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2025-08-18 SMA-03-000007 Testimony Maki Morinoue
From: Maki Morinoue To: LPCtestimonv Subject: Oppose#4 SMA 03-00007 75-5994 Alii Drive Date: Monday,August 18,2025 12:13:13 PM Attachments: Coral reef benefit from reduced land-sea impacts under ocean warmino.pdf Aloha Leeward Planning Commissioners, I strongly oppose SMA 03-00007 at 75-5994 Ali'i Drive. Though it is being requested to revoke, this is now a replacement. It remains a major development valued at over $500,000, and it carries the same serious risks as past shoreline projects. The Kona Coast must be protected. It is our kuleana to use the knowledge we have today—knowledge of sea level rise, climate change, and the vicious cycle of coastal erosion—to make better decisions for tomorrow. Major shoreline development threatens not only private property but also our public beaches, cultural sites, marine ecosystems, and community safety. Seawalls and construction along this fragile coastline accelerate erosion, damage our reefs, and endanger places like Kahalu'u. Respect for our ocean, cultural practices, and natural resources must guide your decisions. Please do not replace one harmful permit with another. Protecting our shoreline is not just policy—it is a responsibility to our people and to future generations. Our community has been calling for a traffic study for the past 10 years, specifically for Ali'i Drive and tsunami zone areas. Asking how much water do we have to keep developing? How do we deal with sewage? Also, warning the County that we are NOT tsunami evacuation ready after several failed attempts since 2017 onward. Attached is a scientific 20-year study that shows land use impacts our ocean health. Thank you for your time. Maki Morinoue Holualoa, ahupua'a Maki Morinoue Share your ALOHA "Aloha Spirit" is the coordination of mind and heart within each person. It brings each person to the self. Each person must think and emote good feelings to others. In the contemplation and presence of the life force, "Aloha," Article Coral reefs benefit from reduced land-sea impacts under ocean warming https://doi.org/10.1038/S4l586-023-06394-w Jamison M.Gove"",Gareth J.Williams2•1 "',Joey Lecky3,Eric Brown 4,Eric Conklin', Received:21 Jul 2022 Chelsie Counse116,Gerald Davis3,Mary K.Donovan',",Kim Falinski',Lindsey Kramer', y Kelly Kozar1O,Ning Litt,Jeffrey A.Maynard 12,Amanda McCutcheon",Sheila A.McKenna10, Accepted:30 June 2023 Brian J.Neilson13,Aryan Safaie14,Christopher Teague",Robert Whittier"& b Gregory P.Asner7,16 Open access Check for updates Coral reef ecosystems are being fundamentally restructured by local human impacts and climate-driven marine heatwaves that trigger mass coral bleaching and mortality'. Reducing local impacts can increase reef resistance to and recovery from bleaching2. However,resource managers lack clear advice on targeted actions that best support coral reefs under climate change'and sector-based governance means most land-and sea-based management efforts remain siloed4.Here we combine surveys of reef change with a unique 20-year time series of land-sea human impacts that encompassed an unprecedented marine heatwave in Hawaii.Reefs with increased herbivorous fish populations and reduced land-based impacts,such as wastewater pollution and urban runoff,had positive coral cover trajectories predisturbance.These reefs also experienced a modest reduction in coral mortality following severe heat stress compared to reefs with reduced fish populations and enhanced land-based impacts. Scenario modelling indicated that simultaneously reducing land-sea human impacts results in a three-to sixfold greater probability of a reef having high reef-builder cover four years postdisturbance than if either occurred in isolation.International effortsto protect 30%of Earth's land and ocean ecosystems by 2030 are underway'.Our results reveal that integrated land-sea management could help achieve coastal ocean conservation goals and provide coral reefs with the best opportunity to persist in our changing climate. Coastal areas contain some of the most biologically diverse and pro- decades3.Its importance was established in the indigenous steward- ductive marine ecosystems on Earth'.But with four times the popula- ship of island ecosystems,which used a decentralized and integrated tion density living within 20 km of the ocean compared to the rest of resource management strategy that extended from the mountains to the world',direct human impacts on local scales are fundamentally the sea1,'"By contrast,contemporary centralized governance means restructuring these important marine communities'.Coastal areas are most terrestrial and ocean management efforts remain siloed4•17•18 As a also affected by stronger and more frequent disturbances fuelled by result,whereas local resource managers have aspired to an integrated human-inducedclimatechange9 These human stressorsare especially land-sea approach",evidence of its efficacy aboveeither approach in acuteon tropical coral reefs where upto 90%ofthe local population live isolation remains wantingand difficult to test.Detecting conservation alongtheshoreline1'.Land-based stressors,such as wastewater pollu- benefits in highly dynamic ecosystems is challenging20,but recent tion,combine with sea-based stressors,such as overfishing,to disrupt studies have identified salient connections between local conditions natural ecological feedbacks on reefs".Corals are further stressed by and coral reef resistance to and recovery potential following mass prolonged periods of anomalously warm ocean temperatures,known as bleachingz,tt,zt-23.Managersthereforerequireunambiguoustargetsfor marine heatwaves12,thatcan cause mass coral bleaching13and mortality the combination of land-sea human impacts they should mitigate to and fundamentally transform reef assemblages14•ts supportcoral reef persistence under climate change.Hamperingthese Reducing human impacts on local scales to maintain ecosystem effortsarea lackof spatially resolved data on local drivers of coral reef integrity has been the guiding model of coral reef conservation for ecosystems over time.Researchers are often forced to use proxies 'Pacific Islands Fisheries Science Center,National Oceanic and Atmospheric Administration(NOAA),Honolulu,HI,USA. School of Ocean Sciences,Bangor University,Menai Bridge,Anglesey, UK.'Pacific Islands Regional Office,National Oceanic and Atmospheric Administration,Honolulu,HI,USA."National Park of American Samoa,Pago Pago,American Samoa,USA.'The Nature Conservancy,Honolulu,HI,USA.'Cooperative Institute for Marine and Atmospheric Research,Honolulu,HI,USA.'Center for Global Discovery and Conservation Science,Arizona State University,Hilo,HI,USA.'School of Geographical Sciences and Urban Planning,Arizona State University,Tempe,AZ,USA.'Hawai'i Wildlife Fund,Kealakekua,HI,USA.10National Park Service, Pacific Island Network Inventory and Monitoring,Hawai'i National Park,HI,USA."Department of Ocean and Resources Engineering,University of Hawai'i at Mwoa,Honolulu,HI,USA. "SymbioSeas,Carolina Beach,INC,USA."Hawai'i Division of Aquatic Resources,Honolulu,HI,USA."Graduate School of Oceanography,University of Rhode Island,Narragansett,RI,USA. "Hawai'i Department of Health,Honolulu,HI,USA."School of Ocean Futures,Arizona State University,Hilo,HI,USA."These authors contributed equally:Jamison M.Gove,Gareth J.Williams. ®e-mail:jamison.gove@noaa.gov;g.j.williams@bangor.ac.uk Nature I www.nature.com I 1 Article a Hawaiian Islands G • Pacific II Ocean Study 11 11 ILL region Human Urban Wastewater Nutrient Sediment Peak Wave population A runoff A pollution A loading A input A rainfall A exposure A b 'Upolu Point O 10- 12 20 � , ' IIR� 30 30 40 § 40 60 50 � °' 50 Y f{i( sas , c 70 s',i a��rg"J S;,' ;, 60 h u 80 „ " a 80 0 90 ,r? qh. 90 100 100 U 4 16 110 ¢ 110 _ 120 126 — "rry 130 _ _ 130+ — — ' 140 140• 150 150 160 —_ A 160 170 N 170 180 , Oh 5 10 km 1 aSouth Point .�O ry0,50 a0,,c0 O .�O 'o ,50 tp O ry00 000 000 ry,,c0 O .�6 6b dab ry,,o6 O ry%o�00 ryryo�O O .�O ry0 00 tp O ryo,,c0 INQ� �ryo 5. Number of people Area Total effluent Total nitrogen Sediment Rainfall Wave power (101 within 15 km) (101 mz ha-1) (1011 ha-1) (kg ha-1) (101 kg ha-1) (101 m3 ha-1) (kW m 1) Permanent reef survey locations • Predisturbance Year I I I I I I I I I O '� `L O b h 60 A O '� `L O b h 6 O O Temporal change d O Response to stdi marine heatwave O O ell O ell, O O O O � '� � '� '� '� '� ry0 ry0 ry0 ry0 ry0 ry0 ry0 le,ry0 ry0 ry0 ry0 ry0 ry0 ry0 ry0 ry0 ry0 ry0 ry0 Decrease ® ®Increase O Four years postdisturbance i ii ii i Permanent reef survey data availability Predisturbance Disturbance Postdisturbance Fig.11 Select local land-sea human impacts and environmental factors on blue hues indicating decreases and red hues indicating increases.Change is coral reefs in our study region in Hawai'i.a,Geographic location of the based on the mean difference between the first5 years(2000-2004)and the Hawaiian Islands.b,Study region with reefsurveys shown for the following: most recentS years(2015-2019)in the time series.This accounted for year-to-year reef trajectories predisturbance(n=23;Fig.2),coraI response tot he2015 variabi I ity in the epi sod ic nature of factors such as wave exposure,rainfa I I and marine he atwave(n=80;Fig.3)and cora I reefs four years postdisturbance sediment input.A subset of factors is shown inc owing to space constraints. (n=55;Fig.4).c,Spatial distribution in annual,high-resolution(100m)data on Additional factors(not shown)include annual rainfal I,phytoplankton biomass, local human impacts and environmenta I factors from 2000 to 2019(coloured ocean tempe rat ure(mean and variabiIity),heat stress,irradiance,fish inggear lines).They axis represents distance along the coastline in kilometres from restrictions,depth and metrics offish biomass.The distribution,change north to south aIongthe study region inb.Vert icaI bar represents the change overtime and variability of all factors are shown in Supplementary Fig.1. overtime(A)for each 100 m section along the coast.A changeover time is high See Extended Data Table land Supplementary Information for detailed (H,d>-50%),moderate(M,0>d<50%)or there isno change(NC,grey),with information on loca I land-sea human impacts and environmentaIfactors. such as population density`and reef accessibility2b,or composite Hawaiian Islands(Extended Data Fig.2).We quantified drivers of coral indices such as'water quality'"that can be affected by anything from reef benthic change atthe scale of individual reefs over 12 years before deforestation 2'toaquaculture28.Such proxiesdo not identifythepolicy disturbance(2003-2014),during and immediately following the marine levers local resource managers can pull and are less likely to result in heatwave(2014-2016)andfouryears postdisturbance(2016-2019).Our management actions or successful conservation outcomes. findings show that simultaneously mitigating local human impacts on Here we present a unique 20-year time series of land-sea human both landand seasupports positivecoral cover trajectories in theabsence impacts and environmental factors known to affect coral reef ecosys- of periodic acute disturbance,reduces coral loss during a marine heat- tem processes across our study region in the Hawaiian Islands(Fig.la). wave and promotes coral reef persistence following severe heat stress. Human factors include urban runoff,wastewater pollution,nutrient loading,sediment input and local restrictions on types offishinggear. Environmental factors include peakand annual rainfall,wave exposure, Reef trajectories predisturbance variability in ocean temperatures and heat stress,irradianceand phyto- Coral cover among reefs surveyed in 2003 was 36.9±2.3%(mean±s.e.; plankton biomass.We also incorporate multiple fish biomass metrics n=23)and changed byless than 3%in the subsequentyears leading up that represent the critical role reef fish play in maintaining coral reef to the2015 marine heatwave(Fig.2a).However,coral cover trajectories ecosystem function"'(see Extended Data Table 1 for a full list of on individual reefs varied considerably over this time period:44%of factors).Wecombined thisdatasetwith recurring,permanently marked reefs showed a positive trajectory(that is,increased coral cover),35% and site-specific underwater survey data on coral reef benthiccommuni- of reefs showed a negative trajectory(that is,decreased coral cover) ties(Fig.1b).Our study reefs spanned large spatiotemporal gradients and the remaining reefs showed no change(Fig.2b).To the best ofour inland-sea human impactsand environmental factors(Fig.1c)that are knowledge,no acute disturbance occurred that can explain thesediver- comparable to coral reef ecosystems globally(Extended Data Fig.1),and gent trajectories.Yet,we did find distinct differences in local conditions which experienced the most severe marine heatwave on record in the between positive and negative trajectory reefs in the years before and 2 1 Nature I www.nature.com a b 0.30 -2003 20 •Positive trajectory .• -2007 15 •Negative trajectory • 0.25 -2011 0 10 . + • w m 2014 m • •' '• 0 0.20 5 - - - - - 0 0 -5 a Range in o-10 ....- 0.05 mean cover U 15 -20 f 0 10 20 30. .40 50 60 70 80 (O O10 OA 00 O°j .�O .�'� .�`1' .�`5 Na Coral cover(%) 'pO ry0 'p 'p ry0 ry0 ry0 ry0 ry0 ry0 ry0 ry0 C Year o • • • • • • N M • • N U U1 F -0.3 -0.2 -0.1 0 0.1 0.2 0.3 Squared canonical correlation(83.2%) d 400 20.0 250 100 10.0 80 2.0 60 1.5 40 m 15 • 1.0 20 0 0.5 t 0 a • 1� 2i 0 0 • • 0� 20 0.5 >° ° 0 40 Z 1.0 60 1.5 10.0 80 ■�■ am" _ r r r o tir �r r F �A \0c5 1 z1 o0 0 �aQe oly G�av >L`0c Q0y \tea o'(`\ �O `ey�� .QO� \act` �,�� �ap� �ap� ,o`° �&� 0 0� Oa ee+ ec Oc Q° ear �oQ `o �a`c `a`c oe a��O�ace t1 1 Lei O<` 1 c 0 ec r� Oa S� �a 0 5r z Fig.21 Reeftrajectories predisturbance and associated local land-sea increasingly more distinct set ofconditions than expected by chance alone). human impacts and environmental factors.a,Coral cover distributions cl,Mean difference(dots)in drop-onejackknife values with upper and lower among surveyed reefs between 2003 and 2014(n=23).b,Coral cover bars representing the respective maximum and minimum differences in local trajectories ofindividual reefs.A reelfwas considered on a positive trajectory human impacts and environmental factors between positive and negative (blue;n=10)or negative trajectory(red;n=8)ifcoral cover between 2003 and trajectory reefs(n=same as in b).Blue and red shaded regions indicate factors 2014 changed by more than 3%.This cut-offwas based on mean coral cover that were greater on reefs that had positive and negative trajectories, range among all 23 reefs for the l2-yearpredisturbance period(range 2.8%;min respectively.Zero line represents equal values.See Extended Data Fig.3for 34.1%;max 36.9%).Reefs with no coral cover change(within±3%)are not shown. the percentage difference in local conditions between positive and negative c,Difference in local conditions between positive versus negative trajectory trajectory reefs.We included all local human impacts and environmental reefs(PERMANOVA,pseudo-F1,17=3.38,P=0.001)visualized along a single factors indto provide a general comparison of local conditions between reefs multivariate axis(capturing the multidimensional and correlated nature ofthe with divergent trajectories.See Fig.lb for reef locations and Supplementary data,Supplementary Fig.2)using a canonical analysis ofprincipal coordinates Fig.3 for predictor variable distributions.See Methods,Extended Data Table 1 (n=same as in b).Allocation success equalled 90 and 87.5%for positive and Supplementary Information for detailed information on local land-sea and negative trajectory reefs,respectively(more than 50%indicates an human impacts and environmental factors. inclusive of this time frame(Fig.2c).For example,the average biomass a 15 km radius).This finding supports the notion that human popula- of all fishes,all herbivorous fishes and groups of herbivorous fishes that tion density is a poor indicator of human-driven land-sea impacts at fill important ecological roles such as scrapers,grazers and browsers30 local scales".We observed minimal differences between positive and were 24-113%(29-214 kg ha-')greater on reefs with positive trajectories negative trajectory reefs relative to fishing gear restrictions,depth, compared to those with negative trajectories(Fig.2d and Extended Data sediment input,ocean temperatures,phytoplankton biomass and Fig.3).These patterns probably reflect positive feedbacks,whereby rainfall.Wave exposure was slightly higher(8.6 kW II on reefs with increasing coral cover promotes habitat suitability for reef fishes,with positive trajectories,but the difference is minor because the entire herbivorous fishes then facilitating coral growth by reducing com- study region is generally protected from large wave events34. petitive exclusion by Fleshyalgae32.By contrast,wastewater pollution, nutrient loading and urban runoff were 46-80%greater on reefs with negative trajectories compared to those with positive trajectories. Coral response to the marine heatWave Despitethese land-based human stressors being comparatively higher In 2015,the Hawaiian Islands experienced the strongest marine Keat- on reefs with negative trajectories,reefswith positive trajectories had wave on record over the past120 years(Extended Data Fig.2).Ocean 63%greater human population density(the number of people within temperatures across our study region were 2.2°C above normal and Nature I www.nature.com 1 3 Article a b Fig.3 1 Local land-sea human impacts and environmental factors that 0.5 modified coral response to the 2015 marine heatwave.a,Historical 29 N 04 (1986-2019)SSTsduringtheseasonalpeak(July-December)averagedacross m the study region;2015 marine heatwave shown in red.b,Maximum DHW 2 .,28 5 0.3 exposure in 2015,a common heat stress metric,among surveyed reefs.All o reefs exceeded the eight DHW threshold expected to produce severe and 0 0.2 widespread coral bleaching and mortality.c,Cora I cover before(2014-2015) 27 d ° and one year following(2016)the marine heatwave among surveyed reefs E a 0'1 (n=80,Fig.1b).The inset represents the distribution of absolute coral cover ~ 0 change.d,The GAMM results(R2=0.79)showing key factors explaining coral 26 9 10 11 12 13 14 response to the marine heatwave.Change accounts for starting condition, DHW CC-weeks) defined as:percentage difference=((Aa.i-Ae,)/Abj)x100,whereAbandAaare P5 d the mean coral cover values at each reef in 2014 or 2015,and 2016,respectively Jul Aug Sep Oct Nov Dec 25 (Methods and Supplementary Fig.4).Positive and negative relationships e`bRI fig^^^egtiootiootiootiotiootio^tio^tio^tio^tio^g o reduce or increase coral loss,respectively.Shaded regions represent 80% confidence intervals.Factors with the strongest model averaged slopes are Year C 65 -25 shown.Total fish biomass and scraper biomass were also important factors in v0.3 ou r mod e Is but had we a k s lopes(re p rese nt i ng less tha n 5%c ha nge;Exte nd ed -50 Data Fig.4).Relative importance of factors among all models(that is,sum of 60 C 02 Al Cc mode I we ig hts a cross a I I mode I s conta i ni ng e ach fa ctor)we re:sed i me nt 0 o -75 -� i np ut(0.99),scra pe r bi omass(0.99),tota I fi sh bi omass(0.90),urba n r u noff 55 0 0.1 1/�y (0.60),phytopl a n kton b ioma ss(0.38),wa stewate r pol l ut ion(0.28),peak 2 t 0.09 0.12 0.16 0.20 rainfall(0.20),nut rient load ing(0.19),grazerbiomass(0.16),DHW(0.08),wave SOIL ho ao�o do.�o o 10 phytoplankton(mg m") power(0.07),depth(0.06)and fishinggear restrictions(0.05).See Extended Coral cover change(/) 25 Data Table 1 for full listof factors included in the analysis,including those 451 removed that were highIycorrelated(r>0.7,see Methods and Supplementary H � ;�►; w 0 Fig.5).See Supplementary Fig.6 for predictor variable distributions. psi 40,11 o m-25 m 35 t g -50 Coral bleaching involves the breakdown of the mutualistic rela- U > 0 tionship between the coral animal and its algal endosymbionts36 6 30 U-75 I o ® A prolonged breakdown in this relationship often results in coral star- 25, " vation and death,as much of the energetic demands of corals are met 2,500 10,000 22,500 by the photosynthetic activity of its endosymbionts36.We found that Urban runoff(m2 her-') reefs with the highest levels of water column h to lankton biomass 20 25 g P Y P (that is,chlorophyll-a)during the marine heatwave showed reduced 15' o coral mortality(Fig.3d).Productivity increases nearshore to tropical islands such as Hawai'i37 and is further concentrated by small-scale 10 -25 ocean processes that attract dense aggregations of plankton38.The ' increase in nutritional subsidies to the coral animal may have helped 5 -50 to reduce coral starvation during the heatwave or provided higher ; "_", 75 energetic reserves that promoted their recovery39.In other regions(for 0 ` example,Great Barrier Reef),high levels of chlorophyll-a are anindica- (2014i2015) (2016) for of poor water quality that drives negative outcomes for corals" Before madne After marine 0 39 625 3,164 10,000 heatwave heatwave Sediment input(kg ha 1) Here,chlorophyll-a was uncorrelated to land-based human impacts (Supplementary Fig.6)and probably reflective of natural gradients in energetic subsidies that facilitated coral survival.Working towards peaked at29.4°C(Fig.3a).Degree heating weeks(DHWs),a widely used locally relevant management strategies requires understanding how heat stress metric for coral reefs,averaged 12 DHWs among surveyed human impacts superimpose on natural biophysical drivers,such as reefs(Fig.3b),far exceeding the eight DHW threshold expected to phytoplankton biomass24,to influence reef ecosystem response to cause severe and widespread coral bleachingandmortality35.Reef sur- acute disturbance. veys performed one year following the marine heatwave showed that Coastal runoff can deliver a broad spectrum of land-based contami- nearly one-quarter of reefs(19outof80)lost morethan20%coral cover nantsthatdegradenearshore water quality,with cascading effects on whereas the hardest-hit reef lost 49%(Fig.3c).But not all reefs expe- coral health41.We found that reefs exposed to the lowest levels ofurban rienced such catastrophic change.Coral cover remained unchanged runoff,and to a lesser extent sediment input,experienced a modest or increased on 18%(14 out of 80)of reefs surveyed.This divergent reduction in coral mortalityfrom the marine heatwave(Fig.3d).Urban ecological response was unexpectedgiven that all reefs were exposed runoff often contains heavy metals and petrochemicals that cause to similarly extreme levels of heat stress(Fig.3b). coral tissue death42 and sediment input can impede the photosyn- Interactions between heat stressand local conditions such asa high thetic capacity of corals and reducegrowthbyburyingcoralcolonies41. abundance of competitive macroalgaecan exacerbatecoral bleaching Together,these stressors can underminethe natural defence abilities and mortality22.However,we lack a detailed understanding of the land- of corals and increase the likelihood of mortality from heatstress40. and sea-based factorsthat mediatecoral responseto marine heatwaves. Although turbid waters may shade corals from excessive sunlightthat Using a generalized additive mixed-modelling framework,we identi- can exacerbate coral bleaching,high levels of heat stress can override fied the land-sea factors that best explained variations in coral cover any protective benefits decreased light may provide43.Existing but change(accountingfor starting cover)among reefs one year after the underused local and national policies such as the Clean Water Act in 2015 marine heatwave in Hawaii(Fig.3d and Extended Data Table 2). the United States provide actionable pathwaysfor marine management 4 1 Nature I www.nature.com a interventions of land-based stressors44.Management strategies that 0.30 leverage such policies to help mitigate coastal runoff,particularly in 0.25 urban areas,may support increased coral survival during severe marine 4 heatwaves. ID P 0.20 We also found thattotal fish biomass and scraper biomass were impor- ° tant factors in our models(Extended Data Table2).Healthyfish popu- 0 0.15 lations provide numerous reef-scale ecosystem functions29,including o some species releasing beneficial nutrient subsidies that increasecoral a 0.10 thermal tolerance45.Scrapers remove fast-growing algal turfs that could otherwise outcompete and overgrow stress-compromised corals30 By 0.051 Low Moderate comparison to phytoplankton biomass and coastal runoff,the slopes 0 5 10 15 20 25 30 35 40 45 50 of the relationships between total fish biomass and scraper biomass Cover of reef-building organisms(%) with heat-driven coral loss Were Weak(Extended Data Fig.4).Intense b Resource Probability marine heatwaves can cause severecoral mortality even on highly pro- management scenario Low Moderate High tected,uninhabited reefswith intact fish population s46,suggestingthat Initial condition Q 0.83 0.17 0.02 extreme heat stress may simplyoverwhelm the functional roles of reef Sea-based only Q 0.30 0.70 0.14 fish over shorttime scales.However,abundant fish populations,in par- Land-based only Q 0.17 0.83 0.26 ticular herbivores,can supportcoral reef recovery potential following Integrated land-sea Q 0.02 0.98 0.80 disturbance 2.Understanding whether this positive relationship holds across gradients in land-based impacts is key for supporting targeted og oa o� 06 05 06 810 o�� fisheries management in coastal marine ecosystems. 640 Coral reefs four years postdisturbance t 490 o The dominant reef-builders in tropical coral reef ecosystems are 360 oa hard corals and crustose coralline algae21.Crustose coralline algae ° are encrusting calcifying algae that fuse the reef framework together 5250 05 and promote coral growth by serving as a successional prerequisite for 6 160 06 coral recruitment and suppressing competitive fleshy algae 21.Given that coral cover can take a decade or more to recover to prebleaching N 90 0- level s41,assessing the total cover of reef-building organisms(hard 40 coral+crustosecora Ili nealgae)is more indicative of coral reef recovery potential following disturbance.Our surveys four years following the 10 2015 marine heatwave found that reef-builder cover ranged from 3.4 0 111110011 to 51.9%(mean of 24.3%±1.7 s.e.;n=55;Fig.4a).Critically,there were 25 50 75 100 125 150 175 200 225 250 275 different reefs with high(more than or equal to the 75th percentile)and „4111l Scraper biomass(kg hal) 41864 low(less than or equal to the 25th percentile)reef-builder cover before Fig.4 1 Local management scenarios thatsupport coral reef persistence and after the marine heatwave.Nearly two-thirds of reefs with high four years postdisturbance.a,Percentage cover ofreef-building organisms reef-builder cover in 2019 did not support such levels ofcover before (hard coral+crustose coralline algae)among reefs surveyed(n=55)in 2019, the marine heatwave.Similarly,we observed a more than 40%change four years following the marine heatwave.colours represent low(<-25th in the location of reefs with low reef-builder cover between 2015 and percentile),moderate(>25th and<75th percentile)or high(>-75th percentile) 2019.This reshuffling of reefs in terms of relative reef-builder cover cover.b,Probability oflow,moderate or high cover ofreef-builders shown in suggested differential coral reef persistence in the years following relation to variations inscraperbiomassand wastewater pollution.Example severe heat stress. scenarios show that simultaneously decreasing wastewater pollution We used an ordinal logistic regression framework to identify the and increasing scraper biomass results in afargreater probability ofhigh local land-sea human impacts and environmental factors that best reef-builder cover(scenario'C')than achievingeither management scenario in supported coral reefpersistence in theyears following the 2015 marine isolation(scenariosA'and'B').The upper(250kgha')and lower(30kgha') heatwave.Decreased wastewater pollution and increased scraper management scenarios for scraper biomass represented the 92nd and 36th biomass were the most important and significant(P<0.05)in pre- percentiles,respectively.We specifically chose250 kg ha-'as it approximates the long-term mean(2003-2019;n=17)scraper biomass inKealakekuaBay,a dictingwhetherareefhadrelativelyhigherreefbuildercoverfour marineprotected area in our study region where no fishing has been allowed years postdisturbance(Extended Data Table3).Pollution from human since1969(Supplementary Fig.11).Similarly,the upper(600,0001 ha-')and waste affects coastal marine ecosystems glObally48and is especially lower(2,5001h')management scenarios chosen for wastewater pollution harmful to corals from untreated sources,such as septic tanks and represented the 95th and 36th percentiles ofthe 2019 distribution, cesspools,which are both common in Hawai'i49.Consequently,high respectively(Supplementary Fig.12).Probability values and lineswere derived concentrations oftoxins and pathogens]each into coastal waters that from the top model from ordinal logistic regression modelling(Extended Data increase coral disease,reduce coral growth and reproduction,and Table3,Methodsand Supplementary Information).Coloursfor low,moderate increase coral susceptibility to bleaching42.These negative impacts and high in bare the same as those in a.See Extended Data Table lforfu111istof on coral persistence aretherefore much reduced in areas ofdecreased local land-sea human impacts and environmental factors included in the wastewater pollution.Scrapers reveal bare substrate as they feed and analysis,includingthose removed that were highly correlated(r>0.7,Methods facilitate the settlement,growth and survival ofcrustose coralline and Supplementary Fig.8).See Supplementary Fig.9for predictor variable algae and corals following acute disturbance30.Beyond these top-down distributions. effects on benthic condition,bottom-up effects of improved habitat quality could be contributing to the positive relationshipwe observed between scraper biomass and higher reef-builder cover.Parrotfish are the dominant scrapers in Hawai'i,and typically have home ranges of Nature I www.nature.com 1 5 Article less than 1 km(ref.50).Furthermore,our scraper biomass estimates in greenhouse gas emissions may buy reefs more time to adapt and were derived from multiple observations across several time points persist into the future.Contemporary governance must therefore following the marine heatwave,rather than a single snapshot estimate. shift towards an integrated approach to align management strategies Such strong site-based fidelity,combined with our recurring surveys, with reef ecosystem processes and the coincident multiscale human suggests that resident scrapers played a key role in promoting higher drivers that affect them'. reef-builder cover rather than the association driven purely byan influx An ambitious effort is underway to protect 30%of Earth's land and of individuals seeking more favourable habitat postdisturbance. sea areas by 2030 as part of the recently adopted Kunming-Montreal Sea-based management efforts are often disconnected from those Global Biodiversity Framework'.The motivation behind the'30 by 30' occurring onland118.We generated management scenarios ofhow is to support ecological resilience,conserve biodiversityand preserve varying scraper biomass(sea-based management)and wastewater pollu- ecosystem services that underpin human well-being53.The 30 by 30 tion(land-based management)influenced the probability of being in has broad participation and is being incorporated into conservation a low,moderate(more than the 25th and less than the 75th percentile) efforts by nations globally.However,our results reveal that sea-based or high reef-builder cover category.Our findings indicate that aninte- management alone is insufficient to mitigate the full spectrum of local grated management approach can result in a positive synergistic human effects on coastal ecosystems such as coral reefs.These efforts outcome for coral reefs(Fig.4b).For example,four years following must therefore explicitly couple the respective 30%land-sea targets the marine heatwave,a reef across our study region with low scraper to realize coastal ocean conservation goals.But in most coastal geo- biomass(forexample,30 kg ha-')and relatively high wastewater pollu- graphies,30%protection is impractical and unethical given the high tion(forexample,600,000 1 ha-')is most likelyto have low reef-builder proportion of peoplethat live nearand depend on these ecosystem S14. cover(83%probability)(Fig.4b,'initial condition').Where scraper Instead,mitigating land-based impacts such as wastewater pollution biomass is higher(for example,250 kg ha-')but wastewater pollution must occur together with fisheries governance for successful con- remains high,there is a 70%probability of moderate reef-builder cover servation outcomes,akin to long-standing indigenous stewardship (scenario A).Conversely,where wastewater pollution is lower(for practices of island ecosystems16.Only by adopting coupled land-sea example,2,5001 ha-'),but scraper biomass remains low,there is an policy measures,alongsideglobal emissions reductions,will coral reef 83%probability of moderate reef-builder cover(scenario B).However, ecosystems and the human communities they support have the best ifboth land and sea management scenarios occur,there isan 80%prob- opportunity for persistence in our changing climate. ability of high reef-builder cover(scenario Q.Combining land and sea management resulted in a three-to sixfold increase in the probability of high reef-builder coverfour years following severe heat stress than Onlinecontent if land or sea were managed in isolation. Any methods,additional references,Nature Portfolio reportingsumma- ries,source data,extended data,supplementary information,acknowl- edgements,peer review information;details of author contributions Conclusion and competing interests;and statements of data and code availability Herewe showthat simultaneously mitigating local land-and sea-based are available at https://doi.org/10.1038/s4l586-023-06394-w. human impacts promotes coral reef persistence before,during and in the years following a historically unprecedented marine heatwave in Hawai'i.Our unique spatiallyand temporally resolved data highlighted 1. Hughes,T.P.et al.Coral reefs in the Anthropocene.Nature 546,82-90(2017). the specific impacts that best correlated with coral reef persistence in 2. 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Skirving,W.et al.CoralTemp and the Coral Reef Watch Coral Bleaching Heat Stress O 4.0International License,which permits use,sharing,adaptation,distribution Product Suite version 3.1.Remote Sens.12,3856(2020). and reproduction in any medium or format,as long as you give appropriate 36. Glynn,P.W.Coral-reef bleaching-ecological perspectives.Coral Reefs 12,1-17(1993). credit to the original author(s)and the source,provide a link to the Creative Commons licence, 37. Gove,J.M.et at.Near-island biological hotspots in barren ocean basins.Nat.Commun.7, and indicate if changes were made.The images or other third party material in this article are 10581(2016). included in the article's Creative Commons licence,unless indicated otherwise in a credit line 38. 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Wooldridge,S.A.Water quality and coral bleaching thresholds:formalising the linkage for the inshore reefs of the Great Barrier Reef,Australia.Mar.Pollut.Bull.58, ©This is a U.S.Government work and not under copyright protection in the US;foreign 745-751(2009). copyright protection may apply 2023 Nature I www.nature.com 1 7 Article Methods Length-weight fitting parameters were obtained from a comprehen- sive assessment of Hawai'i specific parameters56 and FishBase65.Fish Study site species were excluded from fish biomass calculations according to life Hawai'i Island(19.550 N,155.660 W)is the southeastern most island of history characteristics that are not well captured with visual surveys, the Hawaiian Archipelago,located in the northern central Pacific(Fig.1). including cryptic benthic species,nocturnal species,pelagic schooling The western section has roughly 200 km of coastline predominantly species and manta rays. oriented north to south.The coastline contains the longest contiguous reef ecosystem in the main Hawaiian Islands55 and large gradients in Human population.We quantified human population density using human population,local land-sea impacts and environmental factors NASA Gridded Population of theWorld v.4(ref.66).The dataset is avail- that arecomparable to reef ecosystems globally(Extended Data Fig.1). ableatl km resolution at5-year intervals.Linear interpolation was used The region represents an ideal studylocation for resolvingthe land-sea to fill in the missing years and produce annual time steps of human human impacts driving reef ecosystem change and coral trajectories population within 15 km of each 100 mgrid cell across our study region following acute climate-driven disturbance. (Supplementary Fig.12). Reef surveys Wastewater pollution.We calculated wastewater effluent(I ha-'yr') Full details related to sampling design,site selection and survey and nitrogen input(kg ha-'yr')from onsite sewage disposal systems frequency for benthic and reef-fish data collection across our study (for example,cesspools and septic tanks)and injection wells(collec- region are in the Supplementary Information.In brief,underwater tivelyOSDS)in coastal waters at100 m resolution.OnlyOSDS located visual surveys of benthic assemblages were collated from three moni- within a modelled one-year groundwater travel time of the coast were toring programmes for the following years(number of reefs surveyed included in the analysis and nutrients from OSDS were assumed to are in parentheses):2003(23),2007(23),2011(23),2014(40),2015 flow to the nearest point on the shoreline.Wastewater effluent and (40),2016(80),2017(80),2018(15)and 2019(55).All benthic surveys nutrient input were estimated on thebasis of ref.67and discharge rates used permanently marked pins to ensure the same area of reef was and nutrient loading according to ref.68.A Gaussian decay function surveyed over time.High-resolution photographs were collected by was used to estimate dispersal offshore,approaching zero at 2 km using photoquadrats at 1 m intervals along 25 m belt-transects(n=26 (Supplementary Figs.13-15).This samedispersal function wasalsoused photographs per transect).Between 30 and 50 random points were for nutrient input,urban runoff,sediment input and rainfall,which are overlaid on each photograph and the benthic component under each each described below. point was identified to the lowest possible taxonomic level.Percent- age cover of the major functional groups at each reef were used in this Nutrient input.We calculated nutrient input(kg ha-'yr')at100m analysis,namely hard coral and crustose coralline algae.Surveys of resolution as the combination of total nitrogen from OSDS(Waste- reef-fish assemblages were performed along the same permanently water pollution section above)and golf courses.The total golf course marked 25 m transects concurrently with benthic surveys.However, area per watershed was derived from NOAA Coastal Change Analysis reef-fish surveys were performed more frequently(one to six times Program(CCAP)land-useand land-coverdata and Landsat cloud-free per year from 2003 to 2019)than benthic surveys,depending on the composite images created with Google Earth Engine.The golf course reef location and monitoring programme performing the surveys.In area was multiplied byan annual nitrogen application rateof 585 kg ha-' all surveys,fishes were identified to species,sized and enumerated. (refs.69,70)and then by leaching rate of32%7-73to estimate nitrogen To account for differences among programmes in how researchers that either runs off or reaches the groundwater.We also imposed a surveyed reef fish,counts were calibrated using species and method reduction in nitrogen that reached the ocean on the basis of distance specific adjustments56. inland and used subwatershed catchment data'to estimate nutrient transport from golf courses to the coastline(Supplementary Figs.16-18). Local land-sea human impacts and environmental factors Fish biomass.The biomass of fishes at a given reef was measured as Urban runoff.We quantified the total area of impervious surfaces total fishbiomass,herbivore fish biomass and the biomass of browsers, (that is,paved roads,parking lots,sidewalks and roofs)within 10 km grazers and scrapers56.Total fish biomass is an indicator of the overall of the coastline at 100 m resolution for each year from 2000 to 2017 stateofthefish assemblage 57 and is reduced inareasthat have increased (Supplementary Figs.19 and 20).Data wereextracted from NOAACCAP fishing pressures8,s9.InHawai'i,non-commercial nearshore fisheries land-use land-coverdata from1992,2001,2005and 2010.We also digi- dominate,with people fishing for recreational,subsistenceand cultural tized 2017 impervious surface cover from a single cloud-free Landsat purposes60,61 However,the dominant harvesting modesand magnitude 8 image(courtesyof the United States Geological Survey,USGS)(15 m of fishing activities are largely unknown at spatial or temporal scales resolution pan-sharpened).Years in between data availability were relevant to this stud y62.As such,we include total fish biomass in part filled in by linear interpolation. to represent fishing effort on reefs but recognize its shortcomings in capturing reef-and species-specific differences in fishing pressure Rainfall.Wequantified annual rainfall(m3ha-')and peakrainfall(maxi- across our study region.We also include herbivores and subdivisions mum3-day rainfall total,m3ha-')at100 m resolution.Daily rainfall data by feeding guilds that represent important indicators of resilience were generated following refs.75,76.Rainfall from each rain station on coral reefsso,6,61.Browsers are defined as herbivores that feed on was used to derive interpolated surfaces at annual time steps using macroalgae and associated epiphytic material,and are important for Empirical Bayesian Kriging in ArcGIS.Subwatershed catchment data' reducing the cover of larger,more established macroalgae.Grazers were clipped to 0-10 km from the coast and used to calculate rainfall are herbivores that feed largely on small algal turfs,helping to prevent per drainage area(Supplementary Figs.21 and 22). their succession into larger macroalgae,and scrapers are herbivores that closely crop the substrate and open up new space to promote the Sediment input.The Integrated Valuation of Ecosystem Services settlement,growth and survival ofcrustose coral line algae and corals30. and Tradeoffs sediment delivery model was used to derive long-term We followed established methods for calculating fish biomass56. annual average sediment input(kg ha-')reaching the coast77"oat100 m The biomass of individual fishes was estimated using the allometric resolution.We then modulated the long-term annual average sedi- length-weight conversion:W=aTLb,where parameters a and bare mentovertimeby watershed onthebasisof discharge calculated from species-specific constants,TL is total length(cm)and Wis weight(g). peak rainfall data(Rainfall section above).Discharge bywatershed was 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 Article pollution,urban runoff,nutrient loading,phytoplankton biomass and P(y.<j) peak rainfall.A fourth-root transformation was applied to sediment. In P(yt>j) -Cj+Blzil+"'+Bkzik To reduce model overfitting,Pearson's correlation coefficients were calculated among all predictors(Supplementary Fig.5),removing one of each pair of highly correlated(r>0.7)predictors.To further Here,i indexes each of N observations,with categories yi,and the strive for model parsimony,we a priori excluded human population left-hand side of the equation is the logit of the probability of a density from the model-fitting process as it was a poor indicator of reef-bull der category ofjor lower,forj=1(high)or2(moderate).Reefs human-driven land-to-sea impacts on local scales(Figs.1c and 2d and with low reef-builder cover contributed to the regression through cal- Extended Data Fig.3).We also excluded browser biomass as they rep- culation ofthe log odds.Each Cjis an MLE-computed model intercept, resented less than 10%on averageoftotal herbivore biomassacross all and each Bk is the MLE coefficient corresponding to the standardized reefs before,during and postdisturbance.This resulted in thefollowing independent variable zik,for k=1 through n,where n is the variable predictors included in the models(correlated predictors in parentheses number of predictors used in a given candidate model,hence the were removed):total fish biomass,biomass of scrapers,biomass of ellipsis(...).Afundamental component of this model isthe assumption grazers(total herbivore biomass),DHW(SST mean and variability), of proportional odds,or parallel regression,which indicates that Bk wastewater pollution,nutrient input,urban runoff,sediment inputand values are independent of the logitlevelj.The validity of this parallel peak rainfall(annual rainfall correlated with both),wave power,phy- regression assumption was ascertained using Brant's Wald test97,as toplankton biomass(irradiance),fishinggear restrictions and depth. well as a likelihood ratio test(a=0.05). The decision of which correlated predictors to retain was based on a We then calculated the following predictors based on current litera- hypothesis-driven approach,in part whether the given predictor had ture and our hypotheses of the principal factors that drive changes in the potential to directly(for example,sediment input)rather than reef-builder cover across space and time following a major thermal indirectly(for example,annual rainfall driving sediment input)affect disturbance:fish biomass metrics,wastewater pollution,nutrient heat-driven coral loss. loading,urban runoff,annual rainfall,peak rainfall,wave exposure, We incorporated a random spatial factor to account for the possible phytoplankton biomass and irradiance:the mean of all data from 2016 influence of a change in an underlying variable along the coastline not to 2019;sediment was measured as the mean of top three events over quantified in this study.This was done by breaking the coastline up the 2006-2019 time period;SST mean and SST variability:mean of all into discrete 10 km sections running north to south.Section size was data from 2000 to 2018.Notethat2019 was excluded in SST mean and determined using hierarchical clustering based on pairwise Euclid- SSTvariability owing to the marine heatwave that affected Hawai`i21,but can distances between reefs and identifying an inflection point in the occurred after our 2019 fish and benthic surveys;fishing gear restric- intragroupvariance24(SupplementaryFig.7).WefittedGAMMsfor tions involved the marine managed area designation in 2016 and depth all possible candidate models(unique combinations of the predictor was assessed by in-water diver-assessed values. variables)using the UGamm wrapper function,in combination with We used the same process as in the GAMManalysis to removeoutliers the dredge function in the MuMln package94.Nonlinear smoothness in in our predictor variables(above).We then square-root transformed the models was determined using penalized cubic regression splines, the following predictors to down-weightthe influenceof values atthe with the numberof knots(limited to fourto reduce overfitting)spread extreme ends of their distributions:total fish biomass,wastewater evenly throughout each covariate.All possible candidate modelswere pollution,sediment inputand nutrient loading.Pearson's correlation computed(unique combinations of the predictor variables)but limit- coefficients were calculated among all predictors(Supplementary ingthe total number of predictors in anygiven candidate model to five Fig.8),removing highly correlated(r>0.7)predictors.For the rea- to reduce overfitting.We used Akaike's information criterion with abias sons outlined in our GAMM analysis and for continuity,we a priori correction for small sample sizes95(AICc)for model comparison and excluded human population densityand thebiomass ofbrowsers from all models within OAlCc<-2ofthetop model(DAICc=0)are presented the model-fitting process.This resulted in the following predictors in Extended Data Table 2.To visualize the effectof predictor terms on included in the models(correlated predictors in parentheses were coral cover change,we averaged the coefficients fromthe top models removed):total fish biomass,biomass of scrapers,biomass ofgrazers (that is,AAICc<_2)to generate a predicted dataset and set all other (total herbivore biomass),wastewater pollution,nutrient input,sedi- predictor terms to their median value.Finally,we calculated a meas- ment input,urban runoff(phytoplankton biomass),wave exposure, ure of predictor variable relative importance within each candidate fishing gear restrictions and depth.The decision of which correlated model by calculating the sum of AlCc model weightsforeach predictor predictors to retain followed the same logic asour GAMManalysis.The (that is,the sum of model weights across all models containing each mean and variability in SST were excluded given the negligible range predictor;Fig.3). of values among reefs(0.1 and 0.025°C,respectively).All possible candidate models were computed while limiting the total number of Coral reefs four years postdisturbance.Our goal was to assess the predictors in anygiven candidate model to four(to reduce overfitting local land-sea human impacts and environmental factors that best and to account for the lower response variable replication compared explained variations in the cover of reef-building organisms four years to our GAMM analysis).Models were computed using the multino- following the marine heatwave.The cover of reef-building organisms mial logistic regression function mnrfitinMATLAB.We again used for reefs surveyed in 2019(n=SS)were parsed into three categories AICcfor model comparison and all modelswithin DAICc<-2 of thetop on the basis of thefollowing percentiles:low,less than or equal to the model(DAICc=0)arepresented in Extended Data Table3.McFadden's 25th;moderate,more than 25th and lessthan75th;and high,morethan pseudo-R2 was computed for the highest ranked models and ranged or equal to the 75th.We then performed ordinal logistic regression96 from 0.21 to 0.22.Unlike traditional R2 values,McFadden's pseudo-R2 to determine the probability of a given reef having high,moderate or of more than 0.2 represents an excellent fit98.Models Within OAICC<<-2 lowcoverof reef-building organisms onthebasisof the prevailing local of model 1 in Extended Data Table3 demonstrated comparable levels of human impacts and environmental factors(that is,predictor variables; goodness of fit andparsimony""O Many ofthe parameter coefficients Extended Data Table 1).Logit models are multivariate extensions of within these models were sensitive to the underlying variability in the generalized linear regression models that provide parameter estimates dataandtheirestimates did notdiffer significantly from zero(P<0.05). by means of maximum likelihood estimation(MLE)to model the rela- Thetop model contained parameters with covariate estimates signifi- tive log odds of observing a reef-builder cover category or less versus cantly different from zero,namely scraper biomass and wastewater observing the remaining higher categories: pollution.Using model 1,we examined changes in the probability of a given reef having high(more than or equal to the 75th percentile),mod- Reef Watch(https://coraireefwatch.noaa.gov/product/oc/index. erate(more than the 25th and less than the 75th percentile)or low(less php)and ref.80.See Methods and Supplementary Information for than orequal to the 25th percentile)reef-builder cover(Fig.4a)on the more detailed information on the data used to support the findings of basis of variations in thesetwo land-sea predictors(Fig.4b).Probabil- this study. ity curves for high,moderate and low were calculated on the basis of changing scraper biomass and wastewater pollution and holding all other predictors at their mean. Code availability Statistical analyses were performed using the software packages R Resource management scenarios.The resource management sce- (www.r-project.org)v.4.0.2(using libraries gamm4,MuMln,foreach, narios presented in Fig.4b were selected on the basis of the following doMC,ggplot2,gmt,tidyverse,zoo and lubridate)103 MATLAB(www. rationale.We chose 250 kg ha-'as the management target for scraper mathworks.com)using v.2021a(using Statistics and Machine Learn- biomass as this value approximates the long-term mean(2003-2019; ing toolbox),ArcGIS Desktop(www.esri.com)v.10.6 with Advanced n=17)biomass of scraperswithin Kealakekua Bay,a marine protected licensing and extensions Spatial Analyst and Geostatistical Analyst, area where no fishing has been allowed since 1969(Supplementary Integrated Valuation of Ecosystem Services and Tradeoffs Sediment Fig.10).Kealakekua Bay isalsoexposed to numerous land-based stress- Delivery Ratio model(https://naturalcapitaIproject.stanford.edu/ ors,includinghigh levels of wastewater pollution(258,0001h-'in2019). software/invest)and the PERMANOVA+(ref.89)add-on for Primerv.7 As such,our value of 250 kg ha-'represents an estimate of scraper bio- (ref.104).Code is available for download at https://github.com/ mass on a reef with strong fisheries protection but with land-based jamisongove/Coral-Reef-Persistence. stressors present.In addition,we compared our upper(250 kg ha-') and lower(30 kg ha-')scraper biomass values to the distribution Of 55. 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Reprints and permissions information is available at http://www.nature.com/reprints. a b 0 10 20 30 40 50 60 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Coral Cover(rcover) Management(OFished0 Restricted pNo-Take) C d 0 20 40 60 80 100 0 1000 2000 3000 4000 Human Population(103People) Small-Scale Fisheries Market Gravity e f (number of people/(hours of travel2)) 0 25 50 75 1000 0 1000 2000 3000 4000 5000 Nutrient Input from Wastewater Pollution Sediment Input(103 kg km-2) 9 (103 kg per watershed) h 0 5000 10000 15000 20000 25000 30000 0 0.2 0.4 0.6 0.8 1 Tourism(tourist trips per year) Cumulative Pressure 0 10 20 30 40 50 0 500 1000 1500 2000 Wave Exposure(kW m-') Primary Production(mg C m-2 day-') k I -1.5 -1 -0.5 0 0.5 1 1.5 -1.5 -1 -0.5 0 0.5 1 1.5 Historical Thermal Stress Recent Thermal Stress Global Mean±2SD t Hawai'i Mean±2SD Extended DataFig.1 Comparisonof human,environmental,and climate (e.g.,recreational diving and snorkelling)and reef-adjacent(e.g.,provisionof factors for reefs in Hawai'i with coral reef ecosystems globally.Dots rep resent calmwaters,sand beaches,views,and seafood)aspects(sample number same global(lightgrey)and Hawai'i(darkgrey)meanvalues.Error bars represent asinc);h,Cumulative pressure score from stressors to coral reefsper5 kmreef the mean±2 standard deviation(SD).Factors presented are:a,Coral cover containingpixel(unitless);1,Mean wave energy,or wave power(kW m-'),from (percent hard coral;global n=2,584;Hawai'i n=137);b,Proportional reefarea 1979-2009(n=sameasina);j,Mean primary productivity(mgCm'day1) bycountrythat isopen(fished),gear restricted(restricted),or fully restricted between 2003-2013(n=sameasina);k-I,Unitless metric of(k)historical (no-take)to fishing(sample numbersameas ina);c,Human population within (1985-2017)and(I)recent(2014-2017)thermal stress on coral reefs,whereby 5 km in 2018(global n=54,596;Hawai'i n=199);d,Small-scale fisheriesmarket positivevalues represent moredesirable(i.e.,less thermal stress)over the gravity(number of people/(hoursoftravel)'represents human useand fishing respectivetimeframes1"(n=same asinc).The mean for reefs in Hawai'i falls pressure related to the size and accessibility of coral reefs to nearby human within 2SD of theglobal mean forall factors.Data are fromthe following settlementsandmarkets59(n=same asinc);e,Annualinputof nitrogen(103kg) sources:a,b,ijfrom106efrom";c,d,fg,h,k,from107.Factors presented here perwatershed fromwastewater pollutionon coral reefs(global n=38,033; wereobtainedfromglobaldatasetsandwill differ fromthosepresentedwithin Hawai'in=324);fSediment input(103kgkm')to coral reefs(n=sameasin c); our present study owing to the methodological differences as well as differences g,Annual number of tourist visits driven by coral reefs combining on-reef in their spatiotemporal extent and resolution. Article 29 Monthly SST 28.5 —12 Month Moving Average 28 -- Mean±2SD - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 27.5 U 27 d Z'26.5 a cl- 26 25.5 U 't 25 U) CO 24.5 N U) 24 23.5 — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — 23 22.5 1900 1905 1910 1915 1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 Year Extended Data Fig.2 1 Long-term ocean temperature record forHawai'i. time series.Data are from NOAA's Extended Reconstructed SST v5(https:// Monthly sea surface temperature(SST)for the main Hawaiian Islands from www.ncei.noaa.gov/products/land-based-station/noaa-global-temp)and 1900-2020.Dashed lines represent±2 standard deviations(SD)above and val ues shown are the 901h percenti le of monthly SST from within the vicinity of below the long-term mean.Red lineis the 12-month moving average.The2015 the main Hawaiian Islands(18.5to22.5°N;-160.5to-154.5°W). marine heatwave is represented by the highest SST values over the 120-year 125 00 175 0 75 1 U 50 >75 T 25 0 o IL l '- 0 T ® • 25 a 6 6 U 50 0' z� d 75 100 125 ro zAft Aft J` 4 e Extended Data Fig.3 Percent difference in mean drop-onejackknife greater on reefs that had positive and negative trajectories,respectively. values oflocal human impacts and environmental factors between positive Zero line represents equal values.Outliers that fell outside a threshold of±2 and negative trajectory reefs.The percent difference((Vl-V2)/[(Vl+V2)/2]; standard deviations ofthe median were removed prior to analysis.See Fig.lb dots)was quantified by taking the ratio ofthe mean in drop-onejackknife for reeflocations and Fig.2d for mean absolute differences in factor values. values between positive(n=10)and negative(n=8)trajectory reefs(sensu)". See Methods,Extended Data Table 1,and Supplementary Information for Upper and lower bars represent the respective maximum and minimum per detailed information on local land-sea human impacts and environmental cent differences.Blue and red shaded regions indicate factors that were factors. Article 25 25 25 0 0 0 —25 —25 —25 —50 —50 —50 a� ID -75 -75 -75 ARb 0.06 0.09 0.12 0.16 0.20 0 39 625 3164 10000 0 40 160 360 m Phytoplankton(mg m-3) Sediment Input(kg ha-') Scrapers Biomass(kg ha-') Co U 25 25 a� 0 U 0 0 6 U -25 -25 —50 —50 —75 AMML —75 2500 10000 22500 360 640 1440 2560 Urban Runoff(mz ha-') Total Fish Biomass(kg ha-') Extended Data Fig.4 1 Generalized Additive Mixed Model(GAMM)results factorsamongall models(i.e.,sum ofAICc model weights across all models (RZ=0.79)showing key local land-sea human impacts and environmental containing each factor)was as follows:sediment input(0.99),scraper factors that modified coral response to the 2015 marine heatwave.Positive biomass(0.99),tota I fish biomass(0.90),urban runoff(0.60),phytopla nkton and negative relationships reduce or increase coral loss,respectively.Because biomass(0.38),wastewater pollution(0.28),peak rainfall(0.20),nutrient changes in coral cover following disturbance can be affected by variations in loading(0.19),grazer biomass(0.16),DHW(0.08),wave power(0.07),depth starting condition(reefs with higher initial cover have greater scope for loss, (0.06),and fishinggear restrictions(0.05).See Extended Data Table 1 for full list and vice versa)91we modelled relative coral cover change following ref.92to oflocal land-sea human impacts and environmental factors included in the ensure comparability across reefs(see Methods).Median values with shaded analysis,including those removed thatwere highly correlated(Fig.SS).See region representing the 80%confidence interval.The relative importance of Fig.S6 for predictor variable distributions. a b Mean Reef 65 0 • Trajectories±g5%CI • Positive Negative -5 • 60 1 1 ^-10 TT °'0))-20 55 50 15 �-20 • 45 U-25 • • 50 40 • o -30 • 45 835 -35 0 30 -40 0 25 • 40 0 450 5 10 15 20 25 30 35 40 45 50 55 60 65 20 Coral Cover 2015 a 35 15 0 U 10 e iC 30 2014 2016 0 0 U C coi 25 -10 tChange -10 • -15 a-2020 0-20 o 0) 0-30 ) c 15 t-3 -40 a)-355 10 0-40 0-50 • U U CO-45 is-60 o Change o • • 5 U-50 (%difference) U-70 -55 0 5 10 15 20 25 30 35 40 45 50 55 60 65 p -60 Coral Cover 2015 2014 2016 Extended DataFig.5 Coral cover change following the 2015 marine heatwave both prior to,and toa lesser extent,followingthe marine heatwave compared on positiveversus negativecoral cover trajectory reefs.a,Cora I cover on to negative trajectory reefs.c,Positive trajectory reefs experience increased positive(blue;n=10)and negative(red;n=8)trajectory reefs surveyed absolutecoral cover loss followingthe marine heatwave(underlying relationship (see Fig.2b in main manuscript)prior to(2014)and 1-year following(2016)the shown in panel d),but this difference is largely absent once starting coral cover marine heatwave.b,Positive trajectoryreefs havea higher mean coral cover condition is accounted for(underlying relationship shown in panel e). Article Extended Data Table 11 Local land-sea human impacts and environmental factors considered for our analyses Impact or Metric Units Spatial Temporal Temporal Data Source Justification Factor Resolution Resolution Range Abundant reef fish populations support Total Biomass, 1-6 surveys, See reef-scale ecosystem functions such as 29 Fish Herbivores, kg ha-1 25 m per site,per 2003-2019 Supplemental predation and nutrient release . Biomass Grazers,Browsers, ear Information Herbivorous fishes support ecosystem Scrapers y resilience and mitigate the negative effects fleshy algae have on coral survivall0e Human Pop ulation Density people/15 1 km Annual 2000-2019 NASA GPWv4 Human population density is a widely used Population km proxy for local human impacts25109110. High concentrations of toxins(e.g., endocrine disruptors,pathogenic bacteria and viruses,pharmaceuticals,and heavy Wastewater Modified from metals)are found in wastewater Pollution Total Effluent L ha-1 100 m Annual 2000-2019 ref67 pollution"'.These toxins can drive a higher incidence of coral disease,reduced coral growth and reproduction,increased cover of fleshy algae,and increased coral bleaching and subsequent mortality42. See Human-derived nutrient input can promote Nutrient Input Total Nitrogen kg ha-1 100 m Annual 2000-2019 Supplemental rapid algal growth,outcompeting corals Information and disrupting ecosystem function57. Runoff can deliver a broad spectrum of land-based contaminants(e.g.,heavy See metals and household chemicals)that Urban Runoff Impervious m2 ha-1 100 m Annual 2000-2019 Supplemental degrades nearshore water quality,with Surfaces Information cascading effects on coral health41, including the natural defence abilities of corals and increase the likelihood of mortality from heat stress40 Sediment See Sediment input can impede the Input Sediment kg ha' 100 m Annual 2000-2019 Supplemental photosynthetic capacity of corals and Information reduce growth by burying coral colonies4. Large pulses of freshwater from storm events can cause localised die-off of nearshore corals,fish,and other reef- Rainfall Peak Modified from associated or anisms112.Rain events can Rainfall Rainfall m3 ha' 100 m Annual 2000-2019 refs`,? also mobilise high levels of nutrients, sediment,and land-based debris that impact nearshore water quality and coral reef health"'. Gradients in wave exposure and Wave See associated flaw produce varying levels of Exposure Wave Power kW m 1 50 m Hourly 2000-2019 Supplemental disturbance that can play a major role in Information determining coral reef community patternsa4,114,115 Chlorophyll-a is a widely used indicator for changes in phytoplankton production1' that propagates through the food-web117. Phytoplankto s 750 m& Daily& NOAA's Coral Corals can supplement nutritional 4 km 8-day 2002-2014 Reef Watch), requirements through h eterotrophic n Biomass Chlorophyll-a mg m and ref feeding on zooplankton .High levels of chlorophyll-a are also indicative of poor water quality that can have negative outcomes for corals40. Sea-surface Summertime Mean NOAAThe mean and variability in summertime Reef Coral Temperature &Standard °C 5 km Daily 2000-2019 ocean temperature is a widely used metric (SST) Deviation Reef Watch for coral reef resilience57. Degree heating week is the accumulation Degree Heating 5 km NOAA's Coral of heat stress above the coral bleaching Heat Stress Week Reef Daily 2000-2018 Reef Watch threshold over a 12-week period as is the dominant metric in coral reef research to quantify heat stress on corals(e.q.,22). Photosynthetically Einstein NOAA's Coral Excessive irradiance can cause light stress Irradiance Active Radiation m-2 d-' 750 m Daily 2015-2019 Reef Watch that exacerbates coral bleaching 118•"0. Fishing Gear See We use fishing gear restrictions as a Restrictions Gear Rank Categorical NA NA 2000-2019 Supplemental metric to represent spatial fisheries Information management120. Deeper reefs are often less impacted by Depth Depth Metres NA NA 2003-2019 Reef Surveys heat stress compared to those located in shallower depths2 21. See'Local land-sea human impacts and environmental factors'section in Supplemental Information for detailed information on calculating each impact or factor,including data collection methods,data sources and ancillary data sets,and specific tools or software utilized'11121 Extended Data Table 2 1 Summary of generalized additive mixed effects models(GAMM)relating coral response to the 2015 marine heatwave with local land-sea human impacts and environmental factors Significant Land-Sea and Environmental Factors Log Likelihood AICc AAICc Adjusted Rz Model 1.Phytoplankton Biomass,Urban Runoff, Sediment Input,Scraper Biomass,Total Fish -302.98 638.34 0 0.79 Biomass Model 2.Urban Runoff,Sediment Input,Peak -303.93 640.25 1.91 0.78 Rainfall,Scraper Biomass,Total Fish Biomass The top two candidate models are shown.AICc,Akaike's information criterion corrected for small sample size;AAICc,change in AICc across the candidate models(AAICc<2 of the top model are shown);Adjusted R�,proportion of variation in the response variable explained by the candidate model. Article Extended Data Table 31 Summary of ordinal logistic regression(OLR)models relating the per cent cover of reef-builders (hard coral+crustose coralline algae)four years following the 2015 marine heatwave to local land-sea human impacts and environmental factors Model Output AICc AAICc McFadden's pseudo-R Predictors Scraper Wastewater Sediment Input Peak Rainfall Biomass Pollution 70 Coefficients 0.689 -0.867 0.159 0.547 103.28 0 0.22 0 p value 0.031 0.019 0.647 0.130 Predictors Scraper Wastewater Urban Runoff Sediment Input Biomass Pollution N -0 Coefficients 0.705 -0.567 -0.348 0.330 104.72 1.44 0.21 0 p value 0.025 0.115 0.282 0.303 Predictors Depth Scraper Wastewater Peak Rainfall Biomass Pollution co as Coefficients -0.508 0.540 -0.802 0.728 104.73 1.45 0.21 0 p value 0.109 0.088 0.022 0.038 Predictors Scraper Urban Runoff Sediment Input Peak Rainfall Biomass v 0 Coefficients 0.673 -0.655 -0.076 0.493 105.19 1.91 0.21 p value 0.033 0.039 0.813 0.162 AICc,Akaike's information criterion corrected for small sample size;AAICc,change in AICc across the candidate models(AAICc<2 of the top model are shown);McFadden's pseudo-R', proportion of variation explained by candidate model(0.2-0.4 represent an excellent fit)9B.Significant predictors at p<0.05 are in bold. natureportfolio Corresponding author(s): Jamison Gove,Gareth Williams • • Last updated by author(s): Aug 7,2023 • Reporting Summary Nature Portfolio wishes to improve the reproducibility of the work that we publish.This form provides structure for consistency and transparency ' in reporting.For further information on Nature Portfolio policies,see our Editorial Policies and the Editorial Policy Checklist. Statistics For all statistical analyses,confirm that the following items are present in the figure legend,table legend,main text,or Methods section. n/a Confirmed ® The exact sample size(n)for each experimental group/condition,given as a discrete number and unit of measurement ® A statement on whether measurements were taken from distinct samples or whether the same sample was measured repeatedly The statistical test(s)used AND whether they are one-or two-sided ❑ ® Only common tests should be described solely by name;describe more complex techniques in the Methods section. ® A description of all covariates tested ® A description of any assumptions or corrections,such as tests of normality and adjustment for multiple comparisons A full description of the statistical parameters including central tendency(e.g.means)or other basic estimates(e.g.regression coefficient) ❑ ® AND variation(e.g.standard deviation)or associated estimates of uncertainty(e.g.confidence intervals) For null hypothesis testing,the test statistic(e.g.F,t,r)with confidence intervals,effect sizes,degrees of freedom and P value noted ❑ ® Give P values as exact values whenever suitable. ® F-] For Bayesian analysis,information on the choice of priors and Markov chain Monte Carlo settings ❑ ® For hierarchical and complex designs,identification of the appropriate level for tests and full reporting of outcomes ❑ ® Estimates of effect sizes(e.g.Cohen's d,Pearson's r),indicating how they were calculated Our web collection on statistics for bioloaists contains articles on many of the points above. Software and code Policy information about availability of computer code Data collection All data and code that support the findings of this study are available at https://github.com/J`amisongove/Coral-Reef-Persistence. Data analysis Statistical analyses were performed using the software packages R(www.r-project.org)version 4.0.2(using libraries gamm4,MUMIn,foreach, doMC,ggplot2,gmt,tidyverse,zoo,lubridate)(ref.1),Matlab(www.matthworks.com)using v2021a(using Statistics and Machine Learning toolbox),ArcGIS Desktop(www.esri.com)v10.6 with Advanced licensing and extensions Spatial Analyst and Geostatistical Analyst,InVEST Sediment Delivery Ratio model(https://naturaIcapitaIproject.stanford.edu/software/invest),and the PERMANOVA+(ref.2)add-on for Primer version 7(ref.3). 1 Team,R.C.in R Roundation for Statistical Computing(2021). 2 Anderson,M.,Gorley,R.N.&Clarke,R.K.Permanova+for primer:Guide to software and statistical methods(2008). 3 Clarke,K.&Gorley,R.Getting started with PRIMER v7.PRIMER-E:Plymouth,Plymouth Marine Laboratory(2015). For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature,software must be made available to editors and reviewers.We strongly encourage code deposition in a community repository(e.g.GitHub).See the Nature Portfolio guidelines for submitting code&software for further information. Data Policy information about availability of data All manuscripts must include a data availability statement.This statement should provide the following information,where applicable: Accession codes,unique identifiers,or web links for publicly available datasets A description of any restrictions on data availability • • For clinical datasets or third party data,please ensure that the statement adheres to our og IiC • All data that support the findings of this study are available at https://github.com/jamisongove/Coral-Reef-Persistence.Reef fish length-weight parameters were • obtained from FishBase(https://fishbase.org)and ref.1,human population data from NASA Gridded Population of the World v4(https://sedac.ciesin.columbia.edu/ data/set/gpw-v4-population-count-revll),land use and land cover data from the NOAA Coastal Change Analysis Program(https://www.coast.noaa.gov/htdata/ • rasterl/landcover/buIkdownload/),soils data from USDA Gridded Soil Survey Geographic Database(gSSURGO;https://www.nres.usda.gov/resources/data-and- reports/gridded-soil-survey-geographic-gssurgo-database),sub-watershed catchment data from USGS Stream Stats(https://water.usgs.gov/GIS/metadata/usgswrd/ XML/ds680_archydrohucs.xml)(ref.2),watershed and digital elevation model data from USGS National Hydrography Dataset(https://www.usgs.gov/national- hydrography/national-hydrography-dataset),rainfall data from refs.3,4,Landsat 8 satellite image from USGS(https://earthexplorer.usgs.gov/),Landsat 7 and 8 cloud-free composites derived using Google Earth Engine(https://earthengine.google.com/),individual wastewater systems for Hawai'i from refs.5,6,marine managed area designation from ref.80 and downloadable from the State of Hawai'i(https://planning.hawaii.gov/gis),fishing regulations from the State of Hawai'i (https://dlnr.hawaii.gov/dar/fishing/fishing-regulations/),sea surface temperature and degree heating week data from NOAA Coral Reef Watch(https:// coralreefwatch.noaa.gov/product/Skm),ocean color(chlorophyll-a and irradiance)data from NOAA Coral Reef Watch(https://coralreefwatch.noaa.gov/product/oc/ index.php)and ref.7.See Methods and Supplemental Information for more detailed information on the data used to support the findings of this study. 1 Donovan,M.K.et al.Combining fish and benthic communities into multiple regimes reveals complex reef dynamics.Sci.Rep.8,16943(2018). 2 Rea,A.&Skinner,K.D.Geospatial datasets for watershed delineation and characterization used in the Hawai'i StreamStats web application.US Geol.Surv.Data Ser.680,12(2012). 3 Longman,R.J.,Newman,A.J.,Giambelluca,T.W.&Lucas,M.Characterizing the Uncertainty and Assessing the Value of Gap-Filled Daily Rainfall Data in Hawaii.J. Appl.Met.Clim.S9,1261-1276(2020). 4 Longman,R.J.et al.Compilation of climate data from heterogeneous networks across the Hawaiian Islands.Scientific Data S,180012(2018). S DOH.Individual Wastewater System Database.Hawaii Dept.of Health(2017). 6 DOH.Underground Injection Control Permit application files.Hawaii Dept.of Health(2017). 7 Wedding,L.M.et al.Advancing the integration of spatial data to map human and natural drivers on coral reefs.PLoS ONE 13(2018). Human research participants Policy information about studies involving human research participants and Sex and Gender in Research. Reporting on sex and gender NOT APPLICABLE Population characteristics NOT APPLICABLE Recruitment NOT APPLICABLE Ethics oversight NOT APPLICABLE .... ................................................................................ ............ Note that full information on the approval of the study protocol must also be provided in the manuscript. Field-specific reporting Please select the one below that is the best fit for your research.If you are not sure,read the appropriate sections before making your selection. ❑Life sciences ❑ Behavioural&social sciences ® Ecological,evolutionary&environmental sciences For a reference copy of the document with all sections,see nature.com/documents/nr-reporting-summary-flat.pdf Ecological, evolutionary & environmental sciences study design All studies must disclose on these points even when the disclosure is negative. Study description The study tested the hypothesis that mitigating local human impacts facilitates coral reef persistence in the face of climate change- induced disturbance,specifically mass coral bleaching.Our goal was to move beyond commonly used proxies of local human impacts and generate spatially resolved data on specific land-sea human activities to identify actionable outcomes.This further allowed us to quantify the effects mitigating either land-or sea-based human impacts in isolation or simultaneously had on the ability of key reef- building organisms to recover post-disturbance.We achieved this by combining recurring in-water SCUBA surveys of coral reef benthic and fish communities with a 20-year time series of land-sea human impacts and other environmental factors thought to drive coral reef ecosystem processes.Our study included reefs across a broad range of ecological states,large spatiotemporal gradients in land-sea human impacts and environmental factors,and which experienced the most severe marine heatwave on record in the Hawaiian Islands. Research sample We quantified changes in the per cent cover of major reef-building benthic groups(hard coral,crustose coralline algae)and related these to concurrent changes in numerous land-sea human impacts,including urban runoff,wastewater pollution,nutrient loading, • • sediment input,and local restrictions on fishing gear types.Environmental factors included peak and annual rainfall,wave exposure, variability in ocean temperatures and heat stress,irradiance,and phytoplankton biomass.We also incorporated multiple fish biomass • metrics that represent the critical role reef fish play in maintaining coral reef ecosystem dynamics.All human impacts and • environmental factors were chosen based on prior evidence in the literature that they represent key drivers of reef ecosystem processes and were quantified using a variety of modelled and satellite-derived data sources. • • Sampling strategy Underwater visual surveys of shallow-water benthic and reef-fish assemblages were collated from the following three coral reef ecosystem monitoring agencies to maximise spatial and temporal replication across the study region:State of Hawai'i Division of • Aquatic Resources,National Park Service,and The Nature Conservancy.Each program conducted surveys using similar data collection methods(see below)in shallow-water(<30 m)depths over hard-bottom substrate. Data collection All coral reef surveys used a traditional 2S m belt-transect method.Benthic surveys used permanently marked pins to ensure the same area of reef was surveyed overtime.High resolution photographs were collected via photoquadrats at 1 m intervals along 2S m belt-transects(N=26 photographs per transect).Thirty to fifty random points were overlaid on each photograph and the benthic component under each point was identified to the lowest possible taxonomic level.Per cent cover of the major functional groups were used in this analysis,namely hard coral,crustose coralline algae,macroalgae,and turf algae.All data were averaged among each transect and then among all transects for each site(1—4 transects per site,per year,depending on the monitoring program). Surveys of reef-fish assemblages were performed along the same permanently marked 2S m transects concurrently with benthic surveys.In all surveys,fishes were identified to species,sized,and enumerated.To account for differences among programs in how researchers surveyed reef fish,counts were calibrated using species and method specific adjustments previously developed for the region. Timing and spatial scale Underwater visual surveys of benthic assemblages were collated from three monitoring programs for the following years(number of reefs surveyed are in parentheses):2003(23),2007(23),2011(23),2014(40),201S(40),2016(80),2017(80),2018(1S),2019(SS). All benthic surveys used permanently marked pins to ensure the same area of reef was surveyed over time.High resolution photographs were collected via photoquadrats at 1 m intervals along 2S m belt-transects(N=26 photographs per transect).Thirty to fifty random points were overlaid on each photograph and the benthic component under each point was identified to the lowest possible taxonomic level.Per cent cover of the major functional groups at each reef were used in this analysis,namely hard coral and crustose coralline algae.Surveys of reef-fish assemblages were performed along the same permanently marked 2S m transects concurrently with benthic surveys.However,reef fish surveys were performed more frequently(1—6 times per year from 2003— 2019)than benthic surveys,depending on the reef location and monitoring program performing the surveys.In all surveys,fishes were identified to species,sized,and enumerated.To account for differences among programs in how researchers surveyed reef fish, counts were calibrated using species and method specific adjustments.The survey region spanned roughly 200 km of coastline on the island of Hawai'i. Data exclusions Fish species were excluded from fish biomass calculations according to life history characteristics that are not well captured with visual surveys,including cryptic benthic species,nocturnal species,pelagic schooling species,and manta rays.We also accounted for extreme observations of schooling species,which were defined by calculating the upper 99.9%of all individual observations, resulting in 26 observations out of over O.S million,comprised of 11 species.The distribution of individual counts in the entire database for those 11 species was then used to identify observations that fell above the 99.0%quantile of counts for each species individually.These observations were adjusted to the 99.0%quantile for analysis. Other data exclusions include outliers in predictor variables(the local human impacts and environmental factors).Within the section "Coral reef trajectories pre-disturbance", prior to calculating per cent difference,we identified and removed outliers that fell outside a threshold of±2 standard deviations of the median.Within the section"Coral response to the 201S Marine Heatwave", prior to model fitting,we identified the presence of outliers in our predictor variables as any point that fell outside a threshold of±2 standard deviations of the median.We then applied an additional step to retain any point above this threshold that was within 2S% of the maximum predictor value below the threshold.This ensured that no data points were unnecessarily discarded from our formal model-fitting process because of applying an arbitrary threshold cutoff for data inclusion.We used the exact same process to identify and remove outliers within the section"Coral reefs four years post-disturbance"prior to formal model fitting. Reproducibility A description of the methodologies used is provided in the Methods and expanded on substantially for several ofthe human impact and environmental factors in the Supplementary Information.The data and full code necessary to reproduce the findings are available at https://github.comAamisongove/Coral-Reef-Persistence Randomization Survey sites were either randomly or haphazardly chosen by the various monitoring agencies involved in data collection.Sites were separated by a minimum distance(2SO m)and transects within sites were also separated by a minimum distance(S-10 m).To minimise observer bias offish counts,sizing calibration dives were conducted using fish models of known size at the beginning of each field season.Observer crossover training was done using two observers side by side when possible.Benthic cover estimates were quantified by randomly assigning 20 points to each image using post-hoc image analysis programs(Photogrid or Coral Point Count with Excel Extensions)and identifying the benthic group to the lowest taxonomic rank under each point. Blinding All in situ benthic and reef fish surveys were conducted prior to this research question being conceived.The divers carried out the • surveys for the most part without prior knowledge of the local human impacts and environmental factors for their respective survey locations—we later quantified these for each reef location and time ofsurvey,thus blinding in this respect was achieved.In some cases,divers were aware of any local fishing restrictions in effect,but this was unavoidable as many of them specifically survey inside and outside of these zones Did the study involve field work? ®Yes ❑No i Field work, collection and transport Field conditions Because of the nature of collecting underwater benthic information,field conditions must be relatively calm(i.e.,low wind and wave activity)with relatively good underwater visibility(i.e.,>5 m). • • Location Our study site was Hawai'i Island(19.55°N,155.66°W),USA,which is the southeastern most island of the Hawaiian Archipelago, located in the northern central Pacific.The western section has roughly 200 km of coastline that is predominately oriented north to south.The coastline contains the longest contiguous reef ecosystem in the main Hawaiian Islands and large gradients in human • population,local land-sea impacts,and environmental factors.The region represents an ideal study location for resolving the interacting land-sea human impacts driving reef ecosystem change and coral trajectories following acute climate-driven disturbance. All reefs included in this study were in shallow-water(depth<30 m). Access&import/export All survey data were collected with the knowledge and consent of the State of Hawai'i,which has legal jurisdiction of all waters from 0-3 nm of the shoreline.The director of the State of Hawai'i's Division of Aquatic Resources,which is the managing agency of State waters,contributed survey data and both a collaborator and coauthor on this manuscript. Disturbance All surveys were performed by professional scientific divers that aim to minimise contact and disturbance of the reef.No coral reef benthic or fish species were removed from their habitat as part of this effort Reporting for specific materials, systems and methods We require information from authors about some types of materials,experimental systems and methods used in many studies.Here,indicate whether each material, system or method listed is relevant to your study.If you are not sure if a list item applies to your research,read the appropriate section before selecting a response. Materials &experimental systems Methods n/a Involved in the study n/a Involved in the study ® ❑ Antibodies ® F-] ChIP-seq ® F� Eukaryotic cell lines ® F-] Flow cytometry ® F-1 Palaeontology and archaeology ® F-] MRI-based neuroimaging ® ❑ Animals and other organisms ® F-] Clinical data ® F-] Dual use research of concern