Ecology

Calow, P. Towards a definition of functional ecology. Funct. Ecol. 1, 57–61 (1987).Article 

Google Scholar 
Violle, C. et al. Let the concept of trait be functional! Oikos 116, 882–892 (2007).Article 

Google Scholar 
McGill, B. J., Enquist, B. J., Weiher, E. & Westoby, M. Rebuilding community ecology from functional traits. Trends Ecol. Evol. 21, 178–185 (2006).Article 
PubMed 

Google Scholar 
Cadotte, M. W. Functional traits explain ecosystem function through opposing mechanisms. Ecol. Lett. 20, 989–996 (2017).Article 
PubMed 

Google Scholar 
Dehling, D. M. & Stouffer, D. B. Bringing the Eltonian niche into functional diversity. Oikos 127, 1711–1723 (2018).Article 

Google Scholar 
Schleuter, D., Daufresne, M., Massol, F. & Argillier, C. A user’s guide to functional diversity indices. Ecol. Monogr. 80, 469–484 (2010).Article 

Google Scholar 
Leinster, T. & Cobbold, C. A. Measuring diversity: the importance of species similarity. Ecology 93, 477–489 (2012).Article 
PubMed 

Google Scholar 
Carmona, C. P., de Bello, F., Mason, N. W. H. & Lepš, J. Traits without borders: integrating functional diversity across scales. Trends Ecol. Evol. 31, 382–394 (2016).Article 
PubMed 

Google Scholar 
Chao, A. et al. An attribute-diversity approach to functional diversity, functional beta diversity, and related (dis)similarity measures. Ecol. Monogr. 89, e01343 (2019).ADS 
Article 

Google Scholar 
Morris, E. K. et al. Choosing and using diversity indices: insights for ecological applications from the German biodiversity exploratories. Ecol. Evol. 4, 3514–3524 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Kattge, J., Bönisch, G. & D’iaz, S. et al. TRY plant trait database – enhanced coverage and open access. Glob. Change Biol. 26, 119–188 (2020).ADS 
Article 

Google Scholar 
Fajardo, A. & Siefert, A. Intraspecific trait variation and the leaf economics spectrum across resource gradients and levels of organization. Ecology 99, 1024–1030 (2018).Article 
PubMed 

Google Scholar 
Mayfield, M. M. et al. What does species richness tell us about functional trait diversity? Predictions and evidence for responses of species and functional trait diversity to land-use change. Glob. Ecol. Biogeogr. 19, 423–431 (2010).
Google Scholar 
Wieczynski, D. J. et al. Climate shapes and shifts functional biodiversity in forests worldwide. Proc. Natl. Acad. Sci. 116, 587–592 (2019).CAS 
Article 
PubMed 

Google Scholar 
Tilman, D., Isbell, F. & Cowles, J. M. Biodiversity and ecosystem functioning. Annu. Rev. Ecol. Evol. Syst. 45, 471–493 (2014).Article 

Google Scholar 
Petchey, O. L. & Gaston, K. J. Functional diversity: back to basics and looking forward. Ecol. Lett. 9, 741–758 (2006).Article 
PubMed 

Google Scholar 
Bolnick, D. I. et al. Why intraspecific trait variation matters in community ecology. Trends Ecol. Evol. 26, 183–192 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Violle, C. et al. The return of the variance: intraspecific variability in community ecology. Trends Ecol. Evol. 27, 244–252 (2012).Article 
PubMed 

Google Scholar 
Díaz, S. & Cabido, M. Vive la différence: plant functional diversity matters to ecosystem processes. Trends Ecol. Evol. 16, 646–655 (2001).Article 

Google Scholar 
Stuart-Smith, R. D. et al. Integrating abundance and functional traits reveals new global hotspots of fish diversity. Nature 501, 539–542 (2013).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Hillebrand, H., Bennett, D. M. & Cadotte, M. W. Consequences of dominance: a review of evenness effects on local and regional ecosystem processes. Ecology 89, 1510–1520 (2008).Article 
PubMed 

Google Scholar 
Loreau, M. The Challenges of Biodiversity Science. Excellence in Ecology Series (International Ecology Institute, 21385 Oldendorf/Luhe, Germany, 2010).Hulshof, C. M. et al. Intra-specific and inter-specific variation in specific leaf area reveal the importance of abiotic and biotic drivers of species diversity across elevation and latitude. J. Veg. Sci. 24, 921–931 (2013).Article 

Google Scholar 
Siefert, A. et al. A global meta-analysis of the relative extent of intraspecific trait variation in plant communities. Ecol. Lett. 18, 1406–1419 (2015).Article 
PubMed 

Google Scholar 
Dall, S. R. X., Bell, A. M., Bolnick, D. I. & Ratnieks, F. L. W. An evolutionary ecology of individual differences. Ecol. Lett. 15, 1189–1198 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Bolnick, D. I. & Ballare, K. M. Resource diversity promotes among individual diet variation, but not genomic diversity, in lake stickleback. Ecol. Lett. 23, 495–505 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Lande, R. & Arnold, S. J. The measurement of selection on correlated characters. Evolution 37, 1210–1226 (1983).Article 
PubMed 

Google Scholar 
Mullon, C. & Lehmann, L. An evolutionary quantitative genetics model for phenotypic (co)variance under limited dispersal, with an application to socially synergistic traits. Evolution 73, 1695–1728 (2019).Article 
PubMed 

Google Scholar 
Taper, M. L. & Case, T. J. Quantitative genetic models for the coevolution of character displacement. Ecology 66, 355–371 (1985).Article 

Google Scholar 
Engen, S., Grotan, V., Saether, B.-E. & Coste, C. F. D. An evolutionary and ecological community model for distribution of phenotypes and abundances among competing species. Am. Natur. 198, 1 (2021). https://doi.org/10.1086/714529.Kohyama, T. & Takada, T. The stratification theory for plant coexistence promoted by one-sided competition. J. Ecol. 97, 463–471 (2009).Article 

Google Scholar 
Kinzig, A. P., Levin, S. A., Dushoff, J. & Pacala, S. W. Limiting similarity, species packing, and system stability for hierarchical competition-colonization models. Am. Nat. 153, 371–383 (1999).CAS 
Article 
PubMed 

Google Scholar 
Adler, F. R. & Mosquera, J. Is space necessary? Interference competition and limits to biodiversity. Ecology 81, 3226–3232 (2000).Article 

Google Scholar 
Suding, K. N. et al. Scaling environmental change through the community-level: a trait-based response-and-effect framework for plants. Glob. Change Biol. 14, 1125–1140 (2008).ADS 
Article 

Google Scholar 
Loreau, M. & Hector, A. Partitioning selection and complementarity in biodiversity experiments. Nature 412, 72–76 (2001).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Parent, C. E. & Crespi, B. J. Ecological opportunity in adaptive radiation of Galápagos endemic land snails. Am. Nat. 174, 898–905 (2009).Article 
PubMed 

Google Scholar 
Geist, D. J., Snell, H., Snell, H., Goddard, C. & Kurz, M. D. A. Paleogeographic Model of the Galápagos Islands and Biogeographical and Evolutionary Implications. In Geophysical Monograph Series, (eds Harpp, K. S., Mittelstaedt, E., d’Ozouville, N. & Graham, D. W.), chap. 8, 145–166 (2014).Parent, C. E. & Crespi, B. J. Sequential colonization and diversification of Galápagos endemic land snail genus Bulimulus (Gastropoda, Stylommatophora). Evolution 60, 2311–2328 (2006).CAS 
PubMed 

Google Scholar 
Parent, C. E. Diversification on islands: bulimulid land snails of Galápagos. Ph.D. thesis, Simon Fraser University, Burnaby, Canada (2008).Kraemer, A. C., Roell, Y. E., Shoobs, N. F. & Parent, C. E. Does island ontogeny dictate both the accumulation of species richness and functional diversity? Glob. Ecol. Biogeogr. 31, 123–137 (2021).Kraemer, A. C., Philip, C. W., Rankin, A. M. & Parent, C. E. Trade-offs direct the evolution of coloration in Galápagos land snails. Proc. R. Soc. B 286, 20182278 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Barabás, G. & D’Andrea, R. The effect of intraspecific variation and heritability on community pattern and robustness. Ecol. Lett. 19, 977–986 (2016).Article 
PubMed 

Google Scholar 
Barton, N. H., Etheridge, A. M. & Véber, A. The infinitesimal model: definition, derivation, and implications. Theor. Popul. Biol. 118, 50–73 (2017).CAS 
MATH 
Article 
PubMed 

Google Scholar 
Govaert, L. et al. Eco-evolutionary feedbacks—theoretical models and perspectives. Funct. Ecol. 33, 13–30 (2019).Article 

Google Scholar 
Keddy, P. A. & Shipley, B. Competitive hierarchies in herbaceous plant communities. Oikos 54, 234–241 (1989).Article 

Google Scholar 
Allesina, S. et al. Predicting the stability of large structured food webs. Nat. Commun. 6, 7842 (2015).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Kandlikar, G. S., Johnson, C. S., Yan, X., Kraft, N. J. B. & Levine, J. M. Winning and losing with microbes: how microbially mediated fitness differences influence plant diversity. Ecol. Lett. 22, 1178–1191 (2019).PubMed 

Google Scholar 
Reich, P. B. et al. Impacts of biodiversity loss escalate through time as redundancy fades. Science 336, 589–592 (2012).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Spaak, J. W. & De Laender, F. Effects of pigment richness and size variation on coexistence, richness and function in light limited phytoplankton. J. Ecol. 109, 2385–2394 (2021).Article 

Google Scholar 
Parain, E. C., Rohr, R. P., Gray, S. M. & Bersier, L.-F. Increased temperature disrupts the biodiversity–ecosystem functioning relationship. Am. Nat. 193, 227–239 (2019).Article 
PubMed 

Google Scholar 
Gonzalez, A. et al. Scaling-up biodiversity-ecosystem functioning research. Ecol. Lett. 23, 757–776 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Leibold, M. A., Urban, M. C., De Meester, L., Klausmeier, C. A. & Vanoverbeke, J. Regional neutrality evolves through local adaptive niche evolution. Proc. Natl Acad. Sci. USA 116, 2612–2617 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Dieckmann, U. & Doebeli, M. On the origin of species by sympatric speciation. Nature 400, 354–357 (1999).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Edwards, K. F. et al. Evolutionarily stable communities: a framework for understanding the role of trait evolution in the maintenance of diversity. Ecol. Lett. 21, 1853–1868 (2018).Article 
PubMed 

Google Scholar 
Bolnick, D. I., Svanbäck, R., Araujo, M. S. & Persson, L. Comparative support for the niche variation hypothesis that more generalized populations also are more heterogeneous. Proc. Natl Acad. Sci. USA 104, 10075–10079 (2007).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Van Valen, L. Morphological variation and width of ecological niche. Am. Nat. 99, 377–390 (1965).Article 

Google Scholar 
Goodfriend, G. A. Variation in land-snail shell form and size and its causes: a review. Syst. Biol. 35, 204–223 (1986).Article 

Google Scholar 
Machin, J. Structural adaptation for reducing water-loss in three species of terrestrial snail. J. Zool. 152, 55–65 (1967).Article 

Google Scholar 
McMahon, R. F. Thermal tolerance, evaporative water loss, air-water oxygen consumption and zonation of intertidal prosobranchs: a new synthesis. In Progress in Littorinid and Muricid Biology, 241–260 (Springer, Dordrecht, The Netherlands, 1990).Rees, B. B. & Hand, S. C. Heat dissipation, gas exchange and acid-base status in the land snail oreohelix during short-term estivation. J. Exp. Biol. 152, 77–92 (1990).Article 

Google Scholar 
Newkirk, G. F. & Doyle, R. W. Genetic analysis of shell-shape variation in Littorina saxatilis on an environmental cline. Mar. Biol. 30, 227–237 (1975).Article 

Google Scholar 
Seeley, R. H. Intense natural selection caused a rapid morphological transition in a living marine snail. Proc. Natl Acad. Sci. USA 83, 6897–6901 (1986).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Schmidt-Nielsen, K., Taylor, C. R. & Shkolnik, A. Desert snails: problems of heat, water and food. J. Exp. Biol. 55, 385–398 (1971).CAS 
Article 
PubMed 

Google Scholar 
Xavier Jordani, M. et al. Intraspecific and interspecific trait variability in tadpole metacommunitiees from the Brazilian Atlantic rainforest. Ecol. Evol. 9, 4025–4037 (2019).Article 
PubMed 
PubMed Central 

Google Scholar  More

Vorontsova, M. S., Clark, L. G., Dransfield, J., Govaerts, R. H. A. & Baker, W. J. World Checklist of Bamboos and Rattans 102 (Science Press, 2017).
Google Scholar 
Lobovikov, M., Paudel, S., Ball, L., Piazza, M., Guardia, M., Ren, H., Russo, L. & Wu, J. World bamboo resources: a thematic study prepared in the framework of the global forest resources assessment 2005. Food & Agriculture Org., (2007).FAO. Global Forest Resources Assessment 2020: Main report, Rome. Accessed 18 Nov 2021. https://www.fao.org/3/ca9825en/ca9825en.pdf. https://doi.org/10.4060/ca9825en (2020).ISFR http://www.indiaenvironmentportal.org.in/files/file/isfr-fsi-vol1.pdf (Accessed November 18 2021) (2019).Salam, K. Connecting the poor: bamboo, problems and prospect. South Asia Bamboo Foundation (SABF) (2013) retrieved 17 December 2013 from jeevika.org/bamboo/2g-article-fornbda.docx.INBAR. Accessed 18 Nov 2021. https://www.inbar.int/global-programmes/.Osman, A. I., Abdelkader, A., Johnston, C. R., Morgan, K. & Rooney, D. W. Thermal investigation and kinetic modeling of lignocellulosic biomass combustion for energy production and other applications. Ind. Eng. Chem. Res. 56, 12119–12130 (2017).CAS 
Article 

Google Scholar 
Fawzy, S., Osman, A., Doran, J. & Rooney, D. W. Strategies for mitigation of climate change: a review. Environ. Chem. Lett. 18, 2069–2094 (2020).CAS 
Article 

Google Scholar 
IPCC. Global warming of 1.5 °C. In: Masson-Delmotte, V., Zhai, P., Pörtner, H.-O., Roberts, D., Skea, J., Shukla, P. R., Pirani, A., Moufouma-Okia, W., Péan, C., Pidcock, R., Connors, S., Matthews, J. B. R., Chen, Y., Zhou, X., Gomis, M. I., Lonnoy, E., Maycock, T., Tignor, M., & Waterfeld, T. (eds) An IPCC special report on the impacts of global warming of 1.5 °C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and eforts to eradicate poverty (2018). https://www.ipcc.ch/site/assets/uploads/sites/2/2019/06/SR15_Full_Report_High_Res.pdf (Accessed 22 Dec 2019).Osman, A. et al. Conversion of biomass to biofuels and life cycle assessment: a review. Environ. Chem. Lett. 19, 4075–4118 (2021).CAS 
Article 

Google Scholar 
Balajii, M. & Niju, S. Biochar-derived heterogeneous catalysts for biodiesel production. Environ. Chem. Lett. 17, 1447–1469. https://doi.org/10.1007/s10311-019-00885-x (2019).CAS 
Article 

Google Scholar 
Gunarathne, V., Ashiq, A., Ramanayaka, S., Wijekoon, P. & Vithanage, M. Biochar from municipal solid waste for resource recovery and pollution remediation. Environ. Chem. Lett. 17, 1225–1235. https://doi.org/10.1007/s10311-019-00866-0 (2019).CAS 
Article 

Google Scholar 
Lobovikov, M., Schoene, D. & Yping, L. Bamboo in climate change and rural livelihood. Mitig. Adapt. Strateg. Glob. Change 17, 261–276 (2012).Article 

Google Scholar 
Yuen, J. Q., Fung, T. & Ziegler, A. D. Carbon stocks in bamboo ecosystems worldwide: estimates and uncertainties. For. Ecol. Manag. 393, 113–138 (2017).Article 

Google Scholar 
Devi, A. S. & Singh, K. S. Carbon storage and sequestration potential in aboveground biomass of bamboos in North East India. Sci. Rep. 11, 837 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nath, A. J., Lal, R. & Das, A. K. Managing woody bamboos for carbon farming and carbon trading. Glob. Ecol. Conserv. 3, 654–663 (2015).Article 

Google Scholar 
UNFCCC. Thirty-ninth Meeting of the Clean Development Mechanism Executive Board. UN Campus, Langer Eugen, Hermann-Ehlers-Str. 10, 53113 Bonn, Germany (2008).FTFA. Food and Trees for Africa. World’s First Bamboo Carbon Offset Credits Issued under the VCS in the Voluntary Carbon Market. In: trees.co.za (2012).Sharma, R., Wahono, J. & Baral, H. Bamboo as an alternative bioenergy crop and powerful ally for land restoration in Indonesia. Sustainability 10, 4367 (2018).Article 

Google Scholar 
Chin, K. L. et al. Bioenergy production from bamboo: potential source from Malaysia’s perspective. Bioresources 12, 6844–6867 (2017).CAS 
Article 

Google Scholar 
Littlewood, J., Wang, L., Tumbull, C. & Murphy, R. J. Techno-economic potential of bioethanol from bamboo in China. Biotechnol. Biofuels 6, 173–173 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Buckingham, K. et al. The potential of bamboo is constrained by outmoded policy frames. Ambio 40, 544–548 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
IPCC shorturl.at/bguxF (Accessed November 18 2021) (2003).Kempes, C. P., West, G. B., Crowell, K. & Girvan, M. Predicting maximum tree heights and other traits from allometric scaling and resource limitations. PLoS ONE 6(6), e20551 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sileshi, G. W. A critical review of forest biomass estimation models, common mistakes and corrective measures. For. Ecol. Manag. 329, 237–254 (2014).Article 

Google Scholar 
Verma, A. et al. Predictive models for biomass and carbon stocks estimation in Grewia optiva on degraded lands in western Himalaya. Agrofor. Syst. 88(5), 895–905 (2014).Article 

Google Scholar 
Gao, X. et al. Modeling of the height–diameter relationship using an allometric equation model: a case study of stands of Phyllostachys edulis. J. For. Res. 27, 339–347 (2016).CAS 
Article 

Google Scholar 
Huy, B. & Long, T. T. A manual for bamboo forest biomass and carbon assessment, INBAR technical report (2019).https://www.inbar.int/resources/inbar_publications/a-manual-for-bamboo-forest-biomass-and-carbon-assessment/ (Accessed November 18 2021).Brahma, B. et al. A critical review of forest biomass estimation equations in India. Trees For. People 5, 100098. https://doi.org/10.1016/j.tfp.2021.100098 (2021).Article 

Google Scholar 
Yen, T. M., Ji, Y. J. & Lee, J. S. Estimating biomass production and carbon storage for a fast-growing makino bamboo (Phyllostachys makinoi) plant based on the diameter distribution model. For. Ecol. Manag. 260, 339–344. https://doi.org/10.1016/j.foreco.2010.04.021 (2010).Article 

Google Scholar 
FAO. Guidelines on Destructive Measurement for Forest Biomass Estimation (FAO, Rome, 2012).Yen, T. M. Comparing aboveground structure and aboveground carbon storage of an age series of moso bamboo forests subjected to different management strategies. J. For. Res. 20, 1–8 (2015).CAS 
Article 

Google Scholar 
Yuen, J. Q., Fung, T. & Ziegler, A. D. Carbon stocks in bamboo ecosystem worldwide: estimates and uncertainties. For. Ecol. Manag. 393, 113–138 (2017).Article 

Google Scholar 
Nath, A. J., Das, G. & Das, A. K. Above ground standing biomass and carbon storage in village bamboos in North East India. Biomass Bioenergy 33, 1188–1196 (2009).Article 

Google Scholar 
Rawat, R. S., Arora, G., Rawat, V. R. S., Borah, H. R., Singson, M. Z., Chandra, G., Nautiyal, R. & Rawat, J. Estimation of biomass and carbon stock of bamboo species through development of allometric equations. Indian Council of Forestry Research and Education, Dehradun, INDIA (2018).Tripathi, S. K. & Singh, K. P. Productivity and nutrient cycling in recently harvested and mature bamboo savannas in the dry tropics. J. Appl. Ecol. 31, 109–124 (1994).Article 

Google Scholar 
Kaushal, R. et al. Predictive models for biomass and carbon stock estimation in male bamboo (Dendrocalamus strictus L.) in Doon valley, India. Acta Ecol. Sin. 36, 469–476 (2016).Article 

Google Scholar 
Das, D. & Chaturvedi, O. P. Bambusa bambos (L.) Voss plantation in eastern India: I. Culm recruitment, dry matter dynamics and carbon flux. J. Bamboo Rattan 5(1&2), 47–59 (2006).
Google Scholar 
Shanmughavel, P. & Francis, K. Above ground biomass production and nutrient distribution in growing bamboo (Bambusa bambos (L.) Voss). Biomass Bioenergy 10(5/6), 383–91 (1996).CAS 
Article 

Google Scholar 
Seethalakshmi, K. K. & Kumar, M. Bamboos of India: A Compendium. Kerala Forest Research Institute, Peechi and International Network for Bamboo and Rattan, Beijing (1998).Yen, T. M., Ji, Y. J. & Lee, J. S. Estimating biomass production and carbon storage for a fast-growing makino bamboo (Phyllostachys makinoi) plant based on the diameter distribution model. For. Ecol. Manag. 260, 339–344. https://doi.org/10.1016/j.foreco.2010.04.021 (2010).Article 

Google Scholar 
FAO. Guidelines on Destructive Measurement for Forest Biomass Estimation (FAO, Rome, 2012).Huy, B. et al. Allometric equations for estimating tree aboveground biomass in evergreen broadleaf forests of Vietnam. For. Ecol. Manag. 382, 193–205 (2016).Article 

Google Scholar 
Huy, B. et al. Allometric equations for estimating tree aboveground biomass in tropical dipterocarp forests of Vietnam’. Forests 7(180), 1–19 (2016).
Google Scholar 
Huy, B., Poudel, K. P. & Temesgen, H. Aboveground biomass equations for evergreen broadleaf forests in South Central coastal ecoregion of Vietnam: selection of eco-regional or pantropical models’. For. Ecol. Manag. 376, 276–283 (2016).Article 

Google Scholar 
Akaike, H. Information theory as an extension of the maximum likelihood principle’. In Petrov, B. N. & Csaki, F. E. (eds) Proceedings of the 2nd international symposium on information theory. Budapest: Akademiai Kiado, 267–281 (1973).Schwarz, G. E. Estimating the dimension of a model. Ann. Stat. 6(2), 461–464 (1978).MathSciNet 
MATH 
Article 

Google Scholar 
Huy, B. Methodology for developing and cross-validating allometric equations for estimating forest tree biomass. HCM City: Science & Technology, 238 (2017a).Huy, B. Statistical informatics in forestry. HCM City: Science & Technology, 282 (2017b).Huy, B., Tinh, N. T., Poudel, K. P., Frank, B. M. & Temesgen, H. Taxon-specific modeling systems for improving reliability of tree aboveground biomass and its components estimates in tropical dry dipterocarp forests. For. Ecol. Manag. 437, 156–174 (2019).Article 

Google Scholar 
Huy, B., Thanh, G. T., Poudel, K. P. & Temesgen, H. Individual plant allometric equations for estimating aboveground biomass and its components for a common bamboo species (Bambusa procera A. Chev. and A Camus) in tropical forests. Forests 10, 1–17 (2019).Article 

Google Scholar 
Mayer, D. G. & Butler, D. G. Statistical validation. Ecol. Model. 68, 21–32 (1993).Article 

Google Scholar 
Chave, J. et al. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145, 87–99 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Basuki, T. M., Van Laake, P. E., Skidmore, A. K. & Hussin, Y. A. Allometric equations for estimating the aboveground biomass in the tropical lowland Dipterocarp forests’. For. Ecol. Manag. 257, 1684–1694 (2009).Article 

Google Scholar 
Kaushal, R. et al. Rooting behavior and soil properties in different bamboo species of Western Himalayan Foothils, India. Sci. Rep. 10, 4966 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kramer, P. J. & Kozlowski, T. T. Physiology of Wood Plants 628–702 (McGraw Hill, 1979).
Google Scholar 
IPCC Available at http://www.ipcc.ch. AccessedOctober2008 (2008).Yen, T. M., Ji, Y. J. & Lee, J. S. Estimating biomass production and carbon storage for a fast-growing makino bamboo (Phyllostachys makinoi) plant based on the diameter distribution model. For. Ecol. Manag. 260, 339–344 (2010).Article 

Google Scholar 
Inoue, A., Sakamoto, S., Suga, H., Kitazato, H. & Sakuta, K. Construction of one-way volume table for the three major useful bamboos in Japan. J. For. Res. 18, 323–334 (2013).Article 

Google Scholar 
Kralicek, K., Huy, B., Poudel, K. P., Temesgen, H. & Salas, C. Simultaneous estimation of above- and below-ground biomass in tropical forests of Vietnam. For. Ecol. Manag. 390, 147–156 (2017).Article 

Google Scholar 
Montes, N., Gauquelin, W., Badri, V., Bertaudiere, E. H. & Zaoui, A. A non-destructive method for estimating aboveground forest biomass in threatended woodlands. For. Ecol. Manag. 130, 37–46 (2000).Article 

Google Scholar 
Verma, A. et al. Predictive models for biomass and carbon stocks estimation in Grewia optiva on degraded lands in western Himalaya. Agrofor. Syst. 88, 895–905. https://doi.org/10.1007/s10457-014-9734-1 (2014).Article 

Google Scholar 
Singnar, P. et al. Allometric scaling, biomass accumulation and carbon stocks in different aged stands of thin-walled bamboos Schizostachyum dullooa Pseudostachyum polymorphum and Melocanna baccifera. For. Ecol. Manag. 395, 81–91. https://doi.org/10.1016/j.foreco.2017.04.001 (2017).Article 

Google Scholar 
Huang, S., Price, D. & Titus, S. J. Development of ecoregion-based height diameter models for white spruce in boreal forests. For. Ecol. Manag. 129, 125–141 (2000).Article 

Google Scholar 
Yen, T. M. Culm height development, biomass accumulation and carbon storage in an initial growth stage for a fast-growing moso bamboo (Phyllostachy pubescens). Bot. Stud. 57, 10 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Tripathi, S. K. & Singh, K. P. Culm recruitment, dry matter dynamics and carbon flux in recently harvested and mature bamboo savannas in the Indian dry tropics. Ecol. Res. 11, 149–164 (1996).Article 

Google Scholar 
Singh, A. N. & Singh, J. S. Biomass, net primary production and impact of bamboo plantation on soil redevelopment in a dry tropical region. For. Ecol. Manag. 119, 195–207 (1999).Article 

Google Scholar 
Das, D. K. & Chaturvedi, O. P. Bambusa bambos (L.) Voss plantation in eastern India: I. Culm recruitment, dry matter dynamics and carbon flux. J. Bamboo Rattan 5, 47–59 (2006).
Google Scholar 
Shanmughavel, P. & Francis, K. Above ground biomass production and nutrient distribution in growing bamboo (Bambusa bambos (L.) Voss). Biomass Bioenergy 10, 383–391 (1996).CAS 
Article 

Google Scholar 
Arnoult, S. & Brancourt-Hulmel, M. A review on miscanthus biomass production and composition for bioenergy use: genotypic and environmental variability and implications for breeding. Bioenergy Res. 8, 502–526 (2015).CAS 
Article 

Google Scholar 
Nath, A. J., Das, G. & Das, A. K. Above ground standing biomass and carbon storage in village bamboos in North East India. Biomass Bioenergy 33, 1188–1196 (2009).Article 

Google Scholar 
Bargali, S. S., Singh, S. P. & Singh, R. Structure and function of an age series of eucalyptus plantations in central Himalaya I. Dry matter dynamics. Ann. Bot. 69, 405–411 (1992).Article 

Google Scholar 
Rizvi, R. H., Dhyani, S. K., Yadav, R. S. & Ramesh, S. Biomass production and carbon stock of poplar agroforestry systems in Yamunanagar and Saharanpur districts of North western India. Curr. Sci. 100, 736–742 (2011).CAS 

Google Scholar 
Kanime, N. et al. Biomass production and carbon sequestration in different tree-based systems of Central Himalayan Tarai region. For Trees Livelihoods 22(1), 38–50 (2013).Article 

Google Scholar 
Arora, G. et al. Growth, biomass, carbon stocks and sequestration in age series Populus deltoides plantations in Tarai region of central Himalaya. Turk. J. Agric. For. https://doi.org/10.3906/tar-1307-94 (2013).Article 

Google Scholar 
Song, X. et al. Carbon sequestration by Chinese bamboo forests and their ecological benefits: assessment of potential, problems, and future challenges. Environ. Rev. 19, 418–428 (2011).CAS 
Article 

Google Scholar 
Winjum, J. K., Dixon, R. C. & Schroeder, P. E. Carbon storage in forest plantations and their wood products. J. World Resour. Manag. 8, 1–19 (1997).
Google Scholar 
Yadava, A. K. Biomass production and carbon sequestration in different agroforestry systems of Tarai region. Indian For. 136(2), 234–244 (2010).
Google Scholar 
Lou, Y., Li, Y., Buckingham, K., Henley, G. & Zhou, G. Bamboo and Climate change mitigation: a comparative analysis of carbon sequestration. In International Network for Bamboo and Rattan (INBAR), Beijing (2010).Nair, P. K. R., Kumar, B. M. & Nair, V. D. Agroforestry as a strategy for carbon sequestration. J. Plant Nutr. Soil Sci. 172, 10–23 (2009).CAS 
Article 

Google Scholar  More

Spodek, M., Ben-Dov, Y., Protasov, A., Carvalho, C. J. & Mendel, Z. First record of Dactylopius opuntiae (Cockerell) (Hemiptera: Coccoidea: Dactylopiidae) from Israel. Phytoparasitica 42(3), 377–379. https://doi.org/10.1007/s12600-013-0373-2 (2014).Article 

Google Scholar 
García Morales, M., Denno, B. D., Miller, D. R., Miller, G. L., Ben-Dov, Y. & Hardy, N. B. ScaleNet: a literature-based model of scale insect biology and systematic (2016).Bouharroud, R., Amarraque, A. & Qessaoui, R. First report of the Opuntia cochineal scale Dactylopius opuntiae (Hemiptera: Dactylopiidae) in Morocco. EPPO Bull. 46(2), 308–310. https://doi.org/10.1111/epp.12298 (2016).Article 

Google Scholar 
Vanegas-Rico, J. M. et al. Biology and life history of Hyperaspis trifurcata feeding on Dactylopius opuntiae. Biocontrol 61(6), 691–701. https://doi.org/10.1007/s10526-016-9753-0 (2016).Article 

Google Scholar 
Mann, J. Cactus-feeding insects and mites. Bull. US. Nat. Mus. 256, 1–15 (1969).
Google Scholar 
Vanegas-Rico, J. M. et al. Hyperaspis trifurcata (Coleoptera: Coccinellidae) and its parasitoids in Central Mexico. Rev. Colomb. Entomol. 41(2), 194–199 (2015).
Google Scholar 
Lopes, E. B., Albuquerque, I. C., Brito, C. H. & Batista, J. D. L. Velocidade de dispersão de dactylopius opuntiae em palma gigante (opuntia fícus-indica). Rev. Bras. Eng. Agric. Ambient. 6(2), 644–649 (2009).
Google Scholar 
Badii, M. H. & Flores, A. E. Prickly pear cacti pests and their control in Mexico. Fla. Entomol. 84, 503–505. https://doi.org/10.2307/3496379 (2001).Article 

Google Scholar 
Sbaghi, M., Bouharroud, R., Boujghagh, M. & El Bouhssini, M. Sources de résistance d’Opuntia spp. contre la cochenille à carmin, Dactylopius opuntiae, au Maroc. EPPO Bull. 49(3), 585–592. https://doi.org/10.1111/epp.12606 (2019).Article 

Google Scholar 
Khan, H. A. A., Sayyed, A. H., Akram, W., Razald, S. & Ali, M. Predatory potential of Chrysoperla carnea and Cryptolaemus montrouzieri larvae on different stages of the mealybug, Phenacoccus solenopsis: A threat to cotton in South Asia. J. Insect. Sci. 12(1), 147. https://doi.org/10.1673/031.012.14701 (2012).Article 
PubMed Central 

Google Scholar 
El Aalaoui, M., Bouharroud, R., Sbaghi, M., El Bouhssini, M. & Hilali, L. Seasonal biology of Dactylopius opuntiae (Hemiptera: Dactylopiidae) on Opuntia ficus-indica (Caryophyllales: Cactaceae) under field and semi-field conditions in Morocco. Ponte. 1, 259–327. https://doi.org/10.21506/j.ponte.2020.1.17 (2020).Article 

Google Scholar 
Flores, A., Olvera, H., Rodríguez, S. & Barranco, J. Predation potential of Chilocorus cacti (Coleoptera: Coccinellidae) to the prickly pear cacti pest Dactylopius opuntiae (Hemiptera: Dactylopiidae). Neotrop. Entomol. 42(4), 407–411. https://doi.org/10.1007/s13744-013-0139-z (2013).CAS 
Article 
PubMed 

Google Scholar 
Galloway, T. & Handy, R. Immunotoxicity of organophosphorous pesticides. Ecotoxicology 12(1), 345–363. https://doi.org/10.1023/A:1022579416322 (2003).CAS 
Article 
PubMed 

Google Scholar 
Arias-Estévez, M. et al. The mobility and degradation of pesticides in soils and the pollution of groundwater resources. Agric. Ecosyst. Environ. 123(4), 247–260. https://doi.org/10.1016/j.agee.2007.07.011 (2008).CAS 
Article 

Google Scholar 
Palacios-Mendoza, C., Nieto-Hernández, R., Llanderal-Cázares, C. & González-Hernández, H. Efectividad biológica de productos biodegradables para el control de la cochinilla silvestre Dactylopius opuntiae (Cockerell) (Homoptera: Dactylopiidae). Acta. Zool. Mex. 20(3), 99–106 (2004).
Google Scholar 
Borges, L. R. et al. Use of biodegradable products for the control of Dactylopius opuntiae (Hemiptera: Dactylopiidae) in cactus pear. Acta. Hortic. 995, 379–386. https://doi.org/10.17660/ActaHortic.2013.995.49 (2013).Article 

Google Scholar 
Carneiro-Leão, M. P., Tiago, P. V., Medeiros, L. V., da Costa, A. F. & de Oliveira, N. T. Dactylopius opuntiae: Control by the Fusarium incarnatum–equiseti species complex and confirmation of mortality by DNA fingerprinting. J. Pest. Sci. 90(3), 925–933. https://doi.org/10.1007/s10340-017-0841-4 (2017).Article 

Google Scholar 
da Silva Santos, A. C., Oliveira, R. L. S., da Costa, A. F., Tiago, P. V. & de Oliveira, N. T. Controlling Dactylopius opuntiae with Fusarium incarnatum–equiseti species complex and extracts of Ricinus communis and Poincianella pyramidalis. J. Pest. Sci. 89(2), 539–547. https://doi.org/10.1007/s10340-015-0689-4 (2016).Article 

Google Scholar 
Tiago, P. V. et al. Polymorphisms in entomopathogenic fusaria based on inter simple sequence repeats. Biocontrol Sci. Technol. 26(10), 1401–1410. https://doi.org/10.1080/09583157.2016.1210084 (2016).Article 

Google Scholar 
Ramdani, C., Bouharroud, R., Sbaghi, M., Mesfioui, A. & El Bouhssini, M. Field and laboratory evaluations of different botanical insecticides for the control of Dactylopius opuntiae (Cockerell) on cactus pear in Morocco. Int. J. Trop. Insect. Sci. 41(2), 1623–1632. https://doi.org/10.1007/s42690-020-00363-w (2021).Article 

Google Scholar 
El-Aalaoui, M. et al. Comparative toxicity of different chemical and biological insecticides against the scale insect Dactylopius opuntiae and their side effects on the predator Cryptolaemus montrouzieri. Arch. Phytopathol. Plant. Prot. 52(1–2), 155–169. https://doi.org/10.1080/03235408.2019.1589909 (2019).CAS 
Article 

Google Scholar 
El-Aalaoui, M., Bouharroud, R., Sbaghi, M., El Bouhssini, M. & Hilali, L. Predatory potential of eleven native Moroccan adult ladybird species on different stages of Dactylopius opuntiae (Cockerell)(Hemiptera: Dactylopiidae). EPPO Bull. 49(2), 374–379. https://doi.org/10.1111/epp.12565 (2019).Article 

Google Scholar 
El-Aalaoui, M., Bouharroud, R., Sbaghi, M., El Bouhssini, M. & Hilali, L. First study of the biology of Cryptolaemus montrouzieri and its potential to feed on the mealybug Dactylopius opuntiae (Hemiptera: Dactylopiidae) under laboratory conditions in Morocco. Arch. Phytopathol. Plant. Prot. 52(13–14), 1112–1124. https://doi.org/10.1080/03235408.2019.1691904 (2019).CAS 
Article 

Google Scholar 
Lester, P. J., Thistlewood, H. M. A. & Harmsen, R. Some effects of pre-release host-plant on the biological control of Panonychus ulmi by the predatory mite Amblyseius fallacis. Exp. Appl. Acarol. 24(1), 19–33. https://doi.org/10.1023/A:1006345119387 (2000).CAS 
Article 
PubMed 

Google Scholar 
Poinar, G. O. Description and biology of a new insect parasitic rhabditoid, Heterorhabditis bacteriophora n. Gen., n. Sp. (Rhabditida: Heterorhabditidae n. Fam.). Nematol. 21(4), 463–470. https://doi.org/10.1163/187529275X00239 (1976).Article 

Google Scholar 
Boemare, N., Akhurst, R. & Mourant, R. DNA relatedness between Xenorhabdus spp. (Enterobacteriaceae), symbiotic bacteria of entomopathogenic nematodes, and a proposal to transfer Xenorhabdus luminescens to a new genus, Photorhabdus gen-nov.. Int. J. Syst. Bacteriol. 43(2), 249–255. https://doi.org/10.1099/00207713-43-2-249 (1993).CAS 
Article 

Google Scholar 
Gulzar, S., Wakil, W. & Shapiro-Ilan, D. I. Potential use of entomopathogenic nematodes against the soil dwelling stages of onion thrips, Thrips tabaci Lindeman: Laboratory, greenhouse and field trials. Biol. Control. 161, 104677. https://doi.org/10.1016/j.biocontrol.2021.104677 (2021).Article 

Google Scholar 
Adams, B. J. & Nguyen, K. B. Taxonomy and systematics. In Entomopathogenic Nematology (ed. Gaugler, R.) 1–34 (CABI Publishing, 2002).
Google Scholar 
Dowds, B. C. A. & Peters, A. Virulence mechanisms. In Entomopathogenic Nematology (ed. Gaugler, R.) 79–90 (CABI Publishing, 2003).
Google Scholar 
Bal, H. K. & Grewal, P. S. Lateral dispersal and foraging behavior of entomopathogenic nematodes in the absence and presence of mobile and non-mobile hosts. PLoS ONE 10(6), e0129887. https://doi.org/10.1371/journal.pone.0129887 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Lewis, E. E., Gaugler, R. & Harrison, R. Entomopathogenic nematode host finding—response to host contact cues by cruise and ambush foragers. Parasitology 105, 309–315. https://doi.org/10.1017/S0031182000074230 (1992).Article 

Google Scholar 
Campbell, J. F. & Gaugler, R. Nictation behavior and its ecological implications in the host search strategies of entomopathogenic nematodes (Heterorhabditidae and Steinernematidae). Behaviour 126, 155–169 (1993).Article 

Google Scholar 
Lewis, E. E., Gaugler, R. & Harrison, R. Response of cruiser and ambusher entomopathogenic nematodes (Steinernematidae) to host volatile cues. Can. J. Zool. 71, 765–769 (1993).Article 

Google Scholar 
Grewal, P. S., Lewis, E. E., Gaugler, R. & Campbell, J. F. Host finding behavior as a predictor of foraging strategy in entomopathogenic nematodes. Parasitology 108, 207–215 (1994).Article 

Google Scholar 
Poinar, G. O. Biology and taxonomy of Steinernematidae and Heterorhabditidae. In Entomopathogenic Nematodes in Biological cOntrol (eds Gaugler, R. & Kaya, H. K.) 23–62 (CRC Press, 1990).
Google Scholar 
De Waal, J. Y., Wolhlfarter, M. & Malan, A. P. Laboratory bioassays for the differential susceptibility of Planococcus ficus and Pseudococcus viburni (Hemiptera: Pseudococcidae) to entomopathogenic nematodes (Rhabditida: Heterorhabditidae and Steinernematidae). S. Afr. J. Plant. Soil. 24, 243–244 (2007).
Google Scholar 
Lacey, L. A. & Shapiro-Ilan, D. I. Microbial control of insect pests in temperate orchard systems: Potential for incorporation into IPM. Annu. Rev. Entomol. 53(1), 121–144. https://doi.org/10.1146/annurev.ento.53.103106.093419 (2008).CAS 
Article 
PubMed 

Google Scholar 
Van Niekerk, S. & Malan, A. P. Potential of South African entomopathogenic nematodes (Heterorhabditidae and Steinernematidae) for control of the citrus mealybug, Planococcus citri (Pseudococcidae). J. Invertebr. Pathol. 111(2), 166–174. https://doi.org/10.1016/j.jip.2012.07.023 (2012).Article 
PubMed 

Google Scholar 
Půža, V. Control of insect pests by entomopathogenic nematodes. In Principles of Plant Microbe Interactions (ed. Lugtenberg, B.) 175–183 (Springer, 2015).
Google Scholar 
Gulzar, S. et al. Environmental tolerance of entomopathogenic nematodes differs among nematodes arising from host cadavers versus aqueous suspension. J. Invertebr. Pathol. 175, 107452. https://doi.org/10.1016/j.jip.2020.107452 (2020).CAS 
Article 
PubMed 

Google Scholar 
Gulzar, S. et al. Virulence of entomopathogenic nematodes to pupae of Frankliniella fusca (Thysanoptera: Thripidae). J. Econ. Entomol. 114(5), 2018–2023. https://doi.org/10.1093/jee/toab132 (2021).Article 
PubMed 

Google Scholar 
Gulzar, S., Wakil, W. & Shapiro-Ilan, D. I. Combined effect of entomopathogens against Thrips tabaci Lindeman (Thysanoptera: Thripidae): laboratory, greenhouse and field trials. Insects 12(5), 456. https://doi.org/10.3390/insects12050456 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Usman, M. et al. Virulence of entomopathogenic fungi to Rhagoletis pomonella (Diptera: Tephritidae) and interactions with entomopathogenic nematodes. J. Econ. Entomol. 113(6), 2627–2633. https://doi.org/10.1093/jee/toaa209 (2020).Article 
PubMed 

Google Scholar 
Usman, M. et al. Potential of entomopathogenic nematodes against the pupal stage of the apple maggot Rhagoletis pomonella (Walsh) (Diptera: Tephritidae). J. Nematol. 52, e2020–e2079. https://doi.org/10.21307/jofnem-2020-079 (2020).Article 
PubMed Central 

Google Scholar 
Usman, M., Wakil, W. & Shapiro-Ilan, D. I. Entomopathogenic nematodes as biological control agent against Bactrocera zonata and Bactrocera dorsalis (Diptera: Tephritidae). Biol. Control. 163, 104706. https://doi.org/10.1016/j.biocontrol.2021.104706 (2021).Article 

Google Scholar 
Grewal, P. S., Wang, X. & Taylor, R. A. J. Dauer juvenile longevity and stress tolerance in natural populations of entomopathogenic nematodes: Is there a relationship?. Int. J. Parasitol. 32(6), 717–725. https://doi.org/10.1016/S0020-7519(02)00029-2 (2002).CAS 
Article 
PubMed 

Google Scholar 
Benseddik, Y. et al. Occurrence and distribution of entomopathogenic nematodes (Steinernematidae and Heterorhabditidae) in Morocco. Biocontrol. Sci. Technol. 30(10), 1060–1072. https://doi.org/10.1080/09583157.2020.1787344 (2020).Article 

Google Scholar 
Mokrini, F. et al. Potential of Moroccan entomopathogenic nematodes for the control of the Mediterranean fruit fly Ceratitis capitata Wiedemann (Diptera: Tephritidae). Sci. Rep. 10(1), 1–11. https://doi.org/10.1038/s41598-020-76170-7 (2020).CAS 
Article 

Google Scholar 
Gorgadze, O., Bakhtadze, G., Kereselidze, M. & Lortkipanidze, M. The efficacy of entomopathogenic agents against Halyomorpha halys. Int. J. Curr. Res. 9, 62177–62180 (2017).
Google Scholar 
Tarasco, E. & Triggiani, O. Use of Italian EPNs in controlling Rhytidoderes plicatus Oliv, (Coleoptera, Curculionidae) in potted savoy cabbages. IOBC. WPRS. Bull. OILBN. 28, 9–12 (2005).
Google Scholar 
Moreno Salguero, C. A., Bustillo Pardey, A. E., Lopez Nunez, J. C., Castro Valderrama, U. & Ramirez Sanchez, G. D. Virulence of entomopathogenic nematodes to control Aeneolamia varia (Hemiptera: Cercopidae) in sugarcane. Rev. Colomb. Entomol. 38(2), 260–265 (2012).
Google Scholar 
Julià, I., Morton, A., Roca, M. & Garcia-del-Pino, F. Evaluation of three entomopathogenic nematode species against nymphs and adults of the sycamore lace bug, Corythucha ciliata. Biocontrol 65(5), 623–633. https://doi.org/10.1007/s10526-020-10045-8 (2020).CAS 
Article 

Google Scholar 
Sirjani, F. O., Lewis, E. E. & Kaya, H. K. Evaluation of entomopathogenic nematodes against the olive fruit fly, Bactrocera oleae (Diptera: Tephritidae). Biol. Control. 48, 274–7280. https://doi.org/10.1016/j.biocontrol.2008.11.002 (2009).Article 

Google Scholar 
Guide, B. A., Soares, E. A., Itimura, C. R. & Alves, V. S. Entomopathogenic nematodes in the control of cassava root mealybug Dysmicoccus sp. (Hemiptera: Pseudococcidae). Rev. Colomb. Entomol. 42(1), 16–21. https://doi.org/10.25100/socolen.v42i1.6664 (2016).CAS 
Article 

Google Scholar 
Le Vieux, P. D. & Malan, A. P. The potential use of entomopathogenic nematodes to control Planococcus ficus (Signoret) (Hemiptera: Pseudococcidae). S. J. Enol. Vitic. 34(2), 296–306. https://doi.org/10.21548/34-2-1108 (2013).Article 

Google Scholar 
Lewis, E. D., Campbell, J., Griffin, C., Kaya, H. & Peters, A. Behavioral ecology of entomopathogenic nematodes. Biol. Control. 38(1), 66–79. https://doi.org/10.1016/j.biocontrol.2005.11.007 (2006).Article 

Google Scholar 
Rahoo, A. M., Tariq Mukhta, T., Gowen, S. R., Rahoo, R. K. & Abro, S. A. Reproductive potential and host searching ability of entomopathogenic nematode Steinernema feltiae. Pak. J. Zool. 49(1), 229–234. https://doi.org/10.17582/journal.pjz/2017.49.1.229.234 (2017).Article 

Google Scholar 
Selvan, S., Campbell, J. F. & Gaugler, R. Density-dependent effects on entomopathogenic nematodes (Heterorhabditidae and Steinernematidae) within an insect host. J. Invertebr. Pathol. 62(3), 278–284. https://doi.org/10.1006/jipa.1993.1113 (1993).Article 

Google Scholar 
Gaugler, R., Wang, Y. & Campbell, J. F. Aggressive and evasive behaviors in Popillia japonica (Coleoptera: Scarabaeidae) larvae: Defences against entomopathogenic nematode attack. J. Invertebr. Pathol. 64(3), 193–199. https://doi.org/10.1016/S00222011(94)90150-3 (1994).Article 

Google Scholar 
Burjanadze, M., Kharabadze, N. & Chkhidze, N. Testing local isolates of entomopathogenic microorganisms against brown marmorated stink Bug Halyomorpha halys in Georgia. BIO Web Conf. 18, 00006. https://doi.org/10.1051/bioconf/20201800006 (2020).Article 

Google Scholar 
Del Valle, E. E., Dolinski, C. & Souza, R. M. Dispersal of Heterorhabditis baujardi LPP7 (Nematoda: Rhabditida) applied to the soil as infected host cadavers. Int. J. Pest. Manag. 54(2), 115–122. https://doi.org/10.1080/09670870701660579 (2008).Article 

Google Scholar 
Griffin, C. T., Boemare, N. E. & Lewis, E. E. Biology and behavior. In Nematodes as Biocontrol Agents 1st edn (eds Grewal, P. S. et al.) 47–59 (CABI Publishing, 2005).Chapter 

Google Scholar 
Bastidas, B., Portillo, E. & San-Blas, E. Size does matter: The life cycle of Steinernema spp. in micro-insect hosts. J. Invertebr. Pathol. 121, 46–55. https://doi.org/10.1016/j.jip.2014.06.010 (2014).Article 
PubMed 

Google Scholar 
Stokwe, N. F. & Malan, A. P. Susceptibility of the obscure mealybug, Pseudococcus viburni (Signoret) (Pseudococcidae), to South African isolates of entomopathogenic nematodes. Int. J. Pest. Manag. 62(2), 119–128. https://doi.org/10.1080/09670874.2015.1122250 (2016).Article 

Google Scholar 
Stokwe, N. F. & Malan, A. P. Laboratory bioassays to determine susceptibility of woolly apple aphid, Eriosoma lanigerum (Hausmann) (Hemiptera: Aphididae), to entomopathogenic nematodes. Afr. Entomol. 25(1), 123–136. https://doi.org/10.4001/003.025.0123 (2017).Article 

Google Scholar 
Cuthbertson, A. G. et al. Bemisia tabaci: The current situation in the UK and the prospect of developing strategies for eradication using entomopathogens. Insect Sci. 18(1), 1–10. https://doi.org/10.1111/j.1744-7917.2010.01383.x (2011).Article 

Google Scholar 
Van Niekerk, S. & Malan, A. P. Compatibility of Heterorhabditis zealandica and Steinernema yirgalemense with agrochemicals and biological control agents. Afr. Entomol. 22, 49–56 (2014).Article 

Google Scholar 
Van Niekerk, S. & Malan, A. P. Adjuvants to improve aerial control of the citrus mealybug Planococcus citri (Hemiptera: Pseudococcidae) using entomopathogenic nematodes. J. Helminthol. 89(2), 189–195. https://doi.org/10.1017/S0022149X13000771 (2015).CAS 
Article 
PubMed 

Google Scholar 
Aldama-Aguilera, C. & Llanderal-Cázares, C. Grana cochinilla: comparación de métodos de producción en penca cortada. Agrociencia 37(1), 11–19 (2003).
Google Scholar 
Kaya, H. K. & Stock, S. P. Techniques in insect nematology. In Manual of Techniques in Insect Pathology, Biological Techniques Series (ed. Lacey, L. A.) 281–324 (Academic Press, 1997).Chapter 

Google Scholar 
White, C. F. A method for obtaining infective larvae from culture. Science 66, 302–303. https://doi.org/10.1126/science.66.1709.302-a (1927).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Shapiro-Ilan, D. I., Morales-Ramos, J. A. & Rojas, M. G. In vivo production of entomopathogenic nematodes. In Microbial-Based Biopesticides 137–158 (Humana Press, 2016).Chapter 

Google Scholar 
Henderson, C. F. & Tilton, E. W. Tests with acaricides against the brown wheat mite. J. Econ. Entomol. 48(2), 157–161 (1955).CAS 
Article 

Google Scholar 
Abbot, W. S. Method of computing the effectiveness of an insecticide. J. Econ. Entomol. 18(2), 265–267. https://doi.org/10.1093/jee/18.2.265a (1925).Article 

Google Scholar 
Finney, D. J. Probit analysis 3rd edn, 20–63 (Cambridge University Press, 1971).MATH 

Google Scholar 
Haye, T., Wyniger, D. & Gariepy, T. D. Recent range expansion of brown marmorated stink bug in Europe. In Proceedings of the Eighth International Conference on Urban Pests (eds Müller, G. et al.) 309–314 (OOK Press, 2014).
Google Scholar 
Carver, R. H. & Nash, J. G. Doing data analysis with SPSS: version 18.0. (Cengage Learning, 2011). More

Courtillot, V. E. & Renne, P. R. On the ages of flood basalt events. C. R. Geosci. 335, 113–140 (2003).Article 

Google Scholar 
Campbell, I., Czamanske, G., Fedorenko, V., Hill, R. & Stepanov, V. Synchronism of the Siberian Traps and the Permian–Triassic boundary. Science 258, 1760–1763 (1992).Article 

Google Scholar 
Burgess, S. D. & Bowring, S. A. High-precision geochronology confirms voluminous magmatism before, during, and after Earth’s most severe extinction. Sci. Adv. 1, e1500470 (2015).Article 

Google Scholar 
Payne, J. L. & Clapham, M. E. End-Permian mass extinction in the oceans: an ancient analog for the twenty-first century? Annu. Rev. Earth Planet. Sci. 40, 89–111 (2012).Article 

Google Scholar 
Schneebeli-Hermann, E. et al. Evidence for atmospheric carbon injection during the end-Permian extinction. Geology 41, 579–582 (2013).Article 

Google Scholar 
Lee, C. & Lackey, J. Global continental arc flare-ups and their relation to long-term greenhouse conditions. Elements 11, 125–130 (2015).Article 

Google Scholar 
McKenzie, N. R. et al. Continental arc volcanism as the principal driver of icehouse-greenhouse variability. Science 352, 444–447 (2016).Article 

Google Scholar 
Ratschbacher, B. C., Paterson, S. R. & Fischer, T. P. Spatial and depth‐dependent variations in magma volume addition and addition rates to continental arcs: application to global CO2 fluxes since 750 Ma. Geochem. Geophys. Geosyst. 20, 2997–3018 (2019).Article 

Google Scholar 
Soreghan, G. S., Soreghan, M. J. & Heavens, N. G. Explosive volcanism as a key driver of the late Paleozoic ice age. Geology 47, 600–604 (2019).Article 

Google Scholar 
Jones, M. T., Sparks, R. S. J. & Valdes, P. J. The climatic impact of supervolcanic ash blankets. Clim. Dyn. 29, 553–564 (2007).Article 

Google Scholar 
DeCelles, P. G., Ducea, M. N., Kapp, P. & Zandt, G. Cyclicity in cordilleran orogenic systems. Nat. Geosci. 2, 251–257 (2009).Article 

Google Scholar 
Ducea, M. N., Paterson, S. R. & DeCelles, P. G. High-volume magmatic events in subduction systems. Elements 11, 99–104 (2015).Article 

Google Scholar 
Milan, L. A., Daczko, N. R. & Clarke, G. L. Cordillera Zealandia: a Mesozoic arc flare-up on the palaeo-Pacific Gondwana Margin. Sci. Rep. 7, 261 (2017).Article 

Google Scholar 
Gravley, D. M., Deering, C. D., Leonard, G. S. & Rowland, J. V. Ignimbrite flare-ups and their drivers: a New Zealand perspective. Earth Sci. Rev. 162, 65–82 (2016).Article 

Google Scholar 
de Silva, S. L., Riggs, N. R. & Barth, A. P. Quickening the pulse: fractal tempos in continental arc magmatism. Elements 11, 113–118 (2015).Article 

Google Scholar 
Attia, S., Cottle, J. M. & Paterson, S. R. Erupted zircon record of continental crust formation during mantle driven arc flare-ups. Geology 48, 446–451 (2020).Article 

Google Scholar 
Chisholm, E.-K. I., Simpson, C. & Blevin, P. New SHRIMP U–Pb Zircon Ages from the New England Orogen, New South Wales: July 2010–June 2012 (Geoscience Australia, 2014).McPhie, J. Evolution of a non-resurgent cauldron: the Late Permian Coombadjha volcanic complex, northeastern New South Wales, Australia. Geol. Mag. 123, 257–277 (1986).Article 

Google Scholar 
Lackie, M. The magnetic fabric of the Late Permian Dundee Ignimbrite, Dundee, NSW. Explor. Geophys. 19, 481–488 (1988).Article 

Google Scholar 
Stewart, A. Facies in an Upper Permian volcanic succession, Emmaville Volcanics, Deepwater, northeastern New South Wales. Aust. J. Earth Sci. 48, 929–942 (2001).Article 

Google Scholar 
Milan, L. A. et al. A new reconstruction for Permian East Gondwana based on zircon data from ophiolite of the East Australian Great Serpentinite Belt. Geophys. Res. Lett. 48, e2020GL090293 (2021).Article 

Google Scholar 
Rosenbaum, G. The Tasmanides: Phanerozoic tectonic evolution of eastern Australia. Annu. Rev. Earth Planet. Sci. 46, 291–325 (2018).Article 

Google Scholar 
Shaw, S., Flood, R. & Pearson, N. The New England Batholith of eastern Australia: evidence of silicic magma mixing from zircon 176Hf/177Hf ratios. Lithos 126, 115–126 (2011).Article 

Google Scholar 
Kohn, B. et al. Shaping the Australian crust over the last 300 million years: insights from fission track thermotectonic imaging and denudation studies of key terranes. Aust. J. Earth Sci. 49, 697–717 (2002).Article 

Google Scholar 
Metcalfe, I., Crowley, J., Nicoll, R. & Schmitz, M. High-precision U–Pb CA-TIMS calibration of Middle Permian to Lower Triassic sequences, mass extinction and extreme climate-change in eastern Australian Gondwana. Gondwana Res. 28, 61–81 (2015).Article 

Google Scholar 
Laurie, J. et al. Calibrating the Middle and Late Permian palynostratigraphy of Australia to the geologic time-scale via U–Pb zircon CA-IDTIMS dating. Aust. J. Earth Sci. 63, 701–730 (2016).Article 

Google Scholar 
Creech, M. Tuffaceous deposition in the Newcastle Coal Measures: challenging existing concepts of peat formation in the Sydney Basin, New South Wales, Australia. Int. J. Coal Geol. 51, 185–214 (2002).Article 

Google Scholar 
Vajda, V. et al. End-Permian (252 Mya) deforestation, wildfires and flooding—an ancient biotic crisis with lessons for the present. Earth Planet. Sci. Lett. 529, 115875 (2020).Article 

Google Scholar 
Frank, T. D. et al. Pace, magnitude, and nature of terrestrial climate change through the end-Permian extinction in southeastern Gondwana. Geology, 49, 1089–1095 (2021).Grevenitz, P., Carr, P. & Hutton, A. Origin, alteration and geochemical correlation of Late Permian airfall tuffs in coal measures, Sydney Basin, Australia. Int. J. Coal Geol. 55, 27–46 (2003).Article 

Google Scholar 
Phillips, L. et al. U–Pb geochronology and palynology from Lopingian (Upper Permian) coal measure strata of the Galilee Basin, Queensland, Australia. Aust. J. Earth Sci. 65, 153–173 (2018).Article 

Google Scholar 
Siégel, C., Bryan, S., Allen, C., Gust, D. & Purdy, D. Crustal evolution in the New England Orogen, Australia: repeated igneous activity and scale of magmatism govern the composition and isotopic character of the continental crust. J. Petrol., 61, 1–28 (2020).Wang, X. et al. Convergent continental margin volcanic source for ash beds at the Permian–Triassic boundary, South China: constraints from trace elements and Hf-isotopes. Palaeogeogr. Palaeoclimatol. Palaeoecol. 519, 154–165 (2019).Article 

Google Scholar 
Nelson, D. & Cottle, J. Tracking voluminous Permian volcanism of the Choiyoi Province into central Antarctica. Lithosphere 11, 386–398 (2019).Article 

Google Scholar 
He, B., Zhong, Y.-T., Xu, Y.-G. & Li, X.-H. Triggers of Permo-Triassic boundary mass extinction in South China: the Siberian Traps or Paleo-Tethys ignimbrite flare-up? Lithos 204, 258–267 (2014).Article 

Google Scholar 
Cope, T. Phanerozoic magmatic tempos of North China. Earth Planet. Sci. Lett. 468, 1–10 (2017).Article 

Google Scholar 
Sun, Y. et al. Lethally hot temperatures during the Early Triassic greenhouse. Science 338, 366–370 (2012).Article 

Google Scholar 
Jin, Y. et al. Pattern of marine mass extinction near the Permian–Triassic boundary in South China. Science 289, 432–436 (2000).Article 

Google Scholar 
Song, H., Wignall, P. B., Tong, J. & Yin, H. Two pulses of extinction during the Permian–Triassic crisis. Nat. Geosci. 6, 52–56 (2013).Article 

Google Scholar 
Ramezani, J. & Bowring, S. A. Advances in numerical calibration of the Permian timescale based on radioisotopic geochronology. Geol. Soc. Spec. Publ. 450, 51–60 (2018).Article 

Google Scholar 
Joachimski, M. M. et al. Climate warming in the latest Permian and the Permian–Triassic mass extinction. Geology 40, 195–198 (2012).Article 

Google Scholar 
Alroy, J. et al. Phanerozoic trends in the global diversity of marine invertebrates. Science 321, 97–100 (2008).Article 

Google Scholar 
Mundil, R., Ludwig, K. R., Metcalfe, I. & Renne, P. R. Age and timing of the Permian mass extinctions: U/Pb dating of closed-system zircons. Science 305, 1760–1763 (2004).Article 

Google Scholar 
Chen, B. et al. Permian ice volume and palaeoclimate history: oxygen isotope proxies revisited. Gondwana Res. 24, 77–89 (2013).Article 

Google Scholar 
Shen, S. Z. et al. High‐resolution Lopingian (Late Permian) timescale of South China. Geol. J. 45, 122–134 (2010).Article 

Google Scholar 
Shellnutt, J. G., Denyszyn, S. W. & Mundil, R. Precise age determination of mafic and felsic intrusive rocks from the Permian Emeishan large igneous province (SW China). Gondwana Res. 22, 118–126 (2012).Article 

Google Scholar 
Fielding, C. R. et al. Sedimentology of the continental end-Permian extinction event in the Sydney Basin, eastern Australia. Sedimentology 68, 30–62 (2021).Article 

Google Scholar 
Fielding, C. R. et al. Age and pattern of the southern high-latitude continental end-Permian extinction constrained by multiproxy analysis. Nat. Commun. 10, 1–12 (2019).Article 

Google Scholar 
Liu, Z. et al. Osmium-isotope evidence for volcanism across the Wuchiapingian–Changhsingian boundary interval. Chem. Geol. 529, 119313 (2019).Article 

Google Scholar 
Cheng, C. et al. Permian carbon isotope and clay mineral records from the Xikou section, Zhen’an, Shaanxi Province, central China: climatological implications for the easternmost Paleo-Tethys. Palaeogeogr. Palaeoclimatol. Palaeoecol. 514, 407–422 (2019).Article 

Google Scholar 
Gastaldo, R. A. et al. The base of the Lystrosaurus Assemblage Zone, Karoo Basin, predates the end-Permian marine extinction. Nat. Commun. 11, 1–8 (2020).Article 

Google Scholar 
Retallack, G. J. et al. Multiple Early Triassic greenhouse crises impeded recovery from Late Permian mass extinction. Palaeogeogr. Palaeoclimatol. Palaeoecol. 308, 233–251 (2011).Article 

Google Scholar 
Mays, C. et al. Refined Permian–Triassic floristic timeline reveals early collapse and delayed recovery of south polar terrestrial ecosystems. GSA Bull. 132, 1489–1513 (2020).Article 

Google Scholar 
Yugan, J., Jing, Z. & Qinghua, S. Two Phases of the End-Permian Mass Extinction. In Pangea: Global Environments and Resources — Memoir, 17, 813-822 (1994).Williams, M. L., Jones, B. G. & Carr, P. F. The interplay between massive volcanism and the local environment: geochemistry of the Late Permian mass extinction across the Sydney Basin, Australia. Gondwana Res. 51, 149–169 (2017).Article 

Google Scholar 
van der Boon, A. et al. Exploring a link between the Middle Eocene Climatic Optimum and Neotethys continental arc flare-up. Clim. Past 17, 229–239 (2021).Article 

Google Scholar 
Metcalfe, I. Tectonic evolution of Sundaland. Bull. Geol. Soc. Malays. 63, 27–60 (2017).Article 

Google Scholar 
Maravelis, A. G. et al. Re-assessing the Upper Permian stratigraphic succession of the Northern Sydney Basin, Australia, by CA-IDTIMS. Geosciences 10, 474 (2020).Article 

Google Scholar 
Voice, P. J., Kowalewski, M. & Eriksson, K. A. Quantifying the timing and rate of crustal evolution: global compilation of radiometrically dated detrital zircon grains. J. Geol. 119, 109–126 (2011).Article 

Google Scholar 
Watson, E. B., Wark, D. A. & Thomas, J. B. Crystallization thermometers for zircon and rutile. Contrib. Mineral. Petrol. 151, 413–433 (2006).Article 

Google Scholar 
Sláma, J. et al. Plešovice zircon—a new natural reference material for U–Pb and Hf isotopic microanalysis. Chem. Geol. 249, 1–35 (2008).Article 

Google Scholar 
Wiedenbeck, M. et al. Three natural zircon standards for U–Th–Pb, Lu–Hf, trace element and REE analyses. Geostand. Newsl. 19, 1–23 (1995).Article 

Google Scholar 
Mattinson, J. M. Zircon U–Pb chemical abrasion (“CA-TIMS”) method: combined annealing and multi-step partial dissolution analysis for improved precision and accuracy of zircon ages. Chem. Geol. 220, 47–66 (2005).Article 

Google Scholar 
Krogh, T. E. A low contamination method for hydrothermal decomposition of zircon and extraction of U and Pb for isotopic age determination, Geochim. Cosmochim. Acta 37, 485–494 (1973).Article 

Google Scholar 
Gerstenberger, H. & Haase, G. A highly effective emitter substance for mass spectrometric Pb isotope ratio determinations. Chem. Geol. 136, 309–312 (1997).Article 

Google Scholar 
Schmitz, M. D. & Schoene, B. Derivation of isotope ratios, errors, and error correlations for U–Pb geochronology using 205Pb-235U-(233U)-spiked isotope dilution thermal ionization mass spectrometric data. Geochem. Geophys. Geosyst. 8, https://doi.org/10.1029/2006gc001492 (2007).Condon, D. J., Schoene, B., McLean, N. M., Bowring, S. A. & Parrish, R. R. Metrology and traceability of U–Pb isotope dilution geochronology (EARTHTIME tracer calibration part I). Geochim. Cosmochim. Acta 164, 464–480 (2015).Article 

Google Scholar 
Jaffey, A. H., Flynn, K. F., Glendenin, L. E., Bentley, W. C. & Essling, A. M. Precision measurement of half-lives and specific activities of 235U and 238U. Phys. Rev. C 4, 1889–1906 (1971).Article 

Google Scholar 
Hiess, J., Condon, D. J., McLean, N. & Noble, S. R. 238U/235U systematics in terrestrial uranium-bearing minerals. Science 335, 1610–1614 (2012).Article 

Google Scholar 
Crowley, J. L., Schoene, B. & Bowring, S. A. U–Pb dating of zircon in the Bishop Tuff at the millennial scale. Geology 35, 1123–1126 (2007).Article 

Google Scholar 
Ludwig, K. R. User’s manual for Isoplot 3.00 (Berkley Geochronology Center, 2003).Offenburg, A. C. & Pogson, D. J. Geological Map of New England 1:500,000 (Geological Survey of New South Wales, 1973).Cranfield, L. C., Hutton, L. J. & Green, P. M. Geological Map of Ipswich 1:100,000 (Geological Survey of Queensland, 1978).Shaw, S. E. & Flood, R. H. The New England Batholith, eastern Australia: geochemical variations in time and space. J. Geophys. Res. Solid Earth 86, 10530–10544 (1981).Article 

Google Scholar 
Barnes, R. G., Brown, R. E., Brownlow, J. W. & Stroud, W. J. Late Permian volcanics in New England. Q. Notes Geol. Surv. N. South Wales 84, 1–36 (1991).
Google Scholar 
Finlayson, D. M. & Collins, C. D. N. Lithospheric velocity structures under the southern New England Orogen: evidence for underplating at the Tasman Sea margin. Aust. J. Earth Sci. 40, 141–153 (1993).Article 

Google Scholar 
Timothy, C., Geoffrey, L. C., Nathan, R. D., Sandra, P. & Adrianna, R. Orthopyroxene–omphacite- and garnet–omphacite-bearing magmatic assemblages, Breaksea Orthogneiss, New Zealand: oxidation state controlled by high-P oxide fractionation. Lithos 216–217, 1–16 (2015).
Google Scholar 
Chapman, T., Clarke, G. L. & Daczko, N. R. Crustal differentiation in a thickened arc—evaluating depth dependences. J. Petrol. 57, 595–620 (2016).Article 

Google Scholar 
Jagoutz, O. & Behn, M. D. Foundering of lower island-arc crust as an explanation for the origin of the continental Moho. Nature 504, 131–134 (2013).Article 

Google Scholar 
Chapman, J. B., Ducea, M. N., DeCelles, P. G. & Profeta, L. Tracking changes in crustal thickness during orogenic evolution with Sr/Y: an example from the North American Cordillera. Geology 43, 919–922 (2015).Article 

Google Scholar 
Bryant, C. J. A Compendium of Granites of the Southern New England Orogen, Eastern Australia (Geological Survey of New South Wales, 2017).Phillips, G., Landenberger, B. & Belousova, E. A. Building the New England Batholith, eastern Australia—linking granite petrogenesis with geodynamic setting using Hf isotopes in zircon. Lithos 122, 1–12 (2011).Article 

Google Scholar 
Kemp, A., Hawkesworth, C., Collins, W., Gray, C. & Blevin, P. Isotopic evidence for rapid continental growth in an extensional accretionary orogen: the Tasmanides, eastern Australia. Earth Planet. Sci. Lett. 284, 455–466 (2009).Article 

Google Scholar 
Anderson, J. R., Fraser, G. L., McLennan, S. M. & Lewis, C. J. A U–Pb Geochronology Compilation for Northern Australia Report No. 2017/22 (Geoscience Australia, 2017).Belousova, E. A., Griffin, W. L. & O’Reilly, S. Y. Zircon crystal morphology, trace element signatures and Hf isotope composition as a tool for petrogenetic modelling: examples from eastern Australian granitoids. J. Petrol. 47, 329–353 (2005).Article 

Google Scholar 
Bodorkos, S. et al. U–Pb Ages from the Central Lachlan Orogen and New England Orogen, New South Wales Report No. 2016/21 (Geoscience Australia, 2016).Cawood, P. A., Pisarevsky, S. A. & Leitch, E. C. Unraveling the New England orocline, east Gondwana accretionary margin. Tectonics 30, 1–15 (2011).Chisholm, E. I., Blevin, P. L. & Simpson, C. J. New SHRIMP U–Pb Zircon Ages from the New England Orogen, New South Wales: July 2012–June 2014 Report No. 2014/13 (Geoscience Australia, 2014).Chisholm, E. I., Blevin, P. L. & Simpson, C. J. New SHRIMP U–Pb Zircon Ages from the New England Orogen, New South Wales: July 2010–June 2012 Report No. 2014/13 (Geoscience Australia, 2014).Cross, A. & Blevin, P. L. Summary of Results for the Joint GSNSW–GA Geochronology Project Report No. GS2013/0426 (Geoscience Australia, 2013).Craven, S. J., Daczko, N. R. & Halpin, J. A. Thermal gradient and timing of high-T–low-P metamorphism in the Wongwibinda Metamorphic Complex, southern New England Orogen, Australia. J. Metamorph. Geol. 30, 3–20 (2012).Article 

Google Scholar 
Black, L. P. U–Pb Zircon Ages Obtained During 2006/07 for NSW Geological Survey Projects (Geoscience Australia, 2007).Rosenbaum, G., Li, P. & Rubatto, D. The contorted New England Orogen (eastern Australia): new evidence from U–Pb geochronology of early Permian granitoids. Tectonics 31, https://doi.org/10.1029/2011tc002960 (2012).Walthenberg, K., Blevin, P. L., Bull, K. F., Cronin, D. E. & Armistead, S. E. New SHRIMP U–Pb Zircon Ages from the Lachland Orogen and the New England Orogen, New South Wales: Mineral Systems Projects, July 2015–June 2016 Report No. 2016/28 (Geoscience Australia, 2016).Walthenberg, K., Blevin, P. L., Bodorkos, S. & Cronin, D. E. New SHRIMP U–Pb Ages from the New England Orogen, New South Wales: July 2014–June 2015 Report No. 2015/28 (Geoscience Australia, 2015).Jeon, H., Williams, I. S. & Chappell, B. W. Magma to mud to magma: rapid crustal recycling by Permian granite magmatism near the eastern Gondwana margin. Earth Planet. Sci. Lett. 319, 104–117 (2012).Article 

Google Scholar  More

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Singh, B. K., Bardgett, R. D., Smith, P. & Reay, D. S. Microorganisms and climate change: terrestrial feedbacks and mitigation options. Nat. Rev. Microbiol. 8, 779–790 (2010).CAS 
PubMed 
Article 

Google Scholar 
Fierer, N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat. Rev. Microbiol. 15, 579–590 (2017).CAS 
PubMed 
Article 

Google Scholar 
Guerra, C. A. et al. Tracking, targeting, and conserving soil biodiversity. Science 371, 239–241 (2021).CAS 
PubMed 
Article 

Google Scholar 
Fanin, N. et al. Consistent effects of biodiversity loss on multifunctionality across contrasting ecosystems. Nat. Ecol. Evol. 2, 269–278 (2018).PubMed 
Article 

Google Scholar 
Delgado-Baquerizo, M. et al. Multiple elements of soil biodiversity drive ecosystem functions across biomes. Nat. Ecol. Evol. 4, 210–220 (2020).PubMed 
Article 

Google Scholar 
Chen, W. et al. Fertility-related interplay between fungal guilds underlies plant richness-productivity relationships in natural grasslands. New Phytol. 226, 1129–1143 (2020).PubMed 
Article 

Google Scholar 
Semchenko, M. et al. Fungal diversity regulates plant–soil feedbacks in temperate grassland. Sci. Adv. 4, eaau4578 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kohli, M. et al. Stability of grassland production is robust to changes in the consumer food web. Ecol. Lett. 22, 707–716 (2019).PubMed 
Article 

Google Scholar 
Liang, M. et al. Soil microbes drive phylogenetic diversity–productivity relationships in a subtropical forest. Sci. Adv. 5, eaax5088 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tilman, D., Reich, P. B. & Knops, J. M. H. Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 441, 629–632 (2006).CAS 
PubMed 
Article 

Google Scholar 
Yang, G. W., Wagg, C., Veresoglou, S. D., Hempel, S. & Rillig, M. C. How soil biota drive ecosystem stability. Trends Plant Sci. 23, 1057–1067 (2018).CAS 
PubMed 
Article 

Google Scholar 
de Vries, F. T., Griffiths, R. I., Knight, C. G., Nicolitch, O. & Williams, A. Harnessing rhizosphere microbiomes for drought-resilient crop production. Science 368, 270–274 (2020).PubMed 
Article 
CAS 

Google Scholar 
Pörtner, H.O. et al. Scientific outcome of the IPBES-IPCC co-sponsored workshop on biodiversity and climate change (IPBES, 2021).Gessner, M. O. et al. Diversity meets decomposition. Trends Ecol. Evol. 25, 372–380 (2010).PubMed 
Article 

Google Scholar 
Bardgett, R. D. & van der Putten, W. H. Belowground biodiversity and ecosystem functioning. Nature 515, 505–511 (2014).CAS 
PubMed 
Article 

Google Scholar 
Anthony, M. A. et al. Forest tree growth is linked to mycorrhizal fungal composition and function across Europe. ISME J. https://doi.org/10.1038/s41396-021-01159-7 (2022).Jia, Y. Y., van der Heijden, M. G. A., Wagg, C., Feng, G. & Walder, F. Symbiotic soil fungi enhance resistance and resilience of an experimental grassland to drought and nitrogen deposition. J. Ecol. 109, 3171–3181 (2020).Article 
CAS 

Google Scholar 
Delgado-Baquerizo, M. et al. The proportion of soil-borne pathogens increases with warming at the global scale. Nat. Clim. Change 10, 550–554 (2020).Article 

Google Scholar 
Tedersoo, L., Bahram, M. & Zobel, M. How do mycorrhizal associations drive plant population and community biology? Science 367, eaba1223 (2020).CAS 
PubMed 
Article 

Google Scholar 
Guo, X. et al. Climate warming leads to divergent succession of grassland microbial communities. Nat. Clim. Change 8, 813–818 (2018).Article 

Google Scholar 
Põlme, S. et al. FungalTraits: a user-friendly traits database of fungi and fungus-like stramenopiles. Fungal Divers. 105, 1–16 (2020).Article 

Google Scholar 
Egidi, E. et al. A few Ascomycota taxa dominate soil fungal communities worldwide. Nat. Commun. 10, 2369 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Tedersoo, L. et al. Global diversity and geography of soil fungi. Science 346, 1078–1088 (2014).CAS 
Article 

Google Scholar 
Delgado-Baquerizo, M. et al. The influence of soil age on ecosystem structure and function across biomes. Nat. Commun. 11, 4721 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Isbell, F. et al. Biodiversity increases the resistance of ecosystem productivity to climate extremes. Nature 526, 574–577 (2015).CAS 
PubMed 
Article 

Google Scholar 
Steidinger, B. S. et al. Climatic controls of decomposition drive the global biogeography of forest-tree symbioses. Nature 569, 404–408 (2019).CAS 
PubMed 
Article 

Google Scholar 
Wagg, C. et al. Diversity and asynchrony in soil microbial communities stabilizes ecosystem functioning. Elife 10, 3207 (2021).Article 

Google Scholar 
Yang, G. W., Wagg, C., Veresoglou, S. D., Hempel, S. & Rillig, M. C. Plant and soil biodiversity have non-substitutable stabilizing effects on biomass production. Ecol. Lett. 24, 1582–1593 (2021).PubMed 
Article 

Google Scholar 
Chen, L. T. et al. Above- and belowground biodiversity jointly drive ecosystem stability in natural alpine grasslands on the Tibetan Plateau. Glob. Ecol. Biogeogr. https://doi.org/10.1111/geb.13307 (2021).Garcia-Palacios, P., Gross, N., Gaitan, J. & Maestre, F. T. Climate mediates the biodiversity-ecosystem stability relationship globally. Proc. Natl Acad. Sci. USA 115, 8400–8405 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Valencia, E. et al. Synchrony matters more than species richness in plant community stability at a global scale. Proc. Natl Acad. Sci. USA 117, 24345–24351 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Craven, D. et al. Multiple facets of biodiversity drive the diversity-stability relationship. Nat. Ecol. Evol. 2, 1579–1587 (2018).PubMed 
Article 

Google Scholar 
Naeem, S. & Li, S. B. Biodiversity enhances ecosystem reliability. Nature 390, 507–509 (1997).CAS 
Article 

Google Scholar 
Hautier, Y. et al. Eutrophication weakens stabilizing effects of diversity in natural grasslands. Nature 508, 521–525 (2014).CAS 
PubMed 
Article 

Google Scholar 
Jousset, A., Schmid, B., Scheu, S. & Eisenhauer, N. Genotypic richness and dissimilarity opposingly affect ecosystem performance. Ecol. Lett. 14, 537–624 (2011).CAS 
PubMed 
Article 

Google Scholar 
Jiang, L., Pu, Z. & Nemergut, D. R. On the importance of the negative selection effect for the relationship between biodiversity and ecosystem functioning. Oikos 117, 488–493 (2008).Article 

Google Scholar 
Ratzke, C., Barrere, J. & Gore, J. Strength of species interactions determines biodiversity and stability in microbial communities. Nat. Ecol. Evol. 4, 376–383 (2020).PubMed 
Article 

Google Scholar 
Lekberg, Y. et al. Nitrogen and phosphorus fertilization consistently favor pathogenic over mutualistic fungi in grassland soils. Nat. Commun. 12, 3484 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bastida, F. et al. Soil microbial diversity–biomass relationships are driven by soil carbon content across global biomes. ISME J. 15, 2081–2091 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Paruelo, J., Epstein, H. E., Lauenroth, W. K. & Burke, I. C. ANPP estimates from NDVI for the central grassland region of the United States. Ecology 78, 953–958 (1997).Article 

Google Scholar 
Jobbágy, E. G., Sala, O. E. & Paruelo, J. M. Patterns and controls of primary production in the Patagonian steppe: a remote sensing approach. Ecology 83, 307–319 (2002).
Google Scholar 
Oehri, J., Schmid, B., Schaepman-Strub, G. & Niklaus, P. A. Biodiversity promotes primary productivity and growing season lengthening at the landscape scale. Proc. Natl Acad. Sci. USA 114, 10160–10165 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bastos, A., Running, S. W., Gouveia, C. & Trigo, R. M. The global NPP dependence on ENSO: La Niña and the extraordinary year of 2011. J. Geophys. Res. Biogeosci. 118, 1247–1255 (2013).Article 

Google Scholar 
Orwin, K. H. & Wardle, D. A. New indices for quantifying the resistance and resilience of soil biota to exogenous disturbances. Soil Biol. Biochem. 36, 1907–1912 (2004).CAS 
Article 

Google Scholar 
Frankenberg, C. et al. Prospects for chlorophyll fluorescence remote sensing from the Orbiting Carbon Observatory-2. Remote Sens. Environ. 147, 1–12 (2014).Article 

Google Scholar 
Sun, Y. et al. Overview of solar-induced chlorophyll fluorescence (SIF) from the Orbiting Carbon Observatory-2: retrieval, cross-mission comparison, and global monitoring for GPP. Remote Sens. Environ. 209, 808–823 (2018).Article 

Google Scholar 
Zhang, Y., Joiner, J., Alemohammad, S. H., Zhou, S. & Gentine, P. A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks. Biogeosciences 15, 5779–5800 (2018).CAS 
Article 

Google Scholar 
Running, S. W. et al. A continuous satellite-derived measure of global terrestrial primary production. Bioscience 54, 547–560 (2004).Article 

Google Scholar 
Beguería, S. et al. Standardized precipitation evapotranspiration index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. Int. J. Climatol. 34, 3001–3023 (2014).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 
Luo, H. et al. Contrasting responses of planted and natural forests to drought intensity in Yunnan, China. Remote Sens. 8, 635 (2016).Article 

Google Scholar 
Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations—the CRU TS3. 10 Dataset. Int. J. Climatol. 34, 623–642 (2014).Article 

Google Scholar 
Allen, R. G. et al. Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements (FAO, 1998); https://www.fao.org/3/x0490e/x0490e00.htmOksanen, J. et al. Vegan: Community Ecology Package (R Foundation for Statistical Computing, 2013).Legendre, P. et al. Studying beta diversity: ecological variation partitioning by multiple regression and canonical analysis. J. Plant Ecol. 1, 3–8 (2008).Article 

Google Scholar 
Grömping, U. Relative importance for linear regression in R: the package relaimpo. J. Stat. Softw. 17, 1–27 (2006).Article 

Google Scholar 
Lefcheck., J. S. piecewiseSEM: piecewise structural equation modelling in R for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579.Bates, D. et al. lme4: linear mixed-effects models using Eigen and S4. J. Stat. Soft. 67, 1–48 (2014).
Google Scholar  More

This work formulates a general framework of the WFE Nexus at the national level, which includes all pertinent interactions between water, food, and energy sources and demands. Figure 1 depicts the feedbacks involving resource availability and consumption. The causal loops of the developed model for national-scale assessment are shown in Fig. 2. The model depicted in Fig. 2 proposes reducing consumption to reduce the water crisis to the extent possible. By reducing water use and pollution the environmental water requirement can be reduced, thus alleviating the water crisis. This paper’s objective is sustainable management by reducing per capita water use (in the residential section) and per capita energy use (in the domestic, public, and commercial section). The WFE nexus is modeled as a dynamic system for demand management applied to the stocks of energy, surface water, and groundwater resources to calculate their input and output rates (flows) at the national level while providing for environmental flow requirements (Fig. 3). The national modeling approach is of the lumped type, meaning that inputs and outputs to the stocks of water and energy represent totals over an entire country (in the case study, Iran); therefore, the models does not consider intra-country regional variations. The units of water resources and energy resources are expressed in cubic meters and MWh, respectively.Figure 1Feedbacks between resources and uses in the WFE nexus taking into account environmental considerations.Full size imageFigure 2The causal loops of the model developed for simulating the WFE nexus.Full size imageFigure 3Flow diagram of the WFE Nexus system.Full size imageBalance of water resourcesThe study of water exchanges in a country is based on the law of conservation of matter. The following sections present calculations pertinent to the annual balance of surface and groundwater resources.Surface water resourcesThe national runoff generated in a country’s high-elevation areas (or high terrain) and low-elevation areas (plains) is quantified with the following equations:$${preheight}_{t}=HeightCotimes {Precipitation}_{t}$$
(1)

in which ({preheight}_{t}) = volume of precipitation that falls in high-elevation areas during period t, (HeightCo) = the percentage of total precipitation that falls in high-elevation areas, and ({Precipitation}_{t}) = volume of precipitation during period t.$${preplain}_{t}=PlainCotimes {Precipitation}_{t}$$
(2)

in which ({preplain}_{t}) = volume of precipitation that falls in the plains during period t, and (PlainCo) = the percentage of total precipitation that falls in plains (low elevation areas).$${SInflow}_{t}=HeighSInflowCotimes {preheight}_{t}+PlainSInflowCotimes {preplain}_{t}+{OutCSW}_{t}+{Dr}_{t}$$
(3)

in which ({SInflow}_{t}) = the total volume of surface flows during period t, (HeighSInflowCo) = the runoff coefficient in high-elevation areas, (PlainSInflowCo) = the runoff coefficient in the plains, ({OutCSW}_{t}) = the difference between the volume of surface inflow and outflow through a country’s border during period t; and ({Dr}_{t}) = the flow of groundwater resources to surface water resources (i.e., baseflow) during period t.It is possible to calculate the water use after calculating the annual surface water originating by precipitation. Some of the water use by the agricultural, industrial, and municipal sectors becomes return flows. Equations (4) through (9) show how to calculate the surface water use and the water return flows to the surface water sources.$${DomWD}_{t}={Population}_{t}times PerCapitaWatertimes 365$$
(4)

in which ({DomWD}_{t}) = the volume of water use in the municipal sector during period t, ({Population}_{t}) = the population of the country during period t, and (PerCapitaWater) = per capita drinking water use (cubic meters per person per day).$${IndDomWD}_{t}={DomWD}_{t}+{IndWD}_{t}$$
(5)

in which ({IndDomWD}_{t}) = the volume of water use in the municipal and industrial sectors during period t, and ({IndWD}_{t}) = the volume of water use in the industrial sector during period t.The water use by the agricultural sector accounts for the water footprint of agricultural products, which measures their water use per mass of produce, and adjusting the water use by including water losses and agricultural return flows. A separate sub-agent (AGR agent) is introduced to perform the calculations related to the agricultural sector to simplify the dynamic-system model (main model), and the required outputs (BWAgr, GWAgr) of the dynamic system model are called by the agent in the main model (see Figs. 3 and 4). The BWAgr is given by the expression within parentheses in Eq. (6).Figure 4Agricultural subsystem modeled in the AGR agent (shows how to calculate the blue and gray water footprints of agricultural products).Full size image$${AgrWD}_{t}=left(sum_{iin A}{BW}_{i}times {Product}_{i,t}right)times frac{1}{{E}_{Agr}}+OtherAgrWD$$
(6)

in which ({AgrWD}_{t}) = the volume of agricultural water use during period t, ({BW}_{i}) = blue water footprint of agricultural product i (cubic meters per ton), ({Product}_{i,t}) = the amount of production of agricultural product i during period t (tons), ({E}_{Agr}) = the overall irrigation efficiency, (OtherAgrWD) = the volume of water consumed by agricultural products not included in the set A of agricultural products (in cubic meters). The set A includes those agricultural products with the largest yields and shares of the national food basket.$${AgrReW}_{t}={AgrWD}_{t}times AgrReCo$$
(7)

in which ({AgrReW}_{t}) = the volume of water returned from agricultural water use during the period t, and (AgrReCo) = the coefficient of water returned from agricultural water use.$${IndDomReW}_{t}={IndDomWD}_{t}times IndDomReCo$$
(8)

in which ({IndDomReW}_{t}) = the volume of water returned from industrial and municipal water use during period t, and (IndDomReCo) = the coefficient of water returned from industrial and municipal water uses.$${ReSW}_{t}=IndDomReSWCotimes {IndDomReW}_{t}+AgrReSWCotimes {AgrReW}_{t}$$
(9)

in which ({ReSW}_{t}) = the volume of water returned from water uses to surface water resources during period t, (IndDomReSWCo) = the percentage of water returned from municipal and industrial water use to surface water resources, and (AgrReSWCo) = the percentage of water returned from agricultural water use to surface water resources.Water is applied to produce energy, and Eqs. (10) through (15) perform the related calculations. The ({WEIF}_{t}) variable in Eq. (14) is necessary to account for the volume of water saved as a result of the energy savings. A PR model is introduced to account for such water savings (see Fig. 3).$${Diff}_{t} ={OutputE}_{t}-{OutputE}_{t}^{P}$$
(10)

in which ({Diff}_{t})= the difference between the energy used in the main model during period t and the energy used in period t in the PR model, ({OutputE}_{t}) = the sum of energy uses during period t in the main model (the method of calculating ({OutputE}_{t}) is described in detail in “Energy uses”), and ({OutputE}_{t}^{P}) = the sum of energy uses during period t in the PR model. Equations (11) and (12) account for the case when energy use exceeds energy production under current conditions, in which case energy exports are reduced. This prevents additional energy production to meet excess demand, and, consequently, there would not be increases in water use.$${Diff}_{t} le 0,,,{if,,func}_{t}=0$$
(11)
$${Diff}_{t} >0,,,{ if,,func}_{t}={Diff}_{t}$$
(12)

in which ({ iffunc}_{t}) = the amount of energy saved during period t.Equation (13) calculates the water required to produce energy:$${{TotalWE}_{t}=Coal}_{t}times ENwateruseC+{Gas}_{t}times ENwateruseG+{OilPetroleumP}_{t }times ENwateruseO+{Nuclear}_{t}times ENwateruseN+{Elec}_{t}times ENwateruseE$$
(13)

in which ({TotalWE}_{t}) = the volume of water required to produce the energy demand during period t,({Elec}_{t}) = the amount of electricity production during period t (MWh), and (ENwateruseE) = the water required per unit of energy generated by electricity (cubic meters per MWh), all other terms were previously defined.Equation (14) calculates the water savings:$${WEIF}_{t}=sum_{t=1}^{T}frac{{TotalWE}_{t}}{{OutputE}_{t}^{0}}times {if,,func}_{t}$$
(14)

in which ({WEIF}_{t})= the volume of water saved as a result of the energy saved during period t, T = the number of periods of simulation (T = 5 years).Part of the water used to produce energy from coal, oil, petroleum products, and nuclear fuel is accounted for in the industrial sector water use. For this reason, the volume of water to produce energy calculated with Eq. (15) is reduced by that part of water already accounted for in the industrial water use to avoid double accounting.$${WE}_{t}={Coal}_{t}times ENwateruseC+{Gas}_{t}times ENwateruseG+{OilPetroleumP}_{t }times ENwateruseO+{Nuclear}_{t}times ENwateruseN-INDEtimes {IndWD}_{t}-{WEIF}_{t}$$
(15)

in which ({WE}_{t}) = the volume of water required to produce different types of energy (except those included in the industrial sector) during period t, ({Coal}_{t}) = the energy produced with coal during period t (MWh), (ENwateruseC) = the water required per unit of energy produced with coal (cubic meters per MWh),({Gas}_{t}) = the amount of energy produced with natural gas during period t (MWh), (ENwateruseG) = the water required per unit of energy produced with natural gas (cubic meters per MWh), ({OilPetroleumP}_{t}) = the amount of energy produced with crude oil and other petroleum products during period t (MWh), (ENwateruseO) = the water required per unit of energy produced with crude oil and petroleum products (cubic meters per MWh),({Nuclear}_{t}) = the amount of nuclear energy produced during period t (MWh), (ENwateruseN) = the water required per unit of nuclear energy produced (cubic meters per MWh), and (INDE) = the percentage of industrial water use already accounted for in Eq. (5) (which pertains to water used in the coke coal, oil refineries, and nuclear fuel industries).Part of the discharge of springs enters the surface water sources, and this enters the calculation of the input to the surface water-resources stock in Eq. (16):$${InputSW}_{t}= SInflow+{ReSW}_{t}{+ Fountain}_{t}$$
(16)

in which ({InputSW}_{t}) = the volume of inflow water to surface water sources during period t, and ({Fountain}_{t}) = discharge of springs to surface water sources during period t, other terms previously defined.The output of the surface water resources includes water use and the infiltration of surface water into groundwater, the latter calculated with Eq. (17):$${SInflowInf}_{t}={SInflow}_{t}times SInflowInfCo$$
(17)

in which ({SInflowInf}_{t}) = the infiltration volume of surface water during period t, and (SInflowInfCo) = the infiltration coefficient of surface water.The output of the surface water resources stock is calculated using Eq. (18):$${OutputSW}_{t}={AgrSWDCo}_{t}times {AgrWD}_{t}+{IndSWDCo}_{t}times {IndWD}_{t}+{DomSWDCo}_{t}times {DomWD}_{t}+{mathrm{ WE}}_{t}+{SInflowInf}_{t}-{EvSwSea}_{t}$$
(18)

in which ({OutputSW}_{t}) = the output volume of surface water during period t, ({AgrSWDCo}_{t}) = the percentage of gross agricultural water use from surface water resources during period t, ({IndSWDCo}_{t}) = the percentage of industrial water use from surface water resources during period t, ({DomSWDCo}_{t})= the percentage of gross drinking water consumption from surface water sources during period t, and ({EvSwSea}_{t}) = the total volume of evaporation from surface water plus the discharge of surface water to the sea during period t.The balance of surface water resources is calculated based on Eq. (19):$$SWaterleft(tright)=underset{{t}_{0}}{overset{t}{int }}left[{InputSW}_{t}left(Sright)-{OutputSW}_{t}(S)right]dt+SWater(0)$$
(19)

in which (SWaterleft(tright)) = the stock of surface water resources at time t, (SWater(0)) denotes the stock of surface water at the initial time (t = 0).Groundwater resourcesGroundwater resources gain water from deep infiltration of precipitation in the plains and elevated areas from (1) inflows from outside of the study area, (2) infiltration from surface flows and return waters. Groundwater output factors also include the discharge of groundwater resources (wells, springs, and aqueducts), groundwater flow that moves outside the study area and evaporation. Infiltration of precipitation in the plains and in high terrain into groundwater resources is calculated with Eq. (20):$${Inf}_{t}=PrePInfCotimes {preplain}_{t}+PreHInfCotimes {preheight}_{t}$$
(20)

in which ({Inf}_{t}) = the volume of water entering groundwater sources through infiltration of precipitation during period t, (PrePInfCo) = the infiltration coefficient of precipitation in the plains, and (PreHInfCo) = the infiltration coefficient of rainfall in high terrain.Equation (21) calculates the volume of return water that accrues to groundwater resources:$${ReGW}_{t}=IndDomReGWCotimes {IndDomReW}_{t}+AgrReGWCotimes {AgrReW}_{t}$$
(21)

in which ({ReGW}_{t}) = the volume of water returned from water use that accrues to groundwater resources during period t, (IndDomReGWCo) = the percentage of water returned from municipal and industrial water use accruing to groundwater resources, and (AgrReGWCo) = the percentage of water returned from agricultural water use accruing to groundwater resources.The volume of groundwater input is calculated with Eq. (22):$${InputGW}_{t}={Inf}_{t}+{ReGW}_{t}+{SInflowInf}_{t}+{OutCGw }_{t}$$
(22)

in which ({InputGW}_{t}) = the volume of groundwater input during period t, and ({OutCGw }_{t}) = the difference between the volume of groundwater leaving and that entering the country during period t.The volume of groundwater output is calculated with Eq. (23):$${OutputGW}_{t}={AgrGWDCo}_{t}times {AgrWD}_{t}+IndGWDCotimes {IndWD}_{t}+DomGWDCotimes {DomWD}_{t}+{EvGwDr}_{t}$$
(23)

in which ({OutputGW}_{t}) = the volume of groundwater output during period t, ({AgrGWDCo}_{t}) = the percentage of gross agricultural water use from groundwater resources during period t, IndGWDCo = the percentage of industrial water use from groundwater resources during period t, DomGWDCo = the percentage of municipal water use from groundwater resources during period t, and ({EvGwDr }_{t}) = the total volume of evaporation from groundwater plus the drainage of groundwater resources to surface water resources at time t.Equation (24) calculates the annual balance of groundwater resources:$$GWaterleft(tright)=underset{{t}_{0}}{overset{t}{int }}left[{InputGW}_{t}left(Sright)-{OutputGW}_{t}left(Sright)right]dt+GWater(0)$$
(24)

in which GWater(t) = the groundwater resources stock at time t, (GWater(0)) denotes the stock of groundwater at the initial time (t = 0).Energy usesEnergy uses are calculated with Eqs. (25)–(27). The total national energy use includes the agricultural, industrial, transportation, and exports sectors’ energy demands. The energy uses by these sectors do not change during the implementation of the policy, and, consequently do not change the WFE Nexus in that period; therefore, they are not included in the calculations.$${WDTP}_{t}={DomWD}_{t}times {CEIntensity}_{t}$$
(25)

in which ({WDTP}_{t}) = the energy used in the extraction, transmission, distribution, and treatment of water in the water and wastewater system during period t, and ({CEIntensity}_{t}) = the energy intensity in the extraction, transmission, distribution, and treatment of water in water and wastewater systems during the period t (MWh per cubic meter).$${ResComPubED}_{t}=ResComPubPerCapitatimes {Population}_{t}$$
(26)

in which ({ResComPubED}_{t}) = the energy use by the domestic, commercial, and public sectors during period t, and (ResComPubPerCapita) = the per capita energy consumption by the domestic, commercial, and public sectors (MWh per person per year).$${OutputE}_{t}={ResComPubED}_{t}+{WDTP}_{t}$$
(27)
Environmental water needsThe gray water footprint is defined as the volume of freshwater that is required to assimilate the load of pollutants based on natural background concentrations and existing ambient water quality standards. The estimation of the gray water footprint associated with discharges from agricultural production is based on the load of nitrogen fertilizers, which are pervasive in agriculture. The gray water footprint in terms of nitrogen concentration has been estimated by Mekonnen and Hoekstra24,25, as written in Eq. (28):$${GW}_{t}^{Agr}=sum_{iin A}{GW}_{i}times {Product}_{i,t}$$
(28)

in which ({GW}_{t}^{Agr})= the volume of gray water in the agricultural sector during period t, and ({GW}_{i}) = the volume of gray water associated with the production of one ton of agricultural product i (cubic meters per ton)(.)There are no accurate estimates of the concentrations of pollutants per unit of industrial production, or of the concentration of pollutants in municipal wastewater. Therefore, the conservative dilution factor (DF), which is equal to 1 for untreated returned water from the municipal and industrial sectors, is applied in this work. Equation (29) is a simplified equation of the gray water footprint26. The fraction appearing on the right-hand side of Eq. (29) is equal to the DF.$${GW}_{t}^{IndDom}= frac{{C}_{eff}-{C}_{nat}}{{C}_{max}-{C}_{nat}}times {IndDomReW}_{t}times IndDomReUT$$
(29)

in which ({GW}_{t}^{IndDom}) = the gray water footprint of the municipal and industrial sectors during period t, ({C}_{eff}) = the nitrogen concentration in return water (mg/L), ({C}_{nat}) = the natural concentrations of contaminant in surface water (mg/L), ({C}_{max}) = the maximum allowable concentration contaminant in surface water (mg/L), and (IndDomReUT) = the percentage of untreated returned water from the municipal and industrial sectors.The total gray water footprint is obtained by summing the footprints associated with the municipal/industrial and agricultural sectors:$${TotalGW}_{mathrm{t}}={GW}_{t}^{IndDom}+{GW}_{t}^{Agr}$$
(30)

in which ({TotalGW}_{mathrm{t}}) = the volume of gray water from all sectors during period t.This work considers qualitative and quantitative environmental water needs. Equation (31) is used to calculate the total environmental water need. The Tennant method for calculating the riverine environmental flow requirement (or instream flow) stipulates that, based on the conditions of each basin, between 10 to 30% of the average long-term flow of rivers represents the environmental flow requirement27. The sum of these requirements across all the basins equals the environmental requirement of the entire region or country. Yet, by providing 10 to 30% of the average long-term flow of rivers the riverine ecosystem barely emerges from critical conditions, and is far from optimal ecologic functioning. The total environmental water need is equal to the sum of the environmental flow requirement plus the volume of water needed to dilute the contaminants entering the surface water sources:$${ENV}_{t}={TotalGW}_{t}+Tennant$$
(31)

in which ({ENV}_{t}) = the environmental flow requirement during period t, and Tennant = the environmental flow requirement calculated by the Tennant (1976) method.The policy evaluation indexThe available renewable water is calculated with Eq. (32):$${IN}_{t}={OutCGW }_{t}+ {SInflow }_{t}+{ Inf}_{t}-{EvGwDr}_{t}$$
(32)

in which ({IN}_{t})= the renewable water available before the application of environmental constraints during period t.The volume of manageable water is calculated with Eq. (33):$$REWleft(tright)=underset{{t}_{0}}{overset{t}{int }}left[INleft(tright)-ENVleft(tright)right]dt$$
(33)

in which REW (t) = the (cumulative) manageable and exploitable renewable water in the period t-t0.Equation (34) calculates the total water withdrawals by the agricultural, industrial, municipal, and energy production sectors:$${WDW}_{t}={OutputSW }_{t}+ {OutputGW}_{t}- {cheshmeh}_{t}$$
(34)

in which ({WDW}_{t}) = the sum of the withdrawals by the agricultural, industrial, municipal, and energy production sectors during period t.The cumulative water withdrawals are calculated with Eq. (35):$$withdleft(tright)=underset{{t}_{0}}{overset{t}{int }}WDWleft(tright)dt$$
(35)

in which (withdleft(tright)) = the sum of the withdrawals by the agricultural, industrial, municipal and energy production sectors in the horizon t-t0.Equation (36) calculates the water stress index:$${index}_{{t}_{f}}^{MRW}=frac{withd({t}_{f})}{REWleft({t}_{f}right)}times 100$$
(36)

in which ({index}_{{t}_{f}}^{MRW}) = the renewable water stress index at the end of the study period, and ({t}_{f}) = the period marking the end of the study horizon.Once the water and energy model is developed it must be calibrated with observational data prior to its use in predictions, as shown below. More

Crops and their landrace study areasFood crops whose genetic resources are researched and conserved by CGIAR international agricultural research centres or by the CePaCT of the SPC were included in this study. Crop landrace distributions were modelled and conservation analyses conducted within recognized primary and, for some crops, secondary regions of diversity, where these crops were domesticated and/or have been cultivated for very long periods, and where they are, thus, expected to feature high genetic diversity and adaptation to local environmental and cultural factors (Supplementary Tables 1 and 2)9,13. These regions were identified through literature review (Supplementary Information) and confirmed by crop experts.Occurrence dataOur crop landrace group distribution modelling and conservation gap analysis rely on occurrence data, including coordinates of locations where landraces were previously collected for ex situ conservation and reference sightings. For ex situ conservation records, occurrences marked as landraces were retrieved from two major online databases: the Genesys Plant Genetic Resources portal33 and the World Information and Early Warning System on Plant Genetic Resources for Food and Agriculture (WIEWS) of the Food and Agriculture Organization of the United Nations34. Occurrences were also obtained directly from individual international genebank information systems: AfricaRice, the International Transit Centre and Musa Germplasm Information System of Bioversity International35, CePaCT, International Center for Tropical Agriculture (CIAT), International Maize and Wheat Improvement Center (CIMMYT), International Potato Center (CIP), International Center for Agricultural Research in the Dry Areas (ICARDA), International Crops Research Institute for the Semi-arid Tropics (ICRISAT), International Institute of Tropical Agriculture (IITA) and International Rice Research Institute (IRRI), as well as from the United States Department of Agriculture (USDA) Genetic Resources Information Network (GRIN)–Global36 and the Comisión Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO)37. Occurrences were compiled from the Global Biodiversity Information Facility (GBIF), with ‘living specimen’ records classified as ex situ conservation records and the remaining serving as reference sightings for use in distribution modelling. Reference occurrences were also drawn from published literature (Supplementary Information). Duplicated observations within or between data sources were eliminated, with a preference to utilize the most original data. Coordinates were corrected or removed when latitude and longitude were equal to zero or inverted, located in water bodies or in the wrong country or had poor resolution ( 10 (ref. 60). The predictors and whether they were selected for the modelling of each landrace group are presented in Supplementary Table 4.We generated a random sample of pseudo-absences as background points in areas that (1) were within the same ecological land units61 as the occurrence points, (2) were deemed potentially suitable according to a support vector machine classifier that uses all occurrences and predictor variables and (3) were farther than 5 km from any occurrence62. The number of pseudo-absences generated per crop group was ten times its number of unique occurrences.MaxEnt models were fitted through five-fold (K = 5) cross-validation with 80% training and 20% testing. For each fold, we calculated the area under the receiving operating characteristic curve (AUC), sensitivity, specificity and Cohen’s kappa as measures of model performance. To create a single prediction that represents the probability of occurrence for the landrace group, we computed the median across K models. Geographic areas in the form of pixels with probability values above the maximum sum of sensitivity and specificity were treated as the final area of predicted presence13.Ex situ conservation status and gapsThree separate but complementary metrics were developed to compare the geographic and environmental diversity in current ex situ conservation collections to the total geographic and environmental variation across the crop landrace group distribution model and, thus, to identify and quantify ex situ conservation gaps13.A connectivity gap score (SCON) was calculated for each 2.5-arc-minute pixel within the distribution model by drawing a triangle63,64 around each pixel using the three closest genebank accession occurrence locations as vertices and then deriving normalized values for the pixel based on distance to the triangle centroid and vertices13. The SCON of a pixel is high—closer to 1 on a scale of 0–1—when its corresponding triangle is large, when the pixel is close to the centroid of the triangle or when the distance to the vertices is large. A high SCON represents a greater probability of the pixel location being a gap in existing ex situ collections.An accessibility gap score (SACC) was calculated for each 2.5-arc-minute pixel in the distribution model by computing travel time from each pixel to its nearest genebank accession occurrence location based both on distance and the speed of travel, defined by a friction surface13,45. Travel time scores were normalized by dividing pixel values by the longest travel time within the distribution model, with the final score ranging from 0 to 1. A high SACC value for a pixel reflects long travel times from existing genebank collection occurrences and, thus, represents a higher probability of the pixel location being a gap in existing ex situ collections.An environmental gap score (SENV) was calculated for each 2.5-arc-minute pixel in the distribution model by conducting a hierarchical clustering analysis using Ward’s method with all the predictor variables from the distribution modelling. The Mahalanobis distance between each pixel and the environmentally closest genebank accession occurrence location was then computed13. Environmental distance scores were normalized between 0 and 1. A high SENV value for a pixel reflects a large distance to areas with similar environments where landraces have previously been collected for genebank conservation and, thus, represents a higher probability of the pixel location being a gap in existing ex situ collections.Spatial ex situ conservation gaps were determined from the conservation gap scores using a cross-validation procedure to derive a threshold for each score. We created synthetic gaps by removing existing genebank occurrences in five randomly chosen circular areas with a 100 km radius within the distribution model. We then tested whether these artificial gaps could be predicted by our gap analysis, identifying the threshold value of each score that would maximize the prediction of these synthetic gaps. Performance for each of the five gap areas was assessed using AUC, sensitivity and specificity. The average cross-area threshold value was calculated for each score to discern pixels with a high likelihood of finding ex situ conservation gaps and that, thus, were higher priority for further field sampling. These were pixels with combined gap scores above the threshold, assigned a value of 1, as opposed to the relatively well-conserved areas below the threshold, which were assigned a value of 0.The three binary conservation gap scores were then mapped in combination, resulting in pixels across the distribution model with gap values ranging from 0 to 3. Pixels with a value of 0 display no connectivity, accessibility or environmental gaps and are considered well represented ex situ. Pixels with a value of 1 indicate a conservation gap in connectivity, accessibility or the environment; we consider these ‘low-confidence’ gaps. Pixels with a value of 2 indicate gaps in two metrics or ‘medium-confidence’ gaps, and values of 3 indicate gaps across all metrics or ‘high-confidence’ gaps. High-confidence gap areas are displayed on crop-conservation-gap maps (Fig. 2b and Supplementary Information) and conservation hotspot maps across crops (Fig. 4 and Extended Data Figs. 5–8).The representation of crop landrace groups in current ex situ conservation collections was calculated based on the final 1–3 value conservation-gap maps. The complement of the proportion of the modelled distribution considered as a potential conservation gap by any single gap score represents the minimum estimate of current representation; the complement of the proportion considered by all three scores as a gap, which is to say high-confidence gap areas, represents the maximum estimate (Supplementary Tables 1 and 2).While distribution modelling and conservation gap analyses were conducted at the crop landrace group level and results are presented in full in the Supplementary Information, for ease of comparison of results across crops, and to avoid bias towards crops with many landrace groups, we also calculated summary results at the crop level. Crops that had been assessed with geographic differentiations, including maize in Africa and Latin America and yams in the New World and the Old World, were also combined. For spatial results, the pixels in crop landrace group models were summed—that is, constituent landrace group models were combined. The minimum and maximum current conservation representation estimations at the crop level were then calculated based on combined spatial models.GBIF occurrence downloadsThe following occurrence downloads from the Global Biodiversity Information Facility (GBIF; https://www.gbif.org/, 2017−2021) were used: 10.15468/dl.rrntfr, 10.15468/dl.2f2v4h, 10.15468/dl.2ywlb7, 10.15468/dl.lnfelh, 10.15468/dl.ryrmfj, 10.15468/dl.8adf61, 10.15468/dl.nff5ys, 10.15468/dl.erxs6e, 10.15468/dl.vbfgho, 10.15468/dl.mjjk3x, 10.15468/dl.uppz1n, 10.15468/dl.938bgm, 10.15468/dl.hr87hm, 10.15468/dl.k1va80, 10.15468/dl.coqpu2, 10.15468/dl.lkoo9u, 10.15468/dl.e998mp, 10.15468/dl.vfbmm7, 10.15468/dl.tnp478, 10.15468/dl.6zxsea, 10.15468/dl.0lray8, 10.15468/dl.5sjgsw, 10.15468/dl.wkju6h, 10.15468/dl.7xzfvc, 10.15468/dl.autlf5, 10.15468/dl.fe2amw, 10.15468/dl.2zblvz, 10.15468/dl.ddplkj, 10.15468/dl.jbzejg, 10.15468/dl.ej5bha, 10.15468/dl.905pxd, 10.15468/dl.pim1vs, 10.15468/dl.vdridc, 10.15468/dl.b43gyv, 10.15468/dl.nnw3z7, 10.15468/dl.bnt9jc, 10.15468/dl.f5x2cg, 10.15468/dl.ub7zbg, 10.15468/dl.sggf2v, 10.15468/dl.ath5ve, 10.15468/dl.23k3ug, 10.15468/dl.cym376, 10.15468/dl.53bwzk, 10.15468/dl.fsad7h and 10.15468/dl.fm6p7z.Reporting SummaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. 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