Ecology

Delavaux, C. S., Smith‐Ramesh, L. M. & Kuebbing, S. E. Beyond nutrients: a meta‐analysis of the diverse effects of arbuscular mycorrhizal fungi on plants and soils. Ecology 98, 2111–2119 (2017).Lugtenberg, B. & Kamilova, F. Plant-growth-promoting rhizobacteria. Annu. Rev. Microbiol. 63, 541–556 (2009).Article 
CAS 
PubMed 

Google Scholar 
Franche, C., Lindström, K. & Elmerich, C. Nitrogen-fixing bacteria associated with leguminous and non-leguminous plants. Plant Soil 321, 35–59 (2009).Article 
CAS 

Google Scholar 
Razanajatovo, M. et al. Autofertility and self‐compatibility moderately benefit island colonization of plants. Glob. Ecol. Biogeogr. 28, 341–352 (2019).Article 

Google Scholar 
Schrader, J., Wright, I. J., Kreft, H. & Westoby, M. A roadmap to plant functional island biogeography. Biol. Rev. (2021).Herridge, D. F., Peoples, M. B. & Boddey, R. M. Global inputs of biological nitrogen fixation in agricultural systems. Plant Soil 311, 1–18 (2008).Article 
CAS 

Google Scholar 
Vitousek, P. Nutrient cycling and limitation: Hawai’i as a model ecosystem. (Princeton Univ. Press, Princeton, NJ, 2004). Nutrient cycling and limitation: Hawai’i as a model ecosystem. Princeton Univ. Press, Princeton, NJ.Book 

Google Scholar 
Becking, L. G. M. B. Geobiologie of inleiding tot de milieukunde. (WP Van Stockum & Zoon, 1934).Peay, K. G. & Bruns, T. D. Spore dispersal of basidiomycete fungi at the landscape scale is driven by stochastic and deterministic processes and generates variability in plant–fungal interactions. N. Phytol. 204, 180–191 (2014).Article 

Google Scholar 
Delavaux, C. S. et al. Mycorrhizal fungi influence global plant biogeography. Nat. Ecol. Evol. 3, 424 (2019).Article 
PubMed 

Google Scholar 
Duchicela, J., Bever, J. D. & Schultz, P. A. Symbionts as Filters of Plant Colonization of Islands: Tests of Expected Patterns and Environmental Consequences in the Galapagos. Plants 9, 74 (2020).Article 
CAS 
PubMed Central 

Google Scholar 
Delavaux, C. S. et al. Mycorrhizal types influence island biogeography of plants. Commun. Biol. 4, 1–8 (2021).Article 

Google Scholar 
Simonsen, A. K., Dinnage, R., Barrett, L. G., Prober, S. M. & Thrall, P. H. Symbiosis limits establishment of legumes outside their native range at a global scale. Nat. Commun. 8, 1–9 (2017).Article 

Google Scholar 
Poole, P., Ramachandran, V. & Terpolilli, J. Rhizobia: from saprophytes to endosymbionts. Nat. Rev. Microbiol. 16, 291–303 (2018).Article 
CAS 
PubMed 

Google Scholar 
Sprent, J. I., Ardley, J. & James, E. K. Biogeography of nodulated legumes and their nitrogen‐fixing symbionts. N. Phytol. 215, 40–56 (2017).Article 
CAS 

Google Scholar 
Menge, D. N. Hedin, L. O. & Pacala, S. W. Nitrogen and phosphorus limitation over long-term ecosystem development in terrestrial ecosystems. (2012).Lambers, H., Raven, J. A., Shaver, G. R. & Smith, S. E. Plant nutrient-acquisition strategies change with soil age. Trends Ecol. evolution 23, 95–103 (2008).Article 

Google Scholar 
Walker, T. & Syers, J. K. The fate of phosphorus during pedogenesis. Geoderma 15, 1–19 (1976).Article 
CAS 

Google Scholar 
Jin, L. et al. Synergistic interactions of arbuscular mycorrhizal fungi and rhizobia promoted the growth of Lathyrus sativus under sulphate salt stress. Symbiosis 50, 157–164 (2010).Article 
CAS 

Google Scholar 
Afkhami, M. E. & Stinchcombe, J. R. Multiple mutualist effects on genomewide expression in the tripartite association between Medicago truncatula, nitrogen‐fixing bacteria and mycorrhizal fungi. Mol. Ecol. 25, 4946–4962 (2016).Article 
CAS 
PubMed 

Google Scholar 
Larimer, A. L., Clay, K. & Bever, J. D. Synergism and context dependency of interactions between arbuscular mycorrhizal fungi and rhizobia with a prairie legume. Ecology 95, 1045–1054 (2014).Article 
PubMed 

Google Scholar 
Primieri, S., Magnoli, S. M., Koffel, T. S., Stürmer, S. L. & Bever, J. D. Perennial, but not annual legumes synergistically benefit from infection with arbuscular mycorrhizal fungi and rhizobia: a meta‐analysis. N. Phytol. 233, 505-514 (2021).Larimer, A. L., Bever, J. D. & Clay, K. The interactive effects of plant microbial symbionts: a review and meta-analysis. Symbiosis 51, 139–148 (2010).Article 

Google Scholar 
Werner, G. D., Cornwell, W. K., Sprent, J. I., Kattge, J. & Kiers, E. T. A single evolutionary innovation drives the deep evolution of symbiotic N 2-fixation in angiosperms. Nat. Commun. 5, 1–9 (2014).Article 

Google Scholar 
Weigelt, P., König, C. & Kreft, H. GIFT- A global inventory of floras and traits for macroecology and biogeography. J. Biogeogr. 47, 16–43 (2020).Article 

Google Scholar 
Werner, G. D. et al. Symbiont switching and alternative resource acquisition strategies drive mutualism breakdown. Proc. Natl Acad. Sci. 115, 5229–5234 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bamba, M. et al. Wide distribution range of rhizobial symbionts associated with pantropical sea-dispersed legumes. Antonie van. Leeuwenhoek 109, 1605–1614 (2016).Article 
PubMed 

Google Scholar 
Chen, W.-M., Lee, T.-M., Lan, C.-C. & Cheng, C.-P. Characterization of halotolerant rhizobia isolated from root nodules of Canavalia rosea from seaside areas. FEMS Microbiol. Ecol. 34, 9–16 (2000).Article 
CAS 
PubMed 

Google Scholar 
Toma, M. A. et al. Tripartite symbiosis of Sophora tomentosa, rhizobia and arbuscular mycorhizal fungi. Braz. J. Microbiol. 48, 680–688 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Elanchezhian, R., Rajalakshmi, S. & Jayakumar, V. Salt tolerance characteristics of rhizobium species associated with Vigna marina. Indian J. Agric. Sci. 79, 980–985 (2009).CAS 

Google Scholar 
Chapin, F. S., Matson, P. A., Mooney, H. A. & Vitousek, P. M. Principles of Terrestrial Ecosystem Ecology (Springer, 2002).Vitousek, P. M., Walker, L. R., Whiteaker, L. D. & Matson, P. A. Nutrient limitations to plant growth during primary succession in Hawaii Volcanoes National Park. Biogeochemistry 23, 197–215 (1993).Article 

Google Scholar 
Liao, C. et al. Altered ecosystem carbon and nitrogen cycles by plant invasion: a meta-analysis. N. Phytologist 177, 706–714 (2008).Article 
CAS 

Google Scholar 
Woodward, S. A. et al. Use of the Exotic Tree Myrica Faya by Native and Exotic Birds in Hawai’i Volcanoes National Park (University of Hawaii Press, 1990).Vitousek, P. M., Walker, L. R., Whiteaker, L. D., Mueller-Dombois, D. & Matson, P. A. Biological invasion by Myrica faya alters ecosystem development in Hawaii. Science 238, 802–804 (1987).Article 
CAS 
PubMed 

Google Scholar 
Theoharides, K. A. & Dukes, J. S. Plant invasion across space and time: factors affecting nonindigenous species success during four stages of invasion. N. phytologist 176, 256–273 (2007).Article 

Google Scholar 
Kalwij, J. M. Review of ‘The Plant List, a working list of all plant species’. J. Vegetation Sci. 23, 998–1002 (2012).Article 

Google Scholar 
Byng, J. W. et al. An update of the Angiosperm Phylogeny Group classification for the orders and families of flowering plants: APG IV. Botanical J. Linn. Soc. 181, 1–20 (2016).Article 

Google Scholar 
Soudzilovskaia, N. A. et al. FungalRoot: Global online database of plant mycorrhizal associations. N. Phytol. 227, 955–966 (2020).Article 

Google Scholar 
Weigelt, P., König, C. & Kreft, H. GIFT–A global inventory of floras and traits for macroecology and biogeography. J. Biogeogr. 47, 16–43 (2020).Article 

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

Google Scholar 
Danielson, J. J. & Gesch, D. B. “Global multi-resolution terrain elevation data 2010 (GMTED2010),” (US Geological Survey, 2011).Weigelt, P. & Kreft, H. Quantifying island isolation–insights from global patterns of insular plant species richness. Ecography 36, 417–429 (2013).Article 

Google Scholar 
Kreft, H., Jetz, W., Mutke, J., Kier, G. & Barthlott, W. Global diversity of island floras from a macroecological perspective. Ecol. Lett. 11, 116–127 (2008).PubMed 

Google Scholar 
Triantis, K. A., Economo, E. P., Guilhaumon, F. & Ricklefs, R. E. Diversity regulation at macro‐scales: species richness on oceanic archipelagos. Glob. Ecol. Biogeogr. 24, 594–605 (2015).Article 

Google Scholar 
Crase, B., Liedloff, A. C. & Wintle, B. A. A new method for dealing with residual spatial autocorrelation in species distribution models. Ecography 35, 879–888 (2012).Article 

Google Scholar 
Bivand, R. R packages for analyzing spatial data: a comparative case study with areal data. Geogr. Anal. 54, 488–518 (2022).Article 

Google Scholar 
R. C. Team, R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2019).Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting Linear Mixed-Effects Models Using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar  More

Suttle CA. Marine viruses-major players in the global ecosystem. Nat Rev Microbiol. 2007;5:801–12.CAS 
PubMed 

Google Scholar 
Fuhrman JA. Marine viruses and their biogeochemical and ecological effects. Nature 1999;399:541–8.CAS 
PubMed 

Google Scholar 
Rohwer F, Thurber RV. Viruses manipulate the marine environment. Nature 2009;459:207–12.CAS 
PubMed 

Google Scholar 
Breitbart M, Bonnain C, Malki K, Sawaya NA. Phage puppet masters of the marine microbial realm. Nat Microbiol. 2018;3:754–66.CAS 
PubMed 

Google Scholar 
Zimmerman AE, Howard-Varona C, Needham DM, John SG, Worden AZ, Sullivan MB, et al. Metabolic and biogeochemical consequences of viral infection in aquatic ecosystems. Nat Rev Microbiol. 2020;18:21–34.CAS 
PubMed 

Google Scholar 
Rosenwasser S, Ziv C, Creveld SGV, Vardi A. Virocell metabolism: metabolic innovations during host-virus interactions in the ocean. Trends Microbiol. 2016;24:821–32.CAS 
PubMed 

Google Scholar 
Fuchsman CA, Carlson MCG, Garcia Prieto D, Hays MD, Rocap G. Cyanophage host-derived genes reflect contrasting selective pressures with depth in the oxic and anoxic water column of the Eastern Tropical North Pacific. Environ Microbiol. 2021;23:2782–2800.CAS 
PubMed 

Google Scholar 
Roux S, Brum JR, Dutilh BE, Sunagawa S, Duhaime MB, Loy A, et al. Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses. Nature 2016;537:689–93.CAS 
PubMed 

Google Scholar 
Gregory AC, Zayed AA, Conceição-Neto N, Temperton B, Bolduc B, Alberti A, et al. Marine DNA viral macro-and microdiversity from pole to pole. Cell 2019;177:1109–23.CAS 
PubMed 
PubMed Central 

Google Scholar 
Brum JR, Ignacio-Espinoza JC, Roux S, Doulcier G, Acinas SG, Alberti A, et al. Patterns and ecological drivers of ocean viral communities. Science 2015;348:1261498.PubMed 

Google Scholar 
Dion MB, Oechslin F, Moineau S. Phage diversity, genomics and phylogeny. Nat Rev Microbiol. 2020;18:125–38.CAS 
PubMed 

Google Scholar 
Sullivan MB, Waterbury JB, Chisholm SW. Cyanophages infecting the oceanic cyanobacterium Prochlorococcus. Nature 2003;424:1047–51.CAS 
PubMed 

Google Scholar 
Mann NH. Phages of the marine cyanobacterial picophytoplankton. FEMS Microbiol Rev. 2003;27:17–34.CAS 
PubMed 

Google Scholar 
Ni T, Zeng Q. Diel infection of cyanobacteria by cyanophages. Front Mar Sci. 2016;2:123.
Google Scholar 
Flombaum P, Gallegos JL, Gordillo RA, Rincon J, Zabala LL, Jiao N, et al. Present and future global distributions of the marine Cyanobacteria Prochlorococcus and Synechococcus. Proc Natl Acad Sci USA 2013;110:9824–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Biller SJ, Berube PM, Lindell D, Chisholm SW. Prochlorococcus: the structure and function of collective diversity. Nat Rev Microbiol 2015;13:13–27.CAS 
PubMed 

Google Scholar 
Proctor LM, Fuhrman JA. Viral mortality of marine-bacteria and cyanobacteria. Nature 1990;343:60–62.
Google Scholar 
Carlson MCG, Ribalet F, Maidanik I, Durham BP, Hulata Y, Ferron S, et al. Viruses affect picocyanobacterial abundance and biogeography in the North Pacific Ocean. Nat Microbiol 2022;7:570–80.CAS 
PubMed 
PubMed Central 

Google Scholar 
Matteson AR, Loar SN, Pickmere S, DeBruyn JM, Ellwood MJ, Boyd PW, et al. Production of viruses during a spring phytoplankton bloom in the South Pacific Ocean near of New Zealand. FEMS Microbiol Ecol 2012;79:709–19.CAS 
PubMed 

Google Scholar 
Ribalet F, Swalwell J, Clayton S, Jimenez V, Sudek S, Lin Y, et al. Light-driven synchrony of Prochlorococcus growth and mortality in the subtropical Pacific gyre. Proc Natl Acad Sci USA. 2015;112:8008–12.CAS 
PubMed 
PubMed Central 

Google Scholar 
Demory D, Liu R, Chen Y, Zhao F, Coenen AR, Zeng Q, et al. Linking light-dependent life history traits with population dynamics for Prochlorococcus and cyanophage. mSystems 2020;5:e00586–19.CAS 
PubMed 
PubMed Central 

Google Scholar 
Avrani S, Wurtzel O, Sharon I, Sorek R, Lindell D. Genomic island variability facilitates Prochlorococcus-virus coexistence. Nature 2011;474:604–8.CAS 
PubMed 

Google Scholar 
Marston MF, Pierciey FJ Jr, Shepard A, Gearin G, Qi J, Yandava C, et al. Rapid diversification of coevolving marine Synechococcus and a virus. Proc Natl Acad Sci USA 2012;109:4544–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Xiao X, Guo W, Li X, Wang C, Chen X, Lin X, et al. Viral lysis alters the optical properties and biological availability of dissolved organic matter derived from Prochlorococcus picocyanobacteria. Appl Environ Microbiol. 2021;87:e02271–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
Xiao X, Zeng Q, Zhang R, Jiao N. Prochlorococcus viruses—From biodiversity to biogeochemical cycles. Sci China Earth Sci. 2018;61:1728–36.
Google Scholar 
Jover LF, Effler TC, Buchan A, Wilhelm SW, Weitz JS. The elemental composition of virus particles: implications for marine biogeochemical cycles. Nat Rev Microbiol. 2014;12:519–28.CAS 
PubMed 

Google Scholar 
Puxty RJ, Millard AD, Evans DJ, Scanlan DJ. Viruses inhibit CO2 fixation in the most abundant phototrophs on earth. Curr Biol 2016;26:1585–9.CAS 
PubMed 

Google Scholar 
Weitz JS, Stock CA, Wilhelm SW, Bourouiba L, Coleman ML, Buchan A, et al. A multitrophic model to quantify the effects of marine viruses on microbial food webs and ecosystem processes. ISME J. 2015;9:1352–64.PubMed 
PubMed Central 

Google Scholar 
Sullivan MB, Coleman ML, Weigele P, Rohwer F, Chisholm SW. Three Prochlorococcus cyanophage genomes: signature features and ecological interpretations. PLoS Biol. 2005;3:e144.PubMed 
PubMed Central 

Google Scholar 
Sullivan MB, Krastins B, Hughes JL, Kelly L, Chase M, Sarracino D, et al. The genome and structural proteome of an ocean siphovirus: a new window into the cyanobacterial ‘mobilome’. Environ Microbiol. 2009;11:2935–51.CAS 
PubMed 
PubMed Central 

Google Scholar 
Sullivan MB, Huang KH, Ignacio-Espinoza JC, Berlin AM, Kelly L, Weigele PR, et al. Genomic analysis of oceanic cyanobacterial myoviruses compared with T4-like myoviruses from diverse hosts and environments. Environ Microbiol. 2010;12:3035–56.CAS 
PubMed 
PubMed Central 

Google Scholar 
Sabehi G, Shaulov L, Silver DH, Yanai I, Harel A, Lindell D. A novel lineage of myoviruses infecting cyanobacteria is widespread in the oceans. Proc Natl Acad Sci USA 2012;109:2037–42.CAS 
PubMed 
PubMed Central 

Google Scholar 
Huang S, Wang K, Jiao N, Chen F. Genome sequences of siphoviruses infecting marine Synechococcus unveil a diverse cyanophage group and extensive phage-host genetic exchanges. Environ Microbiol. 2012;14:540–58.CAS 
PubMed 

Google Scholar 
Labrie SJ, Frois-Moniz K, Osburne MS, Kelly L, Roggensack SE, Sullivan MB, et al. Genomes of marine cyanopodoviruses reveal multiple origins of diversity. Environ Microbiol. 2013;15:1356–76.CAS 
PubMed 

Google Scholar 
Dekel-Bird NP, Avrani S, Sabehi G, Pekarsky I, Marston MF, Kirzner S, et al. Diversity and evolutionary relationships of T7-like podoviruses infecting marine cyanobacteria. Environ Microbiol. 2013;15:1476–91.CAS 
PubMed 

Google Scholar 
Huang S, Wilhelm SW, Jiao N, Chen F. Ubiquitous cyanobacterial podoviruses in the global oceans unveiled through viral DNA polymerase gene sequences. ISME J. 2010;4:1243–51.PubMed 

Google Scholar 
Baran N, Goldin S, Maidanik I, Lindell D. Quantification of diverse virus populations in the environment using the polony method. Nat Microbiol. 2018;3:62–72.CAS 
PubMed 

Google Scholar 
Chow C-ET, Suttle CA. Biogeography of viruses in the sea. Annu Rev Virol. 2015;2:41–66.CAS 
PubMed 

Google Scholar 
Chen F, Lu JR. Genomic sequence and evolution of marine cyanophage P60: a new insight on lytic and lysogenic phages. Appl Environ Microbiol. 2002;68:2589–94.CAS 
PubMed 
PubMed Central 

Google Scholar 
Huang S, Zhang S, Jiao N, Chen F. Comparative genomic and phylogenomic analyses reveal a conserved core genome shared by estuarine and oceanic cyanopodoviruses. PLoS One. 2015;10:e0142962.PubMed 
PubMed Central 

Google Scholar 
Pope WH, Weigele PR, Chang J, Pedulla ML, Ford ME, Houtz JM, et al. Genome sequence, structural proteins, and capsid organization of the cyanophage Syn5: A “horned’ bacteriophage of marine Synechococcus. J Mol Biol. 2007;368:966–81.CAS 
PubMed 
PubMed Central 

Google Scholar 
Huang S, Sun Y, Zhang S, Long L. Temporal transcriptomes of a marine cyanopodovirus and its Synechococcus host during infection. Microbiologyopen 2021;10:e1150.CAS 
PubMed 

Google Scholar 
Wang K, Chen F. Prevalence of highly host-specific cyanophages in the estuarine environment. Environ Microbiol. 2008;10:300–12.CAS 
PubMed 

Google Scholar 
Chen F, Wang K, Huang S, Cai H, Zhao M, Jiao N, et al. Diverse and dynamic populations of cyanobacterial podoviruses in the Chesapeake Bay unveiled through DNA polymerase gene sequences. Environ Microbiol. 2009;11:2884–92.PubMed 

Google Scholar 
Goldin S, Hulata Y, Baran N, Lindell D. Quantification of T4-like and T7-like cyanophages using the polony method show they are significant members of the virioplankton in the North Pacific Subtropical Gyre. Front Microbiol. 2020;11:1210.PubMed 
PubMed Central 

Google Scholar 
Nasko DJ, Chopyk J, Sakowski EG, Ferrell BD, Polson SW, Wommack KE. Family A DNA polymerase phylogeny uncovers diversity and replication gene organization in the virioplankton. Front Microbiol. 2018;9:3053.PubMed 
PubMed Central 

Google Scholar 
Dekel-Bird NP, Sabehi G, Mosevitzky B, Lindell D. Host-dependent differences in abundance, composition and host range of cyanophages from the Red Sea. Environ Microbiol. 2015;17:1286–99.CAS 
PubMed 

Google Scholar 
Hanson CA, Marston MF, Martiny JBH. Biogeographic variation in host range phenotypes and taxonomic composition of marine cyanophage isolates. Front Microbiol. 2016;7:983.PubMed 
PubMed Central 

Google Scholar 
Rocap G, Larimer FW, Lamerdin J, Malfatti S, Chain P, Ahlgren NA, et al. Genome divergence in two Prochlorococcus ecotypes reflects oceanic niche differentiation. Nature 2003;424:1042–7.CAS 
PubMed 

Google Scholar 
Chen B, Wang L, Song S, Huang B, Sun J, Liu H. Comparisons of picophytoplankton abundance, size, and fluorescence between summer and winter in northern South China Sea. Cont Shelf Res. 2011;31:1527–40.
Google Scholar 
Lindell D, Jaffe JD, Coleman ML, Futschik ME, Axmann IM, Rector T, et al. Genome-wide expression dynamics of a marine virus and host reveal features of co-evolution. Nature 2007;449:83–86.CAS 
PubMed 

Google Scholar 
Zhao Y, Qin F, Zhang R, Giovannoni SJ, Zhang Z, Sun J, et al. Pelagiphages in the Podoviridae family integrate into host genomes. Environ Microbiol. 2019;21:1989–2001.CAS 
PubMed 

Google Scholar 
Leptihn S, Gottschalk J, Kuhn A. T7 ejectosome assembly: A story unfolds. Bacteriophage 2016;6:e1128513.CAS 
PubMed 
PubMed Central 

Google Scholar 
Thompson LR, Zeng Q, Kelly L, Huang KH, Singer AU, Stubbe J, et al. Phage auxiliary metabolic genes and the redirection of cyanobacterial host carbon metabolism. Proc Natl Acad Sci USA 2011;108:E757–64.CAS 
PubMed 
PubMed Central 

Google Scholar 
Zeng Q, Chisholm SW. Marine viruses exploit their host’s two-component regulatory system in response to resource limitation. Curr Biol 2012;22:124–8.CAS 
PubMed 

Google Scholar 
Zeng Q, Bonocora RP, Shub DA. A free-standing homing endonuclease targets an intron insertion site in the psbA gene of cyanophages. Curr Biol. 2009;19:218–22.CAS 
PubMed 

Google Scholar 
Lindell D, Jaffe JD, Johnson ZI, Church GM, Chisholm SW. Photosynthesis genes in marine viruses yield proteins during host infection. Nature 2005;438:86–89.CAS 
PubMed 

Google Scholar 
Breitbart M, Thompson LR, Suttle CA, Sullivan MB. Exploring the vast diversity of marine viruses. Oceanography. 2007;20:135–9.
Google Scholar 
Kazlauskas D, Venclovas C. Computational analysis of DNA replicases in double-stranded DNA viruses: relationship with the genome size. Nucleic Acids Res. 2011;39:8291–305.CAS 
PubMed 
PubMed Central 

Google Scholar 
Liu X, Zhang Q, Murata K, Baker ML, Sullivan MB, Fu C, et al. Structural changes in a marine podovirus associated with release of its genome into Prochlorococcus. Nat Struct Mol Biol. 2010;17:830–6.CAS 
PubMed 
PubMed Central 

Google Scholar 
Dai W, Fu C, Raytcheva D, Flanagan J, Khant HA, Liu XG, et al. Visualizing virus assembly intermediates inside marine cyanobacteria. Nature 2013;502:707–10.CAS 
PubMed 
PubMed Central 

Google Scholar 
Liu R, Liu Y, Chen Y, Zhan Y, Zeng Q. Cyanobacterial viruses exhibit diurnal rhythms during infection. Proc Natl Acad Sci USA 2019;116:14077–82.CAS 
PubMed 
PubMed Central 

Google Scholar 
Maidanik I, Kirzner S, Pekarski I, Arsenieff L, Tahan R, Carlson MCG, et al. Cyanophages from a less virulent clade dominate over their sister clade in global oceans. ISME J. 2022;16:2169–80.CAS 
PubMed 
PubMed Central 

Google Scholar 
Shitrit D, Hackl T, Laurenceau R, Raho N, Carlson MCG, Sabehi G, et al. Genetic engineering of marine cyanophages reveals integration but not lysogeny in T7-like cyanophages. ISME J. 2022;16:488–99.CAS 
PubMed 

Google Scholar 
Liang Y, Wang L, Wang Z, Zhao J, Yang Q, Wang M, et al. Metagenomic analysis of the diversity of DNA viruses in the surface and deep sea of the South China Sea. Front Microbiol. 2019;10:1951.PubMed 
PubMed Central 

Google Scholar 
Pedrós-Alió C, Potvin M, Lovejoy C. Diversity of planktonic microorganisms in the Arctic Ocean. Prog Oceanogr. 2015;139:233–43.
Google Scholar 
Luo E, Eppley JM, Romano AE, Mende DR, DeLong EF. Double-stranded DNA virioplankton dynamics and reproductive strategies in the oligotrophic open ocean water column. ISME J. 2020;14:1304–15.CAS 
PubMed 
PubMed Central 

Google Scholar 
Steidinger BS, Crowther TW, Liang J, Van Nuland ME, Werner GDA, Reich PB, et al. Climatic controls of decomposition drive the global biogeography of forest-tree symbioses. Nature 2019;569:404–8.CAS 
PubMed 

Google Scholar 
Xie X, Wu T, Zhu M, Jiang G, Xu Y, Wang X, et al. Comparison of random forest and multiple linear regression models for estimation of soil extracellular enzyme activities in agricultural reclaimed coastal saline land. Ecol Indic. 2021;120:106925.CAS 

Google Scholar 
Lee SJ, Richardson CC. Choreography of bacteriophage T7 DNA replication. Curr Opin Chem Biol. 2011;15:580–6.CAS 
PubMed 
PubMed Central 

Google Scholar 
Kulczyk AW, Richardson CC. The replication system of bacteriophage T7. Enzymes. 2016;39:89–136.CAS 
PubMed 

Google Scholar 
Benkovic SJ, Valentine AM, Salinas F. Replisome-mediated DNA replication. Annu Rev Biochem. 2001;70:181–208.CAS 
PubMed 

Google Scholar 
Johnson A, O’Donnell M. Cellular DNA replicases: components and dynamics at the replication fork. Annu Rev Biochem. 2005;74:283–315.CAS 
PubMed 

Google Scholar 
Seco EM, Zinder JC, Manhart CM, Lo Piano A, McHenry CS, Ayora S. Bacteriophage SPP1 DNA replication strategies promote viral and disable host replication in vitro. Nucleic Acids Res. 2013;41:1711–21.CAS 
PubMed 

Google Scholar 
Mruwat N, Carlson MCG, Goldin S, Ribalet F, Kirzner S, Hulata Y, et al. A single-cell polony method reveals low levels of infected Prochlorococcus in oligotrophic waters despite high cyanophage abundances. ISME J. 2021;15:41–54.CAS 
PubMed 

Google Scholar 
Moore LR, Rocap G, Chisholm SW. Physiology and molecular phylogeny of coexisting Prochlorococcus ecotypes. Nature 1998;393:464–7.CAS 
PubMed 

Google Scholar 
Puxty RJ, Millard AD, Evans DJ, Scanlan DJ. Shedding new light on viral photosynthesis. Photosynth Res. 2015;126:71–97.CAS 
PubMed 

Google Scholar 
Edwards KF, Steward GF, Schvarcz CR. Making sense of virus size and the tradeoffs shaping viral fitness. Ecol Lett. 2021;24:363–73.PubMed 

Google Scholar 
Moore LR, Coe A, Zinser ER, Saito MA, Sullivan MB, Lindell D, et al. Culturing the marine cyanobacterium Prochlorococcus. Limnol Oceanogr Methods. 2007;5:353–62.CAS 

Google Scholar 
Hyman P, Abedon ST. Bacteriophage host range and bacterial resistance. Adv Appl Microbiol. 2010;70:217–48.CAS 
PubMed 

Google Scholar 
Fridman S, Flores-Uribe J, Larom S, Alalouf O, Liran O, Yacoby I, et al. A myovirus encoding both photosystem I and II proteins enhances cyclic electron flow in infected Prochlorococcus cells. Nat Microbiol. 2017;2:1350–7.CAS 
PubMed 

Google Scholar 
Fang X, Liu Y, Zhao Y, Chen Y, Liu R, Qin QL, et al. Transcriptomic responses of the marine cyanobacterium Prochlorococcus to viral lysis products. Environ Microbiol. 2019;21:2015–28.CAS 
PubMed 

Google Scholar 
John SG, Mendez CB, Deng L, Poulos B, Kauffman AK, Kern S, et al. A simple and efficient method for concentration of ocean viruses by chemical flocculation. Environ Microbiol Rep. 2011;3:195–202.CAS 
PubMed 
PubMed Central 

Google Scholar 
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014;30:2114–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
Schmieder R, Edwards R. Quality control and preprocessing of metagenomic datasets. Bioinformatics 2011;27:863–4.CAS 
PubMed 
PubMed Central 

Google Scholar 
Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10:1–10.
Google Scholar 
Peng Y, Leung HC, Yiu SM, Chin FY. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 2012;28:1420–8.CAS 
PubMed 

Google Scholar 
Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 2014;30:2068–9.CAS 
PubMed 

Google Scholar 
Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.CAS 
PubMed 
PubMed Central 

Google Scholar 
Minh BQ, Schmidt HA, Chernomor O, Schrempf D, Woodhams MD, von Haeseler A, et al. IQ-TREE 2: new models and efficient methods for phylogenetic Inference in the genomic era. Mol Biol Evol. 2020;37:2461–2461.PubMed 
PubMed Central 

Google Scholar 
Hoang DT, Chernomor O, von Haeseler A, Minh BQ, Vinh LS. UFBoot2: improving the ultrafast bootstrap approximation. Mol Biol Evol. 2018;35:518–22.CAS 
PubMed 

Google Scholar 
Martinez-Hernandez F, Fornas O, Lluesma Gomez M, Bolduc B, de la Cruz Pena MJ, Martinez JM, et al. Single-virus genomics reveals hidden cosmopolitan and abundant viruses. Nat Commun. 2017;8:15892.CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhang Z, Qin F, Chen F, Chu X, Luo H, Zhang R, et al. Culturing novel and abundant pelagiphages in the ocean. Environ Microbiol 2021;23:1145–61.CAS 
PubMed 

Google Scholar 
Buchholz HH, Michelsen ML, Bolanos LM, Browne E, Allen MJ, Temperton B. Efficient dilution-to-extinction isolation of novel virus-host model systems for fastidious heterotrophic bacteria. ISME J. 2021;15:1585–98.CAS 
PubMed 
PubMed Central 

Google Scholar 
Qin F, Du S, Zhang Z, Ying H, Wu Y, Zhao G, et al. Newly identified HMO-2011-type phages reveal genomic diversity and biogeographic distributions of this marine viral group. ISME J. 2022;16:1363–75.CAS 
PubMed 
PubMed Central 

Google Scholar  More

Data processingIn 2019, the CETUS data spanning between 2012 and 2017 was published open access through the Flanders Marine Institute (VLIZ) IPT portal and distributed by EMODnet and OBIS, in a first version of the CETUS dataset9. The data collected between 2018 and 2019 was prepared as the 2012–2017 data9. Methods for photographic verification/validation and to evaluate the MMOs experience were applied (see below), in order to include new variables on data quality in an updated version of the dataset. Currently, the CETUS dataset is updated, with a 2nd version available10. It comprises data from 2012 to 2017, with the following two new columns on the observers’ experience: “most experienced observer” and “least experienced observer”; and a new column associated with validation of the sightings’ identifications: “photographic validation”. The results here presented correspond to the analysis of the data from 2012 to 2019, and the open-access dataset will soon be further updated with the 2018–2019 data.Photographic verificationAll the former MMOs who have integrated the CETUS Project, between 2012 and 2019, were contacted and asked to provide any available photographic or video records of cetaceans collected during their on board periods. The collection of sighting’s images was not a requirement of the CETUS protocol, and so these records were obtained opportunistically, with availability and quality depending on several factors: observers on board having personal cameras, camera quality, intention of the observer taking the photograph (e.g., for aesthetic or identification purposes).The images obtained were organized in a folder hierarchy from the year to the day of recording. However, not all the images had metadata up to the day of recording, so these were inserted into the most appropriate hierarchy-level of the folder organization. For each set of records corresponding to a single-taxon sighting, the photos/videos with the better quality or framing (i.e., that allowed for an easier species identification) were selected for that sighting. The remaining photos/videos were only consulted in case of doubt (e.g., to look for additional details that could help with the identification).Verification consisted of the process of matching the photographic/video records with the dataset sighting registers. Whenever possible and ideally, the file metadata was used for the process. However, often, the date and/or time of the file metadata were wrong, non-existent, or in different time zones. In these cases, a conservative methodology was applied using all available information to match as many sightings as possible. An estimation of time lag was attempted (based on, at least, two obvious matches between photographs/videos and dataset registers, e.g., unique sighting of the day, close to the boat, easy/obvious identification). When not possible, further evaluation consisted in assessing whether the sighting and image record was too obvious, and accounting for unique complementary information on the sighting (e.g., the number of animals or the side of the sighting were unique for that day and/or for that species/group).Photographic validationAfter the verification process, the validation of the matched records was carried out, to confirm or correct the species identification of sightings in the 1st version of the CETUS dataset (i.e., reported by the MMOs on board). The validation approach involved, for more dubious identification through the photo/video records, the discussion between four experienced observers of the CETUS team. In cases where no consensual agreement was achieved, an external expert on cetacean identification was also consulted. Identifications made through the photographic/video records required 100% certainty, and these were then compared with the cetacean identifications provided in the 1st version of the CETUS dataset. Then, the occurrence records with originally misidentifications of cetaceans, as well as those records where validation allowed to achieve an identification to a lower taxon, were corrected in the 2nd version of the dataset (i.e., a delphinid sighting validated as common dolphin, will now appear as common dolphin). A new column “photographic validation” was added to the dataset with the following categories: “yes” (i.e., validated with photograph/video), “no” (i.e., not validated with photograph/video), and “to the family” (i.e., validation only to the family taxon).For further analysis, specifically for the model process on the identification success (see below), registers were considered “completely validated” if it was possible to complete the photographic/video identification process up to the species level (then, differentiating if the original identification from the MMOs was or not correct). For Globicephala sp. and Kogia sp., validation to the genus was considered complete, since the species from both genera are visually hardly differentiated, especially at sea.Creating a data quality criteria: evaluating MMOs experienceQuality criteria were created to evaluate the MMOs experience based on the information collected from their curricula vitae (CVs) (alumni MMOs provided as many CVs as the years of their participation in CETUS). The following quality criteria were considered: (i) the experience at sea, (ii) the experience with cetaceans’ ID, (iii) the number of species they have worked with, and (iv) the experience working with the CETUS Project protocol. Each of these quality criteria was ranked from 0 to 5, and then these were summed to generate an evaluation score, on a scale of 0 to 20, attributed to each MMO (Table 4).Table 4 Quality criteria for MMOs evaluation.Full size tableThe MMOs evaluations were computed for each cruise (i.e., the trip from one port to another), considering the experience of the MMOs based on the CV obtained for that year, plus the experience acquired during CETUS participation in previous cruises that year. Since most of the times, the team of observers on board each cruise was constituted by two MMOs, two final evaluation scores were attributed to each cruise in the 2nd version of the CETUS dataset, into two new columns: “most experienced observer” and “least experienced observer”. On rare occasions where there is only one observer on board that cruise, only the evaluation of the single observer was included under the column “most experienced observer”, leaving the column “least experienced observer” as “NULL”. To investigate the experience of MMOs on board, both individually and cumulative (LEO + MEO), the combination of the score values was computed by cruise. These were then trimmed to unique combinations of evaluation scores.The names of observers, previously presented in the online dataset for each cruise, were removed for anonymity purposes, as there is now ancillary information regarding their experience.Model fittingTwo Generalized Additive Models (GAM) were fitted to assess bias on the number of sightings recorded per survey and on the identification success of cetacean species. Details for each model are presented below. Both models were fitted in R (Version 4.1.0). Prior to modelling, Pearson correlations were calculated between all pairs of explanatory variables, considered for each model (see below), to exclude highly correlated variables, considering a threshold of 0.7524,25,28. Since the variables regarding MMOs’ experience were correlated (LEO or MEO correlated with cumulative and mean experience; and cumulative experience correlated with mean experience – Supplementary Fig. S3), these variables were not included in the first fitting stage (backward selection) but included later through forward selection (see below). Multicollinearity among explanatory variables was measured through the Variance Inflation Factor (VIF), with a threshold of 3 (Supplementary Tables S4)24,25,29. After removing the MMOs evaluation scores, no multicollinearity was observed, so all the other variables were kept for the first fitting stage.For model selection, a backward selection was applied to oversaturated models18,24,25,30,31. The Akaike Information Criterion (AIC) was used as a measure of adequation of fitness, choosing the model with the lowest AIC value at each step of the model fitting process, i.e., comparing nested models (larger model incorporating one more explanatory variable compared with the smaller model). If the AIC-difference between the two models was less than 2, an Analysis of Variance (ANOVA), through chi-square test, was used to check if the AIC-difference was significant24,25,32. If this difference was not statistically significant (p  > 0.05), the simplest model (smaller model) was kept. Through a forward selection process, the variables regarding the MMOs evaluation scores were added, one at a time, to the best model obtained in the previous backward selection. After comparing the models with each other (separate variables for LEO + MEO vs. Cumulative Evaluation vs Mean Evaluation), the best model, considering the AIC value, was kept. A final backward selection process was then applied.All GAMs were fitted with the “mgcv” package (https://cran.r-project.org/web/packages/mgcv) and a maximum of four splines (k = 4) was chosen to limit the complexity of smoothers describing the effects of the explanatory variables25,31. If a spline was close to linear (with estimated degrees of freedom of ~1), the smooth term was removed, and a linear function was fitted. To check for model quality, the “gam.check” function was used to verify the diagnostic plots and the adequacy of the number of splines (Supplementary Figs. S5 and S6). Existence of influential data points was assessed (with the threshold of 0.25 for the Hat values), as well as the correlation between model residuals and explanatory variables. In both final models, number of splines was adequate and there were no influential data points or clear correlation between residuals and explanatory variables (Supplementary Figs. S7 and S8)24,32.Bias modelling of number of sightingsTo assess the bias parameters on the number of sightings recorded per survey (i.e., a full day monitoring, from sunrise to sunset), the following detectability factors were considered as explanatory variables: weather conditions (i.e., the minimums and maximums of the sea state, wind state, and visibility), the experience of MMOs (i.e., the evaluation scores of the least and the most experienced observers, as well as the mean and cumulative evaluations of the MMOs experience) and kilometres sampled “on-effort” (i.e., periods of active survey). Sampling periods were divided into “On-effort” and “Off-effort” conditions, based on four meteorological variables: sea state (Douglas scale), wind state (Beaufort scale), visibility (measured in a categorical scale ranging from 0–10 and estimated from the distance to the horizon line and possible reference points at a known range, e.g., ships with an automatic identification system,  > 1000 km), and the occurrence of rain (see Supplementary Table S9)10. For the model fitting, only “on-effort” periods of sampling were considered. Given that the response variable was count data, a Poisson distribution was tested (with a log link function). Then, the resulting first oversaturated model was checked for overdispersion, through a Pearson estimator. Since it tested positive for overdispersion (φ = 1.99), a negative binomial distribution (with a log link function) was fitted.Bias modelling of identification successA binary response variable, based on the success in the species identification for each sighting, was generated, and a model with binomial distribution (with a logit link function) was fitted. As in the previous model, only “on-effort” records were used. The total number of non-successful identifications across the dataset (the 0 s of the model) was extrapolated from the proportion of wrong identifications obtained in the validation process. To calculate this proportion, only complete validated sightings registered “on-effort” were used. Proportions were computed and extrapolated to Odontoceti and Mysticeti, separately. This resulted in 78 non-successful identifications in delphinids, plus 17 misidentifications in baleen whales, i.e., a total of 95 “on-effort” sightings randomly selected from the dataset were defined as unsuccessful identifications (0 s in the response variable for model fitting). The remaining records were considered successful identifications (1 s in the response variable for model fitting). To assess the bias parameters on the identification success, the following independent variables were considered in the analysis: the group (i.e., Group A: Odontoceti sightings, excluding sperm whale (Physeter macrocephalus); and Group B: Mysticeti sightings, plus sperm whale), the size of the group (i.e., the best estimate of the number of animals in a sighting, from the observer’s perspective), sighting distance (i.e., a relative measure according to the scale of the binoculars), weather conditions (i.e., the sea state, wind state, and visibility at the time of each sighting), the experience of MMOs (i.e., the evaluation scores of least and most experienced observers, as well as the mean and cumulative scores of the MMOs teams). Group A and B were settled according to cetacean morphology. However, since sperm whales have closer similarities with Mysticeti species, they were also included in Group B21,33. This categorization was mostly based on body size, as this is likely the main factor, regarding the species morphology, influencing the identification. Group A is constituted by species with a medium length of less than 10 meters, while Group B includes the larger species over 10 meters (Mysticeti plus P. macrocephalus)33. Since in the CETUS Project, different binoculars have been used – with two different reticle scales – it was necessary to standardize binocular distances to the same scale. More

Wright, S. Evolution in Mendelian Populations. Genetics 16, 97–159 (1931).CAS 
PubMed 
PubMed Central 

Google Scholar 
DiBattista, J. D. Patterns of genetic variation in anthropogenically impacted populations. Conserv. Genet. 9, 141–156 (2008).
Google Scholar 
Ellegren, H. & Galtier, N. Determinants of genetic diversity. Nat. Rev. Genet. 17, 422–433 (2016).CAS 
PubMed 

Google Scholar 
Nei, M., Maruyama, T. & Chakraborty, R. The Bottleneck Effect and Genetic Variability in Populations. Evolution 29, 1–10 (1975).PubMed 

Google Scholar 
Frankham, R. Stress and adaptation in conservation genetics. J. Evol. Biol. 18, 750–755 (2005).CAS 
PubMed 

Google Scholar 
Mimura, M. et al. Understanding and monitoring the consequences of human impacts on intraspecific variation. Evol. Appl. 10, 121–139 (2017).PubMed 

Google Scholar 
Whitham, T. G. et al. A framework for community and ecosystem genetics: from genes to ecosystems. Nat. Rev. Genet. 7, 510–523 (2006).CAS 
PubMed 

Google Scholar 
Hughes, A., Inouye, B., Johnson, M., Underwood, N. & Vellend, M. Ecological consequences of genetic diversity. Ecol. Lett. 11, 609–623 (2008).PubMed 

Google Scholar 
Hughes, A. R., Stachowicz, J. J. & Williams, S. L. Morphological and physiological variation among seagrass (Zostera marina) genotypes. Oecologia 159, 725–733 (2009).PubMed 

Google Scholar 
Schweitzer, J. A. et al. Genetically based trait in a dominant tree affects ecosystem processes: Plant genetics impact ecosystems. Ecol. Lett. 7, 127–134 (2004).
Google Scholar 
Hughes, A. R. & Stachowicz, J. J. Genetic diversity enhances the resistance of a seagrass ecosystem to disturbance. Proc. Natl Acad. Sci. USA 101, 8998–9002 (2004).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wimp, G. M. et al. Conserving plant genetic diversity for dependent animal communities. Ecol. Lett. 7, 776–780 (2004).
Google Scholar 
Reusch, T. B. H., Ehlers, A., Hämmerli, A. & Worm, B. Ecosystem recovery after climatic extremes enhanced by genotypic diversity. Proc. Natl Acad. Sci. 102, 2826–2831 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).CAS 
PubMed 

Google Scholar 
Salo, T. & Gustafsson, C. The Effect of Genetic Diversity on Ecosystem Functioning in Vegetated Coastal Ecosystems. Ecosystems 19, 1429–1444 (2016).
Google Scholar 
Zettlemoyer, M. A. & Peterson, M. L. Does Phenological Plasticity Help or Hinder Range Shifts Under Climate Change? Front. Ecol. Evol. 9, 392 (2021).
Google Scholar 
Fei, S. et al. Divergence of species responses to climate change. Sci. Adv. 3, e1603055 (2017).PubMed 
PubMed Central 

Google Scholar 
Yiming, L. et al. Latitudinal gradients in genetic diversity and natural selection at a highly adaptive gene in terrestrial mammals. Ecography 44, 206–218 (2021).
Google Scholar 
Excoffier, L., Foll, M. & Petit, R. J. Genetic Consequences of Range Expansions. Annu. Rev. Ecol. Evol. Syst. 40, 481–501 (2009).
Google Scholar 
Alsos, I. G. et al. Genetic consequences of climate change for northern plants. Proc. R. Soc. B Biol. Sci. 279, 2042–2051 (2012).
Google Scholar 
Stahl, U., Reu, B. & Wirth, C. Predicting species’ range limits from functional traits for the tree flora of North America. Proc. Natl Acad. Sci. 111, 13739–13744 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Van Nuland, M. E. et al. Intraspecific trait variation across elevation predicts a widespread tree species’ climate niche and range limits. Ecol. Evol. 10, 3856–3867 (2020).PubMed 
PubMed Central 

Google Scholar 
Peterson, M. L., Angert, A. L. & Kay, K. M. Experimental migration upward in elevation is associated with strong selection on life history traits. Ecol. Evol. 10, 612–625 (2019).PubMed 
PubMed Central 

Google Scholar 
Vitasse, Y., Signarbieux, C. & Fu, Y. H. Global warming leads to more uniform spring phenology across elevations. Proc. Natl Acad. Sci. 115, 1004–1008 (2018).CAS 
PubMed 

Google Scholar 
Piao, S. et al. Plant phenology and global climate change: Current progresses and challenges. Glob. Change Biol. 25, 1922–1940 (2019).
Google Scholar 
Chen, I.-C., Hill, J., Ohlemüller, R., Roy, D. B. & Thomas, C. Rapid Range Shifts of Species Associated with High Levels of Climate Warming. Science 333, 1024–6 (2011).CAS 
PubMed 

Google Scholar 
Pauls, S. U., Nowak, C., Bálint, M. & Pfenninger, M. The impact of global climate change on genetic diversity within populations and species. Mol. Ecol. 22, 925–946 (2013).PubMed 

Google Scholar 
De Kort, H. et al. Life history, climate and biogeography interactively affect worldwide genetic diversity of plant and animal populations. Nat. Commun. 12, 516 (2021).PubMed 
PubMed Central 

Google Scholar 
Hampe, A. & Petit, R. J. Conserving biodiversity under climate change: the rear edge matters. Ecol. Lett. 8, 461–467 (2005).PubMed 

Google Scholar 
DeMarche, M. L., Doak, D. F. & Morris, W. F. Incorporating local adaptation into forecasts of species’ distribution and abundance under climate change. Glob. Change Biol. 25, 775–793 (2019).
Google Scholar 
Bothwell, H. M. et al. Genetic data improves niche model discrimination and alters the direction and magnitude of climate change forecasts. Ecol. Appl. 31, e02254 (2021).Syfert, M. M., Brummitt, N. A., Coomes, D. A., Bystriakova, N. & Smith, M. J. Inferring diversity patterns along an elevation gradient from stacked SDMs: A case study on Mesoamerican ferns. Glob. Ecol. Conserv. 16, e00433 (2018).
Google Scholar 
Mateo, R. G., Felicísimo, Á. M., Pottier, J., Guisan, A. & Muñoz, J. Do Stacked Species Distribution Models Reflect Altitudinal Diversity Patterns? PLOS ONE 7, e32586 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Ferrier, S. & Guisan, A. Spatial modelling of biodiversity at the community level. J. Appl. Ecol. 43, 393–404 (2006).
Google Scholar 
Ware, I. M. et al. Climate-driven reduction of genetic variation in plant phenology alters soil communities and nutrient pools. Glob. Change Biol. 25, 1514–1528 (2019).
Google Scholar 
Endler, J. A. Geographic variation, speciation, and clines (Princeton University Press, 1977).May, R. M. & Godfrey, J. Biological Diversity: Differences between Land and Sea [and Discussion]. Philos. Trans. Biol. Sci. 343, 105–111 (1994).
Google Scholar 
Des Roches, S. et al. The ecological importance of intraspecific variation. Nat. Ecol. Evol. 2, 57–64 (2018).PubMed 

Google Scholar 
Van Nuland, M. E., Bailey, J. K. & Schweitzer, J. A. Divergent plant–soil feedbacks could alter future elevation ranges and ecosystem dynamics. Nat. Ecol. Evol. 1, 0150 (2017).
Google Scholar 
Richardson, A. D. et al. Influence of spring and autumn phenological transitions on forest ecosystem productivity. Philos. Trans. R. Soc. B Biol. Sci. 365, 3227–3246 (2010).
Google Scholar 
Richardson, A. D. et al. Influence of spring phenology on seasonal and annual carbon balance in two contrasting New England forests. Tree Physiol. 29, 321–321 (2009).CAS 
PubMed 

Google Scholar 
Huntington, T. G. CO2-induced suppression of transpiration cannot explain increasing runoff. Hydrol. Process. 22, 311–314 (2008).
Google Scholar 
Kim, J. H. et al. Warming-Induced Earlier Greenup Leads to Reduced Stream Discharge in a Temperate Mixed Forest Catchment. J. Geophys. Res. Biogeosciences 123, 1960–1975 (2018).
Google Scholar 
Ware, I. M. et al. Climate-driven divergence in plant-microbiome interactions generates range-wide variation in bud break phenology. Commun. Biol. 4, 748 (2021).PubMed 
PubMed Central 

Google Scholar 
Mori, A. S. et al. Biodiversity–productivity relationships are key to nature-based climate solutions. Nat. Clim. Change 11, 543–550 (2021).
Google Scholar 
Woolbright, S. A., Whitham, T. G., Gehring, C. A., Allan, G. J. & Bailey, J. K. Climate relicts and their associated communities as natural ecology and evolution laboratories. Trends Ecol. Evol. 29, 406–416 (2014).PubMed 

Google Scholar 
Naiman, R. J., Décamps, H. & McClain, M. E. Riparia: ecology, conservation, and management of streamside communities (Elsevier Academic Press, 2005).Bayliss, S. L. J., Mueller, L. O., Ware, I. M., Schweitzer, J. A. & Bailey, J. K. Plant genetic variation drives geographic differences in atmosphere–plant–ecosystem feedbacks. Plant-Environ. Interact. 1, 166–180 (2020).
Google Scholar 
Cooke, J. E. K. & Rood, S. B. Trees of the people: the growing science of poplars in Canada and worldwide. This commentary is one of a selection of papers published in the Special Issue on Poplar Research in Canada. Can. J. Bot. 85, 1103–1110 (2007).
Google Scholar 
Evans, L. M., Allan, G. J., Meneses, N., Max, T. L. & Whitham, T. G. Herbivore host- associated genetic differentiation depends on the scale of plant genetic variation examined. Evol. Ecol. 27, 65–81 (2013).
Google Scholar 
Evans, L. M. et al. Geographical barriers and climate influence demographic history in narrowleaf cottonwoods. Heredity 114, 387–396 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hargreaves, A. L., Samis, K. E., Eckert, C. G., Schmitz, A. E. O. J. & Bronstein, E. J. L. Are Species’ Range Limits Simply Niche Limits Writ Large? A Review of Transplant Experiments beyond the Range. Am. Nat. 183, 157–173 (2014).PubMed 

Google Scholar 
Gotelli, N. J. & Stanton-Geddes, J. Climate change, genetic markers and species distribution modelling. J. Biogeogr. 42, 1577–1585 (2015).
Google Scholar 
Cushman, S. A. et al. Landscape genetic connectivity in a riparian foundation tree is jointly driven by climatic gradients and river networks. Ecol. Appl. 24, 1000–1014 (2014).PubMed 

Google Scholar 
Bothwell, H. M. et al. Conserving threatened riparian ecosystems in the American West: Precipitation gradients and river networks drive genetic connectivity and diversity in a foundation riparian tree (Populus angustifolia). Mol. Ecol. 26, 5114–5132 (2017).PubMed 

Google Scholar 
Jimenez-Valverde, A. Sample Size for the evaluation of presence-absence models. Ecol. Indic. 114, 106289 (2020).
Google Scholar 
Hamann, A., Wang, T., Spittlehouse, D. L. & Murdock, T. Q. A Comprehensive, High-Resolution Database of Historical and Projected Climate Surfaces for Western North America. Bull. Am. Meteorol. Soc. 94, 1307–1309 (2013).
Google Scholar 
Lucinda. M. et al. NHDPlus version 2: user guide (Horizon Systems Corporation, 2012).ESRI. ArcMap (ESRI, 2018).Bayliss, S. L. J., Papeş, M., Schweitzer, J. A. & Bailey, J. K. Aggregate population-level models informed by genetics predict more suitable habitat than traditional species-level model across the range of a widespread riparian tree. PLoS One. 17, e0274892 (2022).CAS 
PubMed 
PubMed Central 

Google Scholar 
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 
Franklin, J. Mapping species distributions: spatial inference and prediction (Cambridge University Press, 2009).Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting Linear Mixed-Effects Models Using lme4. J. Stat. Softw. 67, 1–48 (2015). (1).
Google Scholar 
Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57 (2011).
Google Scholar 
Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151 (2006).
Google Scholar 
Pearson, R. G., Raxworthy, C. J., Nakamura, M. & Townsend Peterson, A. Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J. Biogeogr. 34, 102–117 (2007).
Google Scholar 
Swets, J. A. Measuring the Accuracy of Diagnostic Systems. Science 240, 1285–1293 (1988).CAS 
PubMed 

Google Scholar 
Engler, R. et al. 21st century climate change threatens mountain flora unequally across Europe. Glob. Change Biol. 17, 2330–2341 (2011).
Google Scholar 
Randin, C. F. et al. Climate change and plant distribution: local models predict high-elevation persistence. Glob. Change Biol. 15, 1557–1569 (2009).
Google Scholar 
Knutti, R., Masson, D. & Gettelman, A. Climate model genealogy: Generation CMIP5 and how we got there. Geophys. Res. Lett. 40, 1194–1199 (2013).
Google Scholar 
Mateo, R. G., Mokany, K. & Guisan, A. Biodiversity Models: What If Unsaturation Is the Rule? Trends Ecol. Evol. 32, 556–566 (2017).PubMed 
PubMed Central 

Google Scholar 
R. Core Team. R: a language and environment for statistical computing (R Foundation for Statistical Computing, 2020).Oksanen, J. et al. vegan: community ecology package (2020) http://CRAN.R-project.org/package=vegan. More

Theurillat, J.-P. & Guisan, A. Potential impact of climate change on vegetation in the European Alps: A review. Clim. Change 50, 77–109 (2001).CAS 

Google Scholar 
Diaz, H. F. & Eischeid, J. K. Disappearing “alpine tundra” Köppen climatic type in the western United States. Geophys. Res. Lett. 34, L18707 (2007).ADS 

Google Scholar 
Dirnböck, T., Essl, F. & Rabitsch, W. Disproportional risk for habitat loss of high-altitude endemic species under climate change. Glob. Change Biol. 17, 990–996 (2011).ADS 

Google Scholar 
Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).ADS 
CAS 
PubMed 

Google Scholar 
Pauli, H., Gottfried, M., Dirnböck, T., Dullinger, S. & Grabherr, G. Assessing the long-term dynamics of endemic plants at summit habitats. In Alpine Biodiversity in Europe (eds. Nagy, L., Grabherr, G., Körner, C., & Thompson, D. B.) 195–207 (Springer, 2003).Cogoni, D., Sulis, E., Bacchetta, G. & Fenu, G. The unpredictable fate of the single population of a threatened narrow endemic Mediterranean plant. Biodivers. Conserv. 28, 1799–1813 (2019).
Google Scholar 
Cursach, J., Besnard, A., Rita, J. & Fréville, H. Demographic variation and conservation of the narrow endemic plant Ranunculus weyleri. Acta Oecol. 53, 102–109 (2013).ADS 

Google Scholar 
Dibner, R. R., DeMarche, M. L., Louthan, A. M. & Doak, D. F. Multiple mechanisms confer stability to isolated populations of a rare endemic plant. Ecol. Monogr. 89, e01360 (2019).
Google Scholar 
Boyce, M. S., Haridas, C. V., Lee, C. T., NCEAS Stochastic Demography Working Group. Demography in an increasingly variable world. Trends Ecol. Evol. 21, 141–148 (2006).PubMed 

Google Scholar 
Buckley, Y. M. et al. Causes and consequences of variation in plant population growth rate: A synthesis of matrix population models in a phylogenetic context. Ecol. Lett. 13, 1182–1197 (2010).PubMed 

Google Scholar 
Abbott, R. E., Doak, D. F. & DeMarche, M. L. Portfolio effects, climate change, and the persistence of small populations: Analyses on the rare plant Saussurea weberi. Ecology 98, 1071–1081 (2017).PubMed 

Google Scholar 
Villellas, J., Doak, D. F., García, M. B. & Morris, W. F. Demographic compensation among populations: What is it, how does it arise and what are its implications?. Ecol. Lett. 18, 1139–1152 (2015).PubMed 

Google Scholar 
Doak, D. F. & Morris, W. F. Demographic compensation and tipping points in climate-induced range shifts. Nature 467, 959–962 (2010).ADS 
CAS 
PubMed 

Google Scholar 
García-Camacho, R., Albert, M. J. & Escudero, A. Small-scale demographic compensation in a high-mountain endemic: The low edge stands still. Plant Ecol. Divers. 5, 37–44 (2012).
Google Scholar 
Andrello, M. et al. Accounting for stochasticity in demographic compensation along the elevational range of an alpine plant. Ecol. Lett. 23, 870–880 (2020).PubMed 

Google Scholar 
Valladares, F. et al. The effects of phenotypic plasticity and local adaptation on forecasts of species range shifts under climate change. Ecol. Lett. 17, 1351–1364 (2014).PubMed 

Google Scholar 
Ægisdóttir, H. H., Kuss, P. & Stöcklin, J. Isolated populations of a rare alpine plant show high genetic diversity and considerable population differentiation. Ann. Bot. 104, 1313–1322 (2009).PubMed 
PubMed Central 

Google Scholar 
Morente-López, J. et al. Geography and environment shape landscape genetics of Mediterranean alpine species Silene ciliata Poiret. (Caryophyllaceae). Front. Plant Sci. 9, 1698 (2018).PubMed 
PubMed Central 

Google Scholar 
Franks, S. J., Weber, J. J. & Aitken, S. N. Evolutionary and plastic responses to climate change in terrestrial plant populations. Evol. Appl. 7, 123–139 (2014).PubMed 

Google Scholar 
Jeong, H., Cho, Y.-C. & Kim, E. Differential plastic responses to temperature and nitrogen deposition in the subalpine plant species, Primula farinosa subsp. modesta. AoB Plants 13, plab061 (2021).ADS 
PubMed 
PubMed Central 

Google Scholar 
Sulis, E., Bacchetta, G., Cogoni, D. & Fenu, G. From global to local scale: Where is the best for conservation purpose?. Biodivers. Conserv. 30, 183–200 (2021).
Google Scholar 
Hambler, D. & Dixon, J. Primula farinosa L. J. Ecol. 91, 694–705 (2003).
Google Scholar 
Arnold, E. & Richards, A. On the occurrence of unilateral incompatibility in Primula section Aleuritia Duby and the origin of Primula scotica Hook. Bot. J. Linn. Soc. 128, 359–368 (1998).
Google Scholar 
Tribsch, A. Areas of endemism of vascular plants in the eastern Alps in relation to Pleistocene glaciation. J. Biogeogr. 31, 747–760 (2004).
Google Scholar 
Chung, J.-M., Son, S.-W., Kim, S.-Y., Park, G.-W. & Kim, S.-S. Genetic diversity and geographic differentiation in the endangered Primula farinosa subsp. modesta, a subalpine endemic to Korea. Korean J. Plant. Taxon. 43, 236–243 (2013).
Google Scholar 
Lindborg, R. & Ehrlén, J. Evaluating the extinction risk of a perennial herb: Demographic data versus historical records. Conserv. Biol. 16, 683–690 (2002).
Google Scholar 
Caswell, H. Matrix Population Models, 2nd ed (Sinauer Associates Inc, 2000).Salguero-Gómez, R. & De Kroon, H. Matrix projection models meet variation in the real world. J. Ecol. 98, 250–254 (2010).
Google Scholar 
Jongejans, E. et al. Region versus site variation in the population dynamics of three short-lived perennials. J. Ecol. 98, 279–289 (2010).
Google Scholar 
Jongejans, E. & De Kroon, H. Space versus time variation in the population dynamics of three co-occurring perennial herbs. J. Ecol. 93, 681–692 (2005).
Google Scholar 
Suggitt, A. J. et al. Habitat microclimates drive fine-scale variation in extreme temperatures. Oikos 120, 1–8 (2011).
Google Scholar 
Tomimatsu, H. & Ohara, M. Demographic response of plant populations to habitat fragmentation and temporal environmental variability. Oecologia 162, 903–911 (2010).ADS 
PubMed 

Google Scholar 
Kudernatsch, T., Fischer, A., Bernhardt-Römermann, M. & Abs, C. Short-term effects of temperature enhancement on growth and reproduction of alpine grassland species. Basic Appl. Ecol. 9, 263–274 (2008).
Google Scholar 
Kim, E. & Donohue, K. Local adaptation and plasticity of Erysimum capitatum to altitude: Its implications for responses to climate change. J. Ecol. 101, 796–805 (2013).
Google Scholar 
Forbis, T. A. Seedling demography in an alpine ecosystem. Am. J. Bot. 90, 1197–1206 (2003).PubMed 

Google Scholar 
Yenni, G., Adler, P. B. & Ernest, S. M. Strong self-limitation promotes the persistence of rare species. Ecology 93, 456–461 (2012).PubMed 

Google Scholar 
Doak, D. F. Source-sink models and the problem of habitat degradation: General models and applications to the Yellowstone grizzly. Conserv. Biol. 9, 1370–1379 (1995).
Google Scholar 
Lesica, P. & Crone, E. E. Arctic and boreal plant species decline at their southern range limits in the Rocky Mountains. Ecol. Lett. 20, 166–174 (2017).PubMed 

Google Scholar 
Oldfather, M. F. & Ackerly, D. D. Microclimate and demography interact to shape stable population dynamics across the range of an alpine plant. New Phytol. 222, 193–205 (2019).PubMed 

Google Scholar 
Ågren, J., Fortunel, C. & Ehrlén, J. Selection on floral display in insect-pollinated Primula farinosa: Effects of vegetation height and litter accumulation. Oecologia 150, 225–232 (2006).ADS 
PubMed 

Google Scholar 
Ehrlén, J., Syrjänen, K., Leimu, R., Begona Garcia, M. & Lehtilä, K. Land use and population growth of Primula veris: An experimental demographic approach. J. Appl. Ecol. 42, 317–326 (2005).
Google Scholar 
Ehrlén, J. & Morris, W. F. Predicting changes in the distribution and abundance of species under environmental change. Ecol. Lett. 18, 303–314 (2015).PubMed 
PubMed Central 

Google Scholar 
Stubben, C. & Milligan, B. Estimating and analyzing demographic models using the popbio package in R. J. Stat. Softw. 22, 1–23 (2007).
Google Scholar 
Weiss, N. Package ‘wPerm’. https://cran.r-project.org/web/packages/wPerm/wPerm.pdf. (2015).Frossard, J. & Renaud, O. Permutation tests for regression, ANOVA, and comparison of signals: The permuco package. J. Stat. Softw. 99, 1–32 (2021).
Google Scholar  More

Meghana, M. & Shastri, Y. Sustainable valorization of sugar industry waste: Status, opportunities, and challenges. Biores. Technol. 303, 122929 (2020).CAS 

Google Scholar 
Petrescu, D. C., Vermeir, I. & Petrescu-Mag, R. M. Consumer understanding of food quality, healthiness, and environmental impact: a cross-national perspective. IJERPH 17, 169 (2019).PubMed Central 

Google Scholar 
Kassam, A., Friedrich, T., Shaxson, F. & Pretty, J. The spread of conservation agriculture: justification, sustainability and uptake. Int. J. Agric. Sustain. 7, 292–320 (2009).
Google Scholar 
Malviya, M. K. et al. Sugarcane microbiome: role in sustainable production. In Microbiomes and Plant Health 225–242 (Elsevier, 2021). https://doi.org/10.1016/B978-0-12-819715-8.00007-0.Chapter 

Google Scholar 
Sandhu, H. S., Wratten, S. D. & Cullen, R. Organic agriculture and ecosystem services. Environ. Sci. Policy 13, 1–7 (2010).CAS 

Google Scholar 
Schipanski, M. E. et al. Balancing multiple objectives in organic feed and forage cropping systems. Agr. Ecosyst. Environ. 239, 219–227 (2017).
Google Scholar 
Knapp, S. & van der Heijden, M. G. A. A global meta-analysis of yield stability in organic and conservation agriculture. Nat. Commun. 9, 3632 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Bender, S. F., Wagg, C. & van der Heijden, M. G. A. An underground revolution: biodiversity and soil ecological engineering for agricultural sustainability. Trends Ecol. Evol. 31, 440–452 (2016).PubMed 

Google Scholar 
Berendsen, R. L., Pieterse, C. M. J. & Bakker, P. A. H. M. The rhizosphere microbiome and plant health. Trends Plant Sci. 17, 478–486 (2012).CAS 
PubMed 

Google Scholar 
Chialva, M., Lanfranco, L. & Bonfante, P. The plant microbiota: composition, functions, and engineering. Curr. Opin. Biotechnol. 73, 135–142 (2022).CAS 
PubMed 

Google Scholar 
Dastogeer, K. M. G., Tumpa, F. H., Sultana, A., Akter, M. A. & Chakraborty, A. Plant microbiome–an account of the factors that shape community composition and diversity. Curr. Plant Biol. 23, 100161 (2020).
Google Scholar 
Yang, B., Wang, Y. & Qian, P.-Y. Sensitivity and correlation of hypervariable regions in 16S rRNA genes in phylogenetic analysis. BMC Bioinformat. 17, 135 (2016).
Google Scholar 
Nilsson, R. H. et al. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 47, D259–D264 (2019).CAS 
PubMed 

Google Scholar 
Wright, R. J., Gibson, M. I. & Christie-Oleza, J. A. Understanding microbial community dynamics to improve optimal microbiome selection. Microbiome 7, 85 (2019).PubMed 
PubMed Central 

Google Scholar 
Praeg, N. & Illmer, P. Microbial community composition in the rhizosphere of Larix decidua under different light regimes with additional focus on methane cycling microorganisms. Sci. Rep. 10, 22324 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
de Souza, R. S. C. et al. Unlocking the bacterial and fungal communities assemblages of sugarcane microbiome. Sci. Rep. 6, 28774 (2016).ADS 
PubMed 
PubMed Central 

Google Scholar 
Tayyab, M. et al. Sugarcane cultivars manipulate rhizosphere bacterial communities’ structure and composition of agriculturally important keystone taxa. 3 Biotech. 12, 32 (2022).PubMed 

Google Scholar 
Tayyab, M. et al. Sugarcane cultivar-dependent changes in assemblage of soil rhizosphere fungal communities in subtropical ecosystem. Environ. Sci. Pollut. Res. 29, 20795–20807 (2022).
Google Scholar 
Dakora, F. D., Matiru, V. N. & Kanu, A. S. Rhizosphere ecology of lumichrome and riboflavin, two bacterial signal molecules eliciting developmental changes in plants. Front. Plant Sci. 6, 700 (2015).PubMed 
PubMed Central 

Google Scholar 
Chapelle, E., Mendes, R., Bakker, P. A. H. & Raaijmakers, J. M. Fungal invasion of the rhizosphere microbiome. ISME J. 10, 265–268 (2016).CAS 
PubMed 

Google Scholar 
Teheran-Sierra, L. G. et al. Bacterial communities associated with sugarcane under different agricultural management exhibit a diversity of plant growth-promoting traits and evidence of synergistic effect. Microbiol. Res. 247, 126729 (2021).CAS 
PubMed 

Google Scholar 
de Carvalho, L. A. L. et al. Farming systems influence the compositional, structural, and functional characteristics of the sugarcane-associated microbiome. Microbiol. Res. 252, 126866 (2021).PubMed 

Google Scholar 
Henneron, L. et al. Fourteen years of evidence for positive effects of conservation agriculture and organic farming on soil life. Agron. Sustain. Dev. 35, 169–181 (2015).
Google Scholar 
Hartmann, M., Frey, B., Mayer, J., Mäder, P. & Widmer, F. Distinct soil microbial diversity under long-term organic and conventional farming. ISME J. 9, 1177–1194 (2015).PubMed 

Google Scholar 
Tayyab, M. et al. Sugarcane monoculture drives microbial community composition, activity and abundance of agricultural-related microorganisms. Environ. Sci. Pollut. Res. 28, 48080–48096 (2021).CAS 

Google Scholar 
Pang, Z. et al. Soil Metagenomics reveals effects of continuous sugarcane cropping on the structure and functional pathway of rhizospheric microbial community. Front. Microbiol. 12, 627569 (2021).PubMed 
PubMed Central 

Google Scholar 
Orr, C. H., Stewart, C. J., Leifert, C., Cooper, J. M. & Cummings, S. P. Effect of crop management and sample year on abundance of soil bacterial communities in organic and conventional cropping systems. J. Appl. Microbiol. 119, 208–214 (2015).CAS 
PubMed 

Google Scholar 
Brasil. Lei no 10.831, de 23 de dezembro de 2003. Dispõe sobre a agricultura orgânica e dá outras providências. In Publicado no Diário Oficial da União de 24/12/2003 (2003).Europea, C. Reglamento (CE) no 834/2007 del Consejo, de 28 de junio de 2007, sobre producción y etiquetado de los productos ecológicos y por el que se deroga el Reglamento (CEE) no 2092/91. D. Of. Unión Eur. 20, 1–23 (2007).
Google Scholar 
Council of the European Union. 889/2008, “Commission Regulation 889/2008/EC of 5 September 2008 laying down detailed rules for the implementation of Council Regulation (EC) No 834/2007 on organic production and labelling of organic products with regard to organic production, labelling and control”. Off. J. Eur. Union (L) 250, 18–19 (2007).
Google Scholar 
de Andrade, J. C., Cantarella, H. & Quaggio, J. A. Análise química para avaliação da fertilidade de solos tropicais. (2001).Donagema, G. K., de Campos, D. B., Calderano, S. B., Teixeira, W. G. & Viana, J. M. Manual de métodos de análise de solo. In Embrapa Solos-Documentos (INFOTECA-E) (2011).Kassambara, A. ggpubr: ‘ggplot2’ Based Publication Ready Plots. (2020). at R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2020). At Lundberg, D. S., Yourstone, S., Mieczkowski, P., Jones, C. D. & Dangl, J. L. Practical innovations for high-throughput amplicon sequencing. Nat. Methods 10, 999–1002 (2013).CAS 
PubMed 

Google Scholar 
Fadrosh, D. W. et al. An improved dual-indexing approach for multiplexed 16S rRNA gene sequencing on the Illumina MiSeq platform. Microbiome 2, 6 (2014).PubMed 
PubMed Central 

Google Scholar 
Renaud, G., Stenzel, U., Maricic, T., Wiebe, V. & Kelso, J. deML: robust demultiplexing of Illumina sequences using a likelihood-based approach. Bioinformatics 31, 770–772 (2015).CAS 
PubMed 

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 
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).CAS 
PubMed 

Google Scholar 
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Cole, J. R. et al. Ribosomal database project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 42, D633–D642 (2014).CAS 
PubMed 

Google Scholar 
McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8, e61217 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lahti, L. & Shetty, S. Microbiome R package. (2012).Oksanen, J. et al. vegan: Community Ecology Package. (2019). At Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).PubMed 
PubMed Central 

Google Scholar 
Dhariwal, A. et al. MicrobiomeAnalyst: a web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data. Nucleic Acids Res. 45, W180–W188 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Douglas, G. M. et al. PICRUSt2: an improved and extensible approach for metagenome inference. Bioinformatics https://doi.org/10.1101/672295 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Parks, D. H., Tyson, G. W., Hugenholtz, P. & Beiko, R. G. STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics 30, 3123–3124 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kohl, M., Wiese, S. & Warscheid, B. Cytoscape: software for visualization and analysis of biological networks. In Data Mining in Proteomics (eds Hamacher, M. et al.) 291–303 (Humana Press, Totowa, NJ, 2011). https://doi.org/10.1007/978-1-60761-987-1_18.Chapter 

Google Scholar 
Assenov, Y., Ramírez, F., Schelhorn, S.-E., Lengauer, T. & Albrecht, M. Computing topological parameters of biological networks. Bioinformatics 24, 282–284 (2008).CAS 
PubMed 

Google Scholar 
Shen, Z. et al. Deep 16S rRNA pyrosequencing reveals a bacterial community associated with banana fusarium wilt disease suppression induced by bio-organic fertilizer application. PLoS One 9, e98420 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Yun, Y. et al. The relationship between pH and bacterial communities in a single karst ecosystem and its implication for soil acidification. Front. Microbiol. 7, 1955 (2016).PubMed 
PubMed Central 

Google Scholar 
Wu, Y., Zeng, J., Zhu, Q., Zhang, Z. & Lin, X. pH is the primary determinant of the bacterial community structure in agricultural soils impacted by polycyclic aromatic hydrocarbon pollution. Sci. Rep. 7, 40093 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Li, R. et al. Pyrosequencing reveals the influence of organic and conventional farming systems on bacterial communities. PLoS One 7, e51897 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bill, M., Chidamba, L., Gokul, J. K., Labuschagne, N. & Korsten, L. Bacterial community dynamics and functional profiling of soils from conventional and organic cropping systems. Appl. Soil. Ecol. 157, 103734 (2021).
Google Scholar 
Xun, W., Shao, J., Shen, Q. & Zhang, R. Rhizosphere microbiome: Functional compensatory assembly for plant fitness. Comput. Struct. Biotechnol. J. 19, 5487–5493 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Semenov, M. V., Krasnov, G. S., Semenov, V. M. & van Bruggen, A. Mineral and organic fertilizers distinctly affect fungal communities in the crop rhizosphere. JoF 8, 251 (2022).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wang, Z., Li, Y., Li, T., Zhao, D. & Liao, Y. Tillage practices with different soil disturbance shape the rhizosphere bacterial community throughout crop growth. Soil Tillage Res. 197, 104501 (2020).
Google Scholar 
Gdanetz, K. & Trail, F. The wheat microbiome under four management strategies, and potential for endophytes in disease protection. Phytobiom. J. 1, 158–168 (2017).
Google Scholar 
Lazcano, C. et al. The rhizosphere microbiome plays a role in the resistance to soil-borne pathogens and nutrient uptake of strawberry cultivars under field conditions. Sci. Rep. 11, 3188 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Leys, N. M. E. J. et al. Occurrence and phylogenetic diversity of Sphingomonas strains in soils contaminated with polycyclic aromatic hydrocarbons. Appl. Environ. Microbiol. 70, 1944–1955 (2004).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Yin, C. et al. Role of bacterial communities in the natural suppression of rhizoctonia solani bare patch disease of wheat (Triticum aestivum L.). Appl. Environ. Microbiol. 79, 7428–7438 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Stewart, A. & Hill, R. Applications of trichoderma in plant growth promotion. In Biotechnology and Biology of Trichoderma 415–428 (Elsevier, 2014). https://doi.org/10.1016/B978-0-444-59576-8.00031-X.Chapter 

Google Scholar 
Banerjee, S. et al. Network analysis reveals functional redundancy and keystone taxa amongst bacterial and fungal communities during organic matter decomposition in an arable soil. Soil Biol. Biochem. 97, 188–198 (2016).CAS 

Google Scholar 
Andargie, M., Congyi, Z., Yun, Y. & Li, J. Identification and evaluation of potential bio-control fungal endophytes against Ustilagonoidea virens on rice plants. World J. Microbiol. Biotechnol. 33, 120 (2017).PubMed 

Google Scholar 
Orrù, L. et al. How tillage and crop rotation change the distribution pattern of fungi. Front. Microbiol. 12, 634325 (2021).PubMed 
PubMed Central 

Google Scholar 
van der Heijden, M. G. A. & Hartmann, M. Networking in the plant microbiome. PLoS Biol. 14, e1002378 (2016).PubMed 
PubMed Central 

Google Scholar 
Wang, W. et al. Consistent responses of the microbial community structure to organic farming along the middle and lower reaches of the Yangtze River. Sci. Rep. 6, 35046 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Silva, T. M. et al. Degradation of 2,4-D herbicide by microorganisms isolated from Brazilian contaminated soil. Braz. J. Microbiol. 38, 522–525 (2007).
Google Scholar 
Laura, M., Snchez-Salinas, E., Gonzlez, E. D. & Luisa, M. Pesticide biodegradation: mechanisms, genetics and strategies to enhance the process. In Biodegradation – Life of Science (ed. Chamy, R.) (InTech, 2013). https://doi.org/10.5772/56098.Chapter 

Google Scholar 
Upadhyay, L. S. B. & Dutt, A. Microbial detoxification of residual organophosphate pesticides in agricultural practices. In Microbial Biotechnology (eds Patra, J. K. et al.) 225–242 (Springer Singapore, Singapore, 2017). https://doi.org/10.1007/978-981-10-6847-8_10.Chapter 

Google Scholar 
Hassan, Y. I., Lepp, D., He, J. & Zhou, T. Draft genome sequences of Devosia sp. strain 17-2-E-8 and Devosia riboflavina strain IFO13584. Genome Announ. https://doi.org/10.1128/genomeA.00994-14 (2014).Article 

Google Scholar 
Talwar, C. et al. Defining the environmental adaptations of genus Devosia: insights into its expansive short peptide transport system and positively selected genes. Sci. Rep. 10, 1151 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Li, F., Chen, L., Zhang, J., Yin, J. & Huang, S. Bacterial community structure after long-term organic and inorganic fertilization reveals important associations between soil nutrients and specific taxa involved in nutrient transformations. Front. Microbiol. 8, 187 (2017).PubMed 
PubMed Central 

Google Scholar 
Ho, A., Lonardo, D. P. D. & Bodelier, P. L. E. Revisiting life strategy concepts in environmental microbial ecology. Microbiol. Ecol. https://doi.org/10.1093/femsec/fix006 (2017).Article 

Google Scholar 
Lupatini, M., Korthals, G. W., de Hollander, M., Janssens, T. K. S. & Kuramae, E. E. Soil microbiome is more heterogeneous in organic than in conventional farming system. Front. Microbiol. 7, 2064 (2017).PubMed 
PubMed Central 

Google Scholar 
Wang, H. et al. Eight years of manure fertilization favor copiotrophic traits in paddy soil microbiomes. Eur. J. Soil Biol. 106, 103352 (2021).CAS 

Google Scholar 
Fließbach, A., Oberholzer, H.-R., Gunst, L. & Mäder, P. Soil organic matter and biological soil quality indicators after 21 years of organic and conventional farming. Agric. Ecosyst. Environ. 118, 273–284 (2007).
Google Scholar 
Lewin, G. R. et al. Evolution and ecology of Actinobacteria and their bioenergy applications. Annu. Rev. Microbiol. 70, 235–254 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Karanja, E. N. et al. Diversity and structure of prokaryotic communities within organic and conventional farming systems in central highlands of Kenya. PLoS One 15, e0236574 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Francioli, D. et al. Mineral versus organic amendments: microbial community structure, activity and abundance of agriculturally relevant microbes are driven by long-term fertilization strategies. Front. Microbiol. 7, 1446 (2016).PubMed 
PubMed Central 

Google Scholar 
Paungfoo-Lonhienne, C. et al. Nitrogen fertilizer dose alters fungal communities in sugarcane soil and rhizosphere. Sci. Rep. 5, 8678 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Pang, Z. et al. Liming positively modulates microbial community composition and function of sugarcane fields. Agronomy 9, 808 (2019).CAS 

Google Scholar 
Aira, M., Gómez-Brandón, M., Lazcano, C., Bååth, E. & Domínguez, J. Plant genotype strongly modifies the structure and growth of maize rhizosphere microbial communities. Soil Biol. Biochem. 42, 2276–2281 (2010).CAS 

Google Scholar 
Ma, M. et al. Responses of fungal community composition to long-term chemical and organic fertilization strategies in Chinese Mollisols. MicrobiologyOpen 7, e00597 (2018).PubMed 
PubMed Central 

Google Scholar 
Bellenger, J. P., Darnajoux, R., Zhang, X. & Kraepiel, A. M. L. Biological nitrogen fixation by alternative nitrogenases in terrestrial ecosystems: a review. Biogeochemistry 149, 53–73 (2020).
Google Scholar 
Schmidt, J. E. et al. Effects of agricultural management on rhizosphere microbial structure and function in processing tomato plants. Appl. Environ. Microbiol. https://doi.org/10.1128/AEM.01064-19 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Agler, M. T. et al. Microbial hub taxa link host and abiotic factors to plant microbiome variation. PLoS Biol. 14, e1002352 (2016).PubMed 
PubMed Central 

Google Scholar 
Lin, Y. et al. Nitrosospira cluster 8a plays a predominant role in the nitrification process of a subtropical Ultisol under long-term inorganic and organic fertilization. Appl. Environ. Microbiol. 84, e01031-e1118 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Chu, H. et al. Community structure of ammonia-oxidizing bacteria under long-term application of mineral fertilizer and organic manure in a sandy loam soil. Appl. Environ. Microbiol. 73, 485–491 (2007).ADS 
CAS 
PubMed 

Google Scholar 
Xun, W. et al. Specialized metabolic functions of keystone taxa sustain soil microbiome stability. Microbiome 9, 35 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar  More

The African Development Corridors database is publicly available. The visualisation of the database that can be explored interactively here: https://dcp-unep-wcmc.opendata.arcgis.com/. The data is deposited in the Dryad Digital Repository referenced as Thorn, J. P.R., Mwangi, B.; Juffe Bignoli, D., The African Development Corridors Database, Dryad, Dataset, https://doi.org/10.5061/dryad.9kd51c5hw (2022)43. The final data were compiled into an online Master database spreadsheet, using the project code data as the merging attribute of the spatial and tabular database (AfricanDevelopmentCorridorsDatabase2022.csv). The African Development Corridor Database is presented as a GeoPackage file (.gpkg) and ESRI file Geodatabase (.gdb) composed by line and point feature datasets with the 22 associated attributes for all mapped corridors, a table with corridors that could not be mapped (also with the attributes), and a table with all sources consulted for each project code.We created a data standard to ensure a systematic and standardised data collection (Supplementary Table 2). Each data record in the database represents a project within a development corridor. To group all projects within the same development corridor we used a unique identifier composed by three letters that identified the corridor plus a number unique for each project or record. For example, the Lamu port project in Kenya within the Lamu Port South Sudan Ethiopia Transport Corridor (LAPSSET) was represented as LAP000. In this corridor we identified 20 projects, from LAP0001 which is the Lamu Port to LAP0020 which is the Isiolo-Lokichar-Lodwar-Nadapal Highway in Kenya. In addition to the unique identifier for each project, the data standard includes data attributes that provide detailed information about each project. Table 1 describes the attributes included in the database. Supplementary Table 3 summarises the 79 corridors included in the database.Table 1 List of the attributes included in the African Development Corridors Database.Full size tableInfrastructure types and status of development corridors in AfricaThe data consists of a total of 79 corridors consisting of 184 projects (Fig. 2). Of the 12 infrastructure types, the most predominant form of infrastructure in Africa’s development corridors is roads (n = 64, 34.8%), followed by wet ports (n = 38, 20.7%), passenger and freight railways (n = 33, 17.9%), and airports (n = 14, 7.6%). Fewer resort cities, electricity transmission lines, dry ports, industrial parks, and water pipelines comprise development corridors (all ≤ n = 3, 1.6%) (Fig. 3). We acknowledge our study might not include many infrastructure developments that are components projects of larger programmes but are not yet labelled as corridors. A total of 107 (58.7%) projects are operational, 35 (19%) are in progress, 25 (13.6%) are planned, 25 (13.9%) are being upgraded, and 2(1%) are on hold.Fig. 2Map showing the distribution of all the development corridors included in the African Development Corridors Database and their infrastructure type.Full size imageFig. 3Subset of highest frequencies of key attributes captured in the database.Full size imageSpatial distributionThe linear distance of development corridors in Africa is 122,294 km – which approximates to three times the Earth’s circumference, with an average of 1703.84 ± 213.19 km (mean, SE), ranging from 4–11,141 km. In terms of number of projects per country, Kenya has the most projects (n = 34, 18.5%), followed by Tanzania (n = 18, 9.8%), South Africa and Democratic Republic of the Congo (n = 17, 9.2% ea.), Ethiopia (n = 15, 8.2%), Mozambique and Zambia (n = 14, 7.6%), Angola, Uganda, Guinea and Cameroon (n = 12, 6.5%), Namibia (n = 11, 6.0%), Republic of Congo (n = 10, 5.4%), Burundi and Chad (n = 9, 4.9%), Malawi, Senegal, and Zimbabwe (n = 8, 4.4%), and Burkina Faso and Ghana (n = 7, 3.8%). Due to differences in the frequency and quality that countries publish data on infrastructure and development corridor investments, coverage may be lower for some regions, or some periods (i.e., recent, or further in the past).Investments in development corridorsAdjusting for inflation, the total investment of development corridors that is captured in the database ranges between USD 547.29–658.62 billion. The average cost of a corridor ranges between USD 3.46 ± 1.92 billion and USD 4.17 ± 2.04 billion. This is a notable sum, considering the average GDP in sub-Saharan Africa is USD 1.48 billion44. The most expensive development corridor project is the first of the nine Trans African Highway projects at USD 78.20 billion (when adjusted for inflation) – comprising transcontinental roads across Africa. We were able to capture the budget (or at least a proportion of the budget) for 84.7% of all projects.Temporal evolution of growth of development corridorsInvestments started in the 1800s and have increased exponentially (Fig. 4). Over a fifty-year period, the greatest number of investments took place between 1950 and 2000. Spikes in investments occurred particularly around 1900, which was when there was a wave of new imperialism across the continent, followed in the 1960s when many countries across sub-Saharan Africa gained independence. The third spike in investment was in the last decade, particularly since 2013, when we have seen rapid growth in foreign direct investment in Africa under initiatives such as the Belt and Road Initiative. According to the Ernst and Young Africa Attractiveness Survey (2019)45, the largest foreign direct investment (in terms of capital) between 2014–2018 came from China (USD 72,235 million), France (USD 34,172 million), USA (USD 30,885 million), the United Arab Emirates (USD 25,278 million) and the United Kingdom (USD 17,768 million).Fig. 4(a) Temporal evolution of investment in development corridors in Africa. (b) Annual investments per annum in development corridors in Africa (USD maximum, before adjusting for inflation).Full size imageDonors that are funding development corridorsAcross Africa, regional development banks invested the most in development corridors (30.8%), with the African Development Bank funding the majority (24.3%) of all projects. Outside of Africa, the regional development banks that invested in the most projects are the Export-Import Bank of China (n = 13, 3.8%), the European Investment Bank (n = 10, 2.8%) and the Arab Bank for Economic Development in Africa (n = 4, 1.2% ea.). National governments funded about 29.8% of all projects. The Government of Kenya funded the most projects (n = 26; 7.5%), followed by the Governments of Tanzania (n = 7, 2.0%) and South Africa (n = 4, 1.2%). Multilateral banks funded 10.9% of projects – mostly from the World Bank (n = 33, 9.54%) and a few from the International Finance Corporation (n = 4, 1.6%). The international development community funded only 6.1% – of which the OPEC Fund for International Development funded four projects. Private companies continue to invest in a small percentage of development corridors (3.5%), but this is higher than national banks that invest in 3.2%. Regional Economic Community bodies have invested in 15 (4.8%) of all 184 projects. The average number of donors per corridor ranged from one to 12.Weighting of investments by donor typeIn terms capital funded per donor type, Regional Development Banks invested the most (totalling USD 30.72 billion), followed by national governments (USD 20.45 billion). The figure then drops substantially to international development agencies (USD6.17 billion) and multilateral banks (USD 3.76 billion). These results are limited by the fact that we were only able to capture the amount funded delineated by donor type for 22.58% (or USD 70.24 billion) of the minimum of all investments (USD 311.14 billion) before adjusting for inflation.Commodities transportedA total of 147 commodities were captured. The top twenty commodities traded were rice (n = 52, 28.7% of all projects), sugar (27.0%), fish and petroleum (24.3% ea.), passengers (21.6%), textiles (21.1%), maize (19.5%), coffee (18.9%), cement and timber (17.8% ea.) followed by cotton, crude petroleum, vehicle spare parts, beverages, copper, fruit, fertilisers, gold, pharmaceutical products, and tobacco.Beneficiaries and net supplier or receiverApproximately 213 different beneficiaries were identified – predominantly local communities (n = 134 of projects, 72.8%), followed by national and local governments (63.0%), traders (51.1%), agricultural farmers and livestock producers (27.7%), ports (27.2%), industries (25.5%), truck drivers (22.3%), tourists (17.4%), entrepreneurs (12.0%), and logistics companies (11.4%). Almost all (89.1%) of corridors are net receivers and suppliers of commodities, while only 13 (7.1%) are net suppliers and seven are net receivers (3.8%). More

WHO. Global plan for insecticide management. (World Health Organisation, Geneva, Switzerland 130, 2012).WHO. Paludisme: situation mondiale. vol. 2507. World Health Organisation, Geneva, Switzerland, (2020).WHO. Procédures pour tester la résistance aux insecticides chez les moustiques vecteurs du paludisme Seconde édition. (World Health Organisation, Geneva, Switzerland, 2017).WHO. Guidelines for Malaria Vector Control. (World Health Organisation, Geneva, Switzerland, 2019).Churcher, T. S., Lissenden, N., Griffin, J. T., Worrall, E. & Ranson, H. The impact of pyrethroid resistance on the efficacy and effectiveness of bednets for malaria control in Africa. Elife 5, 16090 (2016).
Google Scholar 
Hemingway, J. et al. Averting a malaria disaster: Will insecticide resistance derail malaria control?. Lancet 387, 1785–1788 (2016).PubMed 
PubMed Central 

Google Scholar 
Dabiré, K. R. et al. Trends in insecticide resistance in natural populations of malaria vectors in Burkina Faso, West Africa: 10 Years surveys K. INTECH 32, 479–502 (2012).
Google Scholar 
WHO. WHO Global Malaria Programme: Global Plan for insecticide resistance management. (World Health Organisation, Geneva, Switzerland, 2012).Toe, K. H. et al. Do bednets including piperonyl butoxide offer additional protection against populations of Anopheles gambiae s.l. that are highly resistant to pyrethroids? An experimental hut evaluation in Burkina Faso. Med. Vet. Entomol. 32, 407–416 (2018).CAS 
PubMed 

Google Scholar 
Hien, A. S. et al. Evidence supporting deployment of next generation insecticide treated nets in Burkina Faso: Bioassays with either chlorfenapyr or piperonyl butoxide increase mortality of pyrethroid-resistant Anopheles gambiae. Malar. J. 20, 1–12 (2021).
Google Scholar 
Zoubiri, S. & Baaliouamer, A. Potentiality of plants as source of insecticide principles. J. Saudi Chem. Soc. 18, 925–938 (2014).
Google Scholar 
Tripathi, A. K., Upadhyay, S., Bhuiyan, M. & Bhattacharya, P. R. A review on prospects of essential oils as biopesticide in insect-pest management. J. Pharmacogn. Phytother. 1, 52–63 (2009).CAS 

Google Scholar 
Isman, M. B. Plant essential oils for pest and disease management. Crop Prot. 19, 603–608 (2000).ADS 
CAS 

Google Scholar 
Mossa, A. T. H. Green pesticides: Essential oils as biopesticides in insect-pest management. J. Environ. Sci. Technol. 9, 354–378 (2016).CAS 

Google Scholar 
Lucia, A. et al. Larvicidal effect of Eucalyptus grandis essential oil and turpentine and their major components on Aedes aegypti larvae. J. Am. Mosq. Control Assoc. 23, 299–303 (2007).CAS 
PubMed 

Google Scholar 
Singh, R., Koul, O. & Rup, P. J. Toxicity of some essential oil constituents and their binary mixtures against Chilo partellus (Lepidoptera: Pyralidae). Int. J. Tropical Insect Sci. 29, 93–101 (2009).CAS 

Google Scholar 
Sarma, R., Adhikari, K., Mahanta, S. & Khanikor, B. Combinations of plant essential oil based terpene compounds as larvicidal and adulticidal agent against Aedes aegypti (Diptera: Culicidae). Sci. Rep. 9, 1–13 (2019).ADS 

Google Scholar 
Mansour, S. A., Foda, M. S. & Aly, A. R. Mosquitocidal activity of two Bacillus bacterial endotoxins combined with plant oils and conventional insecticides. Ind. Crops Prod. 35, 44–52 (2012).CAS 

Google Scholar 
Yaméogo, F., Wendgida, D. W., Sombié, A., Sanon, A. & Badolo, A. Insecticidal activity of essential oils from six aromatic plants against Aedes aegypti, dengue vector from two localities of Ouagadougou Burkina Faso. Arthropod. Plant. Interact. 15, 627–634 (2021).
Google Scholar 
Wangrawa, D. W. et al. Essential oils and their binary combinations have synergistic and antagonistic insecticidal properties against Anopheles gambiae s l. (Diptera: Culicidae). Biocatal. Agric. Biotechnol. 42, 102347 (2022).CAS 

Google Scholar 
Drabo, S. F., Olivier, G., Bassolé, I. H. N., Nébié, R. C. & Laurence, M. Susceptibility of MED-Q1 and MED-Q3 biotypes of Bemisia tabaci (Hemiptera: Aleyrodidae) populations to essential and seed oils. J. Econ. Entomol. 110, 1031–1038 (2017).
Google Scholar 
N’Guessan, R., Corbel, V., Akogbéto, M. & Rowland, M. Treated nets and indoor residual reduced efficacy of insecticide-pyrethroid resistance area benin. Emerg. Infect. Dis. 13, 199–206 (2007).PubMed 
PubMed Central 

Google Scholar 
WHO. Standard operating procedure for testing insecticide susceptibility of adult mosquitoes in WHO tube tests. (World Health Organisation, Geneva, Switzerland 2022).Abbott, W. S. A method of computing the effectiveness of an insecticide. J. Econ. Entomol. 18, 265–267 (1925).CAS 

Google Scholar 
Schelz, Z., Molnar, J. & Hohmann, J. Antimicrobial and antiplasmid activities of essential oils. Fitoterapia 77, 279–285 (2006).CAS 
PubMed 

Google Scholar 
Bassolé, I. H. N. & Juliani, H. R. Essential oils in combination and their antimicrobial properties. Molecules 17, 3989–4006 (2012).PubMed 
PubMed Central 

Google Scholar 
WHO. Test Procedures for Insecticide Resistance Monitoring in Malaria Vector Mosquitoes Second edition. (World Health Organisation, Geneva, Switzerland 2016).Tchoumbougnang, F. et al. Activité larvicide sur Anopheles gambiae giles et composition chimique des huiles essentielles extraites de quatre plantes cultivées au Cameroun. Biotechnol. Agron. Soc. Environ. 13, 77–84 (2009).CAS 

Google Scholar 
Ranson, H. & Lissenden, N. Insecticide resistance in African Anopheles mosquitoes: A worsening situation that needs urgent action to maintain malaria control. Trends Parasitol. 32, 187–196 (2016).CAS 
PubMed 

Google Scholar 
Wangrawa, D. et al. Insecticidal activity of local plants essential oils against laboratory and field strains of Anopheles gambiae s. L. (Diptera: Culicidae) from Burkina Faso. J. Econ. Entomol. 111, 2844–2853 (2018).CAS 
PubMed 

Google Scholar 
Gbolade, A. A. & Lockwood, G. B. Toxicity of Ocimum sanctum L. essential oil to Aedes aegypti larvae and its chemical composition. J. Essent. Oil Bearing Plants 11, 148–153 (2008).CAS 

Google Scholar 
Vani, R. S., Cheng, S. F. & Chuah, C. H. Comparative study of volatile compounds from genus Ocimum. Am. J. Appl. Sci. 6, 523–528 (2009).CAS 

Google Scholar 
Bassolé, et al. Ovicidal and larvicidal activity against Aedes aegypti and Anopheles gambiae complex mosquitoes of essential oils extracted from three spontaneous plants of Burkina Faso. Parasitologia 45, 23–26 (2003).
Google Scholar 
Peerzada, N. Chemical composition of the essential oil of Hyptis Suaveolens. Molecules 2, 165–168 (1997).CAS 

Google Scholar 
Ilboudo, Z. et al. Biological activity and persistence of four essential oils towards the main pest of stored cowpeas, Callosobruchus maculatus (F.) (Coleoptera: Bruchidae). J. Stored Prod. Res. 46, 124–128 (2010).CAS 

Google Scholar 
Zulfikar, A. & Sitepu, F. Y. The effect of lemongrass (Cymbopogon nardus) extract as insecticide against Aedes aegypti. Int. J. Mosq. Res. 6, 101–103 (2019).
Google Scholar 
Ojewumi, E. M., Oladipupo, A. A. & Ojewumi, O. E. Oil extract from local leaves an alternative to synthetic mosquito repellants. Pharmacophore 9, 1–6 (2018).
Google Scholar 
Gnankiné, O. & Bassolé, I. H. N. Essential oils as an alternative to pyrethroids resistance against Anopheles species complex giles (Diptera: Culicidae). Molecules 22, 1321 (2017).PubMed Central 

Google Scholar 
Bossou, A. D. et al. Chemical composition and insecticidal activity of plant essential oils from Benin against Anopheles gambiae (Giles). Parasit. Vectors 6, 337 (2013).PubMed 
PubMed Central 

Google Scholar 
Balboné, et al. Essential oils from five local plants: An alternative larvicide for Anopheles gambiae s. I. (Diptera: Culicidae) and Aedes aegypti (Diptera: Culicidae) control in Western Burkina Faso. Front. Trop. Dis. 3, 853405 (2022).
Google Scholar 
Bekele, J. & Hassanali, A. Blend effects in the toxicity of the essential oil constituents of Ocimum kilimandscharicum and Ocimum kenyense (Labiateae) on two post-harvest insect pests. Phytochemistry 57, 385–391 (2001).CAS 
PubMed 

Google Scholar 
Pavela, R. Acute and synergistic effects of some monoterpenoid essential oil compounds on the house fly (Musca domestica). J. Essent. Oil Bearing Plants 11, 451–459 (2008).CAS 

Google Scholar 
Tanprasit, P. Biological control of dengue fever mosquitoes (Aedes aegypti Linn.) using leaf extracts of Chan (Hyptis suaveolens (L) poit.) and hedge flower Lantana camara Linn.). (2005).Park, H. M. et al. Larvicidal activity of myrtaceae essential oils and their components against Aedes aegypti, acute toxicity on Daphnia magna, and aqueous residue. J. Med. Entomol. 48, 405–410 (2011).CAS 
PubMed 

Google Scholar 
Burt, S. Essential oils: Their antibacterial properties and potential applications in foods—A review. Int. J. Food Microbiol. 94, 223–253 (2004).CAS 
PubMed 

Google Scholar 
Abbassy, M. A., Abdelgaleil, S. A. M. & Rabie, R. Y. A. Insecticidal and synergistic effects of Majorana hortensis essential oil and some of its major constituents. Entomol. Exp. Appl. 131, 225–232 (2009).CAS 

Google Scholar 
Chiasson, H., Bélanger, A., Bostanian, N., Vincent, C. & Poliquin, A. Acaricidal properties of Artemisia absinthium and Tanacetum vulgare (Asteraceae) essential oils obtained by three methods of extraction. J. Econ. Entomol. 94, 167–171 (2001).CAS 
PubMed 

Google Scholar 
Luz, T. R. S. A., deMesquita, L. S. S., Amaral, F. M. M. & Coutinho, D. F. Essential oils and their chemical constituents against Aedes aegypti L. (Diptera: Culicidae) larvae. Acta Trop. 212, 105705 (2020).CAS 
PubMed 

Google Scholar 
Deletre, E., Mallent, M., Menut, C., Chandre, F. & Martin, T. Behavioral response of Bemisia tabaci (Hemiptera: Aleyrodidae) to 20 plant extracts. J. Econ. Entomol. 108, 1890–1901 (2015).
Google Scholar 
Berenbaum, M. A. Y. & Neal, J. J. Synergism between myristicin and xanthotoxin, a naturally cooccurring plant toxicant. J. Chem. Ecol. 11, 1349–1358 (1985).CAS 
PubMed 

Google Scholar 
Intirach, J. et al. Chemical constituents and combined larvicidal effects of selected essential oils against Anopheles cracens (Diptera: Culicidae). Psyche (London) https://doi.org/10.1155/2012/591616 (2012).
Google Scholar 
Pavela, R. Acute, synergistic and antagonistic effects of some aromatic compounds on the Spodoptera littoralis Boisd. (Lep., Noctuidae) larvae. Ind. Crops Prod. 60, 247–258 (2014).CAS 

Google Scholar 
Muturi, E. J., Ramirez, J. L., Doll, K. M. & Bowman, M. J. Combined toxicity of three essential oils against Aedes aegypti (Diptera: Culicidae) larvae. J. Med. Entomol. 54, 1684–1691 (2017).CAS 
PubMed 

Google Scholar  More