Ecology

Steffen, W. et al. Planetary boundaries: guiding human development on a changing planet. Science 347, 1259855 (2015).PubMed 
PubMed Central 

Google Scholar 
Kanter, D. R. et al. Nitrogen pollution policy beyond the farm. Nat. Food 1, 27–32 (2020).
Google Scholar 
Tian, H. Q. et al. A comprehensive quantification of global nitrous oxide sources and sinks. Nature 586, 248–256 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Thompson, R. L. et al. Acceleration of global N2O emissions seen from two decades of atmospheric inversion. Nat. Clim. Change 9, 993–998 (2019).CAS 

Google Scholar 
Isobe, K., Allison, S. D., Khalili, B., Martiny, A. C. & Martiny, J. B. H. Phylogenetic conservation of bacterial responses to soil nitrogen addition across continents. Nat. Commun. 10, 2499 (2019).PubMed 
PubMed Central 

Google Scholar 
Dai, Z. M. et al. Long-term nitrogen fertilization decreases bacterial diversity and favors the growth of Actinobacteria and Proteobacteria in agro-ecosystems across the globe. Glob. Change Biol. 24, 3452–3461 (2018).
Google Scholar 
Wallenstein, M., Myrold, D., Firestone, M. & Voytek, M. Environmental controls on denitrifying communities and denitrification rates: insights from molecular methods. Ecol. Appl 16, 2143–2152 (2006).PubMed 
PubMed Central 

Google Scholar 
Scheer, C., Fuchs, K., Pelster, D. E. & Butterbach-Bahl, K. Estimating global terrestrial denitrification from measured N2O:(N2O + N2) product ratios. Curr. Opin. Enviro 47, 72–80 (2020).
Google Scholar 
Inatomi, M., Hajima, T. & Ito, A. Fraction of nitrous oxide production in nitrification and its effect on total soil emission: a meta-analysis and global-scale sensitivity analysis using a process-based model. PLoS One 14, e0219159 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Liang, D. & Robertson, G. P. Nitrification is a minor source of nitrous oxide (N2O) in an agricultural landscape and declines with increasing management intensity. Glob. Change Biol. 27, 5599–5613 (2021).
Google Scholar 
Zumft, W. G. Cell biology and molecular basis of denitrification. Microbiol Mol. Biol. R. 61, 533–616 (1997).CAS 

Google Scholar 
Graf, D. R. H., Jones, C. M. & Hallin, S. Intergenomic comparisons highlight modularity of the denitrification pathway and underpin the importance of community structure for N2O emissions. PLoS One 9, e114118 (2014).PubMed 
PubMed Central 

Google Scholar 
Lycus, P. et al. Phenotypic and genotypic richness of denitrifiers revealed by a novel isolation strategy. ISME J. 11, 2219–2232 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Roco, C. A., Bergaust, L. L., Bakken, L. R., Yavitt, J. B. & Shapleigh, J. P. Modularity of nitrogen-oxide reducing soil bacteria: linking phenotype to genotype. Environ. Microbiol 19, 2507–2519 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hallin, S., Philippot, L., Loffler, F. E., Sanford, R. A. & Jones, C. M. Genomics and ecology of novel N2O-reducing microorganisms. Trends Microbiol 26, 43–55 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Philippot, L., Andert, J., Jones, C. M., Bru, D. & Hallin, S. Importance of denitrifiers lacking the genes encoding the nitrous oxide reductase for N2O emissions from soil. Glob. Change Biol. 17, 1497–1504 (2011).
Google Scholar 
Domeignoz-Horta, L. A. et al. Non-denitrifying nitrous oxide-reducing bacteria—an effective N2O sink in soil. Soil Biol. Biochem 103, 376–379 (2016).CAS 

Google Scholar 
Ramirez, K. S., Craine, J. M. & Fierer, N. Consistent effects of nitrogen amendments on soil microbial communities and processes across biomes. Glob. Change Biol. 18, 1918–1927 (2012).
Google Scholar 
Leff, J. W. et al. Consistent responses of soil microbial communities to elevated nutrient inputs in grasslands across the globe. Proc. Natl. Acad. Sci. USA 112, 10967–10972 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Shi, S. et al. The interconnected rhizosphere: high network complexity dominates rhizosphere assemblages. Ecol. Lett. 19, 926–936 (2016).PubMed 
PubMed Central 

Google Scholar 
Huang, R. L. et al. Plant-microbe networks in soil are weakened by century-long use of inorganic fertilizers. Micro. Biotechnol. 12, 1464–1475 (2019).CAS 

Google Scholar 
Tylianakis, J. M. & Morris, R. J. Ecological networks across environmental gradients. Annu. Rev. Ecol. Evol. S 48, 25–48 (2017).
Google Scholar 
Geisseler, D. & Scow, K. M. Long-term effects of mineral fertilizers on soil microorganisms—a review. Soil Biol. Biochem 75, 54–63 (2014).CAS 

Google Scholar 
Simek, M. & Cooper, J. The influence of soil pH on denitrification: progress towards the understanding of this interaction over the last 50 years. Eur. J. Soil Sci. 53, 345–354 (2002).CAS 

Google Scholar 
Klemedtsson, L., von Arnold, K., Weslien, P. & Gundersen, P. Soil CN ratio as a scalar parameter to predict nitrous oxide emissions. Glob. Change Biol. 11, 1142–1147 (2005).
Google Scholar 
Parn, J. et al. Nitrogen-rich organic soils under warm well-drained conditions are global nitrous oxide emission hotspots. Nat. Commun. 9, 1135 (2018).PubMed 
PubMed Central 

Google Scholar 
Maeda, K. et al. Relative contribution of nirK-and nirS-bacterial denitrifiers as well as fungal denitrifiers to nitrous oxide production from dairy manure compost. Environ. Sci. Technol. 51, 14083–14091 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Coyotzi, S. et al. Agricultural soil denitrifiers possess extensive nitrite reductase gene diversity. Environ. Microbiol 19, 1189–1208 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Nadeau, S. A. et al. Metagenomic analysis reveals distinct patterns of denitrification gene abundance across soil moisture, nitrate gradients. Environ. Microbiol 21, 1255–1266 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Enwall, K., Throbäck, I. N., Stenberg, M., Söderström, M. & Hallin, S. Soil resources influence spatial patterns of denitrifying communities at scales compatible with land management. Appl Environ. Microbiol 76, 2243–2250 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Jones, C. M. & Hallin, S. Ecological and evolutionary factors underlying global and local assembly of denitrifier communities. ISME J. 4, 633–641 (2010).PubMed 

Google Scholar 
Silverman, J. D., Washburne, A. D., Mukherjee, S. & David, L. A. A phylogenetic transform enhances analysis of compositional microbiota data. eLife 6, 5721 (2017).
Google Scholar 
Magurran, A. E. & Henderson, P. A. Explaining the excess of rare species in natural species abundance distributions. Nature 422, 714–716 (2003).CAS 
PubMed 
PubMed Central 

Google Scholar 
Dai, Z. et al. Long-term nitrogen fertilization decreases bacterial diversity and favors the growth of Actinobacteria and Proteobacteriain agro-ecosystems across the globe. Glob. Change Biol. 24, 3452–3461 (2018).
Google Scholar 
Fierer, N. et al. Comparative metagenomic, phylogenetic and physiological analyses of soil microbial communities across nitrogen gradients. ISME J. 6, 1007–1017 (2011).PubMed 
PubMed Central 

Google Scholar 
Naether, A. et al. Environmental factors affect acidobacterial communities below the subgroup level in grassland and forest soils. Appl Environ. Microbiol. 78, 7398–7406 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Navarrete, A. A. et al. Differential response of Acidobacteria subgroups to forest-to-pasture conversion and their biogeographic patterns in the Western Brazilian Amazon. Front. Microbiol. 6, 1443 (2015).PubMed 
PubMed Central 

Google Scholar 
Jones, C. M., Stres, B., Rosenquist, M. & Hallin, S. Phylogenetic analysis of nitrite, nitric oxide, and nitrous oxide respiratory enzymes reveal a complex evolutionary history for denitrification. Mol. Biol. Evol. 25, 1955–1966 (2008).CAS 
PubMed 

Google Scholar 
Kuypers, M. M. M., Marchant, H. K. & Kartal, B. The microbial nitrogen-cycling network. Nat. Rev. Microbiol 16, 263–274 (2018).CAS 
PubMed 

Google Scholar 
Zhou, J., Deng, Y., Luo, F., He, Z. & Yang, Y. Phylogenetic molecular ecological network of soil microbial communities in response to elevated CO2. MBio 2, e00122-00111–e00122-00111 (2011).
Google Scholar 
Huang, R. et al. Plant–microbe networks in soil are weakened by century‐long use of inorganic fertilizers. Micro. Biotechnol. 12, 1464–1475 (2019).CAS 

Google Scholar 
Bar-Massada, A. Complex relationships between species niches and environmental heterogeneity affect species co-occurrence patterns in modelled and real communities. Proc. Royal Soc. B 282, 20150927 (2015).
Google Scholar 
Boccaletti, S., Latora, V., Moreno, Y., Chavez, M. & Hwang, D. U. Complex networks: structure and dynamics. Phys. Rep. 424, 175–308 (2006).
Google Scholar 
Yuan, M. M. et al. Climate warming enhances microbial network complexity and stability. Nat. Clim. Change 11, 343–U100 (2021).
Google Scholar 
Freilich, S. et al. The large-scale organization of the bacterial network of ecological co-occurrence interactions. Nucleic Acids Res. 38, 3857–3868 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Samad, M. D. S. et al. Phylogenetic and functional potential links pH and N2O emissions in pasture soils. Sci. Rep. 6, 35990 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wang, Y. et al. Soil pH as the chief modifier for regional nitrous oxide emissions: new evidence and implications for global estimates and mitigation. Glob. Change Biol. 24, E617–E626 (2018).
Google Scholar 
Jones, C. M. et al. Recently identified microbial guild mediates soil N2O sink capacity. Nat. Clim. Change 4, 801–805 (2014).CAS 

Google Scholar 
Dorsch, P., Braker, G. & Bakken, L. R. Community-specific pH response of denitrification: experiments with cells extracted from organic soils. FEMS Microbiol Ecol. 79, 530–541 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Linton, N. F., Machado, P. V. F., Deen, B., Wagner-Riddle, C. & Dunfield, K. E. Long-term diverse rotation alters nitrogen cycling bacterial groups and nitrous oxide emissions after nitrogen fertilization. Soil Biol. Biochem 149, 107917 (2020).CAS 

Google Scholar 
Xu, X. Y. et al. nosZ clade II rather than clade I determine in situ N2O emissions with different fertilizer types under simulated climate change and its legacy. Soil Biol. Biochem 150, 107974 (2020).CAS 

Google Scholar 
Philippot, L. et al. Loss in microbial diversity affects nitrogen cycling in soil. ISME J. 7, 1609–1619 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Delgado-Baquerizo, M., Grinyer, J., Reich, P. B. & Singh, B. K. Relative importance of soil properties and microbial community for soil functionality: insights from a microbial swap experiment. Funct. Ecol. 30, 1862–1873 (2016).
Google Scholar 
Kottek, M., Grieser, J., Beck, C., Rudolf, B. & Rubel, F. World map of the Köppen–Geiger climate classification updated. Meteorol. Z. 15, 259–263 (2006).
Google Scholar 
Lu, C. Q. & Tian, H. Q. Global nitrogen and phosphorus fertilizer use for agriculture production in the past half century: shifted hot spots and nutrient imbalance. Earth Syst. Sci. Data 9, 181–192 (2017).
Google Scholar 
Van Meter, K. J., Basu, N. B., Veenstra, J. J. & Burras, C. L. The nitrogen legacy: emerging evidence of nitrogen accumulation in anthropogenic landscapes. Environ. Res. Lett. 11, 035014–035013 (2016).
Google Scholar 
Takahashi, S., Tomita, J., Nishioka, K., Hisada, T. & Nishijima, M. Development of a prokaryotic universal primer for simultaneous analysis of bacteria and archaea using next-generation sequencing. PLoS One 9, e105592 (2014).PubMed 
PubMed Central 

Google Scholar 
Zhang, J., Kobert, K., Flouri, T. & Stamatakis, A. PEAR: a fast and accurate illumina paired-end reAd mergeR. Bioinformatics 30, 614–620 (2014).CAS 
PubMed 

Google Scholar 
Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: a versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).PubMed 
PubMed Central 

Google Scholar 
Pruesse, E., Peplies, J. & Glöckner, F. O. SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics 28, 1823–1829 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Ludwig, W. et al. ARB: a software environment for sequence data. Nucleic Acids Res. 32, 1363–1371 (2004).CAS 
PubMed 
PubMed Central 

Google Scholar 
Oksanen J. vegan: Community Ecology Package version 1.8–5 (Semantic Scholar, 2007).McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8, e61217 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Palarea-Albaladejo, J. & Martin-Fernandez, J. A. zCompositions—R package for multivariate imputation of left-censored data under a compositional approach. Chemom. Intell. Lab 143, 85–96 (2015).CAS 

Google Scholar 
Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).
Google Scholar 
Csardi, G. & Nepusz, T. The igraph software package for complex network research. Int. J. Complex Syst. 1695, 1–9 (2006).
Google Scholar 
Menzel, U. RMThreshold: Signal-Noise Separation in Random Matrices by Using Eigenvalue. R Package Version 1.1 edn. https://rdrr.io/cran/RMThreshold/man/RMThreshold-package.html (2016).Gu, Z. G., Gu, L., Eils, R., Schlesner, M. & Brors, B. circlize implements and enhances circular visualization in R. Bioinformatics 30, 2811–2812 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Goenawan, I. H., Bryan, K. & Lynn, D. J. DyNet: visualization and analysis of dynamic molecular interaction networks. Bioinformatics 32, 2713–2715 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Jones, C. M. & Hallin, S. Geospatial variation in co-occurrence networks of nitrifying microbial guilds. Mol. Ecol. 28, 293–306 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Newman, M. E. J. & Girvan, M. Finding and evaluating community structure in networks. Phys. Rev. E 69, 268–215 (2004).
Google Scholar 
Deng, Y. et al. Molecular ecological network analyses. BMC Bioinform. 13, 113 (2012).
Google Scholar 
Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008).CAS 
PubMed 
PubMed Central 

Google Scholar 
Dormann, C. F. et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2012).
Google Scholar 
Kuhn, M. Building predictive models in R using the caret package. J. Stat. Softw. 28, 1–26 (2008).
Google Scholar 
Greenwell, B. M. & Boehmke, B. C. Variable importance plots-an introduction to the vip package. R. J. 12, 343–366 (2020).
Google Scholar 
Molnar, C. iml: An R package for Interpretable. Mach. Learn. J. Open Source Softw. 3, 786 (2018).
Google Scholar  More

Laboratory for Integrative Biodiversity Research (LIBRe), Finnish Museum of Natural History (LUOMUS), University of Helsinki, Helsinki, FinlandStefano Mammola, Jagoba Malumbres-Olarte, Pedro Cardoso, Caroline S. Fukushima, Tuuli Korhonen, Marija Miličić & Joni A. SaarinenMolecular Ecology Group (MEG), Water Research Institute, National Research Council of Italy (CNR-IRSA), Largo Tonolli 50, 28922, Verbania Pallanza, ItalyStefano Mammola & Alejandro MartínezCE3C – Centre for Ecology, Evolution and Environmental Changes / Azorean Biodiversity Group and Universidade dos Açores, Angra do Heroísmo, Azores, PortugalJagoba Malumbres-OlarteAlbert Katz International School for Desert Studies, Ben-Gurion University of the Negev, Sede Boqer Campus, Beersheba, IsraelValeria ArabeskyBlaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer Campus, Beersheba, IsraelValeria Arabesky & Yael LubinColección Nacional de Arácnidos, Instituto de Biología, Universidad Nacional Autónoma de México (UNAM), Mexico City, MexicoDiego Alejandro Barrales-AlcaláEnvironmental Biology Division, Institute of Biological Sciences, College of Arts and Sciences and Museum of Natural History, University of the Philippines Los Banos, 4031, Los Baños, PhilippinesAimee Lynn Barrion-DupoCentro Universitario de Rivera, Universidad de la República, Montevideo, UruguayMarco Antonio BenamúLab. Ecotoxicología de Artrópodos Terrestres, Centro Univeritario de Rivera, Universidad de la República, Montevideo, UruguayMarco Antonio BenamúLaboratorio Ecología del Comportamiento, Instituto de Investigaciones Biológicas clemente Estable (IIBCE), Montevideo, UruguayMarco Antonio BenamúDitsong National Museum of Natural History, PO Box 4197, Pretoria, 0001, South AfricaTharina L. BirdDepartment of Zoology and Entomology, University of Pretoria, Private Bag X20, Hatfield, 0028, South AfricaTharina L. BirdFreelance translator, Verbania Pallanza, ItalyMaria BogomolovaDepartment of Molecular Biology and Genetics, Democritus University of Thrace, Komotini, GreeceMaria ChatzakiDepartment of Life sciences, National Chung Hsing University, No.145 Xingda Rd., South Dist., Taichung City, 402204, TaiwanRen-Chung Cheng & Tien-Ai ChuDepartment of Biology, Macelwane Hall, 3507 Laclede Avenue, Saint Louis University, St. Louis, MO, 63103, USALeticia M. Classen-RodríguezCroatian Biospeleological Society, Rooseveltov trg 6, Zagreb, CroatiaIva Čupić & Martina PavlekProgram Sarjana, Fakultas Biologi, Universitas Gadjah Mada, Yogyakarta, IndonesiaNaufal Urfi Dhiya’ulhaqInsectarium de Montréal, Espace pour la vie, 4101, rue Sherbrooke Est, Montréal, Québec, H1X 2B2, CanadaAndré-Philippe Drapeau PicardSerket, Arachnid Collection of Egypt (ACE), Cairo, EgyptHisham K. El-HennawyErzincan Binali Yıldırım University, Faculty of Science and Arts, Biology Department, 24002, Erzincan, TurkeyMert ElvericiThe National Natural History Collections, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Givat Ram, Jerusalem, 9190401, IsraelZeana Ganem & Efrat Gavish-RegevThe Department of Ecology, Evolution and Behavior, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Givat Ram, Jerusalem, 9190401, IsraelZeana GanemBotswana International University of Science and Technology, Palapye, BotswanaNaledi T. GonnyeUMR CNRS 6553 Ecobio, Université de Rennes, 263 Avenue du Gal Leclerc, CS 74205, 35042, Rennes Cedex, FranceAxel Hacala & Julien PétillonDepartment of Zoology and Entomology, University of the Free State, P.O. Box 339, Bloemfontein, 9300, South AfricaCharles R. Haddad & Zingisile MboDepartment of Zoology, University of Oxford, Oxford, OX1 3PS, United KingdomThomas HesselbergDepartment of Biological Sciences, National University of Singapore, 14 Science Drive 4, Singapore, 117543, SingaporeTammy Ai Tian HoDepartment of Biotechnology, Faculty of Science and Technology, Thammasat University, Rangsit, Pathum Thani, 12121, ThailandThanakorn Into & Booppa PetcharadDept. of Life Science and Systems Biology, University of Torino, Via Accademia Albertina, 13 – 10123, Torino, ItalyMarco Isaia & Veronica NanniUnit of Conservation Biology, Department of Zoology, Bharathiar University, Coimbatore, 641046, Tamilnadu, IndiaDharmaraj JayaramanNational Museum of Namibia, Windhoek, NamibiaNanguei Karuaera5A Sagar Sangeet, SBS Marg, Mumbai, 400005, IndiaRajashree Khalap & Kiran KhalapDepartment of Biological Sciences, Ajou University, Suwon, Republic of KoreaDongyoung KimResearch Centre of the Slovenian Academy of Sciences and Arts, Jovan Hadži Institute of Biology, Ljubljana, SloveniaSimona Kralj-FišerUniversity of Greifswald, Zoological Institute and Museum, General and Systematic Zoology, Loitzerstrasse 26, 17489, Greifswald, GermanyHeidi Land, Shou-Wang Lin & Gabriele UhlDepartment of Natural Resource Sciences, McGill University, 21 111 Lakeshore Road, Sainte-Anne-de-Bellevue, Quebec, H9X 3V9, CanadaSarah Loboda & Catherine ScottDepartment of Biological Science, Macquarie University, Sydney, NSW, 2122, AustraliaElizabeth LoweMitrani Department of Desert Ecology, University in Midreshet Ben-Gurion, Midreshet Ben-Gurion, IsraelYael LubinBioSense Institute – Research Institute for Information Technologies in Biosystems, University of Novi Sad, Dr Zorana Đinđića 1, 21000, Novi Sad, SerbiaMarija MiličićNational Museums of Kenya, Museum Hill, P.O. BOX 40658- 00100, Nairobi, KenyaGrace Mwende KiokoSchool for Advanced Studies IUSS, Science, Technology and Society Department, 25100, Pavia, ItalyVeronica NanniInstitute of Biological Sciences, Faculty of Science, University of Malaya, 50603, Kuala Lumpur, MalaysiaYusoff Norma-RashidDepartment of Animal and Environmental Biology, Federal University, Oye-Ekiti, Ekiti State, NigeriaDaniel NwankwoTe Aka Mātuatua School of Science, University of Waikato, Private Bag 3105, Hamilton, 3240, New ZealandChristina J. PaintingIndependent researcher, Toronto, CanadaAleck PangMuseo Civico di Scienze Naturali “E. Caffi”, Piazza Cittadella, 10, I-24129, Bergamo, ItalyPaolo PantiniRuđer Bošković Institute, Bijenička cesta 54, 10000, Zagreb, CroatiaMartina PavlekBiodiversity Research Laboratory, Moreton Morrell, Warwickshire College University Centre, Warwickshire, United KingdomRichard PearceInstitute for Coastal and Marine Research, Nelson Mandela University, Port Elizabeth, South AfricaJulien PétillonDepartment of Entomology, University of Antananrivo, Antananarivo, MadagascarOnjaherizo Christian RaberahonaSchool of Biological Sciences, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, United StatesLaura Segura-HernándezDepartment of Biological Sciences, University of Toronto Scarborough, 1265 Military Trail, Scarborough, Ontario, M1C 1A4, CanadaLenka SentenskáNatural Sciences, Auckland War Memorial Museum, Parnell, Auckland, 1010, New ZealandLeilani WalkerTe Pūnaha Matatini, University of Auckland, Auckland, New ZealandLeilani WalkerMurang’a University of Technology, Department of Physical & Biological Sciences, P.O.Box 75-10200, Murang’a, KenyaCharles M. WaruiInstitute of Biology and Earth Sciences, Pomeranian University in Słupsk, Arciszewskiego 22a, 76-200, Słupsk, PolandKonrad WiśniewskiZoological Museum, Biodiversity Unit, FI-20014, University of Turku, Turku, FinlandAlireza ZamaniDepartment of Psychology, University of Tennessee, Knoxville, Tennessee, USAAngela ChuangDepartment of Entomology and Nematology, Citrus Research and Education Center, University of Florida, Lake Alfred, Florida, USAAngela ChuangConceptualization: SM, JM-O, CS, AC; Data collection & validation: all authors; Data management: SM, VN, AC; Data analysis & visualization (Figs. 2–5): SM; Summary illustration (Fig. 1): JM-O; Writing (first draft): SM; Writing, contributions: JM-O, CS, AC; All authors read the text, provided comments, suggestions, and corrections, and approved the final version. More

Schemske, D. W. in Foundations of Tropical Forest Biology (eds Chazdon, R. L. & Whitmore, T. C.) 163–173 (Univ. Chicago Press, 2002).Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).
Google Scholar 
Schemske, D. W., Mittelbach, G. G., Cornell, H. V., Sobel, J. M. & Roy, K. Is there a latitudinal gradient in the importance of biotic interactions? Annu. Rev. Ecol. Evol. Syst. 40, 245–269 (2009).
Google Scholar 
Schweiger, O., Settele, J., Kudrna, O., Klotz, S. & Kühn, I. Climate change can cause spatial mismatch of trophically interacting species. Ecology 89, 3472–3479 (2008).PubMed 

Google Scholar 
Hegland, S. J., Nielsen, A., Lázaro, A., Bjerknes, A.-L. & Totland, Ø. How does climate warming affect plant–pollinator interactions? Ecol. Lett. 12, 184–195 (2009).PubMed 

Google Scholar 
Walther, G.-R. Community and ecosystem responses to recent climate change. Philos. Trans. R. Soc. B 365, 2019–2024 (2010).
Google Scholar 
Blois, J. L., Zarnetske, P. L., Fitzpatrick, M. C. & Finnegan, S. Climate change and the past, present and future of biotic interactions. Science 341, 499–504 (2013).CAS 
PubMed 

Google Scholar 
Schleuning, M. et al. Ecological networks are more sensitive to plant than to animal extinction under climate change. Nat. Commun. 7, 13965 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bascompte, J., García, M. B., Ortega, R., Rezende, E. L. & Pironon, S. Mutualistic interactions reshuffle the effects of climate change on plants across the tree of life. Sci. Adv. 5, eaav2539 (2019).PubMed 
PubMed Central 

Google Scholar 
Memmott, J., Craze, P. G., Waser, N. M. & Price, M. V. Global warming and the disruption of plant–pollinator interactions. Ecol. Lett. 10, 710–717 (2007).PubMed 

Google Scholar 
Tylianakis, J. M., Didham, R. K., Bascompte, J. & Wardle, D. A. Global change and species interactions in terrestrial ecosystems. Ecol. Lett. 11, 1351–1363 (2008).PubMed 

Google Scholar 
Dalsgaard, B. et al. Specialization in plant–hummingbird networks is associated with species richness, contemporary precipitation and Quaternary climate-change velocity. PLoS ONE 6, e25891 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Dalsgaard, B. et al. Historical climate-change influences modularity and nestedness of pollination networks. Ecography 36, 1331–1340 (2013).
Google Scholar 
Memmott, J., Waser, N. M. & Price, M. V. Tolerance of pollination networks to species extinctions. Proc. R. Soc. Lond. B 271, 2605–2611 (2004).
Google Scholar 
Kaiser-Bunbury, C. N., Muff, S., Memmott, J., Müller, C. B. & Caflisch, A. The robustness of pollination networks to the loss of species and interactions: a quantitative approach incorporating pollinator behaviour. Ecol. Lett. 13, 442–452 (2010).PubMed 

Google Scholar 
Dáttilo, W. et al. Unravelling Darwin’s entangled bank: architecture and robustness of mutualistic networks with multiple interaction types. Proc. R. Soc. B 283, 20161564 (2016).PubMed 
PubMed Central 

Google Scholar 
Dalsgaard, B. et al. Trait evolution, resource specialization and vulnerability to plant extinctions among Antillean hummingbirds. Proc. R. Soc. B 285, 20172754 (2018).PubMed 
PubMed Central 

Google Scholar 
Gilman, S. E., Urban, M. C., Tewksbury, J., Gilchrist, G. W. & Holt, R. D. A framework for community interactions under climate change. Trends Ecol. Evol. 25, 325–331 (2010).PubMed 

Google Scholar 
Rahbek, C. & Graves, G. R. Multiscale assessment of patterns of avian species richness. Proc. Natl Acad. Sci. USA 98, 4534–4539 (2001).CAS 
PubMed 
PubMed Central 

Google Scholar 
Rahbek, C. & Graves, G. R. Detection of macro-ecological patterns in South American hummingbirds is affected by spatial scale. Proc. R. Soc. Lond. B 267, 2259–2265 (2000).CAS 

Google Scholar 
Dalsgaard, B. et al. The influence of biogeographical and evolutionary histories on morphological trait-matching and resource specialization in mutualistic hummingbird–plant networks. Funct. Ecol. 35, 1120–1133 (2021).
Google Scholar 
Sandel, B. et al. The influence of Late Quaternary climate-change velocity on species endemism. Science 334, 660–664 (2011).CAS 
PubMed 

Google Scholar 
Scherrer, D. & Körner, C. Topographically controlled thermal-habitat differentiation buffers alpine plant diversity against climate warming. J. Biogeogr. 38, 406–416 (2011).
Google Scholar 
Graves, G. R. & Rahbek, C. Source pool geometry and the assembly of continental avifaunas. Proc. Natl Acad. Sci. USA 102, 7871–7876 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
IPCC Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects (eds Barros, V. R. et al.) (Cambridge Univ. Press, 2014).Hoegh-Guldberg, O. et al. in Special Report on Global Warming of 1.5 °C (eds Masson-Delmotte, V. et al.) 175–311 (IPCC, WMO, 2018).Watson, J. E. M., Iwamura, T. & Butt, N. Mapping vulnerability and conservation adaptation strategies under climate change. Nat. Clim. Change 3, 989–994 (2013).
Google Scholar 
Martín González, A. M., Dalsgaard, B. & Olesen, J. M. Centrality measures and the importance of generalist species in pollination networks. Ecol. Complex. 7, 36–43 (2010).
Google Scholar 
Burgos, E. et al. Why nestedness in mutualistic networks? J. Theor. Biol. 249, 307–313 (2007).PubMed 

Google Scholar 
Bersier, L.-F., Banašek-Richter, C. & Cattin, M.-F. Quantitative descriptors of food-web matrices. Ecology 83, 2394–2407 (2002).
Google Scholar 
Thébault, E. & Fontaine, C. Stability of ecological communities and the architecture of mutualistic and trophic networks. Science 329, 853–856 (2010).PubMed 

Google Scholar 
Tylianakis, J. M., Laliberté, E., Nielsen, A. & Bascompte, J. Conservation of species interaction networks. Biol. Conserv. 143, 2270–2279 (2010).
Google Scholar 
Grass, I., Jauker, B., Steffan-Dewenter, I., Tscharntke, T. & Jauker, F. Past and potential future effects of habitat fragmentation on structure and stability of plant–pollinator and host–parasitoid networks. Nat. Ecol. Evol. 2, 1408–1417 (2018).PubMed 

Google Scholar 
Stouffer, D. B. & Bascompte, J. Compartmentalization increases food-web persistence. Proc. Natl Acad. Sci. USA 108, 3648–3652 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Blüthgen, N., Menzel, F. & Blüthgen, N. Measuring specialization in species interaction networks. BMC Ecol. 6, 9 (2006).PubMed 
PubMed Central 

Google Scholar 
Dormann, C. F. & Strauss, R. A method for detecting modules in quantitative bipartite networks. Methods Ecol. Evol. 5, 90–98 (2014).
Google Scholar 
Bascompte, J., Jordano, P., Melián, C. J. & Olesen, J. M. The nested assembly of plant–animal mutualistic networks. Proc. Natl Acad. Sci. USA 100, 9383–9387 (2003).CAS 
PubMed 
PubMed Central 

Google Scholar 
Rahbek, C. et al. Humboldt’s enigma: what causes global patterns of mountain biodiversity? Science 365, 1108–1113 (2019).CAS 

Google Scholar 
Cracraft, J. Historical biogeography and patterns of differentiation within the South American avifauna: areas of endemism. Ornithol. Monogr. 36, 49–84 (1985).
Google Scholar 
Hazzi, N. A., Moreno, J. S., Ortiz-Movliav, C. & Palacio, R. D. Biogeographic regions and events of isolation and diversification of the endemic biota of the tropical Andes. Proc. Natl Acad. Sci. USA 115, 7985–7990 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Jønsson, K. A. et al. Tracking animal dispersal: from individual movement to community assembly and global range dynamics. Trends Ecol. Evol. 31, 204–214 (2016).PubMed 

Google Scholar 
McGuire, J. A. et al. Molecular phylogenetics and the diversification of hummingbirds. Curr. Biol. 24, 910–916 (2014).CAS 
PubMed 

Google Scholar 
Proctor, M., Yeo, P. & Lack, A. The Natural History of Pollination (HarperCollins, 1996).Simberloff, D. S. & Wilson, E. O. Experimental zoogeography of islands: the colonization of empty islands. Ecology 50, 278–296 (1969).
Google Scholar 
Connor, E. F. & Simberloff, D. Species number and compositional similarity of the Galapagos flora and avifauna. Ecol. Monogr. 48, 219–248 (1978).
Google Scholar 
Grant, P. R. & Abbott, I. Interspecific competition, island biogeography and null hypotheses. Evolution 34, 332–341 (1980).CAS 
PubMed 

Google Scholar 
Thomas, C. D. Climate, climate change and range boundaries. Divers. Distrib. 16, 488–495 (2010).
Google Scholar 
Almeida-Neto, M., Guimarães, P., Guimarães, P. R. Jr, Loyola, R. D. & Ulrich, W. A consistent metric for nestedness analysis in ecological systems: reconciling concept and measurement. Oikos 117, 1227–1239 (2008).
Google Scholar 
Simmons, B. I. et al. Moving from frugivory to seed dispersal: incorporating the functional outcomes of interactions in plant–frugivore networks. J. Anim. Ecol. 87, 995–1007 (2018).PubMed 
PubMed Central 

Google Scholar 
Benadi, G., Blüthgen, N., Hovestadt, T. & Poethke, H.-J. Contrasting specialization–stability relationships in plant–animal mutualistic systems. Ecol. Model. 258, 65–73 (2013).
Google Scholar 
Beckett, S. J. Improved community detection in weighted bipartite networks. R. Soc. Open Sci. 3, 140536 (2016).PubMed 
PubMed Central 

Google Scholar 
Sonne, J. et al. Ecological mechanisms explaining interactions within plant–hummingbird networks: morphological matching increases towards lower latitudes. Proc. R. Soc. B 287, 20192873 (2020).PubMed 
PubMed Central 

Google Scholar 
Patefield, W. Algorithm AS 159: an efficient method of generating random R × C tables with given row and column totals. J. R. Stat. Soc. C 30, 91–97 (1981).
Google Scholar 
Dalsgaard, B. et al. Opposed latitudinal patterns of network‐derived and dietary specialization in avian plant–frugivore interaction systems. Ecography 40, 1395–1401 (2017).
Google Scholar 
Dormann, C. F., Gruber, B. & Fründ, J. Introducing the bipartite package: analysing ecological networks. R News 8, 8–11 (2008).Holt, B. G. et al. An update of Wallace’s zoogeographic regions of the world. Science 339, 74–78 (2013).CAS 
PubMed 

Google Scholar 
Two-Minute Gridded Global Relief Data (ETOPO2) v. 2 (NOAA National Geophysical Data Center, 2006); https://doi.org/10.7289/V5J1012QJetz, W. & Rahbek, C. Geographic range size and determinants of avian species richness. Science 297, 1548–1551 (2002).CAS 
PubMed 

Google Scholar 
Dobzhansky, T. Evolution in the tropics. Am. Sci. 38, 209–221 (1950).
Google Scholar 
Currie, D. J., Francis, A. P. & Kerr, J. T. Some general propositions about the study of spatial patterns of species richness. Écoscience 6, 392–399 (1999).
Google Scholar 
Hurlbert et al. The effect of energy and seasonality on avian species richness and community composition. Am. Nat. 161, 83–97 (2003).PubMed 

Google Scholar 
Karger, D. N. et al. Climatologies at high resolution for the Earth’s land surface areas. Sci. Data 4, 170122 (2017).PubMed 
PubMed Central 

Google Scholar 
Mateo, R. G., Felicísimo, Á. M. & Muñoz, J. Effects of the number of presences on reliability and stability of MARS species distribution models: the importance of regional niche variation and ecological heterogeneity. J. Veg. Sci. 21, 908–922 (2010).
Google Scholar 
Blonder, B. et al. Linking environmental filtering and disequilibrium to biogeography with a community climate framework. Ecology 96, 972–985 (2015).PubMed 

Google Scholar 
Vizentin-Bugoni, J., Debastiani, V. J., Bastazini, V. A. G., Maruyama, P. K. & Sperry, J. H. Including rewiring in the estimation of the robustness of mutualistic networks. Methods Ecol. Evol. 11, 106–116 (2020).
Google Scholar 
Rahbek, C., Borregaard, M. K., Hermansen, B., Nogues-Bravo, D. & Fjeldså, J. Definition and Description of the Montane Regions of the World (Center for Macroecology, Evolution and Climate, 2019); https://macroecology.ku.dk/resources/mountain_regions/definition-and-description-of-the-montane-regions-of-the-world_kopi/Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biom. J. 50, 346–363 (2008).PubMed 

Google Scholar  More

Pereira, H. M. et al. Scenarios for global biodiversity in the 21st century. Science 330, 1496–1501 (2010).CAS 
PubMed 

Google Scholar 
Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W. & Courchamp, F. Impacts of climate change on the future of biodiversity. Ecol. Lett. 15, 365–377 (2012).PubMed 
PubMed Central 

Google Scholar 
Chen, I.-C., Hill, J. K., Ohlemüller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011).CAS 
PubMed 

Google Scholar 
Steinbauer, M. J. et al. Accelerated increase in plant species richness on mountain summits is linked to warming. Nature 556, 231–234 (2018).CAS 
PubMed 

Google Scholar 
Feeley, K. J., Bravo-Avila, C., Fadrique, B., Perez, T. M. & Zuleta, D. Climate-driven changes in the composition of New World plant communities. Nat. Clim. Change 10, 965–970 (2020).CAS 

Google Scholar 
Radeloff, V. C. et al. The rise of novelty in ecosystems. Ecol. Appl. 25, 2051–2068 (2015).PubMed 

Google Scholar 
Davis, A. J., Jenkinson, L. S., Lawton, J. H., Shorrocks, B. & Wood, S. Making mistakes when predicting shifts in species range in response to global warming. Nature 391, 783–786 (1998).CAS 
PubMed 

Google Scholar 
Suttle, K. B., Thomsen, M. A. & Power, M. E. Species interactions reverse grassland responses to changing climate. Science 315, 640–642 (2007).CAS 
PubMed 

Google Scholar 
van der Putten, W. H., Macel, M. & Visser, M. E. Predicting species distribution and abundance responses to climate change: why it is essential to include biotic interactions across trophic levels. Proc. R. Soc. B 365, 2025–2034 (2010).
Google Scholar 
Gaüzère, P., Iversen, L. L., Barnagaud, J.-Y., Svenning, J.-C. & Blonder, B. Empirical predictability of community responses to climate change. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2018.00186 (2018).Mangan, S. A. et al. Negative plant–soil feedback predicts tree-species relative abundance in a tropical forest. Nature 466, 752–755 (2010).CAS 
PubMed 

Google Scholar 
Bennett, J. A. et al. Plant–soil feedbacks and mycorrhizal type influence temperate forest population dynamics. Science 355, 181–184 (2017).CAS 
PubMed 

Google Scholar 
Teste, F. P. et al. Plant–soil feedback and the maintenance of diversity in Mediterranean-climate shrublands. Science 355, 173–176 (2017).CAS 
PubMed 

Google Scholar 
Kardol, P., Bezemer, T. M. & van der Putten, W. H. Temporal variation in plant–soil feedback controls succession. Ecol. Lett. 9, 1080–1088 (2006).PubMed 

Google Scholar 
van der Putten, W. H., van Dijk, C. & Peters, B. A. M. Plant-specific soil-borne diseases contribute to succession in foredune vegetation. Nature 362, 53–56 (1993).
Google Scholar 
Bever, J. D. Feedback between plants and their soil communities in an old field community. Ecology 75, 1965–1977 (1994).
Google Scholar 
Bever, J. D., Westover, K. M. & Antonovics, J. Incorporating the soil community into plant population dynamics: the utility of the feedback approach. J. Ecol. 85, 561–573 (1997).
Google Scholar 
Chesson, P. Mechanisms of maintenance of species diversity. Annu. Rev. Ecol. Syst. 31, 343–366 (2000).
Google Scholar 
Bever, J. D. Soil community feedback and the coexistence of competitors: conceptual frameworks and empirical tests. New Phytol. 157, 465–473 (2003).PubMed 

Google Scholar 
Revilla, T. A., Veen, G. F., Eppinga, M. B. & Weissig, F. J. Plant–soil feedbacks and the coexistence of competing plants. Theor. Ecol. 6, 99–113 (2013).
Google Scholar 
Molofsky, J. & Bever, J. D. A novel theory to explain species diversity in landscapes: positive frequency dependence and habitat suitability. Proc. R. Soc. B 269, 2389–2393 (2002).PubMed 
PubMed Central 

Google Scholar 
Ke, P. J. & Wan, J. Effects of soil microbes on plant competition: a perspective from modern coexistence theory. Ecol. Monogr. 90, e01391 (2020).
Google Scholar 
Mack, K. M. L. & Bever, J. D. Coexistence and relative abundance in plant communities are determined by feedbacks when the scale of feedback and dispersal is local. J. Ecol. 102, 1195–1201 (2014).PubMed 
PubMed Central 

Google Scholar 
Bauer, J. T., Mack, K. M. L. & Bever, J. D. Plant–soil feedbacks as drivers of succession: evidence from remnant and restored tallgrass prairies. Ecosphere 6, art158 (2015).
Google Scholar 
Kulmatiski, A., Beard, K. H., Grenzer, J., Forero, L. & Heavilin, J. Using plant–soil feedbacks to predict plant biomass in diverse communities. Ecology 97, 2064–2073 (2016).PubMed 

Google Scholar 
Reinhart, K. O. et al. Globally, plant–soil feedbacks are weak predictors of plant abundance. Ecol. Evol. 11, 1756–1768 (2021).PubMed 
PubMed Central 

Google Scholar 
Casper, B. B. & Castelli, J. P. Evaluating plant–soil feedback together with competition in a serpentine grassland. Ecol. Lett. 10, 394–400 (2007).PubMed 

Google Scholar 
Shannon, S., Flory, S. L. & Reynolds, H. Competitive context alters plant–soil feedback in an experimental woodland community. Oecologia 169, 235–243 (2012).PubMed 

Google Scholar 
Lekberg, Y. et al. Relative importance of competition and plant–soil feedback, their synergy, context dependency and implications for coexistence. Ecol. Lett. 21, 1268–1281 (2018).PubMed 

Google Scholar 
Kostenko, O., van de Voorde, T. F. J., Mulder, P. P. J., van der Putten, W. H. & Bezemer, M. T. Legacy effects of aboveground–belowground interactions. Ecol. Lett. 15, 813–821 (2012).PubMed 

Google Scholar 
Bezemer, M. T. et al. Above- and below-ground herbivory effects on below-ground plant–fungus interactions and plant–soil feedback responses. J. Ecol. 101, 325–333 (2013).
Google Scholar 
Classen, A. T. et al. Direct and indirect effects of climate change on soil microbial and soil microbial–plant interactions: what lies ahead? Ecosphere 6, art130 (2015).
Google Scholar 
McCarthy-Neumann, S. & Kobe, R. K. Site soil-fertility and light availability influence plant–soil feedback. Front. Ecol. Evol. 7, 383 (2019).
Google Scholar 
Smith-Ramesh, L. M. & Reynolds, H. L. The next frontier of plant–soil feedback research: unraveling context dependence across biotic and abiotic gradients. J. Veg. Sci. 28, 484–494 (2017).
Google Scholar 
Crawford, K. M. et al. When and where plant–soil feedback may promote plant coexistence: a meta-analysis. Ecol. Lett. 22, 1274–1284 (2019).PubMed 

Google Scholar 
de Long, J. R., Fry, E. L., Veen, G. F. & Kardol, P. Why are plant–soil feedbacks so unpredictable, and what to do about it? Funct. Ecol. 33, 118–128 (2019).
Google Scholar 
Beals, K. K. et al. Predicting plant–soil feedback in the field: meta-analysis reveals that competition and environmental stress differentially influence PSF. Front. Ecol. Evol. 8, 191 (2020).
Google Scholar 
van der Putten, W. H., Bradford, M. A., Brinkman, P. E., van de Voorde, T. F. J. & Veen, G. F. Where, when and how plant–soil feedback matters in a changing world. Funct. Ecol. 30, 1109–1121 (2016).
Google Scholar 
Pugnaire, F. I. et al. Climate change effects on plant–soil feedbacks and consequences for biodiversity and functioning of terrestrial ecosystems. Sci. Adv. 5, eaaz1834 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Trenberth, K. E. Changes in precipitation with climate change. Clim. Res. 47, 123–138 (2011).
Google Scholar 
Pendergrass, A. G., Knutti, R., Lehner, F., Deser, C. & Sanderson, B. M. Precipitation variability increases in a warmer climate. Sci. Rep. 7, 17966 (2017).PubMed 
PubMed Central 

Google Scholar 
Fierer, N., Schimel, J. P. & Holden, P. A. Influence of drying–rewetting frequency on soil bacterial community structure. Microb. Ecol. 45, 63–71 (2003).CAS 
PubMed 

Google Scholar 
Drenovsky, R. E., Vo, D., Graham, K. J. & Scow, K. M. Soil water content and organic carbon availability are major determinants of soil microbial community composition. Microb. Ecol. 48, 424–430 (2004).CAS 
PubMed 

Google Scholar 
Brockett, B. F., Prescott, C. E. & Grayston, S. J. Soil moisture is the major factor influencing microbial community structure and enzyme activities across seven biogeoclimatic zones in western Canada. Soil Biol. Biochem. 44, 9–20 (2012).CAS 

Google Scholar 
Manzoni, S., Schimel, J. P. & Porporato, A. Responses of soil microbial communities to water stress: results from a meta-analysis. Ecology 93, 930–938 (2012).PubMed 

Google Scholar 
de Vries, F. T. et al. Soil bacterial networks are less stable under drought than fungal networks. Nat. Commun. 9, 3033 (2018).PubMed 
PubMed Central 

Google Scholar 
de Oliveira, T. B. et al. Fungal communities differentially respond to warming and drought in tropical grassland soil. Mol. Ecol. 29, 1550–1559 (2020).PubMed 

Google Scholar 
Eastburn, D. M., McElrone, A. J. & Bilgin, D. D. Influence of atmospheric and climatic change on plant–pathogen interactions. Plant Pathol. 60, 54–69 (2011).
Google Scholar 
Suzuki, N., Rivero, R. M., Shulaev, V., Blumwald, E. & Mittler, R. Abiotic and biotic stress combinations. New Phytol. 203, 32–43 (2014).PubMed 

Google Scholar 
Cavagnaro, T. R. Soil moisture legacy effects: impacts on soil nutrients, plants and mycorrhizal responsiveness. Soil Biol. Biochem. 95, 173–179 (2016).CAS 

Google Scholar 
Crawford, K. M. & Hawkes, C. V. Soil precipitation legacies influence intraspecific plant–soil feedback. Ecology 101, e03142 (2020).PubMed 

Google Scholar 
Fry, E. L. et al. Drought neutralises plant–soil feedback of two mesic grassland forbs. Oecologia 186, 1113–1125 (2018).PubMed 
PubMed Central 

Google Scholar 
Snyder, A. E. & Harmon-Threatt, A. N. Reduced water-availability lowers the strength of negative plant–soil feedbacks of two Asclepias species. Oecologia 190, 425–432 (2019).PubMed 

Google Scholar 
Kulmatiski, A., Beard, K. H., Stevens, J. R. & Cobbold, S. M. Plant–soil feedbacks: a meta-analytical review. Ecol. Lett. 11, 980–992 (2008).PubMed 

Google Scholar 
Brinkman, P. E., van der Putten, W. H., Bakker, E.-J. & Verhoeven, K. J. Plant–soil feedback: experimental approaches, statistical analyses and ecological interpretations. J. Ecol. 98, 1063–1073 (2010).
Google Scholar 
Bever, J. D. Negative feedback within a mutualism: host-specific growth of mycorrhizal fungi reduces plant benefit. Proc. R. Soc. B 269, 2595–2601 (2002).PubMed 
PubMed Central 

Google Scholar 
Castelli, J. P. & Casper, B. B. Intraspecific AM fungal variation contributes to plant–fungal feedback in a serpentine grassland. Ecology 84, 323–336 (2003).
Google Scholar 
Mangan, S. A., Herre, E. A. & Bever, J. D. Specificity between neotropical tree seedlings and their fungal mutualists leads to plant–soil feedback. Ecology 91, 2594–2603 (2010).PubMed 

Google Scholar 
Bever, J. D., Mangan, S. A. & Alexander, H. M. Maintenance of plant species diversity by pathogens. Annu. Rev. Ecol. Evol. Syst. 46, 305–325 (2015).
Google Scholar 
Gilbert, G. S. & Parker, I. M. The evolutionary ecology of plant disease: a phylogenetic perspective. Annu. Rev. Phytopathol. 54, 549–578 (2016).CAS 
PubMed 

Google Scholar 
Milici, V. R., Dalui, D., Mickley, J. G. & Bagchi, R. Responses of plant–pathogen interactions to precipitation: implications for tropical tree richness in a changing world. J. Ecol. 108, 1800–1809 (2020).
Google Scholar 
Kaisermann, A., de Vries, F. T., Griffiths, R. I. & Bardgett, R. D. Legacy effects of drought on plant–soil feedbacks and plant–plant interactions. New Phytol. 215, 1413–1424 (2017).CAS 
PubMed 

Google Scholar 
Revillini, D., Gehring, C. A. & Johnson, N. C. The role of locally adapted mycorrhizas and rhizobacteria in plant–soil feedback systems. Funct. Ecol. 30, 1086–1098 (2016).
Google Scholar 
Ji, B. & Bever, J. D. Plant preferential allocation and fungal reward decline with soil phosphorus: implications for mycorrhizal mutualism. Ecosphere 7, e01256 (2016).
Google Scholar 
Rubin, R. L., van Groenigen, K. J. & Hungate, B. A. Plant growth promoting rhizobacteria are more effective under drought: a meta-analysis. Plant Soil 416, 309–323 (2017).CAS 

Google Scholar 
Brinkman, E. P., Duyts, H., Karssen, G., van der Stoel, C. D. & van der Putten, W. H. Plant-feeding nematodes in coastal sand dunes: occurrence, host specificity and effects on plant growth. Plant Soil 397, 17–30 (2015).CAS 

Google Scholar 
Hoeksema, J. D. et al. A meta-analysis of context-dependency in plant response to inoculation with mycorrhizal fungi. Ecol. Lett. 13, 394–407 (2010).PubMed 

Google Scholar 
Chase, J. M. Community assembly: when should history matter? Oecologia 136, 489–498 (2003).PubMed 

Google Scholar 
Fukami, T. Historical contingency in community assembly: integrating niches, species pools, and priority effects. Annu. Rev. Ecol. Evol. Syst. 46, 1–23 (2015).
Google Scholar 
Reinhart, K. O. & Rinella, M. J. A common soil handling technique can generate incorrect estimates of soil biota effects on plants. New Phytol. 210, 786–789 (2016).PubMed 

Google Scholar 
Mehlich, A. Mehlich-3 soil test extractant: a modification of Mehlich-2 extractant. Commun. Soil Sci. Plant Anal. 15, 1409–1416 (1984).CAS 

Google Scholar 
Rhoades, J. D. in Methods of Soil Analysis: Part 2 (eds Page, A. L. et al.) Ch. 10 (American Society of Agronomy and Soil Science Society of America, 1982).Schofield, R. K. & Taylor, A. W. The measurement of soil pH. Soil Sci. Soc. Am. Proc. 19, 164–167 (1955).CAS 

Google Scholar 
Keeney, D. R. in Methods of Soil Analysis: Part 2 (eds Page, A. L. et al.) Ch. 35 (American Society of Agronomy and Soil Science Society of America, 1982).Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Pauvert, C. et al. Bioinformatics matters: the accuracy of plant and soil fungal community data is highly dependent on the metabarcoding pipeline. Fungal Ecol. 41, 23–33 (2019).
Google Scholar 
Abarenkov, K. et al UNITE QIIME Release for Fungi. Version 04.02.2020 (UNITE Community, 2020).Francioli, D., van Ruijven, J., Bakker, L. & Mommer, L. Drivers of total and pathogenic soil-borne fungal communities in grassland plant species. Fungal Ecol. 48, 100987 (2020).
Google Scholar 
Nhu, H. et al. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 20, 241–248 (2016).
Google Scholar 
Brooks, M. B. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).
Google Scholar 
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Google Scholar 
Lou, J. Entropy and diversity. Oikos 113, 363–375 (2006).
Google Scholar 
Oksanen, J. et al. vegan: Community Ecology Package. R version 2.5–7 https://CRAN.R-project.org/package=vegan (2020).Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2020).
Google Scholar 
Wilensky, U. NetLogo http://ccl.northwestern.edu/netlogo (1999).Salecker, J., Sciaini, M., Meyer, K. M. & Wiegand, K. The NLRX R package: a next-generation framework for reproducible NetLogo model analyses. Methods Ecol. Evol. 10, 1854–1863 (2019).
Google Scholar 
Wickham et al. Welcome to the tidyverse. J. Open Source Softw. 4, 1686 (2019).
Google Scholar 
R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020). More

Ethics oversightThis study was carried out within the frame of our housing and breeding permit (311.4-si) granted by the Landratsamt Starnberg, Germany. Attachment of backpacks was approved by the Regierung von Oberbayern, Germany (ROB-55-2-2532. Vet_02-17-211).Study populationsWe used four zebra finch populations that are genetically differentiated due to founder effects and selection (see Supplementary Fig. 1 & Fig. 2): two domesticated populations (D1 and D2) that have been maintained in captivity in Europe for about 150 years and two populations (W1 and W2) that have been taken from the wild about 10–30 years ago (see Supplementary Fig. 1). We ran all experiments in two independent replicates. We used individuals from populations D1 and W1 for replicate 1 and individuals from D2 and W2 for replicate 2.Breeding experiment Generation 1We created four groups of 36 individuals (9 males and 9 females from both a domesticated and a wild-derived population, two groups within each replicate) and put each group separately in an indoor aviary (5 m × 2.0 m × 2.5 m). All individuals had been reared normally by their genetic parents in similar breeding aviaries, were inexperienced (never mated before) and unfamiliar to all opposite-sex individuals. In replicate 1 (W1 – D1, starting December 2016), birds were 142 ± 32 days old at the start of the experiment (range: 101–191 days); in replicate 2 (W2 – D2, starting March 2017), birds were 241 ± 47 days old (range: 151–306 days). In each aviary, we provided nest material and nest boxes to stimulate breeding and observed pair-bonding behaviour for ca. 60 h spread over 14 days. Two observers recorded all instances of allopreening, sitting in bodily contact, and visiting a nest box together, which reflects pair bonding64.In total, we observed 3166 instances of heterosexual association among the 4 × 36 individuals (Supplementary Table 3). We defined a pair-bond between two opposite-sex individuals if they were recorded in pair-bonding behaviour at least five times (mean: 22 ± 14 SD, range: 5 – 73). This cut-off was chosen (blind to the outcome of data analysis) based on the frequency distribution showing a clear deviation from a random, zero-truncated Poisson distribution (Supplementary Fig. 8). Using this definition, we identified a total of 60 pairs (30 in each replicate). Of all females, 48 and 6 had a pair-bond with one and two males, respectively (18 females remained unpaired). Conversely, 34, 10, and 2 males had a pair-bond with one, two, and three females, respectively (26 males remained unpaired).Cross-fostering for Generation 2 experimentsAfter the breeding experiment of Generation 1, in 2017, we established two different cultural lineages within each genetic population by cross-fostering eggs, either within or between populations (Fig. 3). For this purpose, we used 16 aviaries (four per population), each containing 8 males and 8 females of the same population (Generation 1). Individuals were allowed to freely form pairs and breed. We reciprocally exchanged eggs shortly after laying between two aviaries per population (within-population cross-fostering) and between pairs of aviaries from different populations (between-population cross-fostering). This resulted in four cultural lineages per replicate (DD, DW, WD, and WW; Fig. 3). Each lineage was maintained in two separate breeding aviaries to ensure the availability of unfamiliar opposite-sex Generation 2 individuals from the same line. Offspring remained with their foster parents until they reached sexual maturity, when the following experiment started.Social experiment Generation 2Between December 2017 and March 2018, we put four groups of individuals (two groups for each replicate) in indoor aviaries (same as in Generation 1 experiment). Each group consisted of 10 males and 10 females from each of the cross-fostered groups DD, WW, DW and WD, i.e., a total of 80 birds per aviary, except that one aviary of replicate 2 only consisted of 63 individuals (7DD, 8WW, 8DW and 8WD) due to a shortage of birds. In replicate 1 (W1 – D1, starting December 2017), birds were 170 ± 25 days old at the start of the experiment (range: 105–199 days); in replicate 2 (W2 – D2, starting January 2018), birds were 200 ± 29 days old (range: 120–241 days). We recorded the position of individuals using an automated barcode-based tracking system31. We fitted each individual with a unique machine-readable barcode (Supplementary Fig. 4a) and placed eight cameras (8-megapixel Camera Module V2; RS Components Ltd and Allied Electronics Inc.), each connected to a Raspberry Pi (Raspberry Pi 3 Model Bs; Raspberry Pi Foundation) in each aviary. For 30 consecutive days, the cameras recorded individuals at six perches and at two feeders (Supplementary Fig. 4b, c). Between 05:30 and 20:00, when lights were switched on, each camera took a picture every two seconds.Each day, pictures stored on the Raspberry Pis were downloaded to a central server and processed using customised scripts. The customised software used the PinPoint library in Python65 to identify each barcode in each picture, allowing us to simultaneously track the position and orientation of each individual (Supplementary Fig. 4b) for the duration of the experiment. The tracking system generated 118 million observations across all four aviaries (Supplementary Fig. 4c). From these data, we extracted the average distance between the male and the female (in mm) for each male-female dyad, either daily or across the entire 30-day period (for comparison, such distance data were also extracted for all male-male and all female-female dyads). We used this dataset to identify the nearest opposite-sex individual for each of 151 males and females (55% of these 151 associations were reciprocal). Out of 151 nearest males to females, 74 (49%) paired with that female in the following breeding experiment (see below) and this proportion strongly increased as the average distance between partners decreased (Supplementary Fig. 9).Breeding experiment Generation 2Immediately after the social experiment, we moved each group into a separate semi-outdoor aviary (5 m × 2.5 m × 2.5 m) and provided nest material and nest boxes. During the next 2 months, three observers scored heterosexual associations to identify pair bonds as described for ‘breeding experiment Generation 1’ (ca 300 h per replicate). In total, we observed 6072 associations involving 284 individuals (Supplementary Table 3). Consistent with the previous experiment, we defined a pair-bond when a male-female dyad was observed in pair-bonding behaviour at least five times during the entire experiment (mean: 18 ± 13 SD range: 5 – 61; Supplementary Fig. 8). Using this definition, we identified 147 pairs (79 pairs in replicate 1 and 68 in replicate 2). Of all males, 97, 22 and 2 had a pair-bond with 1, 2 and 3 females, respectively (27 males remained unpaired). Conversely, 99, 21 and 2 females had a pair-bond with 1, 2 and 3 males (26 females remained unpaired).Breeding experiment Generation 3Between April and December 2018, we housed the four cultural lineages (DD, WW, DW and WD) separately again. We placed 8 males and 8 females in each of 16 breeding aviaries (four per lineage) and allowed them to freely form pairs and breed. The offspring belong to four lineages (Fig. 3): two lineages with individuals that were raised by parents that had not been cross-fostered between the domestic and wild-derived population (DDD and WWW) and two lineages with individuals from the same genetic background, but where their parents had been cross-fostered and raised by the other population (DDW and WWD).Between December 2018 and February 2019, we put four groups of 36 birds (two per replicate, i.e., 2 with 18 DDD and 18 DDW individuals and 2 with 18 WWW and 18 WWD individuals; 9 males and 9 females per lineage; Supplementary Table 3) in an outdoor aviary (same as above). In replicate 1 (W1 – D1, starting December 2018), birds were 172 ± 44 days old at the start of the experiment (range: 131–195 days); in replicate 2 (W2 – D2, starting January 2019), birds were 191 ± 40 days old (range: 122–230 days). During 14 days, two observers recorded all pair-bond behaviours as described under ‘breeding experiment Generation 1’. In total, we observed 3378 instances of pair-bond behaviour involving 137 individuals (Supplementary Table 3). As above, we defined a pair-bond when a male-female dyad was observed in pair-bonding behaviour at least five times during the entire experiment (mean: 18 ± 11 SD, range: 5 – 47; Supplementary Fig. 8). We identified 82 pair bonds (37 in replicate 1 and 45 in replicate 2). Of all males, 34, 16, 4 and 1 had a pair-bond with 1, 2, 3 and 4 females, respectively (17 males remained unpaired). Conversely, 42, 16, 1 and 1 females had a pair-bond with 1, 2, 3 and 5 males, respectively (12 females remained unpaired).Morphological measurementsAfter birds had reached sexual maturity ( >100 days of age), we measured body mass (to the nearest 0.1 g), tarsus length (to the nearest 0.1 mm), and wing length (to the nearest 0.5 mm) of all individuals (all measured by WF). We included these three variables in a principal component analysis (PCA) and used the first principal component (PC1, 67% of variation explained) as a measure of body size.Song recording and analysis approachWe recorded the songs of the parental males from Generation 1 (16 aviaries x 8 males = 128 males, of which 122 were successfully recorded between November and December in 2017) and of their offspring (Generation 2; 146 out of 152 males were successfully recorded between March and May 2018). To elicit courtship song, each male was placed together with an unfamiliar female in a metal wire cage (50 cm × 30 cm × 40 cm) equipped with three perches and containing food and water. The cage was placed within one of two identical sound-attenuated chambers. We mounted a Behringer condenser microphone (TC20, Earthworks, USA) at a 45° angle between the ceiling and the side wall of the chamber, such that the distance to each perch was approximately 35 cm. The microphone was connected to a PR8E amplifier (SM Pro Audio, Melbourne, Australia) from which we recorded directly through a M-Audio Delta 44 sound card (AVID Technology GmbH, Hallbergmoos, Germany) onto the hard drive of a computer.Previous studies that quantified differentiation of songs between zebra finch populations using specific song parameters (e.g., duration and frequency measures) largely failed to detect prominent differences12,49,50. We, therefore, used the following two approaches (Sound Analysis Pro and Machine Learning) to quantify the extent to which a given male’s song resembled the songs of other males.Song similarity analysis with SAPUsing Sound Analysis Pro (SAP) version 2011.10427, we quantified song similarity (ranging from 0 to 100) by direct pairwise comparison of song motifs (the main part of a male’s song that is stereotypically repeated and about 0.8 s long, excluding introductory syllables). Pair-wise comparisons of two males (based on one representative motif recording per male) revealed higher within-population similarity than between-population similarity (Supplementary Table 2, data from Generation 1). Further, for offspring that were cross-fostered between populations (N = 73 males from Generation 2) song similarity to their foster father was higher than song similarity to their genetic father (80 versus 68, paired t-test: p  More

Connor, E. F. & Taverner, M. P. The evolution and adaptive significance of the leaf-mining habit. Oikos 79, 6–25. https://doi.org/10.2307/3546085 (1997).Article 

Google Scholar 
Hespenheide, H. A. Bionomics of leaf-mining insects. Annu. Rev. Entomol. 36, 535–560. https://doi.org/10.1146/annurev.en.36.010191.002535 (1991).Article 

Google Scholar 
Kato, M. Structure, organization, and response of a species-rich parasitoid community to host leafminer population dynamics. Oecologia 97, 17–25 (1994).ADS 
Article 

Google Scholar 
López, R., Carmona, D., Vincini, A. M., Monterubbianesi, G. & Caldiz, D. Population dynamics and damage caused by the leafminer Liriomyza huidobrensis Blanchard (Diptera: Agromyzidae), on seven potato processing varieties grown in temperate environment. Neotrop. Entomol. 39, 108–114. https://doi.org/10.1590/S1519-566X2010000100015 (2010).Article 
PubMed 

Google Scholar 
Lopez-Vaamonde, C., Godfray, H. C. J. & Cook, J. M. Evolutionary dynamics of host-plant use in a genus of leaf-mining moths. Evolution 57, 1804–1821. https://doi.org/10.1111/j.0014-3820.2003.tb00588.x (2003).Article 
PubMed 

Google Scholar 
Lopez-Vaamonde, C. et al. Fossil-calibrated molecular phylogenies reveal that leaf-mining moths radiated millions of years after their host plants. J. Evol. Biol. 19, 1314–1326. https://doi.org/10.1111/j.1420-9101.2005.01070.x (2006).CAS 
Article 
PubMed 

Google Scholar 
Scheffer, S. J., Lewis, M. L., Hébert, J. B. & Jacobsen, F. Diversity and host plant-use in North American Phytomyza Holly Leafminers (Diptera: Agromyzidae): Colonization, divergence, and specificity in a host-associated radiation. Ann. Entomol. Soc. Am. 114, 59–69. https://doi.org/10.1093/aesa/saaa034 (2021).CAS 
Article 

Google Scholar 
Tooker, J. F. & Giron, D. The evolution of endophagy in herbivorous insects. Front. Plant Sci. 11, 581816. https://doi.org/10.3389/fpls.2020.581816 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Hawkins, B. A. Pattern and Process in Host-Parasitoid Interactions (Cambridge University Press, 1994).Book 

Google Scholar 
Novotny, V. & Basset, Y. Host specificity of insect herbivores in tropical forests. Proc. R. Soc. B Biol. Sci. 272, 1083–1090. https://doi.org/10.1098/rspb.2004.3023 (2005).Article 

Google Scholar 
Lewis, O. T. et al. Structure of a diverse tropical forest insect-parasitoid community. J. Anim. Ecol. 71, 855–873. https://doi.org/10.1046/j.1365-2656.2002.00651.x (2002).Article 

Google Scholar 
Hirao, T. & Murakami, M. Quantitative food webs of lepidopteran leafminers and their parasitoids in a Japanese deciduous forest. Ecol. Res. 23, 159–168. https://doi.org/10.1007/s11284-007-0351-6 (2008).Article 

Google Scholar 
Pocock, M. J. O., Evans, D. M. & Memmott, J. The robustness and restoration of a network of ecological networks. Science 335, 973–977. https://doi.org/10.1126/science.1214915 (2012).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Leppänen, S. A., Altenhofer, E., Liston, A. D. & Nyman, T. Phylogenetics and evolution of host-plant use in leaf-mining sawflies (Hymenoptera: Tenthredinidae: Heterarthrinae). Mol. Phylogenet. Evol. 64, 331–341. https://doi.org/10.1016/j.ympev.2012.04.005 (2012).Article 
PubMed 

Google Scholar 
Doorenweerd, C., Van Nieukerken, E. J. & Menken, S. B. J. A global phylogeny of leafmining Ectoedemia moths (Lepidoptera: Nepticulidae): Exploring host plant family shifts and allopatry as drivers of speciation. PLoS ONE 10, 1–20. https://doi.org/10.1371/journal.pone.0119586 (2015).CAS 
Article 

Google Scholar 
Nakadai, R. & Kawakita, A. Phylogenetic test of speciation by host shift in leaf cone moths (Caloptilia) feeding on maples (Acer). Ecol. Evol. 6, 4958–4970. https://doi.org/10.1002/ece3.2266 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Opler, P. A. Fossil lepidopterous leaf mines demonstrate the age of some insect-plant relationships. Science 179, 1321–1323. https://doi.org/10.1126/science.179.4080.1321 (1973).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Labandeira, C. C., Dilcher, D. L., Davis, D. R. & Wagner, D. L. Ninety-seven million years of angiosperm-insect association: Paleobiological insights into the meaning of coevolution. Proc. Natl. Acad. Sci. U. S. A. 91, 12278–12282. https://doi.org/10.1073/pnas.91.25.12278 (1994).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Winkler, I. S., Labandeira, C. C., Wappler, T. & Wilf, P. Distinguishing Agromyzidae (Diptera) leaf mines in the fossil record: New taxa from the Paleogene of North America and Germany and their evolutionary implications. J. Paleontol. 84, 935–954. https://doi.org/10.1666/09-163.1 (2010).Article 

Google Scholar 
van Nieukerken, E. J., Doorenweerd, C., Hoare, R. J. B. & Davis, D. R. Revised classification and catalogue of global Nepticulidae and Opostegidae (Lepidoptera, Nepticuloidea). Zookeys 2016, 65–246. https://doi.org/10.3897/zookeys.628.9799 (2016).Article 

Google Scholar 
Maccracken, S. A., Sohn, J.-C., Miller, I. M. & Labandeira, C. C. A new Late Cretaceous leaf mine Leucopteropsa spiralae gen. et sp. nov. (Lepidoptera: Lyonetiidae) represents the first confirmed fossil evidence of the Cemiostominae. J. Syst. Palaeontol. 19, 131–144. https://doi.org/10.1080/14772019.2021.1881177 (2021).Article 

Google Scholar 
Wilf, P., Labandeira, C. C., Johnson, K. R. & Ellis, B. Decoupled plant and insect diversity after the end-Cretaceous extinction. Science 313, 1112–1115. https://doi.org/10.1126/science.1129569 (2006)ADS 
CAS 
Article 
PubMed 

Google Scholar 
Donovan, M. P., Wilf, P., Labandeira, C. C., Johnson, K. R. & Peppe, D. J. Novel insect leaf-mining after the end-Cretaceous extinction and the demise of Cretaceous leaf miners, Great Plains, USA. PLoS ONE 9, e103542. https://doi.org/10.1371/journal.pone.0103542 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Donovan, M. P., Iglesias, A., Wilf, P., Labandeira, C. C. & Cúneo, N. R. Rapid recovery of Patagonian plant–insect associations after the end-Cretaceous extinction. Nat. Ecol. Evol. 1, 0012. https://doi.org/10.1038/s41559-016-0012 (2017).Article 

Google Scholar 
Donovan, M. P., Wilf, P., Iglesias, A., Cúneo, N. R. & Labandeira, C. C. Persistent biotic interactions of a Gondwanan conifer from Cretaceous Patagonia to modern Malesia. Commun. Biol. 3, 708. https://doi.org/10.1038/s42003-020-01428-9 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Labandeira, C. C. The four phases of plant-arthropod associations in deep time. Geol. Acta 4, 409–438. https://doi.org/10.1344/105.000000344 (2006).Article 

Google Scholar 
Labandeira, C. C. Silurian to Triassic plant and hexapod clades and their associations: new data, a review, and interpretations. Arthropod Syst. Phylogen. 64, 53–94 (2006).
Google Scholar 
Wakita, K., Nakagawa, T., Sakata, M., Tanaka, N. & Oyama, N. Phanerozoic accretionary history of Japan and the western Pacific margin. Geol. Mag. https://doi.org/10.1017/s0016756818000742 (2018).Article 

Google Scholar 
Katayama, M. Stratigraphical study on the Mine Series. J. Geol. Soc. Jpn. 46, 127–141. https://doi.org/10.5575/geosoc.46.127 (1939).Article 

Google Scholar 
Maeda, H. & Oyama, N. Stratigraphy and fossil assemblages of the Triassic Mine Group and Jurassic Toyora Group in western Yamaguchi Prefecture. J. Geol. Soc. Japan 125, 585–594. https://doi.org/10.5575/geosoc.2019.0020 (2019).Article 

Google Scholar 
Aizawa, J. Fossil insect-bearing strata of the Triassic Mine Group, Yamaguchi Prefecture. Bull. Kitakyushu Mus. Nat. Hist. Hum. Hist. Ser. A 10, 91–98 (1991).
Google Scholar 
Oyama, N. & Maeda, H. Madygella humioi sp. nov. from the Upper Triassic Mine Group, Southwest Japan: The oldest record of a sawfly (Hymenoptera: Symphyta) in East Asia. Paleontol. Res. 24, 64–71 (2020).Article 

Google Scholar 
Fujiyama, I. Mesozoic insect fauna of East Asia part 1. Introduction and upper Triassic faunas. Bull. Natl. Sci. Mus. 16, 331–386 (1973).
Google Scholar 
Fujiyama, I. Late Triassic insects from Mine, Yamaguchi, Japan, Part 1. Odonata. Bull. Natl. Sci. Mus. Tokyo Ser. C 17, 49–56 (1991).
Google Scholar 
Ueda, K. A Triassic fossil of scorpion fly from Mine, Japan. Bull. Kitakyushu Mus. Nat. Hist. Hum. Hist. Ser. Ser. A 10, 99–103 (1991).
Google Scholar 
Takahashi, F., Ishida, H., Nohara, M., Doi, E. & Taniguchi, S. Occurrence of insect fossils from the Late Triassic Mine Group. Bull. Mine City Mus. Yamaguchi Prefect. Jpn. 13, 1–27 (1997).CAS 

Google Scholar 
Kametaka, M. Provenance of the Upper Triassic mine group Southwest Japan. J. Geol. Soc. Jpn. 105, 651–667 (1999).CAS 
Article 

Google Scholar 
Takahashi, E. & Mikami, T. Triassic. In Geology of Yamaguchi Prefecture (ed. Yamaguchi Museum) 93–108 (Yamaguchi Museum, 1975).Kiminami, K. Atsu Group and Mine Group. In Monograph on Geology of Japan 6, Chugoku Region (ed. Geological Society of Japan) 85–88 (Asakura Publishing Co., Ltd., 2009).Naito, G. Plant Fossils from the Mine Group (Mine City Education Comittee, 2000).
Google Scholar 
Kimura, T. Geographical distribution of Palaeozoic and Mesozoic plants in East and Southeast Asia. Hist. Biogeogr. Plate Tecton. Evol. Jpn. East Asia 1982, 135–200 (1987).
Google Scholar 
Kimura, T., Naito, G. & Ohana, T. Baiera cf. furcata (Lindley and Hutton) Braun from the Carnic Momonoki Formation, Japan. Bull. Natl. Sci. Mus. 9, 91–114 (1983).
Google Scholar 
Katagiri, T. Pallaviciniites oishii (comb. Nov.), a thalloid liverwort from the Late Triassic of Japan. Bryologist 118, 245–251. https://doi.org/10.1639/0007-2745-118.3.245 (2015).Article 

Google Scholar 
Kustatscher, E. et al. Flora of the Late Triassic. In The Late Triassic World, Topics in Geobiology, Vol. 46 (ed. Tanner, L. H.) 545–622 (Springer, 2018). https://doi.org/10.1007/978-3-319-68009-5_13.Oyama, N., Yukawa, H. & Maeda, H. Mesozoic insect fossils of Japan: Significance of the Upper Triassic insect fauna of the Mine Group, Yamaguchi Pref. Bull. Mine City Mus. Yamaguchi Prefect. Jpn. 33, 1–13 (2020).
Google Scholar 
Shcherbakov, D. E., Lukashevich, E. D. & Blagoderov, V. Triassic Diptera and initial radiation of the order. Int. J. Dipterol. Res. 6, 75–115 (1995).
Google Scholar 
Krzemiński, W. & Krzemińska, E. Triassic Diptera: Descriptions, revisions and phylogenetic relations. Acta Zool. Cracov. 46, 153–184 (2003).
Google Scholar 
Blagoderov, V., Grimaldi, D. A. & Fraser, N. C. How time flies for flies: Diverse Diptera from the Triassic of Virginia and early radiation of the order. Am. Mus. Novit. 3572, 1–39. https://doi.org/10.1206/0003-0082(2007)509[1:HTFFFD]2.0.CO;2 (2007).Article 

Google Scholar 
Lukashevich, E. D., Przhiboro, A. A., Marchal-Papier, F. & Grauvogel-Stamm, L. The oldest occurrence of immature Diptera (Insecta), Middle Triassic France. Ann. la Société Entomol. Fr. 46, 4–22. https://doi.org/10.1080/00379271.2010.10697636 (2010).Article 

Google Scholar 
Schmidt, A. R. et al. Arthropods in amber from the Triassic Period. Proc. Natl. Acad. Sci. 109, 14796–14801. https://doi.org/10.1073/pnas.1208464109 (2012).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Lara, M. B. & Lukashevich, E. D. The first Triassic dipteran (Insecta) from South America, with review of Hennigmatidae. Zootaxa 3710, 81–92. https://doi.org/10.11646/zootaxa.3710.1.6 (2013).Article 
PubMed 

Google Scholar 
Kimura, T. & Ohana, T. Some fossil ferns from the Middle Carnic Momonoki Formation, Yamaguchi prefecture, Japan. Bull. Natl. Sci. Mus. Ser. C Geol. Paleontol. 6, 73–92 (1980).
Google Scholar 
Hering, E. M. Biology of the Leaf Miners https://doi.org/10.1007/978-94-015-7196-8. (Springer, 1951).Book 

Google Scholar 
Kirichenko, N. et al. Systematics of Phyllocnistis leaf-mining moths (Lepidoptera, Gracillariidae) feeding on dogwood (Cornus spp.) in Northeast Asia, with the description of three new species. Zookeys 2018, 79–118. https://doi.org/10.3897/zookeys.736.20739 (2018).Article 

Google Scholar 
Cerdeña, J. et al. Phyllocnistis furcata sp. nov.: A new species of leaf-miner associated with Baccharis (Asteraceae) from Southern Peru (Lepidoptera, Gracillariidae). Zookeys 2020, 121–145. https://doi.org/10.3897/zookeys.996.53958 (2020).Article 

Google Scholar 
Elb, P. M., Melo-de-Pinna, G. F. & de Menezes, N. L. Morphology and anatomy of leaf miners in two species of Commelinaceae (Commelina diffusa Burm. F. and Floscopa glabrata (Kunth) Hassk). Acta Bot. Brasilica 24, 283–287. https://doi.org/10.1590/S0102-33062010000100030 (2010).Article 

Google Scholar 
Vasco, A., Moran, R. C. & Ambrose, B. A. The evolution, morphology, and development of fern leaves. Front. Plant Sci. 4, 1–16. https://doi.org/10.3389/fpls.2013.00345 (2013).Article 

Google Scholar 
Eiseman, C. Leafminers of North America. (Charley Eiseman, 2019).Yang, J., Wang, X., Duffy, K. & Dai, X. A preliminary world checklist of fern-mining insects. Biodivers. Data J. 9, e62839. https://doi.org/10.3897/BDJ.9.e62839 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Ding, Q., Labandeira, C. C. & Ren, D. Biology of a leaf miner (Coleoptera) on Liaoningocladus boii (Coniferales) from the Early Cretaceous of northeastern China and the leaf-mining biology of possible insect culprit clades. Arthropod Syst. Phylogen. 72, 281–308 (2014).
Google Scholar 
Boucher, S. Revision of the Canadian species of Amauromyza Hendel (Diptera: Agromyzidae). Can. Entomol. 144, 733–757. https://doi.org/10.4039/tce.2012.80 (2012).Article 

Google Scholar 
Scheirs, J., Vandevyvere, I. & De Bruyn, L. Influence of monocotyl leaf anatomy on the feeding pattern of a grass-mining agromyzid (Diptera). Ann. Entomol. Soc. Am. 90, 646–654 (1997).Article 

Google Scholar 
Boucher, S. Leaf-miner flies (Diptera: Agromyzidae). In Encyclopedia of Entomology (ed. Capinera J. L.) 2163–2169 (Springer, 2008). https://doi.org/10.1007/978-1-4020-6359-6.Eiseman, C. S. New rearing records for muscoid leafminers (Diptera: Anthomyiidae, Scathophagidae) in the United States. Proc. Entomol. Soc. Wash. 120, 25–50. https://doi.org/10.4289/0013-8797.120.1.25 (2018).Article 

Google Scholar 
Meikle, A. A. The insects associated with bracken. Agric. Prog. 14, 58–61 (1937).
Google Scholar 
Lawton, J. H. The structure of the arthropod community on bracken. Bot. J. Linn. Soc. 73, 187–216. https://doi.org/10.1111/j.1095-8339.1976.tb02022.x (1976).Article 

Google Scholar 
Lawton, J. H., MacGarvin, M. & Heads, P. A. Effects of altitude on the abundance and species richness of insect herbivores on bracken. J. Anim. Ecol. 56, 147–160. https://doi.org/10.2307/4805 (1987).Article 

Google Scholar 
Cooper-Driver, Gi. A. Insect-fern associations. Entomol. Exp. Appl. 24, 310–316. https://doi.org/10.1111/j.1570-7458.1978.tb02787.x (1978).Article 

Google Scholar 
Eiseman, C. S. Further Nearctic rearing records for phytophagous muscoid flies (Diptera: Anthomyiidae, Scathophagidae). Proc. Entomol. Soc. Washingt. 122, 595–603. https://doi.org/10.4289/0013-8797.122.3.595 (2020).Article 

Google Scholar 
Santos, M. G. & Maia, V. C. A synopsis of fern galls in Brazil. Biota Neotrop. 18, e20180513. https://doi.org/10.1590/1676-0611-BN-2018-0513 (2018).Article 

Google Scholar 
Peters, R. S. et al. Evolutionary history of the Hymenoptera. Curr. Biol. 27, 1013–1018. https://doi.org/10.1016/j.cub.2017.01.027 (2017). CAS 
Article 
PubMed 

Google Scholar 
Ronquist, F. et al. A total-evidence approach to dating with fossils, applied to the early radiation of the Hymenoptera. Syst. Biol. 61, 973–999. https://doi.org/10.1093/sysbio/sys058 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Needham, J. G., Frost, S. W. & Tothill, B. H. Leaf-Mining Insects (Waverly Press, 1928).
Google Scholar 
Smith, D. R., Eiseman, C. S., Charney, N. D. & Record, S. A new Nearctic Scolioneura (Hymenoptera, Tenthredinidae) mining leaves of Vaccinium (Ericaceae). J. Hymenopt. Res. 43, 1–8. https://doi.org/10.3897/JHR.43.4546 (2015).Article 

Google Scholar 
Zheng, D. et al. Middle-Late Triassic insect radiation revealed by diverse fossils and isotopic ages from China. Sci. Adv. 4, eaat1380. https://doi.org/10.1126/sciadv.aat1380 (2018).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Zhang, S. Q. et al. Evolutionary history of Coleoptera revealed by extensive sampling of genes and species. Nat. Commun. 9, 1–11. https://doi.org/10.1038/s41467-017-02644-4 (2018).ADS 
CAS 
Article 

Google Scholar 
McKenna, D. D. et al. The evolution and genomic basis of beetle diversity. Proc. Natl. Acad. Sci. 116, 24729–24737. https://doi.org/10.1073/pnas.1909655116 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Gimmel, M. L. & Ferro, M. L. General overview of saproxylic Coleoptera. In Saproxylic Insects, Zoological Monographs, Vol. 1 (ed. Ulyshen, M. D.) 51–128 (Springer, 2018). https://doi.org/10.1007/978-3-319-75937-1_2.Labandeira, C. C., Anderson, J. M. & Anderson, H. M. Expansion of arthropod herbivory in Late Triassic South Africa: The Molteno Biota, Aasvoëlberg 411 site and developmental biology of a gall. In The Late Triassic World, Topics in Geobiology Vol. 46 (ed. Tanner, L. H.) 623–719 (Springer International Publishing AG, 2018).Chapter 

Google Scholar 
Fiebrig, K. Eine Schaum bildende Käferlarve Pachyschelus spec. (Bupr. Sap.) Die Ausscheidung von Kautschuk aus der Nahrung und dessen Verwertung zu Schutzzwecken (auch bei Rhynchoten). Z. f. Wiss. Insektenbiol. 4, 333–339 (1908).
Google Scholar 
Bruch, C. Metamórfosis de Pachyschelus undularius (Burm.). Physis 3, 30–36 (1917).
Google Scholar 
Hering, E. M. Neotropische Buprestiden-Minen. Arb. Physiol. Angew. Entomol. 9, 241–249 (1942).
Google Scholar 
Kogan, M. Contribuição ao conhecimento da sistemática e biologia de buprestídeos minadores do gênero Pachyschelus Solier, 1833: (Coleoptera, Buprestidae). Mem. Inst. Oswaldo Cruz 61, 429–457 (1963).CAS 
Article 

Google Scholar 
Kawahara, A. Y. et al. Phylogenomics reveals the evolutionary timing and pattern of butterflies and moths. Proc. Natl. Acad. Sci. 116, 22657–22663. https://doi.org/10.1073/pnas.1907847116 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Van Eldijk, T. J. B. et al. A Triassic-Jurassic window into the evolution of lepidoptera. Sci. Adv. 4, e1701568. https://doi.org/10.1126/sciadv.1701568 (2018).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Sohn, J. C., Labandeira, C. C., Davis, D. & Mitter, C. An annotated catalog of fossil and subfossil Lepidoptera (Insecta: Holometabola) of the world. Zootaxa. https://doi.org/10.11646/zootaxa.3286.1.1 (2012).Doorenweerd, C., Van Nieukerken, E. J., Sohn, J. C. & Labandeira, C. C. A revised checklist of Nepticulidae fossils (Lepidoptera) indicates an Early Cretaceous origin. Zootaxa 3963, 295–334. https://doi.org/10.11646/zootaxa.3963.3.2 (2015).Article 
PubMed 

Google Scholar 
Kawahara, A. Y. et al. A molecular phylogeny and revised higher-level classification for the leaf-mining moth family Gracillariidae and its implications for larval host-use evolution. Syst. Entomol. 42, 60–81. https://doi.org/10.1111/syen.12210 (2017).Article 

Google Scholar 
Mazumdar, J. Phytoliths of pteridophytes. S. Afr. J. Bot. 77, 10–19. https://doi.org/10.1016/j.sajb.2010.07.020 (2011).Article 

Google Scholar 
Trembath-Reichert, E., Wilson, J. P., McGlynn, S. E. & Fischer, W. W. Four hundred million years of silica biomineralization in land plants. Proc. Natl. Acad. Sci. U. S. A. 112, 5449–5454 https://doi.org/10.1073/pnas.1500289112 (2015).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Hunt, J. W., Dean, A. P., Webster, R. E., Johnson, G. N. & Ennos, A. R. A novel mechanism by which silica defends grasses against herbivory. Ann. Bot. 102, 653–656. https://doi.org/10.1093/aob/mcn130 (2008).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Reynolds, O. L., Keeping, M. G. & Meyer, J. H. Silicon-augmented resistance of plants to herbivorous insects: A review. Ann. Appl. Biol. 155, 171–186. https://doi.org/10.1111/j.1744-7348.2009.00348.x (2009).CAS 
Article 

Google Scholar 
Edwards, N. P. et al. Leaf metallome preserved over 50 million years. Metallomics 6, 774–782. https://doi.org/10.1039/C3MT00242J (2014).CAS 
Article 
PubMed 

Google Scholar 
Müller, A. H. Über Hyponome fossiler und rezenter Insekten, erster Beitrag. Freib. Forschungsh. C 366, 7–27 (1982).
Google Scholar 
Beck, A. L. & Labandeira, C. C. Early Permian insect folivory on a gigantopterid-dominated riparian flora from north-central Texas. Palaeogeogr. Palaeoclimatol. Palaeoecol. 142, 139–173. https://doi.org/10.1016/S0031-0182(98)00060-1 (1998).Article 

Google Scholar 
Jarzembowski, E. A. The oldest plant-insect interaction in Croatia: Carboniferous evidence. Geol. Croat. 65(3), 387–392. https://doi.org/10.4154/GC.2012.28 (2002).Article 

Google Scholar 
Donovan, M. P. & Lucas, S. G. Insect herbivory on the Late Pennsylvanian Kinney Brick Quarry Flora, New Mexico, USA. Kinney Brick Quarry Lagerstätte. N. M. Mus. Nat. Hist. Sci. Bull. 84, 193–207 (2021).Potonié, R. Ueber das Rothliegende des Thüringer Waldes. Theil II: Die Flora des Rothliegenden von Thüringen. Abh. Preuss. Geol. Landesanst. 9, 1–298 (1893).
Google Scholar 
Potonié, R. Mitteilungen über mazerierte kohlige Pflanzenfossilien. Z. Bot. 13, 79–88 (1921).Adami-Rodrigues, K. A., Iannuzzi, R. & Pinto, I. D. Permian plant-insect interactions from a Gondwana flora of southern Brazil. Foss. Strat. 51, 106–126 (2004).
Google Scholar 
Krassilov, V. A. & Karasev, E. First evidence of plant–arthropod interaction at the Permian–Triassic boundary in the Volga Basin European Russia. Alavesia 2, 247–252 (2008).
Google Scholar 
Labandeira, C. C., Wilf, P., Johnson, K. & Marsh, F. Guide to insect (and other) damage types on compressed plant fossils. Version 3.0. Smithson. Institution, Washington, DC 25 (2007).Scott, A. C., Anderson, J. M. & Anderson, H. M. Evidence of plant-insect interactions in the Upper Triassic Molteno formation of South Africa. J. Geol. Soc. London. 161, 401–410. https://doi.org/10.1144/0016-764903-118 (2004).Article 

Google Scholar 
Tillyard, R. J. Mesozoic Insects of Queensland No. 9. Orthoptera, and Additions to the Protorthoptera, Odonata, Hemiptera, and Planipennia. Proc. Linn. Soc. N. S. W. 47, 447–470 (1922).
Google Scholar 
Rozefelds, A. C. & Sobbe, I. Problematic insect leaf mines from the Upper Triassic Ipswich Coal Measures of Southeastern Queensland Australia. Alcheringa 11, 51–57 (1987).Article 

Google Scholar 
Wappler, T., Kustatscher, E. & Dellantonio, E. Plant-insect interactions from Middle Triassic (late Ladinian) of Monte Agnello (Dolomites, N-Italy)-Initial pattern and response to abiotic environmental pertubations. PeerJ 2015, e921. https://doi.org/10.7717/peerj.921 (2015).Article 

Google Scholar 
Meller, B., Ponomarenko, A. G., Vasilenko, D. V., Fischer, T. C. & Aschauer, B. First beetle elytra, abdomen (Coleoptera) and a mine trace from Lunz (Carnian, Late Triassic, Lunz-am-See, Austria) and their taphonomical and evolutionary aspects. Palaeontology 54, 97–110. https://doi.org/10.1111/j.1475-4983.2010.01009.x (2011).Article 

Google Scholar 
Vassilenko, D. V. Traces of plant-arthropod interactions from Madygen (Triassic, Kyrgyzstan): Preliminary data. Sovremennaya paleontologia: klassicheskie i noveishie metody 9–16 (2009).Zherikhin, V. V. Insect Trace Fossils. In History of Insects (ed. Rasnitsyn A. P., Quicke, D. L.) 303–324 (Kluwer Academic Publishers, 2010).Schindelin, J. et al. Fiji: An open-source platform for biological-image analysis. Nat. Methods 9, 676–682. https://doi.org/10.1038/nmeth.2019 (2012).CAS 
Article 
PubMed 

Google Scholar 
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).Book 

Google Scholar  More

BioassaysSalmon-louse bioassays were performed by the BC Centre for Aquatic Health Sciences (CAHS) as described in Saksida et al.10. Briefly, motile (i.e., pre-adult and adult) L. salmonis were collected from 11 salmon farms in the Broughton Archipelago (BA) between 2010 and 2021 and transported to CAHS in Campbell River, BC. Within 18 h of collection, healthy lice were separated by sex and randomly placed into petri dishes each containing approximately 10 lice (mean ± SD = 9.6 ± 1.1) and subjected to one of six EMB concentrations (either 0, 31.3, 62.5, 125, 250, and 500 ppb or 0, 62.5, 125, 250, 500, and 1000 ppb, depending on suspected variation in EMB sensitivity11). Each collection corresponded to one bioassay, and each bioassay contained roughly four replicates for each sex (4.0 ± 1.3 for females and 3.6 ± 0.9 for males). After 24 h of EMB exposure, lice were classified as alive if they could swim and attach to the petri dish, or moribund/dead otherwise. Lice were kept at 10 °C throughout the process. In total, 34 bioassays were conducted from 11 farms between October 2010 and November 2021.We analysed the proportion of lice that survived exposure to EMB, using standard statistical descriptions that accounted for within-assay dependencies (generalized linear mixed models (GLMMs) with logit link functions, fitted separately to the data from each bioassay). The models included fixed effects for EMB concentration, sex, and the interaction between the two, as well as a random intercept for petri dish. For each analysis, we centered concentration values and scaled them by one standard deviation. We used the GLMM fits to calculate the effective concentrations at which 50% of the lice survived (EC50) in each bioassay. The GLMM for one bioassay produced a singular fit because there was not enough variation in the female survival data to warrant the random-effects structure. We retained the EC50 values resulting from this singular fit because re-fitting without the random intercept yielded identical EC50 values, and removing the entire bioassay from the overall dataset did not qualitatively affect the subsequent analysis.To assess whether the sensitivity of salmon lice to EMB has decreased over time, we fitted a set of five standard GLMs with gamma error distributions and log link functions to the maximum-likelihood EC50 estimates. Each of these five models included binary effects for sex and for whether the farm’s stock had previously been treated, since both affect EMB sensitivity in lice10. The first model included only these two effects and served as a null model that assumed lice did not evolve EMB resistance over time. The second model added a fixed effect for time (i.e., the number of days since January 1, 2010), while the third model included an interaction between time and sex. The fourth and fifth models were identical to the second and third, but with a quadratic effect for time, to account for possible first-order nonlinearity. We were unable to add an effect for farm due to small sample sizes. We performed model selection using the Akaike Information Criterion penalized for small sample sizes AICc25, treating AICc differences of less than two as being indistinguishable in terms of statistical support and selecting the least complex model when that was the case26. The ΔAICc values for the EC50 models were 48.1, 6.1, 4.9, 0, 1.75, respectively.Field efficacyWe used relative salmon-louse counts after EMB treatment (i.e., the post-treatment count divided by the pre-treatment count) as our measure of EMB field resistance between 2010 and 2021 (higher relative counts imply lower treatment efficacy). We defined “pre-treatment” as one month prior to treatment and “post-treatment” as three months after treatment (roughly when one would expect to find the lowest counts in louse populations previously unexposed to EMB), as in Saksida et al.10. We excluded EMB treatments for which an additional, non-EMB treatment was performed within the following three months. In total, there were 73 EMB treatments for which we were able to calculate relative post-treatment counts.Salmon-louse counts were performed by farm staff as described by Godwin et al.27. In short, salmon-louse counts were usually performed at least one per month by capturing 20 stocked fish in each of three net pens using a box seine net, then placing the fish in an anesthetic bath of tricaine methanesulfonate (TMS, or MS-222) and assessing the fish for motile (i.e., pre-adult and adult) L. salmonis by eye.The treatment dataset included the date and type of every treatment that has been performed on a BA farm (i.e., not just the 11 farms with bioassay data). In total, 88 EMB treatments were conducted between 2010 and 2021, of which we were able to calculate relative post-treatment counts for 73 because some months lacked counts or had a non-EMB treatment performed within the following three months. An additional 22 non-EMB treatments (e.g., freshwater and hydrogen baths) were performed, all since the beginning of 2019, but we excluded these data from our analysis.To determine whether field efficacy of EMB treatments has decreased over time, we used GLM-based “hurdle models”—standard statistical descriptions used to accommodate an over-abundance of zeroes in data being analysed. A hurdle model uses two components—one model for whether a count is nonzero and another for the value of the nonzero count—to predict overall mean count. To this end, we fitted three binomial GLMs paired with three gamma GLMs to the relative-count data, each of the paired models being structurally identical in terms of predictors. All of these submodels included a binary fixed effect for previous treatment, as in the EC50 models. The null pair of submodels included no additional terms, the second pair of submodels included a fixed effect for time (i.e., the number of days since January 1, 2010), and the third pair of submodels included a quadratic effect of time (again, to account for possible first-order deviations nonlinearity). We were unable to add an effect for farm due to small sample sizes. We performed model selection of the hurdle models, again using the Akaike Information Criterion penalized for small sample sizes. The ΔAICc values for the three hurdle models were 39.6, 18.3, and 0, respectively. We performed our analyses in R 3.6.028, using the lme4 package29. More

Base run simulationFigure 6 shows the results of the base run simulation. In this scenario, strong vegetation declines over time, while the empty area and flammable vegetation have increasing trends. As such, more fuel would be available for burning, and the wildfire can burn broader areas. Panel (a) shows an oscillatory trend for the burn rate with an average upward trend (To make sure the oscillatory behavior of the model does not fade, Appendix 4 shows the simulation result for 100 years). The observed pattern in the burn rate can be traced back to the patterns of human ignition (Panel b), and the growing trend of vulnerable properties (Panel c). In addition, the results show the long-term declining trend of strong vegetation in our base line simulation (Panel d); over time, stronger vegetation is replaced by flammable vegetation which can lead to more fire. This change in vegetation composition effectively increases the average burn rate. Over time, with more flammable vegetation and with the expansion of vulnerable properties, the likelihood of human-made ignition increases.Figure 6Base run simulation for a 20-year run of the model.Full size imageCoupling effectsFigure 7 shows how the relation between perceived fire risk and the burn rate influences the system. The black line is the base run simulation for comparison. The blue dashed line depicts the condition in which risk perception changes extremely slowly, and the human system is almost disconnected from the natural system. In this situation, if humans underestimate the fire potential, the system burns down nature, resulting in a catastrophic environmental outcome as depicted in panel (a). Panel (a) shows that the burn rate overshoots in the short term but relatively declines due to less remaining natural resources to burn.Figure 7Coupling effect analysis for 20 years. Human ignition unit is Ignition/year, and vulnerable property unit is a million hectares. Strong vegetation and flammable vegetation are provided as the ratio that each occupied the forest area.Full size imagePanel (b) displays the total burn rate throughout the study time to cast further insight into the burn rate sensitivity to perceived risk. The overall burn rate does not significantly change when the risk perception changes from 0.5 to 2, indicating the difference among burn rates in panel (a) is more about the fluctuation timing, but not the size. However, an additional rise in the sense of risk greatly raises the overall burn rate, as seen in panel (a).In the case of prolonged change in risk perception, human ignition continues to increase (panel c) as the perceived risk changes slowly. Furthermore, vulnerable properties are being built faster than their demolition (panel d). A slighter delay in perception leads to a higher frequency of oscillation as depicted in the graphs by the red dashed lines and a longer delay in a lower frequency oscillation, as shown by the purple graphs. Overall, the results are not much different from the base run. We are losing forests (panel e) and have periodic burn rates of increasing magnitude over time.Policy experimentsHere we examine the impact of implementing four proposed policies introduced in Table 2. To prevent the initial condition and transition periods affecting our comparison of proposed policies, we imposed each policy at the fifth year and compared the total burn rates between 10 and 20 years. Figure 8 shows the effect of these policies on different variables. Figure 8Policy implementation. Note: P1: limits vulnerable property development; P2: prescribed burning; P3: effective firefighting; and P4: Clear cutting. Human ignition unit is Ignition/year, and vulnerable property unit is a million hectares. Strong vegetation and flammable vegetation are provided as the ratio that each occupied the forest area.Full size imagePanels (a) and (b) show the burn rate over time and cumulative, respectively. All four policies reduce the burn-rate magnitude compared to the base run. P3 is more effective in early burning-rate reduction compared to other policies, but they ultimately result in similar behavior. It is worth noticing that P1 has the most effect on long-run fluctuation reduction, although its total effect in the time span is less than P3. It seems that firefighting is more effective in the short run, but it fails to dampen the fluctuation and instead limits its growth. This is partly because of the increase in human ignition and settlement due to the success of firefighting in the short run. As a result, people perceive less fire danger and continue to engage in high-risk activities and expand housing in the WUI. The result is further fluctuation in the burn rate even when P3 is implemented. On the other hand, the WUI expansion limitation policy can effectively reduce the burn-rate fluctuation in a timely manner. Implementing P4 causes a reduction in strong vegetation, which leads to flammable vegetation increase. As flammable vegetation is the main fuel for wildfire, this policy cause increase in fuel availability and an increase in the burning rate.Change in human ignition is provided in panel (c). Different levels of human-made ignition are observable, and the reason is that people adjust their high-risk behavior with burn rate, and not with the number of fires. In the firefighting policy, as for a given level of ignition, the burn rate declines, we observe more risky behavior and more human-made ignition. It is interesting to note that, as panel (c) shows, we end up with more WUI under policies 2, 3, and 4. In fact, the reason is that the firefighting, prescribed burning and clear cutting only affect natural sector of the model, decrease burn rate, which decreases risk perception and in turn result in more WUI development. On the other hand, P1 directly targets WUIs.Panel (e) displays the change in strong vegetation, which shows that P4 causes the most reduction in forest tree cover as it directly removes strong vegetation. P2 also causes a decrease in strong vegetation compared to the base run. The reason is that burning flammable vegetation damages young trees and prevents them from developing into solid vegetation. On the other hand, P3 has the least effect on strong vegetation by slowing the damage to young trees and confining the fire. Panel (f) shows the flammable vegetation dynamic after imposing each policy. P3 and P2 reduce flammable vegetation more than P1. However, there is an important difference in how these policies cause the reduction in flammable vegetation. In comparing panels (a) and (b), we see that while P3 causes further increases in the strong vegetation, P2 causes an increase in the empty area. P4 is the only policy that increases flammable vegetation by removing the strong vegetation and providing an empty area to be filled with young vegetation.Overall, it looks like each policy has some marginal effect on containing wildfire, though the magnitudes of effect are not considerable.Replication of United States dataFor model validation, we investigate its ability to fit a single case, United States’ wildfires from 1996 to 2015. We utilize the United States Department of Agriculture’s wildfire database for the conterminous United States (Short, 2017). The results are shown in Fig. 9. In this figure, simulation of burning rate and human ignition (continuous lines, in black) closely follows the real-world data (dotted lines, in red), and the model fairly replicates the historical trends.Figure 9Burning rate and human ignition per unit of forest area. The black line represents the model result, and the red dotted line represents the historical wildfire activity in the conterminous United States.Full size imageCombination policy implementation analysisTo better understand the impacts of our policies, we run different pairs of policies simultaneously. The results illustrate the nonlinear incremental impacts between policies. Simply put, it appears that the impact of several policies is enforced when combined synergistically. In other words, applying several policies might have a greater overall impact than the sum of the policies’ individual effects and suggests that policymakers should avoid searching for a panacea and adopt a broad range of approaches thoughtfully.The results of multiple policy implementations along with single ones are presented in Fig. 10. For example, P1 and P2 each reduce the total burn rate by 4.9% and 4.5%, respectively. While the summation of these effects is 9.4%, simultaneously implementing P1 and P2 lead to a 13.6% burn-rate reduction—P1 controls the human ignition, and P2 reduces the flammable vegetation stock—together, the burn rate is more affected than if implemented separately. The case is more interesting when P1 and P3 are imposed together. The result is a 38% burn-rate reduction compared to 13.9%, which is the sum of solely implementing each policy. The synergic effect happens because P3 lets the flammable vegetation (mainly young trees) age and become strong vegetation. Furthermore, the P1 also prevents human ignition from growing as fast as a single P3 implementation.Figure 10The nonlinear effect of policies. The benefits of implementing multiple policies differ from the sum of the effect of policies. The figure shows the percent of burn rate reduction. Note: P1: limit vulnerable property development; P2: prescribed burning; P3: effective firefighting; and P4: Clear cutting.Full size imageAn interesting case happens when P2 and P3 are implemented together. The synergic effect is less than the sum of separate implementation, mainly because both policies affect the vegetation dynamic and not the human factor in the wildfire. P2 and P3 both cause a lower initial burn rate, but due to the reduction in perceived risk of wildfire and expansion of WUI, this effect quickly disappears. This is another evidence for the importance of considering the problem as an interconnected natural and human system, where effective policies should address both sides.Finally, an interesting result emerges when all policies impose together. Surprisingly, imposing all policies together does not have the most impact on the total burn rate (32.5%), which is less than the P1 and P3 effect (38.0%). The reason relates mainly to the fact P2 and P4 both cause increase in flammable vegetation after empty area filled, which lead to more burning rate after a delay.Sensitivity analysisWe conducted a series of sensitivity analysis to check the model’s robustness to our assumptions. Specifically, we conducted a Monte-Carlo analysis and changed several parameter values to determine the range of outcomes. The results are reported in Appendix 2. In summary, the focus was on parameters that can take on substantially different values from those assumed in the model, including parameters used for risk perception formulation, its effect on human behavior, such as time to perceive risk and time to change behavior, in addition to fractional burning rate per ignition, average s burning, initial flammable vegetation, initial strong vegetation, human ignition multiplier, and initial vulnerable property. As described in the Appendix, for most of these variables, we changed the corresponding variable up to double its base run value. Moreover, we test different values for initial strong vegetation and initial flammable vegetation changing them between zero and their base run values. Each sensitivity test is the outcome of 2000 simulation runs using a uniformly distributed random distribution of the parameters within the specified intervals. The results are qualitatively robust, and their variability is within reasonable limits (See Figure A1). More