Philippa Kaur

These authors contributed equally: Celso H. L. Silva Junior, Nathália S. Carvalho, Ana C. M. Pessôa.Tropical Ecosystems and Environmental Sciences Laboratory (TREES), São José dos Campos, São Paulo, BrazilCelso H. L. Silva Junior, Nathália S. Carvalho, Ana C. M. Pessôa, João B. C. Reis, Aline Pontes-Lopes, Juan Doblas, Wesley Campanharo, Henrique Cassol, Yosio E. Shimabukuro, Liana O. Anderson & Luiz E. O. C. AragãoInstituto Nacional de Pesquisas Espaciais (INPE), São José dos Campos, São Paulo, BrazilCelso H. L. Silva Junior, Nathália S. Carvalho, Ana C. M. Pessôa, Aline Pontes-Lopes, Juan Doblas, Wesley Campanharo, Henrique Cassol, Luciana Gatti, Ana P. Aguiar, Yosio E. Shimabukuro & Luiz E. O. C. AragãoUniversidade Estadual do Maranhão (UEMA), São Luís, Maranhão, BrazilCelso H. L. Silva JuniorCentro Nacional de Monitoramento e Alertas de Desastres Naturais (CEMADEN), São José dos Campos, São Paulo, BrazilJoão B. C. Reis & Liana O. AndersonUniversity of Bristol, Bristol, UKViola Heinrich & Joanna HouseInstituto de Pesquisa Ambiental da Amazônia (IPAM), Brasília, Distrito Federal, BrazilAne Alencar, Camila Silva & Paulo BrandoUniversidade Estadual de Campinas (UNICAMP), Campinas, São Paulo, BrazilDavid M. LapolaEcología del Paisaje y Modelación de Ecosistemas (ECOLMOD), Universidad Nacional de Colombia (UNAL), Bogota, ColombiaDolors ArmenterasUniversidade de Brasília, Brasília, Distrito Federal, BrazilEraldo A. T. MatricardiUniversity of Oxford, Oxford, UKErika BerenguerLancaster University, Lancaster, UKCamila Silva, Erika Berenguer & Jos BarlowSouth Dakota State University, Brookings, SD, USAIzaya NumataEmpresa Brasileira de Pesquisa Agropecuária (EMBRAPA) Amazônia Oriental, Belém, Pará, BrazilJoice FerreiraUniversity of California, Irvine, CA, USAPaulo BrandoWoodwell Climate Research Center, Falmouth, MA, USAPaulo BrandoInstituto Nacional de Pesquisas da Amazônia (INPA), Manaus, Amazonas, BrazilPhilip M. FearnsideJet Propulsion Laboratory (JPL), Pasadena, CA, USASassan SaatchiUniversity of California, Los Angeles, CA, USASassan SaatchiUniversidade Federal do Acre (UFAC), Cruzeiro do Sul, Acre, BrazilSonaira SilvaUniversity of Exeter, Exeter, UKStephen Sitch & Luiz E. O. C. AragãoStockholm Resilience Centre, Stockholm University, Stockholm, SwedenAna P. AguiarSchool of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL, USACarlos A. SilvaEuropean Commission, Joint Research Centre (JRC), Ispra, VA, ItalyChristelle Vancutsem, Frédéric Achard & René BeuchleCenter for International Forestry Research (CIFOR), Bogor, IndonesiaChristelle Vancutsem More

1.Campbell, W. B. The Cellulose-Water Relationship in Papermaking (Dept Of Interior, Forest Service Bulletin, 1933).
Google Scholar 
2.Przybysz, K. & Wandelt, P. Pulp quality control system Part 3 Fiber strength. Przeglad Pap. 61, 283–286 (2005).
Google Scholar 
3.Horn, R. A. Morphology of Wood Pulp Fiber from Softwoods and Influence on Paper Strength. Research Paper FPL-242 (U.S. Department of Agriculture, 1974).
Google Scholar 
4.Joutsimo, O., Wathén, R. & Tamminen, T. Effects of fiber deformations on pulp sheet properties and fiber strength. Pap. Puu-Pap. Tim. 87, 392–397 (2005).CAS 

Google Scholar 
5.Kerekes, R. & Senger, J. J. Characterizing refining action in low-consistency refiners by forces on fibres. J. Pulp Pap. Sci. 32, 1–8 (2006).CAS 

Google Scholar 
6.Karlström, A. & Eriksson, K. Fiber energy efficiency. Part 2: Forces acting on the refiner bars. Nord. Pulp Pap. Res. J. 06, 332–343 (2014).Article 

Google Scholar 
7.Zeng, X., Retulainen, E., Heinemann, S. & Fu, S. Fibre deformations induced by different mechanical treatment and their effect on zero-span strength. Nord. Pulp Paper Res. J. 27, 335–342 (2012).CAS 
Article 

Google Scholar 
8.Joutsimo, O. & Asikainen, S. Effect of fiber wall pore structure on pulp sheet density of softwood kraft pulp fibers. BioRes. 8, 2719–2737 (2013).Article 

Google Scholar 
9.Tingjie, C. et al. Effect of refining on physical properties and paper strength of pinus massoniana and china fir cellulose fibers. BioRes. 11, 7839–7848 (2016).
Google Scholar 
10.Laine, C., Wang, X. S., Tenkanen, M. & Varhimo, A. Changes in the fiber wall during refining of bleached pine kraft pulp. Holzforschung 58, 233–240 (2004).CAS 
Article 

Google Scholar 
11.Gharehkhani, S. et al. Basic effects of pulp refining on fiber properties: A review. Carbohydr. Polym. 115, 785–803 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.El-Sharkawy, K., Haavisto, S., Koskenhely, K. & Paulapuro, H. Effect of fiber flocculation and filling design on refiner loadability and refining characteristics. BioRes. 3, 403–424 (2008).Article 

Google Scholar 
13.Kerekes, R. Energy and forces in refining. J. Pulp Pap. Sci. 36, 10–15 (2010).CAS 

Google Scholar 
14.O’Rourke, D. Nongovernmental organization strategies to influence global production and consumption. J. Ind. Ecol. 9, 115–128 (2005).Article 

Google Scholar 
15.Holik, H. Handbook of Paper and Board 2nd edn. (Willey-VCH, 2013).Book 

Google Scholar 
16.Przybysz, K. Fibrillation of cellulose fibers. Przemysl Chem. 82, 1149–1151 (2003).CAS 

Google Scholar 
17.Ferritsius, R. et al. Development of fibre properties in full scale HC and LC refining. in 2016 International Mechanical Pulping Conference, Jacksonville, 26–28 (2016).18.Kane, M. W. Beating, fiber length distributions and tensile strength-part. Pulp Pap. Canada 60, 308–359 (1959).
Google Scholar 
19.Hartman, R. R. Mechanical Treatment of Pulps for Property Development. PhD Dissertation, Institute of Paper Science and Technology (1984).20.Constable, M. The paper shredder: Trails of law. Law Text Culture 23, 276–293 (2019).
Google Scholar 
21.Japanese Paper Recycle, Paper Recycling Promotion Center http://www.prpc.or.jp/document/publications/japan/.22.Paper Recycling Facts, University of Southern Indiana https://www.usi.edu/recycle/paper-recycling-facts/.23.Chauhan, V. S., Kumar, N., Kumar, M. & Thapar, S. K. Weighted average fiber length: An important parameter in papermaking. Taiwan Lin Ye Ke Xue 28, 51–65 (2013).
Google Scholar 
24.Wangaard, F. F. & Woodson, G. E. Fiber length–fiber strength, interrelationship for slash pine and its effect on pulp–sheet properties. Wood Sci. 5, 235–240 (1973).
Google Scholar 
25.Perng, Y. S., Wang, I. C., Cheng, Y. L. & Chen, Y. C. Effects of fiber morphological characteristics and refining on handsheet properties. Taiwan Lin Ye Ke Xue 24, 127–139 (2009).
Google Scholar 
26.Choi, E. Y. & Cho, B. U. Effect of beating and water impregnation on fiber swelling and paper properties. J. Korea TAPPI 45, 88–95 (2013).CAS 

Google Scholar 
27.Pruden, B. The effect of fines on paper properties. Pap. Technol. 46, 19–26 (2005).
Google Scholar 
28.Kibblewhite, R. P. Interrelations between pulp refining treatments, fibre and pulp fines quality, and pulp freeness. Pap. Puu-Pap. Tim. 57, 519–526 (1975).
Google Scholar 
29.Olejnik, K. Effect of the free swelling of refined cellulose fibres on the mechanical properties of paper. Fibres Text. East. Eur. 20, 113–116 (2012).CAS 

Google Scholar 
30.Sundblad, S. Predictions of Pulp and Paper Properties Based on Fiber Morphology. Master Thesis in Macromolecular Materials, KTH Vetenskap Och Konst, Stockholm, Sweden (2015).31.Retulainen, E. The Role of Fibre Bonding in Paper Properties (National Technical Information Service, Espoo, 1997).
Google Scholar 
32.Hietanen, S. E. K. Fundamental aspects of the refining process. Pap. Puu-Pap. Tim. 72, 158–170 (1990).CAS 

Google Scholar 
33.Wang, X., Maloney, T. & Paulapuro, H. Fibre fibrillation and its impact on sheet properties. Pap. Puu-Pap. Tim. 89, 148–151 (2007).CAS 

Google Scholar 
34.Lindqvist, H. et al. The effect of fibre properties, fines content and surfactant addition on dewatering, wet and dry web properties. Nord. Pulp Pap. Res. J. 27, 104–111 (2012).MathSciNet 
CAS 
Article 

Google Scholar 
35.Kekäläinen, K., Illikainen, M. & Niinimäki, J. Morphological changes in never-dried kraft fibers under mechanical shearing. Cellulose 19, 879–889 (2012).Article 
CAS 

Google Scholar 
36.Heymer, J. O., Olson, J. A. & Kerekes, R. The role of multiple loading cycles on pulp in refiners. Nord. Pulp Pap. Res. 26, 283–287 (2018).Article 

Google Scholar 
37.Vishtal, A. & Retulainen, E. Boosting the extensibility potential of fibre networks: A review. BioRes. 9, 7933–7983 (2014).Article 

Google Scholar 
38.Cheng, Q., Wang, J., McNeel, J. & Jacobson, P. Water retention value measurements of cellulosic materials using a centrifuge technique. BioRes. 5, 1945–1954 (2010).CAS 

Google Scholar 
39.Scallan, A. M. & Carles, J. The correlation of the water retention value with the fibre saturation point. Sven Papperstidning 75, 699–703 (1972).CAS 

Google Scholar 
40.Bäckström, M., Kolar, M. & Htun, M. Characterisation of fines from unbleached kraft pulps and their impact on sheet properties. Holzforschung 62, 546–552 (2008).Article 
CAS 

Google Scholar 
41.Ferreira, P. J., Matos, S. & Figueiredo, M. M. Size characterization of fibres and fines in hardwood kraft pulps. Part. Part. Syst. Charact. 16, 20–24 (1999).CAS 
Article 

Google Scholar 
42.Ciesielski, K. & Olejnik, K. Application of neural networks for estimation of paper properties based on refined pulp properties. Fibres Text. East. Eur. 5, 126–132 (2014).
Google Scholar 
43.Paavilainen, L. Importance of particle size: fibre length and fines: for the characterization of softwood kraft pulp. Pap. Puu-Pap. Tim. 72, 516–526 (1990).CAS 

Google Scholar 
44.Hai, L. V., Park, H. J. & Seo, Y. B. Effect of PFI mill and Valley beater refining on cellulose degree of polymerization, alpha cellulose contents, and crystallinity of wood and cotton fibers. J. Korea TAPPI 45, 27–33 (2013).CAS 

Google Scholar 
45.Wathén, R. Studies on Fiber Strength and its Effect on Paper Properties. Dissertation for the degree of Doctor of Science in Technology, KCL Communications 11, Helsinki University of Technology (2006).46.Motamedian, H. R., Halilovic, A. E. & Kulachenko, A. Mechanisms of strength and stiffness improvement of paper after PFI refining with a focus on the effect of fines. Cellulose 26, 4099–4124 (2019).Article 

Google Scholar 
47.Nordström, B. & Hermansson, L. Effect of fiber length on formation and strength efficiency in twin-wire roll forming. Nord. Pulp Pap. Res. 32, 119–125 (2017).Article 

Google Scholar 
48.Biermann, C. J. Refining and Pulp Characterization. Handbook of Pulping and Papermaking 138–139 (Academic Press, 1996).
Google Scholar 
49.Jang, H. F. & Seth, R. S. Determining the mean values for fibre physical properties. Nord. Pulp Pap. Res. J. 19, 372–378 (2004).CAS 
Article 

Google Scholar 
50.Bajpai, P. The Pulp and Paper Industry. Pulp and Paper Industry: Emerging Waste Water Treatment Technologies 23–25 (Elesiver, 2017).
Google Scholar 
51.Fišerová, M., Gigac, J. & Balberčák, J. Relationship between fibre characteristics and tensile strength of hardwood and softwood kraft pulps. Cell. Chem. Technol. 44, 249–253 (2010).
Google Scholar 
52.Johansson, A. Correlations Between Fibre Properties and Paper Properties. Master Thesis in Pulp Technology, KTH Vetenskap Och Konst (2011).53.Sjöberg, J. & Höglund, H. Refining system for sack paper pulp: Part 1 HC refining under pressurised conditions and subsequent LC refining. Nord. Pulp Pap. Res. 20, 320–328 (2005).Article 

Google Scholar 
54.Larsson, P. T., Lindström, T., Carlsson, L. A. & Fellers, C. Fiber length and bonding effects on tensile strength and toughness of kraft paper. J. Mater. Sci. 53, 3006–3015 (2018).ADS 
CAS 
Article 

Google Scholar 
55.Watson, A. J. & Dadswell, H. E. Influence of fibre morphology on paper properties. Part 1: fibre length. Appita J. 14, 168–178 (1961).CAS 

Google Scholar 
56.Horn, R. A. Morphology of Pulp Fiber from Hardwoods and Influence on Paper Strength. USDA Forest Service, Research Paper FPL 312, Forest Products Laboratory, 1–10 (1978).57.Seth, R. S. The measurement and significance of fines. Pulp Pap. Canada 104, 41–44 (2003).CAS 

Google Scholar 
58.Odabas, N., Henniges, U., Potthast, A. & Rosenau, T. Cellulosic fines: properties and effects. Prog. Mater. Sci. 83, 574–594 (2016).CAS 
Article 

Google Scholar 
59.Sirviö, J. & Nurminen, I. Systematic changes in paper properties caused by fines. Pulp Pap. Canada 105, 39–42 (2004).
Google Scholar 
60.Bossu, J. et al. Fine cellulosic materials produced from chemical pulp: The combined effect of morphology and rate of addition on paper properties. Nanomaterials 9, 321 (2019).PubMed Central 
Article 
CAS 

Google Scholar 
61.Niskanen, K. (ed.) Paper Physics, Papermaking Science and Technology, Book 16 (Finnish Paper Engineers Association and TAPPI, 1998).
Google Scholar 
62.Maloney, T. C., Todorovic, A. & Paulapuro, H. The effect of fiber swelling on press dewatering. Nord. Pulp Pap. Res. 13, 285–291 (1998).CAS 
Article 

Google Scholar 
63.Fischer, W. J. et al. Pulp fines-characterization, sheet formation, and comparison to microfibrillated cellulose. Polymers 9, 366–378 (2017).PubMed Central 
Article 
CAS 

Google Scholar 
64.Park, J. Y., Melani, L., Lee, H. & Kim, H. J. Effect of pulp fibers on the surface softness component of hygiene paper. Holzforschung 74, 497–504 (2020).CAS 
Article 

Google Scholar 
65.Jonsson, D. K. et al. Energy at your service: Highlighting energy usage systems in the context of energy efficiency analysis. Energy Effic. 4, 355–369 (2011).Article 

Google Scholar  More

1.Fisher MC, Garner TWJ. Chytrid fungi and global amphibian declines. Nat Rev Microbiol. 2020;18:332–43.CAS 
PubMed 
Article 

Google Scholar 
2.Skerratt LF, Berger L, Speare R, Cashins S, McDonald KR, Phillott AD, et al. Spread of chytridiomycosis has caused the rapid global decline and extinction of frogs. Ecohealth. 2007;4:125–34.Article 

Google Scholar 
3.Voyles J, Vredenburg VT, Tunstall TS, Parker JM, Briggs CJ, Rosenblum EB. Pathophysiology in Mountain Yellow-Legged Frogs (Rana muscosa) during a Chytridiomycosis Outbreak. PLoS ONE. 2012;7:e35374.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
4.Voyles J, Young S, Berger L, Campbell C, Voyles WF, Dinudom A, et al. Pathogenesis of Chytridiomycosis, a cause of catastrophic amphibian declines. Science (80-). 2009;326:582–5.CAS 
Article 

Google Scholar 
5.Antwis RE, Harrison XA. Probiotic consortia are not uniformly effective against different amphibian chytrid pathogen isolates. Mol Ecol. 2018;27:577–89.CAS 
PubMed 
Article 

Google Scholar 
6.Muletz-wolz CR, Almario JG, Barnett SE, DiRenzo GV, Martel A, Pasmans F, et al. Inhibition of fungal pathogens across genotypes and temperatures by amphibian skin bacteria. Front Microbiol. 2017;8:1551.7.Kueneman JG, Woodhams DC, Harris R, Archer HM, Knight R, McKenzie VJ, et al. Probiotic treatment restores protection against lethal fungal infection lost during amphibian captivity. Proc R Soc London B Biol Sci. 2016;283:20161553.8.Harris RN, Brucker RM, Walke JB, Becker MH, Schwantes CR, Flaherty DC, et al. Skin microbes on frogs prevent morbidity and mortality caused by a lethal skin fungus. ISME J. 2009;3:818–24.CAS 
PubMed 
Article 

Google Scholar 
9.Bletz MC, Loudon AH, Becker MH, Bell SC, Woodhams DC, Minbiole KP, et al. Mitigating amphibian chytridiomycosis with bioaugmentation: characteristics of effective probiotics and strategies for their selection and use. Ecol Lett. 2013;16:817–20.Article 

Google Scholar 
10.Becker MH, Harris RN, Minbiole KP, Schwantes CR, Rollins-Smith LA, Reinert LK, et al. Towards a better understanding of the use of probiotics for preventing chytridiomycosis in Panamanian golden frogs. Ecohealth. 2011;8:501–6.PubMed 
Article 

Google Scholar 
11.Woodhams DC, Geiger CC, Reinert LK, Rollins-Smith LA, Lam B, Harris RN, et al. Treatment of amphibians infected with chytrid fungus: learning from failed trials with itraconazole, antimicrobial peptides, bacteria, and heat therapy. Dis Aquat Organ. 2012;98:11–25.CAS 
PubMed 
Article 

Google Scholar 
12.Coyte KZ, Schluter J, Foster KR. The ecology of the microbiome: networks, competition, and stability. Science. 2015;350:663–6.CAS 
PubMed 
Article 

Google Scholar 
13.Oh J, Byrd AL, Park M, Kong HH, Segre JA. Temporal stability of the human skin microbiome. Cell. 2016;165:854–66.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Harrison XA, Price SJ, Hopkins K, Leung W, Sergeant C, Garner T. Diversity-stability dynamics of the amphibian skin microbiome and susceptibility to a lethal viral pathogen. Front Microbiol. 2019;10:1–13.CAS 
Article 

Google Scholar 
15.Walke JB, Becker MH, Krinos A, Chang E, Santiago C, Umile TP, et al. Seasonal changes and the unexpected impact of environmental disturbance on skin bacteria of individual amphibians in a natural habitat. FEMS Microbiol Ecol. 2021;97:1–14.Article 

Google Scholar 
16.Bletz MC, Perl R, Bobowski BT, Japke LM, Tebbe CC, Dohrmann AB, et al. Amphibian skin microbiota exhibits temporal variation in community structure but stability of predicted Bd-inhibitory function. ISME J. 2017;11:1521–34.PubMed 
PubMed Central 
Article 

Google Scholar 
17.Familiar López M, Rebollar EA, Harris RN, Vredenburg VT, Hero J. Temporal variation of the skin bacterial community and Batrachochytrium dendrobatidis infection in the terrestrial cryptic frog Philoria loveridgei. Front Microbiol. 2017;8:1–12.Article 

Google Scholar 
18.Longo AV, Savage AE, Hewson I, Zamudio KR. Seasonal and ontogenetic variation of skin microbial communities and relationships to natural disease dynamics in declining amphibians. R Soc Open Sci. 2015;2:140377.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
19.Douglas AJ, Hug LA, Katzenback BA. Composition of the North American wood frog (Rana sylvatica) bacterial skin microbiome and seasonal variation in community structure. Microb Ecol. 2021;81:78–92.CAS 
PubMed 
Article 

Google Scholar 
20.Estrada A, Hughey MC, Medina D, Rebollar EA, Walke JB, Harris RN, et al. Skin bacterial communities of neotropical treefrogs vary with local environmental conditions at the time of sampling. PeerJ. 2019;2019:1–20.
Google Scholar 
21.Longo AV, Zamudio KR. Temperature variation, bacterial diversity and fungal infection dynamics in the amphibian skin. Mol Ecol. 2017;26:4787–97.PubMed 
Article 

Google Scholar 
22.Vredenburg VT, Bingham R, Knapp R, Morgan JAT, Moritz C, Wake D. Concordant molecular and phenotypic data delineate new taxonomy and conservation priorities for the endangered mountain yellow-legged frog. J Zool. 2007;271:361–74.Article 

Google Scholar 
23.Vredenburg VT, McNally S, Sulaeman H, Butler HM, Yap T, Koo MS, et al. Pathogen invasion history elucidates contemporary host pathogen dynamics. PLoS ONE. 2019;14:1–14.Article 
CAS 

Google Scholar 
24.Vredenburg VT, Knapp RA, Tunstall TS, Briggs CJ. Dynamics of an emerging disease drive large-scale amphibian population extinctions. Proc Natl Acad Sci USA. 2010;107:9689–94.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Briggs CJ, Knapp RA, Vredenburg VT. Enzootic and epizootic dynamics of the chytrid fungal pathogen of amphibians. Proc Natl Acad Sci USA. 2010;107:9695–700.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
26.Knapp RA, Fellers GM, Kleeman PM, Miller DA, Vredenburg VT, Rosenblum EB, et al. Large-scale recovery of an endangered amphibian despite ongoing exposure to multiple stressors. Proc Natl Acad Sci USA. 2016;113:11889–94.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
27.Jani AJ, Briggs CJ. The pathogen Batrachochytrium dendrobatidis disturbs the frog skin microbiome during a natural epidemic and experimental infection. Proc Natl Acad Sci USA. 2014;111:E5049–E5058.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
28.Ellison S, Knapp RA, Sparagon W, Swei A, Vredenburg VT. Reduced skin bacterial diversity correlates with increased pathogen infection intensity in an endangered amphibian host. Mol Ecol. 2019;28:127–40.PubMed 
Article 

Google Scholar 
29.Jani AJ, Briggs CJ. Host and aquatic environment shape the amphibian skin microbiome but effects on downstream resistance to the pathogen Batrachochytrium dendrobatidis are variable. Front Microbiol. 2018;9:1–17.Article 

Google Scholar 
30.Jani AJ, Knapp RA, Briggs CJ. Epidemic and endemic pathogen dynamics correspond to distinct host population microbiomes at a landscape scale. Proc R Soc B Biol Sci. 2017;284:20170944.31.Vredenburg VT. Reversing introduced species effects: experimental removal of introduced fish leads to rapid recovery of a declining frog. Proc Natl Acad Sci USA. 2004;101:7646–50.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
32.Joseph MB, Knapp RA. Disease and climate effects on individuals drive post-reintroduction population dynamics of an endangered amphibian. Ecosphere 2018;9:e02499.33.Boyle DG, Boyle DB, Olsen V, Morgan JAT, Hyatt AD. Rapid quantitative detection of chytridiomycosis (Batrachochytrium dendrobatidis) in amphibian samples using real-time Taqman PCR assay. Dis Aquat Organ. 2004;60:141–8.CAS 
PubMed 
Article 

Google Scholar 
34.Hyatt AD, Boyle DG, Olsen V, Boyle DB, Berger L, Obendorf D, et al. Diagnostic assays and sampling protocols for the detection of Batrachochytrium dendrobatidis. Dis Aquat Organ. 2007;73:175–92.CAS 
PubMed 
Article 

Google Scholar 
35.Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
38.Price MN, Dehal PS, Arkin AP. FastTree 2-approximately maximum-likelihood trees for large alignments. PLoS ONE. 2010;5:e9490.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
39.McDonald D, Price MN, Goodrich J, Nawrocki EP, DeSantis TZ, Probst A, et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 2012;6:610–8.CAS 
Article 

Google Scholar 
40.Bokulich NA, Kaehler BD, Rideout JR, Dillon M, Bolyen E, Knight R, et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome. 2018;6:90.PubMed 
PubMed Central 
Article 

Google Scholar 
41.Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825–30.
Google Scholar 
42.Weiss S, Xu ZZ, Peddada S, Amir A, Bittinger K, Gonzalez A, et al. Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome. 2017;5:27.PubMed 
PubMed Central 
Article 

Google Scholar 
43.Faith DP. Conservation evaluation and phylogenetic diversity. Biol Conserv. 1992;61:1–10.Article 

Google Scholar 
44.Pielou EC. The measurement of diversity in different types of biological collections. J Theor Biol. 1966;13:131–44.Article 

Google Scholar 
45.Lozupone C, Knight R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol. 2005;71:8228–35.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Lozupone CA, Hamady M, Kelley ST, Knight R. Quantitative and qualitative β diversity measures lead to different insights into factors that structure microbial communities. Appl Environ Microbiol. 2007;73:1576–85.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
47.Chang Q, Luan Y, Sun F. Variance adjusted weighted UniFrac: a powerful beta diversity measure for comparing communities based on phylogeny. BMC Bioinformatics. 2011;12:118.PubMed 
PubMed Central 
Article 

Google Scholar 
48.Chen J, Bittinger K, Charlson ES, Hoffmann C, Lewis J, Wu GD, et al. Associating microbiome composition with environmental covariates using generalized UniFrac distances. Bioinformatics. 2012;28:2106–13.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.McDonald D, Vázquez-Baeza Y, Koslicki D, McClelland J, Reeve N, Xu Z, et al. Striped UniFrac: enabling microbiome analysis at unprecedented scale. Nat Methods. 2018;15:847–48.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
50.Mandal S, Van Treuren W, White RA, Eggesbø M, Knight R, Peddada SD. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb Ecol Heal Dis. 2015;26:1–7.
Google Scholar 
51.Bokulich NA, Subramanian S, Faith JJ, Gevers D, Gordon JI, Knight R, et al. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat Methods. 2013;10:57–59.CAS 
PubMed 
Article 

Google Scholar 
52.Woodhams DC, Alford RA, Antwis RE, Archer H, Becker MH, Belden LK, et al. Antifungal isolates database of amphibian skin-associated bacteria and function against emerging fungal pathogens. Ecology. 2015;96:595.Article 

Google Scholar 
53.Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4:e2584.PubMed 
PubMed Central 
Article 

Google Scholar 
54.R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2020.55.Lee G, You HJ, Bajaj JS, Joo SK, Yu J, Park S, et al. Distinct signatures of gut microbiome and metabolites associated with significant fibrosis in non-obese NAFLD. Nat Commun. 2020;11:1–13.
Google Scholar 
56.Lloyd-Price J, Mahurkar A, Rahnavard G, Crabtree J, Orvis J, Hall AB, et al. Strains, functions and dynamics in the expanded Human Microbiome Project. Nature. 2017;550:61–66.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
57.Knapp RA, Briggs CJ, Smith TC, Maurer JR. Nowhere to hide: impact of a temperature-sensitive amphibian pathogen along an elevation gradient in the temperate zone. Ecosphere. 2011;2:art93.Article 

Google Scholar 
58.Grice EA, Kong HH, Conlan S, Deming CB, Davis J, Young AC, et al. Topographical and temporal diversity of the human skin microbiome. Science. 2009;324:1190–2.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
59.Bates KA, Clare FC, O’Hanlon S, Bosch J, Brookes L, Hopkins K, et al. Amphibian chytridiomycosis outbreak dynamics are linked with host skin bacterial community structure. Nat Commun. 2018;9:1–11.CAS 
Article 

Google Scholar 
60.Kinney VC, Heemeyer JL, Pessier AP, Lannoo MJ, Samples F. Seasonal pattern of Batrachochytrium dendrobatidis infection and mortality in Lithobates areolatus: affirmation of Vredenburg’ s “10, 000 Zoospore Rule”. PLoS ONE. 2011;6:e16708.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

1.Wilson, E. O. The little things that run the world (The importance and conservation of invertebrates). Conserv. Biol. 1, 344–346 (1987).Article 

Google Scholar 
2.Catalogue of Life. Catalogue of life: 2018 annual checklist. http://www.catalogueoflife.org/annual-checklist/2018/info/ac (2018).3.Stork, N. E. How many species of insects and other terrestrial arthropods are there on earth?. Annu. Rev. Entomol. 63, 31–45 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Dornelas, M. et al. BioTIME: A database of biodiversity time series for the Anthropocene. Glob. Ecol. Biogeogr. 27, 760–786 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
5.Magurran, A. E. et al. Long-term datasets in biodiversity research and monitoring: Assessing change in ecological communities through time. Trends Ecol. Evol. 25, 574–582 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
6.Nielsen, T. F., Sand-Jensen, K., Dornelas, M. & Bruun, H. H. More is less: Net gain in species richness, but biotic homogenization over 140 years. Ecol. Lett. 22, 1650–1657 (2019).Article 

Google Scholar 
7.Hallmann, C. A. et al. More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLoS ONE 12, e0185809 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
8.Seibold, S. et al. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574, 671–674 (2019).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.Wagner, D. L. Insect declines in the Anthropocene. Annu. Rev. Entomol. 65, 457–480 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.Wagner, D. L., Grames, E. M., Forister, M. L., Berenbaum, M. R. & Stopak, D. Insect decline in the Anthropocene: Death by a thousand cuts. PNAS 118, 1–10 (2021).
Google Scholar 
11.Welti, E. A. R., Roeder, K. A., de Beurs, K. M., Joern, A. & Kaspari, M. Nutrient dilution and climate cycles underlie declines in a dominant insect herbivore. Proc. Natl. Acad. Sci. U. S. A. 117, 7271–7275 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Blowes, S. A. et al. The geography of biodiversity change in marine and terrestrial assemblages. Science 366, 339–345 (2019).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Vellend, M. et al. Global meta-analysis reveals no net change in local-scale plant biodiversity over time. Proc. Natl. Acad. Sci. U. S. A. 110, 19456 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Dornelas, M. et al. A balance of winners and losers in the Anthropocene. Ecol. Lett. 22, 847–854 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
15.Rada, S. et al. Protected areas do not mitigate biodiversity declines: A case study on butterflies. Divers. Distrib. 25, 217–224 (2019).Article 

Google Scholar 
16.Dornelas, M. et al. Assemblage time series reveal biodiversity change but not systematic loss. Science 344, 296–299 (2014).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
17.Magurran, A. E., Dornelas, M., Moyes, F. & Henderson, P. A. Temporal β diversity—A macroecological perspective. Glob. Ecol. Biogeogr. 28, 1949–1960 (2019).Article 

Google Scholar 
18.McGill, B. J., Enquist, B. J., Weiher, E. & Westoby, M. Rebuilding community ecology from functional traits. Trends Ecol. Evol. 21, 178–185 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
19.Múrria, C., Iturrarte, G. & Gutiérrez-Cánovas, C. A trait space at an overarching scale yields more conclusive macroecological patterns of functional diversity. Glob. Ecol. Biogeogr. 29, 1729–1742 (2020).Article 

Google Scholar 
20.Violle, C. et al. Let the concept of trait be functional!. Oikos 116, 882–892 (2007).Article 

Google Scholar 
21.Schmera, D., Heino, J., Podani, J., Erős, T. & Dolédec, S. Functional diversity: A review of methodology and current knowledge in freshwater macroinvertebrate research. Hydrobiologia 787, 27–44 (2017).Article 

Google Scholar 
22.Frainer, A., McKie, B. G. & Malmqvist, B. When does diversity matter? Species functional diversity and ecosystem functioning across habitats and seasons in a field experiment. J. Anim. Ecol. 83, 460–469 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
23.Ceballos, G. et al. Accelerated modern human–induced species losses: Entering the sixth mass extinction. Sci. Adv. 1, e1400253 (2015).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
24.Pereira, H. M., Navarro, L. M. & Martins, I. S. Global biodiversity change: The bad, the good, and the unknown. Annu. Rev. Environ. Resour. 37, 25–50 (2012).Article 

Google Scholar 
25.Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution, and protection. Science 344, 1246752 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Sala, O. E. et al. Global biodiversity scenarios for the year 2100. Science 287, 1770–1774 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Habel, J. C., Samways, M. J. & Schmitt, T. Mitigating the precipitous decline of terrestrial European insects: Requirements for a new strategy. Biodivers. Conserv. 28, 1343–1360 (2019).Article 

Google Scholar 
28.Baranov, V., Jourdan, J., Pilotto, F., Wagner, R. & Haase, P. Complex and nonlinear climate-driven changes in freshwater insect communities over 42 years. Conserv. Biol. 34, 1241–1251 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
29.Halsch, C. A. et al. Insects and recent climate change. PNAS 118, 1–9 (2021).Article 
CAS 

Google Scholar 
30.Raven, P. H. & Wagner, D. L. Agricultural intensification and climate change are rapidly decreasing insect biodiversity. PNAS 118, 1–6 (2021).Article 
CAS 

Google Scholar 
31.Soroye, P., Newbold, T. & Kerr, J. Climate change contributes to widespread declines among bumble bees across continents. Science 367, 685–688 (2020).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
32.Jourdan, J., Baranov, V., Wagner, R., Plath, M. & Haase, P. Elevated temperatures translate into reduced dispersal abilities in a natural population of an aquatic insect. J. Anim. Ecol. 88, 1498–1509 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
33.Bowler, D. E. et al. Cross-realm assessment of climate change impacts on species’ abundance trends. Nat. Ecol. Evol. 1, 1–7 (2017).Article 

Google Scholar 
34.Habel, J. C., Ulrich, W., Biburger, N., Seibold, S. & Schmitt, T. Agricultural intensification drives butterfly decline. Insect Conserv. Divers. 12, 289–295 (2019).
Google Scholar 
35.Januschke, K. & Verdonschot, R. C. M. Effects of river restoration on riparian ground beetles (Coleoptera: Carabidae) in Europe. Hydrobiologia 769, 93–104 (2016).Article 

Google Scholar 
36.Koivula, M. Useful model organisms, indicators, or both? Ground beetles (Coleoptera, Carabidae) reflecting environmental conditions. ZooKeys 100, 287–317 (2011).Article 

Google Scholar 
37.Homburg, K., Homburg, N., Schäfer, F., Schuldt, A. & Assmann, T. Carabids.org—a dynamic online database of ground beetle species traits (Coleoptera, Carabidae). Insect Conserv. Divers. 7, 195–205 (2014).38.Kotze, D. J. et al. Forty years of carabid beetle research in Europe—from taxonomy, biology, ecology and population studies to bioindication, habitat assessment and conservation. ZooKeys 100, 55–148 (2011).Article 

Google Scholar 
39.Rainio, J. & Niemelä, J. Ground beetles (Coleoptera: Carabidae) as bioindicators. Biodivers. Conserv. 12, 487–506 (2003).Article 

Google Scholar 
40.Pozsgai, G., Baird, J., Littlewood, N. A., Pakeman, R. J. & Young, M. R. Long-term changes in ground beetle (Coleoptera: Carabidae) assemblages in Scotland. Ecol. Entomol. 41, 157–167 (2016).Article 

Google Scholar 
41.Jambrošić, V. Ž & Šerić, J. L. Long term changes (1990–2016) in carabid beetle assemblages (Coleoptera: Carabidae) in protected forests on Dinaric Karst on Mountain Risnjak, Croatia. EJE 117, 56–67 (2020).
Google Scholar 
42.Marrec, R. et al. Multiscale drivers of carabid beetle (Coleoptera: Carabidae) assemblages in small European woodlands. Glob. Ecol. Biogeogr. 30, 165–182 (2021).Article 

Google Scholar 
43.Ribera, I., Dolédec, S., Downie, I. S. & Foster, G. N. Effect of land disturbance and stress on species traits of ground beetle assemblages. Ecology 82, 1112–1129 (2001).Article 

Google Scholar 
44.Gobbi, M. & Fontaneto, D. Biodiversity of ground beetles (Coleoptera: Carabidae) in different habitats of the Italian Po lowland. Agric. Ecosyst. Environ. 127, 273–276 (2008).Article 

Google Scholar 
45.Cajaiba, R. L. et al. How informative is the response of Ground Beetles’ (Coleoptera: Carabidae) assemblages to anthropogenic land use changes? Insights for ecological status assessments from a case study in the Neotropics. Sci. Total Environ. 636, 1219–1227 (2018).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.Baulechner, D., Diekötter, T., Wolters, V. & Jauker, F. Converting arable land into flowering fields changes functional and phylogenetic community structure in ground beetles. Biol. Cons. 231, 51–58 (2019).Article 

Google Scholar 
47.Hallmann, C. A. et al. Declining abundance of beetles, moths and caddisflies in the Netherlands. Insect Conserv. Divers. 13, 127–139 (2020).Article 

Google Scholar 
48.Brooks, D. R. et al. Large carabid beetle declines in a United Kingdom monitoring network increases evidence for a widespread loss in insect biodiversity. J. Appl. Ecol. 49, 1009–1019 (2012).Article 

Google Scholar 
49.Kotze, D. J. & O’Hara, R. B. Species decline—but why? Explanations of carabid beetle (Coleoptera, Carabidae) declines in Europe. Oecologia 135, 138–148 (2003).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
50.Homburg, K. et al. Where have all the beetles gone? Long-term study reveals carabid species decline in a nature reserve in Northern Germany. Insect Conserv. Divers. 12, 268–277 (2019).
Google Scholar 
51.Thiele, H. U. Carabid Beetles in their Environments: A Study on Habitat Selection by Adaptations in Physiology and Behaviour. (Springer, 1977). https://doi.org/10.1007/978-3-642-81154-8.52.Hengeveld, R. Dynamics of Dutch Beetle Species During the Twentieth Century (Coleoptera, Carabidae). J. Biogeogr. 12, 389–411 (1985).Article 

Google Scholar 
53.Engel, J. et al. Pitfall trap sampling bias depends on body mass, temperature, and trap number: Insights from an individual-based model. Ecosphere 8, e01790 (2017).Article 

Google Scholar 
54.Eyre, M. D., Rushton, S. P., Luff, M. L. & Telfer, M. G. Investigating the relationships between the distribution of British ground beetle species (Coleoptera, Carabidae) and temperature, precipitation and altitude. J. Biogeogr. 32, 973–983 (2005).Article 

Google Scholar 
55.Paetzold, A., Schubert, C. J. & Tockner, K. Aquatic terrestrial linkages along a braided-river: Riparian arthropods feeding on aquatic insects. Ecosystems 8, 748–759 (2005).Article 

Google Scholar 
56.Van Looy, K., Vanacker, S., Jochems, H., de Blust, G. & Dufrêne, M. Ground beetle habitat templets and riverbank integrity. River Res. Appl. 21, 1133–1146 (2005).Article 

Google Scholar 
57.Lambeets, K., Vandegehuchte, M. L., Maelfait, J.-P. & Bonte, D. Understanding the impact of flooding on trait-displacements and shifts in assemblage structure of predatory arthropods on river banks. J. Anim. Ecol. 77, 1162–1174 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
58.Kotze, D. J., Niemelä, J., O’Hara, R. B. & Turin, H. Testing abundance-range size relationships in European carabid beetles (Coleoptera, Carabidae). Ecography 26, 553–566 (2003).Article 

Google Scholar 
59.Barber, H. S. Traps for cave-inhabiting insects. J. Elisha Mitchell Sci. Soc. 46, 259–266 (1931).
Google Scholar 
60.Dunger, W. Praktische Erfahrungen mit Bodenfallen. Entomologische Nachrichten 7, 41–46 (1963).
Google Scholar 
61.Trautner, J. Handfänge als effektive und vergleichbare Methode zur Laufkäfer-Erfassung an Fließgewässern-Ergebnisse eines Tests an der Aich. Angewandte Carabidologie Supplement 1, 139–144 (1999).
Google Scholar 
62.Trautner, J. Laufkäfer – Methoden der Bestandsaufnahme und Hinweise für die Auswertung bei Naturschutz- und Eingriffsplanungen. in Arten- und Biotopschutz in der Planung: Methodische Standards zur Erfassung von Tierartengruppen (ed. Trautner, J.) 145–162 (1992).63.Linke, S., Bailey, R. C. & Schwindt, J. Temporal variability of stream bioassessments using benthic macroinvertebrates. Freshw. Biol. 42, 575–584 (1999).Article 

Google Scholar 
64.Albrecht, L. Grundlagen, Ziele und Methodik der waldökologischen Forschung in Naturreservaten. vol. 1 (1990).65.Renner, K. Faunistisch-ökologische Untersuchungen der Käferfauna pflanzensoziologisch unterschiedlicher Biotope im Evessell-Buch bei Bielefeld-Sennestadt. Ber. Naturw. V. Bielefeld 145–176 (1980).66.Müller-Motzfeld, G. Die Käfer Mitteleuropas. vol. 2 (Springer Spektrum, 2004).67.Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948).MathSciNet 
MATH 
Article 

Google Scholar 
68.Shannon, C. E. & Weaver, W. The Mathematical Theory of Communication. (University of Illinois Press, 1949).69.Simpson, E. H. Measurement of diversity. Nature 163, 688 (1949).ADS 
MATH 
Article 

Google Scholar 
70.Pielou, E. C. Mathematical Ecology. (Wiley, 1977).71.Smith, B. & Wilson, J. B. A consumer’s guide to Evenness indices. Oikos 76, 70–82 (1996).Article 

Google Scholar 
72.Hillebrand, H. et al. Biodiversity change is uncoupled from species richness trends: Consequences for conservation and monitoring. J. Appl. Ecol. 55, 169–184 (2018).Article 

Google Scholar 
73.Schmera, D., Podani, J., Heino, J., Erős, T. & Poff, N. L. A proposed unified terminology of species traits in stream ecology. Freshw. Sci. 34, 823–830 (2015).Article 

Google Scholar 
74.Villéger, S., Grenouillet, G. & Brosse, S. Decomposing functional β-diversity reveals that low functional β-diversity is driven by low functional turnover in European fish assemblages. Glob. Ecol. Biogeogr. 22, 671–681 (2013).Article 

Google Scholar 
75.Laliberté, E. & Legendre, P. A distance-based framework for measuring functional diversity from multiple traits. Ecology 91, 299–305 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
76.Mason, N. W. H., Mouillot, D., Lee, W. G. & Wilson, J. B. Functional richness, functional evenness and functional divergence: The primary components of functional diversity. Oikos 111, 112–118 (2005).Article 

Google Scholar 
77.Pakeman, R. J. Functional trait metrics are sensitive to the completeness of the species’ trait data?. Methods Ecol. Evol. 5, 9–15 (2014).Article 

Google Scholar 
78.Mouillot, D. et al. Functional over-redundancy and high functional vulnerability in global fish faunas on tropical reefs. PNAS 111, 13757–13762 (2014).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
79.Chevene, F., Doléadec, S. & Chessel, D. A fuzzy coding approach for the analysis of long-term ecological data. Freshw. Biol. 31, 295–309 (1994).Article 

Google Scholar 
80.Cornes, R. C., van der Schrier, G., van den Besselaar, E. J. M. & Jones, P. D. An ensemble version of the E-OBS temperature and precipitation data sets. J. Geophys. Res. Atmos. 123, 9391–9409 (2018).Article 

Google Scholar 
81.Haylock, M. R. et al. A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. J. Geophys. Res. Atmos. 113, 1–12 (2008).Article 

Google Scholar 
82.Jourdan, J. et al. Effects of changing climate on European stream invertebrate communities: A long-term data analysis. Sci. Total Environ. 621, 588–599 (2018).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
83.Büttner, G. Corine land cover and land cover change products. in Land Use and Land Cover Mapping in Europe: Practices & Trends (eds. Manakos, I. & Braun, M.) 55–74 (Springer Netherlands, 2014). https://doi.org/10.1007/978-94-007-7969-3_5.84.Erős, T., Czeglédi, I., Tóth, R. & Schmera, D. Multiple stressor effects on alpha, beta and zeta diversity of riverine fish. Sci. Total Environ. 748, 141407 (2020).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
85.Oksanen, J. et al. Vegan: Community ecology package. https://CRAN.R-project.org/package=vegan (2019).86.Sanders, H. L. Marine benthic diversity: A comparative study. Am. Nat. 102, 243–282 (1968).Article 

Google Scholar 
87.Maire, A., Thierry, E., Viechtbauer, W. & Daufresne, M. Poleward shift in large-river fish communities detected with a novel meta-analysis framework. Freshw. Biol. 64, 1143–1156 (2019).Article 

Google Scholar 
88.R Development Core Team. R: A language and environment for statistical computing. R Foundation For Statistical Computing, Vienna, Austria https://www.r-project.org/ (2019).89.Lahti, L. & Shetty, S. Microbiome R package. http://microbiome.github.io (2012).90.Laliberté, E., Legendre, P. & Shipley, B. FD: Measuring functional diversity from multiple traits, and other tools for functional ecology. https://cran.r-project.org/web/packages/FD/citation.html (2014).91.Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J. Stat. Softw. 36, 1–48 (2010).Article 

Google Scholar 
92.Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & R Development Core Team. Nlme: linear and nonlinear mixed effects models. https://CRAN.R-project.org/package=nlme (2020).93.Boscaini, A., Franceschini, A. & Maiolini, B. River ecotones: Carabid beetles as a tool for quality assessment. Hydrobiologia 422, 173–181 (2000).Article 

Google Scholar 
94.Magura, T., Lövei, G. L. & Tóthmérész, B. Does urbanization decrease diversity in ground beetle (Carabidae) assemblages?. Glob. Ecol. Biogeogr. 19, 16–26 (2010).Article 

Google Scholar 
95.Kędzior, R., Szwalec, A., Mundała, P. & Skalski, T. Ground beetle (Coleoptera, Carabidae) life history traits as indicators of habitat recovering processes in postindustrial areas. Ecol. Eng. 142, 105615 (2020).Article 

Google Scholar 
96.Post, D. M. The long and short of food-chain length. Trends Ecol. Evol. 17, 269–277 (2002).Article 

Google Scholar 
97.Pilotto, F. et al. Meta-analysis of multidecadal biodiversity trends in Europe. Nat. Commun. 11, 3486 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
98.Skarbek, C. J., Kobel-Lamparski, A. & Dormann, C. F. Trends in monthly abundance and species richness of carabids over 33 years at the Kaiserstuhl, southwest Germany. Basic Appl. Ecol. 50, 107–118 (2021).Article 

Google Scholar 
99.Chase, J. M. et al. Species richness change across spatial scales. Oikos 128, 1079–1091 (2019).Article 

Google Scholar 
100.Prather, R. M. & Kaspari, M. Plants regulate grassland arthropod communities through biomass, quality, and habitat heterogeneity. Ecosphere 10, e02909 (2019).Article 

Google Scholar 
101.Desender, K., Dekoninck, W., Dufrêne, M. & Maes, D. Changes in the distribution of carabid beetles in Belgium revisited: Have we halted the diversity loss?. Biol. Cons. 143, 1549–1557 (2010).Article 

Google Scholar 
102.Haase, P. et al. The next generation of site-based long-term ecological monitoring: Linking essential biodiversity variables and ecosystem integrity. Sci. Total Environ. 613–614, 1376–1384 (2018).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar  More

1.Schmidt, M. W. et al. Persistence of soil organic matter as an ecosystem property. Nature 478, 49–56 (2011).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Drobnik, T., Greiner, L., Keller, A. & Grêt-Regamey, A. Soil quality indicators-from soil functions to ecosystem services. Ecol. Ind. 94, 151–169 (2018).Article 

Google Scholar 
3.Bradford, M. A. et al. Managing uncertainty in soil carbon feedbacks to climate change. Nat. Clim. Change 6, 751–758 (2016).ADS 
Article 
CAS 

Google Scholar 
4.Viscarra Rossel, R. et al. A global spectral library to characterize the world’s soil. Earth Sci. Rev. 155, 198–230 (2016).5.Amundson, R. & Biardeau, L. Opinion: Soil carbon sequestration is an elusive climate mitigation tool. Proc. Natl. Acad. Sci. 115, 11652–11656 (2018).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
6.Smith, P. et al. How to measure, report and verify soil carbon change to realize the potential of soil carbon sequestration for atmospheric greenhouse gas removal. Glob. Change Biol. 26, 219–241 (2020).ADS 
Article 

Google Scholar 
7.Zhang, S., Yu, Z., Lin, J. & Zhu, B. Responses of soil carbon decomposition to drying-rewetting cycles: A meta-analysis. Geoderma 361, 114069 (2020).ADS 
CAS 
Article 

Google Scholar 
8.Bot, A. & Benites, J. The Importance of Soil Organic Matter: Key to Drought-Resistant Soil and Sustained Food Production. 80 (Food & Agriculture Org., 2005).9.Rawles, W. J. & Brakensiek, D. Estimating soil water retention from soil properties. J. Irrig. Drain. Div. 108, 166–171 (1982).Article 

Google Scholar 
10.Zhao, D., Zhao, X., Khongnawang, T., Arshad, M. & Triantafilis, J. A Vis–NIR spectral library to predict clay in Australian cotton growing soil. Soil Sci. Soc. Am. J. 82, 1347–1357 (2018).ADS 
CAS 
Article 

Google Scholar 
11.Demattê, J. A., Campos, R. C., Alves, M. C., Fiorio, P. R. & Nanni, M. R. Visible–NIR reflectance: A new approach on soil evaluation. Geoderma 121, 95–112 (2004).ADS 
Article 
CAS 

Google Scholar 
12.Viscarra Rossel, R. Fine-resolution multiscale mapping of clay minerals in Australian soils measured with near infrared spectra. J. Geophys. Res. Earth Surf. 116 (2011).13.Viscarra Rossel, R., Walvoort, D., McBratney, A., Janik, L. J. & Skjemstad, J. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 131, 59–75 (2006).14.Viscarra Rossel, R. & Lark, R. Improved analysis and modelling of soil diffuse reflectance spectra using wavelets. Eur. J. Soil Sci. 60, 453–464 (2009).15.Abdi, H. & Williams, L. J. Principal component analysis. Wiley Interdiscip. Rev. Comput. Stat. 2, 433–459 (2010).Article 

Google Scholar 
16.Næs, T. & Martens, H. Principal component regression in NIR analysis: Viewpoints, background details and selection of components. J. Chemom. 2, 155–167 (1988).Article 

Google Scholar 
17.Geladi, P. & Kowalski, B. R. Partial least-squares regression: A tutorial. Anal. Chim. Acta 185, 1–17 (1986).CAS 
Article 

Google Scholar 
18.Rossel, R. V. Robust modelling of soil diffuse reflectance spectra by “bagging-partial least squares regression”. J. Near Infrared Spectrosc. 15, 39–47 (2007).19.Viscarra Rossel, R. & Behrens, T. Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma 158, 46–54 (2010).20.Tsakiridis, N. L., Keramaris, K. D., Theocharis, J. B. & Zalidis, G. C. Simultaneous prediction of soil properties from VNIR–SWIR spectra using a localized multi-channel 1-d convolutional neural network. Geoderma 367, 114208 (2020).ADS 
CAS 
Article 

Google Scholar 
21.Yang, J., Wang, X., Wang, R. & Wang, H. Combination of convolutional neural networks and recurrent neural networks for predicting soil properties using Vis–NIR spectroscopy. Geoderma 380, 114616 (2020).ADS 
CAS 
Article 

Google Scholar 
22.Shen, Z. & Viscarra Rossel, R. A. Automated spectroscopic modelling with optimised convolutional neural networks. Sci. Rep. 11, 208. https://doi.org/10.1038/s41598-020-80486-9 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
23.Li, F., Wang, L., Liu, J., Wang, Y. & Chang, Q. Evaluation of leaf n concentration in winter wheat based on discrete wavelet transform analysis. Remote Sens. 11, 1331 (2019).ADS 
Article 

Google Scholar 
24.Meng, X. et al. Regional soil organic carbon prediction model based on a discrete wavelet analysis of hyperspectral satellite data. Int. J. Appl. Earth Obs. Geoinf. 89, 102111 (2020).Article 

Google Scholar 
25.Jiang, B. Geospatial analysis requires a different way of thinking: The problem of spatial heterogeneity. GeoJournal 80, 1–13 (2015).Article 

Google Scholar 
26.Song, Y., Wang, J., Ge, Y. & Xu, C. An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: Cases with different types of spatial data. GISci. Remote Sens. 57, 593–610 (2020).Article 

Google Scholar 
27.Yang, Z. et al. The effect of environmental heterogeneity on species richness depends on community position along the environmental gradient. Sci. Rep. 5, 1–7 (2015).
Google Scholar 
28.Jenny, H. Factors of Soil Formation (McGraw-Hill, 1941).29.Ye, H. et al. Effects of different sampling densities on geographically weighted regression kriging for predicting soil organic carbon. Spat. Stat. 20, 76–91 (2017).MathSciNet 
Article 

Google Scholar 
30.Viscarra Rossel, R. & Webster, R. Predicting soil properties from the Australian soil visible-near infrared spectroscopic database. Eur. J. Soil Sci. 63. https://doi.org/10.1111/j.1365-2389.2012.01495.x (2012).31.Sila, A., Pokhariyal, G. & Shepherd, K. Evaluating regression-kriging for mid-infrared spectroscopy prediction of soil properties in western Kenya. Geoderma Reg. 10, 39–47 (2017).Article 

Google Scholar 
32.Fotheringham, A. S., Brunsdon, C. & Charlton, M. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships (Wiley, 2003).33.Brunsdon, C., Fotheringham, A. S. & Charlton, M. E. Geographically weighted regression: A method for exploring spatial nonstationarity. Geogr. Anal. 28, 281–298 (1996).Article 

Google Scholar 
34.Bidanset, P. E. & Lombard, J. R. The effect of kernel and bandwidth specification in geographically weighted regression models on the accuracy and uniformity of mass real estate appraisal. J. Prop. Tax Assess. Admin. 11, 5–14 (2014).
Google Scholar 
35.Brunsdon, C., Fotheringham, A. & Charlton, M. Geographically weighted summary statistics? A framework for localised exploratory data analysis. Comput. Environ. Urban Syst. 26, 501–524 (2002).MATH 
Article 

Google Scholar 
36.Comber, A. et al. The GWR route map: A guide to the informed application of geographically weighted regression. arXiv preprint arXiv:2004.06070 (2020).37.Fotheringham, A. S., Yang, W. & Kang, W. Multiscale geographically weighted regression (MGWR). Ann. Am. Assoc. Geogr. 107, 1247–1265 (2017).
Google Scholar 
38.Yu, H. et al. Inference in multiscale geographically weighted regression. Geogr. Anal. 52, 87–106 (2020).Article 

Google Scholar 
39.Wheeler, D. & Tiefelsdorf, M. Multicollinearity and correlation among local regression coefficients in geographically weighted regression. J. Geogr. Syst. 7, 161–187 (2005).Article 

Google Scholar 
40.Harris, P., Fotheringham, A. S. & Juggins, S. Robust geographically weighted regression: A technique for quantifying spatial relationships between freshwater acidification critical loads and catchment attributes. Ann. Assoc. Am. Geogr. 100, 286–306 (2010).Article 

Google Scholar 
41.Harris, P., Brunsdon, C., Lu, B., Nakaya, T. & Charlton, M. Introducing bootstrap methods to investigate coefficient non-stationarity in spatial regression models. Spat. Stat. 21, 241–261 (2017).MathSciNet 
Article 

Google Scholar 
42.Cho, S.-H., Lambert, D. M. & Chen, Z. Geographically weighted regression bandwidth selection and spatial autocorrelation: An empirical example using Chinese agriculture data. Appl. Econ. Lett. 17, 767–772 (2010).Article 

Google Scholar 
43.Lu, B., Yang, W., Ge, Y. & Harris, P. Improvements to the calibration of a geographically weighted regression with parameter-specific distance metrics and bandwidths. Comput. Environ. Urban Syst. 71, 41–57 (2018).Article 

Google Scholar 
44.Arabameri, A., Pradhan, B. & Rezaei, K. Gully erosion zonation mapping using integrated geographically weighted regression with certainty factor and random forest models in gis. J. Environ. Manag. 232, 928–942 (2019).Article 

Google Scholar 
45.Li, X. et al. Mapping soil organic carbon and total nitrogen in croplands of the corn belt of northeast china based on geographically weighted regression kriging model. Comput. Geosci. 135, 104392 (2020).CAS 
Article 

Google Scholar 
46.Cao, K., Diao, M. & Wu, B. A big data-based geographically weighted regression model for public housing prices: A case study in Singapore. Ann. Am. Assoc. Geogr. 109, 173–186 (2019).
Google Scholar 
47.Ge, Y. et al. Geographically weighted regression-based determinants of malaria incidences in northern China. Trans. GIS 21, 934–953 (2017).Article 

Google Scholar 
48.Viscarra Rossel, R. A. & Hicks, W. S. Soil organic carbon and its fractions estimated by visible–near infrared transfer functions. Eur. J. Soil Sci. 66(3), 438–450 (2015).CAS 
Article 

Google Scholar 
49.Wight, J. P., Ashworth, A. J. & Allen, F. L. Organic substrate, clay type, texture, and water influence on NIR carbon measurements. Geoderma 261, 36–43 (2016).ADS 
CAS 
Article 

Google Scholar 
50.Costa, L. R., Tonoli, G. H. D., Milagres, F. R. & Hein, P. R. G. Artificial neural network and partial least square regressions for rapid estimation of cellulose pulp dryness based on near infrared spectroscopic data. Carbohyd. Polym. 224, 115186 (2019).Article 
CAS 

Google Scholar 
51.Murphy, R. J., Schneider, S., Taylor, Z. & Nieto, J. Mapping clay minerals in an open-pit mine using hyperspectral imagery and automated feature extraction. In Vertical Geology, From Remote Sensing to 3D Geological Modelling. Proceedings of the first Vertical Geology Conference, Lausanne, Switzerland, 5–7 (2014).52.Todorova, M. H. & Atanassova, S. L. Near infrared spectra and soft independent modelling of class analogy for discrimination of chernozems, luvisols and vertisols. J. Near Infrared Spectrosc. 24, 271–280 (2016).ADS 
CAS 
Article 

Google Scholar 
53.Stenberg, B., Viscarra Rossel, R., Mouazen, A. & Wetterlind, J. Visible and Near Infrared Spectroscopy in Soil Science, vol. 107 (Academic Press, 2010).54.Harris, P., Fotheringham, A., Crespo, R. & Charlton, M. The use of geographically weighted regression for spatial prediction: An evaluation of models using simulated data sets. Math. Geosci. 42, 657–680 (2010).MathSciNet 
CAS 
MATH 
Article 

Google Scholar 
55.Department of Primary Industries and Regional Development, Western Australia. South West Agricultural Region (dpird-008) (2020).56.Australian Bureau of Statistics. Value of Agricultural Commodities Produced, Australia (2020).57.Department of Primary Industries and Regional Development, Western Australia. Western Australian Wheat Industry (2019).58.Rayment, G. E. & Lyons, D. J. Soil Chemical Methods—Australasia (CSIRO Publishing, 2010).59.Dolui, S. et al. Structural correlation-based outlier rejection (score) algorithm for arterial spin labeling time series. J. Magn. Reson. Imaging 45, 1786–1797 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
60.Pollet, T. V. & van der Meij, L. To remove or not to remove: The impact of outlier handling on significance testing in testosterone data. Adapt. Hum. Behav. Physiol. 3, 43–60 (2017).Article 

Google Scholar 
61.Leys, C., Ley, C., Klein, O., Bernard, P. & Licata, L. Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. J. Exp. Soc. Psychol. 49, 764–766 (2013).Article 

Google Scholar 
62.Mallat, S. G. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11, 674–693 (1989).ADS 
MATH 
Article 

Google Scholar 
63.Whitcher, B. waveslim: Basic Wavelet Routines for One-, Two-, and Three-Dimensional Signal Processing (2020).64.O’brien, R. M. A caution regarding rules of thumb for variance inflation factors. Qual. Quant. 41, 673–690 (2007).65.Akinwande, M. O. et al. Variance inflation factor: As a condition for the inclusion of suppressor variable (s) in regression analysis. Open J. Stat. 5, 754 (2015).Article 

Google Scholar 
66.Webster, R. & Oliver, M. A. Sample adequately to estimate variograms of soil properties. J. Soil Sci. 43, 177–192 (1992).Article 

Google Scholar 
67.Atteia, O., Dubois, J.-P. & Webster, R. Geostatistical analysis of soil contamination in the Swiss Jura. Environ. Pollut. 86, 315–327 (1994).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
68.Brunsdon, C., Fotheringham, S. & Charlton, M. Geographically weighted regression-modelling spatial non-stationarity. J. R. Stat. Soc. Ser. D (The Statistician) 47, 431–443 (1998).Article 

Google Scholar 
69.Gollini, I., Lu, B., Charlton, M., Brunsdon, C. & Harris, P. Gwmodel: An r package for exploring spatial heterogeneity using geographically weighted models. arXiv preprint arXiv:1306.0413 (2013).70.Lu, B., Harris, P., Charlton, M. & Brunsdon, C. The GWmodel R package: Further topics for exploring spatial heterogeneity using geographically weighted models. Geo Spat. Inf. Sci. 17, 85–101 (2014).Article 

Google Scholar 
71.Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer Science & Business Media, 2009).72.Burnham, K. P. & Anderson, D. R. A practical information-theoretic approach. Model selection and multimodel inference 2 (2002).73.R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, Vienna, Austria 2020).74.Mevik, B.-H., Wehrens, R., Liland, K. H. & Hiemstra, P. pls: Partial Least Squares and Principal Component Regression (2020).75.Bivand, R., Yu, D., Nakaya, T. & Garcia-Lopez, M. spgwr: Geographically weighted regression. R Package Version 0.6-34. http://cran.r-project.org/web/packages/spgwr/. Accessed August 30th 2020 (2020). More

Study lakesThe study was conducted in two water bodies created after aquatic restorations of mining pits, Lakes Milada (2.5 km2, high structural complexity; 50° 39′ N, 13° 58′ E) and Most (3.1 km2, low structural complexity lake; 50° 32′ N, 13° 38′ E), in the Czech Republic (Fig. 1). Aquatic restoration took place from 2001 to 2010 in Milada and from 2008 to 2014 in Most. Both lakes are medium-sized (surface area = 252 and 311 hectares, respectively), relatively deep (Zmean = 16 and 22 m, Zmax = 25 and 75 m), oligotrophic (mean summer total phosphorus  More

Because biological materials are often viscoelastic composites, with properties dependent on orientation as well as spatial and temporal scales, all tests were designed to mimic natural conditions of tool use. For example, because of possible anisotropies, indentations were made on contact surfaces instead of cross sections of the “tools”; abrasion resistance was measured in the contact direction. Fracture tests were designed to mimic the tension on the side of a tooth or sting subjected to lateral forces; the velocities in our impact tests were between 0.1 and 1 m/s, typical for tool interactions, though 1/10 as fast as for some organisms48,49.OrganismsAll organisms, except the salmon, were housed alive in our laboratory until just before testing. Samples were measured within 24 h of removal from the organism, and were maintained in high-humidity environments during the preparation period. This is particularly important because mechanical properties can depend strongly on water content and this dependence can differ between materials. For example, non-HEBs can harden more than HEBs with drying30,50,51.

1.

Leafcutter ants, Atta cephalotes, were obtained from colonies we collected in Flores, Guatemala and Arena Forest Reserve, Trinidad. They were maintained on Himalayan blackberry leaves, Rubus armeniacus, Portugese laurel, Prunus lusitanica, and Japanese spurge, Pachysandra terminalis.

2.

Nereid worms, Neanthes brandti (synonymous with Alitta brandti), were collected from trenches dug in bars near the mouth of the Coos River, Charleston, Oregon. They were kept in sea-water in a laboratory refrigerator at about 5 °C.

3.

Scorpions, Hadrurus arizonensis, from Arizona, were obtained from commercial suppliers (e.g. Bugs of America, http://bugsofamerica.com) and fed House crickets, Acheta domesticus, and mealworms, Tenebrio molitor larvae.

4.

Spiders, Araneus diadematus, were collected seasonally around the University of Oregon campus, and kept alive in a laboratory refrigerator at about 5 °C. Tarantulas, Aphonopelma hentzi were obtained from Carolina (https://www.carolina.com).

5.

Chitons, Katharina tunicata, and Cryptochiton stelleri were collected in rocky intertidal regions along the coast near Charleston, Oregon and kept alive in a laboratory refrigerator.

6.

Salmon heads, Oncorhynchus sp., were obtained fresh from seafood stores.

7.

Leaf cutter bees, Megachile rotundata, were obtained from Mason Bees for Sale (www.masonbeesforsale.com).

Hardness, modulus of elasticity and damping measurementsHardness, modulus of elasticity and dynamic mechanical property measurements were made by pressing a sharp diamond probe into specimens and measuring the resulting indentation as it changed in time. A higher modulus of elasticity indicates that a structure is stiffer and suffers less elastic (quickly recovered) deformation. A higher hardness value indicates that the material will undergo less plastic (non-recovered) deformation and thus will have a smaller pit left behind after the indentation. A material with a higher loss tangent will absorb more energy of vibration (higher damping), and is characterized by lagging surface deformation and recovery (viscoelasticity) as the indention force changes. Damping can reduce damage because the energy absorbed and converted to heat is not available for breaking bonds in the material.We used an Atomic Force Microsocpe (AFM; NanoScope IIIa, Digital Instruments, Santa Barbara, CA) with an add-on force/displacement transducer (TriboScope, Hysitron Inc., Minneapolis, Minnesota). The Hysitron transducer held a polished diamond probe in place with capacitors that were used to sense the position of the probe and to impart vertical forces for indenting and imaging the specimen. Measurement regions were selected for minimal slope and surface topography as evidenced by the depth variation in AFM scans and the symmetry of the residual indents.In order to make the most biologically relevant measurements, indentations were made on regions of the external surface of structures that directly contact the environment. However, we also made measurements on cross-sections of the arthropod “tools” as part of preliminary SEM and indentation investigations to ensure that the thickness of epicuticle or other surface material would not distort the results for HEBs. Figure 2A shows an indentation on an un-polished surface—the original surface topography is visible as linear scratches that are small compared to the pyramidal indentation. When we could not avoid indentation-scale topography, we hand-polished the surface with 2000–12,000 grit sandpaper (Micro-mesh sheets, http://micro-surface.com), to smooth the surface on the scale of the test indents.Figure 2Images of testing samples for each of the measured properties. (A) AFM image of a residual indentation on the natural surface of the Mn-HEB region of the sting of a scorpion, Hadrurus arizonensis. Indentations were used in measuring hardness, modulus of elasticity and damping properties. Natural “scratches” are visible on the original surface around the triangular indentation made by the cube-cornered indenter. (B) Mandible of an ant, Atta cephalotes, before and after an abrasion testing session. The tip of the zinc-rich distal tooth in the “before” image has been flattened during a preliminary abrasion session. (C) Images from before and after energy of fracture testing of a 12 μm thick Zn-HEB test piece made from the nearly-flat side-surface of the fang of a spider, Araneus diadematus. The original fang shape is evident with the tip of the fang towards the top and the proximal side to the left. (D) Images from before and after impact resistance testing of a 12 μm thick Zn-HEB test piece made from the fang of a spider, Araneus diadematus. The test piece covers a 50 μm diameter backing-pit milled (using FIB-SEM) into a (reflective) silicon chip. The piece has been shattered from the impact in the image on the right.Full size imageSpecimens were mounted on atomic force microscopy (AFM) specimen disks (TedPella Inc., Redding, CA) in a mound of epoxy composite. The composite was prepared by mixing approximately 0.45 g of 400-grit aluminum oxide powder (Buehler, Evanston, Ill., Ted Pella Inc., Redding CA, or Kramer Industries, Piscataway, NJ—the later preferred because it was less reflective) with 0.075 mL each of resin and hardener (Quick Set Epoxy; Loctite, Rocky Hill, CT, and 5 m Quik-Cure Epoxy, Bob Smith Industries, Atascadero CA). The composite was stiff enough that the unpolished specimen had to be pressed in and could be oriented before curing so that the desired indentation region would retro-reflect a light beam sent through the eyepiece of a dissecting microscope back into the microscope, ensuring that the desired region would be flat for AFM scanning and indentation. The mounted specimens were placed in an oven at 39 °C for at least 1 h to cure the epoxy composite. Samples that were not tested immediately were kept on moist paper in a container in a refrigerator and were tested within 24 h to avoid dehydration and other changes. Additionally, the epoxy composite served as a barrier to reduce loss of water through the cut surfaces.The epoxy composite mounting technique was tested to ensure that small (about the same size as the biological specimens) “floating” glass cover-slip pieces would yield the same hardness and modulus of elasticity values as large, flat-mounted cover-slip pieces, and re-checked when relevant products were changed.In order to test whether there were any rapid changes in hardness or modulus of elasticity of HEBs, we tested a H. arizonensis sting, that was mounted for AFM measurements while still attached to an anaesthetized scorpion. We did not find a significant trend with time for 14 measurements made between 10 or 20 min (Zn-HEB and Mn-HEB respectively) after separating the live scorpion from the sting, and 5 h (largest R2: 0.006). These fast results were also comparable to results made using the standard technique, indicating that the technique described above was sufficient to prevent significant changes from dehydration. We also made preliminary measurements on scorpion joint cuticle, armour teeth, and other non-HEB regions of the cuticle, and found none that were harder than the region at the base of the sting, used here to represent non-HEB cuticle.We used a pyramid-shaped diamond probe with cubic corner facets (90° between the three faces)14,31. The steeper angle of the cubic tip, relative to a more commonly used Berkovich tip, made it easier to avoid surface features such as hairs. The diamond probe was positioned on the specimen using a 30× extra short focus monocular (M1030, Specwell Corporation, Tokyo, Japan). The indentation sequence began with the force being ramped linearly from 0 to 2 milliNewtons (mN) in 0.1 s, maintained at 2 mN for 10 s. The force was then ramped down to 1.5 mN over 0.1 s and then the force was varied sinusoidally at 10 Hz (for 25 cycles) with a peak-to-peak amplitude of 1.0 mN, in order to measure dynamic properties. The force was then ramped to 0 in 0.1 s.Probe-extension (Oliver–Pharr) and image-based measurementsTwo methods of obtaining the modulus of elasticity and hardness were employed14. In the first method, values were obtained only from force–displacement curves using the Oliver–Pharr technique52,53. The modulus of elasticity was obtained from the slope of the force–displacement curve at the beginning of withdrawal of the indenter. Oliver–Pharr hardness values were calculated from the intercept of this sloped line with the line of zero force.In addition to the hardness value obtained from the force–displacement curves, we also obtained a hardness value based on measurements of the size of the residual indentation. These image-based hardness values were calculated as H = F/A, where “F” is the maximum force applied to the probe, and “A” is the projected area of the residual indentation, obtained from the perimeter of the indentation measured on an AFM image (e.g. Fig. 2A) made by scanning the indenting probe itself minutes after indenting the specimen.The Oliver–Pharr method is inaccurate if the indentation force causes the surface of the specimen to move, such as for improperly backed specimens, because it assumes probe extension is a measure of indentation depth. In contrast, the image-based method is nearly insensitive to global displacements of the specimen because it is based only on the applied force and measurements of the residual indentation. We found it useful to obtain both values to check each other: on several occasions differences between the two measured values indicated support problems. This is important for biological specimens with multiple layers, voids, etc. For example, if there is a lumen under the shaft but not the tip, the tip may appear harder because it displaces less. In addition, calibration problems, such as from fractured silica, were quickly identified by differences in the image and Oliver–Pharr values. Finally, the image method is not sensitive to other artifacts, such as “pile-up”, that are associated with estimating contact region from probe extension53,54,55,56.There is a potential difference between the Oliver–Pharr and image hardness values associated with the different time scales. The Oliver–Pharr hardness is measured during the probe withdrawal while the image of the residual indent is obtained a couple of minutes after indentation. If the indent partially recovers in the interim, the image technique would be based on a smaller residual indentation resulting in a higher hardness value. We prefer the image method not only because it is robust to imperfectly supported specimens, but also because we would like our hardness measurement to reflect the long-term indentation damage done to the tips and blades. To test that the indent size had stabilized by the time we measured it, we re-measured an indentation in the zinc-region of a scorpion sting after more than 6 months and found that the indentation diameter had decreased little, by about 15%.We also calculated an image-based modulus of elasticity value that has been suggested for materials that produce “pile-up” artifacts53. The area measured from the image of the residual indentation (used for the image-based hardness), was substituted for the contact area calculated from probe extension in Eq. (6) of Oliver & Farr, 2004 53.Notwithstanding the differences in image-based and probe-extension based (Oliver–Pharr) measurements, there was little practical difference in results, as shown in Fig. 3A,B. The metals and plastics that we measured for Fig. 3 tended to have slightly higher Oliver–Pharr hardness values than image-based values, possibly because of “pile-up” artifacts.Figure 3Comparison of probe-extension (Oliver–Pharr) and image-based techniques for hardness (A) and reduced modulus of elasticity (B) for our indentation data. Pile-up may account for higher (above the line) Oliver–Pharr values of some of the metals and plastics.Full size imageIn the “Results” section, we plot the values obtained using the images, but the Oliver–Pharr values are included along with image-based values in the results table, Table 1.Loss tangentDynamic mechanical properties were measured from the sinusoidal segments of the indentation sequence by comparing the amplitude and phase of the displacement to the applied sinusoidal force57. Phase lags associated with the transducer and electronics were determined assuming zero true-lag from a fused silica standard obtained using the same indentation sequence and force. The loss tangents obtained in this way were for the high-stress regimes associated with indentation, as compared to low-stress tests involving bending without plastic deformation.Calibration for Oliver–Pharr measurementsIn order to obtain the contact area from the indent depth, the shape of the indenting tip must be known. We characterized the indenting tip shape directly using scanning electron microscope images, so that we could make deep, micron-scale indents that would be evident on un-polished biological surfaces. We could not make indents in silica as deep as the indents desired for our biological specimens without fracturing the silica (fracture for the cubic cornered tip began at about 3 mN) so we could not use the usual technique of estimating the tip shape at depth by calibrating with fused silica. The tip shape was characterized by three measurements: first, the angle of the three-sided pyramidal tip (α), second, a measure of the bluntness of the tip (B), the distance between the apex of the tip if it were an ideal pyramid and the actual blunt tip, and third, a measure of the distance from the blunt tip beyond which the shape of the tip was not distinguishable from an ideal pyramid (I). The value “I” was used as a limit: only indents with greater depth than “I” were used to calculate mechanical properties. For these deeper indents, the following description of the projected area (A) of the contact region between the tip and the specimen was used:$$A= frac{(0.433)(4){(D+B)}^{2}}{frac{1}{{mathrm{tan}}^{2}left(frac{alpha }{2}right)}-0.3333},$$where D is the depth of the indent, determined by the extension of the indenting probe, 0.433 is the ratio of the area of an equilateral triangle to the square of the length of a side, and 0.3333 is tan2 (30°). As an example, the tip used for the majority of measurements was characterized by α = 89.9°, B = 115 nm, I = 100 nm. Thus, for indents with a depth greater than 100 nm, for our tip, A = 2.58 (D + 115 nm)2.Measurement of residual indentation areaThe area of the residual indent was measured using an AFM image, obtained minutes after the indentation, using the indenting tip as the imaging tip. To minimize inaccuracies in indent perimeter determination, caused by finite size of the imaging probe or other systematic errors, we calibrated our area measurements so that we obtained a median value of 70 GPa for measurements of the modulus of elasticity on fused silica. Because there is some subjectivity in measuring the size of the indentation, operator-specific calibrations, based on each operator’s measurements of fused silica, were used for most of the measurements.Test piece preparation for impact resistance and energy of fracture measurementsWe measured resistance to impact and fracture using custom miniature versions of testing devices that fracture or damage standardized “test pieces” of materials. We prepared test pieces as follows: the fresh (usually immediately after removal from the organism) specimens were adhered to one end of a glass slide using a marine epoxy (Loctite, Rocky Hill, CT) which required a curing time of 2 h at 39 °C, or with cyanoacrylate adhesive (Krazy Glue, all purpose, Elmer’s Products)14, which required no extra curing time. A flat region ( > 100 μm diameter, but not wide enough to reduce the thickness of the HEB region in the center to less than 12 μm) was polished on a specimen by grinding the slide with the specimen against a sequence of flat 2000, 6000 and 12,000 grit sandpaper (Micro-mesh sheets, http://micro-surface.com). The specimen was removed from the adhesive using a scalpel, inverted, and the polished-flat region was adhered to the glass with a thin film of water and surrounded by a small bead of marine epoxy. The water film kept the epoxy from getting pulled under the specimen by capillary action. The epoxy was cured and the specimen polished to a thickness of 12 ± 2 μm as determined with a digital micrometer, using the same sandpaper sequence. The resulting test piece was then freed by scraping the epoxy from around the edges using a scalpel blade. The area of the pieces varied according to the size of flat regions and was, typically, hundreds of microns on a side.Maintaining hydration was especially important for these test pieces because they were only 12 μm thick and so they could dry quickly. To reduce artifacts from drying or other changes in the tested material, all preparation and testing took place in a ~ 15 m3 enclosure maintained at greater than 90% relative humidity.Although we used FIB-SEM (Focused Ion Beam-Scanning Electron Microscope) to shape specimens of the materials for molecular fragment analysis, we did not use this technique for preparing our micron-scale test pieces for several reasons: potential material property changes caused by beam damage, subjection to vacuum, and because some of the test pieces needed to be large enough that they would be difficult to make with FIB-SEM.Impact resistance measurementsA custom testing device was built to compare the energy required for a swinging pendulum to shatter test pieces of the different materials (Fig. 2D). A 12 ± 2 μm thick test piece was adhered by the moisture in the high-humidity enclosure and held in place with an adhesive (spots of cyanoacrylate, Krazy Glue, all purpose, Elmer’s Products, www.elmers.com, or 5-min epoxy gel) over a 50 μm-diameter circular pit milled in a silicon wafer using a FIB-SEM apparatus. The pendulum, made of carbon fiber and aluminum (length: 0.2 m, moment of inertia: 4.25 x 10−6 kg m2) with a diamond impactor tip polished to a diameter of 20 μm, was held by miniature bearings and electronically released from increasing heights until the test piece fractured. The energy required to fracture the specimen was calculated from the release height from which the pendulum fractured the test piece. This energy was normalized by the measured thickness of the specimens to give joules required per meter of thickness. Nevertheless, we consider this test to be a relative test that is not expected to be generalizable to all impacts, as the energy to fracture is likely to depend not only on thickness but also on variables such as the diameter of the impactor tip and the diameter of the backing hole.This impact test differs from Charpy and Izod tests in that the energy required to fracture the specimen was measured by releasing the pendulum from increasing heights until the specimen fractured, rather than by releasing it from a height sufficient to fracture all specimens, and measuring the residual energy of the pendulum after impact. The advantage of our threshold technique is that the threshold of fracture is likely the biologically important quantity, and direct determination of the fracture threshold avoids the possibility that the energy deposited in a single highly-energetic impact might be partially expended in plastic deformation, leading to an overestimate of the threshold energy. A drawback of our technique is that impacts that do not break the specimen may produce damage that weakens the specimen for subsequent impacts. Nevertheless, all specimens were subjected to the same series of increasingly energetic impacts, until fracture, and were thus comparable.Energy of fracture measurementsWe measured the energy or work required to slowly break a test piece in two (Fig. 2c) using a custom fracture toughness measuring device14. The device drove apart two microscope cover slips bridged by the test piece until it split in two, while recording the required force and the displacement (work is the product of force and incremental distance). This work, divided by the area of the new post-fracture surfaces, is the energy of fracture, reported in Joules per meter squared. It is a measure of the energy required, per unit area, to break the bonds that originally held the two pieces together (as long as the kinetic energy is relatively small—the pieces do not fly away), and is one indication of the resistance of a material to fracture.The length of the fracture was measured using a microscope and multiplied by the thickness of the test piece to obtain the fracture area. This area was used to normalize force–displacement curves. The work of fracture per unit area of the fracture was obtained by numerically integrating these normalized force–displacement curves. The load cell was designed to be stiff in order to minimize storage of energy within the apparatus as the specimen underwent tension58. Fracture planes were perpendicular to the original surface and approximately perpendicular to the long axis of the “tool” (Fig. 1C).The test protocol was altered from that used previously14 because the test pieces used here were smaller. Test pieces were not notched in order to avoid fractures from the notching process, and the specimens were adhered to the test apparatus in place (Krazy Glue, all purpose, Elmer’s Products, www.elmers.com) to avoid premature fracture. To improve the bonding of the cyanoacrylate adhesive to the glass cover slips in the high humidity atmosphere, we treating the cover slips with a 10-s dip in a 2% (by volume) 3-aminopropyltriethoxysilane (Sigma Chemical Co., www.sigmaaldrich.com), 98% acetone solution, followed by rinses in deionized water and air drying. The test protocol for the ant mandibular teeth varied from the others in that whole teeth were fractured instead of 12μ polished test pieces.A consistency test with AFM data was developed to identify cases of imperfect bonding to the cover slips, when part of the measured energy was expended in partially pulling the test piece out of the cyanoacrylate adhesive. For samples subject to this problem (usually specimens with small adhesive contact areas, such as the fang specimen in Fig. 1C), we required that the force–displacement curve be consistent with the stiffness of the test piece, expected from a model based on the shape of the individual test piece and the slope of the force–displacement curve for the insertion portion of the nano-indentation sequence for that material. When a piece partially pulled out and failed this test, the apparent stiffness was much lower than the expected stiffness (from nano-indentation) and slight stretch marks were often visible in the adhesive on close inspection.This stiffness consistency test was also found to be useful in identifying cases where part of the fracture was pre-existing but had not been visible in the test piece. The pre-existing fracture would tend to reduce the effective width of the specimen and thus could be identified by a lower than expected stiffness under tension.Abrasion resistance measurementsWe measured the energy required to abrade away a volume of material from our specimens by holding them against a rotating abrasive disk. The energy used in eroding the material is given by the force of friction multiplied by the distance traveled over the abrasive paper (work is the product of the force and incremental displacement), with units of Joules per meter cubed of volume worn away.The “pin on disk” type testing device, developed for testing pieces of crab cuticle14, was used with modified procedures for the smaller specimens here. Instead of cylindrical core samples, whole, approximately conical tips of teeth, fangs or stings were used (Fig. 2B). The samples were affixed with cyanoacrylate gel adhesive (Maxi-Cure, Bob Smith Industries, Atascadero CA) to a steel pin held in the head of the wear tester. This head was mounted on a custom-made load cell that measured the horizontal force produced by friction between the specimen pin and the abrasive turntable. During the wear test, the specimen pin was held against the turntable with adjustable weights that, for the standard test, produced a downward force of 0.019 Newtons. The surface of the turntable was covered with 600 grit abrasive paper (#413Q, 3 M Corporation, www.3M.com). The turntable rotation period was usually set to about 4 s, resulting in an interaction velocity of 0.027 m/s.The volume worn away was calculated from “before” and “after” measurements of microscope images (image J software) taken from the side (e.g. Fig. 2B) to measure the height of the approximate cone of worn material, and from face-on in order to measure the area of the base and top of the frustum of worn-away material. The horizontal force was recorded continuously during the wear period. The wear rate (w), defined as the volume worn away per unit energy expended, was approximated as follows:$$w= frac{V}{Fd} ,$$where F is the average force of friction measured during the wear period by the load cell, d is the distance traveled by the pin over the abrasive paper and “V” is the worn volume, approximated as a frustum:$$V = 1{/}3,Delta L , (A1 + left( {A1*A2} right)^{1/2} + A2)$$where “A1” and “A2” are the areas of the worn surface before and after the wear sessions (the area of any voids or internal lumens was subtracted from the area of the cross sections) and ΔL, the change in the length of the specimen due to wear. We defined wear resistance as the inverse of the wear rate, 1/w. While we expect this test to be most useful for relative comparisons, and the value is expected to vary somewhat with abrasive properties and normal forces, we found no statistically distinguishable difference in values from 4 samples that were re-run using a ten-times greater force to press them against the abrasive paper14.Molecular composition and nanometer-scale structureIn order to better understand the composition and structure of the HEBs—down to an atomic scale—we examined a representative HEB using Atom Probe Tomography (APT). We checked the APT results and studied their generality using Time-of-Flight–Secondary Ion Mass Spectrometry (ToF–SIMS). Both of these techniques use a pulsed beam (laser and ion respectively) to break the specimen into molecular fragments that are accelerated to a detector; for a particular charge, heavier fragments travel more slowly and arrive later at the detector. The arrival time differences are used to identify the fragments by their mass, giving information about, for example, the atoms attached to zinc atoms in the specimen and, from APT, the spatial distribution of zinc atoms on a nanometer scale.Atom probe tomography (APT)APT is a 3D nanoscale characterization method in which field evaporated ions from a sharpened needle specimen are analyzed by a position-sensitive single-particle detector, in order to provide an isotopically resolved three-dimensional representation of the real-space specimen elemental distribution59. The field evaporation of non-conductive samples is achieved using a pulsed laser focused on the needle specimen apex.A FIB-SEM based lift-out procedure was used to prepare needle-shaped APT specimens using FEI Helios 600i at the University of Oregon CAMCOR facility, and a Helios Dual Beam Nanolab 600 FIB-SEM housed at Environmental Molecular Sciences Laboratory, PNNL.The APT analysis was carried out using a CAMECA LEAP (local electrode atom probe) 4000X HR system equipped with a 355 nm wavelength picosecond pulsed UV laser. A 30 K sample base temperature and a 100 or 200 kHz laser pulse repetition rate was used. Atom probe data reconstruction and analysis was performed using Cameca IVAS software.
Development of APT techniques for these organic materials.APT has not typically been used to examine organic materials, so we began by examining standards (such as zinc picolinate) and adjusting beam current densities and other parameters in both the FIB-SEM preparation of APT samples and in APT itself in order to minimize physical damage detected with SEM and to minimize differences between the chemical formulae and APT results for standards60. We found that current densities often employed in FIB-SEM milling were much too high for our organic specimens, resulting in beam damage visible in SEM.Based on the standards and SEM evidence of damage, we used FIB-SEM currents for producing the sample needles, and APT laser pulses for promoting evaporation, that were similar to or smaller than those used in other investigations of organic materials61,62,63,64,65,66,67,68. We used ≤ 21 pA for the electron beam, ≤ 80 pA for ion beam “trenching”, 7.7 pA for ion beam imaging and for cutting the cantilever “liftout” piece, and ≤ 24 pA for sharpening needles. The results reported here are based on samples analyzed using 10, 20 or 100 pJ laser pulses.Identification of molecular fragments from mass-to-charge ratios is particularly difficult for organic materials because of the many possible fragments of similar mass, and several techniques have been developed to aid in this analysis61,62,63,64,68,69,70,71,72,73.Our identification of zinc-containing fragments was simplified by the pattern of the 3 or 4 main zinc isotopes (Fig. 6A). In addition, we used resources with lists of fragments as a function of mass (e.g. https://webbook.nist.gov/chemistry/mw-ser/). We also cross-checked fragment identification using a Time-of-Flight–Secondary Ion Mass Spectrometry (ToF–SIMS) system.Time of flight–secondary ion mass spectrometry (ToF–SIMS)We used a ToF–SIMS system that had a higher mass-to-charge resolution than the APT system (although it had micron- instead of nanometer-scale spatial resolution) in order to check APT fragment identification. The higher mass sensitivity of the ToF–SIMS system provided additional evidence that, for example, the fragments identified as ZnCN were not actually ZnC2H2, which is only about 0.02% lighter. We also used ToF–SIMS to study the larger-scale spatial distribution of fragments, and similarities with HEBs from other species.We used an ION-TOF ToF–SIMS IV, manufactured by ION-TOF GmbH, Muenster, Germany. The primary ion beam was Bi3+ (25 kV, 10 kHz, 0.4 pA); the static limit (2 × 1012 ions/cm2) was not exceeded. The dimensions of the analysis area varied, but were between 100 × 100 μm and 300 × 300 μm. A low energy electron beam was used for charge neutralization. The spectra were analyzed using the vendor’s software. Chemical maps of peaks of interest were created from the total spectra and used as a basis for retrospective analysis—i.e., pixel-specific extraction of spectra in order to determine the chemical makeup of features of interest. More

1.Sivapalan, M., Savenije, H. H. & Blöschl, G. Socio-hydrology: A new science of people and water. Hydrol. Process. 26(8), 1270–1276 (2012).ADS 
Article 

Google Scholar 
2.Babel, M. S., Das Gupta, A. & Nayak, D. K. A model for optimal allocation of water to competing demands. Water Resour. Manag. 19(6), 693–712 (2005).Article 

Google Scholar 
3.Banihabib, M. E., Zahraei, A. & Eslamian, S. An integrated optimisation model of reservoir and irrigation system applying uniform deficit irrigation. Int. J. Hydrol. Sci. Technol. 5(4), 372–385 (2015).Article 

Google Scholar 
4.Ghahreman, B. & Sepaskhah, A. R. Optimal water allocation of water from a single reservoir to an irrigation project with pre-determined multiple cropping patterns. Irrig. Sci. 21(3), 127–137 (2002).Article 

Google Scholar 
5.Xevi, E. & Khan, S. A multi-objective optimisation approach to water management. J. Environ. Manag. 77(4), 269–277 (2005).CAS 
Article 

Google Scholar 
6.Divakar, L., Babel, M. S., Perret, S. R. & Das Gupta, A. Optimal allocation of bulk water supplies to competing use sectors based on economic criterion—An application to the Chao Phraya River Basin, Thailand. J. Hydrol. 401(1–2), 22–35 (2011).ADS 
Article 

Google Scholar 
7.Roozbahani, R., Abbasi, B., Schreider, S. & Ardakani, A. A multi-objective approach for transboundary river water allocation. Water Resour. Manag. 28(15), 5447–5463 (2014).Article 

Google Scholar 
8.Schnegg, M. & Kiaka, R. D. The economic value of water: The contradictions and consequences of a prominent development model in Namibia. Econ. Anthropol. 6(2), 264–276 (2019).
Google Scholar 
9.Langarudi, S. P., Maxwell, C. M., Bai, Y., Hanson, A. & Fernald, A. Does socioeconomic feedback matter for water models?. Ecol. Econ. 159, 35–45 (2019).Article 

Google Scholar 
10.Keshavarz, M., Karami, E. & Vanclay, F. The social experience of drought in rural Iran. Land Use Policy 30(1), 120–129 (2013).Article 

Google Scholar 
11.Dean, A. J., Fielding, K. S., Lindsay, J., Newton, F. J. & Ross, H. How social capital influences community support for alternative water sources. Sustain. Cities Soc. 27, 457–466 (2016).Article 

Google Scholar 
12.Scanlon, T. et al. The role of social actors in water access in Sub-Saharan Africa: Evidence from Malawi and Zambia. Water Resour. Rural Dev. 8, 25–36 (2016).Article 

Google Scholar 
13.Popovic, T., Kraslawski, A., Heiduschke, R. & Repke. J. Indicators of social sustainability for wastewater treatment processes. In Computer Aided Chemical Engineering, Vol. 723(28) (2014).14.El-Gafy, I. K. E. D. The water poverty index as an assistant tool for drawing strategies of the Egyptian water sector. Ain Shams Eng. J. 9(2), 173–186 (2018).Article 

Google Scholar 
15.Bui, N. T. et al. Social sustainability assessment of groundwater resources: A case study of Hanoi, Vietnam. Ecol. Indic. 93, 1034–1042 (2018).Article 

Google Scholar 
16.Li, C. et al. Three decades of changes in water environment of a large freshwater lake and its relationship with socio-economic indicators. J. Environ. Sci. 77, 156–166 (2019).Article 

Google Scholar 
17.Ahmadi, A., Karamouz, M., Moridi, A. & Han, D. Integrated planning of land use and water allocation on a watershed scale considering social and water quality issues. J. Water Resour. Plan. Manag. 138(6), 671–681 (2012).Article 

Google Scholar 
18.Tu, Y. et al. Administrative and market-based allocation mechanism for regional water resources planning. Resour. Conserv. Recycl. 95, 156–173 (2015).MathSciNet 
Article 

Google Scholar 
19.Kelly, R. A. et al. Selecting among five common modelling approaches for integrated environmental assessment and management. Environ. Model. Softw. 47, 159–181 (2013).Article 

Google Scholar 
20.Wang, K., Davies, E. G. & Liu, J. Integrated water resources management and modeling: A case study of Bow river basin, Canada. J. Clean. Prod. 240, 118242 (2019).Article 

Google Scholar 
21.Zhao, D. et al. Quantifying economic-social-environmental trade-offs and synergies of water-supply constraints: An application to the capital region of China. Water Res. 195, 116986 (2021).CAS 
Article 

Google Scholar 
22.Navarro-Ramírez, V., Ramírez-Hernandez, J., Gil-Samaniego, M. & Rodríguez-Burgueño, J. E. Methodological frameworks to assess sustainable water resources management in industry: A review. Ecol. Indic. 119, 106819 (2020).Article 

Google Scholar 
23.Iran Environment Organization. https://www.doe.ir/portal/home (2013).24.Madani, K. & Mariño, M. A. System dynamics analysis for managing Iran’s Zayandeh-Rud river basin. Water Resour. Manag. 23(11), 2163–2187 (2009).Article 

Google Scholar 
25.Ahmad, S. & Simonovic, S. P. System dynamics modeling of reservoir operations for flood management. J. Comput. Civ. Eng. 14(3), 190–198 (2000).Article 

Google Scholar 
26.Ahmad, S. & Simonovic, S. P. Spatial system dynamics: New approach for simulation of water resources systems. J. Comput. Civ. Eng. 18(4), 331–340 (2004).Article 

Google Scholar 
27.Ahmad, S. & Simonovic, S. P. An intelligent decision support system for management of floods. Water Resour. Manag. 20(3), 391–410 (2006).Article 

Google Scholar 
28.Dong, Q., Zhang, X., Chen, Y. & Fang, D. Dynamic management of a water resources-socioeconomic-environmental system based on feedbacks using system dynamics. Water Resour. Manag. 33(6), 2093–2108 (2019).Article 

Google Scholar 
29.National Statistical Center of Iran. Summary of Industrial Workshop Statistics by Activity. http://www.amar.org.ir (2013).30.Rezaee, A., Bozorg-Haddad, O. & Singh, V. P. Water and society. in Economical, Political, and Social Issues in Water Resources, 257–271 (Elsevier, 2021).Chapter 

Google Scholar 
31.Ministry of Energy, Water and Wastewater Macro Planning Office. Studies on Updating the Country’s Comprehensive Water Plan in Aras, Urmia, Talesh-Anzali Wetland, Sefidrood-Haraz, Haraz-Qarasu, Gorganrood and Atrak Watersheds (Consumption and Needs of Industrial and Mining Water and Production Wastewater in the Base Year (2006)) in the Catchment Area of Urmia), Vol. 8 (2013).32.Hwang, C. L. & Yoon, K. Methods for multiple attribute decision making. in Multiple Attribute Decision Making: Lecture Notes in Economics and Mathematical Systems (Springer, 1981).Chapter 

Google Scholar 
33.Zeyaeyan, S., Fattahi, E., Ranjbar, A. & Vazifedoust, M. Classification of rainfall warnings based on the TOPSIS method. Climate 5(2), 33 (2017).Article 

Google Scholar 
34.Zolghadr-Asli, B., Bozorg-Haddad, O., Enayati, M. & Goharian, E. Developing a robust multi-attribute decision-making framework to evaluate performance of water system design and planning under climate change. Water Resour. Manag. 35(1), 279–298 (2021).Article 

Google Scholar 
35.Opricovic, S. & Tzeng, G. H. Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. Eur. J. Oper. Res. 156(2), 445–455 (2004).Article 

Google Scholar 
36.Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27(3), 379–423 (1948).MathSciNet 
Article 

Google Scholar  More