Ecology

1.Martiny JBH, Bohannan BJM, Brown JH, Colwell RK, Fuhrman JA, Green JL, et al. Microbial biogeography: putting microorganisms on the map. Nat Rev Microbiol. 2006;4:102–12.CAS 
PubMed 

Google Scholar 
2.Caswell H, Cohen JE. Disturbance, interspecific interaction and diversity in metapopulations. Biol J Linn Soc. 1991;42:193–218.
Google Scholar 
3.Tolker-Nielsen T, Molin S. Spatial organization of microbial biofilm communities. Microb Ecol. 2000;40:75–84.CAS 
PubMed 

Google Scholar 
4.Yanni D, Márquez-Zacarías P, Yunker PJ, Ratcliff WC. Drivers of spatial structure in social microbial communities. Curr Biol. 2019;29:R545–50.CAS 
PubMed 

Google Scholar 
5.Ho A, Angel R, Veraart AJ, Daebeler A, Jia Z, Kim SY, et al. Biotic interactions in microbial communities as modulators of biogeochemical processes: methanotrophy as a model system. Front Microbiol. 2016;7:1–11.
Google Scholar 
6.Falkowski PG, Fenchel T, Delong EF. The microbial engines that drive earth’s biogeochemical cycles. Science. 2008;320:1034–9.CAS 
PubMed 

Google Scholar 
7.Overmann J, van Gemerden H. Microbial interactions involving sulfur bacteria: Implications for the ecology and evolution of bacterial communities. FEMS Microbiol Rev. 2000;24:591–9.CAS 
PubMed 

Google Scholar 
8.García-Bayona L, Comstock LE. Bacterial antagonism in host-associated microbial communities. Science. 2018;361:1–11.
Google Scholar 
9.Coyte KZ, Schluter J, Foster KR. The ecology of the microbiome: Networks, competition, and stability. Science. 2015;350:663–6.CAS 
PubMed 

Google Scholar 
10.Wang X, Li X, Ling J. Streptococcus gordonii LuxS/autoinducer-2 quorum-sensing system modulates the dual-species biofilm formation with Streptococcus mutans. J Basic Microbiol. 2017;57:605–16.CAS 
PubMed 

Google Scholar 
11.Hotterbeekx A, Kumar-Singh S, Goossens H, Malhotra-Kumar S. In vivo and In vitro interactions between Pseudomonas aeruginosa and Staphylococcus spp. Front Cell Infect Microbiol. 2017;7:1–13.
Google Scholar 
12.Dal Co A, van Vliet S, Kiviet DJ, Schlegel S, Ackermann M. Short-range interactions govern the dynamics and functions of microbial communities. Nat Ecol Evol. 2020;4:366–75. https://doi.org/10.1038/s41559-019-1080-2.Article 
PubMed 

Google Scholar 
13.Justice NB, Sczesnak A, Hazen TC, Arkin AP. Environmental selection, dispersal, and organism interactions shape community assembly in high-throughput enrichment culturing. Appl Environ Microbiol. 2017;83:1–16.
Google Scholar 
14.Hilker M. New synthesis: parallels between biodiversity and chemodiversity. J Chem Ecol. 2014;40:225–6.CAS 
PubMed 

Google Scholar 
15.Raguso R, Agrawal A, Douglas A, Jander G, Kessler A, Poveda K, et al. The raison d’être of chemical ecology. Ecology. 2015;96:617–30.PubMed 

Google Scholar 
16.Tilman D. Competition and biodiversity in spatially structured habitats. Ecology. 1994;75:2–16.
Google Scholar 
17.Geyrhofer L, Brenner N. Coexistence and cooperation in structured habitats. BMC Ecol. 2020;20:1–15. https://doi.org/10.1186/s12898-020-00281-y.Article 

Google Scholar 
18.Wakano JY, Nowak MA, Hauert C. Spatial dynamics of ecological public goods. Proc Natl Acad Sci USA. 2009;106:7910–4.CAS 
PubMed 
PubMed Central 

Google Scholar 
19.Nemergut DR, Schmidt SK, Fukami T, O’Neill SP, Bilinski TM, Stanish LF, et al. Patterns and processes of microbial community assembly. Microbiol Mol Biol Rev. 2013;77:342–56.PubMed 
PubMed Central 

Google Scholar 
20.Lowery NV, Ursell T. Structured environments fundamentally alter dynamics and stability of ecological communities. Proc Natl Acad Sci USA. 2019;116:379–88.CAS 

Google Scholar 
21.Lee JZ, Craig Everroad R, Karaoz U, Detweiler AM, Pett-Ridge J, Weber PK, et al. Metagenomics reveals niche partitioning within the phototrophic zone of a microbial mat. PLoS ONE. 2018;13:1–19.
Google Scholar 
22.Quinn RA, Comstock W, Zhang T, Morton JT, da Silva R, Tran A, et al. Niche partitioning of a pathogenic microbiome driven by chemical gradients. Sci Adv. 2018;4:1–12.
Google Scholar 
23.Fenchel T, Finlay B. Oxygen and the spatial structure of microbial communities. Biol Rev. 2008;83:553–69.PubMed 

Google Scholar 
24.Esteban DJ, Hysa B, Bartow-McKenney C. Temporal and spatial distribution of the microbial community of winogradsky columns. PLoS ONE. 2015;10:1–21.
Google Scholar 
25.Azam F. Microbial control of oceanic carbon flux: The plot thickens. Science. 1998;280:694–6.CAS 

Google Scholar 
26.McNally L, Brown SP. Building the microbiome in health and disease: niche construction and social conflict in bacteria. Philos Trans R Soc B Biol Sci. 2015;370:1–8.
Google Scholar 
27.Schreiber F, Ackermann M. Environmental drivers of metabolic heterogeneity in clonal microbial populations. Curr Opin Biotechnol. 2020;62:202–11. https://doi.org/10.1016/j.copbio.2019.11.018.CAS 
Article 
PubMed 

Google Scholar 
28.Lopez D, Vlamakis H, Kolter R. Biofilms. Cold Spring Harbor Perspectives in Biology. 2010;2:1–11.
Google Scholar 
29.Picketts STA, Cadenasso ML. Landscape ecology: spatial heterogeneity in ecological systems. NCASI Techn Bull. 1999;2:420.
Google Scholar 
30.Chao L, Levin BR. Structured habitats and the evolution of anticompetitor toxins in bacteria. Proc Natl Acad Sci USA. 1981;78:6324–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
31.Rainey PB, Travisano M. Adaptive radiation in a heterogeneous environment. Nature. 1998;394:69–72.CAS 
PubMed 

Google Scholar 
32.Cardinale BJ. Biodiversity improves water quality through niche partitioning. Nature. 2011;472:86–91.CAS 
PubMed 

Google Scholar 
33.Loreau M, Naeem S, Inchausti P, Bengtsson J, Grime JP, Hector A, et al. Ecology: biodiversity and ecosystem functioning: current knowledge and future challenges. Science. 2001;294:804–8.CAS 
PubMed 

Google Scholar 
34.Wellborn GA, Langerhans RB. Ecological opportunity and the adaptive diversification of lineages. Ecol Evol. 2015;5:176–95.PubMed 

Google Scholar 
35.Czárán TL, Hoekstra RF. Killer-sensitive coexistence in metapopulations of micro-organisms. Proc R Soc B Biol Sci. 2003;270:1373–8.
Google Scholar 
36.West SA, Griffin AS, Gardner A, Diggle SP. Social evolution theory for microorganisms. Nat Rev Microbiol. 2006;4:597–607.CAS 
PubMed 

Google Scholar 
37.Wagner M, Loy A, Nogueira R, Purkhold U, Lee N, Daims H. Microbial community composition and function in wastewater treatment plants. Antonie Van Leeuwenhoek. 2002;81:665–80.CAS 
PubMed 

Google Scholar 
38.Johnson DR, Lee TK, Park J, Fenner K, Helbling DE. The functional and taxonomic richness of wastewater treatment plant microbial communities are associated with each other and with ambient nitrogen and carbon availability. Environ Microbiol. 2015;17:4851–60.CAS 
PubMed 

Google Scholar 
39.Liébana R, Arregui L, Santos A, Murciano A, Marquina D, Serrano S. Unravelling the interactions among microbial populations found in activated sludge during biofilm formation. FEMS Microbiol Ecol. 2016;92:1–13.
Google Scholar 
40.Reasoner DJ, Geldreich EE. A new medium for the enumeration and subculture of bacteria from potable water. Appl Environ Microbiol. 1985;49:1–7.CAS 
PubMed 
PubMed Central 

Google Scholar 
41.Parada AE, Needham DM, Fuhrman JA. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol. 2015;18:1403–14.PubMed 

Google Scholar 
42.Junkins EN, Stevenson BS. Using plate-wash PCR and high-throughput sequencing to measure cultivated diversity for natural product discovery efforts. Front Microbiol. 2021;12:1–14.
Google Scholar 
43.Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17:10–12.
Google Scholar 
44.Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
PubMed 
PubMed Central 

Google Scholar 
45.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:590–6.
Google Scholar 
46.Yilmaz P, Parfrey LW, Yarza P, Gerken J, Pruesse E, Quast C, et al. The SILVA and “all-species Living Tree Project (LTP)” taxonomic frameworks. Nucleic Acids Res. 2014;42:643–8.
Google Scholar 
47.Davis NM, Proctor DM, Holmes SP, Relman DA, Callahan BJ. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome. 2018;6:1–14.
Google Scholar 
48.Wright ES. DECIPHER: Harnessing local sequence context to improve protein multiple sequence alignment. BMC Bioinformatics. 2015;16:1–14. https://doi.org/10.1186/s12859-015-0749-z.CAS 
Article 

Google Scholar 
49.Wright ES. Using DECIPHER v2.0 to analyze big biological sequence data in R. R J. 2016;8:352–9.
Google Scholar 
50.Schliep KP. phangorn: phylogenetic analysis in R. Bioinformatics. 2011;27:592–3.CAS 
PubMed 

Google Scholar 
51.McMurdie PJ, Holmes S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:1–11.
Google Scholar 
52.Willis A, Bunge J. Estimating diversity via frequency ratios. Biometrics. 2015;71:1042–9.PubMed 

Google Scholar 
53.Pielou EC. The measurement of diversity in different types of biological collections. J Theor Biol. 1966;13:131–44.
Google Scholar 
54.Levene H. Robust tests for equality of variances. In: Olkin I, editor. Contributions to probability and statistics: essays in honor of Harold Hotelling. Stanford University Press, Palo Alto, California, USA; 1960. p. 278–92.55.Fox J, Weisberg S. An R companion to applied regression. 3rd ed. Thousand Oaks, CA: Sage; 2019.56.Lozupone C, Knight R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol. 2005;71:8228–35.CAS 
PubMed 
PubMed Central 

Google Scholar 
57.Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: community ecology package. R Package; 2019.58.Martin BD, Witten D, Willis AD. Modeling microbial abundances and dysbiosis with beta-binomial regression. Ann Appl Stat. 2020;14:94–115.PubMed 
PubMed Central 

Google Scholar 
59.Chambers MC, MacLean B, Burke R, Amodei D, Ruderman DL, Neumann S, et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat Biotechnol. 2012;30:918–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
60.Pluskal T, Castillo S, Villar-Briones A, Orešič M. MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics. 2010;11:1–11.61.Myers OD, Sumner SJ, Li S, Barne S, Du X. One step forward for reducing false positive and false negative compound identifications from mass spectrometry metabolomics data: new algorithms for constructing extracted ion chromatograms and detecting chromatographic peaks. Anal Chem. 2017;89:8696–703.CAS 
PubMed 

Google Scholar 
62.Wang M, Carver JJ, Phelan VV, Sanchez LM, Garg N;, Peng Y, et al. Sharing and community curation of mass spectrometry data with GNPS. Nat Biotechnol. 2017;34:828–37.
Google Scholar 
63.Nothias LF, Petras D, Schmid R, Dührkop K, Rainer J, Sarvepalli A, et al. Feature-based molecular networking in the GNPS analysis environment. Nat Methods. 2020;17:905–8. https://doi.org/10.1038/s41592-020-0933-6.CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
64.Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models. Genome Res. 2003;13:2498–504. http://ci.nii.ac.jp/naid/110001910481/.CAS 
PubMed 
PubMed Central 

Google Scholar 
65.R Core Team. R: a language and environment for R Foundation for Statistical Computing. 2018. https://www.r-project.org/.66.Bates D, Mächler M, Bolker BM, Walker SC. Fitting linear mixed-effects models using lme4. J Stat Softw. 2015;67:1–48.
Google Scholar 
67.Djoumbou Feunang Y, Eisner R, Knox C, Chepelev L, Hastings J, Owen G, et al. ClassyFire: automated chemical classification with a comprehensive, computable taxonomy. J Cheminform. 2016;8:1–20.
Google Scholar 
68.O’Brien J, Wright GD. An ecological perspective of microbial secondary metabolism. Curr Opin Biotechnol. 2011;22:552–8. https://doi.org/10.1016/j.copbio.2011.03.010.CAS 
Article 
PubMed 

Google Scholar 
69.Thierbach S, Wienhold M, Fetzner S, Hennecke U. Synthesis and biological activity of methylated derivatives of the Pseudomonas metabolites HHQ, HQNO and PQS. Beilstein J Org Chem. 2019;15:187–93.CAS 
PubMed 
PubMed Central 

Google Scholar 
70.Morales-Soto N, Dunham SJB, Baig NF, Ellis JF, Madukoma CS, Bohn PW, et al. Spatially dependent alkyl quinolone signaling responses to antibiotics in Pseudomonas aeruginosa swarms. J Biol Chem. 2018;293:9544–52.CAS 
PubMed 
PubMed Central 

Google Scholar 
71.Heeb S, Fletcher MP, Chhabra SR, Diggle SP, Williams P, Cámara M. Quinolones: from antibiotics to autoinducers. FEMS Microbiol Rev. 2011;35:247–74.CAS 
PubMed 

Google Scholar 
72.Grollman AP. Inhibitors of protein biosynthesis. II. Mode of action of anisomycin. J Biolog Chem. 1967;242:3226–33. https://doi.org/10.1016/S0021-9258(18)95953-3.CAS 
Article 

Google Scholar 
73.Sobin BA, Tanner FW Jr. Anisomycin, a new anti-protozoan antibiotic. J Am Chem Soc. 1954;76:4053–4053.CAS 

Google Scholar 
74.Gross H, Stockwell VO, Henkels MD, Nowak-Thompson B, Loper JE, Gerwick WH. The genomisotopic approach: a systematic method to isolate products of orphan biosynthetic gene clusters. Chem Biol. 2007;14:53–63.CAS 
PubMed 

Google Scholar 
75.Jang JY, Yang SY, Kim YC, Lee CW, Park MS, Kim JC, et al. Identification of orfamide A as an insecticidal metabolite produced by Pseudomonas protegens F6. J Agric Food Chem. 2013;61:6786–91.CAS 
PubMed 

Google Scholar 
76.Ma Z, Geudens N, Kieu NP, Sinnaeve D, Ongena M, Martins JC, et al. Biosynthesis, chemical structure, and structure-activity relationship of orfamide lipopeptides produced by Pseudomonas protegens and related species. Front Microbiol. 2016;7:1–16.
Google Scholar 
77.Figueira V, Vaz-Moreira I, Silva M, Manaia CM. Diversity and antibiotic resistance of Aeromonas spp. in drinking and waste water treatment plants. Water Res. 2011;45:5599–611.CAS 
PubMed 

Google Scholar 
78.Skwor T, Stringer S, Haggerty J, Johnson J, Duhr S, Johnson M, et al. Prevalence of potentially pathogenic antibiotic-resistant Aeromonas spp. in treated urban wastewater effluents versus recipient riverine populations: a 3-year comparative study. Appl Environ Microbiol. 2020;86:1–16.
Google Scholar 
79.Janda JM, Abbott SL. The genus Aeromonas: taxonomy, pathogenicity, and infection. Clin Microbiol Rev. 2010;23:35–73.CAS 
PubMed 
PubMed Central 

Google Scholar 
80.Rema T, Lawrence JR, Dynes JJ, Hitchcock AP, Korber DR. Microscopic and spectroscopic analyses of chlorhexidine tolerance in Delftia acidovorans biofilms. Antimicrob Agents Chemother. 2014;58:5673–86.PubMed 
PubMed Central 

Google Scholar 
81.Assanta MA, Roy D, Lemay MJ, Montpetit D. Attachment of Arcobacter butzleri, a new waterborne pathogen, to water distribution pipe surfaces. J Food Protect. 2002;65:1240–7.
Google Scholar 
82.Costerton JW, Stewart PS, Greenberg EP. Bacterial biofilms: a common cause of persistent infections. Science. 1999;284:1318–22.CAS 
PubMed 

Google Scholar 
83.Harrison F, Paul J, Massey RC, Buckling A. Interspecific competition and siderophore-mediated cooperation in Pseudomonas aeruginosa. ISME J. 2008;2:49–55.PubMed 

Google Scholar 
84.Inglis RF, Roberts PG, Gardner A, Buckling A. Spite and the scale of competition in Pseudomonas aeruginosa. Am Nat. 2011;178:276–85.PubMed 

Google Scholar 
85.van der Meij A, Worsley SF, Hutchings MI, van Wezel GP. Chemical ecology of antibiotic production by Actinomycetes. FEMS Microbiol Rev. 2017;41:392–416.PubMed 

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

Google Scholar 
87.Kinkel LL, Schlatter DC, Xiao K, Baines AD. Sympatric inhibition and niche differentiation suggest alternative coevolutionary trajectories among Streptomycetes. ISME J. 2014;8:249–56. https://doi.org/10.1038/ismej.2013.175. [Internet]Available fromCAS 
Article 
PubMed 

Google Scholar 
88.Pacala SW, Levin SA. Biologically generated spatial pattern and the coexistence of competing species. In: Tilman D, Kareiva P, editors. Spatial ecology: the role of space in population dynamics and interspecific interactions; Princeton University Press, Princeton, New Jersey, USA; 1997.89.Zhou J, Ning D. Stochastic community assembly: does it matter in microbial ecology? Microbiol Mol Biol Rev. 2017;81:1–32.
Google Scholar 
90.Haig SJ, Quince C, Davies RL, Dorea CC, Collinsa G. The relationship between microbial community evenness and function in slow sand filters. mBio. 2015;6:1–12.
Google Scholar 
91.Wittebolle L, Marzorati M, Clement L, Balloi A, Daffonchio D, Heylen K, et al. Initial community evenness favours functionality under selective stress. Nature. 2009;458:623–6.CAS 
PubMed 

Google Scholar 
92.Davies J, Ryan KS. Introducing the parvome: bioactive compounds in the microbial world. ACS Chem Biol. 2012;7:252–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
93.Bassler BL, Losick R. Bacterially speaking. Cell. 2006;125:237–46.CAS 
PubMed 

Google Scholar 
94.Venturi V. Regulation of quorum sensing in Pseudomonas. FEMS Microbiol Rev. 2006;30:274–91.CAS 
PubMed 

Google Scholar 
95.Granato ET, Meiller-Legrand TA, Foster KR. The evolution and ecology of bacterial warfare. Curr Biol. 2019;29:R521–37. https://doi.org/10.1016/j.cub.2019.04.024.CAS 
Article 
PubMed 

Google Scholar 
96.Estrela S, Brown SP. Community interactions and spatial structure shape selection on antibiotic resistant lineages. PLoS Comput Biol. 2018;14:1–21.CAS 

Google Scholar 
97.Hibbing ME, Fuqua C, Parsek MR, Peterson SB. Bacterial competition: surviving and thriving in the microbial jungle. Nat Rev Microbiol. 2010;8:15–25.CAS 
PubMed 
PubMed Central 

Google Scholar 
98.Garcia-Garcera M, Rocha EPC. Community diversity and habitat structure shape the repertoire of extracellular proteins in bacteria. Nat Commun. 2020;11:1–11. https://doi.org/10.1038/s41467-020-14572-x.CAS 
Article 

Google Scholar  More

1.Butchart, S. H. M. et al. Global biodiversity: indicators of recent declines. Science 328, 1164–1168 (2010).CAS 
Article 

Google Scholar 
2.Buchanan, G. M., Butchart, S. H. M., Chandler, G. & Gregory, R. D. Assessment of national-level progress towards elements of the Aichi Biodiversity Targets. Ecol. Indic. 116, 106497 (2020).Article 

Google Scholar 
3.Butchart, S. H. M. et al. in Global Assessment Report of the Intergovernmental Science–Policy Platform on Biodiversity and Ecosystem Services (eds Berkes, F. & Brooks, T. M.) Ch. 3 (IPBES Secretariat, 2019); https://doi.org/10.5281/zenodo.38320534.Maxwell, S. L. et al. Area-based conservation in the twenty-first century. Nature 586, 217–227 (2020).CAS 
Article 

Google Scholar 
5.Locke, H. Nature needs half: a necessary and hopeful new agenda for protected areas. Nat. N. S. W. 58, 7–17 (2014).
Google Scholar 
6.Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67, 534–545 (2017).Article 

Google Scholar 
7.Dinerstein, E. et al. A global deal for nature: guiding principles, milestones, and targets. Sci. Adv. 5, eaaw2869 (2019).CAS 
Article 

Google Scholar 
8.Mehrabi, Z., Ellis, E. C. & Ramankutty, N. The challenge of feeding the world while conserving half the planet. Nat. Sustain. 1, 409–412 (2018).Article 

Google Scholar 
9.Kok, M. T. J. et al. Assessing ambitious nature conservation strategies within a 2 degree warmer and food-secure world. Preprint at bioRxiv https://doi.org/10.1101/2020.08.04.236489 (2020).10.Rosa, I. M. D. et al. Multiscale scenarios for nature futures. Nat. Ecol. Evol. 1, 1416–1419 (2017).Article 

Google Scholar 
11.Obermeister, N. Local knowledge, global ambitions: IPBES and the advent of multi-scale models and scenarios. Sustain. Sci. 14, 843–856 (2019).Article 

Google Scholar 
12.Pereira, L. M. et al. Developing multiscale and integrative nature–people scenarios using the Nature Futures Framework. People Nat. 2, 1172–1195 (2020).Article 

Google Scholar 
13.Rabin, S. S. et al. Impacts of future agricultural change on ecosystem service indicators. Earth Syst. Dynam. 11, 357–376 (2019).Article 

Google Scholar 
14.Springmann, M. et al. Global and regional health effects of future food production under climate change: a modelling study. Lancet 387, 1937–1946 (2016).Article 

Google Scholar 
15.Springmann, M. et al. Health and nutritional aspects of sustainable diet strategies and their association with environmental impacts: a global modelling analysis with country-level detail. Lancet Planet. Health 2, e451–e461 (2018).Article 

Google Scholar 
16.Dinerstein, E. et al. A “Global Safety Net” to reverse biodiversity loss and stabilize Earth’s climate. Sci. Adv. 6, eabb2824 (2020).Article 

Google Scholar 
17.Locke, H. et al. Three global conditions for biodiversity conservation and sustainable use: an implementation framework. Natl Sci. Rev. https://doi.org/10.1093/nsr/nwz136 (2019).18.Waldron, A. et al. Protecting 30% of the Planet for Nature: Costs, Benefits and Economic Implications (Campaign for Nature, 2020).19.Strassburg, B. B. N. et al. Global priority areas for ecosystem restoration. Nature 586, 724–729 (2020).CAS 
Article 

Google Scholar 
20.O’Neill, B. C. et al. The roads ahead: narratives for Shared Socioeconomic Pathways describing world futures in the 21st century. Glob. Environ. Change 42, 169–180 (2015).21.Riahi, K. et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob. Environ. Change 42, 153–168 (2017).22.Tauli-Corpuz, V., Alcorn, J., Molnar, A., Healy, C. & Barrow, E. Cornered by PAs: adopting rights-based approaches to enable cost-effective conservation and climate action. World Dev. 130, 104923 (2020).Article 

Google Scholar 
23.Kashwan, P. V., Duffy, R., Massé, F., Asiyanbi, A. P. & Marijnen, E. From racialized neocolonial global conservation to an inclusive and regenerative conservation. Environ. Sci. Policy Sustain. Dev. 63, 4–19 (2021).Article 

Google Scholar 
24.The State of Food Security and Nutrition in the World 2017: Building Resilience for Peace and Food Security (FAO, 2017).25.Schleicher, J. et al. Protecting half of the planet could directly affect over one billion people. Nat. Sustain. 2, 1094–1096 (2019).Article 

Google Scholar 
26.Allan, J. R. et al. The minimum land area requiring conservation attention to safeguard biodiversity. Preprint at bioRxiv https://doi.org/10.1101/839977 (2021).27.Brockington, D. & Wilkie, D. Protected areas and poverty. Phil. Trans. R. Soc. B 370, 20140271 (2015).28.Protected Planet Report 2020 (UNEP–WCMC and IUCN, 2021).29.Naidoo, R. et al. Evaluating the impacts of protected areas on human well-being across the developing world. Sci. Adv. 5, eaav3006 (2019).CAS 
Article 

Google Scholar 
30.Dutta, A., Allan, J., Shimray, G., General, S. & Pact, A. I. P. RE: “A ‘Global Safety Net’ to reverse biodiversity loss and stabilize Earth’s climate”. Sci. Adv. 6, eabb2824 (2020).Article 

Google Scholar 
31.Simmons, B. A., Nolte, C. & McGowan, J. Tough questions for the “30 × 30” conservation agenda. Front. Ecol. Environ. 19, 322–323 (2021).Article 

Google Scholar 
32.Jung, M. et al. Areas of global importance for conserving terrestrial biodiversity, carbon and water. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-021-01528-7 (2021).33.The IUCN Red List of Threatened Species Version 2019.2 (IUCN, 2019).34.The World Database of Key Biodiversity Areas (KBA Partnership, 2019); www.keybiodiversityareas.org35.Mogg, S., Fastre, C. & Visconti, P. Targeted expansion of protected areas to maximise the persistence of terrestrial mammals. Preprint at bioRxiv https://doi.org/10.1101/608992 (2019).36.Gurobi Optimizer Reference Manual (Gurobi Optimization, 2019).37.Hanson, J. O. et al. prioritizr: Systematic Conservation Prioritization in R. R package version 5.0.3 https://CRAN.R-project.org/package=prioritizr (2020).38.Hurtt, G., Chini, L., Frolking, S. & Sahajpal, R. Land-Use Harmonization (LUH2) (Global Ecology Laboratory, Univ. Maryland, 2017).39.Protected Planet: The World Database on Protected Areas (WDPA) (UNEP-WCMC and IUCN, accessed April 2019); www.protectedplanet.net40.Dellink, R., Chateau, J., Lanzi, E. & Magné, B. Long-term economic growth projections in the Shared Socioeconomic Pathways. Glob. Environ. Change 42, 200–214 (2017).41.Jones, B. & O’Neill, B. C. Spatially explicit global population scenarios consistent with the Shared Socioeconomic Pathways. Environ. Res. Lett. 11, 84003 (2016).42.van Vuuren, D. P. et al. The representative concentration pathways: an overview. Climatic Change 109, 5–31 (2011).Article 

Google Scholar 
43.Engström, K. et al. Assessing uncertainties in global cropland futures using a conditional probabilistic modelling framework. Earth Syst. Dynam. 7, 893–915 (2016).44.Alexander, P. et al. Drivers for global agricultural land use change: the nexus of diet, population, yield and bioenergy. Glob. Environ. Change 35, 138–147 (2015).Article 

Google Scholar 
45.Popp, A. et al. Land-use transition for bioenergy and climate stabilization: model comparison of drivers, impacts and interactions with other land use based mitigation options. Climatic Change 123, 495–509 (2014).Article 

Google Scholar 
46.GBD Results Tool (IHME, 2020); http://ghdx.healthdata.org/gbd-results-tool47.KC, S. & Lutz, W. The human core of the Shared Socioeconomic Pathways: population scenarios by age, sex and level of education for all countries to 2100. Glob. Environ. Change 42, 181–192 (2017). More

1.Gaston, K. J. Global patterns of biodiversity. Nature 405, 220–227 (2000).CAS 
Article 

Google Scholar 
2.Friis, E. M., Crane, P. R. & Pedersen, K. R. Early Flowers and Angiosperm Evolution (Cambridge Univ. Press, 2011).3.Blomenkemper, P. et al. A hidden cradle of plant evolution in Permian tropical lowlands. Science 362, 1414–1416 (2018).CAS 
Article 

Google Scholar 
4.Kenrick, P. & Crane, P. R. The Origin and Early Diversification of Land Plants: A Cladistic Study (Smithsonian Institution Scholarly Press, 1997).5.Puttick, M. N. et al. The interrelationships of land plants and the nature of the nature of the ancestral embryophyte. Curr. Biol. 28, 733–745 (2018).CAS 
Article 

Google Scholar 
6.Morris, J. L. et al. The timescale of early land plant evolution. Proc. Natl Acad. Sci. USA 115, 2274–2283 (2018).Article 

Google Scholar 
7.Wellman, C. H., Steemans, P. & Vecoli, M. in Early Palaeozoic Biogeography and Palaeogeography (eds Harper, D. & Servais, T.) Ch. 29 (Geological Society of London, 2014).8.Edwards, D. et al. Piecing together the eophytes—a new group of ancient plants containing cryptospores. New Phytol. 233, 1440–1455 (2021).Article 

Google Scholar 
9.Gray, J. The microfossil record of early land plants; advances in understanding of early terrestrialization, 1970–1984. Philos. Trans. R. Soc. Lond. B 309, 167–195 (1985).Article 

Google Scholar 
10.Wellman, C. H. Cryptospores from the type area for the Caradoc Series (Ordovician) in southern Britain. Palaeontology 55, 103–136 (1996).
Google Scholar 
11.Torsvik, T. H. & Cocks, L. R. M. Earth History and Palaeogeography (Cambridge Univ. Press, 2017).12.Harland, W. B. The Geology of Svalbard (Geological Society of London, 1997).13.Davies, N. S., Berry, C. M., Marshall, J. E. A., Wellman, C. H. & Lindemann, F.-J. The Devonian landscape factory: plant–sediment interactions in the Old Red Sandstone of Svalbard and the rise of vegetation as a biogeomorphic agent. J. Geol. Soc. Lond. https://doi.org/10.1144/jgs2020-225 (2021).14.Blieck, A., Goujet, D. & Janvier, P. The vertebrate stratigraphy of the Lower Devonian (Red Bay Group and Wood Bay Formation) of Spitsbergen. Mod. Geol. 11, 197–217 (1987).
Google Scholar 
15.Blom, H. & Goujet, D. Thelodont scales from the Lower Devonian Red Bay Group, Spitsbergen. Palaeontology 45, 795–820 (2002).Article 

Google Scholar 
16.Pernègre, V. N. & Blieck, A. A revised heterostrachan-cased ichthyostratigraphy of the Wood Bay Formation (Lower Devonian, Spitsbergen), and correlation with Russian Arctic archipelagos. Geodiversitas 38, 5–20 (2016).Article 

Google Scholar 
17.Wellman, C. H. & Richardson, J. B. Sporomorph assemblages from the ‘Lower Old Red Sandstone’ of Lorne Scotland. Spec. Pap. Palaeontol. 55, 41–101.18.Richardson J. B. Taxonomy and classification of some new Early Devonian cryptospores from England. Spec. Pap. Palaeontol. 55, 7–40 (1996).19.Steemans, P. Etude palynostratgraphique du Devonian Inferieur dans l’Ouest de l’Europe. Mém. Soc. Géol. Minér. Bretagne 27, 1–453 (1989).
Google Scholar 
20.Rodriguez, R. M. Palinologia de las Formaciones del Silurico Superior-Devonico Inferior de la Cordillera Cantabrica, Noroeste de España (Institución Fray Bernardino de Sahagún, de la Excelentísima Diputación provincial de León y del Servicio de Publicaciones de la Universidad de León, 1983).21.Richardson, J. B., Rodriguez, R. M. & Sutherland, S. J. E. Palynological zonation of Mid-Palaeozoic sequences from the Cantabrian Mountains, NW Spain: implications for inter-regional and interfacies correlation of the Ludfor/Pridoli and Silurian/Devonian boundaries, and plant dispersal patterns. Bull. Nat. Hist. Mus. Lond. 57, 115–162 (2001).
Google Scholar 
22.Rubinstein, C. & Steemans, P. Miospore assemblages from the Silurian–Devonian boundary, in borehole A1–61, Ghadames Basin, Libya. Rev. Palaeobot. Palynol. 118, 397–412 (2002).Article 

Google Scholar 
23.Spina, A. & Vecoli, M. Palynostratigraphy and vegetational change in the Siluro-Devonian of the Ghadamis basin, North Africa. Palaeogeog. Palaeoclimatol. Palaeoecol. 282, 1–18 (2009).Article 

Google Scholar 
24.Hao, S. G. & Gensel, P. G. in Plants Invade the Land (eds Gensel, P. G. & Edwards, D.) 103–119 (Columbia Univ. Press, 2001).25.Wellman, C. H. et al. Spore assemblages from the Lower Devonian Xujiachong Formation from Qujing, Yunnan, China. Palaeontology 55, 583–611 (2012).Article 

Google Scholar 
26.Hao, S. & Xue, J. The Early Devonian Posongchong Flora of Yunnan (Science Press, 2013).27.Edwards, D., & Li, C.-S. Further insights into the Lower Devonian terrestrial vegetation of Sichuan Province, China. Rev. Palaeobot. Palynol. 253, 37–48 (2018).Article 

Google Scholar 
28.Gao, L. Early Devonian spore and acritarchs from the Guijiatum Formation of Qujing, China. Bull. Inst. Geol. Chin. Acad. Sci. 9, 125–136 (1984).
Google Scholar 
29.Tian, J. et al. Late Silurian to early Devonian palynomorphs from Qujing, Yunnan, southwest China. Acta Geol. Sin. 85, 559–568 (2011).Article 

Google Scholar 
30.Høeg, O. A. The Downtonian and Dittonian flora of Spitsbergen. Skr. Svalbard Ishavet 83, 1–229 (1942).
Google Scholar 
31.Morris, J. L., Edwards, D. & Richardson, J. B. in Transformative Paleobotany (eds Krings, M. et al.) 49–67 (Academic Press, 2018).32.McSweeney, F. R., Shimeta, J. & Buckeridge, J. St. J. S. Two new genera of early Tracheophyta (Zosterophyllaceae) from the upper Silurian–Lower Devonian of Victoria, Australia. Alcheringa https://doi.org/10.1080/03115518.2020.1744725 (2020).33.Xue, J. H. et al. Silurian–Devonian terrestrial revolution in South China: taxonomy, diversity, and character evolution of vascular plants in a paleogeographically isolated low-latitude region. Earth Sci. Rev. 180, 92–125 (2018).Article 

Google Scholar 
34.Hao, S. G. et al. Zosterophyllum Penhallow around the Silurian–Devonian boundary of northeastern Yunnan, China. Int. J. Plant Sci. 168, 477–489 (2007).Article 

Google Scholar 
35.Hao, S. G. et al. Earliest rooting system and root: shoot ratio from a new Zosterophyllum plant. New Phytol. 185, 217–225 (2009).Article 

Google Scholar 
36.Xue, J.-Z. Two zosterophyll plants from the Lower Devonian (Lochkovian) Xitun Formation of northeastern Yunnan, China. Acta Geol. Sin. 83, 504–512 (2009).Article 

Google Scholar 
37.Xue, J.-Z. Lochkovian plants from the Xitun Formation of Yunnan, China and their palaeophytogeographical significance. Geol. Mag. 149, 333–344 (2012).Article 

Google Scholar 
38.Sun, Y. et al. Lethally high temperatures during the early Triassic greenhouse. Science 6105, 366–370 (2012).Article 

Google Scholar 
39.Meng, X. Y. & Gai, Z. K. Falxcornus, a new genus of Tridensaspidae (Galeaspida, stem-Gnathostomata) from the Lower Devonian in Qujing, Yunnan, China. Hist. Biol. https://doi.org/10.1080/08912963.2021.1952198 (2021).40.Traverse, A. Paleopalynology 2nd edn (Springer, 2007). More

1.Hungate, B. A., Dukes, J. S., Shaw, M. R., Luo, Y. & Field, C. B. Nitrogen and climate change. Science 302, 1512–1513 (2003).CAS 
Article 

Google Scholar 
2.Wieder, W. R., Cleveland, C. C., Smith, W. K. & Todd-Brown, K. Future productivity and carbon storage limited by terrestrial nutrient availability. Nat. Geosci. 8, 441–444 (2015).CAS 
Article 

Google Scholar 
3.Sulman, B. N. et al. Diverse mycorrhizal associations enhance terrestrial C storage in a global model. Glob. Biogeochem. Cycles 33, 501–523 (2019).CAS 
Article 

Google Scholar 
4.Wieder, W. R., Cleveland, C. C., Lawrence, D. M. & Bonan, G. B. Effects of model structural uncertainty on carbon cycle projections: biological nitrogen fixation as a case study. Environ. Res. Lett. 10, 044016 (2015).5.Shi, M., Fisher, J. B., Brzostek, E. R. & Phillips, R. P. Carbon cost of plant nitrogen acquisition: global carbon cycle impact from an improved plant nitrogen cycle in the Community Land Model. Glob. Change Biol. 22, 1299–1314 (2016).Article 

Google Scholar 
6.Meyerholt, J., Zaehle, S. & Smith, M. J. Variability of projected terrestrial biosphere responses to elevated levels of atmospheric CO2 due to uncertainty in biological nitrogen fixation. Biogeosciences 13, 1491–1518 (2016).CAS 
Article 

Google Scholar 
7.Fisher, J. B. et al. Carbon cost of plant nitrogen acquisition: a mechanistic, globally applicable model of plant nitrogen uptake, retranslocation, and fixation. Glob. Biogeochem. Cycles 24, GB1014 (2010).8.Wang, Y. P. & Houlton, B. Z. Nitrogen constraints on terrestrial carbon uptake: Implications for the global carbon-climate feedback. Geophys. Res. Lett. 36, L24403 (2009).9.Houlton, B. Z., Wang, Y.-P., Vitousek, P. M. & Field, C. B. A unifying framework for dinitrogen fixation in the terrestrial biosphere. Nature 454, 327–330 (2008).CAS 
Article 

Google Scholar 
10.Steidinger, B. S. et al. Climatic controls of decomposition drive the global biogeography of forest-tree symbioses. Nature 569, 404–408 (2019).CAS 
Article 

Google Scholar 
11.van Velzen, R., Doyle, J. J. & Geurts, R. A resurrected scenario: single gain and massive loss of nitrogen-fixing nodulation. Trends Plant Sci. 24, 49–57 (2018).Article 

Google Scholar 
12.Mills, B. et al. Modelling the long-term carbon cycle, atmospheric CO2, and Earth surface temperature from late Neoproterozoic to present day. Gondwana Res. 67, 172–186 (2018).Article 

Google Scholar 
13.Fowler, D. et al. The global nitrogen cycle in the twenty-first century. Philos. Trans. R. Soc. B 368, 20130164 (2013).14.Rogers, A. et al. A roadmap for improving the representation of photosynthesis in Earth system models. New Phytol. 213, 22–42 (2017).Article 

Google Scholar 
15.Prévost, D., Antoun, H. & Bordeleau, L. M. Effects of low temperatures on nitrogenase activity in sainfoin (Onobrychis viciifolia) nodulated by Arctic rhizobia. FEMS Microbiol. Lett. 45, 205–210 (1987).Article 

Google Scholar 
16.Rainbird, R. M., Atkins, C. A. & Pate, J. S. Effect of temperature on nitrogenase functioning in cowpea nodules. Plant Physiol. 73, 392–394 (1983).CAS 
Article 

Google Scholar 
17.Dalton, D. A. & Zobel, D. B. Ecological aspects of nitrogen fixation by Purshia tridentata. Plant Soil 48, 57–80 (1977).CAS 
Article 

Google Scholar 
18.Waughman, G. J. The effect of temperature on nitrogenase activity. J. Exp. Bot. 28, 949–960 (1977).CAS 
Article 

Google Scholar 
19.Wheeler, C. T. The causation of the diurnal changes in nitrogen fixation in the nodules of Alnus glutinosa. New Phytol. 70, 487–495 (1971).Article 

Google Scholar 
20.Schomberg, H. H. & Weaver, R. W. Nodulation, nitrogen fixation, and early growth of arrowleaf clover in response to root temperature and starter nitrogen. Agron. J. 84, 1046 (1992).CAS 
Article 

Google Scholar 
21.Kou-Giesbrecht, S. & Menge, D. N. L. Nitrogen-fixing trees increase soil nitrous oxide emissions: a meta-analysis. Ecology 102, e03415 (2021).22.Bytnerowicz, T. A., Min, E., Griffin, K. L. & Menge, D. N. L. Repeatable, continuous and real‐time estimates of coupled nitrogenase activity and carbon exchange at the whole‐plant scale. Methods Ecol. Evol. 10, 960–970 (2019).Article 

Google Scholar 
23.Menge, D. N. L., Lichstein, J. W. & Ángeles-Pérez, G. Nitrogen fixation strategies can explain the latitudinal shift in nitrogen-fixing tree abundance. Ecology 95, 2236–2245 (2014).Article 

Google Scholar 
24.Staccone, A. et al. A spatially explicit, empirical estimate of tree-based biological nitrogen fixation in forests of the United States. Glob. Biogeochem. Cycles 32, e2019GB006241 (2020).25.Cierjacks, A. et al. Biological flora of the British Isles: Robinia pseudoacacia. J. Ecol. 101, 1623–1640 (2013).Article 

Google Scholar 
26.Benson, D. R. & Dawson, J. O. Recent advances in the biogeography and genecology of symbiotic Frankia and its host plants. Physiol. Plant. 130, 318–330 (2007).CAS 
Article 

Google Scholar 
27.Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
28.Kumarathunge, D. P. et al. Acclimation and adaptation components of the temperature dependence of plant photosynthesis at the global scale. New Phytol. 222, 768–784 (2019).CAS 
Article 

Google Scholar 
29.Heskel, M. A. et al. Convergence in the temperature response of leaf respiration across biomes and plant functional types. Proc. Natl Acad. Sci. USA 113, 3832–3837 (2016).CAS 
Article 

Google Scholar 
30.Kou-Giesbrecht, S. et al. A novel representation of biological nitrogen fixation and competitive dynamics between nitrogen-fixing and non-fixing plants in a land model (GFDL LM4.1-BNF). Biogeosciences 18, 4143–4183 (2021).CAS 
Article 

Google Scholar 
31.Hardy, R. W. F., Holsten, R. D., Jackson, E. K. & Burns, R. C. The acetylene-ethylene assay for N2 fixation: laboratory and field evaluation. Plant Physiol. 43, 1185–1207 (1968).CAS 
Article 

Google Scholar 
32.Cassar, N., Bellenger, J. P., Jackson, R. B., Karr, J. & Barnett, B. A. N2 fixation estimates in real-time by cavity ring-down laser absorption spectroscopy. Oecologia 168, 335–342 (2012).Article 

Google Scholar 
33.Taylor, B. N., Chazdon, R. L. & Menge, D. N. L. Successional dynamics of nitrogen fixation and forest growth in regenerating Costa Rican rainforests. Ecology 100, e02637 (2019).34.Kok, B. A Critical Consideration of the Quantum Yield of Chlorella-Photosynthesis (W. Junk, 1948).35.Liang, L. L. et al. Macromolecular rate theory (MMRT) provides a thermodynamics rationale to underpin the convergent temperature response in plant leaf respiration. Glob. Change Biol. 24, 1538–1547 (2018).Article 

Google Scholar 
36.Gunderson, C. A., O’hara, K. H., Campion, C. M., Walker, A. V. & Edwards, N. T. Thermal plasticity of photosynthesis: the role of acclimation in forest responses to a warming climate. Glob. Change Biol. 16, 2272–2286 (2010).Article 

Google Scholar 
37.Medlyn, B. E. et al. Temperature response of parameters of a biochemically based model of photosynthesis. II. A review of experimental data. Plant Cell Environ. 25, 1167–1179 (2002).CAS 
Article 

Google Scholar 
38.Slot, M. & Winter, K. In situ temperature relationships of biochemical and stomatal controls of photosynthesis in four lowland tropical tree species. Plant Cell Environ. 40, 3055–3068 (2017).CAS 
Article 

Google Scholar 
39.Murphy, B. K. & Stinziano, J. R. A derivation error that affects carbon balance models exists in the current implementation of the modified Arrhenius function. New Phytol. 6, 2371–2381 (2021).40.Yan, W. & Hunt, L. A. An equation for modelling the temperature response of plants using only the cardinal temperatures. Ann. Bot. 84, 607–614 (1999).Article 

Google Scholar 
41.Farquhar, G. D. & Busch, F. A. Changes in the chloroplastic CO2 concentration explain much of the observed Kok effect: a model. New Phytol. 214, 570–584 (2017).CAS 
Article 

Google Scholar 
42.Farquhar, G. D., Von Caemmerer, S. & Berry, J. A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149, 78–90 (1980).CAS 
Article 

Google Scholar 
43.Duursma, R. A. Plantecophys – an R package for analysing and modelling leaf gas exchange data. PLoS ONE 10, e0143346 (2015).44.Bernacchi, C. J., Singsaas, E. L., Pimentel, C., Portis, A. R. Jr & Long, S. P. Improved temperature response functions for models of Rubisco-limited photosynthesis. Plant Cell Environ. 24, 253–260 (2001).CAS 
Article 

Google Scholar 
45.De Kauwe, M. G. et al. A test of the ‘one-point method’ for estimating maximum carboxylation capacity from field-measured, light-saturated photosynthesis. New Phytol. 210, 1130–1144 (2016).Article 

Google Scholar 
46.Bolker, B. M. & R. Core Team. bbmle: Tools for General Maximum Likelihood Estimation (R Foundation for Statistical Computing, 2014).47.Burnham, K. P. & Anderson, D. R. Multimodel inference: understanding AIC and BIC in model selection. Sociol. Methods Res. 33, 261–304 (2004).Article 

Google Scholar 
48.Bolker, B. M. Ecological Models and Data in R (Princeton Univ. Press, 2008).49.Venables, W. & Ripley, B. Modern Applied Statistics with S (Springer, 2002).50.Bytnerowicz, T. A. tbytnero/Bytnerowicz-Akana-Griffin-Menge-N-fix-Temp: Bytnerowicz_Akana_Griffin_Menge_2022_Nature_Plants https://doi.org/10.5281/zenodo.5764790 (2021). More

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

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

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

The Levant region, the major land bridge connecting Africa with Eurasia, was a significant dispersal route for Hominins and fauna during the early Pleistocene1,2,3. But while there are numerous Eurasian early Pleistocene sites, fossil hominin remains are rare and present only at four localities dated between 1.1 and 1.9 Mya4,5,6,7,8,9,10,11: Dmanisi (Georgia), Venta Micena (Orce, Granada), Modjokerto and Sangiran (Java, Indonesia), and Sima De Elefante (Atapuerca, Spain) (Supplementary 2: Table 1; Fig. 1a). In contrast, early Pleistocene east African sites containing Homo cranial remains are more abundant, but postcranial remains are scarcer, and the best-preserved skeleton is Nariokotome KNM-WT 1500012,13.Figure 1‘Ubeidya site locality. (a) Map of Africa and Eurasia with major Pleistocene paleoanthropological sites. Black circles denote sites with no osteological remains; red circles denote sites with human osteological remains. (b) The location of the site of ‘Ubeidiya, south of lake Kineret (Sea of Galilee), on the western banks of the Jordan Valley (red circle) (c) aerial photograph of the excavation plan of ‘Ubeidiya with the location of layer II-23 where UB 10749 was recovered.Full size imageIn the Levant, the only site from this time-period with hominin remains is ‘Ubeidiya at the western escarpment of the Jordan Valley which is a part of the broader Rift Valley (Supplementary 1: Fig. 1b,c). The fossil remains include cranial fragments (UB 1703, 1704, 1705, and 1706), two incisor (UB 1700, UB 335) and a molar (UB 1701), identified as Homo cf. erectus/ergaster14,15,16,17,18. It is important to note that some of these fragments were bulldozed out of the ground preceding the first season, while others are considered intrusive and younger than the surroundings deposits17.In 2018 during a reanalysis of the faunal assemblages done by two of the authors (A. B, and M. B.) a complete vertebral body (UB 10749) with hominin characteristics was found. This is the first hominin postcranial remain found at ‘Ubeidiya securely assigned to early Pleistocene deposits (See “Materials and methods”).Here we assess the taxonomic affinity of UB 10749, its serial location along the spinal column, its chronological and physiological age at death, estimate the specimen’s height and weight, and detect any pathological or taphonomic changes. Based on our findings, we explore the unique developmental characteristics of the UB 10749 within the context of early Homo paleobiology and its implications for hominin dispersal out of Africa.Description of the findingUB 10749 is a complete vertebral body (Fig. 2). The superior plate of the vertebra is oval, with an uneven surface, indicating non-ossification of the vertebral endplate. Similarly, the inferior plate is also oval with marked postero-lateral edges. A small pit is found in the center of both superior and inferior plates. The inferior plate is bilaterally wider than the superior plate. The anterior and lateral walls are smooth and slightly concave i.e., their superior and inferior edges are more prominent than the center. There is no evidence of rib attachment to the body on the lateral walls. The posterior wall can be divided into three parts, the center and right and left lateral thirds. The central part is smooth with two nutritional foramina. The two lateral thirds are located at the junction between the vertebral body and the pedicles. Their surface is uneven, indicating that the pedicle had not yet ossified to the vertebral body. In a lateral view, the vertebra shows a lordotic wedging as the height of the anterior wall is greater than that of the posterior wall (Supplementary 2: Table 2). The oval shape of the vertebral body, the concavity of the inferior plate, the lordotic wedging, and the lack of rib bearing facets all indicate a lower lumbar vertebra, i.e., presacral (PS) 1, PS2, or PS3 (corresponding to L5, L4, and L3 in modern humans).Figure 2UB 10749 vertebral body. (a) Superior view; (b) posterior view; (c) inferior view; (d) anterior view.Full size imageA micro-CT (µCT) scan of UB 10749 (Fig. 3) reveals a well-developed cortical bone on the anterior and lateral walls and the central part of the posterior wall. The cancellous bone at the superior and inferior plates is very thin, as is the bone at the lateral thirds of the posterior wall, indicating that these were not yet ossified. The µCT scan also reveals well-developed canals within the vertebral body –Bastons’ venous plexus19 (Fig. 3c). A small pit at the superior and inferior plates is seen in the mid-sagittal and coronal planes of the CT scan (Fig. 3a, b). A thin vertical region that appears black on the µCT connects the two pits, indicating that this area was not yet ossified.Figure 3µCT scan of UB 10749. (a) Midsagittal section. (b) Coronal section. (c) Horizontal section.Full size imageTaxonomic identificationWe compared UB 10749 to a range of mammalian species from, but not limited to, those present in ‘Ubeidiya such as carnivores (e.g., Ursus, Hyeana, Panthera), Artiodactyla (e.g., Hippopotamus, Praemegaceros), Perissodactyla (Rhinocertidae, Equidae), Proboscidea (Mamuthus, Elephas), and Primates (Homo, Pongo, Gorilla, Theropithecus and Papio).UB 10749 lacks the inward indentation on the posterior wall distinctive of Ursus and is short cranio-caudally, as opposed to the longer vertebral bodies of ungulates.The size, the large vertebral plate, and the relatively short vertebral body of UB 10749 indicates that it belongs to hominoidea. The lordotic wedging and the concavity of the inferior plate further suggests that this is a hominin vertebra20,21.To narrow the taxonomic identification, we compared UB 10749 to a range of extant and extinct hominin species, and to Pan as an outgroup (Supplementary 2: Table 3). The analysis revealed that the best index to which best differentiates between lumbar vertebral bodies of Homo and Pan is ‘superior length to posterior height’ (Fig. 4; Supplementary 2: Table 4). This index also differentiates between Homo and Australopithecus22. Compared to the three presacral vertebrae (PS1–PS3) of hominins and Pan, UB 10749 falls within the range of Homo and outside the range of Pan or Australopithecus. It falls near the position of the vertebrae of KNM-WT-15000, an early Pleistocene sub adult specimen from east Africa. Therefore, we conclude that the vertebra at hand most probably belongs to an early Pleistocene Homo.Figure 4Comparison of UB 10749 to other hominoids. Vertebral body ratio (superior length to posterior height) of each of the lower 3 presacral vertebra in modern humans, neandertals, australopith, chimpanzees, KNM-WT 15000 and UB 10749. Note that UB 10749 is consistently falls within the range of Homo, and beyond the range of chimpanzees and australopith.Full size imageSerial allocation of the vertebral bodyIt is well known, especially in Hominoidea, that there is a vast overlap in the shape of adjacent lumbar vertebral bodies23. We conducted three separate analyses to address this problem: (1) Vertebral wedging i.e., the ratio of posterior height/anterior height which significantly separates the vertebral segments PS1, PS2, and PS3 of modern humans (Supplementary 2: Fig. 1; Supplementary 2: Table 4), (2) A principal component analysis (PCA) of vertebral linear indices (Fig. 5a; Supplementary 2: Table 4), and (3) Geometric morphometrics (GM) shape analysis (Fig. 5b). Vertebral wedging sets UB 10749 as PS2. The vertebral linear indices PCA sets the UB 10749 as either PS2 or PS3, and the GM shape analysis sets the vertebra as either PS1 or PS2. Based on these results, we estimate that the serial allocation of UB 10749 is most likely PS2.Figure 5Serial allocation of UB 10749. (a) PCA of vertebral body ratios of modern humans, KNM-WT 15000, STS 14, and UB 10749 (see Supplementary Table 4). Note the overlap between adults and juvenile in each presacral vertebra. UB 10749 falls within the range of PS2–PS3. Note that KNM-WT 15000 and STS 14 follow the same pattern as modern humans. (b) PCA results for GM shape analysis. UB 10749 falls within the range of PS1, but with proximity to PS2. Note that KNM-WT 15000 and STS 14 follow the same pattern as modern humans. In both analysis: Circles denotes juvenile; Squares denotes adults. Red denotes PS1; Blue denotes PS2; Green denotes PS3.Full size imageAge at deathAge at death is estimated based on level of ossification, relative vertebral size, or vertebral shape. The lack of neural canal ossification in UB 10749 indicates an approximate age of 3–6-years-old compared to modern humans24,25 (Supplementary 2: Fig. 2), although it is important to note that several authors report high variability in pedicle ossification, up to 16-years-old26,27. The absence of vertebral endplate ossification also supports the young age of UB 10749, indicating that the vertebra belongs to an individual that had not reached puberty28.In contrast, based on its size alone, UB 10749 would be assigned an older age, probably between 11 and 15-year-old modern human (Fig. 6a: Supplementary 2: Table 5). However, vertebral size is highly variable with age, and we cannot rule out either a younger or older age. Finally, geometric morphometric principal component shape analysis suggests that UB 10749 falls within the range of 6–10-years-old modern humans (Fig. 6b). This is confirmed by a linear discriminant analysis which also places UB 10749 well within the 6–10 years old group (Supplementary 2: Fig. 4). Considering all the above information, we estimate that the age at death for UB 10749 is between 6 and 12-years-old.Figure 6Age at death of UB 10749. (a) Vertebral body size (combined sample of PS1–PS3) of modern humans, KNM-WT 15000 and UB 10749 (see Supplementary 2: Table 5). UB 10749 falls within the range of 11–15 years or the lower end of adults. (b) PCA results for GM shape analysis of modern human, KNM-WT 15000, STS 14, and UB 10749 vertebral bodies. UB 10749 falls within the range of the 6–10 age group. In both analyses: Red, 0–5 years old; Green, 6–10 years old; Blue, 11–15 years old; Brown, 16-adults.Full size imageHeight and weight estimationHeight (stature) and weight at death is estimated based on a range of equations and growth charts for modern humans (Supplementary 2: Tables 6–8). The estimated average height at death of UB 10749, points to a height of 155 cm. This height is comparable to a 13 years-old boy or a 12.5 years-old girl, based on CDC growth charts. A height of 155 cm is above the 95 percentile of 10 years old and above the 75 percentile for 12 years old modern humans29. As the age estimation for UB 10749 is 6–12 years, it seems that this individual was tall for its age.Weight is estimated based on height or based on chronological age. Based on height, UB 10749 was 45–55 kg, while based on age, the weight of UB 10749 was 20–43 kg (Supplementary 2: Table 7). Since height is a stronger predictor for weight than age30, we estimate the weight at death at about 45–50 kg.A single juvenile vertebral body is not a definitive predictor for adult height and weight. Even more so, the growth pattern of early Pleistocene hominins is unknown. Thus, to cautiously estimate the adult height and weight of UB 10749, calculations were based on several methods: modern American (CDC growth charts), modern Sudanese population31, and chimpanzees32.Assuming UB 10749 was 6–12 years old, based on chimpanzees’ growth charts, it would have reached adult height of 155–192 cm and weighted 50–101 kg. Based on modern American and Sudanese growth charts, UB 10749 displays a range of a height between 168 and 247 cm and a weight between 62 and 173 kg (Supplementary 2: Table 8). The average height and weight predication for adult size of UB 10749 is 198 cm and 100 kg. Although we cannot rule out any of the estimations, based on its size at death and predicated adult size, UB 10749 was most likely a large-bodied hominin33,34,35.TaphonomyVery thin fluviatile deposits are evident on the surface of the vertebra, despite being cleaned during the excavation. Aside from that, there is no apparent taphonomic alteration or post-depositional breakage.PathologyThe completeness of the vertebral body and its bilateral symmetry do not suggest pathological processes or developmental deformities that may have affected the vertebra, such as osteoarthritis, disc herniation, spondylosis, tuberculosis, brucellosis, or scoliosis36. However, in the absence of the vertebral arch, we cannot rule out anterior slippage of the vertebral body, i.e., spondylolisthesis or facet joint deformities. The discrepancy between the size of the vertebral body and the level of ossification is puzzling. The size of UB 10749 is equivalent to an 11–15-year-old modern human, and the level of ossification is equivalent to a 3–6-year-old modern human child. This discrepancy might result from several factors, including developmental or pathological conditions, such as: persistent notochondral canal; hypopituitarism; androgen deficiency; genetic mutation24,37,38 (see Supplementary 2 for discussion regarding possible pathology). While these conditions are rare in modern humans, they cannot be ruled out. Another possibility is that UB 10749 displays a different ossification pattern than observed in modern humans or great apes25,39. More