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Issue: Vol. 6 No. 1: February 2023
Type: Article
Published: 2023-02-28
DOI: 10.11646/palaeoentomology.6.1.8
Page range: 049–057
Abstract views: 388
PDF downloaded: 282

Improved modelling of compositional heterogeneity reconciles phylogenomic conflicts among lacewings

State Key Laboratory of Palaeobiology and Stratigraphy, Nanjing Institute of Geology and Palaeontology, and Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Nanjing 210008, China; School of Earth Sciences, University of Bristol, Life Sciences Building, Tyndall Avenue, Bristol BS8 1TQ, United Kingdom
State Key Laboratory of Palaeobiology and Stratigraphy, Nanjing Institute of Geology and Palaeontology, and Center for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Nanjing 210008, China; School of Earth Sciences, University of Bristol, Life Sciences Building, Tyndall Avenue, Bristol BS8 1TQ, United Kingdom
Department of Entomology, China Agricultural University, Beijing 100193, China
Division of Entomology, Natural History Museum, and Department of Ecology & Evolutionary Biology, University of Kansas, Lawrence, KS, U.S.A.
Neuropterida phylogenomics evolution compositional heterogeneity systematic error

Abstract

Exponential growth of large-scale data for Neuropterida, an iconic group of insects used in behavioural, ecological, and evolutionary studies, has greatly changed our understanding of the origin and evolution of lacewings and their allies. Recent phylogenomic studies of Neuropterida based on mitogenomes, anchored hybrid enrichment (AHE) data, and transcriptomes have yielded a well-resolved and largely congruent phylogeny. Some interfamilial relationships of lacewings, however, remain inconsistent among different phylogenomic studies. Here we re-analysed the genome-scale AHE and transcriptomic data for Neuropterida under the better fitting site-heterogeneous CAT-GTR+G model and recovered a strongly supported and congruent tree for the deeper phylogeny of Neuroptera. Integrating the smaller but more broadly sampled AHE and the larger but less-sampled transcriptomic data, we present a holistic phylogeny of Neuropterida from which to explore patterns of evolution across the clade. Our re-analyses of the largest available datasets of Neuropterida highlight the significance of modelling across-site compositional heterogeneity and model comparison in large-scale phylogenomic studies of insects.

References

  1. Aspöck, U., Haring, E. & Aspöck, H. (2012) The phylogeny of the Neuropterida: long lasting and current controversies and challenges (Insecta: Endopterygota). Arthropod Systematics & Phylogeny, 70, 119–129.
  2. Badano, D., Aspöck, U., Aspöck, H. & Cerretti, P. (2017) Phylogeny of Myrmeleontiformia based on larval morphology (Neuropterida: Neuroptera): Phylogeny of Myrmeleonti-formia. Systematic Entomology, 42, 94–117. https://doi.org/10.1111/syen.12200
  3. Badano, D., Engel, M.S., Basso, A., Wang, B. & Cerretti, P. (2018) Diverse Cretaceous larvae reveal the evolutionary and behavioural history of antlions and lacewings. Nature Communications, 9, 3257. https://doi.org/10.1038/s41467-018-05484-y
  4. Cai, C.Y., Tihelka, E., Giacomelli, M., Lawrence, J.F., Ślipiński, A., Kundrata, R., Yamamoto, S., Thayer, M.K., Newton, A.F., Leschen, R.A.B., Gimmel, M.L., Lü, L., Engel, M.S., Bouchard, P., Huang, D.Y., Pisani, D. & Donoghue, P.C.J. (2022) Integrated phylogenomics and fossil data illuminate the evolution of beetles. Royal Society Open Science, 9, 211771. https://doi.org/10.1098/rsos.211771
  5. Cai, C.Y., Tihelka, E., Pisani, D. & Donoghue, P.C.J. (2020) Data curation and modeling of compositional heterogeneity in insect phylogenomics: A case study of the phylogeny of Dytiscoidea (Coleoptera: Adephaga). Molecular Phylogenetics and Evolution, 147, 106782. https://doi.org/10.1016/j.ympev.2020.106782
  6. Criscuolo, A. & Gribaldo, S. (2010) BMGE (Block Mapping and Gathering with Entropy): a new software for selection of phylogenetic informative regions from multiple sequence alignments. BMC Evolutionary Biology, 10, 210. https://doi.org/10.1186/1471-2148-10-210
  7. Engel, M.S., Winterton, S.L. & Breitkreuz, L.C.V. (2018) Phylogeny and evolution of Neuropterida: where have wings of lace taken us? Annual Review of Entomology, 63, 531–551. https://doi.org/10.1146/annurev-ento-020117-043127
  8. Hoang, D.T., Chernomor, O., von Haeseler, A., Minh, B.Q. & Vinh, L.S. (2018) UFBoot2: Improving the Ultrafast Bootstrap Approximation. Molecular Biology and Evolution, 35, 518–522. https://doi.org/10.1093/molbev/msx281
  9. Inagaki, Y. & Roger, A.J. (2006) Phylogenetic estimation under codon models can be biased by codon usage heterogeneity. Molecular Phylogenetics and Evolution, 40, 428–434. https://doi.org/10.1016/j.ympev.2006.03.020
  10. Jones, J.R. (2019) Total-evidence phylogeny of the owlflies (Neuroptera, Ascalaphidae) supports a new higher-level classification. Zoologica Scripta, 48, 761–782. https://doi.org/10.1111/zsc.12382
  11. Kapli, P., Yang, Z.H. & Telford, M.J. (2020) Phylogenetic tree building in the genomic age. Nature Reviews Genetics, 21, 428–444. https://doi.org/10.1038/s41576-020-0233-0
  12. Lartillot, N. (2020) PhyloBayes: Bayesian phylogenetics using site-heterogeneous models. In: Scornavacca, C., Delsuc, F. & Galtier, N. (Eds), Phylogenetics in the Genomic Era. No commercial publisher, Authors open access book, pp.1.5:1–1.5:16. Available at: https://hal.archives-ouvertes.fr/hal-02535342 (Accessed 10 Feb 2023)
  13. Lartillot, N. (2022) Identifying the best approximating model in Bayesian phylogenetics: Bayes factors, cross-validation or wAIC? bioRxiv, 2022.04.22.489153. https://doi.org/10.1101/2022.04.22.489153
  14. Lartillot, N., Rodrigue, N., Stubbs, D. & Richer, J. (2013) PhyloBayes MPI: Phylogenetic reconstruction with infinite mixtures of profiles in a parallel environment. Systematic Biology, 62, 611–615. https://doi.org/10.1093/sysbio/syt022
  15. Le, S.Q., Dang, C.C. & Gascuel, O. (2012) Modeling protein evolution with several amino acid replacement matrices depending on site rates. Molecular Biology and Evolution, 29, 2921–2936. https://doi.org/10.1093/molbev/mss112
  16. Lozano-Fernandez, J. (2022) A practical guide to design and assess a phylogenomic study. Genome Biology and Evolution, 14 (9), evac129. https://doi.org/10.1093/gbe/evac129
  17. Machado, R.J.P., Gillung, J.P., Winterton, S.L., Garzón-Orduña, I.J., Lemmon, A.R., Lemmon, E.M. & Oswald, J.D. (2019) Owlflies are derived antlions: anchored phylogenomics supports a new phylogeny and classification of Myrmeleontidae (Neuroptera). Systematic Entomology, 44, 418–450. https://doi.org/10.1111/syen.12334
  18. McKenna, D.D., Shin, S., Ahrens, D., Balke, M., Beza-Beza, C., Clarke, D.J., Donath, A., Escalona, H.E., Friedrich, F., Letsch, H., Liu, S.L., Maddison, D., Mayer, C., Misof, B., Murin, P.J., Niehuis, O., Peters, R.S., Podsiadlowski, L., Pohl, H., Scully, E.D., Yan, E.V., Zhou, X., Ślipiński, A. & Beutel, R.G. (2019) The evolution and genomic basis of beetle diversity. Proceedings of the National Academy of Sciences, 116, 24729–24737. https://doi.org/10.1073/pnas.1909655116
  19. Meusemann, K., Trautwein, M., Friedrich, F., Beutel, R.G., Wiegmann, B.M., Donath, A., Podsiadlowski, L., Petersen, M., Niehuis, O., Mayer, C., Bayless, K.M., Shin, S., Liu, S.L., Hlinka, O., Minh, B.Q., Kozlov, A., Morel, B., Peters, R.S., Bartel, D., Grove, S., Zhou, X., Misof, B. & Yeates, D.K. (2020) Are fleas highly modified Mecoptera? Phylogenomic resolution of Antliophora (Insecta: Holometabola). bioRxiv, 2020.11.19.390666. https://doi.org/10.1101/2020.11.19.390666
  20. Misof, B., Liu, S.L., Meusemann, K., Peters, R.S., Donath, A., Mayer, C., Frandsen, P.B., Ware, J., Flouri, T., Beutel, R.G., Niehuis, O., Petersen, M., Izquierdo-Carrasco, F., Wappler, T., Rust, J., Aberer, A.J., Aspöck, U., Aspöck, H., Bartel, D., Blanke, A., Berger, S., Böhm, A., Buckley, T.R., Calcott, B., Chen, J.Q., Friedrich, F., Fukui, M., Fujita, M., Greve, C., Grobe, P., Gu, S.C., Huang, Y., Jermiin, L.S., Kawahara, A.Y., Krogmann, L., Kubiak, M., Lanfear, R., Letsch, H., Li, Y.Y., Li, Z.Y., Li, J.G., Lu, H.R., Machida, R., Mashimo, Y., Kapli, P., McKenna, D.D., Meng, G.L., Nakagaki, Y., Navarrete-Heredia, J.L., Ott, M., Ou, Y.X., Pass, G., Podsiadlowski, L., Pohl, H., von Reumont, B.M., Schütte, K., Sekiya, K., Shimizu, S., Slipinski, A., Stamatakis, A., Song, W.H., Su, X., Szucsich, N.U., Tan, M.H., Tan, X.M., Tang, M., Tang, J.B., Timelthaler, G., Tomizuka, S., Trautwein, M., Tong, X.L., Uchifune, T., Walzl, M.G., Wiegmann, B.M., Wilbrandt, J., Wipfler, B., Wong, T.K.F., Wu, Q., Wu, G.X., Xie, Y.L., Yang, S.Z,, Yang, Q., Yeates, D.K., Yoshizawa, K., Zhang, Q., Zhang, R., Zhang, W.W., Zhang, Y.H., Zhao, J., Zhou, C.R., Zhou, L.L., Ziesmann, T., Zou, S.J., Li, Y.R., Xu, X., Zhang, Y., Yang, H.M., Wang, J., Wang, J., Kjer, K.M. & Zhou, X. (2014) Phylogenomics resolves the timing and pattern of insect evolution. Science, 346, 763–767. https://doi.org/10.1126/science.1257570
  21. Nguyen, L.T., Schmidt, H.A., von Haeseler, A. & Minh, B.Q. (2015) IQ-TREE: A fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Molecular Biology and Evolution, 32, 268–274. https://doi.org/10.1093/molbev/msu300
  22. Philippe, H., Brinkmann, H., Lavrov, D.V., Littlewood, D.T.J., Manuel, M., Wörheide, G. & Baurain, D. (2011) Resolving difficult phylogenetic questions: Why more sequences are not enough. PLoS Biology, 9, e1000602. https://doi.org/10.1371/journal.pbio.1000602
  23. Rota-Stabelli, O., Lartillot, N., Philippe, H. & Pisani, D. (2013) Serine codon-usage bias in deep phylogenomics: Pancrustacean relationships as a case study. Systematic Biology, 62, 121–133. https://doi.org/10.1093/sysbio/sys077
  24. Schwentner, M., Combosch, D.J., Pakes Nelson, J. & Giribet, G. (2017) A phylogenomic solution to the origin of insects by resolving crustacean-hexapod relationships. Current Biology, 27, 1818–1824. https://doi.org/10.1016/j.cub.2017.05.040
  25. Tihelka, E., Cai, C.Y., Giacomelli, M., Lozano-Fernandez, J., Rota-Stabelli, O., Huang, D.Y., Engel, M.S., Donoghue, P.C.J. & Pisani, D. (2021) The evolution of insect biodiversity. Current Biology, 31, R1299–R1311. https://doi.org/10.1016/j.cub.2021.08.057
  26. Tihelka, E., Giacomelli, M., Huang, D.Y., Pisani, D., Donoghue, P.C.J. & Cai, C.Y. (2020) Fleas are parasitic scorpionflies. Palaeoentomology, 3 (6), 641–653. https://doi.org/10.11646/palaeoentomology.3.6.16
  27. Vasilikopoulos, A., Balke, M., Beutel, R.G., Donath, A., Podsiadlowski, L., Pflug, J.M., Waterhouse, R.M., Meusemann, K., Peters, R.S., Escalona, H.E., Mayer, C., Liu, S.L., Hendrich, L., Alarie, Y., Bilton, D.T., Jia, F., Zhou, X., Maddison, D.R., Niehuis, O. & Misof, B. (2019) Phylogenomics of the superfamily Dytiscoidea (Coleoptera: Adephaga) with an evaluation of phylogenetic conflict and systematic error. Molecular Phylogenetics and Evolution, 135, 270–285. https://doi.org/10.1016/j.ympev.2019.02.022
  28. Vasilikopoulos, A., Misof, B., Meusemann, K., Lieberz, D., Flouri, T., Beutel, R.G., Niehuis, O., Wappler, T., Rust, J., Peters, R.S., Donath, A., Podsiadlowski, L., Mayer, C., Bartel, D., Böhm, A., Liu, S.L., Kapli, P., Greve, C., Jepson, J.E., Liu, X.Y., Zhou, X., Aspöck, H. & Aspöck, U. (2020) An integrative phylogenomic approach to elucidate the evolutionary history and divergence times of Neuropterida (Insecta: Holometabola). BMC Evolutionary Biology, 20, 64. https://doi.org/10.1186/s12862-020-01631-6
  29. Wang, H.C., Minh, B.Q., Susko, E. & Roger, A.J. (2018) Modeling site heterogeneity with posterior mean site frequency profiles accelerates accurate phylogenomic estimation. Systematic Biology, 67, 216–235. https://doi.org/10.1093/sysbio/syx068
  30. Wang, Y.Y., Liu, X.Y., Garzón-Orduña, I.J., Winterton, S.L., Yan, Y., Aspöck, U., Aspöck, H. & Yang, D.Y. (2017) Mitochondrial phylogenomics illuminates the evolutionary history of Neuropterida. Cladistics, 33, 617–636. https://doi.org/10.1111/cla.12186
  31. Wang, Y.Y., Zhou, X.F., Wang, L.M., Liu, X.Y., Yang, D. & Rokas, A. (2019) Gene selection and evolutionary modelling affect phylogenomic inference of Neuropterida based on transcriptome data. International Journal of Molecular Sciences, 20, 1072. https://doi.org/10.3390/ijms20051072
  32. Winterton, S.L., Hardy, N.B. & Wiegmann, B.M. (2010) On wings of lace: phylogeny and Bayesian divergence time estimates of Neuropterida (Insecta) based on morphological and molecular data. Systematic Entomology, 35, 349–378. https://doi.org/10.1111/j.1365-3113.2010.00521.x
  33. Winterton, S.L., Lemmon, A., Gillung, J.P., Garzon, I.J., Badano, D., Bakkes, D.K., Breitkreuz, L.C.V., Engel, M.S., Lemmon, E.M., Liu, X.Y., Machado, R.J.P., Skevington, J.H. & Oswald, J.D. (2018) Evolution of lacewings and allied orders using anchored phylogenomics (Neuroptera, Megaloptera, Raphidioptera). Systematic Entomology, 43, 330–354. https://doi.org/10.1111/syen.12278
  34. Withycombe, C.L. (1925) XV. Some aspects of the biology and morphology of the Neuroptera. With special reference to the immature stages and their possible phylogenetic significance. Transactions of the Royal Entomological Society of London, 72, 303–411. https://doi.org/10.1111/j.1365-2311.1925.tb03362.x