Reference genomes and fossils revise bat family phylogeny and biogeography

With over 1,500 species, bats account for more than one fifth of all living mammal species². They are distributed globally, are predominantly nocturnal and adapted to diverse foraging niches, ranging from insectivorous, sanguivorous, carnivorous, piscivorous, frugivorous and nectarivorous species¹. Some species are exceptionally long-lived compared with other mammals of similar body size, capable of living 8–10 times longer than expected, showing few signs of ageing or cancer³. Several species host a diversity of viruses without overt disease symptoms and show unique immune adaptations^4,5. Despite having the smallest mammalian genomes (approximately 2 Gb), they harbour a diverse endogenous genomic virosphere^4,6 and many have active DNA transposons, which is exceptionally rare among mammals⁷.

The acquisition and evolution of these unique traits have been the centre of heated debates, particularly in relation to the evolution of flight and echolocation. This is probably due to the difficulties in reconstructing bat evolutionary history^8,9,10. Challenges have stemmed from: homoplastic and convergent molecular and morphological characters^11,12; conflicting phylogenetic signals¹³; a paucity of chromosome-level bat genome assemblies^1,14; rapid speciation events resulting in incomplete lineage sorting and introgression¹⁵; the dearth of phylogenetic methods required to handle this complexity; and a limited and fragmented fossil record¹⁶.

Indeed, the fossil record has provided important but incomplete insights into the early bat evolution. The oldest bat fossils, dating to the early Eocene (approximately 56–52 million years ago (Ma)), have been recovered from multiple biogeographical regions, including Asia, North America, Europe and Australia, and in some cases consist of well-preserved skeletons from Lagerstätten deposits^16,17,18. These fossils indicate that powered flight and echolocation had evolved by this time^16,17,18. However, beyond this narrow temporal window, the bat fossil record is sparse and highly fragmented, dominated by isolated dental or postcranial elements, with a substantial proportion of bat evolutionary history estimated to be missing^17,18,19. As a result, fossil evidence alone has proven insufficient to resolve the geographical origin and early diversification of bats. Biogeographical inferences integrating fossils with molecular phylogenies have proposed North American¹⁷, African²⁰ or Asian²¹ origins, often suggesting multiple long-distance dispersal events, but these conclusions remain sensitive to methodological limitations and data incompleteness^21,22. In parallel, sparse genomic sampling and reliance on fragmented or low-quality genome assemblies have limited robust phylogenetic inference, hindering efforts to reconstruct and better understand bat evolution.

To overcome these problems, the Bat1K consortium was established to generate and analyse reference-quality genome assemblies from all living bat species (https://bat1k.com). Here we present phase 1 of the project, which includes chromosome-level, long-read assemblies that represent all 21 currently recognized bat families (Fig. 1). We included members of the enigmatic families, Craseonycteridae (Thailand and Myanmar), Mystacinidae (New Zealand) and Myzopodidae (Madagascar), all regionally endemic, and the three most recently recognized bat families, Cistugidae, Rhinonycteridae and Miniopteridae¹. Our species span deep divergences in the most species-rich bat families Phyllostomidae and Vespertilionidae, and include representatives from the families Nycteridae, Thyropteridae, Furipteridae, Noctiliionidae, Mormoopidae and Natalidae (Fig. 1, Supplementary Table 1.1 and Supplementary Note 1).

We used long-read and Hi-C sequencing to generate 42 new reference-quality chromosome-level genome assemblies representing 41 distinct species; 26 of the new genomes are haplotype-resolved assemblies (Supplementary Notes 2 and 3). The 42 new assemblies meet or exceed the key standards of Bat1K and the Earth Biogenome Project^1,23 and feature high gene completeness (Extended Data Fig. 1a–c and Supplementary Notes 4 and 5). Together with pre-existing high-quality assemblies of 62 species, our dataset comprises 103 bat species (Extended Data Fig. 1 and Supplementary Table 1.1). We also included eight outgroups from across the mammalian tree (Supplementary Note 1).

Transposable elements were curated using the pipeline detailed in Supplementary Note 6 and Extended Data Fig. 1d. Thousands of novel consensus sequences were deposited in Dfam²⁴ (Supplementary Table 6.1; Dataset 6.1). We confirmed previous results indicating that several clades experienced extensive recent DNA transposon and rolling circle invasions while also harbouring the retrotransposons typical of most mammals^7,25, as well as the nearly complete cessation of transposable element activity in Pteropodidae^26,27.

Using heatmaps, we compared proportional transposable element accumulation across two transitional divergence periods. The first period compares proportional transposable element accumulation in ‘recent’ bats (approximately 18 Ma to the present) to ‘early’ bat lineages (approximately 18 Ma to 46 Ma; Supplementary Fig. 6.2a). Similarly, we compared proportional transposable element accumulation in early bat lineages (approximately 18 Ma to 46 Ma) to proportional transposable element accumulation in protobats and ancestral mammals (older than approximately 46 Ma; Supplementary Fig. 6.2b).

When comparing proportional transposable element accumulation in early bats versus their mammalian ancestors (Supplementary Fig. 6.2b), we showed that even during this early diversification, transposable elements had begun to accumulate differentially. Long interspersed nuclear elements (LINEs) increased accumulation relative to other transposable elements in miniopterids, molossids, mormoopids and phyllostomids, whereas the reverse occurred in the hipposiderids and the lineages leading to Cistugo and Triaenops. The expansion of Helitrons, exclusive to the vespertilionids⁷, also began early in their history.

When comparing more recent patterns versus the early bat lineages (Supplementary Fig. 6.2a), we noticed that the cessation of LINE accumulation in the pteropodids^7,27 is accompanied by an increase in long terminal repeat accumulation. However, these are relative increases, and the lack of LINE accumulation amplifies the signal from ongoing long terminal repeat accumulation. Finally, after incorporating phylogeny, we found that transposable element accumulation is positively correlated with genome assembly size, in bats overall and within bat families (R²_conditional = 0.88; Extended Data Fig. 2), consistent with previous analyses in mammals⁷, confirming the role of transposable elements in shaping genome structure.

To leverage the high-quality assemblies for phylogenomics, we generated several whole-genome alignments. To avoid potential biases, we used two independent approaches: a reference-based strategy (MULTIZ²⁹) and a reference-free method (Progressive CACTUS³⁰) (Supplementary Note 8). To minimize bias caused by reference species choice, we produced MULTIZ alignments for three phylogenetically informative references: Homo sapiens (MULTIZ Homo Reference (mHR)), Myotis myotis (mMR; Yangochiroptera) and Rhinolophus ferrumequinum (mRR; Yinpterochiroptera); we then compared them with corresponding CACTUS-derived projections (cHR, cMR and cRR).

MULTIZ consistently yielded higher alignment coverages than CACTUS, most likely caused by more sensitive parameter settings and the inclusion of repeat-overlapping alignments by RepeatFiller^31,32 (Extended Data Fig. 4a–c). However, both methods produced comparable and biologically consistent signals. Alignment coverage was strongly and significantly correlated with neutral branch length to the reference genome, indicating that evolutionary divergence, rather than methodological artefact, primarily explains variation in coverage across species (Extended Data Fig. 4d–f).

To reconstruct the evolutionary history of bats and resolve their long-standing phylogenetic controversies, we generated and analysed a comprehensive suite of genomic datasets using our 103 bat genome assemblies and eight non-bat outgroups (111 species; Supplementary Note 1). In addition to the multiple whole-genome alignments, we generated datasets of 1:1 orthologous protein-coding genes, mitochondrial genomes, neutrally evolving windows and neutral single-nucleotide polymorphisms (SNPs). We applied multiple phylogenomic inference approaches and compared the resulting topologies (Supplementary Notes 9–14). We then assessed potential systematic biases in our datasets and methods, ultimately producing a revised, genome-scale phylogeny for bats (Fig. 2). The resulting trees converged on two main topologies, which we refer to as the protein-coding gene tree (T1) and the neutral windows tree (T2), differing only by two branch rearrangements (the position of Emballonuroidea and monophyly of Pipistrellus; Extended Data Fig. 5). All analyses unambiguously supported the monophyly of the suborders Yinpterochiroptera and Yangochiroptera^16,17,18, refuting the monophyly of the echolocating lineages (Microchiroptera; Extended Data Fig. 6). Where applicable, all families were recovered as monophyletic, across all datasets and methods. We resolved long-standing phylogenetic questions regarding relationships within the Phyllostomidae (Extended Data Fig. 7) and the monophyly of Pipistrellus (Extended Data Fig. 8). Whole-mitochondrial analyses largely supported the nuclear phylogenies (Supplementary Note 14). However, our analyses revealed an alternative composition and branching pattern of the superfamilial groups, providing robust support for the revised placement of Myzopodidae within Vespertilionoidea, with Emballonuroidea as sister to that group. These results call for a re-evaluation of the current-consensus bat phylogeny.

We first analysed the protein-coding genes, using TOGA³³ to identify 1:1 orthologues (16,750 genes; Supplementary Note 9). Species trees inferred under the ASTRAL coalescent framework (Supplementary Note 9) and IQ-TREE2 maximum likelihood concatenation analyses (Supplementary Note 10) largely recapitulated previous subordinal relationships of the prevailing consensus phylogeny^{6,16,17,18,34}. The resulting phylogenies were robust to varying levels of node support (Supplementary Note 10). They provided an alternative topology for the placement of the enigmatic family Myzopodidae (Figs. 2 and 3), the mono-generic family of sucker-footed bats endemic to Madagascar.

The placement of this family has been notoriously difficult, arguably stemming from its long branch, with different genomic regions and datasets providing conflicting support for multiple topologies¹³. Although previous studies have placed Myzopodidae within the predominantly neotropical superfamily Noctilionoidea¹⁷, our coalescent and concatenated analysis of protein-coding genes placed Myzopodidae as the earliest branch within the panglobal superfamily Vespertilionoidea (Figs. 2 and 3). Motivated by these inconsistencies, we systematically evaluated alternative placements of Myzopodidae within Vespertilionoidea, Noctilionoidea and Emballonuroidea, or as the earliest diverging lineage within Yangochiroptera (Fig. 3a).

Across inference methods (coalescent and concatenation), protein-coding genes consistently recovered Myzopodidae (M) within Vespertilionoidea (V) (Fig. 3b; coalescent and concatenated bootstrap = 100% and 100%, respectively; Extended Data Fig. 5, Supplementary Fig. 10.1 and Supplementary Notes 9 and 10). However, only 22% of per-locus maximum-likelihood protein-coding gene trees supported this arrangement (M + V), with 16% supporting inclusion within Noctilionoidea (M + N) and 11% supporting a sister relationship with Emballonuroidea (M + E) (Fig. 3b). No analyses supported an early branching position in Yangochiroptera. Using gene concordance factor (gCF) analyses, which quantifies gene tree concordance–discordance, we assessed the level of concordance per gene and found similar support for the alternate positions of Myzopodidae (gCF: 22.89% (M + V), 16.74% (M + N), 10.18% (M + E) and 50.19% (other); Supplementary Note 11). Only 22% of loci supported the position of Myzopodidae as sister to Vespertilionoidea (M + V). This placement received higher support than any alternative (Supplementary Table 11.1 and Supplementary Fig. 11.1). These results reveal extensive discordance among protein-coding genes, most likely driven by the varying selection pressure on the functional proteins they encode and/or a lack of signal impeding the resolution of the true branching patterns, showing how previous bat phylogenies based on protein-coding genes¹⁷ could have been misled^34,35.

As protein-coding genes comprise approximately 2% of the genome and can be vulnerable to model misspecification^6,34 and/or selection bias^34,36, we next analysed the whole-genome signal across multiple partitions and across our different whole-genome alignments. The partitions included neutral intergenic regions, neutrally evolving ‘dark’ SNPs (defined below; Supplementary Note 12), autosomes, the X chromosome and non-overlapping sliding windows with boundaries guided by genic features to avoid splitting genes (Supplementary Notes 9 and 12).

First, following evidence that neutrally evolving regions can minimize systematic biases resulting from selection³⁵, we used per-base phyloP scores and phastCons to exclude both site-wise accelerated or conserved positions and contiguous constrained regions, as well as genic regions, retaining windows 500 bp or more of neutral sequences (total for mHR, mMR and mRR ≈ 43, 48 and 78 Mb, respectively; Supplementary Note 9). Second, we analysed unconstrained and unannotated genomic regions, which we refer to as the ‘dark genome’, by excluding all known annotated regions from our alignments (Supplementary Note 12). We used our phyloP scores to identify and extract the neutral dark SNPs shared by all taxa from these regions (Supplementary Note 12). We further reduced systematic biases, by using CASTER, a phylogenomic method that relaxes the requirement for recombination-free loci and accounts for incomplete lineage sorting (ILS) and rate heterogeneity³⁴. CASTER analyses of both neutral windows (mHR, mMR and mRR bootstrap = 100, 100 and 100%; q1 = 0.51, 0.48 and 0.50%, respectively; Fig. 3c and Supplementary Note 9) and neutral dark SNPs (mHR, mMR and mRR bootstrap = 99.7, 100 and 74.5%; q1 = 0.403, 0.631 and 0.356, respectively; Fig. 3d and Supplementary Note 12) unambiguously supported Myzopodidae as the earliest branch within Vespertilionoidea (M + V; Fig. 2a).

Given that neutral datasets represented only approximately 0.1–3% of genomes, we further tested the robustness of our topology by analysing whole-genome, whole-scaffold and whole-autosome datasets using CASTER (Supplementary Notes 9 and 12). All analyses predominantly supported Myzopodidae as sister to Vespertilionoidea (M + V; Fig. 3e–g and Supplementary Fig. 9.1a–d). In addition, gCF indicated a consistent signal across all datasets, with optimal topologies showing the strongest support for Myzopodidae as sister to Vespertilionoidea (Supplementary Table 11.1, Supplementary Fig. 11.2 and Supplementary Note 11).

In summary, across protein-coding genes, multiple genomic partitions and alignment frameworks, we consistently recovered a revised topology differing from prevailing molecular hypotheses in the placement of Myzopodidae (Fig. 2a). Of note, this topology agrees with the total evidence molecular + morphology-based topology (Fig. 2b). These results warrant a revision of the biogeographical history of bats and the inclusion of Myzopodidae within the superfamily Vespertilionoidea.

One branch that had variable support across datasets and analyses was the position of the superfamily Emballonuroidea within the suborder Yangochiroptera. This branch is notoriously short and challenging to resolve^13,17. We used the same data partitioning and inference approaches (Supplementary Notes 9–12) to evaluate the competing hypotheses (Emballonuroidea (Vespertilionoidea + Noctilionoidea); T1); (Noctilionoidea (Vespertilionoidea + Emballonuroidea); T2); and (Vespertilionoidea (Emballonuroidea + Noctilionoidea); T3)) (Fig. 3h).

Across the protein-coding datasets, both coalescent and concatenated analyses recovered congruent highly supported topologies for T1 (Fig. 3i; coalescent and concatenated bootstrap = 100 and 100%, respectively). However, per-locus maximum-likelihood protein-coding gene trees (T1, T2, T3 and other = 21, 13, 12 and 54%) and gCF (T1, T2, T3 and other = 21, 13, 11 and 55%) showed discordant support per gene, highlighting the conflicting signal in the protein-coding genes, as was expected (Fig. 3i). Neutral-region analyses also revealed discordance across datasets (Fig. 3j,k). CASTER analyses of mHR neutral windows recovered (T2; bootstrap = 89.9%, q1 = 34), but neutral window analyses of both bat-reference MULTIZ alignments (mRM and mRR bootstrap = 100 and 99%; q1 = 35 and 34, respectively) (Fig. 3j) and analyses of neutral dark SNPs (mHR, mRM and mRR bootstrap = 96.4, 100 and 100%; q1 = 0.383, 0.428 and 0.393, respectively; Fig. 3k) optimally supported T1 (Extended Data Fig. 9).

Whole-genome analyses, regardless of alignment reference species and methods, all supported T1 (Fig. 3l). However, analyses using dark SNPs from different evolutionary rate categories resulted in heterogeneous topological support (Extended Data Fig. 9), highlighting discordance across the genomes (Supplementary Note 12). Sliding-window analyses across MULTIZ alignments for all references, provided near similar support for all hypotheses (T1, T2 and T3) (Fig. 3m and Supplementary Fig. 9.1). These results were mirrored by gCF, which indicated a slight overall excess of support for T2 (Supplementary Tables 11.1 and 11.2 and Supplementary Figs. 11.1 and 11.2). Chromosome-level and scaffold-level trees (Fig. 3n) further highlighted this topological heterogeneity (Supplementary Fig. 12.5). Although a slight majority of genomic regions across all references supported T1, the X chromosome in all three references along with a subset of autosomes consistently supported T2 (Fig. 3n and Supplementary Fig. 12.5).

Widespread phylogenetic incongruence among chromosomes indicates that pronounced phylogenomic discordance characterizes the evolutionary history of Yangochiroptera, with different genomic compartments (loci) supporting alternative topologies, particularly T1 and T2. Such heterogeneity in locus tree topology can arise from locus tree estimation errors, ILS and introgression. Although locus tree estimation artefacts cannot be entirely excluded, we consider their influence to be limited given the high concordance of results across multiple alignment and phylogenetic reconstruction strategies (Fig. 3). Likewise, although ILS can generate topological conflict among loci, under ILS alone, the species tree is expected to remain the predominant genomic signal across genomic compartments, including autosomes and the X chromosome^15,35,37,38. Therefore, the extensive chromosome-scale discordance recovered here cannot be explained by ILS alone and is more consistent with the genomic compartmentalization expected under pervasive introgression³⁷ (Supplementary Note 13). Introgression is well known to produce such genomic heterogeneity by causing different genomic regions to retain distinct histories of lineage divergence and secondary gene exchange^39,40. In clades experiencing pervasive introgression, phylogenetic analyses based on the whole genome may therefore recover the dominant history of introgressed ancestry (here T1) rather than the underlying species tree (T2)^39,40. Under this framework, the true branching history of lineages is expected to persist mainly in low-recombining genomic regions, where the incorporation of foreign alleles is more strongly constrained by selection^15,41.

In mammals, the X chromosome, particularly the X-linked recombination desert region (XLRD), is characterized by exceptionally low recombination rates relative to the rest of the genome³⁸, making it less permeable to introgressed ancestry and therefore more likely to retain the underlying species-tree signal. CASTER topologies generated from this XLRD region supported T2 (Extended Data Figs. 5 and 10 and Supplementary Note 13). These results indicate that widespread genomic support for T1 reflects a dominant signal most likely generated by introgression; whereas T2 represents the underlying species tree. Topology (T2) is also congruent with the combined molecular and morphological analyses (Fig. 2b), further supporting a deep evolutionary separation between Noctilionoidea and the sister-assemblage Vespertilionoidea + Emballonuroidea.

Given that Chiroptera is the second-most speciose order of mammals², reconstructing its ancestral karyotype has been challenging. Zoo-fluorescent in situ hybridization (Zoo-FISH) data have suggested that the ancestral bat genome probably comprised 26 conserved chromosomal fragments; however, reconstructions of the ancestral chromosomes have not yet been achieved⁴². We reconstructed the ancestral bat karyotype and the syntenic relationships using LASTZ chain-and-net alignments for the 103 bat assemblies and the eight outgroups, aligned against two references representing Yangochiroptera (M. myotis) and Yinpterochiroptera (R. ferrumequinum). These alignments were analysed with DESCHRAMBLER^43,44 with a 300-kb syntenic fragment resolution (Supplementary Note 15).

We identified 30 reconstructed ancestral chromosome fragments (RACFs) covering 83.5% of the R. ferrumequinum genome and 32 RACFs covering 80.6% of the M. myotis genome. The direct and indirect comparison of RACFs between R. ferrumequinum and M. myotis reconstructions revealed a high consistency of RACF structures. There were only four interchromosomal and seven intrachromosomal differences across the entire genome, involving 1.4% of the ancestral genome sequence (Supplementary Figs. 15.1 and 15.2).

To investigate the reproducibility of our reconstructions, we generated two additional ancestral karyotypes using ingroup assemblies with high coverage of the corresponding references in syntenic fragments (Supplementary Tables 15.1–15.5 and Supplementary Note 15). The final reconstruction consisted of 26 ancestral bat chromosomes (Fig. 4), agreeing with the number of ‘evolutionarily conserved units’ predicted by Zoo-FISH⁴⁵.

Fusions of ancestral chromosomes prevail over fissions in 97% of the bat genomes (n = 100 of 103) and in 95% of the representative species (n = 20 of 21; Fig. 4). This suggests that bat karyotypes evolved through the fusion of ancestral chromosomes with fissions and translocations being less common. For example, ancestral bat chromosome 13 is found as an individual chromosome, or merged with other ancestral chromosomes in 18 of 21 representative species. Comparisons among human and bat chromosomes using Zoo-FISH revealed seven fusions of human chromosome syntenies ancestral to Chiroptera^42,46, of which five were found in our RACFs and two in the ancestral bat chromosomes. These fusions can be seen on the ‘human’ panel of Fig. 4. Our reconstruction also contained all seven mammalian ancestral human chromosome associations reported for the bat ancestor^42,46, all of which were found in RACFs, confirming the high integrity and accuracy of the reconstruction (Fig. 4 and Supplementary Table 15.7).

Cytogenetic studies have highlighted difficulties in resolving the ancestral bat karyotype due to extensive Robertsonian translocations, including multiple recurrent rearrangements^42,47. The small number of fragmented chromosomes suggests that recurrent rearrangements may have complicated ancestral chromosome reconstructions, despite using the highly contiguous assemblies and high-resolution alignments. Although our ancestral chromosome number matches the number of predicted ancestral bat evolutionary conserved units, we cannot exclude the possibility that some of our ancestral chromosomes represent chromosome arms. Many of the RACFs are identifiable as synteny blocks in extant bat karyotypes, which are known to evolve through the fusion of ancestral chromosomes, supporting this interpretation⁴². Alternatively, the X chromosome, which is known to be (sub)metacentric in many mammals, including bats^48,49, was assembled into one RACF, suggesting that our approach is capable of crossing at least some ancestral centromeres. A comparison of our reconstruction with the syntenies of human chromosomes inferred by Zoo-FISH⁴² confirmed the presence of all predicted fusions reported for the bat ancestor.

Resolving and dating the bat phylogeny have been central to understanding the origins of flight, echolocation and other key bat adaptations (Supplementary Note 16). However, determining how fossil bats relate to living families and when the lineages evolved required tackling major challenges: (1) the paucity of either fossils in morphological datasets¹⁶, or of living taxa in dating analyses of combined morphological and molecular characters⁵⁰; (2) bias in inferring phylogenies from saturated⁵¹ and/or non-independent morphological characters⁵²; and (3) using inappropriate evolutionary models that assume both character independence and linear character-state accumulation⁵³.

To overcome these obstacles, we first assembled a large morphological dataset of 44 pre-Quaternary fossil taxa comprising all but four of the living families and four clades of Eocene stem bats¹⁶. We also included a representative of every extant bat family, choosing species to match our genome assemblies (Supplementary Notes 16 and 17). The resulting data matrix (n = 699 characters), consisted of 65 species, 21 extant and 44 extinct, with 11 newly scored in this study. After filtering saturated and non-independent morphological characters⁵⁴ (Supplementary Table 16.2), we identified 480 statistically independent characters and retained a single fossil per family with the most characters scored. We next subsampled a dataset of 10,000 neutrally evolving sites from intergenic neutral windows in the mHR alignment per species, and merged this with our filtered morphological dataset (Supplementary Note 9). We analysed this total evidence dataset by adopting a fossilized birth–death or total evidence dating (TED)⁵⁵ approach that simultaneously models fossilization, taxon sampling and birth–death branching processes⁵⁶ to estimate divergence dates, thus overcoming challenges that hindered past studies.

Previous analyses identified the oldest stem bats by designating outgroups¹⁶, or enforcing bat monophyly to determine the placement of the newly described Eocene taxon: †Vielasia⁵⁰. By contrast, our TED approach directly inferred the root by identifying the oldest branch: a clade containing the extinct families Archaeonycteridae, Onychonycteridae, Icaronycteridae, Hassianycteridae, Palaeochiropterygidae and †Vielasia (Fig. 2b, clade i, 100% posterior probability as a percentage (pp); see Supplementary Fig. 17.2). Previously thought to represent an early diverging paraphyletic grade of stem bats instead of a clade⁵⁷, these families (minus †Vielasia) have nonetheless been referred to as ‘Eochiroptera’ (sensu Van Valen⁵⁸). Including †Vielasia in this new Eochiroptera with medium probability (Fig. 2b, clade ii, 68% pp) bolsters support for the finding by Hand et al.⁵⁰ that the evolution of laryngeal echolocation predates the crown-bat radiation.

We found a second clade of stem Eocene bats that resembles modern Yinpterochiroptera by including lineages inferred as capable of laryngeal echolocation and the first instance of bat herbivory (Fig. 2b, clade iv, 31% pp). †Palaeophyllophora, †Protorhinolophus, †Pseudorhinolophus and †Vaylatsia were previously thought to belong in Rhinolophoidea, but Jones et al.¹⁶ has found them among the earliest diverging clade of bats together with representatives of the extinct family Necromantidae, †Necromantis adichaster and †Cambaya complexus. In common with previous morphological analyses¹⁶, we found a clade comprising the echolocating lineages formerly classified as ‘Rhinolophoidea,’ †Necromantis and †Cambaya, but our result also includes a representative of the family Philisidae (†Witwatia schlosseri). Although support for this clade is weak (31% pp), support for †Aegyptonycteris, the earliest example of bat omnivory⁵⁹, nesting within the former stem ‘Rhinolophoidea’, is moderate (Fig. 2b, clade v, 61% pp). Echolocation use by †Aegyptonycteris is unknown, but our results indicate the ancient, independent evolution of omnivorous or herbivorous taxa from echolocating, presumed insectivorous ancestors. Together with extant instances in Yinpterochiroptera and Phyllostomidae, this result suggests high evolvability for sensory and dietary adaptations in bats.

Past total evidence and morphology-based inferences among living bat lineages are consistent with most genomic analyses, with one exception, the suborder Yinpterochiroptera, supporting instead the monophyly of the echolocating lineages in the traditional suborder Microchiroptera^16,50. We found strong support for yinpterochiropteran monophyly (Fig. 2b, clade vi, 86% pp), indicating concordance in phylogenetic signal for this clade using combined morphological + molecular data and a fossilized birth–death model, without having to constrain the monophyly of Yinpterochiroptera as previously required¹⁶.

How the poor fossil record of bats^17,19 impacts dating analyses is hard to discern⁶⁰. In the first analysis of its kind, an average 73% of molecular branch length was estimated missing from the fossil record constituting unrepresented basal branch lengths (UBBLs)¹⁷. However, such node-based dating analyses tend to overestimate divergence times compared with fossil evidence⁶¹. Our TED approach significantly reduced ghost lineages and thus UBBLs across the bat phylogeny. With updated taxonomic sampling and filtered morphological characters, we found a mean of 48.7% (45.5% upper fossil boundary) from the TED tree, and 50.8% (47.8% lower fossil boundary) branch length missing from each family lineage in the genomic phylogeny (Supplementary Tables 17.2 and 17.5). Although subtle when averaged, UBBL reduction is significant across families (P ≤ 0.005; Supplementary Note 17). Compared with both past and current molecular phylogenies, our TED results reconcile relaxed clock divergences with the limited yet growing bat fossil record.

By leveraging the age of all 44 fossils, results from combined data analyses also track bat macroevolutionary dynamics. Unlike past studies^21,62, we modelled both speciation and extinction rates directly while inferring the phylogeny and including fossil species that enable more robust estimates of extinction rates⁶³. With a mean of 0.314 species per Ma, our speciation rate triples previously published^21,62 single-rate estimates. Similarly, our estimated extinction rate of 0.270 species per Ma was nine times higher than previous studies²¹, yielding a turnover of 0.844 species per Ma (see Supplementary Table 17.4). These results reveal systematic underestimates of both speciation rates and evolutionary turnover in the global bat radiation in previous analyses.

Where bats originated still remains an open question^17,20,21,22. The poor fossil record^19,64 and the fact that all bats fly have hindered its resolution. Flight confers extraordinary dispersal capabilities and results in global distributions, thus obscuring biogeographical signal. Thus, we implemented a dispersal–extinction cladogenesis model with time-dependent, distance-informed dispersal penalties, accounting for continental drift and high bat mobility. We revisited this question by integrating our fossil and extant taxa into a biogeographical framework (Supplementary Note 18).

Previous fossil-informed analyses placed the origin of bats in North America¹⁷, whereas early biogeographical models pointed to Africa²⁰, and more recent time-stratified approaches based on extant taxa alone, inferred it in Asia²¹. Our results instead reveal a late Paleocene ancestor (Fig. 2b, clade i, and Fig. 5a) that originated in Europe (99.2% pp), and whose descendants dispersed most probably to Africa (Figs. 2b, clade iii, and 5a and Supplementary Table 18.5). From this central Europe–Africa hub, we inferred multiple, independent range expansions into the Americas, Asia and Australia as modern bats diversified into their four major superfamilies (Figs. 2 and 5) within a narrow time interval in the early Eocene (Fig. 5b). For example, Yinpterochiroptera originated in Africa 57.1 Ma (Figs. 2b, clade v, and 5c; Africa: 55.6% pp, Europe–Africa: 14.5% pp), expanding into Asia by the middle of the Eocene at the most recent common ancestor (MRCA) of the Hipposideridae, Rhinonycteridae, Rhinolophidae families (Fig. 5c; 26.3% pp for Asia and 31.1% for Asia–Africa).

Despite difficulties resolving the ancestral range of Yangochiroptera, probably because of rapid lineage emergence just after the Paleocene–Eocene thermal maximum (Supplementary Note 18), distinct patterns emerge from many of its constituent superfamilies. For example, the MRCA of Emballonuroidea and Vespertilionoidea resided in Europe and Africa (39.2% pp for Europe, 22.0% pp for Europe–Africa and 18.5% pp for Africa). The MRCA of Emballonuroidea came out of Africa (47.1% pp for Africa and 14.4% for Africa–Europe), whereas Vespertilionoidea, including Myzopodidae, diversified from a European ancestor (46.3% pp for Europe and 24.7% pp for Europe–Africa), with multiple subsequent colonizations of North America by Natalidae, Molossidae and the MRCA of Cistugidae and Vespertilionidae (Fig. 5c).

In the superfamily Noctilionoidea, we inferred a major dispersal from Europe (38.9% pp) into North America (47.2% pp) around 54 Ma (Fig. 5c). Both timing and topology of this scenario favour a North Atlantic landmass route during peak faunal exchange between Europe and North America in the late Eocene⁶⁵, instead of a Beringian pathway²¹ (Fig. 5b). Following this initial expansion, we found strong support for a colonization of South America by the noctilionoids (52.3% pp) between 51 and 44 Ma (Fig. 5c). Our model rejects dispersal between Africa and South America (0.27% pp) by island hopping across the Atlantic²⁰. Instead, the timing and route of noctilionoids reaching the Americas coincides with the expansion of the tropics in the middle latitudes of the Americas and Europe⁶⁶ and the beginning of the uplift of islands of the Panama isthmus⁶⁷, providing habitat and a North American route for this radiation of bats.

We overcame challenges that have hindered our understanding of bat evolutionary history by integrating reference-quality whole-genome assemblies from all living bat families with a comprehensive morphological dataset, including extant and extinct bats. We used innovative phylogenomic analyses accounting for complex evolutionary histories of both lineages and markers and generated a new phylogeny, resolving previous uncertainties at the superfamily level and placing the family Myzopodidae within Vespertilionoidea. We showed that bats, and thus powered flight, most likely originated in Europe in the late Paleocene, refuting previous African, Asian and North American origins, and suggest that the acquisition of laryngeal echolocation predates the diversification of crown group bats, given the placement of the fossil †Vielasia in the oldest Eochiroptera clade. This suggests a close connection between flight and echolocation in the evolution of the ancestral bat lineage. Our analyses showed that Yinpterochiroptera originated in Africa in the early Eocene with later dispersal of modern yinpterochiropteran families into Asia and Europe, with Yangochiroptera most likely originating in Europe within a similar timeframe. The bat superfamilies probably radiated during the Paleocene–Eocene thermal maximum (approximately 56 Ma) coinciding with increased global temperature and environmental change. Chromosomal reconstructions supported 26 ancestral bat chromosomes and showed that modern bat genomes most likely evolved through chromosome fusions instead of fissions or translocations, shedding light on evolutionary constraints pertaining to the smallest mammalian genomes. The multiple genome alignments and datasets represent valuable resources for the genomics community. Given that they are based on the same set of genome assemblies, they enable benchmarking and direct methodological comparisons among alignment approaches. Finally, the methodological framework presented here can be applied to address phylogenomic questions in other taxonomic groups.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Dataset are available on Dryad (https://doi.org/10.5061/dryad.1rn8pk187). All genome accession numbers and/or Bioproject IDs are listed in Supplementary Table 1.1.

Custom scripts used for data analysis are available on GitHub⁶⁸ (https://github.com/Bat1K-21families/21families-analyses).

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Author notes

1. Ariadna E. Morales

   Present address: The City College of New York, New York, NY, USA

2. These authors contributed equally: Yan Liang, William R. Thomas, Evgeny V. Leushkin, Francisco X. Castellanos, Denis M. Larkin, Tom Brown

Authors and Affiliations

1. Senckenberg-Leibniz Institution for Biodiversity and Earth System Research, Senckenberganlage, Frankfurt, Germany

   Ariadna E. Morales, Evgeny V. Leushkin, Alexander Ben Hamadou, Alejandro Gonzales-Irribarren, Carola Greve, Leon Hilgers, Dimitrios-Georgios Kontopoulos, Shenglin Liu, Yury V. Malovichko, Tilman Schell & Michael Hiller

2. University of Montpellier–CNRS–IRD, ISEM, Montpellier, France

   Ariadna E. Morales & Sebastien J. Puechmaille

3. Tree of Life, Wellcome Sanger Institute, Hinxton, Cambridge, UK

   Yan Liang  (梁妍), Eugene W. Myers, Mark Blaxter, Paul Davis, Matthieu Muffato, Guoying Qi & Emma C. Teeling

4. Department of Ecology and Evolution, Stony Brook University, New York, NY, USA

   William R. Thomas & Liliana M. Dávalos

5. Department of Biological Sciences, Texas Tech University, Lubbock, TX, USA

   Francisco X. Castellanos & David A. Ray

6. Instituto Nacional de Biodiversidad, Quito, Ecuador

   Francisco X. Castellanos

7. Department of Comparative Biomedical Sciences, Royal Veterinary College, University of London, London, UK

   Denis M. Larkin & Maksym Prylutskyi

8. Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany

   Tom Brown, Eugene W. Myers, Martin Pippel, Laura Uelze & Sylke Winkler

9. The Arctic University Museum of Norway, UiT-The Arctic University of Norway, Tromso, Norway

   Bastian Fromm

10. School of Biological, Earth and Environmental Sciences, UNSW Sydney, Sydney, New South Wales, Australia

    Suzanne J. Hand

11. School of Biology and Environmental Science, University College Dublin, Dublin, Ireland

    Zixia Huang, Graham M. Hughes, Sarahjane Power, Marek Uvizl & Emma C. Teeling

12. Conway Institute, University College Dublin, Dublin, Ireland

    Graham M. Hughes

13. School of Life Sciences, Arizona State University, Tempe, AZ, USA

    Matthew F. Jones & Nathan S. Upham

14. Department of Natural History, Royal Ontario Museum, Toronto, Ontario, Canada

    Burton K. Lim, Judith L. Eger, Mark D. Engstrom & Thomas W. Horsley

15. School of Biology, University of St Andrews, St Andrews, UK

    Meike Mai, Ine Alvarez van Tussenbroek & Sonja C. Vernes

16. Okinawa Institute of Science and Technology, Okinawa, Japan

    Eugene W. Myers

17. SciLifeLab, National Bioinformatics Infrastructure Sweden (NBIS), Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden

    Martin Pippel

18. DRESDEN concept Genome Center, Dresden, Germany

    Martin Pippel, Laura Uelze & Sylke Winkler

19. Institut Universitaire de France, Paris, France

    Sebastien J. Puechmaille

20. Department of Mammalogy, Division of Vertebrate Zoology, American Museum of Natural History, New York, NY, USA

    Nancy B. Simmons & Melissa R. de Waal

21. The Vertebrate Genome Laboratory, The Rockefeller University, New York, NY, USA

    Linelle Ann L. Abueg, Giulio Formenti, Erich D. Jarvis, Bonhwang Koo, Kirsty McCaffrey, Brian P. O’Toole & Sam Talbot

22. Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA

    Nadav Ahituv, Michael W. Guernsey & Yelena Guttman

23. Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA

    Nadav Ahituv, Michael W. Guernsey & Yelena Guttman

24. Environment Authority, Muscat Governorate, Oman

    Zahran A. AlAbdulsalam

25. Center of Animal Behavior, Smithsonian Tropical Research Institute, Balboa, Panama

    Dineilys V. Aparicio, Nair Cabezón, Dina K. N. Dechmann, Mirjam Knörnschild & Jenna E. Kohles

26. University of California Santa Cruz, Santa Cruz, CA, USA

    Lina M. Arcila Hernández

27. Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada

    Lina M. Arcila Hernández & Mark D. Engstrom

28. Department of Zoology, National Museum, Prague, Czech Republic

    Petr Benda & Marek Uvizl

29. Department of Zoology, Faculty of Science, Charles University, Prague, Czech Republic

    Petr Benda & Marek Uvizl

30. Biodetics Data Solutions, Guelph, Ontario, Canada

    Alex V. Borisenko

31. Ecological and Regulatory Solutions, University of Guelph, Guelph, Ontario, Canada

    Alex V. Borisenko

32. SOH Conservación, Santo Domingo, Dominican Republic

    Jorge Brocca

33. Department of Medicine, KCVI, Oregon Health & Science University, Portland, OR, USA

    Lucia Carbone & Kimberly A. Nevonen

34. Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA

    Lucia Carbone

35. Division of Genetics, Oregon National Primate Research Center, Beaverton, OR, USA

    Lucia Carbone

36. New Cultivar Innovation, New Zealand Institute for Bioeconomy Science Limited, Auckland, New Zealand

    Jose I. Carvajal & Elena Hilario

37. Programme for Emerging Infectious Diseases, Duke-NUS Medical School, Singapore, Singapore

    Wharton O. Y. Chan, Akshamal M. Gamage & Lin-Fa Wang

38. Max Planck Institute of Animal Behavior, Radolfzell, Germany

    Dina K. N. Dechmann & Jenna E. Kohles

39. University of Konstanz, Konstanz, Germany

    Dina K. N. Dechmann & Jenna E. Kohles

40. Institute for Neurobiology, University of Tübingen, Tübingen, Germany

    Annette Denzinger

41. Department of Environmental Science, School of Science, University of Namibia, Windhoek, Namibia

    Seth J. Eiseb

42. National Museum of Namibia, Windhoek, Namibia

    Seth J. Eiseb

43. Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA

    David Enard & M. Elise Lauterbur

44. Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, USA

    Nicole M. Foley & William J. Murphy

45. Center for Comparative Genomics, Texas A&M AgriLife Research, College Station, TX, USA

    Nicole M. Foley & William J. Murphy

46. Department of National Parks and Wildlife, Research and Development Section, Lilongwe, Malawi

    Jackson Fuller & William O. Mgoola

47. Department of Evolutionary Ecology, National Museum of Natural Sciences, CSIC, Madrid, Spain

    Ismael Galván

48. Department of Anatomy, Faculty of Biomedical Sciences, University of Otago, Otago, New Zealand

    Neil J. Gemmell, Joanne E. Gillum & William S. Pearman

49. Instituto de Investigación de Recursos Biológicos Alexander von Humboldt, Bogota, Colombia

    Mailyn A. Gonzalez

50. Negaunee Integrative Research Center, Field Museum of Natural History, Chicago, IL, USA

    Steven M. Goodman

51. Association Vahatra, Antananarivo, Madagascar

    Steven M. Goodman

52. Paratus Sciences, New York, NY, USA

    Jonathan Gray & Ning Zhang

53. Department of Biological Sciences, California State University Stanislaus, Turlock, CA, USA

    Michael W. Guernsey

54. Laboratorio de Bioconservación y Manejo, Escuela Nacional de Ciencias Biológicas, IPN, Mexico City, Mexico

    Edgar G. Gutiérrez & Jorge Ortega

55. Department of Genetics, University of Granada, Granada, Spain

    Michael Hackenberg

56. Bioinformatics Laboratory, Biotechnology Institute & Biomedical Research Centre (CIBM), Granada, Spain

    Michael Hackenberg

57. Institute of Cell Biology and Neuroscience, Faculty of Biosciences, Goethe University Frankfurt, Frankfurt, Germany

    Leon Hilgers, Shenglin Liu, Yury V. Malovichko & Michael Hiller

58. Department of Biological Sciences, Fairleigh Dickinson University, Teaneck, NJ, USA

    Melissa R. de Waal

59. Cobra Collective Guyana, Georgetown, Guyana

    Deirdre M. Jafferally

60. Hughes Medical Institute, Chevy Chase, MD, USA

    Erich D. Jarvis

61. Amazon Conservation Team, Paramaribo, Suriname

    Sahieda A. Joemratie

62. Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany

    Mirjam Knörnschild & York Winter

63. Museum für Naturkunde-Leibniz Institute for Evolution and Biodiversity Science, Berlin, Germany

    Mirjam Knörnschild & Martina Nagy

64. Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA, USA

    Dimitrios-Georgios Kontopoulos

65. Department of Biology, University of Vermont, Burlington, VT, USA

    M. Elise Lauterbur

66. Paul G. Allen School for Global Health, College of Veterinary Medicine, Washington State University, Pullman, WA, USA

    Michael Letko & Stephanie N. Seifert

67. Julie Ann Wrigley Global Futures Laboratory, Walton Center for Planetary Health, Arizona State University, Tempe, AZ, USA

    Harris A. Lewin

68. Department of Molecular Medicine, Aarhus University Hospital (AUH), Aarhus, Denmark

    Shenglin Liu

69. Department of Clinical Medicine, Aarhus University, Aarhus, Denmark

    Shenglin Liu

70. Bragato Research Institute, Lincoln, New Zealand

    Darrell K. Lizamore

71. Science and Research Directorate, Department of Conservation, Wellington, New Zealand

    Brian D. Lloyd

72. Medical Affairs Organization, Illumina, San Diego, CA, USA

    Livia Loureiro

73. Centro de Investigaciones Tropicales, Universidad Veracruzana, Veracruz, Mexico

    M. Cristina MacSwiney G

74. Institute of Evolutionary Ecology and Conservation Genomics, Ulm University, Ulm, Germany

    Dominik W. Melville, Magdalena Meyer, Bismark A. Opoku & Simone Sommer

75. Laboratory of Virology, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, Hamilton, MT, USA

    Vincent J. Munster

76. University of Caen Normandy, INSERM, Normandie University, Caen, France

    Nicolas Nesi

77. Department of Forests, Ministry of Agriculture Rural Development and Environment, Nicosia, Cyprus

    Haris Nicolaou

78. Department of Wildlife and Range Management, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

    Evans E. Nkrumah

79. Bina Hill Institute Youth Learning Centre, Bina Hill, Guyana

    Zacharias Norman

80. Wildlife Conservation Society, Health Program, Bronx, NY, USA

    Sarah H. Olson

81. Wildlife Health Program, Wildlife Conservation Society, Brazzaville, Republic of the Congo

    Alain Ondzie

82. School of Biological Sciences, University of Auckland, Auckland, New Zealand

    William S. Pearman

83. West German Genome Center (WGGC), Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany

    Francy J. Perez-Llanos

84. Biological and Medical Research Center (BMFZ), Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany

    Francy J. Perez-Llanos

85. Department of Veterinary and Biomedical Sciences, College of Veterinary Medicine, University of Minnesota, St. Paul, MN, USA

    Kendra L. Phelps

86. School of Life and Health Sciences, University of Nicosia, Nicosia, Cyprus

    Myrtani Pieri

87. Escuela de Ciencias e Ingeniería, Universidad del Rosario, Bogota, Colombia

    Paola Pulido-Santacruz

88. School of Biology and Center for Research in Biodiversity and Tropical Ecology (CIBET), University of Costa Rica, San Jose, Costa Rica

    Bernal Rodríguez-Herrera

89. Department of Natural Sciences and Mathematics, Pontificia Universidad Javeriana Cali, Cali, Colombia

    Danny Rojas

90. Environmental Management Consultants Guyana, Ogle, East Coast Demerara, Guyana

    Indranee Roopsind

91. Rupununi Wildlife Research Unit, Lethem, Guyana

    Indranee Roopsind

92. School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK

    Stephen J. Rossiter

93. Institute of Biology, Freie Universitat Berlin, Berlin, Germany

    Constance Scharff

94. Wildconscience, Kralendijk, Bonaire, the Netherlands

    Fernando Simal

95. CARMABI Caribbean Research and Management of Biodiversity, Willemstad, Curaçao

    Fernando Simal

96. Princess Maha Chakri Sirindhorn Natural History Museum, Prince of Songkla University, Songkhla, Thailand

    Pipat Soisook

97. Independent Wildlife Consultant, Muscat, Oman

    Andrew Spalton

98. Bat Conservation Research Lab, Milner Research Institute, University of Bath, Bath, UK

    Emma L. Stone

99. African Bat Conservation, Lilongwe, Malawi

    Emma L. Stone

100. Department of Integrative Biology, University of California Berkeley, Berkeley, CA, USA

     Peter H. Sudmant

101. Biodiversity Institute, University of Kansas, Lawrence, KS, USA

     Robert M. Timm

102. Department of Ecology & Evolutionary Biology, University of Kansas, Lawrence, KS, USA

     Robert M. Timm

103. Czech Academy of Sciences, Institute of Vertebrate Biology, Brno, Czech Republic

     Peter Vallo

104. Department of Biology, Pennsylvania State University, University Park, PA, USA

     Juan M. Vazquez

105. Freshwater Quality Monitoring Division, Science and Technology Branch, Environment and Climate Change Canada, Burlington, Ontario, Canada

     Linet C. Watson

106. AEWC Ltd/BatCRU, Fittleworth, UK

     Daniel Whitby

107. Department of Bioinformatics and Genomics, University of North Carolina, Charlotte, NC, USA

     Laurel R. Yohe

108. Otago Genomics Facility, University of Otago, Dunedin, New Zealand

     Monika Zavodna

109. State Key Laboratory of Virology and Biosafety, College of Life Sciences, Wuhan University, Wuhan, China

     Huabin Zhao

110. Consortium on Inter-Disciplinary Environmental Research, Institute for Advanced Computational Science, Stony Brook University, New York, NY, USA

     Liliana M. Dávalos

Contributions

Y.L., W.R.T., E.V.L., F.X.C., D.M.L. and T.B. are equally contributing second authors; and B.F., S.J.H., Z.H., G.M.H., M.F.J., B.K.L., M.M., E.W.M., M. Pippel, S.J.P. and N.B.S. are equally contributing third authors, listed alphabetically. This study was conceptualized and led by the five corresponding authors: E.C.T., M. Hiller, L.M.D., S.C.V. and D.A.R. who led the scientific direction, manuscript writing and editing. Together with the corresponding authors, 18 co-authors led the data analyses and interpretation detailed in the Supplementary Notes and contributed to writing and editing: A.E.M., Y.L., W.R.T., E.V.L., F.X.C., D.M.L., T.B., B.F., S.J.H., Z.H., G.M.H., M.F.J., B.K.L., M. Mai, E.W.M., M. Pippel, S.J.P. and N.B.S. All authors had the opportunity to review and edit the manuscript. Z.A.A., I.A.V.T., D.V.A., L.M.A.H., P.B., A.V.B., J.B., N.C., W.O.Y.C., L.M.D., D.K.N.D., A.D., D.E., M.D.E., I.G., A.M.G., S.M.G., E.G.G., L.H., M.R.d.W., D.M.J., S.A.J., M.K., J.E.K., M.E.L., M.L., B.K.L., B.D.L., L.L., V.J.M., N.N., H.N., E.E.N., Z.N., S.H.O., A.O., B.A.O., J.O., K.L.P., M.Pieri, S.J.P., P.P.-S., D.A.R., B.R.-H., I.R., S.J.R., C.S., S.N.S., N.B.S., A.S., E.L.S., P.H.S., R.M.T., N.S.U., J.M.V., S.C.V., L.-F.W., L.C.W., D.W., Y.W., L.R.Y., H.Z., J.L.E., S.J.E., J.F., M.A.G., T.W.H., M.C.M.G., M.N., D.R., F.S., P.S., W.O.M., E.C.T., M. Hiller, M. Mai and P.V. acquired samples and data. S.W., D.W.M., C.G., A.B.H., A.M.G., D.K.L., E.H., F.J.P.-L., J.I.C., J.E.G., K.A.N., L.C., M. Meyer, M.W.G., M.Z., N.J.G., N.Z., W.O.Y.C., W.S.P., Y.G., S.S., N.A., B.P.O., L.-F.W., S.C.V., E.C.T. and M. Hiller performed genome sequencing. T.B., E.V.L., M. Pippel, T.S., L.A.L.A., K.M., S.T., B.K., G.F., E.D.J., L.U., E.W.M., S.C.V., E.C.T., M.B., M. Hiller and B.P.O. conducted the genome assembly. A.E.M., E.V.L., D.A.R. and M. Hiller performed the assembly statistics. A.E.M., A.G.-I., Y.V.M., L.H., S.L., D.-G.K., E.V.L. and M. Hiller conducted genome annotation and pairwise genome alignments. D.A.R. and L.M.D. performed the transposable element annotation. B.F., Z.H., S.P., M.U., M. Hackenberg, M. Mai and S.C.V. conducted the microRNA analyses. A.E.M., E.V.L., F.X.C., D.A.R. and M. Hiller performed the multiple genome alignments. A.E.M., E.C.T., M. Hiller, A.G.-I., G.M.H., S.J.P., Y.L., N.M.F. and W.J.M. undertook the phylogenomics. M. Prylutskyi, A.E.M., M. Hiller, H.A.L. and D.M.L. performed the ancestral karyotype reconstruction. W.R.T., M.F.J., F.X.C., G.M.H., S.J.H., N.B.S., L.M.D. and E.C.T. contributed fossils, total evidence and node divergence times, and biogeography. A.B.H., T.B., P.D., G.F., A.G.-I., J.G., M.F.J., B.K., M.E.L., E.V.L., Y.L., M. Mai, Y.V.M., A.E.M., M. Pippel, T.S., N.B.S., W.R.T., M. Muffato, G.Q., L.U., E.C.T., M. Hiller, L.M.D., S.C.V. and D.A.R. curated the data and performed the archving.

Corresponding authors

Correspondence to David A. Ray, Sonja C. Vernes, Liliana M. Dávalos, Michael Hiller or Emma C. Teeling.

Competing interests

The authors declare no competing interests.

Peer review information

Nature thanks Yafei Mao, who co-reviewed with Zikun Yang; Josefin Stiller; and Gerald Wilkinson for their contribution to the peer review of this work. Peer reviewer reports are available.

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