# The origin and speciation of orchids

Oscar A Pérez-escobar, Diego Bogarín, Natalia a S Przelomska, James D Ackerman, Juan A Balbuena, Sidonie Bellot, Roland P Bühlmann, Betsaida Cabrera, Jose Aguilar Cano, Martha Charitonidou, et al.

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## To cite this version:

Oscar A Pérez-escobar, Diego Bogarín, Natalia a S Przelomska, James D Ackerman, Juan A Balbuena, et al.. The origin and speciation of orchids. New Phytologist, 2024, 242 (2), pp.700-716. 10.1111/nph.19580. hal-04758619

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The origin and speciation of orchids

Oscar A. Pérez-Escobar¹*† ID, Diego Bogarín²,³* ID, Natalia A. S. Przelomska¹,⁴* ID, James D. Ackerman⁵,
Juan A. Balbuena⁶, Sidonie Bellot¹ ID, Roland P. Bühlmann⁷, Betsaida Cabrera⁸, Jose Aguilar Cano⁹,
Martha Charitonidou¹⁰ ID, Guillaume Chomicki¹¹ ID, Mark A. Clements¹², Phillip Cribb¹, Melanie Fernández²,
Nicola S. Flanagan¹³ ID, Barbara Gravendeel³ ID, Eric Hágsater¹⁴, John M. Halley¹⁰, Ai-Qun Hu¹⁵,
Carlos Jaramillo¹⁶ ID, Anna Victoria Mauad¹⁷, Olivier Maurin¹ ID, Robert Müntz¹⁸ ID, Ilia J. Leitch¹ ID, Lan Li¹⁹,
Raquel Negrão¹, Lizbeth Oses², Charlotte Phillips¹,⁴ ID, Milton Rincon⁹, Gerardo A. Salazar²⁰, Lalita Simpson²¹,
Eric Smidt¹⁷ ID, Rodolfo Solano-Gomez²², Edicson Parra-Sánchez²³ ID, Raymond L. Tremblay⁵,
Cassio van den Berg²⁴, Boris Stefan Villanueva Tamayo⁹, Alejandro Zuluaga²⁵ ID, Alexandre R. Zuntini¹,
Mark W. Chase¹,²⁶†, Michael F. Fay¹†, Fabien L. Condamine²⁷† ID, Felix Forest¹†, Katharina Nargar¹⁹,²¹,²⁸† ID,
Susanne S. Renner²⁹† ID, William J. Baker¹† ID and Alexandre Antonelli¹,³⁰,³¹,³²,³³† ID

¹Royal Botanic Gardens, Kew, London, TW9 3AE, UK; ²Lankester Botanical Garden, University of Costa Rica, P.O. Box 302-7050, Cartago, Costa Rica; ³Naturalis Biodiversity Centre, Leiden, CR 2333, the Netherlands; ⁴University of Portsmouth, Portsmouth, PO1 2DY, UK; ⁵University of Puerto Rico – Rio Piedras, San Juan, PR, 00925-2537, USA; ⁶ICBiBE, Universitat de València, Valencia, 13-46010, Spain; ⁷Swiss Orchid Foundation, Schönenbuch, 4124, Switzerland; ⁸Jardín Botánico Rafael Maria Moscoso, Santo Domingo, 21-9, Dominican Republic; ⁹Jardín Botánico Jose Celestino Mutis, Bogota, 111071, Colombia; ¹⁰University of Ioannina, Ioannina, GR 45110, Greece; ¹¹Durham University, Durham, DH13LE, UK; ¹²Centre for Australian National Biodiversity Research (joint venture between Parks Australia and CSIRO), GPO Box 1700, Canberra, ACT, 2601, Australia; ¹³Universidad Pontificia Javeriana, Seccional Cali, Cali, 760031, Colombia; ¹⁴Herbarium AMO, Mexico City, 11000, Mexico; ¹⁵Singapore Botanic Gardens, 1 Cluny Road, Singapore, 257494, Singapore; ¹⁶Smithsonian Tropical Research Institute, Apartado, Panama City, 0843-03092, Panama; ¹⁷Universidade Federal do Paraná, Curitiba, 19031, Brazil; ¹⁸Reserva Biológica Guaitil, Eisenstadt, 7000, Austria; ¹⁹National Research Collections Australia, Commonwealth Industrial and Scientific Research Organisation (CSIRO), GPO Box 1700, Canberra, ACT, 2601, Australia; ²⁰Universidad Nacional Autónoma de México, Mexico City, 04510, Mexico; ²¹Australian Tropical Herbarium, James Cook University, GPO Box 6811, Cairns, Qld, 4878, Australia; ²²Instituto Politécnico Nacional, CIDIR unidad Oaxaca, Oaxaca, 71230, Mexico; ²³University of Sheffield, Sheffield, S10 2AH, UK; ²⁴Universidade Estadual de Feira de Santana, Feira de Santana, 44036-900, Brazil; ²⁵Universidad del Valle, Cali, 760042, Colombia; ²⁶Department of Environment and Agriculture, Curtin University, Perth, WA, 6102, Australia; ²⁷Institut des Sciences de l'Evolution de Montpellier (Université de Montpellier/CNRS/JRD/EPHE), Place Eugène Bataillon, Montpellier, 34000, France; ²⁸Scientific Research Organisation (CSIRO), GPO Box 1700, Canberra, ACT, 2601, Australia; ²⁹Washington University in St. Louis, St Louis, MO 63130, USA; ³⁰Department of Biological and Environmental Sciences, Gothenburg Global Biodiversity Centre, Gothenburg, 417 56, Sweden; ³¹University of Gothenburg, Gothenburg, 417 56, Sweden; ³²Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, China; ³³Department of Biology, University of Oxford, Oxford, OX1 3SZ, UK

Summary

* Orchids constitute one of the most spectacular radiations of flowering plants. However, their origin, spread across the globe, and hotspots of speciation remain uncertain due to the lack of an up-to-date phylogeographic analysis.

* We present a new Orchidaceae phylogeny based on combined high-throughput and San-
ger sequencing data, covering all five subfamilies, 17/22 tribes, 40/49 subtribes, 285/736
genera, and c. 7% (1921) of the 29 524 accepted species, and use it to infer geographic range
evolution, diversity, and speciation patterns by adding curated geographical distributions from
the World Checklist of Vascular Plants.

* The orchids' most recent common ancestor is inferred to have lived in Late Cretaceous Laurasia. The modern range of Apostasioideae, which comprises two genera with 16 species from India to northern Australia, is interpreted as relictual, similar to that of numerous other groups that went extinct at higher latitudes following the global climate cooling during the Oligocene. Despite their ancient origin, modern orchid species diversity mainly originated over the last 5 Ma, with the highest speciation rates in Panama and Costa Rica.

Authors for correspondence:
Oscar A. Pérez-Escobar
Email: o.perez-escobar@kew.org

William J. Baker
Email: w.baker@kew.org

Alexandre Antonelli
Email: a.antonelli@kew.org

Received: 4 September 2023

Accepted: 4 December 2023

New Phytologist (2024) **242**: 700–716

doi: 10.1111/nph.19580

<footnote>*Lead authors.</footnote>

<footnote>†Senior authors.</footnote>
**Key words:** high-latitude extinction, historical biogeography, Laurasia, macroevolution, Neotropics, Orchidaceae.

* These results alter our understanding of the geographic origin of orchids, previously proposed as Australian, and pinpoint Central America as a region of recent, explosive speciation.

Introduction

The angiosperm tree of life is characterised by the rise and demise of species, leading to species-rich and depauperate lineages coexisting in time and space (Magallón et al., 2019; Tietje et al., 2022). Investigating factors behind angiosperm diversification requires ancient, species-rich clades thriving across the globe and having a fossil record. One such clade is the Orchidaceae. With 29 524 species (Chase et al., 2015; Christenhusz & Byng, 2016; Govaerts et al., 2021), orchids are among the most species-rich groups of flowering plants, with molecular dating studies having estimated their initial diversification at 112–76 million years ago (Ma) (Ramírez et al., 2007; Gustafsson et al., 2010; Chomicki et al., 2015; Givnish et al., 2016; Serna-Sánchez et al., 2021). This diversification time is partly supported by the orchid fossil record, which includes leaf compressions and pollinaria dated from the early Eocene through the mid-Miocene found in different deposits (Ramírez et al., 2007; Conran et al., 2009; Poinar Jr., 2016a,b; Poinar & Rasmussen, 2017). Most of these fossils have been credibly assigned to subfamilies and tribes.

An updated biogeographic study of this old, species-rich, and cosmopolitan family (Fig. 1a–d), which is uniquely diverse in animal and fungal interactions (Selosse et al., 2022; Ackerman et al., 2023; Karremans et al., 2023), and adaptations to different habitats (Fig. 1e,f), will help our understanding of monocot clades with wide distribution ranges and diversity of adaptations (e.g. Arecaceae (Couvreur et al., 2011), Bromeliaceae (Benzing, 2000)). Such a study can build on several in-depth analyses that have focused on subclades of orchids (Bouetard et al., 2010; Guo et al., 2012; Freudenstein & Chase, 2015; Pérez-Escobar et al., 2017; Nauheimer et al., 2018). In a benchmark study of the biogeographic history and diversification of the entire family, Givnish et al. (2016) used a fossil-calibrated plastid tree for 173 genera (out of 736), representing all five subfamilies, along with 10 outgroup species representing six families (Asteliaceae, Blandfordiaceae, Boryaceae, Hypoxidaceae, and Lanariaceae, with Iriidaceae as a more distant outgroup). Using a likelihood approach (Matzke, 2013) and a single consensus ultrametric tree, their analyses relied on a matrix where terminals, usually representing genera, were coded for their entire distribution. The results pointed to an origin and initial diversification of orchids in Australia during the mid-Cretaceous, 120–90 Ma. Specifically, the range estimates for the Orchidaceae stem node indicated Australia as c. 40% likely, Neotropics plus Australia as c. 20%, and all other ranges together as c. 40%. At 90 Ma, Australia was at high latitudes connected to Antarctica and part of the southern supercontinent, Gondwana, with connections to South America, and the authors, therefore, stressed the likely importance of expansion across Antarctica. A Gondwanan origin for Orchidaceae was also suggested by Chase (2001).

Givnish et al. (2016: table 1) also addressed the correlates of diversification rates through time and inferred Southeast Asia as the region with the highest net diversification rates. Studying how diversification rates are linked to geographical variation helps us understand not only the pace at which extant species diversity has accumulated but also the correlated biotic and abiotic variables (Condamine et al., 2013; Velasco & Pinto-Ledezma, 2022). Although orchid species diversity clearly is unevenly distributed across their distribution (Vitt et al., 2023), no study has yet assessed the relationship between the distribution of orchid species richness and underlying speciation rates at a global scale.

Here, we revisit the biogeographic history of Orchidaceae and infer geographic patterns of speciation, using a greatly expanded taxon sampling (1921 species, 285 genera) as compared to previous studies, with a particular focus on early divergences. We generate a phylogenomic framework by combining high-throughput and Sanger sequencing data. Our spatial information derives from herbarium-vouchered, georeferenced occurrence data, sourced from the Global Biodiversity Information Facility (GBIF; https://www.gbif.org) and the RAINBIO mega-database (Dauby et al., 2016). This dataset was further vetted using the spatial distribution data of the World Checklist of Vascular Plants (WCVP; Govaerts et al., 2021). Based on these datasets, we: (1) infer the evolutionary and biogeographical history of the orchids; (2) test the hypothesis of an Australasian origin for their most recent common ancestor; and (3) revisit the hypothesis that Southeast Asia is the region with the highest current diversification rate of orchids.

Materials and Methods

Taxon sampling, DNA library preparation and sequencing

Our phylogenomic framework was assembled using a high-
throughput-sequencing dataset, complemented with Sanger data
for single genes. The high-throughput-sequencing dataset
focused on the genes targeted by the Angiosperms353 probe set
(Johnson et al., 2019; Baker et al., 2022) and includes 448 spe-
cies, representing 285/736 genera, 40/49 subtribes, 17/22 tribes,
and 5/5 subfamilies (in the classification of Chase et al., 2015).
These data were generated by the Plant and Fungal Trees of Life
Project (Baker et al., 2022) at the Royal Botanic Gardens, Kew,
and the Genomics for Australian Plants Consortium
(https://www.genomicsforaustralianplants.com/). Nineteen
monocot species were included as outgroup taxa, with Dioscorea
caucasica (Dioscoreales) used as a rooting terminal, for a total of
467 species in the high-throughput-sequencing dataset. Illumina
sequencing reads were newly produced for 377 samples (vs Pérez-
Escobar et al., 2021b) from expertly curated specimens housed in
the Kew (K) and Australian National Herbarium (CANB),
<figure><img src="image_5.png" /><figcaption>**Fig. 1** Diversity of orchid habitats (a–c), modern distribution range (d) and pollination systems in Orchidaceae (e–g). (a) Tropical cloud forests; (b) Tropical lowland wet forests; (c) High-elevation grasslands; (e) the Neotropical epiphyte *Cycnoches guttulatum* Schltr., pollinated by male euglossine bees collecting aromatic compounds; (f) *Trigona* bee carrying a pollinarium of a Neotropical *Xylobium elongatum* (upper panel: dorsal view; lower panel: side view); (g) the epiphytic and leafless *Dendrophylax sallei* (Rchb.f.) Benth. ex-Rolfe., a Neotropical orchid pollinated by moths. Photos: Oscar A. Pérez-Escobar & Diego Bogarín.</figcaption></figure>

including 23 species sampled from type material and silica-gel dried tissue (Supporting Information Table S1).

DNA extraction was conducted from 1.5 to 4 mg of silica-dried fresh tissue or herbarium specimen tissue, constituting leaf or occasionally flower. We used a sterile steel bead for mechanical disruption in a SPEX® sample prep tissue homogeniser (SPEX Inc., Metusen NJ, USA), applying two to four disruption cycles of 1 min at 1350 Hz. Next, we added 750 µl CTAB with 2% v/v 2-mercaptoethanol (Doyle & Doyle, 1990) for a 30-min incubation at 65°C, followed by either a 4 h incubation at 57°C (silica-dried samples) or an overnight incubation at 57°C (herbarium specimens). We then added 750 µl SEVAG (24 : 1 chloroform : isoamyl alcohol), placed the samples on a shaking incubator for 30 min, and centrifuged the samples to induce phase separation. Next, we separated off the upper phase containing the DNA fraction and added to this a 0.7× volume of isopropanol for a 4–5-d incubation at –20°C to encourage DNA precipitation. Finally, we subjected the samples to two rounds of centrifugation and

supernatant removal, first to remove the isopropanol and second to wash the DNA pellet with 75% ethanol. Subsequently, the DNA pellet was eluted into 70 µl 10 mM Tris-Cl and purified using a paramagnetic bead clean-up method with a 2 : 1 ratio of Ampure XP beads (Beckman Coulter, Brea, CA, USA) to DNA elution. To gauge the size distribution of the genomic DNA and, on this basis, determine which samples might need shearing before library preparation, we visualised the samples on a 1% agarose gel. Using 18–200 ng starting material, we prepared DNA libraries using NEB Next Ultra II Library Prep Kits (New England Biolabs, Ipswich, MA, USA), according to the manufacturer's protocol, but with half-volume reactions. Where possible (silica-dried material and herbarium specimens with DNA of higher integrity), we aimed for insert sizes of c. 350 bp, by shearing the DNA with a Covaris ME220 Focussed Ultrasonicator (Covaris LLC, Woburn, MA, USA). The libraries were indexed with NEBNext Multiplex Oligos for Illumina (New England Biolabs) and amplified using 6–14 PCR cycles. The yield was
estimated using a Quantus fluorometer (Promega), and fragment
size distribution was estimated using either a 4200 TapeStation
system or an Agilent 2100 BioAnalyser (Agilent Technologies,
Santa Clara, CA, USA). Finally, we used the Angiosperms353
probe set to enrich these genomic libraries for 353 low-copy
nuclear genes (Johnson *et al.*, 2019), modifying the equimolar
pooling of libraries into groups of 10–20 libraries per capture
reaction and total input of up to 1 µg. Furthermore, for the her-
barium samples, we applied a lowered hybridisation temperature
– of 62°C and a prolonged hybridisation time – of 40 h. The
sequencing of paired-end genomic libraries (150 bp × 2) was
conducted on an Illumina HiSeq by Macrogen (Geumcheon,
South Korea).

The Sanger sequencing dataset was assembled through the SuperCRUNCH pipeline (Portik & Wiens, 2020). This pipeline was executed using an initial set of 24 172 sequences of the nuclear ribosomal ITS and the plastid *matK* markers – two commonly sequenced, informative loci for Orchidaceae and other angiosperms (Grace *et al.*, 2021) – obtained from GenBank (https://www.ncbi.nlm.nih.gov/nuccore). The following parameters were used: (1) search terms of ‘ITS; ITS1; ITS2; internal transcribed spacer 1; partial sequence; 5.8S ribosomal RNA gene; complete sequence; internal transcribed spacer 2’ for ITS, and ‘*matK*; *trnK*; maturase K; *trnK* gene, intron; and maturase K *matK* gene’ for *matK*; (2) a list of 285 generic (accepted) names that were sampled in the high-throughput-sequencing dataset; (3) similarity searches between sequences using MEGABLAST (Camacho *et al.*, 2009), retaining a maximum of 200 hits per search. After removing duplicated sequences and obvious contaminants (defined as sequences with misplaced positions given current taxonomic views), we retained sequences of nrITS and plastid *matK* for 2060 species, of which 64% had sequences produced from the same voucher specimen (Table S2).

High-throughput and Sanger sequencing data analyses

Illumina libraries were quality-assessed with FASTQC software
v.0.11 (available at https://www.bioinformatics.babraham.ac.
uk/projects/fastqc/). Paired-end reads were quality-filtered and
adapter-trimmed using TRIMGALORE! v.0.6.4 available at
(https://github.com/FelixKrueger/TrimGalore), using the follow-
ing parameters: (1) -q 30 (minimum Phred score), (2) length 20
(minimum read length), (3) retaining read pairs that passed the
quality thresholds. In silico retrieval of the Angiosperms353 coding
loci was conducted using the pipeline HYBPIPER v.2.1.6 (Johnson
et al., 2016) and the same parameters and software described in Pé-
rez-Escobar et al. (2021b; also see Methods S1). Next, low-copy
nuclear and Sanger markers were aligned using the software MAFFT
v.7.4 (Katoh & Standley, 2013) in conjunction with the iterative
refinement method FFT-NS-I, implementing a maximum of 1000
iterations. To ameliorate potential bias introduced by missing data
in phylogenetic analyses, sequences shorter than 50% of the total
alignment length were excluded. Subsequently, alignments were fil-
tered for misaligned positions using TAPER v.1.0 (Zhang
et al., 2021), with a cut-off value of 1 (flag -c), and inspected by

eye with GENEIOUS v.8.0 (available at https://www.geneious.com/).
The Sanger/Angiosperms353 nucleotide alignments are accessible
at doi: 10.6084/m9 figshare.22245940.

Distance-based, maximum likelihood, Bayesian, and multispecies coalescence phylogenomic inferences

We computed maximum likelihood (ML) trees from the indivi-
dual Sanger and Angiosperms353 nucleotide alignments using
RAxML v.8.0 (Stamatakis, 2014) with 500 rapid bootstrap repli-
cates (flags -# 500 and -x) and the GTR+ Γ nucleotide substitu-
tion model. Additionally, a multispecies coalescent (MSC) tree
was inferred from the individual Angiosperms353 gene trees pro-
duced by RAxML using the software ASTRAL-III v.5.6 (Zhang
et al., 2018) and the flag -t 2 (full tree annotation), after collaps-
sing bipartitions with a likelihood bootstrap percentage (LBP)
< 20% (achieved with the function new_ed of the package
newick_utils v.1.6.0, available at https://bioweb.pasteur.fr/
packages/pack@newick-utils@1.6). To visualise the proportion
of gene tree quartets that agreed with the species tree, we pro-
duced quartet support pie charts for every branch represented in
the species tree, using the full annotation produced by ASTRAL-
III. We also visualised the proportion of gene trees in agreement
with each species' tree bipartition using PHYPARTS v.1.0, by label-
ling as informative any gene tree bipartition with a LBP > 20%
(Smith et al., 2015).

Because of the overall topological congruence between the
ITS- and *matK*-derived trees and the Angiosperms353 low-copy
nuclear MSC trees, as well as a goodness-of-fit test (Balbuena
et al., 2013; Pérez-Escobar et al., 2016, see Methods S1;
Notes S1), we proceeded to compute ML phylogenetic trees
from: a supermatrix derived by concatenating the ITS and *matK*
sequences; and a supermatrix derived from the concatenation of
the Angiosperms353 loci, again using RAxML v.8.0 with the
same settings previously specified, considering each of the two
supermatrices as a single partition. Lastly, we inferred a consensus
network using the 500 RAxML bootstrap replicates produced
from the Angiosperms353 supermatrix and SPLITSTREE v.4.0
(Huson & Bryant, 2006), with a mean edge weight and a thresh-
old value of 0.75 to filter out any splits not found in at least 75%
of the bootstrap trees. The trees and consensus network are acces-
sible at doi: 10.6084/m9 figshare.22245940.

Molecular-clock-dating analyses and species-level phylogeny assembly

Absolute age estimation analysis was conducted in two stages
using the Bayesian framework implemented in BEAST v.2.6
(Bouckaert et al., 2019), as follows:

(1) The backbone was inferred by subsampling the Angios-
perms353 low-copy nuclear gene alignments using SORTDATE
v.1.0 (Smith *et al.*, 2018). Here, we selected the top 25 low-copy
nuclear gene alignments with the lowest root-to-tip variance coef-
ficient (i.e. highest clock-likeness). This selection ensured the
representation of the entire generic diversity as sampled by
the ML high-throughput dataset (i.e. 285 genera; see Methods S1; Tables S3, S4). This data subset was imported in BEAUTI v.2.6 (Bouckaert et al., 2019) as unlinked partitions, with the same priors employed by Pérez-Escobar et al. (2021a), as follows: (1) the GTR nucleotide substitution model and a rate heterogeneity among sites modelled by a Γ distribution with four categories; (2) an uncorrelated log-normal relaxed molecular clock in combination with a prior clock rate interval of 0.0001–0.001 substitutions/site/Ma, modelled by a uniform distribution; (3) a birth–death tree process, modelled by a uniform distribution for the birth and relative death rates; (4) three secondary calibration points from Givnish et al. (2015), modelled by normal distributions (σ = 1) and located at the root node, that is, the most recent common ancestor (MRCA) of Dioscoreales, Asparagales, and Liliales (125 Ma), the MRCA of Orchidaceae (89 Ma), and the MRCA of subtribe Goodyerinae (32 Ma); (5) the ML consensus phylogram of generic lineages produced in RAxML v.8.0 as a starting tree; (6) 500 million generations, sampling every 100 000 generations and ensuring that all posterior values reached effective sample sizes (ESS) > 100. The proportion of informative and missing sequences per taxon sample is provided in Table S4. Finally, the support for the backbone ultrametric trees derived from BEAST was assessed by first computing a maximum credibility consensus tree from the Markov chain Monte Carlo (MCMC) posterior trees using TREEANNOTATOR v.2.6 (https://www.beast2.org/treeannotator/), using a burn-in threshold of 10%, and then counting the proportion of bipartitions falling on different support intervals.

(2) A species-level-ultrametric phylogeny of Orchidaceae was produced by inputting the ITS and *matK* alignments as unlinked partitions in *BEAUTI* v.2.6 (Bouckaert *et al.*, 2019), using the same priors as in (1). Furthermore, the ML consensus phylogram produced in RAxML v.8.0 from the ITS-*matK* supermatrix was used as a starting tree.

(3) To try to accommodate phylogenetic uncertainty, we constructed 10 ultrametric species trees through a novel pipeline (Fig. 2) instead of using a single consensus maximum clade credibility tree. We first randomly sampled 10 posterior trees derived from the BEAST analyses conducted on the genus-level Angiosperms353 and the ITS-*matK* Sanger species-level datasets. Then, for each genus represented in the Angiosperms353 chronograms, we pruned its counterpart clade sampled on the species-level Sanger chronogram and proceeded to graft it onto the corresponding stem of the Angiosperms353 chronogram. This operation was conducted on each pair of randomly sampled posterior trees (hence called posterior probability (PP) species trees). A detailed description of the pipeline is provided in the Methods S1.

Biogeographic analyses

Biogeographical state estimations were conducted on the 10 PP
species trees (see previous section). We estimated areas of origin
and geographic range using the ML approach of dispersal-extinc-
tion-cladogenesis (DEC, Ree & Smith, 2008) as implemented in
the C++ version (Beeravolu & Condamine, 2016). We relied on

DEC because in its C++ implementation, is a model that is scalable
to the number of terminals and biogeographical areas that our
study involves and because it models the main biogeographic phe-
nomena that could have shaped the range evolution of the orchid
family at higher taxonomic levels. The eight geographic areas con-
sidered were: (1) Palearctic, defined as Europe, Siberia, Central
Asia, and Western Asia; (2) Nearctic, including all North America
to the north of Tehuantepec in Mexico and excluding the southern
tip of Florida; (3) Neotropics, including Central America, the Car-
ibbean Islands, and South America; (4) Africa, defined as the whole
African continent including Madagascar, and Arabian Peninsula;
(5) Indomalaya, including India, southern China, and Wallacea;
and (6) Australasia, defined as everything east of Heilprin-
Lydekker's Line (Ali & Heaney, 2021). A time-stratified geo-
graphic model specified constraints on area connectivity by coding
0 if any two areas were not connected or 1 if they were connected
during a given period based on paleogeographic reconstructions.
Our first time slice covered the Late Cretaceous and early Palaeo-
cene (100.5-60 Ma), corresponding to a time when Gondwana
and Laurasia had already separated (de Lamotte et al., 2015). The
second time slice covered the late Paleocene to early Oligocene
(60-30 Ma), and the third the early Oligocene to the present (30-
0 Ma). To avoid biased results supporting ancestral areas in favour
of hyperdiverse biomes, our sampling per biogeographic area used
proportional sampling that reflected known diversity across biogeо-
graphic realms, with the Asian and American tropics hosting the
highest levels of species richness (Vitt et al., 2023). Our analyses
included 905 species distributed in the Neotropics (6% of total
known diversity in the area; Govaerts et al., 2021), 623 from the
Indomalaya region (6%), 347 from the Palearctic (5%), 221 from
the Afrotropics (9%), 180 from Australasia (15%), and 44 from
the Nearctic (10%).

Spatial analysis of species diversity and speciation rates

We queried 29 524 accepted orchid species names obtained from
the WCVP through the GBIF (https://www.gbif.org/) and
RAINBIO (https://gdauby.github.io/rainbio/index.html; Dauby
et al., 2016) databases. Our initial step involved accessing the
GBIF database through the R package SPOCC (available at
https://github.com/ropensci/spocc). Using the occ function, we
downloaded up to 1000 records for each queried species name,
ensuring that we only selected records linked to geographical
coordinates and preserved specimens. The RAINBIO repository
was manually accessed. Next, we conducted an automated filter-
ing using the SPECIESGEOCODER package (Töpel et al., 2017) to
exclude duplicated records and those located within urban areas.
Subsequently, we removed occurrences that fell outside global
coastlines. Lastly, to mitigate the influence of misidentified
records on downstream analyses (Maldonado et al., 2015), we fil-
tered out records for which the distribution did not match with
the species distribution per botanical country (level 3 of the
World Geographical Scheme for Recording Plant Distributions;
Brummitt, 2001) provided by the WCVP. The original record
database sourced from GBIF is available at doi: 10.15468/dl.
<figure><img src="image_6.png" /><figcaption>**Fig. 2** An overview of the pipeline used to produce ultrametric NGS-Sanger merged posterior probability species trees (PP species trees). The pipeline uses as input a custom set of randomly sampled MCMC trees derived from independently conducted molecular-clock dating analyses on the genus-level Angiosperms353 and species-level Sanger gene alignments. It then produces a custom set of species-level trees that reflect branch length variation and support in deep (tribe, subtribe and genus) and recent (species) time as informed by the Angiosperms353 and Sanger datasets. A detailed description of the pipeline is provided in the Supporting Information Methods S1.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Fig. 3** Phylogenetic relationships of Orchidaceae. (a) Consensus tree network inferred from 200 bootstrap replicate maximum likelihood (ML) trees derived from the concatenated alignment of 339 low-copy nuclear genes. Circles at nodes represent bipartitions present in > 75% of the bootstrap ML trees (see Supporting Information Figs S1–S6 for a detailed on the consensus tree network). Non monophyletic groups are highlighted in bold and grey. Samples sequenced from typological material are highlighted in bold and pink. (b) Number of bipartitions (bp) with different likelihood bootstrap support (LBS) values derived from an ML tree inferred from the 339 low-copy nuclear gene alignment. (c) Number of bp with different LBS values derived from 339 nuclear ML gene trees. Grey bars represent poorly supported bipartitions, whereas yellow bars represent moderately to maximally supported bipartitions. (d) Normalised Q-score value derived from an ASTRAL-III analysis, computed from 339 nuclear ML gene trees. Photos: Oscar A. Pérez-Escobar, Diego Bogarín, Sebastian Vieria, Kerry Dressler, William J. Baker.</figcaption></figure>

v2gwvx, and the filtered geographical records used in all down-
stream analyses will be available at doi: 10.6084/m9.fishshare.
22245940.

Estimating net diversification rates (speciation (λ) minus extinction (μ)) in plant lineages remains challenging (Louca & Pennell, 2021). Our approach focuses on speciation dynamics, as inferred from tip λ rates. This metric represents contemporary rates of speciation for a given lineage and is less prone to bias than net diversification rates (Title & Rabosky, 2019). To obtain tip λ rates, we fitted a time-dependent model using BAMM v.2.5.0 (Rabosky et al., 2013). This was informed by sampling fractions, contrasting accepted species number per genus from the WCVP database with the number of species sampled in our phylogenetic analyses. Here, 54% of the genera sampled included sequences from 10 to 50% of their known species, 21% included > 50%,

and 25% included < 10% of their known species. This analysis
was conducted on each of the 10 PP species trees generated by
pruning and grafting the genus- and species-level chronograms
produced by BEAST. We initially computed prior values for the
initial λ and shift parameters, using the R package BAMBTOOLS
(Rabosky et al., 2014), and performed 10 million generations,
sampling the MCMC simulations every 10 000 generations. The
BAMM output was analysed in BAMBtools to ensure that all
analyses reached convergence (ESS values > 100). Mean specia-
tion rates at present time (λ tip rates) were extracted for all term-
inals of the phylogenetic trees using the getTipRates function in
BAMBtools, and then linked to the filtered geographical species
occurrences.

Global patterns of species richness were inferred by calculating
the average number of species per political and botanical country,
<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /><figcaption>**Fig. 4** Biogeographic history of the orchid family. (a) Ancestral areas at nodes inferred on the 10 posterior probability species trees as estimated by a DEC model (see the *Materials and Methods* section) and summarised to the five orchid subfamilies (Inset: A summary of the outgroup sampling considered in our study (the MRCA of orchids is indicated with a black star)). (b) A palaeoclimatic and tectonic plate reconstruction at 90-80 Ma from Burgener *et al.* (2023) showing the possible ancestral range of the orchid MRCA as estimated by Givnish *et al.* (2016) and this study. A detailed account of the first and second most likely ancestral areas estimated to the tribe level is provided on Supporting Information Table S5 (complete results including annotated trees with the most likely ancestral area at nodes and alternative ancestral areas, likelihoods and probabilities are available at 10.6084/m9.fishshare.22245940).</figcaption></figure>
and per grid cell (100 km × 100 km), using the filtered occur-
rence records and the average and maximum λ rate values per
grid cell. All calculations were performed in the open-source

software QGIS v.3.0 (available at https://www.qgis.org/
en/site/forusers/download.html) and the R package KEWR
(Walker, 2022).

<figure><img src="image_10.png" /></figure>
**Fig. 5** Geography of speciation and species diversity of Orchidaceae. (a) Global patterns of species richness per grid cell (100 × 100 km), calculated from a curated database of geographical distribution records; reddish colours indicate higher numbers of species per grid cell whereas bluish colours indicate lower numbers of species per grid cell. A map of species richness per botanical country is provided in Supporting Information Fig. S10. (Inset: orchid species numbers reported for the 10 most orchid biodiverse countries). (b) Global patterns of mean λ tip rates (linear scale) per grid cell (100 × 100 km) as derived from the BAMM software; warm colours indicate higher λ tip rates whereas cold colours indicate lower λ tip rates (Inset: a histogram of the mean λ tip rates attained across all grid cells). (c) Global patterns of mean λ tip rates per ecological region, as defined by the WWF, derived from the BAMM software; warm colours indicate higher numbers of mean λ tip rates per ecological region whereas cold colours indicate lower mean λ tip rates. The highlighted geographical areas indicate the five ecoregions with the highest mean λ tip rates (Inset: Maximum and minimum λ tip rate values for ecoregions containing 100 or more geographical distribution records for which λ tip rates were linked).

Results

A new phylogenomic framework for the orchid family

This study combines high-throughput data with Sanger sequen-
cing data to achieve denser taxon sampling at relatively low cost.
Our MSC and ML analyses yielded a well-supported phyloge-
nomic framework for the Orchidaceae, containing 38% of the
currently accepted orchid genera (Figs 3, S1–S8; see Notes S1).
The 10 posterior probability (PP) species trees showed no major
conflicts with the MSC tree produced in ASTRAL, with the sole
exception of the position of *Corymborkis*, which in some instances
was recovered as nested within tribe Gastrodieae (Figs S7, S8).
Please refer to the Discussion section below for a detailed com-
parison of our topology with earlier phylogenomic studies.

Our results reveal the placements of the monospecific genus
*Cooktownia* D.L.Jones (Orchidoideae) and the small genus *Cla-
deria* (two species; Epidendroideae), distributed, respectively,
from the western Indian Ocean to the western Pacific and from
the south-western Pacific to New Zealand. Previous classifications
had placed *Cooktownia robertsii* in the tribe Orchideae, based on
stigmatic chamber characters (Jones, 1997). Our findings now
show that *Cooktownia* is most closely related to *Habenaria*
Willd., an almost cosmopolitan genus from subtribe Orchidinae.
*Claderia viridiflora* had been included in the predominantly Afri-
can subtribe Eulophiinae based on morphological similarities in
the gynostemium and unpublished nrITS sequences (Cribb &
Pridgeon, 2009). Our data place *Claderia* as sister to *Agrostophyl-
lum* Blume and *Earina* Lindl. (Agrostophyllinae, Epidendreae),
based on sequences from the type specimen of *Claderia viridiflora*
collected in 1867. Though the Angiosperms353 gene recovery
was predictably low for this old specimen, we successfully identi-
fied 40 genes, with 23 being informative (Table S1). The place-
ment as sister to *Agrostophyllum* was also found by Niissalo
et al. (2023) based on plastid genomes and the nrITS loci.

Molecular clock dating and biogeographic reconstructions
point to a Laurasian origin of Orchidaceae during the Late
Cretaceous

Our molecular clock models (Materials and Methods section)
estimated the stem age of Orchidaceae to 120 ± 6 Ma (Fig. S7)
and the crown age as 83 ± 10 Ma, corresponding to the Early
and Late Cretaceous, respectively. The results also indicate that
most orchid species originated over the past 5 million years
(Fig. S9). Comparison of stem ages and tip branch lengths

derived from the Angiosperm353 chronograms and crown node
ages of the Sanger chronograms indicated that age discordance
between both datasets was minimal (Methods S1; Figs S10, S11),
thus suggesting that absolute ages obtained from the 10 posterior
probability trees were reliable. Seven of the 10 biogeographical
reconstructions (conducted on the 10 PP species trees) supported
Laurasia (Nearctic + Palearctic) as the place of origin for the most
recent common ancestor of orchids (relative probability = 0.15–
0.30; Table S5; Fig. 4a). The three others supported Laurasia +
Neotropics or Gondwana (Neotropics + Australasia + Antarctica)
as the most likely place of origin for the most recent common
ancestor of orchids (relative probability = 0.14–0.19), followed
by Laurasia (relative probability = 0.14–0.18; Table S5).

Subsequent colonisation of other biogeographical realms
apparently occurred through long-distance dispersals, for exam-
ple, from Indomalaya to the Afrotropics in tribe Vandeae (Epi-
dendroideae) between 35 and 10 Ma, or through stepping-stone
processes from the Palearctic and Nearctic to the Neotropics in
tribe Cymbidieae (Epidendroideae, 30–20 Ma). The early diver-
sification of Orchidoideae appears to have taken place 55–40 Ma,
with dispersals to Australasia and the Palearctic and subsequent
*in situ* Neotropical diversifications (e.g. Cranichidinae), Nearctic
(Spiranthinae), Paleartic, Indomalaya (Goodyerinae) and Afro-
tropics (e.g. Disinae).

Orchid speciation in space and time

We downloaded 795 735 records from the GBIF and RAINBIO
repositories of which 495 755 accessions were retained after filter-
ing out duplicate records and inaccurate distributions sensu the
WCVP database (see Materials and Methods section). Analyses
of political country species richness indicated that Ecuador,
Colombia, and Papua New Guinea are the top three countries in
terms of species richness. Notably, seven out of 10 most orchid
species-rich countries are located in the Neotropics. Analyses
based on the botanical country species richness (as inferred from
the WCVP) yielded similar results (Figs 5a, insert, S12;
Table S6). An analysis of species richness per grid cell derived
from the curated GBIF-RAINBIO dataset showed that Central
America (especially Costa Rica) and the northern Andean region
(particularly Ecuador and Colombia) have the highest levels of
species richness. These geographical patterns of species richness
are in agreement with the species richness distributions indepen-
dently obtained through the WCVP database and support find-
ings of studies conducted at the family level (Vitt et al., 2023)
and in Orchidoideae (Thompson et al., 2023).
Geographical speciation (λ) patterns, as informed from tip λ
rates, did not always coincide with the areas of highest current
species richness. Notably, when considering minimum and maxi-
mum values of speciation rates, virtually all ecoregions assessed

here had one- or even two-fold variation in their speciation rates
(Fig. 5c, inset). Our speciation rate analyses unveiled multiple
accelerations within each subfamily with the fastest tip rates in
Orchidoideae and Epidendroideae, starting from the early

<figure><img src="image_11.png" /></figure>
**Fig. 6** Speciation dynamics of Orchidaceae. (a) One of 10 posterior probability species trees with modelled speciation ($\lambda$) rates along branches (blue colours denote low $\lambda$ rates, reddish colours indicate high $\lambda$ rates). Small circles at nodes denote the most recent common ancestors (MRCA) of orchid tribes sampled in this study. Numbers at nodes indicate the lineages with the highest $\lambda$ tip rates across the entire family. (b) Tip $\lambda$ rate values (maximum, minimum, mean) derived from the 10 posterior probability (PP) species trees for all terminals sampled. The dots are colour-coded by taxonomic groups (tribes and subfamilies). (Inset: boxplot of $\lambda$ tip rates summarised from the 10 PP species trees for the genera associated with the highest $\lambda$ tip rates (their respective totals of accepted species are provided)). Boxplots are colour-coded by tribes (the lower and upper whisker bounds were computed from the largest and smallest values that are within the 1.5 × interquartile range). Photos: Oscar A. Pérez-Escobar, Diego Bogarín, Kerry Dressler.

Miocene (Fig. 6a). These shifts coincided with the initial diversification of species-rich genera such as *Maxillaria* and *Dendrobium*. In other instances, however, rate increases preceded the divergence of species-rich and depauperate clades as seen with *Lepanthes* and *Lepanthopsis* or occurred within genera like *Bulbophyllum* and *Habenaria*. The lineages with the highest speciation rates are Neotropical epiphytes, including the tribe Epidendreae, and the subtribes Maxillariinae and Oncidiinae. The sole exceptions to this trend are the nearly cosmopolitan terrestrial genus *Habenaria* (Batista *et al.*, 2011) and the genera *Bulbophyllum* and *Dendrobium*, which are mostly epiphytic and distributed mainly in tropical Asia (Fig. 6b, Xiang *et al.*, 2016; Simpson *et al.*, 2022).

The most rapidly speciating lineages predominantly occur in south-eastern Central America where they span diverse habitats, from lowland dry and wet forests through cloud forests to high-elevation grasslands, as exemplified by genera like *Epidendrum*, *Lepanthes*, *Maxillaria*, *Pleurothallis*, and *Stelis* (Fig. 6a).

Discussion

Congruence of our global orchid phylogenomic framework with recent orchid phylogenomic studies

The relationships among subfamilies and tribes found here largely agree with the findings of Zhang *et al.* (2023), a study that used between 639 and 1195 nuclear genes from 610 species representing 297 genera. Notable exceptions include the monophyly of Bletiinae within Epidendreae, and the position of Eriopsidinae within Cymbidieae (fig. S2 in Zhang *et al.*, 2023). A detailed view of the summary trees produced by Zhang *et al.* (2023) reveals evidence of intragenomic conflicts (see fig. S3 in Zhang *et al.*, 2023): 46 bipartitions in the MSC trees attained low local posterior probabilities (< 0.5), and LBP (< 70%), suggesting that dominant alternative relationships exist for the branches in question. Zhang *et al.* (2023) recovered the subtribe Bletiinae as paraphyletic (i.e. *Bletia* and *Chysis* as successive sisters to Ponerinae, Laeliinae and Pleurothallidinae), a relationship also found, albeit with low support, by van den Berg *et al.* (2005) and Górniak *et al.* (2010). Unfortunately, voucher information and corresponding sequencing data of Zhang *et al.* (2023), first available online on February 2023) have not been made publicly available (at the time of writing), which limits the extent to which these phylogenomic discrepancies can be evaluated (PRJNA923320 accession code, queried on 26 November 2023 in https://www.ncbi.nlm.nih.gov/, returned zero results).

A Laurasian, Cretaceous origin of Orchidaceae

Our dating and biogeographic analyses inferred an origin of orchids in the late Cretaceous, in agreement with previous estimates (Chase, 2001; Ramírez *et al.*, 2007; Chomicki *et al.*, 2015; Givnish *et al.*, 2016; Zhang *et al.*, 2023), and suggested Laurasia as the likely place of initial diversification of the family. This matches the important role that Laurasia played during the late Cretaceous, fostering the initial diversification of many tropical and warm-temperate flowering plant and animal lineages. Notable examples include the palm family Arecaceae (Baker & Couvrouréur, 2012) and the yams, *Dioscorea* (Dioscoreaceae: Viruel *et al.*, 2015). During the late Cretaceous, Laurasian angiosperms were mostly herbs to small trees with early successional strategies restricted to unstable habitats (Wing & Boucher, 1998; Wing *et al.*, 2012). Subsequently, throughout the Paleogene, much of Laurasia had a monsoon-influenced humid subtropical climate that supported closed canopy, broad-leaved deciduous angiosperm forests that extended into high paleolatitudes beyond the Arctic Circle (Eiserhardt *et al.*, 2017; Korasidis *et al.*, 2022).

Proposing an initial diversification of the orchids in Laurasia contrasts with prior inferences of an Australian origin of the family, followed by entry into the Neotropics via Antarctica (Fig. 4b; Givnish *et al.*, 2016). Those results were due mostly to the outgroup sampling, which used nine species as representatives of Asteliaceae, Blandfordiaceae, Boryaceae, Hypoxidaceae, and Lanariaceae (most of them from Australia), plus one species of Iridaceae. The orchid stem node was, therefore, placed in Australia with a likelihood of *c.* 40%, in Australia + the Neotropics (linked via Antarctica) with *c.* 20%, and in other ranges with together *c.* 40%. Their orchid crown node was placed in Australia with a likelihood of *c.* 20%, in Australia + the Neotropics with *c.* 30%, and in all other ranges with together *c.* 50%. Our broader sampling of orchid groups and biogeographic analyses on multiple trees may lead to less phylogenetic and geographic uncertainty (Rangel *et al.*, 2015).

The subfamily Apostasioideae, sister to all other Orchidaceae and comprising only 16 species in two genera distributed in Japan, India, Nepal, Bhutan, Southeast Asia, New Guinea and northern Australia (Govaerts *et al.*, 2021; Li *et al.*, 2023), is here interpreted as having a relictual range. This would mirror the relictual ranges of numerous other plant groups that were widespread in the Eocene in Eurasia, but which survive only in tropical Southeast Asia today (Manchester *et al.*, 2009; Meseguer & Condamine, 2020). The pattern is known from at least 50 groups of gymnosperms and angiosperms that formerly occurred in Europe and/or North America (Manchester
et al., 2009). Another example in the Orchidaceae of an ancient
relictual range is the subfamily Cypripedioideae (slipper orch-
ids), with five genera and 169 extant species (136 Old World
and 33 New World). The ancestor of the slipper orchids likely
had a continuous distribution in the boreotropics from where
slipper orchids migrated southwards to both sides of the Pacific
Ocean due to the climate cooling in the late Cenozoic (Guo
et al., 2012; Fig. 4).

The Indomalayan region harbours c. 30% of the extant orchid
species, mostly within the Epidendroideae. The so-far oldest
orchid fossil is a hard epidendroid pollinarium found attached to
a fungus gnat preserved in Baltic amber, dated to 55–40 Ma (Poi-
nar & Rasmussen, 2017), documenting the presence of Epi-
droideae at high latitudes in the early Eocene. During this
period, evergreen forests covered northern Europe (Collinson &
Hooker, 2003), and epiphytism likely had already evolved in Epi-
droideae (Chomicki et al., 2015; Collobert et al., 2022). Our
biogeographic models, which did not use the Baltic amber polli-
narium as either a fossil or geographic constraint, inferred that
Epidendroideae were present in the Palearctic (55–50 Ma), con-
sistent with the fossil evidence. Hard pollinia are prevalent in
Epidendroideae and probably appeared early in the history of the
subfamily (e.g. in the MRCA of Sobralieae + remainder of Epi-
droideae; Dressler, 1990; Mosquera-Mosquera et al., 2019).

A Central American hotspot of orchid speciation

The discrepancy between the orchid's extent species richness and speciation rates agrees with the findings of a broad-scale study on global plant diversification rates and species richness, which are often unlinked (Tietje *et al.*, 2022). The low $\lambda$ tip rates in Australia can be attributed to the predominance of terrestrial orchids in southern Australia (Ackerman, 2019), which tend to have lower speciation rates than epiphytic orchids (Givnish *et al.*, 2015). Contrary to the findings of Pérez-Escobar *et al.* (2017), the northern Andes do not appear to host the fastest speciating orchid clades in the American tropics despite being one of the most species-rich areas world-wide (Pérez-Escobar *et al.*, 2022; Parra-Sánchez *et al.*, 2023). Instead, southern Mesoamerica, comprising the moist and seasonal forests of Costa Rica and Panama, has the highest orchid speciation rates per grid cell, matching its outstandingly high levels of species richness also of other groups (Myers *et al.*, 2000; Mittermeier *et al.*, 2011; Crain & Fernández, 2020; Fig. 5c; Table S7).

Central American cloud forests and high-elevation grasslands
are of Pliocene origin, making them some of the youngest Neo-
tropical biomes (Kirby, 2011). The Late Cenozoic shallow sub-
duction of the young hotspot Cocos ridge under the Panama
block (part of the Caribbean plate) is controlling the rapid uplift
of the Cordillera de Talamanca in south-eastern Costa Rica and
eastern Panama (Morell et al., 2011). This ongoing process
started between 5.5 and 3.5 Ma (Grafe et al., 2002), with elevated
mountain uplift rates of c. 1 km per 1 Ma (Driese et al., 2007).
The rapid creation of mountain ranges and valleys under a tropi-
cal climate could generate multiple microhabitats that might have
enhanced speciation rates. Additionally, the geographic position

of Costa Rica and Panama acts as a biological crossroads between
the two biodiversity hotspots of northern Mesoamerica and the
northern Andes, resulting in the current coexistence of northern
and southern groups (Burger, 1980; Kapelle, 2016). These find-
ings support and expand the hypothesis of Central American tro-
pical forests having evolved rapidly and recently (Cano
et al., 2022). Nevertheless, although our analyses of geographical
speciation rates offer new perspectives on the evolution of orchid
diversity, we urge caution in interpreting the relationship
between $\lambda$ and species richness (Tietje et al., 2022). Additionally,
our taxon sampling and species-level spatial distributions are
likely biased due to uneven collection efforts in certain regions,
such as the historically understudied New Guinea (Camara-Leret
et al., 2020).

Conclusions

This study places the initial diversification of orchids in Laurasia
during the Late Cretaceous (83 ± 10 Ma) and interprets the
modern geographic range of Apostasioideae, comprising 16 spe-
cies in Southeast Asia and Northern Australasia, as relictual. The
presence of Orchidaceae in Eurasia and North America during
the Paleogene, followed by extinction and survival in more south-
ern regions resembles the history of at least 50 genera of seed
plants that once occurred in Eurasia, but survive with just a few
species in tropical Southeast Asia. We reject the hypothesis that
Southeast Asia is the region with highest orchid speciation rates
(Givnish et al., 2016). Instead, our results show that southern
Central America, which contains 4.5% of the world's flora and
fauna in just 0.5% of its land surface, has been a hotspot for
orchid speciation since the Pliocene with the highest speciation
rates per grid cell on a global scale.

Acknowledgements

We thank three anonymous reviewers and the editor Marc-André
Selosse for critical comments that helped improve this paper.
This work was funded by grants from the Calleva Foundation to
the Plant and Fungal Trees of Life Project (PAFTOL) at the
Royal Botanic Gardens, Kew. OAPE acknowledges support from
the Sainsbury Orchid fellowship at the Royal Botanic Gardens,
Kew and the Swiss Orchid Foundation. OAPE is grateful to
David and Gill Mathers for valuable feedback. DB acknowledges
Comisión Institucional de Biodiversidad and Vice-rectory of
Research of the University of Costa Rica for issuing the permit
for access to genetic resources under project B8257. AZ acknowl-
edges Ministerio de Medio Ambiente of Colombia for issuing
collection and research permits (Res. No 007 from 14 Feb 2022,
Res. No 1070 from 28 Aug 2015 and Res. 01004 from 7 Jun
2019). KN acknowledges the Genomics for Australian Plants
consortium funded by Bioplatforms Australia (enabled by
NCRIS), the Ian Potter Foundation, Royal Botanic Gardens
Foundation (Victoria), Royal Botanic Gardens Victoria, the
Royal Botanic Gardens and Domain Trust, the Council of Heads
of Australasian Herbaria, CSIRO, Centre for Australian National
Biodiversity Research and the Department of Biodiversity,
Conservation and Attractions, Western Australia. AA acknowl-
edges financial support from the Swedish Research Council
(2019-05191), the Swedish Foundation for Strategic Environ-
mental Research MISTRA (Project BioPath), and the Kew Foun-
dation. We thank Sebastian Vieira and Kerry Dressler for
photographs of *Apostasia, Calochilus, Maxillaria* and *Thelymitra*,
and Pedro Toribio for the help provided during fieldwork.
OAPE and EPS dedicate this paper to Gamaliel Rios (1961), a
countryside farmer with an incredible knowledge on orchid tax-
onomy, and a source of inspiration to OAPE and EPS.

Competing interests

None declared.

Author contributions

OAP-E, DB, SSR, AA, and WJB were involved in conceptualisa-
tion. OAP-E, FLC, DB, and SB were involved in methodology
and formal analysis. OM, LS, and NASP were involved in data
curation. OAP-E, NASP, AZ, DB A-QH, BC, KN, MAC, and
LL were involved in resources. OAP-E, FLC, and SB were
involved in software. NASP, A-QH, KN, LS, ARZ, and LL
were involved in investigation. WJB, FF, IJL, AA, KN, MAC,
OAP-E, RPB, and RM were involved in funding acquisition.
OAP-E, DB, MC, LO, and SB were involved in visualisation.
OAP-E and SSR were involved in writing – original draft. OAP-
E, SSR, CJ, GC, AA, MC, JDA, RLT, FLC, DB, NASP, WJB,
with further contributions from AVM, BG, BSVT, CP, CvdB,
EH, EP-S, ES, GAS, JAB, JAC, JMH, MF, MFF, MR, MWC,
NSF, PC, RN, and RS-G were involved in writing – review and
editing. OAP-E, DB, and NASP are lead authors on this work.
OAP-E, MWC, MFF, FLC, FF, KN, SSR, WJB, and AA are
senior authors on this work.

ORCID

Alexandre Antonelli ID https://orcid.org/0000-0003-1842-9297

William J. Baker ID https://orcid.org/0000-0001-6727-1831

Sidonie Bellot ID https://orcid.org/0000-0001-6355-237X

Diego Bogarín ID https://orcid.org/0000-0002-8408-8841

Martha Charitonidou ID https://orcid.org/0000-0002-9657-8362

Guillaume Chomicki ID https://orcid.org/0000-0003-4547-6195

Fabien L. Condamine ID https://orcid.org/0000-0003-1673-9910

Nicola S. Flanagan ID https://orcid.org/0000-0002-4909-8710

Barbara Gravendeel ID https://orcid.org/0000-0002-6508-0895

Carlos Jaramillo ID https://orcid.org/0000-0002-2616-5079

Ilia J. Leitch ID https://orcid.org/0000-0002-3837-8186

Robert Müntz ID https://orcid.org/0000-0001-9635-2884

Olivier Maurin ID https://orcid.org/0000-0002-4151-6164

Katharina Nargar ID https://orcid.org/0000-0002-0459-5991

Oscar A. Pérez-Escobar ID https://orcid.org/0000-0001-9166-2410

Edicson Parra-Sánchez ID https://orcid.org/0000-0003-2670-3882

Charlotte Phillips ID https://orcid.org/0000-0002-2200-9677

Natalia A. S. Przelomska ID https://orcid.org/0000-0001-9207-4565

Susanne S. Renner ID https://orcid.org/0000-0003-3704-0703

Eric Smidt ID https://orcid.org/0000-0002-1177-1682

Alejandro Zuluaga ID https://orcid.org/0000-0002-5874-6353

Data availability

The data that support the findings of this study are openly avail-
able in https://treeoflife.kew.org/specimen-viewer, the Sequence
Archive Repository of the NCBI (https://www.ncbi.nlm.nih.
gov/, BioProject number PRJNA1037538). Angiosperms353
alignments, sampling fractions per genera employed in BAMM,
the 10 PP species trees and their corresponding ancestral area esti-
mations and speciation rate analyses are provided in FigShare link
at 10.6084/m9.fishshare.22245940.

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Supporting Information

Additional Supporting Information may be found online in the Supporting Information section at the end of the article.

**Fig. S1** A detailed view of phylogenetic relationships between the Apostasioideae, Vanilloideae, and Cypripedioideae subfamilies.

**Fig. S2** A detailed view of phylogenetic relationships within the Orchidoideae subfamily.

**Fig. S3** A detailed view of phylogenetic relationships between early diverging Epidendroideae lineages.

**Fig. S4** A detailed view of phylogenetic relationships within the Cymbidieae.

**Fig. S5** A detailed view of phylogenetic relationships within the Vandeae.

**Fig. S6** A detailed view of phylogenetic relationships within the Epidendreae.

**Fig. S7** A genus-level chronogram of the Orchidaceae produced from 25 low-copy nuclear, clock-like genes in 339 samples.

**Fig. S8** Species-level Maximum Clade Credibility ultrametric tree of the orchid family, derived from the grafting of 500 MCMC trees produced from a *matK*-ITS supermatrix (1940 samples) onto 500 MCMC trees produced from 25 clock-like genes (339 samples).

**Fig. S9** The age of modern orchid diversity (genera and species) as inferred from branch lengths obtained from the 10 PP species trees.

**Fig. S10** Genera crown and stem node age congruence in the Sanger species-level and NGS genus-level consensus phylogenies (as shown in Figs S7, S8), and the frequency of stem and crown

node ages obtained, respectively, from the 10 randomly sampled NGS genus-level and Sanger species-level posterior probability trees.

**Fig. S11** Dot plot and linear regression Pearson tests of terminal lengths (Mega annum (Ma)) and MRCA ages (Ma) from Maximum Clade Credibility (MCC) consensus trees inferred from 10 PP species trees and 500 PP species trees (same tree presented on Fig. S8).

**Fig. S12** Global patterns of species richness per botanical country calculated from the World Checklist of Vascular Plants database.

**Methods S1** Extended methods on high-throughput sequencing data, gene tree incongruence, molecular clock dating and species-level phylogeny assembly analyses.

**Notes S1** Extended results and discussion on phylogenetic relationships of the Orchidaceae and stem and crown node absolute time discordance.

**Table S1** Voucher information, SRA accession nos., taxonomic rank, and proportion of informative and missing Angiosperms353 sequences of the plant material included in the construction of the NGS phylogenomic backbone.

**Table S2** GenBank accession nos. of the samples mined from GenBank and retained for subsequent analysis.

**Table S3** Root-to-tree variance, tree length, and bipartition support of the Angiosperm353 genes as inferred by SortaDate.

**Table S4** The number of informative and missing sequences included in the inference of absolute age estimation analyses for the genus-level orchid backbone.

**Table S5** Ancestral areas and their corresponding probabilities of key nodes of the orchid species PP trees, as inferred by dispersal-extinction-cladogenesis.

**Table S6** Orchid species richness per botanical country as calculated from the WCVP.

**Table S7** Maximum, minimum, and mean tip speciation rates derived from the 10 PP species trees using the software BAMM.

Please note: Wiley is not responsible for the content or functionality of any Supporting Information supplied by the authors. Any queries (other than missing material) should be directed to the *New Phytologist* Central Office.