Ecology

1.Murphy, P. G. & Lugo, A. E. Ecology of tropical dry forest. Ann. Rev. Ecol. Syst. 17, 67–88. https://doi.org/10.1146/annurev.es.17.110186.000435 (1986).Article 

Google Scholar 
2.Hasselquist, N. J., Allen, M. F. & Santiago, L. S. Water relations of evergreen and drought-deciduous trees along a seasonally dry tropical forest chronosequence. Oecologia 164, 881–890. https://doi.org/10.1007/s00442-010-1725-y (2010).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
3.Maass, M. et al. Long-term (33 years) rainfall and runoff dynamics in a tropical dry forest ecosystem in western Mexico: Management implications under extreme hydrometeorological events. For. Ecol. Manage. 426, 7–17. https://doi.org/10.1016/j.foreco.2017.09.040 (2018).Article 

Google Scholar 
4.NOAA. National Weather Service. Climate Prediction Center. Cold and warm episodes by season. http://www.cpc.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml. (Accessed 19 October 2019).5.Detto, M., Wright, J., Calderón, O. & Muller-Landau, H. C. Resource acquisition and reproductive strategies of tropical forest in response to the El Niño-Southern Oscillation. Nat. Commun. 9, 9–13. https://doi.org/10.1038/s41467-018-03306-9 (2018).CAS 
Article 

Google Scholar 
6.Bretfeld, M., Ewers, B. E. & Hal, J. S. Plant water use responses along secondary forest succession during the 2015–2016 El Niño drought in Panama. New Phytol. 219, 885–899. https://doi.org/10.1111/nph.15071 (2018).Article 
PubMed 

Google Scholar 
7.Meakem, V. et al. Role of tree size in moist tropical forest carbon cycling and water deficit responses. New Phytol. 219, 947–958. https://doi.org/10.1111/nph.14633 (2018).CAS 
Article 
PubMed 

Google Scholar 
8.Salmon, Y. et al. Drought impacts on tree phloem: From cell-level responses to ecological significance. Tree Physiol. 39, 173–191. https://doi.org/10.1093/treephys/tpy153 (2019).CAS 
Article 
PubMed 

Google Scholar 
9.Brodribb, T. J., Powers, J., Cochard, H. & Choat, B. Hanging by a thread? Forests and drought. Science 368, 261–266. https://doi.org/10.1126/science.aat7631 (2020).ADS 
CAS 
Article 
PubMed 

Google Scholar 
10.Powers, J. S. et al. A catastrophic tropical drought kills hydraulically vulnerable tree species. Glob. Change Biol. 26, 3122–3133. https://doi.org/10.1111/gcb.15037 (2020).ADS 
Article 

Google Scholar 
11.Wigneron, J. P. et al. Tropical forests did not recover from the strong 2015–2016 El Niño event. Sci. Adv. 6, eaay4603. https://doi.org/10.1126/sciadv.aay4603 (2020).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
12.Martinez-Vilalta, J. & Lloret, F. Drought-induced vegetation shifts in terrestrial ecosystems: The key role of regeneration dynamics. Glob. Planet. Change 144, 94–108. https://doi.org/10.1016/j.gloplacha.2016.07.009 (2016).ADS 
Article 

Google Scholar 
13.Allen, C. D. et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manag. 259, 660–684. https://doi.org/10.1016/j.foreco.2009.09.001 (2010).Article 

Google Scholar 
14.Anderegg, W. R. L. et al. Meta-analysis reveals that hydraulic traits explain cross-species patterns of drought-induced tree mortality across the globe. PNAS 113, 5024–5029. https://doi.org/10.1073/pnas.1525678113 (2016).ADS 
CAS 
Article 
PubMed 

Google Scholar 
15.Greenwood, S. et al. Tree mortality across biomes is promoted by drought intensity, lower wood density and higher specific leaf area. Ecol. Lett. 20, 539–553. https://doi.org/10.1111/ele.12748 (2017).Article 
PubMed 

Google Scholar 
16.Sperry, J. S., Meinzer, F. C. & McCulloh, K. A. Safety and efficiency conflicts in hydraulic architecture: Scaling from tissues to trees. Plant Cell Environ. 31, 632–645. https://doi.org/10.1111/j.1365-3040.2007.01765.x (2008).Article 
PubMed 

Google Scholar 
17.Borchert, R. & Pockman, W. T. Water storage capacitance and xylem tension in isolated branches of temperate and tropical trees. Tree Physiol. 25, 457–466. https://doi.org/10.1093/treephys/25.4.457 (2005).Article 
PubMed 

Google Scholar 
18.Valdez-Hernández, M., Andrade, J. L., Jackson, P. C. & Rebolledo-Vieyra, M. Phenology of five tree species of a tropical dry forest in Yucatán, Mexico: Effects of environmental and physiological factors. Plant Soil 329, 155–171. https://doi.org/10.1007/s11104-009-0142-7 (2010).CAS 
Article 

Google Scholar 
19.Santiago, L. S. et al. Coordination and trade-offs among hydraulic safety, efficiency and drought avoidance traits in Amazonian rainforest canopy tree species. New Phytol. 218, 1015–1024. https://doi.org/10.1111/nph.15058 (2018).Article 
PubMed 

Google Scholar 
20.Bussotti, F., Pollastrini, M., Holland, V. & Bruggemann, W. Functional traits and adaptive capacity of European forests to climate change. Environ. Experim. Bot. 111, 91–113. https://doi.org/10.1016/j.envexpbot.2014.11.006 (2015).Article 

Google Scholar 
21.Reich, P. B. & Borchert, R. Water stress and tree phenology in a tropical dry forest in the lowlands of Costa Rica. J. Ecol. 72(1), 61–74. https://doi.org/10.2307/2260006 (1984).Article 

Google Scholar 
22.Holbrook, N. M., Whitbeck, J. L. & Mooney, H. A. Drought responses of neotropical dry forest trees. In Seasonally Dry Tropical Forests (eds Bullock, S. H. et al.) (Cambridge University Press, 1995). https://doi.org/10.1017/CBO9780511753398.010.
Google Scholar 
23.Wolfe, B. T. & Kursar, T. A. Diverse patterns of stored water use among saplings in seasonally dry tropical forests. Oecologia 179, 925–936. https://doi.org/10.1007/s00442-015-3329-z (2015).ADS 
Article 
PubMed 

Google Scholar 
24.Borchert, R., Rivera, G. & Hagnauer, W. Modification of vegetative phenology in a tropical semi-deciduous forest by abnormal drought and rain. Biotropica 34, 27–39. https://doi.org/10.1111/j.1744-7429.2002.tb00239.x (2002).Article 

Google Scholar 
25.Markesteijn, L., Poorter, L., Paz, H., Sack, L. & Bongers, F. Ecological differentiation in xylem cavitation resistance is associated with stem and leaf structural traits. Plant Cell Environ. 34, 137–148. https://doi.org/10.1111/j.1365-3040.2010.02231.x (2011).Article 
PubMed 

Google Scholar 
26.Aragón-Moreno, A. A., Islebe, G. A., Torrescano-Valle, N. & Arellano-Verdejo, J. Middle and late Holocene mangrove dynamics of the Yucatan Peninsula, Mexico. J. S. Am. Earth Sci. 85, 307–311. https://doi.org/10.1016/j.jsames.2018.05.015 (2018).Article 

Google Scholar 
27.De la Barreda, B., Metcalfe, E. S. & Boyd, D. S. Precipitation regionalization, anomalies and drought occurrence in the Yucatan peninsula, Mexico. Int. J. Climatol. 40(10), 1–15. https://doi.org/10.1002/joc.6474 (2020).Article 

Google Scholar 
28.IPCC. Summary for Policymakers. In: Global warming of 1.5 °C. An IPCC Special Report on the impacts of global warming of 1.5 °C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty (V. Masson-Delmotte, P., Zhai, H. O., Pörtner, D., Roberts, J., Skea, P.R., Shukla, A., Pirani, W., Moufouma-Okia, C., Péan, R., Pidcock, S., Connors, J.B.R., Matthews, Y., Chen, X., Zhou, M. I., Gomis, E., Lonnoy, T., Maycock, M., Tignor, T., Waterfield, eds.). World Meteorological Organization, Geneva, Switzerland. https://www.ipcc.ch/sr15/ (Accessed 15 November 2019).29.Eller, C. B., Rowland, L. & Oliveira, R. S. Modelling tropical forest responses to drought and El Niño with a stomatal optimization model based on xylem hydraulics. Phil. Trans. R. Soc. B 373, 1–12. https://doi.org/10.1098/rstb.2017.0315 (2018).CAS 
Article 

Google Scholar 
30.Feng, X., Porporato, A. & Rodriguez-Iturbe, I. Changes in rainfall seasonality in the tropics. Nat. Clim. Chang. 3(9), 811–815. https://doi.org/10.1038/nclimate1907 (2013).ADS 
Article 

Google Scholar 
31.Goldstein, G. et al. Stem water storage and diurnal patterns of water use in tropical forest canopy trees. Plant Cell Environ. 21, 397–406. https://doi.org/10.1046/j.1365-3040.1998.00273.x (1998).Article 

Google Scholar 
32.Landsberg, J. & Waring, R. Water relations in tree physiology: where to from here?. Tree Physiol. 37, 18–32. https://doi.org/10.1093/treephys/tpw102 (2016).Article 

Google Scholar 
33.Kim, J. S. & Kug, J.-S. Increased atmospheric CO2 growth rate during El Niño driven by reduced terrestrial CO2 capture in the CMIP5 ESMs. J. Clim. 29, 8783–8805. https://doi.org/10.1175/JCLI-D-14-00672.1 (2016).ADS 
Article 

Google Scholar 
34.Kim, J. S., Kug, J.-S. & Jeong, S. Intensification of terrestrial carbon cycle related to El Niño-Southern Oscillation under greenhouse warming. Nat. Commun. 8, 1674. https://doi.org/10.1038/s41467-017-01831-7 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
35.Wang, Q., Cai, W., Zeng, L. & Wang, D. Nonlinear meridional moisture advection and the ENSO-southern China rainfall teleconnection. Geophys. Res. Lett. 45(9), 4353–4360. https://doi.org/10.1029/2018GL077446 (2018).ADS 
Article 

Google Scholar 
36.Wang, Q., Wang, Y., Sui, J., Zhou, W. & Li, D. Effects of weak and strong winter currents on the thermal state of the South China Sea. J. Clim. 34(1), 313–325. https://doi.org/10.1175/JCLI-D-19-0790.1 (2021).ADS 
Article 

Google Scholar 
37.Xie, S.-P. et al. Eastern Pacific ITCZ dipole and ENSO diversity. J. Clim. 31, 4449–4462. https://doi.org/10.1175/JCLI-D-17-0905.1 (2018).ADS 
Article 

Google Scholar 
38.Peng, Q., Xie, S.-P., Wang, D., Zheng, X.-T. & Zhang, H. Coupled ocean–atmosphere dynamics of the 2017 extreme coastal El Niño. Nat. Commun. 10, 298. https://doi.org/10.1038/s41467-018-08258-8 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
39.Peng, Q. et al. Eastern Pacific winds in the evolution of El Niño: implications for ENSO diversity. J. Clim. 33, 3197–3212. https://doi.org/10.1175/JCLI-D-19-0435.1 (2020).ADS 
Article 

Google Scholar 
40.Barkhodarian, A., Saatchi, S. S., Behrangi, A., Loikith, P. C. & Mechoso, C. R. A recent systematic increase in vapor pressure deficit over tropical South America. Sci. Rep. 9, 15331. https://doi.org/10.1038/s41598-019-51857-8 (2019).ADS 
CAS 
Article 

Google Scholar 
41.Meinzer, F. C., James, S. A., Goldstein, G. & Woodruff, D. Whole-tree water transport scales sapwood capacitance in tropical forest canopy trees. Plant Cell Environ. 26, 1147–1155. https://doi.org/10.1046/j.1365-3040.2003.01039.x (2003).Article 

Google Scholar 
42.Luo, Z. et al. Responses of plant water use to a severe summer drought for two subtropical tree species in the central southern China. J. Hydrol. Reg. Stud. 8, 1–9. https://doi.org/10.1016/j.ejrh.2016.08.001 (2016).CAS 
Article 

Google Scholar 
43.Vinya, R., Malhi, Y., Brown, N. & Fisher, J. Functional coordination between branch hydraulic properties and leaf functional traits in miombo woodlands: Implications for water stress management and species habitat preference. Acta Physiol. Plant 34, 1701–1710. https://doi.org/10.1007/s11738-012-0965-3 (2012).CAS 
Article 

Google Scholar 
44.Choat, B., Ball, M. C., Luly, J. G. & Holtum, J. A. M. Hydraulic architecture of deciduous and evergreen dry rainforest tree species from north-eastern Autralia. Trees 19, 305–311. https://doi.org/10.1007/s00468-004-0392-1 (2005).Article 

Google Scholar 
45.Romero, E., González, E. J., Meave, J. A. & Terrazas, T. Wood anatomy of dominant species with contrasting ecological performance in tropical dry forest succession. Plant Biosyst. 154, 524–534. https://doi.org/10.1080/11263504.2019.1651775 (2019).Article 

Google Scholar 
46.Pineda-García, F., Paz, H. & Meinzer, F. C. Drought resistance in early and late secondary successional species from a tropical dry forest: The interplay between xylem resistance to embolism, sapwood water storage and leaf shedding. Plant Cell Environ. 36, 405–418. https://doi.org/10.1111/j.1365-3040.2012.02582.x (2013).Article 
PubMed 

Google Scholar 
47.Choat, B., Sack, L. & Holbrook, M. Diversity of hydraulic traits in nine Cordia species growing in tropical forests with contrasting precipitation. New Phytol. 175, 686–698. https://doi.org/10.1111/j.1469-8137.2007.02137.x (2007).Article 
PubMed 

Google Scholar 
48.Fallas-Cedeño, L., Holbrook, N. M., Rocha, O. J., Vásquez, N. & Gutiérrez-Soto, M. Phenology, lignotubers, and water relations of Cochlospermum vitifolium, a pioneer tropical dry forest tree in Costa Rica. Biotropica 42, 104–111. https://doi.org/10.1111/j.1744-7429.2009.00539.x (2010).Article 

Google Scholar 
49.Quintanar-Isaías, A., Velasquez-Nuñez, M., Solares-Arenas, F., Pérez-Olvera, C. P. & Torre-Blanco, A. Secondary stem anatomy and uses or four drought-deciduous species of a tropical dry forest in Mexico. Rev. Biol. Trop. 53, 29–48. https://doi.org/10.15517/RBT.V53I1-2.14297 (2005).Article 

Google Scholar 
50.Veneklaas, E. J., Santos-Silva, M. P. & den Ouden, F. Determinants of growth rate in Ficus benjamina L. compared to related faster-growing woody and herbaceous species. Sci. Hortic. 93, 75–84. https://doi.org/10.1016/S0304-4238(01)00315-6 (2002).Article 

Google Scholar 
51.Mediavilla, S., Escudero, A. & Heilmeier, H. Internal leaf anatomy and photosynthetic resource-use efficiency: Interspecific and intraspecific comparisons. Tree Physiol. 21, 251–259. https://doi.org/10.1093/treephys/21.4.251 (2001).CAS 
Article 
PubMed 

Google Scholar 
52.Peguero-Pina, J. J., Sancho-Knapik, D. & Gil-Pelegrin, E. Ancientcell structural traits and photosynthesis in today’s environment. J. Exp. Bot. 68, 1389–1392. https://doi.org/10.1093/jxb/erx081 (2017).CAS 
Article 
PubMed Central 

Google Scholar 
53.Kitajima, K. & Poorter, L. Tissue-level leaf toughness, but not lamina thickness, predicts sapling leaf lifespan and shade tolerance of tropical tree species. New Phytol. 186, 708–721. https://doi.org/10.1111/j.1469-8137.2010.03212.x (2010).Article 
PubMed 

Google Scholar 
54.Schwedenman, L., Pendall, E., Sanchez-Bragado, R., Kunert, N. & Holscher, D. Tree water uptake in a tropical plantation varying in tree diversity: Interspecific differences, seasonal shifts and complementary. Ecohydrology 8, 1–12. https://doi.org/10.1002/eco.1479 (2015).Article 

Google Scholar 
55.Reyes-García, C., Andrade, J. L., Simá, J. L., Us-Santamaría, R. & Jackson, P. C. Sapwood to heartwood ratio affects whole-tree water use in dry forest legume and non-legume trees. Trees 26, 1317–1330. https://doi.org/10.1007/s00468-012-0708-5 (2012).Article 

Google Scholar 
56.Santiago, L. et al. Leaf photosynthetic traits scale with hydraulic conductivity and wood density in Panamanian forest canopy trees. Oecologia 140, 543–550. https://doi.org/10.1007/s00442-004-1624-1 (2004).ADS 
CAS 
Article 
PubMed 

Google Scholar 
57.Li, X. et al. Tree hydraulic traits are coordinated and strongly linked to climate-of-origin across a rainfall gradient. Plant Cell Environ. 41, 646–660. https://doi.org/10.1111/pce.13129 (2018).CAS 
Article 
PubMed 

Google Scholar 
58.Querejeta, J. I., Estrada-Medina, H., Allen, M. F., Jiménez-Osorio, J. J. & Ruenes, R. Utilization of bedrock water by Brosimum alicastrum trees growing on shallow soil atop limestone in a dry tropical climate. Plant Soil 287, 187–197. https://doi.org/10.1007/s11104-006-9065-8 (2006).CAS 
Article 

Google Scholar 
59.Scholz, F. G., Phillips, N. G., Bucci, S. J., Meinzer, F. C. & Goldstein, G. Hydraulic capacitance: Biophysics and functional significance of internal water sources in relation to tree size. In Size- and Age-Related Changes in Tree Structure and Function (eds Meinzer, F. C. et al.) 341–362 (Springer, 2011). https://doi.org/10.1007/978-94-007-1242-3_13.
Google Scholar 
60.Bennett, A. C., McDowell, N. G., Allen, C. D. & Anderson-Teixeira, K. J. Larger trees suffer most during drought in forests worldwide. Nat. Plants 139, 1–5. https://doi.org/10.1038/nplants.2015.139 (2015).Article 

Google Scholar 
61.Sobrado, M. A. Embolism vulnerability in drought-deciduous and evergreen species of a tropical dry forest. Acta Oecol. 18, 383–391. https://doi.org/10.1016/S1146-609X(97)80030-6 (1997).ADS 
Article 

Google Scholar 
62.Brodribb, T. J., Holbrook, N. M., Edwards, E. J. & Gutierrez, M. V. Relation between stomatal closure, leaf turgor and xylem vulnerability in eight tropical dry forest trees. Plant Cell Environ. 26, 443–450. https://doi.org/10.1046/j.1365-3040.2003.00975.x (2003).Article 

Google Scholar 
63.Orellana, R., Balam, M. & Bañuelos, I. Balance Ombrotérmico, evaluación climática. In Atlas de procesos territoriales de Yucatán (eds de Fuentes, A. G. et al.) 174–175 (Universidad Autónoma de Yucatán, 1999).
Google Scholar 
64.Instituto Nacional de Estadística Geografía e Informática, 2017. Anuario estadístico y geográfico de Quintana Roo. INEGI, México. https://www.datatur.sectur.gob.mx/ITxEF_Docs/QROO_ANUARIO. (Accessed 12 December 2019).65.Espinoza-Avalos, J., Islebe, G. A. & Hernández-Arana, H. A. El sistema ecológico de la bahía de Chetumal/corozal: Costa occidental del mar caribe (El Colegio de la Frontera Sur, 2009).
Google Scholar 
66.McKee, T.B., Doesken, N.J. & Kelist, J. The relationship of drought frequency and duration to time scale. in American Meteorological Society, Proceedings of the Eighth Conference on Applied Climatology, 17–22 January, Anaheim, California 179–184 (1993).67.Cheval, S. The Standardized Precipitation Index—An overview. Rom. J. Meteorol. 12(1–2), 17–64 (2015).
68.Koide, R. T., Robichaux, R. H., Morse, S. R. & Smith, C. M. Plant water status, hydraulic resistance and capacitance. In Plant Physiological Ecology, Field Methods and Instrumentation (eds Pearcy, R. W. et al.) 161–178 (Chapman and Hall, 1991). https://doi.org/10.1007/978-94-010-9013-1_9.
Google Scholar 
69.Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9(7), 671–675. https://doi.org/10.1038/nmeth.2089 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
70.StatSoft, Inc. STATISTICA (data analysis software system), version 12. www.statsoft.com (2013). More

1.Endler, J. A. Signals, signal conditions, and the direction of evolution. Am. Nat. 139, S125–S153 (1992).Article 

Google Scholar 
2.Endler, J. A. Some general comments on the evolution and design of animal communication systems. Philos. Trans. R. Soc. Lond. B 340, 215–225 (1993).ADS 
CAS 
Article 

Google Scholar 
3.Bakker, T. C. & Mundwiler, B. Female mate choice and male red coloration in a natural three-spined stickleback (Gasterosteus aculeatus) population. Behav. Ecol. 5, 74–80 (1994).Article 

Google Scholar 
4.Molnár, O., Bajer, K., Mészáros, B., Török, J. & Herczeg, G. Negative correlation between nuptial throat colour and blood parasite load in male European green lizards supports the Hamilton-Zuk hypothesis. Naturwissenschaften 100, 551–558 (2013).ADS 
PubMed 
Article 
CAS 

Google Scholar 
5.Wolfenbarger, L. L. Red coloration of male northern cardinals correlates with mate quality and territory quality. Behav. Ecol. 10, 80–90 (1999).Article 

Google Scholar 
6.Endler, J. A. Natural-selection on color patterns in Poecilia reticulata. Evolution 34, 76–91 (1980).PubMed 
Article 

Google Scholar 
7.Marcondes, R. S. & Brumfield, R. T. Fifty shades of brown: Macroevolution of plumage brightness in the Furnariida, a large clade of drab Neotropical passerines. Evolution 73, 704–719 (2019).PubMed 
Article 

Google Scholar 
8.Alberts, A. C. Constraints on the design of chemical communication systems in terrestrial vertebrates. Am. Nat. 139, S62–S89 (1992).9.Campos, S. M. et al. Volatile fatty acid and aldehyde abundances evolve with behavior and habitat temperature in Sceloporus lizards. Behav. Ecol. (2020).10.Stuart-Fox, D. M. & Ord, T. J. Sexual selection, natural selection and the evolution of dimorphic coloration and ornamentation in agamid lizards. Proc. R. Soc. B 271, 2249–2255 (2004).PubMed 
Article 

Google Scholar 
11.Karlson, P. & Lüscher, M. ‘Pheromones’: A new term for a class of biologically active substances. Nature 183, 55–56 (1959).ADS 
CAS 
PubMed 
Article 

Google Scholar 
12.Schmidt, H. R. & Benton, R. Molecular mechanisms of olfactory detection in insects: Beyond receptors. Open Biol. 10, 200252 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
13.Symonds, M. R. & Elgar, M. A. The evolution of pheromone diversity. Trends Ecol. Evol. 23, 220–228 (2008).PubMed 
Article 

Google Scholar 
14.Boulet, M., Charpentier, M. J. & Drea, C. M. Decoding an olfactory mechanism of kin recognition and inbreeding avoidance in a primate. BMC Evol. Biol. 9, 281 (2009).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
15.Scordato, E. S., Dubay, G. & Drea, C. M. Chemical composition of scent marks in the ringtailed lemur (Lemur catta): Glandular differences, seasonal variation, and individual signatures. Chem. Senses 32, 493–504 (2007).CAS 
PubMed 
Article 

Google Scholar 
16.Janssenswillen, S. et al. Origin and diversification of a salamander sex pheromone system. Mol. Biol. Evol. 32, 472–480 (2015).PubMed 
Article 

Google Scholar 
17.Kikuyama, S. et al. Sodefrin: A female-attracting peptide pheromone in newt cloacal glands. Science 267, 1643–1645 (1995).ADS 
CAS 
PubMed 
Article 

Google Scholar 
18.Wabnitz, P. A., Bowie, J. H., Tyler, M. J., Wallace, J. C. & Smith, B. P. Aquatic sex pheromone from a male tree frog. Nature 401, 444–445 (1999).ADS 
CAS 
PubMed 
Article 

Google Scholar 
19.Baeckens, S. et al. Environmental conditions shape the chemical signal design of lizards. Funct. Ecol. 32, 566–580 (2018).Article 

Google Scholar 
20.Martín, J. & López, P. Pheromones and chemical communication in lizards. In Reproductive Biology and Phylogeny of Lizards and Tuatara (eds Rheubert, J. L. et al.) 43–75 (CRC Press, Boca Raton, 2014).
Google Scholar 
21.Silva, L. & Antunes, A. Vomeronasal receptors in vertebrates and the evolution of pheromone detection. Ann. Rev. Anim. Biosci. 5, 353–370 (2017).CAS 
Article 

Google Scholar 
22.Bonadonna, F. & Nevitt, G. A. Partner-specific odor recognition in an Antarctic seabird. Science 306, 835–835 (2004).CAS 
PubMed 
Article 

Google Scholar 
23.Bonadonna, F. & Sanz-Aguilar, A. Kin recognition and inbreeding avoidance in wild birds: The first evidence for individual kin-related odour recognition. Anim. Behav. 84, 509–513 (2012).Article 

Google Scholar 
24.Krause, E. T., Krüger, O., Kohlmeier, P. & Caspers, B. A. Olfactory kin recognition in a songbird. Biol. Lett. 8, 327–329 (2012).Article 

Google Scholar 
25.Baeckens, S. et al. Environmental conditions shape the chemical signal design of lizards. Funct. Ecol. 32, 566–580 (2018).Article 

Google Scholar 
26.Wyatt, T. D. Proteins and peptides as pheromone signals and chemical signatures. Anim. Behav. 97, 273–280 (2014).Article 

Google Scholar 
27.Baeckens, S., Edwards, S., Huyghe, K. & Van Damme, R. Chemical signalling in lizards: An interspecific comparison of femoral pore numbers in Lacertidae. Biol. J. Linn. Soc. 114, 44–57 (2015).Article 

Google Scholar 
28.Ossip-Klein, A. G., Fuentes, J. A., Hews, D. K. & Martins, E. P. Information content is more important than sensory system or physical distance in guiding the long-term evolutionary relationships between signaling modalities in Sceloporus lizards. Behav. Ecol. Sociobiol. 67, 1513–1522 (2013).Article 

Google Scholar 
29.Pincheira-Donoso, D., Hodgson, D. J. & Tregenza, T. Comparative evidence for strong phylogenetic inertia in precloacal signalling glands in a species-rich lizard clade. Evol. Ecol. Res. 10, 11–28 (2008).
Google Scholar 
30.Wang, Z. et al. The draft genomes of soft-shell turtle and green sea turtle yield insights into the development and evolution of the turtle-specific body plan. Nat. Genet. 45, 701–706 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Schwenk, K. Comparative anatomy and physiology of chemical senses in nonavian aquatic reptiles. In Sensory Evolution on the Threshold: Adaptations in Secondarily Aquatic Vertebrates (eds Thewissen, J. H. M. & Nummela, S.) 65–81 (University of California Press, Berkeley, 2008).
Google Scholar 
32.Vieyra, M. L. Olfactory receptor genes in terrestrial, freshwater, and sea turtles: Evidence for a reduction in the number of functional genes in aquatic species. Chelon. Conserv. Biol. 10, 181–187 (2011).Article 

Google Scholar 
33.Mason, R. T. & Parker, M. R. Social behavior and pheromonal communication in reptiles. J. Comp. Physiol. A. 196, 729–749 (2010).CAS 
Article 

Google Scholar 
34.Ehrenfeld, J. G. & Ehrenfeld, D. W. Externally secreting glands of freshwater and sea turtles. Copeia 1973, 305–314 (1973).Article 

Google Scholar 
35.Waagen, G. N. Musk glands in recent turtles. Master of Science thesis, Department of Biology, University of Utah (1972).36.Weldon, P. J., Flachsbarth, B. & Schulz, S. Natural products from the integument of nonavian reptiles. Nat. Prod. Rep. 25, 738–756 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
37.Ibáñez, A. et al. The chemistry and histology of sexually dimorphic mental glands in the freshwater turtle, Mauremys leprosa. PeerJ 8, e9047 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
38.Rose, F. L., Drotman, R. & Weaver, W. G. Electrophoresis of chin gland extracts of Gopherus (tortoises). Comp. Biochem. Physiol. 29, 847–851 (1969).CAS 
Article 

Google Scholar 
39.Winokur, R. M. & Legler, J. M. Chelonian mental glands. J. Morphol. 147, 275–291 (1975).PubMed 
Article 

Google Scholar 
40.Alberts, A. C., Rostal, D. C. & Lance, V. A. Studies on the chemistry and social significance of chin gland secretions in the desert tortoise, Gopherus agassizii. Herpetol. Monogr. 8, 116–124 (1994).Article 

Google Scholar 
41.Kelley, M. D. & Mendonça, M. T. Mental gland secretions as a social cue in gopher tortoises (Gopherus polyphemus): Tortoise presence stimulates and maintains social behaviour with chemical cues. Acta Ethol. 24, 1–8 (2020).Article 

Google Scholar 
42.Rose, F. L. Tortoise chin gland fatty acid composition: Behavioral significance. Comp. Biochem. Physiol. 32, 577–580 (1970).CAS 
Article 

Google Scholar 
43.Pereira, A. G., Sterli, J., Moreira, F. R. & Schrago, C. G. Multilocus phylogeny and statistical biogeography clarify the evolutionary history of major lineages of turtles. Mol. Phylogenet. Evol. 113, 59–66 (2017).PubMed 
Article 

Google Scholar 
44.Grosse, A. M., Sterrett, S. C. & Maerz, J. C. Effects of turbidity on the foraging success of the eastern painted turtle. Copeia 2010, 463–467 (2010).Article 

Google Scholar 
45.Vitt, L. J. & Caldwell, J. P. Herpetology: An Introductory Biology of Amphibians and Reptiles (Academic Press, 2013).
Google Scholar 
46.Thomson, R. C., Spinks, P. Q. & Shaffer, H. B. A global phylogeny of turtles reveals a burst of climate-associated diversification on continental margins. Proc. Natl. Acad. Sci. 118, e2012215118 (2021).PubMed 
Article 
CAS 

Google Scholar 
47.Colston, T. J., Kulkarni, P., Jetz, W. & Pyron, R. A. Phylogenetic and spatial distribution of evolutionary diversification, isolation, and threat in turtles and crocodilians (non-avian archosauromorphs). BMC Evol. Biol. 20, 1–16 (2020).Article 

Google Scholar 
48.Joyce, W. G., Parham, J. F., Lyson, T. R., Warnock, R. C. & Donoghue, P. C. A divergence dating analysis of turtles using fossil calibrations: An example of best practices. J. Paleontol. 87, 612–634 (2013).Article 

Google Scholar 
49.Shaffer, H. B., McCartney-Melstad, E., Near, T. J., Mount, G. G. & Spinks, P. Q. Phylogenomic analyses of 539 highly informative loci dates a fully resolved time tree for the major clades of living turtles (Testudines). Mol. Phylogenet. Evol. 115, 7–15 (2017).PubMed 
Article 

Google Scholar 
50.Beaulieu, J. M., O’Meara, B. C. & Donoghue, M. J. Identifying hidden rate changes in the evolution of a binary morphological character: The evolution of plant habit in campanulid angiosperms. Syst. Biol. 62, 725–737 (2013).PubMed 
Article 

Google Scholar 
51.Joyce, W. G. & Gauthier, J. A. Palaeoecology of Triassic stem turtles sheds new light on turtle origins. Proc. R. Soc. Lond. B 271, 1–5 (2004).Article 

Google Scholar 
52.Quagliata, S., Malentacchi, C., Delfino, C., Brunasso, A. M. & Delfino, G. Adaptive evolution of secretory cell lines in vertebrate skin. Caryologia 59, 187–206 (2006).Article 

Google Scholar 
53.Shi, P. & Zhang, J. Extraordinary diversity of chemosensory receptor gene repertoires among vertebrates. In Chemosensory Systems in Mammals, Fishes, and Insects (eds Meyerhof, W. & Korsching, S.) 1–23 (Springer, Berlin, 2009).
Google Scholar 
54.Swaney, W. T. & Keverne, E. B. The evolution of pheromonal communication. Behav. Brain Res. 200, 239–247 (2009).CAS 
PubMed 
Article 

Google Scholar 
55.Martín, J. & López, P. Effects of global warming on sensory ecology of rock lizards: Increased temperatures alter the efficacy of sexual chemical signals. Funct. Ecol. 27, 1332–1340 (2013).Article 

Google Scholar 
56.Ibáñez, A., López, P. & Martín, J. Discrimination of conspecifics’ chemicals may allow Spanish terrapins to find better partners and avoid competitors. Anim. Behav. 83, 1107–1113 (2012).Article 

Google Scholar 
57.Lewis, C. H., Molloy, S. F., Chambers, R. M. & Davenport, J. Response of common musk turtles (Sternotherus odoratus) to intraspecific chemical cues. J. Herpetol. 41, 349–353 (2007).Article 

Google Scholar 
58.Poschadel, J. R., Meyer-Lucht, Y. & Plath, M. Response to chemical cues from conspecifics reflects male mating preference for large females and avoidance of large competitors in the European pond turtle, Emys orbicularis. Behaviour 143, 569–587 (2006).Article 

Google Scholar 
59.Weaver, W. G. Courtship and combat behavior in Gopherus berlandieri. Bull. Fla. St. Mus. 15, 1–43 (1970).
Google Scholar 
60.Auffenberg, W. On the courtship of Gopherus polyphemus. Herpetologica 22, 113–117 (1966).
Google Scholar 
61.Augustine, L. & Haislip, N. Husbandry and reproduction of the Indochinese box turtle Cuora galbinifrons, Bourret’s box turtle Cuora bourreti and Southern Vietnam box turtle Cuora picturata in North America. Int. Zoo Yearb. 53, 238–249 (2019).Article 

Google Scholar 
62.Liu, Y.-X., Davy, C. M., Shi, H.-T. & Murphy, R. W. Sex in the half-shell: A review of the functions and evolution of courtship behavior in freshwater turtles. Chelon. Conserv. Biol. 12, 84–100 (2013).Article 

Google Scholar 
63.Schilde, M. Beobachtungen zum Fortpflanzungsverhalten von Sacalia bealei und Sacalia quadriocellata. Radiata 14, 30–32 (2005).
Google Scholar 
64.Fritz, U. Courtship behavior and systematics in the subtribe Nectemydina. 2. A comparison above the species level and remarks on the evolution of behaviour elements. Bull. Chicago Herpetol. Soc. 34, 225–236 (1999).
Google Scholar 
65.Martín, J. & López, P. Multimodal sexual signals in male ocellated lizards Lacerta lepida: Vitamin E in scent and green coloration may signal male quality in different sensory channels. Naturwissenschaften 97, 545–553 (2010).ADS 
PubMed 
Article 
CAS 

Google Scholar 
66.Rowe, C. Receiver psychology and the evolution of multicomponent signals. Anim. Behav. 58, 921–931 (1999).CAS 
PubMed 
Article 

Google Scholar 
67.Martins, E. P. et al. Evolving from static to dynamic signals: Evolutionary compensation between two communicative signals. Anim. Behav. 102, 223–229 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
68.Ferrara, C. R., Vogt, R. C. & Sousa-Lima, R. S. Turtle vocalizations as the first evidence of posthatching parental care in chelonians. J. Comp. Psychol. 127, 24 (2013).PubMed 
Article 

Google Scholar 
69.Bulté, G., Germain, R. R., O’Connor, C. M. & Blouin-Demers, G. Sexual dichromatism in the northern map turtle, Graptemys geographica. Chelon. Conserv. Biol. 12, 187–192 (2013).Article 

Google Scholar 
70.Ibáñez, A., Marzal, A., López, P. & Martín, J. Sexually dichromatic coloration reflects size and immunocompetence in female Spanish terrapins, Mauremys leprosa. Naturwissenschaften 100, 1137–1147 (2013).ADS 
PubMed 
Article 
CAS 

Google Scholar 
71.Rowe, J. W., Gradel, J. R., Bunce, C. F. & Clark, D. L. Sexual dimorphism in size and shell shape, and dichromatism of spotted turtles (Clemmys guttata) in southwestern Michigan. Amphibia-Reptilia 33, 443–450 (2013).Article 

Google Scholar 
72.Steffen, J. E., Learn, K. M., Drumheller, J. S., Boback, S. M. & McGraw, K. J. Carotenoid composition of colorful body stripes and patches in the painted turtle (Chrysemys picta) and red-eared slider (Trachemys scripta). Chelon. Conserv. Biol. 14, 56–63 (2015).Article 

Google Scholar 
73.Moll, E. O., Matson, K. E. & Krehbiel, E. B. Sexual and seasonal dichromatism in the Asian river turtle Callagur borneoensis. Herpetologica 37, 181–194 (1981).
Google Scholar 
74.Praschag, P. et al. A new subspecies of Batagur affinis (Cantor, 1847), one of the world’s most critically endangered chelonians (Testudines: Geoemydidae). Zootaxa 2233, 57–68 (2009).Article 

Google Scholar 
75.Praschag, P., Hundsdörfer, A. & Fritz, U. Phylogeny and taxonomy of endangered South and South-east Asian freshwater turtles elucidated by mtDNA sequence variation (Testudines: Geoemydidae: Batagur, Callagur, Hardella, Kachuga, Pangshura). Zool. Scr. 36, 429–442 (2007).Article 

Google Scholar 
76.Fritz, U. Courtship behavior and systematics in the subtribe Nectemydina. 1. The genus Trachemys, especially Trachemys scripta callirostris (Gray, 1855). Bull. Chicago Herpetol. Soc. 33, 225–236 (1998).
Google Scholar 
77.Ferrara, C. R., Vogt, R. C., Eisemberg, C. C. & Doody, J. S. First evidence of the pig-nosed turtle (Carettochelys insculpta) vocalizing underwater. Copeia 105, 29–32 (2017).Article 

Google Scholar 
78.Baeckens, S. & Whiting, M. J. Investment in chemical signalling glands facilitates the evolution of sociality in lizards. Proc. R. Soc. B 288, 20202438 (2021).PubMed 
Article 
PubMed Central 

Google Scholar 
79.Baeckens, S., García-Roa, R., Martín, J. & Van Damme, R. The role of diet in shaping the chemical signal design of lacertid lizards. J. Chem. Ecol. 43, 902–910 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
80.Kopena, R., López, P. & Martín, J. Relative contribution of dietary carotenoids and vitamin E to visual and chemical sexual signals of male Iberian green lizards: An experimental test. Behav. Ecol. Sociobiol. 68, 571–581 (2014).Article 

Google Scholar 
81.Kopena, R., Martín, J., López, P. & Herczeg, G. Vitamin E supplementation increases the attractiveness of males’ scent for female European green lizards. PLoS ONE 6, e19410 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
82.Martin, J., Ortega, J. & Lopez, P. Interpopulational variations in sexual chemical signals of Iberian wall lizards may allow maximizing signal efficiency under different climatic conditions. PLoS ONE 10, e0131492 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
83.Donihue, C. M. et al. Rapid and repeated divergence of animal chemical signals in an island introduction experiment. J. Anim. Ecol. 89, 1458–1467 (2020).PubMed 
Article 

Google Scholar 
84.Novelli, I. A. Estudo morfológico (anatômico e histológico) do sistema tegumentar de Hydromedusa maximiliani (Mikan, 1820) (Testudines, Chelidae) e Phrynops geoffroanus (Schweigger, 1812) (Testudines, Chelidae). Doctoral thesis, Universidade Federal Rural do Rio de Janeiro (2011).85.Crawford, N. G. et al. A phylogenomic analysis of turtles. Mol. Phylogenet. Evol. 83, 250–257 (2015).PubMed 
Article 

Google Scholar 
86.Bonin, F., Devaux, B. & Dupré, A. Turtles of the World (JHU Press, Baltimore, 2006).
Google Scholar 
87.Bour, R. Global diversity of turtles (Chelonii; Reptilia) in freshwater. Hydrobiologia 595, 593–598 (2008).Article 

Google Scholar 
88.Ernst, C. H. & Barbour, R. W. Turtles of the World (Smithsonian Institution Press, Washington DC, 1989).
Google Scholar 
89.Beaulieu, J. M., Oliver, J. C. & O’Meara, B. C. corHMM: Analysis of Binary Character Evolution, https://CRAN.R-project.org/package=corHMM (2017).90.Boyko, J. D. & Beaulieu, J. M. Generalized hidden Markov models for phylogenetic comparative datasets. Methods Ecol. Evol. 12, 468–478 (2021).Article 

Google Scholar 
91.Beaulieu, J. M. & Donoghue, M. J. Fruit evolution and diversification in campanulid angiosperms. Evolution 67, 3132–3144 (2013).PubMed 
Article 

Google Scholar 
92.Burnham, K. P. & Anderson, D. R. Multimodel inference: Understanding AIC and BIC in model selection. Sociol. Methods Res. 33, 261–304 (2004).MathSciNet 
Article 

Google Scholar 
93.Pagel, M. Detecting correlated evolution on phylogenies: A general method for the comparative analysis of discrete characters. Proc. R. Soc. Lond. B 255, 37–45 (1994).ADS 
Article 

Google Scholar 
94.Gray, K. M. & Steidl, R. J. A plant invasion affects condition but not density or population structure of a vulnerable reptile. Biol. Invasions 17, 1979–1988 (2015).Article 

Google Scholar 
95.Edwards, T. et al. The desert tortoise trichotomy: Mexico hosts a third, new sister-species of tortoise in the Gopherus morafkai—G. agassizii group. ZooKeys 562, 131–158 (2016).Article 

Google Scholar  More

1.Hobbs, P. R. & Gupta, R. K. Problems and challenges of no-till farming for the Rice–Wheat systems of the Indo-Gangetic Plains in South Asia. In Sustainable Agriculture and the International Rice–Wheat System (eds Lal, R. et al.) 101–119 (Ohio State University; Marcel Dekker Inc, 2004).
Google Scholar 
2.Saharawat, Y. S. et al. Evaluation of alternative tillage and crop establishment methods in a rice–wheat rotation in north-western IGP. Field Crops Res. 116, 260–267 (2010).Article 

Google Scholar 
3.Jat, R. K. et al. Seven years of conservation agriculture in a rice–wheat rotation of eastern Gangetic Plains of South Asia: Yield trends and economic profitability. Field Crops Res. 164, 199–210 (2014).Article 

Google Scholar 
4.Parihar, C. M. et al. Long-term conservation agriculture and intensified cropping systems: Effects on growth, yield, water, and energy-use efficiency of maize in north-western India. Pedosphere 28(6), 952–963 (2018).Article 

Google Scholar 
5.Shiferaw, B., Prasanna, B. M., Hellin, J. & Banziger, M. Crops that feed the world 6. Past successes and future challenges to the role played by maize in global food security. Food Secur. 3, 30–327 (2011).
Google Scholar 
6.Srinivasan, G., Zaidi, P. H., Prasanna, B. M., Gonzalez, F. & Lesnick, K. eds. Proceedings of the Eighth Asian Regional Maize Workshop: New Technologies for the New Millennium. Bangkok, Thailand, 5–8 August 2002. Mexico, D.F.: CIMMYT (2004).7.FAO. FAOSTAT database 2019. http://www.fao.org/faostat/en/#data/QC (Accessed 17 April 2021). FAO, Rome (2019).8.Aulakh, M. S. & Grant, C. A. Integrated Nutrient Management for Sustainable Crop Production (The Haworth Press, Taylor and Francis Group, 2008).Book 

Google Scholar 
9.Jat, S. L., Parihar, C. M., Singh, A. K., Jat, M. L. & Jat, R. K. Carbon sustainability and productivity of maize based cropping system under conservation agriculture practices in Indo-Gangetic plains. In: Resilient food systems for a changing world, Proceedings of the 5th World Congress of Conservation Agriculture Incorporating 3rd Farming Systems Design Conference, Brisbane Australia, 26-29 September, 2011, p. 110–111 (2011).10.Parihar, C. M. et al. Conservation agriculture in irrigated intensive maize-based systems of north-western India: Effects on crop yields, water productivity and economic profitability. Field Crops Res. 193, 104–116 (2016).Article 

Google Scholar 
11.Jat, M. L. et al. Crop residue management for sustainable production of maize (Zea mays) in dryland ecosystem. Chem. Sci. Rev. Lett. 6(23), 1681–1686 (2017).CAS 

Google Scholar 
12.Pooniya, V. et al. Six years of conservation agriculture and nutrient management in maize–mustard rotation: Impact on soil properties, system productivity and profitability. Field Crops Res. 260, 108002 (2021).Article 

Google Scholar 
13.Gathala, M. K. et al. Effect of tillage and crop establishment methods on physical properties of a medium-textured soil under a seven-year rice–wheat rotation. Soil Sci. Soc. Am. J. 75, 1851–1862 (2011).ADS 
CAS 
Article 

Google Scholar 
14.Ladha, J. K. et al. Integrating crop and resource management technologies for enhanced productivity, profitability and sustainability of the rice-wheat system in South Asia. p. 69–108. In J.K. Ladha et al. (ed.) Integrated crop and resource management in the rice-wheat system of South Asia. IRRI, Los Baños, Philippines (2009).15.Corsi, S. & Muminjanov, H. Conservation Agriculture: Training guide for extension agents and farmers in Eastern Europe and Central Asia. Rome – ISBN 978-92-5-131456-2 (FAO, 2019).16.Balwinder, S., Humphreys, E., Gaydon, D. S. & Yadav, S. Options for increasing the productivity of the rice–wheat system of north-west India while reducing groundwater depletion Part 2. Is conservation agriculture the answer?. Field Crops Res. 173, 81–94 (2015).Article 

Google Scholar 
17.Laik, R. et al. Integration of conservation agriculture with best management practices for improving system performance of the rice–wheat rotation in eastern Indo-Gangetic plains of India. Agric. Ecosyst. Environ. 195, 68–82 (2014).ADS 
Article 

Google Scholar 
18.Govaerts, B. et al. Conservation agriculture as a sustainable option for the central Mexican highlands. Soil Till. Res. 103, 222–230 (2009).Article 

Google Scholar 
19.Jat, M. L. et al. Double no-till and permanent raised beds in maize–wheat rotation of north western Indo-Gangetic plains of India: Effects on crop yields, water productivity, profitability and soil physical properties. Field Crop Res. 149, 291–299 (2013).Article 

Google Scholar 
20.Ludwig, B. et al. Effects of fertilization and soil management on crop yields and carbon stabilization in soils. A review. Agron. Sustain. Dev. 31, 361–372 (2011).Article 

Google Scholar 
21.Powlson, D. S., Stirling, C. M., Thierfelder, C., White, R. P. & Jat, M. L. Does conservation agriculture deliver climate change mitigation through soil carbon sequestration in tropical agro- ecosystems?. Agric. Ecosyst. Environ. 220, 164–174 (2016).CAS 
Article 

Google Scholar 
22.Kandeler, E. et al. Response of soil microbial biomass and enzyme activities to the transient elevation of carbon dioxide in semi-arid grassland. Soil Biol. Biochem. 38, 2448–2460 (2006).CAS 
Article 

Google Scholar 
23.Witt, C., Pasuquin, J. M., Pampolino, M.F., Buresh, R. J. & Dobermann, A. A manual for the development and participatory evaluation of site-specific nutrient management for maize in tropical, favorable environments. International Plant Nutrition Institute, Penang, Malaysia. http://seap.ipni.net (2009).24.Pampolino, M. et al. Development and evaluation of nutrient expert for wheat in South Asia (special issue): Nutrient management for wheat. Better Crops 96(3), 29–31 (2012).
Google Scholar 
25.Johnston, A. M. Nutrient expert-going global with improved fertilizer recommendations. Better Crops.Plant Food. 99(3), 20, (2015).26.Sapkota, T. B. et al. Precision nutrient management in conservation agriculture based wheat production system of north–west India: Profitability, nutrient use efficiency and environmental footprint. Field Crop Res. 155, 233–244 (2014).Article 

Google Scholar 
27.Pooniya, V. et al. ‘Nutrient expert’ assisted site-specific-nutrient-management: An alternative precision fertilization technology for maize–wheat cropping system in South-Asian Indo-Gangetic Plains. Indian J. Agric. Sci. 85(8), 996–1002 (2015).CAS 

Google Scholar 
28.Budhathoki, S. et al. Assessing growth, productivity and profitability of drought tolerant rice using nutrient expert—rice and other precision fertilizer management practices in Lamjung, Nepal. Acta. Sci. Agric. 2(8), 153–158 (2018).
Google Scholar 
29.Satyanarayana, T. et al. Economics of nitrogen fertilizer application in rice, wheat and maize grown in the Indo-Gangetic Plains. Indian J. of Fert. 8(8), 62–71 (2012).
Google Scholar 
30.Satyanarayana, T., Majumdar, K., Pampolino, M., Johnston, A. M. & Jat, M. L. Nutrient ExpertTM: A tool to optimize nutrient use and improve productivity of maize. Better Crops. South Asia 97(1), 21–24 (2013).
Google Scholar 
31.Anonymous. Annual Progress Report Kharif Maize. All India Coordinated Research Project on Maize. Eds. V. Mahajan et al. Indian Institute of Maize Research, PAU Campus, Ludhiana-141 004, India. pp. 1082 (2016).32.Abrol, I. P. & Sangar, S. Sustaining Indian agriculture-conservation agriculture the way forward. Curr. Sci. 91, 1020–2015 (2006).
Google Scholar 
33.Ghosh, P. K. et al. Conservation agriculture towards achieving food security in north-east India. Curr. Sci. 99, 915–921 (2010).
Google Scholar 
34.Parihar, C. M. et al. Soil quality and carbon sequestration under conservation agriculture with balanced nutrition in intensive cereal-based system. Soil Tillage Res. 202, 104653 (2020).Article 

Google Scholar 
35.Mahajan, G., Singh, K. & Gill, M. S. Scope for enhancing and sustaining rice productivity in Punjab (food bowl of India). Afr. J. Agric. Res. 7, 5611–5620 (2012).Article 

Google Scholar 
36.Jat, H. S. et al. Effects of tillage, crop establishment and diversification on soil organic carbon, aggregation, aggregate associated carbon and productivity in cereal systems of semi-arid Northwest India. Soil Till. Res. 190, 128–138 (2019).CAS 
Article 

Google Scholar 
37.Jat, H. S. et al. Assessing soil properties and nutrient availability under conservation agriculture practices in a reclaimed sodic soil in cereal-based systems of North-West India. Arch. Agron. Soil Sci. 64, 531–545 (2018).CAS 
Article 

Google Scholar 
38.Kaschuk, G., Alberton, O. & Hungria, M. Three decades of soil microbial biomass studies in Brazilian ecosystems: Lessons learned about soil quality and indications for improving sustainability. Soil Biol. Biochem. 42, 1–13 (2010).CAS 
Article 

Google Scholar 
39.Sarkar, D. et al. Can sustainability of maize–mustard cropping system be achieved through balanced nutrient management?. Field Crops Res. 225, 9–21 (2018).Article 

Google Scholar 
40.Pooniya, V., Palta, J. A., Chen, Y., Delhaize, E. & Siddique, K. H. M. Impact of the TaMATE1B gene on above and below-ground growth of durum wheat grown on an acid and Al3+-toxic soil. Plant Soil 447, 73–84 (2020).CAS 
Article 

Google Scholar 
41.Pasuquin, J. M. et al. Closing yield gaps in maize production in Southeast Asia through site-specific nutrient management. Field Crops Res. 156, 219–230 (2014).Article 

Google Scholar 
42.Choudhary, K. M. et al. Evaluating alternatives to rice–wheat system in western Indo-Gangetic Plains: Crop yields, water productivity and economic profitability. Field Crop Res. 218, 1–10 (2018).Article 

Google Scholar 
43.Jat, R. K. et al. Ten years of conservation agriculture in a rice-maize rotation of Eastern Gangetic Plains of India: Yield trends, water productivity and economic profitability. Field Crop Res. 232, 1–10 (2019).Article 

Google Scholar 
44.Jat, H. S. et al. Designing profitable, resource use efficient and environmentally sound cereal based systems for the Western Indo-Gangetic plains. Sci. Rep. 10(1), 1–16 (2020).Article 
CAS 

Google Scholar 
45.Thierfelder, C., Amezquita, E. & Stahr, K. Effects of intensifying organic manuring and tillage practices on penetration resistance and infiltration rate. Soil Till. Res. 82, 211–226 (2005).Article 

Google Scholar 
46.Bescansa, P., Imaz, M. J., Virto, I., Enrique, A. & Hoogmoed, W. B. Soil water retention as affected by tillage and residue management in semiarid Spain. Soil Till. Res. 87, 19–27 (2006).Article 

Google Scholar 
47.Lal, R. Sequestering carbon and increasing productivity by conservation agriculture. JSWC. 70, 55A-62A (2015).
Google Scholar 
48.Page, K. L., Dang, Y. P. & Dalal, R. C. The ability of conservation agriculture to conserve soil organic carbon and the subsequent impact on soil physical, chemical, and biological properties and yield. Front. Sustain. Food Syst. 4, 31 (2020).Article 

Google Scholar 
49.Six, J., Elliott, E. T. & Paustian, K. Soil macroaggregate turnover and microaggregate formation: A mechanism for C sequestration under no-tillage agriculture. Soil Bio. Biochem. 32, 2099–2103 (2000).CAS 
Article 

Google Scholar 
50.Tan, Z., Lal, R., Owens, L. & Izaurralde, R. C. Distribution of light and heavy fractions of soil organic carbon as related to land use and tillage practice. Soil Till. Res. 92, 53–59 (2007).Article 

Google Scholar 
51.Blanco-Canqui, H. & Ruis, S. J. No-tillage and soil physical environment. Geoderma 326, 164–200 (2018).ADS 
Article 

Google Scholar 
52.Somasundaram, J. et al. Conservation agriculture effects on soil properties and crop productivity in a semiarid region of India. Soil Res. 57, 187–199 (2019).Article 

Google Scholar 
53.Hansen, N. C., Allen, B. L., Baumhardt, R. L. & Lyon, D. J. Research achievements and adoption of no-till, dryland cropping in the semi-arid US Great Plains. Field Crops Res. 132, 196–203 (2012).Article 

Google Scholar 
54.Dalal, R. C., Wang, W., Allen, D. E., Reeves, S. & Menzies, N. W. Soil nitrogen and nitrogen-use efficiency under long-term no-till practice. Soil Sci. Society Am. J. 75, 2251–2261 (2011).ADS 
CAS 
Article 

Google Scholar 
55.Zhang, H. L., Lal, R., Zhao, X., Xue, J. F. & Chen, F. Opportunities and challenges of soil carbon sequestration by conservation agriculture in China. Adv. Agron. 124, 1–36 (2014).CAS 
Article 

Google Scholar 
56.Mandal, A., Patra, A. K., Singh, D., Swarup, A. & Masto, R. E. Effect of long-term application of manure and fertilizer on biological and biochemical activities in soil during crop development stages. Biores. Tech. 98, 3585–3592 (2007).CAS 
Article 

Google Scholar 
57.Singh, G., Kumar, D., Marwaha, T. S., Singh, A. K. & Srinivasmurthy, K. Conservation tillage and integrated nitrogen management stimulates soil microbial properties under varying water regimes in maize–wheat cropping system in northern India. Arch. Agro. Soil Sci. 57(5), 507–521 (2011).Article 

Google Scholar 
58.Manning, D. A. C. & Renforth, P. Passive sequestration of atmospheric CO2 through coupled plant mineral reactions in urban soils. Environ. Sci. Technol. 47, 135–141 (2013).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
59.Mullen, M., Melhorn, C., Tyler, C. & Duck, D. Biological and biochemical soil properties in no–till corn with different cover crops. J. Soil Water Conserv. 5, 219–224 (1998).
Google Scholar 
60.Pooniya, V. & Shivay, Y. S. Summer green-manuring crops and zinc fertilization on productivity and economics of basmati rice (Oryza sativa L.). Arch. Agron. Soil. Sci. 58(6), 593–616 (2012).Article 

Google Scholar 
61.Pooniya, et al. Influence of summer legume residue–recycling and varietal diversification on productivity, energetics and nutrient dynamics in basmati rice–wheat cropping system of western Indo-Gangetic Plains. J. Plant Nutr. 41(12), 1491–1506 (2018).CAS 
Article 

Google Scholar 
62.Pooniya, V., Shivay, Y. S., Rana, A., Nain, L. & Prasanna, R. Enhancing soil nutrient dynamics and productivity of Basmati rice through residue incorporation and zinc fertilization. Eur. J. Agron. 41, 28–37 (2012).CAS 
Article 

Google Scholar 
63.Walkley, A. J. & Black, I. A. An examination of the Degtjareff method for determination of soil organic matter and a proposed modification of the chromic acid titration method. Soil Sci. 37, 29–38 (1934).ADS 
CAS 
Article 

Google Scholar 
64.Subbiah, B. V. & Asija, G. L. A rapid procedure for estimation of the available nitrogen in soil. Curr. Sci. 25, 259–260 (1956).CAS 

Google Scholar 
65.Olsen, S. R., Cole, C. V., Watanabe, F. S. & Dean, L. Estimation of Available Phosphorus in Soil by Extraction with Sodium Carbonate (USDA, 1954).
Google Scholar 
66.Hanway, J. J. & Heidel, H. Soil Analysis Methods as used in Iowa State College Soil Testing Laboratory, Bulletin 57 (Iowa State College of Agriculture, 1952).
Google Scholar 
67.Babu, S. et al. Impact of land configuration and organic nutrient management on productivity, quality and soil properties under baby corn in Eastern Himalayas. Sci. Rep. 10(1), 1–14 (2020).Article 
CAS 

Google Scholar 
68.Dey, A. et al. Effect of conservation agriculture on soil organic and inorganic carbon sequestration and lability: A study from a rice–wheat cropping system on a calcareous soil of the eastern Indo-Gangetic Plains. Soil Use Manage. 36, 429–438 (2018).Article 

Google Scholar 
69.Ellert, B. H. & Bettany, J. R. Calculation of organic matter and nutrients stored in soils under contrasting management regimes. Can. J. Soil Sci. 75, 529–538 (1995).CAS 
Article 

Google Scholar 
70.Vance, E. D., Brookes, P. C. & Jenkinson, D. S. An extraction method for measuring soil microbial biomass carbon. Soil Biol. Biochem. 19, 703–704 (1987).CAS 
Article 

Google Scholar 
71.Bhushan, L. et al. Saving of water and labor in a rice–wheat system with no-tillage and direct seeding technologies. Agron. J. 99, 1288–1296 (2007).Article 

Google Scholar 
72.Singh, R. P., Das, S. K., Rao, U. M. B. & Reddy, N. Towards Sustainable Dryland Agricultural Practices (CRIDA, 1990).
Google Scholar 
73.Wanjari, R. H., Singh, M. V. & Ghosh, P. K. Sustainable yield index: An approach to evaluate the sustainability of long-term intensive cropping systems in India. J. Sustain. Agric. 24(4), 39–56 (2004).Article 

Google Scholar 
74.Gomez, K. A. & Gomez, A. A. Statistical Procedures for Agricultural Research 2nd edn, 180–209 (Wiley, 1984).
Google Scholar  More

1.Pendergrass, A. G. & Hartmann, D. L. Changes in the distribution of rain frequency and intensity in response to global warming. J. Clim. 27, 8372–8383 (2014).ADS 
Article 

Google Scholar 
2.da Silva, R. M. et al. Rainfall and river flow trends using Mann-Kendall and Sen’s slope estimator statistical tests in the Cobres River basin. Nat. Hazards 77, 1205–1221 (2015).Article 

Google Scholar 
3.Schmidt, N., Lipp, E., Rose, J. & Luther, M. E. ENSO influences on seasonal rainfall and river discharge in Florida. J. Clim. 14, 615–628 (2001).ADS 
Article 

Google Scholar 
4.Gao, Y. et al. Coupled effects of biogeochemical and hydrological processes on C, N, and P export during extreme rainfall events in a purple soil watershed in southwestern China. J. Hydrol. 511, 692–702 (2014).ADS 
CAS 
Article 

Google Scholar 
5.Liu, X. Climatic characteristics of extreme rainstorm events in China. J. Catastrophol. 14, 54–59 (1999).
Google Scholar 
6.Stutter, M. I., Langan, S. J. & Cooper, R. J. Spatial and temporal dynamics of stream water particulate and dissolved N, P and C forms along a catchment transect, NE Scotland. J. Hydrol. 350, 187–202 (2008).ADS 
CAS 
Article 

Google Scholar 
7.Vink, S., Ford, P. W., Bormans, M., Kelly, C. & Turley, C. Contrasting nutrient exports from a forested and an agricultural catchment in south-eastern Australia. Biogeochemistry 84, 247–264 (2007).CAS 
Article 

Google Scholar 
8.Frost, P. C., Stelzer, R. S., Lamberti, G. A. & Elser, J. J. Ecological Stoichiometry of Trophic Interactions in the Benthos: Understanding the Role of C:N: P Ratios in Lentic and Lotic Habitats. J. N. Am. Benthol. Soc. 21, 515–528 (2002).Article 

Google Scholar 
9.Fisher, S. G., Grimm, N. B., Martí, E., Holmes, R. M. & Jones, J. J. B. Material spiraling in stream corridors: a telescoping ecosystem model. Ecosystems 1, 19–34 (1998).CAS 
Article 

Google Scholar 
10.Harrison, P. J., Yin, K., Lee, J. H. W., Gan, J. & Liu, H. Physical–biological coupling in the Pearl River Estuary. Cont. Shelf Res. 28, 1405–141511 (2008).ADS 
Article 

Google Scholar 
11.Corman, J. R. et al. in AGU Fall Meeting (AGU Fall Meeting Abstracts, 2017).12.Hathaway, J. M., Tucker, R. S., Spooner, J. M. & Hunt, W. F. A Traditional analysis of the first flush effect for nutrients in stormwater runoff from two small urban catchments. Water Air Soil Pollut. 223, 5903–5915 (2012).ADS 
CAS 
Article 

Google Scholar 
13.Pionke, H. B., Gburek, W. J., Schnabel, R. R., Sharpley, A. N. & Elwinger, G. F. Seasonal flow, nutrient concentrations and loading patterns in stream flow draining an agricultural hill-land watershed. J. Hydrol. 220, 62–73 (1999).ADS 
CAS 
Article 

Google Scholar 
14.Atkinson, C. L., Golladay, S. W., Opsahl, S. P. & Covich, A. P. Stream discharge and floodplain connections affect seston quality and stable isotopic signatures in a coastal plain stream. J. N. Am. Benthol. Soc. 28, 360–370 (2009).Article 

Google Scholar 
15.Perez, B., Day, J., Justic, D., Lane, R. & Twilley, R. Nutrient stoichiometry, freshwater residence time, and nutrient retention in a river-dominated estuary in the Mississippi Delta. Hydrobiologia 658, 41–54 (2011).CAS 
Article 

Google Scholar 
16.Frost, P. C., Kinsman, L. E., Johnston, C. A. & Larson, J. H. Watershed discharge modulates relationships between landscape components and nutrient ratios in stream seston. Ecology 90, 1631–1640 (2009).PubMed 
Article 

Google Scholar 
17.Green, M. & Finlay, J. Patterns of hydrologic control over stream water total nitrogen to total phosphorus ratios. Biogeochemistry 99, 15–30 (2011).Article 
CAS 

Google Scholar 
18.Fiorini, A. et al. May conservation tillage enhance soil C and N accumulation without decreasing yield in intensive irrigated croplands? Results from an eight-year maize monoculture. Agric. Ecosyst. Environ. 296, 106926 (2020).CAS 
Article 

Google Scholar 
19.Liu, J. et al. Landscape pattern at the class level regulates the stream water nitrogen and phosphorus levels in a Chinese subtropical agricultural catchment. Agric. Ecosyst. Environ. 295, 106897 (2020).CAS 
Article 

Google Scholar 
20.Neill, C., Deegan, L. A., Thomas, S. M. & Cerri, C. C. Deforestation for pasture alters nitrogen and phosphorus in small Amazonian streams. Ecol. Appl. 11, 1817–1828 (2001).Article 

Google Scholar 
21.Castelli, G., Castelli, F. & Bresci, E. Mesoclimate regulation induced by landscape restoration and water harvesting in agroecosystems of the horn of Africa. Agric. Ecosyst. Environ. 275, 54–64 (2019).Article 

Google Scholar 
22.Cui, L. et al. Identifying the influence factors at multiple scales on river water chemistry in the Tiaoxi Basin, China. Ecol. Indic. 92, 228–238 (2018).Article 

Google Scholar 
23.Liang, T. et al. Estimation of ammonia nitrogen load from nonpoint sources in the Xitiao River catchment, China. J. Environ. Sci. 20(10), 1195–1201 (2008).CAS 
Article 

Google Scholar 
24.Li, Z. F., Yang, G. S. & Li, H. P. Estimation of nutrient export coefficient from different land use types in Xitiaoxi watershed. J. Soil Water Conserv. 21(2), 1–4 (2007) ((In Chinese with English Abstract)).ADS 

Google Scholar 
25.Frost, P. C. et al. Landscape predictors of stream dissolved organic matter concentration and physicochemistry in a Lake Superior river watershed. Aquat. Sci. 68(1), 40–51 (2006).CAS 
Article 

Google Scholar 
26.Xu, H., Paerl, H. W., Qin, B., Zhu, G. & Gao, G. Nitrogen and phosphorus inputs control phytoplankton growth in eutrophic Lake Taihu, China. Limnol. Oceanogr. 55, 420–432 (2010).ADS 
CAS 
Article 

Google Scholar 
27.Qu, W., Mike, D. & Wang, S. Multivariate analysis of heavy metal and nutrient concentrations in sediments of Taihu Lake China. Hydrobiologia 450, 83–89 (2001).CAS 
Article 

Google Scholar 
28.Liu, X., Lu, X. & Chen, Y. The effects of temperature and nutrient ratios on Microcystis blooms in Lake Taihu, China: an 11-year investigation. Harmful Algae 10, 337–343 (2011).Article 
CAS 

Google Scholar 
29.Hu, Q. D. et al. Various inflows to Taihu Lake in autumn: spectroscopy characteristics and DOM flux. Environ. Sci. Technol. 38(3), 152–158 (2015) ((In Chinese with English Abstract)).CAS 

Google Scholar 
30.Gao, L., Li, D. J. Y. & Zhang, Y. W. Nutrients and particulate organic matter discharged by the Changjiang (Yangtze River): seasonal variations and temporal trends. J. Geophys. Res. 117, 110 (2012).
Google Scholar 
31.Guo, L. D., Zhang, J. Z. & Guéguen, C. Speciation and fluxes of nutrients (N, P, Si) from the upper Yukon River. Global Biogeochem. Cycles 18, GB1038 (2004).ADS 

Google Scholar 
32.Zhang, J. Z., Kelble, C. R., Fischer, C. J. & Moore, L. Hurricane Katrina induced nutrient runoff from an agricultural area to coastal waters in Biscayne Bay, Florida. Estuarine Coastal Shelf Sci. 84(2), 209–218 (2009).ADS 
CAS 
Article 

Google Scholar 
33.Redfield, A. C. The biological control of chemical factors in the environment. Am. Sci. 46, 205–221 (1958).CAS 

Google Scholar 
34.Zou, L. & MingY, Guo L. Temporal variations of organic carbon inputs into the upper Yukon River: evidence from fatty acids and their stable carbon isotopic compositions in dissolved, colloidal and particulate phases. Organ. Geochem. 37, 944–956 (2006).CAS 
Article 

Google Scholar 
35.Sañudo-Wilhelmy, S. A. et al. The impact of surface-adsorbed phosphorus on phytoplankton Redfield stoichiometry. Nature 432, 897–901 (2004).ADS 
PubMed 
Article 
CAS 

Google Scholar 
36.Michaels, A. F. The ratios of life. Science 300, 906–907 (2003).CAS 
Article 

Google Scholar 
37.Green, M. B. & Wang, D. Watershed flow paths and stream water nitrogen-to-phosphorus ratios under simulated precipitation regimes. Water Resour. Res. 44, W12414 (2008).ADS 

Google Scholar 
38.Green, M. B. & Finlay, J. C. Patterns of hydrologic control over stream water total nitrogen to total phosphorus ratios. Biogeochemistry 99, 15–30 (2010).CAS 
Article 

Google Scholar 
39.Li, L. J. & Zhang, Q. Application of a surface runoff and groundwater coupled model to Xitiaoxi catchment. J. Soil Water Conserv. 22, 56–61 (2008).MathSciNet 
CAS 

Google Scholar 
40.Jordan, T. E., Correll, D. L. & Weller, D. E. Relating nutrient discharges from watersheds to land use and streamflow variability. Water Resour. Res. 33(11), 2579–2590 (1997).ADS 
CAS 
Article 

Google Scholar 
41.Mcclelland, J. W. et al. Particulate organic carbon and nitrogen export from major arctic rivers. Global Biogeochem. Cycles 30, 629–643 (2016).ADS 
CAS 
Article 

Google Scholar 
42.Kang, J., Amoozegar, A., Hesterberg, D. & Osmond, D. L. Phosphorus leaching in a sandy soil as affected by organic and inorganic fertilizer sources. Geoderma 161, 194–201 (2011).ADS 
CAS 
Article 

Google Scholar 
43.Jury, W. A., Gardner, W. R. & Gardner, W. H. Soil physics (Wiley, New York, 1991).
Google Scholar 
44.Bracken, L. J. & Croke, J. The concept of hydrological connectivity and its contribution to understanding runoff-dominated geomorphic systems. Hydrol. Process. 21, 1749–1763 (2007).ADS 
Article 

Google Scholar 
45.Wu, L., Long, T. Y., Liu, X. & Guo, J. S. Impacts of climate and land-use changes on the migration of non-point source nitrogen and phosphorus during rainfall-runoff in the Jialing River Watershed, China. J. Hydrol. 475, 26–41 (2012).ADS 
CAS 
Article 

Google Scholar 
46.Allan, J. D. Landscapes and riverscapes: the influence of land use on stream ecosystems. Annu. Rev. Ecol. Evol. Syst. 6, 257–284 (2004).Article 

Google Scholar 
47.Gergel, S. E., Turner, M. G., Miller, J. R., Melack, J. M. & Stanley, E. H. Landscape indicators of human impacts to riverine systems. Aquat. Sci. 64, 118–128 (2002).CAS 
Article 

Google Scholar 
48.Wang, L., Lyons, J., Kanehl, P. & Gatti, R. Influences of watershed land use on habitat quality and biotic integrity in Wisconsin streams. Fisheries 22, 6–12 (1997).Article 

Google Scholar 
49.Finlay, J. C. Stream size and human influences on ecosystem production in river networks. Ecosphere 2(8), 87 (2011).Article 

Google Scholar 
50.LAWA. German Guidance document for the implementation of the EC Water Framework Directive. http://www.lawa.de/Publikationen.html. (2003).51.Johnson, L., Richards, C., Host, G. & Arthur, J. Landscape influences on water chemistry in Midwestern stream ecosystems. Freshw. Biol. 37, 193–208 (1997).CAS 
Article 

Google Scholar 
52.Gao, Y., Zhu, B., Wang, T. & Wang, Y. Seasonal change of non-point source pollution-induced bioavailable phosphorus loss: a case study of Southwestern China. J. Hydrol. 420, 373–379 (2012).ADS 
Article 
CAS 

Google Scholar 
53.Wohlfart, T. et al. Spatial distribution of soils determines export of nitrogen and dissolved organic carbon from an intensively managed agricultural landscape. Biogeosciences 9, 4513–4525 (2012).ADS 
CAS 
Article 

Google Scholar 
54.Silva, J., Cunha, B. M., Markewitz, D., Krusche, A. & Ferreira, L. Effects of land cover on chemical characteristics of streams in the Cerrado region of Brazil. Biogeochemistry 105, 75–88 (2011).CAS 
Article 

Google Scholar 
55.Ebina, J., Tsutsui, T. & Shirai, T. Simultaneous determination of total nitrogen and total phosphorus in water using peroxodisulfate oxidation. Water Res. 17, 1721–1726 (1983).CAS 
Article 

Google Scholar 
56.Pacini, N. & Gächter, R. Speciation of riverine particulate phosphorus during rain events. Biogeochemistry 47, 87–109 (1999).CAS 

Google Scholar 
57.Dodds, W. K. Misuse of inorganic N and soluble reactive P concentrations to indicate nutrient status of surface waters. J. N. Am. Benthol. Soc. 22, 171–181 (2003).Article 

Google Scholar  More

1.Allen, T. et al. Global hotspots and correlates of emerging zoonotic diseases. Nat. Commun. 8, 1124 (2017).2.Gibb, R. et al. Zoonotic host diversity increases in human-dominated ecosystems. Nature 584, 398–402 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Hassell, J. M., Begon, M., Ward, M. J. & Fèvre, E. M. Urbanization and disease emergence: dynamics at the wildlife–livestock–human interface. Trends Ecol. Evol. 32, 55–67 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
4.Plowright, R. K. et al. Pathways to zoonotic spillover. Nat. Rev. Microbiol. 15, 502–510 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
5.Holmes, E. C., Rambaut, A. & Andersen, K. G. Pandemics: spend on surveillance, not prediction comment. Nature 558, 180–182 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Tabachnick, W. J. Challenges in predicting climate and environmental effects on vector-borne disease episystems in a changing world. J. Exp. Biol. 213, 946–954 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Franklinos, L. H. V., Jones, K. E., Redding, D. W. & Abubakar, I. The effect of global change on mosquito-borne disease. Lancet Inf. Dis. 19, e302–e312 (2019).Article 

Google Scholar 
8.Seabloom, E. W. et al. The community ecology of pathogens: coinfection, coexistence and community composition. Ecol. Lett. 18, 401–415 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
9.Johnson, P. T. J., De Roode, J. C. & Fenton, A. Why infectious disease research needs community ecology. Science 349, 1259504 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
10.Parker, I. M. et al. Phylogenetic structure and host abundance drive disease pressure in communities. Nature 520, 542–544 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
11.Telfer, S. et al. Species interactions in a parasite community drive infection risk in a wildlife population. Science 330, 243–246 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Dallas, T. A., Laine, A.-L. L. & Ovaskainen, O. Detecting parasite associations within multi-species host and parasite communities. Proc. R. Soc. B 286, 20191109 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
13.Weinstein, S., Titcomb, G., Agwanda, B., Riginos, C. & Young, H. Parasite responses to large mammal loss in an African savanna. Ecology 98, 1839–1848 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
14.Anderson, R. & May, R. Infectious Diseases of Humans: Dynamics and Control (Oxford Univ. Press, 1992).15.Keeling, M. J. & Rohani, P. Modeling Infectious Diseases in Humans and Animals (Princeton Univ. Press, 2011).16.Buhnerkempe, M. G. et al. Eight challenges in modelling disease ecology in multi-host, multi-agent systems. Epidemics 10, 26–30 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
17.Cross, P. C., Prosser, D. J., Ramey, A. M., Hanks, E. M. & Pepin, K. M. Confronting models with data: the challenges of estimating disease spillover. Philos. Trans. R. Soc. B 374, 20180435 (2019).Article 

Google Scholar 
18.Johnson, E. E., Escobar, L. E. & Zambrana-Torrelio, C. An ecological framework for modeling the geography of disease transmission. Trends Ecol. Evol. 34, 655–668 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
19.Warton, D. I. et al. So many variables: joint modeling in community ecology. Trends Ecol. Evol. 30, 766–779 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
20.Carlson, C. J. et al. The global distribution of Bacillus anthracis and associated anthrax risk to humans, livestock and wildlife. Nat. Microbiol. 4, 1337–1343 (2019).CAS 
PubMed 
Article 

Google Scholar 
21.Sutherland, W. J. Predicting the ecological consequences of environmental change: a review of the methods. J. Appl. Ecol. 43, 599–616 (2006).Article 

Google Scholar 
22.Getz, W. M. et al. Making ecological models adequate. Ecol. Lett. 21, 153–166 (2018).PubMed 
Article 

Google Scholar 
23.Carlson, C. J., Chipperfield, J. D., Benito, B. M., Telford, R. J. & O’Hara, R. B. Species distribution models are inappropriate for COVID-19. Nat. Ecol. Evol. 4, 770–771 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
24.Evans, M. R. et al. Predictive systems ecology. Proc. R. Soc. B 280, 20131452 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
25.Purves, D. & Pacala, S. Predictive models of forest dynamics. Science 320, 1452–1453 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Harfoot, M. B. J. et al. Emergent global patterns of ecosystem structure and function from a mechanistic general ecosystem model. PLoS Biol. 12, e1001841 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
27.Lafferty, K. D. et al. Parasites in food webs: the ultimate missing links. Ecol. Lett. 11, 533–546 (2008).PubMed 
PubMed Central 
Article 

Google Scholar 
28.Redding, D. W. et al. Impacts of environmental and socio-economic factors on emergence and epidemic potential of Ebola in Africa. Nat. Commun. 10, 4531 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
29.Redding, D. W., Moses, L. M., Cunningham, A. A., Wood, J. & Jones, K. E. Environmental-mechanistic modelling of the impact of global change on human zoonotic disease emergence: a case study of Lassa fever. Methods Ecol. Evol. 7, 646–655 (2016).Article 

Google Scholar 
30.Carlson, C. J., Dallas, T. A., Alexander, L. W., Phelan, A. L. & Phillips, A. J. What would it take to describe the global diversity of parasites? Proc. R. Soc. B 287, 20201841 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
31.Rynkiewicz, E. C., Pedersen, A. B. & Fenton, A. An ecosystem approach to understanding and managing within-host parasite community dynamics. Trends Parasitol. 31, 212–221 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
32.Lello, J. & Hussell, T. Functional group/guild modelling of inter-specific pathogen interactions: a potential tool for predicting the consequences of co-infection. Parasitology 135, 825–839 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
33.Purves, D. et al. Time to model all life on Earth. Nature 493, 295–297 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
34.Kalka, M. B., Smith, A. R. & Kalko, E. K. V. Bats limit arthropods and herbivory in a tropical forest. Science 320, 71 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
35.Lafferty, K. D. et al. A general consumer-resource population model. Science 349, 854–857 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
36.Friedman, J., Higgins, L. M. & Gore, J. Community structure follows simple assembly rules in microbial microcosms. Nat. Ecol. Evol. 1, 0109 (2017).Article 

Google Scholar 
37.Goldford, J. E. et al. Emergent simplicity in microbial community assembly. Science 361, 469–474 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
38.Hatton, I. A. et al. The predator-prey power law: biomass scaling across terrestrial and aquatic biomes. Science 349, 6252 (2015).Article 
CAS 

Google Scholar 
39.Hatton, I. A., Dobson, A. P., Storch, D., Galbraith, E. D. & Loreau, M. Linking scaling laws across eukaryotes. Proc. Natl Acad. Sci. USA 116, 21616 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
40.Locey, K. J. & Lennon, J. T. Scaling laws predict global microbial diversity. Proc. Natl Acad. Sci. USA 113, 5970–5975 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
41.Godon, J. J., Arulazhagan, P., Steyer, J. P. & Hamelin, J. Vertebrate bacterial gut diversity: size also matters. BMC Ecol. 16, 12 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
42.Faust, C. L. et al. Null expectations for disease dynamics in shrinking habitat: dilution or amplification? Philos. Trans. R. Soc. B 372, 20160173 (2017).Article 

Google Scholar 
43.De Leo, G. A. & Dobson, A. P. Allometry and simple epidemic models for microparasites. Nature 379, 720–722 (1996).PubMed 
Article 
PubMed Central 

Google Scholar 
44.Strauss, A. T., Shoemaker, L. G., Seabloom, E. W. & Borer, E. T. Cross‐scale dynamics in community and disease ecology: relative timescales shape the community ecology of pathogens. Ecology 100, e02836 (2019).45.Handel, A. & Rohani, P. Crossing the scale from within-host infection dynamics to between-host transmission fitness: A discussion of current assumptions and knowledge. Philos. Trans. R. Soc. B 370, 20140302 (2015).46.Tibayrenc, M. & Ayala, F. J. Reproductive clonality of pathogens: a perspective on pathogenic viruses, bacteria, fungi, and parasitic protozoa. Proc. Natl Acad. Sci. USA 109, E3305–E3313 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
47.Gorter, F. A., Manhart, M. & Ackermann, M. Understanding the evolution of interspecies interactions in microbial communities. Philos. Trans. R. Soc. B 375, 1798 (2020).Article 
CAS 

Google Scholar 
48.Zeng, Q., Wu, S., Sukumaran, J. & Rodrigo, A. Models of microbiome evolution incorporating host and microbial selection. Microbiome 5, 127 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
49.Zeng, Q., Sukumaran, J., Wu, S. & Rodrigo, A. Neutral models of microbiome evolution. PLoS Comput. Biol. 11, e1004365 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
50.Liautaud, K., van Nes, E. H., Barbier, M., Scheffer, M. & Loreau, M. Superorganisms or loose collections of species? A unifying theory of community patterns along environmental gradients. Ecol. Lett. 22, 1243–1252 (2019).PubMed 
PubMed Central 

Google Scholar 
51.Coyte, K. Z., Schluter, J. & Foster, K. R. The ecology of the microbiome: Networks, competition, and stability. Science 350, 663–666 (2015).CAS 
Article 
PubMed 

Google Scholar 
52.Wright, E. S. & Vetsigian, K. H. Inhibitory interactions promote frequent bistability among competing bacteria. Nat. Commun. 7, 11274 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
53.Sanchez, A. & Gore, J. Feedback between population and evolutionary dynamics determines the fate of social microbial populations. PLoS Biol. 11, e1001547 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
54.Lopatkin, A. J. & Collins, J. J. Predictive biology: modelling, understanding and harnessing microbial complexity. Nat. Rev. Microbiol. 18, 507–520 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
55.Wu, F. et al. A unifying framework for interpreting and predicting mutualistic systems. Nat. Commun. 10, 242 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
56.Restif, O. et al. Model-guided fieldwork: practical guidelines for multidisciplinary research on wildlife ecological and epidemiological dynamics. Ecol. Lett. 15, 1083–1094 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
57.Herzog, S. A., Blaizot, S. & Hens, N. Mathematical models used to inform study design or surveillance systems in infectious diseases: a systematic review. BMC Infect. Dis. 17, 1–10 (2017).Article 

Google Scholar 
58.Cotterill, G. G. et al. Winter feeding of elk in the Greater Yellowstone Ecosystem and its effects on disease dynamics. Philos. Trans. R. Soc. B 373, 20170093 (2018).Article 

Google Scholar 
59.Cross, P. C. et al. Estimating distemper virus dynamics among wolves and grizzly bears using serology and Bayesian state-space models. Ecol. Evol. 8, 8726–8735 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
60.Hopkins, J. B., Ferguson, J. M., Tyers, D. B. & Kurle, C. M. Selecting the best stable isotope mixing model to estimate grizzly bear diets in the Greater Yellowstone Ecosystem. PLoS ONE 12, e0174903 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
61.Schwartz, C. C. et al. Body and diet composition of sympatric black and grizzly bears in the Greater Yellowstone Ecosystem. J. Wildl. Manag. 78, 68–78 (2014).Article 

Google Scholar 
62.Chester, C. C. Yellowstone to Yukon: transborder conservation across a vast international landscape. Environ. Sci. Policy 49, 75–84 (2015).Article 

Google Scholar 
63.Young, H. S. et al. Interacting effects of land use and climate on rodent-borne pathogens in central Kenya. Philos. Trans. R. Soc. B 372, 20160116 (2017).Article 

Google Scholar 
64.Sitters, J., Kimuyu, D. M., Young, T. P., Claeys, P. & Olde Venterink, H. Negative effects of cattle on soil carbon and nutrient pools reversed by megaherbivores. Nat. Sustain. 3, 360–366 (2020).Article 

Google Scholar 
65.Sethi, S. S., Ewers, R. M., Jones, N. S., Orme, C. D. L. & Picinali, L. Robust, real‐time and autonomous monitoring of ecosystems with an open, low‐cost, networked device. Methods Ecol. Evol. 9, 2383–2387 (2018).Article 

Google Scholar 
66.Alfano, N., Dayaram, A. & Tsangaras, K. Non-invasive surveys of mammalian viruses using environmental DNA. Preprint at bioRxiv https://doi.org/10.1101/2020.03.26.009993 (2020)67.Coyte, K. Z. & Rakoff-Nahoum, S. Understanding competition and cooperation within the mammalian gut microbiome. Curr. Biol. 29, R538–R544 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
68.Murray, M. H. et al. Gut microbiome shifts with urbanization and potentially facilitates a zoonotic pathogen in a wading bird. PLoS ONE 15, e0220926 (2020).69.McCallum, H. I. et al. Does terrestrial epidemiology apply to marine systems? Trends Ecol. Evol. 19, 585–591 (2004).Article 

Google Scholar 
70.Wu, S., Carvalho, P. N., Müller, J. A., Manoj, V. R. & Dong, R. Sanitation in constructed wetlands: a review on the removal of human pathogens and fecal indicators. Sci. Total Environ. 541, 8–22 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
71.Lamb, J. B. et al. Seagrass ecosystems reduce exposure to bacterial pathogens of humans, fishes, and invertebrates. Science 355, 731–733 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
72.Janssen, M. A., Walker, B. H., Langridge, J. & Abel, N. An adaptive agent model for analysing co-evolution of management and policies in a complex rangeland system. Ecol. Model. 131, 249–268 (2000).Article 

Google Scholar 
73.Ngonghala, C. N. et al. General ecological models for human subsistence, health and poverty. Nat. Ecol. Evol. 1, 1153–1159 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
74.Hosseini, P. R. et al. Does the impact of biodiversity differ between emerging and endemic pathogens? The need to separate the concepts of hazard and risk. Philos. Trans. R. Soc. B 372, 20160129 (2017).Article 

Google Scholar 
75.Washburne, A. D. et al. Percolation models of pathogen spillover. Philos. Trans. R. Soc. B 374, 20180331 (2019).Article 

Google Scholar 
76.Olival, K. J. et al. Host and viral traits predict zoonotic spillover from mammals. Nature 546, 646–650 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
77.Dobson, A. et al. Habitat loss, trophic collapse, and the decline of ecosystem services. Ecology 87, 1915–1924 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
78.Faust, C. L. et al. Pathogen spillover during land conversion. Ecol. Lett. 21, 471–483 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
79.Sokolow, S. H. et al. Ecological interventions to prevent and manage zoonotic pathogen spillover. Philos. Trans. R. Soc. B 374, 20180342 (2019).Article 

Google Scholar 
80.Kauffman, M. J. et al. Landscape heterogeneity shapes predation in a newly restored predator-prey system. Ecol. Lett. 10, 690–700 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
81.Smith, D. W., Peterson, R. O. & Houston, D. B. Yellowstone after wolves. BioScience 53, 330–340 (2003).Article 

Google Scholar 
82.McNaughton, S. J. Ecology of a grazing ecosystem: the Serengeti. Ecol. Monogr. 55, 259–294 (1985).Article 

Google Scholar 
83.Atkins, J. L. et al. Cascading impacts of large-carnivore extirpation in an African ecosystem. Science 364, 173–177 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
84.Cross, P. C., Edwards, W. H., Scurlock, B. M., Maichak, E. J. & Rogerson, J. D. Effects of management and climate on elk brucellosis in the Greater Yellowstone Ecosystem. Ecol. Appl. 17, 957–964 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
85.Almberg, E. S., Cross, P. C. & Smith, D. W. Persistence of canine distemper virus in the Greater Yellowstone Ecosystem’s carnivore community. Ecol. Appl. 20, 2058–2074 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
86.Holdo, R. M. et al. A disease-mediated trophic cascade in the Serengeti and its implications for ecosystem C. PLoS Biol. 7, e1000210 (2009).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
87.Borer, E. T. in Unsolved Problems in Ecology (eds Dobson, A. P. et al.) 3–15 (Princeton Univ. Press, 2020).88.Kao, R. H. et al. NEON terrestrial field observations: designing continental-scale, standardized sampling. Ecosphere https://doi.org/10.1890/ES12-00196.1 (2012).89.Springer, Y. P. et al. Tick-, mosquito-, and rodent-borne parasite sampling designs for the National Ecological Observatory Network. Ecosphere 7, e01271 (2016).Article 

Google Scholar 
90.Anderson-Teixeira, K. J. et al. CTFS-ForestGEO: a worldwide network monitoring forests in an era of global change. Glob. Chang. Biol. 21, 528–549 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
91.Dobson, A. P. et al. Ecology and economics for pandemic prevention. Science 369, 379–381 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
92.Kress, W. J., Mazet, J. A. K. & Hebert, P. D. N. Opinion: intercepting pandemics through genomics. Proc. Natl Acad. Sci. USA 117, 202009508 (2020).
Google Scholar 
93.Durmuş, S. & Ülgen, K. Comparative interactomics for virus–human protein–protein interactions: DNA viruses versus RNA viruses. FEBS Open Bio 7, 96–107 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
94.Becker, D. J. & Albery, G. F. Expanding host specificity and pathogen sharing beyond viruses. Mol. Ecol. 29, 3170–3172 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
95.Rittershaus, E. S. C., Baek, S. H. & Sassetti, C. M. The normalcy of dormancy: common themes in microbial quiescence. Cell Host Microbe 13, 643–651 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
96.Pedersen, A. B. & Fenton, A. Emphasizing the ecology in parasite community ecology. Trends Ecol. Evol. 22, 133–139 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
97.Schmid-Hempel, P. Immune defence, parasite evasion strategies and their relevance for ‘macroscopic phenomena’ such as virulence. Philos. Trans. R. Soc. B 364, 85–98 (2009).Article 

Google Scholar 
98.Hekstra, D. R. & Leibler, S. Contingency and statistical laws in replicate microbial closed ecosystems. Cell 149, 1164–1173 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
99.Plowright, R. K. et al. Land use-induced spillover: a call to action to safeguard environmental, animal, and human health. Lancet Planet. 5, E237–E245 (2021).Article 

Google Scholar 
100.Barychka, T., Mace, G. & Purves, D. The Madingley General Ecosystem Model predicts bushmeat yields, species extinction rates and ecosystem-level impacts of bushmeat harvesting. Preprint at biorXiv https://doi.org/10.1101/2020.03.02.959718 (2020). More

General framework for eco-coevolutionary transitions from antagonism to mutualismWe develop a general framework in which we model interactions between host species i (density Hi) and its partner species k (density Fk), which are initially purely antagonistic. The model is general, but could be applied broadly to bacterial hosts and parasitic phages or plant hosts and animal or fungal partners, for example. The ecological dynamics of this community (without evolution) are given by:$$frac{d{H}_{i}}{{dt}}={g}_{i}{H}_{i}left(1-mathop{sum}limits_{j}{q}_{{ij}}{H}_{j}right)+{sum }_{k}{f}!_{ik}left[beta left({H}_{i},{F}_{k}right),alpha left({{H}_{i},F}_{k}right)right]$$
(4a)
$$frac{d{F}_{k}}{{dt}}=mathop{sum}limits_{i}{f}_{ki}left[beta left({H}_{i},{F}_{k}right),alpha left({{H}_{i},F}_{k}right)right]-{delta }_{k}{F}_{k}$$
(4b)
The first term of Eq. (4a) describes host population growth in the absence of partner species, where gi is its intrinsic per capita growth rate and qij is the competitive effect of host j on host i for other limiting factors. The general function fik describes the effects of interactions with partner k on host i: β(Hi, Fk) gives the potential mutualism and α(Hi, Fk) describes the antagonism. In Eq. (4b), the general function fki gives the effects of interactions with host i on partner k and δk is the partner’s per capita mortality rate.To derive an explicit eco-coevolutionary model, we apply Equation (4) to model interactions between a single host species and its exclusive partner species (for the sake of simplicity) in terms of host traits xi and partner traits yi (involved in interactions with host i); the ecological dynamics of which are given by:$$frac{1}{{H}_{i}}frac{d{H}_{i}}{{dt}}={g}_{i}left(1-{q}_{i}{H}_{i}right)+frac{bleft[{x}_{i}^{B}right]vleft[{x}_{i}^{V},{y}_{i}^{V}right]{F}_{k}}{{S}_{i}+vleft[{x}_{i}^{V},{y}_{i}^{V}right]{F}_{k}}-hleft[{x}_{i}^{H},{y}_{i}^{H}right]vleft[{x}_{i}^{V},{y}_{i}^{V}right]{F}_{k}$$
(5a)
$$frac{1}{{F}_{k}}frac{d{F}_{k}}{{dt}}=eleft[{y}_{i}^{V},{y}_{i}^{H}right]vleft[{x}_{i}^{V},{y}_{i}^{V}right]hleft[{x}_{i}^{H},{y}_{i}^{H}right]{H}_{i}-{delta }_{k}$$
(5b)
where b is the mutualistic benefits to the host, v is the visitation rate, Si is a saturation constant, h is the costs of antagonism to the host and its benefits to the partner, and e is the partner’s conversion efficiency. The mutualistic and antagonistic interactions are assumed to contribute additively to host population growth and multiplicatively to partner population growth, assumptions that may be valid for many types of interactions, but will not apply universally. To prevent unbounded population growth in the model, the effects of mutualism on host population growth are assumed to saturates with increasing partner density.The function b[xiB] gives the mutualistic benefits of the partner as a function of host trait xiB:$$bleft[{x}_{i}^{B}right]={b}_{{max },i}left(frac{2}{1+{e}^{-{B}_{i}^{{prime} }{x}_{i}^{B}}}-1right)$$
(6a)
where bmax,i gives the maximum mutualistic benefits and ({B}_{i}^{{prime} }) is a saturation constant. The interaction is purely antagonistic when xiB = 0. As xiB increases, the mutualistic benefits b[xiB] increase towards bmax,i.The function v[xiV, yiV] gives visitation rate as a sigmoid function of host trait xiV and partner trait yiV:$$vleft[{x}_{i}^{V},{y}_{i}^{V}right]=frac{{v}_{{max },i}}{1+{e}^{-{V}_{i}^{{prime} }left({x}_{i}^{V}+{y}_{i}^{V}right)}}$$
(6b)
where vmax,i is the maximum visitation rate and ({V}_{i}^{{prime} }) determines how rapidly visitation rate changes as host and partner traits change. As xiV or yiV increase, the visitation rate increases and approaches vmax,i when xiV + yiV → ∞. As xiV or yiV decrease, the visitation rate decreases and approaches zero when xiV + yiV → −∞. Negative values of xiV indicate that the host species is reducing its attraction of the partner species.The function h[xiH, yiH] gives the costs of antagonism to the host and its benefits to the partner, which is described via a sigmoid function of the difference between host trait xiH and partner trait yiH:$$hleft[{x}_{i}^{H},{y}_{i}^{H}right]=frac{{h}_{{max },i}}{1+{e}^{{H}_{i}^{{prime} }left({x}_{i}^{H}-{y}_{i}^{H}right)}}$$
(6c)
where hmax,i gives the maximum antagonism and ({H}_{i}^{{prime} }) determines how antagonism changes as the difference between host and partner traits increases. When xiH > yiH, antagonism declines and approaches zero when xiH – yiH → ∞, while when xiH < yiH, antagonism increases and approaches hmax,i when xiH – yiH → -∞ (unlike xiV, xiH cannot be negative).Partner traits yiV and yiH trade off with conversion efficiency via the function e[yiV, yiH] as defined by:$$eleft[{y}_{i}^{V},{y}_{i}^{H}right]={e}_{{max },i}{e}^{-left({c}_{I,i}^{V}{left({y}_{i}^{V}right)}^{2}+{c}_{I,i}^{H}{left({y}_{i}^{H}right)}^{2}right)}$$ (6d) where emax,i is the maximum conversion efficiency when interacting with host i (when yiV = yiH = 0), and cI,iV and cI,iH determine how rapidly conversion efficiency declines as yiV or yiH increase, thus quantifying the costliness of traits yiV and yiH, respectively. This trade-off shape was chosen because it is unimodal and constrains conversion efficiency to always be positive. Host trade-offs are defined below (Eq. 8c).Host-partner coevolutionary dynamicsWe model coevolution via the adaptive dynamics framework17,18. Coevolution of a mutant host trait ximut and partner trait yimut (for any general traits xi and yi) is given by:$$frac{d{{x}_{i}}^{{mut}}}{dtau }={mu }_{x}{left.frac{partial {W}_{H}left({{x}_{i}}^{{mut}},{x}_{i},{y}_{i}right)}{partial {{x}_{i}}^{{mut}}}right|}_{{{x}_{i}}^{{mut}}={x}_{i}}$$ (7a) $$frac{d{{y}_{i}}^{{mut}}}{dtau }={mu }_{y}{left.frac{partial {W}_{F}left({{y}_{i}}^{{mut}},{y}_{i},{x}_{i}right)}{partial {{y}_{i}}^{{mut}}}right|}_{{{y}_{i}}^{{mut}}={y}_{i}}$$ (7b) where τ is the evolutionary timescale, μx and μy give, respectively, the rates of host and partner evolution, and WH(ximut,xi,yi) and WF(yimut,yi,xi) are the invasion fitness (per capita growth rate when rare) of a mutant host and partner species with trait ximut and yimut in a resident community with trait xi and yi, respectively. The partial derivatives ({left.partial {W}_{H}/partial {{x}_{i}}^{{mut}}right|}_{{{x}_{i}}^{{mut}}={x}_{i}}) and ({left.partial {W}_{F}/partial {{y}_{i}}^{{mut}}right|}_{{{y}_{i}}^{{mut}}={y}_{i}}) are the selection gradients.We model coevolution of mutualistic benefits from the focal partner species (via b), attraction (via v), and defense (via h). The invasion fitness of the mutant host and a mutant partner are given by:$${W}_{H}={g}_{i}left(1-qleft[{{x}_{i}}^{{mut}},{x}_{i}right]{{H}_{i}}^{ast }right)+frac{bleft[{x}_{i}^{B,{mut}}right]vleft[{x}_{i}^{V,{mut}},{y}_{i}^{V}right]{{F}_{k}}^{ast }}{{S}_{i}+vleft[{x}_{i}^{V,{mut}},{y}_{i}^{V}right]{{F}_{k}}^{ast }}-hleft[{x}_{i}^{H,{mut}},{y}_{i}^{H}right]{vleft[{x}_{i}^{V,{mut}},{y}_{i}^{V}right]{F}_{k}}^{ast }$$ (8a) $${W}_{F}=eleft[{y}_{i}^{V,{mut}},{y}_{i}^{H,{mut}}right]vleft[{x}_{i}^{V},{y}_{i}^{V,{mut}}right]hleft[{x}_{i}^{H},{y}_{i}^{H,{mut}}right]{{H}_{i}}^{ast }-{delta }_{k}$$ (8b) where Hi* and Fk* are species’ densities at the ecological equilibrium (of Eq. 5). The functions b, v, h, and e are given by Eq. (6a–d), respectively, where xi and yi are replaced with ximut in Eq. (8a) and yimut in Eq. (8b). The function q[ximut,xi] describes trade-offs between mutant host traits and mutant host competitive ability as defined by:$$qleft[{{x}_{i}}^{{mut}},{x}_{i}right]=1+{c}_{H,i}^{B}left({left({{x}_{i}}^{B,{mut}}right)}^{{s}_{i}^{B}}-{left({{x}_{i}}^{B}right)}^{{s}_{i}^{B}}right)+{c}_{H,i}^{V}left({left({{x}_{i}}^{V,{mut}}right)}^{{s}_{i}^{V}}-{left({{x}_{i}}^{V}right)}^{{s}_{i}^{V}}right)+{c}_{H,i}^{H}left({left({{x}_{i}}^{H,{mut}}right)}^{{s}_{i}^{H}}-{left({{x}_{i}}^{H}right)}^{{s}_{i}^{H}}right)$$ (8c) If ximut > xi for any trait, the competitive effect experienced by the mutant host is increased by an amount taken to be proportional (for simplicity) to the difference between the trait values, ximut – xi, whereas if ximut < xi, the competitive effect experienced by the mutant host is decreased by that amount. The coefficients cH,iB, cH,iV, and cH,iH measure the costs associated with the trade-off for each trait, while the shape parameters siB, siV, and siH define whether the trade-offs are linear (si = 1), concave (si < 1), or convex (si > 1).Mutualism can evolve via the COA for all trade-off shapes (Supplementary Fig. 3). Parameter space plots show that the interaction transitions from antagonism to net mutualism when the costs associated with host traits underlying attraction (cH,iV) and defense (cH,iH) are within a range beyond which there is evolutionary purging of the partner (Supplementary Fig. 3a–c). Only with convex trade-offs can the net antagonism persist. The coevolution of mutualism also requires that the costs associated with partner traits underlying visitation (cFV) and antagonism (cFH) exceed a threshold (Supplementary Fig. 3d–f) below which there is evolutionary purging of the partner (linear or convex trade-offs) or the net antagonism persists (linear or concave trade-offs). Coevolution of mutualism occurs across greater parameter ranges when the trade-offs are linear or slightly concave because costs increase less rapidly than with convex trade-offs.Ecological model of plant-insect interactionsWe tailor the general model (Eq. 4) to model populations of D. wrightii (density Pw) and D. discolor (density Pd) interacting with M. sexta. We scale the model so that Pi = 1 in the absence of M. sexta: thus, Pi >1 indicates that pollination benefits exceed herbivory costs, and Pi < 1 indicates that herbivory costs exceed pollination benefits. The Datura species do not rely obligately on M. sexta and, consistent with ecology of the natural community (Box 1), the model incorporates the alternative host plant, Proboscidea parviflora (density Pp), and the alternative nectar source, Agave palmeri. The ecological dynamics of this community (without evolution) are given by:$$frac{1}{{P}_{i}}frac{d{P}_{i}}{{dt}}=left(1-{P}_{i}right)+frac{{b}_{i}{v}_{i}A}{H+{v}_{i}A}-{h}_{i}{L}_{i}$$ (9a) $$frac{d{L}_{i}}{{dt}}=varepsilon {e}_{i}{v}_{i}{P}_{i}A-{m}_{i}{h}_{i}{L}_{i}-{d}_{i}{L}_{i}$$ (9b) $$frac{{dA}}{{dt}}=mathop{sum}limits_{i}{rho }_{i}{m}_{i}{h}_{i}{L}_{i}-{d}_{A}A$$ (9c) Equation (9a) describes the population dynamics of plant species i (D. wrightii, D. discolor, or P. parviflora). Equation (9b,c) give the dynamics of M. sexta: Li gives the larvae density on plant species i, which recruit into the adult population, A. Pollination is described by the term biviA/(H+viA), where bi is the per capita growth of plant species i due to pollination by the antagonist, vi is the visitation rate to plant species i per antagonist adult, and H is the saturation constant for pollination. Oviposition is given by εeiviPiA, where ei is the oviposition efficiency (number of eggs laid per floral visit) and ε is the fractional increase in egg production due to nectar-feeding at A. palmeri. Floral visits lead to both pollination and oviposition because these behaviors have been shown to be tightly linked in M. sexta19. Pollination and oviposition are given by saturating and linear functions, respectively, based on our data (Supplementary Data 1). Herbivory damage is given by the term hiLi, where hi is the herbivory rate per larvae on plant species i. Larvae mature at rate mihiLi, where mi is the maturation efficiency (fraction of larvae maturing on plant species i). Larval mortality on plant species i is di, adult mortality is dA, and ρi is pupae survival (due to data constraints, we include pupae survival in our estimates of maturation mi, set ρi = 1, and drop ρi from equations hereafter). Equation (9a) gives the dynamics of the alternative larval host plant, P. parviflora (bp = 0 and cannot evolve), which can coevolve attraction and defense. The alternative nectar source, A. palmeri, is incorporated within the model via the parameter ε.Model scalingWithout the antagonist, plant population growth is given by gi (1 – qiPi), where gi is the per capita growth rate of plant species i due to autonomous self-pollination or pollination by other species and qi is plant self-limitation. As qi is very difficult to quantify in nature, we scale the model so that Pi = 1 without the antagonist. We scale plant density ((hat{{P}_{i}}={q}_{i}{P}_{i})), larvae density ((hat{{L}_{i}}={q}_{i}{L}_{i})), herbivory rate ((hat{{h}_{i}}={h}_{i}/{q}_{i})), maturation efficiency ((hat{{m}_{i}}={q}_{i}{m}_{i})), and survival of pupae ((hat{{rho }_{i}}={rho }_{i}/{q}_{i})); where the hats denote scaled quantities and are dropped elsewhere for clarity. Thus, the model is scaled for parameterization, but is not non-dimensionalized. We then scale gi to 1 such that pollination benefits, bi, are estimated by the ratio of the seed set of moth-pollinated flowers to autonomously self-pollinated flowers. Parameter estimates are for scaled quantities.Interaction breakdown boundary for ancestral interaction in a one-plant species communityFor the ancestral insect to persist, its per capita growth rate must be positive when it is rare (i.e., at Pi* = 1, Li* = 0, A* = 0). In stage-structured models, the per capita growth rate is given by the dominant eigenvalue (λD) of the matrix:$$left[begin{array}{cc}-{m}_{i}{h}_{i}-{d}_{i} & varepsilon {e}_{i}{v}_{i}{{P}_{i}}^*\ {m}_{i}{h}_{i} & -{d}_{A}end{array}right]$$which is given by:$${lambda }_{D}=frac{1}{2}left(-{d}_{A}-{d}_{i}-{m}_{i}{h}_{i}+sqrt{{({d}_{A}+{d}_{i}+{m}_{i}{h}_{i})}^{2}-4({d}_{A}left({d}_{i}+{m}_{i}{h}_{i}right)-varepsilon {e}_{i}{v}_{i}{m}_{i}{h}_{i})}right).$$For the insect to persist, λD must have a positive real part, which occurs only when the second term in the square root of λD is negative; i.e., ({d}_{A}left({d}_{i}+{m}_{i}{h}_{i}right)-varepsilon {e}_{i}{v}_{i}{m}_{i}{h}_{i} , , 1). Applying ({f}_{i}=frac{varepsilon {e}_{i}{v}_{i}}{{d}_{A}}) and ({s}_{i}=frac{{m}_{i}{h}_{i}}{{{m}_{i}{h}_{i}+d}_{i}}), where fi is insect lifetime fecundity and si is the larval success (probability of larvae maturing rather than dying), yields Eq. (1).Interaction transition boundary in a one-plant species communityFor the interaction to transition from antagonism to mutualism, equilibrium plant density, Pi* must exceed one (see “Model scaling”). Setting Eq. (9b) to zero and solving for Pi* yields:({{P}_{i}}^{ast }=frac{{{m}_{i}{h}_{i}+d}_{i}}{varepsilon {e}_{i}{v}_{i}}left(frac{{{L}_{i}}^{ast }}{{A}^{ast }}right)). Setting Eq. (9c) to zero and rearranging terms then yields: (frac{{{L}_{i}}^{ast }}{{A}^{ast }}=frac{{d}_{A}}{{m}_{i}{h}_{i}}). Thus, ({{P}_{i}}^{ast }=frac{{{m}_{i}{h}_{i}+d}_{i}}{varepsilon {e}_{i}{v}_{i}}left(frac{{d}_{A}}{{m}_{i}{h}_{i}}right)) and (rearranging slightly) the condition for mutualism to arise is: ({{P}_{i}}^{ast }=left(frac{{d}_{A}}{varepsilon {e}_{i}{v}_{i}}right)left(frac{{{m}_{i}{h}_{i}+d}_{i}}{{m}_{i}{h}_{i}}right) , > , 1). Rearranging and applying ({f}_{i}=frac{varepsilon {e}_{i}{v}_{i}}{{d}_{A}}) and ({s}_{i}=frac{{m}_{i}{h}_{i}}{{{m}_{i}{h}_{i}+d}_{i}}) yields Eq. (2).Interaction breakdown boundary in a one-plant species communityIn the ancestral interaction, insect persistence is evaluated by whether or not it can increase from low density, which yields Eq. (1). Within the net mutualistic region, however, the insect cannot increase from very low density because it cannot buoy plant density sufficiently to maintain a positive per capita growth rate (mathematically, Eq. 1 cannot hold when Eq. 2 is satisfied). The mutualistic region is thus characterized by bistability (see Supplementary Figure 1), and the interaction breakdown boundary is determined by the conditions for the coexistence equilibrium to exist. At the coexistence equilibrium, the larval and adult densities are: ({{L}_{i}}^{ast }=frac{-B+sqrt{{B}^{2}-4{A}_{L}{C}_{L}}}{2{A}_{L}}) and ({A}^{ast }=frac{-B+sqrt{{B}^{2}-4{A}_{A}{C}_{A}}}{{2A}_{A}}), where ({A}_{L}=varepsilon {e}_{i}{{v}_{i}}^{2}{h}_{i}{d}_{A}), ({A}_{A}=varepsilon {e}_{i}{{v}_{i}}^{2}{m}_{i}{{h}_{i}}^{2}), (B=varepsilon {e}_{i}{{v}_{i}}^{2}{m}_{i}{h}_{i}left(frac{1}{{f}_{i}{s}_{i}}+frac{H}{{v}_{i}{m}_{i}}-left(1+{b}_{i}right)right)), ({C}_{L}=varepsilon {e}_{i}{v}_{i}H{d}_{A}left(frac{1}{{f}_{i}{s}_{i}}-1right)), and ({C}_{A}=varepsilon {e}_{i}{v}_{i}{m}_{i}{h}_{i}Hleft(frac{1}{{f}_{i}{s}_{i}}-1right)). For the coexistence equilibrium to exist, either CL and CA must be negative or B must be negative and Li* and A* must be real. CL and CA are negative when fi si > 1, which is Eq. (1) and cannot hold within the mutualistic region because Eq. (2) must be satisfied. However, B is negative when ({f}_{i}{s}_{i}left(left(1+{b}_{i}right)-frac{H}{{v}_{i}{m}_{i}}right) , > , 1), which is approximated by Eq. (3) when the last term is assumed to be small. For Li* and A* to be real, B2 – 4ALCL > 0 and B2 – 4AACA > 0. Assuming that the pollination saturation constant is small (i.e., H ≈ 0) yields CL ≈ CA ≈ 0 such that ({{L}_{i}}^{ast }approx frac{-B}{{A}_{L}}approx frac{{m}_{i}}{{d}_{A}{f}_{i}{s}_{i}}left({f}_{i}{s}_{i}left(1+{b}_{i}right)-1right)) and ({A}^{ast }approx frac{-B}{{A}_{A}}approx frac{1}{{h}_{i}}left({f}_{i}{s}_{i}left(1+{b}_{i}right)-1right)), which are both positive when fi si (1 + bi) > 1 as approximated by Eq. (3).Interaction transition and breakdown boundaries in a two-plant species communityThese boundaries are analytically intractable and are estimated by simulation (see codes provided online).Coevolutionary dynamics of plants and insectThe effects of plant traits xi and insect traits yi on the ecological dynamics of the interactions are given by:$$frac{1}{{P}_{i}}frac{d{P}_{i}}{{dt}}=left(1-{P}_{i}right)+frac{bleft[{x}_{i}^{B}right]vleft[{x}_{i}^{V},{y}_{i}^{V}right]A}{H+vleft[{x}_{i}^{V},{y}_{i}^{V}right]A}-hleft[{x}_{i}^{H},{y}_{i}^{H}right]{L}_{i}$$
(10a)
$$frac{d{L}_{i}}{{dt}}=varepsilon eleft[{y}_{i}^{V},{y}_{i}^{H}right]vleft[{x}_{i}^{V},{y}_{i}^{V}right]{P}_{i}A-{m}_{i}hleft[{x}_{i}^{H},{y}_{i}^{H}right]{L}_{i}-{d}_{i}{L}_{i}$$
(10b)
$$frac{{dA}}{{dt}}=mathop{sum}limits_{i}{m}_{i}hleft[{x}_{i}^{H},{y}_{i}^{H}right]{L}_{i}-{d}_{A}A$$
(10c)
We model coevolution of plant-insect interactions using the adaptive dynamics framework17,18 to link population dynamics and trait coevolution. The coevolution of mutant plant trait xmut and insect trait ymut (for general traits x and y) is given by Equation (7). We model the coevolution of pollination benefits from the antagonist, bi (via mutant plant trait xiB,mut), attraction (via mutant plant trait xiV,mut and mutant insect trait yiV,mut), and defense (via mutant plant trait xiH,mut and mutant insect trait yiH,mut). The invasion fitness of a mutant plant is given by:$${W}_{P,i}left({x}_{i}^{{mut}},{x}_{i},{y}_{i}right)=left(1-qleft[{x}_{i}^{{mut}},{x}_{i}right]{{P}_{i}}^{ast }right)+frac{bleft[{x}_{i}^{B,{mut}}right]vleft[{x}_{i}^{V,{mut}},{y}_{i}^{V}right]{A}^{ast }}{H+vleft[{x}_{i}^{V,{mut}},{y}_{i}^{V}right]{A}^{ast }}-hleft[{x}_{i}^{H,{mut}},{y}_{i}^{H}right]{{L}_{i}}^{ast }$$
(11a)
where Pi*, Li*, and A* are the densities of the plant, insect larvae per plant, and insect adults, respectively, at the ecological equilibrium (of Eq. 10). The functions (b[x_{i}^{B,mut}], v[x_{i}^{V,mut} , y_{i}^{V}],) and (h[x_{i}^{H,mut}, y_{i}^{H}]), describe the effects of mutant plant traits (x_{i}^{B,mut}), (x_{i}^{V,mut}), (x_{i}^{H,mut}), and (x_{i}^{H,mut}) on pollination benefits, attraction, and defense, respectively, which are defined by Eq. (6a–c), where xi is replaced with ximut (where the plant is the host species and the insect is the partner species). The function q[x,mut, xi] defines the trade-offs between mutant plant traits and the competitive ability of mutant plants, which is given by Eq. (8c) (with si = 1). At a coESS, ximut = xi for all traits such that q[ximut, xi] = 1 and the original definition of Pi >1 indicating that pollination benefits exceed herbivory costs is retained when pollination benefits evolve.Invasion fitness of a mutant insect is given by the dominant eigenvalue of its system of equations evaluated at the resident equilibrium. In a one-plant species community, the insect invasion fitness is:$${W}_{I,i}=frac{1}{2}left(-{d}_{A}-{d}_{i}-{m}_{i}{h}_{i}^{{mut}}+sqrt{{left({d}_{A}+{d}_{i}+{m}_{i}{h}_{i}^{{mut}}right)}^{2}-4left({d}_{A}left({d}_{i}+{m}_{i}{h}_{i}^{{mut}}right)-frac{varepsilon {e}_{i}^{{mut}}{v}_{i}^{{mut}}{h}_{i}^{{mut}}{d}_{A}left({d}_{i}+{m}_{i}{h}_{i}right)}{{e}_{i}{v}_{i}{h}_{i}}right)}right)$$
(11b)
where vimut, himut, and eimut are functions describing the effects of mutant insect traits on attraction, defense, and mutant oviposition efficiency, respectively, which are given by Eq. (6b–d), where yi is replaced with yimut. Invasion fitness of a mutant insect in a two-plant species community is given by the dominant eigenvalue of its system of equations evaluated at the resident equilibrium, which is analytically tractable, but sufficiently complicated that we do not include it here (see codes provided online).The curves where the selection gradients (see Eqs. 7) become zero give the evolutionary isoclines for the coevolutionary system. The points where the isoclines intersect give the coevolutionary singularities, which are coevolutionary stable states (coESSs) when they are stable for both plants and the insect. For tractability, the local stability of the coevolutionary singularities was assessed by carefully inspecting the selection gradient of each trait in the neighborhood of its coESS with all other traits held at their coESS as well as by simulating coevolutionary dynamics. Importantly, all three plant traits (xiB, xiV, and xiH) and both insect traits (yiV and yiH) all coevolve simultaneously in the model.Coevolution of the ancestral antagonistic interactionIn the ancestral interaction, pollination by the antagonist is impossible (bi = 0) and thus visitation only contributes to oviposition. From the plant perspective, the selection gradients for attraction and defense in the ancestral interaction are given by:$${left.frac{partial {W}_{P,i}}{partial {x}_{i}^{V,{mut}}}right|}_{{x}_{i}^{{mut}}={x}_{i}}=-{c}_{P,i}^{V}{{P}_{i}}^{ast }$$
(12a)
$${left.frac{partial {W}_{P,i}}{partial {x}_{i}^{H,{mut}}}right|}_{{x}_{i}^{{mut}}={x}_{i}}=frac{{h}_{{max },i}{H}_{i}^{{prime} }{e}^{{H}_{i}^{{prime} }left({x}_{i}^{H}-{y}_{i}^{H}right)}}{{left(1+{e}^{{H}_{i}^{{prime} }left({x}_{i}^{H}-{y}_{i}^{H}right)}right)}^{2}}{{L}_{i}}^{ast }-{c}_{P,i}^{H}{{P}_{i}}^{ast }$$
(12b)
Equation (12a) predicts that selection favors plant traits that reduce attracting the antagonist (e.g., reduced production of volatiles) and lower costs associated with competitive ability. We constrain xiV to be non-negative in the ancestral interaction so that xiV = 0 at the coESS; otherwise, xiV → –∞ and the plant always purges the insect given this model parameterization. Selection balances reduced herbivory damage (first term of Eq. 12b) with costs of reduced competitive ability (second term of Eq. 12b). Selection gradients for insect traits are sufficiently complicated that we do not include them here (see codes provided online); however, selection balances traits that increase visitation and overcome plant defenses with the costs associated with reduced oviposition. The ancestral coESSs are given in Supplementary Table 3.Coevolution of pollination benefits, attraction, and defenseThe evolution of mutant plant traits that allow the antagonist to pollinate it (bimut > 0) initiates the evolution of pollination benefits from the antagonist. The selection gradient for pollination benefits from the antagonist is given by:$${left.frac{partial {W}_{P,i}}{partial {x}_{i}^{B,{mut}}}right|}_{{x}_{i}^{{mut}}={x}_{i}}=frac{2{b}_{{max },i}{B}_{i}^{{prime} }{e}^{-{B}_{i}^{{prime} }{x}_{i}^{B}}vleft[{x}_{i}^{V},{y}_{i}^{V}right]{A}^{ast }}{{left(1+{e}^{-{B}_{i}^{{prime} }{x}_{i}^{B}}right)}^{2}left(H+vleft[{x}_{i}^{V},{y}_{i}^{V}right]{A}^{ast }right)}-{c}_{P,i}^{B}{{P}_{i}}^{ast }$$
(13a)
Equation (13a) shows that plants evolve traits to benefit from floral visits by the antagonist when selection for increased pollination benefits (first term of Eq. 13a) exceeds the costs associated with reduced competitive ability (second term of Eq. 13a).In the model, pollination benefits from the antagonist evolve via Eq. (13a) simultaneously with plant and insect traits affecting attraction and defense. The plant selection gradient for attraction is now:$${left.frac{partial {W}_{P,i}}{partial {x}_{i}^{V,{mut}}}right|}_{{x}_{i}^{{mut}}={x}_{i}}=frac{bleft[{x}_{i}^{B}right]{v}_{{max },i}{V}_{i}^{{prime} }{e}^{-{V}_{i}^{{prime} }left({x}_{i}^{V}+{y}_{i}^{V}right)}H{A}^{ast }}{{left(Hleft(1+{e}^{-{V}_{i}^{{prime} }left({x}_{i}^{V}+{y}_{i}^{V}right)}right)+{v}_{{max },i}{A}^{ast }right)}^{2}}-{c}_{P,i}^{V}{{P}_{i}}^{ast }$$
(13b)
The co-option of the antagonist has fundamentally changed selection on attraction (Eq. 13b vs. Equation 12a), which now balances traits affecting attraction (first term of Eq. 13b) with the costs of reduced competitive ability (second term of Eq. 13b). Co-option of the antagonist also modifies selection on defense (which is still given by Eq. 12b) by changing both trait values and equilibrium densities.Model parameterizationAll ecological parameters are estimated from empirical data. Here we parameterize the saturation constant H, maturation efficiency mi, larval mortality di, and adult mortality dA as well as the parameters for the alternative larval host plant and the alternative nectar source (see “Model validation” for other parameters).We cannot fit the saturation constant H to data because seed set saturates with even a single floral visit. We therefore estimate H as follows: D. wrightii flowers have a 91% chance of setting fruit30; thus, ({v}_{w}A/(H+{v}_{w}A))= 0.91 for a single visit (({v}_{w})A = 1). Solving (1/(H+1))= 0.91 for H yields: H = 0.1. H is assumed to be the same for D. discolor as pollination benefits saturate with a single visit for D. discolor. For maturation efficiency mi, only 0.5% of M. sexta larvae on D. wrightii survive through the final larval instar in nature34; thus, mw = 0.005. As M. sexta suffers 40% lower larval survival on D. discolor (5/8 larvae surviving to pupation) than on D. wrightii (10/10 larvae surviving to pupation) in our experiment19, we estimate that maturation efficiency is ~40% lower on D. discolor than on D. wrightii; i.e., md = (1 – 0.4)mw = 0.003. To estimate larval mortality, we note that larval survival is given by: ({m}_{i}={e}^{-{d}_{i}{D}_{i}}), where Di is development time. M. sexta has a larval stage of ~20 days on D. wrightii35 and there is no difference in development on D. wrightii and D. discolor, at least to the 5th instar19. Solving for di yields: dw ≈ 0.25 and dd ≈ 0.3. Finally, adults live ~5 days in the wild36. Assuming adult mortality is roughly the inverse of the lifespan: dA ≈ 0.2.For the alternative larval host plant, females lay similar numbers of eggs on D. wrightii and P. parviflora34; thus, visitation rate and oviposition efficiency are assumed to be the same as with D. wrightii; i.e., vp = vw and ep = ew. Because P. parviflora plants are of similar size and architecture as D. wrightii34, we assume that herbivory rate on P. parviflora is the same as on D. wrightii; i.e., hp = hw. (see “Model validation” for estimates of vw, ew, and hw). Only 1% of M. sexta larvae on P. parviflora survive through the final larval stage34; thus, mp = 0.01. As larvae have roughly the same development time on P. parviflora as on D. wrightii (~20 days37), solving ({m}_{p}={e}^{-{d}_{p}{D}_{p}}) yields an estimate of larval mortality on P. parviflora of: dp ≈ 0.25.For the alternative nectar source, A. palmeri provides M. sexta with copious amounts of nectar that females likely utilize for egg production38. M. sexta females lay 100–300 eggs/night39. If females foraging exclusively on D. wrightii lay the minimum 100 eggs/night and females that also forage at A. palmeri lay the maximum 300 eggs/night, then A. palmerii is estimated to increase oviposition by a factor of: ε = 3.Model validationPollination benefits (bi), visitation rate (vi), herbivory rate (hi), and oviposition efficiency (ei) all evolve simultaneously in the model. We independently validate the coESSs predicted by the models whenever possible by estimating these parameters using data that were not used to parameterize the models. We estimate bi via the ratio of the seed set of moth-pollinated flowers to autonomously self-pollinated flowers (autonomously self-pollinated seeds germinate as readily as do outcrossed seeds;30). Pollinated D. wrightii and D. discolor flowers set bw = 4.6 ± 0.2 and bd = 3.6 ± 0.1 times more seeds, respectively, than do autonomously self-pollinated flowers (D. wrightii: n = 21 fruit; D. discolor: n = 85 fruit). Moths averaged vw = 4.3 ± 0.6 floral visits to D. wrightii (n = 89 plants) and vd = 2.4 ± 0.4 floral visits to D. discolor (n = 33 plants) in our experiment19. Estimating the herbivory rate is very difficult in nature; however, we can make cursory estimates based on our data. A single M. sexta larvae can consume 1400–1900 cm2 of leaves, which is more than many D. wrightii plants in nature30. Assuming that an average D. wrightii plant supplies larvae with 1400 cm2 of leaves, the variation in leaf consumption (500 cm2) represents ~0.4 plants (=500/1400). Thus, M. sexta larvae are estimated to consume: hw ≈ 1 ± 0.4 D. wrightii plants. M. sexta larvae consumed roughly two times more D. discolor leaf biomass than D. wrightii leaf biomass based on our cursory estimates from our experiments; thus, hd = 2hw ≈ 2 ± 0.8. We estimate oviposition efficiency by the slope of a linear regression of the number of eggs versus the number of floral visits that each plant received from each female moth in our experiments19, which yields: ew = 0.6 ± 0.1 (n = 34 plants) and ed = 0.6 ± 0.2 (n = 24 plants) (Supplementary Data 1).Estimating evolutionary model parametersDirectly estimating evolutionary parameters with data is not possible. We therefore use theory to predict how key parameters affect eco-coevolutionary outcomes and to select reasonable parameter estimates. Our approach is as follows. We set the rates of plant and insect evolution to one (μx = μy = 1); these rates affect the speed of evolution, but not the coESSs. For each trait, we need to estimate the maximum value (bmax,i, vmax,i, hmax,i, and emax,i), the coefficient (({R}_{i}^{{prime} }), ({V}_{i}^{{prime} }), and ({H}_{i}^{{prime} })), and the associated costs (cP,iB, cP,iV, and cP,iH for plant i and cI,iV and cI,iH for the insect). Maximum trait values were chosen to constrain coevolution to a realistic range. We set the coefficients ({R}_{i}^{{prime} }), ({V}_{i}^{{prime} }), and ({H}_{i}^{{prime} }) to one for simplicity because the exact value of any trait x and y are themselves somewhat arbitrary. The costs associated with the traits therefore largely determine the coevolutionary outcomes in the model.We estimate the costs of each trait by systematically varying the costs of plant traits in the one-plant species community given reasonable values for the insect costs and then systematically varying the costs of insect traits while holding plant costs constant at their chosen values (Fig. 5). Parameter space plots show that the interactions transition from antagonism to net mutualism provided that the costs associated with insect traits underlying visitation (cI,iV) exceed a threshold below which the plant and insect engage in an evolutionary arms-race that results in the evolutionary purging of the antagonist (Fig. 5a, b). Only very rarely does the net antagonism persist. We assigned all insect traits a cost of 0.5 (black points in Fig. 5a, b) and then systematically vary the costs of plant traits associated with attraction and defense.Parameter space plots show that interactions transition from antagonism to net mutualism when the costs associated with defense are high relative to the costs associated with attraction (cP,iH > cP,iV); otherwise, coevolution drives evolutionary purging of the antagonist (Fig. 5c, d). When the costs associated with attraction and defense are both fairly high, the net antagonism persists. We assigned values of cP,iH and cP,iV to D. wrightii and D. discolor such that the parameters for D. discolor are closer to the threshold at which evolutionary purging occurs than are those of D. wrightii (Fig. 5d vs. 5c), reflecting the smaller range of ecological parameters over which M. sexta can persist with D. discolor versus with D. wrightii (Fig. 2b vs. 2a). Finally, the costs associated with pollination benefits from the antagonist (cP,iB) must be very high for the net antagonism to persist and we never observed evolutionary purging of the insect within the range of values used (see codes provided online). We assigned values of cP,iB so that pollination benefits to D. wrightii and D. discolor are well below their maximum values. Our estimates of evolutionary parameters are reported in Supplementary Table 2. Evolutionary parameters for P. parviflora are set equal to D. discolor because, in the absence of more information, both species are annual plants that may face broadly similar evolutionary constraints, at least relative to the perennial D. wrightii.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

1.IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (eds Pörtner, H.-O. et al.) (IPCC, 2019).2.Urban, M. C. Accelerating extinction risk from climate change. Science 348, 571–573 (2015).CAS 
Article 

Google Scholar 
3.McCauley, D. J. & Pinsky, M. L. Marine defaunation: animal loss in the global ocean. Science 347, 1255641 (2015).Article 
CAS 

Google Scholar 
4.Hoffmann, A. A. & Sgrò, C. M. Climate change and evolutionary adaptation. Nature 470, 479–485 (2011).CAS 

Google Scholar 
5.Somero, G. N. The physiology of climate change: how potentials for acclimatization and genetic adaptation will determine ‘winners’ and ‘losers’. J. Exp. Biol. 213, 912–920 (2010).CAS 
Article 

Google Scholar 
6.Kearney, M. & Porter, W. Mechanistic niche modelling: combining physiological and spatial data to predict species’ ranges. Ecol. Lett. 12, 334–350 (2009).Article 

Google Scholar 
7.Foden, W. B. et al. Identifying the world’s most climate change vulnerable species: a systematic trait-based assessment of all birds, amphibians and corals. PLoS ONE 8, e65427 (2013).CAS 
Article 

Google Scholar 
8.West, J. M. & Salm, R. V. Resistance and resilience to coral bleaching: implications for coral reef conservation and management. Conserv. Biol. 17, 956–967 (2003).Article 

Google Scholar 
9.Baskett, M. L., Nisbet, R. M., Kappel, C. V., Mumby, P. J. & Gaines, S. D. Conservation management approaches to protecting the capacity for corals to respond to climate change: a theoretical comparison. Glob. Change Biol. 16, 1229–1246 (2010).Article 

Google Scholar 
10.Beyer, H. L. et al. Risk-sensitive planning for conserving coral reefs under rapid climate change. Conserv. Lett. 11, e12587 (2018).Article 

Google Scholar 
11.Walsworth, T. E. et al. Management for network diversity speeds evolutionary adaptation to climate change. Nat. Clim. Change 9, 632–636 (2019).Article 

Google Scholar 
12.Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 359, 80–83 (2018).CAS 
Article 

Google Scholar 
13.Donner, S. D., Skirving, W. J., Little, C. M., Oppenheimer, M. & Hoegh-Guldberg, O. Global assessment of coral bleaching and required rates of adaptation under climate change. Glob. Change Biol. 11, 2251–2265 (2005).Article 

Google Scholar 
14.Frieler, K. et al. Limiting global warming to 2 °C is unlikely to save most coral reefs. Nat. Clim. Change 3, 165–170 (2012).Article 

Google Scholar 
15.Van Hooidonk, R., Maynard, J. A., Manzello, D. & Planes, S. Opposite latitudinal gradients in projected ocean acidification and bleaching impacts on coral reefs. Glob. Change Biol. 20, 103–112 (2014).Article 

Google Scholar 
16.Logan, C. A., Dunne, J. P., Eakin, C. M. & Donner, S. D. Incorporating adaptive responses into future projections of coral bleaching. Glob. Change Biol. 20, 125–139 (2014).Article 

Google Scholar 
17.Bay, R. A., Rose, N. H., Logan, C. A. & Palumbi, S. R. Genomic models predict successful coral adaptation if future ocean warming rates are reduced. Sci. Adv. 3, e1701413 (2017).Article 

Google Scholar 
18.Matz, M. V., Treml, E. A. & Haller, B. C. Estimating the potential for coral adaptation to global warming across the Indo-West Pacific. Glob. Change Biol. 26, 3473–3481 (2020).Article 

Google Scholar 
19.Baskett, M. L., Gaines, S. D. & Nisbet, R. M. Symbiont diversity may help coral reefs survive moderate climate change. Ecol. Appl. 19, 3–17 (2009).Article 

Google Scholar 
20.Matz, M. V., Treml, E. A., Aglyamova, G. V. & Bay, L. K. Potential and limits for rapid genetic adaptation to warming in a Great Barrier Reef coral. PLoS Genet. 14, e1007220 (2018).Article 
CAS 

Google Scholar 
21.Muscatine, L., Falkowski, P. G., Porter, J. W. & Dubinsky, Z. Fate of photosynthetic fixed carbon in light- and shade-adapted colonies of the symbiotic coral Stylophora pistillata. Proc. R. Soc. Lond. B. 222, 181–202 (1984).CAS 
Article 

Google Scholar 
22.Csaszar, N. B., Ralph, P. J., Frankham, R., Berkelmans, R. & van Oppen, M. J. Estimating the potential for adaptation of corals to climate warming. PLoS ONE 5, e9751 (2010).Article 
CAS 

Google Scholar 
23.Howells, E. J. et al. Coral thermal tolerance shaped by local adaptation of photosymbionts. Nat. Clim. Change 2, 116–120 (2012).Article 

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

Google Scholar 
25.Baker, A. C. Flexibility and specificity in coral–algal symbiosis: diversity, ecology, and biogeography of Symbiodinium. Annu. Rev. Ecol. Evol. Syst. 34, 661–689 (2003).26.Berkelmans, R. & van van Oppen, M. J. H. The role of zooxanthellae in the thermal tolerance of corals: a ‘nugget of hope’ for coral reefs in an era of climate change. Proc. R. Soc. Lond. B 273, 2305–2312 (2006).
Google Scholar 
27.National Academies of Sciences, Engineering, and Medicine A research Review of Interventions to Increase the Persistence and Resilience of Coral Reefs (National Academies Press, 2019).28.National Academies of Sciences, Engineering, and Medicine A Decision Framework for Interventions to Increase the Persistence and Resilience of Coral Reefs (National Academies Press, 2019).29.Darling, E. S., Alvarez-Filip, L., Oliver, T. A., McClanahan, T. R. & Côté, I. M. Evaluating life-history strategies of reef corals from species traits. Ecol. Lett. 15, 1378–1386 (2012).Article 

Google Scholar 
30.Chan, N. C. S. & Connolly, S. R. Sensitivity of coral calcification to ocean acidification: a meta-analysis. Glob. Change Biol. 19, 282–290 (2013).Article 

Google Scholar 
31.Hughes, T. P. et al. Global warming transforms coral reef assemblages. Nature 556, 492–496 (2018).CAS 
Article 

Google Scholar 
32.Darling, E. S. et al. Relationships between structural complexity, coral traits, and reef fish assemblages. Coral Reefs 36, 561–575 (2017).Article 

Google Scholar 
33.Howells, E. J. et al. Corals in the hottest reefs in the world exhibit symbiont fidelity not flexibility. Mol. Ecol. 29, 899–911 (2020).CAS 
Article 

Google Scholar 
34.Madin, J. S., Hughes, T. P. & Connolly, S. R. Calcification, storm damage and population resilience of tabular corals under climate change. PLoS ONE 7, e46637 (2012).CAS 
Article 

Google Scholar 
35.Hoegh-Guldberg, O. et al. in Global Warming of 1.5°C (eds Masson-Delmotte, V. et al.) Ch. 3 (IPCC, 2018).36.Darling, E. S. et al. Social–environmental drivers inform strategic management of coral reefs in the Anthropocene. Nat. Ecol. Evol. 3, 1341–1350 (2019).Article 

Google Scholar 
37.Wilkinson, C. R. Global and local threats to coral reef functioning and existence: review and predictions. Mar. Freshw. Res. 50, 867–878 (1999).
Google Scholar 
38.Palumbi, S. R., Barshis, D. J., Traylor-Knowles, N. & Bay, R. A. Mechanisms of reef coral resistance to future climate change. Science 344, 895–898 (2014).CAS 
Article 

Google Scholar 
39.Kleypas, J. A. et al. Larval connectivity across temperature gradients and its potential effect on heat tolerance in coral populations. Glob. Change Biol. 22, 3539–3549 (2016).Article 

Google Scholar 
40.Heron, S. F. et al. Validation of reef-scale thermal stress satellite products for coral bleaching monitoring. Remote Sens. 8, 59 (2016).Article 

Google Scholar 
41.Safaie, A. et al. High frequency temperature variability reduces the risk of coral bleaching. Nat. Commun. 9, 1671 (2018).Article 
CAS 

Google Scholar 
42.Forster, P. M. et al. Evaluating adjusted forcing and model spread for historical and future scenarios in the CMIP5 generation of climate models. J. Geophys. Res. Atmos. 118, 1139–1150 (2013).Article 

Google Scholar 
43.Ziegler, M., Eguíluz, V. & Duarte, C. et al. Rare symbionts may contribute to the resilience of coral–algal assemblages. ISME J 12, 161–172 (2018).Article 

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

Google Scholar 
45.Thornhill, D. J., Xiang, Y. U., Fitt, W. K. & Santos, S. R. Reef endemism, host specificity and temporal stability in populations of symbiotic dinoflagellates from two ecologically dominant Caribbean corals. PLoS ONE 4, e6262 (2009).Article 
CAS 

Google Scholar 
46.Stat, M., Loh, W. K. W., LaJeunesse, T. C., Hoegh-Guldberg, O. & Carter, D. A. Stability of coral–endosymbiont associations during and after a thermal stress event in the southern Great Barrier Reef. Coral Reefs 28, 709–713 (2009).Article 

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

Google Scholar 
48.Schulte, P. M., Healy, T. M. & Fangue, N. A. Thermal performance curves, phenotypic plasticity, and the time scales of temperature exposure. Integr. Comp. Biol. 51, 691–702 (2011).Article 

Google Scholar 
49.Loya, Y. et al. Coral bleaching: the winners and the losers. Ecol. Lett. 4, 122–131 (2001).Article 

Google Scholar 
50.Langmead, O. & Sheppard, C. Coral reef community dynamics and disturbance: a simulation model. Ecol. Modell. 175, 271–290 (2004).Article 

Google Scholar 
51.Chancerelle, Y. Methods to estimate actual surface areas of scleractinian coral at the colony- and community-scale. Oceanol. Acta 23, 211–219 (2000).Article 

Google Scholar 
52.Falkowski, P. G., Dubinsky, Z., Muscatine, L. & Porter, J. W. Light and the bioenergetics of a symbiotic coral. Bioscience 34, 705–709 (1984).CAS 
Article 

Google Scholar 
53.Huston, M. Variation in coral growth rates with depth at Discovery Bay, Jamaica. Coral Reefs 4, 19–25 (1985).Article 

Google Scholar 
54.Hoogenboom, M., Beraud, E. & Ferrier-Pagès, C. Relationship between symbiont density and photosynthetic carbon acquisition in the temperate coral Cladocora caespitosa. Coral Reefs 29, 21–29 (2010).Article 

Google Scholar 
55.Cunning, R. & Baker, A. C. Not just who, but how many: the importance of partner abundance in reef coral symbioses. Front. Microbiol. 5, 400 (2014).Article 

Google Scholar 
56.McClanahan, T., Muthiga, N. & Mangi, S. Coral and algal changes after the 1998 coral bleaching: interaction with reef management and herbivores on Kenyan reefs. Coral Reefs 19, 380–391 (2001).Article 

Google Scholar 
57.Fitt, W. K., McFarland, F. K., Warner, M. E. & Chilcoat, G. C. Seasonal patterns of tissue biomass and densities of symbiotic dinoflagellates in reef corals and relation to coral bleaching. Limnol. Oceanogr. 45, 677–685 (2000).CAS 
Article 

Google Scholar 
58.Eppley, R. W. Temperature and phytoplankton growth in the sea. Fish. Bull. 70, 1063–1085 (1972).
Google Scholar 
59.Jon, N. Biodiversity and ecosystem functioning: a complex adaptive systems approach. Limnol. Oceanogr. 49, 1269–1277 (2004).Article 

Google Scholar 
60.Mousseau, T. A. & Roff, D. A. Natural selection and the heritability of fitness components. Heredity 59, 181–197 (1987).Article 

Google Scholar 
61.Lynch, M. The rate of polygenic mutation. Genet. Res. 51, 137–148 (1988).CAS 
Article 

Google Scholar 
62.Donner, S. D., Rickbeil, G. J. & Heron, S. F. A new, high-resolution global mass coral bleaching database. PLoS ONE 12, e0175490 (2017).Article 
CAS 

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

Google Scholar 
64.Silverstein, R. N., Cunning, R. & Baker, A. C. Change in algal symbiont communities after bleaching, not prior heat exposure, increases heat tolerance of reef corals. Glob. Change Biol. 21, 236–249 (2015).Article 

Google Scholar 
65.Dunne, J. P. et al. GFDL’s ESM2 global coupled climate–carbon Earth system models part I: physical formulation and baseline simulation characteristics. J. Clim. 25, 6646–6665 (2012).Article 

Google Scholar 
66.Dunne, J. P. et al. GFDL’s ESM2 global coupled climate–carbon Earth system models Part II: carbon system formulation and baseline simulation characteristics. J. Clim. 26, 2247–2267 (2012).Article 

Google Scholar 
67.Lough, J. M. & Barnes, D. J. Environmental controls on growth of the massive coral Porites. J. Exp. Mar. Biol. Ecol. 245, 225–243 (2000).CAS 
Article 

Google Scholar 
68.UNEP-WCMC, WorldFish Centre, WRI & TNC. Global Distribution of Warm-water Coral Reefs, Compiled From Multiple Sources Including the Millennium Coral Reef Mapping Project. Version 4.1 (UN Environment World Conservation Monitoring Centre. Data, 2021); https://doi.org/10.34892/t2wk-5t3469.van Hooidonk, R., Maynard, J. A. & Planes, S. Temporary refugia for coral reefs in a warming world. Nat. Clim. Change 3, 508–511 (2013).Article 
CAS 

Google Scholar 
70.Fitt, W., Brown, B., Warner, M. & Dunne, R. Coral bleaching: interpretation of thermal tolerance limits and thermal thresholds in tropical corals. Coral Reefs 20, 51–65 (2001).Article 

Google Scholar 
71.González-Espinosa, P. C. & Donner, S. D. Predicting cold-water bleaching in corals: role of temperature, and potential integration of light exposure. Mar. Ecol. Prog. Ser. 642, 133–146 (2020).Article 

Google Scholar  More

1.Lamichhaney, S. et al. Evolution of Darwin’s finches and their beaks revealed by genome sequencing. Nature 518, 371–375 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Simões, M. et al. The evolving theory of evolutionary radiations. Trends Ecol. Evol. 31, 27–34 (2016).PubMed 
Article 

Google Scholar 
3.Arnegard, M. E. et al. Genetics of ecological divergence during speciation. Nature 511, 307–311 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
4.Hoffmann, A. A. & Sgrò, C. M. Climate change and evolutionary adaptation. Nature 470, 479–485 (2011).CAS 
PubMed 
Article 

Google Scholar 
5.Olsen, J. L. et al. The genome of the seagrass Zostera marina reveals angiosperm adaptation to the sea. Nature 530, 331–335 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Becerra, J. X., Nogeb, K. & Venable, D. L. Macroevolutionary chemical escalation in an ancient plant–herbivore arms race. Proc. Natl Acad. Sci. USA 106, 18062–18066 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Edger, P. P. et al. The butterfly plant arms-race escalated by gene and genome duplications. Proc. Natl Acad. Sci. USA 112, 8362–8366 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
8.Adler, L. S. & Bronstein, J. L. Attracting antagonists: does floral nectar increase leaf herbivory? Ecology 85, 1519–1526 (2004).Article 

Google Scholar 
9.McCall, A. C. & Irwin, R. E. Florivory: the intersection of pollination and herbivory. Ecol. Lett. 9, 1351–1365 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
10.Cook, J. M. & Rasplus, J.-Y. Mutualists with attitude: coevolving fig wasps and figs. Trends Ecol. Evol. 18, 241–248 (2003).Article 

Google Scholar 
11.Zhang, X. et al. Genomes of the banyan tree and pollinator wasp provide insights into fig–wasp coevolution. Cell 183, 875–889 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Herre, E. A., Jandér, K. C. & Machado, C. A. Evolutionary ecology of figs and their associates: recent progress and outstanding puzzles. Annu. Rev. Ecol. Evol. Syst. 39, 439–458 (2008).Article 

Google Scholar 
13.Souza, C. D. et al. Diversity of fig glands is associated with nursery mutualism in fig trees. Am. J. Bot. 102, 1564–1577 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Souto-Vilarós, D. et al. Pollination along an elevational gradient mediated both by floral scent and pollinator compatibility in the fig and fig–wasp mutualism. J. Ecol. Evol. 106, 2256–2273 (2018).
Google Scholar 
15.Wang, R. et al. Loss of top-down biotic interactions changes the relative benefits for obligate mutualists. Proc. R. Soc. B 286, 20182501 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
16.Mori, K. et al. Identification of RAN1 orthologue associated with sex determination through whole genome sequencing analysis in fig (Ficus carica L.). Sci. Rep. 7, 41124 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
17.Proffit, M. et al. Chemical signal is in the blend: bases of plant–pollinator encounter in a highly specialized interaction. Sci. Rep. 10, 10071 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
18.Chen, C. et al. Private channel: a single unusual compound assures specific pollinator attraction in Ficus semicordata. Funct. Ecol. 23, 941–950 (2009).Article 

Google Scholar 
19.Wang, G., Cannon, C. H. & Chen, J. Pollinator sharing and gene flow among closely related sympatric dioecious fig taxa. Proc. R. Soc. B 283, 20152963 (2016).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
20.Yu, H. et al. De novo transcriptome sequencing in Ficus hirta Vahl. (Moraceae) to investigate gene regulation involved in the biosynthesis of pollinator attracting volatiles. Tree Genet. Genomes 11, 91 (2015).Article 

Google Scholar 
21.Soler, C. C. L., Proffit, M., Bessière, J.-M., Hossaert -McKey, M. & Schatz, B. Evidence for intersexual chemical mimicry in a dioicous plant. Ecol. Lett. 15, 978–985 (2012).PubMed 
Article 

Google Scholar 
22.Volf, M. et al. Community structure of insect herbivores is driven by conservatism, escalation and divergence of defensive traits in Ficus. Ecol. Lett. 21, 83–92 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
23.Martinson, E. O., Hackett, J. D., Machado, C. A. & Arnold, A. E. Metatranscriptome analysis of fig flowers provides insights into potential mechanisms for mutualism stability and gall induction. PLoS ONE 10, e0130745 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
24.Zhang, H. et al. Leaf-mining by Phyllonorycter blancardella reprograms the host–leaf transcriptome to modulate phytohormones associated with nutrient mobilization and plant defense. J. Insect Physiol. 84, 114–127 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Schultz, J. C., Edger, P. P., Body, M. & Appel, H. M. A galling insect activates plant reproductive programs during gall development. Sci. Rep. 9, 1833 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
26.The Nasonia Genome Working Group Functional and evolutionary insights from the genomes of three parasitoid Nasonia species. Science 327, 343–348 (2010).PubMed Central 
Article 
CAS 

Google Scholar 
27.Xiao, J.-H. et al. Obligate mutualism within a host drives the extreme specialization of a fig wasp genome. Genome Biol. 14, R141 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
28.Ohri, D. & Khoshoo, T. N. Nuclear DNA contents in the genus Ficus (Moraceae). Plant Syst. Evol. 156, 1–4 (1987).Article 

Google Scholar 
29.Chen, Y., Compton, S. G., Liu, M. & Chen, X.-Y. Fig trees at the northern limit of their range: the distributions of cryptic pollinators indicate multiple glacial refugia. Mol. Ecol. 21, 1687–1701 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
30.Chen, L.-G. et al. Binding affinity characterization of an antennae-enriched chemosensory protein from the white-backed planthopper, Sogatella furcifera (Horváth), with host plant volatiles. Pestic. Biochem. Phys. 152, 1–7 (2018).Article 
CAS 

Google Scholar 
31.Gu, S.-H. et al. Functional characterization and immunolocalization of odorant binding protein 1 in the lucerne plant bug, Adelphocoris lineolatus (GOEZE). Arch. Insect Biochem. Physiol. 77, 81–99 (2011).CAS 
PubMed 
Article 

Google Scholar 
32.Leal, W. S. et al. Reverse and conventional chemical ecology approaches for the development of oviposition attractants for Culex mosquitoes. PLoS ONE 3, e3045 (2008).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
33.Rizzo, W. B. et al. Fatty aldehyde and fatty alcohol metabolism: review and importance for epidermal structure and function. Biochim. Biophys. Acta 1841, 377–389 (2014).CAS 
PubMed 
Article 

Google Scholar 
34.Schwab, W., Davidovich‐Rikanati, R. & Lewinsohn, E. Biosynthesis of plant‐derived flavor compounds. Plant J. 54, 712–732 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
35.Capella, M., Ribone, P. A., Arce, A. L. & Chan, R. L. Arabidopsis thaliana HomeoBox 1 (AtHB1), a Homedomain-Leucine Zipper I (HD-Zip I) transcription factor, is regulated by PHYTOCHROME-INTERACTING FACTOR 1 to promote hypocotyl elongation. New Phytol. 207, 669–682 (2015).CAS 
PubMed 
Article 

Google Scholar 
36.Jiang, W. et al. Two transcription factors TaPpm1 and TaPpb1 co-regulate anthocyanin biosynthesis in purple pericarps of wheat. J. Exp. Bot. 69, 2555–2567 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Guan, R. et al. Draft genome of the living fossil Ginkgo biloba. GigaScience 5, 49 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
38.Mithöfer, A. & Boland, W. Plant defense against herbivores: chemical aspects. Annu. Rev. Plant Biol. 63, 431–450 (2012).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
39.Salazar, D. et al. Origin and maintenance of chemical diversity in a species-rich tropical tree lineage. Nat. Ecol. Evol. 2, 983–990 (2018).PubMed 
Article 

Google Scholar 
40.Després, L., David, J. P. & Gallet, C. The evolutionary ecology of insect resistance to plant chemicals. Trends Ecol. Evol. 22, 298–307 (2007).PubMed 
Article 

Google Scholar 
41.Segar, S. T., Volf, M., Sisol, M., Pardikes, N. & Souto-Vilarós, A. D. Chemical cues and genetic divergence in insects on plants: conceptual cross pollination between mutualistic and antagonistic systems. Curr. Opin. Insect Sci. 32, 83–90 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
42.Cook, J. M. & Segar, S. T. Speciation in fig wasps. Ecol. Entomol. 35, 54–66 (2010).Article 

Google Scholar 
43.Cruaud, A. et al. An extreme case of plant–insect codiversification: figs and fig-pollinating wasps. Syst. Biol. 61, 1029–1047 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
44.Yu, H. et al. Multiple parapatric pollinators have radiated across a continental fig tree displaying clinal genetic variation. Mol. Ecol. 28, 2391–2405 (2019).PubMed 
Article 

Google Scholar 
45.Satler, J. D. et al. Inferring processes of coevolutionary diversification in a community of Panamanian strangler figs and associated pollinating wasps. Evolution 73, 2295–2311 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
46.Wang, G. et al. Genomic evidence of prevalent hybridization throughout the evolutionary history of the fig–wasp pollination mutualism. Nat. Commun. 12, 718 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
47.Hoballah, M. E. et al. Single gene-mediated shift in pollinator attraction in Petunia. Plant Cell 19, 779–790 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Potts, S. G. et al. Global pollinator declines: trends, impacts and drivers. Trends Ecol. Evol. 25, 345–353 (2010).PubMed 
Article 

Google Scholar 
49.Kiers, E. T., Palmer, T. M., Ives, A. R., Bruno, J. F. & Bronstein, J. L. Mutualisms in a changing world: an evolutionary perspective. Ecol. Lett. 13, 1459–1474 (2010).Article 

Google Scholar 
50.Segar, S. T. et al. The role of evolution in shaping ecological networks. Trends Ecol. Evol. 35, 454–466 (2020).PubMed 
Article 

Google Scholar 
51.Stoy, K. S., Gibson, A. K., Gerardo, N. M. & Morran, L. T. A need to consider the evolutionary genetics of host–symbiont mutualisms. J. Evol. Biol. 33, 1656–1668 (2020).PubMed 
Article 

Google Scholar 
52.Marçais, G. & Kingsford, C. A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics 27, 764–770 (2011).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
53.Xiao, C.-L. et al. MECAT: fast mapping, error correction, and de novo assembly for single-molecule sequencing reads. Nat. Methods 14, 1072–1074 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Walker, B. J. et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS ONE 9, e112963 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
55.Pryszcz, L. P. & Gabaldón, T. Redundans: an assembly pipeline for highly heterozygous genomes. Nucleic Acids Res. 44, e113 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
56.Zhang, Z., Schwartz, S., Wagner, L. & Miller, W. A greedy algorithm for aligning DNA sequences. J. Comput. Biol. 7, 203–214 (2000).CAS 
PubMed 
Article 

Google Scholar 
57.English, A. C. et al. Mind the gap: upgrading genomes with Pacific Biosciences RS long-read sequencing technology. PLoS ONE 7, e47768 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
58.Sahlin, K., Chikhi & Arvestad, R. L. Assembly scaffolding with PE-contaminated mate-pair libraries. Bioinformatics 32, 1925–1932 (2016).PubMed 
Article 

Google Scholar 
59.Belton, J. M. et al. Hi-C: a comprehensive technique to capture the conformation of genomes. Methods 58, 268–276 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
60.Servant, N. et al. HiC-Pro: an optimized and flexible pipeline for Hi-C data processing. Genome Biol. 16, 259 (2015).PubMed 
PubMed Central 
Article 
CAS 

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

Google Scholar 
62.Durand, N. C. et al. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Syst. 3, 95–98 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
63.Dudchenko, O. et al. De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds. Science 356, 92–95 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.Simão, F. A., Waterhouse, R. M., Ioannidis, P., Kriventseva, E. V. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31, 3210–3212 (2015).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
65.Wu, T. D. & Watanabe, C. K. GMAP: a genomic mapping and alignment program for mRNA and EST sequences. Bioinformatics 21, 1859–1875 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
66.Benson, G. Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res. 27, 573–580 (1999).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
67.Bao, W., Kojima, K. K. & Kohany, O. Repbase Update, a database of repetitive elements in eukaryotic genomes. Mob. DNA 6, 11 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
68.Xu, Z. & Wang, H. LTR_FINDER: an efficient tool for the prediction of full-length LTR retrotransposons. Nucleic Acids Res. 35, W265–W268 (2007).PubMed 
PubMed Central 
Article 

Google Scholar 
69.Edgar, R. C. & Myers, E. W. PILER: identification and classification of genomic repeats. Bioinformatics 21, i152–i158 (2005).CAS 
PubMed 
Article 

Google Scholar 
70.Price, A. L., Jones, N. C. & Pevzner, P. A. De novo identification of repeat families in large genomes. Bioinformatics 21, i351–i358 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
71.Lowe, T. M. & Eddy, S. R. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 25, 955–964 (1997).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
72.Nawrocki, E. P. & Eddy, S. R. Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics 29, 2933–2935 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
73.Nawrocki, E. P. et al. Rfam 12.0: updates to the RNA families database. Nucleic Acids Res. 43, D130–D137 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
74.Holt, C. & Yandell, M. MAKER2: an annotation pipeline and genome-database management tool for second-generation genome projects. BMC Bioinf. 12, 491 (2011).Article 

Google Scholar 
75.Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
76.Pertea, M. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 33, 290–295 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
77.Johnson, A. D. et al. SNAP: a web-based tool for identification and annotation of proxy SNPs using HapMap. Bioinformatics 24, 2938–2939 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
78.Stanke, M. & Morgenstern, B. AUGUSTUS: a web server for gene prediction in eukaryotes that allows user-defined constraints. Nucleic Acids Res. 33, W465–W467 (2005).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
79.Buels, R. et al. JBrowse: a dynamic web platform for genome visualization and analysis. Genome Biol. 17, 66 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
80.Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
81.Bairoch, A. & Apweiler, R. The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res. 28, 45–48 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
82.Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457–D462 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
83.Jones, P. et al. InterProScan 5: genome-scale protein function classification. Bioinformatics 30, 1236–1240 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
84.Conesa, A. et al. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 21, 3674–3676 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
85.Li, L., Stoeckert, C. J. Jr & Roos, D. S. OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res. 13, 2178–2189 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
86.Li, H. et al. TreeFam: a curated database of phylogenetic trees of animal gene families. Nucleic Acids Res. 34, D572–D580 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
87.Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
88.Capella-Gutierrez, S., Silla-Martinez, J. M. & Gabaldon, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
89.Guindon, S., Delsuc, F., Dufayard, J. F. & Gascuel, O. Estimating maximum likelihood phylogenies with PhyML. Methods Mol. Biol. 537, 113–137 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
90.Yang, Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 1586–1591 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
91.De Bie, T., Cristianini, N., Demuth, J. P. & Hahn, M. W. CAFE: a computational tool for the study of gene family evolution. Bioinformatics 22, 1269–1271 (2006).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
92.Tang, H. et al. Unraveling ancient hexaploidy through multiply-aligned angiosperm gene maps. Genome Res. 18, 1944–1954 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
93.Schwartz, S. et al. Human–mouse alignments with BLASTZ. Genome Res. 13, 103–107 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
94.Bailey, J. A., Yavor, A. M., Massa, H. F., Trask, B. J. & Eichler, E. E. Segmental duplications: organization and impact within the current human genome project assembly. Genome Res. 11, 1005–1017 (2001).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
95.Birney, E., Clamp, M. & Durbin, R. GeneWise and Genomewise. Genome Res. 14, 988–995 (2004).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
96.Ruan, J., Li, H., Chen, Z. & Coghlan, A. TreeFam: 2008 update. Nucleic Acids Res. 36, D735–D740 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
97.Tholl, D. et al. Practical approaches to plant volatile analysis. Plant J. 45, 540–560 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
98.Wen, B., Mei, Z., Zeng, C. & Liu, S. metaX: a flexible and comprehensive software for processing metabolomics data. BMC Bioinform. 18, 183 (2017).Article 
CAS 

Google Scholar 
99.Wen, P. et al. The sex pheromone of a globally invasive honey bee predator, the Asian eusocial hornet, Vespa velutina. Sci. Rep. 7, 12956 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
100.Chen, Y. et al. SOAPnuke: a MapReduce acceleration-supported software for integrated quality control and preprocessing of high-throughput sequencing data. Gigascience 7, 1–6 (2018).PubMed 
PubMed Central 

Google Scholar 
101.Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
102.Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinform. 12, 323 (2011).CAS 
Article 

Google Scholar 
103.Tian, X., Chen, L., Wang, J., Qiao, J. & Zhang, W. Quantitative proteomics reveals dynamic responses of Synechocystis sp. PCC 6803 to next-generation biofuel butanol. J. Proteom. 78, 326–345 (2013).CAS 
Article 

Google Scholar 
104.Wen, B. et al. IQuant: an automated pipeline for quantitative proteomics based upon isobaric tags. Proteomics 14, 2280–2285 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
105.Brosch, M., Yu, L., Hubbard, T. & Choudhary, J. Accurate and sensitive peptide identification with Mascot Percolator. J. Proteome Res. 8, 3176–3181 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
106.Savitski, M. M., Wilhelm, M., Hahne, H., Kuster, B. & Bantscheff, M. A scalable approach for protein false discovery rate estimation in large proteomic data sets. Mol. Cell Proteomics 14, 2394–2404 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
107.Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).CAS 
Article 

Google Scholar 
108.Bruderer, R. et al. Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen-treated three-dimensional liver microtissues. Mol. Cell Proteomics 14, 1400–1410 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
109.Dunn, W. B. et al. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat. Protoc. 6, 1060–1083 (2011).CAS 
PubMed 
Article 

Google Scholar 
110.Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
111.Choi, M. et al. MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments. Bioinformatics 30, 2524–2526 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
112.Wen, X.-L., Wen, P., Dahlsjö, C. A. L., Sillam-Dussès, D. & Šobotník, J. Breaking the cipher: ant eavesdropping on the variational trail pheromone of its termite prey. Proc. R. Soc. B 284, 20170121 (2017).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
113.Bailey, T. L. & Elkan, C. Fitting a mixture model by expectation maximization to discover motifs in biopolymers. Proc. Int. Conf. Intell. Syst. Mol. Biol. 2, 28–36 (1994).CAS 
PubMed 
PubMed Central 

Google Scholar 
114.Lescot, M. et al. PlantCARE, a database of plant cis-acting regulatory elements and a portal to tools for in silico analysis of promoter sequences. Nucleic Acids Res. 30, 325–327 (2002).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
115.Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 9, 559 (2008).Article 
CAS 

Google Scholar 
116.Yip, A. M. & Horvath, S. Gene network interconnectedness and the generalized topological overlap measure. BMC Bioinform. 8, 22 (2007).Article 
CAS 

Google Scholar 
117.Langfelder, P., Zhang, B. & Horvath, S. Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. Bioinformatics 24, 719–720 (2008).CAS 
PubMed 
Article 

Google Scholar 
118.Gendrel, A. V., Lippman, Z., Martienssen, R. & Colot, V. Profiling histone modification patterns in plants using genomic tiling microarrays. Nat. Methods 2, 213–218 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar  More