Philippa Kaur

Effects of temperature on total daily locomotor activitiesTo understand the effect of temperature on the daily behavior of Drosophila species distributed in different temperature regions, we examined the daily locomotor activity at different temperatures in the following 11 sequenced Drosophila species: cosmopolitan (D. melanogaster and D. simulans), tropical (D. ananassae, D. erecta, D. yakuba, and D. sechellia), subtropical (D. willistoni and D. mojavensis), and temperate (D. persimilis, D. pseudoobscura, and D. virilis) species. Using the Drosophila Activity Monitor system25, we were able to analyze the amount of daily locomotor activity quantitatively at five experimental temperatures, i.e., 17 °C, 20 °C, 23 °C, 26 °C, and 29 °C. As the viability of the adults of D. persimilis and D. pseudoobscura was low at 29 °C, these two species were analyzed at only four experimental temperatures. First, we compared the amount of daily locomotor activities among these Drosophila species (Supplementary Fig. 1). The ranges of the total daily activity were quite diverse in these species (Kruskal–Wallis test: χ2 = 833.18, p  More

Hanski, I. Habitat fragmentation and species richness. J. Biogeogr. 42, 989–993. https://doi.org/10.1111/jbi.12478 (2015).Article 

Google Scholar 
Hoffmann, M. et al. The impact of conservation on the status of the world’s vertebrates. Science 330, 1503–1509. https://doi.org/10.1126/science.1194442 (2010).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution, and protection. Science 344, 1246752 (2014).CAS 
Article 

Google Scholar 
Crooks, K. R. Relative sensitivities of mammalian carnivores to habitat fragmentation. Conserv. Biol. 16, 488–502. https://doi.org/10.1046/j.1523-1739.2002.00386.x (2002).Article 

Google Scholar 
Elliot, N. B., Cushman, S. A., Macdonald, D. W. & Loveridge, A. J. The devil is in the dispersers: Predictions of landscape connectivity change with demography. J. Appl. Ecol. 51, 1169–1178. https://doi.org/10.1111/1365-2664.12282 (2014).Article 

Google Scholar 
Carroll, C. Interacting effects of climate change, landscape conversion, and harvest on carnivore populations at the range margin: Marten and lynx in the northern Appalachians. Conserv. Biol. 21, 1092–1104. https://doi.org/10.1111/j.1523-1739.2007.00719.x (2007).Article 
PubMed 

Google Scholar 
Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, 1241484. https://doi.org/10.1126/science.1241484 (2014).CAS 
Article 
PubMed 

Google Scholar 
Farris, Z. J. et al. Hunting, exotic carnivores, and habitat loss: Anthropogenic effects on a native carnivore community, Madagascar. PLOS ONE 10, e0136456. https://doi.org/10.1371/journal.pone.0136456 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Farris, Z. J. et al. Threats to a rainforest carnivore community: A multi-year assessment of occupancy and co-occurrence in Madagascar. Biol. Cons. 210, 116–124. https://doi.org/10.1016/j.biocon.2017.04.010 (2017).Article 

Google Scholar 
Swihart, R. K., Gehring, T. M., Kolozsvary, M. B. & Nupp, T. E. Responses of “resistant” vertebrates to habitat loss and fragmentation: The importance of niche breadth and range boundaries. Divers. Distrib. 9, 1–18. https://doi.org/10.1046/j.1472-4642.2003.00158.x (2003).Article 

Google Scholar 
Caryl, F. M., Quine, C. P. & Park, K. J. Martens in the matrix: The importance of nonforested habitats for forest carnivores in fragmented landscapes. J. Mammal. 93, 464–474. https://doi.org/10.1644/11-MAMM-A-149.1 (2012).Article 

Google Scholar 
Pereboom, V. et al. Movement patterns, habitat selection, and corridor use of a typical woodland-dweller species, the European pine marten (Martes martes), in fragmented landscape. Can. J. Zool. 86, 983–991. https://doi.org/10.1139/Z08-076 (2008).Article 

Google Scholar 
Fleschutz, M. M. et al. Response of a small felid of conservation concern to habitat fragmentation. Biodivers. Conserv. 25, 1447–1463. https://doi.org/10.1007/s10531-016-1118-6 (2016).Article 

Google Scholar 
Gálvez, N. et al. Forest cover outside protected areas plays an important role in the conservation of the Vulnerable guiña Leopardus guigna. Oryx 47, 251–258. https://doi.org/10.1017/S0030605312000099 (2013).Article 

Google Scholar 
Belcher, C. A. Demographics of tiger quoll (Dasyurus maculatus maculatus) populations in south-eastern Australia. Aust. J. Zool. 51, 611–626. https://doi.org/10.1071/ZO02051 (2003).Article 

Google Scholar 
Maxwell, S., Burbidge, A. & Morris, K. Spotted-tailed Quoll (SE mainland and Tas); recovery outline. (1996).Jones, M. E., Rose, R. K. & Burnett, S. Dasyurus maculatus. Mammalian Species 676, 1–9 (2001).Article 

Google Scholar 
Long, K. & Nelson, J. National recovery plan for the spotted-tailed Quoll Dasyurus maculatus. Victorian Department of Sustainability and Environment (2010).Claridge, A. W. et al. Home range of the spotted-tailed quoll (Dasyurus maculatus), a marsupial carnivore, in a rainshadow woodland. Wildl. Res. 32, 7–14. https://doi.org/10.1071/WR04031 (2005).Article 

Google Scholar 
Glen, A. S. & Dickman, C. R. Home range, denning behaviour and microhabitat use of the carnivorous marsupial Dasyurus maculatus in eastern Australia. J. Zool. 268, 347–354. https://doi.org/10.1111/j.1469-7998.2006.00064.x (2006).Article 

Google Scholar 
Körtner, G. et al. Population structure, turnover and movement of spotted-tailed quolls on the New England Tablelands. Wildl. Res. 31, 475–484. https://doi.org/10.1071/WR03041 (2004).Article 

Google Scholar 
Belcher, C. The Largest Surviving Marsupial Carnivore on Mainland Australia: The Tiger or Spotted-Tailed Quoll Dasyurus maculatus, A Nationally Threatened, Forest-Dependent Species 612–623 (Royal Zoological Society of New South Wales, Sydney, 2004).
Google Scholar 
Henderson, T., Fancourt, B. A., Rajaratnam, R., Vernes, K. & Ballard, G. Spatial and temporal interactions between endangered spotted-tailed quolls and introduced red foxes in a fragmented landscape. J. Zool. https://doi.org/10.1111/jzo.12919 (2021).Article 

Google Scholar 
Troy, S. N. Spatial Ecology of the Tasmanian Spotted-Tailed Quoll. Ph.D. Thesis, University of Tasmania, (2014).Jones, M. E. et al. Research supporting restoration aiming to make a fragmented landscape ‘functional’ for native wildlife. Ecol. Manag. Restor. 22, 65–74. https://doi.org/10.1111/emr.12504 (2021).Article 

Google Scholar 
Andersen, G. E., Johnson, C. N., Barmuta, L. A. & Jones, M. E. Use of anthropogenic linear features by two medium-sized carnivores in reserved and agricultural landscapes. Scientific Reports 7, 1–11. https://doi.org/10.1038/s41598-017-11454-z (2017).CAS 
Article 

Google Scholar 
Nichols, J. D. in Applied Ecology and Human Dimensions in Biological Conservation (eds L. M. Verdade, M.C. Lyra-Jorge, & C.I. Pina) 117–131 (Springer, 2014).Royle, J. A., Chandler, R. B., Sollmann, R. & Gardner, B. in Spatial Capture-Recapture (eds J. Andrew Royle, Richard B. Chandler, Rahel Sollmann, & Beth Gardner) 3–19 (Academic Press, 2014).Sollmann, R., Gardner, B. & Belant, J. L. How does spatial study design influence density estimates from spatial capture-recapture models?. PLoS ONE 7, e34575 (2012).ADS 
CAS 
Article 

Google Scholar 
Kalle, R., Ramesh, T., Qureshi, Q. & Sankar, K. Density of tiger and leopard in a tropical deciduous forest of Mudumalai Tiger Reserve, southern India, as estimated using photographic capture–recapture sampling. Acta Theriol. 56, 335–342. https://doi.org/10.1007/s13364-011-0038-9 (2011).Article 

Google Scholar 
Vissia, S., Wadhwa, R. & van Langevelde, F. Co-occurrence of high densities of brown hyena and spotted hyena in central Tuli, Botswana. J. Zool. 314, 143–150. https://doi.org/10.1111/jzo.12873 (2021).Article 

Google Scholar 
Henderson, T., Fancourt, B. A. & Ballard, G. The importance of species-specific survey designs: Prey camera trap surveys significantly underestimate the detectability of endangered spotted-tailed quolls. Aust. Mammalogy https://doi.org/10.1071/AM21039 (2022).Gorta, S. B. Z., Alting, B., Claridge, A. & Henderson, T. Apparent piebald variants in quolls (Dasyurus): Examples of three recent cases in the spotted-tailed quoll Dasyurus maculatus. Aust. Mammalogy 43, 373–377. https://doi.org/10.1071/AM20058 (2021).Article 

Google Scholar 
Kowalksi, M. (https://exifpro.informer.com/2.1/, 2011).Efford, M. in R package version 4.5.3 (2022).R Core Team. (R Foundation for Statistical Computing, Vienna, Austria, 2022).Rovero, F. & Zimmermann, F. Camera Trapping for Wildlife Research (Pelagic Publishing Ltd, London, 2016).
Google Scholar 
Efford, M. Density estimation in live-trapping studies. Oikos 106, 598–610. https://doi.org/10.1111/j.0030-1299.2004.13043.x (2004).Article 

Google Scholar 
Niedballa, J., Sollmann, R., Courtiol, A. & Wilting, A. camtrapR: An R package for efficient camera trap data management. Methods Ecol. Evol. 7, 1457–1462. https://doi.org/10.1111/2041-210X.12600 (2016).Article 

Google Scholar 
Burnham, K. P. & Anderson, D. R. A practical information-theoretic approach. Model Sel. Multimodel Inference 2, 70–71 (2002).MATH 

Google Scholar 
Hamer, R. P. et al. Differing effects of productivity on home-range size and population density of a native and an invasive mammalian carnivore. Wildlife Res. 49, 158–168. https://doi.org/10.1071/WR20134 (2021).Article 

Google Scholar 
Glen, A. S. & Dickman, C. R. Complex interactions among mammalian carnivores in Australia, and their implications for wildlife management. Biol. Rev. 80, 387–401. https://doi.org/10.1017/s1464793105006718 (2005).Article 
PubMed 

Google Scholar 
Glen, A. S., Pennay, M., Dickman, C. R., Wintle, B. A. & Firestone, K. B. Diets of sympatric native and introduced carnivores in the Barrington Tops, eastern Australia. Austral Ecol. 36, 290–296. https://doi.org/10.1111/j.1442-9993.2010.02149.x (2011).Article 

Google Scholar 
Glen, A. S. & Dickman, C. R. Population viability analysis shows spotted-tailed quolls may be vulnerable to competition. Aust Mammalogy 35, 180–183. https://doi.org/10.1071/AM12045 (2013).Article 

Google Scholar 
Graham, C. A., Maron, M. & McAlpine, C. A. Influence of landscape structure on invasive predators: Feral cats and red foxes in the brigalow landscapes, Queensland Australia. Wildl. Res. 39, 661–676. https://doi.org/10.1071/WR12008 (2012).Article 

Google Scholar 
Glen, A. S. Population attributes of the spotted-tailed quoll (Dasyurus maculatus) in north-eastern New South Wales. Aust. J. Zool. 56, 137–142. https://doi.org/10.1071/ZO08025 (2008).Article 

Google Scholar 
Chua, M. A., Sivasothi, N. & Meier, R. Population density, spatiotemporal use and diet of the leopard cat (Prionailurus bengalensis) in a human-modified succession forest landscape of Singapore. Mammal Res. 61, 99–108 (2016).Article 

Google Scholar 
Lorica, M. & Heaney, L. Survival of a native mammalian carnivore, the leopard cat Prionailurus bengalensis Kerr, 1792 (Carnivora: Felidae), in an agricultural landscape on an oceanic Philippine island. J. Threatened Taxa, 4451–4460 (2013).Rajaratnam, R., Sunquist, M., Rajaratnam, L. & Ambu, L. Diet and habitat selection of the leopard cat (Prionailurus bengalensis borneoensis) in an agricultural landscape in Sabah, Malaysian Borneo. J. Trop. Ecol. 23, 209–217 (2007).Article 

Google Scholar 
Belcher, C. A. & Darrant, J. P. Den use by the spotted-tailed quoll Dasyurus maculatus in south-eastern Australia. Aust Mammalogy 28, 59–64. https://doi.org/10.1071/AM06007 (2006).Article 

Google Scholar 
Glen, A. & Dickman, C. Why are there so many spotted-tailed Quolls Dasyurus maculatus in parts of north-eastern New South Wales?. Aust Zool 35, 711–718. https://doi.org/10.7882/az.2011.023 (2011).Article 

Google Scholar 
Hanski, I. Metapopulation ecology (Oxford University Press, Oxford, 1999).
Google Scholar 
Pulliam, H. R. Sources, sinks, and population regulation. Am. Nat. 132, 652–661 (1988).Article 

Google Scholar 
Belcher, C. A. Susceptibility of the tiger quoll, Dasyurus maculatus, and the eastern quoll, D. viverrinus, to 1080-poisoned baits in control programmes for vertebrate pests in eastern Australia. Wildl. Res. 25, 33–40. https://doi.org/10.1071/WR95077 (1998).Article 

Google Scholar 
Schmidt, G. M., Graves, T. A., Pederson, J. C. & Carroll, S. L. Precision and bias of spatial capture–recapture estimates: A multi-site, multi-year Utah black bear case study. Ecological Applications 32, e2618. https://doi.org/10.1002/eap.2618 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
White, G. C. Capture-Recapture and Removal Methods for Sampling Closed Populations (Los Alamos National Laboratory, New Mexico, 1982).
Google Scholar 
Thornton, D. H. & Pekins, C. E. Spatially explicit capture–recapture analysis of bobcat (Lynx rufus) density: Implications for mesocarnivore monitoring. Wildl. Res. 42, 394–404. https://doi.org/10.1071/WR15092 (2015).Article 

Google Scholar 
Sollmann, R. et al. Improving density estimates for elusive carnivores: Accounting for sex-specific detection and movements using spatial capture–recapture models for jaguars in central Brazil. Biol. Cons. 144, 1017–1024 (2011).Article 

Google Scholar 
Green, A. M., Chynoweth, M. W. & Şekercioğlu, Ç. H. Spatially explicit capture-recapture through camera trapping: A review of benchmark analyses for wildlife density estimation. Front. Ecol. Evol. 8, 473. https://doi.org/10.3389/fevo.2020.563477 (2020).Article 

Google Scholar 
du Preez, B. D., Loveridge, A. J. & Macdonald, D. W. To bait or not to bait: a comparison of camera-trapping methods for estimating leopard Panthera pardus density. Biol. Cons. 176, 153–161 (2014).Article 

Google Scholar 
Zimmermann, F., Breitenmoser-Würsten, C., Molinari-Jobin, A. & Breitenmoser, U. Optimizing the size of the area surveyed for monitoring a Eurasian lynx (Lynx lynx) population in the Swiss Alps by means of photographic capture–recapture. Integr. Zool. 8, 232–243 (2013).Article 

Google Scholar 
Dupont, P., Milleret, C., Gimenez, O. & Bischof, R. Population closure and the bias-precision trade-off in spatial capture–recapture. Methods Ecol. Evol. 10, 661–672. https://doi.org/10.1111/2041-210X.13158 (2019).Article 

Google Scholar 
Mergey, M., Helder, R. & Roeder, J. J. Effect of forest fragmentation on space-use patterns in the European pine marten (Martes martes). J. Mammal. 92, 328–335. https://doi.org/10.1644/09-MAMM-A-366.1 (2011).Article 

Google Scholar 
Silmi, M. et al. Activity and ranging behavior of leopard cats (Prionailurus bengalensis) in an oil palm landscape. Frontiers in Environmental Science 9, 651939. https://doi.org/10.3389/fenvs.2021.651939 (2021).Article 

Google Scholar  More

Ravishankara, A. R., Daniel, J. S. & Portmann, R. W. Nitrous Oxide (N2O): The Dominant Ozone-Depleting Substance Emitted in the 21st Century. Science 326, 123–125 (2009).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Tian H. et al. A comprehensive quantification of global nitrous oxide sources and sinks. Nature. 586, 248–256 (2020).WMO. WMO Greenhouse Gas Bulletin: The State of Greenhouse Gases in the Atmosphere Based on Global Observations through 2019. Tech. Rep. (2020).Butterbach-Bahl, K., Stange, F., Papen, H. & Li, C. Regional inventory of nitric oxide and nitrous oxide emissions for forest soils of southeast Germany using the biogeochemical model PnET-N-DNDC. J. Geophys. Res. – Atmospheres 106, 34155–34166 (2001).ADS 
CAS 
Article 

Google Scholar 
Kesik, M. et al. Inventories of N2O and NO emissions from European forest soils. Biogeosciences 2, 353–375 (2005).ADS 
CAS 
Article 

Google Scholar 
Park, S. et al. Trends and seasonal cycles in the isotopic composition of nitrous oxide since 1940. Nat. Geosci. 5, 261–265 (2012).ADS 
CAS 
Article 

Google Scholar 
World Bank. World Development Indicators: Fertiliser consumption (AG.CON.FERT.ZS), https://data.worldbank.org/indicator/AG.CON.FERT.ZS (2019).Tian, H. et al. Global soil nitrous oxide emissions since the preindustrial era estimated by an ensemble of terrestrial biosphere models: Magnitude, attribution, and uncertainty. Glob. Change Biol. 25, 640–659 (2019).ADS 
Article 

Google Scholar 
Hurtt, G. et al. Harmonization of global land-use change and management for the period 850-2100 (LUH2) for CMIP6. Geosci. Model Dev. 13, 5425–5464 (2020).Sebilo, M., Mayer, B., Nicolardot, B., Pinay, G. & Mariotti, A. Long-term fate of nitrate fertilizer in agricultural soils. Proc. Natl Acad. Sci. USA 110, 18185–18189 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Galloway, J. N. et al. Transformation of the Nitrogen Cycle: Recent Trends, Questions, and Potential Solutions. Science 320, 889–892 (2008).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Fowler, D. et al. The global nitrogen cycle in the twenty-first century. Philos. Trans. R. Soc. Lond. Ser. B, Biol. Sci. 368, 1–13 (2013).
Google Scholar 
Roy, E. D., Hammond Wagner, C. R. & Niles, M. T. Hot spots of opportunity for improved cropland nitrogen management across the United States. Environ. Res. Lett. 16, (2021).Lett, S. & Michelsen, A. Seasonal variation in nitrogen fixation and effects of climate change in a subarctic heath. Plant Soil 379, 193–204 (2014).CAS 
Article 

Google Scholar 
Wang, W. et al. Characteristics of Atmospheric Reactive Nitrogen Deposition in Nyingchi City. Sci. Rep. 9, 1–11 (2019).ADS 
Article 
CAS 

Google Scholar 
Verma, P. & Sagar, R. Effect of nitrogen (N) deposition on soil-N processes: a holistic approach. Sci. Rep. 10, 1–16 (2020).Article 
CAS 

Google Scholar 
Peng, J. et al. Global Carbon Sequestration Is Highly Sensitive to Model-Based Formulations of Nitrogen Fixation. Glob. Biogeochem. Cycles 34, 1–15 (2020).Article 
CAS 

Google Scholar 
Leitner, S. et al. Closing maize yield gaps in sub-Saharan Africa will boost soil N2O emissions. Curr. Opin. Environ. Sustain. 47, 95–105 (2020).Article 

Google Scholar 
Venterea, R. T. et al. Challenges and opportunities for mitigating nitrous oxide emissions from fertilized cropping systems. Front. Ecol. Environ. 10, 562–570 (2012).Article 

Google Scholar 
Wagner-Riddle, C., Baggs, E. M., Clough, T. J., Fuchs, K. & Petersen, S. O. Mitigation of nitrous oxide emissions in the context of nitrogen loss reduction from agroecosystems: managing hot spots and hot moments. Curr. Opin. Environ. Sustain. 47, 46–53 (2020).Article 

Google Scholar 
Gruber, N. & Galloway, J. N. An Earth-system perspective of the global nitrogen cycle. Nature 451, 293–296 (2008).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Hartmann, A. A., Barnard, R. L., Marhan, S. & Niklaus, P. A. Effects of drought and N-fertilization on N cycling in two grassland soils. Oecologia 171, 705–717 (2013).ADS 
PubMed 
Article 

Google Scholar 
Inatomi, M., Hajima, T. & Ito, A. Fraction of nitrous oxide production in nitrification and its effect on total soil emission: A meta-analysis and global-scale sensitivity analysis using a process-based model. Plos One 14, e0219159 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li, Z. et al. Global patterns and controlling factors of soil nitrification rate. Glob. Change Biol. 26, 4147–4157 (2020).ADS 
Article 

Google Scholar 
Reichenau, T. G., Klar, C. W. & Schneider, K. Effects of Climate Change on Nitrate Leaching. In Regional Assessment of Global Change Impacts: The Project GLOWA-Danube (eds Mauser, W. & Prasch, M.) 623–629 (Springer, 2016).He, W. et al. Climate change impacts on crop yield, soil water balance and nitrate leaching in the semiarid and humid regions of Canada. PLoS ONE 13, 1–19 (2018).
Google Scholar 
Mas-Pla, J. & Menció, A. Groundwater nitrate pollution and climate change: learnings from a water balance-based analysis of several aquifers in a western Mediterranean region (Catalonia). Environ. Sci. Pollut. Res. 26, 2184–2202 (2019).CAS 
Article 

Google Scholar 
Stuart, M. E., Gooddy, D. C., Bloomfield, J. P. & Williams, A. T. A review of the impact of climate change on future nitrate concentrations in groundwater of the UK. Sci. Total Environ. 409, 2859–2873 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Frank, D. et al. Effects of climate extremes on the terrestrial carbon cycle: Concepts, processes and potential future impacts. Glob. Change Biol. 21, 2861–2880 (2015).ADS 
Article 

Google Scholar 
Mitchell, R. A., Mitchell, V. J., Driscoll, S. P., Franklin, J. & Lawlor, D. W. Effects of increased CO2 concentration and temperature on growth and yield of winter wheat at two levels of nitrogen application. Plant, Cell Environ. 16, 521–529 (1993).CAS 
Article 

Google Scholar 
Eisenhauer, N., Cesarz, S., Koller, R., Worm, K. & Reich, P. B. Global change belowground: Impacts of elevated CO 2, nitrogen, and summer drought on soil food webs and biodiversity. Glob. Change Biol. 18, 435–447 (2012).ADS 
Article 

Google Scholar 
Ri, X. & Prentice, I. C. Terrestrial nitrogen cycle simulation with a dynamic global vegetation model. Glob. Change Biol. 14, 1745–1764 (2008).ADS 
Article 

Google Scholar 
Ri, X., Prentice, I. C., Spahni, R. & Niu, H. S. Modelling terrestrial nitrous oxide emissions and implications for climate feedback. N. Phytologist 196, 472–488 (2012).Article 
CAS 

Google Scholar 
Giltrap, D. L. & Ausseil, A.-G. E. Upscaling NZ-DNDC using a regression based meta-model to estimate direct N2O emissions from New Zealand grazed pastures. Sci. Total Environ. 539, 221–230 (2016).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Thompson, R. L. et al. TransCom N2O model inter-comparison – Part 1: Assessing the influence of transport and surface fluxes on tropospheric N2O variability. Atmos. Chem. Phys. 14, 4349–4368 (2014).ADS 
Article 

Google Scholar 
Thompson, R. L. et al. TransCom N2O model inter-comparison – Part 2: Atmospheric inversion estimates of N2O emissions. Atmos. Chem. Phys. 14, 6177–6194 (2014).ADS 
Article 
CAS 

Google Scholar 
Thompson, R. L. et al. Acceleration of global N2O emissions seen from two decades of atmospheric inversion. Nat. Clim. Change, 8, (2019).Houlton, B. Z. & Bai, E. Imprint of denitrifying bacteria on the global terrestrial biosphere. Proc. Natl Acad. Sci. USA 106, 21713–21716 (2009).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bai, E., Houlton, B. Z. & Wang, Y. P. Isotopic identification of nitrogen hotspots across natural terrestrial ecosystems. Biogeosciences 9, 3287–3304 (2012).ADS 
CAS 
Article 

Google Scholar 
Craine, J. M. et al. Ecological interpretations of nitrogen isotope ratios of terrestrial plants and soils. Plant Soil 396, 1–26 (2015).CAS 
Article 

Google Scholar 
Craine, J. M. et al. Convergence of soil nitrogen isotopes across global climate gradients. Sci. Rep. 5, 1–8 (2015).
Google Scholar 
Toyoda, S. et al. Decadal time series of tropospheric abundance of N2O isotopomers and isotopologues in the northern hemisphere obtained by the long-term observation at Hateruma Island, Japan. J. Geophys. Res. – Atmospheres 118, 1–13 (2013).
Google Scholar 
Harris, E. et al. Tracking nitrous oxide emission processes at a suburban site with semicontinuous, in situ measurements of isotopic composition. J. Geophys. Res. – Atmospheres 122, 1–21 (2017).CAS 

Google Scholar 
Harris, E. et al. Denitrifying pathways dominate nitrous oxide emissions from managed grassland during drought and rewetting. Sci. Adv. 7, eabb7118 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Yu, L. et al. Atmospheric nitrous oxide isotopes observed at the high-altitude research station Jungfraujoch, Switzerland. Atmos. Chem. Phys. 20, 6495–6519 (2020).ADS 
CAS 
Article 

Google Scholar 
Sowers, T., Rodebaugh, A., Yoshida, N. & Toyoda, S. Extending records of the isotopic composition of atmospheric N2O back to 1800 A.D. from air trapped in snow at the South Pole and the Greenland Ice Sheet Project II ice core. Glob. Biogeochem. Cycles 16, 1129 (2002).ADS 
Article 
CAS 

Google Scholar 
Scheer, C., Fuchs, K., Pelster, D. E. & Butterbach-Bahl, K. Estimating global terrestrial denitrification from measured N2O:(N2O + N2) product ratios. Curr. Opin. Environ. Sustainability 47, 72–80 (2020).Article 

Google Scholar 
Pilegaard, K. Processes regulating nitric oxide emissions from soils. Philos. Trans. R. Soc. B: Biol. Sci. 368, 1–8, (2013).Thompson, R. Documentation of N2O flux service: Description of the N2O inversion production chain. Technical report, Copernicus Atmospheric Monitoring Service, CAMS73_2018SC2 -Documentation of N2O flux service (2021).Voigt, C. et al. Nitrous oxide emissions from permafrost-affected soils. Nat. Rev. Earth Environ. 1, 420–434 (2020).ADS 
CAS 
Article 

Google Scholar 
Pan, B., Lam, S. K., Wang, E., Mosier, A. & Chen, D. New approach for predicting nitrification and its fraction of N2O emissions in global terrestrial ecosystems. Environ. Res. Lett. 16, (2021).Corbeels, M., Hofman, G. & Van Cleemput, O. Fate of fertiliser N applied to winter wheat growing on a Vertisol in a Medditerranean environment. Nutrient Cycl. Agroecosystems 53, 249–258 (1999).Article 

Google Scholar 
Jenkinson, D. S., Poulton, P. R., Johnston, A. E. & Powlson, D. S. Turnover of Nitrogen-15-Labeled Fertilizer in Old Grassland. Soil Sci. Soc. Am. J. 68, 865–875 (2004).ADS 
CAS 
Article 

Google Scholar 
Gardner, J. B. & Drinkwater, L. E. The fate of nitrogen in grain cropping systems: A meta-analysis of 15N field experiments. Ecol. Appl. 19, 2167–2184 (2009).PubMed 
Article 

Google Scholar 
Smith, W. et al. Towards an improved methodology for modelling climate change impacts on cropping systems in cool climates. Sci. Total Environ. 728, 138845 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
IPCC. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. (IPCC, Geneva, Switzerland, 2014).Butterbach-Bahl, K., Baggs, E. M., Dannenmann, M., Kiese, R. & Zechmeister-Boltenstern, S. Nitrous oxide emissions from soils: how well do we understand the processes and their controls? Philos. Trans. R. Soc. Lond. Ser. B, Biol. Sci. 368, 1–13 (2013).Congreves, K. A., Wagner-Riddle, C., Si, B. C. & Clough, T. J. Nitrous oxide emissions and biogeochemical responses to soil freezing-thawing and drying-wetting. Soil Biol. Biochem. 117(October 2017), 5–15 (2018).Wagner-Riddle, C. et al. Globally important nitrous oxide emissions from croplands induced by freeze-thaw cycles. Nat. Geosci. 10, 279–283 (2017).ADS 
CAS 
Article 

Google Scholar 
Byers, E., Bleken, M. A. & Dörsch, P. Winter N2O accumulation and emission in sub-boreal grassland soil depend on clover proportion and soil ph. Environ. Res. Commun. 3, (2021).Doersch, P., Sturite, I. & Trier Kjaer, S. High off-season nitrous oxide emissions negate potential soil C-gain from cover crops in boreal cereal cropping (EGU22-3066). EGU General Assembly 2022, https://doi.org/10.5194/egusphere-egu22-3066 (2022).Prokopiou, M. et al. Constraining N2O emissions since 1940 using firn air isotope measurements in both hemispheres. Atmos. Chem. Phys. 17, 4539–4564 (2017).ADS 
CAS 
Article 

Google Scholar 
Yu, L., Harris, E., Lewicka-Szczebak, D. & Mohn J. What can we learn from N2O isotope data? Analytics, processes and modelling. Rap. Commun. Mass Spectr. 34, 1–13 (2020).Smith, K., Thomson, P., Clayton, H., Mctaggart, I. & Conen, F. Effects of temperature, water content and nitrogen fertilisation on emissions of nitrous oxide by soils. Atmos. Environ. 32, 3301–3309 (1998).ADS 
CAS 
Article 

Google Scholar 
Yao, Z. et al. Soil-atmosphere exchange potential of NO and N2O in different land use types of Inner Mongolia as affected by soil temperature, soil moisture, freeze-thaw, and drying-wetting events. J. Geophys. Res. – Atmospheres 115, 1–17 (2010).
Google Scholar 
Cantarel, A. A. M. et al. Four years of experimental climate change modifies the microbial drivers of N 2O fluxes in an upland grassland ecosystem. Glob. Change Biol. 18, 2520–2531 (2012).ADS 
Article 

Google Scholar 
Zhang, Y. et al. Temperature effects on N2O production pathways in temperate forest soils. Sci. Total Environ. 691, 1127–1136 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Wang, Q. et al. Data-driven estimates of global nitrous oxide emissions from croplands. Natl Sci. Rev. 7, 441–452 (2020).CAS 
PubMed 
Article 

Google Scholar 
Rütting, T., Cizungu Ntaboba, L., Roobroeck, D., Bauters, M., Huygens, D. & Boeckx, P. Leaky nitrogen cycle in pristine African montane rainforest soil. Glob. biogeochemical cycles 29, 1754–1762 (2015).ADS 
Article 
CAS 

Google Scholar 
Brookshire, E. N., Gerber, S., Greene, W., Jones, R. T. & Thomas, S. A. Global bounds on nitrogen gas emissions from humid tropical forests. Geophys. Res. Lett. 44, 2502–2510 (2017).ADS 
CAS 
Article 

Google Scholar 
Homyak, P. M. et al. Aridity and plant uptake interact to make dryland soils hotspots for nitric oxide (NO) emissions. PNAS 113, E2608–E2616 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
IPCC. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. (IGES, Japan, 2006).Davidson, E. A., Suddick, E. C., Rice, C. W. & Prokopy, L. S. More Food, Low Pollution (Mo Fo Lo Po): A Grand Challenge for the 21st Century. J. Environ. Qual. 44, 305–311 (2015).CAS 
PubMed 
Article 

Google Scholar 
Cui, X. et al. Global mapping of crop-specific emission factors highlights hotspots of nitrous oxide mitigation. Nature Food, In press, (2021).McDaniel, M. D., Mas-Pla, J. & Kaye, M. W. Do “hot moments” become hotter under climate change? Soil nitrogen dynamics from a climate manipulation experiment in a post-harvest forest. Biogeochemistry. https://doi.org/10.1007/s10533-014-0001-3 (2014).Yu, G. et al. Stabilization of atmospheric nitrogen deposition in China over the past decade. Nat. Geosci. 12, 424–429 (2019).ADS 
CAS 
Article 

Google Scholar 
New, M., Lister, D., Hulme, M. & Makin, I. A high-resolution data set of surface climate over global land areas. Clim. Res. 21, 1–25 (2002).Article 

Google Scholar 
FAO, IIASA, ISRIC, ISSCAS, and JRC. Harmonized World Soil Database (version 1.2). (Technical report, FAO, Rome, Italy and IIASA, Laxenburg, Austria, 2012).Hiederer, R. & Köchy M. Global soil organic carbon estimates and the harmonized world soil database. EUR 25225EN (2012).Trabucco, A. & Zomer, R. Global Aridity Index and Potential Evapo-Transpiration (ET0) Climate Database v2, https://doi.org/10.6084/m9.figshare.7504448.v3 (2019).Slessarev, E. W. et al. Water balance creates a threshold in soil pH at the global scale. Nature 540, 567–569 (2016).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Zinke, P., Stangenberger, A., Post, W., Emanual, E. & Olson, J. WORLDWIDE ORGANIC SOIL CARBON AND NITROGEN DATA. Technical report, Oak Ridge National Laboratory, https://cdiac.ess-dive.lbl.gov/ndps/ndp018.html (2004).Kowalczyk, E. A., Wang, Y. P. & Law, R. M. The CSIRO Atmosphere Biosphere Land Exchange (CABLE) model for use in climate models and as an offline model. CSIRO Mar. Atmos. Res. Pap. 13, 1–42 (2006).
Google Scholar 
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).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Chen, C. et al. Nitrogen isotopic composition of plants and soil in an arid mountainous terrain: South slope versus north slope. Biogeosciences 15, 369–377 (2018).ADS 
CAS 
Article 

Google Scholar 
Brenner, D., Amundson, R., Baisden, T., Kendall, C. & Harden, J. N variation with time in a California annual grassland ecosystem. Geochimica et. Cosmochimica Acta. 65, 4171–4186 (2001).ADS 
CAS 
Article 

Google Scholar 
Xu, Y., He, J., Cheng, W., Xing, X. & Li, L. Natural 15N abundance in soils and plants in relation to N cycling in a rangeland in Inner Mongolia. J. Plant Ecol. 3, 201–207 (2010).Article 

Google Scholar 
Inglett, P. W., Reddy, K. R., Newman, S. & Lorenzen, B. Increased soil stable nitrogen isotopic ratio following phosphorus enrichment: Historical patterns and tests of two hypotheses in a phosphorus-limited wetland. Oecologia 153, 99–109 (2007).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Bissett, A. et al. Introducing BASE: the Biomes of Australian Soil Environments soil microbial diversity database. GigaScience, 5, (2016).Bauters, M. et al. Functional Composition of Tree Communities Changed Topsoil Properties in an Old Experimental Tropical Plantation. Ecosystems 20, 861–871 (2017).CAS 
Article 

Google Scholar 
Bauters, M. et al. Parallel functional and stoichiometric trait shifts in South American and African forest communities with elevation. Biogeosciences 14, 5313–5321 (2017).ADS 
CAS 
Article 

Google Scholar 
Bauters, M. et al. Contrasting nitrogen fluxes in African tropical forests of the Congo Basin. Ecol. Monograp. 89, 1–17 (2019).Bauters, M. et al. Long-term recovery of the functional community assembly and carbon pools in an African tropical forest succession. Biotropica 51, 319–329 (2019).Article 

Google Scholar 
Gallarotti, N. et al. In-depth analysis of N2O fluxes in tropical forest soils of the Congo Basin combining isotope and functional gene analysis. ISME J. (2021).Barthel, M. et al. Low N2O and variable CH4 fluxes from tropical forest soils of the Congo Basin. Nat. Commun. 13, 1–8 (2022).Article 
CAS 

Google Scholar 
Baumgartner, S. et al. Stable isotope signatures of soil nitrogen on an environmental-geomorphic gradient within the Congo Basin. Soil 7, 83–94 (2021).ADS 
CAS 
Article 

Google Scholar 
Chollet, F. Keras, Keras package for Python https://keras.io (2015).IUPAC. Compendium of Chemical Terminology, 2nd ed. (the “Gold Book”), online version created by S.J. Chalk. Blackwell Science Ltd, https://doi.org/10.1351/goldbook (2019).Yu, L. et al. Constraining global N2O budgets with decadal trends of multiple isotope signatures. In preparation, (2022).Machida, T., Nakazawa, T., Fujii, Y., Aoki, S. & Watanabe, O. Increase in the atmospheric nitrous oxide concentration during the last 250 years. Geophys. Res. Lett. 22, 2921–2924 (1995).ADS 
CAS 
Article 

Google Scholar 
Rubino, M. et al. Revised records of atmospheric trace gases CO2, CH4, N2O, and δ13C-CO2 over the last 2000 years from Law Dome, Antarctica. Earth Syst. Sci. Data 11, 473–492 (2019).ADS 
Article 

Google Scholar 
Dorich, C. et al. Improving N2O emission estimates with the global N2O database. Curr. Opin. Environ. Sustainability 47, 13–20 (2020).Article 

Google Scholar 
Mariotti, A. et al. Experimental-determination of Nitrogen Kinetic Isotope Fractionation – Some Principles – Illustration For the Denitrification and Nitrification Processes. Plant Soil 62, 413–430 (1981).CAS 
Article 

Google Scholar 
Möbius, J. Isotope fractionation during nitrogen remineralization (ammonification): Implications for nitrogen isotope biogeochemistry. Geochimica et. Cosmochimica Acta. 105, 422–432 (2013).ADS 
Article 
CAS 

Google Scholar 
Stern, L., Baisden, W. T. & Amundson, R. Processes controlling the oxygen isotope ratio of soil CO2: Analytic and numerical modeling. Geochimica Et. Cosmochimica Acta. 63, 799–814 (1999).ADS 
CAS 
Article 

Google Scholar 
Denk, T. R. A. et al. The nitrogen cycle: A review of isotope effects and isotope modeling approaches. Soil Biol. Biochem. 105, 121–137 (2017).CAS 
Article 

Google Scholar 
Rohe, L. et al. Comparing modified substrate induced respiration with selective inhibition (SIRIN) and N2O isotope approaches to estimate fungal contribution to denitrification in three arable soils under anoxic conditions. Biogeosciences, 18, 4629–4650, https://doi.org/10.5194/bg-18-4629-2021 (2021).Wei, J. et al. N2O and NOx emissions by reactions of nitrite with soil organic matter of a Norway spruce forest. Biogeochemistry 132, 325–342 (2017).CAS 
Article 

Google Scholar 
Clough, T. J. et al. Influence of soil moisture on codenitrification fluxes from a urea-affected pasture soil. Sci. Rep. 7, 1–12 (2017).CAS 
Article 

Google Scholar 
Bai, E. & Houlton, B. Z. Coupled isotopic and process-based modeling of gaseous nitrogen losses from tropical rain forests. Glob. Biogeochemical Cycles 23, 1–10 (2009).
Google Scholar 
Wen, Y. et al. Disentangling gross N2O production and consumption in soil. Sci. Rep. 6, 1–8 (2016).Article 
CAS 

Google Scholar 
Zhang, Y., Liu, X. J., Fangmeier, A., Goulding, K. T. & Zhang, F. S. Nitrogen inputs and isotopes in precipitation in the North China Plain. Atmos. Environ. 42, 1436–1448 (2008).ADS 
CAS 
Article 

Google Scholar 
Unkovich, M. Isotope discrimination provides new insight into biological nitrogen fixation. N. Phytologist 198, 643–646 (2013).CAS 
Article 

Google Scholar 
Beyn, F., Matthias, V., Aulinger, A. & Dähnke, K. Do N-isotopes in atmospheric nitrate deposition reflect air pollution levels? Atmos. Environ. 107, 281–288 (2015).ADS 
CAS 
Article 

Google Scholar 
Vereecken, H. et al. Modeling Soil Processes: Review, Key challenges and New Perspectives. Vadose Zone J. 15, 1–57 (2016).CAS 

Google Scholar 
Lamarque, J. F. et al. Multi-model mean nitrogen and sulfur deposition from the atmospheric chemistry and climate model intercomparison project (ACCMIP): Evaluation of historical and projected future changes. Atmos. Chem. Phys. 13, 7997–8018 (2013).ADS 
Article 
CAS 

Google Scholar 
Schlesinger, W. H. On the fate of anthropogenic nitrogen. Proc. Natl Acad. Sci. USA 106, 203–208 (2009).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Kim, D. G., Hernandez-Ramirez, G. & Giltrap, D. Linear and nonlinear dependency of direct nitrous oxide emissions on fertilizer nitrogen input: A meta-analysis. Agriculture, Ecosyst. Environ. 168, 53–65 (2013).CAS 
Article 

Google Scholar 
Scheer, C. et al. Addressing nitrous oxide: An often ignored climate and ozone threat. Tech. Rep. (2019).Hu, H. W., Chen, D. & He, J. Z. Microbial regulation of terrestrial nitrous oxide formation: Understanding the biological pathways for prediction of emission rates. FEMS Microbiol. Rev. 39, 729–749 (2015).CAS 
PubMed 
Article 

Google Scholar 
Zaehle, S. Terrestrial nitrogen-carbon cycle interactions at the global scale. Philos. Trans. R. Soc. B: Biol. Sci. 368, 1–9 (2013).Jones, P. et al. Hemispheric and large-scale land surface air temperature variations: An extensive revision and an update to 2010. J. Geophys. Res. 117, D05127 (2012).ADS 

Google Scholar 
Olivier, J. & Berdowski, J. EDGAR 3.x by RIVM/TNO. In The Climate System (eds Berdowski, R. G. J. & Heij, B.) 33–77. (Swets and Zeitlinger Publishers, 2001).Crippa, M. et al. High resolution temporal profiles in the Emissions Database for Global Atmospheric Research. Sci. Data 7, 1–17 (2020).Article 

Google Scholar 
Bateman, A. S. & Kelly, S. D. Fertilizer nitrogen isotope signatures. Isotopes Environ. Health Stud. 43, 237–247 (2007).CAS 
PubMed 
Article 

Google Scholar 
Savard, M. M. et al. Nitrate isotopes unveil distinct seasonal N-sources and the critical role of crop residues in groundwater contamination. J. Hydrol. 381, 134–141 (2010).ADS 
CAS 
Article 

Google Scholar 
Bowman, K. P. & Cohen, P. J. Interhemispheric exchange by seasonal modulation of the Hadley circulation. J. Atmos. Sci. 54, 2045–2059 (1997).ADS 
Article 

Google Scholar 
Moseman-Valtierra, S. et al. Short-term nitrogen additions can shift a coastal wetland from a sink to a source of N2O. Atmos. Environ. 45, 4390–4397 (2011).ADS 
CAS 
Article 

Google Scholar 
Brase, L., Bange, H. W., Lendt, R., Sanders, T. & Dähnke, K. High Resolution Measurements of Nitrous Oxide (N2O) in the Elbe Estuary. Front. Mar. Sci. 4, 1–11 (2017).Article 

Google Scholar 
Wells, N. S. et al. Estuaries as Sources and Sinks of N2O Across a Land Use Gradient in Subtropical Australia. Glob. Biogeochemical Cycles 32, 877–894 (2018).ADS 
CAS 
Article 

Google Scholar 
Rayner, P. Data Assimilation using an ensemble of models: A hierarchical approach. Atmos. Chem. Phys. 20, 1–13 (2020).Article 
CAS 

Google Scholar 
Met Office. Cartopy: a cartographic python library with a Matplotlib interface (https://scitools.org.uk/cartopy), (2015). More

IPCC. Climate Change 2021: The Physical Science Basis. (eds Masson-Delmotte, V. et al.) Contribution of working group 1 to the ‘Sixth assessment report of the intergovernmental panel on climate change’ (Cambridge University Press, 2021).Huang, J., Yu, H., Guan, X., Wang, G. & Guo, R. Accelerated dryland expansion under climate change. Nat. Clim. Change 6, 166–171 (2016).Article 

Google Scholar 
Greve, P. et al. Global assessment of trends in wetting and drying over land. Nat. Geosc. 7, 716–721 (2014).CAS 
Article 

Google Scholar 
Lin, L., Gettelman, A., Feng, S. & Fu, Q. Simulated climatology and evolution of aridity in the 21st century. J. Geophys. Res. Atmos. 120, 5795–5815 (2015).Article 

Google Scholar 
Coumou, D. & Rahmstorf, S. A decade of weather extremes. Nat. Clim. Change 2, 491–496 (2012).Article 

Google Scholar 
Williams, A. P. et al. Large contribution from anthropogenic warming to an emerging North American megadrought. Science 368, 314–318 (2020).CAS 
PubMed 
Article 

Google Scholar 
Touma, D., Ashfaq, M., Nayak, M. A., Kao, S.-C. & Diffenbaugh, N. S. A multi-model and multi-index evaluation of drought characteristics in the 21st century. J. Hydrol. 526, 196–207 (2015).Article 

Google Scholar 
Liu, W. et al. Global drought and severe drought-affected populations in 1.5 and 2 °C warmer worlds. Earth Syst. Dyn. 9, 267–283 (2018).Article 

Google Scholar 
Ault, T. R. On the essentials of drought in a changing climate. Science 368, 256–260 (2020).CAS 
PubMed 
Article 

Google Scholar 
Swann, A. L., Hoffman, F. M., Koven, C. D. & Randerson, J. T. Plant responses to increasing CO2 reduce estimates of climate impacts on drought severity. Proc. Natl Acad. Sci. USA 113, 10019–10024 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Milly, P. C. D. & Dunne, K. A. Potential evapotranspiration and continental drying. Nat. Clim. Change 6, 946–949 (2016).Article 

Google Scholar 
Zhou, S. et al. Soil moisture–atmosphere feedbacks mitigate declining water availability in drylands. Nat. Clim. Change 11, 38–44 (2021).Article 

Google Scholar 
Musselman, K. N., Clark, M. P., Liu, C., Ikeda, K. & Rasmussen, R. Slower snowmelt in a warmer world. Nat. Clim. Change 7, 214–219 (2017).Article 

Google Scholar 
Harpold, A. A. et al. Soil moisture response to snowmelt timing in mixed-conifer subalpine forests. Hydrol. Process. 29, 2782–2798 (2015).Article 

Google Scholar 
Lesk, C., Rowhani, P. & Ramankutty, N. Influence of extreme weather disasters on global crop production. Nature 529, 84–87 (2016).CAS 
PubMed 
Article 

Google Scholar 
Choat, B. et al. Triggers of tree mortality under drought. Nature 558, 531–539 (2018).CAS 
PubMed 
Article 

Google Scholar 
Reichstein, M. et al. Climate extremes and the carbon cycle. Nature 500, 287–295 (2013).CAS 
PubMed 
Article 

Google Scholar 
Pecl, G. T. et al. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science 355, eaai9214 (2017).Song, J. et al. A meta-analysis of 1,119 manipulative experiments on terrestrial carbon-cycling responses to global change. Nat. Ecol. Evol. 3, 1309–1320 (2019).PubMed 
Article 

Google Scholar 
Parton, W. et al. Global-scale similarities in nitrogen release patterns during long-term decomposition. Science 315, 361–364 (2007).CAS 
PubMed 
Article 

Google Scholar 
Adair, E. C. et al. Simple three-pool model accurately describes patterns of long-term litter decomposition in diverse climates. Glob. Change Biol. 14, 2636–2660 (2008).Article 

Google Scholar 
Adair, E. C., Parton, W. J., King, J. Y., Brandt, L. A. & Lin, Y. Accounting for photodegradation dramatically improves prediction of carbon losses in dryland systems. Ecosphere 8, e01892 (2017).Article 

Google Scholar 
Chen, M. et al. Simulation of the effects of photodecay on long-term litter decay using DayCent. Ecosphere 7, e01631 (2016).
Google Scholar 
Asao, S., Parton, W. J., Chen, M. & Gao, W. Photodegradation accelerates ecosystem N cycling in a simulated California grassland. Ecosphere 9, e02370 (2018).Article 

Google Scholar 
Berdugo, M. et al. Global ecosystem thresholds driven by aridity. Science 367, 787–790 (2020).CAS 
PubMed 
Article 

Google Scholar 
Feng, S. & Fu, Q. Expansion of global drylands under a warming climate. Atmos. Chem. Phys. 13, 10081–10094 (2013).CAS 
Article 

Google Scholar 
Berg, A. & McColl, K. A. No projected global drylands expansion under greenhouse warming. Nat. Clim. Change 11, 331–337 (2021).Article 

Google Scholar 
Whitford, W. G. & Duval, B. D. Ecology of Desert Systems 2nd edn (Academic Press, 2020).Maestre, F. T. et al. Structure and functioning of dryland ecosystems in a changing world. Annu. Rev. Ecol. Evol. Syst. 47, 215–237 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Schimel, J. P. Life in dry soils: effects of drought on soil microbial communities and processes. Annu. Rev. Ecol. Evol. Syst. 49, 409–432 (2018).Article 

Google Scholar 
Nielsen, U. N. & Ball, B. A. Impacts of altered precipitation regimes on soil communities and biogeochemistry in arid and semi-arid ecosystems. Glob. Change Biol. 21, 1407–1421 (2015).Article 

Google Scholar 
Collins, S. L. et al. A multiscale, hierarchical model of pulse dynamics in arid-land ecosystems. Annu. Rev. Ecol. Evol. Syst. 45, 397–419 (2014).Article 

Google Scholar 
Kim, D.-G., Mu, S., Kang, S. & Lee, D. Factors controlling soil CO2 effluxes and the effects of rewetting on effluxes in adjacent deciduous, coniferous, and mixed forests in Korea. Soil Biol. Biochem. 42, 576–585 (2010).Article 
CAS 

Google Scholar 
Curiel Yuste, J., Janssens, I. A., Carrara, A., Meiresonne, L. & Ceulemans, R. Interactive effects of temperature and precipitation on soil respiration in a temperate maritime pine forest. Tree Physiol. 23, 1263–1270 (2003).CAS 
PubMed 
Article 

Google Scholar 
Savage, K., Davidson, E. A., Richardson, A. D. & Hollinger, D. Y. Three scales of temporal resolution from automated soil respiration measurements. Agric. Meteorol. 149, 2012–2021 (2009).Article 

Google Scholar 
Hao, Y., Wang, Y., Mei, X. & Cui, X. The response of ecosystem CO2 exchange to small precipitation pulses over a temperate steppe. Plant Ecol. 209, 335–347 (2010).Article 

Google Scholar 
Krüger, J. P., Beckedahl, H., Gerold, G. & Jungkunst, H. F. Greenhouse gas emission peaks following natural rewetting of two wetlands in the southern Ukhahlamba-Drakensberg Park, South Africa. S. Afr. Geogr. J. 96, 113–118 (2013).Article 

Google Scholar 
Haverd, V., Ahlström, A., Smith, B. & Canadell, J. G. Carbon cycle responses of semi-arid ecosystems to positive asymmetry in rainfall. Glob. Change Biol. 23, 793–800 (2017).Article 

Google Scholar 
Kim, D. G., Vargas, R., Bond-Lamberty, B. & Turetsky, M. R. Effects of soil rewetting and thawing on soil gas fluxes: a review of current literature and suggestions for future research. Biogeosciences 9, 2459–2483 (2012).CAS 
Article 

Google Scholar 
Barnard, R. L., Blazewicz, S. J. & Firestone, M. K. Rewetting of soil: revisiting the origin of soil CO2 emissions. Soil Biol. Biochem. 147, 107819 (2020).Prieto, I., Armas, C. & Pugnaire, F. I. Water release through plant roots: new insights into its consequences at the plant and ecosystem level. New Phytol. 193, 830–841 (2012).PubMed 
Article 

Google Scholar 
Neumann, R. B. & Cardon, Z. G. The magnitude of hydraulic redistribution by plant roots: a review and synthesis of empirical and modeling studies. New Phytol. 194, 337–352 (2012).PubMed 
Article 

Google Scholar 
Mooney, H. A., Gulmon, S. L., Rundel, P. W. & Ehleringer, J. Further observations on the water relations of Prosopis tamarugo of the northern Atacama desert. Oecologia 44, 177–180 (1980).CAS 
PubMed 
Article 

Google Scholar 
Richards, J. H. & Caldwell, M. M. Hydraulic lift: substantial nocturnal water transport between soil layers by Artemisia tridentata roots. Oecologia 73, 486–489 (1987).CAS 
PubMed 
Article 

Google Scholar 
Caldwell, M. M., Dawson, T. E. & Richards, J. H. Hydraulic lift: consequences of water efflux from the roots of plants. Oecologia 113, 151–161 (1998).PubMed 
Article 

Google Scholar 
Brooks, J. R., Meinzer, F. C., Coulombe, R. & Gregg, J. Hydraulic redistribution of soil water during summer drought in two contrasting Pacific Northwest coniferous forests. Tree Physiol. 22, 1107–1117 (2002).PubMed 
Article 

Google Scholar 
Lee, J. E., Oliveira, R. S., Dawson, T. E. & Fung, I. Root functioning modifies seasonal climate. Proc. Natl Acad. Sci. USA 102, 17576–17581 (2005).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Robinson, J. L., Slater, L. D. & Schäfer, K. V. R. Evidence for spatial variability in hydraulic redistribution within an oak–pine forest from resistivity imaging. J. Hydrol. 430-431, 69–79 (2012).Article 

Google Scholar 
Oliveira, R. S., Dawson, T. E., Burgess, S. S. O. & Nepstad, D. C. Hydraulic redistribution in three Amazonian trees. Oecologia 145, 354–363 (2005).PubMed 
Article 

Google Scholar 
Zapater, M. et al. Evidence of hydraulic lift in a young beech and oak mixed forest using 18O soil water labelling. Trees 25, 885–894 (2011).Article 

Google Scholar 
Sardans, J. & Peñuelas, J. Hydraulic redistribution by plants and nutrient stoichiometry: shifts under global change. Ecohydrology 7, 1–20 (2014).Article 

Google Scholar 
Schenk, H. J. & Jackson, R. B. Rooting depths, lateral root spreads and below‐ground/above‐ground allometries of plants in water‐limited ecosystems. J. Ecol. 90, 480–494 (2002).Article 

Google Scholar 
Choat, B. et al. Global convergence in the vulnerability of forests to drought. Nature 491, 752–755 (2012).CAS 
PubMed 
Article 

Google Scholar 
Wang, L., Kaseke, K. F. & Seely, M. K. Effects of non-rainfall water inputs on ecosystem functions. WIREs Water 4, e1179 (2017).
Google Scholar 
Dawson, T. E. & Goldsmith, G. R. The value of wet leaves. New Phytol. 219, 1156–1169 (2018).PubMed 
Article 

Google Scholar 
Agam, N. & Berliner, P. R. Dew formation and water vapor adsorption in semi-arid environments – a review. J. Arid. Environ. 65, 572–590 (2006).Article 

Google Scholar 
Dirks, I., Navon, Y., Kanas, D., Dumbur, R. & Grünzweig, J. M. Atmospheric water vapor as driver of litter decomposition in Mediterranean shrubland and grassland during rainless seasons. Glob. Change Biol. 16, 2799–2812 (2010).Article 

Google Scholar 
Jacobson, K. et al. Non-rainfall moisture activates fungal decomposition of surface litter in the Namib Sand Sea. PLoS ONE 10, e0126977 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
McHugh, T. A., Morrissey, E. M., Reed, S. C., Hungate, B. A. & Schwartz, E. Water from air: an overlooked source of moisture in arid and semiarid regions. Sci. Rep. 5, 13767 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Gliksman, D. et al. Biotic degradation at night, abiotic degradation at day: positive feedbacks on litter decomposition in drylands. Glob. Change Biol. 23, 1564–1574 (2017).Article 

Google Scholar 
Goldsmith, G. R., Matzke, N. J. & Dawson, T. E. The incidence and implications of clouds for cloud forest plant water relations. Ecol. Lett. 16, 307–314 (2013).PubMed 
Article 

Google Scholar 
Binks, O. et al. Foliar water uptake in Amazonian trees: evidence and consequences. Glob. Change Biol. 25, 2678–2690 (2019).Article 

Google Scholar 
Benzing, D. H. Vulnerabilities of tropical forests to climate change: the significance of resident epiphytes. Clim. Change 39, 519–540 (1998).Article 

Google Scholar 
Evans, S., Todd-Brown, K. E. O., Jacobson, K. & Jacobson, P. Non-rainfall moisture: a key driver of microbial respiration from standing litter in arid, semiarid, and mesic grasslands. Ecosystems 23, 1154–1169 (2020).CAS 
Article 

Google Scholar 
Newell, S. Y., Fallon, R. D., Rodriguez, R. M. C. & Groene, L. C. Influence of rain, tidal wetting and relative-humidity on release of carbon-dioxide by standing-dead salt-marsh plants. Oecologia 68, 73–79 (1985).CAS 
PubMed 
Article 

Google Scholar 
Kuehn, K. A., Steiner, D. & Gessner, M. O. Diel mineralization patterns of standing-dead plant litter: implications for CO2 flux from wetlands. Ecology 85, 2504–2518 (2004).Article 

Google Scholar 
Doerr, S. H., Shakesby, R. A. & Walsh, R. P. D. Soil water repellency: its causes, characteristics and hydro-geomorphological significance. Earth Sci. Rev. 51, 33–65 (2000).Article 

Google Scholar 
Goebel, M.-O., Bachmann, J., Reichstein, M., Janssens, I. A. & Guggenberger, G. Soil water repellency and its implications for organic matter decomposition – is there a link to extreme climatic events? Glob. Change Biol. 17, 2640–26596 (2011).Article 

Google Scholar 
Mao, J., Nierop, K. G. J., Dekker, S. C., Dekker, L. W. & Chen, B. Understanding the mechanisms of soil water repellency from nanoscale to ecosystem scale: a review. J. Soils Sediments 19, 171–185 (2019).Article 

Google Scholar 
Doerr, S. H., Shakesby, R. A., Dekker, L. W. & Ritsema, C. J. Occurrence, prediction and hydrological effects of water repellency amongst major soil and land-use types in a humid temperate climate. Eur. J. Soil Sci. 57, 741–754 (2006).Article 

Google Scholar 
Lebron, I., Robinson, D. A., Oatham, M. & Wuddivira, M. N. Soil water repellency and pH soil change under tropical pine plantations compared with native tropical forest. J. Hydrol. 414-415, 194–200 (2012).CAS 
Article 

Google Scholar 
Buczko, U., Bens, O. & Hüttl, R. F. Variability of soil water repellency in sandy forest soils with different stand structure under Scots pine (Pinus sylvestris) and beech (Fagus sylvatica). Geoderma 126, 317–336 (2005).Article 

Google Scholar 
Dekker, L. W. & Ritsema, C. J. Variation in water content and wetting patterns in Dutch water repellent peaty clay and clayey peat soils. CATENA 28, 89–105 (1996).CAS 
Article 

Google Scholar 
de Blas, E., Almendros, G. & Sanz, J. Molecular characterization of lipid fractions from extremely water-repellent pine and eucalyptus forest soils. Geoderma 206, 75–84 (2013).Article 
CAS 

Google Scholar 
MacDonald, L. H. & Huffman, E. L. Post-fire soil water repellency. Soil Sci. Soc. Am. J. 68, 1729–1734 (2004).CAS 
Article 

Google Scholar 
Hewelke, E. et al. Intensity and persistence of soil water repellency in pine forest soil in a temperate continental climate under drought conditions. Water 10, 1121 (2018).Article 
CAS 

Google Scholar 
Borken, W. & Matzner, E. Reappraisal of drying and wetting effects on C and N mineralization and fluxes in soils. Glob. Change Biol. 15, 808–824 (2009).Article 

Google Scholar 
Siteur, K. et al. Soil water repellency: a potential driver of vegetation dynamics in coastal dunes. Ecosystems 19, 1210–1224 (2016).CAS 
Article 

Google Scholar 
Austin, A. T. & Vivanco, L. Plant litter decomposition in a semi-arid ecosystem controlled by photodegradation. Nature 442, 555–558 (2006).CAS 
PubMed 
Article 

Google Scholar 
King, J. Y., Brandt, L. A. & Adair, E. C. Shedding light on plant litter decomposition: advances, implications and new directions in understanding the role of photodegradation. Biogeochemistry 111, 57–81 (2012).Article 

Google Scholar 
Moorhead, D. L. & Callaghan, T. Effects of increasing ultraviolet B radiation on decomposition and soil organic matter dynamics: a synthesis and modelling study. Biol. Fertil. Soils 18, 19–26 (1994).CAS 
Article 

Google Scholar 
Sulzberger, B., Austin, A. T., Cory, R. M., Zepp, R. G. & Paul, N. D. Solar UV radiation in a changing world: roles of cryosphere-land-water-atmosphere interfaces in global biogeochemical cycles. Photochem. Photobiol. Sci. 18, 747–774 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Austin, A. T., Mendez, M. S. & Ballaré, C. L. Photodegradation alleviates the lignin bottleneck for carbon turnover in terrestrial ecosystems. Proc. Natl Acad. Sci. USA 113, 4392–4397 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Brandt, L. A., King, J. Y., Hobbie, S. E., Milchunas, D. G. & Sinsabaugh, R. L. The role of photodegradation in surface litter decomposition across a grassland ecosystem precipitation gradient. Ecosystems 13, 765–781 (2010).CAS 
Article 

Google Scholar 
Pieristè, M. et al. Solar UV-A radiation and blue light enhance tree leaf litter decomposition in a temperate forest. Oecologia 191, 191–203 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Wu, C. et al. Photodegradation accelerates coarse woody debris decomposition in subtropical Chinese forests. For. Ecol. Manage. 409, 225–232 (2018).Article 

Google Scholar 
Marinho, O. A., Martinelli, L. A., Duarte-Neto, P. J. R., Mazzi, E. A. & King, J. Y. Photodegradation influences litter decomposition rate in a humid tropical ecosystem, Brazil. Sci. Total Environ. 715, 136601 (2020).CAS 
PubMed 
Article 

Google Scholar 
Wang, Q. W. et al. The contribution of photodegradation to litter decomposition in a temperate forest gap and understorey. New Phytol. 229, 2625–2636 (2021).CAS 
PubMed 
Article 

Google Scholar 
Rutledge, S., Campbell, D. I., Baldocchi, D. & Schipper, L. A. Photodegradation leads to increased carbon dioxide losses from terrestrial organic matter. Glob. Change Biol. 16, 3065–3074 (2010).
Google Scholar 
Williamson, C. E. et al. Solar ultraviolet radiation in a changing climate. Nat. Clim. Change 4, 434–441 (2014).Article 

Google Scholar 
Zepp, R. G., Erickson, D. J. III, Paul, N. D. & Sulzberger, B. Effects of solar UV radiation and climate change on biogeochemical cycling: interactions and feedbacks. Photochem. Photobiol. Sci. 10, 261–271 (2011).CAS 
PubMed 
Article 

Google Scholar 
Austin, A. Has water limited our imagination for aridland biogeochemistry? Trends Ecol. Evol. 26, 229–235 (2011).PubMed 
Article 

Google Scholar 
McCalley, C. K. & Sparks, J. P. Abiotic gas formation drives nitrogen loss from a desert ecosystem. Science 326, 837–840 (2009).CAS 
PubMed 
Article 

Google Scholar 
Lee, H., Rahn, T. & Throop, H. L. An accounting of C-based trace gas release during abiotic plant litter degradation. Glob. Change Biol. 18, 1185–1195 (2012).Article 

Google Scholar 
Wang, B., Lerdau, M. & He, Y. Widespread production of nonmicrobial greenhouse gases in soils. Glob. Change Biol. 23, 4472–4482 (2017).Article 

Google Scholar 
Soper, F. M., McCalley, C. K., Sparks, K. & Sparks, J. P. Soil carbon dioxide emissions from the Mojave desert: isotopic evidence for a carbonate source. Geophys. Res. Lett. 44, 245–251 (2017).CAS 
Article 

Google Scholar 
Day, T. A. & Bliss, M. S. Solar photochemical emission of CO2 from leaf litter: sources and significance to C loss. Ecosystems 23, 1344–1361 (2020).CAS 
Article 

Google Scholar 
Throop, H. L. & Belnap, J. Connectivity dynamics in dryland litter cycles: moving decomposition beyond spatial stasis. Bioscience 69, 602–614 (2019).Article 

Google Scholar 
Throop, H. L. & Archer, S. R. Resolving the dryland decomposition conundrum: some new perspectives on potential drivers. Prog. Bot. 70, 171–194 (2009).CAS 

Google Scholar 
Barnes, P. W. et al. in Progress in Botany Vol. 76 (eds Lüttge, U. & Beyschlag, W.) 273–302 (Springer, 2015).Barnes, P. W., Throop, H. L., Hewins, D. B., Abbene, M. L. & Archer, S. R. Soil coverage reduces photodegradation and promotes the development of soil-microbial films on dryland leaf litter. Ecosystems 15, 311–321 (2012).CAS 
Article 

Google Scholar 
Joly, F. X., Kurupas, K. L. & Throop, H. L. Pulse frequency and soil-litter mixing alter the control of cumulative precipitation over litter decomposition. Ecology 98, 2255–2260 (2017).PubMed 
Article 

Google Scholar 
Weber, B., Büdel, B. & Belnap, J. Biological Soil Crusts: An Organizing Principle in Drylands Vol. 226 (Springer, 2016).Belnap, J. & Lange, O. L. Biological Soil Crusts: Structure, Function, and Management (Springer, 2001).Ferrenberg, S., Tucker, C. L. & Reed, S. C. Biological soil crusts: diminutive communities of potential global importance. Front. Ecol. Environ. 15, 160–167 (2017).Article 

Google Scholar 
Belnap, J. The world at your feet: desert biological soil crusts. Front. Ecol. Environ. 1, 181–189 (2003).Article 

Google Scholar 
Rodríguez-Caballero, E. et al. Dryland photoautotrophic soil surface communities endangered by global change. Nat. Geosci. 11, 185–189 (2018).Article 
CAS 

Google Scholar 
Hawkes, C. V. & Flechtner, V. R. Biological soil crusts in a xeric Florida shrubland: composition, abundance, and spatial heterogeneity of crusts with different disturbance histories. Microb. Ecol. 43, 1–12 (2002).CAS 
PubMed 
Article 

Google Scholar 
Langhans, T. M., Storm, C. & Schwabe, A. Community assembly of biological soil crusts of different successional stages in a temperate sand ecosystem, as assessed by direct determination and enrichment techniques. Microb. Ecol. 58, 394–407 (2009).PubMed 
Article 

Google Scholar 
Veluci, R. M., Neher, D. A. & Weicht, T. R. Nitrogen fixation and leaching of biological soil crust communities in mesic temperate soils. Microb. Ecol. 51, 189–196 (2006).CAS 
PubMed 
Article 

Google Scholar 
Cabała, J. & Rahmonov, O. Cyanophyta and algae as an important component of biological crust from the Pustynia Błędowska Desert (Poland). Pol. Bot. J. 49, 93–100 (2004).
Google Scholar 
Thiet, R. K., Boerner, R. E. J., Nagy, M. & Jardine, R. The effect of biological soil crusts on throughput of rainwater and N into Lake Michigan sand dune soils. Plant Soil 278, 235–251 (2005).CAS 
Article 

Google Scholar 
Jentsch, A. & Beyschlag, W. Vegetation ecology of dry acidic grasslands in the lowland area of Central Europe. Flora 198, 3–25 (2003).Article 

Google Scholar 
Dümig, A. et al. Organic matter from biological soil crusts induces the initial formation of sandy temperate soils. CATENA 122, 196–208 (2014).Article 
CAS 

Google Scholar 
Chamizo, S., Cantón, Y., Rodríguez-Caballero, E. & Domingo, F. Biocrusts positively affect the soil water balance in semiarid ecosystems. Ecohydrology 9, 1208–1221 (2016).Article 

Google Scholar 
Couradeau, E. et al. Bacteria increase arid-land soil surface temperature through the production of sunscreens. Nat. Commun. 7, 10373 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Eldridge, D. J. & Greene, R. S. B. Microbiotic soil crusts: a review of their roles in soil and ecological processes in the rangelands of Australia. Aust. J. Soil Res. 32, 389–415 (1994).Article 

Google Scholar 
Elbert, W. et al. Contribution of cryptogamic covers to the global cycles of carbon and nitrogen. Nat. Geosci. 5, 459–462 (2012).CAS 
Article 

Google Scholar 
Delgado-Baquerizo, M., Maestre, F. T., Rodríguez, J. G. P. & Gallardo, A. Biological soil crusts promote N accumulation in response to dew events in dryland soils. Soil Biol. Biochem. 62, 22–27 (2013).CAS 
Article 

Google Scholar 
Meron, E. From patterns to function in living systems: dryland ecosystems as a case study. Annu. Rev. Condens. Matter Phys. 9, 79–103 (2018).Article 

Google Scholar 
Rietkerk, M. et al. Self-organization of vegetation in arid ecosystems. Am. Nat. 160, 524–530 (2002).PubMed 
Article 

Google Scholar 
Meron, E. Vegetation pattern formation: the mechanisms behind the forms. Phys. Today 72, 30–36 (2019).Article 

Google Scholar 
Gandhi, P., Iams, S., Bonetti, S. & Silber, M. in Dryland Ecohydrology 2nd edn (eds D’Odorico, P. et al.) 469–509 (Springer, 2019).Rietkerk, M., Dekker, S. C., de Ruiter, P. C. & van de Koppel, J. Self-organized patchiness and catastrophic shifts in ecosystems. Science 305, 1926–1929 (2004).CAS 
PubMed 
Article 

Google Scholar 
Lejeune, O., Tlidi, M. & Couteron, P. Localized vegetation patches: a self-organized response to resource scarcity. Phys. Rev. E 66, 010901 (2002).CAS 
Article 

Google Scholar 
Belyea, L. R. & Lancaster, J. Inferring landscape dynamics of bog pools from scaling relationships and spatial patterns. J. Ecol. 90, 223–234 (2002).Article 

Google Scholar 
Eppinga, M. B. et al. Regular surface patterning of peatlands: confronting theory with field data. Ecosystems 11, 520–536 (2008).CAS 
Article 

Google Scholar 
Hiemstra, C. A., Liston, G. E. & Reiners, W. A. Observing, modelling, and validating snow redistribution by wind in a Wyoming upper treeline landscape. Ecol. Modell. 197, 35–51 (2006).Article 

Google Scholar 
Crain, C. M. & Bertness, M. D. Community impacts of a tussock sedge: is ecosystem engineering important in benign habitats? Ecology 86, 2695–2704 (2005).Article 

Google Scholar 
Stanton, D. E., Armesto, J. J. & Hedin, L. O. Ecosystem properties self-organize in response to a directional fog-vegetation interaction. Ecology 95, 1203–1212 (2014).PubMed 
Article 

Google Scholar 
van de Koppel, J., van der Wal, D., Bakker, J. P. & Herman, P. M. Self-organization and vegetation collapse in salt marsh ecosystems. Am. Nat. 165, E1–E12 (2005).PubMed 
Article 

Google Scholar 
Rietkerk, M. & van de Koppel, J. Regular pattern formation in real ecosystems. Trends Ecol. Evol. 23, 169–175 (2008).PubMed 
Article 

Google Scholar 
Aguiar, M. R. & Sala, O. E. Patch structure, dynamics and implications for the functioning of arid ecosystems. Trends Ecol. Evol. 14, 273–277 (1999).CAS 
PubMed 
Article 

Google Scholar 
Bera, B. K., Tzuk, O., Bennett, J. J. & Meron, E. Linking spatial self-organization to community assembly and biodiversity. eLife 10, e73819 (2021).Garcia-Moya, E. & McKell, C. M. Contribution of shrubs to the nitrogen economy of a desert-wash plant community. Ecology 51, 81–88 (1970).Article 

Google Scholar 
Peters, D. P. C. et al. Disentangling complex landscapes: new insights into arid and semiarid system dynamics. Bioscience 56, 491–501 (2006).Article 

Google Scholar 
Okin, G. S. et al. Connectivity in dryland landscapes: shifting concepts of spatial interactions. Front. Ecol. Environ. 13, 20–27 (2015).Article 

Google Scholar 
Ludwig, J. A., Wilcox, B. P., Breshears, D. D., Tongway, D. J. & Imeson, A. C. Vegetation patches and runoff–erosion as interacting ecohydrological processes in semiarid landscapes. Ecology 86, 288–297 (2005).Article 

Google Scholar 
Fahnestock, J. T., Povirk, K. L. & Welker, J. M. Ecological significance of litter redistribution by wind and snow in Arctic landscapes. Ecography 23, 623–631 (2000).Article 

Google Scholar 
Schlesinger, W. H. et al. Biological feedbacks in global desertification. Science 247, 1043–1048 (1990).CAS 
PubMed 
Article 

Google Scholar 
Okin, G. S., Sala, O. E., Vivoni, E. R., Zhang, J. & Bhattachan, A. The interactive role of wind and water in functioning of drylands: what does the future hold? Bioscience 68, 670–677 (2018).Article 

Google Scholar 
Finzi, A. C. et al. Responses and feedbacks of coupled biogeochemical cycles to climate change: examples from terrestrial ecosystems. Front. Ecol. Environ. 9, 61–67 (2011).Article 

Google Scholar 
Yuan, Z. Y. et al. Experimental and observational studies find contrasting responses of soil nutrients to climate change. eLife 6, e23255 (2017).Delgado-Baquerizo, M. et al. Decoupling of soil nutrient cycles as a function of aridity in global drylands. Nature 502, 672–676 (2013).CAS 
PubMed 
Article 

Google Scholar 
Jiao, F., Shi, X. R., Han, F. P. & Yuan, Z. Y. Increasing aridity, temperature and soil pH induce soil C-N-P imbalance in grasslands. Sci. Rep. 6, 19601 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wang, X.-G. et al. Changes in soil C:N:P stoichiometry along an aridity gradient in drylands of northern China. Geoderma 361, 114087 (2020).CAS 
Article 

Google Scholar 
Mulder, C. et al. Connecting the green and brown worlds: allometric and stoichiometric predictability of above- and below-ground networks. Adv. Ecol. Res. 49, 69–175 (2013).Article 

Google Scholar 
Yuan, Z. Y. & Chen, H. Y. H. Decoupling of nitrogen and phosphorus in terrestrial plants associated with global changes. Nat. Clim. Change 5, 465–469 (2015).CAS 
Article 

Google Scholar 
Rotenberg, E. & Yakir, D. Contribution of semi-arid forests to the climate system. Science 327, 451–454 (2010).CAS 
PubMed 
Article 

Google Scholar 
Banerjee, T., De Roo, F. & Mauder, M. Explaining the convector effect in canopy turbulence by means of large-eddy simulation. Hydrol. Earth Syst. Sci. 21, 2987–3000 (2017).Article 

Google Scholar 
Teuling, A. J. et al. Contrasting response of European forest and grassland energy exchange to heatwaves. Nat. Geosci. 3, 722–727 (2010).CAS 
Article 

Google Scholar 
Alkama, R. & Cescatti, A. Biophysical climate impacts of recent changes in global forest cover. Science 351, 600–604 (2016).CAS 
PubMed 
Article 

Google Scholar 
Zellweger, F. et al. Forest microclimate dynamics drive plant responses to warming. Science 368, 772–775 (2020).CAS 
PubMed 
Article 

Google Scholar 
Chen, C. et al. China and India lead in greening of the world through land-use management. Nat. Sustain. 2, 122–129 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Huang, K. et al. Enhanced peak growth of global vegetation and its key mechanisms. Nat. Ecol. Evol. 2, 1897–1905 (2018).De Jong, R., Verbesselt, J., Schaepman, M. E. & De Bruin, S. Trend changes in global greening and browning: contribution of short-term trends to longer-term change. Glob. Change Biol. 18, 642–655 (2012).Article 

Google Scholar 
Pan, N. et al. Increasing global vegetation browning hidden in overall vegetation greening: insights from time-varying trends. Remote Sens. Environ. 214, 59–72 (2018).Article 

Google Scholar 
Mueller, T. et al. Human land-use practices lead to global long-term increases in photosynthetic capacity. Remote Sens. 6, 5717–5731 (2014).Article 

Google Scholar 
Beck, P. S. A. et al. Changes in forest productivity across Alaska consistent with biome shift. Ecol. Lett. 14, 373–379 (2011).PubMed 
Article 

Google Scholar 
Myers-Smith, I. H. et al. Complexity revealed in the greening of the Arctic. Nat. Clim. Change 10, 106–117 (2020).Article 

Google Scholar 
Aguirre-Gutiérrez, J. et al. Drier tropical forests are susceptible to functional changes in response to a long-term drought. Ecol. Lett. 22, 855–865 (2019).PubMed 
Article 

Google Scholar 
Peñuelas, J. et al. Shifting from a fertilization-dominated to a warming-dominated period. Nat. Ecol. Evol. 1, 1438–1445 (2017).PubMed 
Article 

Google Scholar 
Stocker, B. D. et al. Drought impacts on terrestrial primary production underestimated by satellite monitoring. Nat. Geosci. 12, 264–270 (2019).CAS 
Article 

Google Scholar 
Berg, A., Sheffield, J. & Milly, P. C. D. Divergent surface and total soil moisture projections under global warming. Geophys. Res. Lett. 44, 236–244 (2017).Article 

Google Scholar 
Davenport, D. W., Breshears, D. D., Wilcox, B. P. & Allen, C. D. Viewpoint: sustainability of piñon-juniper ecosystems – a unifying perspective of soil erosion thresholds. J. Range Manage. 51, 231 (1998).Article 

Google Scholar 
Briske, D. D., Fuhlendorf, S. D. & Smeins, F. E. A unified framework for assessment and application of ecological thresholds. Rangel. Ecol. Manage. 59, 225–236 (2006).Article 

Google Scholar 
Kayler, Z. E. et al. Experiments to confront the environmental extremes of climate change. Front. Ecol. Environ. 13, 219–225 (2015).Article 

Google Scholar 
Haase, P. et al. The next generation of site-based long-term ecological monitoring: linking essential biodiversity variables and ecosystem integrity. Sci. Total Environ. 613–614, 1376–1384 (2018).PubMed 
Article 
CAS 

Google Scholar 
Halbritter, A. H. et al. The handbook for standardised field and laboratory measurements in terrestrial climate‐change experiments and observational studies (ClimEx). Methods Ecol. Evol. 11, 22–37 (2020).Article 

Google Scholar 
De Boeck, H. J. et al. Global change experiments: challenges and opportunities. Bioscience 65, 922–931 (2015).Article 

Google Scholar 
Kreyling, J. et al. To replicate, or not to replicate – that is the question: how to tackle nonlinear responses in ecological experiments. Ecol. Lett. 21, 1629–1638 (2018).De Boeck, H. J. et al. Understanding ecosystems of the future will require more than realistic climate change experiments – a response to Korell et al. Glob. Change Biol. 26, e6–e7 (2020).Article 

Google Scholar 
Hanson, P. J. & Walker, A. P. Advancing global change biology through experimental manipulations: where have we been and where might we go? Glob. Change Biol. 26, 287–299 (2020).Article 

Google Scholar 
Paschalis, A. et al. Rainfall manipulation experiments as simulated by terrestrial biosphere models: where do we stand? Glob. Change Biol. 26, 3336–3355 (2020).Article 

Google Scholar 
Scheffer, M., Carpenter, S. R., Foley, J. A., Folke, C. & Walker, B. Catastrophic shifts in ecosystems. Nature 413, 591–596 (2001).CAS 
PubMed 
Article 

Google Scholar 
Diaz, S. et al. Assessing nature’s contributions to people. Science 359, 270–272 (2018).CAS 
PubMed 
Article 

Google Scholar 
Thonicke, K. et al. Advancing the understanding of adaptive capacity of social‐ecological systems to absorb climate extremes. Earths Future 8, e2019EF001221 (2020). More

Basic information of intervieweesResults of the descriptive analysis summarized in Table 2 show that more than half of the respondents were males (69%) and were on average 41.3 years old while more than 32 years of farming experience. The study area is comprised of multiple ethnic groups (Han, Tibetan, Yugur, Mongolian, Hui, etc.). In most cases, the main livelihood activity of the Ethnic Minorities (Tibetan, Yugur, Mongolian, Hui, etc.) is livestock, while Han people main livelihood activity is farming. The majority of respondents (64%) were minority nationality. The vast majority of the agro-pastoralists (86%) have a primary school education or above, even though only 1% of them have Undergraduate education or Above. The results also reveal that 92% of respondents have access to weather information. The average cultivated land Per household is 10.23 Mu and Grassland is 156.21 Mu, respectively. The average per household income is RMB78000, and agricultural income is RMB52000.Table 2 Descriptive statistics of agro-pastoralist characteristics.Full size tableDue to their long-term farming experience, the agro-pastoralists were expected to have a high-level of understanding of local climate knowledge. Also contributing to this could be the information they receive about climate change and for some, the associated training through agro-pastoralists’ associations. Therefore, they also have a propensity to adapt to adverse conditions resulting from climate change impacts. In addition, the high-level of farming experience, the cultivated-land size, grassland size, Credit loan, Insurance, Village cadres all have a positive impact on the level of agro-pastoralists’ adaptation to new climate scenarios.However, the education level and cadres experience may be the major limiting factors for adopting specific long-term adaptation strategies. Ethnicity and gender are also expected to be key factors influencing awareness and adaptation to climate change. There are differences in relative perception intensity between Ethnic Minority and Han because of their cultural ecology (the main livelihood activity of minorities nationality is livestock, while Han main livelihood activity is farming.). In terms of gender, women in rural areas are less mobile and have less access to information and rights. They are also heavily involved in domestic work. However, men may have easier access to information (socializing, going out to work, etc.) Therefore, male headed households are expected to be more likely to adapt to the impact of climate change.Climate change trend in the study areaFigure 2 shows the trend of annual precipitation, annual rainfall and annual snow at different meteorological stations in the study area. As shown in the Fig. 2, precipitation, rainfall and snow show an increasing trend, but the increase range of snow (0.0325–0.375/a) is significantly lower than that of precipitation (1.22–3.1/a) and rainfall (1.04–2.81/a). Similarly, through the inspection, it is found that the multi-collinearity among precipitation, rainfall and snow at each meteorological station is obvious (most R2  > 0.5, and p  More

Chen, L. et al. Geographic variation in traits of fruit stones and seeds of Melia azedarach. J. Beijing For. Univ. 36, 15–20 (2014).CAS 

Google Scholar 
Angamuthu, D., Purushothaman, I., Kothandan, S. & Swaminathan, R. Antiviral study on Punica granatum L., Momordica charantia L., Andrographis paniculata Nees, and Melia azedarach L., to human herpes virus-3. Eur. J. Integr. Med. 28, 98–108. https://doi.org/10.1016/j.eujim.2019.04.008 (2019).Article 

Google Scholar 
Wang, N. et al. Selective ERK1/2 agonists isolated from Melia azedarach with potent anti-leukemic activity. BMC Cancer 19, 1–9. https://doi.org/10.1186/s12885-019-5914-8 (2019).CAS 
Article 

Google Scholar 
Khoshraftar, Z., Safekordi, A., Shamel, A. & Zaefizadeh, M. Evaluation of insecticidal activity of nanoformulation of Melia azedarach (leaf) extract as a safe environmental insecticide. Int. J. Environ. Sci. Technol. 17, 1159–1170. https://doi.org/10.1007/s13762-019-02448-7 (2020).CAS 
Article 

Google Scholar 
Sivaraj, I., Nithaniyal, S., Bhooma, V., Senthilkumar, U. & Parani, M. Species delimitation of Melia dubia Cav. from Melia azedarach L. complex based on DNA barcoding. Botany 96, 329–336. https://doi.org/10.1139/cjb-2017-0148 (2018).CAS 
Article 

Google Scholar 
Liao, B. et al. Population structure and genetic relationships of Melia Taxa in China assayed with sequence-related amplified polymorphism (SRAP) markers. Forests 7, 81. https://doi.org/10.3390/f7040081 (2016).Article 

Google Scholar 
Wu, L., Kaewmano, A., Fu, P., Wang, W. & Fan, Z. Intra-annual radial growth of Melia azedarach in a tropical moist seasonal forest and its response to environmental factors in Xishuangbanna Southwest China. Acta Ecol. Sin. 40, 6831–6840. https://doi.org/10.5846/stxb202003120508 (2020).Article 

Google Scholar 
Hoegh-Guldberg, O. et al. The human imperative of stabilizing global climate change at 1.5 C. Science 365, eaaw6974. https://doi.org/10.1126/science.aaw6974 (2019).CAS 
Article 
PubMed 

Google Scholar 
López-Tirado, J., Vessella, F., Schirone, B. & Hidalgo, P. J. Trends in evergreen oak suitability from assembled species distribution models: Assessing climate change in south-western Europe. New For. 49, 471–487. https://doi.org/10.1007/s11056-018-9629-5 (2018).Article 

Google Scholar 
Xu, Y. et al. Modelling the effects of climate change on the distribution of endangered Cypripedium japonicum in China. Forests 12, 429. https://doi.org/10.3390/f12040429 (2021).Article 

Google Scholar 
Booth, T. H. Species distribution modelling tools and databases to assist managing forests under climate change. For. Ecol. Manag. 430, 196–203. https://doi.org/10.1016/j.foreco.2018.08.019 (2018).Article 

Google Scholar 
Dyderski, M. K., Paź, S., Frelich, L. E. & Jagodziński, A. M. How much does climate change threaten European forest tree species distributions?. Glob. Change Biol. 24, 1150–1163. https://doi.org/10.1111/gcb.13925 (2018).ADS 
Article 

Google Scholar 
Zhong, Y. et al. A generalized linear mixed model approach to assess emerald ash Borer diffusion. ISPRS Int. J. Geo Inf. 9, 414. https://doi.org/10.3390/ijgi9070414 (2020).Article 

Google Scholar 
Chang, Z., Meng, J., Shi, Y. & Mo, F. Lnc RNA recognition by fusing multiple features and its function prediction. CAAI Trans. Intell. Syst. 13, 928–934. https://doi.org/10.11992/tis.201806008 (2018).Article 

Google Scholar 
Shiferaw, H., Bewket, W. & Eckert, S. Performances of machine learning algorithms for mapping fractional cover of an invasive plant species in a dryland ecosystem. Ecol. Evol. 9, 2562–2574. https://doi.org/10.1002/ece3.4919 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Tang, X., Yuan, Y., Li, X. & Zhang, J. Maximum entropy modeling to predict the impact of climate change on pine wilt disease in China. Front. Plant Sci. 12, 764. https://doi.org/10.3389/fpls.2021.652500 (2021).Article 

Google Scholar 
Chhogyel, N., Kumar, L., Bajgai, Y. & Jayasinghe, L. S. Prediction of Bhutan’s ecological distribution of rice (Oryza sativa L.) under the impact of climate change through maximum entropy modelling. J. Agric. Sci. 158, 25–37. https://doi.org/10.1017/S0021859620000350 (2020).Article 

Google Scholar 
Ahmad, Z. et al. Melia Azedarach impregnated Co and Ni zero-valent metal nanoparticles for organic pollutants degradation: Validation of experiments through statistical analysis. J. Mater. Sci. Mater. Electron. 31, 16938–16950. https://doi.org/10.1007/s10854-020-04250-5 (2020).CAS 
Article 

Google Scholar 
Hijmans, R. J., Huaccho, L. & Zhang, D. In I International Conference on Sweetpotato. Food and Health for the Future 583, 41–49.Luo, M., Wang, H. & Lyu, Z. Evaluating the performance of species distribution models Biomod2 and MaxEnt using the giant panda distribution data. J. Appl. Ecol. 28, 4001–4006. https://doi.org/10.13287/j.1001-9332.201712.011 (2017).Article 

Google Scholar 
Wang, T., Wang, G., Innes, J. L., Seely, B. & Chen, B. ClimateAP: An application for dynamic local downscaling of historical and future climate data in Asia Pacific. Front. Agric. Sci. Eng. 4, 448–458. https://doi.org/10.15302/J-FASE-2017172 (2017).CAS 
Article 

Google Scholar 
Yang, X.-Q., Kushwaha, S., Saran, S., Xu, J. & Roy, P. Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L Lesser Himalayan foothills. Ecol. Eng. 51, 83–87. https://doi.org/10.1016/j.ecoleng.2012.12.004 (2013).CAS 
Article 

Google Scholar 
Pepe, M. S., Cai, T. & Longton, G. Combining predictors for classification using the area under the receiver operating characteristic curve. Biometrics 62, 221–229. https://doi.org/10.1111/j.1541-0420.2005.00420.x (2006).MathSciNet 
Article 
PubMed 
MATH 

Google Scholar 
McHugh, M. L. Interrater reliability: The kappa statistic. Biochem. Med. 22, 276–282. https://hrcak.srce.hr/89395 (2012).Article 

Google Scholar 
Lu, C. Y., Gu, W., Dai, A. H. & Wei, H. Y. Assessing habitat suitability based on geographic information system (GIS) and fuzzy: A case study of Schisandra sphenanthera Rehd. et Wils. in Qinling Mountains, China. Ecol. Model. 242, 105–115. https://doi.org/10.1016/j.ecolmodel.2012.06.002 (2012).Article 

Google Scholar 
Zhang, L. et al. The basic principle of random forest and its applications in ecology: A case study of Pinus yunnanensis. Acta Ecol. Sin. 34, 650–659. https://doi.org/10.5846/stxb201306031292 (2014).Article 

Google Scholar 
Williams, J. N. et al. Using species distribution models to predict new occurrences for rare plants. Divers. Distrib. 15, 565–576. https://doi.org/10.1111/j.1472-4642.2009.00567.x (2009).Article 

Google Scholar 
Akpoti, K., Kabo-Bah, A. T., Dossou-Yovo, E. R., Groen, T. A. & Zwart, S. J. Mapping suitability for rice production in inland valley landscapes in Benin and Togo using environmental niche modeling. Sci. Total Environ. 709, 136165. https://doi.org/10.1016/j.scitotenv.2019.136165 (2020).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Dutra Silva, L., de Brito, A. E., Vieira Reis, F., Bento Elias, R. & Silva, L. Limitations of species distribution models based on available climate change data: a case study in the Azorean forest. Forests 10, 575. https://doi.org/10.3390/f10070575 (2019).Article 

Google Scholar 
Lin, H. Y. et al. Climate-based approach for modeling the distribution of montane forest vegetation in Taiwan. Appl. Veg. Sci. 23, 239–253. https://doi.org/10.1111/avsc.12485 (2020).Article 

Google Scholar 
Zhang, L. et al. Consensus forecasting of species distributions: The effects of niche model performance and niche properties. PLoS ONE 10, e0120056. https://doi.org/10.1371/journal.pone.0120056 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Zhang, H. The optimality of naive Bayes. Am. Assoc. Artif. Intell. 1, 3 (2004).
Google Scholar 
Wang, Q., Nguyen, T.-T., Huang, J. Z. & Nguyen, T. T. An efficient random forests algorithm for high dimensional data classification. Adv. Data Anal. Classif. 12, 953–972. https://doi.org/10.1007/s11634-018-0318-1 (2018).MathSciNet 
Article 
MATH 

Google Scholar 
Zheng-tao, Y., Bin, D., Bo, H., Lu, H. & Jian-yi, G. Word sense disambiguation based on bayes model and information gain. Proc. Int. J. Adv. Sci. Technol. 2, 153–157. https://doi.org/10.1109/FGCN.2008.188 (2009).Article 

Google Scholar 
Yu, B. et al. SubMito-XGBoost: Predicting protein submitochondrial localization by fusing multiple feature information and eXtreme gradient boosting. Bioinformatics 36, 1074–1081. https://doi.org/10.1093/bioinformatics/btz734 (2020).CAS 
Article 
PubMed 

Google Scholar 
Hailu, B. T., Siljander, M., Maeda, E. E. & Pellikka, P. Assessing spatial distribution of Coffea arabica L. in Ethiopia’s highlands using species distribution models and geospatial analysis methods. Ecol. Inf. 42, 79–89. https://doi.org/10.1016/j.ecoinf.2017.10.001 (2017).Article 

Google Scholar 
Ramirez-Reyes, C. et al. Embracing ensemble species distribution models to inform at-risk species status assessments. J. Fish Wildl. Manag. 12, 98–111. https://doi.org/10.3996/JFWM-20-072 (2021).Article 

Google Scholar 
Wisz, M. S. et al. Effects of sample size on the performance of species distribution models. Divers. Distrib. 14, 763–773. https://doi.org/10.1111/j.1472-4642.2008.00482.x (2008).Article 

Google Scholar 
Feng, L., Sun, J., Shi, Y., Wang, G. & Wang, T. Predicting suitable habitats of camptotheca acuminata considering both climatic and soil variables. Forests 11, 891. https://doi.org/10.3390/f11080891 (2020).Article 

Google Scholar 
Wang, T., Campbell, E. M., O’Neill, G. A. & Aitken, S. N. Projecting future distributions of ecosystem climate niches: Uncertainties and management applications. For. Ecol. Manag. 279, 128–140. https://doi.org/10.1016/j.foreco.2012.05.034 (2012).Article 

Google Scholar 
Wang, T., Hamann, A., Spittlehouse, D. L. & Murdock, T. Q. ClimateWNA—high-resolution spatial climate data for western North America. J. Appl. Meteorol. Climatol. 51, 16–29. https://doi.org/10.1175/JAMC-D-11-043.1 (2012).ADS 
Article 

Google Scholar 
Feng, L. et al. Predicting suitable habitats of ginkgo biloba L. fruit forests in China. Clim. Risk Manag. 34, 100364. https://doi.org/10.1016/j.crm.2021.100364 (2021).Article 

Google Scholar 
Wang, T., Hamann, A., Spittlehouse, D. & Carroll, C. Locally downscaled and spatially customizable climate data for historical and future periods for North America. PLoS ONE 11, e0156720. https://doi.org/10.1371/journal.pone.0156720 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Guo, Y. et al. Spatial prediction and delineation of Ginkgo biloba production areas under current and future climatic conditions. Ind. Crops Prod. 166, 113444. https://doi.org/10.1016/j.indcrop.2021.113444 (2021).Article 

Google Scholar 
Jiao, C., Lan, G., Sun, Y., Wang, G. & Sun, Y. Dopamine alleviates chilling stress in watermelon seedlings via modulation of proline content, antioxidant enzyme activity, and polyamine metabolism. J. Plant Growth Regul. 40, 2. https://doi.org/10.1007/s00344-020-10096-2 (2021).CAS 
Article 

Google Scholar 
Thakur, S., Thakur, I. & Sankanur, M. Assessment of genetic diversity in drek (Melia azedarach) using molecular markers. J. Tree Sci. 36, 78–85. https://doi.org/10.5958/2455-7129.2017.00011.5 (2017).Article 

Google Scholar 
Sivasubramaniam, K. et al. Seed priming: Triumphs and tribulations. The Madras Agricultural Journal 98, 197–209. https://www.researchgate.net/publication/267298497 (2011).
Google Scholar 
Xu, L. et al. Effect of salt stress on growth and physiology in Melia azedarach seedlings of six provenances. Int. J. Agric. Biol. 20, 471–480. https://doi.org/10.17957/IJAB/15.0618 (2018).CAS 
Article 

Google Scholar 
Lenoir, J., Gégout, J.-C., Marquet, P., De Ruffray, P. & Brisse, H. A significant upward shift in plant species optimum elevation during the 20th century. Science. 320, 1768–1771. https://doi.org/10.1126/science.1156831 (2008).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Ou-Yang, C.-F. et al. Impact of equatorial and continental airflow on primary greenhouse gases in the northern South China Sea. Environ. Res. Lett. 10, 065005. https://doi.org/10.1088/1748-9326/10/6/065005 (2015).ADS 
CAS 
Article 

Google Scholar 
Liu, B., Zhu, C., Su, J., Ma, S. & Xu, K. Record-breaking northward shift of the western North Pacific subtropical high in July 2018. J. Meteorol. Soc. Japan. 97, 913–925. https://doi.org/10.2151/jmsj.2019-047 (2019).ADS 
Article 

Google Scholar 
Huang, J. et al. Dryland climate change: Recent progress and challenges. Rev. Geophys. 55, 719–778. https://doi.org/10.1002/2016RG000550 (2017).ADS 
Article 

Google Scholar 
Thuiller, W., Lavorel, S., Araújo, M. B., Sykes, M. T. & Prentice, I. C. Climate change threats to plant diversity in Europe. Proc. Natl. Acad. Sci. 102, 8245–8250. https://doi.org/10.1073/pnas.0409902102 (2005).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Waldvogel, A. M. et al. Evolutionary genomics can improve prediction of species’ responses to climate change. Evol. Lett. 4, 4–18. https://doi.org/10.1002/evl3.154 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Vilà-Cabrera, A., Coll, L., Martínez-Vilalta, J. & Retana, J. Forest management for adaptation to climate change in the Mediterranean basin: A synthesis of evidence. For. Ecol. Manag. 407, 16–22. https://doi.org/10.1016/j.foreco.2017.10.021 (2018).Article 

Google Scholar 
He, X., Li, J., Wang, F., Zhang, J. & Chen, X. Variation and selection of Melia azedarach provenances and families. J. Northeast For. Univ. 47, 1–7. https://doi.org/10.13332/j.1000-1522.20170321 (2019).CAS 
Article 

Google Scholar 
Smith, A. B., Alsdurf, J., Knapp, M., Baer, S. G. & Johnson, L. C. Phenotypic distribution models corroborate species distribution models: A shift in the role and prevalence of a dominant prairie grass in response to climate change. Glob. Change Biol. 23, 4365–4375. https://doi.org/10.1111/gcb.13666 (2017).Article 

Google Scholar 
Bellon, M. R., Dulloo, E., Sardos, J., Thormann, I. & Burdon, J. J. In situ conservation—harnessing natural and human-derived evolutionary forces to ensure future crop adaptation. Evol. Appl. 10, 965–977. https://doi.org/10.1111/eva.12521 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Bidak, L. M., Heneidy, S. Z., Halmy, M. W. A. & El-Kenany, E. T. Sustainability potential for Ginkgo biloba L. plantations under climate change uncertainty: An ex-situ conservation perspective. Acta Ecol. Sin. 42, 101–114. https://doi.org/10.1016/j.chnaes.2021.09.012 (2021).Article 

Google Scholar 
Qin, F., Liu, S. & Yu, S. Effects of allelopathy and competition for water and nutrients on survival and growth of tree species in Eucalyptus urophylla plantations. For. Ecol. Manag. 424, 387–395. https://doi.org/10.1016/j.foreco.2018.05.017 (2018).Article 

Google Scholar 
Zabel, F. et al. Global impacts of future cropland expansion and intensification on agricultural markets and biodiversity. Nat. Commun. 10, 1–10. https://doi.org/10.1038/s41467-019-10775-z (2019).ADS 
CAS 
Article 

Google Scholar  More

Bardgett, R. D. et al. Combatting global grassland degradation. Nat. Rev. Earth Environ. 2(10), 720–735. https://doi.org/10.1038/s43017-021-00207-2 (2021).ADS 
Article 

Google Scholar 
O’Mara, F. P. The role of grasslands in food security and climate change. Ann. Bot. 110, 1263–1270. https://doi.org/10.1093/aob/mcs209 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Eze, S., Palmer, S. M. & Chapman, P. J. Soil organic carbon stock in grasslands: Effects of inorganic fertilizers, liming and grazing in different climate settings. J. Environ. Manage. 223, 74–84. https://doi.org/10.1016/j.jenvman.2018.06.013 (2018).CAS 
Article 
PubMed 

Google Scholar 
Makoudi, B. et al. Phosphorus deficiency increases nodule phytase activity of faba bean rhizobia symbiosis. Acta Physiol. Plant 40, 63. https://doi.org/10.1007/s11738-018-2619-6 (2018).CAS 
Article 

Google Scholar 
Stecca, J. D. L. et al. Inoculation of soybean seeds coated with osmoprotector in differentssoil pH’s. Acta Sci. Agron. 41, 9. https://doi.org/10.4025/actasciagron.v41i1.39482 (2019).Article 

Google Scholar 
Afonso, S., Arrobas, M. & Rodrigues, M. Â. Soil and plant analyses to diagnose hop fields irregular growth. J. Soil Sci. Plant Nutr. 20, 1999–2013. https://doi.org/10.1007/s42729-020-00270-6 (2020).CAS 
Article 

Google Scholar 
Crews, T. E. & Peoples, M. B. Legume versus fertilizer sources of nitrogen: ecological tradeoffs and human needs. Agric. Ecosyst. Environ 102(3), 279–297. https://doi.org/10.1016/j.agee.2003.09.018 (2004).Article 

Google Scholar 
Ossler, J. N., Zielinski, C. A. & Heath, K. D. Tripartite mutualism: Facilitation or trade-offs between rhizobial and mycorrhizal symbionts of legume hosts. Am. J. Bot. 102, 1332–1341. https://doi.org/10.3732/ajb.1500007 (2015).CAS 
Article 
PubMed 

Google Scholar 
Backer, R. et al. Plant growth-promoting rhizobacteria: context, mechanisms of action, and roadmap to commercialization of biostimulants for sustainable agriculture. Front. Plant Sci. 9, 1473. https://doi.org/10.3389/fpls.2018.01473 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Keet, J. H., Ellis, A. G., Hui, C. & Le Roux, J. J. Strong spatial and temporal turnover of soil bacterial communities in South Africa’s hyper diverse fynbos biome. Soil Biol. Biochem. 136, 107541. https://doi.org/10.1016/j.soilbio.2019.107541 (2019).CAS 
Article 

Google Scholar 
Fierer, N. & Jackson, R. B. The diversity and biogeography of soil bacterial communities. Proc. Natl. Acad. Sci. USA 103(3), 626–631. https://doi.org/10.1073/pnas.0507535103 (2006).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kracmarova, M. et al. Response of soil microbes and soil enzymatic activity to 20 years of fertilization. Agronomy 10, 1542. https://doi.org/10.3390/agronomy10101542 (2020).CAS 
Article 

Google Scholar 
Wang, C., Liu, D. H. & Bai, E. Decreasing soil microbial diversity is associated with decreasing microbial biomass under nitrogen addition. Soil Biol. Biochem. 120, 126–133. https://doi.org/10.1016/j.soilbio.2018.02.003 (2018).CAS 
Article 

Google Scholar 
Lucas, R. W. et al. A meta-analysis of the effects of nitrogen additions on base cations: Implications for plants, soils, and streams. For. Ecol. Manage. 262, 95–104. https://doi.org/10.1016/j.foreco.2011.03.018 (2011).Article 

Google Scholar 
Wang, Y. et al. Soil pH is a major driver of soil diazotrophic community assembly in Qinghai-Tibet alpine meadows. Soil Biol. Biochem. 115, 547–555. https://doi.org/10.1016/j.soilbio.2017.09.024 (2017).CAS 
Article 

Google Scholar 
Wan, S. et al. Effects of lime application and understory removal on soil microbial communities in subtropical eucalyptus L’Hér. plantations. Forests 10, 338 (2019).Article 

Google Scholar 
Yin, C., Schlatter, D. C., Kroese, D. R., Paulitz, T. C. & Hagerty, C. H. Impacts of lime application on soil bacterial microbiome in dryland wheat soil in the Pacific Northwest. Appl. Soil Ecol. 168, 104113 (2021).Article 

Google Scholar 
Schroeder, K. L., Schlatter, D. C. & Paulitz, T. C. Location-dependent impacts of liming and crop rotation on bacterial communities in acid soils of the Pacific Northwest. Appl. Soil. Ecol. 130, 59–68 (2018).Article 

Google Scholar 
Sudhakaran, M. & Ravanachandar, A. Role of soil enzymes in agroecosystem. Biotica Res. Today 2(6), 443–444 (2020).
Google Scholar 
Lacava, P. T., Machado, P. C. & de Andrade, P. H. M. Phosphate solubilization by endophytes from the tropical plants. Endophytes 3, 207–226 (2021).
Google Scholar 
Nannipieri, P., Giagnoni, L., Landi, L. & Renella, G. Role of Phosphatase Enzymes in Soil. Phosphorus in Action 215–243 (Springer, 2011).Book 

Google Scholar 
Zhang, L. et al. Soil labile organic carbon fractions and soil enzyme activities after 10 years of continuous fertilization and wheat residue incorporation. Sci. Rep. 10(1), 11318. https://doi.org/10.1038/s41598-020-68163-3 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Turner, B. L. Variation in pH optima of hydrolytic enzyme activities in tropical rain forest soils. Appl. Environ. Microbiol. 76, 6485–6493 (2010).ADS 
CAS 
Article 

Google Scholar 
Acosta-Martínez, V., Pérez-Guzmán, L. & Johnson, J. M. Simultaneous determination of β-glucosidase, β-glucosaminidase, acid phosphomonoesterase, and arylsulfatase activities in a soil sample for a biogeochemical cycling index. Appl. Soil Ecol. 142, 72–80. https://doi.org/10.12691/aees-8-6-26 (2019).CAS 
Article 

Google Scholar 
Parham, J. A. & Deng, S. P. Detection, quantification and characterization of β-glucosaminidase activity in soil. Soil Biol. Biochem. 32(8–9), 1183–1190. https://doi.org/10.1016/S0038-0717(00)00034-1 (2000).CAS 
Article 

Google Scholar 
Olajuyigbe, F. M. & Fatokun, C. O. Biochemical characterization of an extremely stable pH-versatile laccase from Sporothrix carnis CPF-05. Int. J. Biol. Macromol. 94, 535–543. https://doi.org/10.1016/j.ijbiomac.2016.10.037 (2017).CAS 
Article 
PubMed 

Google Scholar 
Bhuyan, M. B. et al. Explicating physiological and biochemical responses of wheat cultivars under acidity stress: insight into the antioxidant defense and glyoxalase systems. Physiol. Mol. Biol. Plants 25, 865–879. https://doi.org/10.1007/s12298-019-00678-0 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Delgado-Baquerizo, M., Grinyer, J., Reich, P. B. & Singh, B. K. Relative importance of soil properties and microbial community for soil functionality: Insights from a microbial swap experiment. Funct. Ecol. 30, 1862–1873 (2016).Article 

Google Scholar 
Zhao, L. et al. Mercury methylation in rice paddies and its possible controlling factors in the Hg mining area, Guizhou province, Southwest China. Environ. Pollut. 215, 1–9. https://doi.org/10.1016/j.envpol.2016.05.001 (2016).CAS 
Article 
PubMed 

Google Scholar 
Ward, D., Kirkman, K., Hagenah, N. & Tsvuura, Z. Soil respiration declines with increasing nitrogen fertilization and is not related to productivity in long-term grassland experiments. Soil Biol. Biochem. 115, 415–422. https://doi.org/10.1016/j.soilbio.2017.08.035 (2017).CAS 
Article 

Google Scholar 
Ward, M. et al. Impact of 2019–2020 mega-fires on Australian fauna habitat. Nat. Ecol. Evol. 4(10), 1321–1326. https://doi.org/10.1038/s41559-020-1251-1 (2020).Article 
PubMed 

Google Scholar 
Fynn, R. W. & O’Connor, T. G. Determinants of community organization of a South African mesic grassland. J. Veg. Sci. 16(1), 93–102 (2005).Article 

Google Scholar 
Morris, C. & Fynn, R. The Ukulinga long-term grassland trials: Reaping the fruits of meticulous, patient research. Bull. Grassl. Soc. S. Afr. 11(1), 7–22 (2001).
Google Scholar 
Le Roux, N. P. & Mentis, M. Veld compositional response to fertilization in the tall grassveld of Natal. S. Afr. J. Plant Soil 3(1), 1–10. https://doi.org/10.1080/02571862.1986.10634177 (1986).Article 

Google Scholar 
Tsvuura, Z. & Kirkman, K. P. Yield and species composition of a mesic grassland savannah in South Africa are influenced by long-term nutrient addition. Austral Ecol. 38, 959–970 (2013).Article 

Google Scholar 
Goldman, E. & Green, L. H. Practical Handbook of Microbiology 2nd edn, 864 (CRC Press Taylor and Francis Group, 2008).Book 

Google Scholar 
Akinbowale, O. L., Peng, H. & Barton, M. D. Diversity of tetracycline resistance genes in bacteria from aquaculture sources in Australia. J. Appl. Microbiol. 103(5), 2016–2025 (2007).CAS 
Article 

Google Scholar 
Jackson, C. R., Tyler, H. L. & Millar, J. J. Determination of microbial extracellular enzyme activity in waters, soils, and sediments using high throughput microplate assays. Preparation of substrate and buffer solutions for colorimetric analyses of enzyme. J. Vis. Exp. 80, 1–9. https://doi.org/10.3791/50399 (2013).CAS 
Article 

Google Scholar 
Goyal, M. & Kaur, R. Interactive effect of nitrogen nutrition, nitrate reduction and seasonal variation on oxalate synthesis in leaves of Napier-bajar hybrid (Pennisetum purpureum P. glaucum). Crop Pasture Sci 70, 669–675 (2019).CAS 
Article 

Google Scholar 
Pavlovic, J., Kostic, L., Bosnic, P., Kirkby, E. A. & Nikolic, M. Interactions of silicon with essential and beneficial elements in plants. Front. Plant Sci. 12, 1224. https://doi.org/10.3389/fpls.2021.697592 (2021).Article 

Google Scholar 
Li, Y., Tremblay, J., Bainard, L. D., Cade-Menun, B. & Hamel, C. Long-term effects of nitrogen and phosphorus fertilization on soil microbial community structure and function under continuous wheat production. Environ. Microbiol. 22, 1066–1088 (2020).CAS 
Article 

Google Scholar 
Guo, Z., Han, J., Li, J., Xu, Y. & Wang, X. Effects of long-term fertilization on soil organic carbon mineralization and microbial community structure. PLoS ONE 14, e0211163 (2019).CAS 
Article 

Google Scholar 
Shang, L., Wan, L. I., Zhou, X., Li, S. & Li, X. Effects of organic fertilizer on soil nutrient status, enzyme activity, and bacterial community diversity in Leymus chinensis steppe in Inner Mongolia, China. PLoS ONE https://doi.org/10.1371/journal.pone.0240559 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Gautam, A. et al. Responses of soil microbial community structure and enzymatic activities to long-term application of mineral fertilizer and beef manure. Environ. Sustain. Indic. 8, 10007S. https://doi.org/10.1016/j.indic.2020.100073 (2020).Article 

Google Scholar 
Wang, J., Lu, X., Zhang, J., Wei, G. & Xiong, Y. Regulating soil bacterial diversity, community structure and enzyme activity using residues from golden apple snails. Sci. Rep. 10(1), 1–11 (2020).CAS 
Article 

Google Scholar 
Xu, D., Carswell, A., Zhu, Q., Zhang, F. & de Vries, W. Modelling long-term impacts of fertilization and liming on soil acidification at Rothamsted experimental station. Sci. Total Environ. 713, 136249 (2020).ADS 
CAS 
Article 

Google Scholar 
von Tucher, S., Hörndl, D. & Schmidhalter, U. Interaction of soil pH and phosphorus efficacy: Long-term effects of P fertilizer and lime applications on wheat, barley, and sugar beet. Ambio 47, 41–49 (2018).Article 

Google Scholar 
Leff, J. W. et al. Consistent responses of soil microbial communities to elevated nutrient inputs in grasslands across the globe. Proc. Natl. Acad. Sci. USA 112, 10967–10972 (2015).ADS 
CAS 
Article 

Google Scholar 
Pan, J. et al. Dynamics of soil nutrients, microbial community structure, enzymatic activity, and their relationships along a chronosequence of Pinus massoniana plantations. Forests 12, 376 (2021).Article 

Google Scholar 
Andrés, J. A., Rovera, M., Guiñazú, L. B., Pastor, N. A. & Rosas, S. B. Role of in crop improvement. In Bacteria in Agrobiology: Plant Growth Responses 107–122 (Springer, 2011).Chapter 

Google Scholar 
Jeong, H., Choi, S. K., Ryu, C. M. & Park, S. H. Chronicle of a soil bacterium: Paenibacillus polymyxa E681 as a tiny guardian of plant and human health. Front. Microbiol. 10, 467 (2019).Article 

Google Scholar 
Garbeva, P. V., van Veen, J. A. & van Elsas, J. D. Microbial diversity in soil: Selection of microbial populations by plant and soil type and implications for disease suppressiveness. Annu. Rev. Phytopathol. 42, 243–270. https://doi.org/10.1146/annurev.phyto.42.012604.135455 (2004).CAS 
Article 
PubMed 

Google Scholar 
Sinsabaugh, R. L. & Moorhead, D. L. Resource allocation to extracellular enzyme production: A model for nitrogen and phosphorus control of litter decomposition. Soil Biol. Biochem. 26(10), 1305–1311. https://doi.org/10.1016/0038-0717(94)90211-9 (1994).Article 

Google Scholar 
Xiao, W., Chen, X., Jing, X. & Zhu, B. A meta-analysis of soil extracellular enzyme activities in response to global change. Soil Biol. Biochem. 123, 21–32. https://doi.org/10.1016/j.soilbio.2018.05.001 (2018).CAS 
Article 

Google Scholar 
Billah, M. et al. Phosphorus & phosphate solubilizing bacteria: Keys for sustainable agriculture. Geomicrobiol. J. 36(10), 904–916. https://doi.org/10.1080/01490451.2019.1654043 (2019).CAS 
Article 

Google Scholar 
Turner, B. L., McKelvie, I. D. & Haygarth, P. M. Characterisation of water-extractable soil organic phosphorus by phosphatase hydrolysis. Soil Biol Biochem. 34, 27–35. https://doi.org/10.1016/S0038-0717(01)00144-4 (2002).CAS 
Article 

Google Scholar 
van Aarle, I. M. & Plassard, C. Spatial distribution of phosphatase activity associated with ectomycorrhizal plants related to soil type. Soil Biol. Biochem. 42(2), 324–330. https://doi.org/10.1016/j.soilbio.2009.11.011 (2020).CAS 
Article 

Google Scholar  More

The pre-evaporitic, evaporitic, and post-evaporitic phases are recognized for the late Aptian. These phases are recorded within the K40–K50 sequences (Fig. 2A), and show an average maximum thickness of approximately 650 m in the studied basins. The pre-evaporitic phase is represented by carbonate and siliciclastic deposits formed in fluvial and lacustrine deltaic environments within a large proto-oceanic gulf28 (Fig. 2A). The peak of the evaporitic deposition is recorded in the K50 sequence, with widespread occurrences in the Brazilian equatorial margin. The origin of these deposits is the heat intensification associated with the widening of the Atlantic Ocean. These conditions caused strong evaporation leading to a wide distribution of evaporites (mainly halite and anhydrite gypsum) in the South Atlantic basins. The eastern continental margin of Brazil contains a restricted marine section characterized by evaporites, which are particularly prominent in thickness and occurrence in the Espírito Santo Basin (Itaúnas Member of the Mariricu Formation) and the Sergipe Basin (the Ibura Member of the Muribeca Formation)28. Evaporites form the most prominent evidence of dry climates in the South Atlantic basins11, with evaporation exceeding precipitation. The post-evaporitic phase is characterized by fully marine conditions evidenced by rich assemblages of marine fossils. During this phase, carbonates were deposited, followed by muddy and sandy sediments in shallow-marine and slope environments.Figure 2Paleoclimatic phases scheme and principal component analysis for paleoclimatic phases. (A) Paleoclimatic phases scheme for the late Aptian and the main depositional environments. (B) Principal component plot of bioclimatic groups. (C) Principal component for the pre-evaporitic phase (N = 92), evaporitic phase (N = 78), and post-evaporitic phase (N = 385); see Supplementary Fig. 9 for individual basins.Full size imagePaleovegetationWe identified a rich plant community with 139 spore and pollen genera/morphotypes representing all plant groups: bryophytes (five genera), ferns (58 genera), lycophytes (18 genera), pteridosperms (one genus), gymnosperms (27 genera), and angiosperms (30 genera) (Supplementary Table 2). The inferred systematic affinities at the family level reached 100% in bryophytes, 56.9% in ferns, 100% in lycophytes, 100% in pteridosperms, 92.6% in gymnosperms, and 40.0% in angiosperms, totaling 67.6% of the recorded genera (Supplementary Table 2). Marine elements (e.g., dinoflagellate cysts and microforaminiferal linings) were identified, in particular from the Sergipe and Araripe basins (Fig. 1). Pollen grains from gymnosperms were most abundant, represented mainly by the conifer families Cheirolepidiaceae, Araucariaceae, and Podocarpaceae, although representing different climatic settings. Classopollis (Cheirolepidiaceae) is the most abundant genus in all sections studied, followed by Araucariacites (Araucariaceae). Gymnosperms showed low diversity. Spore-producing plants are the most diverse in the assemblages of all basins (82 genera) and represented by several families of bryophytes, ferns, and lycophytes (e.g., Sphagnaceae, Anemiaceae, Cyatheaceae, Marsileaceae, Selaginellaceae, and Lycopodiaceae). These plant groups depend on water to reproduce and are therefore associated with humid settings.Cicatricosisporites (Anemiaceae) is the third most abundant palynomorph in all the basins, but especially in the northeastern basins (e.g., Sergipe Basin). Angiosperms are among the least abundant; however, they are diverse and include the most abundant and controversial genus Afropollis, herein attributed to angiosperms. In the most recent publication that addressed this question, ref.29 suggest that Afropollis should be treated as an angiosperm genus, although without more precise systematic assignment. The 30 genera/morphotypes of angiosperms are assigned to 8 families, viz., Arecaceae, Chloranthaceae, Euphorbiaceae, Flacourtiaceae, Illiciaceae, Liliaceae, Solanaceae and Trimeniaceae. The second most abundant genus is Stellatopollis also without precise systematic assignment.Spatio-temporal distribution of bioclimatic groupsOn the basis of their botanical affinities, most taxa were classified into five bioclimatic groups [see “Methods” section and Supplementary information], viz., hydrophytes, hygrophytes, tropical lowland flora, upland flora, and xerophytes (Supplementary Table 2) (Fig. 3).Figure 3Relevant palynomorphs of bioclimatic groups: (1) Aequitriradites sp.; (2) Crybelosporites sp.; (3) Perotriletes sp.; (4) Cicatricosisporites sp.; (5) Echinatisporis sp.; (6) Verrucosisporites sp.; (7) Bennettitaepollenites sp.; (8) Stellatopollis sp.; (9) Afropollis sp.; (10) Dejaxpollenites microfoveolatus; (11) Classopollis classoides; (12) Equisetosporites ovatus; (13) Gnetaceaepollenites jansonii; (14) Regalipollenites sp.; (15) Araucariacites sp.; (16) Callialasporites dampieri; (17) Complicatissacus cearensis; (18) Cyathidites sp.. Scale bar 20 µm.Full size imageOverall, the vegetation is dominated by the xerophytic bioclimatic group on account of the very high abundance of Classopollis (Cheirolepidiaceae) (general mean of 60.5%). However, the stratigraphic distribution of the bioclimatic groups in the sections studied (Supplementary Figs. 1–6) indicates wet phases confirmed by the curves of the other bioclimatic groups (hygrophytes, hydrophytes, tropical lowland flora, and upland flora). We used Pearson correlation analysis (Supplementary Fig. 7) to assess the correlation between the bioclimatic groups. The analysis revealed positive correlations between the bioclimatic groups of hygrophytes, hydrophytes, tropical lowland flora, and upland flora, and a negative correlation between these groups and the xerophyte group (Supplementary Fig. 7). The positive correlation between upland flora and hygrophytes confirms previous studies for the Sergipe Basin6,7, suggesting a relation between these groups and the hot and humid climate. The weak negative correlation between tropical lowland flora and upland flora is presumably related to elevation.The upland flora forms the second most abundant bioclimatic group, with an average of 18.9%. The large number of specimens of Araucariacites (Araucariaceae) in this group is notable. The hydrophytes are the least abundant group, with an average of only 1.4%. In this group, the highest values are attributed to the genus Crybelosporites (Marsileaceae).Principal component analyses (PCA) were used to reduce the multidimensional dataset, based on the percent abundance of the bioclimatic groups to a smaller number of dimensions for interpretive analysis. For all sections, two components or axes explain 97.6% of the observed variability (Fig. 2B). Hygrophytes, hydrophytes, tropical lowland flora, and upland flora show positive correlation (positive loading, 0.320, 0.029, 0.006, and 0.468, respectively), whereas xerophytes show a negative relationship (negative loading, − 0.823) on the first axis, which alone explains 83.0% of the variability. In summary, the first axis of the PCA reveals a separation of two major climatic conditions (wet and dry) along the axis (Fig. 2B). The wet conditions include the associations of hygrophytes, hydrophytes, tropical lowland flora, and upland flora, with dry conditions associated with taxa from the xerophyte group. The second axis explains 14.6%, in which hygrophytes, hydrophytes, and tropical lowland flora show a positive correlation relationship (positive loading, 0.719, 0.037, 0.036, respectively), whereas upland flora and xerophytes show a negative relationship (negative loading, − 0.684 and − 0.108, respectively). With respect to the second axis, a polarization between the hygrophytes (positive loading, 0.719) and the upland flora (negative loading, − 0.684) can be interpreted as a lowland–upland trend. The same pattern was recorded for all paleoclimatic phases (Fig. 2C) and sections (Supplementary Fig. 8), that is, the first axis is related to humidity vs. aridity, and the second axis to elevation (lowland vs. upland). This suggests that these two factors, particularly the first one, controlled the vegetation distribution in the late Aptian of the region. As all bioclimatic groups occurred in the three evaporitic phases, these trends in abundance reflect expansion and contraction of the recorded vegetation.Parallel increasing trends of bioclimatic groups mark the pre-evaporitic phase: hygrophytes and upland flora in the Bragança-Viseu, São Luís, Parnaíba, Ceará, Potiguar, and Araripe basins (Supplementary Figs. 1–3 and 5), suggesting that there was a certain amount of moisture in these areas. The xerophytes show the lowest average of this phase (44.1%) (Table 1), whereas hygrophytes show the highest average (27.0%). These humid conditions are confirmed by the highest mean of the Fs/X ratio (Fs/X = 0.4), representing the predominance of spore-producing plants [see Methods section and Supplementary information]. Despite the low abundance of hydrophytes in the sections, a prominent feature is the highest average (2.5%) of this group (Table 1), which is assigned to aquatic environments, confirming relatively wet conditions in this phase. There are no pre-evaporitic samples available from the Sergipe and Espírito Santo basins.Table 1 Average abundance of bioclimatic groups, diversity, Fs/X and marine elements for the paleoclimatic phases.Full size tableThe evaporitic phase is characterized by the highest abundance of the xerophyte bioclimatic group (76.4%) (Table 1), represented mainly by Classopollis (Supplementary Figs. 1–6). A high abundance of xerophytes occurred widely distributed in all basins studied. In this phase, tropical lowland flora is notable, showing an average higher than the overall average (3.3%), particularly in the Bragança-Viseu, São Luís, Parnaíba, and Ceará basins (Supplementary Figs. 1 and 2). This result is related to the moderate to high abundance of the genus Afropollis in these basins. The evaporitic phase is also characterized by the lowest average Fs/X ratio (Fs/X = 0.1) (Table 1), confirming the dominance of xerophytes.The post-evaporitic phase is characterized by the upland flora bioclimatic group (mean = 24.4%) (Table 1). The moderate to high abundance of upland flora in this phase is represented, in particular, by pollen grains of Araucariacites, which represent the high-relief family Araucariaceae. This bioclimatic group is associated with more humid conditions, as confirmed by an Fs/X ratio higher than the overall average (Fs/X = 0.2). The upland flora is significant in all basins, except the Espírito Santo Basin, where xerophytes predominate in both studied phases in this basin.Latitudinal biome distributionsBiome change is a fundamental biological response to climate change. In the study area, the predominance of a specific biome is mainly related to humidity, since all five recorded bioclimatic groups are related to a warm climate (Supplementary Table 2) representing two biomes: tropical xerophytic shrubland and tropical rainforest. In the rainforest biome two phytophysiognomies are recognized: lowland and montane rainforest. The tropical xerophytic shrubland biome predominates in the three paleoclimatic phases, with a wide latitudinal range from the Bragança-Viseu, São Luís, and Parnaíba basins (1° S) to the Espírito Santo Basin (20° S). This wide distribution is compatible with a predominantly arid climate in South America in the late Aptian, as indicated by paleoclimatic maps8,9,15 (Fig. 4A). Most arid and semi-arid ecosystems are mainly controlled by precipitation. Other climate parameters are less important, a condition that simplifies cause-effect interpretations. The PCA (Fig. 2B) demonstrated that the wet–dry trend, which reflects high–low precipitation, was the main determinant in the distribution of the biomes. However, considering all phases, an increasing trend in humidity was observed from the southeast (Espírito Santo Basin) to the northeast (e.g., Potiguar Basin) (Fig. 4B), coinciding with the hot and wet belt attributed to the ITCZ (Fig. 4A)15. The latitudinal distribution of diversity also follows this trend. Diversity increased significantly towards in the basins near the equator. Diversity indices (Shannon – H’) peaked in the Sergipe Basin (H’ = 3.5, CL-47 section) at 11° S. Conversely, the lowest average diversity is recorded in the Espírito Santo Basin (H’ = 1.1) at 20° S. Additionally, there is a clear correlation between high diversity (H’) and humidity (Fs/X ratio) (r = 0.691), regardless of paleoclimatic phase, as evidenced by the synchronicity of the H’ and Fs/X curves (Fig. 5). After data normalization between humidity (Fs/X) and marine elements (dinoflagellate cysts and microforaminifer linings), we performed linear correlation analyses, which showed a weak but positive correlation (r = 0.137). This is due to the fact that pre- evaporitic deposits contain only 19 occurrences of dinoflagellate cysts in 90 samples. Despite this, the curves of Fs/X, marine elements and diversity are synchronous (Fig. 5), suggesting a relation between humidity, diversity, and marine incursions.Figure 4Latitudinal changes in late Aptian biomes from southeast to center-north. (A) Paleoclimatic belts of the late Aptian in South America (climatic belts modified from refer.14). Reconstruction map at 116 Ma modified from ODSN Plate Tectonic Reconstruction Service. The Reconstruction map at 116 Ma was generated by ODSN Plate Tectonic Reconstruction Service (https://www.odsn.de/odsn/services/paleomap/paleomap.html). (B) Late Aptian latitudinal distribution of the tropical xerophytic biome in Brazil. (C) Stratigraphic distribution of biomes for individual basins. (D) Relative Importance of biomes for paleoclimatic phases.Full size imageFigure 5Biome trends in relation to paleoclimatic phases. Change in biomes, diversity, Fs/X ratio and marine elements shown by changepoint analysis plotted against paleoclimatic phases.Full size imageThe pre-evaporitic phase is marked by a certain balance between the biomes (Fig. 4C,D). In the lowlands, the tropical xerophytic shrubland biome predominated in the Bragança- Viseu, São Luís, Parnaíba, and Ceará basins, but in the Potiguar Basin it is co-dominant with the lowland rainforest. The montane rainforest was relatively extensive in this phase, although with several areal changes, and reached its widest extent in the Araripe (7° S) and Potiguar (5° S) basins in response to the deterioration of the tropical xerophytic shrubland biome. These conditions demonstrate that humidity was relatively high at this stage. The pre-evaporitic deposits were characterized by the highest diversity average (H’ = 1.8).The method of indicator species analysis (IndVal) was used to identify the key species of each paleoclimatic phase (Supplementary Table 15). The species identified for the pre-evaporitic phase, Deltoidospora spp. (Cyatheaceae-Dicksoniaceae) related to the montane rainforest, are indicator species for the Bragança-Viseu, São Luís, Parnaíba, and Ceará basins. The Gnetaceaepollenites spp. (Gnetaceae) of the Potiguar Basin and Equisetosporites spp. (Ephedraceae) of the Araripe Basin are related to the tropical xerophytic shrubland biome (Supplementary Table 15). Even for the pre-evaporitic phase, a progressive increase in the tropical xerophytic shrubland biome was observed and interpreted as the start of a climatic deterioration stage (Fig. 4C), which culminated in the evaporitic phase. Shifts in vegetation types may occur when precipitation reaches a threshold value, which means that a regionally synchronous gradual climate change can cause abrupt vegetation shifts. The change from humid to warm and arid conditions (evaporitic phase) is directly related to a decrease in precipitation. This aridization process coincides with the appearance of marine elements (e.g., dinoflagellate cysts). The threshold effect (intense evaporation) is reflected in an abrupt decrease in the abundance of lowland and montane rainforest and a sharp increase to a very high abundance of the tropical xerophytic shrubland biome (Supplementary Figs. 4C and 5). The threshold effect was not detected in the Espírito Santo Basin, where the arid conditions remained stable with minimal shift (expansion and contraction) of the biome. The main representatives of this biome are conifers of the family Cheirolepidiaceae (Classopollis), which were most abundant in lagoons and coastal environments and are often associated with evaporates30,31,32,33,34,35. Even under xeric or water-stressed conditions there was a slight increase in biomes related to a humid climate (lowland and montane rainforest phytophysiognomies) towards the equatorial region, suggesting influence of the ITCZ (Fig. 4A,B).The evaporitic phase was characterized by the lowest diversity average (H’ = 1.2). With modest rainfall, arid regions are generally characterized by fewer species than moister biomes36. However, diversity indices peaked in the Bragança-Viseu, São Luís, and Parnaíba basins (H’ = 2.6, RL-01 section) and along the equatorial margin (2° S) (Supplementary Fig. 1).IndVal emphasizes the xeric conditions in the evaporitic phase by association with the species from the tropical xerophytic shrubland biome: Classopollis spp. (Ceará and Potiguar basins), Classopollis classoides (Sergipe Basin), Classopollis intrareticulatus (Araripe Basin), and Gnetaceaepollenites spp. (Espírito Santo Basin). For the Bragança-Viseu, São Luís, Parnaíba, and Ceará basins, where xeric restrictions are milder, the indicator taxon is Afropollis spp. from the lowland rainforest. This genus shows the weakest negative correlation with xerophytes.After the end of evaporite deposition, all sections indicate climatic stability, which kept the climate hot and arid even in the post-evaporitic phase, although the response was not linear.The shift in the biomes, especially the tropical xerophytic shrubland in the Bragança-Viseu, São Luís, Parnaíba, Ceará, and Araripe basins, occurred in the transition between the evaporitic and post-evaporitic phases, whereas in the Potiguar and Sergipe basins it occurred within the post-evaporitic phase. As indicated in the dendrograms of each section (Supplementary Figs. 1–6), the shift occurred abruptly in all basins, except the Espírito Santo Basin. The tropical rainforest biome (lowland and montane rainforests) replaced the tropical xerophytic shrubland in almost all basins (Fig. 4C). Even the Espírito Santo Basin, far from the influence of the ITCZ, shows a slight increase in lowland rainforest. The changes in the biomes are attributable to threshold effects caused by gradual climate change related to the ITCZ intensification shift and progressive increase in marine influence, indicated by an increase in marine microplankton from an average of 3.9% in the evaporitic phase to 44.1%. The increase in marine influence is reflected in the first major flooding surface observed in the Cretaceous succession27. Thus, a climate amelioration stage was established in the post- evaporitic phase (Fig. 5). In combination with published paleotopographic information25, the bioclimatic groups associated to the humid conditions (hygrophytes, hydrophytes, tropical lowland flora, and upland flora) were combined and visualized to create Fig. 6.Figure 6Reconstruction of the transitional gradient between marine to terrestrial environment (uplands) under ITCZ influence. The illustration is based on paleoflora and environmental information from palynological data from studied sections. Original size illustration: 18 × 24 cm, by Julio Lacerda.Full size imageAccording to refs.7,37, arid conditions are characterized by sea-level lowstands, whereas warm and humid conditions are correlated with sea levels rise, which explains the increase in the tropical rainforest biome (lowland and montane rainforests). The more intense humidity is supported by the results of IndVal for the post-evaporitic phase, with all species related to humid climate: Deltoidospora spp. (Bragança-Viseu, São Luís and Parnaíba basins), Araucariacites limbatus (Ceará Basin), Cicatricosisporites spp. (Potiguar Basin), Cicatricosisporites spp. and Araucariacites australis (Sergipe Basin), Inaperturopollenites spp. (Araripe Basin) and Inaperturopollenites simplex (Espírito Santo Basin).Our results show that the ITCZ combined with the opening of the South Atlantic Ocean during the late Aptian altered vegetation dynamics. As today, the ITCZ influence is stronger in the northeastern and north-central regions of South America. It is notable that the late Aptian climate evolution in the South Atlantic, culminating in higher humidity, was accompanied by an intrinsic relation between plant diversity, humidity, and marine influence. More