Edmonds, R. L. & Eglitis, A. The role of the Douglas-fir beetle and wood borers in the decomposition of and nutrient release from Douglas-fir logs. Can. J. Res. 19, 853–859 (1989).
Hlásny, T. et al. Living with bark beetles: impacts, outlook and management options. In: From Science to Policy 8. European Forest Institute (2019).
Raffa, K. F., Andersson, M. N. & Schlyter, F. Host selection by bark beetles: Playing the odds in a high‐stakes game. In: Adv. Insect Physiol. (eds Blomquist G., Tittinger C.), Vol 50, 1–74. Elsevier Ltd. (2016).
Biedermann, P. H. W. et al. Bark beetle population dynamics in the Anthropocene: challenges and solutions. Trends Ecol. Evol. 34, 914–924 (2019).
Kurz, W. A. et al. Mountain pine beetle and forest carbon feedback to climate change. Nature 452, 987–990 (2008).
Marini, L. et al. Climate drivers of bark beetle outbreak dynamics in Norway spruce forests. Ecography 40, 1426–1435 (2017).
Allen, C. D. et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. Ecol. Manag 259, 660–684 (2010).
Seidl, R., Schelhaas, M.-J., Rammer, W. & Verkerk, P. J. Increasing forest disturbances in Europe and their impact on carbon storage. Nat. Clim. Change 4, 806–810 (2014).
Ebner, G. Significantly more damaged wood in 2019. www.timber-online.net/log_wood/2020/02/significantly-more-damaged-wood-in-2019.html (2020).
Schlyter, F., Birgersson, G., Byers, J. A., Löfqvist, J. & Bergström, G. Field response of spruce bark beetle, Ips typographus, to aggregation pheromone candidates. J. Chem. Ecol. 13, 701–716 (1987).
Andersson, M. N. et al. Peripheral modulation of pheromone response by inhibitory host compound in a beetle. J. Exp. Biol. 213, 3332–3339 (2010).
Schlyter, F., Birgersson, G. & Leufvén, A. Inhibition of attraction to aggregation pheromone by verbenone and ipsenol: Density regulation mechanisms in bark beetle Ips typographus. J. Chem. Ecol. 15, 2263–2277 (1989).
Zhang, Q.-H. & Schlyter, F. Olfactory recognition and behavioural avoidance of angiosperm nonhost volatiles by conifer-inhabiting bark beetles. Agric. For. Entomol. 6, 1–19 (2004).
Andersson, M. N. et al. Antennal transcriptome analysis of the chemosensory gene families in the tree killing bark beetles, Ips typographus and Dendroctonus ponderosae (Coleoptera: Curculionidae: Scolytinae). BMC Genomics 14, 198 (2013).
Yuvaraj, J. K. et al. Putative ligand binding sites of two functionally characterized bark beetle odorant receptors. BMC Biol. 19, 16 (2021).
Hou, X.-Q. et al. Functional evolution of a bark beetle odorant receptor clade detecting monoterpenoids of different ecological origins. Mol. Biol. Evol. msab218, doi.org/10.1093/molbev/msab1218 (2021).
Filipiak, M. & Weiner, J. Nutritional dynamics during the development of xylophagous beetles related to changes in the stoichiometry of 11 elements. Physiol. Entomol. 42, 73–84 (2017).
Kirk, T. K. & Cowling, E. B. Biological decomposition of solid wood. In: The Chemistry of Solid Wood (ed Rowell R.) 455-487. ACS Publications. doi.org/10.1021/ba-1984-0207.ch012 (1984).
Chakraborty, A. et al. Unravelling the gut bacteriome of Ips (Coleoptera: Curculionidae: Scolytinae): Identifying core bacterial assemblage and their ecological relevance. Sci. Rep. 10, 18572 (2020).
Chakraborty, A. et al. Core mycobiome and their ecological relevance in the gut of five Ips bark beetles (Coleoptera: Curculionidae: Scolytinae). Front. Microbiol. 11, 2134 (2020b).
Davis, T. S. The ecology of yeasts in the bark beetle holobiont: A century of research revisited. Microb. Ecol. 69, 723–732 (2015).
Kirisits, T. Fungal associates of european bark beetles with special emphasis on the ophiostomatoid fungi. In: Bark and Wood Boring Insects in Living Trees in Europe, a Synthesis (eds et al.) 181-236. Springer Netherlands (2007).
Christiansen, E. Ceratocystis polonica inoculated in Norway spruce: Blue‐staining in relation to inoculum density, resinosis and tree growth. Eur. J. Pathol. 15, 160–167 (1985).
Kandasamy, D., Gershenzon, J., Andersson, M. N. & Hammerbacher, A. Volatile organic compounds influence the interaction of the Eurasian spruce bark beetle (Ips typographus) with its fungal symbionts. ISME J. 13, 1788–1800 (2019).
Zhao, T. et al. Fungal associates of the tree-killing bark beetle, Ips typographus, vary in virulence, ability to degrade conifer phenolics and influence bark beetle tunneling behavior. Fungal Ecol. 38, 71–79 (2019).
Keeling, C. I. et al. Draft genome of the mountain pine beetle, Dendroctonus ponderosae Hopkins, a major forest pest. Genome Biol. 14, R27 (2013).
Vega, F. E. et al. Draft genome of the most devastating insect pest of coffee worldwide: the coffee berry borer, Hypothenemus hampei. Sci. Rep. 5, 12525 (2015).
Smith, S. G. Chromosome numbers of Coleoptera. II. Heredity 7, 31–48 (1953).
Wang, S., Lorenzen, M. D., Beeman, R. W. & Brown, S. J. Analysis of repetitive DNA distribution patterns in the Tribolium castaneum genome. Genome Biol. 9, R61 (2008).
Herndon, N. et al. Enhanced genome assembly and a new official gene set for Tribolium castaneum. BMC Genomics 21, 47 (2020).
McKenna, D. D. et al. Genome of the Asian longhorned beetle (Anoplophora glabripennis), a globally significant invasive species, reveals key functional and evolutionary innovations at the beetle–plant interface. Genome Biol. 17, 227 (2016).
Eyun, S. I. et al. Molecular evolution of glycoside hydrolase genes in the western corn rootworm (Diabrotica virgifera virgifera). PLoS ONE 9, (2014).
McKenna, D. D. et al. The evolution and genomic basis of beetle diversity. Proc. Natl Acad. Sci. USA 116, 24729–24737 (2019).
Berger, E., Zhang, D., Zverlov, V. V. & Schwarz, W. H. Two noncellulosomal cellulases of Clostridium thermocellum, Cel9I and Cel48Y, hydrolyse crystalline cellulose synergistically. FEMS Microbiol. Lett. 268, 194–201 (2007).
Chu, C.-C. et al. Patterns of differential gene expression in adult rotation-resistant and wild-type western corn rootworm digestive tracts. Evol. Appl. 8, 692–704 (2015).
Pauchet, Y. & Heckel, D. G. The genome of the mustard leaf beetle encodes two active xylanases originally acquired from bacteria through horizontal gene transfer. Proc. R. Soc. B: Biol. Sci. 280, 20131021 (2013).
Fujita, K., Shimomura, K., Yamamoto, K.-I., Yamashita, T. & Suzuki, K. A chitinase structurally related to the glycoside hydrolase family 48 is indispensable for the hormonally induced diapause termination in a beetle. Biochem. Biophys. Res. Commun. 345, 502–507 (2006).
Santamaría, M. E. et al. Digestive proteases in bodies and faeces of the two-spotted spider mite, Tetranychus urticae. J. Insect Physiol. 78, 69–77 (2015).
Schoville, S. D. et al. A model species for agricultural pest genomics: the genome of the Colorado potato beetle, Leptinotarsa decemlineata (Coleoptera: Chrysomelidae). Sci. Rep. 8, 1931 (2018).
Netherer, S. et al. Interactions among Norway spruce, the bark beetle Ips typographus and its fungal symbionts in times of drought. J. Pest Sci. 94, 591–614 (2021).
Schiebe, C. et al. Inducibility of chemical defences in Norway spruce bark is correlated with unsuccessful mass attacks by the spruce bark beetle. Oecologia 170, 183–198 (2012).
Hedges, L. V. Distribution theory for glass’s estimator of effect size and related estimators. J. Educ. Stat. 6, 107–128 (1981).
Everaerts, C., Grégoire, J.-C. & Merlin, J. The toxicity of Norway spruce monoterpenes to two bark beetle species and their associates. In: Mechanisms of Woody Plant Defenses Against Insects (eds Mattson W. J., Levieux J., Bernard-Dagan C.) 335-344. Springer (1988).
Franceschi, V. R., Krokene, P., Christiansen, E. & Krekling, T. Anatomical and chemical defenses of conifer bark against bark beetles and other pests. N. Phytol. 167, 353–376 (2005).
Andersson, M. N. & Newcomb, R. D. Pest control compounds targeting insect chemoreceptors: another silent spring? Front. Ecol. Evol. 5, 5 (2017).
Anderbrant, O., Schlyter, F. & Birgersson, G. Intraspecific competition affecting parents and offspring in the bark beetle Ips typographus. Oikos 45, 89–98 (1985).
Schlyter, F. & Cederholm, I. Separation of the sexes of living spruce bark beetles, Ips typographus (L.), (Coleoptera: Scolytidae). J. Appl. Entomol. 92, 42–47 (1981).
Chin, C. S. et al. Phased diploid genome assembly with single-molecule real-time sequencing. Nat. Methods 13, 1050–1054 (2016).
Kurtz, S. et al. Versatile and open software for comparing large genomes. Genome Biol. 5, R12 (2004).
Simão, F. A., Waterhouse, R. M., Loannidis, P., Kriventseva, E. V. & Zdobnov, E. M. BUSCO: Assessing genome assembly and annotation completeness withqaz single-copy orthologs. Bioinformatics 31, 3210–3212 (2015).
Marçais, G. & Kingsford, C. A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics 27, 764–770 (2011).
Ranallo-Benavidez, T. R., Jaron, K. S. & Schatz, M. C. GenomeScope 2.0 and Smudgeplot for reference-free profiling of polyploid genomes. Nat. Commun. 11, 1432 (2020).
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
Haas, B. J. et al. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat. Protoc. 8, 1494–1512 (2013).
Li, H. et al. The sequence alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
R Core Team. R: A Language and Environment for Statistical Computing.). R Foundation for Statistical Computing (2020).
Kim, D., Langmead, B. & Salzberg, S. L. HISAT: A fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).
Pertea, M. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 33, 290–295 (2015).
Hoff, K. J., Lange, S., Lomsadze, A., Borodovsky, M. & Stanke, M. BRAKER1: Unsupervised RNA-Seq-based genome annotation with GeneMark-ET and AUGUSTUS. Bioinformatics 32, 767–769 (2015).
Holt, C. & Yandell, M. MAKER2: An annotation pipeline and genome-database management tool for second-generation genome projects. BMC Bioinform 12, 491 (2011).
Korf, I. Gene finding in novel genomes. BMC Bioinform. 5, 59 (2004).
Finn, R. D. et al. Pfam: the protein families database. Nucleic Acids Res. 42, D222–D230 (2014).
Potter, S. C. et al. HMMER web server: 2018 update. Nucleic Acids Res. 46, W200–W204 (2018).
Hall, M. R. et al. The crown-of-thorns starfish genome as a guide for biocontrol of this coral reef pest. Nature 544, 231–234 (2017).
Albertin, C. B. et al. The octopus genome and the evolution of cephalopod neural and morphological novelties. Nature 524, 220–224 (2015).
Simakov, O. et al. Insights into bilaterian evolution from three spiralian genomes. Nature 493, 526–531 (2013).
Xu, L. et al. OrthoVenn2: A web server for whole-genome comparison and annotation of orthologous clusters across multiple species. Nucleic Acids Res 47, W52–W58 (2019).
Emms, D. M. & Kelly, S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol. 20, 238–238 (2019).
Han, M. V., Thomas, G. W., Lugo-Martinez, J. & Hahn, M. W. Estimating gene gain and loss rates in the presence of error in genome assembly and annotation using CAFE 3. Mol. Biol. Evol. 30, 1987–1997 (2013).
Nakagawa, S. & Cuthill, I. C. Effect size, confidence interval and statistical significance: a practical guide for biologists. Biol. Rev. Camb. Philos. Soc. 82, 591–605 (2007).
Coe, R. Effect size calculator. Cambridge CEM (2000).
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