Philippa Kaur

Hall, M. J., Burns, A. L., Martin, J. M. & Hochuli, D. F. Flight initiation distance changes across landscapes and habitats in a successful urban coloniser. Urban Ecosyst. https://doi.org/10.1007/s11252-020-00969-5 (2020).Article 

Google Scholar 
Møller, A. P., Samia, D. S. M., Weston, M. A., Guay, P. J. & Blumstein, D. T. Flight initiation distances in relation to sexual dichromatism and body size in birds from three continents. Biol. J. Linn. Soc. 117, 823–831 (2016).
Google Scholar 
Morelli, F. et al. Contagious fear: Escape behavior increases with flock size in European gregarious birds. Ecol. Evol. 9, 6096–6104 (2019).PubMed 
PubMed Central 

Google Scholar 
Samia, D. S. M. et al. Rural-urban differences in escape behavior of European birds across a latitudinal gradient. Front. Ecol. Evol. 5, 66 (2017).ADS 

Google Scholar 
Blumstein, D. T. Developing an evolutionary ecology of fear: How life history and natural history traits affect disturbance tolerance in birds. Anim. Behav. 71, 389–399 (2006).
Google Scholar 
McFarland, D. Oxford companion to animal behavior. (Oxford University Press, 1987).Stankowich, T. & Blumstein, D. T. Fear in animals: A meta-analysis and review of risk assessment. Proc. R. Soc. B Biol. Sci. 272, 2627–2634 (2005).
Google Scholar 
Lima, S. L. Maximizing feeding efficiency and minimizing time exposed to predators: a trade-off in the black-capped chickadee. Oecologia 66, 60–67 (1985).ADS 
PubMed 

Google Scholar 
Sol, D. et al. Risk-taking behavior, urbanization and the pace of life in birds. Behav. Ecol. Sociobiol. 72, 59 (2018).
Google Scholar 
Lockwood, R., Swaddle, J. P. & Rayner, J. M. V. Avian Wingtip Shape Reconsidered: Wingtip Shape Indices and Morphological Adaptations to Migration. J. Avian Biol. 29, 273–292 (1998).
Google Scholar 
Møller, A. P. Birds. in Escaping from predators: An integrative view of escape decisions and refuge use (eds. Cooper, W. E. J. & Blumstein, D. T.) 88–112 (Cambridge University Press, 2015).Møller, A. P. Flight distance of urban birds, predation and selection for urban life. Behav. Ecol. Sociobiol. 63, 63–75 (2008).
Google Scholar 
Fernández-Juricic, E. et al. Relationships of anti-predator escape and post-escape responses with body mass and morphology: a comparative avian study. Evol. Ecol. Res. 8, 731–752 (2006).
Google Scholar 
Weston, M. A., Mcleod, E. M., Blumstein, D. T. & Guay, P. J. A review of flight-initiation distances and their application to managing disturbance to Australian birds. Emu 112, 269–286 (2012).
Google Scholar 
Hemmingsen, A. The relation of shyness (flushing distance) to body size. Spolia Zool Musei Hauniensis 11, 74–76 (1951).
Google Scholar 
Blumstein, D. T. Flight-initiation distance in birds is dependent on intruder starting distance. J. Wildl. Manage. 67, 852–857 (2013).
Google Scholar 
Glover, H. K., Weston, M. A., Maguire, G. S., Miller, K. K. & Christie, B. A. Towards ecologically meaningful and socially acceptable buffers: Response distances of shorebirds in Victoria, Australia, to human disturbance. Landsc. Urban Plan. 103, 326–334 (2011).
Google Scholar 
Geist, C., Liao, J., Libby, S. & Blumstein, D. T. Does intruder group size and orientation affect flight initiation distance in birds?. Anim. Biodivers. Conserv. 28, 69–73 (2001).
Google Scholar 
Mikula, P. Pedestrian density influences flight distances of urban birds. Ardea 102, 53–60 (2014).
Google Scholar 
Piratelli, A. J., Favoretto, G. R. & de Almeida Maximiano, M. F. Factors affecting escape distance in birds. Zoologia 32, 438–444 (2015).Burger, J. & Gochfeld, M. Human activity influence and diurnal and nocturnal foraging of Sanderlings (Calidris alba). Condor 93, 259–265 (1991).
Google Scholar 
Møller, A. P. & Garamszegi, L. Z. Between individual variation in risk-taking behavior and its life history consequences. Behav. Ecol. 23, 843–853 (2012).
Google Scholar 
Ferguson, S. M., Gilson, L. N. & Bateman, P. W. Look at the time: diel variation in the flight initiation distance of a nectarivorous bird. Behav. Ecol. Sociobiol. 73, 147 (2019).
Google Scholar 
Garamszegi, L. Z. & Møller, A. P. Partitioning within-species variance in behaviour to within- and between-population components for understanding evolution. Ecol. Lett. 20, 599–608 (2017).PubMed 

Google Scholar 
Bauer, S. & Hoye, B. J. Migratory animals couple biodiversity and ecosystem functioning worldwide. Science 344, 1242552 (2014).CAS 
PubMed 

Google Scholar 
Dufour, P. et al. Reconstructing the geographic and climatic origins of long-distance bird migrations. J. Biogeogr. 47, 155–166 (2020).
Google Scholar 
Sol, D. et al. Evolutionary divergence in brain size between migratory and resident birds. PLoS ONE 5, e9617 (2010).ADS 
PubMed 
PubMed Central 

Google Scholar 
Bonnet-Lebrun, A. S., Somveille, M., Rodrigues, A. S. L. & Manica, A. Exploring intraspecific variation in migratory destinations to investigate the drivers of migration. Oikos 130, 187–196 (2021).
Google Scholar 
Zurell, D., Gallien, L., Graham, C. H. & Zimmermann, N. E. Do long-distance migratory birds track their niche through seasons?. J. Biogeogr. 45, 1459–1468 (2018).
Google Scholar 
Samia, D. S. M., Nakagawa, S., Nomura, F., Rangel, T. F. & Blumstein, D. T. Increased tolerance to humans among disturbed wildlife. Nat. Commun. 6, 8877 (2015).ADS 
CAS 
PubMed 

Google Scholar 
Ydenberg, R. C. & Dill, L. M. The economics of fleeing from predators. Adv. Study Behav. 16, 229–249 (1986).
Google Scholar 
Cooper, W. E. J. & Blumstein, D. T. Escape behavior: importance, scope, and variables. in Escaping from predators: An integrative view of escape decisions (eds. Cooper, W. E. J. & Blumstein, D. T.) 3–14 (Cambridge University Press, 2015). https://doi.org/10.1017/CBO9781107447189.002.Sayol, F., Sol, D. & Pigot, A. L. Brain size and life history interact to predict urban tolerance in birds. Front. Ecol. Evol. 8, 58 (2020).
Google Scholar 
Sayol, F., Downing, P. A., Iwaniuk, A. N., Maspons, J. & Sol, D. Predictable evolution towards larger brains in birds colonizing oceanic islands. Nat. Commun. 9, 2820 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Tobias, J. A. & Pigot, A. L. Integrating behaviour and ecology into global biodiversity conservation strategies. Philos. Trans. R. Soc. B Biol. Sci. 374, 20190012 (2019).
Google Scholar 
Wilman, H. et al. EltonTraits 1.0: Species-level foraging attributes of the world’s birds and mammals. Ecology 95, 2027 (2014).
Google Scholar 
Kamilar, J. M. & Cooper, N. Phylogenetic signal in primate behaviour, ecology and life history. Philos. Trans. R. Soc. B Biol. Sci. 368, 20120341–22012034 (2013).
Google Scholar 
Machado, J. P., Antunes, A., Borges, R., Gomes, C. & Rocha, A. P. Measuring phylogenetic signal between categorical traits and phylogenies. Bioinformatics https://doi.org/10.1093/bioinformatics/bty800 (2018).Article 

Google Scholar 
Ericson, P. G. P. et al. Diversification of Neoaves: integration of molecular sequence data and fossils. Biol. Lett. 2, 543–547 (2006).PubMed 
PubMed Central 

Google Scholar 
Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).CAS 
PubMed 

Google Scholar 
Schliep, K. P. phangorn: phylogenetic analysis in R. Bioinformatics 27, 592–593 (2011).CAS 

Google Scholar 
Revell, L. J. & Chamberlain, S. A. Rphylip: An R interface for PHYLIP R package. (2014).Blomberg, S. P. & Garland, T. Tempo and mode in evolution: phylogenetic inertia, adaptation and comparative methods. J. Evol. Biol. 15, 899–910 (2003).
Google Scholar 
Keck, F., Rimet, F., Bouchez, A. & Franc, A. Phylosignal: An R package to measure, test, and explore the phylogenetic signal. Ecol. Evol. 6, 2774–2780 (2016).PubMed 
PubMed Central 

Google Scholar 
Münkemüller, T. et al. How to measure and test phylogenetic signal. Methods Ecol. Evol. 3, 743–756 (2012).
Google Scholar 
Kot, M. Adaptation: Statistics and a null model for estimating phylogenetic effects. Syst. Zool. 39, 227–241 (1990).
Google Scholar 
Blomberg, S. P., Garland, T. J. & Ives, A. R. Testing for phylogenetic signal in comparative data: behavioral traits are more labile. Evolution (N. Y.) 57, 717–745 (2003).
Google Scholar 
Pagel, M. Inferring the historical patterns of biological evolution. Nature 401, 877–884 (1999).ADS 
CAS 
PubMed 

Google Scholar 
Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (Springer, 2002).MATH 

Google Scholar 
McCullagh, P. & Nelder, J. A. Generalized Linear Models. (Chapman and Hall, 1989).Pinheiro, J. et al. nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1-140. 1–117 (2019).Nakazawa, M. ‘fmsb’ Functions for Medical Statistics Book with some Demographic Data – R package version 0.6.1. (2017).R Development Core Team. R: A language and environment for statistical computing. (2021).Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S. (Springer, 2002). https://doi.org/10.1007/978-0-387-21706-2. More

Mack, R. N. et al. Biotic invasions: Causes, epidemiology, global consequences, and control. Ecol. Appl. 10(3), 689–710 (2000).
Google Scholar 
Pimentel, D. Biological invasionseconomic and environmental costs of alien plant, animal, and microbe species. No. 577.18 B5/2011. 2011.Jackson, T. Addressing the economic costs of invasive alien species: Some methodological and empirical issues. Int. J. Sustain. Soc. 7(3), 221–240 (2015).
Google Scholar 
Walker, B. H. & Steffen, W. L. Interactive and integrated effects of global change on terrestrial ecosystems. In The Terrestrial Biosphere and Global Change. Implications for Natural and Managed Ecosystems, Synthesis Volume. International Geosphere-Biosphere Program Book Series 4 (eds Walker, B. et al.) 329–375 (Cambridge University Press, 1999).
Google Scholar 
Wilcove, D. S., Rothstein, D., Dubow, J., Phillips, A. & Losos, E. Quantifying threats to imperiled species in the United States. Bioscience 48(8), 607–615 (1998).
Google Scholar 
Robinson, T. W. Introduction, Spread and Areal Extent of Saltcedar [Tamarix] in the Western States (No. 491) (US Government Printing Office, 1965).
Google Scholar 
Marlin, D., Newete, S. W., Mayonde, S. G., Smit, E. R. & Byrne, M. J. Invasive Tamarix (Tamaricaceae) in South Africa: Current research and the potential for biological control. Biol. Invasions 19(10), 2971–2992 (2017).
Google Scholar 
Pearce, C. M. & Smith, D. G. Saltcedar: Distribution, abundance, and dispersal mechanisms, northern Montana, USA. Wetlands 23(2), 215–228 (2003).
Google Scholar 
Newete, S. W., Mayonde, S. & Byrne, M. J. Distribution and abundance of invasive Tamarix genotypes in South Africa. Weed Res. 59(3), 191–200 (2019).CAS 

Google Scholar 
Chew, M. K. The monstering of tamarisk: How scientists made a plant into a problem. J. Hist. Biol. 42(2), 231–266 (2009).PubMed 

Google Scholar 
Richardson, D. M., Macdonald, I. A. W., Hoffmann, J. H. & Henderson, L. Alienplantinvasions. In The Vegetation of Southern Africa (eds Cowling, R. M. et al.) 535–570 (Cambridge University Press, 1997).
Google Scholar 
Ehrenfeld, J. G. Effects of exotic plant invasions on soil nutrient cycling processes. Ecosystems 6(6), 503–523 (2003).CAS 

Google Scholar 
Haubensak, K. A., D’Antonio, C. M. & Alexander, J. Effects of nitrogen-fixing shrubs in Washington and Coastal California1. Weed Technol. 18(sp1), 1475–1479 (2004).
Google Scholar 
Hawkes, C. V., Wren, I. F., Herman, D. J. & Firestone, M. K. Plant invasion alters nitrogen cycling by modifying the soil nitrifying community. Ecol. Lett. 8(9), 976–985 (2005).PubMed 

Google Scholar 
Kourtev, P. S., Ehrenfeld, J. G. & Häggblom, M. Exotic plant species alter the microbial community structure and function in the soil. Ecology 83(11), 3152–3166 (2002).
Google Scholar 
Saggar, S., McIntosh, P. D., Hedley, C. B. & Knicker, H. Changes in soil microbial biomass, metabolic quotient, and organic matter turnover under Hieracium (H. pilosella L.). Biol. Fertility Soils 30(3), 232–238 (1999).CAS 

Google Scholar 
Dudley, T. L., DeLoach, C. J., Levich, J. E. & Carruthers, R. I. Saltcedar invasion of western riparian areas: Impacts and new prospects for control. Trans. N. Am. Wildlife Nat. Resources Conf. 65, 345–381 (2000).
Google Scholar 
Algotsson, E. Biological diversity. In Environmental Management in South Africa 2nd edn (eds Strydom, H. A. & King, N. D.) 97–125 (Juta Cape Town, 2009).
Google Scholar 
Mayonde, S. G., Cron, G. V., Gaskin, J. F. & Byrne, M. J. Tamarix (Tamaricaceae) hybrids: The dominant invasive genotype in Southern Africa. Biol. Invasions 18(12), 3575–3594 (2016).
Google Scholar 
Corbin, J. D. & D’Antonio, C. M. Effects of exotic species on soil nitrogen cycling: Implications for restoration1. Weed Technol. 18(sp1), 1464–1468 (2004).CAS 

Google Scholar 
Marchante, E., Kjøller, A., Struwe, S. & Freitas, H. Soil recovery after removal of the N 2-fixing invasive Acacia longifolia: Consequences for ecosystem restoration. Biol. Invasions 11(4), 813–823 (2009).
Google Scholar 
Magadlela, D. & Mdzeke, N. Social benefits in the Working for Water programme as a public works initiative: Working for water. S. Afr. J. Sci. 100(1–2), 94–96 (2004).
Google Scholar 
Yelenik, S. G., Stock, W. D. & Richardson, D. M. Ecosystem level impacts of invasive Acacia saligna in the South African fynbos. Restor. Ecol. 12(1), 44–51 (2004).
Google Scholar 
Malcolm, G. M., Bush, D. S. & Rice, S. K. Soil nitrogen conditions approach preinvasion levels following restoration of nitrogen-fixing black locust (Robinia pseudoacacia) stands in a Pine-Oak Ecosystem. Restor. Ecol. 16(1), 70–78 (2008).
Google Scholar 
Maron, J. L. & Jefferies, R. L. Bush lupine mortality, altered resource availability, and alternative vegetation states. Ecology 80(2), 443–454 (1999).
Google Scholar 
AgriLASA (Agri Laboratory Association of Southern Africa). 2004. Soil handbook.Okalebo, J.R., Gathua, K.W. & Woomer, P.L. (2002). Laboratory methods of soil and plant analysis: A working manual second edition. Sacred Africa, Nairobi, 21.LECO. 2003. Truspec CN Carbon/Nitrogen Determinator Instructions Manual. LECO Corporation, St Joseph, USA.Suarez, D. L., Wood, J. D. & Lesch, S. M. Effect of SAR on water infiltration under a sequential rain–irrigation management system. Agric. Water Manag. 86(1–2), 150–164 (2006).
Google Scholar 
Dane, J.H., and Hopmans, JW. (2002). Water retention and storage. GC Method of soil analysis. SSSA book series. Madison, Wisconsin, USA. 1692, 671–720.Blakemore, L.C., Searle, P.L. and Daly, B.K. (1987). Methods for chemical analysis of soils. New Zealand Soil Bureau Scientific, Report 80. New Zealand, Lower Hutt: New Zealand Society of Soil Science, 103.Buckham, L.E. (2011). Contrasting growth traits and insect interactions of two Tamarix species and a hybrid (Tamaricaceae) used for mine rehabilitation in South Africa (Doctoral dissertation).Ladenburger, C. G., Hild, A. L., Kazmer, D. J. & Munn, L. C. Soil salinity patterns in Tamarix invasions in the Bighorn Basin, Wyoming, USA. J. Arid Environ. 65(1), 111–128 (2006).ADS 

Google Scholar 
Beukes, P. C. & Ellis, F. Soil and vegetation changes across a Succulent Karoo grazing gradient. Afr. J. Range Forage Sci. 20(1), 11–19 (2003).
Google Scholar 
Liu, M. et al. Monitoring the invasion of Spartina alterniflora using multi-source high-resolution imagery in the Zhangjiang Estuary, China. Remote Sensing 9(6), 539 (2017).ADS 

Google Scholar 
Newete, S. W., Abd Elbasit, M. A. & Araya, T. Soil salinity and moisture content under non-native Tamarix species. Int. J. Phytorem. 22(9), 931–938. https://doi.org/10.1080/15226514.2020.1774503 (2020).CAS 
Article 

Google Scholar 
Whitford, W. G., Anderson, J. & Rice, P. M. Stemflow contribution to the ’fertile island’effect in creosotebush, Larrea tridentata. J. Arid Environ. 35(3), 451–457 (1997).ADS 

Google Scholar 
Li, C., Li, Y. & Ma, J. Spatial heterogeneity of soil chemical properties at fine scales induced by Haloxylon ammodendron (Chenopodiaceae) plants in a sandy desert. Ecol. Res. 26(2), 385–394 (2011).MathSciNet 
CAS 

Google Scholar 
Sookbirsingh, R., Karina, C., Thomas, E.G. & Rusell, RC. (2010). Salt separation processes in the saltcedar Tamarix ramosissima (Lebed.). Commun Soil Sci Plant Anal. 41(10), 1271–1281.Newete, S.W., Allem, S.M., Venter, N. and Byrne, M.J. Tamarix efficiency in salt excretion and physiological tolerance to salt-induced stress in South Africa. Int. J. Phytoremediat. 1–7 (2019).Di Tomaso, J. M. Impact, biology, and ecology of saltcedar (Tamarix spp.) in the southwestern United States. Weed Technol. 12(2), 326–336 (1998).
Google Scholar 
Smith, S. D., Devitt, D. A., Sala, A., Cleverly, J. R. & Busch, D. E. Water relations of riparian plants from warm desert regions. Wetlands 18(4), 687–696 (1998).
Google Scholar 
Lesica, P. & DeLuca, T. H. Is tamarisk allelopathic?. Plant Soil 267(1–2), 357–365 (2004).CAS 

Google Scholar 
Bagstad, K. J., Lite, S. J. & Stromberg, J. C. Vegetation, soils, and hydrogeomorphology of riparian patch types of a dryland river. Western N. Am. Naturalist 66(1), 23–45 (2006).
Google Scholar 
Lehnhoff, E. A., Rew, L. J., Zabinski, C. A. & Menalled, F. D. Reduced impacts or a longer lag phase? Tamarix in the northwestern USA. Wetlands 32(3), 497–508 (2012).
Google Scholar 
Ye, W., Wang, H. X., Gao, J., Liu, H. J. & Yan, L. Simulation of salt ion migration in soil under reclaimed water irrigation. J. Agro-Environ. Sci. 33(5), 1007–1015 (2014).CAS 

Google Scholar 
Yang, S. C. et al. Characterization of soil salinization based on canonical correspondence analysis method in Gansu Yellow River irrigation district of Northwest China. Scientia Agricultura Sinica 47(1), 100–110 (2014).CAS 

Google Scholar 
Zhang, L. H., Chen, P. H., Li, J., Chen, X. B. & Feng, Y. Distribution of soil salt ions around Tamarix chinensis individuals in the Yellow River Delta. Acta Ecol. Sin. 36(18), 5741–5749 (2016).CAS 

Google Scholar 
Zhang, T., Zhan, X., He, J., Feng, H. & Kang, Y. Salt characteristics and soluble cations redistribution in an impermeable calcareous saline-sodic soil reclaimed with an improved drip irrigation. Agric. Water Manag. 197, 91–99 (2018).
Google Scholar 
Yin, C. H., Feng, G. U., Zhang, F., Tian, C. Y. & Tang, C. Enrichment of soil fertility and salinity by tamarisk in saline soils on the northern edge of the Taklamakan Desert. Agric. Water Manag. 97(12), 1978–1986 (2010).
Google Scholar 
Chaudhari, P. R., Ahire, D. V., Ahire, V. D., Chkravarty, M. & Maity, S. Soil bulk density as related to soil texture, organic matter content and available total nutrients of Coimbatore soil. Int. J. Sci. Res. Publ. 3(2), 1–8 (2013).CAS 

Google Scholar 
Tanveera, A., Kanth, T. A., Tali, P. A. & Naikoo, M. Relation of soil bulk density with texture, total organic matter content and porosity in the soils of Kandi Area of Kashmir valley, India. Int. Res. J. Earth Sci. 4(1), 1–6 (2016).
Google Scholar 
Sharma, B. & Bhattacharya, S. Soil bulk density as related to soil texture, moisture content, Ph, electrical conductivity, organic carbon, organic matter content and available macro nutrients of Pandoga sub watershed, Una District of HP (India). Int. J. Eng. Res. Dev. 13(12), 72–76 (2017).
Google Scholar  More

Milner-Gulland, E. J. & Bennett, E. L. Wild meat: The bigger picture. Trends Ecol. Evol. 18, 351–357 (2003).
Google Scholar 
Van Vliet, N. et al. Bushmeat and human health: Assessing the evidence in tropical and sub-tropical forests. Ethnobio. Conserv. 6, 3. https://doi.org/10.15451/ec2017-04-6.3-1-45 (2017).Article 

Google Scholar 
Ingram, D. J. et al. Wild meat is still on the menu: Progress in wild meat research, policy, and practice from 2002 to 2020. Annu. Rev. Environ. Resour. 46, 221–254. https://doi.org/10.1146/annurev-environ-041020-063132 (2021).Article 

Google Scholar 
Golden, C. D., Fernald, L. C. H., Brashares, J. S., Rasolofoniaina, B. J. R. & Kremen, C. Benefits of wildlife consumption to child nutrition in a biodiversity hotspot. P. Natl. Acad. Sci. 108, 19653–19656 (2011).ADS 
CAS 

Google Scholar 
Roe, D. et al. Beyond banning wildlife trade: COVID-19, conservation and development. World Dev. 136, 105121. https://doi.org/10.1016/j.worlddev.2020.105121 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Zhou, W., Orrick, K., Lim, A. & Dove, M. Reframing conservation and development perspectives on bushmeat. Environ. Res. Lett. 17, 011001. https://doi.org/10.1088/1748-9326/ac3db1 (2021).ADS 
Article 

Google Scholar 
Cawthorn, D.-M. & Hoffman, L. C. The bushmeat and food security nexus: A global account of the contributions, conundrums and ethical collisions. Food Res. Int. 76, 906–925 (2015).PubMed Central 

Google Scholar 
Antunes, A. P. et al. A conspiracy of silence: Subsistence hunting rights in the Brazilian Amazon. Land Use Policy 84, 1–11 (2019).
Google Scholar 
Friant, S. et al. Eating bushmeat improves food security in a biodiversity and infectious disease “Hotspot”. EcoHealth 17, 125–138 (2020).PubMed 

Google Scholar 
Fa, J. E., Currie, D. & Meeuwig, J. Bushmeat and food security in the Congo Basin: Linkages between wildlife and people’s future. Environ. Conserv. 30, 71–78 (2003).
Google Scholar 
Borgerson, C., Razafindrapaoly, B., Rajaona, D., Rasolofoniaina, B. J. R. & Golden, C. D. Food insecurity and the unsustainable hunting of wildlife in a UNESCO world heritage site. Front. Sustain. Food Syst. 3, 99. https://doi.org/10.3389/fsufs.2019.00099 (2019).Article 

Google Scholar 
Booth, H. et al. Investigating the risks of removing wild meat from global food systems. Curr. Biol. 31, 1788–1797. https://doi.org/10.1016/j.cub.2021.01.079 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
van Vliet, N., Nebesse, C. & Nasi, R. Bushmeat consumption among rural and urban children from Province Orientale, Democratic Republic of Congo. Oryx 49, 165–174 (2015).
Google Scholar 
Sirén, A. & Machoa, J. Fish, wildlife, and human nutrition in tropical forests: A fat gap?. Interciencia 33, 186–193 (2008).
Google Scholar 
Sarti, F. M. et al. Beyond protein intake: Bushmeat as source of micronutrients in the Amazon. E&S 20, 22 (2015).
Google Scholar 
Hoffman, L. C. What is the role and contribution of meat from wildlife in providing high quality protein for consumption?. Anim. Front. 2, 15 (2012).
Google Scholar 
Fa, J. E. et al. Disentangling the relative effects of bushmeat availability on human nutrition in central Africa. Sci. Rep. 5, 8168. https://doi.org/10.1038/srep08168 (2015).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Castro, T. G., Baraldi, L. G., Muniz, P. T. & Cardoso, M. A. Dietary practices and nutritional status of 0–24-month-old children from Brazilian Amazonia. Public Health Nutr. 12, 2335–2342 (2009).CAS 
PubMed 

Google Scholar 
Mintz, S. W. & Du Bois, C. M. The anthropology of food and eating. Annu. Rev. Anthropol. 31, 99–119 (2002).
Google Scholar 
Lokossou, Y. U. A., Tambe, A. B., Azandjèmè, C. & Mbhenyane, X. Socio-cultural beliefs influence feeding practices of mothers and their children in Grand Popo, Benin. J. Health Popul. Nutr. 40, 33 (2021).PubMed 
PubMed Central 

Google Scholar 
Murphy, S. P. & Allen, L. H. Nutritional importance of animal source foods. J. Nutr. 133, 3932S-3935S (2003).CAS 
PubMed 

Google Scholar 
Neumann, C. G. et al. Animal source foods improve dietary quality, micronutrient status, growth and cognitive function in Kenyan School Children: Background, study design and baseline findings. J. Nutr. 133, 3941S-3949S (2003).CAS 
PubMed 

Google Scholar 
Desalegn, A., Mossie, A. & Gedefaw, L. Nutritional iron deficiency anemia: Magnitude and its predictors among school age children, Southwest Ethiopia: A community based cross-sectional study. PLoS ONE 9, e114059 (2014).ADS 
PubMed Central 

Google Scholar 
Safiri, S. et al. Burden of anemia and its underlying causes in 204 countries and territories, 1990–2019: Results from the Global Burden of Disease Study 2019. J. Hematol. Oncol. 14, 185 (2021).PubMed 
PubMed Central 

Google Scholar 
Vos, T. et al. Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: A systematic analysis for the Global Burden of Disease Study 2015. Lancet 388, 1545–1602 (2016).
Google Scholar 
Investing in the future: a united call to action on vitamin and mineral deficiencies: global report, 2009. (Micronutrient Initiative, 2009).Walker, S. P. et al. Child development: Risk factors for adverse outcomes in developing countries. Lancet 369, 145–157 (2007).PubMed 

Google Scholar 
Saloojee, H. & Pettifor, J. M. Iron deficiency and impaired child development: The relation may be causal, but it may not be a priority for intervention. BMJ 323, 1377–1378 (2001).CAS 
PubMed 
PubMed Central 

Google Scholar 
Neumann, C., Harris, D. M. & Rogers, L. M. Contribution of animal source foods in improving diet quality and function in children in the developing world. Nutr. Res. 22, 193–220 (2002).CAS 

Google Scholar 
Haileselassie, M. et al. Why are animal source foods rarely consumed by 6–23 months old children in rural communities of Northern Ethiopia? A qualitative study. PLoS ONE 15, e0225707 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Victor, R., Baines, S. K., Agho, K. E. & Dibley, M. J. Factors associated with inappropriate complementary feeding practices among children aged 6–23 months in Tanzania: Complementary feeding practices in Tanzania. Matern. Child Nutr. 10, 545–561 (2014).PubMed 

Google Scholar 
Morsello, C. et al. Cultural attitudes are stronger predictors of bushmeat consumption and preference than economic factors among urban Amazonians from Brazil and Colombia. E&S 20, 21 (2015).
Google Scholar 
Parry, L., Barlow, J. & Pereira, H. Wildlife Harvest and Consumption in Amazonia’s Urbanized Wilderness: Wildlife consumption in urbanized Amazonia. Conserv. Lett. 7, 565–574 (2014).
Google Scholar 
Chaves, W. A., Wilkie, D. S., Monroe, M. C. & Sieving, K. E. Market access and wild meat consumption in the central Amazon, Brazil. Biol. Conserv. 212, 240–248 (2017).
Google Scholar 
Dufour, D. L., Piperata, B. A., Murrieta, R. S. S., Wilson, W. M. & Williams, D. D. Amazonian foods and implications for human biology. Ann. Hum. Biol. 43, 330–348 (2016).PubMed 

Google Scholar 
Piperata, B. A. Nutritional status of Ribeirinhos in Brazil and the nutrition transition. Am. J. Phys. Anthropol. 133, 868–878 (2007).PubMed 

Google Scholar 
Garcia, M. T., Granado, F. S. & Cardoso, M. A. Alimentação complementar e estado nutricional de crianças menores de dois anos atendidas no Programa Saúde da Família em Acrelândia, Acre, Amazônia Ocidental Brasileira. Cad. Saúde Pública 27, 305–316 (2011).PubMed 

Google Scholar 
Marques, R. C., Bernardi, J. V. E., Dorea, C. C. & Dórea, J. G. Intestinal parasites, anemia and nutritional status in young children from transitioning Western Amazon. IJERPH 17, 577 (2020).PubMed Central 

Google Scholar 
Granado, F. S., Augusto, R. A., Muniz, P. T. & Cardoso, M. A. Team, the A. S. Anaemia and iron deficiency between 2003 and 2007 in Amazonian children under 2 years of age: Trends and associated factors. Public Health Nutr. 16, 1751–1759 (2013).PubMed 

Google Scholar 
Nogueira-de-Almeida, C. A. et al. Prevalence of childhood anaemia in Brazil: Still a serious health problem: A systematic review and meta-analysis. Public Health Nutr. 24, 6450–6465. https://doi.org/10.1017/S136898002100286X (2021).Article 
PubMed 

Google Scholar 
de Souza, A. A., Mingoti, S. A., Paes-Sousa, R. & Heller, L. Combination of conditional cash transfer program and environmental health interventions reduces child mortality: An ecological study of Brazilian municipalities. BMC Public Health 21, 627 (2021).PubMed 
PubMed Central 

Google Scholar 
Ferreira, H. S. et al. Prevalence of anaemia in Brazilian children in different epidemiological scenarios: An updated meta-analysis. Public Health Nutr. 24, 2171–2184 (2021).PubMed 

Google Scholar 
Leite, M. S. et al. Prevalence of anemia and associated factors among indigenous children in Brazil: Results from the First National Survey of Indigenous People’s Health and Nutrition. Nutr. 12, 69 (2013).
Google Scholar 
WHO, W. H. O. Prevalence of anaemia in children aged 6–59 months (%). https://www.who.int/data/gho/data/indicators/indicator-details/GHO/prevalence-of-anaemia-in-children-under-5-years-(-) (2021).Schreiner, M. A Poverty Probability Index (PPI®) for Brazil (2008). (2010).Walzer, C. COVID-19 and the curse of piecemeal perspectives. Front. Vet. Sci. 7, 582983 (2020).PubMed 
PubMed Central 

Google Scholar 
Carignano, T. P., Morsello, C. & Parry, L. Rural-urban mobility influences wildmeat access and consumption in the Brazilian Amazon. Oryx (In press).Ferreira, M. U. et al. Anemia and iron deficiency in school children, adolescents, and adults: A community-based study in Rural Amazonia. Am. J. Public Health 97, 237–239 (2007).PubMed 
PubMed Central 

Google Scholar 
de Castro, T. G., Silva-Nunes, M., Conde, W. L., Muniz, P. T. & Cardoso, M. A. Anemia e deficiência de ferro em pré-escolares da Amazônia Ocidental brasileira: Prevalência e fatores associados. Cad. Saúde Pública 27, 131–142 (2011).PubMed 

Google Scholar 
Cotta, R. M. M. et al. Social and biological determinants of iron deficiency anemia. Cad. Saúde Pública 27, s309–s320 (2011).PubMed 

Google Scholar 
Chaves, W. A., Valle, D., Tavares, A. S., Morcatty, T. Q. & Wilcove, D. S. Impacts of rural to urban migration, urbanization, and generational change on consumption of wild animals in the Amazon. Conserv. Biol. 35, 1186–1197. https://doi.org/10.1111/cobi.13663 (2020).Article 

Google Scholar 
El Bizri, H. R. et al. Urban wild meat consumption and trade in central Amazonia. Conserv. Biol. 34, 438–448 (2020).PubMed 

Google Scholar 
Chaves, W. A., Valle, D., Tavares, A. S., von Mühlen, E. M. & Wilcove, D. S. Investigating illegal activities that affect biodiversity: The case of wildlife consumption in the Brazilian Amazon. Ecol. Appl. 31, e02402. https://doi.org/10.1002/eap.2402 (2021).Article 
PubMed 

Google Scholar 
Chaves, W. A., Monroe, M. C. & Sieving, K. E. Wild meat trade and consumption in the Central Amazon, Brazil. Hum. Ecol. 47, 733–746 (2019).
Google Scholar 
Ohl-Schacherer, J. et al. The sustainability of subsistence hunting by matsigenka native communities in Manu National Park, Peru. Conserv. Biol. 21, 1174–1185 (2007).PubMed 

Google Scholar 
Shaffer, C. A., Yukuma, C., Marawanaru, E. & Suse, P. Assessing the sustainability of Waiwai subsistence hunting in Guyana by comparison of static indices and spatially explicit, biodemographic models. Anim. Conserv. 21, 148–158 (2018).
Google Scholar 
Pesquisa de orçamentos familiares, 2008–2009. (IBGE, 2010).Aguiar, J. P. L. Tabela de composição de alimentos da Amazônia. Acta Amaz 26, 121–126 (1996).
Google Scholar 
de Bruyn, J. et al. Food composition tables in resource-poor settings: exploring current limitations and opportunities, with a focus on animal-source foods in sub-Saharan Africa. Br. J. Nutr. 116, 1709–1719 (2016).PubMed Central 

Google Scholar 
World Bank. Poverty and Shared Prosperity 2020: Reversals of Fortune. (World Bank, 2020).Coad, L. M. et al. Toward a Sustainable, Participatory and Inclusive Wild Meat Sector. (Center for International Forestry Research (CIFOR) https://doi.org/10.17528/cifor/007046 (2019).Cowlishaw, G., Mendelson, S. & Rowcliffe, J. M. Evidence for post-depletion sustainability in a mature bushmeat market. J. Appl. Ecol. 42, 460–468 (2005).
Google Scholar 
Carignano Torres, P., Morsello, C., Parry, L. & Pardini, R. Forest cover and social relations are more important than economic factors in driving hunting and bushmeat consumption in post-frontier Amazonia. Biol. Conserv. 253, 108823. https://doi.org/10.1016/j.biocon.2020.108823 (2021).Article 

Google Scholar 
Nunes, A. V., Oliveira-Santos, L. G. R., Santos, B. A., Peres, C. A. & Fischer, E. Socioeconomic drivers of hunting efficiency and use of space by traditional Amazonians. Hum. Ecol. 48, 307–315 (2020).
Google Scholar 
Freitas, C. T. et al. Co-management of culturally important species: A tool to promote biodiversity conservation and human well-being. People Nat. 2, 61–81 (2020).
Google Scholar 
Campos-Silva, J. V., Peres, C. A., Antunes, A. P., Valsecchi, J. & Pezzuti, J. Community-based population recovery of overexploited Amazonian wildlife. PECON 15, 266–270 (2017).
Google Scholar 
Nunes, A. V., Peres, C. A., de Constantino, P. A. L., Santos, B. A. & Fischer, E. Irreplaceable socioeconomic value of wild meat extraction to local food security in rural Amazonia. Biol. Conserv. 236, 171–179 (2019).
Google Scholar 
Balarajan, Y., Ramakrishnan, U., Özaltin, E., Shankar, A. H. & Subramanian, S. Anaemia in low-income and middle-income countries. Lancet 378, 2123–2135 (2011).PubMed 

Google Scholar 
Mendes, M. M. et al. Association between iron deficiency anaemia and complementary feeding in children under 2 years assisted by a Conditional Cash Transfer programme. Public Health Nutr. 24, 4080–4090 (2021).PubMed 

Google Scholar 
Brondízio, E. S., de Lima, A. C. B., Schramski, S. & Adams, C. Social and health dimensions of climate change in the Amazon. Ann. Hum. Biol. 43, 405–414 (2016).PubMed 

Google Scholar 
Ingram, D. J. Wild meat in changing times. J. Ethnobiol. 40, 117 (2020).
Google Scholar 
Nunes, A. V., Guariento, R. D., Santos, B. A. & Fischer, E. Wild meat sharing among non-indigenous people in the southwestern Amazon. Behav. Ecol. Sociobiol. 73, 26 (2019).
Google Scholar 
Parry, L. et al. Social vulnerability to climatic shocks is shaped by urban accessibility. Ann. Am. Assoc. Geogr. 108, 125–143 (2018).
Google Scholar 
IBGE, I. B. de G. e E. Censo Demográfico 2010. (2010).IBGE, I. B. de G. e E. Estimativas da população residente para os municípios e para as unidades da federação com data de referência em 1o de julho de 2019. (2019).Cardoso, M. A., Scopel, K. K. G., Muniz, P. T., Villamor, E. & Ferreira, M. U. Underlying factors associated with anemia in amazonian children: A population-based cross-sectional study. PLOS ONE 7, e36341 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Mattiello, V. et al. Diagnosis and management of iron deficiency in children with or without anemia: consensus recommendations of the SPOG Pediatric Hematology Working Group. Eur. J. Pediatr. 179, 527–545 (2020).PubMed 

Google Scholar 
R Core Team. R: The R project for statistical computing. (2015).Zuur, A. F., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer, 2009). https://doi.org/10.1007/978-0-387-87458-6.Book 
MATH 

Google Scholar 
Devereux, S. Social Protection for Rural Poverty Reduction. Rural Transformations Technical Series 1 (2016).Barton, K. Mu-MIn: Multi-model Inference. R Package Version 0.12.2/r18. (2009).Burnham, K. P., Anderson, D. R. & Burnham, K. P. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (Springer, 2002).MATH 

Google Scholar 
Berti, P. R. Intrahousehold distribution of food: A review of the literature and discussion of the implications for food fortification programs. Food Nutr. Bull. 33, S163–S169 (2012).PubMed 

Google Scholar 
Piperata, B. A., Schmeer, K. K., Hadley, C. & Ritchie-Ewing, G. Dietary inequalities of mother–child pairs in the rural Amazon: Evidence of maternal-child buffering?. Soc. Sci. Med. 96, 183–191 (2013).PubMed 
PubMed Central 

Google Scholar  More

Templeton, T. J., Martinsen, E., Kaewthamasorn, M. & Kaneko, O. The rediscovery of malaria parasites of ungulates. Parasitology 143, 1501–1508. https://doi.org/10.1017/S0031182016001141 (2016).Article 
PubMed 

Google Scholar 
Sheather, A. A malaria parasite in the blood of a buffalo. J. Comp. Pathol. Ther. 32, 80026–80027 (1919).Article 

Google Scholar 
Templeton, T. J. et al. Ungulate malaria parasites. Sci. Rep. 6, 23230. https://doi.org/10.1038/srep23230 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kandel, R. C. et al. First report of malaria parasites in water buffalo in Nepal. Vet. Parasitol. Reg. Stud. Rep. 18, 100348. https://doi.org/10.1016/j.vprsr.2019.100348 (2019).Article 

Google Scholar 
Garnham, P. & Edeson, J. Two new malaria parasites of the Malayan mousedeer. Riv. Malariol. 41, 1–8 (1962).CAS 
PubMed 

Google Scholar 
Hoo, C. & Sandosham, A. The early forms of Hepatocystis fieldi and Plasmodium traguli in the Malayan mouse-deer Tragulus javanicus. Med. J. Malays. 22, 299–301 (1968).
Google Scholar 
de Mello, F.d., Paes, S. Sur une plasmodiae du sang des chèvres. Cr. Séanc. Soc. Biol. 88, 829–830 (1923).Kaewthamasorn, M. et al. Genetic homogeneity of goat malaria parasites in Asia and Africa suggests their expansion with domestic goat host. Sci. Rep. 8, 5827. https://doi.org/10.1038/s41598-018-24048-0 (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Rattanarithikul, R. H., Bruce, A., Harbach, R. E., Panthusiri, P. & Coleman, R. E. Illustrated keys to the mosquitoes of Thailand IV. Anopheles. Southeast Asian J Trop Med Public Health. 37 (2006).Walter Reed Biosystematics Unit. 2021. Systematic catalogue of Culicidae. http://mosquitocatalog.org. Last accessed on 20/09/2021.Sinka, M. E. et al. The dominant Anopheles vectors of human malaria in the Asia-Pacific region: occurrence data, distribution maps and bionomic précis. Parasit. Vectors. 4, 89. https://doi.org/10.1186/1756-3305-4-89 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Syafruddin, D. et al. Malaria prevalence in Nias District, North Sumatra Province, Indonesia. Malar. J. 6, 116. https://doi.org/10.1186/1475-2875-6-116 (2007).Article 
PubMed 
PubMed Central 

Google Scholar 
Vantaux, A. et al. Anopheles ecology, genetics and malaria transmission in northern Cambodia. Sci. Rep. 11, 6458. https://doi.org/10.1038/s41598-021-85628-1 (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Manguin, S., Garros, C., Dusfour, I., Harbach, R. & Coosemans, M. Bionomics, taxonomy, and distribution of the major malaria vector taxa of Anopheles subgenus Cellia in Southeast Asia: An updated review. Infect. Genet. Evol. 8, 489–503. https://doi.org/10.1016/j.meegid.2007.11.004 (2008).CAS 
Article 
PubMed 

Google Scholar 
Paredes-Esquivel, C., Donnelly, M. J., Harbach, R. E. & Townson, H. A molecular phylogeny of mosquitoes in the Anopheles barbirostris Subgroup reveals cryptic species: implications for identification of disease vectors. Mol. Phylogenet. Evol. 50, 141–151. https://doi.org/10.1016/j.ympev.2008.10.011 (2009).CAS 
Article 
PubMed 

Google Scholar 
Sungvornyothin, S., Garros, C., Chareonviriyaphap, T. & Manguin, S. How reliable is the humeral pale spot for identification of cryptic species of the Minimus Complex?. J. Am. Mosq. Control. Assoc. 22, 185–191. https://doi.org/10.2987/8756-971X(2006)22[185:HRITHP]2.0.CO;2 (2006).Article 
PubMed 

Google Scholar 
Brosseau, L. et al. A multiplex PCR assay for the identification of five species of the Anopheles barbirostris complex in Thailand. Parasite. Vectors. 12, 223. https://doi.org/10.1186/s13071-019-3494-8 (2019).Article 

Google Scholar 
Taai, K. & Harbach, R. E. Systematics of the Anopheles barbirostris species complex (Diptera: Culicidae: Anophelinae) in Thailand. Zool. J. Linn. Soc. 174, 244–264. https://doi.org/10.1111/zoj.12236 (2015).Article 

Google Scholar 
Dahan-Moss, Y. et al. Member species of the Anopheles gambiae complex can be misidentified as Anopheles leesoni. Malar. J. 19, 1–9. https://doi.org/10.1186/s12936-020-03168-x (2020).CAS 
Article 

Google Scholar 
De Ang, J. X., Yaman, K., Kadir, K. A., Matusop, A. & Singh, B. New vectors that are early feeders for Plasmodium knowlesi and other simian malaria parasites in Sarawak, Malaysian Borneo. Sci. Rep. https://doi.org/10.1038/s41598-021-86107-3 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Van Bortel, W. et al. Confirmation of Anopheles varuna in Vietnam, previously misidentified and mistargeted as the malaria vector Anopheles minimus. Am. J. Trop. Med. Hyg. 65, 729–732. https://doi.org/10.4269/ajtmh.2001.65.729 (2001).Article 
PubMed 

Google Scholar 
Wharton, R., Eyles, D. E., Warren, M., Moorhouse, D. & Sandosham, A. Investigations leading to the identification of members of the Anopheles umbrosus group as the probable vectors of mouse deer malaria. Bull. World. Health. Organ. 29, 357 (1963).CAS 
PubMed 
PubMed Central 

Google Scholar 
Boundenga, L. et al. Haemosporidian parasites of antelopes and other vertebrates from Gabon, Central Africa. PLoS ONE 11, e0148958. https://doi.org/10.1371/journal.pone.0148958 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Martinsen, E. S. et al. Hidden in plain sight: Cryptic and endemic malaria parasites in North American white-tailed deer (Odocoileus virginianus). Sci. Adv. 2, e1501486. https://doi.org/10.1126/sciadv.1501486 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Garnham, P. C. C. Malaria Parasites and Other Haemosporidia (Blackwell Sci. Pub, 1966).
Google Scholar 
Nguyen, A. H. L., Tiawsirisup, S. & Kaewthamasorn, M. Low level of genetic diversity and high occurrence of vector-borne protozoa in water buffaloes in Thailand based on 18S ribosomal RNA and mitochondrial cytochrome b genes. Infect. Genet. Evol. 82, 104304. https://doi.org/10.1016/j.meegid.2020.104304 (2020).CAS 
Article 
PubMed 

Google Scholar 
Hebert, P. D., Cywinska, A. & Ball, S. L. Biological identifications through DNA barcodes. Proc. R. Soc. Lond. B Biol. Sci. 270, 313–321. https://doi.org/10.1098/rspb.2002.2218 (2003).CAS 
Article 

Google Scholar 
Cywinska, A., Hunter, F. & Hebert, P. D. Identifying Canadian mosquito species through DNA barcodes. Med. Vet. Entomol. 20, 413–424. https://doi.org/10.1111/j.1365-2915.2006.00653.x (2006).CAS 
Article 
PubMed 

Google Scholar 
Ogola, E. O., Chepkorir, E., Sang, R. & Tchouassi, D. P. A previously unreported potential malaria vector in a dry ecology of Kenya. Parasites Vectors 12, 80. https://doi.org/10.1186/s13071-019-3332-z (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Maquart, P.-O., Fontenille, D., Rahola, N., Yean, S. & Boyer, S. Checklist of the mosquito fauna (Diptera, Culicidae) of Cambodia. Parasite 28, 60. https://doi.org/10.1051/parasite/2021056 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Saeung, A. et al. Geographic distribution and genetic compatibility among six karyotypic forms of Anopheles peditaeniatus (Diptera: Culicidae) in Thailand. Trop. Biomed. 29, 613–625 (2012).CAS 
PubMed 

Google Scholar 
Tainchum, K., Kongmee, M., Manguin, S., Bangs, M. J. & Chareonviriyaphap, T. Anopheles species diversity and distribution of the malaria vectors of Thailand. Trends Parasitol. 31, 109–119. https://doi.org/10.1016/j.pt.2015.01.004 (2015).Article 
PubMed 

Google Scholar 
Chookaew, S. et al. Anopheles species composition in malaria high-risk areas in Ranong Province. Dis. Cont. J. 46, 483–493. https://doi.org/10.14456/dcj.2020.45 (2020).Article 

Google Scholar 
Reid, J. A. The Anopheles barbirostris group (Diptera, Culicidae). Bull. Entomol. Res. 53, 1–57 (1962).Article 

Google Scholar 
Harrison, B. A. & Scanlon, J. E. Medical entomology studies–II. The subgenus Anopheles in Thailand (Diptera: Culicidae). Contributions of the American Entomological Institute (Ann Arbor) 12 (1): iv + 1–iv 307 (1975).Wang, Y., Xu, J. & Ma, Y. Molecular characterization of cryptic species of Anopheles barbirostris van der Wulp in China. Parasite Vectors 7, 592. https://doi.org/10.1186/s13071-014-0592-5 (2014).CAS 
Article 

Google Scholar 
Wang, G. et al. An evaluation of the suitability of COI and COII gene variation for reconstructing the phylogeny of, and identifying cryptic species in, anopheline mosquitoes (Diptera Culicidae). Mitochondrial DNA Part A. 28, 769–777. https://doi.org/10.1080/24701394.2016.1186665 (2017).CAS 
Article 

Google Scholar 
Davidson, J. R. et al. Molecular analysis reveals a high diversity of Anopheles species in Karama, West Sulawesi, Indonesia. Parasite Vectors https://doi.org/10.1186/s13071-020-04252-6 (2020).Article 

Google Scholar 
Beebe, N. W. DNA barcoding mosquitoes: advice for potential prospectors. Parasitology 145(5), 622–633. https://doi.org/10.1017/S0031182018000343 (2018).CAS 
Article 
PubMed 

Google Scholar 
Gunathilaka, N. Illustrated key to the adult female Anopheles (Diptera: Culicidae) mosquitoes of Sri Lanka. Appl. Entomol. Zool. 52, 69–77. https://doi.org/10.1007/s13355-016-0455-y (2017).Article 
PubMed 

Google Scholar 
WHO. Pictorial identification key of important disease vectors in the WHO South-East Asia Region. https://apps.who.int/iris/handle/10665/332202 (accessed 20 August 2021).Rigg, C. A., Hurtado, L. A., Calzada, J. E. & Chaves, L. F. Malaria infection rates in Anopheles albimanus (Diptera: Culicidae) at Ipetí-Guna, a village within a region targeted for malaria elimination in Panamá. Infect. Genet. Evol. 69, 216–223. https://doi.org/10.1016/j.meegid.2019.02.003 (2019).Article 
PubMed 

Google Scholar 
Torres-Cosme, R. et al. Natural malaria infection in anophelines vectors and their incrimination in local malaria transmission in Darién, Panama. PLoS ONE 16, e0250059. https://doi.org/10.1371/journal.pone.0250059 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Beebe, N. W. & Saul, A. Discrimination of all Members of the Anopheles punctulatus complex by polymerase chain reaction-restriction fragment length polymorphism analysis. Am. J. Trop. Med. Hyg. 53, 478–481. https://doi.org/10.4269/ajtmh.1995.53.478 (1995).CAS 
Article 
PubMed 

Google Scholar 
Perkins, S. L. & Schall, J. J. A molecular phylogeny of malaria parasites recovered from cytochrome b gene sequences. J. Parasitol. 88, 972–978. https://doi.org/10.1645/0022-3395(2002)088[0972:AMPOMP]2.0.CO;2 (2002).CAS 
Article 
PubMed 

Google Scholar 
Snounou, G. et al. High sensitivity of detection of human malaria parasites by the use of nested polymerase chain reaction. Mol. Biochem. Parasitol. 61, 315–320. https://doi.org/10.1016/0166-6851(93)90077-b (1993).CAS 
Article 
PubMed 

Google Scholar 
Hall, T. A. BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic. Acids. Symp. Ser. 41, 95–98 (1999).CAS 

Google Scholar 
Schoener, E. et al. Avian Plasmodium in Eastern Austrian mosquitoes. Malar. J. 16, 389. https://doi.org/10.1186/s12936-017-2035-1 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Ventim, R. et al. Avian malaria infections in western European mosquitoes. Parasitol. Res. 111, 637–645. https://doi.org/10.1007/s00436-012-2880-3 (2012).Article 
PubMed 

Google Scholar 
Huelsenbeck, J. P. & Ronquist, F. MRBAYES: Bayesian inference of phylogeny. Bioinformatics 17, 754–755 (2001).CAS 
Article 

Google Scholar 
Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in Bayesian phylogenetics using tracer 1.7. Syst. Biol. 67, 901–904 (2018).CAS 
Article 

Google Scholar  More

Bethany, J., Giraldo-Silva, A., Nelson, C., Barger, N. N. & Garcia-Pichel, F. Optimizing the production of nursery-based biological soil crusts for restoration of arid land soils. Appl. Environ. Microbiol. 85(15), e00735-e819 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Belnap, J. & Gardner, J. S. Soil microstructure in soils of the colorado plateau—The role of the cyanobacterium Microcoleus-vaginatus. Gt. Basin Nat. 53(1), 40–47 (1993).
Google Scholar 
Belnap, J. Factors influencing nitrogen fixation and nitrogen release in biological soil crusts. Ecol. Stud. Biol. Soil Crusts Struct. Funct. Manag. 150, 241–261 (2001).
Google Scholar 
Cameron, R. E. & Blank, G. B. Desert algae: Soil crusts and diaphanous substrata as algal habitats. Tech. Rep. Jet Propul. Lab. Calif. Technol. 32–971, 1–41 (1966).
Google Scholar 
Friedmann EI, Galun M. Desert algae lichens and fungi. in Desert Biology (Brown Jr, G.W. eds). Vol. 2. 165–212. (Illus Academic Press, Inc., 1974).Maier, S., Tamm, A., Wu, D.A.-O., Caesar, J., Grube, M., & Weber, B.A.-O. Photoautotrophic Organisms Control Microbial Abundance, Diversity, and Physiology in Different Types of Biological Soil Crusts. (1751–7370 (electronic)).Cable, J. M. & Huxman, T. E. Precipitation pulse size effects on Sonoran Desert soil microbial crusts. Oecologia 141(2), 317–324 (2004).ADS 
PubMed 

Google Scholar 
Evans, R. D. & Johansen, J. R. Microbiotic crusts and ecosystem processes. Crit. Rev. Plant Sci. 18(2), 183–225 (1999).
Google Scholar 
Thompson, J. N. et al. Frontiers of ecology. Bioscience 51(1), 15–24 (2001).
Google Scholar 
Warren, S. D., Rosentreter, R. & Pietrasiak, N. Biological soil crusts of the Great Plains: A review. Rangel Ecol. Manag. 1(78), 213–219 (2021).
Google Scholar 
Warren, S. D. et al. Biological soil crust response to late season prescribed fire in a Great Basin Juniper Woodland. Rangel. Ecol. Manag. 68(3), 241–247 (2015).
Google Scholar 
Thomas, A. D., Hoon, S. R. & Linton, P. E. Carbon dioxide fluxes from cyanobacteria crusted soils in the Kalahari. Appl. Soil Ecol. 39, 254–263 (2008).
Google Scholar 
Williams, A. J., Buck, B. J. & Beyene, M. A. Biological soil crusts in the Mojave Desert, USA: Micromorphology and pedogenesis. Soil Sci. Soc. Am. J. 76(5), 1685–1695 (2012).ADS 
CAS 

Google Scholar 
Belnap, J. & Lange, O. L. Ecological studies: Biological soil crusts: Structure, function, and management. Ecol. Stud. Biol. Soil Crusts Struct. Funct. Manag. 150, 1–503 (2001).
Google Scholar 
Jordan, W. R. I. Restoration ecology: A synthetic approach to ecological research. Rehabil. Damaged Ecosyst. 2, 373–384 (1995).
Google Scholar 
Nash, T. H. et al. Photosynthetic patterns of Sonoran desert lichens.1. Environmental considerations and preliminary field-measurements. Flora 172(4), 335–345 (1982).
Google Scholar 
St. Clair, L. L., Johansen, J. R. & Rushforth, S. R. Lichens of soil crust communities in the Intermountain Area of the western United States. Gt Basin Nat. 53(1), 5 (1993).
Google Scholar 
Bowker, M. A., Belnap, J. & Miller, M. E. Spatial modeling of biological soil crusts to support rangeland assessment and monitoring. Rangel. Ecol. Manag. 59(5), 519–529 (2006).
Google Scholar 
Mayland, H. F., McIntosh, T. H. & Fuller, W. H. Fixation of isotopic nitrogen on a semiarid soil by algal crust organisms. Soil Sci. Soc. Am. Proc. 30(1), 56 (1966).ADS 
CAS 

Google Scholar 
McIlvanie, S. K. Grass seedling establishment, and productivity—Overgrazed vs. protected range soils. Ecology 23(2), 228–231 (1942).
Google Scholar 
Webb, R. H. & Wilshire, H. G. Environmental Effects of Off-Road Vehicles : Impacts and Management in Arid Regions (Springer, 1983).
Google Scholar 
Zobel, D. & Antos, J. A decade of recovery of understory vegetation buried by volcanic tephra from Mount St. Helens. Ecol. Monogr. 1, 67 (1997).
Google Scholar 
Condon, L. & Pyke, D. Resiliency of biological soil crusts and vascular plants varies among morphogroups with disturbance intensity. Plant Soil. 12, 433 (2020).
Google Scholar 
Harper, K., & Marble, J. A role for nonvascular plants in management of arid and semiarid rangelands. in Vegetation Science Applications for Rangeland Analysis and Management [Internet] (Tueller, P.T., ed.). Handbook of Vegetation Science. Vol. 14. 135–169. https://doi.org/10.1007/978-94-009-3085-8_7. (Springer, 1988). Evans, R. D. & Belnap, J. Long-term consequences of disturbance on nitrogen dynamics in an arid ecosystem. Ecology 80(1), 150–160 (1999).
Google Scholar 
Sheridan, R. P. Impact of emissions from coal-fired electricity generating facilities on N2-fixing lichens. Bryologist 82(1), 54–58 (1979).CAS 

Google Scholar 
Henriksson, L. E. & Dasilva, E. J. Effects of some inorganic elements on nitrogen-fixation in blue-green-algae and some ecological aspects of pollution. Z. Allg. Mikrobiol. 18(7), 487–494 (1978).CAS 
PubMed 

Google Scholar 
Freebury, C. Lichens and lichenicolous fungi of Grasslands National Park (Saskatchewan, Canada). Opusc Philolichenum 13, 102–121 (2009).
Google Scholar 
Szyja, M. et al. Neglected but potent dry forest players: ecological role and ecosystem service provision of biological soil crusts in the human-modified Caatinga. Front. Ecol. Evol. (Internet). https://doi.org/10.3389/fevo.2019.00482 (2019).Article 

Google Scholar 
Rosentreter, R. Biological soil of crusts of North American drylands: Cryptic diversity at risk. in Reference Module in Earth Systems and Environmental Sciences [Internet]. https://www.sciencedirect.com/science/article/pii/B9780128211397000738 (Elsevier, 2021). Kranz, C. N., McLaughlin, R. A., Johnson, A., Miller, G. & Heitman, J. L. The effects of compost incorporation on soil physical properties in urban soils—A concise review. J. Environ. Manag. 261, 110209 (2020).
Google Scholar 
Barberán, A. et al. Continental-scale distributions of dust-associated bacteria and fungi. Proc. Natl. Acad. Sci. 112(18), 5756 (2015).ADS 
PubMed 
PubMed Central 

Google Scholar 
Kaye, J. P., Groffman, P. M., Grimm, N. B., Baker, L. A. & Pouyat, R. V. A distinct urban biogeochemistry?. Trends Ecol. Evol. 21(4), 192–199 (2006).PubMed 

Google Scholar 
Pavao-Zuckerman, M. A. The nature of urban soils and their role in ecological restoration in cities. Restor. Ecol. 16(4), 642–649 (2008).
Google Scholar 
Pouyat, R., Groffman, P., Yesilonis, I. & Hernandez, L. Soil carbon pools and fluxes in urban ecosystems. Environ. Pollut. 116, S107–S118 (2002).CAS 
PubMed 

Google Scholar 
Behzad, H., Mineta, K., & Gojobori, T. Global Ramifications of Dust and Sandstorm Microbiota. (1759–6653 (electronic)).Warren, S., Clair, L. & Leavitt, S. Aerobiology and passive restoration of biological soil crusts. Aerobiologia 3, 35 (2021).
Google Scholar 
Hall, S. J. et al. Urbanization alters soil microbial functioning in the Sonoran Desert. Ecosystems 12(4), 654–671 (2009).CAS 

Google Scholar 
Ball, B. A. & Guevara, J. A. The nutrient plasticity of moss-dominated crust in the urbanized Sonoran Desert. Plant Soil. 389(1–2), 225–235 (2015).CAS 

Google Scholar 
Allen, C. D. Monitoring environmental impact in the Upper Sonoran lifestyle: A new tool for rapid ecological assessment. Environ. Manag. 43(2), 346–356 (2009).ADS 

Google Scholar 
Evans, R. A. & Love, R. M. The step-point method of sampling: A practical tool in range research. J. Range Manag. 10(5), 208–212 (1957).
Google Scholar 
Coulloudon, B., & National Applied Resource Sciences C. Sampling Vegetation Attributes Interagency Technical Reference [Internet]. http://www.blm.gov/nstc/library/pdf/samplveg.pdf. (Bureau of Land Management : National Business Center, 1999). Faithfull, N. T. Methods in agricultural chemical analysis: A practical handbook. Methods Agric. Chem. Anal. Pract. Handb. 1–22, 1–266 (2002).
Google Scholar 
Kuske, C. R., Yeager, C. M., Johnson, S., Ticknor, L. O. & Belnap, J. Response and resilience of soil biocrust bacterial communities to chronic physical disturbance in arid shrublands. ISME J. 6(4), 886–897 (2012).CAS 
PubMed 

Google Scholar 
Lorenz, K. & Lal, R. Biogeochemical C and N cycles in urban soils. Environ. Int. 35(1), 1–8 (2009).CAS 
PubMed 

Google Scholar 
Chamizo, S., Canton, Y., Lazaro, R., Sole-Benet, A. & Domingo, F. Crust composition and disturbance drive infiltration through biological soil crusts in semiarid ecosystems. Ecosystems 15(1), 148–161 (2012).
Google Scholar 
Kidron, G. J. & Gutschick, V. P. Soil moisture correlates with shrub-grass association in the Chihuahuan Desert. CATENA 107, 71–79 (2013).
Google Scholar 
Kidron, G. J., Monger, H. C., Vonshak, A. & Conrod, W. Contrasting effects of microbiotic crusts on runoff in desert surfaces. Geomorphology 15(139), 484–494 (2012).ADS 

Google Scholar 
Berdugo, M., Soliveres, S. & Maestre, F. T. Vascular plants and biocrusts modulate how abiotic factors affect wetting and drying events in drylands. Ecosystems 17, 1242 (2014).CAS 

Google Scholar 
Maestre, F. T. et al. Changes in biocrust cover drive carbon cycle responses to climate change in drylands. Glob Change Biol. 19, 3835 (2013).ADS 

Google Scholar 
Valenzuela, A. et al. Aerosol radiative forcing during African desert dust events (2005–2010) over southeastern Spain. Atmos. Chem. Phys. 12(21), 10331–10351 (2012).ADS 
CAS 

Google Scholar 
Kaya, S., Basar, U. G., Karaca, M. & Seker, D. Z. Assessment of urban heat islands using remotely sensed data. Ekoloji 21(84), 107–113 (2012).
Google Scholar 
Demmigadams, B. et al. Effect of high light on the efficiency of photochemical energy-conversion in a variety of lichen species with green and blue-green phycobionts. Planta 180(3), 400–409 (1990).CAS 

Google Scholar 
Gauslaa, Y. & Rikkinen, J. What’s behind the pretty colours? A study on the photobiology of lichens. Nord. J. Bot. 17(5), 556–556 (1995).
Google Scholar 
Garciapichel, F. & Castenholz, R. W. Characterization and biological implications of scytonemin, a cyanobacterial sheath pigment. J. Phycol. 27(3), 395–409 (1991).CAS 

Google Scholar 
Garcia-Pichel, F. & Castenholz, R. W. The role of sheath pigments in the adaptation of terrestrial cyanobacteria to near UV radiation. J. Phycol. 27(3 SUPPL), 24–24 (1991).
Google Scholar 
McDonnell, M. J. et al. Ecosystem processes along an urban-to-rural gradient. Urban Ecosyst. 1(1), 21–36 (1997).
Google Scholar 
Pavao-Zuckerman, M. A. & Byrne, L. B. Scratching the surface and digging deeper: Exploring ecological theories in urban soils. Urban Ecosyst. 12(1), 9–20 (2009).
Google Scholar 
Pavao-Zuckerman, M. A. Urban greenscape, soils, and ecosystem functioning in a semi-arid urban ecosystem. J. Nematol. 41(4), 369–370 (2009).
Google Scholar 
Collins, S. L. et al. Pulse dynamics and microbial processes in aridland ecosystems. J. Ecol. 96(3), 413–420 (2008).
Google Scholar 
Noy-Meir, I. Desert ecosystems environment and producers. In Annual Review on Ecology System (Johnston Richard, F. ed.). Vol. 4. 25–51. (Illus Map Annu Rev Inc, 1973). More

Kaplan, J. O., Krumhardt, K. M. & Zimmermann, N. Quat. Sci. Rev. 28, 3016–3034 (2009).Article 

Google Scholar 
Zheng, Z. et al. Proc. Natl Acad. Sci. USA 118, e2022210118 (2021).CAS 
Article 

Google Scholar 
Lewis, S. L. & Maslin, M. A. Nature 519, 171–180 (2015).CAS 
Article 

Google Scholar 
Steffen, W. et al. Anthr. Rev. 2, 81–98 (2015).
Google Scholar 
Ripple, W. J. et al. BioScience 70, 8–12 (2020).
Google Scholar 
Cohen, J. M., Lajeunesse, M. J. & Rohr, J. R. Nat. Clim. Change 8, 224–228 (2018).Article 

Google Scholar 
Vitasse, Y. et al. Biol. Rev. 96, 1816–1835 (2021).Article 

Google Scholar 
Aono, Y. & Kazui, K. Int. J. Climatol. 28, 905–914 (2008).Article 

Google Scholar 
Sparks, T. H. & Carey, P. D. J. Ecol. 83, 321–329 (1995).Article 

Google Scholar 
Ge, Q. et al. J. Geophys. Res. Biogeosci. 119, 301–311 (2014).Article 

Google Scholar 
IPCC Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).Post, E., Steinman, B. A. & Mann, M. E. Sci. Rep. 8, 3927 (2018).Article 

Google Scholar 
Primack, R. B. & Miller-Rushing, A. J. Bioscience 62, 170–181 (2012).Article 

Google Scholar 
Kharouba, H. M. et al. Proc. Natl Acad. Sci. USA 115, 5211–5216 (2018).CAS 
Article 

Google Scholar 
Richardson, A. D. et al. Agric. For. Meteorol. 169, 156–173 (2013).Article 

Google Scholar 
Parker, D. E., Legg, T. P. & Folland, C. K. Int. J. Climatol. 12, 317–342 (1992).Article 

Google Scholar  More

System dynamics methodThe SD method applies systemic processing to simulate complex non-linear dynamics and feedback. Systemic processing resorts to various tools to simulate complex system behavior and performance24. Systems evolve through states, which change with flows. An example of a state variable is water storage in the study of lakes. The SD method simulates changes in system states driven by flows and various feedbacks25.This work employs the SD method to simulate storage change in Lake Urmia in one historical period (1957–2005) and two future periods (2021–2050 and 2051–2080). The lake’s water volume is the state variable, which is governed by inflows (precipitation, surface water inflows, and groundwater inflows) and outflows (evaporation, leakage, and surface water outflows). The lake’s mass balance equation is expressed as:$$S_{t + 1} = intlimits_{t}^{t + 1} {[I_{s} – O_{s} ]ds + S_{t} }$$
(1)
where St+1 , St, Is, and Os denote the lake’s storage at time t + 1, the lake’s storage at time t, the inflow rate to the lake at time s (units of volume/time), and the outflow rate from the lake at time s (units of volume/time), respectively.The SD method employs the Euler and Runge Kutta methods for the solution of differential equations. The software STELLA, Vensim, Powersim, and Dynamo feature SD solvers26. This work applies the widely-used Vensim software27.Climate changeThe data sets needed for modeling Lake Urmia’s storage over the two future periods were generated after simulating the lake’s water balance during the historical period. HADCM3, a coupled atmosphere–ocean general circulation model’s (AOGCM) climate projections were used to generate precipitation and surface temperature projections over the future periods. The AOGCM data at coarse spatial scales were downscaled to the regional scale suitable for lake storage simulation. The commonly used downscaling methods are statistic and dynamic in nature28,29. This works applies the delta-change downscaling method, in which monthly temperature and precipitation differences between the future and historical are calculated by29:$$Delta T_{t} = overline{T}_{GCM,fut,t} – overline{T}_{GCM,hist,t}$$
(2)
$$Delta P_{t} = overline{P}_{GCM,fut,t} – overline{P}_{GCM,hist,t}$$
(3)
where ∆Tt denotes the difference in long-term average temperatures simulated by HADCM3 for the future ((overline{T}_{GCM,fut,t})) and historical ((overline{T}_{GCM,hist,t})) periods in month t (°C); ∆Pt represents the difference in long-term average precipitations simulated by HADCM3 for the future ((overline{P}_{GCM,fut,t})) and historical ((overline{P}_{GCM,hist,t})) periods in month t (mm). Then, ∆Tt and ∆Pt are applied to project the future downscaled data as follows29:$$T_{t} = T_{obs,t} + , Delta T_{t}$$
(4)
$$P_{t} = P_{obs,t} { + }Delta P_{t}$$
(5)
where Tobs,t, and Pobs,t denote respectively the observed temperature (°C) and precipitation (mm) in month t in the baseline period; and Tt and Pt are the downscaled temperature (°C) and precipitation (mm) in month t of the future period, respectively. Delta-change downscaling is a simple yet efficient option when it comes to spatial downscaling of climate change projections (e.g.30,31,32). The gist of this method is to replicate the changing patterns that are projected by the atmospheric ocean general circulation models (AOGCMs) to generate the climate change patterns of hydro-climatic variables on a regional scale. As such, one would simply compute the relative changes in the long-term variations of the variable that is projected by the models within the baseline and future timeframes. These relative changing patterns would be applied to the historical data to project the impact of climate change on a local scale.Rainfall-runoff modelingThe IHACRES (identification of unit hydrographs and component flows from rainfall, evapotranspiration and streamflow) model is herein applied to simulate runoff from precipitation. Ashofteh et al.33 implemented the IHACRES model to investigate the effects of climate change on reservoir performance in agricultural water supply. Ashofteh et al.34 evaluated the probability of flood occurrence in future periods with IHACRES.The IHACRES model includes a non-linear loss module and a linear unit hydrograph module. The non-linear loss module converts the observed rainfall into the effective rainfall, after which the linear unit hydrograph module converts the effective rainfall into the simulated streamflow35. Here, precipitation rk in time step k is converted to effective precipitation uk through the non-linear loss module employing a catchment wetness index sk:$$u_{k} = , s_{k} times , r_{k}$$
(6)
The effective precipitation is converted to the surface runoff in time step k with the linear unit hydrograph module. The parameters of this model can be set through a thorough grid numeric search and trial-and-error. Perhaps, one of the major advantages of the IHACRES model over other commonly-used rainfall-runoff models is its minimal input data requirement (i.e., air temperature and precipitation)31,35.The other alternative for hydrologic simulation is to use data-driven models. Here, the multilayer perceptron (MLP), a variety of the artificial neural network (ANN) method, was also used to simulate runoff. This model consists of an inlet layer, one or several middle (hidden) layer(s), and an output layer. All of the neurons of a layer are connected to the ones in the next layer, forming a network with complete connections. The primary parameters in modeling the neural network of MLP are: (1) the number of neurons in each layer, (2) the number of layers in the network, and (3) the forcing functions. A regular MLP neural network has three layers36. The first and the third layers are respectively the system inputs and outputs. The middle layer consists of neurons that perform calculations on the inputs. Choosing the number of layers in a neural network is made by trial and error37. From a hydrological simulation standpoint the main idea behind this model is to create a suitable artificial neural network that is capable of accurately converting a set of hydro-climatic variables such as precipitation and temperature as input data into streamflow values. It should be noted that, like most data-driven models, the process of opting for a proper neural network architecture (i.e., selecting the number of layers, number of neurons, and the forcing function) is, for the most part, a trial-and-error procedure.One must objectively evaluate the performance of the hydrological models in order to opt for the setting of a suitable parameter. The root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE) are herein employed to assess the performance of the rainfall-runoff model. They are respectively calculated as follows:$$RMSE = sqrt {frac{{sumlimits_{t = 1}^{N} {(x_{t} – y_{t} )^{2} } }}{N}}$$
(7)
$$R^{2} = left( {frac{{sumnolimits_{t = 1}^{N} {(x_{t} – overline{x} ).(y_{t} – overline{y} )} }}{{sqrt {sumnolimits_{t = 1}^{N} {(x_{t} – overline{x} )^{2} } } .sqrt {sumnolimits_{t = 1}^{N} {(y_{t} – overline{y} )^{2} } } }}} right)^{2}$$
(8)
$$MAE = frac{{sumnolimits_{t = 1}^{N} {left| {x_{t} – y_{t} } right|} }}{N}$$
(9)
where xt , yt, and N denote the simulated value in time step t; the observed value in time step t; and the number data values, respectively. Large errors have a disproportionately large effect on RMSE or MAE.Performance criteriaVarious quantitative measures can be used to assess the performance of water resources systems under different strategies. When it comes to water resources planning and management, perhaps, some of the most common performance criteria are the probability-based performance criteria (PBPC) (i.e., reliability, vulnerability, and resiliency)31,38. In this context, reliability represents the probability of successful functioning of a system; resiliency measures the probability of successful functioning following a system failure; lastly, vulnerability is the severity of failure during an operation horizon39,40. The basic idea behind a performance evaluation attribute is to provide a quantitative measure to describe and assess the performance of a system. In the context of water resources planning and management, these measures have proven time and again that they can be reliable options to evaluate a set of strategic management options objectively (see, e.g.40,41,42,43, and44, just to name a few).Operating policyAny water resources system requires something called the “rule curve,” which determines how water is allocated in a given situation45. A common and effective rule curve when it comes to operation of water resource systems is the standard operation policy (SOP). SOP is a simple, and perhaps best-known real-time operation policy in water resources planning and management46. The core principle here is to minimize the water shortage at the current time step with no conservation policy (e.g., hedging rules) in place. The SOP, as a standard rule curve, determines how the operator acts to control a system at any given state of a reservoir47,48. This rule curve is established as an attempt to balance various water demands including but not limited to flood control, hydropower, water supply, and recreation49. A SOP operating system attempts to release water to meet a water demand at the current time, with no regard to the future. Thus, according to the SOP’s principle, the decision-makers, first allocate the available water to meet the demand of the stakeholder with the highest priority. After this first water demand is fully satisfied, the available water can be used for the next demand. Such an allocation process continues until no water is available.Ethics approvalAll authors accept all ethical approvals.Consent to participateAll authors consent to participate.Consent to publishAll authors consent to publish. More

Meng, L. et al. Proc. Natl Acad. Sci. USA 117, 4228 (2020).CAS 
Article 

Google Scholar 
Wohlfahrt, G., Tomelleri, E. & Hammerle, A. Nat. Ecol. Evol. 3, 1668–1674 (2019).Article 

Google Scholar 
Wortman, S. E. & Lovell, S. T. J. Environ. Qual. 42, 1283–1294 (2013).CAS 
Article 

Google Scholar 
Su, Y. et al. Agri. For. Meterol. 280, 107765 (2020).Article 

Google Scholar 
Smith, I. A., Dearborn, V. K. & Hutyra, L. R. PLoS ONE 14, e0215846 (2019).Article 

Google Scholar 
Richardson, A. D. et al. Nature 560, 368–371 (2018).CAS 
Article 

Google Scholar 
Meineke, E. K., Dunn, R. R. & Frank, S. D. Biol. Lett. 10, 20140586 (2014).Article 

Google Scholar 
Liu, J. et al. Tour. Manag. 70, 262–272 (2019).Article 

Google Scholar 
Li, X. et al. Remote Sens. Environ. 222, 267–274 (2019).CAS 
Article 

Google Scholar 
Wang, S. et al. Nat. Ecol. Evol. 3, 1076–1085 (2019).Article 

Google Scholar 
Feeley, K. J. et al. Nat. Clim. Change 10, 965–970 (2020).CAS 
Article 

Google Scholar 
Li, D. et al. Nat. Ecol. Evol. 3, 1661–1667 (2019).Article 

Google Scholar 
Li, X. et al. Earth Syst. Sci. Data 11, 881–894 (2019).Article 

Google Scholar 
Román, M. O. et al. Remote Sens. Environ. 210, 113–143 (2018).Article 

Google Scholar 
Li, X. et al. Remote Sens. Environ. 215, 74–84 (2018).Article 

Google Scholar  More