Philippa Kaur

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

Nayfach S, Roux S, Seshadri R, Udwary D, Varghese N, Schulz F, et al. A genomic catalog of Earth’s microbiomes. Nat Biotechnol. 2021;39:499–509.CAS 
PubMed 

Google Scholar 
Fierer N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat Rev Microbiol. 2017;15:579–90.CAS 
PubMed 

Google Scholar 
Cordovez V, Dini-Andreote F, Carrión VJ, Raaijmakers JM. Ecology and evolution of plant microbiomes. Annu Rev Microbiol. 2019;73:69–88.CAS 
PubMed 

Google Scholar 
Trivedi P, Leach JE, Tringe SG, Sa T, Singh BK. Plant–microbiome interactions: from community assembly to plant health. Nat Rev Microbiol. 2020;18:607–21.CAS 
PubMed 

Google Scholar 
Fitzpatrick CR, Salas-González I, Conway JM, Finkel OM, Gilbert S, Russ D, et al. The plant microbiome: From ecology to reductionism and beyond. Annu Rev Microbiol. 2020;74:81–100.CAS 
PubMed 

Google Scholar 
Schmidt R, Ulanova D, Wick LY, Bode HB, Garbeva P. Microbe-driven chemical ecology: past, present and future. ISME J. 2019;13:2656–63.PubMed 
PubMed Central 

Google Scholar 
Tyc O, Song C, Dickschat JS, Vos M, Garbeva P. The ecological role of volatile and soluble secondary metabolites produced by soil bacteria. Trends Microbiol. 2017;25:280–92.CAS 
PubMed 

Google Scholar 
Romero D, Traxler MF, López D, Kolter R. Antibiotics as signal molecules. Chem Rev. 2011;111:5492–505.CAS 
PubMed 
PubMed Central 

Google Scholar 
Traxler MF, Seyedsayamdost MR, Clardy J, Kolter R. Interspecies modulation of bacterial development through iron competition and siderophore piracy. Mol Microbiol. 2012;86:628–44.CAS 
PubMed 
PubMed Central 

Google Scholar 
Bernal P, Llamas MA, Filloux A. Type VI secretion systems in plant-associated bacteria. Environ Microbiol. 2018;20:1–15.PubMed 

Google Scholar 
Okada BK, Seyedsayamdost MR. Antibiotic dialogues: induction of silent biosynthetic gene clusters by exogenous small molecules. FEMS Microbiol Rev. 2017;41:19–33.CAS 
PubMed 

Google Scholar 
Zhang C, Straight PD. Antibiotic discovery through microbial interactions. Curr Opin Microbiol. 2019;51:64–71.CAS 
PubMed 

Google Scholar 
Traxler MF, Kolter R. Natural products in soil microbe interactions and evolution. Nat Prod Rep. 2015;32:956–70.CAS 
PubMed 

Google Scholar 
Müller DB, Vogel C, Bai Y, Vorholt JA. The plant microbiota: systems-level insights and perspectives. Annu Rev Genet. 2016;50:211–34.PubMed 

Google Scholar 
Anckaert A, Arias AA, Hoff G, Calonne-Salmon M, Declerck S, Ongena M. The use of Bacillus spp. as bacterial biocontrol agents to control plant diseases. In: Köhl J, Ravensberg W, editors. Microbial bioprotectants for plant disease management. Cambridge, UK: Burleigh Dodds Science Publishing; 2022. p. 1–54.Dunlap CA. Taxonomy of registered Bacillus spp. strains used as plant pathogen antagonists. Biol Control. 2019;134:82–86.
Google Scholar 
Ye M, Tang X, Yang R, Zhang H, Li F, Tao F, et al. Characteristics and application of a novel species of Bacillus: Bacillus velezensis. ACS Chem Biol. 2018;13:500–5.CAS 
PubMed 

Google Scholar 
Grubbs KJ, Bleich RM, Santa Maria KC, Allen SE, Farag S, Shank EA, et al. Large-scale bioinformatics analysis of Bacillus genomes uncovers conserved roles of natural products in bacterial physiology. mSystems 2017;2:e00040–17.CAS 
PubMed 
PubMed Central 

Google Scholar 
Harwood CR, Mouillon J-MM, Pohl S, Arnau J. Secondary metabolite production and the safety of industrially important members of the Bacillus subtilis group. FEMS Microbiol Rev. 2018;42:721–38.CAS 
PubMed 
PubMed Central 

Google Scholar 
Köhl J, Kolnaar R, Ravensberg WJ. Mode of action of microbial biological control agents against plant diseases: Relevance beyond efficacy. Front Plant Sci. 2019;10:1–19.
Google Scholar 
Li Y, Rebuffat S. The manifold roles of microbial ribosomal peptide-based natural products in physiology and ecology. J Biol Chem. 2020;295:34–54.Andrić S, Meyer T, Ongena M. Bacillus responses to plant-associated fungal and bacterial communities. Front Microbiol. 2020;11:1350.PubMed 
PubMed Central 

Google Scholar 
Zhang L, Sun C. Fengycins, cyclic lipopeptides from marine Bacillus subtilis strains, kill the plant-pathogenic fungus Magnaporthe grisea by inducing reactive oxygen species production and chromatin condensation. Appl Environ Microbiol. 2018;84:e00445–18.CAS 
PubMed 
PubMed Central 

Google Scholar 
Molina-Santiago C, Vela-Corcía D, Petras D, Díaz-Martínez L, Pérez-Lorente AI, Sopeña-Torres S, et al. Chemical interplay and complementary adaptative strategies toggle bacterial antagonism and co-existence. Cell Rep. 2021;36:109449.CAS 
PubMed 
PubMed Central 

Google Scholar 
Molina-Santiago C, Pearson JR, Navarro Y, Berlanga-Clavero MV, Caraballo-Rodriguez AM, Petras D, et al. The extracellular matrix protects Bacillus subtilis colonies from Pseudomonas invasion and modulates plant co-colonization. Nat Commun. 2019;10:1919.PubMed 
PubMed Central 

Google Scholar 
Almoneafy AA, Kakar KU, Nawaz Z, Li B, Saand MA, Chun-lan Y, et al. Tomato plant growth promotion and antibacterial related-mechanisms of four rhizobacterial Bacillus strains against Ralstonia solanacearum. Symbiosis 2014;63:59–70.CAS 

Google Scholar 
Kakar KU, Duan Y-P, Nawaz Z, Sun G, Almoneafy AA, Hassan MA, et al. A novel rhizobacterium Bk7 for biological control of brown sheath rot of rice caused by Pseudomonas fuscovaginae and its mode of action. Eur J Plant Pathol. 2014;138:819–34.
Google Scholar 
Raynaud X, Nunan N. Spatial ecology of bacteria at the microscale in soil. PLoS ONE. 2014;9:e87217.PubMed 
PubMed Central 

Google Scholar 
Girard L, Lood C, Höfte M, Vandamme P, Rokni-Zadeh H, van Noort V, et al. The ever-expanding Pseudomonas genus: description of 43 new species and partition of the Pseudomonas putida group. Microorganisms. 2021;9:1–24.
Google Scholar 
Hua GKH, Höfte M. The involvement of phenazines and cyclic lipopeptide sessilin in biocontrol of Rhizoctonia root rot on bean (Phaseolus vulgaris) by Pseudomonas sp. CMR12a is influenced by substrate composition. Plant Soil. 2015;388:243–53.CAS 

Google Scholar 
Ma Z, Hoang Hua GKH, Ongena M, Höfte M. Role of phenazines and cyclic lipopeptides produced by Pseudomonas sp. CMR12a in induced systemic resistance on rice and bean. Environ Microbiol Rep. 2016;8:896–904.PubMed 

Google Scholar 
Olorunleke FE, Hua GKH, Kieu NP, Ma Z, Höfte M. Interplay between orfamides, sessilins and phenazines in the control of Rhizoctonia diseases by Pseudomonas sp. CMR12a. Environ Microbiol Rep. 2015;7:774–81.CAS 
PubMed 

Google Scholar 
van Gestel J, Vlamakis H, Kolter R. From cell differentiation to cell collectives: Bacillus subtilis uses division of labor to migrate. PLoS Biol. 2015;13:1–29.
Google Scholar 
Nihorimbere V, Cawoy H, Seyer A, Brunelle A, Thonart P, Ongena M. Impact of rhizosphere factors on cyclic lipopeptide signature from the plant beneficial strain Bacillus amyloliquefaciens S499. FEMS Microbiol Ecol. 2012;79:176–91.CAS 
PubMed 

Google Scholar 
Hoff G, Arias AA, Boubsi F, Pršić J, Meyer T, Ibrahim HMM, et al. Surfactin stimulated by pectin molecular patterns and root exudates acts as a key driver of the Bacillus-plant mutualistic interaction. MBio 2021;12:e01774–21.CAS 
PubMed Central 

Google Scholar 
Andrić S, Meyer T, Rigolet A, Prigent-Combaret C, Höfte M, Balleux G, et al. Lipopeptide interplay mediates molecular interactions between soil bacilli and pseudomonads. Microbiol Spectr. 2021;9:e0203821.PubMed 

Google Scholar 
Pluskal T, Castillo S, Villar-Briones A, Orešič M. MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinform. 2010;11:395.
Google Scholar 
Li W, Godzik A. Cd-hit: A fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 2006;22:1658–9.CAS 
PubMed 

Google Scholar 
Bodenhofer U, Bonatesta E, Horejš-Kainrath C, Hochreiter S. msa: an R package for multiple sequence alignment. Bioinformatics. 2015;31:3997–9.CAS 
PubMed 

Google Scholar 
Paradis E, Claude J, Strimmer K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 2004;20:289–90.CAS 
PubMed 

Google Scholar 
Ivica Letunic PB. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 2021;49:W293–W296.PubMed 
PubMed Central 

Google Scholar 
R Core Team (2020). R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2020.Steinke K, Mohite OS, Weber T, Kovács ÁT. Phylogenetic distribution of secondary metabolites in the Bacillus subtilis species complex. mSystems. 2021;6:2–10.
Google Scholar 
Molinatto G, Puopolo G, Sonego P, Moretto M, Engelen K, Viti C, et al. Complete genome sequence of Bacillus amyloliquefaciens subsp. plantarum S499, a rhizobacterium that triggers plant defences and inhibits fungal phytopathogens. J Biotechnol. 2016;238:56–59.CAS 
PubMed 

Google Scholar 
Fan B, Wang C, Song X, Ding X, Wu L, Wu H, et al. Bacillus velezensis FZB42 in 2018: The gram-positive model strain for plant growth promotion and biocontrol. Front Microbiol. 2018;9:3389.
Google Scholar 
Mansfield J, Genin S, Magori S, Citovsky V, Sriariyanum M, Ronald P, et al. Top 10 plant pathogenic bacteria in molecular plant pathology. Mol Plant Pathol. 2012;13:614–29.PubMed 
PubMed Central 

Google Scholar 
Scholz R, Vater J, Budiharjo A, Wang Z, He Y, Dietel K, et al. Amylocyclicin, a novel circular bacteriocin produced by Bacillus amyloliquefaciens FZB42. J Bacteriol. 2014;196:1842–52.PubMed 
PubMed Central 

Google Scholar 
Lembrechts JJ, van den Hoogen J, Aalto J, Ashcroft MB, De Frenne P, Kemppinen J, et al. Global maps of soil temperature. Glob Chang Biol. 2022;28:3110–44.CAS 
PubMed 
PubMed Central 

Google Scholar 
Blake C, Christensen MN, Kovacs AT. Molecular aspects of plant growth promotion and protection by Bacillus subtilis. Mol Plant-Microbe Interact. 2021;34:15–25.CAS 
PubMed 

Google Scholar 
Arnaouteli S, Bamford NC, Stanley-Wall NR, Kovács ÁT. Bacillus subtilis biofilm formation and social interactions. Nat Rev Microbiol. 2021;19:600–14.CAS 
PubMed 

Google Scholar 
D’aes J, Hua GKH, De Maeyer K, Pannecoucque J, Forrez I, Ongena M, et al. Biological control of Rhizoctonia root rot on bean by phenazine- and cyclic lipopeptide-producing Pseudomonas CMR12a. Phytopathology. 2011;101:996–1004.PubMed 

Google Scholar 
Grandchamp GM, Caro L, Shank EA. Pirated siderophores promote sporulation in Bacillus subtilis. Appl Environ Microbiol. 2017;83:e03293–16.CAS 
PubMed 
PubMed Central 

Google Scholar 
Miethke M, Klotz O, Linne U, May JJ, Beckering CL, Marahiel MA. Ferri-bacillibactin uptake and hydrolysis in Bacillus subtilis. Mol Microbiol. 2006;61:1413–27.CAS 
PubMed 

Google Scholar 
Pi H, Helmann JD. Genome-wide characterization of the fur regulatory network reveals a link between catechol degradation and bacillibactin metabolism in Bacillus subtilis. MBio. 2018;9:1–15.
Google Scholar 
Adler C, Corbalán NS, Seyedsayamdost MR, Pomares MF, de Cristóbal RE, Clardy J, et al. Catecholate siderophores protect bacteria from pyochelin toxicity. PLoS ONE. 2012;7:e46754.CAS 
PubMed 
PubMed Central 

Google Scholar 
Trottmann F, Franke J, Ishida K, García-Altares M, Hertweck C. A pair of bacterial siderophores releases and traps an intercellular signal molecule: an unusual case of natural nitrone bioconjugation. Angew Chem. 2019;58:200–4.CAS 

Google Scholar 
Mongkolsuk S, Helmann JD. Regulation of inducible peroxide stress responses. Mol Microbiol. 2002;45:9–15.CAS 
PubMed 

Google Scholar 
Cox CD, Rinehart KL, Moore ML, Cook JC. Pyochelin: novel structure of an iron-chelating growth promoter for Pseudomonas aeruginosa. Proc Natl Acad Sci USA. 1981;78:4256–60.CAS 
PubMed 
PubMed Central 

Google Scholar 
Youard ZA, Mislin GLA, Majcherczyk PA, Schalk IJ, Reimmann C. Pseudomonas fluorescens CHA0 produces enantio-pyochelin, the optical antipode of the Pseudomonas aeruginosa siderophore pyochelin. J Biol Chem. 2007;282:35546–53.CAS 
PubMed 

Google Scholar 
Ronnebaum TA, Lamb AL. Nonribosomal peptides for iron acquisition: pyochelin biosynthesis as a case study. Curr Opini Struct Biol. 2018;53:1–11.CAS 

Google Scholar 
Seipke RF, Song L, Bicz J, Laskaris P, Yaxley AM, Challis GL, et al. The plant pathogen Streptomyces scabies 87-22 has a functional pyochelin biosynthetic pathway that is regulated by TetR- and AfsR-family proteins. Microbiology. 2011;157:2681–93.CAS 
PubMed 

Google Scholar 
Gu S, Wei Z, Shao Z, Friman VP, Cao K, Yang T, et al. Competition for iron drives phytopathogen control by natural rhizosphere microbiomes. Nat Microbiol. 2020;5:1002–10.CAS 
PubMed 
PubMed Central 

Google Scholar 
Komaki H, Ichikawa N, Hosoyama A, Hamada M, Igarashi Y. In silico analysis of PKS and NRPS gene clusters in arisostatin-and kosinostatin-producers and description of Micromonospora okii sp. nov. Antibiotics. 2021;10:1447.CAS 
PubMed 
PubMed Central 

Google Scholar 
Engelbrecht A, Saad H, Gross H, Kaysser L. Natural products from Nocardia and their role in pathogenicity. Micro Physiol. 2021;31:217–32.
Google Scholar 
Inahashi Y, Zhou S, Bibb MJ, Song L, Al-Bassam MM, Bibb MJ, et al. Watasemycin biosynthesis in Streptomyces venezuelae: thiazoline C-methylation by a type B radical-SAM methylase homologue. Chem Sci. 2017;8:2823–31.CAS 
PubMed 
PubMed Central 

Google Scholar 
Song J, Qiu S, Zhao J, Han C, Wang Y, Sun X, et al. Pseudonocardia tritici sp. nov., a novel actinomycete isolated from rhizosphere soil of wheat (Triticum aestivum L.). Nat Microbiol. 2019;12:470–80.
Google Scholar 
Sayed AM, Abdel-Wahab NM, Hassan HM, Abdelmohsen UR. Saccharopolyspora: an underexplored source for bioactive natural products. J Appl Microbiol. 2020;128:314–29.CAS 
PubMed 

Google Scholar 
Nordstedt NP, Jones ML. Genomic analysis of Serratia plymuthica MBSA-MJ1: A plant growth promoting rhizobacteria that improves water stress tolerance in greenhouse ornamentals. Front Microbiol. 2021;12:653556.PubMed 
PubMed Central 

Google Scholar 
Zhalnina K, Louie KB, Hao Z, Mansoori N, Da Rocha UN, Shi S, et al. Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat Microbiol. 2018;3:470–80.CAS 
PubMed 

Google Scholar 
Takahashi Y, Malisorn K, Kanchanasin P, Phongsopitanun W, Tanasupawat S, Spain AM, et al. Actinomadura rhizosphaerae sp. nov., isolated from rhizosphere soil of the plant Azadirachta indica. ISME J 2018;68:3012–6.
Google Scholar 
Takahashi Y. Genus Kitasatospora, taxonomic features and diversity of secondary metabolites. J Antibiot. 2017;70:506–13.CAS 

Google Scholar 
Bennur T, Kumar AR, Zinjarde S, Javdekar V. Nocardiopsis species: Incidence, ecological roles and adaptations. Microbiol Res. 2015;174:33–47.PubMed 

Google Scholar 
Walterson AM, Stavrinides J. Pantoea: Insights into a highly versatile and diverse genus within the Enterobacteriaceae. J Basic Microbiol. 2015;39:33–47.
Google Scholar 
Sungthong R, Nakaew N. The genus Nonomuraea: a review of a rare actinomycete taxon for novel metabolites. J Basic Microbiol. 2015;55:554–65.PubMed 

Google Scholar 
Müller S, Strack SN, Ryan SE, Kearns DB, Kirby JR. Predation by Myxococcus xanthus induces Bacillus subtilis to form spore-filled megastructures. Appl Environ Microbiol. 2015;81:203–10.PubMed 

Google Scholar 
Straight PD, Fischbach MA, Walsh CT, Rudner DZ, Kolter R. A singular enzymatic megacomplex from Bacillus subtilis. Proc Natl Acad Sci USA. 2007;104:305–10.CAS 
PubMed 

Google Scholar 
Barger SR, Hoefler BC, Cubillos-Ruiz A, Russell WK, Russell DH, Straight PD. Imaging secondary metabolism of Streptomyces sp. Mg1 during cellular lysis and colony degradation of competing Bacillus subtilis. Antonie van Leeuwenhoek. 2012;102:435–45.CAS 
PubMed 

Google Scholar 
Ogran A, Yardeni EH, Keren-Paz A, Bucher T, Jain R, Gilhar O, et al. The plant host induces antibiotic production to select the most-beneficial colonizers. Appl Environ Microbiol. 2019;85:1–15.
Google Scholar 
Rosenberg G, Steinberg N, Oppenheimer-Shaanan Y, Olender T, Doron S, Ben-Ari J, et al. Not so simple, not so subtle: The interspecies competition between Bacillus simplex and Bacillus subtilis and its impact on the evolution of biofilms. npj Biofilms Microbiomes. 2016;2:15027.PubMed 
PubMed Central 

Google Scholar 
Straight PD, Willey JM, Kolter R. Interactions between Streptomyces coelicolor and Bacillus subtilis: Role of surfactants in raising aerial structures. J Bacteriol. 2006;188:4918–25.CAS 
PubMed 
PubMed Central 

Google Scholar 
Hoefler BC, Gorzelnik KV, Yang JY, Hendricks N, Dorrestein PC, Straight PD. Enzymatic resistance to the lipopeptide surfactin as identified through imaging mass spectrometry of bacterial competition. Proc Natl Acad Sci USA. 2012;109:13082–7.CAS 
PubMed 
PubMed Central 

Google Scholar 
Liu Y, Kyle S, Straight PD. Antibiotic stimulation of a Bacillus subtilis migratory response. mSphere 2018;3:e00586–17.CAS 
PubMed 
PubMed Central 

Google Scholar 
Qi G, Zhu F, Du P, Yang X, Qiu D, Yu Z, et al. Lipopeptide induces apoptosis in fungal cells by a mitochondria-dependent pathway. Peptides. 2010;31:1978–86.CAS 
PubMed 

Google Scholar 
McCully LM, Bitzer AS, Seaton SC, Smith LM, Silby MW. Interspecies social spreading: interaction between two sessile soil bacteria leads to emergence of surface motility. mSphere. 2019;4:e00696–18.CAS 
PubMed 
PubMed Central 

Google Scholar 
Flemming HC, Wingender J, Szewzyk U, Steinberg P, Rice SA, Kjelleberg S. Biofilms: an emergent form of bacterial life. Nat Rev Microbiol. 2016;14:563–75.CAS 
PubMed 

Google Scholar 
Townsley L, Shank EA. Natural-product antibiotics: cues for modulating bacterial biofilm formation. Trends Microbiol. 2017;25:1016–26.CAS 
PubMed 
PubMed Central 

Google Scholar 
Sun X, Xu Z, Xie J, Hesselberg-Thomsen V, Tan T, Zheng D, et al. Bacillus velezensis stimulates resident rhizosphere Pseudomonas stutzeri for plant health through metabolic interactions. ISME J. 2022;16:774–87.CAS 
PubMed 

Google Scholar 
Dumas Z, Ross-Gillespie A, Kümmerli R. Switching between apparently redundant iron-uptake mechanisms benefits bacteria in changeable environments. Proc R Soc B Biol Sci. 2013;280:20131055.
Google Scholar 
Lee N, Kim W, Chung J, Lee Y, Cho S, Jang KS, et al. Iron competition triggers antibiotic biosynthesis in Streptomyces coelicolor during coculture with Myxococcus xanthus. ISME J. 2020;14:1111–24.CAS 
PubMed 
PubMed Central 

Google Scholar 
Kramer J, Özkaya Ö, Kümmerli R. Bacterial siderophores in community and host interactions. Nat Rev Microbiol. 2020;18:152–63.CAS 
PubMed 

Google Scholar 
Niehus R, Picot A, Oliveira NM, Mitri S, Foster KR. The evolution of siderophore production as a competitive trait. Evolution. 2017;71:1443–55.CAS 
PubMed 

Google Scholar 
Ho YN, Lee HJ, Hsieh CT, Peng CC, Yang YL. Chemistry and biology of salicyl-capped siderophores. Stud Nat Prod Chem. 2018;59:431–90.Schalk IJ, Rigouin C, Godet J. An overview of siderophore biosynthesis among fluorescent Pseudomonads and new insights into their complex cellular organization. Environ Microbiol. 2020;22:1447–66.PubMed 

Google Scholar 
Deveau A, Gross H, Palin B, Mehnaz S, Schnepf M, Leblond P, et al. Role of secondary metabolites in the interaction between Pseudomonas fluorescens and soil microorganisms under iron-limited conditions. FEMS Microbiol Ecol. 2016;92:1–11.
Google Scholar 
Jenul C, Keim K, Jens J, Zeiler MJ, Schilcher K, Schurr M, et al. Pyochelin biotransformation shapes bacterial competition. bioRxiv. 2022. https://doi.org/10.1101/2022.04.18.486787.Ho YN, Hoo SY, Wang BW, Hsieh CT, Lin CC, Sun CH, et al. Specific inactivation of an antifungal bacterial siderophore by a fungal plant pathogen. ISME J. 2021;15:1858–61.CAS 
PubMed 
PubMed Central 

Google Scholar 
Lopez-Medina E, Fan D, Coughlin LA, Ho EX, Lamont IL, Reimmann C, et al. Candida albicans inhibits Pseudomonas aeruginosa virulence through suppression of pyochelin and pyoverdine biosynthesis. PLoS Pathog. 2015;11:1–34.
Google Scholar 
Meisel JD, Panda O, Mahanti P, Schroeder FC, Kim DH. Chemosensation of bacterial secondary metabolites modulates neuroendocrine signaling and behavior of C. elegans. Cell. 2014;159:267–80.CAS 
PubMed 
PubMed Central 

Google Scholar 
Finkel OM, Castrillo G, Herrera Paredes S, Salas González I, Dangl JL. Understanding and exploiting plant beneficial microbes. Curr Opin Plant Biol. 2017;38:155–63.PubMed 
PubMed Central 

Google Scholar 
Saad MM, Eida AA, Hirt H, Doerner P. Tailoring plant-associated microbial inoculants in agriculture: a roadmap for successful application. J Exp Bot. 2020;71:3878–901.CAS 
PubMed 
PubMed Central 

Google Scholar 
Ansari FA, Ahmad I. Fluorescent Pseudomonas -FAP2 and Bacillus licheniformis interact positively in biofilm mode enhancing plant growth and photosynthetic attributes. Sci Rep. 2019;9:4547.PubMed 
PubMed Central 

Google Scholar 
Domenech J, Reddy MS, Kloepper JW, Ramos B, Gutierrez-Mañero J. Combined application of the biological product LS213 with Bacillus, Pseudomonas or Chryseobacterium for growth promotion and biological control of soil-borne diseases in pepper and tomato. BioControl. 2006;51:245–58.CAS 

Google Scholar 
Powers MJ, Sanabria-Valentín E, Bowers AA, Shank EA. Inhibition of cell differentiation in Bacillus subtilis by Pseudomonas protegens. J Bacteriol. 2015;197:2129–38.CAS 
PubMed 
PubMed Central 

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

Gardner TA, Cote IM, Gill JA, Grant A, Watkinson AR. Long-term region-wide declines in Caribbean corals. Science. 2003;301:958–60.CAS 
PubMed 

Google Scholar 
Knowlton N. The future of coral reefs. Proc Natl Acad Sci USA. 2001;98:5419–25.CAS 
PubMed 
PubMed Central 

Google Scholar 
Worm B, Barbier EB, Beaumont N, Duffy JE, Folke C, Halpern BS, et al. Impacts of biodiversity loss on ocean ecosystem services. Science. 2006;314:787–90.CAS 
PubMed 

Google Scholar 
Dudgeon SR, Aronson RB, Bruno JF, Precht WF. Phase shifts and stable states on coral reefs. Mar Ecol Prog Ser. 2010;413:201–16.
Google Scholar 
Bell JJ, Davy SK, Jones T, Taylor MW, Webster NS. Could some coral reefs become sponge reefs as our climate changes? Glob Climate Change. 2013;19:2613–24.
Google Scholar 
McMurray SE, Henkel TP, Pawlik JR. Demographics of increasing populations of the giant barrel sponge Xestospongia muta in the Florida Keys. Ecology. 2010;91:560–70.PubMed 

Google Scholar 
Bell JJ. The functional roles of marine sponges. Est Coast Shelf Sci. 2008;79:341–53.
Google Scholar 
Lesser MP, Slattery M. Will coral reef sponges be winners in the Anthropocene? Glob Change Biol. 2020;26:3202–11.
Google Scholar 
Pankey MS, Plachetzki DC, Macartney KJ, Gastaldi M, Slattery M, Gochfeld DJ, et al. Co-phylogeny and convergence shape holobiont evolution in sponge-microbe symbioses. Nat Ecol Evol. 2022;6:750–62.
Google Scholar 
Lesser MP, Slattery M, Mobley CD. Biodiversity and functional ecology of mesophotic coral reefs. Ann Rev Ecol Syst. 2018;49:49–71.
Google Scholar 
Diaz MC, Rützler K. Sponges: an essential component of Caribbean coral reefs. Bull Mar Sci. 2001;69:535–46.
Google Scholar 
Wulff JL. Ecological interactions and the distribution, abundance, and diversity of sponges. Adv Mar Biol. 2012;61:273–344.PubMed 

Google Scholar 
Lesser MP. Benthic-pelagic coupling on coral reefs: feeding and growth of Caribbean sponges. J Exp Mar Biol Ecol. 2006;328:277–88.
Google Scholar 
Perea-Blazquez A, Davy SK, Bell JJ. Estimates of particulate organic carbon flowing from the pelagic environment to the benthos through sponge assemblages. PLoS One. 2012;7:e29569.CAS 
PubMed 
PubMed Central 

Google Scholar 
Lesser MP, Slattery M. Ecology of Caribbean sponges: are top-down or bottom-up processes more important? PLoS One. 2013;8:e79799.CAS 
PubMed 
PubMed Central 

Google Scholar 
Pawlik JR. The chemical ecology of sponges on Caribbean reefs: natural products shape natural systems. BioScience. 2011;61:888–98.
Google Scholar 
Slattery M, Gochfeld DJ. Chemical interactions among marine competitors, and host-pathogens. In: Fattorusso, E, Gerwick, WH, Taglialatela-Scafati, O (eds). Handbook of Marine Natural Products. Springer, 2012. pp. 824–59.Thacker RW, Freeman CJ. Sponge-microbe symbioses: recent advances and new directions. Adv Mar Biol. 2012;62:57–112.PubMed 

Google Scholar 
Taylor MW, Radax R, Steger D, Wagner M. Sponge-associated microorganisms: evolution, ecology, and biotechnological potential. Microbiol Biol Rev. 2007;71:295–347.CAS 

Google Scholar 
Schmitt S, Tsai P, Bell J, Fromont J, Ilan M, Lindquist N, et al. Assessing the complex sponge microbiota: core, variable and species-specific bacterial communities in marine sponges. ISME J. 2012;6:564–76.CAS 
PubMed 

Google Scholar 
Gloeckner V, Wehrl M, Moitinho-Silva L, Gernert C, Schupp P, Pawlik JR, et al. The HMA-LMA dichotomy revisited: an electron microscopical survey of 56 sponge species. Biol Bull. 2014;227:78–88.PubMed 

Google Scholar 
Hentschel U, Fieseler L, Wehrl M, Gernert C, Steinert M, Hacker J, et al. Microbial diversity of marine sponges. Prog Mol Subcell Biol. 2003;37:59–88.CAS 
PubMed 

Google Scholar 
Fiore CL, Jarett JK, Olson ND, Lesser MP. Nitrogen fixation and nitrogen transformation in marine symbioses. Trends Microbiol. 2010;18:455–63.CAS 
PubMed 

Google Scholar 
Zhang F, Jonas L, Lin H, Hill RT. Microbially mediated nutrient cycles in marine sponges. FEMS Microbiol Ecol. 2019;95:115.
Google Scholar 
Schläppy M-L, Schöttner SI, Lavik G, Kuypers MMM, de Beer D, Hoffmann F. Evidence of nitrification and denitrification in high and low microbial abundance sponges. Mar Biol. 2010;157:593–602.PubMed 

Google Scholar 
Giles EC, Kamke J, Moitinho-Silva L, Taylor MW, Hentschel U, Ravasi T, et al. Bacterial community profiles in low microbial abundance sponges. FEMS Microbiol Ecol. 2013;83:232–41.CAS 
PubMed 

Google Scholar 
Weisz JB, Lindquist N, Martens CS. Do associated microbial abundances impact marine demosponge pumping rates and tissue densities. Oecologia. 2008;155:367–76.PubMed 

Google Scholar 
de Goeij JM, van Oevelen D, Vermiej MJA, Osinga R, Middelburg JJ, de Goeij AFPM, et al. Surviving in a marine desert: the sponge loop retains resources within coral reefs. Science. 2013;342:108–10.PubMed 

Google Scholar 
de Goeij JM, Lesser MP, Pawlik JR. Nutrient fluxes and ecological functions of coral reef sponges in a changing ocean. In: Carballo, J, Bell, J eds. Climate Change, Ocean Acidification and Sponges. Springer, 2017. pp 373–410.Tanaka Y, Miyajima T, Wtanabe A, Nadaoka K, Yamamoto T, Ogawa H. Distribution of dissolved organic carbon and nitrogen in a coral reef. Coral Reefs. 2011;30:533–41.
Google Scholar 
Lesser MP, Slattery M, Laverick JH, Macartney KJ, Bridge TC. Global community breaks at 61 m on mesophotic coral reefs. Global Ecol Biogeogr. 2019;28:1403–16.
Google Scholar 
Lønborg C, Álvarez-Salgado XA, Duggan S, Carreira C. Organic matter bioavailability in tropical coastal waters: The Great Barrier Reef. Limnol Oceanogr. 2018;63:1015–35.
Google Scholar 
Macartney KJ, Abraham AC, Slattery M, Lesser MP. Growth and feeding in the sponge Agelas tubulata from shallow to mesophotic depths on Grand Cayman Island. Ecosphere. 2021;12:e03764.
Google Scholar 
Ribes M, Coma R, Atkinson MJ, Kinzie RA. Particle removal by coral reef communities: picoplankton is a major source of nitrogen. Mar Ecol Prog Ser. 2003;257:13–23.
Google Scholar 
Ribes M, Coma R, Atkinson MJ, Kinzie RA. Sponges and ascidians control removal of particulate organic nitrogen from coral reef water. Limnol Oceanogr. 2005;50:1480–9.CAS 

Google Scholar 
Maldonado M, Ribes M, van Duyl FC. Nutrient fluxes through sponges: biology, budgets, and ecological implications. Adv Mar Biol. 2012;62:113–82.PubMed 

Google Scholar 
Seutin G, White BN, Boag PT. Preservation of avian blood and tissue samples for DNA analyses. Can J Zool. 1991;69:82–90.CAS 

Google Scholar 
Abraham AC, Gochfeld DJ, Macartney K, Mellow A, Lesser MP, Slattery M. Biochemical variability in sponges across the Caribbean basin. Invertebr Biol. 2021;140:e12341.
Google Scholar 
Sunagawa S, Woodley CM, Medina M. Threatened corals provide underexplored microbial habitats. PLoS One. 2010;5:e9554.PubMed 
PubMed Central 

Google Scholar 
Parada AE, Needham DM, Fuhrman JA. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol. 2016;18:1403–14.CAS 
PubMed 

Google Scholar 
Apprill A, McNally S, Parsons R, Weber L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat Microb Ecol. 2015;75:129–37.
Google Scholar 
Simion P, Phillippe H, Baurain D, Jager M, Richter RJ, Di Franco A, et al. A Large and consistent phylogenomic dataset supports sponges as the sister group to all other animals. Curr Biol. 2017;27:958–67.CAS 
PubMed 

Google Scholar 
Katoh K, Misawa K, Kuma KI, Miyata T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 2002;30:3059–66.CAS 
PubMed 
PubMed Central 

Google Scholar 
Oksanen J, Simpson GL, Blanchet FG, Kindt R, Legendre P, Minchin PR, et al. vegan: Community Ecology Package. R package version 2.5-5. https://CRAN.R-project.org/package=vegan. Released May, 2019.Pinheiro J, Bates D, DebRoy S, Sarkar D, EISPACK Authors, Heisterkamp S, et al. nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1-155. https://svn.r-project.org/R-packages/trunk/nlme/. Released Jan, 2022.Kindt R, Coe R. Tree diversity analysis. A manual and software for common statistical methods for ecological and biodiversity studies. World Agroforestry Centre, ICRAF, 2005. https://www.worldagroforestry.org/publication/tree-diversity-analysis-manual-and-software-common-statistical-methods-ecological-and.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.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 
Westbrook A, Ramsdell J, Schuelke T, Normington L, Bergeron RD, Thomas WK, et al. PALADIN: protein alignment for functional profiling whole metagenome shotgun data. Bioinformatics. 2017;33:1473–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
Robinson MD, McCarthy DG, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.CAS 
PubMed 

Google Scholar 
Li D, Luo R, Liu C-M, Leung C-M, Ting H-F, Sadakane K, et al. MEGAHIT v1.0: A fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods. 2016;102:3–11.CAS 
PubMed 

Google Scholar 
Li H, Durbin R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics. 2009;25:1754–60.CAS 
PubMed 
PubMed Central 

Google Scholar 
Blin K, Shaw S, Kautsar SA, Medema MH, Weber T. The antiSMASH database version 3: increased taxonomic coverage and new query features for modular enzymes. Nucleic Acids Res. 2009;49:D639–43.
Google Scholar 
Conte-Jerpe IE, Thompson PD, Wong CWM, Oliveira NL, Duprey NN, Moynihan MA, et al. Trophic strategy and bleaching resistance in reef-building corals. Sci Adv. 2020;6:eaaz5443.
Google Scholar 
Jackson AL, Inger R, Parnell AC, Bearhop S. Comparing isotopic niche widths among and within communities: SIBER-Stable Isotope Bayesian Ellipses. Anim Ecol. 2011;80:595–602.
Google Scholar 
Thomas T, Moitinho-Silva L, Lurgi M, Björk JR, Easson C, Astudillo-Garcia C, et al. Diversity, structure and convergent evolution of the global sponge microbiome. Nat Comm. 2016;7:11870.CAS 

Google Scholar 
Erwin PM, Coma R, López-Sendino P, Serrano E, Ribes M. Stable symbionts across the HMA-LMA dichotomy: low seasonal and inter-annual variation in sponge-associated bacteria from taxonomically diverse hosts. FEMS Microbiol Ecol. 2015;91:fiv115.PubMed 

Google Scholar 
Moitinho-Silva L, Steinert G, Nielsen S, Hardoim CCP, Wu Y-C, McCormack GP. Predicting the HMA-LMA status in marine sponges by machine learning. Front Microbiol. 2017;8:752.PubMed 
PubMed Central 

Google Scholar 
Campana S, Demey C, Busch K, Hentschel U, Muyzer G, de Goeij J. Marine sponges maintain stable bacterial communities between reef sites with different coral to algae cover ratios. FEMS Microbiol Ecol. 2021;97:fiab115.CAS 
PubMed 
PubMed Central 

Google Scholar 
Freeman CJ, Thacker RW. Complex interactions between marine sponges and their symbiotic microbial communities. Limnol Oceanogr. 2011;56:1577–86.
Google Scholar 
Siegel A, Kamke J, Hochmuth T, Piel J, Richter M, Liang C, et al. Single-cell genomic reveals the lifestyle of Poribacteria, a candidate phylum symbiotically associated with marine sponges. ISME J. 2011;5:61–70.
Google Scholar 
Bayer K, Jahn MT, Slaby BM, Moitinho-Silva L, Hentschel U. Marine sponges as Chloroflexi hot spots: genomic insights and high resolution visualization of an abundant and diverse symbiotic clade. mSystems. 2018;3:e00150–18.CAS 
PubMed 
PubMed Central 

Google Scholar 
Fan L, Reynolds D, Liu M, Thomas T. Functional equivalence and evolutionary convergence in complex communities of microbial sponge symbionts. Proc Natl Acad Sci USA. 2012;109:1878–87.
Google Scholar 
Ribes M, Jiménez E, Yahel G, López-Sendino P, Diez B, Massana R, et al. Functional convergence of microbes associated with temperate marine sponges. Environ Microbiol. 2012;14:1224–39.CAS 
PubMed 

Google Scholar 
Thomas T, Rusch D, DeMaere MZ, Yung PY, Lewis M, Halpern A, et al. Functional genomic signatures of sponge bacteria reveal unique and shared features of symbiosis. ISME J. 2010;4:1557–67.CAS 
PubMed 

Google Scholar 
Fiore CL, Labrie M, Jarett JK, Lesser MP. Transcriptional activity of the giant barrel sponge, Xestospongia muta holobiont: molecular evidence for metabolic interchange. Front Microbiol. 2015;6:364.PubMed 
PubMed Central 

Google Scholar 
Engel S, Pawlik JR. Allelopathic activities of sponge extracts. Mar Ecol Prog Ser. 2000;207:273–82.
Google Scholar 
Gochfeld DJ, Kamel HN, Olson JB, Thacker RW. Trade-offs in defensive metabolite production but not ecological function in healthy and diseased sponges. J Chem Ecol. 2012;38:451–62.CAS 
PubMed 

Google Scholar 
van Duyl FC, Mueller B, Meesters EH. Spatio-temporal variation in stable isotopic signatures (δ13C and δ15N) of sponges on the Saba Bank. PeerJ. 2018;6:e5460.PubMed 
PubMed Central 

Google Scholar 
Fiore CL, Baker DM, Lesser MP. Nitrogen biogeochemistry in the Caribbean sponge, Xestospongia muta: a source or sink of dissolved inorganic nitrogen? PLoS One. 2013;8:e72961.CAS 
PubMed 
PubMed Central 

Google Scholar 
Hudspith M, de Goeij JM, Streekstra M, Kornder NA, Bougoure J, Guagliardo P, et al. Harnessing solar power: photoautotrophy supplements the diet of a low-light dwelling sponge. ISME J. 2022; https://doi.org/10.1038/s41396-022-01254-3.Shih JL, Selph KE, Wall CB, Wallsgrove NJ, Lesser MP, Popp BN. Trophic ecology of the tropical Pacific sponge Mycale gradis inferred from amino acid compound-specific isotopic analyses. Microb Ecol. 2020;79:495–510.CAS 
PubMed 

Google Scholar 
Macartney KJ, Slattery M, Lesser MP. Trophic ecology of Caribbean sponges in the mesophotic zone. Limnol Oceanogr. 2021;66:1113–24.CAS 

Google Scholar 
Southwell MW, Popp BN, Martens CS. Nitrification controls on fluxes and isotopic composition of nitrate from Florida Keys sponges. Mar Chem. 2008;108:96–108.CAS 

Google Scholar 
Lamb K, Swart PK. The carbon and nitrogen isotopic values of particulate organic material from the Florida Keys: a temporal and spatial study. Coral Reefs. 2008;27:351–62.
Google Scholar 
Ferrier-Pagès C, Leal MG. Stable isotopes as tracers of trophic interactions in marine mutualistic symbioses. Ecol Evol. 2019;9:723–40.PubMed 

Google Scholar 
McMurray SE, Stubler AD, Erwin PM, Finelli CM, Pawlik JR. A test of the sponge-loop hypothesis for emergent Caribbean reef sponges. Mar Ecol Prog Ser. 2018;588:1–14.CAS 

Google Scholar 
Freeman CJ, Easson CG, Baker DM. Metabolic diversity and niche structure in sponges from the Miskito Cays, Honduras. PeerJ. 2014;2:e695.PubMed 
PubMed Central 

Google Scholar 
Freeman CJ, Easson CG, Matterson KO, Thacker RW, Baker DM, Paul VJ. Microbial symbionts and ecological divergence of Caribbean sponges: a new perspective on an ancient association. ISME J. 2020;14:1571–83.PubMed 
PubMed Central 

Google Scholar 
Poppell E, Weisz J, Spicer L, Massaro A, Hill A, Hill M. Sponge heterotrophic capacity and bacterial community structure in high‐and low‐microbial abundance sponges. Mar Ecol. 2014;35:414–24.
Google Scholar 
Morganti TM, Ribes M, Yahel G, Coma R. Size is the major determinant of pumping rates in marine sponges. Front Physiol. 2019;10:1474.PubMed 
PubMed Central 

Google Scholar 
Rix L, Ribes M, Coma R, Jahn MT, de Goeij JM, van Oevelen D, et al. Heterotrophy in the earliest gut: a single-cell view of heterotrophic carbon and nitrogen assimilation in sponge-microbe symbioses. ISME J. 2020;14:2554–67.CAS 
PubMed 
PubMed Central 

Google Scholar 
O’Brien PA, Tan S, Yang C, Frade PR, Andreakis N, Smith HA, et al. Diverse coral reef invertebrates exhibit patterns of phylosymbiosis. ISME J. 2020;14:2211–22.PubMed 
PubMed Central 

Google Scholar 
Erwin PM, Thacker RW. Incidence and identity of photosynthetic symbionts in Caribbean coral reef sponge assemblages. J Mar Biol Assoc UK. 2007;87:1683–92.CAS 

Google Scholar 
Palumbi SR. Tactics of acclimation: morphological changes of sponges in an unpredictable environment. Science. 1984;225:1478–80.CAS 
PubMed 

Google Scholar 
Slattery M, Gochfeld DJ, Diaz MC, Thacker RW, Lesser MP. Variability in chemical defense across a shallow to mesophotic depth gradient in the Caribbean sponge Plakortis angulospiculatus. Coral Reefs. 2016;35:11–22.
Google Scholar 
Morganti T, Coma R, Yahel G, Ribes M. Trophic niche separation that facilitates co‐existence of high and low microbial abundance sponges is revealed by in situ study of carbon and nitrogen fluxes. Limnol Oceanogr. 2017;62:1963–83.CAS 

Google Scholar 
Maldonado M. Sponge waste that fuels marine oligotrophic food webs: a re-assessment of its origin and nature. Mar Ecol. 2016;37:477–91.
Google Scholar  More

Williams, R. et al. Prioritizing global marine mammal habitats using density maps in place of range maps. Ecography 37, 212–220 (2014).
Google Scholar 
Tyack, P. L. & Clark, C. W. Communication and acoustic behavior of dolphins and whales in Hearing by whales and dolphins 156–224 (Springer, 2000).Davis, G. E. et al. Exploring movement patterns and changing distributions of baleen whales in the western North Atlantic using a decade of passive acoustic data. Glob. Change Biol. 26, 4812 (2020).ADS 

Google Scholar 
Lomac-MacNair, K. S. et al. Marine mammal visual and acoustic surveys near the Alaskan Colville River Delta. Polar Biol. 42, 441–448 (2018).
Google Scholar 
Keen, E., Hendricks, B., Wray, J., Alidina, H. & Picard, C. Integrating passive acoustic and visual surveys for marine mammals in coastal habitats in 176th Meeting of Acoustical Society of America. 1 edn.Gregr, E. J., Baumgartner, M. F., Laidre, K. L. & Palacios, D. M. Marine mammal habitat models come of age: The emergence of ecological and management relevance. Endang. Species Res. 22, 205–212 (2013).
Google Scholar 
Hastie, G. D., Wilson, B., Wilson, L., Parsons, K. M. & Thompson, P. M. Functional mechanisms underlying cetacean distribution patterns: Hotspots for bottlenose dolphins are linked to foraging. Mar. Biol. 144, 397–403 (2004).
Google Scholar 
Lambert, C., Mannocci, L., Lehodey, P. & Ridoux, V. Predicting cetacean habitats from their energetic needs and the distribution of their prey in two contrasted tropical regions. PLoS ONE 9, e105958 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Huot, Y. et al. Does chlorophyll a provide the best index of phytoplankton biomass for primary productivity studies?. Biogeosci. Discuss. 4, 707–745 (2007).ADS 

Google Scholar 
Etnoyer, P. et al. Sea-surface temperature gradients across blue whale and sea turtle foraging trajectories off the Baja California Peninsula, Mexico. Deep Sea Res. II 53, 340–358 (2006).ADS 

Google Scholar 
Shabangu, F. W. et al. Seasonal occurrence and diel calling behaviour of Antarctic blue whales and fin whales in relation to environmental conditions off the west coast of South Africa. J. Mar. Syst. 190, 25–39 (2019).
Google Scholar 
Haida Nation & Parks Canada Agency. Gwaii Haanas Gina ’Waadluxan Kilguhlga. Land-Sea-People Management Plan. 33 (© Council of the Haida Nation and Her Majesty the Queen in Right of Canada, represented by the Chief Executive Officer of Parks Canada, 2018).Ford, J. K. B. Marine Mammals of British Columbia. (Royal BC Museum, 2014).Allen, A. S., Yurk, H., Vagle, S., Pilkington, J. & Canessa, R. The underwater acoustic environment at SGaan Kinghlas-Bowie Seamount Marine Protected Area: Characterizing vessel traffic and associated noise using satellite AIS and acoustic datasets. Mar. Pollut. Bull. 128, 82–88 (2018).CAS 
PubMed 

Google Scholar 
Ainslie, M. A. Principles of Sonar Performance Modeling. (Springer, 2010).Collins, M. D. A split-step Padé solution for the parabolic equation method. J. Acoust. Soc. Am. 93, 1736–1742 (1993).ADS 

Google Scholar 
Porter, M. B. & Bucker, H. P. Gaussian beam tracing for computing ocean acoustic fields. J. Acoust. Soc. Am. 82, 1349–1359 (1987).ADS 

Google Scholar 
Mouy, X., MacGillivray, A. O., Vallarta, J. H., Martin, B. & Delarue, J. J.-Y. Ambient Noise and Killer Whale Monitoring near Port Metro Vancouver’s Proposed Terminal 2 Expansion Site: July–September 2012. (Technical report by JASCO Applied Sciences for Hemmera, 2012).Ford, J. et al. Distribution and relative abundance of cetaceans in western Canadian waters from ship surveys, 2002–2008. Can. Tech. Rep. Fish. Aquat. Sci. 2913, 51 (2010).
Google Scholar 
Wright, B. M., Nichol, L. M. & Doniol-Valcroze, T. Spatial density models of cetaceans in the Canadian Pacific estimated from 2018 ship-based surveys. DFO Can. Sci. Advis. Sec. Res. Doc. 2021, 49 (2021).
Google Scholar 
Devred, E., Hardy, M. & Hannah, C. Satellite observations of the Northeast Pacific Ocean. Can. Tech. Rep. Hydrogr. Ocean Sci. 335, 46 (2021).
Google Scholar 
Saha, K. et al. NOAA National centers for environmental information. Dataset https://doi.org/10.7289/v52j68xx (2018).Article 

Google Scholar 
NASA Goddard Space Flight Center, Ocean Ecology Laboratory & Ocean Biology Processing Group. (NASA OB.DAAC, Greenbelt, MD, USA. https://doi.org/10.5067/AQUA/MODIS/L3B/CHL/2018. Accessed 3 Feb 2021.Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. B Stat. Methodol. 73, 3–36 (2011).MathSciNet 
MATH 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2021).Ogle, D. H., Wheeler, P. & Dinno, A. FSA: Fisheries Stock Analysis. R package version 0.8.32. https://github.com/droglenc/FSA (2021).Payne, R. S. & McVay, S. Songs of humpback whales. Science 173, 585–597 (1971).ADS 
CAS 
PubMed 

Google Scholar 
Rekdahl, M. L. et al. Non-song social call bouts of migrating humpback whales. J. Acoust. Soc. Am. 137, 3042–3053 (2015).ADS 
PubMed 
PubMed Central 

Google Scholar 
Oswald, J. N., Rankin, S. & Barlow, J. To whistle or not to whistle? Geographic variation in the whistling behavior of small odontocetes. Aquat. Mamm. 34, 288–302 (2008).
Google Scholar 
Rankin, S., Oswald, J., Barlow, J. P. & Lammers, M. Patterned burst-pulse vocalizations of the northern right whale dolphin, Lissodelphis borealis. J. Acoust. Soc. Am. 121, 1213–1218. https://doi.org/10.1121/1.2404919 (2007).Article 
ADS 
PubMed 

Google Scholar 
Arranz, P. et al. Discrimination of fast click-series produced by tagged Risso’s dolphins (Grampus griseus) for echolocation or communication. J. Exp. Biol. 219, 2898–2907. https://doi.org/10.1242/jeb.144295 (2016).Article 
CAS 
PubMed 

Google Scholar 
Halpin, L. R., Towers, J. R. & Ford, J. K. First record of common bottlenose dolphin (Tursiops truncatus) in Canadian Pacific waters. Mar. Biodivers. Rec. 11, 1–5 (2018).
Google Scholar 
Nikolich, K. & Towers, J. R. Vocalizations of common minke whales (Balaenoptera acutorostrata) in an eastern North Pacific feeding ground. Bioacoustics 29, 97–108 (2020).
Google Scholar 
Money, J. H. & Trites, A. W. A preliminary assessment of the status of marine mammal populations and associated research needs for the west coast of Canada. Report No. Final Report, 80 (Fisheries and Oceans Canada, 1998).Gregr, E. J. & Trites, A. W. Predictions of critical habitat for five whale species in the waters of coastal British Columbia. Can. J. Fish. Aquat. Sci. 58, 1265–1285 (2001).
Google Scholar 
Ou, H., Au, W. W. L., Van Parijs, S., Oleson, E. M. & Rankin, S. Discrimination of frequency-modulated Baleen whale downsweep calls with overlapping frequencies. J. Acoust. Soc. Am. 137, 3024–3032. https://doi.org/10.1121/1.4919304 (2015).Article 
ADS 
PubMed 

Google Scholar 
Mellinger, D. K., Stafford, K. M., Moore, S. E., Dziak, R. P. & Matsumoto, H. An overview of fixed passive acoustic observation methods for cetaceans. Oceanography 20, 36–45 (2007).
Google Scholar 
Stafford, K. M., Citta, J. J., Moore, S. E., Daher, M. A. & George, J. E. Environmental correlates of blue and fin whale call detections in the North Pacific Ocean from 1997 to 2002. Mar. Ecol. Prog. Ser. 395, 37–53 (2009).ADS 

Google Scholar 
Burnham, R., Duffus, D. & Mouy, X. The presence of large whale species in Clayoquot Sound and its offshore waters. Cont. Shelf Res. 177, 15–23 (2019).ADS 

Google Scholar 
Burtenshaw, J. C. et al. Acoustic and satellite remote sensing of blue whale seasonality and habitat in the Northeast Pacific. Deep Sea Res. II 51, 967–986 (2004).ADS 

Google Scholar 
Calambokidis, J., Barlow, J., Ford, J. K. B., Chandler, T. E. & Douglas, A. B. Insights into the population structure of blue whales in the Eastern North Pacific from recent sightings and photographic identification. Mar. Mamm. Sci. 25, 816–832 (2009).
Google Scholar 
Jackson, J. M., Thomson, R. E., Brown, L. N., Willis, P. G. & Borstad, G. A. Satellite chlorophyll off the British Columbia Coast, 1997–2010. J. Geophys. Res. Oceans 120, 4709–4728 (2015).ADS 

Google Scholar 
Evans, R., English, P. A., Anderson, S. C., Gauthier, S. & Robinson, C. L. Factors affecting the seasonal distribution and biomass of E. pacifica and T. spinifera along the Pacific coast of Canada: A spatiotemporal modelling approach. PLoS ONE 16, e0249818 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Moore, S. E., Watkins, W. A., Daher, M. A., Davies, J. R. & Dahlheim, M. E. Blue whale habitat associations in the Northwest Pacific: Analysis of remotely-sensed data using a Geographic Information System. Oceanography 15, 1–10 (2002).
Google Scholar 
Lockyer, C. Review of Baleen Whale (Mysticeti) reproduction and implications for management. Rep. Int. Whal. Commn Spec. Issue 6, 27–50 (1984).
Google Scholar 
Ohsumi, S. M. N. Growth of fin whale in the Northern Pacific Ocean. Sci. Rep. Whale Res. Inst. 13, 97–133 (1958).
Google Scholar 
Watkins, W. A. et al. Seasonality and distribution of whale calls in the North Pacific. Oceanography 13, 62–67 (2000).
Google Scholar 
Watkins, W. A., Tyack, P., Moore, K. E. & Bird, J. E. The 20-Hz signals of finback whales (Balaenoptera physalus). J. Acoust. Soc. Am. 82, 1901–1912 (1987).ADS 
CAS 
PubMed 

Google Scholar 
Stafford, K. M., Mellinger, D. K., Moore, S. E. & Fox, C. G. Seasonal variability and detection range modeling of baleen whale calls in the Gulf of Alaska, 1999–2002. J. Acoust. Soc. Am. 122, 3378–3390 (2007).ADS 
PubMed 

Google Scholar 
Koot, B. Winter Behaviour and Population Structure of Fin Whales (Balaenoptera physalus) in British Columbia inferred from passive acoustic data (University of British Columbia, 2015).
Google Scholar 
Pilkington, J. F., Stredulinsky, E. H., Abernethy, R. M. & Ford, J. K. B. Patterns of Fin whale (Balaenoptera physalus) Seasonality and Relative Distribution in Canadian Pacific Waters Inferred from Passive Acoustic Monitoring. DFO Can. Sci. Advis. Sec. Res. Doc. (2018).Best, B. D., Fox, C. H., Williams, R., Halpin, P. H. & Paquet, P. C. Updated Marine Mammal Distribution and Abundance Estimates in British Columbia (Springer, 2015).
Google Scholar 
Clarke, C. & Jamieson, G. Identification of ecologically and biologically significant areas in the Pacific North Coast integrated management area: Phase II: Final report. Can. Tech. Rep. Fish. Aquat. Sci. 2678, 59 (2006).
Google Scholar 
Nichol, L. M. et al. Distribution, movements and habitat fidelity patterns of Fin Whales (Balaenoptera physalus) in Canadian Pacific Waters. DFO Can. Sci. Advis. Sec. Res. Doc. (2018).Nichol, L. M. & Ford, J. K. B. Information in Support of the Identification of Habitat of Special Importance to Fin Whales (Balaenoptera physalus) in Canadian Pacific Waters. DFO Can. Sci. Advis. Sec. Res. Doc. (2018).Mizroch, S. A., Rice, D. W., Zwiefelhofer, D., Waite, J. & Perryman, W. L. Distribution and movements of fin whales in the North Pacific Ocean. Mammal Rev. 39, 193–227 (2009).
Google Scholar 
Širović, A., Williams, L. N., Kerosky, S. M., Wiggins, S. M. & Hildebrand, J. A. Temporal separation of two fin whale call types across the eastern North Pacific. Mar. Biol. 160, 47–57 (2013).PubMed 

Google Scholar 
Flinn, R. D., Trites, A. W., Gregr, E. J. & Perry, R. I. Diets of fin, sei, and sperm whales in British Columbia: an analysis of commercial whaling records, 1963–1967. Mar. Mamm. Sci. 18, 663–679 (2002).
Google Scholar 
Barnes, R. S. K. & Hughes, R. N. An Introduction to Marine Ecology (Wiley, 1999).
Google Scholar 
Romagosa, M. et al. Food talks: 40-hz fin whale calls are associated with prey biomass. Proc. R. Soc. B 288, 20211156 (2021).PubMed 
PubMed Central 

Google Scholar 
Gregr, E. J., Nichol, L., Ford, J. K., Ellis, G. & Trites, A. W. Migration and population structure of northeastern Pacific whales off coastal British Columbia: An analysis of commercial whaling records from 1908–1967. Mar. Mamm. Sci. 16, 699–727 (2000).
Google Scholar 
Williams, R. & Thomas, L. Distribution and abundance of marine mammals in the coastal waters of British Columbia, Canada. J. Cetac. Res. Manage. 9, 15 (2007).
Google Scholar 
Dalla Rosa, L., Ford, J. K. & Trites, A. W. Distribution and relative abundance of humpback whales in relation to environmental variables in coastal British Columbia and adjacent waters. Contin. Shelf Res. 36, 89–104 (2012).ADS 

Google Scholar 
Winn, H. E. & Winn, L. K. The song of the humpback whale Megaptera novaeangliae in the West Indies. Mar. Biol. 47, 97–114. https://doi.org/10.1007/BF00395631 (1978).Article 

Google Scholar 
Baker, C. S. et al. Population characteristics and migration of summer and late-season humpback whales (Megaptera novaeangliae) in southeastern Alaska. Mar. Mamm. Sci. 1, 304–323 (1985).ADS 

Google Scholar 
McSweeney, D., Chu, K., Dolphin, W. & Guinee, L. North Pacific humpback whale songs: A comparison of southeast Alaskan feeding ground songs with Hawaiian wintering ground songs. Mar. Mamm. Sci. 5, 139–148 (1989).
Google Scholar 
Norris, T. F., McDonald, M. & Barlow, J. Acoustic detections of singing humpback whales (Megaptera novaeangliae) in the eastern North Pacific during their northbound migration. J. Acoust. Soc. Am. 106, 506–514 (1999).ADS 
CAS 
PubMed 

Google Scholar 
Clark, C. W. & Clapham, P. J. Acoustic monitoring on a humpback whale (Megaptera novaeangliae) feeding ground shows continual singing into late spring. Proc. R. Soc. Lond. B 271, 1051–1057 (2004).
Google Scholar 
Stimpert, A. K., Peavey, L. E., Friedlaender, A. S. & Nowacek, D. P. Humpback whale song and foraging behavior on an Antarctic feeding ground. PLoS ONE 7, e51214 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kowarski, K., Evers, C., Moors-Murphy, H., Martin, B. & Denes, S. L. Singing through winter nights: Seasonal and diel occurrence of humpback whale (Megaptera novaeangliae) calls in and around the Gully MPA, offshore eastern Canada. Mar. Mamm. Sci. 34, 169–189 (2018).
Google Scholar 
Nichol, L. M., Abernethy, R., Flostrand, L., Lee, T. S. & Ford, J. K. B. Information relevant for the identification of critical habitats of north pacific humpback whales (Megaptera novaeangliae) in British Columbia. DFO Can. Sci. Advis. Sec. Res. Doc. (2010).Williams, R., Erbe, C., Ashe, E. & Clark, C. W. Quiet (er) marine protected areas. Mar. Pollut. Bull. 100, 154–161 (2015).CAS 
PubMed 

Google Scholar 
Gaston, A. J., Pilgrim, N. G. & Pattison, V. Humpback Whale (Megaptera novaeangliae) observations in Laskeek Bay, western Hecate Strait, in spring and early summer, 1990–2018. Can. Field Nat. 133, 263–269 (2019).
Google Scholar 
Robinson, C. L., Gower, J. F. & Borstad, G. Twenty years of satellite observations describing phytoplankton blooms in seas adjacent to Gwaii Haanas National Park Reserve, Canada. Can. J. Remote Sens. 30, 36–43 (2004).ADS 

Google Scholar 
Swartz, S. L., Taylor, B. L. & Rugh, D. J. Gray whale Eschrichtius robustus population and stock identity. Mamm. Rev. 36, 66–84 (2006).
Google Scholar 
Gaston, A. J. & Heise, K. Results of cetacean observations in Laskeek Bay, 1990–2003. Laskeek Bay Res. 55, 1–10 (2004).
Google Scholar 
Ford, J. K. et al. New insights into the northward migration route of gray whales between Vancouver Island, British Columbia, and southeastern Alaska. Mar. Mamm. Sci. 29, 325–337 (2013).
Google Scholar 
Burnham, R. E. & Duffus, D. A. The use of passive acoustic monitoring as a census tool of gray whale (Eschrichtius robustus) migration. Ocean Coast. Manag. 188, 105070 (2020).
Google Scholar 
Best, P. B. Social organization in sperm whales. In Physeter macrocephalus in Behavior of Marine Animals (eds Winn, H. E. & Olla, B. L.) 227–289 (Springer, 1979).
Google Scholar 
Jaquet, N. & Gendron, D. Distribution and relative abundance of sperm whales in relation to key environmental features, squid landings and the distribution of other cetacean species in the Gulf of California, Mexico. Mar. Biol. 141, 591–601 (2002).
Google Scholar 
Rice, D. W. Sperm whale Physeter macrocephalus Linnaeus, 1758. Handb. Mar. Mamm. 4, 177–233 (1989).
Google Scholar 
Whitehead, H. & Arnbom, T. Social organization of sperm whales off the Galapagos Islands, February–April 1985. Can. J. Zool. 65, 913–919 (1987).
Google Scholar 
Whitehead, H. Sperm whale: Physeter macrocephalus. In Encyclopedia of Marine Mammals 3rd edn (eds Würsig, B. et al.) 919–925 (Academic Press, 2018).
Google Scholar 
Mizroch, S. A. & Rice, D. W. Ocean nomads: Distribution and movements of sperm whales in the North Pacific shown by whaling data and Discovery marks. Mar. Mamm. Sci. 29, E136–E165 (2013).
Google Scholar 
Diogou, N. et al. Sperm whale (Physeter macrocephalus) acoustic ecology at Ocean Station PAPA in the Gulf of Alaska-Part 2: Oceanographic drivers of interannual variability. Deep Sea Res. I 150, 103044 (2019).
Google Scholar 
Ford, J. K. & Ellis, G. M. You are what you eat: Foraging specializations and their influence on the social organization and behavior of killer whales. in Primates and Cetaceans 75–98 (Springer, 2014).Ford, J. K. B. et al. Habitats of special importance to resident killer whales (Orcinus orca) off the West Coast of Canada. DFO Can. Sci. Advis. Sec. Res. Doc. (2017).Ford, J. K. B., Stredulinsky, E. H., Ellis, G. M., Durban, J. W. & Pilkington, J. F. Offshore Killer whales in Canadian pacific waters: Distribution, seasonality, foraging ecology, population status and potential for recovery. DFO Can. Sci. Advis. Sec. Res. Doc. (2014).Nichol, L. M. & Shackleton, D. M. Seasonal movements and foraging behaviour of northern resident killer whales (Orcinus orca) in relation to the inshore distribution of salmon (Oncorhynchus spp.) in British Columbia. Can. J. Zool. 74, 983–991 (1996).
Google Scholar 
Olesiuk, P. F., Ellis, G. M. & Ford, J. K. Life History and Population Dynamics of Northern Resident Killer Whales (Orcinus orca) in British Columbia (Canadian Science Advisory Secretariat Ottawa, 2005).
Google Scholar 
Newman, K. & Springer, A. Nocturnal activity by mammal-eating killer whales at a predation hot spot in the Bering Sea. Mar. Mamm. Sci. 24, 990 (2008).
Google Scholar 
Ford, J. K. B. et al. Dietary specialization in two sympatric populations of killer whales (Orcinus orca) in coastal British Columbia and adjacent waters. Can. J. Zool. 76, 1456–1471 (1998).
Google Scholar 
Barrett-Lennard, L. G., Ford, J. K. B. & Heise, K. A. The mixed blessing of echolocation: Differences in sonar use by fish-eating and mammal-eating killer whales. Anim. Behav. 51, 553–565 (1996).
Google Scholar 
Deecke, V. B., Ford, J. K. B. & Slater, P. J. B. The vocal behaviour of mammal-eating killer whales: Communicating with costly calls. Anim. Behav. 69, 395–405 (2005).
Google Scholar 
Ford, J. K. B. Call traditions and vocal dialects of killer whales (Orcinus orca) in British Columbia Ph.D. thesis, University of British Columbia (1984).Baird, R. W. Status of killer whales, Orcinus orca, Canada. Can. Field. Nat. 115, 676–701 (2001).
Google Scholar 
Ford, J. K. B., Stredulinsky, E. H., Towers, J. R. & Ellis, G. M. Information in Support of the Identification of Critical Habitat for Transient Killer Whales (Orcinus orca) off the West Coast of Canada. DFO Can. Sci. Advis. Sec. Res. Doc. (2013).Tyack, P. L., Johnson, M., Soto, N. A., Sturlese, A. & Madsen, P. T. Extreme diving of beaked whales. J. Exp. Biol. 209, 4238–4253 (2006).PubMed 

Google Scholar 
Baumann-Pickering, S. et al. Species-specific beaked whale echolocation signals. J. Acoust. Soc. Am. 134, 2293–2301 (2013).ADS 
PubMed 

Google Scholar 
Pike, G. C. Two records of Berardius bairdi from the coast of British Columbia. J. Mammal. 34, 98–104 (1953).
Google Scholar 
Pike, G. C. & MacAskie, I. Marine mammals of British Columbia. Fish. Res. Board Can. Bull. 171, 1–10 (1969).
Google Scholar 
Willis, P. M. & Baird, R. W. Sightings and strandings of beaked whales on the west coast of. Aquat. Mamm. 24, 21–25 (1998).
Google Scholar 
Jefferson, T. A. Phocoenoides dalli. Mamm. Spec. https://doi.org/10.2307/3504170 (1988).Article 

Google Scholar 
Boyd, C. et al. Estimation of population size and trends for highly mobile species with dynamic spatial distributions. Divers. Distrib. 24, 1–12 (2018).
Google Scholar 
Carretta, J. V., Taylor, B. L. & Chivers, S. J. Abundance and depth distribution of harbor porpoise (Phocoena phocoena) in northern California determined from a 1995 ship survey. Fish. Bull. 99, 29–29 (2001).
Google Scholar 
Willis, P. M. & Baird, R. W. Status of the dwarf sperm whale, Kogia simus, with special reference to Canada. Can. Field Nat. 112, 114–125 (1998).
Google Scholar 
Kyhn, L. A. et al. Clicking in a killer whale habitat: Narrow-band, high-frequency biosonar cliks of harbour porpoise (Phocoena phocoena) and Dall’s porpoise (Phocoenoides dalli). PLoS ONE 8, e63763 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Madsen, P., Carder, D., Bedholm, K. & Ridgway, S. Porpoise clicks from a sperm whale nose—Convergent evolution of 130 kHz pulses in toothed whale sonars?. Bioacoustics 15, 195–206 (2005).
Google Scholar 
Merkens, K. et al. Clicks of dwarf sperm whales (Kogia sima). Mar. Mamm. Sci. 34, 963–978 (2018).
Google Scholar 
Griffiths, E. T. et al. Detection and classification of narrow-band high frequency echolocation clicks from drifting recorders. J. Acoust. Soc. Am. 147, 3511–3522 (2020).ADS 
PubMed 

Google Scholar 
Baird, R. W. & Stacey, P. J. Status of Risso’s Dolphin, Grampus griseus, in Canada. Naturalist 5, 233142 (1991).
Google Scholar 
Benoit-Bird, K. J. & Au, W. W. Prey dynamics affect foraging by a pelagic predator (Stenella longirostris) over a range of spatial and temporal scales. Behav. Ecol. Sociobiol. 53, 364–373 (2003).
Google Scholar 
Benoit-Bird, K. J., Würsig, B. & Mfadden, C. J. Dusky dolphin (Lagenorhynchus obscurus) foraging in two different habitats: active acoustic detection of dolphins and their prey. Mar. Mamm. Sci. 20, 215–231 (2004).
Google Scholar 
Soldevilla, M. S., Wiggins, S. M. & Hildebrand, J. A. Spatial and temporal patterns of Risso’s dolphin echolocation in the Southern California Bight. J. Acoust. Soc. Am. 127, 124–132 (2010).ADS 
PubMed 

Google Scholar 
Soldevilla, M. S., Wiggins, S. M. & Hildebrand, J. A. Spatio-temporal comparison of Pacific white-sided dolphin echolocation click types. Aquat. Biol. 9, 49–62 (2010).
Google Scholar 
Taylor, F. The relationship of midwater trawl catches to sound scattering layers off the coast of northern British Columbia. J. Fish. Board Can. 25, 457–472 (1968).
Google Scholar 
Curtis, K. R., Howe, B. M. & Mercer, J. A. Low-frequency ambient sound in the North Pacific: Long time series observations. J. Acoust. Soc. Am. 106, 3189–3200 (1999).ADS 

Google Scholar 
Aroyan, J. L. et al. Acoustic models of sound production and propagation in Hearing by whales and dolphins 409–469 (Springer, 2000).
Google Scholar 
Cummings, W. C. & Thompson, P. O. Underwater sounds from the blue whale, Balaenoptera musculus. J. Acoust. Soc. Am. 50, 1193–1198 (1971).ADS 

Google Scholar 
McDonald, M. A., Calambokidis, J., Teranishi, A. M. & Hildebrand, J. A. The acoustic calls of blue whales off California with gender data. J. Acoust. Soc. Am. 109, 1728–1735 (2001).ADS 
CAS 
PubMed 

Google Scholar 
Weirathmueller, M. J., Wilcock, W. S. D. & Soule, D. C. Source levels of fin whale 20 Hz pulses measured in the Northeast Pacific Ocean. J. Acoust. Soc. Am. 133, 741–749 (2013).ADS 
PubMed 

Google Scholar 
Vihtakari, M. ggOceanMaps: Plot Data on Oceanographic Maps using ‘ggplot2’. R package version 1.2.14. https://mikkovihtakari.github.io/ggOceanMaps/ (2022). 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

Alvares, F. et al. Old Wolrd Canis spp. with taxonomic ambiguity: Workshop conclusions and recommendations Vairao, Portugal, 28th–30th May 2019. Canid News (Online Edition) (2019).Jackson, S. M. et al. Taxonomy of the dingo: It’s an ancient dog. Aust. Zool. 41, 347–357 (2021).
Google Scholar 
Stephens, D., Wilton, A. N., Fleming, P. J. S. & Berry, O. Death by sex in an Australian icon: A continent-wide survey reveals extensive hybridization between dingoes and domestic dogs. Mol. Ecol. 24, 5643–5656 (2015).CAS 
PubMed 

Google Scholar 
Cairns, K. M., Shannon, L. M., Koler-Matznick, J., Ballard, J. W. O. & Boyko, A. R. Elucidating biogeographical patterns in Australian native canids using genome wide SNPs. PLoS ONE 13, e0198754 (2018).PubMed 
PubMed Central 

Google Scholar 
Fleming, P. J. S., Ballard, G. & Cutter, N. There is no Dingo dilemma: legislation facilitates culling, containment and conservation of Dingoes in New South Wales. Aust. Zool. 41, 408–416 (2021).
Google Scholar 
Corbett, L. K. The Dingo in Australia and Asia. Second edn, (JB Books Australia, 2001).Newsome, T. M. et al. Making a new dog?. Bioscience 67, 374–381 (2017).
Google Scholar 
Wang, G.-D. et al. Out of southern East Asia: the natural history of domestic dogs across the world. Cell Res. 26, 21–33 (2016).PubMed 

Google Scholar 
Smith, B. The Dingo Debate: Origins, Behaviour and Conservation. (CSIRO Publishing, 2015).Jackson, S. M. et al. The dogma of dingoes-taxonomic status of the dingo: A reply to Smith et al. Zootaxa 4564, 198–212 (2019).
Google Scholar 
Zhang, S. J. et al. Genomic regions under selection in the feralization of the dingoes. Nat. Commun. 11, 671 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
Balme, J. & O’Connor, S. Dingoes and Aboriginal social organization in Holocene Australia. J. Archaeol. Sci. Rep. 7, 775–781 (2016).
Google Scholar 
Cairns, K. M. What is a dingo – origins, hybridisation and identity. Aust. Zool. 41(3), 322–337 (2021).
Google Scholar 
Allen, B. L. & West, P. Influence of dingoes on sheep distribution in Australia. Aust. Vet. J. 91, 261–267 (2013).CAS 
PubMed 

Google Scholar 
Fleming, P. J. S. in Carnivores of Australia: Past, Present and Future (eds A.S. Glen & C.R. Dickman) Ch. 6, 105–149 (CSIRO Publishing, 2014).Stephens, D. The molecular ecology of Australian wild dogs: hybridisation, gene flow and genetic structure at multiple geographic scales, The University of Western Australia, (2011).Cairns, K. M., Nesbitt, B. J., Laffan, S. W., Letnic, M. & Crowther, M. S. Geographic hot spots of dingo genetic ancestry in southeastern Australia despite hybridisation with domestic dogs. Conserv. Genet. 21, 77–90 (2020).CAS 

Google Scholar 
Wilton, A. N., Steward, D. J. & Zafiris, K. Microsatellite variation in the Australian dingo. J. Hered. 90, 108–111 (1999).CAS 
PubMed 

Google Scholar 
Allendorf, F. W., Leary, R. F., Spruell, P. & Wenburg, J. K. The problems with hybrids: Setting conservation guidelines. Trends Ecol. Evol. 16, 613–622 (2001).
Google Scholar 
Atkinson, J. An account of the state of agriculture & grazing in New South Wales. (J. Cross, 1826).Massy, C. The Australian Merino: The Story of a Nation (Revised and updated). xxii,1262 (Random House Australia, 2007).Cairns, K. M., Brown, S. K., Sacks, B. N. & Ballard, J. W. O. Conservation implications for dingoes from the maternal and paternal genome: Multiple populations, dog introgression, and demography. Ecol. Evol. 7, 9787–9807 (2017).PubMed 
PubMed Central 

Google Scholar 
Kopelman, N. M., Mayzel, J., Jakobsson, M., Rosenberg, N. A. & Mayrose, I. Clumpak: A program for identifying clustering modes and packaging population structure inferences across K. Mol. Ecol. Resour. 15, 1179–1191 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Driscoll, C., Yamaguchi, N., O’Brien, S. J. & Macdonald, D. W. A suite of genetic markers useful in assessing wildcat (Felis silvestris ssp.)-domestic cat (Felis silvestris catus) admixture. J. Hered. 102(1), S87–S90 (2011).PubMed 
PubMed Central 

Google Scholar 
Bohling, J. H. & Waits, L. P. Factors influencing red wolf–coyote hybridization in eastern North Carolina USA. Biol. Conserv. 184, 108–116 (2015).
Google Scholar 
Fleming, P., Corbett, L., Harden, R. & Thomson, P. in Managing the Impacts of Dingoes and Other Wild Dogs. (Bureau of Rural Sciences, Canberra, 2001).Van Veldhuisen, R. Pipe dreams: A history of water supply in the Wimmera-Mallee (Wimmera Mallee Water, 2001).Newsome, A. The distribution of red kangaroos, Megaleia rufa (Desmarest), about sources of persistent food and water in central Australia. Aust. J. Zool. 13, 289–300 (1965).
Google Scholar 
James, C. D., Landsberg, J. & Morton, S. R. Provision of watering points in the Australian arid zone: A review of effects on biota. J. Arid Environ. 41, 87–121 (1999).ADS 

Google Scholar 
Robinson, J. A., Brown, C., Kim, B. Y., Lohmueller, K. E. & Wayne, R. K. Purging of strongly deleterious mutations explains long-term persistence and absence of inbreeding depression in island foxes. Curr. Biol. 28, 3487-3494.e3484 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Benazzo, A. et al. Survival and divergence in a small group: The extraordinary genomic history of the endangered Apennine brown bear stragglers. PNAS 114, E9589–E9597 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mattucci, F. et al. Genomic approaches to identify hybrids and estimate admixture times in European wildcat populations. Sci. Rep. 9, 11612 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Thomson, P. C., Rose, K. & Kok, N. E. The behavioural ecology of dingoes in north-western Australia. VI. Temporary extra-terrestrial movements and dispersal. Wildl. Res. 19, 585–595 (1992).
Google Scholar 
Newsome, T. M., Ballard, G.-A., Dickman, C. R., Fleming, P. J. S. & van de Ven, R. Home range, activity and sociality of a top predator, the dingo: A test of the Resource Dispersion Hypothesis. Ecography 36, 914–925 (2013).
Google Scholar 
Giglio, R. M., Rocke, T. E., Osorio, J. E. & Latch, E. K. Characterizing patterns of genomic variation in the threatened Utah prairie dog: Implications for conservation and management. Evol. Appl. 14, 1036–1051 (2021).PubMed 

Google Scholar 
Conroy, G. C. et al. Conservation concerns associated with low genetic diversity for K’gari–Fraser Island dingoes. Sci. Rep. 11, 9503 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Frankham, R. Do island populations have less genetic variation than mainland populations?. Heredity 78, 311–327 (1997).PubMed 

Google Scholar 
Funk, W. C. et al. Adaptive divergence despite strong genetic drift: genomic analysis of the evolutionary mechanisms causing genetic differentiation in the island fox (Urocyon littoralis). Mol. Ecol. 25, 2176–2194 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Behrendorff, L. Best-practice dingo management: six lessons from K’gari (Fraser Island). Aust. Zool. 41, 521–533 (2021).
Google Scholar 
van Eeden, L. M., Smith, B. P., Crowther, M. S., Dickman, C. R. & Newsome, T. M. ‘The dingo menace’: An historic survey on graziers’ management of an Australian carnivore. Pac. Conserv. Biol. 25, 245–256 (2019).
Google Scholar 
Whiting, S. D., Long, J. L., Hadden, K. M., Lauder, A. D. K. & Koch, A. U. Insights into size, seasonality and biology of a nesting population of the Olive Ridley turtle in northern Australia. Wildl. Res. 34, 200–210 (2007).
Google Scholar 
Banks, S. C., Hoyle, S. D., Horsup, A., Sunnucks, P. & Taylor, A. C. Demographic monitoring of an entire species (the northern hairy-nosed wombat, Lasiorhinus krefftii) by genetic analysis of non-invasively collected material. Anim. Conserv. 6, 101–107 (2003).
Google Scholar 
Parker, H. G. et al. Genomic analyses reveal the influence of geographic origin, migration, and hybridization on modern dog breed development. Cell Rep. 19, 697–708 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Thackway, R. & Cresswell, I. An Interim Biogeographic Regionalisation for Australia: A Framework for Setting Priorities in the National Reserves System Cooperative Program. Version 4, (Australian Nature Conservation Agency, Reserve Systems Unit, 1995).Bureau of Meteorology & CSIRO. (Bureau of Meteorology, CSIRO and Farmlink, http://www.bom.gov.au/climate/climate-guides/guides/01-Mallee-VIC-Climate-Guide.pdf, 2019).Rowan, J. N. & Downes, R. G. in Soil Conservation Authority of Victoria (ed Brookes, A.C.) 1–55 (Govt. Printer, Melbourne, 1963).Longmire, J. L., Maltbie, M. & Baker, R. J. Use of “lysis buffer” in DNA isolation and its implications for museum collections. Occas. Pap. Mus. Tex. Tech. Univ. 163, 1–3 (1997).
Google Scholar 
Tatler, J., Prowse, T. A. A., Roshier, D. A., Cairns, K. M. & Cassey, P. Phenotypic variation and promiscuity in a wild population of pure dingoes (Canis dingo). J. Zool. Syst. Evol. Res. 59, 311–322 (2020).
Google Scholar 
Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
Francis, R. M. pophelper: An R package and web app to analyse and visualize population structure. Mol. Ecol. Resour. 17, 27–32 (2017).CAS 
PubMed 

Google Scholar 
Wang, J. The computer program structure for assigning individuals to populations: Easy to use but easier to misuse. Mol. Ecol. Resour. 17, 981–990 (2017).CAS 
PubMed 

Google Scholar 
Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software structure: A simulation study. Mol. Ecol. 14, 2611–2620 (2005).CAS 
PubMed 

Google Scholar 
Earl, D. A. & von Holdt, B. M. Structure harvester: A website and program for visualizing structure output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361 (2012).
Google Scholar 
Verity, R. & Nichols, R. A. Estimating the number of subpopulations (K) in structured populations. Genetics 203, 1827–1839 (2016).PubMed 
PubMed Central 

Google Scholar 
Jombart, T. adegenet: A R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).CAS 
PubMed 

Google Scholar 
Jombart, T., Devillard, S. & Balloux, F. Discriminant analysis of principal components: A new method for the analysis of genetically structured populations. BMC Genet. 11, 94 (2010).PubMed 
PubMed Central 

Google Scholar 
Keenan, K. et al. diveRsity: An R package for the estimation and exploration of population genetics parameters and their associated errors. Methods Ecol. Evol. 4, 782–788 (2013).
Google Scholar 
Peakall, R. & Smouse, P. E. genalex 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes 6, 288–295 (2006).
Google Scholar 
Peakall, R. & Smouse, P. E. GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research—an update. Bioinformatics 28, 2537–2539 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lynch, M. & Ritland, K. Estimation of pairwise relatedness with molecular markers. Genetics 152, 1753–1766 (1999).CAS 
PubMed 
PubMed Central 

Google Scholar 
Weir, B. S. & Cockerham, C. C. Estimating F-statistics for the analysis of population structure. Evolution 38, 1358–1370 (1984).CAS 
PubMed 

Google Scholar 
Jost, L. GST and its relatives do not measure differentiation. Mol. Ecol. 17, 4015–4026 (2008).PubMed 

Google Scholar 
Do, C. et al. NeEstimator v2: Re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol. Ecol. Resour. 14, 209–214 (2014).CAS 
PubMed 

Google Scholar 
Shirk, A. J. & Cushman, S. A. sGD: Software for estimating spatially explicit indices of genetic diversity. Mol. Ecol. Resour. 11, 922–934 (2011).CAS 
PubMed 

Google Scholar 
Schnute, J., Boers, N., Haigh, R. & Couture-Beil, A. Introduction to PBSmapping. (2016). More

To build Sentinel2GlobalLULC, we followed two main steps. First, we established a spatio-temporal consensus between 15 global LULC products for 29 LULC classes. Then, we extracted the maximum number of Sentinel-2 RGB images representing each class. Each image is a tile that has 224 × 224 pixels at 10 × 10 m spatial resolution and was built as a cloud-free composite from all the Sentinel-2 images acquired between June 2015 and October 2020. Both tasks were implemented using GEE, an efficient programming, processing and visualisation platform that allowed us to have free manipulation and access to all used LULC products and Sentinel-2 imagery, simultaneously.Finding spatio-temporal agreement across 15 global LULC productsTo establish the spatio-temporal consensus between different LULC products for each one of the 29 LULC classes, we followed four steps: (1)Identification of the LULC products to be used in the consensus, (2)Standardization and harmonization of the LULC legend that was subsequently used to annotate the image tiles, (3)Spatio-temporal aggregation across LULC products, and (4)Spatial reprojection and tile selection based on optimized spatial purity thresholds.Global LULC products selectionThe adopted purity measure for spatio-temporal agreement across the 15 global LULC products we selected from GEE (Table 2) aims to find areas of high consensus to maximize the annotation quality. Spatial and temporal consensus across such rich diversity of LULC products, in terms of spatial resolution, time coverage, satellite source, LULC classes and accuracy, was used as a source of robustness for our subsequent LULC annotation. Products outside GEE were not used due to computing limitations.Table 2 Main characteristics of the 15 global Land-Use and Land-Cover (LULC) products available in Google Earth Engine (GEE) that were combined to find consensus in the global distribution of 29 main LULC classes.Full size tableStandardization and Harmonization of LULC legendsLand cover (LC) data describes the main type of natural ecosystem that occupies an area; either by vegetation types such as shrublands, grasslands and forests, or by other biophysical classes such as permanent snow, bare land and water bodies. Land use (LU) includes the way in which humans modify or exploit an area, such as urban areas or agricultural fields.To build our 29 LULC classes nomenclature, we established a standardization and harmonization approach based on expert knowledge. During this process, we took into account both the needs of different practitioners in the global and regional LULC mapping field and the thematic resolution of the global LULC legends available in GEE. Our nomenclature consists of 23 LC and 6 LU distinct classes identified through specific consensus rules across 15 LULC products (see Table 4). A six-level (L0 to L5) hierarchical structure was adopted in the creation of these 29 LULC classes (Fig. 2). To facilitate the inter-operability of our 29 legends at the finest level L5 across all LULC products and with the widely used FAO’s hierarchical Land Cover Classification System (LCCS)1, we have established an LULC classification system where the 29 classes can be mapped directly to FAO’s LCCS as explained in the table of Supplementary File 1. The LC part in our dataset contains 20 terrestrial ecosystems and 3 aquatic ecosystems. The terrestrial systems are: Barren lands, Grasslands, Permanent snow, Moss and Lichen lands, Close shrublands, Open shrublands, in addition to 12 Forests classes that differed in their tree cover, phenology, and leaf type. The aquatic classes are: Marine water bodies, Continental water bodies, and Wetlands; furthermore, wetlands were divided into 3 classes: Marshlands, Mangroves and Swamps. The LU part is composed of urban areas and 5 coarse cropland types that differed in their irrigation regime and leaf type. In Table 3, you can find the semantic definition of each one of the 29 classes in Sentinel2GlobalLULC. We provided a table in Supplementary File 2, for a more detailed definition of each LULC class.Fig. 2Tree representation of the six-level (L0 to L5) hierarchical structure of the Land-Use and Land-Cover (LULC) classes contained in the Sentinel2GlobalLULC dataset. Outter circular leafs represent the final or most detailed 29 LULC classes (C1 to C29) of level L5. The followed path to define each class is represented through inner ellipses that contain the names of intermediate classes at different levels between the division of the Earth’s surface (square) into LU and LC (level L0) and the final class circle (level L5). All LULC classes belong to three levels at least, except the 12 forest classes that belong to L5 only.Full size imageTable 3 Semantic signification of each one of the 29 Land Use and Land Cover (LULC) classes contained in the Sentinel2GlobalLULC dataset according to the six-level (L0 to L5) hierarchical structure.Full size tableCombining products across time and spaceFor each one of the 29 LULC classes, we combined in space and time the global LULC information among the 15 GEE LULC products. This way, each image was annotated with a LULC class only if all combined products agreed in its corresponding tile (i.e., 100% of agreement in space and time). For each product and LULC type, we first set one or more criteria to create a global mask at the native resolution of the product in which each pixel was classified as 1 or 0 depending on whether it met the criteria for belonging to that LULC type or not, respectively (see first stage in Table 4). For certain LULC classes, some products did not provide any relevant information, so they were not used. For example (Table 4), in Grasslands (C3), Open Shrublands (C4) and Close Shrublands (C5), we combined 14 products, while in UrbanBlUpArea (C29) and Permanent Snow (C23) we only combined 10 and 7 products, respectively.Table 4 First stage of the rule set criteria used to find consensus across the 15 Land-Use and Land-Cover (LULC) products available in Google Earth Engine (GEE) for each of the 29 LULC classes contained in the Sentinel2GlobalLULC dataset.Full size tableThen (see second stage in Table 5), for each LULC type, we calculated the average of all the masks obtained from each product to create a final global probability map from all products with values ranging between 0 and 1. Value 1 meant that all products agreed to assign that pixel to a particular LULC class, while 0 meant that none of the products assigned it to that particular class (Fig. 3). These 0-to-1 values are interpreted as the spatio-temporal purity level of each pixel to belong to a particular LULC class and are provided as metadata with each image.Table 5 Second stage of the rule set criteria used to find consensus across the 15 Land-Use and Land-Cover (LULC) products available in Google Earth Engine (GEE) for each of the 29 LULC classes contained in the Sentinel2GlobalLULC dataset.Full size tableFig. 3Example of the process of building the final global probability map for one of the 29 Land-Use and Land-Cover (LULC) classes (e.g. C1: “Barren”) by means of spatio-temporal agreement of the 15 LULC products available in Google Earth Engine (GEE). The final map is normalized to values between 0 (white, i.e., areas with no presence of C1 in any product) and 1 (black spots, i.e., areas containing or compatible with the presence of C1 in all 15 products), whereas the shades of grey corresponds to the values in between (i.e., areas that did not contain or were not compatible with the presence of C1 in some of the products). This process is divided into two stages: the first stage (the blue part, see details in Table 4) and the second stage (the yellow part, see details in Table 5). LULC products available for several years are represented with superposed rectangles, while single year products are represented with single rectangles. GMP: global probability map, NA: Not Available.Full size imageAs an example of the first stage (see details in Table 4), to specify if a given pixel belongs to Dense Evergreen Needleleaf Forest, we evaluated its tree cover level using “ ≤ “ and “ ≥ “, while for bands containing the leaf type information, we used the equal operator “ = “. For the spatio-temporal combination of multiple criteria we have used the following operators: “AND”,“OR” and “ADD”. For example, we combined the tree cover percentage criteria with the leaf type criteria using “AND” to select forest pixels that met both conditions. To combine many years instances of the same product, we used “ADD”, except for product P13, where we used “AND” to identify permanent water areas only. Whenever we used the “ADD” operator, we normalized pixel values afterwards to bring it back to a probability interval between 0 and 1 using the division by the total number of combined years or criteria.In the second stage (see details in Table 5), we combined for each LULC class the 15 global probability maps previously derived from each product to create a final global probability map (Fig. 3). This combination was carried out using various operators such as “ADD”, “MULTIPLY” and “OR”, depending on the LULC type. When “ADD” was used, the final pixel values were normalized by dividing the final addition value of each pixel by the total number of added products. The “MULTIPLY” operator was mostly used at the end, to remove urban areas from non-urban LULC classes, or to remove water from non-water LULCs. The multiplication operator was also adopted to make sure that a certain criteria was respected in the final probability map. For instance, for the swamp class, we multiplied all pixels in the final stage by a water mask where saline water areas have a value of 0 in order to eliminate mangrove from swamp pixels and vice versa. Finally, we used “OR” operator between different water related products to take advantage of the fact that they complement each other in terms of spatial-temporal coverage and accuracy.In GEE, when two products are aggregated using “ADD”, “MULTIPLY” or any other operator, the output is aggregated at the spatial resolution of the product at the left of the operator. Hence, to maintain the finest spatial resolution in the final probability map, we multiplied everything by product P15 and placed it at the left of the final “MULTIPLY” operation (See Table 5). Hence, all the 29 final probability maps were generated at the P15 spatial resolution of 30 m/pixel (except the urban class C29 which maintained the 30 m/pixel resolution of product P14).Re-projection and Selection of purity thresholdSince our objective was finding pure Sentinel-2 image tiles of 224 × 224 10-m pixels representing each LULC class, we reprojected the 30 m/pixel probability maps to 2240 m/pixel using the spatial mean reducer in GEE. That is, each pixel value at 2240 m resolution was computed using the mean over all the 30m-pixel values contained within it. Hence, the resulting pixel values at 2240 m resolution represent the purity level that each Sentinel-2 image tile of 224 × 224 10-m pixels has. We illustrated the reprojection and selection processes in Fig. 4.Fig. 4Example of the workflow to obtain a Sentinel-2 image tile of 2240 × 2240 m for one of the 29 Land-Use and Land-Cover (LULC) classes (e.g. C1: “Barren”). The process starts with the reprojected final global probability map obtained from stage two (Table 5) and ends with its exportation to the repository of a Sentinel-2 image tile of 224 × 224 pixels. The white rectangle is the only one having a probability value of 1 (Recall that the purity threshold used for Barren was 1, i.e., 100%). The black pixels has a null probability value, while the probability values between 0 and 1 are represented in gray scale levels.Full size imageFor each one of the reprojected maps, we defined a pixel value threshold to decide whether a given 2240 × 2240 m tile was representative of each LULC class or not. Since training DL image classification models needs a large number of high quality (both in terms of image quality and annotation quality) image tiles to reach a good accuracy, when the spatial purity of 100% (full agreement across products in all the pixels of the 224 × 224 tile) resulted in a small number of agreement tiles for a particular class, the purity threshold was decreased for that class until the number of tiles was larger than 1000 or further decreased in less abundant classes to a minimum of 75% of purity. The found purity value is always provided as metadata for each image in the dataset, so the user can always restrict its analysis to those image tiles and classes at any desired purity level. Decreasing the purity threshold down to 75% for the less abundant classes (e.g swamp, mangrove, etc.) was a trade-off between maintaining a good data annotation quality and providing a sufficient number of tiles in each class. In Table 6, we present the number of agreement tiles found at different purity thresholds ranging from 75% to 100% for each LULC class. This spatial purity was not further decreased since machine learning image classification models are known to be robust when the target class is spatially dominant in each training image (it occupies more than 60% of the pixels in the scene)42. On the other hand, when the number of pure tiles for a LULC class was too large to be downloaded (i.e., greater than 14000), we applied a selection algorithm as described in the Supplementary File 3, to download a maximum of 14000 spatially representative images. For this, the world was divided into a one-degree squared cell grid. If a cell contained less than 50 image tiles, we selected them all. If it contained more than 50, we applied that automatic maximum geographic distance algorithm that selected images as far from each other as possible in a number proportional to the number of existing images in that cell. The map in Fig. 6 shows the global distribution of the selected 194877 image tiles contained in Sentinel2GlobalLULC and distributed in 29 LULC classes.Table 6 Summary of the varying number of found and eventually selected Sentinel-2 image tiles of 224 × 224 pixels depending on the different consensus level reached across the 15 Land-Use and Land-Cover (LULC) products available in Google Earth Engine (GEE) for each of the 29 LULC classes contained in the Sentinel2GlobalLULC dataset.Full size tableData extractionSentinel2GlobalLULC provides the user with two types of data: Sentinel-2 RGB images (jpeg and geotif versions) and CSV files with associated metadata. In the following subsections, we describe the process for associating metadata, including the Global Human Modification (GHM) index.Global human modification index extractionAs an additional metadata related to the level of human influence in each image, we calculated for each tile in GEE, the spatial mean of the global human modification index for terrestrial lands43, where 0 means no human modification and 1 means complete transformation. Since the original GHM product was mapped at 1 × 1 km resolution, we reprojected it to 2240 × 2240 m using the same reprojection procedure explained in (Re-projection and Selection of purity threshold).CSV files generationOnce the tiles were selected, for each LULC class we listed the image tiles in descendent order of purity. Metadata included: geographical coordinates of each tile centroid, tile purity value, name and ID of the LULC class, and average GHM index for that tile. Then, we used the geographical coordinates of each tile to identify its exact administrative address geolocation. To implement this reverse geo-referencing operation, we used a free request-unlimited python module called reverse_geocoder. This way, we assigned a country code, two levels of administrative departments, and the locality to each tile.For LULC classes that had more than 14000 pure tiles, we have released the coordinates before and after the distance-based selection in case the user wants to download more tiles or use our consensus coordinates for other purposes.Sentinel-2 RGB images exportationAfter extracting all these pieces of information and grouping them into CSV files, we went back to the geographic center coordinates of each tile and used them to extract the corresponding 224 × 224 Sentinel-2 RGB tiles using GEE. Each exported image was identical to the 2240 × 2240 m area covered by its Sentinel-2 tile.We chose “Sentinel-2 MSI (Multi-Spectral Instrument) product” since it is free and publicly available in GEE at the fine resolution of 10 × 10 m. We chose “Level-1C” (i.e., top-of-atmosphere reflectance) since it provides the longest data availability of Sentinel-2 images without any modification of the data. To build RGB images, we extracted the three bands B4, B3 and B2 that correspond to Red, Green and Blue channels, respectively. More bands available in Sentinel-2 or even in Sentinel-1 images can be incorporated in the future to our dataset. However, computational limitations (i.e., the size of the dataset would be impractical) did not allowed us to handle it as a first goal. In addition, the spatial resolution of the images would be heterogeneous across bands.To minimize the inherent noise due to atmospheric conditions (e.g. clouds, aerosols, smoke, etc.) that could affect the satellite RGB images, every image was built as a temporal aggregation of all images gathered by Sentinel-2 satellites between June 2015 and October 2020. During this aggregation, only the highest quality images in the corresponding image collection were considered, as we firstly discarded all image instances where the cloud probability exceeded 20% according to the metadata provided in their corresponding Sentinel-2 collection. Then, we calculated the 25th-percentile value between all remaining images for each reflectance band (R, G, and B), and built the final image with the obtained 25-percentile values in each pixel for its RGB bands. The 25th-percentile choice was adopted giving its suitability in atmospheric noise reduction44,45,46,47,48.Usually, Sentinel-2 MSI product includes true colour images in JPEG2000 format, except for the “Level-1C” collection used here. The three original bands (B4, B3, and B2) required a saturation mapping of their reflectance values into 0–255 RGB digital values. Thus, we mapped the saturation reflectance of 3558 into 255 to obtain true RGB channels with digital values between 0 and 255. The choice of these mapping numbers was taken from the Sentinel-2 true colour image recommendations section of Sentinel user guidelines. Finally, after exporting the selected tiles for each LULC class as “.tif” images, we converted them into “.jpeg” format using a lossless conversion algorithm.Technical implementationTo implement all our methodology steps, we first created a javascript in GEE for each LULC class. Each script is a multi-task javascript where we implemented a switch command to control which task we want to execute (between the spatio-temporal aggregation task, the spatial reprojection and tiles selection task, or the data exportation task). In each one of these scripts, we selected from GEE LULC datasets repository the 15 LULC products used to build the consensus of that LULC class. Each script was responsible of elaborating the spatio-temporal combination of the selected products and generating the final consensus map for that LULC class as described in the subsection “Combining products across time and space”. Then, it exports the final global probability map as an asset into GEE server storage to make its reprojection faster. In the same script, once the consensus map exportation was done, we imported it from the GEE assets storage and reprojected it to 2240 × 2240 m resolution; then, we exported the new reprojected map into GEE assets storage again to make its analysis and processing faster. Afterwards, we imported the reprojected map into the same script and applied different processing tasks. During this processing phase, many purity threshold values were evaluated. Then, we elaborated in this same script the pure tiles identification and their center coordinates exportation into a CSV file. A distinct GEE script was developed to import, reproject and export the global GHM map. The resulted GHM map was saved as an asset too, then imported and used in each one of the 29 LULC multi-task scripts.A python script was developed separately to read the exported CSV files for each LULC class and apply the reverse geo-referencing on their pure tiles coordinates then add the found geolocalization data (country code, locality…etc) to the original CSV files as new columns. Then, another python script was implemented to read the new resulted CSV files with all their added columns (reverse geo-referencing data, GHM data) and use the center coordinates of each pure tile in that class to export first its corresponding Sentinel-2 satellite geotiff image within GEE through the python API. Finally, after downloading all the selected geotiff images from our Google drive, we created another python script to convert these geotiff images into JPEG format. More