Ecology

Shah, S. et al. Ethno botanical study of medicinal plants of district charsadda, Khyber Pakhtoonkhwa, Pakistan. Int. J. Herb. Med. 8, 67–75 (2020).
Google Scholar 
Hu, R., Lin, C., Xu, W., Liu, Y. & Long, C. Ethnobotanical study on medicinal plants used by Mulam people in Guangxi, China. J. Ethnobiol. Ethnomed. 16, 1–50 (2020).Article 

Google Scholar 
Kooti, W. et al. Effective medicinal plant in cancer treatment, part 2: Review study. J. Evid. Based Complem. Altern. Med. 22, 982–995 (2017).CAS 
Article 

Google Scholar 
Mazalovska, M. & Kouokam, J. C. Transiently expressed mistletoe lectin ii in nicotiana benthamiana demonstrates anticancer activity in vitro. Molecules 25, 2562 (2020).CAS 
PubMed Central 
Article 

Google Scholar 
Shukla, S. & Mehta, A. Anticancer potential of medicinal plants and their phytochemicals: A review. Rev. Bras. Bot. 38, 199–210 (2015).Article 

Google Scholar 
Shaikh, A. M., Shrivastava, B., Apte, K. G. & Navale, S. D. Medicinal plants as potential source of anticancer agents: A review. J. Pharmacogn. Phytochem. APT Res. Found. 5, 291–295 (2016).CAS 

Google Scholar 
Iqbal, J. et al. Plant-derived anticancer agents: A green anticancer approach. Asian Pac. J. Trop. Biomed. 7, 1129–1150 (2017).Article 

Google Scholar 
Zuber, D. Biological flora of Central Europe: Viscum album L. Flora 199, 181–203 (2004).Article 

Google Scholar 
Bar-Sela, G. White-Berry Mistletoe (Viscum album L.) as complementary treatment in cancer: Does it help?. Eur. J. Integr. Med. 3, e55–e62 (2011).Article 

Google Scholar 
Vicaş, S. I., Ruginǎ, D. & Socaciu, C. Comparative study about antioxidant activities of viscum album from different host trees, harvested in different seasons. J. Med. Plants Res. 5, 2237–2244 (2011).
Google Scholar 
Gastauer, M. & Meira-Neto, J. A. A. Updated angiosperm family tree for analyzing phylogenetic diversity and community structure. Acta Bot. Brasilica 31, 191–198 (2017).Article 

Google Scholar 
Varga, I. et al. Changes in the distribution of European mistletoe (Viscum album) in hungary during the last hundred years. Folia Geobot. 49, 559–577 (2014).Article 

Google Scholar 
Lech, P., Żółciak, A. & Hildebrand, R. Occurrence of european mistletoe (Viscum album l.) On forest trees in poland and its dynamics of spread in the period 2008–2018. Forests 11, 83 (2020).Article 

Google Scholar 
Büssing, A., Suzart, K. & Schweizer, K. Differences in the apoptosis-inducing properties of Viscum album L. extracts. Anticancer. Drugs 8, S9–S14 (1997).PubMed 
Article 

Google Scholar 
Maier, G. & Fiebig, H. H. Absence of tumor growth stimulation in a panel of 16 human tumor cell lines by mistletoe extracts in vitro. Anticancer. Drugs 13, 373–379 (2002).CAS 
PubMed 
Article 

Google Scholar 
Orhan, D. D., Aslan, M., Sendogdu, N., Ergun, F. & Yesilada, E. Evaluation of the hypoglycemic effect and antioxidant activity of three Viscum album subspecies (European mistletoe) in streptozotocin-diabetic rats. J. Ethnopharmacol. 98, 95–102 (2005).PubMed 
Article 

Google Scholar 
Ofem, O. E. et al. Effect of crude aqueous leaf extract of Viscum album (mistletoe) in hypertensive rats. Indian J. Pharmacol. 39, 15–19 (2007).Article 

Google Scholar 
Gupta, G. et al. Sedative, antiepileptic and antipsychotic effects of Viscum album L. (Loranthaceae) in mice and rats. J. Ethnopharmacol. 141, 810–816 (2012).CAS 
PubMed 
Article 

Google Scholar 
Orhue, P. O., Edomwande, E. C., Igbinosa, E. & Al, E. Antibacterial activity of extracts of mistletoe (Tapinanthus dodoneifollus (dc) dancer) from Cocoa tree (Theobrama cacao). Int. J. Herbs Pharmacol. Res. 3, 24–29 (2014).
Google Scholar 
Karagöz, A., Önay, E., Arda, N. & Kuru, A. Antiviral potency of mistletoe (Viscum album ssp. album) extracts against human parainfluenza virus type 2 in Vero cells. Phyther. Res. 17, 560–562 (2003).Article 

Google Scholar 
Thronicke, A., Schad, F., Debus, M., Grabowski, J. & Soldner, G. Viscum album L. therapy in oncology—An update on current evidence. Complement. Med. Res. https://doi.org/10.1159/000524184 (2022).Article 
PubMed 

Google Scholar 
Lavastre, V., Cavalli, H., Ratthe, C. & Girard, D. Anti-inflammatory effect of Viscum album agglutinin-I (VAA-I): Induction of apoptosis in activated neutrophils and inhibition of lipopolysaccharide-induced neutrophilic inflammation in vivo. Clin. Exp. Immunol. 137, 272–278 (2004).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ćebović, T., Spasić, S. & Popović, M. Cytotoxic effects of the Viscum album L. extract on ehrlich tumour cells in vivo. Phyther. Res. 22, 1097–1103 (2008).Article 
CAS 

Google Scholar 
Tröger, W. et al. Viscum album [L.] extract therapy in patients with locally advanced or metastatic pancreatic cancer: A randomised clinical trial on overall survival. Eur. J. Cancer 49, 3788–3797 (2013).PubMed 
Article 

Google Scholar 
Ostermann, T. et al. A Systematic review and meta-analysis on the survival of cancer patients treated with a fermented Viscum album L. extract (Iscador): An update of findings. Complement. Med. Res. 27(260), 271 (2020).
Google Scholar 
Loef, M. & Walach, H. Quality of life in cancer patients treated with mistletoe: A systematic review and meta-analysis. BMC Complement. Med. Ther. 20, 227 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Szurpnicka, A., Kowalczuk, A. & Szterk, A. Biological activity of mistletoe: In vitro and in vivo studies and mechanisms of action. Arch. Pharm. Res. 43, 593–629 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kim, S., Kim, K.-C. & Lee, C. Mistletoe (Viscum album) extract targets Axl to suppress cell proliferation and overcome cisplatin- and erlotinib-resistance in non-small cell lung cancer cells. Phytomedicine 36, 183–193 (2017).CAS 
PubMed 
Article 

Google Scholar 
Urech, K. & Baumgartner, S. Chemical constituents of Viscum album L.: Implications for the pharmaceutical preparation of mistletoe. Transl. Res. Biomed. 4(11), 23 (2015).
Google Scholar 
Franz, H., Ziska, P. & Kindt, A. Isolation and properties of three lectins from mistletoe (Viscum album L.). Biochem. J. 195, 481–484 (1981).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ochocka, J. R. & Piotrowski, A. Biologically active compounds from European mistletoe (Viscum album L.). Can. J. Plant Pathol. 24, 21–28 (2002).CAS 
Article 

Google Scholar 
Hajtó, T. et al. Oncopharmacological perspectives of a plant lectin (Viscum album agglutinin-I): Overview of recent results from in vitro experiments and in vivo animal models, and their possible relevance for clinical applications. Evid. Based Complement. Altern. Med. 2, 59–67 (2005).Article 

Google Scholar 
Nazaruk, J. & Orlikowski, P. Phytochemical profile and therapeutic potential of Viscum album L. Nat. Prod. Res. 30, 373–385 (2016).CAS 
PubMed 
Article 

Google Scholar 
Samuelsson, G. Mistletoe toxins. Syst. Zool. 22, 566–569 (1973).CAS 
Article 

Google Scholar 
Debreczeni, J. É., Girmann, B., Zeeck, A., Krätzner, R. & Sheldrick, G. M. Structure of viscotoxin A3: Disulfide location from weak SAD data. Acta Crystallogr. Sect. D Biol. Crystallogr. 59, 2125–2132 (2003).Article 
CAS 

Google Scholar 
Parsakhoo, A. & Jalilvand, H. Effects of ironwood (Parrotia persica c A Meyer ) leaf litter on forest soil nutrients content. Am. J. Agric. Environ. Sci. 5, 244–249 (2009).CAS 

Google Scholar 
Hosseini, S. M. Inscription of the Hyrcanian forests on the UNESCO world heritage list nomination file, UNESCO, 492. Available at: https://whc.unesco.org/en/list/1584/documents/ (2019).Hosseini, S. M. et al. The effects of Viscum album L. on foliar weight and nutrients content of host trees in Caspian forests (Iran). Polish J. Ecol. 55, 579–583 (2007).MathSciNet 
CAS 

Google Scholar 
Sefidi, K., Marvie Mohadjer, M. R., Etemad, V. & Copenheaver, C. A. Stand characteristics and distribution of a relict population of persian ironwood (Parrotia persica CA Meyer) in northern Iran. Flora Morphol. Distrib. Funct. Ecol. Plants 206, 418–422 (2011).Article 

Google Scholar 
Cărăbuş, M. C., Leinemann, L., Curtu, A. L. & Şofletea, N. Preliminary results on the genetic diversity of Carpinus betulus in Carpathian populations. Bull. Transilv. Univ. Brasov, Ser. II For Wood Ind. Agric. Food Eng. 8, 1–6 (2015).
Google Scholar 
Barbasz, A., Kreczmer, B., Rudolphi-Skorska, E. & Sieprawska, A. Biologically active substances in plant extracts from mistletoe Viscum album and trees: fir (Abies alba Mill.), pine (Pinus sylvestris L.) and yew (Taxus baccata L.). Herba Pol. 58, 16–26 (2012).
Google Scholar 
Wójciak-Kosior, M. et al. Evaluation of seasonal changes of triterpenic acid contents in viscum album from different host trees. Pharm. Biol. 55, 1–4 (2017).PubMed 
Article 
CAS 

Google Scholar 
Stefanucci, A. et al. Viscum album L. homogenizer-assisted and ultrasound-assisted extracts as potential sources of bioactive compounds. J. Food Biochem. 44, 1–12 (2020).Article 
CAS 

Google Scholar 
Urech, K., Schaller, G. & Jäggy, C. Viscotoxins, mistletoe lectins and their isoforms in mistletoe (Viscum album L.) extracts Iscador: Analytical results on pharmaceutical processing of mistletoe. Drug Res. 56, 428–434 (2006).CAS 

Google Scholar 
Soursouri, A., Hosseini, S. M. & Fattahi, F. Biochemical analysis of European mistletoe (Viscum album L.) foliage and fruit settled on Persian ironwood (Parrotia persica C. A. Mey) and hornbeam (Carpinus betulus L.). Biocatal. Agric. Biotechnol. 22, 101360 (2019).Article 

Google Scholar 
Önay-Uçar, E., Karagöz, A. & Arda, N. Antioxidant activity of Viscum album ssp. album. Fitoterapia 77, 556–560 (2006).PubMed 
Article 

Google Scholar 
Simona, V., Rugina, D. & Socaciu, C. Antioxidant activities of Viscum album’s leaves from various host trees. Bull. Univ Agric. Sci. Vet. Med. Cluj Napoca Agric. 65, 327–332 (2008).
Google Scholar 
Schaller, G., Urech, K., Grazi, G. & Giannattasio, M. Viscotoxin composition of the three European subspecies of Viscum album. Planta Med. 64, 677–678 (1998).CAS 
PubMed 
Article 

Google Scholar 
Holandino, C. et al. Phytochemical analysis and in vitro anti-proliferative activity of Viscum album ethanolic extracts. BMC Complement. Med. Ther. 20, 215 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zuber, D. & Widmer, A. Phylogeography and host race differentiation in the European mistletoe (Viscum album L.). Mol. Ecol. 18, 1946–1962 (2009).CAS 
PubMed 
Article 

Google Scholar 
Schaller, G., Urech, K. & Giannattasio, M. Cytotoxicity of different viscotoxins and extracts from the European subspecies of Viscum album L. Phyther. Res. 10, 473–477 (1996).CAS 
Article 

Google Scholar 
Eggenschwiler, J. et al. Mistletoe lectin is not the only cytotoxic component in fermented preparations of Viscum album from white fir (Abies pectinata). BMC Complement. Altern. Med. 7, 14 (2007).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Jaggy, C., Musielski, H., Urech, K. & Schaller, G. Quantitative determination of lectins in mistletoe preparations. Arzneimittel-Forschung/Drug Res. 45, 905–909 (1995).CAS 

Google Scholar  More

Liu, H. et al. Optimal nitrogen input for higher efficiency and lower environmental impacts of winter wheat production in China. Agr. Ecosyst. Environ. 224, 1–11 (2016).Article 

Google Scholar 
Guang-hao, L., Gui-gen, C., Wei-ping, L. & Da-lei, L. Differences of yield and nitrogen use efficiency under different applications of slow-release fertilizer in spring maize. J. Integr. Agric. 20(2), 554–564 (2021).Article 

Google Scholar 
Kumar, V. V. Role of Rhizospheric Microbes in Soil 377–398 (Springer, 2018).Book 

Google Scholar 
Ullah, A. et al. Factors affecting the adoption of organic farming in Peshawar-Pakistan. Agric. Sci. 6(06), 587–593 (2015).
Google Scholar 
Cui, Z. et al. Pursuing sustainable productivity with millions of smallholder farmers. Nature 555(7696), 363–366 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Zhang, X. et al. Managing nitrogen for sustainable development. Nature 528(7580), 51–59 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Alzaidi, A. A., Baig, M. B. & Elhag, E. A. An investigation into the farmers ’ attitudes towards organic farming in Riyadh Region–Kingdom of Saudi Arabia. Bulg. J. Agric. Sci. 19(3), 426–431 (2013).
Google Scholar 
Zhihui, W. et al. Combined applications of nitrogen and phosphorus fertilizers with manure increase maize yield and nutrient uptake via stimulating root growth in a long-term experiment. Pedosphere 26(1), 62–73 (2016).Article 
CAS 

Google Scholar 
Guang-hao, L., Gui-gen, C., Wei-ping, L. & Da-lei, L. Differences of yield and nitrogen use efficiency under different applications of slow release fertilizer in spring maize. J. Integr. Agric. 20(2), 554–564 (2020).
Google Scholar 
Zant, W. Is organic fertilizer going to be helpful in bringing a green revolution to sub-Saharan Africa? Economic explorations for Malawi agriculture (Working Paper). International House Hold Survey Network (2010).Barman, M., Paul, S., Choudhury, A. G., Roy, P. & Sen, J. Biofertilizer as prospective input for sustainable agriculture in India. Int. J. Curr. Microbiol. App. Sci. 6(11), 1177–1186 (2017).Article 

Google Scholar 
Kalhapure, A. H., Shete, B. T. & Dhonde, M. B. Integrated nutrient management in maize (Zea Mays L.) for increasing production with sustainability. Int. J. Agric. Food Sci. Technol. 4(3), 2249–3050 (2013).
Google Scholar 
Nazli, R. I., Kuşvuran, A., Inal, I., Demirbaş, A. & Tansi, V. Effects of different organic materials on forage yield and quality of silage maize (Zea mays L.). Turk. J. Agric. For. 38(1), 23–31 (2014).CAS 
Article 

Google Scholar 
Niu, Z. et al. Total factor productivity growth in china’s corn farming: an application of generalized productivity indicator. J. Bus. Econ. Manag. 22(5), 1189–1208 (2021).Article 

Google Scholar 
van Wesenbeeck, C. F. A., Keyzer, M. A., van Veen, W. C. M. & Qiu, H. Can China’s overuse of fertilizer be reduced without threatening food security and farm incomes?. Agric. Syst. 190, 103093 (2021).Article 

Google Scholar 
Ji, Y., Liu, H. & Shi, Y. Will China’s fertilizer use continue to decline? Evidence from LMDI analysis based on crops, regions and fertilizer types. PLoS ONE 15, e0237234 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jiao, X. et al. Grain production versus resource and environmental costs: towards increasing sustainability of nutrient use in China. J. Exp. Bot. 67(17), 4935–4949 (2016).CAS 
PubMed 
Article 

Google Scholar 
Sher, A. et al. Response of maize grown under high plant density; performance, issues and management: a critical review. Adv. Crop Sci. Technol. 5(3), 1–8 (2017).Article 

Google Scholar 
De-yang, S. H. I. et al. Increased plant density and reduced N rate lead to more grain yield and higher resource utilization in summer maize. J. Integr. Agric. 15(11), 2515–2528 (2016).Article 

Google Scholar 
Du, X., Wang, Z., Lei, W. & Kong, L. Increased planting density combined with reduced nitrogen rate to achieve high yield in maize. Sci. Rep. 11(1), 1–12 (2021).CAS 
Article 

Google Scholar 
Li, T., Zhang, W., Yin, J., Chadwick, D., Norse, D., Lu, Y., Liu, X., Chen, X., Zhang, F., Powlson, D., & Dou, Z. Enhanced-efficiency fertilizers are not a panacea for resolving the nitrogen problem (2017).Adu-gyamfi, R. et al. One-time fertilizer briquettes application for maize production in savanna agroecologies of Ghana. Soil Fertil. Crop Prod. 111(6), 3339–3350 (2019).CAS 

Google Scholar 
Jiang, C. et al. Optimal nitrogen application rates of one-time root zone fertilization and the effect of reducing nitrogen application on summer maize. Sustainability 11, 2979 (2019).Article 

Google Scholar 
Jiang, C. et al. One-time root-zone N fertilization increases maize yield, NUE and reduces soil N losses in lime concretion black soil. Sci. Rep. 8(1), 1–10 (2018).ADS 

Google Scholar 
Li, G., Zhao, B., Dong, S., Liu, P. & Vyn, T. J. Impact of controlled release urea on maize yield and nitrogen use efficiency under different water conditions. PLoS ONE 12(7), 1–16 (2017).
Google Scholar 
Sikora, J. et al. Assessment of the efficiency of nitrogen slow-release fertilizers in integrated production of carrot depending on fertilization strategy. Sustainability (Switzerland) 12(5), 1–10 (2020).
Google Scholar 
Tian, C. et al. Effects of a controlled-release fertilizer on yield, nutrient uptake, and fertilizer usage efficiency in early ripening rapeseed (Brassica napus L.). J. Zhejian Univ. Sci. B (Biomed. Biotechnol.) 17(14), 775–786 (2016).CAS 
Article 

Google Scholar 
Tong, D. & Xu, R. Effects of urea and ( NH4)2SO4 on nitrification and acidification of Ultisols from Southern China. J. Environ. Sci. 24(4), 682–689 (2012).CAS 
Article 

Google Scholar 
El-rokiek, K. G., Ahmed, S. A. & Abd-elsamad, E. E. H. Effect of adding urea or ammonium sulphate on some herbicides efficiency in controlling weeds in onion plants. J. Am. Sci. 6(11), 536–543 (2010).
Google Scholar 
FAO. Guidelines for soil description. Enhanced Recovery After Surgery, (2006).Landon, J. Booker Tropical Soil manual: A Handbook for Soil Survey and Agriculture Land Evaluation in the Tropics and Subtropics (2013).Zhao, R. F. et al. Fertilization and nitrogen balance in a wheat-maize rotation system in North China. Agron. J. 98(4), 938–945 (2006).CAS 
Article 

Google Scholar 
Huang, S. et al. Estimation of nitrogen supply for summer maize production through a long-term field trial in china. Agronomy 11(7), 1358 (2021).CAS 
Article 

Google Scholar 
Dong, Y. J. et al. Effects of new coated release fertilizer on the growth of maize. J. Soil Sci. Plant Nutr. 16(3), 637–649 (2016).CAS 

Google Scholar 
Ngosong, C., Bongkisheri, V., Tanyi, C. B., Nanganoa, L. T. & Tening, A. S. Optimizing nitrogen fertilization regimes for sustainable maize (Zea mays L.) production on the volcanic soils of Buea Cameroon. Adv. Agric. 2019, 1–8 (2019).
Google Scholar 
Su, W., Ahmad, S., Ahmad, I. & Han, Q. Nitrogen fertilization affects maize grain yield through regulating nitrogen uptake, radiation and water use efficiency, photosynthesis and root distribution. PeerJ 8, 1–21 (2020).CAS 

Google Scholar 
Sainju, U. M, Ghimire, R., & Pradhan, G.P. Nitrogen Fertilization I: Impact on Crop, Soil, and Environment. IntechOpen https://doi.org/10.5772/intechopen.86028 (2020).Sha, Z. et al. Effect of N stabilizers on fertilizer-N fate in the soil-crop system: a meta- analysis. Agr. Ecosyst. Environ. 2020, 290 (2019).
Google Scholar 
Chen, K. & Vyn, T. J. Post-silking factor consequences for N efficiency changes over 38 years of commercial maize hybrids. Front. Plant Sci. https://doi.org/10.3389/fpls.2017.01737 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Jia, X. P. et al. Farmer’s adoption of improved nitrogen management strategies in maize production in China: an experimental knowledge training. J. Integr. Agric. 12(2), 364–373 (2013).Article 

Google Scholar 
Amanullah,. Rate and timing of nitrogen application influence partial factor productivity and agronomic NUE of maize (Zea mays L.) planted at low and high densities on calcareous soil in northwest Pakistan. J. Plant Nutr. 39(5), 683–690 (2016).CAS 
Article 

Google Scholar 
Draman, A., Almas, L. K. Partial factor productivity, agronomic efficiency, and economic analyses of maize in wheat-maize cropping system in Pakistan. Southern Agricultural Economics Association Annual Meetings, 2009 (January 2009).Yan, P. et al. Interaction between plant density and nitrogen management strategy in improving maize grain yield and nitrogen use efficiency on the North China Plain. Agric. Sci. 154, 978–988 (2016).Article 

Google Scholar 
Oenema, O. Nitrogen use efficiency (NUE) an indicator for the utilization of nitrogen in food systems. EU Nitrogen Expert Panel, January 2017, 1–4 (2015).Venterea, R. T., Coulter, J. A. & Dolan, M. S. Evaluation of intensive “4R” strategies for decreasing nitrous oxide emissions and nitrogen surplus in rainfed corn. J. Environ. Qual. 45(4), 1186–1195 (2016).CAS 
PubMed 
Article 

Google Scholar 
Zhang, C., Ju, X., Powlson, D., Oenema, O. & Smith, P. Nitrogen surplus benchmarks for controlling N pollution in the main cropping systems of China. Environ. Sci. Technol. 53(12), 6678–6687 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Fernández, C., Koop, G. & Steel, M. F. J. Multiple-output production with undesirable outputs multiple-output production with undesirable outputs : an application to nitrogen surplus in agriculture. J. Am. Stat. Assoc. 97(458), 432–442 (2013).MATH 
Article 

Google Scholar 
Børsting, C. F., Kristensen, T., Misciattelli, L., Hvelplund, T. & Weisbjerg, M. R. Reducing nitrogen surplus from dairy farms. Effects of feeding and management. Livest. Prod. Sci. 83(2–3), 165–178 (2003).Article 

Google Scholar 
Liang, K. et al. Reducing nitrogen surplus and environmental losses by optimized nitrogen and water management in double rice cropping system of South China. Agric. Ecosyst. Environ. 286, 106680 (2019).CAS 
Article 

Google Scholar 
Klages, S. et al. Nitrogen surplus-a unified indicator for water pollution in Europe?. Water (Switzerland) 12(4), 1197 (2020).CAS 

Google Scholar 
Muratoglu, A. Grey water footprint of agricultural production: an assessment based on nitrogen surplus and high-resolution leaching runoff fractions in Turkey. Sci. Total Environ. 742, 140553 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Niemiec, M. & Komorowska, M. The use of slow-release fertilizers as a part of optimization of celeriac production technology. Agric. Eng. 22(2), 59–68 (2018).
Google Scholar 
Ranum, P., Peña-Rosas, J. P. & Garcia-Casal, M. N. Global maize production, utilization, and consumption. Ann. N. Y. Acad. Sci. 1312(1), 105–112 (2014).ADS 
PubMed 
Article 

Google Scholar 
HLPE. Biofules and food security. High Level Panel of Experts on Food Security and Nutrition of the Committee on World Food Security, Rome (2013).Karp, A., Beale, M. H., Beaudoin, F. & Eastmond, P. J. Growing innovations for the bioeconomy. Nat. Plants https://doi.org/10.1038/nplants.2015.193 (2015).Article 
PubMed 

Google Scholar 
Chavarria, H., Trigo, E., Villarreal, F., Elverdin, P., & Piñeiro, V. Policy brief bioeconomy: a sustainable development strategy task force 10 sustainable energy, water, and food systems. T20, Saudi Arabia (2020). More

Here we have used existing surveillance to detect an emerging wildlife disease and appraise its impact by combining traditional host and vector screening with utilisation of national datasets generated by citizen scientists. Following the detection of USUV in the UK in 20207, whilst national surveillance identified no further cases of USUV infection in wild birds that year, we discovered a significant cluster of blackbird DIRs and an overlapping regional reduction in reported blackbird observations, possibly indicating disease-mediated population decline. Our investigation also identified mosquito vectors at the index site that were positive for USUV RNA, suggesting that ongoing virus transmission was likely.The most prevalent and notable histological changes in the blackbirds and house sparrow with confirmed USUV infection were those in the liver and spleen, consisting of necrosis and lymphohistiocytic inflammation along with moderate to abundant virus antigen labelling. Whilst neurotropism resulting in brain necrosis and lymphohistiocytic inflammation has been reported in studies which examined large numbers of wild blackbirds with USUV infection in continental Europe4,12, we found minimal evidence of neural lesions in the five wild birds examined in this study. Although, histopathological changes in other tissues were generally non-specific, immunolabelling demonstrated widespread virus antigen distribution in both bird species, which is similar to reports of USUV infection elsewhere4,13,14. Immunolabelling was disproportionately greater in the brain and heart in contrast to the minimal or absent histological changes observed in these organs: similar contrasting results of histological and immunohistochemical examinations of USUV-infected wild birds have previously been reported12. Although only brain and kidney samples were examined using USUV RT-PCR, our findings, together with earlier reports4,14, demonstrate that viral antigen can be detected in abundance in the heart and liver, suggesting that these organs could be useful for molecular diagnostic sampling.A differential for necrotising lesions in European passerines, and a comorbidity detected in blackbirds with USUV infection, is Plasmodium spp. infection4,8,15. DNA of the same Plasmodium spp. as detected in the tissues of USUV-positive blackbirds from the ZSL London Zoo site in 2020 was identified in Cx. pipiens s.l. that fed on blackbird hosts at this site previously in 2015, supporting endemic avian haemoparasite infection of this wild bird species at this location. In contrast to the results reported from USUV-positive blackbirds in the Netherlands4, no exo-erythrocytic stages of haemoprotozoa indicative of avian malaria were observed histologically in the two UK blackbirds positive for Plasmodium DNA. Since histological examination has limited sensitivity, in situ hybridisation could be used to further appraise the clinical significance of this co-infection in the future16.Zoological collections are ideally placed to form part of wildlife disease surveillance networks and have already contributed to flavivirus detection in mainland Europe10,13,17,18. The collection grounds at ZSL London Zoo are well monitored for evidence of morbidity or mortality in synanthropic wildlife; this unusually high level of vigilance is considered the likely explanation for detection of USUV at such a location. Recent import of infected captive birds can be excluded as a potential route of USUV introduction as the COVID-19 pandemic had led to suspension of animal movements into the zoological collection. Following USUV detection in synanthropic wildlife, preemptive management practices were employed to safeguard the health of captive animals (Supplementary Materials 1); there was no evidence of USUV-associated disease in the collection animals.The majority of mosquitoes trapped in 2020 were primarily ornithophagic Cx. pipiens s.l., a known vector for USUV1 and a common species in temperate urban habitats. This mosquito species was also the most frequently detected at the ZSL London Zoo site in 2015, during historical trapping sessions19 and at two zoological collections in northern England20. Bloodmeal analyses from mosquitoes at ZSL London Zoo in 2015 and 2020 demonstrate that this species feeds on both wild and collection birds, as would be expected for a generalist ornithophagic mosquito21. In addition, targeted mosquito surveillance in 2020 confirmed circulating USUV in multiple Cx. pipiens s.l. pools at the index site over a three-week period subsequent to the detection of USUV-associated wild bird mortality. This further demonstrates that local mosquito trapping combined with PCR screening is useful as part of an integrated surveillance programme22 and provides evidence that native vectors in the UK may facilitate the onward transmission of USUV to susceptible hosts following an emergence event.Wild bird flavivirus surveillance in Great Britain integrates submissions from three schemes, each with a different taxonomic focus. These convenience samples inevitably lead to skews in species coverage. Although a common garden bird, the number of blackbirds tested for USUV was modest at 2–8 per annum over the period 2012–2019. A communication programme to raise awareness of blackbirds as a sentinel species for USUV, involving a range of stakeholder communities (e.g. non-governmental organisations, wildlife rehabilitators and veterinary surgeons) could help to increase the volume of submissions and, by extension, the ability to rapidly identify the occurrence of USUV in this species. The potential value of target species as sentinels within wild bird surveillance networks has been highlighted for other pathogens, e.g. highly pathogenic H5N1 avian influenza and West Nile virus23,24. In addition to this passive surveillance focused on disease detection in avian hosts, active targeted serosurveys could be conducted to identify cryptic exposure of subclinically affected birds in the future. Given the logistical challenges around active serosurveys in wild birds, screening of archived samples from captive birds in the zoological collection may provide a means to further appraise the extent of USUV circulation, as has previously been undertaken at other collections in mainland Europe25,26.Local reductions of blackbird populations have been reported following USUV outbreaks in mainland Europe27,28,29, but numbers recorded by the BBS have been stable in the UK and Greater London since 2011 when USUV incursion would be predicted most likely to have occurred on the basis of spatio-temporal patterns of spread in mainland Europe3 until the latest data are available from 2019 (Supplementary Figure 5). Whilst our index site detection of USUV is unlikely to represent the incursion event, and earlier sporadic or localised USUV incidents prior to 2020 may have occurred7, based on historical blackbird population trends it seems plausible that the existing surveillance system enabled rapid detection of this emerging infectious disease.Significant clustering of blackbird DIRs was observed in the Greater London, South East and East of England regions in 2020. These results should be interpreted with care given the potential for biases with these opportunistic data and the absence of confirmed aetiology for the DIRs, however, these findings are consistent with a regional increase in blackbird morbidity and mortality in summer 2020 around the USUV index site. Consequently, it is likely that further blackbirds, in addition to those recovered for PME, were infected and died with USUV. Whilst no evidence of an increase in generalised ill health or neurological disease category blackbird DIRs was found in 2020, particular attention should be paid to early detection of clusters of DIRs of these categories as a potential signal of USUV occurrence in the future.One indicator, the dead bird ringed recovery dataset, did not support increased scale of blackbird mortality in Greater London; however, the dataset is small and vulnerable to variation in observer bias (e.g. related to COVID-19 induced lockdown and travel restrictions). In contrast, using the GBW dataset, we identified a substantial seasonal decline in the blackbird weekly reporting rate which was associated with a concomitant reduction in weekly count in gardens, but not in ecologically similar control species, which was contemporaneous with the period of detected USUV activity in Greater London. These population trends are consistent with a hypothesis of disease-mediated decline. Alternative explanations, such as variation in climate, food availability or bird movement need consideration and are discussed next.Exploration of climate data indicates that, whilst the spring and early summer of 2020 was noteworthy with a high daily temperature average and low rainfall, at the time of USUV detection and the decline in the blackbird reporting rate, these parameters were within historical ranges (Supplementary Table 7). Consequently, while the climate may have been permissive for USUV transmission, there is no evidence to support variation in the weather alone as an explanation for the seasonal pattern of blackbird reporting rate decline; nor were declines observed in the robin or starling data, the control species with similar soil invertebrate diet and therefore similar vulnerability to summer drought. Blackbird, robin and starling populations in the UK are partially migratory; however, birds from mainland Europe do not migrate to overwinter in England until mid-October (i.e. after the decline in blackbird reporting rate occurred): consequently international bird movement does not offer an explanation for the observed regional blackbird decline. During the late summer season, short-distance movement from garden to non-garden habitats typically occurs, during the period of moult; however, the extent of the decline in blackbird reporting rate in gardens that occurred in Greater London in 2020 markedly exceeds that of the historical trend (2011–2019 inclusive; BTO unpubl. data). In summary, despite the fact that surveillance did not confirm further cases of wild bird USUV infection in 2020, and whilst it is not possible to ascribe causality, or exclude the chance that other factors may have contributed to the observed population trend, it remains possible that large-scale blackbird mortality due to USUV occurred in Greater London in summer 2020.Our study and others30 illustrate the need to integrate disease surveillance and long-term population monitoring schemes to evaluate disease impact, and to use control species to explore potential confounding drivers of population change (e.g. climate, food availability). Since GBW reporting rates are generated online in real-time, and nationwide, they offer a tool to rapidly detect changes in species presence (i.e. reporting rate) or flock size in gardens (i.e. weekly maximum count) that can be used to strategically enhance surveillance effort for disease detection. As wild bird ring recovery reports are also submitted online, there is also the potential to develop a complementary system that monitors for trends in occurrence of dead birds that might signal a disease outbreak. The BBS survey provides the most robust available data on population trends to appraise disease impact, however there is a delay of some months until data from this scheme become available. Since repeated incursions have occurred in mainland Europe following first detection1,17,31, it is likely that USUV will emerge in the UK again, either through overwintering or repeat incursion(s). Integrated disease surveillance in combination with bird population monitoring using the various available datasets, as we have capitalised on here, is required to assess whether USUV re-occurs, or becomes endemic, in UK wild birds and to identify any associated population impacts.By combining a range of professional and citizen science datasets our study approach facilitates the rapid detection of an emerging disease in free-living wildlife and enables insights into its incipient impact. We believe this multidisciplinary approach presents a framework for the early detection of disease outbreaks and incursion, thus helping to safeguard animal and public health. Such early warning systems could facilitate prompt mitigation action, for example targeted biosecurity measures and enhanced vigilance by medical and veterinary authorities. In addition, there is opportunity to further develop collaboration with ornithologists through active surveillance of wild birds, as was recently employed to detect West Nile virus in a migratory bird in the Netherlands32. Whilst population monitoring schemes are most developed for wild birds, lessons learned may be applied for the surveillance of diseases affecting other taxa. More

The conceptual flow chart of the process is provided in Fig. 1. We used seven reanalysis SM data (Table 2) masked with soil temperature (ST) and soil freeze/thaw status to calculate water table depth, i.e. the input of TOPMODEL, given the obvious disagreements between the input datasets. The diagnostic algorithms based on TOPMODEL were used following Stocker et al. (ref. 20) and Xi et al. (ref. 25), where the optimized parameters were calibrated with long-term maximum wetland areas from four observation-based wetland datasets (Table 1). Details about these datasets and computational processing are shown as follows.Fig. 1Diagram of workflow for parameter calibration and the simulation of global wetland dynamics.Full size imageTable 2 Key characteristics of seven global soil moisture reanalysis data used in this study.Full size tableReanalysis soil moisture datasetsSeven long-term reanalysis SM datasets used in this study include NCEP-DOE (National Centers for Environmental Prediction-the Department of Energy)26, MERRA-Land (the Modern-Era Retrospective Analysis for Research and Applications)27, MERRA-2 (ref. 28), GLDAS-Noah v2.0 (the Global Land Data Assimilation System)29, GLDAS-Noah v2.1 (ref. 29), ERA5 (European Environment Agency)30,31, and ERA5-Land30,31. Key characteristics of the seven SM data are listed in Table 2. The datasets differ by their spatial and temporal resolutions, the time-period they cover, as well as the definition of the soil layers. More details are provided for each dataset below.

NCEP-DOE
NCEP-DOE is an updated version of the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) Reanalysis 1 project, which uses a state-of-the-art analysis/forecast system to perform data assimilation with past data from 1948 to the present32. NCEP-DOE features the newer physics and observed SM forcing and also eliminates several previous errors, such as oceanic albedo and snowmelt term during the entire period, and snow cover analysis error from 1974 to 1994 (ref. 26). With a spatial resolution of about 210 km, there are two vertical soil layers in NCEP-DOE for both SM and ST: 0–0.1 and 0.1–2 m.

MERRA-Land and MERRA-2
MERRA-Land soil moisture is generated by driving the Goddard Earth Observing System model version 5.7.2 (GEOS-5.7.2) with meteorological forcing from the MERRA reanalysis product27. The precipitation forcing in MERRA-Land merges MERRA precipitation with a gauge-based data product from the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center, and the Catchment land surface model used in MERRA-Land is updated to the “Fortuna-2.5” version27. MERRA-2 intends to replace the original MERRA reanalysis and ingests important new data types28. The Catchment model in MERRA-2 has been updated with rainfall interception and snow model parameters of MERRA-Land, and the precipitation correction is a refined version of MERRA-Land. For MERRA-Land and MERRA-2, there is only one layer for SM from the surface to the bedrock, with “depth-to-rock” depending on local conditions. ST is computed on six vertical soil layers: 0–0.10, 0.10–0.29, 0.29–0.68, 0.68–1.44, 1.44–2.95, and 2.95–12.95 m.

ERA5 and ERA5-Land
ERA5 is the fifth generation ECMWF (European Centre for Medium-Range Weather Forecasts) reanalysis of global climate and weather, replacing ERA-Interim30,31. Based on a decade of developments in model dynamics and data assimilation, there is a significantly enhanced horizontal resolution (31 km), temporal resolution (hourly) and uncertainty estimation. ERA5 covers 1979–2020 and continues to be updated in near-real-time. ERA5-Land is produced with a finer horizontal resolution of 9 km by running the land component of the ERA5 climate reanalysis but without data assimilation. By March of 2021, the ERA5-Land outputs are only available since 1981. SM and ST are computed on four vertical soil layers (0–0.07, 0.07–0.28, 0.28–1, and 1–2.89 m) for both ERA5 and ERA5-Land.

GLDAS-Noah v2.0 and GLDAS-Noah v2.1

GLDAS is a global, moderate-resolution (0.25° × 0.25°) offline terrestrial modeling system developed by NASA Goddard Space Flight Center (GSFC) and the NOAA National Centers for Environmental Prediction29, thus similar to ERA5. To produce optimal fields of land surface variables in near-real-time, it incorporates satellite- and ground-based observations. GLDAS-Noah drives the Noah land surface model and has two components: one forced with the Princeton meteorological forcing data (i.e. GLDAS-Noah v2.0) and the other forced with a combination of model and observation (i.e. GLDAS-Noah v2.1). GLDAS-Noah v2.0 covers the period 1948–2014, while GLDAS-Noah v2.1 is available from 2000 to the present. There are four vertical layers in the Noah land surface model for both ST and SM: 0–0.1, 0.1–0.4, 0.4–1, and 1–2 m.Observation-based wetland/flooded area dataIn terms of large uncertainties in current wetland datasets (Table 1) we selected four widely used and available satellite/satellite-based wetland/flooded area data including GIEMS-2 (ref. 14), RFW (the Regularly Flooded Wetland map)10, WAD2M (a global dataset of Wetland Area and Dynamics for Methane Modeling)33, and G2017 (the pantropical wetland extent from an expert system model)9 for parameter calibration. Among them, GIEMS-2 and WAD2M include monthly wetland dynamics, while RFW and G2017 are static. The comparison of the four wetland datasets is shown in Supplementary Fig. 1; details on each data are provided below.

GIEMS-2
The GIEMS-1 is the first global estimate of monthly inundated areas, derived from passive microwave land surface emissivity34. With a 0.25° × 0.25° resolution, GIEMS-1 documents a mean annual maximum inundated area of 9.5 Mkm2 for 1993–2007 (including open water, wetlands, and rice paddies, but excluding large lakes), which shows good agreement with existing independent, static inventories as well as regional high-resolution synthetic aperture radar observations34. Based on similar retrieval principles with GIEMS-1, GIEMS-2 is developed to less depend on ancillary data with an updated microwave emissivity, and correct a known overestimation over low vegetated areas from GIEMS-1 (ref. 14). The period is extended to 1992–2015 for GIEMS-2 and can be updated with the availability of observations. Globally, the mean annual maximum and long-term maximum inundated extent after removing the rice paddies using the Monthly Irrigated and Rainfed Crop Areas dataset (MIRCA2000)35 for the period 1992–2015 are 6.7 and 10.9 million km2 (hereafter Mkm2; sum of mean annual maximum or long-term maximum inundated extent for each grid cell) respectively. The rice paddies are removed here as they are not natural wetlands and cannot be simulated with TOPMODEL.

RFW
RFW is a static, high-resolution map (15 arc-sec) of regularly flooded wetlands, developed by overlapping flooded areas (permanent wetlands and flooded vegetation classes) for 2008–2012 from the ESA-CCI land cover map36, mean annual maximum inundated areas (including wetlands, rivers, small lakes, and irrigated rice) for 1993–2004 from GIEMS-D15 global inundation extent (downscaled using GIEMS-1)37, and long-term maximum surface water areas for 1984–2015 from JRC global surface water bodies product13. The large permanent lakes and reservoirs are distinguished using the HydroLAKES database38. Globally, RFW covers 9.7% of the land surface area (~13.0 Mkm2) including wetlands, river channels, deltas, and flooded lake margins, but excluding large lakes10. Due to the mean annual maximum or long-term maximum inundation/surface water extent for 1984–2016 from the three wetland data is used, we treated RFW as the long-term maximum wetland extent in this study. Besides, given that GIEMS-D15 includes artificial rice paddies, we removed them with MIRCA2000 from RFW (~11.9 Mkm2 after removing rice paddies).

WAD2M
WAD2M dataset used in this study is an improved version of the SWAMPS v3.2 from Jensen et al. (ref. 15), covering the years 2000 to 2018. With a spatial resolution of 25 km × 25 km, this data was used as input wetland area data of phase 2 of the Global Methane Budget33. Given that the initial SWAMPS failed to detect wetlands lacking surface inundation and to differentiate between lakes, wetlands, and other surface water bodies, Zhang et al. (ref. 33) modified it using a series of independent static wetland distribution data7,9,39,40,41 in an attempt to include missing wetlands under dense canopies. Besides, they removed inland waters (lakes, rivers, and ponds) and rice agriculture with JRC and MIRCA2000, respectively. Globally, the mean annual maximum and long-term maximum wetland extent for the period 2000–2018 estimated by WAD2M are 8.1 Mkm2 and 13.2 Mkm2 (sum of mean annual maximum or long-term maximum inundated extent for each grid cell) respectively.

G2017

G2017 (ref. 9) is a static, pantropical wetland and peatland extent map (covering 60°S–40°N) at 232 m × 232 m resolution, derived from a hybrid expert model system. With three biophysical indices related to wetland and peat formation (long-term water supply exceeding atmospheric water demand, annually or seasonally waterlogged soils, and geomorphological position where water is supplied and retained), G2017 identifies not only permanently and seasonally wetland areas, but also soil wetness and topographic conditions that favor waterlogging in the absence of flooding for the end of the 20th century. Given the broad coverage of different types of wetlands, we also treated this map as long-term maximum wetland areas. This ‘pantropical’ data (60°S to 40°N) offers the advantage to include non-flooded wetland areas that are missed in satellite-based wetland products. However, note that not all detected wetlands or peatlands in G2017 have been observed. Rice agriculture was also removed with MIRCA2000 from G2017. The resulting wetland and peatland area for 60°S–40°N is 4.0 Mkm2.The TOPMODEL-based diagnostic modelTOPMODEL as improved by Stocker et al. (ref. 20) and Xi et al. (ref. 25) was used to calculate the inundated fraction from WTD at grid-scale in this study. Based on the assumptions that the local hydraulic gradient is approximated by the local topographic slope and the water table variations can be assimilated to a succession of steady states with uniform recharge, the classical TOPMODEL establishes an analytical relationship between the soil moisture deficit and the distributions of local topographic index within a catchment. At grid-scale, the analytical relationship can be represented as:$$CT{I}_{i}-overline{CT{I}_{x}}=mathrm{-M}left({{Gamma }}_{i}-overline{{{Gamma }}_{x}}right)$$
(1)
where CTI indicates the topographic index, defined as the log of the ratio of contributing area to the local slope. We used the CTI data at 500 m × 500 m resolution from Marthews et al. (ref. 22), where lakes, reservoirs, mountain glaciers, and ice caps are removed using the Global Lakes and Wetlands Database7. The (overline{CT{I}_{x}}) indicates the average of CTIi of all sub-grids (index i) within the grid cell x. M indicates a tunable parameter that describes the exponential decrease of soil transmissivity with depth21. Γi is the water table of the pixel i and (overline{{{Gamma }}_{{x}}}) is the mean water table of the grid x. When Γi is at the soil surface (i.e. Γi = 0), the threshold (CT{I}_{x}^{* }) above which all pixels are flooded for the grid x is derived:$$CT{I}_{x}^{* }=overline{CT{I}_{x}}+{rm{M}}cdot overline{{{Gamma }}_{x}}$$
(2)
The wetlands area is defined as the flooded areas (i.e. Γ ≤ 0), the flooded fraction in the grid x (fx) being the percentage of pixels with CTIi larger than a threshold (CT{I}_{x}^{* }):$${f}_{x}=frac{1}{{A}_{x}}{sum }_{i}{A}_{i}^{* }$$with$${A}_{i}^{* }=left{begin{array}{c}{A}_{i},if,CT{I}_{i}ge CT{I}_{x}^{* }\ 0,if,CT{I}_{i} < CT{I}_{x}^{* }end{array}right.$$ (3) To reduce the computational costs from the high-resolution CTI data for predicting long time series of wetland area, we used the asymmetric sigmoid function from Stocker et al. (ref. 20) to fit the “empirical” relationship (widehat{{Psi }}) between (widehat{f}) and Γ:$${{rm{psi }}}_{x}left({{Gamma }}_{x}right)={left(1+{v}_{x}cdot {e}^{-{k}_{x}left({{Gamma }}_{x}-{q}_{x}right)}right)}^{-1/{v}_{x}}$$ (4) where vx, kx, qx are three parameters of the function. Given a value of parameter M, the three parameters can be derived with a sequence of Γx spanning a plausible range of values (−1 m to 2 m) and corresponding fx from the initial TOPMODEL approach (Eq. (3)). Thus, the wetlands in our study are defined as the flooded area simulated by TOPMODEL. As for the range of parameter M, Stocker et al. (ref. 20) used a global uniform value for M (M = 8) after testing simulated wetland fraction for a range of M (7, 8, 9). Nevertheless, given that distinct topography, soil types, and other intrinsic characteristics in different regions, we considered M as a tunable, spatially heterogeneous, and grid-specific parameter, with a range of 1–15 following Xi et al. (ref. 25). Thus, for each grid cell x there are 15 choices for M, and then 15 sets of (vx, kx, qx). The optimized parameter combination of (vx, kx, qx) is determined by selecting minimum root-mean-square-error (RMSE) between simulated inundated fractions and observations:$$RMSE=sqrt{frac{{sum }_{i=1}^{n}{left({O}_{i}-{P}_{i}right)}^{2}}{n}}$$ (5) where Oi and Pi are observed and simulated wetland fraction, respectively. n represents the time-series length for wetland extent. For simulations calibrated with RFW and G2017, the RMSE was computed with the long-term maximum (hereafter called MAX) monthly wetland area because the two data sets are static and only record the MAX wetland extent. While for simulations calibrated with GIEMS-2 and WAD2M which include temporal variations of wetland area, we calibrated the parameters with all months, mean seasonal cycle, yearly maximum, and MAX wetland area, but only showed the optimal simulations calibrated with MAX wetland area in this work to keep consistency with RFW and G2017. Besides, to provide more choices for users, we combined all of the four wetland datasets (i.e. the union of long-term maximum wetland extent) to generate a new wetland map (hereafter called MAX_all), and then used the MAX_all to calibrate the parameters to produce seven sets of global wetland extent products with seven soil moisture datasets. The simulations calibrated with yearly maximum wetland area from GIEMS-2 and WAD2M and long-term maximum wetland area from MAX_all are also provided in our resulting products.Finally, to avoid unrealistically high wetland fraction output from the function, the simulated maximum wetland fraction fx is constrained by the observed MAX wetland area with a parameter ({f}_{x}^{max}) (Eq. (6)), which is different from Stocker et al. (ref. 20). The determination of ({f}_{x}^{max}) is analyzed in the supplemental material in detail (Supplementary Text 1). Once the value of (vx, kx, qx) are determined, the wetland fraction fx can be directly derived from the monthly water table Γx according to Eqs. (4) and (6).$${f}_{x}=minleft({{Psi }}_{x}left({{Gamma }}_{x}right),{f}_{x}^{max}right)$$ (6) Calculation of water table depthWater table depth is not computed by land surface models, given their coarse soil vertical discretization. We thus used the saturation deficit of soil moisture (θSD) as a surrogate of water table depth, θSD being defined as an index consisting of saturated volumetric water content and the “actual” soil depth modified by soil freeze/thaw status:$${theta }_{SD}={z}_{{l}_{0}}-{sum }_{l=1}^{{l}_{0}}{theta }_{l}cdot frac{Delta {z}_{l}}{{theta }_{S}}$$ (7) Subscript l represents the lth soil layer, l0 is the number of layers above the first frozen soil layer counted from the top (l = 1 at the soil surface), θl is the monthly volumetric water content in the lth soil layer (m3 m−3), (Delta {z}_{l}) is the thickness of the lth soil layer (m), θS is the saturated volumetric water content (in m3 m−3 units, uniform over depth).As formulated in Eq. (7), ({z}_{{l}_{0}}) is the thickness of all soil layers (or depth to bedrock) when there is no frozen soil layer. If there exists at least one frozen layer, ({z}_{{l}_{0}}) is set to the depth of the uppermost frozen soil layer. We excluded the frozen soil layers here given that some important wetland processes such as methane production and transport are insignificant when the soils are frozen. In high latitudes, the presence of frozen soil layers may lead to an overestimation of the wetland fraction due to relatively large θSD values even if there is little liquid soil water above the uppermost frozen soil layer. Hence, we used monthly soil temperature (ST) at 70 cm, the Global Record of Daily Landscape Freeze/Thaw Status data42, and the Köppen climate classification system43 to refine the frozen mask. When the monthly mean ST at 70 cm is below 0 °C, or soil freezing days are more than 5 in a month, or the grid is classified as the Hot desert (BWh) in the Köppen climate classification system, the wetland fraction for the grid is set to zero. However, it should be noted that the algorithm using the ST at 70 cm could omit some unfrozen soil layers above 70 cm, which could lead to bias in estimation of methane emissions from these unfrozen layers. We provided the global wetland maps in our resulting products, but the potential uncertainties in wetland estimation due to the omitted unfrozen layers should be considered, particularly at high latitudes. We used seven reanalysis SM products to compute θSD to provide the uncertainty in SM input (Table 2). All data are re-interpolated to 0.25° × 0.25° resolution.Evaluation against wetland calibration data and independent satellite productsAlthough we calibrated parameters of the TOPMODEL-based diagnostic model with the observation-based wetland data, to what extent the simulations can reproduce the spatial patterns and temporal dynamics of the calibration wetland data must be evaluated. For spatial patterns, we calculated the RMSE of wetland area between our simulations and corresponding wetland calibration data following Eq. (5), and evaluated the spatial patterns of simulated wetland extent in two wetland hotspots including Amazon basin and Western Siberia lowlands with three independent global/regional water products. For Amazon basin, we used the global surface water dataset from JRC13 (optical satellite images) and the wetland map produced using mosaics of Japanese Earth Resources Satellite (JERS-1) L-band SAR imagery from Hess et al. (ref. 44, hereafter H2015). For West Siberian lowlands, we used JRC and the Boreal–Arctic Wetland and Lake Dataset (BAWLD, only covers the north of ~55°N) produced using an expert assessment and extrapolated using random forest modelling from climate, topography, soils, permafrost conditions, vegetation, wetlands, and surface water extents and dynamics45. For temporal dynamics, since we only used the static wetland area (long-term maximum) from all of the four observation-based wetland products to calibrate parameters, the simulated temporal dynamics can be evaluated with the two dynamic wetland products (GIEMS-2 and WAD2M). Besides, we also used the terrestrial water storage (TWS) from the Gravity Recovery and Climate Experiment (GRACE), which retrieves relative change in TWS from the monthly anomalies of the Earth’s gravity field for 2003–2016 measured by the twin GRACE satellites46,47 to evaluate the simulated temporal dynamics. More

Blosser, E. M. et al. Environmental drivers of seasonal patterns of host utilization by Culiseta melanura (Diptera: Culicidae) in Florida. J. Med. Entomol. 54, 1365–1374 (2017).PubMed 
PubMed Central 

Google Scholar 
Burkett-Cadena, N. D., Hassan, H. K., Eubanks, M. D., Cupp, E. W. & Unnasch, T. R. Winter severity predicts the timing of host shifts in the mosquito Culex erraticus. Biol. Lett. 8, 567–569 (2012).PubMed 
PubMed Central 

Google Scholar 
Gürtler, R. E., Cecere, M. C., Vazquez, D. P., Chuit, R. & Cohen, J. E. Host-feeding patterns of domiciliary Triatoma infestans (Hemiptera: Reduviidae) in northwest Argentina: Seasonal and instar variation. J. Med. Entomol. 33, 15–26 (1996).PubMed 

Google Scholar 
Rabinovich, J. E. et al. Ecological patterns of blood-feeding by kissing-bugs (Hemiptera: Reduviidae: Triatominae). Mem. Inst. Oswaldo Cruz 106, 479–494 (2011).PubMed 

Google Scholar 
Kent, R. J. Molecular methods for arthropod bloodmeal identification and applications to ecological and vector-borne disease studies. Mol. Ecol. Resour. 9, 4–18 (2009).CAS 
PubMed 

Google Scholar 
Cecere, M. C. et al. Host-feeding sources and infection with Trypanosoma cruzi of Triatoma infestans and Triatoma eratyrusiformis (Hemiptera: Reduviidae) from the Calchaqui Valleys in Northwestern Argentina. J. Med. Entomol. 53, 666–673 (2016).CAS 
PubMed 

Google Scholar 
Logue, K. et al. Unbiased characterization of Anopheles mosquito blood meals by targeted high-throughput sequencing. PLoS Negl. Trop. Dis. 10, e0004512 (2016).PubMed 
PubMed Central 

Google Scholar 
Keller, J. I., Schmidt, J. O., Schmoker, A. M., Ballif, B. A. & Stevens, L. Protein mass spectrometry extends temporal blood meal detection over polymerase chain reaction in mouse-fed Chagas disease vectors. Mem. Inst. Oswaldo Cruz 113, e180160 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Borland, E. M. & Kading, R. C. Modernizing the toolkit for arthropod bloodmeal identification. Insects 12, 1–27 (2021).
Google Scholar 
Clark, K., Karsch-Mizrachi, I., Lipman, D. J., Ostell, J. & Sayers, E. W. GenBank. Nucleic Acids Res. 44, D67–D72 (2016).CAS 
PubMed 

Google Scholar 
Hamer, S. A. et al. Comparison of DNA and carbon and nitrogen stable isotope-based techniques for identification of prior vertebrate hosts of ticks. J. Med. Entomol. 52, 1043–1049 (2015).CAS 
PubMed 

Google Scholar 
Scott, M. C., Harmon, J. R., Tsao, J. I., Jones, C. J. & Hickling, G. J. Reverse line blot probe design and polymerase chain reaction optimization for bloodmeal analysis of ticks from the eastern United States. J. Med. Entomol. 49, 697–709 (2012).CAS 
PubMed 

Google Scholar 
Arias-Giraldo, L. M. et al. Identification of blood-feeding sources in Panstrongylus, Psammolestes, Rhodnius and Triatoma using amplicon-based next-generation sequencing. Parasit. Vectors 13, 1–14 (2020).
Google Scholar 
Kieran, T. J. et al. Blood meal source characterization using Illumina sequencing in the Chagas Disease vector Rhodnius pallescens (Hemiptera: Reduviidae) in Panamá. J. Med. Entomol. https://doi.org/10.1093/jme/tjx170 (2017).Article 
PubMed 

Google Scholar 
Dumonteil, E. et al. Detailed ecological associations of triatomines revealed by metabarcoding and next-generation sequencing: Implications for triatomine behavior and Trypanosoma cruzi transmission cycles. Sci. Rep. 8, 1–13 (2018).CAS 

Google Scholar 
Estrada-Franco, J. G. et al. Vertebrate-Aedes aegypti and Culex quinquefasciatus (Diptera)-arbovirus transmission networks: Non-human feeding revealed by meta-barcoding and nextgeneration sequencing. PLoS Negl. Trop. Dis. 14, 1–22 (2020).
Google Scholar 
Campana, M. G. et al. Simultaneous identification of host, ectoparasite and pathogen DNA via in-solution capture. Mol. Ecol. Resour. 16, 1224–1239 (2016).CAS 
PubMed 

Google Scholar 
Klotz, S. A. et al. Free-roaming kissing bugs, vectors of Chagas disease, feed often on humans in the Southwest. Am. J. Med. 127, 421–426 (2014).PubMed 
PubMed Central 

Google Scholar 
Waleckx, E., Suarez, J., Richards, B. & Dorn, P. L. Triatoma sanguisuga blood meals and potential for Chagas Disease, Louisiana, USA. Emerg. Infect. Dis. 20, 2141–2143 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kjos, S. A. et al. Identification of bloodmeal sources and Trypanosoma cruzi infection in triatomine bugs (Hemiptera: Reduviidae) from residential settings in Texas, the United States. J. Med. Entomol. 50, 1126–1139 (2013).PubMed 

Google Scholar 
Gürtler, R. E., Cohen, J. E., Cecere, M. C. & Chuit, R. Shifting host choices of the vector of Chagas Disease, Triatoma Infestans, in relation to the availability of host in houses in North-West Argentina. J. Appl. Ecol. 34, 699–715 (1997).
Google Scholar 
Minuzzi-Souza, T. et al. Molecular bloodmeal analyses reveal that Trypanosoma cruzi-infected, native triatomine bugs often feed on humans in houses in central Brazil. Med. Vet. Entomol. 32, 504–508 (2018).CAS 
PubMed 

Google Scholar 
Lent, H. & Wygodzinsky, P. W. Revision of the Triatominae (Hemiptera, Reduviidae), and their significance as vectors of Chagas’ Disease. Bull. Am. Museum Nat. Hist. 163, 123–520 (1979).
Google Scholar 
World Health Organization. Chagas disease in Latin America: An epidemiological update based on 2010 estimates. Wkly. Epidemiol. Rec. 6, 33–44 (2015).
Google Scholar 
Dorn, P. L. et al. Autochthonous transmission of Trypanosoma cruzi, Louisiana. Emerg. Infect. Dis. 13, 13–15 (2007).
Google Scholar 
Cantey, P. T. et al. The United States Trypanosoma cruzi infection study: Evidence for vector-borne transmission of the parasite that causes Chagas disease among United States blood donors. Transfusion 52, 1922–1930 (2012).PubMed 

Google Scholar 
Garcia, M. N. et al. Molecular identification and genotyping of Trypanosoma cruzi DNA in autochthonous Chagas disease patients from Texas, USA. Infect. Genet. Evol. 49, 151–156 (2017).CAS 
PubMed 

Google Scholar 
Barr, S., Gossett, K. A. & Klei, T. R. Clinical, clinicopathologic, and parasitologic observations of trypanosomiasis in dogs infected with North American Trypanosoma cruzi isolates. Am. J. Vet. Res. 52, 954–960 (1991).CAS 
PubMed 

Google Scholar 
Meyers, A. C., Meinders, M. & Hamer, S. A. Widespread Trypanosoma cruzi infection in government working dogs along the Texas-Mexico border: Discordant serology, parasite genotyping and associated vectors. PLoS Negl. Trop. Dis. 11, 1–19 (2017).
Google Scholar 
Meyers, A. C., Edwards, E. E., Sanders, J. P., Saunders, A. B. & Hamer, S. A. Fatal Chagas myocarditis in government working dogs in the southern United States: Cross-reactivity and differential diagnoses in five cases across six months. Vet. Parasitol. Reg. Stud. Rep. 24, 1–7 (2021).
Google Scholar 
Hodo, C. L. & Hamer, S. A. Toward an ecological framework for assessing reservoirs of vector-borne pathogens: Wildlife reservoirs of Trypanosoma cruzi across the southern United States. ILAR J. 58, 379–392 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Guarneri, A. A., Pereira, M. H. & Diotaiuti, L. Influence of the blood meal source on the development of Triatoma infestans, Triatoma brasiliensis, Triatoma sordida, and Triatoma pseudomaculata (Heteroptera, Reduviidae). J. Med. Entomol. 37, 373–379 (2000).CAS 
PubMed 

Google Scholar 
Pippin, W. F. The biology and vector capability of Triatoma sanguisuga texana Usinger and Triatoma gerstaeckeri (Stål) compared with Rhodnius prolixus (Stål) (Hemiptera: Triatominae). J. Med. Entomol. 7, 30–45 (1970).CAS 
PubMed 

Google Scholar 
Bern, C., Kjos, S., Yabsley, M. J. & Montgomery, S. P. Trypanosoma cruzi and Chagas’ disease in the United States. Clin. Microbiol. Rev. 24, 655–681 (2011).PubMed 
PubMed Central 

Google Scholar 
Kjos, S. et al. Distribution and characterization of canine Chagas disease in Texas. Vet. Parasitol. 152, 249–256 (2008).CAS 
PubMed 

Google Scholar 
Tenney, T. D., Curtis-Robles, R., Snowden, K. F. & Hamer, S. A. Shelter dogs as sentinels for Trypanosoma cruzi transmission across Texas. Emerg. Infect. Dis. 20, 1323–1326 (2014).PubMed 
PubMed Central 

Google Scholar 
Curtis-Robles, R., Wozniak, E. J., Auckland, L. D., Hamer, G. L. & Hamer, S. A. Combining public health education and disease ecology research: Using citizen science to assess Chagas disease entomological risk in Texas. PLoS Negl. Trop. Dis. 9, e0004235 (2015).PubMed 
PubMed Central 

Google Scholar 
Curtis-Robles, R., Hamer, S. A., Lane, S., Levy, M. Z. & Hamer, G. L. Bionomics and spatial distribution of triatomine vectors of Trypanosoma cruzi in Texas and other southern states, USA. Am. J. Trop. Med. Hyg. 98, 113–121 (2018).PubMed 

Google Scholar 
Curtis-Robles, R., Aukland, L. D., Snowden, K. F., Hamer, G. L. & Hamer, S. A. Analysis of over 1500 triatomine vectors from across the US, predominantly Texas, for Trypanosoma cruzi infection and discrete typing units. Infect. Genet. Evol. 58, 171–180 (2018).PubMed 

Google Scholar 
Hodo, C. L., Wilkerson, G. K., Birkner, E. C., Gray, S. B. & Hamer, S. A. Trypanosoma cruzi transmission among captive nonhuman primates, wildlife, and vectors. EcoHealth 15, 426–436 (2018).PubMed 
PubMed Central 

Google Scholar 
Duffy, T. et al. Analytical performance of a multiplex Real-Time PCR assay using TaqMan probes for quantification of Trypanosoma cruzi satellite DNA in blood samples. PLoS Negl. Trop. Dis. 7, e2000 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Piron, M. et al. Development of a real-time PCR assay for Trypanosoma cruzi detection in blood samples. Acta Trop. 103, 195–200 (2007).CAS 
PubMed 

Google Scholar 
Curtis-Robles, R. et al. Parasitic interactions among Trypanosoma cruzi, triatomine vectors, domestic animals, and wildlife in Big Bend National Park along the Texas-Mexico border. Acta Trop. 188, 225–233 (2018).PubMed 

Google Scholar 
Cupp, E. W. et al. Identification of reptilian and amphibian blood meals from mosquitoes in an eastern equine encephalomyelitis virus focus in central Alabama. Am. J. Trop. Med. Hyg. 71, 272–276 (2004).PubMed 

Google Scholar 
Medeiros, M. C. I., Ricklefs, R. E., Brawn, J. D. & Hamer, G. L. Plasmodium prevalence across avian host species is positively associated with exposure to mosquito vectors. Parasitology 142, 1612–1620 (2015).CAS 
PubMed 

Google Scholar 
Hamer, G. L. et al. Host selection by Culex pipiens mosquitoes and West Nile virus amplification. Am. J. Trop. Med. Hyg. 80, 268–278 (2009).PubMed 

Google Scholar 
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).CAS 
PubMed 

Google Scholar 
Hathaway, N. J., Parobek, C. M., Juliano, J. J. & Bailey, J. A. SeekDeep: Single-base resolution de novo clustering for amplicon deep sequencing. Nucleic Acids Res. 46, e21 (2018).CAS 
PubMed 

Google Scholar 
Zeledón, R. et al. An Appraisal of the Status of Chagas Disease in the United States (Elsevier Inc., Amsterdam, 2012).
Google Scholar 
Gorchakov, R. et al. Trypanosoma cruzi infection prevalence and bloodmeal analysis in triatomine vectors of Chagas disease from rural peridomestic locations in Texas, 2013–2014. J. Med. Entomol. 53, 911–918 (2016).CAS 
PubMed 

Google Scholar 
Stevens, L. et al. Vector blood meals and Chagas Disease transmission potential, United States. Emerg. Infect. Dis. 18, 646–650 (2012).PubMed 
PubMed Central 

Google Scholar 
Polonio, R., López-Domínguez, J., Herrera, C. & Dumonteil, E. Molecular ecology of Triatoma dimidiata in southern Belize reveals risk for human infection and the local differentiation of Trypanosoma cruzi parasites. Int. J. Infect. Dis. 108, 320–329 (2021).CAS 
PubMed 

Google Scholar 
Sasaki, H., Rosales, R. & Tabaru, Y. Host feeding profiles of Rhodnius prolixus and Triatoma dimidiata in Guatemala (Hemiptera: Reduviidae: Triatominae). Med. Entomol. Zool. 54, 283–289 (2003).
Google Scholar 
Villalobos, G., Martínez-Hernández, F., de la Torre, P., Laclette, J. P. & Espinoza, B. Entomological indices, feeding sources, and molecular identification of Triatoma phyllosoma (Hemiptera: Reduviidae) one of the main vectors of Chagas disease in the Istmo de Tehuantepec, Oaxaca, Mexico. Am. J. Trop. Med. Hyg. 85, 490–497 (2011).PubMed 
PubMed Central 

Google Scholar 
Mota, J. et al. Identification of blood meal source and infection with Trypanosoma cruzi of Chagas disease vectors using a multiplex cytochrome b polymerase chain reaction assay. Vector Borne Zoonotic Dis. 7, 617–627 (2007).PubMed 

Google Scholar 
Pizarro, J. C. & Stevens, L. A new method for forensic DNA analysis of the blood meal in Chagas disease vectors demonstrated using Triatoma infestans from Chuquisaca, Bolivia. PLoS ONE 3, e3585 (2008).ADS 
PubMed 
PubMed Central 

Google Scholar 
Abad-Franch, F. & Gurgel-Gonçalves, R. The ecology and natural history of wild triatominae in the Americas. In Triatominae—The Biology of Chagas Disease Vectors (eds Guarneri, A. & Lorenzo, M.) 387–445 (Springer Nature Switzerland AG, 2021). https://doi.org/10.1007/978-3-030-64548-9_16.Chapter 

Google Scholar 
Busselman, R. E. & Hamer, S. A. Chagas disease ecology in the United States: Recent advances in understanding Trypanosoma cruzi transmission among triatomines, wildlife, and domestic animals and a quantitative synthesis of vector-host interactions. Annu. Rev. Anim. Biosci. 10, 325–348 (2022).PubMed 

Google Scholar 
Minuzzi-Souza, T. T. C. et al. Vector-borne transmission of Trypanosoma cruzi among captive Neotropical primates in a Brazilian zoo. Parasit. Vectors 9, 1–7. https://doi.org/10.1186/s13071-016-1334-7 (2016).CAS 
Article 

Google Scholar 
Reis, F. C. et al. Trypanosomatid infections in captive wild mammals and potential vectors at the Brasilia Zoo, Federal District, Brazil. Vet. Med. Sci. 6, 248–256. https://doi.org/10.1002/vms3.216 (2020).CAS 
Article 
PubMed 

Google Scholar 
Martínez-Hernández, F., Oria-Martínez, B., Rendón-Franco, E., Villalobos, G. & Muñoz-García, C. I. Trypanosoma cruzi, beyond the dogma of non-infection in birds. Infect. Genet. Evol. https://doi.org/10.1016/j.meegid.2022.105239 (2022).Article 
PubMed 

Google Scholar 
Botto-Mahan, C. et al. Lizards as silent hosts of Trypanosoma cruzi. Emerg. Infect. Dis. 28, 1250–1253. https://doi.org/10.3201/eid2806.220079 (2022).Article 
PubMed 
PubMed Central 

Google Scholar  More

Allen, R. G. et al. Crop evapotranspiration-guidelines for computing crop water requirements-FAO irrigation and drainage paper 56. FAO, Rome 300, D05109 http://www.fao.org/docrep/X0490E/X0490E00.htm (1998).
Google Scholar 
Trenberth, K. E., Fasullo, J. T. & Kiehl, J. Earth’s global energy budget. Bulletin of the American Meteorological Society 90, 311–324, https://doi.org/10.1175/2008BAMS2634.1 (2009).ADS 
Article 

Google Scholar 
Wang, K. & Dickinson, R. E. A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability. Reviews of Geophysics 50, https://doi.org/10.1029/2011RG000373 (2012).Liu, W. Evaluating remotely sensed monthly evapotranspiration against water balance estimates at basin scale in the Tibetan Plateau. Hydrology Research 49, 1977–1990, https://doi.org/10.2166/nh.2018.008 (2018).Article 

Google Scholar 
Valipour, M., Bateni, S. M., Gholami Sefidkouhi, M. A., Raeini-Sarjaz, M. & Singh, V. P. Complexity of forces driving trend of reference evapotranspiration and signals of climate change. Atmosphere 11, 1081, https://doi.org/10.1007/s41748-021-00252-3 (2020).ADS 
Article 

Google Scholar 
Anwar, S. A., Mamadou, O., Diallo, I. & Sylla, M. B. On the influence of vegetation cover changes and vegetation-runoff systems on the simulated summer potential evapotranspiration of Tropical Africa using RegCM4. Earth Systems and Environment 5, 883–897, https://doi.org/10.3390/atmos11101081 (2021).ADS 
Article 

Google Scholar 
Tomas-Burguera, M., Vicente-Serrano, S. M., Beguera, S., Reig, F. & Latorre, B. Reference crop evapotranspiration database in Spain (1961–2014). Earth System Science Data 11, 1917–1930, https://doi.org/10.5194/essd-11-1917-2019 (2019).ADS 
Article 

Google Scholar 
Córdova, M., Carrillo-Rojas, G., Crespo, P., Wilcox, B. & Célleri, R. Evaluation of the Penman-Monteith (FAO 56 PM) Method for Calculating Reference Evapotranspiration Using Limited Data. Mountain Research and Development 35, 230–239, https://doi.org/10.1659/MRD-JOURNAL-D-14-0024.1 (2015).Article 

Google Scholar 
Valle Júnior, L. C. G. d. et al. Evaluation of FAO-56 procedures for estimating reference evapotranspiration using missing climatic data for a Brazilian tropical savanna. Water 13, 1763, https://doi.org/10.3390/w13131763 (2021).Article 

Google Scholar 
Djaman, K., Irmak, S. & Futakuchi, K. Daily reference evapotranspiration estimation under limited data in Eastern Africa. Journal of Irrigation and Drainage Engineering 143, 06016015, https://doi.org/10.1061/(ASCE)IR.1943-4774.0001154 (2017).Article 

Google Scholar 
Čadro, S., Uzunović, M., Žurovec, J. & Žurovec, O. Validation and calibration of various reference evapotranspiration alternative methods under the climate conditions of Bosnia and Herzegovina. International Soil and Water Conservation Research 5, 309–324, https://doi.org/10.1016/j.iswcr.2017.07.002 (2017).Article 

Google Scholar 
Vicente-Serrano, S. M. et al. Recent changes in monthly surface air temperature over Peru, 1964–2014. International Journal of Climatology 38, 283–306, https://doi.org/10.1002/joc.5176 (2018).ADS 
Article 

Google Scholar 
Huerta, A., Aybar, C. & Lavado-Casimiro, W. PISCO temperatura versión 1.1 (PISCOt v1. 1). Lima, Peru: National Meteorology and Hydrology Service of Peru (SENAMHI) https://iridl.ldeo.columbia.edu/SOURCES/.SENAMHI/.HSR/.PISCO/.Temp/ (2018).Lavado Casimiro, W. S., Labat, D., Guyot, J. L. & Ardoin-Bardin, S. Assessment of climate change impacts on the hydrology of the Peruvian Amazon–Andes basin. Hydrological Processes 25, 3721–3734, https://doi.org/10.1002/hyp.8097 (2011).ADS 
Article 

Google Scholar 
Rau, P. et al. Assessing multidecadal runoff (1970–2010) using regional hydrological modelling under data and water scarcity conditions in Peruvian Pacific catchments. Hydrological Processes 33, 20–35, https://doi.org/10.1002/hyp.13318 (2019).ADS 
Article 

Google Scholar 
Olsson, T. et al. Downscaling climate projections for the Peruvian coastal Chancay-Huaral basin to support river discharge modeling with WEAP. Journal of Hydrology: Regional Studies 13, 26–42, https://doi.org/10.1016/j.ejrh.2017.05.011 (2017).Article 

Google Scholar 
Lavado-Casimiro, W., Lhomme, J. P., Labat, D. & Loup, J. Estimating reference evapotranspiration (FAO 56 Penman Monteith) with limited climatic data in the Peruvian Amazon-Andes basin. Revista Peruana Geo-Atmosferica 4, 31–43 (2015).
Google Scholar 
Hargreaves, G. H. & Samani, Z. A. Reference crop evapotranspiration from temperature. Applied engineering in agriculture 1, 96–99, https://doi.org/10.13031/2013.26773 (1985).Article 

Google Scholar 
Laqui, W. et al. Can artificial neural networks estimate potential evapotranspiration in Peruvian highlands? Modeling Earth Systems and Environment 5, 1911–1924, https://doi.org/10.1007/s40808-019-00647-2 (2019).Article 

Google Scholar 
Baigorria, G. A., Villegas, E. B., Trebejo, I., Carlos, J. F. & Quiroz, R. Atmospheric transmissivity: distribution and empirical estimation around the central Andes. International Journal of Climatology 24, 1121–1136, https://doi.org/10.1002/joc.1060 (2004).ADS 
Article 

Google Scholar 
Huerta, A. PISCO potential evapotranspiration, https://iridl.ldeo.columbia.edu/SOURCES/.SENAMHI/.HSR/.PISCO/.PET/.Oudin, L., Michel, C. & Anctil, F. Which potential evapotranspiration input for a lumped rainfall-runoff model?: Part 1—can rainfall-runoff models effectively handle detailed potential evapotranspiration inputs? Journal of Hydrology 303, 275–289, https://doi.org/10.1016/j.jhydrol.2004.08.025 (2005).ADS 
Article 

Google Scholar 
Xiang, K., Li, Y., Horton, R. & Feng, H. Similarity and difference of potential evapotranspiration and reference crop evapotranspiration – a review. Agricultural Water Management 232, 106043, https://doi.org/10.1016/j.agwat.2020.106043 (2020).Article 

Google Scholar 
Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Scientific data 7, 1–18, https://doi.org/10.1038/s41597-020-0453-3 (2020).Article 

Google Scholar 
Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Scientific data 5, 1–12, https://doi.org/10.1038/sdata.2017.191 (2018).Article 

Google Scholar 
Kalnay, E. et al. The NCEP/NCAR 40-year reanalysis project. Bulletin of the American Meteorological Society 77, 437–472, 10.1175/1520-0477(1996)077  2.0.CO;2 (1996).Hersbach, H. et al. The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society 146, 1999–2049, https://doi.org/10.1002/qj.3803 (2020).ADS 
Article 

Google Scholar 
Muñoz Sabater, J. et al. ERA5-Land: a state-of-the-art global reanalysis dataset for land applications. Earth System Science Data 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021 (2021).ADS 
Article 

Google Scholar 
Aybar, C. et al. Construction of a high-resolution gridded rainfall dataset for Peru from 1981 to the present day. Hydrological Sciences Journal 65, 770–785, https://doi.org/10.1080/02626667.2019.1649411 (2020).Article 

Google Scholar 
Llauca, H., Lavado-Casimiro, W., Montesinos, C., Santini, W. & Rau, P. PISCO_HyM_GR2M: A model of monthly water balance in Peru (1981–2020). Water 13, 1048, https://doi.org/10.3390/w13081048 (2021).Article 

Google Scholar 
Gubler, S. et al. The influence of station density on climate data homogenization. International Journal of Climatology 37, 4670–4683, https://doi.org/10.1002/joc.5114 (2017).ADS 
Article 

Google Scholar 
Hunziker, S. et al. Identifying, attributing, and overcoming common data quality issues of manned station observations. International Journal of Climatology 37, 4131–4145, https://doi.org/10.1002/joc.5037 (2017).ADS 
Article 

Google Scholar 
Hunziker, S. et al. Effects of undetected data quality issues on climatological analyses. Climate of the Past 14, 1–20, https://doi.org/10.5194/cp-14-1-2018 (2018).ADS 
Article 

Google Scholar 
Paredes, P., Pereira, L., Almorox, J. & Darouich, H. Reference grass evapotranspiration with reduced data sets: Parameterization of the FAO Penman-Monteith temperature approach and the Hargeaves-Samani equation using local climatic variables. Agricultural Water Management 240, 106210, https://doi.org/10.1016/j.agwat.2020.106210 (2020).Article 

Google Scholar 
Irmak, S., Kabenge, I., Skaggs, K. E. & Mutiibwa, D. Trend and magnitude of changes in climate variables and reference evapotranspiration over 116-yr period in the Platte River Basin, central Nebraska–USA. Journal of Hydrology 420, 228–244, https://doi.org/10.1016/j.jhydrol.2011.12.006 (2012).ADS 
Article 

Google Scholar 
Tomas-Burguera, M., Vicente-Serrano, S. M., Grimalt, M. & Beguera, S. Accuracy of reference evapotranspiration (ETo) estimates under data scarcity scenarios in the Iberian Peninsula. Agricultural water management 182, 103–116, https://doi.org/10.1016/j.agwat.2016.12.013 (2017).Article 

Google Scholar 
Mardikis, M., Kalivas, D. & Kollias, V. Comparison of interpolation methods for the prediction of reference evapotranspiration—an application in Greece. Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) 19, 251–278, https://doi.org/10.1007/s11269-005-3179-2 (2005).Article 

Google Scholar 
McVicar, T. R. et al. Spatially distributing monthly reference evapotranspiration and pan evaporation considering topographic influences. Journal of Hydrology 338, 196–220, https://doi.org/10.1016/j.jhydrol.2007.02.018 (2007).ADS 
Article 

Google Scholar 
Robinson, E. L., Blyth, E. M., Clark, D. B., Finch, J. & Rudd, A. C. Trends in atmospheric evaporative demand in Great Britain using high-resolution meteorological data. Hydrology and Earth System Sciences 21, 1189–1224, https://doi.org/10.5194/hess-21-1189-2017 (2017).ADS 
Article 

Google Scholar 
Mohammadi, B. & Moazenzadeh, R. Performance analysis of daily global solar radiation models in Peru by regression analysis. Atmosphere 12, https://doi.org/10.3390/atmos12030389 (2021).Vicente-Serrano, S. M., Beguería, S., López-Moreno, J. I., García-Vera, M. A. & Stepanek, P. A complete daily precipitation database for northeast Spain: reconstruction, quality control, and homogeneity. International Journal of Climatology 30, 1146–1163, https://doi.org/10.1002/joc.1850 (2010).ADS 
Article 

Google Scholar 
Lanzante, J. R. Resistant, robust and non-parametric techniques for the analysis of climate data: Theory and examples, including applications to historical radiosonde station data. International Journal of Climatology: A Journal of the Royal Meteorological Society 16, 1197–1226, 10.1002/(SICI)1097-0088(199611)16:11 < 1197::AID-JOC89 > 3.0.CO;2-L (1996).ADS 
Article 

Google Scholar 
Wood, W. H., Marshall, S. J., Whitehead, T. L. & Fargey, S. E. Daily temperature records from a mesonet in the foothills of the Canadian Rocky Mountains, 2005–2010. Earth System Science Data 10, 595–607, https://doi.org/10.5194/essd-10-595-2018 (2018).ADS 
Article 

Google Scholar 
Guentchev, G., Barsugli, J. J. & Eischeid, J. Homogeneity of gridded precipitation datasets for the Colorado River basin. Journal of Applied Meteorology and Climatology 49, 2404–2415, https://doi.org/10.1175/2010JAMC2484.1 (2010).ADS 
Article 

Google Scholar 
Oyler, J. W., Ballantyne, A., Jencso, K., Sweet, M. & Running, S. W. Creating a topoclimatic daily air temperature dataset for the conterminous United States using homogenized station data and remotely sensed land skin temperature. International Journal of Climatology 35, 2258–2279, https://doi.org/10.1002/joc.4127 (2015).ADS 
Article 

Google Scholar 
McAfee, S. A., McCabe, G. J., Gray, S. T. & Pederson, G. T. Changing station coverage impacts temperature trends in the Upper Colorado River basin. International Journal of Climatology 39, 1517–1538, https://doi.org/10.1002/joc.5898 (2019).ADS 
Article 

Google Scholar 
Beguería, S., Vicente-Serrano, S. M., Tomás-Burguera, M. & Maneta, M. Bias in the variance of gridded data sets leads to misleading conclusions about changes in climate variability. International Journal of Climatology 36, 3413–3422, https://doi.org/10.1002/joc.4561 (2016).ADS 
Article 

Google Scholar 
Thevakaran, A. & Sonnadara, D. Estimating missing daily temperature extremes in Jaffna, Sri Lanka. Theoretical and applied climatology 132, 145–152, https://doi.org/10.1007/s00704-017-2082-0 (2018).ADS 
Article 

Google Scholar 
Hubbard, K. Spatial variability of daily weather variables in the high plains of the USA. Agricultural and Forest Meteorology 68, 29–41, https://doi.org/10.1016/0168-1923(94)90067-1 (1994).ADS 
Article 

Google Scholar 
Camargo, M. B. & Hubbard, K. G. Spatial and temporal variability of daily weather variables in sub-humid and semi-arid areas of the United States high plains. Agricultural and forest meteorology 93, 141–148, https://doi.org/10.1016/S0168-1923(98)00122-1 (1999).ADS 
Article 

Google Scholar 
Brugnara, Y., Good, E., Squintu, A. A., van der Schrier, G. & Brönnimann, S. The EUSTACE global land station daily air temperature dataset. Geoscience Data Journal 6, 189–204, https://doi.org/10.5285/7925ded722d743fa8259a93acc7073f2 (2019).ADS 
Article 

Google Scholar 
Gonzalez-Hidalgo, J. C., Peña-Angulo, D., Brunetti, M. & Cortesi, N. MOTEDAS: a new monthly temperature database for mainland Spain and the trend in temperature (1951–2010). International Journal of Climatology 35, 4444–4463, https://doi.org/10.1002/joc.4298 (2015).ADS 
Article 

Google Scholar 
Gudmundsson, L., Bremnes, J. B., Haugen, J. E. & Engen-Skaugen, T. Technical note: Downscaling RCM precipitation to the station scale using statistical transformations – a comparison of methods. Hydrology and Earth System Sciences 16, 3383–3390, https://doi.org/10.5194/hess-16-3383-2012 (2012).ADS 
Article 

Google Scholar 
Stanley, T., Kirschbaum, D. B., Huffman, G. J. & Adler, R. F. Approximating long-term statistics early in the global precipitation measurement era. Earth Interactions 21, 1–10, https://doi.org/10.1175/EI-D-16-0025.1 (2017).Article 

Google Scholar 
Haimberger, L. Homogenization of radiosonde temperature time series using innovation statistics. Journal of Climate 20, 1377–1403, https://doi.org/10.1175/JCLI4050.1 (2007).ADS 
Article 

Google Scholar 
Menne, M. J. & Williams, C. N. Homogenization of temperature series via pairwise comparisons. Journal of Climate 22, 1700–1717, https://doi.org/10.1175/2008JCLI2263.1 (2009).ADS 
Article 

Google Scholar 
Vincent, L. A., Zhang, X., Bonsal, B. R. & Hogg, W. D. Homogenization of daily temperatures over Canada. Journal of Climate 15, 1322–1334, 10.1175/1520-0442(2002)015 < 1322:HODTOC > 2.0.CO;2 (2002).ADS 
Article 

Google Scholar 
Jin, M. & Dickinson, R. E. Land surface skin temperature climatology: Benefitting from the strengths of satellite observations. Environmental Research Letters 5, 044004, https://doi.org/10.1088/1748-9326/5/4/044004 (2010).ADS 
Article 

Google Scholar 
Wan, Z., Hook, S. & Hulley, G. MOD11A2 MODIS/Terra land surface temperature/emissivity 8-day l3 global 1 km SIN grid v006. NASA EOSDIS Land Processes DAAC 10, https://doi.org/10.5067/MODIS/MOD11A2.006 (2015).Wilson, A. M. & Jetz, W. Remotely sensed high-resolution global cloud dynamics for predicting ecosystem and biodiversity distributions. PLoS biology 14, e1002415, https://doi.org/10.1371/journal.pbio.1002415 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Danielson, J. J. & Gesch, D. B. Global multi-resolution terrain elevation data 2010 (GMTED2010) (US Department of the Interior, US Geological Survey Washington, DC, USA, 2011).Holden, Z. A., Abatzoglou, J. T., Luce, C. H. & Baggett, L. S. Empirical downscaling of daily minimum air temperature at very fine resolutions in complex terrain. Agricultural and Forest Meteorology 151, 1066–1073, https://doi.org/10.1016/j.agrformet.2011.03.011 (2011).ADS 
Article 

Google Scholar 
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International journal of climatology 37, 4302–4315, https://doi.org/10.1002/joc.5086 (2017).ADS 
Article 

Google Scholar 
Willmott, C. J. & Robeson, S. M. Climatologically aided interpolation (CAI) of terrestrial air temperature. International Journal of Climatology 15, 221–229, https://doi.org/10.1002/joc.3370150207 (1995).ADS 
Article 

Google Scholar 
Hunter, R. D. & Meentemeyer, R. K. Climatologically aided mapping of daily precipitation and temperature. Journal of Applied Meteorology 44, 1501–1510, https://doi.org/10.1175/JAM2295.1 (2005).ADS 
Article 

Google Scholar 
Parmentier, B. et al. Using multi-timescale methods and satellite-derived land surface temperature for the interpolation of daily maximum air temperature in Oregon. International Journal of Climatology 35, 3862–3878, https://doi.org/10.1002/joc.4251 (2015).ADS 
Article 

Google Scholar 
Hengl, T., Heuvelink, G. B. & Rossiter, D. G. About regression-kriging: From equations to case studies. Computers & Geosciences 33, 1301–1315, https://doi.org/10.1016/j.cageo.2007.05.001. Spatial Analysis (2007).Sun, X.-L., Yang, Q., Wang, H.-L. & Wu, Y.-J. Can regression determination, nugget-to-sill ratio and sampling spacing determine relative performance of regression kriging over ordinary kriging? CATENA 181, 104092, https://doi.org/10.1016/j.catena.2019.104092 (2019).Article 

Google Scholar 
Trajkovic, S. & Gocic, M. Evaluation of three wind speed approaches in temperature-based ET 0 equations: a case study in Serbia. Arabian Journal of Geosciences 14, 1–8, https://doi.org/10.1007/s12517-020-06331-5 (2021).Article 

Google Scholar 
Fotheringham, A. S., Brunsdon, C. & Charlton, M. Geographically weighted regression: the analysis of spatially varying relationships (John Wiley & Sons, 2003).Comber, A. et al. A route map for successful applications of geographically weighted regression. Geographical Analysis https://doi.org/10.1111/gean.12316 (2021).Li, X., Zhou, Y., Asrar, G. R. & Zhu, Z. Developing a 1 km resolution daily air temperature dataset for urban and surrounding areas in the conterminous United States. Remote Sensing of Environment 215, 74–84, https://doi.org/10.1016/j.rse.2018.05.034 (2018).ADS 
Article 

Google Scholar 
Wu, J., Zhong, B., Tian, S., Yang, A. & Wu, J. Downscaling of urban land surface temperature based on multi-factor geographically weighted regression. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12, 2897–2911, https://doi.org/10.1109/JSTARS.2019.2919936 (2019).ADS 
Article 

Google Scholar 
Zhang, G., Zhu, S., Zhang, N., Zhang, G. & Xu, Y. Downscaling hourly air temperature of WRF simulations over complex topography: A case study of Chongli District in Hebei Province, China. Journal of Geophysical Research: Atmospheres 127, e2021JD035542, https://doi.org/10.1029/2021JD035542 (2022).ADS 
Article 

Google Scholar 
Callañaupa Gutierrez, S. et al. Seasonal variability of daily evapotranspiration and energy fluxes in the Central Andes of Peru using eddy covariance techniques and empirical methods. Atmospheric Research 261, 105760, https://doi.org/10.1016/j.atmosres.2021.105760 (2021).Article 

Google Scholar 
Zotarelli, L., Dukes, M. D., Romero, C. C., Migliaccio, K. W. & Morgan, K. T. Step by step calculation of the Penman-Monteith Evapotranspiration (FAO-56 Method). Institute of Food and Agricultural Sciences. University of Florida https://edis.ifas.ufl.edu/pdf/AE/AE45900.pdf (2010).Huerta, A. PISCOeo_pm, a reference evapotranspiration gridded database based on FAO Penman-Monteith in Peru. figshare https://doi.org/10.6084/m9.figshare.c.5633182.v3 (2021).Willmott, C. J., Robeson, S. M. & Matsuura, K. A refined index of model performance. International Journal of climatology 32, 2088–2094, https://doi.org/10.1002/joc.2419 (2012).ADS 
Article 

Google Scholar 
Legates, D. R. & McCabe, G. J. A refined index of model performance: a rejoinder. International Journal of Climatology 33, 1053–1056, https://doi.org/10.1002/joc.3487 (2013).ADS 
Article 

Google Scholar 
Durre, I., Menne, M. J., Gleason, B. E., Houston, T. G. & Vose, R. S. Comprehensive automated quality assurance of daily surface observations. Journal of Applied Meteorology and Climatology 49, 1615–1633, https://doi.org/10.1175/2010JAMC2375.1 (2010).ADS 
Article 

Google Scholar 
Huerta, A. & Lavado-Casimiro, W. Atlas de Zonas Áridas del Perú: una evaluación presente y futura (Servicio Nacional de Meteorología e Hidrología del Perú, Lima, Perú, 2021).Singer, M. B. et al. Hourly potential evapotranspiration at 0.1° resolution for the global land surface from 1981-present. Scientific Data 8, 1–13, https://doi.org/10.1038/s41597-021-01003-9 (2021).Article 

Google Scholar 
Pelosi, A. & Chirico, G. Regional assessment of daily reference evapotranspiration: Can ground observations be replaced by blending ERA5-Land meteorological reanalysis and CM-SAF satellite-based radiation data? Agricultural Water Management 258, 107169, https://doi.org/10.1016/j.agwat.2021.107169 (2021).Article 

Google Scholar 
McCuen, R. H. A sensitivity and error analysis cf procedures used for estimating evaporation. JAWRA Journal of the American Water Resources Association 10, 486–497, https://doi.org/10.1111/j.1752-1688.1974.tb00590.x (1974).ADS 
Article 

Google Scholar 
Coleman, G. & DeCoursey, D. G. Sensitivity and model variance analysis applied to some evaporation and evapotranspiration models. Water Resources Research 12, 873–879, https://doi.org/10.1029/WR012i005p00873 (1976).ADS 
Article 

Google Scholar 
Beven, K. A sensitivity analysis of the Penman-Monteith actual evapotranspiration estimates. Journal of Hydrology 44, 169–190, https://doi.org/10.1016/0022-1694(79)90130-6 (1979).ADS 
Article 

Google Scholar 
Hupet, F. & Vanclooster, M. Effect of the sampling frequency of meteorological variables on the estimation of the reference evapotranspiration. Journal of Hydrology 243, 192–204, https://doi.org/10.1016/S0022-1694(00)00413-3 (2001).ADS 
Article 

Google Scholar 
Ali, M. et al. Sensitivity of Penman–Monteith estimates of reference evapotranspiration to errors in input climatic data. Journal of Agrometeorology 11, 1–8, https://journal.agrimetassociation.org/index.php/jam/article/view/1214 (2009).
Google Scholar 
Field, C. B., Jackson, R. B. & Mooney, H. A. Stomatal responses to increased CO2: implications from the plant to the global scale. Plant, Cell & Environment 18, 1214–1225, https://doi.org/10.1111/j.1365-3040.1995.tb00630.x (1995).Article 

Google Scholar 
Roderick, M. L., Greve, P. & Farquhar, G. D. On the assessment of aridity with changes in atmospheric CO2. Water Resources Research 51, 5450–5463, https://doi.org/10.1002/2015WR017031 (2015).ADS 
CAS 
Article 

Google Scholar 
Swann, A. L., Hoffman, F. M., Koven, C. D. & Randerson, J. T. Plant responses to increasing CO2 reduce estimates of climate impacts on drought severity. Proceedings of the National Academy of Sciences 113, 10019–10024, https://doi.org/10.1073/pnas.1604581113 (2016).ADS 
CAS 
Article 

Google Scholar 
Milly, P. C. & Dunne, K. A. Potential evapotranspiration and continental drying. Nature Climate Change 6, 946–949, https://doi.org/10.1038/nclimate3046 (2016).ADS 
Article 

Google Scholar 
Greve, P., Roderick, M. L. & Seneviratne, S. I. Simulated changes in aridity from the last glacial maximum to 4xCO2. Environmental Research Letters 12, 114021, https://doi.org/10.1088/1748-9326/aa89a3 (2017).ADS 
CAS 
Article 

Google Scholar 
Scheff, J. Drought indices, drought impacts, CO2, and warming: a historical and geologic perspective. Current Climate Change Reports 4, 202–209, https://doi.org/10.1007/s40641-018-0094-1 (2018).Article 

Google Scholar 
Swann, A. L. Plants and drought in a changing climate. Current Climate Change Reports 4, 192–201, https://doi.org/10.1007/s40641-018-0097-y (2018).Article 

Google Scholar 
Lemordant, L., Gentine, P., Swann, A. S., Cook, B. I. & Scheff, J. Critical impact of vegetation physiology on the continental hydrologic cycle in response to increasing CO2. Proceedings of the National Academy of Sciences 115, 4093–4098, https://doi.org/10.1073/pnas.1720712115 (2018).ADS 
CAS 
Article 

Google Scholar 
Yang, Y., Roderick, M. L., Zhang, S., McVicar, T. R. & Donohue, R. J. Hydrologic implications of vegetation response to elevated CO2 in climate projections. Nature Climate Change 9, 44–48, https://doi.org/10.1038/s41558-018-0361-0 (2019).ADS 
CAS 
Article 

Google Scholar 
Greve, P., Roderick, M., Ukkola, A. & Wada, Y. The aridity index under global warming. Environmental Research Letters 14, 124006, https://doi.org/10.1088/1748-9326/ab5046 (2019).ADS 
CAS 
Article 

Google Scholar 
Huerta, A. & Gutierrez, L. PISCOeo_pm. figshare https://doi.org/10.6084/m9.figshare.19686738.v1 (2022). More

Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G., Davis, A., Dean, J., Devin, M., Ghemawat, S. & Zheng, X. TensorFlow: Large-scale machine learning on heterogeneous systems. Software available from tensorflow.org. (2015).Alberdi, A., Aizpurua, O., Gilbert, M. T. P. & Bohmann, K. Scrutinizing key steps for reliable metabarcoding of environmental samples. Methods Ecol. Evol. 9, 134–147 (2018).Article 

Google Scholar 
Albert, J. S. & Reis, R. E. One. Introduction to Neotropical freshwaters. In Historical biogeography of Neotropical freshwater fishes (pp. 3-20). University of California Press. (2011).Allard, L., Popée, M., Vigouroux, R. & Brosse, S. Effect of reduced impact logging and small-scale mining disturbances on Neotropical stream fish assemblages. Aquat. Sci. 78, 315–325 (2016).Article 

Google Scholar 
Berry, O. et al. Making environmental DNA (eDNA) biodiversity records globally accessible. Environ. DNA 3(4), 699–705 (2020).Article 

Google Scholar 
Bohmann, K. et al. Environmental DNA for wildlife biology and biodiversity monitoring. Trends Ecol. Evol. 29(6), 358–367 (2014).PubMed 
Article 

Google Scholar 
Bolyen, E. et al. QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science. Nat. Biotechnol. 32, 852–857 (2019).Article 
CAS 

Google Scholar 
Bonder, M. J., Abeln, S., Zaura, E. & Brandt, B. W. Comparing clustering and pre-processing in taxonomy analysis. Bioinformatics 28(22), 2891–2897 (2012).CAS 
PubMed 
Article 

Google Scholar 
Boussarie, G. et al. Environmental DNA illuminates the dark diversity of sharks. Sci. Adv. 4, eaap9661 (2018).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Boyer, F. et al. obitools: A unix-inspired software package for DNA metabarcoding. Mol. Ecology Resour. 16(1), 176–182 (2016).CAS 
Article 

Google Scholar 
Brandt, M.I., Trouche, B., Quintric, L., Günther, B., Wincker, P., Poulain, J. & Arnaud-Haond, S. Bioinformatic pipelines combining denoising and clustering tools allow for more comprehensive prokaryotic and eukaryotic metabarcoding. Molecular Ecology Resources. Accepted (2021).Brosse, S., Melki, F. & Vigouroux, R. Fishes from the Mitaraka mountains (French Guiana). Zoosystema 41, 131–151 (2019).Article 

Google Scholar 
Brown, E. A., Chain, F. J., Crease, T. J., MacIsaac, H. J. & Cristescu, M. E. Divergence thresholds and divergent biodiversity estimates: can metabarcoding reliably describe zooplankton communities?. Ecol. Evol. 5(11), 2234–2251 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Busia, K., George, D. E., Fannjiang, C., Alexander, D.H., Dorfman, E., Poplin, R., Chang, P., & DePris, M. A deep learning approach to pattern recognition for short DNA sequences. BioRxiv (2020).Bylemans, J., Gleeson, D. M., Hardy, C. M. & Furlan, E. Toward an ecoregion scale evaluation of eDNA metabarcoding primers: A case study for the freshwater fish biodiversity of the Murray-Darling Basin (Australia). Ecol. Evol. 8(17), 8697–8712 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Calderón-Sanou, I., Münkemüller, T., Boyer, F., Zinger, L. & Thuiller, W. From environmental DNA sequences to ecological conclusions: How strong is the influence of methodological choices?. J. Biogeogr. 47(1), 193–206 (2020).Article 

Google Scholar 
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13(7), 581–583 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cantera, I., Coutant, O., Jézéuel, C., Decotte, J.B., Dejean, T., Vigouroux, R., Valentini, A. Murienne, J. & Brosse S. Slight deforestation causes harsh biodiversity decline in Amazonian rivers (submitted)Cantera, I., Decotte, J. B., Dejean, T., Murienne, J., Vigouroux, R., Valentini, A., & Brosse, S. Characterizing the spatial signal of environmental DNA in river systems using a community ecology approach. BioRxiv (2020).Cantera, I. et al. Optimizing environmental DNA sampling effort for fish inventories in tropical streams and rivers. Sci. Rep. 9(1), 1–1 (2019).CAS 
Article 

Google Scholar 
Cardoso, Y. P. & Montoya-Burgos, J. I. Unexpected diversity in the catfish Pseudancistrus brevispinis reveals dispersal routes in a Neotropical center of endemism: The Guyanas Region. Mol. Ecol. 18, 947–964 (2009).CAS 
PubMed 
Article 

Google Scholar 
Cilleros, K. et al. Unlocking biodiversity and conservation studies in high-diversity environments using environmental DNA (eDNA): A test with Guianese freshwater fishes. Mol. Ecol. Resour. 19(1), 27–46 (2019).CAS 
PubMed 
Article 

Google Scholar 
Collen, B., Ram, M., Zamin, T. & McRae, L. The tropical biodiversity data gap: Addressing disparity in global monitoring. Trop. Conserv. Sci. 1(2), 75–88 (2008).Article 

Google Scholar 
Cordier, T., Lanzén, A., Apothéloz-Perret-Gentil, L., Stoeck, T. & Pawlowski, J. Embracing environmental genomics and machine learning for routine biomonitoring. Trends Microbiol. 27(5), 387–397 (2019).CAS 
PubMed 
Article 

Google Scholar 
Cordier, T. et al. Ecosystems monitoring powered by environmental genomics: A review of current strategies with an implementation roadmap. Mol. Ecol. 30(13), 2937–2958 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Coutant, O. et al. Detecting fish assemblages with environmental DNA: Does protocol matter? Testing eDNA metabarcoding method robustness. Environ. DNA 3(3), 619–630 (2020).Article 

Google Scholar 
Deiner, K. et al. Environmental DNA metabarcoding: Transforming how we survey animal and plant communities. Mol. Ecol. 26(21), 5872–5895 (2017).PubMed 
Article 

Google Scholar 
Deneu, B., Servajean, M., Bonnet, P., Botella, C., Munoz, F., & Joly, A. Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment. PLoS Comput. Biol. (in press) (2021).de Mérona, B., Tejerina-Garro, F. L. & Vigouroux, R. Fish-habitat relationships in French Guiana rivers: A review. Cybium 36, 7–15 (2012).
Google Scholar 
DiBattista, J. D. et al. Environmental DNA can act as a biodiversity barometer of anthropogenic pressures in coastal ecosystems. Sci. Rep. 10(1), 1–15 (2020).Article 
CAS 

Google Scholar 
Dornelas, M., Madin, E. M., Bunce, M., DiBattista, J. D., Johnson, M., Madin, J. S., Magurran, A. E., McGill, B. J., Pettorelli, N., Pizarro, O. & Williams, S. B. Towards a macroscope: Leveraging technology to transform the breadth, scale and resolution of macroecological data. Glob. Ecol. Biogeogr. (2019).Dufresne, Y., Lejzerowicz, F., Perret-Gentil, L. A., Pawlowski, J. & Cordier, T. SLIM: A flexible web application for the reproducible processing of environmental DNA metabarcoding data. BMC Bioinform. 20(1), 1–6 (2019).Article 

Google Scholar 
Ficetola, G. F., Miaud, C., Pompanon, F. & Taberlet, P. Species detection using environmental DNA from water samples. Biol. Lett. 4(4), 423–425 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
Ficetola, G. F., Taberlet, P. & Coissac, E. How to limit false positives in environmental DNA and metabarcoding?. Mol. Ecol. Resour. 16(3), 604–607 (2016).CAS 
PubMed 
Article 

Google Scholar 
Ficetola, G. F. et al. Replication levels, false presences and the estimation of the presence/absence from eDNA metabarcoding data. Mol. Ecology Resour. 15(3), 543–556 (2015).CAS 
Article 

Google Scholar 
Flynn, J. M., Brown, E. A., Chain, F. J., MacIsaac, H. J. & Cristescu, M. E. Toward accurate molecular identification of species in complex environmental samples: Testing the performance of sequence filtering and clustering methods. Ecol. Evol. 5(11), 2252–2266 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Gold, Z. et al. eDNA metabarcoding bioassessment of endangered fairy shrimp (Branchinecta spp.). Conserv. Genet. Resour. 12, 685–690 (2020).Article 

Google Scholar 
Grünig, M., Razavi, E., Calanca, P., Mazzi, D., Wegner, J. D., & Pellissier, L. Applying deep neural networks to predict incidence and phenology of plant pests and diseases. Ecosphere (accepted) (2021).Helaly, M. A., Rady, S., & Aref, M. M. Convolutional neural networks for biological sequence taxonomic classification: A comparative study. In International Conference on Advanced Intelligent Systems and Informatics (pp. 523–533). Springer, Cham (2019).Holman, L. E. et al. Animals, protists and bacteria share marine biogeographic patterns. Nat. Ecol. Evol. 5(6), 738–746 (2021).PubMed 
Article 

Google Scholar 
Iknayan, K. J., Tingley, M. W., Furnas, B. J. & Beissinger, S. R. Detecting diversity: Emerging methods to estimate species diversity. Trends Ecol. Evol. 29(2), 97–106 (2014).PubMed 
Article 

Google Scholar 
Jarman, S. N., Berry, O. & Bunce, M. The value of environmental DNA biobanking for long-term biomonitoring. Nat. Ecol. Evol. 2(8), 1192–1193 (2018).PubMed 
Article 

Google Scholar 
Juhel, J. B., Utama, R. S., Marques, V., Vimono, I. B., Sugeha, H. Y., Kadarusman, Pouyaud, L., Dejean, T., Mouillot, D. & Hocdé, R. Accumulation curves of environmental DNA sequences predict coastal fish diversity in the coral triangle. Proc. R. Soc. B 287(1930), 20200248 (2020).Kopp, W., Monti, R., Tamburrini, A., Ohler, U. & Akalin, A. Deep learning for genomics using Janggu. Nat. Commun. 11(1), 1–7 (2020).Article 
CAS 

Google Scholar 
Le Bail, P. Y. et al. Updated checklist of the freshwater and estuarine fishes of French Guiana. Cybium 36(1), 293–319 (2012).
Google Scholar 
LeCun, Y. et al. Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989).Article 

Google Scholar 
Li, W. et al. Validating eDNA measurements of the richness and abundance of anurans at a large scale. J. Anim. Ecol. 90(6), 1466–1479 (2021).PubMed 
Article 

Google Scholar 
Lopes, C. M. et al. eDNA metabarcoding: A promising method for anuran surveys in highly diverse tropical forests. Mol. Ecol. Resour. 17(5), 904–914 (2017).CAS 
PubMed 
Article 

Google Scholar 
Makiola, A. et al. Key questions for next-generation biomonitoring. Front. Environ. Sci. 7, 197 (2020).Article 

Google Scholar 
Marques, V. et al. Blind assessment of vertebrate taxonomic diversity across spatial scales by clustering environmental DNA metabarcoding sequences. Ecography 43(12), 1779–1790 (2020).Article 

Google Scholar 
Marques, V. et al. GAPeDNA: Assessing and mapping global species gaps in genetic databases for eDNA metabarcoding. Divers. Distrib. 27(10), 1880–1892 (2020).Article 

Google Scholar 
Mathon, L. et al. Benchmarking bioinformatic tools for fast and accurate eDNA metabarcoding species identification. Mol. Ecol. Resour. 21(7), 2565–2579 (2021).CAS 
PubMed 
Article 

Google Scholar 
McGee, K. M., Robinson, C. & Hajibabaei, M. Gaps in DNA-based biomonitoring across the globe. Front. Ecol. Evol. 7, 337 (2019).Article 

Google Scholar 
Murienne, J. et al. Aquatic eDNA for monitoring French Guiana biodiversity. Biodivers. Data J. 7, e37518 (2019).Nugent, C. M. & Adamowicz, S. J. Alignment-free classification of COI DNA barcode data with the Python package Alfie. Metabarcoding Metagenomics 4, e55815 (2020).Pagni, M. et al. Density-based hierarchical clustering of pyro-sequences on a large scale-the case of fungal ITS1. Bioinformatics 29(10), 1268–1274 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Papa, Y., Le Bail, P. Y. & Covain, R. Genetic landscape clustering of a large DNA barcoding dataset reveals shared patterns of genetic divergence among freshwater fishes of the Maroni Basin. Authorea Preprints (2020).Piro, V. C., Dadi, T. H., Seiler, E., Reinert, K. & Renard, B. Y. ganon: Precise metagenomics classification against large and up-to-date sets of reference sequences. Bioinformatics 36(Supplement 1), i12–i20 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Polanco Fernández, A., Marques, V., Fopp, F., Juhel, J. B., Borrero-Pérez, G. H., Cheutin, M. C., Eme, D. & Pellissier, L. Comparing environmental DNA metabarcoding and underwater visual census to monitor tropical reef fishes. Environ. DNA 3, 142–156 (2021).Polanco, A. et al. Comparing the performance of 12S mitochondrial primers for fish environmental DNA across ecosystems. Environ. DNA 3(6), 1113–1127 (2021).Article 

Google Scholar 
Polanco Fernández, A., Martinezguerra, M. M., Marques, V., Francisco Villa-Navarro, Borrero-Pérez, G. H., Cheutin, M. C., Dejean, T., Hocdé, R., Juhel, J. B., Maire, E., Manel, S. & Pellissier, L. Recovering aquatic and terrestrial biodiversity in a tropical estuary using environmental DNA. Biotropica 53(6), 1606–1619 (2021).Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: A versatile open source tool for metagenomics. PeerJ 4, 1–22 (2016).Article 

Google Scholar 
Rojahn, J., Gleeson, D. M., Furlan, E., Haeusler, T. & Bylemans, J. Improving the detection of rare native fish species in environmental DNA metabarcoding surveys. Aquat. Conserv. Mar. Freshw. Ecosyst. 31(4), 990–997 (2021).Article 

Google Scholar 
Ruppert, K. M., Kline, R. J. & Rahman, M. S. Past, present, and future perspectives of environmental DNA (eDNA) metabarcoding: A systematic review in methods, monitoring, and applications of global eDNA. Glob. Ecol. Conserv. 17, e00547 (2019).Article 

Google Scholar 
Sato, Y., Miya, M., Fukunaga, T., Sado, T. & Iwasaki, W. MitoFish and MiFish pipeline: A mitochondrial genome database of fish with an analysis pipeline for environmental DNA metabarcoding. Mol. Biol. Evol. 35(6), 1553–1555 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Schirmer, M. et al. Insight into biases and sequencing errors for amplicon sequencing with the Illumina MiSeq platform. Nucleic Acids Res. 43(6), e37 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Schnell, I. B., Bohmann, K. & Gilbert, M. T. P. Tag jumps illuminated–reducing sequence-to-sample misidentifications in metabarcoding studies. Mol. Ecol. Resour. 15(6), 1289–1303 (2015).CAS 
PubMed 
Article 

Google Scholar 
Sepulveda, A. J., Nelson, N. M., Jerde, C. L. & Luikart, G. Are environmental DNA methods ready for aquatic invasive species management?. Trends Ecol. Evol. 35, 668–678 (2020).PubMed 
Article 

Google Scholar 
Shokralla, S., Spall, J. L., Gibson, J. F. & Hajibabaei, M. Next-generation sequencing technologies for environmental DNA research. Mol. Ecol. 21(8), 1794–1805 (2012).CAS 
PubMed 
Article 

Google Scholar 
Shorten, C. & Khoshgoftaar, T. A survey on image data augmentation for deep learning. J. Big Data 6, 60 (2019).Article 

Google Scholar 
Singer, G. A. C., Fahner, N. A., Barnes, J. G., McCarthy, A. & Hajibabaei, M. Comprehensive biodiversity analysis via ultra-deep patterned flow cell technology: A case study of eDNA metabarcoding seawater. Sci. Rep. 9(1), 1–12 (2019).ADS 
CAS 
Article 

Google Scholar 
Su, G. et al. Human impacts on global freshwater fish biodiversity. Science 371(6531), 835 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Taberlet, P., Bonin, A., Coissac, E. & Zinger, L. Environmental DNA: For Biodiversity Research and Monitoring (Oxford University Press, Oxford, 2018).Book 

Google Scholar 
Taberlet, P., Coissac, E., Pompanon, F., Brochmann, C. & Willerslev, E. Towards next-generation biodiversity assessment using DNA metabarcoding. Mol. Ecol. 21(8), 2045–2050 (2012).CAS 
PubMed 
Article 

Google Scholar 
Thomsen, P. F. & Willerslev, E. Environmental DNA-An emerging tool in conservation for monitoring past and present biodiversity. Biol. Conserv. 183, 4–18 (2015).Article 

Google Scholar 
Thuiller, W., Lafourcade, B., Engler, R. & Araújo, M. B. BIOMOD–A platform for ensemble forecasting of species distributions. Ecography 32(3), 369–373 (2009).Article 

Google Scholar 
Valentini, A. et al. Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding. Mol. Ecol. 25(4), 929–942 (2016).CAS 
PubMed 
Article 

Google Scholar 
West, K. et al. Large-scale eDNA metabarcoding survey reveals marine biogeographic break and transitions over tropical north-western Australia. Divers. Distrib. 27(10), 1942–1957 (2021).Article 

Google Scholar  More

Engel, M. S. A new interpretation of the oldest fossil bee (Hymenoptera: Apidae). Am. Mus. Novit. 3296, 1–11 (2000).Article 

Google Scholar 
Michener, C. D. The Bees of the World 2nd edn, (John Hopkins University Press, 2007).Klein, A. M. et al. Importance of pollinators in changing landscapes for world crops. Proc. R. Soc. B. 274, 303–313 (2007).PubMed 
Article 

Google Scholar 
Fürst, M., McMahon, D. P., Osborne, J. L., Paxton, R. J. & Brown, M. J. F. Disease associations between honeybees and bumblebees as a threat to wild pollinators. Nature 506, 364–366 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
McMahon, D. P., Wilfert, L., Paxton, R. J. & Brown, M. J. F. Emerging viruses in bees: from molecules to ecology. Adv. Virus Res. 101, 251–291 (2015).Article 

Google Scholar 
Koch, H., Abrol, D. P., Li, J. & Schmid-Hempel, P. Diversity of evolutionary patterns of bacterial gut associates of corbiculate bees. Mol. Ecol. 22, 2028–2044 (2013).CAS 
PubMed 
Article 

Google Scholar 
McFrederick, Q. S. et al. Environment or kin: whence do bees obtain acidophilic bacteria? Mol. Ecol. 21, 1754–1768 (2012).PubMed 
Article 

Google Scholar 
McFrederick, Q. S., Wcislo, W. T., Hout, M. C. & Mueller, U. G. Host species and developmental stage, but not host social structure, affects bacterial community structure in social polymorphic bees. FEMS Microbiol. Ecol. 88, 398–406 (2014).CAS 
PubMed 
Article 

Google Scholar 
McFrederick, Q. S. et al. Flowers and wild megachilid bees share microbes. Microb. Ecol. 73, 188–200 (2017).PubMed 
Article 

Google Scholar 
Jones, J. C. et al. The gut microbiome is associated with behavioural task in honey bees. Insectes Sociaux 65, 419–429 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kristensen, T. N., Schonherz, A., Rohde, P. D., Sorensen, J. G. & Loeschcke, V. Strong experimental support for the hologenome hypothesis revealed from Drosophila melanogaster selection lines. bioRxiv https://doi.org/10.1101/2021.09.09.459587 (2021)Bovo, S., Utzeri, V. J., Ribani, A., Cabbri, R. & Fontanesi, L. Shotgun sequencing of honey DNA can describe honey bee derived environmental signatures and the honey bee hologenome complexity. Sci. Rep. 10, 1–17 (2020).Article 
CAS 

Google Scholar 
Dharampal, P. S., Carlson, C., Currie, C. R. & Steffan, S. A. Pollen-borne microbes shape bee fitness. Proc. R. Soc. B. 286, 20182894 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Graystock, P., Rehan, S. M. & McFrederick, Q. S. Hunting for healthy microbiomes: determining the core microbiomes of Ceratina, Megalopta, and Apis bees and how they associate with microbes in bee collected pollen. Conserv. Genet. 18, 701–711 (2017).Article 

Google Scholar 
Engel, P. et al. The bee microbiome: impact on bee health and model for evolution and ecology of host-microbe interactions. MBio 7, e02164–15 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Voulgari-Kokota, A., McFrederick, Q. S., Steffan-Dewenter, I. & Keller, A. Drivers, diversity, and functions of the solitary-bee microbiota. Trends Microbiol 27, 1034–1044 (2019).CAS 
PubMed 
Article 

Google Scholar 
Rothman, J. A., Leger, L., Graystock, P., Russell, K. & McFrederick, Q. S. The bumble bee microbiome increases survival of bees exposed to selenate toxicity. Environ. Microbiol. 21, 3417–3429 (2019).CAS 
Article 

Google Scholar 
Engel, P., Martinson, V. G. & Moran, N. A. Functional diversity within the simple gut microbiota of the honey bee. PNAS 109, 11002–11007 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Engel, P. & Moran, N. A. Functional and evolutionary insights into the simple yet specific gut microbiota of the honey bee from metagenomic analysis. Gut Microbes 4, 60–65 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Kwong, W. K. et al. Dynamic microbiome evolution in social bees. Sci. Adv. 3, e1600513 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Breeze, T. D., Bailey, A. P., Balcombe, K. G. & Potts, S. G. Pollination services in the UK: How important are honeybees? Agric. Ecosyst. Environ. 142, 137–143 (2011).Article 

Google Scholar 
Dharampal, P. S., Hetherington, M. C. & Steffan, S. A. Microbes make the meal: oligolectic bees require microbes within their host pollen to thrive. Ecol. Entomol. 45, 1418–1427 (2020).Article 

Google Scholar 
Keller, A. et al. (More than) hitchhikers through the network: the shared microbiome of bees and flowers. Curr. Opin. Insect 44, 8–15 (2021).Article 

Google Scholar 
Hugenholtz, P. & Tyson, G. W. Metagenomics. Nature 455, 481–483 (2008).CAS 
PubMed 
Article 

Google Scholar 
Galbraith, D. A. et al. Investigating the viral ecology of global bee communities with high- throughput metagenomics. Sci. Rep. 8, 8879 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Regan, T. et al. Characterisation of the British honey bee metagenome. Nat. Commun. 9, 1–13 (2018).CAS 
Article 

Google Scholar 
Bovo, S. et al. Shotgun metagenomics of honey DNA: Evaluation of a methodological approach to describe a multi-kingdom honey bee derived environmental DNA signature. PLOS ONE 13, e0205575 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Schoonvaere, K. et al. Unbiased RNA shotgun metagenomics in social and solitary wild bees detects associations with eukaryote parasites and new viruses. PLOS ONE 11, e0168456 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Cox-Foster, D. L. et al. A metagenomic survey of microbes in honey bee colony collapse disorder. Science 318, 283–287 (2007).CAS 
PubMed 
Article 

Google Scholar 
Rehan, S. M., Leys, R. & Schwarz, M. P. A mid-cretaceous origin of sociality in xylocopine bees with only two origins of true worker castes. PLOS ONE 7, e34690 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rehan, S. M. Small carpenter bees (Ceratina). Encyclopedia of Social Insects (ed Chris, S.) (Springer, 2020).Sakagami, S. F. & Maeta, Y. Multifemale nests and rudimentary castes in the normally solitary bee Ceratina japonica (Hymenoptera: Xylocopinae). J. Kans. Entomol. 57, 639–656 (1984).
Google Scholar 
Huisken, J. L., Shell, W. A., Pare, H. K. & Rehan, S. M. The influence of social environment on cooperating and conflict in an incipiently social bee, Ceratina calcarata. Behav. Ecol. 75, 74 (2021).Article 

Google Scholar 
Rehan, S. M., Glastad, K. M., Lawson, S. P. & Hunt, B. G. The genome and methylome of a subsocial small carpenter bee, Ceratina calcarata. GBE 8, 1401–1410 (2016).PubMed 
PubMed Central 

Google Scholar 
Rehan, S. M. et al. Conserved genes underlie phenotypic plasticity in an incipiently social bee. GBE 10, 2749–2758 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Arsenault, S. V., Hunt, B. G. & Rehan, S. M. The effect of maternal care on gene expression and DNA methylation in a subsocial bee. Nat. Commun. 9, 3468 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Shell, W. A. et al. Sociality sculpts similar patterns of molecular evolution in two independently evolved lineages of eusocial bees. Comms. Biol. 4, 1–9 (2021).Article 
CAS 

Google Scholar 
Dew, R. M., McFrederick, Q. S. & Rehan, S. M. Diverse diets with consistent core microbiome in wild bee pollen provisions. Insects 11, 49 (2020).Article 

Google Scholar 
Lawson, S. P., Kennedy, K. & Rehan, S. M. Pollen composition significantly impacts development and survival of the native small carpenter bee, Ceratina calcarata. Ecol. Entomol. 46, 232–239 (2021).Article 

Google Scholar 
Oppenheimer, R. L., Shell, W. A. & Rehan, S. M. Phylogeography and population genetics of the Australian small carpenter bee, Ceratina australensis. Biol. J. Linn. Soc. 124, 747–755 (2018).Article 

Google Scholar 
McFrederick, Q. S. & Rehan, S. M. Wild bee pollen usage and microbial communities co- vary across landscapes. Microb. Ecol. 77, 513–522 (2018).PubMed 
Article 
CAS 

Google Scholar 
Rehan, S. M., Richards, M. H. & Schwarz, M. P. Sociality in the Australian small carpenter bee Ceratina (Neoceratina) australensis. Insectes Sociaux 57, 403–412 (2010).Article 

Google Scholar 
Harpur, B. A. & Rehan, S. M. Connecting social polymorphism to single nucleotide polymorphism: population genomics of the small carpenter bee, Ceratina australensis. Biol. J. Linn. Soc. 132, 945–954 (2021).Article 

Google Scholar 
Neu, A. T., Allen, E. E. & Roy, K. Defining and quantifying the core microbiome: challenges and prospects. PNAS 118, e2104429118 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lawson, S. P., Ciaccio, K. N. & Rehan, S. M. Maternal manipulation of pollen provisions affects worker production in a small carpenter bee. Behav. Ecol. 70, 1891–1900 (2016).Article 

Google Scholar 
Ganeshprasad, D. N., Jani, K., Shouche, Y. S. & Sneharani, A. H. Gut bacterial inhabitants of open nested honey bee, Apis florea. Preprint at https://assets.researchsquare.com/files/rs-225332/v1/ddf21abe-2456-4f45-af61-4ba3e81d16e7.pdf?c=1641312753 (2021).Rothman, J. A., Cox-Foster, D. L., Andrikopoulos, C. & McFrederick, Q. S. Diet breadth affects bacterial identity but not diversity in the pollen provisions of closely related polylectic and oligolectic bees. Insects 11, 1–13 (2020).Article 

Google Scholar 
Cohen, H., McFrederick, Q. S. & Philpott, S. M. Environment shapes the microbiome of the blue orchard bee, Osmia lignaria. Microb. Ecol. 80, 897–907 (2020).PubMed 
Article 

Google Scholar 
Dew, R. M., Rehan, S. M. & Schwarz, M. P. Biogeography and demography of an Australian native bee Ceratina australensis (Hymenoptera: Apidae) since the last glacial maximum. J. Hymenopt. Res. 49, 25–41 (2016).Article 

Google Scholar 
Pinto-Tomás, A. A. et al. Symbiotic nitrogen fixation in the fungus gardens of leaf-cutter ants. Science 326, 1120–1123 (2009).PubMed 
Article 
CAS 

Google Scholar 
Walterson, A. M. & Stavrinides, J. Pantoea insights into a highly versatile and diverse genus within the Enterobacteriaceae. FEMS Microbiol. Rev. 39, 968–984 (2015).CAS 
PubMed 
Article 

Google Scholar 
Scheiner, R., Strauß, S., Thamm, M., Farré-Armengol, G. & Junker, R. R. The bacterium Pantoea ananatis modifies behavioral responses to sugar solutions in honeybees. Insects 11, 692 (2020).PubMed Central 
Article 

Google Scholar 
Leonhardt, S. D. & Kaltenpoth, M. Microbial communities of three sympatric Australian stingless bee species. Plos ONE 9, e105718 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bailey, L. & Ball, B. V. Honey Bee Pathology (Academic Press, 1991).Tham, V. L. Isolation of Streptococcus pluton from the larvae of European honey bees in Australia. Aust. Vet. J. 54, 406–407 (1978).CAS 
PubMed 
Article 

Google Scholar 
Bowman, J. The genus Flavobacterium. Prokaryotes 7, 481–531 (2006).
Google Scholar 
Voordouw, G. The genus Desulovibrio: The centennial. Appl. Environ. Microbiol. 61, 2813–2819 (1995).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Singaravelen, N., Nee’man, G., Inbar, M. & Izhaki, I. Feeding responses of free-flying honeybees to secondary compounds mimicking floral nectars. J. Chem. Ecol. 31, 2791–2804 (2005).Article 
CAS 

Google Scholar 
Baracchi, D., Marples, A., Jenkins, A. J., Leitch, A. R. & Chittka, L. Nicotine in floral nectar pharmacologically influences bumblebee learning of floral features. Sci. Rep. 7, 1951 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Adler, L. S. & Irwin, R. E. Ecological costs and benefits of defenses in nectar. Ecology 86, 2968–2978 (2005).Article 

Google Scholar 
Bally, J. et al. Nicotiana paulineana, a new Australian species in Nicotiana section Suaveolentes. Aust. Syst. Bot. 34, 477–484 (2021).Article 

Google Scholar 
Coenye, T. & Vandamme, P. Diversity and significance of Burkholderia species occupying diverse ecology niches. Environ. Microbiol. 5, 719–729 (2003).CAS 
PubMed 
Article 

Google Scholar 
Levy, A., Merritt, A. J., Aravena-Roman, M., Hodge, M. M. & Inglis, T. J. J. Expanded range of Burkholderia species in Australia. Am. J. Trop. Med. Hyg. 78, 599–604 (2008).CAS 
PubMed 
Article 

Google Scholar 
Kaltenpoth, M. & Flórez, L. V. Versatile and dynamic symbioses between insects and Burkholderia bacteria. Annu. Rev. Entomol. 65, 145–170 (2019).PubMed 
Article 
CAS 

Google Scholar 
Foley, K., Fazio, G., Jensen, A. B. & Hughes, W. O. H. Nutritional limitation and resistance to opportunistic Aspergillus parasites in honey bee larvae. J. Invertebr. Pathol. 111, 68–73 (2012).PubMed 
Article 

Google Scholar 
Yoder, J. A. et al. Fungicide contamination reduces beneficial fungi in bee bread based on an area-wide field study in honey bee, Apis mellifera, colonies. J. Toxicol. Environ. Health Part A 76, 587–600 (2013).CAS 
Article 

Google Scholar 
Yun, J.-H., Jung, M.-J., Kim, P. S. & Bae, J.-W. Social status shapes the bacterial and fungal gut communities of the honey bee. Sci. Rep. 8, 1–11 (2018).
Google Scholar 
Dew, R. M., Silva, D. P. & Rehan, S. M. Range expansion of an already widespread bee under climate change. GECCO 17, e00584 (2019).
Google Scholar 
Cambra, M., Capote, N. & Myrta, A. & Llácer, G. Plum pox virus and the estimated costs associated with sharka disease. EPPO Bull. 36, 202–204 (2006).Article 

Google Scholar 
Roberts, J. M. K., Ireland, K. B., Tay, W. T. & Paini, D. Honey bee-assisted surveillance for early plant virus detection. Ann. Appl. Biol. 173, 285–293 (2018).CAS 
Article 

Google Scholar 
Elliott, B. et al. Pollen diets and niche overlap of honey bees and native bees in protected areas. BAAE 50, 169–180 (2021).
Google Scholar 
Porrini, C. et al. Use of honey bees as bioindicators of environmental pollution in Italy. in Honey bees: estimating the environmental impact of chemicals (eds Devillers, J. & Pham-Delegue, M.-H.) (Taylor & Francis Press, 2002).Kennedy, P., Higginson, A. D., Radford, A. N. & Sumner, S. Altruism in a volatile world. Nature 555, 359–362 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rubin, B. E. R., Sanders, J. G., Turner, K. M., Pierce, N. E. & Kocher, S. D. Social behaviour in bees influences the abundance of Sodalis (Enterobacteriaceae) symbionts. R. Soc. Open Sci. 5, 180369 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Mohr, K. I. & Tebbe, C. C. Diversity and phylotype consistency of bacteria in the guts of three bee species (Apoidea) at an oilseed rape field. Environ. Microbiol. 8, 258–272 (2006).CAS 
PubMed 
Article 

Google Scholar 
Raymann, K. & Moran, N. A. The role of the gut microbiome in health and disease of adult honey bee workers. Curr. Opin. Insect Sci. 26, 97–104 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Amin, F. A. Z. et al. Probiotic properties of Bacillus strains isolated from stingless bee (Heterotrigona itama) honey collected across Malaysia. Int. J. Envrion. Res. Public Health 17, 1–15 (2020).
Google Scholar 
Takeshita, K. & Kikuchi, Y. Riptortus pedestris and Burkholderia symbiont: an ideal model system for insect-microbe symbiotic associations. Res. Microbiol. 168, 175–187 (2017).PubMed 
Article 

Google Scholar 
Martinson, V. G. et al. A simple and distinctive microbiota associated with honey bees and bumble bees. Mol. Ecol. 20, 619–628 (2011).PubMed 
Article 

Google Scholar 
D’Alvise, P. et al. The impact of winter feed type on intestinal microbiota and parasites in honey bees. Apidologie 49, 252–264 (2018).Article 
CAS 

Google Scholar 
Wang, L. et al. Dynamic changes of gut microbial communities of bumble bee queens through important life stages. mSystems 4, e00631–19 (2019).PubMed 
PubMed Central 

Google Scholar 
Kapheim, K. M., Johnson, M. M. & Jolley, M. Composition and acquisition of the microbiome in solitary, ground-nesting alkali bees. Sci. Rep. 11, 2993 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Abdelazez, A. et al. Potential benefits of Lactobacillus plantarum as probiotic and its advantages in human health and industrial applications: A review. Adv. Environ. Biol. 12, 16–27 (2018).CAS 

Google Scholar 
Frese, S. A. et al. The evolution of host specialization in the vertebrate gut symbiont Lactobacillus reuteri. PLoS Genet 7, e1001314 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Duar, R. M. et al. Lifestyles in transition: evolution and natural history of the genus Lactobacillus. FEMS Microbiol. Rev. 41, S27–S48 (2017).PubMed 
Article 

Google Scholar 
Tejerina, M. R., Cabana, M. J. & Benitez-Ahrendts, M. R. Strains of Lactobacillus spp. reduce chalkbrood in Apis mellifera. J. Invertebr. Pathol. 178, 107521 (2021).CAS 
PubMed 
Article 

Google Scholar 
Vásquez, A. et al. Symbionts as major modulators of insect health: Lactic acid bacteria and honeybees. PLOS ONE 7, e33188 (2012).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Voulgari-Kokota, A., Steffan-Dewenter, I. & Keller, A. Susceptibility of red mason bee larvae to bacterial threats due to microbiome exchange with imported pollen provisions. Insects 11, 1–14 (2020).Article 

Google Scholar 
Steffan, S. A. et al. Omnivory in bees: Elevated trophic positions among all major bee families. Am. Nat. 194, 414–421 (2019).PubMed 
Article 

Google Scholar 
Hurst, P. S. Social biology of Exoneurella tridentata, an allodapine bee with morphological castes and perennial colonies. Unpublished D. Phil. Thesis (Flinders University, 2001).Chalita, M. et al. Improved metagenomic taxonomic profiling using a curated core gene- based bacterial database reveals unrecognized species in the genus Streptococcus. Pathogens 9, 204 (2021).Article 

Google Scholar 
Rehan, S. M. & Toth, A. L. Climbing the social ladder: molecular evolution of sociality. Trends Ecol. Evol. 30, 426–433 (2015).PubMed 
Article 

Google Scholar 
Shell, W. A. & Rehan, S. M. Behavioral and genetic mechanisms of social evolution: insights from incipiently and facultatively social bees. Apidologie 49, 13–30 (2018).CAS 
Article 

Google Scholar 
Kirby, K. S. Isolation and characterization of ribosomal ribonucleic acid. Biochem. J. 96, 266–269 (1956).Article 

Google Scholar 
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2019).Article 
CAS 

Google Scholar 
Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Tsilimigras, M. C. B. & Fodor, A. A. Compositional data analysis of the microbiome: fundamentals, tools, and challenges. Ann. Epidemiol. 26, 330–335 (2016).PubMed 
Article 

Google Scholar 
Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol. 20, 257 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nurk, S., Meleshko, D., Korobeynikov, A. & Pevzner, P. A. metaSPAdes: a new versatile metagenomic assembler. Genome Res 27, 824–834 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Altschul, S. F. et al. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).CAS 
PubMed 
Article 

Google Scholar 
Oksanen, J. et al. Package ‘vegan’. Community Ecology package, version 2, 1–295 (2013).Hammer, Ø., Harper, D. A. T. & Ryan, P. D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 9 (2001).
Google Scholar 
Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).CAS 
PubMed 
Article 

Google Scholar 
Mina, R., Haixu, T. & Yuzhen, Y. FragGeneScan: predicting genes in short and error-prone reads. Nucleic Acids Res. 38, e191 (2010).Article 
CAS 

Google Scholar 
Kanehisa, M., Sato, Y. & Morishima, K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J. Mol. Biol. 428, 726–731 (2016).CAS 
PubMed 
Article 

Google Scholar 
Huerta-Cepas, J. et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol. Biol. Evol. 34, 2115–2122 (2017).CAS 
PubMed 
PubMed Central 
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. 15, 1–21 (2014).Article 
CAS 

Google Scholar 
Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinf 9, 599 (2008).Article 
CAS 

Google Scholar 
Langfelder, P. & Horvath, S. Tutorials for the WGCNA package. https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/Tutorials/ (2016).Liaw, A. & Wiener, M. Classification and regression by randomForest. R. N. 2, 18–22 (2002).
Google Scholar 
Kuhn, M. Building predictive models in R using the caret package. J. Stat. Softw. 28, 1–26 (2008).Article 

Google Scholar 
Paluszynska, A. Structure mining and knowledge extraction from random forest with applications to The Cancer Genome Atlas project. Master’s Thesis (University of Warsaw, 2017). More