More stories

  • in

    The impact of 1.5 °C and 2.0 °C global warming on global maize production and trade

    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

  • in

    Cross-cutting research themes for future mangrove forest research

    Sievers, M. et al. Trends Ecol. Evol. 34, 807–817 (2019).Article 

    Google Scholar 
    Barbier, E. B. et al. Ecol. Monogr. 81, 169–193 (2011).Article 

    Google Scholar 
    zu Ermgassen, P. S. E. et al. Estuar. Coast. Shelf Sci. 248, 107159 (2021).Article 

    Google Scholar 
    Spalding, M. & Parrett, C. L. Mar. Policy 110, 103540 (2019).Article 

    Google Scholar 
    Dahdouh-Guebas, F. et al. Estuar. Coast. Shelf Sci. 248, 106942 (2021).Article 

    Google Scholar 
    Dahdouh-Guebas, F. et al. Front. Mar. Sci. 7, 603651 (2020).Article 

    Google Scholar 
    Friess, D. A. & McKee, K. L. in Dynamic Sedimentary Environments of Mangrove Coasts (eds Sidik, F. & Friess, D.A.) Ch. 7 (Elsevier, 2021).Lee, S. Y. et al. Glob. Ecol. Biogeogr. 23, 726–743 (2014).Article 

    Google Scholar 
    Goldberg, L., Lagomasino, D., Thomas, N. & Fatoyinbo, T. Glob. Change Biol. 26, 5844–5855 (2020).Article 

    Google Scholar 
    Cannicci, S. et al. Proc. Natl Acad. Sci. USA 118, e2016913118 (2021).CAS 
    Article 

    Google Scholar 
    Bouillon, S., Koedam, N., Raman, A. & Dehairs, F. Oecologia 130, 441–448 (2002).CAS 
    Article 

    Google Scholar 
    Adame, M. F. et al. Glob. Chang. Biol. 27, 2856–2866 (2021).CAS 
    Article 

    Google Scholar 
    Pittman, S. et al. Mar. Ecol. Prog. Ser. 663, 1–29 (2021).Article 

    Google Scholar 
    Nagelkerken, I., Sheaves, M. T., Baker, R. & Connolly, R. M. Fish Fish. 16, 362–371 (2015).Article 

    Google Scholar 
    Huxham, M., Whitlock, D., Githaiga, M. & Dencer-Brown, A. Curr. For. Rep. 4, 101–110 (2018).
    Google Scholar 
    Bryan-Brown, D. N. et al. Sci. Rep. 10, 7117 (2020).CAS 
    Article 

    Google Scholar 
    Curnick, D. J. et al. Science 363, 239–239 (2019).Article 

    Google Scholar 
    Dahdouh-Guebas, F. & Cannicci, S. Front. Mar. Sci. 8, 799543 (2021).Article 

    Google Scholar 
    Bruelheide, H. et al. Nat. Ecol. Evol. 2, 1906–1917 (2018).Article 

    Google Scholar 
    Harvey, B. P., Marshall, K. E., Harley, C. D. G. & Russell, B. D. Trends Ecol. Evol. 37, 20–29 (2021).Article 

    Google Scholar 
    Rahman, M. M. et al. Nat. Commun. 12, 3875 (2021).CAS 
    Article 

    Google Scholar 
    Yando, E. S. et al. Biol. Conserv. 263, 109355 (2021).Article 

    Google Scholar 
    Krauss, K. W. & Osland, M. J. Ann. Bot. 125, 213–234 (2020).PubMed 

    Google Scholar 
    Asbridge, E. F. et al. Estuar. Coast. Shelf Sci. 228, 106353 (2019).Article 

    Google Scholar 
    Sippo, J. Z., Lovelock, C. E., Santos, I. R., Sanders, C. J. & Maher, D. T. Estuar. Coast. Shelf Sci. 215, 241–249 (2018).Article 

    Google Scholar 
    Erftemeijer, P. L. A. & Hamerlynck, O. J. Coast. Res. 42, 228–235 (2005).
    Google Scholar 
    Abhik, S. et al. Sci. Rep. 11, 20411 (2021).CAS 
    Article 

    Google Scholar 
    Osland, M. J., Day, R. H. & Michot, T. C. Divers. Distrib. 26, 1366–1382 (2020).Article 

    Google Scholar 
    Dahdouh-Guebas, F. et al. Curr. Biol. 15, 579–586 (2005).CAS 
    Article 

    Google Scholar 
    Turschwell, M. P. et al. Biol. Conserv. 247, 108637 (2020).Article 

    Google Scholar 
    Saintilan, N. et al. Science 368, 1118–1121 (2020).CAS 
    Article 

    Google Scholar 
    Xie, D. et al. Environ. Res. Lett. 15, 114033 (2020).Article 

    Google Scholar 
    Ewel, K. C., Twilley, R. R. & Ong, J. E. Glob. Ecol. Biogeogr. Lett. 7, 83–94 (1998).Article 

    Google Scholar 
    Dahdouh-Guebas, F. in Vers une Nouvelle Synthèse Ecologique: de L’écologie Scientifique au Développement Durable. (ed. Meerts, P.) 182–193 (Centre Paul Duvigneaud de Documentation Ecologique, 2013).Gallup, L., Sonnenfeld, D. A. & Dahdouh-Guebas, F. Ocean Coast. Manage. 185, 105001 (2020).Article 

    Google Scholar 
    Rist, S. & Dahdouh-Guebas, F. Environ. Dev. Sustain. 8, 467–493 (2006).Article 

    Google Scholar 
    Foell, J., Harrison, E. & Stirrat, R. L. Participatory Approaches to Natural Resource Management: The Case of Coastal Zone Management in the Puttalam District, Sri Lanka. Project R6977 (School of African and Asian Studies, University of Sussex, 2000).Beymer-Farris, B. A. & Bassett, T. J. Glob. Environ. Change 22, 332–341 (2012).Article 

    Google Scholar 
    Lovelock, C. E. & Brown, B. M. Nat. Ecol. Evol. 3, 1135 (2019).Article 

    Google Scholar 
    Dahdouh-Guebas, F. et al. J. Ethnobiol. Ethnomed. 2, 24 (2006).CAS 
    Article 

    Google Scholar  More

  • in

    Genomic basis for early-life mortality in sharpsnout seabream

    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

  • in

    Effect of DNA methylation, modified by 5-azaC, on ecophysiological responses of a clonal plant to changing climate

    Thuiller, W., Lavorel, S., Araujo, M. B., Sykes, M. T. & Prentice, I. C. Climate change threats to plant diversity in Europe. Proc. Natl. Acad. Sci. USA 102, 8245–8250. https://doi.org/10.1073/pnas.0409902102 (2005).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Fagundez, J. Heathlands confronting global change: Drivers of biodiversity loss from past to future scenarios. Ann. Bot. 111, 151–172. https://doi.org/10.1093/aob/mcs257 (2013).Article 
    PubMed 

    Google Scholar 
    Nicotra, A. B. et al. Plant phenotypic plasticity in a changing climate. Trends Plant Sci. 15, 684–692. https://doi.org/10.1016/j.tplants.2010.09.008 (2010).CAS 
    Article 
    PubMed 

    Google Scholar 
    Dubin, M. J. et al. DNA methylation in Arabidopsis has a genetic basis and shows evidence of local adaptation. Elife 4, 25. https://doi.org/10.7554/eLife.05255 (2015).Article 

    Google Scholar 
    Herrera, C. M., Medrano, M. & Bazaga, P. Comparative spatial genetics and epigenetics of plant populations: Heuristic value and a proof of concept. Mol. Ecol. 25, 1653–1664. https://doi.org/10.1111/mec.13576 (2016).CAS 
    Article 
    PubMed 

    Google Scholar 
    Richards, C. L. et al. Ecological plant epigenetics: Evidence from model and non-model species, and the way forward. Ecol. Lett. 20, 1576–1590. https://doi.org/10.1111/ele.12858 (2017).Article 
    PubMed 

    Google Scholar 
    Münzbergová, Z., Latzel, V., Šurinová, M. & Hadincová, V. DNA methylation as a possible mechanism affecting ability of natural populations to adapt to changing climate. Oikos 128, 124–134. https://doi.org/10.1111/oik.05591 (2019).CAS 
    Article 

    Google Scholar 
    Thiebaut, F., Hemerly, A. S. & Ferreira, P. C. G. A role for epigenetic regulation in the adaptation and stress responses of non-model plants. Front. Plant Sci. 10, 25. https://doi.org/10.3389/fpls.2019.00246 (2019).Article 

    Google Scholar 
    Verhoeven, K. J. F., Vonholdt, B. M. & Sork, V. L. Epigenetics in ecology and evolution: What we know and what we need to know. Mol. Ecol. 25, 1631–1638. https://doi.org/10.1111/mec.13617 (2016).Article 
    PubMed 

    Google Scholar 
    Lisch, D. How important are transposons for plant evolution?. Nat. Rev. Genet. 14, 49–61. https://doi.org/10.1038/nrg3374 (2013).CAS 
    Article 
    PubMed 

    Google Scholar 
    Paszkowski, J. Controlled activation of retrotransposition for plant breeding. Curr. Opin. Biotechnol. 32, 200–206. https://doi.org/10.1016/j.copbio.2015.01.003 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    Becker, C. et al. Spontaneous epigenetic variation in the Arabidopsis thaliana methylome. Nature 480, 245-U127. https://doi.org/10.1038/nature10555 (2011).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Schmitz, R. J. et al. Transgenerational epigenetic instability is a source of novel methylation variants. Science 334, 369–373. https://doi.org/10.1126/science.1212959 (2011).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bossdorf, O., Richards, C. L. & Pigliucci, M. Epigenetics for ecologists. Ecol. Lett. 11, 106–115. https://doi.org/10.1111/j.1461-0248.2007.01130.x (2008).Article 
    PubMed 

    Google Scholar 
    Walsh, M. R. et al. Local adaptation in transgenerational responses to predators. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2015.2271 (2016).Article 

    Google Scholar 
    Foust, C. M. et al. Genetic and epigenetic differences associated with environmental gradients in replicate populations of two salt marsh perennials. Mol. Ecol. 25, 1639–1652. https://doi.org/10.1111/mec.13522 (2016).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Gugger, P. F., Fitz-Gibbon, S., Pellegrini, M. & Sork, V. L. Species-wide patterns of DNA methylation variation in Quercus lobata and their association with climate gradients. Mol. Ecol. 25, 1665–1680. https://doi.org/10.1111/mec.13563 (2016).CAS 
    Article 
    PubMed 

    Google Scholar 
    Herrera, C. M. & Bazaga, P. Untangling individual variation in natural populations: Ecological, genetic and epigenetic correlates of long-term inequality in herbivory. Mol. Ecol. 20, 1675–1688. https://doi.org/10.1111/j.1365-294X.2011.05026.x (2011).CAS 
    Article 
    PubMed 

    Google Scholar 
    Medrano, M., Herrera, C. M. & Bazaga, P. Epigenetic variation predicts regional and local intraspecific functional diversity in a perennial herb. Mol. Ecol. 23, 4926–4938. https://doi.org/10.1111/mec.12911 (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    Herrera, C. M., Medrano, M. & Bazaga, P. Comparative epigenetic and genetic spatial structure of the perennial herb Helleborus foetidus: Isolation by environment, isolation by distance, and functional trait divergence. Am. J. Bot. 104, 1195–1204. https://doi.org/10.3732/ajb.1700162 (2017).Article 
    PubMed 

    Google Scholar 
    Sheldon, E. L., Schrey, A., Andrew, S. C., Ragsdale, A. & Griffith, S. C. Epigenetic and genetic variation among three separate introductions of the house sparrow (Passer domesticus) into Australia. R. Soc. Open Sci. https://doi.org/10.1098/rsos.172185 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gaspar, B., Bossdorf, O. & Durka, W. Structure, stability and ecological significance of natural epigenetic variation: A large-scale survey in Plantago lanceolata. New Phytol. 221, 1585–1596. https://doi.org/10.1111/nph.15487 (2019).Article 
    PubMed 

    Google Scholar 
    Medrano, M., Alonso, C., Bazaga, P., Lopez, E. & Herrera, C. M. Comparative genetic and epigenetic diversity in pairs of sympatric, closely related plants with contrasting distribution ranges in south-eastern Iberian mount. Aob Plants https://doi.org/10.1093/aobpla/plaa013 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wang, M. Z., Li, H. L., Li, J. M. & Yu, F. H. Correlations between genetic, epigenetic and phenotypic variation of an introduced clonal herb. Heredity 124, 146–155. https://doi.org/10.1038/s41437-019-0261-8 (2020).Article 
    PubMed 

    Google Scholar 
    Miryeganeh, M. & Saze, H. Epigenetic inheritance and plant evolution. Popul. Ecol. 62, 17–27. https://doi.org/10.1002/1438-390x.12018 (2020).Article 

    Google Scholar 
    Becklin, K. M. et al. Examining plant physiological responses to climate change through an evolutionary lens. Plant Physiol. 172, 635–649. https://doi.org/10.1104/pp.16.00793 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Szymanska, R., Slesak, I., Orzechowska, A. & Kruk, J. Physiological and biochemical responses to high light and temperature stress in plants. Environ. Exp. Bot. 139, 165–177. https://doi.org/10.1016/j.envexpbot.2017.05.002 (2017).CAS 
    Article 

    Google Scholar 
    Agrawal, A. A., Erwin, A. C. & Cook, S. C. Natural selection on and predicted responses of ecophysiological traits of swamp milkweed (Asclepias incarnata). J. Ecol. 96, 536–542. https://doi.org/10.1111/j.1365-2745.2008.01365.x (2008).Article 

    Google Scholar 
    Azhar, A., Sathornkich, J., Rattanawong, R. & Kasemsap, P. Responses of chlorophyll fluorescence, stomatal conductance, and net photosynthesis rates of four rubber (Hevea brasiliensis) genotypes to drought. Adv. Rubber 844, 11–14. https://doi.org/10.4028/www.scientific.net/AMR.844.11 (2014).CAS 
    Article 

    Google Scholar 
    Bussotti, F., Pancrazi, M., Matteucci, G. & Gerosa, G. Leaf morphology and chemistry in Fagus sylvatica (beech) trees as affected by site factors and ozone: Results from CONECOFOR permanent monitoring plots in Italy. Tree Physiol. 25, 211–219. https://doi.org/10.1093/treephys/25.2.211 (2005).CAS 
    Article 
    PubMed 

    Google Scholar 
    Carlson, J. E., Adams, C. A. & Holsinger, K. E. Intraspecific variation in stomatal traits, leaf traits and physiology reflects adaptation along aridity gradients in a South African shrub. Ann. Bot. 117, 195–207. https://doi.org/10.1093/aob/mcv146 (2016).Article 
    PubMed 

    Google Scholar 
    De Frenne, P. et al. Temperature effects on forest herbs assessed by warming and transplant experiments along a latitudinal gradient. Glob. Change Biol. 17, 3240–3253. https://doi.org/10.1111/j.1365-2486.2011.02449.x (2011).ADS 
    Article 

    Google Scholar 
    Reinhardt, K., Castanha, C., Germino, M. J. & Kueppers, L. M. Ecophysiological variation in two provenances of Pinus flexilis seedlings across an elevation gradient from forest to alpine. Tree Physiol. 31, 615–625. https://doi.org/10.1093/treephys/tpr055 (2011).Article 
    PubMed 

    Google Scholar 
    Yamori, W., Hikosaka, K. & Way, D. A. Temperature response of photosynthesis in C-3, C-4, and CAM plants: Temperature acclimation and temperature adaptation. Photosynth. Res. 119, 101–117. https://doi.org/10.1007/s11120-013-9874-6 (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    Stojanova, B. et al. Adaptive differentiation of Festuca rubra along a climate gradient revealed by molecular markers and quantitative traits. PLoS One https://doi.org/10.1371/journal.pone.0194670 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Han, S. K. & Wagner, D. Role of chromatin in water stress responses in plants. J. Exp. Bot. 65, 2785–2799. https://doi.org/10.1093/jxb/ert403 (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    Han, S. K. & Torii, K. U. Lineage-specific stem cells, signals and asymmetries during stomatal development. Development 143, 1259–1270. https://doi.org/10.1242/dev.127712 (2016).CAS 
    Article 
    PubMed 

    Google Scholar 
    Torii, K. U. Stomatal differentiation: The beginning and the end. Curr. Opin. Plant Biol. 28, 16–22. https://doi.org/10.1016/j.pbi.2015.08.005 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    Tricker, P. J., Gibbings, J. G., Lopez, C. M. R., Hadley, P. & Wilkinson, M. J. Low relative humidity triggers RNA-directed de novo DNA methylation and suppression of genes controlling stomatal development. J. Exp. Bot. 63, 3799–3813. https://doi.org/10.1093/jxb/ers076 (2012).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Vrablova, M., Hronkova, M., Vrabl, D., Kubasek, J. & Santrucek, J. Light intensity-regulated stomatal development in three generations of Lepidium sativum. Environ. Exp. Bot. 156, 316–324. https://doi.org/10.1016/j.envexpbot.2018.09.012 (2018).CAS 
    Article 

    Google Scholar 
    Tricker, P. J., Lopez, C. M. R., Gibbings, G., Hadley, P. & Wilkinson, M. J. Transgenerational, dynamic methylation of stomata genes in response to low relative humidity. Int. J. Mol. Sci. 14, 6674–6689. https://doi.org/10.3390/ijms14046674 (2013).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Puy, J. et al. Improved demethylation in ecological epigenetic experiments: Testing a simple and harmless foliar demethylation application. Methods Ecol. Evol. 9, 744–753. https://doi.org/10.1111/2041-210x.12903 (2018).Article 

    Google Scholar 
    Kosová, V., Hájek, T., Hadincová, V. & Münzbergová, Z. The importance of ecophysiological traits in response of Festuca rubra to changing climate. Physiol. Plant. 174, e13608. https://doi.org/10.1111/ppl.13608 (2022).CAS 
    Article 
    PubMed 

    Google Scholar 
    Maricle, B. R. & Adler, P. B. Effects of precipitation on photosynthesis and water potential in Andropogon gerardii and Schizachyrium scoparium in a southern mixed grass prairie. Environ. Exp. Bot. 72, 223–231. https://doi.org/10.1016/j.envexpbot.2011.03.011 (2011).Article 

    Google Scholar 
    Münzbergová, Z. et al. Plant origin, but not phylogeny, drive species ecophysiological response to projected climate. Front. Plant Sci. 11, 400. https://doi.org/10.3389/fpls.2020.00400 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Beerling, D. J. & Chaloner, W. G. The impact of atmospheric CO2 and temperature change on stomatal density—observations from Quercus robur lammas leaves. Ann. Bot. 71, 231–235. https://doi.org/10.1006/anbo.1993.1029 (1993).CAS 
    Article 

    Google Scholar 
    Tang, Y. L. et al. Heat stress induces an aggregation of the light-harvesting complex of photosystem II in spinach plants. Plant Physiol. 143, 629–638. https://doi.org/10.1104/pp.106.090712 (2007).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jahns, P. & Holzwarth, A. R. The role of the xanthophyll cycle and of lutein in photoprotection of photosystem II. BBA-Bioenerget. 1817, 182–193. https://doi.org/10.1016/j.bbabio.2011.04.012 (2012).CAS 
    Article 

    Google Scholar 
    Baker, N. R. & Rosenqvist, E. Applications of chlorophyll fluorescence can improve crop production strategies: An examination of future possibilities. J. Exp. Bot. 55, 1607–1621. https://doi.org/10.1093/jxb/erh196 (2004).CAS 
    Article 
    PubMed 

    Google Scholar 
    Baker, H. G. In The Genetics of Colonizing Species (eds Baker, H. G. & Stebbins, G. L.) 147–168 (Academic Press, 1965).
    Google Scholar 
    Bartlett, M. K. et al. Global analysis of plasticity in turgor loss point, a key drought tolerance trait. Ecol. Lett. 17, 1580–1590. https://doi.org/10.1111/ele.12374 (2014).Article 
    PubMed 

    Google Scholar 
    Raven, J. A. Selection pressures on stomatal evolution. New Phytol. 153, 371–386. https://doi.org/10.1046/j.0028-646X.2001.00334.x (2002).CAS 
    Article 
    PubMed 

    Google Scholar 
    Zhang, F. F. et al. Effects of CO2 enrichment on growth and development of Impatiens hawkeri. Sci. World J. https://doi.org/10.1100/2012/601263 (2012).ADS 
    Article 

    Google Scholar 
    Gonzalez, A. P. R. et al. Stress-induced memory alters growth of clonal off spring of white clover (Trifolium repens). Am. J. Bot. 103, 1567–1574. https://doi.org/10.3732/ajb.1500526 (2016).CAS 
    Article 
    PubMed 

    Google Scholar 
    Jones, P. A., Taylor, S. M. & Wilson, V. L. Inhibition of DNA methylation by 5-azacytidine. Recent Results Cancer Res. 84, 202–211 (1983).CAS 
    PubMed 

    Google Scholar 
    Meineri, E., Skarpaas, O., Spindelbock, J., Bargmann, T. & Vandvik, V. Direct and size-dependent effects of climate on flowering performance in alpine and lowland herbaceous species. J. Veg. Sci. 25, 275–286. https://doi.org/10.1111/jvs.12062 (2014).Article 

    Google Scholar 
    Šurinová, M., Hadincová, V., Vandvik, V. & Münzbergová, Z. Temperature and precipitation, but not geographic distance, explain genetic relatedness among populations in the perennial grass Festuca rubra. J. Plant Ecol. 12, 730–741. https://doi.org/10.1093/jpe/rtz010 (2019).Article 

    Google Scholar 
    Münzbergová, Z., Hadincová, V., Skálová, H. & Vandvik, V. Genetic differentiation and plasticity interact along temperature and precipitation gradients to determine plant performance under climate change. J. Ecol. 105, 1358–1373. https://doi.org/10.1111/1365-2745.12762 (2017).Article 

    Google Scholar 
    Klanderud, K., Vandvik, V. & Goldberg, D. The importance of biotic vs abiotic drivers of local plant community composition along regional bioclimatic gradients. PLoS One 10, e0130205. https://doi.org/10.1371/journal.pone.0130205 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Meineri, E., Skarpaas, O. & Vandvik, V. Modeling alpine plant distributions at the landscape scale: Do biotic interactions matter?. Ecol. Model. 231, 1–10. https://doi.org/10.1016/j.ecolmodel.2012.01.021 (2012).Article 

    Google Scholar 
    Meineri, E., Spindelbock, J. & Vandvik, V. Seedling emergence responds to both seed source and recruitment site climates: A climate change experiment combining transplant and gradient approaches. Plant Ecol. 214, 607–619. https://doi.org/10.1007/s11258-013-0193-y (2013).Article 

    Google Scholar 
    Vandvik, V., Klanderud, K., Meineri, E., Maren, I. E. & Topper, J. Seed banks are biodiversity reservoirs: Species-area relationships above versus below ground. Oikos 125, 218–228. https://doi.org/10.1111/oik.02022 (2016).Article 

    Google Scholar 
    Stojanova, B. et al. Evolutionary potential of a widespread clonal grass under changing climate. J. Evol. Biol. 32, 1057–1068. https://doi.org/10.1111/jeb.13507 (2019).Article 
    PubMed 

    Google Scholar 
    Osorio-Montalvo, P., Saenz-Carbonell, L. & De-la-Pena, C. 5-azacytidine: A promoter of epigenetic changes in the quest to improve plant somatic embryogenesis. Int. J. Mol. Sci. 19, 20. https://doi.org/10.3390/ijms19103182 (2018).CAS 
    Article 

    Google Scholar 
    Hurlbert, S. H. Pseudoreplication and the design of ecological field experiments. Ecol. Monogr. 54, 187–211. https://doi.org/10.2307/1942661 (1984).Article 

    Google Scholar 
    Münzbergová, Z. & Hadincová, V. Transgenerational plasticity as an important mechanism affecting response of clonal species to changing climate. Ecol. Evol. 7, 5236–5247. https://doi.org/10.1002/ece3.3105 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Oksanen, L. Logic of experiments in ecology: Is pseudoreplication a pseudoissue?. Oikos 94, 27–38. https://doi.org/10.1034/j.1600-0706.2001.11311.x (2001).Article 

    Google Scholar 
    Johnson, S. N., Gherlenda, A. N., Frew, A. & Ryalls, J. M. W. The importance of testing multiple environmental factors in legume-insect research: Replication, reviewers, and rebuttal. Front. Plant Sci. 7, 489. https://doi.org/10.3389/fpls.2016.00489 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hurlbert, S. H. On misinterpretations of pseudoreplication and related matters: A reply to Oksanen. Oikos 104, 591–597. https://doi.org/10.1111/j.0030-1299.2004.12752.x (2004).Article 

    Google Scholar 
    Scheepens, J. F. & Stocklin, J. Flowering phenology and reproductive fitness along a mountain slope: Maladaptive responses to transplantation to a warmer climate in Campanula thyrsoides. Oecologia 171, 679–691. https://doi.org/10.1007/s00442-012-2582-7 (2013).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Gugger, S., Kesselring, H., Stoecklin, J. & Hamann, E. Lower plasticity exhibited by high- versus mid-elevation species in their phenological responses to manipulated temperature and drought. Ann. Bot. 116, 953–962. https://doi.org/10.1093/aob/mcv155 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bezemer, T. M., Thompson, L. J. & Jones, T. H. Poa annua shows inter-generational differences in response to elevated CO2. Glob. Change Biol. 4, 687–691. https://doi.org/10.1046/j.1365-2486.1998.00184.x (1998).ADS 
    Article 

    Google Scholar 
    Cavieres, L. A. & Arroyo, M. T. K. Seed germination response to cold stratification period and thermal regime in Phacelia secunda (Hydrophyllaceae)—altitudinal variation in the mediterranean Andes of central Chile. Plant Ecol. 149, 1–8. https://doi.org/10.1023/a:1009802806674 (2000).Article 

    Google Scholar 
    Souther, S., Lechowicz, M. J. & McGraw, J. B. Experimental test for adaptive differentiation of ginseng populations reveals complex response to temperature. Ann. Bot. 110, 829–837. https://doi.org/10.1093/aob/mcs155 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Matias, L. & Jump, A. S. Impacts of predicted climate change on recruitment at the geographical limits of Scots pine. J. Exp. Bot. 65, 299–310. https://doi.org/10.1093/jxb/ert376 (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    Zhang, H. X. et al. Germination shifts of C-3 and C-4 species under simulated global warming scenario. PLoS One 9, e105139. https://doi.org/10.1371/journal.pone.0105139 (2014).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Maxwell, K. & Johnson, G. N. Chlorophyll fluorescence—a practical guide. J. Exp. Bot. 51, 659–668 (2000).Ashraf, M. & Harris, P. J. C. Photosynthesis under stressful environments: An overview. Photosynthetica 51, 163–190. https://doi.org/10.1007/s11099-013-0021-6 (2013).CAS 
    Article 

    Google Scholar 
    Majekova, M., Martinkova, J. & Hajek, T. Grassland plants show no relationship between leaf drought tolerance and soil moisture affinity, but rapidly adjust to changes in soil moisture. Funct. Ecol. 33, 774–785. https://doi.org/10.1111/1365-2435.13312 (2019).Article 

    Google Scholar 
    Volis, S., Ormanbekova, D., Yermekbayev, K., Song, M. S. & Shulgina, I. Multi-approaches analysis reveals local adaptation in the emmer wheat (Triticum dicoccoides) at macro—but not micro-geographical scale. PLoS One 10, 19. https://doi.org/10.1371/journal.pone.0121153 (2015).CAS 
    Article 

    Google Scholar 
    Younginger, B. S., Sirova, D., Cruzan, M. B. & Ballhorn, D. J. Is biomass a reliable estimate of plant fitness?. Appl. Plant Sci. https://doi.org/10.3732/apps.1600094 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    R Development Core Team. Version 4.0.3 A language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2011).
    Google Scholar 
    Bossdorf, O., Arcuri, D., Richards, C. L. & Pigliucci, M. Experimental alteration of DNA methylation affects the phenotypic plasticity of ecologically relevant traits in Arabidopsis thaliana. Evol. Ecol. 24, 541–553. https://doi.org/10.1007/s10682-010-9372-7 (2010).Article 

    Google Scholar 
    Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P. R., O’Hara, R. B., Simpson, G. L., Solymos, P., Stevens, M. H. H., Szoecs, E. & Wagner, H. (2020). vegan: Community Ecology Package. R package version 2.5-7.Lande, R. & Arnold, S. J. The measurement of selection on correlated characters. Evolution 37, 1210–1226. https://doi.org/10.2307/2408842 (1983).Article 
    PubMed 

    Google Scholar 
    Rolhauser, A. G., Nordenstahl, M., Aguiar, M. R. & Pucheta, E. Community-level natural selection modes: A quadratic framework to link multiple functional traits with competitive ability. J. Ecol. 107, 1457–1468. https://doi.org/10.1111/1365-2745.13094 (2019).Article 

    Google Scholar 
    Yan, W. M., Zhong, Y. Q. W. & Shangguan, Z. P. Contrasting responses of leaf stomatal characteristics to climate change: A considerable challenge to predict carbon and water cycles. Glob. Change Biol. 23, 3781–3793. https://doi.org/10.1111/gcb.13654 (2017).ADS 
    Article 

    Google Scholar 
    González, A. P. R., Dumalasová, V., Rosenthal, J., Skuhrovec, J. & Latzel, V. The role of transgenerational effects in adaptation of clonal offspring of white clover (Trifolium repens) to drought and herbivory. Evol. Ecol. 31, 345–361. https://doi.org/10.1007/s10682-016-9844-5 (2017).Article 

    Google Scholar 
    Shi, W. et al. Transient stability of epigenetic population differentiation in a clonal invader. Front. Plant Sci. https://doi.org/10.3389/fpls.2018.01851 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Quan, J., Münzbergová, Z. & Latzel, V. Time dynamics of stress legacy in clonal transgenerational effects: A case study on Trifolium repens. Ecol. Evol. https://doi.org/10.1002/ece3.8959 (2022).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Harris, C. J. et al. A DNA methylation reader complex that enhances gene transcription. Science 362, 1182. https://doi.org/10.1126/science.aar7854 (2018).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zhang, K. R., Cheng, X. L., Shu, X., Liu, Y. & Zhang, Q. F. Linking soil bacterial and fungal communities to vegetation succession following agricultural abandonment. Plant Soil 431, 19–36. https://doi.org/10.1007/s11104-018-3743-1 (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    Xiao, X. L. et al. A group of SUVH methyl-DNA binding proteins regulate expression of the DNA demethylase ROS1 in Arabidopsis. J. Integr. Plant Biol. 61, 110–119. https://doi.org/10.1111/jipb.12768 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    Gallego-Bartolome, J. DNA methylation in plants: Mechanisms and tools for targeted manipulation. New Phytol. 227, 38–44. https://doi.org/10.1111/nph.16529 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    Wang, Z. W., Bossdorf, O., Prati, D., Fischer, M. & van Kleunen, M. Transgenerational effects of land use on offspring performance and growth in Trifolium repens. Oecologia 180, 409–420. https://doi.org/10.1007/s00442-015-3480-6 (2016).ADS 
    Article 
    PubMed 

    Google Scholar 
    Muir, C. D., Pease, J. B. & Moyle, L. C. Quantitative genetic analysis indicates natural selection on leaf phenotypes across wild tomato species (Solanum sect. Lycopersicon; Solanaceae). Genetics 198, 1629. https://doi.org/10.1534/genetics.114.169276 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ramirez-Valiente, J. A. et al. Natural selection and neutral evolutionary processes contribute to genetic divergence in leaf traits across a precipitation gradient in the tropical oak Quercus oleoides. Mol. Ecol. 27, 2176–2192. https://doi.org/10.1111/mec.14566 (2018).Article 
    PubMed 

    Google Scholar 
    Jueterbock, A. et al. The seagrass methylome is associated with variation in photosynthetic performance among clonal shoots. Front. Plant Sci. 11, 19. https://doi.org/10.3389/fpls.2020.571646 (2020).Article 

    Google Scholar 
    Ganguly, D. R., Crisp, P. A., Eichten, S. R. & Pogson, B. J. The Arabidopsis DNA methylome is stable under transgenerational drought stress. Plant Physiol. 175, 1893–1912. https://doi.org/10.1104/pp.17.00744 (2017).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ganguly, D. R., Crisp, P. A., Eichten, S. R. & Pogson, B. J. Maintenance of pre-existing DNA methylation states through recurring excess-light stress. Plant Cell Environ. 41, 1657–1672. https://doi.org/10.1111/pce.13324 (2018).CAS 
    Article 
    PubMed 

    Google Scholar 
    Nixon, P. J., Michoux, F., Yu, J. F., Boehm, M. & Komenda, J. Recent advances in understanding the assembly and repair of photosystem II. Ann. Bot. 106, 1–16. https://doi.org/10.1093/aob/mcq059 (2010).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Perez, T. M. & Feeley, K. J. Photosynthetic heat tolerances and extreme leaf temperatures. Funct. Ecol. 34, 2236–2245. https://doi.org/10.1111/1365-2435.13658 (2020).Article 

    Google Scholar 
    Kitayama, K., Pattison, R., Cordell, S., Webb, D. & MuellerDombois, D. Ecological and genetic implications of foliar polymorphism in Metrosideros polymorpha Gaud (Myrtaceae) in a habitat matrix on Mauna Loa, Hawaii. Ann. Bot. 80, 491–497. https://doi.org/10.1006/anbo.1996.0473 (1997).Article 

    Google Scholar 
    Konopkova, A. et al. Nucleotide polymorphisms associated with climate and physiological traits in silver fir (Abies alba Mill.) provenances. Flora 250, 37–43. https://doi.org/10.1016/j.flora.2018.11.012 (2019).Article 

    Google Scholar 
    Baer, A., Wheeler, J. K. & Pittermann, J. Limited hydraulic adjustments drive the acclimation response of Pteridium aquilinum to variable light. Ann. Bot. 125, 691–700. https://doi.org/10.1093/aob/mcaa006 (2020).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hao, X. F., Jin, Z. P., Wang, Z. Q., Qin, W. S. & Pei, Y. X. Hydrogen sulfide mediates DNA methylation to enhance osmotic stress tolerance in Setaria italic L.. Plant Soil 453, 355–370. https://doi.org/10.1007/s11104-020-04590-5 (2020).CAS 
    Article 

    Google Scholar 
    Colaneri, A. C. & Jones, A. M. Genome-wide quantitative identification of DNA differentially methylated sites in Arabidopsis seedlings growing at different water potential. PLoS One 8, 10. https://doi.org/10.1371/journal.pone.0059878 (2013).CAS 
    Article 

    Google Scholar 
    Becker, C. & Weigel, D. Epigenetic variation: Origin and transgenerational inheritance. Curr. Opin. Plant Biol. 15, 562–567. https://doi.org/10.1016/j.pbi.2012.08.004 (2012).CAS 
    Article 
    PubMed 

    Google Scholar 
    Spens, A. E. & Douhovnikoff, V. Epigenetic variation within Phragmites australis among lineages, genotypes, and ramets. Biol. Invas. 18, 2457–2462. https://doi.org/10.1007/s10530-016-1223-1 (2016).Article 

    Google Scholar 
    Herrera, C. M., Pozo, M. I. & Bazaga, P. Jack of all nectars, master of most: DNA methylation and the epigenetic basis of niche width in a flower-living yeast. Mol. Ecol. 21, 2602–2616. https://doi.org/10.1111/j.1365-294X.2011.05402.x (2012).CAS 
    Article 
    PubMed 

    Google Scholar 
    Herrera, C. M. & Bazaga, P. Epigenetic correlates of plant phenotypic plasticity: DNA methylation differs between prickly and nonprickly leaves in heterophyllous Ilex aquifolium (Aquifoliaceae) trees. Bot. J. Linn. Soc. 171, 441–452. https://doi.org/10.1111/boj.12007 (2013).Article 

    Google Scholar 
    Keller, T. E., Lasky, J. R. & Yi, S. V. The multivariate association between genomewide DNA methylation and climate across the range of Arabidopsis thaliana. Mol. Ecol. 25, 1823–1837. https://doi.org/10.1111/mec.13573 (2016).CAS 
    Article 
    PubMed 

    Google Scholar 
    Madliger, C. L., Love, O. P., Hultine, K. R. & Cooke, S. J. The conservation physiology toolbox: Status and opportunities. Conserv. Physiol. 6, 16. https://doi.org/10.1093/conphys/coy029 (2018).CAS 
    Article 

    Google Scholar 
    Münzbergová, Z. & Haisel, D. Effects of polyploidization on the contents of photosynthetic pigments are largely population-specific. Photosynth. Res. 140, 289–299. https://doi.org/10.1007/s11120-018-0604-y (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    Balachandran, S. et al. Concepts of plant biotic stress. Some insights into the stress physiology of virus-infected plants, from the perspective of photosynthesis. Physiol. Plant. 100, 203–213. https://doi.org/10.1034/j.1399-3054.1997.1000201.x (1997).CAS 
    Article 

    Google Scholar 
    Pavlíková, Z., Holá, D., Vlasáková, B., Procházka, T. & Münzbergová, Z. Physiological and fitness differences between cytotypes vary with stress in a grassland perennial herb. PLoS One https://doi.org/10.1371/journal.pone.0188795 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zhang, B. B., Zhang, H., Jing, Q. & Wang, J. X. Light pollution on the growth, physiology and chlorophyll fluorescence response of landscape plant perennial ryegrass (Lolium perenne L.). Ecol. Indic. 115, 9. https://doi.org/10.1016/j.ecolind.2020.106448 (2020).CAS 
    Article 

    Google Scholar 
    Cameron, D. D., Geniez, J. M., Seel, W. E. & Irving, L. J. Suppression of host photosynthesis by the parasitic plant Rhinanthus minor. Ann. Bot. 101, 573–578. https://doi.org/10.1093/aob/mcm324 (2008).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Molina-Montenegro, M. A., Salgado-Luarte, C., Oses, R. & Torres-Diaz, C. Is physiological performance a good predictor for fitness? Insights from an invasive plant species. PLoS One 8, 9. https://doi.org/10.1371/journal.pone.0076432 (2013).CAS 
    Article 

    Google Scholar 
    dos Santos, V. & Ferreira, M. J. Are photosynthetic leaf traits related to the first-year growth of tropical tree seedlings? A light-induced plasticity test in a secondary forest enrichment planting. For. Ecol. Manage. 460, 9. https://doi.org/10.1016/j.foreco.2020.117900 (2020).Article 

    Google Scholar 
    Shi, Q. W. et al. Phosphorus-fertilisation has differential effects on leaf growth and photosynthetic capacity of Arachis hypogaea L.. Plant Soil 447, 99–116. https://doi.org/10.1007/s11104-019-04041-w (2020).CAS 
    Article 

    Google Scholar 
    Madriaza, K., Saldana, A., Salgado-Luarte, C., Escobedo, V. M. & Gianoli, E. Chlorophyll fluorescence may predict tolerance to herbivory. Int. J. Plant Sci. 180, 81–85. https://doi.org/10.1086/700583 (2019).Article 

    Google Scholar 
    Franks, P. J., Drake, P. L. & Beerling, D. J. Plasticity in maximum stomatal conductance constrained by negative correlation between stomatal size and density: An analysis using Eucalyptus globulus. Plant Cell Environ. 32, 1737–1748. https://doi.org/10.1111/j.1365-3040.2009.002031.x (2009).Article 
    PubMed 

    Google Scholar 
    Belluau, M. & Shipley, B. Linking hard and soft traits: Physiology, morphology and anatomy interact to determine habitat affinities to soil water availability in herbaceous dicots. PLoS One 13, 25. https://doi.org/10.1371/journal.pone.0193130 (2018).CAS 
    Article 

    Google Scholar 
    Jerbi, A. et al. High biomass yield increases in a primary effluent wastewater phytofiltration are associated to altered leaf morphology and stomatal size in Salix miyabeana. Sci. Total Environ. 738, 12. https://doi.org/10.1016/j.scitotenv.2020.139728 (2020).CAS 
    Article 

    Google Scholar 
    Sakoda, K. et al. Higher stomatal density improves photosynthetic induction and biomass production in Arabidopsis under fluctuating light. Front. Plant Sci. 11, 11. https://doi.org/10.3389/fpls.2020.589603 (2020).Article 

    Google Scholar 
    Liu, J. Y. et al. Effect of summer warming on growth, photosynthesis and water status in female and male Populus cathayana: Implications for sex-specific drought and heat tolerances. Tree Physiol. 40, 1178–1191. https://doi.org/10.1093/treephys/tpaa069 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    Griffin, P. T., Niederhuth, C. E. & Schmitz, R. J. A comparative analysis of 5-azacytidine- and zebularine-induced DNA demethylation. G3 Genes Genomes Genet. 6, 2773–2780. https://doi.org/10.1534/g3.116.030262 (2016).CAS 
    Article 

    Google Scholar 
    Zhang, Y. X. et al. Application of 5-azacytidine induces DNA hypomethylation and accelerates dormancy release in buds of tree peony. Plant Physiol. Biochem. 147, 91–100. https://doi.org/10.1016/j.plaphy.2019.12.010 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    Sammarco, I., Muenzbergova, Z. & Latzel, V. DNA methylation can mediate local adaptation and response to climate change in the clonal plant Fragaria vesca: Evidence from a European-scale reciprocal transplant experiment. Front. Plant Sci. https://doi.org/10.3389/fpls.2022.827166 (2022).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Atighi, M. R., Verstraeten, B., De Meyer, T. & Kyndt, T. Genome-wide DNA hypomethylation shapes nematode pattern-triggered immunity in plants. New Phytol. 227, 545–558. https://doi.org/10.1111/nph.16532 (2020).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Nowicka, A. et al. Comparative analysis of epigenetic inhibitors reveals different degrees of interference with transcriptional gene silencing and induction of DNA damage. Plant J. 102, 68–84. https://doi.org/10.1111/tpj.14612 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    Christman, J. K. 5-Azacytidine and 5-aza-2 ’-deoxycytidine as inhibitors of DNA methylation: Mechanistic studies and their implications for cancer therapy. Oncogene 21, 5483–5495. https://doi.org/10.1038/sj.onc.1205699 (2002).CAS 
    Article 
    PubMed 

    Google Scholar 
    Issa, J. P. J. & Kantarjian, H. M. Targeting DNA methylation. Clin. Cancer Res. 15, 3938–3946. https://doi.org/10.1158/1078-0432.ccr-08-2783 (2009).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Amoah, S. et al. A hypomethylated population of Brassica rapa for forward and reverse epi-genetics. BMC Plant Biol. https://doi.org/10.1186/1471-2229-12-193 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    McGuigan, K., Hoffmann, A. A. & Sgro, C. M. How is epigenetics predicted to contribute to climate change adaptation? What evidence do we need?. Philos. Trans. R. Soc. B Biol. Sci. 376, 10. https://doi.org/10.1098/rstb.2020.0119 (2021).Article 

    Google Scholar 
    Sano, H., Kamada, I., Youssefian, S., Katsumi, M. & Wabiko, H. A single treatment of rice seedlings with 5-azacytidine induces heritable dwarfism and undermethylation of genomic DNA. Mol. Gen. Genet. 220, 441–447. https://doi.org/10.1007/bf00391751 (1990).CAS 
    Article 

    Google Scholar 
    Kondo, H., Ozaki, H., Itoh, K., Kato, A. & Takeno, K. Flowering induced by 5-azacytidine, a DNA demethylating reagent in a short-day plant, Perilla frutescens var. crispa. Physiol. Plant. 127, 130–137. https://doi.org/10.1111/j.1399-3054.2005.00635.x (2006).CAS 
    Article 

    Google Scholar 
    Kumpatla, S. P. & Hall, T. C. Longevity of 5-azacytidine-mediated gene expression and re-establishment of silencing in transgenic rice. Plant Mol. Biol. 38, 1113–1122. https://doi.org/10.1023/a:1006071018039 (1998).CAS 
    Article 
    PubMed 

    Google Scholar 
    Lira-Medeiros, C. F. et al. Epigenetic variation in mangrove plants occurring in contrasting natural environment. PLoS One https://doi.org/10.1371/journal.pone.0010326 (2010).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Raj, S. et al. Clone history shapes Populus drought responses. Proc. Natl. Acad. Sci. USA 108, 12521–12526. https://doi.org/10.1073/pnas.1103341108 (2011).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Richards, C. L., Schrey, A. W. & Pigliucci, M. Invasion of diverse habitats by few Japanese knotweed genotypes is correlated with epigenetic differentiation. Ecol. Lett. 15, 1016–1025. https://doi.org/10.1111/j.1461-0248.2012.01824.x (2012).Article 
    PubMed 

    Google Scholar 
    Platt, A., Gugger, P. F., Pellegrini, M. & Sork, V. L. Genome-wide signature of local adaptation linked to variable CpG methylation in oak populations. Mol. Ecol. 24, 3823–3830. https://doi.org/10.1111/mec.13230 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    Pfeifer, G. P. Mutagenesis at methylated CpG sequences. DNA Methyl. Basic Mech. 301, 259–281 (2006).CAS 
    Article 

    Google Scholar 
    Walsh, C. P. & Xu, G. L. Cytosine methylation and DNA repair. DNA Methyl. Basic Mech. 301, 283–315 (2006).CAS 
    Article 

    Google Scholar  More

  • in

    Canopy arthropod declines along a gradient of olive farming intensification

    Seibold, S. et al. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574, 671–674 (2019).ADS 
    CAS 
    PubMed 

    Google Scholar 
    van Klink, R. et al. Meta-analysis reveals declines in terrestrial but increases in freshwater insect abundances. Science 368, 417–420 (2020).ADS 
    PubMed 

    Google Scholar 
    Wagner, D. L. Insect declines in the anthropocene. Annu. Rev. Entomol. 65, 457–480 (2020).CAS 
    PubMed 

    Google Scholar 
    Wilson, E. O. The little things that run the world (the importance and conservation of invertebrates). Conserv. Biol. 1, 344–346 (1987).
    Google Scholar 
    Isaacs, R., Tuell, J., Fiedler, A., Gardiner, M. & Landis, D. Maximizing arthropod-mediated ecosystem services in agricultural landscapes: The role of native plants. Front. Ecol. Environ. 7, 196–203 (2009).
    Google Scholar 
    Hallmann, C. A. et al. More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLoS ONE 12, e0185809 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Raven, P. H. & Wagner, D. L. Agricultural intensification and climate change are rapidly decreasing insect biodiversity. Proc. Natl. Acad. Sci. U.S.A. 118, e2002548117 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Neves, B. & Pires, I. M. The Mediterranean diet and the increasing demand of the olive oil sector: Shifts and environmental consequences. Region. 5, 101–112 (2018).
    Google Scholar 
    Silveira, A. et al. The sustainability of agricultural intensification in the early 21st century: Insights from the olive oil production in Alentejo (Southern Portugal). In Changing Societies: Legacies and Challenges. The Diverse Worlds of Sustainability, 247–275 (2018).Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Salomone, R. & Ioppolo, G. Environmental impacts of olive oil production: A Life Cycle Assessment case study in the province of Messina (Sicily). J. Clean. Prod. 28, 88–100 (2012).
    Google Scholar 
    Rallo, L. et al. High-density olive plantations. Hortic. Rev. Am. Soc. Hortic. Sci. 41, 303–383 (2013).
    Google Scholar 
    Santos, S. A. P., Pereira, J. A., Torres, L. M. & Nogueira, A. J. A. Evaluation of the effects, on canopy arthropods, of two agricultural management systems to control pests in olive groves from north-east of Portugal. Chemosphere 67, 131–139 (2007).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Gkisakis, V., Volakakis, N., Kollaros, D., Bàrberi, P. & Kabourakis, E. M. Soil arthropod community in the olive agroecosystem: Determined by environment and farming practices in different management systems and agroecological zones. Agric. Ecosyst. Environ. 218, 178–189 (2016).
    Google Scholar 
    Beaufoy, G. EU Policies for Olive Farming. Unsustainable on all counts (WWF and Birdlife International, Brussels, 2001).
    Google Scholar 
    EFNCP. The environmental impact of olive oil production in the EU: Practical options for improving the environmental impact. European Forum on Nature Conservation and Pastoralism & Asociación para el Análisis y Reforma de la Política Agro-rural, Brussels. https://ec.europa.eu/environment/agriculture/pdf/oliveoil.pdf (2000).Vanwalleghem, T. Quantifying the effect of historical soil management on soil erosion rates in Mediterranean olive orchards. Agric. Ecosyst. Environ. 142, 341–351 (2011).
    Google Scholar 
    Simões, M. P., Belo, A. F., Pinto-Cruz, C. & Pinheiro, A. C. Natural vegetation management to conserve biodiversity and soil water in olive orchards. Span. J. Agric. Res. 12, 633–643 (2014).
    Google Scholar 
    Milgroom, J., Soriano, M. A., Garrido, J. M., Gómez, J. A. & Fereres, E. The influence of a shift from conventional to organic olive farming on soil management and erosion risk in southern Spain. Renew. Agric. Food Syst. 22, 1–10 (2007).
    Google Scholar 
    Lodolini, E. M. & Neri, D. Organic olive farming. African J. Agric. Res. 8, 6426–6434 (2013).
    Google Scholar 
    Rallo, L. Iberian olive growing in a time of change. Chron. Horticult. 49, 27–30 (2010).
    Google Scholar 
    Diez, C. M. et al. Cultivar and tree density as key factors in the long-term performance of super high-density olive orchards. Front. Plant Sci. 7, 1–13 (2016).
    Google Scholar 
    Allen, H. D., Randall, R. E., Amable, G. S. & Devereux, B. J. The impact of changing olive cultivation practices on the ground flora of olive groves in the Messara and Psiloritis regions, Crete, Greece. L. Degrad. Dev. 17, 249–327 (2006).
    Google Scholar 
    Herrera, J. M., Costa, P., Medinas, D., Marques, J. T. & Mira, A. Community composition and activity of insectivorous bats in Mediterranean olive farms. Anim. Conserv. 18, 557–566 (2015).
    Google Scholar 
    Costa, A. et al. Structural simplification compromises the potential of common insectivorous bats to provide biocontrol services against the major olive pest Prays oleae. Agric. Ecosyst. Environ. 287, 106708 (2020).
    Google Scholar 
    Morgado, R. et al. A Mediterranean silent spring? The effects of olive farming intensification on breeding bird communities. Agric. Ecosyst. Environ. 288, 106694 (2020).
    Google Scholar 
    Ruano, F. et al. Use of arthropods for the evaluation of the olive-orchard management regimes. Agric. For. Entomol. 6, 111–120 (2004).
    Google Scholar 
    Jerez-Valle, C., García, P. A., Campos, M. & Pascual, F. A simple bioindication method to discriminate olive orchard management types using the soil arthropod fauna. Appl. Soil Ecol. 76, 42–51 (2014).
    Google Scholar 
    Carpio, A. J., Castro, J. & Tortosa, F. S. Arthropod biodiversity in olive groves under two soil management systems: Presence versus absence of herbaceous cover crop. Agric. For. Entomol. 21, 58–68 (2018).
    Google Scholar 
    Rey, P. J. et al. Landscape-moderated biodiversity effects of ground herb cover in olive groves: Implications for regional biodiversity conservation. Agric. Ecosyst. Environ. 277, 61–73 (2019).
    Google Scholar 
    Mccomb, W. C. & Noble, R. E. Invertebrate use of natural tree cavities and vertebrate nest boxes. Am. Midl. Nat. 107, 163–172 (1982).
    Google Scholar 
    Bovyn, R. A., Lordon, M. C., Grecco, A. E., Leeper, A. C. & LaMontagne, J. M. Tree cavity availability in urban cemeteries and city parks. J. Urban Ecol. 5, 1–9 (2019).
    Google Scholar 
    Ribera, I., Dolédec, S., Downie, I. & Foster, G. Effect of land disturbance and stress on species traits of ground beetle assemblages. Ecology 82, 1112–1129 (2001).
    Google Scholar 
    Barbaro, L. & van Halder, I. Linking bird, carabid beetle and butterfly life-history traits to habitat fragmentation in mosaic landscapes. Ecography 32, 321–333 (2009).
    Google Scholar 
    Steffan-Dewenter, I. & Tscharntke, T. Butterfly community structure in fragmented habitats. Ecol. Lett. 3, 449–456 (2000).
    Google Scholar 
    Gámez-Virués, S. et al. Landscape simplification filters species traits and drives biotic homogenization. Nat. Commun. 6, 8568 (2015).ADS 
    PubMed 

    Google Scholar 
    Medinas, D. et al. Road effects on bat activity depend on surrounding habitat type. Sci. Total Environ. 660, 340–347 (2019).ADS 
    CAS 
    PubMed 

    Google Scholar 
    INE. Estatísticas Agrícolas – 2018. Lisboa. Instituto Nacional de Estatística. https://www.ine.pt/xportal/xmain?xpid=INE&xpgid=ine_publicacoes&PUBLICACOESpub_boui=358629204&PUBLICACOESmodo=2 (2019).Rodríguez-Cohard, J. C., Sánchez-Martínez, J. D. & Garrido-Almonacid, A. Strategic responses of the European olive-growing territories to the challenge of globalization. Eur. Plan. Stud. 28, 2261–2283 (2020).
    Google Scholar 
    Reis, P. O olival em Portugal. Dinâmicas, tecnologias e relação com o desenvolvimento rural. Instituto Nacional de Investigação Agrária e Veterinária. http://www.iniav.pt/fotos/editor2/caderno_olivalemportugal.pdf (2014).Yi, Z., Jinchao, F., Dayuan, X., Weiguo, S. & Axmacher, J. C. A comparison of terrestrial arthropod sampling methods. J. Resour. Ecol. 3, 174–182 (2012).
    Google Scholar 
    Leather, S. R. Insect Sampling in Forest Ecosystems (Wiley-Blackwell, New Jersey, 2008).
    Google Scholar 
    Paredes, D., Cayuela, L. & Campos, M. Synergistic effects of ground cover and adjacent vegetation on natural enemies of olive insect pests. Agric. Ecosyst. Environ. 173, 72–80 (2013).
    Google Scholar 
    Porcel, M., Cotes, B., Castro, J. & Campos, M. The effect of resident vegetation cover on abundance and diversity of green lacewings (Neuroptera: Chrysopidae) on olive trees. J. Pest Sci. 90, 195–206 (2017).
    Google Scholar 

    Álvarez, H. A. et al. Semi-natural habitat complexity affects abundance and movement of natural enemies in organic olive orchards. Agric. Ecosyst. Environ. 285, 106618 (2019).
    Google Scholar 
    Paredes, D., Cayuela, L., Gurr, G. M. & Campos, M. Is ground cover vegetation an effective biological control enhancement strategy against olive pests?. PLoS ONE 10, e0117265 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Gkisakis, V. D. et al. Olive canopy arthropods under organic, integrated, and conventional management. The effect of farming practices, climate and landscape. Agroecol. Sustain. Food Syst. 42, 843–858 (2018).
    Google Scholar 
    Sanz-Cortés, F. et al. Phenological growth stages of olive trees (Olea europaea). Ann. Appl. Biol. 140, 151–157 (2002).
    Google Scholar 
    Rodríguez, E., González, B. & Campos, M. Natural enemies associated with cereal cover crops in olive groves. Bullet. Insectol. 65, 43–49 (2012).
    Google Scholar 
    Morente, M., Campos, M. & Ruano, F. Evaluation of two different methods to measure the effects of the management regime on the olive-canopy arthropod community. Agric. Ecosyst. Environ. 259, 111–118 (2018).
    Google Scholar 
    Cardenas, M., Pascual, F., Campos, M. & Pekar, S. The spider assemblage of olive groves under three management systems. Environ. Entomol. 44, 509–518 (2015).PubMed 

    Google Scholar 
    Hegazi, E. M. et al. Seasonality in the occurrence of two lepidopterous olive pests in Egypt. Insect Sci. 18, 565–574 (2011).
    Google Scholar 
    Markó, V., Keresztes, B., Fountain, M. T. & Cross, J. V. Prey availability, pesticides and the abundance of orchard spider communities. Biol. Control 48, 115–124 (2009).
    Google Scholar 
    Picchi, M. S., Marchi, S., Albertini, A. & Petacchi, R. Organic management of olive orchards increases the predation rate of overwintering pupae of Bactrocera oleae (Diptera: Tephritidae). Biol. Control 108, 9–15 (2017).
    Google Scholar 
    Caruso, T. & Migliorini, M. Micro-arthropod communities under human disturbance: Is taxonomic aggregation a valuable tool for detecting multivariate change? Evidence from Mediterranean soil oribatid coenoses. Acta Oecol. 30, 46–53 (2006).ADS 

    Google Scholar 
    Schipper, A. M., Lotterman, K., Geertsma, M., Leuven, R. S. E. W. & Hendriks, A. J. Using datasets of different taxonomic detail to assess the influence of floodplain characteristics on terrestrial arthropod assemblages. Biodivers. Conserv. 19, 2087–2110 (2010).
    Google Scholar 
    Timms, L. L., Bowden, J. J., Summerville, K. S. & Buddle, C. M. Does species-level resolution matter? Taxonomic sufficiency in terrestrial arthropod biodiversity studies. Insect Conserv. Divers. 6, 453–462 (2013).
    Google Scholar 
    Unwin, D. M. A Key to the Families of British Beetles (Field Studies Council, 1984).Goulet, H. & Huber, J. Hymenoptera of the World: An identification Guide to Families. (Agriculture Canada publication, 1993).Johnson, N. F. & Triplehorn, C. A. Borror and DeLong’s Introduction to the Study of Insects 7th edn. (Thomson Brooks/Cole, Belmont, 2005).
    Google Scholar 
    Fletcher, M. J., and updates. Identification keys and checklists for the leafhoppers, planthoppers and their relatives occurring in Australia and neighbouring areas (Hemiptera: Auchenorrhyncha). https://idtools.dpi.nsw.gov.au/keys/auch/index.html (2009).Mata, L. & Goula, M. Clave de familias de Heterópteros de la Península Ibérica (Insecta, Hemiptera, Heteroptera). Versión 1. Publicaciones del Centre de Recursos de Biodiversitat Animal, Universitat de Barcelona. http://www.ub.edu/crba/publicacions/Clau%20heteropters/Volum4_Clave_de_Familias_de_Heteropteros_de_la_P.Iberica.pdf (2011).Oosterbroek, P. The European families of the Diptera. Identification, diagnosis, biology. (Royal Dutch Society for Natural History (KNNV) Publishing, Utrecht, 2015).World Spider Catalog. Version 19. Natural History Museum Bern. http://wsc.nmbe.ch (2018).Campos, M. Lacewing in Andalusian olive orchards. In Lacewing in the Crop Environment (eds McEwen, P. et al.) 492–497 (Cambridge University Press, Cambridge, 2001).
    Google Scholar 
    Wilson, E. O. & Hölldobler, B. The rise of the ants: A phylogenetic and ecological explanation. Proc. Natl. Acad. Sci. U. S. A. 102, 7411–7414 (2005).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Martínez-Núñez, C. et al. Ant community potential for pest control in olive groves: Management and landscape effects. Agric. Ecosyst. Environ 305, 107185 (2021).
    Google Scholar 
    Bianchi, F. J. J. A., Booij, C. J. H. & Tscharntke, T. Sustainable pest regulation in agricultural landscapes: A review on landscape composition, biodiversity and natural pest control. Proc. R. Soc. B. 273, 1715–1727 (2006).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Holland, J. M. et al. Semi-natural habitats support biological control, pollination and soil conservation in Europe. A review. Agron. Sustain. Dev. 37, 31 (2017).
    Google Scholar 

    Paredes, D. et al. Landscape simplification increases Bactrocera oleae abundance in olive groves: Adult population dynamics in different land uses. J. Pest Sci. https://doi.org/10.1007/s10340-022-01489-1 (2022).Article 

    Google Scholar 
    Thies, C., Roschewitz, I. & Tscharntke, T. The landscape context of cereal aphid–parasitoid interactions. Proc. R. Soc. B. 285, 203–210 (2005).
    Google Scholar 
    Pinto-Correia, T., Ribeiro, N. & Sá-Sousa, P. Introducing the montado, the cork and holm oak agroforestry system of Southern Portugal. Agrofor. Syst. 82, 99–104 (2011).
    Google Scholar 
    Morgado, R. et al. Drivers of irrigated olive grove expansion in Mediterranean landscapes and associated biodiversity impacts. Landsc. Urban Plan. 225, 104429 (2022).
    Google Scholar 
    Direção-Geral do Território. Carta de Uso e Ocupação do Solo de Portugal Continental para 2015 (COS2015). http://www.dgterritorio.pt/dados_abertos/cos/ (2015).Roswell, M., Dushoff, J. & Winfree, R. A conceptual guide to measuring species diversity. Oikos 130, 321–338 (2021).
    Google Scholar 
    Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: An R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol. Evol. 7, 1451–1456 (2016).
    Google Scholar 
    Penado, A. et al. From pastures to forests: Changes in Mediterranean wild bee communities after rural land abandonment. Insect Conserv. Divers. 15, 325–336 (2022).
    Google Scholar 
    Ovaskainen, O. & Abrego, N. Joint Species Distribution Modelling. With Applications in R. (Cambridge University Press, 2020).Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).
    Google Scholar 
    Macgregor-Fors, I. & Payton, M. E. Contrasting diversity values: Statistical inferences based on overlapping confidence intervals. PLoS ONE 8, e56794 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Tikhonov, G. et al. Joint species distribution modelling with the r-package Hmsc. Methods Ecol. Evol. 11, 442–447 (2019).
    Google Scholar 
    R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria, 2021).
    Google Scholar 
    Wickramasinghe, L. P., Harris, S. H., Jones, G. & Jennings, N. V. Abundance and species richness of nocturnal insects on organic and conventional farms: Effects of agricultural intensification on bat foraging. Conserv. Biol. 8, 1283–1292 (2004).
    Google Scholar 
    Galloway, A. D., Seymour, C. L., Gaigher, R. & Pryke, J. S. Organic farming promotes arthropod predators, but this depends on neighbouring patches of natural vegetation. Agric. Ecosyst. Environ. 310, 107295 (2021).
    Google Scholar 
    Hevia, V., Ortega, J., Azcárate, F. M., López, C. A. & González, J. A. Exploring the effect of soil management intensity on taxonomic and functional diversity of ants in Mediterranean olive groves. Agric. For. Entomol. 21, 109–118 (2019).
    Google Scholar 
    Vitanović, E. et al. Arthropod communities within the olive canopy as bioindicators of different management systems. Span. J. Agric. Res. 16, e0301 (2018).
    Google Scholar 
    Vasconcelos, S. et al. Long-term consequences of agricultural policy decisions: How are forests planted under EEC regulation 2080/92 affecting biodiversity 20 years later?. Biol. Conserv. 236, 393–403 (2019).
    Google Scholar 
    Tscharntke, T. et al. When natural habitat fails to enhance biological pest control—five hypotheses. Biol. Conserv. 204, 449–458 (2016).
    Google Scholar 
    Ortega, M., Pascual, S. & Rescia, A. J. Spatial structure of olive groves and scrublands affects Bactrocera oleae abundance: A multi-scale analysis. Basic Appl. Ecol. 17, 696–705 (2016).
    Google Scholar 
    Martínez-Núñez, C. et al. Direct and indirect effects of agricultural practices, landscape complexity and climate on insectivorous birds, pest abundance and damage in olive groves. Agric. Ecosyst. Environ. 304, 107145 (2020).
    Google Scholar 
    Paredes, D., Karp, D. S., Chaplin-Kramer, R., Benítez, E. & Campos, M. Natural habitat increases natural pest control in olive groves: Economic implications. J. Pest Sci. 92, 1111–1121 (2019).
    Google Scholar 
    Attwood, S. J., Maron, M., House, P. N. & Zammit, C. Do arthropod assemblages display globally consistent responses to intensified agricultural land use and management?. Glob. Ecol. Biogeogr. 17, 585–599 (2008).
    Google Scholar 
    Miranda, M. A., Miquel, M., Terrassa, J., Melis, N. & Monerris, M. Parasitism of Bactrocera oleae (Diptera; Tephritidae) by Psyttalia concolor (Hymenoptera; Braconidae) in the Balearic Islands (Spain). J. Appl. Entomol. 132, 798–805 (2008).
    Google Scholar 
    Álvarez, H. A., Morente, M., Campos, M. & Ruano, F. L. madurez de las cubiertas vegetales aumenta la presencia de enemigos naturales y la resiliencia de la red trófica de la copa del olivo. Ecosistemas 28, 92–106 (2019).
    Google Scholar 
    Rusch, A., Valantin-Morison, M., Sarthou, J. P. & Roger-Estrade, J. Biological control of insect pests in agroecosystems. Effects of crop management, farming systems, and seminatural habitats at the landscape scale: A review. Adv. Agron. 109, 219–259 (2010).
    Google Scholar 
    Greenop, A., Cook, S. M., Wilby, A., Pywell, R. F. & Woodcock, B. A. Invertebrate community structure predicts natural pest control resilience to insecticide exposure. J. Appl. Ecol. 57, 2441–2453 (2020).CAS 

    Google Scholar 
    Porcel, M., Ruano, F., Cotes, B., Peña, A. & Campos, M. Agricultural management systems affect the green lacewing community (Neuroptera: Chrysopidae) in olive orchards in southern Spain. Environ. Entomol. 42, 97–106 (2013).CAS 
    PubMed 

    Google Scholar 
    Stamou, G. P. Arthropods of Mediterranean-Type Ecosystems (Springer, 2012).Santos, J. L. et al. A farming systems approach to linking agricultural policies with biodiversity and ecosystem services. Front. Ecol. Environ. 19, 168–175 (2021).
    Google Scholar 
    Ribeiro, P. F. et al. An applied farming systems approach to infer conservation-relevant agricultural practices for agri-environment policy design. Land Use Policy 58, 165–172 (2016).
    Google Scholar 
    Herrera, J. M. et al. A food web approach reveals the vulnerability of biocontrol services by birds and bats to landscape modification at regional scale. Sci. Rep. 11, 1–10 (2021).
    Google Scholar 
    Solomou, A. D. & Sfougaris, A. I. Bird community characteristics as indicators of sustainable management in olive grove ecosystems of Central Greece. J. Nat. Hist. 49, 301–325 (2015).
    Google Scholar 
    Piñeiro, V. et al. A scoping review on incentives for adoption of sustainable agricultural practices and their outcomes. Nat Sustain. 3, 809–820 (2020).
    Google Scholar  More

  • in

    High deforestation trajectories in Cambodia slowly transformed through economic land concession restrictions and strategic execution of REDD+ protected areas

    Deforestation trajectories and economic driversCambodia has undergone significant forest loss in recent decades—with 2.6 million hectares of forest cover loss occurring since 2001, equating to 29.5% of forest cover7 and 1.45 billion tonnes of CO2 emissions8. The deforestation rates have increased by 76% in the last decade (2011–2021) compared to the previous (2001–2010; Fig. 1b)7. We find forest loss has occurred within three distinct Phases demonstrated by changepoint analysis: (1) Phase 1: steady rise from 2000 to 2009 (average = 0.82%/year), (2) Phase 2: peak years from 2010 to 2013 (average = 2.3%/year), (3) Phase 3: moderate phase from 2014 to 2021 (average = 1.6%/year). Whilst the annual rate of deforestation has declined since the Phase 2, Cambodia currently has the highest country-level annual rate of forest loss globally7, illustrating the relentless deforestation spreading across the landscape. Critically, much of this forest loss and degradation is occurring in mature primary forests (Fig. 1b), which hold significant carbon and are home to rich biodiversity and keystone species17,18,19.
    This deforestation in Cambodia has been attributed to the widespread development of Economic Land Concessions (ELCs), the expansion of numerous agricultural frontiers and relentless illegal logging20,21,22. These drivers have been abetted by the establishment of an extensive national road network (Fig. 1a)20—developed to promote economic growth and urban–rural connectivity23. The majority (88.4%) of these roads have been funded by foreign governments (the People’s Republic of China: 38.5%, Japan: 37.9%, and Republic of Korea: 12.0%)18—all of whom have established land concessions within Cambodia’s borders24 through the allocation of state land into private land for long-term industrial plantations22,25. The expansion of ELCs across Cambodia (average addition of 105,000 ha/year of ELC land since 1998) has been directly attributed to up to 40% of the country’s deforestation21, with further indirect impacts due to encroachment into rural community lands (indigenous areas, community forests, subsistence agricultural fields). This results in landlessness, poverty, and land conflicts, forcing communities to migrate in search of arable land, further contributing to the growing degradation and destruction of forests22,26,27,28,29.Strategic government interventionProtected areas expanded across Cambodia in 1993 following a royal decree26; the legal details of which were delineated in the 2008 Protected Areas Law, introducing protected categories, wildlife corridors and strict laws prohibiting development9. While over 80 protected areas currently exist covering 35% of Cambodian land10, they are still under substantial threat30. In further efforts to curb deforestation, the Royal Government of Cambodia ordered the suspension of new ELCs and revocation of a subset of existing ELCs in 2012 (Order 01BB)31. This resulted in a reduction of ELCs from a peak of ~ 2.1 million ha in 2012 to ~ 1.6 million ha from 2014 onward (Fig. 1b), with a significant positive correlation between the quantity of land classified as ELCs and the country-level deforestation rate (R = 0.87, p  More

  • in

    Brain de novo transcriptome assembly of a toad species showing polymorphic anti-predatory behavior

    Sample collection and RNA preparationWe analyzed 6 adult yellow-bellied toad individuals representative of distinct behavioral profiles, i.e. prolonged unken-reflex display vs no unken-reflex display (thereafter referred as “ + ” and “-“, respectively). Behavioral profiles were scored as in Chiocchio et al.12: 3 toads showed prolonged unken-reflex (+), whereas the other 3 did not show unken-reflex (−), as reported in Table 1. Sampling procedures were approved by the Italian Ministry of Ecological Transition and the Italian National Institute for Environmental Protection and Research (ISPRA; permit number: 20824, 18-03-2020). After dissection, brain tissue was immediately stored in RNAprotect Tissue Reagent (Quiagen) until RNA extraction. RNA extractions were performed using the RNeasy Plus Kit (Quiagen), according to the manufacturer’ instructions. RNA quality and concentration were assessed by means of both a spectrophotometer and a Bioanalyzer (Agilent Cary60 UV-vis and Agilent 2100, respectively – Agilent Technologies, Santa Clara, USA).Table 1 Summary of the 6 libraries deposited in the Sequence Read Archive (SRA) of NCBI, in terms of number of raw and trimmed reads per sample.Full size tableLibrary preparation and sequencingLibrary preparation and RNA sequencing were performed by NOVOGENE (UK) COMPANY LIMITED using Illumina NovaSeq platform. Library construction was carried out using the NEBNext® Ultra ™ RNA Library Prep Kit for Illumina®, following manufacturer instructions. Briefly, after the quality control check, the mRNA sample was isolated from the total RNA by using magnetic beads made of oligos d(T)25 (i.e. polyA-tail mRNA enrichment). Subsequently, mRNA was randomly fragmented, and a cDNA synthesis step proceeded using random hexamers and the reverse transcriptase enzyme. Once the synthesis of the first chain has finished, the second chain was synthesized with the addition of the Illumina buffer, dNTPs, RNase H and polymerase I of E.coli, by means of the Nick translation method. Then, the resulting products went through purification, repair, A-tailing and adapter ligation. Fragments of the appropriate size were enriched by PCR, the indexed P5 and P7 primers were introduced, and the final products were purified. Finally, the Illumina Novaseq 6000 sequencing system was used to sequence the libraries, through a paired-end 150 bp (PE150) strategy. We obtained on average 52.7 million reads for each library. The sequencing data are available at the NCBI Sequence Read Archive (project ID PRJNA76401320).Pre-assembly processing stageA total of 316,329,573 pairs of reads was generated by Illumina sequencing. All of them went to a cleaning analytic step. The quality of the raw reads was assessed with the FastQC 0.11.5 tool (http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc), in order to estimate the RNAseq quality profiles. The quality estimators were generated for both the raw and trimmed data. The quality assessment metrics for trimmed data were aggregated across all samples into a single report for a summary visualization with MultiQC software tool21 v.1.9 (see Fig. 1). To remove low quality bases and adapter sequences, raw reads were also analyzed through a quality trimming step with Trimmomatic22, v.0.39 (setting the option SLIDINGWINDOW: 4: 15, MINLEN: 36, and HEADCROP: 13). All the unpaired reads were discarded. After the cleaning step and removal of low-quality reads, 297,354,405 clean reads (i.e. 94% of raw reads) were maintained for building the de novo transcriptome assembly (see Table 1).Fig. 1The cleaned reads from all samples were assessed with FastQC and visualized with MultiQC. (a) Read count distribution for mean sequence quality. (b) Mean quality scores distribution. (c) Read length distribution. (d) Per Sequence GC Content.Full size image
    De novo transcriptome assembly and quality assessmentAs there is no reference genome for B. pachypus, we performed a de novo transcriptome assembly procedure. The workflow of the bioinformatic pipelines is shown in Fig. 2. All the described bioinformatics analyses were performed on the high-performance computing systems provided by ELIXIR-IT HPC@CINECA23.Fig. 2Workflow of the bioinformatic pipeline, from raw input data to annotated contigs, for the de novo transcriptome assembly of B. pachypus.Full size imageTo construct an optimized de novo transcriptome, avoiding chimeric transcripts, we employed rnaSPAdes24, a tool for de novo transcriptome assembly from RNA-Seq data implemented in the SPAdes v.3.14.1 package. rnaSPAdes automatically detected two k-mer sizes, approximately one third and half of the maximal read length (the two detected k-mer sizes were 45 and 67 nucleotides, respectively). At this stage, a total of 1,118,671 assembled transcripts were generated by rnaSPAdes runs, with an average length of 689.41 bp and an N50 of 1474 bp (Table 2).Table 2 Similarity rate of newly assembled transcripts versus the de novo transcriptome of B. pachypus.Full size tableResults from the assembly procedures were validated through three independent validator algorithms implemented in BUSCO25 v.4.1.4, DETONATE26 v.1.11 and TransRate27 v.1.0.3. These tools generate several metrics used as a guide to evaluate error sources in the assembly process and provide evidence about the quality of the assembled transcriptome. Busco provides a quantitative measure of transcriptome quality and completeness, based on evolutionarily-informed expectations of gene content from the near-universal, ultra-conserved eukaryotic proteins (eukaryota_odb9) database. Detonate (DE novo TranscriptOme rNa-seq Assembly with or without the Truth Evaluation) is a reference-free evaluation method based on a novel probabilistic model that depends only on the assembly and the RNA-Seq reads used to construct it. Transrate generates standard metrics and remapping statistics. No reference protein sequences were used for the assessment with Transrate. The main metrics resulted from the assembly validators are shown in Table 2 (“Before CD-HIT-est” column). After this triple assessment validation step, the result of the assembly procedure become the input for the CD-HIT-est v.4.8.128 program, a hierarchical clustering tool used to avoid redundant transcripts and fragmented assemblies common in the process of de novo assembly, providing unique genes. CD-HIT-est was run using the default parameters, corresponding to a similarity of 95%. Subsequently, a second validation step was launched on the CD-HIT-est output file. To refine the final transcriptome dataset, a further hierarchical clustering step was performed by running CORSET v1.0629. Then, the output of CORSET was validated by BUSCO, and quality assessment was performed with HISAT230,31 by mapping the trimmed reads to the reference transcriptome (unigenes). Results from all validation steps are shown in Table 2 and discussed in the “Technical Validation” paragraph.Finally, the CORSET output was run on TransDecoder32,33, the current standard tool that identifies long open read frames (ORFs) in assembled transcripts, using default parameters. TransDecoder by default performs ORF prediction on both strands of assembled transcripts regardless of the sequenced library. It also ranks ORFs based on their completeness, and determines if the 5 ‘end is incomplete by looking for any length of AA codons upstream of a start codon (M) without a stop codon. We adopted the “Longest ORF” rule and selected the highest 5 AUG (relative to the inframe stop codon) as the translation start site.Transcriptome annotationWe employed different kinds of annotations for the de novo assembly. We introduced DIAMOND34, an open-source algorithm based on double indexing that is 20,000 times faster than BLASTX on short reads and has a similar degree of sensitivity. Like BLASTX, DIAMOND attempts to determine exhaustively all significant alignments for a given query. Most sequence comparison programs, including BLASTX, follow the seed-and-extend paradigm. In this two-phase approach, users search first for matches of seeds (short stretches of the query sequence) in the reference database, and this is followed by an ‘extend’ phase that aims to compute a full alignment. The following parameter settings were applied: DIAMOND-fast DIAMOND BLASTX-t 48 -k 250 -min-score 40; DIAMOND-sensitive: DIAMOND BLASTX -t 48 -k 250 -sensitive -min-score 40.Contigs were aligned with DIAMOND on Nr, SwissProt and TrEMBL to retrieve the corresponding best annotations. An annotation matrix was then generated by selecting the best hit for each database. Following the analysis of BLASTX against Nr, SwissProt and TremBL, we obtained respectively: 123,086 (64.57%), 77,736 (40.78%), 122,907 (64.48%) contigs. The results obtained following the analysis with BLASTP against Nr, SwissProt and TrEMBL were 96,321 (50.53%), 57,877 (30.36%) and 97,256 (51.02%) contigs respectively. All the information on the resulting datasets is resumed in Table 3.Table 3 Summary of homology annotation hits on the different databases queried in this study.Full size tableThe output obtained by the BLASTX annotation consisted in a total of 77391 sequences simultaneously mapped on the three queried databases (i.e., Nr, SwissProt and TrEMBL). The output obtained following the BLASTP annotation consisted in a total of 57704 sequences simultaneously mapped on the three databases. Venn diagrams are presented in Fig. 3, showing the redundancy of the annotations in the different databases for both DIAMOND BLASTX (Fig. 3a) and DIAMOND BLASTP (Fig. 3b). Furthermore, the ten most represented species and the ten hits of the gene product obtained respectively with BLASTX and BLASTP by mapping the transcripts against the reference database Nr are shown in Figs. 4 and 5. Since BLASTX translated nucleotide sequence searches against protein sequences the BLASTX results are more exhaustive than BLASTP results. Contigs were also processed with InterProScan35 to detect InterProScan signatures. The InterPro database (http://www.ebi.ac.uk/interpro/) integrates together predictive models or ‘signatures’ representing protein domains, families and functional sites from multiple, diverse source databases: Gene3D, PANTHER, Pfam, PIRSF, PRINTS, ProDom, PROSITE, SMART, SUPERFAMILY and TIGRFAMs. The obtained InterProScan results for all the unigenes are available on Figshare in the form of Tab Separated Values (tsv) file format, which includes the GO and KEGG annotated contigs, respectively.Fig. 3Venn diagrams for the number of contigs annotated with DIAMOND (BLASTX (a) and BLASTP (b) functions) against the three databases: Nr, SwissProt, TREMBL.Full size imageFig. 4Most represented species and gene product hits. Top 10 best species (a) and protein (b) hits present in the reference database (Nr, BLASTX).Full size imageFig. 5Most represented species and gene product hits. Top 10 best species (a) and protein (b) hits present in the reference database (Nr, BLASTP).Full size imageComparison with Bombina orientalis brain transcriptomeWe compared the brain de novo transcriptome of B. pachypus with the brain de novo transcriptome of B. orientalis, recently produced in the frame of a prey-catching conditioning experiment17,18. The B. orientalis transcriptome resource was downloaded from GEO archive of NCBI (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE171766). To make the datasets comparable, we first performed ORF prediction on B. orientalis trascriptome using Transdecoder, using default settings. Results from the B. orientalis trascriptome ORF prediction are available in Figshare at the following link https://doi.org/10.6084/m9.figshare.20319633/). We also applied the makedb function implemented in DIAMOND to create the protein database index. Then, we aligned the B. pachypus predicted coding sequences and proteins (query files) against the B. orientalis protein database (reference) using DIAMOND BLASTX and BLASTP, respectively. We obtained 167041 matches from BLASTX and 156248 matches for BLASTP. Results from the BLASTX and BLASTP comparisons, and the most matched proteins, are available on Figshare36 (link available in next paragraph). More

  • in

    Low functional vulnerability of fish assemblages to coral loss in Southwestern Atlantic marginal reefs

    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