Ecology

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.Bai, Y., Henry, J. & Wlodkowic, D. Chemosensory avoidance behaviors of marine amphipods Allorchestes compressa revealed using a millifluidic perfusion technology. Biomicrofluidics 14, 014110 (2020).CAS 
Article 

Google Scholar 
2.Bownik, A. Daphnia swimming behaviour as a biomarker in toxicity assessment: a review. Sci. Total Environ. 601–602, 194–205 (2017).Article 

Google Scholar 
3.Libralato, G., Prato, E., Migliore, L., Cicero, A. M. & Manfra, L. A review of toxicity testing protocols and endpoints with Artemia spp. Ecol. Indic. 69, 35–49 (2016).CAS 
Article 

Google Scholar 
4.Henry, J. & Wlodkowic, D. Towards high-throughput chemobehavioural phenomics in neuropsychiatric drug discovery. Mar. Drugs 17, 340 (2019).CAS 
Article 

Google Scholar 
5.Morgana, S., Estévez-Calvar, N., Gambardella, C., Faimali, M. & Garaventa, F. A short-term swimming speed alteration test with nauplii of Artemia franciscana. Ecotoxicol. Environ. Saf. 147, 558–564 (2018).CAS 
Article 

Google Scholar 
6.Bartolomé, M. C. & Sánchez-Fortún, S. Acute toxicity and inhibition of phototaxis induced by benzalkonium chloride in Artemia franciscana larvae. Bull. Environ. Contam. Toxicol. 75, 1208–1213 (2005).Article 

Google Scholar 
7.Hellou, J. Behavioural ecotoxicology, an “early warning” signal to assess environmental quality. Environ. Sci. Pollut. Res. Int. 18, 1–11 (2011).CAS 
Article 

Google Scholar 
8.Campana, O. & Wlodkowic, D. Ecotoxicology goes on a chip: embracing miniaturized bioanalysis in aquatic risk assessment. Environ. Sci. Technol. 52, 932–946 (2018).CAS 
Article 

Google Scholar 
9.De Esch, C., Slieker, R., Wolterbeek, A., Woutersen, R. & de Groot, D. Zebrafish as potential model for developmental neurotoxicity testing. A mini review. Neurotoxicol. Teratol. 34, 545–553 (2012).Article 

Google Scholar 
10.Blackiston, D., Shomrat, T., Nicolas, C. L., Granata, C. & Levin, M. A second-generation device for automated training and quantitative behavior analyses of molecularly-tractable model organisms. PLoS ONE 5, e14370 (2010).Article 

Google Scholar 
11.Franco-Restrepo, J. E., Forero, D. A. & Vargas, R. A. A review of freely available, open-source software for the automated analysis of the behavior of adult. zebrafish. Zebrafish 16, 223–232 (2019).PubMed 

Google Scholar 
12.Henry, J., Rodriguez, A. & Wlodkowic, D. Impact of digital video analytics on accuracy of chemobehavioural phenotyping in aquatic toxicology. PeerJ 7, e7367 (2019).Article 

Google Scholar 
13.Henry, J. & Wlodkowic, D. High-throughput animal tracking in chemobehavioral phenotyping: current limitations and future perspectives. Behav. Processes 180, 104226 (2020).Article 

Google Scholar 
14.Garcia, G. R., Noyes, P. D. & Tanguay, R. L. Advancements in zebrafish applications for 21st century toxicology. Pharmacol. Ther. 161, 11–21 (2016).CAS 
Article 

Google Scholar 
15.Rennekamp, A. J. & Peterson, R. T. 15 years of zebrafish chemical screening. Curr. Opin. Chem. Biol. 24, 58–70 (2015).CAS 
Article 

Google Scholar 
16.Cartlidge, R. & Wlodkowic, D. Caging of planktonic rotifers in microfluidic environment for sub-lethal aquatic toxicity tests. Biomicrofluidics 12, 044111 (2018).Article 

Google Scholar 
17.Kohler, S. A., Parker, M. O. & Ford, A. T. Shape and size of the arenas affect amphipod behaviours: implications for ecotoxicology. PeerJ 6, e5271 (2018).Article 

Google Scholar 
18.Kohler, S. A., Parker, M. O. & Ford, A. T. Species-specific behaviours in amphipods highlight the need for understanding baseline behaviours in ecotoxicology. Aquat. Toxicol. 202, 173–180 (2018).CAS 
Article 

Google Scholar 
19.Kohler, S. A., Parker, M. O. & Ford, A. T. High-throughput screening of psychotropic compounds: impacts on swimming behaviours in Artemia franciscana. Toxics 9, 64 (2021).Article 

Google Scholar 
20.Inoue, T., Hoshino, H., Yamashita, T., Shimoyama, S. & Agata, K. Planarian shows decision-making behavior in response to multiple stimuli by integrative brain function. Zoolog. Lett. 1, 7 (2015).Article 

Google Scholar 
21.Truong, L. et al. Multidimensional in vivo hazard assessment using zebrafish. Toxicol. Sci. 137, 212–233 (2014).CAS 
Article 

Google Scholar 
22.Zhang, S., Hagstrom, D., Hayes, P., Graham, A. & Collins, E.-M. S. Multi-behavioral endpoint testing of an 87-chemical compound library in freshwater planarians. Toxicol. Sci. 167, 26–44 (2019).CAS 
Article 

Google Scholar 
23.Akiyama, Y., Agata, K. & Inoue, T. Spontaneous behaviors and wall-curvature lead to apparent wall preference in planarian. PLoS ONE 10, e0142214 (2015).Article 

Google Scholar 
24.Blaser, R. E. & Rosemberg, D. B. Measures of anxiety in zebrafish (Danio rerio): dissociation of black/white preference and novel tank test. PLoS ONE 7, e36931 (2012).CAS 
Article 

Google Scholar 
25.Harro, J. Animals, anxiety, and anxiety disorders: how to measure anxiety in rodents and why. Behav. Brain Res. 352, 81–93 (2018).Article 

Google Scholar 
26.Faimali, M. et al. Old model organisms and new behavioral end-points: swimming alteration as an ecotoxicological response. Mar. Environ. Res. 128, 36–45 (2017).CAS 
Article 

Google Scholar 
27.Rashid, M. T. et al. Artemia swarm dynamics and path tracking. Nonlinear Dyn. 68, 555–563 (2012).Article 

Google Scholar 
28.Forward, R. B. & Rittschof, D. Brine shrimp larval photoresponses involved in diel vertical migration: activation by fish mucus and modified amino sugars. Limnol. Oceanogr. 44, 1904–1916 (1999).CAS 
Article 

Google Scholar 
29.Gerhardt, A. Aquatic behavioral ecotoxicology—prospects and limitations. Hum. Ecol. Risk Assess. 13, 481–491 (2007).Article 

Google Scholar 
30.Ford, A. T. et al. The role of behavioral ecotoxicology in environmental protection. Environ. Sci. Technol. 55, 5620–5628 (2021).CAS 
Article 

Google Scholar 
31.Bolker, B. M. et al. Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol. Evol. 24, 127–135 (2009).Article 

Google Scholar  More

1.Gattuso, J.-P. et al. Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios. Science 349, aac4722 (2015).
Google Scholar 
2.Caldeira, K. & Wickett, M. E. Anthropogenic carbon and ocean pH. Nature 425, 365 (2003).CAS 

Google Scholar 
3.Hönisch, B. et al. The geological record of ocean acidification. Science 335, 1058–1063 (2012).
Google Scholar 
4.Turley, C. & Gattuso, J.-P. Future biological and ecosystem impacts of ocean acidification and their socioeconomic-policy implications. Curr. Opin. Environ. Sustain. 4, 278–286 (2012).
Google Scholar 
5.San Martin, V. A. et al. Linking social preferences and ocean acidification impacts in mussel aquaculture. Sci. Rep. 9, 4719 (2019).
Google Scholar 
6.Falkenberg, L. et al. Ocean acidification and human health. Int. J. Environ. Res. Public Health 17, 4563 (2020).CAS 

Google Scholar 
7.Loewe, M. & Rippin, N. The Sustainable Development Goals of the Post-2015 Agenda. Comments on the OWG and SDSN Proposals (German Development Institute 2015).8.Doney, S. C. et al. The impacts of ocean acidification on marine ecosystems and reliant human communities. Annu. Rev. Environ. Resour. 45, 83–112 (2020).
Google Scholar 
9.Ekstrom, J. et al. Vulnerability and adaptation of US shellfisheries to ocean acidification. Nat. Clim. Change 5, 207–214 (2015).
Google Scholar 
10.Ponce Oliva, R. D. et al. Ocean acidification, consumers’ preferences, and market adaptation strategies in the mussel aquaculture industry. Ecol. Econ. 158, 42–50 (2019).
Google Scholar 
11.Quatrinni, A. M. et al. Palaeoclimate ocean conditions shaped the evolution of corals and their skeletons through deep time. Nat. Ecol. Evol. 4, 1531–1538 (2020).
Google Scholar 
12.Thomsen, J. et al. Naturally acidified habitat selects for ocean acidification-tolerant mussels. Sci. Adv. 3, e1602411 (2017).
Google Scholar 
13.Rastrick, S. S. P. et al. Using natural analogues to investigate the effects of climate change and ocean acidification on Northern ecosystems. ICES J. Mar. Sci. 75, 2299–2311 (2018).
Google Scholar 
14.Hall-Spencer, J. M. et al. Volcanic carbon dioxide vents reveal ecosystem effects of ocean acidification. Nature 454, 96–99 (2008).CAS 

Google Scholar 
15.Agostini, S. et al. Ocean acidification drives community shifts towards simplified non-calcified habitats in a subtropical–temperate transition zone. Sci. Rep. 8, 11354 (2018).
Google Scholar 
16.Riquelme-Bugueño, R. et al. Diel vertical migration into anoxic and high-pCO2 waters: acoustic and net-based krill observations in the Humboldt Current. Sci. Rep. 10, 17181 (2020).
Google Scholar 
17.Pérez et al. Riverine discharges impact physiological traits and carbon sources for shell carbonate in the marine intertidal mussel Perumytilus purpuratus. Limnol. Oceanogr. 61, 969–983 (2016).
Google Scholar 
18.Vargas, C. A. et al. Species-specific responses to ocean acidification should account for local adaptation and adaptive plasticity. Nat. Ecol. Evol. 1, 0084 (2017).
Google Scholar 
19.Saavedra et al. Local habitat influences on feeding and respiration of the intertidal mussels Perumytilus purpuratus exposed to increased pCO2 levels. Estuaries Coast. 41, 1118–1129 (2018).CAS 

Google Scholar 
20.Riebesell, U. & Gattuso, J.-P. Lessons learned from ocean acidification research. Nat. Clim. Change 5, 12–14 (2015).CAS 

Google Scholar 
21.Tilbrook, B. et al. An enhanced ocean acidification observing network: from people to technology to data synthesis and information exchange. Front. Mar. Sci. 6, 337 (2019).
Google Scholar 
22.Barry, J. P., Hall-Spencer, J. M. and Tyrrell, T. in Guide to Best Practices for Ocean Acidification Research and Data Reporting (eds Riebesell, U. et al.) Ch. 3 (Publications Office of the European Union, 2010).23.Vargas, C. A. et al. Influence of glacier melting and river discharges on the nutrient distribution and DIC recycling in the southern Chilean Patagonia. J. Geophys. Res. Biogeosci. 123, 256–270 (2018).
Google Scholar 
24.Feely, R. A. et al. Evidence for upwelling of corrosive ‘acidified’ water onto the Continental Shelf. Science 320, 1490–1492 (2008).CAS 

Google Scholar 
25.Vargas, C. A. et al. Riverine and corrosive upwelling waters influences on the carbonate system in the coastal upwelling area off Central Chile: implications for coastal acidification events. J. Geophys. Res. Biogeosci. 121, 1468–1483 (2016).
Google Scholar 
26.Cao, Z. et al. Dynamics of the carbonate system in a large continental shelf system under the influence of both a river plume and coastal upwelling. J. Geophys. Res. Oceans 116, G02010 (2010).
Google Scholar 
27.Feely, R. A. et al. The combined effects of ocean acidification, mixing, and respiration on pH and carbonate saturation in an urbanized estuary. Est. Coast. Shelf Sci. 88, 442–449 (2010).CAS 

Google Scholar 
28.Cai, W.-J. et al. Acidification of subsurface coastal waters enhanced by eutrophication. Nat. Geosci. 4, 766–770 (2011).CAS 

Google Scholar 
29.Kwiatkowski, L. et al. Nighttime dissolution in a temperate coastal ocean ecosystem increases under acidification. Sci. Rep. 6, 22984 (2016).CAS 

Google Scholar 
30.Wolfe, K., Nguyen, H. D., Davey, M. & Byrne, M. Characterizing biogeochemical fluctuations in a world of extremes: a synthesis for temperate intertidal habitats in the face of global change. Glob. Change Biol. 26, 3858–3879 (2020).
Google Scholar 
31.Shaw, E. C., Phinn, S. R., Tilbrook, B. & Steven, A. Natural in situ relationships suggest coral reef calcium carbonate production will decline with ocean acidification. Limnol. Oceanogr. 60, 777–788 (2015).
Google Scholar 
32.Takeshita, Y. et al. Coral reef carbonate chemistry variability at different functional scales. Front. Mar. Sci. 5, 175 (2018).
Google Scholar 
33.Brodeur, J. R. et al. Chesapeake Bay inorganic carbon: spatial distribution and seasonal variability. Front. Mar. Sci. 6, 99 (2019).
Google Scholar 
34.Hoshijima, U. & Hofmann, G. E. Variability of seawater chemistry in a kelp forest environment is linked to in situ transgenerational effects in the purple sea urchin, Strongylocentrotus purpuratus. Front. Mar. Sci. 6, 62 (2019).
Google Scholar 
35.Koweek, D. A. et al. A year in the life of a central California kelp forest: physical and biological insights into biogeochemical variability. Biogeosciences 14, 31–44 (2017).CAS 

Google Scholar 
36.Cornwall, C. E. & Hurd, C. L. Experimental design in ocean acidification research: problems and solutions. ICES J. Mar. Sci. 73, 572–581 (2016).
Google Scholar 
37.Kapsenberg, L. & Hofmann, G. E. Ocean pH time-series and drivers of variability along the northern Channel Islands, California, USA. Limnol. Oceanogr. 61, 953–968 (2016).
Google Scholar 
38.Hofmann, G. E. et al. High-frequency dynamics of ocean pH: a multi-ecosystem comparison. PLoS ONE 6, e28983 (2011).CAS 

Google Scholar 
39.Baumann, H. Experimental assessments of marine species sensitivities to ocean acidification and co-stressors: how far have we come? Can. J. Zool. 97, 399–408 (2019).
Google Scholar 
40.Cornwall, C. E. et al. Diurnal fluctuations in seawater pH influence the response of a calcifying macroalga to ocean acidification. Proc. R. Soc. B 280, 20132201 (2013).
Google Scholar 
41.Rivest, E. B., Comeau, S. & Cornwall, C. E. The role of natural variability in shaping the response of coral reef organisms to climate change. Curr. Clim. 3, 271–281 (2017).
Google Scholar 
42.Sanford, E. & Kelly, M. W. Local adaptation in marine invertebrates. Annu. Rev. Mar. Sci. 3, 509–535 (2011).
Google Scholar 
43.Lewis, C. N. et al. Sensitivity to ocean acidification parallels natural pCO2 gradients experienced by Arctic copepods under winter sea ice. Proc. Natl Acad. Sci. USA 110, E4960–E4967 (2013).CAS 

Google Scholar 
44.Spalding, M. D. et al. Marine ecoregions of the world: a bioregionalization of coastal and shelf areas. BioScience 57, 573–583 (2007).
Google Scholar 
45.Aguilera, V. M., Vargas, C. A. & Dewitte, B. Intraseasonal hydrographic variations and nearshore carbonates system off northern Chile during the 2015 El Niño event. J. Geophys. Res. Biogeosci. 125, e2020JG005704 (2020).CAS 

Google Scholar 
46.Fassbender, A. J. et al. Seasonal carbonate chemistry variability in marine surface waters of the US Pacific Northwest. Earth Syst. Sci. Data 10, 1367–1401 (2018).
Google Scholar 
47.Reum, J. C. P. et al. Seasonal carbonate chemistry covariation with temperature, oxygen, and salinity in a fjord estuary: implications for the design of ocean acidification experiments. PLoS ONE 9, e89619 (2014).
Google Scholar 
48.Wallace, R. B. et al. Coastal ocean acidification: the other eutrophication problem. Estuar. Coast. Shelf Sci. 148, 1–13 (2014).CAS 

Google Scholar 
49.Rutgersson, A. et al. The annual cycle of carbon dioxide and parameters influencing the air–sea carbon exchange in the Baltic Proper. J. Mar. Syst. 74, 381–394 (2008).
Google Scholar 
50.Clargo, N. M., Salt, L. A., Thomas, H. & de Baar, H. J. W. Rapid increase of observed DIC and pCO2 in the surface waters of the North Sea in the 2001–2011 decade ascribed to climate change superimposed by biological processes. Mar. Chem. 177, 566–581 (2015).CAS 

Google Scholar 
51.Ericson, Y. et al. Temporal variability in surface water pCO2 in Adventfjorden (West Spitsbergen) with emphasis on physical and biogeochemical drivers. J. Geophys. Res. Oceans 123, 4888–4905 (2018).CAS 

Google Scholar 
52.Geilfus, N.-X. et al. Spatial and temporal variability of seawater pCO2 within the Canadian Arctic Archipelago and Baffin Bay during the summer and autumn 2011. Cont. Shelf Res. 156, 1–10 (2018).
Google Scholar 
53.Islam, F. et al. Sea surface pCO2 and O2 dynamics in the partially ice-covered Arctic Ocean. J. Geophys. Res. Oceans 122, 1425–1438 (2016).
Google Scholar 
54.Copin-Montégut, C., Bégovic, M. & Merlivat, L. Variability of the partial pressure of CO2 on diel to annual time scales in the Northwestern Mediterranean Sea. Mar. Chem. 85, 169–189 (2004).
Google Scholar 
55.Pardo, P. C. et al. Surface ocean carbon dioxide variability in South Pacific boundary currents and Subantarctic waters. Sci. Rep. 9, 7592 (2019).
Google Scholar 
56.Gagliano, M., McCormick, M. I., Moore, J. A. & Depczynski, M. The basics of acidification: baseline variability of pH on Australian coral reefs. Mar. Biol. 157, 1849–1856 (2010).CAS 

Google Scholar 
57.Takeshita, Y. et al. Including high-frequency variability in coastal acidification projections. Biogeosciences 12, 5853–5870 (2015).
Google Scholar 
58.Carter, H. A., Ceballos-Osuna, L., Miller, N. A. & Stillman, J. H. Impact of ocean acidification on metabolism and energetics during early life stages of the intertidal porcelain crab Petrolisthes cinctipes. J. Exp. Biol. 216, 1412–1422 (2013).CAS 

Google Scholar 
59.Ceballos-Osuna, L., Carter, H. A., Miller, N. A. & Stillman, J. H. Effects of ocean acidification on early life-history stages of the intertidal porcelain crab Petrolisthes cinctipes. J. Exp. Biol. 216, 1405–1411 (2013).CAS 

Google Scholar 
60.Miller, S. H. et al. Effect of elevated pCO2 on metabolic responses of porcelain crab (Petrolisthes cinctipes) larvae exposed to subsequent salinity stress. PLoS ONE 9, e109167 (2014).
Google Scholar 
61.Bayne, B. L. Metabolic expenditure. Dev. Aquacult. Fish. Sci. 41, 331–415 (2017).
Google Scholar 
62.Waldbusser, G. G. et al. Slow shell building, a possible trait for resistance to the effects of acute ocean acidification. Limnol. Oceanogr. 61, 1969–1983 (2016).
Google Scholar 
63.Dorey, N., Lancon, P., Thorndyke, M. & Dupont, S. Assessing physiological tipping point for sea urchin larvae exposed to a broad range of pH. Glob. Change Biol. 19, 3355–3367 (2013).
Google Scholar 
64.Kelly, M. W., Padilla-Gamiño, J. L. & Hofmann, G. E. Natural variation and the capacity to adapt to ocean acidification in the keystone sea urchin Strongylocentrotus purpuratus. Glob. Change Biol. 19, 2536–2546 (2015).
Google Scholar 
65.Gaitán-Espitia, J. D. et al. Spatio–temporal environmental variation mediates geographical differences in phenotypic responses to ocean acidification. Biol. Lett. 13, 20160865 (2017).
Google Scholar 
66.Calosi, P. et al. Distribution of sea urchins living near shallow water CO2 vents is dependent upon species acid–base and ion-regulatory abilities. Mar. Pollut. Bull. 73, 470–484 (2013).CAS 

Google Scholar 
67.Foo, S. A., Dworjanyn, S. A., Poore, A. G. B. & Byrne, M. Adaptive capacity of the habitat modifying sea urchin Centrostephanus rodgersii to ocean warming and ocean acidification: performance of early embryos. PLoS ONE 7, e42497 (2012).CAS 

Google Scholar 
68.Chan, K. Y. K., Grünbaum, D., Arnberg, M. & Dupont, S. Impacts of ocean acidification on survival, growth, and swimming behaviours differ between larval urchins and brittlestars. ICES J. Mar. Sci. 73, 951–996 (2016).
Google Scholar 
69.Stumpp, M. et al. Acidified seawater impacts sea urchin larvae pH regulatory systems relevant for calcification. Proc. Natl Acad. Sci. USA 109, 18192–18197 (2012).CAS 

Google Scholar 
70.Stumpp, M. et al. Digestion in sea urchin larvae impaired under ocean acidification. Nat. Clim. Change 3, 1044–1049 (2013).CAS 

Google Scholar 
71.Thor, P. & Dupont, S. Transgenerational effects alleviate severe fecundity loss during ocean acidification in a ubiquitous planktonic copepod. Glob. Change Biol. 21, 2261–2271 (2015).
Google Scholar 
72.Gibbin, E. M. et al. The evolution of phenotypic plasticity under global change. Sci. Rep. 7, 17253 (2017).
Google Scholar 
73.Gibbin, E. M. et al. Can multi-generational exposure to ocean warming and acidification lead to the adaptation of life history and physiology in a marine metazoan? J. Exp. Biol. 220, 551–563 (2017).
Google Scholar 
74.Dam, H. G. et al. Rapid, but limited, zooplankton adaptation to simultaneous warming and acidification. Nat. Clim. Change 11, 780–786 (2021).
Google Scholar 
75.Byrne, M. Impact of ocean warming and ocean acidification on marine invertebrate life history stages: vulnerabilities and potential for persistence in a changing ocean. Oceanogr. Mar. Biol. 49, 1–42 (2011).
Google Scholar 
76.Kroeker, K. J., Kordas, R. L., Crim, R. N. & Singh, G. G. Meta-analysis reveals negative yet variable effects of ocean acidification on marine organisms. Ecol. Lett. 13, 1419–1434 (2010).
Google Scholar 
77.Kroeker et al. Impacts of ocean acidification on marine organisms: quantifying sensitivities and interaction with warming. Glob. Change Biol. 19, 1884–1896 (2013).
Google Scholar 
78.Takahashi, T., Sutherland, S. C. & Kozyr, A. LDEO Database (Version 2019): Global Ocean Surface Water Partial Pressure of CO2 Database: Measurements Performed During 1957–2019 (NCEI Accession 0160492) Version 9.9 (National Oceanic and Atmospheric Administration National Centers for Environmental Information); https://doi.org/10.3334/CDIAC/OTG.NDP088(V2015)79.Manly, B. F. J. Randomization, Bootstrap and Monte Carlo Methods in Biology (CRC Press, 1997).80.Martinez, W. L. & Martinez, A. R. Computational Statistics Handbook with MATLAB (CRC Press, 2002). 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

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

Study designThe primary data source for this study is the cross-sectional 2013–2014 Demographic and Health Surveys for the DRC which is joined with remote-sensed environmental measures and land use data for mining and logging concessions extracted to DHS survey cluster locations. The DHS was administered using a multi-stage cluster survey design to represent the population of the DRC26. Briefly, survey clusters were selected to be representative of all 26 DRC provinces. Within clusters, households were randomly selected proportional to the population size, and within each household, adults ages 15–59 years were consented, interviewed, and asked to provide a dried blood spot (DBS) sample. Only adults who provided a DBS and consented for biospecimen use in future studies were included in this analysis. The outcome of prevalent malaria infections in the DRC was measured through PCR detection of the P. falciparum lactate dehydrogenase gene from DBS samples collected during DHS administration as described previously12.The main exposures were residence within 15 km of a mining concession and residence within 15 km of a logging concession. Additional covariates included individual-level variables for participant age, sex, use of a long-lasting insecticidal net (LLIN), education, and occupation; household variables for wealth, house roofing material, and the ratio of the number of household members using a bed-net to the total number of household members; and cluster variables for elevation, temperature, precipitation, vegetation, percentage of land cover identified as cropland, grassland, forest, and flooded/swamp land. All individual and household variables were obtained through the DHS. Occupation was recoded such that the manual labor and army category included laborers in mining and logging industries. Cluster variables were extracted from various satellite imagery platforms and other spatial datasets; the methods are described in more detail in the “Appendix”. The main exposures were extracted from geographic data sources as described below.Mining and logging concession data were obtained from the Global Forest Watch online repository27. Mining concessions were subset to only include operations that were active or in remediation spanning the DHS study years (2013–2014); logging concessions only included active operations during 2013. Distance to a mining or logging concession was measured from each cluster location to the boundary of a concession. Clusters were considered exposed to mining or logging if they were located within 15 km of a concession. This distance was chosen to account for the estimated 10 km maximum flight distance of a blood-fed mosquito5, with an additional 5 km to compensate for boundaries and non-residential land near the concessions. This range also accounts for the 5–10 km random spatial offset implemented by the DHS. Locations of mining and logging concessions along with cluster locations were mapped across the DRC. All maps were created in ArcGIS version 10.7.1, shapefiles for administrative boundaries were obtained from GADM.org.Data analysisCharacteristics of the study population were evaluated across quantiles of P. falciparum cluster prevalence and grouped by individual, household, and cluster level variables. To further examine distributions of malaria interventions and risk factors such as age, sex, LLIN use, occupation, household wealth, and household roof materials by mining and logging exposure, we compared mining exposed and logging exposed clusters with mining and logging doubly unexposed clusters stratified by urban and rural residence.We then modelled the prevalence odds of malaria across the DRC using hierarchical logistic regression models to account for the nested structure of the DHS data and to allow for inclusion of spatially varying effects. Models were implemented in a Bayesian framework using Integrated Nested Laplace Approximation (INLA) and stochastic partial differential equations for spatial effects28. In all models, we included two separate indicator terms for proximity to a mining concession and to a logging concession; since these areas are non-overlapping, the referent condition for each of these exposures is therefore locations exposed neither to mining nor to logging.The model fitting process followed two approaches. The first approach evaluated population-level effects of mining and logging on malaria prevalence adjusting for covariates and accounting for cluster-level random effects, which were assumed to vary independently across clusters. The second approach retained covariates and the cluster-level random intercept from the first model and additionally incorporated a spatial field to account for confounding due to space. For the spatial approach, two models were constructed. The first included a spatially varying intercept which borrowed information from neighboring cluster locations assuming a Gaussian random field. The second spatial model explored possible residual confounding due to environmental covariates by allowing spatially varying slopes for temperature, precipitation, vegetation, elevation, and land cover classes while including both independently and spatially varying intercepts across clusters. We introduced spatially varying slopes to account for the unobserved vector population across the DRC. Temperature, precipitation, vegetation, elevation, and various land cover classes have been shown to influence vector composition, survival, and competence for P. falciparum5,23,25, and associations with these covariates may vary due to their effects on the unobserved vector population. Using the spatial modelling approach, we also constructed a smoothed predicted prevalence map of malaria across the DRC, additional details are in the “Appendix”.For all models, confounding variables were selected based on a directed acyclic graph analysis and retained for adjustment if the 95% uncertainty interval (UI) of the variable excluded the null. Variables were coded as they were presented in the DHS with the exception of collapsing wealth into moderate or higher versus low wealth and recategorization of occupation as: professional, sales, or services; not working; manual labor or army; and agricultural work. All environmental variables were coded as continuous and scaled. Land cover variables were coded in intervals of 10 percentage points. Model comparison was done using Deviance Information Criterion (DIC), with the best fitting model having the smallest DIC29. All models were run using the ‘INLA’ package in R version 4.0.428, additional details are described in the “Appendix”.Differences in urban and rural residence were considered an important potential source of bias. Urban residence has been associated with lower prevalence of malaria due to many factors including different vector habitats, better access to healthcare, improved housing construction, and overall higher wealth4,12. To address possible bias introduced by urban residence, we stratified all models by urban and rural residence based on the DHS classification of clusters as urban or rural.A discrete set of confounding variables was identified from fixed effect models for mining and logging in rural and urban areas. The final adjustment set included age, sex, LLIN use, household wealth, temperature, precipitation, vegetation, and elevation. These variables had statistical or substantive significance and were adjusted for in all consecutive analyses.Ethical approval for this study was obtained from the University of North Carolina Institutional Review Board (UNC IRB# 20-3175) and the Kinshasa School of Public Health. Informed consent was obtained from all participants and all methods were conducted in accordance with guidelines and regulations set forth by the UNC IRB and the Kinshasa School of Public Health. More