Ecology

We used the IUCN Red List criteria34,35 and methods developed in other global status-assessment efforts36,37 to assess 10,078 reptile species for extinction risk. We additionally include recommended Red List categories for 118 turtle species38, for a total of 10,196 species covered, representing 89% of the 11,341 described reptile species as of August 202039.Data compilationWe compiled assessment data primarily through regional in-person and remote (that is, through phone and email) workshops with species experts (9,536 species) and consultation with IUCN Species Survival Commission Specialist Groups and stand-alone Red List Authorities (442 species, primarily marine turtles, terrestrial and freshwater turtles, iguanas, sea snakes, mainland African chameleons and crocodiles). We conducted 48 workshops between 2004 and 2019 (Supplementary Table 1). Workshop participants provided information to complete the required species assessment fields (geographical distribution, population abundance and trends, habitat and ecological requirements, threats, use and trade, literature) and draw a distribution map. We then applied the Red List criteria34 to this information to assign a Red List category: extinct, extinct in the wild, critically endangered, endangered, vulnerable, near threatened, least concern and data deficient. Threatened species are those categorized as critically endangered, endangered and vulnerable.TaxonomyWe used The Reptile Database39 as a taxonomic standard, diverging only to follow well-justified taxonomic standards from the IUCN Species Survival Commission40. We could not revisit new descriptions for most regions after the end of the original assessment, so the final species list is not fully consistent with any single release of The Reptile Database.Distribution mapsWhere data allowed, we developed distribution maps in Esri shapefile format using the IUCN mapping guidelines41 (1,003 species). These maps are typically broad polygons that encompass all known localities, with provisions made to show obvious discontinuity in areas of unsuitable habitat. Each polygon is coded according to species’ presence (extant, possibly extant or extinct) and origin (native, introduced or reintroduced)41. For some regions covered in workshops (Caucasus, Southeast Asia, much of Africa, Australia and western South America), we collaborated with the Global Assessment of Reptile Distributions (GARD) (http://www.gardinitiative.org/) to provide contributing experts with a baseline species distribution map for review. Although refined maps were returned to the GARD team, not all of these maps have been incorporated into the GARD.Habitat preferencesWhere known, species habitats were coded using the IUCN Habitat Classification Scheme (v.3.1) (https://www.iucnredlist.org/resources/habitat-classification-scheme). Species were assigned to all habitat classes in which they are known to occur. Where possible, habitat suitability (suitable, marginal or unknown) and major importance (yes or no) was recorded. Habitat data were available for 9,484 reptile species.ThreatsAll known historical, current and projected (within 10 years or 3 generations, whichever is the longest; generation time estimated, when not available, from related species for which it is known; generation time recorded for 76.3% of the 186 species categorized as threatened under Red List criteria A and C1, the only criteria using generation length) threats were coded using the IUCN Threats Classification Scheme v.3.2 (https://www.iucnredlist.org/resources/threat-classification-scheme), which follows a previously published study42. Where possible, the scope (whole ( >90%), majority (50–90%), minority (30%), rapid ( >20%), slow but notable ( More

Bell-Pedersen, D., Cassone, V. M., Earnest, D. J., Golden, S. S. & Hardin, P. E. Circadian rhythms from multiple oscillators: Lessons from diverse organisms. Nat. Rev. Drug Discov. 4, 121–130 (2005).Article 
CAS 

Google Scholar 
Taylor, B. & Jones, M. D. The circadian rhythm of flight activity in the mosquito Aedes aegypti (L.): The phase-setting effects of light-on and light-off. J. Exp. Biol. 51, 59–70 (1969).CAS 
PubMed 
Article 

Google Scholar 
Jones, M. D. R. The programming of circadian flight-activity in relation to mating and the gonotrophic cycle in the mosquito. Physiol. Entomol. 6, 307–313 (1981).Article 

Google Scholar 
Lee, H., Yang, Y., Liu, Y., Teng, H. & Sauman, I. Circadian control of permethrin-resistance in the mosquito Aedes aegypti. Physiol. Entomol. 56, 1219–1223 (2010).
Google Scholar 
Ptitsyn, A. A. et al. Rhythms and synchronization patterns in gene expression in the Aedes aegypti mosquito. BMC Genom. 12, 153 (2011).CAS 
Article 

Google Scholar 
Rund, S. S. C., Hou, T. Y., Ward, S. M., Collins, F. H. & Duf, G. E. Genome-wide profiling of diel and circadian gene expression in the malaria vector Anopheles gambiae. Proc. Natl. Acad. Sci. USA. 108, 419–444 (2011).Article 

Google Scholar 
Rund, S. S. C., Gentile, J. E. & Duffield, G. E. Extensive circadian and light regulation of the transcriptome in the malaria mosquito Anopheles gambiae. BMC Genom. 14, 218 (2013).CAS 
Article 

Google Scholar 
Leming, M. T., Rund, S. S. C., Behura, S. K., Duffield, G. E. & O’Tousa, J. E. A database of circadian and diel rhythmic gene expression in the yellow fever mosquito Aedes aegypti. BMC Genom. 15, 1–9 (2014).Article 
CAS 

Google Scholar 
Faria, N. R. et al. Establishment and cryptic transmission of Zika virus in Brazil and the Americas. Nature 546, 406–410 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Araujo, M. S., Guo, F. & Rosbash, M. Video recording can conveniently assay mosquito locomotor activity. Sci. Rep. 10, 1–9 (2020).Article 
CAS 

Google Scholar 
Lima-Camara, T. N. et al. Dengue infection increases the locomotor activity of Aedes aegypti females. PLoS ONE 6, 1–5 (2011).Article 
CAS 

Google Scholar 
Das, S. & Dimopoulos, G. Molecular analysis of photic inhibition of blood-feeding in Anopheles gambiae. BMC Physiol. 19, 1–19 (2008).
Google Scholar 
Gentile, C. et al. Circadian clock of Aedes aegypti: Effects of blood-feeding, insemination and RNA interference. Mem. Inst. Oswaldo Cruz 108, 80–87 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Meireles-filho, A. C. A. & Kyriacou, C. P. Circadian rhythms in insect disease vectors. Mem. Inst. Oswaldo Cruz 108, 48–58 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Yuan, Q., Metterville, D., Briscoe, A. D. & Reppert, S. M. Insect cryptochromes: Gene duplication and loss define diverse ways to construct insect circadian clocks. Mol. Biol. Evol. 24, 948–955 (2007).CAS 
PubMed 
Article 

Google Scholar 
Gentile, C., Rivas, G. B. S., Meireles-Filho, A. C. A., Lima, J. B. P. & Peixoto, A. A. Circadian expression of clock genes in two mosquito disease vectors: Cry2 is different. J. Biol. Rhythms 24, 444–451 (2009).CAS 
PubMed 
Article 

Google Scholar 
Zhang, Y., Markert, M. J., Groves, S. C., Hardin, P. E. & Merlin, C. Vertebrate-like CRYPTOCHROME 2 from monarch regulates circadian transcription via independent repression of CLOCK and BMAL1 activity. Proc. Natl. Acad. Sci. USA. 114, E7516–E7525 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Matthews, B. J. et al. Improved reference genome of Aedes aegypti informs arbovirus vector control. Nature 563, 501–507 (2018).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Baylies, M. K., Bargiello, T. A., Jackson, F. R. & Young, M. W. Changes in abundance or structure of the per gene product can alter periodicity of the Drosophila clock. Nature 48, 1986–1988 (1987).
Google Scholar 
Sehgal, A., Price, J. L., Man, B. & Young, M. W. Loss of circadian behavioral rhythms and per RNA oscillations in the Drosophila mutant timeless. Science 263, 1603–1606 (1994).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Allada, R., White, N. E., So, W. V., Hall, J. C. & Rosbash, M. A mutant Drosophila homolog of mammalian clock disrupts circadian rhythms and transcription of period and timeless. Cell 93, 791–804 (1998).CAS 
PubMed 
Article 

Google Scholar 
Rutila, J. E., Maltseva, O. & Rosbash, M. The timSL mutant affects a restricted portion of the drosophila melanogaster circadian cycle. J. Biol. Rhythms 13, 380–392 (1998).CAS 
PubMed 
Article 

Google Scholar 
Rund, S. S. C. et al. Daily rhythms in antennal protein and olfactory sensitivity in the malaria mosquito Anopheles gambiae. Sci. Rep. 3, 1–9 (2013).Article 

Google Scholar 
Meireles-Filho, A. C. A. et al. The biological clock of an hematophagous insect: Locomotor activity rhythms, circadian expression and downregulation after a blood meal. FEBS Lett. 580, 2–8 (2006).CAS 
PubMed 
Article 

Google Scholar 
Tallon, A. K., Hill, S. R. & Ignell, R. Sex and age modulate antennal chemosensory-related genes linked to the onset of host seeking in the yellow-fever mosquito, Aedes aegypti. FEBS Lett. https://doi.org/10.1038/s41598-018-36550-6 (2019).Article 

Google Scholar 
Hug, N., Longman, D. & Cáceres, J. F. Mechanism and regulation of the nonsense-mediated decay pathway. Nucleic Acids Res. 44, 1483–1495 (2015).Article 

Google Scholar 
Hardin, P. E. Molecular genetic analysis of circadian timekeeping in Drosophila. Adv. Genet. 74, 147 (2011).
Google Scholar 
Tauber, E., Roe, H., Costa, R., Hennessy, J. M. & Kyriacou, C. P. Temporal mating isolation driven by a behavioral gene in Drosophila. Curr. Biol. 13, 140–145 (2003).CAS 
PubMed 
Article 

Google Scholar 
Rutila, J. E. et al. Cycle is a second bHLH-PAS clock protein essential for circadian rhythmicity and transcription of Drosophila period and timeless. Cell 93, 805–814 (1998).CAS 
PubMed 
Article 

Google Scholar 
Lin, F.-J., Song, W., Meyer-Bernstein, E., Naidoo, N. & Sehgal, A. Photic signaling by cryptochrome in the Drosophila circadian system. Mol. Cell. Biol. 21, 7287–7294 (2001).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Yadav, P., Thandapani, M. & Sharma, V. K. Interaction of light regimes and circadian clocks modulate timing of pre-adult developmental events in Drosophila. BMC Dev. Biol. 14, 1–12 (2014).Article 
CAS 

Google Scholar 
Jones, M. & Reiter, P. Entrainment of the pupation and adult activity rhythms during development in the mosquito Anopheles gambiae. Nature 254, 242–244 (1968).ADS 
Article 

Google Scholar 
Nayar, J. K. The pupation rhythm in Aedes taeniorhynchus (Diptera: Culicidae). II. Ontogenetic timing, rate of development, and endogenous diurnal rhythm of pupation. Ann. Entomol. Soc. Am. 60, 946–971 (1967).CAS 
PubMed 
Article 

Google Scholar 
Nijhout, H. F. et al. The developmental control of size in insects. Wiley Interdiscip. Rev. Dev. Biol. 3, 113–134 (2014).PubMed 
Article 

Google Scholar 
Kaneko, M., Hamblen, M. J. & Hall, J. C. Involvement of the period gene in developmental time-memory: Effect of the per(Short) mutation on phase shifts induced by light pulses delivered to Drosophila larvae. J. Biol. Rhythms 15, 13–30 (2000).CAS 
PubMed 
Article 

Google Scholar 
Srivastava, M., James, A., Varma, V., Sharma, V. K. & Sheeba, V. Environmental cycles regulate development time via circadian clock mediated gating of adult emergence. BMC Dev. Biol. 18, 1–10 (2018).Article 
CAS 

Google Scholar 
Duffield, G. E. et al. Circadian programs of transcriptional activation, signaling, and protein turnover revealed by microarray analysis of mammalian cells. Curr. Biol. 12, 551–557 (2002).CAS 
PubMed 
Article 

Google Scholar 
Menon, A., Varma, V. & Sharma, V. K. Rhythmic egg-laying behaviour in virgin females of fruit flies Drosophila melanogaster. Chronobiol. Int. 31, 433–441 (2014).PubMed 
Article 

Google Scholar 
Kyriacou, C. P., Oldroyd, M., Wood, J., Sharp, M. & Hill, M. Clock mutations alter developmental timing in drosophila. Heredity 64, 395–401 (1990).PubMed 
Article 

Google Scholar 
Allada, R. & Chung, B. Y. Circadian organization of behavior and physiology in Drosophila. Annu. Rev. Physiol. 72, 605–624 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lima-Camara, T. N., Lima, J. B. P., Bruno, R. V. & Peixoto, A. A. Effects of insemination and blood-feeding on locomotor activity of Aedes albopictus and Aedes aegypti (Diptera: Culicidae) females under laboratory conditions. Parasit. Vectors 7, 1–8 (2014).Article 

Google Scholar 
Krishnan, B., Dryer, S. E. & Hardin, P. E. Circadian rhythms in olfactory responses of Drosophila melanogaster. Nature 400, 375–378 (1999).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Delventhal, R. et al. Dissection of central clock function in Drosophila through cell-specific CRISPR-mediated clock gene disruption. Elife 8, 48305 (2019).Article 

Google Scholar 
Nayar, J. K. & Sauerman, D. M. The effect of light regimes on the circadian rhythm of flight activity in the mosquito Aedes taeniorhynchus. J. Exp. Biol. 54, 745–756 (1971).CAS 
PubMed 
Article 

Google Scholar 
Granados-Fuentes, D., Tseng, A. & Herzog, E. D. A circadian clock in the olfactory bulb controls olfactory responsivity. J. Neurosci. 26, 12219–12225 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Eilerts, D. F., Vandergiessen, M., Bose, E. A. & Broxton, K. Odor-specific daily rhythms in the olfactory sensitivity and behavior of Aedes aegypti mosquitoes. Insects 9, 147 (2018).PubMed Central 
Article 

Google Scholar 
Tanoue, S., Krishnan, P., Krishnan, B., Dryer, S. E. & Hardin, P. E. Circadian clocks in antennal neurons are necessary and sufficient for olfaction rhythms in Drosophila. Curr. Biol. 14, 638–649 (2004).CAS 
PubMed 
Article 

Google Scholar 
Wang, G. et al. Clock genes and environmental cues coordinate Anopheles pheromone synthesis, swarming, and mating. Science 371, 411–415 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Sakai, T. & Ishida, N. Circadian rhythms of female mating activity governed by clock genes in Drosophila. Proc. Natl. Acad. Sci. USA. 98, 9221–9225 (2001).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Petersen, G., Hall, J. C. & Rosbash, M. The period gene of Drosophila carries species-specific behavioral instructions. EMBO J. 7, 3939–3947 (1988).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cabrera, M. & Jaffe, K. An aggregation pheromone modulates lekking behavior in the vector mosquito Aedes aegypti (Diptera: Culicidae). J. Am. Mosq. Control Assoc. 23, 1–10 (2007).PubMed 
Article 

Google Scholar 
Montague, T. G., Cruz, J. M., Gagnon, J. A., Church, G. M. & Valen, E. CHOPCHOP: A CRISPR/Cas9 and TALEN web tool for genome editing. Nucleic Acids Res. 42, 401–407 (2014).Article 
CAS 

Google Scholar 
Labun, K., Montague, T. G., Gagnon, J. A., Thyme, S. B. & Valen, E. CHOPCHOP v2: A web tool for the next generation of CRISPR genome engineering. Nucleic Acids Res. 44, W272–W276 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bassett, A. R., Tibbit, C., Ponting, C. P. & Liu, J. L. Highly efficient targeted mutagenesis of Drosophila with the CRISPR/Cas9 system. Cell Rep. 4, 220–228 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhu, H. et al. The two CRYs of the butterfly. Curr. Biol. 15, 730 (2005).Article 
CAS 

Google Scholar 
McDonald, M. J., Rosbash, M. & Emery, P. Wild-type circadian rhythmicity is dependent on closely spaced e boxes in the Drosophila timeless promoter. Mol. Cell. Biol. 21, 1207–1217 (2001).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chang, D. C. & Reppert, S. M. A novel c-terminal domain of drosophila PERIOD inhibits dCLOCK:CYCLE-mediated transcription. Curr. Biol. 13, 654–658 (2003).Article 
CAS 

Google Scholar  More

Zhang, Y., Wu, J. & Xu, B. Human health risk assessment of groundwater nitrogen pollution in Jinghui canal irrigation area of the loess region, northwest China. Environ. Earth Sci. 77, 273 (2018).Article 
CAS 

Google Scholar 
Zhang, D. et al. Potential spreading risks and disinfection challenges of medical wastewater by the presence of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) viral RNA in septic tanks of Fangcang Hospital. Sci. Total Environ. 741, 140445 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ahmed, W. et al. First confirmed detection of SARS-CoV-2 in untreated wastewater in Australia: A proof of concept for the wastewater surveillance of COVID-19 in the community. Sci. Total Environ. 728, 138764 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Haramoto, E., Malla, B., Thakali, O. & Kitajima, M. First environmental surveillance for the presence of SARS-CoV-2 RNA in wastewater and river water in Japan. Sci. Total Environ. 737, 140405 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Naddeo, V. & Liu, H. Editorial Perspectives: 2019 novel coronavirus (SARS-CoV-2): What is its fate in urban water cycle and how can the water research community respond?. Environ. Sci. Water Res. Technol. 6, 1213–1216 (2020).CAS 
Article 

Google Scholar 
Cornelisen, C. D., Gillespie, P. A., Kirs, M., Young, R. G. & Harwood, V. J. Motueka River plume facilitates transport of ruminant faecal contaminants into shellfish growing waters, Tasman Bay, New Zealand. N. Z. J. Mar. Freshw. Res. 45, 477–495 (2011).Article 

Google Scholar 
Devane, M. L., Moriarty, E. M., Wood, D., Webster-Brown, J. & Gilpin, B. J. The impact of major earthquakes and subsequent sewage discharges on the microbial quality of water and sediments in an urban river. Sci. Total Environ. 485–486, 666–680 (2014).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Duttagupta, S. et al. Achieving sustainable development goal for clean water in India: Influence of natural and anthropogenic factors on groundwater microbial pollution. Environ. Manag. 66, 42–755 (2020).Article 

Google Scholar 
Huelsen, T. et al. Domestic wastewater treatment with purple phototrophic bacteria using a novel continuous photo anaerobic membrane bioreactor. Water Res. 100, 486–495 (2016).Article 
CAS 

Google Scholar 
Johnson, D. R. et al. The functional and taxonomic richness of wastewater treatment plant microbial communities are associated with each other and with ambient nitrogen and carbon availability. Environ. Microbiol. 17(12), 4851–4860 (2015).CAS 
PubMed 
Article 

Google Scholar 
Lei, Z. J. M. Effects of phosphorus addition on soil microbial biomass and community composition in three forest types in tropical China. Soil Biol. Biochem. 44(1), 31–38 (2012).Article 
CAS 

Google Scholar 
Jian, L. Effects of nitrogen and phosphorus addition on soil microbial community in a secondary tropical forest of China. Biol. Fertil. Soils 51, 207–215 (2015).Article 
CAS 

Google Scholar 
Yu, S. X., Pang, Y. L., Wang, Y. C., Li, J. L. & Qin, S. Spatial variation of microbial communities in sediments along the environmental gradients from Xiaoqing River to Laizhou Bay. Mar. Pollut. Bull. 76, 1048–1056 (2017).
Google Scholar 
Reidl, J. & Klose, K. E. Vibrio cholerae and cholera: Out of the water and into the host. FEMS Microbiol. Rev. 26(2), 125–139 (2002).CAS 
PubMed 
Article 

Google Scholar 
Chin, C.-S. et al. The origin of the Haitian cholera outbreak strain. N. Engl. J. Med. 364, 33–42 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
Minoru, K., Miho, F., Mao, T., Yoko, S. & Kanae, M. KEGG: New perspectives on genomes, pathways, diseases and drugs. Nucl. Acids Res. 45, D353–D361 (2017).Article 
CAS 

Google Scholar 
Zieliński, W. et al. The prevalence of drug-resistant and virulent Staphylococcus spp. in a municipal wastewater treatment plant and their spread in the environment. Environ. Int. 143, 105914 (2020).PubMed 
Article 
CAS 

Google Scholar 
Dietrich, J. E. S. & Doherty, T. M. Interaction of Mycobacterium tuberculosis with the host: Consequences for vaccine development. APMIS 117, 440–457 (2009).CAS 
PubMed 
Article 

Google Scholar 
Velayati, A. A. et al. Identification and genotyping of Mycobacterium tuberculosis isolated from water and soil samples of a metropolitan city. Chest 147, 1094–1102 (2015).PubMed 
Article 

Google Scholar 
Pereira, M. I. & Medeiros, J. A. Role of Helicobacter pylori in gastric mucosa-associated lymphoid tissue lymphomas. World J. Gastroenterol. 20, 684–698 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
West, A. P., Millar, M. R. & Tompkins, D. S. Effect of physical environment on survival of Helicobacter pylori. J. Clin. Pathol. 45, 228–231 (1992).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Miller, W. A. et al. Salmonella spp., Vibrio spp., Clostridium perfringens, and Plesiomonas shigelloides in marine and freshwater invertebrates from coastal California ecosystems. Microb. Ecol. 52, 198–206 (2006).CAS 
PubMed 
Article 

Google Scholar 
McCarthy, S. A. Effects of temperature and salinity on survival of toxigenic Vibrio cholerae O1 in seawater. Microb Ecol 31, 167–175 (1996).CAS 
PubMed 
Article 

Google Scholar 
Heaney, N. et al. Effects of softwood biochar on the status of nitrogen species and elements of potential toxicity in soils. Ecotoxicol. Environ. Saf. 166, 383–389 (2018).CAS 
PubMed 
Article 

Google Scholar 
Wang, Z. B., Miao, M. S., Kong, Q. & Ni, S. Q. Evaluation of microbial diversity of activated sludge in a municipal wastewater treatment plant of northern China by high-throughput sequencing technology. Desalin. Water Treat. 57, 1–6 (2016).Article 
CAS 

Google Scholar 
Wang, Z. et al. Weak magnetic field: A powerful strategy to enhance partial nitrification. Water Res. 120, 190–198 (2017).CAS 
PubMed 
Article 

Google Scholar 
Zhang, X. et al. Reduction of nitrous oxide emissions from partial nitrification process by using innovative carbon source (mannitol). Bioresour. Technol. 218, 789–795 (2016).CAS 
PubMed 
Article 

Google Scholar 
Liu, X. et al. N2O emission and bacterial community dynamics during realization of the partial nitrification process. RSC Adv. 8, 24305–24311 (2018).ADS 
CAS 
Article 

Google Scholar 
Lv, L., Ren, L. F., Ni, S. Q., Gao, B. Y. & Wang, Y. N. The effect of magnetite on the start-up and N2O emission reduction of the anammox process. RSC Adv. 6, 99989–99996 (2016).ADS 
CAS 
Article 

Google Scholar 
Yang, S., Liebner, S., Alawi, M., Ebenhöh, O. & Wagner, D. Taxonomic database and cut-off value for processing mcrA gene 454 pyrosequencing data by MOTHUR. J. Microbiol. Methods 103, 3–5 (2014).CAS 
PubMed 
Article 

Google Scholar 
Xu, F. et al. Electricity production and evolution of microbial community in the constructed wetland-microbial fuel cell. Chem. Eng. J. 339, 479–486 (2018).CAS 
Article 

Google Scholar 
Bu, C. et al. Dissimilatory nitrate reduction to ammonium in the yellow river estuary: Rates, abundance, and community diversity. Sci. Rep. 7, 6830 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Zhou, J., Fries, M. R., Cheesanford, J. C. & Tiedje, J. M. Phylogenetic analyses of a new group of denitrifiers capable of anaerobic growth of toluene and description of Azoarcus tolulyticus sp. nov.. Int. J. Syst. Bacteriol. 194, 500–506 (1995).Article 

Google Scholar 
Casanova, L., Rutala, W. A., Weber, D. J. & Sobsey, M. D. Survival of surrogate coronaviruses in water. Water Res. 43, 1893–1898 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Elreedy, A. et al. Unraveling the capability of graphene nanosheets and γ-Fe2O3 nanoparticles to stimulate anammox granular sludge. J. Environ. Manag. 277, 111495 (2021).CAS 
Article 

Google Scholar 
Ismail, S. et al. Response of anammox bacteria to short-term exposure of 1,4-dioxane: Bacterial activity and community dynamics. Sep. Purif. Technol. 266, 118539 (2021).CAS 
Article 

Google Scholar 
Shen, X., Xu, M., Li, M., Zhao, Y. & Shao, X. Response of sediment bacterial communities to the drainage of wastewater from aquaculture ponds in different seasons. Sci. Total Environ. 717, 137180 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Ismail, S. et al. Fatigue of anammox consortia under long-term 1,4-dioxane exposure and recovery potential: N-kinetics and microbial dynamics. J. Hazard. Mater. 414, 125533 (2021).CAS 
PubMed 
Article 

Google Scholar 
Li, H. F., Li, B. Z., Wang, E. T., Yang, J. S. & Yuan, H. L. Removal of low concentration of phosphorus from solution by free and immobilized cells of Pseudomonas stutzeri YG-24. Desalination 286, 242–247 (2012).CAS 
Article 

Google Scholar 
Xia, J., Ye, L., Ren, H. & Zhang, X. X. Microbial community structure and function in aerobic granular sludge. Appl. Microbiol. Biotechnol. 102(9), 3967–3979 (2018).CAS 
PubMed 
Article 

Google Scholar 
Akizuki, S. et al. Effects of substrate COD/NO2-N ratio on simultaneous methanogenesis and short-cut denitrification in the treatment of blue mussel using acclimated sludge. Biochem. Eng. J. 99, 16–23 (2015).CAS 
Article 

Google Scholar 
Liao, K. et al. Use of convertible flow cells to simulate the impacts of anthropogenic activities on river biofilm bacterial communities. Sci. Total Environ. 653, 148–156 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Marassi, R. J. et al. Performance and toxicity assessment of an up-flow tubular microbial fuel cell during long-term operation with high-strength dairy wastewater. J. Clean. Prod. 259, 120882 (2020).CAS 
Article 

Google Scholar 
Langille, M. G. I. et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol. 31, 814–821 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Medema, G. J., Schets, F. M., Teunis, P. F. M. & Havelaar, A. H. Sedimentation of free and attached Cryptosporidium oocysts and Giardia cysts in water. Appl. Environ. Microbiol. 64, 4460–4466 (1998).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Igbinosa, E. O., Obi, L. C. & Okoh, A. I. Occurrence of potentially pathogenic vibrios in final effluents of a wastewater treatment facility in a rural community of the Eastern Cape Province of South Africa. Res. Microbiol. 160, 531–537 (2009).CAS 
PubMed 
Article 

Google Scholar 
Goh, S. G., Bayen, S., Burger, D., Kelly, B. C. & Gin, Y. H. Occurrence and distribution of bacteria indicators, chemical tracers and pathogenic vibrios in Singapore coastal waters. Mar. Pollut. Bull. 114, 627–634 (2016).PubMed 
Article 
CAS 

Google Scholar 
Cui, Q., Huang, Y., Wang, H. & Fang, T. Diversity and abundance of bacterial pathogens in urban rivers impacted by domestic sewage. Environ. Pollut. 249, 24–35 (2019).CAS 
PubMed 
Article 

Google Scholar 
Suzuki, Y. et al. Growth and antibiotic resistance acquisition of Escherichia coli in a river that receives treated sewage effluent. Sci. Total Environ. 690, 696–704 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Silva, D. C. V. R. et al. Predicting zebrafish spatial avoidance triggered by discharges of dairy wastewater: An experimental approach based on self-purification in a model river. Environ. Pollut. 266, 115325 (2020).CAS 
PubMed 
Article 

Google Scholar 
Wagner, I. & Zalewski, M. Temporal changes in the abiotic/biotic drivers of selfpurification in a temperate river. Ecol. Eng. 94, 275–285 (2016).Article 

Google Scholar 
Clements, W. H. & Rohr, J. R. Community responses to contaminants: Using basic ecological principles to predict ecotoxicological effects. Environ. Toxicol. Chem. 28, 1789–1800 (2009).CAS 
PubMed 
Article 

Google Scholar 
Ismail, S. & Tawfik, A. Comprehensive study for Anammox process via multistage anaerobic baffled reactors. E3S Web Conf. 22, 4–11 (2017).Article 
CAS 

Google Scholar  More

Shope, R. E., MacNamara, L. G. & Mangold, R. Report on the deer mortality, epizootic hemorrhagic disease of deer. NJ Outdoors 6, 17–21 (1955).
Google Scholar 
Trainer, D. O. Epizootic hemorrhagic disease of deer. J. Wildl. Dis. 28, 377–381 (1964).
Google Scholar 
Shope, R. E., MacNamara, L. G. & Mangold, R. A virus-induced epizootic hemorrhagic disease of the Virginia white-tailed deer (Odocoileus virginianus). J. Exp. Med. 111, 155–170 (1960).CAS 
PubMed 

Google Scholar 
Chalmers, G. A., Vance, H. N. & Mitchell, G. J. An outbreak of epizootic hemorrhagic disease in wild ungulates in Alberta. Wildl. Dis. 4, 1–6 (1964).
Google Scholar 
Stallknecht, D. E. et al. Apparent increase of reported hemorrhagic disease in the midwestern and northeastern USA. J. Wildl. Dis. 51, 348–361 (2015).PubMed 

Google Scholar 
Ruder, M. G. et al. The first 10 years (2006–2015) of epizootic hemorrhagic disease virus serotype 6 in the USA. J. Wildl. Dis. 53, 901–905 (2017).PubMed 

Google Scholar 
Pybus, M. J., Ravi, M. & Pollock, C. Epizootic hemorrhagic disease in Alberta, Canada. J. Wildl. Dis. 50, 720–722 (2014).PubMed 

Google Scholar 
Ruder, M. G. et al. Transmission and epidemiology of bluetongue and epizootic hemorrhagic disease in North America: current perspectives, research gaps, and future directions. Vector-Borne Zoonotic Dis. 15, 348–363 (2015).PubMed 

Google Scholar 
Rivera, N. A. et al. Bluetongue and epizootic hemorrhagic disease in the United States of America at the wildlife: livestock interface. Pathogens 10, 915 (2021).PubMed 

Google Scholar 
Mellor, P. S., Boorman, J. & Baylis, M. Culicoides biting midges: their role as arbovirus vectors. Annu. Rev. Entomol. 45, 307–340 (2000).CAS 
PubMed 

Google Scholar 
Pfannenstiel, R. S. et al. Management of North American Culicoides biting midges: current knowledge and research needs. Vector-Borne Zoonotic Dis. 15, 374–384 (2015).PubMed 

Google Scholar 
Mcgregor, B. L. et al. Vector competence of Florida Culicoides insignis (Diptera: Ceratopogonidae) for epizootic hemorrhagic disease virus serotype-2. (2021). https://doi.org/10.3390/v13030410.Vigil, S. L. et al. Apparent range expansion of Culicoides (Hoffmania) insignis (Diptera: Ceratopogonidae) in the Southeastern United States. https://doi.org/10.1093/jme/tjy036.Mullen, G. R. & Murphree, C. S. Chapter 13-biting midges (Ceratopogonidae). in (eds. Mullen, G. R. & Durden, L. A. B. T.-M. and V. E. (Third E.) 213–236 (Academic Press, 2019). https://doi.org/10.1016/B978-0-12-814043-7.00013-3.Werner, D., Groschupp, S., Bauer, C. & Kampen, H. Breeding Habitat Preferences of major Culicoides Species (Diptera: Ceratopogonidae) in Germany. Int. J. Environ. Res. Public Health 17, 5000 (2020).
Google Scholar 
Tabachnick, W. J., Smartt, C. T. & Rutledge-Connelly, C. R. Bluetongue: ENY-743/IN768, 4/2008. EDIS 2008, (2008).Schmidtmann, E. T., Bobian, R. J. & Belden, R. P. Soil chemistries define aquatic habitats with immature populations of the Culicoides variipennis complex (Diptera: Ceratopogonidae). J. Med. Entomol. 37, 58–64 (2000).CAS 
PubMed 

Google Scholar 
Schmidtmann, E. T. et al. Distribution of Culicoides sonorensis (Diptera: Ceratopogonidae) in Nebraska, South Dakota, and North Dakota: clarifying the epidemiology of bluetongue disease in the Northern great plains region of the United States. J. Med. Entomol. 48, 634–643 (2011).CAS 
PubMed 

Google Scholar 
Mullens, B. A. & Holbrook, F. R. Temperature effects on the gonotrophic cycle of Culicoides variipennis (Diptera: Ceratopogonidae). J. Am. Mosq. Control Assoc. 7, 588–591 (1991).CAS 
PubMed 

Google Scholar 
Lysyk, T. J. & Dergousoff, S. J. Distribution of Culicoides sonorensis (Diptera: Ceratopogonidae) in Alberta, Canada. J. Med. Entomol. 51, 560–571 (2014).CAS 
PubMed 

Google Scholar 
Christensen, S. A., Ruder, M. G., Williams, D. M., Porter, W. F. & Stallknecht, D. E. The role of drought as a determinant of hemorrhagic disease in the eastern United States. Glob. Chang. Biol. 26, 3799–3808 (2020).ADS 
PubMed 

Google Scholar 
Lysyk, T. J. & Danyk, T. Effect of temperature on life history parameters of adult Culicoides sonorensis (Diptera: Ceratopogonidae) in relation to geographic origin and vectorial capacity for bluetongue virus. J. Med. Entomol. 44, 741–751 (2007).CAS 
PubMed 

Google Scholar 
Wittmann, E. J., Mellor, P. S. & Baylis, M. Effect of temperature on the transmission of orbiviruses by the biting midge, Culicoides sonorensis. Med. Vet. Entomol. 16, 147–156 (2002).CAS 
PubMed 

Google Scholar 
Brand, S. P. C. & Keeling, M. J. The impact of temperature changes on vector-borne disease transmission: Culicoides midges and bluetongue virus. J. R. Soc. Interface 14, 20160481 (2017).PubMed 

Google Scholar 
Couvillion, C. E., Nettles, V. F., Davidson, W. R., Pearson, J. E. & Gustafson, G. A. Hemorrhagic disease among white-tailed deer in the Southeast from 1971 through 1980. Proc. US Anim. Hlth. Assoc. 85, 522–537 (1981).
Google Scholar 
Zarnke, R. L. Serologic survey for selected microbial pathogens in Alaskan wildlife. J. Wildl. Dis. 19, 324–329 (1983).CAS 
PubMed 

Google Scholar 
Howerth, E. W., Stallknecht, D. E. & Kirkland, P. D. Bluetongue, epizootic hemorrhagic disease, and other orbivirus-related diseases. Infect. Dis. Wild Mammals https://doi.org/10.1002/9780470344880.ch3 (2001).Article 

Google Scholar 
Stevens, G., McCluskey, B., King, A., O’Hearn, E. & Mayr, G. Review of the 2012 epizootic hemorrhagic disease outbreak in domestic ruminants in the United States. PLoS ONE 10, 1–11 (2015).
Google Scholar 
Fischer, J. R. et al. An epizootic of hemorrhagic disease in white-tailed deer (Odocoileus virginianus) in Missouri: necropsy findings and population impact. J. Wildl. Dis. 31, 30–36 (1995).CAS 
PubMed 

Google Scholar 
Pierce, B. EHD outbreak takes toll on white-tailed deer population. Bozeman Daily Chronicle (2011).Gaydos, J. K., Davidson, W. R., Mead, D. G., Howerth, E. W. & Stallknecht, D. E. Innate resistance to epizootic hemorrhagic disease in white-tailed deer. J. Wildl. Dis. 38, 713–719 (2002).PubMed 

Google Scholar 
Stallknecht, D. E. & Howerth, E. W. Epidemiology of bluetongue and epizootic haemorrhagic disease in wildlife: surveillance methods. Vet. Ital. 40, 203–207 (2004).CAS 
PubMed 

Google Scholar 
Hedman, H. D. et al. Spatial analysis of chronic wasting disease in free-ranging white-tailed deer (Odocoileus virginianus) in Illinois, 2008–2019. Transbound. Emerg. Dis. 68, 2376–2383 (2020).PubMed 

Google Scholar 
Baygents, G. & Bani-Yaghoub, M. Cluster analysis of hemorrhagic disease in Missouri’s white-tailed deer population: 1980–2013. BMC Ecol. 18, 35 (2018).PubMed 

Google Scholar 
French, S. K., Pearl, D. L., Peregrine, A. S. & Jardine, C. M. Spatio-temporal clustering of Baylisascaris procyonis, a zoonotic parasite, in raccoons across different landscapes in southern Ontario. Spat. Spatiotemporal. Epidemiol. 35, 100371 (2020).PubMed 

Google Scholar 
Kulldorff, M., Heffernan, R., Hartman, J., Assunção, R. & Mostashari, F. A space-time permutation scan statistic for disease outbreak detection. PLoS Med. 2, 0216–0224 (2005).
Google Scholar 
Allison, A. B. et al. Detection of a novel reassortant epizootic hemorrhagic disease virus (EHDV) in the USA containing RNA segments derived from both exotic (EHDV-6) and endemic (EHDV-2) serotypes. J. Gen. Virol. 91, 430–439 (2010).CAS 
PubMed 

Google Scholar 
Allen, S. E. et al. Epizootic hemorrhagic disease in white-tailed deer, Canada. Emerg. Infect. Dis. 25, 832–834 (2019).PubMed 

Google Scholar 
Boyer, T. C., Ward, M. P., Wallace, R. L. & Singer, R. S. Regional seroprevalence of bluetongue virus in cattle in Illinois and western Indiana. Am. J. Vet. Res. 68, 1212–1219 (2007).PubMed 

Google Scholar 
Pedersen, K. et al. Serologic Evidence of various arboviruses detected in white-tailed deer (Odocoileus virginianus) in the United States. Am. J. Trop. Med. Hyg. 97, 319–323 (2017).PubMed 

Google Scholar 
Garrett, E. F. et al. Clinical disease associated with epizootic hemorrhagic disease virus in cattle in Illinois. J. Am. Vet. Med. Assoc. 247, 190–195 (2015).PubMed 

Google Scholar 
Boyer, T. C., Ward, M. P. & Singer, R. S. Climate, landscape, and the risk of orbivirus exposure in cattle in Illinois and western Indiana. Am. J. Trop. Med. Hyg. 83, 789–794 (2010).PubMed 

Google Scholar 
Cauvin, A. et al. Antibodies to epizootic hemorrhagic disease virus (EHDV) in farmed and wild Florida white-tailed deer (Odocoileus virginianus). J. Wildl. Dis. 56, 208–213 (2020).CAS 
PubMed 

Google Scholar 
McGregor, B. L. et al. Host use patterns of Culicoides spp. biting midges at a big game preserve in Florida, USA, and implications for the transmission of orbiviruses. Med. Vet. Entomol. 33, 110–120 (2019).CAS 
PubMed 

Google Scholar 
Berke, O. Exploratory disease mapping: kriging the spatial risk function from regional count data. 11, 1–11 (2004).Svoboda, M. et al. The drought monitor. Bull. Am. Meterol. Soc. 83, 1181–1190 (2002).ADS 

Google Scholar 
NOAA National Centers for Environmental Information. State of the Climate: National Climate Report for Annual 2012. https://www.ncdc.noaa.gov/sotc/national/201213. (Accessed: 5th February 2022)Calzolari, M. & Albieri, A. Could drought conditions trigger Schmallenberg virus and other arboviruses circulation?. Int. J. Health Geogr. 12, 6–10 (2013).
Google Scholar 
Zuliani, A. et al. Modelling the northward expansion of Culicoides sonorensis (Diptera: Ceratopogonidae) under future climate scenarios. PLoS ONE 10, 1–23 (2015).
Google Scholar 
Burns, D. Diseases caused by arthropods and other noxious animals. in Rook’s Textbook of Dermatology 1555–1618 (Blackwell Publishing, 2004).Mullens, B. A. A quantitative survey of Culicoides variipennis (Diptera: Ceratopogonidae) in dairy waste water ponds in Southern California. J. Med. Entomol. 26, 559–565 (1989).CAS 
PubMed 

Google Scholar 
Wang, D., Hejazi, M., Cai, X. & Valocchi, A. J. Climate change impact on meteorological, agricultural, and hydrological drought in central Illinois. Water Resour. Res. 47, 9527 (2011).ADS 

Google Scholar 
Tomasek, B. J., Williams, M. M. II. & Davis, A. S. Changes in field workability and drought risk from projected climate change drive spatially variable risks in Illinois cropping systems. PLoS ONE 12, e0172301 (2017).PubMed 

Google Scholar 
Casey, C. L., Rathbun, S. L., Stallknecht, D. E. & Ruder, M. G. Spatial analysis of the 2017 outbreak of hemorrhagic disease and physiographic region in the eastern United States. Viruses 13, 550 (2021).CAS 
PubMed 

Google Scholar 
Berry, B. S., Magori, K., Perofsky, A. C., Stallknecht, D. E. & Park, A. W. Wetland cover dynamics drive hemorrhagic disease patterns in white-tailed deer in the United States. J. Wildl. Dis. 49, 501–509 (2013).PubMed 

Google Scholar 
Uslu, U. & Dik, B. Chemical characteristics of breeding sites of Culicoides species (Diptera: Ceratopogonidae). Vet. Parasitol. 169, 178–184 (2010).CAS 
PubMed 

Google Scholar 
Lysyk, T. J. Abundance and species composition of Culicoides (Diptera : Ceratopogonidae) at cattle facilities in southern Alberta, Canada. (2006).Erram, D., Blosser, E. M. & Cadena, N. B. Habitat associations of Culicoides species (Diptera : Ceratopogonidae) abundant on a commercial cervid farm in Florida, USA. Parasit. Vectors https://doi.org/10.1186/s13071-019-3626-1 (2019).Article 
PubMed 

Google Scholar 
Jones, R. H. Observations on the larval habitats of some North American species of Culicoides (Diptera: Ceratopogonidae). Ann. Entomol. Soc. Am. 54, 702–710 (1961).
Google Scholar 
Schmidtmann, E. T., Jones, C. J. & Gollands, B. Comparative host-seeking activity of Culicoides (Diptera: Ceratopogonidae) attracted to pastured livestock in central New York State, USA. J. Med. Entomol. 17, 221–231 (1980).
Google Scholar 
Schlichting, P. E. Summary of 2019–2020 Illinois deer seasons. Illinois Dep. Nat. Resour. 1–12 (2020).Orange, J. P. et al. Evidence of epizootic hemorrhagic disease virus and bluetongue virus exposure in nonnative ruminant species in northern Florida. J. Zoo Wildl. Med. 51, 745–751 (2021).PubMed 

Google Scholar 
Purse, B. V. et al. Impacts of climate, host and landscape factors on Culicoides species in Scotland. Med. Vet. Entomol. 26, 168–177 (2012).CAS 
PubMed 

Google Scholar 
Searle, K. R. et al. Identifying environmental drivers of insect phenology across space and time: Culicoides in Scotland as a case study. Bull. Entomol. Res. 103, 155–170 (2013).ADS 
CAS 
PubMed 

Google Scholar 
Shimizu, S., Toyota, I., Arishima, T. & Goto, Y. Frequency of serological cross-reactions between Ibaraki and bluetongue viruses using the agar gel immunodiffusion test. Vet. Ital. 40, 583–586 (2004).CAS 
PubMed 

Google Scholar 
Alkhamis, M. A. et al. Global emergence and evolutionary dynamics of bluetongue virus. Sci. Rep. 10, 21677 (2020).ADS 
CAS 
PubMed 

Google Scholar 
Cottingham, S. L., White, Z. S., Wisely, S. M. & Campos-Krauer, J. M. A Mortality-based description of EHDV and BTV prevalence in farmed white-tailed deer (Odocoileus virginianus) in Florida, USA. Viruses 13, 1443 (2021).CAS 
PubMed 

Google Scholar 
Nettles, V. F., Davidson, W. R. & Stallknecht, D. E. Surveillance for hemorrhagic disease in white-tailed deer and other wild ruminants, 1980-1989. In Proceeding of the Annual Conference of the Southeastern Association of Fish and Wildlife Agencies. 46, 138–146 (1992).Maclachlan, N. J., Zientara, S., Wilson, W. C., Richt, J. A. & Savini, G. Bluetongue and epizootic hemorrhagic disease viruses: recent developments with these globally re-emerging arboviral infections of ruminants. Curr. Opin. Virol. 34, 56–62 (2019).PubMed 

Google Scholar 
Savini, G. et al. Epizootic haemorragic disease. Res. Vet. Sci. 91, 1–17 (2011).CAS 
PubMed 

Google Scholar 
Kedmi, M. et al. The association of winds with the spread of EHDV in dairy cattle in Israel during an outbreak in 2006. Prev. Vet. Med. 96, 152–160 (2010).PubMed 

Google Scholar 
Mayo, C. E. et al. Seasonal and interseasonal dynamics of bluetongue virus infection of dairy cattle and Culicoides sonorensis Midges in Northern California: implications for virus overwintering in temperate zones. PLoS ONE 9, e106975 (2014).ADS 
PubMed 

Google Scholar 
USGS National Wildlife Health Center. Wildlife Health Information Sharing Partnership-event reporting system (WHISPers). https://www.nwhc.usgs.gov/whispers/.Lenoch, J. & Nguyen, N. WHISPers, the USGS-NWHC Wildlife Health event reporting system. Proc. Wildl. Dis. Assoc. 8, 2579 (2016).
Google Scholar 
Brooks, J. W. Postmortem changes in animal carcasses and estimation of the postmortem interval. Vet. Pathol. 53, 929–940 (2016).CAS 
PubMed 

Google Scholar 
Pilz, J. & Spöck, G. Why do we need and how should we implement Bayesian Kriging methods. Stoch. Environ. Res. Risk Assess. 22, 621–632 (2007).MathSciNet 

Google Scholar 
Krivoruchko, K. Empirical Bayesian Kriging. ArcUser Fall 6, (2012).Ord, J. K. & Getis, A. Local spatial autocorrelation statistics: distributional issues and an application. Geogr. Anal. 27, 286–306 (1995).
Google Scholar 
Kulldorff, M. & Information Management Services Inc. SaTScanTM v 9.6: Software for the spatial and space-time scan statistics. (2018).Kulldorff, M., Athas, W. F., Feuer, E. J., Miller, B. A. & Key, C. R. Evaluating cluster alarms: a space-time scan statistic and brain cancer in Los Alamos, New Mexico. Am. J. Public Health 88, 1377–1380 (1998).CAS 
PubMed 

Google Scholar  More

Minimum distance to weak efficient frontierBriec and Charnes et al. first proposed the Minimum distance to weak efficient frontier (MinDW) model39,40, which can be expressed as (m + n) linear programming ((m) is the number of input indicators and (n) is the number of output indicators), assuming that the input variable is (x) and the output variable is (y). The specific formula is shown in Eq. (1):$$ begin{aligned} & max beta_{z} ,z = 1,2, ldots ,m + n \ & s.t.left{ begin{gathered} sumnolimits_{j = 1}^{q} {alpha_{j} x_{rj} + beta_{z} e_{r} le x_{rk} ,r = 1,2, ldots ,m} hfill \ sumnolimits_{j = 1}^{q} {alpha_{j} x_{ij} + beta_{z} e_{i} ge y_{ik} ,i = 1,2, ldots ,n} hfill \ alpha_{j} ge 0 hfill \ end{gathered} right. \ end{aligned} $$
(1)
(e_{r}) and (e_{i}) are constants. In the programming formula, only one (e) is equal to 1, and the others are 0, that is shown in Eq. (2):$$ begin{aligned} & e_{r} = 1;{text{ if}}; , r = z; , e_{r} = 0 , ;{text{if}}; , r ne z \ & e_{i} = 1 , ;{text{if}}; , i = z – m; , e_{r} = 0 , ;{text{if}}; , i ne z – m \ end{aligned} $$
(2)
The efficiency value of model is expressed as Eq. (3):$$ theta_{z}^{*} = frac{{1 – frac{1}{m}sumnolimits_{r = 1}^{m} {frac{{beta_{z}^{*} e_{r} }}{{x_{rk} }}} }}{{1 + frac{1}{n}sumnolimits_{i = 1}^{n} {frac{{beta_{z}^{*} e_{i} }}{{y_{ik} }}} }} $$
(3)
The efficiency value of MinDW model is expressed as (theta_{max }^{*} = max (theta_{z}^{*} ,z = 1,2, cdots ,m + n)), and the maximum efficiency value corresponds to the minimum (beta^{*}), that is the nearest distance to the frontier.This paper uses the MinDW model with negative output to conduct empirical analysis. The method can be expressed as (m + n + d) linear programming ((m) is the number of inputs, (n) is the number of desirable output, (d) is the number of unexpected output), assuming that the input variable is (x), the desirable output variable is (y), and the undesirable output variable is (f). The specific formula is shown in Eq. (4):$$ begin{aligned} & max beta_{z} ,z = 1,2, ldots ,m + n + d \ & s.t.left{ begin{gathered} sumnolimits_{j = 1}^{q} {alpha_{j} x_{rj} + beta_{z} e_{r} le x_{rk} ,r = 1,2, ldots ,m} hfill \ sumnolimits_{j = 1}^{q} {alpha_{j} x_{ij} – beta_{z} e_{i} ge y_{ik} ,i = 1,2, ldots ,n} hfill \ sumnolimits_{j = 1}^{q} {alpha_{j} x_{lj} + beta_{z} e_{l} le f_{lk} ,l = 1,2, ldots ,d} hfill \ alpha_{j} ge 0 hfill \ end{gathered} right. \ end{aligned} $$
(4)
(e_{r}), (e_{i}) and (e_{l}) are constants. In the programming formula, only one (e) is equal to 1, and the others are 0, that is shown in Eq. (5):$$ begin{aligned} & e_{r} = 1;{text{ if}}; , r = z; , e_{r} = 0 , ;{text{if}}; , r ne z \ & e_{i} = 1 , ;{text{if }};i = z – m; , e_{r} = 0 , ;{text{if}}; , i ne z – m \ & e_{l} = 1 , ;{text{if}}; , l = z – m – n; , e_{l} = 0 , ;{text{if}}; , l ne z – m – n \ end{aligned} $$
(5)
The efficiency value of model is expressed as Eq. (6):$$ theta_{z}^{*} = frac{{1 – frac{1}{m}sumnolimits_{r = 1}^{m} {frac{{beta_{z}^{*} e_{r} }}{{x_{rk} }}} }}{{1 + frac{1}{n + d}left( {sumnolimits_{i = 1}^{n} {frac{{beta_{z}^{*} e_{i} }}{{y_{ik} }}} + sumnolimits_{l = 1}^{d} {frac{{beta_{z}^{*} e_{l} }}{{f_{lk} }}} } right)}} $$
(6)
The efficiency value of MinDW model is expressed as (theta_{max }^{*} = max (theta_{z}^{*} ,z = 1,2, cdots ,m + n + d)), and the maximum efficiency value corresponds to the minimum (beta^{*}), which means the nearest distance to the frontier.The efficiency value of MinDW model will not be less than the efficiency value of directional distance function model with any direction vector or other distance types (such as radial model and SBM model). In other words, the efficiency value of MinDW model is the largest. Combined with the above process, we can define the common boundary ((beta^{meta*})) and the model is as Eq. (7):$$ begin{aligned} & beta^{meta*} = max frac{{1 – frac{1}{m}sumnolimits_{r = 1}^{m} {frac{{beta_{z} e_{r} }}{{x_{rk} }}} }}{{1 + frac{1}{n + d}left( {sumnolimits_{i = 1}^{n} {frac{{beta_{z} e_{i} }}{{y_{ik} }}} + sumnolimits_{l = 1}^{d} {frac{{beta_{z} e_{l} }}{{f_{lk} }}} } right)}} \ & s.t.left{ begin{gathered} sumnolimits_{j = 1}^{{q_{m} }} {alpha_{j} x_{rj} + beta_{z} e_{r} le x_{rk} ,r = 1,2, cdots ,m} hfill \ sumnolimits_{j = 1}^{{q_{m} }} {alpha_{j} x_{ij} – beta_{z} e_{i} ge y_{ik} ,i = 1,2, cdots ,n} hfill \ sumnolimits_{j = 1}^{{q_{m} }} {alpha_{j} x_{lj} + beta_{z} e_{l} le f_{lk} ,l = 1,2, cdots ,d} hfill \ alpha_{j} ge 0 hfill \ end{gathered} right. \ end{aligned} $$
(7)
Similarly, the efficiency value of DMU relative to the scale frontier ((beta^{scale*})) can be obtained by the Eq. (8):$$ begin{aligned} & beta^{scale*} = max frac{{1 – frac{1}{m}sumnolimits_{r = 1}^{m} {frac{{beta_{z} e_{r} }}{{x_{rk} }}} }}{{1 + frac{1}{n + d}left( {sumnolimits_{i = 1}^{n} {frac{{beta_{z} e_{i} }}{{y_{ik} }}} + sumnolimits_{l = 1}^{d} {frac{{beta_{z} e_{l} }}{{f_{lk} }}} } right)}} \ & s.t.left{ begin{gathered} sumnolimits_{j = 1}^{{q_{s} }} {alpha_{j} x_{rj} + beta_{z} e_{r} le x_{rk} ,r = 1,2, ldots ,m} hfill \ sumnolimits_{j = 1}^{{q_{s} }} {alpha_{j} x_{ij} – beta_{z} e_{i} ge y_{ik} ,i = 1,2, ldots ,n} hfill \ sumnolimits_{j = 1}^{{q_{s} }} {alpha_{j} x_{lj} + beta_{z} e_{l} le f_{lk} ,l = 1,2, ldots ,d} hfill \ alpha_{j} ge 0 hfill \ end{gathered} right. \ end{aligned} $$
(8)
Finally, in the common frontier model, the technology gap ratio (TGR) is equal to the ratio of the efficiency value of the common frontier to the scale frontier41. The formula is as Eq. (9):$$ TGR^{MinDW} = frac{{beta^{meta*} }}{{beta^{scale*} }} $$
(9)
(beta^{meta*}) and (beta^{scale*}) represent the optimal solution of formula (7) and formula (8), respectively. Obviously, (0 le TGR le 1). TGR is used to measure the distance between the optimal production technology and the potential optimal technology of a group, and identify whether there are any differences in LHG under different groups. The closer the TGR is to 1, the closer the technology level is to the optimal potential technology level. Conversely, it shows the larger gap between the technology level and the potential optimal technology level.Metafrontier-Malmquist–Luenberger indexMalmquist productivity index is widely used in the study of dynamic efficiency change trend, and has good adaptability to multiple input–output data and panel data analysis. The actual production process often contains unexpected output. After Chung et al. proposed Malmquist–Luenberger (ML) index, any Malmquist index with undesired output can be called ML index42. Oh constructed the Global-Malmquist–Luenberger index43. All the evaluated DMUs are included in the global reference set, which avoids the phenomenon of infeasible solution in VRS. The global reference set constructed in this paper is as Eqs. (10)–(11):$$ Q^{G} left( x right) = Q^{1} left( {x^{1} } right) cup Q^{2} left( {x^{2} } right) cup cdots cup Q^{T} left( {x^{T} } right) $$
(10)
$$ Q^{t} left( {x^{t} } right) = left{ {left( {y^{t} ,f^{t} } right)left| {x^{t} ;can;produce} right.;left( {y^{t} ,f^{t} } right)} right} $$
(11)
This paper takes MML index as the LHG.$$ begin{aligned} MML_{t – 1}^{t} & = sqrt {frac{{1 – D_{t – 1} left( {x^{t} ,y^{t} ,f^{t} ;y^{t} , – f^{t} } right)}}{{1 – D_{t – 1} left( {x^{t – 1} ,y^{t – 1} ,f^{t – 1} ;y^{t – 1} , – f^{t – 1} } right)}} times frac{{1 – D_{t} left( {x^{t} ,y^{t} ,f^{t} ;y^{t} , – f^{t} } right)}}{{1 – D_{t} left( {x^{t – 1} ,y^{t – 1} ,f^{t – 1} ;y^{t – 1} , – f^{t – 1} } right)}}} \ & = sqrt {frac{{1 – D_{t – 1} left( {x^{t – 1} ,y^{t – 1} ,f^{t – 1} ;y^{t – 1} , – f^{t – 1} } right)}}{{1 – D_{t} left( {x^{t – 1} ,y^{t – 1} ,f^{t – 1} ;y^{t – 1} , – f^{t – 1} } right)}} times frac{{1 – D_{t – 1} left( {x^{t} ,y^{t} ,f^{t} ;y^{t} , – f^{t} } right)}}{{1 – D_{t} left( {x^{t} ,y^{t} ,f^{t} ;y^{t} , – f^{t} } right)}}} \ & ;;;;; times frac{{1 – D_{t} left( {x^{t} ,y^{t} ,f^{t} ;y^{t} , – f^{t} } right)}}{{1 – D_{t – 1} left( {x^{t – 1} ,y^{t – 1} ,f^{t – 1} ;y^{t – 1} , – f^{t – 1} } right)}} \ end{aligned} $$
(12)
Next, it further decompose the MML index into efficiency change (EC) and technology change (TC). The specific formula is shown in Eqs. (13)–(14):$$ TC_{t – 1}^{t} = sqrt {frac{{1 – D_{t – 1} left( {x^{t – 1} ,y^{t – 1} ,f^{t – 1} ;y^{t – 1} , – f^{t – 1} } right)}}{{1 – D_{t} left( {x^{t – 1} ,y^{t – 1} ,f^{t – 1} ;y^{t – 1} , – f^{t – 1} } right)}} times frac{{1 – D_{t – 1} left( {x^{t} ,y^{t} ,f^{t} ;y^{t} , – f^{t} } right)}}{{1 – D_{t} left( {x^{t} ,y^{t} ,f^{t} ;y^{t} , – f^{t} } right)}}} $$
(13)
$$ EC_{t – 1}^{t} = frac{{1 – D_{t} left( {x^{t} ,y^{t} ,f^{t} ;y^{t} , – f^{t} } right)}}{{1 – D_{t – 1} left( {x^{t – 1} ,y^{t – 1} ,f^{t – 1} ;y^{t – 1} , – f^{t – 1} } right)}} $$
(14)
where (left( {x^{t – 1} ,y^{t – 1} ,f^{t – 1} } right)) and (left( {x^{t} ,y^{t} ,f^{t} } right)) represent the input, expected output and unexpected output of t-1 and t, respectively. (TC_{t – 1}^{t}) is the devotion to LHG raise of DMU’s technical progress from (t – 1) to (t). And (EC_{t – 1}^{t}) represents the devotion to LHG raise of DMU’s efficiency improvement from (t – 1) to (t). The higher the value is, the larger the devotion is. The (MML) index is recorded as (MI). The value of (MI) is the LHG. The green total factor productivity index of laying hens breeding under the common frontier and scale frontier are as Eqs. (15)–(16):$$ metaMI_{t – 1}^{t} = sqrt {frac{{1 – D_{{_{t – 1} }}^{m} left( {x^{t} ,y^{t} ,f^{t} ;y^{t} , – f^{t} } right)}}{{1 – D_{{_{t – 1} }}^{m} left( {x^{t – 1} ,y^{t – 1} ,f^{t – 1} ;y^{t – 1} , – f^{t – 1} } right)}} times frac{{1 – D_{{_{t} }}^{m} left( {x^{t} ,y^{t} ,f^{t} ;y^{t} , – f^{t} } right)}}{{1 – D_{{_{t} }}^{m} left( {x^{t – 1} ,y^{t – 1} ,f^{t – 1} ;y^{t – 1} , – f^{t – 1} } right)}}} $$
(15)
$$ groupMI_{t – 1}^{t} = sqrt {frac{{1 – D_{{_{t – 1} }}^{g} left( {x^{t} ,y^{t} ,f^{t} ;y^{t} , – f^{t} } right)}}{{1 – D_{{_{t – 1} }}^{g} left( {x^{t – 1} ,y^{t – 1} ,f^{t – 1} ;y^{t – 1} , – f^{t – 1} } right)}} times frac{{1 – D_{{_{t} }}^{g} left( {x^{t} ,y^{t} ,f^{t} ;y^{t} , – f^{t} } right)}}{{1 – D_{{_{t} }}^{g} left( {x^{t – 1} ,y^{t – 1} ,f^{t – 1} ;y^{t – 1} , – f^{t – 1} } right)}}} $$
(16)
For the DMUs with scale heterogeneity, we can measure the technology gap between the group frontier and the common frontier, which is caused by the specific group structure.Data and variablesBased on the research of the existing literature36, this paper selects five indexes to build the input–output indicator system. Details are as below:

1.

Input variables:

(1)

Quantity of concentrated forage. Mainly includes seeds of crops and their by-products.

(2)

Quantity of grain consumption. Quantity of grain consumed is the quantity of grain consumed by laying hens when they are raised. For example: corn, sorghum, broken rice, wheat, barley, wheat bran, etc.

(3)

Material expenses. The sum of water and fuel power costs, labor costs, and medical epidemic prevention fees. Water and fuel power costs include water, electricity, coal and other fuel power costs; labor costs mean the human management cost of each laying hen from the brood stage to the laying stage; medical and epidemic prevention costs include the cost of disease prevention and control.

2.

Positive output Main product production, which is the egg production per layer.

3.

Negative output Total discharge. According to the calculation method of The Manual of Pollutant Discharge Coefficient, Eq. (17) is used to calculate the COD, TN, and the TP of each layer. Then, according to the calculation method of class GB3838-2002 water quality standard in V, Eq. (18) is used to calculate the total discharge.

$$ POLLUTANTS = FP(FD) times Days $$
(17)
$$ TOTAL , POLLUTANTS = frac{COD}{{40}} + frac{TN}{2} + frac{TP}{{0.4}} $$
(18)
where, (FP(FD)) is the pollution discharge coefficient and the (Days) is the average raising days. Descriptive statistics of input and output indicators are shown in Table 1.Table 1 Descriptive statistics of input and output indicators.Full size tableThe quantity of concentrate, the quantity of food consumed, the cost of labor, the cost of medical treatment all come from “National Agricultural Product Cost and Benefit Data Compilation”. The pollutant discharge coefficient of laying hens is derived from “The Manual of Pollutant Discharge Coefficient”. According to the definition of scale in above two materials, a small scale 300–1000 laying hens, a medium scale 1000–10,000 laying hens, and a large scale greater than 10,000 laying hens are grouped to calculate cost efficiency.From 2004 to 2018, this paper selects 24 major egg-producing provinces (municipalities) in China as samples, after eliminating singular data in the three scales and averaging the missing data, the final small-scale group is left with 7 provinces including Liaoning, Shandong, Henan, Heilongjiang, Jilin, Shanxi, and Shaanxi; the medium-scale group is the remaining 21 provinces of Beijing, Hebei, Jiangsu, Liaoning, Shandong, Tianjin, Zhejiang, Anhui, Henan, Heilongjiang, Jilin, Hubei, Inner Mongolia, Shanxi, Yunnan, Gansu, Ningxia, Shaanxi, Sichuan, Xinjiang, Chongqing; the large-scale group has 18 provinces, including Beijing, Fujian, Guangdong, Henan, Jiangsu, Liaoning, Shandong, Tianjin, Anhui, Henan, Heilongjiang, Hubei, Jilin, Shanxi, Yunnan, Gansu, Sichuan and Chongqing.As is shown in Table 2, after dividing the provinces by region, the eastern region has 10 provinces (municipalities): Liaoning, Shandong, Beijing, Hebei, Jiangsu, Tianjin, Zhejiang, Fujian, Guangdong, Henan. The central region has 7 provinces (autonomous region): Henan, Heilongjiang, Jilin, Shanxi, Anhui, Hubei, Inner Mongolia. The western region has 7 provinces (municipalities): Shaanxi, Gansu, Ningxia, Sichuan, Xinjiang, Chongqing, Yunnan.Table 2 Samples selected from 2004–2018.Full size table More

Link activationContingency tables corresponding to the three cases described in the “Methods” section are shown in Table 1. This Table is quite revealing in several ways. The most interesting aspect is that the highest probability of link establishment occurs when an agreement is activated (Operational Activation in t).Table 1 Contingency tables.Full size tableIn this case, the probability of activation of a new link is 8.8%—namely, the ratio of new activation 7.3% to the total number of links that were not active at year t-1 (82.6%)—which is significantly higher than in the case of links not covered by a commercial agreement (No Trade Agreement), amounting to 1.4%.Therefore, the findings show that operational activation is associated with creating new trade relations between two particular countries. The third set, which considers links where a trade agreement exists in both years (t-1) and t (Trade Agreement in t-1 and t), also shows a consistent activation probability of 6%. This result confirms the assumption that the coverage of a commercial agreement, and not only its implementation, encourages the genesis of new links.Moreover, Table 1 suggests some interesting considerations on trade persistence. To establish these probabilities, we focus on the row totals in which a trade relationship is present at year (t-1), i.e., 28.8% in the case Trade Agreement in t-1 and t. The presence of an agreement influences in a positive way the probability of maintaining a trade relationship. In fact, when a trade agreement is present in both years, (t-1) and t, the probability of preserving the trade relationship is 87.1% ((frac{25.1}{28.8}times {100})), while when a trade agreement is activated at year t, the probability slightly decreases to 81.6%. In cases where trade agreements are missing (No Trade Agreement in t) we observe the probability of retaining a relationship decreases to 77.3%.Another interesting aspect concerns the probability of link deactivation. Once more, the coverage of a trade agreement favors a lower likelihood of deactivation of existing links. The ratio of the percentage of links that were active at year (t-1) and are no more active at year t to the total is 22.7% ((frac{1}{4.4}times {100})) in the case of a lack of agreement. This probability decreases to 18.4% ((frac{3.2}{17.4}times {100})) if we consider only the year of activation of the agreement (Operational Activation), and drops to 12.8% ((frac{3.7}{28.8}times {100})) when looking at agreements present in both years.Together, these results provide insights into the role of trade agreements in the network topology of cereal trade. While the establishment of a trade agreement promotes the potential for new trade links, the presence of the agreement in two consecutive years allows both to maintain an existing relationship and reduce the likelihood of link shutdowns.Flow variationsIn this second part, we study the impact of trade agreements on existing trade flows, analyzing the relationship between the flows at time t and the flows at time (t-1) in each of the three cases described in the “Methods” section—i.e., No trade agreements, Operational Activation in t, and Trade agreement in t-1 and t—measured in US$, Kcal and m(^3) of virtual water.Figure 3Kernel Density scatterplot between trade flows of cereals at time t (on the y-axis) and time (t-1) (on the x-axis) for the three different sets: No trade agreements (column a), Operational Activation in t (b), and Trade agreement in (t-1) and t (c). Panels in the first, second and third row refer to flows in US$, Kcal, and virtual water (m(^3)), respectively. Flow values are shown on a logarithmic scale. The color bar indicates probability densities, and the bisector is highlighted. Notice (i) the higher volumes in the case of flows covered by trade agreement and (ii) a a less relevant increase in volume when the flows are seen in the virtual water lens.Full size imageFigure 3 shows three different scatterplots for each unit of measure (US$ and Kcal and m(^3)). The scatterplots are colored by Kernel Density Estimation (KDE), a non-parametric technique for probability density functions. KDE aims to take a finite sample of data and infer the underlying probability density function. Figure 3 relates the flows at time (t-1) with the flows at time t, both reported on a logarithmic scale since the quantities span several orders of magnitude. Let’s start focusing on flows in terms of dollars and kilocalories. What stands out from the figure is the displacement of the flows toward higher values when they are covered by trade agreements (Trade Agreement in t-1 and t), compared to the case where flows have no trade agreement.We have quantitative evidence of this result by looking at Table 2 where the average flows in both years are shown. The average values of flows in both US$ and Kcal are much higher when there is a trade agreement over time (Trade agreement in t-1 and t). Flows have an average value of (6.13times 10^{7})$, larger than the mean of (3.05times 10^{7})$ achieved by flows not covered by a trade agreement. By comparing the distributions of the two distinct sets with different dimensions by applying the non-parametric Mann-Whitney test, we stand to evaluate this result as extremely significant (p-value approximately 0).Table 2 Average values of trade flows and flow variation index (rho _{ij}) for each of the three sets, in US$ (a), Kcal (b), and Virtual water (VW, m(^3)). The bar indicates the average operator.Full size tableAlso, while operational activation plays a crucial role in creating new links in the global cereal trade, it does not appear to hold central importance in driving flow increases. The average value of flows in both years (t-1) and t are, in fact, smaller than those not covered by trade agreements.The view appears slightly different when we look at the values in terms of virtual water (VW, m(^3)), i.e., the sum of the blue and green components. Flows with a commercial agreement show higher averages values than those not covered by agreements (see panel (c) of Table 2), but the increase is significantly lower than the one recorded in the other two units (US$ and Kcal). The increase recorded in dollars is about 100%, while in terms of virtual water this increase is less than 30%. In the next subsection, we will focus on this peculiar behavior, which reveals a different water content of the goods traded along links covered or not by agreements.Another significant result that emerges from Fig. 3 is the smaller amplitude (around the bisector) of the cloud in the case of link covered by agreements in both years (t-1) and t. This is confirmed by comparing the weighted average of the absolute value of the inter-annual flow variation index (overline{rho _{ij}}_{w}) (weights are the flows traded in the year (t-1)). The index (rho _{ij}) is used to highlight cases where the activation or the presence of the agreement generates a significant flow increase.Larger (rho _{ij}) values correspond to larger average variations from year (t-1) to year t. Accordingly, we observe that in the presence of trade agreement at time (t-1) and t a smaller (rho _{ij}) value of 24.79 percentage points (p.p) is found (see panel (a) of Table 2).Considering all the units (US$, Kcal, and m(^3)), this value is about half of the average inter-annual variation that occurs when there is no trade agreement. Hence, the presence of a commercial agreement over time reduces large fluctuations, stabilizing the year-to-year variations.To shed light on the response of water flows to the occurrence of the agreement, we refer to water productivity (WP)34, both in economic and nutritional terms. Table 3 shows that the Nutritional WP for the total virtual water is, on average, 35% higher in the flows under a trade agreement than in flows that are not under any treaty, while the Economic WP is 62% higher. We also analyze the two virtual water components, blue and green, separately.Interestingly, for blue water in the presence of a trade agreement, the Nutritional WP and the Economic WP for the flows covered by trade agreement are, on average, 68% and 93% higher than for the flows not covered by agreements. In other words, for one cubic meter of water used for grain production, more kilo-calories and dollars are exchanged when an agreement is in place, and this difference is even more significant in terms of blue water.Table 3 Average of nutritional ((mathrm {kcal/m^3})) and economic ((mathrm {US$/m^3})) water productivity (WP) for the total, blue and green virtual water.Full size tableWe also investigate in detail which products contribute most to the imbalance between flows in terms of kcal or water. To this aim, Fig. 4 reports the nutritional WP for each grain item distinguishing whether or not there is a commercial agreement (similar results occur if the economic WP is considered).The figure highlights that the nutritional WP is generally higher in the case where flows are covered by trade agreements (green bars). The most noticeable cases are Maize and Wheat, which are also the most traded products: the value of nutritional WP increases from 1978 (mathrm {kcal/m^3}) (No trade agreement) to 2851 (mathrm {kcal/m^3}) in case of a trade agreement for Wheat, and from 4471 (mathrm {kcal/m^3}) to 5026 for Maize.Figure 4The bar chart shows the nutritional WP for each cereal product in the two sets of Trade agreement in t-1 and t (in green) and No trade agreement (in red). The number over the bars represents the percentage of kcal traded for each product compared to the total kcal of all cereals. Note that green bars are higher than the red ones in 80% of cases.Full size imageA few products have a higher nutritional WP value when the flows are not involved in any treaty, e.g., Rye. This behavior can be traced back to a few flows that dominate the market between countries not linked by trade agreements. For example, trade in Rye in 2014 is attributable to just two major flows in terms of caloric intake relative to water quantity (notably, one between Germany and Japan, the other between Russia and Turkey).Figure 4 clearly shows that grains characterized by greater water efficiency generally move along the links covered by agreements.Performance of trade agreements in increasing flowOur results show that links covered by agreements exhibit larger flows than links not covered by treaties. We also intend to obtain information about the possible flow increase under a specific agreement.As mentioned in the “Methods” section, we selected only those operating links when the agreement came into force to evaluate the variation index ((rho _a)) under a specific treaty. Consequently, since there are trade agreements that came into force before the time interval considered, these are excluded from this analysis. As a result, the total number of agreements selected for this analysis is 99, 61 of which show an increase (positive (rho _{a}) values), while the remaining 38 exhibits a decrease in the flux intensities compared to the overall global trend. We present in Table 5 the results for positive (rho _{a}) variations, while trade agreements with negative (rho _{a}) values are reported in Supplementary Material (5). We provide this analysis in terms of economic flows (US$), but very similar results are obtained if calories (kcal) or virtual water (m(^3)) are chosen as the unit of measure.Table 4 Flow values in millions of dollars in year t and percent changes (rho _{a}) from (t-1) to t for each trade agreement.Full size tableWhat stands out in Table 4 is that most of the positive percentage changes occur in Europe and Central Asia regions. This may be due to long-term commercial activities in Europe, which are supported by the geographical proximity of the countries, as well as the wide variety of political and economic treaties among them. Europe, in fact, is characterized by a fourfold increase in cereal production since the 1960s due to the adoption of the Common Agricultural Policy, which has intensified trade in Europe and towards external markets30.A closer inspection of Table 4 shows that among the agreements with the most significant flows that showed the greatest increases, we find EEA (European Economic Area) in Europe and Central Asia, Japan-ASEAN in East Asia and Pacific, and COMESA in Sub-Saharan Africa.With lower flow values but large increases ((rho _{a})) due to the entry into force of trade agreements, the India-Sri Lanka agreement in South Asia stands out above all others. Also, the treaty signed in 2013 between EU-Colombia and Peru shows significant variations in terms of the percentage of flow increase, but the volume of the corresponding flow is inferior when compared with other trade agreements. On the other hand, the North American Free Trade Agreement (NAFTA), which became effective in 1994, has a lower (rho _{a}) value, but the flows on which the variation is calculated are significantly higher. More

Pandolfi JM, Connolly SR, Marshall DJ, Cohen AL. Projecting coral reef futures under global warming and ocean acidification. Science. 2011;333:418–22.CAS 
Article 

Google Scholar 
Baird AH, Marshall PA. Mortality, growth and reproduction in scleractinian corals following bleaching on the Great Barrier Reef. Mar Ecol Prog Ser. 2002;237:133–41.Article 

Google Scholar 
Lewis CL, Coffroth MA. The acquisition of exogenous algal symbionts by an octocoral after bleaching. Science. 2004;304:1490–2.CAS 
Article 

Google Scholar 
Matsuda SB, Chakravarti LJ, Cunning R, Huffmyer AS, Nelson CE, Gates RD, et al. Temperature mediated acquisition of rare heterologous symbionts promotes survival of coral larvae under ocean warming. Glob Chang Biol. 2022;28:2006–25.Article 

Google Scholar 
Thornhill DJ, Howells EJ, Wham DC, Steury TD, Santos SR. Population genetics of reef coral endosymbionts (Symbiodinium, Dinophyceae). Mol Ecol 2017;26:2640–59.CAS 
Article 

Google Scholar 
Diaz-Almeyda EM, Prada C, Ohdera AH, Moran H, Civitello DJ, Iglesias-Prieto R, et al. Intraspecific and interspecific variation in thermotolerance and photoacclimation in Symbiodinium dinoflagellates. Proc R Soc B. 2017;284:20171767.Article 

Google Scholar 
Howells EJ, Beltran VH, Larsen NW, Bay LK, Willis BL, van Oppen MJH. Coral thermal tolerance shaped by local adaptation of photosymbionts. Nat Clim Change. 2012;2:116–20.Article 

Google Scholar 
Voolstra CR, Buitrago-Lopez C, Perna G, Cardenas A, Hume BCC, Radecker N, et al. Standardized short-term acute heat stress assays resolve historical differences in coral thermotolerance across microhabitat reef sites. Glob Change Biol. 2020;26:4328–43.Article 

Google Scholar 
Behrendt L, Salek MM, Trampe EL, Fernandez VI, Lee KS, Kuhl M, et al. Phenochip: a single-cell phenomic platform for high-throughput photophysiological analyses of microalgae. Sci Adv. 2020;6:eabb2754.CAS 
Article 

Google Scholar 
Torda G, Donelson JM, Aranda M, Barshis DJ, Bay L, Berumen ML, et al. Rapid adaptive responses to climate change in corals. Nat Clim Change. 2017;7:627–36.Article 

Google Scholar 
Buerger P, Alvarez-Roa C, Coppin CW, Pearce SL, Chakravarti LJ, Oakeshott JG, et al. Heat-evolved microalgal symbionts increase coral bleaching tolerance. Sci Adv. 2020;6:eaba2498.CAS 
Article 

Google Scholar 
Kavousi J, Denis V, Sharp V, Reimer JD, Nakamura T, Parkinson JE. Unique combinations of coral host and algal symbiont genotypes reflect intraspecific variation in heat stress responses among colonies of the reef-building coral, Montipora digitata. Mar Biol. 2020;167:23.CAS 
Article 

Google Scholar 
Parkinson JE, Baums IB. The extended phenotypes of marine symbioses: ecological and evolutionary consequences of intraspecific genetic diversity in coral–algal associations. Front Microbiol. 2014;5:445.Article 

Google Scholar 
Andersson M, Johansson S, Bergman H, Xiao L, Behrendt L, Tenje M. A microscopy-compatible temperature regulation system for single-cell phenotype analysis— demonstrated by thermoresponse mapping of microalgae. Lab Chip. 2021;21:1694–705.CAS 
Article 

Google Scholar 
Hume B, D’Angelo C, Burt J, Baker AC, Riegl B, Wiedenmann J. Corals from the Persian/Arabian Gulf as models for thermotolerant reef-builders: prevalence of clade C3 Symbiodinium, host fluorescence and ex situ temperature tolerance. Mar Pollut Bull. 2013;72:313–22.CAS 
Article 

Google Scholar 
Karim W, Nakaema S, Hidaka M. Temperature effects on the growth rates and photosynthetic activities of Symbiodinium cells. J Mar Sci Eng. 2015;3:368–81.Article 

Google Scholar 
Takahashi S, Yoshioka-Nishimura M, Nanba D, Badger MR. Thermal acclimation of the symbiotic alga Symbiodinium spp. alleviates photobleaching under heat stress. Plant Physiol. 2013;161:477–85.CAS 
Article 

Google Scholar 
Robison JD, Warner ME. Differential impacts of photoacclimation and thermal stress on the photobiology of four different phylotypes of Symbiodinium (Pyrrhophyta). J Phycol. 2006;42:568–79.CAS 
Article 

Google Scholar 
Calabrese F, Voloshynoyska I, Musat F, Thullner M, Schlomann M, Richnow HH, et al. Quantitation and comparison of phenotypic heterogeneity among single cells of monoclonal microbial populations. Front Microbiol. 2019;10:2814.Article 

Google Scholar 
Martins BMC, Locke JOW. Microbial individuality: How single-cell heterogeneity enables population level strategies. Curr Opin Microbiol. 2015;24:104–12.CAS 
Article 

Google Scholar  More

Sutton, M. A. et al. The European Nitrogen Assessment: Sources, Effects and Policy Perspectives (Cambridge Univ. Press, 2011).Withers, P. J. A. & Haygarth, P. M. Agriculture, phosphorus and eutrophication: A European perspective. Soil Use Manag. 23, 1–4 (2007).Article 

Google Scholar 
Heffer, P. Assessment of Fertilizer Use by Crop at the Global Level (IFA, 2008).Wassen, M. J., Schrader, J., van Dijk, J. & Eppinga, M. B. Phosphorus fertilization is eradicating the niche of northern Eurasia’s threatened plant species. Nat. Ecol. Evol. 5, 67–73 (2021).Article 

Google Scholar 
Penuelas, J., Janssens, I. A., Ciais, P., Obersteiner, M. & Sardans, J. Anthropogenic global shifts in biospheric N and P concentrations and ratios and their impacts on biodiversity, ecosystem productivity, food security, and human health. Glob. Change Biol. 26, 1962–1985 (2020).Article 

Google Scholar 
Stokstad, E. Nitrogen crisis threatens Dutch environment — and economy. Science 366, 1180–1181 (2019).Article 

Google Scholar 
Dentener, F. et al. Nitrogen and sulfur deposition on regional and global scales: A multimodel evaluation. Global Biogeochem. Cycles 20, GB4003 (2006).Article 

Google Scholar 
Garske, B., Stubenrauch, J. & Ekardt, F. Sustainable phosphorus management in European agricultural and environmental law. RECIEL 29, 107–117 (2020).Article 

Google Scholar 
A Farm to Fork Strategy for a Fair, Healthy and Environmentally-friendly Food System (COM(2020) 381 final: European Commission, 2020); https://knowledge4policy.ec.europa.eu/publication/communication-com2020381-farm-fork-strategy-fair-healthy-environmentally-friendly-food_en More