• Energy Research
• Open Access close
• Closed Access close
• Embargo close
• GB close
• FR close
• IT close

Variable list for files: SW wind - Section table on Skomer (Standardised).csv / NW wind - Section table on Skomer (Standardised).csv / SE wind - Section table on Skomer (Standardised).csv /NE wind - Section table on Skomer (Standardised).csv and SW wind - Sections on Skokholm (Standardised).csv FID: Row ID (for use in ArcGIs) Count: Number of guillemots per section Area: Total area of each section () Density: Density of guillemots per section (number of birds/ Area) X_Centre: X coordinate of the central point of each section Y_Centre: Y coordinate of the central point of each section Sector: Section ID MeanUMedian; MeanUIQR, MeanUSkewness, MeanUCV: Median, interquartile range,skewness and coefficient of variation of mean wind speed per section HorizontalMedian;HorizontalIQR,HorizontalSkewness,HorizontalCV: Median, interquartile range,skewness and coefficient of variation of horizontal wind speed per section PMedian;PIQR,PSkewness,PCV: Median, interquartile range,skewness and coefficient of variation of preessure per section TKEMedian;TKEIQR,TKESkewness,TKECV: Median, interquartile range,skewness and coefficient of variation of turbulent kinetic energy per section TIMedian;TIIQR,TISkewness,TICV: Median, interquartile range,skewness and coefficient of variation of turbulence intensity per section U_2Median;lU_2IQR;U_2Skewness;U_2CV: Median, interquartile range,skewness and coefficient of variation of vertical wind speed per section EpsilonMedian;EpsilonIQR,EpsilonSkewness,EpsilonCV: Median, interquartile range,skewness and coefficient of variation of turbulent dissipation rate per section NutMedian;NutIQR,NutSkewness,NutCV: Median, interquartile range,skewness and coefficient of variation of kinematic viscosity per section GustsMedian;GustsIQR,GustsSkewness,GustsCV: Median, interquartile range,skewness and coefficient of variation of instataneous gusts per section MeanSectorSlope: Mean slope per section ColPresence: Binomial variable, indicating whether a section has birds or not. This variable varies with classification, based on either the count of birds or the density per section Variable list for file: Section table on Skomer - with Mean cliff orientation and Slope (NOT-Standardised).csv FID: Row ID (for use in ArcGIs) Count: Number of guillemots per section Area: Total area of each section () Density: Density of guillemots per section (number of birds/ Area) X_Centre: X coordinate of the central point of each section Y_Centre: Y coordinate of the central point of each section Sector: Section ID MeanSectorSlope: Mean slope per section MeanSectorAspectCircular: Mean cliff orientation per section ApsectClass: Factor indicating whether the mean cliff orientation is lee- or windward to the SW wind ColPresence: Binomial variable, indicating whether a section has birds or not. This variable varies with classification, based on either the count of birds or the density per section Variable list for file: SW wind - Sections on Skokholm to predict colonies using cliff orientation and slope model from Skomer (NON - Standardised).csv FID: Row ID (for use in ArcGIs) Count: Number of guillemots per section Area: Total area of each section () Density: Density of guillemots per section (number of birds/ Area) Sector: Section ID MeanSectorSlope: Mean slope per section MeanSectorAspectCircular: Mean cliff orientation per section Wind is fundamentally related to shelter and flight performance: two factors that are critical for birds at their nest sites. Despite this, airflows have never been fully integrated into models of breeding habitat selection, even for well-studied seabirds. Here we use computational fluid dynamics to provide the first assessment of whether flow characteristics (including wind speed and turbulence) predict the distribution of seabird colonies, taking common guillemots (Uria aalge) breeding on Skomer island as our study system. This demonstrates that occupancy is driven by the need to shelter from both wind and rain/ wave action, rather than airflow characteristics alone. Models of airflows and cliff orientation both performed well in predicting high quality habitat in our study site, identifying 80% of colonies and 93% of avoided sites, as well as 73% of the largest colonies on a neighbouring island. This suggests generality in the mechanisms driving breeding distributions, and provides an approach for identifying habitat for seabird reintroductions considering current and projected wind speeds and directions. Methods detailed in manuscript: https://doi.org/10.1111/ecog.05733.

# 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.

This readme file provides all data and R codes used to perform the analyses presented in Figs. 2-4 of the main text and Supplementary Information Figures S1-S2-S3. FIGURE 2 - Seasonally_dated_GDs.txt: Contains information on the timing (Season) of rockfall (GD) in a given tree (Id) and a given year (yr) over the past 100 years. Inv refers to the operators which analyzed growth disturbances in the tree-ring series. Lat / Long refers to the position of the tree in CH1903/ Swiss Grid projection. Intensity (1-4) refers to (1), intermediate (2) and strong (3) GD. Intensity 4 was attributed to injuries (I). Only the 408 GD rated 3 (strong TRD) and 4 (injuries) were used in Fig. 2. Acronyms used for Response_type read as follows: TRD: Tangential rows of traumatic resin ducts; I: Injuries. Acronyms used for Season refer to Dormancy (1_D), early (2_EE), middle (3_ME) and late (4_LE) earlywood, whereas a GD found in the latewood was attributed to either the early (5_EL) or late (6_LL) latewood. - Trends_in_seasonality_R1.R: The data contained in "Seasonally_dated_GDs" were processed with the R script "Trends_in_Seasonality.R". This seasonal trend analysis code is inspired by work published by Schlögl et al. (2021; https://doi.org/10.1016/j.crm.2021.100294) and Heiser et al. (2022; https://doi.org/10.1029/2011JF002262). FIGURE 3-4-S1 - Tasch_GD.txt: Contains the raw data on rockfall impacts (GD) in a given year (yr) as found in all trees available in that same year (Sample_depth) as well as the cumulated diameter at breast height (cumulated_DBH) of all trees present in that same year. - Rockfall_frequency_climate.R: The data contained in "Tasch_GD.txt" were processed with the R script "Rockfall_frequency_climate.R". - The temperature (Imfeld23_tmp.txt) and precipitation (Imfeld23_prc.txt) data used in Fig. 3 are from the Imfeld et al. 2023 (10.5194/cp-19-703-2023) gridded dataset (1x1 km lat/long) and were extracted at the grid point centered on the Täschgufer site. - The script set with temperature series enables to compute Fig. 4 (l.149:216) and Fig. 3 (l. 216:330); the script set with precipitation series enables to compute Fig. S1 FIGURE S2 - Tasch_GD.txt: Contains the raw data on rockfall impacts (GD) at the Täschgufer site in a given year (yr) as found in all trees available in that same year (Sample_depth) as well as the cumulated diameter at breast height (cumulated_DBH) of all trees present in that same year. - Rockfall_frequency_borehole.R: is adapted from "Rockfall_frequency_climate.R" to work with the borehole dates. - Corvatsch0_6R1: Contains the Corvatsch borehole temperature series (2000-2020, 0.6m depth) (Hoelzle, M. et al. https://doi.org/10.5194/essd-14-1531-2022, 2022). FIGURE S3 - Plattje_GD.txt: Contains the raw data on rockfall impacts (GD) at the Plattje site in a given year (yr) as found all trees available in that same year (Sample_depth) as well as the cumulated diameter at breast height (cumulated_DBH) of all trees present in that same year. - - Rockfall_frequency_climate_Plattje.R: The data contained in "Plattje_GD.txt" were processed with the R script "Rockfall_frequency_climate_Plattje.R". - The temperature (Imfeld23_tmp_Plattje.txt) and precipitation (Imfeld23_prc_Plattje.txt) data used in Fig. 3 are from Imfeld et al. 2023 (10.5194/cp-19-703-2023) gridded dataset (1x1 km lat/long) and were extracted at the grid point centered on the Plattje site.

The 2004 stimulation was aimed at improving the hydraulic performances of the well GPK4. This borehole was stimulated twice. The present test corresponds to the first stimulation of GPK4. Included are the data from a pre-stimulation low flow injection test that was performed to evaluate the initial injectivity of GPK4.

Data type: Experimental measurements, correlations and Van't Hoff plot. Date format: .opj. Origin of the data: Experimental pressure composition isotherm measurements. Data generated by a home-made Sieverts’ type apparatus from CNRS, ICMPE, Thiais, France. Software needed to plot the data: Origin.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.MOHC.HadGEM3-GC31-MM.ssp126' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The HadGEM3-GC3.1-N216ORCA025 climate model, released in 2016, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N216; 432 x 324 longitude/latitude; 85 levels; top level 85 km), land: JULES-HadGEM3-GL7.1, ocean: NEMO-HadGEM3-GO6.0 (eORCA025 tripolar primarily 0.25 deg; 1440 x 1205 longitude/latitude; 75 levels; top grid cell 0-1 m), seaIce: CICE-HadGEM3-GSI8 (eORCA025 tripolar primarily 0.25 deg; 1440 x 1205 longitude/latitude). The model was run by the Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK (MOHC) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, ocean: 25 km, seaIce: 25 km.

The aggregation of data concerned 103 Italian cities and for each city 45 indicators were considered

Raw data and corresponding data analysis (Microsoft Office Excel, Origin) supporting Journal of American Chemical Society publication: "Photoreduction of CO2 with a formate dehydrogenase driven by photosystem II using a semi-artificial Z-scheme architecture". Data include: three-electrode and two-electrode electrochemistry and photoelectrochemistry, data analysis and product quantification.