Philippa Kaur

Sala, E. et al. Protecting the global ocean for biodiversity, food and climate. Nature 592, 397–402 (2021).ADS 
CAS 
Article 

Google Scholar 
Gillispie, C. C., Gratton-Guinness, I. & Fox, R. Pierre-Simon Laplace, 1749-1827: A Life in Exact Science (Princeton Univ. Press, 1999).Dinmore, T. A., Duplisea, D. E., Rackham, B. D., Maxwell, D. L. & Jennings, S. Impact of a large-scale area closure on patterns of fishing disturbance and the consequences for benthic communities. ICES J. Mar. Sci. 60, 371–380 (2003).Article 

Google Scholar 
Hiddink, J. G., Hutton, T., Jennings, S. & Kaiser, M. J. Predicting the effects of area closures and fishing effort restrictions on the production, biomass, and species richness of benthic invertebrate communities. ICES J. Mar. Sci. 63, 822–830 (2006).Article 

Google Scholar 
Greenstreet, S. P. R., Fraser, H. M. & Piet, G. J. Using MPAs to address regional-scale ecological objectives in the North Sea: modelling the effects of fishing effort displacement. ICES J. Mar. Sci. 66, 90–100 (2009).Article 

Google Scholar 
Suuronen, P. et al. A path to a sustainable trawl fishery in Southeast Asia. Rev. Fish. Sci. Aquac. 28, 499–517 (2020).Article 

Google Scholar 
Amoroso, R. O. et al. Bottom trawl fishing footprints on the world’s continental shelves. Proc. Natl Acad. Sci. USA 115, E10275–E10282 (2018).CAS 
Article 

Google Scholar 
Atwood, T. B., Witt, A., Mayorga, J., Hammill, E. & Sala, E. Global patterns in marine sediment carbon stocks. Front. Mar. Sci. 7, 165 (2020).Article 

Google Scholar 
Smeaton, C., Hunt, C. A., Turrell, W. R. & Austin, W. E. N. Marine sedimentary carbon stocks of the United Kingdom’s exclusive economic zone. Front. Earth Sci. 9, 593324 (2021).Article 

Google Scholar 
Legge, O. et al. Carbon on the northwest European shelf: contemporary budget and future influences. Front. Mar. Sci. 7, 143 (2020).Article 

Google Scholar 
Melnychuk, M. C. et al. Identifying management actions that promote sustainable fisheries. Nat. Sustain. 4, 440–449 (2021).Article 

Google Scholar  More

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Google Scholar  More

Clemens, R. et al. Oats, more than just a whole grain: an introduction. Br. J. Nutr. 112, S1–S3 (2014).CAS 
PubMed 
Article 

Google Scholar 
Stewart, D. & Mcdougal, G. Oat agriculture, cultivation and breeding targets: implications for human nutrition and health. Br. J. Nutr. 2, 50–57 (2014).Article 
CAS 

Google Scholar 
Ren, C. Z. et al. “Twelfth Five-Year” Development Report of China’s Oat and Buckwheat Industry. Xi’an: Shaanxi Science and Technology Press, 2011–2015 (2016).Gleick, P. H. & Palaniappan, M. Peak water limits to freshwater withdrawal and use. Proc. Indian Natl. Sci. Acad. 107, 11155–11162 (2010).ADS 
CAS 

Google Scholar 
Yu, L., Zhao, X., Gao, X. & Siddique, K. H. M. Improving/maintaining water-use efficiency and yield of wheat by deficit irrigation: A global meta-analysis. Agric. Water Manag. 228, 105906 (2020).Article 

Google Scholar 
Bai, W., Zhang, H., Liu, B., Wu, Y. & Song, J. Effects of super-absorbent polymers on the physical and chemical properties of soil following different wetting and drying cycles. Soil Use Manag. 26, 253–260 (2010).Article 

Google Scholar 
Döll, P. Impact of climate change and variability on irrigation requirements: A global perspective. Clim. Change 54, 269–293 (2002).ADS 
Article 

Google Scholar 
Harris, F. et al. The water use of Indian diets and socio- demographic factors related to dietary blue water footprint. Sci. Total Environ. 587, 128–136 (2017).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Unesco. Water and jobs: Facts and figures. Perugia, Italy: UNESCO, World Water Assessment Program. Retrieved from http://unesdoc.unesco.org/images/0024/002440/244041e.pdf (2016).Landi, A. et al. Land suitability evaluation for surface, sprinkle and drip irrigation methods in Fakkeh Plain. Iran. J. Appl. Anim. Sci. 8, 3646–3653 (2008).ADS 

Google Scholar 
Kang, S. et al. Improving agricultural water productivity to ensure food security in China under changing environment: From research to practice. Agric. Water Manag. 179, 5–17 (2017).Article 

Google Scholar 
Yang, D. et al. Effect of drip irrigation on wheat evapotranspiration, soil evaporation and transpiration in Northwest China. Agric. Water Manag. 232, 106001 (2020).Article 

Google Scholar 
Xu, S. T., Zhang, L., Neil, B. & McLaughlin, Mi. Effect of synthetic and natural water absorbing soil amendment soil physical properties under potato production in a semi-arid region. Soil Till. Res. 148, 31–39 (2015).Article 

Google Scholar 
Roper, M. M., Ward, P. R., Keulen, A. F. & Hill, J. R. Under no-tillage and stubble retention, soil water content and crop growth are poorly related to soil water repellency. Soil Till. Res. 126, 143–150 (2013).Article 

Google Scholar 
Zhao, H. et al. Ridge-furrow with full plasticfilm mulching improves water use efficiency and tuber yields of potato in a se miarid rainfed ecosystem. Field Crop Research. 161, 137–148 (2014).Article 

Google Scholar 
Li, J. et al. Effects of micro-sprinkling with different irrigation amount on grain yield and water use efficiency of winter wheat in the North China Plain. Agric. Water Manag. 224, 105736 (2019).Article 

Google Scholar 
Chouhan, S. S., Awasthi, M. K. & Nema, R. K. Studies on water productivity and yields responses of wheat based on drip irrigation systems in clay loam soil. Indian J. Sci. Technol. 8, 650 (2015).Article 

Google Scholar 
Liao, L., Zhang, L. & Bengtsson, L. Soil moisture variation and water consumption of spring wheat and their effects on crop yield under drip irrigation. Irrigat. Drainag. Syst. 22, 253–270 (2008).Article 

Google Scholar 
Jha, S. K. et al. Response of growth, yield and water use efficiency of winter wheat to different irrigation methods and scheduling in North China Plain. Agric. Water Manag. 217, 292–302 (2019).Article 

Google Scholar 
Yan, Z., Fengxin, W., Qi, Z., Kaijing, Y. & Youliang, Z. Effect of drip tape distance and irrigation amount on spring wheat yield and water use efficiency. Chin. Agric. Sci. Bull. 32, 194–199 (2016).
Google Scholar 
Chen, R. et al. Lateral spacing in drip-irrigated wheat: the effects on soil moisture, yield, and water use efficiency. Field Crop Res. 179, 52–62 (2015).Article 

Google Scholar 
Shock, C. C., Feibert, E.B.G., & Saunders, L. D. Water management for drip-irrigated spring wheat. Annual Rep. Med. Chem.. 2007 (2005).Bhardwaj, A. K., Shainberg, I., Goldstein, D., Warrington, D. N. & Levy, G. J. Water retention and hydraulic conductivity of cross-linked polyacrylamides in sandy soils. Soil Sci. Soc. Am. J. 71, 406–412 (2007).ADS 
CAS 
Article 

Google Scholar 
Demitri, C., Scalera, F., Madaghiele, M., Sannino, A. & Maffezzoli, A. Potential of cellulose-based superabsorbent hydrogels as water reservoir in agriculture. Int. J. Polym. Sci. 2013, 1–6 (2013).Article 
CAS 

Google Scholar 
Islam, M. R. et al. Effectiveness of a water-saving super-absorbent polymer in soil water conservation for corn (Zea mays L.) based on eco- physiological parameters. J. Agric. Food Sci. 91, 1998–2005 (2011).CAS 
Article 

Google Scholar 
Nazarli, H., Zardashti, M. R., Darvishzadeh, R. & Najafi, S. The effect of water stress and polymer on water use efficiency, yield, and several morphological traits of sunflower under greenhouse condition. Notulae Scientia Biologicae. 2, 53–58 (2010).Article 

Google Scholar 
Huettermann, A., Orikiriza, L. J. & Agaba, H. Application of superabsorbent polymers for improving the ecological chemistry of degraded or polluted lands. Clean: Soil, Air, Water 37, 517–526 (2009).CAS 

Google Scholar 
Jain, N. K., Meena, H. N. & Bhaduri, D. Improvement in productivity, water use efficiency, and soil nutrient dynamics of summer peanut (Arachis hypogaea L) through use of polythene mulch, hydrogel, and nutrient management. Commun. Soil Sci. Plant Anal. 48, 549–564 (2017).CAS 
Article 

Google Scholar 
Shekari, F., Javanmard, A. & Abbasi, A. Effects of super absorbent polymer application on yield and yield components of rapeseed. Notulae Scientia Biologicae. 7, 361–366 (2015).Article 

Google Scholar 
Wang, L. et al. Drip irrigation mode and water-retaining agent on growth regulation and water-saving effect of small coffee. Chin. J. Drainag. Irrigat. Mech. Eng. 33, 796–801 (2015).
Google Scholar 
Liu, P. et al. Effects of soil treatments on soil moisture and soybean yield under the condition of underground drip irrigation. Water Saving Irrigat. 25–28 (2019).Li, R. et al. Effects of water-retaining agent on soil water, fertilizer and corn yield under drip irrigation. J. Drainag. Irrigat. Mech. Eng. 36, 1337–1344 (2018).
Google Scholar 
Ma, B. L., Biswas, D. K., Zhou, Q. P. & Ren, C. Z. Comparisons among cultivars of wheat, hulled and hulless oats: Effects of N fertilization on growth and yield. Can. J. Plant Sci. 92, 1213–1222 (2012).Article 

Google Scholar 
He, W. Effects of different irrigation methods on photosynthesis and soil biological characteristics of oat. Inner Mongolia: Hohhot, Inner Mongolia Agricultural University Master’s Thesis (2013).Wu, N. et al. Effects of water-retaining agent dosage on the yield and quality of naked oats under two irrigation methods. J. Crops 35, 1552–1557 (2009).CAS 

Google Scholar 
Gee, G.W., Bauder, J.W.,. Particle-size analysis. In: Klute, A. (Ed.), Methods of Soil Analysis, Part 1. Soil Science Society of America, South Segoe Road, Madison, WI 53711 USA. 383–409 (1986).Lu, R. Soil Agricultural Chemical Analysis Method (China Agricultural Science and Technology Press, 2000).
Google Scholar 
Wang, D. Water use efficiency and optimal supplemental irrigation in a high yield wheat field. Field Crop Res. 213, 213–220 (2017).Article 

Google Scholar 
Chen, Y. et al. Straw strips mulch on furrows improves water use efficiency and yield of potato in a rainfed semiarid area. Agric. Water Manag. 211, 142–151 (2019).Article 

Google Scholar 
Finn, D. et al. Effect of added nitrogen on plant litter decomposition depends on initial soil carbon and nitrogen stoichiometry. Soil Biol. Biochem. 91, 160–168 (2015).CAS 
Article 

Google Scholar 
Mo, F., Wang, J. Y., Xiong, Y. C., Nguluu, S. N. & Li, F. M. Ridge-furrow mulching system in semiarid Kenya: A promising solution to improve soil water availability and maize productivity. Eur. J. Agron. 80, 124–136 (2016).Article 

Google Scholar 
Luo, C. L. et al. Dual plastic film and straw mulching boosts wheat productivity and soil quality under the El Nino in semiarid Kenya. Sci. Total Environ. 738, 139808 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Bengough, A. G. Water dynamics of the root zone: Rhizosphere biophysics and its control on soil hydrology. Vadose Zone Journal. 11, 1–6 (2012).Article 

Google Scholar 
Zobel, R. W. Plant Roots: Rowth, Activity and Interaction with Soils. Crop Sci. 46, 2699 (2006).Article 

Google Scholar 
Scholl, P. et al. Root induced changes of effective 1D hydraulic properties in a soil column. Plant Soil 381, 193–213 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Williams, S. M. & Weil, R. R. Crop cover root channels may alleviate soil compaction effects on soybean crop. Soil Sci. Soc. Am. J. 68, 1403–1409 (2010).Article 

Google Scholar 
Farrell, C., Ang, X. Q. & Rayner, J. P. Water-retention additives increase plant available water in green roof substrates. Ecol. Eng. 52, 112–118 (2013).Article 

Google Scholar 
Agaba, H. et al. Effects of hydrogel amendment to different soils on plant available water and survival of trees under drought conditions. Clean: Soil, Air, Water 38, 328–335 (2010).CAS 

Google Scholar 
Wu, L., Liu, M. Z. & Liang, R. Preparation and properties of a double-coated slow release NPK compound fertilizer with superabsorbent and water-retention. Biores. Technol. 99, 547–554 (2008).CAS 
Article 

Google Scholar 
Afshar, R. K. et al. Interactive effect of deficit irrigation and soil organic amendments on seed yield and flavonolignan production of milk thistle (Silybum marianum L. Gaertn.). Ind. Crops Prod. 58, 166–172 (2014).CAS 
Article 

Google Scholar 
Wang, L. Effects of different sowing dates and fertilizer rates on the growth and yield of oats in Yinshan hilly area. Hohhot, Inner Mongolia Agricultural University Master’s Thesis (2020).Liu, Y. G. et al. Influence of planting density on the yield of naked oats and its constituent factors. J. Wheat Crops 28, 140–143 (2008).
Google Scholar 
Jia, Z. F. Effects of sowing rate and row spacing on grain quality of naked oat. Seed. 32, 67–69 (2013).
Google Scholar 
Lascano, R. J. & Van Bavel, C. H. M. Stimulation and measurement of evaporation from bare soil. Soil Sci. Soc. Am. J. 50, 1127–1132 (1986).ADS 
Article 

Google Scholar 
Lv, P. et al. Effects of descending distance under wide sowing conditions on wheat yield and dry matter accumulation and transport. J. Wheat Crops 40, 1–6 (2020).
Google Scholar 
Sun, H. Y. et al. Effects of different row spacing on evapotranspiration and yield of winter wheat in wheat fields. Chin. J. Agric. Eng. 1, 22–26 (2006).
Google Scholar 
Li, G. X. et al. Effects of sowing row spacing on yield and water use efficiency of dryland wheat in different years. Agric. Technol. Equipm. 1, 22–26 (2012).ADS 

Google Scholar 
Chen, S. Y. et al. Effects of planting row spacing on soil evaporation and water use in winter wheat fields. Chin. J. Ecol. Agric. 14, 86–89 (2006).
Google Scholar 
Yang, Y. H. et al. Effects of water-retaining agent on soil moisture and utilization of winter wheat at different growth stages. Chin. J. Agric. Eng. 26, 19–26 (2010).
Google Scholar 
Yang, Y. H. et al. Effects of different moisture conservation tillage measures on water consumption characteristics and annual water use of wheat and maize. North China Agric. J. 32, 103–110 (2017).
Google Scholar 
Du, S. N. et al. Effects of Water and PAM Application Modes on Soil Moisture and Maize Growth. Chin. J. Agric. Eng. 24, 30–35 (2008).
Google Scholar 
Tian, L. et al. Effects of combined application of water-retaining agent and microbial fertilizer on dry matter accumulation, distribution, transport and yield of dry oat. J. Ecol. 39, 2996–3003 (2020).
Google Scholar  More

Lehtonen12 presents three simple models with the same broad structure: a single mutant individual with divergent mating behaviour arises in a population of ‘residents’ that all play the same strategy, and the success of that mutant is then followed (Figs. 1, 2). Specifically, Lehtonen investigates the fitness benefits of increased mating for mutant males in comparison to mutant females. Two important parameters can be varied: (i) the degree of anisogamy (defined here as the ratio of sperm number to egg number), which captures how divergent males and females are in the size (and thus number) of gametes they produce, and (ii) the efficiency of fertilisation, which determines how easily gametes can find and fuse with each other. If fertilisation is highly efficient, then gametes of the less numerous type will achieve nearly full fertilisation; on the other hand, inefficient fertilisation can result in gametes of both sexes going unfertilised.Fig. 2: Structure of the three models of Lehtonen12, showing differences in mating behaviour between resident males (green), resident females (blue) and mutant males and females (both yellow).For illustration, we suppose that females produce four eggs each and males produce eight sperm (the anisogamy ratio in nature is typically much higher). In Model 1, resident individuals spawn monogamously in a ‘nest’ (black outline), whereas mutant males and females can bring additional partners to their nest to spawn in a group. In Model 2, resident individuals divide their gametes equally among m spawning groups, each consisting of m individuals of each sex (shown here with m = 2). Mutant males and females instead divide their gametes among a larger or smaller number of groups, mmutant (shown here with mmutant = 4). In Model 3, there is a further sex asymmetry in addition to anisogamy: Fertilisation takes place inside the female’s body. Resident individuals mate with m partners (shown here with m = 2), whereas mutant males and females mate with a larger or smaller number of partners, mmutant (shown here with mmutant = 4).Full size imageIn the first two models, fertilisation is external and no assumptions are made about pre-existing differences between the sexes apart from the number of gametes they produce. In other words, males and females are identical except that males produce sperm in greater numbers than females produce eggs. In Model 1, resident individuals are assumed to mate monogamously, whereas a mutant can monopolise multiple partners of the opposite sex (Fig. 2). Importantly, both male and female mutants can bring additional partners back to their ‘nest’ to spawn in a group. When fertilisation is highly efficient, females can fertilise all of their eggs by bringing back a single male, and there is simply no benefit (in this model) of seeking further partners (Fig. 1A). In contrast, anisogamy means that males always produce at least some gametes in excess, and thus can benefit from seeking additional mates. When fertilisation is inefficient, however, both sexes benefit from increasing the concentration of opposite-sex gametes at their ‘nest’ (Fig. 1B). This latter benefit is sex-symmetric, whereas the former continues to apply only to males. As a consequence, the Bateman gradients are always steeper for males than for females (Fig. 1A, B), confirming Bateman’s argument.Model 2 similarly assumes external fertilisation, but in this case the resident males and females meet in groups consisting of m individuals of each sex (Fig. 2). Fertilisation occurs via group spawning. It is assumed that each resident individual divides its gametes evenly across M groups, whereas mutant individuals can instead spread their gametes over a larger or smaller number of groups (note that the author assumes that M = m, but this assumption could be relaxed without undermining the core argument). Spreading gametes out across a larger number of spawning groups does not increase the concentration of opposite-sex gametes they encounter (Fig. 2). However, a mutant that spreads its gametes more widely reduces the density of its own gametes across those groups in which it spawns. This in turn results in there being more opposite-sex gametes for each gamete of the mutant’s sex in those groups. For example, in Fig. 2, mutant males spawn in twice as many groups as resident males and thereby halve the density of their own sperm in each group. The resulting egg-to-sperm ratio of (frac{4}{6}=frac{2}{3}) is more favourable than the ratio of (frac{4}{8}=frac{1}{2}) that the resident males experience. Mutant females can similarly increase local sperm-to-egg ratios by spreading their eggs over more groups. However, in contrast to males, this only leads to fitness benefit if fertilisation is inefficient, and even then the benefit to females is very modest (scarcely perceptible in Fig. 1D). Gamete spreading reduces wasteful competition among the mutants’ own gametes for fertilisation. Such ‘local’ gamete competition, like gamete competition more generally, is stronger among sperm than among eggs because sperm are more numerous under anisogamy13,14. Consequently, as in Model 1, Bateman gradients are always steeper in males (Fig. 1C, D). Recall that the results of the above models emerge in the absence of any assumptions beyond the sex difference in the number of gametes produced.The third and final model allows for a further pre-existing difference between the sexes in addition to anisogamy: internal fertilisation, which is common and widespread in animals (Fig. 2)15. Each female is assumed to mate with m males, while each male divides his gametes evenly among m females. As in the previous two models, males benefit more than females from additional matings under most conditions. However, in the particular case where fertilisation is highly inefficient and the ratio of sperm to eggs is not too large, the pattern can theoretically reverse, such that female Bateman gradients exceed their male counterparts (Fig. 1F). The reason is that the effects of gamete concentration are asymmetric under internal fertilisation: Multiple mating by a female increases the local concentration of sperm its eggs experience, whereas a male’s multiple mating does not increase the concentration of eggs around its sperm (Fig. 2). Under conditions of severe sperm limitation—due to both weak anisogamy and highly inefficient fertilisation—this can lead to females benefitting more from additional matings than males (Fig. 1F). Although intriguing, it is unclear whether this finding has any empirical relevance, as sperm limitation is probably rarely severe in internal fertilisers. Under more realistic conditions of moderate to high fertilisation rates, sex differences in the degree of local gamete competition once again become decisive, and male Bateman gradients exceed their female counterparts (Fig. 1E). More

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

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

Sampling of coastal communitiesHere, we integrated data from five different projects that had surveyed coastal communities across five countries47,48,49,50. Between 2009 and 2015, we conducted socioeconomic surveys in 72 sites from Indonesia (n = 25), Madagascar (n = 6), Papua New Guinea (n = 10), the Philippines (n = 25), and Tanzania (Zanzibar) (n = 6). Site selection was for broadly similar purposes- to evaluate the effects of various coastal resource management initiatives (collaborative management, integrated conservation and development projects, recreational fishing projects) on people’s livelihoods in rural and peri-urban villages. Within each project, sites were purposively selected to be representative of the broad range of socioeconomic conditions (e.g., population size, levels of development, integration to markets) experienced within the region. We did not survey strictly urban locations (i.e., major cities). Because our sampling was not strictly random, care should be taken when attempting to make inferences beyond our specific study sites.We surveyed between 13 and 150 households per site, depending on the population of the communities and the available time to conduct interviews per site. All projects employed a comparable sampling design: households were either systematically (e.g., every third house), randomly sampled, or in the case of three villages, every household was surveyed (a census) (see Supplementary Data file). Respondents were generally the household head, but could have been other household members if the household head was not available during the study period (i.e. was away). In the Philippines, sampling protocol meant that each village had an even number of male and female respondents. Respondents gave verbal consent to be interviewed.The following standard methodology was employed to assess material style of life, a metric of material assets-based wealth48,51. Interviewers recorded the presence or absence of 16 material items in the household (e.g., electricity, type of walls, type of ceiling, type of floor). We used a Principal Component Analysis on these items and kept the first axis (which explained 34.2% of the variance) as a material wealth score. Thus, each community received a mean material style of life score, based on the degree to which surveyed households had these material items, which we then scaled from 0 to 1. We also conducted an exploratory analysis of how material style of life has changed in two sites in Papua New Guinea (Muluk and Ahus villages) over fifteen and sixteen-year time span across four and five-time periods (2001, 2009, 2012, 2016, and 2002, 2009, 2012, 2016, 2018), respectively, that have been surveyed since 2001/200252. These surveys were semi-panel data (i.e. the community was surveyed repeatedly, but we did not track individuals over each sampling interval) and sometimes occurred in different seasons. For illustrative purposes, we plotted how these villages changed over time along the first two principal components.SensitivityWe asked each respondent to list all livelihood activities that bring in food or income to the household and rank them in order of importance. Occupations were grouped into the following categories: farming, cash crop, fishing, mariculture, gleaning, fish trading, salaried employment, informal, tourism, and other. We considered fishing, mariculture, gleaning, fish trading together as the ‘fisheries’ sector, farming and cash crop as the ‘agriculture’ sector and all other categories into an ‘off-sector’.We then developed three distinct metrics of sensitivity based on the level of dependence on agriculture, fisheries, and both sectors together. Each metric incorporates the proportion of households engaged in a given sector (e.g., fisheries), whether these households also engage in occupations outside of this sector (agriculture and salaried/formal employment; referred to as ‘linkages’ between sectors), and the directionality of these linkages (e.g., whether respondents ranked fisheries as more important than other agriculture and salaried/formal employment) (Eqs. 1–3)$${{{{{{rm{S}}}}}}}_{{{{{{rm{A}}}}}}}=,frac{{{{{{rm{A}}}}}}}{{{{{{rm{A}}}}}}+{{{{{rm{NA}}}}}}},times ,frac{{{{{{rm{N}}}}}}}{{{{{{rm{A}}}}}}+{{{{{rm{NA}}}}}}},times ,frac{left(frac{{{{{{{rm{r}}}}}}}_{{{{{{rm{a}}}}}}}}{2}right),+,1}{{{{{{{rm{r}}}}}}}_{{{{{{rm{a}}}}}}}+,{{{{{{rm{r}}}}}}}_{{{{{{rm{na}}}}}}}+1}$$
(1)
$${{{{{{rm{S}}}}}}}_{{{{{{rm{F}}}}}}}=,frac{{{{{{rm{F}}}}}}}{{{{{{rm{F}}}}}}+{{{{{rm{NF}}}}}}},times ,frac{{{{{{rm{N}}}}}}}{{{{{{rm{F}}}}}}+{{{{{rm{NF}}}}}}},times ,frac{left(frac{{{{{{{rm{r}}}}}}}_{{{{{{rm{f}}}}}}}}{2}right),+,1}{{{{{{{rm{r}}}}}}}_{{{{{{rm{f}}}}}}}+,{{{{{{rm{r}}}}}}}_{{{{{{rm{nf}}}}}}}+1}$$
(2)
$${{{{{{rm{S}}}}}}}_{{{{{{rm{AF}}}}}}}=,frac{{{{{{rm{AF}}}}}}}{{{{{{rm{AF}}}}}}+{{{{{rm{NAF}}}}}}},times ,frac{{{{{{rm{N}}}}}}}{{{{{{rm{AF}}}}}}+{{{{{rm{NAF}}}}}}},times ,frac{left(frac{{{{{{{rm{r}}}}}}}_{{{{{{rm{af}}}}}}}}{2}right),+,1}{{{{{{{rm{r}}}}}}}_{{{{{{rm{af}}}}}}}+,{{{{{{rm{r}}}}}}}_{{{{{{rm{naf}}}}}}}+1}$$
(3)
where ({{{{{{rm{S}}}}}}}_{{{{{{rm{A}}}}}}}), ({{{{{{rm{S}}}}}}}_{{{{{{rm{F}}}}}}}) and ({{{{{{rm{S}}}}}}}_{{{{{{rm{AF}}}}}}}) are a community’s sensitivity in the context of agriculture, fisheries and both sectors, respectively. A, F and AF are the number of households relying on agriculture-related occupations within that community, fishery-related and agriculture- and fisheries-related occupations within the community, respectively. NA, NF and NAF are the number of households relying on non-agriculture-related, non-fisheries-related, and non-agriculture-or-fisheries-related occupations within the community, respectively. N is the number of households within the community. ({{{{{{rm{r}}}}}}}_{{{{{{rm{a}}}}}}}), ({{{{{{rm{r}}}}}}}_{{{{{{rm{f}}}}}}}) and ({{{{{{rm{r}}}}}}}_{{{{{{rm{af}}}}}}}) are the number of times agriculture-related, fisheries-related and agriculture-and-fisheries-related occupations were ranked higher than their counterpart, respectively. ({{{{{{rm{r}}}}}}}_{{{{{{rm{na}}}}}}}), ({{{{{{rm{r}}}}}}}_{{{{{{rm{nf}}}}}}}) and ({{{{{{rm{r}}}}}}}_{{{{{{rm{naf}}}}}}}) are the number of times non-agriculture, non-fisheries, and non-agriculture-and-fisheries-related occupations were ranked higher than their counterparts. As with the material style of life, we also conducted an exploratory analysis of how joint agriculture-fisheries sensitivity has changed over time in a subset of sites (Muluk and Ahus villages in Papua New Guinea) that have been sampled since 2001/200252. Although our survey methodology has the potential for bias (e.g. people might provide different rankings based on the season, or there might be gendered differences in how people rank the importance of different occupations53), our time-series analysis suggest that seasonal and potential respondent variation do not dramatically alter our community-scale sensitivity metric.ExposureTo evaluate the exposure of communities to the impact of future climates on their agriculture and fisheries sectors, we used projections of production potential from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) Fast Track phase 3 experiment dataset of global simulations. Production potential of agriculture and fisheries for each of the 72 community sites and 4746 randomly selected sites from our study countries with coastal populations >25 people/km2 were projected to the mid-century (2046–2056) under two emission scenarios (SSP1-2.6, and SSP5-8.5) and compared with values from a reference historical period (1983–2013).For fisheries exposure (EF), we considered relative change in simulated total consumer biomass (all modelled vertebrates and invertebrates with a trophic level >1). For each site, the twenty nearest ocean grid cells were determined using the Haversine formula (Supplementary Fig. 5). We selected twenty grid cells after a sensitivity analysis to determine changes in model agreement based on different numbers of cells used (1, 3, 5, 10, 20, 50, 100; Supplementary Figs. 6–7), which we balanced off with the degree to which larger numbers of cells would reduce the inter-site variability (Supplementary Fig. 8). We also report 25th and 75th percentiles for the change in marine animal biomass across the model ensemble. Projections of the change in total consumer biomass for the 72 sites were extracted from simulations conducted by the Fisheries and marine ecosystem Model Intercomparison Project (FishMIP3,54). FishMIP simulations were conducted under historical, SSP1-2.6 (low emissions) and SSP5-8.5 (high emissions) scenarios forced by two Earth System Models from the most recent generation of the Coupled Model Intercomparison project (CMIP6);55 GFDL-ESM456 and IPSL-CM6A-LR57. The historical scenario spanned 1950–2014, and the SSP scenarios spanned 2015–2100. Nine FishMIP models provided simulations: APECOSM58,59, BOATS60,61, DBEM2,62, DBPM63, EcoOcean64,65, EcoTroph66,67, FEISTY68, Macroecological69, and ZooMSS11. Simulations using only IPSL-CM6A-LR were available for APECOSM and DBPM, while the remaining 7 FishMIP models used both Earth System Model forcings. This resulted in 16 potential model runs for our examination of model agreement, albeit with some of these runs being the same model forced with two different ESMs. Thus, the range of model agreement could range from 8 (half model runs indicating one direction of change, and half indicating the other) to 16 (all models agree in direction of change). Model outputs were saved with a standardised 1° spatial grid, at either a monthly or annual temporal resolution.For agriculture exposure (EA), we used crop model projections from the Global Gridded Crop model Intercomparison Project (GGCMI) Phase 314, which also represents the agriculture sector in ISIMIP. We used a window of 11×11 cells centred on the site and removed non-land cells (Supplementary Fig. 5). The crop models use climate inputs from 5 CMIP6 ESMs (GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL), downscaled and bias-adjusted by ISIMIP and use the same simulation time periods. We considered relative yield change in three rain-fed and locally relevant crops: rice, maize, and cassava, using outputs from 4 global crop models (EPIC-IIASA, LPJmL, pDSSAT, and PEPIC), run at 0.5° resolution. These 4 models with 5 forcings generate 20 potential model runs for our examination of model agreement. Yield simulations for cassava were only available from the LPJmL crop model. All crop model simulations assumed no adaptation in growing season and fertilizer input remained at current levels. Details on model inputs, climate data, and simulation protocol are provided in ref. 14. At each site, and for each crop, we calculated the average change (%) between projected vs. historical yield within 11×11 cell window. We then averaged changes in rice, maize and cassava to obtain a single metric of agriculture exposure (EA).We also obtained a composite metric of exposure (EAF) by calculating each community’s average change in both agriculture and fisheries:$${{{{{{rm{E}}}}}}}_{{{{{{rm{AF}}}}}}}=,frac{{{{{{{rm{E}}}}}}}_{{{{{{rm{A}}}}}}}+,{{{{{{rm{E}}}}}}}_{{{{{{rm{F}}}}}}}}{2}$$
(4)
Potential ImpactWe calculated relative potential impact as the Euclidian distance from the origin (0) of sensitivity and exposure.Sensitivity testTo determine whether our sites displayed a particular exposure bias, we compared the distributions of our sites and 4746 sites that were randomly selected from 47,460 grid cells within 1 km of the coast of the 5 countries we studied which had population densities >25 people/km2, based on the SEDAC gridded populating density of the world dataset (https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density-rev11/data-download).We used Cohen’s D to determine the size of the difference between our sites and the randomly selected sites.Validating ensemble modelsWe attempted a two-stage validation of the ensemble model projections. First, we reviewed the literature on downscaling of ensemble models to examine whether downscaling validation had been done for the ecoregions containing our study sites.While no fisheries ensemble model downscaling had been done specific to our study regions, most of the models of the ensemble have been independently evaluated against separate datasets aggregated at scales down to Large Marine Ecosystems (LMEs) or Exclusive Economic Zones (EEZs) (see11). For example, the DBEM was created with the objective of understanding the effects of climate change on exploited marine fish and invertebrate species2,70. This model roughly predicts species’ habitat suitability; and simulates spatial population dynamics of fish stocks to output biomass and maximum catch potential (MCP), a proxy of maximum sustainable yield2,62,71. Compared with spatially-explicit catch data from the Sea Around Us Project (SAUP; www.seaaroundus.org)70 there were strong similarities in the responses to warming extremes for several EEZs in our current paper (Indonesia and Philippines) and weaker for the EEZs of Madagascar, Papua New Guinea, and Tanzania. At the LME level, DBEM MCP simulations explained about 79% of the variation in the SAUP catch data across LMEs72. The four LMEs analyzed in this paper (Agulhas Current; Bay of Bengal; Indonesian Sea; and Sulu-Celebes Sea) fall within the 95% confidence interval of the linear regression relationship62. Another example, BOATS, is a dynamic biomass size-spectrum model parameterised to reproduce historical peak catch at the LME scale and observed catch to biomass ratios estimated from the RAM legacy stock assessment database (in 8 LMEs with sufficient data). It explained about 59% of the variability of SAUP peak catch observation at the LME level with the Agulhas Current, Bay of Bengal, and Indonesian Sea catches reproduced within +/-50% of observations61. The EcoOcean model validation found that all four LMEs included in this study fit very close to the 1:1 line for overserved and predicted catches in 200064,65. DBPM, FEISTY, and APECOSM have also been independently validated by comparing observed and predicted catches. While the models of this ensemble have used different climate forcings when evaluated independently, when taken together the ensemble multi-model mean reproduces global historical trends in relative biomass, that are consistent with the long term trends and year-on-year variation in relative biomass change (R2 of 0.96) and maximum yield estimated from stock assessment models (R2 of 0.44) with and without fishing respectively11.Crop yield estimates simulated by GGCMI crop models have been evaluated against FAOSTAT national yield statistics14,73,74. These studies show that the models, and especially the multi-model mean, capture large parts of the observed inter-annual yield variability across most main producer countries, even though some important management factors that affect observed yield variability (e.g., changes in planting dates, harvest dates, cultivar choices, etc.) are not considered in the models. While GCM-based crop model results are difficult to validate against observations, Jägermeyr et al14. show that the CMIP6-based crop model ensemble reproduces the variability of observed yield anomalies much better than CMIP5-based GGCMI simulations. In an earlier crop model ensemble of GGCMI, Müller et al.74 show that most crop models and the ensemble mean are capable of reproducing the weather-induced yield variability in countries with intensely managed agriculture. In countries where management introduces strong variability to observed data, which cannot be considered by models for lack of management data time series, the weather-induced signal is often low75, but crop models can reproduce large shares of the weather-induced variability, building trust in their capacity to project climate change impacts74.We then attempted to validate the models in our study regions. For the crop models, we examined production-weighted agricultural projections weighted by current yields/production area (Supplementary Fig. 1). We used an observational yield map (SPAM2005) and multiplied it with fractional yield time series simulated by the models to calculate changes in crop production over time, which integrates results in line with observational spatial patterns. The weighted estimates were not significantly different to the unweighted ones (t = 0.17, df = 5, p = 0.87). For the fisheries models, our study regions were data-poor and lacked adequate stock assessment data to extend the observed global agreement of the sensitivity of fish biomass to climate during our reference period (1983-2013). Instead, we provide the degree of model run agreement about the direction of change in the ensemble models to ensure transparency about the uncertainty in this downscaled application.AnalysesTo account for the fact that communities were from five different countries we used linear mixed-effects models (with country as a random effect) for all analyses. All averages reported (i.e. exposure, sensitivity, and model agreement) are estimates from these models. In both our comparison of fisheries and agriculture exposure and test of differences between production-weighted and unweighted agriculture exposure we wanted to maintain the paired nature of the data while also accounting for country. To accomplish this we used the differences between the exposure metrics as the response variable (e.g. fisheries exposure minus agriculture exposure), testing whether these differences are different from zero. We also used linear mixed-effects models to quantify relationships between the material style of life and potential impacts under different mitigation scenarios (SSP1-2.6 and 8.5), estimating standard errors from 1000 bootstrap replications. To further explore whether these relationships between the material style of life and potential impacts were driven by exposure or sensitivity, we conducted an additional analysis to quantify relationships between the material style of life and: 1) joint fisheries and agricultural sensitivity; 2) joint fisheries and agricultural exposure under different mitigation scenarios. We present both the conditional R2 (i.e., variance explained by both fixed and random effects) and the marginal R2 (i.e., variance explained by only the fixed effects) to help readers compare among the material style of life relationships.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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