2025-08-18 SMA-03-000007 Testimony Maki Morinoue

Home

calculated following ref.81.Sediment load was assumed to scale with 2000-2014.Fishing gear restrictions were from marine managed discharge according to a approximate ratings curve following ref.82 area designation at the onset of reef surveys and the depth came from (Supplementary Figs.23 and 24). in-water diver-assessed values. The difference in local human impacts and environmental factors Fishing gear restrictions.We created a categorical value for local fish- between positiveand negative trajectory reefswere then calculated as inggear restrictions usingregulation information and marine managed the difference in the mean drop-one jackknife values for each impact area boundary designations updated from ref.80.All regulations were or factor88.Upper and lower bars in Fig.2d represent the respective evaluated for prohibition of gear categories in relation to fishing for maximum and minimum differences in drop-one jackknife values reef finfish species over time:line fishing,lay nets,spear fishing and between positive and negative trajectory reefs.Before calculating aquarium collection.Ranked fishing gear categories are as follows: the drop-one jackknife values,we identified and removed outliers (1)full no-take,(2)no lay net,spear or aquarium,(3)no lay net or aquarium, that fell outside a threshold of+2standard deviations of the median. (4)no lay net,(5)no aquarium and(6)open to all gear types(Supple- We formally tested for a difference in the local conditions of positive mentaryTable 1 and Supplementary Fig.25). versus negative trajectory reefs using a multivariate permutational analysis of variance(PERMANOVA)89 based on a Euclidean distance Sea surface temperature and heat stress.The mean and variability similarity matrix,type III(partial)sums-of-squares and unrestricted (that is,standard deviation)in summertime sea surface temperature permutationsof the normalized data.Wevisualized the results in Fig.2c (SST)was calculated over a90-day window centred on the maximum using a constrained analysis of principal coordinates90 and calculated value of a 7-day moving window average for each SST pixel(Supple- the cross-validation allocation success(a measure of group distinct- mentary Fig.26).Mean regional temperature(Fig.3a)wascalculated by ness)from the leave-one-out procedure of the constrained analysis of takingthe 7-day runningmean of daily values and then averagingacross principal coordinates analysis. all coastal pixels within our study region.Heat stress on reefs during the 2015 marine heatwave was assessed using DHW35,a widely used Coral response to the 2015 marine heatwave.Our goal was to as- metric by coral reef scientists across the world.All data were NOANs sess the local land-sea human impacts and environmental factors Coral Reef Watch v.3.1,available daily at5 km resolution35. that best explained changes in coral cover as a consequence of the 2015 marine heatwave.Any potential to observe change,however, Phytoplankton biomass and irradiance.We used satellite derived could be influenced by variations in starting condition.Reefs with chlorophyll-a(mg m-3;a proxyfor phytoplankton biomass)and irradi- higher initial cover(such as those on positive coral cover trajectories ance(E m-Z d-')from two sources.The long-term mean(2002-2013)in predisturbance,Fig.2b)had greater scope for loss and vice versa91 8-day,4 km data were obtained from ref.80 and shown in Fig.2d and (Extended Data Fig.S).To account for this and ensure comparability Extended Data Fig.3.All subsequent analysis used thevisible-infrared across reefs(Supplementary Fig.4)we calculated coral cover change imaging/radiometer suite,which has high spatial(750 m)and tempo- following ref.92 as: ral(daily)resolution data starting in 2014(provided by NOANs Coral Reef Watch).All data were quality controlled and masked to account %differenceA=[(Az,,;-Ab,;)/AbJ j X 100 for cloud cover(Supplementary Information)and optically shallow waters following ref.83(Supplementary Fig.27). whereAb and A.are the mean coral cover values at each reef in 2014 or 2015,and 2016,respectively. Wave exposure.Wave power(kW m-')combines wave height and Wethen calculated the following predictors based on current litera- period and provides a more representative metric of wave exposure ture and our hypotheses of the principal factors that drive changes in than wave heightalone'.A series of nestedgrids(fromglobal to 50 m) coral cover owing to severe heat stress(Extended Data Table 1).Fish using WAVEWATCH III"'and Simulating Waves Nearshore116were used biomass metrics included the mean of fish data that were coupled to quantify wave transformation over the reef environment at SO m, with benthic surveys:2014(n=40)or 2015(n=40)and 2016(n=80); at hourly intervals across our study region from ref.87 and updated human population,wastewater pollution,nutrient loading,urban run- for this study.Annual data were then generated for each 50 m grid off,annual rainfall,peak rainfall and wave exposure were taken from cell by taking the mean of the top 97.5%in daily maximum wave power the mean of all data from 2012 to 2016,sediment was measured from (Supplementary Fig.28). the mean of the top three events from 2006 to 2016;SST mean and SST variability were taken from the mean from 2000 to 2014;DH W Depth.Depth of the reef floor(m)was measured using diver depth was the maximum value for 2015;phytoplankton biomass and irra- gauges during the in-water reef surveys. diance was the mean from June to November 2015,representing the time inclusive of the marine heatwave;fishing gear restrictions was Statistical analyses the marine managed area designation before the marine heatwave Coral reef trajectories predisturbance.We quantified the change (2014 or 2015,depending on the reef surveyed)and depth came from in coral cover at 23 reefs from 2003 to 2014.A reef was considered to in-water diver-assessed values. have a positive trajectory or negative trajectory if coral cover from We then tested for correlations between coral loss and our suite of the 2003 survey to the 2014 survey increased or decreased bygreater predictor variables using a generalized additive mixed-effects mod- than 3%,respectively(Fig.2b).This cut-off was based on the range in elling(GAMM)framework24 with the gamm4(ref.93)package for R mean coral cover among all 23 reefs across the 12-year period(range (www.r-project.org)v.4.0.2.Before model fitting,we identified the 2.8%;minimum 34.1%;maximum of 36.9%).We then quantified local presence of outliers in our predictor variables as any point that fell human impacts and environmental factors at each reef as follows: outside a threshold of±2 standard deviations of the median.We then fish biomass metrics were from the mean of all annual surveys for appliedan additional stepto retain any point above this threshold that each year from 2003 to 2014;human population,wastewater pol- was within 25%of the maximum predictorvalue below thethreshold. lution,nutrient loading,urban runoff,annual rainfall,peak rainfall, This ensured that no data points were unnecessarily discarded from SST mean and SST variability from the mean of all data from 2000 to our formal model-fitting process because of applying an arbitrary 2014.Phytoplankton biomass and irradiance were from the maximum threshold cut-off for data inclusion.The following predictors were monthly climatology from 2002 to 2013.Sedimentand waveexposure square-root transformed to down-weightthe influenceof values at the came from the mean of the top five events from each year spanning extreme ends of their distributions:all fish biomass metrics,wastewater