Philippa Kaur

Ellison, W. T., Southall, B. L., Clark, C. W. & Frankel, A. S. A new context-based approach to assess marine mammal behavioral responses to anthropogenic sounds. Conserv. Biol. 26, 21–28 (2012).CAS 
PubMed 
Article 

Google Scholar 
Williams, R., Clark, C. W., Ponirakis, D. & Ashe, E. Acoustic quality of critical habitats for three threatened whale populations. Anim. Conserv. 17, 174–185 (2014).Article 

Google Scholar 
Halliday, W. D., Pine, M. K. & Insley, S. J. Underwater noise and arctic marine mammals: Review and policy recommendations. Environ. Rev. 28, 438–448 (2020).Article 

Google Scholar 
Kvadsheim, P. H. et al. Impact of Anthropogenic Noise on the Marine Environment: Status of Knowledge and Management (Springer, 2020).
Google Scholar 
Weilgart, L. S. & Whitehead, H. Distinctive vocalizations from mature male sperm whales (Physeter macrocephalus). Can. J. Zool. 66, 1931–1937 (1988).Article 

Google Scholar 
Simon, M., Stafford, K. M., Beedholm, K., Lee, C. M. & Madsen, P. T. Singing behavior of fin whales in the Davis Strait with implications for mating, migration and foraging. J. Acoust. Soc. Am. 128, 3200 (2010).ADS 
PubMed 
Article 

Google Scholar 
Alves, D., Amorim, M. C. P. & Fonseca, P. J. Assessing acoustic communication active space in the Lusitanian toadfish. J. Exp. Biol. 219, 1122–1129 (2016).PubMed 

Google Scholar 
Linnenschmidt, M., Teilmann, J., Akamatsu, T., Dietz, R. & Miller, L. A. Biosonar, dive, and foraging activity of satellite tracked harbor porpoises (Phocoena phocoena). Mar. Mamm. Sci. 29, E77–E97 (2013).Article 

Google Scholar 
Giorli, G. & Goetz, K. T. Foraging activity of sperm whales (Physeter macrocephalus) off the east coast of New Zealand. Sci. Rep. 9, 12182 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Baumgartner, M. F. & Fratantoni, D. M. Diel periodicity in both sei whale vocalization rates and the vertical migration of their copepod prey observed from ocean gliders. Limnol. Oceanogr. 53, 2197–2209 (2008).ADS 
Article 

Google Scholar 
Urazghildiiev, I. R. & Van Parijs, S. M. Automatic grunt detector and recognizer for Atlantic cod (Gadus morhua). J. Acoust. Soc. Am. 139, 2532–2540 (2016).ADS 
PubMed 
Article 

Google Scholar 
Ladich, F. Ecology of sound communication in fishes. Fish Fish. 20, 552–563 (2019).Article 

Google Scholar 
Radford, C. A., Stanley, J. A., Simpson, S. D. & Jeffs, A. G. Juvenile coral reef fish use sound to locate habitats. Coral Reefs 30, 295–305 (2011).ADS 
Article 

Google Scholar 
Pierpoint, C. Harbour porpoise (Phocoena phocoena) foraging strategy at a high energy, near-shore site in south-west Wales, UK. J. Mar. Biol. Assoc. UK 88, 1167–1173 (2008).Article 

Google Scholar 
Pijanowski, B. C. et al. Soundscape ecology: The science of sound in the landscape. Bioscience 61, 203–216 (2011).Article 

Google Scholar 
Stanley, J. A., Radford, C. A. & Jeffs, A. G. Location, location, location: Finding a suitable home among the noise. Proc. R. Soc. B Biol. Sci. 279, 3622–3631 (2012).Article 

Google Scholar 
Buscaino, G. et al. Temporal patterns in the soundscape of the shallow waters of a Mediterranean marine protected area. Sci. Rep. 6, 1–13 (2016).Article 
CAS 

Google Scholar 
Gasc, A., Francomano, D., Dunning, J. B. & Pijanowski, B. C. Future directions for soundscape ecology: The importance of ornithological contributions. Auk 134, 215–228 (2017).Article 

Google Scholar 
Putland, R. L., Constantine, R. & Radford, C. A. Exploring spatial and temporal trends in the soundscape of an ecologically significant embayment. Sci. Rep. 7, 5713 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pieretti, N., LoMartire, M., Farina, A. & Danovaro, R. Marine soundscape as an additional biodiversity monitoring tool: A case study from the Adriatic Sea (Mediterranean Sea). Ecol. Indic. 83, 13–20 (2017).Article 

Google Scholar 
Gillespie, D., Palmer, L., Macaulay, J., Sparling, C. & Hastie, G. Passive acoustic methods for tracking the 3D movements of small cetaceans around marine structures. PLoS ONE 15, e0229058 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Van Parijs, S. et al. Management and research applications of real-time and archival passive acoustic sensors over varying temporal and spatial scales. Mar. Ecol. Prog. Ser. 395, 21–36 (2009).ADS 
Article 

Google Scholar 
Warren, V. E., McPherson, C., Giorli, G., Goetz, K. T. & Radford, C. A. Marine soundscape variation reveals insights into baleen whales and their environment: a case study in central New Zealand. R. Soc. Open Sci. 8, 201503 (2021).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ahonen, H. et al. The underwater soundscape in western Fram Strait: Breeding ground of Spitsbergen’s endangered bowhead whales. Mar. Pollut. Bull. 123, 97–112 (2017).CAS 
PubMed 
Article 

Google Scholar 
Hildebrand, J. A. Anthropogenic and natural sources of ambient noise in the ocean. Mar. Ecol. Prog. Ser. 395, 5–20 (2009).ADS 
Article 

Google Scholar 
Ross, D. Ship sources of ambient noise. IEEE J. Ocean. Eng. 30, 257–261 (2005).ADS 
Article 

Google Scholar 
Popper, A. N. & Hawkins, A. The Effects of Noise on Aquatic Life Vol. 730 (Springer, 2012).Book 

Google Scholar 
Hubert, J. et al. Effects of broadband sound exposure on the interaction between foraging crab and shrimp: A field study. Environ. Pollut. 243, 1923–1929 (2018).CAS 
PubMed 
Article 

Google Scholar 
Weilgart, L. The Impact of Ocean Noise Pollution on Fish and Invertebrates (Springer, 2018).
Google Scholar 
Kvadsheim, H., Sivle, L. D., Hansen, R. R. & Karlsen, H. E. Effekter av Menneskeskapt støy på Havmiljø Rapport til Miljødirektoratet om Kunnskapsstatus FFI-RAPPORT. (2017).Parks, S. E., Johnson, M., Nowacek, D. & Tyack, P. L. Individual right whales call louder in increased environmental noise. Biol. Lett. 7, 33–35 (2011).PubMed 
Article 

Google Scholar 
Meh, F. et al. Humpback whales Megaptera novaeangliae alter calling behavior in response to natural sounds and vessel noise. Mar. Ecol. Prog. Ser. 607, 251–268 (2018).Article 

Google Scholar 
Leroy, E. C., Royer, J.-Y., Bonnel, J. & Samaran, F. Long-term and seasonal changes of large whale call frequency in the Southern Indian ocean. J. Geophys. Res. Ocean. 123, 8568–8580 (2018).ADS 
Article 

Google Scholar 
Parks, S. E., Clark, C. W. & Tyack, P. L. Short- and long-term changes in right whale calling behavior: The potential effects of noise on acoustic communication. J. Acoust. Soc. Am. 122, 3725–3731 (2007).ADS 
PubMed 
Article 

Google Scholar 
Clark, C. et al. Acoustic masking in marine ecosystems: intuitions, analysis, and implication. Mar. Ecol. Prog. Ser. 395, 201–222 (2009).ADS 
Article 

Google Scholar 
PAME. Underwater Noise in the Arctic: A State of Knowledge Report (PAME, 2019).
Google Scholar 
Beszczynska-Möller, A., Woodgate, R., Lee, C., Melling, H. & Karcher, M. A synthesis of exchanges through the main oceanic gateways to the Arctic Ocean. Oceanography 24, 82–99 (2011).Article 

Google Scholar 
Ramm, T. Hungry During Migration? Humpback Whale Movement from the Barents Sea to a Feeding Stopover in Northern Norway Revealed by Photo-ID Analysis. (MSc thesis. UiT The Arctic University of Norway, 2020).Broms, F. et al. Recent research on the migratory destinations of humpback whales (Megaptera novaeangliae) from a mid-winter feeding stop-over area in Northern Norway. in Recent research on the migratory destinations of humpback whales (Megaptera novaeangliae) from a mid-winter feeding stop-over area in Northern Norway (ed. Wenzel, F. W.) (European Cetacean Society Special Publication Series, 2015).Aniceto, A. S. et al. Arctic marine data collection using oceanic gliders: Providing ecological context to cetacean vocalizations. Front. Mar. Sci. 7, 547 (2020).Article 

Google Scholar 
Jourdain, E. & Vongraven, D. Humpback whale (Megaptera novaeangliae) and killer whale (Orcinus orca) feeding aggregations for foraging on herring (Clupea harengus) in Northern Norway. Mamm. Biol. 86, 27–32 (2017).Article 

Google Scholar 
Christiansen, J. S., Mecklenburg, C. W. & Karamushko, O. V. Arctic marine fishes and their fisheries in light of global change. Glob. Chang. Biol. 20, 352–359 (2014).ADS 
PubMed 
Article 

Google Scholar 
Rødland, E. S. & Bjørge, A. Residency and abundance of sperm whales (Physeter macrocephalus) in the Bleik Canyon, Norway. Mar. Biol. Res. 11, 974–982 (2015).Article 

Google Scholar 
Nøttestad, L. et al. Prey selection of offshore killer whales Orcinus orca in the Northeast Atlantic in late summer: spatial associations with mackerel. Mar. Ecol. Prog. Ser. 499, 275–283 (2014).ADS 
Article 

Google Scholar 
Bjørge, A., Aarefjord, H., Kaarstad, S., Kleivane, L. & Øien, N. Harbour porpoise (Phocoena phocoena) in Norwegian waters (Springer, 1991).
Google Scholar 
Gjøseter, H. et al. Fisken og Havet. https://doi.org/10.1111/maec.12075 (2010).Article 

Google Scholar 
ICES. ICES Report on Ocean Climate 2009 No.304. (2010).ICES. Report of the Working Group on Widely Distributed Stocks (WGWIDE). (2010).Rey, F. Phytoplankton: The grass of the sea. In The Norwegian Sea Ecosystem (ed. Skjoldal, H. R.) 97–136 (Academic Press, 2004).
Google Scholar 
Huse, G. et al. Effects of interactions between fish populations on ecosystem dynamics in the Norwegian Sea : Results of the INFERNO project. Mar. Biol. Res. 8, 415–419 (2012).Article 

Google Scholar 
Godø, O. R., Johnsen, S. & Torkelsen, T. The LoVe ocean observatory is in operation. Mar. Technol. Soc. J. 48, 24–30 (2014).Article 

Google Scholar 
Cooke, J. G. Balaenoptera physalus. The IUCN Red List of Threatened Species: e.T2478A50349982 (2018).Leonard, D. & Øien, N. Estimated abundances of Cetacean species in the Northeast Atlantic from Norwegian Shipboard Surveys Conducted in 2014–2018. NAMMCO Sci. Publ. 11, 4694 (2020).
Google Scholar 
Øygard, S. H. Simulations of Acoustic Transmission Loss of Fin Whale Calls Reaching the LoVe Ocean Observatory. (MSc thesis. University of Bergen, 2018).Steiner, L. et al. A link between male sperm whales, Physeter macrocephalus, of the Azores and Norway. J. Mar. Biol. Assoc. UK 92, 1751–1756 (2012).Article 

Google Scholar 
Olafsen, T., Winther, U., Olsen, Y. & Skjermo, J. Verdiskaping Basert på Produktive hav i 2050 1–76 (Springer, 2012).
Google Scholar 
Wenz, G. M. Acoustic ambient noise in the ocean: Spectra and sources. J. Acoust. Soc. Am. 34, 1936–1956 (1962).ADS 
Article 

Google Scholar 
Klinck, H. et al. Seasonal presence of cetaceans and ambient noise levels in polar waters of the North Atlantic. J. Acoust. Soc. Am. 132, 176–181 (2012).Article 

Google Scholar 
Burnham, R. E., Duffus, D. A. & Mouy, X. The presence of large whale species in Clayoquot Sound and its offshore waters. Cont. Shelf Res. 177, 15–23 (2019).ADS 
Article 

Google Scholar 
Romagosa, M. et al. Baleen whale acoustic presence and behaviour at a Mid-Atlantic migratory habitat, the Azores Archipelago. Sci. Rep. 10, 61489 (2020).
Google Scholar 
Tervo, O. Acoustic Behaviour of Bowhead Whales Balaena mysticetus in Disko Bay, Western Greenland. PhD thesis. (2011).Magnúsdóttir, E. E. & Lim, R. Subarctic singers: Humpback whale (Megaptera novaeangliae) song structure and progression from an Icelandic feeding ground during winter. PLoS ONE 14, e0210057 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Samaran, F. et al. Seasonal and geographic variation of southern blue whale subspecies in the Indian Ocean. PLoS ONE 8, e70 (2013).Article 

Google Scholar 
Norris, T. F., Dunleavy, K. J., Yack, T. M. & Ferguson, E. L. Estimation of minke whale abundance from an acoustic line transect survey of the Mariana Islands. Mar. Mammal Sci. 33, 574 (2017).Article 

Google Scholar 
Marques, T. A. et al. Estimating animal population density using passive acoustics. Biol. Rev. Camb. Philos. Soc. 88, 287–309 (2013).PubMed 
Article 

Google Scholar 
Dunlop, R. A. The effects of vessel noise on the communication network of humpback whales. R. Soc. Open Sci. 6, 190967 (2019).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Christensen, I., Haug, T. & Øien, N. A review of feeding and reproduction in large baleen whales (Mysticeti) and sperm whales Physeter macrocephalus in Norwegian and adjacent waters. ICES J. Mar. Sci. 49, 341–355 (1992).Article 

Google Scholar 
Aniceto, A. S. et al. Monitoring marine mammals using unmanned aerial vehicles: quantifying detection certainty. Ecosphere 9, e02122 (2018).Article 

Google Scholar 
Pedersen, G., Storheim, E., Sivle, L. D., Godø, O. R. & Ødegaard, L. A. Concurrent passive and active acoustic observations of high-latitude shallow foraging sperm whales (Physeter macrocephalus) and mesopelagic prey layer. J. Acoust. Soc. Am. 141, 1–10 (2017).Article 

Google Scholar 
Vogel, E. F. The influence of herring (Clupea harengus) biomass and distribution on killer whale (Orcinus orca) movements on the Norwegian shelf (UiT The Arctic University of Norway, 2020).
Google Scholar 
Williams, R. et al. Impacts of anthropogenic noise on marine life: Publication patterns, new discoveries, and future directions in research and management. Ocean Coast. Manag. 115, 17–24 (2015).Article 

Google Scholar 
Garibbo, S. et al. Low-frequency ocean acoustics: Measurements from the Lofoten-Vesterålen Ocean Observatory, Norway. (2020). https://doi.org/10.1121/2.0001324.Dekeling, R. P. A. et al. Monitoring Guidance for Underwater Noise in European Seas, Part I: Executive Summary (Springer, 2014).
Google Scholar 
Erbe, C. International regulation of underwater noise. Acoust. Aust. 41, 1–10 (2013).
Google Scholar 
Halliday, W. D., Insley, S. J., Hilliard, R. C., de Jong, T. & Pine, M. K. Potential impacts of shipping noise on marine mammals in the western Canadian Arctic. Mar. Pollut. Bull. 123, 73–82 (2017).CAS 
PubMed 
Article 

Google Scholar 
Halliday, W. D. et al. Underwater sound levels in the Canadian Arctic, 2014–2019. Mar. Pollut. Bull. 168, 112437 (2021).CAS 
PubMed 
Article 

Google Scholar 
Ødegaard, L., Pedersen, G. & Johnsen, E. Underwater Noise From Wind At the High North Love Ocean Observatory. UACE 2019 Conf. Proc. 359–366 (2019).Zhang, G., Forland, T. N., Johnsen, E., Pedersen, G. & Dong, H. Measurements of underwater noise radiated by commercial ships at a cabled ocean observatory. Mar. Pollut. Bull. 153, 110948 (2020).CAS 
PubMed 
Article 

Google Scholar 
Maystrenko, Y. P., Olesen, O., Gernigon, L. & Gradmann, S. Deep structure of the Lofoten–Vesterålen segment of the Mid-Norwegian continental margin and adjacent areas derived from 3-D density modeling. J. Geophys. Res. Solid Earth 122, 1402–1433 (2017).ADS 
Article 

Google Scholar 
Gillespie, D. et al. PAMGUARD: Semiautomated, open source software for real-time acoustic detection and localization of cetaceans. J. Acoust. Soc. Am. 125, 2547–2547 (2009).ADS 
Article 

Google Scholar 
Hollander, M. & Wolfe, D. A. Nonparametric Statistical Methods (Wiley, 1973).MATH 

Google Scholar 
Vogel, E. F. et al. Killer whale movements on the Norwegian shelf are associated with herring density. Mar. Ecol. Prog. Ser. 665, 217–231 (2021).ADS 
Article 

Google Scholar 
Garcia, H. A. et al. Temporal-spatial, spectral, and source level distributions of fin whale vocalizations in the Norwegian Sea observed with a coherent hydrophone array. ICES J. Mar. Sci. 76, 268–283 (2019).Article 

Google Scholar 
Davis, G. E. et al. Exploring movement patterns and changing distributions of baleen whales in the western North Atlantic using a decade of passive acoustic data. Glob. Chang. Biol. 26, 4812–4840 (2020).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Risch, D. et al. Minke whale acoustic behavior and multi-year seasonal and diel vocalization patterns in Massachusetts Bay, USA. Mar. Ecol. Prog. Ser. 489, 279–295 (2013).ADS 
Article 

Google Scholar 
Le Tixerant, M., Le Guyader, D., Gourmelon, F. & Queffelec, B. How can Automatic Identification System (AIS) data be used for maritime spatial planning?. Ocean Coast. Manag. 166, 18–30 (2018).Article 

Google Scholar 
Team, R. C. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2020).Sumner, M. D. The Tag Location Problem. 133 (2011).Sumner, M. D., Wotherspoon, S. J. & Hindell, M. A. Bayesian estimation of animal movement from archival and satellite tags. PLoS ONE 4, e7324 (2009).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Halliday, W. D. et al. The coastal Arctic marine soundscape near Ulukhaktok, Northwest Territories, Canada. Polar Biol. 43, 623–636 (2020).Article 

Google Scholar 
Ezzet, F. & Pinheiro, J. Linear, generalized linear, and nonlinear mixed effects models. Pharm. Sci. Quant. Pharmacol. 1, 103–135. https://doi.org/10.1002/9780470087978.ch4 (2006).Article 

Google Scholar 
Mazerolle, M. J. AICcmodavg: Model Selection and Multimodel Inference Based on (Q)AIC(c). (2020).Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).MATH 
Book 

Google Scholar 
Pante, E. & Simon-Bouhet, B. marmap: A package for importing, plotting and analyzing bathymetric and topographic data in R. PLoS ONE 8, e73051 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wessel, P. & Walter, H. F. S. A global self-consistent, hierarchical, high-resolution shoreline database. J. Geophys. Res. 101, 8741–8743 (1996).ADS 
Article 

Google Scholar 
Sueur, J., Aubin, T. & Simonis, C. Equipment review: Seewave, a free modular tool for sound analysis and synthesis. Bioacoustics 18, 213–226 (2008).Article 

Google Scholar 
The Mathworks Inc. MATLAB (R2019a). (MathWorks Inc., 2019).Merchant, N. D. et al. Measuring acoustic habitats. Methods Ecol. Evol. 6, 257–265 (2015).PubMed 
PubMed Central 
Article 

Google Scholar  More

Diomedenema dinarctos, a parasitic worm that infests penguins, is named after the Greek deinos, meaning terrible, and arktos, or bear, because of its resemblance to a menacing teddy bear.Credit: Bronwen Presswell and Jerusha Bennett

What scientists choose to name parasitic worms could say more about the researchers than the organism they are studying.A study1 examining the names of nearly 3,000 species of parasitic worm discovered in the past 20 years reveals a markedly higher proportion named after male scientists than after female scientists — and a growing appetite for immortalizing friends and family members in scientific names.The analysis uncovers ongoing biases in taxonomy — the classification of organisms — and could be used as a jumping-off point for rethinking how scientists name species, says study co-author Robert Poulin, an ecological parasitologist at the University of Otago in Dunedin, New Zealand.“When you name something, it is now named forever. I think it’s worth giving some thought to what names we choose,” he says. The research was published on 11 May in Proceedings of the Royal Society B.As the worm turnsSpecies names often describe how an organism looks or where it was found. But since the nineteenth century, they have also been used to immortalize scientists. The parasite that causes the intestinal disease giardiasis, for instance, was named after French zoologist Alfred Giard.Wondering how naming practices had changed, Poulin and his colleagues combed through papers published between 2000 and 2020 that describe roughly 2,900 new species of parasitic worm. The team found that well over 1,500 species were named after their host organism, where they were found or a prominent feature of their anatomy.Many others were named after people, ranging from technical assistants to prominent politicians (Baracktrema obamai, a species found in Malaysian freshwater turtles, was named after former US president Barack Obama). But just 19% of the 596 species named after eminent scientists were named after women, a percentage that essentially didn’t budge over the decades (see ‘Parasite name game’).

Source: Ref. 1

This could be because of a historical dearth of female figures in the field, says Janine Caira, a parasite taxonomist at the University of Connecticut in Storrs. But another possibility is that the work of past female scientists often goes unrecognized, says Tanapan Sukee, a parasitologist at the University of Melbourne in Australia.Sukee has named two species of parasitic worm after now-deceased Australian biologist Patricia Mawson, who was a key player in the characterization of marsupial parasites. For most of her career, Mawson worked part-time as a technician, and she was often designated second author on papers describing species she had discovered, Sukee says. Similar situations could explain why so few parasites are named after women.Poulin and his colleagues also noticed an upward trend in the number of parasites named after friends and family members of the scientists who formally described them. Some researchers even name species after pets: Rhinebothrium corbatai is a freshwater stingray parasite named after the first author’s Welsh terrier, Corbata.Poulin says this should be discouraged. Species are almost never named after the person who described them, and Poulin argues that names honouring parents, children or spouses could be seen as a way to get around this convention.And besides, “I don’t have any friends or family who want a parasite named after them!” says Sukee. More

IPCC (2013) Climate Change: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker TF, Qin D Qin, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V & Midgley PM (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp (2013).Graham, L.P. Projections of Future Anthropogenic Climate Change [in:] Assessment of Climate Change for the Baltic Sea Basin. Regional Climate Studies. Bolle H.J., Menenti M., Rasool I. Series Editors Springer-Verlag Berlin Heidelberg s.133–220 (2008).Früchtenich, E., Bock, J., Feucht, V., Früchtenich W. Reactions of three European oak species ( Q. robur, Q. petraea and Q. ilex ) to repetitive summer drought in sandy soil. Trees, Forests and People 5: 100093 (2021).Gray, S. B. & Brady, S. M. Plant developmental responses to climate change. Dev. Biol. 419, 64–77 (2016).CAS 
Article 

Google Scholar 
Willliams, A. & De Vries, F. T. Plant root exudation under drought: Implications for ecosystem functioning. New Phytol. 225, 1899–1905 (2019).Article 

Google Scholar 
Canarini, A., Merchant, A. & Dijkstra, F. A. Drought effects on Helianthus annuus and Glycine max metabolites: From phloem to root exudates. Rhizosphere 2, 85–97 (2016).Article 

Google Scholar 
De Vries, F. T. et al. Changes in root-exudate-induced respiration reveal a novel mechanism through which drought affects ecosystem carbon cycling. New Phytol. 224, 132–145 (2019).Article 

Google Scholar 
Phillips, R. P., Finzi, A. C. & Bernhardt, E. S. Enhanced root exudation indu ces microbial feedbacks to N cycling in a pine forest under long-term CO2 fumigation. Ecol. Lett. 14, 187–194 (2011).Article 

Google Scholar 
Meier, I. C. et al. Root exudation of mature beech forests across a nutrient availability gradient: The role of root morphology and fungal activity. New Phytol. 226, 583–594 (2020).CAS 
Article 

Google Scholar 
Gianfreda, L. Enzymes of importance to rhizosphere processes. J. Soil Sci. Plant Nutr. 15, 283–306 (2015).
Google Scholar 
Małek, S., Ważny, R., Błońska, E., Jasik, M. & Lasota, J. Soil fungal diversity and biological activity as indicators of fertilization strategies in a forest ecosystem after spruce disintegration in the Karpaty Mountains. Sci. Total Environ. 751, 142335 (2021).ADS 
Article 

Google Scholar 
Zuccarini, P., Asensio, D., Ogaya, R., Sardans, J. & Penuelas, J. Effects of seasonal and decadal warming on soil enzymatic activity in a P-deficient Mediterranean shrubland. Glob. Change Biol. 26, 3698–3714 (2019).ADS 
Article 

Google Scholar 
Sing, S. et al. Soil organic carbon cycling in response to simulated soil moisture variation under field conditions. Sci. Rep. 11, 10841 (2021).ADS 
Article 

Google Scholar 
Sardans, J. & Penuelas, J. Drought decreases soil enzyme activity in a Mediterranean Quercus ilex L. forest. Soil Biol. Biochem. 37, 455–461 (2005).CAS 
Article 

Google Scholar 
Czúcz, B., Gálhidy, L. & Mátyás, C. Present and forecasted xeric climatic limits of beech and sessile oak distribution at low altitudes in Central Europe. Ann. For. Sci. 68, 99–108. https://doi.org/10.1007/s13595-011-0011-4 (2011).Article 

Google Scholar 
Sáenz-Romero, C. et al. Adaptive and plastic responses of Quercus petraea populations to climate across Europe. Glob. Change Biol. 23, 2831–2847 (2018).ADS 
Article 

Google Scholar 
Regulation of the Minister of the Environment. Detailed requirements for the forest reprudactive material (Dz. U. Nr 31, poz. 272) (in Polish) (2004).Phillips, R. P., Erlitz, Y., Bier, R. & Bernhardt, E. S. New approach for capturing soluble root exudates in forest soils. Funct. Ecol. 22, 990–999. https://doi.org/10.1111/j.1365-2435.2008.01495.x (2008).Article 

Google Scholar 
Ostonen, I., Lõhmus, K. & Lasn, R. The role of soil conditions in fine root ecomorphology in Norway spruce (Picea abies (L.) Karst.). Plant Soil 208, 283–292 (1999).CAS 
Article 

Google Scholar 
Pritsch, K. et al. A rapid and highly sensitive method for measuring enzyme activities in single mycorrhizal tips using 4-methylumbelliferone-labelled fluorogenic substrates in a microplate system. J. Microbiol. Methods 58, 233–241 (2004).CAS 
Article 

Google Scholar 
Sanaullah, M., Razavi, B. S., Blagodatskaya, E. & Kuzyakov, Y. Spatial distribution and catalytic mechanisms of β-glucosidase activity at the root-soil interface. Biol. Fert. Soils 52, 505–514 (2016).CAS 
Article 

Google Scholar 
R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.Hartmann, H. Will a 385million year-struggle for light become a struggle for water and for carbon?–how trees may cope with more frequent climate change-type drought events. Glob. Change Biol. 17, 642–655 (2011).ADS 
Article 

Google Scholar 
Brunner, I., Herzog, C., Dawes, M. A., Arend, M. & Sperisen, C. How tree roots respond to drought. Front. Plant Sci. 6, 547 (2015).Article 

Google Scholar 
Markesteijn, L. & Poorter, L. Seedling root morphology and biomass allocation of 62 tropical tree species in relation to drought- and shade-tolerance. J. Ecol. 97, 311–325 (2009).Article 

Google Scholar 
Poorter, L. & Markesteijn, L. Seedling Traits Determine Drought Tolerance of Tropical Tree Species. Biotropica 40, 321–331 (2008).Article 

Google Scholar 
Ostonen, I. et al. Specific root length as an indicator of environmental change. Plant Biosyst. 141, 426–442 (2007).Article 

Google Scholar 
Lozano, Y. M., Aguilar-Triqueros, C. A., Flaig, I. C. & Rillig, M. C. Root trait responses to drought are more heterogeneous than leaf trait responses. Funct. Ecol. 34, 2224–2235 (2020).Article 

Google Scholar 
De Vries, F. T., Brown, C. & Stevens, C. J. Grassland species root response to drought: consequences for soil carbon and nitrogen availability. Plant Soil 409, 297–312 (2016).Article 

Google Scholar 
Sell, M. et al. Responses of fine root exudation, respiration and morphology in three early successional ree species to increased air humidity and different soil nitrogen sources. Tree Physiol. 42, 557–569. https://doi.org/10.1093/treephys/tpab118 (2021).Article 

Google Scholar 
Karlowsky, S. et al. Drought-induced accumulation of root exudates supports post-drought recovery of microbes in mountain grassland. Front. Plant Sci. 9, 1593. https://doi.org/10.3389/fpls.2018.01593 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Fuchslueger, L., Bahn, M., Fritz, K., Hasibeder, R. & Richter, A. Experimental drought reduces the transfer of recently fixed plant carbon to soil microbes and alters the bacterial community composition in a mountain meadow. New Phytol. 201, 916–927. https://doi.org/10.1111/nph.12569 (2014).CAS 
Article 
PubMed 

Google Scholar 
Gargallo-Garriga, A. et al. Root exudate metabolomes change under drought and show limited capacity for recovery. Sci. Rep. 8, 12696. https://doi.org/10.1038/s41598-018-30150-0 (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Zhang, X., Dippold, M. A., Kuzyakov, Y. & Razavi, B. S. Spatial pattern of enzyme activities depends on root exudate composition. Soil Biol. Biochem. 133, 83–93. https://doi.org/10.1016/j.soilbio.2019.02.010 (2019).CAS 
Article 

Google Scholar 
Hommel, R. et al. Impact of interspecific competition and drought on the allocation of new assimilates in trees. Plant Biol. 18, 785–796. https://doi.org/10.1111/plb.12461 (2016).CAS 
Article 
PubMed 

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

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

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

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

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