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This is the data to "High summer temperatures amplify functional differences between coral- and algae-dominated reef communities". All information on how the dataset was collected can be found in the manuscript. Abstract of the manuscript: Shifts from coral to algal dominance are expected to increase in tropical coral reefs as a result of anthropogenic disturbances. The consequences for key ecosystem functions such as primary productivity, calcification, and nutrient recycling are poorly understood, particularly under changing environmental conditions. We used a novel in situ incubation approach to compare functions of coral- and algae-dominated communities in the central Red Sea bi-monthly over an entire year. In situ gross and net community primary productivity, calcification, dissolved organic carbon fluxes, dissolved inorganic nitrogen fluxes, and their respective activation energies were quantified to describe the effects of seasonal changes. Overall, coral-dominated communities exhibited 30% lower net productivity and 10 times higher calcification than algae-dominated communities. Estimated activation energies indicated a higher thermal sensitivity of coral-dominated communities. In these communities, net productivity and calcification were negatively correlated with temperature (>40% and >65% reduction, respectively, with +5°C increase from winter to summer), while carbon losses via respiration and dissolved organic carbon release were more than doubled at higher temperatures. In contrast, algae-dominated communities doubled net productivity in summer, while calcification and dissolved organic carbon fluxes were unaffected. These results suggest pronounced changes in community functioning associated with phase shifts. Algae-dominated communities may outcompete coral-dominated communities due to their higher productivity and carbon retention to support fast biomass accumulation while compromising the formation of important reef framework structures. Higher temperatures likely amplify these functional differences, indicating a high vulnerability of ecosystem functions of coral-dominated communities to temperatures even below coral bleaching thresholds. Our results suggest that ocean warming may not only cause but also amplify coral-algal phase shifts in coral reefs. Usage information: The data Excel file contains two data sheets: Sheet 1 (Community composition): This sheet gives the community composition of the assessed benthic communities in % cover of functional groups. Sheet 2 (Metabolism): This sheet contains the metabolic data of the benthic communities. All abbreviations and units are in the related publication.

Future ocean acidification (OA) will affect physiological traits of marine species, with calcifying species being particularly vulnerable. As OA entails high energy demands, particularly during the rapid juvenile growth phase, food supply may play a key role in the response of marine organisms to OA. We experimentally evaluated the role of food supply in modulating physiological responses and biomineralization processes in juveniles of the Chilean scallop, Argopecten purpuratus, that were exposed to control (pH 8.0) and low pH (pH 7.6) conditions using three food supply treatments (high, intermediate, and low). We found that pH and food levels had additive effects on the physiological response of the juvenile scallops. Metabolic rates, shell growth, net calcification, and ingestion rates increased significantly at low pH conditions, independent of food. These physiological responses increased significantly in organisms exposed to intermediate and high levels of food supply. Hence, food supply seems to play a major role modulating organismal response by providing the energetic means to bolster the physiological response of OA stress. On the contrary, the relative expression of chitin synthase, a functional molecule for biomineralization, increased significantly in scallops exposed to low food supply and low pH, which resulted in a thicker periostracum enriched with chitin polysaccharides. Under reduced food and low pH conditions, the adaptive organismal response was to trade-off growth for the expression of biomineralization molecules and altering of the organic composition of shell periostracum, suggesting that the future performance of these calcifiers will depend on the trajectories of both OA and food supply. Thus, incorporating a suite of traits and multiple stressors in future studies of the adaptive organismal response may provide key insights on OA impacts on marine calcifiers. In order to allow full comparability with other ocean acidification data sets, the R package seacarb (Gattuso et al, 2015) was used to compute a complete and consistent set of carbonate system variables, as described by Nisumaa et al. (2010). In this dataset the original values were archived in addition with the recalculated parameters (see related PI). The date of carbonate chemistry calculation is 2016-05-16. Supplement to: Ramajo, L; Marbà, Núria; Prado, Luis; Peron, Sophie; Lardies, Marco A; Rodriguez-Navarro, Alejandro; Vargas, C A; Lagos, Nelson A; Duarte, Carlos Manuel (2016): Biomineralization changes with food supply confer juvenile scallops (Argopecten purpuratus) resistance to ocean acidification. Global Change Biology, 22(6), 2025-2037

In the future, rivers may export more pollutants to coastal waters, driven by socio-economic development, increased material consumption, and climate change. However, existing scenarios often ignore multi-pollutant problems. Here, we aim to explore future trends in river exports of nutrients (N and P), plastics (macro and micro), and emerging contaminants (triclosan and diclofenac) at the sub-basin scale in the world by developing and applying the process-based MARINA-Multi model for diverging scenarios. In our MARINA-Multi (Model to Assess River Inputs of pollutaNts to the seAs) model, we implemented two new multi-pollutant scenarios: “Sustainability-driven Future” (SD) and “Economy-driven Future” (ED). In ED, river exports of nutrients and microplastics will double by 2100 globally. For SD, a decrease of up to 83% is projected for all pollutants by 2100. Diffuse sources such as fertilizers are largely responsible for increasing nutrient pollution in the two scenarios. Point sources namely sewage systems are largely responsible for increasing microplastic pollution in the ED scenario. In both scenarios, the Indian Ocean will receive up to 400% more pollutants from rivers by 2100 because of growing population, urbanization, and poor waste management in the African and Asian basins. The situation is different for the Mediterranean Sea and the Pacific Ocean (mainly less future pollution) and the Atlantic Ocean and Arctic Ocean (more or less future pollution depending on sub-basin and scenario). Globally, 56-78% of people are expected to live in more polluted river basins in the future, challenging sustainable development goals for clean waters.

Canopy-forming seaweeds, as primary producers and foundation species, provide key ecological services. Their responses to multiple stressors associated with climate change could therefore have important knock-on effects on the functioning of coastal ecosystems. We examined interactive effects of UVB radiation and warming on juveniles of three habitat-forming subtidal seaweeds from Western Australia–Ecklonia radiata, Scytothalia dorycarpa and Sargassum sp. Fronds were incubated for 14 days at 16–30°C with or without UVB radiation and growth, health status, photosynthetic performance, and light absorbance measured. Furthermore, we used empirical models from the metabolic theory of ecology to evaluate the sensitivity of these important seaweeds to ocean warming. Results indicated that responses to UVB and warming were species specific, with Sargassum showing highest tolerance to a broad range of temperatures. Scytothalia was most sensitive to elevated temperature based on the reduced maximum quantum yields of PSII; however, Ecklonia was most sensitive, according to the comparison of activation energy calculated from Arrhenius’ model. UVB radiation caused reduction in the growth, physiological responses and thallus health in all three species. Our findings indicate that Scytothalia was capable of acclimating in response to UVB and increasing its light absorption efficiency in the UV bands, probably by up-regulating synthesis of photoprotective compounds. The other two species did not acclimate over the two weeks of exposure to UVB. Overall, UVB and warming would severely inhibit the growth and photosynthesis of these canopy-forming seaweeds and decrease their coverage. Differences in the sensitivity and acclimation of major seaweed species to temperature and UVB may alter the balance between species in future seaweed communities under climate change. XiaoWernberg_Temp_UV_PLoSone_raw_dataRaw data on growth, photosynthetic yield, Health status and absorption.XiaoWernberg_Temp_UV_PLoSone_raw data.xlsx

# Systematic review of the uncertainty of coral reef futures under climate change, datasets Published paper resulting from this data can be found at: ## Summary This study conducted a systematic review of 79 published articles projecting coral reef responses to future climate change. This dataset contains qualitative and quantitative data extracted from the published studies, including model types, geographic focus, and projected impacts on coral reefs. ## Description of the data and file structure ### Supplementary Data File **Extracted Data**: **Source data for effect size calculations (n=8 published studies).** * Short.reference used to identify the published study from which the data were extracted. See Full Reference List within this Read.Me file * Scenario.ID identifies the individual scenario within each published study, numbered sequentially as scenario 1 (S1), scenario 2 (S2) * N.c is n/number of model runs for control scenario * N.e is n/number of model runs for future end-of-century (experimental) scenario * M.c is the Model estimate for baseline scenario * M.e is the model estimate of end of century projections * Sd.c is the standard deviation of end of century projection estimates * Sd.e is the standard deviation of the baseline scenario estimates ### Supplementary\_Data1 **Summary Database: Overview of the dataset including study details, geographic focus, spatial scale, modeling approach, and examined stressors.** * Author(s) describes the authors of the published studies from which the data were extracted. See Full Reference List within this Read.Me file * Year refers to the year of publication of the published studies * Ref number identifies the full reference in the Full Reference List within this Read.Me file * Approach type classified the models into five broad categories of methodologies: (a) ‘excess heat’/threshold models, (b) population dynamic models, (c) species distribution models, (d) ecological-evolutionary models, and (e) projective meta-analyses of published data (see published study for formal definitions). In a few cases where studies could not be categorized, the model type was recorded as ‘other’ * Focal projection(s) units is the unit in which the published studies delivered their projections * Spatial scale refers to the spatial scale of the projections published, classified as either regional or global * Geographic focus refers to the region the projections were formulated for (e.g. Great Barrier Reef, Australia) * Major stressor(s) examined refer to the main drivers that were used to parameterize the models (e.g. warming, ocean acidification) ### Supplementary\_Data2 **Complete Database: Detailed information from all 79 reviewed studies (qualitative characteristics)** * Unique_ID is a random unique ID assigned to each of the published papers within the dataset * Author_list is a comprehensive list of all authors of the published studies within the dataset * Article_ttle is the title of the published article * Source_journal is the scientific journal in which the article was published * Publication_year refers to the year of publication of the published studies * Times_cited is the number of citations received by the published studies according to the Thomson Reuters Web of Science database on March 6, 2023. * Model_category classified the models into five broad categories of methodologies: (a) ‘excess heat’/threshold models, (b) population dynamic models, (c) species distribution models, (d) ecological-evolutionary models, and (e) meta-analyses of published data (see published study for formal definitions). In a few cases where studies could not be categorized, the model type was recorded as ‘other’ * Model_technique refers to the method used to model heat stress (thermal threshold technique versus continuous variable technique). For studies to be classified as threshold techniques, the use of these metrics had to form the primary framework of the models that delivered projections. The second technique represents approaches that abandon the central threshold concept to focus on empirical relationships between continuous variables. * if_TM_Threshold type records the type of thermal threshold used. N/a is used when the study did not use a thermal threshold, or it was not clearly reported. * Focal_projection_unit records the units in which the published studies delivered their projections. * Spatial_scale refers to the spatial scale of the projections published, classified as either regional or global. * Reported_geographic_focus refers to the region the projections were formulated for (e.g. Great Barrier Reef, Australia) * Drivers_used_summary records a summary of drivers used to parameterise the models. * Underlying model structure/ description is a summary of the model structure and its purpose * Key_assumptions is a description of the main assumptions made by the model * Future_scenarios_examined refers to the exact future emissions pathways used * Model_geographic_resolution records the spatial resolution of the model output * Downscaled_yes_no records yes for when downscaling techniques were used to improve spatial resolution and no when downscaling techniques were not used * Downscaled_method records which type of downscaling technique was used (either statistical or dynamic). N/A is used when the study did not use a downscaling technique * Study_purpose is a summary of the published study's aims and its findings * Study advantages is a synthesis of the published study's key advantages * Study_gaps is a synthesis of the published study's key limitations ### Supplementary\_Data 3 **Exploratory Meta-analysis Database: Scenario descriptions for data included in the effect size analysis.** * Author(s) describes the authors of the published studies from which the data were extracted. See Full Reference List within this Read.Me file * Year refers to the year of publication of the published studies * Ref is a number that identifies the full reference in the Full Reference List within this Read.Me file * Scenario identifies the individual scenario within each published study, numbered sequentially as scenario 1 (S1), scenario 2 (S2) * Scenario description is a summary of the future scneario modelled * Reported warming refers to the future emissions pathway used to model future warming * Classified warming categorizes these warming levels into different scenarios of 1.5 - 2ºC, 2 - 4ºC, and >4ºC represent projections at the end-of-century (years 2090-2100) * Reported projection unit is the unit in which the published studies delivered their projections * Classified projection unit represents the categories in which the projection units were analysed (e.g. % reef cells at risk) ### Klein\_et\_al.,\_2024 * **R script:** Script used for exploratory meta-analysis ## Reference List We use numbers that reference the sources we used to collect our data. Below is a list of the sources and their corresponding numbers. Supplementary References 1 Page, M. J. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372, n71, doi:10.1136/bmj.n71 (2021). 2 Khalil, I., Muslim, A. M., Hossain, M. S. & Atkinson, P. M. Modelling and forecasting the effects of increasing sea surface temperature on coral bleaching in the Indo-Pacific region. International Journal of Remote Sensing 44, 194-216 (2023). 3 Abe, H., Kumagai, N. H. & Yamano, H. Priority coral conservation areas under global warming in the Amami Islands, Southern Japan. Coral Reefs 41, 1637-1650 (2022). 4 Sully, S., Hodgson, G. & van Woesik, R. Present and future bright and dark spots for coral reefs through climate change. Global Change Biology 28, 4509-4522, doi: (2022). 5 DeFilippo, L. B. et al. Assessing the potential for demographic restoration and assisted evolution to build climate resilience in coral reefs. Ecological applications 32, e2650 (2022). 6 Holstein, D. M., Smith, T. B., van Hooidonk, R. & Paris, C. B. Predicting coral metapopulation decline in a changing thermal environment. Coral Reefs 41, 961-972, doi:10.1007/s00338-022-02252-9 (2022). 7 Raharinirina, N. A., Acevedo-Trejos, E. & Merico, A. Modelling the acclimation capacity of coral reefs to a warming ocean. PLOS Computational Biology 18, e1010099 (2022). 8 Chollett, I. et al. Planning for resilience: Incorporating scenario and model uncertainty and trade‐offs when prioritizing management of climate refugia. Global Change Biology 28, 4054-4068 (2022). 9 Setter, R. O., Franklin, E. C. & Mora, C. Co-occurring anthropogenic stressors reduce the timeframe of environmental viability for the world’s coral reefs. PLOS Biology 20, e3001821, doi:10.1371/journal.pbio.3001821 (2022). 10 McWhorter, J. K., Halloran, P. R., Roff, G., Skirving, W. J. & Mumby, P. J. Climate refugia on the Great Barrier Reef fail when global warming exceeds 3° C. Global Change Biology 28, 5768-5780 (2022). 11 Kalmus, P., Ekanayaka, A., Kang, E., Baird, M. & Gierach, M. Past the precipice? Projected coral habitability under global heating. Earth's Future 10, e2021EF002608 (2022). 12 McWhorter, J. K. et al. The importance of 1.5°C warming for the Great Barrier Reef. Global Change Biology 28, 1332-1341, doi: (2022). 13 Klein, S. G. et al. Projecting coral responses to intensifying marine heatwaves under ocean acidification. Global Change Biology n/a, doi: (2021). 14 Adam, A. A. et al. Diminishing potential for tropical reefs to function as coral diversity strongholds under climate change conditions. Diversity and Distributions 27, 2245-2261 (2021). 15 Cant, J. et al. The projected degradation of subtropical coral assemblages by recurrent thermal stress. Journal of Animal Ecology 90, 233-247 (2021). 16 Principe, S. C., Acosta, A. L., Andrade, J. E. & Lotufo, T. M. Predicted shifts in the distributions of Atlantic reef-building corals in the face of climate change. Frontiers in Marine Science 8, 673086 (2021). 17 Strona, G. et al. Global tropical reef fish richness could decline by around half if corals are lost. Proceedings of the Royal Society B 288, 20210274 (2021). 18 Bleuel, J., Pennino, M. G. & Longo, G. O. Coral distribution and bleaching vulnerability areas in Southwestern Atlantic under ocean warming. Scientific Reports 11, 1-12 (2021). 19 Cornwall, C. E. et al. Global declines in coral reef calcium carbonate production under ocean acidification and warming. Proceedings of the National Academy of Sciences 118, e2015265118, doi:doi:10.1073/pnas.2015265118 (2021). 20 McManus, L. C. et al. Evolution and connectivity influence the persistence and recovery of coral reefs under climate change in the Caribbean, Southwest Pacific, and Coral Triangle. Global change biology 27, 4307-4321 (2021). 21 McClanahan, T. R. & Azali, M. K. Environmental Variability and Threshold Model’s Predictions for Coral Reefs. Frontiers in Marine Science 8, doi:10.3389/fmars.2021.778121 (2021). 22 Zuo, X. et al. Spatially Modeling the Synergistic Impacts of Global Warming and Sea-Level Rise on Coral Reefs in the South China Sea. Remote Sensing 13, 2626 (2021). 23 McManus, L. C. et al. Extreme temperature events will drive coral decline in the Coral Triangle. Global Change Biology 26, 2120-2133 (2020). 24 Rodriguez, L., García, J. J., Tuya, F. & Martínez, B. Environmental factors driving the distribution of the tropical coral Pavona varians: predictions under a climate change scenario. Marine Ecology 41, 1-12 (2020). 25 Cacciapaglia, C. W. & van Woesik, R. Reduced carbon emissions and fishing pressure are both necessary for equatorial coral reefs to keep up with rising seas. Ecography 43, 789-800, doi: (2020). 26 Matz, M. V., Treml, E. A. & Haller, B. C. Estimating the potential for coral adaptation to global warming across the Indo‐West Pacific. Global Change Biology (2020). 27 Kubicek, A., Breckling, B., Hoegh-Guldberg, O. & Reuter, H. Climate change drives trait-shifts in coral reef communities. Scientific Reports 9, 3721, doi:10.1038/s41598-019-38962-4 (2019). 28 Rodriguez, L., Martínez, B. & Tuya, F. Atlantic corals under climate change: modelling distribution shifts to predict richness, phylogenetic structure and trait-diversity changes. Biodiversity and Conservation 28, 3873-3890, doi:10.1007/s10531-019-01855-z (2019). 29 Jones, L. A. et al. Coupling of palaeontological and neontological reef coral data improves forecasts of biodiversity responses under global climatic change. Royal Society Open Science 6, 182111 (2019). 30 Yan, H. et al. Regional coral growth responses to seawater warming in the South China Sea. Science of the total environment 670, 595-605 (2019). 31 Woesik, R. v., Köksal, S., Ünal, A., Cacciapaglia, C. W. & Randall, C. J. Predicting coral dynamics through climate change. Scientific reports 8, 17997 (2018). 32 Wolff, N. H., Mumby, P. J., Devlin, M. & Anthony, K. R. N. Vulnerability of the Great Barrier Reef to climate change and local pressures. Global Change Biology 24, 1978-1991, doi:10.1111/gcb.14043 (2018). 33 Cacciapaglia, C. & van Woesik, R. Marine species distribution modelling and the effects of genetic isolation under climate change. Journal of Biogeography 45, 154-163 (2018). 34 Kornder, N. A., Riegl, B. M. & Figueiredo, J. Thresholds and drivers of coral calcification responses to climate change. Global Change Biology 24, 5084-5095, doi: (2018). 35 Langlais, C. et al. Coral bleaching pathways under the control of regional temperature variability. Nature Climate Change 7, 839-844 (2017). 36 Kendall, M. S., Poti, M. & Karnauskas, K. B. Climate change and larval transport in the ocean: fractional effects from physical and physiological factors. Global Change Biology 22, 1532-1547, doi: (2016). 37 Yara, Y. et al. Potential future coral habitats around Japan depend strongly on anthropogenic CO 2 emissions. Aquatic biodiversity conservation and ecosystem services, 41-56 (2016). 38 Van Hooidonk, R. et al. Local-scale projections of coral reef futures and implications of the Paris Agreement. Scientific reports 6, 39666 (2016). 39 Schleussner, C.-F. et al. Differential climate impacts for policy-relevant limits to global warming: the case of 1.5 C and 2 C. Earth system dynamics 7, 327-351 (2016). 40 Ainsworth, T. D. et al. Climate change disables coral bleaching protection on the Great Barrier Reef. Science 352, 338-342, doi:doi:10.1126/science.aac7125 (2016). 41 Cooper, J. K., Spencer, M. & Bruno, J. F. Stochastic dynamics of a warmer Great Barrier Reef. Ecology 96, 1802-1811 (2015). 42 Bozec, Y.-M. & Mumby, P. J. Synergistic impacts of global warming on the resilience of coral reefs. Philosophical Transactions of the Royal Society B: Biological Sciences 370, 20130267 (2015). 43 Bozec, Y. M., Alvarez‐Filip, L. & Mumby, P. J. The dynamics of architectural complexity on coral reefs under climate change. Global change biology 21, 223-235 (2015). 44 van Hooidonk, R., Maynard, J. A., Liu, Y. & Lee, S. K. Downscaled projections of Caribbean coral bleaching that can inform conservation planning. Global change biology 21, 3389-3401 (2015). 45 Kwiatkowski, L., Cox, P., Halloran, P. R., Mumby, P. J. & Wiltshire, A. J. Coral bleaching under unconventional scenarios of climate warming and ocean acidification. Nature Climate Change 5, 777-781 (2015). 46 Maynard, J. et al. Projections of climate conditions that increase coral disease susceptibility and pathogen abundance and virulence. Nature Climate Change 5, 688-694 (2015). 47 Descombes, P. et al. Forecasted coral reef decline in marine biodiversity hotspots under climate change. Global Change Biology 21, 2479-2487 (2015). 48 Freeman, L. A. Robust performance of marginal Pacific coral reef habitats in future climate scenarios. PLoS One 10, e0128875 (2015). 49 Cacciapaglia, C. & van Woesik, R. Reef‐coral refugia in a rapidly changing ocean. Global Change Biology 21, 2272-2282 (2015). 50 Mumby, P. J., Wolff, N. H., Bozec, Y.-M., Chollett, I. & Halloran, P. Operationalizing the Resilience of Coral Reefs in an Era of Climate Change. Conservation Letters 7, 176-187, doi: (2014). 51 Yara, Y., Fujii, M., Yamano, H. & Yamanaka, Y. Projected coral bleaching in response to future sea surface temperature rises and the uncertainties among climate models. Hydrobiologia 733, 19-29 (2014). 52 Logan, C. A., Dunne, J. P., Eakin, C. M. & Donner, S. D. Incorporating adaptive responses into future projections of coral bleaching. Global Change Biology 20, 125-139 (2014). 53 van Hooidonk, R., Maynard, J. A., Manzello, D. & Planes, S. Opposite latitudinal gradients in projected ocean acidification and bleaching impacts on coral reefs. Global Change Biology 20, 103-112, doi: (2014). 54 Ortiz, J. C., González-Rivero, M. & Mumby, P. J. An ecosystem-level perspective on the host and symbiont traits needed to mitigate climate change impacts on Caribbean coral reefs. Ecosystems 17, 1-13 (2014). 55 Lane, D. R. et al. Quantifying and valuing potential climate change impacts on coral reefs in the United States: Comparison of two scenarios. PloS one 8, e82579 (2013). 56 Kennedy, E. V. et al. Avoiding coral reef functional collapse requires local and global action. Current Biology 23, 912-918 (2013). 57 van Hooidonk, R., Maynard, J. A. & Planes, S. Temporary refugia for coral reefs in a warming world. Nature Climate Change 3, 508-511, doi:10.1038/nclimate1829 (2013). 58 Frieler, K. et al. Limiting global warming to 2 C is unlikely to save most coral reefs. Nature Climate Change 3, 165 (2013). 59 Ortiz, J. C., González‐Rivero, M. & Mumby, P. J. Can a thermally tolerant symbiont improve the future of Caribbean coral reefs? Global change biology 19, 273-281 (2013). 60 Freeman, L. A., Kleypas, J. A. & Miller, A. J. Coral reef habitat response to climate change scenarios. PloS one 8, e82404 (2013). 61 Couce, E., Ridgwell, A. & Hendy, E. J. Future habitat suitability for coral reef ecosystems under global warming and ocean acidification. Global Change Biology 19, 3592-3606, doi: (2013). 62 Couce, E., Irvine, P. J., Gregoire, L., Ridgwell, A. & Hendy, E. Tropical coral reef habitat in a geoengineered, high‐CO2 world. Geophysical Research Letters 40, 1799-1805 (2013). 63 Wooldridge, S. A. et al. Safeguarding coastal coral communities on the central Great Barrier Reef (Australia) against climate change: realizable local and global actions. Climatic Change 112, 945-961 (2012). 64 Meissner, K., Lippmann, T. & Sen Gupta, A. Large-scale stress factors affecting coral reefs: open ocean sea surface temperature and surface seawater aragonite saturation over the next 400 years. Coral Reefs 31, 309-319 (2012). 65 van Hooidonk, R. & Huber, M. Effects of modeled tropical sea surface temperature variability on coral reef bleaching predictions. Coral Reefs 31, 121-131, doi:10.1007/s00338-011-0825-4 (2012). 66 Teneva, L. et al. Predicting coral bleaching hotspots: the role of regional variability in thermal stress and potential adaptation rates. Coral Reefs 31, 1-12 (2012). 67 Yara, Y. et al. Ocean acidification limits temperature-induced poleward expansion of coral habitats around Japan. Biogeosciences 9, 4955-4968 (2012). 68 Edwards, H. J. et al. How much time can herbivore protection buy for coral reefs under realistic regimes of hurricanes and coral bleaching? Global Change Biology 17, 2033-2048 (2011). 69 Anthony, K. R. N. et al. Ocean acidification and warming will lower coral reef resilience. Global Change Biology 17, 1798-1808, doi: (2011). 70 Hoegh-Guldberg, O. Coral reef ecosystems and anthropogenic climate change. Regional Environmental Change 11, 215-227 (2011). 71 Hoeke, R. K., Jokiel, P. L., Buddemeier, R. W. & Brainard, R. E. Projected changes to growth and mortality of Hawaiian corals over the next 100 years. PloS one 6, e18038 (2011). 72 McLeod, E. et al. Warming seas in the Coral Triangle: coral reef vulnerability and management implications. Coastal Management 38, 518-539 (2010). 73 Baskett, M. L., Gaines, S. D. & Nisbet, R. M. Symbiont diversity may help coral reefs survive moderate climate change. Ecological Applications 19, 3-17 (2009). 74 Vivekanandan, E., Ali, M. H., Jasper, B. & Rajagopalan, M. Vulnerability of corals to warming of the Indian seas: a projection for the 21st century. Current Science, 1654-1658 (2009). 75 Donner, S. D. Coping with commitment: projected thermal stress on coral reefs under different future scenarios. PLoS One 4, e5712 (2009). 76 Buddemeier, R. W. et al. A modeling tool to evaluate regional coral reef responses to changes in climate and ocean chemistry. Limnology and Oceanography: Methods 6, 395-411 (2008). 77 Donner, S. D., Skirving, W. J., Little, C. M., Oppenheimer, M. & Hoegh‐Guldberg, O. Global assessment of coral bleaching and required rates of adaptation under climate change. Global Change Biology 11, 2251-2265 (2005). 78 McNeil, B. I., Matear, R. J. & Barnes, D. J. Coral reef calcification and climate change: The effect of ocean warming. Geophysical Research Letters 31 (2004). 79 Guinotte, J., Buddemeier, R. & Kleypas, J. Future coral reef habitat marginality: temporal and spatial effects of climate change in the Pacific basin. Coral reefs 22, 551-558 (2003). 80 Hoegh-Guldberg, O. Climate change, coral bleaching and the future of the world's coral reefs. Marine and freshwater research 50, 839-866 (1999). Climate change impact syntheses, such as those by the Intergovernmental Panel on Climate Change (IPCC), consistently assert that limiting global warming to 1.5°C is unlikely to safeguard most of the world’s coral reefs. This prognosis primarily stems from 'excess heat’ threshold models, which assume that widespread coral bleaching predictably occurs when temperatures accumulate beyond a specific threshold. Our systematic review of research projecting coral reef futures to climate change (n=79) revealed that 'excess heat' models constituted only one third (32%) of all studies but attracted a high proportion (68%) of citations in the field. We observed that most methods employed deterministic cause-and-effect rules rather than probabilistic relationships, impeding the field's ability to estimate uncertainties of coral reef futures. In attempting to assess the consistency of projected impacts, we aimed to identify common coral reef metrics under the same emissions scenarios. However, disparate choices in metrics and emissions scenarios hindered a cohesive synthesis and limited the exploratory analysis to a small fraction of available studies. We found substantial discrepancies in expected impacts to coral reefs, suggesting that some 'excess heat' models may project more extreme impacts than other methods. Drawing on lessons from the field of climate change science, we propose that an IPCC ensemble-like approach to generating probabilistic projections for coral reef futures is feasible. Successful implementation will require improved coordination among modeling efforts to select common output metrics and emission scenarios, addressing existing geographical biases, among other gaps in current modeling efforts. We conducted a comprehensive literature search using the Thomson Reuters Web of Science database to identify studies that projecting the impacts of climate change on shallow tropical and sub-tropical coral reefs. This search, adhering to PRISMA guidelines, yielded 2705 peer-reviewed articles, which we refined to 79 relevant articles published between 1999 and 2023 based on a specific selection criteria (Dataset 1). These studies were categorized into five major methodology types and further classified based on their approaches to simulating heat stress. Key characteristics such as the model output variables, spatial scale, and geographic area of each study were extracted, along with their methodological approaches, assumptions, and the techniques used.Our study aimed to assess and compare the projected impacts and uncertainties of various model types using a meta-analysis approach. The database of 79 studies was considered for inclusion in the exploratory meta-analysis based on specific criteria (view published article and supplementary methods for detailed list and Supplementary Figure 1). Briefly, to enable a meaningful analysis, we identified the three most frequently used model outputs in our database. Among those, only studies that provided: 1) sufficient data for projection estimates and uncertainty measures to be reliably extracted or calculated, 2) reported end-of-century projections, and 3) used a baseline period between 2000 and 2015, were selected for the exploratory meta-analysis. In cases where projection and uncertainty estimates were presented in figures, values were extracted using PlotDigitizer, where possible.

This supplemental datasheet provides the country-level data used for calculation in the manuscript "Possible but Rare: Safe and Just Satisfaction of National Human Needs in Terms of Ecosystem Services." For a summary of the results and insights, check the main text. Please check the supplemental information to follow the detailed calculations using this data. 

# Building functional and sustainable pharmacovigilance systems - an analysis of pharmacovigilance development across high-, middle- and low-income countries [https://doi.org/10.5061/dryad.2547d7wzs](https://doi.org/10.5061/dryad.2547d7wzs) ## Description of the data and file structure ## 1. Interview guide - presented as Supplemental File 1 (.docx) Qualitative data were collected through semi-structured interviews built around the theme ‘Strategies to build functional and sustainable pharmacovigilance systems – an analysis of pharmacovigilance implementation in high-, middle- and low-income countries’. An interview guide (Additional file 1) was developed based on the study’s objective and on the previously performed scoping review(1). The interview guide was divided into five sections with a total of 30 questions: 1. Key informant’s role in the national pharmacovigilance (PV) system 2. Organization of the national PV system 3. PV and health system development 4. PV and pharmaceutical development 5. The way forward in building functional and sustainable PV systems The interview guide was reviewed by the co-authors for clarity and validity of questions, then pilot-tested by a PV expert not directly involved in the research. Verbal informed consent for the use of information provided in the research and for the interview recording was obtained at the start of each interview. The interviews were conducted by the first author via Zoom videoconferencing (Zoom Video Communications Inc.) over a nine-month period, from November 2021 to July 2022. ## 2. Codebook - presented as Supplemental File 3 (.docx) The Framework Method was used for the thematic analysis of qualitative data (2). The respondents’ statements from the interviews were transcribed verbatim by the first author and deductive coding was used to code the transcript. For this purpose, a codebook was developed. The codebook outlined the categories, codes, and subcodes with the corresponding description of the codes and assumptions or rationales for collecting the data. ## 3. Qualitative data framework matrix - presented as Supplemental File 4 (.xlsx) The Framework Method was used for the thematic analysis of qualitative data (2). The respondents’ statements from the interviews were transcribed verbatim by the first author, and deductive coding was used to code the transcript. The codes were clustered into categories and Microsoft Excel (Microsoft Corporation, 2016) was used to summarize and ‘chart’ each transcript into a matrix consisting of subcodes, codes and categories. Twelve categories, clustered into four themes based on the objectives of the interviews emerged from the qualitative study. The analytical level used for the analysis of the interviews was the categories: 1. Triggers and motivation for PV 2. Core PV challenges 3. Organisation and structure for PV 4. Stakeholder coordination 5. Procedures for PV activities 6. PV system financing 7. Focus of capacity building 8. Strategic plan for systematic approach 9. Influence of healthcare systems on PV growth 10. Leveraging the health system for PV in LMIC 11. The influence of the pharmaceutical development 12. Engaging marketing authorisation holders (MAH) in PV To avoid the possibility of identifying the respondent through their job title and the organisation for which they worked, the respondents’ countries, organisations and job titles are not included in the framework matric. Furthermore, any responses that could provide a hint for possibly identifying the respondents were edited such that the text is completely anonymised. ## 4. Online survey questionnaire - presented as Supplemental File 2 (.docx) Quantitative data were collected using a standardised questionnaire (Additional file 2), informed by the interview questions. The questionnaire was set up in ODK ([https://opendatakit.org](https://opendatakit.org)) and pilot-tested by a PV expert not directly involved in the research. The questionnaire, in both English and French, was distributed by email and WhatsApp Messenger (WhatsApp Inc.) between November 2022 and February 2023 and three reminders were sent to non-respondents. ## 5. Checklist for Mixed Methods Research - presented as Supplemental File 5 (.docx) The Checklist for Mixed Methods Search (MMR) Manuscript Preparation and Review was consulted when preparing the manuscript. ## 6. Survey data PivotTable (.xlsx) Quantitative data collected in ODK will be extracted as CSV (comma-separated values) file and cleaned. There were initially 31 variables, representing each question in the online questionnaire. After cleaning, variable B3 and B4 were merged. To avoid the possibility of identifying the respondent through their, job title and the organisation for which they worked, the respondents’ countries, organisations and job titles are not included in the survey data table provided (variables A1 to A3). Text fields were restructured and standardised to facilitate analysis. Twenty-seven variables were included in the analysis: 1. Years of PV experience 2. Influence on PV system 3. Assessment with WHO-GBT 4. Maturity level 5. Other assessment 6. Competent staff 7. Health system influences PV 8. Health system levels 9. PV integrated at each level 10. Opinion on integrating PV at each level 11. Pharmaceutical development influencing PV 12. Legal provisions for industry 13. Industry involved in PV 14. Industry fees used for PV 15. Opinion on using industry fees for PV 16. Strategic PV plan 17. Other plan 18. Stakeholder coordination 19. PV system financing 20. Is there PV without external financing 21. Donor alignment 22. PV priority area 23. Proportion of advanced activities 24. Vaccine contribution 25. PV tools contribution 26. PV approach adequate 27. PV recommendation ## Sharing/Access information All data sources used in the submission have been appropriately cited and referenced in the publication. Data were derived from the following sources: · Qualitative data were collected through semi-structured interviews with key informants representing national and global PV stakeholders (National Regulatory Authorities, National Immunization Programs, Non-Governmental Organisations, technical and donor agencies) · Quantitative data were collected using a standardised questionnaire provided by key informants representing national and global PV stakeholders Study design The study had a convergent parallel mixed methods design, consisting of qualitative and quantitative methods. Qualitative research contained semi-structured interviews. To expand the breadth and range of the study, a quantitative survey was conducted, focusing on the same thematic questions as the semi-structured interviews. Sampling, setting and study population To ensure adequate representation of national and global PV stakeholders, study participants included representatives from the NRA, National Immunisation Programmes (NIP), and global technical and donor agencies (hereafter referred to as Technical and Financial Partners [TFP]). For the interviews, countries were selected based on the publicly available information corresponding to their PV maturity levels, such that all PV maturity levels were adequately represented. Potential participants were contacted via email addresses provided by their organisations, or via regional PV mailing lists. In addition, authors of articles included in a scoping review of strategies to build PV in LMIC, conducted within the context of this research, were contacted by provided email addresses . At least one LMIC from each WHO Region was identified. Sampling was purposive, based on informants’ knowledge and expertise in PV and their decision-making position within the national and global PV organisations concerned. It was deemed sufficient to interview eight to twelve key informants, based on evidence suggesting that saturation can be achieved in a narrow range of interviews particularly in studies with relatively homogenous study populations and narrowly defined objectives. For the survey, the identification of countries and participants was the same as for the interview. The number of participants was determined based on full membership of the WHO Programme for International Drug Monitoring (PIDM) at the start of the research (i.e., approx. 90 out of 131 LMIC), indicating that at least the minimum requirements for a functional PV system were present. For both qualitative and quantitative research, the definite sample was determined by the willingness of potential informants to participate in the research. For this study, the countries were categorised according to World Bank Group country classifications. Data collection Qualitative data were collected through semi-structured interviews built around the topic ‘Strategies to build functional and sustainable pharmacovigilance systems – an analysis of pharmacovigilance implementation in high-, middle- and low-income countries’. An interview guide (Supplemental File 1) was developed based on the study’s objective and on the previously performed scoping review (21). The interview guide was divided into five sections with a total of 30 questions: 1) key informant’s role in the national PV system; 2) organisation of the national PV system; 3) PV and health system development; 4) PV and pharmaceutical development; and 5) ensuring functional and sustainable PV systems. The interview guide was reviewed by the co-authors for clarity and validity of questions, then pilot-tested by a PV expert not directly involved in the research. Sixteen key informants were invited by email to participate in the interviews and were provided with an overview of the research. Verbal informed consent to record the interview and to use of the information provided by the key informant in the research was obtained at the start of each interview. The interviews were conducted by the first author using Zoom videoconferencing (Zoom Video Communications Inc.) over a nine-month period (November 2021 to July 2022). The duration of the interviews ranged from 60 to 90 minutes. Quantitative data were collected using a standardised questionnaire (Supplemental File 2), informed by the interview questions. The questionnaire was set up in ODK (https://opendatakit.org) and pilot-tested by a PV expert not directly involved in the research. The questionnaire, in both English and French, was distributed to 80 persons by email and WhatsApp Messenger (WhatsApp Inc.) between November 2022 and February 2023; three reminders were sent to non-respondents. Data analysis and interpretation The Framework Method was used for the thematic analysis of qualitative data. The respondents’ statements from the interviews were verbatim transcribed by the first author and deductive coding was used to code the transcript. For this purpose, a codebook was developed with the predefined codes organized into corresponding categories based on the research objectives (Supplemental File 3). The codes were assigned to the transcribed data, line by line, and related sub-codes were created to improve the accuracy of the analysis. The codes were then clustered into categories. Using Microsoft Excel (Microsoft Corporation, 2016) each transcript was summarized and the data were ‘charted’ into a matrix consisting of sub-codes, codes and categories (Supplemental File 4). The data were interpreted, with the identification of patterns, relationships, differences and similarities leading to new thematic groups. Quantitative data collected in ODK were exported into Microsoft Excel 2016 for analysis using a PivotTable. Data analysis consisted of descriptive statistics, primarily frequencies and percentages for categorical variables. The Checklist for Mixed Methods Search (MMR) Manuscript Preparation and Review was consulted when preparing the manuscript (see Supplemental File 5 for the completed checklist). Background Detecting, assessing and preventing adverse events and other medicine-related issues necessitate a functional pharmacovigilance system. In many low- and middle-income countries (LMIC), key elements of functional pharmacovigilance, such as effective organisation and procedures for vigilance activities are missing. With increased access to essential and novel medicines in LMIC, and taking into consideration other factors that can influence medicine use and the safety profile of medicines such as the healthcare system, socio-political and genetic factors, LMIC must establish and maintain functional pharmacovigilance systems to ensure adequate safety surveillance of authorised medicines. Objectives This research aims to analyse the development of pharmacovigilance systems across high-, middle- and low-income countries and to carve out essential elements for functionality and sustainability of pharmacovigilance systems in LMIC. Design A convergent parallel mixed methods design, combining qualitative and quantitative methods. Methods Qualitative and quantitative research consisted of semi-structured interviews and an online survey, respectively. Results Twelve key informants from ten countries were interviewed and 52 respondents from 36 countries completed the online survey. From the qualitative and quantitative data, we identified nine essential elements for sustainable pharmacovigilance development in LMIC: understanding the drivers of pharmacovigilance development; adequately resolving core system challenges; implementing an efficient organisational structure and good governance; establishing procedures for pharmacovigilance activities; ensuring availability of qualified and trained staff; identifying alternate sources of financing; having a strategic development plan; adequately leveraging the health system; and effectively integrating the pharmaceutical sector in the national pharmacovigilance system. Conclusions Findings from this research revealed progress in pharmacovigilance systems in LMIC in the last decade, though significant efforts are still needed to develop these systems to meet global standards. Developing the different areas emerging from this research, within the framework of a holistic, fit-for-purpose pharmacovigilance system strengthening, would enable a comprehensive progression from basic to functional and thus sustainable pharmacovigilance systems in LMIC.

This dataset includes data on sea surface temperatures, sea ice concentration, sea ice seasonality, salinity, runoff form the Greenland ice sheet, cholorophyll a, and a litterature review. The data is divided into six regions around Greenland stretching 200 km of the coastline. Each region spans 9 degrees latitude.