Ecology

1.Bálint M, Domisch S, Engelhardt CHM, Haase P, Lehrian S, Sauer J, et al. Cryptic biodiversity loss linked to global climate change. Nat Clim Chang. 2011;1:313–8.Article 

Google Scholar 
2.Parmesan C, Yohe G. A globally coherent fingerprint of climate change impacts across natural systems. Nature. 2003;421:37–42.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Blois JL, Zarnetske PL, Fitzpatrick MC, Finnegan S. Climate change and the past, present, and future of biotic interactions. Science. 2013;341:499–504.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Haines A, Ebi K. The imperative for climate action to protect health. N. Engl J Med. 2019;380:263–73.PubMed 
Article 
PubMed Central 

Google Scholar 
5.Deutsch CA, Tewksbury JJ, Tigchelaar M, Battisti DS, Merrill SC, Huey RB, et al. Increase in crop losses to insect pests in a warming climate. Science. 2018;361:916–9.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Kattwinkel M, Jan-Valentin K, Foit K, Liess M. Climate change, agricultural insecticide exposure, and risk for freshwater communities. Ecol Appl. 2011;21:2068–81.PubMed 
Article 
PubMed Central 

Google Scholar 
7.Moe SJ, De Schamphelaere K, Clements WH, Sorensen MT, Van den Brink PJ, Liess M. Combined and interactive effects of global climate change and toxicants on populations and communities. Environ Toxicol Chem. 2013;32:49–61.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
8.Potts SG, Biesmeijer JC, Kremen C, Neumann P, Schweiger O, Kunin WE. Global pollinator declines: trends, impacts and drivers. Trends Ecol Evol. 2010;25:345–53.PubMed 
Article 
PubMed Central 

Google Scholar 
9.Moran EV, Alexander JM. Evolutionary responses to global change: Lessons from invasive species. Ecol Lett. 2014;17:637–49.PubMed 
Article 
PubMed Central 

Google Scholar 
10.Harwood AD, You J, Lydy MJ. Temperature as a toxicity identification evaluation tool for pyrethroid insecticides: toxicokinetic confirmation. Environ Toxicol Chem. 2009;28:1051–8.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
11.Guo L, Su M, Liang P, Li S, Chu D. Effects of high temperature on insecticide tolerance in whitefly Bemisia tabaci (Gennadius) Q biotype. Pestic Biochem Physiol. 2018;150:97–104.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Mao K, Jin R, Li W, Ren Z, Qin X, He S, et al. The influence of temperature on the toxicity of insecticides to Nilaparvata lugens (Stål). Pestic Biochem Physiol. 2019;156:80–86.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Verheyen J, Delnat V, Stoks R. Increased daily temperature fluctuations overrule the ability of gradual thermal evolution to offset the increased pesticide toxicity under global warming. Environ Sci Technol. 2019;53:4600–8.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Moran NA. Symbiosis as an adaptive process and source of phenotypic complexity. Proc Natl Acad Sci USA. 2007;104:8627–3863.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
15.Kikuchi Y, Hayatsu M, Hosokawa T, Nagayama A, Tago K, Fukatsu T. Symbiont-mediated insecticide resistance. Proc Natl Acad Sci USA. 2012;109:8618–22.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
16.Jones RM, Desai C, Darby TM, Luo L, Wolfarth AA, Scharer CD, et al. Lactobacilli modulate epithelial cytoprotection through the Nrf2 pathway. Cell Rep. 2015;12:1217–25.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
17.Cheng D, Guo Z, Riegler M, Xi Z, Liang G, Xu Y. Gut symbiont enhances insecticide resistance in a significant pest, the oriental fruit fly Bactrocera dorsalis (Hendel). Microbiome. 2017;5:13.PubMed 
PubMed Central 
Article 

Google Scholar 
18.Pang R, Chen M, Yue L, Xing K, Li T, Kang K, et al. A distinct strain of Arsenophonus symbiont decreases insecticide resistance in its insect host. PLoS Genet. 2018;14:e1007725.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
19.Kikuchi Y, Tada A, Musolin DL, Hari N, Hosokawa T, Fujisaki K, et al. Collapse of insect gut symbiosis under simulated climate change. mBio. 2016;7:e01578–16.PubMed 
PubMed Central 
Article 

Google Scholar 
20.Corbin C, Heyworth ER, Ferrari J, Hurst GDD. Heritable symbionts in a world of varying temperature. Heredity. 2017;118:10–20.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
21.Jia FX, Yang MS, Yang WJ, Wang JJ. Influence of continuous high temperature conditions on Wolbachia infection frequency and the fitness of Liposcelis tricolor (Psocoptera: Liposcelididae). Environ Entomol. 2009;38:1365–72.PubMed 
Article 
PubMed Central 

Google Scholar 
22.Burke G, Fiehn O, Moran N. Effects of facultative symbionts and heat stress on the metabolome of pea aphids. ISME J. 2010;4:242–52.PubMed 
Article 
PubMed Central 

Google Scholar 
23.Fan Y, Wernegreen JJ. Can’t take the heat: high temperature depletes bacterial endosymbionts of ants. Micro Ecol. 2013;66:727–33.Article 

Google Scholar 
24.Hussain M, Akutse KS, Ravindran K, Lin Y, Bamisile BS, Qasim M, et al. Effects of different temperature regimes on survival of Diaphorina citri and its endosymbiotic bacterial communities. Environ Microbiol. 2017;19:3439–49.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Engl T, Eberl N, Gorse C, Krüger T, Schmidt THP, Plarre R, et al. Ancient symbiosis confers desiccation resistance to stored grain pest beetles. Mol Ecol. 2018;27:2095–108.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Zhang XJ, Yu XP, Chen JM. High Temperature effects on yeast-like endosymbiotes and pesticide resistance of the small brown planthopper, Laodelphax striatellus. Rice Sci. 2008;15:326–30.CAS 
Article 

Google Scholar 
27.Zhang B, Zuo TQ, Li HG, Sun LJ, Wang SF, Zhang CY, et al. Effect of heat shock on the susceptibility of Frankliniella occidentalis (Thysanoptera: Thripidae) to insecticides. J Integr Agric. 2016;15:2309–18.CAS 
Article 

Google Scholar 
28.Karimzadeh R, Javanshir M, Hejazi MJ. Individual and combined effects of insecticides, inert dusts and high temperatures on Callosobruchus maculatus (Coleoptera: Chrysomelidae). J Stored Prod Res. 2020;89:10693.Article 

Google Scholar 
29.Michigan State University. Arthropod Pesticide Resistance Database (APRD). East Lansing: Michigan State University; 2020. http://www.pesticideresistance.com/.30.Ju JF, Bing XL, Zhao DS, Guo Y, Xi Z, Hoffmann AA, et al. Wolbachia supplement biotin and riboflavin to enhance reproduction in planthoppers. ISME J. 2019;14:676–87.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
31.Zhang Y, Tang T, Li W, Cai T, Li J, Wan H. Functional profiling of the gut microbiomes in two different populations of the brown planthopper. Nilaparvata lugens J Asia Pac Entomol. 2018;21:1309–14.Article 

Google Scholar 
32.Ye YH, Seleznev A, Flores HA, Woolfit M, McGraw EA. Gut microbiota in Drosophila melanogaster interacts with Wolbachia but does not contribute to Wolbachia-mediated antiviral protection. J Invertebr Pathol. 2017;143:18–25.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
33.Yamada R, Floate KD, Riegler M, O’Neill SL. Male development time influences the strength of Wolbachia-induced cytoplasmic incompatibility expression in Drosophila melanogaster. Genetics. 2007;177:801–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Wari D, Kabir MA, Mujiono K, Hojo Y, Shinya T, Tani A, et al. Honeydew-associated microbes elicit defense responses against brown planthopper in rice. J Exp Bot. 2019;70:1683–96.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
35.Miller ALE, Tindall K, Leonard BR. Bioassays for monitoring insecticide resistance. J Vis Exp. 2010;46:2129.
Google Scholar 
36.Zhang J, Zhang Y, Wang Y, Yang Y, Cang X, Liu Z. Expression induction of P450 genes by imidacloprid in Nilaparvata lugens: a genome-scale analysis. Pestic Biochem Physiol. 2016;132:59–64.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
37.Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2-ΔΔCT method. Methods. 2001;25:402–8.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
38.Noda H, Koizumi Y, Zhang Q, Deng K. Infection density of Wolbachia and incompatibility level in two planthopper species, Laodelphax striatellus and Sogatella furcifera. Insect Biochem Mol Biol. 2001;31:727–37.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
39.Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011. https://doi.org/10.14806/ej.17.1.20040.Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.Katoh K, Misawa K, Kuma KI, Miyata T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 2002;30:3059–66.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Bokulich NA, Kaehler BD, Rideout JR, Dillon M, Bolyen E, Knight R, et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome. 2018;6:90.PubMed 
PubMed Central 
Article 

Google Scholar 
43.Liu S, Ding Z, Zhang C, Yang B, Liu Z. Gene knockdown by intro-thoracic injection of double-stranded RNA in the brown planthopper, Nilaparvata lugens. Insect Biochem Mol Biol. 2010;40:666–71.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
44.Tai V, James ER, Nalep CA, Scheffrahn RH, Perlman SJ, Keelinga PJ. The role of host phylogeny varies in shaping microbial diversity in the hindguts of lower termites. Appl Environ Microbiol. 2015;81:1059–70.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
45.Bale JS, Hayward SAL. Insect overwintering in a changing climate. J Exp Biol. 2010;213:980–94.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.Rahmstorf S, Cazenave A, Church JA, Hansen JE, Keeling RF, Parker DE, et al. Recent climate observations compared to projections. Science. 2007;316:709.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
47.Radchuk V, Reed T, Teplitsky C, van de Pol M, Charmantier A, Hassall C, et al. Adaptive responses of animals to climate change are most likely insufficient. Nat Commun. 2019;10:3019.Article 
CAS 

Google Scholar 
48.Iwamura T, Guzman-Holst A, Murray KA. Accelerating invasion potential of disease vector Aedes aegypti under climate change. Nat Commun. 2020;11:2130.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.Li J, Mao T, Wang H, Lu Z, Qu J, Fang Y, et al. The CncC/keap1 pathway is activated in high temperature-induced metamorphosis and mediates the expression of Cyp450 genes in silkworm, Bombyx mori. Biochem Biophys Res Commun. 2019;541:1045–50.Article 
CAS 

Google Scholar 
50.Kalsi M, Palli SR. Transcription factor cap n collar C regulates multiple cytochrome P450 genes conferring adaptation to potato plant allelochemicals and resistance to imidacloprid in Leptinotarsa decemlineata (Say). Insect Biochem Mol Biol. 2017;83:1–12.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
51.Kalsi M, Palli SR. Transcription factors, CncC and Maf, regulate expression of CYP6BQ genes responsible for deltamethrin resistance in Tribolium castaneum. Insect Biochem Mol Biol. 2015;65:47–56.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.Misra JR, Lam G, Thummel CS. Constitutive activation of the Nrf2/Keap1 pathway in insecticide-resistant strains of Drosophila. Insect Biochem Mol Biol. 2013;43:1116–24.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
53.Tang B, Cheng Y, Li Y, Li W, Ma Y, Zhou Q, et al. Adipokinetic hormone regulates cytochrome P450-mediated imidacloprid resistance in the brown planthopper, Nilaparvata lugens. Chemosphere. 2020;259:127490.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Cheng Y, Li Y, Li W, Song Y, Zeng R, Lu K. Inhibition of hepatocyte nuclear factor 4 confers imidacloprid resistance in Nilaparvata lugens via the activation of cytochrome P450 and UDP-glycosyltransferase genes. Chemosphere. 2021;263:128269.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
55.Li Y, Liu X, Wang N, Zhang Y, Hoffmann AA, Guo H. Background-dependent Wolbachia-mediated insecticide resistance in Laodelphax striatellus. Environ Microbiol. 2020;22:2653–63.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.Berticat C, Rousset F, Raymond M, Berthomieu A, Weill M. High Wolbachia density in insecticide-resistant mosquitoes. Proc R Soc B Biol Sci. 2002;269:1413–6.Article 

Google Scholar 
57.Zhang G, Hussain M, O’Neill SL, Asgari S. Wolbachia uses a host microRNA to regulate transcripts of a methyltransferase, contributing to dengue virus inhibition in Aedes aegypti. Proc Natl Acad Sci USA. 2013;110:10276–81.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
58.Bi J, Sehgal A, Williams JA, Wang YF. Wolbachia affects sleep behavior in Drosophila melanogaster. J Insect Physiol. 2018;107:81–88.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
59.Roughgarden J, Gilbert SF, Rosenberg E, Zilber-Rosenberg I, Lloyd EA. Holobionts as units of selection and a model of their population dynamics and evolution. Biol Theory. 2018;13:44–65.Article 

Google Scholar 
60.Pan X, Zhou G, Wu J, Bian G, Lu P, Raikhel AS, et al. Wolbachia induces reactive oxygen species (ROS)-dependent activation of the Toll pathway to control dengue virus in the mosquito Aedes aegypti. Proc Natl Acad Sci USA. 2012;109:E23–31.PubMed 
Article 
PubMed Central 

Google Scholar 
61.Gong JT, Li Y, Li TP, Liang Y, Hu L, Zhang D, et al. Stable introduction of plant-virus-inhibiting Wolbachia into planthoppers for rice protection. Curr Biol. 2020;30:4837–45.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
62.Elzaki MEA, Li ZF, Wang J, Xu L, Liu N, Zeng RS, et al. Activiation of the nitric oxide cycle by citrulline and arginine restores susceptibility of resistant brown planthoppers to the insecticide imidacloprid. J Hazard Mater. 2020;396:122755.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
63.Werren JH. Biology of Wolbachia. Annu Rev Entomol. 1997;42:587–609.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
64.Kokou F, Sasson G, Nitzan T, Doron-Faigenboim A, Harpaz S, Cnaani A, et al. Host genetic selection for cold tolerance shapes microbiome composition and modulates its response to temperature. Elife. 2018;77:e36398.Article 

Google Scholar  More

1.Hoffman, E. A., Schueler, F. W. & Blouin, M. S. Effective population sizes and temporal stability of genetic structure in Rana pipiens, the northern leopard frog. Evolution 58, 2536–2545. https://doi.org/10.1111/j.0014-3820.2004.tb00882.x (2004).Article 
PubMed 

Google Scholar 
2.Hoeck, P. E. A., Bollmer, J. L., Parker, P. G. & Keller, L. F. Differentiation with drift: A spatio-temporal genetic analysis of Galapagos mockingbird populations (Mimus spp.). Philos. Trans. R. Soc. B 365, 1127–1138. https://doi.org/10.1098/rstb.2009.0311 (2010).Article 

Google Scholar 
3.Maebe, K. et al. A century of temporal stability of genetic diversity in wild bumblebees. Sci. Rep. 6, 38289. https://doi.org/10.1038/srep38289 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
4.Schmid, S. et al. Spatial and temporal genetic dynamics of the grasshopper Oedaleus decorus revealed by museum genomics. Ecol. Evol. 8, 1480–1495. https://doi.org/10.1002/ece3.3699 (2018).Article 
PubMed 

Google Scholar 
5.Garant, D., Dodson, J. J. & Bernatchez, L. Ecological determinants and temporal stability of the within-river population structure in Atlantic salmon (Salmo salar). Mol. Ecol. 9, 615–628. https://doi.org/10.1046/j.1365-294X.2000.00909.x (2000).CAS 
Article 
PubMed 

Google Scholar 
6.Jonsdottir, O. D. B., Danielsdottir, A. K. & Naevdal, G. Genetic differentiation among Atlantic cod (Gadus morhua) in Icelandic waters: Temporal stability. ICES J. Mar. Sci. 58, 114–122. https://doi.org/10.1006/jmsc.2000.0995 (2001).Article 

Google Scholar 
7.Rojas-Hernandez, N., Veliz, D., Riveros, M. P., Fuentes, J. P. & Pardo, L. M. Highly connected populations and temporal stability in allelic frequencies of a harvested crab from the Southern Pacific coast. PLoS ONE 11, 1–18. https://doi.org/10.1371/journal.pone.0166029 (2016).CAS 
Article 

Google Scholar 
8.Vera, M. et al. Current genetic status, temporal stability and structure of the remnant wild European flat oyster populations: Conservation and restoring implications. Mar. Biol. 163, 1–17. https://doi.org/10.1007/s00227-016-3012-x (2016).Article 

Google Scholar 
9.Richards, C. M., Emery, S. N. & McCauley, D. E. Genetic and demographic dynamics of small populations of Silene latifolia. Heredity 90, 181–186. https://doi.org/10.1038/sj.hdy.6800214 (2003).CAS 
Article 
PubMed 

Google Scholar 
10.Mhemmed, G., Kamel, H. & Chedly, A. Does habitat fragmentation reduce genetic diversity and subpopulation connectivity?. Ecography 31, 751–756. https://doi.org/10.1111/j.1600-0587.2008.05622.x (2008).Article 

Google Scholar 
11.Gomaa, N. H., Montesinos-Navarro, A., Alonso-Blanco, C. & Picó, F. X. Temporal variation in genetic diversity and effective population size of Mediterranean and subalpine Arabidopsis thaliana populations. Mol. Ecol. 20, 3540–3554. https://doi.org/10.1111/j.1365-294X.2011.05193.x (2011).Article 
PubMed 

Google Scholar 
12.Hinkson, K. M. & Richter, S. C. Temporal trends in genetic data and effective population size support efficacy of management practices in critically endangered dusky gopher frogs (Lithobates sevosus). Ecol. Evol. 6, 2667–2678. https://doi.org/10.1002/ece3.2084 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
13.Savage, W. K., Fremier, A. K. & Shaffer, H. B. Landscape genetics of alpine Sierra Nevada salamanders reveal extreme population subdivision in space and time. Mol. Ecol. 19, 3301–3314. https://doi.org/10.1111/j.1365-294X.2010.04718.x (2010).Article 
PubMed 

Google Scholar 
14.Ficetola, G. F., Garner, T. W. J., Wang, J. & De Bernardi, F. Rapid selection against inbreeding in a wild population of a rare frog. Evol. Appl. 4, 30–38. https://doi.org/10.1111/j.1752-4571.2010.00130.x (2011).Article 
PubMed 

Google Scholar 
15.Richter, S. C. & Nunziata, S. O. Survival to metamorphosis is positively related to genetic variability in a critically endangered amphibian species. Anim. Conserv. 17, 265–274. https://doi.org/10.1111/acv.12088 (2014).Article 

Google Scholar 
16.Holmes, I. & Crawford, A. Temporal population genetic instability in range-edge western toads, anaxyrus boreas. J. Hered. 106, 45–56. https://doi.org/10.1093/jhered/esu068 (2015).Article 
PubMed 

Google Scholar 
17.Munwes, I. et al. The change in genetic diversity down the core-edge gradient in the eastern spadefoot toad (Pelobates syriacus). Mol. Ecol. 19, 2675–2689. https://doi.org/10.1111/j.1365-294X.2010.04712.x (2010).CAS 
Article 
PubMed 

Google Scholar 
18.Agasyan, A., Tuniyev, B., Isailovic, J. C., Lymberakis, P., Andrén, C., Cogalniceanu, D. et al. Pelobates syriacus. The IUCN Red List of Threatened Species. www.iucnredlist.org (2009).19.Gafny, S. The biology and ecology of the Syrian spadefoot toad Pelobates syriacus in Israel (MSc thesis). Tel Aviv University (1986).20.Hollar, A. R., Choi, J., Grimm, A. T. & Buchholz, D. R. Higher thyroid hormone receptor expression correlates with short larval periods in spadefoot toads and increases metamorphic rate. Gen. Comp. Endocrinol. 173, 190–198. https://doi.org/10.1016/j.ygcen.2011.05.013 (2011).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
21.Székely, P., Tudor, M. & Cogalniceanu, D. Effect of habitat drying on the development of the Eastern spadefoot toad (Pelobates syriacus) tadpoles. Amphib. Reptil. 31, 425–434. https://doi.org/10.1163/156853810791769536 (2010).Article 

Google Scholar 
22.Storz, B. L. & Travis, J. Temporally dissociated, trait-specific modifications underlie phenotypic polyphenism in Spea multiplicata tadpoles, which suggests modularity. Sci. World J. 7, 715–726. https://doi.org/10.1100/tsw.2007.159 (2007).Article 

Google Scholar 
23.Mahlstein, I., Portmann, R. W., Daniel, J. S., Solomon, S. & Knutti, R. Perceptible changes in regional precipitation in a future climate. Geophys. Res. Lett. 39, 1–5. https://doi.org/10.1029/2011GL050738 (2012).Article 

Google Scholar 
24.Fisher, R. The Genetical Theory of Natural Selection (Oxford University Press, 1930).Book 

Google Scholar 
25.Bürger, R. The maintenance of genetic variation: A functional analytic approach to quantitative genetic models. In Population Genetics and Evolution (ed. de Jong, G.) 63–72 (Springer, 1988).Chapter 

Google Scholar 
26.Parsons, P. A. Evolutionary rates: Stress and species boundaries. Ann. Rev. Ecol. Syst. 22, 1–18. https://doi.org/10.1146/annurev.es.22.110191.000245 (1991).Article 

Google Scholar 
27.Safriel, U. N., Volis, S. & Kark, S. Core and peripheral populations and global climate change. Isr. J. Plant Sci. 42, 331–345. https://doi.org/10.1080/07929978.1994.10676584 (1994).Article 

Google Scholar 
28.Wan, Q. H., Wu, H., Fujihara, T. & Fang, S. G. Which genetic marker for which conservation genetics issue?. Electrophoresis 25, 2165–2176. https://doi.org/10.1002/elps.200305922 (2004).CAS 
Article 
PubMed 

Google Scholar 
29.Faircloth, B. C. MSATCOMMANDER: Detection of microsatellite repeat arrays and automated, locus-specific primer design. Mol. Ecol. Resour. 8, 92–94. https://doi.org/10.1111/j.1471-8286.2007.01884.x (2008).CAS 
Article 
PubMed 

Google Scholar 
30.Gafny, S. & Gasith, A. Rainpools in Israel. Internal report, the Nature and Parks Authority of Israel (2005).31.Pielou, E. C. The measurement of diversity in different types of biological collections. J. Theor. Biol. 13, 131–144. https://doi.org/10.1016/0022-5193(67)90048-3 (1966).Article 

Google Scholar 
32.Peakall, R. & Smouse, P. E. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research-an update. Bioinformatics 28, 2537–2539. https://doi.org/10.1093/bioinformatics/bts460 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
33.Van Oosterhout, C., Hutchinson, W. F., Wills, D. P. M. & Shipley, P. MICRO-CHECKER: Software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538. https://doi.org/10.1111/j.1471-8286.2004.00684.x (2004).CAS 
Article 

Google Scholar 
34.Rousset, F. GENEPOP’007: A complete re-implementation of the GENEPOP software for Windows and Linux. Mol. Ecol. Resour. 8, 103–106. https://doi.org/10.1111/j.1471-8286.2007.01931.x (2008).Article 
PubMed 

Google Scholar 
35.Nei, M. & Li, W. H. Mathematical model for studying genetic variation in terms of restriction endonucleases. Proc. Nat. Acad. Sci. U.S.A. 76, 5269–5273. https://doi.org/10.1073/pnas.76.10.5269 (1979).ADS 
CAS 
Article 
MATH 

Google Scholar 
36.Excoffier, L. & Lischer, H. E. L. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–656. https://doi.org/10.1111/j.1755-0998.2010.02847.x (2010).Article 
PubMed 

Google Scholar 
37.Saltelli, A. Making best use of model valuations to compute sensitivity indices. Comput. Phys. Commun. 145, 280–297. https://doi.org/10.1016/S0010-4655(02)00280-1 (2002).ADS 
CAS 
Article 
MATH 

Google Scholar 
38.Robinson, M. R. et al. The Impact of environmental heterogeneity on genetic architecture in a wild population of soay sheep. Genetics 181, 1639–1648. https://doi.org/10.1534/genetics.108.086801 (2009).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
39.Charmantier, A. & Garant, D. Environmental quality and evolutionary potential: lessons from wild populations. Proc. R. Soc. B 272, 1415–1425. https://doi.org/10.1098/rspb.2005.3117 (2005).Article 
PubMed 
PubMed Central 

Google Scholar 
40.Stanescu, F., Iosif, R., Szkely, P., Szkely, D. & Cogalniceanu, D. Mass migration of Pelobates syriacus (Boettger, 1889) metamorphs. Herpetozoa 29, 87–89 (2016).
Google Scholar 
41.Levin, N., Elron, E. & Gasith, A. Decline of wetland ecosystems in the coastal plain of Israel during the 20th century: Implications for wetland conservation and management. Landsc. Urban Plan. 92, 220–232. https://doi.org/10.1016/j.landurbplan.2009.05.009 (2009).Article 

Google Scholar 
42.Waples, R. S. & Teel, D. J. Conservation genetics of pacific salmon I. Temporal changes in allele frequency. Conser. Biol. 4, 144–156. https://doi.org/10.1111/j.1523-1739.1990.tb00103.x (1990).Article 

Google Scholar 
43.Chen, N. et al. Allele frequency dynamics in a pedigreed natural population. Proc. Nat. Acad. Sci. U.S.A. 116, 2158–2164. https://doi.org/10.1073/pnas.1813852116 (2019).CAS 
Article 

Google Scholar 
44.Ballon, Y. The effects of different light regimes on activity rhythms of the eastern spadefoot toad (Pelobates syriacus). M.Sc. thesis. Department of Zoology. Tel-Aviv University (2015).45.Cogalniceanu, D. et al. Age and body size in populations of two syntopic spadefoot toads (genus Pelobates) at the limit of their ranges. J. Herpetol. 48, 537–545. https://doi.org/10.1670/13-101 (2014).Article 

Google Scholar 
46.Dimmitt, M. Environmental correlates of emergence in spadefoot toads (Scaphiopus). J. Herpetol. 14, 21–29. https://doi.org/10.2307/1563871 (1980).Article 

Google Scholar 
47.Crispo, E. & Chapman, L. J. Population genetic structure across dissolved oxygen regimes in an African cichlid fish. Mol. Ecol. 17, 2134–2148. https://doi.org/10.1111/j.1365-294X.2008.03729.x (2008).CAS 
Article 
PubMed 

Google Scholar 
48.Crispo, E. & Chapman, L. J. Temporal variation in population genetic structure of a riverine African cichlid fish. J. Hered. 101, 97–106. https://doi.org/10.1093/jhered/esp078 (2010).Article 
PubMed 

Google Scholar 
49.Kitanishi, S., Ikeda, T. & Yamamoto, T. Short-term temporal instability in fine-scale genetic structure of masu salmon. Freshw. Biol. 62, 1655–1664. https://doi.org/10.1111/fwb.12978 (2017).Article 

Google Scholar  More

1.Reynolds, M. P. & Ortiz, R. Climate change and crop production, Chap. Adapting Crops to Climate Change: A Summary. 1–8 (CABI, 2010).2.Lal, R. et al. Management to mitigate and adapt to climate change. J. Soil Water Conserv. 66, 276–285 (2011).Article 

Google Scholar 
3.FAO. A Framework for Land Evaluation. Food and Agriculture Organization of the United Nations, Soils Bulletin No.32. (FAO, Rome, 1976).4.FAO. Land Evaluation. Towards a Revised Framework, Food and Agriculture Organization of the United Nations, Land and Water Discussion Paper No.6. (FAO, Rome, 2007).5.Abbas, H., Shier, W. & Cartwright, R. Effect of temperature, rainfall and planting date on aflatoxin and fumonisin contamination in commercial Bt and non-Bt corn hybrids in Arkansas. Phytoprotection 88, 41–50 (2007).CAS 
Article 

Google Scholar 
6.Brenneman, T. B., Wilson, D. M. & Beaver, R. W. Effects of diniconazole on Aspergillus populations and aflatoxin formation in peanut under irrigated and nonirrigated conditions. Plant Dis. 77, 608–612 (1993).CAS 
Article 

Google Scholar 
7.Wang, T., Zhang, E., Chen, X., Li, L. & Liang, X. Identification of seed proteins associated with resistance to preharvested aflatoxin contamination in peanut (Arachis hypogaea). BMC Plant Biol. 10, 267 (2010).CAS 
Article 

Google Scholar 
8.Gasperini, A. M. et al. Resilience of biocontrol for aflatoxin minimization strategies: Climate change abiotic factors may affect control in non-GM and GM-maize cultivars. Front. Microbiol. 10, 2525 (2019).Article 

Google Scholar 
9.Barrett, J. R. Liver cancer and aflatoxin. Environ. Heal. Perspect. 113, 837–838 (2005).
Google Scholar 
10.US Food and Drug Administration. Bad Bug Book, Foodborne Pathogenic Microorganisms and Natural Toxins, 2nd edn. (Center for Food Safety Applied Nutrition, 2004).11.Marchese, S. et al. Aflatoxin B1 and M1: Biological properties and their involvement in cancer development. Toxins 10, 214 (2018).Article 

Google Scholar 
12.Guo, B., Chen, Z.-Y., Lee, R. D. & Scully, B. T. Drought stress and preharvest aflatoxin contamination in agricultural commodity: Genetics, genomics and proteomics. J. Integr. Plant Biol. 50, 1281–1291 (2008).CAS 
Article 

Google Scholar 
13.Horn, B. W. et al. Sexual reproduction in Aspergillus flavus sclerotia naturally produced in corn. Phytopathology 104, 75–85 (2014).Article 

Google Scholar 
14.Payne, G. A. & Widstrom, N. W. Aflatoxin in maize. Critical Rev. Plant Sci. 10, 423–440 (1992).CAS 
Article 

Google Scholar 
15.Kerry, R., Ortiz, B. V., Ingram, B. R. & Scully, B. T. A spatio-temporal investigation of risk factors for aflatoxin contamination of corn in southern Georgia, USA using geostatistical methods. Crop. Prot. 94, 144–158 (2017).CAS 
Article 

Google Scholar 
16.Yoo, E., Kerry, R., Ingram, B., Ortiz, B. & Scully, B. Defining and characterizing Aflatoxin contamination risk areas for corn in Georgia, USA: Adjusting for collinearity and spatial correlation. Spatial Stat. 28, 84–104 (2018).MathSciNet 
Article 

Google Scholar 
17.FAO (Food and Agriculture Organization). Worldwide regulations for mycotoxins in food and feed in 2003. in FAO Food and Nutrition Paper 81 (2004).18.Mazumder, P. M. & Sasmal, D. Mycotoxins—Limits and regulations. Anc. Sci. Life 20, 1 (2001).CAS 
PubMed 
PubMed Central 

Google Scholar 
19.US Food and Drug Administration. CPG Sec. 683.100 Action Levels for Aflatoxins in Animal Feeds. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/cpg-sec-683100-action-levels-aflatoxinsanimal-feeds (2019).20.European Commission. Commission Regulation (EC) No 1881/2006 of 19 December 2006 setting maximum levels for certain contaminants in foodstuffs. Off. J. Eur. Union 364 (2006).21.Commission, E. Commission Regulation (EC) No 165/2010 of 26 February 2010 amending Regulation (EC) No 1881/2006 setting maximum levels for certain contaminants in foodstuffs as regards aflatoxins. Off. J. Eur. Union 50, 8–12 (2010).
Google Scholar 
22.Medina, A., Rodriguez, A. & Magan, N. Effect of climate change on Aspergillus flavus and aflatoxin B1 production. Front. Microbiol. 5, 348 (2014).Article 

Google Scholar 
23.Medina, A., Rodríguez, A., Sultan, Y. & Magan, N. Climate change factors and Aspergillus flavus: Effects on gene expression, growth and aflatoxin production. World Mycotoxin J. 8, 171–179 (2015).Article 

Google Scholar 
24.Garcia-Cela, E. et al. Unveiling the effect of interacting forecasted abiotic factors on growth and Aflatoxin B1 production kinetics by Aspergillus flavus. Fungal Biol. (2020).25.Widstrom, N. W., Forster, M. J., Martin, W. K. & Wilson, D. M. Agronomic performance in the southeastern United States of maize hybrids containing tropical germplasm. Maydica 41, 59–63 (1996).
Google Scholar 
26.Damianidis, D. et al. Evaluating a generic drought index as a predictive tool for aflatoxin contamination of corn: From plot to regional level. Crop Prot. 113, 64–74 (2018).CAS 
Article 

Google Scholar 
27.Salvacion, A. et al. Effect of rainfall and maximum temperature on corn aflatoxin in the southeastern U. S coastal plain. in Proceedings of the Climate Information for Managing Risks. Orlando, Florida (2011).28.Damianidis, D. et al. Minimum temperature, rainfall, and agronomic management impacts on corn grain aflatoxin contamination. Agron. J 110(5), 1697–1708 (2018).Article 

Google Scholar 
29.Navarro, F., Ingram, B., Kerry, R., Ortiz, B. V. & Scully, B. T. A web-based GIS decision support tool for determining corn aflatoxin risk: A case study data from Southern Georgia, USA. Adv. Anim. Biosci. 8, 718 (2017).Article 

Google Scholar 
30.Battilani, P. et al. Aflatoxin B1 contamination in maize in Europe increases due to climate change. Nat. Sci. Rep. 6, 24328 (2016).ADS 
CAS 
Article 

Google Scholar 
31.Thomson, A. M. et al. RCP4.5: A pathway for stabilization of radiative forcing by 2100. Clim. Change 109(1), 77–94 (2011).32.Schwalm, C. R., Glendon, S. & Duffy, P. B. RCP8.5 tracks cumulative CO2 emissions. PNAS USA 117(33), 19656–19657 (2012).33.Stocker, T. F. et al. Climate change 2013: The physical science basis. in Contribution of Working Group I to the Fifth Assessment Report of IPCC the Intergovernmental Panel on Climate Change (2014).34.Wacoo, A. P., Wendiro, D., Vuzi, P. C. & Hawumba, J. F. Methods for detection of aflatoxins in agricultural food crops. J. Appl. Chem. 2014, 706291 (2014).35.Kerry, R. & Oliver, M. A. Determining the effect of asymmetric data on the variogram. I. Underlying asymmetry. Comput. Geosci. 33, 1212–1232 (2007).ADS 
Article 

Google Scholar 
36.Webster, R. & Oliver, M. A. Sample adequately to estimate variograms of soil properties. J. Soil Sci. 43(1), 177–192 (1992).Article 

Google Scholar 
37.Monestiez, P., Dubroca, L., Bonnin, E., Durbec, J. P. & Guinet, C. Geostatistical modelling of spatial distribution of Balaenoptera physalus in the Northwestern Mediterranean Sea from sparse count data and heterogeneous observation efforts. Ecol. Model. 193, 615–628 (2006).Article 

Google Scholar 
38.Hegewisch, K. C. & Abatzoglou, J. T. ‘Future Time Series’ Web Tool. NW Climate Toolbox. https://climatetoolbox.org/. Accessed June 2019.39.Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).ADS 
Article 

Google Scholar  More

Andersson M (2006) Condition-dependent indicators in sexual selection: development of theory and tests. In: Lucas JR, Simmons LW (eds) Essays in Animal Behaviour: Celebrating 50 Years of Animal Behaviour. Elsevier Academic Press, p 255–269.Badyaev AV, Hill GE (2000) Evolution of sexual dichromatism: contribution of carotenoid- versus melanin-based colouration. Biol J Linn Soc 69:153–172Article 

Google Scholar 
Bates D, Maechler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67:1–48Article 

Google Scholar 
Bednekoff PA, Houston AI (1994) Avian daily foraging patterns: effects of digestive constraints and variability. Evol Ecol 8:36–52Article 

Google Scholar 
Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol 57:289–300.Bonduriansky R, Rowe L (2005) Sexual selection, genetic architecture, and the condition dependence of body shape in the sexually dimorphic fly Prochyliza xanthostoma (Piophilidae). Evolution 59:138–151PubMed 
Article 
PubMed Central 

Google Scholar 
Brooks R, Kemp DJ (2001) Can older males deliver the good genes? TREE 16:308–313CAS 
PubMed 
PubMed Central 

Google Scholar 
Butler D (2009) ASReml R package version 3.0. VSN International Ltd, Hemel Hempstead, UK
Google Scholar 
Cassey P, Ewen J, Blackburn T, Hauber M, Vorobyev M, Marshall N (2008) Eggshell colour does not predict measures of maternal investment in eggs of Turdus thrushes. Naturwissenschaften 95:713–721CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Charmantier A, Wolak ME, Grégoire A, Fargevieille A, Doutrelant C (2017) Colour ornamentation in the blue tit: quantitative genetic (co) variances across sexes. Heredity 118:125CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Class B, Kluen E, Brommer JE (2019) Tail colour signals performance in blue tit nestlings. J Evol Biol 32:913–920PubMed 
Article 
PubMed Central 

Google Scholar 
Cotton S, Fowler K, Pomiankowski A (2004) Do sexual ornaments demonstrate heightened condition-dependent expression as predicted by the handicap hypothesis? Proc R Soc Lond B Biol Sci 271:771–783Article 

Google Scholar 
Cuthill IC (2006) Color perception. In: Hill GE, McGraw KJ (eds) Bird coloration, Vol. 1. Harvard University Press, p 3–40.D’Alba L, Van Hemert C, Spencer KA, Heidinger BJ, Gill L, Evans NP et al. (2014) Melanin-based color of plumage: role of condition and of feathers’ microstructure. Integr Comp Biol 54:633–644PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
Dale J, Dey CJ, Delhey K, Kempenaers B, Valcu M (2015) The effects of life history and sexual selection on male and female plumage colouration. Nature 527:367–370CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Darwin C (1871) The Descent of Man and Selection in Relation to Sex. Princeton University Press.Delhey K, Burger C, Fiedler W, Peters A (2010) Seasonal changes in colour: a comparison of structural, melanin-and carotenoid-based plumage colours. PLoS ONE 5:e11582PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Delhey K, Delhey V, Kempenaers B, Peters A (2015) A practical framework to analyze variation in animal colors using visual models. Behav Ecol 26:367–375Article 

Google Scholar 
Delhey K, Hall ML, Kingma SA, Peters A (2013) Increased conspicuousness can explain the match between visual sensitivities and blue plumage colours in fairy-wrens. Proc R Soc Lond B Biol Sci 284:20121771
Google Scholar 
Delhey K, Peters A (2008) Quantifying variability of avian colours: Are signalling traits more variable? PLoS ONE 3:e1689.Delhey K, Szecsenyi B, Nakagawa S, Peters A (2017) Conspicuous plumage colours are highly variable. Proc R Soc Lond B Biol Sci 284:20162593
Google Scholar 
Dobson AE, Schmidt DJ, Hughes JM (2019) Heritability of plumage colour morph variation in a wild population of promiscuous, long-lived Australian magpies. Heredity 123:349–358PubMed 
PubMed Central 
Article 

Google Scholar 
Dunn PO, Whittingham LA, Pitcher TE (2001) Mating systems, sperm competition, and the evolution of sexual dimorphism in birds. Evolution 55:161–175CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Endler JA, Mielke PW (2005) Comparing entire colour patterns as birds see them. Biol J Linn Soc 86:405–431Article 

Google Scholar 
Endler JA, Westcott DA, Madden JR, Robson T (2005) Animal visual systems and the evolution of color patterns: sensory processing illuminates signal evolution. Evolution 59:1795–1818PubMed 
Article 
PubMed Central 

Google Scholar 
Evans MR, Barnard P (1995) Variable sexual ornaments in scarlet-tufted malachite sunbirds (Nectarinia johnstoni) on Mount Kenya. Biol J Linn Soc 54:371–381Article 

Google Scholar 
Evans SR, Sheldon BC (2012) Quantitative genetics of a carotenoid-based color: heritability and persistent natal environmental effects in the great tit. Am Nat 179:79–94PubMed 
Article 
PubMed Central 

Google Scholar 
Fan M, D’Alba L, Shawkey MD, Peters A, Delhey K (2019) Multiple components of feather microstructure contribute to structural plumage colour diversity in fairy-wrens. Biol J Linn Soc 128:550–568Article 

Google Scholar 
Fan M, Hall ML, Kingma SA, Mandeltort LM, Hidalgo Aranzamendi N, Delhey K et al. (2017) No fitness benefits of early molt in a fairy-wren: relaxed sexual selection under genetic monogamy? Behav Ecol 28:1055–1067Article 

Google Scholar 
Fan M, Teunissen N, Hall ML, Hidalgo Aranzamendi N, Kingma SA, Roast M et al. (2018) From ornament to armament or loss of function? Breeding plumage acquisition in a genetically monogamous bird. J Anim Ecol 87:1274–1285PubMed 
Article 
PubMed Central 

Google Scholar 
Garant D, Sheldon BC, Gustafsson L (2004) Climatic and temporal effects on the expression of secondary sexual characters: genetic and environmental components. Evolution 58:634–644PubMed 
Article 
PubMed Central 

Google Scholar 
Gosden TP, Chenoweth SF (2011) On the evolution of heightened condition dependence of male sexual displays. J Evol Biol 24:685–692CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Guilford T, Dawkins MS (1991) Receiver psychology and the evolution of animal signals. Anim Behav 42:1–14Article 

Google Scholar 
Guindre‐Parker S, Love OP (2014) Revisiting the condition‐dependence of melanin‐based plumage. J Avian Biol 45:29–33Article 

Google Scholar 
Hadfield JD (2010) MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J Stat Softw 33:1–22Article 

Google Scholar 
Hadfield JD, Burgess MD, Lord A, Phillimore AB, Clegg SM, Owens IP (2006) Direct versus indirect sexual selection: genetic basis of colour, size and recruitment in a wild bird. Proc R Soc Lond B Biol Sci 273:1347–1353
Google Scholar 
Hadfield JD, Nutall A, Osorio D, Owens IPF (2007) Testing the phenotypic gambit: phenotypic, genetic and environmental correlations of colour. J Evol Biol 20:549–557CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Hall ML, Kingma SA, Peters A (2013) Male songbird indicates body size with low-pitched advertising songs. PLoS ONE 8:e56717CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hall ML, Peters A (2009) Do male paternity guards ensure female fidelity in a duetting fairy-wren? Behav Ecol 20:222–228Article 

Google Scholar 
Hamilton WD, Zuk M (1982) Heritable true fitness and bright birds: a role for parasites? Science 218:384–387CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Hart NS (2001) Variations in cone photoreceptor abundance and the visual ecology of birds. J Comp Physiol A 187:685–697CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Hart NS, Hunt DM (2007) Avian visual pigments: characteristics, spectral tuning, and evolution. Am Nat 169(S1):S7–S26PubMed 
Article 
PubMed Central 

Google Scholar 
Hawkins GL, Hill GE, Mercadante A (2012) Delayed plumage maturation and delayed reproductive investment in birds. Biol Rev 87:257–274PubMed 
Article 
PubMed Central 

Google Scholar 
Hegyi G, Szigeti B, Török J, Eens M (2007) Melanin, carotenoid and structural plumage ornaments: information content and role in great tits Parus major. J Avian Biol 38:698–708Article 

Google Scholar 
Hidalgo Aranzamendi N (2017) Life-history variation in a tropical cooperative bird: ecological and social effects on productivity. PhD Thesis, Monash University.Hidalgo Aranzamendi N, Hall ML, Kingma SA, Sunnucks P, Peters A (2016) Incest avoidance, extrapair paternity, and territory quality drive divorce in a year-round territorial bird. Behav Ecol 27:1808–1819.Hidalgo Aranzamendi N, Hall ML, Kingma SA, van de Pol M, Peters A (2019) Rapid plastic breeding response to rain matches peak prey abundance in a tropical savanna bird. J Anim Ecol 88:1799–1811PubMed 
Article 
PubMed Central 

Google Scholar 
Hill GE (2006) Environmental regulation of ornamental coloration. In: Hill GE, McGraw KJ (eds) Bird coloration, Vol. 1. Harvard University Press. p 507–560.Hill GE, Brawner WR (1998) Melanin-based plumage colouration in the house finch is unaffected by coccidial infection. Proc R Soc Lond B Biol Sci 265:1105–1109Article 

Google Scholar 
Johnstone RA, Rands SA, Evans MR (2009) Sexual selection and condition‐dependence. J Evol Biol 22:2387–2394CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Kappers EF, de Vries C, Alberda A, Forstmeier W, Both C, Kempenaers B (2018) Inheritance patterns of plumage coloration in common buzzards Buteo buteo do not support a one-locus two-allele model. Biol Lett 14:20180007PubMed 
PubMed Central 
Article 

Google Scholar 
Kemp DJ, Rutowski RL (2007) Condition dependence, quantitative genetics, and the potential signal content of iridescent ultraviolet butterfly coloration. Evolution 61:168–183PubMed 
Article 
PubMed Central 

Google Scholar 
Kendeigh SC, Kontogiannis JE, Mazac A, Roth RR (1969) Environmental regulation of food intake by birds. Comp Biochem Physiol 31:941–957Article 

Google Scholar 
Keyser AJ, Hill GE (2000) Structurally based plumage coloration is an honest signal of quality in male blue grosbeaks. Behav Ecol 11:202–209Article 

Google Scholar 
Kingma SA, Hall ML, Arriero E, Peters A (2010) Multiple benefits of cooperative breeding in purple-crowned fairy-wrens: a consequence of fidelity? J Anim Ecol 79:757–768PubMed 
PubMed Central 

Google Scholar 
Kingma SA, Hall ML, Peters A (2011) No evidence for offspring sex-ratio adjustment to social or environmental conditions in cooperatively breeding purple-crowned fairy-wrens. Behav Ecol Sociobiol 65:1203–1213Article 

Google Scholar 
Kingma SA, Hall ML, Segelbacher G, Peters A (2009) Radical loss of an extreme extra-pair mating system. BMC Ecol 9:15PubMed 
PubMed Central 
Article 

Google Scholar 
Kokko H (1997) Evolutionarily stable strategies of age-dependent sexual advertisement. Behav Ecol Sociobiol 41:99–107Article 

Google Scholar 
Kuznetsova A, Brockhoff PB, Christensen RHB (2015) Package ‘lmerTest’ R version 20–33.Lantz SM, Karubian J (2016) Male Red-backed Fairywrens appear to enhance a plumage-based signal via adventitious molt. The Auk 133:338–346Article 

Google Scholar 
Lind O, Chavez J, Kelber A (2014) The contribution of single and double cones to spectral sensitivity in budgerigars during changing light conditions. J Comp Physiol A 200:197–207Article 

Google Scholar 
Lind O, Henze MJ, Kelber A, Osorio D (2017) Coevolution of coloration and colour vision? Philos Trans R Soc Lond B Biol Sci 372:20160338PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Lindström Å, Visser GH, Daan S (1993) The energetic cost of feather synthesis is proportional to basal metabolic rate. Physiol Zool 66:490–510Article 

Google Scholar 
Manning JT (1985) Choosy females and correlates of male age. J Theor Biol 116:349–354Article 

Google Scholar 
Masello JF, Lubjuhn T, Quillfeldt P (2008) Is the structural and psittacofulvin-based coloration of wild burrowing parrots Cyanoliseus patagonus condition dependent? J Avian Biol 39:653–662Article 

Google Scholar 
McGraw KJ (2006) Mechanics of melanin coloration in birds. In: Hill GE, McGraw KJ (eds) Bird coloration, Vol. 1. Harvard University Press. p 243–294.McGraw KJ, Mackillop EA, Dale J, Hauber ME (2002) Different colors reveal different information: how nutritional stress affects the expression of melanin- and structurally based ornamental plumage. J Exp Biol 205:3747–3755PubMed 
Article 
PubMed Central 

Google Scholar 
McGraw KJ, Safran RJ, Wakamatsu K (2005) How feather colour reflects its melanin content. Funct Ecol 19:816–821Article 

Google Scholar 
McQueen A, Delhey K, Barzan FR, Naimo AC, Peters A (2021) Male fairy-wrens produce and maintain vibrant breeding colors irrespective of individual quality. Behav Ecol 32:178–187Article 

Google Scholar 
Merilä J, Sheldon BC (1999) Genetic architecture of fitness and nonfitness traits: empirical patterns and development of ideas. Heredity 83:103–109PubMed 
Article 
PubMed Central 

Google Scholar 
Meunier J, Pinto SF, Burri R, Roulin A (2011) Eumelanin-based coloration and fitness parameters in birds: a meta-analysis. Behav Ecol Sociobiol 65:559–567Article 

Google Scholar 
Ödeen A, Pruett-Jones S, Driskell AC, Armenta JK, Håstad O (2012) Multiple shifts between violet and ultraviolet vision in a family of passerine birds with associated changes in plumage coloration. Proc R Soc Lond B Biol Sci 279:1269–1276
Google Scholar 
Olsson P, Lind O, Kelber A (2017) Chromatic and achromatic vision: parameter choice and limitations for reliable model predictions. Behav Ecol 29:273–282Article 

Google Scholar 
Owens IPF, Hartley IR (1998) Sexual dimorphism in birds: why are there so many different forms of dimorphism? Proc R Soc Lond B Biol Sci 265:397–407Article 

Google Scholar 
Peters A, Delhey K, Andersson S, Van Noordwijk H, Förschler MI (2008) Condition‐dependence of multiple carotenoid‐based plumage traits: an experimental study. Funct Ecol 22:831–839Article 

Google Scholar 
Peters A, Kingma SA, Delhey K (2013) Seasonal male plumage as a multi-component sexual signal: Insights and opportunities. Emu 113:232–247Article 

Google Scholar 
Peters A, Kurvers RHJM, Roberts ML, Delhey K (2011) No evidence for general condition-dependence of structural plumage colour in blue tits: an experiment. J Evol Biol 24:976–987CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Pomiankowski A, Møller AP (1995) A resolution of the lek paradox. Proc R Soc Lond B Biol Sci 260:21–29Article 

Google Scholar 
Price T, Schluter D (1991) On the low heritability of life‐history traits. Evolution 45:853–861PubMed 
Article 
PubMed Central 

Google Scholar 
Proulx SR, Day T, Rowe L (2002) Older males signal more reliably. Proc R Soc Lond B Biol Sci 269:2291–2299Article 

Google Scholar 
Prum RO (2006) Anatomy, physics, and evolution of structural colours. In: Hill GE, McGraw KJ (eds) Bird coloration, Vol. 1. Harvard University Press. p 295–353.Prum RO (2010) The Lande–Kirkpatrick mechanism is the null model of evolution by intersexual selection: implications for meaning, honesty, and design in intersexual signals. Evolution 64:3085–3100PubMed 
Article 
PubMed Central 

Google Scholar 
R Core Team (2017) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available at https://www.R-project.org/.Reinhold K (2011) Variation in acoustic signalling traits exhibits footprints of sexual selection. Evolution 65:738–745PubMed 
Article 
PubMed Central 

Google Scholar 
Renoult JP, Kelber A, Schaefer HM (2017) Colour spaces in ecology and evolutionary biology. Biol Rev 92:292–315PubMed 
Article 
PubMed Central 

Google Scholar 
Roast MJ, Aranzamendi NH, Fan M, Teunissen N, Hall MD, Peters A (2020) Fitness outcomes in relation to individual variation in constitutive innate immune function. Proc R Soc Lond B Biol Sci 287:20201997
Google Scholar 
Roulin A (2016) Condition‐dependence, pleiotropy and the handicap principle of sexual selection in melanin‐based colouration. Biol Rev 91:328–348PubMed 
Article 
PubMed Central 

Google Scholar 
Roulin A, Ducrest AL (2013) Seminars in cell and developmental biology, Vol 24, no 6–7. Academic Press. p 594–608.Rowe L, Houle D (1996) The lek paradox and the capture of genetic variance by condition dependent traits. Proc R Soc Lond B Biol Sci 263:1415–1421Article 

Google Scholar 
Rowley I, Russell E (1997) Fairy-wrens and Grasswrens: Maluridae. Oxford University Press.Taylor PD, Williams GC (1982) The lek paradox is not resolved. Theor Popul Biol 22:392–409Article 

Google Scholar 
Schodde R (1982) The Fairy-wrens: a monograph of the Maluridae. Lansdowne Editions.Shawkey MD, D’Alba L (2017) Interactions between colour-producing mechanisms and their effects on the integumentary colour palette. Philos Trans R Soc Lond B Biol Sci 372:20160536PubMed 
PubMed Central 
Article 

Google Scholar 
Tibbetts EA (2010) The condition dependence and heritability of signaling and nonsignaling color traits in paper wasps. Am Nat 175:495–503PubMed 
Article 
PubMed Central 

Google Scholar 
Trivers RL (1972) Parental investment and sexual selection. In: Campbell B (ed) Sexual Selection and the Descent of Man 1871–1971. Heinemann. p 136–179.Vorobyev M, Osorio D, Bennett AT, Marshall NJ, Cuthill IC (1998) Tetrachromacy, oil droplets and bird plumage colours. J Comp Physiol A 183:621–633CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
White TE (2020) Structural colours reflect individual quality: a meta-analysis. Biol Lett 16:20200001PubMed 
PubMed Central 
Article 

Google Scholar 
Wilson AJ, Reale D, Clements MN, Morrissey MM, Postma E, Walling CA et al. (2010) An ecologist’s guide to the animal model. J Anim Ecol 79:13–26PubMed 
Article 
PubMed Central 

Google Scholar  More

Data sourcesNo single comprehensive dataset of planktonic foraminiferal distributional records currently exists. Instead, these data are available from a wide range of sources in many different structures. Some of these sources are compilations of existing data (e.g., Neptune14,15,16, ForCenS21), and others derive from individual sampling sites (e.g. ocean drilling expeditions). Triton combines these disparate sources (Fig. 1) to produce a single spatio-temporal dataset of Cenozoic planktonic foraminifera with updated and consistent taxonomy, age models, and paleo-coordinates.Neptune is currently the most comprehensive database of fossil plankton data, with records exclusively from the DSDP, ODP and IODP representing planktonic foraminifera, calcareous nannofossils, diatoms, radiolaria and dinoflagellates14,15,16. A subset of these sites is included in Neptune, representing those with the most continuous sampling through time. The raw data from Neptune form the core of our dataset. All foraminiferal occurrences for the Cenozoic (i.e. last 66 Ma) were downloaded using the GTS 2012 timescale. In the download options, all questionable identifications and invalid taxa were removed, as were records that had been identified as reworked.In addition to Neptune, three other compilation datasets were included in Triton: ForCenS21, which consists of global core-top samples; the Eocene data from Fenton, et al.8 created based on literature searches for planktonic foraminiferal data in the Eocene; and the land-based records from Lloyd, et al.22 that were created from literature searches. The marine records in Lloyd, et al.22 were not included, as they were obtained from Neptune.Following preliminary compilation of existing datasets, we identified all legacy DSDP, ODP and IODP cores missing from Triton. The online DESCLogik (http://web.iodp.tamu.edu/DESCReport/) and Pangaea17 databases were then mined for .csv files containing planktonic foraminiferal species count data for the missing cores, supplemented with data from AWI_Paleo (URI: http://www.awi.de/en/science/geosciences/marine-geology.html), GIK/IFG (URI: http://www.ifg.uni-kiel.de/), MARUM (URI: https://www.marum.de/index.html), and QUEEN (URI: http://ipt.vliz.be/eurobis/resource?r=pangaea_2747). All additional cores were assessed individually by inspecting the scientific drilling proceedings to determine whether sites were suitable to contribute to our dataset. The primary assessment criterion was identification of continuous sedimentary sections, wherein two or more confidently assigned consecutive chronostratigraphic tie points existed to allow for construction of age models.In addition to these longer cores, many sediment sampling projects have produced planktonic foraminiferal distribution data from shorter cores that tend to correspond in age to the last few million years. The website PANGAEA17 (www.pangaea.de) has been used as a repository for most of these occurrence data. This website was searched using the terms “plank* AND foram”, with resulting datasets downloaded using the R package ‘pangaear’23. These datasets were filtered to exclude records collected using multinets, sediment traps or box cores, as these methods produce samples not easily correlated to sediment cores. Column names allowed for further filtering to exclude records with no species-level data, records that had only isotopic data (rather than abundance data), or records with no age controls.Data processingThe data sources underpinning Triton serve their records in different formats. Therefore, processing was necessary to convert records into a unified framework, with one species per row for each sample and associated metadata (see below for details). Some metadata could be used without modification when available (e.g. water depth, data source), whereas other data needed processing to ensure consistency (e.g. abundance, paleo-coordinates, age). Without this processing, samples from different sources were not directly comparable. Where data were not available, they were set to NA. Those records with missing data in crucial columns (species name, abundance, age, and paleo-coordinates) were removed from the final dataset. All data processing was performed using R v. 3.6.124.Taxonomic consistency is essential to enable comparison of datasets created at different times. The species and synonymy lists used in Triton are based on the Paleogene Atlases20,25,26, with additional information from mikrotax27 (http://www.mikrotax.org/pforams/). These sources were supplemented, when necessary with more up to date literature including Poole and Wade28 and Lam and Leckie29. (A full list of the taxonomic sources can be found in the PFdata.xlsx file18.) A synonymy list was generated to convert species names to the senior synonym. At the same time, typographic errors were corrected. For example, Globototalia flexuosa should be Globorotalia flexuosa. Exclusively Mesozoic taxa were omitted, as were all instances when species names were unclear or imprecise (i.e. not at the species level). Junior synonyms were merged with their senior synonyms and their abundances summed, although the original names and abundances are also retained in the processed dataset. For presence/absence samples, these numerical merged abundances were set to one (i.e. present). The full species list and list of synonyms can be found in the accompanying data.Abundance data for planktonic foraminifera are provided in different formats: presence/absence, binned abundance, relative abundance, species counts, and number of specimens per gram. These metrics were converted into numeric relative abundance to make comparisons easier, although both the original abundance value and its numeric version are retained, as is a record of the abundance type. Presence/absence data were converted to a binary format (one for present; zero for absent). Species counts were converted to relative percent abundances based on the total number of specimens in the sample (this was calculated where it was not already recorded). When full counts were not performed, binned abundances were frequently used. These binned abundances were converted into numeric abundances based on the sequence. So, for example, the categorical labels of N, P, R, F, C, A, D (indicating none, present, rare, few, common, abundant, dominant) were converted to a numerical sequence of 0 to 6. As the meaning of letters can depend on the context (e.g. ‘A’ could be absent or abundant), conversion was done in a semi-automated fashion on a sample-by-sample basis. A value of 0.01 was assigned to records where an inconsistent abundance was recorded (e.g. samples with mostly numeric counts but a few species were designated ‘P’, indicating presence). Samples with zero abundance were retained in the full dataset to provide an indication of sampling.The age of samples were recorded in multiple ways. For some samples, age models provide precise numerical estimates of the age (e.g., those in Neptune). Other samples are dated relative to stratigraphic events such as biostratigraphic zones (including benthic and planktonic foraminifera, diatoms, radiolarians and nannofossils) or magnetic reversals. In this case, ages sometimes needed to be converted to reflect revised age estimates. The start and end dates of biostratigraphic zones are defined in relation to events in marker species, e.g. their speciation, extinction or acme events. All such marker events were updated to their most recent estimates and tuned to the GTS 2020 timescale19. The process of updating included correction of synonymies. Additional care was taken to ensure the correct interpretation of abbreviations (e.g. determining whether LO meant lowest occurrence or last occurrence) based on the entire list of events for a study. Where up-to-date ages were not available or events were ambiguous, they were removed from the age models.The marker events defining a zone can depend on the zonal scheme used. For example, Berggren30 defined the base of the planktonic foraminifera zone M8 as the first occurrence of Fohsella fohsi. Wade et al.31 used this same event to define the base of M9. Therefore, the zonal scheme was recorded when collecting age models, to accurately convert ages to the GTS 2020 time scale. Some marker events have different ages depending on the ocean basin or latitude, and these differences are not necessarily well studied31,32. Where these differences in marker events have been recorded, the coordinates of a site were used to determine whether sites were in the Atlantic or Indo-Pacific Ocean, and whether they were tropical or temperate (with the division at 23.5° latitude). However, this is an area where more research is needed to improve the accuracy of higher-latitude dating32. Magnetostratigraphic ages were also tuned to the GTS 2020 timescale.We constructed new age models for samples not already assigned a numeric age. Where the depths of biostratigraphic events were already recorded, these were converted directly to GTS 2020. Where samples were not given any ages, often the case for the cores collected in the early days of ocean drilling, ages were reconstructed from the shipboard and post-cruise biostratigraphic data available in DESCLogik, Pangaea, and drilling publications. For holes where no tie point data were retrievable, biostratigraphic count data were extracted directly from drilling publications, and biostratigraphic events were assigned via GTS 2020. The first and last occurrences in raw shipboard biostratigraphic data often do not represent true datums, and careful assessment of the shipboard, and post-cruise literature was a prerequisite to confidently assigning chronostratigraphic datums. Tie point depths were assigned as the midpoint depth between the core sample before and after an event. For example, for an extinction event, the recorded depth was the midway point between the last recorded occurrence of a species and the first sample from which the species is absent. All sites were assessed individually to determine the age of the seafloor. Where IODP reports or sample-based publications strictly stated that the sediment surface (i.e. 0.00 meters below seafloor (mbsf)) was deemed to be “Holocene”, “Recent” or “Modern” in age, an additional 0 Ma tie point was assigned appropriately. All samples present outside the maximum/minimum age tie points for that site were removed, as they could not be confidently assigned an age. During assessment, individual drilling reports were investigated for geological structures. Where features such as unconformities, reverse faults, stratigraphic inversions, décollements, and major slips and slumps were identified, separate age models were generated for individual intact stratal intervals to account for potential externally emplaced or repeated strata (see “Age models” and “Triton working” in the figshare data repository18). Similarly, age gaps of greater than 10% of the age range of the core were classified as hiatuses, leading to separate age models (see Fig. 4). Cores of denser sediments that have been sampled using rotary drilling will often have only ~50–60% recovery in a core (9.5 m)33. As it is not possible to determine where the recovered core material came from within this length, all intact core pieces are grouped together as a continuous section from the section top, regardless of where the pieces were sourced (e.g. 4.5 m of recovered material will be recorded as 0–4.5 m of cored interval even if some came from 9–9.5 m). Consequently, age estimates within cores where recovery was low, typically the samples collected longer ago, will necessarily be less certain.Fig. 4Different age model estimates applied to core material from IODP Site U1499A in the South China Sea. Mag – mean age based only on the magnetostratigraphic marker events. Zones – mean age based on all the marker events. Int Mag – interpolation of the points between the magnetostratigraphic marker events. Interp – interpolation between the full set of marker events. Model – the model of age as a function of depth. Note the hiatus between 50 and 100 m. For the shallower section of the dataset, with only three data points, a simple linear model was used. For the deeper section, a GAM smooth was fitted. For this site, the model predictions were chosen as the best fit.Full size imageUsing the updated marker event ages, we created age-depth plots and modelled the best fit to the data. There are different ways of creating these models, and multiple methods were applied to each core. The one that provided the best fit to the original data was chosen (the different age models are available in “Age models” in the figshare data repository18). These choices were confirmed manually (see Fig. 4). The simplest age model used interpolation of the marker events to create ‘zones’ and assign estimated ages assuming a continuous sedimentation rate between the start and end of each of these zones. Where the events do not provide a continuous sequence (e.g. gaps in the zonal markers), age estimates were assigned as the mean of that zone with error estimates of the width of the zone. Where magnetostratigraphic events were present they were given preference. This method leads to different estimates of sedimentation rate for each zone. The more complex age model estimates a smoother sedimentation rate. When there were fewer than 5 marker events, a linear model of age as a function of depth was fitted for the entire core. For larger datasets, generalised additive models (GAMs) for the same variables were used, to allow for variation in sedimentation rates through time. GAMs were run using the mgcv R library, with a gamma value of 1.134. The type of age model used in the analysis was recorded. Where appropriate, the number of points and the r2 of the model are recorded to give an indication of the accuracy of the age model.The latitude and longitude coordinates of samples were recorded in decimal degrees. For all samples except modern ones, plate tectonic reconstructions were necessary to determine the coordinates at which the sample was originally deposited. Reconstructions were performed using the Matthews, et al.35 plate motion model, which is an updated version of the Seton, et al.36 model used by Neptune. Comparisons of age models35,36,37,38,39 suggest this model is most appropriate for the deep sea environment where most of the samples occur, and is able to assign coordinates to significantly more sites than the Scotese39 GPlates model. This test was performed with a subset of the data (10633 unique sites); the Matthews, et al.35 model provided paleocoordinates for 95% of the data, whilst the GPlates model only provided coordinates for 17% of the data. The calculation of paleocoordinates was automated using an adaptation of https://github.com/macroecology/mapast.When sediment samples are derived from multiple sources, duplication will inevitably occur. All such duplicated records, identified based on the combination of species, abundance, sample depth, and coordinate values, were removed. Additionally, working on an individual record level, species that occurred significantly outside their known ranges were flagged (following updated age models) on the assumption these records were misidentifications, contamination or re-working. Records were classified as falling significantly outside their known range if they were more than 5 Ma outside the species’ range in the Palaeogene (66-23 Ma) and more than 2 Ma in the Neogene (23-0 Ma). These values were chosen based on the tradeoff between removing reworked specimens and allowing for some errors in the age estimates. Age estimates for older samples tend to be less precise. Ages were obtained from Lamyman et al. (in prep) and are available in “PFdata” in the figshare data repository18. In total, 10,990 suspect records were flagged (~2% of all records). More

Based on the present-day distribution of photosynthetic bacteria31, we assume a competitive advantage for anoxygenic photosynthetic bacteria in early environments where electron donors such as Fe2+, H2S, or H2 were present. We also assume the contemporaneous existence of environments where cyanobacterial populations could thrive, providing a seedbed for migration. Non-marine waters provide an example of the latter, supported by the branching of non-marine taxa from basal nodes in cyanobacterial phylogenies44,45 and also by the presence of stromatolites in Archean lacustrine successions46, despite the likelihood that many Archean lakes and rivers had low levels of potential electron donors such as Fe2+ and H2S47.Following Jones et al.40 and Ozaki et al.42, we use Fe (iron) and P (phosphorus) to represent the environment, which is similar to the H2 and P employed in other studies48,49. The logic of this choice is that in Archean oceans, Fe2+ is thought to have been the principal electron donor for anoxygenic photosynthesis50,51, whereas P governed total rates of photosynthesis. (Kasting14 argued that H2 was key to photosynthesis on the early Earth, a view supported by low iron concentrations in some early Archean stromatolites52.). In any event, under the conditions of low P availability thought to have characterized early oceans25,40,49,53,54,55, anoxygenic photosynthesis would have depleted limiting nutrients before alternative electron donors were exhausted. In consequence, rates of photosynthetic oxygen production would be low. As iron availability declined and/or P availability increased, the biosphere would inevitably reach a point where P would remain after Fe2+ had been depleted, expanding the range of environments where cyanobacteria are favored by natural selection42.Our model keeps track of the abundances of anoxygenic photosynthetic bacteria (APB), x1, cyanobacteria, x2, and three crucial chemicals: iron(II) (Fe2+), y1, phosphate (PO43−), y2, and dioxygen (O2), z. Both types of bacteria require phosphate for reproduction. APB needs iron(II) (or some other suitable reductant) as an electron donor in photosynthesis. The following five equations describe the reproduction and death of APB and of cyanobacteria as well as the dynamics of iron(II), phosphate, and dioxygen:$${rm{APB}}: {dot{x}}_{1} ={x}_{1}{y}_{1}{y}_{2}-{x}_{1}+{u}_{1}\ {rm{Cyano}}: {dot{x}}_{2} =c{x}_{2}{y}_{2}-{x}_{2}+{u}_{2}\ {{rm{Fe}}}^{2+}: {dot{y}}_{1} ={f}_{1}-{y}_{1}-{x}_{1}{y}_{1}{y}_{2}-{y}_{1}z\ {{rm{PO}}_{4}}^{3-}: {dot{y}}_{2} ={f}_{2}-{y}_{2}-{x}_{1}{y}_{1}{y}_{2}-{x}_{2}{y}_{2}\ {{rm{O}}}_{2}: dot{z} =a{x}_{2}{y}_{2}-bz-{y}_{1}z$$
(1)
Here, we have omitted to write symbols for those rate constants that, for understanding the GOE, can be set to one without loss of generality (Supplementary Note 1). Each remaining rate constant is a free parameter. Equations (1) thus satisfy redox balance by construction. We are left with a system that has five main parameters: c specifies the rate of reproduction of cyanobacteria; f1 and f2 denote the rates of supply of iron(II) and phosphate, respectively; a denotes biogenic production of oxygen; b denotes geochemical consumption of oxygen. Note that iron(II) and phosphate are also removed by geochemical processes at a rate proportional to their abundance. In addition, iron(II) is used up during anoxygenic photosynthesis, and iron(II) reacts with oxygen and is thereby removed from the system. Phosphate is used up during the growth of APB and cyanobacteria. (We investigate extensions of the model that incorporate bounded bacterial growth rates and organic carbon in Supplementary Note 2 and Supplementary Note 3, respectively.)We posit iron(II) as the primary electron donor for anoxygenic photosynthesis, and for simplicity of presentation, we refer to y1 and f1 in this context. However, as noted above, y1 and f1 can similarly represent the abundances and influxes of other alternative electron donors, especially dihydrogen (H2)56,57 and hydrogen sulfide (H2S)58. Our model, its analytical solution, and the conclusions that follow hold equally well by considering any of these electron donors or all together.We also include small migration rates, u1 and u2, which allow for the possibility that APB and cyanobacteria persist in privileged sites from which they can migrate into the main arena of competition. On the Archean Earth, these parameters could have been affected by the flow of water and by surface winds. For the mathematical analysis presented in the main text, we assume that these rates are negligibly small.The GOE represents the transition from a world dominated by APB (Equilibrium E1) to one that is dominated by cyanobacteria (Equilibrium E2) (Figs. S1, S2). On a slowly changing planet, the abundances of APB and cyanobacteria and of the three chemicals are approximately in steady state. Therefore, we consider the fixed points of Eqs. (1).Pure equilibriaIn the absence of APB and cyanobacteria, the abiotic equilibrium abundances of iron(II) and of phosphate are given by f1 and f2, respectively, and there is no oxygen in the system. If f1f2  > 1, then APB can emerge. Subsequently, the system settles to Equilibrium E1, where only APB are present and there is still no oxygen. E1 is stable against invasion of cyanobacteria if$${f}_{1}-{f}_{2}, > ,frac{(c+1)(c-1)}{c}.$$
(2)
This condition can be fulfilled if the influx of iron, f1, is large enough, or if the influx of phosphate, f2, is small enough. The term on the right-hand side of the inequality is an increasing function of the reproductive rate, c, of cyanobacteria.If cf2  > 1, then the system admits another equilibrium, E2, where only cyanobacteria are present and oxygen is abundant. Equilibrium E2 is stable against invasion of APB if$$a(c{f}_{2}-1), > ,(b+c)({f}_{1}-c).$$
(3)
The left-hand side of the inequality is positive. If the right-hand side is negative (that is, if f1  ,c(a-1).$$
(4)
Condition (4) is understood as follows. If b is sufficiently large, then there is not enough atmospheric oxygen for rusting to render E2 stable against invasion of APB before E1 loses stability; the result is stable coexistence. But if b is sufficiently small, then rusting causes E2 to become stable before E1 becomes unstable. The critical value of b therefore depends on the input of atmospheric oxygen for Equilibrium E2; it is an increasing function of the reproductive rate of cyanobacteria and of their rate of production of oxygen.If a  c(a − 1). Figure 3 shows gradual oxygenation due to decreasing f1. In this case, the transition occurs via the mixed equilibrium, (hat{E}), where both types of bacteria coexist (Fig. 4). A subsequent increase in f1 can cause APB to regain dominance (Fig. S3a).Fig. 3: The GOE can be triggered by a decline in the influx of iron(II) and is gradual if b  > c(a − 1).Equilibrium E1 (APB dominate) loses stability and Equilibrium E2 (cyanobacteria dominate) gains stability when f1 drops below ({f}_{1}^{* }) and (f_1^{prime}), respectively. We set f2 = 80, c = 10, a = 10, b = 100, and u1 = u2 = 10−3. a We simulate Eqs. (8) from Supplementary Note 1 with α1 = α2 = β1 = β2 = 1, and we set f1 = 100 − 40(t/105). t* denotes the time at which Equilibrium E1 loses stability. b There is stable coexistence of both types of bacteria for (f_1^{prime} , More

1.Walcott, C. D. Smithson. Misc. Collect. 57, 17–40 (1911).
Google Scholar 
2.Sepkoski, J. J. Jr. Paleobiology 10, 246–267 (1984).Article 

Google Scholar 
3.Hughes, N. C. Curr. Sci. 110, 774–775 (2016).
Google Scholar 
4.Kühl, G., Briggs, D. E. G. & Rust, J. Science 323, 771–773 (2009).Article 

Google Scholar 
5.Moysiuk, J., Smith, M. R. & Caron, J.-B. Nature 541, 394–397 (2017).CAS 
Article 

Google Scholar 
6.Yang, X. et al. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-021-01490-4 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
7.Sánchez, M. Embryos in Deep Time (Univ. California Press, 2012).8.Fusco, G., Hong, P. S. & Hughes, N. C. Proc. R. Soc. Lond. B 281, 20133037 (2014).
Google Scholar 
9.Hughes, N. C., Hong, P. S., Hou, J. & Fusco, G. Front. Ecol. Evol. 5, 37 (2017).Article 

Google Scholar 
10.Hopkins, M. J. Pap. Palaeontol. 7, 985–1002 (2020).Article 

Google Scholar 
11.Moczek, A. P. et al. Evol. Dev. 17, 198–219 (2015).Article 

Google Scholar 
12.Walossek, D. & Müller, K. J. Lethaia 23, 409–427 (1990).Article 

Google Scholar 
13.Fu, D., Ortega-Hernández, J., Daley, A. C., Zhang, X. & Shu, D. BMC Evol. Biol. 18, 147 (2018).Article 

Google Scholar 
14.Hughes, N. C., Kříž, J., MacQuaker, J. H. S. & Huff, W. D. Bull. Geosci. 89, 219–238 (2014).Article 

Google Scholar 
15.Hartnoll, R. G. & Bryant, A. D. J. Crustac. Biol 10, 14–19 (1990).Article 

Google Scholar 
16.Minelli, A. & Fusco, G. Evolving Pathways–Key Themes in Evolutionary Developmental Biology (Cambridge Univ. Press, 2008). More

Effects of waterlogging stress on leaf morphology in mulberry seedlingsFigure 1 shows the change in the leaf morphology of mulberry seedlings under different submergence depths. The results showed that the seedlings under both SS and HS could grow well, and there were 3 slightly wilted leaves on average under FS. There were 3 wilted leaves and 2 defoliated leaves on average in the HS group after 10 days of flooding, and a few adventitious roots began to appear at the base of the stem. In the SS group, there slight wilting and falling of mulberry leaves were observed on the 15th day after submergence, and there were 5 wilting leaves and a few adventitious roots per plant. In the SS group, there were 3 defoliated leaves and 2 wilted leaves per mulberry seedling, and no adventitious roots developed. The HS group showed an average of 7 adventitious roots per plant. Additionally, there were 8 wilted leaves, 10 defoliated leaves and 4 brown spots per plant under HS.Figure 1Effect of submergence stress on leaf morphology in Morus alba: (a) The number of curled or wilted leaves per plant; (b) The number of brown spots or rotten leaves per plant; (c) The number of fallen leaves per plant; (d) The number of adventitious roots. This figure was drawn using Origin Pro 2021 v. 9.8.0.200.Full size imageEffects of waterlogging stress on initial fluorescence (Fo), and maximum fluorescence (Fm) under dark adaptation in mulberry leavesThe initial fluorescence value (Fo) and the maximum fluorescence value (Fm) of mulberry seedlings significantly decreased over time. Figure 2a shows that the Fo values of mulberry seedlings under SS, HS, and FS decreased by 31.27%, 22.51%, and 42.45%, respectively, on day 4 and were significantly different (p  More