Ecology

1.Filkins LM, O’Toole GA. Cystic fibrosis lung infections: polymicrobial, complex, and hard to treat. PLoS Pathog. 2015;11:e1005258.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
2.Paterson IK, Hoyle A, Ochoa G, Baker-Austin C, Taylor NGH. Optimising antibiotic usage to treat bacterial infections. Sci Rep. 2016;6:37853.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
3.Andrews JM. Determination of minimum inhibitory concentrations. J Antimicrob Chemother. 2001;48:5–16.CAS 
PubMed 
Article 

Google Scholar 
4.Brook I. Inoculum effect. Rev Infect Dis. 1989;11:361–8.CAS 
PubMed 
Article 

Google Scholar 
5.Karslake J, Maltas J, Brumm P, Wood KB. Population density modulates drug inhibition and gives rise to potential bistability of treatment outcomes for bacterial infections. PLOS Comput Biol. 2016;12:e1005098.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
6.Udekwu KI, Parrish N, Ankomah P, Baquero F, Levin BR. Functional relationship between bacterial cell density and the efficacy of antibiotics. J Antimicrob Chemother. 2009;63:745–57.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
7.Sweeney E, Sabnis A, Edwards AM, Harrison F. Effect of host-mimicking medium and biofilm growth on the ability of colistin to kill Pseudomonas aeruginosa. Microbiology. 2020;166:1171–80.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
8.Walters MC, Roe F, Bugnicourt A, Franklin MJ, Stewart PS. Contributions of antibiotic penetration, oxygen limitation, and low metabolic activity to tolerance of Pseudomonas aeruginosa biofilms to ciprofloxacin and tobramycin. Antimicrob Agents Chemother. 2003;47:317–23.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
9.Nguyen D, Joshi-Datar A, Lepine F, Bauerle E, Olakanmi O, Beer K, et al. Active starvation responses mediate antibiotic tolerance in biofilms and nutrient-limited bacteria. Science. 2011;334:982–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.Høiby N, Bjarnsholt T, Givskov M, Molin S, Ciofu O. Antibiotic resistance of bacterial biofilms. Int J Antimicrob Agents. 2010;35:322–32.PubMed 
Article 
CAS 

Google Scholar 
11.Olsen I. Biofilm-specific antibiotic tolerance and resistance. Eur J Clin Microbiol Infect Dis. 2015;34:877–86.CAS 
PubMed 
Article 

Google Scholar 
12.Macia MD, Rojo-Molinero E, Oliver A. Antimicrobial susceptibility testing in biofilm-growing bacteria. Clin Microbiol Infect. 2014;20:981–90.CAS 
PubMed 
Article 

Google Scholar 
13.Thieme L, Hartung A, Tramm K, Klinger-Strobel M, Jandt KD, Makarewicz O, et al. MBEC versus MBIC: the lack of differentiation between biofilm reducing and inhibitory effects as a current problem in biofilm methodology. Biol Proced Online. 2019;21:18.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
14.Bottery MJ, Pitchford JW, Friman V-P. Ecology and evolution of antimicrobial resistance in bacterial communities. ISME J. 2021;15:939–48.PubMed 
Article 

Google Scholar 
15.Smith AL, Fiel SB, Mayer-Hamblett N, Ramsey B, Burns JL. Susceptibility testing of Pseudomonas aeruginosa isolates and clinical response to parenteral antibiotic administration: lack of association in cystic fibrosis. Chest. 2003;123:1495–502.CAS 
PubMed 
Article 

Google Scholar 
16.Radlinski L, Conlon B. Antibiotic efficacy in the complex infection environment. Curr Opin MicrobioL 2018;42:19–24.CAS 
PubMed 
Article 

Google Scholar 
17.Vos MGJ, de, Zagorski M, McNally A, Bollenbach T. Interaction networks, ecological stability, and collective antibiotic tolerance in polymicrobial infections. PNAS. 2017;114:10666–71.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
18.Adamowicz EM, Flynn J, Hunter RC, Harcombe WR. Cross-feeding modulates antibiotic tolerance in bacterial communities. ISME J. 2018;12:2723–35.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
19.Aranda-Díaz A, Obadia B, Dodge R, Thomsen T, Hallberg ZF, Güvener ZT, et al. Bacterial interspecies interactions modulate pH-mediated antibiotic tolerance. eLife. 2020;9:e51493.PubMed 
PubMed Central 
Article 

Google Scholar 
20.Vega NM, Gore J. Collective antibiotic resistance: mechanisms and implications. Curr Opin Microbiol. 2014;21:28–34.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
21.Beaudoin T, Yau YCW, Stapleton PJ, Gong Y, Wang PW, Guttman DS, et al. Staphylococcus aureus interaction with Pseudomonas aeruginosa biofilm enhances tobramycin resistance. NPJ Biofilms Microbiomes. 2017;3:25.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
22.Orazi G, O’Toole GA. Pseudomonas aeruginosa alters Staphylococcus aureus sensitivity to vancomycin in a biofilm model of cystic fibrosis infection. mBio. 2017;8:e00873–17.PubMed 
PubMed Central 
Article 

Google Scholar 
23.Sorg RA, Lin L, Doorn GS, van, Sorg M, Olson J, Nizet V, et al. Collective resistance in microbial communities by intracellular antibiotic deactivation. PLOS Biol. 2016;14:e2000631.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
24.Perlin MH, Clark DR, McKenzie C, Patel H, Jackson N, Kormanik C, et al. Protection of Salmonella by ampicillin-resistant Escherichia coli in the presence of otherwise lethal drug concentrations. Proc R Soc B. 2009;276:3759–68.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Flynn JM, Cameron LC, Wiggen TD, Dunitz JM, Harcombe WR, Hunter RC. Disruption of cross-feeding inhibits pathogen growth in the sputa of patients with cystic fibrosis. mSphere. 2020;5:e00343–20.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
26.Gurney J, Brown SP, Kaltz O, Hochberg ME. Steering phages to combat bacterial pathogens. Trends Microbiol. 2020;28:85–94.CAS 
PubMed 
Article 

Google Scholar 
27.Waters VJ, Kidd TJ, Canton R, Ekkelenkamp MB, Johansen HK, LiPuma JJ, et al. Reconciling antimicrobial susceptibility testing and clinical response in antimicrobial treatment of chronic cystic fibrosis lung infections. Clin Infect Dis. 2019;69:1812–6.CAS 
PubMed 
Article 

Google Scholar 
28.Somayaji R, Parkins MD, Shah A, Martiniano SL, Tunney MM, Kahle JS, et al. Antimicrobial susceptibility testing (AST) and associated clinical outcomes in individuals with cystic fibrosis: a systematic review. J Cyst Fibros. 2019;18:236–43.CAS 
PubMed 
Article 

Google Scholar 
29.Raghuvanshi R, Vasco K, Vázquez-Baeza Y, Jiang L, Morton JT, Li D, et al. High-resolution longitudinal dynamics of the cystic fibrosis sputum microbiome and metabolome through antibiotic therapy. mSystems. 2020;5:e00292–20.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Cystic Fibrosis Trust. UK cystic fibrosis registry annual data report 2019. 2020. [online] Available at: https://www.cysticfibrosis.org.uk/sites/default/files/2020-12/2019%20Registry%20Annual%20Data%20report_Sep%202020.pdf [Accessed 5 June 2021].31.Nixon GM, Armstrong DS, Carzino R, Carlin JB, Olinsky A, Robertson CF, et al. Clinical outcome after early Pseudomonas aeruginosa infection in cystic fibrosis. J Pediatr. 2001;138:699–704.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
32.Sánchez MB. Antibiotic resistance in the opportunistic pathogen Stenotrophomonas maltophilia. Front Microbiol. 2015;6:658.PubMed 
PubMed Central 
Article 

Google Scholar 
33.Salsgiver EL, Fink AK, Knapp EA, LiPuma JJ, Olivier KN, Marshall BC, et al. Changing epidemiology of the respiratory bacteriology of patients with cystic fibrosis. Chest. 2016;149:390–400.PubMed 
PubMed Central 
Article 

Google Scholar 
34.Cystic Fibrosis Trust. Antibiotic treatment for cystic fibrosis. 2009. [online] Available at: https://www.cysticfibrosis.org.uk/sites/default/files/2020-11/Anitbiotic%20Treatment.pdf [Accessed 7 June 2021].35.Denton M, Todd NJ, Littlewood JM. Role of anti-pseudomonal antibiotics in the emergence of Stenotrophomonas maltophilia in cystic fibrosis patients. Eur J Clin Microbiol Infect Dis. 1996;15:402–5.CAS 
PubMed 
Article 

Google Scholar 
36.Esposito A, Pompilio A, Bettua C, Crocetta V, Giacobazzi E, Fiscarelli E, et al. Evolution of Stenotrophomonas maltophilia in cystic fibrosis lung over chronic infection: a genomic and phenotypic population study. Front Microbiol. 2017;8:1590.PubMed 
PubMed Central 
Article 

Google Scholar 
37.Pompilio A, Crocetta V, De Nicola S, Verginelli F, Fiscarelli E, Di Bonaventura G. Cooperative pathogenicity in cystic fibrosis: Stenotrophomonas maltophilia modulates Pseudomonas aeruginosa virulence in mixed biofilm. Front Microbiol. 2015;6:951.PubMed 
PubMed Central 
Article 

Google Scholar 
38.Dalbøge CS, Hansen CR, Pressler T, Høiby N, Johansen HK. Chronic pulmonary infection with Stenotrophomonas maltophilia and lung function in patients with cystic fibrosis. J Cyst Fibros. 2011;10:318–25.PubMed 
Article 

Google Scholar 
39.Okazaki A, Avison MB. Induction of L1 and L2 β-lactamase production in Stenotrophomonas maltophilia is dependent on an AmpR-type regulator. Antimicrob Agents Chemother. 2008;52:1525–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
40.Yurtsev EA, Chao HX, Datta MS, Artemova T, Gore J. Bacterial cheating drives the population dynamics of cooperative antibiotic resistance plasmids. Mol Syst Biol. 2013;9:683.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.Bottery MJ, Wood AJ, Brockhurst MA. Selective conditions for a multidrug resistance plasmid depend on the sociality of antibiotic resistance. Antimicrob Agents Chemother. 2016;60:2524–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Palmer KL, Aye LM, Whiteley M. Nutritional cues control Pseudomonas aeruginosa multicellular behavior in cystic fibrosis sputum. J Bacteriol. 2007;189:8079–87.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Artemova T, Gerardin Y, Dudley C, Vega NM, Gore J. Isolated cell behavior drives the evolution of antibiotic resistance. Mol Syst Biol. 2015;11:822.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
44.Harrison E, Wood AJ, Dytham C, Pitchford JW, Truman J, Spiers A, et al. Bacteriophages limit the existence conditions for conjugative plasmids. mBio. 2015;6:e00586–15.PubMed 
PubMed Central 

Google Scholar 
45.Hall JPJ, Wood AJ, Harrison E, Brockhurst MA. Source–sink plasmid transfer dynamics maintain gene mobility in soil bacterial communities. PNAS. 2016;113:8260–5.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Regoes RR, Wiuff C, Zappala RM, Garner KN, Baquero F, Levin BR. Pharmacodynamic functions: a multiparameter approach to the design of antibiotic treatment regimens. Antimicrob Agents Chemother. 2004;48:3670–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
47.Yu G, Baeder DY, Regoes RR, Rolff J. Predicting drug resistance evolution: insights from antimicrobial peptides and antibiotics. Proc R Soc B. 2018;285:20172687.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
48.Zhanel GG, Simor AE, Vercaigne L, Mandell L. Imipenem and meropenem: comparison of in vitro activity, pharmacokinetics, clinical trials and adverse effects. Can J Infect Dis. 1998;9:215–28.CAS 
PubMed 
PubMed Central 

Google Scholar 
49.Gould VC, Okazaki A, Avison MB. β-Lactam resistance and β-lactamase expression in clinical Stenotrophomonas maltophilia isolates having defined phylogenetic relationships. J Antimicrob Chemother. 2006;57:199–203.CAS 
PubMed 
Article 

Google Scholar 
50.European Cystic Fibrosis Society Patient Registry. ECFS patient registry annual data report 2018. 2020. [online] Available at: https://www.ecfs.eu/sites/default/files/general-content-files/working-groups/ecfs-patient-registry/ECFSPR_Report_2018_v1.4.pdf [Accessed 7 June 2021].51.Radlinski L, Rowe SE, Kartchner LB, Maile R, Cairns BA, Vitko NP, et al. Pseudomonas aeruginosa exoproducts determine antibiotic efficacy against Staphylococcus aureus. PLoS Biol. 2017;15:e2003981.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
52.Harrison FY. Microbial ecology of the cystic fibrosis lung. Microbiology. 2007;153:917–23.CAS 
PubMed 
Article 

Google Scholar 
53.Keel RA, Sutherland CA, Crandon JL, Nicolau DP. Stability of doripenem, imipenem and meropenem at elevated room temperatures. Int J Antimicrob Agents. 2011;37:184–5.CAS 
PubMed 
Article 

Google Scholar 
54.Okazaki A, Avison MB. Aph(3′)-IIc, an Aminoglycoside resistance determinant from Stenotrophomonas maltophilia. Antimicrob Agents Chemother. 2007;51:359–60.CAS 
PubMed 
Article 

Google Scholar 
55.Li X-Z, Zhang L, McKay GA, Poole K. Role of the acetyltransferase AAC(6′)-Iz modifying enzyme in aminoglycoside resistance in Stenotrophomonas maltophilia. J Antimicrob Chemother. 2003;51:803–11.CAS 
PubMed 
Article 

Google Scholar 
56.Frost I, Smith WPJ, Mitri S, Millan AS, Davit Y, Osborne JM, et al. Cooperation, competition and antibiotic resistance in bacterial colonies. ISME J. 2018;12:1582–93.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
57.Yin C, Yang W, Meng J, Lv Y, Wang J, Huang B. Co-infection of Pseudomonas aeruginosa and Stenotrophomonas maltophilia in hospitalised pneumonia patients has a synergic and significant impact on clinical outcomes. Eur J Clin Microbiol Infect Dis. 2017;36:2231–5.CAS 
PubMed 
Article 

Google Scholar 
58.Waters V, Yau Y, Prasad S, Lu A, Atenafu E, Crandall I, et al. Stenotrophomonas maltophilia in cystic fibrosis: serologic response and effect on lung disease. Am J Respir Crit Care Med. 2011;183:635–40.PubMed 
Article 

Google Scholar 
59.Goss CH, Mayer-Hamblett N, Aitken ML, Rubenfeld GD, Ramsey BW. Association between Stenotrophomonas maltophilia and lung function in cystic fibrosis. Thorax. 2004;59:955–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Mojica MF, Ouellette CP, Leber A, Becknell MB, Ardura MI, Perez F, et al. Successful treatment of bloodstream infection due to metallo-β-lactamase-producing Stenotrophomonas maltophilia in a renal transplant patient. Antimicrob Agents Chemother. 2016;60:5130–4.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.McCutcheon JG, Dennis JJ. The potential of phage therapy against the emerging opportunistic pathogen Stenotrophomonas maltophilia. Viruses. 2021;13:1057.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
62.Rossi E, La Rosa R, Bartell JA, Marvig RL, Haagensen JAJ, Sommer LM, et al. Pseudomonas aeruginosa adaptation and evolution in patients with cystic fibrosis. Nat Rev Microbiol. 2021;19:331–42.CAS 
PubMed 
Article 

Google Scholar 
63.Davies EV, James CE, Brockhurst MA, Winstanley C. Evolutionary diversification of Pseudomonas aeruginosa in an artificial sputum model. BMC Microbiol. 2017;17:3.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
64.Bara JJ, Matson Z, Remold SK. Life in the cystic fibrosis upper respiratory tract influences competitive ability of the opportunistic pathogen Pseudomonas aeruginosa. R Soc Open Sci. 2018;5:180623.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
65.Bartell JA, Sommer LM, Haagensen JAJ, Loch A, Espinosa R, Molin S, et al. Evolutionary highways to persistent bacterial infection. Nat Commun. 2019;10:629.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
66.Estrela S, Brown SP. Community interactions and spatial structure shape selection on antibiotic resistant lineages. PLOS Comput Biol. 2018;14:e1006179.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
67.McNally L, Bernardy E, Thomas J, Kalziqi A, Pentz J, Brown SP, et al. Killing by Type VI secretion drives genetic phase separation and correlates with increased cooperation. Nat Commun. 2017;8:14371.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
68.Burmølle M, Webb JS, Rao D, Hansen LH, Sørensen SJ, Kjelleberg S. Enhanced biofilm formation and increased resistance to antimicrobial agents and bacterial invasion are caused by synergistic interactions in multispecies biofilms. Appl Environ Microbiol. 2006;72:3916–23.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
69.Willner D, Haynes MR, Furlan M, Schmieder R, Lim YW, Rainey PB, et al. Spatial distribution of microbial communities in the cystic fibrosis lung. ISME J. 2012;6:471–4.CAS 
PubMed 
Article 

Google Scholar 
70.Turner KH, Wessel AK, Palmer GC, Murray JL, Whiteley M. Essential genome of Pseudomonas aeruginosa in cystic fibrosis sputum. PNAS. 2015;112:4110–5.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
71.Kirchner S, Fothergill JL, Wright EA, James CE, Mowat E, Winstanley C. Use of artificial sputum medium to test antibiotic efficacy against Pseudomonas aeruginosa in conditions more relevant to the cystic fibrosis lung. J Vis Exp. 2012;64:e3857.
Google Scholar 
72.Harrison F, Diggle SP. An ex vivo lung model to study bronchioles infected with Pseudomonas aeruginosa biofilms. Microbiology. 2016;162:1755–60.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
73.Harrington NE, Sweeney E, Harrison F. Building a better biofilm – formation of in vivo-like biofilm structures by Pseudomonas aeruginosa in a porcine model of cystic fibrosis lung infection. Biofilm. 2020;2:100024.PubMed 
PubMed Central 
Article 

Google Scholar 
74.Bricio-Moreno L, Sheridan VH, Goodhead I, Armstrong S, Wong JKL, Waters EM, et al. Evolutionary trade-offs associated with loss of PmrB function in host-adapted Pseudomonas aeruginosa. Nat Commun. 2018;9:2635.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
75.Castellani S, Di Gioia S, di Toma L, Conese M. Human cellular models for the investigation of lung inflammation and mucus production in cystic fibrosis. Anal Cell Pathol. 2018;2018:3839803.Article 
CAS 

Google Scholar 
76.Levin-Reisman I, Ronin I, Gefen O, Braniss I, Shoresh N, Balaban NQ. Antibiotic tolerance facilitates the evolution of resistance. Science. 2017;355:826–30.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
77.Wistrand-Yuen E, Knopp M, Hjort K, Koskiniemi S, Berg OG, Andersson DI. Evolution of high-level resistance during low-level antibiotic exposure. Nat Commun. 2018;9:1599.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
78.Choi K-H, Schweizer HP. mini-Tn7 insertion in bacteria with single attTn7 sites: example Pseudomonas aeruginosa. Nat Protoc. 2006;1:153–61.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
79.Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and Its applications to single-cell Sequencing. J Comput Biol. 2012;19:455–77.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
80.Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.CAS 
Article 

Google Scholar 
81.Onoue Y, Mori M. Amino acid requirements for the growth and enterotoxin production by Staphylococcus aureus in chemically defined media. Int J Food Microbiol. 1997;36:77–82.CAS 
PubMed 
Article 

Google Scholar 
82.Kumar S, Stecher G, Li M, Knyaz C, Tamura K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol. 2018;35:1547–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
83.Guindon S, Dufayard J-F, Lefort V, Anisimova M, Hordijk W, Gascuel O. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst Biol. 2010;59:307–21.CAS 
PubMed 
Article 

Google Scholar 
84.Lefort V, Longueville J-E, Gascuel O. SMS: smart model selection in PhyML. Mol Biol Evol. 2017;34:2422–4.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
85.Kuznetsov A, Bollin CJ. NCBI Genome Workbench: desktop software for comparative genomics, visualization, and GenBank data submission. Methods Mol Biol. 2021;2231:261–95.CAS 
PubMed 
Article 

Google Scholar 
86.Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25:1754–60.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

1.Andersson, M. Sexual Selection (Princeton University Press, 1996).
Google Scholar 
2.Clutton-Brock, T. H. The Evolution of Parental Care (Princeton University Press, 1991).Book 

Google Scholar 
3.Olsson, M., Shine, R., Wapstra, E., Ujvari, B. & Madsen, T. Sexual dimorphism in lizard body shape: The roles of sexual selection and fecundity selection. Evolution 56, 1538–1542 (2002).Article 

Google Scholar 
4.McPherson, F. J. & Chenoweth, P. J. Mammalian sexual dimorphism. Anim. Reprod. Sci. 131, 109–122 (2012).Article 
CAS 

Google Scholar 
5.Shine, R. The evolution of large body size in females: A critique of Darwin’s “fecundity advantage” model. Am. Nat. 131, 124–131 (1988).Article 

Google Scholar 
6.Fairbairn, D. J. et al. (eds) Sex, Size and Gender Roles: Evolutionary Studies of Sexual Size Dimorphism (Oxford University Press, 2007).
Google Scholar 
7.Slatkin, M. Ecological causes of sexual dimorphism. Evolution 38, 622–630 (1984).Article 

Google Scholar 
8.Shine, R. Ecological causes for the evolution of sexual dimorphism: A review of the evidence. Q. Rev. Biol. 64, 419–461 (1989).Article 
CAS 

Google Scholar 
9.Herrel, A., Spithoven, L., Van Damme, R. & De Vree, F. Sexual dimorphism of head size in Gallotia galloti: Testing the niche divergence hypothesis by functional analyses. Funct. Ecol. 13, 289–297 (1999).Article 

Google Scholar 
10.Pearson, D., Shine, R. & How, R. Sex-specific niche partitioning and sexual size dimorphism in Australian pythons (Morelia spilota imbricata). Biol. J. Linn. Soc. 77, 113–125 (2002).Article 

Google Scholar 
11.Hierlihy, C. A., Garcia-Collazo, R., Chavez Tapia, C. B. & Mallory, F. F. Sexual dimorphism in the lizard Sceloporus siniferus: Support for the intraspecific niche divergence and sexual selection hypotheses. Salamandra 49, 1–6 (2013).
Google Scholar 
12.Vitt, L. J. & Cooper, W. E. Jr. The evolution of sexual dimorphism in the skink Eumeces laticeps: An example of sexual selection. Can. J. Zool. 63, 995–1002 (1985).Article 

Google Scholar 
13.Shine, R. Intersexual dietary divergence and the evolution of sexual dimorphism in snakes. Am. Nat. 138, 103–122 (1991).Article 

Google Scholar 
14.Fitzgerald, M. & Shine, R. Mate-guarding in free-ranging Carpet Pythons (Morelia spilota). Aust. Zool. 39, 434–439 (2018).Article 

Google Scholar 
15.Cundall, D. & Greene, H. W. Feeding in snakes. In Feeding: Form, Function, and Evolution in Tetrapod Vertebrates (ed. Schwenk, K.) 293–333 (Academic Press, 2000).Chapter 

Google Scholar 
16.Goiran, C., Dubey, S. & Shine, R. Effects of season, sex and body size on the feeding ecology of turtle-headed sea snakes (Emydocephalus annulatus) on IndoPacific inshore coral reefs. Coral Reefs 32, 527–538 (2013).ADS 
Article 

Google Scholar 
17.Shine, R., Bonnet, X., Elphick, M. J. & Barrott, E. G. A novel foraging mode in snakes: Browsing by the sea snake Emydocephalus annulatus (Serpentes, Hydrophiidae). Funct. Ecol. 18, 16–24 (2004).Article 

Google Scholar 
18.Lynch, T. P. The Behavioural Ecology of the Olive Sea Snake, Aipysurus laevis. PhD thesis, James Cook University (2000).19.Borczyk, B., Paśko, Ł, Kusznierz, J. & Bury, S. Sexual dimorphism and skull size and shape in the highly specialized snake species, Aipysurus eydouxii (Elapidae: Hydrophiinae). PeerJ 9, e11311 (2021).Article 

Google Scholar 
20.Queral-Regil, A. & King, R. B. Evidence for phenotypic plasticity in snake body size and relative head dimensions in response to amount and size of prey. Copeia 1998, 423–429 (1998).Article 

Google Scholar 
21.Bonnet, X., Shine, R., Naulleau, G. & Thiburce, C. Plastic vipers: influence of food intake on the size and shape of Gaboon vipers (Bitis gabonica). J. Zool. 255, 341–351 (2001).Article 

Google Scholar 
22.Sanders, K. L., Lee, M. S., Leys, R., Foster, R. & Keogh, J. S. Molecular phylogeny and divergence dates for Australasian elapids and sea snakes (Hydrophiinae): Evidence from seven genes for rapid evolutionary radiations. J. Evol. Biol. 21, 682–695 (2008).Article 
CAS 

Google Scholar 
23.Aubret, F. & Shine, R. Genetic assimilation and the postcolonization erosion of phenotypic plasticity in island tiger snakes. Curr. Biol. 19, 1932–1936 (2009).Article 
CAS 

Google Scholar 
24.McCarthy, C. J. Adaptations of sea snakes that eat fish eggs; with a note on the throat musculature of Aipysurus eydouxi (Gray, 1849). J. Nat. Hist. 21, 1119–1128 (1987).Article 

Google Scholar 
25.Shine, R., Shine, T. G., Brown, G. P. & Goiran, C. Life history traits of the sea snake Emydocephalus annulatus, based on a 17-yr study. Coral Reefs 39, 1407–1414 (2020).Article 

Google Scholar 
26.Segall, M., Cornette, R., Fabre, A. C., Godoy-Diana, R. & Herrel, A. Does aquatic foraging impact head shape evolution in snakes? Proc. R. Soc. B 283, 20161645 (2016).Article 

Google Scholar 
27.Avolio, C., Shine, R. & Pile, A. J. The adaptive significance of sexually dimorphic scale rugosity in sea snakes. Am. Nat. 167, 728–738 (2006).Article 

Google Scholar 
28.Sherratt, E., Rasmussen, A. R. & Sanders, K. L. Trophic specialization drives morphological evolution in sea snakes. R. Soc. Open Sci. 5, 172141 (2018).ADS 
Article 

Google Scholar 
29.Frédérich, B. & Parmentier, E. (eds) Biology of Damselfishes (CRC Press, 2016).
Google Scholar 
30.Heatwole, H. Sea Snakes 2nd edn. (Krieger Publishing Company, 1999).
Google Scholar 
31.Lukoschek, V. & Shine, R. Sea snakes rarely venture far from home. Ecol. Evol. 2, 1113–1121 (2012).Article 

Google Scholar 
32.Shine, R., Shine, T. & Shine, B. Intraspecific habitat partitioning by the sea snake Emydocephalus annulatus (Serpentes, Hydrophiidae): The effects of sex, body size, and colour pattern. Biol. J. Linn. Soc. 80, 1–10 (2003).Article 

Google Scholar 
33.Goiran, C., Brown, G. P. & Shine, R. Niche partitioning within a population of sea snakes is constrained by ambient thermal homogeneity and small prey size. Biol. J. Linn. Soc. 129, 644–651 (2020).Article 

Google Scholar  More

Seasonal changes in weather, food availability and mice body conditionThe weather was hot and dry during summer (temperature: 24.42 ± 0.36 °C; total rainfall: 0.60 mm) and temperatures were lower and rainfall was higher during the winter months (temperature: 13.47 ± 0.45 °C; total rainfall: 39.60 mm; LM: N = 138, F = 368.4, P  More

1.Amat, J. A. & Masero, J. A. How Kentish plovers, Charadrius alexandrinus, cope with heat stress during incubation. Behav. Ecol. Sociobiol. 56, 26–33 (2004).Article 

Google Scholar 
2.du Plessis, K. L., Martin, R. O., Hockey, P. A. R., Cunningham, S. J. & Ridley, A. R. The costs of keeping cool in a warming world: Implications of high temperatures for foraging, thermoregulation and body condition of an arid-zone bird. Glob. Chang. Biol. 18, 3063–3070 (2012).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Cunningham, S. J., Martin, R. O. & Hockey, P. A. R. Can behaviour buffer the impacts of climate change on an arid-zone bird?. Ostrich 86, 119–126 (2015).Article 

Google Scholar 
4.Smit, B. et al. Behavioural responses to heat in desert birds: implications for predicting vulnerability to climate warming. Clim. Chang. Responses 3, 1–14 (2016).Article 

Google Scholar 
5.McNab, B.K. The Physiological Ecology of Vertebrates: A View from Energetics (Cornell University Press, 2002).6.Cunningham, S. J., Gardner, J. L. & Martin, R. O. Opportunity costs and the response of birds and mammals to climate warming. Front. Ecol. Environ. 1, 1–8. https://doi.org/10.1002/fee.2324 (2021).Article 

Google Scholar 
7.Wolf, B. O., Wooden, K. M. & Walsberg, G. E. The use of thermal refugia by two small desert birds. Condor 98(2), 424–428 (1996).Article 

Google Scholar 
8.Cook, T. R. et al. Parenting in a warming world: Thermoregulatory responses to heat stress in an endangered seabird. Conserv. Physiol. 8, 1–13 (2020).Article 

Google Scholar 
9.Speakman, J. R. & Król, E. Maximal heat dissipation capacity and hyperthermia risk: Neglected key factors in the ecology of endotherms. J. Anim. Ecol. 79, 726–746 (2010).PubMed 
PubMed Central 

Google Scholar 
10.Nilsson, J. Å. & Nord, A. Testing the heat dissipation limit theory in a breeding passerine. Proc. R. Soc. B Biol. Sci. 285, 1 (2018).
Google Scholar 
11.Nord, A. & Nilsson, J. Å. Heat dissipation rate constrains reproductive investment in a wild bird. Funct. Ecol. 33, 250–259 (2019).Article 

Google Scholar 
12.Tapper, S., Nocera, J. J. & Burness, G. Heat dissipation capacity influences reproductive performance in an aerial insectivore. J. Exp. Biol. 223, 1 (2020).
Google Scholar 
13.Buckley, L. B., Ehrenberger, J. C. & Angilletta, M. J. Thermoregulatory behaviour limits local adaptation of thermal niches and confers sensitivity to climate change. Funct. Ecol. 29, 1038–1047 (2015).Article 

Google Scholar 
14.Edwards, E. K., Mitchell, N. J. & Ridley, A. R. The impact of high temperatures on foraging behaviour and body condition in the Western Australian Magpie Cracticus tibicen dorsalis. Ostrich 86, 137–144 (2015).Article 

Google Scholar 
15.Thompson, M. L., Cunningham, S. J. & McKechnie, A. E. Interspecific variation in avian thermoregulatory patterns and heat dissipation behaviours in a subtropical desert. Physiol. Behav. 188, 311–323 (2018).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
16.Kemp, R. et al. Sublethal fitness costs of chronic exposure to hot weather vary between sexes in a threatened desert lark. Emu 120, 216–229 (2020).Article 

Google Scholar 
17.Funghi, C., McCowan, L. S. C., Schuett, W. & Griffith, S. C. High air temperatures induce temporal, spatial and social changes in the foraging behaviour of wild zebra finches. Anim. Behav. 149, 33–43 (2019).Article 

Google Scholar 
18.Pattinson, N. B. et al. Heat dissipation behaviour of birds in seasonally hot arid-zones: are there global patterns?. J. Avian Biol. 51, 1–11 (2020).Article 

Google Scholar 
19.Moyer-Horner, L., Mathewson, P. D., Jones, G. M., Kearney, M. R. & Porter, W. P. Modeling behavioral thermoregulation in a climate change sentinel. Ecol. Evol. 5, 5810–5822 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
20.Moore, D., Stow, A. & Kearney, M. R. Under the weather?—The direct effects of climate warming on a threatened desert lizard are mediated by their activity phase and burrow system. J. Anim. Ecol. 87, 660–671 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
21.Bladon, A. J. et al. Behavioural thermoregulation and climatic range restriction in the globally threatened ethiopian bush-crow Zavattariornis stresemanni. Ibis 161(3), 546–558. https://doi.org/10.1111/ibi.12660 (2019).Article 

Google Scholar 
22.Conradie, S. R., Woodborne, S. M., Cunningham, S. J. & McKechnie, A. E. Chronic, sublethal effects of high temperatures will cause severe declines in southern African arid-zone birds during the 21st century. Proc. Natl. Acad. Sci. USA 116, 14065–14070 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
23.Enriquez-Urzelai, U. et al. The roles of acclimation and behaviour in buffering climate change impacts along elevational gradients. J. Anim. Ecol. 89, 1722–1734 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
24.Albright, T. P. et al. Mapping evaporative water loss in desert passerines reveals an expanding threat of lethal dehydration. Proc. Natl. Acad. Sci. USA 114, 2283–2288 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
25.Dawson, W. R. Evaporative losses of water by birds. Comp. Biochem. Physiol. Part A Physiol. 71, 495–509 (1982).Article 
CAS 

Google Scholar 
26.Wolf, B. O. & Walsberg, G. E. Respiratory and cutaneous evaporative water loss at high environmental temperatures in a small bird. J. Exp. Biol. 199, 451–457 (1996).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
27.Calder, W. A. & Smichdt-Nielsen, K. Evaporative cooling and respiratory alkalosis in the pigeon. Proc. Natl. Acad. Sci. USA 55(4), 750–756. https://doi.org/10.1073/pnas.55.4.750 (1966).ADS 
Article 
PubMed 
PubMed Central 
CAS 

Google Scholar 
28.Bartholomew, G. A. The role of behavior in the temperature regulation of the masked booby. Condor 68, 523–535. https://doi.org/10.2307/1366261 (1966).Article 

Google Scholar 
29.Bryant, D. M. Heat stress in tropical birds: behavioural thermoregulation during flight. Ibis (Lond. 1859). 125, 313–323 (1983).30.Tattersall, G. J., Andrade, D. V. & Abe, A. S. Heat exchange from the toucan bill reveals a controllable vascular thermal radiator. Science (80-. ). 325, 468–470 (2009).31.Van De Ven, T. M. F. N., Martin, R. O., Vink, T. J. F., McKechnie, A. E. & Cunningham, S. J. Regulation of heat exchange across the hornbill beak: Functional similarities with toucans?. PLoS ONE 11, 1–14 (2016).
Google Scholar 
32.Van Vuuren, A. K., Kemp, L. V. & McKechnie, A. E. The beak and unfeathered skin as heat radiators in the southern ground-hornbill. J. Avian Biol. 51, 1–7 (2020).
Google Scholar 
33.Winkler, D.W., Billerman, S.M. & Lovette, I.J. Storks (Ciconiidae), version 1.0. In Birds of the World (S. M. Billerman, B. K. Keeney, P. G. Rodewald, and T. S. Schulenberg, Editors). Cornell Lab of Ornithology (2020) https://doi.org/10.2173/bow.ciconi2.0134.Kahl, P. M. Thermoregulation in the wood stork, with special reference to the role of the legs. Physiol Zool. 36(2), 141–151 (1963).Article 

Google Scholar 
35.Steen, I. & Steen, J. B. The Importance of the Legs in the Thermoregulation of Birds. Acta Physiol. Scand. 63, 285–291 (1965).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
36.Hainsworth, F. R. Saliva spreading, activity and body temperature regulation in the rat. Am J Physiol. 212, 1288–1292 (1967).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
37.Gentry, R. L. Thermoregulatory behavior of eared seals. Behaviour 46(2), 73–93. https://doi.org/10.1163/156853973×00175 (1973).Article 
PubMed 
CAS 
PubMed Central 

Google Scholar 
38.Sturbaum, B. A. & Riedesel, M. L. Dissipation of stored body heat by the ornate box turtle, Terrapene ornata. Comp. Biochem. Physiol. Part A Physiol. 58, 93–97 (1977).Article 

Google Scholar 
39.Marder, J., Porat, I., Raber, P. & Adler, J. Acid-base balance and body temperature regulation of heat stressed Psammomys obesus (Gerbillinae): The effect of bicarbonate loss via saliva spreading. Physiol Zool. 56(3), 389–396. https://doi.org/10.1086/physzool.56.3.30152603 (1983).Article 

Google Scholar 
40.Hatch, D. E. Energy conserving and heat dissipating mechanisms of the turkey vulture. Auk 87(1), 111–124. https://doi.org/10.2307/4083662 (1970).Article 

Google Scholar 
41.Cooper, J. & Siegfried, W. R. Behavioural responses of young cape gannets Sula capensis to high ambient temperatures. Mar. Behav. Physiol. 3, 211–220 (1976).Article 

Google Scholar 
42.Thomas, B. T. Maguari Stork Nesting: Juvenile Growth and Behavior. Auk 101, 812–823 (1984).Article 

Google Scholar 
43.Hancock, J.A., Kushlan, J.A. & Kahl, M.P. Storks, Ibises and Spoonbills of the World (Academic Press, 1992).44.Townsend, H., Huyvaert, K. P., Hodum, P. J. & Anderson, D. J. Nesting distributions of Galapagos boobies (Aves: Sulidae): an apparent case of amensalism. Oecologia 132, 419–427. https://doi.org/10.1007/s00442-002-0992-7 (2002).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
45.Finkelstein, M., Kuspa, Z., Snyder, N.F. & Schmitt, N.J. California condor (Gymnogyps californianus), version 2.0. In The Birds of North America (P. G. Rodewald, Editor). Cornell Lab of Ornithology (2015). https://doi.org/10.2173/bna.61046.Czenze, Z. J. et al. Regularly drinking desert birds have greater evaporative cooling capacity and higher heat tolerance limits than non-drinking species. Funct. Ecol. 34, 1589–1600 (2020).Article 

Google Scholar 
47.Nudds, R. L. & Oswald, S. A. An interspecific test of Allen’s rule: Evolutionary implications for endothermic species. Evolution (N. Y). 61, 2839–2848 (2007).48.Symonds, M. R. E. & Tattersall, G. J. Geographical variation in bill size across bird species provides evidence for Allen’s rule. Am. Nat. 176, 188–197 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
49.Galván, I., Rodríguez-Martínez, S. & Carrascal, L. M. Dark pigmentation limits thermal niche position in birds. Funct. Ecol. 32, 1531–1540 (2018).Article 

Google Scholar 
50.Wilman, H. et al. EltonTraits 1 . 0 : Species-level foraging attributes of the world ’ s birds and mammals. Ecology 95, 2027 (2014).51.Brooke, M. D. L. Ecological factors influencing the occurrence of ‘flash marks’ in wading birds. Funct. Ecol. 12, 339–346 (1998).Article 

Google Scholar 
52.Maclean, I. M. D., Mosedale, J. R. & Bennie, J. J. Microclima: An r package for modelling meso- and microclimate. Methods Ecol Evol. 10(2), 280–290. https://doi.org/10.1111/2041-210X.13093 (2019).Article 

Google Scholar 
53.Hadfield, A. J. Package ‘ MCMCglmm ’. https://cran.r-project.org/web/packages/MCMCglmm/ (2019)54.Jetz, W., Thomas, G.H., Joy, J.B., Hartmann, K. & Mooers, A.O. 2012. The global diversity of birds in space and time. Nature. 491(7424): 444–448 (2012). https://doi.org/10.1038/nature1163155.Revell, M.L.J. Package ‘ phytools ’ https://cran.r-project.org/web/packages/phytools/ (2020)56.Freckleton, R. P., Harvey, P. H. & Pagel, M. Phylogenetic analysis and comparative data: A test and review of evidence. Am. Nat. 160, 712–726 (2002).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
57.Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, (2015).58.Crawley, M.J. The R Book (John Wiley & Sons, 2013).59.Barton, K. Package MuMin: Multi-model Inference https://cran.r-project.org/web/packages/MuMIn/index.html (2020).60.Grueber, C. E., Nakagawa, S., Laws, R. J. & Jamieson, I. G. Multimodel inference in ecology and evolution: Challenges and solutions. J. Evol. Biol. 24, 699–711 (2011).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
61.Symonds, M. R. E. & Moussalli, A. A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike’s information criterion. Behav. Ecol. Sociobiol. 65, 13–21 (2011).Article 

Google Scholar 
62.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ (2020).63.Bakken, G. S. & Angilletta, M. J. How to avoid errors when quantifying thermal environments. Funct. Ecol. 28, 96–107 (2014).Article 

Google Scholar 
64.van Dyk, M., Noakes, M. J. & McKechnie, A. E. Interactions between humidity and evaporative heat dissipation in a passerine bird. J. Comp. Physiol. B. 189, 299–308. https://doi.org/10.1007/s00360-019-01210-2 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
65.Webster, M.D., Campbell, G.S. & King, J.R. Cutaneous resistance to water-vapor diffusion in pigeons and the role of the plumage. Physiol. Zool. 58(1): 58–70 (1985). http://www.jstor.org/stable/30161220.66.Battley, P. F., Rogers, D. I., Piersma, T. & Koolhaas, A. Behavioural evidence for heat-load problems in Great Knots in tropical Australia fuelling for long-distance flight. Emu 103, 97–103 (2003).Article 

Google Scholar 
67.Piersma, T. & van Gils, J.A. The Flexible Phenotype: A Body-Centered Integration of Ecology, Physiology, and Behavior (Oxford University Press, 2011).68.Fitzpatrick, M. J., Mathewson, P. D. & Porter, W. P. Validation of a mechanistic model for non-invasive study of ecological energetics in an endangered wading bird with counter-current heat exchange in its legs. PLoS ONE 10, 1–34 (2015).Article 
CAS 

Google Scholar 
69.Lustick, S., Battersby, B. & Kelty, M. Effects of insolation on juvenile herring gull energetics and behavior. Ecologia. 60(4), 673–678. https://doi.org/10.2307/1936603 (1979).Article 

Google Scholar 
70.Ward, J. M., Blount, J. D., Ruxton, G. D. & Houston, D. C. The adaptive significance of dark plumage for birds in desert environments. Ardea 90, 311–323 (2002).
Google Scholar 
71.Nicolaï, M. P. J., Shawkey, M. D., Porchetta, S., Claus, R. & D’Alba, L. Exposure to UV radiance predicts repeated evolution of concealed black skin in birds. Nat. Commun. 11, (2020).72.Mitchell, D. et al. Revisiting concepts of thermal physiology: Predicting responses of mammals to climate change. J. Anim. Ecol. 87, 956–973 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
73.Walsberg, G. E., Campbell, G. S. & King, J. R. Animal coat color and radiative heat gain: A re-evaluation. J. Comp. Physiol. B 126, 211–222 (1978).Article 

Google Scholar 
74.McFarland, D. J. & Baher, E. Factors affecting feather posture in the barbary dove. Anim. Behav. 16, 171–177 (1968).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
75.Hohtola, E., Rintamäki, H. & Hissa, R. Shivering and ptiloerection as complementary cold defense responses in the pigeon during sleep and wakefulness. J Comp Physiol. 136, 77–81. https://doi.org/10.1007/BF00688626 (1980).Article 

Google Scholar 
76.Kahl, P. M. Spread-wing postures and their possible functions in the Ciconiidae. Auk 88(4), 715–722. https://doi.org/10.2307/4083833 (1971).Article 

Google Scholar 
77.Dawson, T. J., Robertshaw, D. & Taylor, C. R. Sweating in the kangaroo: A cooling mechanism during exercise, but not in the heat. Am J Physiol. 227(2), 494–498. https://doi.org/10.1152/ajplegacy.1974.227.2.494 (1974).Article 
PubMed 
CAS 
PubMed Central 

Google Scholar 
78.Hoffman, T. C. M., Walsberg, G. E. & DeNardo, D. F. Cloacal evaporation: an important and previously undescribed mechanism for avian thermoregulation. J. Exp. Biol. 210, 741–749 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
79.Graves, G. R. Urohidrosis and tarsal color in Cathartes vultures (Aves: Cathartidae). Proc. Biol. Soc. Washingt. 132, 56–64 (2019).Article 

Google Scholar 
80.Torres, R. & Velando, A. Male preference for female foot colour in the socially monogamous blue-footed booby, Sula nebouxii.. Anim. Behav. 69, 59–65 (2005).Article 

Google Scholar 
81.López-Rull, I., Lifshitz, N., Macías Garcia, C., Graves, J. A. & Torres, R. Females of a polymorphic seabird dislike foreign-looking males. Anim. Behav. 113, 31–38 (2016).82.Gutiérrez, J. S. & Soriano-Redondo, A. Laterality in foraging phalaropes promotes phenotypically assorted groups. Behav. Ecol. 31, 1429–1435 (2021).Article 

Google Scholar 
83.Jarić, I. et al. iEcology: Harnessing large online resources to generate ecological insights. Trends Ecol. Evol. 35, 630–639 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
84.Vrettos, M., Reynolds, C. & Amar, A. Malar stripe size and prominence in peregrine falcons vary positively with solar radiation: support for the solar glare hypothesis. Biol. Lett. 17, 20210116. https://doi.org/10.1098/rsbl.2021.0116 (2021).Article 
PubMed 
PubMed Central 

Google Scholar  More

1.Ashman, T.-L. et al. Pollen limitation of plant reproduction: Ecological and evolutionary causes and consequences. Ecology 85, 2408–2421 (2004).Article 

Google Scholar 
2.Ricketts, T. H. et al. Landscape effects on crop pollination services: Are there general patterns?. Ecol. Lett. 11, 499–515 (2008).Article 

Google Scholar 
3.Rollin, O. & Garibaldi, L. A. Impacts of honeybee density on crop yield: A meta-analysis. J. Appl. Ecol. 56, 1152–1163. https://doi.org/10.1111/1365-2664.13355 (2019).Article 

Google Scholar 
4.Bennett, J. M. et al. Land use and pollinator dependency drives global patterns of pollen limitation in the Anthropocene. Nat. Commun. 11, 3999. https://doi.org/10.1038/s41467-020-17751-y (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
5.Aizen, M. A. & Harder, L. D. Expanding the limits of the pollen-limitation concept: Effects of pollen quantity and quality. Ecology 88, 271–281 (2007).Article 

Google Scholar 
6.Igic, B. & Kohn, J. R. The distribution of plant mating systems: Study bias against obligately outcrossing species. Evolution 60, 1098–1103 (2006).Article 

Google Scholar 
7.Abrol, D. P. Pollination Biology: Biodiversity and Conservation and Agricultural Production. Applied Pollination: Present Scenario 55–83 (Springer, 2012).
Google Scholar 
8.Frankel, R. & Galun, E. Pollination Mechanisms, Reproduction and Plant Breeding Vol. 2 (Springer Verlag, 1977).Book 

Google Scholar 
9.Schneider, D., Goldway, M., Rotman, N., Adato, I. & Stern, R. A. Cross-pollination improves ‘Orri’ mandarin fruit yield. Sci. Hortic. 122, 380–384 (2009).Article 

Google Scholar 
10.Fattahi, R., Mohammadzedeh, M. & Khadivi-Khub, A. Influence of different pollen sources on nut and kernel characteristics of hazelnut. Sci. Hortic. 173, 15–19 (2014).Article 

Google Scholar 
11.Żurawicz, E., Studnicki, M., Kubik, J. & Pruski, K. A careful choice of compatible pollinizers significantly improves the size of fruits in red raspberry (Rubus idaeus L.). Sci. Hortic. 235, 253–257 (2018).Article 

Google Scholar 
12.Garibaldi, L. A. et al. Wild pollinators enhance fruit set of crops regardless of honey bee abundance. Science 339, 1608–1611. https://doi.org/10.1126/science.1230200 (2013).ADS 
CAS 
Article 
PubMed 

Google Scholar 
13.Willcox, B. K., Aizen, M. A., Cunningham, S. A., Mayfield, M. M. & Rader, R. Deconstructing pollinator community effectiveness. Curr. Opin. Insect. Sci. 21, 98–104. https://doi.org/10.1016/j.cois.2017.05.012 (2017).Article 
PubMed 

Google Scholar 
14.Richards, T. E. et al. Relationships between nut size, kernel quality, nutritional composition and levels of outcrossing in three macadamia cultivars. Plants 9, 228 (2020).CAS 
Article 

Google Scholar 
15.van Nocker, S. & Gardiner, S. E. Breeding better cultivars, faster: Applications of new technologies for the rapid deployment of superior horticultural tree crops. Hortic. Res. 1, 14022. https://doi.org/10.1038/hortres.2014.22 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
16.Isaacs, R. & Kirk, A. K. Pollination services provided to small and large highbush blueberry fields by wild and managed bees. J. Appl. Ecol. 47, 841–849 (2010).Article 

Google Scholar 
17.Brittain, C., Kremen, C., Garber, A. & Klein, A.-M. Pollination and plant resources change the nutritional quality of almonds for human health. PLoS ONE 9, e90082. https://doi.org/10.1371/journal.pone.0090082 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
18.Klatt, B. K. et al. Bee pollination improves crop quality, shelf life and commercial value. Proc. R. Soc. B 281, 20132440 (2014).Article 

Google Scholar 
19.Crane, J. et al. in The Avocado: Botany, Production and Uses (eds. Schaffer, B., Wolstenholme, B. N. & Whiley, A. W.) 200–233 (CABI, 2013).20.Duarte, P. F., Chaves, M. A., Borges, C. D. & Mendonça, C. R. B. Avocado: Characteristics, health benefits and uses. Ciênc. Rural 46, 747–754. https://doi.org/10.1590/0103-8478cr20141516 (2016).CAS 
Article 

Google Scholar 
21.Dreher, M. L. & Davenport, A. J. Hass avocado composition and potential health effects. Crit. Rev. Food Sci. Nutr. 53, 738–750. https://doi.org/10.1080/10408398.2011.556759 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
22.Araújo, R. G., Rodriguez-Jasso, R. M., Ruiz, H. A., Pintado, M. M. E. & Aguilar, C. N. Avocado by-products: Nutritional and functional properties. Trends Food Sci. Technol. 80, 51–60. https://doi.org/10.1016/j.tifs.2018.07.027 (2018).CAS 
Article 

Google Scholar 
23.Lerman-Garber, I., Ichazo-Cerro, S., Zamora-González, J., Cardoso-Saldaña, G. & Posadas-Romero, C. Effect of a high-monounsaturated fat diet enriched with avocado in NIDDM patients. Diabetes Care 17, 311–315. https://doi.org/10.2337/diacare.17.4.311 (1994).CAS 
Article 
PubMed 

Google Scholar 
24.López, L. R. et al. Monounsaturated fatty acid (avocado) rich diet for mild hypercholesterolemia. Arch. Med. Res. 27, 519–523 (1996).
Google Scholar 
25.Kris-Etherton, P. M. et al. High-monounsaturated fatty acid diets lower both plasma cholesterol and triacylglycerol concentrations. Am. J. Clin. Nutr. 70, 1009–1015 (1999).CAS 
Article 

Google Scholar 
26.Trueman, S. J., Richards, S., McConchie, C. A. & Turnbull, C. G. N. Relationships between kernel oil content, fruit removal force and abscission in macadamia. Aust. J. Exp. Agric. 40, 859–866 (2000).Article 

Google Scholar 
27.Stout, A. B. A Study in Cross-Pollination of Avocados in Southern California (New York Botanical Garden, 1923).
Google Scholar 
28.Blanke, M. M. & Lovatt, C. J. Anatomy and transpiration of the avocado inflorescence. Ann. Bot. 71, 543–547. https://doi.org/10.1006/anbo.1993.1070 (1993).Article 

Google Scholar 
29.Salazar-García, S., Garner, L. C. & Lovatt, C. J. in The Avocado: Botany, Production and Uses. Reproductive Biology (eds. Schaffer, B., Wolstenholme, B. N. & Whiley, A. W.) 118–167 (CABI, 2013).30.Garner, L. C. & Lovatt, C. J. The relationship between flower and fruit abscission and alternate bearing of ‘Hass’ avocado. J. Am. Soc. Hortic. Sci. 133, 3–10. https://doi.org/10.21273/jashs.133.1.3 (2008).Article 

Google Scholar 
31.Vithanage, V. The role of the European honeybee (Apis mellifera L.) in avocado pollination. J. Hortic. Sci. 65, 81–86. https://doi.org/10.1080/00221589.1990.11516033 (1990).Article 

Google Scholar 
32.Perez-Balam, J. et al. The contribution of honey bees, flies and wasps to avocado (Persea americana) pollination in southern Mexico. J. Pollinat. Ecol. 8, 42–47 (2012).Article 

Google Scholar 
33.Ying, Z., Davenport, T. L. R., Zhang, T., Schnell, R. J. & Tondo, C. L. Selection of highly informative microsatellite markers to identify pollen donors in “Hass” avocado orchards. Plant Mol. Biol. Rep. 27, 374–380 (2009).CAS 
Article 

Google Scholar 
34.Alcaraz, M. & Hormaza, J. Influence of physical distance between cultivars on yield, outcrossing rate and selective fruit drop in avocado (Persea americana, Lauraceae). Ann. Appl. Biol. 158, 354–361 (2011).Article 

Google Scholar 
35.Borrone, J. W. et al. Outcrossing in Florida avocados as measured using microsatellite markers. J. Am. Soc. Hortic. Sci. 133, 255–261 (2008).Article 

Google Scholar 
36.Schnell, R. J. et al. Outcrossing between ‘Bacon’ pollinizers and adjacent ‘Hass’ avocado trees and the description of two new lethal mutants. HortScience 44, 1522. https://doi.org/10.21273/hortsci.44.6.1522 (2009).Article 

Google Scholar 
37.Degani, C., Goldring, A., Adato, I., El-Batsri, R. & Gazit, S. Pollen parent effect on outcrossing rate, yield, and fruit characteristics of `Fuerte’ avocado. HortScience 25, 471. https://doi.org/10.21273/hortsci.25.4.471 (1990).Article 

Google Scholar 
38.Sedgley, M. & Annells, C. M. Flowering and fruit-set response to temperature in the avocado cultivar ‘Hass’. Sci. Hortic. 14, 27–33. https://doi.org/10.1016/0304-4238(81)90075-3 (1981).Article 

Google Scholar 
39.Degani, C., El-Batsri, R. & Gazit, S. Outcrossing rate, yield, and selective fruit abscission in “Ettinger” and “Ardith” avocado plots. J. Am. Soc. Hortic. Sci. 122, 813–817 (1997).Article 

Google Scholar 
40.Ying, Z. et al. Re-evaluation of the roles of honeybees and wind on pollination in avocado. J. Hortic. Sci. Biotechnol. 84, 255–260. https://doi.org/10.1080/14620316.2009.11512513 (2009).Article 

Google Scholar 
41.Sapir, G. et al. Synergistic effects between bumblebees and honey bees in apple orchards increase cross pollination, seed number and fruit size. Sci. Hortic. 219, 107–117. https://doi.org/10.1016/j.scienta.2017.03.010 (2017).Article 

Google Scholar 
42.Stern, R., Eisikowitch, D. & Dag, A. Sequential introduction of honeybee colonies and doubling their density increases cross-pollination, fruit-set and yield in ‘Red Delicious’ apple. J. Hortic. Sci. Biotechnol. 76, 17–23. https://doi.org/10.1080/14620316.2001.11511320 (2001).Article 

Google Scholar 
43.Kämper, W., Trueman, S. J., Ogbourne, S. M. & Wallace, H. M. Pollination services in macadamia depend on across-orchard transport of cross pollen. J. Appl. Ecol. (under review).44.Robbertse, P. J., Coetzer, L. A., Johannsmeier, M. F., Köhne, J. S. & Morudu, T. M. Hass Yield and Fruit Size as Influenced by Pollination and Pollen Donor—A Joint Progress Report 63–67 (South African Avocado Growers’ Association Yearbook, 1996).
Google Scholar 
45.Araújo, E., Costa, M., Chaud-Netto, J. & Fowler, H. G. Body size and flight distance in stingless bees (Hymenoptera: Meliponini): Inference of flight range and possible ecological implications. Braz. J. Biol. 64, 563–568 (2004).Article 

Google Scholar 
46.Jalali-Khanabadi, B.-A., Mozaffari-Khosravi, H. & Parsaeyan, N. Effects of almond dietary supplementation on coronary heart disease lipid risk factors and serum lipid oxidation parameters in men with mild hyperlipidemia. J. Altern. Complement. Med. 16, 1279–1283 (2010).Article 

Google Scholar 
47.Kaiser, C. & Wolstenholme, B. N. Aspects of delayed harvest of ‘Hass’ avocado (Persea americana Mill.) fruit in a cool subtropical climate. I. Fruit lipid and fatty acid accumulation. J. Hortic. Sci. 69, 437–445. https://doi.org/10.1080/14620316.1994.11516473 (1994).CAS 
Article 

Google Scholar 
48.Smil, V. Phosphorus in the environment: Natural flows and human interferences. Annu. Rev. Environ. Resour. 25, 53–88. https://doi.org/10.1146/annurev.energy.25.1.53 (2000).Article 

Google Scholar 
49.Bangerth, F. Calcium-related physiological disorders of plants. Annu. Rev. Phytopathol. 17, 97–122. https://doi.org/10.1146/annurev.py.17.090179.000525 (1979).CAS 
Article 

Google Scholar 
50.Witney, G. W., Hofman, P. J. & Wolstenholme, B. N. Effect of cultivar, tree vigour and fruit position on calcium accumulation in avocado fruits. Sci. Hortic. 44, 269–278. https://doi.org/10.1016/0304-4238(90)90127-Z (1990).CAS 
Article 

Google Scholar 
51.Matoh, T. & Kobayashi, M. Boron and calcium, essential inorganic constituents of pectic polysaccharides in higher plant cell walls. J. Plant Res. 111, 179–190 (1998).CAS 
Article 

Google Scholar 
52.Hopkirk, G., White, A., Beever, D. J. & Forbes, S. K. Influence of postharvest temperatures and the rate of fruit ripening on internal postharvest rots and disorders of New Zealand ‘Hass’ avocado fruit. N. Z. J. Crop Hortic. Sci. 22, 305–311. https://doi.org/10.1080/01140671.1994.9513839 (1994).Article 

Google Scholar 
53.Meir, S. et al. Prolonged storage of `Hass’ avocado fruit using modified atmosphere packaging. Postharvest Biol. Technol. 12, 51–60. https://doi.org/10.1016/S0925-5214(97)00038-0 (1997).CAS 
Article 

Google Scholar 
54.Flitsanov, U., Mizrach, A., Liberzon, A., Akerman, M. & Zauberman, G. Measurement of avocado softening at various temperatures using ultrasound. Postharvest Biol. Technol. 20, 279–286 (2000).Article 

Google Scholar 
55.Hofman, P. J., Bower, J. & Woolf, A. in The Avocado: Botany, Production and Uses. Harvesting, Packing, Postharvest Technology, Transport and Processing (eds. Schaffer, B., Wolstenholme, B. N. & Whiley, A. W.) 489–540 (CABI, 2013).56.McGeehan, S. L. & Naylor, D. V. Automated instrumental analysis of carbon and nitrogen in plant and soil samples. Commun. Soil Sci. Plant Anal. 19, 493–505. https://doi.org/10.1080/00103628809367953 (1988).CAS 
Article 

Google Scholar 
57.Rayment, G. E. & Higginson, F. R. Australian Laboratory Handbook of Soil and Water Chemical Methods (Inkata, 1992).
Google Scholar 
58.Munter, R. C. & Grande, R. A. in Developments in Atomic Plasma Spectrochemical Analysis. Plant Tissue and Soil Extract Analysis by ICP-Atomic Emission Spectrometry (ed. Byrnes, R. M.) 653–672 (Heyden, 1981).59.Martinie, G. D. & Schilt, A. A. Wet oxidation efficiencies of perchloric acid mixtures for various organic substances and the identities of residual matter. Anal. Chem. 48, 70–74. https://doi.org/10.1021/ac60365a032 (1976).CAS 
Article 

Google Scholar 
60.Bai, S. H. et al. Nutritional quality of almond, canarium, cashew and pistachio and their oil photooxidative stability. J. Food Sci. Technol. 56, 792–798. https://doi.org/10.1007/s13197-018-3539-6 (2019).CAS 
Article 
PubMed 

Google Scholar 
61.Ivanova, N. V., Fazekas, A. J. & Hebert, P. D. N. Semi-automated, membrane-based protocol for DNA isolation from plants. Plant Mol. Biol. Rep. 26, 186–198 (2008).CAS 
Article 

Google Scholar 
62.Kämper, W., Cooke, J., Trueman, S. J. & Ogbourne, S. M. Detection of single nucleotide polymorphisms (SNPs) in avocado cultivars, Persea americana (Lauraceae). Appl. Plant Sci. (submitted).63.Jordon-Thaden, I. E. et al. A basic ddRADseq two-enzyme protocol performs well with herbarium and silica-dried tissues across four genera. Appl. Plant Sci. 8, e11344–e11344. https://doi.org/10.1002/aps3.11344 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
64.Sharon, D. et al. An integrated genetic linkage map of avocado. Theor. Appl. Genet. 95, 911–921 (1997).CAS 
Article 

Google Scholar 
65.Borrone, J. W., Schnell, R. J., Violi, H. A. & Ploetz, R. C. Seventy microsatellite markers from Persea americana Miller (avocado) expressed sequence tags. Mol. Ecol. Resour. 7, 439–444 (2007).CAS 
Article 

Google Scholar 
66.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2015).
Google Scholar 
67.Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. lmerTest package: Tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).Article 

Google Scholar  More

1.Voss, R. S. & Emmons, L. H. Mammalian diversity in Neotropical lowland rainforests: A preliminary assessment. Bull. Am. Museum Nat. Hist. 230, 1–115 (1996).
Google Scholar 
2.Barnosky, A. D. et al. Variable impact of late-Quaternary megafaunal extinction in causing ecological state shifts in North and South America. Proc. Natl. Acad. Sci. U. S. A. 113, 856–861 (2016).ADS 
CAS 
PubMed 
Article 

Google Scholar 
3.Croft, D. A., Engelman, R. K., Dolgushina, T. & Wesley, G. Diversity and disparity of sparassodonts (Metatheria) reveal non-analogue nature of ancient South American mammalian carnivore guilds. Proc. R. Soc. B 285, 20172012 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
4.Fariña, R. A. Trophic relationships among Lujanian mammals. Evol. Theory 11, 125–134 (1996).
Google Scholar 
5.Fariña, R. A. & Blanco, R. E. Megatherium the Stabber. Proc. R. Soc. B Biol. Sci. 263, 1725–1729 (2006).ADS 

Google Scholar 
6.Tejada-Lara, J. V. et al. Body mass predicts isotope enrichment in herbivorous mammals. Proc. R. Soc. B Biol. Sci. 285, 20181020 (2018).Article 
CAS 

Google Scholar 
7.de Muizon, C. & McDonald, H. G. An aquatic sloth from the Pliocene of Peru. Nature 375, 224–227 (1995).ADS 
Article 

Google Scholar 
8.Croft, D. A. The middle Miocene (Laventan) Quebrada Honda Fauna, southern Bolivia and a description of its notoungulates. Palaeontology 50, 277–303 (2007).Article 

Google Scholar 
9.Boecklen, W. J., Yarnes, C. T., Cook, B. A. & James, A. C. On the use of stable isotopes in trophic ecology. Annu. Rev. Ecol. Evol. Syst. 42, 411–440 (2011).Article 

Google Scholar 
10.Lee-Thorp, J. J., Sealy, J. J. C. & van der Merwe, N. J. N. Stable carbon isotope ratio differences between bone collagen and bone apatite, and their relationship to diet. J. Archaeol. Sci. 32, 1459–1470 (1989).
Google Scholar 
11.Clementz, M. T., Fox-Dobbs, K., Wheatley, P. V., Koch, P. L. & Doak, D. F. Revisiting old bones: Coupled carbon isotope analysis of bioapatite and collagen as an ecological and palaeoecological tool. Geol. J. 44, 605–620 (2009).CAS 
Article 

Google Scholar 
12.Tejada, J. V. et al. Comparative isotope ecology of western Amazonian rainforest mammals. Proc. Natl. Acad. Sci. https://doi.org/10.1073/pnas.2007440117 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
13.Robinson, D. δ15N as an integrator of the nitrogen cycle. Trends Ecol. Evol. 16, 153–162 (2001).CAS 
PubMed 
Article 

Google Scholar 
14.McMahon, K. W. & McCarthy, M. D. Embracing variability in amino acid δ15N fractionation: Mechanisms, implications, and applications for trophic ecology. Ecosphere 7, 1–26 (2016).Article 

Google Scholar 
15.McClelland, J. W. & Montoya, J. P. Trophic relationships and the nitrogen isotopic composition of amino acids in plankton. Ecology 83, 2173–2180 (2002).Article 

Google Scholar 
16.Chikaraishi, Y., Ogawa, N. O., Doi, H. & Ohkouchi, N. 15N/14N ratios of amino acids as a tool for studying terrestrial food webs: A case study of terrestrial insects (bees, wasps, and hornets ). Ecol. Res. 26, 835–844 (2011).Article 

Google Scholar 
17.Popp, B. N. et al. Insight into the trophic ecology of yellowfin tuna, Thunnus albacares, from compound- specific nitrogen isotope analysis of proteinaceous amino acids. In Stable Isotopes as Indicators of Ecological Change (eds Dawson, T. E. & Siegwolf, R. T. W.) 173–190 (Elsevier Inc., 2007).
Google Scholar 
18.Naito, Y. I., Honch, N. V., Chikaraishi, Y., Ohkouchi, N. & Yoneda, M. Quantitative evaluation of marine protein contribution in ancient diets based on nitrogen isotope ratios of individual amino acids in bone collagen: An investigation at the Kitakogane Jomon Site. Am. J. Phys. Anthropol. 143, 31–40 (2010).PubMed 
Article 

Google Scholar 
19.O’Connell, T. C. ‘Trophic’ and ‘source’ amino acids in trophic estimation: A likely metabolic explanation. Oecologia 184, 317–326 (2017).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Chikaraishi, Y., Ogawa, N. O. & Ohkouchi, N. Further evaluation of the trophic level estimation based on nitrogen isotopic composition of amino acids. In Earth, Life, and Isotopes (eds Ohkouchi, N. et al.) 37–51 (Kyoto Universy Press, 2010).
Google Scholar 
21.Steffan, S. A. et al. Trophic hierarchies illuminated via amino acid isotopic analysis. PLoS ONE 8, 1–10 (2013).Article 
CAS 

Google Scholar 
22.Chikaraishi, Y., Kashiyama, Y., Ogawa, N. O., Kitazato, H. & Ohkouchi, N. Metabolic control of nitrogen isotope composition of amino acids in macroalgae and gastropods: Implications for aquatic food web studies. Mar. Ecol. Prog. Ser. 342, 85–90 (2007).ADS 
CAS 
Article 

Google Scholar 
23.Naito, Y. I. et al. Ecological niche of Neanderthals from Spy Cave revealed by nitrogen isotopes of individual amino acids in collagen. J. Hum. Evol. 93, 82–90 (2016).PubMed 
Article 

Google Scholar 
24.Nielsen, J. M., Popp, B. N. & Winder, M. Meta-analysis of amino acid stable nitrogen isotope ratios for estimating trophic position in marine organisms. Oecologia https://doi.org/10.1007/s00442-015-3305-7 (2015).Article 
PubMed 

Google Scholar 
25.Décima, M., Landry, M. R. & Popp, B. N. Environmental perturbation effects on baseline δ15N values and zooplankton trophic flexibility in the southern California current ecosystem. Limnol. Oceanogr. 58, 624–634 (2013).ADS 
Article 
CAS 

Google Scholar 
26.Jarman, C. L. et al. Diet of the prehistoric population of Rapa Nui (Easter Island, Chile) shows environmental adaptation and resilience. Am. J. Phys. Anthropol. https://doi.org/10.1002/ajpa.23273 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
27.Kendall, I. P. et al. Compound-specific δ15N values express differences in amino acid metabolism in plants of varying lignin content. Phytochemistry 161, 130–138 (2019).CAS 
PubMed 
Article 

Google Scholar 
28.Ramirez, M. D., Besser, A. C., Newsome, S. D. & McMahon, K. W. Meta-analysis of primary producer amino acid δ15N values and their influence on trophic position estimation. Methods Ecol. Evol. https://doi.org/10.1111/2041-210X.13678 (2021).Article 

Google Scholar 
29.Hebert, C. E., Popp, B. N., Fernie, K. J., Rattner, B. A. & Wallsgrove, N. Amino acid specific stable nitrogen isotope values in avian tissues: Insights from captive American kestrels and wild herring gulls. Environ. Sci. Technol. 50, 12928–12937 (2016).ADS 
CAS 
PubMed 
Article 

Google Scholar 
30.Chikaraishi, Y. et al. Determination of aquatic food-web structure based on compound-specific nitrogen isotopic composition of amino acids. Limnol. Oceanogr. 7, 740–750 (2009).CAS 
Article 

Google Scholar 
31.Steffan, S. A. et al. Microbes are trophic analogs of animals. Proc. Natl. Acad. Sci. 112, 15119–15124 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
32.Kendall, I. P., Lee, M. R. F. & Evershed, R. P. The effect of trophic level on individual amino acid δ15N values in a terrestrial ruminant food web. Sci. Technol. Archaeol. Res. 3, 135–145 (2017).
Google Scholar 
33.Matthews, C. J. D., Ruiz-Cooley, R. I., Pomerleau, C. & Ferguson, S. H. Amino acid δ15N underestimation of cetacean trophic positions highlights limited understanding of isotopic fractionation in higher marine consumers. Ecol. Evol. 10, 3450–3462 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
34.Styring, A. K., Sealy, J. C. & Evershed, R. P. Resolving the bulk δ15N values of ancient human and animal bone collagen via compound-specific nitrogen isotope analysis of constituent amino acids. Geochim. Cosmochim. Acta 74, 241–251 (2010).ADS 
CAS 
Article 

Google Scholar 
35.Lorrain, A. et al. Nitrogen and carbon isotope values of individual amino acids: A tool to study foraging ecology of penguins in the Southern Ocean. Mar. Ecol. Prog. Ser. 391, 293–306 (2009).ADS 
CAS 
Article 

Google Scholar 
36.Lorrain, A. et al. Nitrogen isotopic baselines and implications for estimating foraging habitat and trophic position of yellowfin tuna in the Indian and Pacific Oceans. Deep. Res. Part II Top. Stud. Oceanogr. 113, 188–198 (2015).ADS 
CAS 
Article 

Google Scholar 
37.Hartman, G. Are elevated δ15N values in herbivores in hot and arid environments caused by diet or animal physiology?. Funct. Ecol. 25, 122–131 (2011).Article 

Google Scholar 
38.Hartman, G. & Danin, A. Isotopic values of plants in relation to water availability in the Eastern Mediterranean region. Oecologia 162, 837–852 (2010).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
39.Hansen, R. M. Shasta ground sloth food habits, Rampart Cave, Arizona. Paleobiology 4, 302–319 (1978).Article 

Google Scholar 
40.McDonald, H. G. & Morgan, G. S. Ground Sloths of New Mexico. Foss. Rec. 3 New. Mex. Museum Nat. Hist. Sci. Bull. 53, 652–663 (2011).
Google Scholar 
41.Poinar, H. N. Molecular coproscopy: Dung and diet of the extinct ground sloth Nothrotheriops shastensis. Science 281, 402–406 (1998).ADS 
CAS 
PubMed 
Article 

Google Scholar 
42.Clack, A. A., MacPhee, R. D. E. & Poinar, H. N. Mylodon darwinii DNA sequences from ancient fecal hair shafts. Ann. Anat. 194, 26–30 (2012).CAS 
PubMed 
Article 

Google Scholar 
43.Höss, M., Dilling, A., Currant, A. & Pääbo, S. Molecular phylogeny of the extinct ground sloth Mylodon darwinii. Proc. Natl. Acad. Sci. U. S. A. 93, 181–185 (1996).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Moore, D. M. Post-glacial vegetation in the South Patagonian territory of the giant ground sloth, Mylodon. Bot. J. Linn. Soc. 77, 177–202 (1978).Article 

Google Scholar 
45.Bargo, M. S., Toledo, N. & Vizcaino, S. F. Muzzle of South American Pleistocene ground sloths (Xenarthra, Tardigrada). J. Morphol. 267, 248–263 (2006).PubMed 
Article 

Google Scholar 
46.Rasmussen, M. et al. Response to comment by Goldberg et al. on ‘DNA from Pre-Clovis human coprolites in Oregon, North America’. Science 325, 148 (2009).ADS 
CAS 
Article 

Google Scholar 
47.Janis, C. M. Correlations between craniodental anatomy and feeding in ungulates: Reciprocal illumination between living and fossil taxa. In Functional Morphology in Vertebrate Paleontology (ed. Thomason, J.) 76–98 (Cambridge U Press, 1995).
Google Scholar 
48.Clauss, M., Nunn, C., Fritz, J. & Hummel, J. Evidence for a tradeoff between retention time and chewing efficiency in large mammalian herbivores. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 154, 376–382 (2009).PubMed 
Article 
CAS 

Google Scholar 
49.Vizcaino, S. F., Bargo, M. S. & Cassini, G. H. Dental occlusal surface area in relation to body mass, food habits and other biologic features in fossil xenarthrans. Ameghiniana 43, 11–26 (2006).
Google Scholar 
50.McNab, B. K. Energetics, population biology, and distribution of xenarthrans, living and extinct. In The Ecology of Arboreal Folivores 219–232 (Smithsonian Press, 1985).51.Davis, L. B. & Birkbak, R. C. On the transfer of energy in layers of fur. Biophys. J. 14, 249–268 (1974).PubMed 
PubMed Central 
Article 

Google Scholar 
52.Clauss, M. et al. The maximum attainable body size of herbivorous mammals: Morphophysiological constraints on foregut, and adaptations of hindgut fermenters. Oecologia 136, 14–27 (2003).ADS 
CAS 
PubMed 
Article 

Google Scholar 
53.Fariña, R. A., Czerwonogora, A. & Di Giacomo, M. Splendid oddness: Revisiting the curious trophic relationships of South American Pleistocene mammals and their abundance. An. Acad. Bras. Cienc. 86, 311–331 (2014).PubMed 
Article 

Google Scholar 
54.Zhu, D. et al. The large mean body size of mammalian herbivores explains the productivity paradox during the Last Glacial Maximum. Nat. Ecol. Evol. 2, 640–649 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
55.Zimov, S. A., Zimov, N. S., Tikhonov, A. N. & Chapin, I. S. Mammoth steppe: A high-productivity phenomenon. Quat. Sci. Rev. 57, 26–45 (2012).ADS 
Article 

Google Scholar 
56.Hannides, C. C. S., Popp, B. N., Landry, M. R. & Graham, B. S. Quantification of zooplankton trophic position in the North Pacific Subtropical Gyre using stable nitrogen isotopes. Limnol. Oceanogr. 54, 50–61 (2009).ADS 
CAS 
Article 

Google Scholar  More

1.Oerke, E.-C. Crop losses to pests. J. Agric. Sci. 144, 31–43 (2006).Article 

Google Scholar 
2.R4P Network. Trends and challenges in pesticide resistance detection. Trends Plant Sci. 21, 834–853 (2016).3.Heap, I. M. The international herbicide-resistant weed database. http://www.weedscience.org/Home.aspx (2021).4.Délye, C., Jasieniuk, M. & Le Corre, V. Deciphering the evolution of herbicide resistance in weeds. Trends Genet. 29, 649–658 (2013).PubMed 
Article 
CAS 

Google Scholar 
5.Gaines, T. A. et al. Mechanisms of evolved herbicide resistance. J. Biol. Chem. 295, 10307–10330 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
6.Murphy, B. P. & Tranel, P. J. Target-site mutations conferring herbicide resistance. Plants 8, 382 (2019).CAS 
PubMed Central 
Article 
PubMed 

Google Scholar 
7.Beckie, H. J. & Tardif, F. J. Herbicide cross resistance in weeds. Crop Prot. 35, 15–28 (2012).CAS 
Article 

Google Scholar 
8.Han, H. et al. Cytochrome P450 CYP81A10v7 in Lolium rigidum confers metabolic resistance to herbicides across at least five modes of action. Plant J. 105, 79–92 (2021).CAS 
PubMed 
Article 

Google Scholar 
9.Kreiner, J. M. et al. Multiple modes of convergent adaptation in the spread of glyphosate-resistant Amaranthus tuberculatus. Proc. Natl. Acad. Sci. 116, 21076–21084 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.Milani, A. et al. Population structure and evolution of resistance to acetolactate synthase (ALS)-inhibitors in Amaranthus tuberculatus in Italy. Pest Manag. Sci. 77, 2971–2980 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
11.Clements, D. R. et al. Adaptability of plants invading North American cropland. Agric. Ecosyst. Environ. 104, 379–398 (2004).Article 

Google Scholar 
12.Essl, F. et al. Biological flora of the British Isles: Ambrosia artemisiifolia. J. Ecol. 103, 1069–1098 (2015).Article 

Google Scholar 
13.Cowbrough, M. J., Brown, R. B. & Tardif, F. J. Impact of common ragweed (Ambrosia artemisiifolia) aggregation on economic thresholds in soybean. Weed Sci. 51, 947–954 (2003).CAS 
Article 

Google Scholar 
14.Swinton, S. M., Buhler, D. D., Forcella, F., Gunsolus, J. L. & King, R. P. Estimation of crop yield loss due to interference by multiple weed species. Weed Sci. 42, 103–109 (1994).Article 

Google Scholar 
15.Bassett, I. J. & Crompton, C. W. The biology of Canadian weeds: Ambrosia artemisiifolia L. and A. psilostachya DC. Can. J. Plant Sci. 55, 463–476 (1975).16.Chauvel, B., Dessaint, F., Cardinal-Legrand, C. & Bretagnolle, F. The historical spread of Ambrosia artemisiifolia L. France from herbarium records. J. Biogeogr. 33, 665–673 (2006).Article 

Google Scholar 
17.Sala, C. A., Bulos, M., Altieri, E. & Ramos, M. L. Genetics and breeding of herbicide tolerance in sunflower. Helia 35, 57–69 (2012).Article 

Google Scholar 
18.Yu, Q. & Powles, S. B. Resistance to AHAS inhibitor herbicides: Current understanding. Pest Manag. Sci. 70, 1340–1350 (2014).CAS 
PubMed 
Article 

Google Scholar 
19.Tranel, P. J., Wright, T. R. & Heap, I. M. ALS mutations from resistant weeds. http://www.weedscience.com (2021).20.Patzoldt, W. L., Tranel, P. J., Alexander, A. L. & Schmitzer, P. R. A common ragweed population resistant to cloransulam-methyl. Weed Sci. 49, 485–490 (2001).CAS 
Article 

Google Scholar 
21.Rousonelos, S. L., Lee, R. M., Moreira, M. S., VanGessel, M. J. & Tranel, P. J. Characterization of a common ragweed (Ambrosia artemisiifolia) population resistant to ALS- and PPO-inhibiting herbicides. Weed Sci. 60, 335–344 (2012).CAS 
Article 

Google Scholar 
22.Zheng, D., Patzoldt, W. L. & Tranel, P. J. Association of the W574L ALS substitution with resistance to cloransulam and imazamox in common ragweed (Ambrosia artemisiifolia). Weed Sci. 53, 424–430 (2005).CAS 
Article 

Google Scholar 
23.Van Wely, A. C. et al. Glyphosate and acetolactate synthase inhibitor resistant common ragweed (Ambrosia artemisiifolia L.) in southwestern Ontario. Can. J. Plant Sci. 95, 335–338 (2015)24.Marsan-Pelletier, F., Vanasse, A., Simard, M.-J. & Cuerrier, M.-E. Survey of imazethapyr-resistant common ragweed (Ambrosia artemisiifolia L.) in Quebec. Phytoprotection 99, 36–44 (2019).25.Owen, M. D. & Zelaya, I. A. Herbicide-resistant crops and weed resistance to herbicides. Pest Manag. Sci. 61, 301–311 (2005).CAS 
PubMed 
Article 

Google Scholar 
26.Duke, S. O. & Powles, S. B. Glyphosate: A once-in-a-century herbicide. Pest Manag. Sci. 64, 319–325 (2008).CAS 
PubMed 
Article 

Google Scholar 
27.Barnes, E. R., Knezevic, S. Z., Sikkema, P. H., Lindquist, J. L. & Jhala, A. J. Control of glyphosate-resistant common ragweed (Ambrosia artemisiifolia L.) in glufosinate-resistant soybean [Glycine max (L.) Merr]. Front. Plant Sci. 8, 1455 (2017).28.Tranel, P. J. & Wright, T. R. Resistance of weeds to ALS-inhibiting herbicides: What have we learned?. Weed Sci. 50, 700–712 (2002).CAS 
Article 

Google Scholar 
29.Li, J., Li, M., Gao, X. & Fang, F. A novel amino acid substitution Trp574Arg in acetolactate synthase (ALS) confers broad resistance to ALS-inhibiting herbicides in crabgrass (Digitaria sanguinalis). Pest Manag. Sci. 73, 2538–2543 (2017).CAS 
PubMed 
Article 

Google Scholar 
30.Duggleby, R. G., Pang, S. S., Yu, H. & Guddat, L. W. Systematic characterization of mutations in yeast acetohydroxyacid synthase. Interpretation of herbicide-resistance data. Eur. J. Biochem. 270, 2895–2904 (2003).31.Jung, S.-M. et al. Amino acid residues conferring herbicide resistance in tobacco acetohydroxyacid synthase. Biochem. J. 383, 53–61 (2004).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
32.Owen, M. J., Walsh, M. J., Llewellyn, R. S. & Powles, S. B. Widespread occurrence of multiple herbicide resistance in Western Australian annual ryegrass (Lolium rigidum) populations. Aust. J. Agric. Res. 58, 711–718 (2007).CAS 
Article 

Google Scholar 
33.Owen, M. J., Martinez, N. J. & Powles, S. B. Multiple herbicide-resistant Lolium rigidum (annual ryegrass) now dominates across the Western Australian grain belt. Weed Res. 54, 314–324 (2014).CAS 
Article 

Google Scholar 
34.Délye, C. Nucleotide variability at the acetyl coenzyme A carboxylase gene and the signature of herbicide selection in the grass weed Alopecurus myosuroides (Huds.). Mol. Biol. Evol. 21, 884–892 (2004).35.Délye, C., Clément, J. A. J., Pernin, F., Chauvel, B. & Le Corre, V. High gene flow promotes the genetic homogeneity of arable weed populations at the landscape level. Basic Appl. Ecol. 11, 504–512 (2010).Article 

Google Scholar 
36.Délye, C., Pernin, F. & Scarabel, L. Evolution and diversity of the mechanisms endowing resistance to herbicides inhibiting acetolactate-synthase (ALS) in corn poppy (Papaver rhoeas L.). Plant Sci. 180, 333–342 (2011).37.Sudheesh, M. An analysis of polygenic herbicide resistance evolution and its management based on a population genetics approach. Basic Appl. Ecol. 16, 104–111 (2015).Article 

Google Scholar 
38.Bullock, J. M. Assessing and controlling the spread and the effects of common ragweed in Europe. Report, Contractor: Natural environment research Council UK (2012).39.Yu, Q., Nelson, J. K., Zheng, M. Q., Jackson, J. & Powles, S. B. Molecular characterisation of resistance to ALS-inhibiting herbicides in Hordeum leporinum biotypes. Pest Manag. Sci. 63, 918–927 (2007).CAS 
PubMed 
Article 

Google Scholar 
40.Simard, M.-J., Laforest, M., Soufiane, B., Benoit, D. L. & Tardif, F. Linuron resistant common ragweed (Ambrosia artemisiifolia) populations in Quebec carrot fields: presence and distribution of target-site and non-target site resistant biotypes. Can. J. Plant Sci. 98, 345–352 (2017).
Google Scholar 
41.Ganie, Z., Jugulam, M., Varanasi, V. & Jhala, A. J. Investigating mechanism of glyphosate resistance in a common ragweed (Ambrosia artemisiifolia L.) biotype from Nebraska. Can. J. Plant Sci. (2017). https://doi.org/10.1139/CJPS-2017-0036.42.Duhoux, A., Carrère, S., Duhoux, A. & Délye, C. Transcriptional markers enable identification of rye-grass (Lolium sp.) plants with non-target-site-based resistance to herbicides inhibiting acetolactate-synthase. Plant Sci. 257, 22–36 (2017).43.Gardin, J. A. C., Gouzy, J., Carrère, S. & Délye, C. ALOMYbase, a resource to investigate non-target-site-based resistance to herbicides inhibiting acetolactate-synthase (ALS) in the major grass weed Alopecurus myosuroides (black-grass). BMC Genomics 16, 590 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
44.Torra, J. et al. Target-site and non-target-site resistance mechanisms confer multiple and cross- resistance to ALS and ACCase inhibiting herbicides in Lolium rigidum from Spain. Front. Plant Sci. 12, 625138 (2021).45.Manley, B. S., Hatzios, K. K. & Wilson, H. P. Absorption, translocation, and metabolism of chlorimuron and nicosulfuron in imidazolinone-resistant and susceptible smooth pigweed (Amaranthus hybridus). Weed Technol. 13, 759–764 (1999).CAS 
Article 

Google Scholar 
46.Jeffers, G. M., O’Donovan, J. T. & Hall, L. M. Wild mustard (Brassica kaber) resistance to ethametsulfuron but not to other herbicides. Weed Technol. 10, 847–850 (1996).CAS 
Article 

Google Scholar 
47.Veldhuis, L. J., Hall, L. M., O’Donovan, J. T., Dyer, W. & Hall, J. C. Metabolism-based resistance of a wild mustard (Sinapis arvensis L.) biotype to ethametsulfuron-methyl. J. Agric. Food Chem. 48, 2986–2990 (2000).48.Scarabel, L., Pernin, F. & Délye, C. Occurrence, genetic control and evolution of non-target-site based resistance to herbicides inhibiting acetolactate synthase (ALS) in the dicot weed Papaver rhoeas. Plant Sci. 238, 158–169 (2015).CAS 
PubMed 
Article 

Google Scholar 
49.Nakka, S., Thompson, C. R., Peterson, D. E. & Jugulam, M. Target site-based and non-target site based resistance to ALS Inhibitors in Palmer Amaranth (Amaranthus palmeri). Weed Sci. 65, 681–689 (2017).Article 

Google Scholar 
50.Meyer, L. et al. New gSSR and EST-SSR markers reveal high genetic diversity in the invasive plant Ambrosia artemisiifolia L. and can be transferred to other invasive Ambrosia species. PLOS ONE 12, e0176197 (2017).51.Van Boheemen, L. A. et al. Multiple introductions, admixture and bridgehead invasion characterize the introduction history of Ambrosia artemisiifolia in Europe and Australia. Mol. Ecol. 26, 5421–5434 (2017).PubMed 
Article 

Google Scholar 
52.Délye, C. et al. Harnessing the power of next-generation sequencing technologies to the purpose of high-throughput pesticide resistance diagnosis. Pest Manag. Sci. 76, 543–552 (2020).PubMed 
Article 
CAS 

Google Scholar 
53.Délye, C., Matéjicek, A. & Gasquez, J. PCR-based detection of resistance to acetyl-CoA carboxylase-inhibiting herbicides in black-grass (Alopecurus myosuroides Huds) and ryegrass (Lolium rigidum Gaud). Pest Manag. Sci. 58, 474–478 (2002).PubMed 
Article 
CAS 

Google Scholar 
54.Duggleby, R. G., McCourt, J. A. & Guddat, L. W. Structure and mechanism of inhibition of plant acetohydroxyacid synthase. Plant Physiol. Biochem. 46, 309–324 (2008).CAS 
PubMed 
Article 

Google Scholar 
55.Leigh, J. W. & Bryant, D. POPART: full-feature software for haplotype network construction. Methods Ecol. Evol. 6, 1110–1116 (2015).Article 

Google Scholar 
56.Neff, M. M., Neff, J. D., Chory, J. & Pepper, A. E. dCAPS, a simple technique for the genetic analysis of single nucleotide polymorphisms: Experimental applications in Arabidopsis thaliana genetics. Plant J. 14, 387–392 (1998).CAS 
PubMed 
Article 

Google Scholar 
57.Délye, C. & Boucansaud, K. A molecular assay for the proactive detection of target site-based resistance to herbicides inhibiting acetolactate synthase in Alopecurus myosuroides. Weed Res. 48, 97–101 (2008).Article 

Google Scholar 
58.Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2ddCT method. Methods 25, 402–408 (2001).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
59.Bustin, S. A. et al. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin. Chem. 55, 611–622 (2009).CAS 
PubMed 
Article 

Google Scholar  More

In this study, the HFLUX model was coupled with the SCEM-UA algorithm for analyzing the uncertainties of the model inputs. The specific procedures started with selecting the inputs of the HFLUX model. With the linked HFLUX and SCEM-UA model and implementation of an iteration scheme, the uncertainty of each of the selected inputs was obtained based on the ranges (minimum and maximum values) of the input data/parameters and the Latin hypercube sampling. The simulations were then compared against the observed data to evaluate the performance of the SCEM-UA algorithm. These steps are depicted in Fig. 1.Figure 1Flowchart for the uncertainty analysis.Full size imageRiver water temperatures simulated by the HFLUX modelRiver water temperature affects the water quality and the ecosystem health, and hence control of river water temperature is important to mitigation of its adverse effects1. The HFLUX model was used to simulate the streamflow temperatures at different locations and times. The model is highly flexible in terms of choosing the solution methods for solving the governing equations and selecting the energy budget terms such as shortwave solar radiation, latent heat flux, and sensible heat transfer flux. The model input data include the initial spatial and temporal temperature conditions, stream geometry data, discharge data, and meteorological data8. The water balance and energy balance equations are respectively given by8:$$frac{partial A}{{partial t}} + frac{partial Q}{{partial x}} = mathop qnolimits_{L}$$
(1)
$$frac{{partial left( {Amathop Tnolimits_{w} } right)}}{partial t} + frac{{partial left( {Qmathop Tnolimits_{w} } right)}}{partial x} = mathop qnolimits_{L} mathop Tnolimits_{L} + R$$
(2)
$$R = frac{{Bmathop varphi nolimits_{total} }}{{mathop rho nolimits_{w} mathop Cnolimits_{w} }}$$
(3)
where A is the cross section area of the stream (m2), x is the distance along the stream (m), t is the time (s), Q is the discharge of the stream (m3/s), qL is the lateral inflow per unit stream length (m2/s), Tw is the stream temperature ((^circ C)), TL is the temperature of the lateral inflow ((^circ C)), R is the energy flux (source or sink) per unit stream length ((^circ C) m2/s), B is the width of the stream (m), (mathop varphi nolimits_{total}) is the total energy flux to the stream per surface area (W/m2), (mathop rho nolimits_{w}) is the density of water (kg/m3), and (mathop Cnolimits_{w}) is the specific heat of water (J/kg (^circ C)). Equation (3) is based on a thermal datum of 0 (^circ C) and the impact on the absolute value of the advective heat flux term. In Eq. (2), if qL is negative, the first term on the right-hand side of the equation becomes a loss of qLTw. Also, dispersive heat transport that is omitted in Eq. 2 is negligible when the longitudinal change in water temperature is small in comparison to the temporal changes8.SCEM-UA algorithmThe SCEM-UA algorithm provides posterior distribution functions for the model parameters and input data by generating an initial sample from the parameter space. First, the indicators of n, q, and s that are respectively dimension (the number of investigate inputs), number of complexes (the population to be divided), and population (the number of sample points) are determined for the algorithm. Then, the algorithm searches the sampling points in the feasible space and sorts the points according to the density. The algorithm determines the sequence and complexes based on those points. The sequence is the first q points of the population and complexes are a collection of m points from the population. Note that m = s/q. In the next step, the points of each complex are sorted based on the density, which can be mathematically expressed as20:$$left{ {begin{array}{*{20}c} {mathop alpha nolimits^{k} le T,,,,,,,,,mathop theta nolimits^{t + 1} = Nleft( {mathop theta nolimits^{t} ,,mathop Cnolimits_{n}^{2} mathop Sigma nolimits^{k} } right)} \ {mathop alpha nolimits^{k} > T,,,,,,,,mathop theta nolimits^{t + 1} = Nleft( {mathop mu nolimits^{k} ,,mathop Cnolimits_{n}^{2} mathop Sigma nolimits^{k} } right)} \ end{array} } right.$$
(4)
where k = 1,2,…,q, α is the ratio of the mean posterior density of the m points of complexes to the mean posterior density of the last m generated points of sequences, (theta) is the points of complexes, ({c}_{n}=frac{2.4}{sqrt{n}}) , (T={10}^{6}), (mu) is the mean, and ∑ denotes the covariance. To investigate the new points created by the algorithm, the points of complexes are replaced by20:$$left{ {begin{array}{*{20}l} {Omega ge Zquad replace,best,member,of,mathop Cnolimits^{k} ,with,mathop theta nolimits^{t + 1} } \ {Omega < Zquad mathop theta nolimits^{t + 1} = mathop theta nolimits^{t} ,,,,,,,,,,,,,,,,,,,,,} \ end{array} } right.$$ (5) where (mathop Cnolimits^{k}) is the Kth complex, Z is drawn from the uniform distribution in the range of 0–1, and Ω is calculated by20:$$Omega = frac{{Pleft( {left. {mathop theta nolimits^{t + 1} } right|y} right)}}{{Pleft( {left. {mathop theta nolimits^{t} } right|y} right)}}$$ (6) where (Pleft( {left. {mathop theta nolimits^{t + 1} } right|y} right)) and (Pleft( {left. {mathop theta nolimits^{t} } right|y} right)) are the posterior probability distributions for (mathop theta nolimits^{t + 1}) and (mathop theta nolimits^{t}), respectively. Then, the algorithm examines the following condition for each complex. If it is rejected, the algorithm replaces the worst member ({c}^{k})(the point with the lowest density) with ({theta }^{t+1}) 20.$$mathop Gamma nolimits^{k} le T,,and,,Pleft( {{{mathop theta nolimits^{t + 1} } mathord{left/ {vphantom {{mathop theta nolimits^{t + 1} } y}} right. kern-nulldelimiterspace} y}} right) < ,Pleft( {{{mathop Cnolimits_{m}^{k} } mathord{left/ {vphantom {{mathop Cnolimits_{m}^{k} } y}} right. kern-nulldelimiterspace} y}} right)$$ (7) where ({Gamma }^{k}) is the ratio of the posterior density of the best (the point with the highest density) to the posterior density of the worst member of ({c}^{k}). The last step is to examine (beta) and L. Note that (beta) = 1 and L = m/10. If (beta < L), (beta = beta + 1) and the algorithm returns to sort complex points. Otherwise, the algorithm examines the Gelman and Rubin convergence6, and eventually provides the posterior distribution functions20. The value of the Gelman and Rubin convergence should be less than 1.2. The Gelman and Rubin convergence is examined by:$$R = sqrt {frac{g - 1}{g} + frac{q + 1}{{q.g}}frac{B}{W}}$$ (8) where g is the number of iterations within each sequence, B is the variance between the q sequence means, and W is the average of the q within-sequence variances for the parameter under consideration20.Study AREAMeadowbrook Creek was selected to test the methods proposed in this study8. The creek flows through the City of Syracuse in New York. Thus, this catchment consists of high residential and industrial land covers, which contribute runoff to the main channel. The creek is about 4 km long. A portion of this creek (475 m long) was selected for the modeling for a period of June 13–19, 2012 in this study. The upstream boundary condition in the HFLUX model was set based on the water temperature of the creek observed at the upstream station8. The uncertainty of the model inputs was examined at three selected points as shown in Fig. 2. Note that the input values at these three points had greater relative changes than the changes at other locations, which provided the possibility to improve the evaluation of the algorithm performance. In addition, these three locations had the same sampling of the selected input data. During the simulation period, the streamflow velocity varied within a range of 0.06–0.63 (m/s). The daily temperature changed between 8.9 and 28.2 °C. The relative humidity, used to calculate the total energy flux to the stream per surface area, changed from 36 to 93%. The creek bed mainly consisted of clay, cobbles, sand, and gravel materials. The basic statistics of the data/variables used in the HFLUX model are presented in Table 1. Figure 2 shows the study area, the creek, and the three selected points for analysis.Figure 2Study area and the locations of three evaluation sections (the gray enlarged map shows the State of New York), the map in this Figure is created by Google Earth 7.0.2.8415 (https://google.com/earth/versions).Full size imageTable 1 Basic statistics of the data/variables used in the HFLUX model.Full size tableEthical approvalAll authors accept all ethical approvals.Consent to participateAll authors consent to participate.Consent to publishAll authors consent to publish. More