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During the summer field season in 2012, Benthic GeoScience Inc. (Benthic) mobilized under contract with Ocean Renewable Power Company (ORPC) in order to conduct precise geospatial measurements of the seafloor accomplishing a preliminary Site Characterization Study for the ORPC East Forelands Tidal Energy Power Project. This study included a high-density bathymetric survey, acoustic reflective intensity imagery, and an assessment of the physical character of the ORPC East Forelands Tidal Energy Power Project environment. The Multibeam Echosounder (MBES) survey included a large area surrounding the East Forelands of Cook Inlet in the vicinity of Nikiski, Alaska. Included in this submission are the report for the East Forelands Site Characterization Study and the accompanying data from the survey as described below. The digital deliverables from this effort include: - Comprehensive Site Characterization Report (Format: PDF, Ver. 1.1, March 2013) - Comprehensive 3D Fledermaus Presentation (Format: SCENE, Ver. 1.1, March 2013) - Bathymetric Surface (Format: ASCVer. 1, March 2013) - Slope Gradient Surface (Format: ASCVer. 1, March 2013) - Comprehensive Acoustic Intensity Image (Format: TIF/TWFVer. 1, March 2013) - Geologic Seafloor Interpretation Surface (Format: ASCVer. 1.0, March 2013) - Comprehensive Google Earth Presentation (Format: KMZVer. 1.1, March 2013)

Nets are towed obliquely at approx. 1 knot, from the surface to approx. 175 m. Towing time is approx. 20 minutes. Zooplankton (weak swimmers >200µm) are collected using oblique tows of a 1 m**2 net (3m length) with 202µm mesh Nitex netting.

Nets are towed obliquely at approx. 1 knot, from the surface to approx. 175 m. Towing time is approx. 20 minutes. Zooplankton (weak swimmers >200µm) are collected using oblique tows of a 1 m**2 net (3m length) with 202µm mesh Nitex netting.

NREL Modular Ocean Instrumentation System (MOIS) data files for the Azura grid-connected deployment at the 30-meter berth of the US Navys Wave Energy Test Site (WETS 30m Site) at the Kaneohe Marine Corps Base Hawaii (MCBH) on the windward (northeast) coast of the island of Oahu, HI. See general documentation describing specifics of the data files and formats in a separate NREL submission (linked below).

This realistic ocean simulation was run using the Coastal and Regional Ocean COmmunity model (CROCO), based on the Regional Ocean Modelling System (ROMS), which has 60 terrain-following vertical levels. This output (WOES 0.25) is the largest grid of a triply nested configuration: WOES I, WOES II and WOES III, with horizontal resolutions of ~22.5, 7.5 and 2.5 km respectively. Monthly ouputs of the 0.25 degree GLORYS ocean reanalysis is used to force the boundaries of WOES I. The surface forcing for this model is provided by a bulk formulation using daily ERA-Interim atmospheric reanalysis (with a resolution of ~80 km) and using a relative wind approach. The output is saved as daily averages, in monthly netcdf files spanning January 1993 - December 2014. WOES 0.25 spans 55.7degS to 3.18388 degS and 10degW to 102.25degE and covers most of the Southern Subtropical Indian Ocean and a part of the Southern Atlantic Ocean. Model output includes: averaged free-surface (zeta), averaged vertically integrated u-momentum component (ubar), averaged vertically integrated v-momentum component (vbar), averaged u-momentum component (u), averaged v-momentum component (v), averaged potential temperature (temp), averaged salinity (salt), averaged vertical momentum component (w). Numerical computations were performed on the IDRIS (Institut du Developpement et des Ressources en Informatique Scientifique) IBM "ADA" computer facility (under grant A0020107630)

This realistic ocean simulation was run using the Coastal and Regional Ocean COmmunity model (CROCO), based on the Regional Ocean Modelling System (ROMS), which has 60 terrain-following vertical levels. This output (WOES 0.25) is the largest grid of a triply nested configuration: WOES I, WOES II and WOES III, with horizontal resolutions of ~22.5, 7.5 and 2.5 km respectively. Monthly ouputs of the 0.25 degree GLORYS ocean reanalysis is used to force the boundaries of WOES I. The surface forcing for this model is provided by a bulk formulation using daily ERA-Interim atmospheric reanalysis (with a resolution of ~80 km) and using a relative wind approach. The output is saved as daily averages, in monthly netcdf files spanning January 1993 - December 2014. WOES 0.25 spans 55.7degS to 3.18388 degS and 10degW to 102.25degE and covers most of the Southern Subtropical Indian Ocean and a part of the Southern Atlantic Ocean. Model output includes: averaged free-surface (zeta), averaged vertically integrated u-momentum component (ubar), averaged vertically integrated v-momentum component (vbar), averaged u-momentum component (u), averaged v-momentum component (v), averaged potential temperature (temp), averaged salinity (salt), averaged vertical momentum component (w). Numerical computations were performed on the IDRIS (Institut du Developpement et des Ressources en Informatique Scientifique) IBM "ADA" computer facility (under grant A0020107630)

Poleward range extensions by warm-adapted sea urchins are switching temperate marine ecosystems from kelp-dominated to barren-dominated systems that favour the establishment of range-extending tropical fishes. Yet, such tropicalization may be buffered by ocean acidification, which reduces urchin grazing performance and the urchin barrens that tropical range-extending fishes prefer. Using ecosystems experiencing natural warming and acidification, we show that ocean acidification could buffer warming-facilitated tropicalization by reducing urchin populations (by 87%) and inhibiting the formation of barrens. This buffering effect of CO2 enrichment was observed at natural CO2 vents that are associated with a shift from a barren-dominated to a turf-dominated state, which we found is less favourable to tropical fishes. Together, these observations suggest that ocean acidification may buffer the tropicalization effect of ocean warming against urchin barren formation via multiple processes (fewer urchins and barrens) and consequently slow the increasing rate of tropicalization of temperate fish communities. In order to allow full comparability with other ocean acidification data sets, the R package seacarb (Gattuso et al, 2021) 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 by seacarb is 2021-07-26.

# 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. 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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.

During three cruises in the Mid Atlantic Ridge area in 2016 and 2017, we studied the biomass of mesopelagic fish down to a depth of 600 m and identified and quantified the species composition of the catches. The biomass density was estimated considering the volume of water filtered by the cross-sectional area of the trawl blinded with 16 mm meshes (130 m–2), the distanced covered by the trawl (m) at a towing speed (transformed to m s–1) the effective tow time (min) and the catch (kg).