Philippa Kaur

Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).Article 
ADS 
CAS 

Google Scholar 
Chen, I.-C., Hill, J. K., Ohlemüller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011).Article 
ADS 
CAS 

Google Scholar 
Bowler, D. E. et al. Cross-realm assessment of climate change impacts on species’ abundance trends. Nat. Ecol. Evol. 1, 1–7 (2017).Article 

Google Scholar 
Barrett, J. E. et al. Persistent effects of a discrete warming event on a polar desert ecosystem. Glob. Change Biol. 14, 2249–2261 (2008).Article 
ADS 

Google Scholar 
Gooseff, M. N. et al. Decadal ecosystem response to an anomalous melt season in a polar desert in Antarctica. Nat. Ecol. Evol. 1, 1334–1338 (2017).Article 

Google Scholar 
Iknayan, K. J. & Beissinger, S. R. In transition: Avian biogeographic responses to a century of climate change across desert biomes. Glob. Change Biol. 26, 3268–3284 (2020).Article 
ADS 

Google Scholar 
Conradie, S. R., Woodborne, S. M., Cunningham, S. J. & McKechnie, A. E. Chronic, sublethal effects of high temperatures will cause severe declines in southern African arid-zone birds during the 21st century. Proc. Natl Acad. Sci. USA 116, 14065–14070 (2019).Article 
ADS 
CAS 

Google Scholar 
du Plessis, K. L., Martin, R. O., Hockey, P. A. R., Cunningham, S. J. & Ridley, A. R. The costs of keeping cool in a warming world: implications of high temperatures for foraging, thermoregulation and body condition of an arid-zone bird. Glob. Change Biol. 18, 3063–3070 (2012).Article 
ADS 

Google Scholar 
Ward, D. The Biology of Deserts (OUP Oxford, 2016).Reid, V. W. et al. Millennium Ecosystem Assessment, 2005. In Ecosystems and Human Well-being: Synthesis (Island Press, 2005).Zhou, L., Chen, H. & Dai, Y. Stronger warming amplification over drier ecoregions observed since 1979. Environ. Res. Lett. 10, 064012 (2015).Article 
ADS 

Google Scholar 
Hoegh-Guldberg, O. et al. 2018: Impacts of 1.5ºC Global Warming on Natural and Human Systems. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty (eds Masson-Delmotte, V. et al.) Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 175-312, https://doi.org/10.1017/9781009157940.005.Albright, T. P. et al. Mapping evaporative water loss in desert passerines reveals an expanding threat of lethal dehydration. Proc. Natl Acad. Sci. USA 114, 2283–2288 (2017).Article 
ADS 
CAS 

Google Scholar 
Friedrich, T., Timmermann, A., Tigchelaar, M., Timm, O. E. & Ganopolski, A. Nonlinear climate sensitivity and its implications for future greenhouse warming. Sci. Adv. 2, e1501923 (2016).Article 
ADS 

Google Scholar 
Kearney, M. R. & Porter, W. P. NicheMapR—an R package for biophysical modelling: the microclimate model. Ecography 40, 664–674 (2017).Article 

Google Scholar 
Huey, R. B. et al. Predicting organismal vulnerability to climate warming: roles of behaviour, physiology and adaptation. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 367, 1665–1679 (2012).Article 

Google Scholar 
Kearney, M. & Porter, W. Mechanistic niche modelling: combining physiological and spatial data to predict species’ ranges. Ecol. Lett. 12, 334–350 (2009).Article 

Google Scholar 
Bicudo, J. E. P., Buttemer, W. A., Chappell, M. A., Pearson, J. T. & Bech, C. Ecological and Environmental Physiology of Birds Vol. 2 (Oxford University Press, 2010).McKechnie, A. E. & Wolf, B. O. Climate change increases the likelihood of catastrophic avian mortality events during extreme heat waves. Biol. Lett. 6, 253–256 (2010).Article 

Google Scholar 
Riddell, E. A. et al. Exposure to climate change drives stability or collapse of desert mammal and bird communities. Science 371, 633–636 (2021).Article 
ADS 
CAS 

Google Scholar 
Williams, J. B. & Tieleman, B. I. Physiological adaptation in desert birds. BioScience 55, 416–425 (2005).Article 

Google Scholar 
Iknayan, K. J. & Beissinger, S. R. Collapse of a desert bird community over the past century driven by climate change. Proc. Natl Acad. Sci. USA 115, 8597–8602 (2018).Article 
ADS 
CAS 

Google Scholar 
Albright, T. P. et al. Combined effects of heat waves and droughts on avian communities across the conterminous United States. Ecosphere 1, art12 (2010).Article 

Google Scholar 
Cruz-McDonnell, K. K. & Wolf, B. O. Rapid warming and drought negatively impact population size and reproductive dynamics of an avian predator in the arid southwest. Glob. Change Biol. 22, 237–253 (2016).Article 
ADS 

Google Scholar 
Dawson, W. R. Temperature Regulation and Water Requirements of the Brown and Abert Towhees, Pipilo Fuscus and Pipilo Aberti.[With Plates.] (University of California Press, 1954).Riddell, E. A., Iknayan, K. J., Wolf, B. O., Sinervo, B. & Beissinger, S. R. Cooling requirements fueled the collapse of a desert bird community from climate change. Proc. Natl. Acad. Sci. USA https://doi.org/10.1073/pnas.1908791116 (2019).Wolf, B. Global warming and avian occupancy of hot deserts; a physiological and behavioral perspective. Rev. Chil. Hist. Nat. 73, 395–400 (2000).Article 

Google Scholar 
Kier, G. et al. A global assessment of endemism and species richness across island and mainland regions. Proc. Natl Acad. Sci. USA 106, 9322–9327 (2009).Article 
ADS 
CAS 

Google Scholar 
Ioffe, S. Improved consistent sampling, weighted Minhash and L1 sketching. In Proceedings of the 2010 IEEE International Conference on Data Mining 246–255 (IEEE Computer Society, 2010).Losos, E., Hayes, J., Phillips, A., Wilcove, D. & Alkire, C. Taxpayer-subsidized resource extraction harms species. BioScience 45, 446–455 (1995).Article 

Google Scholar 
Rodríguez-Estrella, R. Land use changes affect distributional patterns of desert birds in the Baja California peninsula, Mexico. Divers. Distrib. 13, 877–889 (2007).Article 

Google Scholar 
Stralberg, D. et al. Climate-change refugia in boreal North America: what, where, and for how long? Front. Ecol. Environ. 18, 261–270 (2020).Article 

Google Scholar 
Hinkel, J. et al. Sea-level rise scenarios and coastal risk management. Nat. Clim. Change 5, 188–190 (2015).Article 
ADS 

Google Scholar 
He, Q. & Silliman, B. R. Climate change, human impacts, and coastal ecosystems in the anthropocene. Curr. Biol. 29, R1021–R1035 (2019).Article 
CAS 

Google Scholar 
C. B. D. Zero Draft of the Post-2020 Global Biodiversity Framework CBD/WG2020/2/3. https://www.cbd.int/doc/c/efb0/1f84/a892b98d2982a829962b6371/wg2020-02-03-en.pdf Convention on Biology Diversity, Montreal, Canada (2020).Jung, M. et al. A global map of terrestrial habitat types. Sci. Data 7, 256 (2020).Article 

Google Scholar 
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 
Meigs, P. World distributions of arid and semi-arid homoclimates. In Review of Research on Arid Zone Hydrology (UNESCO, 1953).Holt, B. G. et al. An update of Wallace’s zoogeographic regions of the world. Science 339, 74–78 (2013).Article 
ADS 
CAS 

Google Scholar 
Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015. Sci. Data 5, 170191 (2018).Article 

Google Scholar 
Qin, Y. et al. Agricultural risks from changing snowmelt. Nat. Clim. Change 10, 459–465 (2020).Article 
ADS 

Google Scholar 
Kearney, M. R. & Porter, W. P. NicheMapR – an R package for biophysical modelling: the microclimate model. Ecography 40, 664–674 (2017).Article 

Google Scholar 
Pattinson, N. B. et al. Heat dissipation behaviour of birds in seasonally hot arid-zones: are there global patterns? J. Avian Biol. 51, e02350 (2020).Smith, E. K., O’Neill, J., Gerson, A. R. & Wolf, B. O. Avian thermoregulation in the heat: resting metabolism, evaporative cooling and heat tolerance in Sonoran Desert doves and quail. J. Exp. Biol. 218, 3636–3646 (2015).Article 

Google Scholar 
Smith, E. K., O’Neill, J. J., Gerson, A. R., McKechnie, A. E. & Wolf, B. O. Avian thermoregulation in the heat: resting metabolism, evaporative cooling and heat tolerance in Sonoran Desert songbirds. J. Exp. Biol. 220, 3290–3300 (2017).
Google Scholar 
Kearney, M. NicheMapR: R implementation of Niche Mapper software for biophysical modelling. https://github.com/mrke/NicheMapR. (2020).Cunningham, S. J., Martin, R. O. & Hockey, P. A. Can behaviour buffer the impacts of climate change on an arid-zone bird? Ostrich 86, 119–126 (2015).Article 

Google Scholar 
Czenze, Z. J. et al. Regularly drinking desert birds have greater evaporative cooling capacity and higher heat tolerance limits than non-drinking species. Funct. Ecol. 34, 1589–1600 (2020).Article 

Google Scholar 
Smit, B. et al. Avian thermoregulation in the heat: phylogenetic variation among avian orders in evaporative cooling capacity and heat tolerance. J. Exp. Biol. 221, jeb174870 (2018).Worcester, S. E. The scaling of the size and stiffness of primary flight feathers. J. Zool. 239, 609–624 (1996).Article 

Google Scholar 
Wang, X., Nudds, R. L., Palmer, C. & Dyke, G. J. Size scaling and stiffness of avian primary feathers: implications for the flight of Mesozoic birds. J. Evol. Biol. 25, 547–555 (2012).Article 
CAS 

Google Scholar 
McKechnie, A. E., Gerson, A. R. & Wolf, B. O. Thermoregulation in desert birds: scaling and phylogenetic variation in heat tolerance and evaporative cooling. J. Exp. Biol. 224, jeb229211 (2021).Flint, L. E., Flint, A. L., Thorne, J. H. & Boynton, R. Fine-scale hydrologic modeling for regional landscape applications: the California Basin Characterization Model development and performance. Ecol. Process. 2, 25 (2013).Article 

Google Scholar 
Handbook of the Birds of the World and BirdLife International. Handbook of the Birds of the World and BirdLife International digital checklist of the birds of the world. Version 5. http://datazone.birdlife.org/userfiles/file/Species/Taxonomy/HBW-BirdLife_Checklist_v5_Dec20.zip (2020).Brooks, T. M. et al. Measuring terrestrial Area of Habitat (AOH) and its utility for the IUCN red list. Trends Ecol. Evol. 34, 977–986 (2019).Article 

Google Scholar 
Pastore, M. Overlapping: a R package for estimating overlapping in empirical distributions. J. Open Source Softw. 3, 1023 (2018).Article 
ADS 

Google Scholar 
UNEP-WCMC and IUCN, Protected Planet: The World Database on Protected Areas (WDPA) [Online], June 2021, Cambridge, UK: UNEP-WCMC and IUCN www.protectedplanet.net (2021).Butchart, S. H. M. et al. Shortfalls and solutions for meeting national and global conservation area targets. Conserv. Lett. 8, 329–337 (2015).Article 

Google Scholar 
Dudley, N. Guidelines for Applying Protected Area Management Categories (ICUN, 2008).Mangiafico, S. rcompanion: Functions to Support Extension Education Program Evaluation. https://CRAN.R-project.org/package=rcompanion. (2021).Crawford, C. L., Estes, L. D., Searchinger, T. D. & Wilcove, D. S. Consequences of underexplored variation in biodiversity indices used for land-use prioritization. Ecol. Appl. 31, e02396 (2021).Article 

Google Scholar 
Gorelick, N. et al. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).Article 
ADS 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020). More

Hsu-Kim H, Kucharzyk KH, Zhang T, Deshusses MA. Mechanisms regulating mercury bioavailability for methylating microorganisms in the aquatic environment: A critical review. Environ Sci Technol. 2013;47:2441–56.Article 
CAS 

Google Scholar 
Podar M, Gilmour CC, Brandt CC, Soren A, Brown SD, Crable BR, et al. Global prevalence and distribution of genes and microorganisms involved in mercury methylation. Sci Adv. 2015;1:e1500675.Article 

Google Scholar 
Liu YR, Johs A, Bi L, Lu X, Hu HW, Sun D, et al. Unraveling microbial communities associated with methylmercury production in paddy soils. Environ Sci Technol. 2018;52:13110–8.Article 
CAS 

Google Scholar 
Lee C-S, Fisher NS. Bioaccumulation of methylmercury in a marine copepod. Environ Toxicol Chem. 2017;36:1287–93.Article 
CAS 

Google Scholar 
Parks JM, Johs A, Podar M, Bridou R, Hurt RAJ, Smith SD, et al. The genetic basis for bacterial mercury methylation. Science 2013;339:1332–5.Article 
CAS 

Google Scholar 
McDaniel EA, Peterson BD, Stevens SLR, Tran PQ, Anantharaman K, McMahon KD. Expanded phylogenetic diversity and metabolic flexibility of mercury-methylating microorganisms. mSystems 2020;5:e00299–20.Article 
CAS 

Google Scholar 
Cooper CJ, Zheng K, Rush KW, Johs A, Sanders BC, Pavlopoulos GA, et al. Structure determination of the HgcAB complex using metagenome sequence data: Insights into microbial mercury methylation. Commun Biol. 2020;3:320.Article 
CAS 

Google Scholar 
Kerin EJ, Gilmour CC, Roden E, Suzuki MT, Coates JD, Mason RP. Mercury methylation by dissimilatory iron-reducing bacteria. Appl Environ Microbiol. 2006;72:7919–21.Article 
CAS 

Google Scholar 
Gilmour CC, Podar M, Bullock AL, Graham AM, Brown SD, Somenahally AC, et al. Mercury methylation by novel microorganisms from new environments. Environ Sci Technol. 2013;47:11810–20.Article 
CAS 

Google Scholar 
Capo E, Bravo AG, Soerensen AL, Bertilsson S, Pinhassi J, Feng C, et al. Deltaproteobacteria and Spirochaetes-like bacteria are abundant putative mercury methylators in oxygen-deficient water and marine particles in the Baltic Sea. Front Microbiol. 2020;11:574080.Article 

Google Scholar 
Gionfriddo CM, Tate MT, Wick RR, Schultz MB, Zemla A, Thelen MP, et al. Microbial mercury methylation in Antarctic sea ice. Nat Microbiol. 2016;1:16127.Article 
CAS 

Google Scholar 
Jones DS, Walker GM, Johnson NW, Mitchell CPJ, Coleman Wasik JK, Bailey JV. Molecular evidence for novel mercury methylating microorganisms in sulfate-impacted lakes. ISME J. 2019;13:1659–75.Article 
CAS 

Google Scholar 
Christensen GA, Gionfriddo CM, King AJ, Moberly JG, Miller CL, Somenahally AC, et al. Determining the reliability of measuring mercury cycling gene abundance with correlations with mercury and methylmercury concentrations. Environ Sci Technol. 2019;53:8649–63.Article 
CAS 

Google Scholar 
Villar E, Cabrol L, Heimburger-Boavida LE. Widespread microbial mercury methylation genes in the global ocean. Environ Microbiol Rep. 2020;12:277–87.Article 
CAS 

Google Scholar 
Lin H, Ascher DB, Myung Y, Lamborg CH, Hallam SJ, Gionfriddo CM, et al. Mercury methylation by metabolically versatile and cosmopolitan marine bacteria. ISME J. 2021;15:1810–25.Article 
CAS 

Google Scholar 
King JK, Kostka JE, Frischer ME, Saunders FM, Jahnke RA. A quantitative relationship that demonstrates mercury methylation rates in marine sediments are based on the community composition and activity of sulfate-reducing bacteria. Environ Sci Technol. 2001;35:2491–6.Article 
CAS 

Google Scholar 
Regnell O, Watras CJ. Microbial mercury methylation in aquatic environments: A critical review of published field and laboratory studies. Environ Sci Technol. 2019;53:4–19.Article 
CAS 

Google Scholar 
Xie R, Wang Y, Huang D, Hou J, Li L, Hu H, et al. Expanding Asgard members in the domain of Archaea sheds new light on the origin of eukaryotes. Sci China Life Sci. 2022;65:818–29.Article 
CAS 

Google Scholar 
Seitz KW, Dombrowski N, Eme L, Spang A, Lombard J, Sieber JR, et al. Asgard archaea capable of anaerobic hydrocarbon cycling. Nat Commun. 2019;10:1822.Article 

Google Scholar 
Zaremba-Niedzwiedzka K, Caceres EF, Saw JH, Backstrom D, Juzokaite L, Vancaester E, et al. Asgard archaea illuminate the origin of eukaryotic cellular complexity. Nature 2017;541:353–8.Article 
CAS 

Google Scholar 
Liu Y, Makarova KS, Huang W-C, Wolf YI, Nikolskaya AN, Zhang X, et al. Expanded diversity of Asgard archaea and their relationships with eukaryotes. Nature 2021;593:553–7.Article 
CAS 

Google Scholar 
Zhang JW, Dong HP, Hou LJ, Liu Y, Ou YF, Zheng YL, et al. Newly discovered Asgard archaea Hermodarchaeota potentially degrade alkanes and aromatics via alkyl/benzyl-succinate synthase and benzoyl-CoA pathway. ISME J. 2021;15:1826–43.Article 
CAS 

Google Scholar 
Cai M, Liu Y, Yin X, Zhou Z, Friedrich MW, Richter-Heitmann T, et al. Diverse Asgard archaea including the novel phylum Gerdarchaeota participate in organic matter degradation. Sci China Life Sci. 2020;63:886–97.Article 
CAS 

Google Scholar 
Baker BJ, De Anda V, Seitz KW, Dombrowski N, Santoro AE, Lloyd KG. Diversity, ecology and evolution of Archaea. Nat Microbiol. 2020;5:887–900.Article 
CAS 

Google Scholar 
Farag Ibrahim F, Zhao R, Biddle Jennifer F, Atomi H. “Sifarchaeota,” a novel Asgard phylum from Costa Rican sediment capable of polysaccharide degradation and anaerobic methylotrophy. Appl Environ Micro. 2021;87:e02584–20.
Google Scholar 
Adam PS, Borrel G, Brochier-Armanet C, Gribaldo S. The growing tree of Archaea: new perspectives on their diversity, evolution and ecology. ISME J. 2017;11:2407–25.Article 

Google Scholar 
Cai M, Richter-Heitmann T, Yin X, Huang W-C, Yang Y, Zhang C, et al. Ecological features and global distribution of Asgard archaea. Sci Total Environ. 2021;758:143581.Article 
CAS 

Google Scholar 
Zhang C-J, Chen Y-L, Sun Y-H, Pan J, Cai M-W, Li M. Diversity, metabolism and cultivation of archaea in mangrove ecosystems. Mar Life Sci Tech. 2020;3:252–62.Article 

Google Scholar 
Dai SS, Yang Z, Tong Y, Chen L, Liu SY, Pan R, et al. Global distribution and environmental drivers of methylmercury production in sediments. J Hazard Mater. 2021;407:124700.Article 
CAS 

Google Scholar 
Tang WL, Liu YR, Guan WY, Zhong H, Qu XM, Zhang T. Understanding mercury methylation in the changing environment: Recent advances in assessing microbial methylators and mercury bioavailability. Sci Total Environ. 2020;714:136827.Article 
CAS 

Google Scholar 
Tsui MTK, Finlay JC, Balogh SJ, Nollet YH. In situ production of methylmercury within a stream channel in northern California. Environ Sci Technol. 2010;44:6998–7004.Article 
CAS 

Google Scholar 
Liu Y, Zhou Z, Pan J, Baker BJ, Gu JD, Li M. Comparative genomic inference suggests mixotrophic lifestyle for Thorarchaeota. ISME J. 2018;12:1021–31.Article 
CAS 

Google Scholar 
Lei P, Zhong H, Duan D, Pan K. A review on mercury biogeochemistry in mangrove sediments: Hotspots of methylmercury production? Sci Total Environ. 2019;680:140–50.Article 
CAS 

Google Scholar 
Beckers F, Rinklebe J. Cycling of mercury in the environment: Sources, fate, and human health implications: A review. Crit Rev Env Sci Tec. 2017;47:693–794.Article 
CAS 

Google Scholar 
de Oliveira DC, Correia RR, Marinho CC, Guimaraes JR. Mercury methylation in sediments of a Brazilian mangrove under different vegetation covers and salinities. Chemosphere 2015;127:214–21.Article 

Google Scholar 
Li R, Xu H, Chai M, Qiu GY. Distribution and accumulation of mercury and copper in mangrove sediments in Shenzhen, the world’s most rapid urbanized city. Environ Moni Assess. 2016;188:87.Article 

Google Scholar 
O’Connor D, Hou D, Ok YS, Mulder J, Duan L, Wu Q, et al. Mercury speciation, transformation, and transportation in soils, atmospheric flux, and implications for risk management: A critical review. Environ Int. 2019;126:747–61.Article 

Google Scholar 
Obrist D, Kirk JL, Zhang L, Sunderland EM, Jiskra M, Selin NE. A review of global environmental mercury processes in response to human and natural perturbations: Changes of emissions, climate, and land use. Ambio 2018;47:116–40.Article 

Google Scholar 
Capo E, Peterson BD, Kim M, Jones DS, Acinas SG, Amyot M, et al. A consensus protocol for the recovery of mercury methylation genes from metagenomes. Mol Ecol Resour. 2022; https://doi.org/10.1111/1755-0998.13687.Gionfriddo CM, Wymore AM, Jones DS, Wilpiszeski RL, Lynes MM, Christensen GA, et al. An improved hgcAB primer set and direct high-throughput sequencing expand Hg-methylator diversity in nature. Front Microbiol. 2020;11:541554.Article 

Google Scholar 
Yu R-Q, Barkay T. Chapter two – microbial mercury transformations: Molecules, functions and organisms. Adv Appl Microbiol. 2022;118:31–90.Article 

Google Scholar 
Chételat J, Richardson MC, MacMillan GA, Amyot M, Poulain AJ. Ratio of methylmercury to dissolved organic carbon in water explains methylmercury bioaccumulation across a latitudinal gradient from north-temperate to arctic lakes. Environ Sci Technol. 2018;52:79–88.Article 

Google Scholar 
Liu Y-R, Dong J-X, Han L-L, Zheng Y-M, He J-Z. Influence of rice straw amendment on mercury methylation and nitrification in paddy soils. Environ Pollut. 2016;209:53–9.Article 
CAS 

Google Scholar 
Moreau JW, Gionfriddo CM, Krabbenhoft DP, Ogorek JM, DeWild JF, Aiken GR, et al. The effect of natural organic matter on mercury methylation by Desulfobulbus propionicus 1pr3. Front Microbiol. 2015;6:1389.Article 

Google Scholar 
Chen C-F, Ju Y-R, Chen C-W, Dong C-D. The distribution of methylmercury in estuary and harbor sediments. Sci Total Environ. 2019;691:55–63.Article 
CAS 

Google Scholar 
Bravo AG, Bouchet S, Guédron S, Amouroux D, Dominik J, Zopfi J. High methylmercury production under ferruginous conditions in sediments impacted by sewage treatment plant discharges. Water Res. 2015;80:245–55.Article 
CAS 

Google Scholar 
Wang H, Su J, Zheng T, Yang X. Insights into the role of plant on ammonia-oxidizing bacteria and archaea in the mangrove ecosystem. J Soil Sediment. 2015;15:1212–23.Article 
CAS 

Google Scholar 
Imachi H, Nobu MK, Nakahara N, Morono Y, Ogawara M, Takaki Y, et al. Isolation of an archaeon at the prokaryote–eukaryote interface. Nature 2020;577:519–25.Article 
CAS 

Google Scholar 
Zhou J, Riccardi D, Beste A, Smith JC, Parks JM. Mercury methylation by HgcA: Theory supports carbanion transfer to Hg(II). Inorg Chem. 2014;53:772–7.Article 
CAS 

Google Scholar 
Smith Steven D, Bridou R, Johs A, Parks Jerry M, Elias Dwayne A, Hurt Richard A, et al. Site-directed mutagenesis of HgcA and HgcB reveals amino acid residues important for mercury methylation. Appl Environ Micro. 2015;81:3205–17.Article 
CAS 

Google Scholar 
Sousa FL, Neukirchen S, Allen JF, Lane N, Martin WF. Lokiarchaeon is hydrogen dependent. Nat Microbiol. 2016;1:16034.Article 
CAS 

Google Scholar 
Schaefer JK, Rocks SS, Zheng W, Liang L, Gu B, Morel FMM. Active transport, substrate specificity, and methylation of Hg(II) in anaerobic bacteria. Proc Natl Acad Sci USA 2011;108:8714.Article 
CAS 

Google Scholar 
Sakai S, Imachi H, Hanada S, Ohashi A, Harada H, Kamagata Y. Methanocella paludicola gen. nov., sp. nov., a methane-producing archaeon, the first isolate of the lineage ‘Rice Cluster I’, and proposal of the new archaeal order Methanocellales ord. nov. Int J Syst Evol Microbiol. 2008;58:929–36.Article 

Google Scholar 
Dridi B, Fardeau ML, Ollivier B, Raoult D, Drancourt M. Methanomassiliicoccus luminyensis gen. nov., sp. nov., a methanogenic archaeon isolated from human faeces. Int J Syst Evol Microbiol. 2012;62:1902–7.Article 
CAS 

Google Scholar 
Dietz R, Sonne C, Basu N, Braune B, O’Hara T, Letcher RJ, et al. What are the toxicological effects of mercury in arctic biota? Sci Total Environ. 2013;443:775–90.Article 
CAS 

Google Scholar 
Gilmour Cynthia C, Bullock Allyson L, McBurney A, Podar M, Elias Dwayne A, Lovley Derek R. Robust mercury methylation across diverse methanogenic archaea. mBio 2018;9:e02403–17.
Google Scholar 
Pan J, Chen Y, Wang Y, Zhou Z, Li M. Vertical distribution of Bathyarchaeotal communities in mangrove wetlands suggests distinct niche preference of Bathyarchaeota subgroup 6. Micro Ecol. 2019;77:417–28.Article 

Google Scholar 
Zhang C-J, Pan J, Duan C-H, Wang Y-M, Liu Y, Sun J, et al. Prokaryotic diversity in mangrove sediments across southeastern China fundamentally differs from that in other biomes. mSystems 2019;4:e00442–19.Article 
CAS 

Google Scholar 
Peng Y, Leung HC, Yiu SM, Chin FY. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 2012;28:1420–8.Article 
CAS 

Google Scholar 
Li D, Liu C-M, Luo R, Sadakane K, Lam T-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 2015;31:1674–6.Article 
CAS 

Google Scholar 
Hyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinforma. 2010;11:119.Article 

Google Scholar 
Zhang C-J, Pan J, Liu Y, Duan C-H, Li M. Genomic and transcriptomic insights into methanogenesis potential of novel methanogens from mangrove sediments. Microbiome. 2020;8:94.Article 
CAS 

Google Scholar 
Kang DD, Froula J, Egan R, Wang Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 2015;3:e1165.Article 

Google Scholar 
Sieber CMK, Probst AJ, Sharrar A, Thomas BC, Hess M, Tringe SG, et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat Microbiol. 2018;3:836–43.Article 
CAS 

Google Scholar 
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.Article 
CAS 

Google Scholar 
Olm MR, Brown CT, Brooks B, Banfield JF. dRep: A tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 2017;11:2864–8.Article 
CAS 

Google Scholar 
Chaumeil PA, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 2019;36:1925–7.
Google Scholar 
Kanehisa M, Sato Y, Morishima K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J Mol Biol. 2016;428:726–31.Article 
CAS 

Google Scholar 
Huerta-Cepas J, Forslund K, Szklarczyk D, Jensen LJ, von Mering C, Bork P. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol Biol Evol. 2017;34:2115–22.Article 
CAS 

Google Scholar 
Finn RD, Clements J, Eddy SR. HMMER web server: interactive sequence similarity searching. Nucleic Acids Res. 2011;39:W29–W37.Article 
CAS 

Google Scholar 
Eddy SR. Accelerated profile HMM searches. PLoS Comput Biol. 2011;7:e1002195.Article 
CAS 

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

Google Scholar 
Capella-Gutiérrez S, Silla-Martínez JM, Gabaldón T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 2009;25:1972–3.Article 

Google Scholar 
Nguyen L-T, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2015;32:268–74.Article 
CAS 

Google Scholar 
Price MN, Dehal PS, Arkin AP. FastTree 2 – approximately maximum-likelihood trees for large alignments. Plos ONE. 2010;5:e9490.Article 

Google Scholar 
Letunic I, Bork P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 2016;44:W242–5.Article 
CAS 

Google Scholar 
Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature 2021;596:583–9.Article 
CAS 

Google Scholar 
Trott O, Olson AJ. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31:455–61.CAS 

Google Scholar 
Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9.Article 
CAS 

Google Scholar 
Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinforma (Oxf, Engl). 2010;26:841–2.Article 
CAS 

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

Google Scholar  More

Crowther, T. W. et al. Mapping tree density at a global scale. Nature 525, 201–205 (2015).Article 
CAS 

Google Scholar 
Asner, G. P. et al. A universal airborne LiDAR approach for tropical forest carbon mapping. Oecologia 168, 1147–1160 (2012).Article 

Google Scholar 
Walker, A. P. et al. Predicting long‐term carbon sequestration in response to CO2 enrichment: How and why do current ecosystem models differ? Glob. Biogeochem. Cy. 29, 476–495 (2015).Article 
CAS 

Google Scholar 
Madrigal-González, J. et al. Climate reverses directionality in the richness–abundance relationship across the World’s main forest biomes. Nat. Commun. 11, 1–7 (2020).Article 

Google Scholar 
Stephenson, N. L. Climatic control of vegetation distribution: the role of the water balance. Am. Nat. 135, 649–670 (1990).Article 

Google Scholar 
Weiskittel, A. R., Maguire, D. A., Monserud, R. A. Development of a hybrid model for intensively managed Douglas-fir in the Pacific Northwest. In Forest Growth and Timber Quality, 49 (USDA, Portland, 2009).Paoli, G. D., Curran, L. M. & Slik, J. W. F. Soil nutrients affect spatial patterns of aboveground biomass and emergent tree density in southwestern Borneo. Oecologia 155, 287–299 (2008).Article 

Google Scholar 
Yoda, K., Kira, T., Ogawa, H. & Hozami, K. Self-thinning in overcrowded pure stands under cultivated and natural conditions. J. Biol. Osaka City Univ. 14, 107–129 (1963).
Google Scholar 
Westoby, M. The self-thinning rule. Adv. Ecol. Res. 14, 167–225 (1984).Article 

Google Scholar 
Weiner, J. & Freckleton, R. P. Constant final yield. Annu. Rev. Ecol. Evol. S. 41, 173–192 (2010).Article 

Google Scholar 
Pillet, M. et al. Disentangling competitive vs. climatic drivers of tropical forest mortality. J. Ecol. 106, 1165–1179 (2018).Article 

Google Scholar 
Schluter, D. Experimental evidence that competition promotes divergence in adaptive radiation. Science 266, 798–801 (1994).Article 
CAS 

Google Scholar 
Pacala, S.W. & Levin, S.A. Biologically generated spatial pattern and the coexistence of competing species. Spatial Ecology: The Role of Space in Population Dynamics and Interspecific Interactions. (Princeton University Press, Princeton, NJ, p. 204-232, 1997).Asefa, M., Cao, M., Zhang, G., Ci, X. & Li, J. Yang Environmental filtering structures tree functional traits combination and lineages across space in tropical tree assemblages. Sci. Rep. 7, 1–10 (2017).Article 
CAS 

Google Scholar 
Pretzsch, H. & Biber, P. Tree species mixing can increase maximum stand density. Can. J. For. Res. 46, 1179–1193 (2016).Article 

Google Scholar 
Escudero, A. et al. Every bit helps: The functional role of individuals in assembling any plant community, from the richest to monospecific ones. J. Veg. Sci. 32, e13059 (2021).Article 

Google Scholar 
Jump, A. S. et al. Structural overshoot of tree growth with climate variability and the global spectrum of drought-induced forest dieback. Glob. Chang. Biol. 23, 3742–3757 (2017).Article 

Google Scholar 
M. Takyu, Y. Kubota, S.I. Aiba, T. Seino, T. Nishimura. Pattern of changes in species diversity, structure and dynamics of forest ecosystems along latitudinal gradients in East Asia. In Forest Ecosystems and Environments (Springer, Tokyo, 2005), pp. 49–58.Rivoire, M. & Le, G. A. Moguedec, generalized self-thinning relationship for multi-species and mixed-size forests. Ann. Sci. 69, 207–219 (2012).Article 

Google Scholar 
Salas‐Eljatib, C. & Weiskittel, A. R. Evaluation of modelling strategies for assessing self‐thinning behaviour and carrying capacity. Ecol. Evol. 8, 10768–10779 (2018).Article 

Google Scholar 
Schietti, J. et al. Forest structure along a 600 km transect of natural disturbances and seasonality gradients in central‐southern Amazonia. J. Ecol. 104, 1335–1346 (2016).Article 

Google Scholar 
Vanclay, J. K. & Sands, P. J. Calibrating the self-thinning frontier. For. Ecol. Manag. 259, 81–85 (2009).Article 

Google Scholar 
Sapijanskas, J., Paquette, A., Potvin, C., Kunert, N. & Loreau, M. Tropical tree diversity enhances light capture through crown plasticity and spatial and temporal niche differences. Ecology 95, 2479–2492 (2014).Article 

Google Scholar 
Lieth, H. Modeling the primary productivity of the world. In H. Lieth & R. H. Whittaker, eds. Primary Productivity of the Biosphere (Springer-Verlag, New York, New York, USA, 1975), pp. 237–264.Grieser, J., Gommes, R., Cofield, S., Bernardi, M. World Maps of Climatological net Primary Production of Biomass, NPP. Food and Agriculture Organization of the United Nations. (GEONETWORK. FAO, Rome, Italy, 2006).Hengl, T. et al. SoilGrids250m: global gridded soil information based on machine learning. PLoS One 12, e0169748 (2017).Article 

Google Scholar 
Wood, S. N. Stable and efficient multiple smoothing parameter estimation for generalized additive models. J. Am. Stat. Assoc. 99, 673–686 (2004).Article 

Google Scholar 
J.B. Grace. Structural Equation Modeling and Natural Systems. (Cambridge University Press, Cambridge, 2006).Aho, K., Derryberry, D. & Peterson, T. Model selection for ecologists: the worldviews of AIC and BIC. Ecology 95, 631–636 (2014).Article 

Google Scholar 
R Core Team (2021). R: A language and environment for statistical computing. (R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/).Wood, S., Scheipl, F. & Wood, M. S. Package ‘gamm4’. Am. Stat. 45, 339 (2017).
Google Scholar 
Lefcheck, J. S. piecewiseSEM: Piecewise structural equation modelling in r for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).Article 

Google Scholar 
Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. R. Core Team nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1-148 (2020).Latham J, Cumani R, Rosati I, Bloise M. FAO Global Land Cover (GLC-SHARE) Database Beta-Release 1.0, Land and Water Division. 2014. http://www.fao.org/uploads/media/glc-share-doc.pdf. More

Evapotranspiration measurements and above-ground biomassFigure 1 shows daily evapotranspiration (ET, mm day−1) of each CC tested before mowing (DOY, day of the year, 184) and at 2, 8, 17 and 25 days after mowing (DOY 190, 196, 205 and 213); bare soil was also included as a reference. Before mowing, ET rates showed significant differences between and within the three groups. CR plants had a mean ET of 8.1 mm day−1, which was lower, compared to the other two groups (10.6 and 18.6 mm day−1 for GR and LE, respectively) and the bare soil control (8.5 mm day−1). On DOY 184, values as high as 9.4 (Glechoma hederacea L., GH) and 9.8 mm day−1 (Trifolium subterraneum L. cv. Denmark, TS) were found (Fig. 1), while ranging around 7 mm day-1, Dichondra repens J.R.Forst. & G.Forst. (DR), Hieracium pilosella L. (HP), and Sagina subulata (Swartz) C. Presl (SS) ET were lower than soil evaporation itself.Figure 1Vertical bars represent the daily water use as referred to unit of soil (ET, mm day−1) for the bare soil (yellow) and all the cover crop species as divided into creeping plants (shades of blue), legumes (shades of green) and grasses (shades of orange). Evapotranspiration was measured though a gravimetric method before (i.e. − 4) and at 2, 8, 17 and 25 days after mowing. ET data are mean values ± SE (n = 4).Full size imageOn the same day, a large ET variation was recorded within the GR group as Festuca arundinacea Schreb. cv. Thor (FA) scored the highest daily ET values (13.4 mm day−1), whereas in Festuca ovina L. cv. Ridu (FO), water loss was reduced by 45% (7.5 mm day−1). Within the 15 CCs, LE registered the highest pre-mowing ET with Trifolium michelianum Savi cv. Bolta (TM) peaking at 22.6 mm day−1. However, within LE, Medicago polymorpha L. cv. Scimitar (MP) showed ET values as low as 12.1 mm day−1 (Fig. 1).Two days after mowing, all tested CCs recorded ET values lower than 9 mm day−1 (Fig. 1). Moreover, water use reduction among LE ranged between 56% (M. polymorpha, MP) and 73% (T. michelianum, TM), such that T. michelianum (TM, 6.1 mm day−1), Medicago truncatula Gaertn. cv. Paraggio (MT, 5.6 mm day−1) and M. polymorpha (MP, 5.2 mm day−1) registered ET values lower than the bare soil (7.0 mm day−1). Even though registering a consistent ET reduction after mowing, GR retained ET rates slightly higher than bare soil, except for F. ovina (FO), which recorded the lowest at 6.3 mm day−1. Subsequent samplings showed that most of the CCs had a progressive recovery in water use (Fig. 1) and data taken 17 days after mowing confirmed that Lotus corniculatus L. cv. Leo (LC) and all GR fetched pre-mowing ET rates. Medicago lupulina L. cv. Virgo (ML) registered a partial recovery with similar rates (about 13 mm day−1) at 17 and 25 days after the mowing event. F. ovina and all remaining LE stayed below 10 mm day−1 with ET values close to the control until the end of the trial. At 17 days from grass cutting, under a quite high exceeding-the-pot biomass, both G. hederacea (GH) and T. subterraneum (TS) reached ET values as high as 12.0 and 11.4 mm day−1, respectively. On the other hand, D. repens (DR), H. pilosella (HP), and S. subulata (SS) even though with slightly higher ET values than those registered at the beginning of the trial (DOY 184), remained close to the soil evaporation rates until DOY 213.Aboveground dry clipped biomass at the first mowing date (ADW_MW1, DOY 188) showed large differences among groups, as represented in Table 1. ADW_MW1 within LE was quite variable, as values ranged between 274.3 g m−2 (M. polymorpha, MP) and 750.0 g m−2 (T. michelianum, TM). With a mean value of 565.9 g m−2, LE aboveground biomass was 80% higher than the mean GR ADW_MW1 (110.2 g m-2). F. ovina (FO) scored the lowest value at 48.4 g m−2 among grasses, while within the creeping group, G. hederacea (GH) and T. subterraneum (TS) had biomass development outside the pot edges totalling 89.6 g m−2 and 23.2 g m−2, respectively.Table 1 Aboveground dry biomass clipped at the first mowing event (ADW _MW1), the corresponding leaf area surface index (LAI) and water use per leaf area unit (ETLEAF) of all cover crops tested.Full size tableLeaf area index (LAI, m2 m−2) at mowing showed the highest values in LE with LAI peaking at 12.4 (Table 1). Among GR, LAI did not show significant differences, being around 1.2. Concerning CR, LAI was assessed at 0.2 and 0.8 for T. subterraneum (TS) and G. hederacea (GH) respectively, while LAI estimated through photo analysis ranged between 1.3 (D. repens, DR) and 3.6 (T. subterraneum TS).Evapotranspiration per leaf area unit (ETLEAF) was notably higher in GR, ranging between 7.75 (F. ovina, FO) and 9.22 (Lolium perenne L. cv. Playfast, LP) mm m−2 day−1 (Table 1). In descending order, ETLEAF was the highest in D. repens (DR, 5.46 mm m−2 day−1). Similar ETLEAF was found when comparing some LE and CR species such as M. truncatula (MT, 3.40 mm m−2 day−1), M. lupulina (ML, 4.05 mm m−2 day−1), G. hederacea (GH, 3.68 mm m−2 day−1), H. pilosella (HP, 3.86 mm m-2 day-1) and T. subterraneum (TS, 2.74 mm m−2 day−1). T. michelianum (TM), with 1.81 mm m-2 day-1 scored the lowest ETLEAF of all species (Table 1).Plotting LAI versus the before-mowing ET yielded a significant quadratic relationship (R2  > 0.76) (Fig. 2a) which helped to distinguish two different data clouds. Till LAI values of about 6, the model was linear, having at its lower end all GR and CR species with the inclusion of M. polymorpha (MP) as a legume, while, at the other end, M. truncatula (MT), L. corniculatus (LC) and M. lupulina (ML) were grouped together. T. michelianum (TM) was isolated from all CCs at 22.56 mm day−1.Figure 2Panel (a): quadratic regression of leaf area index (LAI, m2 m−2) vs cover crop evapotranspiration per unit of soil (ET, mm day−1). Each data point is mean value ± SE (n = 4). The quadratic model equation is y = − 0.128×2 + 2.9968x + 5.4716, R2 = 0.76. Panel (b): the quadratic regression between LAI corresponding to the clipped biomass (m2 m−2) and cover crop ET reduction (%). Each data point is mean value ± SE (n = 4). Quadratic model equation is y = − 0.8985×2 + 16.503x + 5.1491, R2 = 0.94.Full size imageWhen regressing the fraction of ET reduction, compared to pre-mowing values vs LAI (Fig. 2b), the same quadratic model achieved a very close fit (R2 = 0.94, p  1 mm) root diameters as affected by soil cover.Full size tableThe highest values of diameter class length (DCL, mm cm−3) for very fine roots (DCL_VF,  1.0 mm) roots although, most notably, L. corniculatus roots showed the highest abundance for both DCL_M (23.08 cm cm−3) and DCL_C (0.54 cm cm−3).At the 10–20 cm soil depth, GR confirmed the highest values for both very fine and fine roots, with F. arundinacea reaching maximum DCL of 2.269 and 5.215 cm cm-3, respectively (Table 2). L. corniculatus largely outscored any other species for both medium and coarse root diameter (6.173 and 0.037 cm cm−3, respectively), with F. arundinacea ranking second (3.157 and 0.016 cm cm−3, respectively).The highest root dry weight (RDW, mg cm-3) within the topsoil layer was reached by L. corniculatus (8.7 mg cm−3) and F. arundinacea (7.6 mg cm-3). Notably, such values were significantly higher than those recorded on the remaining species, except for the F. arundinacea vs F. rubra commutata comparison (Table 2). At 10–20 depth, scant variation was recorded in RDW measured in grasses, whereas L. corniculatus held its supremacy within legumes (4.5 mg cm−3). Within the creeping type, D. repens (DR) and G. hederacea (GH) scored RDW values as high as those determined for grass species (namely F. arundinacea , P. pratensis and F. rubra commutata), whereas S. subulata (SS) essentially had no root development.Soil aggregates and mean weight diameter (MWD)Table 3 reports the proportional aggregate weight (g kg−1) for both 0–10 and 10–20 cm soil depths. Compared to bare soil, the largest increase in large macroaggregates (LM,  > 2000 µm) in the top 10 cm of soil was achieved by L. corniculatus with 461 g kg−1. L. corniculatus differed from the rest of the LE group, whose grand mean (90 g kg−1) was the lowest of the three tested groups. As a legume, T. subterraneum (TS, 122 g kg−1) recorded the lowest values compared to fellow CR species, ranging between 211 (D. repens, DR) and 316 g kg−1 (G. hederacea, GH). GR recorded LM values slightly lower than those of CR, with a mean value of 217 vs 224 g kg-1.Table 3 Proportional aggregate weight (g kg−1) of sand-free aggregate-size fractions acquired from wet sieving as affected by soil cover and mean weight diameter (MWD). Aggregate-size fraction divided as macroaggregates with large size ( > 2 mm, LM) and small size (2 mm—250 μm, sM), microaggregates (250 μm—53 μm, m), and silt and clay ( More

Laundré, J. W., Hernández, L. & Altendorf, K. B. Wolves, elk, and bison: reestablishing the “landscape of fear” in Yellowstone National Park, U.S.A.. Can. J. Zool. 79, 1401–1409 (2001).Article 

Google Scholar 
Laundré, J. W., Hernandez, L. & Ripple, W. J. The landscape of fear: Ecological implications of being afraid. Open Ecol. J. 3, 1–7 (2010).Article 

Google Scholar 
Suraci, J. P., Clinchy, M., Zanette, L. Y. & Wilmers, C. C. Fear of humans as apex predators has landscape-scale impacts from mountain lions to mice. Ecol. Lett. 22, 1578–1586 (2019).Article 

Google Scholar 
Miller, S. G., Knight, R. L. & Miller, C. K. Wildlife responses to pedestrians and dogs. Wildl. Soc. B. 29, 124–132 (2001).
Google Scholar 
Larson, C., Reed, S., Merenlender, A. M. & Crooks, K. R. Effects of recreation on animals revealed as widespread through a global systemic review. PLoS ONE 11, 1–21 (2016).Article 

Google Scholar 
Balmford, A. et al. Walk on the wild side: Estimating the global magnitude of visits to protected areas. PLoS Biol 13, 1–6 (2015).Article 

Google Scholar 
Baker, A. D. & Leberg, P. L. Impacts of human recreation on carnivores in protected areas. PLoS Biol 13, 1–21 (2018).
Google Scholar 
Schulze, K. et al. An assessment of threats to terrestrial protected areas. Cons. Lett. 11, 1–10 (2018).Article 

Google Scholar 
Suraci, J. P. et al. Disturbance type and species life history predict mammal responses to humans. Glob. Change Biol. 27, 3718–3731 (2021).Article 
CAS 

Google Scholar 
Reilly, M. L., Tobler, M. W., Sonderegger, D. L. & Beier, P. Spatial and temporal response of wildlife to recreational activities in the San Francisco Bay ecoregion. Biol. Conserv. 207, 117–126 (2017).Article 

Google Scholar 
Naidoo, R. & Burton, A. C. Relative effects of recreational activities on a temperate terrestrial wildlife assemblage. Conserv. Sci. Pract. 2, e271 (2020).
Google Scholar 
Nickel, B. A., Suraci, J. P., Allen, M. L. & Wilmers, C. C. Human presence and human footprint have non-equivalent effects on wildlife spatiotemporal habitat use. Biol. Conserv. 241, 1–11 (2020).Article 

Google Scholar 
Blanchet, F. G., Cazelles, K. & Gravel, D. Co-occurrence is not evidence of ecological interactions. Ecol. Lett. 23, 1050–1063 (2020).Article 

Google Scholar 
Poggiato, G. et al. On the interpretations of joint modeling in community ecology. TREE 36, 391–401 (2021).
Google Scholar 
Bates, A. E., Primack, R. B., Moraga, P. & Duarte, C. M. COVID-19 pandemic and associated lockdown as a “Global Human Confinement Experiment” to investigate biodiversity conservation. Biol. Conserv. 248, 1–6 (2020).Article 

Google Scholar 
Rutz, C. et al. COVID-19 lockdown allows researchers to quantify the effects of human activity on wildlife. Nat. Ecol. Evol. 4, 1156–1159 (2020).Article 

Google Scholar 
Wang, Y., Allen, M. L. & Wilmers, C. C. Mesopredator spatial and temporal responses to large predators and human development in the Santa Cruz Mountains of California. Biol. Conserv. 190, 23–33 (2015).Article 

Google Scholar 
Lewis, J. S. et al. Human activity influences wildlife populations and activity patterns: implications for spatial and temporal refuges. Ecosphere 12, 1–16 (2021).Article 

Google Scholar 
Corradini, A. et al. Effects of cumulated outdoor activity on wildlife habitat use. Biol. Conserv. 253, 108818 (2021).Article 

Google Scholar 
Soule, M. E. et al. Dynamics of rapid extinctions of chaparral-requiring birds in urban habitat islands. Conserv. Biol. 2, 75–92 (1988).Article 

Google Scholar 
Feit, B., Feit, A. & Letnic, M. Apex predators decouple population dynamics between mesopredators and their prey. Ecosystems 22, 1606–1617 (2019).Article 

Google Scholar 
Berger, J. Fear, human shields and the redistribution of prey and predators in protected areas. Biol. Lett. 3, 620–623 (2007).Article 

Google Scholar 
Sarmento, W., Biel, M. & Berger, J. Redistribution, human shields and loss of migratory behavior in the crown of the continent. Intermt. J. Sci. 22, 2016 (2016).
Google Scholar 
Gaynor, K. M., Hojnowski, C. E., Carter, N. H. & Brashares, J. S. The influence of human disturbance on wildlife nocturnality. Science 360, 1232–1235 (2018).Article 
ADS 
CAS 

Google Scholar 
Microsoft. AI for Earth camera trap image processing API (2020).Niedballa, J., Sollmann, R., Courtiol, A. & Wilting, A. camtrapR: an R package for efficient camera trap data management. Methods Ecol. Evol. 7, 1457–1462 (2016).Article 

Google Scholar 
MacKenzie, D. I., Nichols, J. D., Hines, J. E., Knutson, M. G. & Franklin, A. B. Estimating site occupancy, colonization, and local extinction when a species is detected imperfectly. Ecology 84, 2200–2207. https://doi.org/10.1890/02-3090 (2003).Article 

Google Scholar 
MacKenzie, D. I. et al. Occupancy estimation and modeling (Elsevier, 2018).
Google Scholar 
Fiske, I. & Chandler, R. unmarked: An R package for fitting hierarchical models of wildlife occurrence and abundance. J. Stat. Soft. 4, 1–23 (2011).
Google Scholar 
Mazerolle, M. J. AICcmodavg: Model selection and multimodel inference based on (Q)AIC(c). R package version 2.3-1 (2020). https://cran.r-project.org/package=AICcmodavg.Ladle, A., Steenweg, R., Shepherd, B. & Boyce, M. S. The role of human outdoor recreation in shaping patterns of grizzly bear-black bear co-occurrence. PLoS ONE 13, 1–16 (2018).Article 

Google Scholar 
Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).Article 

Google Scholar 
Ridout, M. S. & Linkie, M. Estimating overlap of daily activity patterns from camera trap data. J. Agric. Biol. Environ. Stat. 14, 322–337 (2009).Article 
MathSciNet 
MATH 

Google Scholar 
Agostinelli, C. & Lund, U. R package ‘circular’: circular statistics (version 0.4-94.1 (2022). https://r-forge.r-project.org/projects/circular/.Santos, F. et al. Prey availability and temporal partitioning modulate felid coexistence in Neotropical forests. PLoS ONE 14, 1–23 (2019).Article 

Google Scholar 
Olea, P. P., Iglesias, N. & Mateo-Tomás, P. Temporal resource partitioning mediates vertebrate coexistence at carcasses: the role of competitive and facilitative interactions. Basic Appl. Ecol. 60, 63–75 (2022).Article 

Google Scholar 
Shilling, F. et al. A reprieve from US wildlife mortality on roads during the COVID-19 pandemic. Biol. Conserv. 256, 109013 (2021).Article 

Google Scholar 
Behera, A. K. et al. The impacts of COVID-19 lockdown on wildlife in Deccan Plateau. India. Sci. Total Environ. 822, 153268 (2022).Article 
ADS 
CAS 

Google Scholar 
Procko, M., Naidoo, R., LeMay, V. & Burton, A. C. Human impacts on mammals in and around a protected area before, during, and after COVID-19 lockdowns. Conserv. Sci. Pract. 4, e12743. https://doi.org/10.1111/csp2.12743 (2022).Article 

Google Scholar 
Sanderfoot, O. V., Kaufman, J. D. & Gardner, B. Drivers of avian habitat use and detection of backyard birds in the Pacific Northwest during COVID-19 pandemic lockdowns. Sci. Rep. 12, 12655 (2022).Article 
ADS 
CAS 

Google Scholar 
Nevin, J. A. & Grace, R. C. Behavioral momentum and the law of effect. Behav. Brain Sci. 23, 73–90 (2000).Article 
CAS 

Google Scholar 
Kautz, T. M. et al. Large carnivore response to human road use suggests a landscape of coexistence. Glob. Ecol. Conserv. 30, e01772 (2021).Article 

Google Scholar 
Frey, S., Volpe, J. P., Heim, N. A., Paczkowski, J. & Fisher, J. T. Move to nocturnality not a universal trend in carnivore species on disturbed landscapes. Oikos 129, 1128–1140 (2020).Article 

Google Scholar 
Schwartz, C. C. et al. Contrasting activity patterns of sympatric and allopatric black and grizzly bears. J. Wildl. Manag. 74, 1628–1638 (2010).Article 

Google Scholar 
Fortin, J. K. et al. Impacts of human recreation on brown bears (Ursus arctos): a review and new management tool. PLoS ONE 11, 1–26 (2016).Article 

Google Scholar 
Kendall, K. C. et al. Grizzly bear density in Glacier National Park. Montana. J. Wildl. Manag. 72, 1693–1705 (2008).Article 

Google Scholar 
Stetz, J. B., Kendall, K. C. & Macleod, A. C. Black bear density in Glacier National Park, Montana. Wildl. Soc. Bull. 38, 60–70 (2014).Article 

Google Scholar 
Sargeant, A. B. & Allen, S. H. Observed interactions between coyotes and red foxes. J. Mamm. 70, 631–633 (1989).Article 

Google Scholar 
Newsome, T. M. & Ripple, W. J. A continental scale trophic cascade from wolves through coyotes to foxes. J. Anim. Ecol. 84, 49–59 (2015).Article 

Google Scholar 
Naylor, L. M., Wisdom, M. J. & Anthony, R. G. Behavioral responses of North American elk to recreational activity. J. Wildl. Manag. 73, 328–338 (2009).Article 

Google Scholar 
Sarmento, W. M. & Berger, J. Human visitation limits the utility of protected areas as ecological baselines. Biol. Conserv. 212, 316–326 (2017).Article 

Google Scholar 
Garber, S. D. & Burger, J. A 20-year study documenting the relationship between turtle decline and human recreation. Ecol. Appl. 5, 1151–1162 (1995).Article 

Google Scholar 
Winter, P. L., Selin, S., Cerveny, L. & Bricker, K. Outdoor recreation, nature-based tourism, and sustainability. Sustainability 12, 1–12 (2020).
Google Scholar 
Geffroy, B., Samia, D. S. M., Bessa, E. & Blumstein, D. T. How nature-based tourism might increase prey vulnerability to predators. TREE 30, 755–765 (2015).
Google Scholar 
Eagles, P. F. J., McCool, S. F. & Haynes, C. D. Sustainable Tourism in Protected Areas: Guidelines for Planning and Management Vol. 8 (IUCN, 2002).Book 

Google Scholar  More

ParticipantsIn total, 80, 63, and 56 participants in the EM, CM, and control groups, respectively, initially agreed to participate in this study. Nevertheless, 28, 12, and 13 individuals in the EM, CM, and control groups, respectively, withdrew their participation and did not bring any fecal samples to the study site. After providing fecal samples, 10 and 6 individuals with EM and CM, respectively, reported intake of probiotics and were excluded from the analysis. No participant in the control group consumed probiotics during the study period. Eventually, 42, 45, and 43 participants in the EM, CM, and control groups, respectively, were enrolled (Fig. 1). The demographic and clinical characteristics of participants are summarized in Table 1. All participants with EM and CM used acute treatments for migraine. Moreover, 25 (59.5%) and 27 (60.0%) participants with EM and CM, respectively, received prophylactic treatment for migraine. Of the 42 participants with EM, 20 used anti-epileptic medications, 11 used beta blockers, 2 used an anti-depressant, and 1 used a calcium-channel blocker for prophylactic treatment. Of the 45 participants with CM, 23 used anti-epileptic medications, 8 used beta blockers, 1 used an anti-depressant, and no participant used calcium-channel blockers for prophylactic treatment. No participant in the EM, CM, and control groups was infected with SARS-CoV-2 before or during participation in the study.Figure 1Flow of participants in a study on the composition of gut microbiota in participants with episodic or chronic migraine.Full size imageTable 1 Demographic and clinical characteristics of participants with episodic and chronic migraine and the control.Full size tableCollection of 16 s RNA sequencing dataWe obtained 7,802,425 read sequences, accounting for 99.8% of the valid sequences from the fecal samples of 130 participants. According to barcode and primer sequence filtering, an average of 59,305 (range, 3716–90,832) observed sequences per sample was recovered for downstream analysis. Thus, 2,242,325 sequences were obtained from the controls for phylogenetic analysis, whereas 2,747,952 and 2,812,148 sequences were obtained from the EM and CM groups, respectively.Microbial diversityAlpha diversity was defined as microbial community richness and evenness. Alpha diversities in the genus richness, as evaluated by Chao1 (Fig. 2A), Shannon (Fig. 2B), and Simpson (Fig. 2C) indices, did not differ significantly among the EM, CM, and control groups. Beta diversity represented the community composition dissimilarity between samples. PCoA with the weighted UniFrac distance (Fig. 3A and Supplementary Fig. S1A, p = 0.176, permutational multivariate analysis of variance [PERMANOVA]), the unweighted UniFrac distance (Fig. 3B and Supplementary Fig. S1B, p = 0.132, PERMANOVA), and the Bray–Curtis dissimilarity index (Fig. 3C and Supplementary Fig. S1C, p = 0.220, PERMANOVA) for beta diversity at the genus level among the EM, CM, and control groups revealed that these three groups could not be separated.Figure 2Alpha diversity at the genus level using Chao1 (A), Shannon (B), and Simpson (C) indices*,†. *Controls (green) and participants with episodic migraine (blue) and chronic migraine (yellow). †In the box plots, the lower boundary of the box indicates the 25th percentile; a blue line within the box marks the median, and the upper boundary of the box indicates the 75th percentile. Whiskers above (red) and below the box (green) indicate the highest and the lowest values, respectively.Full size imageFigure 3Beta diversity of microbiota in principal coordinate analysis plot with the weighted UniFrac distance (A), the unweighted UniFrac distance (B) and the Bray–Curtis dissimilarity index (C)*. *Controls (green) and participants with episodic migraine (blue) and chronic migraine (yellow).Full size imageRelative abundance of fecal microbes between participants with EM and the controlRelative abundance of fecal microbes at the phylum level did not differ significantly among participants in the control, EM, and CM groups (Supplementary Fig. S2). Moreover, Tissierellales (p = 0.001) and Tissierellia (p = 0.001) were more abundant in the EM group than that in the control group at the order and class levels, respectively (Fig. 4A). At the family level, Peptoniphilaceae (p = 0.001) and Eubacteriaceae (p = 0.045) occurred at a significantly higher proportion in the EM group than that in the control group. Furthermore, at the genus level, the abundance of 11 genera differed significantly between the two groups, including one more abundant and 10 less abundant genera in the EM group. Catenibacterium (p = 0.031) and Olsenella (p = 0.038) had the highest relative abundance in the control and EM groups, respectively.Figure 4Taxonomic differences in fecal microbiota among participants. The fold change (log2) denotes the difference in relative abundance between participants with episodic migraine and the control (A), between those with chronic migraine and the control (B), and between those with episodic and chronic migraine (C). CM chronic migraine; EM episodic migraine.Full size imageRelative abundance of fecal microbes between participants with CM and the controlThe analysis results at the class, order, family, genus, and species levels between CM and control groups are illustrated in Fig. 4B. Tissierellia (p = 0.001), Tissierellales (p = 0.001), and Peptoniphilaceae (p = 0.001) were more abundant in the CM group than that in the control group at the class, order, and family levels, respectively; however, at the genus level, the abundances of 18 genera differed significantly, including four more abundant and 14 less abundant genera in the CM group than in the control group.Relative abundance of fecal microbes between participants with EM and CMThe analysis results at the class, order, family, and genus levels between CM and EM groups are summarized in Fig. 4C. At the class level, Bacilli (p = 0.033) were less abundant in the CM group than that in the EM group; however, at the order level, Selenomonadales (p = 0.016) and Lactobacillales (p = 0.034) were less abundant in the CM group than that in the EM group. Moreover, at the class level, Selenomonadaceae (p = 0.016) and Prevotellaceae (p = 0.012) were less abundant in the CM group than that in the EM group. Furthermore, at the genus level, PAC001212_g (p = 0.019) revealed relative positive predominancy in the CM groups, whereas Prevotella (p = 0.019), Holdemanella (p = 0.009), Olsenella (p = 0.033), Adlercreutzia (p = 0.018), and Coprococcus (p = 0.040) revealed relative positive predominancy in the EM group.Association among fecal microbiota and clinical characteristics and comorbidities of migraineAmong the five genera (Roseburia, Eubacterium_g4, Agathobacter, PAC000195_g, and Catenibacterium) depicting predominance or less-predominance both in EM and CM groups, we conducted additional analyses for clinical characteristics and migraine comorbidities.Combining the results of the 42 and 45 participants with EM and CM, respectively, the Poisson regression analysis for relative abundance of microbiota revealed that a higher composition of PAC000195_g (p = 0.040) was significantly associated with lower headache frequency (Table 2). Furthermore, Agathobacter (p = 0.009) had a negative association with severe headache intensity (Table 3). Anxiety was associated with Catenibacterium (p = 0.027); however, depression did not reveal any association with the five genera (Table 3).Table 2 The association between headache frequency and the relative abundance of microbiota.*Full size tableTable 3 The association of severe headache intensity and comorbidities with the relative abundance of microbiota*.Full size tableRelative abundance of fecal microbes in participants with EM based on prophylactic treatmentAlpha and beta diversities in participants with EM did not differ significantly based on their prophylactic treatment (Supplementary Figs S3A–C, S4A–C, and S5A–C). At the genus level, Klebsiella (p = 0.009), Enterobacteriaceae_g (p = 0.006), and Faecalibacterium (p = 0.046) were more abundant in the prophylactic group than the non-prophylactic group (Supplementary Fig. S6A).Relative abundance of fecal microbes in participants with CM based on prophylactic treatmentAlpha and beta diversities in participants with CM did not differ significantly based on prophylactic treatment (Supplementary Figs S7A–C, S8A–C, and S9A–C). Emergencia (p = 0.043), Ruthenibacterium (p = 0.005), Eggerthella (p = 0.003), PAC000743_g (p = 0.034), and Anaerostipes (p = 0.039) were more abundant in the prophylactic group, whereas PAC000196_g (p = 0.049), Fusicatenibacter (p = 0.028), and Faecalibacterium (p = 0.021) were more abundant in the non-prophylactic group at the genus level (Supplementary Fig. S6B). More

Mace, G. M. et al. Aiming higher to bend the curve of biodiversity loss. Nat. Sustain. 1, 448–451 (2018).Article 

Google Scholar 
Díaz, S., et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).Díaz, S. et al. Set ambitious goals for biodiversity and sustainability. Science 370, 411 (2020).Article 

Google Scholar 
Locke, H., et al. A Nature-Positive World: The Global Goal for Nature (Wildlife Conservation Society, 2020); https://library.wcs.org/doi/ctl/view/mid/33065/pubid/DMX3974900000.aspxOpen-ended Working Group on the Post-2020 Global Biodiversity Framework. First Draft of the Post-2020 Global Biodiversity Framework CBD/WG2020/3/3 (Convention on Biological Diversity, 2021).Open-Ended Working Group on the Post-2020 Global Biodiversity Framework. Draft Recommendation Submitted by the Co-Chairs CBD/WG2020/4/L.2-ANNEX (Convention on Biological Diversity, 2022).Environment Act 2021 (UK) (HM Government, 2021); https://www.legislation.gov.uk/ukpga/2021/30/contents/enactedBull, J. W. & Strange, N. The global extent of biodiversity offset implementation under no net loss policies. Nat. Sustain. 1, 790–798 (2018).Article 

Google Scholar 
Prendeville, S., Cherim, E. & Bocken, N. Circular cities: mapping six cities in transition. Environ. Innov. Soc. Transit. 26, 171–194 (2018).de Silva, G. C., Regan, E. C., Pollard, E. H. B. & Addison, P. F. E. The evolution of corporate no net loss and net positive impact biodiversity commitments: understanding appetite and addressing challenges. Bus. Strategy Environ. 28, 1481–1495 (2019).Article 

Google Scholar 
zu Ermgassen, S. O. S. E. et al. Exploring the ecological outcomes of mandatory biodiversity net gain using evidence from early‐adopter jurisdictions in England. Conserv. Lett. 14, e12820 (2021).Article 

Google Scholar 
McGlyn, J., et al. Science-Based Targets for Nature: Initial Guidance for Business (Science Based Targets Network, 2020); https://sciencebasedtargetsnetwork.org/resource-repository/zu Ermgassen, S. O. S. E. et al. Are corporate biodiversity commitments consistent with delivering ‘nature-positive’ outcomes? A review of ‘nature-positive’ definitions, company progress and challenges. J. Clean. Prod. 379, 134798 (2022).Article 

Google Scholar 
Addison, P. F. E., Bull, J. W. & Milner‐Gulland, E. J. Using conservation science to advance corporate biodiversity accountability. Conserv. Biol. 33, 307–318 (2019).Article 

Google Scholar 
Smith, T. et al. Biodiversity means business: reframing global biodiversity goals for the private sector. Conserv. Lett. 13, e12690 (2020).Article 

Google Scholar 
Maron, M. et al. Setting robust biodiversity goals. Conserv. Lett. https://doi.org/10.1111/conl.12816 (2021).Newing, H. & Perram, A. What do you know about conservation and human rights? Oryx 53, 595–596 (2019).Article 

Google Scholar 
Standard on Biodiversity Offsets (The Business and Biodiversity Offsets Programme, 2012).Arlidge, W. N. S., et al. A mitigation hierarchy approach for managing sea turtle captures in small-scale fisheries. Front. Mar. Sci. 7, 49 (2020).Squires, D. & Garcia, S. The least-cost biodiversity impact mitigation hierarchy with a focus on marine fisheries and bycatch issues. Conserv. Biol. 32, 989–997 (2018).Article 

Google Scholar 
Booth, H., Squires, D. & Milner-Gulland, E. J. The mitigation hierarchy for sharks: a risk-based framework for reconciling trade-offs between shark conservation and fisheries objectives. Fish Fish. 21, 269–289 (2020).Article 

Google Scholar 
Gupta, T. et al. Mitigation of elasmobranch bycatch in trawlers: a case study in Indian fisheries. Front. Mari. Sci. 7, 571 (2020).Budiharta, S. et al. Restoration to offset the impacts of developments at a landscape scale reveals opportunities, challenges and tough choices. Global Environ. Change 52, 152–161 (2018).Article 

Google Scholar 
Bull, J. W. et al. Net positive outcomes for nature. Nat. Ecol. Evol. 4, 4–7 (2020).Article 

Google Scholar 
Arlidge, W. N. S. et al. A global mitigation hierarchy for nature conservation. BioScience 68, 336–347 (2018).Article 

Google Scholar 
Milner-Gulland, E. J. et al. Four steps for the Earth: mainstreaming the post-2020 global biodiversity framework. One Earth 4, 75–87 (2021).Article 
ADS 

Google Scholar 
Wolff, A., Gondran, N. & Brodhag, C. Detecting unsustainable pressures exerted on biodiversity by a company. Application to the food portfolio of a retailer. J. Clean. Prod. 166, 784–797 (2017).Article 

Google Scholar 
FAOSTAT Analytical Brief 15 Land Use and Land Cover Statistics: Global, Regional and Country Trends, 1990–2018 (FAO, 2020).Williams, D. R. et al. Proactive conservation to prevent habitat losses to agricultural expansion. Nat. Sustain. 4, 314–322 (2021).Article 

Google Scholar 
Leclère, D. et al. Bending the curve of terrestrial biodiversity needs an integrated strategy. Nature 585, 551–556 (2020).Article 
ADS 

Google Scholar 
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 
Clark, M. A., Springmann, M., Hill, J. & Tilman, D. Multiple health and environmental impacts of foods. Proc. Natl Acad. Sci. USA 116, 23357 (2019).Article 
ADS 
CAS 

Google Scholar 
Willett, W. et al. Food in the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems. Lancet 393, 447–492 (2019).Article 

Google Scholar 
Poore, J. & Nemecek, T. Reducing food’s environmental impacts through producers and consumers. Science 360, 987 (2018).Article 
ADS 
CAS 

Google Scholar 
Wiedmann, T., Lenzen, M., Keyßer, L. T. & Steinberger, J. K. Scientists’ warning on affluence. Nat. Commun. 11, 3107 (2020).Article 
ADS 
CAS 

Google Scholar 
Benton, T. G. et al. A ‘net zero’ equivalent target is needed to transform food systems. Nat. Food 2, 905–906 (2021). 2021.Article 

Google Scholar 
Crenna, E., Sinkko, T. & Sala, S. Biodiversity impacts due to food consumption in Europe. J. Clean. Prod. 227, 378–391 (2019).Article 
CAS 

Google Scholar 
Bull, J. W., et al. Analysis: the biodiversity footprint of the University of Oxford. Nature 604, 420–424 (2022).Harrington, R. A., Adhikari, V., Rayner, M. & Scarborough, P. Nutrient composition databases in the age of big data: foodDB, a comprehensive, real-time database infrastructure. BMJ Open 9, e026652 (2019).Article 

Google Scholar 
Chaudhary, A., Verones, F., De Baan, L. & Hellweg, S. Quantifying land use impacts on biodiversity: combining species–area models and vulnerability indicators. Environ. Sci. Technol. 49, 9987–9995 (2015).Article 
ADS 
CAS 

Google Scholar 
Winter, L., Lehmann, A., Finogenova, N. & Finkbeiner, M. Including biodiversity in life cycle assessment—state of the art, gaps and research needs. Environ. Impact Assess. Rev. 67, 88–100 (2017).Article 

Google Scholar 
Chaudhary, A. & Kastner, T. Land use biodiversity impacts embodied in international food trade. Global Environ. Change 38, 195–204 (2016).Article 

Google Scholar 
Lenzen, M. et al. International trade drives biodiversity threats in developing nations. Nature 486, 109–112 (2012).Article 
ADS 
CAS 

Google Scholar 
Bates, B., et al. National Diet and Nutrition Survey Years 1 to 9 of the Rolling Programme (2008/2009–2016/2017): Time Trend and Income Analyses (Public Health England & Food Standards Agency, 2019).Stewart, C., Piernas, C., Cook, B. & Jebb, S. A. Trends in UK meat consumption: analysis of data from years 1–11 (2008–09 to 2018–19) of the National Diet and Nutrition Survey rolling programme. Lancet Planet. Health 5, e699–e708 (2021).Article 

Google Scholar 
Nielsen, K. S. et al. Improving climate change mitigation analysis: a framework for examining feasibility. One Earth 3, 325–336 (2020).Article 
ADS 

Google Scholar 
Selinske, M. J. et al. We have a steak in it: eliciting interventions to reduce beef consumption and its impact on biodiversity. Conserv. Lett. 13, e12721 (2020).Article 

Google Scholar 
Hollands, G. J. et al. The TIPPME intervention typology for changing environments to change behaviour. Nat. Hum. Behav. 1, 1–9 (2017).Article 

Google Scholar 
Marteau, T. M., Hollands, G. J. & Fletcher, P. C. Changing human behavior to prevent disease: the importance of targeting automatic processes. Science 337, 1492–1495 (2012).Article 
ADS 
CAS 

Google Scholar 
Michie, S., van Stralen, M. M. & West, R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement. Sci. 6, 42 (2011).Article 

Google Scholar 
Moran, D., Giljum, S., Kanemoto, K. & Godar, J. From satellite to supply chain: new approaches connect earth observation to economic decisions. One Earth 3, 5–8 (2020).Article 
ADS 

Google Scholar 
Godar, J., Suavet, C., Gardner, T. A., Dawkins, E. & Meyfroidt, P. Balancing detail and scale in assessing transparency to improve the governance of agricultural commodity supply chains. Environ. Res. Lett. 11, 035015 (2016).Article 
ADS 

Google Scholar 
DeFries, R. S., Fanzo, J., Mondal, P., Remans, R. & Wood, S. A. Is voluntary certification of tropical agricultural commodities achieving sustainability goals for small-scale producers? A review of the evidence. Environ. Res. Lett. 12, 033001 (2017).Article 
ADS 

Google Scholar 
Bull, J. W., Suttle, K. B., Gordon, A., Singh, N. J. & Milner-Gulland, E. J. Biodiversity offsets in theory and practice. Oryx 47, 369–380 (2013).Article 

Google Scholar 
zu Ermgassen, S. O. S. E. et al. The ecological outcomes of biodiversity offsets under “no net loss” policies: a global review. Conserv. Lett. 12, e12664 (2019).Article 

Google Scholar 
Waddock, S. Achieving sustainability requires systemic business transformation. Glob. Sustain. 3, e12 (2020).Travers, H., Walsh, J., Vogt, S., Clements, T. & Milner-Gulland, E. J. Delivering behavioural change at scale: what conservation can learn from other fields. Biol. Conserv. 257, 109092 (2021).Article 

Google Scholar 
Gaupp, F. et al. Food system development pathways for healthy, nature-positive and inclusive food systems. Nat. Food 2, 928–934 (2021).Article 

Google Scholar 
Astill, J. et al. Transparency in food supply chains: a review of enabling technology solutions. Trends Food Sci. Technol. 91, 240–247 (2019).Article 
CAS 

Google Scholar 
Poore, J & Nemecek, T. Full Excel model: life-cycle environmental impacts of food drink products. Oxford University Research Archive https://ora.ox.ac.uk/objects/uuid:a63fb28c-98f8-4313-add6-e9eca99320a5 (2018).Clark, M., et al. Estimating the environmental impacts of 57,000 food products. Proc. Natl Acad. Sci. USA 119, e2120584119 (2022).Clark, M., et al. Supplemental Data for ‘Estimating the environmental impacts of 57,000 food products’. Oxford University Research Archive https://ora.ox.ac.uk/objects/uuid:4ad0b594-3e81-4e61-aefc-5d869c799a87 (2022).Bianchi, F., Dorsel, C., Garnett, E., Aveyard, P. & Jebb, S. A. Interventions targeting conscious determinants of human behaviour to reduce the demand for meat: a systematic review with qualitative comparative analysis. IJBNPA 15, 102 (2018).
Google Scholar 
Bianchi, F., Garnett, E., Dorsel, C., Aveyard, P. & Jebb, S. A. Restructuring physical micro-environments to reduce the demand for meat: a systematic review and qualitative comparative analysis. Lancet Planet. Health 2, e384–e397 (2018).Article 

Google Scholar 
Hillier-Brown, F. C. et al. The impact of interventions to promote healthier ready-to-eat meals (to eat in, to take away or to be delivered) sold by specific food outlets open to the general public: a systematic review. Obes. Rev. 18, 227–246 (2017).Article 
CAS 

Google Scholar 
von Philipsborn, P. et al. Environmental interventions to reduce the consumption of sugar-sweetened beverages and their effects on health. Cochrane Database Syst. Rev. 6, Cd012292 (2019).
Google Scholar 
Attwood, S., Voorheis, P., Mercer, C., Davies, K. & Vennard, D. Playbook for Guiding Diners toward Plant-Rich Dishes in Food Service (World Resources Institute, 2020); https://www.wri.org/research/playbook-guiding-diners-toward-plant-rich-dishes-food-serviceGarnett, E. E., Balmford, A., Sandbrook, C., Pilling, M. A. & Marteau, T. M. Impact of increasing vegetarian availability on meal selection and sales in cafeterias. Proc. Natl Acad. Sci. USA 116, 20923 (2019).Article 
ADS 
CAS 

Google Scholar 
Reinders, M. J., Huitink, M., Dijkstra, S. C., Maaskant, A. J. & Heijnen, J. Menu-engineering in restaurants—adapting portion sizes on plates to enhance vegetable consumption: a real-life experiment. IJBNPA 14, 41 (2017).
Google Scholar 
Brunner, F., Kurz, V., Bryngelsson, D. & Hedenus, F. Carbon label at a university restaurant—label implementation and evaluation. Ecol. Econ. 146, 658–667 (2018).Article 

Google Scholar 
McClain, A. D., Hekler, E. B. & Gardner, C. D. Incorporating prototyping and iteration into intervention development: a case study of a dining hall-based intervention. J. Am. Coll. Health 61, 122–131 (2013).Article 

Google Scholar 
de Vaan, J. Eating Less Meat: How to Stimulate the Choice for a Vegetarian Option without Inducing Reactance. MSc thesis, Radboud Univ. (2018). More

Reeburgh, W. S. Oceanic methane biogeochemistry. Chem. Rev. 107, 486–513 (2007).Article 
CAS 

Google Scholar 
Chadwick, G. L. et al. Comparative genomics reveals electron transfer and syntrophic mechanisms differentiating methanotrophic and methanogenic archaea. PLoS Biol. 20, e3001508 (2022).Article 

Google Scholar 
Haroon, M. F. et al. Anaerobic oxidation of methane coupled to nitrate reduction in a novel archaeal lineage. Nature 500, 567–570 (2013).Article 
CAS 

Google Scholar 
Hallam, S. J. et al. Reverse methanogenesis: testing the hypothesis with environmental genomics. Science 305, 1457–1462 (2004).Article 
CAS 

Google Scholar 
McGlynn, S. E. Energy metabolism during anaerobic methane oxidation in ANME Archaea. Microbes Environ. 32, 5–13 (2017).Article 

Google Scholar 
Beal, E. J., House, C. H. & Orphan, V. J. Manganese- and iron-dependent marine methane oxidation. Science 325, 184–187 (2009).Article 
CAS 

Google Scholar 
McGlynn, S. E., Chadwick, G. L., Kempes, C. P. & Orphan, V. J. Single cell activity reveals direct electron transfer in methanotrophic consortia. Nature 526, 531–535 (2015).Article 
CAS 

Google Scholar 
Wegener, G., Krukenberg, V., Riedel, D., Tegetmeyer, H. E. & Boetius, A. Intercellular wiring enables electron transfer between methanotrophic archaea and bacteria. Nature 526, 587–590 (2015).Article 
CAS 

Google Scholar 
Cai, C. et al. A methanotrophic archaeon couples anaerobic oxidation of methane to Fe(III) reduction. ISME J. 12, 1929–1939 (2018).Article 
CAS 

Google Scholar 
Ettwig, K. F. et al. Archaea catalyze iron-dependent anaerobic oxidation of methane. Proc. Natl Acad. Sci. USA 113, 12792–12796 (2016).Article 
CAS 

Google Scholar 
Leu, A. O. et al. Anaerobic methane oxidation coupled to manganese reduction by members of the Methanoperedenaceae. ISME J. 14, 1030–1041 (2020).Article 
CAS 

Google Scholar 
Leu, A. O. et al. Lateral gene transfer drives metabolic flexibility in the anaerobic methane-oxidizing archaeal family Methanoperedenaceae. mBio 11, e01325-20 (2020).Cai, C. et al. Response of the anaerobic methanotrophic archaeon Candidatus ‘Methanoperedens nitroreducens’ to the long-term ferrihydrite amendment. Front. Microbiol. 13, 799859 (2022).Arshad, A. et al. A metagenomics-based metabolic model of nitrate-dependent anaerobic oxidation of methane by Methanoperedens-like Archaea. Front. Microbiol. 6, 1423 (2015).Article 

Google Scholar 
Raghoebarsing, A. A. et al. A microbial consortium couples anaerobic methane oxidation to denitrification. Nature 440, 918–921 (2006).Article 
CAS 

Google Scholar 
Walker, D. J. F. et al. The archaellum of Methanospirillum hungatei is electrically conductive. mBio 10, e00579-19 (2019).Article 
CAS 

Google Scholar 
Krukenberg, V. et al. Gene expression and ultrastructure of meso- and thermophilic methanotrophic consortia. Environ. Microbiol. 20, 1651–1666 (2018).Article 
CAS 

Google Scholar 
Schubert, C. J. et al. Evidence for anaerobic oxidation of methane in sediments of a freshwater system (Lago di Cadagno). FEMS Microbiol. Ecol. 76, 26–38 (2011).Article 
CAS 

Google Scholar 
Stahl, D. A. & Amann, R. in Nucleic Acid Techniques in Bacterial Systematics (eds Stackebrandt, E. & Goodfellow, M.) 205–248 (Wiley, 1991).Wallner, G., Amann, R. & Beisker, W. Optimizing fluorescent in situ hybridization with rRNA-targeted oligonucleotide probes for flow cytometric identification of microorganisms. Cytometry 14, 136–143 (1993).Article 
CAS 

Google Scholar 
Bowers, R. M. et al. Minimum information about a single amplified genome (MISAG) and a metagenome- assembled genome (MIMAG) of Bacteria and Archaea. Nat. Biotechnol. 36, 660 (2018).Article 
CAS 

Google Scholar 
Vo, C. H., Goyal, N., Karimi, I. A. & Kraft, M. First observation of an acetate switch in a methanogenic autotroph (Methanococcus maripaludis S2). Microbiol. Insights 13, 1178636120945300 (2020).Article 

Google Scholar 
Cai, C. et al. Acetate production from anaerobic oxidation of methane via intracellular storage compounds. Environ. Sci. Technol. 53, 7371–7379 (2019).Article 
CAS 

Google Scholar 
Ratcliff, W. C. & Denison, R. F. Bacterial persistence and bet hedging in Sinorhizobium meliloti. Commun. Integr. Biol. 4, 98–100 (2011).Article 
CAS 

Google Scholar 
Ma, K., Schicho, R. N., Kelly, R. M. & Adams, M. W. Hydrogenase of the hyperthermophile Pyrococcus furiosus is an elemental sulfur reductase or sulfhydrogenase: evidence for a sulfur-reducing hydrogenase ancestor. Proc. Natl Acad. Sci. USA 90, 5341–5344 (1993).Article 
CAS 

Google Scholar 
Simon, G.-C. et al. Response of the anaerobic methanotroph “Candidatus Methanoperedens nitroreducens” to oxygen stress. Appl. Environ. Microbiol. 84, e01832-18 (2018).
Google Scholar 
van der Star, W. R. L. et al. The membrane bioreactor: a novel tool to grow anammox bacteria as free cells. Biotechnol. Bioeng. 101, 286–294 (2008).Article 

Google Scholar 
Duggin, I. G. et al. CetZ tubulin-like proteins control archaeal cell shape. Nature 519, 362–365 (2015).Article 
CAS 

Google Scholar 
Schwarzer, S., Rodriguez-Franco, M., Oksanen, H. M. & Quax, T. E. F. Growth phase dependent cell shape of Haloarcula. Microorganisms 9, 231 (2021).Article 
CAS 

Google Scholar 
Dang, H. Y. & Lovell, C. R. Microbial surface colonization and biofilm development in marine environments. Microbiol. Mol. Biol. Rev. 80, 91–138 (2016).Article 
CAS 

Google Scholar 
Howard-Varona, C., Hargreaves, K. R., Abedon, S. T. & Sullivan, M. B. Lysogeny in nature: mechanisms, impact and ecology of temperate phages. ISME J. 11, 1511–1520 (2017).Article 

Google Scholar 
Pires, D. P., Melo, L. D. R. & Azeredo, J. Understanding the complex phage–host interactions in biofilm communities. Annu. Rev. Virol. 8, 73–94 (2021).Canchaya, C., Proux, C., Fournous, G., Bruttin, A. & Brüssow, H. Prophage genomics. Microbiol. Mol. Biol. Rev. 67, 238–276 (2003).Article 
CAS 

Google Scholar 
Zhang, X. et al. Polyhydroxyalkanoate-driven current generation via acetate by an anaerobic methanotrophic consortium. Water Res. 221, 118743 (2022).Article 
CAS 

Google Scholar 
Knittel, K., Lösekann, T., Boetius, A., Kort, R. & Amann, R. Diversity and distribution of methanotrophic Archaea at cold seeps. Appl. Environ. Microbiol. 71, 467–479 (2005).Article 
CAS 

Google Scholar 
Orphan, V. J., House, C. H., Hinrichs, K.-U., McKeegan, K. D. & DeLong, E. F. Multiple archaeal groups mediate methane oxidation in anoxic cold seep sediments. Proc. Natl Acad. Sci. USA 99, 7663–7668 (2002).Article 
CAS 

Google Scholar 
Orphan, V. J. et al. Geological, geochemical, and microbiological heterogeneity of the seafloor around methane vents in the Eel River Basin, offshore California. Chem. Geol. 205, 265–289 (2004).Article 
CAS 

Google Scholar 
Ackermann, M. A functional perspective on phenotypic heterogeneity in microorganisms. Nat. Rev. Microbiol. 13, 497–508 (2015).Article 
CAS 

Google Scholar 
Robinson, R. W. Life cycles in the methanogenic archaebacterium Methanosarcina mazei. Appl. Environ. Microbiol. 52, 17–27 (1986).Article 
CAS 

Google Scholar 
Daims, H., Stoecker, K. & Wagner, M. in Molecular Microbial Ecology (eds Osborn, A. M. & Smith, C. J.) 213–239 (Taylor & Francis, 2005).Ludwig, W. et al. ARB: a software environment for sequence data. Nucleic Acids Res. 32, 1363–1371 (2004).Article 
CAS 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).Article 
CAS 

Google Scholar 
Yilmaz, L. S., Parnerkar, S. & Noguera, D. R. mathFISH, a web tool that uses thermodynamics-based mathematical models for in silico evaluation of oligonucleotide probes for fluorescence in situ hybridization. Appl. Environ. Microbiol. 77, 1118–1122 (2011).Article 
CAS 

Google Scholar 
Stoecker, K., Dorninger, C., Daims, H. & Wagner, M. Double labeling of oligonucleotide probes for fluorescence in situ hybridization (DOPE-FISH) improves signal intensity and increases rRNA accessibility. Appl. Environ. Microbiol. 76, 922–926 (2010).Article 
CAS 

Google Scholar 
Fuchs, B. M., Glockner, F. O., Wulf, J. & Amann, R. Unlabeled helper oligonucleotides increase the in situ accessibility to 16S rRNA of fluorescently labeled oligonucleotide probes. Appl. Environ. Microbiol. 66, 3603–3607 (2000).Article 
CAS 

Google Scholar 
Manz, W., Amann, R., Ludwig, W., Wagner, M. & Schleifer, K.-H. Phylogenetic oligodeoxynucleotide probes for the major subclasses of Proteobacteria: problems and solutions. Syst. Appl. Microbiol. 15, 593–600 (1992).Article 

Google Scholar 
Ostle, A. G. & Holt, J. G. Nile blue A as a fluorescent stain for poly-beta-hydroxybutyrate. Appl. Environ. Microbiol. 44, 238–241 (1982).Article 
CAS 

Google Scholar 
Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, e1 (2013).Article 
CAS 

Google Scholar 
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).Article 
CAS 

Google Scholar 
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 1091 (2019).Article 
CAS 

Google Scholar 
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).Article 
CAS 

Google Scholar 
Eren, A. M., Vineis, J. H., Morrison, H. G. & Sogin, M. L. A filtering method to generate high quality short reads using Illumina paired-end technology. PLoS ONE 8, e66643 (2013).Article 

Google Scholar 
Minoche, A. E., Dohm, J. C. & Himmelbauer, H. Evaluation of genomic high-throughput sequencing data generated on Illumina HiSeq and genome analyzer systems. Genome Biol. 12, R112 (2011).Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).Article 
CAS 

Google Scholar 
Warren, R. L. et al. LINKS: scalable, alignment-free scaffolding of draft genomes with long reads. GigaScience 4, 35 (2015).Article 

Google Scholar 
Walker, B. J. et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS ONE 9, e112963 (2014).Article 

Google Scholar 
Wick, R. R., Schultz, M. B., Zobel, J. & Holt, K. E. Bandage: interactive visualization of de novo genome assemblies. Bioinformatics 31, 3350–3352 (2015).Article 
CAS 

Google Scholar 
Wick, R. R. et al. Trycycler: consensus long-read assemblies for bacterial genomes. Genome Biol. 22, 266 (2021).Article 
CAS 

Google Scholar 
Kolmogorov, M., Yuan, J., Lin, Y. & Pevzner, P. A. Assembly of long, error-prone reads using repeat graphs. Nat. Biotechnol. 37, 540–546 (2019).Article 
CAS 

Google Scholar 
Wick, R. R. & Holt, K. E. Benchmarking of long-read assemblers for prokaryote whole genome sequencing. F1000Res. 8, 2138 (2021).Vaser, R. & Šikić, M. Time- and memory-efficient genome assembly with Raven. Nat. Comput. Sci. 1, 332–336 (2021).Article 

Google Scholar 
Wick, R. R. & Holt, K. E. Polypolish: short-read polishing of long-read bacterial genome assemblies. PLoS Comput. Biol. 18, e1009802 (2022).Article 
CAS 

Google Scholar 
Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927 (2019).
Google Scholar 
Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018).Article 
CAS 

Google Scholar 
Poplin, R. et al. Scaling accurate genetic variant discovery to tens of thousands of samples. Preprint at bioRxiv https://doi.org/10.1101/201178 (2017).Article 

Google Scholar 
Heller, D. & Vingron, M. SVIM: structural variant identification using mapped long reads. Bioinformatics 35, 2907–2915 (2019).Article 
CAS 

Google Scholar 
Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).Article 
CAS 

Google Scholar 
Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).Article 
CAS 

Google Scholar 
Suzek, B. E., Huang, H., McGarvey, P., Mazumder, R. & Wu, C. H. UniRef: comprehensive and non-redundant UniProt reference clusters. Bioinformatics 23, 1282–1288 (2007).Article 
CAS 

Google Scholar 
Tatusov, R. L. et al. The COG database: an updated version includes eukaryotes. BMC Bioinformatics 4, 41 (2003).Article 

Google Scholar 
Finn, R. D. et al. The Pfam protein families database: towards a more sustainable future. Nucleic Acids Res. 44, D279–D285 (2016).Article 
CAS 

Google Scholar 
Haft, D. H. et al. TIGRFAMs and genome properties in 2013. Nucleic Acids Res. 41, D387–D395 (2013).Article 
CAS 

Google Scholar 
Zhou, Z. et al. METABOLIC: high-throughput profiling of microbial genomes for functional traits, metabolism, biogeochemistry, and community-scale functional networks. Microbiome 10, 33 (2022).Article 
CAS 

Google Scholar 
Eddy, S. R. Accelerated profile HMM searches. PLoS Comput. Biol. 7, e1002195 (2011).Article 
CAS 

Google Scholar 
Bateman, A. et al. The Pfam protein families database. Nucleic Acids Res. 32, D138–D141 (2004).Article 
CAS 

Google Scholar 
Amann, R. I. et al. Combination of 16S rRNA-targeted oligonucleotide probes with flow cytometry for analyzing mixed microbial populations. Appl. Environ. Microbiol. 56, 1919–1925 (1990).Article 
CAS 

Google Scholar 
Daims, H., Brühl, A., Amann, R., Schleifer, K. H. & Wagner, M. The domain-specific probe EUB338 is insufficient for the detection of all Bacteria: development and evaluation of a more comprehensive probe set. Syst. Appl. Microbiol. 22, 434–444 (1999).Article 
CAS 

Google Scholar 
Schmid, M. C. et al. Biomarkers for in situ detection of anaerobic ammonium-oxidizing (anammox) bacteria. Appl. Environ. Microbiol. 71, 1677–1684 (2005).Article 
CAS 

Google Scholar 
Yu, N. Y. et al. PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics 26, 1608–1615 (2010).Article 
CAS 

Google Scholar 
Bendtsen, J. D., Nielsen, H., von Heijne, G. & Brunak, S. Improved prediction of signal peptides: SignalP 3.0. J. Mol. Biol. 340, 783–795 (2004).Article 

Google Scholar  More