serve to represent the completeness of the bat fossil record just as well as other metrics. 35 36 37 255 To assess completeness with respect to characters we used traditional CCM2 38 39 256 (CCM2a, Mannion and Upchurch 2010) where skeletal regions are weighted by assigned 40 41 257 numbers of characters, and the physical completeness of individual elements are used as a 42 43 44 258 guide for the presence of characters within each element. For example, if a femur is complete 45 46 259 it is assumed all the characters of that femur are obtainable. However, this might not 47 48 260 necessarily always be the case as bones frequently suffer surface damage and erosion. We 49 50 51 261 used an extensive osteological character list (699 distinct characters) provided by NBS and 52 53 262 specifically designed to include fossil chiropterans (see Brown et al. 2019). The number of 54 55 263 characters per skeletal region (e.g. skull, mandible, axial skeleton etc.) were counted and used 56 57 58 264 to determine the relative character proportions assigned to each (Brown et al. 2019: Table 59 60 265 S1). These relative proportions were used to score completeness values per taxon. The

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12 1 2 3 266 individual completeness of elements was based on the composite completeness of the 4 5 6 267 elements derived from both left and right bilateral sides of the skeleton, as the distinction 7 8 268 between left and right sides is irrelevant for phylogenetic character scoring in symmetrical 9 10 269 animals such as bats. Characters pertaining to the calcar were removed from the proportions 11 12 for extinct taxa to keep consistent body proportions between metrics, as mentioned above. 13 270 14 15 271 Character 698 and 699, were omitted from the analyses as they pertain to the baculum, a 16 17 272 gender-specific element. Characters that were not discrete to a specific element were assigned 18 19 273 a proportion of the character (e.g. character X references both the humerus and femur: 20 21 22 274 humerus and femur were assigned 50% of a character for determination of relative 23 24 275 proportions). 25 26 27 276 28 29 30 277 Time bins and comparative data 31 32 33 278 For analyses over geological time, stage-level time bins were used between the Ypresian 34 35 279 (early Eocene) and Messinian (late Miocene). Subsequently, epoch-level time bins were used 36 37 due to the short durations of the Pliocene and Pleistocene. The completeness for each metric 38 280 39 40 281 was calculated for each time bin as the mean and median of the completeness scores for all 41 42 282 species and genera (separately) occurring within that time bin. 43 44 45 283 Two different estimates of fossil record sampling were used. All fossil records of 46 47 284 Cenozoic tetrapods from terrestrial depositional settings were downloaded from the PaleoDB 48 49 th 50 285 on 15 May 2018 (see Brown et al. 2019). Numbers of geological formations yielding 51 52 286 tetrapod fossils (tetrapod-bearing formations, TBFs) and numbers of PaleoDB ‘collections’ 53 54 287 (= localities) yielding tetrapod fossils (tetrapod-bearing collections, TBCs) were calculated 55 56 57 288 from this download. TBFs and TBCs were used instead of counts of numbers of formations 58 59 289 yielding bats, in order to provide a more global estimate of sampling, reducing the possibility 60

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13 1 2 3 290 of signal “redundancy” (Benton et al. 2011). It is worth noting, however, that recent 4 5 6 291 simulation studies have indicated that using a wider clade does not necessarily remove the 7 8 292 issue of redundancy (Brocklehurst 2015, Dunhill et al. 2018). 9 10 11 293 Bat completeness metrics were also compared with those from other tetrapod groups 12 13 294 for which completeness data has been compiled. SCM2 data were compared to values for 14 15 295 sauropodomorphs (Mannion and Upchurch 2010), pelycosaurs (Brocklehurst and Fröbisch 16 17 18 296 2014), ichthyosaurs (Cleary et al. 2015), parareptiles (Verrière et al. 2016), and plesiosaurs 19 20 297 (Tutin and Butler 2017). CCM data were compared to those for sauropodomorphs (Mannion 21 22 298 and Upchurch 2010), birds (Brocklehurst et al. 2012), anomodonts (Walther and Fröbisch 23 24 25 299 2013) pelycosaurs (Brocklehurst and Fröbisch 2014), pterosaurs (Dean et al. 2016), 26 27 300 parareptiles (Verrière et al. 2016), plesiosaurs (Tutin and Butler 2017), and Cretaceous and 28 29 301 Paleogene eutherian mammals (Davies et al. 2017). Mannion and Upchurch (2010) presented 30 31 32 302 their sauropodomorph data in substage time bins, but this was recalculated into stage-level 33 34 303 time bins by Dean et al. (2016). 35 36 37 304 SCM2 and CCM2 results were further compared between five major subgroups of 38 39 305 Chiroptera (sensu Teeling et al. 2012): the family Pteropodidae, the superfamilies 40 41 306 Rhinolophoidea, Emballonuroidea, Noctilionoidea, Vespertilionoidea and superfamily-level 42 43 44 307 incertae sedis taxa, mostly consisting of basal chiropterans. 45 46 47 308 Previous studies (Brocklehurst et al. 2012; Dean et al. 2016) of completeness metrics 48 49 309 have attempted to parse out the influence of sites of exceptional preservation (Lagerstätten). 50 51 310 In this study only Konservat-Lagerstätten were considered, which are here defined as 52 53 54 311 deposits that preserve articulated fossils and associated soft tissues under unique depositional 55 56 312 circumstances. Three Lagerstätten relevant to bats were recognised: Green River Formation, 57 58 313 USA (Ypresian, Jepsen 1966; Simmons et al. 2008); Messel Shale, Germany (Lutetian, 59 60

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14 1 2 3 314 Russell and Sige 1970; Simmons and Geisler 1998); and the Shanwang Formation, China 4 5 6 315 (Burdigalian, Yang and Yang 1994). Completeness scores of species from these three 7 8 316 Lagerstätten were excluded from some analyses as noted below. 9 10 11 317 Unlike some previous analyses of completeness (e.g. Brocklehurst et al. 2012), we do 12 13 318 not attempt to make comparisons between completeness metrics and estimates of species 14 15 319 richness that attempt to account for variable spatiotemporal sampling of the fossil record. 16 17 18 320 This is because sampling of the bat record is insufficient to allow diversity to be estimated 19 20 321 using the most rigorous and appropriate diversity estimators (e.g. shareholder quorum 21 22 322 subsampling; Alroy 2010; Close et al. 2018). We do, however, make comparisons between 23 24 25 323 sampled (i.e. uncorrected) species counts and completeness. 26 27 28 324 29 30 31 325 Statistical analyses 32 33 34 326 All statistical analyses were conducted in R. Time series plots were produced using the 35 36 327 package strap (Bell and Lloyd 2015). Non-temporal pairwise comparisons of populations of 37 38 328 completeness values for bats and other tetrapod groups were made using non-parametric 39 40 329 Mann-Whitney-Wilcoxon tests, which assess differences in the population medians and 41 42 43 330 distribution. Mann-Whitney-Wilcoxon tests were also used to compare the validity of 44 45 331 different completeness metrics, completeness between the major subgroups of Chiroptera, 46 47 332 across northern and southern hemispheres, different major continents, and between caves and 48 49 50 333 normal fluvio-marine deposits (for all time bins). False discovery rate (FDR; Benjamini and 51 52 334 Hochberg 1995) corrections were used where appropriate to correct for multiple comparisons. 53 54 335 For comparisons of more than two datasets (e.g. comparing populations of values for 55 56 57 336 different continental regions), Kruskal-Wallis tests were used. 58 59 60

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15 1 2 3 337 Time series comparisons compared changes in bat completeness metrics through time 4 5 6 338 to one another, between species and generic levels, as well as to temporal variation in 7 8 339 observed species richness, fossil record sampling (TBFs and TBCs), and time bin length. We 9 10 340 used generalized least-squares regression (GLS) with a first order autoregressive model 11 12 (corARMA) fitted to the data using the function gls() in the R package nlme v. 3.1–137 13 341 14 15 342 (Pinheiro et al. 2018). GLS reduces the chance of overestimating statistical significance of 16 17 343 regression lines due to serial correlation. Time series were ln-transformed prior to analysis to 18 19 344 ensure normality and homoskedasticity of residuals. We calculated likelihood-ratio based 20 21 2 22 345 pseudo-R values using the function r.squaredLR() of the R package MuMIn (Bartoń 2018). 23 24 346 We also tested whether combinations of completeness scores, fossil record sampling and time 25 26 347 bin length provided significant explanations of observed species richness, by fitting GLS 27 28 29 348 autoregressive models to combinations of potential explanatory variables. Results were 30 31 349 compared using Akaike's information criterion for small sample sizes (AICc) and Akaike 32 33 350 weights were calculated to identify the best combination of explanatory variables from those 34 35 tested. AICc was calculated using the function AICc() of the R package qpcR (Spiess 2018), 36 351 37 38 352 and Akaike weights calculated using the aic.w() function of the R package phytools (Revell 39 40 353 2017). 41 42 43 354 Ordinary least squares (OLS) linear regressions were used to test for trends in overall 44 45 355 patterns of completeness, observed taxonomic diversity and sampling metrics when 46 47 48 356 compared to geological time, as well as to test the relationship between species completeness 49 50 357 and the year in which that species was named. 51 52 53 358 54 55 56 359 RESULTS 57 58 59 360 Changes in mean and median completeness through time. 60

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16 1 2 3 361 All SCM2 metrics show highly consistent patterns through time (Fig. 2A), with the modified 4 5 6 362 SCM2 metrics (CSCM2 2D and 3D) being consistently slightly offset towards higher values 7 8 363 than the standard SCM2 metrics. Mean completeness is initially comparatively high (c. 7– 9 10 364 8%) in the Ypresian and rises to a peak in the Lutetian (c. 38–39%). Subsequently, 11 12 completeness is very low (c. 2–7%) and almost flat from the Bartonian to the Pliocene, with 13 365 14 15 366 just a single peak of moderately higher values (c. 7–9%) in the Aquitanian. The Pleistocene 16 17 367 has SCM2 values (c. 11–16%) that are substantially higher than other post-Lutetian intervals. 18 19 20 368 When Lagerstätten taxa are removed, there is little variation in SCM2 completeness 21 22 369 values prior to the Pleistocene (Fig. 2B), with the previously comparatively high Ypresian 23 24 25 370 and Lutetian values being greatly diminished. There is still a moderate peak in completeness 26 27 371 in the Aquitanian, and a more substantial peak in the Pleistocene. Median completeness 28 29 372 values (including Lagerstätten taxa; Fig. 2C) are similar to the mean values excluding 30 31 32 373 Lagerstätten taxa: they are consistently very low (c. 1–5%), with the exception of higher 33 34 374 values in the Aquitanian (c. 6–10%) and the Pleistocene (c. 7–11%). 35 36 37 375 CCM2 patterns (Fig. 2A) are broadly similar to, although always higher than, those 38 39 376 for SCM2, in that they show high values in the Lutetian (c. 49%), after which there is 40 41 377 relatively little variation in completeness (c. 22–29%). The Pleistocene does not form a 42 43 44 378 distinct peak, in contrast to the SCM2 data. The Lutetian peak disappears when Lagerstätten 45 46 379 taxa are excluded (Fig. 2B). 47 48 49 380 BSCM2 is high in the Ypresian (c. 13%) and Lutetian (c. 40%) when Lagerstätten 50 51 381 taxa are included (Fig. 2A), and then virtually flat (c. 8–10%) subsequently, with no peaks in 52 53 54 382 the Aquitanian or Pleistocene. When Lagerstätten taxa are excluded then BSCM2 is virtually 55 56 383 flat throughout the Cenozoic (Fig. 2B), and median BSCM2 values form a perfectly straight 57 58 384 line (Fig. 2C). 59 60

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17 1 2 3 385 No significant correlation is recovered when all data points for SCM2 (p = 0.306; R2 4 5 6 386 = 0.0001) are compared to geological time using OLS, whereas CCM2 has a very weak trend 7 8 387 towards decreasing values towards the present (p = 0.02; R2 = 0.01) (Brown et al. 2019: Fig. 9 10 388 S1). When Lagerstätten taxa are excluded then there is a significant trend towards increasing 11 12 SCM2 values towards the Recent (p = 7.17e-12; R2 = 0.10), but not for CCM2 values (p = 13 389 14 15 390 0.135; R2 = 0.003). The significant trend for SCM2 data is lost when Pleistocene data are 16 17 391 excluded (p = 0.433; R2 = 0.03). 18 19 20 392 Generic level completeness metrics show significantly different temporal patterns to 21 22 393 species level metrics (Brown et al. 2019: Fig. S2). In most time bins completeness is higher 23 24 25 394 for genera, but generic completeness drops below species completeness in the Lutetian 26 27 395 skeletal metrics. All generic time series have higher completeness in the Ypresian (20% 28 29 396 SCM2 2D, 34.2% CCM2) and Bartonian (19.8% SCM2 2D, 43.8% CCM2), and peaks in the 30 31 32 397 Chattian (20.1% SCM2 2D, 46.4% CCM2) and Aquitanian (20.7% SCM2 2D, 44.3% CCM2) 33 34 398 which are not revealed in the species level data. 35 36 37 399 38 39 40 400 Differences between completeness metrics 41 42 43 401 Non-temporal comparisons using Mann-Whitney-Wilcox tests show that there are no 44 45 402 significant differences between SCM2 metrics generated using either 2D or 3D approaches 46 47 403 (Table 1). The modified SCM2 approach (CSCM2), which does not consider left and right 48 49 50 404 sides of the body separately, has significantly higher median completeness values than the 51 52 405 standard SCM2 approach (adjusted p = 0.0002–0.0003; Table 1). CCM2 median 53 54 406 completeness is significantly higher than that of all SCM2 (adjusted p = 1.05E-72 to 3.91E- 55 56 57 407 61) and BSCM2 (adjusted p = 1.42E-47) approaches (Table 1). Indeed, median completeness 58 59 408 for CCM2 (21.14%) is nearly an order of magnitude greater than median completeness for 60

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18 1 2 3 409 SCM2 (2.8%, using the 2D approach), and more than four times greater than median 4 5 6 410 completeness for CSCM2 (4.48%, using the 2D approach). BSCM2 shows a tiny interquartile 7 8 411 range, indicating very limited variation in BSCM2 values. Violin plots emphasise the bottom- 9 10 412 heavy nature of the distributions of completeness values (Brown et al. 2019: Fig. S3). 11 12 13 413 Time series comparisons of mean values using GLS find significant relationships 14 15 414 between all completeness metrics (Table 2; Fig. 2A). The strongest relationships are between 16 17 2 18 415 different SCM2 metrics (pseudo-R = 0. 979–0.996). CCM2 is strongly correlated with 19 20 416 SCM2 metrics (pseudo-R2 = 0.564–0.61), and both SCM2 and CCM2 are significantly 21 22 417 correlated with BSCM2 (pseudo-R2 = 0.673–0.735). However, scatterplots of data for SCM2 23 24 25 418 versus CCM2, SCM2 versus BSCM2 and CCM2 versus BSCM2 suggest that these 26 27 419 relationships are potentially influenced by a single outlier, the Lutetian, for which 28 29 420 completeness values are anomalously high due to the presence of the Messel Lagerstätte. 30 31 32 421 When Lagerstätten taxa are excluded from the time series (Fig. 2B), the relationships 33 34 422 between SCM2 metrics remain strongly significant (Table 3). However, the relationships 35 36 423 between SCM2 metrics and CCM2 are non-significant, as are those between CCM2 and 37 38 424 BSCM2 and SCM2 and BSCM2 (Table 3). 39 40 41 425 All generic and species level time series lack significant correlation apart from 42 43 44 426 BSCM2 when Lagerstätten taxa are included (Brown et al. 2019: Table S2). Excluding 45 46 427 Lagerstätten taxa removes any correlation between the two taxonomic resolutions (Brown et 47 48 428 al. 2019: Table S3). 49 50 51 429 Given the strength of correlation between temporal changes in the different SCM2 52 53 54 430 metrics, subsequent analyses and comparisons are focused on the following datasets only: 55 56 431 SCM2 (2D approach), CSCM2 (2D approach) and CCM2. We do not focus further on 57 58 59 60

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19 1 2 3 432 BSCM2 given the highly limited variation present within taxon scores for this metric (see 4 5 6 433 Discussion). 7 8 9 434 10 11 12 435 Comparisons with taxonomic diversity and sampling 13 14 Raw taxonomic diversity (Fig. 2D) fluctuates from the Ypresian to the Miocene, with peaks 15 436 16 17 437 in the Priabonian (40 species), Rupelian (35 species), Burdigalian (52 species) and 18 19 438 Serravallian (38 species), and troughs in the Bartonian (10 species), Aquitanian (8 species), 20 21 439 Langhian (17 species) and Tortonian–Messinian (17 and 13 species, respectively). Diversity 22 23 24 440 then begins to increase in the Pliocene (68 species), and Pleistocene diversity (165 species) 25 26 441 far exceeds that of all earlier intervals. There is no significant overall trend in diversity 27 28 442 through time (p = 0.127; R2 = 0.1153). Excluding the small number of Lagerstätten has no 29 30 31 443 substantial impact on perceived taxonomic diversity patterns beyond substantially reducing 32 33 444 taxon counts for the Lutetian. 34 35 36 445 TBFs show a general trend of increases through time (p = 0.0003; R2 = 0.65), with 37 38 446 lower numbers of formations from the Ypresian to the Rupelian, followed by considerably 39 40 447 higher values from the Chattian to the Pleistocene (Brown et al. 2019: Fig. S4). A broadly 41 42 2 43 448 similar but marginally non-significant pattern is seen in TBCs (p = 0.06; R = 0.196), 44 45 449 although TBCs are marked by an enormous peak in numbers during the Pleistocene, with 46 47 450 values 2–3 times higher than in preceding stages. 48 49 50 451 The best models for observed taxonomic diversity were those including either only 51 52 53 452 CCM2 or only SCM2 as explanatory variables, followed by the models involving only time 54 55 453 bin length as an explanatory variable (Brown et al. 2019: Table S4). However, all of these 56 57 454 models were non-significant with negligible R2 values. 58 59 60 455

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20 1 2 3 456 Comparisons between taxonomic groups 4 5 6 457 For comparisons of SCM2, bats show significantly lower median completeness values than 7 8 9 458 all taxonomic groups to which they were compared (Table 4; Fig. 3). Indeed, the upper 10 11 459 quartile of the bat distribution falls below the lower quartile of the distributions of all of the 12 13 460 other groups, with the exception of pelycosaurs. The distribution of SCM2 values for bats is 14 15 461 very bottom-heavy (Fig. 3), and this is more extreme than in other groups that also show 16 17 18 462 bottom-heavy SCM2 distributions, such as sauropodomorphs and pelycosaurs. 19 20 21 463 As noted above, CCM2 values for bats are higher than SCM2 values, but they are still 22 23 464 significantly lower than most other groups to which they were compared (Table 5; Fig. 4), 24 25 465 with the exception of Mesozoic birds and pelycosaurs. Median CCM2 values for bats 26 27 28 466 (21.14%) are moderately lower than for pelycosaurs (25.6%) but higher than Mesozoic birds 29 30 467 (15.57%). Bird data have a substantially greater interquartile range than that of bats (61.6% 31 32 468 versus 21.9%) and a more bimodal distribution. 33 34 35 469 There is no statistical difference in the distribution of SCM2 and CCM2 values 36 37 between any bat subgroup (Brown et al. 2019: Table S5 and S6). Kruskal-Wallis tests further 38 470 39 40 471 reveal no significant difference between the distribution of values in either SCM2 (p=0.2312) 41 42 472 or CCM2 (p=0.5497). Pteropodidae and Noctilionoidea are the only groups that are not 43 44 473 represented by complete and near complete taxa. All groups share a very similar bottom- 45 46 47 474 heavy distribution of completeness values except Pteropodidae, which has a strongly bimodal 48 49 475 distribution with a much larger interquartile range (Brown et al. 2019: Fig. S5 and S6). 50 51 52 476 53 54 55 477 Spatial and environmental comparisons 56 57 58 478 Mann-Whitney-Wilcox tests indicate that bat specimens recovered solely from Lagerstätten 59 60 479 are considerably more complete than those derived from either non-Lagerstätten fluvio-

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21 1 2 3 480 marine settings (SCM2, p = 3.736e-08; CCM2, p = 4.189e-08) or caves (SCM2, p = 2.612e- 4 5 6 481 08; CCM2, p = 5.72e-08). However, specimens derived from cave deposits are significantly 7 8 482 more complete than specimens discovered from fluviolacustrine and marine regimes, even 9 10 483 when included Lagerstätten (SCM2, p = 2.2E-16; CCM2, p = 9.5E-11; Fig. 5). For northern 11 12 versus southern hemisphere comparisons, results are non-significant for SCM2 (p = 0.343), 13 484 14 15 485 but significant for CCM2 (p = 0.001), with completeness higher in the northern hemisphere 16 17 486 (Fig. 6). Kruskal-Wallis tests indicate significant differences occur between median 18 19 487 completeness values for different continental regions for both SCM2 (p = 1.29e-13) and 20 21 22 488 CCM2 (1.55e-09). Most notably, median SCM2 values for North America are significantly 23 24 489 higher than those for all other continental regions with the exception of Australasia, whereas 25 26 490 for CCM2, median values for Europe are significantly higher than those for all other 27 28 29 491 continental regions with the exception of Australasia (Brown et al. 2019: Table S7 and S8; 30 31 492 Brown et al. 2019: Fig. S7 and S8). 32 33 34 493 35 36 37 494 Completeness and date of discovery 38 39 40 495 OLS recovered a significant relationship between date of discovery and SCM2, with more 41 42 496 recently discovered species being known from less complete material, but the explanatory 43 44 497 power is very low (p = 2.40E-06; adjusted R2 = 0.047). This relationship is significant even 45 46 2 47 498 when Lagerstätten taxa are excluded (p = 1.11e-09; adjusted R = 0.08). The same result is 48 49 499 recovered for comparisons of date of discovery and CCM2 (p = 4.89e-08; adjusted R2 = 50 51 500 0.06), also when Lagerstätten taxa are excluded (p = 1.11e-09; adjusted R2 = 0.08) (Fig. 7A- 52 53 th 54 501 B). A collector’s curve shows few discoveries from the early part of the 19 century up until 55 56 502 the 1950s, followed by a steep rise in the rate of discoveries in the second half of the 20th 57 58 59 60

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22 1 2 3 503 century. There is no sign of any decline in the rates of discovery towards the present day (Fig. 4 5 6 504 7C). 7 8 9 505 10 11 12 506 DISCUSSION 13 14 Comparison of completeness metrics 15 507 16 17 508 There is no significant difference between the time series or non-temporal distributions of 18 19 20 509 SCM2 values for bats derived from either 2D or 3D methods (Table 1-2). This suggests that 21 22 510 completeness estimates generated by simpler (e.g. 2D skeletal drawings) methods of 23 24 511 calculating body proportions are statistically indistinguishable from those generated by more 25 26 27 512 detailed methods. Efforts to develop highly accurate means of calculating body proportions 28 29 513 (e.g. Brocklehurst and Fröbisch 2014; Verrière et al. 2016; our use of 3D CT scans) may be 30 31 514 unnecessarily precise and time consuming given these results. 32 33 34 515 Using CSCM2 significantly increases the median and distribution of completeness scores 35 36 37 516 in comparison to the original SCM2 (Table 1, Fig S3). Calculating SCM2 by separating the 38 39 517 left and right sides of the skeleton therefore significantly underestimates the completeness of 40 41 518 the bat fossil record. SCM2 2D seems to be most strongly altered when using the composite 42 43 519 metric. However, time series comparisons show that mean and median CSCM2 scores are 44 45 46 520 only minimally higher than SCM2 per time bin, and the original and composite metrics show 47 48 521 the same temporal trends. 49 50 51 522 BSCM2 scores are consistently higher than other skeletal completeness metrics. Its 52 53 523 temporal patterns lack the resolution of the other metrics (Fig. 2A-C), and their distribution is 54 55 56 524 unlike the other skeletal metrics (Brown et al. 2019: Fig. S3), with an unrepresentative 57 58 525 absence of taxon completeness scores below ~4%. Unlike with ichthyosaurs (Cleary et al. 59 60 526 2015), it is likely this metric is too coarse to accurately estimate the completeness of the bat

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23 1 2 3 527 fossil record, and is possibly inadequate for use with groups known from very limited fossil 4 5 6 528 material. 7 8 9 529 CCM2 has higher mean and median time series, and significantly higher non-temporal 10 11 530 median and distribution of completeness values in comparison to the skeletal completeness 12 13 531 metrics (Table 1-3; Fig. 2A-C). The large differences between the character- and skeletal- 14 15 532 based metrics is likely because teeth are the most common element recovered in the bat fossil 16 17 18 533 record and because of the high proportion of dental characters (42% of all phylogenetic 19 20 534 characters) compared to the skeletal mass of the dentition (1.92% of total skeleton). This 21 22 535 highlights the different aspects of fossil record quality that are assessed by character- and 23 24 25 536 skeletal-based metrics. Even though skeletal material is sparse in the bat fossil record, it is 26 27 537 still relatively rich in character information. 28 29 30 538 All mean time series are significantly correlated with each other, suggesting that the same 31 32 539 temporal signal is recovered regardless of the metric used. However, removing Lagerstätten 33 34 35 540 taxa from the time series renders non-significant the correlations between BSCM2 and SCM2 36 37 541 and CCM2 and SCM2 (Table 3), suggesting that Lagerstätten can have a strong influence on 38 39 542 metric results and our interpretations of the bat fossil record. 40 41 42 543 The significant changes and lack of correlation between the species and generic time 43 44 544 series suggests that the quality of the fossil record can strongly differ depending on the 45 46 47 545 taxonomic resolution used. The higher generic compared to species completeness for most 48 49 546 time bins is expected since most genera are known from multiple species and therefore 50 51 547 incorporate many more specimens. The drop in mean generic completeness in the Lutetian 52 53 54 548 below species completeness levels is surprising considering that the stage contains many 55 56 549 Lagerstätten taxa. This, however, is likely due to the addition of more fragmentary taxonomic 57 58 550 records to the analyses that are indeterminate at species level. 59 60

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24 1 2 3 551 Teeling et al. (2005) used ghost lineages from a molecular phylogeny representing 30 4 5 6 552 genera to estimate that 61% of the bat fossil record is missing and that first appearances are 7 8 553 underestimated by 73%. Eiting and Gunnell (2009) later concluded the generic bat fossil 9 10 554 record was 12% complete using phylogenetically independent extinction rate and 11 12 preservation potential to assess fossil occurrence ranges. These studies assessed very different 13 555 14 15 556 concepts of ‘completeness’ (Benton and Storrs 1994; Foote and Sepkoski 1999; Benton et al. 16 17 557 2000; Benton et al. 2011) to the metrics used here. However, it is interesting that the mean 18 19 558 generic character (27.8% CCM2, ~70% incomplete) and skeletal completeness (12.6% SCM2 20 21 22 559 2D, 14% SCM2 3D, 14.6% CSCM2 2D, 16.8% CSCM2 3D and 13.8% BSCM2) converge 23 24 560 on relatively similar values to the previous studies, respectively. This suggests the overall 25 26 561 signals of a comparatively poor bat fossil record are consistent regardless of the approach 27 28 29 562 used. 30 31 32 563 33 34 35 564 Taxonomic comparisons 36 37 Bats seem to have the least complete skeletal fossil record of any tetrapod group previously 38 565 39 40 566 studied using the skeletal completeness metric, but their character completeness is more 41 42 567 similar to some other groups (Fig. 3-4). The extremely poor skeletal record is most likely due 43 44 568 to the fragile nature of bat skeletons, and the very common preservation and reporting of 45 46 47 569 isolated teeth relative to other skeletal elements. Bat teeth, like those of most fossil mammals, 48 49 570 are highly character-rich and therefore allow for the recognition of new species from very 50 51 571 limited fossil material. Even with this in mind, bat CCM2 is comparatively high, with a non- 52 53 54 572 temporal median score higher than that of Mesozoic birds. However, the similarity between 55 56 573 CCM2 scores for bats and birds may not be that surprising considering their similar ecologies 57 58 574 (e.g. flight capability) and the likely similar taphonomic biases acting upon their records 59 60

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25 1 2 3 575 (Brocklehurst et al. 2012; Gardner et al. 2016) (see below). There are large differences 4 5 6 576 apparent visually between the two CCM2 distributions, but the median values are similar. 7 8 577 The much larger interquartile range and more bimodal distribution of Mesozoic birds reflects 9 10 578 their more common preservation in Lagerstätten deposits (34.7% and 2.5% in Mesozoic birds 11 12 and fossil bats, respectively [Brocklehurst et al. 2012; this study]). 13 579 14 15 580 Although bats and early eutherians both represent subgroups within mammals, early 16 17 18 581 eutherian CCM2 values (from Davies et al. 2017) are significantly higher than those of bats. 19 20 582 This may in part reflect differential character scoring between the groups. Early eutherians 21 22 583 share similar dental character proportions (37%; Davies et al. 2017) to bats (42%), but the 23 24 25 584 proportion of cranial characters (34%) used in phylogenetic studies of early eutherians is 26 27 585 much greater than in bats (5.6%), whereas the proportion of postcranial characters is 28 29 586 substantially reduced (26% in early eutherians, 52% in bats). This means the bat character 30 31 32 587 completeness is more reliant on infrequently preserved (or identified/reported) postcranial 33 34 588 material than the early eutherian record. In future, it would be interesting to see how skeletal 35 36 589 completeness metrics for other eutherian groups compare to the extremely poor skeletal 37 38 590 record of bats. 39 40 41 591 In contrast, the statistical and visual similarity between the distributions of CCM2 42 43 44 592 scores in the bat and pelycosaur records makes little morphological, ecological and 45 46 593 taphonomic sense. Brocklehurst and Fröbisch (2014) concluded that before the widespread 47 48 594 use of cladistics, many pelycosaur species were named on the basis of limited material, 49 50 51 595 leading to taxonomic “over-splitting” and therefore artificially reducing the quality of the 52 53 596 pelycosaur fossil record. This could explain its similarity with the bat fossil record which by 54 55 597 its nature is poor but taxonomically informative. 56 57 58 59 60

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26 1 2 3 598 No significant differences in the completeness of the six major chiropteran subgroups 4 5 6 599 were found in our study, as all share similar median SCM2 and CCM2 values. Pteropodidae, 7 8 600 however, have an unusual bimodal distribution that differs visually from that of the other 9 10 601 subgroups. This record is likely not statistically different from the other subgroups because of 11 12 the extended interquartile range and the small sample size (n=7). This, like previous 13 602 14 15 603 assessments (Gunnell and Simmons 2005; Teeling et al. 2005; Eiting and Gunnell 2009), 16 17 604 emphasises how comparatively poor the pteropodid fossil record is. The low completeness of 18 19 605 pteropodid specimens is perhaps unexpected given that they are the first major group to 20 21 22 606 diverge (Simmons 2005) and also have the largest and thus most robust skeletons (Norberg 23 24 607 and Norberg 2012; Brown et al. 2013), possibly giving them a greater opportunity to be 25 26 608 represented in the fossil record. The lack of a well-sampled fossil record for this important 27 28 29 609 group is troubling for understanding the evolutionary development of all bats, but may 30 31 610 potentially stem from aspects of pteropodid distribution and ecology (e.g. their tendency to 32 33 611 occupy tropical wooded environments and avoidance of caves by many species), as well as 34 35 potential origin of this clade in tropical Australasia (Almeida et al. 2016) (see below). 36 612 37 38 613 39 40 41 614 Sampling biases 42 43 44 615 Palaeoecology and Palaeoenvironment. The generally poor fossil record of bats is not 45 46 47 616 surprising considering their small size, delicate skeletons, and their preference for 48 49 617 environments rarely preserved in the fossil record (e.g. forests, caves). 58% of all species 50 51 618 included in our dataset are solely derived from cave deposits, whereas only 34% are solely 52 53 54 619 derived from non-Lagerstätten fluvio-marine settings, and 2.5% from exceptional 55 56 620 Lagerstätten deposits. 57 58 59 60

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27 1 2 3 621 Lagerstätten understandably recover the most complete bat specimens and therefore 4 5 6 622 represent our best chances to enhance our knowledge of early bat evolution, handicapped 7 8 623 only by their temporal and spatial restrictions. The much more widespread cave, fissure fill 9 10 624 and sinkhole deposits on average preserve significantly more complete (for both SCM2 and 11 12 CCM2) bat specimens than fluvio-marine deposits, but as yet have not produced even a 13 625 14 15 626 single published complete bat skeleton. Though they represent more frequent sources of bat 16 17 627 remains, the absence of complete material could well be a limiting factor in their usefulness 18 19 628 for understanding bat evolution. 20 21 22 629 The Pleistocene sees a large increase in species richness and a peak in non-Lagerstätten 23 24 25 630 influenced skeletal completeness. The Pleistocene is the only stage that has individual taxa 26 27 631 known from more than 50% of the skeleton, with the exclusion of those stages with 28 29 632 Lagerstätten deposits (Brown et al. 2019: Fig. S1). 88% of all Pleistocene species are known 30 31 32 633 from caves, and 81% are known solely from them. Caves are known to preserve highly useful 33 34 634 fossil records for mammals, having often very large sample sizes (perhaps tens of thousands 35 36 635 of fossils) and preserving behavioural processes and palaeoecological signals (Jass and 37 38 636 George 2010). Jass and George (2010) found that in the USA, cave deposits account for a 39 40 41 637 high proportion (62%) of individual late Pleistocene mammal species records in comparison 42 43 638 to non-cave deposits. They further showed that late Pleistocene bats have a comparatively 44 45 639 high occurrence rate in caves. As some bat cave remains are autochthonous, sampling the 46 47 48 640 actual caves that were previously occupied by fossil bats greatly increases the likelihood of 49 50 641 preserving more complete skeletal material. Furthermore, Pleistocene deposits have likely 51 52 642 undergone the least taphonomic and diagenetic processes of any time bin and a number may 53 54 55 643 be unlithified, which have been demonstrated to better preserve specimens (Kowalewski et 56 57 644 al. 2006; Hendy 2009). The heightened preservation of cave deposits in the Pleistocene could 58 59 645 explain the increase in completeness of this time period. 60

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28 1 2 3 646 4 5 6 647 Geographic and human biases. Bats have an almost global distribution. The generally more 7 8 9 648 complete remains known from the northern hemisphere therefore most likely reflect 10 11 649 heightened historical collection effort and fortunate preservation regimes. Europe has the 12 13 650 highest median CCM2 of any continental region, likely resulting from long historical interest, 14 15 651 the presence of large spatial and temporal fissure fill deposits such as the Quercy 16 17 18 652 Phosphorites (France) and the exceptional preservation of the Messel Oil Shales (Germany). 19 20 653 North America has the highest SCM2 values, likely because 83% of its taxa are known from 21 22 654 cave deposits, 72% solely so. Asia, by contrast, has a comparatively limited record, with only 23 24 25 655 42 occurrences and only 33% of taxa known from caves. The southern hemisphere has a 26 27 656 significantly lower CCM2 in comparison to the northern hemisphere. This likely reflects a 28 29 657 relatively limited record and the lack of any complete taxa due to the absence of Lagerstätten. 30 31 32 658 South America has a better SCM2 record than Europe, possibly because 55% of taxa are 33 34 659 derived from cave deposits (all Pleistocene), but constitutes one of the poorer CCM2 records. 35 36 660 Australasia seems to have relatively high completeness scores because of the small sample 37 38 661 size (24 taxa) and the fact that 50% of known taxa are derived from cave deposits. Africa has 39 40 41 662 moderate CCM2 scores but one of the poorest SCM2 records, possibly because only 22% are 42 43 663 known from caves, but 93% of taxa have at least one occurrence in fluvio-marine formations. 44 45 664 Africa, Asia, Australasia and South America potentially hold a wealth of unexplored and 46 47 48 665 undiscovered information on bat fossils, and more effort may be required to sample the bat 49 50 666 record in new geographical localities. 51 52 53 667 The negative correlation between completeness and discovery date suggests 54 55 668 researchers have named more bat species based on less complete material as time has passed. 56 57 58 669 However, the correlations are very weak and explain only small amounts of variation. The 59 60 670 patterns observed likely reflect the extreme paucity of the bat fossil record during the 19th

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29 1 2 3 671 century in comparison to the major increase in recognition of new bat species in recent 4 5 6 672 decades (Fig. 7) since an explosion of finds in the 1950s and the improvement of techniques 7 8 673 for fossil discovery (Morgan and Czaplewski 2012). 9 10 11 674 12 13 14 675 Macroevolutionary understanding 15 16 17 676 The vast majority of the bat fossil record is known from isolated teeth and jaw fragments, 18 19 677 which fortunately are diagnostic enough to identify many different species. As such, there is 20 21 678 no correlation between taxon completeness and changes in bat diversity through time. 22 23 24 679 However, such a record only provides limited insight. Near complete specimens from 25 26 680 Lagerstätten are often the only way to answer important biological and evolutionary 27 28 681 questions about the early history of the group. Such questions include how body sizes in the 29 30 31 682 group evolved (Giannini et al. 2012), or whether laryngeal echolocation evolved multiple 32 33 683 times convergently, or evolved just once and was lost in pteropodids (Teeling et al. 2012; 34 35 684 Teeling et al. 2016). Even then there are fundamental disagreements as to what 36 37 morphological markers are actually informative for identifying such features and behavioural 38 685 39 40 686 traits in fossils (Simmons et al. 2008; 2010; Veselka et al. 2010; Teeling et al. 2012). Given 41 42 687 the paucity of fossil data, much research attention has focused on genomic, molecular and 43 44 688 morphological studies of extant taxa, which have made great strides in deciphering bat 45 46 47 689 relationships and answering some of these important questions (Springer et al. 2001; Teeling 48 49 690 et al. 2002; Pederson and Timm 2012; Tsagkogeorga et al. 2013; Wang et al. 2017). A more 50 51 691 even representation of different skeletal elements, such as seen in other tetrapod groups, 52 53 54 692 would provide a more enriched understanding of the evolution of different aspects of bat 55 56 693 biology and functional morphology. However, the Pleistocene sees an increase in skeletal 57 58 694 completeness and diversity, but lacks a peak in CCM2. As explained earlier, this may be a 59 60

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30 1 2 3 695 consequence of non-dental elements having comparatively little morphological character 4 5 6 696 weight and therefore little impact on average phylogenetic understanding. Complete 7 8 697 skeletons from Lagerstätten deposits are necessary to significantly enhance the morphology- 9 10 698 based phylogenetic datasets. 11 12 13 699 With relatively little fluctuation except for the Lagerstätten peak in the Lutetian, bat 14 15 700 completeness is fairly constant through time and does not correlate with geological age, 16 17 18 701 changes in tetrapod bearing formations, collections or taxon richness. This suggests the 19 20 702 general poor quality of the bat fossil record has been unaffected by large scale evolutionary 21 22 703 and environmental changes through the Cenozoic, or by the geological outcrop availability 23 24 25 704 and the number of sampled localities. Moreover, there are many chronological and 26 27 705 geographical gaps in the record (Teeling et al. 2005; Eiting and Gunnell 2009; Morgan and 28 29 706 Czaplewski 2012). As previously discussed (Gunnell and Simmons 2005), the lack of 30 31 32 707 unambiguous Paleocene bat fossils is a major barrier to understanding the origins of the 33 34 708 group. Molecular and combined-data phylogenies (Teeling et al. 2005; Miller-Butterworth et 35 36 709 al. 2007; Meredith et al. 2013; O’Leary et al. 2013; Lei and Dong 2016) have estimated that 37 38 710 bats first evolved just after the Cretaceous-Palaeogene boundary, meaning that early bats 39 40 41 711 existed during almost the entirety of the Paleocene without leaving a discernible fossil record. 42 43 712 This leaves a ~10 Myr gap (Miller-Butterworth et al. 2007) between their origin and the first 44 45 713 unambiguous bat fossils. The lack of any taxonomically useful Paleocene fossils is puzzling. 46 47 48 714 Bats have recently been hypothesized to have evolved within forested environments 49 50 715 (Giannini 2012). The relatively poor preservation of these environments in the fossil record 51 52 716 (Newell 1959; Raup 1988) and potential geographic isolation of early bats (Smith et al. 2012) 53 54 55 717 may explain the lack of a Paleocene record. The metrics we employ here do not take into 56 57 718 consideration this large chronological gap in the bat fossil record and can possibly present a 58 59 719 false sense of completeness, as this gap is removed from analyses. This must be considered 60

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31 1 2 3 720 when interpreting our findings, and future studies must address the absent Paleocene bat 4 5 6 721 record. The quality of early Eocene bat record is a dramatic improvement, and has been 7 8 722 hypothesized to relate to an insect radiation at the PETM fuelling diversification and 9 10 723 radiation of five bat superfamilies within five million years (Teeling et al. 2005). Bats 11 12 exploiting a more varied tapestry of environments and niches in the early Eocene potentially 13 724 14 15 725 enhanced the likelihood of individuals being preserved in the fossil record. Though 16 17 726 convincing arguments have been presented (Giannini 2012), without Paleocene fossils 18 19 727 researchers can only theorise as to how and why ancestral bats transitioned to flying 20 21 22 728 locomotion and the unique bat morphology, as the first bats known from complete specimens 23 24 729 have all the unique anatomical features of extant forms (Gunnell and Simmons 2005). 25 26 27 730 To try and address some of the temporal and spatial gaps in the bat fossil record 28 29 731 highlighted by this study, our results might inform future exploration, and be utilised in 30 31 32 732 combination with ancestral area reconstructions (Ruedi et al. 2012; Ruedi et al. 2013; 33 34 733 Almeida et al. 2016) and innovative predictive modelling techniques (Anemone et al. 2011; 35 36 734 Conroy et al. 2012; Wills et al. 2018), to locate new and productive fossil localities. 37 38 39 735 40 41 42 736 CONCLUSIONS 43 44 45 737 Quantitative analysis of the bat fossil record reveals it to be comparatively distinct and 46 47 738 derived almost entirely from either isolated teeth, fragmentary elements or complete 48 49 50 739 skeletons. 51 52 53 740 - Bats have the poorest skeletal completeness of any previously assessed tetrapod 54 55 741 group, but considerably higher character completeness, similar to that previously 56 57 742 found for pelycosaurs and birds. 58 59 60

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32 1 2 3 743 - There are no major differences in the completeness of the six major bat subgroups, 4 5 6 744 but Pteropodidae is notable for its extremely limited record and highly bimodal 7 8 745 distribution of scores. 9 10 746 - There is little fluctuation in completeness through time for any metric, except for a 11 12 13 747 peak in the Lutetian, and a skeletal high in the Pleistocene. Temporal completeness 14 15 748 patterns reveal significant differences depending on the taxonomic resolution used. 16 17 749 Observed bat diversity is not correlated with completeness metrics, likely because 18 19 750 highly diagnostic teeth allow for low level taxonomic assignments. 20 21 22 751 - All complete and near complete bat specimens are derived from Lagerstätten. 23 24 752 - Bat fossils from caves are generally more complete than those from fluvio-marine 25 26 753 deposits. However, the former environments have limited use as no complete 27 28 29 754 specimens have ever been described from a cave deposit. 30 31 755 - The heightened historical research interest and presence of Lagerstätten potentially 32 33 756 explain the significantly higher character completeness in the northern hemisphere in 34 35 36 757 comparison to the southern hemisphere. 37 38 758 - Simple 2D measures of skeletal body proportions reveal the same completeness 39 40 759 signals as more accurate 3D models. BSCM2 is possibly too coarse a metric to 41 42 43 760 accurately represent the quality and changes in the bat fossil record. 44 45 46 761 47 48 762 ACKNOWLEDGEMENTS 49 50 51 763 The authors would like to thank Dr Jessie Maisano (University of Texas), DigiMorph.org, 52 53 54 764 and NSF grants DEB-9873663 and IIS-9874781 for CT data used in this study. We thank all 55 56 765 contributors to the Paleobiology Database: this is Paleobiology Database official publication 57 58 766 XXX. This research was originally completed as EEB’s BSci research in Palaeobiology & 59 60

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33 1 2 3 767 Palaeoenvironments at the University of Birmingham. Review comments from Neil 4 5 6 768 Brocklehurst, Sally Thomas and Nicholas Czaplewski improved the final manuscript. This 7 8 769 research was partially funded by the European Union's Horizon 2020 research and innovation 9 10 770 programme under grant agreement 637483 (ERC Starting Grant TERRA to R.J.B.). 11 12 13 771 14 15 16 772 REFERENCES 17 18 19 773 ALMEIDA, F. C., GIANNINI, N. P. and SIMMONS, N. B. 2016. The evolutionary history 20 21 774 of the African fruit bats (Chiroptera: Pteropodidae). Acta Chiropterologica, 18, 73-108. 22 23 24 775 ALROY, J. 2010. Geographical, environmental and intrinsic biotic controls on Phanerozoic 25 26 27 776 marine diversification. Palaeontology, 53, 1211-1235. 28 29 30 777 ANEMONE, R. L., EMERSON, C. W. and CONROY, G. C. 2011. Finding fossils in new 31 32 778 ways: An artificial neural network approach to predicting the location of productive fossil 33 34 779 localities. Evolutionary Anthropology: Issues, News, and Reviews, 20, 169-180. 35 36 37 780 ARITA, H. T. and FENTON, M. B. 1997. Flight and echolocation in the ecology and 38 39 evolution of bats. Trends in Ecology & Evolution, 12, 53-58. 40 781 41 42 782 BARTOŃ, K. 2018. MuMIn: Multi-model inference. R package version 1.42.1. 43 44 45 783 BEARDMORE, S. R., ORR, P. J., MANZOCCHI, T., FURRER, H. and JOHNSON, C. 46 47 48 784 2012. Death, decay and disarticulation: modelling the skeletal taphonomy of marine reptiles 49 50 785 demonstrated using Serpianosaurus (Reptilia; Sauropterygia). Palaeogeography, 51 52 786 Palaeoclimatology, Palaeoecology, 337, 1-13. 53 54 55 787 BELL, M. A. and LLOYD, G. T. 2015. strap: an R package for plotting phylogenies against 56 57 58 788 stratigraphy and assessing their stratigraphic congruence. Palaeontology, 58, 379-389. 59 60

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42 1 2 3 967 SPRINGER, M. S., TEELING, E. C., MADSEN, O., STANHOPE, M. J. and DE JONG, W. 4 5 6 968 W. 2001. Integrated fossil and molecular data reconstruct bat echolocation. Proceedings of 7 8 969 the National Academy of Sciences of the USA, 98, 6241-6246. 9 10 11 970 SWARTZ, S. M., BENNETT, M. B. and CARRIER, D. R. 1992. Wing bone stresses in free 12 13 971 flying bats and the evolution of skeletal design for flight. Nature, 359, 726-729. 14 15 16 972 — and MIDDLETON, K. M. 2008. Biomechanics of the bat limb skeleton: scaling, material 17 18 973 properties and mechanics. Cells Tissues Organs, 187, 59-84. 19 20 21 974 — IRIARTE-DÍAZ, J., RISKIN, D. K. and BREUER, K. S. 2012. A bird? A plane? No, it’s a 22 23 24 975 bat: an introduction to the biomechanics of bat flight. 317-352. In GUNNELL, G. F. and 25 26 976 SIMMONS, N. B. (eds). Evolutionary History of Bats: Fossils, Molecules and Morphology. 27 28 977 Cambridge University Press, 560 pp. 29 30 31 978 TEELING, E. C., SPRINGER, M. S., MADSEN, O., BATES, P., O'BRIEN, S. J. and 32 33 34 979 MURPHY, W. J. 2005. A molecular phylogeny for bats illuminates biogeography and the 35 36 980 fossil record. Science, 307, 580-584. 37 38 39 981 — MADSEN, O., VAN DEN BUSSCHE, R. A., DE JONG, W. W., STANHOPE, M. J. and 40 41 982 SPRINGER, M. S. 2002. Microbat paraphyly and the convergent evolution of a key 42 43 983 innovation in Old World rhinolophoid microbats. Proceedings of the National Academy of 44 45 46 984 Sciences of the USA, 99, 1431-1436. 47 48 49 985 — DOOL, S. and SPRINGER, M. S. 2012. Phylogenies, fossils and functional genes: the 50 51 986 evolution of echolocation in bats. 1-22. In GUNNELL, G. F. and SIMMONS, N. B. (eds). 52 53 987 Evolutionary History of Bats: Fossils, Molecules and Morphology. Cambridge University 54 55 56 988 Press, 560 pp. 57 58 59 60

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43 1 2 3 989 — JONES, G. and ROSSITER, S. J. 2016. Phylogeny, genes, and hearing: implications for 4 5 6 990 the evolution of echolocation in bats. 25-54. In FENTON, M. B., GRINNELL, A. D., 7 8 991 POPPER, A. N. and FAY, R. R. (eds). Bat Bioacoustics. Springer, 304 pp. 9 10 11 992 TEMPLIN, R. J. 2000. The spectrum of animal flight: insects to pterosaurs. Progress in 12 13 993 Aerospace Sciences, 36, 393-436. 14 15 16 994 TSAGKOGEORGA, G., PARKER, J., STUPKA, E., COTTON, J. A. and ROSSITER, S. J. 17 18 995 2013. Phylogenomic analyses elucidate the evolutionary relationships of bats. Current 19 20 21 996 Biology, 23, 2262-2267. 22 23 24 997 TSANG, S. M., CIRRANELLO, A. L., BATES, P. J. J. and SIMMONS N. B. 2015. The 25 26 998 roles of taxonomy and systematics in bat conservation. 503-538. In KINGSON, T. and 27 28 999 VOIGHT, C. (ed.) Bats in the Anthropocene: Conservation of Bats in a Changing World. 29 30 31 1000 Springer International, 606 pp. 32 33 34 1001 TUTIN, S. L. and BUTLER, R. J. 2017. The completeness of the fossil record of plesiosaurs, 35 36 1002 marine reptiles from the Mesozoic. Acta Palaeontologica Polonica, 62, 563. 37 38 39 1003 VERRIÈRE, A., BROCKLEHURST, N. and FRÖBISCH, J. 2016. Assessing the 40 41 1004 completeness of the fossil record: comparison of different methods applied to parareptilian 42 43 1005 tetrapods (Vertebrata: Sauropsida). Paleobiology, 42, 680-695. 44 45 46 1006 VESELKA, N., MCERLAIN, D. D., HOLDSWORTH, D. W., EGER, J. L., CHHEM, R. K., 47 48 49 1007 MASON, M. J., BRAIN, K. L., FAURE, P. A. and FENTON, M. B. 2010. A bony 50 51 1008 connection signals laryngeal echolocation in bats. Nature, 463, 939. 52 53 54 1009 WALTHER, M. and FRÖBISCH, J. 2013. The quality of the fossil record of anomodonts 55 56 1010 (Synapsida, Therapsida). Comptes Rendus Palevol, 12, 495-504. 57 58 59 60

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44 1 2 3 1011 WANG, Z., ZHU, T., XUE, H., FANG, N., ZHANG, J., ZHANG, L., PANG, J., TEELING, 4 5 6 1012 E. and ZHANG, S. 2017. Prenatal development supports a single origin of laryngeal 7 8 1013 echolocation in bats. Nature Ecology & Evolution, 1, 0021. 9 10 11 1014 WILLS, S., CHOINIERE, J. N. and BARRETT, P. M. 2018. Predictive modelling of fossil- 12 13 1015 bearing locality distributions in the Elliot Formation (Upper Triassic–Lower Jurassic), South 14 15 1016 Africa, using a combined multivariate and spatial statistical analyses of present-day 16 17 18 1017 environmental data. Palaeogeography, Palaeoclimatology, Palaeoecology, 489, 186-197. 19 20 21 1018 WILSON, D. E. and REEDER, D. M. 2005. Mammal Species of the World. A Taxonomic and 22 23 1019 Geographic Reference (3rd ed). Johns Hopkins University Press, 2142 pp. 24 25 26 1020 WIMSATT, W. 2012. Biology of bats. Elsevier, 420 pp. 27 28 29 1021 YANG, H. and YANG, S. 1994. The Shanwang fossil biota in eastern China: a Miocene 30 31 1022 Konservat‐Lagerstätte in lacustrine deposits. Lethaia, 27, 345-354. 32 33 34 1023 35 36 37 1024 FIGURE CAPTIONS 38 39 40 1025 FIG 1. Scientifically-informed skeletal reconstructions of (a) Icaronycteris index and (b) 41 42 1026 Myotis myotis modified from Wimsatt (2012), used to calculate the 2D weighted skeletal 43 44 45 1027 proportions for ‘extinct’ and ‘extant’ taxa respectively; and (c) a CT-scanned Myotis 46 47 1028 daubentonii specimen, used to calculate the 3D weighted skeletal proportions. For detailed 48 49 1029 breakdown of weightings see Brown et al. (2018). 50 51 52 1030 FIG 2. Changes in bat completeness and raw diversity through time: (a) mean scores, based 53 54 55 1031 on all taxa and data; (b) mean scores, excluding Lagerstätten taxa; (c) median scores, based 56 57 1032 on all taxa and data; and (d) raw species richness. 58 59 60

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45 1 2 3 1033 FIG 3. Distribution of bat SCM2 scores in comparison to other tetrapod groups. Comparative 4 5 6 1034 taxa: synapsid-grade pelycosaurs (Brocklehurst and Fröbisch 2014); parareptiles (Verrière et 7 8 1035 al. 2016); ichthyosaurs (Cleary et al. 2015); plesiosaurs (Tutin and Butler 2017); and 9 10 1036 sauropodomorphs (Mannion and Upchurch 2010). 11 12 13 1037 FIG 4. Distribution of bat CCM2 scores in comparison to other tetrapod groups. Comparative 14 15 1038 taxa: eutherian mammals (Davies et al. 2017); synapsid-grade pelycosaurs (Brocklehurst and 16 17 18 1039 Fröbisch 2014); parareptiles (Verrière et al. 2016); anomodonts (Walther and Fröbisch 2013); 19 20 1040 plesiosaurs (Tutin and Butler 2017); pterosaurs (Dean et al. 2016); sauropodomorphs 21 22 1041 (Mannion and Upchurch 2010); and birds (Brocklehurst et al. 2012). 23 24 25 1042 FIG 5. Distribution of bat SCM2 (light grey) and CCM2 (dark grey) scores between ‘caves’ 26 27 28 1043 (caves, fissure fills, sink holes) and fluvio-marine deposits, including Lagerstätten taxa. 29 30 31 1044 FIG 6. Distribution of bat SCM2 (light grey) and CCM2 (dark grey) scores between the 32 33 1045 northern and southern hemispheres. 34 35 36 1046 FIG 7. Changes in taxonomic completeness and collection rate over historical time. 37 38 1047 Publication date in correlation with (a) SCM2, (b) CCM2, and in comparison, to (c) 39 40 1048 cumulative species count. 41 42 43 1049 44 45 46 1050 TABLE CAPTIONS 47 48 49 1051 TABLE 1. Results of comparisons of the population median and distribution of completeness 50 51 52 1052 values for different completeness metrics using Mann-Whitney-Wilcoxon tests. Statistically 53 54 1053 significant results indicated in bold. 55 56 57 58 59 60

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46 1 2 3 Test 4 5 statistic p-value following FDR 6 Dataset 1 Dataset 2 (W) p-value corrections 7 8 SCM2 2D SCM2 3D 93615 0.338 0.338 9 CSCM2 2D CSCM2 3D 93587 0.334 0.338 10 11 SCM2 2D CSCM2 2D 83036 0.0002 0.0003 12 SCM2 3D CSCM2 3D 83419 0.0003 0.0003 13 14 SCM2 2D CCM2 28584 1.31E-73 1.05E-72 15 CSCM2 2D CCM2 34521 9.78E-62 3.91E-61 16 17 SCM2 2D BSCM2 47996 9.34E-40 3.74E-39 18 19 CCM2 BSCM2 151565 5.34E-48 1.42E-47 20 1054 21 22 23 1055 TABLE 2. Results of pairwise comparisons between time series of completeness values 24 25 1056 using GLS. Statistically significant results indicated in bold. 26 27 28 Comparison Slope t-value p-value R2 29 30 SCM2 2D ~ SCM2 3D 1.038 60.27 <0.0001 0.996 31 32 CSCM2 2D ~ CSCM2 3D 1.029 32.27 <0.0001 0.989 33 SCM2 2D ~ CSCM2 2D 1.191 31.86 <0.0001 0.988 34 35 SCM2 3D ~ CSCM2 3D 1.194 34.21 <0.0001 0.989 36 37 SCM2 2D ~ CCM2 2.709 4.14 0.0014 0.564 38 CSCM2 2D ~ CCM2 2.374 4.57 0.0006 0.61 39 40 SCM2 2D ~ BSCM2 1.349 5.81 0.0001 0.735 41 42 BSCM2 ~ CCM2 1.538 4.93 0.0004 0.673 43 44 45 1057 46 47 48 1058 TABLE 3. Results of pairwise comparisons between time series of completeness values 49 50 1059 using GLS and following removal of Lagerstätten taxa. Statistically significant results 51 52 indicated in bold. 53 1060 54 55 Comparison Slope t-value p-value R2 56 57 SCM2 2D ~ SCM2 3D 1.012 45.19 <0.0001 0.994 58 CSCM2 2D ~ CSCM2 3D 0.949 27.76 <0.0001 0.985 59 60

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47 1 2 3 SCM2 2D ~ CSCM2 2D 1.233 20.98 <0.0001 0.973 4 5 SCM2 3D ~ CSCM2 3D 1.174 23.57 <0.0001 0.978 6 SCM2 2D ~ CCM2 1.542 1.25 0.2366 0.106 7 8 CSCM2 2D ~ CCM2 1.278 1.33 0.2090 0.114 9 10 SCM2 2D ~ BSCM2 1.527 1.49 0.1612 0.161 11 BSCM2 ~ CCM2 -0.394 -1.49 0.1627 0.166 12 13 1061 14 15 1062 TABLE 4. Results of comparisons of the population median and distribution of SCM2 values 16 17 18 1063 for bats (2D approach) to other taxonomic groups using Mann-Whitney-Wilcoxon tests. 19 20 1064 Statistically significant results indicated in bold. 21 22 23 p-value 24 following 25 26 FDR 27 Dataset 1 Dataset 2 Test statistic (W) p-value corrections 28 29 Bat SCM2 Ichthyosaur SCM2 2808 3.19E-43 1.60E-42 30 Bat SCM2 Plesiosaur SCM2 4456 7.91E-42 1.98E-41 31 32 Bat SCM2 Sauropodomorpha SCM2 12438 5.25E-39 8.74E-39 33 Bat SCM2 Parareptilia SCM2 4253 2.57E-25 3.22E-25 34 35 Bat SCM2 Pelycosauria SCM2 8249 6.77E-21 6.77E-21 36 1065 37 38 39 1066 TABLE 5. Results of comparisons of the population median and distribution of CCM2 40 41 1067 values for bats to other taxonomic groups using Mann-Whitney-Wilcoxon tests. Statistically 42 43 44 1068 significant results indicated in bold. 45 46 p-value following 47 48 Dataset 1 Dataset 2 Test statistic (W) p-value FDR corrections 49 Bat CCM2 Bird CCM2 28427 0.902 0.902 50 51 Bat CCM2 Pterosaur CCM2 27999 4.69E-07 6.25E-07 52 Bat CCM2 Plesiosaur CCM2 11098 3.64E-20 7.28E-20 53 54 Bat CCM2 Sauropodomorpha. CCM2 27465 3.96E-08 6.33E-08 55 Bat CCM2 Anomodontia CCM2 2134 9.90E-40 3.96E-39 56 57 Bat CCM2 Parareptilia CCM2 5447 5.28E-21 1.41E-20 58 Bat CCM2 Pelycosauria CCM2 18747.5 0.079 0.090 59 60

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48 1 2 3 Bat CCM2 Early Eutheria CCM2 29122.5 2.36E-73 1.89E-72 4 5 1069 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 FIG 1. Scientifically-informed skeletal reconstructions of (a) Icaronycteris index and (b) Myotis myotis 46 modified from Wimsatt (2012), used to calculate the 2D weighted skeletal proportions for ‘extinct’ and 47 ‘extant’ taxa respectively; and (c) a CT-scanned Myotis daubentonii specimen, used to calculate the 3D weighted skeletal proportions. For detailed breakdown of weightings see Brown et al. (2018). 48 49 80x129mm (300 x 300 DPI) 50 51 52 53 54 55 56 57 58 59 60 Palaeontology Palaeontology Page 104 of 109

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 FIG 2. Changes in bat completeness and raw diversity through time: (a) mean scores, based on all taxa and 46 data; (b) mean scores, excluding Lagerstätten taxa; (c) median scores, based on all taxa and data; and (d) 47 raw species richness. 48 116x224mm (300 x 300 DPI) 49 50 51 52 53 54 55 56 57 58 59 60 Palaeontology Page 105 of 109 Palaeontology

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 FIG 3. Distribution of bat SCM2 scores in comparison to other tetrapod groups. Comparative taxa: synapsid- 45 grade pelycosaurs (Brocklehurst and Fröbisch 2014); parareptiles (Verrière et al. 2016); ichthyosaurs 46 (Cleary et al. 2015); plesiosaurs (Tutin and Butler 2017); and sauropodomorphs (Mannion and Upchurch 2010). 47 48 107x125mm (300 x 300 DPI) 49 50 51 52 53 54 55 56 57 58 59 60 Palaeontology Palaeontology Page 106 of 109

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 FIG 4. Distribution of bat CCM2 scores in comparison to other tetrapod groups. Comparative taxa: eutherian mammals (Davies et al. 2017); synapsid-grade pelycosaurs (Brocklehurst and Fröbisch 2014); parareptiles 45 (Verrière et al. 2016); anomodonts (Walther and Fröbisch 2013); plesiosaurs (Tutin and Butler 2017); 46 pterosaurs (Dean et al. 2016); sauropodomorphs (Mannion and Upchurch 2010); and birds (Brocklehurst et 47 al. 2012). 48 49 109x126mm (300 x 300 DPI) 50 51 52 53 54 55 56 57 58 59 60 Palaeontology Page 107 of 109 Palaeontology

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 FIG 5. Distribution of bat SCM2 (light grey) and CCM2 (dark grey) scores between ‘caves’ (caves, fissure 19 fills, sink holes) and fluvio-marine deposits, including Lagerstätten taxa. 20 21 161x54mm (300 x 300 DPI) 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Palaeontology Palaeontology Page 108 of 109

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 FIG 6. Distribution of bat SCM2 (light grey) and CCM2 (dark grey) scores between the northern and 19 southern hemispheres. 20 164x55mm (300 x 300 DPI) 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Palaeontology Page 109 of 109 Palaeontology

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 FIG 7. Changes in taxonomic completeness and collection rate over historical time. Publication date in 46 correlation with (a) SCM2, (b) CCM2, and in comparison, to (c) cumulative species count. 47 109x189mm (300 x 300 DPI) 48 49 50 51 52 53 54 55 56 57 58 59 60 Palaeontology