Philippa Kaur

1.Oke, T. R. The heat island of the urban boundary layer: characteristics, causes and effects. In Wind climate in cities (eds Cermak, J. E. et al.) 81–107 (Springer, 1995). https://doi.org/10.1007/978-94-017-3686-2_5.
Google Scholar 
2.Wang, C., Wang, Z. H. & Yang, J. Cooling effect of urban trees on the built environment of contiguous United States. Earth’s Future 6, 1066–1081. https://doi.org/10.1029/2018EF000891 (2018).ADS 
Article 

Google Scholar 
3.Pauleit, S. Urban street tree plantings: identifying the key requirements. Proc. Inst. Civ. Eng. Munic. Eng. 156, 43–50. https://doi.org/10.1680/muen.2003.156.1.43 (2003).Article 

Google Scholar 
4.Frantzeskaki, N. et al. Nature-based solutions for urban climate change adaptation: linking science, policy, and practice communities for evidence-based decision-making. BioScience 69, 455–466. https://doi.org/10.1093/biosci/biz042 (2019).Article 

Google Scholar 
5.Armson, D., Stringer, P. & Ennos, A. R. The effect of tree shade and grass on surface and globe temperatures in an urban area. Urban For. Urban Green. 11, 245–255. https://doi.org/10.1016/j.ufug.2012.05.002 (2012).Article 

Google Scholar 
6.Oke, T. R. The micrometeorology of the urban forest. Philos. Trans. R. Soc. Lond. B 324, 335–349. https://doi.org/10.1098/rstb.1989.0051 (1989).ADS 
Article 

Google Scholar 
7.Chow, W. T. & Brazel, A. J. Assessing xeriscaping as a sustainable heat island mitigation approach for a desert city. Build. Environ. 47, 170–181. https://doi.org/10.1016/j.buildenv.2011.07.027 (2012).Article 

Google Scholar 
8.Wang, K., Ma, Q., Wang, X. & Wild, M. Urban impacts on mean and trend of surface incident solar radiation. Geophys. Res. Lett. 41, 4664–4668. https://doi.org/10.1002/2014GL060201 (2014).ADS 
Article 

Google Scholar 
9.Bassuk, N. & Whitlow, T. Environmental stress in street trees. Arboricult. J. 12, 195–201. https://doi.org/10.1080/03071375.1988.9746788 (1988).Article 

Google Scholar 
10.Nowak, D. J., Kuroda, M. & Crane, D. E. Tree mortality rates and tree population projections in Baltimore, Maryland, USA. Urban For. Urban Green. 2, 139–147. https://doi.org/10.1078/1618-8667-00030 (2004).Article 

Google Scholar 
11.Monteith, J. . & Unsworth, M. . Principles of Environmental Physics: Plants, Animals, and the Atmosphere 4th edn. (Elsevier Ltd., 2013).
Google Scholar 
12.Simon, H. et al. Modeling transpiration and leaf temperature of urban trees—a case study evaluating the microclimate model ENVI-met against measurement data. Landsc. Urban Plan. 174, 33–40. https://doi.org/10.1016/j.landurbplan.2018.03.003 (2018).Article 

Google Scholar 
13.González-Dugo, M. P., Moran, M. S., Mateos, L. & Bryant, R. Canopy temperature variability as an indicator of crop water stress severity. Irrig. Sci. 24, 1–8. https://doi.org/10.1007/s00271-005-0023-7 (2006).Article 

Google Scholar 
14.Han, M., Zhang, H., DeJonge, K. C., Comas, L. H. & Trout, T. J. Estimating maize water stress by standard deviation of canopy temperature in thermal imagery. Agricult. Water Manag. 177, 400–409. https://doi.org/10.1016/j.agwat.2016.08.031 (2016).Article 

Google Scholar 
15.Hou, M., Tian, F., Zhang, T. & Huang, M. Evaluation of canopy temperature depression, transpiration, and canopy greenness in relation to yield of soybean at reproductive stage based on remote sensing imagery. Agricult. Water Manag. 222, 182–192. https://doi.org/10.1016/j.agwat.2019.06.005 (2019).Article 

Google Scholar 
16.Heilman, J. L., Brittin, C. L. & Zajicek, J. M. Water use by shrubs as affected by energy exchange with building walls. Agricult. For. Meteorol. 48, 345–357. https://doi.org/10.1016/0168-1923(89)90078-6 (1989).ADS 
Article 

Google Scholar 
17.Perini, K. & Magliocco, A. Effects of vegetation, urban density, building height, and atmospheric conditions on local temperatures and thermal comfort. Urban For. Urban Green. 13, 495–506. https://doi.org/10.1016/j.ufug.2014.03.003 (2014).Article 

Google Scholar 
18.Soer, G. J. Estimation of regional evapotranspiration and soil moisture conditions using remotely sensed crop surface temperatures. Remote Sens. Environ. 9, 27–45. https://doi.org/10.1016/0034-4257(80)90045-0 (1980).ADS 
Article 

Google Scholar 
19.Spronken-Smith, R. A. & Oke, T. R. The thermal regime of urban parks in two cities with different summer climates. Int. J. Remote Sens. 19, 2085–2104. https://doi.org/10.1080/014311698214884 (1998).ADS 
Article 

Google Scholar 
20.Rahman, M. A. et al. Traits of trees for cooling urban heat islands: a meta-analysis. Build. Environ. 170, 106606. https://doi.org/10.1016/j.buildenv.2019.106606 (2020).Article 

Google Scholar 
21.Leuzinger, S., Vogt, R. & Körner, C. Tree surface temperature in an urban environment. Agricult. For. Meteorol. 150, 56–62. https://doi.org/10.1016/j.agrformet.2009.08.006 (2010).ADS 
Article 

Google Scholar 
22.Meier, F. & Scherer, D. Spatial and temporal variability of urban tree canopy temperature during summer 2010 in Berlin, Germany. Theor. Appl. Climatol. 110, 373–384. https://doi.org/10.1007/s00704-012-0631-0 (2012).ADS 
Article 

Google Scholar 
23.Ballester, C., Jiménez-Bello, M. A., Castel, J. R. & Intrigliolo, D. S. Usefulness of thermography for plant water stress detection in citrus and persimmon trees. Agric. For. Meteorol. 168, 120–129. https://doi.org/10.1016/j.agrformet.2012.08.005 (2013).ADS 
Article 

Google Scholar 
24.Jones, H. G. Application of thermal imaging and infrared sensing in plant physiology and ecophysiology. Adv. Botan. Res. 41, 107–163. https://doi.org/10.1016/s0065-2296(04)41003-9 (2004).ADS 
Article 

Google Scholar 
25.Givoni, B. Impact of planted areas on urban environmental quality: a review. Atmos. Environ. Part B Urban Atmos. 25, 289–299. https://doi.org/10.1016/0957-1272(91)90001-U (1991).ADS 
Article 

Google Scholar 
26.Kalnay, E. & Cai, M. Impact of urbanization and land-use change on climate. Nature 423, 528–531. https://doi.org/10.1038/nature01675 (2003).ADS 
CAS 
Article 
PubMed 

Google Scholar 
27.A. Coutts, A. et al. Impacts of water sensitive urban design solutions on human thermal comfort. p. 20 (online link: https://watersensitivecities.org.au/wp-content/uploads/2016/07/TMR_B3-1_WSUD_thermal_comfort_no2.pdf) (2014).28.Coutts1, A. et al. The Impacts of WSUD Solutions on Human Thermal Comfort Green Cities and Micro-climate-B3.1-2-2014 Contributing Authors. Tech. Rep. (1968).29.Gunawardena, K. R., Wells, M. J. & Kershaw, T. Utilising green and bluespace to mitigate urban heat island intensity. Sci. Total Environ. 584–585, 1040–1055. https://doi.org/10.1016/j.scitotenv.2017.01.158 (2017).ADS 
CAS 
Article 
PubMed 

Google Scholar 
30.Völker, S., Baumeister, H., Classen, T., Hornberg, C. & Kistemann, T. Evidence for the temperature-mitigating capacity of urban blue space—a health geographic perspective. Erdkunde 67, 355–371. https://doi.org/10.3112/erdkunde.2013.04.05 (2013).Article 

Google Scholar 
31.Hu, L. & Li, Q. Greenspace, bluespace, and their interactive influence on urban thermal environments. Environ. Res. Lett. 15, 034041. https://doi.org/10.1088/1748-9326/ab6c30 (2020).ADS 
Article 

Google Scholar 
32.Theeuwes, N. E., Solcerová, A. & Steeneveld, G. J. Modeling the influence of open water surfaces on the summertime temperature and thermal comfort in the city. J. Geophys. Res. Atmos. 118, 8881–8896. https://doi.org/10.1002/jgrd.50704 (2013).ADS 
Article 

Google Scholar 
33.Ziter, C. D., Pedersen, E. J., Kucharik, C. J. & Turner, M. G. Scale-dependent interactions between tree canopy cover and impervious surfaces reduce daytime urban heat during summer. Proc. Natl. Acad. Sci. U. S. A. 116, 7575–7580. https://doi.org/10.1073/pnas.1817561116 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
34.Johnson, S., Ross, Z., Kheirbek, I. & Ito, K. Characterization of intra-urban spatial variation in observed summer ambient temperature from the New York City Community Air Survey. Urban Clim. 31, 100583. https://doi.org/10.1016/j.uclim.2020.100583 (2020).Article 

Google Scholar 
35.Wetherley, E. B., McFadden, J. P. & Roberts, D. A. Megacity-scale analysis of urban vegetation temperatures. Remote Sens. Environ. 213, 18–33. https://doi.org/10.1016/j.rse.2018.04.051 (2018).ADS 
Article 

Google Scholar 
36.Zhou, W., Wang, J. & Cadenasso, M. L. Effects of the spatial configuration of trees on urban heat mitigation: a comparative study. Remote Sens. Environ. 195, 1–12. https://doi.org/10.1016/j.rse.2017.03.043 (2017).ADS 
Article 

Google Scholar 
37.Weng, Q., Lu, D. & Schubring, J. Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies. Remote Sens. Environ. 89, 467–483. https://doi.org/10.1016/j.rse.2003.11.005 (2004).ADS 
Article 

Google Scholar 
38.Hulley, G., Hook, S., Fisher, J. & Lee, C. ECOSTRESS, A NASA Earth-Ventures Instrument for studying links between the water cycle and plant health over the diurnal cycle. In International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2017-July, 5494–5496 (Institute of Electrical and Electronics Engineers Inc., 2017). https://doi.org/10.1109/IGARSS.2017.8128248.39.Wood, S. N. Low-rank scale-invariant tensor product smooths for generalized additive mixed models. Biometrics 62, 1025–1036. https://doi.org/10.1111/j.1541-0420.2006.00574.x (2006).MathSciNet 
Article 
PubMed 
MATH 

Google Scholar 
40.Duan, S.-B. et al. Estimation of diurnal cycle of land surface temperature at high temporal and spatial resolution from clear-sky MODIS data. Remote Sens. 6, 3247–3262. https://doi.org/10.3390/rs6043247 (2014).ADS 
Article 

Google Scholar 
41.Hu, L., Sun, Y., Collins, G. & Fu, P. Improved estimates of monthly land surface temperature from MODIS using a diurnal temperature cycle (DTC) model. ISPRS J. Photogramm. Remote Sens. 168, 131–140. https://doi.org/10.1016/j.isprsjprs.2020.08.007 (2020).ADS 
Article 

Google Scholar 
42.New York City Department of Information Technology and Telecommunications (NYC DoITT). Land Cover Raster Data 6-inch Resolution (2017).43.New York City Department of Information Technology and Telecommunications (NYC DoITT). New York City Building Footprint (2017).44.Wood, S. N. Generalized Additive Models: An Introduction with R 2nd edn. (CRC Press, 2017).Book 

Google Scholar 
45.Cârlan, I., Mihai, B. A., Nistor, C. & Große-Stoltenberg, A. Identifying urban vegetation stress factors based on open access remote sensing imagery and field observations. Ecol. Inform. 55, 101032. https://doi.org/10.1016/j.ecoinf.2019.101032 (2020).Article 

Google Scholar 
46.Cregg, B. & Dix, M. E. Tree moisture stress and insect damage in urban areas in relation to heat island effects. J. Arboricult. 27, 8–17 (2001).
Google Scholar 
47.Novem, D. & Falxa, N. United States Department of Agriculture The Urban Forest of New York City. Tech. Rep. https://doi.org/10.2737/NRS-RB-117 (2018).48.Shaker, R. R., Altman, Y., Deng, C., Vaz, E. & Forsythe, K. W. Investigating urban heat island through spatial analysis of New York City streetscapes. J. Clean. Prod. 233, 972–992. https://doi.org/10.1016/j.jclepro.2019.05.389 (2019).Article 

Google Scholar 
49.Meir, T., Orton, P. M., Pullen, J., Holt, T. & Thompson, W. T. Forecasting the New York City urban heat island and sea breeze during extreme heat events. Weather Forecast. 28, 1460–1477. https://doi.org/10.1175/WAF-D-13-00012.1 (2013).ADS 
Article 

Google Scholar 
50.Ramamurthy, P., González, J., Ortiz, L., Arend, M. & Moshary, F. Impact of heatwave on a megacity: an observational analysis of New York City during July 2016. Environ. Res. Lett. 12, 054011. https://doi.org/10.1088/1748-9326/aa6e59 (2017).ADS 
Article 

Google Scholar 
51.Gedzelman, S. D. et al. Mesoscale aspects of the Urban Heat Island around New York City. Theor. Appl. Climatol. 75, 29–42. https://doi.org/10.1007/s00704-002-0724-2 (2003).ADS 
Article 

Google Scholar 
52.Cavender, N. & Donnelly, G. Intersecting urban forestry and botanical gardens to address big challenges for healthier trees, people, and cities. Plants People Planet 1, 315–322. https://doi.org/10.1002/ppp3.38 (2019).Article 

Google Scholar 
53.Marias, D. E., Meinzer, F. C. & Still, C. Impacts of leaf age and heat stress duration on photosynthetic gas exchange and foliar nonstructural carbohydrates in Coffea arabica. Ecol. Evol. 7, 1297–1310. https://doi.org/10.1002/ece3.2681 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
54.Brune, M. Urban trees under climate change. Potential impacts of dry spells and heat waves in three Germany regions in the 1950s. Report 20, Climate Service Center Germany, Hamburg 123 (2016).55.Jim, C. Y. Green-space preservation and allocation for sustainable greening of compact cities. Cities 21, 311–320. https://doi.org/10.1016/j.cities.2004.04.004 (2004).Article 

Google Scholar 
56.Santamouris, M. Cooling the cities—a review of reflective and green roof mitigation technologies to fight heat island and improve comfort in urban environments. Sol. Energy 103, 682–703. https://doi.org/10.1016/j.solener.2012.07.003 (2014).ADS 
Article 

Google Scholar 
57.Drescher, M. Urban heating and canopy cover need to be considered as matters of environmental justice. Proc. Natl. Acad. Sci. U. S. A. 116, 26153–26154. https://doi.org/10.1073/pnas.1917213116 (2019).ADS 
CAS 
Article 
PubMed Central 

Google Scholar 
58.Roloff, A. . Bäume in der Stadt (Ulmer, E, 2013).
Google Scholar 
59.Bruse, M. ENVI-met 3.0: Updated Model Overview. Tech. Rep. (2004).60.US Census Bureau. State and County Quick Facts https://www.census.gov/quickfacts/fact/table/US/PST045219 (2019).61.Shorris, A. Cool Neighborhoods NYC: A Comprehensive Approach to Keep Communities Safe in Extreme Heat. Tech. Rep.62.Hulley, G. ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) Mission Level 2 Product Specification Document. Tech. Rep. (2018).63.Stark, P. & Parker, R. Bounded-Variable Least-squares: An Algorithm and Applications. Tech. Rep. 394, (1995). More

1.Collingsworth, P. D. et al. Climate change as a long-term stressor for the fisheries of the Laurentian Great Lakes of North America. Rev. Fish Biol. Fish. 27, 363–391 (2017).Article 

Google Scholar 
2.Hilborn, R. & Walters, C. J. Quantitative Fisheries Stock Assessment: Choice, Dynamics and the Uncertainty (Routledge, Chapman and Hall Inc., 1992).Book 

Google Scholar 
3.Bean, C. W., Winfield, I. J. & Fletcher, J. M. Stock assessment of the Arctic charr (Salvelinus alpinus) population in Loch Ness, UK in stock assessment. In Inland Fisheries (ed. Cowx, I. G.) 206–223 (Blackwell Scientific Publications, 1996).
Google Scholar 
4.Emmrich, M. et al. Strong correspondence between gillnet catch per effort and hydroacoustically derived fish biomass in stratified lakes. Freshw. Biol. 57, 2436–2448 (2012).Article 

Google Scholar 
5.CEN (European Committee for Standardization). Water quality – sampling of fish with multi-mesh gillnets. European Committee for Standardization, European Standard EN 14757:2015 (Brussels, 2015).6.Murphy, B. & Willis, D. W. Fisheries Techniques 2nd edn. (American Fisheries Society, 1996).
Google Scholar 
7.Kinzelbach, R. Neozoans in European waters—Exemplifying the worldwide process of invasion and species mixing. Cell. Mol. Life Sci. 51, 526–538 (1995).CAS 
Article 

Google Scholar 
8.Cerwenka, A. F. Phenotypic and genetic differentiation of invasive gobies in the upper Danube River. Dissertation (Technische
Universität München, 2014).9.Byström, P. et al. Declining coastal piscivore populations in the Baltic Sea: Where and when do sticklebacks matter?. Ambio 44, 462–471 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
10.Ustups, D. et al. Diet overlap between juvenile flatifish and the invasive round goby in the central Baltic Sea. J. Sea Res. 107, 121–129 (2016).ADS 
Article 

Google Scholar 
11.Jackson, D. A. & Harvey, H. H. Qualitative and quantitative sampling of lake fish communities. Can. J. Fish. Aquat. Sci. 54, 2807–2813 (1997).Article 

Google Scholar 
12.Argyle, R. L. Acoustics as a tool for the assessment of Great Lakes forage fishes. Fish. Res. 14, 179–196 (1992).Article 

Google Scholar 
13.Jurvelius, J., Leinikki, J., Mamylov, V. & Pushkin, S. Stock assessment of pelagic three-spined stickleback (Gasterosteus aculeatus): A simultaneous up- and down-looking echo-sounding study. Fish. Res. 27, 227–241 (1996).Article 

Google Scholar 
14.Horne, J. K. Acoustic approaches to remote species identification: A review. Fish. Oceanogr. 9, 356–371 (2000).Article 

Google Scholar 
15.Godlewska, M., Świerzowski, A. & Winfield, I. J. Hydroacoustics as a tool for studies of fish and their habitat. Ecohydrol. Hydrobiol. 4, 417–427 (2004).
Google Scholar 
16.Muška, M. et al. Real-time distribution of pelagic fish: combining hydroacoustics, GIS and spatial modelling at a fine spatial scale. Sci. Rep. 8, 5381. https://doi.org/10.1038/s41598-018-23762-z (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
17.Berger, L. et al. Acoustic target classification. ICES Coop. Res. Rep. https://doi.org/10.17895/ices.pub.4567 (2018).ADS 
Article 

Google Scholar 
18.Emmrich, M., Helland, I. P., Busch, S., Schiller, S. & Mehner, T. Hydroacoustic estimates of fish densities in comparison with stratified pelagic trawl sampling in two deep, coregonid-dominated lakes. Fish. Res. 105, 178–186 (2010).Article 

Google Scholar 
19.DuFour, M. R., Qian, S. S., Mayer, C. M. & Vandergoot, C. S. Embracing uncertainty to reduce bias in hydroacoustic species apportionment. Fish. Res. https://doi.org/10.1016/j.fishres.2020.105750 (2021).Article 

Google Scholar 
20.Cabreira, A. G., Tripode, M. & Madirolas, A. Artificial neural networks for fish-species identification. ICES J. Mar. Sci. 66, 1119–1129 (2009).Article 

Google Scholar 
21.Robotham, H., Bosch, P., Gutierrez-Estrada, J., Castillo, J. & Pulido-Calvo, I. Acoustic identification of small pelagic fish species in Chile using support vector machines and neural networks. Fish. Res. 102, 115–122 (2010).Article 

Google Scholar 
22.Taylor, J. C. & Maxwell, D. L. Hydroacoustics: lakes and reservoirs. in Salmonid Field Protocols Handbook: Techniques for Assessing Status and Trends in Salmon and Trout Populations (ed. Johnson, D. H. et al.) 153–172 (American Fisheries Society in association with State of the Salmon, 2007).23.Parker-Stetter, S. L., Rudstam, L. G., Sullivan, L. G. & Warner, D. M. Standard operating procedures for fisheries acoustic surveys in the Great Lakes. Great Lakes Fisheries Commission Special Publication 09-01 (2009).24.Guillard, J., Perga, M. E., Colon, M. & Angeli, N. Hydroacoustic assessment of young-of-the-year perch, Perca fluviatilis, population dynamics in an oligotrophic lake (Lake Annecy, France). Fish. Manag. Ecol. 13, 319–327 (2006).Article 

Google Scholar 
25.Winfield, I. J., Fletcher, J. M., James, J. B. & Bean, J. B. Assessment of fish populations in still waters using hydroacoustics and survey gill netting: Experiences with Arctic charr (Salvelinus alpinus) in the UK. Fish. Res. 96, 30–38 (2009).Article 

Google Scholar 
26.Yule, D. L., Lori, M. E., Cachera, S., Colon, M. & Guillard, J. Comparing two fish sampling standards over time: Largely congruent results but with caveats. Freshw. Biol. 58, 2074–2088 (2013).Article 

Google Scholar 
27.DuFour, M. R., Song, S. Q., Mayer, C. M. & Vandergoot, C. S. Evaluating catchability in a large-scale gillnet survey using hydroacoustics: Making the case for coupled surveys. Fish. Res. 211, 309–318 (2019).Article 

Google Scholar 
28.Haralabous, J. & Georgakarakos, S. Artificial neural networks as a tool for species identification of fish schools. ICES J. Mar. Sci. 53, 173–180 (1996).Article 

Google Scholar 
29.Zakharia, M. E., Magand, F., Hetroit, F. & Diner, N. Wideband sounder for fish species identification at sea. ICES J. Mar. Sci. 53, 203–208 (1996).Article 

Google Scholar 
30.Fernandes, P. G. Classification trees for species identification of fish-school echo traces. ICES J. Mar. Sci. 66, 1073–1080 (2009).Article 

Google Scholar 
31.Eckmann, R. A hydroacoustic study of the pelagic spawning behavior of whitefish (Coregonus lavaretus) in lake constance. Can. J. Fish. Aquat. Sci. 48, 995–1002 (1991).Article 

Google Scholar 
32.Eckmann, R. & Engesser, B. Reconstructing the build-up of a pelagic stickleback (Gasterosteus aculeatus) population using hydroacoustics. Fish. Res. 210, 189–192 (2018).Article 

Google Scholar 
33.Peltonen, H., Ruuhijärvi, J., Malinen, T. & Horppila, J. Estimation of roach (Rutilus rutilus (L.)) and smelt (Osmerus eperlanus (L.)) stocks with virtual population analysis. Hydroacoustics and Gillnet CPUE. Fish. Res. 44, 25–36 (1999).Article 

Google Scholar 
34.MacLennan, D. N., Fernandes, P. G. & Dalen, J. A consistent approach to definitions and symbols in fisheries acoustics. ICES J. Mar. Sci. 59, 365–369 (2002).Article 

Google Scholar 
35.Korneliussen, R. J. The acoustic identification of Atlantic mackerel. ICES J. Mar. Sci. 67, 1749–1758 (2010).Article 

Google Scholar 
36.Langkau, M. C., Balk, H., Schmidt, M. B. & Borcherding, J. Can acoustic shadows identify fish species? A novel application of imaging sonar data. Fish. Manag. Ecol. 19, 313–322 (2012).Article 

Google Scholar 
37.Boswell, K. M., Wilson, M. P. & Cowan, J. H. Jr. A semiautomated approach to estimating fish size, abundance, and behavior from dual-frequency identification sonar (DIDSON) data. N. Am. J. Fish. Manag. 28, 799–807 (2008).Article 

Google Scholar 
38.Crossman, J. A., Martel, G., Johnson, P. N. & Bray, K. The use of Dual-Frequency Identification SONar (DIDSON) to document white sturgeon activity in the Columbia River, Canada. J. Appl. Ichthyol. 27, 53–57 (2011).Article 

Google Scholar 
39.Rakowitz, G. et al. Use of high-frequency imaging sonar (DIDSON) to observe fish behavior towards a surface trawl. Fish. Res. 123–124, 37–48 (2012).Article 

Google Scholar 
40.Skowronski, M. D. & Harris, J. G. Automatic detection of microchiroptera echolocation calls from field recordings using machine learning algorithms. J. Acoust. Soc. Am. 119, 1817–1833 (2005).Article 

Google Scholar 
41.Witten, I. H. & Frank, E. Data Mining: Practical Machine Learning Tools and Techniques 4th edn. (Morgan Kaufmann USA, 2017).MATH 

Google Scholar 
42.Jordan, M. I. & Mitchell, T. M. Machine learning: Trends, perspectives, and prospects. Science 349(6245), 255–260 (2015).ADS 
MathSciNet 
CAS 
MATH 
Article 

Google Scholar 
43.Jech, J. M., Lawson, G. L. & Lavery, A. C. Wideband (15–260 kHz) acoustic volume backscattering spectra of Northern krill (Meganyctiphanes norvegica) and butterfish (Peprilus triacanthus). ICES J. Mar. Sc. 74, 2249–2261 (2017).Article 

Google Scholar 
44.Lavery, A. C., Bassett, C., Lawson, G. L. & Jech, J. M. Exploiting signal processing approaches for broadband echosounders. ICES J. of Mar. Sci. 74, 2262–2275 (2017).Article 

Google Scholar 
45.Bassett, C., De Robertis, A. & Wilson, C. D. Broadband echosounder measurements of the frequency response of fishes and euphausiids in the Gulf of Alaska. ICES J. Mar. Sci. 75, 1131–1142 (2018).Article 

Google Scholar 
46.Demer, D. A. et al. 2016 USA–Norway EK80 Workshop Report: Evaluation of a wideband echosounder for fisheries and marine ecosystem science. ICES Coop. Res. Rep. https://doi.org/10.17895/ices.pub.2318 (2017).Article 

Google Scholar 
47.Tuzlukov, V. Signal Processing in Radar Systems 1st edn. (CRC Press Taylor & Francis Group USA, 2013).MATH 

Google Scholar 
48.Baer, J., Eckmann, R., Rösch, R., Arlinghaus, R. & Brinker, A. Managing upper lake constance fishery in a multi-sector policy landscape: Beneficiary and victim of a century of anthropogenic trophic change. In Inter-Sectoral Governance of Inland Fisheries (eds Song, A. M. et al.) 32–47 (TBTI Publication Series, 2017).
Google Scholar 
49.Roch, S., von Ammon, L., Geist, J. & Brinker, A. Foraging habits of invasive three-spined sticklebacks (Gasterosteus aculeatus)—Impacts on fisheries yield in Upper Lake Constance. Fish. Res. 204, 172–180 (2018).Article 

Google Scholar 
50.Rösch, R., Baer, J. & Brinker, A. Impact of the invasive three-spined stickleback (Gasterosteus aculeatus) on relative abundance and growth of native pelagic whitefish (Coregonus wartmanni) in Upper Lake Constance. Hydrobiol. 824, 255–270 (2018).Article 
CAS 

Google Scholar 
51.Balk, H., & Lindem, T. Sonar4 and Sonar5-Pro Post processing systems Operator manual, version 6.0.3. (2018).52.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017).
Google Scholar 
53.Liaw, A. & Wiener, M. Classification and regression by randomForest. R News. 2, 18–22 (2002).
Google Scholar 
54.Degan, D. J. & Wilson, W. Comparison of four hydroacoustic frequencies for sampling pelagic fish populations in Lake Texoma. N. Am. J. Fish. Manag. 15, 924–932 (1995).Article 

Google Scholar 
55.Godlewska, M. et al. Hydroacoustic measurements at two frequencies: 70 and 120 kHz—Consequences for fish stock estimation. Fish. Res. 96, 11–16 (2009).Article 

Google Scholar 
56.Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).Article 
MATH 

Google Scholar 
57.Cutler, D. R. et al. Random forests for classification in ecology. Ecology 88, 2783–2792 (2007).PubMed 
Article 

Google Scholar 
58.Oppel, S. et al. Comparison of five modelling techniques to predict the spatial distribution and abundance of seabirds. Biol. Cons. 156, 94–104 (2012).Article 

Google Scholar 
59.Kuhn, M. Caret: Classification and Regression Training. R package version 6.0-81. https://CRAN.R-project.org/package=caret (2018).60.Archer, K. J. & Kimes, R. V. Empirical characterization of random forest variable importance measures. Comput. Stat. Data Anal. 52, 2249–2260 (2008).MathSciNet 
MATH 
Article 

Google Scholar 
61.Lawson, G. J., Barange, M. & Fréon, P. Species identification of pelagic fish schools on the South African continental shelf using acoustic descriptors and ancillary information. ICES J. Mar. Sci. 58, 275–287 (2001).Article 

Google Scholar 
62.Simmonds, E. J., Armstrong, F. & Copland, P. J. Species identification using wideband backscatter with neural network and discriminant analysis. ICES J. Mar. Sci. 53, 189–195 (1996).Article 

Google Scholar 
63.Bergström, U. et al. Stickleback increase in the Baltic Sea—A thorny issue for coastal predatory fish. Estuar. Coast. Shelf Sci. 163, 134–142 (2015).ADS 
Article 

Google Scholar 
64.Pepin, T. & Shears, T. H. Influence of body size and alternate prey abundance on the risk of predation to fish larvae. Mar. Ecol. Prog. Ser. 128, 279–285 (1995).ADS 
Article 

Google Scholar 
65.Frouzová, J., Kubečka, J., Balk, H. & Frouz, J. Target strength of some European fish species and its dependence onfish body parameters. Fish. Res. 75, 86–96 (2005).Article 

Google Scholar 
66.Marques, D. A., Lucek, K., Sousa, V. C., Excoffier, L. & Seehausen, O. Admixture between old lineages facilitated contemporary ecological speciation in Lake Constance stickleback. Nat. Commun. 10, 4240. https://doi.org/10.1038/s41467-019-12182-w (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar  More

Study site and subject detailsThe study was conducted at the Inkawu Vervet Project (IVP) in a 12,000-hectare private game reserve: Mawana (28° 00.327 S, 031° 12.348 E) in KwaZulu Natal province, South Africa. The vegetation of the study site consisted in a savannah characterized by a mosaic of grasslands and clusters of trees of the typical savannah thornveld, bushveld and thicket patches. We studied two groups of wild vervet monkeys (Chlorocebus pygerythrus): ‘Noha’ (NH) and ‘Kubu’ (KB). NH was composed of 34 individuals (6 adult males; 9 adult females; 6 juvenile males; 7 juvenile females; 5 infant males; 1 infant female) and KB was composed of 19 individuals (1 adult male; 6 adult females; 3 juvenile males; 4 juvenile females; 3 infant males; 2 infant females; Table S1). Males were considered as adults once they dispersed, and females were considered as adults after they gave their first birth. Individuals that did not fulfil these criteria were considered as juveniles28 and infants were aged less than 1 year old. In EWA models, infants and juveniles were lumped in a single category “juveniles”. Each group had been habituated to the presence of human observers: since 2010 for NH and since 2013 for KB. All individuals were identifiable thanks to portrait photographs and specific individual body and face features (scars, colours, shape etc.).This research adhered to the “Guidelines for the use of animals in research” of Association for Study of Animal Behaviour, was approved by the relevant local authority, Ezemvelo KZN Wildlife, South Africa and complied with the ARRIVE guidelines.Hierarchy establishmentAgonistic interactions (aggressor behaviour: stare, chase, attack, hit, bite, take place; victim behaviour: retreat, flee, leave, avoid, jump aside) were collected from May 2018 to October 2018, aside from experiment days, on all the adults and juveniles of both groups via ad libitum sampling50 and food competition tests (i.e. corn provided to the whole group from a plastic box). Data were collected by CC, MBC and different observers from the IVP team. Before beginning data collection, observers had to pass an inter-observer reliability test with 80% reliability for each data category between two observers. Data were collected on tablets (Vodacom Smart Tab 2) equipped with Pendragon version 8.Individual hierarchical ranks were determined by the outcome of dyadic agonistic interactions recorded ad libitum and through food competition tests using Socprog software version 2.751. Hierarchies in both groups were significantly linear (NH: h′ = 0.27; P  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