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

Animals such as this orangutan in Indonesia are endangered because of illegal deforestation.Credit: Jami Tarris/Future Publishing via Getty

Funding battles stymie plan to protect global biodiversityScientists are frustrated with slow progress towards a new deal to protect the natural world. Government officials from around the globe met in Geneva, Switzerland, on 14–29 March to find common ground on a draft of the deal, known as the post-2020 global biodiversity framework, but discussions stalled.The framework so far sets out 4 broad goals, including slowing species extinction, and 21 mostly quantitative targets, such as protecting at least 30% of the world’s land and seas. It is part of an international treaty known as the United Nations Convention on Biological Diversity, and aims to address the global biodiversity crisis, which could see one million plant and animal species go extinct in the next few decades.Many who were at the meeting say that disagreements over funding for biodiversity conservation were the main hold-up in negotiations. For example, the draft deal proposed that US$10 billion of funding per year should flow from developed nations to low- and middle-income countries to help them to implement the biodiversity framework. But many think this is not enough.Negotiators say they will now have to meet again before a highly anticipated UN biodiversity summit later this year, where the deal was to be signed.‘Collegiality’ influences researchers’ promotion prospectsUniversities in North America often consider how well researchers interact with each other when making decisions about who gets promoted, a study has found, even though these factors are not formally acknowledged in review guidelines.A researcher’s performance is usually assessed according to three pillars: research, teaching and service. But in recent years, there has been a push from some academics to add another pillar: collegiality. Many say that the concepts of cooperation, collaboration and respect, which broadly fall under the definition of collegiality, are important to the functioning of laboratories and research teams.DeDe Dawson, an academic librarian at the University of Saskatchewan in Saskatoon, Canada, and colleagues analysed more than 860 review, promotion and tenure documents from different departments at 129 universities in the United States and Canada to get a sense of how often collegiality is taken into account.The study, published on 6 April (D. Dawson et al. PLoS ONE 17, e0265506; 2022), found that the concept of collegiality was widespread: the word ‘collegiality’ and related terms, such as ‘citizenship’ or ‘professionalism’, appeared 507 times in 213 of the documents, suggesting that it was often taken into account in evaluations. But just 85 documents included a definition of the term, and fewer still explained how it was measured or used in assessments.

Source: D. Dawson et al. PLoS ONE 17, e0265506 (2022)

Collegiality was mentioned most often in research-intensive institutions (see ‘Academia’s fourth pillar’). The authors say that this could be because the behaviour involved is valued in research groups.Dawson and her colleagues warn that relying on collegiality in performance reviews without adequate guidance could introduce bias, as those in charge fill in the blanks with their own definitions.“We need to make sure that we don’t use collegiality to exclude others that may communicate or interact differently,” says Sujay Kaushal, a geologist at the University of Maryland in College Park, who has previously studied collegiality. More

United Nations. World Population Prospects: The 2017 Revision, Key Findings and Advance Tables. Working Paper No. ESA/P/WP/248 (UN-DESA, 2017).Costello, C. et al. The future of food from the sea. Nature 588, 95–100 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
IPCC. IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (2019).FAO. Mapping Supply and Demand for Animal-Source Foods to 2030 (2011).Foley, J. A. et al. Global consequences of land use. Science 309, 570–574 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
DeFries, R. S., Rudel, T., Uriarte, M. & Hansen, M. Deforestation driven by urban population growth and agricultural trade in the twenty-first century. Nat. Geosci. 3, 178–181 (2010).ADS 
CAS 
Article 

Google Scholar 
Rockström, J. et al. Future water availability for global food production: the potential of green water for increasing resilience to global change. Water Resour. Res. 45, W00A12 (2009).Article 

Google Scholar 
IPCC. IPCC Special Report on Climate Change and Land (2019).Poore, J. & Nemecek, T. Reducing food’s environmental impacts through producers and consumers. Science 360, 987–992 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
FAO. The State of World Fisheries and Aquaculture 2020: Sustainability in Action (2020).Bryndum‐Buchholz, A. et al. Twenty-first-century climate change impacts on marine animal biomass and ecosystem structure across ocean basins. Glob. Change Biol. 25, 459–472 (2019).ADS 
Article 

Google Scholar 
Cheung, W. W. L., Dunne, J., Sarmiento, J. L. & Pauly, D. Integrating ecophysiology and plankton dynamics into projected maximum fisheries catch potential under climate change in the Northeast Atlantic. ICES J. Mar. Sci. 68, 1008–1018 (2011).Article 

Google Scholar 
Froehlich, H. E., Gentry, R. R. & Halpern, B. S. Global change in marine aquaculture production potential under climate change. Nat. Ecol. Evol. 2, 1745–1750 (2018).PubMed 
Article 

Google Scholar 
Handisyde, N., Telfer, T. C. & Ross, L. G. Vulnerability of aquaculture-related livelihoods to changing climate at the global scale. Fish Fish. 18, 466–488 (2017).Article 

Google Scholar 
Szuwalski, C. S. & Hollowed, A. B. Climate change and non-stationary population processes in fisheries management. ICES J. Mar. Sci. 73, 1297–1305 (2016).Article 

Google Scholar 
Pinsky, M. L. et al. Preparing ocean governance for species on the move. Science 360, 1189–1191 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Gaines, S. D. et al. Improved fisheries management could offset many negative effects of climate change. Sci. Adv. 4, eaao1378 (2018).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Free, C. M. et al. Realistic fisheries management reforms could mitigate the impacts of climate change in most countries. PLoS ONE 15, e0224347 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Clapp, J. Food self-sufficiency: making sense of it, and when it makes sense. Food Policy 66, 88–96 (2017).Article 

Google Scholar 
Barange, M., Bahri, T., Beveridge, M. & Cochrane, K. L. Impacts of Climate Change on Fisheries and Aquaculture: Synthesis of Current Knowledge, Adaptation and Mitigation Options. Fisheries and Aquaculture Technical Paper No. 627 (FAO, 2018).Lester, S. E. et al. Marine spatial planning makes room for offshore aquaculture in crowded coastal waters. Nat. Commun. 9, 945 (2018).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cottrell, R. S., Blanchard, J. L., Halpern, B. S., Metian, M. & Froehlich, H. E. Global adoption of novel aquaculture feeds could substantially reduce forage fish demand by 2030. Nat. Food 1, 301–308 (2020).Article 

Google Scholar 
Hua, K. et al. The future of aquatic protein: implications for protein sources in aquaculture diets. One Earth 1, 316–329 (2019).ADS 
Article 

Google Scholar 
Chavanne, H. et al. A comprehensive survey on selective breeding programs and seed market in the European aquaculture fish industry. Aquacult. Int. 24, 1287–1307 (2016).Article 

Google Scholar 
Troell, M., Jonell, M. & Henriksson, P. J. G. Ocean space for seafood. Nat. Ecol. Evol. 1, 1224–1225 (2017).PubMed 
Article 

Google Scholar 
European Union. Commission Regulation (EC) No 710/2009 of 5 August 2009 Amending Regulation (EC) No 889/2008 laying down detailed rules for the implementation of Council Regulation (EC) No 834/2007, as regards laying down detailed rules on organic aquaculture animal and seaweed production. http://data.europa.eu/eli/reg/2009/710/oj (2009).Golden, C. D. et al. Aquatic foods to nourish nations. Nature 598, 315–320 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Davies, I. P. et al. Governance of marine aquaculture: pitfalls, potential, and pathways forward. Mar. Policy 104, 29–36 (2019).Article 

Google Scholar 
Gentry, R. R. et al. Exploring the potential for marine aquaculture to contribute to ecosystem services. Rev. Aquacult. 12, 499–512 (2020).Article 

Google Scholar 
Troell, M. et al. Ecological engineering in aquaculture — potential for integrated multi-trophic aquaculture (IMTA) in marine offshore systems. Aquaculture 297, 1–9 (2009).Article 

Google Scholar 
Froehlich, H. E., Jacobsen, N. S., Essington, T. E., Clavelle, T. & Halpern, B. S. Avoiding the ecological limits of forage fish for fed aquaculture. Nat. Sustain. 1, 298–303 (2018).Article 

Google Scholar 
Øverland, M., Mydland, L. T. & Skrede, A. Marine macroalgae as sources of protein and bioactive compounds in feed for monogastric animals. J. Sci. Food Agric. 99, 13–24 (2019).PubMed 
Article 
CAS 

Google Scholar 
Besson, M. et al. Environmental impacts of genetic improvement of growth rate and feed conversion ratio in fish farming under rearing density and nitrogen output limitations. J. Clean. Prod. 116, 100–109 (2016).Article 

Google Scholar 
Froehlich, H. E., Runge, C. A., Gentry, R. R., Gaines, S. D. & Halpern, B. S. Comparative terrestrial feed and land use of an aquaculture-dominant world. Proc. Natl Acad. Sci. USA 115, 5295–5300 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Aguilar-Manjarrez, J., Soto, D., Brummett, R. E. Aquaculture Zoning, Site Selection and Area Management under the Ecosystem Approach to Aquaculture (FAO, 2017).Soto, D. et al. In Impacts Of Climate Change on Fisheries and Aquaculture: Synthesis of Current Knowledge, Adaptation and Mitigation Options Ch. 26 (FAO, 2018).Darwin, C. The Variation of Animals and Plants Under Domestication (John Murray, 1868).Gjedrem, T., Robinson, N. & Rye, M. The importance of selective breeding in aquaculture to meet future demands for animal protein: a review. Aquaculture 350–353, 117–129 (2012).Article 

Google Scholar 
Antonello, J. et al. Estimates of heritability and genetic correlation for body length and resistance to fish pasteurellosis in the gilthead sea bream (Sparus aurata L.). Aquaculture 298, 29–35 (2009).Article 

Google Scholar 
Saillant, E., Dupont-Nivet, M., Haffray, P. & Chatain, B. Estimates of heritability and genotype–environment interactions for body weight in sea bass (Dicentrarchus labrax L.) raised under communal rearing conditions. Aquaculture 254, 139–147 (2006).Article 

Google Scholar 
Klinger, D. H., Levin, S. A. & Watson, J. R. The growth of finfish in global open-ocean aquaculture under climate change. Proc. R. Soc. B 284, 20170834 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Salayo, N. D., Perez, M. L., Garces, L. R. & Pido, M. D. Mariculture development and livelihood diversification in the Philippines. Mar. Policy 36, 867–881 (2012).Article 

Google Scholar 
Boyce, D. G., Lotze, H. K., Tittensor, D. P., Carozza, D. A. & Worm, B. Future ocean biomass losses may widen socioeconomic equity gaps. Nat. Commun. 11, 2235 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sumaila, U. R. et al. Benefits of the Paris Agreement to ocean life, economies, and people. Sci. Adv. 5, eaau3855 (2019).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
United Nations. Transforming Our World: The 2030 Agenda for Sustainable Development (United Nations, 2017).Hilborn, R. et al. Effective fisheries management instrumental in improving fish stock status. Proc. Natl Acad. Sci. USA 117, 2218–2224 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Free, C. M. et al. Impacts of historical warming on marine fisheries production. Science 363, 979–983 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Costello, C. et al. Global fishery prospects under contrasting management regimes. Proc. Natl Acad. Sci. USA 113, 5125–5129 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ye, Y. & Gutierrez, N. L. Ending fishery overexploitation by expanding from local successes to globalized solutions. Nat. Ecol. Evol. 1, 0179 (2017).Article 

Google Scholar 
Leape, J. et al. Technology, Data and New Models for Sustainably Managing Ocean Resources (World Resources Institute, 2020).Anderson, C. R. et al. Scaling up from regional case studies to a global harmful algal bloom observing system. Front. Mar. Sci. 6, 250 (2019).Article 

Google Scholar 
Dunn, D. C., Maxwell, S. M., Boustany, A. M. & Halpin, P. N. Dynamic ocean management increases the efficiency and efficacy of fisheries management. Proc. Natl Acad. Sci. USA 113, 668–673 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
FAO. Aquaculture Development: 7. Aquaculture Governance and Sector Development (2017).Oyinlola, M. A., Reygondeau, G., Wabnitz, C. C. C., Troell, M. & Cheung, W. W. L. Global estimation of areas with suitable environmental conditions for mariculture species. PLoS ONE 13, e0191086 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Jackson, A. Fish in-fish out ratio explained. Aquacult. Eur. 34, 5–10 (2009).
Google Scholar 
Tacon, A. G. J. & Metian, M. Feed matters: satisfying the feed demand of aquaculture. Rev. Fish. Sci. Aquacult. 23, 1–10 (2015).Article 

Google Scholar 
Tacon, A. G. J. & Metian, M. Global overview on the use of fish meal and fish oil in industrially compounded aquafeeds: trends and future prospects. Aquaculture 285, 146–158 (2008).CAS 
Article 

Google Scholar 
World Bank. Population, Total (2020); https://data.worldbank.org/indicator/SP.POP.TOTLEdwards, P., Zhang, W., Belton, B. & Little, D. C. Misunderstandings, myths and mantras in aquaculture: its contribution to world food supplies has been systematically over reported. Mar. Policy 106, 103547 (2019).Article 

Google Scholar 
Roberts, P. Conversion Factors for Estimating the Equivalent Live Weight of Fisheries Products (The Food and Agriculture Organization of the United Nations, 1998).R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).Kaschner, K. et al. AquaMaps: Predicted Range Maps for Aquatic Species https://www.aquamaps.org/ (2019).García Molinos, J. et al. Climate velocity and the future global redistribution of marine biodiversity. Nat. Clim. Change 6, 83–88 (2016).ADS 
Article 

Google Scholar 
Cashion, T., Le Manach, F., Zeller, D. & Pauly, D. Most fish destined for fishmeal production are food-grade fish. Fish Fish. 18, 837–844 (2017).Article 

Google Scholar 
Froehlich, H. E., Gentry, R. R. & Halpern, B. S. Synthesis and comparative analysis of physiological tolerance and life-history growth traits of marine aquaculture species. Aquaculture 460, 75–82 (2016).Article 

Google Scholar 
Thorson, J. T., Munch, S. B., Cope, J. M. & Gao, J. Predicting life history parameters for all fishes worldwide. Ecol. Appl. 27, 2262–2276 (2017).PubMed 
Article 

Google Scholar 
Froese, R. & Pauly, D. FishBase http://www.fishbase.org (2021).Palomares, M. & Pauly, D. SeaLifeBase http://www.sealifebase.org (2019).FAO. Cultured Aquatic Species (2019).Dunne, J. P. et al. GFDL’s ESM2 global coupled climate–carbon Earth system models. Part I: physical formulation and baseline simulation characteristics. J. Clim. 25, 6646–6665 (2012).ADS 
Article 

Google Scholar 
Dunne, J. P. et al. GFDL’s ESM2 global coupled climate–carbon Earth system models. Part II: carbon system formulation and baseline simulation characteristics. J. Clim. 26, 2247–2267 (2013).ADS 
Article 

Google Scholar 
Song, Z. et al. Centuries of monthly and 3-hourly global ocean wave data for past, present, and future climate research. Sci. Data 7, 226 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Gentry, R. R. et al. Mapping the global potential for marine aquaculture. Nat. Ecol. Evol. 1, 1317–1324 (2017).PubMed 
Article 

Google Scholar 
Barton, A. et al. Impacts of coastal acidification on the Pacific Northwest shellfish industry and adaptation strategies implemented in response. Oceanography 25, 146–159 (2015).Article 

Google Scholar 
Froehlich, H. E., Smith, A., Gentry, R. R. & Halpern, B. S. Offshore aquaculture: I know it when I see it. Front. Mar. Sci. 4, 154 (2017).Article 

Google Scholar 
World Bank. Adjusted Net National Income per Capita (Current US$) (2019); https://data.worldbank.org/indicator/NY.ADJ.NNTY.PC.CDWorld Bank. Pump Price for Diesel Fuel (US$ per liter) (2019); https://data.worldbank.org/indicator/EP.PMP.DESL.CDPiburn, J. wbstats: programmatic access to the World Bank API. R package v.1.0.4 https://cran.r-project.org/web/packages/wbstats/index.html (2018).Rubino, M. (ed.) Offshore Aquaculture in the United States: Economic Considerations, Implications & Opportunities NOAA Technical Memorandum NMFS F/SPO-103 (US Department of Commerce, 2008).Jackson, A. & Newton, R. Project to Model the Use of Fisheries By-products in the Production of Marine Ingredients, with Special Reference to the Omega 3 Fatty Acids EPA and DHA (Institute Of Aquaculture, University Of Stirling And IFFO, 2016). More

Szulkin, M. et al. How to quantify urbanization when testing for urban evolution?. Urban Evol. Biol. https://doi.org/10.1093/oso/9780198836841.003.0002 (2020).Article 

Google Scholar 
Slabbekoorn, H. Songs of the city: Noise-dependent spectral plasticity in the acoustic phenotype of urban birds. Anim. Behav. https://doi.org/10.1016/j.anbehav.2013.01.021 (2013).Article 

Google Scholar 
Christiansen, N. A., Fryirs, K. A., Green, T. J. & Hose, G. C. The impact of urbanisation on community structure, gene abundance and transcription rates of microbes in upland swamps of Eastern Australia. PLoS ONE https://doi.org/10.1371/journal.pone.0213275 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Alberti, M. et al. Global urban signatures of phenotypic change in animal and plant populations. Proc. Natl. Acad. Sci. USA https://doi.org/10.1073/pnas.1606034114 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
McFall-Ngai, M. M. et al. Animals in a bacterial world, a new imperative for the life sciences. Proc. Natl. Acad. Sci. https://doi.org/10.1073/pnas.1218525110 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Zilber-Rosenberg, I. & Rosenberg, E. Role of microorganisms in the evolution of animals and plants: the hologenome theory of evolution. FEMS Microbiol. Rev. https://doi.org/10.1111/j.1574-6976.2008.00123.x (2008).Article 
PubMed 

Google Scholar 
Trevelline, B. K., Fontaine, S. S., Hartup, B. K. & Kohl, K. D. Conservation biology needs a microbial renaissance: A call for the consideration of host-associated microbiota in wildlife management practices. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2018.2448 (2019).Article 

Google Scholar 
Jarrett, C., Powell, L. L., McDevitt, H., Helm, B. & Welch, A. J. Bitter fruits of hard labour: diet metabarcoding and telemetry reveal that urban songbirds travel further for lower-quality food. Oecologia https://doi.org/10.1007/s00442-020-04678-w (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Zollinger, S. A. et al. Traffic noise exposure depresses plasma corticosterone and delays offspring growth in breeding zebra finches. Conserv. Physiol. https://doi.org/10.1093/conphys/coz056 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Sprau, P., Mouchet, A. & Dingemanse, N. J. Multidimensional environmental predictors of variation in avian forest and city life histories. Behav. Ecol. https://doi.org/10.1093/beheco/arw130 (2017).Article 

Google Scholar 
Teyssier, A. et al. Inside the guts of the city: Urban-induced alterations of the gut microbiota in a wild passerine. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2017.09.035 (2018).Article 
PubMed 

Google Scholar 
Murray, M. H. et al. Gut microbiome shifts with urbanization and potentially facilitates a zoonotic pathogen in a wading bird. PLoS ONE https://doi.org/10.1371/journal.pone.0220926 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Fuirst, M., Veit, R. R., Hahn, M., Dheilly, N. & Thorne, L. H. Effects of urbanization on the foraging ecology and microbiota of the generalist seabird Larus argentatus. PLoS ONE https://doi.org/10.1371/journal.pone.0209200 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Phillips, J. N., Berlow, M. & Derryberry, E. P. The effects of landscape urbanization on the gut microbiome: An exploration into the gut of urban and rural white-crowned sparrows. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2018.00148 (2018).Article 

Google Scholar 
Berlow, M., Phillips, J. N. & Derryberry, E. P. Effects of urbanization and landscape on gut microbiomes in white-crowned sparrows. Microb. Ecol. https://doi.org/10.1007/s00248-020-01569-8 (2020).Article 
PubMed 

Google Scholar 
Cox, L. M. et al. Altering the intestinal microbiota during a critical developmental window has lasting metabolic consequences. Cell https://doi.org/10.1016/j.cell.2014.05.052 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Knutie, S. A., Wilkinson, C. L., Kohl, K. D. & Rohr, J. R. Early-life disruption of amphibian microbiota decreases later-life resistance to parasites. Nat. Commun. 8, 1–8 (2017).CAS 
Article 

Google Scholar 
Sudyka, J., Di Lecce, I., Wojas, L., Rowiński, P. & Szulkin, M. Nest-boxes alter the reproductive ecology of urban cavity-nesters in a species-dependent way. https://doi.org/10.32942/OSF.IO/WP9MN.
Maziarz, M., Broughton, R. K. & Wesołowski, T. Microclimate in tree cavities and nest-boxes: Implications for hole-nesting birds. For. Ecol. Manag. https://doi.org/10.1016/j.foreco.2017.01.001 (2017).Article 

Google Scholar 
Thompson, M. J., Capilla-Lasheras, P., Dominoni, D. M., Réale, D. & Charmantier, A. Phenotypic variation in urban environments: mechanisms and implications. Trends Ecol. Evol. 37, 171–182 (2022).CAS 
Article 

Google Scholar 
Salmón, P. et al. Continent-wide genomic signatures of adaptation to urbanisation in a songbird across Europe. Nat. Commun. 12, 1–14 (2021).ADS 
Article 

Google Scholar 
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. https://doi.org/10.1186/s13059-014-0550-8 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Sackey, B. A., Mensah, P., Collison, E. & Sakyi-Dawson, E. Campylobacter, Salmonella, Shigella and Escherichia coli in live and dressed poultry from metropolitan Accra. Int. J. Food Microbiol. https://doi.org/10.1016/S0168-1605(01)00595-5 (2001).Article 
PubMed 

Google Scholar 
Benskin, C. M. W. H., Wilson, K., Jones, K. & Hartley, I. R. Bacterial pathogens in wild birds: A review of the frequency and effects of infection. Biol. Rev. https://doi.org/10.1111/j.1469-185X.2008.00076.x (2009).Article 
PubMed 

Google Scholar 
Hansell, M. & Overhill, R. Bird nests and construction behaviour. Bird Nests Constr. Behav. https://doi.org/10.1017/cbo9781139106788 (2000).Article 

Google Scholar 
Siddiqui, S. H., Khan, M., Kang, D., Choi, H. W. & Shim, K. Meta-analysis and systematic review of the thermal stress response: Gallus gallus domesticus show low immune responses during heat stress. Front. Physiol. 13, 31 (2022).Article 

Google Scholar 
Sepulveda, J. & Moeller, A. H. The effects of temperature on animal gut microbiomes. Front. Microbiol. https://doi.org/10.3389/fmicb.2020.00384 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Kohl, K. D. & Yahn, J. Effects of environmental temperature on the gut microbial communities of tadpoles. Environ. Microbiol. https://doi.org/10.1111/1462-2920.13255 (2016).Article 
PubMed 

Google Scholar 
Teyssier, A. et al. Diet contributes to urban-induced alterations in gut microbiota: Experimental evidence from a wild passerine. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2019.2182 (2020).Article 

Google Scholar 
Benskin, C. M. W. H., Rhodes, G., Pickup, R. W., Wilson, K. & Hartley, I. R. Diversity and temporal stability of bacterial communities in a model passerine bird, the zebra finch. Mol. Ecol. https://doi.org/10.1111/j.1365-294X.2010.04892.x (2010).Article 
PubMed 

Google Scholar 
Garrett, W. S. et al. Enterobacteriaceae Act in concert with the gut microbiota to induce spontaneous and maternally transmitted colitis. Cell Host Microbe https://doi.org/10.1016/j.chom.2010.08.004 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Videvall, E. et al. Early-life gut dysbiosis linked to juvenile mortality in ostriches. BMC Microbiome 8, 1–13 (2020).Article 

Google Scholar 
Hooper, L. V. & MacPherson, A. J. Immune adaptations that maintain homeostasis with the intestinal microbiota. Nat. Rev. Immunol. https://doi.org/10.1038/nri2710 (2010).Article 
PubMed 

Google Scholar 
Borre, Y. E. et al. Microbiota and neurodevelopmental windows: Implications for brain disorders. Trends Mol. Med. https://doi.org/10.1016/j.molmed.2014.05.002 (2014).Article 
PubMed 

Google Scholar 
Jones, E. L. & Leather, S. R. Invertebrates in urban areas: A review. Eur. J. Entomol. https://doi.org/10.14411/eje.2012.060 (2012).Article 

Google Scholar 
Wilkin, T. A., King, L. E. & Sheldon, B. C. Habitat quality, nestling diet, and provisioning behaviour in great tits Parus major. J. Avian Biol. https://doi.org/10.1111/j.1600-048X.2009.04362.x (2009).Article 

Google Scholar 
Pollock, C. J., Capilla-Lasheras, P., McGill, R. A. R., Helm, B. & Dominoni, D. M. Integrated behavioural and stable isotope data reveal altered diet linked to low breeding success in urban-dwelling blue tits (Cyanistes caeruleus). Sci. Rep. https://doi.org/10.1038/s41598-017-04575-y (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Davidson, G. L. et al. Diet induces parallel changes to the gut microbiota and problem solving performance in a wild bird. Sci. Rep. https://doi.org/10.1038/s41598-020-77256-y (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Bodawatta, K. H. et al. Flexibility and resilience of great tit (Parus major) gut microbiomes to changing diets. Anim. Microbiome 2021(3), 1–14 (2021).
Google Scholar 
Baniel, A. et al. Seasonal shifts in the gut microbiome indicate plastic responses to diet in wild geladas. Microbiome 9, 1–20 (2021).Article 

Google Scholar 
Sullam, K. E. et al. Environmental and ecological factors that shape the gut bacterial communities of fish: A meta-analysis. Mol. Ecol. https://doi.org/10.1111/j.1365-294X.2012.05552.x (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Martiny, J. B. H. et al. Microbial biogeography: Putting microorganisms on the map. Nat. Rev. Microbiol. https://doi.org/10.1038/nrmicro1341 (2006).Article 
PubMed 

Google Scholar 
Lucass, C., Eens, M. & Müller, W. When ambient noise impairs parent-offspring communication. Environ. Pollut. https://doi.org/10.1016/j.envpol.2016.03.015 (2016).Article 
PubMed 

Google Scholar 
Kight, C. R. & Swaddle, J. P. How and why environmental noise impacts animals: An integrative, mechanistic review. Ecol. Lett. https://doi.org/10.1111/j.1461-0248.2011.01664.x (2011).Article 
PubMed 

Google Scholar 
Cui, B., Gai, Z., She, X., Wang, R. & Xi, Z. Effects of chronic noise on glucose metabolism and gut microbiota-host inflammatory homeostasis in rats. Sci. Rep. https://doi.org/10.1038/srep36693 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Campo, J. L., Gil, M. G. & Dávila, S. G. Effects of specific noise and music stimuli on stress and fear levels of laying hens of several breeds. Appl. Anim. Behav. Sci. https://doi.org/10.1016/j.applanim.2004.08.028 (2005).Article 

Google Scholar 
Injaian, A. S., Taff, C. C. & Patricelli, G. L. Experimental anthropogenic noise impacts avian parental behaviour, nestling growth and nestling oxidative stress. Anim. Behav. https://doi.org/10.1016/j.anbehav.2017.12.003 (2018).Article 

Google Scholar 
Cui, B. et al. Effects of chronic noise exposure on the microbiome-gut-brain axis in senescence-accelerated prone mice: Implications for Alzheimer’s disease. J. Neuroinflammation https://doi.org/10.1186/s12974-018-1223-4 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Wei, L. et al. Constant light exposure alters gut microbiota and promotes the progression of steatohepatitis in high fat diet rats. Front. Microbiol. https://doi.org/10.3389/fmicb.2020.01975 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Chatelain, M. et al. Replicated, urban-driven exposure to metallic trace elements in two passerines. Sci. Rep. 11, 1–10 (2021).Article 

Google Scholar 
Chatelain, M. et al. Urban metal pollution explains variation in reproductive outputs in great tits and blue tits. Sci. Total Environ. 776, 145966 (2021).ADS 
CAS 
Article 

Google Scholar 
Rosenfeld, C. S. Gut dysbiosis in animals due to environmental chemical exposures. Front. Cell. Infect. Microbiol. 7, 396 (2017).Article 

Google Scholar 
Sommer, F. & Bäckhed, F. The gut microbiota-masters of host development and physiology. Nat. Rev. Microbiol. https://doi.org/10.1038/nrmicro2974 (2013).Article 
PubMed 

Google Scholar 
Tomiałojć, L. & Wesołowski, T. Diversity of the Białowieza forest avifauna in space and time. J. Ornithol. https://doi.org/10.1007/s10336-003-0017-2 (2004).Article 

Google Scholar 
Corsini, M. et al. Growing in the city: Urban evolutionary ecology of avian growth rates. Evol. Appl. https://doi.org/10.1111/eva.13081 (2021).Article 
PubMed 

Google Scholar 
Teyssier, A., Lens, L., Matthysen, E. & White, J. Dynamics of gut microbiota diversity during the early development of an avian host: Evidence from a cross-foster experiment. Front. Microbiol. https://doi.org/10.3389/fmicb.2018.01524 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Tremblay, I., Thomas, D., Blondel, J., Perret, P. & Lambrechts, M. M. The effect of habitat quality on foraging patterns, provisioning rate and nestling growth in Corsican Blue Tits Parus caeruleus. Ibis (Lond 1859). 147, 17–24 (2005).Article 

Google Scholar 
Corsini, M., Marrot, P. & Szulkin, M. Quantifying human presence in a heterogeneous urban landscape. Behav. Ecol. https://doi.org/10.1093/beheco/arz128 (2019).Article 

Google Scholar 
Corsini, M., Dubiec, A., Marrot, P. & Szulkin, M. Humans and tits in the city: Quantifying the effects of human presence on great tit and blue tit reproductive trait variation. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2017.00082 (2017).Article 

Google Scholar 
Kyba, C. C. M. et al. High-resolution imagery of earth at night: New sources, opportunities and challenges. Remote Sens. https://doi.org/10.3390/rs70100001 (2015).Article 

Google Scholar 
Maraci, Ö. et al. The gut microbial composition is species-specific and individual-specific in two species of estrildid finches, the Bengalese finch and the zebra finch. Front. Microbiol. https://doi.org/10.3389/fmicb.2021.619141 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Engel, K. et al. Individual- and species-specific skin microbiomes in three different estrildid finch species revealed by 16S amplicon sequencing. Microb. Ecol. https://doi.org/10.1007/s00248-017-1130-8 (2017).Article 
PubMed 

Google Scholar 
Magoč, T. & Salzberg, S. L. FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics https://doi.org/10.1093/bioinformatics/btr507 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal https://doi.org/10.14806/ej.17.1.200 (2011).Article 

Google Scholar 
Schloss, P. D. et al. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. https://doi.org/10.1128/AEM.01541-09 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics https://doi.org/10.1093/bioinformatics/btq461 (2010).Article 
PubMed 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. https://doi.org/10.1093/nar/gks1219 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2020).
Google Scholar 
Clarke, K. R., Gorley, R., Somerfield, P. & Warwick, R. Change in Marine Communities: an Approach to Statistical Analysis and Interpretation 3rd edn (Prim. Plymouth, 2014).Shannon, C. E. The mathematical theory of communication. MD Comput. https://doi.org/10.2307/410457 (1997).Article 
PubMed 

Google Scholar 
Faith, D. P. Conservation evaluation and phylogenetic diversity. Biol. Conserv. https://doi.org/10.1016/0006-3207(92)91201-3 (1992).Article 

Google Scholar 
Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
Fox, J. et al. The car Package. R (2012).Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. https://doi.org/10.1111/j.2041-210x.2009.00001.x (2010).Article 

Google Scholar 
DHARMa: Residual diagnostics for hierarchical (multi-level/mixed) regression models. https://cran.r-project.org/web/packages/DHARMa/vignettes/DHARMa.html.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).Book 

Google Scholar 
McMurdie, P. J. & Holmes, S. Phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE https://doi.org/10.1371/journal.pone.0061217 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B https://doi.org/10.1111/j.2517-6161.1995.tb02031.x (1995).Article 
MATH 

Google Scholar 
Whittaker, R. H. Vegetation of the Siskiyou mountains Oregon and California. Ecol. Monogr. https://doi.org/10.2307/1948435 (1960).Article 

Google Scholar 
Paulson, J. metagenomeSeq: Statistical analysis for sparse high-throughput sequencing. Bioconductor.Jp (2014).Bray, J. R. & Curtis, J. T. An ordination of the upland forest communities of southern Wisconsin. Ecol. Monogr. https://doi.org/10.2307/1942268 (1957).Article 

Google Scholar 
Lozupone, C. A., Hamady, M., Kelley, S. T. & Knight, R. Quantitative and qualitative β diversity measures lead to different insights into factors that structure microbial communities. Appl. Environ. Microbiol. https://doi.org/10.1128/AEM.01996-06 (2007).Article 
PubMed 
PubMed Central 

Google Scholar 
Oksanen, J. et al. Package ‘vegan’ Title Community Ecology Package Version 2.5-6. cran.ism.ac.jp (2019).Anderson, M. J. & Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral Ecol. https://doi.org/10.1046/j.1442-9993.2001.01070.x (2001).Article 

Google Scholar 
Clarke, K. R. & Ainsworth, M. A method of linking multivariate community structure to environmental variables. Mar. Ecol. Prog. Ser. https://doi.org/10.3354/meps092205 (1993).Article 

Google Scholar 
QGIS Development Team. QGIS Geographic Information System (Open Source Geospatial Foundation, 2019).
Google Scholar  More