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    Resistance evolution can disrupt antibiotic exposure protection through competitive exclusion of the protective species

    Laxminarayan R, Duse A, Wattal C, Zaidi AKM, Wertheim HFL, Sumpradit N, et al. Antibiotic resistance—the need for global solutions. Lancet Infect Dis. 2013;13:1057–98.PubMed 
    Article 

    Google Scholar 
    Murray CJ, Ikuta KS, Sharara F, Swetschinski L, Robles Aguilar G, Gray A, et al. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet. 2022;399:629–55.CAS 
    Article 

    Google Scholar 
    O’Neil J. Antimicrobial resistance: tackling a crisis for the health and wealth of nations. The review on antimicrobial resistance. 2014. https://amr-review.org/sites/default/files/AMRReviewPaper-Tacklingacrisisforthehealthandwealthofnations_1.pdf.Pang Z, Raudonis R, Glick BR, Lin T-J, Cheng Z. Antibiotic resistance in Pseudomonas aeruginosa: mechanisms and alternative therapeutic strategies. Biotechnol Adv. 2019;37:177–92.CAS 
    PubMed 
    Article 

    Google Scholar 
    Vandeplassche E, Tavernier S, Coenye T, Crabbé A. Influence of the lung microbiome on antibiotic susceptibility of cystic fibrosis pathogens. Eur Respir Rev. 2019;28:190041.PubMed 
    Article 

    Google Scholar 
    Wheatley R, Diaz Caballero J, Kapel N, de Winter FHR, Jangir P, Quinn A, et al. Rapid evolution and host immunity drive the rise and fall of carbapenem resistance during an acute Pseudomonas aeruginosa infection. Nat Commun. 2021;12:2460.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    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 
    Allison DG, Matthews MJ. Effect of polysaccharide interactions on antibiotic susceptibility of Pseudomonas aeruginosa. J Appl Bacteriol. 1992;73:484–8.CAS 
    PubMed 
    Article 

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

    Google Scholar 
    Bottery MJ, Matthews JL, Wood AJ, Johansen HK, Pitchford JW, Friman V-P. Inter-species interactions alter antibiotic efficacy in bacterial communities. ISME J. 2022;16:812–21.CAS 
    PubMed 
    Article 

    Google Scholar 
    Elias S, Banin E. Multi-species biofilms: living with friendly neighbors. FEMS Microbiol Rev. 2012;36:990–1004.CAS 
    PubMed 
    Article 

    Google Scholar 
    Hoffman LR, Deziel E, D’Argenio DA, Lepine F, Emerson J, McNamara S, et al. Selection for Staphylococcus aureus small-colony variants due to growth in the presence of Pseudomonas aeruginosa. Proc Natl Acad Sci USA. 2006;103:19890–5.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Molina-Santiago C, Daddaoua A, Fillet S, Duque E, Ramos J-L. Interspecies signalling: Pseudomonas putida efflux pump TtgGHI is activated by indole to increase antibiotic resistance: Antibiotic resistance. Environ Microbiol. 2014;16:1267–81.CAS 
    PubMed 
    Article 

    Google Scholar 
    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. https://doi.org/10.1128/mBio.00873-17.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    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 Biol Sci. 2009;276:3759–68.CAS 
    Article 

    Google Scholar 
    Ryan RP, Fouhy Y, Garcia BF, Watt SA, Niehaus K, Yang L, et al. Interspecies signalling via the Stenotrophomonas maltophilia diffusible signal factor influences biofilm formation and polymyxin tolerance in Pseudomonas aeruginosa. Mol Microbiol. 2008;68:75–86.CAS 
    PubMed 
    Article 

    Google Scholar 
    Sherrard LJ, McGrath SJ, McIlreavey L, Hatch J, Wolfgang MC, Muhlebach MS, et al. Production of extended-spectrum β -lactamases and the potential indirect pathogenic role of Prevotella isolates from the cystic fibrosis respiratory microbiota. Int J Antimicrob Agents. 2016;47:140–5.CAS 
    PubMed 
    Article 

    Google Scholar 
    Tognon M, Köhler T, Gdaniec BG, Hao Y, Lam JS, Beaume M, et al. Co-evolution with Staphylococcus aureus leads to lipopolysaccharide alterations in Pseudomonas aeruginosa. ISME J. 2017;11:2233–43.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Adamowicz EM, Muza M, Chacón JM, Harcombe WR. Cross-feeding modulates the rate and mechanism of antibiotic resistance evolution in a model microbial community of Escherichia coli and Salmonella enterica. PLOS Pathog. 2020;16:e1008700.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    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 
    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 
    Klümper U, Recker M, Zhang L, Yin X, Zhang T, Buckling A, et al. Selection for antimicrobial resistance is reduced when embedded in a natural microbial community. ISME J. 2019;13:2927–37.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Scheuerl T, Hopkins M, Nowell RW, Rivett DW, Barraclough TG, Bell T. Bacterial adaptation is constrained in complex communities. Nat Commun. 2020;11:754.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Sorg RA, Lin L, van Doorn GS, 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 
    Kulczycki LL, Kostuch M, Bellanti JA. A clinical perspective of cystic fibrosis and new genetic findings: relationship of CFTR mutations to genotype-phenotype manifestations. Am J Med Genet. 2003;116A:262–7.PubMed 
    Article 

    Google Scholar 
    Flume PA, Mogayzel PJ, Robinson KA, Rosenblatt RL, Quittell L, Marshall BC. Cystic fibrosis pulmonary guidelines: pulmonary complications: hemoptysis and pneumothorax. Am J Respir Crit Care Med. 2010;182:298–306.PubMed 
    Article 

    Google Scholar 
    Belkin RA, Henig NR, Singer LG, Chaparro C, Rubenstein RC, Xie SX, et al. Risk factors for death of patients with cystic fibrosis awaiting lung transplantation. Am J Respir Crit Care Med. 2006;173:659–66.PubMed 
    Article 

    Google Scholar 
    Martin C, Hamard C, Kanaan R, Boussaud V, Grenet D, Abély M, et al. Causes of death in French cystic fibrosis patients: the need for improvement in transplantation referral strategies! J Cyst Fibros. 2016;15:204–12.PubMed 
    Article 

    Google Scholar 
    Döring G, Conway SP, Heijerman HGM, Hodson ME, Høiby N, Smyth A, et al. Antibiotic therapy against Pseudomonas aeruginosa in cystic fibrosis: a European consensus. Eur Respir J. 2000;16:749.PubMed 
    Article 

    Google Scholar 
    Marshall B, Faro A, Brown W, Elbert A, Fink A, Cromwell E, et al. Patient registry, annual data report. Bethesda, Maryland: Cystic Fibrosis Foundation; 2020. https://www.cff.org/sites/default/files/2021-11/Patient-Registry-Annual-Data-Report.pdf.Vongthilath R, Richaud Thiriez B, Dehillotte C, Lemonnier L, Guillien A, Degano B, et al. Clinical and microbiological characteristics of cystic fibrosis adults never colonized by Pseudomonas aeruginosa: analysis of the French CF registry. PLOS ONE. 2019;14:e0210201.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Zolin A, Orenti A, Jung A, van Rens J. ECFSPR annual report 2019. Denmark: European Cystic Fibrosis Society Patient Registry; 2021. https://www.ecfs.eu/sites/default/files/general-content-files/working-groups/ecfs-patient-registry/ECFSPR_Report_2019_v1_16Feb2022.pdf.Conrad D, Haynes M, Salamon P, Rainey PB, Youle M, Rohwer F. Cystic fibrosis therapy: a community ecology perspective. Am J Respir Cell Mol Biol. 2013;48:150–6.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Filkins LM, Graber JA, Olson DG, Dolben EL, Lynd LR, Bhuju S, et al. Coculture of Staphylococcus aureus with Pseudomonas aeruginosa drives S. aureus towards fermentative metabolism and reduced viability in a cystic fibrosis model. J Bacteriol. 2015;197:2252–64.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Zhao J, Schloss PD, Kalikin LM, Carmody LA, Foster BK, Petrosino JF, et al. Decade-long bacterial community dynamics in cystic fibrosis airways. Proc Natl Acad Sci USA. 2012;109:5809–14.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ballestero S, Vírseda I, Escobar H, Suárez L, Baquero F. Stenotrophomonas maltophilia in cystic fibrosis patients. Eur J Clin Microbiol Infect Dis. 1995;14:728–9.CAS 
    PubMed 
    Article 

    Google Scholar 
    Gladman G, Connor PJ, Williams RF, David TJ. Controlled study of Pseudomonas cepacia and Pseudomonas maltophilia in cystic fibrosis. Arch Dis Child. 1992;67:192–5.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Goss CH. Association between Stenotrophomonas maltophilia and lung function in cystic fibrosis. Thorax. 2004;59:955–9.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Parkins MD, Floto RA. Emerging bacterial pathogens and changing concepts of bacterial pathogenesis in cystic fibrosis. J Cyst Fibros. 2015;14:293–304.CAS 
    PubMed 
    Article 

    Google Scholar 
    Goss CH, Otto K, Aitken ML, Rubenfeld GD. Detecting Stenotrophomonas maltophilia does not reduce survival of patients with cystic fibrosis. Am J Respir Crit Care Med. 2002;166:356–61.PubMed 
    Article 

    Google Scholar 
    Alonso A, Martínez JL. Multiple antibiotic resistance in Stenotrophomonas maltophilia. Antimicrob Agents Chemother. 1997;41:1140–2.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Zhang L, Li XZ, Poole K. Multiple antibiotic resistance in Stenotrophomonas maltophilia: involvement of a multidrug efflux system. Antimicrob Agents Chemother. 2000;44:287–93.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Abda EM, Krysciak D, Krohn-Molt I, Mamat U, Schmeisser C, Förstner KU, et al. Phenotypic heterogeneity affects Stenotrophomonas maltophilia K279a colony morphotypes and β-lactamase expression. Front Microbiol. 2015;6:1373.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 
    Walsh TR, Hall L, Assinder SJ, Nichols WW, Cartwright SJ, MacGowan AP, et al. Sequence analysis of the L1 metallo-β-lactamase from Xanthomonas maltophilia. Biochim Biophys Acta. 1994;1218:199–201.CAS 
    PubMed 
    Article 

    Google Scholar 
    Yang Z, Liu W, Cui Q, Niu W, Li H, Zhao X, et al. Prevalence and detection of Stenotrophomonas maltophilia carrying metallo-I2-lactamase blaL1 in Beijing, China. Front Microbiol. 2014;5:692.Kataoka D, Fujiwara H, Kawakami T, Tanaka Y, Tanimoto A, Ikawa S, et al. The indirect pathogenicity of Stenotrophomonas maltophilia. Int J Antimicrob Agents. 2003;22:601–6.CAS 
    PubMed 
    Article 

    Google Scholar 
    Winstanley C, O’Brien S, Brockhurst MA. Pseudomonas aeruginosa evolutionary adaptation and diversification in cystic fibrosis chronic lung infections. Trends Microbiol. 2016;24:327–37.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    McGuigan L, Callaghan M. The evolving dynamics of the microbial community in the cystic fibrosis lung. Environ Microbiol. 2015;17:16–28.PubMed 
    Article 

    Google Scholar 
    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 
    Mahrt N, Tietze A, Künzel S, Franzenburg S, Barbosa C, Jansen G, et al. Bottleneck size and selection level reproducibly impact evolution of antibiotic resistance. Nat Ecol Evol. 2021;5:1233–1242.Govaert L, Altermatt F, De Meester L, Leibold MA, McPeek MA, Pantel JH, et al. Integrating fundamental processes to understand eco‐evolutionary community dynamics and patterns. Funct Ecol. 2021;35:2138–55.Article 

    Google Scholar 
    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 
    Souza Barbosa F, Capra Pezzi L, Tsao M, Oliveira TF, Manoela Dias Macedo S, Schapoval EES, et al. Stability and degradation products of imipenem applying High‐Resolution Mass Spectrometry: an analytical study focused on solutions for infusion. Biomed Chromatogr. 2018;33:4471.Verpooten G, Verbist L, Buntinx A, Entwistle L, Jones K, Broe M. The pharmacokinetics of imipenem (thienamycin-formamidine) and the renal dehydropeptidase inhibitor cilastatin sodium in normal subjects and patients with renal failure. Br J Clin Pharmacol. 1984;18:183–93.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Li H, Luo Y-F, Williams BJ, Blackwell TS, Xie C-M. Structure and function of OprD protein in Pseudomonas aeruginosa: from antibiotic resistance to novel therapies. Int J Med Microbiol. 2012;302:63–8.CAS 
    PubMed 
    Article 

    Google Scholar 
    Kousser C, Clark C, Sherrington S, Voelz K, Hall RA. Pseudomonas aeruginosa inhibits Rhizopus microsporus germination through sequestration of free environmental iron. Sci Rep. 2019;9:5714.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Schalk IJ, Guillon L. Pyoverdine biosynthesis and secretion in Pseudomonas aeruginosa: implications for metal homeostasis: pyoverdine biosynthesis. Environ Microbiol. 2013;15:1661–73.CAS 
    PubMed 
    Article 

    Google Scholar 
    Duan X, Pan Y, Cai Z, Liu Y, Zhang Y, Liu M, et al. rpoS-mutation variants are selected in Pseudomonas aeruginosa biofilms under imipenem pressure. Cell Biosci. 2021;11:138.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Zhu K, Chen S, Sysoeva TA, You L. Universal antibiotic tolerance arising from antibiotic-triggered accumulation of pyocyanin in Pseudomonas aeruginosa. PLOS Biol. 2019;17:e3000573.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    El-Fouly MZ, Sharaf AM, Shahin AAM, El-Bialy HA, Omara AMA. Biosynthesis of pyocyanin pigment by Pseudomonas aeruginosa. J Radiat Res Appl Sci. 2015;8:36–48.CAS 
    Article 

    Google Scholar 
    Baron SS, Rowe JJ. Antibiotic action of pyocyanin. Antimicrob Agents Chemother. 1981;20:814–20.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kimura M, Ohta T. The average number of generations until fixation of a mutant gene in a finite population. Genetics. 1969;61:763–71.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Meirelles LA, Perry EK, Bergkessel M, Newman DK. Bacterial defenses against a natural antibiotic promote collateral resilience to clinical antibiotics. PLOS Biol. 2021;19:e3001093.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hall JPJ, Harrison E, Brockhurst MA. Competitive species interactions constrain abiotic adaptation in a bacterial soil community. Evol Lett. 2018;2:580–9.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Scanlan PD, Hall AR, Blackshields G, Friman V-P, Davis MR, Goldberg JB, et al. Coevolution with bacteriophages drives genome-wide host evolution and constrains the acquisition of abiotic-beneficial mutations. Mol Biol Evol. 2015;32:1425–35.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Finkel SE. Long-term survival during stationary phase: evolution and the GASP phenotype. Nat Rev Microbiol. 2006;4:113–20.CAS 
    PubMed 
    Article 

    Google Scholar 
    Gefen O, Fridman O, Ronin I, Balaban NQ. Direct observation of single stationary-phase bacteria reveals a surprisingly long period of constant protein production activity. Proc Natl Acad Sci. 2014;111:556–61.CAS 
    PubMed 
    Article 

    Google Scholar 
    Fang Z, Zhang L, Huang Y, Qing Y, Cao K, Tian G, et al. OprD mutations and inactivation in imipenem-resistant Pseudomonas aeruginosa isolates from China. Infect Genet Evol. 2014;21:124–8.CAS 
    PubMed 
    Article 

    Google Scholar 
    Hirabayashi A, Kato D, Tomita Y, Iguchi M, Yamada K, Kouyama Y, et al. Risk factors for and role of OprD protein in increasing minimal inhibitory concentrations of carbapenems in clinical isolates of Pseudomonas aeruginosa. J Med Microbiol. 2017;66:1562–72.CAS 
    PubMed 
    Article 

    Google Scholar 
    Huang H, Jeanteur D, Pattus F, Hancock REW. Membrane topology and site-specific mutagenesis of Pseudomonas aeruginosa porin OprD. Mol Microbiol. 1995;16:931–41.CAS 
    PubMed 
    Article 

    Google Scholar 
    Fournier D, Richardot C, Müller E, Robert-Nicoud M, Llanes C, Plésiat P, et al. Complexity of resistance mechanisms to imipenem in intensive care unit strains of Pseudomonas aeruginosa. J Antimicrob Chemother. 2013;68:1772–80.CAS 
    PubMed 
    Article 

    Google Scholar 
    Kao C-Y, Chen S-S, Hung K-H, Wu H-M, Hsueh P-R, Yan J-J, et al. Overproduction of active efflux pump and variations of OprD dominate in imipenem-resistant Pseudomonas aeruginosa isolated from patients with bloodstream infections in Taiwan. BMC Microbiol. 2016;16:107.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Ocampo-Sosa AA, Cabot G, Rodríguez C, Roman E, Tubau F, Macia MD, et al. Alterations of OprD in carbapenem-intermediate and -susceptible strains of Pseudomonas aeruginosa isolated from patients with bacteremia in a Spanish multicenter study. Antimicrob Agents Chemother. 2012;56:1703–13.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Shu J-C, Kuo A-J, Su L-H, Liu T-P, Lee M-H, Su I-N, et al. Development of carbapenem resistance in Pseudomonas aeruginosa is associated with OprD polymorphisms, particularly the amino acid substitution at codon 170. J Antimicrob Chemother. 2017;72:2489–95.CAS 
    PubMed 
    Article 

    Google Scholar 
    Pernet E, Guillemot L, Burgel P-R, Martin C, Lambeau G, Sermet-Gaudelus I, et al. Pseudomonas aeruginosa eradicates Staphylococcus aureus by manipulating the host immunity. Nat Commun. 2014;5:5105.CAS 
    PubMed 
    Article 

    Google Scholar 
    Briaud P, Camus L, Bastien S, Doléans-Jordheim A, Vandenesch F, Moreau K. Coexistence with Pseudomonas aeruginosa alters Staphylococcus aureus transcriptome, antibiotic resistance and internalization into epithelial cells. Sci Rep. 2019;9:16564.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Khare A, Tavazoie S. Multifactorial competition and resistance in a two-species bacterial system. PLOS Genet. 2015;11:e1005715.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Mashburn LM, Jett AM, Akins DR, Whiteley M. Staphylococcus aureus serves as an iron source for Pseudomonas aeruginosa during in vivo coculture. J Bacteriol. 2005;187:554–66.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Cirz RT, O’Neill BM, Hammond JA, Head SR, Romesberg FE. Defining the Pseudomonas aeruginosa SOS response and its role in the global response to the antibiotic ciprofloxacin. J Bacteriol. 2006;188:7101–10.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    García-Contreras R, Nuñez-López L, Jasso-Chávez R, Kwan BW, Belmont JA, Rangel-Vega A, et al. Quorum sensing enhancement of the stress response promotes resistance to quorum quenching and prevents social cheating. ISME J. 2015;9:115–25.PubMed 
    Article 
    CAS 

    Google Scholar 
    Moradali MF, Ghods S, Rehm BHA. Pseudomonas aeruginosa lifestyle: a paradigm for adaptation, survival, and persistence. Front Cell Infect Microbiol 2017;7:39.Vogt SL, Green C, Stevens KM, Day B, Erickson DL, Woods DE, et al. The stringent response is essential for Pseudomonas aeruginosa virulence in the rat lung agar bead and Drosophila melanogaster feeding models of infection. Infect Immun. 2011;79:4094–104.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Baron SS, Terranova G, Rowe JJ. Molecular mechanism of the antimicrobial action of pyocyanin. Curr Microbiol. 1989;18:223–30.CAS 
    Article 

    Google Scholar 
    Castañeda-Tamez P, Ramírez-Peris J, Pérez-Velázquez J, Kuttler C, Jalalimanesh A, Saucedo-Mora MÁ, et al. Pyocyanin restricts social cheating in Pseudomonas aeruginosa. Front Microbiol. 2018;9:1348.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Fontoura R, Spada JC, Silveira ST, Tsai SM, Brandelli A. Purification and characterization of an antimicrobial peptide produced by Pseudomonas sp. strain 4B. World J Microbiol Biotechnol. 2009;25:205–13.CAS 
    Article 

    Google Scholar 
    Hassan HM, Fridovich I. Mechanism of the antibiotic action pyocyanine. J Bacteriol. 1980;141:156–63.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Machan ZA, Pitt TL, White W, Watson D, Taylor GW, Cole PJ, et al. Interaction between Pseudomonas aeruginosa and Staphylococcus aureus: description of an antistaphylococcal substance. J Med Microbiol. 1991;34:213–7.CAS 
    PubMed 
    Article 

    Google Scholar 
    Raji El Feghali PA, Nawas T. Pyocyanin: a powerful inhibitor of bacterial growth and biofilm formation. Madridge J Case Rep Stud. 2018;3:101–7.Article 

    Google Scholar 
    Saha S, Thavasi R, Jayalakshmi S. Phenazine pigments from Pseudomonas aeruginosa and their application as antibacterial agent and food colourants. Res J Microbiol. 2008;3:122–8.CAS 
    Article 

    Google Scholar 
    Schiessl KT, Hu F, Jo J, Nazia SZ, Wang B, Price-Whelan A, et al. Phenazine production promotes antibiotic tolerance and metabolic heterogeneity in Pseudomonas aeruginosa biofilms. Nat Commun. 2019;10:762.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Jagmann N, Brachvogel H-P, Philipp B. Parasitic growth of Pseudomonas aeruginosa in co-culture with the chitinolytic bacterium Aeromonas hydrophila: parasitic growth of Pseudomonas aeruginosa. Environ Microbiol. 2010;12:1787–802.CAS 
    PubMed 
    Article 

    Google Scholar 
    Noto MJ, Burns WJ, Beavers WN, Skaar EP. Mechanisms of pyocyanin toxicity and genetic determinants of resistance in Staphylococcus aureus. J Bacteriol. 2017;199:00221–17.Venkataraman A, Rosenbaum MA, Perkins SD, Werner JJ, Angenent LT. Metabolite-based mutualism between Pseudomonas aeruginosa PA14 and Enterobacter aerogenes enhances current generation in bioelectrochemical systems. Energy Environ Sci. 2011;4:4550.CAS 
    Article 

    Google Scholar 
    Waite RD, Qureshi MR, Whiley RA. Modulation of behaviour and virulence of a high alginate expressing Pseudomonas aeruginosa strain from cystic fibrosis by oral commensal bacterium Streptococcus anginosus. PLOS ONE. 2017;12:e0173741.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Whooley MA, McLoughlin AJ. The regulation of pyocyanin production in Pseudomonas aeruginosa. Eur J Appl Microbiol Biotechnol. 1982;15:161–6.CAS 
    Article 

    Google Scholar 
    Elbargisy RM. Optimization of nutritional and environmental conditions for pyocyanin production by urine isolates of Pseudomonas aeruginosa. Saudi J Biol Sci. 2021;28:993–1000.CAS 
    PubMed 
    Article 

    Google Scholar 
    Gupta S, Laskar N, Kadouri DE. Evaluating the effect of oxygen concentrations on antibiotic sensitivity, growth, and biofilm formation of human pathogens. Microbiol Insights. 2016;9. https://doi.org/10.4137/MBI.S40767.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Worlitzsch D, Tarran R, Ulrich M, Schwab U, Cekici A, Meyer KC, et al. Effects of reduced mucus oxygen concentration in airway Pseudomonas infections of cystic fibrosis patients. J Clin Investig. 2002;109:317–25.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Skurnik D, Roux D, Cattoir V, Danilchanka O, Lu X, Yoder-Himes DR, et al. Enhanced in vivo fitness of carbapenem-resistant oprD mutants of Pseudomonas aeruginosa revealed through high-throughput sequencing. Proc Natl Acad Sci USA. 2013;110:20747–52.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Higgins S, Heeb S, Rampioni G, Fletcher MP, Williams P, Cámara M. Differential regulation of the phenazine biosynthetic operons by quorum sensing in Pseudomonas aeruginosa PAO1-N. Front Cell Infect Microbiol. 2018;8:252.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Dragoš A, Martin M, Falcón García C, Kricks L, Pausch P, Heimerl T, et al. Collapse of genetic division of labour and evolution of autonomy in pellicle biofilms. Nat Microbiol. 2018;3:1451–60.PubMed 
    Article 
    CAS 

    Google Scholar 
    Cuthbertson L, Walker AW, Oliver AE, Rogers GB, Rivett DW, Hampton TH, et al. Lung function and microbiota diversity in cystic fibrosis. Microbiome. 2020;8:45.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Rogers GB, Carroll MP, Serisier DJ, Hockey PM, Jones G, Bruce KD. Characterization of bacterial community diversity in cystic fibrosis lung infections by use of 16S ribosomal DNA terminal restriction fragment length polymorphism profiling. J Clin Microbiol. 2004;42:5176–83.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Workentine ML, Sibley CD, Glezerson B, Purighalla S, Norgaard-Gron JC, Parkins MD, et al. Phenotypic heterogeneity of Pseudomonas aeruginosa populations in a cystic fibrosis patient. PLoS ONE. 2013;8:e60225.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Valdezate S. Persistence and variability of Stenotrophomonas maltophilia in cystic fibrosis patients, Madrid, 1991-8. Emerg Infect Dis. 2001;7:113–22.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    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 
    Jeon YD, Jeong WY, Kim MH, Jung IY, Ahn MY, Ann HW, et al. Risk factors for mortality in patients with Stenotrophomonas maltophilia bacteremia. Medicine. 2016;95:e4375.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Sherrard LJ, Tunney MM, Elborn JS. Antimicrobial resistance in the respiratory microbiota of people with cystic fibrosis. Lancet Lond Engl. 2014;384:703–13.CAS 
    Article 

    Google Scholar 
    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 

    Google Scholar 
    Jelsbak L, Johansen HK, Frost A-L, Thøgersen R, Thomsen LE, Ciofu O, et al. Molecular epidemiology and dynamics of Pseudomonas aeruginosa populations in lungs of cystic fibrosis patients. Infect Immun. 2007;75:2214–24.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Yeung ATY, Parayno A, Hancock REW. Mucin promotes rapid surface motility in Pseudomonas aeruginosa. mBio. 2012;3:300073–12.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;64:3857.Hill DB, Long RF, Kissner WJ, Atieh E, Garbarine IC, Markovetz MR, et al. Pathological mucus and impaired mucus clearance in cystic fibrosis patients result from increased concentration, not altered pH. Eur Respir J. 2018;52:1801297.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Benoni G, Cuzzolin L, Bertrand C, Puchetti V, Velo G. Imipenem kinetics in serum, lung tissue and pericardial fluid in patients undergoing thoracotomy. J Antimicrob Chemother. 1987;20:725–8.CAS 
    PubMed 
    Article 

    Google Scholar 
    Radhakrishnan M, Jaganath A, Rao GSU, Kumari HBV. Nebulized imipenem to control nosocomial pneumonia caused by Pseudomonas aeruginosa. J Crit Care. 2008;23:148–50.CAS 
    PubMed 
    Article 

    Google Scholar 
    Wenzler E, Fraidenburg DR, Scardina T, Danziger LH. Inhaled antibiotics for gram-negative respiratory infections. Clin Microbiol Rev. 2016;29:581–632.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    The European Committee on Antimicrobial Susceptibility Testing. Breakpoint tables for interpretation of MICs and zone diameters.Version 12.0, 2022. http://www.eucast.org.Kang D, Revtovich AV, Chen Q, Shah KN, Cannon CL, Kirienko NV. Pyoverdine-dependent virulence of Pseudomonas aeruginosa isolates from cystic fibrosis patients. Front Microbiol. 2019;10:2048.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Martin LW, Reid DW, Sharples KJ, Lamont IL. Pseudomonas siderophores in the sputum of patients with cystic fibrosis. BioMetals. 2011;24:1059–67.CAS 
    PubMed 
    Article 

    Google Scholar 
    Caldwell CC, Chen Y, Goetzmann HS, Hao Y, Borchers MT, Hassett DJ, et al. Pseudomonas aeruginosa exotoxin pyocyanin causes cystic fibrosis airway pathogenesis. Am J Pathol. 2009;175:2473–88.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    O’Loughlin CT, Miller LC, Siryaporn A, Drescher K, Semmelhack MF, Bassler BL. A quorum-sensing inhibitor blocks Pseudomonas aeruginosa virulence and biofilm formation. Proc Natl Acad Sci USA. 2013;110:17981–6.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Sass G, Nazik H, Penner J, Shah H, Ansari SR, Clemons KV, et al. Studies of Pseudomonas aeruginosa mutants indicate pyoverdine as the central factor in inhibition of Aspergillus fumigatus biofilm. J Bacteriol. 2018;200:00345–17.Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.CAS 
    PubMed 
    PubMed Central 
    Article 

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

    Google Scholar 
    Deatherage DE, Barrick JE. Identification of mutations in laboratory-evolved microbes from next-generation sequencing data using breseq. In: Sun L, Shou W, editors. Engineering and Analyzing Multicellular Systems. New York, NY: Springer New York; 2014. p. 165–88.Robinson JT, Thorvaldsdóttir H, Winckler W, Guttman M, Lander ES, Getz G, et al. Integrative genomics viewer. Nat Biotechnol. 2011;29:24–6.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Thorvaldsdottir H, Robinson JT, Mesirov JP. Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief Bioinform. 2013;14:178–92.CAS 
    PubMed 
    Article 

    Google Scholar  More

  • in

    STEM learning communities promote friendships but risk academic segmentation

    Xie, Y., Fang, M. & Shauman, K. STEM education. Annu. Rev. Sociol. 41, 331–357 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Chen, X. STEM attrition: College students’ paths into and out of STEM fields. National Center for Education Statistics. Retrieved from http://ies.ed.gov/pubsearch/pubsinfo.asp?pubid=2014001rev. Accessed 22 September 2021.Huang G, Taddese N, Walter E (2000) Entry and persistence of women and minorities in college science and engineering education. National Center for Education Statistics. Retrieved from https://eric.ed.gov/?id=ED566411. Accessed 22 September 2021.Hurtado, S., Eagan, K., & Chang, M. Degrees of Success: Bachelor’s Degree Completion Rates among Initial STEM Majors (Higher Education Research Institute, Los Angeles, CA) (2010).National Science Foundation, Broadening Participation Working Group (2014) Pathways to broadening participation in response to the CEOSE 2011–2012 recommendation. National Science Foundation. Retrieved from https://www.nsf.gov/pubs/2015/nsf15037/nsf15037.pdf. Accessed 22 Sep 2021.James, S. M. & Singer, S. R. From the NSF: The National Science Foundation’s investments in broadening participation in science, technology, engineering, and mathematics education through research and capacity building. CBE Life Sci. Educ. 15(3), 1–8 (2016).Article 

    Google Scholar 
    Smith, B. L., MacGregor, J., Matthews, R. & Gabelnick, F. Learning communities: Reforming undergraduate education (Jossey-Bass, 2004).
    Google Scholar 
    Andrade, M. S. Learning communities: Examining positive outcomes. J. Coll. Stud. Ret. 9(1), 1–20 (2007).Article 

    Google Scholar 
    Maton, K. I., Pollard, S. A., McDougall Weise, T. V. & Hrabowski, F. A. Meyerhoff Scholars Program: A strengths-based, institution-wide approach to increasing diversity in science, technology, engineering, and mathematics. Mt Sinai J. Med. 79(5), 610–623 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Dagley, M., Georgiopoulos, M., Reece, A. & Young, C. Increasing retention and graduation rates through a STEM learning community. J. Coll. Stud. Ret. 18(2), 167–182 (2016).Article 

    Google Scholar 
    National Survey of Student Engagement (2015) Engagement Insights: Survey Findings on the Quality of Undergraduate Education—Annual Results 2015 (Bloomington, IN).Tinto, V. Leaving college: Rethinking the causes and cures of student attrition (University of Chicago Press, 1987).
    Google Scholar 
    Tinto, V. Learning better together: The impact of learning communities on student success. Higher Educ. Monogr. Ser. 1(8), 1–8 (2003).
    Google Scholar 
    Otto, S., Evins, M. A., Boyer-Pennington, M. & Brinthaupt, T. M. Learning communities in higher education: Best practices. Journal of Student Success and Retention 2(1), 1–20 (2015).
    Google Scholar 
    Boda, Z., Elmer, T., Vörös, A. & Stadtfeld, C. Short-term and long-term effects of a social network intervention on friendships among university students. Sci. Rep. 10(1), 1–2 (2020).Article 
    CAS 

    Google Scholar 
    Hotchkiss, J. L., Moore, R. E. & Pitts, M. M. Freshman learning communities, college performance, and retention. Educ. Econ. 14(2), 197–210 (2006).Article 

    Google Scholar 
    Whalen, D. F. & Shelley, M. C. Academic success for STEM and non-STEM majors. J. STEM Educ. 11(1), 45–60 (2010).
    Google Scholar 
    Xu, D., Solanki, S., McPartlan, P. & Sato, B. EASEing students into college: The impact of multidimensional support for underprepared students. Educ. Res. 47(7), 435–450 (2018).Article 

    Google Scholar 
    Jaffee, D., Carle, A., Phillips, R. & Paltoo, L. Intended and unintended consequences of first-year learning communities: An initial investigation. J. First-Year Exp. Stud. Trans. 20(1), 53–70 (2008).
    Google Scholar 
    Tinto, V. & Goodsell, A. Freshman interest groups and the first-year experience: Constructing student communities in a large university. J. First Year Exp. Stud. Trans. 6(1), 7–28 (1994).
    Google Scholar 
    Domizi, D. Student perceptions about their informal learning experiences in a first-year residential learning community. J. First Year Exp. Stud. Transit. 20(1), 97–110 (2008).
    Google Scholar 
    Lee, D. S. & Lemieux, T. Regression discontinuity designs in economics. J. Econ. Lit. 2, 281–355 (2010).Article 

    Google Scholar 
    Jacob, R., Zhu, P., Somers, M.A., & Bloom, H. A Practical Guide to Regression Discontinuity (MDRC, New York, NY, 2012).Hays, R. B. & Oxley, D. Social network development and functioning during a life transition. J. Pers. Soc. Psychol. 50(2), 305–313 (1986).CAS 
    PubMed 
    Article 

    Google Scholar 
    Freeman, T. M., Anderman, L. H. & Jensen, J. M. Sense of belonging in college freshmen at the classroom and campus levels. J. Exp. Educ. 75(3), 203–220 (2007).Article 

    Google Scholar 
    Zumbrunn, S., McKim, C., Buhs, E. & Hawley, L. R. Support, belonging, motivation, and engagement in the college classroom: A mixed method study. Instr. Sci. 42(5), 661–684 (2014).Article 

    Google Scholar 
    Hasan, S. & Bagde, S. The mechanics of social capital and academic performance in an Indian college. Am. Sociol. Rev. 78(6), 1009–1032 (2013).Article 

    Google Scholar 
    Stadtfeld, C., Vörös, A., Elmer, T., Boda, Z. & Raabe, I. J. Integration in emerging social networks explains academic failure and success. Proc. Natl. Acad. Sci. USA 116(3), 792–797 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Kraemer, B. A. The academic and social integration of Hispanic students into college. Rev. High Educ. 20(2), 163–179 (1997).Article 

    Google Scholar 
    Nora, A. Two-year colleges and minority students’ educational aspirations: Help or hindrance. Higher Educ. Handb. Theory Res. 9(3), 212–247 (1993).
    Google Scholar 
    McCabe, J.M. Connecting in College: How Friendship Networks Matter for Academic and Social Success (University of Chicago Press, Chicago, IL, 2016).Felten, P., & Lambert, L. M. Relationship-rich Education: How Human Connections Drive Success in College (Johns Hopkins University Press, Baltimore, MD, 2020).Hallinan, M. T. The peer influence process. Stud. Educ. Eval. 7(3), 285–306 (1981).Article 

    Google Scholar 
    Thomas, S. L. Ties that bind: A social network approach to understanding student integration and persistence. J. Higher Educ. 71(5), 591–615 (2000).
    Google Scholar 
    Turetsky, K. M., Purdie-Greenaway, V., Cook, J. E., Curley, J. P. & Cohen, G. L. A psychological intervention strengthens students’ peer social networks and promotes persistence in STEM. Sci. Adv. 6(45), 1–10 (2020).Article 

    Google Scholar 
    Dokuka, S., Valeeva, D. & Yudkevich, M. How academic achievement spreads: The role of distinct social networks in academic performance diffusion. PLoS ONE 15(7), 1–16 (2020).Article 
    CAS 

    Google Scholar 
    Epstein, J. L. & Karweit, N. (eds) Friends in school: Patterns of selection and influence in secondary schools (Academic Press, 1983).
    Google Scholar 
    Feld, S. L. The focused organization of social ties. AJS 86(5), 1015–1035 (1981).
    Google Scholar 
    Rivera, M. T., Soderstrom, S. B. & Uzzi, B. Dynamics of dyads in social networks: Assortative, relational, and proximity mechanisms. Annu. Rev. Sociol. 36, 91–115 (2010).Article 

    Google Scholar 
    Mollenhorst, G., Volker, B. & Flap, H. Changes in personal relationships: How social contexts affect the emergence and discontinuation of relationships. Soc. Netw. 37, 65–80 (2014).Article 

    Google Scholar 
    Thomas, R. J. Sources of friendship and structurally induced homophily across the life course. Sociol Perspect 62(6), 822–843 (2019).Article 

    Google Scholar 
    Kubitschek, W. N. & Hallinan, M. T. Tracking and students’ friendships. Soc. Psychol. Q 46, 1–5 (1998).Article 

    Google Scholar 
    Frank, K. A., Muller, C. & Mueller, A. S. The embeddedness of adolescent friendship nominations: The formation of social capital in emergent network structures. AJS 119(1), 216–253 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    Kossinets, G. & Watts, D. J. Origins of homophily in an evolving social network. AJS 115(2), 405–450 (2009).
    Google Scholar 
    Wimmer, A. & Lewis, K. Beyond and below racial homophily: ERG models of a friendship network documented on Facebook. AJS 116(2), 583–642 (2010).PubMed 

    Google Scholar 
    Hallinan, M. T. & Sørensen, A. B. Ability grouping and student friendships. Am. Educ. Res. J. 51, 485–499 (1985).Article 

    Google Scholar 
    Leszczensky, L. & Pink, S. Ethnic segregation of friendship networks in school: Testing a rational-choice argument of differences in ethnic homophily between classroom-and grade-level networks. Soc. Netw. 42, 18–26 (2015).Article 

    Google Scholar 
    DiMaggio, P. & Garip, F. Network effects and social inequality. Annu. Rev. Sociol. 54, 93–118 (2012).Article 

    Google Scholar 
    Johnson, A. M. ‘“I can turn it on when i need to”’: Pre-college Integration, culture, and peer academic engagement among black and Latino/a engineering Students. Sociol. Educ. 56, 1–20 (2019).Article 

    Google Scholar 
    Perry, B. L., Pescosolido, B. A. & Borgatti, S. P. Egocentric network analysis: Foundations, methods, and models (Cambridge University Press, 2018).Book 

    Google Scholar 
    Wasserman, S. & Faust, K. Social network analysis: Methods and applications (Cambridge University Press, 1994).MATH 
    Book 

    Google Scholar 
    Hartup, W. W. & Stevens, N. Friendships and adaptation in the life course. Psychol. Bull. 121(3), 355 (1997).Article 

    Google Scholar 
    Vaquera, E. & Kao, G. Do you like me as much as I like you? Friendship reciprocity and its effects on school outcomes among adolescents. Soc. Sci. Res. 37(1), 55–72 (2008).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Imbens, G. W. & Lemieux, T. Regression discontinuity designs: A guide to practice. J. Econom. 142(2), 615–635 (2008).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    Imbens, G. W. & Angrist, J. D. Identification and estimation of local average treatment effects. Econometrica 62(2), 467–475 (1994).MATH 
    Article 

    Google Scholar 
    Robins, G., Pattison, P., Kalish, Y. & Lusher, D. An introduction to exponential random graph (p*) models for social networks. Soc. Netw. 29(2), 173–191 (2007).Article 

    Google Scholar 
    Handcock, M. S., Hunter, D. R., Butts, C. T., Goodreau, S. M. & Morris, M. Statnet: Software tools for the representation, visualization, analysis and simulation of network data. J. Stat. Softw. 24(1), 1548–7660 (2008).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Calonico, S., Cattaneo, M. D. & Titiunik, R. Optimal data-driven regression discontinuity plots. J. Am. Stat. Assoc. 110(512), 1753–1769 (2015).MathSciNet 
    CAS 
    MATH 
    Article 

    Google Scholar 
    Duxbury, S. W. The problem of scaling in exponential random graph models. Sociol. Methods Res. https://doi.org/10.1177/0049124120986178:1-39 (2021).MathSciNet 
    Article 

    Google Scholar 
    McPherson, M., Smith-Lovin, L. & Cook, J. M. Birds of a feather: Homophily in social networks. Annu. Rev. Sociol. 27(1), 415–444 (2001).Article 

    Google Scholar 
    Kadushin, C. Understanding social networks: Theories, concepts, and findings (Oxford University Press, 2012).
    Google Scholar 
    Flashman, J. Academic achievement and its impact on friend dynamics. Sociol. Educ. 85(1), 61–80 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Carrell, S. E., Sacerdote, B. I. & West, J. E. From natural variation to optimal policy? The importance of endogenous peer group formation. Econometrica 81(3), 855–882 (2013).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    Cox, A. B. Cohorts, ‘“siblings”,’ and mentors: Organizational structures and the creation of social capital. Sociol. Educ. 90(1), 47–63 (2017).Article 

    Google Scholar 
    Valente, T. W. Network interventions. Science 337(6090), 49–53 (2012).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Nunn, L. M. College belonging: How first-year and first-generation students navigate campus life (Rutgers University Press, 2021).Book 

    Google Scholar 
    Garlick, R. Academic peer effects with different group assignment policies: Residential tracking versus random assignment. Am. Econ. J. Appl. Econ. 10(3), 345–369 (2018).Article 

    Google Scholar 
    Carrell, S. E., Fullerton, R. L. & West, J. E. Does your cohort matter? Measuring peer effects in college achievement. J. Labor. Econ. 27(3), 439–464 (2009).Article 

    Google Scholar 
    Lomi, A., Snijders, T. A., Steglich, C. E. & Torló, V. J. Why are some more peer than others? Evidence from a longitudinal study of social networks and individual academic performance. Soc. Sci. Res. 40(6), 1506–1520 (2011).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Poldin, O., Valeeva, D. & Yudkevich, M. Which peers matter: How social ties affect peer-group effects. Res. High Educ. 57(4), 448–468 (2016).Article 

    Google Scholar 
    Raabe, I. J., Boda, Z. & Stadtfeld, C. The social pipeline: How friend influence and peer exposure widen the STEM gender gap. Sociol. Educ. 92(2), 105–123 (2019).Article 

    Google Scholar 
    Burt, R. S. Structural holes and good ideas. AJS 110(2), 349–399 (2004).
    Google Scholar 
    Oakes, J. Keeping track: How schools structure inequality (Yale University Press, 2005).
    Google Scholar 
    Park JJ et al. (2021) Who are you studying with? The role of diverse friendships in STEM and corresponding inequality. Res. High Educ. https://doi.org/10.1007/s11162-021-09638-8.Marsden, P. V. & Campbell, K. E. Measuring tie strength. Soc. Forces 63(2), 482–501 (1984).Article 

    Google Scholar 
    Mattie, H., Engø-Monsen, K., Ling, R. & Onnela, J. P. Understanding tie strength in social networks using a local “bow tie” framework. Sci. Rep. 8(1), 1–9 (2018).CAS 
    Article 

    Google Scholar 
    Sørensen, A. B. Organizational differentiation of students and educational opportunity. Sociol. Educ. 43(4), 355–376 (1970).Article 

    Google Scholar  More

  • in

    Competition for pollinators destabilizes plant coexistence

    Potts, S. et al. Global pollinator declines: trends, impacts and drivers. Trends Ecol. Evol. 25, 345–353 (2010).Article 

    Google Scholar 
    Thomann, M., Imbert, E., Devaux, C. & Cheptou, P.-O. Flowering plants under global pollinator decline. Trends Plant Sci. 18, 353–359 (2013).CAS 
    Article 

    Google Scholar 
    Pauw, A. Can pollination niches facilitate plant coexistence? Trends Ecol. Evol. 28, 30–37 (2013).Article 

    Google Scholar 
    Johnson, C. A. How mutualisms influence the coexistence of competing species. Ecology 102, e03346 (2021).PubMed 

    Google Scholar 
    Tilman, D. Resource Competition and Community Structure (Princeton Univ. Press, 1982).Tilman, D. Constraints and tradeoffs: toward a predictive theory of competition and succession. Oikos 58, 3–15 (1990).Article 

    Google Scholar 
    Chesson, P. Mechanisms of maintenance of species diversity. Annu. Rev. Ecol. Syst. 31, 343–358 (2000).Article 

    Google Scholar 
    Mitchell, R. J., Flanagan, R. J., Brown, B. J., Waser, N. M. & Karron, J. D. New frontiers in competition for pollination. Ann. Bot. 103, 1403–1413 (2009).Article 

    Google Scholar 
    Morales, C. L. & Traveset, A. A meta-analysis of impacts of alien vs. native plants on pollinator visitation and reproductive success of co-flowering native plants. Ecol. Lett. 12, 716–728 (2009).Article 

    Google Scholar 
    Jones, E. I., Bronstein, J. L. & Ferrière, R. The fundamental role of competition in the ecology and evolution of mutualisms. Ann. N. Y. Acad. Sci. 1256, 66–88 (2012).ADS 
    Article 

    Google Scholar 
    Memmott, J., Waser, N. M. & Price, M. V. Tolerance of pollination networks to species extinctions. Proc. R. Soc. Lond. B 271, 2605–2611 (2004).Article 

    Google Scholar 
    Bascompte, J. & Jordano, P. Mutualistic Networks (Princeton University Press, 2013).Bascompte, J. Mutualism and biodiversity. Curr. Biol. 29, R467–R470 (2019).CAS 
    Article 

    Google Scholar 
    Chesson, P. Updates on mechanisms of maintenance of species diversity. J. Ecol. 106, 1773–1794 (2018).Article 

    Google Scholar 
    Levin, D. A. & Anderson, W. W. Competition for pollinators between simultaneously flowering species. Am. Nat. 104, 455–467 (1970).Article 

    Google Scholar 
    Kunin, W. & Iwasa, Y. Pollinator foraging strategies in mixed floral arrays: density effects and floral constancy. Theor. Popul. Biol. 49, 232–263 (1996).CAS 
    Article 

    Google Scholar 
    Lanuza, J. B., Bartomeus, I. & Godoy, O. Opposing effects of floral visitors and soil conditions on the determinants of competitive outcomes maintain species diversity in heterogeneous landscapes. Ecol. Lett. 21, 865–874 (2018).Article 

    Google Scholar 
    Thomson, J. Spatial and temporal components of resource assessment by flower-feeding insects. J. Anim. Ecol. 50, 49–59 (1981).Article 

    Google Scholar 
    Knight, T. M. et al. Reflections on, and visions for, the changing field of pollination ecology. Ecol. Lett. 21, 1282–1295 (2018).MathSciNet 
    CAS 
    Article 

    Google Scholar 
    Biella, P. et al. Experimental loss of generalist plants reveals alterations in plant-pollinator interactions and a constrained flexibility of foraging. Sci. Rep. 9, 7376 (2019).ADS 
    Article 

    Google Scholar 
    Brosi, B. & Briggs, H. M. Single pollinator species losses reduce floral fidelity and plant reproductive function. Proc. Natl Acad. Sci. USA 110, 13044–13048 (2013).ADS 
    CAS 
    Article 

    Google Scholar 
    Addicott, J. F. in The Biology of Mutualism (ed. Boucher, D. H.) 217–247 (Croom Helm, 1985).Knight, T. M. et al. Pollen limitation of plant reproduction: pattern and process. Annu. Rev. Ecol. Evol. Syst. 36, 467–497 (2005).Article 

    Google Scholar 
    Bartomeus, I., Saavedra, S., Rohr, R. P. & Godoy, O. Experimental evidence of the importance of multitrophic structure for species persistence. Proc. Natl Acad. Sci. USA 118, e2023872118 (2021).CAS 
    Article 

    Google Scholar 
    Levine, J. M., Bascompte, J., Adler, P. B. & Allesina, S. Beyond pairwise mechanisms of species coexistence in complex communities. Nature 546, 56–64 (2017).ADS 
    CAS 
    Article 

    Google Scholar 
    Saavedra, S. et al. A structural approach for understanding multispecies coexistence. Ecol. Monogr. 87, 470–486 (2017).Article 

    Google Scholar 
    Rinella, M. J., Strong, D. J. & Vermeire, L. T. Omitted variable bias in studies of plant interactions. Ecology 101, e03020 (2020).Article 

    Google Scholar  More

  • in

    Comparison of entomological impacts of two methods of intervention designed to control Anopheles gambiae s.l. via swarm killing in Western Burkina Faso

    Study sites and swarm characterizationThe survey was conducted in 10 villages in south-western Burkina Faso especially around the district of Bobo-Dioulasso, Santitougou (N11° 17′ 16″, W4° 13′ 04″), Kimidougou (N11° 17′ 53″; W4° 14′ 11″), Nastenga (N10.96871; W003.23477), Zeyama (N10.87638; W 003.26145), Mogobasso (N11° 25′ 31″, W4° 06′ 08″), Synbekuy (N11° 53′ 28″, W3° 44′ 02″), Ramatoulaye (N11° 33′ 39″, W3° 57′ 05″) Syndombokuy (N11° 53′ 06″, W3° 43′ 19″), Lampa (N11.16464; W 003.6374) et Syndounkuy (N11.14541; W 003.05141) (Fig. 1). All villages are located north of Bobo-Dioulasso, on the national road 10 (N10), ranged from 20 and 90 km. The region is characterised by wooded savannah located in south-western Burkina Faso, and the mean annual rainfall is about 1200 mm. The rainy season extends from May to October and the dry season from November to April. Malaria transmission in the area extends from June to November. However, residual transmission may occur beyond this period in specific locations. An. gambiae is the major malaria vector following by An. coluzzii and An. Arabiensis. Villages were chosen to represent similar ecological and entomological settings, they are middle sized and relatively isolated from one another.Figure 1Localization of the study sites in south-western Burkina Faso. This map was created under QGIS version 2.18 Las Palmas. link: https://changelog.qgis.org/en/qgis/version/2.18.0/Full size imageSpray Application Against Mosquito Swarms (SAMS) consisted of spraying diluted insecticide (Actellic 50: tap water with 1:20 concentration) at dusk by trained volunteer teams. They used the innovative technology of targeted swarm spraying with handheld sprayers and conventional broadcast space spray with backpack sprayers to achieve maximum effect. The spraying activities were conducted in eight of the ten villages. The target swarm spray was used in the four villages Kimidougou, Nastenga, Ramatoulaye and Syndombokuy. The broadcast space spray was applied in four other villages, Zeyama, Mogobasso, Lampa and Syndounkuy. The two remaining villages, Santidougou and Synbekuy were chosen as controls (Fig. 1). In each village, the potential swarm markers and the positive swarm sites were identified and geo-referenced using GPS. All concessions also were geo-referenced and labelled using paint.Procedure of the interventionTargeted swam spraying using handheld sprayersTargeted swarm spraying was carried out in four villages. Members of each team and volunteers from the selected villages were trained to target the swarms and apply an appropriate amount of spray each time. After the pre-intervention phase, all swarm sites scattered through the villages were repaired and swarm characteristics recorded. At 30 min before dusk (the estimated swarming time), a volunteer was placed in each compound with a sprayer. The objective of each volunteer was to destroy any swarm in the compound by applying insecticide with the handheld sprayer (Fig. 2A,B). Screening of the compound was continued for about 30 min until it was dark and no mosquitoes were visible. A single operator was able to effectively target 5 to 10 swarms per spray evening, depending on the distribution of swarms across the village. Spraying was carried out for 10 successive days throughout each village. The period of spraying approximately covered the period of pre-imaginal mosquito stages and was renewed after 45 days. The quantity of insecticide used was measured daily, in order to determine with precision the total quantity of insecticide used during targeted spraying.Figure 2Volunteer spraying swarms using handheld sprayers (A,B). Backpack spraying activities (C,D).Full size imageConventional broadcast spraying using Backpack sprayersThe broadcast spraying was also carried out in 4 villages but, unlike the targeted spraying, there was no direct targeting of swarms. At swarming time (estimated around 30 min at dusk) two volunteers with backpack sprayers ran through the entire village along paths between the compounds while spraying insecticide (Fig. 2C,D). As with the targeted spraying procedure, the broadcast spraying was carried out for 10 successive days in all 4 villages simultaneously, and spraying recommenced after 45 days. The quantity of insecticide used was measured daily, in order to determine with precision the total quantity of insecticide used during targeted spraying.Evaluation of the interventionA year prior to the intervention, baseline entomological data was collected in both villages to estimate mosquito density, human biting rate, female insemination rate, age structure of females and entomological inoculation rate29. The same parameters were evaluated immediately before and after intervention. The pre- and post-intervention evaluation of the abovementioned parameters were carried in both control and intervention villages at the same time. In both pre-intervention and post-intervention phases, two methods of mosquito collection were performed in each village, the human landing catch (HLC), indoor and outdoor in 4 houses for 4 successive nights, the pyrethroid spray catch (PSC) in the same10 houses and 10 randomly selected houses. To identify these, all houses in each village were coded and these codes were used to randomly select those to be sampled. All sampled sites were mapped using a global positioning system (GPS). Collected anopheline mosquitoes were sorted by taxonomic status, physiological status, and sex. Approximately, the ovaries of 200 females/month/village (100 females indoor and 100 females outdoor) were dissected to determine the physiological age, and parous females were subsequently subjected to ELISA assays to determine Plasmodium sporozoite rates. Data produced from indoor and outdoor mosquito collections were then used to estimate mosquito densities, their spatial distribution, produce a map identifying hotspots where the highest mosquito densities and biting occurred within the village, female age structure and quantify the intensity of malaria transmission. The impact of the spray was measured to see how it affected each of these parameters in the intervention villages compared to the controls.Statistical analysisThe resting mosquito abundance was assessed as the number of mosquitoes per house, the human biting rate assessed as the number of bites per person per night, the parity rate assessed as the percentage of parous females, and the insemination rate assessed as the percentage of the inseminated females. The list above defined the key entomological parameters to determine the dynamic of An. gambie s.l. populations and malaria transmission. The generalized estimating equation (GEE) method was used to estimate population averaged effect of intervention on various outcome measurements. As the GEE models do not require distributional assumptions but only specification of the mean and variance structure, they are more robust against misspecification of higher-order features of the data, and are useful when the main interest is in population averaged effects of an intervention or treatment. However, because they do not use a full likelihood model, they cannot be used for individual-specific inference30,31. Despite this shortcoming, their robustness to different types of correlation structures in the data (due to temporal ordering of measurements, or other hierarchical structure in data) makes them attractive for analyses of this type. GEE models were run in R version 3.6.232, using the package “geepack”33 for three datasets on insemination and parity rate, number of bites per person per night (NBPN), and density of adult male and female mosquitoes. To clean and plot the data the “tidyverse” family of R packages34 were used.Ethical considerationsThis study did not involve human patients. The full protocol of the study was submitted to the Institutional Ethics Committee of the “Institut de Recherche en Sciences de la Sante” for review and approval (A17-2016/CEIRES). In accordance with the approval, presentations of the project were given to the study site villagers and requests for their participation were made. During these visits the objectives, protocol and expected results were explained and discussed, as well as the implications for the households willing to take part in this study. A written consent form was signed or marked with fingerprint by the head of the households before any activity could take place in his compound. Insecticides used in this study are approved for use by the Burkina Faso insecticide regulation authority. More

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    A long-term reconstructed TROPOMI solar-induced fluorescence dataset using machine learning algorithms

    Canadell, J. G. et al. Contributions to accelerating atmospheric CO2 growth from economic activity, carbon intensity, and efficiency of natural sinks. Proc. Natl. Acad. Sci. 104, 18866–18870 (2007).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Beer, C. et al. Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate. Science 329, 834–838 (2010).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Park, T. et al. Changes in timing of seasonal peak photosynthetic activity in northern ecosystems. Global. Change. Biol. 25, 2382–2395 (2019).ADS 

    Google Scholar 
    Wang, T. et al. Emerging negative impact of warming on summer carbon uptake in northern ecosystems. Nat. Commun. 9, 5391 (2018).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Farquhar, G. D., Von Caemmerer, S. & Berry, J. A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149, 78–90 (1980).CAS 
    PubMed 

    Google Scholar 
    Chen, J. M. et al. Effects of foliage clumping on the estimation of global terrestrial gross primary productivity. Global. Biogeochem. Cy 26, GB1019 (2012).ADS 

    Google Scholar 
    De Pury, D. G. G. & Farquhar, G. D. Simple scaling of photosynthesis from leaves to canopies without the errors of big-leaf models. Plant Cell Environ. 20, 537–557 (1997).
    Google Scholar 
    Zhang, Y. et al. Development of a coupled carbon and water model for estimating global gross primary productivity and evapotranspiration based on eddy flux and remote sensing data. Agr. Forest. Meteorol. 223, 116–131 (2016).ADS 

    Google Scholar 
    Monteith, J. L. Climate and the efficiency of crop production in Britain. Philos. Trans. R. Soc. Lond., B, Biol. Sci. 281, 277–294 (1977).ADS 

    Google Scholar 
    Running, S. W. et al. A continuous satellite-derived measure of global terrestrial primary production. Bioscience. 54, 547–560 (2004).
    Google Scholar 
    Yuan, W. et al. Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data. Remote Sens. Environ. 114, 1416–1431 (2010).ADS 

    Google Scholar 
    Ruimy, A., Dedieu, G. & Saugier, B. TURC: A diagnostic model of continental gross primary productivity and net primary productivity. Global. Biogeochem. Cy 10, 269–285 (1996).ADS 
    CAS 

    Google Scholar 
    Jung, M. et al. The FLUXCOM ensemble of global land-atmosphere energy fluxes. Sci. Data 6, 190076 (2019).
    Google Scholar 
    Bodesheim, P., Jung, M., Gans, F., Mahecha, M. D. & Reichstein, M. Upscaled diurnal cycles of land–atmosphere fluxes: a new global half-hourly data product. Earth Syst. Sci. Data 10, 1327–1365 (2018).ADS 

    Google Scholar 
    Joiner, J. et al. Estimation of Terrestrial Global Gross Primary Production (GPP) with Satellite Data-Driven Models and Eddy Covariance Flux Data. Remote Sens. 10, 1346 (2018).ADS 

    Google Scholar 
    Xiao, J. et al. Data-driven diagnostics of terrestrial carbon dynamics over North America. Agr. Forest. Meteorol. 197, 142–157 (2014).ADS 

    Google Scholar 
    Ichii, K. et al. New data-driven estimation of terrestrial CO2 fluxes in Asia using a standardized database of eddy covariance measurements, remote sensing data, and support vector regression. J. Geophys. Res. Biogeosci. 122, 767–795 (2017).CAS 

    Google Scholar 
    Cai, W. et al. Improved estimations of gross primary production using satellite-derived photosynthetically active radiation. J. Geophys. Res. Biogeosci. 119, 110–123 (2014).
    Google Scholar 
    Ma, J., Yan, X., Dong, W. & Chou, J. Gross primary production of global forest ecosystems has been overestimated. Sci. Rep. 5, 10820 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Cai, W. et al. Large Differences in Terrestrial Vegetation Production Derived from Satellite-Based Light Use Efficiency Models. Remote Sens. 6, 8945–8965 (2014).ADS 

    Google Scholar 
    Jung, M. et al. Uncertainties of modeling gross primary productivity over Europe: A systematic study on the effects of using different drivers and terrestrial biosphere models. Global. Biogeochem. Cy 21, GB4021 (2007).ADS 

    Google Scholar 
    Yuan, W. et al. Global comparison of light use efficiency models for simulating terrestrial vegetation gross primary production based on the LaThuile database. Agr. Forest. Meteorol. 192-193, 108–120 (2014).ADS 

    Google Scholar 
    Frankenberg, C. et al. New global observations of the terrestrial carbon cycle from GOSAT: Patterns of plant fluorescence with gross primary productivity. Geophys. Res. Lett. 38, L17706 (2011).ADS 

    Google Scholar 
    Joiner, J. et al. Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: methodology, simulations, and application to GOME-2. Atmos Meas Tech 6, 2803–2823 (2013).
    Google Scholar 
    Frankenberg, C. et al. Prospects for chlorophyll fluorescence remote sensing from the Orbiting Carbon Observatory-2. Remote Sens. Environ. 147, 1–12 (2014).ADS 

    Google Scholar 
    Joiner, J. et al. Filling-in of near-infrared solar lines by terrestrial fluorescence and other geophysical effects: simulations and space-based observations from SCIAMACHY and GOSAT. Atmos Meas Tech 5, 809–829 (2012).CAS 

    Google Scholar 
    Köhler, P. et al. Global Retrievals of Solar‐Induced Chlorophyll Fluorescence With TROPOMI: First Results and Intersensor Comparison to OCO‐2. Geophys. Res. Lett. 45, 10,456–410,463 (2018).
    Google Scholar 
    Joiner, J. et al. First observations of global and seasonal terrestrial chlorophyll fluorescence from space. Biogeosciences 8, 637–651 (2011).ADS 
    CAS 

    Google Scholar 
    Guanter, L. et al. Retrieval and global assessment of terrestrial chlorophyll fluorescence from GOSAT space measurements. Remote Sens. Environ. 121, 236–251 (2012).ADS 

    Google Scholar 
    Du, S. et al. Retrieval of global terrestrial solar-induced chlorophyll fluorescence from TanSat satellite. Sci. Bull. 63, 1502–1512 (2018).
    Google Scholar 
    Baker, N. R. Chlorophyll fluorescence: a probe of photosynthesis in vivo. Annu. Rev. Plant Biol. 59, 89–113 (2008).CAS 
    PubMed 

    Google Scholar 
    Drusch, M. et al. The FLuorescence EXplorer Mission Concept—ESA’s Earth Explorer 8. Ieee. T. Geosci. Remote 55, 1273–1284 (2017).ADS 

    Google Scholar 
    Guanter, L. et al. The TROPOSIF global sun-induced fluorescence dataset from the Sentinel-5P TROPOMI mission. Earth Syst. Sci. Data, 13, 5423–5440 (2021).Roesch, A. Use of Moderate-Resolution Imaging Spectroradiometer bidirectional reflectance distribution function products to enhance simulated surface albedos. J. Geophys. Res. 109 (2004).Wan, Z. New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product. Remote Sens. Environ. 140, 36–45 (2014).ADS 

    Google Scholar 
    Sulla-Menashe, D., Gray, J. M., Abercrombie, S. P. & Friedl, M. A. Hierarchical mapping of annual global land cover 2001 to present: The MODIS Collection 6 Land Cover product. Remote Sens. Environ. 222, 183–194 (2019).ADS 

    Google Scholar 
    Su, W., Charlock, T. P., Rose, F. G. & Rutan, D. Photosynthetically active radiation from Clouds and the Earth’s Radiant Energy System (CERES) products. J. Geophys. Res. 112 (2007).Still, C. J., Berry, J. A., Collatz, G. J. & Defries, R. S. Global distribution of C3and C4vegetation: Carbon cycle implications. Global. Biogeochem. Cy 17, 6-1-6-14 (2003).Zhang, Y. et al. Spatio‐temporal convergence of maximum daily light‐use efficiency based on radiation absorption by canopy chlorophyll. Geophys. Res. Lett. 45, 3508–3519 (2018).ADS 

    Google Scholar 
    Zhang, Z. et al. The potential of satellite FPAR product for GPP estimation: An indirect evaluation using solar-induced chlorophyll fluorescence. Remote Sens. Environ. 240, 111686 (2020).ADS 

    Google Scholar 
    Baker, N. R. Chlorophyll Fluorescence: A Probe of Photosynthesis In Vivo. Annu. Rev. Plant. Biol. 59, 89–113 (2008).CAS 
    PubMed 

    Google Scholar 
    Du, S., Liu, L., Liu, X. & Hu, J. Response of canopy solar-induced chlorophyll fluorescence to the absorbed photosynthetically active radiation absorbed by chlorophyll. Remote Sens. 9, 911 (2017).ADS 

    Google Scholar 
    Rossini, M. et al. Analysis of Red and Far-Red Sun-Induced Chlorophyll Fluorescence and Their Ratio in Different Canopies Based on Observed and Modeled Data. Remote Sens. 8, 412 (2016).ADS 

    Google Scholar 
    Verrelst, J. et al. Global sensitivity analysis of the SCOPE model: What drives simulated canopy-leaving sun-induced fluorescence? Remote Sens. Environ. 166, 8–21 (2015).ADS 

    Google Scholar 
    Zhang, Q. et al. Estimating light absorption by chlorophyll, leaf and canopy in a deciduous broadleaf forest using MODIS data and a radiative transfer model. Remote Sens. Environ. 99, 357–371 (2005).ADS 

    Google Scholar 
    Zhang, Y., Joiner, J., Alemohammad, S. H., Zhou, S. & Gentine, P. A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks. Biogeosciences 15, 5779–5800 (2018).ADS 
    CAS 

    Google Scholar 
    Li, X. & Xiao, J. A Global, 0.05-Degree Product of Solar-Induced Chlorophyll Fluorescence Derived from OCO-2, MODIS, and Reanalysis Data. Remote Sens. 11, 517 (2019).ADS 

    Google Scholar 
    Yu, L., Wen, J., Chang, C. Y., Frankenberg, C. & Sun, Y. High‐Resolution Global Contiguous SIF of OCO‐2. Geophys. Res. Lett. 46, 1449–1458 (2019).ADS 

    Google Scholar 
    Ma, Y., Liu, L., Chen, R., Du, S. & Liu, X. Generation of a Global Spatially Continuous TanSat Solar-Induced Chlorophyll Fluorescence Product by Considering the Impact of the Solar Radiation Intensity. Remote Sens. 12, 2167 (2020).ADS 

    Google Scholar 
    Gentine, P. & Alemohammad, S. H. Reconstructed Solar‐Induced Fluorescence: A Machine Learning Vegetation Product Based on MODIS Surface Reflectance to Reproduce GOME‐2 Solar‐Induced Fluorescence. Geophys. Res. Lett. 45, 3136–3146 (2018).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wen, J. et al. A framework for harmonizing multiple satellite instruments to generate a long-term global high spatial-resolution solar-induced chlorophyll fluorescence (SIF). Remote Sens. Environ. 239, 111644 (2020).ADS 

    Google Scholar 
    Yang, X. et al. Solar‐induced chlorophyll fluorescence that correlates with canopy photosynthesis on diurnal and seasonal scales in a temperate deciduous forest. Geophys. Res. Lett. 42, 2977–2987 (2015).ADS 
    CAS 

    Google Scholar 
    Hain, C. R., Crow, W. T., Mecikalski, J. R., Anderson, M. C. & Holmes, T. An intercomparison of available soil moisture estimates from thermal infrared and passive microwave remote sensing and land surface modeling. J. Geophys. Res. 116, D15107 (2011).ADS 

    Google Scholar 
    Anderson, M. C., Norman, J. M., Mecikalski, J. R., Otkin, J. A. & Kustas, W. P. A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 2. Surface moisture climatology. J. Geophys. Res. 112, D11112 (2007).ADS 

    Google Scholar 
    Scherrer, D., Bader, M. K.-F. & Körner, C. Drought-sensitivity ranking of deciduous tree species based on thermal imaging of forest canopies. Agr. Forest. Meteorol. 151, 1632–1640 (2011).ADS 

    Google Scholar 
    Duveiller, G. et al. A spatially downscaled sun-induced fluorescence global product for enhanced monitoring of vegetation productivity. Earth Syst. Sci. Data 12, 1101–1116 (2020).ADS 

    Google Scholar 
    Zhang, Y. et al. A global moderate resolution dataset of gross primary production of vegetation for 2000–2016. Sci. Data 4, 170165 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Chen, T. & Guestrin, C. in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 785-794 (Association for Computing Machinery).Hengl, T. et al. SoilGrids250m: Global gridded soil information based on machine learning. PLOS ONE 12, e0169748 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Li, Y., Li, M., Li, C. & Liu, Z. Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms. Sci. Rep. 10, 9952 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Tan, W., Wei, C., Lu, Y. & Xue, D. Reconstruction of All-Weather Daytime and Nighttime MODIS Aqua-Terra Land Surface Temperature Products Using an XGBoost Approach. Remote Sens. 13, 4723 (2021).ADS 

    Google Scholar 
    Adnan, M., Alarood, A. A. S., Uddin, M. I. & Ur Rehman, I. Utilizing grid search cross-validation with adaptive boosting for augmenting performance of machine learning models. PeerJ Comput. Sci. 8, e803 (2022).PubMed 
    PubMed Central 

    Google Scholar 
    Chen, X. A long-term reconstructed TROPOMI solar-induced fluorescence dataset using machine learning algorithms. figshare https://doi.org/10.6084/m9.figshare.19336346.v2 (2022).Guanter, L. et al. Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proc. Natl. Acad. Sci. 111, E1327–E1333 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pierrat, Z. et al. Diurnal and seasonal dynamics of solar‐induced chlorophyll fluorescence, vegetation indices, and gross primary productivity in the boreal forest. J. Geophys. Res. Biogeosci., e2021JG006588 (2022).Magney, T. S. et al. Mechanistic evidence for tracking the seasonality of photosynthesis with solar-induced fluorescence. Proc. Natl. Acad. Sci. 116, 11640–11645 (2019).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Grossmann, K. et al. PhotoSpec: A new instrument to measure spatially distributed red and far-red Solar-Induced Chlorophyll Fluorescence. Remote Sens. Environ. 216, 311–327 (2018).ADS 

    Google Scholar 
    Li, Z. et al. Solar-induced chlorophyll fluorescence and its link to canopy photosynthesis in maize from continuous ground measurements. Remote Sens. Environ. 236, 111420 (2020).ADS 

    Google Scholar 
    Magney, T. S. et al. Mechanistic evidence for tracking the seasonality of photosynthesis with solar-induced fluorescence. Proc. Natl. Acad. Sci. 201900278 (2019).Wei, X., Wang, X., Wei, W. & Wan, W. Use of Sun-Induced Chlorophyll Fluorescence Obtained by OCO-2 and GOME-2 for GPP Estimates of the Heihe River Basin, China. Remote Sens. 10, 2039 (2018).ADS 

    Google Scholar 
    Walther, S. et al. Satellite chlorophyll fluorescence measurements reveal large‐scale decoupling of photosynthesis and greenness dynamics in boreal evergreen forests. Global. Change. Biol. 22, 2979–2996 (2016).ADS 

    Google Scholar 
    Köhler, P., Guanter, L. & Joiner, J. A linear method for the retrieval of sun-induced chlorophyll fluorescence from GOME-2 and SCIAMACHY data. Atmos. Meas. Tech. 8, 2589–2608 (2015).
    Google Scholar 
    Parazoo, N. C. et al. Towards a Harmonized Long‐Term Spaceborne Record of Far‐Red Solar‐Induced Fluorescence. J. Geophys. Res. Biogeosci. 124, 2518–2539 (2019).
    Google Scholar 
    Pastorello, G. et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci. Data 7 (2020).Reichstein, M. et al. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Global. Change. Biol. 11, 1424–1439 (2005).ADS 

    Google Scholar 
    Lasslop, G. et al. Separation of net ecosystem exchange into assimilation and respiration using a light response curve approach: critical issues and global evaluation. Global. Change. Biol. 16, 187–208 (2010).ADS 

    Google Scholar 
    Chen, C. et al. China and India lead in greening of the world through land-use management. Nat. Sustain. 2, 122–129 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Tong, X. et al. Increased vegetation growth and carbon stock in China karst via ecological engineering. Nat. Sustain. 1, 44–50 (2018).
    Google Scholar 
    Miettinen, J., Shi, C. & Liew, S. C. Deforestation rates in insular Southeast Asia between 2000 and 2010. Global. Change. Biol. 17, 2261–2270 (2011).ADS 

    Google Scholar 
    De, S. V. et al. Land use patterns and related carbon losses following deforestation in South America. Environ. Res. Lett. 10, 124004 (2015).ADS 

    Google Scholar 
    Huete, A. et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83, 195–213 (2002).ADS 

    Google Scholar 
    Still, C. J., Berry, J. A., Collatz, G. J. & Defries, R. S. ISLSCP II C4 Vegetation Percentage, ORNL Distributed Active Archive Center, https://doi.org/10.3334/ORNLDAAC/932 (2009).Pierrat, Z. & Stutz, J. Tower-based solar-induced fluorescence and vegetation index data for Southern Old Black Spruce forest, Zenodo, https://doi.org/10.5281/ZENODO.5884643 (2022).Magney, T. et al. Canopy and needle scale fluorescence data from Niwot Ridge, Colorado 2017-2018, CaltechDATA, https://doi.org/10.22002/D1.1231 (2019).Wan, Z., Hook, S. & Hulley, G. MOD11C1 MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 0.05Deg CMG V006, NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MODIS/MOD11C1.006 (2015).Friedl, M. & Sulla-Menashe, D. MCD12C1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 0.05Deg CMG V006, NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MODIS/MCD12C1.006 (2015).Schaaf, C. & Wang, Z. MCD43C4 MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global 0.05Deg CMG V006, NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MODIS/MCD43C4.006 (2015).Doelling, D. CERES Level 3 SYN1DEG-DAYTerra+Aqua HDF4 file – Edition 4A, NASA Langley Atmospheric Science Data Center DAAC, https://doi.org/10.5067/TERRA+AQUA/CERES/SYN1DEGDAY_L3.004A (2017). More

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    Repatriation of a historical North Atlantic right whale habitat during an era of rapid climate change

    Descamps, S. et al. Diverging phenological responses of Arctic seabirds to an earlier spring. Glob. Change Biol. 25, 4081–4091 (2019).ADS 
    Article 

    Google Scholar 
    Ramp, C., Delarue, J., Palsbøll, P. J., Sears, R. & Hammond, P. S. Adapting to a warmer ocean—seasonal shift of baleen whale movements over three decades. PLoS ONE 10, e0121374 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Insley, S. J., Halliday, W. D., Mouy, X. & Diogou, N. Bowhead whales overwinter in the Amundsen Gulf and Eastern Beaufort Sea. R. Soc. Open Sci. 8, 1 (2021).Article 

    Google Scholar 
    Heide-Jørgensen, M. P., Laidre, K. L., Quakenbush, L. T. & Citta, J. J. The Northwest Passage opens for bowhead whales. Biol. Lett. 8, 270–273 (2012).PubMed 
    Article 

    Google Scholar 
    Durant, J., Hjermann, D., Ottersen, G. & Stenseth, N. Climate and the match or mismatch between predator requirements and resource availability. Clim. Res. 33, 271–283 (2007).Article 

    Google Scholar 
    Staudinger, M. D. et al. It’s about time: A synthesis of changing phenology in the Gulf of Maine ecosystem. Fish. Oceanogr. 28, 532–566 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Miller-Rushing, A. J., Høye, T. T., Inouye, D. W. & Post, E. The effects of phenological mismatches on demography. Philos. Trans. R. Soc. B Biol. Sci. 365, 3177–3186 (2010).Article 

    Google Scholar 
    Edwards, M. & Richardson, A. J. Impact of climate change on marine pelagic phenology and trophic mismatch. Nature 430, 881–884 (2004).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Record, N. et al. Rapid climate-driven circulation changes threaten conservation of endangered North Atlantic right whales. Oceanography 32, 1 (2019).Article 

    Google Scholar 
    MacLeod, C. Global climate change, range changes and potential implications for the conservation of marine cetaceans: a review and synthesis. Endanger. Species Res. 7, 125–136 (2009).Article 

    Google Scholar 
    Learmonth, J. A. et al. Potential effects of climate change on marine mammals. Oceanogr. Mar. Biol. Annu. Rev. 44, 431–464 (2006).
    Google Scholar 
    Pinsky, M. L., Worm, B., Fogarty, M. J., Sarmiento, J. L. & Levin, S. A. Marine taxa track local climate velocities. Science 341, 1239–1242 (2013).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Pershing, A. J. et al. Slow adaptation in the face of rapid warming leads to collapse of the Gulf of Maine cod fishery. Science 350, 809–812 (2015).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Gulf of Maine Research Institute. Gulf of Maine Warming Update: 2021 the Hottest Year on Record. (2022).Saba, V. S. et al. Enhanced warming of the Northwest Atlantic Ocean under climate change. J. Geophys. Res. Oceans 121, 118–132 (2016).ADS 
    Article 

    Google Scholar 
    Friedland, K. D. et al. Trends and change points in surface and bottom thermal environments of the US Northeast Continental Shelf Ecosystem. Fish. Oceanogr. 29, 396–414 (2020).Article 

    Google Scholar 
    Nye, J., Link, J., Hare, J. & Overholtz, W. Changing spatial distribution of fish stocks in relation to climate and population size on the Northeast United States continental shelf. Mar. Ecol. Prog. Ser. 393, 111–129 (2009).ADS 
    Article 

    Google Scholar 
    Kress, S. W., Shannon, P. & O’Neal, C. Recent changes in the diet and survival of Atlantic puffin chicks in the face of climate change and commercial fishing in midcoast Maine, USA. FACETS 1, 27–43 (2017).Article 

    Google Scholar 
    Davis, G. E. et al. Exploring movement patterns and changing distributions of baleen whales in the western North Atlantic using a decade of passive acoustic data. Glob. Change Biol. 26, 4812–4840 (2020).ADS 
    Article 

    Google Scholar 
    Pace, R. M., Corkeron, P. J. & Kraus, S. D. State-space mark-recapture estimates reveal a recent decline in abundance of North Atlantic right whales. Ecol. Evol. 7, 8730–8741 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Meyer-Gutbrod, E. L. & Greene, C. H. Uncertain recovery of the North Atlantic right whale in a changing ocean. Glob. Change Biol. 24, 455–464 (2018).ADS 
    Article 

    Google Scholar 
    Sorochan, K. A. et al. North Atlantic right whale (Eubalaena glacialis) and its food: (II) interannual variations in biomass of Calanus spp. on western North Atlantic shelves. J. Plankton Res. 41, 687–708 (2019).Article 

    Google Scholar 
    Friedland, K. D. et al. Spring bloom dynamics and zooplankton biomass response on the US Northeast Continental Shelf. Cont. Shelf Res. 102, 47–61 (2015).ADS 
    Article 

    Google Scholar 
    Meyer-Gutbrod, E., Greene, C., Davies, K. & Johns, D. Ocean regime shift is driving collapse of the North Atlantic right whale population. Oceanography 34, 22–31 (2021).Article 

    Google Scholar 
    Knowlton, A., Hamilton, P., Marx, M., Pettis, H. & Kraus, S. Monitoring North Atlantic right whale Eubalaena glacialis entanglement rates: A 30 yr retrospective. Mar. Ecol. Prog. Ser. 466, 293–302 (2012).ADS 
    Article 

    Google Scholar 
    Davies, K. T. A. & Brillant, S. W. Mass human-caused mortality spurs federal action to protect endangered North Atlantic right whales in Canada. Mar. Policy 104, 157–162 (2019).Article 

    Google Scholar 
    Kraus, S. D. & Rolland, R. M. Right whales in the urban ocean. in The urban whale: North Atlantic right whales at the crossroads 1–38 (Harvard University Press, 2010). https://doi.org/10.2307/j.ctv1pnc1q9.Winn, H. E., Price, C. A. & Sorensen, P. W. The distributional biology of the right whale (Eubalaena glacialis) in the western North Atlantic. Rep. Int. Whal. Comm. Spec. 10, 129–138 (1986).
    Google Scholar 
    Mayo, C. A. & Marx, M. K. Surface foraging behaviour of the North Atlantic right whale, Eubalaena glacialis, and associated zooplankton characteristics. Can. J. Zool. 68, 2214–2220 (1990).Article 

    Google Scholar 
    Mayo, C. A. et al. Distribution, demography, and behavior of North Atlantic right whales (Eubalaena glacialis) in Cape Cod Bay, Massachusetts, 1998–2013. Mar. Mammal Sci. 34, 979–996 (2018).Article 

    Google Scholar 
    Pendleton, D. E. et al. Regional-scale mean copepod concentration indicates relative abundance of North Atlantic right whales. Mar. Ecol. Prog. Ser. 378, 211–225 (2009).ADS 
    Article 

    Google Scholar 
    Kenney, R. D., Winn, H. E. & Macaulay, M. C. Cetaceans in the Great South Channel, 1979–1989: right whale (Eubalaena glacialis). Cont. Shelf Res. 15, 385–414 (1995).ADS 
    Article 

    Google Scholar 
    Brown, M. W. et al. Recovery strategy for the North Atlantic right whale (Eubalaena glacialis) in Atlantic Canadian waters. in Species at risk act recovery strategy series (Fisheries and Oceans Canada, 2009).Weinrich, M. T., Kenney, R. D. & Hamilton, P. K. Right whales (Eubalaena glacialis) on Jeffreys Ledge: a habitat of unrecognized importance?. Mar. Mammal Sci. 16, 326–337 (2000).Article 

    Google Scholar 
    Cole, T. et al. Evidence of a North Atlantic right whale Eubalaena glacialis mating ground. Endanger. Species Res. 21, 55–64 (2013).Article 

    Google Scholar 
    Ganley, L., Brault, S. & Mayo, C. What we see is not what there is: estimating North Atlantic right whale Eubalaena glacialis local abundance. Endanger. Species Res. 38, 101–113 (2019).Article 

    Google Scholar 
    Simard, Y., Roy, N., Giard, S. & Aulanier, F. North Atlantic right whale shift to the Gulf of St. Lawrence in 2015, revealed by long-term passive acoustics. Endanger. Species Res. 40, 271–284 (2019).Article 

    Google Scholar 
    Leiter, S. et al. North Atlantic right whale Eubalaena glacialis occurrence in offshore wind energy areas near Massachusetts and Rhode Island, USA. Endanger. Species Res. 34, 45–59 (2017).Article 

    Google Scholar 
    Stone, K. M. et al. Distribution and abundance of cetaceans in a wind energy development area offshore of Massachusetts and Rhode Island. J. Coast. Conserv. 21, 527–543 (2017).Article 

    Google Scholar 
    Vanderlaan, A., Taggart, C., Serdynska, A., Kenney, R. & Brown, M. Reducing the risk of lethal encounters: Vessels and right whales in the Bay of Fundy and on the Scotian Shelf. Endanger. Species Res. 4, 283–297 (2008).Article 

    Google Scholar 
    National Marine Fisheries Service. Endangered and threatened species; critical habitat for endangered North Atlantic right whale. Fed. Regist. 80, 9314–9345 (2015).
    Google Scholar 
    National Marine Fisheries Service. Taking of marine mammals incidental to commercial fishing operations; Atlantic large whale take reduction plan regulations; Atlantic coastal fisheries cooperative management act provisions; American lobster fishery. Fed. Regist. 85, 86878–86900 (2020).
    Google Scholar 
    Reeves, R. R., Breiwick, J. M. & Mitchell, E. D. History of whaling and estimated kill of right whales, Balaena glacialis, in the Northeastern United States, 1620–1924. Mar. Fish. Rev. 36, 1 (1999).
    Google Scholar 
    Allen, G. M. The whalebone whales of New England. Mem. Boston Soc. Nat. Hist. 8, 107–322 (1915).ADS 

    Google Scholar 
    CETAP (Cetacean and Turtle Assessment Program). A characterization of marine mammals and turtles in the mid- and North- Atlantic areas of the U.S. Outer Continental Shelf, final report. (1982).Kenney, R. D. & Vigness-Raposa, K. J. Marine mammals and sea turtles of Narragansett Bay, Block Island Sound, Rhode Island Sound, and nearby waters: An analysis of existing data for the Rhode Island Ocean Special Area Management Plan. in Rhode Island Ocean Special Area Management Plan; Volume 2 Appendix A: Technical Reports for the Rhode Island Ocean Special Area Management Plan. 701–1037 (Rhode Island Coastal Resources Management Council, Wakefield, RI, 2010).Pendleton, D. et al. Weekly predictions of North Atlantic right whale Eubalaena glacialis habitat reveal influence of prey abundance and seasonality of habitat preferences. Endanger. Species Res. 18, 147–161 (2012).MathSciNet 
    Article 

    Google Scholar 
    Kraus, S. D., Kenney, R. D. & Thomas, L. A framework for studying the effects of offshore wind development on marine mammals and turtles. (2019). Report prepared for the Massachusetts Clean Energy Center, Boston, MA, and the Bureau of Ocean Energy Management, Office of Renewable Energy Programs, Sterling, VA. Anderson Cabot Center for Ocean Life, New England Aquarium, Boston, MA. 48 pp.Quintana-Rizzo, E. et al. Residency, demographics, and movement patterns of North Atlantic right whales Eubalaena glacialis in an offshore wind energy development area in southern New England, USA. Endanger. Species Res. 45, 251–268 (2021).Article 

    Google Scholar 
    Taylor, J. K. D., Kenney, R. D., LeRoi, D. J. & Kraus, S. D. Automated vertical photography for detecting pelagic species in multitaxon aerial surveys. Mar. Technol. Soc. J. 48, 36–48 (2014).Article 

    Google Scholar 
    Hamilton, P. K., Knowlton, A. R. & Marx, M. K. Right whales tell their own stories: the photo-identification catalog. in The urban whale: North Atlantic right whales at the crossroads 75–104 (Harvard University Press, 2010).Buckland, S. T., Anderson, D. R., Burnham, K. P. & Laake, J. L. Distance sampling: Estimating abundance of biological populations Vol. 50 (Chapman and Hall, 1993).MATH 
    Book 

    Google Scholar 
    R: The R Project for Statistical Computing. https://www.r-project.org/.Miller, D. L., Rexstad, E., Thomas, L., Marshall, L. & Laake, J. L. Distance Sampling in R. J. Stat. Softw. 89, 1–28 (2019).Article 

    Google Scholar 
    Eberhardt, L. L., Chapman, D. G. & Gilbert, J. R. A review of marine mammal census methods. Wildl. Monogr. 1, 3–46 (1979).
    Google Scholar 
    Durant, S. M. et al. Long-term trends in carnivore abundance using distance sampling in Serengeti National Park, Tanzania: Serengeti carnivore trends. J. Appl. Ecol. 48, 1490–1500 (2011).Article 

    Google Scholar 
    Reeves, R. R. & Mitchell, E. The Long Island, New York, right whale fishery: 1650–1924. Rep. Int. Whal. Comm. 10, 201–220 (1986).
    Google Scholar 
    Davis, G. E. et al. Long-term passive acoustic recordings track the changing distribution of North Atlantic right whales (Eubalaena glacialis) from 2004 to 2014. Sci. Rep. 7, 13460 (2017).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Jackson, J. et al. Have whales returned to a historical hotspot of industrial whaling? The pattern of southern right whale Eubalaena australis recovery at South Georgia. Endanger. Species Res. 43, 323–339 (2020).Article 

    Google Scholar 
    Carroll, E. L. et al. Reestablishment of former wintering grounds by New Zealand southern right whales. Mar. Mammal Sci. 30, 206–220 (2014).Article 

    Google Scholar 
    Charlton, C. et al. Southern right whales (Eubalaena australis) return to a former wintering calving ground: Fowlers Bay, South Australia. Mar. Mammal Sci. 35, 1438–1462 (2019).Article 

    Google Scholar 
    Garrigue, C. et al. Searching for humpback whales in a historical whaling hotspot of the Coral Sea, South Pacific. Endanger. Species Res. 42, 67–82 (2020).Article 

    Google Scholar 
    Clapham, P. J., Aguilar, A. & Hatch, L. T. Determining spatial and temporal scales for management: lessons from whaling. Mar. Mammal Sci. 24, 183–201 (2008).Article 

    Google Scholar 
    Watkins, W. A. & Schevill, W. E. Right whale feeding and baleen rattle. J. Mammal. 57, 58–66 (1976).Article 

    Google Scholar 
    Beardsley, R. C. et al. Spatial variability in zooplankton abundance near feeding right whales in the Great South Channel.. Deep Sea Res Part II Top. Stud. Oceanogr. 43, 1601–1625 (1996).ADS 
    Article 

    Google Scholar 
    Wishner, K. F. et al. Copepod patches and right whales in the Great South Channel off New England. Bull. Mar. Sci. 43, 825–844 (1988).ADS 

    Google Scholar 
    Baumgartner, M., Cole, T., Clapham, P. & Mate, B. North Atlantic right whale habitat in the lower Bay of Fundy and on the SW Scotian Shelf during 1999–2001. Mar. Ecol. Prog. Ser. 264, 137–154 (2003).ADS 
    Article 

    Google Scholar 
    Moore, M. J. & van der Hoop, J. M. The painful side of trap and fixed net fisheries: Chronic entanglement of large whales. J. Mar. Biol. 2012, 1–4 (2012).Article 

    Google Scholar  More

  • in

    Coastal ecological impacts from pumice rafts

    Massive drift of pumice along the northeastern coast of Okinawa IslandA large amount of pumice stones reached and was deposited along the northeastern coast of Okinawa Island, that were brought by strong seasonal northeasterly winds (Supplementary Video 1). The pumice was thought to be brought by the Kuroshio countercurrent from sites near the Ogasawara Archipelago 1300 km away. Because the Kuroshio countercurrent is composed of various medium-sized eddies in the ocean, the current does not always flow in one direction and as a continuous flow27,28. The pumice drift was more strongly controlled by the seasonal northwesterly winds to be transported to Okinawa across the Philippine Sea (Fig. 1a). The pumice raft reached the northern part of Okinawa approximately 2 months after the eruption (Figs. 2, 3 and 4). According to a very recent report, the pumice clasts were drifting ashore in Thailand (traveling 4000 km-long distance) across the South China Sea within half a year of this eruption29. Most pumice stones were gray, but some pumice was banded, and others were black reflecting some compositional variation25,29 (Fig. 2d,e). The Kuroshio Current is faster than the Kuroshio countercurrent27, so some pumice clasts have already reached the main island of Japan25. Tracking the dispersal of the pumice will allow a better forecasting model based on observed raft trajectories by considering exact wind effects in the Philippine Sea30.Figure 2An example of a natural beach on Okinawa Island where pumice has washed ashore. (a) Appearance of natural sandy beaches on the northern part of Okinawa Island (Ibu beach, Kunigami Village, 26°75′57.88″ N, 128°32′23.32″ E). Photo was taken on 24 October 2021. Pumice drifted onto the sandy beach and formed a striped pattern. The white-capped waves indicate on the place where the reef edge exist. The white arrow points to the mangrove river estuary corresponding to Fig. 9. (b) Estimation of the pumice sedimentation depth on the original sand beach surface. (c) The high tide zone of the natural sandy beach is covered with pumice pebbles and stones. Yellow arrows indicate black pumice stones. Scale bar: 10 cm. (d, e) Front and back of examples of relatively large pumice stones from the same beach. The left image is mostly light brown, whereas the right image is almost black. Scale bars: 5 cm.Full size imageFigure 3Short-term migration of pumice from beaches as revealed by stationary observations. These four photos were taken at two sites on northern Okinawa Island on two consecutive days, 23 and 24 October 2021. (a, b) A sandy beach along the Sate Coast (26°78′84.56″ N, 128°22′30.57″ E). It was windy on the first day, and pumice stones were washed up with the waves. Almost all the pumice stones were removed from the beach and transported offshore on the following day. The black arrow in photo (a) indicates Cape Hedo, the northernmost tip of Okinawa Island. (c, d) At this gravelly beach (26°80′83.25″ N, 128°23′38.56″ E), pumice fully covers the seawall on the first day, but all of the pumice stones washed away, leaving the original gravels, on the following day. The white arrow in each photo indicates an identical marker stone placed on the beach. Weather data of northern Okinawa (https://www.data.jma.go.jp/obd/stats/etrn/view/daily_a1.php?prec_no=91&block_no=0901&year=2021&month=10&day=23&view=g_wsp) and tidal data (Naha: 26°13′ N, 127°40′ E) (https://www.data.jma.go.jp/gmd/kaiyou/db/tide/genbo/genbo.php) are provided by Japan Meteorological Agency.Full size imageFigure 4Pumice stones settled by marine organisms. (a) Pumice collected from Ibu beach on 31 October 2021. Two marine benthos coexist close together on a pumice stone. Scale bar: 1 cm. (b) Enlarged image of the Lepas barnacle. Scale bar: 3 mm. (c) Enlarged image of the bryozoan. Scale bar: 3 mm. (d) Stereo microscopic image of pumice pebbles of a few millimeters in diameter collected from Ibu beach on 15 January 2022. The light brown coloration indicates some algal/cyanobacterial growth on the pumice. Scale bar 1 mm. (e) Red autofluorescence was detected from pumice pebbles. Image corresponds to (d). Autofluorescence from microalgae was confirmed by Supplementary Fig. 2. Scale bar 1 mm. (f) Enlarged image of the center of the figure of (e) shows red autofluorescent signals with a diameter of 10–30 µm. Scale bar: 200 µm.Full size imageChanges in the coastal landscape: natural beaches and estuariesMarine calcifiers, including corals, calcareous algae, and foraminifers, produce white sandy beaches on Okinawa Island. However, the gray pumice drifting ashore changed the white sand beach, especially along the northeastern coastline. We observed several lines of pumice aggregations, suggesting that pumice was brought ashore by wavefronts several times produced by a strong north wind at the tide lines (Supplementary Video 1; Fig. 2a). At the same sampling site, the thickest depth of beached pumice was more than 30 cm (Fig. 2b; Supplementary Video 2). Most of the pumice stones were from 0.5 cm to 3 cm in diameter, with a few black pumice stones included (Fig. 2c: yellow arrow). Pumice stones arrived at the estuaries of some brackish rivers (Fig. 8, Supplementary Fig. 1a) and mangrove forests in northwest Okinawa (Fig. 9).Pumice stones and pumice rafts show dynamic behavior in a short period. We captured photographs 24 h apart at two positions on the shore of Okinawa, which allowed us to compare the pumice dynamics during this period (Fig. 3). Within that time frame, there were two high tides, and the tide level changed by up to 170 cm. As seen in Fig. 3a, on the first day, the coast was covered with pumice, and floating pumice could be seen on the seafront. The north wind was strong that day, as shown by the relatively high waves near the shore as well as white‐crested waves near the reef edge. By the following day, most of the pumice had been moved offshore by tides and winds (Fig. 3b), indicating that newly beached pumice raft deposits were removed quickly from open beach areas. At another site on a gravelly beach, pumice fully covered the seawall on the first day, but almost all of the pumice stones were washed away, leaving the original gravels, on the following day (Fig. 3c,d). Japan Meteorological Agency (Oku station: 232 m above sea level, latitude 26°50.1, longitude 128°16.3′) reported that northerly winds were blowing (mean wind speed: 3.4 m/s) on 23rd October in northern Okinawa. The following day, the wind direction changed to the east-southeast; blowing offshore (mean wind speed: 2.9 m/s), resulting in the dramatic removal of pumice form the coast (Fig. 3). These observations indicate that surface winds rather than ocean currents had a strong influence on the raft trajectory and residence time on beaches, and are consistent with past research5. These observations lead us to expect that the pumice rafts will disappear from the coast of Okinawa fairly quickly, but in fact, there have been many cases where they have come back again in a few days. Although the overall amount of pumice drifting has been decreasing, a small amount of pumice has been drifting in coastal area of Okinawa in May, 202231. It is unlikely that large amounts of pumice will drift repeatedly throughout Okinawa Prefecture as reported in this report, but it should be noted that detached pumice material remains in beach and river runoff.Biofouling of sessile organisms on pumice arriving to OkinawaIt is noteworthy that the pumice rafts traveled over the deep Philippine Sea for over 2 months, and on arrival in Okinawa there was little to no biofouling of the pumice (Fig. 2). Some stranded pumices observed on Okinawa beaches had become habitats for sessile organisms (Fig. 4), as reported in previous studies1,2,3,4,5,6,29. Goose barnacles (Lepas sp.) without external damage to the shell were the most abundant species observed on the pumice (Fig. 4b). Lepas is a common biofouling taxon distributed globally and plays a role in biofouling as a foundation organism. The shell growth rate is more than 1 mm/day in some Lepas species32 suggesting that the Lepas had been growing on the pumice for about two weeks. Measurements of the shell size of Lepas attached to the pumice collections conducted in the same area (Supplementary Video 2) showed a bias toward larger sizes in the second collection (5.92 ± 3.86 mm (average ± S.D.), n = 75, 13 November 2021) than in the first one (3.43 ± 1.08 mm, n = 21, 31 October 2021), and significant differences were detected between the measurement periods (Mann–Whitney U test, p  More

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    Connecting nutritional facts with the traditional ranking of ethnobotanically used fodder grasses by local farmers in Central Punjab of Pakistan

    Harun, N., Chaudhry, A. S., Shaheen, S., Ullah, K. & Khan, F. Ethnobotanical studies of fodder grass resources for ruminant animals, based on the traditional knowledge of indigenous communities in Central Punjab Pakistan. J. Ethnobiol. Ethnomed. 13(1), 56. https://doi.org/10.1186/s13002-017-0184-5 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Shaheen, H., Qureshi, R., Qaseem, M. F. & Bruschi, P. The fodder grass resources for ruminants: A indigenous treasure of local communities of Thal desert Punjab, Pakistan. PLoS One 15(3), e0224061. https://doi.org/10.1371/journal.pone.0224061 (2020).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Huston, J. E. Forage utilization and nutrient requirements of the goat1. J. Dairy Sci. 61(7), 988–993. https://doi.org/10.3168/jds.S0022-0302(78)83679-0 (1978).Article 

    Google Scholar 
    Wilson, A. D., Leigh, J. H., Hindley, N. L. & Mulham, W. E. Comparison of the diets of goats and sheep on a Casuarina cristata–Heterodendrum oleifolium woodland community in western New South Wales. Aust. J. Exp. Agric. 15(72), 45–53. https://doi.org/10.1071/EA9750045 (1975).CAS 
    Article 

    Google Scholar 
    Grünwaldt, E. G., Pedrani, A. R. & Vich, A. I. Goat grazing in the arid piedmont of Argentina. Small Ruminants Res. 13(3), 211–216. https://doi.org/10.1016/0921-4488(94)90066-3 (1994).Article 

    Google Scholar 
    Aganga, A. A., Omphile, U. J., Thema, T. & Baitshotlhi, J. C. Chemical composition of napier grass (Pennisetum purpureum) at different stages of growth and napier grass silages with additives. J. Biosci. 5(4), 493–496. https://doi.org/10.3923/jbs.2005.493.496 (2005).Article 

    Google Scholar 
    Ganskopp, D. & Bohnert, D. Nutritional dynamics of 7 Northern Great Basin grasses. J. Range Manage. 54, 640–647. https://doi.org/10.2307/4003664 (2001).Article 

    Google Scholar 
    Capstaff, N. M. & Miller, A. J. Improving the yield and nutritional quality of forage crops. Front. Plant Sci. 9, 535. https://doi.org/10.3389/fpls.2018.00535 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Arzani, H., Basiri, M., Khatibi, F. & Ghorbani, G. Nutritive value of some Zagros Mountain rangeland species. Small Ruminants Res. 65(1–2), 128–135. https://doi.org/10.1016/j.smallrumres.2005.05.033 (2006).Article 

    Google Scholar 
    Keba, H. T., Madakadze, I. C., Angassa, A. & Hassen, A. Nutritive value of grasses in semi-arid rangelands of Ethiopia, Local experience based herbage preference evaluation versus laboratory analysis. Asian-Aust. J. Anim. Sci. 26(3), 366. https://doi.org/10.5713/ajas.2012.12551 (2013).Article 

    Google Scholar 
    Dhungana, S., Tripathee, H. P., Puri, L., Timilsina, Y. P. & Devkota, K. P. Nutritional analysis of locally preferred fodder trees of Middle Hills of Nepal, a case study from Hemja VDC, Kaski District, Nepal. J. Sci. Technol. 13(2), 39–44. https://doi.org/10.3126/njst.v13i2.7712 (2012).Article 

    Google Scholar 
    Talore, D. G. Evaluation of major feed resources in crop-livestock mixed farming systems, southern Ethiopia, Indigenous knowledge versus laboratory analysis results. J. Agric. Rural Dev. 116(2), 157–166 (2015). http://nbn-resolving.de/urn:nbn:de:hebis:34-2015061048507.Geng, Y. et al. Nutrient value of wild fodder species and the implications for improving the diet of mithun (Bos frontalis) in Dulongjiang area, Yunnan Province, China. Plant Diversity 42(6), 455–463. https://doi.org/10.1016/j.pld.2020.09.007 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sayed, M. A. I., Kulkarni, S., Kulkarni, D., Pande, A. & Kauthale, V. Nutritional study of local fodder species in Ahmednagar district of western Maharashtra. Agric. Sci. Digest A Res. J. 37(2), 154–156. https://doi.org/10.18805/asd.v37i2.7979 (2017).Article 

    Google Scholar 
    Evitayani, L. W., Fariani, A., Ichinohe, T. & Fujihara, T. Study on nutritive value of tropical forages in North Sumatra, Indonesia. Asian-Aust. J. Anim. Sci. 17(11), 1518–1523. https://doi.org/10.5713/ajas.2004.1518 (2004).Article 

    Google Scholar 
    Kanak, A. R., Khan, M. J., Debi, M. R., Pikar, M. K. & Aktar, M. Nutritive value of three fodder species at different stages of maturity, Bangladesh. J. Anim. Sci. 41(2), 90–95. https://doi.org/10.3329/bjas.v41i2.14123 (2012).Article 

    Google Scholar 
    Rahim, I., Maselli, D., Rueff, H. & Wiesmann, U. Indigenous fodder trees can increase grazing accessibility for landless and mobile pastoralists in northern Pakistan. Pastoral. Res. Policy Pract. 1(2), 1–2. https://doi.org/10.1186/2041-7136-1-2 (2011).Article 

    Google Scholar 
    Sultan, J., Inam-ur-rahim, I., Nawaz, H., Yaqoob, M. & Javed, I. Mineral composition, palatability and digestibility of free rangeland grasses of northern grasslands of Pakistan. Pak. J. Bot. 40(5), 2059–2070 (2008).CAS 

    Google Scholar 
    Bano, G., Islam, M., Ahmad, S., Aslam, S. & Koukab, S. Seasonal variation in nutritive value of Chrysopogon aucheri (boiss) stapf., and Cymbopogon jwarancusa (jones) schult., in highland Balochistan, Pakistan. Pak. J. Bot. 41(2), 511–517 (2009).CAS 

    Google Scholar 
    Rafay, M., Khan, R. A., Yaqoob, S. & Ahmad, M. Nutritional evaluation of major range grasses from Cholistan Desert. Pak. J. Nutr. 12(1), 23–29. https://doi.org/10.3923/pjn.2013.23.29 (2013).CAS 
    Article 

    Google Scholar 
    Sultan, J. I., Manzoor, M. N., Shahzad, M. A. & Nisa M. Nutritional profile and in situ digestion kinetics of some irrigated grasses at pre-bloom stage. In International Conference on Biology, Environment and Chemistry 455–463 (2011). https://doi.org/10.3923/pjn.2013.23.29.Ahmed, K. et al. Proximate analysis, Relative feed values of various forage plants for ruminants investigated in a semi-arid region of Punjab, Pakistan. J. Agric. Sci. 27(6), 302. https://doi.org/10.4236/as.2013.46043 (2013).Article 

    Google Scholar 
    Manzoor, M. N., Sultan, J. I., Nisa, M. U. & Bilal, M. Q. Nutritive evaluation and in-situ digestibility of irrigated grasses. J. Anim. Plant Sci. 23, 1223–1227 (2013).CAS 

    Google Scholar 
    Sultan, J. I., Rahim, I. U., Nawaz, H. & Yaqoob, M. Nutritive value of marginal land grasses of northern grasslands of Pakistan. Pak. J. Bot. 39(4), 1071–1082 (2007).
    Google Scholar 
    Khan, R. I., Alam, M. R. & Amin, M. R. Effect of season and fertilizer on species composition and nutritive value of native grasses. Asian-Aust. J. Anim. Sci. 12(8), 1222–1227. https://doi.org/10.5713/ajas.1999.1222 (1999).Article 

    Google Scholar 
    Grant, K., Kreyling, J., Dienstbach, L. F. H., Beierkuhnlein, C. & Jentsch, A. Water stress due to increased intra-annual precipitation variability reduced forage yield but raised forage quality of a temperate grassland. Agric. Ecosyst. Environ. 186, 11–22. https://doi.org/10.1016/j.agee.2014.01.013 (2014).Article 

    Google Scholar 
    Ray, D. K., Gerber, J. S., MacDonald, G. K. & West, P. C. Climate variation explains a third of global crop yield variability. Nat. Commun. 6(1), 1–9. https://doi.org/10.1038/ncomms6989 (2015).CAS 
    Article 

    Google Scholar 
    Egeru, A. et al. Land cover and soil properties influence on forage quantity in a semiarid region in East Africa. Appl. Environ. Soil Sci. https://doi.org/10.1155/2019/6874268 (2019).Article 

    Google Scholar 
    Mertens, D. R. Interpretation of forage analysis reports. In 30th National Alfalfa symposium. Las vegas, NV. (2000).Hussain, F. & Durrani, M. J. Nutritional evaluation of some forage plants from Harboi Rangeland, Kalat, Pakistan. Pak. J. Bot. 41(3), 1137–1154 (2009).CAS 

    Google Scholar 
    Ammar, H., López, S., Bochi-Brum, O., García, R. & Ranilla, M. J. Composition and in vitro digestibility of leaves and stems of grasses and legumes harvested from permanent mountain meadows at different stages of maturity. J. Anim. Feed Sci. 8(4), 599–610. https://doi.org/10.22358/jafs/69184/1999 (1999).Article 

    Google Scholar 
    Faichney, G. J., Gordon, G. L. R., Welch, R. J. & Rintoul, A. J. Effect of dietary free lipid on anaerobic fungi and digestion in the rumen of sheep. Aust. J. Agric. Res. 53(5), 519–527. https://doi.org/10.1071/AR01143 (2002).CAS 
    Article 

    Google Scholar 
    Khan, S., Anwar, K., Kalim, K., Saeed, A. & Shah, S. Z. Nutritional evaluation of some top fodder tree leaves and shrubs of District Dir (Lower), Pakistan as a quality livestock feed. Int. J. Curr. Microbiol. Appl. Sci. 3(5), 941–947 (2014).
    Google Scholar 
    Tudsri, S. & Kaewkunya, C. Effect of leucaena row spacing and cutting intensity on the growth of leucaena and three associated grasses in Thailand. Asian Aust. J. Anim. Sci. 15(7), 986–991 (2002).
    Google Scholar 
    Nasrullah, M., Niimi, R., Akashi, X. & Kawamura, O. Nutritive evaluation of forage plants grown in South Sulawesi, Indonesia. Asian Aust. J. Anim. Sci. 16(5), 693–701. https://doi.org/10.5713/ajas.2004.63 (2003).CAS 
    Article 

    Google Scholar 
    Yahaya, M. S., Kawai, M., Takahashi, J. & Matsuoka, S. The effects of different moisture content and ensiling time on silo degradation of structural carbohydrate of orchard grass. Asian Aust. J. Anim. Sci. 15(2), 213–217. https://doi.org/10.5713/ajas.2002.213 (2002).Article 

    Google Scholar 
    Norton, B. W. Differences between species in forage quality. In Nutritional Limits to Animal Production from Pastures, proceedings of an international symposium held at St. Lucia, Queensland, Australia, UK. Commonwealth Agricultural Bureaux, (1982).National Research council. Nutrient Requirements of Dairy Cattle 7th edn. (National Academy Press, 2001).
    Google Scholar 
    Nogueira Filho, J. C. M., Fondevila, M., Urdaneta, A. B. & Ronquillo, M. G. In vitro microbial fermentation of tropical grasses at an advanced maturity stage. Anim. Feed Sci. Technol. 83(2), 145–157. https://doi.org/10.1016/S0377-8401(99)00123-6 (2000).CAS 
    Article 

    Google Scholar 
    National Research Council. Nutrient Requirements of Sheep, Vol ***5 (National Academies Press, 1985).
    Google Scholar 
    Erickson, P. S. & Kalscheur, K. F. Nutrition and feeding of dairy cattle. In Animal Agriculture pp 157–180 (2020).Holechek, J. L., Pieper, R. D. & Herbel, C. H. Range Management Principles and Practices 5th edn. (Prentice-Hall, 2004).
    Google Scholar 
    Saro, C. et al. Effect of dietary crude protein on animal performance, blood biochemistry profile, ruminal fermentation parameters and carcass and meat quality of heavy fattening Assaf lambs. Animals 10(11), 2177 (2020).PubMed Central 

    Google Scholar 
    Buckmaster, D. R. Forage Looses, Equal Economic Looses Agricultural Engineer Fact Shell PM-107 (The Pennsylvania State University, 1990).
    Google Scholar 
    Paulson, J., Jung, H., Raeth-Knight, M. & Linn, J. Grass vs. legume forages for dairy cattle (2008). https://conservancy.umn.edu/bitstream/handle/11299/204154/SF95_M658a-69-2008_magr56173.pdf?sequence=1.Lüscher, A., Mueller-Harvey, I., Soussana, J. F., Rees, R. M. & Peyraud, J. L. Potential of legume-based grassland–livestock systems in Europe: A review. Grass Forage Sci. 69(2), 206–228. https://doi.org/10.1111/gfs.12124 (2014).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Van Soest, P. J. Nutritional Ecology of the Ruminant 2nd edn. (Cornell University Press, 1994).
    Google Scholar 
    Tucak, M., Ravlic, M., Horvat, D. & Cupic, T. Improvement of forage nutritive quality of alfalfa and red clover through plant breeding. Agronomy 11(11), 2176. https://doi.org/10.3390/agronomy11112176 (2021).CAS 
    Article 

    Google Scholar 
    Harper, K. & McNeill, D. The role iNDF in the regulation of feed intake and the importance of its assessment in subtropical ruminant systems (the role of iNDF in the regulation of forage intake). Agriculture 5(3), 778–790. https://doi.org/10.3390/agriculture5030778 (2015).CAS 
    Article 

    Google Scholar 
    Singh, G. P. & Oosting, S. J. A model for describing the energy value of straws. Indian Dairyman XLI 322–327 (1992). https://agris.fao.org/agris-search/search.do?recordID=NL2012083374.Reed, J. A. & Goe, M. R. Estimating the Nutritive Value of Cereal Crop Residues, Implications for developing feeding standards for draught animals. ILCA Bulletin (1989). https://hdl.handle.net/10568/4610.Kumar, K. & Soni, A. Nutrient evaluation of common vegetation of Rajasthan, Pennisetum typholdenum, Cenchrus ciliaris, Cenchrus setigerus and Lasiurus sindicus. Int. J. Plant Anim. Environ. Sci. 4(1), 177–183 (2014).CAS 

    Google Scholar 
    Kramberger, B. & Klemenčič, S. Effect of harvest date on the chemical composition and nutritive value of Cerastium holosteoides. Grass Forage Sci. 58(1), 12–16. https://doi.org/10.1046/j.1365-2494.2003.00346.x (2003).CAS 
    Article 

    Google Scholar 
    Raffrenato, E. et al. Effect of lignin linkages with other plant cell wall components on in vitro and in vivo neutral detergent fiber digestibility and rate of digestion of grass forages. J. Dairy Sci. 100(10), 8119–8131. https://doi.org/10.3168/jds.2016-12364 (2017).CAS 
    Article 
    PubMed 

    Google Scholar 
    McDonald, P. et al. Animal nutrition. Pearson UK https://doi.org/10.1088/1755-1315/951/1/012013 (2022).Article 

    Google Scholar 
    Brown, P. H., Graham, R. D. & Nicholas, D. G. D. The effect of manganese and nitrate supply on the level of phenolics and lignin in young wheat plant. Plant Soil 81, 437–440 (1984).CAS 

    Google Scholar 
    Mbwile, R. P. & Uden, P. Effects of age and season on growth and nutritive value of Rhodes grass (Chloris gayana cv. Kunthi). Anim. Feed Sci. Technol. 65, 87–98 (1997).
    Google Scholar 
    Hameed, M., Naz, N., Ahmad, M. S. A. & Islam-ud-Din, R. A. Morphological adaptations of some grasses from the salt range, Pakistan. Pak. J. Bot. 40(4), 1571–1578 (2008).
    Google Scholar 
    Makkar, H. P. S. Effects and fate of tannins in ruminant animals, adaptation to tannins, and strategies to overcome detrimental effects of feeding tannin-rich feeds. Small Rumin. Res. 49(3), 241–256. https://doi.org/10.1016/S0921-4488(03)00142-1 (2003).ADS 
    Article 

    Google Scholar 
    Patra, A. K. Nutritional management in organic livestock farming for improved ruminant health and production—an overview. Livestock Res. Rural Dev. 19(3), 41 (2007).
    Google Scholar 
    Akande, K. E., Doma, U. D., Agu, H. O. & Adamu, H. M. Major antinutrients found in plant protein sources: Their effect on nutrition. Pak. J. Nutr. 9(8), 827–832 (2010).CAS 

    Google Scholar 
    Tadele, Y. Important anti-nutritional substances and inherent toxicants of feeds. Food Sci. Qual. Manage. 36, 40–47 (2015).
    Google Scholar 
    D’Mello, J.F. Farm animal metabolism and nutrition. Cabi Publishing. UK. (2000). https://www.researchgate.net/profile/Adegbola-Adesogan/publication/242151831_What_are_feeds_worth_A_critical_evaluation_of_selected_nutritive_value_methods/links/5852780c08aef7d030a4e95b/What-are-feeds-worth-A-critical-evaluation-of-selected-nutritive-value-methods.pdf.Panhwar, F. Anti-nutritional Factors in Oil Seeds as Aflatoxin in Ground Nut (Digitalverlag GmbH, 2005).
    Google Scholar 
    Huang, J. et al. Tree defence and bark beetles in a drying world: Carbon partitioning, functioning and modelling. New Phytol. 225(1), 26–36. https://doi.org/10.1111/nph.16173 (2020).Article 
    PubMed 

    Google Scholar 
    Min, B. R., Barry, T. N., Attwood, G. T. & McNabb, W. C. The effect of condensed tannins on the nutrition and health of ruminants fed fresh temperate forages, a review. Anim. Feed Sci. Technol. 106(1–4), 3–19 (2003).CAS 

    Google Scholar 
    Muetzel, S., Hoffmann, E. M. & Becker, K. Supplementation of barley straw with Sesbania pachycarpa leaves in vitro: Effects on fermentation variables and rumen microbial population structure quantified by ribosomal RNA targeted probes. Br. J. Nutr. 89(4), 445–453 (2003).CAS 
    PubMed 

    Google Scholar 
    Yao, L. H. et al. Flavonoids in food and their health benefits. Plant Foods Hum. Nutr. 59(3), 113–122 (2004).CAS 
    PubMed 

    Google Scholar 
    Tracy, B. F. et al. Resilience in forage and grazinglands. Crop Sci. 58(1), 31–42 (2018).
    Google Scholar 
    Ehsen, S. et al. Secondary metabolites as anti-nutritional factors in locally used halophytic forage/fodder. Pak. J. Bot. 48(2), 629–636 (2016).CAS 

    Google Scholar 
    Mudzwiri, M. Evaluation of traditional South African leafy plants for their safety in human consumption. Doctoral Dissertation (2007).Francis, G., Kerem, Z., Makkar, H. P. & Becker, K. The biological action of saponins in animal systems, A review. Brit. J. Nutr. 88(6), 587–605 (2002).CAS 
    PubMed 

    Google Scholar 
    Duke, J. Phytochemical and ethnobotanical databases (2000).Terrill, T. H., Rowan, A. M., Douglas, G. B. & Barry, T. N. Determination of extractable and bound condensed tannin concentrations in forage plants, protein concentrate meals and cereal grains. J. Sci. Food Agric. 58(3), 321–329. https://doi.org/10.1002/jsfa.2740580306 (1992).CAS 
    Article 

    Google Scholar 
    Barry, T. N. & McNabb, W. C. The implications of condensed tannins on the nutritive value of temperate forages fed to ruminants. Br. J. Nutr. 81(4), 263–272 (1999).CAS 
    PubMed 

    Google Scholar 
    Kallah, S. K., Bale, J. D., Abdullahi, U. S., Mohammed, I. R. & Lawai, R. Nutrient composition of native forms of semi-arid and dry-humid savannahs of Nigeria. Anim. Feed Sci. Technol. 84, 137–145 (2000).CAS 

    Google Scholar 
    Megersa, E., Mengistu, A. & Asebe, G. Nutritional characterization of selected fodder species in Abol and Lare Districts of Gambella Region, Ethiopia. J. Nutr. Food Sci. 7(2), 2–6 (2017).
    Google Scholar 
    Van Soest, P. J. & Robertson, J. B. Analysis of Forages and Fibrous Foods (Cornell University, 1985).
    Google Scholar 
    Moore, K. J. & Jung, H. G. Lignin and fiber digestion. J. Range Manag. 54(4), 420–430 (2001).
    Google Scholar 
    Ramirez, R. G., Haenlein, G. F. W., Garcia-Castillo, C. G. & Nunez-Gonzalez, M. A. Protein, lignin and mineral contents and In-Situ dry matter digestibility of native Mexican grasses consumed by range goats. Small Ruminant Resour. 52(3), 261–269 (2004).
    Google Scholar 
    Ronquillo, M. G., Fondevila, M., Urdaneta, A. B. & Newman, Y. In vitro gas production from buffel grass Cenchrus ciliaris L. fermentation in relation to the cutting interval, the level of nitrogen fertilisation and the season of growth. Anim. Feed Sci. Technol. 72(1–2), 19–32 (1998).
    Google Scholar 
    Mlay, P. S. et al. Feed value of selected tropical grasses, legumes and concentrates. Vet. Arch. 76(1), 53–63 (2006).
    Google Scholar 
    Arif, M. et al. In vitro digestibility of selected forages in Sargodha district, Pakistan. In Vitro 6(3), 62–72 (2016).CAS 

    Google Scholar 
    Revell, D. K., Baker, S. K. & Purser, B. B. Estimates of the intake and digestion of nitrogen by sheep grazing a Mediterranean pasture as it matures senesces. Aust. Soc. Anim. Prod. 20, 217–220 (1994).
    Google Scholar 
    Cherney, D. J. R., Mertens, D. R. & Moore, J. E. Intake and digestibility by withers as influenced by forage morphology at three levels of forage offering. J. Anim. Sci. 68(12), 4387–4399. https://doi.org/10.2527/1990.68124387x (1990).CAS 
    Article 
    PubMed 

    Google Scholar 
    Lichtenberg, V. L. & Hemken, R. W. Hay quality. In: Grazing Management: An Ecological Perspective. Timber Press, Portland, Oregon USA (1985). https://www.pakbs.org/pjbot/PDFs/40(1)/PJB40(1)249.pdf.de Oliveira, C. V. et al. Urea supplementation in rumen and post-rumen for cattle fed a low-quality tropical forage. Brit. J. Nutr. 124(11), 1166–1178. https://doi.org/10.1017/S0007114520002251 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    Rufino, L. M. et al. Effects of the amount and frequency of nitrogen supplementation on intake, digestion, and metabolism in cattle fed low-quality tropical grass. Anim. Feed Sci. Technol. 260, 114367 (2020).CAS 

    Google Scholar 
    Njidda, A. A. Determining dry matter degradability of some semi-arid browse species of north-eastern Nigeria using the in vitro technique. Nigerian J. Basic Appl. Sci. 18(2), 160–167. https://doi.org/10.4314/njbas.v18i2.64306 (2014).Article 

    Google Scholar 
    Rakib-Uz-Zaman, S. M. et al. Ethnobotanical study and phytochemical profiling of Heptapleurum hypoleucum leaf extract and evaluation of its antimicrobial activities against diarrhea-causing bacteria. J. Genet. Engl. Biotechnol. https://doi.org/10.1186/s43141-020-00030-0 (2020).Article 

    Google Scholar 
    Rodrigues, E. & de Oliveira, D. R. Ethnopharmacology: A laboratory science?. Rodriguésia 71, 25 (2020).
    Google Scholar 
    Kellogg, E. A. Poaceae. In The Families and Genera of Vascular Plants (ed. Kubtizki, K.) (Springer, 2014).
    Google Scholar 
    Horwitz W. & Latimer G. W. Official methods of analysis of AOAC International. 18th Ed. Gaithersburg, Md. AOAC International (2005). https://doi.org/10.1071/EA9750045.Makkar, H. P., Siddhuraju, P. & Becker, K. Plant Secondary Metabolites (Humana Press, 2007).
    Google Scholar 
    Tilley, J. M. & Terry, R. A. A two stage technique for the in vitro digestion of forage crops. Grass Forage Sci. 18(2), 104–111. https://doi.org/10.1111/j.1365-2494.1963.tb00335.x (1963).CAS 
    Article 

    Google Scholar  More