Ecology

Angélil, O. et al. An independent assessment of anthropogenic attribution statements for recent extreme temperature and rainfall events. J. Clim. 30(1), 5–16 (2017).ADS 

Google Scholar 
Rosenzweig, C. et al. Coordinating AgMIP data and models across global and regional scales for 1.5°C and 2.0°C assessments. Philos. Trans. R. Soc. A. 376, 20160455 (2018).ADS 

Google Scholar 
Mitchell, D. et al. Half a degree additional warming, prognosis and projected impacts (HAPPI): Background and experimental design. Geosci. Model Dev. 10, 571–583 (2017).ADS 
CAS 

Google Scholar 
Coumou, D. & Rahmstorf, S. A decade of weather extremes. Nat. Clim. Change 2, 491–496 (2012).ADS 

Google Scholar 
IPCC: Summary for Policymakers. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change 4–6 (Cambridge University Press, 2013).Diffenbaugh, N. S. et al. Quantifying the influence of global warming on unprecedented extreme climate events. PNAS 114(19), 4881–4886 (2016).ADS 

Google Scholar 
Tai, A. P. K., Martin, M. V. & Heald, C. L. Threat to future global food security from climate change and ozone air pollution. Nat. Clim. Change 4, 817–821 (2014).ADS 
CAS 

Google Scholar 
Román-Palacios, C. & Wiens, J. J. Recent responses to climate change reveal the drivers of species extinction and survival. PNAS 117(8), 4211–4217 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
Dong, W. H., Liu, Z., Liao, H., Tang, Q. H. & Li, X. E. New climate and socio-economic scenarios for assessing global human health challenges due to heat risk. Clim. Change 130(4), 505–518 (2015).ADS 

Google Scholar 
Brown, S. C., Wigley, T. M. L., Otto-Bliesner, B. L., Rahbek, C. & Fordham, D. A. Persistent Quaternary climate refugia are hospices for biodiversity in the Anthropocene. Nat. Clim. Change 10, 244–248 (2020).ADS 

Google Scholar 
Fischer, H., Amelung, D. & Said, N. The accuracy of German citizens’ confidence in their climate change knowledge. Nat. Clim. Change 9, 776–780 (2020).ADS 

Google Scholar 
Hasegawa, T. et al. Risk of increased food insecurity under stringent global climate change mitigation policy. Nat. Clim. Change 8, 699–703 (2018).ADS 

Google Scholar 
Lobell, D. B., Schlenker, W. & Costa-Roberts, J. Climate trends and global crop production since 1980. Science 333, 616–620 (2011).ADS 
CAS 
PubMed 

Google Scholar 
UNFCCC. The Paris Agreement. 2015, https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris-agreement.Roche, K. R., Müller-Itten, M., Dralle, D. N., Bolster, D. & Müller, M. F. Climate change and the opportunity cost of conflict. PNAS 117(4), 1935–1940 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Challinor, A. J. et al. A meta-analysis of crop yield under climate change and adaptation. Nat. Clim. Change 4, 287–291 (2014).ADS 

Google Scholar 
Lobell, D. B. et al. Prioritizing climate change adaptation needs for food security in 2030. Science 319, 607–610 (2017).
Google Scholar 
Lv, S. et al. Yield gap simulations using ten maize cultivars commonly planted in Northeast China during the past five decades. Agric. For. Meteorol. 205, 1–10 (2015).ADS 

Google Scholar 
Chao, W., Kehui, C. & Shah, F. Heat stress decreases rice grain weight: Evidence and physiological mechanisms of heat effects prior to flowering. Int. J. Mol. Sci. 23(18), 10922 (2022).
Google Scholar 
Chao, W. et al. Estimating the yield stability of heat-tolerant rice genotypes under various heat conditions across reproductive stages: A 5-year case study. Sci. Rep. 11, 13604 (2021).ADS 

Google Scholar 
IPCC. Food security and food production systems. In Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel of Climate Change 485–533 (Cambridge University Press, 2014).Tigchelaar, M., Battisti, D. S., Naylor, R. L. & Ray, D. K. Future warming increases probability of globally synchronized maize production shocks. PNAS 115(26), 6644–6649 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Zhao, C. et al. Temperature increase reduces global yields of major crops in four independent estimates. PNAS 114, 9326–9331 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Diffenbaugh, N. S., Hertel, T. W., Scherer, M. & Verma, M. Response of corn markets to climate volatility under alternative energy futures. Nat. Clim. Change 2, 514–518 (2012).ADS 

Google Scholar 
Jensen, H. G. & Anderson, K. Grain price spikes and beggar-thy-neighbor policy responses: A global economywide analysis. World Bank Econ. Rev. 31, 158–175 (2017).
Google Scholar 
Fraser, E. D. G., Simelton, E., Termansen, M., Gosling, S. N. & South, A. “Vulnerability hotspots”: Integrating socio-economic and hydrological models to identify where cereal production may decline in the future due to climate change induced drought. Agric. For. Meteorol. 170, 195–205 (2013).ADS 

Google Scholar 
Puma, M. J., Bose, S., Chon, S. Y. & Cook, B. I. Assessing the evolving fragility of the global food system. Environ. Res. Lett. 10, 024007 (2015).ADS 

Google Scholar 
Wheeler, T. & Braun, J. V. Climate change impacts on global food security. Science 341(6145), 508–513 (2013).ADS 
CAS 
PubMed 

Google Scholar 
Lunt, T., Jones, A. W., Mulhern, W. S., Lezaks, D. P. M. & Jahn, M. M. Vulnerabilities to agricultural production shocks: An extreme, plausible scenario for assessment of risk for the insurance sector. Clim. Risk Manag. 13, 1–9 (2016).
Google Scholar 
Jägermeyr, J. & Frieler, K. Spatial variations in crop growing seasons pivotal to reproduce global fluctuations in maize and wheat yields. Sci. Adv. 4(11), eaat4517 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Elliott, J. et al. Characterizing agricultural impacts of recent large-scale US droughts and changing technology and management. Agric. Syst. 159, 275–281 (2017).
Google Scholar 
Tack, J., Barkley, A. & Nalley, L. L. Effect of warming temperatures on US wheat yields. Proc. Natl. Acad. Sci. 112, 6931–6936 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Tao, F., Zhang, Z., Liu, J. & Yokozawa, M. Modelling the impacts of weather and climate variability on crop productivity over a large area: A new super-ensemblebased probabilistic projection. Agric. For. Meteorol. 149, 1266–1278 (2009).ADS 

Google Scholar 
Parent, B. et al. Maize yields over Europe may increase in spite of climate change, with an appropriate use of the genetic variability of flowering time. PNAS 115(42), 10642–10647 (2018).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Yang, C. Y., Fraga, H., Ieperen, W. V. & Santos, J. A. Assessment of irrigated maize yield response to climate change scenarios in Portugal. Agric. Water Manag. 184, 178–190 (2017).
Google Scholar 
Miller, S. A. & Moore, F. C. Climate and health damages from global concrete production. Nat. Clim. Change https://doi.org/10.1038/s41558-020-0733-0 (2020).Article 

Google Scholar 
Kassie, B. T. et al. Exploring climate change impacts and adaptation options for maize production in the Central Rift Valley of Ethiopia using different climate change scenarios and crop models. Clim. Change 129, 145–158 (2015).ADS 

Google Scholar 
Tao, F. & Zhang, Z. Climate change, high-temperature stress, rice productivity, and water use in Eastern China: A new superensemble-based probabilistic projection. J. Appl. Meteorol. Climatol. 52, 531–551 (2013).ADS 

Google Scholar 
Glotter, M. & Elliott, J. Simulating US agriculture in a modern Dust Bowl drought. Nat. Plants 3, 16193 (2016).PubMed 

Google Scholar 
Challinor, A. J., Koehler, A. K., Ramirez-Villegas, J., Whitfield, S. & Das, B. Current warming will reduce yields unless maize breeding and seed systems adapt immediately. Nat. Clim. Change 6, 954–958 (2016).ADS 

Google Scholar 
Cammarano, D. et al. Using historical climate observations to understand future climate change crop yield impacts in the Southeastern US. Clim. Change 134, 311–326 (2016).ADS 

Google Scholar 
Etten, J. V. et al. Crop variety management for climate adaptation supported by citizen science. PNAS 116(10), 4194–4199 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Urban, D. W., Sheffield, J. & Lobell, D. B. The impacts of future climate and carbon dioxide changes on the average and variability of US maize yields under two emission scenarios. Environ. Res. Lett. 10, 045003 (2015).ADS 

Google Scholar 
IPCC. Summary for policymakers. In Global Warming of 1.5°C. An IPCC Special Report on the Impacts of Global Warming of 1.5°C Above Pre-industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate Change, Sustainable Development, and Efforts to Eradicate Poverty 32 (World Meteorological Organization, 2018).Ruane, A. C., Goldberg, R. & Chryssanthacopoulos, J. Climate forcing datasets for agricultural modeling: Merged products for gap-filling and historical climate series estimation. Agr. For. Meteorol. 200, 233–248 (2015).
Google Scholar 
Hempel, S., Frieler, K., Warszawski, L., Schewe, J. & Piontek, F. A trendpreserving bias correction-the ISI-MIP approach. Earth Syst. Dyn. 4, 219–236 (2013).ADS 

Google Scholar 
Monfreda, C., Ramankutty, N. & Foley, J. A. Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Glob. Biogeochem. Cycles 22, 1022 (2008).ADS 

Google Scholar 
You, L.Z., et al. Spatial Production Allocation Model (SPAM) 2000 Version 3.2. http://mapspam.info (2015).Hoogenboom, G., et al. Decision Support System for Agrotechnology Transfer (DSSAT) Version 4.6 (DSSAT Foundation, 2015). http://dssat.net (2015).Sacks, W. J., Deryng, D., Foley, J. A. & Ramankutty, N. Crop planting dates: An analysis of global patterns. Glob. Ecol. Biogeogr. 19, 607–620 (2010).
Google Scholar 
Batjes, H.N. A Homogenized Soil Data File for Global Environmental Research: A Subset of FAO. ISRIC and NRCS Profiles (Version 1.0). Working Paper and Preprint 95/10b (International Soil Reference and Information Centre, 1995).Xiong, W. et al. Can climate-smart agriculture reverse the recent slowing of rice yield growth in China?. Agric. Ecosyst. Environ. 196, 125–136 (2014).
Google Scholar 
Hertel, T. W. Global Trade Analysis: Modeling and Applications 5–30 (Cambridge University Press, 1997).
Google Scholar 
Corong, E. L., Hertel, T. W., McDougall, R., Tsigas, M. E. & Mensbrugghe, D. V. The standard GTAP model, version 7. J. Glob. Econ. Anal. 2(1), 1–119 (2017).
Google Scholar 
Ciscar, J. C. et al. Physical and economic consequences of climate change in Europe. PNAS 108, 2678–2683 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hsiang, S. et al. Estimating economic damage from climate change in the United States. Science 356(6345), 1362–1369 (2017).ADS 
CAS 
PubMed 

Google Scholar 
Taheripour, F., Hertel, T. W. & Liu, J. The role of irrigation in determining the global land use impacts of biofuels. Energy Sustain. Soc. 3(1), 4 (2013).
Google Scholar 
Ali, T., Huang, J. K. & Yang, J. Impact assessment of global and national biofuels developments on agriculture in Pakistan. Appl. Energy 104, 466–474 (2013).
Google Scholar 
Yang, J., Huang, J. K., Qiu, H. G., Rozelle, S. & Sombilla, M. A. Biofuels and the greater Mekong Subregion: Assessing the impact on prices, production and trade. Appl. Energy 86, S37–S46 (2009).
Google Scholar 
Horridge, M. SplitCom, programs to disaggregate a GTAP sector (Centre of Policy Studies, Vitorial University). https://www.copsmodels.com/splitcom.htm (2005).Taylor, K. E., Stouffer, B. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).ADS 

Google Scholar 
Zhou, B. T., Wen, H. Q. Z., Xu, Y., Song, L. C. & Zhang, X. B. Projected changes in temperature and precipitation extremes in China by the CMIP5 multimodel ensembles. J. Clim. 27, 6591–6611 (2014).ADS 

Google Scholar 
Knutti, R., Rogelj, J., Sedláček, J. & Ficher, E. M. A scientific critique of the two-degree climate change target. Nat. Geosci. 9(1), 1–6 (2015).
Google Scholar 
Rogelj, J. et al. Energy system transformations for limiting end-of-century warming to below 1.5°C. Nat. Clim. Change 5(6), 519–527 (2015).ADS 

Google Scholar 
Friedlingstein, P. et al. Persistent growth of CO2 emissions and implications for reaching climate targets. Nat. Geosci. 7(10), 709–715 (2014).ADS 
CAS 

Google Scholar 
Azar, C., Johansson, D. J. A. & Mattsson, N. Meeting global temperature targets the role of bioenergy with carbon capture and storage. Environ. Res. Lett. 8(3), 1345–1346 (2013).
Google Scholar 
Liu, B. et al. Testing the responses of four wheat crop models to heat stress at anthesis and grain filling. Glob. Change Biol. 22, 1890–1903 (2016).ADS 

Google Scholar 
Elad, Y. & Pertot, I. Climate change impacts on plant pathogens and plant diseases. J. Crop Improv. 28, 99–139 (2014).CAS 

Google Scholar 
Challinora, A. J. et al. Improving the use of crop models for risk assessment and climate change adaptation. Agric. Syst. 159, 296–306 (2018).
Google Scholar 
Bassu, S. et al. How do various maize crop models vary in their responses to climate change factors?. Glob. Change Biol. 20, 2301–2320 (2014).ADS 

Google Scholar 
Wang, N. et al. Increased uncertainty in simulated maize phenology with more frequent supra-optimal temperature under climate warming. Eur. J. Agron. 71, 19–33 (2015).
Google Scholar 
Rosenzweig, C. et al. Assessing agricultural risks of climate change in the twenty-first century in a global gridded crop model intercomparison. PNAS 111, 3268–3273 (2014).ADS 
CAS 
PubMed 

Google Scholar  More

Sale, P. F. & Steneck, R. S. Critical Science Gaps Impede Use of No-take Fishery Reserves (University of Maine/University of New Hampshire Sea Grant College Program, 2005).Book 

Google Scholar 
Hilborn, R. & Walters, C. J. Quantitative Fisheries Stock Assessment: Choice, Dynamics and Uncertainty (Springer, 2013).
Google Scholar 
Hamilton, S. L., Regetz, J. & Warner, R. R. Postsettlement survival linked to larval life in a marine fish. Proc. Natl. Acad. Sci. 105, 1561–1566 (2008).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Raventos, N. & Macpherson, E. Effect of pelagic larval growth and size-at-hatching on post-settlement survivorship in two temperate labrid fish of the genus Symphodus. Mar. Ecol. Prog. Ser. 285, 205–211 (2005).ADS 
Article 

Google Scholar 
Johnson, D. W., Christie, M. R., Stallings, C. D., Pusack, T. J. & Hixon, M. A. Using post-settlement demography to estimate larval survivorship: A coral reef fish example. Oecologia 179, 729–739 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Garrido, S. et al. Born small, die young: Intrinsic, size-selective mortality in marine larval fish. Sci. Rep. 5, 17065 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Shima, J. S. et al. Reproductive phenology across the lunar cycle: Parental decisions, offspring responses, and consequences for reef fish. Ecology 101, e03086 (2020).PubMed 
Article 

Google Scholar 
Pini, J., Planes, S., Rochel, E., Lecchini, D. & Fauvelot, C. Genetic diversity loss associated to high mortality and environmental stress during the recruitment stage of a coral reef fish. Coral Reefs 30, 399–404 (2011).ADS 
Article 

Google Scholar 
Bourret, V., Dionne, M. & Bernatchez, L. Detecting genotypic changes associated with selective mortality at sea in Atlantic salmon: Polygenic multilocus analysis surpasses genome scan. Mol. Ecol. 23, 4444–4457 (2014).CAS 
PubMed 
Article 

Google Scholar 
Planes, S. & Lenfant, P. Temporal change in the genetic structure between and within cohorts of a marine fish, Diplodus sargus, induced by a large variance in individual reproductive success. Mol. Ecol. 11, 1515–1524 (2002).CAS 
PubMed 
Article 

Google Scholar 
Planes, S. & Romans, P. Evidence of genetic selection for growth in new recruits of a marine fish. Mol. Ecol. 13, 2049–2060 (2004).CAS 
PubMed 
Article 

Google Scholar 
Davidson, W. S. Adaptation genomics: Next generation sequencing reveals a shared haplotype for rapid early development in geographically and genetically distant populations of rainbow trout. Mol. Ecol. 21, 219–222 (2012).CAS 
PubMed 
Article 

Google Scholar 
Carreras, C. et al. East is east and west is west: Population genomics and hierarchical analyses reveal genetic structure and adaptation footprints in the keystone species Paracentrotus lividus (Echinoidea). Divers. Distrib. 26, 382–398 (2020).Article 

Google Scholar 
Carreras, C. et al. Population genomics of an endemic Mediterranean fish: Differentiation by fine scale dispersal and adaptation. Sci. Rep. 7, 43417 (2017).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Babbucci, M. et al. An integrated genomic approach for the study of mandibular prognathism in the European seabass (Dicentrarchus labrax). Sci. Rep. 6, 38673 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Barbanti, A. et al. Helping decision making for reliable and cost-effective 2b-RAD sequencing and genotyping analyses in non-model species. Mol. Ecol. Resour. 20, 795–806 (2020).CAS 
Article 

Google Scholar 
Torrado, H., Carreras, C., Raventos, N., Macpherson, E. & Pascual, M. Individual-based population genomics reveal different drivers of adaptation in sympatric fish. Sci. Rep. 10, 12683 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Xuereb, A. et al. Asymmetric oceanographic processes mediate connectivity and population genetic structure, as revealed by RADseq, in a highly dispersive marine invertebrate (Parastichopus californicus). Mol. Ecol. 27, 2347–2364 (2018).PubMed 
Article 

Google Scholar 
Benestan, L. et al. Seascape genomics provides evidence for thermal adaptation and current-mediated population structure in American lobster (Homarus americanus). Mol. Ecol. 25, 5073–5092 (2016).PubMed 
Article 

Google Scholar 
Lu, F. et al. Switchgrass genomic diversity, ploidy, and evolution: Novel insights from a network-based SNP discovery protocol. PLoS Genet. 9, e1003215 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wang, S., Meyer, E., McKay, J. K. & Matz, M. V. 2b-RAD: A simple and flexible method for genome-wide genotyping. Nat. Methods 9, 808–810 (2012).CAS 
PubMed 
Article 

Google Scholar 
Raventos, N. & Macpherson, E. Planktonic larval duration and settlement marks on the otoliths of Mediterranean littoral fishes. Mar. Biol. 138, 1115–1120 (2001).Article 

Google Scholar 
Torrado, H. et al. Impact of individual early life traits in larval dispersal: A multispecies approach using backtracking models. Prog. Oceanogr. 192, 102518 (2021).Article 

Google Scholar 
Schunter, C. et al. A novel integrative approach elucidates fine-scale dispersal patchiness in marine populations. Sci. Rep. 9, 10796 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hixon, M. A. & Carr, M. H. Synergistic predation, density dependence, and population regulation in marine fish. Science 277, 946–949 (1997).CAS 
Article 

Google Scholar 
Macpherson, E. et al. Mortality of juvenile fishes of the genus Diplodus in protected and unprotected areas in the western Mediterranean Sea. Mar. Ecol. Prog. Ser. 160, 135–147 (1997).ADS 
Article 

Google Scholar 
Macpherson, E. Ontogenetic shifts in habitat use and aggregation in juvenile sparid fishes. J. Exp. Mar. Biol. Ecol. 220, 127–150 (1998).Article 

Google Scholar 
Eckert, G. J. Estimates of adult and juvenile mortality for labrid fishes at One Tree Reef, Great Barrier Reef. Mar. Biol. 95, 167–171 (1987).Article 

Google Scholar 
Pascual, M., Rives, B., Schunter, C. & Macpherson, E. Impact of life history traits on gene flow: A multispecies systematic review across oceanographic barriers in the Mediterranean Sea. PLoS ONE 12, e0176419 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Schunter, C. et al. Matching genetics with oceanography: Directional gene flow in a Mediterranean fish species. Mol. Ecol. 20, 5167–5181 (2011).CAS 
PubMed 
Article 

Google Scholar 
Ciotti, B. J. & Planes, S. Within-generation consequences of postsettlement mortality for trait composition in wild populations: An experimental test. Ecol. Evol. 9, 2550–2561 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Yoklavich, M. M. & Bailey, K. M. Hatching period, growth and survival of young walleye pollock Theragra chalcogramma as determined from otolith analysis. Mar. Ecol. Prog. Ser. 64, 13–23 (1990).ADS 
Article 

Google Scholar 
Cargnelli, L. M. & Gross, M. R. The temporal dimension in fish recruitment: Birth date, body size, and size-dependent survival in a sunfish (bluegill: Lepomis macrochirus). Can. J. Fish. Aquat. Sci. 53, 360–367 (1996).Article 

Google Scholar 
Moginie, B. F. & Shima, J. S. Hatch date and growth rate drives reproductive success in nest-guarding males of a temperate reef fish. Mar. Ecol. Prog. Ser. 592, 197–206 (2018).ADS 
Article 

Google Scholar 
Sponaugle, S., Boulay, J. N. & Rankin, T. L. Growth- and size-selective mortality in pelagic­larvae of a common reef fish. Aquat. Biol. 13, 263–273 (2011).Article 

Google Scholar 
Biro, P. A., Abrahams, M. V., Post, J. R. & Parkinson, E. A. Behavioural trade-offs between growth and mortality explain evolution of submaximal growth rates. J. Anim. Ecol. 75, 1165–1171 (2006).PubMed 
Article 

Google Scholar 
Litvak, M. K. & Leggett, W. C. Age and size-selective predation on larval fishes: the bigger-is-better hypothesis revisited. Mar. Ecol. Prog. Ser. 81, 13–24 (1992).ADS 
Article 

Google Scholar 
D’Alessandro, E. K., Sponaugle, S. & Cowen, R. K. Selective mortality during the larval and juvenile stages of snappers (Lutjanidae) and great barracuda Sphyraena barracuda. Mar. Ecol. Prog. Ser. 474, 227–242 (2013).ADS 
Article 

Google Scholar 
Meekan, M. G. et al. Bigger is better: Size-selective mortality throughout the life history of a fast-growing clupeid, Spratelloides gracilis. Mar. Ecol. Progress Ser. 317, 237–244 (2006).ADS 
Article 

Google Scholar 
Takasuka, A., Aoki, I. & Mitani, I. Evidence of growth-selective predation on larval Japanese anchovy Engraulis japonicus in Sagami Bay. Mar. Ecol. Prog. Ser. 252, 223–238 (2003).ADS 
Article 

Google Scholar 
Sanford, E. & Kelly, M. W. Local adaptation in marine invertebrates. Ann. Rev. Mar. Sci. 3, 509–535 (2011).PubMed 
Article 

Google Scholar 
Raventos, N., Torrado, H., Arthur, R., Alcoverro, T. & Macpherson, E. Temperature reduces fish dispersal as larvae grow faster to their settlement size. J. Anim. Ecol. 90, 1419–1432 (2021).PubMed 
Article 

Google Scholar 
Logsdon, N. J., Deshpande, A., Harris, B. D., Rajashankar, K. R. & Walter, M. R. Structural basis for receptor sharing and activation by interleukin-20 receptor-2 (IL-20R2) binding cytokines. Proc. Natl. Acad. Sci. 109, 12704–12709 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Eldon, B., Riquet, F., Yearsley, J., Jollivet, D. & Broquet, T. Current hypotheses to explain genetic chaos under the sea. Curr. Zool. 62, 551–566 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Macpherson, E., Gordoa, A. & Garcia-Rubies, A. Biomass size spectra in littoral fishes in protected and unprotected areas in the NW Mediterranean. Estuarine Coast. Shelf Sci. 55, 777–788 (2002).ADS 
Article 

Google Scholar 
Garcia-Rubies, A. & Zabala I Limousin, M. Effects of total fishing prohibition on the rocky fish assemblages of Medes Islands marine reserve (NW Mediterranean). Sci. Mar. 54(4), 317–328 (1990).
Google Scholar 
Vigliola, L. et al. Spatial and temporal patterns of settlement among sparid fishes of the genus Diplodus in the northwestern Mediterranean. Mar. Ecol. Prog. Ser. 168, 45–56 (1998).ADS 
Article 

Google Scholar 
Dixon, P. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14, 927–930 (2003).Article 

Google Scholar 
Catchen, J., Hohenlohe, P. A., Bassham, S., Amores, A. & Cresko, W. A. Stacks: An analysis tool set for population genomics. Mol. Ecol. 22, 3124–3140 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Goudet, J. hierfstat, a package for r to compute and test hierarchical F-statistics. Mol. Ecol. Notes 5, 184–186 (2005).Article 

Google Scholar 
Jombart, T. adegenet: A R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).CAS 
PubMed 
Article 

Google Scholar 
Wickham, H. ggplot2. (2009). https://doi.org/10.1007/978-0-387-98141-3.Forester, B. R., Lasky, J. R., Wagner, H. H. & Urban, D. L. Comparing methods for detecting multilocus adaptation with multivariate genotype-environment associations. Mol. Ecol. 27, 2215–2233 (2018).CAS 
PubMed 
Article 

Google Scholar 
Natsidis, P., Tsakogiannis, A., Pavlidis, P., Tsigenopoulos, C. S. & Manousaki, T. Phylogenomics investigation of sparids (Teleostei: Spariformes) using high-quality proteomes highlights the importance of taxon sampling. Commun. Biol. 2, 400 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Huerta-Cepas, J. et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol. Biol. Evol. 34, 2115–2122 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Al-Shahrour, F. et al. FatiGO: A functional profiling tool for genomic data: Integration of functional annotation, regulatory motifs and interaction data with microarray experiments. Nucleic Acids Res. 35, W91–W96 (2007).PubMed 
PubMed Central 
Article 

Google Scholar 
Supek, F., Bošnjak, M., Škunca, N. & Šmuc, T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS ONE 6, e21800 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wang, M., Zhao, Y. & Zhang, B. Efficient test and visualization of multi-set intersections. Sci. Rep. 5, 16923 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

The pathosystemPlantago lanceolata L. is a perennial monoecious ribwort plantain that reproduces both clonally via the production side rosettes, and sexually via wind pollination. Seeds drop close to the mother plant and usually form a long-term seed bank47. Podospharea plantaginis (Castagne; U. Braun and S. Takamatsu) (Erysiphales, Ascomycota) is an obligate biotrophic powdery mildew that infects only P. lanceolata and requires living host tissue through its life cycle48. It completes its life cycle as localized lesions on host leaves, only the haustorial feeding roots penetrating the leaf tissue to feed nutrients from its host. Infection causes significant stress for host plant and may increase the host mortality31. The interaction between P. lanceolata and P. plantaginis is strain-specific, whereby the same host genotype may be susceptible to some pathogen genotypes while being resistant to others49. The putative resistance mechanism includes two steps. First, resistance occurs when the host plant first recognizes the attacking pathogen and blocks its growth. When the first step fails and infection takes place, the host may mitigate infection development. Both resistance traits vary among host genotypes49.Approximately 4000 P. lanceolata populations form a network covering an area of 50 × 70 km in the Åland Islands, SW of Finland. Disease incidence (0/1) in these populations has been recorded systematically every year in early September since 2001 by approximately 40 field assistants, who record the occurrence of the fungus P. plantaginis in the local P. lanceolata populations30. At this time, disease symptoms are conspicuous as infected plants are covered by white mycelia and conidia. The coverage (m2) of P. lanceolata in the meadows was recorded between 2001 and 2008 and is used as an estimate of host population size. In the field survey two technicians estimate Plantago population size by visually estimating how much ground/other vegetation P. lanceolata foliage covers (m2) in each meadow. The proportion of P. lanceolata plants in each population suffering from drought is also estimated annually in the survey. Data on average rainfall (mm) in July and August was estimated separately for each population using detailed radar-measured rainfall (obtained by Finnish Meteorological Institute) and it was available for years 2001–2008.Host population connectivity (SH)27 for each local population i was computed with the formula that takes into account the area of host coverage (m²) of all host populations surveyed, denoted with (Aj), and their spatial location compared to other host populations. We assume that the distribution of dispersal distances from a location are described by negative exponential distribution. Under this assumption, the following formula (1) quantifies for a focal population i, the effect of all other host populations, taking into account their population sizes and how strongly they are connected through immigration to it:$${S}_{i}^{H}=mathop{sum}limits_{jne i}{{{{{rm{e}}}}}}^{{-alpha d}_{{ij}}}sqrt{{A}_{j}}.$$
(1)
here, dij is the Euclidian distance between populations i and j and 1/α equals the mean dispersal distance, which was set to be two kilometres based on results from a previous study16.The annual survey data has demonstrated that P. plantaginis infects annually 2–16% of all host populations and persists as a highly dynamic metapopulation through extinctions and re-colonizations of local populations16. The number of host populations has remained relatively stable over the study period49. The first visible symptoms of P. plantaginis infection appear in late June as white-greyish lesions consisting of mycelium supporting the dispersal spores (conidia) that are carried by wind to the same or new host plants. Six to eight clonally produced generations follow one another in rapid succession, often leading to local epidemic with substantial proportion of the infected hosts by late summer within the host local population. Podosphaera plantaginis produces resting structures, chasmothecia, that appear towards the end of growing season in August–September31. Between 20% and 90% of the local pathogen populations go extinct during the winter, and thus the recolonization events play an important role in the persistence of the pathogen regionally16.Inoculation assay: Effect of connectivity and disease history on phenotypic disease resistanceHost and pathogen material for the experimentTo examine whether the diversity and level of resistance vary among host populations depending on their degree of connectivity (SH) and disease history, we selected 20 P. lanceolata populations for an inoculation assay. These populations occur in different locations in the host network, and were selected based on their connectivity values (S H of selected populations was 37–110 in isolated and 237–336 in highly connected category, Fig. 1). We did not include host populations in the intermediate connectivity category that was used in the population dynamic analyses in the inoculation assay due to logistic constraints. Podosphaera plantaginis is an obligate biotrophic pathogen that requires living host tissue throughout its life cycle, and obtaining sufficient inoculum for experiments is extremely time and space consuming. In both isolated and highly connected categories, half of the populations (IDs 193, 260, 311, 313, 337, 507, 1821, 1999, 2818 and 5206) were healthy during the study years 2001–2014, while half of the populations (IDs 271, 294, 309, 321, 490, 609, 1553, 1556, 1676 and 1847) were infected by P. plantaginis for several years during the same period. We collected P. lanceolata seeds from randomly selected ten individual plants around the patch area from each host population in August 2014.To acquire inoculum for the assay, we collected the pathogen strains as infected leaves, one leaf from ten plant individuals from four additional host populations (IDs 3301, 4684, 1784, and 3108) in August 2014. None of the pathogen populations were same as the sampled host populations and hence, the strains used in the assay all represent allopatric combinations. Both host and pathogen populations selected for the study were separated by at least two kilometres. The collected leaves supporting infection were placed in Petri dishes on moist filter paper and stored at room temperature until later use.Seeds from ten mother plants from each population were sown in 2:1 mixture of potting soil and sand, and grown in greenhouse conditions at 20 ± 2 °C (day) and 16 ± 2 °C (night) with 16:8 L:D photoperiod. Due to the low germination rate of collected seeds, population 260 (isolated and healthy population) was excluded from the study. Seedlings of ten different mother plants were randomly selected among the germinated plants for each population (n = 190), and grown in individual pots until the plants were eight weeks old.The pathogen strains were purified through three cycles of single colony inoculations and maintained on live, susceptible leaves on Petri dishes in a growth chamber 20 ± 2 °C with 16:8 L:D photoperiod. Every two weeks, the strains were transferred to fresh P. lanceolata leaves. Purified powdery mildew strains (M1–M4), one representing each allopatric population (3301, 4684, 1784 and 3108), were used for the inoculation assay. To produce enough sporulating fungal material, repeated cycles of inoculations were performed before the assay.Inoculation assay quantifying host resistance phenotypesIn order to study how the phenotypic resistance of hosts varies depending on population connectivity and infection history, we scored the resistance of 190 host genotypes, ten individuals from each study populations (n = 19), in an inoculation assay. Here, one detached leaf from each plant was exposed to a single pathogen strain (M1–M4) by brushing spores gently with a fine paintbrush onto the leaf. Leaves were placed on moist filter paper in Petri dishes and kept in a growth chamber at 20 ± 2 with a 16/8D photoperiod. All the inoculations were repeated on two individual Petri plates, leading to 760 host genotype—pathogen genotype combinations and a total of 1520 inoculations (19 populations * 10 plant genotypes * 4 pathogen strains * 2 replicates). We then observed and scored the pathogen infection on day 12 post inoculation, under dissecting microscope. The resulting plant phenotypic response was scored as 0 = susceptible (infection) when mycelium and conidia were observed on the leaf surface, and as 1 = resistance (no infection), when no developing lesions could be detected under a dissecting microscope. A genotype was defined resistant only if both inoculated replicates showed similar response (1), and susceptible if one or both replicates became infected (0).Statistical analysesBayesian spatio-temporal INLA model of the changes in host population sizeTo study how the pathogen infection influences on host population growth, we analyzed the relative change in host population size (m2) (defined as population size (t) − population size (t−1))/population size (t−1)) between consecutive years utilizing data from 2001 to 2008 in response to pathogen presence-absence status at t−1 (Supplementary Table 2). To assess whether this depends on host population connectivity, we estimated the separate effects of pathogen presence/absence in the previous year for connectivity categories—high-, low, and intermediate—that were based on the 0.2 and 0.8 quantiles of the host-connectivity values (Fig. 1A and Supplementary Figs. 1, 2). This allowed us to directly assess and compare the effect of the pathogen on host population growth in the extreme categories between isolated and highly connected host populations which were represented in the sampling for the inoculation study (Fig. 2).As covariates, we included the proportion (0–100%) of dry host plants measured each year within each local population as well as data on the amount of rainfall at the summer months (June, July, and August) obtained from the satellite images, as these were suggested be relevant for this pathosystem in an earlier analysis16. Observations where the change in host population size, or the host population coverage had absolute values larger than their 0.99 quantiles in the whole data, were regarded as outliers and omitted from the analysis. Before the analyses, all the continuous covariates were scaled and centred, and the categorical variables were transformed into binary variables.The relative changes in local host population size between consecutive years was analyzed by a Bayesian spatio-temporal statistical model that simultaneously considers the effects of a set of biologically meaningful predictors. The linear predictor thus consists of two parts (2,3):$$beta {X}_{t}+{z}_{t}{A}_{t}$$
(2)
where (beta) represents the correlation coefficients corresponding to the effects of environmental covariates, ({z}_{t}) corresponds to the spatiotemporal random effect, and ({X}_{t}) and ({A}_{t}) project these to the observation locations. For ({z}_{t}) we assume that the observations from a location in consecutive time points (t−1) and t are described by 1st order autoregressive process:$${z}_{t}=varphi {z}_{t-1}+{w}_{t}$$
(3)
where ({w}_{t}) corresponds to spatially structured zero-mean random noise, for which a Matern covariance function is assumed. Statistical inference then targets jointly the covariate effects (beta), the temporal autocorrelation (varphi), and the hyperparameters describing the spatial autocorrelation in wt. From these the overall variance, as well as spatial range—a distance after which spatial autocorrelation ceases to be significant—can be inferred (Supplementary Fig. 3). For more detailed description of the structure of the statistical model and how to do efficient inference with it using R-INLA, we refer to refs. 16,50.Identification of resistance phenotypesThe phenotype composition of each study population was defined by individual plant responses to the four pathogen strains, where each response could be “susceptible = 0” or “resistant = 1”. For example, a phenotype “1111” refers to a plant resistant to all four pathogen strains. The diversity of distinct resistance phenotypes within populations was estimated using the Shannon diversity index as implemented in the vegan software package51. The Shannon diversity index for all four study groups was then analyzed using a linear model with class predictors population type (well-connected or isolated), infection history (healthy or infected), and their interaction.Analysis of population connectivity and infection history effects on host resistanceTo test whether host population resistance varied depending on connectivity (SH) and infection history, we analyzed the inoculation responses (0 = susceptible, 1=resistant) of each host-pathogen combination by using a logit mixed-effect model in the lme4 package52. The model included the binomial dependent variable (resistance-susceptible; 1/0), and class predictors population type (well-connected or isolated), infection history (healthy or infected), mildew strain (M1, M2, M3, and M4) and their interactions. Plant individual and population were defined as random effects, with plant genotype (sample) hierarchically nested under population. Model fit was assessed using chi-square tests on the log-likelihood values to compare different models and significant interactions, and the best model was selected based on AIC-values. P-values for regression coefficients were obtained by using the car package53. We ran all the analyses in R software54.The metapopulation modelWe model the ecological and co-evolutionary dynamics of host and pathogen metapopulations to understand key features of the experimental system that impact on the qualitative patterns observed. The structure and parameters in our model are therefore not estimated using experimental data, but rather are chosen to cover a range of possibilities (e.g., low vs high transmission rates, variation in trade-off shapes for fitness costs). We construct the metapopulations in two stages to account for relatively well and poorly connected demes. All demes are identical in quality (i.e., no differences in intrinsic birth or death rates between demes) and only differ in their connectivity. Our metapopulation consists of an outer network of 20 demes, equally spaced around the unit square (0.2 units apart), and a 7×7 inner lattice of demes at a minimum distance of 0.2 units from the outer network (Fig. 3A), giving a total of 69 demes. Demes that are separated by a Euclidean distance of at most 0.2 are then connected to each other. This means that populations near the centre of the metapopulation are highly connected, while those on the boundary of the metapopulation are poorly connected. This also has the effect of making connections between well and poorly connected demes assortative (i.e., well/poorly connected demes tend to be connected to well/poorly connected demes). We relax the assumption of assortativity in a second type of network by randomly reassigning connections between demes, while maintaining the same degree distribution. (i.e., the probability of two demes being connected is proportionate to their degree). While well connected demes still have more connections to other well connected demes than to poorly connected demes, they are not more likely to be connected to a well connected deme than by chance based on the degree distribution. In both types of network structure, we classify a deme as well-connected if it is in the top 20% of the degree distribution and poorly connected if it is in the bottom 20%.We model the genetics using a multilocus gene-for-gene framework with haploid host and pathogen genotypes characterized by (L) biallelic loci, where 0 and 1 represent the presence and absence, respectively, of resistance and infectivity alleles. Host genotype (i) and pathogen genotype (j) are represented by binary strings: ({x}_{i}^{1}{x}_{i}^{2}ldots {x}_{i}^{L}) and ({y}_{j}^{1}{y}_{j}^{2}ldots {y}_{j}^{L}). Resistance acts multiplicatively such that the probability of host (i) being infected when challenged by pathogen (j) is ({Q}_{{ij}}={sigma }^{{d}_{{ij}}}), where (sigma) is the reduction in infectivity per effective resistance allele and ({d}_{{ij}}={sum }_{k=1}^{L}{x}_{i}^{k}big(1-{y}_{j}^{k}big)) is the number of effective resistance alleles (i.e., the number of loci where hosts have a resistance allele but pathogens do not have a corresponding infectivity allele). Hosts and pathogens with more resistance or infectivity alleles are assumed to pay higher fitness costs, ({c}_{H}left(iright)) eq. (4) and ({c}_{P}left(jright)) eq. (5) with:$${c}_{H}left(iright)={c}_{H}^{1}left(frac{1-{{{{{rm{e}}}}}}^{frac{{c}_{H}^{2}}{L}{sum }_{k=1}^{L}{x}_{i}^{k}}}{1-{{{{{rm{e}}}}}}^{{c}_{H}^{2}}}right)$$
(4)
and$${c}_{P}left(jright)={c}_{P}^{1}left(frac{1-{{{{{rm{e}}}}}}^{frac{{c}_{P}^{2}}{L}{sum }_{k=1}^{L}{y}_{j}^{k}}}{1-{{{{{rm{e}}}}}}^{{c}_{P}^{2}}}right)$$
(5)
where (0 , < , {c}_{H}^{1},; {c}_{P}^{1},le, 1) control the overall strength of the costs (i.e., the maximum proportional reduction in reproduction (hosts) or transmission rate (pathogens)) and ({c}_{H}^{2},; {c}_{P}^{2}in {{mathbb{R}}}_{ne 0}) control the shape of the trade-off. When ({c}_{H}^{2},; {c}_{P}^{2}, < , 0) the costs decelerate (increasing returns) and when ({c}_{H}^{2},; {c}_{P}^{2}, > , 0) the costs accelerate the costs accelerate (decreasing returns) (Supplementary Fig. 4). This formulation, therefore, allows for a wide-range of trade-off shapes that may occur in nature.The dynamics of the (finite) host and pathogen populations are modelled stochastically using the tau-leap method with a fixed step size of (tau=1). For population (p), the mean host birth rate at time (t) for host (i) (6) is$${B}_{i}^{p}left(tright)=left(aleft(1-{c}_{H}left(iright)right)-q{N}_{p}left(tright)right){S}_{i}^{p}left(tright)$$
(6)
where (a) is the maximum per-capita birth rate, (q) is the strength of density-dependent competition on births, ({N}_{p}left(tright)={S}_{i}^{p}left(tright)+{I}_{icirc }^{p}left(tright)) is the local host population size, ({S}_{i}^{p}left(tright)) and ({I}_{icirc }^{p}left(tright)={sum }_{j=1}^{n}{I}_{{ij}}^{p}left(tright)) are the local sizes of susceptible and infected individuals of genotype (i), and ({I}_{{ij}}^{p}left(tright)) is the local size of hosts of genotype (i) infected by pathogen (j). Host mutations occur at an average rate of ({mu }_{H}) per loci (limited to at most one mutation per time step), so that the mean number of mutations from host type (i) to ({i}^{{prime} }) is ({mu }_{H}{m}_{i{i}^{{prime} }}{B}_{i}^{p}left(tright)), where ({m}_{i{i}^{{prime} }}=1) if genotypes (i) and ({i}^{{prime} }) differ at exactly one locus, and is 0 otherwise.The mean local mortalities for susceptible and infected individuals are (b{S}_{i}^{p}left(tright)) and (left(b+alpha right){I}_{{ij}}^{p}left(tright)), respectively, where (b) is the natural mortality rate and (alpha) is the disease-associated mortality rate. The average number of infected hosts that recover is (gamma {I}_{{ij}}^{p}left(tright)), where (gamma) is the recovery rate.The mean number of new local infections of susceptible host type (i) by pathogen (j) eq. (7) is:$${INF}_{{ij}}^{p}left(tright)=beta left(1-{c}_{P}left(jright)right){Q}_{{ij}}{S}_{i}^{p}left(tright){Y}_{j}^{p}left(tright)$$
(7)
where (beta) is the baseline transmission rate and ({Y}_{j}^{p}left(tright)) is the local number of pathogen propagules following mutation and dispersal. Pathogen mutations occur in a similar manner to host mutations, with mutations from type (j) to ({j}^{{prime} }) occurring at rate ({mu }_{P}{m}_{j{j}^{{prime} }}{I}_{circ j}^{p}left(tright)) where ({mu }_{P}) is the mutation rate per loci (limited to at most one mutation per timestep) and ({I}_{circ j}^{p}left(tright)={sum }_{i=1}^{n}{I}_{{ij}}^{p}left(tright)) is the local number of pathogen (j.) Following mutation, the local number of pathogens of type (j) eq. (8) is:$${W}_{j}^{p}left(tright)={I}_{circ j}^{p}left(tright)left(1-{mu }_{P}Lright)+{mu }_{P}{m}_{j{j}^{{prime} }}{I}_{circ j}^{p}left(tright)$$
(8)
Pathogen dispersal occurs following mutation at a rate of (rho) between connected demes, given by the adjacency matrix ({G}_{{pr}}), with ({G}_{varSigma p}) the total number of connections for deme (p). The mean local number of pathogen propagules following mutation and dispersal eq. (9) is therefore:$${Y}_{j}^{p}left(tright)={W}_{j}^{p}left(tright)left(1-rho {G}_{varSigma p}right)+rho mathop {sum }limits_{r=1}^{{M}_{varSigma }}{G}_{{pr}}{W}_{j}^{r}left(tright)$$
(9)
We focus our parameter sweep on: (i) the structure of the network (assortative or random connections); (ii) the strength (left({c}_{H}^{1},; {c}_{P}^{1}right)) and shape (left({c}_{H}^{2},; {c}_{P}^{2}right)) of the trade-offs; (iii) the transmission rate (left(beta right)); and (iv) the dispersal rate (left(rho right)), fixing the remaining parameters as described in Supplementary Table 1 (preliminary investigations suggested they had less of an impact on the qualitative outcome) and conducting 100 simulations per parameter set. For each simulation we initially seed all populations with the most susceptible host type and place the least infective pathogen type in one of the well-connected populations to minimize the risk of early extinction. We then solve the dynamics for 10,000 time steps (preliminary investigations indicated this was a sufficient period for the metapopulations to reach a quasi-equilibrium in terms of overall resistance). We calculate the average level of resistance (proportion of loci with a resistance allele) between time steps 4001 and 5000 (transient dynamics) and over the final 1000 time steps (long-term dynamics) for well and poorly connected demes, categorized according to whether the disease is present in (infected) or absent from (uninfected) the local population at a given time point and discarding simulations where the pathogen is driven globally extinct.We compare the mean level of resistance in infected/uninfected poorly/well-connected populations across all simulations to the empirical results. We say that a simulation is a qualitative ‘match’ for the empirical findings if: (i) in poorly connected demes, the infected populations are on average at least 5% more resistant than uninfected populations; and (ii) in well-connected demes, the uninfected populations are on average at least 5% more resistant than infected populations. In other words, if ({R}_{{CS}}) is the mean resistance for a population with connectivity (C) ((C=W) and (C=P) for well and poorly connected demes, respectively) and infection status (S) ((S=U) and (S=I) for uninfected and infected populations, respectively), then a parameter set is a qualitative ‘match’ for the empirical findings if ({R}_{{WU}} > 1.05{R}_{{WI}}) and (1.05{R}_{{PI}}, > , 1.05{R}_{{PU}}). If these criteria are not met, then the parameter set is a qualitative ‘mismatch’ for the empirical findings. The model is not intended to be a replica of an empirical metapopulation, but rather is used to reveal the key factors which lead to qualitatively similar distributions of resistance and disease incidences observed in the study of the Åland islands. Hence, the purpose of the model is to determine which biological factors are likely to be crucial to the patterns observed herein.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Birks, H. J. B. Strengths and weaknesses of quantitative climate reconstructions based on late-quaternary biological proxies. Open Ecol. J. 3, 68–110 (2011).Article 

Google Scholar 
Chevalier, M. et al. Pollen-based climate reconstruction techniques for late Quaternary studies. Earth-Sci. Rev. 210, 103384 (2020).Article 

Google Scholar 
Bartlein, P. J. et al. Pollen-based continental climate reconstructions at 6 and 21 ka: a global synthesis. Clim. Dyn. 37, 775–802 (2011).Article 

Google Scholar 
Brierley, C. M. et al. Large-scale features and evaluation of the PMIP4-CMIP6 midHolocene simulations. Clim. Past 16, 1847–1872 (2020).Kageyama, M. et al. The PMIP4 Last Glacial Maximum experiments: preliminary results and comparison with the PMIP3 simulations. Clim. Past 17, 1065–1089 (2021).Article 

Google Scholar 
Harrison, S. BIOME 6000 DB classified plotfile version 1. https://doi.org/10.17864/1947.99. (2017).Loarie, S. R. et al. The velocity of climate change. Nature 462, 1052–1055 (2009).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Svenning, J. C. & Sandel, B. Disequilibrium vegetation dynamics under future climate change. Am. J. Bot. 100, 1266–1286 (2013).PubMed 
Article 

Google Scholar 
Neilson, R. P. et al. Forecasting regional to global plant migration in response to climate change. BioScience 55 https://academic.oup.com/bioscience/article/55/9/749/285963 (2005).Normand, S. et al. Postglacial migration supplements climate in determining plant species ranges in Europe. Proc. R. Soc. B: Biol. Sci. 278, 3644–3653 (2011).Article 

Google Scholar 
Seltzer, A. M. et al. Widespread six degrees Celsius cooling on land during the Last Glacial Maximum. Nature 593, 228–232 (2021).Tierney, J. E. et al. Glacial cooling and climate sensitivity revisited. Nature 584, 569 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Ray, N. & Adams, J. M. A GIS-based Vegetation Map of the World at the Last Glacial Maximum (25,000-15,000 BP). Internet Archaeol. 11, https://doi.org/10.11141/ia.11.2 (2001).Birks, H. J. B. & Willis, K. J. Alpines, trees, and refugia in Europe. Plant Ecol. Divers. 1, 147–160 (2008).Article 

Google Scholar 
Roberts, D. R. & Hamann, A. Glacial refugia and modern genetic diversity of 22 western North American tree species. Proc. R. Soc. B: Biol. Sci. 282, 20142903 (2015).Clark, J. S. Why trees migrate so fast: Confronting theory with dispersal biology and the paleorecord. Am. Nat. 152, 204–224 (1998).CAS 
PubMed 
Article 

Google Scholar 
Jackson, S. & Overpeck, J. Responses of plant populations and communities to environmental changes of the late Quaternary. Paleobiology 26, 194–220 (2000).Article 

Google Scholar 
Williams, J. W., Ordonez, A. & Svenning, J.-C. A unifying framework for studying and managing climate-driven rates of ecological change. Nat. Ecol. Evol. 5, 17–26 (2021).Harrison, S. P. & Goñi, M. F. S. Global patterns of vegetation response to millennial-scale variability and rapid climate change during the last glacial period. Quat. Sci. Rev. 29, 2957–2980 (2010).ADS 
Article 

Google Scholar 
Williams, J. W., Post, D. M., Cwynar, L. C., Lotter, A. F. & Levesque, A. J. Rapid and widespread vegetation responses to past climate change in the North Atlantic region. Geology 30, 971–974 (2002).ADS 
CAS 
Article 

Google Scholar 
Giesecke, T., Brewer, S., Finsinger, W., Leydet, M. & Bradshaw, R. H. W. Patterns and dynamics of European vegetation change over the last 15,000 years. J. Biogeogr. 44, 1441–1456 (2017).Article 

Google Scholar 
Ordonez, A. & Williams, J. W. Climatic and biotic velocities for woody taxa distributions over the last 16 000 years in eastern North America. Ecol. Lett. 16, 773–781 (2013).PubMed 
Article 

Google Scholar 
Svenning, J.-C. & Skov, F. Limited filling of the potential range in European tree species. Ecol. Lett. 7, 565–573 (2004).Article 

Google Scholar 
Talluto, M. V., Boulangeat, I., Vissault, S., Thuiller, W. & Gravel, D. Extinction debt and colonization credit delay range shifts of eastern North American trees. Nat. Ecol. Evol. 1, 1–6 (2017).Article 

Google Scholar 
Pearson, R. G. & Dawson, T. P. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Glob. Ecol. Biogeogr. 12, 361–371 (2003).Article 

Google Scholar 
Webb, T. Is vegetation in equilibrium with climate? How to interpret late-Quaternary pollen data. Vegetatio 67, 75–91 (1986).Article 

Google Scholar 
Jackson, S. T. & Williams, J. W. Modern analogs in quaternary paleoecology: Here today, gone yesterday, gone tomorrow? Annu. Rev. Earth Planet. Sci. 32, 495–537 (2004).ADS 
CAS 
Article 

Google Scholar 
Cao, X., Tian, F., Dallmeyer, A. & Herzschuh, U. Northern Hemisphere biome changes ( >30°N) since 40 cal ka BP and their driving factors inferred from model-data comparisons. Quat. Sci. Rev. 220, 291–309 (2019).ADS 
Article 

Google Scholar 
He, F. Simulating transient climate evolution of the last deglaciation with CCSM3 Dissertation at the University of Wisconsin – Madison (2011).Osman, M. B. et al. Globally resolved surface temperatures since the Last Glacial Maximum. Nature 599, 239–244 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Shakun, J. D. et al. Global warming preceded by increasing carbon dioxide concentrations during the last deglaciation. Nature 484, 49–54 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Alley, R. B. The Younger Dryas cold interval as viewed from central Greenland. in Quaternary Science Reviews vol. 19 213–226 (Pergamon, 2000).He, C. et al. Hydroclimate footprint of pan-Asian monsoon water isotope during the last deglaciation. Sci. Adv. 7, eabe2611 (2021).ADS 
PubMed 
Article 

Google Scholar 
Reick, C. H., Raddatz, T., Brovkin, V. & Gayler, V. Representation of natural and anthropogenic land cover change in MPI-ESM. J. Adv. Modeling Earth Syst. 5, 459–482 (2013).Prentice, I. C., Guiot, J., Huntley, B., Jolly, D. & Cheddadi, R. Reconstructing biomes from palaeoecological data: a general method and its application to European pollen data at 0 and 6 ka. Clim. Dyn. 12, 185–194 (1996).Article 

Google Scholar 
Dallmeyer, A., Claussen, M. & Brovkin, V. Harmonising plant functional type distributions for evaluating Earth system models. Clim 15, 335–366 (2019).
Google Scholar 
Ni, J., Cao, X., Jeltsch, F. & Herzschuh, U. Biome distribution over the last 22,000 yr in China. Palaeogeogr. Palaeoclimatol. Palaeoecol. 409, 33–47 (2014).Article 

Google Scholar 
Williams, J. W. & Jackson, S. T. Novel climates, no-analog communities, and ecological surprises. Front. Ecol. Environ. 5, 475–482 (2007).Article 

Google Scholar 
Sobol, M. K., Scott, L. & Finkelstein, S. A. Reconstructing past biomes states using machine learning and modern pollen assemblages: a case study from Southern Africa. Quat. Sci. Rev. 212, 1–17 (2019).ADS 
Article 

Google Scholar 
Marinova, E. et al. Pollen‐derived biomes in the Eastern Mediterranean–Black Sea–Caspian‐Corridor. J. Biogeogr. 45, 484–499 (2018).Article 

Google Scholar 
Cao, X. et al. Pollen-based quantitative land-cover reconstruction for northern Asia covering the last 40 ka cal BP. Clim. Past 15, 1503–1536 (2019).Article 

Google Scholar 
Geng, R. et al. Modern pollen assemblages from lake sediments and soil in East Siberia and relative pollen productivity estimates for major taxa. Front. Ecol. Evol. 10, 508 (2022).Article 

Google Scholar 
Cao, X. et al. A taxonomically harmonized and temporally standardized fossil pollen dataset from Siberia covering the last 40 kyr. Earth Syst. Sci. Data 12, 119–135 (2020).ADS 
Article 

Google Scholar 
Sugita, S. Theory of quantitative reconstruction of vegetation I: pollen from large sites REVEALS regional vegetation composition. Holocene 17, 229–241 (2007).ADS 
Article 

Google Scholar 
Githumbi, E. et al. European pollen-based REVEALS land-cover reconstructions for the Holocene: Methodology, mapping and potentials. Earth Syst. Sci. Data 14, 1581–1619 (2022).ADS 
Article 

Google Scholar 
Snell, R. S. et al. Using dynamic vegetation models to simulate plant range shifts. Ecography 37, 1184–1197 (2014).Article 

Google Scholar 
Bullock, J. M. et al. Modelling spread of British wind-dispersed plants under future wind speeds in a changing climate. J. Ecol. 100, 104–115 (2012).Article 

Google Scholar 
Svenning, J. C., Normand, S. & Skov, F. Postglacial dispersal limitation of widespread forest plant species in nemoral Europe. Ecography 31, 316–326 (2008).Article 

Google Scholar 
Herzschuh, U. et al. Glacial legacies on interglacial vegetation at the Pliocene-Pleistocene transition in NE Asia. Nat. Commun. 7, 1–11 (2016).Article 

Google Scholar 
Herzschuh, U. Legacy of the Last Glacial on the present‐day distribution of deciduous versus evergreen boreal forests. Glob. Ecol. Biogeogr. 29, 198–206 (2020).Article 

Google Scholar 
Väliranta, M. et al. Plant macrofossil evidence for an early onset of the Holocene summer thermal maximum in northernmost Europe. Nat. Commun. 6, 1–8 (2015).Article 

Google Scholar 
Schulte, L., Li, C., Livsovski, S. & Herzschuh, U. Forest-permafrost feedbacks and glacial refugia help explain the unequal distribution of larch across continents. J. Biogeogr. 9, 0305–0270 (2022).
Google Scholar 
Davis, M. B., Shaw, R. G. & Etterson, J. R. Evolutionary responses to changing climate. Ecology 86, 1704–1714 (2005).Article 

Google Scholar 
Urban, M. C., Tewksbury, J. J. & Sheldon, K. S. On a collision course: competition and dispersal differences create no-analogue communities and cause extinctions during climate change. Proc. R. Soc. B Biol. Sci. 279, 2072–2080 (2012).Article 

Google Scholar 
Pennington, W. Lags in adjustment of vegetation to climate caused by the pace of soil development. Evidence from Britain. Vegetatio 67, 105–118 (1986).Article 

Google Scholar 
MacDonald, G. M., Kremenetski, K. V. & Beilman, D. W. Climate change and the northern Russian treeline zone. Philos. Trans. R. Soc. B: Biol. Sci. 363, 2285–2299 (2008).CAS 
Article 

Google Scholar 
Prentice, I. C., Bartlein, P. J. & Webb, T. Vegetation and climate change in eastern North America since the last glacial maximum. Ecology 72, 2038–2056 (1991).Article 

Google Scholar 
Cao, X. Y., Herzschuh, U., Telford, R. J. & Ni, J. A modern pollen-climate dataset from China and Mongolia: Assessing its potential for climate reconstruction. Rev. Palaeobot. Palynol. 211, 87–96 (2014).Article 

Google Scholar 
Leroy, S. A. G., Arpe, K., Mikolajewicz, U. & Wu, J. Climate simulations and pollen data reveal the distribution and connectivity of temperate tree populations in eastern Asia during the Last Glacial Maximum. Clim 16, 2039–2054 (2020).
Google Scholar 
Kaufman, D. et al. A global database of Holocene paleotemperature records. Sci. Data 7, 115 (2020).Mottl, O. et al. Global acceleration in rates of vegetation change over the past 18,000 years. Science 372, 860–864 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Nolan, C. et al. Past and future global transformation of terrestrial ecosystems under climate change. Science 361, 920–923 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Mauritsen, T. et al. Developments in the MPI-M Earth System Model version 1.2 (MPI-ESM1.2) and Its Response to Increasing CO2. J. Adv. Model. Earth Syst. 11, 998–1038 (2019).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Reick, C. et al. JSBACH 3—The land component of the MPI Earth System Model: documentation of version 3.2. Hamburg: MPI für Meteorologie. Berichte zur Erdsystemforsch. (2021).Brovkin, V., Raddatz, T., Reick, C. H., Claussen, M. & Gayler, V. Global biogeophysical interactions between forest and climate. Geophys. Res. Lett. 36, L07405 (2009).ADS 
Article 

Google Scholar 
Berger, A. L. Long-term variations of daily insolation and Quaternary climatic changes. J. Atmos. Sci. 35, 2361–2367 (1978).ADS 
Article 

Google Scholar 
Köhler, P., Nehrbass-Ahles, C., Schmitt, J., Stocker, T. F. & Fischer, H. A 156 kyr smoothed history of the atmospheric greenhouse gases CO2, CH4, and N2O and their radiative forcing. Earth Syst. Sci. Data 9, 363–387 (2017).ADS 
Article 

Google Scholar 
Tarasov, L., Dyke, A. S., Neal, R. M. & Peltier, W. R. A data-calibrated distribution of deglacial chronologies for the North American ice complex from glaciological modeling. Earth Planet. Sci. Lett. 315–316, 30–40 (2012).ADS 
Article 

Google Scholar 
Loana Meccia, V. & Mikolajewicz, U. Interactive ocean bathymetry and coastlines for simulating the last deglaciation with the Max Planck Institute Earth System Model (MPI-ESM-v1.2). Geosci. Model Dev. 11, 4677–4692 (2018).ADS 
Article 

Google Scholar 
Riddick, T., Brovkin, V., Hagemann, S. & Mikolajewicz, U. Dynamic hydrological discharge modelling for coupled climate model simulations of the last glacial cycle: the MPI-DynamicHD model version 3.0. Geosci. Model Dev. 11, 4291–4316 (2018).ADS 
Article 

Google Scholar 
Kapsch, M., Mikolajewicz, U., Ziemen, F. & Schannwell, C. Ocean response in transient simulations of the last deglaciation dominated by underlying ice‐sheet reconstruction and method of meltwater distribution. Geophys. Res. Lett. 49, e2021GL096767 (2022).ADS 
Article 

Google Scholar 
Murton, J. B., Bateman, M. D., Dallimore, S. R., Teller, J. T. & Yang, Z. Identification of Younger Dryas outburst flood path from Lake Agassiz to the Arctic Ocean. Nature 464, 740–743 (2010).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Rehfeld, K., Marwan, N., Heitzig, J. & Kurths, J. Comparison of correlation analysis techniques for irregularly sampled time series. Nonlinear Process. Geophys. 18, 389–404 (2011).ADS 
Article 

Google Scholar 
Braconnot, P. et al. Evaluation of climate models using palaeoclimatic data. Nat. Clim. Change 2, 417–424 (2012).ADS 
Article 

Google Scholar 
Cao, X. Y., Ni, J., Herzschuh, U., Wang, Y. B. & Zhao, Y. A late Quaternary pollen dataset from eastern continental Asia for vegetation and climate reconstructions: set up and evaluation. Rev. Palaeobot. Palynol. 194, 21–37 (2013).Article 

Google Scholar 
Bigelow, N. H. et al. Climate change and Arctic ecosystems: 1. Vegetation changes north of 55°N between the last glacial maximum, mid-Holocene, and present. J. Geophys. Res. Atmos. 108, 8170 (2003).Ramankutty, N. & Foley, J. A. Estimating historical changes in global land cover: Croplands from 1700 to 1992. Glob. Biogeochem. Cycles 13, 997–1027 (1999).ADS 
CAS 
Article 

Google Scholar 
Berger, A. & Loutre, M. F. Insolation values for the climate of the last 10 million years. Quat. Sci. Rev. 10, 297–317 (1991).ADS 
Article 

Google Scholar 
Deplazes, G. et al. Links between tropical rainfall and North Atlantic climate during the last glacial period. Nat. Geosci. 6, 213–217 (2013).ADS 
CAS 
Article 

Google Scholar 
Wessel, P. et al. Generic mapping tools: improved version released. EOS Trans. AGU 94, 409–410 (2013).ADS 
Article 

Google Scholar  More

Schmidt, J. Breeding places and migrations of the eel. Nature 111, 51–54 (1923).ADS 
Article 

Google Scholar 
Tucker, D. W. A new solution to the Atlantic eel problem. Nature 183, 495–501 (1959).ADS 
Article 

Google Scholar 
Voorhis, A. D. & Hersey, J. B. Oceanic thermal fronts in the Sargasso Sea. J. Geophys. Res. 69(18), 3809–3814 (1964).ADS 
Article 

Google Scholar 
Kleckner, R. C. & McCleave, J. D. The northern limit of spawning by Atlantic eels (Anguilla spp.) in the Sargasso Sea in relation to thermal fronts and surface water masses. J. Mar. Res. 46, 647–667 (1988).Article 

Google Scholar 
Ullman, D. S., Cornillon, P. C. & Shan, Z. On the characteristics of subtropical fronts in the North Atlantic. J. Geophys. Res: Oceans 112, C01010 (2007).ADS 

Google Scholar 
Miller, M. J. et al. Spawning by the European eel across 2000 km of the Sargasso Sea. Biol. Lett. 15, 20180835 (2019).Article 

Google Scholar 
Westerberg, H. et al. Larval abundance across the European eel spawning area: An analysis of recent and historic data. Fish. 19, 890–902 (2018).
Google Scholar 
Halliwell, G. R. Jr., Olson, D. B. & Peng, G. Stability of the Sargasso Sea subtropical frontal zone. J. Phys. Oceanogr. 24(6), 1166–1183 (1994).ADS 
Article 

Google Scholar 
van Ginneken, V. J. T. & Maes, G. E. The European eel (Anguilla anguilla, Linnaeus), its lifecycle, evolution and reproduction: A literature review. Rev. Fish Biol. Fish. 15, 367–398 (2005).Article 

Google Scholar 
Friedland, K. D., Miller, M. J. & Knights, B. Oceanic changes in the Sargasso Sea and declines in recruitment of the European eel. ICES J. Mar. Sci. 64, 519–530 (2007).Article 

Google Scholar 
Jacoby, D. M. P. et al. Synergistic patterns of threat and the challenges facing global anguillid eel conservation. Glob. Ecol. Conserv. 4, 321–333 (2015).Article 

Google Scholar 
Béguer-Pon, M. et al. Tracking anguillid eels: Five decades of telemetry-based research. Mar. Freshw. Res. 69, 199 (2018).Article 

Google Scholar 
Righton, D. et al. Important questions to progress science and sustainable management of anguillid eels. Fish 22, 762–788 (2021).
Google Scholar 
Aoyama, J. Life history and evolution of migration in catadromous eels (genus Anguilla). Aquat. Bio Sci. Monogr. 2, 1–42 (2009).
Google Scholar 
Tsukamoto, K., Aoyama, J. & Miller, M. J. Migration, speciation, and the evolution of diadromy in anguillid eels. Can. J. Fish. Aquat. Sci. 59, 1989–1998 (2002).Article 

Google Scholar 
Tesch, F.-W. Telemetric observations on the spawning migration of the eel (Anguilla anguilla) west of the European continental shelf. Env. Biol. Fish. 3, 203–209 (1978).Article 

Google Scholar 
Aarestrup, K. et al. Oceanic spawning migration of the European eel (Anguilla anguilla). Science 325, 1660 (2009).ADS 
CAS 
Article 

Google Scholar 
Westerberg, H. et al. Behaviour of stocked and naturally recruited European eels during migration. Mar. Ecol. Prog. Ser. 496, 145–157 (2014).ADS 
Article 

Google Scholar 
Amilhat, E. et al. First evidence of European eels exiting the Mediterranean Sea during their spawning migration. Sci. Rep. 6, 21817 (2016).ADS 
CAS 
Article 

Google Scholar 
Righton, D. et al. Empirical observations of the spawning migration of European eels: The long and dangerous road to the Sargasso Sea. Sci. Adv. 2, e1501694 (2016).ADS 
Article 

Google Scholar 
Verhelst, P. et al. Mapping silver eel migration routes in the North Sea. Sci Rep. 12, 318 (2022).ADS 
CAS 
Article 

Google Scholar 
Kuroki, M. et al. Hatching time and larval growth of Atlantic eels in the Sargasso Sea. Mar. Biol. 164, 118. https://doi.org/10.1007/s00227-017-3150-9 (2017).Article 

Google Scholar 
Acton, L. et al. What is the Sargasso Sea? The problem of fixing space in a fluid ocean. Polit. Geogr. 68, 86–100 (2019).Article 

Google Scholar 
GEBCO Compilation Group. GEBCO 2020 Grid. https://doi.org/10.5285/a29c5465-b138-234d-e053-6c86abc040b9 (2020).Miller, M. J. & Hanel, R. The Sargasso Sea Subtropical Gyre: The spawning and larval development area of both freshwater and marine eels. Sargasso Sea Alliance Science Report Series, 7, 20 pp (2011).Munk, P. et al. Oceanic fronts in the Sargasso Sea control the early life and drift of Atlantic eels. Proc. Biol. Sci. 277, 3593–3599 (2010).PubMed 
PubMed Central 

Google Scholar 
Béguer-Pon, M., Castonguay, M., Shan, S., Benchetrit, J. & Dodson, J. J. Direct observations of American eels migrating across the continental shelf to the Sargasso Sea. Nat. Commun. 6, 8705 (2015).ADS 
Article 

Google Scholar 
Westin, L. The spawning migration of European silver eel (Anguilla anguilla L.) with particular reference to stocked eel in the Baltic. Fish. Res. 38(3), 257–270 (1998).
Article 

Google Scholar 
Tesch, F.-W., Wendt, T. & Karlsson, L. Influence of geomagnetism on the activity and orientation of the eel, Anguilla anguilla (L.), as evident from laboratory experiments. Ecol. Freshw. Fish 1(1), 52–60 (1992).Article 

Google Scholar 
Tesch, F.-W. The Eel (Blackwell Science, Oxford, UK, 2003).Book 

Google Scholar 
Durif, C. M. F. et al. Magnetic compass orientation in the European eel. PLoS ONE 8(3), e59212 (2013).ADS 
CAS 
Article 

Google Scholar 
Schabetsberger, R. et al. Hydrographic features of anguillid spawning areas: Potential signposts for migrating eels. Mar. Ecol. Prog. Ser. 554, 141–155 (2016).ADS 
Article 

Google Scholar 
Naisbett-Jones, L. C., Putman, N. F., Stephenson, J. F., Ladak, S. & Young, K. A. A magnetic map leads juvenile European eels to the Gulf stream. Curr. Biol. 27, 1236–1240 (2017).CAS 
Article 

Google Scholar 
Dekker, W. Status of the European eel stock and fisheries. In Eel Biology (eds Aida, K. et al.) 237–254 (Springer, New York, 2003).Chapter 

Google Scholar 
Drouineau, H. et al. Freshwater eels: A symbol of the effects of global change. Fish Fish (Oxf) 19, 903–930 (2018).Article 

Google Scholar 
ICES. Joint EIFAAC/ICES/GFCM Working Group on Eels (WGEEL). ICES Scientific Reports. 2(85) (2020).Pike, C., Crook, V. & Gollock, M. Anguilla anguilla. The IUCN Red List of Threatened Species 2020: e.T60344A152845178 (2020).Durif, C., Dufour, S. & Elie, P. The silvering process of Anguilla anguilla: A new classification from the yellow resident to the silver migrating stage. J. Fish. Biol. 66, 1025–1043 (2005).Article 

Google Scholar 
Pankhurst, N. W. Relation of visual changes to the onset of sexual maturation in the European eel Anguilla Anguilla (L.). J. Fish Biol. 21, 127–140 (1982).Article 

Google Scholar 
Økland, F., Thorstad, E. B., Westerberg, H., Aarestrup, K. & Metcalfe, J. D. Development and testing of attachment methods for pop-up satellite archival transmitters in European eel. Anim. Biotelem. 1, 3 (2013).Article 

Google Scholar  More

Birkeland, C. Coral Reefs in the Anthropocene (Springer, 2015).Book 

Google Scholar 
Kleypas, J. A., Mcmanus, J. W. & Meñez, L. A. B. Environmental limits to coral reef development: Where do we draw the line?. Am. Zool. 39(1), 146–159. https://doi.org/10.1093/icb/39.1.146 (1999).Article 

Google Scholar 
Perry, C. T. & Larcombe, P. Marginal and non-reef-building coral environments. Coral Reefs 22, 427–432. https://doi.org/10.1007/s00338-003-0330-5 (2003).Article 

Google Scholar 
Wilkinson, C. R. Global and local threats to coral reef functioning and existence: review and predictions. Mar. Freshw. Res. 50, 867–878. https://doi.org/10.1071/mf99121 (1999).Article 

Google Scholar 
Mies, M. et al. South atlantic coral reefs are major global warming refugia and less susceptible to bleaching. Front. Mar. Sci. 7, 514. https://doi.org/10.3389/fmars.2020.00514 (2020).Article 

Google Scholar 
Leão, Z. M. A. N. et al. Brazilian coral reefsin a period of global change: A synthesis. Braz. J. Oceanogr. 64, 97–116. https://doi.org/10.1590/S1679-875920160916064sp2 (2016).Article 

Google Scholar 
Coker, D. J., Wilson, S. K. & Pratchett, M. S. Importance of live coral habitat for reef fishes. Rev. Fish Biol. Fish. 24, 89–126. https://doi.org/10.1007/s11160-013-9319-5 (2014).Article 

Google Scholar 
Alvarez-Filip, L., Gill, J. A. & Dulvy, N. K. Complex reef architecture supports more small-bodied fishes and longer food chains on Caribbean reefs. Ecosphere 2, 118. https://doi.org/10.1890/ES11-00185.1 (2011).Article 

Google Scholar 
Wilson, S. K., Graham, N. A. J., Pratchett, M. S., Jones, G. P. & Polunin, N. V. C. Multiple disturbances and the global degradation of coral reefs: Are reef fishes at risk or resilient?. Glob. Change Biol. 12, 2220–2234. https://doi.org/10.1111/j.1365-2486.2006.01252.x (2006).ADS 
Article 

Google Scholar 
Sully, S., Burkepile, D. E., Donovan, M. K., Hodgson, G. & van Woesik, R. A global analysis of coral bleaching over the past two decades. Nat. Commun. 10, 1264. https://doi.org/10.1038/s41467-019-09238-2 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Bellwood, D. R., Hughes, T. P., Folke, C. & Nystrom, M. Confronting the coral reef crisis. Nature 429, 827–833. https://doi.org/10.1038/nature02691 (2004).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Hughes, T. P. et al. climate change, human impacts, and the resilience of coral reefs. Science 301, 929–933. https://doi.org/10.1126/science.1085046 (2003).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Holbrook, N. J. et al. Keeping pace with marine heatwaves. Nat. Rev. Earth Environ. 1, 482–493. https://doi.org/10.1038/s43017-020-0068-4 (2020).ADS 
Article 

Google Scholar 
Bleuel, J., Pennino, M. G. & Longo, G. O. Coral distribution and bleaching vulnerability areas in Southwestern Atlantic under ocean warming. Sci. Rep. 11, 12833. https://doi.org/10.1038/s41598-021-92202-2 (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Fontoura, L. et al. The macroecology of reef fish agonistic behaviour. Ecography 43, 1278–1290. https://doi.org/10.1111/ecog.05079 (2020).Article 

Google Scholar 
Inagaki, K. Y., Pennino, M. G., Floeter, S. R., Hay, M. E. & Longo, G. O. Trophic interactions will expand geographically but be less intense as oceans warm. Glob. Change Biol. 26, 6805–6812. https://doi.org/10.1111/gcb.15346 (2020).ADS 
Article 

Google Scholar 
Longo, G. O., Hay, M. E., Ferreira, C. E. L. & Floeter, S. R. Trophic interactions across 61 degrees of latitude in the Western Atlantic. Glob. Ecol. Biogeogr. 28, 107–117. https://doi.org/10.1111/geb.12806 (2019).Article 

Google Scholar 
Pratchett, M. S. et al. Effects of climate-induced coral bleaching on coral-reef fishes: Ecological and economic consequences. Oceanogr. Mar. Biol. Annu. Rev. 46, 251–296. https://doi.org/10.1201/9781420065756.ch6 (2008).Article 

Google Scholar 
Graham, N. A. J. et al. Lag effects in the impacts of mass coral bleaching on coral reef fish, fisheries, and ecosystems. Conserv. Biol. 21, 1291–1300. https://doi.org/10.1111/j.1523-1739.2007.00754.x (2007).Article 
PubMed 

Google Scholar 
Strona, G. et al. Global tropical reef fish richness could decline by around half if corals are lost. Proc. R. Soc. B 288, 20210274. https://doi.org/10.1098/rspb.2021.0274 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
McClenachan, L. Extinction risk in reef fishes 199–207 (Cambridge University Press, 2015).
Google Scholar 
Power, M. E. et al. Challenges in the quest for keystones. Bioscience 46, 609–620. https://doi.org/10.2307/1312990 (1996).Article 

Google Scholar 
Pereira, P. H. C. et al. The influence of multiple factors upon reef fish abundance and species richness in a tropical coral complex. Ichthyol. Res. 61, 375–384. https://doi.org/10.1007/s10228-014-0409-8 (2014).Article 

Google Scholar 
Coni, E. O. C. et al. An evaluation of the use of branching fire-corals (Millepora spp.) as refuge by reef fish in the Abrolhos Bank, eastern Brazil. Environ. Biol. Fish. 96, 45–55. https://doi.org/10.1007/s10641-012-0021-6 (2013).Article 

Google Scholar 
Graham, N. A. J. et al. Extinction vulnerability of coral reef fishes. Ecol. Lett. 14, 341–348. https://doi.org/10.1111/j.1461-0248.2011.01592.x (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Cornwell, W. K., Schwilk, D. W. & Ackerly, D. D. A trait-based test for habitat filtering: convex hull volume. Ecology 87, 1465–1471. https://doi.org/10.1890/0012-9658(2006)87[1465:ATTFHF]2.0.CO;2 (2006).Article 
PubMed 

Google Scholar 
Mouillot, D., Graham, N. A. J., Villéger, S., Mason, N. W. H. & Bellwood, D. R. A functional approach reveals community responses to disturbances. Trends Ecol. Evol. 28(3), 167–177. https://doi.org/10.1016/j.tree.2012.10.004 (2013).Article 
PubMed 

Google Scholar 
Pimiento, C. et al. Functional diversity of marine megafauna in the Anthropocene. Sci. Adv. 6, 7650. https://doi.org/10.1126/sciadv.aay7650 (2020).ADS 
Article 

Google Scholar 
Loiola, M. et al. Structure of marginal coral reef assemblages under different turbidity regime. Mar. Environ. Res. 147, 138–148. https://doi.org/10.1016/j.marenvres.2019.03.013 (2019).CAS 
Article 
PubMed 

Google Scholar 
Aued, A. W. et al. Large-scale patterns of benthic marine communities in the Brazilian Province. PLoS ONE 13, e0198452. https://doi.org/10.1371/journal.pone.0198452 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Leão, Z. M. A. N., Kikuchi, R. K. P. & Testa, V. Corals and Coral Reefs of Brazil 9–52 (Elsevier Publisher, 2003).
Google Scholar 
Pinheiro, H. T. et al. South-western Atlantic reef fishes: Zoogeographical patterns and ecological drivers reveal a secondary biodiversity centre in the Atlantic Ocean. Divers. Distrib. 24, 951–965. https://doi.org/10.1111/ddi.12729 (2018).Article 

Google Scholar 
Floeter, S. R. et al. Atlantic reef fish biogeography and evolution. J. Biogeogr. 35, 22–47. https://doi.org/10.1111/j.1365-2699.2007.01790.x (2008).Article 

Google Scholar 
Cord, I. et al. Brazilian marine biogeography: A multi-taxa approach for outlining sectorization. Mar. Biol. 169(5), 61. https://doi.org/10.1007/s00227-022-04045-8 (2022).Article 

Google Scholar 
Leal, I. C. S., Araújo, M. E. D., Cunha, S. R. D. & Pereira, P. H. C. The influence of fire-coral colony size and agonistic behaviour of territorial damselfish on associated coral reef fish communities. Mar. Environ. Res. 108, 45–54. https://doi.org/10.1016/j.marenvres.2015.04.009 (2015).CAS 
Article 
PubMed 

Google Scholar 
Kéry, M. & Royle, J. A. Applied hierarchical modeling in ecology: Analysis of distribution abundance and species richness in R and BUGS. In Prelude and Static Models Vol. 1 (eds Kéry, M. & Royle, J. A.) (Academic Press, 2016).MATH 

Google Scholar 
Hadj-Hammou, J., Mouillot, D. & Graham, N. A. J. Response and effect traits of coral reef fish. Front. Mar. Sci. https://doi.org/10.3389/fmars.2021.640619 (2021).Article 

Google Scholar 
McLean, M. et al. Trait similarity in reef fish faunas across the world’s oceans. PNAS 118(12), e2012318118. https://doi.org/10.1073/pnas.2012318118 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Brandl, S. J. et al. Coral reef ecosystem functioning: eight core processes and the role of biodiversity. Front. Ecol. Environ. 17, 445–454. https://doi.org/10.1002/fee.2088 (2019).Article 

Google Scholar 
Eggertsen, L. et al. Seaweed beds support more juvenile reef fish than seagrass beds in a south-western Atlantic tropical seascape. Estuar. Coast. Shelf S. 196, 97–108. https://doi.org/10.1016/j.ecss.2017.06.041 (2017).ADS 
Article 

Google Scholar 
Mouillot, D. et al. Functional over-redundancy and high functional vulnerability in global fish faunas on tropical reefs. PNAS 111, 13757–13762. https://doi.org/10.1073/pnas.1317625111 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Briggs, J. C. Marine Zoogeography (McGraw-Hill, 1974).
Google Scholar 
Garcia, G. S., Dias, M. S. & Longo, G. O. Trade-off between number and length of remote videos for rapid assessments of reef fish assemblages. J. Fish Biol. 99(3), 896–904. https://doi.org/10.1111/jfb.14776 (2021).Article 
PubMed 

Google Scholar 
Quimbayo, J. P. et al. Life-history traits, geographical range, and conservation aspects ofreef fishes from the Atlantic and Eastern Pacific. Ecology 102, e03298. https://doi.org/10.1002/ecy.3298 (2021).Article 
PubMed 

Google Scholar 
Katsanevakis, S. et al. Monitoring marine populations and communities: methods dealing with imperfect detectability. Aquat. Biol. 16, 31–52. https://doi.org/10.3354/ab00426 (2012).Article 

Google Scholar 
Villéger, S., Mason, N. W. H. & Mouillot, D. New multidimensional functional diversity indices for a multifaceted framework in functional ecology. Ecology 89, 2290–2301. https://doi.org/10.1890/07-1206.1 (2008).Article 
PubMed 

Google Scholar 
Maire, E., Grenouillet, G., Brosse, S. & Villéger, S. How many dimensions are needed to accurately assess functional diversity? A pragmatic approach for assessing the quality of functional spaces. Glob. Ecol. Biogeogr. 24, 728–740. https://doi.org/10.1111/geb.12299 (2015).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021)Kellner, K. jagsUI: A Wrapper Around ‘rjags’ to Streamline ‘JAGS’ Analyses. R package version 1.5.2. https://CRAN.R-project.org/package=jagsUI (2021)Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).Book 

Google Scholar 
Ferreira, C. E. L., Gonçalves, J. E. A. & Coutinho, R. Community structure of fishes and habitat complexity on a tropical rocky shore. Environ. Biol. Fish. 61, 353–369 (2001).Article 

Google Scholar 
Fulton, C. J. et al. Macroalgal meadow habitats support fish and fisheries in diverse tropical seascapes. Fish Fish. 21, 700–717. https://doi.org/10.1111/faf.12455 (2020).Article 

Google Scholar 
Ferreira, L. C. L. et al. Different responses of massive and branching corals to a major heatwave at the largest and richest reef complex in South Atlantic. Mar. Biol. 168, 54. https://doi.org/10.1007/s00227-021-03863-6 (2021).CAS 
Article 

Google Scholar 
Lonzetti, B. C., Vieira, E. A. & Longo, G. O. Ocean warming can help zoanthids outcompete branching hydrocorals. Coral Reefs 41, 175–189. https://doi.org/10.1007/s00338-021-02212-9 (2022).Article 

Google Scholar 
Grillo, A. C., Candido, C. F., Giglio, V. J. & Longo, G. O. Unusual high coral cover in a Southwestern Atlantic subtropical reef. Mar. Biodivers. 51, 77. https://doi.org/10.1007/s12526-021-01221-9 (2021).Article 

Google Scholar 
Matheus, Z. et al. Benthic reef assemblages of the Fernando de Noronha Archipelago, tropical South-west Atlantic: Effects of depth, wave exposure and cross-shelf positioning. PLoS ONE 14(1), e0210664. https://doi.org/10.1371/journal.pone.0210664 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Meirelles, P. M. et al. Baseline assessment of mesophotic reefs of the vitória-trindade seamount chain based on water quality, microbial diversity, benthic cover and fish biomass data. PLoS ONE 10(6), e0130084. https://doi.org/10.1371/journal.pone.0130084 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ferreira, C. E. L., Floeter, S. R., Gasparini, J. L., Ferreira, B. P. & Joyeux, J. C. Trophic structure patterns of Brazilian reef fishes: A latitudinal comparison. J. Biogeogr. 31, 1093–1106. https://doi.org/10.1111/j.1365-2699.2004.01044.x (2004).Article 

Google Scholar 
Fontoura, L. et al. Climate-driven shift in coral morphological structure predicts decline of juvenile reef fishes. Glob. Change Biol. 26, 557–567. https://doi.org/10.1111/gcb.14911 (2020).ADS 
Article 

Google Scholar 
MacNeil, M. A. et al. Accounting for detectability in reef-fish biodiversity estimates. Mar. Ecol.-Prog. Ser. 367, 249–260. https://doi.org/10.3354/meps07580 (2008).ADS 
Article 

Google Scholar 
Capitani, L., de Araujo, J. N., Vieira, E. A., Angelini, R. & Longo, G. O. Ocean warming will reduce standing biomass in a Tropical Western Atlantic reef ecosystem. Ecosystems 25, 843–857. https://doi.org/10.1007/s10021-021-00691-z (2022).Article 

Google Scholar 
Fogliarini, C. O., Longo, G. O., Francini-Filho, R. B., McClenachan, L. & Bender, M. G. Sailing into the past: Nautical charts reveal changes over 160 years in the largest reef complex in the South Atlantic Ocean. PECON 20(3), 231–239. https://doi.org/10.1007/10.1016/j.pecon.2022.05.003 (2022).Article 

Google Scholar 
Gasparini, J. L., Floeter, S. R., Ferreira, C. E. L. & Sazima, I. Marine ornamental trade in Brazil. Biodivers. Conserv. 14, 2883–2899. https://doi.org/10.1007/s10531-004-0222-1 (2005).Article 

Google Scholar 
Francini-Filho, R. B. et al. Brazil 163–198 (Springer, 2019).
Google Scholar 
Bellwood, D. R., Goatley, C. H. R. & Bellwood, O. The evolution of fishes and corals on reefs: Form, function and interdependence. Biol. Rev. 92, 878–901. https://doi.org/10.1111/brv.12259 (2017).Article 
PubMed 

Google Scholar 
Nunes, L. T. et al. Ecology of Prognathodes obliquus, a butterflyfish endemic to mesophotic ecosystems of St. Peter and St. Paul’s Archipelago. Coral Reefs 38, 955–960. https://doi.org/10.1007/s00338-019-01822-8 (2019).ADS 
Article 

Google Scholar 
Liedke, A. et al. Abundance, diet, foraging and nutritional condition of the banded butterflyfish (Chaetodon striatus) along the western Atlantic. Mar. Biol. 163, 6. https://doi.org/10.1007/s00227-015-2788-4 (2016).CAS 
Article 

Google Scholar  More

Vega, F. E., Rosenquist, E. & Collins, W. Global project needed to tackle coffee crisis. Nature 425, 343 (2003).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Craparo, A. C. W., Van Asten, P. J. A., Läderach, P., Jassogne, L. T. P. & Grab, S. W. Coffea arabica yields decline in Tanzania due to climate change: global implications. Agric. For. Meteorol. 207, 1–10 (2015).ADS 
Article 

Google Scholar 
Davis, A. P. et al. High extinction risk for wild coffee species and implications for coffee sector sustainability. Sci. Adv. 5, eaav3473 (2019).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Davis, A. P., Gole, T. W., Baena, S. & Moat, J. The impact of climate change on indigenous arabica coffee (Coffea arabica): predicting future trends and identifying priorities. PLoS ONE 7, e47981 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Davis, A. P., Mieulet, D., Moat, J., Sarmu, D. & Haggar, J. Arabica-like flavour in a heat-tolerant wild coffee species. Nat. Plants 7, 413–418 (2021).CAS 
PubMed 
Article 

Google Scholar 
Moat, J., Gole, T. W. & Davis, A. P. Least concern to endangered: applying climate change projections profoundly influences the extinction risk assessment for wild Arabica coffee. Global Change Biol. 25, 390–403 (2019).ADS 
Article 

Google Scholar 
Moat, J. et al. Resilience potential of the Ethiopian coffee sector under climate change. Nat. Plants 3, 17081 (2017).PubMed 
Article 

Google Scholar 
Kath, J. et al. Not so robust: Robusta coffee production is highly sensitive to temperature. Global Change Biol. 26, 3677–3688 (2020).ADS 
Article 

Google Scholar 
Liu, L. et al. Soil moisture dominates dryness stress on ecosystem production globally. Nat. Commun. 11, 1–9 (2020).ADS 
CAS 

Google Scholar 
Grossiord, C. et al. Plant responses to rising vapor pressure deficit. New Phytol. 226, 1550–1566 (2020).PubMed 
Article 

Google Scholar 
IPCC Climate Change 2022: Impacts, Adaptation, and Vulnerability (eds. Pörtner, H.-O. et al.) (Cambridge Univ. Press, 2022).Burke, M. et al. Higher temperatures increase suicide rates in the United States and Mexico. Nat. Clim. Change 8, 723–729 (2018).ADS 
Article 

Google Scholar 
Burke, M., Hsiang, S. M. & Miguel, E. Global non-linear effect of temperature on economic production. Nature 527, 235–239 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Duffy, K. A. et al. How close are we to the temperature tipping point of the terrestrial biosphere? Sci. Adv. 7, eaay1052 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Scheffer, M. et al. Early-warning signals for critical transitions. Nature 461, 53–59 (2009).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Schneider, S. H. Abrupt non-linear climate change, irreversibility and surprise. Global Environ. Change 14, 245–258 (2004).Article 

Google Scholar 
Lenton, T. M. Early warning of climate tipping points. Nat. Clim. Change 1, 201–209 (2011).ADS 
Article 

Google Scholar 
Lenton, T. M. et al. Climate tipping points—too risky to bet against. Nature. 575, 592–595 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Lobell, D. B., Bänziger, M., Magorokosho, C. & Vivek, B. Nonlinear heat effects on African maize as evidenced by historical yield trials. Nat. Clim. Change 1, 42–45 (2011).ADS 
Article 

Google Scholar 
Lobell, D. B., Deines, J. M. & Tommaso, S. D. Changes in the drought sensitivity of US maize yields. Nat. Food 1, 729–735 (2020).Article 

Google Scholar 
Lobell, D. B. et al. Greater sensitivity to drought accompanies maize yield increase in the US Midwest. Science 344, 516–519 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Rigden, A., Mueller, N., Holbrook, N., Pillai, N. & Huybers, P. Combined influence of soil moisture and atmospheric evaporative demand is important for accurately predicting US maize yields. Nat. Food 1, 127–133 (2020).Article 

Google Scholar 
Schlenker, W. & Roberts, M. J. Nonlinear temperature effects indicate severe damages to US crop yields under climate change. Proc. Natl Acad. Sci. USA 106, 15594–15598 (2009).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
McDowell, N. G. et al. Mechanisms of woody-plant mortality under rising drought, CO2 and vapour pressure deficit. Nat. Rev. Earth Environ. 3, 294–308 (2022).ADS 
CAS 
Article 

Google Scholar 
Sinclair, T. R. et al. Limited-transpiration response to high vapor pressure deficit in crop species. Plant Sci. 260, 109–118 (2017).CAS 
PubMed 
Article 

Google Scholar 
López, J., Way, D. A. & Sadok, W. Systemic effects of rising atmospheric vapor pressure deficit on plant physiology and productivity. Global Change Biol. 27, 1704–1720 (2021).ADS 
Article 

Google Scholar 
McDowell, N. G. & Allen, C. D. Darcy’s law predicts widespread forest mortality under climate warming. Nat. Clim. Change 5, 669–672 (2015).ADS 
Article 

Google Scholar 
Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
You, L., Wood, S., Wood-Sichra, U. & Wu, W. Generating global crop distribution maps: from census to grid. Agric. Syst. 127, 53–60 (2014).Article 

Google Scholar 
Fong, Y., Huang, Y., Gilbert, P. B. & Permar, S. R. chngpt: threshold regression model estimation and inference. BMC Bioinformatics 18, 1–7 (2017).Article 

Google Scholar 
Qin, Y. et al. Agricultural risks from changing snowmelt. Nat. Clim. Change 10, 459–465 (2020).ADS 
Article 

Google Scholar 
Forster, P. M., Maycock, A. C., McKenna, C. M. & Smith, C. J. Latest climate models confirm need for urgent mitigation. Nat. Clim. Change 10, 7–10 (2020).ADS 
Article 

Google Scholar 
Forster, P. M. et al. Projections of when temperature change will exceed 2 °C above pre-industrial levels. Nat. Clim. Change 10, 407–412 (2011).
Google Scholar 
Joshi, M., Hawkins, E., Sutton, R., Lowe, J. & Frame, D. Projections of when temperature change will exceed 2 °C above pre-industrial levels. Nat. Clim. Change 1, 407–412 (2011).ADS 
Article 

Google Scholar 
IPCC, 2021: Summary for Policymakers. In Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, in press).Lobell, D. B. et al. The critical role of extreme heat for maize production in the United States. Nat. Clim. Change 3, 497–501 (2013).ADS 

Google Scholar 
Sinclair, T. R., Hammer, G. L. & Van Oosterom, E. J. Potential yield and water-use efficiency benefits in sorghum from limited maximum transpiration rate. Funct. Plant Biol. 32, 945–952 (2005).PubMed 
Article 

Google Scholar 
Martins, M. Q. et al. Protective response mechanisms to heat stress in interaction with high [CO2] conditions in Coffea spp. Front. Plant Sci. 7, 947 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Rodrigues, W. P. et al. Long‐term elevated air [CO2] strengthens photosynthetic functioning and mitigates the impact of supra‐optimal temperatures in tropical Coffea arabica and C. canephora species. Global Change Biol. 22, 415–431 (2016).ADS 
Article 

Google Scholar 
Ghini, R. et al. Coffee growth, pest and yield responses to free-air CO2 enrichment. Clim. Change 132, 307–320 (2015).ADS 
Article 

Google Scholar 
Rakocevic, M. et al. The vegetative growth assists to reproductive responses of Arabic coffee trees in a long-term FACE experiment. Plant Growth Regul. 91, 305–316 (2020).CAS 
Article 

Google Scholar 
Hammer, G. L. et al. Designing crops for adaptation to the drought and high‐temperature risks anticipated in future climates. Crop Sci. 60, 605–621 (2020).Article 

Google Scholar 
Gennari, P., Rosero-Moncayo, J. & Tubiello, F. N. The FAO contribution to monitoring SDGs for food and agriculture. Nat. Plants 5, 1196–1197 (2019).PubMed 
Article 

Google Scholar 
Lesk, C., Rowhani, P. & Ramankutty, N. Influence of extreme weather disasters on global crop production. Nature 529, 84–87 (2016).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Ortiz-Bobea, A., Ault, T. R., Carrillo, C. M., Chambers, R. G. & Lobell, D. B. Anthropogenic climate change has slowed global agricultural productivity growth. Nat. Clim. Change 11, 306–312 (2021).ADS 
Article 

Google Scholar 
Davis, A. P. et al. Hot coffee: the identity, climate profiles, agronomy, and beverage characteristics of Coffea racemosa and C. zanguebariae. Front. Sustain. Food Syst. 5, 740137 (2021).Article 

Google Scholar 
Sarmiento-Soler, A. et al. Disentangling effects of altitude and shade cover on coffee fruit dynamics and vegetative growth in smallholder coffee systems. Agric. Ecosyst. Environ. 326, 107786 (2022).CAS 
Article 

Google Scholar 
Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. B 73, 3–36 (2011).MathSciNet 
MATH 
Article 

Google Scholar 
Barton, K. MuMIn: multi-model inference. R-Forge http://r-forge.r-project.org/projects/mumin/ (2009).R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing https://www.r-project.org/ (2021).Harrison, X. A. et al. A brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ 6, e4794 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Najafi, E., Devineni, N., Khanbilvardi, R. M. & Kogan, F. Understanding the changes in global crop yields through changes in climate and technology. Earths Future 6, 410–427 (2018).ADS 
CAS 
Article 

Google Scholar 
Ovalle-Rivera, O. et al. Assessing the accuracy and robustness of a process-based model for coffee agroforestry systems in Central America. Agrofor. Syst. 94, 2033–2051 (2020).Article 

Google Scholar 
Varma, S. & Simon, R. Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics 7, 1–8 (2006).Article 

Google Scholar 
Yuan, W. et al. Increased atmospheric vapor pressure deficit reduces global vegetation growth. Sci. Adv. 5, eaax1396 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Son, H. & Fong, Y. Fast grid search and bootstrap-based inference for continuous two-phase polynomial regression models. Environmetrics 32, e2664 (2021).MathSciNet 
Article 

Google Scholar 
Wintgens, J. N. et al. Coffee: Growing, Processing, Sustainable Production. A Guidebook for Growers, Processors, Traders, and Researchers (Wiley, 2004). More