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    Convergence in water use efficiency within plant functional types across contrasting climates

    Arneth, A. et al. Terrestrial biogeochemical feedbacks in the climate system. Nat. Geosci. 3, 525–532 (2010).CAS 
    Article 

    Google Scholar 
    Green, J. K. et al. Regionally strong feedbacks between the atmosphere and terrestrial biosphere. Nat. Geosci. 10, 410–414 (2017).CAS 
    Article 

    Google Scholar 
    Heimann, M. & Reichstein, M. Terrestrial ecosystem carbon dynamics and climate feedbacks. Nature 451, 289–292 (2008).CAS 
    Article 

    Google Scholar 
    Beer, C. et al. Temporal and among-site variability of inherent water use efficiency at the ecosystem level. Glob. Biogeochem. Cycles 23, 1–13 (2009).Article 

    Google Scholar 
    Keenan, T. F. et al. Increase in forest water-use efficiency as atmospheric carbon dioxide concentrations rise. Nature 499, 324–327 (2013).CAS 
    Article 

    Google Scholar 
    Frank, D. C. et al. Water-use efficiency & transpiration across European forests during the Anthropocene. Nat. Clim. Change 5, 579–583 (2015).CAS 
    Article 

    Google Scholar 
    Mastrotheodoros, T. et al. Linking plant functional trait plasticity and the large increase in forest water use efficiency. J. Geophys. Res. Biogeosci. 122, 2393–2408 (2017).Article 

    Google Scholar 
    Lavergne, A. et al. Observed and modelled historical trends in the water-use efficiency of plants and ecosystems. Glob. Change Biol. 25, 2242–2257 (2019).Article 

    Google Scholar 
    Huxman, T. E. et al. Convergence across biomes to a common rain-use efficiency. Nature 429, 651–654 (2004).CAS 
    Article 

    Google Scholar 
    Yang, Y. et al. Contrasting responses of water use efficiency to drought across global terrestrial ecosystems. Sci. Rep. 6, 23284 (2016).CAS 
    Article 

    Google Scholar 
    Huang, L. et al. A global examination of the response of ecosystem water-use efficiency to drought based on MODIS data. Sci. Total Environ. 601–602, 1097–1107 (2017).Article 

    Google Scholar 
    Reichstein, M. et al. Severe drought effects on ecosystem CO2 and H2O fluxes at three Mediterranean evergreen sites: revision of current hypotheses? Glob. Change Biol. 8, 999–1017 (2002).Article 

    Google Scholar 
    Reichstein, M. et al. Inverse modeling of seasonal drought effects on canopy CO2/H2O exchange in three Mediterranean ecosystems. J. Geophys. Res. Atmos. 108, 4726 (2003).Article 

    Google Scholar 
    Cooley, S. S. et al. Assessing regional drought impacts on vegetation and evapotranspiration: a case study in Guanacaste, Costa Rica. Ecol. Appl. 29, e01834 (2019).Article 

    Google Scholar 
    Medrano, H., Flexas, J. & Galmés, J. Variability in water use efficiency at the leaf level among Mediterranean plants with different growth forms. Plant Soil 317, 17–29 (2008).Article 

    Google Scholar 
    Soh, W. K. et al. Rising CO2 drives divergence in water use efficiency of evergreen and deciduous plants. Sci. Adv. 5, eaax7906 (2019).CAS 
    Article 

    Google Scholar 
    Wang, M., Chen, Y., Wu, X. & Bai, Y. Forest-type-dependent water use efficiency trends across the northern hemisphere. Geophys. Res. Lett. 45, 8283–8293 (2018).Article 

    Google Scholar 
    Enquist, B. et al. Scaling from traits to ecosystems: developing a general trait driver theory via integrating trait-based and metabolic scaling theories. Adv. Ecol. Res. 52, 249–318 (2015).Article 

    Google Scholar 
    Gross, N. et al. Functional trait diversity maximizes ecosystem multifunctionality. Nat. Ecol. Evol. 1, 0132 (2017).Article 

    Google Scholar 
    Bagousse‐Pinguet, Y. L. et al. Testing the environmental filtering concept in global drylands. J. Ecol. 105, 1058–1069 (2017).Article 

    Google Scholar 
    Ponce Campos, G. E. et al. Ecosystem resilience despite large-scale altered hydroclimatic conditions. Nature 494, 349–352 (2013).CAS 
    Article 

    Google Scholar 
    Fisher, J. B. et al. The future of evapotranspiration: global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources. Water Resour. Res. 53, 2618–2626 (2017).Article 

    Google Scholar 
    Xue, B.-L. et al. Global patterns, trends, and drivers of water use efficiency from 2000 to 2013. Ecosphere 6, art174 (2015).Article 

    Google Scholar 
    Fisher, J. B. et al. ECOSTRESS: NASA’s next generation mission to measure evapotranspiration from the International Space Station. Water Resour. Res. 56, e2019WR026058 (2020).Article 

    Google Scholar 
    Higgins, M. A. et al. Geological control of floristic composition in Amazonian forests. J. Biogeogr. 38, 2136–2149 (2011).Article 

    Google Scholar 
    De Kauwe, M. G., Keenan, T. F., Medlyn, B. E., Prentice, I. C. & Terrer, C. Satellite based estimates underestimate the effect of CO2 fertilization on net primary productivity. Nat. Clim. Change 6, 892–893 (2016).Article 

    Google Scholar 
    Huang, M. et al. Seasonal responses of terrestrial ecosystem water-use efficiency to climate change. Glob. Change Biol. 22, 2165–2177 (2016).Article 

    Google Scholar 
    Lin, Y.-S. et al. Optimal stomatal behaviour around the world. Nat. Clim. Change 5, 459–464 (2015).CAS 
    Article 

    Google Scholar 
    Medlyn, B. E. et al. Reconciling the optimal and empirical approaches to modelling stomatal conductance. Glob. Change Biol. 17, 2134–2144 (2011).Article 

    Google Scholar 
    Peters, W. et al. Increased water-use efficiency and reduced CO2 uptake by plants during droughts at a continental scale. Nat. Geosci. 11, 744–748 (2018).CAS 
    Article 

    Google Scholar 
    Cheng, L. et al. Recent increases in terrestrial carbon uptake at little cost to the water cycle. Nat. Commun. 8, 110 (2017).Article 

    Google Scholar 
    Fisher, J. B. ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS): Level-3 Evapotranspiration L3(ET_PT-JPL) Algorithm Theoretical Basis Document. Jet Propulsion Laboratory, California Institute of Technology (2018).Running, S. W. et al. A continuous satellite-derived measure of global terrestrial primary production. BioScience 54, 547–560 (2004).Article 

    Google Scholar 
    Heinsch, F. et al. Evaluation of remote sensing based terrestrial productivity from MODIS using regional tower eddy flux network observations. IEEE Trans. Geosci. Remote Sens. 44, 1908–1925 (2006).Article 

    Google Scholar 
    Zhao, M., Heinsch, F., Nemani, R. & Running, S. Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sens. Environ. 95, 164–176 (2005).Article 

    Google Scholar 
    Ryu, Y. et al. Integration of MODIS land and atmosphere products with a coupled-process model to estimate gross primary productivity and evapotranspiration from 1 km to global scales. Glob. Biogeochem. Cycles 25, GB4017 (2011).Article 

    Google Scholar  More

  • in

    Recent expansion of oil palm plantations into carbon-rich forests

    Xu, Y. et al. Annual oil palm plantation maps in Malaysia and Indonesia from 2001 to 2016. Earth Syst. Sci. Data 12, 847–867 (2020).Article 

    Google Scholar 
    Meijaard, E. et al. The environmental impacts of palm oil in context. Nat. Plants 6, 1418–1426 (2020).Article 

    Google Scholar 
    Guillaume, T. et al. Carbon costs and benefits of Indonesian rainforest conversion to plantations. Nat. Commun. 9, 2388 (2018).Article 

    Google Scholar 
    Ordway, E. M. & Asner, G. P. Carbon declines along tropical forest edges correspond to heterogeneous effects on canopy structure and function. Proc. Natl Acad. Sci. USA 117, 7863–7870 (2020).CAS 
    Article 

    Google Scholar 
    Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850 (2013).CAS 
    Article 

    Google Scholar 
    Santoro, M. et al. The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations. Earth Syst. Sci. Data 13, 3927–3950 (2021).Article 

    Google Scholar 
    The World Database on Protected Areas (WDPA) (UNEP-WCMC and IUCN, accessed 12 February 2020); www.protectedplanet.netMahmud, A., Rehrig, M. & Hills, G. Improving the Livelihoods of Palm Oil Smallholders: The Role of the Private Sector (FSG, 2010).Lasco, R. Forest carbon budgets in Southeast Asia following harvesting and land cover change. Sci. China 45, 55–64 (2002).Article 

    Google Scholar 
    Historical Greenhouse Gas Emissions (Climate Watch, accessed 6 October 2021); https://www.climatewatchdata.org/Euler, M., Schwarze, S., Siregar, H. & Qaim, M. Oil palm expansion among smallholder farmers in Sumatra, Indonesia. J. Agric. Econ. 67, 658–676 (2016).Article 

    Google Scholar 
    Donofrio, S., Rothrock, P. & Leonard, J. J. F. T. Supply Change: Tracking Corporate Commitments to Deforestation-free SupplyChains, 2017 (Forest Trends, 2017).Rist, L., Feintrenie, L. & Levang, P. The livelihood impacts of oil palm: smallholders in Indonesia. Biodivers. Conserv. 19, 1009–1024 (2010).Article 

    Google Scholar 
    Saadun, N. et al. Socio-ecological perspectives of engaging smallholders in environmental-friendly palm oil certification schemes. Land Use Policy 72, 333–340 (2018).Article 

    Google Scholar 
    Hansen, M. C., Stehman, S. V. & Potapov, P. V. Quantification of global gross forest cover loss. Proc. Natl Acad. Sci. USA 107, 8650 (2010).CAS 
    Article 

    Google Scholar 
    Santoro, M. & Cartus, O. ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the year 2017 v.1 (Centre for Environmental Data Analysis, 2019); https://doi.org/10.5285/bedc59f37c9545c981a839eb552e4084Busch, J. et al. Reductions in emissions from deforestation from Indonesia’s moratorium on new oil palm, timber, and logging concessions. Proc. Natl Acad. Sci. USA 112, 1328–1333 (2015).CAS 
    Article 

    Google Scholar 
    McGarigal, K., Cushman, S. A. & Ene, E. FRAGSTATS v.4: spatial pattern analysis program for categorical and continuous maps (Univ. Massachusetts, 2012). More

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    Logging elevated the probability of high-severity fire in the 2019–20 Australian forest fires

    Bowman, D., Williamson, G. J., Gibson, R. K., Bradstock, R. A. & Keenan, R. J. The severity and extent of the Australia 2019–20 Eucalyptus forest fires are not the legacy of forest management. Nat. Ecol. Evol. 5, 1003–1010 (2021).Article 

    Google Scholar 
    Lindenmayer, D. B., Kooyman, R., Taylor, C., Ward, M. & Watson, J. Recent Australian wildfires made worse by logging and associated forest management. Nat. Ecol. Evol. 4, 898–900 (2020).Article 

    Google Scholar 
    Gould, J. S., Knight, I. & Sullivan, A. L. Physical modelling of leaf scorch height from prescribed fires in young Eucalyptus sieberi regrowth forests in South-Eastern Australia. Int. J. Wildl. Fire 7, 7–20 (1997).Article 

    Google Scholar 
    Keith, D. Ocean Shores to Desert Dunes: The Native Vegetation of NSW and the ACT (Department of Environment and Conservation NSW, 2004).Burrows, N. Predicting canopy scorch height in jarrah forests. CALM Sci. 2, 267–274 (1997).
    Google Scholar 
    Penney, G., Habibi, D. & Cattani, M. Firefighter tenability and its influence on wildfire suppression. Fire Saf. J. 106, 38–51 (2019).Article 

    Google Scholar 
    Sharples, J. J. et al. Natural hazards in Australia: extreme bushfire. Clim. Change 139, 85–99 (2016).Article 

    Google Scholar 
    Attiwill, P. M. et al. Timber harvesting does not increase fire risk and severity in wet eucalypt forests of Southern Australia. Conserv. Lett. 7, 341–354 (2014).Article 

    Google Scholar 
    Lindenmayer, D., Taylor, C. & Blanchard, W. Empirical analyses of the factors influencing fire severity in southeastern Australia. Ecosphere 12, e03721 (2021).Article 

    Google Scholar 
    Taylor, C., Blanchard, W. & Lindenmayer, D. B. Does forest thinning reduce fire severity in Australian eucalypt forests? Conserv. Lett. 14, e12766 (2020).
    Google Scholar 
    Taylor, C., McCarthy, M. A. & Lindenmayer, D. B. Non-linear effects of stand age on fire severity. Conserv. Lett. 7, 355–370 (2014).Article 

    Google Scholar 
    Furlaud, J. M., Prior, L. D., Williamson, G. J. & Bowman, D. M. J. S. Fire risk and severity decline with stand development in Tasmanian giant Eucalyptus forest. For. Ecol. Manag. 502, 119724 (2021).Article 

    Google Scholar 
    Price, O. F. & Bradstock, R. A. The efficacy of fuel treatment in mitigating property loss during wildfires: insights from analysis of the severity of the catastrophic fires in 2009 in Victoria, Australia. J. Environ. Manag. 113, 146–157 (2012).Article 

    Google Scholar 
    Taylor, C. & Lindenmayer, D. B. The adequacy of Victoria’s protected areas for conserving its forest-dependent fauna. Austral Ecol. 44, 1076–1090 (2019).Article 

    Google Scholar 
    Taylor, C., Blanchard, W. & Lindenmayer, D. B. What are the relationships between thinning and fire severity? Austral Ecol. https://doi.org/10.1111/aec.13096 (2021).La Sala, A. Thinning Regrowth Eucalypts Native Forest Silviculture Technical Bulletin No. 13 (Forestry Tasmania, 2001).Cary, G. J., Blanchard, W., Foster, C. N. & Lindenmayer, D. B. Effects of altered fire intervals on critical timber production and conservation values. Int. J. Wildl. Fire 30, 322–328 (2021).Article 

    Google Scholar 
    Filkov, A. I. et al. The determinants of crown fire runs during extreme wildfires in broadleaf forests in Australia. Adv. For. Fire Res. https://doi.org/10.14195/978-989-26-16-506_190; http://hdl.handle.net/10316.2/44517 (2018).Lindenmayer, D. B., Hobbs, R. J., Likens, G. E., Krebs, C. J. & Banks, S. C. Newly discovered landscape traps produce regime shifts in wet forests. Proc. Natl Acad. Sci. USA 108, 15887–15891 (2011).CAS 
    Article 

    Google Scholar  More

  • in

    Elevated extinction risk of cacti under climate change

    Boyle, T. H. & Anderson, E. in Cacti: Biology and Uses (ed. Nobel, P. S.) 125–141 (Univ. California Press, 2002).Gibson, A. C. & Nobel, P. S. The Cactus Primer (Harvard Univ. Press, 1986).Bravo Hollis, H. & Sánchez Mejorada, H. Las Cactáceas de México (Univ. Nacional Autónoma de México, 1978).Goettsch, B. et al. High proportion of cactus species threatened with extinction. Nat. Plants 1, 15142 (2015).CAS 
    PubMed 

    Google Scholar 
    Benavides, E., Breceda, A. & Anadón, J. D. Winners and losers in the predicted impact of climate change on cacti species in Baja California. Plant Ecol. 222, 29–44 (2021).
    Google Scholar 
    Nobel, P. S. Responses of some North American CAM plants to freezing temperatures and doubled CO2 concentrations: implications of global climate change for extending cultivation. J. Arid. Environ. 34, 187–196 (1996).
    Google Scholar 
    Reyes-García, C. & Andrade, J. L. Crassulacean acid metabolism under global climate change. N. Phytol. 181, 754–757 (2009).
    Google Scholar 
    Smith, S. D., Didden-Zopfy, B. & Nobel, P. S. High-temperature responses of North American cacti. Ecology 65, 643–651 (1984).
    Google Scholar 
    Larios, E., González, E. J., Rosen, P. C., Pate, A. & Holm, P. Population projections of an endangered cactus suggest little impact of climate change. Oecologia 192, 439–448 (2020).PubMed 

    Google Scholar 
    Esparza-Olguı́n, L., Valverde, T. & Vilchis-Anaya, E. Demographic analysis of a rare columnar cactus (Neobuxbaumia macrocephala) in the Tehuacan Valley, Mexico. Biol. Conserv. 103, 349–359 (2002).
    Google Scholar 
    Seal, C. E. et al. Thermal buffering capacity of the germination phenotype across the environmental envelope of the Cactaceae. Glob. Change Biol. 23, 5309–5317 (2017).
    Google Scholar 
    Huang, J., Yu, H., Guan, X., Wang, G. & Guo, R. Accelerated dryland expansion under climate change. Nat. Clim. Change 6, 166–171 (2016).
    Google Scholar 
    Gurvich, D. E. et al. Combined effect of water potential and temperature on seed germination and seedling development of cacti from a mesic Argentine ecosystem. Flora 227, 18–24 (2017).
    Google Scholar 
    Nuzhyna, N., Baglay, K., Golubenko, A. & Lushchak, O. Anatomically distinct representatives of Cactaceae Juss. family have different response to acute heat shock stress. Flora 242, 137–145 (2018).
    Google Scholar 
    Andrade, J. L. & Nobel, P. S. Microhabitats and water relations of epiphytic cacti and ferns in a lowland neotropical forest. Biotropica 29, 261–270 (1997).
    Google Scholar 
    Williams, D. G., Hultine, K. R. & Dettman, D. L. Functional trade-offs in succulent stems predict responses to climate change in columnar cacti. J. Exp. Bot. 65, 3405–3413 (2014).PubMed 

    Google Scholar 
    Aragón-Gastélum, J. L. et al. Induced climate change impairs photosynthetic performance in Echinocactus platyacanthus, an especially protected Mexican cactus species. Flora Morphol. Distrib. Funct. Ecol. Plants 209, 499–503 (2014).
    Google Scholar 
    Martorell, C., Montañana, D. M., Ureta, C. & Mandujano, M. C. Assessing the importance of multiple threats to an endangered globose cactus in Mexico: cattle grazing, looting and climate change. Biol. Conserv. 181, 73–81 (2015).
    Google Scholar 
    Dávila, P., Téllez, O. & Lira, R. Impact of climate change on the distribution of populations of an endemic Mexican columnar cactus in the Tehuacán-Cuicatlán Valley, Mexico. Plant Biosyst. 147, 376–386 (2013).
    Google Scholar 
    Conver, J. L., Foley, T., Winkler, D. E. & Swann, D. E. Demographic changes over >70 yr in a population of saguaro cacti (Carnegiea gigantea) in the northern Sonoran Desert. J. Arid. Environ. 139, 41–48 (2017).
    Google Scholar 
    Carrillo-Angeles, I. G., Suzán-Azpiri, H., Mandujano, M. C., Golubov, J. & Martínez-Ávalos, J. G. Niche breadth and the implications of climate change in the conservation of the genus Astrophytum (Cactaceae). J. Arid. Environ. 124, 310–317 (2016).
    Google Scholar 
    de Cavalcante, A. M. B. & de Duarte, A. S. Modeling the distribution of three cactus species of the Caatinga biome in future climate scenarios. Int. J. Ecol. Environ. Sci. 45, 191–203 (2019).
    Google Scholar 
    de Cavalcante, A. M. B., de Duarte, A. S. & Ometto, J. P. H. B. Modeling the potential distribution of Epiphyllum phyllanthus (L.) Haw. under future climate scenarios in the Caatinga biome. An. Acad. Bras. Cienc. 92, 351–358 (2020).
    Google Scholar 
    Tellez-Valdes, O. & DiVila-Aranda, P. Protected areas and climate change: a case study of the cacti in the Tehuacan-Cuicatlan biosphere reserve, Mexico. Conserv. Biol. 17, 846–853 (2003).
    Google Scholar 
    dos Santos Simões, S., Zappi, D., da Costa, G. M., de Oliveira, G. & Aona, L. Y. S. Spatial niche modelling of five endemic cacti from the Brazilian Caatinga: past, present and future. Austral Ecol. 45, 1–13 (2019).
    Google Scholar 
    Gorostiague, P., Sajama, J. & Ortega-Baes, P. Will climate change cause spatial mismatch between plants and their pollinators? A test using Andean cactus species. Biol. Conserv. 226, 247–255 (2018).
    Google Scholar 
    Butler, C. J., Wheeler, E. A. & Stabler, L. B. Distribution of the threatened lace hedgehog cactus (Echinocereus reichenbachii) under various climate change scenarios. J. Torre. Bot. Soc. 139, 46–55 (2012).
    Google Scholar 
    Johnson, C. N. Species extinction and the relationship between distribution and abundance. Nature 394, 272–274 (1998).CAS 

    Google Scholar 
    Thuiller, W., Lavorel, S. & Araújo, M. B. Niche properties and geographical extent as predictors of species sensitivity to climate change. Glob. Ecol. Biogeogr. 14, 347–357 (2005).
    Google Scholar 
    Enquist, B. J. Cyberinfrastructure for an integrated botanical information network to investigate the ecological impacts of global climate change on plant biodiversity. Preprint at PeerJ https://doi.org/10.7287/peerj.preprints.2615v2 (2016).Buisson, L., Thuiller, W., Casajus, N., Lek, S. & Grenouillet, G. Uncertainty in ensemble forecasting of species distribution. Glob. Change Biol. 16, 1145–1157 (2010).
    Google Scholar 
    Thuiller, W., Guéguen, M., Renaud, J., Karger, D. N. & Zimmermann, N. E. Uncertainty in ensembles of global biodiversity scenarios. Nat. Commun. 10, 1446 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Goettsch, B., Durán, A. P. & Gaston, K. J. Global gap analysis of cactus species and priority sites for their conservation. Conserv. Biol. 33, 369–376 (2018).PubMed 

    Google Scholar 
    Maitner, B. S. et al. The bien R package: A tool to access the Botanical Information and Ecology Network (BIEN) database. Methods Ecol. Evol. 9, 373–379 (2018).
    Google Scholar 
    Karger, D. N. et al. Climatologies at high resolution for the Earth’s land surface areas. Sci. Data 4, 170122 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Sanderson, B. M., Knutti, R. & Caldwell, P. A representative democracy to reduce interdependency in a multimodel ensemble. J. Clim. 28, 5171–5194 (2015).
    Google Scholar 
    Brodzik, M. J., Billingsley, B., Haran, T., Raup, B. & Savoie, M. H. EASE-Grid 2.0: Incremental but significant improvements for Earth-gridded data sets. ISPRS Int. J. Geo-Inf. 1, 32–45 (2012).
    Google Scholar 
    Venter, O. et al. Global terrestrial human footprint maps for 1993 and 2009. Sci. Data 3, 160067 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Phillips, S. maxnet: Fitting ‘maxent’ species distribution models with ‘glmnet’. R package version 0.1.4. https://CRAN.R-project.org/package=maxnet (2017).Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    Dormann, C. F. et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).
    Google Scholar 
    Franklin, S. B., Gibson, D. J., Robertson, P. A., Pohlmann, J. T. & Fralish, J. S. Parallel analysis: a method for determining significant principal components. J. Veg. Sci. 6, 99–106 (1995).
    Google Scholar 
    Roberts, D. R. et al. Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography 40, 913–929 (2017).
    Google Scholar 
    Merow, C., Smith, M. J. & Silander, J. A. A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography 36, 1058–1069 (2013).
    Google Scholar 
    Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).
    Google Scholar 
    Calabrese, J. M., Certain, G., Kraan, C. & Dormann, C. F. Stacking species distribution models and adjusting bias by linking them to macroecological models. Glob. Ecol. Biogeogr. 23, 99–112 (2014).
    Google Scholar 
    R Core Team R: A Language and Environment for Statistical Computing Version 3.6.0 (R Foundation for Statistical Computing, 2019). https://www.R-project.org/ More

  • in

    Mutualism promotes insect fitness by fungal nutrient compensation and facilitates fungus propagation by mediating insect oviposition preference

    Franco FP, Túler AC, Gallan DZ, Gonçalves FG, Favaris AP, Peñaflor MFGV, et al. Fungal phytopathogen modulates plant and insect responses to promote its dissemination. ISME J. 2021;15:3522–33.CAS 

    Google Scholar 
    Huang H, Ren L, Li H, Schmidt A, Gershenzon J, Lu Y, et al. The nesting preference of an invasive ant is associated with the cues produced by actinobacteria in soil. PLoS Pathog. 2020;16:e1008800.CAS 

    Google Scholar 
    Angleró-Rodríguez YI, Blumberg BJ, Dong Y, Sandiford SL, Pike A, Clayton AM, et al. A natural Anopheles-associated Penicillium chrysogenum enhances mosquito susceptibility to Plasmodium infection. Sci Rep. 2016;6:34084.
    Google Scholar 
    Davis TS, Landolt PJ. A survey of insect assemblages responding to volatiles from a ubiquitous fungus in an agricultural landscape. J Chem Ecol. 2013;39:860–8.CAS 

    Google Scholar 
    Flury P, Vesga P, Dominguez-Ferreras A, Tinguely C, Ullrich CI, Kleespies RG, et al. Persistence of root-colonizing Pseudomonas protegens in herbivorous insects throughout different developmental stages and dispersal to new host plants. ISME J. 2018;13:860–72.
    Google Scholar 
    Kandasamy D, Gershenzon J, Andersson MN, Hammerbacher A. Volatile organic compounds influence the interaction of the Eurasian spruce bark beetle (Ips typographus) with its fungal symbionts. ISME J. 2019;13:1788–800.CAS 

    Google Scholar 
    Keesey IW, Koerte S, Khallaf MA, Retzke T, Guillou A, Grosse-Wilde E, et al. Pathogenic bacteria enhance dispersal through alteration of Drosophila social communication. Nat Commun. 2017;8:265.
    Google Scholar 
    Paul GB, Gerhard F, Elżbieta R, Alexandra S, Arne H, Sébastien L, et al. Yeast, not fruit volatiles mediate Drosophila melanogaster attraction, oviposition and development. Funct Ecol. 2012;26:1365–2435.
    Google Scholar 
    Ganter PF. Yeast and invertebrate associations. In: Gábor P, Carlos R, editors. Biodiversity and ecophysiology of yeasts. Berlin, Heidelberg: Springer; 2006. pp 303–70.Anagnostou C, Legrand EA, Rohlfs M. Friendly food for fitter flies?—Influence of dietary microbial species on food choice and parasitoid resistance in Drosophila. Oikos. 2010;119:533–41.
    Google Scholar 
    Günther CS, Knight SJ, Jones R, Goddard MR. Are Drosophila preferences for yeasts stable or contextual? Ecol Evol. 2019;9:8075–86.
    Google Scholar 
    Luo Y, Johnson JC, Chakraborty TS, Piontkowski A, Gendron CM, Pletcher SD. Yeast volatiles double starvation survival in Drosophila. Sci Adv. 2021;7:eabf8896.CAS 

    Google Scholar 
    Fogleman S. Coadaptation of Drosophila and yeasts in their natural habitat. J Chem Ecol. 1986;12:1037–55.
    Google Scholar 
    Droby S, Eick A, Macarisin D, Cohen L, Rafael G, Stange R, et al. Role of citrus volatiles in host recognition, germination and growth of Penicillium digitatum and Penicillium italicum. Postharvest Biol Tec. 2008;49:386–96.CAS 

    Google Scholar 
    Stensmyr MC, Dweck HK, Farhan A, Ibba I, Strutz A, Mukunda L, et al. A conserved dedicated olfactory circuit for detecting harmful microbes in Drosophila. Cell. 2012;151:1345–57.CAS 

    Google Scholar 
    Melo N, Wolff GH, Costa-da-Silva AL, Arribas R, Triana MF, Gugger M, et al. Geosmin attracts Aedes aegypti mosquitoes to oviposition sites. Curr Biol. 2020;30:127–34.CAS 

    Google Scholar 
    Wei DD, He W, Lang N, Miao ZQ, Xiao LF, Dou W, et al. Recent research status of Bactrocera dorsalis: Insights from resistance mechanisms and population structure. Arch Insect Biochem. 2019;102:e21601.CAS 

    Google Scholar 
    Han P, Wang X, Niu CY, Dong YC, Zhu JQ, Desneux N. Population dynamics, phenology, and overwintering of Bactrocera dorsalis (Diptera: Tephritidae) in Hubei Province, China. J Pest Sci. 2011;84:289–95.
    Google Scholar 
    Duyck PF, David P, Quilici S. A review of relationships between interspecific competition and invasions in fruit flies (Diptera: Tephritidae). Ecol Entomol. 2004;29:511–20.
    Google Scholar 
    Wen T, Zheng L, Dong S, Gong Z, Sang M, Long X, et al. Rapid detection and classification of citrus fruits infestation by Bactrocera dorsalis (Hendel) based on electronic nose. Postharvest Biol Tec. 2019;147:156–65.
    Google Scholar 
    Li X, Yang H, Wang T, Wang J, Wei H. Life history and adult dynamics of Bactrocera dorsalis in the citrus orchard of Nanchang, a subtropical area from China: implications for a control timeline. ScienceAsia. 2019;45:212–20.
    Google Scholar 
    Chalupowicz D, Veltman B, Droby S, Eltzov E. Evaluating the use of biosensors for monitoring of Penicillium digitatum infection in citrus fruit. Sens Actuat B-Chem. 2020;311:127896.CAS 

    Google Scholar 
    Turlings TC, Lengwiler UB, Bernasconi ML, Wechsler D. Timing of induced volatile emissions in maize seedlings. Planta. 1998;207:146–52.CAS 

    Google Scholar 
    Wang B, Dong W, Li H, D’Onofrio C, Bai P, Chen R, et al. Molecular basis of (E)-β-farnesene-mediated aphid location in the predator Eupeodes corollae. Curr Biol. 2022;32:951–62.CAS 

    Google Scholar 
    Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2− ΔΔCT method. Methods. 2001;25:402–8.CAS 

    Google Scholar 
    Cellar NA, De Nison JE, Seipelt CT, Twohig M, Burgess JA. Title of subordinate document. In: Dramatic improvements in assay reproducibility for water-soluble vitamins using ACQUITY UPLC and the Ultra-Sensitive Xevo TQ-S Mass Spectrometer. 2013. https://www.waters.com/webassets/cms/library/docs/720004690en.pdf.Ren FR, Sun X, Wang TY, Yan JY, Yao YL, Li CQ, et al. Pantothenate mediates the coordination of whitefly and symbiont fitness. ISME J. 2021;15:1655–67.CAS 

    Google Scholar 
    Batta YA. Quantitative postharvest contamination and transmission of Penicillium expansum (Link) conidia to nectarine and pear fruit by Drosophila melanogaster (Meig.) adults. Postharvest Biol Tec. 2006;40:190–6.
    Google Scholar 
    Rohlfs M. Clash of kingdoms or why Drosophila larvae positively respond to fungal competitors. Front Zool. 2005;2:2.
    Google Scholar 
    Becher PG, Bengtsson M, Hansson BS, Witzgall P. Flying the fly: long-range flight behavior of Drosophila melanogaster to attractive odors. J Chem Ecol. 2010;36:599–607.CAS 

    Google Scholar 
    Dionigi C, Ahten T, Wartelle L. Effects of several metals on spore, biomass, and geosmin production by Streptomyces tendae and Penicillium expansum. J Ind Microbiol Biot. 1996;17:84–88.CAS 

    Google Scholar 
    Jin S, Zhou X, Gu F, Zhong G, Yi X. Olfactory plasticity: variation in the expression of chemosensory receptors in Bactrocera dorsalis in different physiological states. Front Physiol. 2017;8:672.
    Google Scholar 
    Li H, Ren L, Xie M, Gao Y, He M, Hassan B, et al. Egg-surface bacteria are indirectly associated with oviposition aversion in Bactrocera dorsalis. Curr Biol. 2020;30:4432–40.CAS 

    Google Scholar 
    Liu Y, Cui Z, Si P, Liu Y, Zhou Q, Wang G. Characterization of a specific odorant receptor for linalool in the Chinese citrus fly Bactrocera minax (Diptera: Tephritidae). Insect Biochem Molec. 2020;122:103389.CAS 

    Google Scholar 
    Ju JF, Bing XL, Zhao DS, Guo Y, Hong XY. Wolbachia supplement biotin and riboflavin to enhance reproduction in planthoppers. ISME J. 2019;14:1–12.
    Google Scholar 
    Liu F, Wickham JD, Cao Q, Lu M, Sun J. An invasive beetle–fungus complex is maintained by fungal nutritional-compensation mediated by bacterial volatiles. ISME J. 2020;14:2829–42.CAS 

    Google Scholar 
    Douglas AE. The B vitamin nutrition of insects: the contributions of diet, microbiome and horizontally acquired genes. Curr Opin Insect Sci. 2017;23:65–69.
    Google Scholar 
    Honda K, Ômura H, Hayashi N, Abe F, Yamauchi T. Conduritols as oviposition stimulants for the danaid butterfly, Parantica sita, identified from a host plant, Marsdenia tomentosa. J Chem Ecol. 2004;30:2285–96.CAS 

    Google Scholar 
    Soldano A, Alpizar YA, Boonen B, Franco L, Lopez-Requena A, Liu G, et al. Gustatory-mediated avoidance of bacterial lipopolysaccharides via TRPA1 activation in Drosophila. Elife. 2016;5:e13133.
    Google Scholar 
    Hussain A, Üçpunar HK, Zhang M, Loschek LF, Grunwald Kadow IC. Neuropeptides modulate female chemosensory processing upon mating in Drosophila. PLoS Biol. 2016;14:e1002455.
    Google Scholar 
    Stötefeld L, Holighaus G, Schütz S, Rohlfs M. Volatile-mediated location of mutualist host and toxic non-host microfungi by Drosophila larvae. Chemoecology. 2015;5:271–83.
    Google Scholar 
    Gou B, Liu Y, Guntur A, Stern U, Yang HC. Mechanosensitive neurons on the internal reproductive tract contribute to egg-laying-induced acetic acid attraction in Drosophila. Cell Rep. 2014;9:522–30.CAS 

    Google Scholar 
    Mezzera C, Brotas M, Gaspar M, Pavlou HJ, Goodwin SF, Vasconcelos ML. Ovipositor extrusion promotes the transition from courtship to copulation and signals female acceptance in Drosophila melanogaster. Curr Biol. 2020;30:3736–48.CAS 

    Google Scholar 
    Teimoori-Boghsani Y, Ganjeali A, Cernava T, Müller H, Asili J, Berg G. Endophytic fungi of native Salvia abrotanoides plants reveal high taxonomic diversity and unique profiles of secondary metabolites. Front Microbiol. 2020;10:3013–20.
    Google Scholar 
    Holden JT, Furman C, Snell EE. D-alanine and the vitamin B6 content of microorganisms. J Biol Chem. 1949;178:789–97.CAS 

    Google Scholar 
    Michalkova V, Benoit JB, Weiss BL, Attardo GM, Aksoy S. Vitamin B6 generated by obligate symbionts is critical for maintaining proline homeostasis and fecundity in tsetse flies. Appl Environ Micro. 2014;80:5844–53.
    Google Scholar 
    Ren FR, Sun X, Wang TY, Yao YL, Huang YZ, Zhang X, et al. Biotin provisioning by horizontally transferred genes from bacteria confers animal fitness benefits. ISME J. 2020;14:2542–53.CAS 

    Google Scholar 
    Salem H, Bauer E, Strauss AS, Vogel H, Marz M, Kaltenpoth M. Vitamin supplementation by gut symbionts ensures metabolic homeostasis in an insect host. Proc Biol Sci. 2014;281:20141838.
    Google Scholar  More

  • in

    Variation in the ratio of compounds in a plant volatile blend during transmission by wind

    Beyaert, I. & Hilker, M. Plant odour plumes as mediators of plant–insect interactions. Biol. Rev. 89, 68–81 (2014).
    Google Scholar 
    Simpraga, M., Takabayashi, J. & Holopainen, J. K. Language of plants: Where is the word?. J. Integr. Plant Biol. 58, 343–349 (2016).CAS 

    Google Scholar 
    Bruce, T. J. A., Wadhams, L. J. & Woodcock, C. M. Insect host location: A volatile situation. Trends Plant Sci. 10, 269–274 (2005).CAS 

    Google Scholar 
    Bruce, T. J. A. & Pickett, J. A. Perception of plant volatile blends by herbivorous insects—Finding the right mix. Phytochemistry 72, 1605–1611 (2011).CAS 

    Google Scholar 
    Raguso, R. A. Wake up and smell the roses: The ecology and evolution of floral scent. Annu. Rev. Ecol. Evol. S. 39, 549–569 (2008).
    Google Scholar 
    Schiestl, F. P. The evolution of floral scent and insect chemical communication. Ecol. Lett. 13, 643–656 (2010).
    Google Scholar 
    Arimura, G., Kost, C. & Boland, W. Herbivore-induced, indirect plant defences. Biochim. Biophys. Acta. 1734, 91–111 (2005).CAS 

    Google Scholar 
    Hare, J. D. Ecological role of volatiles produced by plants in response to damage by herbivorous insects. Annu. Rev. Entomol. 56, 161–180 (2011).CAS 

    Google Scholar 
    Laothawornkitkul, J., Taylor, J. E., Paul, N. D. & Hewitt, C. N. Biogenic volatile organic compounds in the earth system. New Phytol. 183, 27–51 (2009).CAS 

    Google Scholar 
    Dicke, M., van Loon, J. J. A. & Soler, R. Chemical complexity of volatiles from plant induced by multiple attack. Nature Chem. Biol. 5, 317–324 (2009).CAS 

    Google Scholar 
    Loreto, F. & Schnitzler, J. P. Abiotic stresses and induced BVOCs. Trends Plant Sci. 15, 154–166 (2010).CAS 

    Google Scholar 
    Tasin, M. et al. Synergism and redundancy in a plant volatile blend attracting grapevine moth females. Phytochemistry 68, 203–209 (2007).CAS 

    Google Scholar 
    Riffell, J. A., Lei, H., Christensen, T. A. & Hildebrand, J. G. Characterization and coding of behaviorally significant odor mixtures. Curr. Biol. 19, 335–340 (2009).CAS 

    Google Scholar 
    Riffell, J. A., Lei, H. & Hildebrand, J. G. Neural correlates of behavior in the moth Manduca sexta in response to complex odors. Proc. Natl. Acad. Sci. USA 106, 19219–19226 (2009).ADS 
    CAS 

    Google Scholar 
    Atema, J. Eddy chemotaxis and odor landscapes: Exploration of nature with animal sensors. Biol. Bull. 191, 129–138 (1996).CAS 

    Google Scholar 
    Conchou, L. et al. Insect odorscapes: From plant volatiles to natural olfactory scenes. Front. Physiol. 10, 972 (2019).
    Google Scholar 
    Riffell, J. A., Abrell, L. & Hildebrand, J. G. Physical processes and real-time chemical measurement of the insect olfactory environment. J. Chem. Ecol. 34, 837–853 (2008).CAS 

    Google Scholar 
    Mylne, K. R., Davidson, M. J. & Thomson, D. J. Concentration fluctuation measurements in tracer plumes using high and low frequency response detectors. Bound-Lay. Meteorol. 79, 225–242 (1996).ADS 

    Google Scholar 
    Finelli, C. M., Pentcheff, N. D., Zimmer-Faust, R. K. & Wethey, D. S. Odor transport in turbulent flows: Constraints on animal navigation. Limnol. Oceanogr. 44, 1056–1071 (1999).ADS 
    CAS 

    Google Scholar 
    Murlis, J., Elkinton, J. S. & Cardé, R. T. Odor plumes and how insects use them. Annu. Rev. Entomol. 37, 505–532 (1992).
    Google Scholar 
    Murlis, J., Willis, M. A. & Cardé, R. T. Spatial and temporal structures of pheromone plumes in fields and forests. Physiol. Entomol. 25, 211–222 (2000).CAS 

    Google Scholar 
    Kennedy, J. S. The visual response of flying mosquitoes. Proc. Zool. Soc. London Ser. A 109, 221–242 (1940).
    Google Scholar 
    Bursell, E. Observations on the orientation of tsetse flies (Glossina pallidipes) to wind-borne odours. Physio. Entomol. 9, 133–137 (1984).
    Google Scholar 
    Murlis, J., Elkinton, J. S. & Cardé, R. T. Odor plumes and how insects use them. Annu. Rev. Entomol. 37, 505–532 (1992).
    Google Scholar 
    Kennedy, J. S., Ludlow, A. R. & Sanders, C. J. Guidance of flying male moths by wind-borne sex-pheromone. Physiol. Entomol. 6, 395–412 (1981).
    Google Scholar 
    Koehl, M. A. R. The fluid mechanics of arthropod sniffing in turbulent odor plumes. Chem. Senses 31, 93–105 (2006).CAS 

    Google Scholar 
    Baker, T. C., Willis, M. A., Haynes, K. F. & Phelan, P. L. A pulsed cloud of sex pheromone elicits upwind flight in male moths. Physiol. Entomol. 10, 257–265 (1985).
    Google Scholar 
    Willis, M. A. & Baker, T. C. Effects of intermittent and continuous pheromone stimulation on the flight behavior of the oriental fruit moth, Grapholita molesta. Physiol. Entomol. 9, 341–358 (1984).
    Google Scholar 
    Mafraneto, A. & Cardé, R. T. Fine-scale structure of pheromone plumes modulates upwind orientation of flying moths. Nature 369, 142–144 (1994).ADS 
    CAS 

    Google Scholar 
    Mafraneto, A. & Cardé, R. T. Dissection of the pheromone-modulated flight of moths using single-pulse response as a template. Experientia 52, 373–379 (1996).CAS 

    Google Scholar 
    Vickers, N. J. & Baker, T. C. Reiterative responses to single strands of odor promote sustained upwind flight and odor source location by moths. Proc. Natl. Acad. Sci. USA 91, 5756–5760 (1994).ADS 
    CAS 

    Google Scholar 
    Lei, H., Riffell, J. A., Gage, S. L. & Hildebrand, J. G. Contrast enhancement of stimulus intermittency in a primary olfactory network and its behavioral significance. J. Biol. 8, 21 (2009).
    Google Scholar 
    Kuenen, L. & Carde, R. T. Strategies for recontacting a lost pheromone plume: Casting and upwind flight in the male gypsy moth. Physiol. Entomol. 19, 15–29 (1994).
    Google Scholar 
    Vickers, N. J. & Baker, T. C. Latencies of behavioral response to interception of filaments of sex pheromone and clean air influence flight track shape in Heliothis virescens (F.) males. J. Comp. Physiol. A. 178, 831–847 (1996).
    Google Scholar 
    Vickers, N. J. Mechanisms of animal navigation in odor plumes. Biol. Bull. 198, 203–212 (2000).CAS 

    Google Scholar 
    Cardé, R. T. & Willis, M. A. Navigational strategies used by insects to find distant, wind-borne sources of odor. J. Chem. Ecol. 34, 854–866 (2008).
    Google Scholar 
    Willis, M. A. & Baker, T. C. Effects of varying sex pheromone component ratios on the zigzagging flight movements of the oriental fruit moth, Grapholita molesta. J. Insect. Behav. 1, 357–371 (1988).
    Google Scholar 
    Voskamp, K. E., Den Otter, C. J. & Noorman, N. Electroantennogram responses of tsetse flies (Glossina pallidipes) to host odours in an open field and riverine woodland. Physiol. Entomol. 23, 176–183 (1998).
    Google Scholar 
    Cai, X. M., Xu, X. X., Bian, L., Luo, Z. X. & Chen, Z. M. Measurement of volatile plant compounds in field ambient air by thermal desorption–gas chromatography–mass spectrometry. Anal. Bioanal. Chem. 407, 9105–9114 (2015).CAS 

    Google Scholar 
    Zollner, G. E., Torr, S. J., Ammann, C. & Meixner, F. X. Dispersion of carbon dioxide plumes in African woodland: implications for host-finding by tsetse flies. Physiol. Entomol. 29, 381–394 (2004).
    Google Scholar 
    McFrederick, Q. S., Kathilankal, J. C. & Fuentes, J. D. Air pollution modifies floral scent trails. Atmos. Environ. 42, 2336–2348 (2008).ADS 
    CAS 

    Google Scholar 
    Yuan, J. S., Himanen, S. J., Holopainen, J. K., Chen, F. & NealStewart, C. Jr. Smelling global climate change: mitigation of function for plant volatile organic compounds. Trends Ecol. Evol. 24, 323–331 (2009).
    Google Scholar 
    Weissburg, M. J. The fluid dynamical context of chemosensory behavior. Biol. Bull. 198, 188–202 (2000).CAS 

    Google Scholar 
    Atkinson, R. & Arey, J. Gas-phase tropospheric chemistry of biogenic volatile organic compounds: A review. Atmos. Environ. 37, 197–219 (2003).ADS 

    Google Scholar 
    Helmig, D., Bocquet, F., Pollmann, J. & Revermann, T. Analytical techniques for sesquiterpene emission rate studies in vegetation enclosure experiments. Atmos. Environ. 38, 557–572 (2004).ADS 
    CAS 

    Google Scholar 
    Riffell, J. A, Shlizerman, E., Sanders, E., Abrell, L., Medina, B., Hinterwirth, A. J. & NathanKutz, J. Flower discrimination by pollinators in a dynamic chemical environment. Science 344, 1515–1518 (2014).Shorey, H. H. Animal communication by pheromones (Academic Press, 1976).Cardé, R. T. & Charlton, R. E. Olfactory sexual communication in Lepidoptera: Strategy, sensitivity and selectivity In Insect communication (ed. Lewis, T.) 241–265 (Academic Press, 1984).Elkinton, J. S., Schal, C., Ono, T. & Carde, R. T. Pheromone puff trajectory and upwind flight of male gypsy moths in a forest. Physiol. Entomol. 12, 399–406 (1987).
    Google Scholar 
    Baker, T. C., Fadamiro, H. Y. & Cosse, A. A. Moth uses fine tuning for odour resolution. Nature 393, 530 (1998).ADS 
    CAS 

    Google Scholar 
    Szyszka, P., Stierle, J. S., Biergans, S. & Galizia, C. G. The speed of smell: Odor-object segregation within milliseconds. PLoS One 7, e36096 (2012).ADS 
    CAS 

    Google Scholar 
    Hildebrand, J. G. Analysis of chemical signals by nervous systems. Proc. Natl. Acad. Sci. USA 92, 67–74 (1995).ADS 
    CAS 

    Google Scholar 
    Cai, X. M. et al. Field background odour should be taken into account when formulating a pest attractant based on plant volatiles. Sci. Rep. 7, 41818 (2017).ADS 
    CAS 

    Google Scholar 
    Xu, X. X. et al. Does background odor in tea gardens mask attractants? Screening and application of attractants for Empoasca onukii Matsuda. J. Econ. Entomol. 110, 2357–2363 (2017).CAS 

    Google Scholar 
    Hare, J. D. & Sun, J. J. Production of induced volatiles by Datura wrightii in response to damage by insects: Effect of herbivore species and time. J. Chem. Ecol. 37, 751–764 (2011).CAS 

    Google Scholar 
    Mumm, R., Tiemann, T., Schulz, S. & Hilker, M. Analysis of volatiles from black pine (Pinus nigra): Significance of wounding and egg deposition by a herbivorous sawfly. Phytochemistry 65, 3221–3230 (2004).CAS 

    Google Scholar  More

  • in

    Metabarcoding the Antarctic Peninsula biodiversity using a multi-gene approach

    Meredith M, Sommerkorn M, Cassotta S, Derksen C, Ekaykin A, Hollowed A. IPCC special report on the ocean and cryosphere in a changing climate In: Pörtner H-O, Roberts D, Masson-Delmotte V, Zhai P, Tignor M, Poloczanska Eea, editors. 2022; chapter 3: https://doi.org/10.1017/9781009157964 (in press).Rozema PD, Venables HJ, van de Poll WH, Clarke A, Meredith MP, Buma AGJ. Interannual variability in phytoplankton biomass and species composition in northern Marguerite Bay (West Antarctic Peninsula) is governed by both winter sea ice cover and summer stratification. Limnol Oceanogr. 2017;62:235–52.Article 

    Google Scholar 
    Venables HJ, Clarke A, Meredith MP. Wintertime controls on summer stratification and productivity at the western Antarctic Peninsula. Limnol Oceanogr. 2013;58:1035–47.Article 

    Google Scholar 
    Barnes DKA, Souster T. Reduced survival of Antarctic benthos linked to climate-induced iceberg scouring. Nat Clim Change. 2011;1:365–8.Article 

    Google Scholar 
    Grange L, Tyler P, Peck L, Cornelius N. Long-term interannual cycles of the gametogenic ecology of the Antarctic brittle star Ophionotus victoriae. Mar Ecol Prog Ser. 2004;278:141–55.Article 

    Google Scholar 
    Schratzberger M, Ingels J. Meiofauna matters: The roles of meiofauna in benthic ecosystems. J Exp Mar Biol Ecol. 2018;502:12–25.Article 

    Google Scholar 
    Mayor D, Thornton B, Jenkins H, Felgate S. Microbiota: the living foundation. In: Beninger P, editor. Mudflat ecology. Switzerland AG: Springer Nature 2018. p. 43–61.Fonseca VG, Sinniger F, Gaspar JM, Quince C, Creer S, Power DM, et al. Revealing higher than expected meiofaunal diversity in Antarctic sediments: a metabarcoding approach. Sci Rep. 2017;7:6094.CAS 
    Article 

    Google Scholar 
    Vause BJ, Morley SA, Fonseca VG, Jazdzewska A, Ashton GV, Barnes DKA, et al. Spatial and temporal dynamics of Antarctic shallow soft-bottom benthic communities: ecological drivers under climate change. BMC Ecol. 2019;19:27.Article 

    Google Scholar 
    Danovaro R, Scopa M, Gambi C, Fraschetti S. Trophic importance of subtidal metazoan meiofauna: evidence from in situ exclusion experiments on soft and rocky substrates. Mar Biol. 2007;152:339–50.Article 

    Google Scholar 
    Watzin MC. The effects of meiofauna on settling macrofauna: meiofauna may structure macrofaunal communities. Oecologia. 1983;59:163–6.Article 

    Google Scholar 
    Schmidt JL, Deming JW, Jumars PA, Keil RG. Constancy of bacterial abundance in surficial marine sediments. Limnol Oceanogr. 1998;43:976–82.Article 

    Google Scholar 
    Whitman WB, Coleman DC, Wiebe WJ. Prokaryotes: the unseen majority. Proc Natl Acad Sci USA. 1998;95:6578–83.CAS 
    Article 

    Google Scholar 
    Burdige DJ. Preservation of organic matter in marine sediments: controls, mechanisms, and an imbalance in sediment organic carbon budgets? Chem Rev. 2007;107:467–85.CAS 
    Article 

    Google Scholar 
    Zou K, Thébault E, Lacroix G, Barot S. Interactions between the green and brown food web determine ecosystem functioning. Funct Ecol. 2016;30:1454–65.Article 

    Google Scholar 
    Anderson TR, Pond DW, Mayor DJ. The role of microbes in the nutrition of detritivorous invertebrates: a stoichiometric analysis. Front Microbiol. 2016;7:2113.
    Google Scholar 
    Lacoste E, Piot A, Archambault P, McKindsey CW, Nozais C. Bioturbation activity of three macrofaunal species and the presence of meiofauna affect the abundance and composition of benthic bacterial communities. Mar Environ Res. 2018;136:62–70.CAS 
    Article 

    Google Scholar 
    Bonaglia S, Nascimento FJ, Bartoli M, Klawonn I, Bruchert V. Meiofauna increases bacterial denitrification in marine sediments. Nat Commun. 2014;5:5133.CAS 
    Article 

    Google Scholar 
    Riemann F, Helmke E. Symbiotic relations of sediment-agglutinating nematodes and bacteria in detrital habitats: the enzyme-sharing concept. Mar Ecol. 2002;23:93–113.CAS 
    Article 

    Google Scholar 
    dos Santos GAP, Derycke S, Fonseca-Genevois VG, Coelho LCBB, Correia MTS, Moens T. Differential effects of food availability on population growth and fitness of three species of estuarine, bacterial-feeding nematodes. J Exp Mar Biol Ecol. 2008;355:27–40.Article 

    Google Scholar 
    Zeppilli D, Sarrazin J, Leduc D, Arbizu PM, Fontaneto D, Fontanier C, et al. Is the meiofauna a good indicator for climate change and anthropogenic impacts? Mar Biodivers. 2015;45:505–35.Article 

    Google Scholar 
    Moens T, Beninger PG. Meiofauna: an inconspicuous but important player in Mudflat ecology. In: Beninger P, editor. Mudflat ecology aquatic ecology series. 7. Switzerland: Springer; 2018.Webb AL, Hughes KA, Grand MM, Lohan MC, Peck LS. Sources of elevated heavy metal concentrations in sediments and benthic marine invertebrates of the western Antarctic Peninsula. Sci Total Environ. 2020;698:134268.CAS 
    Article 

    Google Scholar 
    Brown KM, Fraser KP, Barnes DK, Peck LS. Links between the structure of an Antarctic shallow-water community and ice-scour frequency. Oecologia. 2004;141:121–9.Article 

    Google Scholar 
    Stoeck T, Bass D, Nebel M, Christen R, Jones MDM, Breiner H-W, et al. Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. Mol Ecol. 2010;19:21–31.CAS 
    Article 

    Google Scholar 
    Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 2012;6:1621–4.CAS 
    Article 

    Google Scholar 
    Leray M, Yang JY, Meyer CP, Mills SC, Agudelo N, Ranwez V, et al. A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: application for characterizing coral reef fish gut contents. Front Zool. 2013;10:34.Article 

    Google Scholar 
    Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17:10–2.Article 

    Google Scholar 
    Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
    Article 

    Google Scholar 
    Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinform. 2009;10:421.Article 

    Google Scholar 
    McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One. 2013;8:e61217.CAS 
    Article 

    Google Scholar 
    Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.CAS 
    Article 

    Google Scholar 
    Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL. GenBank. Nucleic Acids Res. 2005;33:D34–8.CAS 
    Article 

    Google Scholar 
    Wentworth CK. A scale of grade and class terms for clastic sediments. J Geol. 1922;30:377–92.Article 

    Google Scholar 
    Dean WE. Determination of carbonate and organic matter in calcareous sediments and sedimentary rocks by loss on ignition; comparison with other methods. J Sediment Res. 1974;44:242–8.CAS 

    Google Scholar 
    Tatzber M, Stemmer M, Spiegel H, Katzlberger C, Haberhauer G, Gerzabek MH. An alternative method to measure carbonate in soils by FT-IR spectroscopy. Environ Chem Lett. 2007;5:9–12.CAS 
    Article 

    Google Scholar 
    Hsieh CH, Reiss CS, Hunter JR, Beddington JR, May RM, Sugihara G. Fishing elevates variability in the abundance of exploited species. Nature. 2006;443:859–62.CAS 
    Article 

    Google Scholar 
    Elbrecht V, Braukmann TWA, Ivanova NV, Prosser SWJ, Hajibabaei M, Wright M, et al. Validation of COI metabarcoding primers for terrestrial arthropods. Peer J. 2019;7:e7745–e.Article 

    Google Scholar 
    Kirse A, Bourlat SJ, Langen K, Fonseca VG. Unearthing the potential of Soil eDNA metabarcoding—towards best practice advice for invertebrate biodiversity assessment. Front. Ecol. Evol. 2021;9:630560.Article 

    Google Scholar 
    Zhang GK, Chain FJJ, Abbott CL, Cristescu ME. Metabarcoding using multiplexed markers increases species detection in complex zooplankton communities. Evolut Appl. 2018;11:1901–14.CAS 
    Article 

    Google Scholar 
    Marquina D, Andersson AF, Ronquist F. New mitochondrial primers for metabarcoding of insects, designed and evaluated using in silico methods. Mol Ecol Resour. 2019;19:90–104.CAS 
    Article 

    Google Scholar 
    Leasi F, Sevigny JL, Laflamme EM, Artois T, Curini-Galletti M, de Jesus Navarrete A, et al. Biodiversity estimates and ecological interpretations of meiofaunal communities are biased by the taxonomic approach. Commun Biol. 2018;1:112.Article 

    Google Scholar 
    Giebner H, Langen K, Bourlat SJ, Kukowka S, Mayer C, Astrin JJ, et al. Comparing diversity levels in environmental samples: DNA sequence capture and metabarcoding approaches using 18S and COI genes. Mol Ecol Resour. 2020;20:1333–45.CAS 
    Article 

    Google Scholar 
    Vanhove S, Lee HJ, Beghyn M, Gansbeke DV, Brockington S, Vincx M. The Metazoan Meiofauna in its biogeochemical environment: the case of an Antarctic coastal sediment. J Mar Biol Assoc UK. 1998;78:411–34.Article 

    Google Scholar 
    Pasotti F, Saravia LA, De Troch M, Tarantelli MS, Sahade R, Vanreusel A. Benthic Trophic Interactions in an Antarctic Shallow Water Ecosystem Affected by Recent Glacier Retreat. PLoS ONE. 2015;10:e0141742.Article 

    Google Scholar 
    Griffiths JR, Kadin M, Nascimento FJA, Tamelander T, Tornroos A, Bonaglia S, et al. The importance of benthic-pelagic coupling for marine ecosystem functioning in a changing world. Global Change Biology. 2017;23:2179–96.Article 

    Google Scholar 
    Virta L, Gammal J, Järnström M, Bernard G, Soininen J, Norkko J, et al. The diversity of benthic diatoms affects ecosystem productivity in heterogeneous coastal environments. Ecology. 2019;100:e02765.Article 

    Google Scholar 
    Malviya S, Scalco E, Audic S, Vincent F, Veluchamy A, Poulain J, et al. Insights into global diatom distribution and diversity in the world’s ocean. Proc Natl Acad Sci USA. 2016;113:E1516–25.CAS 
    Article 

    Google Scholar 
    Forster D, Dunthorn M, Mahe F, Dolan JR, Audic S, Bass D, et al. Benthic protists: the under-charted majority. Fems Microbiol Ecol. 2016;92:fiw120.Article 

    Google Scholar 
    Fonseca VG, Carvalho GR, Nichols B, Quince C, Johnson HF, Neill SP, et al. Metagenetic analysis of patterns of distribution and diversity of marine meiobenthic eukaryotes. Glob Ecol Biogeogr. 2014;23:1293–302.Article 

    Google Scholar 
    O’Malley MA. The nineteenth century roots of ‘everything is everywhere’. Nat Rev Microbiol. 2007;5:647–51.Article 

    Google Scholar 
    Pasotti F, Manini E, Giovannelli D, Wölfl A-C, Monien D, Verleyen E, et al. Antarctic shallow water benthos in an area of recent rapid glacier retreat. Mar Ecol. 2015;36:716–33.Article 

    Google Scholar 
    Molari M, Janssen F, Vonnahme TR, Wenzhöfer F, Boetius A. The contribution of microbial communities in polymetallic nodules to the diversity of the deep-sea microbiome of the Peru Basin (4130–4198 m depth). Biogeosciences. 2020;17:3203–22.CAS 
    Article 

    Google Scholar 
    Signori CN, Thomas F, Enrich-Prast A, Pollery RCG, Sievert SM. Microbial diversity and community structure across environmental gradients in Bransfield Strait, Western Antarctic Peninsula. Front Microbiol. 2014;5:647.Article 

    Google Scholar 
    Ozturk RC, Feyzioglu AM, Altinok I. Prokaryotic community and diversity in coastal surface waters along the Western Antarctic Peninsula. Pol Sci. 2021;31:100764.Article 

    Google Scholar 
    Ghiglione JF, Murray AE. Pronounced summer to winter differences and higher wintertime richness in coastal Antarctic marine bacterioplankton. Environ Microbiol. 2012;14:617–29.CAS 
    Article 

    Google Scholar 
    Luria CM, Ducklow HW, Amaral-Zettler LA. Marine bacterial, archaeal and eukaryotic diversity and community structure on the continental shelf of the western Antarctic Peninsula. Aquat Microbial Ecol. 2014;73:107–21.Article 

    Google Scholar 
    Cao S, He J, Zhang F, Lin L, Gao Y, Zhou Q. Diversity and community structure of bacterioplankton in surface waters off the northern tip of the Antarctic Peninsula. Pol Res. 2019;38:3491.Article 

    Google Scholar 
    Walsh EA, Kirkpatrick JB, Rutherford SD, Smith DC, Sogin M, D’Hondt S. Bacterial diversity and community composition from seasurface to subseafloor. ISME J. 2016;10:979–89.Article 

    Google Scholar 
    Kiko R, Werner I, Wittmann A. Osmotic and ionic regulation in response to salinity variations and cold resistance in the Arctic under-ice amphipod Apherusa glacialis. Pol Biol. 2009;32:393–8.Article 

    Google Scholar 
    Zeppilli D, Leduc D, Fontanier C, Fontaneto D, Fuchs S, Gooday AJ, et al. Characteristics of meiofauna in extreme marine ecosystems: a review. Mar Biodivers. 2018;48:35–71.Article 

    Google Scholar 
    Arnosti C, Joergensen BB, Sagemann J, Thamdrup B. Temperature dependence of microbial degradation of organic matter in marine sediments: polysaccharide hydrolysis, oxygen consumption, and sulfate reduction. Mar Ecol Prog Ser. 1998;165:59–70.CAS 
    Article 

    Google Scholar 
    Fabiano M, Danovaro R. Enzymatic activity, bacterial distribution, and organic matter composition in sediments of the ross sea (Antarctica). Appl Environ Microbiol. 1998;64:3838–45.CAS 
    Article 

    Google Scholar 
    Kujawinski EB, Longnecker K, Barott KL, Weber RJM, Kido Soule, MC. Microbial community structure affects marine dissolved organic matter composition. Front Mar Sci. 2016;3:45.Article 

    Google Scholar 
    Barrett JE, Virginia RA, Hopkins DW, Aislabie J, Bargagli R, Bockheim JG, et al. Terrestrial ecosystem processes of Victoria Land, Antarctica. Soil Biol Biochem. 2006;38:3019–34.CAS 
    Article 

    Google Scholar 
    Ganzert L, Lipski A, Hubberten H-W, Wagner D. The impact of different soil parameters on the community structure of dominant bacteria from nine different soils located on Livingston Island, South Shetland Archipelago, Antarctica. Fems Microbiol Ecol. 2011;76:476–91.CAS 
    Article 

    Google Scholar 
    Rusch A, Huettel M, Reimers CE, Taghon GL, Fuller CM. Activity and distribution of bacterial populations in Middle Atlantic Bight shelf sands. Fems Microbiol Ecol. 2003;44:89–100.CAS 
    Article 

    Google Scholar 
    Hemkemeyer M, Dohrmann AB, Christensen BT, Tebbe CC. Bacterial preferences for specific soil particle size fractions revealed by community analyses. Front Microbiol. 2018;9:149.Article 

    Google Scholar 
    Giere O. Meiobenthology: the microscopic motile fauna of aquatic sediments. 2nd ed: Springer-Verlag Berlin Heidelberg; 2009. 527 p.Fonseca VG, Carvalho GR, Sung W, Johnson HF, Power DM, Neill SP, et al. Second-generation environmental sequencing unmasks marine metazoan biodiversity. Nat Commun. 2010;1:98.Article 

    Google Scholar 
    Pitcher RC, Lawton P, Ellis N, Smith SJ, Incze LS, Wei C-L, et al. Exploring the role of environmental variables in shaping patterns of seabed biodiversity composition in regional-scale ecosystems. J Appl Ecol. 2012;49:670–9.Article 

    Google Scholar 
    Rose A, Ingels J, Raes M, Vanreusel A, Arbizu PM. Long-term iceshelf-covered meiobenthic communities of the Antarctic continental shelf resemble those of the deep sea. Heidelberg: Springer; 2014. 743–62 p.Gonçalves-Araujo R, de Souza MS, Tavano VM, Garcia CAE. Influence of oceanographic features on spatial and interannual variability of phytoplankton in the Bransfield Strait, Antarctica. J Mar Syst. 2015;142:1–15.Article 

    Google Scholar 
    Learman DR, Henson MW, Thrash JC, Temperton B, Brannock PM, Santos SR, et al. Biogeochemical and microbial variation across 5500 km of Antarctic surface sediment implicates organic matter as a driver of benthic community structure. Front Microbiol. 2016;7:284.Article 

    Google Scholar 
    Ghiglione JF, Galand PE, Pommier T, Pedros-Alio C, Maas EW, Bakker K, et al. Pole-to-pole biogeography of surface and deep marine bacterial communities. Proc Natl Acad Sci USA. 2012;109:17633–8.CAS 
    Article 

    Google Scholar 
    Rosli N, Leduc D, Rowden A, Probert P. Review of recent trends in ecological studies of deep-sea meiofauna, with focus on patterns and processes at small to regional spatial scales. Mar Biodivers. 2017;48:13–34.Article 

    Google Scholar 
    Ruff SE, Probandt D, Zinkann A-C, Iversen M, Klaas C, Schwabe L, et al. Indications for algae-degrading benthic microbial communities in deep-sea sediments along the Antarctic Polar Front. Deep Sea Res Part II: Top Stud Oceanogr. 2014;108:6–16.Article 

    Google Scholar 
    El-Serehy HA, Al-Rasheid KA, Al-Misned FA, Al-Talasat AA, Gewik MM. Microbial-meiofaunal interrelationships in coastal sediments of the Red Sea. Saudi J Biol Sci. 2016;23:327–34.CAS 
    Article 

    Google Scholar 
    Danovaro R, Company JB, Corinaldesi C, D’Onghia G, Galil B, Gambi C, et al. Deep-sea biodiversity in the Mediterranean Sea: the known, the unknown, and the unknowable. PLoS ONE. 2010;5:e11832.Article 

    Google Scholar 
    Mussmann M, Pjevac P, Kruger K, Dyksma S. Genomic repertoire of the Woeseiaceae/JTB255, cosmopolitan and abundant core members of microbial communities in marine sediments. ISME J. 2017;11:1276–81.CAS 
    Article 

    Google Scholar 
    Hinger I, Pelikan C, Mußmann M. Role of the ubiquitous bacterial family Woeseiaceae for N2O production in marine sediments. Geophys Res Abstracts. 2019;21:17441.
    Google Scholar 
    Hoffmann K, Bienhold C, Buttigieg PL, Knittel K, Laso-Pérez R, Rapp JZ, et al. Diversity and metabolism of Woeseiales bacteria, global members of marine sediment communities. ISME J. 2020;14:1042–56.CAS 
    Article 

    Google Scholar 
    Mare MF. A study of a marine benthic community with special reference to the microorganisms. J Mar Biol Assoc UK. 1942;25:517–54.Article 

    Google Scholar 
    Bott TL, Borchardt MA. Grazing of protozoa, bacteria, and diatoms by Meiofauna in lotic epibenthic communities. J North Am Bentholog Soc. 1999;18:499–513.Article 

    Google Scholar 
    Griffiths HJ. Antarctic marine biodiversity-what do we know about the distribution of life in the Southern Ocean? PLoS ONE. 2010;5:e11683.Article 

    Google Scholar 
    Convey P, Chown SL, Clarke A, Barnes DKA, Bokhorst S, Cummings V, et al. The spatial structure of Antarctic biodiversity. Ecol Monogr. 2014;84:203–44.Article 

    Google Scholar 
    Li L, Ma ZS. Species sorting and neutral theory analyses reveal archaeal and bacterial communities are assembled differently in hot springs. Front Bioeng Biotechnol. 2020;8:464.Article 

    Google Scholar 
    Lee JE, Buckley HL, Etienne RS, Lear G. Both species sorting and neutral processes drive assembly of bacterial communities in aquatic microcosms. Fems Microbiol Ecol. 2013;86:288–302.CAS 
    Article 

    Google Scholar 
    Gansfort B, Fontaneto D, Zhai M. Meiofauna as a model to test paradigms of ecological metacommunity theory. Hydrobiologia. 2020;847:2645–63.Article 

    Google Scholar 
    Convey P, Peck LS. Antarctic environmental change and biological responses. Sci Adv. 2019;5:eaaz0888.CAS 
    Article 

    Google Scholar  More

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    Estimating the expected planting area of double- and single-season rice in the Hunan-Jiangxi region of China by 2030

    China is the world’s most populous country, with a population of over 1.4 billion people, or 18% of the world human population1. However, China has only about 9% of the 1.4 billion hectares of total arable land in the world2. The question of “who will feed China?” raised by Dr. Lester R. Brown in 1995 is still worthy of consideration today, and ensuring food security remains a top priority for the Chinese government3.Rice is the staple food on dining-tables of over 65% of the population in China; thus, adequate rice production is critical to ensure food security in China4. In order to produce more rice on the limited amount of arable land available, double-season rice cropping systems, which involve successively growing early-season rice (ESR) and late-season rice (LSR) from March to November within a single calendar year, have been extensively developed in southern China5. The development of double-season rice cropping systems has made a considerable contribution toward achieving rice self-sufficiency in China6.Hunan and Jiangxi are the top two double-season rice producing provinces in China7. However, in recent years, the planting area devoted to double-season rice has sharply decreased in the Hunan-Jiangxi region as a result of the conversion from double- to single-season rice (SSR) cropping systems (referred as the rice “double-to-single” phenomenon) (Fig. 1A). A reduced rural labor supply and rising labor wages due to urbanization and economic growth are the key driving forces for the rice “double-to-single” phenomenon11. Fortunately, the rice “double-to-single” phenomenon has not resulted in a decrease in total rice production in the Hunan-Jiangxi region (Fig. 1B). During the most recent 10 years (2011–2020), the total rice production in the Hunan-Jiangxi region has been ranged from 45.3 to 48.7 million tons (Mt) with an average of 46.6 Mt, and the contribution of the Hunan-Jiangxi region to rice production in China has been maintained at ~ 22%.Figure 1(A) Planting areas (million hectares, Mha) for early-season rice (ESR), late-season rice (LSR), and single-season rice (SSR) in the Hunan-Jiangxi region and (B) total rice production (million tons, Mt) in the Hunan-Jiangxi region and the contribution of the Hunan-Jiangxi region to total rice production in China from 2011 to 2020. In (B), the dashed line represents the average rice production during 2011–2020. The rice planting area and total rice production in the Hunan-Jiangxi region were calculated based on data collected from the Hunan Provincial Bureau of Statistics8 and the Jiangxi Provincial Bureau of Statistics9. The contribution of the Hunan-Jiangxi region to rice production in China is the percentage of total rice production in the Hunan-Jiangxi region to the total rice production in China. Data for total rice production in China were collected from the National Bureau of Statistics of China10.Full size imageBecause China’s population is still growing12, China must continue to increase rice production. The domestic demand for rice grain in China is expected to reach 217 Mt by 2030, when the population of China is expected to stabilize6. To meet this demand, the Hunan-Jiangxi region will need to produce 47.7 Mt of rice grains, assuming that the contribution of the Hunan-Jiangxi region to rice production in China remains at the level of the most recent 10 years (~ 22%) (Fig. 1B). This expected rice production (ERP) is 1.1 Mt higher than the average total rice production during the most recent 10 years. In order to avoid the negative effect of the “double-to-single” phenomenon on achieving the ERP in the Hunan-Jiangxi region by 2030, it is necessary to estimate how much planting area of double-season rice will be needed in this region by this point in time.The ERP can be expressed by the following formula: ERP = EPAESR × EGYESR + EPALSR × EGYLSR + EPASSR × EGYSSR, where EGYESR, EGYLSR, and EGYSSR are the estimated grain yields of ESR, LSR, and SSR, respectively; and EPAESR, EPALSR, and EPASSR are the estimated planting areas for ESR, LSR, and SSR, respectively. We assume the following conditions in the Hunan-Jiangxi region by 2030: (1) the total paddy field area will be maintained in the range of 4.57–5.02 million hectares (Mha) that was planted during the years 2011–20208,9; (2) the ratio of EPALSR to EPAESR is the same as the average ratio of planting area of LSR to ESR during 2011–2020 (i.e., 1.07) (Fig. 1A); (3) EPASSR is the difference between the total paddy field area and the EPALSR; and (4) EGYESR, EGYLSR, and EGYSSR are projected under three scenarios: (1) constant yield scenario, (2) 5% yield increase scenario, and (3) 10% yield increase scenario (Fig. 2). The baseline yield for all three scenarios is the average grain yields during 2011–2020. The EPAESR, EPALSR, and EPASSR in the Hunan-Jiangxi region needed to achieve the expected rice production by 2030 were obtained by solving the above formula and are shown in Fig. 3.Figure 2(A) Grain yields from 2011 to 2020 and (B) estimated grain yields by 2030 under three scenarios for early-season rice (ESR), late-season rice (LSR), and single-season rice (SSR) in the Hunan-Jiangxi region. The grain yields from 2011 to 2020 were calculated based on data collected from the Hunan Provincial Bureau of Statistics8 and the Jiangxi Provincial Bureau of Statistics9. In (A), ns denotes non-significant trend at the 0.05 probability level (Statistix 8.0, Analytical software, Tallahassee, FL, USA). In (B), the baseline yield for all three scenarios is the average grain yields during 2011–2020.Full size imageFigure 3Estimated planting areas for early-season rice (ESR), late-season rice (LSR), and single-season rice (SSR) in the Hunan-Jiangxi region that will be required to achieve the expected rice production by 2030 under three scenarios: (A) constant yield scenario, (B) 5% yield increase scenario, and (C) 10% yield increase scenario. Mha is million hectares.Full size imageThe results presented in Fig. 3 provide guidance and models for the government’s decision-making process in the planning planting areas for ESR, LSR, and SSR in the Hunan-Jiangxi region. In brief, farmers will need to plant 2.55–3.18 Mha of ESR, 2.73–3.40 Mha of LSR, and 1.17–2.29 Mha of SSR under the constant yield scenario, 2.09–2.72 Mha of ESR, 2.24–2.91 Mha of LSR, and 1.66–2.78 Mha of SSR under the 5% yield increase scenario, and 1.67–2.31 Mha of ESR, 1.79–2.47 Mha of LSR, and 2.10–3.23 Mha of SSR under the 10% yield increase scenario in the Hunan-Jiangxi region by 2030 depending on the total available paddy field area.One thing to note here is that the actual planting areas for ESR (2.44 Mha) and LSR (2.57 Mha) in 2020 are below the estimated lower limits of planting areas for ESR (2.55 Mha) and LSR (2.73 Mha) that will be needed by 2030 under the constant yield scenario, while the actual planting area for SSR in 2020 (2.42 Mha) is above the estimated upper limit for SSR (2.29 Mha) that will be needed by 2030 under the constant yield scenario (Figs. 1A and 3A). This finding indicates that it is urgent to avoid a further aggravated “double-to-single” phenomenon while maintaining the total paddy field area in the Hunan-Jiangxi region. Because it is not an easy task to maintain the total paddy field area under the projected scenario for urban expansion13, the government should prepare an alternative to reverse the “double-to-single” phenomenon in the Hunan-Jiangxi region. Increasing the mechanized level of farming operation and improving economic returns to farmers are two key aspects for the government to take into account to promote the development of double-season rice.Although the current planting area of double-season rice can fully meet the requirement for achieving the ERP in the Hunan-Jiangxi region by 2030 under both the 5% and 10% yield increase scenarios, there is some difficulty in reaching the yield increase targets. In recent years, the planting area of high-quality rice varieties has been rapidly increased in China14. However, grain yield is generally not very high for high-quality rice varieties, although no genetic linkage has been identified between grain yield and quality in rice15. Hence, great efforts are required to develop rice varieties with both high quality and high yield. In addition, rice yields are determined not only by the variety but also by environments and crop management practices. Soil nutrient deficiencies, unfavorable climatic conditions (e.g., heat, cold, and drought), and pest infestations have always been major yield-limiting factors for rice production in China16. Therefore, great efforts are also required to: (1) improve soil fertility of low- and medium-yielding rice fields and optimize nutrient management practices; (2) develop climate-smart agriculture practices for alleviating climatic stresses; and (3) promote integrated pest management practices. More