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    Fitness costs associated with a GABA receptor mutation conferring dieldrin resistance in Aedes albopictus

    Agnew P, Berticat C, Bedhomme S, Sidobre C, Michalakis Y (2004) Parasitism increases and decreases the costs of insecticide resistance in mosquitoes. Evolution 58:579–586CAS 
    PubMed 
    Article 

    Google Scholar 
    Ahmad NA, Endersby-Harshman NM, Mohd Mazni NR, Mohd Zabari NZA, Amran SNS, Ridhuan Ghazali MK et al. (2020) Characterization of sodium channel mutations in the Dengue vector mosquitoes Aedes aegypti and Aedes albopictus within the context of ongoing Wolbachia releases in Kuala Lumpur, Malaysia. Insects 11:529PubMed Central 
    Article 

    Google Scholar 
    Alout H, Ndam NT, Sandeu MM, Djégbe I, Chandre F, Dabiré RK et al. (2013) Insecticide resistance alleles affect vector competence of Anopheles gambiae s.s. for Plasmodium falciparum field isolates. PLoS ONE 8:e63849CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Andreasen MH, ffrench-Constant RH (2002) In situ hybridization to the Rdl locus on polytene chromosome 3L of Anopheles stephensi. Med Vet Entomol 16:452–455CAS 
    PubMed 
    Article 

    Google Scholar 
    Assogba BS, Djogbénou LS, Milesi P, Berthomieu A, Perez J, Ayala D et al. (2015) An ace-1 gene duplication resorbs the fitness cost associated with resistance in Anopheles gambiae, the main malaria mosquito. Sci Rep. 5:14529CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Assogba BS, Milesi P, Djogbénou LS, Berthomieu A, Makoundou P, Baba-Moussa LS et al. (2016) The ace-1 locus is amplified in all resistant Anopheles gambiae mosquitoes: fitness consequences of homogeneous and heterogeneous duplications. PloS Biol 14:e2000618PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Atyame CM, Alout H, Mousson L, Vazeille M, Diallo M, Weill M et al. (2019) Insecticide resistance genes affect Culex quinquefasciatus vector competence for West Nile virus. Proc Biol Sci 286:20182273CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Auteri M, La Russa F, Blanda V, Torina A (2018) Insecticide resistance associated with kdr mutations in Aedes albopictus: an update on worldwide evidences. Biomed Res Int 2018:e3098575Article 

    Google Scholar 
    Berticat C, Boquien G, Raymond M, Chevillon C (2002) Insecticide resistance genes induce a mating competition cost in Culex pipiens mosquitoes. Genet Res 79:41–47Berticat C, Duron O, Heyse D, Raymond M (2004) Insecticide resistance genes confer a predation cost on mosquitoes, Culex pipiens. Genet Res 83:189–196CAS 
    PubMed 
    Article 

    Google Scholar 
    Bhatia SC, Deobhankar RB (1963) Reversion of dieldrin-resistance in the field population of A. culicifacies in Maharashtra State (erstwhile Bombay State), India. Indian J Malariol 17:339–351CAS 
    PubMed 

    Google Scholar 
    Bonizzoni M, Gasperi G, Chen X, James AA (2013) The invasive mosquito species Aedes albopictus: current knowledge and future perspectives. Trends Parasitol 29:460–468PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Bourguet D, Guillemaud T, Chevillon C, Raymond M (2004) Fitness costs of insecticide resistance in natural breeding sites of the mosquito Culex pipiens. Evolution 58:128–135PubMed 
    Article 

    Google Scholar 
    Brooke BD, Hunt RH, Coetzee M (2000) Resistance to dieldrin + fipronil assorts with chromosome inversion 2La in the malaria vector Anopheles gambiae. Med Vet Entomol 14:190–194CAS 
    PubMed 
    Article 

    Google Scholar 
    Buckingham SD, Biggin PC, Sattelle BM, Brown LA, Sattelle DB (2005) Insect GABA receptors: splicing, editing, and targeting by antiparasitics and insecticides. Mol Pharm 68:942–951CAS 
    Article 

    Google Scholar 
    Chen H, Li K, Wang X, Yang X, Lin Y, Cai F et al. (2016) First identification of kdr allele F1534S in VGSC gene and its association with resistance to pyrethroid insecticides in Aedes albopictus populations from Haikou City, Hainan Island, China. Infect Dis Poverty 5:31PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Davari B, Vatandoost H, Oshaghi MA, Ladonni H, Enayati AA, Shaeghi M et al. (2007) Selection of Anopheles stephensi with DDT and dieldrin and cross-resistance spectrum to pyrethroids and fipronil. Pestic Biochem Physiol 89:97–103CAS 
    Article 

    Google Scholar 
    Delatte H, Paupy C, Dehecq JS, Thiria J, Failloux AB, Fontenille D (2008) Aedes albopictus, vector of Chikungunya and Dengue viruses in Reunion Island: biology and control. Parasite 15:3–13CAS 
    PubMed 
    Article 

    Google Scholar 
    Deng J, Guo Y, Su X, Liu S, Yang W, Wu Y et al. (2021) Impact of deltamethrin-resistance in Aedes albopictus on its fitness cost and vector competence. PLoS Negl Trop Dis 15:e0009391CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Djogbénou L, Weill M, Hougard J-M, Raymond M, Akogbéto M, Chandre F (2007) Characterization of insensitive acetylcholinesterase (ace-1R) in Anopheles gambiae (Diptera: Culicidae): resistance levels and dominance. J Med Entomol 44:805–810PubMed 

    Google Scholar 
    Du W, Awolola TS, Howell P, Koekemoer LL, Brooke BD, Benedict MQ et al. (2005) Independent mutations in the Rdl locus confer dieldrin resistance to Anopheles gambiae and An. arabiensis. Insect Mol Biol 14:179–183CAS 
    PubMed 
    Article 

    Google Scholar 
    Duron O, Labbé P, Berticat C, Rousset F, Guillot S, Raymond M et al. (2006) High Wolbachia density correlates with cost of infection for insecticide resistant Culex pipiens mosquitoes. Evolution 60:303–314CAS 
    PubMed 
    Article 

    Google Scholar 
    ffrench-Constant RH, Rocheleau TA, Steichen JC, Chalmers AE (1993) A point mutation in a Drosophila GABA receptor confers insecticide resistance. Nature 363:449–451CAS 
    PubMed 
    Article 

    Google Scholar 
    ffrench-Constant RH, Anthony N, Aronstein K, Rocheleau T, Stilwell G (2000) Cyclodiene insecticide resistance: from molecular to population genetics. Annu Rev Entomol 45:449–466CAS 
    PubMed 
    Article 

    Google Scholar 
    Fox J, Weisberg S (2019) An R companion to applied regression, 3rd edn. SAGE, Thousand Oaks California, https://socialsciences.mcmaster.ca/jfox/Books/Companion/
    Google Scholar 
    Freeman JC, Smith LB, Silva JJ, Fan Y, Sun H, Scott JG (2021) Fitness studies of insecticide resistant strains: lessons learned and future directions. Pest Manag Sci 77:3847–3856CAS 
    PubMed 
    Article 

    Google Scholar 
    Gratz NG (2004) Critical review of the vector status of Aedes albopictus. Med Vet Entomol 18:215–227CAS 
    PubMed 
    Article 

    Google Scholar 
    Grau-Bové X, Tomlinson S, O’Reilly AO, Harding NJ, Miles A, Kwiatkowski D et al. (2020) Evolution of the insecticide target Rdl in African Anopheles is driven by interspecific and interkaryotypic introgression. Mol Biol Evol 37:2900–2917PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Grigoraki L, Lagnel J, Kioulos I, Kampouraki A, Morou E, Labbé P et al. (2015) Transcriptome profiling and genetic study reveal amplified carboxylesterase genes implicated in temephos resistance, in the Asian tiger mosquito Aedes albopictus. PLoS Negl Trop Dis 9:e0003771PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hamon J, Garret-Jones C (1962) Insecticide-resistance in major vectors of malaria, and its operational importance. Bull World Health Organ, Geneva
    Google Scholar 
    Hartley CJ, Newcomb RD, Russell RJ, Yong CG, Stevens JR, Yeates DK et al. (2006) Amplification of DNA from preserved specimens shows blowflies were preadapted for the rapid evolution of insecticide resistance. Proc Natl Acad Sci USA 103:8757–8762CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hemingway J, Ranson H (2000) Insecticide resistance in insect vectors of human disease. Annu Rev Entomol 45:371–391CAS 
    PubMed 
    Article 

    Google Scholar 
    Hemingway J, Hawkes NJ, McCarroll L, Ranson H (2004) The molecular basis of insecticide resistance in mosquitoes. Insect Biochem Mol Biol 34:653–665CAS 
    PubMed 
    Article 

    Google Scholar 
    Hosie AM, Baylis HA, Buckingham SD, Sattelle DB (1995) Actions of the insecticide fipronil, on dieldrin-sensitive and -resistant GABA receptors of Drosophila melanogaster. Br J Pharm 115:909–912CAS 
    Article 

    Google Scholar 
    Ishak IH, Riveron JM, Ibrahim SS, Stott R, Longbottom J, Irving H et al. (2016) The Cytochrome P450 gene CYP6P12 confers pyrethroid resistance in kdr-free Malaysian populations of the Dengue vector Aedes albopictus. Sci Rep. 6:24707CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kasai S, Ng LC, Lam-Phua SG, Tang CS, Itokawa K, Komagata O et al. (2011) First detection of a putative knockdown resistance gene in major mosquito vector, Aedes albopictus. Jpn J Infect Dis 64:217–221CAS 
    PubMed 
    Article 

    Google Scholar 
    Kliot A, Ghanim M (2012) Fitness costs associated with insecticide resistance. Pest Manag Sci 68:1431–1437CAS 
    PubMed 
    Article 

    Google Scholar 
    Kolaczinski J, Curtis C (2001) Laboratory evaluation of fipronil, a phenylpyrazole insecticide, against adult Anopheles (Diptera: Culicidae) and investigation of its possible cross-resistance with dieldrin in Anopheles stephensi. Pest Manag Sci 57:41–45CAS 
    PubMed 
    Article 

    Google Scholar 
    Kraemer MU, Sinka ME, Duda KA, Mylne AQ, Shearer FM, Barker CM et al. (2015) The global distribution of the arbovirus vectors Aedes aegypti and Ae. albopictus. Elife 4:e08347PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Labbé P, David J-P, Alout H, Milesi P, Djogbénou L, Pasteur N et al. (2017) 14 – Evolution of resistance to insecticide in disease vectors. In: Tibayrenc M (ed) Genetics and Evolution of Infectious Diseases, Second Edition. Elsevier, London, p 313–339Chapter 

    Google Scholar 
    Latreille AC, Milesi P, Magalon H, Mavingui P, Atyame CM (2019) High genetic diversity but no geographical structure of Aedes albopictus populations in Réunion Island. Parasit Vectors 12:597PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lebon C, Alout H, Zafihita S, Dehecq JS, Weill M, Tortosa P et al. (2022) Spatio-temporal dynamics of a dieldrin resistance gene in Aedes albopictus and Culex quinquefasciatus populations from Reunion Island. J Insect Sci 22:4PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lebon C, Soupapoule K, Wilkinson DA, Goff GL, Damiens D, Gouagna LC (2018) Laboratory evaluation of the effects of sterilizing doses of γ-rays from Caesium-137 source on the daily flight activity and flight performance of Aedes albopictus males. PLoS ONE 13:e0202236PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Li Y, Xu J, Zhong D, Zhang H, Yang W, Zhou G et al. (2018) Evidence for multiple-insecticide resistance in urban Aedes albopictus populations in southern China. Parasit Vectors 11:4PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Low VL, Vinnie-Siow WY, Lim YAL, Tan TK, Leong CS, Chen CD et al. (2015) First molecular genotyping of A302S mutation in the gamma aminobutyric acid (GABA) receptor in Aedes albopictus from Malaysia. Trop Biomed 32:554–556CAS 
    PubMed 

    Google Scholar 
    McKenzie BA, Wilson AE, Zohdy S (2019) Aedes albopictus is a competent vector of Zika virus: a meta-analysis. PLoS ONE 14:e0216794CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Milesi P, Pocquet N, Labbé P (2013) BioRssay: A R script for bioassay analyses. http://www.isem.univ-montp2.fr/recherche/equipes/genomique-de-ladaptation/personnel/labbepierrick/Moyes CL, Vontas J, Martins AJ, Ng LC, Koou SY, Dusfour I et al. (2017) Contemporary status of insecticide resistance in the major Aedes vectors of arboviruses infecting humans. PLoS Negl Trop Dis 11:e0005625PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ozoe Y, Kita T, Ozoe F, Nakao T, Sato K, Hirase K (2013) Insecticidal 3-benzamido-N-phenylbenzamides specifically bind with high affinity to a novel allosteric site in housefly GABA receptors. Pestic Biochem Physiol 107:285–292CAS 
    PubMed 
    Article 

    Google Scholar 
    Paupy C, Ollomo B, Kamgang B, Moutailler S, Rousset D, Demanou M et al. (2009) Comparative role of Aedes albopictus and Aedes aegypti in the emergence of Dengue and Chikungunya in central Africa. Vector Borne Zoonotic Dis 10:259–266Article 

    Google Scholar 
    Peakall R, Smouse PE (2012) GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update. Bioinformatics 28:2537–2539CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Platt N, Kwiatkowska RM, Irving H, Diabaté A, Dabire R, Wondji CS (2015) Target-site resistance mutations (kdr and RDL), but not metabolic resistance, negatively impact male mating competiveness in the malaria vector Anopheles gambiae. Heredity 115:243–252CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    R Core Team (2019) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/
    Google Scholar 
    Ranson H, Burhani J, Lumjuan N, Black WCI (2010) Insecticide resistance in Dengue vectors. TropIKA.net [online] 1. http://journal.tropika.net/scielo.php?script=sci_arttext&pid=S2078-86062010000100003&lng=en&nrm=iso. Accessed 03 March 2022Raymond M, Berticat C, Weill M, Pasteur N, Chevillon C (2001) Insecticide resistance in the mosquito Culex pipiens: what have we learned about adaptation? Genetica 112–113:287–296PubMed 
    Article 

    Google Scholar 
    Renault P, Solet J-L, Sissoko D, Balleydier E, Larrieu S, Filleul L et al. (2007) A major epidemic of Chikungunya virus infection on Réunion Island, France, 2005–2006. Am J Trop Med Hy 77:727–731Article 

    Google Scholar 
    Rowland M (1991a) Behaviour and fitness of γHCH/dieldrin resistant and susceptible female Anopheles gambiae and An. stephensi mosquitoes in the absence of insecticide. Med Vet Entomol 5:193–206CAS 
    PubMed 
    Article 

    Google Scholar 
    Rowland M (1991b) Activity and mating competitiveness of γHCH/dieldrin resistant and susceptible male and virgin female Anopheles gambiae and An. stephensi mosquitoes, with assessment of an insecticide-rotation strategy. Med Vet Entomol 5:207–222CAS 
    PubMed 
    Article 

    Google Scholar 
    Russell VL (2021) Emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.5.5.1. https://CRAN.R-project.org/package=emmeansSu X, Guo Y, Deng J, Xu J, Zhou G, Zhou T et al. (2019) Fast emerging insecticide resistance in Aedes albopictus in Guangzhou, China: alarm to the Dengue epidemic. PLoS Negl Trop Dis 13:e0007665CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Tantely ML, Tortosa P, Alout H, Berticat C, Berthomieu A, Rutee A et al. (2010) Insecticide resistance in Culex pipiens quinquefasciatus and Aedes albopictus mosquitoes from La Réunion Island. Insect Biochem Mol Biol 40:317–324CAS 
    PubMed 
    Article 

    Google Scholar 
    Taskin BG, Dogaroglu T, Kilic S, Dogac E, Taskin V (2016) Seasonal dynamics of insecticide resistance, multiple resistance, and morphometric variation in field populations of Culex pipiens. Pestic Biochem Physiol 129:14–27CAS 
    PubMed 
    Article 

    Google Scholar 
    Taylor‐Wells J, Brooke BD, Bermudez I, Jones AK (2015) The neonicotinoid imidacloprid, and the pyrethroid deltamethrin, are antagonists of the insect Rdl GABA receptor. J Neurochem 135:705–713PubMed 
    Article 

    Google Scholar 
    Therneau T (2015) A Package for Survival Analysis in S. R package version 2.38. https://CRAN.R-project.org/package=survivalThompson M, Shotkoski F, ffrench-Constant R (1993) Cloning and sequencing of the cylodienne insecticide resistance from the yellow fewer Aedes aegypti. FEBS Lett 325:187–190CAS 
    PubMed 
    Article 

    Google Scholar 
    Tsetsarkin KA, Vanlandingham DL, McGee CE, Higgs S (2007) A single mutation in Chikungunya virus affects vector specificity and epidemic potential. PLoS Pathog 3:e201PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Vontas J, Kioulos E, Pavlidi N, Morou E, della Torre A, Ranson H (2012) Insecticide resistance in the major Dengue vectors Aedes albopictus and Aedes aegypti. Pestic Biochem Physiol 104:126–131CAS 
    Article 

    Google Scholar 
    Wondji CS, Dabire RK, Tukur Z, Irving H, Djouaka R, Morgan JC (2011) Identification and distribution of a GABA receptor mutation conferring dieldrin resistance in the malaria vector Anopheles funestus in Africa. Insect Biochem Mol Biol 41:484–491CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Xu J, Bonizzoni M, Zhong D, Zhou G, Cai S, Li Y et al. (2016) Multi-country survey revealed prevalent and novel F1534S mutation in voltage-gated sodium channel (VGSC) gene in Aedes albopictus. PLoS Negl Trop Dis 10:e0004696PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Yang C, Huang Z, Li M, Feng X, Qiu X (2017) RDL mutations predict multiple insecticide resistance in Anopheles sinensis in Guangxi, China. Malar J 16:482PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Zhou X, Yang C, Liu N, Li M, Tong Y, Zeng X et al. (2019) Knockdown resistance (kdr) mutations within seventeen field populations of Aedes albopictus from Beijing China: first report of a novel V1016G mutation and evolutionary origins of kdr haplotypes. Parasit Vectors 12:180PubMed 
    PubMed Central 
    Article 

    Google Scholar  More

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    Pollinator biological traits and ecological interactions mediate the impacts of mosquito-targeting malathion application

    Garibaldi, L. A. et al. Stability of pollination services decreases with isolation from natural areas despite honey bee visits. Ecol. Lett. 14(10), 1062–1072 (2011).PubMed 
    Article 

    Google Scholar 
    Kremen, C. et al. Pollination and other ecosystem services produced by mobile organisms: A conceptual framework for the effects of land-use change. Ecol. Lett. 10(4), 299–314 (2007).PubMed 
    Article 

    Google Scholar 
    Kluser, S. & Peduzzi, P. Global pollinator decline: A literature review. Preprint at http://archive-ouverte.unige.ch/unige 32258 (2007).Potts, S. G. et al. Global pollinator declines: Trends, impacts and drivers. Trends Ecol. Evol. 25(6), 345–353 (2010).PubMed 
    Article 

    Google Scholar 
    Rhodes, C. J. Pollinator decline—an ecological calamity in the making?. Sci. Prog. 101(2), 121–160 (2018).PubMed 
    Article 

    Google Scholar 
    Huang, H. & D’Odorico, P. Critical transitions in plant-pollinator systems induced by positive inbreeding-reward-pollinator feedbacks. Iscience 23(2), 100819 (2020).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Krishnan, N. et al. Assessing field-scale risks of foliar insecticide applications to monarch butterfly (Danaus plexippus) larvae. Environ. Toxicol. Chem. 39(4), 923–941 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Bargar, T. A., Hladik, M. L. & Daniels, J. C. Uptake and toxicity of clothianidin to monarch butterflies from milkweed consumption. PeerJ 8, e8669 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Emmel, T. C. & Tucker, J. C. In Mosquito Control Pesticides: Ecological Impacts and Management Alternatives (eds Emmel, T. C. & Tucker, J. C.) 105 (Scientific Publishers, 1991).Johnson, R. M., Ellis, M. D., Mullin, C. A. & Frazier, M. Pesticides and honey bee toxicity–USA. Apidologie 41(3), 312–331 (2010).CAS 
    Article 

    Google Scholar 
    Olaya-Arenas, P., Scharf, M. E. & Kaplan, I. Do pollinators prefer pesticide-free plants? An experimental test with monarchs and milkweeds. J. Appl. Ecol. 57(10), 2019–2030 (2020).CAS 
    Article 

    Google Scholar 
    Berryman, A. A. What causes population cycles of forest Lepidoptera?. Trends Ecol. Evol. 11(1), 28–32 (1996).CAS 
    PubMed 
    Article 

    Google Scholar 
    Elkinton, J. & Boettner, G. Benefits and harm caused by the introduced generalist tachinid, Compsilura concinnata North America. Biol. Control 57(2), 277–288 (2012).
    Google Scholar 
    Beschta, R. L. & Ripple, W. J. Riparian vegetation recovery in Yellowstone: The first two decades after wolf reintroduction. Biol. Conserv. 198, 93–103 (2016).Article 

    Google Scholar 
    Oberhauser, K. et al. Lacewings wasps and fliesoh my insect enemies take a bite out of monarchs. In Monarchs in a Changing World: Biology and Conservation of an iconic insect (eds Oberhauser, K. S. et al.) 71–82 (Cornell University Press, 2015).Chapter 

    Google Scholar 
    Zalucki, M. P., Clarke, A. R. & Malcolm, S. B. Ecology and behavior of first instar larval Lepidoptera. Annu. Rev. Entomol. 47(1), 361–393 (2002).CAS 
    PubMed 
    Article 

    Google Scholar 
    Hermann, S. L., Blackledge, C., Haan, N. L., Myers, A. T. & Landis, D. A. Predators of monarch butterfly eggs and neonate larvae are more diverse than previously recognised. Sci. Rep. 9(1), 1–9 (2019).CAS 
    Article 

    Google Scholar 
    McCoshum, S. M., Andreoli, S. L., Stenoien, C. M., Oberhauser, K. S. & Baum, K. A. Species distribution models for natural enemies of monarch butterfly (Danaus plexippus) larvae and pupae: Distribution patterns and implications for conservation. J. Insect Conserv. 20(2), 223–237 (2016).Article 

    Google Scholar 
    Geest, E. A., Wolfenbarger, L. L. & McCarty, J. P. Recruitment, survival and parasitism of monarch butterflies (Danaus plexippus) in milkweed gardens and conservation areas. J. Insect Conserv. 23(2), 211–224 (2019).Article 

    Google Scholar 
    Stenoien, C. et al. Monarchs in decline: A collateral landscape-level effect of modern agriculture. Insect Sci. 25(4), 528–541 (2018).PubMed 
    Article 

    Google Scholar 
    Crone, E. E., Pelton, E. M., Brown, L. M., Thomas, C. C. & Schultz, C. B. Why are monarch butterflies declining in the west? Understanding the importance of multiple correlated drivers. Ecol. Appl. 29(7), e01975 (2019).PubMed 
    Article 

    Google Scholar 
    Brower, L. P. et al. Effect of the 2010–2011 drought on the lipid content of monarchs migrating through Texas to overwintering sites in Mexico. In The Monarchs in a Changing World: Biology and Conservation of an Iconic Butterfly (eds Oberhauser, K. S. et al.) 117–129 (Cornell University Press, 2015).
    Google Scholar 
    Thogmartin, W. E. et al. Monarch butterfly population decline in North America: Identifying the threatening processes. R. Soc. Open Sci. 4(9), 170760 (2017).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Olaya-Arenas, P. & Kaplan, I. Quantifying pesticide exposure risk for monarch caterpillars on milkweeds bordering agricultural land. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2019.00223 (2019).
    Article 

    Google Scholar 
    Olaya-Arenas, P., Hauri, K., Scharf, M. E. & Kaplan, I. Larval pesticide exposure impacts monarch butterfly performance. Sci. Rep. 10(1), 1–12 (2020).Article 

    Google Scholar 
    Cameron, S. A. et al. Patterns of widespread decline in North American bumble bees. PNAS 108(2), 662–667 (2011).ADS 
    MathSciNet 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Epstein, L. Fifty years since silent spring. Annu. Rev. Phytopathol. 52, 377–402 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Rayor, L. S. Effects of monarch larval host plant chemistry and body size on Polistes wasp predation. In The Monarch Butterfly Biology and Conservation (eds Oberhauser, K. S. & Solensky, M. J.) 39–46 (Cornell University Press, 2004).
    Google Scholar 
    Baker, A. M. & Potter, D. A. Invasive paper wasp turns urban pollinator gardens into ecological traps for monarch butterfly larvae. Sci. Rep. 10(1), 1–7 (2020).Article 

    Google Scholar 
    Castellanos, I. & Barbosa, P. Dropping from host plants in response to predators by a polyphagous caterpillar. J. Lepid. Soc. 65(4), 270–272 (2011).
    Google Scholar 
    Kessler, S. C. et al. Bees prefer foods containing neonicotinoid pesticides. Nature 521(7550), 74–76 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Liao, L.-H., Wu, W.-Y. & Berenbaum, M. R. Behavioral responses of honey bees (Apis mellifera) to natural and synthetic xenobiotics in food. Sci. Rep. 7(1), 1–8 (2017).Article 

    Google Scholar 
    Musser, R. O. et al. Caterpillar saliva beats plant defences. Nature 416(6881), 599–600 (2002).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Schmidt, J. & Smith, J. Host examination walk and oviposition site selection of Trichogramma minutum: Studies on spherical hosts. J. Insect Behav. 2(2), 143–171 (1989).Article 

    Google Scholar 
    Ramos, R. S. et al. Investigation of the lethal and behavioral effects of commercial insecticides on the parasitoid wasp Copidosoma truncatellum. Chemosphere 191, 770–778 (2018).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Chareonviriyaphap, T. et al. Pesticide avoidance behavior in Anopheles albimanus, a malaria vector in the Americas. J. Am. Mosq. Control Assoc. 13(2), 171–183 (1997).CAS 
    PubMed 

    Google Scholar 
    Nansen, C., Baissac, O., Nansen, M., Powis, K. & Baker, G. Behavioral avoidance-will physiological insecticide resistance level of insect strains affect their oviposition and movement responses?. PLoS ONE 11(3), e0149994 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Martini, X., Kincy, N. & Nansen, C. Quantitative impact assessment of spray coverage and pest behavior on contact pesticide performance. Pest Manag. Sci. 68(11), 1471–1477 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Bull, D. & Coleman, R. Effects of pesticides on Trichogramma spp. Southwest. Entomol. Suppl. 8, 156–168 (1985).CAS 

    Google Scholar 
    Thubru, D., Firake, D. & Behere, G. Assessing risks of pesticides targeting lepidopteran pests in cruciferous ecosystems to eggs parasitoid, Trichogramma brassicae (Bezdenko). Saudi J. Biol. Sci. 25(4), 680–688 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Selwood, K. & Zimmer, H. Refuges for biodiversity conservation: A review of the evidence. Biol. Conserv. 245, 108502 (2020).Article 

    Google Scholar 
    Chmiel, J. A., Daisley, B. A., Pitek, A. P., Thompson, G. J. & Reid, G. Understanding the effects of sublethal pesticide exposure on honey bees: A role for probiotics as mediators of environmental stress. Front. Ecol. Evol. 8, 22 (2020).Article 

    Google Scholar 
    Chittka, L., Williams, N., Rasmussen, H. & Thomson, J. Navigation without vision: Bumblebee orientation in complete darkness. Proc. R. Soc. B 266(1414), 45–50 (1999).PubMed Central 
    Article 

    Google Scholar 
    Young, M. W. & Kay, S. A. Time zones: A comparative genetics of circadian clocks. Nat. Rev. Genet. 2(9), 702–715 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    Mallet, J. Gregarious roosting and home range in Heliconius butterflies. Natl. Geogr. Res. 2(2), 198–215 (1986).
    Google Scholar 
    Chang, Y.-M. et al. Roosting site usage, gregarious roosting and behavioral interactions during roost-assembly of two Lycaenidae butterflies. Zool. Stud. 59, e10 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Vulinec, K. Collective security aggregation by insects as a defence. In Insect Defences. Adaptive Mechanisms of Prey and Predators (eds Evans, D. L. & Schmidt, J. O.) 251–288 (State University of New York, 1990).
    Google Scholar 
    Salcedo, C. Environmental elements involved in communal roosting in Heliconius butterflies (Lepidoptera: Nymphalidae). Environ. Entomol. 39(3), 907–911 (2010).PubMed 
    Article 

    Google Scholar 
    Giordano, B. V., McGregor, B. L., Runkel, A. E. IV. & Burkett-Cadena, N. D. Distance diminishes the effect of deltamethrin exposure on the monarch butterfly, Danaus plexippus. J. Am. Mosq. Control Assoc. 36(3), 181–188 (2020).PubMed 
    Article 

    Google Scholar 
    Matzrafi, M. Climate change exacerbates pest damage through reduced pesticide efficacy. Pest Manag. Sci. 75(1), 9–13 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Hewitt, A. Spray drift: Impact of requirements to protect the environment. Crop Prot. 19(8–10), 623–627 (2000).Article 

    Google Scholar 
    Nail, K. R., Stenoien, C. & Oberhauser, K. S. Immature monarch survival: Effects of site characteristics, density and time. Ann. Entomol. Soc. 108(5), 680–690 (2015).Article 

    Google Scholar 
    Payne, C. C. & Mertens, P. P. Cytoplasmic polyhedrosis viruses. In The Reoviridae (ed. Joklik, K.) 425–504 (Springer, 1983).Chapter 

    Google Scholar 
    Zalucki, M. P. et al. It’s the first bites that count: Survival of first-instar monarchs on milkweeds. Austral. Ecol. 26(5), 547–555 (2001).Article 

    Google Scholar 
    Salvato, M. Influence of mosquito control chemicals on butterflies (Nymphalidae, Lycaenidae, Hesperiidae) of the lower Florida keys. J. Lepid. Soc. 55(1), 8–14 (2001).
    Google Scholar 
    Frey, D. F. & Leong, K. L. Can microhabitat selection or differences in ‘catchability’ explain male-biased sex ratios in overwintering populations of monarch butterflies?. Anim. Behav. 45(5), 1025 (1993).Article 

    Google Scholar 
    Macgregor, C. J. & Scott-Brown, A. S. Nocturnal pollination: An overlooked ecosystem service vulnerable to environmental change. Emerg. Top. Life Sci. 4(1), 19–32 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar  More

  • in

    Protecting the Amazon forest and reducing global warming via agricultural intensification

    Study regions and recent trends in land use changeOur analysis focuses on four biomes (referred to as regions in the rest of the text), accounting for nearly all soybean area in Brazil: the Pampa, the Atlantic Forest, the Cerrado and the Amazon (Supplementary Section 1). Soybean production is negligible in the Pantanal and the Caatinga, so these two regions were excluded from our analysis. We focused on soybean-based systems in Brazil, either those that include one crop per year (single soybean) or those including a second-crop maize. In the latter system, soybean is sown in September–October, and maize is sown right after the soybean harvest in late January–February. Single soybean is common in the Pampa, where the drier climate does not allow double cropping. In contrast, higher precipitation allows double cropping in the Amazon, the Cerrado and most of the Atlantic Forest (Supplementary Section 2).Recent trends in yield, area and production for soybean and second-crop maize were derived from official statistics for the 2007–2019 period16. We fitted linear models to derive the annual rate of yield improvement and harvested area for soybean and second-crop maize, separately for each region (Fig. 1 and Extended Data Fig. 1). Land use change arising from soybean expansion was estimated using data from the MapBiomas project (v.5.0)10 (Supplementary Table 1). Our estimation of land use change accounted for the time lag between land conversion and the beginning of soybean production, which can include transitional stages such as the cultivation of upland rice or short-term pasture-based livestock systems42. To account for this, we looked at the new land brought into soybean production during the 2008–2019 period, and we analysed how much of this land was under a different land use type (forest, savannah, grassland, pasture or other crops) in 2000 (Extended Data Fig. 2).Estimation of yield potential and yield gapsWe used results on yield potential for Brazil that we generated through the Global Yield Gap Atlas project43 using well-validated process-based crop models and the best available sources of weather, soil and management data. Briefly, we selected 32 sites to portray the distribution of the soybean harvested area within the country, following protocols that ensure representativeness and a reasonable coverage of the national crop area44. The 32 sites collectively accounted for half of the soybean harvested area in Brazil. These sites were located within agro-climatic zones accounting for 86% of the national soybean production and accounted for 72–92% of the soybean area in each region. Following protocols that gave preference to measured data at a high level of spatial and temporal resolution45, we collected databases on weather, soil, management and crop yields for soybean for each site, and also for second-crop maize at those sites where double-cropping is practised (Supplementary Tables 2 and 3 and Supplementary Section 3).Yield potential was simulated for widespread cultivars in each region using the CROPGRO soybean model embedded in DSSAT v.4.546 and the Hybrid-Maize model47. Both models simulate crop growth and development on a daily time step. Growth rates are determined by simulating both CO2 assimilation and respiration, with partitioning coefficients to different organs dependent on developmental stage. The model phenological coefficients were calibrated to portray the crop cycle of the most dominant cultivars in each region in Brazil. We used generic default coefficients for growth-related model internal parameters such as photosynthesis, respiration, leaf area expansion, light interception, biomass partitioning and grain filling. In all cases, simulations of yield potential assumed the absence of insect pests, weeds and diseases and no nutrient limitations. In simulating yield potential, both models account for solar radiation, photoperiod, temperature, and the timing and amount of rainfall as well as soil properties influencing crop water balance.We first evaluated the CROPGRO and Hybrid-Maize models on the ability to reproduce measured phenology and yields across 40 well-managed experiments located across the four regions. The models showed satisfactory performance at reproducing the measured values (Extended Data Fig. 3). We then simulated soybean yield potential for the dominant agricultural soils at each site (usually two or three), as determined from the soil maps generated by the Radambrasil project48. The simulations were based on long-term (1999–2018) measured daily weather data retrieved from the Brazilian Institute of Meteorology49. Soybean yield potential was simulated for each year of the time series. We also simulated yield potential for second-crop maize for those sites where double-cropping is practised. To do so, we used sowing dates and cultivar maturities that maximize the overall productivity of the soybean–maize system; these sowing dates and cultivar maturities are within the current ranges in each region21,28. To estimate the average yield potential for each site, we weighted the simulated values for each soil type by soil area fraction at each site. In all cases, the simulations assumed no limitations to crop growth due to nutrient deficiencies or incidence of biotic stresses such as weeds, insect pests and pathogens. The results were upscaled from site to region and then to country following van Bussel et al.44. Briefly, the average yield potential for each region was estimated by averaging the simulated yields across the sites located within each region, weighing sites according to their share of the soybean area within each region. A similar approach was followed to upscale yield potential from region to the national level. Details on crop modelling, data sources and upscaling are provided in Supplementary Section 3.The average farmer yield was calculated separately for soybean and second-crop maize on the basis of the average yield reported over the 2012–2017 period for the municipalities that overlap with each site, weighing municipalities on the basis of their share of the soybean or maize area within each site16. Including more years before 2012 would have led to a biased estimate of average actual yield due to the technological yield trend in Brazil. Average farmer yields were estimated at the region and country levels following the same upscaling approach as for yield potential. Finally, the exploitable yield gap was calculated as the difference between attainable yield and average farmer yield. The attainable yield was calculated as 80% of the simulated yield potential, which is considered a reasonable yield for farmers with adequate access to inputs, markets and technical information (Supplementary Section 2).Assessing scenarios of intensification and land use changeWe explored three scenarios with different soybean and maize yields and areas by 2035 and assessed their outcomes in terms of production, land use change and GWP (Supplementary Table 4). A 15-year future timespan is long enough to facilitate the implementation of long-term policies, investments and technologies devoted to closing the exploitable yield gap and to implement land-use policies, but it is short enough to minimize long-term effects from climate change on crop yields and cropping systems. In the BAU scenario, historical (2007–2019) trends of soybean and second-crop maize area and yield (Extended Data Fig. 1) remain unchanged in all regions between the baseline year (2019) and the final year (2035). Likewise, soybean area expands following the same pattern of land use change observed during 2008–2019 (Extended Data Fig. 2).To explore the available opportunity for increasing production on the existing production area, we considered an NCE scenario in which there is no physical expansion of cropland while full closure of the exploitable yield gap occurs in the regions where the current yield gaps are small (the Pampa and the Atlantic Forest), and 50% closure of the exploitable yield gap takes place in regions where the current yield gaps are large (the Amazon and the Cerrado) (Supplementary Table 4). These rates are comparable to historical yield gains in the Pampa and the Atlantic Forest. A scenario of full yield closure in the Amazon and the Cerrado would have been unrealistic, as it would have required rates of yield improvement that are three to four times higher than historical rates, much higher than those in the Pampa and the Atlantic Forest, and well beyond those reported for main soybean-producing countries. In the case of second-crop maize, we assumed full closure of the exploitable yield gap by 2035 because historical rates of yield improvement are adequate to reach that yield level. Regarding second-crop maize area, we projected the proportion of double-cropping to increase from the current 47% (Amazon), 39% (Cerrado) and 31% (Atlantic Forest) to 100%, 70% and 50%, respectively, as determined on the basis of the degree of water limitation in each region (Supplementary Section 4).Finally, we explored a third scenario of intensification plus target area expansion (INT), in which identical yield gain rates and the adoption of double-cropping equivalent to those in the NCE scenario were assumed, but with physical expansion of the soybean–maize system allowed in low-C ecosystems (that is, pastures and grasslands). In this scenario, soybean expansion is limited to 5% of existing pastures and grasslands in the Pampa, the Atlantic Forest and the Cerrado (total of 5.7 Mha) as a result of a parallel intensification in the pasture-based livestock sector that frees up land for soybean production. The latter would require an increase of current stocking rates, not only for freeing up 5% of the area for soybean cultivation but also to meet the projected 7% beef production increase during the study period (2020–2035)17. Hence, an overall 12% increase in stocking rates would be required within our 15-year timeframe, which is a reasonable target as reported in previous studies and based on current trends in stocking rates16,29,32,33.Another assumption is that the yield potential of pasture and grasslands converted for soybean production is similar to that in existing soybean areas in each region. Cropland expansion into grassland and pastures was allowed in all regions except for the Amazon to prevent ‘leaking’ effects and the impact of road development on land clearing50,51. Similarly, the conversion of area cultivated with food crops for soybean production was not allowed to avoid the negative impact of indirect land use change52.Estimation of GWP and gross incomeWe estimated GHG emissions, including carbon dioxide (CO2), methane (CH4) and nitrous oxides (N2O), associated with land conversion (GHGLUC) and crop production (GHGPROD) for the baseline year (2019) and for the three scenarios by year 2035 (BAU, NCE and INT). GHGLUC includes emissions associated with changes in C stocks from aboveground and belowground biomass when land is converted for soybean production (GHGBIO), as well as GHG emissions derived from changes in soil organic C (GHGSOC). For each land use type, annual GHGBIO was estimated on the basis of the difference between C stocks of the land use type that was converted for production (Supplementary Table 5) and, depending on the scenario and region, the average C stocks of the new cropping system53,54,55:$${mathrm{GHG}}_{{mathrm{BIO}}} = {sum} {left( {{mathrm{TDM}}_i-{mathrm{TDM}}_{{mathrm{crop}}}} right) times A_i}$$
    (1)
    where i is the land cover type, TDM is the total dry matter (tC ha−1) in land cover type i and in cropland (crop), and Ai is the annual area converted from land use type i for soybean cultivation (Supplementary Table 4). C stocks for single soybean and soybean–second-crop maize systems were assumed at 2 and 5 tC ha−1, respectively53,54,55. Changes in SOC stocks were estimated following the Intergovernmental Panel on Climate Change 2019 guidelines54, available country-specific emission factors56 and the SOC values estimated for each region57,58:$${mathrm{GHG}}_{{mathrm{SOC}}} = {sum} {left( {{mathrm{SOC}}_{{mathrm{REF}},i} times F_{{mathrm{LU}}}} right) times A_i}$$
    (2)
    where SOCREF is the SOC stock for mineral soils in the upper 30 cm for the reference condition (tC ha−1)57 in land cover type i (Supplementary Table 5), and FLU is the stock change factor for SOC land-use systems for a particular land use (Supplementary Table 4). Because no-till is the predominant soil management strategy in Brazil59, we used FLU = 0.96 for natural vegetation converted to no-till annual crop production, and FLU = 1.16 for pasture and grassland converted to no-till annual crop production56. Because we wanted to assess the full impact of the three scenarios (BAU, NCE and INT) on GWP, we assigned all GHGBIO and GHGSOC derived from land conversion to the first year after land conversion and expressed them as CO2 equivalents by multiplying changes in C stocks by 3.67.Annual GHG emissions derived from soybean and second-crop maize production (GHGPROD) were calculated for each scenario and included those derived from manufacturing, packaging and transportation of agricultural inputs, fossil fuel use for field operations, soil N2O emissions derived from the application of nitrogen (N) fertilizer, and domestic grain transportation. For the baseline year (2019), annual GHG emissions from N, phosphorous (P) and potassium (K) fertilizers and other inputs (lime, pesticides and fuel) were calculated on the basis of current average input rates for soybean and second-crop maize in each region as derived from the crop management data collected for each region (Supplementary Table 6 and Supplementary Section 3.4). To calculate GHG emissions associated with manufacturing, packaging and transportation of N, P and K fertilizers and lime, we used specific updated emissions factors for South America60, selecting those fertilizer sources that are most commonly used for soybean and second-crop maize production: urea (N), monoammonium phosphate (P) and potassium chloride (K). Our calculations also included the extra lime application that is needed to correct soil acidity in converted areas. Emission factors associated with seed production, pesticides and diesel were derived from ref. 61. Soil N2O emissions derived from N fertilizer application were calculated assuming an N2O emission factor of 1% of the applied N fertilizer on the basis of the country-specific emission factor62. Emissions derived from domestic grain transportation for each region were estimated using the GHGs per ton of grain as reported by previous studies for each region63. We assumed that inputs other than nutrient fertilizer will not change relative to the baseline in the BAU scenario. In the INT scenario, applied inputs were calculated on the basis of those reported for current high-yield fields where the yield gap is small. We estimated fertilizer nutrient rates for the three scenarios following a nutrient-balance approach that depends on the projected yield for each scenario (Supplementary Table 6 and Supplementary Section 3.4).GHGPROD in the baseline year (2019) and for the three scenarios in 2035 (BAU, NCE and INT) was estimated for each region by multiplying the emissions per unit of area by the annual soybean harvested area, summing them to estimate GHG emissions at the national level. Overall 100-year GWP was estimated as the sum of GHGLUC and GHGPROD, both expressed as CO2e to account for the higher warming potential of CH4 and N2O, which are 25 and 298 times the intensity of CO2 on a per mass basis, respectively. The gross income was estimated for each scenario by multiplying the annual crop production by the average price for soybean and maize grain during the past ten years (US$453 and US$184 per t for soybean and maize, respectively1). Finally, to combine the environmental and economic impacts into one metric, we calculated the GWP intensity as the ratio between GWP and gross income.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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    Isotopic evidence that aestivation allows malaria mosquitoes to persist through the dry season in the Sahel

    Adamou, A. et al. The contribution of aestivating mosquitoes to the persistence of Anopheles gambiae in the Sahel. Malar. J. 10, 151 (2011).PubMed 
    PubMed Central 

    Google Scholar 
    Huestis, D. L. et al. Seasonal variation in metabolic rate, flight activity and body size of Anopheles gambiae in the Sahel. J. Exp. Biol. 215, 2013–2021 (2012).PubMed 
    PubMed Central 

    Google Scholar 
    Huestis, D. L. et al. Variation in metabolic rate of Anopheles gambiae and A. arabiensis in a Sahelian village. J. Exp. Biol. 214, 2345–2353 (2011).PubMed 
    PubMed Central 

    Google Scholar 
    Lehmann, T. et al. Aestivation of the African malaria mosquito, Anopheles gambiae in the Sahel. Am. J. Trop. Med. Hyg. 83, 601–606 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    Yaro, A. S. et al. Dry season reproductive depression of Anopheles gambiae in the Sahel. J. Insect Physiol. 58, 1050–1059 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Omer, S. M. & Cloudsley-Thompson, J. L. Survival of female Anopheles gambiae Giles through a 9-month dry season in Sudan. Bull. World Health Organ. 42, 319 (1970).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Omer, S. M. & Cloudsley-Thompson, J. L. Dry season biology of Anopheles gambiae Giles in the Sudan. Nature 217, 879–880 (1968).
    Google Scholar 
    Holstein, M. H. Biology of Anopheles gambiae (1954). World Health Organization.Andrade, C. M. et al. Increased circulation time of Plasmodium falciparum underlies persistent asymptomatic infection in the dry season. Nat. Med. 26, 1929–1940 (2020).CAS 
    PubMed 

    Google Scholar 
    Coulibaly, D. et al. Spatio-temporal dynamics of asymptomatic malaria: bridging the gap between annual malaria resurgences in a Sahelian environment. Am. J. Trop. Med. Hyg. 27, 1761–1769 (2017).
    Google Scholar 
    Gillies, M. & Wilkes, T. A study of the age-composition of populations of Anopheles gambiae Giles and A. funestus Giles in north-eastern Tanzania. Bull. Entomol. Res. 56, 237–262 (1965).CAS 
    PubMed 

    Google Scholar 
    Gillies, M. T. & De Meillon, B. The Anophelinae of Africa south of the Sahara (Ethiopian Zoogeographical Region) (Johannesburg: South African Institute for Medical Research, 1968).Dao, A. et al. Signatures of aestivation and migration in Sahelian malaria mosquito populations. Nature 516, 387–390 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Thomson, J. G. Malaria in Nyasaland. Proc. R. Soc. Med. 28, 391–404 (1934).
    Google Scholar 
    Huestis, D. L. et al. Windborne long-distance migration of malaria mosquitoes in the Sahel. Nature 574, 404–408 (2019).CAS 
    PubMed 

    Google Scholar 
    Lambert, B., North, A., Burt, A. & Godfray, H. C. J. The use of driving endonuclease genes to suppress mosquito vectors of malaria in temporally variable environments. Malar. J. 17, 154 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Verhulst, N. O., Loonen, J. A. C. M. & Takken, W. Advances in methods for colour marking of mosquitoes. Parasit. Vectors 6, 200 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    Hagler, J. R. & Jackson, C. G. Methods for marking insects: current techniques and future prospects. Annu. Rev. Entomol. 46, 511–543 (2001).CAS 
    PubMed 

    Google Scholar 
    Hamer, G. L. et al. Dispersal of adult culex mosquitoes in an urban West Nile virus hotspot: a mark–capture study incorporating stable isotope enrichment of natural larval habitats. PLoS Negl. Trop. Dis. 8, e2768 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Hamer, G. L. et al. Evaluation of a stable isotope method to mark naturally-breeding larval mosquitoes for adult dispersal studies. J. Med. Entomol. 49, 61–70 (2012).CAS 
    PubMed 

    Google Scholar 
    Opiyo, M. A. et al. Using stable isotopes of carbon and nitrogen to mark wild populations of Anopheles and Aedes mosquitoes in south-eastern Tanzania. PLoS ONE 11, e0159067 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Hood-Nowotny, R., Mayr, L. & Knols, B. Use of carbon-13 as a population marker for Anopheles arabiensis in a sterile insect technique (SIT) context. Malar. J. 5, 6 (2006).PubMed 
    PubMed Central 

    Google Scholar 
    Hood-Nowotny, R. & Knols, B. G. J. Stable isotope methods in biological and ecological studies of arthropods. Entomol. Exp. Appl. 124, 3–16 (2007).CAS 

    Google Scholar 
    Hood-Nowotny, R. et al. Intrinsic and synthetic stable isotope marking of tsetse flies. J. Insect Sci. 11, 79 (2011).PubMed 
    PubMed Central 

    Google Scholar 
    Atzrodt, J., Derdau, V., Kerr, W. J. & Reid, M. Deuterium- and tritium-labelled compounds: applications in the life sciences. Angew. Chem. Int. Ed. 57, 1758–1784 (2018).CAS 

    Google Scholar 
    Copia, L., Wassenaar, L. I., Terzer-Wassmuth, S., Belachew, D. L. & Araguas-Araguas, L. J. Comparative evaluation of 2H- versus 3H-based enrichment factor determination on the uncertainty and accuracy of low-level tritium analyses of environmental waters. Appl. Radiat. Isot. 176, 109850 (2021).CAS 
    PubMed 

    Google Scholar 
    Begon, M., Harper, J. & Townsend, C. Ecology: Individuals, Populations and Communities (Blackwell Science, 1996).Faiman, R. et al. Marking mosquitoes in their natural larval sites using 2H-enriched water: a promising approach for tracking over extended temporal and spatial scales. Methods Ecol. Evol. 10, 1274–1285 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Florkin, M. Chemical Zoology: Arthropoda Part B (Academic Press, 2014).Hackman, R. H. & Goldberg, M. Studies on chitin VI. The nature of alpha-and beta-chitins. Aust. J. Biol. Sci. 18, 935–946 (1965).CAS 
    PubMed 

    Google Scholar 
    Faiman, R. et al. Quantifying flight aptitude variation in wild Anopheles gambiae in order to identify long-distance migrants. Malar. J. 19, 263 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Huestis, D. L. & Lehmann, T. Ecophysiology of Anopheles gambiae s.l.: persistence in the Sahel. Infect. Genet. Evol. 28, 648–661 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Lehmann, T. et al. Seasonal variation in spatial distributions of Anopheles gambiae in a Sahelian village: evidence for aestivation. J. Med. Entomol. 51, 27–38 (2014).PubMed 

    Google Scholar 
    Costantini, C. et al. Density, survival and dispersal of Anopheles gambiae complex mosquitoes in a West African Sudan savanna village. Med. Vet. Entomol. 10, 203–219 (1996).CAS 
    PubMed 

    Google Scholar 
    Toure, Y. T. et al. Mark–release–recapture experiments with Anopheles gambiae s.l. in Banambani Village, Mali, to determine population size and structure. Med. Vet. Entomol. 12, 74–83 (1998).CAS 
    PubMed 

    Google Scholar 
    Faiman, R. et al. A novel fluorescence and DNA combination for versatile, long-term marking of mosquitoes. Methods Ecol. Evol. https://doi.org/10.1111/2041-210X.13592 (2021).Brattström, O., Bensch, S., Wassenaar, L. I., Hobson, K. A. & Åkesson, S. Understanding the migration ecology of European red admirals Vanessa atalanta using stable hydrogen isotopes. Ecography 33, 720–729 (2010).
    Google Scholar 
    Hobson, K. A., Jinguji, H., Ichikawa, Y., Kusack, J. W. & Anderson, R. C. Long-distance migration of the globe skimmer dragonfly to Japan revealed using stable hydrogen (δ 2H) isotopes. Environ. Entomol. 50, 247–255 (2020).
    Google Scholar 
    Schilling, E. G. et al. Phenological and isotopic evidence for migration as a life history strategy in Aeshna canadensis (family: Aeshnidae) dragonflies. Ecol. Entomol. 46, 209–219 (2021).
    Google Scholar 
    Girard, P., Hillaire-Marcel, C. & Oga, M. S. Determining the recharge mode of Sahelian aquifers using water isotopes. J. Hydrol. 197, 189–202 (1997).CAS 

    Google Scholar 
    Gutiérrez-Expósito, C., Ramírez, F., Afán, I., Forero, M. & Hobson, K. A. Toward a deuterium feather isoscape for sub-Saharan Africa: progress, challenges and the path ahead. PLoS ONE https://doi.org/10.1371/journal.pone.0135938 (2015).Lutz, A., Thomas, J. M. & Panorska, A. Environmental controls on stable isotope precipitation values over Mali and Niger, West Africa. Environ. Earth Sci. 62, 1749–1759 (2011).CAS 

    Google Scholar 
    Risi, C. et al. Understanding the Sahelian water budget through the isotopic composition of water vapor and precipitation. J. Geophys. Res. Atmos. 115, 1–23 (2010).
    Google Scholar 
    Tremoy, G. et al. A 1-year long δ18O record of water vapor in Niamey (Niger) reveals insightful atmospheric processes at different timescales. Geophys. Res. Lett. 39, 1–5 (2012).
    Google Scholar 
    Terzer‐Wassmuth, S., Wassenaar, L. I., Welker, J. M., Araguás-Araguás, L. J. Improved high‐resolution global and regionalized isoscapes of δ18O, δ2H and d‐excess in precipitation. Hydrol. Process. 35 (2021).Hobson, K. A. et al. A multi-isotope (δ13C, δ15N, δ2H) feather isoscape to assign Afrotropical migrant birds to origins. Ecosphere 3, art44 (2012).
    Google Scholar 
    Diuk-Wasser, M. A. et al. Effect of rice cultivation patterns on malaria vector abundance in rice-growing villages in Mali. Am. J. Trop. Med. Hyg. 76, 869–874 (2007).PubMed 

    Google Scholar 
    Sogoba, N. et al. Malaria transmission dynamics in Niono, Mali: the effect of the irrigation systems. Acta Trop. 101, 232–240 (2007).PubMed 

    Google Scholar 
    Florio, J. et al. Diversity, dynamics, direction, and magnitude of high-altitude migrating insects in the Sahel. Sci. Rep. 10, 20523 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wilkins, E. E., Howell, P. I. & Benedict, M. Q. IMP PCR primers detect single nucleotide polymorphisms for Anopheles gambiae species identification, Mopti and Savanna rDNA types, and resistance to dieldrin in Anopheles arabiensis. Malar. J. 5, 125 (2006).PubMed 
    PubMed Central 

    Google Scholar 
    Wassenaar, L. I. & Hobson, K. A. Comparative equilibration and online technique for determination of non-exchangeable hydrogen of keratins for use in animal migration studies. Isotopes Environ. Health Stud. 39, 211–217 (2003).CAS 
    PubMed 

    Google Scholar 
    Chesson, L. A., Podlesak, D. W., Cerling, T. E. & Ehleringer, J. R. Evaluating uncertainty in the calculation of non-exchangeable hydrogen fractions within organic materials. Rapid Commun. Mass Spectrom. 23, 1275–1280 (2009).CAS 
    PubMed 

    Google Scholar 
    Schimmelmann, A. Determination of the concentration and stable isotopic composition of nonexchangeable hydrogen in organic matter. Anal. Chem. 63, 2456–2459 (1991).CAS 

    Google Scholar 
    Speakman, J. Doubly Labelled Water: Theory and Practice (Chapman & Hall, 1997).Base SAS 9.4 Procedures Guide (SAS Institute, 2015).Cade, B. S. & N, B. R. A gentle introduction to quantile regression for ecologists. Front. Ecol. Environ. 1, 412–420 (2003).
    Google Scholar 
    SAS/STAT® 15.1 User’s Guide (SAS Institute, 2018).Mcclintock, B. T. et al. Uncovering ecological state dynamics with hidden Markov models. Ecol. Lett. 23, 1878–1903 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Issam, M., Naulet, N., Martin, M. L. & Martin, G. J. A site-specific and multielement approach to the determination of liquid–vapor isotope fractionation parameters: the case of alcohols. J. Phys. Chem. 94, 8303–8309 (1990).
    Google Scholar 
    Linderstrøm-Lang, C. U. & Vaslow, F. Isotope effect on the vapor pressures of water–ethanol and deuterium oxide–ethanol-d mixtures. J. Phys. Chem. 72, 2645–2650 (1968).
    Google Scholar 
    Ventura, M. & Jeppesen, E. Effects of fixation on freshwater invertebrate carbon and nitrogen isotope composition and its arithmetic correction. Hydrobiologia 632, 297–308 (2009).CAS 

    Google Scholar  More

  • in

    Oceanic vertical migrators in a warming world

    Seibel, B. A. & Birk, M. A. Nat. Clim. Change https://doi.org/10.1038/s41558-022-01491-6 (2022).Article 

    Google Scholar 
    Urban, M. C. et al. Science 353, aad8466 (2016).Article 

    Google Scholar 
    Pörtner, H. O. & Knust, R. Science 315, 95–97 (2007).Article 

    Google Scholar 
    Verberk, W. C. E. P., Bilton, D. T., Calosi, P. & Spicer, J. I. Ecology 92, 1565–1572 (2011).Article 

    Google Scholar 
    Rubalcaba, J. G., Verberk, W. C., Hendriks, A. J., Saris, B. & Woods, H. A. Proc. Natl Acad. Sci. USA 117, 31963–31968 (2020).CAS 
    Article 

    Google Scholar 
    Deutsch, C. et al. Proc. Natl Acad. Sci. USA 119, e2201345119 (2022).CAS 
    Article 

    Google Scholar 
    Stramma, L. et al. Nat. Clim. Change 2, 33–37 (2012).CAS 
    Article 

    Google Scholar 
    Vergés, A. et al. Proc. R. Soc. B 281, 20140846 (2014).Article 

    Google Scholar  More

  • in

    Early Mars habitability and global cooling by H2-based methanogens

    Cockell, C. S. et al. Habitability: a review. Astrobiology 16, 89–117 (2016).ADS 
    Article 

    Google Scholar 
    Michalski, J. R. et al. The Martian subsurface as a potential window into the origin of life. Nat. Geosci. 11, 21–26 (2018).ADS 
    Article 

    Google Scholar 
    Fairén, A. G. et al. Stability against freezing of aqueous solutions on early Mars. Nature 459, 401–404 (2009).ADS 
    Article 

    Google Scholar 
    Clifford, S. M. et al. Depth of the Martian cryosphere: Revised estimates and implications for the existence and detection of subpermafrost groundwater. J. Geophys. Res. 115, E07001 (2010).ADS 
    Article 

    Google Scholar 
    Rivera-Valentín, E. G., Chevrier, V. F., Soto, A. & Martínez, G. Distribution and habitability of (meta)stable brines on present-day Mars. Nat. Astron. 4, 756–761 (2020).ADS 
    Article 

    Google Scholar 
    Stevens, A. H., Patel, M. R. & Lewis, S. R. Numerical modelling of the transport of trace gases including methane in the subsurface of Mars. Icarus 250, 587–594 (2015).ADS 
    Article 

    Google Scholar 
    Sholes, S. F., Krissansen-Totton, J. & Catling, D. C. A maximum subsurface biomass on mars from untapped free energy: CO and H2 as potential antibiosignatures. Astrobiology 19, 655–668 (2019).ADS 
    Article 

    Google Scholar 
    Wordsworth, R. D. The climate of early Mars. Annu. Rev. Earth Planet. Sci. 44, 381–408 (2016).ADS 
    Article 

    Google Scholar 
    Liu, J. et al. Anoxic chemical weathering under a reducing greenhouse on early Mars. Nat. Astron. 5, 503–509 (2021).ADS 
    Article 

    Google Scholar 
    Battistuzzi, F. U., Feijao, A. & Hedges, S. B. A genomic timescale of prokaryote evolution: insights into the origin of methanogenesis, phototrophy, and the colonization of land. BMC Evol. Biol. 4, 44 (2004).Article 

    Google Scholar 
    Martin, W. F. & Sousa, F. L. Early microbial evolution: the age of anaerobes. Cold Spring Harbor Perspect. Biol 8, a018127 (2016).Article 

    Google Scholar 
    Sauterey, B. et al. Co-evolution of primitive methane-cycling ecosystems and early Earth’s atmosphere and climate. Nat. Commun. 11, 2705 (2020).ADS 
    Article 

    Google Scholar 
    Affholder, A. et al. Bayesian analysis of Enceladus’s plume data to assess methanogenesis. Nat. Astron. 5, 805–814 (2021).ADS 
    Article 

    Google Scholar 
    Wordsworth, R. et al. Transient reducing greenhouse warming on early Mars. Geophys. Res. Lett. 44, 665–671 (2017).ADS 
    Article 

    Google Scholar 
    Turbet, M., Boulet, C. & Karman, T. Measurements and semi-empirical calculations of CO2 + CH4 and CO2 + H2 collision-induced absorption across a wide range of wavelengths and temperatures. Application for the prediction of early Mars surface temperature. Icarus 346, 113762 (2020).Article 

    Google Scholar 
    Price, P. B. & Sowers, T. Temperature dependence of metabolic rates for microbial growth, maintenance, and survival. Proc. Nat. Acad. Sci. USA 101, 4631–4636 (2004).ADS 
    Article 

    Google Scholar 
    Taubner, R.-S. et al. Biological methane production under putative Enceladus-like conditions. Nat. Commun. 9, 748 (2018).ADS 
    Article 

    Google Scholar 
    Ramirez, R. M. A warmer and wetter solution for early Mars and the challenges with transient warming. Icarus 297, 71–82 (2017).ADS 
    Article 

    Google Scholar 
    Kharecha, P., Kasting, J. & Siefert, J. A coupled atmosphere–ecosystem model of the early Archean Earth. Geobiology 3, 53–76 (2005).Article 

    Google Scholar 
    Tarnas, J. D. et al. Radiolytic H2 production on Noachian Mars: implications for habitability and atmospheric warming. Earth Planet. Sci. Lett. 502, 133–145 (2018).ADS 
    Article 

    Google Scholar 
    Yung, Y. L. et al. Methane on Mars and habitability: challenges and responses. Astrobiology 18, 1221–1242 (2018).ADS 
    Article 

    Google Scholar 
    Knutsen, E. W. et al. Comprehensive investigation of Mars methane and organics with ExoMars/NOMAD. Icarus 357, 114266 (2021).Article 

    Google Scholar 
    Cockell, C. S. Trajectories of martian habitability. Astrobiology 14, 182–203 (2014).ADS 
    Article 

    Google Scholar 
    Westall, F. et al. Biosignatures on Mars: What, where, and how? Implications for the search for Martian life. Astrobiology 15, 998–1029 (2015).ADS 
    Article 

    Google Scholar 
    Lepot, K. Signatures of early microbial life from the Archean (4 to 2.5 Ga) eon. Earth Sci. Rev. 209, 103296 (2020).Article 

    Google Scholar 
    Fastook, J. L. & Head, J. W. Glaciation in the late noachian icy highlands: Ice accumulation, distribution, flow rates, basal melting, and top-down melting rates and patterns. Planet. Space Sci. 106, 82–98 (2015).ADS 
    Article 

    Google Scholar 
    Fassett, C. I. & Head, J. W. Valley network-fed, open-basin lakes on Mars: distribution and implications for Noachian surface and subsurface hydrology. Icarus 198, 37–56 (2008).ADS 
    Article 

    Google Scholar 
    Tanaka, K. L. et al. Geologic Map of Mars: U.S. Geological Survey Scientific Investigations Map 3292, Scale 1000,000 (US Geological Survey, 2014); https://doi.org/10.3133/sim3292Sun, V. Z. & Stack, K. M. Geologic Map of Jezero Crater and the Nili Planum Region, Mars: U.S. Geological Survey Scientific Investigations Map 3464, Scale 1000 (US Geological Survey, 2020); https://doi.org/10.3133/sim3464Ward, P. The Medea Hypothesis (Princeton Univ. Press, 2009).Chopra, A. & Lineweaver, C. H. The Case for a Gaian bottleneck: the biology of habitability. Astrobiology 16, 7–22 (2016).ADS 
    Article 

    Google Scholar 
    Arney, G. et al. The Pale Orange Dot: The Spectrum and Habitability of Hazy Archean Earth. Astrobiology 16, 873–899 (2016).Batalha, N. et al. Testing the early Mars H2-CO2 greenhouse hypothesis with a 1-D photochemical model. Icarus 258, 337–349 (2015).ADS 
    Article 

    Google Scholar 
    Stüeken, E. E. et al. Isotopic evidence for biological nitrogen fixation by molybdenum-nitrogenase from 3.2 Gyr. Nature 520, 666–669 (2015).ADS 
    Article 

    Google Scholar 
    Cockell, C. S. et al. Minimum units of habitability and their abundance in the universe. Astrobiology 21, 481–489 (2021).ADS 
    Article 

    Google Scholar 
    Adams, D. et al. Nitrogen fixation at early Mars. Astrobiology 21, 968–980 (2021).ADS 
    Article 

    Google Scholar 
    Fergason, R. L., Hare, T. M. and Laura, J. HRSC and MOLA Blended Digital Elevation Model at 200m v2. Astrogeology PDS Annex (US Geological Survey, 2018); http://bit.ly/HRSC_MOLA_Blend_v0Sauterey, B. MarsEcosys v.1.0. Zenodo https://doi.org/10.5281/zenodo.6963348 (2022). More

  • in

    Unique thermal sensitivity imposes a cold-water energetic barrier for vertical migrators

    Robison, B. H. Conservation of deep pelagic biodiversity. Conserv. Biol. 23, 847–858 (2009).
    Google Scholar 
    Fernandez-Alamo, M. A. & Färber-Lorda, J. Zooplankton and the oceanography of the eastern tropical Pacific: a review. Prog. Oceanogr. 69, 318–359 (2006).
    Google Scholar 
    Bianchi, D., Galbraith, E. D., Carozza, D. A., Mislan, K. A. S. & Stock, C. A. Intensification of open-ocean oxygen depletion by vertically migrating animals. Nat. Geosci. 6, 545–548 (2013).CAS 

    Google Scholar 
    Steinberg, D. K. & Landry, M. R. Zooplankton and the ocean carbon cycle. Annu. Rev. Mar. Sci. 9, 413–444 (2017).
    Google Scholar 
    Kiko, R. & Hauss, H. On the estimation of zooplankton-mediated active fluxes in oxygen minimum zones regions. Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00741 (2019).Longhurst, A., Bedo, A., Harrison, W., Head, E. & Sameoto, D. Vertical flux of respiratory carbon by oceanic diel migrant biota. Deep Sea Res. Part I 37, 685–694 (1990).CAS 

    Google Scholar 
    Elder, L. E. & Seibel, B. A. The thermal stress response to diel vertical migration in the hyperiid amphipod, Phronima sedentaria. Comp. Biochem. Physiol. A 187, 20–26 (2015).CAS 

    Google Scholar 
    Tremblay, N., Gomez-Gutierrez, J., Zenteno-Savin, T., Robinson, C. J. & Sanchez-Velascoa, L. Role of oxidative stress in seasonal and daily vertical migration of three krill species in the Gulf of California. Limnol. Oceanogr. 55, 2570–2584 (2010).CAS 

    Google Scholar 
    Lopes, A. R. et al. Oxidative stress in deep scattering layers: heat shock response and antioxidant enzymes activities of myctophid fishes thriving in oxygen minimum zones. Deep Sea Res. Part I 82, 10–16 (2013).CAS 

    Google Scholar 
    Seibel, B. A., Schneider, J., Kaartvedt, S., Wishner, K. F. & Daly, K. L. Hypoxia tolerance and metabolic suppression in oxygen minimum zone euphausiids: implications for ocean deoxygenation and biogeochemical cycles. Integr. Comp. Biol. https://doi.org/10.1093/icb/icw091 (2016).Seibel, B. A. et al. Metabolic suppression during protracted exposure to hypoxia in the jumbo squid, Dosidicus gigas, living in an oxygen minimum zone. J. Exp. Biol. 217, 2710–2716 (2014).
    Google Scholar 
    Wishner, K. F. et al. Ocean deoxygenation and zooplankton: very small oxygen differences matter. Sci. Adv. 4, eaau5180 (2018).CAS 

    Google Scholar 
    Koslow, J. A., Goericke, R., Lara-Lopez, A. & Watson, W. Impact of declining intermediate-water oxygen on deepwater fishes in the California Current. Mar. Ecol. Prog. Ser. 436, 207–218 (2011).
    Google Scholar 
    Oschlies, A. A committed fourfold increase in ocean oxygen loss. Nat. Commun. 12, 2307 (2021).CAS 

    Google Scholar 
    Wishner, K. F., Seibel, B. A. & Outram, D. Ocean deoxygenation and copepods: coping with oxygen minimum zone variability. Biogeosciences 17, 2315–2339 (2020).
    Google Scholar 
    Stramma, L. et al. Expansion of oxygen minimum zones may reduce available habitat for tropical pelagic fishes. Nat. Clim. Change 2, 33–37 (2012).CAS 

    Google Scholar 
    Köhn, E. E., Münnich, M., Vogt, M., Desmmet, F. & Gruber, N. Strong habitat compression by extreme shoaling events of hypoxic waters in the Eastern Pacific. J. Geophys. Res. Oceans 127, e2022JC018429 (2022).
    Google Scholar 
    Poloczanska, E. S. et al. Global imprint of climate change on marine life. Nat. Clim. Change 3, 919–925 (2013).
    Google Scholar 
    Pinsky, M. L., Selden, R. L. & Kitchel, Z. J. Climate-driven shifts in marine species ranges: scaling from organisms to communities. Annu. Rev. Mar. Sci. 12, 153–179 (2020).
    Google Scholar 
    Cavole, L. M. et al. Biological impacts of the 2013–2015 warm-water anomaly in the northeast Pacific: winners, losers, and the future. Oceanography 29, 273–285 (2016).
    Google Scholar 
    Lavaniegosa, B. E., Jiménez-Herrera, M. A. & Ambriz-Arreola, I. Unusually low euphausiid biomass during the warm years of 2014–2016 in the transition zone of the California Current. Deep Sea Res. Part II 1, 69–170 (2019).
    Google Scholar 
    Lilly, L. E. & Ohman, M. D. Euphausiid spatial displacements and habitat shifts in the southern California Current system in response to El Niño variability. Prog. Oceanogr. 193, 102544 (2021).
    Google Scholar 
    Zeidberg, L. D. & Robison, B. H. Invasive range expansion by the Humboldt squid, Dosidicus gigas, in the eastern North Pacific. Proc. Natl Acad. Sci. USA 104, 12948–12950 (2007).CAS 

    Google Scholar 
    Szesciorka, A. R. et al. Timing is everything: drivers of interannual variability in blue whale migration. Sci. Rep. 10, 7710 (2020).CAS 

    Google Scholar 
    Hoving, H.-J. et al. Extreme plasticity in life‐history strategy allows a migratory predator (jumbo squid) to cope with a changing climate. Glob. Change Biol. 19, 2089–2103 (2013).
    Google Scholar 
    Boscolo-Galazzo, F. et al. Temperature controls carbon cycling and biological evolution in the ocean twilight zone. Science 371, 1148–1152 (2021).CAS 

    Google Scholar 
    Deutsch, C., Ferrel, A., Seibel, B. A., Pörtner, H.-O. & Huey, R. B. Climate change tightens a metabolic constraint on marine habitats. Science 348, 1132–1135 (2015).CAS 

    Google Scholar 
    Seibel, B. A. & Deutsch, C. Oxygen supply capacity in animals evolves to meet maximum demand at the current oxygen partial pressure regardless of size or temperature. J. Exp. Biol. 223, jeb210492 (2020).
    Google Scholar 
    Deutsch, C., Penn, J. L. & Seibel, B. A. Diverse hypoxia and thermal tolerances shape biogeography of marine animals. Nature 585, 557–562 (2020).CAS 

    Google Scholar 
    Childress, J. J. Are there physiological and biochemical adaptations of metabolism in deep-sea animals? Trends Ecol. Evol. 10, 30–36 (1995).CAS 

    Google Scholar 
    Seibel, B. A. & Drazen, J. C. The rate of metabolism in marine animals: environmental constraints, ecological demands and energetic opportunities. Philos. Trans. R. Soc. B. 362, 2061–2078 (2007).CAS 

    Google Scholar 
    Seibel, B. A. et al. Oxygen supply capacity breathes new life into the critical oxygen partial pressure (Pcrit). J. Exp. Biol. 224, jeb242210 (2021).
    Google Scholar 
    Childress, J. J. & Seibel, B. A. Life at stable low oxygen: adaptations of animals to oceanic oxygen minimum layers. J. Exp. Biol. 201, 1223–1232 (1998).CAS 

    Google Scholar 
    Garcia, H. E., et al. World Ocean Atlas 2018, Volume 3: Dissolved Oxygen, Apparent Oxygen Utilization, and Oxygen Saturation (NOAA/NESDIS, 2019).Locarnini, R. A., et. al. World Ocean Atlas 2018, Volume 1: Temperature (NOAA/NESDIS, 2019).Maas, A. E., Frazar, S., Outram, D., Seibel, B. A. & Wishner, K. F. Fine-scale vertical distribution of macroplankton and micronekton in an eastern tropical North Pacific in association with an oxygen minimum zone. J. Plankton Res. 36, 1557–1575 (2014).
    Google Scholar 
    Rosa, R. & Seibel, B. A. Synergistic effect of climate-related variables suggests future physiological impairment in a top oceanic predator. Proc. Natl Acad. Sci. USA 52, 20776–20780 (2008).
    Google Scholar 
    Halsey, L. G., Killen, S. S., Clark, T. D. & Norin, T. Exploring key issues of aerobic scope interpretation in ectotherms: absolute versus factorial. Rev. Fish. Biol. Fish. 28, 405–415 (2018).
    Google Scholar 
    Peterson, C. C., Nagy, K. A. & Diamond, J. Sustained metabolic scope. Proc. Natl Acad. Sci. USA 87, 2324–2328 (1990).CAS 

    Google Scholar 
    Seibel, B. A., Luu, B. E., Tessier, S. N., Towanda, T. & Storey, K. B. Metabolic suppression in the pelagic crab, Pleuroncodes planipes, in oxygen minimum zones. Comp. Biochem. Physiol. A 224, 88–97 (2018).CAS 

    Google Scholar 
    Hadj-Moussa, H., Logan, S. M., Seibel, B. A. & Storey, K. B. Potential role for microRNA in regulating hypoxia-induced metabolic suppression in the jumbo squid? BBA Gene Regul. Mech. 1861, 586–593 (2018).CAS 

    Google Scholar 
    Torres, J. J. & Childress, J. J. Relationship of oxygen consumption to swimming speed in Euphausia pacifica. Mar. Biol. 74, 79–86 (1983).
    Google Scholar 
    Cohen, J. H. & Forward, R. B. Jr. Zooplankton diel vertical migration—a review of proximate control. Oceanogr. Mar. Biol. Annu. Rev. 47, 77–110 (2009).
    Google Scholar 
    Gilly, W. F. et al. Locomotion and behavior of Humboldt squid, Dosidicus gigas, in relation to natural hypoxia in the Gulf of California, Mexico. J. Exp. Biol. 215, 3175–3190 (2012).
    Google Scholar 
    Jaffe, J. S., Ohman, M. D. & De Robertis, A. Sonar estimates of daytime activity levels of Euphausia pacifica in Saanich inlet. Can. J. Fish. Aquat. Sci. 56, 2000–2010 (1999).
    Google Scholar 
    Klevjer, T. A. & Kaartvedt, S. Krill (Meganyctiphanes norvegica) swim faster at night. Limnol. Oceanogr. 56, 765–774 (2011).
    Google Scholar 
    Backus, R. H. et al. Ceratoscopelus maderensis: pecuiiar sound-scattering layer identified with this myctophid fish. Science 160, 991–993 (1968).CAS 

    Google Scholar 
    Barham, E. G. in Proceedings of an International Symposium on Biological Sound Scattering in the Ocean (ed. Farquhar, G. B.) 100–118 (Superintendent of Documents, 1971).Sanders, N. K. & Childress, J. J. A comparison of the respiratory function of the haemocyanins of vertically migrating and non-migrating pelagic, deep-sea Oplophorid shrimps. J. Exp. Biol. 152, 167–187 (1990).
    Google Scholar 
    Seibel, B. A. Critical depth in the jumbo squid, Dosidicus gigas (Ommastrephidae), living in oxygen minimum zones II. Blood-oxygen binding. Deep Sea Res. Part II 95, 139–144 (2013).CAS 

    Google Scholar 
    Pörtner, H.-O., Bock, C. & Mark, F. C. Oxygen- and capacity-limited thermal tolerance: bridging ecology and physiology. J. Exp. Biol. 220, 2685–2696 (2017).
    Google Scholar 
    Laffoley, D. & Baxter, J. M. Ocean Deoxygenation: Everyone’s Problem—Causes, Impacts, Consequences and Solutions (IUCN, 2019).Birk, M. A. Respirometry: Tools for Conducting and Analyzing Respirometry Experiments. R version 1.4.0 http://cran.r-project.org/package=respirometry (2021).Huang, B. et al. Improvements of the daily optimum interpolation sea surface temperature (DOISST) Version 2.1. J. Clim. 34, 2923–2939 (2021).
    Google Scholar  More

  • in

    Resolving malaria’s dry-season dilemma

    Seasonal fluctuations in animal population dynamics are among the most fundamental attributes of life on Earth. A long recognized but poorly understood example is the dramatic seasonal fluctuation in the abundance of malaria vectors in the semi-arid savannah and Sahel regions of Africa. In these regions, the vector mosquitoes largely disappear during a prolonged 3- to 8-month dry season, when lack of rain causes the aquatic larval habitats to disappear. As a result, malaria transmission plummets. When the rains return, the mosquito vectors rapidly reappear, leading to a resurgence of malaria transmission. How the vector populations are able to persist through the prolonged dry season and rapidly rebound with the onset of rains is referred to as the ‘dry-season malaria paradox’, and has remained an enduring mystery of malariology for nearly 100 years. Writing in Nature Ecology & Evolution, Faiman et al.1 help to resolve this mystery by using an innovative isotopic labelling strategy: they demonstrate that at least approximately 20% of the local population of the malaria vector Anopheles coluzzi in the West African Sahel survive the dry season locally by undergoing summer dormancy, known as aestivation. More