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

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

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

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

As stated above this study aims to shed light on the deep uncertainties that are associated with the climate change phenomenon. The seasonally-averaged surface air temperature, hereafter simply referred to as temperature, was selected as the non-conditional climatic variable to be monitored within the Karkheh River Basin, Iran, during the baseline period (1975–2005). The CORDEX datasets (RCP 8.5) were employed to make climate-change projections.The first step in the proposed framework is to identify the most suitable theoretical distribution function to represent the stochastic behavior patterns of both historical and climate change data sets. Such identification considered the following theoretical distributions: normal, lognormal, exponential, Weibull, 3-parameter Weibull, extreme value, gamma, logistic, and loglogistic. It is important to note here that the primary strategy in this study is to analyze the data from a numeric standpoint without any presumption about the stochastic structure of the data44. As such, the study would opt for any distribution that is deemed fittest to describe the data. A summary of the fitted distributions to represent the prior distributions and likelihood functions is found in Tables S1 through S4 (see the Appendix). Furthermore, the climate-change period was divided into three mutually exclusive time frames which are short-term (2010–2039), mid-term (2040–2069), and long-term (2070–2099) future to gain a better understanding of the evoluton of future temperature changes.With Bayes’ theorem in mind, a Markov Chain Monte Carlo (MCMC) method was then applied to merge the prior distributions and the likelihood functions and to generate a sample set from the posterior distribution set. After a series of trials-and-errors, the sample size for the MCMC algorithm was set to be 1000 (n = 1000). These generated sample sets were then used to specify the most suitable theoretical pdfs to represent the posterior distribution functions. Figure 3, for instance, illustrates the most appropriate theoretical distribution that could represent the posterior distribution for the Seimareh sub-basin during spring under the short-term period.Figure 3The step-by-step process of computing the posterior pdf: (a) the prior distribution of Seimareh sub-basin during spring and the likelihood function of this sub-basin in the short-term future; (b) the histogram of the generated samples; and (c) the posterior distribution.Full size imageFigure 4 demonstrates the frequency with which each specific theoretical distribution functions was deemed the most suitable to characterize the prior, likelihood, and posterior distributions. Analyzing the fitted pdfs in Fig. 4 reveals an important point about the nature of RCMs’ raw projections. Specifically, the most frequently chosen distribution function for prior and posterior distributions is the 3-parameter Weibull. As for the likelihood function, however, it was the normal distribution that outperformed other available alternatives. Furthermore, the type of selected theoretical distribution for prior and posterior pdfs seems far more diverse compared to those from the likelihood functions. In fact, the likelihood functions were only limited to three types of distributions, most of which are normal distributions. Keep in mind that these functions are the most suitable pdfs that were fitted to the RCMs projected results. The cause behind this notion might be traced back to the nature of RCMs’ projections. RCMs operate at a finer horizontal resolution than GCMs, and thus they provide localized and high-resolution detailed climatic information that can be of importance for many management purposes, especially in regions with complex topography. However, the analyzed data revealed that among the distributions fitted for the likelihood function the normal distribution was found to be the best distribution to describe the data 70% of the time. This could be interpreted as signaling that employing RCMs’ raw projections, especially for regions that have considerable volatility in their climatic variables, should be used with caution, and further adjustment to the raw projected data may be required in some cases. Note that from a statistical standpoint, the normal distribution is not heavy-tailed, and as such, may not be the best way to portray this data. The fact that, in most cases, it has been selected as the best way to portray the stochastic nature of the likelihood function (i.e., RCM’s projections) means that innate characteristics of these data might prevent them to truly represent these types of variables on their own.Figure 4The frequency of using each individual theoretical pdfs as prior, likelihood function, and posterior distributions.Full size imageFigure 5 provides additional information regarding the frequency in which each individual theoretical distributions were deemed suitable to represent the posterior pdfs. While posterior distribution sets are, indeed, the most diverse in terms of the number of different types of distributions, a significant proportion of fitted pdfs (approximately 52%), however, are fitted by the 3-parameter Weibull distribution. Further information regarding the fitted distributions to represent the posterior pdfs is found in Tables S5 to S7 (Appendix).Figure 5The frequency of using different theoretical distributions as posterior pdfs.Full size imageThe computed posterior distribution functions can be interpreted as modified representations of the stochastic behavior of temperature variable concerning the short-term, mid-term, and long-term climate change projections. In that spirit, employing the confidence interval of 95%, the average temperature of the entire basin is depicted in Fig. 6 associated with historical and climate change conditions. Two sets of behavior patterns are observed. The first one is a broad trend in summer. The second pattern describes the rest of the seasons. In summer (Fig. 6b) the presence of a mild, yet, steady positive trend (upward) is detected. Here, one can expect the average temperature of the basin to increase steadily with the passage of time. As for the rest of the seasons, while it seems that the average temperature of the basin would experience a mild drop in the short-term, the temperature would begin to rise with a steady trend with time. In spring (Fig. 6a) and autumn (Fig. 6c) time series, it is projected that the expected average temperature in the basin would eventually surpass those that had been experienced in the baseline condition in the mid-term and long-term future. Concerning winter temperature it is seen in Fig. 6d that it is projected to increase over time. Yet, it has been estimated that it might not reach the observed average temperature of the basin in neither of the expressed time frames. Of course, given the upward trend in the data, this temperature would indeed be reached in a longer timeframe. It is worth noting that these patterns are in line with the idea that the earlier impacts of climate change are to amplify the historical patterns in climatic variables. That is why the data show a slight drop in colder seasons and an uptick in the warmer ones. That is, of course, until eventually, a new climatic equilibrium is reached on a global scale. At this point, the temperature as shown here would start to increase gradually.Figure 6The historical and simulated average temperatures of the entire basin with the 95%confidence interval in (a) spring; (b) summer; (c) autumn; and (d) winter.Full size imageThe other notable implication that can be understood by analyzing Fig. 6 is the variation in the width of the confidence intervals under baseline and climate change conditions. In comparison to the baseline condition, the length of the 95% confidence intervals would dramatically decrease under climate change conditions. This shrinking indicates that the generated results are more densely surrounding the central tendency measure herein chosen as the mean (μ) of the data. This notion is still in line with the idea that RCM’s projections mostly resemble the stochastic characteristic of normal distributions. A normal distribution is by nature not a heavily-tailed distribution, meaning that it rarely generates tail values. Even though the MCMC framework has mitigated this effect to some extent, they inevitably inherit this stochastic property from the likelihood functions.Again, to truly understand the obtained results here, one must first acknowledge how Bayesian models work. The main idea behind a Bayesian-based framework is to adjust the prior assumptions about a stochastic phenomenon through observed samples. In this case, the prior information represents the historical data, and the likelihood function (i.e., the samples) is obtained from RCM projects. As can be seen here, while RCMs’ projections might be perfectly capable of portraying the normal behavior of a variable under climate change conditions, which is usually sufficient for most lumped evaluation of climate change impact assessments, they might not be suitable to study extreme hydro-climatic events. The main problem with the raw RCM projections is that they follow a normal distribution, which is a symmetric distribution. Figure 4 suggests that while the MCMC framework here is mitigating this impact the final projections inherit this property from the likelihood functions. This simply means that while any RCM-based projection is perfectly suitable to understand the general outline of the climate change impacts, they are not the best option to study extreme events because even by modifying their pdfs, they rarely generate truly extreme values. The average temperatures in all sub-basins under baseline and climate change conditions are summarized in Table 1.Table 1 The average surface air temperature in all sub-basins under baseline and climate change conditions (°C).Full size tableAs for the impact of climate change, it is clear that these data are associated with deep uncertainty; that is, the parameters used to describe the stochastic behavior of a variable may be subjected to some degree of uncertainty. These parameters, μ for one, may also be represented by a pdf of their own. This study focuses on highlighting this type of deep uncertainty that might interfere with the central tendency measure μ.The deep uncertainty in this instance dictates that the recorded parameters for each posterior distribution are not deterministic values. While for a given prior distribution and likelihood function the MCMC would lead to a specific type of posterior pdf, the parameters that are used to define this pdf (e.g., μ), could vary each time the algorithm is used. If this variation is mild, there is more certainty about the nature of the variable’s stochastic behavior pattern (i.e., the posterior distribution function). If it is determined that the parameters are experiencing severe variations then the deep uncertain environment would leave the decision-makers unsure about the variables’ stochastic behavior pattern.With that idea in mind the combination of prior distributions and likelihood functions was executed for 100 times, and in each iteration the mean of 1000 samples was recorded. A theoretical distribution function was then fitted to the recorded values. Naturally, if the recorded values are generally close to one another numerically, the parameters of the computed posterior pdfs are less subjected to deep uncertainty. If, however, these values show significant fluctuation then the deep uncertainty of climate change would impede predictions of the stochastic behavior pattern of temperature. Figure 7, for example, portrays the uncertainty of the computed μ parameter for Seimareh sub-basin in spring under short-term future condition.Figure 7The uncertainty of the computed μ parameter for Seimareh sub-basin in spring under short-term future condition demonstrated by (a) a histogram and (b) a probability distribution function.Full size imageFigure 8 demonstrates the number of times each theoretical distribution was chosen to portray the stochastic behavior of the μ parameter. As can be seen here, the normal and lognormal distributions are the most common pdfs used to describe the variation in the μ parameter. One should also note the fact that about 65% of the distributions used to describe the future condition are normal distributions. The list of fitted pdfs is summarized in Tables S8 to S10.Figure 8The frequency with which each theoretical distribution was found suitable to describe the stochastic distribution of the μ parameter.Full size imageTable 2 summarizes the variation in the computed μ parameter in each given sub-basin. It is seen in Table 2 the 95% confidence interval of the μ parameter in all cases ranges between ± 0.1 and ± 0.3 °C. In 55% of the cases, this interval was found not to be more than ± 0.1 °C, and, furthermore, in 97% of them the interval was less than ± 0.2 °C. Needless to say, a widened confidence interval for the μ parameter can only signal that the deep uncertainty has a more pronounced impact on the temperature’s stochastic behavior. As for the case of the spring data set of the Seimareh sub-basin under the short-term condition, or the case of the Gharesou sub-basin’s winter data series under short-term period, the confidence interval for the μ parameter is estimated to be ± 0.3 °C wide. This indicates that compared to other projected posterior pdfs there is less certainty about the predicted stochastic behavior pattern of temperature variable for these particular cases. As shown in Table 2 in some cases, the variation in the projected μ temperature’ posterior pdfs is decreasing over time (for a given season over different timeframes). As discussed earlier, this was interpreted as the deep uncertainties of the climate change projections, meaning that lower volatility in this measure indicates that the said variable is less affected by the deep uncertainty of the climate-change phenomenon. This observation is in line with the general belief that, in the near future, the climate change phenomenon is most likely to intensify the historical patterns in climatic variable, but gradually we expect to see an upward trend in temperature in the longer run45. In this case, there is more volatility in the earlier time frames, but as time progresses, this volatility seems to decrease in some cases. This means that the obtained projections are showing less uncertainty about the outline behavior of the parameter for the long-term future as the models that are simulating the climatic behavior under climate change conditions have already reached a new equilibrium by that point.Table 2 The variation in the computed μ of the temperature’ posterior pdfs.Full size table More