Philippa Kaur

Alizon S, de Roode JC, Michalakis Y (2013) Multiple infections and the evolution of virulence. Ecol Lett 16(4):556–67PubMed 

Google Scholar 
Bian G, Zhou G, Lu P, Xi Z (2013) Replacing a native Wolbachia with a novel strain results in an increase in endosymbiont load and resistance to dengue virus in a mosquito vector. PLoS Negl Trop Dis 7(6):e2250PubMed 
PubMed Central 

Google Scholar 
Bjørnstad ON, Harvill ET (2005) Evolution and emergence of Bordetella in humans. Trends Microbiol 13(8):355–9PubMed 

Google Scholar 
Bosch TC (2013) Cnidarian-microbe interactions and the origin of innate immunity in metazoans. Annu Rev Microbiol 67:499–518CAS 
PubMed 

Google Scholar 
Bull JJ, Turelli M (2013) Wolbachia versus dengue: Evolutionary forecasts. Evol Med Public Health 2013(1):197–207PubMed 
PubMed Central 

Google Scholar 
Cabreiro F, Gems D (2013) Worms need microbes too: microbiota, health and aging in Caenorhabditis elegans. EMBO Mol Med 5(9):1300–10CAS 
PubMed 
PubMed Central 

Google Scholar 
Chen F, Krasity BC, Peyer SM, Koehler S, Ruby EG, Zhang X et al. (2017) Bactericidal permeability-increasing proteins shape host-microbe interactions. mBio 8:e00040–17CAS 
PubMed 
PubMed Central 

Google Scholar 
Chrostek E, Pelz-Stelinski K, Hurst GDD, Hughes GL (2017) Horizontal Transmission of Intracellular Insect Symbionts via Plants. Front Microbiol 8:2237PubMed 
PubMed Central 

Google Scholar 
Chrostek E, Teixeira L (2015) Mutualism breakdown by amplification of Wolbachia genes. PLoS Biol 13(2):e1002065PubMed 
PubMed Central 

Google Scholar 
Cisani G, Varaldo PE, Grazi G, Soro O (1982) High-level potentiation of lysostaphin anti-staphylococcal activity by lysozyme. Antimicrob Agents Chemother 21(4):531–5CAS 
PubMed 
PubMed Central 

Google Scholar 
Clark LC, Hodgkin J (2014) Commensals, probiotics and pathogens in the Caenorhabditis elegans model. Cell Microbiol 16(1):27–38CAS 
PubMed 

Google Scholar 
Coolon JD, Jones KL, Todd TC, Carr BC, Herman MA (2009) Caenorhabditis elegans genomic response to soil bacteria predicts environment-specific genetic effects on life history traits. PLOS Genet 5:e1000503PubMed 
PubMed Central 

Google Scholar 
Dierking K, Yang W, Schulenburg H (2016) Antimicrobial effectors in the nematode Caenorhabditis elegans: an outgroup to the Arthropoda. Philos Trans R Soc Lond B Biol Sci 371:1695
Google Scholar 
Dong Y, Manfredini F, Dimopoulos G (2009) Implication of the mosquito midgut microbiota in the defense against malaria parasites. PLoS Pathog 5(5):e1000423PubMed 
PubMed Central 

Google Scholar 
Drew GC, King KC (2022) More or less? The effect of symbiont density in protective mutualisms. Am Nat 199(4):443–54PubMed 

Google Scholar 
Ford SA, Kao D, Williams D, King KC (2016) Microbe-mediated host defence drives the evolution of reduced pathogen virulence. Nat Commun 7:13430CAS 
PubMed 
PubMed Central 

Google Scholar 
Ford SA, King KC (2016) Harnessing the Power of Defensive Microbes: Evolutionary Implications in Nature and Disease Control. PLoS Pathog 12(4):e1005465PubMed 
PubMed Central 

Google Scholar 
Ford SA, King KC (2021) In Vivo Microbial Coevolution Favors Host Protection and Plastic Downregulation of Immunity. Mol Biol Evol 38(4):1330–1338CAS 
PubMed 

Google Scholar 
Frank SA (1996) Models of parasite virulence. Q Rev Biol 71(1):37–78CAS 
PubMed 

Google Scholar 
Félix MA, Braendle C (2010) The natural history of Caenorhabditis elegans. Curr Biol 20(22):R965–9PubMed 

Google Scholar 
Garsin DA, Sifri CD, Mylonakis E, Qin X, Singh KV, Murray BE et al. (2001) A simple model host for identifying Gram-positive virulence factors. Proc Natl Acad Sci USA 98(19):10892–7CAS 
PubMed 
PubMed Central 

Google Scholar 
Gerardo NM, Parker BJ (2014) Mechanisms of symbiont-conferred protection against natural enemies: an ecological and evolutionary framework. Curr Opin Insect Sci 4:8–14PubMed 

Google Scholar 
Gravato-Nobre MJ, Hodgkin J (2005) Caenorhabditis elegans as a model for innate immunity to pathogens. Cell Microbiol 7(6):741–51CAS 
PubMed 

Google Scholar 
Habets MG, Rozen DE, Brockhurst MA (2012) Variation in Streptococcus pneumoniae susceptibility to human antimicrobial peptides may mediate intraspecific competition. Proc Biol Sci 279(1743):3803–11CAS 
PubMed 
PubMed Central 

Google Scholar 
Heath BD, Butcher RD, Whitfield WG, Hubbard SF (1999) Horizontal transfer of Wolbachia between phylogenetically distant insect species by a naturally occurring mechanism. Curr Biol 9(6):313–6CAS 
PubMed 

Google Scholar 
Heikkilä MP, Saris PE (2003) Inhibition of Staphylococcus aureus by the commensal bacteria of human milk. J Appl Microbiol 95(3):471–8PubMed 

Google Scholar 
Hoffmann AA, Ross PA, Rašić G (2015) Wolbachia strains for disease control: ecological and evolutionary considerations. Evol Appl 8(8):751–68PubMed 
PubMed Central 

Google Scholar 
Hope IA (1999) C. elegans: a practical approach. Oxford University Press, Oxford
Google Scholar 
Huigens ME, de Almeida RP, Boons PA, Luck RF, Stouthamer R (2004) Natural interspecific and intraspecific horizontal transfer of parthenogenesis-inducing Wolbachia in Trichogramma wasps. Proc Biol Sci 271(1538):509–15CAS 
PubMed 
PubMed Central 

Google Scholar 
Jaenike J, Polak M, Fiskin A, Helou M, Minhas M (2007) Interspecific transmission of endosymbiotic Spiroplasma by mites. Biol Lett 3(1):23–5CAS 
PubMed 

Google Scholar 
Kaltenpoth M, Engl T (2014) Defensive microbial symbionts in Hymenoptera. Funct Ecol 28(2):315–27
Google Scholar 
King KC (2019) Quick guide: defensive symbionts. Curr Biol 29:R78–R80CAS 
PubMed 

Google Scholar 
King KC, Brockhurst MA, Vasieva O, Paterson S, Betts A, Ford SA et al. (2016) Rapid evolution of microbe-mediated protection against pathogens in a worm host. ISME J 10(8):1915–24CAS 
PubMed 
PubMed Central 

Google Scholar 
Kong C, Tan MW, Nathan S (2014) Orthosiphon stamineus protects Caenorhabditis elegans against Staphylococcus aureus infection through immunomodulation. Biol Open 3(7):644–55PubMed 
PubMed Central 

Google Scholar 
Kopylova E, Noé L, Touzet H (2012) SortMeRNA: Fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 14(24):3211–17
Google Scholar 
Koziel J, Potempa J (2013) Protease-armed bacteria in the skin. Cell Tissue Res 351:325–37CAS 
PubMed 

Google Scholar 
Lysenko ES, Ratner AJ, Nelson AL, Weiser JN (2005) The role of innate immune responses in the outcome of interspecies competition for colonization of mucosal surfaces. PLoS Pathog 1(1):e1PubMed 
PubMed Central 

Google Scholar 
Magalhaes T, Bergren NA, Bennett SL, Borland EM, Hartman DA, Lymperopoulos K et al. (2019) Induction of RNA interference to block Zika virus replication and transmission in the mosquito Aedes aegypti. Insect Biochem Mol Biol 111:103169CAS 
PubMed 
PubMed Central 

Google Scholar 
Margolis E, Yates A, Levin BR (2010) The ecology of nasal colonization of Streptococcus pneumoniae, Haemophilus influenzae and Staphylococcus aureus: the role of competition and interactions with host’s immune response. BMC Microbiol 10:59PubMed 
PubMed Central 

Google Scholar 
Marra A, Hanson MA, Kondo S, Erkosar B, Lemaitre B (2021) Drosophila Antimicrobial Peptides and Lysozymes Regulate Gut Microbiota Composition and Abundance. mBio 12(4):e0082421CAS 
PubMed 

Google Scholar 
Martinez J, Cogni R, Cao C, Smith S, Illingworth CJ, Jiggins FM (2016) Addicted? Reduced host resistance in populations with defensive symbionts. Proc Biol Sci 283:1833
Google Scholar 
Martín-Platero AM, Valdivia E, Ruíz-Rodríguez M, Soler JJ, Martín-Vivaldi M, Maqueda M et al. (2006) Characterization of antimicrobial substances produced by Enterococcus faecalis MRR 10-3, isolated from the uropygial gland of the hoopoe (Upupa epops). Appl Environ Microbiol 72(6):4245–9PubMed 
PubMed Central 

Google Scholar 
Mason KL, Stepien TA, Blum JE, Holt JF, Labbe NH, Rush JS et al. (2011) From commensal to pathogen: translocation of Enterococcus faecalis from the midgut to the hemocoel of Manduca sexta. MBio 2(3):e00065–11PubMed 
PubMed Central 

Google Scholar 
Matthews AC, Mikonranta L, Raymond B (2019) Shifts along the parasite-mutualist continuum are opposed by fundamental trade-offs. Proc Biol Sci 286(1900):20190236CAS 
PubMed 
PubMed Central 

Google Scholar 
May G, Nelson P (2014) Defensive mutualisms: do microbial interactions within hosts drive the evolution of defensive traits? Funct Ecol 28(2):356–63
Google Scholar 
Mejía LC, Herre EA, Sparks JP, Winter K, García MN, Van Bael SA et al. (2014) Pervasive effects of a dominant foliar endophytic fungus on host genetic and phenotypic expression in a tropical tree. Front Microbiol 5:479PubMed 
PubMed Central 

Google Scholar 
Mergaert P (2018) Role of antimicrobial peptides in controlling symbiotic bacterial populations. Nat prod Rep. 35(4):336–56CAS 
PubMed 

Google Scholar 
Metcalf CJE, Koskella B (2019) Protective microbiomes can limit the evolution of host pathogen defense. Evol Lett 3:534–43PubMed 
PubMed Central 

Google Scholar 
Montalvo-Katz S, Huang H, Appel MD, Berg M, Shapira M (2013) Association with soil bacteria enhances p38-dependent infection resistance in Caenorhabditis elegans. Infect Immun 81(2):514–20CAS 
PubMed 
PubMed Central 

Google Scholar 
Moreira LA, Iturbe-Ormaetxe I, Jeffery JA, Lu G, Pyke AT, Hedges LM et al. (2009) A Wolbachia symbiont in Aedes aegypti limits infection with dengue, Chikungunya, and Plasmodium. Cell 139(7):1268–78PubMed 

Google Scholar 
O’Neill SL, Ryan PA, Turley AP, Wilson G, Retzki K, Iturbe-Ormaetxe I et al. (2018) Scaled deployment of Wolbachia to protect the community from Aedes transmitted arboviruses. Gates Open Res 2:36PubMed 

Google Scholar 
Oliver KM, Campos J, Moran NA, Hunter MS (2008) Population dynamics of defensive symbionts in aphids. Proc Biol Sci 275(1632):293–9PubMed 

Google Scholar 
Oliver KM, Smith AH, Russell JA (2014) Defensive symbiosis in the real world ‘96 advancing ecological studies of heritable, protective bacteria in aphids and beyond. Funct Ecol 28(2):341–55
Google Scholar 
Pan X, Pike A, Joshi D, Bian G, McFadden MJ, Lu P et al. (2018) The bacterium Wolbachia exploits host innate immunity to establish a symbiotic relationship with the dengue vector mosquito Aedes aegypti. ISME J 12(1):277–88CAS 
PubMed 

Google Scholar 
Parker BJ, Barribeau SM, Laughton AM, de Roode JC, Gerardo NM (2011) Non-immunological defense in an evolutionary framework. Trends Ecol Evol 26(5):242–8PubMed 

Google Scholar 
Pastar I, O’Neill K, Padula L, Head CR, Burgess JL, Chen V et al. (2020) Staphylococcus epidermidis Boosts Innate Immune Response by Activation of Gamma Delta T Cells and Induction of Perforin-2 in Human Skin. Front Immunol 11:550946CAS 
PubMed 
PubMed Central 

Google Scholar 
Pees B, Kloock A, Nakad R, Barbosa C, Dierking K (2017) Enhanced behavioral immune defenses in a C. elegans C-type lectin-like domain gene mutant. Dev Comp Immunol 74:237–42CAS 
PubMed 

Google Scholar 
Peleg AY, Tampakakis E, Fuchs BB, Eliopoulos GM, Moellering RC, Mylonakis E (2008) Prokaryote-eukaryote interactions identified by using Caenorhabditis elegans. Proc Natl Acad Sci USA 105(38):14585–90CAS 
PubMed 
PubMed Central 

Google Scholar 
Petersen C, Dirksen P, Schulenburg H (2015) Why we need more ecology for genetic models such as C. elegans. Trends Genet 31(3):120–7CAS 
PubMed 

Google Scholar 
Pimentel H, Bray NL, Puente S, Melsted P, Pachter L (2017) Differential analysis of RNA-seq incorporating quantification uncertainty. Nat Methods 14(7):687–90CAS 
PubMed 

Google Scholar 
Portal-Celhay C, Blaser MJ (2012) Competition and resilience between founder and introduced bacteria in the Caenorhabditis elegans gut. Infect Immun 80(3):1288–99CAS 
PubMed 
PubMed Central 

Google Scholar 
Raberg L, de Roode JC, Bell AS, Stamou P, Gray D, Read AF (2006) The role of immune-mediated apparent competition in genetically diverse malaria infections. Am Nat 168(1):41–53PubMed 

Google Scholar 
Rafaluk-Mohr C, Ashby B, Dahan DA, King KC (2018) Mutual fitness benefits arise during coevolution in a nematode-defensive microbe model. Evol Lett 2(3):246–56PubMed 
PubMed Central 

Google Scholar 
Ragland SA, Criss AK (2017) From bacterial killing to immune modulation: Recent insights into the functions of lysozyme. PLoS Pathog 13(9):e1006512PubMed 
PubMed Central 

Google Scholar 
Rancès E, Ye YH, Woolfit M, McGraw EA, O’Neill SL (2012) The relative importance of innate immune priming in Wolbachia-mediated dengue interference. PLoS Pathog 8(2):e1002548PubMed 
PubMed Central 

Google Scholar 
Raudvere U, Kolberg L, Kuzmin I, Arak T, Adler P, Peterson H et al. (2019) g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res 47(W1):W191–W198CAS 
PubMed 
PubMed Central 

Google Scholar 
Raymann K, Shaffer Z, Moran NA (2017) Antibiotic exposure perturbs the gut microbiota and elevates mortality in honeybees. PLoS Biol 15(3):e2001861PubMed 
PubMed Central 

Google Scholar 
Rossouw W, Korsten L (2017) Cultivable microbiome of fresh white button mushrooms. Lett Appl Microbiol 64(2):164–70CAS 
PubMed 

Google Scholar 
Russell JA, Moran NA (2005) Horizontal transfer of bacterial symbionts: heritability and fitness effects in a novel aphid host. Appl Environ Microbiol 71(12):7987–94CAS 
PubMed 
PubMed Central 

Google Scholar 
Ryu H, Kim SH, Lee HY, Bai JY, Nam YD, Bae JW et al. (2008) Innate immune homeostasis by the homeobox gene Caudal and commensal-gut mutualism in Drosophila. Science 319:777–82CAS 
PubMed 

Google Scholar 
Sellegounder D, Liu Y, Wibisono P, Chen CH, Leap D, Sun J (2019) Neuronal GPCR NPR-8 regulates C. elegans defense against pathogen infection. Sci Adv 5(11):eaaw4717CAS 
PubMed 
PubMed Central 

Google Scholar 
Sifri CD, Begun J, Ausubel FM, Calderwood SB (2003) Caenorhabditis elegans as a model host for Staphylococcus aureus pathogenesis. Infect Immun 71(4):2208–17CAS 
PubMed 
PubMed Central 

Google Scholar 
Singh UB, Malviya D, Wasiullah, Singh S, Pradhan JK, Singh BP et al. (2016) Bio-protective microbial agents from rhizosphere eco-systems trigger plant defense responses provide protection against sheath blight disease in rice (Oryza sativa L.). Microbiol Res 192:300–12CAS 
PubMed 

Google Scholar 
Trevelline BK, Fontaine SS, Hartup BK, Kohl KD (2019) Conservation biology needs a microbial renaissance: a call for the consideration of host-associated microbiota in wildlife management practices. Proc Biol Sci 286(1895):20182448PubMed 
PubMed Central 

Google Scholar 
Ulrich Y, Schmid-Hempel P (2012) Host modulation of parasite competition in multiple infections. Proc Biol Sci 279(1740):2982–9PubMed 
PubMed Central 

Google Scholar 
Vaishnava S, Yamamoto M, Severson KM, Ruhn KA, Yu X, Koren O et al. (2011) The antibacterial lectin RegIIIgamma promotes the spatial segregation of microbiota and host in the intestine. Science 334(653):255–8CAS 
PubMed 
PubMed Central 

Google Scholar 
Varahan S, Iyer VS, Moore WT, Hancock LE (2013) Eep confers lysozyme resistance to enterococcus faecalis via the activation of the extracytoplasmic function sigma factor SigV. J Bacteriol 195(14):3125–34CAS 
PubMed 
PubMed Central 

Google Scholar 
Visvikis O, Ihuegbu N, Labed SA, Luhachack LG, Alves AF, Wollenberg AC et al. (2014) Innate host defense requires TFEB-mediated transcription of cytoprotective and antimicrobial genes. Immunity 40(6):896–909CAS 
PubMed 
PubMed Central 

Google Scholar 
Vorburger C, Ganesanandamoorthy P, Kwiatkowski M (2013) Comparing constitutive and induced costs of symbiont-conferred resistance to parasitoids in aphids. Ecol Evol 3(3):706–13PubMed 
PubMed Central 

Google Scholar 
Wang S, Dos-Santos ALA, Huang W, Liu KC, Oshaghi MA, Wei G et al. (2017) Driving mosquito refractoriness to Plasmodium falciparum with engineered symbiotic bacteria. Science 357(6358):1399–1402CAS 
PubMed 

Google Scholar 
Wilke AB, Marrelli MT (2015) Paratransgenesis: a promising new strategy for mosquito vector control. Parasit Vectors 8:342PubMed 
PubMed Central 

Google Scholar 
Wong D, Bazopoulou D, Pujol N, Tavernarakis J, Ewbank J (2007) Genome-wide investigation reveals pathogen-specific and shared signatures in the response of Caenorhabditis elegans to infection. Genome Biol 8:R194PubMed 
PubMed Central 

Google Scholar  More

WHO. Global plan for insecticide management. (World Health Organisation, Geneva, Switzerland 130, 2012).WHO. Paludisme: situation mondiale. vol. 2507. World Health Organisation, Geneva, Switzerland, (2020).WHO. Procédures pour tester la résistance aux insecticides chez les moustiques vecteurs du paludisme Seconde édition. (World Health Organisation, Geneva, Switzerland, 2017).WHO. Guidelines for Malaria Vector Control. (World Health Organisation, Geneva, Switzerland, 2019).Churcher, T. S., Lissenden, N., Griffin, J. T., Worrall, E. & Ranson, H. The impact of pyrethroid resistance on the efficacy and effectiveness of bednets for malaria control in Africa. Elife 5, 16090 (2016).
Google Scholar 
Hemingway, J. et al. Averting a malaria disaster: Will insecticide resistance derail malaria control?. Lancet 387, 1785–1788 (2016).PubMed 
PubMed Central 

Google Scholar 
Dabiré, K. R. et al. Trends in insecticide resistance in natural populations of malaria vectors in Burkina Faso, West Africa: 10 Years surveys K. INTECH 32, 479–502 (2012).
Google Scholar 
WHO. WHO Global Malaria Programme: Global Plan for insecticide resistance management. (World Health Organisation, Geneva, Switzerland, 2012).Toe, K. H. et al. Do bednets including piperonyl butoxide offer additional protection against populations of Anopheles gambiae s.l. that are highly resistant to pyrethroids? An experimental hut evaluation in Burkina Faso. Med. Vet. Entomol. 32, 407–416 (2018).CAS 
PubMed 

Google Scholar 
Hien, A. S. et al. Evidence supporting deployment of next generation insecticide treated nets in Burkina Faso: Bioassays with either chlorfenapyr or piperonyl butoxide increase mortality of pyrethroid-resistant Anopheles gambiae. Malar. J. 20, 1–12 (2021).
Google Scholar 
Zoubiri, S. & Baaliouamer, A. Potentiality of plants as source of insecticide principles. J. Saudi Chem. Soc. 18, 925–938 (2014).
Google Scholar 
Tripathi, A. K., Upadhyay, S., Bhuiyan, M. & Bhattacharya, P. R. A review on prospects of essential oils as biopesticide in insect-pest management. J. Pharmacogn. Phytother. 1, 52–63 (2009).CAS 

Google Scholar 
Isman, M. B. Plant essential oils for pest and disease management. Crop Prot. 19, 603–608 (2000).ADS 
CAS 

Google Scholar 
Mossa, A. T. H. Green pesticides: Essential oils as biopesticides in insect-pest management. J. Environ. Sci. Technol. 9, 354–378 (2016).CAS 

Google Scholar 
Lucia, A. et al. Larvicidal effect of Eucalyptus grandis essential oil and turpentine and their major components on Aedes aegypti larvae. J. Am. Mosq. Control Assoc. 23, 299–303 (2007).CAS 
PubMed 

Google Scholar 
Singh, R., Koul, O. & Rup, P. J. Toxicity of some essential oil constituents and their binary mixtures against Chilo partellus (Lepidoptera: Pyralidae). Int. J. Tropical Insect Sci. 29, 93–101 (2009).CAS 

Google Scholar 
Sarma, R., Adhikari, K., Mahanta, S. & Khanikor, B. Combinations of plant essential oil based terpene compounds as larvicidal and adulticidal agent against Aedes aegypti (Diptera: Culicidae). Sci. Rep. 9, 1–13 (2019).ADS 

Google Scholar 
Mansour, S. A., Foda, M. S. & Aly, A. R. Mosquitocidal activity of two Bacillus bacterial endotoxins combined with plant oils and conventional insecticides. Ind. Crops Prod. 35, 44–52 (2012).CAS 

Google Scholar 
Yaméogo, F., Wendgida, D. W., Sombié, A., Sanon, A. & Badolo, A. Insecticidal activity of essential oils from six aromatic plants against Aedes aegypti, dengue vector from two localities of Ouagadougou Burkina Faso. Arthropod. Plant. Interact. 15, 627–634 (2021).
Google Scholar 
Wangrawa, D. W. et al. Essential oils and their binary combinations have synergistic and antagonistic insecticidal properties against Anopheles gambiae s l. (Diptera: Culicidae). Biocatal. Agric. Biotechnol. 42, 102347 (2022).CAS 

Google Scholar 
Drabo, S. F., Olivier, G., Bassolé, I. H. N., Nébié, R. C. & Laurence, M. Susceptibility of MED-Q1 and MED-Q3 biotypes of Bemisia tabaci (Hemiptera: Aleyrodidae) populations to essential and seed oils. J. Econ. Entomol. 110, 1031–1038 (2017).
Google Scholar 
N’Guessan, R., Corbel, V., Akogbéto, M. & Rowland, M. Treated nets and indoor residual reduced efficacy of insecticide-pyrethroid resistance area benin. Emerg. Infect. Dis. 13, 199–206 (2007).PubMed 
PubMed Central 

Google Scholar 
WHO. Standard operating procedure for testing insecticide susceptibility of adult mosquitoes in WHO tube tests. (World Health Organisation, Geneva, Switzerland 2022).Abbott, W. S. A method of computing the effectiveness of an insecticide. J. Econ. Entomol. 18, 265–267 (1925).CAS 

Google Scholar 
Schelz, Z., Molnar, J. & Hohmann, J. Antimicrobial and antiplasmid activities of essential oils. Fitoterapia 77, 279–285 (2006).CAS 
PubMed 

Google Scholar 
Bassolé, I. H. N. & Juliani, H. R. Essential oils in combination and their antimicrobial properties. Molecules 17, 3989–4006 (2012).PubMed 
PubMed Central 

Google Scholar 
WHO. Test Procedures for Insecticide Resistance Monitoring in Malaria Vector Mosquitoes Second edition. (World Health Organisation, Geneva, Switzerland 2016).Tchoumbougnang, F. et al. Activité larvicide sur Anopheles gambiae giles et composition chimique des huiles essentielles extraites de quatre plantes cultivées au Cameroun. Biotechnol. Agron. Soc. Environ. 13, 77–84 (2009).CAS 

Google Scholar 
Ranson, H. & Lissenden, N. Insecticide resistance in African Anopheles mosquitoes: A worsening situation that needs urgent action to maintain malaria control. Trends Parasitol. 32, 187–196 (2016).CAS 
PubMed 

Google Scholar 
Wangrawa, D. et al. Insecticidal activity of local plants essential oils against laboratory and field strains of Anopheles gambiae s. L. (Diptera: Culicidae) from Burkina Faso. J. Econ. Entomol. 111, 2844–2853 (2018).CAS 
PubMed 

Google Scholar 
Gbolade, A. A. & Lockwood, G. B. Toxicity of Ocimum sanctum L. essential oil to Aedes aegypti larvae and its chemical composition. J. Essent. Oil Bearing Plants 11, 148–153 (2008).CAS 

Google Scholar 
Vani, R. S., Cheng, S. F. & Chuah, C. H. Comparative study of volatile compounds from genus Ocimum. Am. J. Appl. Sci. 6, 523–528 (2009).CAS 

Google Scholar 
Bassolé, et al. Ovicidal and larvicidal activity against Aedes aegypti and Anopheles gambiae complex mosquitoes of essential oils extracted from three spontaneous plants of Burkina Faso. Parasitologia 45, 23–26 (2003).
Google Scholar 
Peerzada, N. Chemical composition of the essential oil of Hyptis Suaveolens. Molecules 2, 165–168 (1997).CAS 

Google Scholar 
Ilboudo, Z. et al. Biological activity and persistence of four essential oils towards the main pest of stored cowpeas, Callosobruchus maculatus (F.) (Coleoptera: Bruchidae). J. Stored Prod. Res. 46, 124–128 (2010).CAS 

Google Scholar 
Zulfikar, A. & Sitepu, F. Y. The effect of lemongrass (Cymbopogon nardus) extract as insecticide against Aedes aegypti. Int. J. Mosq. Res. 6, 101–103 (2019).
Google Scholar 
Ojewumi, E. M., Oladipupo, A. A. & Ojewumi, O. E. Oil extract from local leaves an alternative to synthetic mosquito repellants. Pharmacophore 9, 1–6 (2018).
Google Scholar 
Gnankiné, O. & Bassolé, I. H. N. Essential oils as an alternative to pyrethroids resistance against Anopheles species complex giles (Diptera: Culicidae). Molecules 22, 1321 (2017).PubMed Central 

Google Scholar 
Bossou, A. D. et al. Chemical composition and insecticidal activity of plant essential oils from Benin against Anopheles gambiae (Giles). Parasit. Vectors 6, 337 (2013).PubMed 
PubMed Central 

Google Scholar 
Balboné, et al. Essential oils from five local plants: An alternative larvicide for Anopheles gambiae s. I. (Diptera: Culicidae) and Aedes aegypti (Diptera: Culicidae) control in Western Burkina Faso. Front. Trop. Dis. 3, 853405 (2022).
Google Scholar 
Bekele, J. & Hassanali, A. Blend effects in the toxicity of the essential oil constituents of Ocimum kilimandscharicum and Ocimum kenyense (Labiateae) on two post-harvest insect pests. Phytochemistry 57, 385–391 (2001).CAS 
PubMed 

Google Scholar 
Pavela, R. Acute and synergistic effects of some monoterpenoid essential oil compounds on the house fly (Musca domestica). J. Essent. Oil Bearing Plants 11, 451–459 (2008).CAS 

Google Scholar 
Tanprasit, P. Biological control of dengue fever mosquitoes (Aedes aegypti Linn.) using leaf extracts of Chan (Hyptis suaveolens (L) poit.) and hedge flower Lantana camara Linn.). (2005).Park, H. M. et al. Larvicidal activity of myrtaceae essential oils and their components against Aedes aegypti, acute toxicity on Daphnia magna, and aqueous residue. J. Med. Entomol. 48, 405–410 (2011).CAS 
PubMed 

Google Scholar 
Burt, S. Essential oils: Their antibacterial properties and potential applications in foods—A review. Int. J. Food Microbiol. 94, 223–253 (2004).CAS 
PubMed 

Google Scholar 
Abbassy, M. A., Abdelgaleil, S. A. M. & Rabie, R. Y. A. Insecticidal and synergistic effects of Majorana hortensis essential oil and some of its major constituents. Entomol. Exp. Appl. 131, 225–232 (2009).CAS 

Google Scholar 
Chiasson, H., Bélanger, A., Bostanian, N., Vincent, C. & Poliquin, A. Acaricidal properties of Artemisia absinthium and Tanacetum vulgare (Asteraceae) essential oils obtained by three methods of extraction. J. Econ. Entomol. 94, 167–171 (2001).CAS 
PubMed 

Google Scholar 
Luz, T. R. S. A., deMesquita, L. S. S., Amaral, F. M. M. & Coutinho, D. F. Essential oils and their chemical constituents against Aedes aegypti L. (Diptera: Culicidae) larvae. Acta Trop. 212, 105705 (2020).CAS 
PubMed 

Google Scholar 
Deletre, E., Mallent, M., Menut, C., Chandre, F. & Martin, T. Behavioral response of Bemisia tabaci (Hemiptera: Aleyrodidae) to 20 plant extracts. J. Econ. Entomol. 108, 1890–1901 (2015).
Google Scholar 
Berenbaum, M. A. Y. & Neal, J. J. Synergism between myristicin and xanthotoxin, a naturally cooccurring plant toxicant. J. Chem. Ecol. 11, 1349–1358 (1985).CAS 
PubMed 

Google Scholar 
Intirach, J. et al. Chemical constituents and combined larvicidal effects of selected essential oils against Anopheles cracens (Diptera: Culicidae). Psyche (London) https://doi.org/10.1155/2012/591616 (2012).
Google Scholar 
Pavela, R. Acute, synergistic and antagonistic effects of some aromatic compounds on the Spodoptera littoralis Boisd. (Lep., Noctuidae) larvae. Ind. Crops Prod. 60, 247–258 (2014).CAS 

Google Scholar 
Muturi, E. J., Ramirez, J. L., Doll, K. M. & Bowman, M. J. Combined toxicity of three essential oils against Aedes aegypti (Diptera: Culicidae) larvae. J. Med. Entomol. 54, 1684–1691 (2017).CAS 
PubMed 

Google Scholar  More

Ahmed MZ, Li SJ, Xue X, Yin XJ, Ren SX, Jiggins FM et al. (2015) The Intracellular bacterium Wolbachia uses parasitoid wasps as phoretic vectors for efficient horizontal transmission. PLoS Pathog 11:1–19
Google Scholar 
Arai H, Hirano T, Akizuki N, Abe A, Nakai M, Kunimi Y et al. (2019) Multiple infection and reproductive manipulations of Wolbachia in Homona magnanima (Lepidoptera: Tortricidae). Microb Ecol 77:257–266PubMed 

Google Scholar 
Arai H, Lin SR, Nakai M, Kunimi Y, Inoue MN (2020) Closely related male-killing and nonmale-killing Wolbachia strains in the oriental tea tortrix Homona magnanima. Microb Ecol 79:1011–1020CAS 
PubMed 

Google Scholar 
Bailey NW, Zuk M (2008) Changes in immune effort of male field crickets infested with mobile parasitoid larvae. J Insect Physiol 54:96–104CAS 
PubMed 

Google Scholar 
Ballad JWO, Hatzidakis J, Karr TL, Kreitman M (1996) Reduced variation in Drosophila simulans mitochondrial DNA. Genetics 144:1519–1528
Google Scholar 
Birch LC (1948) The intrinsic rate of natural increase of an insect population. J Anim Ecol 17:15–26
Google Scholar 
Capobianco IIIF, Nandkumar S, Parker JD (2018) Wolbachia affects survival to different oxidative stressors dependent upon the genetic background in Drosophila melanogaster. Physiol Entomol 43:239–244
Google Scholar 
Danthanarayana W (1975) Factors determining variation in fecundity of the light brown apple moth, Epiphyas postvittana (Walker) (Tortricidae). Aust J Zool 23:309–319
Google Scholar 
Dean MD (2006) A Wolbachia-associated fitness benefit depends on genetic background in Drosophila simulans. Proc R Soc B 273:1415–1420CAS 
PubMed 
PubMed Central 

Google Scholar 
Deseo KV (1971) Study of factors influencing the fecundity and fertility of codling moth (Laspeyresia pomonella L., Lepidoptera, Tortricidae). Acta Phytopathol Hun 6:243–252
Google Scholar 
Dobson SL, Rattanadechakul W, Marsland EJ (2004) Fitness advantage and cytoplasmic incompatibility in Wolbachia single-and superinfected Aedes albopictus. Heredity 93:135–142CAS 
PubMed 

Google Scholar 
Duron O, Bouchon D, Boutin S, Bellamy L, Zhou L, Engelstädter J, Hurst GD (2008) The diversity of reproductive parasites among arthropods: Wolbachia do not walk alone. BMC Biol 6:1–12
Google Scholar 
Engelstädter J, Telschow A, Hammerstein P (2004) Infection dynamics of different Wolbachia-types within one host population. J Theor Biol 231:345–55PubMed 

Google Scholar 
Fleury F, Vavre F, Ris N, Fouillet P, Boulétreau M (2000) Physiological cost induced by the maternally-transmitted endosymbiont Wolbachia in the Drosophila parasitoid Leptopilina heterotoma. Parasitology 121:493–500PubMed 

Google Scholar 
Frank SA (1998) Dynamics of cytoplasmic incompatibility with multiple Wolbachia infections. J Theor Biol 192:213–18CAS 
PubMed 

Google Scholar 
Frank SA, Hurst LD (1996) Mitochondria and male disease. Nature 383:224–224CAS 
PubMed 

Google Scholar 
Fry AJ, Palmer MR, Rand DM (2004) Variable fitness effects of Wolbachia infection in Drosophila melanogaster. Heredity 93:379–389CAS 
PubMed 

Google Scholar 
Gómez-Valero L, Soriano-Navarro M, Pérez-Brocal V, Heddi A, Moya A, García-Verdugo JM, Latorre A (2004) Coexistence of Wolbachia with Buchnera Aphidicola and a secondary symbiont in the aphid Cinara cedri. J Bacteriol 186:6626–33PubMed 
PubMed Central 

Google Scholar 
Hoffmann AA, Turelli M, Harshman LG (1990) Factors affecting the distribution of cytoplasmic incompatibility in Drosophila simulans. Genetics 126:933–948CAS 
PubMed 
PubMed Central 

Google Scholar 
Hornett EA, Charlat S, Duplouy AMR, Davies N, Roderick GK, Wedell N et al. (2006) Evolution of male-killer suppression in a natural population. PLoS Biol 4:1643–1648CAS 

Google Scholar 
Hough JA, Pimentel D (1978) Influence of host foliage on development, survival, and fecundity of the gypsy moth. Environ Entomol 7:97–102
Google Scholar 
Ikeda T, Ishikawa H, Sasaki T (2003) Infection density of Wolbachia and level of cytoplasmic incompatibility in the Mediterranean flour moth, Ephestia kuehniella. J Invertebr Pathol 84:1–5PubMed 

Google Scholar 
Ishii T, Nakai M, Okuno S, Takatsuka J, Kunimi Y (2003) Characterization of Adoxophyes honmai single-nucleocapsid nucleopolyhedrovirus: morphology, structure, and effects on larvae. J Invertebr Pathol 83:206–214CAS 
PubMed 

Google Scholar 
Kondo N, Shimada M, Fukatsu T (2005) Infection density of Wolbachia endosymbiont affected by coinfection and host genotype. Biol Lett 1:488–491PubMed 
PubMed Central 

Google Scholar 
Lu P, Bian G, Pan X, Xi Z (2012) Wolbachia induces density-dependent inhibition to dengue virus in mosquito cells. PLoS Negl Trop D 6:1–8CAS 

Google Scholar 
Maia AHN, Luiz AJB, Campanhola C (2000) Statistical inference on associated fertility life table parameters using jackknife technique: computational aspects. J Econ Entomol 93:511–518
Google Scholar 
Mazzetto F, Gonella E, Alma A (2015) Wolbachia infection affects female fecundity in Drosophila suzukii. Bull Insectol 68:153–157
Google Scholar 
Meyer JS, Ingersoll CG, McDonald LL, Boyce MS (1986) Estimating uncertainty in population growth rates: jackknife vs. bootstrap techniques. Ecology 67:1156–1166
Google Scholar 
Moreira LA, Iturbe-Ormaetxe I, Jeffery JA, Lu G, Pyke AT, Hedges LM et al. (2009) A Wolbachia symbiont in Aedes aegypti limits infection with dengue, Chikungunya, and Plasmodium. Cell 139:1268–1278PubMed 

Google Scholar 
Mouton L, Henri H, Bouletreau M, Vavre F (2006) Effect of temperature on Wolbachia density and impact on cytoplasmic incompatibility. Parasitology 132:49–56CAS 
PubMed 

Google Scholar 
Narita S, Nomura M, Kageyama D (2007) Naturally occurring single and double infection with Wolbachia strains in the butterfly Eurema hecabe: transmission efficiencies and population density dynamics of each Wolbachia strain. FEMS Microb Ecol 61:235–245CAS 

Google Scholar 
Pigeault R, Braquart-Varnier C, Marcadé I, Mappa G, Mottin E, Sicard M (2014) Modulation of host immunity and reproduction by horizontally acquired Wolbachia. J Insect Physiol 70:125–133CAS 
PubMed 

Google Scholar 
Rancès E, Ye YH, Woolfit M, McGraw EA, O´Neill SL (2012) The relative importance of innate immune priming in Wolbachia-mediated dengue interference. PLoS Pathog 8:e1002548. https://doi.org/10.1371/journal.ppat.1002548Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
R Core Team (2021) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/Stevanovic AL, Arnold PA, Johnson KN (2015) Wolbachia -mediated antiviral protection in Drosophila larvae and adults following oral infection. Appl Environ Micro 81:8215–8223CAS 

Google Scholar 
Takamatsu T, Arai H, Abe N, Nakai M, Kunimi Y, Inoue MN (2021) Coexistence of two male-killers and their impact on the development of oriental tea tortrix Homona magnanima. Microb Ecol 81:193–202CAS 
PubMed 

Google Scholar 
Takehana A, Katsuyama T, Yano T, Oshima Y, Takada H, Aigaki T et al. (2002) Overexpression of a pattern-recognition receptor, peptidoglycan-recognition protein-LE, activates imd/relish-mediated antibacterial defense and the prophenoloxidase cascade in Drosophila larvae. Proc Natl Acad Sci USA 99:13705–13710CAS 
PubMed 
PubMed Central 

Google Scholar 
Takatsuka J, Okuno S, Ishii T, Nakai M, Kunimi Y (2010) Fitness-related traits of entomopoxviruses isolated from Adoxophyes honmai (Lepidoptera: Tortricidae) at three localities in Japan. J Invertebr Pathol 105:121–131PubMed 

Google Scholar 
Teixeira L, Ferreira Á, Ashburner M (2008) The Bacterial symbiont Wolbachia induces resistance to RNA viral infections in Drosophila melanogaster. PLoS Biol 6:e1000002. https://doi.org/10.1371/journal.pbio.1000002Article 
CAS 
PubMed Central 

Google Scholar 
Thomas P, Kenny N, Eyles D, Moreira LA, O´Neill SL, Asgari S (2011) Infection with the wMel and wMelPop strains of Wolbachia leads to higher levels of melanization in the hemolymph of Drosophila melanogaster, Drosophila simulans and Aedes aegypti. Dev Comp Immunol 35:360–365CAS 
PubMed 

Google Scholar 
Tsuruta K, Wennmann JT, Kunimi Y, Inoue MN, Nakai M (2018) Morphological properties of the occlusion body of Adoxophyes orana granulovirus. J Invertebr Pathol 154:58–64CAS 
PubMed 

Google Scholar 
Turelli M, Hoffmann AA (1991) Rapid spread of an inherited incompatibility factor in California Drosophila. Nature 353:440–442CAS 
PubMed 

Google Scholar 
Vautrin E, Vavre F (2009) Interactions between vertically transmitted symbionts: cooperation or conflict? Trends Microbiol 17:95–99CAS 
PubMed 

Google Scholar 
Vavre F, Fleury F, Lepetit D, Fouillet P, Boulétreau M (1999) Phylogenetic evidence for horizontal transmission of Wolbachia in host- parasitoid associations. Mol Biol Evol 16:1711–1723CAS 
PubMed 

Google Scholar 
Vollmer J, Schiefer A, Schneider T, Jülicher K, Johnston KL, Taylor MJ et al. (2013) Requirement of lipid II biosynthesis for cell division in cell wall-less Wolbachia, endobacteria of arthropods and filarial nematodes. Int J Med Microbiol 303:140–149CAS 
PubMed 

Google Scholar 
Voronin D, Guimarães AF, Molyneux GR, Johnston KL, Ford L, Taylor MJ (2014) Wolbachia lipoproteins: abundance, localization and serology of Wolbachia peptidoglycan associated lipoprotein and the Type IV Secretion System component, VirB6 from Brugia malayi and Aedes albopictus. Parasite Vector 7:462
Google Scholar 
Watanabe M, Miura K, Hunter MS, Wajnberg E (2011) Superinfection of cytoplasmic incompatibility-inducing Wolbachia is not additive in Orius strigicollis (Hemiptera: Anthocoridae). Heredity 106:642–648CAS 
PubMed 

Google Scholar 
Weeks AR, Turelli M, Harcombe WR, Reynolds KT, Hoffmann AA (2007) From parasite to mutualist: rapid evolution of Wolbachia in natural populations of Drosophila. PLoS Biol 5:0997–1005CAS 

Google Scholar 
Werren JH, Baldo L, Clark ME (2008) Wolbachia: Master manipulators of invertebrate biology. Nat Rev Microbiol 6:741–751CAS 
PubMed 

Google Scholar 
Xue X, Li S, Ahmed MZ, Barro PJ, Ren S, Qiu B (2012) Inactivation of Wolbachia reveals its biological roles in whitefly host. PLoS One 7:e48148. https://doi.org/10.1371/journal.pone.0048148Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zug R, Hammerstein P (2012) Still a host of hosts for Wolbachia: analysis of recent data suggests that 40% of terrestrial arthropod species are infected. PLoS One 7:e38544. https://doi.org/10.1371/journal.pone.0038544Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zug R, Hammerstein P (2015) Wolbachia and the insect immune system: what reactive oxygen species can tell us about the mechanisms of Wolbachia-host interactions. Front Microbiol 6:1–16
Google Scholar  More

Alvares, F. et al. Old Wolrd Canis spp. with taxonomic ambiguity: Workshop conclusions and recommendations Vairao, Portugal, 28th–30th May 2019. Canid News (Online Edition) (2019).Jackson, S. M. et al. Taxonomy of the dingo: It’s an ancient dog. Aust. Zool. 41, 347–357 (2021).
Google Scholar 
Stephens, D., Wilton, A. N., Fleming, P. J. S. & Berry, O. Death by sex in an Australian icon: A continent-wide survey reveals extensive hybridization between dingoes and domestic dogs. Mol. Ecol. 24, 5643–5656 (2015).CAS 
PubMed 

Google Scholar 
Cairns, K. M., Shannon, L. M., Koler-Matznick, J., Ballard, J. W. O. & Boyko, A. R. Elucidating biogeographical patterns in Australian native canids using genome wide SNPs. PLoS ONE 13, e0198754 (2018).PubMed 
PubMed Central 

Google Scholar 
Fleming, P. J. S., Ballard, G. & Cutter, N. There is no Dingo dilemma: legislation facilitates culling, containment and conservation of Dingoes in New South Wales. Aust. Zool. 41, 408–416 (2021).
Google Scholar 
Corbett, L. K. The Dingo in Australia and Asia. Second edn, (JB Books Australia, 2001).Newsome, T. M. et al. Making a new dog?. Bioscience 67, 374–381 (2017).
Google Scholar 
Wang, G.-D. et al. Out of southern East Asia: the natural history of domestic dogs across the world. Cell Res. 26, 21–33 (2016).PubMed 

Google Scholar 
Smith, B. The Dingo Debate: Origins, Behaviour and Conservation. (CSIRO Publishing, 2015).Jackson, S. M. et al. The dogma of dingoes-taxonomic status of the dingo: A reply to Smith et al. Zootaxa 4564, 198–212 (2019).
Google Scholar 
Zhang, S. J. et al. Genomic regions under selection in the feralization of the dingoes. Nat. Commun. 11, 671 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
Balme, J. & O’Connor, S. Dingoes and Aboriginal social organization in Holocene Australia. J. Archaeol. Sci. Rep. 7, 775–781 (2016).
Google Scholar 
Cairns, K. M. What is a dingo – origins, hybridisation and identity. Aust. Zool. 41(3), 322–337 (2021).
Google Scholar 
Allen, B. L. & West, P. Influence of dingoes on sheep distribution in Australia. Aust. Vet. J. 91, 261–267 (2013).CAS 
PubMed 

Google Scholar 
Fleming, P. J. S. in Carnivores of Australia: Past, Present and Future (eds A.S. Glen & C.R. Dickman) Ch. 6, 105–149 (CSIRO Publishing, 2014).Stephens, D. The molecular ecology of Australian wild dogs: hybridisation, gene flow and genetic structure at multiple geographic scales, The University of Western Australia, (2011).Cairns, K. M., Nesbitt, B. J., Laffan, S. W., Letnic, M. & Crowther, M. S. Geographic hot spots of dingo genetic ancestry in southeastern Australia despite hybridisation with domestic dogs. Conserv. Genet. 21, 77–90 (2020).CAS 

Google Scholar 
Wilton, A. N., Steward, D. J. & Zafiris, K. Microsatellite variation in the Australian dingo. J. Hered. 90, 108–111 (1999).CAS 
PubMed 

Google Scholar 
Allendorf, F. W., Leary, R. F., Spruell, P. & Wenburg, J. K. The problems with hybrids: Setting conservation guidelines. Trends Ecol. Evol. 16, 613–622 (2001).
Google Scholar 
Atkinson, J. An account of the state of agriculture & grazing in New South Wales. (J. Cross, 1826).Massy, C. The Australian Merino: The Story of a Nation (Revised and updated). xxii,1262 (Random House Australia, 2007).Cairns, K. M., Brown, S. K., Sacks, B. N. & Ballard, J. W. O. Conservation implications for dingoes from the maternal and paternal genome: Multiple populations, dog introgression, and demography. Ecol. Evol. 7, 9787–9807 (2017).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 
Driscoll, C., Yamaguchi, N., O’Brien, S. J. & Macdonald, D. W. A suite of genetic markers useful in assessing wildcat (Felis silvestris ssp.)-domestic cat (Felis silvestris catus) admixture. J. Hered. 102(1), S87–S90 (2011).PubMed 
PubMed Central 

Google Scholar 
Bohling, J. H. & Waits, L. P. Factors influencing red wolf–coyote hybridization in eastern North Carolina USA. Biol. Conserv. 184, 108–116 (2015).
Google Scholar 
Fleming, P., Corbett, L., Harden, R. & Thomson, P. in Managing the Impacts of Dingoes and Other Wild Dogs. (Bureau of Rural Sciences, Canberra, 2001).Van Veldhuisen, R. Pipe dreams: A history of water supply in the Wimmera-Mallee (Wimmera Mallee Water, 2001).Newsome, A. The distribution of red kangaroos, Megaleia rufa (Desmarest), about sources of persistent food and water in central Australia. Aust. J. Zool. 13, 289–300 (1965).
Google Scholar 
James, C. D., Landsberg, J. & Morton, S. R. Provision of watering points in the Australian arid zone: A review of effects on biota. J. Arid Environ. 41, 87–121 (1999).ADS 

Google Scholar 
Robinson, J. A., Brown, C., Kim, B. Y., Lohmueller, K. E. & Wayne, R. K. Purging of strongly deleterious mutations explains long-term persistence and absence of inbreeding depression in island foxes. Curr. Biol. 28, 3487-3494.e3484 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Benazzo, A. et al. Survival and divergence in a small group: The extraordinary genomic history of the endangered Apennine brown bear stragglers. PNAS 114, E9589–E9597 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mattucci, F. et al. Genomic approaches to identify hybrids and estimate admixture times in European wildcat populations. Sci. Rep. 9, 11612 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Thomson, P. C., Rose, K. & Kok, N. E. The behavioural ecology of dingoes in north-western Australia. VI. Temporary extra-terrestrial movements and dispersal. Wildl. Res. 19, 585–595 (1992).
Google Scholar 
Newsome, T. M., Ballard, G.-A., Dickman, C. R., Fleming, P. J. S. & van de Ven, R. Home range, activity and sociality of a top predator, the dingo: A test of the Resource Dispersion Hypothesis. Ecography 36, 914–925 (2013).
Google Scholar 
Giglio, R. M., Rocke, T. E., Osorio, J. E. & Latch, E. K. Characterizing patterns of genomic variation in the threatened Utah prairie dog: Implications for conservation and management. Evol. Appl. 14, 1036–1051 (2021).PubMed 

Google Scholar 
Conroy, G. C. et al. Conservation concerns associated with low genetic diversity for K’gari–Fraser Island dingoes. Sci. Rep. 11, 9503 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Frankham, R. Do island populations have less genetic variation than mainland populations?. Heredity 78, 311–327 (1997).PubMed 

Google Scholar 
Funk, W. C. et al. Adaptive divergence despite strong genetic drift: genomic analysis of the evolutionary mechanisms causing genetic differentiation in the island fox (Urocyon littoralis). Mol. Ecol. 25, 2176–2194 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Behrendorff, L. Best-practice dingo management: six lessons from K’gari (Fraser Island). Aust. Zool. 41, 521–533 (2021).
Google Scholar 
van Eeden, L. M., Smith, B. P., Crowther, M. S., Dickman, C. R. & Newsome, T. M. ‘The dingo menace’: An historic survey on graziers’ management of an Australian carnivore. Pac. Conserv. Biol. 25, 245–256 (2019).
Google Scholar 
Whiting, S. D., Long, J. L., Hadden, K. M., Lauder, A. D. K. & Koch, A. U. Insights into size, seasonality and biology of a nesting population of the Olive Ridley turtle in northern Australia. Wildl. Res. 34, 200–210 (2007).
Google Scholar 
Banks, S. C., Hoyle, S. D., Horsup, A., Sunnucks, P. & Taylor, A. C. Demographic monitoring of an entire species (the northern hairy-nosed wombat, Lasiorhinus krefftii) by genetic analysis of non-invasively collected material. Anim. Conserv. 6, 101–107 (2003).
Google Scholar 
Parker, H. G. et al. Genomic analyses reveal the influence of geographic origin, migration, and hybridization on modern dog breed development. Cell Rep. 19, 697–708 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Thackway, R. & Cresswell, I. An Interim Biogeographic Regionalisation for Australia: A Framework for Setting Priorities in the National Reserves System Cooperative Program. Version 4, (Australian Nature Conservation Agency, Reserve Systems Unit, 1995).Bureau of Meteorology & CSIRO. (Bureau of Meteorology, CSIRO and Farmlink, http://www.bom.gov.au/climate/climate-guides/guides/01-Mallee-VIC-Climate-Guide.pdf, 2019).Rowan, J. N. & Downes, R. G. in Soil Conservation Authority of Victoria (ed Brookes, A.C.) 1–55 (Govt. Printer, Melbourne, 1963).Longmire, J. L., Maltbie, M. & Baker, R. J. Use of “lysis buffer” in DNA isolation and its implications for museum collections. Occas. Pap. Mus. Tex. Tech. Univ. 163, 1–3 (1997).
Google Scholar 
Tatler, J., Prowse, T. A. A., Roshier, D. A., Cairns, K. M. & Cassey, P. Phenotypic variation and promiscuity in a wild population of pure dingoes (Canis dingo). J. Zool. Syst. Evol. Res. 59, 311–322 (2020).
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 
Francis, R. M. pophelper: An R package and web app to analyse and visualize population structure. Mol. Ecol. Resour. 17, 27–32 (2017).CAS 
PubMed 

Google Scholar 
Wang, J. The computer program structure for assigning individuals to populations: Easy to use but easier to misuse. Mol. Ecol. Resour. 17, 981–990 (2017).CAS 
PubMed 

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. & von Holdt, 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 
Verity, R. & Nichols, R. A. Estimating the number of subpopulations (K) in structured populations. Genetics 203, 1827–1839 (2016).PubMed 
PubMed Central 

Google Scholar 
Jombart, T. adegenet: A R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).CAS 
PubMed 

Google Scholar 
Jombart, T., Devillard, S. & Balloux, F. Discriminant analysis of principal components: A new method for the analysis of genetically structured populations. BMC Genet. 11, 94 (2010).PubMed 
PubMed Central 

Google Scholar 
Keenan, K. et al. diveRsity: An R package for the estimation and exploration of population genetics parameters and their associated errors. Methods Ecol. Evol. 4, 782–788 (2013).
Google Scholar 
Peakall, R. & Smouse, P. E. genalex 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes 6, 288–295 (2006).
Google Scholar 
Peakall, R. & Smouse, P. E. GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research—an update. Bioinformatics 28, 2537–2539 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lynch, M. & Ritland, K. Estimation of pairwise relatedness with molecular markers. Genetics 152, 1753–1766 (1999).CAS 
PubMed 
PubMed Central 

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 
Jost, L. GST and its relatives do not measure differentiation. Mol. Ecol. 17, 4015–4026 (2008).PubMed 

Google Scholar 
Do, C. et al. NeEstimator v2: Re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol. Ecol. Resour. 14, 209–214 (2014).CAS 
PubMed 

Google Scholar 
Shirk, A. J. & Cushman, S. A. sGD: Software for estimating spatially explicit indices of genetic diversity. Mol. Ecol. Resour. 11, 922–934 (2011).CAS 
PubMed 

Google Scholar 
Schnute, J., Boers, N., Haigh, R. & Couture-Beil, A. Introduction to PBSmapping. (2016). More

Wang, S., Liu, Q. & Zhang, D. Karst rocky desertification in southwestern China: Geomorphology, landuse, impact and rehabilitation. Land Degrad. Dev. 15(2), 115–121 (2004).
Google Scholar 
Jiang, M. et al. Geologic factors leadingly drawing the macroecological pattern of rocky desertification in southwest China. Sci. Total Environ. 458–460, 419–426 (2013).
Google Scholar 
Jiang, Z., Lian, Y. & Qin, X. Rocky desertification in Southwest China: Impacts, causes, and restoration. Earth-Sci. Rev. 132, 1–12 (2014).ADS 

Google Scholar 
Xu, E., Zhang, H. & Li, M. Object-based mapping of karst rocky desertification using a support vector machine. Land Degrad. Dev. 26(2), 158–167 (2012).
Google Scholar 
Li, Y., Bai, X., Wang, S. & Tian, Y. Integrating mitigation measures for karst rocky desertification land in the Southwest mountains of China. Carbonates Evaporites 34, 1095–1106 (2018).
Google Scholar 
Lan, J. Responses of soil organic carbon components and their sensitivity to karst rocky desertification control measures in Southwest China. J. Soil. Sediment. 21, 978–989 (2020).
Google Scholar 
Gao, J., Du, F., Zuo, L. & Jiang, Y. Integrating ecosystem services and rocky desertification into identification of karst ecological security pattern. Landscape Ecol. 36, 2113–2133 (2020).
Google Scholar 
Huang, X. et al. Driving factors and prediction of rock desertification of non-tillage lands in a karst basin, Southwest China. Pol. J. Environ. Stud. 30(4), 3627–3635 (2021).CAS 

Google Scholar 
Chen, S., Zhou, Z., Yan, L. & Li, B. Quantitative evaluation of ecosystem health in a karst area of South China. Sustain. Basel 8(10), 975 (2016).
Google Scholar 
Liu, F., He, B. Y. & Kou, J. F. Landsat thermal remote sensing to investigate the present situation and variation characteristics of karst rocky desertification in Pingguo County of Guangxi, Southwest China. Sci. Soil Water Conserv. 15(02), 125–131 (2017).
Google Scholar 
Zhang, X., Shang, K., Cen, Y., Shuai, T. & Sun, Y. Estimating ecological indicators of karst rocky desertification by linear spectral unmixing method. Int. J. Appl. Earth Obs. Geoinf. 31, 86–94 (2014).ADS 
CAS 

Google Scholar 
Zhang, Z., Ouyang, Z., Xiao, Y., Xiao, Y. & Xu, W. Using principal component analysis and annual seasonal trend analysis to assess karst rocky desertification in southwestern China. Environ. Monit. Assess. 189(6), 1–19 (2017).
Google Scholar 
Li, S. & Wu, H. Mapping karst rocky desertification using Landsat 8 images. Remote Sens. Lett. 6(9), 657–666 (2015).
Google Scholar 
Yang, S. X., Lin, H., Hou, F., Zhang, L. P. & Hu, Z. L. Estimating karst area vegetation coverage by pixel unmixing. Bull. Surv. Mapp. 5, 23–27 (2014).
Google Scholar 
Xiong, Y., Yue, Y. M. & Wang, K. L. Comparative study of indicator extraction for assessment of karst rocky desertification based on hyperion and ASTER images. Bull. Soil Water Conserv. 33(03), 186–190 (2013).
Google Scholar 
Dai, G., Sun, H., Wang, B., Huang, C., Wang, W., Yao, Y., et al. Assessment of karst rocky desertification from the local to regional scale based on unmanned aerial vehicle images: Acase-study of Shilin County, Yunnan Province, China. Land Degrad. Dev. 1–14 (2021).Pu, J., Zhao, X., Dong, P., Wang, Q. & Yue, Q. Extracting information on rocky desertification from satellite images: A comparative study. Remote Sens. 13(13), 2497 (2021).ADS 

Google Scholar 
Yue, Y. M. et al. Remote sensing of indicators for evaluating karst rocky desertification. Procedia Environ. Sci. 15(04), 722–736 (2011).
Google Scholar 
Huang, Q. & Cai, Y. Spatial pattern of Karst rock desertification in the middle of Guizhou Province. Southwestern China. Environ. Geol. 52(7), 1325–1330 (2006).MathSciNet 

Google Scholar 
Wang, J., Li, S., Li, H., Luo, H. & Wang, M. Classifying indices and remote sensing image characters of rocky desertification lands: a case of karst region in Northern Guangdong Province. J. Desert Res. 5, 765–770 (2007).
Google Scholar 
Chen, F. et al. Assessing spatial-temporal evolution processes and driving forces of karst rocky desertification. Geocarto Int. 1–22 (2019).Qi, X., Zhang, C. & Wang, K. Comparing remote sensing methods for monitoring karst rocky desertification at sub-pixel scales in a highly heterogeneous karst region. Sci. Rep-UK https://doi.org/10.1038/s41598-019-49730-9 (2019).Article 

Google Scholar 
Yue, Y. et al. Spectral indices for estimating ecological indicators of karst rocky desertification. Int. J. Remote Sens. 31(8), 2115–2122 (2010).
Google Scholar 
Yan, Y., Hu, B. Q., Han, Q. Y. & Li, Y. L. Early warning for karst rocky desertification in agricultural land base on the 3S and ANN technique: A case study in Du’an County, Guangxi. Carsologica Sin. 31(01), 52–58 (2012).
Google Scholar 
Zhang, J. et al. Spectral analysis of seasonal rock and vegetation changes for detecting karst rocky desertification in southwest China. Int. J. Appl. Earth Obs. Geoinf. https://doi.org/10.1016/j.jag.2021.102337 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Liu, Y., Wang, J. & Deng, X. Rocky land desertification and its driving forces in the karst areas of rural Guangxi, Southwest China. J. Mt. Sci-Engl. 5(4), 350–357 (2008).
Google Scholar 
Li, Y., Xie, J., Luo, G., Yang, H. & Wang, S. The evolution of a karst rocky desertification land ecosystem and its driving forces in the Houzhaihe Area, China. J. Ecol. 5, 501–512 (2015).
Google Scholar 
Zhang, Y. R., Zhou, Z. F. & Ma, S. B. Rocky desertification and climate change characteristics in typical karst area of Guizhou Province over past two decades. Environ. Sci. Technol. 37(09), 192–197 (2014).
Google Scholar 
Bai, X. Y., Wang, S. J., Chen, Q. W. & Cheng, A. Y. Constrains of lithological background of carbonate rock on spatio-temporal evolution of karst rocky desertification land. Earth Sci. 35(4), 691–696 (2010).
Google Scholar 
Li, L. & Xiong, K. Study on peak-cluster-depression rocky desertification landscape evolution and human activity-influence in South of China. Eur. J. Remote Sens. 1–9 (2020).Yao, Y. H., Shuo, N. D. Z., Zhang, J. Y., Hu, Y. F. & Kou, Z. X. Spatiotemporal characteristics of karst rocky desertification and the impact of human activities from 2010 to 2015 in Guanling County, Guizhou Province. Prog. Geogr. 38(11), 1759–1769 (2019).
Google Scholar 
Shi, K., Yang, Q. & Li, Y. Are karst rocky desertification areas affected by increasing human activity in Southern China? An empirical analysis from nighttime light data. Int. J. Environ. Res. Public Health. 16(21), 4175 (2019).PubMed Central 

Google Scholar 
Luo, X. L. et al. Analysis on the spatio- temporal evolution process of rocky desertification in Southwest Karst area. Acta Ecol. Sin. 41(02), 680–693 (2021).
Google Scholar 
Yang, Q., Jiang, Z., Yuan, D., Ma, Z. & Xie, Y. Temporal and spatial changes of karst rocky desertification in ecological reconstruction region of Southwest China. Envirov. Earth Sci. 72(11), 4483–4489 (2014).
Google Scholar 
Zhang, C., Qi, X., Wang, K., Zhang, M. & Yue, Y. The application of geospatial techniques in monitoring karst vegetation recovery in southwest China. Prog. Phys. Geog. 41(4), 450–477 (2017).
Google Scholar 
Ying, B., Xiao, S., Xiong, K., Cheng, Q. & Luo, J. Comparative studies of the distribution characteristics of rocky desertification and land use/land cover classes in typical areas of Guizhou province, China. Envirov. Earth Sci. 71(2), 631–645 (2013).
Google Scholar 
Luo, X. et al. Analysis on the spatio-temporal evolution process of rocky desertification in Southwest Karst area. Acta Ecol. Sin. 41(2), 680–693 (2021).
Google Scholar 
Chong, G. et al. Characteristics of changes in karst rocky desertification in southtern and western china and driving mechanisms. Chin. Geogr. Sci. 31, 1082–1096 (2021).
Google Scholar 
Guo, B. et al. A novel-optimal monitoring model of rocky desertification based on feature space models with typical surface parameters derived from LANDSAT_8 OLI. Degrad. Dev. 32(17), 5023–5036 (2021).
Google Scholar 
Chen, F. et al. Spatio-temporal evolution and future scenario prediction of karst rocky desertification based on CA–Markov model. Arab. J. Geosci. 14, 1262 (2021).
Google Scholar 
Wu, X., Liu, H., Huang, X. & Zhou, T. Human driving forces: Analysis of rocky desertification in karst region in Guanling County, Guizhou Province. Chin. Geogr. Sci. 21(5), 600–608 (2011).
Google Scholar 
Chen, H. et al. The evolution of rocky desertification and its response to land use changes in Wanshan Karst area, Tongren City, Guizhou Province, China. J. Agr. Resour. Environ. 37(01), 24–35 (2020).
Google Scholar 
Zerrouki, N., Dairi, A., Harrou, F., Zerrouki, Y. & Sun, Y. Efficient land desertification detection using a deep learning-driven generative adversarial network approach: A case study. Concurr. Comp-Pract. E. https://doi.org/10.1002/cpe.6604 (2021).Article 

Google Scholar 
Keskin, H., Grunwald, S. & Harris, W. Digital mapping of soil carbon fractions with machine learning. Geoderma 339, 40–58 (2019).ADS 
CAS 

Google Scholar 
Tian, Y. et al. Aboveground mangrove biomass estimation in Beibu Gulf using machine learning and UAV remote sensing. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2021.146816 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Xi, H. et al. Spatio-temporal characteristics of rocky desertification in typical Karst areas of Southwest China: A case study of Puding county, Guizhou province. Acta Ecol. Sin. 38(24), 8919–8933 (2018).
Google Scholar 
Deng, Y. et al. Relationship among land surface temperature and LUCC, NDVI in typical karst area. Sci. Rep-UK. 296–306 (2018).Li, S. M., Yu, L. W., Gan, S. & Yang, Y. M. Study on inversion relationship between vegetation lndex and leaf area index of rocky desertification area in southeast Yunnan based on ETM+. J. Kunming Univ. Sci. Technol. (Natl Sci.) 40(06), 31–36 (2015).
Google Scholar 
Yan, X. & Cai, Y. Multi-Scale anthropogenic driving forces of karst rocky desertification in Southwest China. Land Degrad. Dev. 26(2), 193–200 (2013).
Google Scholar 
Meyer, H., Reudenbach, C., Wollauer, S. & Nauss, T. Importance of spatial predictor variable selection in machine learning applications – Moving from data reproduction to spatial prediction. Ecol. Model. https://doi.org/10.1016/j.ecolmodel.2019.108815 (2019).Article 

Google Scholar 
Cracknell, M. & Reading, A. Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information. Comput. Geosci-UK 63, 22–33 (2014).
ADS 

Google Scholar 
Feng, K. et al. Monitoring desertification using machine-learning techniques with multiple indicators derived from MODIS images in Mu Us Sandy Land, China. Remote Sens. 14, 2663. https://doi.org/10.3390/rs14112663 (2022).Article 
ADS 

Google Scholar 
Belgiu, M. & Drăguţ, L. Random Forest in remote sensing: A review of applications and future directions. ISPRS J. Photogramm 114, 24–31 (2016).
Google Scholar 
Chutia, D., Bhattacharyya, D. K., Sarma, K. K., Kalita, R. & Sudhakar, S. Hyperspectral remote sensing classifications: A perspective survey. Trans. GIS https://doi.org/10.1111/tgis.12164 (2015).Article 

Google Scholar 
Song, T. Q., Peng, W. X., Du, H., Wang, K. & Zeng, F. Occurrence spatial-temporal dynamics and regulation strategies of karst rocky desertification in southwest China. Acta Ecol. Sin. 34(18), 5328–5341 (2014).
Google Scholar 
Zhu, L.F. Study on the Spatial-Temporal Variation of Vegetation Coverage and Karst Rocky Desertification based on MODIS Data. Ph.D. Dissertation, Southwestern University. Chongqing, China (2018).Yang, Q. et al. Spatio-temporal evolution of rocky desertification and its driving forces in karst areas of Northwestern Guangxi, China. Environ. Earth Sci. 64, 383–393 (2011).
Google Scholar 
Mishra, N. & Chaudhuri, G. Spatio-temporal analysis of trends in seasonal vegetation productivity across Uttarakhand, Indian Himalayas, 2000–2014. Appl. Geogr. 56, 29–41 (2015).
Google Scholar 
Zhang, X., Shang, K., Cen, Y., Shuai, T. & Sun, Y. Estimating ecological indicators of karst rocky desertification by linear spectral unmixing method. Int. J. Appl. Earth. Obs. 31, 86–94 (2014).CAS 

Google Scholar 
Reshef, D. et al. Detecting novel associations in large data sets. Science 334(6062), 1518–1524 (2011).ADS 
CAS 
PubMed 
PubMed Central 
MATH 

Google Scholar 
Li, W. et al. Concentration estimation of dissolved oxygen in Pearl River Basin using input variable selection and machine learning techniques. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2020.139099 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Abdelhakim, A., El, H., Luis, E., Salah, E. & Abdelghani, C. Retrieving crop albedo based on radar sentinel-1 and random forest. Approach. Remote Sens. 13(16), 3181 (2021).ADS 

Google Scholar 
Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M. & Rigol-Sanchez, J. P. An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS J. Photogramm 67, 93–104 (2012).
Google Scholar 
Dharumarajan, S., Bishop, T., Hegde, R. & Singh, S. Desertification vulnerability index-an effective approach to assess desertification processes: A case study in Anantapur District, Andhra Pradesh, India. Land Degrad. Dev. 29(1), 150–161 (2017).
Google Scholar 
Li, P. et al. Dynamic monitoring of desertification in ningdong based on landsat images and machine learning. Sustainability 14, 7470. https://doi.org/10.3390/su14127470 (2022).Article 

Google Scholar 
Pacheco, A. D. P., Junior, J. A. D. S., Ruiz-Armenteros, A. M. & Henriques, R. F. F. Assessment of k-nearest neighbor and random forest classifiers for mapping forest fire areas in Central Portugal using landsat-8, sentinel-2, and terra imagery. Remote Sens. 13, 1345. https://doi.org/10.3390/rs13071345 (2021).Article 
ADS 

Google Scholar  More

DataSea Surface temperature (1850–2019)Sea Surface Temperature (SST, °C) from 1850 to 2019 originated from the COBE SST2 1° × 1° gridded dataset74, https://psl.noaa.gov/data/gridded/data.cobe2.html. SST data were interpolated on a 0.25° latitude × 0.25° longitude grid on a monthly scale from 1850 to 2019.BathymetryBathymetry (m) came from GEBCO Bathymetric Compilation Group 2019 (The GEBCO_2019 Grid—a continuous terrain model of the global oceans and land). Data are provided by the British Oceanographic Data Centre, National Oceanography Centre, NERC, UK. doi:10/c33m. (https://www.bodc.ac.uk/data/published_data_library/catalogue/10.5285/836f016a-33be-6ddc-e053-6c86abc0788e/). These data were interpolated on a 0.25° latitude × 0.25° longitude grid.Biological dataDaily mass concentration of chlorophyll-a in seawater (mg/m3) originated from the Glob Colour project (http://www.globcolour.info/). The product merges together all the daily data from satellites (MODIS, SeaWIFS, VIIRS) available from September 1997 to December 2019, on a 4 km resolution spatial grid. These data were interpolated on a daily scale on a 0.25° latitude × 0.25° longitude grid. These data were only used to map the average maximum standardised SSB (mdSSB) around the North Sea (Fig. 1a). When long-term changes in mdSSB were examined, we used modelled chlorophyll data (see section “Climate projections” below).Cod recrutment at age 1, Spawning Stock Biomass (SSB) and fishing effort F for 1963–2019 originated from ICES35.We used a plankton index of larval cod survival, which was an update of the index proposed by Beaugrand and colleagues33. Based on data from the Continuous Plankton Recorder (CPR)75, the index is based on the simultaneous consideration of six key biological parameters important for the diet and growth of cod larvae and juveniles in the North Sea:76,77 (i) Total calanoid copepod biomass as a quantitative indicator of food for larval cod, (ii) mean size of calanoid copepods as a qualitative indicator of food, (iii-iv) the abundance of the two dominant congeneric species Calanus finmarchicus and C. helgolandicus, (v) the genus Pseudocalanus and (vi) the taxonomic group euphausiids. A standardised Principal Component Analysis (PCA) is performed on the six plankton indicators for each month from March to September for the period 1958–2017 (table 60 years × 7 months-6 indicators). The plankton index is simply the first principal component of the PCA33.Climate projectionsClimate projections for SST and mass concentration of chlorophyll in seawater (kg m−3) originated from the Coupled Model Intercomparison Project Phase 6 (CMIP6)5 and were provided by the Earth System Grid Federation (ESGF). We used the projections known as Shared Socioeconomic Pathways (SSP) 245 and 585 corresponding respectively to a medium and a high radiative forcing by 2100 (2.5 W m−2 and 8.5 W m−2)78. The daily simulations of four different models (i.e. CNRM-ESM2-1, GFDL-ESM4, IPSL-CM6A-LR, and UKESM1-0-LL) covering the time period 1850–2014 (historical simulation) and 2015–2100 (future projections for the two SSPs scenarios) were used. All the data were interpolated on a 0.25° by 0.25° regular grid. Key references (i.e. DOI and dataset version) are provided in Supplementary Text 1. Long-term changes in modelled SSB were based on these data (including modelled daily chlorophyll data).The FishClim modelLet Kt be the maximum standardised Spawning Stock Biomass (mdSSB hereafter) that can be reached by a fish stock at time t for a given environmental regime φt. Xt+1, standardised SSB (dSSB hereafter) at time t+1 was calculated from dSSB at time t as follows:$${X}_{t+1}={X}_{t}+r{X}_{t}left(1-frac{{X}_{t}}{{K}_{t}}right)-alpha {X}_{t}$$
(1)
α is the fishing intensity that varies between 0 (i.e. no fishing) and 1 (i.e. 100% of SSB fished in a year). It is important to note that α (see Eq. (10)) should not be mistaken with ICES fishing effort F79 (calculated from SSB). The second term of Eq. (1) is the intrinsic growth rate of the fish stock that is a function of both Kt and the population growth rate r (r was fixed to 0.5 in most analyses, but see Fig. 3d however where r varied from 0.25 to 0.75). The population growth rate r is highly influenced by the life history traits of a species80 but also by environmental variability54,55,81. Here, the population growth rate was assumed to be constant in space and time and the influence of environmental variability occurred exclusively through its effects on Kt. We made this choice to not multiply the sources of complexity and errors (i.e. population growth rate is very difficult to assess and varies with age80). The third term reflects the part of dSSB that is lost by fishing. Note that natural mortality is not explicitly integrated in Eq. (1) because this process is difficult to assess with confidence at the scale of our study. Here, we assumed that the second term of Eq. (1) implicitly considered this process; when K increases, it is likely that natural mortality diminishes, especially at age 134. We tested this assumption below. Most of the time when fishing occurs, Xt {y}_{{{{rm{opt}}}}}$$
(3)
Here yopt= 5.4 °C and t1 and t2 were fixed to 5.7 °C and 4 °C, respectively, so that the thermal niche was close to that assessed by Beaugrand and colleagues31 (Supplementary Fig. 2). This Supplementary Figure compares the thermal response curve we chose in the present study with the data analysed in Beaugrand and colleagues31. The figure shows that the response curve (red curve) is close to the histogram showing the number of geographical cells with a cod occurrence as a function of temperature varying between −2 °C (frozen seawater) and 20 °C.Because t1  > t2, the niche was slightly negative asymmetrical (Supplementary Fig. 1). U1(y) was the first component of mdSSB along the thermal gradient y. c was the maximum value of mdSSB; c was fixed to 1 so that mdSSB varied between 0 and 184,85. y was the value of SST. Slight variations in the different parameters of the niche did not alter either the spatial patterns in the distribution of mdSSB nor the correlations with recruitment.To model the bathymetric niche of cod, we used a trapezoidal function. Changes in mdSSB, U2, along bathymetry, were assessed using four points (θ1, θ2, θ3, θ4):$$begin{array}{cc}{{U}}_2({{z}})=0 & {{{{{{{rm{When}}}}}}; z}}le {{{{rm{theta }}}}}_{1}end{array}$$
(4)
$$begin{array}{cc}{{U}}_2({{z}})=frac{z-{theta }_{1}}{{theta }_{2}-{theta }_{1}}c & {{{{{rm{When}}}}}},{{{{rm{theta }}}}}_{1} < {{z}}le {{{{rm{theta }}}}}_{2}end{array}$$ (5) $$begin{array}{cc}{{U}}_2({{z}})={{c}} & {{{rm{When}}}},{{{{rm{theta }}}}}_{2} < {{z}} < {{{{rm{theta }}}}}_{3}end{array}$$ (6) $${{U}}_2begin{array}{cc}(z)=frac{{theta }_{4}-z}{{theta }_{4}-{theta }_{3}}c & {{{rm{When}}}},{{{{rm{theta }}}}}_{3}le {{z}} < {{{{rm{theta }}}}}_{4}end{array}$$ (7) $$begin{array}{cc}{{{rm{U}}}}_2({{z}})=0 & {{{rm{When}}}}; {{{rm{z}}}}ge {{{theta }}}_{4}end{array}$$ (8) With θ2 ≥ θ1, θ3 ≥ θ2 and θ4≥ θ3 and y the bathymetry; θ1 = 0, θ2 = 10−4, θ3 = 200 and θ4 = 600 m (Supplementary Fig. 1). These parameters were retrieved from the litterature86,87. Here also c, the maximum abundance reached by the target species was fixed to 1 and U2 varied between 0 and 1. Trapezoidal niches have been used frequently to model the spatial distribution of fish and marine mammals88,89.The trophic niche was modelled by a rectangular function on a daily basis. To the best of our knowledge, no information on the trophic niche is available. We modelled the trophic niche by fixing U3 to 1 when chlorophyll-a concentration was higher than 0.05 mg m−3 during a minimum period of 15 days and 0 otherwise (Supplementary Fig. 1). This minimum of chlorophyll was implemented as a proxy for suitable food, which has been shown to be important in the North Atlantic for cod recruitment and distribution6,33.There exists two ways to combine the different ecological dimensions of a niche: (i) use an additive or (ii) a multiplicative model82,90. We used a multiplicative model because when one dimension is associated to a nil abundance, the resulting abundance combining all dimensions is also nil in contrast to an additive model; therefore only one unsuitable environmental value may explain a nil abundance. All dimensions were associated to abundance values that varied between 0 and 190.Therefore, maximum dSSB, K, for a given environmental regime E was given by multiplying the three niches (thermal, bathymetric and trophic):$$K=mathop{prod }limits_{i=1}^{p}{U}_{i}$$ (9) where p = 3, the three dimensions of the niche.AnalysesMapping of maximum standardised SSBmdSSB is close to the “dynamic B0” approach; B0 is the SSB in the absence of fishing (generally expressed in tonnes)51 whereas mdSSB is the SSB in the absence of fishing standardised between 0 and 1 and assessed from the knowledge of the niche of the species. We first assessed mdSSB in the North-east Atlantic (around UK) at a spatial resolution of 0.25° latitude × 0.25° longitude on a daily basis from 1850 to 2019. For this analysis, FishClim was run on monthly COBE SST (1850–2019), mean bathymetry and a climatology of daily mass concentration of chlorophyll-a in seawater from the Glob Colour project (see Data section). We then calculated an annual average based on the main seasonal productive period around UK, i.e. from March to October90. Finally, we averaged all years to examine spatial patterns in mean mdSSB (Fig. 1a).Temporal changes in maximum standardised SSBWe assessed average long-term changes in mdSSB in the North Sea (51°N–62°N and 3°W–9.5°E); the annual average was calculated from March to October because this is a period of high production90 . We compared long-term changes in mdSSB with cod recruitment at age 1, a plankton index of larval cod survival based on the period March to October33, and ICES-based SSB35 for 1963-2019 (Fig. 1b–d).Correlation analyses with modelled maximum standardised SSBPearson correlations between long-term changes in mdSSB (average for the North Sea, 51°N–62°N and 3°W–9.5°E) and cod recruitment at age 1 in decimal logarithm35, a plankton index of larval cod survival in the North Sea33, and observed ICES SSB in decimal logarithm35 for the period 1963–2019 were calculated (Fig. 1b–d). The same analysis was performed between assessed fishing intensity α from our FishClim model and fishing effort F35 in the North Sea (Fig. 1e). The probability of significance of the coefficients of correlation was adjusted to correct for temporal autocorrelation91.Assessment of fishing intensity from ICES spawning stock biomassUsing North Sea ICES SSB, we applied Eq. (1) to assess fishing intensity α:$$alpha =1+rleft(1-frac{{X}_{t}}{{K}_{t}}right)-frac{{X}_{t+1}}{{X}_{t}}$$ (10) With Xt+1 and Xt the ICES dSSB (in decimal logarithm). Standardisation of ICES SSB, necessary for this analysis, was complicated because many different kinds of standardisation were achievable so long as X remained strictly above 0 (i.e. full cod extirpation, not observed so far35) and strictly below min(K) (i.e. all black curves always below all points of the blue curve were possible, Supplementary Fig. 3). Indeed, ICES SSB includes exploitation and environmental fluctuations whereas K (i.e. mdSSB) integrates only environmental forcing; the difference is mainly caused by the negative influence of fishing. We chose the black curve (ICES SSB) that maximised the correlation between α (fishing intensity in the FishClim model) and F (ICES fishing effort)35.Reconstruction of long-term changes in ICES spawning stock biomassThe estimation of α allowed us to reconstruct long-term changes in cod (ICES) dSSB and to examine the respective influence of fishing and CIEC by means of Eq. (1) (“Methods”) using four hypothetical scenarios (Fig. 1f). First, we fixed fishing intensity and considered exclusively environmental variations through its influence on dSSB. (i–ii) We assessed long-term changes in dSSB from long-term variation in observed mdSSB (called Kt in Eq. (1)) with a constant level of exploitation fixed to (i) minimum (upper blue curve, i.e. the lowest fishing intensity observed in 1963–2019) or (ii) maximum (lower blue curve, i.e. the highest fishing intensity observed in 1963–2019).Second, we fixed the environmental influence on dSSB and considered variations in fishing intensity. We estimated long-term changes in dSSB from long-term variation in estimated α with a constant mdSSB fixed to (iii) minimum (lower red curve, i.e. the lowest mdSSB observed in 1963–2019) or (iv) maximum (upper red curve, i.e. the highest mdSSB observed in 1963–2019). It was possible to compare long-term changes in reconstructed (ICES) dSSB (thick black curve in Fig. 1f) with these four hypothetical scenarios (Fig. 1f); note that these comparisons were not affected by the choice we made earlier on the standardisation of (ICES) SSB.Quantification of the respective influence of fishing and climate/environment on spawning stock biomassUsing the previous curves, we examined the respective influence of fishing and CIEC on reconstructed (ICES) dSSB (Fig. 2). First, the influence of fishing was investigated by estimating the residuals between reconstructed (ICES) dSSB based on long-term changes in mdSSB (i.e. Kt in Eq. (1)) and α (thick black curves in Fig. 1f) and modelled dSSB based on fluctuating fishing intensity α and invariant K (average of the two red curves in Fig. 1f). This calculation led to the red curve in Fig. 2b. Next, we performed the opposite procedure to examine the influence of CIEC on dSSB (i.e. invariant fishing intensity α based on the two blue curves in Fig. 1f). This calculation led to the blue curve in Fig. 2b.A cluster analysis, based on a matrix years × three time series with (i) long-term changes in reconstructed standardised (ICES) SSBs, (ii) fishing and (iii) CIEC, was performed to identify key periods (vertical dashed lines in Fig. 2). We standardised each variable between 0 and 1 and used an Euclidean distance to assess the year (1963–2019) × year (1963–2019) square matrix so that each variable contributed equally to each association coefficient. We used an agglomerative hierarchical clustering technique using average linkage, which was a good compromise between the two extreme single and complete clustering techniques92. In this paper, we were only interested in the timing between the different time periods (i.e. the groups of years) revealed by the cluster analysis (Fig. 2).We also calculated an index of fishing influence (ε, expressed in percentage) by means of two indicators γ and δ, which were slightly different to the ones we used above. The first one, γ, was modelled dSSB with fluctuating fishing intensity and a constant mdSSB based on the best suitable environment observed during 1963–2019 (only the upper red curve in Fig. 1f; fishing influence). The second one, δ, was modelled dSSB based on fluctuating environment and fishing intensity (black curve in Fig. 1f) on modelled dSSB based on a fluctuating environment but a constant fishing intensity fixed to the lowest value of the time series (only the upper blue curve in Fig. 1f; environmental influence). The index of fishing influence (ε, expressed in percentage) was calculated as follows:$$varepsilon =frac{100gamma }{gamma +delta }$$ (11) For each period of 1963–2019 identified by the cluster analysis, we quantified the influence of fishing (and therefore the environment) using a Jackknife procedure93,94. The resampling procedure recalculated ε by removing each time 1 year of the time period, which allowed us to provide a range of values (i.e. minimum and maximum) in addition to the average value (bar{varepsilon }) calculated for each interval, including the whole period (Fig. 2c).Long-term changes in modelled spawning stock biomass (1850–2019, 2020–2100 and 2020-2300)We modelled mdSSB (Kt in Eq. (1)) using outputs from four Earth System models (ESMs) based on two scenarios of SST/Chlorophyll changes (i.e. SSP245 and SSP585) for the period 1850–2100 (and for one scenario and one ESM until 2300; Fig. 3).For the period 1850–2019, we used daily SST/Chlorophyll changes from the four ESMs to estimate potential changes in mdSSB (thin dashed black curves in Fig. 3a). An average of mdSSB was also calculated (thick green curve in Fig. 3a).For the period 2020–2100, we showed all potential changes in mdSSB based on the four ESMs and both scenarios SSP245 (thin dashed blue curves in Fig. 3a) and SSP585 (thin dashed red curves). An average of mdSSB was also calculated for scenarios SSP245 (thick continuous blue curve) and SSP585 (thick continuous red curve). In addition, we assessed dSSB based on a constant standardised catch fixed to the average of 2008–2019, the last period identified by the cluster analysis (G5, i.e. (alpha X) = 0.03 in Eq. (1)), and the average values of all ESMs for SSP245 (thick dashed blue curve in Fig. 3a) and SSP585 (thick dashed red curve). This analysis was performed to show how a constant catch might alter long-term changes in mdSSB. When Xt (Eq. (1)) reached 0.1, the stock was considered as fully extirpated.Understanding how fishing and climate/environment interact now and in the futureWe modelled dSSB as a function of fishing intensity α and CIEC to show how fishing and the environment interact (Fig. 3b, c). We calculated dSSB for fishing intensity between α = 0 and α = 0.5 every step Ɵ = 0.001 and for mdSSB between K = 0 and K = 1 every step Ɵ = 0.001 to represent values of dSSB as a function of fishing and CIEC. We then superimposed reconstructed ICES dSSB (1963–2019) on the diagram for three periods: 1963–1985 (high SSB), 1986–1999 (pronounced reduction in SSB), and 2000–2019 (low SSB). Maximum standardised SSB for 2020–2100 (or 2300 exclusively for Scenario SSP 585 of IPSL ESM) assessed from four ESMs and scenarios SSP245 and SSP585 were also superimposed. Fishing intensity is unpredictable for 2020–2100 and so we arbitrarily fixed it constant between 0.08 and 0.17 in increments of 0.1 for display purposes, starting by ESMs based on scenario SSP 245 followed by scenario SSP 585 (Fig. 3b). When Xt (Eq. (1)) reached 0.1, the stock was considered as fully extirpated.We calculated an index of sensitivity of dSSB as a function of fishing intensity and CIEC. To do so, we first calculated sensitivity of dSSB to fishing intensity α. Index ζi was calculated at point i from dSSB X and fishing intensity α at i−1 and i+1 (see also Eq. (1)):$$begin{array}{cc}{zeta }_{i}=frac{left|{X}_{i+1}-{X}_{i-1}right|}{left|{alpha }_{i+1}-{alpha }_{i-1}right|} & {{{rm{with}}}},{{{rm{min }}}}(alpha )+{{uptheta }}le ile {{{rm{max }}}}(alpha )-{{uptheta }}end{array}$$ (12) With min(α) = 0, max(α) = 0.5 and Ɵ = 0.001.Similarly, we calculated sensitivity of dSSB to K. Index ηj was calculated at point j from dSSB X and mdSSB K at j−1 and j+1 (see also Eq. (1)):$$begin{array}{cc}{eta }_{j}=frac{left|{X}_{j+1}-{X}_{j-1}right|}{left|{K}_{j+1}-{K}_{j-1}right|} & {{{rm{with}}}},{{{rm{min }}}}left(Kright)+{{{rm{theta }}}}le {{j}}le {{{rm{max }}}}({{{rm{K}}}})-{{uptheta }}end{array}$$ (13) With min(K) = 0, max(K) = 1 and Ɵ = 0.001.Then, we summed the two indices to assess the joint sensitivity of dSSB to fishing intensity Z and mdSSB H:$${{{{bf{I}}}}}_{{{i}},{{j}}}={{{bf{Z}}}}({{{{rm{zeta }}}}}_{{{i}}})+{{{bf{H}}}}({eta }_{{{j}}})$$ (14) Matrix I was subsequently standardised between 0 and 1:$${{{{boldsymbol{I}}}}}^{{{{boldsymbol{* }}}}}=frac{{{{boldsymbol{I}}}}-min ({{{boldsymbol{I}}}})}{max left({{{boldsymbol{I}}}}right)-min ({{{boldsymbol{I}}}})}$$ (15) With I* the matrix of sensitivity of dSSB to fishing intensity and mdSSB standardised between 0 and 1 (Fig. 3c).Number of years needed for recovery after stock collapseWe investigated how the number of years needed for a stock to recover after stock collapse (i.e. dSSB=0.05 in Eq. (1); i.e. 10% of mdSSB) varied as a function of mdSSB (between 0 and 1 by increment of 0.001); this was only influenced by the environmental regime φt and population growth rate r. For this analysis, we fixed a target dSSB of 0.4 (vertical dashed green vertical line in Fig. 3d) and three different values of r: 0.25, 0.5 and 0.75. We simulated a hypothetical moratorium with a fishing intensity α = 0 in Eq. (1).Here, stock collapse was defined as dSSB ≤ 0.1 × mdSSB, i.e. when the dSSB reached less than 10% of the unfished biomass mdSSB. This threshold corresponds to values usually defined in the literature; e.g. Pinsky and colleagues95 defined a collapse when landings are below 10% the average of the five highest landings recorded for more than 2 years, Worm and colleagues69 defined stock collapse when the biomass becomes lower than 10% of the unfished biomass, Andersen96 when it is lower than 20% and Thorpe and De Oliveira67 when it is lower than 10–20%.Potential consequences of fisheries management and climate-induced environmental changesWe examined how fishing and CIEC may affect cod stocks and their exploitation around UK with a focus in the North Sea (Figs. 4, 5). For these analyses, we averaged long-term changes in modelled dSSB corresponding to each scenario (all thin dashed blue and thin red curves in Fig. 3a for SSP245 and 585, respectively). In these analyses, the stock was considered fully extirpated when Xt (Eq. (1)) reached 0.1.Year of cod extirpation for 2020–2100 We estimated year of cod extirpation from 2020 to 2100 in each geographical cell based on (i) a constant fishing intensity (α = 0.04) in time and space, and (ii) an adjusted fishing intensity using the concept of Mean Sustainable Yield (MSY). The choice of α = 0.04 did not alter our conclusions; a lower or a higher value delayed or speed cod extirpation in a predictable way, respectively. In fisheries, MSY is defined as the maximum catch (abundance or biomass) that can be removed from a population over an indefinite period with dX/dt = 0, with X for dSSB and t for time. Despite some criticisms about MSY66, the concept remains a key paradigm in fisheries management35,63. We used this concept to show that controlling fishing intensity delayed cod extirpation. From Eq. (1), we calculated fishing intensity, called αMSYt, so that X remained above XMSYt at all time t:$${alpha }_{{{{{rm{MSY}}}}t}}=rleft(1-frac{{X}_{{{{{rm{MSY}}}}t}}}{{K}_{t}}right)$$ (16) In this analysis, we fixed XMSY t = Kt/2. We assessed ({alpha }_{{{{{rm{MSY}}}}t}}) from Eq. (16) and then estimated dSSB from ({alpha }_{{{{{rm{MSY}}}}t}}) and Kt (based on averaged SSP245 and SSP585) by means of Eq. (1). Although results were displayed at the scale of the north-east Atlantic (around UK), we calculated the difference in year of cod extirpation between scenarios of warming (SSP245 and SSP585) and between scenarios of cod management (constant versus adjusted—MSY— fishing intensity). Differences were presented by means of histograms (Fig. 4). From each histogram, we calculated the median of the differences in year of cod extirpation E97. Pooled standardised catch by 2100 (2020–2100) In term of fishing exploitation, we assessed pooled standardised catch (i.e. pooled dSSB) in 2100 (2020–2100), again for two scenarios of CIEC (SSP245 and 585) and two scenarios of cod management (constant versus adjusted—MSY—fishing intensity; Fig. 5). We then calculated the percentage of reduction in pooled standardised catch caused by fishing or the intensity of warming. Finally, we assessed the median of the percentage of reduction in pooled standardised catch for the North Sea area (51°N–62°N and 3°W–9.5°E). The goal of this analysis was to demonstrate that controlling fishing intensity optimises cod exploitation. Statistics and reproducibilityAll statistical analyses can be reproduced from the equations provided in the text, the cited references or the data available in Supplementary Data.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

To build Sentinel2GlobalLULC, we followed two main steps. First, we established a spatio-temporal consensus between 15 global LULC products for 29 LULC classes. Then, we extracted the maximum number of Sentinel-2 RGB images representing each class. Each image is a tile that has 224 × 224 pixels at 10 × 10 m spatial resolution and was built as a cloud-free composite from all the Sentinel-2 images acquired between June 2015 and October 2020. Both tasks were implemented using GEE, an efficient programming, processing and visualisation platform that allowed us to have free manipulation and access to all used LULC products and Sentinel-2 imagery, simultaneously.Finding spatio-temporal agreement across 15 global LULC productsTo establish the spatio-temporal consensus between different LULC products for each one of the 29 LULC classes, we followed four steps: (1)Identification of the LULC products to be used in the consensus, (2)Standardization and harmonization of the LULC legend that was subsequently used to annotate the image tiles, (3)Spatio-temporal aggregation across LULC products, and (4)Spatial reprojection and tile selection based on optimized spatial purity thresholds.Global LULC products selectionThe adopted purity measure for spatio-temporal agreement across the 15 global LULC products we selected from GEE (Table 2) aims to find areas of high consensus to maximize the annotation quality. Spatial and temporal consensus across such rich diversity of LULC products, in terms of spatial resolution, time coverage, satellite source, LULC classes and accuracy, was used as a source of robustness for our subsequent LULC annotation. Products outside GEE were not used due to computing limitations.Table 2 Main characteristics of the 15 global Land-Use and Land-Cover (LULC) products available in Google Earth Engine (GEE) that were combined to find consensus in the global distribution of 29 main LULC classes.Full size tableStandardization and Harmonization of LULC legendsLand cover (LC) data describes the main type of natural ecosystem that occupies an area; either by vegetation types such as shrublands, grasslands and forests, or by other biophysical classes such as permanent snow, bare land and water bodies. Land use (LU) includes the way in which humans modify or exploit an area, such as urban areas or agricultural fields.To build our 29 LULC classes nomenclature, we established a standardization and harmonization approach based on expert knowledge. During this process, we took into account both the needs of different practitioners in the global and regional LULC mapping field and the thematic resolution of the global LULC legends available in GEE. Our nomenclature consists of 23 LC and 6 LU distinct classes identified through specific consensus rules across 15 LULC products (see Table 4). A six-level (L0 to L5) hierarchical structure was adopted in the creation of these 29 LULC classes (Fig. 2). To facilitate the inter-operability of our 29 legends at the finest level L5 across all LULC products and with the widely used FAO’s hierarchical Land Cover Classification System (LCCS)1, we have established an LULC classification system where the 29 classes can be mapped directly to FAO’s LCCS as explained in the table of Supplementary File 1. The LC part in our dataset contains 20 terrestrial ecosystems and 3 aquatic ecosystems. The terrestrial systems are: Barren lands, Grasslands, Permanent snow, Moss and Lichen lands, Close shrublands, Open shrublands, in addition to 12 Forests classes that differed in their tree cover, phenology, and leaf type. The aquatic classes are: Marine water bodies, Continental water bodies, and Wetlands; furthermore, wetlands were divided into 3 classes: Marshlands, Mangroves and Swamps. The LU part is composed of urban areas and 5 coarse cropland types that differed in their irrigation regime and leaf type. In Table 3, you can find the semantic definition of each one of the 29 classes in Sentinel2GlobalLULC. We provided a table in Supplementary File 2, for a more detailed definition of each LULC class.Fig. 2Tree representation of the six-level (L0 to L5) hierarchical structure of the Land-Use and Land-Cover (LULC) classes contained in the Sentinel2GlobalLULC dataset. Outter circular leafs represent the final or most detailed 29 LULC classes (C1 to C29) of level L5. The followed path to define each class is represented through inner ellipses that contain the names of intermediate classes at different levels between the division of the Earth’s surface (square) into LU and LC (level L0) and the final class circle (level L5). All LULC classes belong to three levels at least, except the 12 forest classes that belong to L5 only.Full size imageTable 3 Semantic signification of each one of the 29 Land Use and Land Cover (LULC) classes contained in the Sentinel2GlobalLULC dataset according to the six-level (L0 to L5) hierarchical structure.Full size tableCombining products across time and spaceFor each one of the 29 LULC classes, we combined in space and time the global LULC information among the 15 GEE LULC products. This way, each image was annotated with a LULC class only if all combined products agreed in its corresponding tile (i.e., 100% of agreement in space and time). For each product and LULC type, we first set one or more criteria to create a global mask at the native resolution of the product in which each pixel was classified as 1 or 0 depending on whether it met the criteria for belonging to that LULC type or not, respectively (see first stage in Table 4). For certain LULC classes, some products did not provide any relevant information, so they were not used. For example (Table 4), in Grasslands (C3), Open Shrublands (C4) and Close Shrublands (C5), we combined 14 products, while in UrbanBlUpArea (C29) and Permanent Snow (C23) we only combined 10 and 7 products, respectively.Table 4 First stage of the rule set criteria used to find consensus across the 15 Land-Use and Land-Cover (LULC) products available in Google Earth Engine (GEE) for each of the 29 LULC classes contained in the Sentinel2GlobalLULC dataset.Full size tableThen (see second stage in Table 5), for each LULC type, we calculated the average of all the masks obtained from each product to create a final global probability map from all products with values ranging between 0 and 1. Value 1 meant that all products agreed to assign that pixel to a particular LULC class, while 0 meant that none of the products assigned it to that particular class (Fig. 3). These 0-to-1 values are interpreted as the spatio-temporal purity level of each pixel to belong to a particular LULC class and are provided as metadata with each image.Table 5 Second stage of the rule set criteria used to find consensus across the 15 Land-Use and Land-Cover (LULC) products available in Google Earth Engine (GEE) for each of the 29 LULC classes contained in the Sentinel2GlobalLULC dataset.Full size tableFig. 3Example of the process of building the final global probability map for one of the 29 Land-Use and Land-Cover (LULC) classes (e.g. C1: “Barren”) by means of spatio-temporal agreement of the 15 LULC products available in Google Earth Engine (GEE). The final map is normalized to values between 0 (white, i.e., areas with no presence of C1 in any product) and 1 (black spots, i.e., areas containing or compatible with the presence of C1 in all 15 products), whereas the shades of grey corresponds to the values in between (i.e., areas that did not contain or were not compatible with the presence of C1 in some of the products). This process is divided into two stages: the first stage (the blue part, see details in Table 4) and the second stage (the yellow part, see details in Table 5). LULC products available for several years are represented with superposed rectangles, while single year products are represented with single rectangles. GMP: global probability map, NA: Not Available.Full size imageAs an example of the first stage (see details in Table 4), to specify if a given pixel belongs to Dense Evergreen Needleleaf Forest, we evaluated its tree cover level using “ ≤ “ and “ ≥ “, while for bands containing the leaf type information, we used the equal operator “ = “. For the spatio-temporal combination of multiple criteria we have used the following operators: “AND”,“OR” and “ADD”. For example, we combined the tree cover percentage criteria with the leaf type criteria using “AND” to select forest pixels that met both conditions. To combine many years instances of the same product, we used “ADD”, except for product P13, where we used “AND” to identify permanent water areas only. Whenever we used the “ADD” operator, we normalized pixel values afterwards to bring it back to a probability interval between 0 and 1 using the division by the total number of combined years or criteria.In the second stage (see details in Table 5), we combined for each LULC class the 15 global probability maps previously derived from each product to create a final global probability map (Fig. 3). This combination was carried out using various operators such as “ADD”, “MULTIPLY” and “OR”, depending on the LULC type. When “ADD” was used, the final pixel values were normalized by dividing the final addition value of each pixel by the total number of added products. The “MULTIPLY” operator was mostly used at the end, to remove urban areas from non-urban LULC classes, or to remove water from non-water LULCs. The multiplication operator was also adopted to make sure that a certain criteria was respected in the final probability map. For instance, for the swamp class, we multiplied all pixels in the final stage by a water mask where saline water areas have a value of 0 in order to eliminate mangrove from swamp pixels and vice versa. Finally, we used “OR” operator between different water related products to take advantage of the fact that they complement each other in terms of spatial-temporal coverage and accuracy.In GEE, when two products are aggregated using “ADD”, “MULTIPLY” or any other operator, the output is aggregated at the spatial resolution of the product at the left of the operator. Hence, to maintain the finest spatial resolution in the final probability map, we multiplied everything by product P15 and placed it at the left of the final “MULTIPLY” operation (See Table 5). Hence, all the 29 final probability maps were generated at the P15 spatial resolution of 30 m/pixel (except the urban class C29 which maintained the 30 m/pixel resolution of product P14).Re-projection and Selection of purity thresholdSince our objective was finding pure Sentinel-2 image tiles of 224 × 224 10-m pixels representing each LULC class, we reprojected the 30 m/pixel probability maps to 2240 m/pixel using the spatial mean reducer in GEE. That is, each pixel value at 2240 m resolution was computed using the mean over all the 30m-pixel values contained within it. Hence, the resulting pixel values at 2240 m resolution represent the purity level that each Sentinel-2 image tile of 224 × 224 10-m pixels has. We illustrated the reprojection and selection processes in Fig. 4.Fig. 4Example of the workflow to obtain a Sentinel-2 image tile of 2240 × 2240 m for one of the 29 Land-Use and Land-Cover (LULC) classes (e.g. C1: “Barren”). The process starts with the reprojected final global probability map obtained from stage two (Table 5) and ends with its exportation to the repository of a Sentinel-2 image tile of 224 × 224 pixels. The white rectangle is the only one having a probability value of 1 (Recall that the purity threshold used for Barren was 1, i.e., 100%). The black pixels has a null probability value, while the probability values between 0 and 1 are represented in gray scale levels.Full size imageFor each one of the reprojected maps, we defined a pixel value threshold to decide whether a given 2240 × 2240 m tile was representative of each LULC class or not. Since training DL image classification models needs a large number of high quality (both in terms of image quality and annotation quality) image tiles to reach a good accuracy, when the spatial purity of 100% (full agreement across products in all the pixels of the 224 × 224 tile) resulted in a small number of agreement tiles for a particular class, the purity threshold was decreased for that class until the number of tiles was larger than 1000 or further decreased in less abundant classes to a minimum of 75% of purity. The found purity value is always provided as metadata for each image in the dataset, so the user can always restrict its analysis to those image tiles and classes at any desired purity level. Decreasing the purity threshold down to 75% for the less abundant classes (e.g swamp, mangrove, etc.) was a trade-off between maintaining a good data annotation quality and providing a sufficient number of tiles in each class. In Table 6, we present the number of agreement tiles found at different purity thresholds ranging from 75% to 100% for each LULC class. This spatial purity was not further decreased since machine learning image classification models are known to be robust when the target class is spatially dominant in each training image (it occupies more than 60% of the pixels in the scene)42. On the other hand, when the number of pure tiles for a LULC class was too large to be downloaded (i.e., greater than 14000), we applied a selection algorithm as described in the Supplementary File 3, to download a maximum of 14000 spatially representative images. For this, the world was divided into a one-degree squared cell grid. If a cell contained less than 50 image tiles, we selected them all. If it contained more than 50, we applied that automatic maximum geographic distance algorithm that selected images as far from each other as possible in a number proportional to the number of existing images in that cell. The map in Fig. 6 shows the global distribution of the selected 194877 image tiles contained in Sentinel2GlobalLULC and distributed in 29 LULC classes.Table 6 Summary of the varying number of found and eventually selected Sentinel-2 image tiles of 224 × 224 pixels depending on the different consensus level reached across the 15 Land-Use and Land-Cover (LULC) products available in Google Earth Engine (GEE) for each of the 29 LULC classes contained in the Sentinel2GlobalLULC dataset.Full size tableData extractionSentinel2GlobalLULC provides the user with two types of data: Sentinel-2 RGB images (jpeg and geotif versions) and CSV files with associated metadata. In the following subsections, we describe the process for associating metadata, including the Global Human Modification (GHM) index.Global human modification index extractionAs an additional metadata related to the level of human influence in each image, we calculated for each tile in GEE, the spatial mean of the global human modification index for terrestrial lands43, where 0 means no human modification and 1 means complete transformation. Since the original GHM product was mapped at 1 × 1 km resolution, we reprojected it to 2240 × 2240 m using the same reprojection procedure explained in (Re-projection and Selection of purity threshold).CSV files generationOnce the tiles were selected, for each LULC class we listed the image tiles in descendent order of purity. Metadata included: geographical coordinates of each tile centroid, tile purity value, name and ID of the LULC class, and average GHM index for that tile. Then, we used the geographical coordinates of each tile to identify its exact administrative address geolocation. To implement this reverse geo-referencing operation, we used a free request-unlimited python module called reverse_geocoder. This way, we assigned a country code, two levels of administrative departments, and the locality to each tile.For LULC classes that had more than 14000 pure tiles, we have released the coordinates before and after the distance-based selection in case the user wants to download more tiles or use our consensus coordinates for other purposes.Sentinel-2 RGB images exportationAfter extracting all these pieces of information and grouping them into CSV files, we went back to the geographic center coordinates of each tile and used them to extract the corresponding 224 × 224 Sentinel-2 RGB tiles using GEE. Each exported image was identical to the 2240 × 2240 m area covered by its Sentinel-2 tile.We chose “Sentinel-2 MSI (Multi-Spectral Instrument) product” since it is free and publicly available in GEE at the fine resolution of 10 × 10 m. We chose “Level-1C” (i.e., top-of-atmosphere reflectance) since it provides the longest data availability of Sentinel-2 images without any modification of the data. To build RGB images, we extracted the three bands B4, B3 and B2 that correspond to Red, Green and Blue channels, respectively. More bands available in Sentinel-2 or even in Sentinel-1 images can be incorporated in the future to our dataset. However, computational limitations (i.e., the size of the dataset would be impractical) did not allowed us to handle it as a first goal. In addition, the spatial resolution of the images would be heterogeneous across bands.To minimize the inherent noise due to atmospheric conditions (e.g. clouds, aerosols, smoke, etc.) that could affect the satellite RGB images, every image was built as a temporal aggregation of all images gathered by Sentinel-2 satellites between June 2015 and October 2020. During this aggregation, only the highest quality images in the corresponding image collection were considered, as we firstly discarded all image instances where the cloud probability exceeded 20% according to the metadata provided in their corresponding Sentinel-2 collection. Then, we calculated the 25th-percentile value between all remaining images for each reflectance band (R, G, and B), and built the final image with the obtained 25-percentile values in each pixel for its RGB bands. The 25th-percentile choice was adopted giving its suitability in atmospheric noise reduction44,45,46,47,48.Usually, Sentinel-2 MSI product includes true colour images in JPEG2000 format, except for the “Level-1C” collection used here. The three original bands (B4, B3, and B2) required a saturation mapping of their reflectance values into 0–255 RGB digital values. Thus, we mapped the saturation reflectance of 3558 into 255 to obtain true RGB channels with digital values between 0 and 255. The choice of these mapping numbers was taken from the Sentinel-2 true colour image recommendations section of Sentinel user guidelines. Finally, after exporting the selected tiles for each LULC class as “.tif” images, we converted them into “.jpeg” format using a lossless conversion algorithm.Technical implementationTo implement all our methodology steps, we first created a javascript in GEE for each LULC class. Each script is a multi-task javascript where we implemented a switch command to control which task we want to execute (between the spatio-temporal aggregation task, the spatial reprojection and tiles selection task, or the data exportation task). In each one of these scripts, we selected from GEE LULC datasets repository the 15 LULC products used to build the consensus of that LULC class. Each script was responsible of elaborating the spatio-temporal combination of the selected products and generating the final consensus map for that LULC class as described in the subsection “Combining products across time and space”. Then, it exports the final global probability map as an asset into GEE server storage to make its reprojection faster. In the same script, once the consensus map exportation was done, we imported it from the GEE assets storage and reprojected it to 2240 × 2240 m resolution; then, we exported the new reprojected map into GEE assets storage again to make its analysis and processing faster. Afterwards, we imported the reprojected map into the same script and applied different processing tasks. During this processing phase, many purity threshold values were evaluated. Then, we elaborated in this same script the pure tiles identification and their center coordinates exportation into a CSV file. A distinct GEE script was developed to import, reproject and export the global GHM map. The resulted GHM map was saved as an asset too, then imported and used in each one of the 29 LULC multi-task scripts.A python script was developed separately to read the exported CSV files for each LULC class and apply the reverse geo-referencing on their pure tiles coordinates then add the found geolocalization data (country code, locality…etc) to the original CSV files as new columns. Then, another python script was implemented to read the new resulted CSV files with all their added columns (reverse geo-referencing data, GHM data) and use the center coordinates of each pure tile in that class to export first its corresponding Sentinel-2 satellite geotiff image within GEE through the python API. Finally, after downloading all the selected geotiff images from our Google drive, we created another python script to convert these geotiff images into JPEG format. More

The African Development Corridors database is publicly available. The visualisation of the database that can be explored interactively here: https://dcp-unep-wcmc.opendata.arcgis.com/. The data is deposited in the Dryad Digital Repository referenced as Thorn, J. P.R., Mwangi, B.; Juffe Bignoli, D., The African Development Corridors Database, Dryad, Dataset, https://doi.org/10.5061/dryad.9kd51c5hw (2022)43. The final data were compiled into an online Master database spreadsheet, using the project code data as the merging attribute of the spatial and tabular database (AfricanDevelopmentCorridorsDatabase2022.csv). The African Development Corridor Database is presented as a GeoPackage file (.gpkg) and ESRI file Geodatabase (.gdb) composed by line and point feature datasets with the 22 associated attributes for all mapped corridors, a table with corridors that could not be mapped (also with the attributes), and a table with all sources consulted for each project code.We created a data standard to ensure a systematic and standardised data collection (Supplementary Table 2). Each data record in the database represents a project within a development corridor. To group all projects within the same development corridor we used a unique identifier composed by three letters that identified the corridor plus a number unique for each project or record. For example, the Lamu port project in Kenya within the Lamu Port South Sudan Ethiopia Transport Corridor (LAPSSET) was represented as LAP000. In this corridor we identified 20 projects, from LAP0001 which is the Lamu Port to LAP0020 which is the Isiolo-Lokichar-Lodwar-Nadapal Highway in Kenya. In addition to the unique identifier for each project, the data standard includes data attributes that provide detailed information about each project. Table 1 describes the attributes included in the database. Supplementary Table 3 summarises the 79 corridors included in the database.Table 1 List of the attributes included in the African Development Corridors Database.Full size tableInfrastructure types and status of development corridors in AfricaThe data consists of a total of 79 corridors consisting of 184 projects (Fig. 2). Of the 12 infrastructure types, the most predominant form of infrastructure in Africa’s development corridors is roads (n = 64, 34.8%), followed by wet ports (n = 38, 20.7%), passenger and freight railways (n = 33, 17.9%), and airports (n = 14, 7.6%). Fewer resort cities, electricity transmission lines, dry ports, industrial parks, and water pipelines comprise development corridors (all ≤ n = 3, 1.6%) (Fig. 3). We acknowledge our study might not include many infrastructure developments that are components projects of larger programmes but are not yet labelled as corridors. A total of 107 (58.7%) projects are operational, 35 (19%) are in progress, 25 (13.6%) are planned, 25 (13.9%) are being upgraded, and 2(1%) are on hold.Fig. 2Map showing the distribution of all the development corridors included in the African Development Corridors Database and their infrastructure type.Full size imageFig. 3Subset of highest frequencies of key attributes captured in the database.Full size imageSpatial distributionThe linear distance of development corridors in Africa is 122,294 km – which approximates to three times the Earth’s circumference, with an average of 1703.84 ± 213.19 km (mean, SE), ranging from 4–11,141 km. In terms of number of projects per country, Kenya has the most projects (n = 34, 18.5%), followed by Tanzania (n = 18, 9.8%), South Africa and Democratic Republic of the Congo (n = 17, 9.2% ea.), Ethiopia (n = 15, 8.2%), Mozambique and Zambia (n = 14, 7.6%), Angola, Uganda, Guinea and Cameroon (n = 12, 6.5%), Namibia (n = 11, 6.0%), Republic of Congo (n = 10, 5.4%), Burundi and Chad (n = 9, 4.9%), Malawi, Senegal, and Zimbabwe (n = 8, 4.4%), and Burkina Faso and Ghana (n = 7, 3.8%). Due to differences in the frequency and quality that countries publish data on infrastructure and development corridor investments, coverage may be lower for some regions, or some periods (i.e., recent, or further in the past).Investments in development corridorsAdjusting for inflation, the total investment of development corridors that is captured in the database ranges between USD 547.29–658.62 billion. The average cost of a corridor ranges between USD 3.46 ± 1.92 billion and USD 4.17 ± 2.04 billion. This is a notable sum, considering the average GDP in sub-Saharan Africa is USD 1.48 billion44. The most expensive development corridor project is the first of the nine Trans African Highway projects at USD 78.20 billion (when adjusted for inflation) – comprising transcontinental roads across Africa. We were able to capture the budget (or at least a proportion of the budget) for 84.7% of all projects.Temporal evolution of growth of development corridorsInvestments started in the 1800s and have increased exponentially (Fig. 4). Over a fifty-year period, the greatest number of investments took place between 1950 and 2000. Spikes in investments occurred particularly around 1900, which was when there was a wave of new imperialism across the continent, followed in the 1960s when many countries across sub-Saharan Africa gained independence. The third spike in investment was in the last decade, particularly since 2013, when we have seen rapid growth in foreign direct investment in Africa under initiatives such as the Belt and Road Initiative. According to the Ernst and Young Africa Attractiveness Survey (2019)45, the largest foreign direct investment (in terms of capital) between 2014–2018 came from China (USD 72,235 million), France (USD 34,172 million), USA (USD 30,885 million), the United Arab Emirates (USD 25,278 million) and the United Kingdom (USD 17,768 million).Fig. 4(a) Temporal evolution of investment in development corridors in Africa. (b) Annual investments per annum in development corridors in Africa (USD maximum, before adjusting for inflation).Full size imageDonors that are funding development corridorsAcross Africa, regional development banks invested the most in development corridors (30.8%), with the African Development Bank funding the majority (24.3%) of all projects. Outside of Africa, the regional development banks that invested in the most projects are the Export-Import Bank of China (n = 13, 3.8%), the European Investment Bank (n = 10, 2.8%) and the Arab Bank for Economic Development in Africa (n = 4, 1.2% ea.). National governments funded about 29.8% of all projects. The Government of Kenya funded the most projects (n = 26; 7.5%), followed by the Governments of Tanzania (n = 7, 2.0%) and South Africa (n = 4, 1.2%). Multilateral banks funded 10.9% of projects – mostly from the World Bank (n = 33, 9.54%) and a few from the International Finance Corporation (n = 4, 1.6%). The international development community funded only 6.1% – of which the OPEC Fund for International Development funded four projects. Private companies continue to invest in a small percentage of development corridors (3.5%), but this is higher than national banks that invest in 3.2%. Regional Economic Community bodies have invested in 15 (4.8%) of all 184 projects. The average number of donors per corridor ranged from one to 12.Weighting of investments by donor typeIn terms capital funded per donor type, Regional Development Banks invested the most (totalling USD 30.72 billion), followed by national governments (USD 20.45 billion). The figure then drops substantially to international development agencies (USD6.17 billion) and multilateral banks (USD 3.76 billion). These results are limited by the fact that we were only able to capture the amount funded delineated by donor type for 22.58% (or USD 70.24 billion) of the minimum of all investments (USD 311.14 billion) before adjusting for inflation.Commodities transportedA total of 147 commodities were captured. The top twenty commodities traded were rice (n = 52, 28.7% of all projects), sugar (27.0%), fish and petroleum (24.3% ea.), passengers (21.6%), textiles (21.1%), maize (19.5%), coffee (18.9%), cement and timber (17.8% ea.) followed by cotton, crude petroleum, vehicle spare parts, beverages, copper, fruit, fertilisers, gold, pharmaceutical products, and tobacco.Beneficiaries and net supplier or receiverApproximately 213 different beneficiaries were identified – predominantly local communities (n = 134 of projects, 72.8%), followed by national and local governments (63.0%), traders (51.1%), agricultural farmers and livestock producers (27.7%), ports (27.2%), industries (25.5%), truck drivers (22.3%), tourists (17.4%), entrepreneurs (12.0%), and logistics companies (11.4%). Almost all (89.1%) of corridors are net receivers and suppliers of commodities, while only 13 (7.1%) are net suppliers and seven are net receivers (3.8%). More