Philippa Kaur

Gilbert, S. F., Sapp, J. & Tauber, A. I. A symbiotic view of life: we have never been individuals. Q. Rev. Biol. 87, 325–341 (2012).PubMed 
Article 

Google Scholar 
Bass, D., Stentiford, G. D., Wang, H.-C., Koskella, B. & Tyler, C. R. The pathobiome in animal and plant diseases. Trends Ecol. Evol. 34, 996–1008 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Husnik, F. & Keeling, P. J. The fate of obligate endosymbionts: reduction, integration, or extinction. Curr. Opin. Genet. Dev. 58-59, 1–8 (2019).CAS 
PubMed 
Article 

Google Scholar 
Berg, G. et al. Microbiome definition re-visited: old concepts and new challenges. Microbiome 8, 103 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Kwong, W. K. & Moran, N. A. Gut microbial communities of social bees. Nat. Rev. Microbiol. 14, 374–384 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hammer, T. J., Janzen, D. H., Hallwachs, W., Jaffe, S. P. & Fierer, N. Caterpillars lack a resident gut microbiome. Proc. Nat Acad. Sci. USA 114, 9641–9646 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Holt, C. C., van der Giezen, M., Daniels, C. L., Stentiford, G. D. & Bass, D. Spatial and temporal axes impact ecology of the gut microbiome in juvenile European lobster (Homarus gammarus). ISME J. 14, 531–543 (2020).PubMed 
Article 

Google Scholar 
Pollock, F. J. et al. Coral-associated bacteria demonstrate phylosymbiosis and cophylogeny. Nat. Commun. 9, 4921 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Thomas, T. et al. Diversity, structure and convergent evolution of the global sponge microbiome. Nat. Commun. 7, 11870 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Engelberts, J. P. et al. Characterization of a sponge microbiome using an integrative genome-centric approach. ISME J. 14, 1100–1110 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ley, R. E. et al. Evolution of mammals and their gut microbes. Science 320, 1647–1651 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mallot, E. K. & Amato, K. R. Host specificity of the gut microbiome. Nat. Rev. Microbiol. 19, 639–653 (2021).Article 
CAS 

Google Scholar 
Colston, T. J. & Jackson, C. R. Microbiome evolution along divergent branches of the vertebrate tree of life: what is known and unknown. Mol. Ecol. 25, 3776–3800 (2016).PubMed 
Article 

Google Scholar 
Levin, D. et al. Diversity and functional landscapes in the microbiota of animals in the wild. Science 372, eabb5352 (2021).CAS 
PubMed 
Article 

Google Scholar 
Nishida, A. H. & Ochman, H. Rates of gut microbiome divergence in mammals. Mol. Ecol. 27, 1884–1897 (2013).Article 

Google Scholar 
Brooks, A. W., Kohl, K. D., Brucker, R. M., van Opstal, E. J. & Bordenstein, S. R. Phylosymbiosis: relationships and functional effects of microbial communities across host evolutionary history. PLoS Biol. 14, e2000225 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Mazel, F. et al. Is host filtering the main driver of phylosymbiosis across the tree of life? mSystems 3, https://doi.org/10.1128/mSystems.00097-18 (2018).Lutz, H. L. et al. Ecology and host identity outweigh evolutionary history in shaping the bat microbiome. mBio 4, 6 (2019).
Google Scholar 
Grond, K. et al. No evidence for phylosymbiosis in Western chipmunk species. FEMS Microbiol. Ecol. 96, fiz182 (2020).CAS 
PubMed 
Article 

Google Scholar 
Song, S. J. et al. Comparative analyses of vertebrate gut microbiomes reveal convergence between birds and bats. mBio 11, 1 (2020).Article 

Google Scholar 
Trevelline, B. K., Sosa, J., Hartup, B. K. & Kohl, K. D. A bird’s-eye view of phylosymbiosis: weak signatures of phylosymbiosis among all 15 species of cranes. Proc. R. Soc. B 287, 20192988 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Muegge, B. D. et al. Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Science 332, 970–974 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Youngblut, N. D. et al. Host diet and evolutionary history explain different aspects of gut microbiome diversity among vertebrate clades. Nat. Commun. 10, 2200 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Amato, K. R. et al. Evolutionary trends in host physiology outweigh dietary niche in structuring primate gut microbiomes. ISME J. 13, 576–587 (2019).CAS 
PubMed 
Article 

Google Scholar 
Moeller, A. H. et al. Social behavior shapes the chimpanzee pan-microbiome. Sci. Adv. 2, e1500997 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Eckert, E. M., Anicic, N. & Fontaneto, D. Freshwater zooplankton microbiome composition is highly flexible and strongly influenced by the environment. Mol. Ecol. 30, 1545–1558 (2021).PubMed 
Article 

Google Scholar 
Yatsunenko, T. et al. Human gut microbiome viewed across age and geography. Nature 486, 222–228 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bik, H. M. Microbial metazoa are microbes too. mSystems 4, e00109–e00119 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Schuelke, T., Pereira, T. J., Hardy, S. M. & Bik, H. M. Nematode-associated microbial taxa do not correlate with host phylogeny, geographic region or feeding morphology in marine sediment habitats. Mol. Ecol. 27, 1930–1951 (2018).PubMed 
Article 

Google Scholar 
Guidetti, R. et al. Further insights in the Tardigrada microbiome: phylogenetic position and prevalence of infection of four new Alphaproteobacteria putative endosymbionts. Zool. J. Linn. Soc. 188, 925–937 (2020).Article 

Google Scholar 
Giere, O. Meiobenthology (Springer-Verlag, 2009).Laumer, C. E. et al. Revisiting metazoan phylogeny with genomic sampling of all phyla. Proc. R. Soc. B 286, 20190831 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hammer, T. J., Sanders, J. G. & Fierer, N. Not all animals need a microbiome. FEMS Microbiol. Lett. 366, fnz117 (2019).CAS 
PubMed 
Article 

Google Scholar 
Alejandre-Colomo, C. et al. Cultivable Winogradskyella species are genomically distinct from the sympatric abundant candidate species. ISME Commun. 1, 51 (2021).Article 

Google Scholar 
Husnik, F. et al. Bacterial and archaeal symbioses with protists. Curr. Biol. 31, R862–R877 (2021).CAS 
PubMed 
Article 

Google Scholar 
Salje, J. Cells within cells: Rickettsiales and the obligate intracellular bacterial lifestyle. Nat. Rev. Microbiol. 19, 375–390 (2021).CAS 
PubMed 
Article 

Google Scholar 
Neave, M. J., Apprill, A., Ferrier-Pagès, C. & Voolstra, C. R. Diversity and function of prevalent symbiotic marine bacteria in the genus Endozoicomonas. Appl. Microbiol. Biotechnol. 100, 8315–8324 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Weiland-Bräuer, N. et al. Composition of bacterial communities associated with Aurelia aurita changes with compartment, life stage, and population. Appl. Environ. Microbiol. 81, 6038–6052 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bik, E. M. et al. Marine mammals harbor unique microbiotas shaped by and yet distinct from the sea. Nat. Commun. 7, 10516 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Burns, A. R. et al. Contribution of neutral processes to the assembly of gut microbial communities in the zebrafish over host development. ISME J. 10, 655–664 (2016).CAS 
PubMed 
Article 

Google Scholar 
McFall-Ngai, M. Adaptive immunity: care for the community. Nature 445, 153 (2007).CAS 
PubMed 
Article 

Google Scholar 
Ruehland, C. & Dubilier, N. Gamma- and epsilonproteobacterial ectosymbionts of a shallow-water marine worm are related to deep-sea hydrothermal vent ectosymbionts. Environ. Microbiol. 12, 2312–2326 (2010).CAS 
PubMed 

Google Scholar 
Gruber-Vodicka, H. R. et al. Two intracellular and cell-type specific bacterial symbionts in the placozoan Trichoplax H2. Nat. Microbiol. 4, 1465–1474 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Schockaert, E. R. in Methods for the Examination of Organismal Diversity in Soils and Sediments (ed. Hall, G. S.) 211–225 (CABI, 1996).Higgins, R. P. in Introduction to the Study of Meiofauna (eds. Higgins, R. P. and Thiel, H.) 328–331 (SIP, 1988).Schram, M. D. & Davison, P. G. Irwin Loops—a history and method of constructing homemade loops. Trans. Kans. Acad. Sci. 115, 35–40 (1903).Article 

Google Scholar 
Medlin, L., Elwood, H. J., Stickel, S. & Sogin, M. L. The characterization of enzymatically amplified eukaryotic 16S-like rRNA-coding regions. Gene 71, 491–499 (1988).CAS 
PubMed 
Article 

Google Scholar 
Bower, S. M. et al. Preferential PCR amplification of parasitic protistan small subunit rDNA from metazoan tissues. J. Eukaryot. Microbiol. 51, 325–332 (2004).CAS 
PubMed 
Article 

Google Scholar 
Comeau, A. M., Li, W. K. W., Tremblay, J.-E., Carmack, E. C. & Lovejoy, C. Arctic ocean microbial community structure before and after the 2007 record sea ice minimum. PLoS ONE 6, e27492 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhang, R.-Y. et al. Design of targeted primers based on 16S rRNA sequences in meta-transcriptomic datasets and identification of a novel taxonomic group in the Asgard archaea. BMC Microbiol. 20, 25 (2020).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Lane, D. J. in Nucleic Acid Techniques in Bacterial Systematics (eds Stackebrandt, E. & Goodfellow, M) 115–175 (Wiley, 1991).Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414 (2016).CAS 
PubMed 
Article 

Google Scholar 
Marcel, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10 (2011).
Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).Callahan, B. J. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 
Article 

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

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).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Davis, N. M., Proctor, D. M., Holmes, S. P., Relman, D. A. & Callahan, B. J. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6, 226 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).Love, M. I., Huber, W. & Anders, S. Moderate estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).Kurtz, Z. D. Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput. Biol. 11, e1004226 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Csardi, G. & Nepusz, T. The igraph Software Package for Complex Network Research (InterJournal, 2006).Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).Kolde, R. pheatmap: pretty heatmaps. R package version 1.0.12 https://CRAN.R-project.org/package=pheatmap (2015).Lin, H. & Das Peddada, S. Analysis of composition of microbiomes with bias correction. Nat. Commun. 11, 3514 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Oksanen, J. vegan: Community Ecology Package. R package version 2.5.7 https://CRAN.R-project.org/package=vegan (2020).Rouse, G., Pleijel, F. & Tilic, E. Annelida (Oxford Univ. Press, 2022).Ahmed, M. & Holovachov, O. Twenty years after De Ley and Blaxter—How far did we progress in understanding the phylogeny of the phylum Nematoda? Animals 11, 3479 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Van Steenkiste, N. W. L., Herbert, E. R. & Leander, B. S. Species diversity in the marine microturbellarian Astrotorhynchus bifidus sensu lato (Platyhelminthes: Rhabdocoela) from the Northeast Pacific Ocean. Mol. Phylogenet. Evol. 120, 259–273 (2018). More

Crawford, J. E. et al. Efficient production of male Wolbachia-infected Aedes aegypti mosquitoes enables large-scale suppression of wild populations. Nat. Biotechnol. 38, 482–492 (2020).CAS 
Article 

Google Scholar 
Xi, Z., Khoo, C. C. H. & Dobson, S. L. Wolbachia establishment and invasion in an Aedes aegypti laboratory population. Science 310, 326–328 (2005).CAS 
Article 

Google Scholar 
Phuc, H. K. et al. Late-acting dominant lethal genetic systems and mosquito control. BMC Biol. 5, 11 (2007).Article 

Google Scholar 
Kandul, N. P. et al. Transforming insect population control with precision guided sterile males with demonstration in flies. Nat. Commun. 10, 84 (2019).CAS 
Article 

Google Scholar 
Kyrou, K. et al. A CRISPR–Cas9 gene drive targeting doublesex causes complete population suppression in caged Anopheles gambiae mosquitoes. Nat. Biotechnol. 36, 1062–1066 (2018).CAS 
Article 

Google Scholar 
Kittayapong, P. et al. Combined sterile insect technique and incompatible insect technique: the first proof-of-concept to suppress Aedes aegypti vector populations in semi-rural settings in Thailand. PLoS Negl. Trop. Dis. 13, e0007771 (2019).Article 

Google Scholar 
Zheng, X. et al. Incompatible and sterile insect techniques combined eliminate mosquitoes. Nature 572, 56–61 (2019).CAS 
Article 

Google Scholar 
Ryan, P. A. et al. Establishment of wMel Wolbachia in Aedes aegypti mosquitoes and reduction of local dengue transmission in Cairns and surrounding locations in northern Queensland, Australia. Gates Open Res. 3, 1547 (2019).Article 

Google Scholar 
Indriani, C. et al. Reduced dengue incidence following deployments of Wolbachia-infected Aedes aegypti in Yogyakarta, Indonesia: a quasi-experimental trial using controlled interrupted time series analysis. Gates Open Res. 4, 50 (2020).Velez, I. D. et al. The impact of city-wide deployment of Wolbachia-carrying mosquitoes on arboviral disease incidence in Medellín and Bello, Colombia: study protocol for an interrupted time-series analysis and a test-negative design study. F1000Res. 8, 1327 (2020).Article 

Google Scholar 
Durovni, B. et al. The impact of large-scale deployment of Wolbachia mosquitoes on dengue and other Aedes-borne diseases in Rio de Janeiro and Niterói, Brazil: study protocol for a controlled interrupted time series analysis using routine disease surveillance data. F1000Res. 8, 1328 (2020).Article 

Google Scholar 
O’Connor, L. et al. Open release of male mosquitoes infected with a Wolbachia biopesticide: field performance and infection containment. PLoS Negl. Trop. Dis. 6, e1797 (2012).Article 

Google Scholar 
Nazni, W. A. et al. Establishment of Wolbachia strain wAlbB in Malaysian populations of Aedes aegypti for dengue control. Curr. Biol. 29, 4241–4248 (2019).CAS 
Article 

Google Scholar 
Klassen, W. & Curtis, C. F. In: Sterile Insect Technique (eds Dyck, V. A., Hendrichs, J. & Robinson, A. S.) 3–36 (Springer-Verlag, 2005).Fried, M. Determination of sterile-insect competitiveness. J. Econ. Entomol. 64, 869–872 (1971).Article 

Google Scholar 
Bouyer, J. et al. Field performance of sterile male mosquitoes released from an uncrewed aerial vehicle. Sci. Robot. 5, eaba6251 (2020).Article 

Google Scholar 
Krafsur, E. S., Whitten, C. J. & Novy, J. E. Screwworm eradication in North and Central America. Parasitol. Today 3, 131–137 (1987).CAS 
Article 

Google Scholar 
Hendrichs, J., Ortiz, G., Liedo, P. & Schwarz, A. Six years of successful medfly program in Mexico and Guatemala. In: Fruit Flies of Economic Importance (ed Cavalloro, R.) 353–365 (A. A. Balkema, 1983).Helinski, M. E. H., Parker, A. G. & Knols, B. G. J. Radiation-induced sterility for pupal and adult stages of the malaria mosquito Anopheles arabiensis. Malar. J. 5, 41 (2006).Article 

Google Scholar 
Helinski, M. E. H., Parker, A. G. & Knols, B. G. J. Radiation biology of mosquitoes. Malar. J. 8 Suppl 2, S6 (2009).Benedict, M. Q. & Robinson, A. S. The first releases of transgenic mosquitoes: an argument for the sterile insect technique. Trends Parasitol. 19, 349–355 (2003).Article 

Google Scholar 
Culbert, N. J. et al. Longevity of mass-reared, irradiated and packed male Anopheles arabiensis and Aedes aegypti under simulated environmental field conditions. Parasit. Vectors 11, 603 (2018).CAS 
Article 

Google Scholar 
Culbert, N. J. et al. A rapid quality control test to foster the development of genetic control in mosquitoes. Sci. Rep. 8, 16179 (2018).Article 

Google Scholar 
Bentley, D. R. et al. Accurate whole human genome sequencing using reversible terminator chemistry. Nature 456, 53–59 (2008).CAS 
Article 

Google Scholar 
Carlson, R. The pace and proliferation of biological technologies. Biosecur. Bioterror. 1, 203–214 (2003).Article 

Google Scholar 
The Wolbachia Project–Singapore Consortium & Ching, N. L. Wolbachia-mediated sterility suppresses Aedes aegypti populations in the urban tropics. Preprint at https://www.medrxiv.org/content/10.1101/2021.06.16.21257922v1 (2021).Soh, S. et al. Economic impact of dengue in Singapore from 2010 to 2020 and the cost-effectiveness of Wolbachia interventions. PLoS Global Public Health https://doi.org/10.1371/journal.pgph.0000024 (2021). More

Traynor, K. S. et al. Varroa destructor: A complex parasite, crippling honey bees worldwide. Trends Parasitol. 36, 592–606. https://doi.org/10.1016/j.pt.2020.04.004 (2020).CAS 
Article 
PubMed 

Google Scholar 
Rosenkranz, P., Aumeier, P. & Ziegelmann, B. Biology and control of Varroa destructor. J. Invertebr. Pathol. 103, S96–S119. https://doi.org/10.1016/j.jip.2009.07.016 (2010).Article 
PubMed 

Google Scholar 
Noel, A., Le Conte, Y. & Mondet, F. Varroa destructor: how does it harm Apis mellifera honey bees and what can be done about it?. Emerg. Top. Life Sci. 4, 45–57. https://doi.org/10.1042/ETLS20190125 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Boncristiani, H. et al. World honey bee health: the global distribution of western honey bee (Apis mellifera L.) pests and pathogens. Bee World 98, 2–6 (2020).Article 

Google Scholar 
Ramsey, S. D. et al. Varroa destructor feeds primarily on honey bee fat body tissue and not hemolymph. Proc. Natl. Acad. Sci. U.S.A. 116, 1792–1801. https://doi.org/10.1073/pnas.1818371116 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Di Prisco, G. et al. A mutualistic symbiosis between a parasitic mite and a pathogenic virus undermines honey bee immunity and health. Proc. Natl. Acad. Sci. USA 113, 3203–3208. https://doi.org/10.1073/pnas.1523515113 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Mondet, F. et al. Antennae hold a key to Varroa-sensitive hygiene behaviour in honey bees. Sci. Rep. 5, 10454. https://doi.org/10.1038/srep10454 (2015).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
McMenamin, A. J. & Genersch, E. Honey bee colony losses and associated viruses. Curr. Opin. Insect Sci. 8, 121–129. https://doi.org/10.1016/j.cois.2015.01.015 (2015).Article 
PubMed 

Google Scholar 
Beaurepaire, A. et al. Diversity and global distribution of viruses of the western honey bee Apis mellifera. Insects 11, 239. https://doi.org/10.3390/insects11040239 (2020).Article 
PubMed Central 

Google Scholar 
Levin, S. et al. New viruses from the ectoparasite mite Varroa destructor infesting Apis mellifera and Apis cerana. Viruses 11, 94. https://doi.org/10.3390/v11020094 (2019).CAS 
Article 
PubMed Central 

Google Scholar 
Chen, G. et al. A new strain of virus discovered in China specific to the parasitic mite Varroa destructor poses a potential threat to honey bees. Viruses 13, 679. https://doi.org/10.3390/v13040679 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kraberger, S. et al. Genome sequences of two single-stranded DNA viruses identified in Varroa destructor. Genome Announc. 6, e00107-00118. https://doi.org/10.1128/genomeA.00107-18 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Haddad, N., Horth, L., Al-Shagour, B., Adjlane, N. & Loucif-Ayad, W. Next-generation sequence data demonstrate several pathogenic bee viruses in Middle East and African honey bee subspecies (Apis mellifera syriaca, Apis mellifera intermissa) as well as their cohabiting pathogenic mites (Varroa destructor). Virus Genes 54, 694–705. https://doi.org/10.1007/s11262-018-1593-9 (2018).CAS 
Article 
PubMed 

Google Scholar 
Wilfert, L. et al. Deformed wing virus is a recent global epidemic in honeybees driven by Varroa mites. Science 351, 594–597. https://doi.org/10.1126/science.aac9976 (2016).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Remnant, E. J., Mather, N., Gillard, T. L., Yagound, B. & Beekman, M. Direct transmission by injection affects competition among RNA viruses in honeybees. Proc. Royal Soc. B 286, 20182452. https://doi.org/10.1098/rspb.2018.2452 (2019).CAS 
Article 

Google Scholar 
Martin, S. J. & Brettell, L. E. Deformed wing virus in honeybees and other insects. Ann. Rev. Virol. 6, 49–69. https://doi.org/10.1146/annurev-virology-092818-015700 (2019).CAS 
Article 

Google Scholar 
Martin, S. J. et al. Global honey bee viral landscape altered by a parasitic mite. Science 336, 1304–1306. https://doi.org/10.1126/science.1220941 (2012).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Loope, K. J., Baty, J. W., Lester, P. J. & Wilson Rankin, E. E. Pathogen shifts in a honeybee predator following the arrival of the Varroa mite. Proc. Royal Soc. B 286, 20182499. https://doi.org/10.1098/rspb.2018.2499 (2019).CAS 
Article 

Google Scholar 
McMahon, D. P. et al. Elevated virulence of an emerging viral genotype as a driver of honeybee loss. Proc. Royal Soc. B https://doi.org/10.1098/rspb.2016.0811 (2016).Article 

Google Scholar 
Grindrod, I., Kevill, J. L., Villalobos, E. M., Schroeder, D. C. & Ten Martin, S. J. years of Deformed wing virus (DWV) in Hawaiian honey bees (Apis mellifera), the dominant DWV-A variant is potentially being replaced by variants with a DWV-B coding sequence. Viruses 13, 969. https://doi.org/10.3390/v13060969 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kevill, J. L., Stainton, K. C., Schroeder, D. C. & Martin, S. J. Deformed wing virus variant shift from 2010 to 2016 in managed and feral UK honey bee colonies. Arch. Virol. 166, 2693–2702. https://doi.org/10.1007/s00705-021-05162-3 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Grozinger, C. M. & Flenniken, M. L. Bee viruses: Ecology, pathogenicity, and impacts. Annu. Rev. Entomol. 64, 205–226. https://doi.org/10.1146/annurev-ento-011118-111942 (2019).CAS 
Article 
PubMed 

Google Scholar 
Natsopoulou, M. E. et al. The virulent, emerging genotype B of Deformed wing virus is closely linked to overwinter honeybee worker loss. Sci. Rep. 7, 5242. https://doi.org/10.1038/s41598-017-05596-3 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Iwasaki, J. M., Barratt, B. I., Lord, J. M., Mercer, A. R. & Dickinson, K. J. The New Zealand experience of varroa invasion highlights research opportunities for Australia. Ambio 44, 694–704. https://doi.org/10.1007/s13280-015-0679-z (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Solignac, M. et al. The invasive Korea and Japan types of Varroa destructor, ectoparasitic mites of the Western honeybee (Apis mellifera), are two partly isolated clones. Proc. Royal Soc. B 272, 411–419. https://doi.org/10.1098/rspb.2004.2853 (2005).Article 

Google Scholar 
Hall, R. J. et al. Apicultural practice and disease prevalence in Apis mellifera, New Zealand: A longitudinal study. J. Apic. Res. 60, 644–658. https://doi.org/10.1080/00218839.2021.1936422 (2021).Article 

Google Scholar 
Mondet, F., de Miranda, J. R., Kretzschmar, A., Le Conte, Y. & Mercer, A. R. On the front line: quantitative virus dynamics in honeybee (Apis mellifera L) colonies along a new expansion front of the parasite Varroa destructor. PLoS Pathog. 10, e1004323 (2014).Article 

Google Scholar 
McFadden, A. M. J. et al. Israeli acute paralysis virus not detected in Apis mellifera in New Zealand in a national survey. J. Apic. Res. 53, 520–527. https://doi.org/10.3896/ibra.1.53.5.03 (2015).Article 

Google Scholar 
Dobelmann, J., Felden, A. & Lester, P. J. Genetic strain diversity of multi-host RNA viruses that infect a wide range of pollinators and associates is shaped by geographic origins. Viruses 12, 358. https://doi.org/10.3390/v12030358 (2020).CAS 
Article 
PubMed Central 

Google Scholar 
Gruber, M. A. M. et al. Single-stranded RNA viruses infecting the invasive argentine ant Linepithema humile. Sci. Rep. 7, 3304. https://doi.org/10.1038/s41598-017-03508-z (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Brenton-Rule, E. C. et al. The origins of global invasions of the German wasp (Vespula germanica) and its infection with four honey bee viruses. Biol. Invasions 20, 3445–3460. https://doi.org/10.1007/s10530-018-1786-0 (2018).Article 

Google Scholar 
Lester, P. J., Buick, K. H., Baty, J. W., Felden, A. & Haywood, J. Different bacterial and viral pathogens trigger distinct immune responses in a globally invasive ant. Sci. Rep. 9, 5780. https://doi.org/10.1038/s41598-019-41843-5 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Lester, P. J. et al. No evidence of enemy release in pathogen and microbial communities of common wasps (Vespula vulgaris) in their native and introduced range. PLoS ONE 10, e0121358. https://doi.org/10.1371/journal.pone.0121358 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Levin, S., Sela, N. & Chejanovsky, N. Two novel viruses associated with the Apis mellifera pathogenic mite Varroa destructor. Sci. Rep. 6, 37710. https://doi.org/10.1038/srep37710 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Cornman, S. R. et al. Genomic survey of the ectoparasitic mite Varroa destructor, a major pest of the honey bee Apis mellifera. BMC Genom. 11, 602. https://doi.org/10.1186/1471-2164-11-602 (2010).CAS 
Article 

Google Scholar 
Gauthier, L. et al. The Apis mellifera filamentous virus genome. Viruses 7, 3798–3815. https://doi.org/10.3390/v7072798 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Giuffre, C., Lubkin, S. R. & Tarpy, D. R. Does viral load alter behavior of the bee parasite Varroa destructor?. PLoS ONE 14, e0217975. https://doi.org/10.1371/journal.pone.0217975 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
De Smet, L. et al. BeeDoctor, a versatile MLPA-based diagnostic tool for screening bee viruses. PLoS ONE 7, e47953. https://doi.org/10.1371/journal.pone.0047953 (2012).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Runckel, C. et al. Temporal analysis of the honey bee microbiome reveals four novel viruses and seasonal prevalence of known viruses, Nosema, and Crithidia. PLoS ONE 6, e20656. https://doi.org/10.1371/journal.pone.0020656 (2011).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Remnant, E. J. et al. A diverse range of novel RNA viruses in geographically distinct honey bee populations. J. Virol. 91, e00158. https://doi.org/10.1128/JVI.00158-17 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Navajas, M. et al. New Asian types of Varroa destructor: a potential new threat for world apiculture. Apidologie 41, 181–193. https://doi.org/10.1051/apido/2009068 (2010).CAS 
Article 

Google Scholar 
Kearse, M. et al. Geneious basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649. https://doi.org/10.1093/bioinformatics/bts199 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Huelsenbeck, J. P. & Ronquist, F. MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics 17, 754–755. https://doi.org/10.1093/bioinformatics/17.8.754 (2001).CAS 
Article 
PubMed 

Google Scholar 
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120. https://doi.org/10.1093/bioinformatics/btu170 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kim, D., Langmead, B. & Salzberg, S. L. HISAT: A fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360. https://doi.org/10.1038/nmeth.3317 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Wallberg, A. et al. A hybrid de novo genome assembly of the honeybee, Apis mellifera, with chromosome-length scaffolds. BMC Genom. 20, 275. https://doi.org/10.1186/s12864-019-5642-0 (2019).Article 

Google Scholar 
Techer, M. A. et al. Divergent evolutionary trajectories following speciation in two ectoparasitic honey bee mites. Commun. Biol. 2, 357. https://doi.org/10.1038/s42003-019-0606-0 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Haas, B. J. et al. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat. Protoc. 8, 1494–1512. https://doi.org/10.1038/nprot.2013.084 (2013).CAS 
Article 
PubMed 

Google Scholar 
Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using Diamond. Nat. Methods 12, 59–60. https://doi.org/10.1038/nmeth.3176 (2015).CAS 
Article 
PubMed 

Google Scholar 
National Center for Biotechnology Information (NCBI). Bethesda (MD), National Library of Medicine (US), National Center for Biotechnology Information; [1988]–[cited 2017 Apr 06]. Available from: https://www.ncbi.nlm.nih.gov/.Camacho, C. et al. BLAST+: Architecture and applications. BMC Bioinform. 10, 421. https://doi.org/10.1186/1471-2105-10-421 (2009).CAS 
Article 

Google Scholar 
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419. https://doi.org/10.1038/nmeth.4197 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
R: A language and environment for statistical computing v. 4.0.2 (R Foundation for Statistical Computing, Vienna, Austria, 2020).Oksanen, J. et al. vegan: community ecology package. (R package version 2.4–0. https://CRAN.R-project.org/package=vegan., 2016).Li, D. et al. Molecular detection of small hive beetle Aethina tumida Murray (Coleoptera: Nitidulidae): DNA barcoding and development of a real-time PCR assay. Sci. Rep. 8, 9623. https://doi.org/10.1038/s41598-018-27603-x (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Anderson, D. L. & Trueman, J. W. H. Varroa jacobsoni (Acari: Varroidae) is more than one species. Exp. Appl. Acarol. 24, 165–189. https://doi.org/10.1023/a:1006456720416 (2000).CAS 
Article 
PubMed 

Google Scholar 
Bradford, E. L., Christie, C. R., Campbell, E. M. & Bowman, A. S. A real-time PCR method for quantification of the total and major variant strains of the Deformed wing virus. PLoS ONE 12, e0190017. https://doi.org/10.1371/journal.pone.0190017 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Dynes, T. L. et al. Fine scale population genetic structure of Varroa destructor, an ectoparasitic mite of the honey bee (Apis mellifera). Apidologie 48, 93–101. https://doi.org/10.1007/s13592-016-0453-7 (2017).Article 

Google Scholar 
Maggi, M. et al. Genetic structure of Varroa destructor populations infesting Apis mellifera colonies in Argentina. Exp. Appl. Acarol. 56, 309–318. https://doi.org/10.1007/s10493-012-9526-0 (2012).CAS 
Article 
PubMed 

Google Scholar 
Hasegawa, N., Techer, M. & Mikheyev, A. S. A toolkit for studying Varroa genomics and transcriptomics: preservation, extraction, and sequencing library preparation. BMC Genom. 22, 54. https://doi.org/10.1186/s12864-020-07363-7 (2021).CAS 
Article 

Google Scholar 
Gisder, S. & Genersch, E. Direct evidence for infection of Varroa destructor mites with the bee-pathogenic Deformed wing virus variant B, but not variant A, via fluorescence in situ hybridization analysis. J. Virol. 95, e01786. https://doi.org/10.1128/JVI.01786-20 (2021).CAS 
Article 
PubMed Central 

Google Scholar 
Gisder, S., Aumeier, P. & Genersch, E. Deformed wing virus: replication and viral load in mites (Varroa destructor). J. Gen. Virol. 90, 463–467. https://doi.org/10.1099/vir.0.005579-0 (2009).CAS 
Article 
PubMed 

Google Scholar 
Yue, C. & Genersch, E. RT-PCR analysis of Deformed wing virus in honeybees (Apis mellifera) and mites (Varroa destructor). J. Gen. Virol. 86, 3419–3424. https://doi.org/10.1099/vir.0.81401-0 (2005).CAS 
Article 
PubMed 

Google Scholar 
Posada-Florez, F. et al. Deformed wing virus type A, a major honey bee pathogen, is vectored by the mite Varroa destructor in a non-propagative manner. Sci. Rep. 9, 12445. https://doi.org/10.1038/s41598-019-47447-3 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Budge, G. E. et al. Chronic bee paralysis as a serious emerging threat to honey bees. Nat. Commun. 11, 2164. https://doi.org/10.1038/s41467-020-15919-0 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Graystock, P. et al. The Trojan hives: pollinator pathogens, imported and distributed in bumblebee colonies. J. Appl. Ecol. 50, 1207–1215. https://doi.org/10.1111/1365-2664.12134 (2013).Article 

Google Scholar 
Roberts, J. M. K., Simbiken, N., Dale, C., Armstrong, J. & Anderson, D. L. Tolerance of honey bees to Varroa mite in the absence of deformed wing virus. Viruses https://doi.org/10.3390/v12050575 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Brettell, L. E. & Martin, S. J. Oldest Varroa tolerant honey bee population provides insight into the origins of the global decline of honey bees. Sci. Rep. 7, 45953. https://doi.org/10.1038/srep45953 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Herrero, S. et al. Identification of new viral variants specific to the honey bee mite Varroa destructor. Exp. Appl. Acarol. 79, 157–168. https://doi.org/10.1007/s10493-019-00425-w (2019).CAS 
Article 
PubMed 

Google Scholar 
Dobelmann, J. et al. Fitness in invasive social wasps: the role of variation in viral load, immune response and paternity in predicting nest size and reproductive output. Oikos 126, 1208–1218. https://doi.org/10.1111/oik.04117 (2017).CAS 
Article 

Google Scholar 
Shojaei, A., Nourian, A., Khanjani, M. & Mahmoodi, P. The first molecular characterization of Lake Sinai virus in honey bees (Apis mellifera) and Varroa destructor mites in Iran. J. Apic. Res. https://doi.org/10.1080/00218839.2021.1921467 (2021).Article 

Google Scholar 
Hartmann, U., Forsgren, E., Charriere, J. D., Neumann, P. & Gauthier, L. Dynamics of Apis mellifera filamentous Virus (AmFV) infections in honey bees and relationships with other parasites. Viruses 7, 2654–2667. https://doi.org/10.3390/v7052654 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Nanetti, A., Bortolotti, L. & Cilia, G. Pathogens spillover from honey bees to other arthropods. Pathogens 10, 1044. https://doi.org/10.3390/pathogens10081044 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Norton, A. M., Remnant, E. J., Buchmann, G. & Beekman, M. Accumulation and competition amongst Deformed wing virus genotypes in naive Australian honeybees provides insight into the increasing global prevalence of genotype B. Front. Microbiol. 11, 620. https://doi.org/10.3389/fmicb.2020.00620 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Mordecai, G. J. et al. Superinfection exclusion and the long-term survival of honey bees in Varroa-infested colonies. ISME J. 10, 1182–1191. https://doi.org/10.1038/ismej.2015.186 (2016).CAS 
Article 
PubMed 

Google Scholar  More

Ambitious new targets are needed to conserve nature by protecting parks and species.Credit: Tang Dehong/VCG/Getty

It will take ample time and money to slow the world’s catastrophic loss of plant and animal species — and right now, both are running dangerously low. This year, nations are due to agree to an action plan to protect global biodiversity at the 15th Conference of the Parties (COP15) to the United Nations Convention on Biological Diversity. But the meeting is already two years late because of the pandemic, and China, which will host the conference in Kunming, has yet to set a new date.Now, conflicts over financing are adding to the tension. Conservation groups and advocates suggest that rich nations must donate at least US$60 billion annually to help less-affluent ones to fund projects such as protecting areas where wildlife can thrive and tackling the illegal wildlife trade that is driving hundreds of species to extinction. This is much more than the $4 billion to $10 billion that they are estimated to be spending today, and well below the amount they are giving low- and middle-income countries (LMICs) to fight climate change, which reached around $50 billion in 2019 according to one estimate. Yet limited overseas development funds are spread ever thinner as donors deal with the pandemic and now the fallout from Russia’s invasion of Ukraine. This is where COP15 is meant to deliver: as well as agreeing to the action plan, called the Global Biodiversity Framework, nations will be encouraged to pledge more money.A mix of public and private money has started to trickle in. Currently, biodiversity funding on the table ahead of COP15 amounts to roughly $5.2 billion per year, according to estimates by a group of five leading conservation organizations. Most comes from six governments, including France, the United Kingdom and Japan, and the European Union. In April, the Global Environment Facility (GEF) — a multilateral fund to support international environmental agreements — announced that, over the next four years, around $1.9 billion will go to projects dedicated to biodiversity. However, it’s unclear how much of this will come from the coffers that donor countries have already pledged.Some cash for conservation is coming from private philanthropic donors — such as $2 billion committed by entrepreneur Jeff Bezos last year. And starting in 2020, a group of financial institutions (now 89 of them) promised to annually report their financing activities and investments that affect biodiversity, and to move away from those that do harm — a form of ecological accounting that could help to shrink the budget needed to protect biodiversity. Donors will need to reach much deeper into their pockets to meet the demands of LMICs, the custodians of much of the world’s biodiversity. In March, a group of LMICs, led by Gabon, asked for $100 billion per year in new funding when officials met in Geneva, Switzerland, to discuss progress on the Global Biodiversity Framework. The LMICs want the money placed in a new multilateral fund for biodiversity, separate from, but complementary to, the GEF.Aside from cash, the fund will need to find a new home and structure — and there are a few options. A proposal from Brazil, circulated at the Geneva meeting, suggests the fund be governed by a board of 24 members, with an equal number from rich and lower-income nations. The board would be responsible for funding decisions and would prioritize projects that help to achieve the biodiversity convention’s goals. The pitch generated interest among some countries, but also concerns that it’s an attempt by Brazil to divert attention from its failure over the past few years to protect the Amazon rainforest and prevent other environmental harm.Another option is the Kunming Biodiversity Fund, which China announced in October last year to help LMICs to safeguard their ecosystems. It allocated 1.5 billion yuan (US$223 million) to seed the fund and invited other countries to contribute, but so far none has. Sources knowledgeable about the fund say that donor countries are reluctant to pitch in because China is holding on too tightly to the reins and is not involving others in its deliberations. Details of how the fund will operate are scarce, but Nature has learnt that China is floating the idea of housing it at the Asian Infrastructure Investment Bank (AIIB), based in Beijing. Set up in 2016, the AIIB has $100 billion in total capital and 105 members, including Germany, France and the United Kingdom. The AIIB has big green plans. By 2025, it wants half of all infrastructure projects it finances to focus on climate issues. With rigorous oversight and transparency, the AIIB would make a good home for the Kunming fund.As countries prepare to meet in Nairobi on 20–26 June in a last-ditch attempt to push the biodiversity framework forwards before COP15, China, as the host, must urgently provide stronger leadership on financing, including more transparency and engagement. Progress will require quick, generous contributions from donor nations — which should prioritize grants, not loans, for biodiversity projects.Holding the COP15 meeting must be a priority, too. As China tightens restrictions in the face of a COVID-19 surge, some researchers fear that delays will stretch on, stalling conservation work and leaving less time to meet biodiversity targets. China must either commit to holding the meeting this year or let it proceed elsewhere. One option being quietly discussed is moving the meeting to Canada — home of the United Nations biodiversity convention’s secretariat — and this deserves consideration. The world needs an ambitious biodiversity plan now — nature cannot wait. More

Literature reviewWe conducted a comprehensive review of relevant literature published since 1995. Studies were extracted from the China National Knowledge Infrastructure and Web of Science using the following keywords: “N (nitrogen) loss OR NO (nitric oxide) emission OR N2O (nitrous oxide) emission OR NH3 (ammonia volatilization) emission OR NO3− (nitric leaching) OR N (nitrogen) runoff AND wheat AND China”. We excluded the following types of experiment: experiments not covering the entire wheat growing season, experiments conducted in greenhouses or laboratories, experiments without zero-N control, and experiments including manure, controlled release fertilizer, or inhibitors. In total, we extracted 941 observations from 138 articles, consisting of 121 observations of NO emission, 383 of N2O emission, 185 of NH3 emission, 188 of NO3− leaching, and 64 of Nr runoff. We also extracted data on N application rates, and climate and soil variables (Fig. 1). Missing climate data were obtained from China Meteorological Data Network (https://data.cma.cn/), miss values of soil organic carbon (SOC) and total N content were obtained from the National Scientific Fertilizer Network (http://kxsf.soilbd.com/), and missing soil silt, clay, sand content, bulk density, cation exchange capacity (CEC), and pH data were obtained from the Harmonized World Soil Database (HWSD) v. 1.2 (http://www.fao.org/soils-portal/soil-survey/soilmaps-and-databases/harmonized-world-soildatabase-v12/en). Based on this dataset, the EFs of Nr loss pathways were calculated by the following equation:$$E{F}_{i}=left({E}_{treatment}{rm{-}}{E}_{control}right){rm{/}}N;applied$$
(1)
where i = 1–5, represented NO, N2O, NH3, NO3− leaching and Nr runoff, respectively. Etreatment is the loss rate of experimental treatments with applied N fertilizer, Econtrol is the loss rate of experimental control without applied N fertilizer, and N applied is the N application rate corresponding to Etreatment. The resulting data was used to develop RF models to predict EFs of the five Nr loss pathways.Fig. 1The generate framework of the Nr loss from Chinese wheat system (Nr-Wheat) 1.0 database.Full size imageRF modelsRF models outperformed empirical models in previous studies15,18,19. We employed RF models to predict the EFs of NO, N2O, NH3, NO3− leaching, and Nr runoff. Environmental factors were selected via redundancy analysis20. Redundancy analysis, a basic ordination technique for gradients analysis, produces an ordination summarizing the variation in several response variables that can be best explained by a matrix of explanatory variables based on multiple linear regression. We conducted redundancy analysis using Canoco 5 to further analyze the effects of 10 environmental factors, including 4 soil physical factors (bulk density, silt, clay, and sand content), 4 soil chemical factors (pH, SOC, CEC and total N content), and 2 weather factors (total rainfall and mean temperature during the wheat growing period) of different EFs. Ultimately, the dataset of each pathway contained an ensemble of different environmental factors (Table 1).Table 1 Environmental factors were employed to build RF model for each pathway and total explanatory rates.Full size tableWhen establishing the RF model, the first step was to select k features from a total of m (k  More

The custom code used to clean occurrence records and construct SDMs is available at (github.com/RhettRautsaw/ VenomMaps). We used the following R16 packages for data cleaning, manipulation, species distribution modeling, and Shiny app creation: tidyverse17 readxl18, data.table19, sf20, sp21,22, rgdal23, raster24, smoothr25, ape26, phytools27, argparse28, parallel16, memuse29, dismo30, rJava31, concaveman32, spThin33, usdm34, ENMeval35, kuenm36, shiny37, leaflet38, leaflet.extras39, leaflet.extras240, RColorBrewer41, ggpubr42, ggtext43, and patchwork44.Updating occurrence record taxonomyOur goal was to update and reconstruct the distributions of New World pitvipers. We used the Reptile Database45 (May 2021) as our primary source for current taxonomy which included the following genera: Agkistrodon, Atropoides, Bothriechis, Bothrocophias, Bothrops, Cerrophidion, Crotalus, Lachesis, Metlapilcoatlus, Mixcoatlus, Ophryacus, Porthidium, and Sistrurus. However, to ensure we captured all New World pitvipers records, we incorporated all members of the family Viperidae (all vipers and pitvipers) into our pipeline for updating occurrence record taxonomy (i.e., to account for errors in the recorded latitude, longitude, or if subfamily was not recorded).First, we collected global occurrence records for “Viperidae” from GBIF (downloaded 2021-08-19)46, Bison (downloaded 2021-08-19)47, HerpMapper (only New World taxa; downloaded 2021-08-19)48, Brazilian Snake Atlas49, BioWeb (downloaded 2021-07-07)50, unpublished data/databases from RMR, GJV, EPH, LRVA, MM, and CLP, and georeferenced literature records totaling 373,673 species-level records, 292,425 of which are New World pitvipers. Given the fluidity of taxonomy, records were often associated with outdated names. For example, Crotalus mitchelli pyrrhus was elevated to Crotalus pyrrhus51, but may still be recorded as the former in a given repository (e.g., GBIF). To correct taxonomy in our database, we checked records against a list of synonyms found on the Reptile Database and compared them to current taxonomy. If species and subspecies columns matched the same taxon (or no subspecies was recorded), then species IDs were not altered. If species and subspecies IDs did not match the same taxon, we updated taxonomy by minimizing the number of changes required to a given character string. We then manually checked all changes.Constructing distribution mapsNext, we collected preliminary distribution maps from the International Union for Conservation of Nature (IUCN; downloaded 2018-11-27)52, Global Assessment of Reptile Distributions (GARD) v1.153, Heimes54, Campbell and Lamar55, and unpublished maps. We manually curated distribution maps for all New World pitvipers in QGIS using the occurrence records, previous distribution maps, and recent publications for each taxon (note that distributions for Old World Viperidae have not yet been updated). We used a digital relief map (maps-for-free.com) and The Nature Conservancy Terrestrial Ecoregions (TNG.org)56 to identify clear distribution boundaries (e.g., mountains). We then clipped the final distributions to a land boundary (GADM v3.6)57 and smoothed the distribution using the the “chaikin” method in the R package smoothr25.Occurrence-distribution overlapOur initial taxonomy check was only concerned with records for which a subspecies was recorded and had since been elevated to species status. Therefore, many records with no assigned subspecies likely remained associated with an incorrect or outdated generic and/or specific identification. Fortunately, taxonomic changes are typically associated with changes in the species’ expected distribution. For example, when Crotalus simus was resurrected from C. durissus, the distribution of C. durissus was split: the northern portion of its range in Central America now represented the resurrected species (C. simus) and the southern portion of its range remained C. durissus55. Yet, occurrence records in Central America often remain labelled as C. durissus in data repositories. Therefore, we spatially joined records with the newly reconstructed species distribution maps to determine if they overlapped with their expected distribution (Old World taxa were joined with the GARD 1.1 distributions53).Briefly, we developed a custom function (occ_cleaner.R) to perform the spatial join and update taxonomy. First, we calculated the distance for each record to the 20 nearest distributions within 50 km (full overlap resulted in a distance of 0 m). Next, we calculated the phylogenetic distance between the recorded species ID and each species with which that record overlapped using the tree from Zaher et al.58 and adding taxa based on recent clade-specific publications (bind.tip2.R; see github.com/RhettRautsaw/VenomMaps for full list of references and details). If records overlapped with their expected species, no changes were made. If records fell outside of their expected distribution, we filtered the potential overlapping and nearby species (within 50 km) to minimize phylogenetic distance. If multiple species were equally distant (i.e., share the same common ancestor), we attempted to minimize geographic distance. If multiple species remained equally distant in both phylogenetic and geographic distance, we flagged the record to be manually checked. We also flagged records if a species’ taxonomy had changed and records were additionally flagged as potentially dubious if the taxonomic change had a phylogenetic divergence greater than 5 million years. We manually checked all flagged records and returned records to their original species ID if species identity remained uncertain. We flagged these records as potentially dubious, along with records that fell outside of their expected distribution (within 50 km), and removed all flagged records for species distribution modeling. Our final cleaned database contained 344,998 global records, of which 275,087 were New World pitvipers.Species distribution modelingWe attempted to infer SDMs for the 158 species of New World pitvipers currently recognized by the Reptile Database (May 2021) and additionally modeled the three subspecies of Crotalus ravus separately based on recommendations for species status elevation by Blair et al.59 for a total of 160 species. We developed a unix-executable R script (autokuenm.R) designed to take occurrence records, distribution maps, and environmental data and prepare these data for species distribution modeling with kuenm36. We chose to use kuenm – and MaxEnt v3.4.460 – because it has been shown to have good predictive power61 and fine-tuning of this algorithm has performance comparable to more computationally intensive ensembles62,63. Additionally, MaxEnt allows for flexibility in parameter selection64 and can function entirely with presence data14.Prior to autokuenm, to account for sampling/spatial bias during SDM, we created a bias file by using the pooled New World pitviper occurrence records as representative background data65,66,67,68. Specifically, we converted occurrence records to a raster and performed two-dimensional kernel density estimation (kde2d) with the MASS package with default settings69 and rescaled the kernel density by a factor of 1000 and rounded to three decimal places. This was then used as input to factor out sampling bias by MaxEnt. We then ran autokuenm, which is designed to subset/partition the cleaned occurrence records for a given species and prepare additional files for SDM. We first defined M-areas – or areas accessible to a given species – using the World Wildlife Fund Terrestrial Ecoregions70. Biogeographic regions represent distributional limits for many species and are reasonable hypotheses for the areas accessible to a given species71,72. To do this, we created alpha hulls from the subset of occurrence records for a given species using concaveman32 with default settings. We then identified regions with at least 20% of the region covered by the alpha hull and merged these regions together to form our final M-area. All environmental layers and the bias file were cropped to this M-area which was used as the geographic extent for modeling. We then randomly selected 5% of records to function as an independent test set for final model evaluation. Next, we generated 2000 random background points across the cropped environmental layers and used ENMeval to partition occurrence records into four sets using the checkerboard2 pattern35. Note that the background points here were not used in MaxEnt. One of the four partitions was selected at random to be used as the testing set; the remaining three partitions were used for training the MaxEnt models. If the number of occurrence records in the independent test set was less than five, then we used the training partition for final model creation and used the testing partition for final model evaluation.We tested the top-contributing variables from three sets of environmental layers: (1) bioclimatic variables, (2) EarthEnv topographic variables73, and (3) a combination of these variables. To select the top-contributing variables in each set, we wrote a custom function (SelectVariables) which used a combination of MaxEnt permutation importance and Variable Inflation Factors (VIF) to remove collinearity while keeping the variables that contributed the most to the model. Compared with variable selection via principal component analysis loadings, the permutation importance and VIF methodology demonstrated significant improvement in MaxEnt model fit. First, we designed SelectVariables to run MaxEnt using dismo::maxent with default settings and then extracted the permutation importance. We removed variables if they had 0% permutation importance. Next, we calculated VIF with usdm::vif and then iteratively removed variables by selecting the variables with two highest VIF values and removing whichever variable had the lowest permutation importance. We then recalculated VIF and repeated the process until the maximum VIF value was less than 10. Finally, we recalculated permutation importance with the remaining variables using dismo::maxent with default settings and removed variables with less than 1% permutation importance to create the final variable sets. This process was done for each species independently.With the final environmental variable, testing, and training sets, we generated SDMs using kuenm. First, we created candidate calibration models with multiple combinations of regularization multipliers (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 2, 3, 4, 5, 6, 8, 10), feature classes (l, q, h, lq, lp, lt, lh, qp, qt, qh, pt, ph, th, lqp, lqt, lqh, lpt, lph, lth, qpt, qph, qth, pth, lqpt, lqph, lqth, lpth, qpth, lqpth), and sets of environmental predictors (bioclimatic, topographic, combination) totaling 2,958 candidate models per species. We then ran each model in parallel using GNU Parallel74. Next, we evaluated the candidate models and selected the best models using statistical significance (partial ROC), prediction ability (omission rates; OR), and model complexity (AICc) with the “kuenm_ceval” function with default settings. Specifically, models were only considered if they were statistically significant and had an OR less than 5%. If no models passed the OR criteria, the models with the minimal OR were considered. Finally, any remaining models were filtered to those within 2 AICc of the top model (Supplementary Table 1). In addition to evaluating and comparing all models together, we evaluated bioclimatic-only and combination-only models separately since these two sets of environmental variables were expected to be the best performing models given the ubiquity of bioclimatic variables in species distribution modeling (Supplementary Table 1).We generated 10 bootstrap replicates for each of the “best” calibration models using the “kuenm_mod” function. We also performed jackknifing to assess variable importance and models were output in raw format. We evaluated the final models using “kuenm_feval” with default settings. To select the best model for each comparative set (i.e., all, bioclimatic-only, and combination-only sets), we filtered the final evaluation results to minimize the OR and maximize the AUC ratio (Supplementary Table 2). If multiple models remained and were considered equally competitive, we averaged these models together (Supplementary Table 3). Because we performed three different set of comparisons, there were three “best” models per species, so we again aimed to minimize the OR and maximize the AUC ratio to select a final model for each species (Supplementary Table 4). We then converted our final models into cloglog format for visualization and threshold the models using a 10th percentile training presence cutoff (Fig. S2). Both conversion and thresholding functions are provided as R functions (raw2log, raw2clog, raster_threshold in functions.R; github.com/RhettRautsaw/VenomMaps). More

This Hubble image captures a set of galaxies that are unusual because they seem not to have dark matter.Credit: NASA/ESA/P. van Dokkum, Yale Univ.

Galaxies without dark matter baffle astronomersScientists have long thought that galaxies cannot form without the gravitational pull of the mysterious material known as dark matter. But one group of astronomers thinks it might have observed a line of 11 galaxies that don’t contain any of the substance, and could all have been created in an ancient collision (P. van Dokkum et al. Nature 605, 435–439; 2022).This kind of system could be used to learn about how galaxies form, and about the nature of dark matter itself. However, some researchers are not convinced that the claim is much more than a hypothesis.The finding centres on two galaxies, called DF2 and DF4, that were described in 2018 and 2019. Their stars moved so slowly that the pull of dark matter was not needed to explain their orbits, so the team concluded that the galaxies contained no dark matter.In the latest research, scientists identified between three and seven new candidates for dark-matter-free galaxies in a line between DF2 and DF4, as well as strange, faint galaxies at either end.“If proven right, this could certainly be exciting for galaxy formation. However, the jury is still out,” says Chervin Laporte, an astronomer at the University of Barcelona in Spain.Northern Australian tree deaths double in 35 yearsThe rate at which trees are dying in the old-growth tropical forests of northern Australia each year has doubled since the 1980s, and researchers say climate change is probably to blame.The findings, published in Nature on 18 May, come from an extraordinary record of tree deaths catalogued at 24 sites in the tropical forests of northern Queensland over the past 49 years (D. Bauman et al. Nature https://doi.org/hv67; 2022).The research team recorded that 2,305 trees across 81 key species had died since 1971. But from the mid-1980s, tree mortality risk increased from an average of 1% a year to 2% a year (see ‘Increasing death rate’). Of the 81 tree species that the team studied, 70% showed an increase in mortality risk over the study period.The study found that the rise in death rate occurred at the same time as a long-term trend of increases in the atmospheric vapour pressure deficit, which is the difference between the amount of water vapour that the atmosphere can hold and the amount of water it does hold at a given time. The higher the deficit, the more water trees lose through their leaves, which can lead to sustained stress and eventually tree death.

Europe’s first farming populations descend mostly from farmers in the Anatolian peninsula, in what is now Turkey.Credit: Fatih Kurt/Anadolu Agency/Getty

Ancient DNA maps ‘dawn of farming’Sometime before 12,000 years ago, nomadic hunter-gatherers in the Middle East made one of the most important transitions in human history: they began staying put and took to farming.Two ancient-DNA studies have now homed in on the identity of the hunter-gatherers who settled down.Researchers sequenced the genomes of 15 hunter-gatherers and early farmers who lived in southwest Asia and Europe, along a key migration routes into Europe — the Danube River (N. Marchi et al. Cell https://doi.org/gp49rr; 2022).The team found that ancient farmers in Anatolia — now Turkey — descended from repeated mixing between distinct hunter-gatherer groups from Europe and the Middle East. These groups first split at the height of the last Ice Age, some 25,000 years ago. Modelling suggests that the western groups nearly died out, before rebounding as the climate warmed.Once established in Anatolia, the researchers found, early farmers moved west into Europe in a stepping-stone-like way, beginning around 8,000 years ago. They mixed occasionally — but not extensively — with local hunter-gatherers.The findings chime with those of a similar ancient-genomics study posted on the bioRxiv preprint server this month (M. E. Allentoft. et al. Preprint at bioRxiv https://doi.org/hv7g; 2022). More

Khamraev, Sh. R. & Bezborodov, Yu. G. Results of research on the reduction of physical evaporation of moisture from the cotton fields. Sci. World 2(33), 86–93 (2016).
Google Scholar 
Khan, A. U. et al. Production of organic fertilizers from rocket seed (Eruca sativa L.), chicken peat and Moringa oleifera leaves for growing linseed under water deficit stress. Sustainability 13(1), 1–19 (2021).CAS 

Google Scholar 
Patil Shirish, S., Kelkar Tushar, S. & Bhalerao Satish, A. Mulching: A soil and water conservation practice. Res. J. Agric For. Sci. 1(3), 26–29 (2013).
Google Scholar 
Matkovic, A. et al. Mulching as a physical weed control method applicable in medicinal plants cultivations. J. Lekovite Sirovine 35, 37–51 (2015).Article 

Google Scholar 
Nawaz, A., Lal, R., Shrestha, R. K. & Farooq, M. Mulching affects soil properties and greenhouse gas emissions under long-term no-till and plough-till systems in alfisol of Central Ohio. Land Degrad. Dev. 28(2), 673–681 (2016).Article 

Google Scholar 
Brant, V. et al. Splash erosion in maize crops under conservation management in combination with Shallow Strip-tillage before Sowing. Soil Water Res. 12(2), 106–116 (2017).CAS 
Article 

Google Scholar 
Kumar, R. et al. Effect of plant spacing and organic mulch on growth, yield and quality of natural sweetener plant Stevia and soil fertility in western Himalayas. Int. J. Plant Prod. 8(3), 311–334 (2014).ADS 

Google Scholar 
Seleiman, M. F. & Kheir, A. M. S. Maize productivity, heavy metals uptake and their availability in contaminated clay and sandy alkaline soils as affected by inorganic and organic amendments. Chemosphere 204, 514–522 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Seleiman, M. F. & Kheir, A. M. S. Saline soil properties, quality and productivity of wheat grown with bagasse ash and thiourea in different climatic zones. Chemosphere 193, 538–546 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Chakraborty, D. et al. Effect of mulching on soil and plant water status, and the growth and yield of wheat (Triticum aestivum L.) in a semi-arid environment. Agric. Water Manag. 95(12), 1323–1334 (2008).Article 

Google Scholar 
Ahmad, Z. I., Ansar, M., Iqbal, M. & Minhas, N. M. Effect of planting geometry and mulching on moisture conservation, weed control and wheat growth under rainfed conditions. Pak. J. Bot. 39(4), 1189–1195 (2007).
Google Scholar 
Teame, G. Effect of organic mulches and land preparation methods on soil moisture and sesame productivity. Afr. J. Agric. Res. 12(38), 2836–2843 (2017).Article 

Google Scholar 
Lehar, L., Wardiyati, T., Moch Dawam, M. & Suryanto, A. Influence of mulch and plant spacing on yield of Solanum tuberosum L. cv. Nadiya at medium altitude. Int. Food Res. J. 24(3), 1338–1344 (2017).CAS 

Google Scholar 
Arash, K. The evaluation of water use efficiency in common bean (Phaseolus vulgaris L.) in irrigation condition and mulch. Sci. Agric. 2(3), 60–64 (2013).
Google Scholar 
Artyszak, A., Gozdowski, D. & Kucińska, K. The yield and technological quality of sugar beet roots cultivated in mulches. Plant Soil Environ. 60(10), 464–469 (2014).Article 

Google Scholar 
Brittaine, R. & Lutaladio, N. Jatropha: A Smallholder Bioenergy Crop. The Potential for Pro-poor Development Integrated Crop Management, Vol. 8 (IFAD/FAO, 2010). http://www.fao.orgElbehri, A., Segerstedt, A. & Liu, P. Biofuels and the sustainability challenge: A global assessment of sustainability issues, trends and policies for biofuels and related feedstocks. Food and Agric. Organ. United Nations (FAO) xvi-174 (2013).King, A. J. et al. Potential of Jatropha curcas as a source of renewable oil and animal feed. J. Exp. Bot. 60(10), 2897–2905 (2009).CAS 
PubMed 
Article 

Google Scholar 
Raheman, H. 14 Jatropha. Handbook of Bioenergy Crop Plants, 315–345 (2012).Ullah, F., Bano, A. & Nosheen, A. Sustainable measures for biodiesel. Effects 36(23), 2621–2628 (2014).CAS 

Google Scholar 
Irshad, M. et al. Evaluation of Jatropha curcas L. leaves mulching on wheat growth and biochemical attributes under water stress. BMC Plant Biol. 21(1), 1–12 (2021).Article 
CAS 

Google Scholar 
Dieye, T. et al. The effect of Jatropha curcas L. leaf litter decomposition on soil carbon and nitrogen status and bacterial community structure (Senegal). J. Soil Sci. Environ Manag. 7(3), 32–44 (2016).CAS 
Article 

Google Scholar 
Kafi, M. & Salehi, M. Kochia scoparia as a model plant to explore the impact of water deficit on halophytic communities. Pak. J. Bot. 44, 257–262 (2012).
Google Scholar 
Yang, Y. M., Liu, X. J., Li, W. Q. & Li, C. Z. Effect of different mulch materials on winter wheat production in desalinized soil in Heilonggang region of North China. J. Zhejiang Univ. Sci. B 7(11), 858–867 (2006).PubMed 
PubMed Central 
Article 

Google Scholar 
Xie, Z. K., Wang, Y. J. & Li, F. M. Effect of plastic mulching on soil water use and spring wheat yield in arid region of northwest China. Agric. Water Manag. 75(1), 71–83 (2005).Article 

Google Scholar 
Khan, R. H., Anwar-ul-Haq, K. & Sajjad, M. R. Effect of different types of mulches on grain yield and yield components of wheat (Triticum aestivum) under rainfed condition. J. Biol. Agric. Healthc. 4(12), 85–91 (2014).
Google Scholar 
Weidhuner, A., Afshar, R. K., Luo, Y., Battaglia, M. & Sadeghpour, A. Particle size affects nitrogen and carbon estimate of a wheat cover crop. Agron. J. 111(6), 3398–3402 (2019).CAS 
Article 

Google Scholar 
Ding, Z. et al. The integrated effect of salinity, organic amendments, phosphorus fertilizers, and deficit irrigation on soil properties, phosphorus fractionation and wheat productivity. Sci. Rep. 10(1), 1–13 (2020).Article 
CAS 

Google Scholar 
Rummana, S., Amin, A. K. M. R., Islam, M. S. & Faruk, G. M. Effect of irrigation and mulch materials on growth and yield of wheat. Bang. Agron. J. 21(1), 71–76 (2018).Article 

Google Scholar 
Richard, L. A. Diagnosis and improvement of saline and alkaline soils. Handbook No. 60 (US Depart. Agric., 1954).McLean, E. O. Soil pH and lime requirement. Methods of Soil Analysis: Part 2 Chemical and Microbiological Properties, Vol. 9, 199–224 (1983).Walkley, A. A critical examination of a rapid method for determining organic carbon in soils—Effect of variations in digestion conditions and of inorganic soil constituents. Soil Sci. 63, 251–264 (1947).ADS 
CAS 
Article 

Google Scholar 
Singleton, V. L., Orthofer, R. & Lamuela-Raventos, R. M. Analysis of total phenols and other oxidation substrates and antioxidants by means of Folin–Ciocalteu reagent. Methods Enzymol. 299, 152–178 (1999).CAS 
Article 

Google Scholar 
Vance, E. D., Brookes, P. C. & Jenkinson, D. S. An extraction method for measuring soil microbial biomass C. Soil Biol. Biochem. 19, 703–707 (1987).CAS 
Article 

Google Scholar 
Bremner, J. M. & Mulvaney, C. S. Nitrogen-total. In Methods of Soil Analysis. Part 2. Chemical and Microbiological Properties (eds Page, A. L. et al.) 595–624 (Soil Sci. Society America, 1982).
Google Scholar 
Steel, R. G. D., Torrie, J. H. & Dickey, D. A. Principles and Procedures of Statistics: A Biometrical Approach 3rd edn, 246 (McGraw-Hill, 1997).
Google Scholar 
Brady, N. C. & Weil, R. R. Soil colloids: Seat of soil chemical and physical acidity. Nat. Prop. Soils 5(13), 311–358 (2008).
Google Scholar 
Scharenbroch, B. C. & Lloyd, J. E. Particulate organic matter and soil nitrogen availability in urban landscapes. Arboricul. Urb. For. 32(4), 180–191 (2006).Article 

Google Scholar 
Bhadha, J. H., Capasso, J. M., Khatiwada, R., Swanson, S. & LaBorde, C. Raising soil organic matter content to improve water holding capacity. UF/IFAS 1–5 (2017).Chalker-Scott, L. Impact of mulches on landscape plants and the environment—A review. J. Environ. Hortic. 25(4), 239–249 (2007).Article 

Google Scholar 
Liu, Z., Fu, B., Zheng, X. & Liu, G. Plant biomass, soil water content and soil N:P ratio regulating soil microbial functional diversity in a temperate steppe: A regional scale study. Soil Biol. Biochem. 42(3), 445–450 (2010).CAS 
Article 

Google Scholar 
Bai, S. H., Blumfield, T. J. & Reverchon, F. The impact of mulch type on soil organic carbon and nitrogen pools in a sloping site. Biol. Fertil. Soils 50(1), 37–44 (2014).Article 

Google Scholar 
Yang, H. et al. The combined effects of maize straw mulch and no-tillage on grain yield and water and nitrogen use efficiency of dry-land winter wheat (Triticum aestivum L.). Soil Tillage Res. 197, 104485 (2020).Article 

Google Scholar 
Li, X. J. et al. Abscisic acid pretreatment enhances salt tolerance of rice seedlings: Proteomic evidence. Biochim. Biophys. Acta (BBA) Proteins Proteomics 1804(4), 929–940 (2010).CAS 
Article 

Google Scholar 
Fang, S., Xie, B., Liu, D. & Liu, J. Effects of mulching materials on nitrogen mineralization, nitrogen availability and poplar growth on degraded agricultural soil. New For. 41(2), 147–162 (2011).Article 

Google Scholar 
Houghton, J. T. Climate Change 2001: The Scientific Basis 419–470 (2001).Johnson, D. et al. Plant community composition affects the biomass, activity and diversity of microorganisms in limestone grassland soil. Eur. J. Soil Sci. 54(4), 671–678 (2003).Article 

Google Scholar 
Johnson, M. J., Lee, K. Y. & Scow, K. M. DNA finger printing reveals links among agricultural crops, soil properties, and the composition of soil microbial communities. Geoderma 114, 279–303 (2003).ADS 
Article 

Google Scholar 
Nielsen, N. M., Winding, A. & Binnerup, S. Microorganisms as Indicators of Soil Health 15–16 (Ministry of the Environment, National Environ. Res. Inst., 2002).
Google Scholar 
Wilkinson, S. C. et al. PLFA profiles of microbial communities in decomposing conifer litters subject to moisture stress. Soil Biol. Biochem. 34(2), 189–200 (2002).CAS 
Article 

Google Scholar 
Drenovsky, R. E., Vo, D., Graham, K. J. & Scow, K. M. Soil water content and organic carbon availability are major determinants of soil microbial community composition. Microb. Ecol. 48(3), 424–430 (2004).CAS 
PubMed 
Article 

Google Scholar 
Liu, Y. Y., Yao, H. Y. & Huang, C. Y. Influence of soil moisture regime on microbial community diversity and activity in a paddy soil. Acta Pedol. Sin. 43, 828–834 (2006).
Google Scholar 
Jensen, K. D., Beier, C., Michelsen, A. & Emmett, B. A. Effects of experimental drought on microbial processes in two temperate heathlands at contrasting water conditions. Appl. Soil Ecol. 24(2), 165–176 (2003).Article 

Google Scholar 
Stoklosa, A., Hura, T., Stupnicka-Rodzynkiewicz, E., Dabkowska, T. & Lepiarczyk, A. The influence of plant mulches on the content of phenolic compounds in soil and primary weed infestation of maize. Acta. Agron. Bot. 61(2), 205–219 (2008).
Google Scholar 
Ohno, T. Oxidation of phenolic acid derivatives by soil and its relevance to allelopathic activity. J. Environ. Qual. 30(5), 1631–1635 (2001).CAS 
PubMed 
Article 

Google Scholar 
Farooq, S., Shahid, M., Khan, M. B., Hussain, M. & Farooq, M. Improving the productivity of bread wheat by good management practices under terminal drought. J. Agric. Crop Sci. 201(3), 173–188 (2015).Article 

Google Scholar 
Madani, A., Rad, A. S., Pazoki, A., Nourmohammadi, G. & Zarghami, R. Wheat (Triticum aestivum L.) grain filling and dry matter partitioning responses to source: Sink modifications under postanthesis water and nitrogen deficiency. Acta Sci. Agron. 32, 145–151 (2010).CAS 
Article 

Google Scholar 
Deng, X. P., Shan, L., Zhang, H. & Turner, N. C. Improving agricultural water use efficiency in arid and semiarid areas of China. Agric. Water Manag. 80(1–3), 23–40 (2006).Article 

Google Scholar 
Athar, H. R., Khan, A. & Ashraf, M. Inducing salt tolerance in wheat by exogenously applied ascorbic acid through different modes. J. Plant Nutr. 32, 1799–1817 (2009).CAS 
Article 

Google Scholar 
Luo, et al. Dual plastic film and straw mulching boosts wheat productivity and soil quality under the El Nino in semiarid Kenya. Sci. Total Environ. 738, 139808 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Duan, et al. Improvement of wheat productivity and soil quality by arbuscular mycorrhizal fungi is density-and moisture-dependent. Agron. Sustain. Dev. 41(1), 1–12 (2021).Article 
CAS 

Google Scholar  More