Philippa Kaur

Dewey-Mattia D, Manikonda K, Hall AJ, Wise ME, Crowe SJ. Surveillance for foodborne disease outbreaks—United States, 2009–2015. MMWR Surveillance Summaries. 2018;67:1.PubMed Central 
Article 

Google Scholar 
CDC. Ongoing Multistate Outbreak of Escherichia coli serotype O157:H7 Infections Associated With Consumption of Fresh Spinach – United States. JAMA. 2006;296:2195–6.Article 

Google Scholar 
Jay MT, Cooley M, Carychao D, Wiscomb GW, Sweitzer RA, Crawford-Miksza L, et al. Escherichia coli O157: H7 in feral swine near spinach fields and cattle, central California coast. Emerg Infect Dis. 2007;13:1908.PubMed 
PubMed Central 
Article 

Google Scholar 
Cooley M, Carychao D, Crawford-Miksza L, Jay MT, Myers C, Rose C, et al. Incidence and tracking of Escherichia coli O157: H7 in a major produce production region in California. PLoS One. 2007;2:e1159.PubMed 
PubMed Central 
Article 

Google Scholar 
Mukherjee A, Mammel MK, LeClerc JE, Cebula TA. Altered Utilization of N-Acetyl-d-Galactosamine by Escherichia coli O157:H7 from the 2006 Spinach Outbreak. J Bacteriol. 2008;190:1710–7.CAS 
PubMed 
Article 

Google Scholar 
Macarisin D, Patel J, Bauchan G, Giron JA, Sharma VK. Role of Curli and Cellulose Expression in Adherence of Escherichia coli O157:H7 to Spinach Leaves. Foodborne Pathog Dis. 2012;9:160–7.CAS 
PubMed 
Article 

Google Scholar 
Carter MQ, Louie JW, Huynh S, Parker CT. Natural rpoS mutations contribute to population heterogeneity in Escherichia coli O157:H7 strains linked to the 2006 US spinach-associated outbreak. Food Microbiol. 2014;44:108–18.CAS 
PubMed 
Article 

Google Scholar 
Park S, Navratil S, Gregory A, Bauer A, Srinath I, Szonyi B, et al. Farm management, environment, and weather factors jointly affect the probability of spinach contamination by generic Escherichia coli at the preharvest stage. Appl Environ Microbiol. 2014;80:2504–15.PubMed 
PubMed Central 
Article 

Google Scholar 
CDC. Investigation Details. 2021 [updated 2021; cited]; Available from: https://www.cdc.gov/ecoli/2021/o157h7-02-21/details.html.Karp DS, Gennet S, Kilonzo C, Partyka M, Chaumont N, Atwill ER, et al. Comanaging fresh produce for nature conservation and food safety. Proc Natl Acad Sci. 2015;112:11126–31.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jones MS, Fu Z, Reganold JP, Karp DS, Besser TE, Tylianakis JM, et al. Organic farming promotes biotic resistance to foodborne human pathogens. J Appl Ecol. 2019;56:1117–27.Article 

Google Scholar 
Holden N, Pritchard L, Toth I. Colonization outwith the colon: plants as an alternative environmental reservoir for human pathogenic enterobacteria. FEMs Microbiol Rev. 2009;33:689–703.CAS 
PubMed 
Article 

Google Scholar 
Holden N. You are what you can find to eat: bacterial metabolism in the rhizosphere. Curr Issues Mol Biol. 2019;30:1–16.Coulthurst S. The Type VI secretion system: a versatile bacterial weapon. Microbiology. 2019;165:503–15.CAS 
PubMed 
Article 

Google Scholar 
Liao H, Li X, Bai Y, Cui P, Wen C, Liu C, et al. Herbicide selection promotes antibiotic resistance in soil microbiomes. Mol Biol Evolut. 2021;38:2337–50.CAS 
Article 

Google Scholar 
Yaron S, Römling U. Biofilm formation by enteric pathogens and its role in plant colonization and persistence. Microb Biotechnol. 2014;7:496–516.PubMed 
PubMed Central 
Article 

Google Scholar 
Wright KM, Chapman S, McGeachy K, Humphris S, Campbell E, Toth IK, et al. The endophytic lifestyle of Escherichia coli O157:H7: quantification and internal localization in roots. Phytopathology. 2013;103:333–40.PubMed 
Article 

Google Scholar 
Dinu L-D, Bach S. Induction of viable but nonculturable Escherichia coli O157:H7 in the phyllosphere of lettuce: a food safety risk factor. Appl Environ Microbiol. 2011;77:8295–302.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Crozier L, Marshall J, Holmes A, Wright KM, Rossez Y, Merget B, et al. The role of l-arabinose metabolism for Escherichia coli O157:H7 in edible plants. Microbiology. 2021;167:1–12.Franz E, Semenov AV, Van Bruggen AHC. Modelling the contamination of lettuce with Escherichia coli O157:H7 from manure-amended soil and the effect of intervention strategies. J Appl Microbiol. 2008;105:1569–84.CAS 
PubMed 
Article 

Google Scholar 
Gu G, Hu J, Cevallos-Cevallos JM, Richardson SM, Bartz JA, van Bruggen AHC. Internal colonization of salmonella enterica serovar typhimurium in tomato plants. PLoS One. 2011;6:e27340.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Crozier L, Hedley PE, Morris J, Wagstaff C, Andrews SC, Toth I, et al. Whole-transcriptome analysis of verocytotoxigenic Escherichia coli O157:H7 (Sakai) suggests plant-species-specific metabolic responses on exposure to spinach and lettuce extracts. Front Microbiol. 2016;12:1088. 7
Google Scholar 
Jacob C, Melotto M. Human pathogen colonization of lettuce dependent upon plant genotype and defense response activation. Front Plant Sci. 2020;30:10.
Google Scholar 
Launders N, Locking ME, Hanson M, Willshaw G, Charlett A, Salmon R, et al. A large Great Britain-wide outbreak of STEC O157 phage type 8 linked to handling of raw leeks and potatoes. Epidemiol Infect. 2016;144:171–81.CAS 
PubMed 
Article 

Google Scholar 
Berendsen RL, Pieterse CMJ, Bakker PAHM. The rhizosphere microbiome and plant health. Trends Plant Sci. 2012;17:478–86.CAS 
PubMed 
Article 

Google Scholar 
Schenkel D, Deveau A, Niimi J, Mariotte P, Vitra A, Meisser M, et al. Linking soil’s volatilome to microbes and plant roots highlights the importance of microbes as emitters of belowground volatile signals. Environ Microbiol. 2019;21:3313–27.Article 

Google Scholar 
Teixeira PJPL, Colaianni NR, Fitzpatrick CR, Dangl JL. Beyond pathogens: microbiota interactions with the plant immune system. Curr Opin Microbiol. 2019;49:7–17.CAS 
PubMed 
Article 

Google Scholar 
Darlison J, Mogren L, Rosberg A-K, Grudén M, Minet A, Liné C, et al. Leaf mineral content govern microbial community structure in the phyllosphere of spinach (Spinacia oleracea) and rocket (Diplotaxis tenuifolia). Sci Total Environ. 2019;675:501–12.CAS 
PubMed 
Article 

Google Scholar 
Lopez-Velasco G, Carder PA, Welbaum GE, Ponder MA. Diversity of the spinach (Spinacia oleracea) spermosphere and phyllosphere bacterial communities. FEMS Microbiol Lett. 2013;346:146–54.CAS 
PubMed 
Article 

Google Scholar 
Daniel S, Goldlust K, Quebre V, Shen M, Lesterlin C, Bouet J-Y, et al. Vertical and Horizontal Transmission of ESBL Plasmid from Escherichia coli O104:H4. Genes. 2020;11:1207.CAS 
PubMed Central 
Article 

Google Scholar 
Orgiazzi A, Bardgett RD, Barrios E, Behan-Pelletier V, Briones MJI, Chotte J-L, et al. Global soil biodiversity atlas. European Commission; 2016.Vorholt JA, Vogel C, Carlström CI, Müller DB. Establishing causality: opportunities of synthetic communities for plant microbiome research. Cell Host Microbe. 2017;22:142–55.CAS 
PubMed 
Article 

Google Scholar 
Fierer N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat Rev Microbiol. 2017;15:579–90.CAS 
PubMed 
Article 

Google Scholar 
Latz E, Eisenhauer N, Rall BC, Scheu S, Jousset A. Unravelling linkages between plant community composition and the pathogen-suppressive potential of soils. Scientific Reports. 2016;6:23584.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lapsansky ER, Milroy AM, Andales MJ, Vivanco JM. Soil memory as a potential mechanism for encouraging sustainable plant health and productivity. Curr Opin Biotechnol. 2016;38:137–42.CAS 
PubMed 
Article 

Google Scholar 
Chapelle E, Mendes R, Bakker PAHM, Raaijmakers JM. Fungal invasion of the rhizosphere microbiome. ISME Journal. 2016;10:265–8.CAS 
PubMed 
Article 

Google Scholar 
Schikora A, Jackson RW, Van Overbeek L, Holden N. Editorial: plants as alternative hosts for human and animal pathogens – second edition. Front Microbiol. [Editorial] 2020;14:11.
Google Scholar 
Lebeis SL. Greater than the sum of their parts: characterizing plant microbiomes at the community-level. Curr Opin Plant Biol. 2015;24:82–6.CAS 
PubMed 
Article 

Google Scholar 
Kinnunen M, Dechesne A, Proctor C, Hammes F, Johnson D, Quintela-Baluja M, et al. A conceptual framework for invasion in microbial communities. ISME J. 2016;10:2773–9.PubMed 
PubMed Central 
Article 

Google Scholar 
Uyttendaele M, Jaykus LA, Amoah P, Chiodini A, Cunliffe D, Jacxsens L, et al. Microbial hazards in irrigation water: standards, norms, and testing to manage use of water in fresh produce primary production. Compr Rev Food Sci Food Saf. 2015;14:336–56.Article 

Google Scholar 
Litchman E. Invisible invaders: non‐pathogenic invasive microbes in aquatic and terrestrial ecosystems. Ecol Lett. 2010;13:1560–72.PubMed 
Article 

Google Scholar 
Blackburn TM, Lockwood JL, Cassey P. The influence of numbers on invasion success. Mol Ecol. 2015;24:1942–53.PubMed 
Article 

Google Scholar 
Hawkes CV, Connor EW. Translating Phytobiomes from Theory to Practice: Ecological and Evolutionary Considerations. Phytobiomes. Journal. 2017;1:57–69.
Google Scholar 
Meyer KM, Leveau JH. Microbiology of the phyllosphere: a playground for testing ecological concepts. Oecologia. 2012;168:621–9.PubMed 
Article 

Google Scholar 
Jousset A, Schulz W, Scheu S, Eisenhauer N. Intraspecific genotypic richness and relatedness predict the invasibility of microbial communities. ISME J. 2011;5:1108–14.PubMed 
PubMed Central 
Article 

Google Scholar 
Martínez-Vaz BM, Fink RC, Diez-Gonzalez F, Sadowsky MJ. Enteric pathogen-plant interactions: molecular connections leading to colonization and growth and implications for food safety. Microbes Environ. 2014;29:123–35.Alegbeleye OO, Singleton I, Sant’Ana AS. Sources and contamination routes of microbial pathogens to fresh produce during field cultivation: a review. Food Microbiol. 2018;73:177–208.PubMed 
PubMed Central 
Article 

Google Scholar 
Johannessen GS, Bengtsson GB, Heier BT, Bredholt S, Wasteson Y, Rørvik LM. Potential uptake of Escherichia coli O157: H7 from organic manure into crisphead lettuce. Appl Environ Microbiol. 2005;71:2221–5.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Fett WF. Inhibition of Salmonella enterica by plant-associated pseudomonads in vitro and on sprouting alfalfa seed. J Food Prot. 2006;69:719–28.PubMed 
Article 

Google Scholar 
Brandl MT, Cox CE, Teplitski M. Salmonella interactions with plants and their associated microbiota. Phytopathology. 2013;103:316–25.PubMed 
Article 

Google Scholar 
Thao S, Brandl MT, Carter MQ. Enhanced formation of shiga toxin-producing Escherichia coli persister variants in environments relevant to leafy greens production. Food Microbiol. 2019;84:103241.PubMed 
Article 

Google Scholar 
Devarajan N, McGarvey JA, Scow K, Jones MS, Lee S, Samaddar S, et al. Cascading effects of composts and cover crops on soil chemistry, bacterial communities and the survival of foodborne pathogens. J Appl Microbiol. 2021;131:1564–77.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Williams TR, Moyne A-L, Harris LJ, Marco ML. Season, irrigation, leaf age, and Escherichia coli inoculation influence the bacterial diversity in the lettuce phyllosphere. PLoS One. 2013;8:e68642.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhang Y, Jewett C, Gilley J, Bartelt-Hunt SL, Snow DD, Hodges L, et al. Microbial communities in the rhizosphere and the root of lettuce as affected by Salmonella-contaminated irrigation water. FEMS Microbiol Ecol. 2018;94:fiy135.CAS 
PubMed 
Article 

Google Scholar 
Jarvis KG, White JR, Grim CJ, Ewing L, Ottesen AR, Beaubrun JJ-G, et al. Cilantro microbiome before and after nonselective pre-enrichment for Salmonella using 16S rRNA and metagenomic sequencing. BMC Microbiol. 2015;15:1–13.CAS 
Article 

Google Scholar 
Allard SM, Callahan MT, Bui A, Ferelli AMC, Chopyk J, Chattopadhyay S, et al. Creek to rable: tracking fecal indicator bacteria, bacterial pathogens, and total bacterial communities from irrigation water to kale and radish crops. Sci Total Environ. 2019;666:461–71.CAS 
PubMed 
Article 

Google Scholar 
Gu G, Yin H-B, Ottesen A, Bolten S, Patel J, Rideout S, et al. Microbiomes in ground water and alternative irrigation water, and spinach microbiomes impacted by irrigation with different types of water. Phytobiomes J. 2019;3:137–47.Article 

Google Scholar 
Obayomi O, Edelstein M, Safi J, Mihiret M, Ghazaryan L, Vonshak A, et al. The combined effects of treated wastewater irrigation and plastic mulch cover on soil and crop microbial communities. Biology Fertility Soils. 2020;56:729–42.CAS 
Article 

Google Scholar 
Truchado P, Gil MI, Suslow T, Allende A. Impact of chlorine dioxide disinfection of irrigation water on the epiphytic bacterial community of baby spinach and underlying soil. PLoS One. 2018;13:e0199291.PubMed 
PubMed Central 
Article 

Google Scholar  More

Alvarez-Filip, L., Dulvy, N. K., Gill, J. A., Côté, I. M. & Watkinson, A. R. Flattening of Caribbean coral reefs: region-wide declines in architectural complexity. Proc. R. Soc. B: Biol. Sci. 276, 3019–3025 (2009).Article 

Google Scholar 
Hughes, T. P. et al. Global warming transforms coral reef assemblages. Nature 556, 492 (2018).CAS 
PubMed 
Article 

Google Scholar 
Drury, C. & Lirman, D. Genotype by environment interactions in coral bleaching. Proc. R. Soc. B Biol. Sci., https://doi.org/10.1098/rspb.2021.0177 (2021).Kenkel, C. D., Almanza, A. T. & Matz, M. V. Fine-scale environmental specialization of reef-building corals might be limiting reef recovery in the Florida Keys. Ecology 96, 3197–3212 (2015).PubMed 
Article 

Google Scholar 
Howells, E. J., Abrego, D., Meyer, E., Kirk, N. L. & Burt, J. A. Host adaptation and unexpected symbiont partners enable reef‐building corals to tolerate extreme temperatures. Glob. Change Biol. 22, 2702–2714 (2016).Article 

Google Scholar 
Thomas, L. et al. Mechanisms of thermal tolerance in reef-building corals across a fine-grained environmental mosaic: lessons from Ofu, American Samoa. Front. Mar. Sci., https://doi.org/10.3389/fmars.2017.00434 (2018).Thomas, L., López, E. H., Morikawa, M. K. & Palumbi, S. R. Transcriptomic resilience, symbiont shuffling, and vulnerability to recurrent bleaching in reef‐building corals. Mol. Ecol. 28, 3371–3382 (2019).PubMed 
Article 

Google Scholar 
Barshis, D. J. et al. Genomic basis for coral resilience to climate change. Proc. Natl Acad. Sci. USA 110, 1387–1392 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Guest, J. R. et al. Contrasting patterns of coral bleaching susceptibility in 2010 suggest an adaptive response to thermal stress. PLoS ONE 7, e33353 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Matz, M. V., Treml, E. A. & Haller, B. C. Estimating the potential for coral adaptation to global warming across the Indo‐West Pacific. Glob. Chang. Biol. 26, 3473–3481 (2020).Bay, R. A., Rose, N. H., Logan, C. A. & Palumbi, S. R. Genomic models predict successful coral adaptation if future ocean warming rates are reduced. Sci. Adv. 3, e1701413 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Quigley, K. M., Bay, L. K. & van Oppen, M. J. Genome‐wide SNP analysis reveals an increase in adaptive genetic variation through selective breeding of coral. Mol. Ecol. 29, 2176–2188 (2020).Howells, E. J. et al. Enhancing the heat tolerance of reef-building corals to future warming. Sci. Adv. 7, eabg6070 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
LaJeunesse, T. C. et al. Systematic revision of symbiodiniaceae highlights the antiquity and diversity of coral endosymbionts. Curr. Biol. 28, 2570–2580 (2018).CAS 
PubMed 
Article 

Google Scholar 
Rowan, R. Coral bleaching: thermal adaptation in reef coral symbionts. Nature 430, 742 (2004).CAS 
PubMed 
Article 

Google Scholar 
Sampayo, E. M., Ridgway, T., Bongaerts, P. & Hoegh-Guldberg, O. Bleaching susceptibility and mortality of corals are determined by fine-scale differences in symbiont type. Proc. Natl Acad. Sci. USA 105, 10444–10449 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Maire, J. et al. Intracellular bacteria are common and taxonomically diverse in cultured and in hospite algal endosymbionts of coral reefs. ISME J., 15, 2028–2042 (2021).Ziegler, M., Seneca, F. O., Yum, L. K., Palumbi, S. R. & Voolstra, C. R. Bacterial community dynamics are linked to patterns of coral heat tolerance. Nat. Commun. 8, 14213 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
van Oppen, M. J. & Blackall, L. L. Coral microbiome dynamics, functions and design in a changing world. Nat. Rev. Microbiol. 17, 557–567 (2019).PubMed 
Article 
CAS 

Google Scholar 
Fuller, Z. L. et al. Population genetics of the coral Acropora millepora: Toward genomic prediction of bleaching. Science 369 (2020).Dixon, G. B. et al. Genomic determinants of coral heat tolerance across latitudes. Science 348, 1460–1462 (2015).CAS 
PubMed 
Article 

Google Scholar 
Jin, Y. K. et al. Genetic markers for antioxidant capacity in a reef-building coral. Sci. Adv. 2, e1500842 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Cooke, I. et al. Genomic signatures in the coral holobiont reveal host adaptations driven by Holocene climate change and reef specific symbionts. Sci. Adv. 6, eabc6318 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Bay, R. A. & Palumbi, S. R. Multilocus adaptation associated with heat resistance in reef-building corals. Curr. Biol. 24, 2952–2956 (2014).CAS 
PubMed 
Article 

Google Scholar 
Drury, C. Resilience in reef-building corals: the ecological and evolutionary importance of the host response to thermal stress. Mol. Ecol. 00, 1–18 (2019).CAS 

Google Scholar 
Quigley, K. M., Willis, B. L. & Bay, L. K. Heritability of the Symbiodinium community in vertically-and horizontally-transmitting broadcast spawning corals. Sci. Rep. 7, 8219 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Van Hooidonk, R., Maynard, J. & Planes, S. Temporary refugia for coral reefs in a warming world. Nat. Clim. Change 3, 508 (2013).Article 
CAS 

Google Scholar 
Quigley, K. M., Warner, P. A., Bay, L. K. & Willis, B. L. Unexpected mixed-mode transmission and moderate genetic regulation of Symbiodinium communities in a brooding coral. Heredity, 121, 524–536 (2018).Cunning, R., Ritson-Williams, R. & Gates, R. D. Patterns of bleaching and recovery of Montipora capitata in Kāne’ohe Bay, Hawai’i, USA. Mar. Ecol. Prog. Ser. 551, 131–139 (2016).CAS 
Article 

Google Scholar 
Dilworth, J., Caruso, C., Kahkejian, V. A., Baker, A. C. & Drury, C. Host genotype and stable differences in algal symbiont communities explain patterns of thermal stress response of Montipora capitata following thermal pre-exposure and across multiple bleaching events. Coral Reefs, https://doi.org/10.1007/s00338-020-02024-3 (2020).Rocha de Souza, M. et al. Community composition of coral-associated Symbiodiniaceae is driven by fine-scale environmental gradients. bioRxiv https://doi.org/10.1101/2021.11.10.468165 (2021).Innis, T., Cunning, R., Ritson-Williams, R., Wall, C. & Gates, R. Coral color and depth drive symbiosis ecology of Montipora capitata in Kāne’ohe Bay, O’ahu, Hawai’i. Coral Reefs 37, 423–430 (2018).Article 

Google Scholar 
Shore-Maggio, A., Runyon, C. M., Ushijima, B., Aeby, G. S. & Callahan, S. M. Differences in bacterial community structure in two color morphs of the Hawaiian reef coral Montipora capitata. Appl. Environ. Microbiol. 81, 7312–7318 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Roach, T. N., Dilworth, J., Jones, A. D., Quinn, R. A. & Drury, C. Metabolomic signatures of coral bleaching history. Nat. Ecol. Evol., 5, 495–503 (2021).Baird, A. H., Guest, J. R. & Willis, B. L. Systematic and biogeographical patterns in the reproductive biology of scleractinian corals. Annu. Rev. Ecol., Evolution, Syst. 40, 551–571 (2009).Article 

Google Scholar 
Caruso, C. et al. Genetic patterns in Montipora capitata across an environmental mosaic in Kāne’ohe Bay. bioRxiv https://doi.org/10.1101/2021.10.07.463582 (2021).Rose, N. H., Bay, R. A., Morikawa, M. K. & Palumbi, S. R. Polygenic evolution drives species divergence and climate adaptation in corals. Evolution 72, 82–94 (2017).PubMed 
Article 

Google Scholar 
Rose, N. H. et al. Genomic analysis of distinct bleaching tolerances among cryptic coral species. Proc. R. Soc. B 288, 20210678 (2021).PubMed 
Article 

Google Scholar 
Forsman, Z. H. et al. Ecomorph or endangered coral? DNA and microstructure reveal Hawaiian species complexes: Montipora dilatata/flabellata/turgescens & M. patula/verrilli. PLoS ONE 5, e15021 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dixon, G., Abbott, E. & Matz, M. Meta‐analysis of the coral environmental stress response: Acropora corals show opposing responses depending on stress intensity. Mol. Ecol. 29, 2855–2870 (2020).CAS 
PubMed 
Article 

Google Scholar 
Lim, S., Kim, D. G. & Kim, S. ERK-dependent phosphorylation of the linker and substrate-binding domain of HSP70 increases folding activity and cell proliferation. Exp. Mol. Med. 51, 1–14 (2019).CAS 
PubMed 
Article 

Google Scholar 
Yancey, P. H. et al. Betaines and dimethylsulfoniopropionate as major osmolytes in cnidaria with endosymbiotic dinoflagellates. Physiol. Biochem. Zool. 83, 167–173 (2010).CAS 
PubMed 
Article 

Google Scholar 
Hill, R., Li, C., Jones, A., Gunn, J. & Frade, P. Abundant betaines in reef-building corals and ecological indicators of a photoprotective role. Coral Reefs 29, 869–880 (2010).Article 

Google Scholar 
Ngugi, D. K., Ziegler, M., Duarte, C. M. & Voolstra, C. R. Genomic blueprint of glycine betaine metabolism in coral metaorganisms and their contribution to reef nitrogen budgets. iScience 23, 101120 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Williams, A. et al. Metabolome shift associated with thermal stress in coral holobionts. bioRxiv https://doi.org/10.1101/2020.06.04.134619 (2021).Sakamoto, A. & Murata, N. The role of glycine betaine in the protection of plants from stress: clues from transgenic plants. Plant, Cell Environ. 25, 163–171 (2002).CAS 
Article 

Google Scholar 
Burg, M. B. & Ferraris, J. D. Intracellular organic osmolytes: function and regulation. J. Biol. Chem. 283, 7309–7313 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chen, T. H. & Murata, N. Glycinebetaine protects plants against abiotic stress: mechanisms and biotechnological applications. Plant Cell Environ. 34, 1–20 (2011).PubMed 
Article 
CAS 

Google Scholar 
Petronini, P., De Angelis, E., Borghetti, A. & Wheeler, K. Effect of betaine on HSP70 expression and cell survival during adaptation to osmotic stress. Biochem. J. 293, 553–558 (1993).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Padilla-Gamiño, J. L., Pochon, X., Bird, C., Concepcion, G. T. & Gates, R. D. From parent to gamete: vertical transmission of Symbiodinium (Dinophyceae) ITS2 sequence assemblages in the reef building coral Montipora capitata. PLoS ONE 7, e38440 (2012).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Cunning, R. & Baker, A. C. Thermotolerant coral symbionts modulate heat stress‐responsive genes in their hosts. Mol. Ecol. 29, 2940–2950 (2020).CAS 
PubMed 
Article 

Google Scholar 
Buerger, P. et al. Heat-evolved microalgal symbionts increase coral bleaching tolerance. Sci. Adv. 6, eaba2498 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mayfield, A. B. & Gates, R. D. Osmoregulation in anthozoan–dinoflagellate symbiosis. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 147, 1–10 (2007).PubMed 
Article 
CAS 

Google Scholar 
Chan, W. Y., Peplow, L. M., Menéndez, P., Hoffmann, A. A. & van Oppen, M. J. Interspecific hybridization may provide novel opportunities for coral reef restoration. Front. Mar. Sci. 5, 160 (2018).Article 

Google Scholar 
Rose, N. H., Seneca, F. O. & Palumbi, S. R. Gene networks in the wild: identifying transcriptional modules that mediate coral resistance to experimental heat stress. Genome Biol. Evolution 8, 243–252 (2016).CAS 
Article 

Google Scholar 
Ruiz-Jones, L. J. & Palumbi, S. R. Tidal heat pulses on a reef trigger a fine-tuned transcriptional response in corals to maintain homeostasis. Sci. Adv. 3, e1601298 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Chakravarti, L. J., Beltran, V. H. & van Oppen, M. J. Rapid thermal adaptation in photosymbionts of reef‐building corals. Glob. Change Biol. 23, 4675–4688 (2017).Article 

Google Scholar 
Little, A. F., Van Oppen, M. J. & Willis, B. L. Flexibility in algal endosymbioses shapes growth in reef corals. Science 304, 1492–1494 (2004).CAS 
PubMed 
Article 

Google Scholar 
Quigley, K., Randall, C., van Oppen, M. & Bay, L. Assessing the role of historical temperature regime and algal symbionts on the heat tolerance of coral juveniles. Biol. Open 9, bio047316 (2020).Matsuda, S. et al. Coral bleaching susceptibility is predictive of subsequent mortality within but not between coral species. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2020.00178 (2020).Ritson-Williams, R. & Gates, R. D. Coral community resilience to successive years of bleaching in Kane ‘ohe Bay, Hawai ‘i. Coral Reefs. 39, 757–769 (2020).Hancock, J. et al. Coral husbandry for ocean futures: leveraging abiotic factors to increase survivorship, growth and resilience in juvenile Montipora capitata. Mar. Ecol. Prog. Ser., https://doi.org/10.3354/meps13534 (2020).Falconer, D. S. Introduction To Quantitative Genetics (Pearson, 1960).Cunning, R. & Baker, A. C. Excess algal symbionts increase the susceptibility of reef corals to bleaching. Nat. Clim. Change 3, 259–262 (2012).Article 

Google Scholar 
Cunning, R., Gillette, P., Capo, T., Galvez, K. & Baker, A. Growth tradeoffs associated with thermotolerant symbionts in the coral Pocillopora damicornis are lost in warmer oceans. Coral Reefs 34, 155–160 (2015).Article 

Google Scholar 
Alonge, M. et al. RaGOO: fast and accurate reference-guided scaffolding of draft genomes. Genome Biol. 20, 1–17 (2019).Article 

Google Scholar 
Shumaker, A. et al. Genome analysis of the rice coral Montipora capitata. Sci. Rep. 9, 2571 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).Article 

Google Scholar 
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Korneliussen, T. S., Albrechtsen, A. & Nielsen, R. ANGSD: analysis of next generation sequencing data. BMC Bioinforma. 15, 356 (2014).Article 

Google Scholar 
Skotte, L., Korneliussen, T. S. & Albrechtsen, A. Estimating individual admixture proportions from next generation sequencing data. Genetics 195, 693–702 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Oksanen, J. et al. vegan: Community Ecology Package. R package version 1.17-2. R Development Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2010).
Google Scholar 
Wright, R. M., Aglyamova, G. V., Meyer, E. & Matz, M. V. Gene expression associated with white syndromes in a reef building coral, Acropora hyacinthus. BMC Genomics 16, 371 (2015).PubMed 
PubMed Central 
Article 
CAS 

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

Google Scholar 
Hivert, V., Leblois, R., Petit, E. J., Gautier, M. & Vitalis, R. Measuring genetic differentiation from Pool-seq data. Genetics 210, 315–330 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Yi, X. et al. Sequencing of 50 human exomes reveals adaptation to high altitude. Science 329, 75–78 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pluskal, T., Castillo, S., Villar-Briones, A. & Orešič, M. MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinforma. 11, 1–11 (2010).Article 
CAS 

Google Scholar 
Wang, M. et al. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nat. Biotechnol. 34, 828–837 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nothias, L.-F. et al. Feature-based molecular networking in the GNPS analysis environment. Nat. Methods 17, 905–908 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dührkop, K. et al. SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information. Nat. methods 16, 299–302 (2019).PubMed 
Article 
CAS 

Google Scholar 
Ludwig, M., Fleischauer, M., Dührkop, K., Hoffmann, M. A. & Böcker, S. in Computational Methods and Data Analysis for Metabolomics 185–207 (Springer, 2020).Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinforma. 9, 1–13 (2008).Article 
CAS 

Google Scholar 
Pedersen, H. K. et al. A computational framework to integrate high-throughput ‘-omics’ datasets for the identification of potential mechanistic links. Nat. Protoc. 13, 2781–2800 (2018).CAS 
PubMed 
Article 

Google Scholar 
Pei, G., Chen, L. & Zhang, W. in Methods in enzymology 585 135–158 (Elsevier, 2017).Sumner, L. W. et al. Proposed minimum reporting standards for chemical analysis. Metabolomics 3, 211–221 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

Pimentel, D. et al. Economic and environmental threats of alien plant, animal, and microbe invasions. Agric. Ecosyst. Environ. 84, 1–20 (2001).
Google Scholar 
Wilcove, D. S. & Chen, L. Y. Management costs for endangered species. Conserv. Biol. 12, 1405–1407 (1998).
Google Scholar 
Singer, M. C., Wee, B., Hawkins, S. & Butcher, M. Rapid natural and anthropogenic diet evolution: three examples from checkerspot butterflies in The Evolutionary Biology of Herbivorous Insects: Speciation, Specialization and Radiation (ed. Tilmon, K. J.). 311–324. (University of California Press, 2008).Ruesink, J. L., Parker, I. M., Groom, M. J. & Kareiva, P. M. Reducing the risks of nonindigenous species introductions. Bioscience 45, 465–477 (1995).
Google Scholar 
Rhymer, J. M. & Simberloff, D. Extinction by hybridization and introgression. Annu. Rev. Ecol. Syst. 27, 83–109 (1996).
Google Scholar 
Vitousek, P. M., D’Antonio, C. M., Loope, L. L. & Westbrooks, R. Biological invasions as global environmental change. Am. Sci. 84, 468–478 (1996).ADS 

Google Scholar 
Daszak, P., Cunningham, A. A. & Hyatt, A. D. Emerging infectious diseases of wildlife-threats to biodiversity and human health. Science 287, 443–449 (2000).ADS 
CAS 
PubMed 

Google Scholar 
Lockwood, J. L., Cassey, P. & Blackburn, T. The role of propagule pressure in explaining species invasions. Trends Ecol. Evol. 20, 223–228 (2005).PubMed 

Google Scholar 
Blackburn, T. M. & Jeschke, J. M. Invasion success and threat status: two sides of a different coin?. Ecography 32, 83–88 (2009).
Google Scholar 
Facon, B. et al. A general eco-evolutionary framework for understanding bioinvasions. Trends Ecol. Evol. 21, 130–135 (2006).PubMed 

Google Scholar 
Ellstrand, N. C. & Schierenbeck, K. A. Hybridization as a stimulus for the evolution of invasiveness in plants?. Proc. Natl. Acad. Sci. USA 97, 7043–7050 (2000).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Verhoeven, K. J. F., Macel, M., Wolfe, L. M. & Biere, A. Population admixture, biological invasions and the balance between local adaptation and inbreeding depression. Proc. R. Soc. B-Biol. Sci. 278, 2–8 (2011).
Google Scholar 
Brevik, K., Lindström, L., McKay, S. D. & Chen, Y. H. Transgenerational effects of insecticides-implications for rapid pest evolution in agroecosystems. Curr. Opin. Insect Sci. 26, 34–40 (2018).PubMed 

Google Scholar 
Kirk, W. D. J. & Terry, L. I. The spread of the western flower thrips Frankliniella occidentalis (Pergande). Agr. Forest. Entomol. 5, 301–310 (2003).
Google Scholar 
Piiroinen, S., Lyytinen, A. & Lindström, L. Stress for invasion success? Temperature stress of preceding generations modifies the response to insecticide stress in an invasive pest insect. Evol. Appl. 6, 313–323 (2013).PubMed 

Google Scholar 
Margus, A. et al. Sublethal pyrethroid insecticide exposure carries positive fitness effects over generations in a pest insect. Sci. Rep. 9, 1–10 (2019).CAS 

Google Scholar 
Vais, H., Williamson, M. S., Devonshire, A. L. & Usherwood, P. N. R. The molecular interactions of pyrethroid insecticides with insect and mammalian sodium channels. Pest Manag. Sci. 57, 877–888 (2001).CAS 
PubMed 

Google Scholar 
Smith, L. B., Kasai, S. & Scott, J. G. Voltage-sensitive sodium channel mutations S989P+ V1016G in Aedes aegypti confer variable resistance to pyrethroids, DDT and oxadiazines. Pest Manag. Sci. 74, 737–745 (2018).CAS 
PubMed 

Google Scholar 
Guerrero, F. D., Jamroz, R. C., Kammlah, D. & Kunz, S. E. Toxicological and molecular characterization of pyrethroid-resistant horn flies, Haematobia irritans: Identification of kdr and super-kdr point mutations. Insect Biochem. Mol. 27, 745–755 (1997).CAS 

Google Scholar 
Morin, S. et al. Mutations in the Bemisia tabaci para sodium channel gene associated with resistance to a pyrethroid plus organophosphate mixture. Insect Biochem. Mol. 32, 1781–1791 (2002).CAS 

Google Scholar 
Kasai, S. et al. First detection of a putative knockdown resistance gene in major mosquito vector, Aedes albopictus. Jpn. J. Infect. Dis. 64, 217–221 (2011).CAS 
PubMed 

Google Scholar 
Brito, L. P. et al. Assessing the effects of Aedes aegypti kdr mutations on pyrethroid resistance and its fitness cost. PLoS ONE 8, e60678 (2013).ADS 
MathSciNet 

Google Scholar 
De Barro, P. J., Liu, S. S., Boykin, L. M. & Dinsdale, A. B. Bemisia tabaci: A statement of species status. Annu. Rev. Entomol. 56, 1–19 (2011).PubMed 

Google Scholar 
Perring, T. M. The Bemisia tabaci species complex. Crop Prot. 20, 725–737 (2001).
Google Scholar 
Navas-Castillo, J., Fiallo-Olivé, E. & Sánchez-Campos, S. Emerging virus diseases transmitted by whiteflies. Annu. Rev. Phytopathol. 49, 219–248 (2011).CAS 
PubMed 

Google Scholar 
Mugerwa, H. et al. African ancestry of new world, Bemisia tabaci-whitefly species. Sci. Rep. 8, 2734 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Kanakala, S. & Ghanim, M. Global genetic diversity and geographical distribution of Bemisia tabaci and its bacterial endosymbionts. PLoS ONE 14, e0213946 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hu, J. et al. New putative cryptic species detection and genetic network analysis of Bemisia tabaci (Hemiptera: Aleyrodidae) in China based on mitochondrial COI sequences. Mitochondr. DNA Part DNA Mapp. Seq. Anal. 29, 474–484 (2018).Vyskocilova, S., Tay, W. T., van Brunschot, S., Seal, S. & Colvin, J. An integrative approach to discovering cryptic species within the Bemisia tabaci whitefly species complex. Sci. Rep. 8, 10886 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Cheek, S. & Macdonald, O. Statutory controls to prevent the establishment of Bemisia tabaci in the United Kingdom. Pestic. Sci. 42, 135–137 (1994).CAS 

Google Scholar 
Horowitz, A. R. et al. Biotype Q of Bemisia tabaci identified in Israel. Phytoparasitica 31, 94–98 (2003).
Google Scholar 
Basit, M. Status of insecticide resistance in Bemisia tabaci: Resistance, cross-resistance, stability of resistance, genetics and fitness costs. Phytoparasitica 47, 207–225 (2019).CAS 

Google Scholar 
Horowitz, A. R., Kontsedalov, S., Khasdan, V. & Ishaaya, I. Biotypes B and Q of Bemisia tabaci and their relevance to neonicotinoid and pyriproxyfen resistance. Arch. Insect Biochem. Physiol. 58, 216–225 (2005).CAS 
PubMed 

Google Scholar 
Horowitz, A. R., Ghanim, M., Roditakis, E., Nauen, R. & Ishaaya, I. Insecticide resistance and its management in Bemisia tabaci species. J. Pest. Sci. 93, 893–910 (2020).
Google Scholar 
Delatte, H. et al. A new silverleaf-inducing biotype Ms of Bemisia tabaci (Hemiptera: Aleyrodidae) indigenous to the islands of the south-west Indian Ocean. B. Entomol. Res. 95, 29–35 (2005).CAS 

Google Scholar 
Peterschmitt, M. et al. First report of tomato yellow leaf curl virus in Réunion Island. Plant Dis. 83, 303 (1999).CAS 
PubMed 

Google Scholar 
Delatte, H., Lett, J.-M., Lefeuvre, P., Reynaud, B. & Peterschmitt, M. An insular environment before and after TYLCV introduction in Tomato Yellow Leaf Curl Virus Disease: Management, Molecular Biology, Breeding for Resistance (ed. Czosnek, H.). 13–23. (Springer, 2007).Delatte, H. et al. Microsatellites reveal extensive geographical, ecological and genetic contacts between invasive and indigenous whitefly biotypes in an insular environment. Genet. Res. 87, 109–124 (2006).CAS 
PubMed 

Google Scholar 
Delatte, H. et al. Genetic diversity, geographical range and origin of Bemisia tabaci (Hemiptera: Aleyrodidae) Indian Ocean Ms. B. Entomol. Res. 101, 487–497 (2011).CAS 

Google Scholar 
Thierry, M. et al. Mitochondrial, nuclear, and endosymbiotic diversity of two recently introduced populations of the invasive Bemisia tabaci MED species in La Réunion. Insect. Conserv. Divers. 8, 71–80 (2015).
Google Scholar 
Tsagkarakou, A. et al. Molecular diagnostics for detecting pyrethroid and organophosphate resistance mutations in the Q biotype of the whitefly Bemisia tabaci (Hemiptera: Aleyrodidae). Pestic. Biochem. Phys. 94, 49–54 (2009).CAS 

Google Scholar 
Delatte, H. et al. Differential invasion success among biotypes: case of Bemisia tabaci. Biol. Invasions 11, 1059–1070 (2009).
Google Scholar 
Chu, D., Tao, Y.-L., Zhang, Y.-J., Wan, F.-H. & Brown, J. K. Effects of host, temperature and relative humidity on competitive displacement of two invasive Bemisia tabaci biotypes [Q and B]. Insect Sci. 19, 595–603 (2012).
Google Scholar 
Chu, D., Wan, F. H., Zhang, Y. J. & Brown, J. K. Change in the biotype composition of Bemisia tabaci in Shandong Province of China from 2005 to 2008. Environ. Entomol. 39, 1028–1036 (2010).PubMed 

Google Scholar 
Pascual, S. & Callejas, C. Intra- and interspecific competition between biotypes B and Q of Bemisia tabaci (Hemiptera: Aleyrodidae) from Spain. B. Entomol. Res. 94, 369–375 (2004).CAS 

Google Scholar 
Pan, H. et al. Insecticides promote viral outbreaks by altering herbivore competition. Ecol. Appl. 25, 1585–1595 (2015).PubMed 

Google Scholar 
Shatters, R. G. et al. Population genetics of Bemisia tabaci biotypes B and Q from the Mediterranean and the U.S. inferred using microsatellite markers. in Fourth International Bemisia Workshop International Whitefly Genomics Workshop (3–8 December 2006). (Duck Key: USDA/ARS US Horticultural Research Laboratory, 2006).McKenzie, C. L. & Osborne, L. S. Bemisia tabaci MED (Q biotype) (Hemiptera: Aleyrodidae) in Florida is on the move to residential landscapes and may impact open-field agriculture. Fla. Entomol. 100, 481–484 (2017).
Google Scholar 
Guo, X.-J. et al. Diversity and genetic differentiation of the whitefly Bemisia tabaci species complex in China based on mtCOI and cDNA-AFLP analysis. J. Integr. Agr. 11, 206–214 (2012).CAS 

Google Scholar 
Prabhaker, N., Castle, S., Henneberry, T. J. & Toscano, N. C. Assessment of cross-resistance potential to neonicotinoid insecticides in Bemisia tabaci (Hemiptera: Aleyrodidae). B. Entomol. Res. 95, 535–543 (2005).CAS 

Google Scholar 
Taquet, A. et al. Insecticide resistance and fitness cost in Bemisia tabaci (Hemiptera: Aleyrodidae) invasive and resident species in La Réunion Island. Pest Manag. Sci. 76, 1235–1244 (2020).CAS 
PubMed 

Google Scholar 
Elfekih, S. et al. Genome-wide analyses of the Bemisia tabaci species complex reveal contrasting patterns of admixture and complex demographic histories. PLoS ONE 13, e0190555 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Thierry, M. et al. Symbiont diversity and non-random hybridization among indigenous (Ms) and invasive (B) biotypes of Bemisia tabaci. Mol. Ecol. 20, 2172–2187 (2011).CAS 
PubMed 

Google Scholar 
Gauthier, N. et al. Genetic structure of Bemisia tabaci Med populations from home-range countries, inferred by nuclear and cytoplasmic markers: impact on the distribution of the insecticide resistance genes. Pest Manag. Sci. 70, 1477–1491 (2014).CAS 
PubMed 

Google Scholar 
Alon, M. et al. Multiple origins of pyrethroid resistance in sympatric biotypes of Bemisia tabaci (Hemiptera: Aleyrodidae). Insect Biochem. Mol. 36, 71–79 (2006).CAS 

Google Scholar 
Vassiliou, V. et al. Insecticide resistance in Bemisia tabaci from Cyprus. Insect Sci. 18, 30–39 (2011).CAS 

Google Scholar 
Gnankiné, O., Hema, O., Namountougou, M., Mouton, L. & Vavre, F. Impact of pest management practices on the frequency of insecticide resistance alleles in Bemisia tabaci (Hemiptera: Aleyrodidae) populations in three countries of West Africa. Crop Prot. 104, 86–91 (2018).
Google Scholar 
Cahill, M., Byrne, F. J., Gorman, K., Denholm, I. & Devonshire, A. L. Pyrethroid and organophosphate resistance in the tobacco whitefly Bemisia tabaci (Homoptera: Aleyrodidae). B. Entomol. Res. 85, 181–187 (1995).CAS 

Google Scholar 
Weill, M. et al. Insecticide resistance: A silent base prediction. Curr. Biol. 14, 552–553 (2004).
Google Scholar 
Bouvier, J.-C. et al. Deltamethrin resistance in the codling moth (Lepidoptera: Tortricidae): Inheritance and number of genes involved. Heredity (Edinb) 87, 456–462 (2001).CAS 

Google Scholar 
Calvert, L. A. et al. Morphological and mitochondrial DNA marker analyses of whiteflies (Homoptera: Aleyrodidae) colonizing cassava and beans in Colombia. Ann. Entomol. Soc. Am. 94, 512–519 (2001).CAS 

Google Scholar 
Tocko-Marabena, B. K. et al. Genetic diversity of Bemisia tabaci species colonizing cassava in Central African Republic characterized by analysis of cytochrome c oxidase subunit I. PLoS ONE 12, e0182749 (2017).PubMed 
PubMed Central 

Google Scholar 
Ally, H. M. et al. What has changed in the outbreaking populations of the severe crop pest whitefly species in cassava in two decades?. Sci. Rep. 9, 1–13 (2019).CAS 

Google Scholar 
Kearse, M. et al. Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649 (2012).PubMed 
PubMed Central 

Google Scholar 
Van Oosterhout, C., Hutchinson, W. F., Wills, D. P. M. & Shipley, P. MICRO-CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538 (2004).
Google Scholar 
Weir, B. S. & Cockerham, C. C. Estimating F-statistics for the analysis of population structure. Evolution 38, 1358–1370 (1984).CAS 
PubMed 

Google Scholar 
Raymond, M. GENEPOP (version 1.2): Population genetics software for exact tests and ecumenicism. J. Hered. 86, 248–249 (1995).
Google Scholar 
Piry, S., Luikart, G. & Cornuet, J. M. BOTTLENECK: a computer program for detecting recent reductions in the effective population size using allele frequency data. J. Hered. 90, 502–503 (1999).
Google Scholar 
Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol. Ecol. 14, 2611–2620 (2005).CAS 
PubMed 

Google Scholar 
Earl, D. A. & VonHoldt, B. M. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361 (2012).
Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. https://www.r-project.org/ (2020).Jombart, T. & Ahmed, I. Adegenet 1.3–1: New tools for the analysis of genome-wide SNP data. Bioinformatics 27, 3070–3071 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kopelman, N. M., Mayzel, J., Jakobsson, M., Rosenberg, N. A. & Mayrose, I. Clumpak: A program for identifying clustering modes and packaging population structure inferences across K. Mol. Ecol. Resour. 15, 1179–1191 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Slatkin, M. Isolation by distance in equilibrium and non-equilibrium populations. Evolution 47, 264–279 (1993).PubMed 

Google Scholar 
Vähä, J.-P. & Primmer, C. R. Efficiency of model-based Bayesian methods for detecting hybrid individuals under different hybridization scenarios and with different numbers of loci. Mol. Ecol. 15, 63–72 (2006).PubMed 

Google Scholar  More

Our online sampling methods largely follow protocols detailed in3,4, though we limited our online searches to online shops and did not extend to social media. Large portions of code are directly re-used from those papers, although we provide modified code with this paper additionally. For keyword searches and data review we used R v.4.1.149 via RStudio v.1.4.110350, and made wide use of functions supplied by the anytime v.0.3.951, assertthat v.0.2.152, dplyr v.1.0.753, glue v.1.4.254, lazyeval v.0.2.255, lubridate v.1.7.1056, magrittr v.2.0.157, 17urr v.0.3.458, reshape2 v.1.4.459, stringr v.1.4.060, and tidyr v.1.1.361 other specific package uses are listed during the methods description. We used the grateful v.0.0.362 package to generate citations for all R packages. Code and data used to produce figures and summary data are also available at: 10.5281/zenodo.5758541.Website sampling and searchWe searched for contemporary arachnid selling websites using the Google search engine and targeted nine languages (English, French, Spanish, German, Portuguese, Japanese, Czech, Polish, Russian). Terms were created to be inclusive, so only spiders and scorpions were on the initial search string as specialist groups may exist for either, but are unlikely for smaller arachnid groups, which were often listed under “other” in online shops. Terms were selected to be encompassing so that any sites listing variants of “spider” or mentioning arachnid in the chosen language were selected. Whilst some groups such as tarantulas are more popular as pets such sites will not omit translations of spider and should also be captured in the search, hence Terraristika (as was shown in previous analysis of amphibians and reptiles) listed the greatest number of species, despite not being a specialist site. We used the localised versions of each of these languages with the following Boolean search strings:

English: (Spider OR scorpion OR arachnid) AND for sale

French: (Araignée OR scorpion OR arachnide) AND à vendre

Spanish: (Araña OR escorpión OR arácnido) AND en venta

German: (Arachnoid OR Spinne OR Skorpion OR Spinnentier) AND zum Verkauf

Portuguese: (Aranha OR escorpião OR aracnídeo) AND à venda

Japanese: (クモ OR サソリ OR クモ型類) AND (中村彰宏 OR 販売)

Czech: (Pavouk OR Štír OR pavoukovec) AND prodej

Polish: (Pająk OR Skorpion OR pajęczak) AND sprzedaż

Russian: Продажа пауков OR скорпионов

We undertook these searches in a private window in the Firefox v.92.0.1 browser63 to limit the impacts of search history. These keywords were used to identify sites which may be selling arachnids, which could then be checked before a comprehensive scrape.For each language, we downloaded the first 15 pages of results between 2021-06-06 and 2021-07-07 (or fewer in the result that the search returned fewer than 15 pages: German 8 pages and Spanish 14 pages). This resulted in ~1270 sites that could potentially be selling arachnids. After removing duplicates and simplifying the URLs (so all ended in.com,.org,. co.uk etc.; Code S1), we reviewed each site for the following criteria (2021-07-31 to 2021-08-02): whether they sell arachnids, the type of site (trade or classified ads), the order arachnids were listed in (e.g., date or alphabetical), the best search method for gather species appearances (see below for hierarchical search methods), a refined target URL listing species inventory, the number of pages within the website potentially required to cycle through, and if the search method required a crawl, whether the site explicitly forbade crawling data collection and whether we could limit the crawl’s scope with a filter on downstream URLs. Finally, we assigned all suitable sites with a unique ID. We have made a censored version of the website review results available in Data S1. In addition to the systematic search for arachnid trade, we added 43 websites discovered ad hoc from links on previously discovered sites (many listed online shops), those listed in other journal articles on invertebrate trade (i.e.,6) or from discussion with informed colleagues (between 2021-08-07 and 2021-09-15). After reviewing these ad hoc sites (2021-08-07 to 2021-09-15), we had a combined total of 111 sites to attempt to search for the appearance of arachnid species.Our searches of websites took one of five forms (Code S2), designed to minimise server load and limit the number of irrelevant pages searched, while ensuring we captured the pages listing species. We prioritised using the lowest/simplest search method possible for each site.Single page or PDFFor websites that listed their entire arachnid stock on a single page, we retrieved that single page using the downloader v.0.4 package64. In cases where the inventory was listed in a PDF, we manually downloaded the PDF and used pdftools v.3.0.165 to assess the text.CycleSome websites had large stocklists split across multiple pages that could be accessed sequentially. In these cases, we used the downloader v.0.4 package64 to retrieve each page, as we cycled from page 1 to the terminal page identified during the website review stage. Two sites required a slight modification to the page cycling process: as the sequential pages were not defined by pages, but by the number of adverts displayed. In these instances, we cycled through all adverts 20 adverts at a time (i.e., matching the default number displayed at a time by the site). For all cycling we implemented a 10 s cooldown between requests to limit server load.Level 1 crawlFor websites that split their stock between multiple pages, but with no sequential ordering, we used a level 1 crawl, via the Rcrawler v.0.1.9.1 package66 to access them all. For example, where a site had an “arachnid for sale” page, but full species names existed only in linked pages (e.g., “tarantulas”, “scorpions”).Cycle and level 1 crawlSome websites required a combined approach, where stock was split sequentially across pages, and the species identities (i.e., scientific names) required accessing the pages linked to from the sequential pages. In these cases, we ran the initial sequential sampling followed by a level 1 crawl.Level 2 crawlWhere level 1 crawls were insufficient to cover all species sold on a site, we used a level 2 crawl to reach all pages listing species. This tended to be the case on websites with multiple categories to classify and split their stock (e.g., “arachnid”—“spider”—“tarantula”).For all crawls, we used a cooldown of 20 s between requests to limit server load, and where possible we limited the scope of the crawl (i.e., linked pages to be retrieved) using a key phrase common to all stock listing pages (e.g., “/category=arachnid/”).In addition to the sampling of contemporary sites, we explored the archived pages available for https://www.terraristik.com via the Internet Archive (2002–201967). Terraristika had been previously shown as a major contributor to traded species lists4, and the website’s age and accessibility via the internet archive meant it was one of the few websites where temporal sampling was feasible. We used pages retrieved via the Internet Archive’s Wayback machine API68, via code created for3,4. The code used was based on the wayback v.0.4.0 package69, but additionally made httr v.1.4.270, jsonlite v.1.7.271, downloader v.0.464, lubridate v.1.7.1056, and tibble v.3.1.3 packages72 (Code S3).Keyword generationWe relied on multiple sources to build a list of arachnid species (spiders, scorpions and uropygi). For spiders we used the WSC (ref. 18; https://wsc.nmbe.ch/dataresources; accessed 2021-09-18). We filtered the WSC dataset to remove subspecies, then used a combination of rvest v.1.0.173, dplyr v.1.0.753, and stringr v.1.4.0 packages60 (see Code S4) to query the online version of the WSC database to retrieve all synonyms for each species. Where the synonyms were listed with an abbreviated genus, we replace the abbreviation with the first instance of a genus that matched the first letter of the abbreviation.We combined the WSC data with a list manually retrieved from the Scorpion Files74 (https://www.ntnu.no/ub/scorpion-files/index.php; accessed 2021-09-19). For the uropygi species, we combined species listings from Integrated Taxonomic Information System (ITIS75; https://www.itis.gov/servlet/SingleRpt/RefRpt?search_type=source&search_id=source_id&search_id_value=1209 and https://www.itis.gov/servlet/SingleRpt/SingleRpt?search_topic=TSN&anchorLocation=SubordinateTaxa&credibilitySort=TWG%20standards%20met&rankName=ALL&search_value=82710&print_version=SCR&source=from_print#SubordinateTaxa; accessed 2021-09-19) and the Western Australian Museum76 (http://www.museum.wa.gov.au/catalogues-beta/browse/uropygi; accessed 2021-09-19). We were unable to source reliable data on all scorpion and uropygi synonyms; therefore, we used all names listed from all sources, but made note of those names considered nomen dubium. Our final keyword list contained 52,111 species, 94,184 different species names, with mean of 1.81 SE ± 0.01 terms per species (range 1–61). For summaries of total species, we relied on the species classed as accepted by the species databases (WSC, Scorpion Files, ITIS and the Western Australian Museum).Keyword searchWe successfully retrieved 3020 pages from 103 websites (mean = 28.78 SE ± 11.42, range: 1–1077), and used a further 4668 previously archived pages. To prepare each of the retrieved web pages for keyword searching, we removed all double spaces, html elements, and non-alpha-numeric characters, replacing them with single spaces (Code S5). For this process we used rvest v.1.0.173, XML v.3.99.0.877, and xml2 v.1.3.278 packages. This process increased the chances that genus and species epithets would appear in a compatible format when compared to our keyword list. The process was not able to repair abbreviated genera, or aid detection where genus and species epithet were not reported side-by-side.Due to the large number of species we were forced to adapt previous searching methods, instead implementing a hierarchical genus-species search (Code S6). We searched each retrieved page for any mention of genera, then only searched for species that were contained within that genus. We did not differentiate whether the genus was currently accepted or old, so if a species had ever belonged to a genus it was included in the second stage of the search. The specifics of the keyword search used case-insensitive fixed string matching (via the stringr v.1.4.0 package60). While collation string matching would have helped detect species with differently coded ligatures or diacritic marks, the occurrence of ligature and diacritic marks are infrequent in scientific names and did not warrant the considerably increased computational costs.Via the keyword search we recorded all instances of genus matches, species matches, the website ID, and the page number. We also collected the words surrounding a genus match (3 prior and four after) as a means of exploring common terms that may be used to describe the genera.We provide the compiled outputs from searching contemporary and historic pages in Data S2–S4. Prior to combining these two datasets into a final list of traded species, and summarising the overall patterns, we cleaned out instances of spurious genera and species detections. Predominantly this included short genera names that could appear at the start of longer words (e.g., terms such as: “rufus”, “Dia”, “Diana”, “Mala”, “Inca”, “Pero”, “May”, “Janus”, “Yukon”, “Lucia”, “Zora”, “Beata”, “Neon”, “Prima”, “Meta”, “Patri”, “Enna”, “Maso”, “Mica”, “Perro”; we already implemented a filter that required genera to be preceded by a space and thus these were not part of the species name). We are confident these genera should be excluded, as none had species detected within them.Trade database and third-party dataWe downloaded United States Fish and Wildlife Service’s LEMIS data compiled by79,80 from https://doi.org/10.5281/zenodo.3565869 (Data S5). We filtered the LEMIS data to records where the class was listed as Arachnida (Code S6).We downloaded the Gross imports data from the CITES trade database from the website and filtered to Class Arachnid, years 1975–202181 (accessed 2021-09-15; Data S6), and downloaded the CITES appendices filtered to arachnids82 (Data S7).We downloaded the IUCN Redlist assessments for arachnids from https://www.iucnredlist.org83 (accessed 2021-09-15; Data S8).Species summary and visualisationWe compiled all sources of trade data (online, LEMIS, CITES) into a single dataset detailing which genera/species had been detected in each source (Data S9 and Code S7). We used two criteria to determine detection, whether there was an exact match with an accepted genus/species or whether there was a match to any historically used genera/species name. Because of splits in genera, the “ANY genera” matching is likely overly generous. For broad summaries we rely on the “ANY species” name matching.We used cowplot v.1.1.184, ggplot2 v.3.3.585, ggpubr v.0.4.086, ggtext v.0.1.187, scales v.1.1.188, scico v.1.2.089, and UpSetR v.1.4.090 to generate summary visuals (Code S8; Code S9). We added additional details to the upset plot and modified the position of plot labels using Affinity Designer v.1.10.391. We also used Affinity Designer to create the Uropygid silhouette for Fig. 1. We obtained public domain licensed spider and scorpion silhouettes from http://phylopic.org/ (https://phylopic.org/image/d7a80fdc0-311f-4bc5-b4fc-1a45f4206d27/; http://phylopic.org/image/4133ae32-753e-49eb-bd31-50c67634aca1/).Descriptions and coloursWe explored the lag time between species descriptions, and their detection in LEMIS or online trade (Code S10). We relied on the description dates provided alongside the lists of species names. Unlike the broader summaries, we restricted explorations of lag times to species detected only via exact matches (operating under the assumption that newly described species traded swiftly after description would be using the modern accepted name). We distinguished between those species detected only in the complementary data, as the earliest trade date was not known; therefore, our summaries of lag time are based on those species detected in a particular year either via LEMIS or temporal online trade.Following a visual inspection of sites where we often noticed listings with either colour or localities (e.g., “Chilobrachys spp. “Electric Blue” 0.1.3. Chilobrachys sp. “Kaeng Krachan” 0.1.0. Chilobrachys spp. “Prachuap Khiri Khan”: Data S9). We explored the words that surrounded detected genera. After using the forcats v.0.5.192, stringr v.1.4.060, and tidytext v.0.3.193 package to compile common terms and remove English stop words, we determine colour was frequently mentioned (Code S11). To filter out non-colour words, we used wikipedia’s list of colours (https://en.wikipedia.org/wiki/List_of_colors:_N%E2%80%93Z). Once cleaned, we further removed terms that are ambiguously colour related (e.g., “space”, “racing”, “photo”, “boy”, “bean”, “blaze”, “jungle”, “mountain”, “dune”, “web”, “colour”, “rainforest”, “tree”, “sea”). We then summarised this data as the counts of instances where a genus appeared alongside a given colour term (n.b., counts are therefore impacted by any underlying imbalances in how many times a site mentioned a genus). We plotted all colours using the same hex codes listed on the wikipedia page, with the exception of “cobalt”, “grey”, “metallic”, “slate”, “electric”, “dark”, “sheen”, and “chocolate” that required manual linking to a hex code.Summary of trade numbersWe summarised LEMIS data using a number of filters (Code S12). Following3,4,94, we limited our summaries to items that feasibly can be considered to represent whole individuals (LEMIS code = Dead animal BOD, live eggs (EGL), dead specimen (DEA), live specimen (LIV), specimen (SPE), whole skin (SKI), entire animal trophy (TRO)). We describe the portion of trade that is prevented (i.e., seized, where disposition == “S”). We classed non-commercial trade as anything listed as for Biomedical research (M), Scientific (S), or Reintroduction/introduction into the wild (Y). For captive vs. wild summaries, we treated all Animals bred in captivity (C and F), Commercially bred (D), and Specimens originating from a ranching operation (R) as originating from captivity. We only included animals listed as Specimens taken from the wild (W) in wild counts. The few instances that fell outside of our defined captive vs. wild categorisation are treated as other. For summaries of wild capture per genus, we relied entirely on LEMIS’s listings of genera, making no effort to determine synonymisations. We did filter out those listed only as “Non-CITES entry” or NA. We used the countrycode v.1.3.095 package to help plot the LEMIS countries of origin. Taxonomy represents an ongoing challenge, we were limited to recognising the species listed in the aforementioned databases, generating synonym lists from these sources, and attempting to reconcile these lists. Rapid rates of species description means that compiling comprehensive lists can be challenging, and species may be traded under junior synonyms or old names, and newer descriptions may not have been added to sites96. We were also limited to platforms that advertised using text not images, as images can be challenging to identify accurately.MappingMapping species is challenging due to the lack of standardised data on species distributions. Spider distributions were mapped based on the data in the World Spider Catalogue (Data S12). Firstly, the localities associated with each species were collated into four spreadsheets based on the data provided in the WSC (WSC18; https://wsc.nmbe.ch/dataresources; accessed 2021-09-18), these listed (1) country, (2) region, (3) “to” (where the range was given as one country to another) and (4) Island.Before processing any “introduced” localities were removed, the four sheets were then checked for any simple spelling errors (in islands file) or mislistings (i.e., regions in the islands file). Country data were cross-referenced with the names of country provided by Thematic Mapper to standardise them (https://thematicmapping.org/; Data S11). This was done by uploading data into Arcmap and using joins and connects to connect it to the standard country name file, and any which could not be paired were corrected to ensure all could be successfully digitised.Regions were digitised based on accepted names of different regions and included 33 different regions (see supplements) for each of these the standard accepted area within each of these regions was searched online to determine the accepted boundaries. These were then selected from the Thematic mapper, exported and labelled with the corresponding region. Once this was completed for all 33 regions they were merged and exported to a geodatabase. The spreadsheet listing regional preferences of each species was also uploaded to Arcmap 10.3, then exported into the geodatabase, then connected to a regional map using joins and relates to connect the regional preferences from the spreadsheet to the shapefiles. The new dbf was then exported to provide a listing of each species and each country in the region it was connected to, and then copied into the same csv as the corrected country listings.For preferences listed as “to” we first separated each country listed in the “to” listings into a separate column, then developed a list of species and each of the countries listed in the “to” list (which was frequently between 5–6). These were then corrected to the standard names from thematic mapper for both countries and the regions used in the previous section. We then merged the countries and regions file and added fields of geometry in ArcMap to provide a centroid for each designated area. This table was then exported and joined and connected to the species in the “to” file. This data was then converted to point form and turned to a point file, then a minimum convex polygon (convex hulls) developed for each species to connect the regions between all those listed. These species specific minimum convex polygons were then intersected with the countries from Thematic mapper, and then dissolve was used to form a shapefile that just listed species and all the countries between those ranges. This was then exported and merged with the listings from countries and regions.The islands file included both independent islands (which needed names corrected, or archipelago names given) and those that fall within a national designation. For those islands we replaced the island name with that of the country, as listings of species may be particularly poor, and tiny non-independent islands are not visible in the global-scale analysis.This forth database table was then merged with the former three, and remove duplicates used to remove any duplicate entries, as species often had individual countries listed in additions to regions or “to”. This was then uploaded into Arcmap and exported to a geodatabase file then connected to the original Thematic mapper file and exported to the geodatabase to yield 134,187 connections between species and countries. This was then connected to our main analysis to include the trade status, and CITES and IUCN Redlist status for each species for further analysis.Scorpion data was considerably messier than that on the world spider catalogue. Firstly, we downloaded all scorpion data from iNaturalist and GBIF97,98 (search; scorpions), removed duplicates, then cross-referenced these with the thematic mapper file within Quantum GIS. Species listed in regions where they were clearly not native (i.e., a species listed in the UK when the rest of that species or genus were in Australia) were removed, and all extinct species were excluded.In addition, all the “update files” were downloaded from the “Scorpion files”, the PDFs collated then using smallpdf tools the tables were extracted into excel form and cleaned to include just species and country listing. This was added to the countries listed for species within99 and100 though this was restricted to a subset of species. The data were all collated into an excel file with the species name, and country listing. This was then added to all the data from https://scorpiones.pl/maps/. These maps have a good coverage of species countries, but are apparently no longer being updated (Jan Ove Rein pers comm 2021) hence the need for further data to provide complete and updated and comprehensive coverage for all species. Country names were then standardised based on the Thematic Mapper standards (Data S13 and Data S11). Species names were then cross-referenced to those listed in the Scorpion files, any not matching were checked as synonyms and converted to the accepted name (though the only collated data for Scorpion synonyms was on French-language Wikipedia, i.e., see https://fr.wikipedia.org/wiki/Bothriurus). Once all country and species names were corrected this provided a listing of 4059 species-country associations. These were then associated with country files in the same way as spiders. We plotted spider and scorpion species/genera, as well as LEMIS origins, using ggplot285, combining Thematic world border data (https://thematicmapping.org/) with summaries of species/genera/and trade levels. Species listed in a single-country (and thus more likely to be country endemic) were also counted using summary statistics, so that species most vulnerable to trade could be noted separately.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

PreliminariesTo unify all the five classes of two-by-two games, Yamamoto et al.35 introduced the weightlifting game. In this game, each player either cooperates or defects in carrying a weight. Players who carry the weight pay a cost, (cge 0). The weight is successfully lifted with probability ({p}_{i}), where (i=mathrm{0,1},2) is the total number of cooperators and ({p}_{i}) increases with the number of cooperators (i). If the cooperators succeed, both players receive a benefit (b >0). However, in case of failure, both players gain nothing. The pay-off of the cooperators is (b{p}_{i}-c), and the pay-off of the defectors is (b{p}_{i}) (Table 2). In terms of the parameters (Delta {p}_{1}={p}_{1}-{p}_{0}) and (Delta {p}_{2}={p}_{2}-{p}_{1}), which represents the increase in the probability of success due to an additional cooperator, the following inequalities are obtained for the pay-offs (R, T, S), and (P) (Table 1):

(i)

(Delta {p}_{1} >c/b) for (S >P),

(ii)

(Delta {p}_{2} >c/b) for (R >T), and

(iii)

(Delta {p}_{1}+Delta {p}_{2} >c/b) for (R >P).

Table 2 Pay-off table of two-person weightlifting game.Full size tablePD satisfies only (iii), CH satisfies (i) and (iii), SH satisfies (ii) and (iii), DT satisfies none of the three conditions, and CT satisfies all three. In 2021, Chiba et al.1 studied the evolution of cooperation in society by incorporating environmental value in the weightlifting game. They found that the evolution of cooperation seems to follow a DT to DT trajectory, which can explain the rise and fall of human societies.The ({varvec{n}})-player weightlifting gameIn this study, we generalize the weightlifting game to (n)-players. Suppose (n) self-interested and rational individuals selected from a population of infinite size. The (n) players are asked to lift a weight. Each individual (or player) can decide to either carry the weight (cooperate, (C)) or not carry/pretend to carry the weight (defect, (D)). Players who decide to carry the weight can either succeed or fail. The probability of successful weightlifting is denoted by ({p}_{i}), (i=mathrm{0,1},dots ,n), where (i) indicates the number of cooperators (henceforth, (i) always represents the number of cooperators). The probability of success increases with the number of individuals cooperating, and it may remain less than unity even if all (n) individuals cooperate. Players who decide to carry the weight pay a cost, (cge 0), regardless of the outcome, while those who defect need not pay anything. If the cooperators succeed, all (n) individuals receive a benefit (bge 0). There is no penalty for failure. We use the expected gains/losses of the players as the pay-off. If there are (i-1) cooperative players, then the pay-off of (j) is ({B}_{C}left(iright)=b{p}_{i}-c) when (j) cooperates and ({B}_{D}left(i-1right)=b{p}_{i-1}) when (j) defects. The number of cooperators differs by one, since in ({B}_{C}left(iright)), there is an additional cooperator, which is (j) him- or herself. To decide whether to cooperate or defect, all players weigh their expected gain and rationally choose the option with the highest expected gain. The graphical outline of this game is illustrated in Fig. 1 (see also Supplementary Figure S1 for the flow of the game). The pay-off table for a four-player game is shown as an example in Table 3. Here, player (1) is the innermost row (strategies are listed in the second column of the table), player (2) is the innermost column (strategies are listed in the second row of the table), and the succeeding players take the succeeding rows or columns (we enter the first player as a row player and the following player as a column player and continue in this order). Each cell represents players’ pay-offs, with the first component being the pay-off for the first player, the second for the second player, and so on. For instance, consider the entry in the first row and third column, where players (1, 2) and (3) cooperate but player (4) defects. The pay-offs of players (1) to (3) are ({B}_{C}(3)), while the pay-off of player (4) is ({B}_{D}left(3right)). In the above example, there are as many row players as column players because the number of players is even. However, we can have one more player in the rows than in the columns if there is an odd number of players.Figure 1A schematic diagram of the n-player weightlifting game. In this game, players decide whether to cooperate or defect in carrying the weight. Cooperators need to pay a cost. The weightlifting can either succeed or fail. In case of success, all players receive a benefit. In case of failure, all players receive nothing. The player’s pay-off depends on the benefit, cost and probability of success. Each player decides whether to cooperate or defect so as to maximize the expected gain.Full size imageTable 3 Pay-off table of four-player weightlifting game.Full size tableNash equilibrium and pareto optimal strategiesHere we present the Nash equilibrium and Pareto optimal strategies of the (n)-player weightlifting game in terms of the cost-to-benefit ratio (c/b) and probability of success ({p}_{i}). The Nash equilibrium consists of the best responses of each player. Players have no incentive to deviate from this strategy profile since deviation will not increase an individual’s pay-off if the other players maintain the same strategy. If ({B}_{C}(i)ge {B}_{D}(i-1)), the best response of player (j) is to cooperate, but if ({B}_{C}(i)le {B}_{D}(i-1)), the best response is to defect.We have (Delta {p}_{i}={p}_{i}-{p}_{i-1}ge 0) for the increase in the probability of success because the probability ({p}_{i}) increases with the number of cooperators (i). It is convenient to divide cases depending on whether (Delta {p}_{i} >c/b) or (Delta {p}_{i} More

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DataAll data was processed and analyzed using R (R Core Team, Version 4.0.3).Dengue case data were collected and shared by the Alcaldía de Medellín, Secretaría de Salud. In Medellin, dengue case surveillance is conducted by public health institutions that classify and report all cases that meet the WHO clinical dengue case criteria for a probable case to Medellin’s Secretaría de Salud through SIVIGILA (“el Sistema Nacional de Vigilancia en Salud Publica). All case data were de-identified and aggregated to the SIT Zone level.Human public transit usage and movement data were collected and shared by the Área Metropolitana del Valle de Aburrá for 50–200 respondents per SIT Zone. The “Encuestas Origen Destino” (Origen Destination Surveys) were conducted in 2005, 2011, and 2016 and published in 2006, 2012, and 2017, with survey methods described by the Área Metropolitana del Valle de Aburrá25. Survey respondents include a randomly selected subset of all Medellin residents in each SIT zone regardless of whether they use public transit or not. Survey respondents reported the start and end locations, purpose for travel, and mode of travel for all movement over the last 24 h from the time the survey was administered. Respondents reported all modes of movement, including public transit, private transit, and movement on foot. The results of the survey published in 2017 are published online by the Área Metropolitana del Valle de Aburrá26, and select data are available through the geodata-Medellin open data portal27. The results and data of the survey published in 2012 are not publicly available and were obtained directly from the Área Metropolitana del Valle de Aburrá.The public transit usage survey data were also used to extract socioeconomic data to the SIT zone; surveyors also reported basic demographic data including household Estrato, which was averaged per SIT zone to estimate zone socioeconomic status. “Estrato” measures socioeconomic status on a scale from 1 (lowest) to 6 (highest). This system is used by the government of Colombia to allocate public services and subsidies (Law 142, 1994). Data from the public transit usage survey were used to extract socioeconomic status data because it is the only location available where the spatial scale of the data matched the spatial scale of the SIT zone.Data on the location of Medellín public transit lines was downloaded as shape files from the geodata-Medellín open data portal27 and subset for each year to the set of transit lines that was available in that year. Data on the opening date of each Medellín public transit line was taken from the Medellín metro website28.Because census data at the zone level were not available for this study and only exists for 2005 and 2018, we used population estimates for each year downloaded from the WorldPop project29 and aggregated by SIT zone. The accuracy of WorldPop estimates were checked against available census data for 2005 and 2018 at the comuna level, accessed via the geodata- Medellín open data portal27.Ethical considerationsNo human subjects research was conducted. All data used was de-identified, and the analysis was conducted on a database of cases meeting the clinical criteria for dengue with no intervention or modification of biological, physical, psychological, or social variables. All methods were performed in accordance with the relevant guidelines and regulations.Data analysisQuantifying public transit usage and distance from nearest transit lineTo quantify public transit usage, we determined if each respondent reported using the metro, metroplus, or ruta alimentadora (supplementary bus route system integrated with the metro system) in the last 24 h. We then calculated the percent of respondents using the public transit system at least once for each SIT zone.To quantify the distance to the nearest public transit line, we calculated the distance from the center point of each zone to the closest metro, metroplus, tranvía, metrocable, ruta alimentadora, or escalera eléctrica. This was recalculated for each year, including new transit lines that were added within that year.Spatial autoregressive models of dengue incidenceDengue incidence per year at the level of the SIT zone was modeled using a fixed effects spatial panel model by maximum likelihood (R package splm30) as described in31. Our fixed effects were socioeconomic status, distance from public transit, a two-way interaction between these factors, and year. To weight dengue cases by population per SIT zone, the model contained a log offset of population per zone per year. Dengue case counts were log transformed after adding one to account for zones with zero dengue cases in a given year. Year was analyzed as a categorical variable to avoid smoothing epidemic years. All continuous variables were scaled to enable comparison of effect size. Because these panel models require balanced data across time, data was truncated to SIT zones that had data for all years available (247 remaining of 291). Spatial dependency was evaluated, and the model was selected using the Hausman specification test and locally robust panel Lagrange Multiplier tests for spatial dependence. Based on a significant Hausman specification test result, which indicates a poor specification of the random effect model, a fixed effect model was chosen. This result is supported by the fact that we had a nearly exhaustive sample of SIT zones in the Medellin metro area. Lagrange multiplier tests were used to determine the most appropriate spatial dependency specifications. Based on the results of the Lagrange multiplier tests, a Spatial Autoregressive (SAR) model was the most appropriate to incorporate spatial dependency; a SAR model considers that the number of dengue cases in a SIT zone depends on the number in neighboring zones.Because public transit usage was a measurement taken during just two of the study years, we constructed an additional fixed effects spatial panel model by maximum likelihood model of dengue incidence in just 2011 and 2016 that included ridership as an additional predictor variable. Our fixed effects were year, socioeconomic status, distance from public transit, a two-way interaction between socioeconomic status and distance from public transit, percent utilizing public transit, and a two-way interaction between socioeconomic status and percent utilizing public transit. As in our model of all years, the model contained a log offset of population per zone per year and dengue case counts were log transformed after adding one to account for zones with zero dengue cases in a given year, year was analyzed as a categorical variable, and all continuous variables were scaled to enable comparison of effect size. The data was truncated to SIT zones that had data for all years available (251 remaining of 291). We used the same model selection process, and again a fixed effect model was chosen, and based on the results of the Lagrange multiplier tests, a Spatial Autoregressive (SAR) model was determined the most appropriate to incorporate spatial dependency. More

Australia’s tropical rainforests are some of the oldest in the world.Credit: Alexander Schenkin

The rate of tree dying in the old-growth tropical forests of northern Australia each year has doubled since the 1980s, and researchers say climate change is probably to blame.The findings, published today in Nature1, come from an extraordinary record of tree deaths catalogued at 24 sites in the tropical forests of northern Queensland over the past 49 years.“Trees are such long-living organisms that it really requires huge amounts of data to be able to detect changes in such rare events as the death of a tree,” says lead author David Bauman, a plant ecologist at the University of Oxford, UK. The sites were initially surveyed every two years, then every three to four years, he explains, and the analysis focused on 81 key species.Bauman and his team recorded that 2,305 of these trees have died since 1971. But they calculated that, from the mid-1980s, tree mortality risk increased from an average of 1% a year to 2% a year (See ‘Increasing death rate’).

Bauman says that trees help to slow global warming because they absorb carbon dioxide, so an increase in tree deaths reduces forests’ carbon-capturing ability. “Tropical forests are critical to climate change, but they’re also very vulnerable to it,” he explains.Climate changeThe study found that the rise in death rate occurred at the same time as a long-term trend of increases in the atmospheric vapour pressure deficit, which is the difference between the amount of water vapour that the atmosphere can hold and the amount of water it does hold at a given time. The higher the deficit, the more water trees lose through their leaves. “If the evaporative demand at the leaf level can’t be matched by water absorption in fine roots, it can lead to leaves wilting, whole branches dying and, if the stress is sustained, to tree death,” Bauman says.The researchers looked at other climate-related trends — including rising temperatures and an estimate of drought stress in soils — but they found that the drying atmosphere had the strongest effect. “What we show is that this increase [in tree mortality risk] also closely followed the increase in atmospheric water stress, or the drying power of air, which is a consequence of the temperature increase due to climate change,” Bauman explains.Of the 81 tree species that the team studied, 70% showed an increase in mortality risk over the study period, including the Moreton Bay chestnut (Castanospermum australe), white aspen (Medicosma fareana) and satin sycamore (Ceratopetalum succirubrum).The authors also saw differences in mortality in the same tree species across plots, depending on how high the atmospheric vapour pressure deficit was in each plot.“This is one data set where the trees have been monitored in reasonably good detail since the early ’70s, and this is a really top-notch analysis of it,” says Belinda Medlyn, an ecosystem scientist at University of Western Sydney, Australia.But she says that more experiments are needed to determine whether the vapour pressure deficit is the biggest climate-related contributor to the increase in tree deaths. More