bioRxiv preprint doi: https://doi.org/10.1101/2021.01.26.428346; this version posted January 27, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

1 AGE, ORIGIN, AND BIOGEOGRAPHY: UNVEILING THE FACTORS 2 BEHIND THE DIVERSIFICATION OF DUNG BEETLES

3 Orlando Schwery1, 2 * and Brian C. O’Meara2

4 1 New Mexico Consortium, Los Alamos, NM, USA

5 2 Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, TN, 6 USA

7 * Corresponding author: [email protected]

8 Keywords: dung beetles, Scarabaeinae, diversification, speciation, extinction, macroevolution, 9 phylogenetics, biogeography

10

11 Abstract

12 The remarkable diversity and global distribution of dung beetles has long attracted the interest of 13 researchers. However, there is still an ongoing debate on their origin, the reasons behind their 14 diversity, and their path to global distribution. The two most prominent hypotheses regarding 15 their origin and biogeographic history involve either vicariance events after the breakup of 16 Gondwana, or an African origin and subsequent dispersal. One of the key reasons why the 17 question is still disputed is a dependence on knowing the age of the dung beetles – a Mesozoic 18 origin would favor the scenario of Gondwanan vicariance, a Cenozoic origin would suggest the 19 out-of-Africa scenario. To help settle this longstanding question, we provide a taxonomically 20 expanded phylogeny, with divergence times estimated under two calibration schemes suggesting 21 an older or younger origin respectively. Using model-based inference, we estimate the ancestral 22 area of the group and test for the influence of ranges on diversification rates. Our results support 23 the hypothesis of an old age for Scarabaeinae and Gondwanan origin but remain ambiguous 24 about the exact relation of range on lineage diversification.

25 1 bioRxiv preprint doi: https://doi.org/10.1101/2021.01.26.428346; this version posted January 27, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

26 Introduction

27 Dung beetles are a remarkable group of insects. Their unusual lifestyle requiring the dung of 28 other animals to feed and reproduce gave rise to a host of morphological and behavioral 29 specializations, as adaptations to the various ecological peculiarities they face in their worldwide 30 distribution (Hanski & Cambefort 1991). While their diversity of around 5,300 species is 31 comparatively modest among beetles, their dung-processing activity makes them one of the most 32 important groups of insects, both ecologically (Nichols et al. 2008) and economically (Losey & 33 Vaughan 2006). Their unusual life history has attracted considerable interest of researchers, in 34 ecology and evolution (Hanski & Cambefort 1991; Scholtz, Davis & Kryger 2009), conservation 35 (Spector 2006), and even developmental biology (Moczek 2011). Despite that, key aspects 36 concerning their origin and the factors behind their diversity are still unknown and subject of 37 debate in the field. The ecology of extant species has been well studied (Hanski & Cambefort 38 1991), but the main hindrance to understanding historical, evolutionary aspects of their biology 39 is the lack of a comprehensive and reliable phylogeny (Tarasov & Génier 2015), and with that, 40 validation for their taxonomy (Tarasov & Dimitrov 2016).

41 Probably the two most debated questions of dung beetle evolution revolve around their 42 geographic origin and what led to their current diversity and distribution. A relation to their 43 associated dung producers is suspected (Scholtz, Davis & Kryger 2009; Gunter et al. 2016), but 44 disagreement exists over whether the success of dung beetles has always been an association 45 with mammals, or whether dinosaurs were involved early in their history. Regarding their 46 geographic origin and subsequent spread, the main competing hypotheses are whether it was 47 Gondwanan-vicariance (Davis, Scholtz & Philips 2002) or dispersal out of Africa (Sole & 48 Scholtz 2010). The answers to all these hypotheses partially hinge on the question of how old the 49 group is. It has long been debated whether Scarabaeinae are of Mesozoic or Cenozoic origin – 50 the former would make Gondwanan vicariance and feeding on dinosaur dung plausible, the latter 51 would rule it out. Earlier attempts to determine the age of dung beetles using various approaches 52 led to widely different estimates, ranging from mid Mesozoic to early Cenozoic (Hanski & 53 Cambefort 1991; Scholtz & Chown 1995; Krell 2000; Davis, Scholtz & Philips 2002; Krell 54 2006). More recent attempts using phylogenies also ranged from Cretaceous to Eocene-

2 bioRxiv preprint doi: https://doi.org/10.1101/2021.01.26.428346; this version posted January 27, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

55 Oligocene (Wirta, Orsini & Hanski 2008; Ahrens, Schwarzer & Vogler 2014; Mlambo, Sole & 56 Scholtz 2015; Gunter et al. 2016), shifting the support for early and late origins of Scarabaeinae 57 over time (Scholtz, Davis & Kryger 2009). At this time, the field is still divided: an old Mesozoic 58 origin of dung beetles, and with that a biogeographical scenario of Gondwanan vicariance and 59 subsequent dispersal has been supported by some studies (Davis, Scholtz & Philips 2002; Gunter 60 et al. 2016; Gunter et al. 2018), whereas a young Cenozoic origin and therefore an out-of-Africa 61 dispersal scenario has been supported by others (Monaghan et al. 2007; Sole & Scholtz 2010; 62 Davis, Scholtz & Sole 2016).

63 To address these questions, we provide an extended phylogeny of Scarabaeinae, with divergence 64 times estimates based on calibrations reflecting two different assumptions of their maximal age. 65 We use this phylogeny and the inferred age of the group to address the question of their 66 geographical origin using model-based methods for ancestral range estimation; and the question 67 of how it relates to their lineage diversification using range-dependent diversification rate 68 estimation. In particular, focusing on their Gondwanan origin and subsequent dispersals to areas 69 of (what was formerly) Laurasia and Madagascar, we address the question of whether the 70 dispersal into new areas was promoting dispersal, or whether their place of origin is the main 71 source of diversification. Finally, we test a hypothesis that links dung beetle diversity to the dung 72 producers present in different regions (Davis & Scholtz 2001; Scholtz, Davis & Kryger 2009). 73 Specifically, linking beetle diversity to the size of mammal dung producers, and the diversity of 74 different dung types they produced, suggesting that areas with larger mammals and more diverse 75 droppings would allow for a more diverse dung beetle fauna.

76 Materials and Methods

77 Phylogenetic Analysis

78 We wrote a script in R (R Development Core Team 2014) using the packages reutils (Schöfl 79 2016) and seqinr (Charif et al. 2005) to query GenBank (Sayers et al. 2019) for any nucleotide 80 sequences matching the organism label “Scarabaeinae” and that carried either “COI”, “16S”, 81 “18S”, “28S”, or “CAD” in the title. We downloaded the resulting accessions using the packages 82 ape (Paradis, Claude & Strimmer 2004) and insect (Wilkinson et al. 2018), removed any 3 bioRxiv preprint doi: https://doi.org/10.1101/2021.01.26.428346; this version posted January 27, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

83 duplicates and saved them as FASTA files. The 28S accessions included both 28SD2 and 28SD3 84 sequences, which were sorted and aligned separately. The retrieved COI sequences largely 85 covered two adjacent regions of the gene, and since only few accessions covered both regions, 86 we decided to split them into two separate alignments (further called COI-1 and COI-2 87 respectively). If a marker had taxa with several accessions, the longest sequence was chosen 88 unless it proved to be clearly different from the other accessions for that taxon. Taxa which were 89 only determined to genus level were only included if they were the only representative of that 90 genus.

91 The accessions of the seven markers were aligned separately using AliView v.1.26 (Larsson 92 2014): They were aligned using MAFFT globalpair, and then visually inspected and adjusted 93 manually where necessary. During this process, any sequences that showed considerable 94 mismatch with the rest of the alignment were submitted to BLAST (Altschul et al. 1997) to 95 detect any mislabeled sequences from other organismal groups. Similarly, quick RAxML 96 (Stamatakis 2014) runs were performed for each aligned marker separately, and were tested for 97 generic monophyly and long branches. Generic monophyly was tested using the package 98 MonoPhy (Schwery & O’Meara 2016). Long branches were determined using the package ips; 99 terminal branches were considered exceptionally long if their length was more than 0.25 times 100 the maximum tip height in the tree, or if the product of their length and height was more than two 101 times the interquartile range away from the third quartile of all tip length and tip height products 102 in the tree. Any taxa that stood out by branch length or placement were checked using BLAST as 103 well. The accession numbers of the sequences used in the final alignment can be found in Table 104 S1.

105 Poorly aligned or divergent regions in each alignment were removed using Gblocks (Castresana 106 2000) (settings: minimum bases for conserved regions >0.5x alignment length, minimum for 107 flanking regions >0.7x alignment length, maximum contiguous nonconserved sites: 8, minimum 108 block length: 4 (noncoding) or 5 (coding), gap positions allowed with > half of sequences having 109 gap). Finally, all alignments were concatenated using the package evobiR (Blackmon & Adams 110 2015). Partitioning the alignment by each marker (the two marker parts in case of COI), we used 111 PartitionFinder v.2.1.1 (Lanfear et al. 2017) to determine the best substitution models for each

4 bioRxiv preprint doi: https://doi.org/10.1101/2021.01.26.428346; this version posted January 27, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

112 partition, using the greedy algorithm (Lanfear et al. 2012), and PhyML (Guindon et al. 2010). 113 Additionally, the package ClockstaR (Duchene, Molak & Ho 2014) was used to determine 114 whether the partitions should have different clock models.

115 Using the concatenated alignment and the determined substitution models, a maximum 116 likelihood phylogeny was constructed with RAxML v.8.2.4 (Stamatakis 2014), using the rapid 117 hill climbing algorithm with 100 rapid bootstrap samples. The ingroup and four clades that 118 would later be used for fossil calibrations (see below) were constrained to be monophyletic. 119 Because of branch support issues, we used the online implementation of RogueNaRok (Aberer, 120 Krompass & Stamatakis 2013) to find potential rogue taxa. Taxa whose exclusion would lead to 121 a raw improvement of more than 1 were inspected and 136 sequences were eventually dropped 122 and a final RAxML tree was built from this reduced alignment. To account for weakly supported 123 clade relationships in subsequent analyses, ten sets of bootstrapped alignments were created, for 124 each of which a separate tree was built in the same manner as from the full alignment. 125 Monophyly on genus and tribe level was assessed for all trees using the R package MonoPhy 126 (Schwery & O’Meara 2016).

127 Divergence Time Estimation

128 Reliable Scarabaeinae fossils that have recently been re-examined by Tarasov et al. (2016) were 129 chosen to calibrate tree nodes during divergence time estimation. Of the 35 fossils previously 130 assigned to Scarabaeinae, they considered only 21 to be assigned reliably on the basis of their 131 morphological characters. From among these, the earliest fossils with confident generic 132 placement were chosen for each genus that we could assign a node to. The minimal age of the 133 fossils was used as minimum constraint for the stem age of the corresponding clade. The oldest 134 reliable fossil dung beetle, Lobateuchus parisii, was used to constrain the subfamily, and the 135 minimal age estimate for Juraclopus rohendorfi, the oldest fossil of the family Scarabaeidae, was 136 used as the minimal age constraint for the crown of the whole tree.

137 In order to get times to constrain the maximal age of these clades, we consulted previous studies 138 that had estimated the age of Scarabaeinae. On the younger end of the spectrum, some studies 139 refer to Wirta, Orsini and Hanski (2008), or Mlambo, Sole and Scholtz (2015) for young

5 bioRxiv preprint doi: https://doi.org/10.1101/2021.01.26.428346; this version posted January 27, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

140 estimates of the age of dung beetles (33.9ma or 56ma respectively). However, neither of those 141 estimates are particularly useful to calibrate the age of the whole group, as Wirta, Orsini and 142 Hanski (2008) focused on Helictopleurini of Madagascar, and Mlambo, Sole and Scholtz (2015) 143 exclusively on African dung beetles. While some more dated phylogenies of clades within the 144 subfamily exist, the only examples that include an age for the whole group are of higher taxa in 145 which the dung beetles are nested. Ahrens, Schwarzer and Vogler (2014) constructed a tree of 146 146 taxa of Scarabaeoidea, in which the crown Scarabaeinae was estimated at 89.6ma (ranging 147 from 83.5-98.1ma), while Gunter et al. (2016) in a phylogeny of 445 taxa of Scarabaeoidea 148 estimated it to range from 118.8-131.6ma. While various studies estimated the ages within 149 Scarabaeoidea (Ahrens, Schwarzer & Vogler 2014; McKenna et al. 2015; Toussaint et al. 2017), 150 the ages of Scarabaeinae and Scarabaeidae could not always be obtained from them – 151 consequently, we relied solely on Gunter et al. (2016) for an age estimate of Scarabaeidae 152 (116.85-199.64ma).

153 Because no clade can be older than the clade in which it is nested, we constrained the maximal 154 stem ages for all constrained genera (as well as the maximal crown age of all Scarabaeinae) to be 155 the maximal estimated age of the Scarabaeinae, and the maximal crown age of the whole tree 156 (being the stem age of Scarabaeinae) to be the maximal estimated age of Scarabaeidae. Given the 157 disagreement of estimated ages in the literature, we set up two different sets of constraints: an 158 ‘old’ one, with the Scarabaeinae and Scarabaeidae ages as estimated by Gunter et al. (2016), and 159 a ‘young’ one, with the Scarabaeinae and Scarabaeidae ages as estimated by Ahrens, Schwarzer 160 and Vogler (2014) – with the latter actually being the age of Scarabaeoidea, due to the lack of 161 available ages for the actual Scarabaeidae (apart from that by Gunter et al. (2016)). While 162 McKenna et al. (2015) actually estimated a younger age for Scarabaeoidea, their estimate is 163 younger than the age of the oldest fossil of that group, which is why we chose to use the next 164 older estimate by Ahrens, Schwarzer and Vogler (2014) ranging from 167.2-181.8ma. The 165 different fossil constraints used can be seen in Table 2.

166 Those two sets of age constraints were then used to estimate divergence times for the ML tree 167 obtained through RAxML and all ten bootstrap trees, using penalized likelihood in treePL (Smith

6 bioRxiv preprint doi: https://doi.org/10.1101/2021.01.26.428346; this version posted January 27, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

168 & O'Meara 2012). We performed a thorough search with random cross validation, which was 169 preceded by a preliminary run to estimate the best tuning parameters and smoothing factor.

170 Occurrence Data and Ancestral Range Estimation

171 We used the package rgbif (Chamberlain et al. 2016; Chamberlain & Boettiger 2017) to 172 download occurrence data from GBIF (http://data.gbif.org) for all taxa determined to species 173 level. Using an R script, the retrieved records were cleaned from empty or invalid entries, with 174 regards to the basis of record, identification date, country and coordinates, and sets with unique 175 countries or coordinates per taxon respectively were made. Name validity of unavailable taxa 176 was checked using the package taxize (Chamberlain & Szocs 2013), and eventually searched on 177 genus level. For any taxa that were still missing, occurrence information was searched for in the 178 literature.

179 We defined the areas taxa occur in as continents (Africa, Asia, Europe, North America, South 180 America, Oceania), and defined Madagascar as a separate area, given the high number of taxa 181 endemic to it (Miraldo, Wirta & Hanski 2011). However, we did not define India as a separate 182 area, and considered it part of Asia. India has a peculiar tectonic history of initially staying 183 connected to Madagascar until breaking off around 87ma and drifting northwards to collide with 184 Asia around 55-43ma (Seton et al. 2012). Not explicitly including it as a separate area means a 185 major simplification for the model, but also ignoring potentially relevant possible scenarios. For 186 example, lineages that have entered India from Madagascar before their breakup, could have 187 been evolving in relative isolation there until coming into contact with Eurasia. However, we 188 suspect that testing that kind of scenario would require more detailed geographical resolution, 189 and thus may warrant a dedicated separate study.

190 Each taxon’s range was defined as the one or several areas it occurred in, based on the collected 191 information from GBIF and the literature. Where information on continent was missing, it was 192 inferred from coordinates and countries of occurrence, and inconsistencies between these were 193 checked and corrected. Taxa occurring in many continents were inspected for the extent of 194 overlap and reliability of the records. In doing so, we also paid attention to species that were

7 bioRxiv preprint doi: https://doi.org/10.1101/2021.01.26.428346; this version posted January 27, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

195 introduced to areas by humans, and removed such occurrences from the species’ range, thereby 196 only assigning its presumed natural range.

197 Given the potential age of the group, it is conceivable that tectonic plate movement played a role 198 in their dispersal and distribution. We thus constructed a stratified dispersal matrix representing 199 the changing strength of dispersal barriers between the continents over time. We divided the last 200 200ma before the present into five time slices, following the tectonic events described in Seton et 201 al. (2012), stretching from 200-150ma (Pangaea intact), 150-110ma (breakup of Pangaea into 202 Gondwana and Laurasia, Madagascar breaking off of Africa, though still connected via 203 Antarctica), 110-50ma (breakup of Gondwana into Africa, Australia-Antarctica, and South 204 America, the latter still connected to Antarctica via a land bridge), 50-20ma (Australia separates 205 from Antarctica, Laurasia breakup into Laurentia and Eurasia (Hosner, Braun & Kimball 2015), 206 South America properly disconnected from Antarctica (Reguero et al. 2014)), and 20-0ma (land 207 bridges (some temporary) establish at Beringia (Hosner, Braun & Kimball 2015), the Isthmus of 208 Panama, and between Australia and Asia, and Eurasia and Africa).

209 Inspired by Toussaint, Bloom and Short (2017), we recognized dispersal barriers of different 210 strengths: 1) directly adjacent areas (barrier of strength 0), 2) areas connected by land bridge 211 (0.15), 3) areas separated by a small distance of water (0.25), and 4) by a large distance of water 212 (0.75). Having to pass through another area was considered a barrier of strength 0.5, and the case 213 of facing more than 3 barriers was assigned a strength of 0.95; the corresponding dispersal 214 multiplier was 1 - strength of barriers. The route of smallest resistance was picked for each 215 possible dispersal case, adding up the different barriers encountered (matrices see Table S4).

216 While it would make sense that different dispersal barriers at different times would affect the 217 dispersal dynamics of the group, it is also possible that only time differences or only dispersal 218 barriers did. Furthermore, the partially arbitrary choice of time intervals could affect the 219 inference as well. Thus, we also calculated averaged dispersal matrices to be used without time 220 stratification. For this purpose, the dispersal multipliers of each time slice were weighted by the 221 duration of that time slice relative to the total time from the beginning of that slice until the 222 present. These weighted multipliers were added up for each transition and then divided by the 223 sum of weights, so they would add to one. This weighting is intended to represent the fact that 8 bioRxiv preprint doi: https://doi.org/10.1101/2021.01.26.428346; this version posted January 27, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

224 the more recent positioning of the continental plates should have had a higher impact on the 225 current distribution of extant taxa. Two matrices (for 3 and 4 time slices respectively, see Table 226 S5) were constructed that way, to be used for either the phylogeny dated with the younger or 227 older calibration times.

228 We used the package BioGeoBEARS (Matzke 2013b; Matzke 2013a) to estimate the ancestral 229 ranges of the dung beetles. The package implements some of the most popular models for 230 ancestral range estimation, DEC (Ree & Smith 2008), DIVA (Ronquist 1997), and BayArea 231 (Landis et al. 2013), in the same framework, allowing them to easily be compared against each 232 other to test different biogeographical hypotheses. While the DEC model is a maximum 233 likelihood implementation just as originally implemented in Lagrange, the DIVA model was 234 originally implemented using parsimony, and in BioGeoBEARS the processes DIVA assumes are 235 modeled under maximum likelihood. Similarly, BayArea was originally implemented as 236 Bayesian, and is represented in BioGeoBEARS as a maximum likelihood interpretation of the 237 same. Thus, these two models should be referred to as DIVALIKE and BAYAREALIKE 238 respectively.

239 A popular feature of BioGeoBEARS is the addition of “jump-dispersal” to any of these models. 240 By adding the additional jump parameter (“+j”), one allows for founder events in the model, 241 meaning that at a speciation event, one descending lineage stays in the ancestral range, while the 242 other descendant jumps to a new area (Matzke 2014). However, there has recently been some 243 debate regarding the validity of the +j models (Ree & Sanmartín 2018; Klaus & Matzke 2019), 244 and this type of cladogenetic event is believed to be more important in island systems than in 245 non-island clades (Matzke 2013b). For those reasons, we decided not to employ the j parameter.

246 However, since the dispersal multiplier matrices defined above are somewhat arbitrary, we do 247 pair them with a parameter (w) that scales the matrix and that can be optimized as well, 248 constituting the +w models (Dupin et al. 2017). The parameter w is used to exponentiate the 249 dispersal multiplier before it is multiplied by the dispersal rate and is 1 by default. Thus, if the 250 multipliers play an important role in the biogeographical history of the group, w is likely to be 251 estimated to be larger than 1, whereas it should be estimated to be lower than 1 if the dispersal 252 matrix does not add much to explain the patterns. While this does not allow to modulate the 9 bioRxiv preprint doi: https://doi.org/10.1101/2021.01.26.428346; this version posted January 27, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

253 relative strengths of the different multipliers, it seems reasonable to expect that w would be 254 estimated to downweigh the importance of a grossly unrealistic set of multipliers.

255 We employed each of the three base models (DEC, DIVALIKE, BAYAREALIKE) in four 256 different ways: 1) as a basic model (estimating d and e), 2) with the non-stratified manual 257 dispersal multiplier matrix and estimated w parameter, 3) as a time stratified model, and 4) as a 258 time stratified model with a manual dispersal multiplier matrix for each time slice, and estimated 259 w parameter. In all those analyses, we constrained the maximal range any lineage can occupy to 260 3, as none of the extant species occupy more than 3 areas. To account for the branch support 261 issues, we ran all these models both on the trees with old and young calibrations, as well as the 262 ten bootstrap trees each.

263 Finally, preliminary analyses produced curious results, particularly in the time stratified model, 264 where lineages within clades that where entirely present in one area (e.g. Madagascar) would 265 commonly disperse to a neighboring area (e.g. Africa) right after a speciation event, only to 266 return to the ancestral area again. It was presumed that clades in which every species inhabited 267 the same two areas (e.g. the Americas) would be responsible for this, as models as DEC do not 268 include the required scenario (cladogenesis in widespread taxa where both daughters inherit 269 widespread range). Thus, such cases required the above pattern of one daughter losing and re- 270 gaining the widespread range, thereby inflating the inferred dispersal rate and forcing other 271 lineages to do the same. We therefore created test data sets where we identified the area in which 272 species most commonly co-occurred (the Americas), or the two pairs of most co-inhabited areas 273 (the Americas and Eurasia), and coded those as one, thus getting a 6-area and 5-area dataset to be 274 analyzed separately.

275 Diversification Analysis

276 We tested two sets of biogeography-related hypotheses: whether areas with larger mammals and 277 more diverse droppings are associated with higher diversification rates of dung beetles, and 278 whether diversification rates were raised when dung beetles gained access to new habitats, 279 dispersing from Gondwana to Madagascar or Laurasia. The former hypothesis was derived from 280 Davis and Scholtz (2001); (also elaborated in Scholtz, Davis & Kryger 2009). They classify

10 bioRxiv preprint doi: https://doi.org/10.1101/2021.01.26.428346; this version posted January 27, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

281 mammalian dung into four types based on size and physico-chemical characteristics: 1) small dry 282 pellets from small to medium herbivores, 2) small odiferous droppings from omni- or carnivores, 283 3) large, dry, course-fibered droppings from large non-ruminant herbivores, and 4) large, moist, 284 fine-fibered pads from large ruminants. The number of those types of dung available, as well as 285 the (fairly correlated) body size of mammals was tallied for different biogeographical regions, 286 and shown to relate to aspects of dung beetle diversity (Davis & Scholtz 2001; Scholtz, Davis & 287 Kryger 2009).

288 Explicitly biogeographic diversification models such as GeoSSE (Goldberg, Lancaster & Ree 289 2011) or GeoHiSSE (Beaulieu & O'Meara 2016; Caetano, O'Meara & Beaulieu 2018) only test 290 diversification between two areas (and three ranges: endemic to one or the other area, or 291 widespread), making explicitly testing hypotheses involving more areas (not to speak of all 292 seven) impossible. However, in the case of only three areas, testing different combinations of 293 two against each other can still yield relevant insights. To this end, we re-coded the distribution 294 data from above to merge occurrences 1) of areas with large mammals and diverse droppings 295 (Afro-Eurasia: Africa, Europe, and Asia), of medium sized mammals and less diverse droppings 296 (North and South America), and those of those with small mammals and the least diverse 297 droppings (East Gondwanan Fragments: Oceania and Madagascar).; and 2) those of the areas 298 formerly making up Gondwana (South America, Africa, and Oceania), those of former Laurasia 299 (North America, Europe, and Asia), while keeping Madagascar as an area of its own. We then 300 formatted these two data sets to suit the different GeoSSE tests: a set where we join the areas 301 with medium sized mammals and intermediate droppings-diversity with the areas of large 302 mammals and diverse droppings, and one where we join them with the small mammal and low 303 droppings-diversity instead. Another set of where we join Madagascar with Gondwana, where 304 we join it with Laurasia, and where we leave it as an area separate from the rest.

305 For each of the resulting five data sets (and for both the young and old tree respectively), we 306 inferred maximum likelihood estimates with GeoSSE, under a set of different model constraints. 307 The constraints used were combinations of equal speciation between areas, equal extinction 308 between areas, equal dispersal rates between areas, and variation where speciation was set to 309 zero in one area and forced to be equal in the other and widespread lineages, and vice versa. The

11 bioRxiv preprint doi: https://doi.org/10.1101/2021.01.26.428346; this version posted January 27, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

310 full and constrained models were then compared using likelihood ratio tests and their AIC 311 score. The best models for each combination of data set and tree were subsequently used to get 312 posterior distributions of the parameter estimates using MCMC, with an exponential prior related 313 to the Kendall-Moran estimate for net diversification rate (Kendall 1949; Moran 1951). A short 314 preliminary chain of 100 generations was run and the distances between the 5% and 95% 315 quantiles for each parameter were used to set the tuning parameter w for the slice sampler. Then, 316 each dataset was run for 20,000 generations. Convergence was assessed by the convergence 317 parameter of the function, by visual inspection of the log likelihood trace, and calculating 318 effective sample size using effectiveSize from the R package coda (Plummer et al. 2004). We 319 then compared the 95% quantiles of the posterior distribution for all estimated parameters to see 320 whether they overlap.

321 Results

322 Phylogeny and Divergence Times

323 Out of 122 genera represented in this phylogeny, 33 were monophyletic, 28 were non- 324 monophyletic, and 61 were monotypic (Table S2). When considering the bootstrap trees, 14 325 genera were consistently monophyletic, 22 consistently non-monophyletic, whereas the 326 remaining 25 varied between trees. On the tribal level, we recovered Eucraniini, Ateuchini, 327 Eurysterniini, Gymnopleurini, and Sisyphini as monophyletic (Table S3). Of these, only the last 328 three consistently so, with Onitini being monophyletic in some bootstrap replicates. Upon closer 329 inspection of the reasons for each tribe’s non-monophyly (Table 1), we see that at least part of it 330 results from taxonomic issues. For Scarabaeini, Phanaeini, and Onitini, non-monophyly is due to 331 a few incertae sedis taxa, most of which monotypic. Oniticellini and Onthophagini have similar 332 issues where one intruder of the former is a member of the latter, but also the whole of 333 Oniticellini is nested within Onthophagini. Dichotomiini, Deltochilini and Canthonini are 334 scattered in clumps across the tree, with the latter two intermingling a lot, while Coprini come 335 out in three separate clades.

336 The estimated ages of the calibrated nodes for both the young and old calibration are given in 337 Table 2. It is apparent that under both calibration schemes, the crown age of the Scarabaeinae has 12 bioRxiv preprint doi: https://doi.org/10.1101/2021.01.26.428346; this version posted January 27, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

338 hit its constrained maximum age, while the stem ages of the constrained nodes are not reaching 339 their maximum constraints but are often meeting or exceeding the minimum age constraint of the 340 subfamily’s crown. The bootstrap branch support values can be seen in Figure 1.

341 Ancestral Range Estimation

342 For all analyses of the 7-area data set on the young and old maximum likelihood trees and their 343 respective sets of 10 bootstrap trees, the analyses recovered the unstratified DEC model with 344 manual dispersal multipliers (DEC+w) as the best-fitting model. In all trees, w – the exponent for 345 the dispersal multipliers – was estimated to be larger than 1 (1.71-2.83), indicating its weight in 346 the dispersal process being higher than the initial dispersal matrix suggested. The estimated 347 dispersal and extirpation rates vary between the trees but are comparable and at the very least in 348 the same order of magnitude. For the 6-area and 5-area datasets, which model was inferred to fit 349 best varied widely among the different trees.

350 The ancestral range of the whole subfamily was estimated to be Africa, Oceania, and South 351 America for both the young and old tree. However, in the alternative trees based on bootstrap 352 replicates, the estimated ancestral range could include Madagascar instead of South America or 353 instead of Oceania, or could just include Africa and Oceania (Table S6). Interestingly, 354 reconstructions with root states other than Africa, Oceania, and South America tended to be more 355 ambiguous. Overall, the estimated ancestral ranges seemed to suggest that numerous clades 356 mostly stay in the same areas (particularly Africa, Oceania, and Madagascar), with some more 357 dispersal within the Americas and Eurasia, as well as within the clade comprising the 358 Onthophagini and Oniticellini (Figure S1).

359 Diversification Analyses

360 The best fitting models for each data set and tree, according to likelihood ratio tests and AIC 361 scores, are given in Table 3. The MCMC analyses converged and yielded reasonably high 362 effective sampling sizes with no value below 295. The 95% quantiles of the posterior 363 distributions for each parameter estimate is given in Table 4.

13 bioRxiv preprint doi: https://doi.org/10.1101/2021.01.26.428346; this version posted January 27, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

364 Regardless of whether the areas with medium sized mammals and intermediate diversity in 365 droppings were joined with the areas of large or small mammals, there was no significant 366 difference between them in terms of diversification rates (if the best model did not constrain 367 them; Figure 2, Figure 3). When the areas of medium and small mammals are joined, the 368 analysis attributes any difference in diversity between them to higher dispersal out of Afro- 369 Eurasia into the other areas.

370 Despite some differences in which model fitted the Gondwanan origin data best, the results 371 between the old tree (Figure 4) and the young tree (Figure 5) are largely consistent. They show 372 that lineages in Gondwana have a significantly lower speciation rate than lineages outside of it, 373 while lineages in Laurasia disperse into the other areas at a higher rate than vice-versa, while 374 there are no significant differences in either diversification nor dispersal between Madagascar 375 and the other areas.

376 Discussion

377 Phylogeny and Taxonomy

378 With 541 represented in-group species, this is to date the most inclusive dated species-level 379 molecular phylogeny of Scarabaeinae. Most previously inferred phylogenies of the group were 380 either constrained to specific sub-clades or regions (e.g. Davis & Scholtz 2001; Wirta, Orsini & 381 Hanski 2008; Sole & Scholtz 2010; Wirta et al. 2010; Mlambo, Sole & Scholtz 2015; 382 Breeschoten et al. 2016; Gunter et al. 2018), or part of a larger phylogeny where the main focus 383 accordingly was not or not only on dung beetles (Ahrens, Schwarzer & Vogler 2014; Kim & 384 Farrell 2015; Gunter et al. 2016; Toussaint et al. 2017). A recent large un-dated phylogeny by 385 Tarasov and Dimitrov (2016) based on 8 gene regions had a similar amount of terminals (547 386 with outgroup), though it constitutes a smaller sample of actual species diversity, as many 387 species were represented by multiple accessions and many were not determined to species level. 388 The levels of generic and tribal monophyly in this new phylogeny (Table S2, Table S3) and the 389 causes of it (Table 1) would suggest a reasonable level of agreement between this tree and 390 current taxonomy. The lack of support for many groupings within the tree (Figure 1), particularly 391 in the backbone, is cause for concern, as it not only suggests shortcomings in the inferred tree, 14 bioRxiv preprint doi: https://doi.org/10.1101/2021.01.26.428346; this version posted January 27, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

392 but also casts doubt upon the reliability of analyses results derived from that tree. Other attempts 393 at phylogenies of the overall Scarabaeinae were plagued with similar patterns of low branch 394 support (Tarasov & Dimitrov 2016), which suggests a general issue in the study of this group. 395 Molecular phylogenies have challenged the traditional classification, particularly the tribal 396 monophyly of Canthonini and Dichotomiini (Monaghan et al. 2007), as well as Coprini, 397 Onthophagini and Oniticellini, while the monophyly of the remaining tribes still seems supported 398 (Scholtz, Davis & Kryger 2009). Tarasov and Dimitrov (2016) note how their own results are 399 consistent with those of previous phylogenetic studies of Scarabaeinae (Ocampo & Hawks 2006; 400 Monaghan et al. 2007; Vaz-de-Mello 2007; Wirta, Orsini & Hanski 2008; Sole & Scholtz 2010; 401 Wirta et al. 2010; Mlambo, Sole & Scholtz 2014; Gunter et al. 2016), as well as with a large 402 phylogeny based on morphology (Tarasov & Génier 2015). They furthermore observed that the 403 studies to date tend to resolve old nodes and more recent nodes, but not intermediate ones, and 404 that the same set of problematic tribes mentioned above are consistently not monophyletic. All of 405 this seems to be reflected in our results as well, particularly when considering the extent of 406 monophyly problems (Table 1). Using the suggested new classification by Tarasov and Dimitrov 407 (2016) does not yield much improvement. While Eucraniini and Eurysternini are strictly 408 monophyletic in both, monophyly gained through reclassification in Dichotomiini, Canthonini, 409 and Scarabaeini is offset by the loss of it in Ateuchini, Gymnopleurini, and Sisyphini. Both 410 classifications show similar consistency across the set of bootstrap trees.

411 Ancestral Range Estimation and the Origin of Scarabaeinae

412 The two main hypotheses regarding where dung beetles originated are an origin in Gondwana 413 followed by vicariance events after the breakup of the supercontinent (Hanski & Cambefort 414 1991; Davis & Scholtz 2001; Davis, Scholtz & Philips 2002), and an origin in Africa and 415 subsequent dispersal out of it (Sole & Scholtz 2010). One major point of conflict between those 416 two ideas was the age of Scarabaeinae: Gondwanan vicariance would necessitate the group to be 417 of Mesozoic, rather than Cenozoic origin, as they would have to exist and be widespread enough 418 before the continental breakup (110ma according to Sanmartin and Ronquist (2004), 93ma 419 according to Scotese (1993)) in order for vicariance events to be plausible.

15 bioRxiv preprint doi: https://doi.org/10.1101/2021.01.26.428346; this version posted January 27, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

420 To answer the question of biogeographic origin in absence of an appropriate phylogeny, some 421 workers relied on classification (Hanski & Cambefort 1991; Davis & Scholtz 2001), considering 422 widespread tribes to be ancient and predating the Gondwana-breakup, in turn giving rise to 423 younger, less widespread tribes. Later attempts combined the relative age of phylogenies 424 (Monaghan et al. 2007; Wirta, Orsini & Hanski 2008) with fast and slow rates of molecular 425 sequence divergence in insects to get maximal and minimal divergence time estimates (Scholtz, 426 Davis & Kryger 2009), concluding that even the slowest known divergence rates would not 427 support the idea of a pre-Gondwanan-breakup origin of dung beetles. Sole and Scholtz (2010) 428 subsequently used a time calibrated phylogeny of the African representatives of Canthonini and 429 Dicotomiini to address the question, finding the divergence times between dung beetles and their 430 outgroup, and of the crown of dung beetles to be considerably younger than the breakup of 431 Gondwana (56ma and 40ma respectively), thus further supporting the later out-of-Africa 432 scenario.

433 As for the divergence times inferred in this study (Table 2), the older age of 131.6ma would 434 place the origin of dung beetles well before the complete breakup of Gondwana, whereas the 435 younger estimate of 98.1ma appears ambiguous, depending on when the actual separation of 436 Gondwana was completed, and depending on the accuracy of this age estimate. In either case 437 however, the inference of older origins, similar to other recent estimates that considered more 438 than just a subset of Scarabaeinae (Ahrens, Schwarzer & Vogler 2014; Gunter et al. 2016), 439 makes a Gondwanan origin and thus the potential for vicariance after its breakup seem like a 440 plausible option again.

441 The reconstruction of what is essentially Gondwana as the ancestral range at the crown of our 442 phylogenies seems to further support the idea of Gondwanan-vicariance. However, the DEC 443 model is known to have a bias towards widespread ancestors (Clark et al. 2008; Ree & Smith 444 2008; Buerki et al. 2011; Matzke 2014), even if not as strongly as DIVA (Kodandaramaiah 445 2010). Therefore, since we constrained the number of areas a lineage can maximally inhabit to 446 three, the inference of those three areas as the origin could possibly be an artefact. With regards 447 to the branch support issues, it would appear that the consistency with which the same model 448 was preferred across all trees using the 7-area dataset, and the relative stability of the inferred

16 bioRxiv preprint doi: https://doi.org/10.1101/2021.01.26.428346; this version posted January 27, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

449 origin to be Gondwana, or parts thereof (Table S6), could be seen as a sign that the result is 450 robust enough. However, the wildly varying best supported model under the reduced area 451 datasets (6 and 5 areas) is cause for concern. It could be argued, that leaving those pairs or areas 452 separate (North and South America, Europe and Asia respectively), might lead to a distribution 453 of areas across clades that is not broken up when they are rearranged in the bootstrap trees, thus 454 implying the same dispersal mechanisms. On the contrary, lumping them could lead to single- 455 distribution clades being broken up, thus changing the number of implied dispersal and 456 vicariance events. However, this is rather speculative and requires further investigation. Finally, 457 while the high estimates for w suggest that the specified dispersal multipliers are a relevant 458 improvement of the model overall, this does not guarantee that they are an accurate 459 representation of dung beetle dispersal probabilities at the given times. While they might capture 460 some large-scale dispersal constraints, the relative magnitude of the dispersal multipliers 461 between specific continents could still be inaccurate, e.g. because of the way the barriers were 462 specified and the values they were assigned. Sensitivity tests, or even adding an approach using 463 actual distance, could help to inform us about this.

464 In any case, given the branch support issues, we would refrain from a too detailed interpretation 465 of the ranges reconstructed at internal nodes. However, it is notable that the reconstruction 466 suggests that many clades can be found which seem to be predominantly confined to one (or 467 few) areas, particularly to Oceania, Madagascar, and Africa, with some more dispersal in 468 American or Eurasian clades. An exception seems to be the clade comprising Onthophagini and 469 Oniticellini, which seems to have much more area changes. This could indicate that the 470 Onthophagini and Oniticellini are different from the remaining dung beetles in their 471 biogeographic history and maybe their dispersal abilities. It could however also just reflect a lack 472 of sampling in that clade.

473 Diversification Analyses

474 The GeoSSE results for the hypothesis on whether mammal size and available dung-diversity in 475 different biogeographical areas affected the diversification rates of Scarabaeinae seem to suggest 476 that this is not the case. The increased dispersal from Afro-Eurasia into the other regions is 477 certainly interesting and could reflect different plausible dispersal events. But given that the test 17 bioRxiv preprint doi: https://doi.org/10.1101/2021.01.26.428346; this version posted January 27, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

478 was not set up to address these alternatives, we would suggest exercising care not to 479 overinterpret this pattern. When Davis and Scholtz (2001) reported on the patterns among 480 mammals, the diversity of their droppings, and dung beetle diversity in different areas of the 481 world, they noted that it was particularly related to tribal diversity (and generic diversity within 482 tribes) in those areas, but less correlated to genus or species diversity. They interpret this as a 483 sign that the influence of these patterns in mammal body size and droppings on dung beetle 484 diversification happened earlier in their evolutionary history, around the time when the tribes 485 split. While the idea that mammal dung availability could have been a crucial influence on beetle 486 diversification in the past appears very sensible, it would seem that the arguments presented in 487 this particular case are problematic. Firstly, it relates an extant pattern in mammals to past effects 488 in beetles assuming that the distribution of mammals and their droppings across the world was 489 comparable between then and now. However, those extant patterns were almost certainly not 490 constant over the timespan of dung beetle diversification, with the most recent event that 491 changed those patterns already being the Late Quaternary megafaunal extinctions (Stuart 2015). 492 Furthermore, assuming from the ages in this current tree (Table 2), as well as past estimates (e.g. 493 Gunter et al. 2016), the ages of many tribes may either be older than the rise of mammal 494 diversity, or would at least coincide with a time when mammal diversity would not be expected 495 to compare well to the extant one. Finally, considering the fact that the pattern does not correlate 496 well with species level diversity, the method we employed might also be suboptimal to test the 497 implied scenario of past influence, and an approach that allows the influence of mammal size and 498 droppings to be constrained to particular time-intervals might be more suitable.

499 The result of the Gondwanan origin GeoSSE analysis might initially seem surprising, as a large 500 portion of Scarabaeinae species diversity is found in areas that were formerly Gondwana 501 (Scholtz, Davis & Kryger 2009) and the former-Gondwanan lineages were not underrepresented 502 in comparison to the others (Gondwana: 320 taxa, Laurasia: 143 taxa, Madagascar: 114 taxa). 503 But considering the possible Gondwanan origin of the whole group, and the perceived inertia of 504 clades (diversifying in an area rather than dispersing more), one might suggest that the larger 505 absolute number of species in former Gondwanaland results from diversifying at a lower rate but 506 over longer timespans than the on average probably younger Laurasian or Malagasy lineages. 507 This would also explain the lack of rate difference between the latter and Laurasia and 18 bioRxiv preprint doi: https://doi.org/10.1101/2021.01.26.428346; this version posted January 27, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

508 Gondwana combined. The higher dispersal out of Laurasia was not expected, assuming the 509 general direction of dung beetle dispersal was outward from Gondwana. But in the light of 510 higher dispersal outside of Gondwana, this could reflect the dispersal of few lineages out of 511 Gondwana, where they diversified, and subsequently a few of those Laurasian taxa returned to 512 former Gondwana (e.g. in the case of taxa which returned from North America to South America 513 after closing of the isthmus). The lack of difference in dispersal rate between Madagascar and the 514 rest is plausible as well, knowing that most Malagasy dung beetles are part of one of few clades 515 that entered Madagascar and diversified there, with very few dispersals back (Miraldo et al. 516 2011; Sole et al. 2011). This is also reflected by the fact that the inferred rate between 517 Madagascar and the rest is comparatively low (Table 4). All in all, those results would support 518 the hypothesis that access to new areas was associated with a rise of diversification rates in dung 519 beetles. However, more fine-scale analyses are needed to confirm the implied scenarios behind 520 this. Also, while overall sampling frequency in this phylogeny was considered in this analysis, it 521 cannot be ruled out that some of these results are artefacts of uneven sampling between groups 522 within the subfamily. Until an even more complete tree of Scarabaeinae is available, finding 523 ways to correct for such sampling biases would be advisable.

524 Acknowledgements

525 We would like to express our thankfulness for helpful discussions and comments at various 526 stages of this project to Kimberly Sheldon, Nicole Gunter, Sergei Tarasov, Nick Matzke, Jim 527 Fordyce, Colin Sumrall, Dave Bapst, Michelle Lawing, Jeremy Beaulieu, Luna Sanchez-Reyes, 528 Veronica Brown, Marisol Sanchez, Todd Pierson, Hailee Korotkin, Claire Winfrey, Jess Dreyer, 529 and Maggie Mamantov.

530 Finally, we thank the Society of Systematic Biologists and the Systematics Association for their 531 financial support towards this project.

532

19 bioRxiv preprint doi: https://doi.org/10.1101/2021.01.26.428346; this version posted January 27, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

533 References

534 Aberer, A.J., Krompass, D. & Stamatakis, A. (2013) Pruning Rogue Taxa Improves Phylogenetic 535 Accuracy: An Efficient Algorithm and Webservice. Systematic Biology, 62, 162-166. 536 Ahrens, D., Schwarzer, J. & Vogler, A.P. (2014) The evolution of scarab beetles tracks the 537 sequential rise of angiosperms and mammals. Proceedings of the Royal Society B- 538 Biological Sciences, 281. 539 Altschul, S.F., Madden, T.L., Schaffer, A.A., Zhang, J.H., Zhang, Z., Miller, W. & Lipman, D.J. 540 (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search 541 programs. Nucleic Acids Research, 25, 3389-3402. 542 Beaulieu, J.M. & O'Meara, B.C. (2016) Detecting Hidden Diversification Shifts in Models of 543 Trait-Dependent Speciation and Extinction. Systematic Biology, 65, 583-601. 544 Blackmon, H. & Adams, R. (2015) EvobiR: tools for comparative analyses and teaching 545 evolutionary biology. 546 Breeschoten, T., Doorenweerd, C., Tarasov, S. & Vogler, A.P. (2016) Phylogenetics and 547 biogeography of the dung beetle genus Onthophagus inferred from mitochondrial 548 genomes. Molecular Phylogenetics and Evolution, 105, 86-95. 549 Buerki, S., Forest, F., Alvarez, N., Nylander, J.A.A., Arrigo, N. & Sanmartin, I. (2011) An 550 evaluation of new parsimony-based versus parametric inference methods in 551 biogeography: a case study using the globally distributed plant family Sapindaceae. 552 Journal of Biogeography, 38, 531-550. 553 Caetano, D.S., O'Meara, B.C. & Beaulieu, J.M. (2018) Hidden state models improve state‐ 554 dependent diversification approaches, including biogeographical models. Evolution, 72, 555 2308-2324. 556 Castresana, J. (2000) Selection of conserved blocks from multiple alignments for their use in 557 phylogenetic analysis. Molecular Biology and Evolution, 17, 540-552. 558 Chamberlain, S., Ram, K., Barve, V. & Mcglinn, D. (2016) rgbif: Interface to the Global 559 “Biodiversity” Information Facility “API”. R package version 0.9. 8. 560 Chamberlain, S.A. & Boettiger, C. (2017) R Python, and Ruby clients for GBIF species 561 occurrence data. PeerJ Preprints. 562 Chamberlain, S.A. & Szocs, E. (2013) taxize: taxonomic search and retrieval in R. 563 F1000Research, 2, 191-191. 564 Charif, D., Thioulouse, J., Lobry, J.R. & Perriere, G. (2005) Online synonymous codon usage 565 analyses with the ade4 and seqinR packages. Bioinformatics, 21, 545-547. 566 Clark, J.R., Ree, R.H., Alfaro, M.E., King, M.G., Wagner, W.L. & Roalson, E.H. (2008) A 567 comparative study in ancestral range reconstruction methods: retracing the uncertain 568 histories of insular lineages. Systematic Biology, 57, 693-707.

20 bioRxiv preprint doi: https://doi.org/10.1101/2021.01.26.428346; this version posted January 27, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

569 Davis, A.L., Scholtz, C.H. & Sole, C.L. (2016) Biogeographical and co-evolutionary origins of 570 scarabaeine dung beetles: Mesozoic vicariance versus Cenozoic dispersal and dinosaur 571 versus mammal dung. Biological Journal of the Linnean Society, 120, 258-273. 572 Davis, A.L.V. & Scholtz, C.H. (2001) Historical vs. ecological factors influencing global 573 patterns of scarabaeine dung beetle diversity. Diversity and Distributions, 7, 161-174. 574 Davis, A.L.V., Scholtz, C.H. & Philips, T.K. (2002) Historical biogeography of scarabaeine 575 dung beetles. Journal of Biogeography, 29, 1217-1256. 576 Duchene, S., Molak, M. & Ho, S.Y.W. (2014) ClockstaR: choosing the number of relaxed-clock 577 models in molecular phylogenetic analysis. Bioinformatics, 30, 1017-1019. 578 Dupin, J., Matzke, N.J., Särkinen, T., Knapp, S., Olmstead, R.G., Bohs, L. & Smith, S.D. (2017) 579 Bayesian estimation of the global biogeographical history of the Solanaceae. Journal of 580 Biogeography, 44, 887-899. 581 Goldberg, E.E., Lancaster, L.T. & Ree, R.H. (2011) Phylogenetic Inference of Reciprocal Effects 582 between Geographic Range Evolution and Diversification. Systematic Biology, 60, 451- 583 465. 584 Guindon, S., Dufayard, J.F., Lefort, V., Anisimova, M., Hordijk, W. & Gascuel, O. (2010) New 585 Algorithms and Methods to Estimate Maximum-Likelihood Phylogenies: Assessing the 586 Performance of PhyML 3.0. Systematic Biology, 59, 307-321. 587 Gunter, N.L., Monteith, G.B., Cameron, S.L. & Weir, T.A. (2018) Evidence from Australian 588 mesic zone dung beetles supports their Gondwanan origin and Mesozoic diversification 589 of the Scarabaeinae. Insect Systematics & Evolution, 1, 1-27. 590 Gunter, N.L., Weir, T.A., Slipinksi, A., Bocak, L. & Cameron, S.L. (2016) If Dung Beetles 591 (Scarabaeidae: Scarabaeinae) Arose in Association with Dinosaurs, Did They Also Suffer 592 a Mass Co-Extinction at the K-Pg Boundary? Plos One, 11. 593 Hanski, I. & Cambefort, Y. (1991) Dung beetle ecology. Dung beetle ecology., i-xii, 1-481. 594 Hosner, P.A., Braun, E.L. & Kimball, R.T. (2015) Land connectivity changes and global cooling 595 shaped the colonization history and diversification of New World quail (Aves: 596 Galliformes: Odontophoridae). Journal of Biogeography, 42, 1883-1895. 597 Kendall, D.G. (1949) Stochastic processes and population growth. Journal of the Royal 598 Statistical Society. Series B (Methodological), 11, 230-282. 599 Kim, S.I. & Farrell, B.D. (2015) Phylogeny of world stag beetles (Coleoptera: Lucanidae) 600 reveals a Gondwanan origin of Darwin's stag beetle. Molecular Phylogenetics and 601 Evolution, 86, 35-48. 602 Klaus, K.V. & Matzke, N.J. (2019) Statistical Comparison of Trait-dependent Biogeographical 603 Models indicates that Podocarpaceae Dispersal is influenced by both Seed Cone Traits 604 and Geographical Distance. Systematic Biology. 605 Kodandaramaiah, U. (2010) Use of dispersal–vicariance analysis in biogeography–a critique. 606 Journal of Biogeography, 37, 3-11.

21 bioRxiv preprint doi: https://doi.org/10.1101/2021.01.26.428346; this version posted January 27, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

607 Krell, F.-T. (2000) The fossil record of Mesozoic and Tertiary Scarabaeoidea (Coleoptera: 608 Polyphaga). Invertebrate Systematics, 14, 871-905. 609 Krell, F.-T. (2006) Fossil record and evolution of Scarabaeoidea (Coleoptera: Polyphaga). The 610 Coleopterists Bulletin, 60, 120-143. 611 Landis, M.J., Matzke, N.J., Moore, B.R. & Huelsenbeck, J.P. (2013) Bayesian Analysis of 612 Biogeography when the Number of Areas is Large. Systematic Biology, 62, 789-804. 613 Lanfear, R., Calcott, B., Ho, S.Y.W. & Guindon, S. (2012) PartitionFinder: Combined Selection 614 of Partitioning Schemes and Substitution Models for Phylogenetic Analyses. Molecular 615 Biology and Evolution, 29, 1695-1701. 616 Lanfear, R., Frandsen, P.B., Wright, A.M., Senfeld, T. & Calcott, B. (2017) PartitionFinder 2: 617 New Methods for Selecting Partitioned Models of Evolution for Molecular and 618 Morphological Phylogenetic Analyses. Molecular Biology and Evolution, 34, 772-773. 619 Larsson, A. (2014) AliView: a fast and lightweight alignment viewer and editor for large 620 datasets. Bioinformatics, 30, 3276-3278. 621 Losey, J.E. & Vaughan, M. (2006) The economic value of ecological services provided by 622 insects. Bioscience, 56, 311-323. 623 Matzke, N.J. (2013a) BioGeoBEARS: BioGeography with Bayesian (and likelihood) 624 evolutionary analysis in R Scripts. R package, version 0.2, 1, 2013. 625 Matzke, N.J. (2013b) Probabilistic Historical Biogeography: New Models for Founder-Event 626 Speciation, Imperfect Detection, and Fossils Allow Improved Accuracy and Model- 627 Testing. Frontiers of Biogeography, 5, 242-248. 628 Matzke, N.J. (2014) Model Selection in Historical Biogeography Reveals that Founder-Event 629 Speciation Is a Crucial Process in Island Clades. Systematic Biology, 63, 951-970. 630 McKenna, D.D., Farrell, B.D., Caterino, M.S., Farnum, C.W., Hawks, D.C., Maddison, D.R., 631 Seago, A.E., Short, A.E.Z., Newton, A.F. & Thayer, M.K. (2015) Phylogeny and 632 evolution of Staphyliniformia and Scarabaeiformia: forest litter as a stepping stone for 633 diversification of nonphytophagous beetles. Systematic Entomology, 40, 35-60. 634 Miraldo, A., Hewitt, G.M., Paulo, O.S. & Emerson, B.C. (2011) Phylogeography and 635 demographic history of Lacerta lepida in the Iberian Peninsula: multiple refugia, range 636 expansions and secondary contact zones. Bmc Evolutionary Biology, 11. 637 Miraldo, A., Wirta, H. & Hanski, I. (2011) Origin and diversification of dung beetles in 638 Madagascar. Insects, 2, 112-127. 639 Mlambo, S., Sole, C.L. & Scholtz, C.H. (2014) Affinities of the Canthonini dung beetles of the 640 Eastern Arc Mountains. Organisms Diversity & Evolution, 14, 115-120. 641 Mlambo, S., Sole, C.L. & Scholtz, C.H. (2015) A molecular phylogeny of the African 642 Scarabaeinae (Coleoptera: Scarabaeidae). Arthropod Systematics & Phylogeny, 73, 303- 643 321.

22 bioRxiv preprint doi: https://doi.org/10.1101/2021.01.26.428346; this version posted January 27, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

644 Moczek, A. (2011) Evolution and development: onthophagus beetles and the evolutionary 645 developmental genetics of innovation, allometry and plasticity. Ecology and evolution of 646 dung beetles, 126-151. 647 Monaghan, M.T., Inward, D.J.G., Hunt, T. & Vogler, A.P. (2007) A molecular phylogenetic 648 analysis of the Scarabaeinae (dung beetles). Molecular Phylogenetics and Evolution, 45, 649 674-692. 650 Moran, P. (1951) Estimation methods for evolutive processes. Journal of the Royal Statistical 651 Society: Series B (Methodological), 13, 141-146. 652 Nichols, E., Spector, S., Louzada, J., Larsen, T., Amequita, S., Favila, M.E. & Scarabaeinae Res, 653 N. (2008) Ecological functions and ecosystem services provided by Scarabaeinae dung 654 beetles. Biological Conservation, 141, 1461-1474. 655 Ocampo, F.C. & Hawks, D.C. (2006) Molecular phylogenetics and evolution of the food 656 relocation behaviour of the dung beetle tribe Eucraniini (Coleoptera : Scarabaeidae : 657 Scarabaeinae). Invertebrate Systematics, 20, 557-570. 658 Paradis, E., Claude, J. & Strimmer, K. (2004) APE: Analyses of Phylogenetics and Evolution in 659 R language. Bioinformatics, 20, 289-290. 660 Plummer, M., Best, N., Cowles, K. & Vines, K. (2004) CODA: Output Analysis and Diagnostics 661 for MCMC, R package version 0.13-3, 2008. CRAN: The Comprehensive R Archive 662 Network. 663 R Development Core Team (2014) R: A language and environment for statistical computing. R 664 Foundation for Statistical Computing, Vienna, Austria. 665 Ree, R.H. & Sanmartín, I. (2018) Conceptual and statistical problems with the DEC+ J model of 666 founder‐event speciation and its comparison with DEC via model selection. Journal of 667 Biogeography, 45, 741-749. 668 Ree, R.H. & Smith, S.A. (2008) Maximum likelihood inference of geographic range evolution 669 by dispersal, local extinction, and cladogenesis. Systematic Biology, 57, 4-14. 670 Reguero, M.A., Gelfo, J.N., López, G.M., Bond, M., Abello, A., Santillana, S.N. & Marenssi, 671 S.A. (2014) Final Gondwana breakup: the Paleogene South American native ungulates 672 and the demise of the South America–Antarctica land connection. Global and Planetary 673 Change, 123, 400-413. 674 Ronquist, F. (1997) Dispersal-vicariance analysis: A new approach to the quantification of 675 historical biogeography. Systematic Biology, 46, 195-203. 676 Sanmartin, I. & Ronquist, F. (2004) Southern hemisphere biogeography inferred by event-based 677 models: plant versus animal patterns. Systematic Biology, 53. 678 Sayers, E.W., Cavanaugh, M., Clark, K., Ostell, J., Pruitt, K.D. & Karsch-Mizrachi, I. (2019) 679 GenBank. Nucleic Acids Research, 47, D94-D99. 680 Schöfl, G. (2016) reutils: Talk to the NCBI EUtils. R package, version 0.2.3. 681 Scholtz, C. & Chown, S. (1995) The evolution of habitat use and diet in the Scarabaeoidea: a 682 phylogenetic approach [pp. 355–374]. Biology, Phylogeny, and Classification of 23 bioRxiv preprint doi: https://doi.org/10.1101/2021.01.26.428346; this version posted January 27, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

683 Coleoptera: Papers Celebrating the 80th Birthday of Roy A. Crowson. J. Pakaluk and SA 684 Slipinski (eds.). Muzeum i Instytut Zoologii PAN, Warszawa, Poland. 685 Scholtz, C.H., Davis, A.L.V. & Kryger, U. (2009) Evolutionary biology and conservation of 686 dung beetles. Pensoft Sofia. 687 Schwery, O. & O’Meara, B.C. (2016) MonoPhy: a simple R package to find and visualize 688 monophyly issues. PeerJ Computer Science, 2, e56. 689 Scotese, C. (1993) Maps of generalized continental positions and orography produced by the 690 palaeomap project, University of Texas, Arlington. Terrestrial ecosystems through time. 691 Seton, M., Müller, R., Zahirovic, S., Gaina, C., Torsvik, T., Shephard, G., Talsma, A., Gurnis, 692 M., Turner, M. & Maus, S. (2012) Global continental and ocean basin reconstructions 693 since 200 Ma. Earth-Science Reviews, 113, 212-270. 694 Smith, S.A. & O'Meara, B.C. (2012) treePL: divergence time estimation using penalized 695 likelihood for large phylogenies. Bioinformatics, 28, 2689-2690. 696 Sole, C.L. & Scholtz, C.H. (2010) Did dung beetles arise in Africa? A phylogenetic hypothesis 697 based on five gene regions. Molecular Phylogenetics and Evolution, 56, 631-641. 698 Sole, C.L., Wirta, H., Forgie, S.A. & Scholtz, C.H. (2011) Origin of Madagascan Scarabaeini 699 dung beetles (Coleoptera: Scarabaeidae): dispersal from Africa. Insect Systematics & 700 Evolution, 42, 29-40. 701 Spector, S. (2006) Scarabaeine dung beetles (Coleoptera: Scarabaeidae: Scarabaeinae): an 702 invertebrate focal taxon for biodiversity research and conservation. The Coleopterists 703 Bulletin, 60, 71-83. 704 Stamatakis, A. (2014) RAxML version 8: a tool for phylogenetic analysis and post-analysis of 705 large phylogenies. Bioinformatics, 30, 1312-1313. 706 Stuart, A.J. (2015) Late Quaternary megafaunal extinctions on the continents: a short review. 707 Geological Journal, 50, 338-363. 708 Tarasov, S. & Dimitrov, D. (2016) Multigene phylogenetic analysis redefines dung beetles 709 relationships and classification (Coleoptera: Scarabaeidae: Scarabaeinae). Bmc 710 Evolutionary Biology, 16. 711 Tarasov, S. & Génier, F. (2015) Innovative Bayesian and parsimony phylogeny of dung beetles 712 (Coleoptera, Scarabaeidae, Scarabaeinae) enhanced by ontology-based partitioning of 713 morphological characters. Plos One, 10, e0116671. 714 Tarasov, S., Vaz-de-Mello, F.Z., Krell, F.T. & Dimitrov, D. (2016) A review and phylogeny of 715 Scarabaeine dung beetle fossils (Coleoptera: Scarabaeidae: Scarabaeinae), with the 716 description of two Canthochilum species from Dominican amber. Peerj, 4. 717 Toussaint, E.F., Bloom, D. & Short, A.E. (2017) Cretaceous West Gondwana vicariance shaped 718 giant water scavenger beetle biogeography. Journal of Biogeography, 44, 1952-1965. 719 Toussaint, E.F.A., Seidel, M., Arriaga-Varela, E., Hajek, J., Kral, D., Sekerka, L., Short, A.E.Z. 720 & Fikacek, M. (2017) The peril of dating beetles. Systematic Entomology, 42, 1-10.

24 bioRxiv preprint doi: https://doi.org/10.1101/2021.01.26.428346; this version posted January 27, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

721 Vaz-de-Mello, F. (2007) Revision taxonomica e analysis phylogenetico de la tribu Ateuchini. 722 Xalapa, Veracruz, Mexico: Instituto de Ecologia AC. 723 Wilkinson, S.P., Davy, S.K., Bunce, M. & Stat, M. (2018) Taxonomic identification of 724 environmental DNA with informatic sequence classification trees. 725 Wirta, H., Orsini, L. & Hanski, I. (2008) An old adaptive radiation of forest dung beetles in 726 Madagascar. Molecular Phylogenetics and Evolution, 47, 1076-1089. 727 Wirta, H., Viljanen, H., Orsini, L., Montreuil, O. & Hanski, I. (2010) Three parallel radiations of 728 Canthonini dung beetles in Madagascar. Molecular Phylogenetics and Evolution, 57, 729 710-727. 730

25 bioRxiv preprint doi: https://doi.org/10.1101/2021.01.26.428346; this version posted January 27, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

731 Tables and Figures

Table 1 Tribal Monophyly-Issues Dung Beetles Monophyly status and reasons for non-monophyly for all tribes in the full matrix trees. Mon.=monophyly-status, #Tips=number of taxa assigned to this tribe, Tips=number of additional taxa in this clade (descendants of same MRCA), #Intr=number of tribes among intruder taxa, Intruders=Genera or Tribes intruding (numbers in parentheses are numbers of tips, all taxa incertae sedis unless indicated otherwise), #Outl.=number of taxon-members outside the main clade.

Tribe Mon. #Tips Tips #Intr. Intruders #Outl. Outliers Eucraniini Yes 8 0 0 NA Ateuchini Yes 2 0 0 NA Eurysternini Yes 6 0 0 NA Gymnopleurini Yes 8 0 0 NA Sisyphini Yes 6 0 0 NA Scarabaeini No 37 1 1 Neateuchus (1) 0 Phanaeini No 44 2 1 Diabroctis (1), 0 Dendropaemon (1) Onitini No 7 2 1 Cheironitis (2) 0 Oniticellini No 36 10 2 Tiniocellus (2), 0 Tragiscus (1), Drepanocerus (4), Cyptochirus (1), Heterosyphus (1), Proagoderus (1, Onthophagini) Onthophagini No 119 53 1 Hyalonthophagus (2), 16 Onthophagus diabolicus Cleptocaccobius (1), and 15 more. Euonthophagus (2), Milichus (1) Deltochilini No 132 414 1 Canthonini (2) 112 Arachnodes emmae and 111 more. Canthonini No 52 494 1 Deltochilini (7) 25 Megathopa villosa and 24 more. Coprini No 23 501 1 Paracopris (1) 10 Coptodactyla storeyi and 9 more. Dichotomiini No 36 510 0 24 Canthidium haroldi and 23 more.

26 bioRxiv preprint doi: https://doi.org/10.1101/2021.01.26.428346; this version posted January 27, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

M

a l a g o n i e l l a _ p u n c t i c o l l i s D

e

l t o

c D h

M e i l l u e t o m g c a _ h p D p t C h i D s s l s e o u o s u _ e e D l s m p p e t P n i l u i M t o u a e r d e l _ o d a i s c i s c _ l s s o s e s t g c r o s o i

h i D L u _ n n v o a u g i h u r a p s n t

i n l a A e i b i i s j c c s a e l s l D t i a u a r l u a n r s a z b l o h o o u i l r u o t u o a p c A r t r e e p i u w n h m a h s o d s i c t s p A i e t s o i u t m c i l y a s a a l r o l c t s n m u C o l t e k c s l _ S a c _ e t r a h _ r i a i l T s e t p u _ o i h c _ _ s a s a _ m p d c h b s s o s o c b i a f n i c _ d s s n u o m c u i s a s a C i n C r c i y _ i u s t i l u a h i p o h _ a s o r n i s l h i u H m d o a u r h b S h u s l i r x n o r u p s a e _ _ r C r o n c g r m e u r o C h e l i C r i i u d p e e l h a n o a a a b i n e c C _ _ n m u s p C s l a s u p o l u r a A r e p a i s a d m u r u i e d e s s i l o e a e _ e v y o n b i o t s n m m l u C e o e t r t r e o o r d a n z p _ o s e s u o r h n h _ m b m s l l n C b s p s a y i t S b i p d m a e i b c s e p d

a a c o u a m n s n t e e s c p o _ r n e o c n C o p m m u a r l p o h a r r _ e d a s u _ r i o r e _ t s i a d E t l l o a c l _ a s o

C c s t n _ s c a i o s e n e s i r p o c a u h s i u o C t l t i e h o p m d n t u o s s r n c _ d y s d e _ s n s s n o e i o g i o i

c h o u i n s i i i i a r _ e t _ r c _ _ e l s o s t s n n c n i o a o n a a c _ r e m s r r t b u p s d o n e _ o t p n a i _ _ s C x a n _ n s l d n n h i a s d s d o h C m a m l h v _ p s a _ t n u l o n n _ u a _ n g l a a n _ a i _ e n i h a m e m r d i h i r i t h s s s a e m l u h r _ o i c a o k y o t o e s o o e l _ r l s n i _ p n c i f u r s J t o s o p l d a m i e s h d a _ y y y m y p _ l d o a s u e n c i r c e g a e c m l h o i a _ n n n d g n i A l l n a s n o s l a e o s o e a c t C f p o s c e a e i s e n n n o s g s p r _ e g g _ l n o a g d g i d o a _ e t _ p _ a p f a e o a o _ p e c a e C h r n _ d _ e h p r d _ d a a e f l r s u z e n r n e a i u a a u t h h s c h a n m u o A s i n s c s h n o r r l r h r t i s i r h D p o n c _ m C O n s l q a r u u a o d l o e o s o e r s s u s o u h d t c n u s i s o i c u f _ y e A b h n _ e c a i e n a i c _ s i l a A u m y n a u r e a a i m C n d e e x n i s s _ g n e n c o a p t _ s p n o d l a m n p _ a l v a r g c s a h r d a C r d g h o i i h c e a h u e y n d k _ i r P _ i t o r h n P P n u r s n h d h a n o m b n e f i r o s c d e i G n u a c n 9 c o s i o d s u i t a o u s A d r n _ _ i d u i s n c o u h i d 6 i u c c _ r o s r o h n h e e r A u s p n u a m t s u s P n d u a n s A h m a n u A o n n g 7 p a a s a i o r e y c s a n s c n i p c s a a c h t a 0 r s i l _ n e d s r _ r s A n _ n a s C h c _ l i o r h e p u i u c h o e t n o r n r r u r t t i i h a a C u c i c e O n _ a m r s n h l h s i p i u s e n a a A a e t P i l s e o A c s i r _ d c n o g s a d d P P s s A a r e g i a s i u h A c d r n t a o a s a o c l u o s n s l s _ u i c _ s i i C p x h l e u r a p t C a h i a i c e c o P u a r A o p h e h l l s a m o r e n i i e e y O y n _ P a n c a r s p v n i i r s l o r a l e y 8 l u r o O c s o c r p n p r a s s v a d e o s a r u r o s _ m e u _ a u _ A i n n o a p C o x h d e o _ u i o p O n r r A d p o m e C e A l i s s n n i i a d h l e p x t s 7 A u s e e s u o t n i h r p y a v d 7 c A d n s i f v l a o l m b D r o e i u 7 r o o w e u g o o h y x a _ a i A c C o s i r P s i l p e P u e c b t i c a _ p h l n e e e o h p u e _ a e C p p s y r a e r l s 7 n n n m n p a t e _ s e 1 A a m r l l s a o r a S n h a i e e s e d a n u p i p r p n o h r e t s u s n r o n e m h n n b u o u d h h u g n p o m s r y u _ u t r n i s o u e o P a e i 3 o m o m r s n 3 P d p a t s _ o a a a i s s h n r e c _ r p n u r s i 1 c A p r n m n a p a l r e n l h n u s A y e r _ i _ o r _ 9 P n G r n n c e i e _ o n 4 s l t _ s d h n s _ 3 c a t u o n o o e o a 8 _ o i k _ i p h r _ s a a s k 0 m n l i a a a o u l r i h r p c r p 5 r n a r e c o p u o n i a 9 a G y s h C p e a p n c n e _ b i i s y 8 s t o u o d m e n s a b 4 0 r c e d m a a a h n s _ n o r l d A o u e o r e t h u o 9 l o _ h A c r i P o u u a h _ a u a b l v 9 t l r t r i o t a o s n e a a _ m _ n i 4 a G n t l 4 3 A o c h p s s a n e s a e s a 8 p e p c n c o n s o m n e b e y r 0 o l _ j m u P a _ e b f _ 3 4 6 c o _ r a r _ n e s u _ r i a p r 7 0 n o o u a o t P a u e a u 7 m o e n s q o o 3 2 a 7 h n e u d o e 8 A 6 n p e l p r o d i p 9 n s a n 3 c s n g s l c s s s s i 1 7 h y n n l t o e _ h u p e n n 7 3 o i a a P s l u 2 6 5 o o n a l n 9 p _ n e i 9 o u o 5 m o _ p o 2 9 s a s h u n _ i l t c d c r p r c b d u n e _ a 7 h _ r c e i r u g 5 3 G m n o t r o u 3 m n s h 7 _ h a u n _ a s d e e v i d l i 9 4 o t e _ o u a a P b la r o a a l e s 1 a y e c l t o l i t s a 2 n a 3 e o e s c n _ c n i u e 6 8 r c n i n i m P n e h a a n e _ s y n la n 7 m n e r p n l a n l u _ h i i 0 G e m k _ a a t n s m j 4 9 3 T T l t e o u a a n u 7 1 e m o h e n i t e 9 8 A l t h e s a h u i c b t d 1 o r s c a i o 8 0 m r e n r e u s 5 p c t t l _ a s i l c f s u a 4 8 6 2 T e t n p C m u i u a a i e a i 0 8 e p n m e s e l is _ a q s t f n d 7 9 o e c s a i 4 n m 8 7 l e t n 1 1 T c o a e d b e l s e s n m u r s 1 l o a c e u s i 2 1 u i i m a r e i u 8 5 T n m e e _ n s o l s t _ u _ u s 9 7 0 p n l u C o c r h e 7 0 l M l i i e h e h e o s l 7 3 s s c a u o m a s p s u 9 9 1 9 e p q _ la s r t i t j t y u P n n i 7 6 5 0 m o p u a l t a i u i s l _ l 1 6 0 8 m T s a e t n n r s a h s s e h s s 4 5 4 1 o s e i i y t l c p u 8 1 6 e n e o _ s h e g m t a p o ie l s m i P _ e o a i t 8 0 n o p m n lu l fr r _ u _ s r s 0 1 T n g n s n l m o 7 6 6 4 0 0 m T s t i a r s t t u h e a a n 7 5 2 2 3 e o s v a y o tu s is _ _ e r s P P i a g s a 9 2 1 e m m u e i l s e e e n s c e 4 1 6 0 i s p p s s s p s h h u n o r i e 2 7 8 2 6 9 e s C s e u m s a e e id c s id li a P _ i la n 5 1 T e b o e g a _ _ u u r d _ o n i a s a u s u 1 3 T _ _ l n t d i s t i l o a n n h a s u 8 0 T o n p a la s s u _ e s i _ l t r s a _ m e rt s s 6 4 1 2 s o i v S l u s s o to e c s o c o u e a a g u i _ 7 8 9 1 h u v e n u b u t c id a ic a e e e P n u e s a 4 6 4 p i h r u o u m tu a c p e p m i n a P u u a 0 7 1 t n a n l n u i a a to d id e a in P P P h P s h a th _ c 6 a b n u a i p a l l _ p p c E i ir E f g e ti s e d 6 s g a a s o _ a ia h h a _ a t y h 3 3 8 2 1 8 o c S p o E a t v s _ a a h h _ n u e e i 6 S a e e _ p p p u E c h s a n a w h s m s l 8 8 9 h _ L e s e p _ e a m e n P n n a a t m 7 le 4 9 8 p C s g _ _ n a s d r e a h a e n a o e _ a i 7 4 a L u L s s a E p e i _ s id e s e a P e a u a g w u lu l n o 0 s 5 4 2 a e n p E d to s _ o a i u n h u e e n d e u n 6 9 7 6 t v u u s i c e s ct n i ns s a a s u s u e s n n s 9 2 9 9 0 S s u a n n e to a d e e _ e n _ s _ s r e _ a s 3 5 li a p a a L s u c p i id pa l bo ee P t e _ v _ i n s r 7 0 5 e p p s ic1 a to o e a b P h r u a i i _ i a i i 8 p S u r4 E t E h P a ia s e n g n a w l s s 9 o L e e u d s s e p ac c _ h ga h _ u d n d m l 9 3 i u i 0 s a h a n n e a e 2 2 n L L o it s l a E p a e n a n a g q s y e i g i 5 2 8 3 r a h 2 1 5 E p d m a n a e u u _ m u x th n 6 7 o h n lo t 6 9 9 E i s_ i m a e u la a p io s a e 5 9 5 1 6 4 ic _ il n sp to e ki _ e u s_ r dr yr n o r 3 4 6 9 5 M d s v e i c id s s u s y is id o 9n i 0 6 2 5 6 4 _ u _ id m 0 pa o n de E E s_ _ e _ e is 7 9 1 s n s c e 3 E t a i A u u n fu c t n 7 a u c h ac h to n c cr E i r o e s 2 2 8 1 u p n o 8 p s_ c o ra a n m io ra xe 6 1 n e a _ s_ 8 E e pa G m n ni n ro s e n 4 4 1 0 8 3 a L p s u id E l io iu u e d us n s 4 4 4 8 p e u c 4 to yp p m m a s is 1 3 e L n e 7 6 c An A h so _ _ ra is 8 2 7 L a o 5 8 5 a s o n o id ar pl b 8 1 3 0 9 p r p tu m o de e a a du 9 4 9 7 e p E ta io m r s ch ni s 7 1 9 5 6 L o 4 c P p io us _c n co _ 2 0 2 C 9 n a C G so p _ a o ll lo 8 0 9 2 0 pu Pa ch an ly id so st vi id e b 4 6 _ ch ys th p e id er fr es oc 1 8 4 on y o i h s_ e q on e 0 9 2 h so ma di od h s_ ui s p 6 1 3 nt m _ K K um er et b lin ha 7 9 7 a Pa P a_ str he Kh he _ u er ilo u 9lu ic s us ch a be ia pe e pe ha s_ o b s 8s 0 ch lli at P ys ch nn tus r_ pe r r ce cly a 2 O co lis n Pa ac om ys ig n r_ _s old n ta 7 ri a la ch hy a om se igr b ub i tr 3 5 6 0 i ab n p ys so _d a n oa on a al 1 4 2 y gl dio om om m en _g i e el en is 9 6 re _ ri c P a a_ tic ar ne lii eu 5 7 to la e s_ ac _ro rod ol iep us s 7 _s ty _m u P hy tu rig lis in 2 7 la ac la om nis ac P so nd u us 1 ty od ty ist a lu sp hy ac m ige es 8 1 ac pt ac h a ific ic _ so hy a_s nu i 8 8 6 d o d p pt ir lic bia Pa ma som ch s 2 3 9 7 8 to C to Am ru _m mp m ch _a a inz 8 5 2 8 op op ter la si na s yso esc _va i C C in iel x_ dig ptu r Pa m ula le 2 9 9 5 3 _ rz py u m e lex chy a_e piu flor 6 7 9 lla a o se co nig p s Pac som nd s ae zie emryg P in x_ sim ide hy a_ roe 3 ar D io x_ py lis m_ le llo S som hip ydi 9 m D py go ia iu ia tico car a_ po 8 8 4 8 7 De go ry tib ob tib ac is S D ab gle cra 8 8 ry io x_ nth m_ _fr ens ca rep Dre aeu nto tes 7 Dio D py ro iu m i tas raba an pan s_c ni 8 5 8 5 go Pa ob biu oki _ta ta eus opo op ica 9 1 7 7 1 1 3 1 2 ry nth tho co ta i ara cta _ga dus odu tric 9 8 4 5 5 Dio no on _ ula ch noc xpe Sc len _p s_c osu 3 2 7 1 A ud ium cic dit icra _ine ti ara us rox os s se ob fas bow D ara hod bae imu tatu 5 P nth ia_ is_ noc esc um us_b s s 9 6 O mb ps s eus 7 icra ra_d vex Sca ohe 7 na lyro icro ma 2 D oca con sis Sca raba ma 2 7 4 4 Ig de _m pyg ran um_ uen raba eus ni 1 8 2 3 0 6 2 B xys s_ 1 Dic hidi maq Sc eus _fla 7 6 ro oxy 9 Byrr _na visi arab _bri vico 0 3 1 9 0 3 U Ur ium _da Sca aeus ttoni rnis s rrhid anus xus Pa raba _hip 3 2 9 nutu 3 9 5 By akw rado chylo eus_ pias 9 5 8 cor 9 Nam s_pa nsis Sca mera adam 9 5 1 9 ter_ yolu bosie raba _fem asto ap roed s_ou S eus_ oral r tosc End philu s s cara rugos is 2 6 9 3 ole 8 Silva lpinu laris ntosu baeu us B 9 1 9 lus_a _simi tome S Scara s_wes 1 ecko niola anus_ carab baeus twoo 9 2 3 4 5 P antho nikw aeus_ _caffe di 4 9 liusc Oute viettei r 1 A Scar 7 0 6 2 Scara abaeus 6 10 9 3 llum Scarab baeus_ _satyr 6 1 8 9 _pusi Neateu aeus_ra goryi us oloma status ullus chus_pr dama 8 Odont rus_co s urus_p Scara oboscid ropho culatu Delople baeus_za eus 5 4 Sa _tuber nis Sc mbezian 5 2 7 5 4 phorus nitidipen s arabaeus us 9 7 Saro orhina_ _armatu _opatroides Scara _deludens 3 Copt ergerius Ataenius Sca baeus_sac 7 4 9 0 Frankenb armatus sp rabaeus_thp er 7 7 0 armatus_ araphytus_ lia_sp hon 9 7 9 0 4 4 0 ergerius_ P Aegia Scarabaeus_p 5 8 2 Frankenb ius 7 1 Leotric Trichill hillum_sp idium_pilosum 2 8 C 3 2 tragicus anthidium_guanacaste 6 4 Dialytellus_ hodius_lividus Rhyparus_sp Canth 5 9 9 5 9 2 8 2 Ap odius_aegrotus idium_thalassinum 9 Aph Canthidium_rufinum 1 9 7 Dichotomius_boreus 1 6 0 Podotenus_fulviventris _tasmaniae 8 7 Dichotomius_laevicollis 7 8 3 0 Acrossidius 9 6 Onthophagus_hecate_hecate Dichotomius_parcepunctatus 6 2 8 8 1 5 Onthophagus_hecate Dichotomius_yucatanus 3 2 Onthophagus_ us_semisquamosus 2 2 9 orpheus Dichotomi inatus 1 6 Onthophag Dichotomius_gem 3 2 0 us_crinitus otomius_sericeus 0 Onthophagus_pennsyl Dich omius_bos 6 2 vanicus Dichot 9 5 7 4 Onthophagus_cos 8 8 8 Onthop cineus mius_nisus _viduus 0 On hagus_marginic Dichoto Ateuchus um 9 8 thophagus_h ollis chus_vidu 8 3 1 5 On aematopus Ateu nsis thophagus_ _ecuadore 4 4 Ontho bidentatus Ateuchus adorense 8 4 0 5 9 5 phagus_ch hus_ecu ridanus 2 4 Onthop ampioni Ateuc hus_flo sis 9 1 4 Onth hagus_sh Ateuc floriden 1 O ophagus arpi uchus_ yge 9 1 8 nthopha _batesi Ate hrysop gus_inc chus_c pygus Ont ensus Ateu chryso 9 7 2 5 3 9 1 9 7 Onth hophag uchus_ ophag us_stoc Ate xus 6 4 us_acu kwelli s_infle 8 1 Ontho minatu ternu lus 00 phagu s Eurys gustu 9 4 1 Ontho s_xan s_an 6 3 Ontho phagu thome sternu ejus 9 On phag s_pra rus Eury _pleb 8 thoph us_cl ecelle rnus tinus On On agus ypeat ns uryste velu is 6 4 9t3hop thoph _fod us E rnus_ ticoll s 5 8 5 9 hagu agus iens ryste ama s inutu 6 s_as _len Eu us_h ibaeu s_m lis 7 Onth peru zi stern _car ano era s 2 9 6 O opha lus Eury rnus N _hum ratu 7 2 4 ntho gus ryste nos colo 6 1 6 6 pha _bab Eu Na s_bi 9 4 O gus irus ano tus 2 ntho O _bab sa N gna kii 4 9 9 7 7 8 4 On pha nth irus rosi ans ns 6 thop gus oph soid rub s_h nite i 9 Ont hag _sin agus es os_ ano os_ tte 1 hop us_ icus _ob Nan N an _vie 3 6 4 1 5 5 hag vid scu N os tus 7 O On us_ uus rior Nan ita s 4 3 3 O ntho tho tau dub atu 3 0 2 4 9 nth ph pha rinu os_ ype lis 9 On oph agu gus s an _cl nta i 4 O O tho ag s_b _so N nos ide ras 9 1 4 O nt nth pha us_ ive liv Na cc rie i 8 0 n hop op gu tau rte agu s_o pey on 9 0 tho ha hag s_i rus x s no s_ vad is 9 8 3 ph gu us llyr Na ano s_ ns s 4 O agu s_m _au icu N no oe tu nth s_ a str s Na mb ota tus 9 7 3 op pu mm ali no in la 8 5 8 1 3 9 7 O ha gn illa s ma s_b cu 8 5 2 nth gu ax tu s_ no ima s 1 4 On op s_c s no Na _b su 6 1 O tho ha ap Na os ibo tus 0 nt ph gu ell an scr ta s 7 0 O ho ag s_ a N mi nc ns ni 8 0 O nth ph u ne se ipu ce en 9 9 4 6 3 O n o ag s_e os _ m es ip sis 7 3 4 n th ph us v ten os se n lat n 5 O th op ag _ an o an s_ ya s_ ye 0 1 n op ha us oc idu cer N de _c ru its 8 8 1 1 0 0 8 th O ha gu _f hro s us o rus p b us 3 3 op nth g s_ er m hn p am om n s 9 3 0 h o us p ox er ac am ol _z lia u i 6 3 9 0 O ag ph _s en us Ar ol ot s ba lic ot 5 9 3 0 O nt On us a lo ta ot Ap ru _ al ill i 5 0 7 n ho th _ gu an ca Ap mp es et m n 5 3 0 9 th p o lam s_ ei nt la od m s_ do is 5 2 1 op ha ph i m hu to n s_ ru a ns us 2 O h g ag na jo s o ch ru p _v e at is 1 n a us u tu be Ap ra p m s on ul ns s 1 th O gu _ s_ s rg A m la ru dr c e tu ii 0 2 0 O O op nt s_ qu co i la to p n a jy ta k 9 8 6 0 0 O n nt h h m a n to o am to im je o ns 2 8 7 1 nt th h a op u dr se po Ap ol si dr ro rin a s 9 h op op gu h lg ip n A ot it a a d _h e u 6 O op M h h s ag ra us ta p oh qu m ua s a ce 9 is n O h il a ag _d u ve tu ne A b _ s_ q ru en ri s 6 4 5 7 1 O t n a ic gu u u s_ i la us m us u _ p l e 4 n 1 5 0 3 9 8 n ho th gu hu s s_ nn tu tu _a r pr us m he s s 3 ne 2 t p o s s _ h i r s s p m r la _ s_ si a O h h p _ _ ve a ng rb ru am la p o s u n f s 6 n op a ha ru ap rm ag i al p ol o m ot ru pr e a cu 6 t h g g b ic i m t ot la p p a is m a s 5 5 7 9 h a us u ic a ic la po p to A m am e s o s p u 1 7 8 1 O op g _ s u li ul to A A o la l ng n s n a i o i s c 9 7 0 n h u g _p n s a o p o to a e i ra y n _ n u ri 0 th a s_ ra h du tu p A ot o r a s _ r o s a is ss a O o g p n a l s A p p o in n s s d s u r is 0 n p u r u n u A A s_ ra s ae is ili u a r si a rm lo r h 0 t O h s o la a s u a i z s t t m e r m o o st la 5 4 3 O h n a _ n t ei r d s e n b n a d a _ if m o a s 3 O n o t g g us u d p _ n t e u ta h n h s u s c lu s 0 0 5 O t p h u la s e m s oe ha bo s r _ a at u g s_ e _ i u 7 3 8 7 3 5 O n n h h o s b s la ru b a s_ fo is s_ c si i u s s h e 7 7 2 4 6 th th o a p _ ra o p s m u e r i a r x i _ iu p 1 n o o p g ha o t t m _ o s b p r t a e s _ n 6 8 t h u v u o m o n p _ r s s e 7 8 O O h p p a s g a s p a n s a is m o o e th a u r s a 9 7 5 o h h _ u tu l u l c s i a u 8 5 n g A o a r i a o c M a u h s i _ 4 t n p a a u r s s t m p m p C li io c i t r th s s 7 9 7 6 O O h th h g g s u _ o _ e e l a s a a r 7 0 5 6 7 o a u u _ fi g p do am s h e t r C h a a u 4 6 0 1 0 O n n p o g s s l c ro A u l ru o H H e a t C h in s 5 7 7 0 7 1 0 2 th th h p u _ _ a ap s e to p B M th a t ir ri 9 9 8 7 0 0 n o o a h s l m ti i s s o a C a p s 1 0 6 t p a _ e e g ll e p p m c C la u 0 O h O h p g g f m l e u p _ la E i x 8 3 o h u u r i n s u s A ta h 0 6 1 O n p n a a s s a u ta a n u to _ e s 8 O t h t g g _ _ c r e c r o e s v i 8 0 8 3 n O h h u u o t u p p M n r 7 6 t O n o a o s p s ic ta u o t 4 0 9 h n t p g s im o s t m A n c s 9 3 2 O o n t h u p _ _ a r u la ri e 1 5 O th h o h s h v c c i n s i 0 n l o i _ v s s 5 p o p a _ a a o ic is i t p s il n s s 1 0 0 n t h o p g g c e s o s u u i 3 0 h h a o E u s ia u t s 6 7 1 3 t a p h u u c n l p n t lu s s s n 2 9 1 4 2 h o h a n a l A i _ i li a u u s 8 8 1 1 p g a g s d s o is r s s o l t n 0 3 4 o u a g _ _ b i u a u e ia u e id u 9 4 6 9 p h g u s a m i p q a m c b l t 9 a s u s l t n b d i p a 6 0 4 4 h _ u s _ ty u a i a i o 4 6 e E e m o i a l 2 a g s s r _ b _ l 3 7 0 m _ m l d i s o g c lc v 9 g u _ o i p s o s e 8 7 2 n c i _ u s g y _ r u _ i u s a n u e c u u E s s i p s u s g z 8 3 O _ r e r e s s n n c s t s 2 s g c d r u i u s _ u s n u a i i 2 n _ v b h t _ i _ l 4 7 9 i a u in ir c n s s in _ r n f r 8 t n e n u i s r i e u i s i _ w 4 6 h r a l c r i p l u ir s p u r r b 7 u t o o iu n i s r 5 o i l p E e w n i p u n e e a s O C O t c i s r s r u i r E i p u f d l s o E p a s n E _ l r i n r f i n i 5 n O a ic u i E i i E n t l E u n h n s i s h p p ir i a i _ s a u 9 O t n c s o s p r n t p u h o O t a u u E i c s u l 3 9 O c h r l E p n i 2 n O t g n E m e t t u 2 6 5 O o o n h n o o n p _ u 2 9 1 O u i h E e l u n m 1 n t n t i r s i d a u 4 h p t n o O b p s i _ E i k i l i a 9 7 n t t h h s t _ i 8 n O O h o h h t p i h _ p s i k h d t 4 t o h n o u n i i m m _ e t h p a o h a n E u n o r p u 0 h n n o p o t p s n s n m _ _ s a o O h g p a h h _ g i 9 p h p i u s k o i 6 a O O o t t g g r O l i s u s s e n O p O h h a u h o a b u i _ i m r i e p n h a h r X e s 8 1 s u p s p a b u s m d _ e 9 n n n h o o t O a g a g a g in iv t r e d d 2 2 h n s _ b _ s s i r u 0 4 p p h _ g h u E i u s 9 t r 2 t t t a n g u u g _ o s e s a m e e d e 9 8 h h h a t o v a u u l n 1 8 g h h h s u s u s d n x 3 0 u u s c i e d a a 7 t i v a l a d o 7 a 8 9 8 o o o g p h g _ b s e c e r m l i u o a a s _ n s _ s a u g t n u l s X o r n _ 3 u p r _ b a l r 0 p p u h o _ a _ n o 0 p u a e 4 1 O s p g g _ i c r l i i o s a f r r c _ s 0 H n s u u l s s u p g e e 4 O h s a b m b i l i p a d 9 O h h _ h u u p c t e n o c s i 0 _ s a _ _ c a p 3 2 n O t u n a B r p _ i z n 2 a _ g h a e i o r 4 s _ o 3 y a o 5 a o a s n g t s a s s 0 n C s y f c b s s r a d o 8 t n r u u s e b i b i o M l a s a 1 a g g f _ _ u a r r n a i o 1 h O H n g c g o n t u i 1 t u t t t 1 t O C a b s s t a i u i n u c M e f a h H u m m o s g t t r a g 9 l u u h d M u 1 u e i s t w 1 5 o t h O o t a u c a n y o B c s n c a s t s 3 n h c r _ u t n u h i o y n a s s r i i e a n o n _ t 4 o s e s t s u 1 l a i n s s s r h 1 p n l s y a t r 0 t n c o r s _ a u r 4 o o t c _ _ _ e i n t s O 6 p a _ O a M P s i h p t n o a a u O u c 0 h h o p _ v 9 t _ s P _ s e 5 p t c m a p n s i l t n s c e 8 h l n h h d v r r l d u h o h h s o s i s i 1 o b i p t i s f 3 a o y i i u c l 3 h C o r u t t a i i g 5 5 t e i a r u i 7 3 a o i i u s s n g 7 i a t s p p a o i h i s p i _ g n h p b i n j u g l r e e 2 n p e m s s o o 3 a p l u p e s o a a g 0 g h p p l i s 1 _ u s r p o g h e n u d o 9 7 t n s f u v 2 u g i s r s i u t u l o b 5 h h u e s s O s i f l h _ u n 5 u a u h e s a i o l a s s a 7 p a p u s m 7 4 s _ r a i a 9 3 u t r e e n a r l a 9 o a s s t l u p s a 7 9 s g s a t l s i i c u u i s 0 9 _ h g n u o m a e a l _ r s g o _ a h t 3 9 p s n e w u n o u 7 _ u _ g m d _ 8 a u r _ i z s m _ c s i s a h d c u _ h u g u o h c P h g i a s 8 h s f u s D s i e l _ s g a a c a u 9 u r c m t o l c s a a s r s H u 8 s i C c a b s i i _ _ h u i u u s s t r l c i s g g 9 l _ n g u 5 g _ t e e r k a c i s _ c 0 a r h h _ n 9 t s u _ _ u u a g c s o a i s a u y a f e h t l g s 8 i u l p a p s s e e _ u o l s s i t g 5 c i u f _ o n h o l b u i u s a s s n C y u u e d c r u u u o r u s l y s r b _ s r e a H n i e s s t g p n c s _ o y c h p H g _ o H o p t i i s h h i h u i H u r P p h i 9 i u u r e 7 t o a u e a s o a s p H d u p p a c c g n i S t e e s h o D d _ i p n e l n s n h g l 6 u t u o i 5 e h y y f h a a H e c i t C i _ p s n o a l l i o r c l s c e i i s s _ p y s d p O p _ h o l i c i y i n l n H c e c u h s e g o e t y t H i c s r H o y s o t s c e o L H s e t o t S S a H H l t p s p a u i i h O n i e H s l o i o n u m _ P s H e t s n h n u e H H o i i p c _ _ e H o s o t t p g e e a l e e S r s p p h o p u v O H e l p s i l r s o t S a e l s O i e e i l p i c i e n o a n n l e t t e H l o l e p n _ u l H c i t l c l i i l r u c e l P s l o e e H l c u g l l i l e y y t c c l x y d i s r s l i u p o r i i c t e O i t i o h e u g i c e c t H s n u t g e l c o o s t t c u i h s o s o s h e e e u e i r o l i t f y u i r o o t p l r a n l l i l t e r c t H a c g r o p t r a c u r c u e

u p i g v l c r o i l o u i o p H s S b o r o g u c p p u h l c u a a t i c o u

t u h v p l e c s l r e l u e l e c a _ e o p p s t o l l e o L p t p i n i e s t l e s s p i e l e r d s c a l p s b _ b a o s o t e e s l o _ c s u _ i l l n l p e u u s e u s i l k _ l _ i a i _ u l o n e o r _ e u t a o _ e f e _ c _ s i _ p u s u t i p u m s u r

r w o e s H _ l u r _ u f c l N i s r h i u p u i a r t r e s t u p h m u u n i P s _ t l u r u g u l r a t T u _ p s a r t t o t s r o e u n u e a e d l u r r u e u s m u s o r e t H l r r u u s H l a n g n l e n l u s e p P t i u n u o n u s o l l g u u u r u r l e s e _ s p n u l o O r a r s _ a o r s i e _ u e i l b _ l i n s _ g r e _ s c O u s u e s r _ s t e r _ c e u O l t

s l a c _ c a e a h e u y m t p u r l e i _ s a c s _ d s t u _ _ c u a g s n m g i o u r e r e g r _ o m i c p u f o c a l p i s c u e s o i u u t u r r i s i a d i o _ f n n i l s s r c s n r u r i o e e b s d s p s o r t i i l L u _ g o i _ u r s g r b p u _ o t o r s r e n s s a f o a t u u u _ u n n u r r i l n e i a u d n u _ h i l l a t u c s e l s p c s s n r d c i a i i p l t t r a i q s c a i i s a c e s n b m n i e c n _ a O t n o c i i h y i u o h r l p

e c n i c o m t _ u o p _ o c t e o p f r u i v o g u p _ p i l i r a

c g e o e i E s d i f l o l c

s e a u u n n l u l i O e a r t a i u s i i r a o h a n l l r o n e r l u c s e o r l u u i d s s i i r u a l i _ l c u t u s l s s h E D a i y m d o i s r u m l r u l s _ D t s t n r D p s s u i l c n s

i i e s i u l i s n e s i f e u a p l d e i _ i v s o l l

T _ a a r s t c a l c u _ i O s e i i c i t t r t v d D e t s i u n u i a i e

i c n u

c e u s c s

n i r n a n e s

t c s t b

i o s

r o a r s o d

n u i i u t n i u g o E

E a s u a r r

E i u T

s

Figure 1: Branch Support Dung Beetle Phylogeny. Bootstrap branch length support on the un-dated RAxML phylogeny. Colder colors indicating higher branch support, decreasing branch width with decreasing support (terminal branches are black).

27

0.2 bioRxiv preprint doi: https://doi.org/10.1101/2021.01.26.428346; this version posted January 27, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Table 2 Node Calibrations and Estimated Ages. For each node age calibration, the corresponding clade, fossil, location on the branch (crown or stem) are given. For each calibration scheme (‘young’ vs. ‘old’), the age constraints used and the estimated node ages on crown and stem, are indicated for each calibrated node, followed by the difference between the estimates.

Young Old Age Diff. Stem/ Clade Fossil Min Max Crown Stem Min Max Crown Stem crown Min Max cons. cons. age age cons. cons. age age Copris Copris kartlinus stem 3.6 98.1 85.80 85.89 3.6 131.6 59.80 59.87 26.00 26.02 Gymnopleurus Gymnopleurus sisyphus stem 13.5 98.1 35.53 52.97 13.5 131.6 50.45 74.30 14.91 21.33

Onthophagus Onthophagus bisontinus stem 13.5 98.1 57.74 70.81 13.5 131.6 85.03 100.58 27.29 29.77

Heliocopris Heliocopris antiquus stem 18.7 98.1 1.46 78.79 18.7 131.6 2.09 110.90 0.63 32.11

Scarabaeinae Lobateuchus parisii crown 53 98.1 98.10 53 131.6 131.60 33.50 Scarabaeidae Juraclopus rohdendorfi crown 152 181.8 152 199.64

28 bioRxiv preprint doi: https://doi.org/10.1101/2021.01.26.428346; this version posted January 27, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Table 3 Best Fitting Models for Biogeographcal Diversification Hypotheses. Best fitting models of ML GeoSSE analyses for each data set and each tree, as determined by likelihood ratio tests and AIC scores. Models where lambda was constrained to be equal in both areas, also constrained lambda to be zero for widespread taxa.

Tested Pairing Young Tree Old Tree Large Mammals vs. rest equal mu, equal mu, equal dispersal equal dispersal Small Mammals vs. rest equal lambda, equal lambda, equal mu equal mu Gondwana vs. rest equal mu equal mu, equal dispersal Laurasia vs . rest full model equal lambda, equal mu Madagascar vs. rest equal dispersal equal mu, equal dispersal

29 bioRxiv preprint doi: https://doi.org/10.1101/2021.01.26.428346; this version posted January 27, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Table 4 Posterior Distribution of GeoSSE Rate Estimates. 95% quantiles of posterior distributions for diversification and dispersal rates estimated under the best scoring model for each data set and each tree. s=speciation rate, x=extinction rate, r=net diversification rate (s-x), d= dispersal rate. 732

Quant. sA sB sAB xA xB rA rB rAB dA dB Likelihood Pairings Young Tree

Only Large 2.50% 0.0968 0.0942 0.0382 0.0192 0.0636 0.0600 0.0382 0.0008 -2436.8114 Mam. Vs. rest 97.50% 0.1449 0.1437 3.9306 0.0785 0.0803 0.0799 3.9306 0.0021 -2428.7662

Only Small 2.50% 0.1076 0.0342 0.0613 0.0002 0.0015 -2436.8855 Mam. vs. rest 97.50% 0.1552 0.0922 0.0756 0.0014 0.0042 -2431.4368

Madagascar 2.50% 0.1041 0.0930 0.1085 0.0006 0.0191 0.0770 0.0602 0.1085 0.0003 -2381.0849 vs. rest 97.50% 0.2102 0.1423 8.0402 0.1276 0.0798 0.1158 0.0756 8.0402 0.0010 -2373.0650

Laurasia 2.50% 0.0819 0.0863 0.0016 0.0006 0.0160 0.0581 0.0528 0.0016 0.0223 0.0003 -2619.1510 vs . rest 97.50% 0.1308 0.1409 0.0651 0.0674 0.0857 0.0928 0.0724 0.0651 0.0577 0.0036 -2610.9780

Gondwana 2.50% 0.0964 0.0683 0.0002 0.0008 0.0863 0.0593 0.0002 0.0051 0.0028 -2629.9069 vs. rest 97.50% 0.1228 0.0924 0.0309 0.0305 0.1054 0.0721 0.0309 0.0156 0.0056 -2622.8756

Pairings Old Tree

Only Large 2.50% 0.0636 0.0619 0.0286 0.0093 0.0448 0.0426 0.0286 0.0005 -2632.4797 Mam. vs. rest 97.50% 0.0952 0.0942 1.5142 0.0486 0.0560 0.0557 1.5142 0.0013 -2624.6484

Only Small 2.50% 0.0698 0.0183 0.0435 0.0001 0.0010 -2633.0250 Mam. vs. rest 97.50% 0.0994 0.0546 0.0530 0.0009 0.0029 -2627.6812

Madagascar 2.50% 0.0726 0.0575 0.0952 0.0043 0.0558 0.0437 0.0952 0.0002 -2578.7214 vs. rest 97.50% 0.1066 0.0887 6.9666 0.0438 0.0753 0.0544 6.9666 0.0007 -2571.5387

Laurasia 2.50% 0.0602 0.0103 0.0394 0.0137 0.0002 -2810.4804 vs . rest 97.50% 0.0851 0.0443 0.0513 0.0327 0.0027 -2805.3483

Gondwana 2.50% 0.0627 0.0477 0.0001 0.0003 0.0580 0.0437 0.0001 0.0028 -2829.6036 vs. rest 97.50% 0.0780 0.0611 0.0174 0.0152 0.0702 0.0512 0.0174 0.0045 -2822.8190

30 bioRxiv preprint doi: https://doi.org/10.1101/2021.01.26.428346; this version posted January 27, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

sA xB rA dB sB rB 2.5 sAB 2.5 rAB 2000

40

2.0 2.0

1500

30

1.5 1.5

y y y y

t t t t

i i i i

s s s s

n n n n

e e e e

d d d d

1000

y y y y

t t t t

i i i i

l l l l

i i i i

b b b

b 20

a a a a

b b b b

o o o o

r r r r

P P P P

1.0 1.0

500 10

0.5 0.5

0.0 0 0.0 0

0 5 10 15 20 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0 5 10 15 20 0.0005 0.0010 0.0015 0.0020

Speciation rate Extinction rate NetDiv rate Dispersal rate

sB xB rB dA dB

2000

50 40 150

40 1500

30

100

y

y y

30 y

t

t

t

t

i

i

i

i

s

s

s

s

n

n

n

n

e

e

e

e

d

d

d

d

y

y

y

y

t

t

t

t

i

i

i

i

l

l

l

l

i

i

i

i

b

b b

b 1000

a

a

a

a

b

b

b

b

o

o

o

o

r

r r

20 r

P

P

P P

20

50

500 10

10

0 0 0 0

0.06 0.07 0.08 0.09 0.10 0.11 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.040 0.045 0.050 0.055 0.000 0.001 0.002 0.003 0.004 Speciation rate Extinction rate NetDiv rate Dispersal rate Figure 2: GeoSSE Results Dung Producers Old Tree. Posterior distributions of area-dependent rates estimated by GeoSSE. Left to right: speciation rates, extinction rates, net diversification rates (speciation – extinction), dispersal rates. Top: rates in areas with large and medium sized mammals and high and intermediate droppings- diversity (Afro-Eurasia and Americas, blue), and rates in areas with small sized mammals and low droppings-diversity (East Gondwanan Fragments, yellow). Bottom: rates in areas with large sized mammals and high droppings-diversity (Afro-Eurasia, yellow), and rates of areas with low and medium sized mammals and low and intermediate droppings-diversity (Americas and East Gondwanan Fragments, blue). Rates of widespread taxa are colored green; where only one rate was estimated, it is colored blue. Dispersal rates of an area reflect dispersal out of said area.

31 bioRxiv preprint doi: https://doi.org/10.1101/2021.01.26.428346; this version posted January 27, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

sA xB rA dB 1.2 1.2 sB rB sAB rAB

1200

25

1.0 1.0

1000

20

0.8 0.8

800

15

y y y y

t t t t

i i i i

s s s s

n n n n

e e e

e 0.6 0.6

d d d d

y y y y

t t t t

i i i i

l l l

l 600

i i i i

b b b b

a a a a

b b b b

o o o o

r r r r

P P P P

10

0.4 0.4 400

5 0.2 0.2 200

0.0 0 0.0 0

0 10 20 30 40 0.00 0.02 0.04 0.06 0.08 0.10 0 10 20 30 40 0.0005 0.0010 0.0015 0.0020 0.0025 0.0030

Speciation rate Extinction rate NetDiv rate Dispersal rate

sB xB rB dA dB

30 25 100

25

1000 20 80

20

y

y

y

y

t

t

t

t

i

i

i

i

s

s s

15 60 s

n

n

n

n

e

e

e

e

d

d

d

d

y

y

y

y

t

t

t

t

i

i

i

i

l

l

l

l

i

i

i

i

b

b

b

b

a

a

a

a

b

b

b

b

o

o o

15 o

r

r

r

r

P

P

P P

500 10 40

10

5 20 5

0 0 0 0

0.10 0.12 0.14 0.16 0.18 0.02 0.04 0.06 0.08 0.10 0.12 0.055 0.060 0.065 0.070 0.075 0.080 0.000 0.001 0.002 0.003 0.004 0.005 0.006 0.007 Speciation rate Extinction rate NetDiv rate Dispersal rate Figure 3: GeoSSE Result Dung Producers Young Tree. Posterior distributions of area-dependent rates estimated by GeoSSE. Left to right: speciation rates, extinction rates, net diversification rates (speciation – extinction), dispersal rates. Top: rates in areas with large and medium sized mammals and high and intermediate droppings- diversity (Afro-Eurasia and Americas, blue), and rates in areas with small sized mammals and low droppings-diversity (East Gondwanan Fragments, yellow). Bottom: rates in areas with large sized mammals and high droppings-diversity (Afro-Eurasia, yellow), and rates of areas with low and medium sized mammals and low and intermediate droppings-diversity (Americas and East Gondwanan Fragments, blue). Rates of widespread taxa are colored green; where only one rate was estimated, it is colored blue. Dispersal rates of an area reflect dispersal out of said area.

32 bioRxiv preprint doi: https://doi.org/10.1101/2021.01.26.428346; this version posted January 27, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

sA 120 xB rA dB sB rB sAB rAB

200

800 150 100

150

80 600

100

y y y y

t t t t

i i i i

s s s s

n n n n

e e e

e 60

d d d d

y y y y

t t t t

i i i i

l l l l

i i i

i 100

b b b b

a a a a

b b b b

o o o o

r r r

r 400

P P P P

40

50

50 200

20

0 0 0 0

0.00 0.02 0.04 0.06 0.08 0.000 0.005 0.010 0.015 0.020 0.025 0.00 0.02 0.04 0.06 0.0020 0.0025 0.0030 0.0035 0.0040 0.0045 0.0050 0.0055

Speciation rate Extinction rate NetDiv rate Dispersal rate

sB 50 xB rB dA dB

60 120 400

40

50 100

300

30

40 80

y

y

y

y

t

t

t

t

i

i

i

i

s

s

s

s

n

n

n

n

e

e

e

e

d

d

d

d

y

y

y

y

t

t

t

t

i

i

i

i

l

l

l

l

i

i

i

i

b

b

b

b

a

a

a

a

b

b b

30 60 b

o

o o

o 200

r

r

r

r

P

P

P P 20

20 40

100 10

10 20

0 0 0 0

0.06 0.07 0.08 0.09 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.035 0.040 0.045 0.050 0.055 0.00 0.01 0.02 0.03 0.04

Speciation rate Extinction rate NetDiv rate Dispersal rate

sA xB rA dB sB rB sAB rAB

40

3000

2500

1.0 30 1.0

2000

y

y

y

y

t

t

t

t

i

i

i

i

s

s

s

s

n

n

n

n

e

e

e

e

d

d

d

d

y

y

y

y

t

t

t

t

i

i

i

i

l

l

l

l

i

i i

20 i

b

b

b

b

a

a

a

a

b

b

b

b

o

o o

o 1500

r

r

r

r

P

P

P P

0.5 0.5

1000

10

500

0.0 0 0.0 0

0 5 10 15 20 25 30 35 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0 5 10 15 20 25 30 35 0.0002 0.0004 0.0006 0.0008 0.0010 0.0012 Speciation rate Extinction rate NetDiv rate Dispersal rate Figure 4: GeoSSE Results Out-Of-Gondwana Old Tree. Posterior distributions of area-dependent rates estimated by GeoSSE. Left to right: speciation rates, extinction rates, net diversification rates (speciation – extinction), dispersal rates. Top: rates in Laurasia and Madagascar (blue), and rates in Gondwana (yellow). Center: rates in Laurasia (blue), and rates in Gondwana and Madagascar (yellow). Bottom: rates in Madagascar (blue), and rates in Gondwana and Laurasia. Rates of widespread taxa are colored green; where only one rate was estimated, it is colored blue. Dispersal rates of an area reflect dispersal out of said area.

33 bioRxiv preprint doi: https://doi.org/10.1101/2021.01.26.428346; this version posted January 27, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

100 sA xB 120 rA dA sB rB dB sAB rAB

50 500

100

80

40 400

80

60

30 300

y y y y

t t t t

i i i i

s s s s

n n n

n 60

e e e e

d d d d

y y y y

t t t t

i i i i

l l l l

i i i i

b b b b

a a a a

b b b b

o o o o

r r r r

P P P P 40

20 200 40

20 10 100 20

0 0 0 0

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.00 0.01 0.02 0.03 0.04 0.05 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.005 0.010 0.015 0.020

Speciation rate Extinction rate NetDiv rate Dispersal rate

sA xB rA dA sB rB dB sAB rAB 40 80

20

300

30 60

15

y

y

y

y

t

t t

t 200

i

i

i

i

s

s

s

s

n

n

n

n

e

e

e

e

d

d

d

d

y

y

y

y

t

t t

20 40 t

i

i

i

i

l

l

l

l

i

i

i

i

b

b

b

b

a

a

a

a

b

b

b

b

o

o

o

o

r

r

r

r

P

P

P P 10

100 10 20

5

0 0 0 0

0.00 0.05 0.10 0.15 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.00 0.05 0.10 0.15 0.00 0.02 0.04 0.06 0.08

Speciation rate Extinction rate NetDiv rate Dispersal rate

sA xA rA dB sB xB rB sAB rAB

25

2000

20

1.0 1.0

1500

15

y

y

y

y

t

t

t

t

i

i

i

i

s

s

s

s

n

n

n

n

e

e

e

e

d

d

d

d

y

y

y

y

t

t

t

t

i

i

i

i

l

l

l

l

i

i

i

i

b

b

b

b

a

a

a

a

b

b b

b 1000

o

o

o

o

r

r

r

r

P

P

P P

10 0.5 0.5

500

5

0.0 0 0.0 0

0 5 10 15 20 25 30 35 0.00 0.05 0.10 0.15 0 5 10 15 20 25 30 35 0.0005 0.0010 0.0015 0.0020 Speciation rate Extinction rate NetDiv rate Dispersal rate Figure 5: GeoSSE Results Out-Of-Gondwana Young Tree. Posterior distributions of area-dependent rates estimated by GeoSSE. Left to right: speciation rates, extinction rates, net diversification rates (speciation – extinction), dispersal rates. Top: rates in Laurasia and Madagascar (blue), and rates in Gondwana (yellow). Center: rates in Laurasia (blue), and rates in Gondwana and Madagascar (yellow). Bottom: rates in Madagascar (blue), and rates in Gondwana and Laurasia. Rates of widespread taxa are colored green; where only one rate was estimated, it is colored blue. Dispersal rates of an area reflect dispersal out of said area.

34