Philippa Kaur

Moberg, F. & Folke, C. Ecological goods and services of coral reef ecosystems. Ecol. Econ. 29, 215–233 (1999).Article 

Google Scholar 
Woodhead, A. J., Hicks, C. C., Norström, A. V., Williams, G. J. & Graham, N. A. J. Coral reef ecosystem services in the Anthropocene. Funct. Ecol. 33, 1023–1034 (2019).
Google Scholar 
Hughes, T. P. et al. Coral reefs in the Anthropocene. Nature 546, 82 (2017).Article 
ADS 
CAS 

Google Scholar 
Hughes, T. P., Kerry, J. T. & Simpson, T. Large-scale bleaching of corals on the Great Barrier Reef. Ecology 99, 501 (2017).Article 

Google Scholar 
Anthony, K. R. N., Kline, D. I., Diaz-Pulido, G., Dove, S. & Hoegh-Guldberg, O. Ocean acidification causes bleaching and productivity loss in coral reef builders. PNAS 105, 17442–17446 (2008).Article 
ADS 
CAS 

Google Scholar 
Courtial, L., Roberty, S., Shick, J. M., Houlbrèque, F. & Ferrier-Pagès, C. Interactive effects of ultraviolet radiation and thermal stress on two reef-building corals. Limnol. Oceanogr. 62, 1000–1013 (2017).Article 
ADS 

Google Scholar 
Jessen, C. et al. In-situ effects of eutrophication and overfishing on physiology and bacterial diversity of the Red Sea Coral Acropora hemprichii. PLoS ONE 8, e62091 (2013).Article 
ADS 
CAS 

Google Scholar 
Jessen, C., Roder, C., Villa Lizcano, J. F., Voolstra, C. R. & Wild, C. In-situ effects of simulated overfishing and eutrophication on benthic coral reef algae growth, succession, and composition in the Central Red Sea. PLoS ONE 8, e66992 (2013).Article 
ADS 
CAS 

Google Scholar 
Fabricius, K. E. Effects of terrestrial runoff on the ecology of corals and coral reefs: Review and synthesis. Mar. Pollut. Bull. 50, 125–146 (2005).Article 
CAS 

Google Scholar 
Hughes, T. P. et al. Climate change, human impacts, and the resilience of coral reefs. Science 301, 929–933 (2003).Article 
ADS 
CAS 

Google Scholar 
Fabricius, K. E., Cséke, S., Humphrey, C. & De’ath, G. Does trophic status enhance or reduce the thermal tolerance of scleractinian corals? A review, experiment and conceptual framework. PLoS ONE 8, e54399 (2013).Article 
ADS 
CAS 

Google Scholar 
McLachlan, R. H., Price, J. T., Solomon, S. L. & Grottoli, A. G. Thirty years of coral heat-stress experiments: A review of methods. Coral Reefs 39, 885–902 (2020).Article 

Google Scholar 
Fabricius, K. E. Factors determining the resilience of coral reefs to eutrophication: A review and conceptual model. In Coral Reefs: An Ecosystem in Transition (eds Dubinsky, Z. & Stambler, N.) (Springer, 2011).
Google Scholar 
Tilstra, A. et al. Light induced intraspecific variability in response to thermal stress in the hard coral Stylophora pistillata. PeerJ. https://doi.org/10.7717/PEERJ.3802/ (2017).Article 

Google Scholar 
Connolly, S. R., Lopez-Yglesias, M. A. & Anthony, K. R. N. Food availability promotes rapid recovery from thermal stress in a scleractinian coral. Coral Reefs 31, 951–960 (2012).Article 
ADS 

Google Scholar 
Coles, S. L. & Brown, B. E. Coral bleaching—Capacity for acclimatization and adaptation. Adv. Mar. Biol. 46, 183 (2003).Article 
CAS 

Google Scholar 
Rosenberg, E., Koren, O., Reshef, L., Efrony, R. & Zilber-Rosenberg, I. The role of microorganisms in coral health, disease and evolution. Nat. Rev. Microbiol. 5, 355–362 (2007).Article 
CAS 

Google Scholar 
Szmant, A. M. Nutrient enrichment on coral reefs: Is it a major cause of coral reef decline? Estuaries 25, 743–766 (2002).Article 
CAS 

Google Scholar 
Atkinson, M. J., Carlson, B. & Crow, G. L. Coral growth in high-nutrient, low-pH seawater: A case study of corals cultured at the Waikiki Aquarium, Honolulu, Hawaii. Coral Reefs 14, 215–223 (1995).Article 
ADS 

Google Scholar 
Bongiorni, L., Shafir, S., Angel, D. & Rinkevich, B. Survival, growth and gonad development of two hermatypic corals subjected to in situ fish-farm nutrient enrichment. Mar. Ecol. Prog. Ser. 253, 137–144 (2003).Article 
ADS 

Google Scholar 
Grigg, R. W. Coral reefs in an urban embayment in Hawaii: A complex case history controlled by natural and anthropogenic stress. Coral Reefs 14, 253–266 (1995).Article 
ADS 

Google Scholar 
Fabricius, K. E. & De’ath, G. Identifying ecological change and its causes: A case study on coral reefs. Ecol. Appl. 14, 1448–1465 (2004).Article 

Google Scholar 
Ferrier-Pagès, C., Gattuso, J. P., Dallot, S. & Jaubert, J. Effect of nutrient enrichment on growth and photosynthesis of the zooxanthellate coral Stylophora pistillata. Coral Reefs 19, 103–113 (2000).Article 

Google Scholar 
Rosset, S., Wiedenmann, J., Reed, A. J. & D’Angelo, C. Phosphate deficiency promotes coral bleaching and is reflected by the ultrastructure of symbiotic dinoflagellates. Mar. Pollut. Bull. 118, 180–187 (2017).Article 
CAS 

Google Scholar 
Ban, S. S., Graham, N. A. J. & Connolly, S. R. Evidence for multiple stressor interactions and effects on coral reefs. Glob. Change Biol. 20, 681–697 (2014).Article 
ADS 

Google Scholar 
Wiedenmann, J. et al. Nutrient enrichment can increase the susceptibility of reef corals to bleaching. Nat. Clim. Change 3, 160–164 (2012).Article 
ADS 

Google Scholar 
Rädecker, N. et al. Heat stress destabilizes symbiotic nutrient cycling in corals. PNAS. https://doi.org/10.1073/pnas.2022653118 (2021).Article 

Google Scholar 
LaJeunesse, T. C. et al. Systematic revision of symbiodiniaceae highlights the antiquity and diversity of coral endosymbionts. Curr. Biol. 28, 2570–2580 (2018).Article 
CAS 

Google Scholar 
Falkowski, P. G., Dubinsky, Z., Muscatine, L. & McCloskey, L. Population control in symbiotic corals—Ammonium ions and organic materials maintain the density of zooxanthellae. Bioscience 43, 606–611 (1993).Article 

Google Scholar 
Muscatine, L. & Pool, R. R. Regulation of numbers of intracellular algae. Proc. R. Soc. Lond. Ser. B Biol. Sci. 204, 131–139 (1979).ADS 
CAS 

Google Scholar 
Muller-Parker, G., D’Elia, C. F. & Cook, C. B. Interactions between corals and their symbiotic algae. Coral Reefs Anthr. https://doi.org/10.1007/978-94-017-7249-5_5 (2015).Article 

Google Scholar 
Rädecker, N., Pogoreutz, C., Voolstra, C. R., Wiedenmann, J. & Wild, C. Nitrogen cycling in corals: The key to understanding holobiont functioning? Trends Microbiol. 23, 490–497 (2015).Article 

Google Scholar 
Steinberg, R. K., Dafforn, K. A., Ainsworth, T. & Johnston, E. L. Know thy anemone: A review of threats to octocorals and anemones and opportunities for their restoration. Front. Mar. Sci. 7, 590 (2020).Article 

Google Scholar 
Inoue, S., Kayanne, H., Yamamoto, S. & Kurihara, H. Spatial community shift from hard to soft corals in acidified water. Nat. Clim. Change 3, 683–687 (2013).Article 
ADS 
CAS 

Google Scholar 
Wild, C. & Naumann, M. S. Effect of active water movement on energy and nutrient acquisition in coral reef-associated benthic organisms. PNAS 110, 8767–8768 (2013).Article 
ADS 
CAS 

Google Scholar 
Fox, H. E., Pet, J. S., Dahuri, R. & Caldwell, R. L. Recovery in rubble fields: Long-term impacts of blast fishing. Mar. Pollut. Bull. 46, 1024–1031 (2003).Article 
CAS 

Google Scholar 
Benayahu, Y. & Loya, Y. Settlement and recruitment of a soft coral: Why is Xenia macrospiculata a successful colonizer? Bull. Mar. Sci. 36, 177–188 (1985).
Google Scholar 
Norström, A. V., Nyström, M., Lokrantz, J. & Folke, C. Alternative states on coral reefs: Beyond coral-macroalgal phase shifts. Mar. Ecol. Prog. Ser. 376, 293–306 (2009).Article 
ADS 

Google Scholar 
Reverter, M., Helber, S. B., Rohde, S., De Goeij, J. M. & Schupp, P. J. Coral reef benthic community changes in the Anthropocene: Biogeographic heterogeneity, overlooked configurations, and methodology. Glob. Change Biol. 28, 1956–1971 (2022).Article 

Google Scholar 
Karcher, D. B. et al. Nitrogen eutrophication particularly promotes turf algae in coral reefs of the central Red Sea. PeerJ 2020, 1–25 (2020).
Google Scholar 
El-Khaled, Y. C. et al. Nitrogen fixation and denitrification activity differ between coral- and algae-dominated Red Sea reefs. Sci. Rep. 11, 1–15 (2021).Article 

Google Scholar 
Ruiz-Allais, J. P., Benayahu, Y. & Lasso-Alcalá, O. M. The invasive octocoral Unomia stolonifera (Alcyonacea, Xeniidae) is dominating the benthos in the Southeastern Caribbean Sea. Mem. la Fund La Salle Ciencias Nat. 79, 63–80 (2021).
Google Scholar 
Ruiz Allais, J. P., Amaro, M. E., McFadden, C. S., Halász, A. & Benayahu, Y. The first incidence of an alien soft coral of the family Xeniidae in the Caribbean, an invasion in eastern Venezuelan coral communities. Coral Reefs 33, 287 (2014).Article 
ADS 

Google Scholar 
Baum, G., Januar, I., Ferse, S. C. A., Wild, C. & Kunzmann, A. Abundance and physiology of dominant soft corals linked to water quality in Jakarta Bay, Indonesia. PeerJ 2016, 1–29 (2016).
Google Scholar 
Menezes, N. M. et al. New non-native ornamental octocorals threatening a South-west Atlantic reef. J. Mar. Biol. Assoc. U.K. https://doi.org/10.1017/S0025315421000849 (2022).Article 

Google Scholar 
Mantelatto, M. C., da Silva, A. G., dos Louzada, T. S., McFadden, C. S. & Creed, J. C. Invasion of aquarium origin soft corals on a tropical rocky reef in the southwest Atlantic. Brazil. Mar. Pollut. Bull. 130, 84–94 (2018).Article 
CAS 

Google Scholar 
Simancas-Giraldo, S. M. et al. Photosynthesis and respiration of the soft coral Xenia umbellata respond to warming but not to organic carbon eutrophication. PeerJ 9, e11663 (2021).Article 

Google Scholar 
Vollstedt, S., Xiang, N., Simancas-Giraldo, S. M. & Wild, C. Organic eutrophication increases resistance of the pulsating soft coral Xenia umbellata to warming. PeerJ 2020, 1–16 (2020).
Google Scholar 
Thobor, B. et al. The pulsating soft coral Xenia umbellata shows high resistance to warming when nitrate concentrations are low. Sci. Rep. https://doi.org/10.1038/s41598-022-21110-w (2022).Article 

Google Scholar 
Costa, O. S., Leão, Z. M. A. N., Nimmo, M. & Attrill, M. J. Nutrification impacts on coral reefs from northern Bahia, Brazil. Hydrobiologia 440, 307–315 (2000).Article 
CAS 

Google Scholar 
Fleury, B. G., Coll, J. C., Tentori, E., Duquesne, S. & Figueiredo, L. Effect of nutrient enrichment on the complementary (secondary) metabolite composition of the soft coral Sarcophyton ebrenbergi (Cnidaria: Octocorallia: Alcyonaceae) of the Great Barrier Reef. Mar. Biol. 136, 63–68 (2000).Article 
CAS 

Google Scholar 
Bednarz, V. N., Naumann, M. S., Niggl, W. & Wild, C. Inorganic nutrient availability affects organic matter fluxes and metabolic activity in the soft coral genus Xenia. J. Exp. Biol. 215, 3672–3679 (2012).CAS 

Google Scholar 
Bruno, J. F., Petes, L. E., Harvell, C. D. & Hettinger, A. Nutrient enrichment can increase the severity of coral diseases. Ecol. Lett. 6, 1056–1061 (2003).Article 

Google Scholar 
Ezzat, L., Maguer, J.-F.F., Grover, R. & Ferrier-Pagès, C. Limited phosphorus availability is the Achilles heel of tropical reef corals in a warming ocean. Sci. Rep. 6, 1–11 (2016).Article 

Google Scholar 
Tanaka, Y., Grottoli, A. G., Matsui, Y., Suzuki, A. & Sakai, K. Effects of nitrate and phosphate availability on the tissues and carbonate skeleton of scleractinian corals. Mar. Ecol. Prog. Ser. 570, 101–112 (2017).Article 
ADS 
CAS 

Google Scholar 
Liu, G., Strong, A. E., Skirving, W. & Arzayus, L. F. Overview of NOAA coral reef watch program’s near-real time satellite global coral bleaching monitoring activities. In Proc. 10th International Coral Reef Symposium, 1783–1793 (2006).Bellworthy, J. & Fine, M. Beyond peak summer temperatures, branching corals in the Gulf of Aqaba are resilient to thermal stress but sensitive to high light. Coral Reefs 36, 1071–1082 (2017).Article 
ADS 

Google Scholar 
Rex, A., Montebon, F. & Yap, H. T. Metabolic responses of the scleractinian coral Porites cylindrica Dana to water motion. I. Oxygen flux studies. J. Exp. Mar. Biol. Ecol. 186, 33–52 (1995).Article 

Google Scholar 
Long, M. H., Berg, P., de Beer, D. & Zieman, J. C. In situ coral reef oxygen metabolism: An eddy correlation study. PLoS ONE 8, e58581 (2013).Article 
ADS 
CAS 

Google Scholar 
Fabricius, K. E. & Klumpp, D. W. Widespread mixotrophy in reef-inhabiting soft corals: The influence of depth, and colony expansion and contraction on photosynthesis. Mar. Ecol. Prog. Ser. 125, 195–204 (1995).Article 
ADS 

Google Scholar 
Bradford, M. M. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal. Biochem. 72, 248–254 (1976).Article 
CAS 

Google Scholar 
Raimonet, M., Guillou, G., Mornet, F. & Richard, P. Macroalgae δ15N values in well-mixed estuaries: Indicator of anthropogenic nitrogen input or macroalgae metabolism? Estuar. Coast. Shelf Sci. 119, 126–138 (2013).Article 
ADS 
CAS 

Google Scholar 
Furla, P., Galgani, I., Durand, I. & Allemand, D. Sources and mechanisms of inorganic carbon transport for coral calcification and photosynthesis. J. Exp. Biol. 203, 3445–3457 (2000).Article 
CAS 

Google Scholar 
Hughes, A. D., Grottoli, A. G., Pease, T. K. & Matsui, Y. Acquisition and assimilation of carbon in non-bleached and bleached corals. Mar. Ecol. Prog. Ser. 420, 91–101 (2010).Article 
ADS 
CAS 

Google Scholar 
Rau, G. H., Takahashi, T. & Des Marais, D. J. Latitudinal variations in plankton delta C-13—Implications for CO2 and productivity in past oceans. Nature 341, 516–518 (1989).Article 
ADS 
CAS 

Google Scholar 
McMahon, K. W., Hamady, L. L. & Thorrold, S. R. A review of ecogeochemistry approaches to estimating movements of marine animals. Limnol. Oceanogr. 58, 697–714 (2013).Article 
ADS 
CAS 

Google Scholar 
Muscatine, L., Porter, J. W. & Kaplan, I. R. Resource partitioning by reef corals as determined from stable isotope composition. Mar. Biol. 100, 185–193 (1989).Article 

Google Scholar 
Swart, P. K. et al. The isotopic composition of respired carbon dioxide in scleractinian corals: Implications for cycling of organic carbon in corals. Geochim. Cosmochim. Acta 69, 1495–1509 (2005).Article 
ADS 
CAS 

Google Scholar 
Rodrigues, L. J. & Grottoli, A. G. Calcification rate and the stable carbon, oxygen, and nitrogen isotopes in the skeleton, host tissue, and zooxanthellae of bleached and recovering Hawaiian corals. Geochim. Cosmochim. Acta 70, 2781–2789 (2006).Article 
ADS 
CAS 

Google Scholar 
Grottoli, A. G. & Rodrigues, L. J. Bleached Porites compressa and Montipora capitata corals catabolize δ13C-enriched lipids. Coral Reefs 30, 687–692 (2011).Article 
ADS 

Google Scholar 
Levas, S. J., Grottoli, A. G., Hughes, A., Osburn, C. L. & Matsui, Y. Physiological and biogeochemical traits of bleaching and recovery in the mounding species of coral porites lobata: Implications for resilience in mounding corals. PLoS ONE 8, 32–35 (2013).Article 

Google Scholar 
Schoepf, V. et al. Annual coral bleaching and the long-term recovery capacity of coral. Proc. R. Soc. B Biol. Sci. 282, 20151887 (2015).Article 

Google Scholar 
Lesser, M. P. et al. Nitrogen fixation by symbiotic cyanobacteria provides a source of nitrogen for the scleractinian coral Montastraea cavernosa. Mar. Ecol. Prog. Ser. 346, 143–152 (2007).Article 
ADS 
CAS 

Google Scholar 
Carpenter, E. J., Harvey, H. R., Brian, F. & Capone, D. G. Biogeochemical tracers of the marine cyanobacterium Trichodesmium. Deep Sea Res. I Oceanogr. Res. Pap. 44, 27–38 (1997).Article 
ADS 
CAS 

Google Scholar 
Lachs, L. et al. Effects of tourism-derived sewage on coral reefs: Isotopic assessments identify effective bioindicators. Mar. Pollut. Bull. 148, 85–96 (2019).Article 
CAS 

Google Scholar 
Kürten, B. et al. Influence of environmental gradients on C and N stable isotope ratios in coral reef biota of the Red Sea, Saudi Arabia. J. Sea Res. 85, 379–394 (2014).Article 
ADS 

Google Scholar 
Core Team, R. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).Wickham, H. et al. Welcome to the Tidyverse. J. Open Source Softw. 4, 1686 (2019).Article 
ADS 

Google Scholar 
Kassambara, A. ggpubr: ‘ggplot2’ Based Publication Ready Plots. R Package Version 0.4.0 (2020).Kassambara, A. rstatix: Pipe-Friendly Framework for Basic Statistical Tests. R Package Version 0.7.0 (2021).Contreras-Silva, A. I. et al. A meta-analysis to assess long-term spatiotemporal changes of benthic coral and macroalgae cover in the Mexican Caribbean. Sci. Rep. 10, 1–12 (2020).Article 

Google Scholar 
Ledlie, M. H. et al. Phase shifts and the role of herbivory in the resilience of coral reefs. Coral Reefs 26, 641–653 (2007).Article 
ADS 

Google Scholar 
Kuffner, I. B. & Toth, L. T. A geological perspective on the degradation and conservation of western Atlantic coral reefs. Conserv. Biol. 30, 706–715 (2016).Article 

Google Scholar 
Hughes, T. P. Catastrophes, phase shifts, and large-scale degradation of a Caribbean Coral Reef. Science 265, 1547–1551 (1994).Article 
ADS 
CAS 

Google Scholar 
de Bakker, D. M., Meesters, E. H., Bak, R. P. M., Nieuwland, G. & van Duyl, F. C. Long-term shifts in coral communities on shallow to deep reef slopes of Curaçao and Bonaire: Are there any winners? Front. Mar. Sci. 3, 247 (2016).Article 

Google Scholar 
Mergner, H. & Svoboda, A. Productivity and seasonal changes in selected reef areas in the Gulf of Aqaba (Red Sea). Helgoländer Meeresun. 30, 383–399 (1977).Article 

Google Scholar 
Schlichter, D., Svoboda, A. & Kremer, B. P. Functional autotrophy of Heteroxenia fuscescens (Anthozoa: Alcyonaria): Carbon assimilation and translocation of photosynthates from symbionts to host. Mar. Biol. 78, 29–38 (1983).Article 
CAS 

Google Scholar 
Al-Sofyani, A. A. & Niaz, G. R. A comparative study of the components of the hard coral Seriatopora hystrix and the soft coral Xenia umbellata along the Jeddah coast, Saudi Arabia. Rev. Biol. Mar. Oceanogr. 42, 207–219 (2007).Article 

Google Scholar 
McCloskey, L. R., Wethey, D. S. & Porter, J. W. Measurement and interpretation of photosynthesis and respiration in reef corals. In Coral Reefs: Research Methods (eds Stoddart, D. R. & Johannes, R. E.) 379–396 (United Nations Educational, Scientific and Cultural Organization, 1978).
Google Scholar 
Baker, D. M., Freeman, C. J., Wong, J. C. Y., Fogel, M. L. & Knowlton, N. Climate change promotes parasitism in a coral symbiosis. ISME J. 12, 921–930 (2018).Article 
CAS 

Google Scholar 
Hoegh-Guldberg, O. & Smith, G. J. The effect of sudden changes in temperature, light and salinity on the population density and export of zooxanthellae from the reef corals Stylophora pistillata Esper and Seriatopora hystrix Dana. J. Exp. Mar. Biol. Ecol. 129, 279–303 (1989).Article 

Google Scholar 
Iglesias-Prieto, R., Matta, J. L., Robins, W. A. & Trench, R. K. Photosynthetic response to elevated temperature in the symbiotic dinoflagellate Symbiodinium microadriaticum in culture. Proc. Natl. Acad. Sci. 89, 10302–10305 (1992).Article 
ADS 
CAS 

Google Scholar 
Béraud, E., Gevaert, F., Rottier, C. & Ferrier-Pagès, C. The response of the scleractinian coral Turbinaria reniformis to thermal stress depends on the nitrogen status of the coral holobiont. J. Exp. Biol. 216, 2665–2674 (2013).
Google Scholar 
Kremien, M., Shavit, U., Mass, T. & Genin, A. Benefit of pulsation in soft corals. Proc. Natl. Acad. Sci. U.S.A. 110, 8978–8983 (2013).Article 
ADS 
CAS 

Google Scholar 
Grover, R. et al. Coral uptake of inorganic phosphorus and nitrogen negatively affected by simultaneous changes in temperature and pH. PLoS ONE 6, 1–10 (2011).
Google Scholar 
Cardini, U. et al. Microbial dinitrogen fixation in coral holobionts exposed to thermal stress and bleaching. Environ. Microbiol. 18, 2620–2633 (2016).Article 
CAS 

Google Scholar 
Cardini, U. et al. Functional significance of dinitrogen fixation in sustaining coral productivity under oligotrophic conditions. Proc. R. Soc. B Biol. Sci. 282, 20152257 (2015).Article 

Google Scholar 
Santos, H. F. et al. Climate change affects key nitrogen-fixing bacterial populations on coral reefs. ISME J. 8, 2272–2279 (2014).Article 

Google Scholar 
Tilstra, A. et al. Relative diazotroph abundance in symbiotic red sea corals decreases with water depth. Front. Mar. Sci. 6, 372 (2019).Article 

Google Scholar 
Klinke, A. et al. Impact of phosphate enrichment on the susceptibility of the pulsating soft coral Xenia umbellata to ocean warming. Front. Mar. Sci. 9, 1026321 (2022).Article 

Google Scholar 
Rädecker, N. et al. Heat stress reduces the contribution of diazotrophs to coral holobiont nitrogen cycling. ISME J. https://doi.org/10.1038/s41396-021-01158-8 (2021).Article 

Google Scholar 
Swart, P. K., Saied, A. & Lamb, K. Temporal and spatial variation in the δ15N and δ13C of coral tissue and zooxanthellae in Montastraea faveolata collected from the Florida reef tract. Limnol. Oceanogr. 50, 1049–1058 (2005).Article 
ADS 
CAS 

Google Scholar 
Grottoli, A. G., Tchernov, D. & Winters, G. Physiological and biogeochemical responses of super-corals to thermal stress from the Northern Gulf of Aqaba, Red Sea. Front. Mar. Sci. 4, 215 (2017).Article 

Google Scholar 
Dubinsky, Z. & Stambler, N. Marine pollution and coral reefs. Glob. Change Biol. 2, 511–526 (1996).Article 
ADS 

Google Scholar 
Loya, Y., Lubinevsky, H., Rosenfeld, M. & Kramarsky-Winter, E. Nutrient enrichment caused by in situ fish farms at Eilat, Red Sea is detrimental to coral reproduction. Mar. Pollut. Bull. 49, 344–353 (2004).Article 
CAS 

Google Scholar 
Costa, O. S., Nimmo, M. & Attrill, M. J. Coastal nutrification in Brazil: A review of the role of nutrient excess on coral reef demise. J. S. Am. Earth Sci. 25, 257–270 (2008).Article 

Google Scholar 
Tait, D. R. et al. The influence of groundwater inputs and age on nutrient dynamics in a coral reef lagoon. Mar. Chem. 166, 36–47 (2014).Article 
CAS 

Google Scholar 
Guan, Y., Hohn, S., Wild, C. & Merico, A. Vulnerability of global coral reef habitat suitability to ocean warming, acidification and eutrophication. Glob. Change Biol. 26, 5646–5660 (2020).Article 
ADS 

Google Scholar 
Hall, E. R. et al. Eutrophication may compromise the resilience of the Red Sea coral Stylophora pistillata to global change. Mar. Pollut. Bull. 131, 701–711 (2018).Article 
CAS 

Google Scholar 
Naumann, M. S. et al. Organic matter release by dominant hermatypic corals of the Northern Red Sea. Coral Reefs 29, 649–659 (2010).Article 
ADS 

Google Scholar 
Wild, C. et al. Coral mucus functions as an energy carrier and particle trap in the reef ecosystem. Nature 428, 66–70 (2004).Article 
ADS 
CAS 

Google Scholar  More

Bovet, D. & Vauclair, J. Picture recognition in animals and humans. Behav. Brain. Res. 109, 143–165 (2000).Article 
CAS 

Google Scholar 
Anonymous. Tinder for Orangutans. Dublin Zoo. https://www.dublinzoo.ie/news/tinder-for-orangutans (2020).Henley, J. “Tinder for Orangutans”: Dutch zoo to let female choose mate on a tablet. The Guardian. https://www.theguardian.com/environment/2017/jan/31/tinder-for-orangutans-dutch-zoo-to-let-female-choose-mate-on-a-tablet (2017).Gierszewski, S. et al. The virtual lover: variable and easily guided 3D fish animations as an innovative tool in mate-choice experiments with sailfin mollies-II validation. Curr. Zool. 6, 65–74 (2017).Article 

Google Scholar 
Dolins, F. L., Klimowicz, C., Kelley, J. & Menzel, C. R. Using virtual reality to investigate comparative spatial cognitive abilities in chimpanzees and humans. Am. J. Primat. 76, 496–513 (2014).Article 

Google Scholar 
Faria, J. J. et al. A novel method for investigating the collective behaviour of fish: Introducing ‘Robofish’. Behav. Ecol. Sociobiol. 64, 1211–1218 (2010).Article 

Google Scholar 
Kozak, E. C. & Uetz, G. W. Male courtship signal modality and female mate preference in the wolf spider Schizocosa ocreata: results of digital multimodal playback studies. Curr. Zool. 65, 705–711 (2019).Article 

Google Scholar 
Loukola, O. J., Perry, C. J., Coscos, L. & Chittka, L. Bumblebees show cognitive flexibility by improving on an observed complex behavior. Science 355, 833–836 (2017).Article 
ADS 
CAS 

Google Scholar 
MacLaren, R. D. Evidence of an emerging female preference for an artificial male trait and the potential for spread via mate choice copying in Poecilia latipinna. Ethology 125, 575–586 (2019).
Google Scholar 
Rönkä, K. et al. Geographic mosaic of selection by avian predators on hindwing warning colour in a polymorphic aposematic moth. Ecol. Lett. 23, 1654–1663 (2020).Article 

Google Scholar 
Rosenthal, G. G., Rand, A. S. & Ryan, M. J. The vocal sac as a visual cue in anuran communication: An experimental analysis using video playback. Anim. Behav. 68, 55–58 (2004).Article 

Google Scholar 
Thurley, K. & Ayaz, A. Virtual reality systems for rodents. Curr. Zool. 63, 109–119 (2017).Article 

Google Scholar 
Ware, E. L., Saunders, D. R. & Troje, N. F. Social interactivity in pigeon courtship behavior. Curr. Zool. 63, 85–95 (2017).Article 

Google Scholar 
Wang, D. et al. The influence of model quality on self-other mate choice copying. Pers. Ind. Diff. 17, 110481 (2021).Article 

Google Scholar 
Gray, J. R., Pawlowski, V. & Willis, M. A. A method for recording behavior and multineuronal CNS activity from tethered insects flying in virtual space. J. Neurosci. Meth. 120, 211–223 (2002).Article 

Google Scholar 
Strauss, R., Schuster, S. & Götz, K. G. Processing of artificial visual feedback in the walking fruit fly Drosophila melanogaster. J. Exp. Biol. 200, 1281–1296 (1997).Article 
CAS 

Google Scholar 
Kemppainen, J. et al. Binocular mirror-symmetric microsaccadic sampling enables Drosophila hyperacute 3D vision. PNAS 119, e2109717119 (2022).Article 
CAS 

Google Scholar 
Bowers, R. I., Place, S. S., Todd, P. M., Penke, L. & Asendorpf, J. B. Generalization in mate-choice copying in humans. Behav. Ecol. 23, 112–124 (2012).Article 

Google Scholar 
Pruett-Jones, S. Independent versus nonindependent mate choice: do females copy each other? Am. Nat. 140, 1000–1006 (1992).Article 
CAS 

Google Scholar 
Dagaeff, A.-C., Pocheville, A., Nöbel, S., Isabel, G. & Danchin, E. Drosophila mate copying correlates with atmospheric pressure in a speed learning situation. Anim. Behav. 121, 163–174 (2016).Article 

Google Scholar 
Mery, F. et al. Public versus personal information for mate copying in an invertebrate. Curr. Biol. 19, 730–734 (2009).Article 
CAS 

Google Scholar 
Danchin, E. et al. Cultural flies: Conformist social learning in fruitflies predicts long-lasting mate-choice traditions. Science 362, 1025–1030 (2018).Article 
ADS 
CAS 

Google Scholar 
Monier, M., Nöbel, S., Isabel, G. & Danchin, E. Effects of a sex ratio gradient on female mate-copying and choosiness in Drosophila melanogaster. Curr. Zool. 64, 251–258 (2018).Article 

Google Scholar 
Monier, M., Nöbel, S., Danchin, E. & Isabel, G. Dopamine and serotonin are both required for mate-copying in Drosophila melanogaster. Front. Behav. Neurosci. 12, 334 (2019).Article 

Google Scholar 
Nöbel, S., Allain, M., Isabel, G. & Danchin, E. Mate copying in Drosophila melanogaster males. Anim. Behav. 141, 9–15 (2018).Article 

Google Scholar 
Nöbel, S., Danchin, E. & Isabel, G. Mate-copying for a costly variant in Drosophila melanogaster females. Behav. Ecol. 29, 1150–1156 (2018).Article 

Google Scholar 
Dukas, R. Natural history of social and sexual behavior in fruit flies. Sci. rep. 10, 1–11 (2020).Article 

Google Scholar 
Chouinard-Thuly, L. et al. Technical and conceptual considerations for using animated stimuli in studies of animal behavior. Curr. Zool. 63, 5–19 (2017).Article 

Google Scholar 
Nöbel, S. et al. Female fruit flies copy the acceptance, but not the rejection, of a mate. Behav. Ecol. 33, 1018–1024 (2022)Article 

Google Scholar 
Bretman, A., Westmancoat, J. D., Gage, M. J. G. & Chapman, T. Males use multiple, redundant cues to detect mating rivals. Curr. Biol. 21, 617–622 (2011).Article 
CAS 

Google Scholar 
Greenspan, R. J. & Ferveur, J. F. Courtship in drosophila. Ann. Rev. Gen. 34, 205 (2000).Article 
CAS 

Google Scholar 
Grillet, M., Dartevelle, L. & Ferveur, J. F. A Drosophila male pheromone affects female sexual receptivity. Proc. Roy. Soc. B. 273, 315–323 (2006).Article 
CAS 

Google Scholar 
Borst, A. Drosophila’s view on insect vision. Curr. Biol. 19, R36–R47 (2009).Article 
CAS 

Google Scholar 
Paulk, A., Millard, S. & van Swinderen, B. Vision in Drosophila: Seeing the world through a model´s eye. Ann. Rev. Entomol. 58, 313–332 (2013).Article 
CAS 

Google Scholar 
Antony, C. & Jallon, J. M. The chemical basis for sex recognition in Drosophila melanogaster. J. Insect. Physiol. 28, 873–880 (1982).Article 
CAS 

Google Scholar 
Keesey, I. W. et al. Adult frass provides a pheromone signature for Drosophila feeding and aggregation. J. Chem. Ecol. 42, 739–747 (2016).Article 
CAS 

Google Scholar 
Talyn, B. C. & Bowse, H. B. The role of courtship song in sexual selection and species recognition by female Drosophila melanogaster. Anim. Behav. 68, 1165–1180 (2004).Article 

Google Scholar 
von Schilcher, F. The function of pulse song and sine song in the courtship of Drosophila melanogaster. Anim. Behav. 24, 622–6251976 (1976).Article 

Google Scholar 
McGregor, P. K. et al. Design of playback experiments: The Thornbridge hall NATO ARW consensus. In Playback and Studies of Animal Communication (ed. McGregor, P.) 1–9 (Plenum Press, New York, 1992).Chapter 

Google Scholar 
Richmond, J. The three Rs. In The UFAW Handbook on the Care and Management of Laboratory and Other Research Animals (eds Hubrecht, R. & Kirkwood, J.) 5–22 (Wiley-Blackwell, Hoboken, 2002).
Google Scholar 
Russell, W. M. S. & Burch, R. L. The Principles of Humane Experimental Technique (Methuen & Co Ltd, 1959).
Google Scholar 
Schlupp, I., Ryan, M. & Waschulewski, M. Female preferences for naturally-occurring novel male traits. Behaviour 136, 519–527 (1999).Article 

Google Scholar 
Witte, K. & Klink, K. No pre-existing bias in sailfin molly females, Poecilia latipinna, for a sword in males. Behaviour 142, 283–303 (2005).Article 

Google Scholar 
Gerlai, R. Animated images in the analysis of zebrafish behavior. Curr. Zool. 63, 35–44 (2017).Article 

Google Scholar 
Ioannou, C. C., Guttal, V. & Couzin, I. D. Predatory fish select for coordinated collective motion in virtual prey. Science 337, 1212–1215 (2012).Article 
ADS 
CAS 

Google Scholar 
Little, A. C., Jones, B. C. & DeBruine, L. M. Preferences for variation in masculinity in real male faces change across the menstrual cycle: Women prefer more masculine faces when they are more fertile. Pers. Ind. Diff. 45, 478–482 (2008).Article 

Google Scholar 
Little, A. C., Jones, B. C. & DeBruine, L. M. Facial attractiveness: Evolutionary based research. Phil. Trans. R. Soc. B. 366, 1638–1659 (2011).Article 

Google Scholar 
Morrison, E. R., Clark, A. P., Tiddeman, B. P. & Penton-Voak, I. S. Manipulating shape cues in dynamic human faces: Sexual dimorphism is preferred in female but not male faces. Ethology 116, 1234–1243 (2010).Article 

Google Scholar 
Kacsoh, B. Z., Bozler, J., Ramaswami, M. & Bosco, G. Social communication of predator-induced changes in Drosophila behavior and germ line physiology. eLife. 4, e07423 (2015).Article 

Google Scholar 
Caruana, N. & Seymour, K. Objects that induce face pareidolia are prioritized by the visual system. Brit. J. Psychol. 113, 496–507 (2022).Article 

Google Scholar 
Agrawal, S., Safarik, S. & Dickinson, M. The relative roles of vision and chemosensation in mate recognition of Drosophila melanogaster. J. Exp. Biol. 217, 2796–2805 (2014).
Google Scholar 
R Development Core Team. R: A Language and Environment for Statistical Computing (Austria, Vienna, 2021).
Google Scholar 
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Fox, J. & Weisberg, S. An {R} Companion to Applied Regression 2nd edn. (Sage Publishing, London, 2001).
Google Scholar  More

Correction to: Communications Biology https://doi.org/10.1038/s42003-020-1022-1, published online 11 June 2020.In the original version of the Perspective, a unit conversion error affected calculations for cereal rye, triticale, barley, and oats. Further, berseem clover yield estimates were mistranscribed from the original source. These mistakes led to errors in Supplementary Data 1, Figure 2 and in the presentation of the data in the text.Supplementary Data 1 has now been replaced with a file containing the correct numbers.Figure 2 has been corrected:Original figure 2New figure 2The Abstract stated: “In this Perspective, we estimate land use requirements to supply the United States maize production area with cover crop seed, finding that across 18 cover crops, on average 3.8% (median 2.0%) of current production area would be required, with the popular cover crops rye and hairy vetch requiring as much as 4.5% and 11.9%, respectively”.The text should read: “In this Perspective, we estimate land use requirements to supply the United States maize production area with cover crop seed, finding that across 18 cover crops, on average 2.4% (median 2.1%) of current production area would be required, with the popular cover crops rye and hairy vetch requiring as much as 4.8% and 11.9%, respectively”.In the 1st paragraph of the right hand column on page 2, the text said: “(…), we find that the land requirements for production of cover crop seed would be on average 1.4 million hectares (median 746,000 ha), which is equivalent to 3.8% (median 2.0%) of the U.S. maize farmland. Rye (Secale cereale L.) – a midrange seed yielding cover crop and one of the most commonly used in the corn belt, would require as much as 1,661,000 hectares (4.5% of maize farmland), (…)”The text should read: “(…) we find that the land requirements for production of cover crop seed would be on average 892,526 hectares (median 774,417 ha), which is equivalent to 2.4% (median 2.1%) of the U.S. maize farmland. Rye (Secale cereale L.) – a midrange seed yielding cover crop and one of the most commonly used in the corn belt, would require as much as 1,779,770 hectares (4.8% of maize farmland), (…)”On page 3, second paragraph the text said: “Cover cropping the entire U.S. maize area would require the equivalent of as much as 18% (rye) to 49% (hairy vetch) (…)”The text should read: “Cover cropping the entire U.S. maize area would require the equivalent of as much as 19% (rye) to 49% (hairy vetch) (…)”This errors have now been corrected in the Perspective Article. More

Aerial imagesWe use publicly available aerial images of Rwanda at 0.25 × 0.25 m2 resolution, collected in June–August of 2008 and 2009. The images were acquired from 3,000 m altitude above ground level, originally with a mean ground resolution of 0.22 × 0.22 m2 pixel size then resampled to 0.25 × 0.25 m2, using a Vexcel UltraCam-X aerial digital photography camera34. The images exhibit a red, green and blue band stored under 8 bit unsigned integer format. The aerial images cover 96% of the country and the remaining 4% was filled with satellite images from WorldView-2, Ikonos, Spot and QuickBird satellite sensors which are part of the publicly available dataset.Environmental dataWe use locally available climate data: mean annual rainfall, mean annual temperature and elevation data (10 × 10 m2 resolution) to assess relationships between tree density, crown cover and environmental gradients. We also use land cover data to extract the spatial extent of plantations, forest, farmland, and urban and built-up areas for our landscape stratification. Climate data were obtained from the Rwanda Meteorological Agency as daily records from 1971 to 2017. The national forest map was manually created in 2012 using on-screen digitizing techniques over the 2008 aerial images35. A forest was defined as ‘a group of trees higher than 7 m and a tree cover of more than 10% or trees able to reach these thresholds in situ on a land of about 0.25 ha or more’51. A shrub was defined as ‘a group of perennial trees smaller than 7 m at maturity and a canopy cover of more than 10% on a land of about 0.25 ha or more’. The forest dataset was composed of 105,690 forest polygons, classified as either natural forest (closed natural forest, degraded natural forest, bamboo stand, wooded savanna and shrubland) or ‘forest plantations’ (Eucalyptus spp., eucalyptus; Pinus spp., pine; Callitris spp., callitris; Cupressus spp., cypress; Acacia mearnsii, black wattle; Acacia melanoxylon, melanoxylon; Grevillea robusta, grevillea; Maesopsis eminii, maesopsis; Alnus acuminata, alnus; Jacaranda mimosifolia, jacaranda; mixed species, mixed; and others) (Extended Data Fig. 7i). We separate shrubland from natural forest and merged it with savanna into the class ‘savannas and shrublands’. We further separated tree plantations and grouped them into Eucalyptus and non-Eucalyptus plantations. Then, a farmland map was acquired from the Rwanda Land Management and Use Authority (RLMUA)52 and overlaid with the 2012 forest cover map as a reference to clean the overlapping parts, under an assumption that the overlap is due to land use dynamics. Finally, a layer marking urban and built-up areas was acquired from RLMUA as well and the same preprocessing step as done for farmlands was applied. The combination of the land cover datasets resulted in our stratification scheme with six classes: natural forests, savannas and shrublands, Eucalyptus plantations, non-Eucalyptus plantations, farmland and urban and built-up.Mapping of individual trees using deep learningWe used the open-source framework developed by ref. 17 to map individual tree crowns. The framework uses a deep neural network based on the U-Net architecture53,54. We trained the network using 97,574 manually delineated tree crowns spread over 103 areas/bounding boxes representing the full range of biogeographical conditions found across Rwanda. To cope with the challenge of separating touching tree crowns, we used a higher weight for boundary areas between crowns, as suggested in refs. 17,53. Crown sizes in the predictions were found to be 27% smaller as compared to the manual delineations within the 103 training areas, due to the applied boundary weight that emphasizes gaps between tree crowns. Therefore, to calculate the real canopy cover, we extended each predicted tree crown by 27% and dissolved the touching crowns into continuous features. We counted single tree crowns for each hectare presented here as tree density and the percentage of each hectare covered by the extended tree crowns as canopy cover.We developed a postprocessing method that separates clumped tree crowns and fills any gap inside a single crown (Extended Data Fig. 2). Our postprocessing method, which we refer to as detect centre and relabel (DCR), determines the crown centres in the model predictions assuming that tree crowns have a round shape and then relabels the model predictions on the basis of weighted distances to the identified crown centres. First, DCR performs a distance transform, computing for each pixel the Euclidean distance to the nearest pixel predicted as background. Let the transformed image be distance-transformed (DT). Then an m × m maximum filter is applied to DT, where m depends on the size of the smallest object to be separated. We store all pixels for which the original DT value is the same before and after max-filtering. These pixels are the instance centres as they are furthest away from the boundary and have the highest distance values within the area defined by m. In the case of several connected instance centres in regions where multiple connected pixels have the same distance from the background, only a single instance centre is kept. Finally, each pixel x predicted as a crown in the original image is assigned to its nearest instance centre, where the distance function penalizes background pixels on the connecting line between the instance centre and x.Allometry for biomass and carbon stock estimationGenerally, allometric equations define a statistical relationship between structural properties of a tree and its biomass55,56. In our case, we assume a relationship between the crown area and aboveground biomass (AGB), which varies between biomes36. Since destructive AGB measurements are rare, we established biome-specific relationships between crown diameter (CD) derived from the crown area (CD = 2√(crown area/π)) and stem diameter at breast height (DBH) (equations (3) and (6)). DBH has been shown to be highly correlated with AGB36,37,38,39,40. We then used established relationships from literature to derive AGB from DBH for savannas and shrublands (equation (4)), tree plantations (equation (5)) and natural forests (equation (7)). AGB was predicted for each tree and summed for 1 ha grids to derive AGB in the unit Mg per ha. Values were multiplied by 0.47 (refs. 57,58) to derive aboveground carbon (AGC). Summed numbers over land cover classes are considered as carbon stocks. The bias as reported here was calculated following the approach from ref. 36 reporting the relative systematic error in per cent:$$mathrm {bias} = frac{1}{N}mathop {sum}limits_{i = 1}^N {frac{{(Y_{mathrm {obs}} – Y_{mathrm {pred}})}}{{Y_{mathrm {obs}}}}}times 100$$
(1)
The error for the evaluation with NFI data was defined by:$$mathrm{bias} = frac{{left| {mathop {sum}nolimits_N {(Y_{mathrm{obs}} – Y_{mathrm{pred}})} } right|}}{{left| {mathop {sum}nolimits_N {Y_{mathrm{obs}}} } right|}}$$
(2)
For trees outside natural forests, we used the database from ref. 36 including 10,591 field-measured trees from woodlands and savanna plus 952 samples from agroforestry landscapes in Kenya37 to establish a linear relationship between CD and DBH (Extended Data Fig. 3a). The Kenyan dataset is compatible with the trees in Rwanda. To ensure compatibility, the Kenya data contained open-grown trees most of which are of the same families or genus as in Rwanda grown under the same conditions, the latter factor shown to be important for generalizing37.A major axis regression (average of four runs each 50% of the data) led to equation (3):$${{{mathrm{DBH}}}}_{{{{mathrm{predicted}}}}},{{{mathrm{in}}}},{{{mathrm{cm}}}} = – 4.665 + 5.102 times {{{mathrm{CD}}}}$$
(3)
Equation (3) showed a reasonable performance with a very low bias (average of four runs on the 50% not used to establish the equation (3)): r² = 0.71; slope = 0.95; root mean square error (RMSE) = 6.2 cm; relative RMSE (rRMSE) = 42%; bias = 1%). We tested equation (3) on an independent dataset from Kenya consisting of 93 trees where AGB was destructively measured (Fig. 3b). The Kenyan database provides an uncommon opportunity to use destructive samples in which the carbon mass is not estimated indirectly and the relationship between crown area and carbon is direct: we do not need to invoke a second allometry to derive the dependent variable. All trees were open-grown trees in the same growing conditions as the agricultural areas of Rwanda. On these 93 trees, DBH can be predicted reasonably well from CD using equation (3) (r² = 0.84; slope = 0.86; RMSE = 8 cm; rRMSE = 25%; bias = 6%). We then applied an allometric equation from literature37 established for non-forest trees in East Africa to estimate AGB from DBHpredicted and compared the predicted AGB with the destructively measured AGB (r² = 0.81; RMSE = 511 kg; rRMSE = 55%; bias = 25%) showing an acceptable performance (Extended Data Fig. 3c) but indicating a systematic bias, which will be further tested with biome-specific field data (next section). We apply equation (4) to estimate AGB for trees outside forests in Rwanda in savannas and shrublands:$${{{mathrm{AGB}}}}_{{{{mathrm{predicted}}}}},{{{mathrm{in}}}},{{{mathrm{kg}}}} = 0.091 times {{mathrm{DBH}}_{{mathrm{predicted}}}}^{2.472}$$
(4)
Given the different structure of trees in farmlands, urban and built-up areas and plantations as compared to trees in natural forests and in natural non-forest areas, we used a different equation for trees in these areas. It was established in Rwanda using destructive samples from tree plantations39:$${{{mathrm{AGB}}}}_{{{{mathrm{predicted}}}}},{{{mathrm{in}}}},{{{mathrm{kg}}}} = 0.202 times {{mathrm{DBH}}_{{mathrm{predicted}}}}^{2.447}$$
(5)
A different CD–DBH relationship was established for natural forests. Here, we conducted a field campaign in December 2021 sampling 793 overstory trees in Rwanda’s protected natural forest. We measured both CD and DBH and established a logarithmic major axis regression model with a Baskerville correction59 between the two variables to predict DBH from CD (Extended Data Fig. 3d). We did four runs each using 50% of the data to establish equation (6) (average of the four runs) and the other 50% to test the performance also averaged over the four runs (r² = 0.71; slope = 0.99; RMSE = 13 cm; rRMSE = 45%; bias = 19%). Note that CD is extended by 27% to account for underestimations of touching crowns in dense forests (see previous section):$$begin{array}{l}{mathrm{DBH}}_{{mathrm{predicted}}},{mathrm{in}},{mathrm{cm}} = left({mathrm{exp}}left(1.154 + 1.248 times {mathrm{ln}}({mathrm{CD}} times 1.27) right)right.\left. times left({mathrm{exp}}(0.3315^2/2) right) right)end{array}$$
(6)
We then used a state-of-the-art allometric equation established for tropical forests38 to predict AGB from DBH for natural forests in Rwanda:$$begin{array}{l}{{{mathrm{AGB}}}}_{{{{mathrm{predicted}}}}},{{{mathrm{in}}}},{{{mathrm{kg}}}} = {{{mathrm{exp}}}}Big[ {1.803 – 0.976{{{E}}} + 0.976,{{{mathrm{ln}}}}left( rho right)}\+ 2.673;{{{mathrm{ln}}}}left( {{{{mathrm{DBH}}}}} right) – 0.0299left[ {{{{mathrm{ln}}}}left( {{{mathrm{DBH}}}} right)} right]^2 Big]end{array}$$
(7)
where E measures the environmental stress38 (a gridded layer is accessible via https://chave.ups-tlse.fr/pantropical_allometry.htm) and ρ is the wood density. Here, we used a fixed number (0.54), which is the average wood density for 6,161 trees from ref. 40, weighted according to the abundance of the species in the plots. The relative error was calculated by the quadratic mean of the intraplot and interplot variations, which is 18.2% (Extended Data Table 1b). No destructive AGB measurements were found that showed a similar CD–DBH relationship as we measured during the field trip in Rwanda’s forest. We could thus not evaluate the performance for natural forests at tree level but had to rely on plot-level comparisons (next section).Evaluation and uncertainties of the allometryBiomass estimations without direct measurements of height or DBH inevitably include a relatively high level of uncertainty at tree level38,60. Uncertainty does not only originate from the CD to DBH conversion but also the equation converting DBH to AGB. As shown in the previous section, no strong systematic bias could be detected for the CD to DBH conversion but the evaluation of the CD-based AGB prediction with an independent dataset from destructively measured AGB revealed a bias of 25%. However, this comparison (Extended Data Fig. 3c) may not be representative for an entire country having a variety of landscapes and tree species, so a systematic propagation is unlikely. We also did not have sufficient field data to evaluate the conversions in natural forests. Here, we used data from 15 natural forest plots with 6,161 trees published by ref. 40 and ref. 41 and directly compared the summed biomass of the trees we predicted over their plots. The median measured biomass for the plots is 121 MgC ha−1 and we predict a median biomass of 81 MgC ha−1 (plot-based rRMSE = 54%; bias = 11%; bias on summed plots = 26%). The overall underestimation by our prediction is not necessarily a model bias but may be partly explained by the contribution of the understory trees, which cannot be captured by aerial images. Interestingly, our C stock estimates are in the same range of magnitude as global biomass products43,44,45,61 (Extended Data Fig. 4), indicating that overstory tree-level carbon stock assessments are possible from optical very high resolution images, even in tropical forests. Several global products overestimated biomass for non-forest areas like savannas or croplands, which is probably because they are calibrated in denser forests. The most recent products of ref. 42 and ref. 61 are much closer to the estimates from our results and the NFI. This is also seen in the grid-based correlation matrix where ref. 42 correlates best with our map, followed by ref. 61.We further use NFI data from 2014 to measure the uncertainty of the final carbon stock estimates and evaluate if systematic differences between AGB predictions and field assessments can be found for different land cover classes (Extended Data Table 1). For the NFI data, a total of 373 plots with 2,415 trees were measured and species-specific allometric equations applied62. To identify systematic errors at landscape scale, we extracted averaged values for areas around the plots from our predictions and calculated statistics on averages over all plots. Interestingly, our predictions for farmlands only show a bias of 5.9%: we estimate on average 2.46 MgC ha−1 and the inventories measure 2.37 MgC ha−1 on their 150 plots. For savanna and shrublands, we estimate 4.16 MgC ha−1 while inventories measure 3.31 MgC ha−1 (bias = 18.9%). For plantations, we estimate lower values (8.16 compared to 16.79 MgC ha−1; bias = 52.6%). To calculate the total uncertainty on country-wide C stock estimates, we weighted the bias from the different classes according to their relative area. We estimate a total uncertainty on the carbon stock predictions of 16.9% at the national scale (Extended Data Table 1).We found a very low bias for estimated C density in farmlands (5.9% bias) which make up most of the areas outside natural forests in Rwanda (Extended Data Table 1, Extended Data Fig. 6). The high bias for plantations can be explained by three factors: large bare areas considered part of plantations by the manual delineation of plantation areas (Extended Data Fig. 1); regular harvesting and continual thinning which keep many plantation trees young and small; and the fact that our aerial images are from 2008 while plantation trees have grown until 2014 with a few new NFI plots initiated after 2008. The bias in savannas and shrublands can be explained by the following factors: the presence of multistemed trees with large crowns such as Acacia spp. and Ficus spp. among others; the fact that a crown-based method overestimates C stocks of shrubs with a small height; and presence of shrub trees with both small height and small (multiple) stems. If tree-level based carbon stock assessments derived from crown diameter as presented here should become standard to complement national inventories, a database with sufficient samples to evaluate for systematic errors needs to be established for each biome and inventory and satellite/aerial image-based methods need to be further harmonized.To further quantify the error propagation of the CD to DBH conversion for our application, we established four equations each randomly using 50% of the dataset and predicted the carbon stock for each tree in Rwanda with each equation. We did this separately for natural forests and trees outside natural forests. We calculated the rRMSE between the aggregated carbon stocks for each hectare. We averaged the rRMSE for each land cover class and show that the uncertainty for all classes does not exceed 5% (Extended Data Table 2a).Evaluation and uncertainties of tree crown mappingWe created an independent test dataset, which was never seen during training and was also not used to optimize hyperparameters. The test set consists of 6,591 manually labelled trees located in 15 random 1 ha plots (Extended Data Fig. 5). Thanks to the size of the country, the plots represent all rainfall zones and three major landscapes of the country. The plot-level comparison yielded very high correlations between the predictions and the labels and is shown in Extended Data Fig. 5. We also calculated a confusion matrix showing an overall per pixel accuracy of 96.2%, a true positive rate of 79.6% and a false positive rate of 6.8% (Extended Data Table 2b). Trees outside natural forests are easy to spot and count for the human eye, so we have confidence in the plot-based evaluation. However, it is often challenging in natural forests. Here, we used again the field measurements from 15 plots with 6,161 trees40,41. We find that we underestimate the total tree count by 22.6%, which may, at least partly, be explained by understory trees hidden by overstory trees and which are, therefore, not visible in our images. New field campaigns are needed to better understand and calibrate our results and possibly correct for systematic bias.Application and evaluation beyond RwandaWe acquired 83 Skysat scenes at 80 cm for Tanzania, Burundi, Uganda, Rwanda and Kenya. The model trained on the 25 cm resolution aerial images of Rwanda from 2008 was directly applied on the Skysat images. Forest and non-forest areas were manually delineated to decide which allometric equation to use for the carbon stock conversion. We randomly selected 150 1 × 1 km2 patches and aggregated the predicted carbon density per patch and compared the results with previously published maps42,43,44,45. Results show that the model can directly be applied to comparable landscapes on different datasets. Note, however, that accurate carbon stock predictions need local adjustments with field data. We then tested the tree crown model transferability on aerial images from California (NAIP; 60 cm) and France (20 cm) and found that the model delivers realistic results without any local training or calibration (Extended Data Figure 8).Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. More

IPCC. In IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (IPCC, 2019).IOC-UNESCO. Global Ocean Science Report 2020-Charting Capacity for Ocean Sustainability (UNESCO Publishing, 2020).Sala, E. et al. Protecting the global ocean for biodiversity, food and climate. Nature 592, 397–402 (2021).Article 
CAS 

Google Scholar 
Boyce, D. G., Lotze, H. K., Tittensor, D. P., Carozza, D. A. & Worm, B. Future ocean biomass losses may widen socioeconomic equity gaps. Nat. Commun. 11, 1–11 (2020).Article 

Google Scholar 
Foundation Prince Albert II of Monaco. “Which Knowledge for Which Sustainable Ocean Governance?” in Livre de restitution de la Monaco Ocean Week 2021 (2021).Swilling, M. et al. The Ocean Transition: What to learn from System Transitions (World Resources Institute, 2020).OECD. The Ocean Economy in 2016 (OECD Publishing, 2016).High Level Panel for a Sustainable Ocean Economy. Transformations for a Sustainable Ocean Economy – a vision for Protection, Production and Prosperity (2020).Landrigan, P. J. et al. Human health and ocean pollution. Ann. Global Health 86, 151 (2020).Article 

Google Scholar 
OECD. Development Co-operation Report 2016: the Sustainable Development Goals as Business Opportunities (OECD Publishing, 2016).OECD. Development Co-operation Report 2020: Learning from Crises, Building Resilience (OECD Publishing, 2020).Hoegh-Guldberg, O. et al. The Ocean as a Solution to Climate Change: Five Opportunities for Action. (World Resources Institute, 2019).Gattuso, J. P. et al. Ocean solutions to address climate change and its effects on marine ecosystems. Front. Mar. Sci. 5, 337 (2018).Article 

Google Scholar 
Heinze, C. et al. The quiet crossing of ocean tipping points. Proc. Natl Acad. Sci. USA 118, e2008478118 (2021).Article 
CAS 

Google Scholar 
IPBES. Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (eds. Brondizio, E. S., Settele, J., Díaz, S. & Ngo, H. T.) (IPBES Secretariat, 2019).IPCC. IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (IPCC, 2019).Nash, K. L. et al. Planetary boundaries for a blue planet. Nat. Ecol. Evol. 1, 1625–1634 (2017).Article 

Google Scholar 
UN General Assembly. General Assembly Resolution Declaration of Principles Governing the Seabed and Ocean Floor. A/RES/25/2749. (1970).Brodie Rudolph, T. et al. A transition to sustainable ocean governance. Nat. Commun 11, 1–14 (2020).Article 

Google Scholar 
Claudet, J., Amon, D. J. & Blasiak, R. Opinion: transformational opportunities for an equitable ocean commons. Proc. Natl Acad. Sci. USA 118, e2117033118 (2021).Article 
CAS 

Google Scholar 
Laffoley, D. et al. Evolving the narrative for protecting a rapidly changing ocean, post‐ COVID‐19. Aquatic Conserv. 31, 1512–1534 (2021).Article 
CAS 

Google Scholar 
Folke, C. et al. Our future in the Anthropocene biosphere. Ambio 50, 834–869 (2021).Article 

Google Scholar 
Bennett, N. J. et al. Towards a sustainable and equitable blue economy. Nat. Sustain. 2, 991–993 (2019).Article 

Google Scholar 
United Nations General Assembly. Oceans and the law of the sea A/RES/72/73 (5 December 2017).De Santo, E. M. et al. Protecting biodiversity in areas beyond national jurisdiction: an earth system governance perspective. Earth Syst. Governance 2, 100029 (2019).Röckmann, C., van Leeuwen, J., Goldsborough, D., Kraan, M. & Piet, G. The interaction triangle as a tool for understanding stakeholder interactions in marine ecosystem based management. Mar. Pol. 52, 155–162 (2015).Article 

Google Scholar 
Kotzé, L. J. Fragmentation revisited in the context of global environmental law and governance. SALJ 131, 548–582 (2014).
Google Scholar 
Claudet, J. et al. A roadmap for using the UN decade of ocean science for sustainable development in support of science, policy, and action. One Earth 2, 34–42 (2020).Article 

Google Scholar 
Pörtner, H. O. et al. IPBES-IPCC Co-sponsored Workshop Report on Biodiversity and Climate Change (IPBES and IPCC, 2021).Picourt, L. et al. Swimming the Talk: How to Strengthen Collaboration and Synergies between the Climate and Biodiversity Conventions? (Ocean & Climate Platform, 2021).Valdes, L. The UN architecture for ocean science knowledge and governance. Chapter 18. In Handbook on the Economics and Management of Sustainable Oceans (eds. Paulo A.L.D. Nunes, P.A.L.D., Svensson, L. E. & Markandya, A. (Edward Elgar Publishing, 2017).Valdés, L. Mees, J. & Enevoldsen, H. International organizations supporting ocean science. In IOC-UNESCO, Global Ocean Science Report—The current status of ocean science around the world (eds. Valdés, L. et al.) 146–169 (UNESCO, 2017).Fawkes, K., Ferse, S., Scheffers, A. & Cummins, V. Learning from experience: what the emerging global marine assessment community can learn from the social processes of other global environmental assessments. Anthropocene Coasts 4, 87–114 (2021).Article 

Google Scholar 
Tessnow-von Wysocki, I. & Vadrot, A. B. M. The voice of science on marine biodiversity negotiations: a systematic literature review. Front. Mar. Sci. 7, 614282 (2020).Article 

Google Scholar 
Dalton, K. et al. Marine-related learning networks: shifting the paradigm toward collaborative ocean governance. Front. Mar. Sci. 7, 1–16 (2020).Article 

Google Scholar 
Gerbara, M. F. Understanding international bricolage. What drives behaviour change towards sustainable land use in the Eastern Amazon? Int. J. Commons 13, 1 (2019).
Google Scholar 
Jabbour, J. & Flachsland, C. 40 years of global environmental assessments: a retrospective analysis. Environ. Sci. Policy 77, 193–202 (2017).Article 

Google Scholar 
Messerli, P. et al. Expansion of sustainability science needed for the SDGs. Nat. Sustain. 2 10, 892–894 (2019).Article 

Google Scholar 
The Because the Ocean Initiative. Ocean for climate – Ocean-related measures in climate strategies (Nationally determined contributions, national adaptation plans, adaptation communications and national policy frameworks) (2019).Vieross, M. K. et al. Considering indigenous peoples and local communities in the governance of the global ocean commons. Mar. Pol. 119, 104039 (2020).Article 

Google Scholar 
Halpern, B. et al. Spatial and temporal changes in cumulative human impacts on the world’s ocean. Nat. Commun. 6, 1–7 (2015).Article 

Google Scholar 
Watson-Wright, W., & Valdes, J.L. Fragmented Governance of Our One Global Ocean. In The Future of Ocean Governance and Capacity Development – Essays in Honor of Elisabeth Mann Borgese (1918–2002) 16–22 (Brill, Nijhoff, 2019).United Nations Framework Convention on Climate Change. Chile Madrid Time for Action. FCCC/CO/2019/13.Add.1 Decision 1/CP (2020).Fawkes, K. & Cummins, V. Beneath the surface of the first world ocean assessment: an investigation into the global process’ support for sustainable development. Front. Mar. Sci. 6, 612 (2019).Article 

Google Scholar 
Bayliss-Brown, G., Cavaleri Gerhardinger, L. & Starger, C. Networked knowledge to action in support of ocean sustainability. Coast. Manage. 4, 4, 235–237 (2020).
Google Scholar 
Gerhardinger, L. C., Holzkämper, E., de Andrade, M. M., Corrêa, M. R. & Turra, A. Envisioning ocean governability transformations through network-based marine spatial planning. Marit. Stud. 21, 1, 131–152 (2022).Article 

Google Scholar 
Wyborn, C. et al. Imagining transformative biodiversity futures. Nat. Sustain. 3, 670–672 (2021).Article 

Google Scholar 
Jacobs, S. et al. Use your power for good: plural valuation of nature – the Oaxaca statement. Glob. Sustain. 3, e8 (2020).Article 

Google Scholar 
Herbst, D. F., Gerhardinger, L. C., Vila-Nova, D. A., de Carvalho, F. G. & Hanazaki, N. Integrated and deliberative multidimensional assessment of a subtropical coastal-marine ecosystem (Babitonga bay, Brazil). Ocean Coast. Manag. 196, 105279 (2020).Article 

Google Scholar 
McCrory, G., Holmén, J., Schäpke, N. & Holmberg, J. Sustainability-oriented labs in transitions: an empirically grounded typology. Environ. Innov. Soc. Transit. 43, 99–117 (2022).Article 

Google Scholar 
Gerhardinger, L. C., Andrade, M. M. de, Corrêa, M. R., & Turra, A. Crafting a sustainability transition experiment for the Brazilian blue economy. Mar. Pol. 120, 104157 (2020).Pereira, L., Sitas, N., Ravera, F., Jimenez-Aceituno, A. & Merrie, A. Building capacities for transformative change towards sustainability: imagaination in Intergovernmental Science-Policy Processes. Elem. Sci.Anth 7, 35 (2019).Article 

Google Scholar 
Flannery, W., Toonen, H., Jay, S. & Vince, J. A critical turn in marine spatial planning. Marit. Stud. 1987, 223–228 (2020).Article 

Google Scholar 
Clarke, J. & Flannery, W. The post-political nature of marine spatial planning and modalities for its re-politicisation. J. Envir. Policy Plan. 22 2, 170–183 (2020).Article 

Google Scholar 
von Schuckmann, K. et al. Copernicus marine service ocean state report 5th issue. J. Oper.Oceanogr. 14, 1–185 (2021).
Google Scholar 
Mercator International. Digital twin of the ocean. https://www.mercator-ocean.eu/en/digital-twin-ocean/ (2022).Geomar. Digital twin ocean. https://www.geomar.de/en/research/irf/digital-twin-ocean (2022).Creative Commons. https://creativecommons.org/licenses/ (2022).Orchid. Connecting research and researchers. https://orcid.org/#:~:text=ORCID%20provides%20a%20persistent%20digital,%2C%20peer%20review%2C%20and%20more (2022).Jasanoff, S. Technologies of humility. Nature 450, 33 (2007).Article 
CAS 

Google Scholar 
Pörtner, H.-O. et al. Technical summary. In Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds Pörtner, H.-O. et al.) (Cambridge Univ. Press, 2022).Pereira, L. M., Hichert, T., Hamann, M., Preiser, R. & Biggs, R. Using futures methods to create transformative spaces: visions of a good anthropocene in Southern Africa. Ecol. Soc. 23, 1, https://doi.org/10.5751/ES-09907-230119 (2018).Article 

Google Scholar 
TWI2050 Report. Transformations to Achieve the Sustainable Development Goals. Report prepared by World in 2050 Initiative. International Institute for Applied Systems Analysis (IIASA). www.twi2050.org (2018).Mitchell, R. B., Clark, W. C., Cash, D. W., & Dickson, N. M. Global Environmental Assessments: Information and Influence (MIT Press, 2016).Norström, A. V. et al. Principles for knowledge co-production in sustainability research. Nat. Sustain. 3, 182–190 (2020).Article 

Google Scholar 
Galland, G., Harrould-Kolieb, E. & Herr, D. The ocean and climate change policy. Clim. Pol. 12, 6, 764–771 (2012).Article 

Google Scholar 
Pereira, L. M. et al. Developing multiscale and integrative nature–people scenarios using the nature futures framework. People Nat. 2, 1172–1195 (2020).Article 

Google Scholar 
Evans, K. et al. Transferring complex scientific knowledge to useable products for society: the role of the global integrated ocean assessment and challenges in the effective delivery of ocean knowledge. Front. Environ. Sci 9, 626532 (2021).Article 

Google Scholar 
United Nations Ocean Conference. An international panel for ocean sustainability side event. (2022).Foundation Prince Albert II of Monaco. “Why an IPOS” in Livre de restitution de la Monaco Ocean Week 2022 (2022).Convention on Biodiversity. Open ended working group on the post 2020 global biodiversity framework. 3rd meeting. First Draft of the post-2020 global biodiversity framework (2021).Sitas, N. et al. Exploring the usefulness of scenario archetypes in science-policy processes: experience across IPBES assessments. Ecol. Soc. 24, 35 (2019).Article 

Google Scholar 
Laffoley, D. et al. The forgotten ocean: why COP26 must call for vastly greater ambition and urgency to address ocean change. Aquatic Conserv. 32, 1–12 (2021).
Google Scholar 
Martin, M. A. et al. Ten new insights in climate science 2021: a horizon scan. Glob.Sustain. 4, 1–20 (2021).Article 

Google Scholar 
Poli, R. Anticipation: what about turning the human and social sciences upside down? Futures 64, 15–18 (2014).Article 

Google Scholar 
Dasgupta, P. The Economics of Biodiversity: The Dasgupta Review (HM Treasury, 2021).Sumaila, U. R. et al. Financing a sustainable ocean economy. Nat. Commun. 12, 3259 (2021).Article 
CAS 

Google Scholar 
Muiderman, K., Gupta, A., Vervoort, J. & Biermann, F. Four approaches to anticipatory climate governance: different conceptions of the future and implications for the present. WIREs Clim. Change 11, e673 (2020).Article 

Google Scholar 
Obermeister, N. Local knowledge, global ambitions: IPBES and the advent of multi-scale models and scenarios. Sustain. Sci. 14, 843–856 (2019).Article 

Google Scholar 
Vadrot, A., Rankovic, A., Lapeyre, R., Aubert, P. & Laurans, Y. Why are social sciences and humanitites needed in the workds of IPBES? A systematic review of the literature. Innovation 31, S78–S100 (2018).
Google Scholar 
Edenhofer, O. & Kowarsch, M. Cartography of pathways: a new model for environemntal policy assessments. Enviro.Sci.Policy 51, 56–64 (2015).Article 

Google Scholar 
Kowarsch, M. et al. An road map for global assessments. Nat. Clim. Change 7, 379–382 (2017).Article 

Google Scholar 
European Commission Press Release. International Ocean Governance: EU’s Contribution for Setting the Course of a Blue Planet. https://ec.europa.eu/commission/presscorner/detail/en/IP_22_3742 (2022).Seafood Business for Ocean Stewardship (SeaBOS). http://www.seabos.org/ (2022). More

Cantwell-Jones, A. et al. Global plant diversity as a reservoir of micronutrients for humanity. Nat. Plants https://doi.org/10.1038/s41477-022-01100-6 (2022).Swenson, N. G. Phylogenetic imputation of plant functional trait databases. Ecography 37, 105–110 (2014).Article 

Google Scholar 
Swenson, N. G. et al. Phylogeny and the prediction of tree functional diversity across novel continental settings. Glob. Ecol. Biogeogr. 26, 553–562 (2017).Article 

Google Scholar 
Molina-Venegas, R. et al. Assessing among-lineage variability in phylogenetic imputation of functional trait datasets. Ecography 41, 1740–1749 (2018).Article 

Google Scholar 
Guénard, G., Legendre, P. & Peres-Neto, P. Phylogenetic eigenvector maps: a framework to model and predict species traits. Methods Ecol. Evol. 4, 1120–1131 (2013).Article 

Google Scholar 
Guénard, G., Ohe, P. C., von der, Walker, S. C., Lek, S. & Legendre, P. Using phylogenetic information and chemical properties to predict species tolerances to pesticides. Proc. R. Soc. B 281, 20133239 (2014).Article 

Google Scholar 
Ezekiel, M. Methods of Correlation Analysis (John Wiley and Sons, 1930).Johnson, T. F., Isaac, N. J. B., Paviolo, A. & González-Suárez, M. Handling missing values in trait data. Glob. Ecol. Biogeogr. 30, 51–62 (2021).Article 

Google Scholar 
Debastiani, V. J., Bastazini, V. A. G. & Pillar, V. D. Using phylogenetic information to impute missing functional trait values in ecological databases. Ecol. Inform. 63, 101315 (2021).Article 

Google Scholar 
Goolsby, E. W., Bruggeman, J. & Ané, C. Rphylopars: fast multivariate phylogenetic comparative methods for missing data and within-species variation. Methods Ecol. Evol. 8, 22–27 (2017).Article 

Google Scholar  More

Deng, Y., Li, X., Shi, F. & Hu, X. Woody plant encroachment enhanced global vegetation greening and ecosystem water-use efficiency. Glob. Ecol. Biogeogr. 30, 2337–2353 (2021).Article 

Google Scholar 
Brandt, J., Haynes, M., Kuemmerle, T., Waller, D. & Radeloff, V. Regime shift on the roof of the world: alpine meadows converting to shrublands in the southern Himalayas. Biol. Conserv. 158, 116–127 (2013).Article 

Google Scholar 
García Criado, M., Myers-Smith, I. H., Bjorkman, A. D., Lehmann, C. E. R. & Stevens, N. Woody plant encroachment intensifies under climate change across tundra and savanna biomes. Glob. Ecol. Biogeogr. 29, 925–943 (2020).Article 

Google Scholar 
van Auken, O. Causes and consequences of woody plant encroachment into western North American grasslands. J. Environ. Manage. 90, 2931–2942 (2009).Article 
CAS 

Google Scholar 
Bond, W. J., Midgley, G. F. & Woodward, F. I. The importance of low atmospheric CO2 and fire in promoting the spread of grasslands and savannas. Glob. Chang. Biol. 9, 973–982 (2010).Article 

Google Scholar 
D’Odorico, P., Okin, G. S. & Bestelmeyer, B. T. A synthetic review of feedbacks and drivers of shrub encroachment in arid grasslands. Ecohydrology 5, 520–530 (2012).Article 

Google Scholar 
Kulmatiski, A. & Beard, K. H. Woody plant encroachment facilitated by increased precipitation intensity. Nat. Clim. Change 3, 833–837 (2013).Article 
CAS 

Google Scholar 
Eldridge, D. J. & Soliveres, S. Are shrubs really a sign of declining ecosystem function? Disentangling the myths and truths of woody encroachment in Australia. Aust. J. Bot. 62, 594–608 (2015).Article 

Google Scholar 
Domine, F., Barrere, M. & Morin, S. The growth of shrubs on high Arctic tundra at Bylot Island: impact on snow physical properties and permafrost thermal regime. Biogeosciences 13, 6471–6486 (2016).Article 

Google Scholar 
Maestre, F. T., Callaway, R. M., Valladares, F. & Lortie, C. J. Refining the stress-gradient hypothesis for competition and facilitation in plant communities. J. Ecol. 97, 199–205 (2009).Article 

Google Scholar 
Eldridge, D. J. et al. Impacts of shrub encroachment on ecosystem structure and functioning: towards a global synthesis. Ecol. Lett. 14, 709–722 (2011).Article 

Google Scholar 
Archer, S. R. & Predick, K. I. An ecosystem services perspective on brush management: research priorities for competing land-use objectives. J. Ecol. 102, 1394–1407 (2014).Article 

Google Scholar 
Eldridge, D. J. & Ding, J. Remove or retain: ecosystem effects of woody encroachment and removal are linked to plant structural and functional traits. N. Phytol. 229, 2637–2646 (2020).Article 

Google Scholar 
Albrecht, M. A., Becknell, R. E. & Long, Q. Habitat change in insular grasslands: woody encroachment alters the population dynamics of a rare ecotonal plant. Biol. Conserv. 196, 93–102 (2016).Article 

Google Scholar 
Stanton, R. A. et al. Shrub encroachment and vertebrate diversity: a global meta-analysis. Glob. Ecol. Biogeogr. 27, 368–379 (2017).Article 

Google Scholar 
Archer, S. R. et al. in Rangeland Systems: Processes, Management and Challenges (ed. Briske, D.) 25–84 (Springer, 2017).Anadón, J. D., Sala, O. E., Turner, B. L. & Bennett, E. M. Effect of woody-plant encroachment on livestock production in North and South America. Proc. Natl Acad. Sci. USA 111, 12948–12953 (2014).Article 

Google Scholar 
Maestre, F. T. et al. Structure and functioning of dryland ecosystems in a changing world. Annu. Rev. Eco. Evol. Syst. 47, 215–237 (2016).Article 

Google Scholar 
Teague, W. et al. Sustainable management strategies for mesquite rangeland: the Waggoner Kite project. Rangelands 19, 4–9 (1997).
Google Scholar 
Hamilton, W. T., McGinty, A., Ueckert, D. N., Hanselka, C. W. & Lee, M. R. Brush Management: Past, Present, Future (A&M Univ. Press, 2004).Bestelmeyer, B. T. et al. The grassland–shrubland regime shift in the southwestern United States: misconceptions and their implications for management. BioScience 68, 678–690 (2018).Article 

Google Scholar 
Ding, J. & Eldridge, D. J. Contrasting global effects of woody plant removal on ecosystem structure, function and composition. Perspect. Plant Ecol. Evol. Syst. 39, 125460 (2019).Article 

Google Scholar 
Huxman, T. E. et al. Ecohydrological implication of woody plant encroachment. Ecology 86, 308–319 (2005).Article 

Google Scholar 
Schmutz, E. M., Cable, D. R. & Warwick, J. J. Effect of shrub removal on the vegetation of a semidesert grass-shrub range. Rangel. Ecol. Manag. 12, 34–37 (1959).Article 

Google Scholar 
Noble, J. C. & Walker, P. Integrated shrub management in semi-arid woodlands of eastern Australia: a systems-based decision support model. Agric. Syst. 88, 332–359 (2006).Article 

Google Scholar 
Eldridge, D. J. et al. The pervasive and multifaceted influence of biocrusts on water in the world’s drylands. Glob. Chang. Biol. 26, 6003–6014 (2020).Article 

Google Scholar 
Bestelmeyer, B. T., Goolsby, D. P. & Archer, S. R. Spatial perspectives in state-and-transition models: a missing link to land management. J. Appl. Ecol. 48, 746–757 (2011).Article 

Google Scholar 
Riginos, C. & Young, T. P. Positive and negative effects of grass, cattle, and wild herbivores on Acacia saplings in an East African savanna. Oecologia 153, 985–995 (2007).Article 

Google Scholar 
Soliveres, S. et al. Plant diversity and ecosystem multifunctionality peak at intermediate levels of woody cover in global drylands. Glob. Ecol. Biogeogr. 23, 1408–1416 (2014).Article 

Google Scholar 
Soliveres, S. & Eldridge, D. J. Do changes in grazing pressure and the degree of shrub encroachment alter the effects of individual shrubs on understorey plant communities and soil function? Funct. Ecol. 28, 530–537 (2013).Article 

Google Scholar 
Maestre, F. T., Bowker, M. A., Puche, M., Hinojosa, M. B. & Escudero, A. Shrub encroachment can reverse desertification in semi-arid Mediterranean grasslands. Ecol. Lett. 12, 930–941 (2010).Article 

Google Scholar 
Abreu, R. C. R., Durigan, G., Melo, A. C. G., Pilon, N. A. L. & Hoffmann, W. A. Facilitation by isolated trees triggers woody encroachment and a biome shift at the savanna-forest transition. J. Appl. Ecol. 58, 2650–2660 (2021).Article 

Google Scholar 
North, M., Oakley, B., Fiegener, R. & Barbour, G. M. Influence of light and soil moisture on Sierran mixed-conifer understory communities. Plant Ecol. 177, 13–24 (2005).Article 

Google Scholar 
Muvengwi, J., Mbiba, M., Jimu, L., Mureva, A. & Dodzo, B. An assessment of the effectiveness of cut and ring barking as a method for control of invasive Acacia mearnsii in Nyanga National Park, Zimbabwe. For. Ecol. Manag. 427, 1–6 (2018).Article 

Google Scholar 
Abella, S. R. & Chiquoine, L. P. The good with the bad: when ecological restoration facilitates native and non-native species. Restor. Ecol. 27, 343–351 (2019).Article 

Google Scholar 
Bestelmeyer, B., Ward, J., Herrick, E. J. & Tugel, A. J. Fragmentation effects on soil aggregate stability in a patchy arid grassland. Rangel. Ecol. Manag. 59, 406–415 (2006).Article 

Google Scholar 
Okin, G. S., Gillette, D. A. & Herrick, J. E. Multi-scale controls on and consequences of aeolian processes in landscape change in arid and semi-arid environments. J. Arid. Environ. 65, 253–275 (2006).Article 

Google Scholar 
Hu, X., Li, X. Y., Zhao, Y., Gao, Z. & Zhao, S. J. Changes in soil microbial community during shrub encroachment process in the Inner Mongolia grassland of northern China. Catena 202, 105230 (2021).Article 
CAS 

Google Scholar 
D’Odorico, P. et al. Positive feedback between microclimate and shrub encroachment in the northern Chihuahuan desert. Ecosphere 1, 1–11 (2010).Article 

Google Scholar 
Eldridge, D. J., Soliveres, S., Bowker, M. A. & Val, J. Grazing dampens the positive effects of shrub encroachment on ecosystem functions in a semi‐arid woodland. J. Appl. Ecol. 50, 1028–1038 (2013).Article 

Google Scholar 
Daryanto, S., Eldridge, D. J. & Throop, H. L. Managing semi-arid woodlands for carbon storage: grazing and shrub effects on above- and belowground carbon. Agric. Ecosyst. Environ. 169, 1–11 (2013).Article 

Google Scholar 
Paynter, Q. & Flanagan, G. J. Integrating herbicide and mechanical control treatments with fire and biological control to manage an invasive wetland shrub, Mimosa pigra. J. Appl. Ecol. 41, 615–629 (2004).Article 

Google Scholar 
Throop, H. L., Reichmann, L. G., Sala, O. E. & Archer, S. R. Response of dominant grass and shrub species to water manipulation: an ecophysiological basis for shrub invasion in a Chihuahuan Desert grassland. Oecologia 169, 373–383 (2012).Article 

Google Scholar 
Brantley, S. T. & Young, D. R. Shifts in litterfall and dominant nitrogen sources after expansion of shrub thickets. Oecologia 155, 337–345 (2008).Article 

Google Scholar 
Ding, J. & Eldridge, D. J. The fertile island effect varies with aridity and plant patch type across an extensive continental gradient. Plant Soil 459, 173–183 (2020).Article 

Google Scholar 
Mihoč, M. et al. Soil under nurse plants is always better than outside: a survey on soil amelioration by a complete guild of nurse plants across a long environmental gradient. Plant Soil 408, 31–41 (2016).Article 

Google Scholar 
Ochoa-Hueso, R. et al. Soil fungal abundance and plant functional traits drive fertile island formation in global drylands. J. Ecol. 106, 242–253 (2018).Article 
CAS 

Google Scholar 
Soliveres, S., Eldridge, D. J., Hemmings, F. & Maestre, F. T. Nurse plant effects on plant species richness in drylands: the role of grazing, rainfall and species specificity. Perspect. Plant Ecol. Evol. Syst. 14, 402–410 (2012).Article 

Google Scholar 
Schlesinger, W. et al. Biological feedbacks in global desertification. Science 147, 1043–1048 (1990).Article 

Google Scholar 
Ding, J. & Eldridge, D. J. Climate and plants regulate the spatial variation in soil multifunctionality across a climatic gradient. Catena 201, 105233 (2021).Article 
CAS 

Google Scholar 
Ding, J., Travers, S. K., Delgado-Baquerizo, M. & Eldridge, D. J. Multiple trade-offs regulate the effects of woody plant removal on biodiversity and ecosystem functions in global rangelands. Glob. Chang. Biol. 26, 709–720 (2020).Article 

Google Scholar 
De Soyza, A. G., Whitford, W. G., Martinez-Meza, E. & Van Zee, J. W. Variation in creosotebush (Larrea tridentata) canopy morphology in relation to habitat, soil fertility and associated annual plant communities. Am. Nat. 137, 13–26 (1997).Article 

Google Scholar 
Breemen, N. V. Nutrient cycling strategies. Plant Soil 168, 321–326 (1995).Li, J., Gilhooly, W. P. III., Okin, G. S. & Blackwell, J. III. Abiotic processes are insufficient for fertile island development: a 10-year artificial shrub experiment in a desert grassland. Geophys. Res. Lett. 44, 2245–2253 (2017).Article 

Google Scholar 
Ward, D. et al. Large shrubs increase soil nutrients in a semi-arid savanna. Geoderma 310, 153–162 (2018).Article 
CAS 

Google Scholar 
Miwa, C. Persistence of Western Juniper Resource Islands following Canopy Removal. MSc thesis, Oregon State Univ. (2007).Zhou, L. et al. Shrub-encroachment induced alterations in input chemistry and soil microbial community affect topsoil organic carbon in an Inner Mongolian grassland. Biogeochemistry 136, 311–324 (2017).Article 
CAS 

Google Scholar 
Kwok, A. B. C. & Eldridge, D. J. The influence of shrub species and fine-scale plant density on arthropods in a semiarid shrubland. Rangel. J. 38, 381–389 (2016).Article 

Google Scholar 
Young, J. A., Evans, R. A. & Rimbey, C. Weed control and revegetation following western juniper (Juniperus occidentalis) control. Weed Sci. 33, 513–517 (1985).Article 

Google Scholar 
Wiedemann, H. T. & Kelly, P. J. Turpentine (Eremophila sturtii) control by mechanical uprooting. Rangel. J. 23, 173–181 (2001).Article 

Google Scholar 
Bowker, M. A., Belnap, J., Chaudhary, V. B. & Johnson, N. C. Revisiting classic water erosion models in drylands: the strong impact of biological soil crusts. Soil Biol. Biochem. 40, 2309–2316 (2008).Article 
CAS 

Google Scholar 
Ding, J. & Eldridge, D. J. Biotic and abiotic effects on biocrust cover vary with microsite along an extensive aridity gradient. Plant Soil 450, 429–441 (2020).Article 
CAS 

Google Scholar 
Blaum, N., Seymour, C., Rossmanith, E., Schwager, M. & Jeltsch, F. Changes in arthropod diversity along a land use driven gradient of shrub cover in savanna rangelands: identification of suitable indicators. Biodivers. Conserv. 18, 1187–1199 (2009).Article 

Google Scholar 
Eldridge, D. J., Poore, A., Ruiz-Colmenero, M., Letnic, M. & Soliveres, S. Ecosystem structure, function and composition in rangelands are negatively affected by livestock grazing. Ecol. Appl. 26, 1273–1283 (2016).Article 

Google Scholar 
Maestre, F. T. & Cortina, J. Insights into ecosystem composition and function in a sequence of degraded semiarid steppes. Restor. Ecol. 12, 494–502 (2004).Article 

Google Scholar 
Nakagawa, S. in Ecological Statistics: Contemporary Theory and Application (eds Fox, G. A. et al.) Ch. 4 (Oxford Univ. Press, 2015).Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
Zomer, R. J., Trabucco, A., Bossio, D. A. & Verchot, L. V. Climate change mitigation: a spatial analysis of global land suitability for clean development mechanism afforestation and reforestation. Agric. Ecosyst. Environ. 126, 67–80 (2008).Article 

Google Scholar 
Tavşanoğlu, Ç. & Pausas, J. G. A functional trait database for mediterranean basin plants. Sci. Data 5, 180135 (2018).Article 

Google Scholar 
The PLANTS Database (USDA, 2019); https://plants.usda.gov/Kattge, J. et al. TRY—a global database of plant traits. Glob. Chang. Biol. 17, 2905–2935 (2011).Article 

Google Scholar 
Hedges, L. V., Gurevitch, J. & Curtis, P. S. The meta-analysis of response ratios in experimental ecology. Ecology 80, 1150–1156 (1999).Article 

Google Scholar 
Mallen-Cooper, M. et al. Global synthesis reveals strong multifaceted effects of eucalypts on soils. Glob. Ecol. Biogeogr. 31, 1667–1678 (2022).Article 

Google Scholar 
Chen, X., Chen, H. Y. & Chang, S. X. Meta-analysis shows that plant mixtures increase soil phosphorus availability and plant productivity in diverse ecosystems. Nat. Ecol. Evol. 6, 1112–1121 (2022).Article 

Google Scholar 
Noble, D. W. A., Lagisz, M., O’dea, R. E. & Nakagawa, S. Nonindependence and sensitivity analyses in ecological and evolutionary meta-analyses. Mol. Ecol. 26, 2410–2425 (2017).Article 

Google Scholar 
Nakagawa, S. & Santos, E. Methodological issues and advances in biological meta-analysis. Ecol. Evol. 26, 1253–1274 (2012).Article 

Google Scholar 
Grace, J. B. Structural Equation Modeling and Natural Systems (Cambridge Univ. Press, 2006).Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J. Stat. Softw. 36, 1–48 (2010).Article 

Google Scholar 
Archer, E. rfPermute v2.1.1 (R Foundation for Statistical Computing, 2010).Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).Stefan, V. & Levin, S. plotbiomes: plot Whittaker biomes with ggplot2 (R package version 0009001, 2021).Kahle, D. & Wickham, H. ggmap: spatial visualization with ggplot2. R. J. 5, 144–161 (2013).Article 

Google Scholar 
R Core Team. MOSR connections (R Foundation for Statistical Computing, 2013). More

Baker, A. C., Glynn, P. W. & Riegl, B. Climate change and coral reef bleaching: an ecological assessment of long-term impacts, recovery trends and future outlook. Estuar. Coast Shelf Sci. 80, 435–471 (2008).Article 

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

Google Scholar 
Normille, D. El Niño’s warmth devastating reefs worldwide. Science 352, 2015–2016 (2016).
Google Scholar 
Morikawa, M. K. & Palumbi, S. R. Using naturally occurring climate resilient corals to construct bleaching-resistant nurseries. Proc Natl Acad Sci USA 116, 10586–10591 (2019).Safaie, A. et al. High frequency temperature variability reduces the risk of coral bleaching. Nat. Commun. 9, 1671 (2018).Article 

Google Scholar 
Thomas, L. et al. Mechanisms of thermal tolerance in Reef-building corals across a fine-grained environmental mosaic: lessons from Ofu. Am. Samoa. Front Mar. Sci. 4, 434 (2018).Article 

Google Scholar 
Kenkel, C. D., Meyer, E. & Matz, M. V. Gene expression under chronic heat stress in populations of the mustard hill coral (Porites astreoides) from different thermal environments. Mol. Ecol. 22, 4322–4334 (2013).Article 
CAS 

Google Scholar 
Gomulkiewicz, R. & Holt, R. D. When does evolution by natural selection prevent extinction? Evolution 49, 201–207 (1995).
Google Scholar 
Bruno, J. F., Siddon, C. E., Witman, J. D., Colin, P. L. & Toscano, M. A. El Niño related coral bleaching in Palau, western Caroline Islands. Coral Reefs 20, 127–136 (2001).Article 

Google Scholar 
Golbuu, Y. et al. Palau’s coral reefs show differential habitat recovery following the 1998-bleaching event. Coral Reefs 26, 319–332 (2007).Article 

Google Scholar 
van Woesik, R. et al. Climate-change refugia in the sheltered bays of Palau: analogs of future reefs. Ecol. Evol. 2, 2474–2484 (2012).Article 

Google Scholar 
Barkley, H. C. & Cohen, A. L. Skeletal records of community-level bleaching in Porites corals from Palau. Coral Reefs 35, 1407–1417 (2016).Article 

Google Scholar 
Gouezo, M. et al. Drivers of recovery and reassembly of coral reef communities. Proc. R. Soc. B Biol. Sci. 286, 20182908 (2019).Shamberger, K. E. F. et al. Diverse coral communities in naturally acidified waters of a Western Pacific reef. Geophys. Res. Lett. 41, 499–504 (2014).Article 

Google Scholar 
Barkley, H. C. et al. Changes in coral reef communities across a natural gradient in seawater pH. Sci. Adv. 1, e1500328 (2015).Article 

Google Scholar 
Fabricius, K. E., Mieog, J. C., Colin, P. L., Idip, D. & van Oppen, M. J. H. Identity and diversity of coral endosymbionts (zooxanthellae) from three Palauan reefs with contrasting bleaching, temperature and shading histories. Mol. Ecol. 13, 2445–2458 (2004).Article 
CAS 

Google Scholar 
Anthony, K. R. N., Kline, D. I., Diaz-Pulido, G., Dove, S. & Hoegh-Guldberg, O. Ocean acidification causes bleaching and productivity loss in coral reef builders. Proc. Natl Acad. Sci. USA 105, 17442–17446 (2008).Article 
CAS 

Google Scholar 
Gibbin, E. M., Putnam, H. M., Gates, R. D., Nitschke, M. R. & Davy, S. K. Species-specific differences in thermal tolerance may define susceptibility to intracellular acidosis in reef corals. Mar. Biol. 162, 717–723 (2015).Article 
CAS 

Google Scholar 
Boulay, J. N., Hellberg, M. E., Cortés, J. & Baums, I. B. Unrecognized coral species diversity masks differences in functional ecology. Proc. R. Soc. B Biol. Sci. 281, 20131580 (2013).Baums, I. B., Boulay, J. N., Polato, N. R. & Hellberg, M. E. No gene flow across the Eastern Pacific Barrier in the reef-building coral Porites lobata. Mol. Ecol. 21, 5418–5433 (2012).Article 

Google Scholar 
Forsman, Z. H., Wellington, G. M., Fox, G. E. & Toonen, R. J. Clues to unraveling the coral species problem: Distinguishing species from geographic variation in Porites across the Pacific with molecular markers and microskeletal traits. PeerJ 3, e751 (2015).Article 

Google Scholar 
Levas, S. J., Grottoli, A. G., Hughes, A., Osburn, C. L. & Matsui, Y. Physiological and biogeochemical traits of bleaching and recovery in the mounding species of coral Porites lobata: implications for resilience in mounding corals. PLoS ONE 8, e63267 (2013).Article 
CAS 

Google Scholar 
Linsley, B. K. et al. Coral carbon isotope sensitivity to growth rate and water depth with Paleo-sea level implications. Nat. Commun. 10, 1–9 (2019).
Google Scholar 
Peyrot-Clausade, M., Hutchings, P. & Richard, G. Temporal variations of macroborers in massive Porites lobata on Moorea, French Polynesia. Coral Reefs 11, 161–166 (1992).Article 

Google Scholar 
Nanami, A. & Nishihira, M. Microhabitat association and temporal stability in reef fish assemblages on massive Porites microatolls. Ichthyol. Res. 51, 165–171 (2004).Article 

Google Scholar 
Cantin, N. E. & Lough, J. M. Surviving coral bleaching events: porites growth anomalies on the Great Barrier Reef. PLoS ONE 9, e88720 (2014).Article 

Google Scholar 
Carilli, J. E., Norris, R. D., Black, B., Walsh, S. M. & Mcfield, M. Century-scale records of coral growth rates indicate that local stressors reduce coral thermal tolerance threshold. Glob. Chang Biol. 16, 1247–1257 (2010).Article 

Google Scholar 
Cantin, N. E., Cohen, A. L., Karnauskas, K. B., Tarrant, A. M. & McCorkle, D. C. Ocean warming slows coral growth in the central Red Sea. Science 329, 322–325 (2010).Article 
CAS 

Google Scholar 
Lough, J. M. & Cooper, T. F. New insights from coral growth band studies in an era of rapid environmental change. Earth Sci. Rev. 108, 170–184 (2011).Article 
CAS 

Google Scholar 
Mollica, N. R. N. et al. Skeletal records of bleaching reveal different thermal thresholds of Pacific coral reef assemblages. Coral Reefs 38, 743–757 (2019).Article 

Google Scholar 
Barkley, H. C. et al. Repeat bleaching of a central Pacific coral reef over the past six decades (1960–2016). Commun. Biol. 1, 177 (2018).DeCarlo, T. M. & Cohen, A. L. Dissepiments, density bands and signatures of thermal stress in Porites skeletons. Coral Reefs 36, 749–761 (2017).Article 

Google Scholar 
DeCarlo, T. M. et al. Acclimatization of massive reef-building corals to consecutive heatwaves. Proc. R. Soc. B 286, 20190235 (2019).DeCarlo, T. M. The past century of coral bleaching in the Saudi Arabian central Red Sea. PeerJ 8, e10200 (2020).Article 

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

Google Scholar 
Fabricius, K. E. Effects of irradiance, flow, and colony pigmentation on the temperature microenvironment around corals: Implications for coral bleaching? Limnol. Oceanogr. 51, 30–37 (2006).Article 

Google Scholar 
Edmunds, P. J., Putnam, H. M. & Gates, R. D. Photophysiological consequences of vertical stratification of Symbiodinium in tissue of the coral Porites lutea. Biol. Bull. 223, 226–235 (2012).Article 
CAS 

Google Scholar 
Smith, L. W., Wirshing, H., Baker, A. C. & Birkeland, C. Environmental versus genetic influences on growth rates of the corals Pocillopora eydouxi and Porites lobata. Pac. Sci. 62, 57–69 (2008).Article 

Google Scholar 
Kenkel, C. D. & Bay, L. K. Exploring mechanisms that affect coral cooperation: symbiont transmission mode, cell density and community composition. PeerJ 2018, e6047 (2018).Article 

Google Scholar 
Sunde, J., Yıldırım, Y., Tibblin, P. & Forsman, A. Comparing the performance of microsatellites and RADseq in population genetic studies: analysis of data for Pike (Esox lucius) and a synthesis of previous studies. Front. Genet. 11, 218 (2020).Article 

Google Scholar 
Barkley, H. C., Cohen, A. L., McCorkle, D. C. & Golbuu, Y. Mechanisms and thresholds for pH tolerance in Palau corals. J. Exp. Mar. Biol. Ecol. 489, 7–14 (2017).Article 
CAS 

Google Scholar 
Mollica, N. R. et al. Ocean acidification affects coral growth by reducing skeletal density. Proc. Natl Acad. Sci. USA 115, 1754–1759 (2018).Article 
CAS 

Google Scholar 
DeCarlo, T. M. et al. Coral macrobioerosion is accelerated by ocean acidification and nutrients. Geology 43, 7–10 (2014).Article 

Google Scholar 
Manzello, D. P. et al. Role of host genetics and heat-tolerant algal symbionts in sustaining populations of the endangered coral Orbicella faveolata in the Florida Keys with ocean warming. Glob. Chang Biol. 25, 1016–1031 (2019).Article 

Google Scholar 
Rippe, J. P., Dixon, G., Fuller, Z. L., Liao, Y. & Matz, M. Environmental specialization and cryptic genetic divergence in two massive coral species from the Florida Keys Reef Tract. Mol. Ecol. 1–17 https://doi.org/10.1111/mec.15931 (2021).Schoepf, V. et al. Thermally variable, macrotidal Reef habitats promote rapid recovery from mass coral bleaching. Front. Mar. Sci. 7, 245 (2020).Article 

Google Scholar 
Dixon, G. B. et al. Genomic determinants of coral heat tolerance across latitudes. Science 348, 1460–1462 (2015).Article 
CAS 

Google Scholar 
Baums, I. B. et al. Considerations for maximizing the adaptive potential of restored coral populations in the western Atlantic. Ecol. Appl. 29, 1–23 (2019).Article 

Google Scholar 
Gosselin, L. A. & Qian, P.-Y. Juvenile mortality in benthic marine invertebrates. Mar. Ecol. Prog. Ser. 146, 265–282 (1997).Article 

Google Scholar 
Gouezo, M. et al. Modelled larval supply predicts coral population recovery potential following disturbance. Mar. Ecol. Prog. Ser. 661, 127–145 (2021).Golbuu, Y., Gouezo, M., Kurihara, H., Rehm, L. & Wolanski, E. Long-term isolation and local adaptation in Palau’s Nikko Bay help corals thrive in acidic waters. Coral Reefs 35, 909–918 (2016).Article 

Google Scholar 
Golbuu, Y. et al. Predicting coral recruitment in Palau’s complex reef archipelago. PLoS ONE 7, e50998 (2012).Article 
CAS 

Google Scholar 
Barshis, D. J., Birkeland, C., Toonen, R. J., Gates, R. D. & Stillman, J. H. High-frequency temperature variability mirrors fixed differences in thermal limits of the massive coral Porites lobata (Dana, 1846). J. Exp. Biol. jeb.188581 https://doi.org/10.1242/jeb.188581 (2018).Shamberger, K. E. F., Lentz, S. J. & Cohen, A. L. Low and variable ecosystem calcification in a coral reef lagoon under natural acidification. Limnol. Oceanogr. https://doi.org/10.1002/lno.10662 (2017).Cacciapaglia, C. & van Woesik, R. Climate-change refugia: shading reef corals by turbidity. Glob. Chang Biol. 22, 1145–1154 (2016).Article 

Google Scholar 
Anthony, K. R. Enhanced energy status of corals on coastal, high-turbidity reefs. Mar. Ecol. Prog. Ser. 319, 111–116 (2006).Article 

Google Scholar 
Houlbrèque, F. & Ferrier-Pagès, C. Heterotrophy in tropical scleractinian corals. Biol. Rev. Camb. Philos. Soc. 84, 1–17 (2009).Article 

Google Scholar 
Aichelman, H. E. et al. Heterotrophy mitigates the response of the temperate coral Oculina arbuscula to temperature stress. Ecol. Evol. 6, 6758–6769 (2016).Article 

Google Scholar 
Gómez‐Corrales, M. & Prada, C. Cryptic lineages respond differently to coral bleaching. Mol. Ecol. 0, 1–9 (2020).
Google Scholar 
Fifer, J. E., Yasuda, N., Yamakita, T., Bove, C. B. & Davies, S. W. Genetic divergence and range expansion in a western North Pacific coral. Sci. Total Environ. 152423 https://doi.org/10.1016/J.SCITOTENV.2021.152423 (2021).Euclide, P. T. et al. Attack of the PCR clones: rates of clonality have little effect on RAD-seq genotype calls. Mol. Ecol. Resour. 20, 66–78 (2020).Article 
CAS 

Google Scholar 
Noonan, S. H. C., DiPerna, S., Hoogenboom, M. O. & Fabricius, K. E. Effects of variable daily light integrals and elevated CO2 on the adult and juvenile performance of two Acropora corals. Mar. Biol. 169, 1–15 (2022).Article 

Google Scholar 
Martins, C. P. P. et al. Growth response of reef-building corals to ocean acidification is mediated by interplay of taxon-specific physiological parameters. Front. Mar. Sci. 0, 879 (2022).
Google Scholar 
Bairos-Novak, K. R., Hoogenboom, M. O., van Oppen, M. J. H. & Connolly, S. R. Coral adaptation to climate change: meta-analysis reveals high heritability across multiple traits. Glob. Chang. Biol. 27, 5694–5710 (2021).Article 
CAS 

Google Scholar 
Kenkel, C. D., Setta, S. P. & Matz, M. V. Heritable differences in fitness-related traits among populations of the mustard hill coral, Porites astreoides. Heredity 115, 509–516 (2015).Article 
CAS 

Google Scholar 
Dziedzic, K. E., Elder, H., Tavalire, H. & Meyer, E. Heritable variation in bleaching responses and its functional genomic basis in reef-building corals (Orbicella faveolata). Mol. Ecol. 28, 2238–2253 (2019).Article 

Google Scholar 
Quigley, K. M., Bay, L. K. & Oppen, M. J. H. Genome‐wide SNP analysis reveals an increase in adaptive genetic variation through selective breeding of coral. Mol. Ecol. 2176–2188 https://doi.org/10.1111/mec.15482 (2020).Veron, J. E. N. Corals of the World (Australian Institute of Marine Science, 2000).Polato, N. R., Concepcion, G. T., Toonen, R. J. & Baums, I. B. Isolation by distance across the Hawaiian Archipelago in the reef-building coral Porites lobata. Mol. Ecol. 19, 4661–4677 (2010).Article 
CAS 

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).Article 

Google Scholar 
Puritz, J. B., Hollenbeck, C. M. & Gold, J. R. dDocent: a RADseq, variant-calling pipeline designed for population genomics of non-model organisms. PeerJ 2, e431 (2014).Article 

Google Scholar 
Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).Article 
CAS 

Google Scholar 
Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. ArXiv (2013).Garrison, E. & Marth, G. Haplotype-based variant detection from short-read sequencing. ArXiv (2012).Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).Article 
CAS 

Google Scholar 
Kopelman, N. M., Mayzel, J., Jakobsson, M. & Rosenberg, N. A. CLUMPAK: a program for identifying clustering modes and packaging population structure inferences across K. Mol. Ecol. Resour. 15, 1179–1191 (2015).Article 
CAS 

Google Scholar 
Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).Article 
CAS 

Google Scholar 
Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol. Ecol. 14, 2611–2620 (2005).Article 
CAS 

Google Scholar 
Puechmaille, S. J. The program STRUCTURE does not reliably recover the correct population structure when sampling is uneven: subsampling and new estimators alleviate the problem. Mol. Ecol. Resour. 16, 608–627 (2016).Article 

Google Scholar 
Jombart, T. & Ahmed, I. adegenet 1.3-1: new tools for the analysis of genome-wide SNP data. Bioinformatics 27, 3070–3071 (2011).Article 
CAS 

Google Scholar 
Goudet, J. Hierfstat, a package for R to compute and test hierarchical F-statistics. Molecular Ecology Notes. 5, 184–186 (2005).Zeileis, A. & Grothendieck, G. zoo: S3 infrastructure for regular and irregular time series. J. Stat. Softw. 14, 1–27 (2005).Ryan, J. A. & Ulrich, J. M. xts: eXtensible Time Series. Package at https://cran.r-project.org/package=xts (2018).LaJeunesse, T. C. Diversity and community structure of symbiotic dinoflagellates from Caribbean coral reefs. Mar. Biol. 141, 387–400 (2002).Article 

Google Scholar 
LaJeunesse, T. C. & Trench, R. K. Biogeography of two species of Symbiodinium (Freudenthal) inhabiting the intertidal sea anemone Anthopleura elegantissima (Brandt). Biol. Bull. 199, 126–134 (2000).Article 
CAS 

Google Scholar  More