Ecology

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

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

Johannesson, K., Smolarz, K., Grahn, M. & André, C. The future of baltic sea populations: Local extinction or evolutionary rescue?. Ambio 40, 179–190 (2011).Article 
CAS 

Google Scholar 
Reusch, T. B. H. et al. The Baltic Sea as a time machine for the future coastal ocean. Sci. Adv. 4, eaar8195 (2018).Article 
ADS 

Google Scholar 
Kahru, M. & Elmgren, R. Multidecadal time series of satellite-detected accumulations of cyanobacteria in the Baltic Sea. Biogeosciences 11, 3619–3633 (2014).Article 
ADS 

Google Scholar 
Kahru, M., Elmgren, R. & Savchuk, O. P. Changing seasonality of the Baltic Sea. Biogeosciences 13, 1009–1018 (2016).Article 
ADS 

Google Scholar 
Hjerne, O., Hajdu, S., Larsson, U., Downing, A. S. & Winder, M. Climate driven changes in timing, composition and magnitude of the Baltic Sea phytoplankton spring bloom. Front. Mar. Sci. 6, 482 (2019).Article 

Google Scholar 
Bianchi, T. S. et al. Cyanobacterial blooms in the Baltic Sea: Natural or human-induced?. Limnol. Oceanogr. 45, 716–726 (2000).Article 
ADS 
CAS 

Google Scholar 
Poutanen, E.-L. & Nikkilä, K. Carotenoid pigments as tracers of cyanobacterial blooms in recent and post-glacial sediments of the Baltic Sea. Ambio 30, 179–183 (2001).Article 
CAS 

Google Scholar 
Andersson, A., Höglander, H., Karlsson, C. & Huseby, S. Key role of phosphorus and nitrogen in regulating cyanobacterial community composition in the northern Baltic Sea. Estuar. Coast. Shelf Sci. 164, 161–171 (2015).Article 
CAS 

Google Scholar 
Olofsson, M., Suikkanen, S., Kobos, J., Wasmund, N. & Karlson, B. Basin-specific changes in filamentous cyanobacteria community composition across four decades in the Baltic Sea. Harmful Algae 91, 101685 (2020).Article 
CAS 

Google Scholar 
Rolff, C. & Elfwing, T. Increasing nitrogen limitation in the Bothnian Sea, potentially caused by inflow of phosphate-rich water from the Baltic Proper. Ambio 44, 601–611 (2015).Article 
CAS 

Google Scholar 
Eriksson Wiklund, A.-K., Dahlgren, K., Sundelin, B. & Andersson, A. Effects of warming and shifts of pelagic food web structure on benthic productivity in a coastal marine system. Mar. Ecol. Prog. Ser. 396, 13–25 (2009).Article 
ADS 

Google Scholar 
Wikner, J. & Andersson, A. Increased freshwater discharge shifts the trophic balance in the coastal zone of the northern Baltic Sea. Glob. Change Biol. 18, 2509–2519 (2012).Article 
ADS 

Google Scholar 
Gulati, R. D. & Demott, W. R. The role of food quality for zooplankton: remarks on the state-of-the-art, perspectives and priorities. Freshw. Biol. 38, 16 (1997).Article 

Google Scholar 
Martin-Creuzburg, D., von Elert, E. & Hoffmann, K. H. Nutritional constraints at the cyanobacteria- Daphnia magna interface: The role of sterols. Limnol. Oceanogr. 53, 456–468 (2008).Article 
ADS 

Google Scholar 
Hedberg, P., Albert, S., Nascimento, F. J. A. & Winder, M. Effects of changing phytoplankton species composition on carbon and nitrogen uptake in benthic invertebrates. Limnol. Oceanogr. 66, 469–480 (2021).Article 
ADS 
CAS 

Google Scholar 
Gorokhova, E. Toxic cyanobacteria Nodularia spumigena in the diet of Baltic mysids: Evidence from molecular diet analysis. Harmful Algae 8, 264–272 (2009).Article 
CAS 

Google Scholar 
Karlson, A. M. L., Gorokhova, E. & Elmgren, R. Nitrogen fixed by cyanobacteria is utilized by deposit-feeders. PLoS ONE 9, e104460 (2014).Article 
ADS 

Google Scholar 
Karlson, A. M. L. et al. Nitrogen fixation by cyanobacteria stimulates production in Baltic food webs. Ambio 44, 413–426 (2015).Article 
CAS 

Google Scholar 
Lesutienė, J., Bukaveckas, P. A., Gasiūnaitė, Z. R., Pilkaitytė, R. & Razinkovas-Baziukas, A. Tracing the isotopic signal of a cyanobacteria bloom through the food web of a Baltic Sea coastal lagoon. Estuar. Coast. Shelf Sci. 138, 47–56 (2014).Article 
ADS 

Google Scholar 
Rolff, C. Seasonal variation in d13C and d15N of size-fractionated plankton at a coastal station in the northern Baltic proper. Mar. Ecol. Prog. Ser. 203, 47–65 (2000).Article 
ADS 
CAS 

Google Scholar 
Koski, M., Engström, J. & Viitasalo, M. Reproduction and survival of the calanoid copepod Eurytemora affinis fed with toxic and non-toxic cyanobacteria. Mar. Ecol. Prog. Ser. 186, 187–197 (1999).Article 
ADS 

Google Scholar 
Koski, M. et al. Calanoid copepods feed and produce eggs in the presence of toxic cyanobacteria Nodularia spumigena. Limnol. Oceanogr. 47, 878–885 (2002).Article 
ADS 

Google Scholar 
Schmidt, K. & Jónasdóttir, S. Nutritional quality of two cyanobacteria: How rich is ‘poor’ food?. Mar. Ecol. Prog. Ser. 151, 1–10 (1997).Article 
ADS 

Google Scholar 
Kankaanpää, H., Vuorinen, P. J., Sipiä, V. & Keinänen, M. Acute effects and bioaccumulation of nodularin in sea trout (Salmo trutta m. trutta L.) exposed orally to Nodularia spumigena under laboratory conditions. Aquat. Toxicol. 61, 155–168 (2002).Article 

Google Scholar 
Persson, K.-J., Bergström, K., Mazur-Marzec, H. & Legrand, C. Differential tolerance to cyanobacterial exposure between geographically distinct populations of Perca fluviatilis. Toxicon 76, 178–186 (2013).Article 
CAS 

Google Scholar 
Monserrat, J. M., Yunes, J. O. S. & Bianchini, A. Effects of Anabaena Spiroides (cyanobacteria) aqueous extracts on the acetylcholinesteraseactivity of aquatic species. Environ. Toxicol. Chem. 20, 1228–1235 (2001).Article 
CAS 

Google Scholar 
Lehtonen, K. K. et al. Accumulation of nodularin-like compounds from the cyanobacterium Nodularia spumigena and changes in acetylcholinesterase activity in the clam Macoma balthica during short-term laboratory exposure. Aquat. Toxicol. 64, 461–476 (2003).Article 
CAS 

Google Scholar 
Fulton, M. H. & Key, P. B. Acetylcholinesterase inhibition in esturai fish and invertebrates as an indicator of organophoshorus insecticide exposure and effects. Environ. Toxicol. Chem. 20, 37–45 (2001).Article 
CAS 

Google Scholar 
DeMott, W. R., Zhang, Q.-X. & Carmichael, W. W. Effects of toxic cyanobacteria and purified toxins on the survival and feeding of a copepod and three species of Daphnia. Limnol. Oceanogr. 36, 1346–1357 (1991).Article 
ADS 
CAS 

Google Scholar 
Hogfors, H. et al. Bloom-forming cyanobacteria support copepod reproduction and development in the Baltic Sea. PLoS ONE 9, e112692 (2014).Article 
ADS 

Google Scholar 
Motwani, N. H., Duberg, J., Svedén, J. B. & Gorokhova, E. Grazing on cyanobacteria and transfer of diazotrophic nitrogen to zooplankton in the Baltic Sea: Cyanobacteria blooms support zooplankton growth. Limnol. Oceanogr. 63, 672–686 (2018).Article 
ADS 

Google Scholar 
Gorokhova, E., El-Shehawy, R., Lehtiniemi, M. & Garbaras, A. How copepods can eat toxins without getting sick: Gut bacteria help zooplankton to feed in cyanobacteria blooms. Front. Microbiol. 11, 589816 (2021).Article 

Google Scholar 
Elmgren, R. Structure and dynamics of Baltic benthos communities, with particular reference to the relationship between macro- and meiofauna. Kieler Meeresforsch. Sonderh. 4, 1–22 (1978).
Google Scholar 
Laine, A. O. Distribution of soft-bottom macrofauna in the deep open Baltic Sea in relation to environmental variability. Estuar. Coast. Shelf Sci. 57, 87–97 (2003).Article 
ADS 
CAS 

Google Scholar 
Hill, C., Quigley, M. A., Cavaletto, J. F. & Gordon, W. Seasonal changes in lipid content and composition in the benthic amphipods Monoporeia afinis and Pontoporeia femorata. Limnol. Oceanogr. 37, 1280–1289 (1992).Article 
ADS 
CAS 

Google Scholar 
Lehtonen, K. K. Ecophysiology of the benthic amphipod Monoporeia affinis in an open-sea area of the northern Baltic Sea: Seasonal variations in body composition, with bioenergetic considerations. Mar. Ecol. Prog. Ser. 143, 87–98 (1996).Article 
ADS 

Google Scholar 
Karlson, A. M. L., Nascimento, F. J. A. & Elmgren, R. Incorporation and burial of carbon from settling cyanobacterial blooms by deposit-feeding macrofauna. Limnol. Oceanogr. 53, 2754–2758 (2008).Article 
ADS 

Google Scholar 
Karlson, A. M. L. & Mozūraitis, R. Deposit-feeders accumulate the cyanobacterial toxin nodularin. Harmful Algae 12, 77–81 (2011).Article 
CAS 

Google Scholar 
Savage, C. Tracing the influence of sewage nitrogen in a coastal ecosystem using stable nitrogen isotopes. Ambio 34, 145–150 (2005).Article 

Google Scholar 
Newsome, S. D., Del Rio, C. M., Bearhop, S. & Phillips, D. L. A niche for isotopic ecology. Front. Ecol. Environ. 5, 429–436 (2007).Article 

Google Scholar 
Layman, C. A., Arrington, D. A., Montaña, C. G. & Post, D. M. Can stable isotope ratio provide for community-wide mesures of trophic structure?. Ecology 88, 42–48 (2007).Article 

Google Scholar 
Jackson, A. L., Inger, R., Parnell, A. C. & Bearhop, S. Comparing isotopic niche widths among and within communities: SIBER—Stable isotope Bayesian ellipses in R: Bayesian isotopic niche metrics. J. Anim. Ecol. 80, 595–602 (2011).Article 

Google Scholar 
Blomqvist, S. & Lundgren, L. A benthic sled for sampling soft bottoms. Helgol. Meeresunters. 50, 453–456 (1996).Article 

Google Scholar 
Karlson, A. M. L., Nascimento, F. J. A., Näslund, J. & Elmgren, R. Higher diversity of deposit-feeding macrofauna enhances phytodetritus processing. Ecology 91, 1414–1423 (2010).Article 

Google Scholar 
Mazur-Marzec, H., Tymińska, A., Szafranek, J. & Pliński, M. Accumulation of nodularin in sediments, mussels, and fish from the Gulf of Gdańsk, southern Baltic Sea. Environ. Toxicol. 22, 101–111 (2007).Article 
ADS 
CAS 

Google Scholar 
van de Bund, W., Ólafsson, E., Modig, H. & Elmgren, R. Effects of the coexisting Baltic amphipods Monoporeia affinis and Pontoporeia femorata on the fate of a simulated spring diatom bloom. Mar. Ecol. Prog. Ser. 212, 107–115 (2001).Article 
ADS 

Google Scholar 
Larsson, U., Hobro, R. & Wulff, F. Dynamics of a Phytoplankton Spring Bloom in a Coastal Area of the Northern Baltic Proper (University of Stockholm, 1986).
Google Scholar 
Heiskanen, A.-S. Factors Governing Sedimentation and Pelagic Nutrient Cycles in the Northern Baltic Sea: = Sedimentaatioon ja Ravinteiden Kiertoon Vaikuttavat Tekijät Pohjoisen Ltämeren Ulapaekosysteemissä (Finnish Environment Institute, 1998).
Google Scholar 
Nadon, M.-O. & Himmelman, J. H. Stable isotopes in subtidal food webs: Have enriched carbon ratios in benthic consumers been misinterpreted?. Limnol. Oceanogr. 51, 2828–2836 (2006).Article 
ADS 
CAS 

Google Scholar 
Gorokhova, E. Shifts in rotifer life history in response to stable isotope enrichment: Testing theories of isotope effects on organismal growth. Methods Ecol. Evol. 9, 269–277 (2017).Article 

Google Scholar 
Karlson, A. M. L., Reutgard, M., Garbaras, A. & Gorokhova, E. Isotopic niche reflects stress-induced variability in physiological status. R. Soc. Open Sci. 5, 171398 (2018).Article 
ADS 

Google Scholar 
del Rio, C. M., Wolf, N., Carleton, S. A. & Gannes, L. Z. Isotopic ecology 10 years after a call for more laboratory experiments. Biol. Rev. 84, 91–111 (2009).Article 

Google Scholar 
Ledesma, M., Gorokhova, E., Holmstrand, H., Garbaras, A. & Karlson, A. M. L. Nitrogen isotope composition of amino acids reveals trophic partitioning in two sympatric amphipods. Ecol. Evol. 10, 10773–10784 (2020).Article 

Google Scholar 
Bocquené, G. & Galgani, F. Biological Effects of Contaminants: Cholinesterase Inhibitation by Organophosphate and Carbamate Compounds (ICES Techniques in Marine Environmental Science (TIMES). Report., 1998). https://doi.org/10.17895/ices.pub.5048.
Book 

Google Scholar 
Ellman, G. L., Courtney, K. D., Andres, V. & Featherstone, R. M. A new and rapid colorimetric determination of acetylcholinesterase activity. Biochem. Pharmacol. 7, 88–95 (1961).Article 
CAS 

Google Scholar 
Jarek, S. mvnormtest: Normality test for multivariate variables. (2012). R package version 0.1-9. https://CRAN.R-project.org/package=mvnormtestR Core Team. R: A Language and Environment for Statistical Computing. (2021).Nascimento, F. J. A., Karlson, A. M. L., Näslund, J. & Gorokhova, E. Settling cyanobacterial blooms do not improve growth conditions for soft bottom meiofauna. J. Exp. Mar. Biol. Ecol. 368, 138–146 (2009).Article 

Google Scholar 
Roche-Mayzaud, O., Mayzaud, P. & Biggs, D. Medium-term acclimation of feeding and of digestive and metabolic enzyme activity in the neritic copepod Acartia clause. I. Evidence from laboratory experiments. Mar. Ecol. Prog. Ser. 69, 25–40 (1991).Article 
ADS 
CAS 

Google Scholar 
Stuart, V., Head, E. J. H. & Mann, K. H. Seasonal changes in the digestive enzyme levels of the amphipod Corophium volutator (Pallas) in relation to diet. J. Exp. Mar. Biol. Ecol. 88, 243–256 (1985).Article 
CAS 

Google Scholar 
Schwarzenberger, A., Ilić, M. & Von Elert, E. Daphnia populations are similar but not identical in tolerance to different protease inhibitors. Harmful Algae 106, 102062 (2021).Article 
CAS 

Google Scholar 
Schwarzenberger, A. & Fink, P. Gene expression and activity of digestive enzymes of Daphnia pulex in response to food quality differences. Comp. Biochem. Physiol. B 218, 23–29 (2018).Article 
CAS 

Google Scholar 
Sipiä, V. O. et al. Bioaccumulation and detoxication of nodularin in tissues of flounder (Platichthys flesus), mussels (Mytilus edulis, Dreissena polymorpha), and clams (Macoma balthica) from the Northern Baltic Sea. Ecotoxicol. Environ. Saf. 53, 305–311 (2002).Article 

Google Scholar 
Bolnick, D. I. et al. The ecology of individuals: Incidence and implications of individual specialization. Am. Nat. 161, 1–28 (2003).Article 
MathSciNet 

Google Scholar 
MacArthur, R. H. & Pianka, E. R. On optimal use of a patchy environment. Am. Nat. 100, 603–609 (1966).Article 

Google Scholar 
Wiklund, A.-K.E., Sundelin, B. & Rosa, R. Population decline of amphipod Monoporeia affinis in Northern Europe: Consequence of food shortage and competition?. J. Exp. Mar. Biol. Ecol. 367, 81–90 (2008).Article 

Google Scholar 
Leonardsson, K., Sörlin, T., Samberg, H. & Sorlin, T. Does Pontoporeia affinis (Amphipoda) optimize age at reproduction in the Gulf of Bothnia?. Oikos 52, 328 (1988).Article 

Google Scholar 
Eriksson Wiklund, A.-K. & Andersson, A. Benthic competition and population dynamics of Monoporeia affinis and Marenzelleria sp. in the northern Baltic Sea. Estuar. Coast. Shelf Sci. 144, 46–53 (2014).Article 
ADS 

Google Scholar 
Karlson, A. M. L. et al. Linking consumer physiological status to food-web structure and prey food value in the Baltic Sea. Ambio 49, 391–406 (2020).Article 
CAS 

Google Scholar 
Olofsson, M. Nitrogen fixation estimates for the Baltic Sea indicate high rates for the previously overlooked Bothnian Sea. Ambio https://doi.org/10.1007/s13280-020-01331-x (2021).Article 

Google Scholar  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

All methods were performed in accordance with the relevant guidelines and regulations.Study siteThe laboratory experiments on regeneration and wash resistance were conducted at the KCMUCo-PAMVERC Insecticide Testing Facility; while experimental hut study was carried out at Harusini, the facility’s field site located at Mabogini village (S03˚22.764’ E03˚720.793’), adjacent to Lower Moshi rice irrigation scheme in north-eastern Tanzania. The dominant vector at this site is An. arabiensis with moderate level of resistance to pyrethroids conferred by both oxidase and esterase activities32. In this study, pyrethroid-resistant laboratory reared An. gambiae Muleba-Kis mosquitoes were released into the huts for the release-recapture experiment.Test systemsNon-blood fed, 2–5 day old females of susceptible An. gambiae s.s. Kisumu strain and pyrethroid resistant An. gambiae s.s Muleba-Kis strain were used for the evaluation of efficacy in the laboratory (phase I). The Muleba-Kis strain has been colonized for more than 8 years and it is resistant to permethrin with fixed L1014S kdr frequency and metabolic resistance through increased oxidase activity has also been reported21. Only An. gambiae s.s Muleba-Kis were used in release-recapture experiments. The Kisumu strain is fully susceptible to insecticides and free of any detectable insecticide resistance mechanisms. The strain originated from Kisumu, Kenya and has been colonized for many years in laboratory. At the KCMUCo-PAMVERC Moshi insectary, the adult Kisumu strain mosquitoes are reared at a temperature of 24–27 °C, 75 ± 10% relative humidity (RH) and maintained under a dark:light regime of 12:12 h. The Muleba-Kis mosquitoes used for the release-recapture experiments were reared in the field insectary under ambient temperature and relative humidity and treated as previously explained21. The susceptibility status of these colonies is checked every three months using WHO susceptibility test33 and, CDC bottle bioassay test34. The colonies are regularly genotyped for kdr mutations using TaqMan assays35. To maintain the resistance of Muleba-Kis, larvae are frequently selected with alpha-cypermethrin.Regeneration timeTo determine the regeneration time of the insecticide-treated blankets, blankets were cut into 25 × 25 cm pieces and tested before washing and then washed and dried three times consecutively following WHO recommended procedures for LLINs36. The pieces were then re-tested after one, two, three, six and seven days post-washing using WHO cylinders against susceptible An. gambiae s.s (Kisumu).Graphs for 24-h mortality and 60 min knock down (KD) correlating to insecticide bioavailability, as measured by 3 min exposure in cylinder bioassays, were established before and after washing blanket pieces three times consecutively in a day, and tested within a maximum of seven days post-washing. The time in days required to reach initial mortality or 60 min KD plateau is the period required for full regeneration of insecticide-treated blanket.Wash resistanceWHO cylinder bioassays36 were used to assess the wash resistance for the blanket pieces washed 0, 5, 10, 15 and 20 times at the intervals equivalent to the regeneration time. Four pieces cut from 4 permethrin and 4 untreated blankets were used as positive and negative control respectively, against 4 pieces cut from 4 PBO–permethrin blankets.Bioassay proceduresFive, non-blood fed, 2–5 day old An. gambiae Kisumu or An. gambiae Muleba-Kis mosquitoes were exposed for 3 min or 30 min to blanket pieces in WHO cylinder. Bioassays were carried out at 27 ± 2 °C and 75 ± 10% RH. Knock-down was scored after 60 min post-exposure and mortality after 24 h. Fifty mosquitoes (5 mosquitoes per cylinder) were used on each 25 × 25 cm piece of blanket sample. After exposure, the mosquitoes were held for 24 h with access to 10% glucose solution in the paper cups covered with a net material. Mosquitoes exposed to untreated blanket were referred as a negative control.WHO tunnel test methodBlanket pieces which recorded ≤ 80% mortality in cylinder bioassay were tested in the tunnel assay using WHO guidelines. The tunnel was made of an acrylic square cylinder (25 cm in height, 25 cm in width, and 60 cm in length) divided into two sections using a blanket-covered frame fitted into a slot across the tunnel. During the assays a guinea pig was held in a small wooden cage (as a bait) in one of the sections and 50, non-blood fed, female An. gambiae Kisumu or An. gambiae Muleba-Kis aged 5–8 days were released in the other section at dusk and left overnight (13 h) for experimentation at 27 ± 2 °C and 75 ± 10% RH. The blanket surface was deliberately holed (nine 1-cm holes) to allow mosquitoes to contact the blanket material and penetrate to the baited chamber. Treated blankets were tested concurrently together with an untreated blanket. Scoring for the numbers of mosquitoes found alive or dead, fed or unfed, in each section were done in the morning. Mosquitoes found alive were removed and held in paper cups with labels corresponding to each tunnel sections under controlled conditions (25–27 °C and 75–85% RH) and fed on 10% glucose solution to monitor for delayed mortality post exposurely. Outcomes recorded were: mosquito penetration, blood feeding and mortality.Washing of blankets and whole nets for hut trialBlankets and whole nets were separately washed following WHOPES guidelines. In brief, each blanket/net was washed in Savon de Marseilles soap solution (2 g/L) for 10 min: 3 min stirring, 4 min soaking, then another 3 min stirring. This was followed by 2 rinse cycles of the same duration with water only. The water pH was 6 for all washes. The mean water hardness was within the WHOPES limit of ≤ 89 ppm. All nets used in the experimental hut study were cut with holes (4 cm × 4 cm) to simulate the conditions of a torn net. While nets were washed 20 times as per guidelines, blankets were only washed 10 times. To simulate a situation in emergence situations where washing is less frequent due to water scarcity30,31.Experimental hut trial:experimental hut designExperimental hut study was done in Lower Moshi using typical East African experimental huts design as described in the WHOPES35. Huts were constructed with brick walls and featured with cement plaster on the inside and a ceiling board, a metal iron sheet roof, open eaves with window and veranda traps on each side and window traps. Slight modifications from the original structure were made by installing metal eave baffles on two sides. The baffles allow mosquito entry but prevent exits. The window traps were used to collect mosquitoes that tend to exit the huts.Test item labelling, washing and perforatingBoth blankets and LLINs for the trial were distinctively labelled with fabric labels that withstand washes. For wash resistance, the blankets and nets were separately washed according to a protocol adapted from the standard WHO washing procedure36 at the interval equivalent to the regeneration time established in the laboratory for blanket and LLIN respectively. Before testing in the experimental huts, all nets were deliberately holed i.e. 30 holes measuring 4 × 4 cm were made in each net, 9 holes in each of the long side panels, and 6 holes at each short side (head- and foot-side panels) to enhance blood-feeding on the control arm.Test items packagingEach blanket and net were sealed in a plastic bag and then packed in the large plastic container. Each container was labelled for a single treatment to avoid cross contamination between test items.Experimental hut decontaminationA cone assay with 10 susceptible mosquitoes was performed on one wall per hut to rule out any contamination of the wall surface. Only huts with 24 h mortality of susceptible mosquitoes  More

La Sorte, F. A., Johnston, A. & Ault, T. R. Global trends in the frequency and duration of temperature extremes. Clim. Change 166, 1–2 (2021).Article 
ADS 

Google Scholar 
Hansen, J., Sato, M., Ruedy, R., Lo, K. & Medina-Elizade, M. Global temperature change. Proc. Natl. Acad. Sci. U.S.A. 103(39), 14288–14293 (2006).Article 
ADS 
CAS 

Google Scholar 
Borchert, R., Robertson, K., Schwartz, M. D. & Williams-Linera, G. Phenology of temperate trees in tropical climates. Int. J. Biometeorol. 50, 57–65 (2005).Article 
ADS 

Google Scholar 
Misra, G., Sarah, A. & Menzel, A. Ground and satellite phenology in alpine forests are becoming more heterogeneous across higher elevations with warming. Agric. For. Meteorol. 303, 108383 (2021).Article 
ADS 

Google Scholar 
Zuo, Z., Xiao, D. & Qiong, H. Role of the warming trend in global land surface air temperature variations. Sci. China Earth Sci. 6, 866–871 (2021).Article 
ADS 

Google Scholar 
Ling, Y. et al. Assessing the accuracy of forest phenological extraction from sentinel-1 C-band backscatter measurements in deciduous and coniferous forests. Remote Sens. 14(3), 674 (2022).Article 
ADS 

Google Scholar 
Zhang, H., Yuan, W., Liu, S., Dong, W. & Fu, Y. Sensitivity of flowering phenology to changing temperature in China. J. Geophys. Res. Biogeosci. 120(8), 1658–1665 (2015).Article 

Google Scholar 
Cho, J. G. et al. Apple phenology occurs earlier across South Korea with higher temperatures and increased precipitation. Int. J. Biometeorol. 65, 265–276 (2020).Article 

Google Scholar 
Li, C. et al. Response of vegetation phenology to the interaction of temperature and precipitation changes in Qilian mountains. Remote Sens. 14(5), 1248 (2022).Article 
ADS 

Google Scholar 
Berra, E. F. & Gaulton, R. Remote sensing of temperate and boreal forest phenology: A review of progress, challenges and opportunities in the intercomparison of in-situ and satellite phenological metrics. For. Ecol. Manage. 480, 118663 (2021).Article 

Google Scholar 
Zhang, Y. & Li, M. A new method for monitoring start of season (SOS) of forest based on multisource remote sensing. Int. J. Appl. Earth Obs. Geoinf. 104, 102556 (2021).
Google Scholar 
Zhang, X. et al. Monitoring vegetation phenology using MODIS. Remote Sens. Environ. 84(3), 471–475 (2003).Article 
ADS 

Google Scholar 
Thapa, S., Garcia Millan, V. E. & Eklundh, L. Assessing forest phenology: A multi-scale comparison of near-surface (UAV, spectral reflectance sensor, PhenoCam) and Satellite (MODIS, Sentinel-2) remote sensing. Remote Sens. 13, 1597 (2021).Article 
ADS 

Google Scholar 
Bórnez, K., Descals, A., Verger, A. & Peñuelas, J. Land surface phenology from VEGETATION and PROBA-V data: Assessment over deciduous forests. Int. J. Appl. Earth Observ. Geoinf. 84, 101974 (2020).
Google Scholar 
Yu, L., Yan, Z. & Zhang, S. Forest phenology shifts in response to climate change over China–Mongolia–Russia international economic corridor. Forests 11, 757 (2020).Article 

Google Scholar 
Lara, C. et al. Climatic regulation of vegetation phenology in protected areas along Western South America. Remote Sens. 13, 2590 (2021).Article 
ADS 

Google Scholar 
Silveira, E. M. O. et al. Forest phenoclusters for Argentina based on vegetation phenology and climate. Ecol. Appl. 32, 2526 (2022).Article 

Google Scholar 
Tatalovich, Z., Wilson, J. P. & Cockburn, M. A comparison of thiessen polygon, kriging, and spline models of potential UV exposure. Cartogr. Geogr. Inf. Sci. 33, 217–231 (2006).Article 

Google Scholar 
Choubin, B. et al. Spatiotemporal dynamics assessment of snow cover to infer snowline elevation mobility in the mountainous regions. Cold Reg. Sci. Technol. 167, 102870 (2019).Article 

Google Scholar 
Rojas, R., Flexas, J. & Coopman, R. E. Particularities of the highest elevation treeline in the world: Polylepis tarapacana Phil. as a model to study ecophysiological adaptations to extreme environments. Flora 292, 152076 (2022).Article 

Google Scholar 
Du, J. et al. Interacting effects of temperature and precipitation on climatic sensitivity of spring vegetation green-up in arid mountains of China. Agric. For. Meteorol. 269–270, 71–77 (2019).Article 
ADS 

Google Scholar 
Du, J. et al. Daily minimum temperature and precipitation control on spring phenology in arid-mountain ecosystems in China. Int. J. Climatol. 40, 2568–2579 (2020).Article 

Google Scholar 
He, Z. et al. Impacts of recent climate extremes on spring phenology in arid-mountain ecosystems in China. Agric. For. Meteorol. 260–261, 31–40 (2018).Article 
ADS 

Google Scholar 
He, Z. et al. Assessing temperature sensitivity of subalpine shrub phenology in semi-arid mountain regions of China. Agric. For. Meteorol. 213, 42–52 (2015).Article 
ADS 

Google Scholar 
Mu, C., Lu, H., Wang, B., Bao, X. & Cui, W. Short-term effects of harvesting on carbon storage of boreal Larix gmelinii–Carex schmidtii forested wetlands in Daxing’anling, northeast China. For. Ecol. Manage. 293, 140–148 (2013).Article 

Google Scholar 
Hu, T. et al. Effects of fire on soil respiration and its components in a Dahurian larch (Larix gmelinii) forest in northeast China: Implications for forest ecosystem carbon cycling. Geoderma 402, 115273 (2021).Article 
ADS 
CAS 

Google Scholar 
Nyikadzino, B., Chitakira, M. & Muchuru, S. Rainfall and runoff trend analysis in the Limpopo river basin using the Mann Kendall statistic. Phys. Chem. Earth 117, 102870 (2020).Article 

Google Scholar 
Gocic, M. & Trajkovic, S. Analysis of changes in meteorological variables using Mann-Kendall and Sen’s slope estimator statistical tests in Serbia. Glob. Planet. Change 100, 172–182 (2013).Article 
ADS 

Google Scholar 
Fang, Y. et al. Changing contribution rate of heavy rainfall to the rainy season precipitation in Northeast China and its possible causes. Atmos. Res. 197, 437–445 (2017).Article 

Google Scholar 
Piao, S. et al. Changes in satellite-derived vegetation growth trend in temperate and boreal Eurasia from 1982 to 2006. Glob. Change Biol. 17, 3228–3239 (2011).Article 
ADS 

Google Scholar 
Ahas, R., Aasa, A., Menzel, A., Fedotova, V. G. & Scheifinger, H. Changes in European spring phenology. Int. J. Climatol. 22, 1727–1738 (2002).Article 

Google Scholar 
Liang, L., Henebry, G. M., Liu, L., Zhang, X. & Hsu, L. C. Trends in land surface phenology across the conterminous United States (1982–2016) analyzed by NEON domains. Ecol. Appl. 31, e02323 (2021).Article 

Google Scholar 
Fu, Y. H. et al. Decreasing control of precipitation on grassland spring phenology in temperate China. Glob. Ecol. Biogeogr. 30, 490–499 (2020).Article 

Google Scholar 
Aze, T. Unraveling ecological signals from a global warming event of the past. Proc. Natl. Acad. Sci. U.S.A. 119, e2201495119 (2022).Article 

Google Scholar 
Menzel, A., Estrella, N. & Testka, A. Temperature response rates from long-term phenological records. Climate Res. 30, 21–28 (2005).Article 
ADS 

Google Scholar 
Wang, H., Liu, D., Lin, H., Montenegro, A. & Zhu, X. NDVI and vegetation phenology dynamics under the influence of sunshine duration on the Tibetan plateau. Int. J. Climatol. 35, 687–698 (2015).Article 

Google Scholar 
Lesica, P. & Kittelson, P. M. Precipitation and temperature are associated with advanced flowering phenology in a semi-arid grassland. J. Arid Environ. 74, 1013–1017 (2010).Article 
ADS 

Google Scholar 
Shen, M., Piao, S., Cong, N., Zhang, G. & Jassens, I. A. Precipitation impacts on vegetation spring phenology on the Tibetan Plateau. Glob. Change Biol. 21, 3647–3656 (2015).Article 
ADS 

Google Scholar 
Li, Z. et al. Spatio-temporal responses of cropland phenophases to climate change in Northeast China. J. Geog. Sci. 22, 29–45 (2012).Article 
CAS 

Google Scholar 
Badeck, F. W. et al. Responses of spring phenolgy to climate change. New Phytol. 162, 295–309 (2004).Article 

Google Scholar 
Peng, H., Xia, H., Chen, H., Zhi, P. & Xu, Z. Spatial variation characteristics of vegetation phenology and its influencing factors in the subtropical monsoon climate region of southern China. PLoS ONE 16, e0250825 (2021).Article 
CAS 

Google Scholar 
Zhang, J. et al. NIRv and SIF better estimate phenology than NDVI and EVI: Effects of spring and autumn phenology on ecosystem production of planted forests. Agric. For. Meteorol. 315, 108819 (2022).Article 
ADS 

Google Scholar 
Yu, X., Zhuang, D., Hou, X. & Chen, H. Forest phenological patterns of Northeast China inferred from MODIS data. J. Geog. Sci. 15, 239–246 (2005).Article 

Google Scholar 
Chen, X. & Xu, L. Phenological responses of Ulmus pumila (Siberian Elm) to climate change in the temperate zone of China. Int. J. Biometeorol. 56, 695–706 (2012).Article 
ADS 

Google Scholar 
Ma, X., Bai, H., He, Y. & Li, S. The vegetation RSP of Qinling Mountains based on the NDVI and the response of temperature to it. Appl. Mech. Mater. 700, 394–399 (2014).Article 

Google Scholar  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