DOI: 10.5061/dryad.905qftttq
title: Data from: Non-adaptive radiation promotes phenotypic diversification and convergent evolution of aposematic mimicry in a highly diverse genus of Megaloptera

description: Evolutionary radiations are considered key processes underlying the origin
 of biodiversity. Notably, the mechanisms driving these radiations can vary
 across organisms and often involve a complex interplay of abiotic and
 biotic factors. Empirical studies on evolutionary history are crucial for
 validation of multiple hypothesis regarding the mode of evolutionary
 radiations. Within the obligatory aquatic insect order Megaloptera, the
 genus Protohermes is the most speciose clade with 90 described species,
 accounting for around 22 % of the total ordinal diversity. Protohermes
 species are featured by the limited dispersal ability, primarily occurring
 across the Oriental region, and a range of diversified phenotypes, e.g.,
 highly divergent genital characters, and mimetic coloration alongside
 shifts in biological rhythm—from nocturnal to diurnal activities. Here, we
 infer the spatiotemporal mode of diversification and associated driving
 factors of the Protohermes radiation as a test case for exploring the
 processes and potential mechanisms of evolutionary radiations. We present
 the first time-calibrated phylogeny of Protohermes using genome-scale data
 of ultraconserved elements (UCEs) and mitochondrial genes with a
 comprehensive taxon sampling. Our results reveal a mid-Cretaceous stem age
 of Protohermes, followed by a recent and steady diversification during the
 Neogene. Estimation of historical biogeography suggests the genus likely
 originated from a broad range including the Himalaya-Hengduan Mountains +
 Indochina + Borneo, with the first two areas serving as the center of
 early diversification. Our results further suggest that vicariance events,
 likely attributed to the Cenozoic Himalayan orogeny as well as climate
 change in East Asia, triggered speciation that coincided with the
 accumulation of genital divergence. Further enhancement of genital and
 phenotypic diversification may have been promoted by secondary contacts of
 allopatric or parapatric lineages following the build-up of species
 richness, likely facilitating species coexistence and lineage
 accumulation. We argue that the current species diversity of Protohermes
 likely resulted from a non-adaptive radiation. Our results highlight the
 role of geographic vicariance and sexual selection in driving the species
 and phenotypic diversification in insects.
description: # Data Files for "Non-adaptive radiation promotes phenotypic
 diversification and convergent evolution of aposematic mimicry in a highly
 diverse genus of Megaloptera" ## Authors * **Data Author**: Yuezheng
 Tu * Contact: [tuyuezheng@126.com](mailto:tuyuezheng@126.com) * **Article
 Authors**: Tu Y.Z., Li X.K., Hayashi F., Zhang F., Yang D., Condamine
 F.L., Liu X.Y. ## Date Created 2025-04-22 --- ## Data Directory Structure
 **Data/** ├── **1.1-assemblies/** # de novo assembled genomes (.fa) used
 in the present study ├── **1.2-UCE-probe/** │ └──
 **Protohermes-v1-master-probe-list-DUPE-SCREENED.fasta** #
 lineage-specific UCE probe set deseigned for the present study ├──
 **1.3-matrix_alignments/** │ ├── **1.3.1-mtPRO/** *#* algnments of
 mitochondrial protein-coding genes in the mtPRO matrix │ ├──
 **1.3.2-uceCORY90/** *#* algnments of UCE loci in the uceCORY90 matrix │
 ├── **1.3.3-ucePRO70/** *#* algnments of UCE loci in the ucePRO70 matrix │
 └── **1.3.4-ucePRO90/** *#* algnments of UCE loci in the ucePRO90 matrix
 ├── **2.1-phylogeny_IQTree/** *#* concatenated alignments (.fas) and
 partitioning schemes (.txt) used for phylogenetic reconstruction in
 IQ-Tree 2 and their results (.treefile) │ ├── **2.1.1-Corydalinae/** │ │
 ├── **uceCORY90_FcC_supermatrix.fas** # concatenated matrix for the
 uceCORY90 dataset │ │ ├── **uceCORY90_partition_by_locus.txt** #
 partitioning scheme (by UCE locus) for maximum likelihood (ML)
 phylogenetic inference │ │ ├── **uceCORY90_partition_by_locus.treefile** #
 result from IQ-TREE 2 (dataset partitioned by locus) │ │ ├──
 **uceCORY90_partition_by_swsc-en.txt**  # partitioning scheme (by SWSC-EN)
 for ML phylogenetic inference │ │ └──
 **uceCORY90_partition_by_swsc-en.treefile** # result from IQ-TREE 2
 (dataset partitioned by SWSC-EN) │ └── **2.1.2-Protohermes/** │ ├──
 **2.1.2.1-ucePRO70/** │ │ ├── **ucePRO70_FcC_supermatrix.fas** #
 concatenated matrix for the ucePRO70 dataset │ │ ├──
 **ucePRO70_partition_by_locus.txt** # partitioning scheme (by UCE locus)
 for ML phylogenetic inference │ │ └──
 **ucePRO70_partition_by_locus.treefile**  # result from IQ-TREE 2 (dataset
 partitioned by locus) │ ├── **2.1.2.2-ucePRO90/** │ │ ├──
 **ucePRO90_FcC_supermatrix.fas** # concatenated matrix for the ucePRO90
 dataset │ │ ├── **ucePRO90_partition_by_locus.txt** # partitioning scheme
 (by UCE locus) for ML phylogenetic inference │ │ ├──
 **ucePRO90_partition_by_locus.treefile**  # result from IQ-TREE 2 (dataset
 partitioned by locus) │ │ ├── **ucePRO90_partition_by_swsc-en.txt** #
 partitioning scheme (by SWSC-EN) for ML phylogenetic inference │ │ └──
 **ucePRO90_partition_by_swsc-en.treefile** # result from IQ-TREE 2
 (dataset partitioned by SWSC-EN) │ ├── **2.1.2.3-ucePRO90+coi/** │ │ ├──
 **ucePRO90swsc+coi_supermatrix.fas** # concatenated matrix for the
 ucePRO90+coi dataset, including UCE loci and the mitochondrial *COI* gene
 │ │ ├── **ucePRO90swsc-en+coi_supermatrix_partition.txt** # partitioning
 scheme (UCE loci by SWSC-EN) for ML phylogenetic inference │ │ └──
 **ucePRO90swsc-en+coi_supermatrix_partition.treefile** # result from
 IQ-TREE 2 │ └── **2.1.2.4-mtPRO/** │ ├── **mtPRO_FcC_supermatrix.fas** #
 concatenated matrix for the mtPRO dataset (including 13 protein-coding
 mitochondrial genes) │ ├── **mtPRO_partition.txt** # partitioning scheme
 (by gene locus) for ML phylogenetic inference │ └──
 **mtPRO_partition.treefile** # result from IQ-TREE 2 ├──
 **2.2-phylogeny_ASTRAL/** # gene trees used for phylogenetic
 reconstruction in ASTER │ ├── **uceCORY90all.gene.tre** # gene trees of
 the uceCORY90 matrix │ ├── **ucePRO70all.gene.tre** # gene trees of
 the ucePRO70 matrix │ └── **ucePRO90all.gene.tre** # gene trees of
 the ucePRO90 matrix ├── **3-BEAST_divergence_time/** │ ├──
 **3.1-Corydalinae/** │ │ ├── **3.1.1-SortaDate-PartitionFinder/** │ │ │
 ├── **gene_selected.txt** # list of the 50 uce loci selected by SortaDate
 for the dating analyses of Corydalinae │ │ │ └── **best_scheme.txt** #
 partitioning scheme and best-fitting models for the dataset estimated by
 PartitionFinder 2.1.1 │ │ ├── **3.1.2-path_sampling/** # input files
 (.xml) for the path sampling (PS) analyses for different branching process
 priors, and the log files (.log) from the analyses │ │ │ ├──
 **3.1.2.1-birth-death/** │ │ │ │ ├── **PS_cory1120pt_bd.xml** # input file
 for PS analysis (birth-death model) │ │ │ │ └── **path_sampling.log** #
 output from the PS analysis │ │ │ └── **3.1.2.2-yule/** │ │ │ ├──
 **PS_cory1120pt_yule.xml**  # input file for PS analysis (yule model) │ │
 │ └── **path_sampling.log** # output from the PS analysis │ │ └──
 **3.1.3-divergence_time_bd/** │ │ ├── **cory1120pt_bd.xml** # input file
 for the divergence estimation of Corydalinae using the birth-death model
 as branch process prior in BEAST 2.6.1 │ │ └── **cory1120pt_bd.tre** #
 maximum clade credibility (MCC) tree summarized from the dating results │
 └── **3.2-Protohermes/** │ ├── **3.2.1-SortaDate-PartitionFinder/** │ │
 ├── **gene_selected.txt** # list of the 50 uce loci selected by SortaDate
 for the dating analyses of Protohermes │ │ └── **best_scheme.txt** #
 partitioning scheme and best-fitting models for the dataset estimated by
 PartitionFinder 2.1.1 │ ├── **3.2.2-path_sampling/** # input files (.xml)
 for the path sampling analyses for different branching process priors, and
 the log files (.log) from the analyses │ │ ├── **3.2.2.1-birth-death/** │
 │ │ ├── **PS_pro1121pt_bd.xml** # input file for PS analysis (birth-death
 model) │ │ │ └── **path_sampling.log** # output from the PS analysis │ │
 └── **3.2.2.2-yule/** │ │ ├── **PS_pro1121pt_yule.xml** # input file for
 PS analysis (yule model) │ │ └── **path_sampling.log** # output from the
 PS analysis │ └── **3.2.3-divergence_time_bd/** │ ├── **pro1121pt_bd.xml**
 # input file for the divergence estimation of Protohermes using the
 birth-death model as branch process prior in BEAST 2.6.1 │ ├──
 **pro1125pt_bd.tre** # maximum clade credibility (MCC) tree summarized
 from the dating results │ └── **pro.tre** # MCC tree of Protohermes with
 outgroups removed; this time-calibrated tree was used in all the following
 analyses ├── **4.1-BioGeoBEARS/** │ ├── **PRO_geog2.data** # geography
 data used for biogeographic analyses in BioGeoBEARS │ └── **PBSM/** #
 rates estimated from BSM simulations ├── **4.2-Phenotypic_evolution/** │
 ├── **pro_coloration.csv** # coloration data used for ancestral state
 estimation (ASE) in phytools │ └── **phenotype_pPCA/** │ ├──
 **phenotypic_data.xlsx** # original data of body markings (mean values of
 each taxa) │ ├── **pro_pheno.csv** # penotypic data used for the
 phylogenetic principal components analysis (pPCA) in phytools │ └──
 **pro_ppc.csv** # results from pPCA ├── **4.3-Genital_evolution/** │ ├──
 **4.3.1-Data/** │ │ ├── **Trait_description.docx** # description for the
 coded genital characters │ │ ├── **Pro_genitalia_matrix.nex** # matrix of
 genital characters │ │ └── **character_dependency.txt** # character
 dependency matrix for the PCoA analysis │ └── **4.3.2-PCoA_results/** │
 ├── **Pro_genitalia_m_distance.csv** # MORD distance matrix of genital
 characters │ └── **Pro_genitalia_pcov.csv** # output from PCoA analysis
 ├── **4.4-Eco_evolution/** │ ├── **4.4.1-Data/** │ │ └──
 **Pro_mean_env.csv** # Mean values of ecological trait, represented by
 environmental variables obtained from WorldClim database and Hansen et al.
 (2013) using occurrence records of Protohermes species. Details for the
 variables are listed in Supplementary Appendix S1. │ └──
 **4.4.2-pPCA_output/** │ └── **pro_epc.csv** # result from pPCA analysis
 ├── **4.5-HiSSE/** │ ├── **pro_coloration.csv** # binary coloration data
 coded for the HiSSE analyses (coding strategy described in the
 "material and methods" section of the main text) │ └──
 **pro_marking.csv** # binary wingmarking data used in the HiSSE
 analyses (detailed coding strategy described in the "material and
 methods" section of the main text; 1 = wing markings present, 0 =
 wing markings absent) ├── **4.6-GeoHiSSE/** # biogeographic data coded for
 the GeoHiSSE analyses (detailed coding strategy described in the
 "material and methods" section of the main text; 1 = endemic to
 the targeted area, 2 = absent from the targeted area, 0 = distributed both
 in the targeted area and other area)  │ ├── **pro_geo_Borneo.csv** #
 geographic data (targeted area: Boreno) │ ├──
 **pro_geo_Himalayas_HDM.csv** # geographic data (targeted area:
 Himalayas_HDM) │ ├── **pro_geo_Indochina.csv** # geographic data (targeted
 area: Indochina) │ └── **pro_geo_SChina.csv** # geographic data (targeted
 area: S. China) ├── **4.7-ClaDS/** │ └── **ClaDS_commands.txt** # commands
 used for the clads analysis in the julia package PANDA.jl ├──
 **4.8-RevBayes/** │ ├── **pro_EBD.Rev** # script used for time-depedent
 diversification analysis in RevBayes │ ├── **mcmc_EBD_HSMRF_temp.Rev** #
 script used for paleotemperature-dependent diversification analysis in
 RevBayes │ └── **mcmc_EBD_HSMRF_monsoon.Rev** # script used for
 paleomonsoon-dependent diversification analysis in RevBayes └──
 **4.9-piecewiseSEM/** ├── **Grids_selected_data_sd2.csv** # input data for
 piecewiseSEM analysis (methods used to process raw data are described in
 supplementary Appendix S1) ├── **piecewiseSEM.R** # script for
 piecewiseSEM analysis └── **SEM_result.txt** # results from piecewiseSEM
 analysis