More stories

  • in

    Short-term mercury exposure disrupts muscular and hepatic lipid metabolism in a migrant songbird

    Bowler, D. E. et al. Mapping human pressures on biodiversity across the planet uncovers anthropogenic threat complexes. People Nat. 2, 380–394 (2020).Article 

    Google Scholar 
    Persson, L. et al. Outside the safe operating space of the planetary boundary for novel entities. Environ. Sci. Technol. https://doi.org/10.1021/acs.est.1c04158 (2022).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    United Nations Environment Programme (UNEP). 2019. Global Mercury Assessment 2018. UN Environment Programme, Chemicals and Health Branch Geneva, Switzerland. https://www.unep.org/resources/publication/global-mercury-assessment-2018Rimmer, C. C., Miller, E. K., McFarland, K. P., Taylor, R. J. & Faccio, S. D. Mercury bioaccumulation and trophic transfer in the terrestrial food web of a montane forest. Ecotoxicology 19, 697–709 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    Cristol, D. A. et al. The movement of aquatic mercury through terrestrial food webs. Science 320, 335 (2008).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Evers, D. The effects of methylmercury on wildlife: A comprehensive review and approach for interpretation. Encycl. Anthropocene 5, 181–194 (2018).Article 

    Google Scholar 
    Whitney, M. C. & Cristol, D. A. Impacts of sublethal mercury exposure on birds: a detailed review. Rev. Environ. Contam. Toxicol. 244, 113–163 (2017).
    Google Scholar 
    Seewagen, C. L. Threats of environmental mercury to birds: Knowledge gaps and priorities for future research. Bird Conserv. Int. 20, 112–123 (2010).Article 

    Google Scholar 
    Seewagen, C. L. The threat of global mercury pollution to bird migration: Potential mechanisms and current evidence. Ecotoxicology 29, 1254–1267 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Ma, Y., Branfireun, B. A., Hobson, K. A. & Guglielmo, C. G. Evidence of negative seasonal carry-over effects of breeding ground mercury exposure on survival of migratory songbirds. J. Avian Biol. 49, jav-01656 (2018).Article 

    Google Scholar 
    Newton, I. Can conditions experienced during migration limit the population levels of birds?. J. Ornithol. 147, 146–166 (2006).Article 

    Google Scholar 
    Klaassen, M., Hoye, B. J., Nolet, B. A. & Buttemer, W. A. Ecophysiology of avian migration in the face of current global hazards. Philos. Trans. R. Soc. B 367, 1719–1732 (2020).Article 

    Google Scholar 
    Zurell, D., Graham, C. H., Gallien, L., Thuiller, W. & Zimmermann, N. E. Long-distance migratory birds threatened by multiple independent risks from global change. Nat. Clim. Chang. 8, 992–996 (2018).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Seewagen, C. L., Ma, Y., Morbey, Y. E. & Guglielmo, C. G. Stopover departure behavior and flight orientation of spring-migrant Yellow-rumped Warblers (Setophaga coronata) experimentally exposed to methylmercury. J. Ornithol. 160, 617–624 (2019).Article 

    Google Scholar 
    Seewagen, C. L. Blood mercury levels and the stopover refueling performance of a long-distance migratory songbird. Can. J. Zool. 91, 41–45 (2013).CAS 
    Article 

    Google Scholar 
    Adams, E. M., Williams, K. A., Olsen, B. J. & Evers, D. C. Mercury exposure in migrating songbirds: Correlations with physical condition. Ecotoxicology 29, 1240–1253 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Ma, Y., Perez, C. R., Branfireun, B. A. & Guglielmo, C. G. Dietary exposure to methylmercury affects flight endurance in a migratory songbird. Environ. Pollut. 234, 894–901 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Gerson, A. R., Cristol, D. A. & Seewagen, C. L. Environmentally relevant methylmercury exposure reduces the metabolic scope of a model songbird. Environ. Pollut. 246, 790–796 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Jenni, L. & Jenni-Eiermann, S. Fuel supply and metabolic constraints in migrating birds. J. Avian Biol. 29, 521–552 (1998).Article 

    Google Scholar 
    McWilliams, S. R., Guglielmo, C., Pierce, B. & Klaassen, M. Flying, fasting, and feeding in birds during migration: A nutritional and physiological ecology perspective. J. Avian Biol. 35, 377–393 (2004).Article 

    Google Scholar 
    Guglielmo, C. G. Move that fatty acid: Fuel selection and transport in migratory birds and bats. Integr. Comp. Biol. 50, 336–345 (2010).PubMed 
    Article 

    Google Scholar 
    Guglielmo, C. G. Obese super athletes: Fat-fueled migration in birds and bats. J. Exp. Biol. 221(Suppl_1), 165753 (2018).Article 

    Google Scholar 
    Kawakami, T. et al. Differential effects of cobalt and mercury on lipid metabolism in the white adipose tissue of high-fat diet-induced obesity mice. Toxicol. Appl. Pharmacol. 258, 32–42 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Yadetie, F. et al. Global transcriptome analysis of Atlantic cod (Gadus morhua) liver after in vivo methylmercury exposure suggests effects on energy metabolism pathways. Aquat. Toxicol. 126, 314–325 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    Park, K. & Seo, E. Association between toenail mercury and metabolic syndrome is modified by selenium. Nutrients 8, 424 (2016).PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Caito, S. W., Newell-Caito, J., Martell, M., Crawford, N. & Aschner, M. Methylmercury induces metabolic alterations in Caenorhabditis elegans: Role for C/EBP transcription factor. Toxicol. Sci. 174, 112–123 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Edmonds, S. T., O’Driscoll, N. J., Hillier, N. K., Atwood, J. L. & Evers, D. C. Factors regulating the bioavailability of methylmercury to breeding rusty blackbirds in northeastern wetlands. Environ. Pollut. 171, 148–154 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Rowse, L. M., Rodewald, A. D., Mažeika, S. & Sullivan, P. Pathways and consequences of contaminant flux to Acadian flycatchers (Empidonax virescens) in urbanizing landscapes of Ohio, USA. Sci. Total Environ. 485, 461–467 (2014).ADS 
    PubMed 
    Article 
    CAS 

    Google Scholar 
    Marsh, R. L. Catabolic enzyme activities in relation to premigratory fattening and muscle hypertrophy in the gray catbird (Dumetella carolinensis). J. Comp. Physiol. 141, 417–423 (1981).CAS 
    Article 

    Google Scholar 
    Guglielmo, C. G., Haunerland, N. H., Hochachka, P. W. & Williams, T. D. Seasonal dynamics of flight muscle fatty acid binding protein and catabolic enzymes in a migratory shorebird. Am. J. Physiol.-Regul. Integr. Comp. Physiol. 282(5), R1405–R1413 (2002).CAS 
    PubMed 
    Article 

    Google Scholar 
    Maillet, D. & Weber, J. M. Relationship between n-3 PUFA content and energy metabolism in the flight muscles of a migrating shorebird: Evidence for natural doping. J. Exp. Biol. 210, 413–420 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    Weber, J. M. Metabolic fuels: Regulating fluxes to select mix. J. Exp. Biol. 214, 286–294 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    Feige, J. N., Gelman, L., Michalik, L., Desvergne, B. & Wahli, W. From molecular action to physiological outputs: Peroxisome proliferator-activated receptors are nuclear receptors at the crossroads of key cellular functions. Prog. Lipid. Res. 45, 120–159 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    Bensinger, S. J. & Tontonoz, P. Integration of metabolism and inflammation by lipid-activated nuclear receptors. Nature 454, 470–477. https://doi.org/10.1038/nature07202 (2008).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Ynalvez, R., Gutierrez, J. & Gonzalez-Cantu, H. Mini-review: Toxicity of mercury as a consequence of enzyme alteration. Biometals 29, 781–788 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Gerson, A. R. & Guglielmo, C. G. Energetics and metabolite profiles during early flight in American robins (Turdus Migratorius). J. Comp. Physiol. B. 183, 983–991 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    Price, E. R., McFarlan, J. T. & Guglielmo, C. G. Preparing for migration? The effects of photoperiod and exercise on muscle oxidative enzymes, lipid transporters, and phospholipids in white-crowned sparrows. Physiol. Biochem. Zool. 83, 252–262 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    Bradley, S. S., Dick, M. F., Guglielmo, C. G. & Timoshenko, A. V. Seasonal and flight-related variation of galectin expression in heart, liver and flight muscles of yellow-rumped warblers (Setophaga coronata). Glycoconj. J. 34, 603–611 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    McFarlan, J. T., Bonen, A. & Guglielmo, C. G. Seasonal upregulation of fatty acid transporters in flight muscles of migratory white-throated sparrows (Zonotrichia albicollis). J. Exp. Biol. 212, 2934–2940 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    Zhang, Y., King, M. O., Harmon, E., Eyster, K. & Swanson, D. L. Migration-induced variation of fatty acid transporters and cellular metabolic intensity in passerine birds. J. Comp. Physiol. B. 185, 797–810 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Dick, M. F. & Guglielmo, C. G. Dietary polyunsaturated fatty acids influence flight muscle oxidative capacity but not endurance flight performance in a migratory songbird. Am. J. Physiol.-Regul. Integr. Compar. Physiol. 316(4), R362–R375 (2019).CAS 
    Article 

    Google Scholar 
    Schmittgen, T. D. & Livak, K. J. Analyzing real-time PCR data by the comparative CT method. Nat. Protoc. 3, 1101–1108 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    Bittencourt, L. O. et al. Oxidative biochemistry disbalance and changes on proteomic profile in salivary glands of rats induced by chronic exposure to methylmercury. Oxid. Med. Cell. Longev. https://doi.org/10.1155/2017/5653291 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Shi, Q., Sun, N., Kou, H., Wang, H. & Zhao, H. Chronic effects of mercury on Bufo gargarizans larvae: Thyroid disruption, liver damage, oxidative stress and lipid metabolism disorder. Ecotoxicol. Environ. Saf. 164, 500–509 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Nøstbakken, O. J. et al. Dietary methylmercury alters the proteome in Atlantic salmon (Salmo salar) kidney. Aquat. Toxicol. 108, 70–77 (2012).PubMed 
    Article 
    CAS 

    Google Scholar 
    Zink, E. M. Comparison of the mercury induced proteomes of Escherichia coli MG1655 with and without the NR1 plasmid. MSc thesis, Washington State University, Pullman, WA (2009).Lundgren, B. O. & Kiessling, K. H. Seasonal variation in catabolic enzyme activities in breast muscle of some migratory birds. Oecologia 66, 468–471 (1985).ADS 
    PubMed 
    Article 

    Google Scholar 
    Banerjee, S. & Chaturvedi, C. M. Migratory preparation associated alterations in pectoralis muscle biochemistry and proteome in Palearctic-Indian emberizid migratory finch, red-headed bunting, Emberiza bruniceps. Comp. Biochem. Physiol. D Genom. Proteom. 17, 9–25 (2016).CAS 

    Google Scholar 
    Dick, M. F. The long haul: migratory flight preparation and performance in songbirds. Ph.D. dissertation, University of Western Ontario, London, Canada (2017).Driedzic, W. R., Crowe, H. L., Hicklin, P. W. & Sephton, D. H. Adaptations in pectoralis muscle, heart mass, and energy metabolism during premigratory fattening in semipalmated sandpipers (Calidris pusilla). Can. J. Zool. 71, 1602–1608 (1993).Article 

    Google Scholar 
    De Moranville, K. J. et al. PPAR expression, muscle size and metabolic rates across the gray catbird’s annual cycle are greatest in preparation for fall migration. J. Exper. Biol. 222, 198028 (2019).Article 

    Google Scholar 
    Zajac, D. M., Cerasale, D. J., Landman, S. & Guglielmo, C. G. Behavioral and physiological effects of photoperiod-induced migratory state and leptin on Zonotrichia albicollis: II. Effects on fatty acid metabolism. Gen. Comp. Endocrinol. 174, 269–275 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    Tinant, G. et al. Methylmercury displays pro-adipogenic properties in rainbow trout preadipocytes. Chemosphere 263, 127917 (2021).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Cambier, S. et al. At environmental doses, dietary methylmercury inhibits mitochondrial energy metabolism in skeletal muscles of the zebra fish (Danio rerio). Int. J. Biochem. Cell Biol. 41, 791–799 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    Ferain, A. et al. Transcriptional effects of phospholipid fatty acid profile on rainbow trout liver cells exposed to methylmercury. Aquat. Toxicol. 199, 174–187 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Börchers, T., Højrup, P., Nielsen, S. U., Roepstorff, P., Spener, F., Knudsen, J. Revision of the amino acid sequence of human heart fatty acid-binding protein. In Cellular Fatty Acid-binding Proteins 127–133 (Springer, Boston, 1990).Dörmann, P. et al. Amino acid exchange and covalent modification by cysteine and glutathione explain isoforms of fatty acid-binding protein occurring in bovine liver. J. Biol. Chem. 268, 16286–16292 (1993).PubMed 
    Article 

    Google Scholar 
    Su, X. & Abumrad, N. A. Cellular fatty acid uptake: A pathway under construction. Trends Endocrinol. Metab. 20(2), 72–77 (2009).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    van Oort, M. M. et al. Each of the four intracellular cysteines of CD36 is essential for insulin-or AMP-activated protein kinase-induced CD36 translocation. Arch. Physiol. Biochem. 120, 40–49 (2014).PubMed 
    Article 
    CAS 

    Google Scholar 
    Wang, G., Bonkovsky, H. L., de Lemos, A. & Burczynski, F. J. Recent insights into the biological functions of liver fatty acid binding protein 1. J. Lipid Res. 56, 2238–2247 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Vallee, B. L. & Ulmer, D. D. Biochemical effects of mercury, cadmium, and lead. Annu. Rev. Biochem. 41, 91–128 (1972).CAS 
    PubMed 
    Article 

    Google Scholar 
    Aschner, M. & Syversen, T. Methylmercury: Recent advances in the understanding of its neurotoxicity. Ther. Drug Monit. 27, 278–283 (2005).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kenow, K. P., Meyer, M. W., Hines, R. K. & Karasov, W. H. Distribution and accumulation of mercury in tissues of captive-reared common loon (Gavia immer) chicks. Environ. Toxicol. Chem. 26, 1047–1055 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    Varian-Ramos, C. W., Whitney, M., Rice, G. W. & Cristol, D. A. Form of dietary methylmercury does not affect total mercury accumulation in the tissues of zebra finch. Bull. Environ. Contam. Toxicol. 99, 1–8 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Rizzetti, D. A. et al. Chronic mercury at low doses impairs white adipose tissue plasticity. Toxicology 418, 41–50 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Richter, C. A. et al. Methylmercury-induced changes in gene transcription associated with neuroendocrine disruption in largemouth bass (Micropterus salmoides). Gen. Comp. Endocrinol. 203, 215–224 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Barnes, D. M., Hanlon, P. R. & Kircher, E. A. Effects of inorganic HgCl2 on adipogenesis. Toxicol. Sci. 75(2), 368–377 (2003).CAS 
    PubMed 
    Article 

    Google Scholar 
    Corder, K. R., DeMoranville, K. J., Russell, D. E., Huss, J. M. & Schaeffer, P. J. Annual life-stage regulation of lipid metabolism and storage and association with PPARs in a migrant species: the gray catbird (Dumetella carolinensis). J. Exp. Biol. 219, 3391–3398 (2016).PubMed 

    Google Scholar 
    DeMoranville, K. J., Carter, W. A., Pierce, B. J. & McWilliams, S. R. Flight training in a migratory bird drives metabolic gene expression in the flight muscle but not liver, and dietary fat quality influences select genes. Am. J. Physiol.-Regul. Integr. Compar. Physiol. 319(6), R637–R652 (2020).CAS 
    Article 

    Google Scholar 
    Gavrilova, O. et al. Liver peroxisome proliferator-activated receptor γ contributes to hepatic steatosis, triglyceride clearance, and regulation of body fat mass. J. Biol. Chem. 278(36), 34268–34276 (2003).CAS 
    PubMed 
    Article 

    Google Scholar 
    Bedoucha, M., Atzpodien, E. & Boelsterli, U. A. Diabetic KKAy mice exhibit increased hepatic PPARγ1 gene expression and develop hepatic steatosis upon chronic treatment with antidiabetic thiazolidinediones. J. Hepatol. 35, 17–23 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    Egeler, O., Williams, T. D. & Guglielmo, C. G. Modulation of lipogenic enzymes, fatty acid synthase and Δ 9-desaturase, in relation to migration in the western sandpiper (Calidris mauri). J. Comp. Physiol. B 170, 169–174 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    Klaper, R. et al. Use of a 15k gene microarray to determine gene expression changes in response to acute and chronic methylmercury exposure in the fathead minnow (Pimephales promelas). J. Fish Biol. 72, 2207–2280 (2008).CAS 
    Article 

    Google Scholar 
    Calow, P. Physiological costs of combating chemical toxicants: Ecological implications. Comp. Biochem. Physiol. C 100, 3–6 (1991).CAS 
    PubMed 
    Article 

    Google Scholar 
    Spalding, M. G. et al. Histologic, neurologic, and immunologic effects of methylmercury in captive great egrets. J. Wildl. Dis. 36, 423–435 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    Carlson, J. R., Cristol, D. & Swaddle, J. P. Dietary mercury exposure causes decreased escape takeoff flight performance and increased molt rate in European starlings (Sturnus vulgaris). Ecotoxicology 23, 1464–1473 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Faaborg, J. et al. Conserving migratory land birds in the New World: Do we know enough?. Ecol. Appl. 20, 398–418 (2010).PubMed 
    Article 

    Google Scholar 
    Duijns, S. et al. Body condition explains migratory performance of a long-distance migrant. Proc. R. Soc. B https://doi.org/10.1098/rspb.2017.1374 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Single-cell stable isotope probing in microbial ecology

    Neufeld JD, Wagner M, Murrell JC. Who eats what, where and when? Isotope-labelling experiments are coming of age. ISME J. 2007;1:103–10.CAS 
    PubMed 
    Article 

    Google Scholar 
    Boschker HTS, Nold SC, Wellsbury P, Bos D, de Graaf W, Pel R, et al. Direct linking of microbial populations to specific biogeochemical processes by 13C-labelling of biomarkers. Nature. 1998;392:801–5Jehmlich N, Schmidt F, von Bergen M, Richnow H-H, Vogt C. Protein-based stable isotope probing (Protein-SIP) reveals active species within anoxic mixed cultures. ISME J. 2008;2:1122–33.CAS 
    PubMed 
    Article 

    Google Scholar 
    Radajewski S, Ineson P, Parekh NR, Colin Murrell J. Stable-isotope probing as a tool in microbial ecology. Nature. 2000;403:646–9.CAS 
    PubMed 
    Article 

    Google Scholar 
    Manefield M, Whiteley AS, Griffiths RI, Bailey MJ. RNA stable isotope probing, a novel means of linking microbial community function to phylogeny. Appl Environ Microbiol. 2002;68:5367–73.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Berry D, Mader E, Lee TK, Woebken D, Wang Y, Zhu D, et al. Tracking heavy water (D2O) incorporation for identifying and sorting active microbial cells. Proc Natl Acad Sci USA. 2015;112:E194–203.CAS 
    PubMed 

    Google Scholar 
    Jehmlich N, Vogt C, Lünsmann V, Richnow HH, von Bergen M. Protein-SIP in environmental studies. Curr Opin Biotechnol. 2016;41:26–33.CAS 
    PubMed 
    Article 

    Google Scholar 
    Haichar, FEZ, Achouak W, Christen R, Heulin T, et al. Identification of cellulolytic bacteria in soil by stable isotope probing. Environ Microbiol. 2007;9:625–34Rangel-Castro JI, Ignacio Rangel-Castro J, Killham K, Ostle N, Nicol GW, Anderson IC, et al. Stable isotope probing analysis of the influence of liming on root exudate utilization by soil microorganisms. Environ Microbiol. 2005;7:828–38.CAS 
    PubMed 
    Article 

    Google Scholar 
    Wang Y, Song Y, Tao Y, Muhamadali H, Goodacre R, Zhou N-Y, et al. Reverse and multiple stable isotope probing to study bacterial metabolism and interactions at the single cell level. Anal Chem. 2016;88:9443–50.CAS 
    PubMed 
    Article 

    Google Scholar 
    Sharma K, Palatinszky M, Nikolov G, Berry D, Shank EA. Transparent soil microcosms for live-cell imaging and non-destructive stable isotope probing of soil microorganisms. Elife. 2020;9:e56275.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lee KS, Landry Z, Pereira FC, Wagner M, Berry D, Huang WE, et al. Raman microspectroscopy for microbiology. Nat. Rev. Methods Primers. 2021;1:80.CAS 
    Article 

    Google Scholar 
    Hatzenpichler R, Krukenberg V, Spietz RL, Jay ZJ. Next-generation physiology approaches to study microbiome function at single cell level. Nat Rev Microbiol. 2020;18:241–56.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Wagner M. Single-cell ecophysiology of microbes as revealed by raman microspectroscopy or secondary ion mass spectrometry imaging. Ann Rev Microbiol. 2009;63:411–29Lennon JT, Jones SE. Microbial seed banks: the ecological and evolutionary implications of dormancy. Nat Rev Microbiol. 2011;9:119–30.CAS 
    PubMed 
    Article 

    Google Scholar 
    Lewis K. Persister cells, dormancy and infectious disease. Nat Rev Microbiol. 2006;5:48–56.PubMed 
    Article 
    CAS 

    Google Scholar 
    Nielsen KM, Johnsen PJ, Bensasson D, Daffonchio D. Release and persistence of extracellular DNA in the environment. Environ Biosafety Res. 2007;6:37–53.CAS 
    PubMed 
    Article 

    Google Scholar 
    Blazewicz SJ, Barnard RL, Daly RA, Firestone MK. Evaluating rRNA as an indicator of microbial activity in environmental communities: limitations and uses. ISME J. 2013;7:2061–8.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Nocker A, Sossa-Fernandez P, Burr MD, Camper AK. Use of propidium monoazide for live/dead distinction in microbial ecology. Appl Environ Microbiol. 2007;73:5111–7.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Tawakoli PN, Al-Ahmad A, Hoth-Hannig W, Hannig M, Hannig C. Comparison of different live/dead stainings for detection and quantification of adherent microorganisms in the initial oral biofilm. Clin Oral Investig. 2013;17:841–50.CAS 
    PubMed 
    Article 

    Google Scholar 
    Netuschil L, Auschill TM, Sculean A, Arweiler NB. Confusion over live/dead stainings for the detection of vital microorganisms in oral biofilms-which stain is suitable? BMC Oral Health. 2014;14:2.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hatzenpichler R, Connon SA, Goudeau D, Malmstrom RR, Woyke T, Orphan VJ. Visualizing in situ translational activity for identifying and sorting slow-growing archaeal−bacterial consortia. Proc Natl Acad Sci USA. 2016;113:E4069–78.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kuru E, Hughes HV, Brown PJ, Hall E, Tekkam S, Cava F, et al. In Situ probing of newly synthesized peptidoglycan in live bacteria with fluorescent D-amino acids. Angew Chem Int Ed Engl. 2012;51:12519–23.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kopf SH, McGlynn SE, Green-Saxena A, Guan Y, Newman DK, Orphan VJ. Heavy water and15N labelling with NanoSIMS analysis reveals growth rate-dependent metabolic heterogeneity in chemostats. Environ Microbiol. 2015;17:2542–56Kopf SH, Sessions AL, Cowley ES, Reyes C, Van Sambeek L, Hu Y, et al. Trace incorporation of heavy water reveals slow and heterogeneous pathogen growth rates in cystic fibrosis sputum. Proc Natl Acad Sci USA. 2016;113:E110–6.CAS 
    PubMed 
    Article 

    Google Scholar 
    Neubauer C, Kasi AS, Grahl N, Sessions AL, Kopf SH, Kato R, et al. Refining the Application of Microbial Lipids as Tracers of Staphylococcus aureus Growth Rates in Cystic Fibrosis Sputum. J Bacteriol. 2018;200:e00365–18.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Haider S, Wagner M, Schmid MC, Sixt BS, Christian JG, Häcker G, et al. Raman microspectroscopy reveals long-term extracellular activity of Chlamydiae. Mol Microbiol. 2010;77:687–700.CAS 
    PubMed 
    Article 

    Google Scholar 
    Kloehn J, Boughton BA, Saunders EC, O’Callaghan S, Binger KJ, McConville MJ. Identification of Metabolically Quiescent Leishmania mexicana Parasites in Peripheral and Cured Dermal Granulomas Using Stable Isotope Tracing Imaging Mass Spectrometry. mBio. 2021;12:e00129–21.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kong L, Setlow P, Li Y-Q. Direct analysis of water content and movement in single dormant bacterial spores using confocal Raman microspectroscopy and Raman imaging. Anal Chem. 2013;85:7094–101.CAS 
    PubMed 
    Article 

    Google Scholar 
    Knudsen SM, Cermak N, Delgado FF, Setlow B, Setlow P, Manalis SR. Water and small-molecule permeation of dormant Bacillus subtilis spores. J Bacteriol. 2016;198:168–77.CAS 
    PubMed 
    Article 

    Google Scholar 
    Chen D, Huang S-S, Li Y-Q. Real-time detection of kinetic germination and heterogeneity of single Bacillus spores by laser tweezers Raman spectroscopy. Anal Chem. 2006;78:6936–41.CAS 
    PubMed 
    Article 

    Google Scholar 
    Devictor V, Clavel J, Julliard R, Lavergne S, Mouillot D, Thuiller W, et al. Defining and measuring ecological specialization. J Appl Ecol. 2010;47:15–25.Article 

    Google Scholar 
    Pereira FC, Berry D. Microbial nutrient niches in the gut. Environ Microbiol. 2017;19:1366–78.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Shakya M, Lo C-C, Chain PSG. Advances and challenges in metatranscriptomic analysis. Front Genet. 2019;10:904.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Berry D, Loy A. Stable-Isotope probing of human and animal microbiome function. Trends Microbiol. 2018;26:999–1007.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Terrado R, Pasulka AL, Lie AA-Y, Orphan VJ, Heidelberg KB, Caron DA. Autotrophic and heterotrophic acquisition of carbon and nitrogen by a mixotrophic chrysophyte established through stable isotope analysis. ISME J. 2017;11:2022–34.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Dekas AE, Parada AE, Mayali X, Fuhrman JA, Wollard J, Weber PK, et al. Characterizing Chemoautotrophy and Heterotrophy in Marine Archaea and Bacteria With Single-Cell Multi-isotope NanoSIP. Front Microbiol. 2019;10:2682.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Wegener G, Bausch M, Holler T, Thang NM, Mollar XP, Kellermann MY, et al. Assessing sub-seafloor microbial activity by combined stable isotope probing with deuterated water and 13C-bicarbonate. Environ Microbiol. 2019;14:1517–27Jing X, Gou H, Gong Y, Su X, Xu L, Ji Y, et al. Raman-activated cell sorting and metagenomic sequencing revealing carbon-fixing bacteria in the ocean. Environ Microbiol. 2018;20:2241–55.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Xu J, Zhu D, Ibrahim AD, Allen CCR, Gibson CM, Fowler PW, et al. Raman deuterium isotope probing reveals microbial metabolism at the single-cell level. Anal Chem. 2017;89:13305–12.CAS 
    PubMed 
    Article 

    Google Scholar 
    Zhang M, Hong W, Abutaleb NS, Li J, Dong P-T, Zong C, et al. Rapid determination of antimicrobial susceptibility by stimulated Raman scattering imaging of D2O metabolic incorporation in a single bacterium. Adv Chem Microsc Life Sci Transl Med. 2021.Lima C, Muhamadali H, Xu Y, Kansiz M, Goodacre R. Imaging Isotopically Labeled Bacteria at the Single-Cell Level Using High-Resolution Optical Infrared Photothermal Spectroscopy. Anal Chem. 2021;93:3082–8.CAS 
    PubMed 
    Article 

    Google Scholar 
    Ackermann M. A functional perspective on phenotypic heterogeneity in microorganisms. Nat Rev Microbiol. 2015;13:497–508.CAS 
    PubMed 
    Article 

    Google Scholar 
    Balaban NQ, Merrin J, Chait R, Kowalik L, Leibler S. Bacterial persistence as a phenotypic switch. Science. 2004;305:1622–5.CAS 
    PubMed 
    Article 

    Google Scholar 
    Maamar H, Raj A, Dubnau D. Noise in gene expression determines cell fate in Bacillus subtilis. Science. 2007;317:526–9.CAS 
    PubMed 
    Article 

    Google Scholar 
    Emonet T, Cluzel P. Relationship between cellular response and behavioral variability in bacterial chemotaxis. Proc Natl Acad Sci USA. 2008;105:3304–9.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ozbudak EM, Thattai M, Lim HN, Shraiman BI, Van Oudenaarden A. Multistability in the lactose utilization network of Escherichia coli. Nature. 2004;427:737–40.CAS 
    PubMed 
    Article 

    Google Scholar 
    Kiviet DJ, Nghe P, Walker N, Boulineau S, Sunderlikova V, Tans SJ. Stochasticity of metabolism and growth at the single-cell level. Nature. 2014;514:376–9.CAS 
    PubMed 
    Article 

    Google Scholar 
    Kotte O, Volkmer B, Radzikowski JL, Heinemann M. Phenotypic bistability in Escherichia coli’s central carbon metabolism. Mol Syst Biol. 2014;10:736.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    New AM, Cerulus B, Govers SK, Perez-Samper G, Zhu B, Boogmans S, et al. Different levels of catabolite repression optimize growth in stable and variable environments. PLoS Biol. 2014;12:e1001764.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Solopova A, van Gestel J, Weissing FJ, Bachmann H, Teusink B, Kok J, et al. Bet-hedging during bacterial diauxic shift. Proc Natl Acad Sci USA. 2014;111:7427–32.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Schreiber F, Littmann S, Lavik G, Escrig S, Meibom A, Kuypers MMM, et al. Phenotypic heterogeneity driven by nutrient limitation promotes growth in fluctuating environments. Nat Microbiol. 2016;1:16055.CAS 
    PubMed 
    Article 

    Google Scholar 
    Nikolic N, Schreiber F, Dal Co A, Kiviet DJ, Bergmiller T, Littmann S, et al. Cell-to-cell variation and specialization in sugar metabolism in clonal bacterial populations. PLoS Genet. 2017;13:e1007122.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Takhaveev V, Heinemann M. Metabolic heterogeneity in clonal microbial populations. Curr Opin Microbiol. 2018;45:30–8.CAS 
    PubMed 
    Article 

    Google Scholar 
    Altschuler SJ, Wu LF. Cellular heterogeneity: do differences make a difference? Cell. 2010;141:559–63.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Beaumont HJE, Gallie J, Kost C, Ferguson GC, Rainey PB. Experimental evolution of bet hedging. Nature. 2009;462:90–3.CAS 
    PubMed 
    Article 

    Google Scholar 
    Calabrese F, Voloshynovska I, Musat F, Thullner M, Schlömann M, Richnow HH, et al. Quantitation and comparison of phenotypic heterogeneity among single cells of monoclonal microbial populations. Front Microbiol. 2019;10:2814.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Zimmermann M, Escrig S, Hübschmann T, Kirf MK, Brand A, Inglis RF, et al. Phenotypic heterogeneity in metabolic traits among single cells of a rare bacterial species in its natural environment quantified with a combination of flow cell sorting and NanoSIMS. Front Microbiol. 2015;6:243.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Zimmermann M, Escrig S, Lavik G, Kuypers MMM, Meibom A, Ackermann M, et al. Substrate and electron donor limitation induce phenotypic heterogeneity in different metabolic activities in a green sulphur bacterium. Environ Microbiol Rep. 2018;10:179–83.CAS 
    PubMed 
    Article 

    Google Scholar 
    Sheik AR, Muller EE, Audinot J-N, Lebrun LA, Grysan P, Guignard C, et al. In situ phenotypic heterogeneity among single cells of the filamentous bacterium Candidatus Microthrix parvicella. ISME J. 2016;10:1274–9.CAS 
    PubMed 
    Article 

    Google Scholar 
    Seyedsayamdost MR, Case RJ, Kolter R, Clardy J. The Jekyll-and-Hyde chemistry of Phaeobacter gallaeciensis. Nat Chem. 2011;3:331–5.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ferrier-Pagès C, Leal MC. Stable isotopes as tracers of trophic interactions in marine mutualistic symbioses. Ecol Evol. 2019;9:723–40.PubMed 
    Article 

    Google Scholar 
    Pasulka AL, Thamatrakoln K, Kopf SH, Guan Y, Poulos B, Moradian A, et al. Interrogating marine virus-host interactions and elemental transfer with BONCAT and nanoSIMS-based methods. Environ Microbiol. 2018;20:671–92.CAS 
    PubMed 
    Article 

    Google Scholar 
    Kopp C, Domart-Coulon I, Escrig S, Humbel BM, Hignette M, Meibom A. Subcellular investigation of photosynthesis-driven carbon assimilation in the symbiotic reef coral Pocillopora damicornis. mBio. 2015;6:e02299–14.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Rädecker N, Pogoreutz C, Gegner HM, Cárdenas A, Roth F, Bougoure J, et al. Heat stress destabilizes symbiotic nutrient cycling in corals. Proc Natl Acad Sci U S A. 2021;118:e2022653118.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Krueger T, Bodin J, Horwitz N, Loussert-Fonta C, Sakr A, Escrig S, et al. Temperature and feeding induce tissue level changes in autotrophic and heterotrophic nutrient allocation in the coral symbiosis – a NanoSIMS study. Sci Rep. 2018;8:12710.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Gibbin E, Gavish A, Krueger T, Kramarsky-Winter E, Shapiro O, Guiet R, et al. Vibrio coralliilyticus infection triggers a behavioural response and perturbs nutritional exchange and tissue integrity in a symbiotic coral. ISME J. 2019;13:989–1003.CAS 
    PubMed 
    Article 

    Google Scholar 
    Rix L, Ribes M, Coma R, Jahn MT, de Goeij JM, van Oevelen D, et al. Heterotrophy in the earliest gut: a single-cell view of heterotrophic carbon and nitrogen assimilation in sponge-microbe symbioses. ISME J. 2020;14:2554–67.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Thomas T, Moitinho-Silva L, Lurgi M, Björk JR, Easson C, Astudillo-García C, et al. Diversity, structure and convergent evolution of the global sponge microbiome. Nat Commun. 2016;7:11870.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Mills MM, Turk-Kubo KA, van Dijken GL, Henke BA, Harding K, Wilson ST, et al. Unusual marine cyanobacteria/haptophyte symbiosis relies on N2 fixation even in N-rich environments. ISME J. 2020;14:2395–406.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Turk-Kubo KA, Mills MM, Arrigo KR, van Dijken G, Henke BA, Stewart B, et al. UCYN-A/haptophyte symbioses dominate N2 fixation in the Southern California Current System. ISME Commun. 2021;1:1–13.Article 

    Google Scholar 
    Moore CM, Mills MM, Arrigo KR, Berman-Frank I, Bopp L, Boyd PW, et al. Processes and patterns of oceanic nutrient limitation. Nat Geosci. 2013;6:701–10.CAS 
    Article 

    Google Scholar 
    Scheller S, Yu H, Chadwick GL, McGlynn SE, Orphan VJ. Artificial electron acceptors decouple archaeal methane oxidation from sulfate reduction. Science. 2016;351:703–7.CAS 
    PubMed 
    Article 

    Google Scholar 
    Pereira FC, Wasmund K, Cobankovic I, Jehmlich N, Herbold CW, Lee KS, et al. Rational design of a microbial consortium of mucosal sugar utilizers reduces Clostridiodes difficile colonization. Nat Commun. 2020;11:5104.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Mooshammer M, Kitzinger K, Schintlmeister A, Ahmerkamp S, Nielsen JL, Nielsen PH, et al. Flow-through stable isotope probing (Flow-SIP) minimizes cross-feeding in complex microbial communities. ISME J. 2021;15:348–53.CAS 
    PubMed 
    Article 

    Google Scholar 
    Drescher K, Nadell CD, Stone HA, Wingreen NS, Bassler BL. Solutions to the public goods dilemma in bacterial biofilms. Curr Biol. 2014;24:50–5.CAS 
    PubMed 
    Article 

    Google Scholar 
    Słomka J, Alcolombri U, Secchi E, Stocker R, Fernandez VI. Encounter rates between bacteria and small sinking particles. New J Phys. 2020;22:043016.Article 

    Google Scholar 
    Alcolombri U, Peaudecerf FJ, Fernandez VI, Behrendt L, Lee KS, Stocker R. Sinking enhances the degradation of organic particles by marine bacteria. Nat Geosci. 2021;14:775–80.CAS 
    Article 

    Google Scholar 
    University of Massachusetts Amherst Massachusetts Lynn Margulis, Margulis L, Fester R. Symbiosis as a source of evolutionary innovation: speciation and morphogenesis. MIT Press; 1991. 454 p.Legin AA, Schintlmeister A, Sommerfeld NS, Eckhard M, Theiner S, Reipert S, et al. Nano-scale imaging of dual stable isotope labeled oxaliplatin in human colon cancer cells reveals the nucleolus as a putative node for therapeutic effect. Nanoscale Adv. 2021;3:249–62.CAS 
    Article 

    Google Scholar 
    Schaible GA, et al. Correlative SIP-FISH-Raman-SEM-NanoSIMS links identity, morphology, biochemistry, and physiology of environmental microbes. ISME COMMUN. 2022;2:52.Article 

    Google Scholar 
    Yu G-H, Chi Z-L, Kappler A, Sun F-S, Liu C-Q, Teng HH, et al. Fungal nanophase particles catalyze iron transformation for oxidative stress removal and iron acquisition. Curr Biol. 2020;30:2943–50.e4.CAS 
    PubMed 
    Article 

    Google Scholar 
    Subirana MA, Riemschneider S, Hause G, Dobritzsch D, Schaumlöffel D, Herzberg M. High spatial resolution imaging of subcellular macro and trace element distribution during phagocytosis. Metallomics. 2022;14:mfac011.PubMed 
    Article 

    Google Scholar 
    Bonnin EA, Fornasiero EF, Lange F, Turck CW, Rizzoli SO. NanoSIMS observations of mouse retinal cells reveal strict metabolic controls on nitrogen turnover. BMC Mol Cell Biol. 2021;22:5.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Jo MC, Liu W, Gu L, Dang W, Qin L. High-throughput analysis of yeast replicative aging using a microfluidic system. Proc Natl Acad Sci U S A. 2015;112:9364–9.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Anggraini D, Ota N, Shen Y, Tang T, Tanaka Y, Hosokawa Y, et al. Recent advances in microfluidic devices for single-cell cultivation: methods and applications. Lab Chip. 2022;22:1438–68.CAS 
    PubMed 
    Article 

    Google Scholar 
    Eriksen R, Daria V, Gluckstad J. Fully dynamic multiple-beam optical tweezers. Opt Express. 2002;10:597–602.PubMed 
    Article 

    Google Scholar 
    Dai X, Fu W, Chi H, Mesias VSD, Zhu H, Leung CW, et al. Optical tweezers-controlled hotspot for sensitive and reproducible surface-enhanced Raman spectroscopy characterization of native protein structures. Nat Commun. 2021;12:1292.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Collins DJ, Morahan B, Garcia-Bustos J, Doerig C, Plebanski M, Neild A. Two-dimensional single-cell patterning with one cell per well driven by surface acoustic waves. Nat Commun. 2015;6:8686.CAS 
    PubMed 
    Article 

    Google Scholar 
    Hu F, Shi L, Min W. Biological imaging of chemical bonds by stimulated Raman scattering microscopy. Nat Methods. 2019;16:830–42.CAS 
    PubMed 
    Article 

    Google Scholar 
    Ge X, Pereira FC, Mitteregger M, Berry D, Zhang M, Hausmann B, et al. SRS-FISH: A high-throughput platform linking microbiome metabolism to identity at the single-cell level. Proc Natl Acad Sci U S A. 2022;119:e2203519119.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Vandergrift GW, Kew W, Lukowski JK, Bhattacharjee A, Liyu AV, Shank EA, et al. Imaging and direct sampling capabilities of nanospray desorption electrospray ionization with absorption-mode 21 Tesla Fourier transform ion cyclotron resonance mass spectrometry. Anal Chem. 2022;94:3629–36.CAS 
    PubMed 
    Article 

    Google Scholar 
    Harrison JP, Berry D. Vibrational spectroscopy for imaging single microbial cells in complex biological samples. Front Microbiol. 2017;8:675.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Mayali X. NanoSIMS: microscale quantification of biogeochemical activity with large-scale impacts. Ann Rev Mar Sci. 2020;12:449–67.PubMed 
    Article 

    Google Scholar 
    Alexandrov T. Spatial metabolomics and imaging mass spectrometry in the age of artificial intelligence. Annu Rev Biomed Data Sci. 2020;3:61–87.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Boschker HTS, Middelburg JJ. Stable isotopes and biomarkers in microbial ecology. FEMS Microbiol Ecol. 2002;40:85–95.CAS 
    PubMed 
    Article 

    Google Scholar 
    Mayali X, Weber PK, Nuccio E, Lietard J, Somoza M, Blazewicz SJ, et al. Chip-SIP: Stable Isotope Probing analyzed with rRNA-targeted microarrays and nanoSIMS. Methods Mol Biol. 2019;2046:71–87.PubMed 
    Article 

    Google Scholar 
    Chokkathukalam A, Kim D-H, Barrett MP, Breitling R, Creek DJ. Stable isotope-labeling studies in metabolomics: new insights into structure and dynamics of metabolic networks. Bioanalysis. 2014;6:511–24.CAS 
    PubMed 
    Article 

    Google Scholar 
    Hiller K, Metallo CM, Kelleher JK, Stephanopoulos G. Nontargeted elucidation of metabolic pathways using stable-isotope tracers and mass spectrometry. Anal Chem. 2010;82:6621–8.CAS 
    PubMed 
    Article 

    Google Scholar 
    Rusconi R, Garren M, Stocker R. Microfluidics expanding the frontiers of microbial ecology. Annu Rev Biophys. 2014;43:65–91.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lee KS, Pereira FC, Palatinszky M, Behrendt L, Alcolombri U, Berry D, et al. Optofluidic Raman-activated cell sorting for targeted genome retrieval or cultivation of microbial cells with specific functions. Nat Protoc. 2021;16:634–76.CAS 
    PubMed 
    Article 

    Google Scholar 
    Wagner M, Haider S. New trends in fluorescence in situ hybridization for identification and functional analyses of microbes. Curr Opin Biotechnol. 2012;23:96–102.CAS 
    PubMed 
    Article 

    Google Scholar  More

  • in

    Nonreproductive effects are more important than reproductive effects in a host feeding parasitoid

    Godfray, H. C. Parasitoids: Behavioural and Evolutionary Ecology (Princeton University Press, 1994).Book 

    Google Scholar 
    Jervis, M. A., Ellers, J. & Harvey, J. A. Resource acquisition, allocation, and utilization in parasitoid reproductive strategies. Annu. Rev. Entomol. 53, 361–385 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    Jervis, M. A. & Kidd, N. A. C. Host-feeding strategies in hymenopteran parasitoids. Biol. Rev. 61, 395–434 (1986).Article 

    Google Scholar 
    Cebolla, R., Vanaclocha, P., Urbaneja, A. & Tena, A. Overstinging by hymenopteran parasitoids causes mutilation and surplus killing of hosts. J. Pest Sci. 91, 327–339 (2018).Article 

    Google Scholar 
    Abram, P. K., Brodeur, J., Urbaneja, A. & Tena, A. Nonreproductive effects of insect parasitoids on their hosts. Annu. Rev. Entomol. 64, 259–276 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Münster-Swendsen, M. Population cycles of the spruce needle miner in Denmark driven by interactions with insect parasitoids. In Population Cycles: The Case for Trophic Interactions (ed. Berryman, A. A.) 29–43 (Oxford University Press, 2002).
    Google Scholar 
    Abram, P. K., Brodeur, J., Burte, V. & Boivin, G. Parasitoid-induced host egg abortion: an underappreciated component of biological control services provided by egg parasitoids. Biol. Control 98, 52–60 (2016).Article 

    Google Scholar 
    Vinson, S. B. & Iwantsch, G. F. Host suitability for insect parasitoids. Annu. Rev. Entomol. 25, 397–419 (1980).Article 

    Google Scholar 
    Heimpel, G. E. & Collier, T. R. The evolution of host-feeding behaviour in insect parasitoids. Biol. Rev. 71, 373–400 (1996).Article 

    Google Scholar 
    Heimpel, G. E., Rosenheim, J. A. & Adams, J. M. Behavioral ecology of host feeding in Aphytis melinus parasitoid. Nor. J. Agric. Sci. 6, 101–115 (1994).
    Google Scholar 
    Heimpel, G. E. & Rosenheim, J. A. Dynamic host feeding by the parasitoid Aphytis melinus: the balance between current and future reproduction. J. Anim. Ecol. 64, 153–167 (1995).Article 

    Google Scholar 
    Choi, W. I., Yoon, T. J. & Ryoo, M. I. Host-size-dependent feeding behaviour and progeny sex ratio of Anisopteromalus calandrae (Hym., Pteromalidae). J. Appl. Entomol. 125, 71–77 (2001).Article 

    Google Scholar 
    Burger, J. M. S., Hemerik, L., Leteren, J. C. & Vet, L. E. M. Reproduction now or later: optimal host-handling strategies in the whitefly parasitoid Encasia formosa. Oikos 106, 117–130 (2004).Article 

    Google Scholar 
    Guillemaud, T. et al. The tomato borer, Tuta absoluta, invading the Mediterranean Basin, originates from a single introduction from Central Chile. Sci. Rep. 5, 8371 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Desneux, N., Luna, M. G., Guillemaud, T. & Urbaneja, A. The invasive South American tomato pinworm, Tuta absoluta, continues to spread in Afro-Eurasia and beyond: the new threat to tomato world production. J. Pest Sci. 84, 403–408 (2011).Article 

    Google Scholar 
    Desneux, N. et al. Biological invasion of European tomato crops by Tuta absoluta: ecology, geographic expansion and prospects for biological control. J. Pest Sci. 83, 197–215 (2010).Article 

    Google Scholar 
    Biondi, A., Guedes, R. N. C., Wan, F. H. & Desneux, N. Ecology, worldwide spread and management of the invasive South American tomato pinworm, Tuta absoluta: past, present and future. Annu. Rev. Entomol. 63, 239–258 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Campos, M. R., Biondi, A., Adiga, A., Guedes, R. N. C. & Desneux, N. From the Western Palaearctic region to beyond: Tuta absoluta 10 years after invading Europe. J. Pest Sci. 90, 787–796 (2017).Article 

    Google Scholar 
    Han, P. et al. Are we ready for the invasion of Tuta absoluta? Unanswered key questions for elaborating an integrated pest management package in Xinjiang, China. Entomol. Gen. 38, 125 (2018).
    Google Scholar 
    Han, P. et al. Tuta absoluta continues to disperse in Asia: damage, ongoing management and future challenges. J. Pest Sci. 92, 1317–1327 (2019).Article 

    Google Scholar 
    Mansour, R. et al. Occurrence, biology, natural enemies and management of Tuta absoluta in Africa. Entomol. Gen. 38, 83–111 (2018).Article 

    Google Scholar 
    Zhang, G. F. et al. Outbreak of the South American tomato leafminer, Tuta absoluta, in the Chinese mainland: geographic and potential host range expansion. Pest Manag. Sci. 77, 5475–5488 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Desneux, N. et al. Integrated pest management of Tuta absoluta: practical implementations across different world regions. J. Pest Sci. 95, 17–39 (2022).Article 

    Google Scholar 
    Wang, M. H. et al. Polygyny of Tuta absoluta may affect sex pheromone-based control techniques. Entomol. Gen. 41, 357–367 (2021).Article 

    Google Scholar 
    Rostami, E., Madadi, H., Abbasipour, H., Allahyari, H. & Cuthbertson, A. G. S. Pest density influences on tomato pigment contents: the South American tomato pinworm scenario. Entomol. Gen. 40, 195–205 (2020).Article 

    Google Scholar 
    Desneux, N., Decourtye, A. & Delpuech, J. M. The sublethal effects of pesticides on beneficial arthropods. Annu. Rev. Entomol. 52, 81–106 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    Gebiola, M., Bernardo, U., Ribes, A. & Gibson, G. A. P. An integrative study of Necremnus Thomson (Hymenoptera: Eulophidae) associated with invasive pests in Europe and North America: taxonomic and ecological implications. Zool. J. Linn. Soc. 173, 352–423 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Naselli, M. et al. Insights into food webs associated with the South American tomato pinworm. Pest Manag. Sci. 73, 1352–1357 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Campos, M. R. et al. Impact of a shared sugar food source on biological control of Tuta absoluta by the parasitoid Necremnus tutae. J. Pest Sci. 93, 207–218 (2020).Article 

    Google Scholar 
    Zhang, Y. B. et al. Host selection behavior of the host-feeding parasitoid Necremnus tutae on Tuta absoluta. Entomol. Gen. https://doi.org/10.1127/entomologia/2021/1246 (2021).Article 

    Google Scholar 
    Bodino, N., Ferracini, C. & Tavella, L. Is host selection influenced by natal and adult experience in the parasitoid Necremnus tutae (Hymenoptera: Eulophidae)?. Anim. Behav. 112, 221–228 (2016).Article 

    Google Scholar 
    Biondi, A., Desneux, N., Amiens-Desneux, E., Siscaro, G. & Zappalà, L. Biology and developmental strategies of the Palaearctic parasitoid, Bracon nigricans (Hymenoptera: Braconidae) on the Neotropical moth Tuta absoluta (Lepidoptera: Gelechiidae). J. Econ. Entomol. 106, 1638–1647 (2013).PubMed 
    Article 

    Google Scholar 
    Foltyn, S. & Gerling, D. The parasitoids of the aleyrodid Bemisia tabaci in Israel. Development, host preference and discrimination of the aphelinid Eretmocerus mundus. Entomol. Exp. Appl. 38, 255–260 (1985).Article 

    Google Scholar 
    Zhang, Y. B., Yang, N. W., Sun, L. Y. & Wan, F. H. Host instar suitability in two invasive whiteflies for the naturally occurring parasitoid Eretmocerus hayati in China. J. Pest Sci. 88(2), 1612–1618 (2015).
    Google Scholar 
    Lebreton, S., Darrouzet, E. & Chevrier, C. Could hosts considered as low quality for egg-laying be considered as high quality for host-feeding?. J. Insect Physiol. 55, 694–699 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    Calvo, F. J., Soriano, J. D., Bolckmans, K. & Belda, J. E. Host instar suitability and life-history parameters under different temperature regimes of Necremnus artynes on Tuta absoluta. Biocontrol Sci. Technol. 23(7), 803–815 (2013).Article 

    Google Scholar 
    Chailleux, A., Desneux, N., Arnó, J. & Gabarra, R. Biology of two key Palaearctic larval ectoparasitoids when parasitizing the invasive pest Tuta absoluta. J. Pest Sci. 87(3), 441–448 (2014).Article 

    Google Scholar 
    Asgari, S. & Rivers, D. B. Venom proteins from endoparasitoid wasps and their role in host-parasite interactions. Annu. Rev. Entomol. 56, 313–335 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    Abram, P. K., Gariepy, T. D., Boivin, G. & Brodeur, J. An invasive stink bug as an evolutionary trap for an indigenous egg parasitoid. Biol. Invasions 16, 1387–1395 (2014).Article 

    Google Scholar 
    Schlaepfer, M. A., Sherman, P. W., Blossey, B. & Runge, M. C. Introduced species as evolutionary traps. Ecol. Lett. 8, 241–246 (2005).Article 

    Google Scholar 
    van Driesche, R. G., Bellotti, A., Herrera, C. J. & Castello, J. A. Host feeding and ovipositor insertion as sources of mortality in the mealybug Phenacoccus herreni caused by two encyrtids, Epidinocarsis diversicornis and Acerophagus coccois. Entomol. Exp. Appl. 44, 97–100 (1987).Article 

    Google Scholar 
    Barrett, B. & Brunner, J. Types of parasitoid-induced mortality, host stage preferences, and sex ratios exhibited by Pnigalio flavipes (Hymenoptera: Eulophidae) using Phyllonorycter elmaella (Lepidoptera: Gracillaridae) as a host. Environ. Entomol. 19, 803–807 (1990).Article 

    Google Scholar 
    Huang, Y., Loomans, A. J. M., van Lenteren, J. C. & Xu, R. M. Hyperparasitism behavior of the autoparasitoid Encarsia tricolor on two secondary host species. BioControl 54, 411–424 (2009).Article 

    Google Scholar 
    Patel, K. J., Schuster, D. J. & Smerage, G. H. Density dependent parasitism and host-killing of Liriomyza trifolii (Diptera: Agromyzidae) by Diglyphus intermedius (Hymenoptera: Eulophidae). Fla. Entomol. 86, 8–14 (2003).Article 

    Google Scholar 
    Lauziere, I., Perez-Lachaud, G. & Bordeur, J. Influence of host density on the reproductive strategy of Cephalonomia stephanoderis, a parasitoid of the coffee berry borer. Entomol. Exp. Appl. 92, 21–28 (1999).Article 

    Google Scholar 
    Blanckenhorn, W. U. The evolution of body size: what keeps organisms small?. Quart. Rev. Biol. 75(4), 385–407 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    Idriss, G. E. A., Mohamed, S. A., Khamis, F., Plessis, H. D. & Ekesi, S. Biology and performance of two indigenous larval parasitoids on Tuta absoluta (Lepidoptera: Gelechiidae) in Sudan. Biocontrol Sci. Technol. 28(6), 614–628 (2018).Article 

    Google Scholar 
    Blanckenhorn, W. U., Preziosi, R. F. & Fairbairn, D. J. Time and energy constraints and the evolution of sexual size dimorphism-to eat or to mate?. Evol. Ecol. 9, 369–381 (1995).Article 

    Google Scholar 
    Blomqvist, D., Johansson, O. C., Unger, U., Larsson, M. & Flodin, L. A. Male aerial display and reversed sexual size dimorphism in the dunlin. Anim. Behav. 54, 1291–1299 (1997).CAS 
    PubMed 
    Article 

    Google Scholar 
    Simmons, L. W., Tomkins, J. L. & Hunt, J. Sperm competition games played by dimorphic male beetles. Proc. R. Soc. Lond. B 266, 145–150 (1999).Article 

    Google Scholar 
    Madsen, T. & Shine, R. Costs of reproduction influence the evolution of sexual size dimorphism in snakes. Evolution 48, 1389–1397 (1994).PubMed 
    Article 

    Google Scholar 
    Blanckenhorn, W. U., Morf, C., Mühlhäuser, C. & Reusch, T. Spatiotemporal variation in selection on body size in the dung fly Sepsis cynipsea. J. Evol. Biol. 9, 369–381 (1999).
    Google Scholar  More

  • in

    Life table construction for crapemyrtle bark scale (Acanthococcus lagerstroemiae): the effect of different plant nutrient conditions on insect performance

    USDA, N. Census of Horticultural Specialties (USDA, 2014).
    Google Scholar 
    USDA, N. Census of Horticultural Specialties (USDA, 2019).
    Google Scholar 
    Soliman, A. S. & Shanan, N. T. The role of natural exogenous foliar applications in alleviating salinity stress in Lagerstroemia indica L. seedlings. J. Appl. Hortic. 19, 35–45 (2017).Article 

    Google Scholar 
    Chappell, M. R., Braman, S. K., Williams-Woodward, J. & Knox, G. J. J. o. E. H. Optimizing plant health and pest management of Lagerstroemia spp. in commercial production and landscape situations in the southeastern United States: A review. 30, 161–172 (2012).Gu, M., Merchant, M., Robbins, J. & Hopkins, J. Crape Myrtle Bark Scale: A New Exotic Pest. Texas A&M AgriLife Ext. Service. EHT 49 (2014).Kondo, T., Gullan, P. J. & Williams, D. J. Coccidology. The study of scale insects (Hemiptera: Sternorrhyncha: Coccoidea). Ciencia y Tecnología Agropecuaria 9, 55–61 (2008).Article 

    Google Scholar 
    Jiang, N. & Xu, H. Observertion on Eriococcus lagerostroemiae Kuwana. J. Anhui Agric. Coll. 25, 142–144 (1998).
    Google Scholar 
    He, D., Cheng, J., Zhao, H. & Chen, S. Biological characteristic and control efficacy of Eriococcus lagerstroemiae. Chin. Bull. Entomol. 45, 812–814 (2008).
    Google Scholar 
    Harcourt, D. The development and use of life tables in the study of natural insect populations. Annu. Rev. Entomol. 14, 175–196 (1969).Article 

    Google Scholar 
    Leslie, P. H. On the use of matrices in certain population mathematics. Biometrika 33, 183–212 (1945).MathSciNet 
    CAS 
    PubMed 
    MATH 
    Article 

    Google Scholar 
    Birch, L. The intrinsic rate of natural increase of an insect population. J. Anim. Ecol., 15–26 (1948).Chi, H. Life-table analysis incorporating both sexes and variable development rates among individuals. Environ. Entomol. 17, 26–34 (1988).Article 

    Google Scholar 
    Chi, H. & Liu, H. Two new methods for the study of insect population ecology. Bull. Inst. Zool. Acad. Sin 24, 225–240 (1985).
    Google Scholar 
    Fathipour, Y. & Maleknia, B. in Ecofriendly Pest Management for Food Security (ed Omkar) 329–366 (Academic Press, 2016).Auad, A. et al. The impact of temperature on biological aspects and life table of Rhopalosiphum padi (Hemiptera: Aphididae) fed with signal grass. Fla. Entomol. 569–577 (2009).Qu, Y. et al. Sublethal and hormesis effects of beta-cypermethrin on the biology, life table parameters and reproductive potential of soybean aphid Aphis glycines. Ecotoxicology 26, 1002–1009 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Araujo, E. S., Benatto, A., Mogor, A. F., Penteado, S. C. & Zawadneak, M. A. Biological parameters and fertility life table of Aphis forbesi Weed, 1889 (Hemiptera: Aphididae) on strawberry. Braz. J. Biol. 76, 937–941. https://doi.org/10.1590/1519-6984.04715 (2016).CAS 
    Article 
    PubMed 

    Google Scholar 
    Krishnamoorthy, S. V. & Mahadevan, N. R. Life table studies of sugarcane scale, Melanaspis glomerata G. J. Entomol. Res. 27, 203–212 (2003).
    Google Scholar 
    Uematsu, H. Studies on life table for an armored scale insect, Aonidiella taxus Leonardi (Homoptera: Diaspididae). J. Fac. Agric. Kyushu Univ. (1979).Hill, M. G., Mauchline, N. A., Hall, A. J. & Stannard, K. A. Life table parameters of two armoured scale insect (Hemiptera: Diaspididae) species on resistant and susceptible kiwifruit (Actinidia spp.) germplasm. N. Z. J. Crop Hortic. Sci. 37, 335–343 (2009).Article 

    Google Scholar 
    Yong, C. X. W. Z. C. & Shaoyun, Z. J. Y. S. W. Age-specific life table of chinese white wax scale (Ericerus pela) natural population and analysis of death key factors. Scientia Silvae Sinica 9 (2008).Rosado, J. F. et al. Natural biological control of green scale (Hemiptera: Coccidae): a field life-table study. Biocontrol. Sci. Technol. 24, 190–202 (2014).Article 

    Google Scholar 
    Fand, B. B., Gautam, R. D., Chander, S. & Suroshe, S. S. Life table analysis of the mealybug, Phenacoccus solenopsis Tinsley (Hemiptera: Pseudococcidae) under laboratory conditions. J. Entomol. Res. 34, 175–179 (2010).
    Google Scholar 
    Vargas-Madríz, H. et al. Life and fertility table of Bactericera cockerelli (Hemiptera: Triozidae), under different fertilization treatments in the 7705 tomato hybrid. Rev. Chil. entomol. 39 (2014).Huang, Y. B. & Chi, H. Age-stage, two-sex life tables of Bactrocera cucurbitae (Coquillett)(Diptera: Tephritidae) with a discussion on the problem of applying female age-specific life tables to insect populations. Insect Sci. 19, 263–273 (2012).Article 

    Google Scholar 
    Saska, P. et al. Leaf structural traits rather than drought resistance determine aphid performance on spring wheat. J. Pest. Sci. 94, 423–434 (2021).Article 

    Google Scholar 
    Ma, K., Tang, Q., Xia, J., Lv, N. & Gao, X. Fitness costs of sulfoxaflor resistance in the cotton aphid, Aphis gossypii Glover. Pestic. Biochem. Physiol. 158, 40–46 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Ullah, F. et al. Fitness costs in clothianidin-resistant population of the melon aphid, Aphis gossypii. PLoS ONE 15, e0238707 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Güncan, A. & Gümüş, E. Influence of different hazelnut cultivars on some demographic characteristics of the filbert aphid (Hemiptera: Aphididae). J. Econ. Entomol. 110, 1856–1862 (2017).PubMed 
    Article 

    Google Scholar 
    Bailey, R., Chang, N.-T., Lai, P.-Y. & Hsu, T.-C. Life table of cycad scale, Aulacaspis yasumatsui (Hemiptera: Diaspididae), reared on Cycas in Taiwan. J. Asia Pac. Entomol. 13, 183–187 (2010).Article 

    Google Scholar 
    Wang, Z., Chen, Y. & Diaz, R. Temperature-dependent development and host range of crapemyrtle bark scale, Acanthococcus lagerstroemiae (Kuwana)(Hemiptera: Eriococcidae). Fla. Entomol. 102, 181–186 (2019).Article 

    Google Scholar 
    Zhang, Z.-J. et al. A determining factor for insect feeding preference in the silkworm, Bombyx mori. PLoS Biol. 17, e3000162 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Wang, Z., Chen, Y., Diaz, R. & Laine, R. A. Physiology of crapemyrtle bark scale, Acanthococcus lagerstroemiae (Kuwana), associated with seasonally altered cold tolerance. J. Insect Physiol. 112, 1–8 (2019).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Suh, S.-J. Notes on some parasitoids (Hymenoptera: Chalcidoidea) associated with Acanthococcus lagerstroemiae (Kuwana)(Hemiptera: Eriococcidae) in the Republic of Korea. Insecta mundi 0690, 1–5 (2019).
    Google Scholar 
    Meindl, G. A., Bain, D. J. & Ashman, T.-L. Edaphic factors and plant–insect interactions: Direct and indirect effects of serpentine soil on florivores and pollinators. Oecologia 173, 1355–1366 (2013).ADS 
    PubMed 
    Article 

    Google Scholar 
    Wielgolaski, F. E. Phenological modifications in plants by various edaphic factors. Int. J. Biometeorol. 45, 196–202 (2001).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Uchida, R. in Plant nutrient management in Hawaii’s soils (ed Raymond S. Uchida James A. Silva) 31–55 (University of Hawaii at Manoa, College of Agriculture & Tropical Resources, 2000).Flanders, S. E. Observations on host plant induced behavior of scale insects and their endoparasites. Can. Entomol. 102, 913–926 (1970).Article 

    Google Scholar 
    Yang, T.-C. & Chi, H. Life tables and development of Bemisia argentifolii (Homoptera: Aleyrodidae) at different temperatures. J. Econ. Entomol. 99, 691–698 (2006).PubMed 
    Article 

    Google Scholar 
    Tuan, S. J., Lee, C. C. & Chi, H. Population and damage projection of Spodoptera litura (F.) on peanuts (Arachis hypogaea L.) under different conditions using the age-stage, two-sex life table. Pest Manag. Sci. 70, 805–813 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Vafaie, E. et al. Seasonal population patterns of a new scale pest, Acanthococcus lagerstroemiae Kuwana (Hemiptera: Sternorrhynca: Eriococcidae), of Crapemyrtles in Texas, Louisiana, and Arkansas. J. Environ. Hortic. 38, 8–14 (2020).Article 

    Google Scholar 
    Vafaie, E. K. Bark and systemic insecticidal control of Acanthococcus (= Eriococcus) lagerstroemiae (Hemiptera: Eriococcidae) on Potted Crapemyrtles, 2017. Arthropod manag. tests 44, tsy109 (2019).Vafaie, E. K. & Knight, C. M. J. A. M. T. Bark and systemic insecticidal control of Acanthococcus (= Eriococcus) lagerstroemiae (Crapemyrtle Bark Scale) on Landscape Crapemyrtles, 2016. 42, tsx130 (2017).Vafaie, E. & Gu, M. Insecticidal control of crapemyrtle bark scale on potted crapemyrtles, Fall 2018. Arthropod. Manag. Tests 44, tsz061 (2019).Article 

    Google Scholar 
    Aktar, M. W., Sengupta, D. & Chowdhury, A. J. I. t. Impact of pesticides use in agriculture: their benefits and hazards. 2, 1 (2009).Grafton-Cardwell, E. & Vehrs, S. Monitoring for organophosphate-and carbamate-resistant armored scale (Homoptera: Diaspididae) in San Joaquin valley citrus. J. Econ. Entomol. 88, 495–504 (1995).CAS 
    Article 

    Google Scholar 
    Almarinez, B. J. M. et al. Biological control: A major component of the pest management program for the invasive coconut scale insect, Aspidiotus rigidus Reyne, in the Philippines. Insects 11, 745 (2020).PubMed Central 
    Article 

    Google Scholar 
    Grout, T. & Richards, G. Value of pheromone traps for predicting infestations of red scale, Aonidiella aurantii (Maskell)(Hom., Diaspididae), limited by natural enemy activity and insecticides used to control citrus thrips, Scirtothrips aurantii Faure (Thys., Thripidae). J. Appl. Entomol. 111, 20–27 (1991).Article 

    Google Scholar 
    Grafton-Cardwell, E., Millar, J., O’Connell, N. & Hanks, L. Sex pheromone of yellow scale, Aonidiella citrina (Homoptera: Diaspididae): Evaluation as an IPM tactic. J. Agric. Urban. Entomol. 17, 75–88 (2000).CAS 

    Google Scholar 
    Jactel, H., Menassieu, P., Lettere, M., Mori, K. & Einhorn, J. Field response of maritime pine scale, Matsucoccus feytaudi Duc. (Homoptera: Margarodidae), to synthetic sex pheromone stereoisomers. J. Chem. Ecol. 20, 2159–2170 (1994).CAS 
    PubMed 
    Article 

    Google Scholar 
    Mendel, Z. et al. Outdoor attractancy of males of Matsucoccus josephi (Homoptera: Matsucoccidae) and Elatophilus hebraicus (Hemiptera: Anthocoridae) to synthetic female sex pheromone of Matsucoccus josephi. J. Chem. Ecol. 21, 331–341 (1995).CAS 
    PubMed 
    Article 

    Google Scholar 
    Zada, A. et al. Sex pheromone of the citrus mealybug Planococcus citri: Synthesis and optimization of trap parameters. J. Econ. Entomol. 97, 361–368 (2004).CAS 
    PubMed 
    Article 

    Google Scholar 
    Zhang, Z. & Shi, Y. Studies on the Morphology and Biology of Eriococcus Lagerstroemiae Kuwana. J. Shandong Agri. Univ. 2 (1986).Savopoulou-Soultani, M., Papadopoulos, N. T., Milonas, P. & Moyal, P. Abiotic factors and insect abundance. PSYCHE 2012 (2012).Vandegehuchte, M. L., de la Pena, E. & Bonte, D. Relative importance of biotic and abiotic soil components to plant growth and insect herbivore population dynamics. PLoS ONE 5, e12937 (2010).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Clavijo McCormick, A. Can plant–natural enemy communication withstand disruption by biotic and abiotic factors?. Ecol. Evol. 6, 8569–8582 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Nebapure, S. M. & Sagar, D. Insect-plant interaction: A road map from knowledge to novel technology. Karnataka J. Agric. Sci. 28, 1–7 (2015).
    Google Scholar 
    Murashige, T. & Skoog, F. A revised medium for rapid growth and bio assays with tobacco tissue cultures. Physiol. Plant. 15, 473–497 (1962).CAS 
    Article 

    Google Scholar 
    Hogendorp, B. K., Cloyd, R. A. & Swiader, J. M. Effect of nitrogen fertility on reproduction and development of citrus mealybug, Planococcus citri Risso (Homoptera: Pseudococcidae), feeding on two colors of coleus Solenostemon scutellarioides L. Codd. Environ. Entomol. 35, 201–211 (2006).Article 

    Google Scholar 
    Lema, K. & Mahungu, N. in Tropical root crops: Production and uses in Africa: proceedings of the Second Triennial Symposium of the International Society for Tropical Root Crops-Africa Branch held in Douala, Cameroon, 14-19 Aug. 1983. (IDRC, Ottawa, ON, CA).McClure, M. S. Dispersal of the scale Fiorinia externa (Homoptera: Diaspididae) and effects of edaphic factors on its establishment on hemlock. Environ. Entomol. 6, 539–544 (1977).Article 

    Google Scholar 
    Salama, H., Amin, A. & Hawash, M. Effect of nutrients supplied to citrus seedlings on their susceptibility to infestation with the scale insects Aonidiella aurantii (Maskell) and Lepidosaphes beckii (Newman)(Coccoidea). Zeitschrift für Angewandte Entomologie 71, 395–405 (1972).Article 

    Google Scholar 
    Rasmann, S. & Pellissier, L. in Climate Change and Insect Pests Vol. 8 (ed P. Niemelä C. Björkman) 38–53 (Wallingford, UK: CAB Int., 2015).Wang, Z. & Li, S. Effects of nitrogen and phosphorus fertilization on plant growth and nitrate accumulation in vegetables. J. Plant Nutr. 27, 539–556 (2004).CAS 
    Article 

    Google Scholar 
    Da Costa, P. B. et al. The effects of different fertilization conditions on bacterial plant growth promoting traits: Guidelines for directed bacterial prospection and testing. Plant Soil. 368, 267–280 (2013).Article 

    Google Scholar 
    Dong, H., Kong, X., Li, W., Tang, W. & Zhang, D. Effects of plant density and nitrogen and potassium fertilization on cotton yield and uptake of major nutrients in two fields with varying fertility. Field Crops Res. 119, 106–113 (2010).Article 

    Google Scholar 
    Aulakh, M., Dev, G. & Arora, B. Effect of sulphur fertilization on the nitrogen–sulphur relationships in alfalfa (Medicago sativa L. Pers.). Plant Soil. 45, 75–80 (1976).CAS 
    Article 

    Google Scholar 
    Powell, G., Tosh, C. R. & Hardie, J. Host plant selection by aphids: Behavioral, evolutionary, and applied perspectives. Annu. Rev. Entomol. 51, 309–330 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    Sauge, M. H., Grechi, I. & Poëssel, J. L. Nitrogen fertilization effects on Myzus persicae aphid dynamics on peach: Vegetative growth allocation or chemical defence?. Entomol. Exp. Appl. 136, 123–133 (2010).CAS 
    Article 

    Google Scholar 
    Chen, Y., Serteyn, L., Wang, Z., He, K. & Francis, F. Reduction of plant suitability for corn leaf aphid (Hemiptera: Aphididae) under elevated carbon dioxide condition. Environ. Entomol. (2019).Miller, D. R. & Kosztarab, M. Recent advances in the study of scale insects. Annu. Rev. Entomol. 24, 1–27 (1979).CAS 
    Article 

    Google Scholar 
    Hardy, N. B., Peterson, D. A. & Normark, B. B. Scale insect host ranges are broader in the tropics. Biol. Lett. 11, 20150924 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Chen, Q. et al. Age-stage, two-sex life table of Parapoynx crisonalis (Lepidoptera: Pyralidae) at different temperatures. PLoS ONE 12, e0173380 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Li, X. et al. Density-dependent demography and mass-rearing of Carposina sasakii (Lepidoptera: Carposinidae) incorporating life table variability. J. Econ. Entomol. 112, 255–265 (2019).PubMed 
    Article 

    Google Scholar 
    Ning, S., Zhang, W., Sun, Y. & Feng, J. Development of insect life tables: comparison of two demographic methods of Delia antiqua (Diptera: Anthomyiidae) on different hosts. Sci. Rep. 7, 1–10 (2017).ADS 
    Article 

    Google Scholar 
    TWOSEX-MSChart: A computer program for the age-stage, two-sex life table analysis (2020).Goodman, D. Optimal life histories, optimal notation, and the value of reproductive value. Am. Nat. 119, 803–823 (1982).MathSciNet 
    Article 

    Google Scholar 
    Efron, B. & Tibshirani, R. J. An Introduction to the Bootstrap (CRC Press, 1994).MATH 
    Book 

    Google Scholar  More

  • in

    Complex extracellular biology drives surface competition during colony expansion in Bacillus subtilis

    Riley M, Gordon D. The ecological role of bacteriocins in bacterial competition. Trends Microbiol. 1999;7:129–33.CAS 
    PubMed 
    Article 

    Google Scholar 
    Griffin A, West S, Buckling A. Cooperation and competition in pathogenic bacteria. Nature. 2004;430:1024–7.CAS 
    PubMed 
    Article 

    Google Scholar 
    Velicer G, Vos M. Sociobiology of the myxobacteria. Annu Rev Microbiol. 2009;63:599–623.CAS 
    PubMed 
    Article 

    Google Scholar 
    Brockhurst M, Habets M, Libberton B, Buckling A, Gardner A. Ecological drivers of the evolution of public-goods cooperation in bacteria. Ecology. 2010;91:334–40.PubMed 
    Article 

    Google Scholar 
    Drescher K, Nadell CD, Stone HA, Wingreen NS, Bassler BL. Solutions to the public goods dilemma in bacterial biofilms. Curr Biol. 2014;24:50–55.CAS 
    PubMed 
    Article 

    Google Scholar 
    van Gestel J, Weissing FJ, Kuipers OP, Kovács ÁT. Density of founder cells affects spatial pattern formation and cooperation in Bacillus subtilis biofilms. ISME J. 2014;8:2069–79.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Henrichsen J. Bacterial surface translocation: a survey and a classification. Bacteriol Rev. 1972;36:478–503.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    van Gestel J, Vlamakis H, Kolter R. From cell differentiation to cell collectives: Bacillus subtilis uses division of labor to migrate. PLoS Biol. 2015;13:e1002141.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Hölscher T, Kovács ÁT. Sliding on the surface: bacterial spreading without an active motor. Environ Microbiol. 2017;19:2537–45.PubMed 
    Article 

    Google Scholar 
    Kearns D. A field guide to bacterial swarming motility. Nat Rev Microbiol. 2010;8:634–44.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Nogales J, Bernabéu-Roda L, Cuéllar V, Soto M. ExpR is not required for swarming but promotes sliding in Sinorhizobium meliloti. J Bacteriol. 2012;194:2027–35.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Murray T, Kazmierczak B. Pseudomonas aeruginosa exhibits sliding motility in the absence of type IV pili and flagella. J Bacteriol. 2008;190:2700–8.CAS 
    PubMed 
    Article 

    Google Scholar 
    Kinsinger R, Shirk M, Fall R. Rapid surface motility in Bacillus subtilis is dependent on extracellular surfactin and potassium ion. J Bacteriol. 2003;185:5627–31.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Grau RR, De Oña P, Kunert M, Leñini C, Gallegos-Monterrosa R, Mhatre E, et al. A duo of potassium-responsive histidine kinases govern the multicellular destiny of Bacillus subtilis. MBio. 2015;6:e00581–15.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kobayashi K, Iwano M. BslA(YuaB) forms a hydrophobic layer on the surface of Bacillus subtilis biofilms. Mol Microbiol. 2012;85:51–66.CAS 
    PubMed 
    Article 

    Google Scholar 
    Hobley L, Ostrowski A, Rao FV, Bromley KM, Porter M, Prescott AR, et al. BslA is a self-assembling bacterial hydrophobin that coats the Bacillus subtilis biofilm. Proc Natl Acad Sci USA. 2013;110:13600–5.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Seminara A, Angelini T, Wilking J, Vlamakis H, Ebrahim S, Kolter R, et al. Osmotic spreading of Bacillus subtilis biofilms driven by an extracellular matrix. Proc Natl Acad Sci USA. 2012;109:1116–21.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kafri M, Metzl-Raz E, Jona G, Barkai N. The cost of protein production. Cell Rep. 2016;14:22–31.CAS 
    PubMed 
    Article 

    Google Scholar 
    Sexton D, Schuster M. Nutrient limitation determines the fitness of cheaters in bacterial siderophore cooperation. Nat Commun. 2017;8:230.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Xavier J, Kim W, Foster K. A molecular mechanism that stabilizes cooperative secretions in Pseudomonas aeruginosa. Mol Microbiol. 2011;79:166–79.CAS 
    PubMed 
    Article 

    Google Scholar 
    Tai JSB, Mukherjee S, Nero T, Olson R, Tithof J, Nadell CD, et al. Social evolution of shared biofilm matrix components. Proc Natl Acad Sci USA. 2022;119:e2123469119.PubMed 
    Article 

    Google Scholar 
    Branda SS, Chu F, Kearns DB, Losick R, Kolter R. A major protein component of the Bacillus subtilis biofilm matrix. Mol Microbiol. 2006;59:1229–38.CAS 
    PubMed 
    Article 

    Google Scholar 
    Martin M, Dragoš A, Hölscher T, Maróti G, Bálint B, Westermann M, et al. De novo evolved interference competition promotes the spread of biofilm defectors. Nat Commun. 2017;8:15127.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Dragoš A, Kiesewalter H, Martin M, Hsu C-Y, Hartmann R, Wechsler T, et al. Division of labor during biofilm matrix production. Curr Biol. 2018;28:1903–13.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Martin M, Dragoš A, Schäfer D, Maróti G, Kovács ÁT. Cheaters shape the evolution of phenotypic heterogeneity in Bacillus subtilis biofilms. ISME J. 2020;14:2302–12.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Otto SB, Martin M, Schäfer D, Hartmann R, Drescher K, Brix S, et al. Privatization of biofilm matrix in structurally heterogeneous biofilms. mSystems. 2020;5:e00425–20.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Arnaouteli S, Bamford NC, Stanley-Wall NR, Kovács ÁT. Bacillus subtilis biofilm formation and social interactions. Nat Rev Microbiol. 2021;19:600–14.CAS 
    PubMed 
    Article 

    Google Scholar 
    Kovács ÁT, Dragoš A. Evolved Biofilm: review on the experimental evolution studies of Bacillus subtilis pellicles. J Mol Biol. 2019;431:4749–59.Dragos A, Lakshmanan N, Martin M, Horvath B, Maroti G, Falcon Garcia C, et al. Evolution of exploitative interactions during diversification in Bacillus subtilis biofilms. FEMS Microbiol Ecol. 2018;94:fix155.Article 
    CAS 

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

    Google Scholar 
    van Gestel J, Bareia T, Tenennbaum B, Dal Co A, Guler P, Aframian N, et al. Short-range quorum sensing controls horizontal gene transfer at micron scale in bacterial communities. Nat Commun. 2021;12:2324.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Gore J, Youk H, Van Oudenaarden A. Snowdrift game dynamics and facultative cheating in yeast. Nature. 2009;459:253–6.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Konkol MA, Blair KM, Kearns DB. Plasmid-encoded comI inhibits competence in the ancestral 3610 strain of Bacillus subtilis. J Bacteriol. 2013;195:4085–93.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hölscher T, Dragoš A, Gallegos-Monterrosa R, Martin M, Mhatre E, Richter A, et al. Monitoring spatial segregation in surface colonizing microbial populations. J Vis Exp. 2016;2016:e54752.
    Google Scholar 
    Morris R, Schor M, Gillespie R, Ferreira A, Baldauf L, Earl C, et al. Natural variations in the biofilm-associated protein BslA from the genus Bacillus. Sci Rep. 2017;7:6730.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Dogsa I, Brloznik M, Stopar D, Mandic-Mulec I. Exopolymer diversity and the role of levan in Bacillus subtilis biofilms. PLoS One. 2013;8:e62044.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Branda SS, González-Pastor JE, Ben-Yehuda S, Losick R, Kolter R. Fruiting body formation by Bacillus subtilis. Proc Natl Acad Sci USA. 2001;98:11621–6.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lenski RE, Rose M, Simpson S, Tadler S. Long-term experimental evolution in Escherichia coli. I Adaptation and divergence during 2,000 generations. Am Nat. 1991;138:1315–41.Article 

    Google Scholar 
    Hallatschek O, Hersen P, Ramanathan S, Nelson DR. Genetic drift at expanding frontiers promotes gene segregation. Proc Natl Acad Sci USA. 2007;104:19926–30.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Slatkin M, Excoffier L. Serial founder effects during range expansion: a spatial analog of genetic drift. Genetics. 2012;191:171–81.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    MacLean R, Fuentes-Hernandez A, Greig D, Hurst L, Gudelj I. A mixture of ‘cheats’ and ‘co-operators’ can enable maximal group benefit. PLoS Biol. 2010;8:e1000486.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Kearns DB. Division of labour during Bacillus subtilis biofilm formation. Mol Microbiol. 2008;67:229–31.CAS 
    PubMed 
    Article 

    Google Scholar 
    Kiesewalter HT, Lozano-Andrade CN, Wibowo M, Strube ML, Maróti G, Snyder D, et al. Genomic and chemical diversity of Bacillus subtilis secondary metabolites against plant pathogenic fungi. mSystems. 2021;6:e00770–20.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Stefanic P, Mandic-Mulec I. Social interactions and distribution of Bacillus subtilis pherotypes at microscale. J Bacteriol. 2009;191:1756–64.CAS 
    PubMed 
    Article 

    Google Scholar 
    Even-Tov E, Omer Bendori S, Valastyan J, Ke X, Pollak S, Bareia T, et al. Social evolution selects for redundancy in bacterial quorum sensing. PLoS Biol. 2016;14:e1002386.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Kalamara M, Spacapan M, Mandic-Mulec I, Stanley-Wall N. Social behaviours by Bacillus subtilis: quorum sensing, kin discrimination and beyond. Mol Microbiol. 2018;110:863–78.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Aframian N, Eldar A. A bacterial tower of Babel: Quorum-Sensing signaling diversity and its evolution. Annu Rev Microbiol. 2020;74:587–606.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kiesewalter HT, Lozano-Andrade CN, Strube ML, Kovács ÁT. Secondary metabolites of Bacillus subtilis impact the assembly of soil-derived semisynthetic bacterial communities. Beilstein J Org Chem. 2020;16:2983–98.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Dragoš A, Kovács ÁT. The peculiar functions of the bacterial extracellular matrix. Trends Microbiol. 2017;25:257–66.PubMed 
    Article 
    CAS 

    Google Scholar 
    Kovács ÁT. Impact of spatial distribution on the development of mutualism in microbes. Front Microbiol. 2014;5:649.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Zhang F, Kwan A, Xu A, Süel G. A synthetic quorum sensing system reveals a potential private benefit for public good production in a biofilm. PLoS One. 2015;10:e0132948.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Bruce J, West S, Griffin A. Functional amyloids promote retention of public goods in bacteria. Proc Biol Sci. 2019;286:20190709.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ma L, Conover M, Lu H, Parsek M, Bayles K, Wozniak D. Assembly and development of the Pseudomonas aeruginosa biofilm matrix. PLoS Pathog. 2009;5:e1000354.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Hartmann R, Jeckel H, Jelli E, Singh PK, Vaidya S, Bayer M, et al. Quantitative image analysis of microbial communities with BiofilmQ. Nat Microbiol. 2021;6:151–6.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Dar D, Dar N, Cai L, Newman DK. Spatial transcriptomics of planktonic and sessile bacterial populations at single-cell resolution. Science. 2021;373:eabi4882.CAS 
    PubMed 
    Article 

    Google Scholar 
    Lozano-Andrade CN, Nogueira CG, Wibowo M, Kovács ÁT. Establishment of a transparent soil system to study Bacillus subtilis chemical ecology. bioRxiv. 2022. https://doi.org/10.1101/2022.01.10.475645.Article 

    Google Scholar  More

  • in

    Comprehensive climatic suitability evaluation of peanut in Huang-Huai-Hai region under the background of climate change

    Overview of the study areaBased on the actual cultivation of peanuts, the Huang-Huai-Hai region is selected as the study area (Fig. 1). The main body of the study area is the Huang-Huai-Hai Plain (North China Plain), which is a typical alluvial plain resulting from extensive sediment deposition carried by the Yellow River, the Huaihe River and the Haihe River and their tributaries, and the hills in central and southern Shandong Peninsula adjacent to it. Administrative zones include 5 provinces, 2 cities, 53 cities and 376 counties (districts). In China, The Huang-Huai-Hai region is an important production and processing centre for agricultural products, with a total land area of 4.10 × 105 square kilometers and cultivated fields of 2.15 × 107 hm2, accounting for 4.3% and 16.3% of the total amount of the country, respectively. It belongs to temperate continental monsoon climate with distinct seasons, accumulated temperature of 3600–4800 degrees above 10 °C, frost-free period of 170–200 days and annual precipitation of 500–950 mm27. The Huang-huai-hai region is the largest peanut growing area, accounting for more than 50% of the country’s peanut production and area28.Figure 1Location of the study areas. The figure was made in the ArcGIS 10.2 platform (https://www.esri.com/en-us/home).Full size imageData sourcesThe data used in the study mainly include meteorological data, geographic information data and crop data. The meteorological data comes from China Meteorological Information Center (http://data.cma.cn), including the daily maximum temperature (℃), daily minimum temperature (℃), daily average temperature (℃), daily precipitation (mm) and daily average wind speed (M/s) observed by 186 ground observation meteorological stations in the Huang-Huai-Hai region from 1960 to 2019 (Fig. 1). Geographic information data include elevation DEM data (resolution of 1 km × 1 km) and land use data in the study area, which are from the resource and environmental science and data center of Chinese Academy of Sciences (http://www.resdc.cn). Crop data, including peanut sowing area and yield data, are derived from the statistical yearbooks of provinces and cities in the study area and China Agricultural Technology Network (http://www.cast.net.cn).Data processingMeteorological data processingAnusplin software is a tool to interpolate multivariate data based on ordinary thin disks and local thin disk spline functions, enabling the introduction of covariates for simultaneous spatial interpolation of multiple surfaces, suitable for meteorological data time series29. First, the Anusplin software is used to spatially interpolate the meteorological data and suitability data of the peanut growing season (April to September) from 1960 to 2019 based on the elevation data with a resolution of 1 km × 1 km. The Inverse Distance Weight (IDW) interpolation can make the meteorological data after Anusplin interpolation maintain consistency with the original data, and is able to improve the interpolation accuracy. Finally, the meteorological and suitability data set with a resolution of 1 km × 1 km is obtained. ArcGIS and MATLAB software were used to count the median of regional meteorological factors in agricultural fields of different cities (counties), and the meteorological factors and suitability of different periods of peanut growth season in each city (county) were obtained.Yield data processingMany factors affect crop yield formation, which can be generally divided into three main categories: meteorological conditions, agronomic and technological measures, and stochastic factors. Agricultural technical measures reflect the development level of social production in a certain historical period and become time technology trend output, which is referred to as trend output for short, and meteorological production reflects short period yield components that are affected by meteorological elements. Stochastic factors account for a small proportion and are often ignored in actual calculations30. The specific calculation is as follows:$$Y={Y}_{t}+{Y}_{w}$$
    (1)

    where Y is the actual yield (single production) of the crop, Yt is the trend yield, and Yw is the meteorological yield.In this paper, a straight-line sliding average method is used to simulate the trend yield. The straight-line sliding average method is a very commonly used method to model yield, and it considers the change in the time series of yield within a certain stage as a linear function, showing a straight line, as the stage continuously slides, the straight line continuously changes the position, and the backward slip reflects the continuous change in the evolution trend of the yield history31. The regression models in each stage are obtained in turn, and the mean value of each linear sliding regression simulation value at each time point is taken as its trend yield value. The linear trend equation at some stage is:$${Y}_{i}left(tright)={a}_{i}+{b}_{i}t$$
    (2)
    where i = n-k + 1, is the number of equations; k is the sliding step; n is the number of sample sequences; t is the time serial number. Yi(t) is the function value of each equation at point t. there are q function values at point t. the number of q is related to n and k. Calculate the average value of each function value at each point:$$overline{{Y }_{i}(t)}=frac{1}{q}sum_{j=1}^{q}{Y}_{i}left(tright)$$
    (3)
    Connecting the (overline{{Y }_{i}(t)}) value of each point can represent the historical evolution trend of production. Its characteristics depend on the value of k. Only when k is large enough, the trend yield can eliminate the influence of short cycle fluctuation. After comparison and considering the length of yield series, k is taken as 5 in this paper.After the trend yield is obtained, the meteorological yield is calculated using Eq. (1), then the relative meteorological production is$${Y}_{r}=frac{{Y}_{w}}{{Y}_{t}}$$
    (4)
    The relative meteorological yield shows that the relative variability of yield fluctuation deviating from the trend, that is, the amplitude of yield fluctuation, is not affected by time and space, and is comparable. However, when the value is negative, it indicates that the meteorological conditions are unfavorable to the overall crop production, and the crop yield reduction, that is, the yield reduction rate32.Characteristics of spatial and temporal distribution of climatic resources in the Huang-Huai-Hai regionCollect meteorological resource data from 1960 to 2019. Taking 1960–1989 as the first three decades of the study and 1990–2019 as the last three decades, the climatic resource changes of peanut growth in the Huang-Huai-Hai region are analyzed by interpolation of heat resources (average temperature), water resources (precipitation) and light resources (sunshine hours) in the study area in two periods combined with topographic factors.Establishment of suitability modelAccording to the definition of phenological time and growth period of peanut planting practice in the Huang-Huai-Hai region, the growth season of peanut is divided into three growth periods and five growth stages (Table 1). Temperature, precipitation and sunshine hours are the necessary meteorological factors to determine the normal development of peanut. Therefore, combined with climatic resources in the study area, temperature, precipitation and sunshine suitability model was introduced to quantitatively analyze the suitability of peanut planting.Table 1 Division of peanut growth periods.Full size tableTemperature suitability modelTemperature is a very important factor in the growth period of peanut, and the change of temperature in different growth periods will have a great influence on the yield and quality of peanut. As a warm-loving crop, accumulated temperature plays a decisive role in the budding condition and nutrient growth stage of peanut. Temperature determines the quality of fruit and the final yield of peanut. Beta function33 is used to calculate temperature suitability, which is universal for crop-temperature relationship. The specific calculation is as follows:$${F}_{i}left(tright)=frac{(t-{t}_{1}){({t}_{h}-t)}^{B}}{({t}_{0}-{t}_{1}){({t}_{h}-{t}_{0})}^{B}}$$
    (5)
    where the value of B is shown in$$B=frac{{t}_{h}-{t}_{0}}{{t}_{0}-{t}_{1}}$$
    (6)
    where Fi(t) is the temperature suitability of a certain growth period; t is the daily average temperature of peanut at a certain development stage; t1, th and t0 are the lower limit temperature, upper limit temperature and appropriate temperature required for each growth period of peanut. Refer to the corresponding index system and combined with the peanut production practice in Huang-Huai-Hai region34,35,36, determine the three base point temperature of peanut in each growth period, as shown in the Table 2.Table 2 Three fundamental points temperature and crop coefficient of peanut at each growth stage in the study area.Full size tablePrecipitation suitability modelPeanut has a long growth period, which is nearly half a year. Insufficient or excessive water during the growth period has a great impact on the growth and development, pod yield and quality of peanut. Combined with the actual situation of Huang-Huai-Hai region and peanut precipitation / water demand index, the water suitability function is determined and calculated as follows:$${text{F}}_{{text{i}}} left( {text{r}} right) = left{ {begin{array}{*{20}l} {frac{{text{r}}}{{0.9{text{ET}}_{{text{c}}} }}} hfill & {r < 0.9E{text{T}}_{{text{c}}} } hfill \ 1 hfill & {0.9E{text{T}}_{{text{c}}} le r le 1.2E{text{T}}_{{text{c}}} } hfill \ {frac{{1.2{text{ET}}_{{text{c}}} }}{{text{r}}}} hfill & {r > 1.2E{text{T}}_{{text{c}}} } hfill \ end{array} } right.$$
    (7)
    where Fi(r) is the water suitability of a certain growth period; r is the accumulated precipitation of peanut in a certain development period; ETc is the water demand of peanut in each growth period.$${mathrm{ET}}_{mathrm{c}}={mathrm{K}}_{mathrm{c}}cdot {mathrm{ET}}_{0}$$
    (8)
    where Kc is the peanut crop coefficient (Table 2) and ET0 is the crop reference evapotranspiration, which is calculated by the Penman Monteith method recommended by the international food and Agriculture Organization (FAO).Sunshine suitability modelSunshine hours are an important condition for photosynthesis. The “light compensation point” and “light saturation point” of peanut are relatively high, and more sunshine hours are required for photosynthesis. Under certain conditions of water, temperature and carbon dioxide, photosynthesis increases or decreases with the increase or decrease of light. Relevant studies show that when the sunshine hours reach more than 55% of the available sunshine hours, the crops reach the appropriate state to reflect the light37. The following formula is used to calculate the sunshine suitability of peanut in each growth period.$${mathrm{F}}_{mathrm{i}}left(mathrm{s}right)=left{begin{array}{l}frac{mathrm{S}}{{mathrm{S}}_{0}} quad S{mathrm{S}}_{0}end{array}right.$$
    (9)
    where Fi(s) is the sunshine suitability of peanut in a certain development period, S is the actual sunshine hours in a certain growth period, S0 is 55% of the sunshine hours (L0), and the calculation method of L0 refers to the following formula.$${mathrm{L}}_{0}=frac{2mathrm{t}}{15}$$
    (10)
    $$mathrm{sin}frac{mathrm{t}}{2}=sqrt{frac{mathrm{sin}(45^circ -frac{mathrm{varnothing }-updelta -upgamma }{2})times mathrm{sin}(45^circ +frac{mathrm{varnothing }-updelta -upgamma }{2})}{mathrm{cosvarnothing }times mathrm{cosdelta }}}$$
    (11)
    where Φ is the geographic latitude, δ is the declination, γ is the astronomical refraction, t is the angle.Comprehensive suitability modelPeanut has different needs for meteorological elements such as temperature, sunshine and precipitation in different growth periods. In order to analyze the impact of meteorological factors in different growth periods on yield, correlation analysis was conducted between the suitability of temperature, precipitation and sunshine in each growth period and the relative meteorological yield of peanut, and the correlation coefficient of each growth period divided by the sum of the correlation coefficients of the whole growth period was used as the weight coefficient of the suitability of temperature, precipitation and sunshine in each growth period (Table 3). The climatic suitability of each single element in peanut growing season is calculated by using formulas (12) and (13):Table 3 The weight coefficients of climatic suitability at each growth stage.Full size table$$left{begin{array}{c}{mathrm{b}}_{mathrm{ti}}=frac{{mathrm{a}}_{mathrm{ti}}}{sum_{mathrm{i}=1}^{mathrm{n}}{mathrm{a}}_{mathrm{ti}}}\ {mathrm{b}}_{mathrm{ri}}=frac{{mathrm{a}}_{mathrm{ri}}}{{sum }_{mathrm{i}=1}^{mathrm{n}}{mathrm{a}}_{mathrm{ri}}}\ {mathrm{b}}_{mathrm{si}}=frac{{mathrm{a}}_{mathrm{si}}}{{sum }_{mathrm{i}=1}^{mathrm{n}}{mathrm{a}}_{mathrm{si}}}end{array}right.$$
    (12)
    $$left{begin{array}{c}F(t)={sum }_{mathrm{i}=1}^{mathrm{n}}left[{mathrm{b}}_{mathrm{ti}}{mathrm{F}}_{mathrm{i}}(mathrm{t})right]\ F(r)={sum }_{mathrm{i}=1}^{mathrm{n}}left[{mathrm{b}}_{mathrm{ri}}{mathrm{F}}_{mathrm{i}}(mathrm{r})right]\ F(s)={sum }_{mathrm{i}=1}^{mathrm{n}}left[{mathrm{b}}_{mathrm{si}}{mathrm{F}}_{mathrm{i}}(mathrm{s})right]end{array}right.$$
    (13)
    where bti, bri and bsi are the weight coefficients of temperature, precipitation and sunshine suitability in the i growth period respectively, ati, ari and asi are the correlation coefficients between temperature, precipitation and sunshine suitability and meteorological impact index of peanut yield in the i growth period respectively, and F(t), F(r) and F(s) are the temperature, precipitation and sunshine suitability in peanut growth season respectively.Then, the geometric average method is used to obtain the comprehensive suitability of peanut growth season, as shown in formula (14).$$F(S)=sqrt[3]{F(t)times F(r)times F(s)}$$
    (14)
    Verification of climatic zoning resultsDrought and flood disaster indexOn the basis of previous studies, in view of the different water demand of peanut in different development stages, this paper adds the water demand of peanut in different development stages as an important index to calculate, and constructs a standardized precipitation crop water demand index (SPRI) that can comprehensively characterize the drought and flood situation of peanut, so as to judge and analyze the occurrence of drought and flood disasters of peanut.Step 1: calculate the difference D between precipitation and crop water demand at each development stage$${D}_{i}={P}_{i}-{ET}_{ci}$$
    (15)
    where Pi is the precipitation in the i development period (mm), and ETci is the crop water demand in the i development period (mm).Step 2: normalize the data sequence.Since there are negative values in the original sequence, it is necessary to normalize the data when calculating the standardized precipitation crop water demand index. The normalized value is the SPRI value. The normalization method and drought and flood classification are consistent with SPEI index38,39,40.Chilling injury indexBased on the results of previous studies41, the abnormal percentage of caloric index was selected as the index of low-temperature chilling injury of peanut to judge and analyze the occurrence of chilling injury in different growth stages. The specific calculation process and formula are as follows:Step 1: calculate the caloric index of different development stages.Combined with the growth and development characteristics of peanut and considering the appropriate temperature, lower limit temperature and upper limit temperature at different growth stages of peanut, the caloric index can reflect the response of crops to environmental heat conditions. The average value of daily heat index is taken as the heat index of growth stage to reflect the influence of heat conditions in different growth stages on crop growth and development. Refer to formulas (5) and (6) to calculate the heat index Fi(t) at different development stages.Step 2: calculate the percentage of heat index anomaly$${I}_{ci}=frac{{F}_{i}(t)-overline{{F }_{i}(t)}}{overline{{F }_{i}(t)}}times 100%$$
    (16)
    where Ici is the Chilling injury index of stage i, Fi(t) is the heat index of stage i, and (overline{{F }_{i}(t)}) is the average value of the heat index of stage i over the years.Heat injury indexBased on the results of previous studies42, taking the average temperature of 26 °C, 30 °C and 28 °C and the daily maximum temperature of 35 °C, 35 °C and 37 °C as the critical temperature index to identify the heat damage of peanut in three growth stages, if this condition is met and lasts for more than 3 days, it will be recorded as a high temperature event.Disaster frequencyDisaster frequency (Pi) is defined as the ratio of the number of years of disaster at a certain station to the total number of years in the study period43, which is calculated by formula (17).$${P}_{i}=frac{n}{N}times 100%$$
    (17)
    where n is the number of years of disaster events to some extent at a certain growth period at a certain station, and N is the total number of years. More

  • in

    Co-occurrence networks reveal more complexity than community composition in resistance and resilience of microbial communities

    Testing H1 and H2 at community composition levelAs noted above, the simple fact that fungi grow more slowly than bacteria is the basis of the hypotheses that (H1) fungal communities should be more resistant than bacterial communities to drought stress, and (H2) that fungal communities should be less resilient than bacterial communities when the stress is relieved by rewetting18. In addition to growth rate, these two hypotheses may be related to differences in the form of growth between fungi and bacteria. For example, multicellular hyphal growth versus unicellular division or the greater thickness of fungal cell walls as compared to those of bacteria47,48. We tested H1 and H2 at the community composition level by blending the fungal and bacterial datasets generated from the same leaf, root, rhizosphere and soil samples collected from field-grown sorghum that had been either irrigated as a control, or subjected to preflowering drought followed by regular wetting beginning at flowering10,11.We followed the approach of Shade et al.17 to detect resistance and resilience, which had been developed for univariate variables, e.g., richness. For multivariate data, e.g., community composition, we modified it by calculating pairwise community dissimilarity for two groups: within-group (control-control pairs, drought-drought pairs, or rewetting-rewetting pairs), and between-group (control-drought pairs, or control-rewetting pairs). Ecological resistance to drought stress is detected by comparing compositional dissimilarity of between-group pairs (control-drought pairs) against within-group pairs (control-control pairs and drought-drought pairs) for each of the droughted weeks (weeks 3–8). Ecological resilience to rewetting is detected by assessing, from before to after rewetting, the change in the difference of compositional dissimilarity between within-group pairs and between-group pairs. Here, the point just before rewetting was week 8 and the points after rewetting were weeks 9–17. A t-test was used to assess the statistical significance of the differences in resistance or resilience between bacterial and fungal communities at each time point for each compartment.To account for the different resolutions of ITS and 16 S, we compared bacterial 16 S OTUs against both fungal ITS, species-level OTUs as well the fungal family level (Supplementary Fig. 1). The results of analyses using either fungal families or OTUs are consistent. Out of 36 comparisons (15 roots, 15 rhizospheres and 6 soils), different family and OTUs results were detected in four instances. In two of these, significances detected by OTUs were not detected by family (root, weeks 4 and 17) and, in the other two cases, significances detected by family were not detected by OTUs (rhizosphere, weeks 7 and 8). (Fig. 1). We report only results that are consistent at both the species and family levels (Fig. 1).In line with our first hypothesis, H1, we found that the resistance to drought stress for fungal mycobiomes was consistently stronger than that for bacterial microbiomes for weeks 5 in root, weeks 4–6 in rhizosphere, and weeks 4 and 6–8 in soil (Fig. 1, Supplementary Fig. 1 and Supplementary Table 2). In support of our second hypothesis, H2, when the stress of pre-flowering drought was relieved by rewetting, we found that the resilience of the bacterial communities was consistently higher than that for the fungi in weeks 9–16 in root, and weeks 11–17 in rhizosphere (Fig. 1, Supplementary Fig. 1 and Supplementary Table 2).Surprisingly, we found that resilience was stronger for fungal than bacterial communities in the first week (week 9) of rewetting in the rhizosphere (Fig. 1, Supplementary Fig. 1 and Supplementary Table 2). This high resilience of fungi may be associated with the quick growth of sorghum roots when rewetted. The rhizosphere zone around these newly formed roots may be quickly colonized by soil fungi, a community that was weakly affected by drought. This result suggests that re-assembly of the rhizosphere microbial community is more complex than previously expected.The finding that fungal community composition in the soil is not shaped by drought prevented us from further detecting resilience (Fig. 1). Note fungal community in early leaves was excluded from analysis due to the high proportion of non-fungal reads in sequencing11.Testing H1 and H2 at all-correlation levelNext, we moved from the comparison of whole communities to correlation among individual bacterial and fungal taxa to test the hypotheses about resistance, H1, and resilience, H2. As noted above, previous research provided the foundation for the stress gradient hypothesis, which predicts an increase in positive associations in stress32,33,34,35,36,37. Further, ecological modeling predicts that negative associations promote stability40. Concerning specific associations, studies of Arabidopsis and associated microbes reported that positive associations are favored within kingdoms, i.e., within bacteria or within fungi, while negative associations predominate between kingdoms38,39. Given these foundations, concerning H1, we expected an increase in the proportion of positive correlation by drought stress that would be strongest for B-B, followed by F-F, and lastly by B-F; for H2 we expected rewetting to cause a decrease in the proportion of positive correlation, again most strongly for B-B, followed by F-F, and lastly by B-F.Overall, at the all-correlation level, we found no consistent support for the differences postulated for bacterial and fungal responses in H1. For example, strong increases in the proportion of positive correlations under drought could be found in all microbial pairings for some compartments (B-B in leaf and root, F-F in rhizosphere and soil, and B-F in root and rhizosphere) (Fig. 2a, Supplementary Figs. 2, 3). Neither did we find consistent support for the differences ascribed to bacteria and fungi in H2 as the strongest decreases in the proportion of positive correlations during rewetting occurred at F-F in rhizosphere and soil, and B-B in leaf and root (Fig. 2b, Supplementary Figs. 2, 3).Fig. 2: Correlations of microbes in drought stress and drought relief.Estimates of combined correlations (row a) show an increase in positive correlations under drought stress across the four compartments (root, black; rhizosphere, blue; soil, red; leaf, green). Data points underlying the lines in the figure are provided in the alternative version in Supplementary Fig. 2. This result is in line with the stress gradient hypothesis which posits that stressful environments favor positive associations because competition will be less intense than in benign environments32,33,36,37. Note that positive trends in combined correlations can arise in two ways. First, from an increase of positive correlations (row b) that exceeds the rise in negative correlations (row c), e.g., Leaf bacterial-bacterial (Bac-Bac) correlations or rhizosphere fungal-fungal (Fun-Fun) correlations in the drought period (Negative correlations in row C values are multiplied by −1 to facilitate comparison). Second, from a decrease in negative correlations that exceeds a decrease in positive correlations, e.g., root bacterial-bacterial correlations or root bacterial-fungal (Bac-Fun) correlations in drought. Combined (a), positive (b) and negative (c) estimates of correlation (Spearman’s rho, ρ) are given for four compartments (root, rhizosphere, soil and leaf), and three types of correlations (Bacterium-Bacterium, Fungus-Fungus, Bacterium-Fungus). T-tests (two sided) were carried out for linear mixed effect modelling that incorporates link type and compartments as random factors. Detailed distribution densities of correlations are presented in Supplementary Fig. 3. Source data are provided as a Source Data file.Full size imageWe found support for the stress gradient hypothesis because drought increased the relative frequency of positive correlations among microbial taxa (Fig. 2a, Supplementary Figs. 2, 3). The increases were due, largely, to B-B correlations in leaf and F-F correlations in the rhizosphere during drought, when the relative frequency of positive correlations was increased (Fig. 2b, Supplementary Figs. 2, 3) and the frequencies of negative correlations were decreased or weakly increased (Fig. 2c, Supplementary Figs. 2, 3). Less obvious increases in the relative frequency of positive correlations (such as B-B in root, F-F in soil, and B-F in root and rhizosphere) occurred where drought reduced both positive and negative correlations, but the losses of negative correlations exceeded those of positive correlations (Fig. 2, Supplementary Figs. 2, 3).In support of the expectation that correlations would be more negative between taxonomic groups than within taxonomic groups, we found that the relative frequency of positive correlations was generally lower for B-F than B-B and F-F correlations (Fig. 2, Supplementary Figs. 2, 3). Moreover, as ecological modeling has indicated that negative associations should promote stability of communities40, we hypothesize that B-F correlations would be more stable than B-B and F-F networks in response to drought stress. However, we found no support for this hypothesis, as B-F correlations (for example in root) did not always show the least response to drought stress (Fig. 2, Supplementary Figs. 2, 3).Testing H1 and H2 at species co-occurrence levelFor our final test of H1 (resistance) and H2 (resilience) we focused on co-occurrence networks based on significant, positive correlations. These networks have been reported to be destabilized for bacteria but not for fungi in mesocosms subject to drought stress19, and shown to be disrupted for bacteria in natural vegetation studied over gradients of increasing aridity41,42. Using these results as guides, for H1 we expected that drought stress should disrupt co-occurrence networks most strongly for B-B, followed by F-F, and lastly by B-F. For H2 we expected that relief of stress by rewetting should strengthen microbial co-occurrence networks most strongly for B-B, followed by F-F, and lastly by B-F.For this test we constructed microbial co-occurrence networks using significant positive pairwise correlations between microbial taxa, B-B, F-F and B-F, and compared the network complexity between fully irrigated control and drought, and between control and rewetting following drought. In general, we found no consistent support for the difference between bacteria and fungi inherent in H1. Rhizosphere was the one compartment where B-B vertices dropped and F-F vertices rose in response to drought, as expected, but this result was offset in root and soil, where vertices dropped in all networks, B-B, F-F and B-F (Figs. 3, 4; Supplementary Figs. 4, 5). Analysis by co-occurrence networks highlighted the differences between plant compartments. In root drought strongly disrupted networks of B-B, B-F and F-F, but in the other three compartments, network disruption was weaker, and networks were even enhanced by drought for F-F in rhizosphere and B-B in leaf (Figs. 3, 4).Fig. 3: Networks of significant positive cross-taxonomic group correlations (bacteria and fungi).a Fungal operational taxonomic units (OTUs) (blue) and bacterial OTUs (black) are graphed as nodes. Significant positive Spearman correlations are graphed as edges (ρ  > 0.6, false discovery rate adjusted P  More

  • in

    CaliPopGen: A genetic and life history database for the fauna and flora of California

    Population genetic data collection from primary data sourcesFigure 4 describes the overall data collection workflow for the four datasets that comprise CaliPopGen. We first identified literature potentially containing population genetic data for California by querying the Web of Science Core Collection (https://webofknowledge.com/) for relevant literature from 1900 to 2020 with the terms: topic = (California*) AND topic = (genetic* OR genomic*) AND topic = (species OR taxa* OR population*). We included only empirical peer-reviewed literature and excluded unreviewed preprints. In using these search terms, our goal was to broadly identify genetic papers focused on California with population or species-level analyses, while avoiding purely phylogenetic studies or those focused on agricultural or model species. This resulted in 4,942 unique records.Fig. 4Flow chart of the data collection process that generated the CaliPopGen databases.Full size imageWe next screened titles and abstracts to retain articles that: (1) provided data on populations of species which are self-sustaining without anthropogenic involvement; (2) included at least some eukaryote species; (3) included population(s) sampled within California; (4) mentioned measures of genetic diversity or differentiation; and (5) were not reviews (thus restricting our search to only primary literature). We retained 1869 studies after this first pass of literature screening (see Technical Validation for estimate of inter- and intra-screener bias).Our second, more in-depth screening pass involved reading the full text of these 1869 studies. We had two goals. First, we confirmed that retained papers fully met all five of our inclusion criteria (the first screen was very liberal with respect to these criteria, and many papers failed to meet at least one criterion after close reading). Second, we eliminated papers where the data were not presented in a way that allowed us to extract population-level information. For example, many of the more systematics-focused studies pooled samples from large, somewhat ill-defined regions (“Sierra Nevada” or “Southern California”); if such regions were larger than 50 km in a linear dimension, we deemed them unusable for making geographically-informative inferences. Other studies presented summaries of population data, often in the form of phylogenetic networks or trees, but did not include information on actual population genetic parameters and therefore were not relevant to our database. We retained 528 publications after this second pass.From this set of papers, we extracted species, locality, and genetic data for each California population or sampling locality described in each study (Fig. 3A). This included Latin binomial/trinomial, English common name, population identifiers, and geographic coordinates of sampling sites. We also noted population/sampling localities that were interpreted as comprised of interspecific hybrids, and listed both parental species. We collected population genetic diversity and differentiation statistics for each unique genetic marker for each population/sampling locality; as a result, a sampling locality may have multiple entry rows, one for each locus or marker type. Parameters extracted for each population/marker combination include sample size, genetic marker type, gene targets, number of loci, years of sampling, and reported values for effective population size (Ne), expected (HE) and observed (HO,) heterozygosity, nucleotide diversity (π, pi), alleles-per-locus (APL), allelic richness (AR), percent polymorphic loci (PPL), haplotype diversity (HDIV), inbreeding coefficient (e.g. FIS, FIT, GIS), and pairwise population genetic comparison parameters (FST, GST, DST, Nei’s D, Jost’s D, or phi). We note that while there are technical differences between allelic richness and alleles-per-locus, source literature often used the terms interchangeably, and we include the parameters and their values as named in the source. We define marker type as the general category of genetic marker used (e.g., “microsatellite” or “nuclear”), while gene targets are the specific locus/loci (e.g., “COI”). We present these data in two separate datasets, one containing all population-level genetic summary statistics (Dataset 121, see Fig. 3C and detailed description in Table 1) and a second for estimates of pairwise genetic differentiation (Dataset 221, see Fig. 3D and detailed description in Table 2).Table 1 Description of the population genetic data in Dataset 121.Full size tableTable 2 Description of the pairwise genetic distance data in Dataset 221.Full size tableAll genetic data were extracted directly from the source literature. However, we also updated or added to the metadata for these population genetic values in several ways. We included kingdom, phylum, and a lower-level taxonomic grouping for each species (usually class), and updated scientific and common names based on the currently accepted taxonomy of the Global Biodiversity Information Facility22. When geographic coordinates were not provided for a sampling locality, as was frequently the case in the older literature, we used Google Maps (https://www.google.com/maps) to georeference localities based on either in-text descriptions or embedded figure maps guided by permanent landmarks like a bend in a river or administrative boundaries. Because this can only yield approximate coordinates, we recorded estimated accuracy as the radius of our best estimate of possible error in kilometers. If coordinates were provided in degree/minute/seconds, we used Google Maps to translate them to decimal degrees. In cases where coordinates were not provided and locality descriptions were too vague to determine coordinates with less than 50 km estimated coordinate error, we did not attempt to extract coordinates but still provide the genetic data. All coordinates are provided in the web Mercator projection (EPSG:3857). We excluded studies that reported genetic parameter values only for samples aggregated regionally (“Southern California” or “Sierra Nevada”). If marker type was not explicitly included, we classified marker type based on the gene targets reported, if provided.Life history trait data collectionTo increase the utility of CaliPopGen, we also assembled data on life history traits for all animal (Dataset 321) and plant (Dataset 421) species contained in Datasets 121 and 221. We assembled trait data that have previously been shown to correlate with genetic diversity, including those related to reproduction, life cycle, and body size, as well as conservation status (e.g.23,24,25,26,). Life history data were compiled by first referencing large online repositories, often specific to taxonomic groups, like the TRY plant trait database27, and the Royal Botanic Gardens Kew Seed Information Database28. If trait data for species of interest were unavailable from these compilations, we conducted keyword literature searches for each combination of species and life history trait, and extracted data from the primary literature. When data were not available for the subspecies or species for which we had genetic data, we report values for the next closest taxonomic level, up to and including family, as available in the literature.For both animals and plants, we defined habitat types as marine, freshwater, diadromous, amphibious, or terrestrial. Marine species include those that are found in brackish or wetland-marine habitats, as well as bird species that primarily reside in marine habitats. Freshwater species include those that are found in wetland-freshwater habitats, as well as species that primarily reside in freshwater. The diadromous category includes fish species that are catadromous or anadromous. We considered species to be amphibious if they have an obligatory aquatic stage in their life cycle, but also spend a significant portion of their life cycle on land. Terrestrial species were defined as those that spend most of their life cycle on land and are not aquatic for any portion of their life cycle. In a few cases (e.g., waterbirds that are both freshwater and marine, semi-aquatic reptiles), a species could reasonably be placed in more than one category, and we did our best to identify the primary life history category for such taxa. If the taxonomic identity of an entry was hybrid between species or subspecies, this was noted in the speciesID column and no life history data were reported.The CaliPopGen Animal Life History Traits Dataset 321 (description of dataset in Table 3) includes habitat type, lifespan, fecundity, lifetime reproductive success, age at sexual maturity, number of breeding events per year, mode of reproduction, adult length and mass, California native status, listing status under the US Endangered Species Act (ESA), listing status under the California Endangered Species Act (CESA), and status as a California Species of Special Concern (SSC). For some traits, value ranges were recorded–for example, minimum to maximum lifespan. In other cases, we recorded single values and, when available, a definition of this single value, (for example, minimum, average, or maximum lifespan). We report either the range of the age of sexual maturity (minimum to maximum), or a single value, depending on the available literature. For sexually dimorphic species, we report female adult length and weight when available, because female body size often correlates with fecundity. Across animal taxonomic groups, different measures of body size and length measurements are often used, reflecting community consensus on how to measure size. Given this variation, we report the type of length measurement, if available, as Standard Length (SL), Fork Length (FL), Total Length (TL), Snout-to-Vent Length (SVL), Straight-Line Carapace (SLC), or Wingspan (WS).Table 3 Description of the animal life-history data in Dataset 321.Full size tableThe CaliPopGen Plant Life History Traits Dataset 421 (description of dataset in Table 4) includes habitat type, lifespan, life cycle, adult height, self-compatibility, monoecious or dioecious, mode of reproduction, pollination and seed dispersal modes, mass per seed, California native status, NatureServe29 element ranks (global and state ranks, see Table 5 for definitions), listing status under the Federal Endangered Species Act (ESA), and listing status under the California Endangered Species Act (CESA). In contrast to most animal species, plant lifespan was typically reported as a single value. We define life cycles as the following: Annual: completes full life cycle in one year; Biennial: completes full life cycle in two years; Perennial: completes full life cycle in more than two years; Perennial-Evergreen: perennial and retains functional leaves throughout the year; Perennial-Deciduous: perennial and loses all leaves synchronously for part of the year. Some species are variable (for example, have annual and biennial individuals), and in those cases we attempted to characterize the most common modality.Table 4 Description of the plant life-history data in Dataset 421.Full size tableTable 5 Description of the Conservation status (Heritage Rank) from California Natural Diversity Database29.Full size tableBecause of the paucity of data available for chromists and fungi, we did not extract life history trait data for the relatively few species in these taxonomic groups.Data visualization and summaryWe used the R-package raster (v3.1–5) to visualize the spatial extent of the data in CaliPopGen in Fig. 3. Panel (A) shows a summary plot of all unique populations of both the Population Genetic Diversity in Dataset 121 and the Pairwise Population Differentiation in Dataset 221. Panel (B) shows the total number of unique populations in each California terrestrial ecoregion. Panel (C) depicts all data entries of Population Genetic Diversity Dataset 121, summed for each 20×20 km grid cell. Panel (D) shows the density of pairwise straight lines drawn between pairs of localities in the Pairwise Population Differentiation Dataset 221, depicted as the total number of lines per 20×20 km grid cell. The number of populations and species of both Datasets 121 & 221 are summarized for each marine and terrestrial ecoregion in Table 6.Table 6 Summary of total numbers of populations and species per California ecoregion, separately for population genetic and pairwise datasets.Full size table More