Ecology

1.Spiertz, J. H. J. Nitrogen, sustainable agriculture and food security: a review. Agron. Sustain. Dev. 30, 43–55. https://doi.org/10.1051/agro:2008064 (2010).CAS 
Article 

Google Scholar 
2.Kowalchuk, G. A. & Stephen, J. R. Ammonia-oxidizing bacteria: a model for molecular microbial ecology. Annu. Rev. Microbiol. 55, 485–529. https://doi.org/10.1146/annurev.micro.55.1.485 (2001).CAS 
Article 
PubMed 

Google Scholar 
3.Zumft, W. G. Cell biology and molecular basis of denitrification. Microbiol. Mol. Biol. Rev. 61, 533–616 (1997).CAS 
PubMed 
PubMed Central 

Google Scholar 
4.Gelfand, I. & Yakir, D. Influence of nitrite accumulation in association with seasonal patterns and mineralization of soil nitrogen in a semi-arid pine forest. Soil Biol. Biochem. 40, 415–424. https://doi.org/10.1016/j.soilbio.2007.09.005 (2008).CAS 
Article 

Google Scholar 
5.Subbarao, G. V. et al. Scope and strategies for regulation of nitrification in agricultural systems-challenges and opportunities. Crit. Rev. Plant Sci. 25, 303–335. https://doi.org/10.1080/07352680600794232 (2006).CAS 
Article 

Google Scholar 
6.Shen, T., Stieglmeier, M., Dai, J., Urich, T. & Schleper, C. Responses of the terrestrial ammonia-oxidizing archaeon Ca. Nitrososphaera viennensis and the ammonia-oxidizing bacterium Nitrosospira multiformis to nitrification inhibitors. FEMS Microbiol. Lett. 344, 121–129, https://doi.org/10.1111/1574-6968.12164 (2013).7.Prosser, J. I., Head, I. M. & Stein, L. Y. in The Prokaryotes – Alphaproteobacteria and Betaproteobacteria (ed DeLong Rosenberg E., E.F., Lory, S., Stackebrandt, E., Thompson, F.) 901–918 (Springer-Verlag, 2014).8.Hayatsu, M. et al. An acid-tolerant ammonia-oxidizing gamma-proteobacterium from soil. ISME J. 11, 1130–1141. https://doi.org/10.1038/ismej.2016.191 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
9.Alves, R.J.E., Minh, B.Q, Urich, T., von Haeseler, A. & Schleper, C. Unifying the global phylogeny and environmental distribution of ammonia-oxidising archaea based on amoA genes. Nat. Commun. 9, https://doi.org/10.1038/s41467-018-03861-1 (2018).10.Wang, H. Et al. Distinct distribution of archaea from soil to freshwater to estuary: implications of archaeal composition and function in different environments. Front. Microbiol. 11. https://doi.org/10.3389/fmicb.2020.576661 (2020).11.Prosser, J. I. & Nicol, G. W. Archaeal and bacterial ammonia-oxidisers in soil: the quest for niche specialisation and differentiation. Trends Microbiol. 20, 523–531. https://doi.org/10.1016/j.tim.2012.08.001 (2012).CAS 
Article 
PubMed 

Google Scholar 
12.Pester, M. et al. amoA-based consensus phylogeny of ammonia-oxidizing archaea and deep sequencing of amoA genes from soils of four different geographic regions. Environ. Microbiol. 14, 525–539. https://doi.org/10.1111/j.1462-2920.2011.02666.x (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
13.Spang, A. et al. The genome of the ammonia-oxidizing Candidatus Nitrososphaera gargensis: insights into metabolic versatility and environmental adaptations. Environ. Microbiol. 14, 3122–3145. https://doi.org/10.1111/j.1462-2920.2012.02893.x (2012).CAS 
Article 
PubMed 

Google Scholar 
14.Daims, H. et al. Complete nitrification by Nitrospira bacteria. Nature 528, 504–509. https://doi.org/10.1038/nature16461 (2015).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
15.van Kessel, M. A. et al. Complete nitrification by a single microorganism. Nature 528, 555–559. https://doi.org/10.1038/nature16459 (2015).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
16.Junier, P. et al. Phylogenetic and functional marker genes to study ammonia-oxidizing microorganisms (AOM) in the environment. Appl. Microbiol. Biotechnol. 85, 425–440. https://doi.org/10.1007/s00253-009-2228-9 (2010).CAS 
Article 
PubMed 

Google Scholar 
17.Leininger, S. et al. Archaea predominate among ammonia-oxidizing prokaryotes in soils. Nature 442, 806–809. https://doi.org/10.1038/nature04983 (2006).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
18.Zhalnina, K. et al. Ca. Nitrososphaera and Bradyrhizobium are inversely correlated and related to agricultural practices in long-term field experiments. Front. Microbiol. 4, 104, https://doi.org/10.3389/fmicb.2013.00104 (2013).19.Pjevac, P. et al. AmoA-targeted polymerase chain reaction primers for the specific detection and quantification of comammox Nitrospira in the environment. Front. Microbiol. 8, 1508. https://doi.org/10.3389/fmicb.2017.01508 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
20.Palomo, A., Dechesne, A. & Smets, B. F. Genomic profiling of Nitrospira species reveals ecological success of comammox Nitrospira. bioRxiv, 612226, https://doi.org/10.1101/612226 (2019).21.Poghosyan, L. et al. Metagenomic recovery of two distinct comammox Nitrospira from the terrestrial subsurface. Environ. Microbiol. 21, 3627–3637. https://doi.org/10.1111/1462-2920.14691 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
22.Palomo, A. et al. Comparative genomics sheds light on niche differentiation and the evolutionary history of comammox Nitrospira. ISME J. 12, 1779–1793. https://doi.org/10.1038/s41396-018-0083-3 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
23.Strous, M. et al. Deciphering the evolution and metabolism of an anammox bacterium from a community genome. Nature 440, 790–794. https://doi.org/10.1038/nature04647 (2006).ADS 
Article 
PubMed 

Google Scholar 
24.De Boer, W. & Kowalchuk, G. A. Nitrification in acid soils: micro-organisms and mechanisms. Soil Biol. Biochem. 33, 853–866. https://doi.org/10.1016/s0038-0717(00)00247-9 (2001).Article 

Google Scholar 
25.Tourna, M. et al. Nitrososphaera viennensis, an ammonia oxidizing archaeon from soil. Proc. Natl. Acad. Sci. USA. 108, 8420–8425. https://doi.org/10.1073/pnas.1013488108 (2011).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
26.Arp, D. J., Chain, P. S. G. & Klotz, M. G. The impact of genome analyses on our understanding of ammonia-oxidizing bacteria. Annu. Rev. Microbiol. 61, 503–528 (2007).CAS 
Article 

Google Scholar 
27.Simon, J. & Klotz, M. G. Diversity and evolution of bioenergetic systems involved in microbial nitrogen compound transformations. Biochim. Biophys. Acta 114–135, 2013. https://doi.org/10.1016/j.bbabio.2012.07.005 (1827).CAS 
Article 

Google Scholar 
28.Walker, C. B. et al. Nitrosopumilus maritimus genome reveals unique mechanisms for nitrification and autotrophy in globally distributed marine crenarchaea. Proc. Natl. Acad. Sci. USA 107, 8818–8823. https://doi.org/10.1073/pnas.0913533107 (2010).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
29.Li, C. Y., Hu, H. W., Chen, Q. L., Chen, D. L. & He, J. Z. Comammox Nitrospira play an active role in nitrification of agricultural soils amended with nitrogen fertilizers. Soil Biol. Biochem. 138, https://doi.org/10.1016/j.soilbio.2019.107609 (2019).30.Li, C. Y., Hu, H. W., Chen, Q. L., Chen, D. L. & He, J. Z. Niche differentiation of clade A comammox Nitrospira and canonical ammonia oxidizers in selected forest soils. Soil Biol. Biochem. 149, https://doi.org/10.1016/j.soilbio.2020.107925 (2020).31.Daims, H., Lucker, S. & Wagner, M. A new perspective on microbes formerly known as nitrite-oxidizing bacteria. Trends Microbiol. 24, 699–712. https://doi.org/10.1016/j.tim.2016.05.004 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
32.Castelle, C. J. et al. Extraordinary phylogenetic diversity and metabolic versatility in aquifer sediment. Nat. Commun. 4, 2120. https://doi.org/10.1038/ncomms3120 (2013).ADS 
Article 
PubMed 

Google Scholar 
33.Sorokin, D. Y. et al. Nitrification expanded: discovery, physiology and genomics of a nitrite-oxidizing bacterium from the phylum Chloroflexi. ISME J. 6, 2245–2256. https://doi.org/10.1038/ismej.2012.70 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
34.Lucker, S. et al. A Nitrospira metagenome illuminates the physiology and evolution of globally important nitrite-oxidizing bacteria. Proc. Natl. Acad. Sci. USA. 107, 13479–13484. https://doi.org/10.1073/pnas.1003860107 (2010).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
35.Mendum, T. A., Sockett, R. E. & Hirsch, P. R. Use of molecular and isotopic techniques to monitor the response of autotrophic ammonia-oxidizing populations of the beta subdivision of the class Proteobacteria in arable soils to nitrogen fertilizer. Appl. Environ. Microbiol. 65, 4155–4162 (1999).ADS 
CAS 
Article 

Google Scholar 
36.Hirsch, P. R. et al. Soil resilience and recovery: rapid community responses to management changes. Plant Soil 412, 283–297. https://doi.org/10.1007/s11104-016-3068-x (2017).CAS 
Article 
PubMed 

Google Scholar 
37.Hirsch, P. R., Mauchline, T. H. & Clark, I. M. Culture-independent molecular techniques for soil microbial ecology. Soil Biol. Biochem. 42, 878–887. https://doi.org/10.1016/j.soilbio.2010.02.019 (2010).CAS 
Article 

Google Scholar 
38.Vetrovsky, T. & Baldrian, P. The variability of the 16S rRNA gene in bacterial genomes and its consequences for bacterial community analyses. PLoS ONE 8, e57923. https://doi.org/10.1371/journal.pone.0057923 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
39.Fu, Q.L., Clark, I.M., Zhu, J., Hu, H.Q. & Hirsch, P.R The short-term effects of nitrification inhibitors on the abundance and expression of ammonia. and nitrite oxidizers in a long-term field experiment comparing land management. Biol Fertil Soils. 54, 163–172. https://doi.org/10.1007/s00374-017-1249-2 (2018).40.Bollmann, A., Schmidt, I., Saunders, A. M. & Nicolaisen, M. H. Influence of starvation on potential ammonia-oxidizing activity and amoA mRNA levels of Nitrosospira briensis. Appl. Environ. Microbiol. 71, 1276–1282. https://doi.org/10.1128/aem.71.3.1276-1282.2005 (2005).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
41.Li, C. Y., Hu, H. W., Chen, Q. L., Chen, D. L. & He, J. Z. Growth of comammox Nitrospira is inhibited by nitrification inhibitors in agricultural soils. J. Soils Sediments 20, 621–628. https://doi.org/10.1007/s11368-019-02442-z (2020).CAS 
Article 

Google Scholar 
42.Koch, H., van Kessel, M. A. H. J. & Lücker, S. Complete nitrification: insights into the ecophysiology of comammox Nitrospira. Appl. Microbiol. Biotechnol. 103, 177–189. https://doi.org/10.1007/s00253-018-9486-3 (2019).CAS 
Article 
PubMed 

Google Scholar 
43.Placella, S. A. & Firestone, M. K. Transcriptional response of nitrifying communities to wetting of dry soil. Appl. Environ. Microbiol. 79, 3294–3302. https://doi.org/10.1128/AEM.00404-13 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
44.Hirsch, P. R. et al. Starving the soil of plant inputs for 50 years reduces abundance but not diversity of soil bacterial communities. Soil Biol. Biochem. 41, 2021–2024. https://doi.org/10.1016/j.soilbio.2009.07.011 (2009).CAS 
Article 

Google Scholar 
45.Clark, I. M., Buchkina, N., Jhurreea, D., Goulding, K. W. & Hirsch, P. R. Impacts of nitrogen application rates on the activity and diversity of denitrifying bacteria in the Broadbalk Wheat Experiment. Philos. Trans. R. Soc. Lond. B Biol. Sci. 367, 1235–1244, https://doi.org/10.1098/rstb.2011.0314 (2012).46.Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Meth. 12, 59–60 (2015).CAS 
Article 

Google Scholar 
47.Huson, D. H., Auch, A. F., Qi, J. & Schuster, S. C. MEGAN analysis of metagenomic data. Genome Res. 17, 377–386. https://doi.org/10.1101/gr.5969107 (2007).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
48.Katoh, K. & Standley, D. M. MAFFT Multiple Sequence Alignment Software Version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780. https://doi.org/10.1093/molbev/mst010 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar  More

1.Feldhaar, H. Bacterial symbionts as mediators of ecologically important traits of insect hosts. Ecol. Entomol. 36, 533–543 (2011).Article 

Google Scholar 
2.Popa, V., Deziel, E., Lavallee, R., Bauce, E. & Guertin, C. The complex symbiotic relationships of bark beetles with microorganisms: A potential practical approach for biological control in forestry. Pest Manag. Sci. 68, 963–975. https://doi.org/10.1002/ps.3307 (2012).CAS 
Article 
PubMed 

Google Scholar 
3.Qadri, M., Short, S., Gast, K., Hernandez, J. & Wong, A.C.-N. Microbiome innovation in agriculture: Development of microbial based tools for insect pest management. Front. Sustain. Food Syst. 4, 547751. https://doi.org/10.3389/fsufs (2020).Article 

Google Scholar 
4.Vasanthakumar, A., Handelsman, J., Schloss, P. D., Bauer, L. S. & Raffa, K. F. Gut microbiota of an invasive subcortical beetle, Agrilus planipennis Fairmaire, across various life stages. Environ. Entomol. 37, 1344–1353 (2008).PubMed 
Article 

Google Scholar 
5.Zhang, Z., Jiao, S., Li, X. & Li, M. Bacterial and fungal gut communities of Agrilus mali at different developmental stages and fed different diets. Sci. Rep. 8, 15634. https://doi.org/10.1038/s41598-018-34127-x (2018).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
6.Franzini, P. Z., Ramond, J.-B., Scholtz, C. H., Sole, C. L., Ronca, S. & Cowan, D. A. The gut microbiomes of two Pachysoma MacLeay desert dung beetle species (Coleoptera: Scarabaeidae: Scarabaeinae) feeding on different diets. PLoS ONE 11, e0161118 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
7.Colman, D. R., Toolson, E. C. & Takacs-Vesbach, C. Do diet and taxonomy influence insect gut bacterial communities?. Mol. Ecol. 21, 5124–5137 (2012).CAS 
PubMed 
Article 

Google Scholar 
8.Kim, J. M. Choi, M.-Y., Kim, J.-W., Lee, S. A., Ahn, J.-H., Song, J., Kim, S.-H. & Weon, H.-Y. Effects of diet type, developmental stage, and gut compartment in the gut bacterial communities of two Cerambycidae species (Coleoptera). J. Microbiol. 55, 21–30 (2017).CAS 
PubMed 
Article 

Google Scholar 
9.Ferguson, L. V.  Dhakal, P., Lebenzon, J. E., Heinrichs, D. E., Bucking, C., & Sinclair B. J. Seasonal shifts in the insect gut microbiome are concurrent with changes in cold tolerance and immunity. Funct. Ecol. 32, 2357–2368 (2018).Article 

Google Scholar 
10.Mason, C. J., Hanshew, A. S. & Raffa, K. F. Contributions by host trees and insect activity to bacterial communities in Dendroctonus valens (Coleoptera: Curculionidae) galleries, and their high overlap with other microbial assemblages of bark beetles. Environ. Entomol. 45, 348–356. https://doi.org/10.1093/ee/nvv184 (2015).CAS 
Article 
PubMed 

Google Scholar 
11.Mogouong, J., Constant, P., Lavallée, R. & Guertin, C. Gut microbiome of the emerald ash borer, Agrilus planipennis Fairmaire, and its relationship with insect population density. FEMS Microbiol. Ecol. https://doi.org/10.1093/femsec/fiaa141 (2020).Article 
PubMed 

Google Scholar 
12.Moran, N. A. & Yun, Y. Experimental replacement of an obligate insect symbiont. Proc. Natl. Acad. Sci. 112, 2093–2096 (2015).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
13.Borcard, D., Legendre, P. & Drapeau, P. Partialling out the spatial component of ecological variation. Ecology 73, 1045–1055 (1992).Article 

Google Scholar 
14.Peres-Neto, P. R., Legendre, P., Dray, S. & Borcard, D. Variation partitioning of species data matrices: Estimation and comparison of fractions. Ecology 87, 2614–2625 (2006).PubMed 
Article 

Google Scholar 
15.Cappaert, D., McCullough, D. G., Poland, T. M. & Siegert, N. W. Emerald ash borer in North America: A research and regulatory challenge. (2005).16.Kovacs, K. F., Haight, R. G., McCullough, D. G., Mercader, R. J., Siegert, N. W. & Liebhold, A. M. Cost of potential emerald ash borer damage in U.S. communities, 2009–2019. Ecol. Econ. 69, 569–578 (2010).Article 

Google Scholar 
17.Aukema, J. E., Leung, B., Kovacs, K., Chivers, C., Britton, K. O., Englin, J., Frankel, S. J., Haight, R. G., Holmes, T. P., Liebhold, A. M., McCullough, D. G. & Von Holle, B. Economic impacts of non-native forest insects in the continental United States. PLoS ONE 6, e24587 (2011).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
18.Poland, T. M. & McCullough, D. G. Emerald ash borer: Invasion of the urban forest and the threat to North America’s ash resource. J. For. 104, 118–124 (2006).
Google Scholar 
19.Herms, D. A. & McCullough, D. G. Emerald ash borer invasion of North America: History, biology, ecology, impacts, and management. Annu. Rev. Entomol. 59, 13–30. https://doi.org/10.1146/annurev-ento-011613-162051 (2014).CAS 
Article 
PubMed 

Google Scholar 
20.McCullough, D. G. Challenges, tactics and integrated management of emerald ash borer in North America. For. Int. J. For. Res. 93, 197–211 (2020).
Google Scholar 
21.Gandhi, K. J. & Herms, D. A. North American arthropods at risk due to widespread Fraxinus mortality caused by the alien emerald ash borer. Biol. Invasions 12, 1839–1846 (2010).Article 

Google Scholar 
22.Slesak, R. A., Lenhart, C. F., Brooks, K. N., D’Amato, A. W. & Palik, B. J. Water table response to harvesting and simulated emerald ash borer mortality in black ash wetlands in Minnesota, USA. Can. J. For. Res. 44, 961–968 (2014).Article 

Google Scholar 
23.Wielkopolan, B. & Obrepalska-Steplowska, A. Three-way interaction among plants, bacteria, and coleopteran insects. Planta 244, 313–332. https://doi.org/10.1007/s00425-016-2543-1 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
24.Howe, G. A. & Jander, G. Plant immunity to insect herbivores. Annu. Rev. Plant Biol. 59, 41–66 (2008).CAS 
PubMed 
Article 

Google Scholar 
25.Stam, J. M., Kroes, A., Li, Y., Gols, R., van Loon, J. J. A., Poelman, E. H. & Dicke, M. Plant interactions with multiple insect herbivores: from community to genes. Annu. Rev. Plant Biol. 65, 689–713 (2014).CAS 
PubMed 
Article 

Google Scholar 
26.Douglas, A. E. Multiorganismal insects: Diversity and function of resident microorganisms. Annu. Rev. Entomol. 60, 17–34 (2015).CAS 
PubMed 
Article 

Google Scholar 
27.Vorholt, J. A. Microbial life in the phyllosphere. Nat. Rev. Microbiol. 10, 828 (2012).CAS 
PubMed 
Article 

Google Scholar 
28.Shikano, I., Rosa, C., Tan, C.-W. & Felton, G. W. Tritrophic interactions: Microbe-mediated plant effects on insect herbivores. Annu. Rev. Phytopathol. 55, 313–331 (2017).CAS 
PubMed 
Article 

Google Scholar 
29.Schowalter, T. D. Insect Ecology: An Ecosystem Approach (Academic Press, 2016).
Google Scholar 
30.Oliverio, A. M., Gan, H., Wickings, K. & Fierer, N. A DNA metabarcoding approach to characterize soil arthropod communities. Soil Biol. Biochem. 125, 37–43 (2018).CAS 
Article 

Google Scholar 
31.Lennon, J. T., Muscarella, M. E., Placella, S. A. & Lehmkuhl, B. K. How, when, and where relic DNA affects microbial diversity. MBio 9, e00637-e618. https://doi.org/10.1128/mBio.00637-18 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
32.Humphrey, P. T. & Whiteman, N. K. Insect herbivory reshapes a native leaf microbiome. Nat. Ecol. Evol. 4, 221–229 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
33.Yutthammo, C., Thongthammachat, N., Pinphanichakarn, P. & Luepromchai, E. Diversity and activity of PAH-degrading bacteria in the phyllosphere of ornamental plants. Microb. Ecol. 59, 357–368 (2010).CAS 
PubMed 
Article 

Google Scholar 
34.Kadivar, H. & Stapleton, A. E. Ultraviolet radiation alters maize phyllosphere bacterial diversity. Microb. Ecol. 45, 353–361 (2003).CAS 
PubMed 
Article 

Google Scholar 
35.Thapa, S. & Prasanna, R. Prospecting the characteristics and significance of the phyllosphere microbiome. Ann. Microbiol. 68, 229–245 (2018).CAS 
Article 

Google Scholar 
36.Kembel, S. W., O’Connor, T. K., Arnold, H. K., Hubbell, S. P., Wright, S. J. & Green, J. L. Relationships between phyllosphere bacterial communities and plant functional traits in a neotropical forest. Proc. Natl. Acad. Sci. 111, 13715–13720 (2014).37.Biedermann, P. H. & Vega, F. E. Ecology and evolution of insect–fungus mutualisms. Annu. Rev. Entomol. 65, 431–455 (2020).CAS 
PubMed 
Article 

Google Scholar 
38.Fischer, R., Ostafe, R. & Twyman, R. M. In: Yellow Biotechnology II: Insect Biotechnology in Plant Protection and Industry. Ch. Cellulases from insects, 51–64 (Springer, 2013).39.Watanabe, H. & Tokuda, G. Cellulolytic systems in insects. Ann. Rev. Entomol. 55, 609–632 (2010).CAS 
Article 

Google Scholar 
40.Mittapalli, O., Bai, X., Mamidala, P., Rajarapu, S. P., Bonello, P. & Herms, D. A. Tissue-specific transcriptomics of the exotic invasive insect pest emerald ash borer (Agrilus planipennis). PLoS ONE 5, e13708 (2010).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
41.Vacheron, J., Péchy-Tarr, M., Brochet, S., Heiman, C. M., Stojiljkovic, M., Maurhofer, M. & Keel, C. T6SS contributes to gut microbiome invasion and killing of an herbivorous pest insect by plant-beneficial Pseudomonas protegens. ISME J. 13, 1318–1329. https://doi.org/10.1038/s41396-019-0353-8 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
42.Smith, C. C., Snowberg, L. K., Caporaso, J. G., Knight, R. & Bolnick, D. I. Dietary input of microbes and host genetic variation shape among-population differences in stickleback gut microbiota. ISME J. 9, 2515–2526 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Agler, M. T., Ruhe, J., Kroll, S., Morhenn, C., Kim, S.-T., Weigel, D. & Kemen, E. M. Microbial hub taxa link host and abiotic factors to plant microbiome variation. PLoS Biol. 14, e1002352 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
44.Gupta, A. & Nair, S. Dynamics of insect-microbiome interaction influence host and microbial symbiont. Front. Microbiol. 11, 1357 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
45.AFSQ. La clé forestière. https://afsq.org/cle-forestiere/accueil.html. Association forestière du Sud du Québec (2018).46.Comeau, A. M., Li, W. K. W., Tremblay, J. -É., Carmack, E. C. & Lovejoy, C. Arctic Ocean Microbial Community Structure before and after the 2007 Record Sea Ice Minimum. PLoS ONE 6, e27492. https://doi.org/10.1371/journal.pone.0027492 (2011).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
47.Toju, H., Tanabe, A. S., Yamamoto, S. & Sato, H. High-coverage ITS primers for the DNA-based identification of ascomycetes and basidiomycetes in environmental samples. PLoS ONE 7, e40863. https://doi.org/10.1371/journal.pone.0040863 (2012).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
48.Edgar, R. C. UNOISE2: Improved error-correction for Illumina 16S and ITS amplicon sequencing. BioRxiv 081257 (2016).49.Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
50.Callahan, B. J., McMurdie, P. J. & Holmes, S. P. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 11, 2639 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
51.Glassman, S. I. & Martiny, J. B. H. Broadscale ecological patterns are robust to use of exact sequence variants versus operational taxonomic units. mSphere 3, e00148-e118. https://doi.org/10.1128/mSphere.00148-18 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
52.Cole, J. R., Wang, Q., Fish, J. A., Chai, B., McGarrell, D. M., Sun, Y.,  Brown, C. T.,  Porras-Alfaro, A., Kuske, C. R. & Tiedje J. M. Ribosomal database project: Data and tools for high throughput rRNA analysis. Nucleic Acids Res. 42, D633-642. https://doi.org/10.1093/nar/gkt1244 (2014).CAS 
Article 
PubMed 

Google Scholar 
53.Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
54.Chen, Y. & Poland, T. M. Interactive influence of leaf age, light intensity, and girdling on green ash foliar chemistry and emerald ash borer development. J. Chem. Ecol. 35, 806–815. https://doi.org/10.1007/s10886-009-9661-1 (2009).CAS 
Article 
PubMed 

Google Scholar 
55.Bi, J. L., Toscano, N. C. & Madore, M. A. Effect of urea fertilizer application on soluble protein and free amino acid content of cotton petioles in relation to silverleaf whitefly (Bemisia argentifolii) populations. J. Chem. Ecol. 29, 747–761. https://doi.org/10.1023/a:1022880905834 (2003).CAS 
Article 
PubMed 

Google Scholar 
56.Torti, S. D., Dearing, M. D. & Kursar, T. A. Extraction of phenolic compounds from fresh leaves: A comparison of methods. J. Chem. Ecol. 21, 117–125 (1995).CAS 
PubMed 
Article 

Google Scholar 
57.Hagerman, A. E. Extraction of tannin from fresh and preserved leaves. J. Chem. Ecol. 14, 453–461 (1988).CAS 
PubMed 
Article 

Google Scholar 
58.Beauchemin, N. J., Furnholm,T., Lavenus, J., Svistoonoff, S., Doumas, P., Bogusz, D., Laplaze, L. & Tisa L. S. Casuarina root exudates alter the physiology, surface properties, and plant infectivity of Frankia sp. strain CcI3. Appl. Environ. Microbiol. 78, 575–580 (2012).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
59.Garg, B. Plant Analysis: Comprehensive Methods and Protocols (Scientific Publishers, 2012).
Google Scholar 
60.Wellburn, R. The spectral determination of chlorophylls a and b, as well as total carotenoids, using various solvents with spectrophotometers of different resolution. J. Plant Physiol. 144, 307–313 (1994).CAS 
Article 

Google Scholar 
61.Marquis, R. J., Newell, E. A. & Villegas, A. C. Non-structural carbohydrate accumulation and use in an understorey rain-forest shrub and relevance for the impact of leaf herbivory. Funct. Ecol. 11, 636–643. https://doi.org/10.1046/j.1365-2435.1997.00139.x (1997).Article 

Google Scholar 
62.Garcia, A. M. N., Moumen, A., Ruiz, D. Y. & Alcaide, E. M. Chemical composition and nutrients availability for goats and sheep of two-stage olive cake and olive leaves. Anim. Feed Sci. Technol. 107, 61–74 (2003).Article 
CAS 

Google Scholar 
63.Van Soest, P. V., Robertson, J. & Lewis, B. Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. J. Dairy Sci. 74, 3583–3597 (1991).PubMed 
Article 

Google Scholar 
64.Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P. R. & O’Hara, R. B. Package ‘vegan’. R package version 2.5-6 (2019)65.Borcard, D., Gillet, F. & Legendre, P. Numerical Ecology with R (Springer, 2018).MATH 
Book 

Google Scholar 
66.Kembel, S. W., Eisen, J. A., Pollard, K. S. & Green, J. L. The phylogenetic diversity of metagenomes. PLoS ONE 6, e23214 (2011).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
67.Faith, D. P. Conservation evaluation and phylogenetic diversity. Biol. Cons. 61, 1–10 (1992).Article 

Google Scholar 
68.Kembel, S. W.,  Cowan, P. D., Helmus, M. R., Cornwell, W. K., Morlon, H., Ackerly, D. D., Blomberg, S. P., & Webb, C. O. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).CAS 
PubMed 
Article 

Google Scholar 
69.Legendre, P. & De Cáceres, M. Beta diversity as the variance of community data: Dissimilarity coefficients and partitioning. Ecol. Lett. 16, 951–963 (2013).PubMed 
Article 

Google Scholar 
70.Dray, S., Bauman, D., Blanchet, G., Borcard, D., Clappe, S., Guenard, G., Jombart, T., Larocque, G., Legendre, P., Madi, N, Wagner H. H. Package ‘adespatial’, version 0.3-14. R Package version 2.5.6 (2018).71.De Cáceres, M., Legendre, P. & Moretti, M. Improving indicator species analysis by combining groups of sites. Oikos 119, 1674–1684 (2010).Article 

Google Scholar 
72.De Caceres, M., Jansen, F. & Caceres, D. Package ‘indicspecies’, version 1.7.9. R package version 2.5.6 (2016).73.Blanchet, F. G., Legendre, P. & Borcard, D. Forward selection of explanatory variables. Ecology 89, 2623–2632 (2008).PubMed 
Article 

Google Scholar  More

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript. More

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript. More

1.CONAB—Companhia Nacional de Abastecimento. Acompanhamento da Safra Brasileira de Grãos. V.7 – SAFRA 2019/20 – N. 12 – Décimo segundo levantamento. https://www.conab.gov.br/info-agro/safras (2020).2.Panizzi, A. R. & Corrêa-Ferreira, B. S. Dynamics in the insect fauna adaption to soybeans in the tropics. Trends Entomol. 1, 71–88 (1997).
Google Scholar 
3.Cattelan, A. J. & Dall’Agnol, A. The rapid soybean growth in Brazil. Oilseeds Fats Crops Lipids 25, D102 (2018).
Google Scholar 
4.Freitas, P. L. & Landers, J. N. The transformation of agriculture in Brazil through development and adoption of Zero Tillage Conservation Agriculture. Int. Soil Wat. Cons. Res. 2, 35–46 (2014).
Google Scholar 
5.Brookes, G., Taheripour, F. & Tyner, W. E. The contribution of glyphosate to agriculture and potential impact of restrictions on use at the global level. GM Crops Food 8, 216–228 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
6.Bueno, R. C. O. F., Bueno, A. F., Moscardi, F., Parra, J. R. P. & Hoffmann-Campo, C. B. Lepidopteran larva consumption of soybean foliage: Basis for developing multiple-species economic thresholds for pest management decisions. Pest Manag. Sci. 67, 170–174 (2011).CAS 
PubMed 
Article 

Google Scholar 
7.Panizzi, A. R. History and contemporary perspectives of the integrated pest management of soybean in Brazil. Neotrop. Entomol. 42, 119–127 (2013).CAS 
PubMed 
Article 

Google Scholar 
8.Bortolotto, A. et al. The use of soybean integrated pest management in Brazil: A review. Embrapa Soja-Artigo em periódico indexado (ALICE) Agron. Sci. Biotechnol. 1, 25–32 (2015).
Google Scholar 
9.CIB; AGROCONSULT. Impactos Econômicos e Sócio-ambientais da Tecnologia de Plantas Resistentes a Insetos no Brasil – Análise Histórica, Perspectivas e Desafios Futuros. http://apps.agr.br/wp-content/uploads/2018/12/Impactos-do-Milho-Bt-no-Brasil.pdf (2018).10.Brookes, G. The farm level economic and environmental contribution of Intacta soybeans in South America: The first five years. GM Crops Food 9, 140–151 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
11.Macrae, T. C. et al. Laboratory and field evaluations of transgenic soybean exhibiting high-dose expression of a synthetic Bacillus thuringiensis cry1A gene for control of Lepidoptera. J. Econ. Entomol. 98, 577–587 (2005).PubMed 
Article 

Google Scholar 
12.Bernardi, O. et al. Assessment of the high-dose concept and level of control provided by MON 87701× MON 89788 soybean against Anticarsia gemmatalis and Pseudoplusia includens (Lepidoptera: Noctuidae) in Brazil. Pest Manag. Sci. 68, 1083–1091 (2012).CAS 
PubMed 
Article 

Google Scholar 
13.Bernardi, O. et al. High levels of biological activity of Cry1Ac protein expressed on MON 87701× MON 89788 soybean against Heliothis virescens (Lepidoptera: Noctuidae). Pest Manag. Sci. 70, 588–594 (2014).CAS 
PubMed 
Article 

Google Scholar 
14.Dourado, P. M. et al. High susceptibility to Cry1Ac and low resistance allele frequency reduce the risk of resistance of Helicoverpa armigera to Bt soybean in Brazil. PLoS ONE 11, e0161388 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
15.Bernardi, O. et al. Low susceptibility of Spodoptera cosmioides, Spodoptera eridania and Spodoptera frugiperda (Lepidoptera: Noctuidae) to genetically-modified soybean expressing Cry1Ac protein. Crop Prot. 58, 33–40 (2014).CAS 
Article 

Google Scholar 
16.Edgerton, M. D. et al. Transgenic insect resistance traits increase corn yield and yield stability. Nat. Biotechnol. 30, 493–496 (2012).CAS 
PubMed 
Article 

Google Scholar 
17.Lu, Y., Wu, K., Jiang, Y., Guo, Y. & Desneux, N. Widespread adoption of Bt cotton and insecticide decrease promotes biocontrol services. Nature 487, 362–365 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
18.Carrière, Y. et al. Long-term regional suppression of pink bollworm by Bacillus thuringiensis cotton. Proc. Natl. Acad. Sci. USA 100, 1519–1523 (2003).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
19.Hutchison, W. D. et al. Areawide suppression of European corn borer with Bt maize reaps savings to non-Bt maize growers. Science 330, 222–225 (2010).ADS 
CAS 
PubMed 
Article 

Google Scholar 
20.Wu, K. M., Lu, Y. H., Feng, H. Q., Jiang, Y. Y. & Zhao, J. Z. Suppression of cotton bollworm in multiple crops in China in areas with Bt toxin–containing cotton. Science 321, 1676–1678 (2008).ADS 
CAS 
PubMed 
Article 

Google Scholar 
21.Dively, G. P. et al. Regional pest suppression associated with widespread Bt maize adoption benefits vegetable growers. Proc. Natl. Acad. Sci. USA 115, 3320–3325 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
22.Lu, Y. et al. Mirid bug outbreaks in multiple crops correlated with wide-scale adoption of Bt cotton in China. Science 328, 1151–1154 (2010).ADS 
CAS 
PubMed 
Article 

Google Scholar 
23.Zhao, J. H., Ho, P. & Azadi, H. Benefits of Bt cotton counterbalanced by secondary pests? Perceptions of ecological change in China. Environ. Monit. Assess. 173, 985–994 (2011).PubMed 
Article 

Google Scholar 
24.Gould, F. Sustainability of transgenic insecticidal cultivars: Integrating pest genetics and ecology. Annu. Rev. Entomol. 43, 701–726 (1998).CAS 
PubMed 
Article 

Google Scholar 
25.Van Rensburg, J. B. J. First report of field resistance by the stem borer, Busseola fusca (Fuller) to Bt-transgenic maize. S. Afr. J. Plant Soil 24, 147–151 (2007).Article 

Google Scholar 
26.Storer, N. P. et al. Discovery and characterization of field resistance to Bt maize: Spodoptera frugiperda (Lepidoptera: Noctuidae) in Puerto Rico. J. Econ. Entomol. 103, 1031–1038 (2010).PubMed 
Article 

Google Scholar 
27.Dhurua, S. & Gujar, G. T. Field-evolved resistance to Bt toxin Cry1Ac in the pink bollworm, Pectinophora gossypiella (Saunders) (Lepidoptera: Gelechiidae), from India. Pest Manag. Sci. 67, 898–903 (2011).CAS 
PubMed 
Article 

Google Scholar 
28.Gassmann, A. J., Petzold-Maxwell, J. L., Keweshan, R. S. & Dunbar, M. W. Field-evolved resistance to Bt maize by western corn rootworm. PLoS ONE 6, e22629 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
29.Farias, J. R. et al. Field-evolved resistance to Cry1F maize by Spodoptera frugiperda (Lepidoptera: Noctuidae) in Brazil. Crop Prot. 64, 150–158 (2014).ADS 
Article 

Google Scholar 
30.Fatoretto, J. C., Michel, A. P., Silva Filho, M. C. & Silva, N. Adaptive potential of fall armyworm (Lepidoptera: Noctuidae) limits Bt trait durability in Brazil. J. Integr. Pest Manag. 8, 17 (2017).Article 

Google Scholar 
31.Silva, C. S. et al. Population expansion and genomic adaptation to agricultural environments of the soybean looper, Chrysodeixis includens. Evol. Appl. 13, 2071–2085 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
32.Herzog, D. C. Sampling soybean looper on soybean. In Sampling Methods in Soybean Entomology (eds Koogan, M. & Herzog, D. C.) 141–168 (Springer, 1980).Chapter 

Google Scholar 
33.Sosa-Gómez, D. R. et al. Manual de Identificação de Insetos e Outros Invertebrados da Cultura da Soja (Embrapa Soja-Documentos (INFOTECA-E), 2014).
Google Scholar 
34.Gilligan, T. M. & Passoa, S. C. LepIntercept–An identification resource for intercepted Lepidoptera larvae. (Identification Technology Program (ITP), 2014). http://idtools.org/id/leps/lepintercept/key.html.35.R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2020). https://www.R-project.org/.36.Kaster, M. & Farias, J. R. B. Regionalização dos Testes de Valor de Cultivo e Uso e da indicação de Cultivares de Soja-terceira Aproximação (Embrapa Soja-Documentos (INFOTECA-E), 2012).
Google Scholar 
37.Sosa-Gómez, D. R., Delpin, K. E., Moscardi, F. & Nozaki, M. D. H. The impact of fungicides on Nomuraea rileyi (Farlow) Samson epizootics and on populations of Anticarsia gemmatalis Hübner (Lepidoptera: Noctuidae), on soybean. Neotrop. Entomol. 32, 287–291 (2003).Article 

Google Scholar 
38.Specht, A., Paula-Moraes, S. V. & Sosa-Gómez, D. R. Host plants of Chrysodeixis includens (Walker) (Lepidoptera, Noctuidae, Plusiinae). Rev. Bras. Entomol. 59, 343–345 (2015).Article 

Google Scholar 
39.Andrade, K. et al. Bioecological characteristics of Chrysodeixis includens (Lepidoptera: Noctuidae) fed on different hosts. Austral. Entomol. 55, 449–454 (2016).Article 

Google Scholar 
40.Moonga, M. N. & Davis, J. A. Partial life history of Chrysodeixis includens (Lepidoptera: Noctuidae) on summer hosts. J. Econ. Entomol. 109, 1713–1719 (2016).CAS 
PubMed 
Article 

Google Scholar 
41.Specht, A. et al. Biotic potential and life tables of Chrysodeixis includens (Lepidoptera: Noctuidae), Rachiplusia nu, and Trichoplusia ni on soybean and forage turnip. J. Insect Sci. 19, 8 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
42.Zulin, D., Ávila, C. J. & Schlick-Souza, E. C. Population fluctuation and vertical distribution of the soybean looper (Chrysodeixis includes) in soybean culture. Am. J. Plant Sci. 9, 1544–1556 (2018).Article 

Google Scholar 
43.Stacke, R. F. et al. Field-evolved resistance to chitin synthesis inhibitor insecticides by soybean looper, Chrysodeixis includens (Lepidoptera: Noctuidae), in Brazil. Chemosphere 259, 127499 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
44.Stacke, R. F. et al. Inheritance of lambda-cyhalothrin resistance, fitness costs and cross-resistance to other pyrethroids in soybean looper, Chrysodeixis includens (Lepidoptera: Noctuidae). Crop Prot. 131, 105096 (2020).CAS 
Article 

Google Scholar 
45.Yano, S. A. et al. High susceptibility and low resistance allele frequency of Chrysodeixis includens (Lepidoptera: Noctuidae) field populations to Cry1Ac in Brazil. Pest Manag. Sci. 72, 1578–1584 (2016).CAS 
PubMed 
Article 

Google Scholar 
46.Silva, M. T. B. & Moscardi, F. Field efficacy of the nucleopolyhedrovirus of Anticarsia gemmatalis Hübner (Lepidoptera: Noctuidae): Effect of formulations, water pH, volume and time of application, and type of spray nozzle. Neotrop. Entomol. 31, 75–83 (2002).Article 

Google Scholar 
47.Herzog, D. C. & Todd, J. W. Sampling velvetbean caterpillar on soybean. In Sampling Methods in Soybean Entomology (eds Koogan, M. & Herzog, D. C.) 107–140 (Springer, 1980).Chapter 

Google Scholar 
48.Panizzi, A. R., Oliveira, L. J. & Silva, J. J. Survivorship, larval development and pupal weight of Anticarsia gemmatalis (Hübner) (Lepidoptera: Noctuidae) feeding on potential leguminous host plants. Neotrop. Entomol. 33, 563–567 (2004).Article 

Google Scholar 
49.Leite, N. A., Alves-Pereira, A., Corrêa, A. S., Zucchi, M. I. & Omoto, C. Demographics and genetic variability of the new world bollworm (Helicoverpa zea) and the old world bollworm (Helicoverpa armigera) in Brazil. PLoS ONE 9, e113286 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
50.Leite, N. A. et al. Pan-American similarities in genetic structures of Helicoverpa armigera and Helicoverpa zea (Lepidoptera: Noctuidae) with implications for hybridization. Environ. Entomol. 46, 1024–1034 (2017).CAS 
PubMed 
Article 

Google Scholar 
51.Sosa-Gómez, D. R. et al. Timeline and geographical distribution of Helicoverpa armigera (Hübner) (Lepidoptera, Noctuidae: Heliothinae) in Brazil. Rev. Bras. Entomol. 60, 101–104 (2016).Article 

Google Scholar 
52.Dourado, P. M. et al. Host plant use of Helicoverpa spp. (Lepidoptera: Noctuidae) in the Brazilian agricultural landscape. Pest Manag. Sci. 77, 780–794 (2021).CAS 
PubMed 
Article 

Google Scholar 
53.Czepak, C., Albernaz, K. C., Vivan, L. M., Guimarães, H. O. & Carvalhais, T. First reported occurrence of Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae) in Brazil. Pesqui. Agropecu. Trop. 43, 110–113 (2013).Article 

Google Scholar 
54.Gomes, E. S., Santos, V. & Ávila, C. J. Biology and fertility life table of Helicoverpa armigera (Lepidoptera: Noctuidae) in different hosts. Entomol. Sci. 20, 419–426 (2017).Article 

Google Scholar 
55.Luttrell, R. G. & Mink, J. S. Damage to cotton fruiting structures by the fall armyworm, Spodoptera frugiperda (Lepidoptera: Noctuidae). J. Cotton Sci. 3, 35–44 (1999).
Google Scholar 
56.Martinelli, S., Barata, R. M., Zucchi, M. I., DeCastroSilva-Filho, M. & Omoto, C. Molecular variability of Spodoptera frugiperda (Lepidoptera: Noctuidae) populations associated to maize and cotton crops in Brazil. J. Econ. Entomol. 99, 519–526 (2006).CAS 
PubMed 
Article 

Google Scholar 
57.Barros, E. M., Torres, J. B., Ruberson, J. R. & Oliveira, M. D. Development of Spodoptera frugiperda on different hosts and damage to reproductive structures in cotton. Ent. Exp. Appl. 137, 237–245 (2010).Article 

Google Scholar 
58.Silva, D. M. D. et al. Biology and nutrition of Spodoptera frugiperda (Lepidoptera: Noctuidae) fed on different food sources. Sci. Agric. 74, 18–31 (2017).Article 

Google Scholar 
59.Machado, E. P. et al. Cross-crop resistance of Spodoptera frugiperda selected on Bt maize to genetically-modified soybean expressing Cry1Ac and Cry1F proteins in Brazil. Sci. Rep. 10, 1–9 (2020).Article 
CAS 

Google Scholar 
60.Montezano, D. G. et al. Host plants of Spodoptera frugiperda (Lepidoptera: Noctuidae) in the Americas. Afr. Entomol. 26, 286–300 (2018).Article 

Google Scholar 
61.Nagoshi, R. N. & Meagher, R. L. Review of fall armyworm (Lepidoptera: Noctuidae) genetic complexity and migration. Fla. Entomol. 91, 546–554 (2008).
Google Scholar 
62.Westbrook, J. K., Nagoshi, R. N., Meagher, R. L., Fleischer, S. J. & Jairam, S. Modeling seasonal migration of fall armyworm moths. Int. J. Biometeorol. 60, 255–267 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
63.Garcia, A. G., Ferreira, C. P., Godoy, W. A. & Meagher, R. L. A computational model to predict the population dynamics of Spodoptera frugiperda. J. Pest Sci. 92, 429–441 (2019).Article 

Google Scholar 
64.Diez-Rodríguez, G. I. & Omoto, C. Herança da resistência de Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae) a lambda-cialotrina. Neotrop. Entomol. 30, 311–316 (2001).Article 

Google Scholar 
65.Carvalho, R. A., Omoto, C., Field, L. M., Williamson, M. S. & Bass, C. Investigating the molecular mechanisms of organophosphate and pyrethroid resistance in the fall armyworm Spodoptera frugiperda. PLoS ONE 8, e62268 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
66.Nascimento, A. R. B. et al. Genetic basis of Spodoptera frugiperda (Lepidoptera: Noctuidae) resistance to the chitin synthesis inhibitor lufenuron. Pest Manag. Sci. 72, 810–815 (2016).PubMed 
Article 
CAS 

Google Scholar 
67.Okuma, D. M. et al. Inheritance and fitness costs of Spodoptera frugiperda (Lepidoptera: Noctuidae) resistance to Spinosad in Brazil. Pest Manag. Sci. 74, 1441–1448 (2018).CAS 
PubMed 
Article 

Google Scholar 
68.Bolzan, A. et al. Selection and characterization of the inheritance of resistance of Spodoptera frugiperda (Lepidoptera: Noctuidae) to chlorantraniliprole and cross-resistance to other diamide insecticides. Pest Manag. Sci. 75, 2682–2689 (2019).CAS 
PubMed 
Article 

Google Scholar 
69.Lira, E. C. et al. Resistance of Spodoptera frugiperda (Lepidoptera: Noctuidae) to spinetoram: Inheritance and cross-resistance to spinosad. Pest Manag. Sci. 76, 2674–2680 (2020).CAS 
PubMed 
Article 

Google Scholar 
70.Paulillo, L. C. M. et al. Changes in midgut endopeptidase activity of Spodoptera frugiperda (Lepidoptera: Noctuidae) are responsible for adaptation to soybean proteinase inhibitors. J. Econ. Entomol. 93, 892–896 (2000).CAS 
PubMed 
Article 

Google Scholar 
71.Silva-Brandão, K. L. et al. Transcript expression plasticity as a response to alternative larval host plants in the speciation process of corn and rice strains of Spodoptera frugiperda. BMC Genom. 18, 1–15 (2017).Article 
CAS 

Google Scholar 
72.Montezano, D. G., Specht, A., Sosa-Gomez, D. R., Roque-Specht, V. F. & Barros, N. M. Immature stages of Spodoptera eridania (Lepidoptera: Noctuidae): Developmental parameters and host plants. J. Insect Sci. 14, 238 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
73.Santos, K. B., Meneguim, A. M. & Neves, P. M. O. J. Biologia de Spodoptera eridania (Cramer) (Lepidoptera: Noctuidae) em diferentes hospedeiros. Neotrop. Entomol. 34, 903–910 (2005).Article 

Google Scholar 
74.Justiniano, W., Fernandes, M. G. & Viana, C. L. T. P. Diversity, composition and population dynamics of arthropods in the genetically modified soybeans Roundup Ready® RR1 (GT 40-3-2) and Intacta RR2 PRO (MON87701 x MON89788). J. Agric. Sci. 6, 33 (2014).
Google Scholar 
75.Specht, A. et al. Owlet moths (Lepidoptera: Noctuoidea) associated with Bt and non-Bt soybean in the Brazilian savanna. Braz. J. Biol. 79, 248–256 (2019).PubMed 
Article 

Google Scholar 
76.Specht, A. & Roque-Specht, V. F. Immature stages of Spodoptera cosmioides (Lepidoptera: Noctuidae): Developmental parameters and host plants. Zoologia 33, e20160053 (2016).Article 

Google Scholar 
77.Habib, M. E. M., Paleari, M. L. & Amaral, M. E. C. Effect of three larval diets on the development of the armyworm, Spodoptera latifascia Walker, 1856 (Lepidoptera: Noctuidae). Rev. Bras. Zool. 1, 177–182 (1983).Article 

Google Scholar 
78.Silva, D. M. et al. Biology of Spodoptera eridania and Spodoptera cosmioides (Lepidoptera: Noctuidae) on different host plants. Fla. Entomol. 100, 752–760 (2017).Article 

Google Scholar 
79.Tomquelski, G. V. & Maruyama, L. C. T. Lagarta-da-macã em soja. Rev. Cultiv. 117, 20–22 (2009).
Google Scholar 
80.Blanco, C. A. Heliothis virescens and Bt cotton in the United States. GM Crops Food 3, 201–212 (2012).PubMed 
Article 

Google Scholar 
81.Barrionuevo, M. J., Murúa, M. G., Goane, L., Meagher, R. & Navarro, F. Life table studies of Rachiplusia nu (Guenée) and Chrysodeixis (= Pseudoplusia) includens (Walker) (Lepidoptera: Noctuidae) on artificial diet. Fla. Entomol. 95, 944–951 (2012).Article 

Google Scholar 
82.Specht, A. et al. Ocorrência de Rachiplusia nu (Guenée) (Lepidoptera: Noctuidae) em Fumo (Nicotiana tabacum L.) no Rio Grande do Sul. Neotrop. Entomol. 35, 705–706 (2006).PubMed 
Article 

Google Scholar 
83.Trentin, L. B. et al. The complete genome of Rachiplusia nu nucleopolyhedrovirus (RanuNPV) and the identification of a baculoviral CPD-photolyase homolog. Virology 534, 64–71 (2019).CAS 
PubMed 
Article 

Google Scholar 
84.Perini, C. R. et al. Genetic structure of two Plusiinae species suggests recent expansion of Chrysodeixis includens in the American continent. Agric. For. Entomol. 23, 2502–3260 (2020).
Google Scholar 
85.Bacalhau, F. B. et al. Performance of genetically modified soybean expressing the Cry1A. 105, Cry2Ab2, and Cry1Ac proteins against key Lepidopteran pests in Brazil. J. Econ. Entomol. 113, 2883–2889 (2020).PubMed 
Article 

Google Scholar 
86.Machado, E. P. et al. Survival and development of Spodoptera eridania, Spodoptera cosmioides and Spodoptera albula (Lepidoptera: Noctuidae) on genetically-modified soybean expressing Cry1Ac and Cry1F proteins. Pest Manag. Sci. 76, 4029–4035 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
87.Horikoshi, R. J. et al. Effective dominance of resistance of Spodoptera frugiperda to Bt maize and cotton varieties: Implications for resistance management. Sci. Rep. 6, 1–8 (2016).Article 
CAS 

Google Scholar  More

1.Kumar, V. et al. Can productivity and profitability be enhanced in intensively managed cereal systems while reducing the environmental footprint of production? Assessing sustainable intensification options in the breadbasket of India. Agric. Ecosyst. Environ. 252, 132–147 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
2.Ladha, J. K. et al. How extensive are yield declines in long-term rice-wheat experiments in Asia?. For. Crop. Res. 81, 159–180 (2003).Article 

Google Scholar 
3.Bhatt, R., Kukal, S. S., Busari, M. A., Arora, S. & Yadav, M. Sustainability issues on rice–wheat cropping system. Int. Soil Water Conserv. Res. 4, 64–74 (2016).Article 

Google Scholar 
4.Sidhu, H. S. et al. Sub-surface drip fertigation with conservation agriculture in a rice-wheat system: A breakthrough for addressing water and nitrogen use efficiency. Agric. Water Manag. 216, 273–283 (2019).Article 

Google Scholar 
5.Singh, M. et al. intercomparison of crop establishment methods for improving yield and profitability in the rice-wheat system of Eastern India. For. Crop. Res. 250, 107776 (2020).Article 

Google Scholar 
6.Jat, H. S. et al. Energy use efficiency of crop residue management for sustainable energy and agriculture conservation in NW India. Renew. Energy 155, 1372–1382 (2020).Article 

Google Scholar 
7.Lohan, S. K. et al. Burning issues of paddy residue management in north-west states of India. Renew. Sustain. Energy Rev. 81, 693–706 (2018).Article 

Google Scholar 
8.Buhler, D. D. Weed population responses to weed control practices. II. Residual effects on weed populations, control, and Glycine max yield. Weed Sci. 47, 423–426 (1999).CAS 
Article 

Google Scholar 
9.Armengot, L. et al. Tillage as a driver of change in weed communities: A functional perspective. Agric. Ecosyst. Environ. 222, 276–285 (2016).Article 

Google Scholar 
10.Chhokar, R. S., Singh, S., Sharma, R. K. & Singh, M. Influence of straw management on Phalaris minor Retz control. Indian J. Weed Sci. 41, 150–156 (2009).
Google Scholar 
11.Chhokar, R. S. & Malik, R. K. Isoproturon-resistant Littleseed Canarygrass (Phalaris minor) and its response to alternate herbicides. Weed Technol. 16, 116–123 (2002).CAS 
Article 

Google Scholar 
12.Oerke, E. C. Crop losses to pests. J. Agric. Sci. 144, 31–43 (2006).Article 

Google Scholar 
13.Hassan, G., Khan, I., Khan, H. & Munir, M. Effect of different herbicides on weed density and some agronomic traits of wheat. Pak. J. Weed Sci. Res. 11, 17–22 (2005).
Google Scholar 
14.Chhokar, R. S. Ã., Sharma, R. K., Jat, G. R., Pundir, A. K. & Gathala, M. K. Effect of tillage and herbicides on weeds and productivity of wheat under rice–wheat growing system. Crop Prot. 26, 1689–1696 (2007).CAS 
Article 

Google Scholar 
15.Yadav, D. B., Yadav, A., Punia, S. S. & Chauhan, B. S. Management of herbicide-resistant Phalaris minor in wheat by sequential or tank-mix applications of pre- and post-emergence herbicides in north-western Indo-Gangetic Plains. Crop Prot. 89, 239–247 (2016).Article 

Google Scholar 
16.Harker, K. N. & O’Donovan, J. T. Recent weed control, weed management, and integrated weed management. Weed Technol. 27, 1–11 (2013).Article 

Google Scholar 
17.Scherner, A., Melander, B. & Kudsk, P. Vertical distribution and composition of weed seeds within the plough layer after eleven years of contrasting crop rotation and tillage schemes. Soil Tillage Res. 161, 135–142 (2016).Article 

Google Scholar 
18.Hillocks, R. J. Farming with fewer pesticides: EU pesticide review and resulting challenges for UK agriculture. Crop Prot. 31, 85–93 (2012).Article 

Google Scholar 
19.Nikolić, L., Šeremešić, S., Ljevnaić-Mašić, B., Latković, D. & Konstantinović, B. Weeds and their ecological indicator values in a long-term experiment. Appl. Ecol. Environ. Res. 18, 4775–4790 (2020).Article 

Google Scholar 
20.Blubaugh, C. K. & Kaplan, I. Tillage compromises weed seed predator activity across developmental stages. Biol. Control 81, 76–82 (2015).Article 

Google Scholar 
21.Nichols, V., Verhulst, N., Cox, R. & Govaerts, B. Weed dynamics and conservation agriculture principles: A review. For. Crop. Res. 183, 56–68 (2015).Article 

Google Scholar 
22.Sepat, S. et al. Effects of weed control strategy on weed dynamics, soybean productivity and profitability under conservation agriculture in India. For. Crop. Res. 210, 61–70 (2017).Article 

Google Scholar 
23.Sharma, P., Singh, M. K., Verma, K. & Prasad, S. K. Changes in the weed seed bank in long-term establishment methods trials under rice-wheat cropping system. Agronomy 10, 1–14 (2020).
Google Scholar 
24.Plaza-Bonilla, D. et al. Carbon management in dryland agricultural systems. A review. Agron. Sustain. Dev. 35, 1319–1334 (2015).Article 

Google Scholar 
25.Nandan, R. et al. Viable weed seed density and diversity in soil and crop productivity under conservation agriculture practices in rice-based cropping systems. Crop Prot. 136, 105210 (2020).CAS 
Article 

Google Scholar 
26.Choudhary, M., Vivek Sharma, P. C., Yadav, A. K. & Jat, H. S. Influence of management practices on weed dynamics, crop productivity and profitability in Wheat under Rice-Wheat cropping system in reclaimed sodic soils. J. Soil Salin. Water Qual. 9, 78–83 (2017).
Google Scholar 
27.Menalled, F. D., Smith, R. G., Dauer, J. T. & Fox, T. B. Impact of agricultural management on carabid communities and weed seed predation. Agric. Ecosyst. Environ. 118, 49–54 (2007).Article 

Google Scholar 
28.Shahzad, M., Farooq, M. & Hussain, M. Weed spectrum in different wheat-based cropping systems under conservation and conventional tillage practices in Punjab, Pakistan. Soil Tillage Res. 163, 71–79 (2016).Article 

Google Scholar 
29.Kumar, V. et al. Weed management strategies to reduce herbicide use in zero-till Rice–Wheat cropping systems of the Indo-Gangetic Plains. Weed Technol. 27, 241–254 (2013).Article 

Google Scholar 
30.Weisberger, D., Nichols, V. & Liebman, M. Does diversifying crop rotations suppress weeds? A meta-analysis. PLoS One 14, 1–12 (2019).
Google Scholar 
31.Melander, B. et al. European perspectives on the adoption of nonchemical weed management in reduced-tillage systems for arable crops. Weed Technol. 27, 231–240 (2013).Article 

Google Scholar 
32.Nandan, R. et al. Comparative assessment of the relative proportion of weed morphology, diversity, and growth under new generation tillage and crop establishment techniques in rice-based cropping systems. Crop Prot. 111, 23–32 (2018).Article 

Google Scholar 
33.Westerman, P., Luijendijk, C. D., Wevers, J. D. A. & Van Der Werf, W. Weed seed predation in a phenologically late crop. Weed Res. 51, 157–164 (2011).Article 

Google Scholar 
34.Honek, A., Martinkova, Z. & Jarosik, V. Ground beetles (Carabidae) as seed predators. Eur. J. Entomol. 100, 531–544 (2003).Article 

Google Scholar 
35.Jat, H. S. et al. Temporal changes in soil microbial properties and nutrient dynamics under climate smart agriculture practices. Soil Tillage Res. 199, 104595 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Liebman, M. & Davis, A. S. Integration of soil, crop and weed management in low-external-input farming systems. Weed Res. 20, 27–47 (2000).Article 

Google Scholar 
37.Lee, S. H., Yoo, S. H., Choi, J. Y. & Bae, S. Assessment of the impact of climate change on drought characteristics in the Hwanghae Plain, North Korea using time series SPI and SPEI: 1981–2100. Water (Switzerland) 9, 20 (2017).
Google Scholar 
38.Jat, H. S. et al. Re-designing irrigated intensive cereal systems through bundling precision agronomic innovations for transitioning towards agricultural sustainability in North-West India. Sci. Rep. 9, 1–14 (2019).ADS 
CAS 
Article 

Google Scholar 
39.Jat, H. S. et al. Climate Smart Agriculture practices improve soil organic carbon pools, biological properties and crop productivity in cereal-based systems of north-west India. CATENA 181, 104059 (2019).CAS 
Article 

Google Scholar 
40.Hernández Plaza, E., Navarrete, L. & González-Andújar, J. L. Intensity of soil disturbance shapes response trait diversity of weed communities: The long-term effects of different tillage systems. Agric. Ecosyst. Environ. 207, 101–108 (2015).Article 

Google Scholar 
41.Trichard, A., Ricci, B., Ducourtieux, C. & Petit, S. The spatio-temporal distribution of weed seed predation differs between conservation agriculture and conventional tillage. Agric. Ecosyst. Environ. 188, 40–47 (2014).Article 

Google Scholar 
42.Franke, A. C. et al. Phalaris minor seedbank studies: Longevity, seedling emergence and seed production as affected by tillage regime. Weed Res. 47, 73–83 (2007).Article 

Google Scholar 
43.Usman, K. et al. Integrated weed management through tillage and herbicides for Wheat production in Rice-Wheat cropping system in northwestern Pakistan. J. Integr. Agric. 11, 946–953 (2012).CAS 
Article 

Google Scholar 
44.Naresh, R. K. et al. Conservation agriculture improving soil quality for sustainable production systems under smallholder farming conditions in north west India: A review. Int. J. life Sci. Biotechnol. Pharm. Res. 2, 65 (2013).CAS 

Google Scholar 
45.Kumar, V. et al. Conservation agriculture (CA)-based practices reduced weed problem in wheat and caused shifts in weed seedbank community in rice-wheat cropping systems. In Weed Science for Sustainable Agriculture, Environment, and Biodiversity. (Eds. Rao A. N and Yaduraju N. T), Proceedings of 25th Asian Pacific Weed Science Society Conference 142 (2015).46.Malik, R. K., Kumar, V. & McDonald, A. Conservation agriculture-based resource-conserving practices and weed management in the rice-wheat cropping systems of the Indo-Gangetic Plains. Indian J. Weed Sci. 50, 218 (2018).Article 

Google Scholar 
47.Roth, C. M., Shroyer, J. P. & Paulsen, G. M. Allelopathy of sorghum on wheat under several tillage systems. Agron. J. 20, 855–860 (2000).Article 

Google Scholar 
48.Ayodele, O. P. & Aluko, O. A. Weed management strategies for conservation agriculture and environmental sustainability in Nigeria. IOSR J. Agric. Vet. Sci. Ver. I(10), 2319–2372 (2017).
Google Scholar 
49.Crutchfield, D. A., Wicks, G. A. & Burnside, O. C. Effect of winter wheat (Triticum aestivum) straw mulch level on weed control. Weed Sci. 34, 110–114 (1986).CAS 
Article 

Google Scholar 
50.Khanh, T. D., Xuan, T. D. & Chung, I. M. Rice allelopathy and the possibility for weed management. Ann. Appl. Biol. 151, 325–339 (2007).CAS 
Article 

Google Scholar 
51.Santín-Montanyá, M. I., Martín-Lammerding, D., Walter, I., Zambrana, E. & Tenorio, J. L. Effects of tillage, crop systems and fertilization on weed abundance and diversity in 4-year dry land winter wheat. Eur. J. Agron. 48, 43–49 (2013).Article 

Google Scholar 
52.Heggenstaller, A. H., Liebman, M. & Anex, R. P. Growth analysis of biomass production in sole-crop and double-crop corn systems. Crop Sci. 49, 2215–2224 (2009).Article 

Google Scholar 
53.Liebman, M. & Mohler, C. L. Weeds and the soil environment. In Ecological Management of Agricultural Weeds, (M. Liebman et al. eds.) 210–268 (2001). https://doi.org/10.1071/ea04026.54.Shrestha, A., Jeffrey, P. M. & Lanini, W. T. Subsurface drip irrigation as a weed management tool for conventional and conservation tillage Tomato (Lycopersicon esculentum Mill.) production in semi-arid agroecosystems. J. Sustain. Agric. 31, 37–41 (2007).Article 

Google Scholar 
55.Coolong, T. Using irrigation to manage weeds: A focus on drip irrigation. In Weed and Pest Control—Conventional and New Challenges (ed. Goyal, M. R.) 162–182 (Apple Academic Press, 2013).
Google Scholar 
56.Chhokar, R. S., Malik, R. K. & Balyan, R. S. Effect of moisture stress and seeding depth on germination of littleseed Canarygrass (Phalaris minor Retz.). Indian J. Weed Sci. 31, 78–79 (1999).
Google Scholar 
57.Singh, R., Gajri, P. R., Gill, K. S. & Khera, R. Puddling intensity and nitrogen use efficiency of rice (Oryza sativa) on a sandy loam soil of Punjab. Indian J. Agric. Sci. 65, 749–751 (1995).
Google Scholar 
58.Bajwa, A., Anjum, S. A. & Tanveer, M. Impact of fertilizer use on weed management in conservation agriculture—a review. Paki. J. Agric. Res. 69, 20 (2014).
Google Scholar 
59.Derksen, D. A., Anderson, R. L., Blackshaw, R. E. & Maxwell, B. Weed dynamics and management in the Northern Great Plains. Agron. J. 94, 174–185 (2002).Article 

Google Scholar 
60.Gathala, M. K. et al. Effect of tillage and crop establishment methods on physical properties of a medium-textured soil under a seven-year Rice-Wheat rotation. Soil Sci. Soc. Am 75, 1851–1863 (2011).CAS 
Article 

Google Scholar 
61.Dong, H., Ma, Y., Wu, H., Jiang, W. & Ma, X. Germination of Solanum nigrum l (black nightshade) in response to different abiotic factors. Planta Daninha 2016, 1–12 (2020).
Google Scholar 
62.Farooq, M., Flower, K. C., Jabran, K., Wahid, A. & Siddique, K. H. M. Crop yield and weed management in rainfed conservation agriculture. Soil Tillage Res. 117, 172–183 (2011).Article 

Google Scholar 
63.Swanton, C. J., Clements, D. R. & Derksen, D. A. Weed succession under conservation tillage: A hierarchical framework for research and management. Weed Technol. 7, 286–297 (1993).Article 

Google Scholar 
64.Humphreys, E., Kukal, S. S., Christen, E. W. & Hira, G. S. Halting the groundwater decline in North-West India—which crop technologies will be Winners?. Adv. Agron. 109, 155–217 (2010).Article 

Google Scholar 
65.Gomez, K. A. & Gomez, A. Statistical Procedures for Agricultural Research. (1984).66.Microsoft Corporation. (2010). Microsoft Excel. https://office.microsoft.com/excel. More

1.Fones, H. N. et al. Threats to global food security from emerging fungal and oomycete crop pathogens. Nat. Food 1, 332–342 (2020).
Google Scholar 
2.Chaloner, T. M., Gurr, S. J. & Bebber, D. P. Geometry and evolution of the ecological niche in plant-associated microbes. Nat. Commun. 11, 2955 (2020).CAS 

Google Scholar 
3.Bebber, D. P. Range-expanding pests and pathogens in a warming world. Annu. Rev. Phytopathol. 53, 335–356 (2015).CAS 

Google Scholar 
4.Bebber, D. P. et al. Many unreported crop pests and pathogens are probably already present. Glob. Change Biol. 25, 2703–2713 (2019).
Google Scholar 
5.Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Syst. 37, 637–669 (2006).
Google Scholar 
6.Bebber, D. P., Ramotowski, M. A. T. & Gurr, S. J. Crop pests and pathogens move polewards in a warming world. Nat. Clim. Change 3, 985–988 (2013).
Google Scholar 
7.Yan, Y., Wang, Y.-C., Feng, C.-C., Wan, P.-H. M. & Chang, K. T.-T. Potential distributional changes of invasive crop pest species associated with global climate change. Appl. Geogr. 82, 83–92 (2017).
Google Scholar 
8.Elith, J. & Leathwick, J. R. Species distribution models: ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. Syst. 40, 677–697 (2009).
Google Scholar 
9.Kearney, M. & Porter, W. Mechanistic niche modelling: combining physiological and spatial data to predict species’ ranges. Ecol. Lett. 12, 334–350 (2009).
Google Scholar 
10.Bondeau, A. et al. Modelling the role of agriculture for the 20th century global terrestrial carbon balance. Glob. Change Biol. 13, 679–706 (2007).
Google Scholar 
11.Bregaglio, S., Donatelli, M. & Confalonieri, R. Fungal infections of rice, wheat, and grape in Europe in 2030–2050. Agron. Sustain. Dev. 33, 767–776 (2013).
Google Scholar 
12.Bebber, D. P. Climate Change effects on Black Sigatoka disease of banana. Philos. Trans. R. Soc. B 374, 20180269 (2019).
Google Scholar 
13.Delgado-Baquerizo, M. et al. The proportion of soil-borne pathogens increases with warming at the global scale. Nat. Clim. Change 10, 550–554 (2020).
Google Scholar 
14.Ostberg, S., Schewe, J., Childers, K. & Frieler, K. Changes in crop yields and their variability at different levels of global warming. Earth Syst. Dyn. 9, 479–496 (2018).
Google Scholar 
15.Rosenzweig, C. et al. Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc. Natl Acad. Sci. USA 111, 3268–3273 (2014).CAS 

Google Scholar 
16.Magarey, R. D., Sutton, T. B. & Thayer, C. L. A simple generic infection model for foliar fungal plant pathogens. Phytopathology 95, 92–100 (2005).CAS 

Google Scholar 
17.Bebber, D. P., Holmes, T. & Gurr, S. J. The global spread of crop pests and pathogens. Glob. Ecol. Biogeogr. 23, 1398–1407 (2014).
Google Scholar 
18.Soberón, J. & Nakamura, M. Niches and distributional areas: concepts, methods, and assumptions. Proc. Natl Acad. Sci. USA 106, 19644–19650 (2009).
Google Scholar 
19.Bebber, D. P., Holmes, T., Smith, D. & Gurr, S. J. Economic and physical determinants of the global distributions of crop pests and pathogens. N. Phytol. 202, 901–910 (2014).
Google Scholar 
20.Sparks, A. H., Forbes, G. A., Hijmans, R. J. & Garrett, K. A. Climate change may have limited effect on global risk of potato late blight. Glob. Change Biol. 20, 3621–3631 (2014).
Google Scholar 
21.Chen, X. M. Epidemiology and control of stripe rust [Puccinia striiformis f. sp. tritici] on wheat. Can. J. Plant Pathol. 27, 314–337 (2005).
Google Scholar 
22.Zhan, J. & McDonald, B. A. Thermal adaptation in the fungal pathogen Mycosphaerella graminicola. Mol. Ecol. 20, 1689–1701 (2011).
Google Scholar 
23.Robin, C., Andanson, A., Saint-Jean, G., Fabreguettes, O. & Dutech, C. What was old is new again: thermal adaptation within clonal lineages during range expansion in a fungal pathogen. Mol. Ecol. 26, 1952–1963 (2017).
Google Scholar 
24.Rowlandson, T. et al. Reconsidering leaf wetness duration determination for plant disease management. Plant Dis. 99, 310–319 (2014).
Google Scholar 
25.IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).26.Dunn, R. J. H., Willett, K. M., Ciavarella, A. & Stott, P. A. Comparison of land surface humidity between observations and CMIP5 models. Earth Syst. Dyn. 8, 719–747 (2017).
Google Scholar 
27.Větrovský, T. et al. A meta-analysis of global fungal distribution reveals climate-driven patterns. Nat. Commun. 10, 5142 (2019).
Google Scholar 
28.Liu, X. et al. Warming affects foliar fungal diseases more than precipitation in a Tibetan alpine meadow. N. Phytol. 221, 1574–1584 (2019).
Google Scholar 
29.IPCC Climate Change 2014: Impacts, Adaptation, and Vulnerability (eds Field, C. B. et al.) (Cambridge Univ. Press, 2014).30.Sohl, T. L., Wimberly, M. C., Radeloff, V. C., Theobald, D. M. & Sleeter, B. M. Divergent projections of future land use in the United States arising from different models and scenarios. Ecol. Model. 337, 281–297 (2016).
Google Scholar 
31.Müller, C. et al. Exploring uncertainties in global crop yield projections in a large ensemble of crop models and CMIP5 and CMIP6 climate scenarios. Environ. Res. Lett. 16, 034040 (2021).
Google Scholar 
32.Folberth, C. et al. Parameterization-induced uncertainties and impacts of crop management harmonization in a global gridded crop model ensemble. PLoS ONE 14, e0221862 (2019).CAS 

Google Scholar 
33.Monfreda, C., Ramankutty, N. & Foley, J. A. Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Glob. Biogeochem. Cycles 22, GB1022 (2008).
Google Scholar 
34.Portmann, F. T., Siebert, S. & Döll, P. MIRCA2000—global monthly irrigated and rainfed crop areas around the year 2000: a new high-resolution data set for agricultural and hydrological modeling. Glob. Biogeochem. Cycles 24, 1–24 (2010).
Google Scholar 
35.Liu, J., Williams, J. R., Zehnder, A. J. B. & Yang, H. GEPIC—modelling wheat yield and crop water productivity with high resolution on a global scale. Agric. Syst. 94, 478–493 (2007).
Google Scholar 
36.Liu, W. et al. Global investigation of impacts of PET methods on simulating crop–water relations for maize. Agric. Meteorol. 221, 164–175 (2016).
Google Scholar 
37.Williams, J. R. & Sharpley, A. N. EPIC—Erosion/Productivity Impact Calculator: 1. Model Documentation (USDA, 1989).38.Watanabe, M. et al. Improved climate simulation by MIROC5: mean states, variability, and climate sensitivity. J. Clim. 23, 6312–6335 (2010).
Google Scholar 
39.Collins, W. J. et al. Development and evaluation of an Earth-system model—HadGEM2. Geosci. Model Dev. 4, 1051–1075 (2011).
Google Scholar 
40.Dunne, J. P. et al. GFDL’s ESM2 global coupled climate–carbon earth system models. Part I: physical formulation and baseline simulation characteristics. J. Clim. 25, 6646–6665 (2012).
Google Scholar 
41.Dufresne, J.-L. et al. Climate change projections using the IPSL-CM5 Earth system model: from CMIP3 to CMIP5. Clim. Dyn. 40, 2123–2165 (2013).
Google Scholar 
42.Bebber, D. P., Chaloner, T. M. & Gurr, S. J. Fungal and Oomycete Cardinal Temperatures (the Togashi Dataset) (Dryad, 2020); https://doi.org/10.5061/DRYAD.TQJQ2BVW643.Viswanath, K. et al. Simulation of leaf blast infection in tropical rice agro-ecology under climate change scenario. Clim. Change 142, 155–167 (2017).
Google Scholar 
44.Boixel, A.-L., Delestre, G., Legeay, J., Chelle, M. & Suffert, F. Phenotyping thermal responses of yeasts and yeast-like microorganisms at the individual and population levels: proof-of-concept, development and application of an experimental framework to a plant pathogen. Microb. Ecol. 78, 42–56 (2019).
Google Scholar 
45.Hijmans, R. J. et al. raster: Geographic data analysis and modeling. R package v.3.1-5 (2020).46.Yan, W. & Hunt, L. A. An equation for modelling the temperature response of plants using only the cardinal temperatures. Ann. Bot. 84, 607–614 (1999).
Google Scholar 
47.Wiens, J. A., Stralberg, D., Jongsomjit, D., Howell, C. A. & Snyder, M. A. Niches, models, and climate change: assessing the assumptions and uncertainties. Proc. Natl Acad. Sci. USA 106, 19729–19736 (2009).CAS 

Google Scholar 
48.Chen, Y. A new methodology of spatial cross-correlation analysis. PLoS ONE 10, e0126158 (2015).
Google Scholar  More

ModelWe model a tri-trophic food chain of one plant, one herbivore and one predator population on one or two habitat patches and complex meta-food-webs consisting of 10 plants and 30 animals in different landscapes containing 50 patches. The feeding dynamics are constant overall patches and are determined by the allometric food-web model by Schneider et al. 201633. We integrate dispersal as species-specific biomass flux between habitat patches according to Ryser et al. 201934. With the use of a dynamic bioenergetic model we formulate feeding and dispersal dynamics in terms of ordinary differential equations. The rate of change in biomass densities of a species are the sum of its biomass loss by metabolism, being preyed upon and emigration and its biomass gain by feeding and immigration. For detailed equations and for model parameters see section Equations and parameters and the supplement (Supplementary Table 1).Local food-web dynamicsFollowing the allometric food-web model by Schneider et al. 201633 each species is fully characterised by its average adult body mass. For the complex food-web log10 body masses were randomly drawn from a uniform distribution from 0 to 3 for plants and from 2 to 6 for animals. For the food chain the plant body mass was set to 102, the herbivore body mass to 104 and the predator body mass to 106. We set mass ratios of the herbivore to the plant and the predator to the herbivore to the optimum of 100, thus the respective resource being a one-hundredth of its consumer’s body mass. This simplifies feeding efficiency rates (see section Equations and parameters; Li,j, Eq. (5)) to 1 in the case of a food chain. Trophic dynamical parameters, such as metabolic rates and feeding rates, scale with body masses of model species. Also, we assume a type-II functional response for the food chain and a slight nonlinearity of the functional response in the food web as this stabilises persistence in more complex systems. Compared to Ryser et al 2019, capture rates were reduced to 5% to achieve viable food chains and food webs to increase the stability in the absence of interference competition.Nutrient modelWe have an underlying nutrient model with one nutrient that is driving the nutrient uptake and therefore the growth rate of the plant population11,33. The nutrient model consists of one nutrient, a nutrient turnover rate of 0.25 and a nutrient supply concentration. The nutrient supply concentration was varied to get eutrophic and oligotrophic patches (see Setup).Spatial dynamicsWe model dispersal between local communities as a dynamic process of emigration and immigration, assuming dispersal to occur at the same timescale as the local population dynamics35. Thus, biomass flows change dynamically between local populations and the dispersal dynamics directly influence local population dynamics and vice versa25.Dispersal rates of animals are modelled with an adaptive emigration rate depending on the net growth rate on the given patch. Dispersal ranges depend on the body masses of our model species with larger species having a higher dispersal range. We model a hostile matrix between habitat patches that does not allow feeding interactions to occur during dispersal. Depending on the scenario, we define a landscape with one, two or 50 patches. In cases with two or 50 patches, their locations are spatially explicit and were chosen in a way that the distances between reflect the dispersal loss of the predator across the matrix hostility gradient.Emigration and immigrationBased on empirical observations36 and previous theoretical frameworks13,22,37, we assume that the maximum dispersal distance of animal species increases with their body mass. For simplicity, we do not let the plants disperse, as they do not move themselves and the dispersal of plant propagules strongly depends on their dispersal strategy. We model emigration rates as a function of each species’ per capita net growth rate, which is summarising local conditions such as resource availability, predation pressure, and inter- and intra-specific competition25 (but see Sensitivity Analyses for dispersal models with constant dispersal or non-body-mass-scaled dispersal ranges). Dispersal losses scale linearly with the distance between two patches and are 100% in scenarios with only one patch or when the distance between the two patches surpasses the dispersal range of an animal. Even though we model dispersal losses according to dispersal distances, this loss term could also represent any other sort of dispersal loss. For numerical reasons, we did not allow dispersal flows smaller than 10−10.Numerical simulationsWe initialised each local population with a biomass density randomly sampled from a uniform probability density within the interval (0,10). Starting from these random initial conditions, we numerically simulated food web and dispersal dynamics over 100,000 time steps by integrating the system of differential equations implemented in C++ using procedures of the SUNDIALS CVODE solver version 2.7.0 (backward differentiation formula with absolute and relative error tolerances of 10−10) and the time series of biomass densities were saved for last 10,000 time steps. For numerical reasons, a local population was considered extinct and was set to 0 once its biomass density dropped below 10−20. Based on the empirically derived metabolic rates, these 100,000 time steps correspond to ~11 years. Our model does, however, not account for time spent for organisms’ other non-trophic activities such as sleeping or mating. Thus, the time scales of the simulation should only be compared with caution to natural time scales of population dynamics. Transient dynamics usually equilibrate within the first few thousand time steps.Equations and parametersOur model formulates the change of biomass densities over time in ordinary differential equations. Given the empirical origin of metabolic rates used in our model, one time step corresponds to an hour and body masses are in mg, areas of patches are not defined. The feeding links (i.e. who eats whom) are constant overall patches and are as well as the feeding dynamics determined by the allometric food-web model by Schneider et al. 201633. We integrate dispersal as species-specific biomass flow between habitat patches. Using ordinary differential equations to describe the feeding and dispersal dynamics, the rate of change in biomass density Bi,z of species i on patch z is given by$$frac{d{B}_{i,z}}{{dt}}[{mg}* {{{{{{{mathrm{Area}}}}}}}}^{-1}* {h}^{-1}]={B}_{i,z}mathop{sum}limits_{j}{e}_{j}{F}_{{ij},z}-mathop{sum}limits_{j}{{B}_{j,z}F}_{{ji},z}-{x}_{i}{B}_{i,z}-{E}_{i,z}+{I}_{i,z}({{{{{rm{for}}}}}}; {{{{{rm{animals}}}}}})$$
(1)
$$frac{d{B}_{i,z}}{{dt}}[{mg}* {{{{{{{mathrm{Area}}}}}}}}^{-1}* {h}^{-1}]={r}_{i}{G}_{i}{B}_{i,z}-mathop{sum}limits_{j}{B}_{j,z}{F}_{{ji},z}-{x}_{i}{B}_{i,z}({{{{{rm{for}}}}}}; {{{{{rm{plants}}}}}})$$
(2)
with the first three terms describing local trophic dynamics and the last two terms describing emigration, Ei,z (Eq. 9), and immigration, Ii,z (Eq. 11). For simplicity, we do not let plants disperse. Trophic dynamics are driven by following three processes. First, predation or herbivory on species j with assimilation efficiency e (ej = 0.545, if j is a plant, typical for herbivory; ej = 0.906 if j is an animal, typical for carnivory38) and the functional response Fij,z (Eq. 3) for animals, and a nutrient dependent growth (Eq. 7) for plants. Second, losses due to predation or herbivory, respectively. Third, losses by metabolic demands with xi = xAmi−0.305 with scaling constant xA = 0.141 (tenfold laboratory metabolic rate39 at a temperature of 20° Celsius to represent field metabolic rates) for animals and xi = xPmi−0.25 with xP = 0.138 for plants. We used a dynamic nutrient model (Eq. 8) as the energetic basis of our food web. Each species i is fully characterised by its average adult body mass mi. Body masses determine the interaction strengths of feeding links as well as the metabolic demands of species. Data from empirical feeding interactions are used to parametrise the functions that characterise the optimal prey body mass and the location and width of the feeding niche of a predator33. From each mi a unimodal attack kernel, called feeding efficiency Lij is constructed which determines the probability of consumer species i to attack and capture an encountered resource species j. We model Lij as an asymmetrical hump-shaped Ricker’s function (Eq. 5) that is maximised for an energetically optimal resource body mass (optimal consumer-resource body mass ratio Ropt = 100) and has a width of γ. The maximum of the feeding efficiency Lij equals 1. Supplementary table 1 is an overview of the standard parameter set for the equations. See also Schneider et al. 201633 for further information regarding the allometric food-web model.Functional response$${F}_{{ij},z}=frac{{omega }_{i}{b}_{i,j}{R}_{j,z}^{1+q}}{1+{omega }_{i}{sum }_{k}{b}_{{ik}}{h}_{{ik}}{R}_{k,z}^{1+q}}cdot frac{1}{{m}_{i}}$$
(3)
Per unit biomass feeding rate of consumer i as function of the biomass density of the resource Rj, with bi,j, resource-specific capture coefficient (Eq. 4); hi,j, resource-specific handling time (Eq. 6); ωi = 1/(number of resource species of i), an inefficiency parameter for generalists assuming that generalist are less adapted in for example search patterns or hunting strategies to a specific prey species; and q, the Hill coefficient for nonlinearities in density dependency (if q = 0 it is a Type-II functional response, if q = 1 it is a Type-III functional response).Capture coefficient$${b}_{{ij}}=f{a}_{k}{m}_{i}^{{beta }_{i}}{m}_{j}^{{beta }_{j}}{L}_{{ij}}$$
(4)
Resource-specific capture coefficient of consumer species i on resource species j scaling the feeding kernel Lij by a power function of consumer and resource body mass, assuming that the encounter rate between consumer and resource scales with their respective movement speed. This body mass scaling of encounter rates is assumed to occur before the attempt of a predator to capture its prey is made. We differentiate between carnivorous and herbivorous interactions with each comprising a constant scaling factor for their capture coefficients ak with k ∈ 0, 1 (a0 = 15 for carnivorous species and a1 = 3500 for herbivorous species). For plant resources, ({m}_{j}^{{beta }_{j}}) was replaced with the constant value of 1 (as plants do not move).Feeding efficiency$${L}_{i,j}={left(frac{{m}_{i}}{{m}_{j}{R}_{{{{{{{mathrm{opt}}}}}}}}}{e}^{1-frac{{m}_{i}}{{m}_{j}{R}_{{{{{{{mathrm{opt}}}}}}}}}}right)}^{gamma }$$
(5)
The probability of consumer i to attack and capture an encountered resource j (which can be either plant or animal), described by an asymmetrical hump-shaped curve (Ricker’s function), centered around an optimal consumer-resource body mass ratio Ropt = 10033 and with γ that that affects the width of the hump. An increase in γ results in a decrease in the width.Handling time$${h}_{{ij}}={h}_{0}{m}_{i}^{{eta }_{i}}{m}_{j}^{{eta }_{j}}$$
(6)
The time consumer i needs to kill, ingest, and digest resource species j, with scaling constant h0 = 0.4 and allometric exponents ηi = −0.48 and ηj = −0.6640.Growth factor for plants$${G}_{i}=frac{N}{{K}_{i}+N}$$
(7)
Species-specific growth factor of plants determined dynamically by the nutrient; with Ki, half-saturation densities determining the nutrient uptake efficiency assigned randomly for each plant species i and (uniform distribution within (0.1, 0.2)).Nutrient dynamics$$frac{d{N}_{z}}{{dt}}=Dleft(S-Nright)-mathop{sum}limits_{i,z}{r}_{i}{G}_{i}{P}_{i,z}$$
(8)
Rate of change of nutrient concentration N of nutrient on patch z, with global turnover rate D = 0.25, determining the rate at which nutrients are refreshed and the nutrient supply concentration S.Generating landscapesWe generated different fragmented landscapes, represented by random geometric graphs, by randomly drawing the locations of Z patches from a uniform distribution between 0 and 1 for x- and y-coordinates, respectively.DispersalWe model dispersal between local communities as a dynamic process of emigration and immigration, assuming dispersal to occur at the same timescale as the local population dynamics. Thus, biomass flows dynamically between local populations and the dispersal dynamics directly influence local population dynamics and vice versa. We model a hostile matrix between habitat patches that does not allow for feeding interactions to occur during dispersal. The total rate of emigration of animal species i from patch z is$${E}_{i,z}={d}_{i,z}{B}_{i,z}$$
(9)
with di,z as the corresponding per capita dispersal rate. We model di,z as$${d}_{i,z}=frac{a}{1+{{{{{{rm{e}}}}}}}^{-b({x}_{i}-{v}_{i,z})}}$$
(10)
with a, the maximum dispersal rate, b = 10, a parameter determining the shape of the dispersal rate, xi, the inflection point determined by the metabolic demands per unit biomass of species i, and υi,z, the net growth rate of species i on patch z. The net growth rate consists of the biomass gain by feeding, the biomass loss by being fed upon and the metabolic loss (({v}_{i,z}=frac{{B}_{i,z}mathop{sum}limits_{j}{e}_{j}{F}_{{ij},z}-mathop{sum}limits_{j}{{B}_{j,z}F}_{{ji},z}-{x}_{i}{B}_{i,z}}{{B}_{i,z}})). We chose to model di,z as a function of each species’ net growth rate to account for emigration triggers, such as resource availability, predation pressure, and inter- and intra-specific competition. If for example an animal species’ net growth is positive, there is no need for dispersal and emigration will be low. However, if the local environmental conditions deteriorate, the growing incentives to search for a better habitat increase the fraction of individuals emigrating.ImmigrationThe rate of immigration of biomass density of species i into patch z follows$${I}_{i,z}=mathop{sum}limits_{n,epsilon, {N}_{z}}{E}_{i,n}{max }(1-{delta }_{i,{nz}},0)frac{{max }(1-{delta }_{i,{nz}},0)}{mathop{sum}limits_{m,epsilon, {N}_{n}}{max }(1-{delta }_{i,{nz}},0)}$$
(11)
where Nz and Nn are the sets of all patches within the dispersal range of species i on patches z and n, respectively. In this equation, Ei,n is the emigration rate of species i from patch n, ({max }(1-{delta }_{i,{nz}},0)) is the fraction of successfully dispersing biomass, i.e. the fraction of biomass not lost to the matrix, and δi,nz is the distance between patches n and z relative to species i’s maximum dispersal distance δi (see below paragraph Maximum dispersal distance). The term (frac{{max }(1-{delta }_{i,{nz}},0)}{mathop{sum}limits_{m,epsilon, {N}_{n}}{max }(1-{delta }_{i,nz},0)})determines the fraction of biomass of species i emigrating from source patch n towards target patch z. This fraction depends on the relative distance between the patches, δi,nz, and the relative distances to all other potential target patches m of species i on the source patch n, δi,nm. Thus, the flow of biomass is greatest between patches with small distances to account for the logic that the first patch dispersing organism come across is closer. In other words, the further a destination is, the more likely it is to come across another patch before.For numerical reasons, we did not allow for dispersal flows with Ii,z  More