Systematics and biogeography of the Holarctic dragonfly genus Somatochlora (Anisoptera: Corduliidae)

The Striped Emeralds (Somatochlora) are a Holartic group of medium-sized metallic green dragonflies that mainly inhabit bogs and seepages, alpine streams, lakes, channels and lowland brooks. With 42 species they are the most diverse genus within Corduliidae (Odonata: Anisoptera). Systematic, taxonomic, and biogeographic resolution within Somatochlora remains unclear, with numerous hypotheses of relatedness based on wing veins, male claspers (epiproct and paraprocts), and nymphs. Furthermore, Somatochlora borisi was recently described as a new genus (Corduliochlora) based on 17 morphological characters, but its position with respect to Somatochlora is unclear. We present a phylogenetic reconstruction of Somatochlora using Anchored Hybrid Enrichment (AHE) sequences of 40/42 Somatochlora species (including C. borisi). Our data recovers the monophyly of Somatochlora, with C. borisi recovered as sister to the remaining Somatochlora. We also recover three highly supported clades and one of mixed support; this lack of resolution is most likely due to incomplete lineage sorting, third codon position saturation based on iterative analyses run on variations of our dataset, and hybridization. Furthermore, we constructed a dataset for all species based on 20 morphological characters from the literature which were used to evaluate phylogenetic groups recovered with molecular data; the data support the validity of Corduliochlora as a genus distinct from Somatochlora. Finally, divergence time estimation and biogeographic analysis indicate Somatochlora originated in the Western North Hemisphere during the Miocene, with three dispersal events to the Eastern North Hemisphere (11, 7, and 5 Ma respectively) across the Beringian Land Bridge.

Methods
Taxon Sampling

We acquired specimens of Somatochlora from natural history collections. Specimens sampled from collections in the American Museum of Natural History (AMNH), Florida State Collection of Arthropods (FSCA), Naturalis Biodiversity Center (RMNH), National Museum of Natural History Museum (NMNH), and Monte L. Bean Life Sciences Museum at Brigham Young University (BYU). In total, we sampled 40 of the 42 current species of Somatochlora. We selected a large number of outgroups to establish a robust phylogenetic placement for the Somatochlora. We sampled outgroups from Corduliidae (Hemicordulia, Procordulia, Guadalca, Paracordulia, Neurocordulia, Navicordulia, Helocordulia, Cordulia, Epitheca, Metaphya, Pentathemis, Aeschnosoma, and Antipodochlora), other established families within the superfamily Libelluloidea (Libellulidae: Pantala, Libellula, Orthetrum, Macromiidae: Macromia, Epophthalmia, Synthemistidae: Eusynthemis, Choristhemis, Gomphomacromia), and families within Cavilabiata (Chlorogomphidae: Chlorogomphus, Cordulegastridae: Cordulegaster, Anotogaster, Neopetalia: Neopetalia punctata). Specimen provenance data including locality, date, author, collector, and determiner, are listed in Supplemental Table S1.

DNA Extraction and Sequencing: 

We removed the hind leg from individual specimens of each species using sterilized forceps, and extracted DNA using ZYMOBIOMICS DNA miniprep kits (Irvine, CA). We quantified DNA yield using a Qubit 4 fluorometer, and sent DNA extracts to RAPID Genomics (Gainesville, Florida) for library preparation and sequencing. Loci were amplified using Anchored Hybrid Enrichment (AHE) probes modified from Bybee et al. (2021), consisting of 1,306 loci (Goodman et al. 2023). Probes sets were originally created by scanning for 941 exons commonly shared across insects using published data from 24 odonate transcriptomes (Futahashi et al. 2015, Suvorov et al. 2017) as well as two assembled genomes from Bybee et al. (2021). An additional 211 functional loci were sequenced, focusing on vision, flight, and immunity (Bybee et al. 2021; Goodman et al. 2023). We sequenced loci of representatives of each genus using the full 1,306 probe set (500kb), while a subset of 92 loci (20kb) was sequenced for the remaining species. Raw AHE reads can be obtained from Dryad digital repository number: https://datadryad.org/stash/share/nII28qPnpxLswmsOQC0Nxj6JP_HXeF1DmaI0H8vgZlU, 

while loci coverage for each species can be obtained from Supplemental Table S1. 

AHE Assembly and Analysis:

We trimmed adaptors from raw reads using fastp (Tang and Wong 2001) and checked for quality using multiQC (Ewels et al. 2016). We followed methods outlined in (Breinholt et al. 2018) to assemble and assign orthology to each target capture locus with a few modifications. In brief, we assembled each locus individually using iterative baited assembly with SPAdes (Prjibelski et al. 2020) and reference loci from the chromosome-length genome assembly of Tanypteryx hageni (Petaluridae) (Tolman et al. 2023b). We then screened each locus for orthology by first ensuring that the locus did not have BLAST hits to multiple places in the genome, and secondly, by ensuring best reciprocal hits between the reference and the query sequence. We performed subsequent analyses using assemblies of the probe region of our loci, as preliminary analyses recovered increased noise and reduced phylogenetic support using probe + flanking regions if a low number of loci was recovered from any taxon, as well as flanking regions expressing high variability in alignment.

Phylogenetic Analysis:

We generated multiple sequence alignments for each locus using the ‘MAFFT-linsi’ algorithm in MAFFT v.7.475 (Katoh and Standley 2013), and trimmed alignments using a 0.75 threshold cutoff using trimAI v1.2 (Capella-Gutiérrez et al. 2009). We concatenated the alignment using FASconCAT v1.11 (Kück and Meusemann 2010), and generated an initial partitioning scheme using relaxed clustering with the model fixed to GTR + G for each subset in IQtree v2.1.3 (Minh et al. 2020b). We then selected the best nucleotide substitution model for each subset in the partitioning scheme using ModelFinder and estimated a maximum likelihood tree (ML). We estimated branch support using SH-like approximate likelihood ratio tests (SH-aLRT) and 1,000 ultrafast bootstrap replicates (UFboot) in IQtree v2.1.3 (Guindon et al. 2010, Kalyaanamoorthy et al. 2017). To assess the degree of incomplete lineage sorting (ILS), we first reconstructed ML trees for each locus with 1,000 ultrafast bootstrap replicates and performed a coalescent-based species tree estimation in ASTRAL2 v5.6.1 using local posterior probabilities (LPP) (Mirarab and Warnow 2015). As an additional metric to assess the degree of ILS as well as introgression (hybridization), we calculated gene concordance factors (gCF) and site concordance factors (sCF), using our concatenated ML tree and our loci trees (Minh et al. 2020a); gCF and sCF calculates the proportion of genes and informative sites respectively, which support the bipartition (split) defined by the branches within our ML tree (Minh et al. 2020a). We identify nodes of high support possessing bootstrap (BS) and SH-aLRT values > 90, and LPP values >0.90. We identify regions of high gene and site concordance (gCF and sCF respectively) possessing values >0.7. We rooted the tree using Neopetaliidae.

Post-Hoc Modifications of Phylogeny

Preliminary phylogenetic analyses recovered S. georgiana (Coppery Emerald) as sister to Libellulidae with mixed support (SH-alrt: 100 UFBoot: 100, LPP: 0.39).  We checked for contaminants among aligned loci sequenced for S. georgiana by blasting them to the National Center for Biotechnology Information (NCBI) Basic Local Alignment Search Tool (BLAST) Database using the blastn command (Chen et al. 2015). Top predicted identity blast hits (percent identity) for our loci belonged to annotated assemblies of Zygoptera including Ischnura elegans (Price et al. 2022), Pantala flavescens (Liu et al. 2022), as well as other arthropod genomes including Megalopta genalis (Hymenoptera: Halictidae), Zootermopsis nevadensis (Blattodea: Archotermopsidae), Cryptotermes secundus (Blattodea: Kalotermitidae), and Neodiprion fabricii (Hymenoptera: Tenthredinoidea) (Terrapon et al. 2014, Jones et al. 2015, Kapheim et al. 2020, Lin et al. 2021, Herrig et al. 2023). Furthermore, 24 of our loci did not recover any blast hits, most likely due to locus fragment motifs being unique among Anisoptera (Bybee et al. 2021, Goodman et al. 2023), and lacking arthropod homologues within Genbank. However, S. hineana was recovered as the top predicted blast hit for two legacy genes (Cytochrome Oxygenase 1: Locus 2001, and Cytochrome Oxygenase b: Locus 2000), suggesting genes are still usable to place S. georgiana within the genus. To utilize S. georgiana within our analysis, we first reconstructed ML trees for each locus with 1,000 ultrafast bootstrap replicates. Using a custom-built python script, we 1. rooted each gene tree with Neopetaliidae, 2. determined the most closely related taxa to S. georgiana using branch length as a proxy, 3. calculated the bootstrap support values of S. georgiana + sister taxon, 4. retained the sequences of S. georgiana from loci where it is sister to a Somatochlora species with high bootstrap support, 5. realigned, trimmed, and concatenated our revised locus list as outlined in our methods 6. reran our ML phylogenetic tree in IQtree. We verified the results of our code through visual inspection of our gene trees. In total, we retained 37/92 loci for S. georgiana, recovering it within Somatochlora with high support across metrics (SH-alrt: 100 UFBoot: 100, LPP: 1.0). As this method of loci pruning is new, and heavily dependent on correct taxonomy of species, we verified our specimen of S. georgiana using dichotomous keys from Garrison et al. (2006) and Walker (1925). All subsequent analyses utilize this new pruned-loci dataset, which herein will be referred to as our ‘no-coded’ dataset.

Preliminary analyses also recovered discrepancies in topology of Somatochlora between our no-coded ML and ASTRAL trees suggesting ILS and hybridization, as also made evident by our low gCF and sCF scores across Somatochlora (Fig. 1). However, we also hypothesize that these differences are the result of 3rd codon site saturation, where the signal to noise ratio is skewed, reducing resolution (Yang 1996, Simmons et al. 2006, Parvathy et al. 2022). Codon usage bias has been more commonly observed high throughput molecular datasets as well as insect genomes (Behura and Severson 2012, 2013, Breinholt and Kawahara 2013, Sharma and Uddin 2014, Galtier et al. 2018). To test for 3rd codon site saturation, and using another custom-built python script, we conducted two analyses. We set the reading frame of our no-coded alignments of all our loci to reduce the amount of stop codons. Next, we 1. replaced the third-codon position to ‘R’ for purine nucleotides (A and G), and ‘Y’ for pyrimidines (C and T) across all our loci (herein referred to as ‘RY-third’ analysis) 2. recoded all nucleotides as R’s or Y’s for all of our loci (herein referred to as ‘RY-all’ analysis). 3. for each of these two datasets, we realigned, trimmed, and concatenated our loci as outlined in our methods. 4. reconstructed new ML and ASTRAL trees for both of our RY-coded loci datasets. RY-coding of nucleotides is common practice to reduce noise in data as it reflects signal coming from transitions and transversions (Woese et al. 1991, Phillips et al. 2004, Harshman et al. 2008, Kück and Meusemann 2010, White et al. 2011, Chen et al. 2014, Timmermans et al. 2016, Simmons 2017). Custom-built python and bash scripts were created with the assistance of ChatGPT (OpenAI. 2023) and provided in the Supplemental Information.

Fossil Selection and Time Divergence Analysis:

Taxonomy of odonate fossils relies predominantly on wing characters due to their high preservation potential, and plethora of venational traits (Fraser and Tillyard 1957). However, most researchers acknowledge that wing venation is highly prone to convergence and should be used in conjunction with other traits if available (Fraser and Tillyard 1957, Gloyd 1959, Hennig 1981, Fleck et al. 2008). Amber fossils of adult and nymphal Odonata are rare in the fossil record (Wighton and Wilson 1986, Bechly 1996, Karr and Clapham 2015, Schädel and Bechly 2016, Zheng and Jarzembowski 2020, Boudet et al. 2023)(See table 1 in Schaedel et al. (2020)) (paleodb.com), limiting analyses pertaining to accessory genitalic, thoracic, penile, or nymphal traits.  Kohli et al. (2016) published a list of vetted fossil calibrations for Odonata, as part of the Fossil Calibration Database (fossilcalibrations.org), providing recommendations for fossil selection covering the breadth of taxa in our phylogeny. We chose fossil calibrations for the crown nodes for Cavilabiata, Macromiidae, Corduliidae, Libellulidae, and Corduliidae + Libellulidae. Phylogenetic and age justifications for divergence time estimation of our fossils are outlined in Kohli et al. (2016) and (Kohli et al. 2021b) (Table 1).

Fossil Validation

We surveyed two additional tentative Somatochlora fossils as calibration points, extending our sampling beyond Kohli et al. (2021b). We used the five principles outlined by (Parham et al. 2012) and Ksepka et al. (2015) as best calibration practices. In brief, the five criteria are as follows: 1. Fossil accession number for fossil and referrals, 2. Apomorphy-based or phylogenetic analysis, 3. Reconciliation of morphological and molecular data, 4. Locality and stratigraphic data for fossil taxa 5. Radioisotopic age or numeric age references for fossils.

Two putative fossils of Somatochlora exist, the older being S. oregonica Cockerell, 1927 from Central Oregon, which is estimated to be Oligocene in origin (33.9 – 28.4 Ma). However, we are skeptical of this identification since the fossil is only of the upper half of either the forewing or hindwing, from the nodus to the pterostigma, possessing the first radial vein (R1), and the first and second medial veins (M1 & M2) (Needham et al. 2000). Within his diagnosis,  Cockerell (1927) draws similarities of the fossil to other species of Somatochlora by the first two postnodal cross veins distal to the pterostigma being obliquely angled, while the subnodal veins are perpendicular. Cockerell (1927) also states that doubling of cells occurs at the 7th cross vein between R1 and M2. However, these traits are quite variable among North American Somatochlora (Walker 1925, Needham 1930, Walker and Corbet 1975, Needham et al. 2000, Garrison et al. 2006). Cockerell (1927) also states that the first subnodal cell is very long, roughly equal to three postnodal cells. Although this is the case with some Somatochlora species, other North American corduliid genera also possess this trait including Dorocordulia, Epitheca, Helocordulia, and Neurocordulia (Needham et al. 2000, Garrison et al. 2006). Finally, Cockerell (1927) states that at the second cell below the pterostigma possesses an oblique cross vein which can be found in S. arctica. However, no Somatochlora species, including S. arctica possess a second cross vein below the pterostigma. Overall, we exclude this fossil for calibration, as the traits mentioned do not provide strong enough synapomorphies to place the fossil into Somatochlora.

The second fossil, Somatochlora brisaci (Nel et al. 1996) is significantly younger, with a Miocene origin (8.7 – 5.3 Ma), discovered in a deposit from Southeastern France. The fossil is a near-complete hindwing except for a few posterior regions of the wing margin missing near the third and fourth medial veins, the cubital (C) and anal (A) veins. Within the description, Nel et al. (1996) performed a parsimony analysis on the fossil, comparing it to other corduliid, synthemistid, and macromiid genera. The authors conclude that the S. brisaci does seem to be related to Somatochlora, but forms an unresolved trichotomy with the genera Antipodochlora and Guadalca. Somatochlora brisaci was temporarily attributed to Somatochlora because the arculus is midway between the first two antenodal crossveins, but the authors acknowledge several key traits which separate it from recent species; the most noticeable being the triangle possessing four cells (See Supplemental Table S2 and Appendix I).

Using our no-coded ML phylogeny, we performed several additional morphological analyses to verify the fossil placement of S. brisaci. Using two independent lists of wing trait characters from (Nel et al. 1996) and Ware (2008) we scored the wings of all extant species within our phylogeny. We traced each trait onto our ML phylogeny using Mesquite v3.81 (Maddison and Maddison 2007) using a parsimony-based reconstruction of character history, retaining characters which possessed high phylogenetic signal within families and genera, and excluding the ones which exhibited homoplasy (Retention Index > .70) (Farris 1989, Kälersjö et al. 1999). After removing homoplasious characters, we then combined both revised Ware et al. and Nel et al datasets; if the two datasets had similar characters, such redundancies were removed. We then scored the wing traits of S. brisaci using this morphological trait set and performed phylogenetic analyses using parsimony in TNT v1.6. (Goloboff et al. 2008), Bayesian Inference (BI) using MrBayes v3.2 (Huelsenbeck and Ronquist 2001), and Maximum Likelihood (ML) using IQtree v2.1.3 (See Supplemental Information). We applied an MK model of discrete character evolution for our BI and ML analyses (Lewis 2001).

Since fossil choice and placement can drastically affect the outcome of divergence time analysis (Kohli et al. 2021b), we ran divergence time estimation under four different scenarios. The first scenario designated as ‘No Fossil’ we exclude S. brisaci from our analysis for a total of six remaining fossil calibrations. The second scenario designated as ‘Somatochlora node’ we place S. brisaci on the node of the Somatochlora. The third scenario designated as ‘Guadalca node’ we place S. brisaci on the node of Guadalca. The fourth scenario designated as ‘Antipodochlora node’, we place S. brisaci on the node of Antipodochlora (Table 1).

All divergence time analyses were conducted on the nucleotide dataset in MCMCtree as implemented in the software package PAML v.4.7a (Yang 2007) using an ultrametric (equal branch lengths) version of our no-coded ML tree. We used our full unpartitioned dataset due to computational limits since our dataset consists of over 1000 loci. Fossil calibrations were set using uniform prior distributions with hard upper and lower bounds (Table 1). Our root maximum age was set at 158.1 million years, based on the earliest fossil within Cavilabiata (Juralibellula ningchengensis) (Huang and Nel 2007). We set default parameters for defining prior distribution and used the General Time Reversible (GTR) nucleotide substitution model for calculating the hessian matrix for our dataset. For each scenario, we performed two independent MCMC runs with 500,000 iterations, sampling every 100 trees with a 2000 tree burn-in, and checked for convergence using Tracer v. 1.6 (Drummond and Rambaut 2007). Finally, we examined prior distributions of each run to ensure reasonable fossil choices and placement on the tree (Warnock et al. 2012). Divergence time estimates and outputs from all the four scenarios are provided in our Supplemental Information.

Biogeographic Analysis:

We estimated the ancestral range of Somatochlora excluding outgroups with the maximum likelihood R package BioGeoBears v1.1.2 (Matzke 2013) using our no-coded Bayesian-estimated time-calibrated phylogeny from MCMCtree. We chose BioGeoBears due to its customization of dispersal rates, time stratification events, and comparison of different likelihood and Bayesian dispersal models. Furthermore, BioGeoBears incorporates a new parameter called the founder-event speciation (J), which allows for the possibility that a new population could colonize a new area via a ‘jumping dispersal event’ (Matzke 2013). We conducted the analysis three ways: (1) using the Dispersal Extinction Cladogenesis (DEC, DEC + j) model (Ree and Smith 2008), (2) using a likelihood implementation of the Dispersal-vicariance analysis (DIVA, DIVA + j) (Ronquist 1997), and (3) using a Bayesian-like implementation of area estimation (BayArea-like, BayArea + j) (Matzke 2013). Although (Ree and Sanmartín 2018) highlight conceptual and statistical issues with the DEC + j model, recent work has validated +j models as valid in AICc comparisons (Matzke 2022).  Wallace’s biogeographic regions are commonly used when estimating ancestral ranges for insects, (Lohman et al. 2011, Toussaint et al. 2019a, Toussaint et al. 2019b, Toussaint et al. 2021a, Toussaint et al. 2021b, Tseng et al. 2022, Kawahara et al. 2023), but delineations among biogeographic regions may vary across studies; this is for many reasons, such as variable dispersal among taxa, and many insect taxa have evolutionary histories that predate modern continents (Olson et al. 2001, Holt et al. 2013). As such, we chose modified Wallacean biogeographic regions which are not only hypothesized as being broad geographic ranges of Somatochlora (Walker 1925, Walker and Corbet 1975, Allen et al. 1985), but have been used previously in inferring biodiversity of Odonata in the Nearctic and Palearctic regions (Abbott et al. 2022, Kalkman et al. 2022). We used 3 geographic ranges, A. IndoMalay region (Referred to as Indo-Malay in Olson et al. (2001) including the Chinese provinces of Sichuan, Hubei, Anhui, and Jiangsu), B. Eastern North Hemisphere (defined as Palearctic by Olson et al. (2001)), and C. Western North Hemisphere (defined as Nearctic by Olson et al. (2001), which does not include the three mountain ranges of northern and central Mexico.

We tested for statistical differences of constrained versus unconstrained models (ex: DIVA and DIVA + j), using the p-value of likelihood ratio test (LRT). We determined the most optimal model for explaining our data using the criterion of the highest negative log-likelihood (-LnL), and the lowest Akaike criterion (AIC). Furthermore, we performed an unstratified analysis (without areas allowed/adjacency files, dispersal matrices, maximum number of areas, and dispersal multipliers) due to the wide geographic range of Somatochlora.

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