More stories

  • in

    Cultivation and biogeochemical analyses reveal insights into methanogenesis in deep subseafloor sediment at a biogenic gas hydrate site

    1.Macdonald IR, Guinasso NL, Sassen R, Brooks JM, Lee L, Scott KT. Gas hydrate that breaches the sea-floor on the continental-slope of the Gulf-of-Mexico. Geology. 1994;22:699–702.CAS 

    Google Scholar 
    2.Kvenvolden KA. A review of the geochemistry of methane in natural gas hydrate. Org Geochem. 1995;23:997–1008.CAS 

    Google Scholar 
    3.Milkov AV. Molecular and stable isotope compositions of natural gas hydrates: a revised global dataset and basic interpretations in the context of geological settings. Org Geochem. 2005;36:681–702.CAS 

    Google Scholar 
    4.Cragg BA, Parkes RJ, Fry JC, Weightman AJ, Rochelle PA, Maxwell JR. Bacterial populations and processes in sediments containing gas hydrates (ODP Leg 146: Cascadia Margin). Earth Planet Sc Lett. 1996;139:497–507.CAS 

    Google Scholar 
    5.Yoshioka H, Maruyama A, Nakamura T, Higashi Y, Fuse H, Sakata S, et al. Activities and distribution of methanogenic and methane-oxidizing microbes in marine sediments from the Cascadia Margin. Geobiology. 2010;8:223–33.CAS 
    PubMed 

    Google Scholar 
    6.Yoshioka H, Sakata S, Cragg BA, Parkes RJ, Fujii T. Microbial methane production rates in gas hydrate-bearing sediments from the eastern Nankai Trough, off central Japan. Geochem J. 2009;43:315–21.CAS 

    Google Scholar 
    7.Heuer VB, Inagaki F, Morono Y, Kubo Y, Spivack AJ, Viehweger B, et al. Temperature limits to deep subseafloor life in the Nankai Trough subduction zone. Science. 2020;370:1230–4.CAS 
    PubMed 

    Google Scholar 
    8.Wellsbury P, Goodman K, Cragg BA, Parkes RJ. The geomicrobiology of deep marine sediments from Blake Ridge containing methane hydrate (sites 994, 995 and 997). Proc Ocean Drill Program Sci results. 2000;164:379–91.
    Google Scholar 
    9.Bidle KA, Kastner M, Bartlett DH. A phylogenetic analysis of microbial communities associated with methane hydrate containing marine fluids and sediments in the Cascadia margin (ODP site 892B). Fems Microbiol Lett. 1999;177:101–8.CAS 
    PubMed 

    Google Scholar 
    10.Reed DW, Fujita Y, Delwiche ME, Blackwelder DB, Sheridan PP, Uchida T, et al. Microbial communities from methane hydrate-bearing deep marine sediments in a forearc basin. Appl Environ Microbiol. 2002;68:3759–70.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    11.Briggs BR, Inagaki F, Morono Y, Futagami T, Huguet C, Rosell-Mele A, et al. Bacterial dominance in subseafloor sediments characterized by methane hydrates. FEMS Microbiol Ecol. 2012;81:88–98.CAS 
    PubMed 

    Google Scholar 
    12.Kendall MM, Boone DR. Cultivation of methanogens from shallow marine sediments at Hydrate Ridge, Oregon. Archaea. 2006;2:31–38.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    13.Fry JC, Parkes RJ, Cragg BA, Weightman AJ, Webster G. Prokaryotic biodiversity and activity in the deep subseafloor biosphere. FEMS Microbiol Ecol. 2008;66:181–96.CAS 
    PubMed 

    Google Scholar 
    14.Nunoura T, Takaki Y, Shimamura S, Kakuta J, Kazama H, Hirai M, et al. Variance and potential niche separation of microbial communities in subseafloor sediments off Shimokita Peninsula, Japan. Environ Microbiol. 2016;18:1889–906.CAS 
    PubMed 

    Google Scholar 
    15.Mikucki JA, Liu Y, Delwiche M, Colwell FS, Boone DR. Isolation of a methanogen from deep marine sediments that contain methane hydrates, and description of Methanoculleus submarinus sp. nov. Appl Environ Microbiol. 2003;69:3311–6.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    16.Weng C-Y, Chen S-C, Lai M-C, Wu S-Y, Lin S, Yang TF, et al. Methanoculleus taiwanensis sp. nov., a methanogen isolated from deep marine sediment at the deformation front area near Taiwan. Int J Syst Evol Micr. 2015;65:1044–9.CAS 

    Google Scholar 
    17.Kendall MM, Liu Y, Sieprawska-Lupa M, Stetter KO, Whitman WB, Boone DR. Methanococcus aeolicus sp. nov., a mesophilic, methanogenic archaeon from shallow and deep marine sediments. Int J Syst Evol Microbiol. 2006;56:1525–9.CAS 
    PubMed 

    Google Scholar 
    18.Strąpoć D, Ashby M, Wood L, Levinson R, Huizinga B. Significant contribution of methyl/methanol-utilising methanogenic pathway in a subsurface biogas environment. In: Skovhus T, Whitby C, editors. Applied microbiology and molecular biology in oilfield systems. Dordrecht: Springer; 2010. p. 211–6.19.Guo H, Liu R, Yu Z, Zhang H, Yun J, Li Y, et al. Pyrosequencing reveals the dominance of methylotrophic methanogenesis in a coal bed methane reservoir associated with Eastern Ordos Basin in China. Int J Coal Geol. 2012;93:56–61.CAS 

    Google Scholar 
    20.Katayama T, Yoshioka H, Muramoto Y, Usami J, Fujiwara K, Yoshida S, et al. Physicochemical impacts associated with natural gas development on methanogenesis in deep sand aquifers. ISME J. 2015;9:436–46.CAS 
    PubMed 

    Google Scholar 
    21.Yanagawa K, Tani A, Yamamoto N, Hachikubo A, Kano A, Matsumoto R, et al. Biogeochemical cycle of methanol in anoxic deep-sea sediments. Microbes Environ. 2016;31:190–3.PubMed 
    PubMed Central 

    Google Scholar 
    22.Colwell F, Matsumoto R, Reed D. A review of the gas hydrates, geology, and biology of the Nankai Trough. Chem Geol. 2004;205:391–404.CAS 

    Google Scholar 
    23.Uchida T, Waseda A, Namikawa T. Methane accumulation and high concentration of gas hydrate in marine and terrestrial sandy sediments. In: Collett T, Johnson A, Knapp C, Boswell R, editors. Natural gas hydrates: energy resource potential and associated geologic hazards. Tulsa: American Association of Petroleum Geologists Memoir 89; 2009. p. 401–13.24.Katayama T, Yoshioka H, Takahashi HA, Amo M, Fujii T, Sakata S. Changes in microbial communities associated with gas hydrates in subseafloor sediments from the Nankai Trough. FEMS Microbiol Ecol. 2016;92:fiw093.PubMed 

    Google Scholar 
    25.Oba M, Sakata S, Fujii T. Archaeal polar lipids in subseafloor sediments from the Nankai Trough: Implications for the distribution of methanogens in the deep marine subsurface. Org Geochem. 2015;78:153–60.CAS 

    Google Scholar 
    26.Noguchi S, Shimoda N, Takano O, Oikawa N, Inamori T, Saeki T, et al. 3-D internal architecture of methane hydrate-bearing turbidite channels in the eastern Nankai Trough, Japan. Mar Pet Geol. 2011;28:1817–28.
    Google Scholar 
    27.Fujii T, Suzuki K, Takayama T, Tamaki M, Komatsu Y, Konno Y, et al. Geological setting and characterization of a methane hydrate reservoir distributed at the first offshore production test site on the Daini-Atsumi Knoll in the eastern Nankai Trough, Japan. Mar Pet Geol. 2015;66:310–22.CAS 

    Google Scholar 
    28.Kanno T, Fukuhara M, Osawa O, Chee S, Takekoshi M, Wang X, et al. Estimation of geothermal gradient in marine gas-hydrate-bearing formation in the Eastern Nankai Trough. Beijing, China: Proceedings of the 8th International Conference on Gas Hydrates (ICGH8–2014); 2014.29.Kaneko M, Takano Y, Ogawa NO, Sato Y, Yoshida N, Ohkouchi N. Estimation of methanogenesis by quantification of coenzyme F430 in marine sediments. Geochem J. 2016;50:453–60.CAS 

    Google Scholar 
    30.Kaneko M, Takano Y, Chikaraishi Y, Ogawa NO, Asakawa S, Watanabe T, et al. Quantitative analysis of coenzyme F430 in environmental samples: a new diagnostic tool for methanogenesis and anaerobic methane oxidation. Anal Chem. 2014;86:3633–8.CAS 
    PubMed 

    Google Scholar 
    31.Katayama T, Kamagata Y Cultivation of Methanogens. Hydrocarbon and lipid microbiology protocols. In: McGenity T, Timmis K, Nogales B, editors. Springer protocols handbooks. Berlin, Heidelberg: Springer; 2016. p. 177–95.32.Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol. 2009;75:7537–41.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    33.Pruesse E, Quast C, Knittel K, Fuchs BM, Ludwig W, Peplies J, et al. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 2007;35:7188–96.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    34.Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S. MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol Biol Evol. 2011;28:2731–9.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    35.Jobb G, von Haeseler A, Strimmer K. TREEFINDER: a powerful graphical analysis environment for molecular phylogenetics. BMC Evol Biol. 2004;4:18.PubMed 
    PubMed Central 

    Google Scholar 
    36.Whiticar MJ. Carbon and hydrogen isotope systematics of bacterial formation and oxidation of methane. Chem Geol. 1999;161:291–314.CAS 

    Google Scholar 
    37.Scheller S, Goenrich M, Thauer RK, Jaun B. Methyl-coenzyme M reductase from methanogenic archaea: Isotope effects on the formation and anaerobic oxidation of methane. J Am Chem Soc. 2013;135:14975–84.CAS 
    PubMed 

    Google Scholar 
    38.Diekert G, Konheiser U, Piechulla K, Thauer RK. Nickel requirement and factor F430 content of methanogenic bacteria. J Bacteriol. 1981;148:459–64.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    39.Mayr S, Latkoczy C, Krüger M, Günther D, Shima S, Thauer RK, et al. Structure of an F430 variant from archaea associated with anaerobic oxidation of methane. J Am Chem Soc. 2008;130:10758–67.CAS 
    PubMed 

    Google Scholar 
    40.House CH, Orphan VJ, Turk KA, Thomas B, Pernthaler A, Vrentas JM, et al. Extensive carbon isotopic heterogeneity among methane seep microbiota. Environ Microbiol. 2009;11:2207–15.CAS 
    PubMed 

    Google Scholar 
    41.Lloyd KG, Alperin MJ, Teske A. Environmental evidence for net methane production and oxidation in putative ANaerobic MEthanotrophic (ANME) archaea. Environ Microbiol. 2011;13:2548–64.CAS 
    PubMed 

    Google Scholar 
    42.Laso-Pérez R, Wegener G, Knittel K, Widdel F, Harding KJ, Krukenberg V, et al. Thermophilic archaea activate butane via alkyl-coenzyme M formation. Nature. 2016;539:396–401.PubMed 

    Google Scholar 
    43.Inagaki F, Nunoura T, Nakagawa S, Teske A, Lever M, Lauer A, et al. Biogeographical distribution and diversity of microbes in methane hydrate-bearing deep marine sediments on the Pacific Ocean Margin. Proc Natl Acad Sci USA. 2006;103:2815–20.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    44.Marchesi JR, Weightman AJ, Cragg BA, Parkes RJ, Fry JC. Methanogen and bacterial diversity and distribution in deep gas hydrate sediments from the Cascadia Margin as revealed by 16S rRNA molecular analysis. FEMS Microbiol Ecol. 2001;34:221–8.CAS 
    PubMed 

    Google Scholar 
    45.Nunoura T, Inagaki F, Delwiche ME, Colwell FS, Takai K. Subseafloor microbial communities in methane hydrate-bearing sediment at two distinct locations (ODP Leg 204) in the Cascadia Margin. Microbes Environ. 2008;23:317–25.PubMed 

    Google Scholar 
    46.Cord-Ruwisch R, Ollivier B. Interspecific hydrogen transfer during methanol degradation by Sporomusa acidovorans and hydrogenophilic anaerobes. Arch Microbiol. 1986;144:163–5.CAS 

    Google Scholar 
    47.Heijthuijsen JHFG, Hansen TA. Interspecies hydrogen transfer in co-cultures of methanol-utilizing acidogens and sulfate-reducing or methanogenic bacteria. FEMS Microbiol Ecol. 1986;2:57–64.
    Google Scholar 
    48.Eichler B, Schink B. Oxidation of primary aliphatic alcohols by Acetobacterium carbinolicum sp. nov., a homoacetogenic anaerobe. Arch Microbiol. 1984;140:147–52.CAS 

    Google Scholar 
    49.Parkes RJ, Cragg B, Roussel E, Webster G, Weightman A, Sass H. A review of prokaryotic populations and processes in sub-seafloor sediments, including biosphere: geosphere interactions. Mar Geol. 2014;352:409–25.CAS 

    Google Scholar 
    50.Imachi H, Aoi K, Tasumi E, Saito Y, Yamanaka Y, Saito Y, et al. Cultivation of methanogenic community from subseafloor sediments using a continuous-flow bioreactor. ISME J. 2011;5:1913–25.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    51.Newberry CJ, Webster G, Cragg BA, Parkes RJ, Weightman AJ, Fry JC. Diversity of prokaryotes and methanogenesis in deep subsurface sediments from the Nankai Trough, Ocean Drilling Program Leg 190. Environ Microbiol. 2004;6:274–87.PubMed 

    Google Scholar 
    52.Orsi WD, Edgcomb VP, Christman GD, Biddle JF. Gene expression in the deep biosphere. Nature 2013;499:205–8.CAS 
    PubMed 

    Google Scholar 
    53.Vigneron A, L’Haridon S, Godfroy A, Roussel EG, Cragg BA, Parkes RJ, et al. Evidence of active methanogen communities in shallow sediments of the sonora margin cold seeps. Appl Environ Microbiol. 2015;81:3451–9.CAS 
    PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Species delimitation and mitonuclear discordance within a species complex of biting midges

    1.De Queiroz, K. Species concepts and species delimitation. Syst. Biol. 56, 879–886. https://doi.org/10.1080/10635150701701083 (2007).Article 
    PubMed 

    Google Scholar 
    2.Coyne, J. A. & Orr, H. A. Speciation (Sinauer Associates Inc, 2004).
    Google Scholar 
    3.Endler, J. A. Gene flow and population differentiation: studies of clines suggest that differentiation along environmental gradients may be independent of gene flow. Science 179, 243–250 (1973).CAS 
    PubMed 
    ADS 

    Google Scholar 
    4.Mayr, E. Systematics and the Origin of Species, from the Viewpoint of a Zoologist (Harvard University Press, 1999).
    Google Scholar 
    5.Richardson, J. L., Urban, M. C., Bolnick, D. I. & Skelly, D. K. Microgeographic adaptation and the spatial scale of evolution. Trends Ecol. Evol. 29, 165–176 (2014).PubMed 

    Google Scholar 
    6.Nosil, P. Ernst Mayr and the integration of geographic and ecological factors in speciation. Biol. J. Lin. Soc. 95, 26–46 (2008).
    Google Scholar 
    7.Kisel, Y. & Barraclough, T. G. Speciation has a spatial scale that depends on levels of gene flow. Am. Nat. 175, 316–334 (2010).PubMed 

    Google Scholar 
    8.Leliaert, F. et al. DNA-based species delimitation in algae. Eur. J. Phycol. 49, 179–196 (2014).
    Google Scholar 
    9.Carstens, B. C., Pelletier, T. A., Reid, N. M. & Satler, J. D. How to fail at species delimitation. Mol. Ecol. 22, 4369–4383 (2013).PubMed 

    Google Scholar 
    10.Schlick-Steiner, B. C. et al. Integrative taxonomy: a multisource approach to exploring biodiversity. Annu. Rev. Entomol. 55, 421–438 (2010).CAS 
    PubMed 

    Google Scholar 
    11.Capblancq, T., Mavárez, J., Rioux, D. & Després, L. Speciation with gene flow: evidence from a complex of alpine butterflies (Coenonympha, Satyridae). Ecol. Evol. 9, 6444–6457 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    12.Pedraza-Marrón, C. d. R. et al. Genomics overrules mitochondrial DNA, siding with morphology on a controversial case of species delimitation. Proc. R. Soc. B 286, 20182924 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    13.Hinojosa, J. C. et al. A mirage of cryptic species: genomics uncover striking mitonuclear discordance in the butterfly Thymelicus sylvestris. Mol. Ecol. 28, 3857–3868 (2019).PubMed 

    Google Scholar 
    14.Nygren, A. et al. A mega-cryptic species complex hidden among one of the most common annelids in the North East Atlantic. PLoS ONE 13, e0198356 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    15.Thielsch, A., Knell, A., Mohammadyari, A., Petrusek, A. & Schwenk, K. Divergent clades or cryptic species? Mito-nuclear discordance in a Daphnia species complex. BMC Evol. Biol. 17, 1–9 (2017).
    Google Scholar 
    16.Eyer, P. A. & Hefetz, A. Cytonuclear incongruences hamper species delimitation in the socially polymorphic desert ants of the Cataglyphis albicans group in Israel. J. Evol. Biol. 31, 1828–1842 (2018).CAS 
    PubMed 

    Google Scholar 
    17.Borkent, A. Biology of Disease Vectors. 2nd edn, i–xxiii + 1–785 (Elsevier Academic Press, 2004).18.Mellor, P., Boorman, J. & Baylis, M. Culicoides biting midges: their role as arbovirus vectors. Annu. Rev. Entomol. 45, 307–340 (2000).CAS 
    PubMed 

    Google Scholar 
    19.Rushton, J. & Lyons, N. Economic impact of Bluetongue: a review of the effects on production. Veterinaria italiana 51, 401–406 (2015).PubMed 

    Google Scholar 
    20.Tabachnick, W. J. Culicoides vriipennis and Bluetongue-Virus eidemiology in the United States. Annu. Rev. Entomol. 41, 23–43. https://doi.org/10.1146/annurev.en.41.010196.000323 (1996).CAS 
    Article 
    PubMed 

    Google Scholar 
    21.Wirth, W. W. & Jones, R. H. The North American Subspecies of Culicoides variipennis (Diptera, Heleidae). U. S. Dep. Agric. Tech. Bull 1170, 1–35 (1957).
    Google Scholar 
    22.Holbrook, F. R. et al. Sympatry in the Culicoides variipennis Complex (Diptera: Ceratopogonidae): a Taxonomic Reassessment. J. Med. Entomol. 37, 65–76. https://doi.org/10.1603/0022-2585-37.1.65 (2000).CAS 
    Article 
    PubMed 

    Google Scholar 
    23.Hopken, M. W. Pathogen Vectors at the Wildlife-Livestock Interface: Molecular Approaches to Elucidating Culicoides (Diptera: Ceratopogonidae) Biology (University of Colorado, 2016).
    Google Scholar 
    24.Shults, P. A Study of the Taxonomy, Ecology, and Systematics of Culicoides Species (Diptera: Ceratopogonidae) Including those Associated with Deer Breeding Facilities in Southeast Texas (Texas A&M University, 2015).
    Google Scholar 
    25.Velten, R. K. & Mullens, B. A. Field morphological variation and laboratory hybridization of Culicoides variipennis sonorensis and C. v. occidentalis (Diptera:Ceratopogonidae) in southern California. J. Med. Entomol. 34, 277–284 (1997).CAS 
    PubMed 

    Google Scholar 
    26.Fontaine, M. C. et al. Extensive introgression in a malaria vector species complex revealed by phylogenomics. Science 347, 1258522 (2015).PubMed 

    Google Scholar 
    27.Bolnick, D. I. & Otto, S. P. The magnitude of local adaptation under genotype-dependent dispersal. Ecol. Evol. 3, 4722–4735 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    28.Slatkin, M. Isolation by distance in equilibrium and non-equilibrium populations. Evolution 47, 264–279 (1993).PubMed 

    Google Scholar 
    29.Pante, E. et al. Species are hypotheses: avoid connectivity assessments based on pillars of sand. Mol. Ecol. 24, 525–544 (2015).PubMed 

    Google Scholar 
    30.Jacquet, S. et al. Colonization of the Mediterranean basin by the vector biting midge species Culicoides imicola: an old story. Mol. Ecol. 24, 5707–5725. https://doi.org/10.1111/mec.13422 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    31.Onyango, M. G. et al. Genotyping of whole genome amplified reduced representation libraries reveals a cryptic population of Culicoides brevitarsis in the Northern Territory, Australia. BMC Genomics 17, 769. https://doi.org/10.1186/s12864-016-3124-1 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    32.Onyango, M. G. et al. Delineation of the population genetic structure of Culicoides imicola in East and South Africa. Parasit. Vectors 8, 660. https://doi.org/10.1186/s13071-015-1277-4 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    33.Mignotte, A. et al. High dispersal capacity of Culicoides obsoletus (Diptera: Ceratopogonidae), vector of bluetongue and Schmallenberg viruses, revealed by landscape genetic analyses. Parasit. Vectors 14, 1–14 (2021).
    Google Scholar 
    34.Sanders, C. J. & Carpenter, S. Assessment of an immunomarking technique for the study of dispersal of Culicoides biting midges. Infect. Genet. Evol. 28, 583–587 (2014).PubMed 

    Google Scholar 
    35.Kluiters, G., Swales, H. & Baylis, M. Local dispersal of palaearctic Culicoides biting midges estimated by mark-release-recapture. Parasit. Vectors 8, 86 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    36.Ducheyne, E. et al. Quantifying the wind dispersal of Culicoides species in Greece and Bulgaria. Geospat. Health 10, 177–189 (2007).
    Google Scholar 
    37.Purse, B. V. et al. Climate change and the recent emergence of bluetongue in Europe. Nat. Rev. Microbiol. 3, 171–181 (2005).CAS 
    PubMed 

    Google Scholar 
    38.Jacquet, S. et al. Range expansion of the Bluetongue vector, Culicoides imicola, in continental France likely due to rare wind-transport events. Sci. Rep. https://doi.org/10.1038/srep27247 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    39.Rundle, H. D. & Nosil, P. Ecological speciation. Ecol. Lett. 8, 336–352 (2005).
    Google Scholar 
    40.Wang, I. J. & Bradburd, G. S. Isolation by environment. Mol. Ecol. 23, 5649–5662 (2014).PubMed 

    Google Scholar 
    41.Shults, P. A Study of Culicoides Biting Midges in the Subgenus Monoculicoides: Population Genetics, Taxonomy, Systematics, and Control. Ph.D. thesis, Texas A&M University (2021).42.Jewiss-Gaines, A., Barelli, L. & Hunter, F. F. First records of Culicoides sonorensis (Diptera: Ceratopogonidae), a known vector of bluetongue virus, Southern Ontario. J. Med. Entomol. 54, 757–762. https://doi.org/10.1093/jme/tjw215 (2017).CAS 
    Article 
    PubMed 

    Google Scholar 
    43.Chan, K. M. & Levin, S. A. Leaky prezygotic isolation and porous genomes: rapid introgression of maternally inherited DNA. Evolution 59, 720–729 (2005).CAS 
    PubMed 

    Google Scholar 
    44.Harrison, R. G. Hybrid zones: windows on evolutionary process. Oxf. Surv. Evol. Biol. 7, 69–128 (1990).
    Google Scholar 
    45.Harrison, R. G. Animal mitochondrial DNA as a genetic marker in population and evolutionary biology. Trends Ecol. Evol. 4, 6–11 (1989).CAS 
    PubMed 

    Google Scholar 
    46.Després, L. One, Two or More Species? Mitonuclear Discordance and Species Delimitation. Molecular ecology 28(17), 3845–3847 (2019).PubMed 

    Google Scholar 
    47.Janes, J. K. et al. The K= 2 conundrum. Mol. Ecol. 26, 3594–3602 (2017).PubMed 

    Google Scholar 
    48.De Meester, L., Vanoverbeke, J., Kilsdonk, L. J. & Urban, M. C. Evolving perspectives on monopolization and priority effects. Trends Ecol. Evol. 31, 136–146 (2016).PubMed 

    Google Scholar 
    49.Ballard, J. W. O., Chernoff, B. & James, A. C. Divergence of mitochondrial DNA is not corroborated by nuclear DNA, morphology, or behavior in Drosophila simulans. Evolution 56, 527–545 (2002).PubMed 

    Google Scholar 
    50.Behura, S., Sahu, S., Mohan, M. & Nair, S. Wolbachia in the Asian rice gall midge, Orseolia oryzae (Wood-Mason): Correlation between host mitotypes and infection status. Insect Mol. Biol. 10, 163–171 (2001).CAS 
    PubMed 

    Google Scholar 
    51.Covey, H. et al. Cryptic Wolbachia (Rickettsiales: Rickettsiaceae) detection and prevalence in Culicoides (Diptera: Ceratopogonidae) midge populations in the United States. J. Med. Entomol. 57, 1262–1269. https://doi.org/10.1093/jme/tjaa003 (2020).Article 
    PubMed 

    Google Scholar 
    52.Pagès, N., Muñoz-Muñoz, F., Verdún, M., Pujol, N. & Talavera, S. First detection of Wolbachia-infected Culicoides (Diptera: Ceratopogonidae) in Europe: Wolbachia and Cardinium infection across Culicoides communities revealed in Spain. Parasit. Vectors 10, 582. https://doi.org/10.1186/s13071-017-2486-9 (2017).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    53.Pilgrim, J. et al. Cardinium symbiosis as a potential confounder of mtDNA based phylogeographic inference in Culicoides imicola (Diptera: Ceratopogonidae), a vector of veterinary viruses. Parasit. Vectors 14, 100. https://doi.org/10.1186/s13071-020-04568-3 (2021).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    54.Hare, M. P. Prospects for nuclear gene phylogeography. Trends Ecol. Evol. 16, 700–706 (2001).
    Google Scholar 
    55.Onyango, M. G. et al. Assessment of population genetic structure in the arbovirus vector midge, Culicoides brevitarsis (Diptera: Ceratopogonidae), using multi-locus DNA microsatellites. Vet. Res. 46, 108. https://doi.org/10.1186/s13567-015-0250-8 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    56.Fonseca, D. M., Smith, J. L., Kim, H.-C. & Mogi, M. Population genetics of the mosquito Culex pipiens pallens reveals sex-linked asymmetric introgression by Culex quinquefasciatus. Infect. Genet. Evol. 9, 1197–1203 (2009).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    57.Goubert, C., Minard, G., Vieira, C. & Boulesteix, M. Population genetics of the Asian tiger mosquito Aedes albopictus, an invasive vector of human diseases. Heredity 117, 125–134 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    58.Lehmann, T. et al. Microgeographic structure of Anopheles gambiae in western Kenya based on mtDNA and microsatellite loci. Mol. Ecol. 6, 243–253 (1997).CAS 
    PubMed 

    Google Scholar 
    59.Chapuis, M.-P. & Estoup, A. Microsatellite null alleles and estimation of population differentiation. Mol. Biol. Evol. 24, 621–631. https://doi.org/10.1093/molbev/msl191 (2006).CAS 
    Article 
    PubMed 

    Google Scholar 
    60.Manni, M. et al. Molecular markers for analyses of intraspecific genetic diversity in the Asian Tiger mosquito, Aedes albopictus. Parasit. Vectors 8, 1–11 (2015).
    Google Scholar 
    61.Arntzen, J. W., Jehle, R., Bardakci, F., Burke, T. & Wallis, G. P. Asymmetric viability of reciprocal-cross hybrids between Crested and Marbled Newts (Triturus cristatus and T. marmoratus). Evolution 63, 1191–1202. https://doi.org/10.1111/j.1558-5646.2009.00611.x (2009).Article 
    PubMed 

    Google Scholar 
    62.Gibeaux, R. et al. Paternal chromosome loss and metabolic crisis contribute to hybrid inviability in Xenopus. Nature 553, 337. https://doi.org/10.1038/nature25188 (2018).CAS 
    Article 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    63.Werren, J. H., Baldo, L. & Clark, M. E. Wolbachia: master manipulators of invertebrate biology. Nat. Rev. Microbiol. 6, 741 (2008).CAS 
    PubMed 

    Google Scholar 
    64.Servedio, M. R. & Kirkpatrick, M. The effects of gene flow on reinforcement. Evolution 51, 1764–1772. https://doi.org/10.1111/j.1558-5646.1997.tb05100.x (1997).Article 
    PubMed 

    Google Scholar 
    65.Howard, D. J. Reinforcement: origin, dynamics, and fate of an evolutionary hypothesis. Hybrid zones and the evolutionary process, 46–69 (1993).66.Yukilevich, R. Asymmetrical patterns of speciation uniquely support reinforcement in Drosophila. Evolution 66, 1430–1446. https://doi.org/10.1111/j.1558-5646.2011.01534.x (2012).Article 
    PubMed 

    Google Scholar 
    67.Downes, J. A. The Culicoides variipennis complex: a necessary re-alignment of nomenclature (Diptera: Ceratopogonidae). Can. Entomol. 110, 63–69 (1978).
    Google Scholar 
    68.Toews, D. P. & Brelsford, A. The biogeography of mitochondrial and nuclear discordance in animals. Mol. Ecol. 21, 3907–3930 (2012).CAS 
    PubMed 

    Google Scholar 
    69.Smith, H. & Mullens, B. A. Seasonal activity, size, and parity of Culicoides occidentalis (Diptera: Ceratopogonidae) in a coastal southern California salt marsh. J. Med. Entomol. 40, 352–355. https://doi.org/10.1603/0022-2585-40.3.352 (2003).Article 
    PubMed 

    Google Scholar 
    70.Linley, J. The effect of salinity on oviposition and egg hatching in Culicoides variipennis sonorensis (Diptera: Ceratopogonidae). J. Am. Mosq. Control Assoc. 2, 79–82 (1986).CAS 
    PubMed 

    Google Scholar 
    71.Gerry, A. C. & Mullens, B. A. Response of Male Culicoides variipennis sonorensis (Diptera: Ceratopogonidae) to carbon dioxide and observations of mating behavior on and near cattle. J. Med. Entomol. 35, 239–244. https://doi.org/10.1093/jmedent/35.3.239 (1998).CAS 
    Article 
    PubMed 

    Google Scholar 
    72.Nolan, D. V. et al. Rapid diagnostic PCR assays for members of the Culicoides obsoletus and Culicoides pulicaris species complexes, implicated vectors of bluetongue virus in Europe. Vet. Microbiol. 124, 82–94 (2007).CAS 
    PubMed 

    Google Scholar 
    73.Sebastiani, F. et al. Molecular differentiation of the Old World Culicoides imicola species complex (Diptera, Ceratopogonidae), inferred using random amplified polymorphic DNA markers. Mol. Ecol. 10, 1773–1786 (2001).CAS 
    PubMed 

    Google Scholar 
    74.Carlson, D. Identification of mosquitoes of Anopheles gambiae species complex A and B by analysis of cuticular components. Science 207, 1089–1091 (1980).CAS 
    PubMed 
    ADS 

    Google Scholar 
    75.Palacios, G. et al. Characterization of the Sandfly fever Naples species complex and description of a new Karimabad species complex (genus Phlebovirus, family Bunyaviridae). J. Gen. Virol. 95, 292 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    76.Rivas, G., Souza, N. & Peixoto, A. A. Analysis of the activity patterns of two sympatric sandfly siblings of the Lutzomyia longipalpis species complex from Brazil. Med. Vet. Entomol. 22, 288–290 (2008).CAS 
    PubMed 

    Google Scholar 
    77.Wilson, W. C. et al. Current status of bluetongue virus in the Americas. Bluetongue 10, 197–220 (2009).
    Google Scholar 
    78.Allen, S. E. et al. Epizootic Hemorrhagic Disease in White-Tailed Deer, Canada. Emerg. Infect. Dis. 25, 832–834. https://doi.org/10.3201/eid2504.180743 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    79.McGregor, B. L. et al. Field data implicating Culicoides stellifer and Culicoides venustus (Diptera: Ceratopogonidae) as vectors of epizootic hemorrhagic disease virus. Parasit. Vectors 12, 258. https://doi.org/10.1186/s13071-019-3514-8 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    80.Shults, P., Ho, A., Martin, E. M., McGregor, B. L. & Vargo, E. L. Genetic diversity of Culicoides stellifer (Diptera: Ceratopogonidae) in the Southeastern United States compared with sequences from Ontario, Canada. J. Med. Entomol. 57, 1324–1327. https://doi.org/10.1093/jme/tjaa025 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    81.Mallet, J. Hybridization as an invasion of the genome. Trends Ecol. Evol. 20, 229–237 (2005).PubMed 

    Google Scholar 
    82.Ciota, A. T., Chin, P. A. & Kramer, L. D. The effect of hybridization of Culex pipiens complex mosquitoes on transmission of West Nile virus. Parasit. Vectors 6, 1–4 (2013).
    Google Scholar 
    83.Meiswinkel, R., Gomulski, L., Delécolle, J., Goffredo, M. & Gasperi, G. The taxonomy of Culicoides vector complexes-unfinished business. Vet. Ital. 40, 151–159 (2004).CAS 
    PubMed 

    Google Scholar 
    84.Ewels, P., Magnusson, M., Lundin, S. & Käller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics (Oxford, England) 32, 3047–3048. https://doi.org/10.1093/bioinformatics/btw354 (2016).CAS 
    Article 

    Google Scholar 
    85.Andrews, S. Babraham bioinformatics-FastQC a quality control tool for high throughput sequence data. https://www.bioinformatics.babraham.ac.uk/projects/fastqc (2010).86.Rochette, N. C., Rivera-Colón, A. G. & Catchen, J. M. Stacks 2: Analytical methods for paired-end sequencing improve RADseq-based population genomics. Mol. Ecol. 28, 4737–4754 (2019).CAS 
    PubMed 

    Google Scholar 
    87.Morales-Hojas, R. et al. The genome of the biting midge Culicoides sonorensis and gene expression analyses of vector competence for bluetongue virus. BMC Genomics 19, 624. https://doi.org/10.1186/s12864-018-5014-1 (2018).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    88.Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics (Oxford, England) 25, 1754–1760 (2009).CAS 

    Google Scholar 
    89.Pante, E. et al. Use of RAD sequencing for delimiting species. Heredity 114, 450–459 (2015).CAS 
    PubMed 

    Google Scholar 
    90.Benestan, L. M. et al. Conservation genomics of natural and managed populations: building a conceptual and practical framework. Mol. Ecol. 25, 2967–2977 (2016).PubMed 

    Google Scholar 
    91.Lischer, H. E. & Excoffier, L. PGDSpider: an automated data conversion tool for connecting population genetics and genomics programs. Bioinformatics (Oxford, England) 28, 298–299 (2012).CAS 

    Google Scholar 
    92.Pina-Martins, F., Silva, D. N., Fino, J. & Paulo, O. S. Structure_threader: An improved method for automation and parallelization of programs structure, fastStructure and MavericK on multicore CPU systems. Mol. Ecol. Resour. 17, e268–e274 (2017).CAS 
    PubMed 

    Google Scholar 
    93.Raj, A., Stephens, M. & Pritchard, J. K. Variational Inference of Population Structure in Large SNP Datasets. bioRxiv 10, 001073 (2013).
    Google Scholar 
    94.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.http://www.R-project.org/ (2013).95.Jombart, Thibaut, and Caitlin Collins. A tutorial for discriminant analysis of principal components (DAPC) using adegenet 2.0. 0. London: Imperial College London, MRC Centre for Outbreak Analysis and Modelling (2015).96.Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics (Oxford, England) 30, 1312–1313 (2014).CAS 

    Google Scholar 
    97.Leaché, A. D., Banbury, B. L., Felsenstein, J., De Oca, A.N.-M. & Stamatakis, A. Short tree, long tree, right tree, wrong tree: New acquisition bias corrections for inferring SNP phylogenies. Syst. Biol. 64, 1032–1047 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    98.Pattengale, N. D., Alipour, M., Bininda-Emonds, O. R., Moret, B. M. & Stamatakis, A. How many bootstrap replicates are necessary?. J. Comput. Biol. 17, 337–354 (2010).MathSciNet 
    CAS 
    PubMed 

    Google Scholar 
    99.Trifinopoulos, J., Nguyen, L.-T., von Haeseler, A. & Minh, B. Q. W-IQ-TREE: A fast online phylogenetic tool for maximum likelihood analysis. Nucleic Acids Res. 44, W232–W235 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    100.Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K., Von Haeseler, A. & Jermiin, L. S. ModelFinder: Fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    101.Nguyen, L.-T., Schmidt, H. A., Von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).CAS 
    PubMed 

    Google Scholar 
    102.Hoang, D. T., Chernomor, O., Von Haeseler, A., Minh, B. Q. & Vinh, L. S. UFBoot2: improving the ultrafast bootstrap approximation. Mol. Biol. Evol. 35, 518–522 (2018).CAS 
    PubMed 

    Google Scholar 
    103.Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: Assessing the performance of PhyML 30. Syst. Biol. 59, 307–321. https://doi.org/10.1093/sysbio/syq010 (2010).CAS 
    Article 
    PubMed 

    Google Scholar 
    104.Rousset, F. genepop’007: a complete re‐implementation of the genepop software for Windows and Linux. Molecular ecology resources 8(1), 103–106 (2008).
    Google Scholar 
    105.Rousset, F. Genetic differentiation between individuals. J Evol Biol 13, 58–62 (2000).
    Google Scholar 
    106.Loiselle, B. A., Sork, V. L., Nason, J. & Graham, C. Spatial genetic structure of a tropical understory shrub, Psychotria officinalis (Rubiaceae). Am. J. Bot. 82, 1420–1425 (1995).
    Google Scholar 
    107.Hardy, O. & Vekemans, X. SPAGeDi 1.5. A program for Spatial Pattern Analysis of Genetic Diversity. User’s manual http://ebe.ulb.ac.be/ebe/SPAGeDi_files/SPAGeDi_1.5_Manual.pdf. Université Libre de Bruxelles, Brussells, Belgium.[Google Scholar] (2015).108.Jay, F., Sjödin, P., Jakobsson, M. & Blum, M. G. Anisotropic isolation by distance: the main orientations of human genetic differentiation. Mol. Biol. Evol. 30, 513–525 (2013).CAS 
    PubMed 

    Google Scholar 
    109.Piry, S. et al. Mapping Averaged Pairwise Information (MAPI): a new exploratory tool to uncover spatial structure. Methods Ecol. Evol. 7, 1463–1475 (2016).
    Google Scholar 
    110.Kearse, M. et al. Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics (Oxford, England) 28, 1647–1649. https://doi.org/10.1093/bioinformatics/bts199 (2012).Article 

    Google Scholar 
    111.Hopken, M. W. Pathogen Vectors at The Wildlife-Livestock Interface: Molecular Approaches to Elucidating Culicoides (Diptera: Ceratopogonidae) Ph.D. thesis, Colorado State University (2016).112.Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    113.Bandelt, H. J., Forster, P. & Rohl, A. Median-joining networks for inferring intraspecific phylogenies. Mol. Biol. Evol. 16, 37–48. https://doi.org/10.1093/oxfordjournals.molbev.a026036 (1999).CAS 
    Article 
    PubMed 

    Google Scholar  More

  • in

    Completely predatory development is described in a braconid wasp

    The presents study indicates that Bracon predatorius generally oviposits during early stages of gall development (Fig. 1d) on galls induced by Aceria doctersi mostly on tender leaves (Fig 1a–c) and rarely on petioles and stems13. The number of B. predatorius larvae in parasitized galls ranged from 1–27 (n=93). Eighty-five percent of the examined galls (n=109) were parasitized by B. predatorius. Different development stages of larvae (Fig. 1f,g) and pupae (Fig. 1i) of B. predatorius were found together in some large galls (n=31) (Fig. 1i), which suggests multiple oviposition at different stages of gall development. Dissection of leaf galls two hours after oviposition by B. predatorius always revealed only a single egg (n=8). No live A. doctersi individuals were found close to the parasitoid wasp pupae (Fig. 1h). Aceria doctersi galls parasitised by B. predatorius have also been found in Kodakara (Thrissur district, Kerala) about 100 km away from the type locality in Kozhikode.The larval stages of B. predatorius feed on both juvenile and adults of A. doctersi (Fig 2d–f, Supplementary Video 1) which usually remain close to the erineal hairs on which they feed16; no egg predation occurs. Young larvae of B. predatorius wriggle through in between erineal hairs (Supplementary Video 1). They use their sickle-shaped mandibles (Fig 3b–e) to hunt mites (Supplementary Video 1). Continuous outward and inward movement of mandibles of B. predatorius larvae occurs along with the wriggling movement (Supplementary Video 1). The final instar larvae of B. predatorius are the most active and they feed voraciously at the rate of 5–7 A. doctersi individuals/min (n=8) (Supplementary Video 1).Figure 2Predatory behaviour of Bracon predatorius Ranjith & Quicke sp. nov. (a–c) Relationships between presence/absence and number of B. predatorius, gall size and numbers of mites (median, upper and lower quartiles, 1.5 × interquartile range and outliers): (a) galls without Bracon predatorius (n = 16) are significantly smaller than those with one or more Bracon predatorius (n = 93) (t = 3.7592, DF = 97.265, p-value = 0.000291), (b) galls without Bracon predatorius contain significantly more mites than those with (t = 6.308, DF = 15.877, p-value = 0.0001), (c) mite number as a function of number of Bracon predatorius larvae (only in parasitised galls) with gall volume as co-variate (n = 93, adjusted R2 = 0.4657,F = 21.13 on 3 and 89DF, p-value = 0.0001), gall volume and interaction were non-significant. (d–f) Sequential images of predatory behaviour of Bracon predatorius.Full size imageFigure 3Final instar larval cephalic structure of Bracon predatorius Ranjith & Quicke sp. nov. (a–d) Slide microphotographs of larval head capsule and mandible (a) macerated head capsule in anterior view, (b) head capsule, in dorsal view, (c) head capsule (in part), ventral view, (d) right mandible, in dorsal view, (e) anterior view of living final instar larva of B. predatorius consuming mite.Full size imageUnattacked galls were significantly smaller than those containing B. predatorius (means 217 and 595 respectively; p More

  • in

    Viral tag and grow: a scalable approach to capture and characterize infectious virus–host pairs

    Improving our understanding of “viral tagging” flow cytometric signalsVT is a deceptively simple idea whereby a mixture of natural viruses are labeled with a DNA-binding fluorescent dye and ‘bait’ hosts infected by these stained viruses can be detected with flow cytometry via the fluorescent shift of “viral-tagged cells” [38, 39] (Fig. 1A, B). These viral-tagged cells can then be sorted, and the viral DNA separated using isotopic fractionation (the DNA of the cultured host is pre-labeled with “heavy” DNA) to access the metagenomes of the viruses that were experimentally determined to have infected these cell types. However, in practice, VT has been only minimally adopted by the community [43], presumably because it requires costly equipment (a high-performance flow sorter) and diverse technical expertise (flow cytometry, phage biology, and bioinformatics), while lacking sufficient benchmarking. To the latter, we sought to use a cultured phage-host model system (Pseudoalteromonas strain H71, hereafter H71, and its specific myophage PSA-HM1, hereafter HM1) to systematically assess the impact of various multiplicities of infection (MOIs; the ratio of the number of virus particles to the number of target cells, [48]) on the resultant VT signals. Further, we sought to augment VT to add an “and grow” capability whereby scalable single-virus cultivation, characterization, and sequencing could be enabled (Fig. 1C).Fig. 1: Overview of viral tagging, and the variant developed here—viral tag and grow.A Viruses are labeled with a green fluorescent dye and then mixed with potential host bacteria. B Fluorescence detection of individual cells with fluorescently-labeled viruses (FLVs) by flow cytometer. The flow cytometry plot (side scatter or forward scatter versus green fluorescence) shows the expected locations of FLV-tagged (VTs) and nontagged cells (NTs), which are flow-cytometrically green positive and negative, respectively. C Single-cell sorting of VTs is followed by subsequent amplification of infectious viruses. Single VTs are sorted into a 96-well plate that contains host culture. Culture growth is monitored by measuring optical density (OD) over time. A decrease in the OD curve from VT-containing wells (relative to the phage-negative control) indicates cell lysis by progeny viruses produced from a single isolated VT cell.Full size imageTo gain a better understanding of the biology behind VT signatures, we examined how H71 interacts with HM1, a phage specific for this host, and HS8, a phage that does not adsorb to this host – both assayed via flow cytometry and microscopy (for details, see Methods and online protocol, https://www.protocols.io/view/viral-tagging-and-grow-a-scalable-approach-to-captbwutpewn?form=MY01SV&OCID=MY01SV). Briefly, phages were stained with SybrGold (fluoresces green upon blue-light excitation) and for microscopy, H71 cells were stained with DAPI (fluoresces blue upon blue-light excitation, 4′,6-diamidino-2-phenylindole), as previously described [39, 49]. Replicate cultures of stained cells were then mixed with fluorescently-labeled phages (either HM1 or HS8 in each treatment) at infective MOIs = 1, 2, and 4, then these infections were incubated for 10 min, and processed (centrifuged and resuspended; see Methods for details) three times to remove free phages (see Methods for details). For microscopy, the relative fraction of virus-tagged (VTs) and nontagged cells (NTs) was measured from the available cells up to ~500 cells for each sample. For flow cytometry, cell detection was optimized to minimize background noise [50], and negative controls consisted of stained and washed sheath buffer and filtered Q water samples, as previously described [39].Overall, the resulting VT experiments were robust and informative. First, our cell-only optimizations resulted in controls that were impeccably clean (see representative cytograms and gating counts in Fig. 2A–C and  Supplementary Fig. S1). Second, in “virus addition” treatments, the resultant VT signal was distinct for specific (HM1) versus nonspecific (HS8) phages. Specifically, adding HM1 at MOIs = 1, 2, and 4 corresponded to VT population shifts of an average of 25%, 50%, and 80%, respectively, while NT populations proportionally decreased (Fig. 2D, E, linear regression r2 = 0.98). In contrast, for all tested MOIs of the nonspecific HS8 phage, the shifted populations were negligible (range: ~1.0–1.9%) and uncorrelated (Supplementary Fig. S2A, B; r2 = 0.14).Fig. 2: Flow cytometric and microscopic analyses of Pseudoalteromonas-phage associations.A Hierarchical gating for detection of Pseudoalteromonas strain H71 (hereafter, H71) and its subpopulations of viral tagged (VTs) and nontagged cells (NTs). A parent gate was drawn on H71 cells using FSC vs. SSC (Fig. S1) and represented in two types of contour and dot plots (left and right in the top of the gray box, respectively). From this gate, green-positive (VT) and -negative (NT) populations were sub-gated in the green fluorescence vs. SSC (right, dot plot) and quantified as percentage fractions of a parent population (bar charts in the gray box). B, C Flow cytometric plots of sheath buffer only (B) and stained/washed sheath buffer without phages (C) (see Methods and Fig. S1). D Flow cytometric detections for H71 cells (~106/ml) that were incubated with fluorescently-labeled specific phage HM1 at MOIs of 1, 2, and 4, respectively (from left to right). E Linear regression relationships between the MOIs (x-axis) and the percentages (Y-axis) of flow cytometric VT (green) and NT (black) populations for phage HM1 at MOIs of 1, 2, and 4, respectively. R-square values are represented. F DAPI (4′,6-diamidino-2-phenylindole, blue)-stained H71 cells were mixed with fluorescent phages HM1 (SybrGold, green) at MOIs of 1, 2, and 4, respectively (Methods for details). Above, the merged images of phage-host mixtures (Additional images are shown in Figs. S4–7). Below, an enlarged view of four regions selected from the above images. Interpretations of virus-tagged cells, nontagged cells, and “free” viruses are represented in the results and discussion and methods, respectively. Arrows point to phages found on the margin of bacterial cells. Scale bar, 2 µm. Microscopic observations for nonspecific phage HS8-H71 are shown in Fig. S8. G Correlation between the MOI (x-axis) and the microscopic fractions (y-axis) of VTs (green) and NTs (black) for phage HM1 at MOIs of 1, 2, and 4, respectively. R-square value is shown. H Impact of cell physiology on viral tagging signals. H71 cells (~106/ml) in the early log, late log, and stationary phase were infected by phage HM1 at MOIs of 1 (Left) and 4 (Right), respectively. Percentages of tagged populations were measured at the time point after fluorescently-labeled HM1 were inoculated for 20 min at various MOIs followed by centrifugation and resuspension to remove free viruses (see Methods for details). Each test was done in duplicate (error bars show standard deviations).Full size imageDespite observing a strong linear correlation between MOI and %VT for HM1, it was surprising that even at high MOIs = 1, 2, and 4, the resultant population shifts were 1.2- to 2.5-fold less than expected from theory alone based on Poisson distribution (see Supplementary Fig. S3). To investigate this, we used microscopy to inspect for virus clumping, positioning relative to cell surfaces, and background noise. These results revealed spot-like green signals of various sizes outside of host cells, which we interpreted as free viruses, and this was true even (a) at these higher MOIs, and (b) despite centrifugation to remove free viruses following incubation (see Methods; Fig. 2F and  Supplementary Figs. S4–7). We suspect these unincorporated SYBR-stained particles are viral aggregates, possibly due to host cell parts and/or debris in the lysate [51,52,53] or tangling of phage tails [54]. Prior work has shown that these and other mechanisms that decrease the accessibility of viral particles to host receptors could reduce observed infectious particles [48].Our third key observation in these experiments rested with an improved understanding of the ‘signal shift’ between VT and NT populations in the flow cytogram across varied MOIs. Again, comfortably, increasing the MOI pushed VT signals toward higher fluorescence, with NTs decreasing proportionally (Fig. 2F). We posited that such increased “VT” signal could result from multiple phages adsorbing per cell. Indeed, microscopy visualization of ~500 single cells per treatment revealed that the number of detectable phages per infected cell increased proportionally to the MOI (Fig. 2F, G and  Supplementary Figs. S4–6). For example, of the tagged cells, few (~14%) cells exhibited multiple phages adsorbed at an MOI = 1, whereas those numbers increased drastically at MOIs = 2 and 4, where most (~55% and 67%) tagged cells exhibited multiple adsorbed phages per cell. As a negative control, we examined VT signals for a nonspecific phage, and this revealed that virtually all of the 545 single cells that were examined were nontagged (99.3%) even at an MOI = 10 (Supplementary Fig. S7). Presumably, the remaining ~0.7% of cells that appeared to have a phage adsorbed represent promiscuous, reversible binding to nonhost cells as is known to occur in other phage model systems [39]. Mechanistically, multiple phages can bind to a single host cell. For example, under very high-titer infection conditions (e.g., MOI = 100) phages can distribute over an entire cell surface [55], presumably accessing broadly distributed receptors [56]. Prior VT work has demonstrated strong VT signals under very high MOI (e.g., MOI = 1000) conditions [43], though no optimization experiments were presented to understand these patterns and the false positives that would result from free phages coincidently sorted (see further discussion later).Finally, we re-evaluated the impact of cell physiology (e.g., early, middle, and late log phase host growth) and adsorption time (e.g., 20 min intervals from 0 to 120 min) on Pseudoalteromonas VT signals—and did so at two MOIs = 1 and 4, respectively (Fig. 2H). At both MOIs tested, growth phase was seen to impact the VT signals, with late log phase cells showing the highest fluorescent shift for VT cells in contrast to signals that were reduced in early log phase cells and nearly absent from stationary phase cells (Fig. 2H). This finding is consistent with our prior optimizations with Pseudoalteromonas phage-host model systems [39]. However, we observed that VT signals were optimal at 20 min after adsorption (see Methods) and, rather than stay high as we had previously observed, these experiments revealed that the VT signals were reduced by nearly half at subsequent time points. Though conflicting with our prior work [39], these current experiments employ hierarchical gating (Supplementary Fig. S1; see Methods), which we feel more appropriately quantify these patterns. This is because we interpret the signal reduction to be due to the lysis of first-adsorbed tagged cells and/or the injection of fluorescent DNA of the adsorbed virus(es) into cells as the latent period of phage HM1 for H71 cells under these conditions dictates [24]. Indeed, it has been reported that for phage lambda—E.coli system, the injection of fluorescent phage DNA followed by signal diffusion inside the cells decreased ~40% of the overall signal intensities of individual virus–host pairs [57].Together, though an extensive set of experiments, these findings are largely confirmatory with our prior work characterizing Pseudoalteromonas phages [39]. However, and critically, our prior work failed to rigorously investigate these phenomena with respect to their (i) flow cytogram population signatures, (ii) single-cell microscopy imaging, and (iii) hierarchically gated tagged-cell timing estimates. We hope that these additional clarifications here provide a better mechanistic understanding of VT signals, and encourage wider adoption of this promising high-throughput method to identify viruses that infect a particular host.Introducing VT and grow: VT coupled to plate-based cultivation assaysGiven this improved understanding of the VT signal, we next sought to expand VT to include an “and grow” capability to scalably capture and characterize viruses linked to hosts (conceptually presented in Fig. 1C). Pragmatically, this should also help resolve long-standing questions of (i) what fraction of VT cells lead to productive infections (i.e., does adsorption equal infection?, [45]), and (ii) whether sample processing (e.g., laser detection, sheath fluid growth inhibition [37, 58]) or cell density effects resulting from single-cell sorts [59, 60] would prohibit downstream growth assays.To this end, we used the Pseudoalteromonas-virus HM1 model system to optimize sorting and growth conditions. Specifically, we wondered how many cells from sorted populations would be required to observe lysis (both dynamically, and terminally) under various MOI conditions. To test this, viral-tagged cells (the “VT” treatment) or nontagged cells (the “NT” treatment) were sorted into individual wells of a 96-well plate containing growth medium; fresh host cells were added, and growth-lysis curves were established by measuring optical density (OD) over time (see Methods). Treatment variables included the number of cells sorted (n = 1, 3, or 9) and infection conditions (MOI = 1 or 4), while controls included (i) NT cells to control for false-positive culture lyses by free viruses coincidently sorted with target cells, and (ii) sorting process controls against host cell lysis and growth in plates consisting of wells containing cultures with and without phage HM1, respectively. For all experiments, cells were infected during late-exponential phase for 10 min, followed by dilution to halt further infection, and centrifugation to remove free viruses (see Methods, [41]).We first analyzed the reduced-titer MOI = 1 infection. When only single cells were sorted, the growth curves from those wells as compared to those of phage-free controls, showed that more than half (56%; 20/36) of the VT wells with detectably reduced OD, whereas only a single NT well (8%; 1/12) showed such a decrease (Fig. 3A). This low rate of false-positive culture lysis in NT wells suggests that in most of the VT wells, progeny phages produced from an isolated parent VT—not free viruses―infect and lyse the host culture (For more details, see the burst size distribution of sorted single VTs below). Presumably, the 16 VT wells that did not lyse were due to one of the following: (i) reduced viability of isolated VTs through multiple steps of sample preparation or sorting with high sheath pressure [37, 58], (ii) possible reversible virus adsorption from the VT cell prior to well capture, and/or (iii) mis-diagnoses due to the weak fluorescent shift of singly-VT cells as is a known challenge in fluorescence-based cell sorting [58, 61].Fig. 3: Evaluation of viral growth assay under various infection conditions.Two liquid cultures of Pseudoalteromonas strain H71 (105/ml) in the late-logarithmic growth phase were infected by specific phage HM1 at MOIs of 1 and 4, respectively. From each infected culture, varying numbers of tagged (VT) and nontagged (NT) cells were sorted into individual wells of a 96-well plate containing growth medium followed by the addition of fresh host cells (104 cells per well). Positive and negative controls (host culture with HM1 at an MOI of 0.1 and without HM1, respectively) were included in each plate (see Methods for details). From top to bottom, left to right in panels (A) MOI = 1 and (B) MOI = 4, respectively, pie charts depict the percentages of lysed (yellow) and nonlysed (gray) wells from the total wells containing the given numbers (n = 1, 3, and 9) of isolated VTs and NTs. Culture lysis for VT- and NT-containing wells was determined by comparing their growth curves (next to each pie chart, black lines) to those of negative (red) and positive controls (blue). The X-axis indicates the OD590nm and the Y-axis, the time in hours.Full size imageTo assess the MOI = 1 infections further, we evaluated the data for wells containing more than 1 cell sorted to each well. This revealed that sorting 3 or 9 cells improved the fraction of wells lysed in the VT treatments to 88 and 100%, respectively, but this came at the cost of increased false positives in the NT treatment (pie charts in Fig. 3A). The latter is likely due to the same challenges described above of differentiating the NT from VT populations when signal intensity was relatively low. Given the 96-well plate format, these experiments demonstrate the ability to follow growth kinetics for each well (time course OD figures in Fig. 3A). This revealed that single VT cell sorts had delayed lysis relative to the multiple-cell sorts and hints at the power such kinetics data could provide for scalably characterizing new en masse captured phage isolates from field samples. Stepping back, however, it is promising that the number of sorted cells per well, for both VT and NT wells, was linearly proportional to the percentages of lysed wells (r2 = 0.73 and 0.99), respectively (Supplementary Fig. S8). This suggests a robustness and repeatability for these experiments.Beyond the fraction of the VT and NT wells displaying clear lysis, the kinetics of lysis—particularly for single-cell sorts—can be a valuable first read-out for variability in virus infection dynamics. To assess this in our dataset, we examined the kinetics of OD readings through 20 h (growth-lysis curves in Fig. 3A). Focusing on the 36 wells containing a single VT cell, 20 lysed (reported above), but their lysis kinetics drastically differed—some wells showed stepwise decreases after early increases in OD and the others a very low or no increase followed by the curve recovery. Similar lysis patterns have been observed in other phage-host systems, where host culture growth depended on phage concentration, with suppression of host cells increasing with higher phage titers and vice versa [62, 63]. Our observation of the well-to-well variation in culture lysis is likely due to different progeny production from isolated VT per well, relating to the stochasticity of viral infection [37, 64,65,66,67]. However, the stochastic infection alone cannot explain such diverse lysis patterns, given the random nature of diffusion and contact of progeny particles from infected cells to neighboring susceptible cells in the fluid (i.e., the host culture) [68, 69]. Either biological or physical infection process, or both, could impact varied lysis pattern. Further experiments are required to test this hypothesis (e.g., single-cell burst size assay, [37]; see below).Finally, given that flow cytometric population separation was critical for optimizing lysis success and that simply sorting more cells comes at the cost of increased false-positive lysis, we next explored the impact of increasing the per-cell fluorescent VT signal with MOI = 4 infections. Indeed, sorting from these better-resolved populations improved our per-well lysis results as all of the VT wells lysed, and this was the case whether sorting 1, 3, or 9 cells per well (pie charts in Fig. 3B). For the NT wells, false positives were less problematic, but they did remain a minor problem as some wells (4–8%) lysed, and this increased in the multiple-cell sorted wells. Though VT and NT populations are likely better resolved, thereby reducing false-positive lysis in the NT wells from the MOI = 1 infections, presumably the higher MOI infections lead to free viruses being coincidently co-sorted in the sort droplets. Notably, the kinetic read-outs (growth-lysis curves in Fig. 3B) were relatively invariable, possibly suggesting that the much higher number of viruses-per-cell in these infections obscured virus-to-virus variability in life history traits [66, 67, 70].Together, these experiments provide strong baseline data for assessing the impact of VT signal quality, MOIs, and growth data and hint that the approach may also open up new windows into variation in trait space across virus isolates.New biology enabled by viral tag and grow: a window into “viral individuality”?A major challenge in viral ecology is scaling from the handful of viruses that might be well characterized to the millions of virus types in an average seawater or field sample. While diversity surveys have come a long way (e.g., hundreds of thousands of viruses in a single study [23]), the pragmatic challenges of taking physiological measurements across many viral isolates leaves modeling efforts with very little empirical data on virus life history traits, severely bottlenecking the viruses brought into predictive models [71]. Further, microbiologists have revealed that even among “clonal” isolates, there can be remarkable phenotypic heterogeneity, or “microbial individuality” [72,73,74]; does the same exist for viruses? Hints that there is such “virus individuality” among DNA viruses, including phages, are emerging with data demonstrating variability in single-cell burst size (progeny per infected cell), with up to ~100-fold differences and these differences attributed to stochastic events such as variation in starting points in cell size, growth stage, and resources [37, 64,65,66].Of particular interest in understanding ‘virus individuality’ are recent single-cell analyses developed for a Synechococcus phage-host model system that revealed a wide range of burst sizes (from 2 to 200 infective viruses/cell) within a laboratory clonal isolate [37]. Methodologically, this approach sorts cells—infected or not—into wells (e.g., of a 96-well plate) and follows their infection dynamics. This has the benefit of assessing a single cell’s growth-lysis curve in each well. However, a drawback is that experiments are more conveniently done at high MOI conditions (e.g., an MOI = 3 was used) to get larger numbers of wells lysing among the randomly sorted cells (see Methods). Increasing MOI will lead to more virus-containing and, therefore, lysing wells, subsequently greatly increasing the number of cells with multiple viruses attached such that it will confound measurements of lysis dynamics since they will be a function of both virus-to-virus ‘individuality’ and an unknown, but variable per-cell MOI [70, 75].Inspired by this latter work, we sought to improve such single-cell growth-lysis assays in ways that might leverage the scalability of VT + Grow. For these experiments, we wanted to reduce the MOI (to MOI = 0.5) since theory predicts that most (77%) of the infected cells would be singly infected (Poisson distribution), but keep it high enough to have a reasonably separated VT cell population (see Methods). After cells and viruses were mixed, individual VT cells were sorted into different wells containing growth medium, plates were incubated to allow lysis of the single sorted VT cell, and the number of plaques per well were determined by pour plate plaque assays (Fig. 4A; see Methods for details). This operationally single-cell burst size assay showed a wide range of infective viruses per cell (2 to 397, X-axis) from a total of 72 individual cells assessed (Y-axis) (on average = 100; Fig. 4B), with similar average population burst sizes of 110 ± 15 [24]. Though a clonal virus isolate, these findings suggest, just as seen for cyanophages [37], that stochastic events must dictate the specific burst size for any given interaction. However, unlike the prior work, it is unlikely that cells with multiple viruses adsorbed any of this signal since such events should be much rarer at an MOI = 0.5 instead of MOI = 3. This suggests that these stochastic events are of a biological nature, which we posit might mechanistically result from the timing of initial virus–host interactions and/or cell-to-cell or virus-to-virus variation in nonheritable traits such as per-cell nutrient stores. If we interpret such infected cell variability as ecologically relevant variation in “virocells” (sensu [13, 76, 77]), then these findings open a window into “virus individuality” via a more scalable and controllable characterization approach than previously available.Fig. 4: Distribution of virus burst sizes per single viral-tagged cell.A Schematic overview of single-cell assay for viral burst size determination by viral tagging and grow. In the latent period of infection, single viral-tagged cells (VTs) were sorted by flow cytometer from Pseudoalteromonas sp. H71 cells infected by phage HM1 at an MOI of 0.5 (see Methods for details). Following sorting single VTs into different wells of the 96-well plate containing growth medium (MSM), the plate was incubated to allow for viral progenies to release from infected cells. The number of viruses produced per VT was then determined by the number of plaques per poured plate using the traditional plaque assay. B Distribution of viral burst size from individual tagged cells. The number of progeny viruses (X-axis) per cell (Y-axis) are represented in bins of 20, with the exception of the first bin excluding single plaques. The number (n) of individual tagged cells assessed is represented at the top right corner.Full size imageLimitations and future development opportunities for VT and GrowThough these efforts provide a more robust foundation for broadening the use of VT related methods, there remain challenges. First, researchers must be aware that VT is not a simple method, and its success depends on instrument calibration and ultraclean sample processing to establish maximally separated VT and NT populations (see the link below for details on flow cytometric setup and optimization). Second, sorting purity, particularly in field applications, will be challenged by suboptimal VT flow cytometric signatures, e.g., mis-identification of NT cells. Though this can be overcome with very high MOI infections (e.g., 1000 viruses per cell, [43]), two issues remain: (i) the effective MOIs cannot be measured in field samples (and thus, unknown), and (ii) at such high MOIs, the experiments will suffer from coincident sorting of free viruses that will increase false positives. Another factor that could affect sorting purity is nonviral DNA in the environmental sample, whether it is associated with bacterial cells or not, which could be coincidently sorted. It is thus necessary to ensure that prior to any VT work, environmental samples are properly processed or treated for the removal of nonviral genes and other materials (e.g., filtration and/or centrifugation). Fortunately, the “and grow” approach added to VT provides an additional screening step whereby false-negatives and false positives can be discerned via growth-lysis monitoring. Further, the “and grow” component, a plate-based assay, enables faster and more scalable lysis screening (e.g., 96-well format) than the time- and labor-intensive traditional plaque assay [62, 63]. Third, viral aggregates that alter the effective MOI infection conditions could lead to confounding results when comparing results across laboratories. Here, we invite efforts to find and optimize approaches to reduce viral aggregates (e.g., detergents, sonication, syringe pumping), and until viral aggregates are eliminated, to microscopically examine the state of free viruses in new sample types, particularly for outlier results. Fourth, the methods remain dependent upon a cultivable host, and though VT has been applied to multiple heterotroph and cyanobacterial phage-host pairs [39], two big unknowns remain: (i) how will the “and grow” processing impact growth of these strains, and (ii) will non-marine model systems be amenable to these approaches. The in-depth optimizations presented here for a Pseudoalteromonas phage-host model system serve a foundation for understanding other target virus–host pairs. To this end, we suggest deep investigation for any new model systems being studied, and as information becomes more broadly available, invite a community-standards and benchmarking approach to determine ideal setups for infectious conditions (e.g., growth curve, MOIs) and instrumental parameters. To facilitate this, we have established a VT forum on the Viral Ecology VERVE Net living protocols at protocols.io (below) as a way to empower and broadly engage researchers interested in these new methods and the many variants that could blossom from this base. Specifically, the details for viral and bacterial sample processing can be found at https://www.protocols.io/view/viral-tagging-and-grow-a-scalable-approach-to-capt-bwutpewn?form=MY01SV&OCID=MY01SV and for flow cytometric optimization at https://www.protocols.io/view/bd-influx-cell-sorter-start-up-and-shut-427down-for-v-bv8cn9sw. Both protocols provide additional notes for critical steps to improve methodological reproducibility and/or sensitivity, and particularly for the latter, it will be updated regularly to better optimize, calibrate, and standardize a flow cytometer. More

  • in

    Exploring rhizo-microbiome transplants as a tool for protective plant-microbiome manipulation

    1.Mendes R, Raaijmakers JM. Cross-kingdom similarities in microbiome functions. ISME J. 2015;9:1905–7.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    2.Berg G, Rybakova D, Fischer D, Cernava T, Vergès M-CC, Charles T, et al. Microbiome definition re-visited: old concepts and new challenges. Microbiome. 2020;8:103.PubMed 
    PubMed Central 

    Google Scholar 
    3.Hall AB, Tolonen AC, Xavier RJ. Human genetic variation and the gut microbiome in disease. Nat Rev Genet. 2017;18:690–9.CAS 
    PubMed 

    Google Scholar 
    4.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 
    5.Ramírez-Puebla ST, Servín-Garcidueñas LE, Jiménez-Marín B, Bolaños LM, Rosenblueth M, Martínez J, et al. Gut and root microbiota commonalities. Appl Environ Microbiol. 2013;79:2–9.PubMed 
    PubMed Central 

    Google Scholar 
    6.Lu T, Ke M, Lavoie M, Jin Y, Fan X, Zhang Z, et al. Rhizosphere microorganisms can influence the timing of plant flowering. Microbiome. 2018;6:231.PubMed 
    PubMed Central 

    Google Scholar 
    7.Zhang J, Liu Y-X, Zhang N, Hu B, Jin T, Xu H, et al. NRT1.1B is associated with root microbiota composition and nitrogen use in field-grown rice. Nat Biotechnol. 2019;37:676–84.CAS 
    PubMed 

    Google Scholar 
    8.Kwak MJ, Kong HG, Choi K, Kwon SK, Song JY, Lee J, et al. Rhizosphere microbiome structure alters to enable wilt resistance in tomato. Nat Biotechnol. 2018;36:1100–9.CAS 

    Google Scholar 
    9.Li H, La S, Zhang X, Gao L, Tian Y. Salt-induced recruitment of specific root-associated bacterial consortium capable of enhancing plant adaptability to salt stress. ISME J. 2021;15:2865–82.CAS 
    PubMed 

    Google Scholar 
    10.Xu L, Dong Z, Chiniquy D, Pierroz G, Deng S, Gao C, et al. Genome-resolved metagenomics reveals role of iron metabolism in drought-induced rhizosphere microbiome dynamics. Nat Commun. 2021;12:3209.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    11.Levy M, Kolodziejczyk AA, Thaiss CA, Elinav E. Dysbiosis and the immune system. Nat Rev Immunol. 2017;17:219–32.CAS 
    PubMed 

    Google Scholar 
    12.Lee SM, Kong HG, Song GC, Ryu CM. Disruption of Firmicutes and Actinobacteria abundance in tomato rhizosphere causes the incidence of bacterial wilt disease. ISME J. 2021;15:330–47.CAS 
    PubMed 

    Google Scholar 
    13.Stacy A, Andrade-Oliveira V, McCulloch JA, Hild B, Oh JH, Perez-Chaparro PJ, et al. Infection trains the host for microbiota-enhanced resistance to pathogens. Cell. 2021;184:615–.e17.CAS 
    PubMed 

    Google Scholar 
    14.Sanders ME, Merenstein DJ, Reid G, Gibson GR, Rastall RA. Probiotics and prebiotics in intestinal health and disease: from biology to the clinic. Nat Rev Gastroenterol Hepatol. 2019;16:605–16.PubMed 

    Google Scholar 
    15.Bhattacharyya PN, Jha DK. Plant growth-promoting rhizobacteria (PGPR): emergence in agriculture. World J Microbiol Biotechnol. 2012;28:1327–50.CAS 
    PubMed 

    Google Scholar 
    16.Bashan Y, de-Bashan LE, Prabhu SR, Hernandez J-P. Advances in plant growth-promoting bacterial inoculant technology: formulations and practical perspectives (1998–2013). Plant Soil. 2014;378:1–33.CAS 

    Google Scholar 
    17.Zmora N, Zilberman-Schapira G, Suez J, Mor U, Dori-Bachash M, Bashiardes S, et al. Personalized gut mucosal colonization resistance to empiric probiotics is associated with unique host and microbiome features. Cell. 2018;174:1388–.e21.CAS 

    Google Scholar 
    18.Kaakoush NO. Fecal transplants as a microbiome-based therapeutic. Curr Opin Microbiol. 2020;56:16–23.CAS 
    PubMed 

    Google Scholar 
    19.Kassam Z, Lee CH, Yuan Y, Hunt RH. Fecal microbiota transplantation for Clostridium difficile infection: systematic review and meta-analysis. Am J Gastroenterol. 2013;108:500–8.PubMed 

    Google Scholar 
    20.Baruch EN, Youngster I, Ben-Betzalel G, Ortenberg R, Lahat A, Katz L, et al. Fecal microbiota transplant promotes response in immunotherapy-refractory melanoma patients. Science. 2021;371:602–9.CAS 

    Google Scholar 
    21.Weller DM, Raaijmakers JM, Gardener BBM, Thomashow LS. Microbial populations responsible for specific soil suppressiveness to plant pathogens. Annu Rev Phytopathol. 2002;40:309–48.CAS 
    PubMed 

    Google Scholar 
    22.Gopal M, Gupta A, Thomas GV. Bespoke microbiome therapy to manage plant diseases. Front Microbiol. 2013;4:355.PubMed 
    PubMed Central 

    Google Scholar 
    23.Raaijmakers JM, Bonsall RF, Weller DM. Effect of population density of Pseudomonas fluorescens on production of 2,4-diacetylphloroglucinol in the rhizosphere of wheat. Phytopathology. 1999;89:470–5.CAS 
    PubMed 

    Google Scholar 
    24.Mazurier S, Corberand T, Lemanceau P, Raaijmakers JM. Phenazine antibiotics produced by fluorescent pseudomonads contribute to natural soil suppressiveness to Fusarium wilt. ISME J. 2009;3:977–91.CAS 
    PubMed 

    Google Scholar 
    25.Siddiqui ZA, Shakeel U. Screening of Bacillus isolates for potential biocontrol of the wilt disease complex of pigeon pea (Cajanus cajan) under greenhouse and small-scale field conditions. J Plant Pathol. 2007;89:179–83.
    Google Scholar 
    26.Yadav K, Damodaran T, Dutt K, Singh A, Muthukumar M, Rajan S, et al. Effective biocontrol of banana fusarium wilt tropical race 4 by a bacillus rhizobacteria strain with antagonistic secondary metabolites. Rhizosphere. 2021;18:100341.
    Google Scholar 
    27.Meng Q, Yin J, Rosenzweig N, Douches D, Hao JJ. Culture-based assessment of microbial communities in soil suppressive to potato common scab. Plant Dis. 2012;96:712–7.PubMed 

    Google Scholar 
    28.Carrión VJ, Cordovez V, Tyc O, Etalo DW, de Bruijn I, de Jager VCL, et al. Involvement of Burkholderiaceae and sulfurous volatiles in disease-suppressive soils. ISME J. 2018;12:2307–21.PubMed 
    PubMed Central 

    Google Scholar 
    29.Gómez Expósito R, de Bruijn I, Postma J, Raaijmakers JM. Current insights into the role of rhizosphere bacteria in disease suppressive soils. Front Microbiol. 2017;8:2529.PubMed 
    PubMed Central 

    Google Scholar 
    30.Raaijmakers JM, Mazzola M. Soil immune responses. Science. 2016;352:1392–3.CAS 

    Google Scholar 
    31.Bakker PAHM, Pieterse CMJ, de Jonge R, Berendsen RL. The soil-borne legacy. Cell. 2018;172:1178–80.CAS 
    PubMed 

    Google Scholar 
    32.Schlatter D, Kinkel L, Thomashow L, Weller D, Paulitz T. Disease suppressive soils: new insights from the soil microbiome. Phytopathology. 2017;107:1284–97.PubMed 

    Google Scholar 
    33.Kyselková M, Kopecký J, Frapolli M, Défago G, Ságová-Marecková M, Grundmann GL, et al. Comparison of rhizobacterial community composition in soil suppressive or conducive to tobacco black root rot disease. ISME J. 2009;3:1127–38.PubMed 

    Google Scholar 
    34.Rosenzweig N, Tiedje JM, Quensen JF, Meng Q, Hao JJ. Microbial communities associated with potato common scab-suppressive soil determined by pyrosequencing analyses. Plant Dis. 2012;96:718–25.PubMed 

    Google Scholar 
    35.Cha JY, Han S, Hong H-J, Cho H, Kim D, Kwon Y, et al. Microbial and biochemical basis of a Fusarium wilt-suppressive soil. ISME J. 2016;10:119–29.CAS 
    PubMed 

    Google Scholar 
    36.Liu X, Zhang S, Jiang Q, Bai Y, Shen G, Li S, et al. Using community analysis to explore bacterial indicators for disease suppression of tobacco bacterial wilt. Sci Rep. 2016;6:36773.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    37.Mendes R, Kruijt M, de Bruijn I, Dekkers E, van der Voort M, Schneider JHM, et al. Deciphering the rhizosphere microbiome for disease-suppressive bacteria. Science. 2011;332:1097–100.CAS 

    Google Scholar 
    38.Wei Z, Gu Y, Friman VP, Kowalchuk GA, Xu Y, Shen Q, et al. Initial soil microbiome composition and functioning predetermine future plant health. Sci Adv. 2019;5:eaaw0759.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    39.Rillig MC, Antonovics J, Caruso T, Lehmann A, Powell JR, Veresoglou SD, et al. Interchange of entire communities: microbial community coalescence. Trends Ecol Evol. 2015;30:470–6.PubMed 

    Google Scholar 
    40.Deng S, Caddell DF, Xu G, Dahlen L, Washington L, Yang J, et al. Genome wide association study reveals plant loci controlling heritability of the rhizosphere microbiome. ISME J. 2021;15:3181–94.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    41.Mendes LW, Mendes R, Raaijmakers JM, Tsai SM. Breeding for soil-borne pathogen resistance impacts active rhizosphere microbiome of common bean. ISME J. 2018;12:3038–42.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    42.Hu J, Wei Z, Kowalchuk GA, Xu Y, Shen Q, Jousset A. Rhizosphere microbiome functional diversity and pathogen invasion resistance build up during plant development. Environ Microbiol. 2020;22:5005–18.PubMed 

    Google Scholar 
    43.Schreiter S, Ding G-C, Heuer H, Neumann G, Sandmann M, Grosch R, et al. Effect of the soil type on the microbiome in the rhizosphere of field-grown lettuce. Front Microbiol. 2014;5:144.PubMed 
    PubMed Central 

    Google Scholar 
    44.Wei Z, Hu J, Gu Y, Yin S, Xu Y, Jousset A, et al. Ralstonia solanacearum pathogen disrupts bacterial rhizosphere microbiome during an invasion. Soil Biol Biochem. 2018;118:8–17.CAS 

    Google Scholar 
    45.Jiang G, Wei Z, Xu J, Chen H, Zhang Y, She X, et al. Bacterial wilt in China: History, current status, and future perspectives. Front Plant Sci. 2017;8:1549.PubMed 
    PubMed Central 

    Google Scholar 
    46.Manda RR, Addanki VA, Srivastava S. Bacterial wilt of solanaceous crops. Int J Chem Stud. 2020;8:1048–57.CAS 

    Google Scholar 
    47.Barik S, Reddy AC, Ponnam N, Kumari M, C AG, Reddy DCL, et al. Breeding for bacterial wilt resistance in eggplant (Solanum melongena L.): progress and prospects. Crop Prot. 2020;137:105270.CAS 

    Google Scholar 
    48.Wei Z, Yang X, Yin S, Shen Q, Ran W, Xu Y. Efficacy of Bacillus-fortified organic fertiliser in controlling bacterial wilt of tomato in the field. Appl Soil Ecol. 2011;48:152–9.
    Google Scholar 
    49.Park E-J, Lee S-D, Chung E-J, Lee M-H, Um H-Y, Murugaiyan S, et al. MicroTom – A model plant system to study bacterial wilt by Ralstonia solanacearum. Plant Pathol J. 2007;23:239–44.
    Google Scholar 
    50.Schandry N. A practical guide to visualization and statistical analysis of R. solanacearum infection data using R. Front Plant Sci. 2017;8:623.PubMed 
    PubMed Central 

    Google Scholar 
    51.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 
    52.Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 2013;41:e1.CAS 
    PubMed 

    Google Scholar 
    53.Cole JR, Wang Q, Fish JA, Chai B, McGarrell DM, Sun Y, et al. Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 2014;42:D633–642.CAS 

    Google Scholar 
    54.Katoh K, Misawa K, Kuma K, 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 
    55.Price MN, Dehal PS, Arkin AP. FastTree 2-approximately maximum-likelihood trees for large alignments. PLoS ONE. 2010;5:e9490.PubMed 
    PubMed Central 

    Google Scholar 
    56.Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    57.Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.CAS 

    Google Scholar 
    58.Dixon P. VEGAN, a package of R functions for community ecology. J Veg Sci. 2003;14:927–30.
    Google Scholar 
    59.Shenhav L, Thompson M, Joseph TA, Briscoe L, Furman O, Bogumil D, et al. FEAST: fast expectation-maximization for microbial source tracking. Nat Methods. 2019;16:627–32.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    60.Mendiburu F de. agricolae: Statistical procedures for agricultural research. R package version 1.3–5. 2021.61.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 
    62.Robinson MD, McCarthy DJ, Smyth GK. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.CAS 
    PubMed 

    Google Scholar 
    63.Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12:R60.PubMed 
    PubMed Central 

    Google Scholar 
    64.Deng Y, Jiang Y-H, Yang Y, He Z, Luo F, Zhou J. Molecular ecological network analyses. BMC Bioinformatics. 2012;13:113.PubMed 
    PubMed Central 

    Google Scholar 
    65.Zhou J, Deng Y, Luo F, He Z, Tu Q, Zhi X. Functional molecular ecological networks. mBio. 2010;1:e00169–10.PubMed 
    PubMed Central 

    Google Scholar 
    66.Ma B, Wang Y, Ye S, Liu S, Stirling E, Gilbert JA, et al. Earth microbial co-occurrence network reveals interconnection pattern across microbiomes. Microbiome. 2020;8:82.PubMed 
    PubMed Central 

    Google Scholar 
    67.Kuntal BK, Chandrakar P, Sadhu S, Mande SS. ‘NetShift’: a methodology for understanding ‘driver microbes’ from healthy and disease microbiome datasets. ISME J. 2019;13:442–54.PubMed 

    Google Scholar 
    68.Douglas GM, Maffei VJ, Zaneveld JR, Yurgel SN, Brown JR, Taylor CM, et al. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol. 2020;38:685–8.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    69.Choi K, Choi J, Lee PA, Roy N, Khan R, Lee HJ, et al. Alteration of bacterial wilt resistance in tomato plant by microbiota transplant. Front Plant Sci. 2020;11:1186.PubMed 
    PubMed Central 

    Google Scholar 
    70.Davar D, Dzutsev AK, McCulloch JA, Rodrigues RR, Chauvin J-M, Morrison RM, et al. Fecal microbiota transplant overcomes resistance to anti-PD-1 therapy in melanoma patients. Science. 2021;371:595–602.CAS 

    Google Scholar 
    71.D’Haens GR, Jobin C. Fecal microbial transplantation for diseases beyond recurrent Clostridium difficile infection. Gastroenterology. 2019;157:624–36.PubMed 

    Google Scholar 
    72.Gough E, Shaikh H, Manges AR. Systematic review of intestinal microbiota transplantation (fecal bacteriotherapy) for recurrent Clostridium difficile infection. Clin Infect Dis. 2011;53:994–1002.PubMed 

    Google Scholar 
    73.Durack J, Lynch SV. The gut microbiome: relationships with disease and opportunities for therapy. J Exp Med. 2019;216:20–40.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    74.Danne C, Rolhion N, Sokol H. Recipient factors in faecal microbiota transplantation: one stool does not fit all. Nat Rev Gastroenterol Hepatol. 2021;18:503–13.PubMed 

    Google Scholar 
    75.Jiang G, Wang N, Zhang Y, Zhang Y, Yu J, Zhang Y, et al. The relative importance of soil moisture in predicting bacterial wilt disease occurrence. Soil Ecol Lett. 2021;3:356–66.76.Wei Z, Friman VP, Pommier T, Geisen S, Jousset A, Shen Q. Rhizosphere immunity: targeting the underground for sustainable plant health management. Front Agric Sci Eng. 2020;7:317–28.
    Google Scholar 
    77.Hu J, Wei Z, Friman VP, Gu SH, Wang X-F, Eisenhauer N, et al. Probiotic diversity enhances rhizosphere microbiome function and plant disease suppression. mBio. 2016;7:e01790–16.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    78.Bakker PAHM, Doornbos RF, Zamioudis C, Berendsen RL, Pieterse CMJ. Induced systemic resistance and the rhizosphere microbiome. Plant Pathol J. 2013;29:136–43.PubMed 
    PubMed Central 

    Google Scholar 
    79.Wei Z, Yang T, Friman VP, Xu Y, Shen Q, Jousset A. Trophic network architecture of root-associated bacterial communities determines pathogen invasion and plant health. Nat Commun. 2015;6:8413.CAS 
    PubMed 

    Google Scholar 
    80.Li M, Wei Z, Wang J, Jousset A, Friman V-P, Xu Y, et al. Facilitation promotes invasions in plant-associated microbial communities. Ecol Lett. 2019;22:149–58.PubMed 

    Google Scholar 
    81.Mendes LW, Raaijmakers JM, de Hollander M, Mendes R, Tsai SM. Influence of resistance breeding in common bean on rhizosphere microbiome composition and function. ISME J. 2018;12:212–24.PubMed 

    Google Scholar 
    82.Rosales PF, Bordin GS, Gower AE, Moura S. Indole alkaloids: 2012 until now, highlighting the new chemical structures and biological activities. Fitoterapia. 2020;143:104558.CAS 
    PubMed 

    Google Scholar 
    83.Sarbu LG, Bahrin LG, Babii C, Stefan M, Birsa ML. Synthetic flavonoids with antimicrobial activity: a review. J Appl Microbiol. 2019;127:1282–90.CAS 
    PubMed 

    Google Scholar 
    84.Madadi E, Mazloum-Ravasan S, Yu JS, Ha JW, Hamishehkar H, Kim KH. Therapeutic application of betalains: a review. Plants. 2020;9:E1219.PubMed 

    Google Scholar 
    85.Ryan RP, Monchy S, Cardinale M, Taghavi S, Crossman L, Avison MB, et al. The versatility and adaptation of bacteria from the genus Stenotrophomonas. Nat Rev Microbiol. 2009;7:514–25.CAS 
    PubMed 

    Google Scholar 
    86.Kolton M, Erlacher A, Berg G, Cytryn E. The Flavobacterium genus in the plant holobiont: ecological, physiological, and applicative insights. In: Castro-Sowinski S, editor. Microbial models: from environmental to industrial sustainability. Singapore: Springer; 2016. p. 189–207.87.Haas D, Défago G. Biological control of soil-borne pathogens by fluorescent pseudomonads. Nat Rev Microbiol. 2005;3:307–19.CAS 
    PubMed 

    Google Scholar 
    88.Fira D, Dimkić I, Berić T, Lozo J, Stanković S. Biological control of plant pathogens by Bacillus species. J Biotechnol. 2018;285:44–55.CAS 
    PubMed 

    Google Scholar  More

  • in

    Matrix condition mediates the effects of habitat fragmentation on species extinction risk

    1.Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).ADS 
    CAS 
    PubMed 

    Google Scholar 
    2.Newbold, T. et al. Has land use pushed terrestrial biodiversity beyond the planetary boundary? A global assessment. Science 353, 288–291 (2016).ADS 
    CAS 
    PubMed 

    Google Scholar 
    3.Maxwell, S. L., Fuller, R. A., Brooks, T. M. & Watson, J. E. M. Biodiversity: the ravages of guns, nets and bulldozers. Nature 536, 143 (2016).ADS 
    CAS 
    PubMed 

    Google Scholar 
    4.Betts, M. G. et al. Global forest loss disproportionately erodes biodiversity in intact landscapes. Nature 547, 441–444 (2017).CAS 
    PubMed 

    Google Scholar 
    5.Haddad, N. M. et al. Habitat fragmentation and its lasting impact on Earth’s ecosystems. Sci. Adv. 1, e1500052 (2015).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    6.Fahrig, L. Ecological responses to habitat fragmentation per se. Annu. Rev. Ecol. Evol. Syst. 48, 1–23 (2017).
    Google Scholar 
    7.Fletcher, R. J. et al. Is habitat fragmentation good for biodiversity? Biol. Conserv. 226, 9–15 (2018).
    Google Scholar 
    8.Fahrig, L. Habitat fragmentation: a long and tangled tale. Glob. Ecol. Biogeogr. 28, 33–41 (2019).
    Google Scholar 
    9.Fahrig, L. et al. Is habitat fragmentation bad for biodiversity? Biol. Conserv. 230, 179–186 (2019).
    Google Scholar 
    10.Miller-Rushing, A. J. et al. How does habitat fragmentation affect biodiversity? A controversial question at the core of conservation biology. Biol. Conserv. 232, 271–273 (2019).
    Google Scholar 
    11.Fahrig, L. Effects of habitat fragmentation on biodiversity. Annu. Rev. Ecol. Evol. Syst. 34, 487–515 (2003).
    Google Scholar 
    12.Fahrig, L. Rethinking patch size and isolation effects: the habitat amount hypothesis. J. Biogeogr. 40, 1649–1663 (2013).
    Google Scholar 
    13.Hanski, I. Habitat fragmentation and species richness. J. Biogeogr. 42, 989–993 (2015).
    Google Scholar 
    14.Pfeifer, M. et al. Creation of forest edges has a global impact on forest vertebrates. Nature 551, 187–191 (2017).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    15.Betts, M. G. et al. Extinction filters mediate the global effects of habitat fragmentation on animals. Science 366, 1236–1239 (2019).ADS 
    CAS 
    PubMed 

    Google Scholar 
    16.Pardini, R. et al. Beyond the fragmentation threshold hypothesis: regime shifts in biodiversity across fragmented landscapes. PLoS ONE 5, e13666 (2010).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    17.Villard, M.-A. & Metzger, J. P. Beyond the fragmentation debate: a conceptual model to predict when habitat configuration really matters. J. Appl. Ecol. 51, 309–318 (2014).
    Google Scholar 
    18.Prugh, L. R., Hodges, K. E., Sinclair, A. R. E. & Brashares, J. S. Effect of habitat area and isolation on fragmented animal populations. Proc. Natl Acad. Sci. USA 105, 20770–20775 (2008).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    19.Franklin, J. F. & Lindenmayer, D. B. Importance of matrix habitats in maintaining biological diversity. Proc. Natl Acad. Sci. USA 106, 349–350 (2009).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    20.MacArthur, R. H. & Wilson, E. O. The Theory of Island Biogeography (Princeton University Press, 1967).21.Haila, Y. A conceptual genealogy of fragmentation research: from island biogeography to landscape ecology. Ecol. Appl. 12, 321–334 (2002).
    Google Scholar 
    22.Watson, D. M. A conceptual framework for studying species composition in fragments, islands and other patchy ecosystems. J. Biogeogr. 29, 823–834 (2002).
    Google Scholar 
    23.Watson, J. E. M., Whittaker, R. J. & Freudenberger, D. Bird community responses to habitat fragmentation: how consistent are they across landscapes? J. Biogeogr. 32, 1353–1370 (2005).
    Google Scholar 
    24.Mendenhall, C. D., Karp, D. S., Meyer, C. F. J., Hadly, E. A. & Daily, G. C. Predicting biodiversity change and averting collapse in agricultural landscapes. Nature 509, 213–217 (2014).ADS 
    CAS 
    PubMed 

    Google Scholar 
    25.Daily, G. C., Ceballos, G., Pacheco, J., Suzán, G. & Sánchez‐Azofeifa, A. Countryside biogeography of Neotropical mammals: conservation opportunities in agricultural landscapes of Costa Rica. Conserv. Biol. 17, 1814–1826 (2003).
    Google Scholar 
    26.Green, R. E., Cornell, S. J., Scharlemann, J. P. W. & Balmford, A. Farming and the fate of wild nature. Science 307, 550–555 (2005).ADS 
    CAS 
    PubMed 

    Google Scholar 
    27.Perfecto, I. & Vandermeer, J. Biodiversity conservation in tropical agroecosystems. Ann. N. Y. Acad. Sci. 1134, 173–200 (2008).ADS 
    PubMed 

    Google Scholar 
    28.Law, E. A. & Wilson, K. A. Providing context for the land-sharing and land-sparing debate. Conserv. Lett. 8, 404–413 (2015).
    Google Scholar 
    29.Phalan, B. T. What have we learned from the land sparing-sharing model? Sustainability 10, 1760 (2018).
    Google Scholar 
    30.Balmford, B., Green, R. E., Onial, M., Phalan, B. & Balmford, A. How imperfect can land sparing be before land sharing is more favourable for wild species? J. Appl. Ecol. 56, 73–84 (2019).
    Google Scholar 
    31.Prevedello, J. A. & Vieira, M. V. Does the type of matrix matter? A quantitative review of the evidence. Biodivers. Conserv. 19, 1205–1223 (2010).
    Google Scholar 
    32.Ferreira, A. S., Peres, C. A., Bogoni, J. A. & Cassano, C. R. Use of agroecosystem matrix habitats by mammalian carnivores (Carnivora): a global-scale analysis. Mammal. Rev. 48, 312–327 (2018).
    Google Scholar 
    33.Battin, J. When good animals love bad habitats: ecological traps and the conservation of animal populations. Conserv. Biol. 18, 1482–1491 (2004).
    Google Scholar 
    34.Martin, L. J., Blossey, B. & Ellis, E. Mapping where ecologists work: biases in the global distribution of terrestrial ecological observations. Front. Ecol. Environ. 10, 195–201 (2012).
    Google Scholar 
    35.Di Marco, M., Ferrier, S., Harwood, T. D., Hoskins, A. J. & Watson, J. E. M. Wilderness areas halve the extinction risk of terrestrial biodiversity. Nature 573, 582–585 (2019).ADS 
    PubMed 

    Google Scholar 
    36.Fahrig, L. et al. Functional landscape heterogeneity and animal biodiversity in agricultural landscapes. Ecol. Lett. 14, 101–112 (2011).PubMed 
    PubMed Central 

    Google Scholar 
    37.Arroyo‐Rodríguez, V. et al. Designing optimal human-modified landscapes for forest biodiversity conservation. Ecol. Lett. 23, 1404–1420 (2020).PubMed 

    Google Scholar 
    38.Purvis, A., Gittleman, J. L., Cowlishaw, G. & Mace, G. M. Predicting extinction risk in declining species. Proc. R. Soc. Lond. B Biol. Sci. 267, 1947–1952 (2000).CAS 

    Google Scholar 
    39.Fisher, D. O., Blomberg, S. P. & Owens, I. P. F. Extrinsic versus intrinsic factors in the decline and extinction of Australian marsupials. Proc. R. Soc. Lond. B Biol. Sci. 270, 1801–1808 (2003).
    Google Scholar 
    40.Cardillo, M. et al. Multiple causes of high extinction risk in large mammal species. Science 309, 1239–1241 (2005).ADS 
    CAS 
    PubMed 

    Google Scholar 
    41.Davidson, A. D., Hamilton, M. J., Boyer, A. G., Brown, J. H. & Ceballos, G. Multiple ecological pathways to extinction in mammals. Proc. Natl Acad. Sci. USA 106, 10702–10705 (2009).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    42.Di Marco, M., Collen, B., Rondinini, C. & Mace, G. M. Historical drivers of extinction risk: using past evidence to direct future monitoring. Proc. R. Soc. B Biol. Sci. 282, 20150928 (2015).
    Google Scholar 
    43.Di Marco, M., Venter, O., Possingham, H. P. & Watson, J. E. M. Changes in human footprint drive changes in species extinction risk. Nat. Commun. 9, 4621 (2018).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    44.Rondinini, C., Marco, M. D., Visconti, P., Butchart, S. H. M. & Boitani, L. Update or outdate: long-term viability of the IUCN Red List. Conserv. Lett. 7, 126–130 (2014).
    Google Scholar 
    45.Bland, L. M. et al. Cost-effective assessment of extinction risk with limited information. J. Appl. Ecol. 52, 861–870 (2015).
    Google Scholar 
    46.Crooks, K. R. et al. Quantification of habitat fragmentation reveals extinction risk in terrestrial mammals. Proc. Natl Acad. Sci. USA 114, 7635–7640 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    47.Lucas, P. M., González‐Suárez, M. & Revilla, E. Range area matters, and so does spatial configuration: predicting conservation status in vertebrates. Ecography 42, 1103–1114 (2019).
    Google Scholar 
    48.Arregoitia, L. D. V. Biases, gaps, and opportunities in mammalian extinction risk research. Mammal. Rev. 46, 17–29 (2016).
    Google Scholar 
    49.Rondinini, C. et al. Global habitat suitability models of terrestrial mammals. Philos. Trans. R. Soc. B Biol. Sci. 366, 2633–2641 (2011).
    Google Scholar 
    50.Venter, O. et al. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nat. Commun. 7, 12558 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    51.Williams, B. A. et al. Change in terrestrial human footprint drives continued loss of intact ecosystems. One Earth 3, 371–382 (2020).
    Google Scholar 
    52.Tucker, M. A. et al. Moving in the Anthropocene: global reductions in terrestrial mammalian movements. Science 359, 466–469 (2018).ADS 
    CAS 
    PubMed 

    Google Scholar 
    53.Hoffmann, M. et al. The impact of conservation on the status of the world’s vertebrates. Science 330, 1503–1509 (2010).ADS 
    CAS 
    PubMed 

    Google Scholar 
    54.Breiman, L. Random forests. Mach. Learn 45, 5–32 (2001).MATH 

    Google Scholar 
    55.Laurance, W. F. Ecological correlates of extinction proneness in Australian tropical rain forest mammals. Conserv. Biol. 5, 79–89 (1991).
    Google Scholar 
    56.Viveiros de Castro, E. B. & Fernandez, F. A. S. Determinants of differential extinction vulnerabilities of small mammals in Atlantic forest fragments in Brazil. Biol. Conserv. 119, 73–80 (2004).
    Google Scholar 
    57.Reider, I. J., Donnelly, M. A. & Watling, J. I. The influence of matrix quality on species richness in remnant forest. Landsc. Ecol. 33, 1147–1157 (2018).
    Google Scholar 
    58.Ewers, R. M. & Didham, R. K. Confounding factors in the detection of species responses to habitat fragmentation. Biol. Rev. 81, 117–142 (2006).PubMed 

    Google Scholar 
    59.Schipper, J. et al. The status of the world’s land and marine mammals: diversity, threat, and knowledge. Science 322, 225–230 (2008).ADS 
    CAS 
    PubMed 

    Google Scholar 
    60.Tracewski, Ł. et al. Toward quantification of the impact of 21st-century deforestation on the extinction risk of terrestrial vertebrates. Conserv. Biol. 30, 1070–1079 (2016).PubMed 

    Google Scholar 
    61.Cardillo, M. et al. The predictability of extinction: biological and external correlates of decline in mammals. Proc. R. Soc. B Biol. Sci. 275, 1441–1448 (2008).
    Google Scholar 
    62.Murray, K. A., Arregoitia, L. D. V., Davidson, A., Marco, M. D. & Fonzo, M. M. I. D. Threat to the point: improving the value of comparative extinction risk analysis for conservation action. Glob. Change Biol. 20, 483–494 (2014).ADS 

    Google Scholar 
    63.Rondinini, C., Wilson, K. A., Boitani, L., Grantham, H. & Possingham, H. P. Tradeoffs of different types of species occurrence data for use in systematic conservation planning. Ecol. Lett. 9, 1136–1145 (2006).PubMed 

    Google Scholar 
    64.Galán-Acedo, C. et al. The conservation value of human-modified landscapes for the world’s primates. Nat. Commun. 10, 152 (2019).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    65.Watling, J. I., Nowakowski, A. J., Donnelly, M. A. & Orrock, J. L. Meta-analysis reveals the importance of matrix composition for animals in fragmented habitat. Glob. Ecol. Biogeogr. 20, 209–217 (2011).
    Google Scholar 
    66.Fahrig, L. & Rytwinski, T. Effects of roads on animal abundance: an empirical review and synthesis. Ecol. Soc. 14, 21 (2009).67.Woinarski, J. C. Z., Burbidge, A. A. & Harrison, P. L. Ongoing unraveling of a continental fauna: decline and extinction of Australian mammals since European settlement. Proc. Natl Acad. Sci. USA 112, 4531–4540 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    68.May, S. A. & Norton, T. W. Influence of fragmentation and disturbance on the potential impact of feral predators on native fauna in Australian forest ecosystems. Wildl. Res 23, 387–400 (1996).
    Google Scholar 
    69.Peres, C. A. Synergistic effects of subsistence hunting and habitat fragmentation on Amazonian Forest vertebrates. Conserv. Biol. 15, 1490–1505 (2001).
    Google Scholar 
    70.Laurance, W. F. & Useche, D. C. Environmental synergisms and extinctions of tropical species. Conserv. Biol. 23, 1427–1437 (2009).PubMed 

    Google Scholar 
    71.Côté, I. M., Darling, E. S. & Brown, C. J. Interactions among ecosystem stressors and their importance in conservation. Proc. R. Soc. B Biol. Sci. 283, 20152592 (2016).
    Google Scholar 
    72.Didham, R. K., Kapos, V. & Ewers, R. M. Rethinking the conceptual foundations of habitat fragmentation research. Oikos 121, 161–170 (2012).
    Google Scholar 
    73.Ruffell, J., Banks‐Leite, C. & Didham, R. K. Accounting for the causal basis of collinearity when measuring the effects of habitat loss versus habitat fragmentation. Oikos 125, 117–125 (2016).
    Google Scholar 
    74.Morante‐Filho, J. C. et al. Direct and cascading effects of landscape structure on tropical forest and non-forest frugivorous birds. Ecol. Appl. 28, 2024–2032 (2018).PubMed 

    Google Scholar 
    75.Sodhi, N. S., Koh, L. P., Brook, B. W. & Ng, P. K. L. Southeast Asian biodiversity: an impending disaster. Trends Ecol. Evol. 19, 654–660 (2004).PubMed 

    Google Scholar 
    76.Bland, L. M., Collen, B., Orme, C. D. L. & Bielby, J. Predicting the conservation status of data-deficient species. Conserv. Biol. 29, 250–259 (2015).PubMed 

    Google Scholar 
    77.Segan, D. B., Murray, K. A. & Watson, J. E. M. A global assessment of current and future biodiversity vulnerability to habitat loss–climate change interactions. Glob. Ecol. Conserv. 5, 12–21 (2016).
    Google Scholar 
    78.Maron, M., Simmonds, J. S. & Watson, J. E. M. Bold nature retention targets are essential for the global environment agenda. Nat. Ecol. Evol. 2, 1194–1195 (2018).PubMed 

    Google Scholar 
    79.IUCN. IUCN Red List of Threatened Species. Version 2021-1. (2021).80.IUCN. A global standard for the identification of Key Biodiversity Areas. Version 1.0. (IUCN, Gland, 2016).81.Crooks, K. R., Burdett, C. L., Theobald, D. M., Rondinini, C. & Boitani, L. Global patterns of fragmentation and connectivity of mammalian carnivore habitat. Philos. Trans. R. Soc. B Biol. Sci. 366, 2642–2651 (2011).
    Google Scholar 
    82.Ripple, W. J., Bradshaw, G. A. & Spies, T. A. Measuring forest landscape patterns in the cascade range of Oregon, USA. Biol. Conserv. 57, 73–88 (1991).
    Google Scholar 
    83.Li, B.-L. & Archer, S. Weighted mean patch size: a robust index for quantifying landscape structure. Ecol. Model. 102, 353–361 (1997).
    Google Scholar 
    84.Di Marco, M., Rondinini, C., Boitani, L. & Murray, K. A. Comparing multiple species distribution proxies and different quantifications of the human footprint map, implications for conservation. Biol. Conserv. 165, 203–211 (2013).
    Google Scholar 
    85.IUCN. IUCN Red List of Threatened Species. Version 2012-1. (2012).86.Cutler, D. R. et al. Random forests for Classification in ecology. Ecology 88, 2783–2792 (2007).PubMed 

    Google Scholar 
    87.Jetz, W., Carbone, C., Fulford, J. & Brown, J. H. The scaling of animal space use. Science 306, 266–268 (2004).ADS 
    CAS 
    PubMed 

    Google Scholar 
    88.McNab, B. K. The influence of food habits on the energetics of eutherian mammals. Ecol. Monogr. 56, 1–19 (1986).
    Google Scholar 
    89.Tucker, M. A., Ord, T. J. & Rogers, T. L. Evolutionary predictors of mammalian home range size: body mass, diet and the environment. Glob. Ecol. Biogeogr. 23, 1105–1114 (2014).
    Google Scholar 
    90.Murphy, M. A., Evans, J. S. & Storfer, A. Quantifying Bufo boreas connectivity in Yellowstone National Park with landscape genetics. Ecology 91, 252–261 (2010).PubMed 

    Google Scholar 
    91.Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).
    Google Scholar 
    92.Cohen, J. Statistical Power Analysis for the Behavioral Sciences. (Academic Press, 1988).93.ESRI. ArcGIS Pro version 2.8.2, https://www.esri.com/en-us/home (2021).94.R Core Team. R: A Language and Environment for Statistical Computing. https://www.R-project.org/ (2017).95.Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2, 18–22 (2002).
    Google Scholar 
    96.Kuhn, M. Building predictive models in R using the caret package. J. Stat. Softw. 28, 1–26 (2008).
    Google Scholar 
    97.Molnar, C. & Schratz, P. iml: Interpretable Machine Learning. R package version 0.10.1, https://CRAN.R-project.org/package=iml (2020).98.Torchiano, M. effsize: Efficient Effect Size Computation. R package version 0.8.1, https://CRAN.R-project.org/package=effsize (2020).99.Chamberlain, S. rredlist: ‘IUCN’ Red List Client. R package version 0.7.0, https://CRAN.R-project.org/package=rredlist (2020).100.Smith, F. A. et al. Body mass of late Quaternary mammals. Ecology 84, 3403–3403 (2003).
    Google Scholar 
    101.Jones, K. E. et al. PanTHERIA: a species-level database of life history, ecology, and geography of extant and recently extinct mammals. Ecology 90, 2648–2648 (2009).
    Google Scholar 
    102.Tacutu, R. et al. Human ageing genomic resources: integrated databases and tools for the biology and genetics of ageing. Nucleic Acids Res. 41, D1027–D1033 (2013).CAS 
    PubMed 

    Google Scholar 
    103.Verde Arregoitia, L. D., Blomberg, S. P. & Fisher, D. O. Phylogenetic correlates of extinction risk in mammals: species in older lineages are not at greater risk. Proc. R. Soc. B Biol. Sci. 280, 20131092 (2013).
    Google Scholar 
    104.Faurby, S. et al. PHYLACINE 1.2: the phylogenetic atlas of mammal macroecology. Ecology 99, 2626–2626 (2018).PubMed 

    Google Scholar 
    105.Wilman, H. et al. EltonTraits 1.0: species-level foraging attributes of the world’s birds and mammals. Ecology 95, 2027–2027 (2014).
    Google Scholar 
    106.Kissling, W. D. et al. Establishing macroecological trait datasets: digitalization, extrapolation, and validation of diet preferences in terrestrial mammals worldwide. Ecol. Evol. 4, 2913–2930 (2014).PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Ecology and genetic structure of the invasive spotted lanternfly Lycorma delicatula in Japan where its distribution is slowly expanding

    Surveys at the two sites in Kanazawa City showed that the 1st instar larvae had hatched by June 2020 (Fig. 1). After 1 June, the population age structure changed every two weeks until the emergence of 4th instar larvae, which were numerous on 15 and 28 July. Adults were mainly detected on 12 August. This suggests that L. delicatula has a univoltine life cycle in this region, as reported in South Korea22 and Pennsylvania, USA3. The results also indicate that the 1st to 3rd instar larvae molt approximately every two weeks, and the period of development from the 4th instar to the adult phase is approximately one month in this region.The patterns in which adults were captured differed significantly between females and males (Fig. 2). In August, a larger number of females were captured than males. After mid-September, when breeding began, the numbers of females and males captured were approximately equal. Our survey revealed that all individuals had reached adulthood by late August (Fig. 1). Hence, it is unlikely that males emerged much later than females, at least in the survey area up to three meters above the ground. Domingue et al.23 reported a similar female bias just after adult emergence based on a survey of large A. altissima trees up to four meters above the ground. They reported all-female aggregations on the trunks and exposed roots of larger A. altissima trees in the same period as that observed in this study (Fig. S2c,d). Female aggregation is suggested to be a behavior that causes them to crowd into a limited area to feed on optimal resources for producing viable egg masses23. It has also been reported that a high proportion of males are distributed on smaller trees of A. altissima, Vitis sp., and other plant species; however, the number of L. delicatula males on such plants are remarkably lower than those on the larger A. altissima23. Therefore, it is not fully understood why there were fewer males during the early adult emergence period in the survey areas. It is possible that males are distributed in higher positions of the host trees in the early stage of adult development. During the breeding season, courtship behaviour by males (Movie S1) and mating (Movie S2) were frequently observed in the survey area, as previously reported24. Males might change their distribution to nearer ground level during these periods. To clarify this, it will be necessary to expand the survey area to the upper parts of trees in the future.Lycorma delicatula is known to be polyphagous but feeds mainly on A. altissima1,3,4,8,25. In the present study, most L. delicatula were observed on A. altissima (Fig. S2a–d), although some individuals were also observed on wild grapevine A. glandulosa var. heterophylla (Fig. S2e). Wild grapevine is also a favourite host plant of L. delicatula, as previously reported3,8,26. In addition to the host plants, many egg masses were laid on non–plant materials such as building walls (Fig. S2f), as reported previously3,4,8,27.This study showed that most of the eggs of L. delicatula were covered with waxy deposits (99/100 egg masses), as reported previously3,8. The role of wax in L. delicatula is thought to protect eggs from environmental and biotic factors such as natural enemies14,28. In this study, we obtained data supporting the possibility that wax functions against some environmental factors. We observed a significant decrease in the number of eggs per egg mass in exposed environments compared to that in sheltered environments due to peeling off, likely a result of wind and rainfall action. When the wax was removed, the egg numbers per egg mass decreased further (Fig. 3). Moreover, this study showed that the hatching rate of overwintered eggs was significantly reduced when the wax was removed from the egg mass that formed in exposed places (Fig. 4). These results suggest that egg survival is greatly affected by environmental factors, such as wind and rainfall, and that wax may play a role in protecting eggs from these factors. To clarify this, a more detailed analysis should be conducted in an environment where the amount and intensity of wind and rainfall are strictly controlled.To determine the genetic structure of L. delicatula populations in Japan, we conducted a phylogenetic analysis using ND2 and ND6 gene sequences for the samples collected from nine sites in the Hokuriku region and one site in the Okayama Prefecture (Fig. S1a,b, and Table S1). The occurrence of L. delicatula was recently confirmed from Okayama18; in this population, in addition to individuals with white hindwings, many individuals with blue-green coloured hindwings28 have also been reported18. In our analysis, we included both colour types collected from Okayama, and the gene sequence data obtained in previous studies11,21. The results showed that all the samples were classified into one of nine different lineages (i.e. haplotypes), whose geographic distributions were almost consistent with the results of the previous study by Du et al.21. All samples collected from the Hokuriku region (Fig. S1b) in Japan, except for that from Hakusan (JPN_IKHS), had identical sequences and belonged to the same clade as samples from the northwestern area of China (Fig. 5 and Fig. S1a). However, both hindwing colour variations (white and blue-green) from Okayama had identical sequences, and belonged to the same haplotype as the samples from the central area of China, South Korea, and the USA (Fig. 5 and Fig. S1). These results indicate that the genetic structure of L. delicatula in Japan is divided into at least two groups and supports that each group has a history of invasion and colonisation from different regions. Interestingly, this study revealed that the sample collected from Hakusan in Japan in 2010 (site no. 2 in Fig. S1b and Table S1) belonged to the same haplotype as the samples from the central areas of China, South Korea, and the USA, but not to those collected from the same Hokuriku region in Japan in 2020 (Fig. 5 and Fig. S1b). This may indicate that in the last decade, the central China haplotype previously existing in the Hokuriku area has been replaced by the northwestern China haplotype. To clarify this, a more detailed analysis using high-resolution markers7,21,29 and a larger sample size, including old, preserved specimens that were captured during the first invasion into the Hokuriku area, is required.Lycorma delicatula has rapidly expanded its distribution in several countries. In South Korea, the first specimen-confirmed report of L. delicatula was published in 2004. Thereafter, its distribution expanded throughout South Korea, and population densities increased by 20114,8. In the USA, it was first detected in Pennsylvania in 20149, and by 2021, had expanded its distribution into 12 other surrounding states4,10 (Fig. S1c). In contrast, in Japan, the distribution of L. delicatula has been limited to the Hokuriku region (Fig. S1b) since it was first reported in the Ishikawa Prefecture in 200914 until it was detected in Osaka Prefecture in 201717, even though the preferred host plant, A. altissima, is distributed throughout Japan19,20. Various biotic and/or abiotic factors seem to be involved in this relatively slow expansion of distribution in Hokuriku, Japan. The most likely factor is the influence of climate, as shown previously22,30,31. Hokuriku has a large amount of precipitation, including snowfall in winter. For example, mean annual precipitation in Kanazawa is 2401.5 mm32, much higher than that of Philadelphia (1060.0 mm), and Seoul (1460.0 mm)33. Precipitation appears to cause a decrease in egg viability (Figs. 3 and 4). This might explain the suppressed distributional range expansion of L. delicatula from Hokuriku, although it would be necessary to confirm that egg mortality in the Hokuriku region is higher than in other regions in future studies. In addition, indigenous predators and parasitoids in the region may play an important role in suppressing the population of L. delicatula, which should also be explored in future research.In Japan, L. delicatula has recently been found in Osaka17 and Okayama18, which are warm regions with relatively low-precipitation (mean annual precipitation in these areas are 1338.3 mm and 1143.1 mm, respectively32). The Okayama population has the same haplotype as the one that has rapidly increased in South Korea and the USA (Fig. 5 and Fig. S1). This may mean that the southwestern region of Japan is at high risk of L. delicatula invasion. Hence, detailed monitoring of L. delicatula is needed in these regions. Simultaneous preventative action to control the spread of L. delicatula is also required. Control using pesticides may adversely affect the indigenous species, therefore alternative methods should be used. Further verification on the vulnerability of dewaxed eggs of L. delicatula to precipitation (Figs. 3 and 4) is needed, but this study has provided valuable insights into how this pest insect could be managed in an environmentally friendly way. A deeper understanding of the specific ecology of invasive alien species is necessary for sustainable environmental conservation. More

  • in

    Relative effects of land conversion and land-use intensity on terrestrial vertebrate diversity

    cSAR modelWe used the numerical cSAR model16 to calculate native species loss of four taxonomic groups (mammals, amphibians, reptiles, birds) caused by 45 LU types that were mapped onto a reference 5 × 5 arcmin grid (we also call individual grid cells landscapes in the following) of the global land area excluding Greenland and Antarctica. Calculations were based on (a) gridded LU-intensity and LU-type information (see below), (b) effects of LU-intensity on species richness derived from recently published meta-analyses5,21, and (c) information on species distributions and habitat affiliations from IUCN and Birdlife International databases41,42. For presentation of results, we aggregated the calculated effects of the 45 LU-types into those of six broad LU-types (cropland (30 annual crop types); pastures (non-grassland converted to grassland); grazing land (natural/ near-natural areas with livestock grazing); builtup (sealed areas); plantations (11 permanent crop types plus timber plantations), and forests (natural/ near-natural forest under forestry); see Supplementary Data 2 for details).In the below formulae, we use the following indices: g = taxonomic group, n = grid cell, b = broad LU-type. We calculated the total number of native species losses for each taxonomic group g and grid cell n as$${{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{loss}}}}}}}={{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}}times left(1-{left(frac{{{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}}}^{{{{{{rm{cur}}}}}}}+{sum }_{{{{{{rm{b}}}}}}=1}^{{{{{{{rm{b}}}}}}}_{{{{{{rm{n}}}}}}}}{{{{{{rm{h}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}times {{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}}{{{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}}}right)}^{{{{{{{rm{z}}}}}}}_{{{{{{rm{n}}}}}}}}right)$$
    (1)
    Here, ({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}}) is the potential species richness in pristine ecosystems, ({{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}}}^{{{{{{rm{cur}}}}}}}) is the pristine ecosystem area where no LU occurs (in m2), ({{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}}) is the grid cell’s terrestrial area (in m2), ({{{{{{rm{h}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) is the affinity parameter, ({{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) is the area of the LU-type, and ({{{{{{rm{z}}}}}}}_{{{{{{rm{n}}}}}}}) is the grid cell’s SAR exponent taken from ref. 43. The model’s components are described below.Potential species richness ({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}})
    We defined potential species richness of a landscape as the number of species for which the area-of-habitat (AOH) under pre-human or pristine conditions overlap the landscape (here referred to as native species and native AOH). Following ref. 44, we used range maps of all mammal, reptile and amphibia species provided by the IUCN45 and bird species by Birdlife International46 databases to calculate gridded species richness via, first, overlapping each species’ range polygons with a 5 × 5 arcmin reference raster, second, constraining the resulting list of species per raster cell to those adapted to the pristine ecosystem(s) of these raster cell as defined in ref. 25, and, third, constraining the resulting list of species by each species’ elevational range, also provided by the IUCN42. Here, we are interested in the total historical range of extant species46 and hence included all parts of the range where the species were indicated as (i) Extant, Probably Extant, Possibly Extinct, Extinct and Presence Uncertain, (ii) Native and Reintroduced, and (iii) Resident or present during the Breeding Season or the Non-breeding Season, in the cited data sources.We first rasterized each species’ range polygons using the raster and fasterize packages in R47. Second, for each terrestrial grid cell in our reference raster, we created a species list by extracting each species’ gridded range using the velox package in R. Third, we ascertained that each cell’s species list contained only terrestrial species by excluding species which exclusively have aquatic habitat affiliations. The species’ habitat affiliations were directly taken from the IUCN and Birdlife databases42,46. Fourth, we removed species from this cell’s species list which, according to the IUCN, are not affiliated with that cell’s pristine ecosystem. We therefore manually assigned the habitats distinguished in the ICUN habitat affiliation scheme to one or several of the 14 broad ecosystem types distinguished and mapped in ref. 25 (Supplementary Data 4). The maps in the referenced study “approximate the original extent of natural communities prior to major land-use change”48 and, hence, represent pristine ecosystems or potential vegetation types. Fifth, we excluded species whose elevational range did not overlap the elevational range of the grid cell using the GMTED2010 dataset (www.usgs.gov). These refinement steps were taken because species’ range maps usually deliver coarse-scale extent of occurrence rather than AOH information44. Finally, we counted the species identities in each grid cell as ({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}}). The species lists created in this step were also used for later steps, referred to in the appropriate sections.Areas of pristine ecosystems (({{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}}}^{{{{{{rm{cur}}}}}}}),({{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}})) and LU-types (({{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}))The potential pristine ecosystem area ({{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}}) is defined as the cell’s entire terrestrial area (excluding water bodies as defined by the land mask of the HYDE 3.2.1 database49). As the area of pristine ecosystems currently found in each grid cell (({{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}}}^{{{{{{rm{cur}}}}}}})), we used the proportion of ({{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}}) marked as wilderness and non-productive/ snow areas as described below. The area of each of the 45 LU-types within each grid cell (({{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}})) was extracted from respective land cover and LU maps applying the approach outlined in ref. 50 – with 2010 as year of reference wherever possible (Supplementary Data 2).Following ref. 50, builtup land, total cropland (including annual and permanent crops/ plantations), permanent pastures (areas used as pastures for more than five years) and rangeland (available in the two sub-categories natural and converted) extents were taken from the LU database HYDE 3.2.149 which was adapted to include rural infrastructure areas by assigning 5% of each grid cell’s cropland area to builtup land. We then split the total cropland cover into areas used for 41 different annual and permanent crops by integrating data from the Spatial Production Allocation Model (SPAM) for 201051,52 and adjusting them to cropland extent in the data from ref. 50. To comply with the IUCN habitats classification scheme42, some of these crops were grouped into the plantation category (permanent crops), while the remainder was grouped into the cropland category (annual crops; see Supplementary Data 2 for details).Wilderness areas were derived from the combination of human footprint data, i.e., a spatially explicit inventory of human artefact density available for 1993 and 200953,54 and intact forest landscape data for 2000 and 201355. Core wilderness areas without human use were defined as having a value of zero human footprint and, in forests, being part of an intact forest landscape55. Within forests, the additional category of peripheral wilderness was introduced for areas where either only zero human footprint is recorded, or only an intact forest landscape exists.The area remaining in each grid cell after allocating the above land cover types represents area covered by used forests and other land with mixed land uses56. Hence, in addition to the approach in ref. 50, forests were split into deciduous and coniferous forests based on the description of the ESA CCI land cover categories57. This distinction was necessary for the differentiated allocation of wood harvest (see below). A further refinement was applied by identifying plantation forests, defined as areas in non-forest biomes converted to forests for forestry and areas in forest biomes converted to non-native forest types58, which were linked to the IUCN habitat class plantations (Supplementary Data 2).As in ref. 50, the remaining area not allocated to any of the land cover or LU types above is denoted as “other land, maybe grazed”56. These lands, typically treeless or bearing scattered tress, were allocated to converted grasslands on areas that potentially carry forests or to natural grassland on areas where the potential vegetation would not consist of forests25.To arrive at the six broad LU-type aggregates compatible with the IUCN and Birdlife habitat affiliation schemes42,46 and PREDICTS categories21 (needed for quantifying LU-intensity effects, see below in section “Affinity parameter” for details), we rearranged and aggregated the described LU layers as needed (see Supplementary Data 2 for an overview). (a) Builtup remained as described above. (b) Cropland was defined as annual crops, covering the respective 29 SPAM categories plus fodder. (c) Pastures were defined as areas where pristine ecosystems were converted to grasslands and includes permanent pastures and converted rangelands from HYDE 3.2.149, plus those parts of “other land maybe grazed” located in forest25. (d) Grazing land was defined as natural or near-natural areas where grazing occurs and includes natural rangelands from HYDE 3.2.149, plus 50% of each grid cell’s open forest area and 25% of each grid cell’s peripheral wilderness area, the latter two assumed to be only occasionally grazed and hence given low grazing intensity (see below), plus those parts of “other land maybe grazed” located in non-forest25. (e) Forests were defined as forests where forestry occurs and includes 100% of each grid cell’s closed forest area, 50% of each grid cell’s open forest area, and 25% of each grid cell’s peripheral wilderness area, the latter two assumed to be only occasionally used for forestry and given low intensity (see below). (f) Plantations were defined as areas where pristine ecosystems were converted into plantation-like LU and include the 11 SPAM categories representing permanent / plantation crops, plus used forests identified by ref. 58 as plantations (see above). As stated above, these aggregated broad LU-types were needed to align the different LU categorizations used in the different data sources with each other. The effects on biodiversity were then calculated on each of the 45 LU-types and afterwards aggregated to the six broad LU-types to give a better overview.Continuous LU-intensity indices (({{{{{{rm{LUI}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}))We constructed continuous LU-intensity indices ({{{{{{rm{LUI}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) for each of the 45 LU-types based on gridded management descriptors15. For this purpose, we used two different sets of intensity indicators (called Set 1 and Set 2) to compare and combine their impact on predicted species loss. We used two indicator sets to account for the multidimensional nature of LU-intensity9,12 and to include a wide range of available data products. For an overview of which data products and assumptions went into the individual sets, please refer to Supplementary Data 2.Set 1Set 1 is taken from the human appropriation of net primary production (HANPP) framework, a socioecological indicator basically describing the LU mediated extraction of biotic resources in the context of global biogeochemical cycles23. We used the ratio of HANPPharv to NPPpot as a systemic metric to assess LU-intensity12,22, with HANPPharv being harvested or extracted biomass and NPPpot being NPP of potential natural vegetation, i.e. the vegetation existing under current climate conditions in the hypothetical absence of LU23. The ratio HANPPharv/NPPpot relates harvest to the productivity potential of the land where the harvest takes place and is, thus, robust against geographic differences in natural productivity. As it is related to energy availability in ecosystem food chains, it may be linked to the species-energy relationship, the strongest correlate of spatial biodiversity patterns at larger scales59.For calculating NPPpot, LPJ-GUESS60 version 4.0.1 was used in its standard configuration but with nitrogen limitation disabled and forced by the CRU-NCEP climate data61,62 aggregated from 6-hourly to monthly fields.HANPPharv of all LU-types except builtup was calculated based on the FAOSTAT database by principally accounting total biomass flows via conversion and expansion factors as outlined in ref. 63. As a special case, HANPPharv of built-up was assumed to be half of the actual NPP, which was defined as 1/3 of the potential vegetation in ref. 64. This results in a constant intensity on built-up land of ~17% of NPPpot.HANPPharv of permanent and non-permanent crops was spatially downscaled following 40 permanent and non-permanent crop-specific production patterns from the Spatially-Disaggregated Crop Production Statistics Database (SPAM52), merging minor SPAM categories such as “robusta coffee” and “arabica coffee” to ensure consistency with FAOSTAT reporting. Additionally, we added the LU-type fodder, which was downscaled following NPPpot patterns.Harvest of natural and plantation forest is reported by FAOSTAT in the four categories industrial roundwood, wood fuel, and coniferous and deciduous. We allocated industrial roundwood harvest to closed forests, while we split wood fuel harvest in proportion to productivity between closed and open forests, independently for deciduous and coniferous forests, respectively. For Set 1, we assumed forestry harvest to follow the patterns of forest NPPpot65. These intensity definitions were used for both natural and plantation forest.Reported harvest on grazing land and pastures was allocated following patterns of aboveground NPP accessible for grazing as reported in ref. 63. Following the assumption that systems with low natural productivity allow for a lower maximum harvest than systems with high productivity, we assigned a maximum harvest intensity of 40% at a level of accessible NPP of 20 gC/m² and increased this linearly to a maximum grazing intensity of 80% at 250 gC/m². Such, harvest was concentrated on grazing land and pastures with high productivity. In cases where the calculated national grazing land and pasture harvest demand surpassed NPP availability on grassland, we used information on fertilization rates on grassland66 to either adjust NPP or harvest data: NPP was boosted in countries where more than 5% of overall fertilizer consumption was applied to grasslands, while countries where no relevant fertilization of grasslands occurred, the reported harvest demand was reduced accordingly, assuming it will be met from other sources. This intensity definition was applied to both (natural) gazing land and (converted) pastures.Set 2For the LU-intensity indicator Set 2, we used published data from different sources. For cropland we used the input metric nitrogen application rates (in kg N/ha of cropland)12,22, available for 17 major non-permanent crops67,68. For crops from the SPAM categories (see above) not covered by these data, we used the within-grid-cell area-weighted average of other crops in the same cell. For areas designated as cropland in our data (see above) but not in the available N application data, we assumed national average values of the respective crop.For pastures and grazing land, we used gridded livestock information69. We used information on the typical weight per animal to calculate livestock units70 and aggregated the data for all ruminant species (buffalo, horses, cattle, sheep, goats). This data on livestock numbers per grid cell was then divided by land area per grid cell to arrive at livestock densities, which were applied to the extent of grazing land and pastures. Please note that this dataset contains information on the number of livestock (per species group) per area in a grid cell and thereby differs from the grazing intensity metric applied in Set 171, as grazing animals may be fed from other sources than grassland72.For builtup, we aggregated a 1 km built-up area density map for 201473 to the target resolution of 5 arc min and used it as is as intensity indicator.For natural and plantation forest, we used the same data as described above for Set 1, but we assumed forestry harvest to follow another pattern. We calculated the difference between potential and actual biomass stocks74 and allocated forestry harvest within each country according to these patterns, i.e., the share of national forest harvest allocated to a forest cell corresponds to its share in the national difference between potential and actual biomass stocks.Scaling of LU-intensity indicesFor the purpose of applying linear functions on species richness loss caused by LU-intensity (see below, affinity parameter), we scaled each LU-intensity indicator to values between 0 and 1, with 0 being no intensity (hypothetical) and 1 being the intensity threshold above which an increase of intensity causes no further increase of species loss. This threshold is not necessarily the highest recorded value of an intensity indicator, as effects may be regionally variable. We therefore winsorized some LUI indicators to that intensity threshold before scaling them (dividing by this threshold). These thresholds were defined as follows.In Set 1, maximum intensity was assumed to be reached at harvesting 100% of NPPpot on cropland.In forests (natural and plantation), maximum intensity was derived from ref. 75, which limits sustainably harvestable aboveground biomass in forests to 30% of NPPpot. In concordance with the HANPP framework, we included the belowground biomass destroyed by forestry using biome-specific factors76.On grazing land and pastures, maximum intensity was defined as removal of all NPP accessible for grazing. This considers only the aboveground and non-woody parts of NPPpot. The maximum removable aboveground share was estimated as 50% of NPPpot, and the proportion of non-woody vegetation was estimated as 30% (in closed-canopy land cover types) or 100% (on open land cover types)71. HANPPharv/NPPpot was assumed to be at its maximum intensity level when the maximum level of grazing intensity, as described above, was reached. The resulting thresholds are in line with literature77,78, and assume that maximum intensities will be reached faster in systems with low natural productivity.In Set 2, for all crop types (permanent and non-permanent) except legumes, N application rates were capped at 150 kg N/ha, i.e., we assumed that 150 kg N/ha was the maximum LU-intensity on cropland, beyond which no further species richness loss occurs, i.e., after which an increase of N application rates causes no further increase in species loss based on ref. 79. For legumes, under the assumption that they need less N fertilizer due to their N-fixing capabilities, we assumed the following cap values, based on information provided in ref. 80: beans and lentils at 110 kg N/ha, chickpeas at 100 kg N/ha, soybean at 70 kg N/ha and cowpeas, pigeon peas and other pulses at kg N/ha 90.For pastures and grazing land, maximum intensity was defined as the per biome 80th percentile of livestock-density.The intensity of builtup area was not winsorized.Affinity parameter (({{{{{{rm{h}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}))The affinity parameter ({{{{{{rm{h}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) can be regarded as a LU-intensity dependent weighting factor for the area of each of the 45 LU-types used here. For low affinity, i.e., a small fraction of native species is left due to LU, the area of this LU-type (({{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) in formula 1) is down-weighted, resulting in higher species loss ({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{loss}}}}}}}) (and vice versa). The affinity parameter consists of two terms, (a) ({{{{{{rm{r}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}), the fraction of species affiliated with a given LU-type, and (b) ({{{{{{rm{f}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}), the fraction of ({{{{{{rm{r}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) that remains when LU-intensity (({{{{{{rm{LUI}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}})) rises to a particular level.The fraction of species affiliated with a certain LU-type under minimal ({{{{{{rm{LUI}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) (({{{{{{rm{r}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}})) is based on the habitat affiliation information taken from the IUCN Red List API45 and BirdLife data46 cross-tabulated with our mapped LU-types (Supplementary Data 2). We calculated ({{{{{{rm{r}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) by dividing the number of species affiliated with a certain LU-type (({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}^{{{{{{{rm{pot}}}}}}; {{{{{rm{LU}}}}}}}})) by the number of native species expected in this cell under pristine ecosystem conditions (({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}})) as$${{{{{{rm{r}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}=frac{{{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}^{{{{{{{rm{pot}}}}}}; {{{{{rm{LU}}}}}}}}}{{{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}}}$$
    (2)
    Please note that for the two unconverted broad LU-types grazing land and forests, respectively (see above), we assumed no land conversion prior to its use, leading to ({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}^{{{{{{{rm{pot}}}}}}; {{{{{rm{LU}}}}}}}}={{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}}). We further assumed that the whole fraction of LU-type affiliated species ({{{{{{rm{r}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) are present in a given LU-type as long as ({{{{{{rm{LUI}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) is minimal (here 0.83, we argue that extrapolation outside the measured intensity range is uncertain, and that an increase in LUI above 0.83 (i.e., Intense) might not necessarily result in even stronger effects on SR. See Supplementary Fig. 5, which illustrates the results of these considerations and shows the continuous effect of ({{{{{{rm{LUI}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) on SR used in this study.The affinity parameter ({{{{{{rm{h}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) was then calculated as follows and inserted into formula 1 (cSAR model).$${{{{{{rm{h}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}={left({{{{{{rm{r}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}right)}^{1/{{{{{{rm{z}}}}}}}_{{{{{{rm{n}}}}}}}}times {left({{{{{{rm{f}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}right)}^{1/{{{{{{rm{z}}}}}}}_{{{{{{rm{n}}}}}}}}$$
    (5)
    Species loss caused by LU-intensity (({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{loss}}}}}}{{{{{rm{int}}}}}}}))In order to calculate the relative impact of LU-intensity on species richness, we re-ran the model with ({{{{{{rm{LUI}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) = 0 in all grid cells and LU types, thereby effectively setting ({{{{{{rm{f}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) = 1 and ({{{{{{rm{h}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}={{{{{{rm{r}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}). The results of this model can be considered as delivering the land conversion effect without any possible enhancement by intensification. In addition, we designed a hypothetical, back-of-the-envelope intensification scenario where ({{{{{{rm{LUI}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) = 1 in all grid cells and LU-types.The contribution of intensity to the species richness loss was then calculated as$${{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{loss}}}}}}{{{{{rm{int}}}}}}}=left({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{loss}}}}}}}-{{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{{rm{loss}}}}}}; {{{{{rm{conv}}}}}}}}right)/{{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}}$$
    (6)
    With ({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{{rm{loss}}}}}}; {{{{{rm{conv}}}}}}}}) being the results of the ({{{{{{rm{LUI}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) = 0 model and ({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{loss}}}}}}}) from Eq. (1).Native area-of-habitat loss of individual speciesThe cSAR model calculates by how many species the native species pool is reduced in response to LU in each 5 arcmin grid cell. However, it does not identify the individual species lost. To estimate each species’ native AOH loss, we randomly drew the predicted number of species lost from the native species pool of each cell.First, we rounded the number of species lost as calculated by the cSAR model to the next integer for losses from both conversion (({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{{rm{loss}}}}}}; {{{{{rm{conv}}}}}}}})) and intensification (here taken as ({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{loss}}}}}}}-{{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{{rm{loss}}}}}}; {{{{{rm{conv}}}}}}}}), see section above: “Species loss caused by LU-intensity”). To avoid rounding all values below 0.5 to 0, and, hence, to underestimate low levels of species loss, particularly in species-poor regions, we used a two-step rounding routine. First, prior to actual rounding, we randomly decided whether a number is rounded to the next higher or lower integer, with the likelihood of either decision depending on the decimal number’s (positive or negative) distance to 0.5 (i.e., the decimal number gave the likelihood of rounding up). Second, we took the species list used to generate ({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}}) (see above under potential species richness) and modified it to either contain only species affiliated or unaffiliated with each LU-type, yielding two species lists for each grid cell and LU-type, respectively. The list of species affiliated with a particular LU-type was then used to select species predicted to get lost due to intensification, while the list of species not affiliated with it was used to select species lost due to conversion.From each grid cell, we then randomly drew as many species from these lists as determined by the rounding routine above, considering each LU-type and whether the number of lost species was caused by intensification or conversion. However, in each cell, each species could only be drawn once, independently of whether it was affiliated with several LU-types. As a consequence, the order in which LU-types are considered when drawing species is relevant for the outcome of the calculation. For instance, species simultaneously unaffiliated with cropland and affiliated with natural forest may never be drawn in response to intensification of natural forest if losses due to conversion into cropland are always handled first. Therefore, we randomly iterated the sequence by which LU-types were considered, i.e., the order of LU-types, in the random draw routine in each of 100 repeated runs.We repeated the random-draws 100 times to yield a representative sample and processed the resulting 100 lists of species-per-cell losses in the following way. For each of the 100 runs, we summed the areas of all cells each species was drawn from, i.e., predicted to be lost, across all LU-types and within individual LU-types, yielding 100 area sums per species (one per run). From these 100 areas, we calculated the mean and the 0.025th and 0.975th quantiles as 95% confidence intervals (CIs). The means and CIs were then divided by the species’ global AOH (sums of cell areas in native range), thereby yielding the proportional global AOH loss attributable to current LU in general, and to different LU-types or land conversion vs. LU-intensity in particular.Description/ presentation of resultsAll cSAR model calculations were based on global land use maps that distinguish 45 LU-types as described above. For the sake of simplicity, we present results aggregated to the six broad LU-types cropland, pastures, natural grazing land, built-up, plantations, and forests (natural/ near-natural forest under forestry; see Supplementary Data 2). All calculated SR decreases are expressed in percentage losses relative to ({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}}).Summary statistics mentioned in the text and Supplementary Data 1, 5 and 6 were calculated as follows. Global, biome-wide and nation-wide average species losses due to conversion, LU-intensity or both were calculated as cell-area weighted means across all cells with native terrestrial vertebrate species either excluding or including wilderness areas (which, for this purpose, are defined as cells where the sum of all LU area equals 0). The percentual land area exceeding a certain threshold of calculated SR decline were calculated by dividing the area sum of all cells exceeding that threshold by the area sum of all cells with native species excluding wilderness.Differences among average AOH losses (across all taxonomic groups) mentioned in the text and Supplementary Data 3 were modelled using generalized linear models assuming a binomial distribution (proportional AOH loss between 0 and 1), each species’ mean AOH loss (mean of 100 random draw runs) as response, and either (a) IUCN categories, (b) land use types, or (c) taxonomic group as predictor variables. Differences between predictor variable levels were then alculated by multiple comparisons via p-values adjusted with the Tukey method. A p-value of  More