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# Coral reef state influences resilience to acute climate-mediated disturbances\_Table S1 [https://doi.org/10.5061/dryad.rfj6q57gz](https://doi.org/10.5061/dryad.rfj6q57gz) The dataset provides a summary of all publications included in the analysis for this study and the key statistics obtained from the studies and used in the analyses. The dataset includes details about the publication, spatial identifiers (e.g. realm, province, ecoregion) unique site code, information on the disturbance type and timing, the pre-and post-disturbance coral cover, the 5-year annual recovery rate, the recovery shape and recovery completeness classifications. Please see details Methods in the journal article "Coral reef state influences resilience to acute climate-mediated disturbances" as published in Global Ecology and Biogeography. ## Description of the data and file structure Each column provides the following information: | Column | Detail | | ------ | ------ | | Realm | All studies were assigned to an ‘ecoregion’, ‘province’ and ‘realm’ based on their spatial location in Spalding et al. (2007)’s spatial classification system for coastal and shelf waters. | | Province | All studies were assigned to an ‘ecoregion’, ‘province’ and ‘realm’ based on their spatial location in Spalding et al. (2007)’s spatial classification system for coastal and shelf waters. | | Ecoregion | All studies were assigned to an ‘ecoregion’, ‘province’ and ‘realm’ based on their spatial location in Spalding et al. (2007)’s spatial classification system for coastal and shelf waters. | | Unique study identifier | Unique identifiers for the lowest sampling unit in the dataset. In cases where there were data for different regions, reefs, islands/atolls, sites, reef zones, depths, and/or multiple disturbances within a publication or time-series, data from these publications were divided into separate ‘studies’. | | Publication/Dataset | Unique identifiers for the publication or dataset (generally the surname of the first author followed by the year of publication). | | Publication title | Title of the publication or dataset from which the data were sourced. | | Publication year | Year the publication from the which the data were sourced was published. | | Country/Territory | Name of the country or location from which the data came. | | Site latitude | Latitude of the study site from where the data came. | | Site longitude | Longitude of the study site from where the data came. | | Disturbance type | Classification of disturbance: Temperature stress, Cyclone/ severe storm, Runoff or Multiple. | | Disturbance.year | Year of the disturbance. | | Mean coral cover pre-disturbance | Pre-disturbance coral cover as extracted from the publication or dataset as the closest data point prior to disturbance. If there is an NA value in this column then there was no pre-disturbance data available and a measure of impact was not calculated. | | Mean coral cover post-disturbance | Post-disturbance coral cover as extracted from the publication or dataset as the closest data point prior to disturbance. If there is an NA value in this column then there was no pre-disturbance data available and a measure of impact was not calculated. | | Impact (lnRR) | Impact measure: the log response ratio of pre- to post-disturbance percentage coral cover. If there is an NA value in this column then there was no pre-disturbance data available and a measure of impact was not calculated. | | Time-averaged recovery rate | Recovery rate as percentage coral cover per year in the approximate 5-year time window following disturbance. See main Methods text in manuscript for more detail. If there is an NA value in this column then the available time-series following disturbance did not satisfy the criteria for inclusion in the calculation of recovery rate. | | Recovery shape | Recovery shape category: linear, accelerating, decelerating, logistic, flatline or null. If there is an NA value in this column then the available time-series following disturbance did not satisfy the criteria for inclusion in classification of recovery shape. | | Recovery completeness | Recovery completeness category: complete recovery – coral is observed to reach its pre-disturbance coral cover, signs of recovery – a positive trajectory but not reaching pre-disturbance cover in the time period examined, undetermined – no clear pattern in recovery, the null model was the top model, no recovery – the null model was the top model but the linear model had slope and standard error in slope near zero and further decline – the top model had a negative trend. If there is an NA value in this column then the available time-series following disturbance did not satisfy the criteria for inclusion in classification of recovery shape. | | Reference | Source for the data. | ## Sharing/Access information Data was derived from the following sources: **Appendix 1. Full list of references providing the data used in impact and recovery analyses supporting Table S1** Arceo, H. O., Quibilan, M. C., Aliño, P. M., Lim, G., & Licuanan, W. Y. (2001). Coral bleaching in Philippine reefs: Coincident evidences with mesoscale thermal anomalies. Bulletin of Marine Science, 69(2), 579-593. Aronson, R. B., Precht, W. F., Toscano, M. A., & Koltes, K. H. (2002). The 1998 bleaching event and its aftermath on a coral reef in Belize. Marine Biology, 141(3), 435-447. Aronson, R. B., Sebens, K. P., & Ebersole, J. P. (1994). Hurricane Hugo's impact on Salt River submarine canyon, St. Croix, US Virgin Islands. Proceedings of the colloquium on global aspects of coral reefs, Miami, 1993, 189-195. Bahr, K. D., Rodgers, K. S., & Jokiel, P. L. (2017). Impact of three bleaching events on the reef resiliency of Kāne'ohe Bay, Hawai'i. Frontiers in Marine Science, 4(DEC). Baird, A. H., Álvarez-Noriega, M., Cumbo, V. R., Connolly, S. R., Dornelas, M., & Madin, J. S. (2018). Effects of tropical storms on the demography of reef corals. Marine Ecology Progress Series, 606, 29-38. Barranco, L. M., Carriquiry, J. D., Rodríguez-Zaragoza, F. A., Cupul-Magaña, A. L., Villaescusa, J. A., & Calderón-Aguilera, L. E. (2016). Spatiotemporal variations of live coral cover in the Northern Mesoamerican reef system, Yucatan Peninsula, Mexico. Scientia Marina, 80(2), 143-150. Bastidas, C., Bone, D., Croquer, A., Debrot, D., Garcia, E., Humanes, A., . . . Rodríguez, S. (2012). Massive hard coral loss after a severe bleaching event in 2010 at Los Roques, Venezuela. Revista de Biologia Tropical, 60(SUPPL. 1), 29-37. Booth, D. J., & Beretta, G. A. (2002). Changes in a fish assemblage after a coral bleaching event. Marine Ecology Progress Series, 245, 205-212. Brandl, S. J., Emslie, M. J., & Ceccarelli, D. M. (2016). Habitat degradation increases functional originality in highly diverse coral reef fish assemblages. Ecosphere, 7(11). Brown, D., & Edmunds, P. J. (2013). Long-term changes in the population dynamics of the Caribbean hydrocoral Millepora spp. Journal of Experimental Marine Biology and Ecology, 441, 62-70. Brown, V. B., Davies, S. A., & Synnot, R. N. (1990). Long-term Monitoring of the Effects of Treated Sewage Effluent on the Intertidal Macroalgal Community Near Cape Schanck, Victoria, Australia. Botanica Marina, 33(1), 85-98. Bruckner, A. W., Coward, G., Bimson, K., & Rattanawongwan, T. (2017). Predation by feeding aggregations of Drupella spp. inhibits the recovery of reefs damaged by a mass bleaching event. Coral Reefs, 36(4), 1181-1187. Burt, J. A., Paparella, F., Al-Mansoori, N., Al-Mansoori, A., & Al-Jailani, H. (2019). Causes and consequences of the 2017 coral bleaching event in the southern Persian/Arabian Gulf. Coral Reefs. Bythell, J. (1997). Assessment of the impacts of hurricanes Marilyn and Luis and post-hurricane community dynamics at Buck Island Reef National Monument as part of the long-term coral reef monitoring program in the north-eastern Caribbean. Retrieved from Newcastle, United Kingdom: Coles, S. L., & Brown, E. K. (2007). Twenty-five years of change in coral coverage on a hurricane impacted reef in Hawai'i: The importance of recruitment. Coral Reefs, 26(3), 705-717. Connell, J. H., Hughes, T. P., Wallace, C. C., Tanner, J. E., Harms, K. E., & Kerr, A. M. (2004). A long‐term study of competition and diversity of corals. Ecological Monographs, 74(2), 179-210. Couch, C. S., Burns, J. H. R., Liu, G., Steward, K., Gutlay, T. N., Kenyon, J., . . . Kosaki, R. K. (2017). Mass coral bleaching due to unprecedented marine heatwave in Papahānaumokuākea Marine National Monument (Northwestern Hawaiian Islands). PLoS ONE, 12(9). Crabbe, M. J. C. (2014). Evidence of initial coral community recovery at Discovery Bay on Jamaica’s north coast. Revista de Biologia Tropical, 62, 137-140. Crosbie, A. J., Bridge, T. C., Jones, G., & Baird, A. H. (2019). Response of reef corals and fish at Osprey Reef to a thermal anomaly across a 30 m depth gradient. Marine Ecology Progress Series, 622, 93-102. Darling, E. S., McClanahan, T. R., & Côté, I. M. (2010). Combined effects of two stressors on Kenyan coral reefs are additive or antagonistic, not synergistic. Conservation Letters, 3(2), 122-130. De Bakker, D. M., Meesters, E. H., Bak, R. P. M., Nieuwland, G., & Van Duyl, F. C. (2016). Long-term Shifts in Coral Communities On Shallow to Deep Reef Slopes of Curaçao and Bonaire: Are There Any Winners? Frontiers in Marine Science, 3(247). Depczynski, M., Gilmour, J. P., Ridgway, T., Barnes, H., Heyward, A. J., Holmes, T. H., . . . Wilson, S. K. (2013). Bleaching, coral mortality and subsequent survivorship on a West Australian fringing reef. Coral Reefs, 32(1), 233-238. Diaz-Pulido, G., McCook, L. J., Dove, S., Berkelmans, R., Roff, G., Kline, D. I., . . . Hoegh-Guldberg, O. (2009). Doom and Boom on a Resilient Reef: Climate Change, Algal Overgrowth and Coral Recovery. PLoS ONE, 4(4). Dollar, S. J., & Tribble, G. W. (1993). Recurrent storm disturbance and recovery: a long-term study of coral communities in Hawaii. Coral Reefs, 12(3-4), 223-233. Donner, S. D., Kirata, T., & Vieux, C. (2010). Recovery from the 2004 coral bleaching event in the Gilbert Islands, Kiribati. Atoll Research Bulletin(587), 1-25. Edmunds, P. J. (2013). Decadal-scale changes in the community structure of coral reefs of St. John, US Virgin Islands. Marine Ecology Progress Series, 489, 107-123. Edmunds, P. J. (2018). Implications of high rates of sexual recruitment in driving rapid reef recovery in Mo’orea, French Polynesia. Scientific Reports, 8(1). Edmunds, P. J. (2019). Three decades of degradation lead to diminished impacts of severe hurricanes on Caribbean reefs. Ecology, 100(3). Edward, J. K. P., Mathews, G., Diraviya Raj, K., Laju, R. L., Selva Bharath, M., Arasamuthu, A., . . . Malleshappa, H. (2018). Coral mortality in the Gulf of Mannar, southeastern India, due to bleaching caused by elevated sea temperature in 2016. Current Science, 114(9), 1967-1972. Edwards, A. J., Clark, S., Zahir, H., Rajasuriya, A., Naseer, A., & Rubens, J. (2001). Coral bleaching and mortality on artificial and natural reefs in Maldives in 1998, sea surface temperature anomalies and initial recovery. Marine Pollution Bulletin, 42(1), 7-15. Emslie, M. J., Bray, P., Cheal, A. J., Johns, K. A., Osborne, K., Sinclair-Taylor, T., & Thompson, C. A. (2020). Decades of monitoring have informed the stewardship and ecological understanding of Australia's Great Barrier Reef. Biological Conservation, 252, 108854. Fenner, D. P. (1991). Effects of Hurricane Gilbert on coral reefs, fishes and sponges at Cozumel, Mexico. Bulletin of Marine Science, 48(3), 719-730. Fox, M. D., Carter, A. L., Edwards, C. B., Takeshita, Y., Johnson, M. D., Petrovic, V., . . . Smith, J. E. (2019). Limited coral mortality following acute thermal stress and widespread bleaching on Palmyra Atoll, central Pacific. Coral Reefs. García-Sais, J. R., Williams, S. M., & Amirrezvani, A. (2017). Mortality, recovery, and community shifts of scleractinian corals in Puerto Rico one decade after the 2005 regional bleaching event. PeerJ, 2017(7). Garpe, K. C., Yahya, S. A. S., Lindahl, U., & Öhman, M. C. (2006). Long-term effects of the 1998 coral bleaching event on reef fish assemblages. Marine Ecology Progress Series, 315, 237-247. Gilmour, J. P., Cook, K. L., Ryan, N. M., Puotinen, M. L., Green, R. H., Shedrawi, G., . . . Oades, D. (2019). The state of Western Australia’s coral reefs. Coral Reefs. Gilmour, J. P., Smith, L. D., Heyward, A. J., Baird, A. H., & Pratchett, M. S. (2013). Recovery of an isolated coral reef system following severe disturbance. Science, 340(6128), 69-71. Glynn, P. W. (1984). Widespread coral mortality and the 1982-1983 El Niño warming event. Environmental Conservation, 11(2), 133-146. Glynn, P. W., Enochs, I. C., Afflerbach, J. A., Brandtneris, V. W., & Serafy, J. E. (2014). Eastern Pacific reef fish responses to coral recovery following El Niño disturbances. Marine Ecology Progress Series, 495, 233-247. Gouezo, M., Golbuu, Y., Van Woesik, R., Rehm, L., Koshiba, S., & Doropoulos, C. (2015). Impact of two sequential super typhoons on coral reef communities in Palau. Marine Ecology Progress Series, 540, 73-85. Guest, J. R., Tun, K., Low, J., Vergés, A., Marzinelli, E. M., Campbell, A. H., . . . Steinberg, P. D. (2016). 27 years of benthic and coral community dynamics on turbid, highly urbanised reefs off Singapore. Scientific Reports, 6. Guillemot, N., Chabanet, P., & Le Pape, O. (2010). Cyclone effects on coral reef habitats in New Caledonia (South Pacific). Coral Reefs, 29(2), 445-453. Guzmán, H. M., & Cortés, J. (2001). Changes in reef community structure after fifteen years of natural disturbances in the Eastern Pacific (Costa Rica). Bulletin of Marine Science, 69(1), 133-149. Guzman, H. M., Cortes, J., Richmond, R. H., & Glynn, P. W. (1987). Effects of "El Nino - Southern oscillation' 1982/83 in the coral reefs at Isla del Cano, Costa Rica. Revista de Biologia Tropical, 35(2), 325-332. Haapkylä, J., Melbourne-Thomas, J., Flavell, M., & Willis, B. L. (2013). Disease outbreaks, bleaching and a cyclone drive changes in coral assemblages on an inshore reef of the Great Barrier Reef. Coral Reefs, 32(3), 815-824. Hagan, A., & Spencer, T. (2008). Reef resilience and change 1998–2007, Alphonse Atoll, Seychelles. Paper presented at the Proc 11th Int Coral Reef Symp. Harii, S., Hongo, C., Ishihara, M., Ide, Y., & Kayanne, H. (2014). Impacts of multiple disturbances on coral communities at Ishigaki Island, Okinawa, Japan, during a 15 year survey. Marine Ecology Progress Series, 509, 171-180. Harrison, H. B., Álvarez-Noriega, M., Baird, A. H., Heron, S. F., MacDonald, C., & Hughes, T. P. (2018). Back-to-back coral bleaching events on isolated atolls in the Coral Sea. Coral Reefs. Holbrook, S. J., Adam, T. C., Edmunds, P. J., Schmitt, R. J., Carpenter, R. C., Brooks, A. J., . . . Briggs, C. J. (2018). Recruitment Drives Spatial Variation in Recovery Rates of Resilient Coral Reefs. Scientific Reports, 8(1). Hongo, C., & Yamano, H. (2013). Species-Specific Responses of Corals to Bleaching Events on Anthropogenically Turbid Reefs on Okinawa Island, Japan, over a 15-year Period (1995-2009). PLoS ONE, 8(4). Huang, H., Yang, Y., Li, X., Yang, J., Lian, J., Lei, X., . . . Zhang, J. (2014). Benthic community changes following the 2010 Hainan flood: Implications for reef resilience. Marine Biology Research, 10(6), 601-611. Hughes, T. P. (1994). Catastrophes, phase shifts, and large-scale degradation of a Caribbean coral reef. Science, 265(5178), 1547-1551. Jokiel, P. L., Hunter, C. L., Taguchi, S., & Watarai, L. (1993). Ecological impact of a fresh-water "reef kill" in Kaneohe Bay, Oahu, Hawaii. Coral Reefs, 12(3-4), 177-184. Jones, A. M., & Berkelmans, R. (2014). Flood impacts in Keppel Bay, Southern Great Barrier Reef in the aftermath of cyclonic rainfall. PLoS ONE, 9(1). Jonker, M., Johns, K., & Osborne, K. (2008). Surveys of benthic reef communities using underwater digital photography and counts of juveniles. Long-term monitoring of the Great Barrier Reef Standard Operation Procedure Number 10. Retrieved from Townsville: Kuo, C. Y., Yuen, Y. S., Meng, P. J., Ho, P. H., Wang, J. T., Liu, P. J., . . . Chen, C. A. (2012). Recurrent Disturbances and the Degradation of Hard Coral Communities in Taiwan. PLoS ONE, 7(8). Lam, V. Y. Y., Chaloupka, M., Thompson, A., Doropoulos, C., & Mumby, P. J. (2018). Acute drivers influence recent inshore Great Barrier Reef dynamics. Proceedings of the Royal Society B: Biological Sciences, 285(1890). Lambo, A. L., & Ormond, R. F. G. (2006). Continued post-bleaching decline and changed benthic community of a Kenyan coral reef. Marine Pollution Bulletin, 52(12), 1617-1624. Lamy, T., Galzin, R., Kulbicki, M., Lison de Loma, T., & Claudet, J. (2016). Three decades of recurrent declines and recoveries in corals belie ongoing change in fish assemblages. Coral Reefs, 35(1), 293-302. Lamy, T., Legendre, P., Chancerelle, Y., Siu, G., & Claudet, J. (2015). Understanding the spatio-temporal response of coral reef fish communities to natural disturbances: Insights from beta-diversity decomposition. PLoS ONE, 10(9). Liddell, W. D., & Ohlhorst, S. L. (1992). Ten years of disturbance and change on a Jamaican fringing reef. Paper presented at the 7th Int. Coral Reef Symp. Lirman, D., Glynn, P. W., Baker, A. C., & Morales, G. E. L. (2001). Combined effects of three sequential storms on the huatulco coral reef tract, mexico. Bulletin of Marine Science, 69(1), 267-278. Lovell, E., & Sykes, H. Rapid recovery from bleaching events-Fiji Coral Reef Monitoring Network Assessment of hard coral cover from. Loya, Y., Sakai, K., Yamazato, K., Nakano, Y., Sambali, H., & Van Woesik, R. (2001). Coral bleaching: The winners and the losers. Ecology Letters, 4(2), 122-131. Lozano-Montes, H. M., Keesing, J. K., Grol, M. G., Haywood, M. D. E., Vanderklift, M. A., Babcock, R. C., & Bancroft, K. (2017). Limited effects of an extreme flood event on corals at Ningaloo Reef. Estuarine, Coastal and Shelf Science, 191, 234-238. Madin, J. S., Baird, A. H., Bridge, T. C. L., Connolly, S. R., Zawada, K. J. A., & Dornelas, M. (2018). Cumulative effects of cyclones and bleaching on coral cover and species richness at Lizard Island. Marine Ecology Progress Series, 604, 263-268. Magdaong, E. T., Fujii, M., Yamano, H., Licuanan, W. Y., Maypa, A., Campos, W. L., . . . Martinez, R. (2014). Long-term change in coral cover and the effectiveness of marine protected areas in the Philippines: A meta-analysis. Hydrobiologia, 733(1), 5-17. McField, M. (2000). Influence of disturbance on coral reef community structure in Belize. Paper presented at the Proc 9th Int Coral Reef Symp. Monaco, M. E., Friedlander, A. M., Caldow, C., Hile, S. D., Menza, C., & Boulon, R. H. (2009). Long-term monitoring of habitats and reef fish found inside and outside the U.S. Virgin Islands Coral Reef National Monument: A comparative assessment. Caribbean Journal of Science, 45(2-3), 338-347. Montefalcone, M., Morri, C., & Bianchi, C. N. (2018). Long-term change in bioconstruction potential of Maldivian coral reefs following extreme climate anomalies. Global Change Biology, 24(12), 5629-5641. Morgan, K. M., Perry, C. T., Johnson, J. A., & Smithers, S. G. (2017). Nearshore turbid-zone corals exhibit high bleaching tolerance on the Great Barrier Reef following the 2016 ocean warming event. Frontiers in Marine Science, 4. Obura, D., Gudka, M., Rabi, F. A., Gian, S. B., Bijoux, J., Freed, S., . . . Sola, E. (2017). Coral Reef Status Report for the Western Indian Ocean (2017). Paper presented at the Nairobi Convention. Obura, D., & Mangubhai, S. (2011). Coral mortality associated with thermal fluctuations in the Phoenix Islands, 2002-2005. Coral Reefs, 30(3), 607-619. Ostrander, G. K., Armstrong, K. M., Knobbe, E. T., Gerace, D., & Scully, E. P. (2000). Rapid transition the structure of a coral reef community: The effects of coral bleaching and physical disturbance. Proceedings of the National Academy of Sciences of the United States of America, 97(10), 5297-5302. Pereira, M. A. M., & Gonçalves, P. M. B. (2004). Effects of the 2000 southern Mozambique floods on a marginal coral community: The case at Xai-Xai. African Journal of Aquatic Science, 29(1), 113-116. Perry, C. T. (2003). Reef development at Inhaca Island, Mozambique: Coral communities and impacts of the 1999/2000 southern African floods. Ambio, 32(2), 134-139. Phongsuwan, N., Chankong, A., Yamarunpatthana, C., Chansang, H., Boonprakob, R., Petchkumnerd, P., . . . Bundit, O. A. (2013). Status and changing patterns on coral reefs in Thailand during the last two decades. Deep-Sea Research Part II: Topical Studies in Oceanography, 96, 19-24. Reyes-Bonilla, H., Carriquiry, J. D., Leyte-Morales, G. E., & Cupul-Magaña, A. L. (2002). Effects of the El Niño-Southern Oscillation and the anti-El Niño event (1997-1999) on coral reefs of the western coast of México. Coral Reefs, 21(4), 368-372. Ridgway, T., Inostroza, K., Synnot, L., Trapon, M., Twomey, L., & Westera, M. (2016). Temporal patterns of coral cover in the offshore Pilbara, Western Australia. Marine Biology, 163(9). Riegl, B. (2002). Effects of the 1996 and 1998 positive sea-surface temperature anomalies on corals, coral diseases and fish in the Arabian Gulf (Dubai, UAE). Marine Biology, 140(1), 29-40. Rioja-Nieto, R., Chiappa-Carrara, X., & Sheppard, C. (2012). Effects of hurricanes on the stability of reef-associated landscapes. Ciencias Marinas, 38(1), 47-55. Rogers, C. S., Gilnack, M., & Fitz Iii, H. C. (1983). Monitoring of coral reefs with linear transects: A study of storm damage. Journal of Experimental Marine Biology and Ecology, 66(3), 285-300. Rousseau, Y., Galzin, R., & Maréchal, J. P. (2010). Impact of hurricane Dean on coral reef benthic and fish structure of Martinique, French West Indies. Cybium, 34(3), 243-256. Russ, G. R., & Leahy, S. M. (2017). Rapid decline and decadal-scale recovery of corals and Chaetodon butterflyfish on Philippine coral reefs. Marine Biology, 164(1). Ruzicka, R. R., Colella, M. A., Porter, J. W., Morrison, J. M., Kidney, J. A., Brinkhuis, V., . . . Colee, J. (2013). Temporal changes in benthic assemblages on Florida Keys reefs 11 years after the 1997/1998 El Niño. Marine Ecology Progress Series, 489, 125-141. Sheppard, C. R. C. (1999). Coral decline and weather patterns over 20 years in the Chagos Archipelago, central Indian Ocean. Ambio, 28(6), 472-478. Shulman, M. J., & Robertson, D. R. (1996). Changes in the coral reefs of San Bias, Caribbean Panama: 1983 to 1990. Coral Reefs, 15(4), 231-236. Smith, T. B., Brandt, M. E., Calnan, J. M., Nemeth, R. S., Blondeau, J., Kadison, E., . . . Rothenberger, P. (2013). Convergent mortality responses of Caribbean coral species to seawater warming. Ecosphere, 4(7). Steneck, R. S., Arnold, S. N., Boenish, R., de León, R., Mumby, P. J., Rasher, D. B., & Wilson, M. W. (2019). Managing Recovery Resilience in Coral Reefs Against Climate-Induced Bleaching and Hurricanes: A 15 Year Case Study From Bonaire, Dutch Caribbean. Frontiers in Marine Science, 6(265). Stobart, B., Teleki, K., Buckley, R., Downing, N., & Callow, M. (2005). Coral recovery at Aldabra Atoll, Seychelles: Five years after the 1998 bleaching event. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 363(1826), 251-255. Torda, G., Sambrook, K., Cross, P., Sato, Y., Bourne, D. G., Lukoschek, V., . . . Willis, B. L. (2018). Decadal erosion of coral assemblages by multiple disturbances in the Palm Islands, central Great Barrier Reef. Scientific Reports, 8(1). Trapon, M. L., Pratchett, M. S., & Penin, L. (2011). Comparative effects of different disturbances in coral reef habitats in Moorea, French Polynesia. Journal of Marine Biology, 2011. Tsounis, G., & Edmunds, P. J. (2017). Three decades of coral reef community dynamics in St. John, USVI: A contrast of scleractinians and octocorals. Ecosphere, 8(1). Van Woesik, R., De Vantier, L. M., & Glazebrook, J. S. (1995). Effects of Cyclone "Joy' on nearshore coral communities of the Great Barrier Reef. Marine Ecology Progress Series, 128(1-3), 261-270. Van Woesik, R., Sakai, K., Ganase, A., & Loya, Y. (2011). Revisiting the winners and the losers a decade after coral bleaching. Marine Ecology Progress Series, 434, 67-76. Vercelloni, J., Kayal, M., Chancerelle, Y., & Planes, S. (2019). Exposure, vulnerability, and resiliency of French Polynesian coral reefs to environmental disturbances. Scientific Reports, 9(1). Walsh, W. J. (1983). Stability of a coral reef fish community following a catastrophic storm. Coral Reefs, 2(1), 49-63. Wilkinson, C. (2004). Status of coral reefs of the world: 2004 (Vol. 2). Queensland, Australia: Global Coral Reef Monitoring Network. Wilkinson, C. R., & Souter, D. (2008). Status of Caribbean coral reefs after bleaching and hurricanes in 2005. Wismer, S., Tebbett, S. B., Streit, R. P., & Bellwood, D. R. (2019). Spatial mismatch in fish and coral loss following 2016 mass coral bleaching. Science of the Total Environment, 650, 1487-1498. Woolsey, E., Bainbridge, S. J., Kingsford, M. J., & Byrne, M. (2012). Impacts of cyclone Hamish at One Tree Reef: Integrating environmental and benthic habitat data. Marine Biology, 159(4), 793-803. Aim: Understand the interplay between resistance and recovery on coral reefs, and investigate dependence on pre- and post-disturbance states, to inform generalisable reef resilience theory across large spatial and temporal scales. Location: Tropical coral reefs globally. Time period: 1966 to 2017. Major taxa studied: Scleratinian hard corals. Methods: We conducted a literature search to compile a global dataset of total coral cover before and after acute storms, temperature stress, and coastal runoff from flooding events. We used meta-regression to identify variables that explained significant variation in disturbance impact, including disturbance type, year, depth, and pre-disturbance coral cover. We further investigated the influence of these same variables, as well as post-disturbance coral cover and disturbance impact, on recovery rate. We examined the shape of recovery, assigning qualitatively distinct, ecologically relevant, population growth trajectories: linear, logistic, logarithmic (decelerating), and a second-order quadratic (accelerating). Results: We analysed 427 disturbance impacts and 117 recovery trajectories. Accelerating and logistic were the most common recovery shapes, underscoring non-linearities and recovery lags. A complex but meaningful relationship between the state of a reef pre- and post-disturbance, disturbance impact magnitude, and recovery rate was identified. Fastest recovery rates were predicted for intermediate to large disturbance impacts, but a decline in this rate was predicted when more than ~75% of pre-disturbance cover was lost. We identified a shifting baseline, with declines in both pre-and post-disturbance coral cover over the 50 year study period. Main conclusions: We breakdown the complexities of coral resilience, showing interplay between resistance and recovery, as well as dependence on both pre- and post-disturbance states, alongside documenting a chronic decline in these states. This has implications for predicting coral reef futures and implementing actions to enhance resilience. The dataset provides a summary of all studies included in the analysis and the key statistics obtained from the studies and used in the analyses for the manuscript entitled "Coral reef state influences resilience to acute climate-mediated disturbances" as published in Global Ecology and Biogeography. The dataset includes details about the publication, spatial identifiers (e.g. realm, province, ecoregion) unique site code, information on the disturbance type and timing, the pre-and post-disturbance coral cover, the 5-year annual recovery rate, the recovery shape and recovery completeness classifications. Please see details Methods in the journal article "Coral reef state influences resilience to acute climate-mediated disturbances" as published in Global Ecology and Biogeography.

Near continuous methane and CO2 fluxes measured along a transect on an ombrotrophic fen in Southern Sweden from August 2017-September 2019 using an automated greenhouse gas flux platform SkyLine2D. The impacts of drought (in 2018 the mire experienced drought conditions) and different vegetation types (sedge, heather, sphagnum or open water; 6 replicated for each) on the fluxes were determined. Fluxes were measured within collars of 20-cm diameter, 4-min at each collar. CH4 and CO2 fluxes were detected using a Licor infrared gas analyser (IRGA, LI-8100, Licor, NE, USA) to measure CO2 and a cavity ringdown laser (CRD, LGR U-GGA-91, Los Gatos Research, CA USA) to measure both CO2 and CH4. Fluxes of CO2 and CH4 were calculated using linear regression; a deadband of at least 20 seconds was allowed for the chamber headspace to mix and a window of 90 seconds was used for CO2 and 240 seconds used for CH4. Fluxes were adjusted for area, air temperature and gas volume. Further adjustment was made to the CO2 fluxes during daylight hours based upon the light response curve to account for attenuation of light by the chamber material, after. All data manipulation and analyses were carried out using SAS 9.4 (SAS Institute, CA 161 USA). GHG flux data (for both CO2 and CH4) were quality controlled in the first instance using the R2 statistic of the CO2 flux measurement, with values < 0.9 discarded. Measurements passing this threshold were then assessed using the output statistics from the regression calculation of CH4 fluxes, where regressions with a P value < 0.05 were accepted, while those that did not were treated as zero flux. Data outliers were defined as those ± 1.96 standard errors of the mean flux value for each collar and were excluded from the analyses. Data were further filtered to account for overestimation of fluxes during still atmospheric night-time conditions. Using the procedure fluxes where the mean CO2 concentration for the 20 second period before and after chamber closure dropped by more than 25 ppm where discounted. Net ecosystem exchange and methane fluxes were measured from a hemi-boreal ombrotrophic fen in Southern Sweden. An automated chamber system, SkyLine2D, was used to measure the fluxes near-continuously from August 2017 to September 2019. Four ecotypes were identified: sphagnum (Sphagnum spp), eriophorum, heather and water, to assess how these different ecotypes would respond to drought. The 2018 drought allowed comparison of fluxes between drought and non-drought years (May to September), and their recovery the following year.

Large-scale reforestation can potentially bring both benefits and risks to the water cycle, which needs to be better quantified under future climates to inform reforestation decisions. We identified 477 water-insecure basins worldwide accounting for 44.6% (380.2 Mha) of the global reforestation potential. As many of these basins are in the Asia-Pacific, we used regional coupled land-climate modelling for the period 2041–2070 to reveal that reforestation increases evapotranspiration and precipitation for most water-insecure regions over the Asia-Pacific. This resulted in a statistically significant increase in water yield (p < 0.05) for the Loess Plateau-North China Plain, Yangtze Plain, Southeast China and Irrawaddy regions. Precipitation feedback was influenced by the degree of initial moisture limitation affecting soil moisture response and thus evapotranspiration, as well as precipitation advection from other reforested regions and moisture transport away from the local region. Reforestation also reduces the probability of extremely dry months in most of the water-insecure regions. However, some regions experience non-significant declines in net water yield due to heightened evapotranspiration outstripping increases in precipitation, or declines in soil moisture and advected precipitation. This dataset contains raw data outputs for Teo et al. (2022), Global Change Biology. Please see the published paper for further details on methods. For enquiries, please contact the corresponding authors: hcteo [at] u.nus.edu or lianpinkoh [at] nus.edu.sg.  Shapefiles can be opened with any GIS program such as ArcMap or QGIS. CSV files can be opened with any spreadsheet program such as Microsoft Excel or OpenOffice.

Purpose This study aims to consider environmental sustainability, a global challenge under the preview of sustainable development goals, highlighting the significance of knowledge economy in attaining sustainable aggregate demand behavior globally. For this purpose, 155 countries that have data available from 1995 to 2021 were selected. The purpose of selecting these countries is to test the global responsibility of the knowledge economy to attain environmental sustainability. Design/methodology/approach Results are estimated with the help of panel quantile regression. The empirical existence of aggregate demand-based environmental Kuznets curve (EKC) was tested using non-linear tests. Moreover, principal component analysis has been incorporated to construct the knowledge economy index. Findings U-shaped aggregate demand-based EKC at global level is validated. However, environmental deterioration increases with an additional escalation after US$497.945m in aggregate demand. As a determinant, the knowledge economy is reducing CO2 emissions. The knowledge economy has played a significant role in global responsibility, shifting the EKC downward and extending the CO2 reduction phase for every selected country. Further, urbanization, energy intensity, financial development and trade openness significantly deteriorate the environmental quality. Originality/value This study contains the empirical existence of aggregate demand-based EKC. The role of the knowledge economy is examined through an index which is calculated by using four pillars of the knowledge economy (technology, innovations, education and institutions). This study is based on a combined panel of all the countries for which the data was available.

AbstractThe integration of power plants and desalination systems has attracted increasing attention over the past few years as an effective solution to tackle sustainable development and climate change issues. In this light, this paper introduces a novel modelling and optimization approach for a combined-cycle power plant (CCPP) integrated with reverse osmosis (RO) and multi-effect distillation (MED) desalination systems. The integrated CCPP and RO–MED desalination system is thermodynamically modelled utilizing MATLAB and EES software environments, and the results are validated via Thermoflex software simulations. Comprehensive energy, exergic, exergoeconomic, and exergoenvironmental (4E) analyses are performed to assess the performance of the integrated system. Furthermore, a new multi-objective water cycle algorithm (MOWCA) is implemented to optimize the main performance parameters of the integrated system. Finally, a real-world case study is performed based on Iran's Shahid Salimi Neka power plant. The results reveal that the system exergy efficiency is increased from 8.4 to 51.1% through the proposed MOWCA approach, and the energy and freshwater costs are reduced by 8.4% and 29.4%, respectively. The latter results correspond to an environmental impact reduction of 14.2% and 33.5%. Hence, the objective functions are improved from all exergic, exergoeconomic, and exergoenvironmental perspectives, proving the approach to be a valuable tool towards implementing more sustainable combined power plants and desalination systems.

Although the United Nations’ Sustainable Development Goals (SDGs) advocate, through SDG 4 and SDG 10, equitable quality education and the reduction of inequalities within and between countries, respectively, few studies have examined how inequalities in regional sustainability influence higher education. Therefore, this study aims to examine the relationship between regional sustainability and higher education in China using fixed-effects panel modelling. A systematic force framework showing how regional sustainability drives higher education was constructed from economic, social, and environmental perspectives, and the endogeneity in the process of how regional sustainability affects higher education was explored by introducing one-year lagged values as instrumental variables. Our results show that regional sustainability has a significant impact on higher educational attainment in China, with differing effects in the eastern, central, and western regions, respectively. In central China, economic sustainability plays a significant positive role in higher educational attainment; in the western region, economic and social sustainability have stronger positive effects, while environmental sustainability has significantly negative effects. In terms of policy implications, our findings can be used to support regional development policies to promote regional higher education.

The increasing depletion of natural resources, combined with a wider set of pressures on the environment, has, in recent years, highlighted the need for a more efficient use of energy and a development process that involves alternative energy sources. Energy efficiency has received much attention as a solution, implying both monetary and emissions savings. However, the latter may be partially offset by the income and demand effects of the former, both in more efficient sectors and in spreading to the wider economy. This is the problem of rebound effects. Taking Spain as a case study, and introducing an energy-related CGE model that develops the inclusion of renewables, this paper evaluates a combination of efficiency initiatives to deliver both reduced energy use by households and a more sustainable supply of energy. Our findings suggest that a package aimed at improving efficiency in household electricity and petroleum use, combined with a more competitive supply of energy from renewable sources, may be the only way to get reductions in all energy use, and thus benefit the economy. Specifically, we consider how this package may lead to positive economic impacts and associated rebound effects, where the latter are focused on a greener energy supply.

Global change drivers that modify the quality and quantity of litter inputs to soil affect greenhouse gas fluxes, and thereby constitute a feedback to climate change. Carbon cycling in the Yukon-Kuskokwim (Y-K) River Delta, a subarctic wetland system, is influenced by landscape variations in litter quality and quantity generated by herbivores (migratory birds) that create ‘grazing lawns’ of short stature, nitrogen-rich vegetation. To identify the mechanisms by which these changes in litter inputs affect soil carbon balance, we independently manipulated qualities and quantities of litter representative of levels found in the Y-K Delta in a fully factorial microcosm experiment. We measured carbon dioxide (CO2) fluxes from these microcosms weekly. To help us identify how litter inputs influenced greenhouse gas fluxes, we sequenced soil fungal and bacterial communities, and measured soil microbial biomass carbon, dissolved carbon, inorganic nitrogen, and enzyme activity. We found that positive correlations between litter input quantity and CO2 flux were dependent upon litter type, due to differences in litter stoichiometry and changes to the structure of decomposer communities, especially the soil fungi. These community shifts were particularly pronounced when litter was added in the form of herbivore feces, and in litter input treatments that induced nitrogen limitation (i.e., senesced litter). The sensitivity of carbon cycling to litter quality and quantity in this system demonstrates that herbivores can strongly impact greenhouse gas fluxes through their influence on plant growth and tissue chemistry.

Study PopulationThe target population of the study were women aged 18 years to 69 years from households in Mwea East sub County that have experienced climate change events. As shown in table 3.1 below, the total population of female in Mwea East sub County in this age category was estimated at 38,734 (Kenya National Bureau of Statistics (KNBS)Volume III, table 2.5, (2019).Sample SizeA sample size of 449 respondents was determined as adequate for statistical analysis for the study using an online sample size calculator (calculator.net, 2021). 95% confidence level and 4.6% margin of error was used to calculate the sample size of 449 respondents determining the level of accuracy of the sample from the total estimated population of 38,734 women aged 18-69 years in Mwea East sub County.Data CollectionThe administration of the questionnaire was done by the Principal Investigator (PI) along with the KIIs, which were conducted after the questionnaire had been administered. The questionnaires were administered by 11 data collection assistants who were trained by the researcher. One of the 11 data collectors was the team leader. The researcher collected data in 5 of the households to demonstrate and practice the data collection process. Data AnalysisQuantitative and qualitative data were analyzed and triangulated to validate the findings. The quantitative data was analyzed using a combination of the IBM SPSS techniques including frequencies, cross-tabulations, bivariate statistics, means, correlations and descriptive ratio statistics. Qualitative data from both respondents and key informants’ interviews were documented using filed notes and thematically analyzed. The analysis from both sets of data was then merged to present the results. Study PopulationThe target population of the study were women aged 18 years to 69 years from households in Mwea East sub County that have experienced climate change events. As shown in table 3.1 below, the total population of female in Mwea East sub County in this age category was estimated at 38,734 (Kenya National Bureau of Statistics (KNBS)Volume III, table 2.5, (2019).Sample SizeA sample size of 449 respondents was determined as adequate for statistical analysis for the study using an online sample size calculator (calculator.net, 2021). 95% confidence level and 4.6% margin of error was used to calculate the sample size of 449 respondents determining the level of accuracy of the sample from the total estimated population of 38,734 women aged 18-69 years in Mwea East sub County.Data CollectionThe administration of the questionnaire was done by the Principal Investigator (PI) along with the KIIs, which were conducted after the questionnaire had been administered. The questionnaires were administered by 11 data collection assistants who were trained by the researcher. One of the 11 data collectors was the team leader. The researcher collected data in 5 of the households to demonstrate and practice the data collection process. Data AnalysisQuantitative and qualitative data were analyzed and triangulated to validate the findings. The quantitative data was analyzed using a combination of the IBM SPSS techniques including frequencies, cross-tabulations, bivariate statistics, means, correlations and descriptive ratio statistics. Qualitative data from both respondents and key informants’ interviews were documented using filed notes and thematically analyzed. The analysis from both sets of data was then merged to present the results.