Loading...
HomeMy WebLinkAboutCOM 0377.315 2024-2026From: Maki Morinoue Sent: Sunday, August 17, 2025 9:19 PM To: Council Testimony Cc: Kimball, Heather, Kagiwada, Jennifer; Onishi, Dennis; Kierkiewicz, Ashley, Kanealii- Kleinfelder, Matt; Galimba, Michelle M.; Villegas, Rebecca; Inaba, Holeka; Hustace, James Subject: Please SUPPORT Res. 234-25 Attachments: Coral reef benefit from reduced land -sea impacts under ocean warmingwf o c a =cam Aloha Chair and Council Members, co, -nc- I stand in full support of Resolution 234-25 with the amendments made to pro�ct €h health, safety, and cultural integrity of Hawaii Island. This is a historic and inioorta'rrt: opportunity for our residents. I find this a historical and essential conversatio�to have, and applaud this opportunity to support something positive. Thank you. We ask that you unanimously Vote in SUPPORT of Resolution 234-25 that will help end Pohakuloa live fire and bombing practice, and further contamination and destruction of the landscape, soil and air quality in the Pohakuloa Training Area, rejecting land swaps and lease renewals with the military unless specific stipulations are established, with the U.S. Military adhering to our Hawai'i State Constitution and motto through the Ka Pa'akai Analysis on the area. I have been noticing a significant increase in the number of dead birds near the playground of the Gilbert Kahele Recreation Area. During a road trip to Hilo, I counted more than four dead birds in close proximity to each other in this park near the playground on two different occasions. I didn't have time to walk around the park, but I was curious if there was more to see, as it looked very unusual. I found it highly alarming that I no longer take my family to this park to play and rest. I hear the shootings and see military practice dropping things from the sky, and observe when it is dry, the dirt flying through the wind and blowing for miles. It gets on our car, and I stay in the car so as not to breathe the air near the Pohakuloa area. The Board of Land and Natural Resources recognized this danger when they denied the U.S. Army's Final Environmental Impact Statement in May 2025—its second failure to meet the requirements of our laws. The Army's recent, partial clean-up efforts are not acts of goodwill; they are strategic moves to secure more land and reframe their public image. True stewardship means consistent respect for cultural sites and environmental health —not compliance only when more land is at stake. Pohakuloa is culturally sacred. It is also centrally located at a high elevation and greatly impacts our island's ecological health. We cannot allow further poisoning of our land, air, and water under the guise of national defense. Protecting this area,is protecting our people, our culture, and our future. Comm. .3I Ref. To: 1 Ref. Date 9 I urge you to adopt Resolution 234-25 without delay. Attached is a scientific article from our island, titled "Coral Reefs Benefit from Reduced Land -Sea Impacts under Ocean Warming." Their land -to -sea impact data along the west side of our island. What we do on land impacts our water and ocean. It is connected. It's a fact we can not deny. Mahalo, Maki Morinoue Holualoa z Article Coral reefs benefit from reduced land -sea impacts under ocean warming https-//doi.org/l0.1038/s4l586-023-06394-w Received: 21 July 2022 Accepted: 30 June 2023 Openaccess ® Check for updates Jamison M. GoveL1®,Gareth J. Williams29®,Joey Lecky", Eric Browns, Eric Conklin', Chelsie Counselle, Gerald Davis", Mary K. Donovan)`, Kim FalinskP, Lindsey Kramer", Kelly Kozart", Ning Li", Jeffrey A. Maynard", Amanda McCutcheon'", Sheila A. McKenna'", Brian J. NeiLson", Aryan Safaie16, Christopher Teague'", Robert Whittier" & Gregory P. Asner'•1e 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 bleaching. 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 covertrajectoriespredisturbance. 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- ductive marine ecosystems on Earth'. Butwith four times the popula- tion density living within 20 km of the ocean compared to the rest of the world', direct human impacts on local scales are fundamentally restructuringthese important marine communitiess. Coastal areas are also affected by stronger and more frequent disturbances fuelled by human -induced climate change'. These human stressorsare especially acute on tropical coral reefs where up to 90%of the local population live alongtheshorelinem. Land -based stressors, such as wastewater pollu- tion, combine with sea -based stressors, such as overfishing, to disrupt natural ecological feedbacks on reefs". Corals are further stressed by prolonged periods ofanomalouslywarm ocean temperatures, known as marine heatwaves, that can cause mass coral bleaching and mortality and fundamentally transform reef assemblages". Reducing human impacts on local scales to maintain ecosystem integrity has been the guiding model of coral reef conservation for decades'- Its importance was established in the indigenous steward- ship of island ecosystems, which used a decentralized and integrated resource management strategy that extended from the mountains to theses"". By contrast, contemporary centralized governance means most terrestrial and ocean management efforts remain siloed417.0. As a result, wh ereas local resource managers have aspired to an integrated land -sea approach",evidenceofits efficacy above either approachin isolation remains wanting and difffcultto test. Detecting conservation benefits in highly dynamic ecosystems is challenging', but recent studies have identified salient connections between local conditions and coral reef resistance to and recovery potential following mass bleachingzu.a-'oManagers therefore require unambiguous targets for the combination of land -sea human impacts they should mitigate to support coral reef persistence under climate change. Hamperingthese efforts area lack of spatially resolved data on local drivers of coral reef ecosystems over time. Researchers are often forced to use proxies 'Pacific Island.Fisherle. 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 Amercan Samoa, Pago Pago, American Sanwa, USA. 'The Nature Conservancy,Honolulu,HI, USA. 'Cooperativelnstitute 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, Kealakekw, HI, USA.1ONationd Park Service. Pacific Island Network Inventory and Monitoring. Hami'l National Park, HI, USA. "Department of Ocean and Resources Engineering, University of Hawari atM`anoa, Honolulu, HI, USA. "SymbioSeas, Carolina Beach, NC, USA. "Hawarl Division of Aquatic Resources, Honolulu, HI, USA. "Graduate School of Oceanography, University of Rhode Island, Narragansett. RI. USA. "Hawaf! Department of Health, Honolulu, HI, USA. 1°Schootof Ocean Futures, Arizona State University, Hilo, HI, USA. 'Thew authors contributed equally. Jamison M. Gcve, Gareth]. Williams. ®email: jamison.gove@noaa.gov; g.lwilliams@bangor.ac.uk Nature I www.nature.com 17 Article O 051 LL Not R.._ '*rI sad rem�on Human Urban Wastewater Nutrient Sediment Peak Wave population A runoff A pollution A leading a Input A rainfall A exposure A Number of people Area Towleflluent Total nflrogen (103 within 15 lma (103 m3 hat) (1031 hat) (kg ha l) Permanent reef survey locations • Predisturbance o Response to 2015 marine heatwave Four years postdisturbance permanent reef survey data availability Sediment (103 kg ha -I) Rainfa0 (103 m3 hat) Wave power (kW m-1) Year I ,.pl �{I I I I I' I I I I' I 1 I ,^Temporalchange IqA ,�,9 Decrease®MMM Increase Predwwbance Disturbance Postdlstwbance Fig.11 Select local land -sea human Impacts and environmental factors on coral reefs in our study region in Hawal'l. a, Geographic location of the H awalian Islands. b, Study region with reefsurveys shown for the following: reef trajectories predisturbance in = 23; Fig. 2).coral response to the 2015 marine heatwave in = 90; Fig. 3)aad coral reefs four years postdisturbance in =55; Fig.4). c, Spatial distribution in annual, high -resolution (100 m) data on local human impacts and environmental factors from 2000to 2019 (coloured I ines). They axis represents distance along the coastline in kilometres from north to south along the study region in b.VerticaI be r re presents the change overtime(A) for each 100 msection along the coast. A change over time is high (H,A 250%), moderate (M, 0 > A <50%)or there is no change (NC,grey),with such as population density24•25 and reef accessibility`, or composite Indices such as'water quality"' that can be affected by anything from deforestation 21to aquaculture2B. Such proxiesdo not identifythepolicy levers local resource managers can pull and are less likely to result in management actions or successful conservation outcomes. Here we present a unique 20-year time series of land -sea human Impacts and environmental factors known to affect coral reef ecosys- tem processes across ourstudyregion in the Hawaiian Islands (Fig.1a). Human factors include urban runoff, wastewater pollution, nutrient loading, sediment input and local restrictions on types of Fishing gear. Environmental factors include peakand annual rainfall,wave exposure, variability in ocean temperatures and heat stress, irradiance and phyto- plankton biomass. We also incorporate multiple fish biomass metrics that represent the critical role reef fish play in maintaining coral reef ecosystem function 39-J1(see Extended Data Table 1 for a full list of factors). We combined thisdatasetwith recurring, permanentlymarked and site -specific underwatersurvey data on coral reefbenthiccommuni- ties (Fig.1b).Our study reefs spanned large spatiotemporal gradients In land -sea human impactsand environmental factors (Fig.1c) that are comparableto coral reefecosystemsglobally (Extended Data Fig.1), and which experienced the most severe marine heatwave on record in the blue hues indicating de creases and red hues Ind icatingIncreases. Change Is based on the mean difference between the firsts years (2000-20.04) and the mostrecent5 years (2015-2019) In the time series. This accounted foryear-to-year variability in the episodic nature of factors such as wave exposure, rainfall and sediment input. Asubset of factors Is shown in c owing to space constraints. Additional factors (not shown) include annual rainfall, phytoplankton biomass, ocean temperature (mean and variability), heat stress, irradiance, fishing gear restrictions, depth and metrics of fish biomass. The distribution, change over time and variabilityof all factors are shown in Supplementary Fig.1. See Extended Data Table 1 and Sup p lem e n tary Information for detailed Information on local land -sea human impacts and environmental factors. Hawaiian Islands (Extended Data Fig.2). We quantified drivers of coral reef benthic change atthe scale of individual reefs over 12 years before disturbance (2003-2014), duringand immediatelyfollowingthe marine heatwave (2014-2016) and four years postdisturbance (2016-2019).Our findings show that simultaneously mitigating local human impacts on both land and sea supports positive coral covertrajectories in the absence of periodic acute disturbance, reduces coral loss during a marine heat - wave and promotes coral reef persistence following severe heat stress. Reef trajectories predisturbance Coral coveramong reefs surveyed in 2003was 36.9 ± 2.3% (mean ± s.e.; n = 23) and changed byless than 3% in thesubsequentyears leading up to the2015 marine heatwave (Fig.2a). However, coral covertrajectories on individual reefs varied considerably over this time period: 44%of reefs showed a positive trajectory (that is, increased coral cover), 35% of reefs showed a negative trajectory (that is, decreased coral cover) and the remaining reefs showed no change (Fig.2b).To the best ofour knowledge, noacutedisturbance occurred that can explainthese diver- gent trajectorles.Yet,wedid find distinctdifferences in local conditions between positive and negativetrajectory reefs in the years before and 2 1 Nature I www.nature.com 0.30 -20M -20W 0.25 - 2011 - 2014 N 0.20 0 0.15 C 0 0.10 ° Ran9eln 0.05 mean cover Coral cover (%) -0.2 f 20 •PoshKe taifcory ,,... 15 • Ne9ative imiectory10 _ t 8-5 -0-10 -15 l_"-- -20 Year ® • M so • • so -at 0 0.1 0.2 0.3 Squared ranoniml conelation (83.2%) 20.0 100 20 15 10 0.5 0 05 1.0 1.5 10.0 J q 4 I iTa,9asad + °e96aB ac Sao maPy�`��a3a0��c P 3e a a�0 Ca S W 3o0e, vP �cC T3 .va o �a Fig. 21 Reef trajectorlespredisturbance and associated local land -sea human Impacts and environmental factors. a, Coral cover distributions among surveyed reefs between 2003 and 2014 (n = 23). b, Coral cover trajectories of! ndividual reefs. A reeFwas considered on a positive trajectory (blue; n=10) or negative trajectory(red;n= 8) lfcoralcoverbetween 2003and 2014changed by more than 3%. This cut-off was based on mean coral cover range amo ng all23 reefs for the 12•year p redisturbance period (range 2.8%;min 34.1%; max36.9%): Reefs with nocoral coverchange (within±3%) are notshown. c, Difference in local conditions between positiveversus negative trajectory reefs (PERMANOVA, pseudo-Ft,e=3.38,P= 0.001) visualized alonga single muitivariate axis (capturing the multidimensional and correlated nature of the data, Supplementary Fig.2) usinga canonical analysisof principal coordinates (n= same as In b). Allocation success equalled 90 and 87.5%forpositive and negative trajectory reefs, respectively (more than50%indicates an Inclusive of this time frame (Fig.2c). For example, theaverage biomass of all fishes, all herbivorous fishes and groups of herbivorous fishes that fill important ecological roles such as scrapers,grazers and browsers30 were 24-113%(29-214 kg ha-') greater on reefswith positivetrajectories compared to thosewith negativetrajectories (Fig.2d and Extended Data Fig, 3). These patterns probably reflect positive feedbacks, whereby increasing coral cover promotes habitat suitability for reef fishes,with herbivorous fishes then facilitating coral growth by reducing com- petitive exclusion by Fleshy algaeS2. By contrast, wastewater pollution, nutrient loading and urban runoff were 46-80%greater on reefs with negative trajectories compared to those with positive trajectories. Despitethese land -based human stressors being comparatively higher on reefswith negativetrajectories, reefswith positivetrajectories had 63%greater human population density (the number of people within increasingly more distinctset of conditions than expected bychancealone). d, Mean difference (dots) in drop•onejackknife values with upper and lower bars representing the respective maximum and minimum differences in local human impacts and environmental factors between positive and negative trajectory reefs (n=sameas In b). Blue and red shaded regions Indicate factors that were greater on reefs that had positive and negative trajectories, respectively.Zero line representsequal values. SeeExtended Data Fig.3 for the percentage differencein local conditions between positive and negative trajectory reefs. We )ncludedall local human impacts and environmental factors in dto provide a general comparison of local conditionsbetween reefs withdivergenttrajectories. See Fig. lb for reefiocations and Supplementary Fig.3 forpredictor variabledistributions. See Methods, Extended Data Table I and Supplementary Information fordetailed information onlocai land -sea human Impacts and environmental factors. a 15 km radius). This finding supports the notion that human popula- tion density is a poor indicator of human -driven land -sea impacts at local scales". We observed minimal differences between positive and negative trajectory reefs relative to fishing gear restrictions, depth, sediment input, ocean temperatures, phytoplankton biomass and rainfall. Wave exposure was slightly higher (8.6 kW m) on reefs with positive trajectories, but the difference is minor because the entire study region is generally protected from large wave events34. Coral response to the marine heatwave In 2015, the Hawaiian Islands experienced the strongest marine heat - wave on record over the past 120 years (Extended Data Fig. 2).Ocean temperatures across our study region were 2.2 °C above normal and Nature I www.nature.com 1 3 Article 40 a Year 2 0.3 0 0.2 Y fl o.1 n` 4p po3o ryo �o o ,so R, Coral cover change (%) DHW CC -weeks) '/4 0.09 0.12 0.1a Pbyloplankton (mg m-) Urban mnoff (ms her) 25 -75 (2014/2015) (2016) \ oaten, marine After marine 0 39 625 3,16410,0 heatwave heatwave Sediment input (kg ba-1) peaked at29,4eC (Fig.3a). Degree heatingweeks (DH Ws), a widelyused heat stress metric for coral reefs, averaged 12 DHWs among surveyed reefs (Fig.3b), far exceeding the eight DHW threshold expected to cause severe and widespread coral bleaching and mortality35. Reef sur- veys performed one year following the marine heatwave showed that nearly one-quarterof reefs (19 out of 80) lost more than 20%coral cover whereas the hardest -hit reef lost 49%(Fig.3c). But not all reefs expe- rienced such catastrophic change. Coral cover remained unchanged or increased on 18% (14 out of 80) of reefs surveyed. This divergent ecological response was unexpectedgiven that all reefs were exposed to similarly extreme levels of heat stress (Fig. 3b). Interactions between heat stress and local co nditions such as a high abundance of competitive macroalgae can exacerbate coral bleaching andm rrtalityu.However, we lack adetailed understandingoftheland- and sea -based factors that mediate coral response to marine heatwaves. Using a generalized additive mixed -modelling framework, we Identi- fled the land -sea factors that best explained variations in coral cover change (accounting for starting cover) among reefs one yea rafter the 2015 marine heatwave in Hawai'i (Fig. 3d and Extended Data Table 2). Fig.31 Local land -sea human Impactsand environmentalfactorsthat modified coral response to the 2015 marine heatwave. a, Historical (1986-2019) SSTs during the seasonal peak (July -December) averaged across the study reglon; 2015 marine heatwave shown in red. b, Maximum DHW exposure in 2015, a common heat stress metric, among surveyed reefs. All reefs exceeded the eight DHW threshold expected to produce severe and widespread coral bleaching and mortality. c, Cora Icover before(2014-2015) and one year following(2016)the marine heatwave among surveyed reefs In = 80, Fig.1b). The Inset represents the distribution of absolute coral cover change. d, The GAMM results (R2= 0.79) showingkey factorsexplaining coral response to the marine heatwave. Change accounts for starting condition, defined as: percentage difference= ((Am-Asa)lA,,,l) x 100,whereA,andA,are the mean coral cover values at each reef in 2014 or 2015, and 2016. respectively (Methods and Supplementary Fig.4). Positive and negative relationships reduce or increase coral loss,respectively. Shaded regions represent80% confidence intervals. Factors with the strongest model averaged slopes are shown. Total fish biomass and scraper biomass were also important factors in our models but had weak slopes (representing less than 5%change; Extended Data Fig.4). Relative Importance of factors among all models (that Is, sum of AICc model weights across all models containing each factor) were: sediment Input (0.99), scraper biomass (0.99), total fish biomass (0.90), urban runoff (0.60), phytoplankton biomass (0.38), wastewater pollution (0.28), peak rainfall (0.20), nutrient loading (0.19), grazerblomass (0.16), DHW (0.08), wave power (0.07),depth (0.06) and fishinggear restrictions (0.05). See Extended Data Table lfor full listof factors included in the analysis, Including those removed that were highly correlated 10.7, see Methods and Supplementary Fig.5). See Supplementary Fig.6 for predictor variable distributions. Coral bleaching involves the breakdown of the mutualistic rela- tionship between the coral animal and its algal endosymbionts3A A prolonged breakdown in this relationship often results in coral star- vation and death, as much of the energetic demands of corals are met by the photosynthetic activity of its endosymbionts36. We found that reefs with the highest levels of water column phytoplankton biomass (that is, chlorophyll -a) during the marine heatwave showed reduced coral mortality (Fig.3d). Productivity increases nearshore to tropical islands such as Hawa i i37 and is further concentrated by small-scale ocean processes that attract dense aggregations of plankton 38. The increase in nutritional subsidies to the coral animal may have helped to reduce coral starvation during the heatwave or provided higher energetic reserves that promoted their recovery39.1 n other regions (for example, Great Barrier Reef), high levels ofchlorophyll-care an indica- tor of poor water quality that drives negative outcomes for corals"' 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. Workingtowards locally relevant management strategies requires understanding how human impacts superimpose on natural biophysical drivers, such as phytoplankton biomass`, to influence reef ecosystem response to acute disturbance. Coastal runoff can deliver a broad spectrum of land -based contami- nants that degrade nearshore water quality, with cascadingeffectson coral health". We found that reefs exposed to the lowest levelsofurban runoff, and to a lesser extent sediment input, experienced a modest reduction in coral mortalityfrom themarine heatwave (Fig.3d). Urban runoff often contains heavy metals and petrochemicals that cause coral tissue death42 and sediment input can impede the photosyn- thetic capacityofcorals and reducegrowth byburying coral colonies''. Together, these stressors can undermine the natural defence abilities of corals and increase the likelihood of mortality from heat stress40. Although turbid waters may shade corals from excessive sunlight that can exacerbate coral bleaching, high levels of heatstress can override any protective benefits decreased light may provided. Existing but underused local and national policies such as the Clean Water Act in the United States provide actionablepathways for marine management 4 1 Nature I www.nature.com a 0.30 0.25 N N e 0.20 6 0 ? 0.15 e 6 0.10 0.05 810 640 490 a 360 `a 250 6 4i 160 N a V 90 9 40 �10 1 Cover of reef -building organisms (%) Resource Probability management scenario Low Moderate High Initial mmiltlon, © 0.83 0.17 0.02 Sea -based only 0 0.30 0.70 0.14 Land -based only 0 0.17 0.83 0.26 Integrated land -sea 0 0.02 0.98 0.80 a� Scraper biomass (kg he 4004 Fig. 41 Local managementscenarlos thatsupport coral reef persistence four years postdisturbance.a, Percentage cover of reef -building organisms (hard coral+crustose coralline algae)among reefs surveyed (n =55) in 2019, four years follow] ngthe marine heatwave. Colours rep resent low (525th percentile), moderate (>25th and <75th percentile) or high 0!75th percentile) cover. It, Probability of low, moderate or high cover o f reef-b antlers shown in relation to variations Inscraper biomass and wastewater pollution. Example scenarios show that slatultaneously decreasing wastewater pollution and increasing scraper biomass results in a far greater probability of high reef-buIIder cover (scenario'C')than ach leving efthe r management scenario in isolation (scenarios Nand'B'). The upper (250 kg ha')and lower (30 kg ha') management scenarios for scraper biomass represented the 92nd and 36th percentiles, respectively. We specificallychose 250 kg he-' as it approxi mares the long-term mean (2003-2019; a=17) scraper biomass in Kealakekua Bay, a marine protected area In our study region where no if shing has been allowed since 1969 (Supplementary Fig. 11). Similarly, the upper (600,0001 he-') and lower (2,5001 h-1) management scenarios chosen for wastewater pollution represented the 95th and 36th percentiles of the 2019 distribution, res pectively (Sup pie mentary Fig. 12). Probabill ty values and lines were derived from the top model from ordinal logistic regression modelling (Extended Data Table3, Methods and Supplementary information). Colours for low, moderate and high In bare the same as those in a. See Extended Data Table 1 forfull list of local land -sea human impacts and environmental factors included in the analysis, including those removed that were highly correlated (r>0.7, Methods and Su p plemen tary Fig. 8). See Supp le me mary Fig.9 for pred ictor variable distributions. interventions of land -based stressors44. Management strategies that leverage such policies to help mitigate coastal runoff, particularly in urban areas, may support increased coral survival during severe marine heatwaves. We also found thattotal fish biomassandscraperbiomasswere impor- tant factors in our models (Extended Data Table 2). Healthyfish popu- lations provide numerous reef -scale ecosystem functions', including somespecies releasingbenef icial nutrientsubsidies that increase coral thermal tolemnce4s- Scrapers remove fast-growing algal turfs that could otherwiseoutcompete and overgrow stress -compromised coraIS30. By comparison to phytoplankton biomass and coastal runoff, the slopes of the relationships between total fish biomass and scraper biomass with heat -driven coral loss were weak (Extended Data Fig.4). Intense marine heatwaves can cause severecoral mortality even on highly pro- tected, uninhabited reefswith intact fish populations46, suggesting that extreme heat stress maysimply overwhelm the functional roles of reef fish overshorttime scales. However, abundant fish populations, in par- ticular herbivores, can support coral reef recovery potential following disturbance=. Understandingwhether this positive relationship holds acrossgradients in land -based impactsis key for supporting targeted fisheries management in coastal marine ecosystems. Coral reefs fouryears postdisturbance The dominant reef -builders in tropical coral reef ecosystems are hard corals and crustose coralline a Igae25. Crustose coralline algae are encrusting calcifying algae that fuse the reef framework together and promote coral growth by servingas a successional prerequisite for coral recruitment and suppressing competitive Fleshy algae'-`. Given that coral cover can taken decade or more to recover to prebleaching levels47, assessing the total cover of reef -building organisms (hard coral +crustosecomllinealgae) is more indicativeofcoral reef recovery potential followingdisturbance. Oursurveys four years following the 2015 marine heatwave found that reef -builder cover ranged from 3.4 to 51.9%(mean of 24.3%± 1.7 s.e.; n =55; Fig.4a). Critically, there were different reefswith high (more than orequal to the 75th percentile) and low (lessthan or equal to the 25th percentile) reef -builder cover before and after the marine heatwave. Nearly two-thirds of reefs with high reef -builder cover in 2019 did not support such levels of cover before the marine heatwave. Similarly, we observed a more than 40%change in the location of reefs with low reef -builder cover between 2015 and 2019. This reshuffling of reefs in terms of relative reef -builder cover suggested differential coral reef persistence in the years following severe heat stress. We used an ordinal logistic regression framework to identify the local land -sea human impacts and environmental factors that best supported coral reef persistence in theyears followingthe2O15 marine heatwave. Decreased wastewater pollution and increased scraper biomass were the most important and significant (P<O.O5) in pre- dicting whether a reef had relatively higher reef -builder cover four years postdisturbance (Extended Data Table3). Pollution from human waste affects coastal marine ecosystems globally4' and is especially harmful to corals from untreated sources, such as septic tanks and cesspools, which are both common in Hawai'i49- Consequently, high concentrations of toxinsand pathogens leach into coastal waters that increase coral disease, reduce coral growth and reproduction, and increase coral susceptibility to bleaching42. These negative impacts on coral persistence aretherefore much reduced in areas of decreased wastewater pollution.Scrapers reveal bare substrate as they feed and facilitate the settlement, growth and survival of crustose coralline algae and corals following acute disturbanceJO. Beyond these top -down effects on benthic condition, bottom -up effects of improved habitat quality could becontributing to the positive relationship we observed between scraper biomass and higher reef-buildercover. Parrotffsh are the dominant scrapers in Hawai f, 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 were derived from multiple observations across several time points followingthe marine heatwave, rather than a singlesnapshot estimate. Such strong site -based fidelity, combined with our recurring surveys, suggests that resident scrapers played a key role in promoting higher reef -builder cover ratherthan theassociation driven purely byan influx of individuals seeking more favourable habitat postdisturbance. Sea -based management efforts are often disconnected from those occurring on land" ". We generated management scenarios of how varying scraper biomass (sea -based management) and wastewater pollu- tion (land -based management) influenced the probability of being in a low, moderate (more than the 25th and less than the 75th percentile) or high reef -builder cover category. Our findings indicate that an inte- grated management approach can result in a positive synergistic outcome for coral reefs (Fig. 4b). For example, four years following the marine heatwave, a reef across our study region with low scraper biomass (forexample, 30 kg ha 1) and relatively high wastewater pollu- tion (forexample, 600,000 I ha-') is most likelyto have low reef -builder cover (83% probability) (Fig. 4b,'initia I condition'). Where scraper biomass is higher (forexample, 250 kg ha') butwastewater pollution remains high, there is a 70%probability of moderate reef -builder cover (scenario A). Conversely, where wastewater pollution is lower (for example, 2,5001 ha'), butscraper biomass remains low, there is an 83%probability of moderate reef -builder cover (scenario B). However, if both land and sea managementscenariosoccur, there isan 80% prob- abilityof 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 cover four years following severe heatstress than if land or sea were managed in isolation. Conclusion Hereweshowthat simultaneouslymitigating local land- and sea -based human impacts promotes coral reef persistence before, during and in the years Following a historically unprecedented marine heatwave in Hawai'i.Our unique spatially and temporally resolved data highlighted the specific impacts that bestcorrelated with coral reef persistence m each ofthesetempoml periods. Forexample, thebiomassof all reef -Fish groupswas associated with positive reef trajectories overthe 12 years leadingup tothe marine heatwave. By contrast,scraperbiorrasswas the only fishgroupassoclated with positive outcomes for reefs four years - following severe heat stress. This suggests that reef fish play essential functional roles at different points in time and that particular feeding andbehaviours are probably critical for reef persistence followingacute distrubanceJO. Similarly, land -based impacts consistently emerged as driving negative coral reef outcomes, but the combination of stress - ors changed depending on the observational time window in ques- tion. Highly resolved data on the local human impacts that drive reef ecosystem trajectories over time are unlikely to be available in most regions. However, our overarching finding that integrated land -sea managementbenef its coral reefs under ocean warming, is applicable to populated reefs globally. The local human impacts we identify here represent the direct or proximate drivers of reef condition in our study. These in turn are dic- tated by an array of distal socioeconomic and cultural factors such as human migration and urbanization, finance, trade and tourism that indirectly affect how people interact with coral reefs'-'. Distal human drivers also underpin climate change that Is driving severe marine heatwaves that trigger mass coral bleaching at global scales. Increases In future ocean temperatures and the frequency and sever- ity of coral bleaching events"could simply overwhelm the positive effects of local management actions on coral reefs. However, there is substantial variation in the projected rates of ocean warming within and among countries under reduced emissions scenariossz, Actions thatsupport coral reef persistence locally alongsideglobal reductions in greenhouse gas emissions may buy reefs more time to adapt and persist into the future. Contemporary governance most therefore shift towards an integrated approach to align management strategies with reef ecosystem processes and the coincident multiscale human drivers that affect them'. An ambitious effort is underway to protect 30% of Earth's land and sea areas by 2030 as part of the recently adopted Kunming -Montreal Global Biodiversity Frameworks. The motivation behind the'30 by 30' is to support ecological resilience, conserve bfodiversityand preserve ecosystem services that underpin human well-being53. The 30 by 30 has broad participation and is being incorporated into conservation efforts by nations globally. However, our results reveal that sea -based managementalone is insufficient to mitigatethe full spectrum of local human effects on coastal ecosystems such as coral reefs. These efforts must therefore explicitly couple the respective 30% land -sea targets to realize coastal ocean conservation goals. But in most coastal geo- graphies, 30% protection is impractical and unethical given the high proportion of peoplethat live near and depend on these ecosystems54. Instead, mitigating land -based impacts such as wastewater pollution must occur together with fisheries governance for successful con- servation outcomes, akin to long-standing indigenous stewardship practices of island ecosystems". only by adopting coupled land -sea policy measures, alongsideglobal emissions reductions, will coral reef ecosystems and the human communities they support have the best opportunity for persistence in our changing climate. Onlinecontent Any methods, additional references, Nature Portfolio reportingsumma- ries, source data, extended data, supplementary information, acknowl- edgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi.org/10.1038/s4l586.023-06394-w. 1. Hughes, T. P. at at. Coral reefs in the Anthropocene. Nature 546, 82-90(2017). 2. Graham, N. A. I., Jennings, S., MacNeil. M. A. Mouillot,D.& Wilson, S. K. Predicting climate -driven regime shifts versus rebound potential in coral reefs. Nature518, 94-97 (2015). 3. McLeod, E. at A The future of resilience -based management in coral reef ecosystems. J. Environ. Manage. 233, 291-301(2019). 4. Tallaard,S. at aL Implementing integrated waaet management in a sector -based governance system. Ocean Coast. Manage. 67, 39-53 (2012). 5. CBD.Kunming-Montmal Global Biodiversity Framework. In Nco. Conference ofparlJes to the Convention on Biological Diversity Fifteenth Meeting CBD/COP115/L25(2022). B. Tittenso, D. P, at at. Global Panama and predictors of marine biodiversityacross tam Nature 466,1098-1101(2010). 7. Kummu,M. at al. Over the hills and further away from coast: global geospatial patterns of human and environment over the 20th-21st centuries. Environ. Res. Lett. 11, 034010 (2016). B. He, O.&Silliman,B.R. Climate change, human impacts, and coastal ecosystems in the Anthropocene. Corr. Siol. 29, R1021-RIO35(2019). 9. Doney,S.C.at at. Climate change impacts on marine ecosystems. Annu. Rev. Mar. Sol. 4, 11-37(2012). 10. Andrew, N. L., Bright, R, de la Rost, L.,Tech, S.1.& Vickers, M. Coastal proximity of poputo bore in 22 Pacific Island muntries and territories. PLo.S ONE 14, a 0223249 (2019). 11. MacNeil, M. A. at at. Water quality mediates resitience on the Great Barrier Reef. Nat Ecol. Evd 3, 620-627(2019). 12. Oliver, E. C. J. at al. Longer and more frequent marine heatwaves over the past century. Nat. Commun. 9, 1324(201 B). 13. Hughes, T.P.at at. Global warming and recurrent mass bleaching of coral. Nature 543, 373-377(2017), 14. Hughes, T. P. at at. Global warming transforms moral reef assemblages. Nature 556, 492-495(201B). 15. Edgar, 0.J.at at. Continent -wide declines In shallow reef Ufa over a decade of ocean warming. Nature 615, BSB-865(2023). 16. Winter, K.B. et at Indigenous stewardship through novel approaches to collaborative management in Hawari. E.I. Soc.28, 26 (2023). 17. Sandin,S.A. at at Harnessing islend-ocean onnnsaticns to maximize marine benefits of island conservation. Proc. Nad Aced. Si. USA 119,.2122354119 (2022). 18. Halpern, R. S., Lester, S. E. & McLeod, K. L. Placing marine protected areas onto the ecosystem -based management seascape. Prop. Natl Arad. Scf. USA 107, 1B312-18317 (2010). 19. Marshall, P. A., Schuttenberg, H. Z.&WestJ. M. A Reef Manager's Guide to Coral Bleaching (Grant Battier Reef Marine Park Authority, 2006). 6 1 Nature I www.nature.com 20. Mumby,P.J., Chaloupka,M.,Bozec,Y:M.,Smnmk,R.S.&Montero Sam, I. Revisiting the evidentiary basis for ecological cascades with conservation impacts. Conserv. Lett. 15, el2847(2022). 21. Aerer,G.P. eteL Mapped coral mortality and refugla in an archipetego-slate marine heat wave. Proc. NatfAcad. Scl. USA 119,.2123331119 (2022). 22. Donovan. M. K. at at. Local conditions magnify coralloss after marine heatwaves.Science 37Z 977-980 (2021). 23. Baum, J.K.at at. Transformation of coral communities subjected to an unprecedented heatwave is modulated by local disturbance.So/. Adv. 9, eabg5615(2023). 24. Williams,G. J., Gave, 1. M., Eynaud, Y., Zgllezym i, B. J.& Sandin, S. A. Local human impacts decoupte natural biophysical relationships on Pacific carat reefs. Ecagmphy 38, 751-761(2015). 25. Smith, J. E. at .1. Re-evaluating the health of carat reef communities: baselines and evidence for human impacts across the central Pacific. Prcc. R. Sm. B. 283, 20151985 (2016). 26. Maire,E.at at. How accessible are coral reefs to people? A global assessment based on travel time. Scot Lett 19, 351-360 (2016). 27. Maine. J. at al. Human deforestation outweighs future climate change impacts of sedimentation on coral reefs. Nat. Commun. 4,1986 (2013). 28. Hozumi, A.,Hong, P. Y., Kaaravedt, S., Resisted, A.&Jones, B. H. Water quality, seasonality, and trajectory of an agtlawllure-wastewater plume in the Red Sea. Aquacult. Envimn. Inter. 10, 61-77(2018). 29. Brandt, S.J. at al. Coral reef ecosystem functioning: eight core processes and the role of biodiversity. Front. Ecol. Environ. 17, 445-454 (2019). 30. Bellwood, D.R.,Hughes, T.P., Folke,C.&Nysirfim, M. Confronting the coral reef crisis. Nature 429,827-833(2004). 31. Cinnee J. E. at al. Meeting fisheries, ecosystem function, and biodiversity, goats in human -dominated world. Science 368, 307-311(2020). 32. Rarec,Y:M., Yakob,L., Bejamno, S. & Mumby, P. 1. Reciprocal facilitation and nondintsrity maintain habitat engineering on coral reefs. Oikoe 12Z 428-440 (2013). 33. Cinner, J. E., Graham, N. A. J., Huchery, C.&MacNeil, M. A. Global effects of local human population density and distance to markets on the condition of coral reef fisheries. Comerv. Sint. 27,453-458(2013). 34. Stops,J.E. at aL Wave energy resources along the Hawaiian Island chain. Renew. Energy SS, 305-321(2013). 35. Skirving, W. at at. CoralTemp and the Coral Reef Watch Coral Bleaching Heat Stress Product Suite version 3.1. Remote Sens. 12, 3856 (2020). 36. Glynn, P. W. Coral -reef bleaching- ecological perspectives. Coral Reefs 12, 1-17(1993). 37. Gave, J.M. at al. Near -Island biological holspots in barren ocean basins. Nat. Commun. 7, 10581(2016). 38. Whitney. J.L. at al. Surface slicks are pelagic nurseries for diverse ocean fauna. ScL Rep. 11, 3197 (2021). 39. Grettaii,A.a,Rodrigues, L.J.&Pelardy,J.E. Heterotrophic plasticity and resilience in bleached corals. Nature 440,1186-1189 (2006). 40. Wooldridge, S. A. Water quality and coral bleaching thresholds: formalising the linkage for the inshore reefs of the Great Barrier Reef, Australia. Mar. Polite. Bu1L 58, 745-751(2009). 41. Fabriclus.K.E.Effects of terrestrial runoff on the ecology of corals and coral reefs review and synthesis. Mar. Pollan. Buff. 50, 125-146 (2G05). 42. Nalley,E.M.at al. Water quality thresholds for coastal contaminant impacts on corals: a systematic review and meta -analysis. W. Total Environ. 794,148632 (2021). 43. Carlson, R. R., U, J., Crowder, L. B.&Asrrer, G P. Large-scale effects of turbidity on coral bleaching in the Hawaiian islands. Front. Mar, Sci. 9, 969472 (2022). 44. Carlson, R.R.,Foo,S.A., Bums. 1.H.R.&Asrrer,G.P. Untapped policy venues to protect coral reef ecosystems. Proc. Nall Aced. Sol. USA 119, e2117562119 (202Z). 45. Shantz, A.A. at at. Positive interactions between ..rate and damaelfish increaseeoral resistance to temperature stress. Glob. Change Biol. 29, 417-431(2023). 46. Vargas -Angel, B. at at. El Nino -associated catastrophic carat mortality at Jarvis Island. central Equatorial Pacific. Coral Beefs 38, 781-741(2019). 47. Gilmour, J. P. Smith, L. D., Hayward, A. I., Baird, A. H.& Pratchett, M. S. Recovery of an isolated coral reef system fallowing severe disturbance. Science 340, 69-71(2013). 48. Tuhotske,C. at al. Mapping global inputs and impacts from of human sewage in coastal ecosystems. PLaS ONE 16, e0256898(2021). 49. Messacapo,M.at at. Review article:Hawafys cesspool problem: review and recommendations for water resources and human health. J. Cont. War. Res. Ed. 170, 35-75 (2020). 50. Meyer, C.G., Papastamatiou,Y.P.& Clark, T.B. Differential movement patterns and site fidelity among trophic groups of reef fishes in a Hawaiian marine protected area. Mar. Skit 157,1499-1511(2010). 51. Clutter, J.E.&Kitfinger,J.N. in Ecology of Fishes on Coral Reefs (ad. Mora, C.) 215-22D (Cambridge Univ. Press, 2015). 52. van Hmidonk. R. at at. Local -scale projections ofcoral reef futures and implications of the Paris Agreement. SoL ReA 6,39666 (2016). 53. Dinerstein, E. at at. A global deal for nature: guiding principles, milestones, and targets. &I. Adv. S, eaaw2869(2019). 54. Obura, D.O. at at. Achieving nature. and people -positive future. One Earth 6,105-117 (2023). Publisher nests Springer Nature remains neutral with regard to Jurisdictional claims in published maps and instiunianal affiliations. cc- f Open AccessThisarifcle is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or formal as long as you give appropriate credit to the original authors) and the source, provide link to the Creative Commons licence, and Indicate if changes were made. The images or other third party material in this article are included In the article's Creative Commons licence, unless Indicated otherwise In a credit line to the material If material is not included in the article's Creative Commons licence and your intended use is not Permitted by statutory regulation or exceeds the permitted use, you will need Wobtain permission directly, from the copyright holder. To view a copy of this licence, visit hup://crwa vecorvnons.org/Ucems/by/4.0/. ®This is US. Government work and not under copyright protection in the US; foreign 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 Hawaii specific parameters' and FishBase65 Fish Study site Hawai'i Island (19.550 N,155.660 W) is the southeastern most island of the Hawaiian Archipelago, located in the northern central Pacific (Fig.1). The western section has roughly 200 km of coastline predominantly oriented north to south.The coastlinecontainsthe longestcontiguous reef ecosystem in the main Hawaiian Islands"s and large gradients in human population, local land -sea impacts and environmental factors thatarecomparable to reefecosystemsglobally (Extended Data Fig.1). The region represents an ideal study l ocati on for resolving the land -sea human impacts driving reef ecosystem change and coral trajectories following acute climate -driven disturbance. Reefsurveys Full details related to sampling design, site selection and survey frequency for benthic and reef -fish data collection across our study region are in the Supplementary Information. In brief, underwater visual surveys of benthic assemblageswere collated from three moni- toring programmes for the followingyears (number of reefs surveyed are in parentheses): 2003 (23), 2007 (23), 2011(23), 2014 (40), 2015 (40), 2016 (80), 2017 (80), 2018 (15) and 2019 (55). All benthic surveys used permanently marked pins to ensure the sa me area of reef was surveyed overtime. High -resolution photographs were collected by using photoquadrats at 1 m intervalsalong 25 m belt-transects (n = 26 photographs per transect). Between 30 and 50 random points were overlaid on each photograph and the benthic component under each point was identified to the lowest possible taxonomic level. Percent- age cover of the major functional groups at each reefwere used in this analysis, namely hard coral and crustose coralline algae. Surveys of reef -fish assemblages were performed along the same permanently marked 25 m transects concurrently with benthic surveys. However, reef -fish surveys were performed more Frequently (one to six times per year from 2003 to 2019) than benthic surveys, depending on the reef location and monitoring programme performing the surveys. In all surveys, fishes were identified to species, sized and enumerated. To account for differences among programmes in how researchers surveyed reef fish, counts were calibrated using species and method specific adjustmen&6. Local land -sea human impacts and environmental factors Fish biomass. The biomass of fishes at a given reef was measured as total fishbiomass, herbivore fish biomass and the biomass of browsers, grazers and scrapers56. Total fish biomass is an indicatorof the overall state of the Fish assemblagenand is reduced in areas thathave increased Fishing pressure". In Hawaii, non-commercial nearshore fisheries dominate, with people fishingforrecreational, subsistence and cultural purposes6 '. However, the dominant harvesting modesand magnitude of fishing activities are largely unknown at spatial or temporal scales relevant to this study62. As such, we include total fish biomass in part to represent fishing effort on reefs but recognize its shortcomings in capturing reef- and species -specific differences in Fishing pressure across our study region. We also include herbivores and subdivisions by feeding guilds that represent important indicators of resilience on coral reefs"'b'. Browsers are defined as herbivores that feed on macroalgae and associated epiphytic material, and are important for reducing the cover of larger, more established macroalgae. Grazers are herbivores that feed largely on small algal turfs, helping to prevent their succession into larger macroalgae, and scrapers are herbivores thatcloselycrop the substrate and open up new space to promote the settlement,growth and survival ofcrustose comllinealgae and corals'0 We followed established methods for calculating fish biomass" The biomass of Individual Fishes was estimated using the allometric length -weight conversion: W =aT L', where parameters a and b are species -specific constants, TL is total length (cm) and W is weight (g). specieswere excluded from fish biomass calculations according to life history characteristics that are not well captured with visual surveys, Includingcryptic benthic species, nocturnal species, pelagic schooling species and manta rays. Human population. We quantified human population density using NASA Gridded Population of the World v.4 (ref.66). The dataset is avail- ableatl km resolution at5-year Intervals. Linear interpolation was used to fill in the missing years and produce annual time steps of human population within 15 km ofeach 100 mgrid cell across ourstudy region (Supplementary Fig.12). Wastewater pollution. We calculated wastewater effluent (I ha ' yr) and nitrogen input (kg ha ' yr ') from onsite sewage disposal systems (for example, cesspools and septic tanks) and injection wells (collec- tively OSDS) in coastalwaters at 100 m resolution. OnlyOSDS located within a modelled one-yeargroundwater travel time of the coast were included in the analysis and nutrients from OSDS were assumed to Flow to the nearest point on the shoreline. Wastewater effluent and nutrient inputwere estimated on thebasis of ref. 67and discharge rates and nutrient loading according to ref. 68. A Gaussian decay function was used to estimate dispersal offshore, approaching zero at 2 km (SupplementaryFigs.13-15).This samedispersal function wasalsoused for nutrient input, urban runoff, sediment input and rainfall, which are each described below. Nutrient input. We calculated nutrient input (kg ha ' yr ') at 100 m resolution as the combination of total nitrogen from OSDS (Waste- water pollution section above) and golf courses. The total golf course area per watershed was derived from NOAA Coastal Change Analysis Program (CCAP) land -use and land -cover data and Lan dsat cloud -Free composite images created with Google Earth Engine. The golf course area was multiplied by an annual nitrogen appiication rateof585 kg ha-' (refs.69,70) and then by a leaching rate of 32%"-"to estimate nitrogen that either runs off or reaches the groundwater. We also imposed a reduction in nitrogen that reached the ocean on the basis of distance Inland and used subwatershed catchment data' to estimate nutrient transport fromgol fcourses to thecoastline (Supplementary Figs. 16-18). Urban runoff. We quantified the total area of impervious surfaces (that is, paved roads, parking lots, sidewalks and roofs) within 10 km of the coastline at 100 m resolution for each year from 2000 to 2017 (Supplementary Figs.19 and 20). Data were extracted from NOAA CCAP land -use la nd-cover data from 1992, 2001, 2005 and 2010. We also digi- tized 2017 impervious surface cover from a single cloud -free Landsat 8 Image (courtesy of the United States Geological Survey, USGS) (15 m resolution pan -sharpened). Years 1n between data availability were filled In by linear interpolation. Rainfall. We quantified annual rainfall (m' ha-') and peakrainfall (maxi- mum3-day rainfall total, m' ha') at100 m resolution. Dailyrainfall data were generated following refs. 75,76. Rainfall from each rain station was used to derive interpolated surfaces at annual time steps using Empirical Bayesfan Kriging in ArcGIS. Subwatershed catchmentdataJ4 were clipped to 0-10 km from the coast and used to calculate rainfall per drainage area (Supplementary Figs.21 and 22). Sediment Input. The Integrated Valuation of Ecosystem Services and Tradeoffs sediment delivery model was used to derive long-term annualaverage sedimentinput(kg ha') reachingthe coast' wat100 m resolution. We then modulated the long-term annual average sedi- ment overtime by watershed on the basis of dischargecalculated from peak rainfall data (Rainfall section above). Discharge bywatershed was calculated following ref.81. Sediment load was assumed to scale with discharge according to a approximate ratings curve following ref. 82 (Supplementary Figs. 23 and 24). Fishing gear restrictions. We created a categorical value for local fish- ing gear restrictions using regulation information and marine managed area boundary designations updated from ref.80. All regulationswere evaluated for prohibition of gear categories in relation to fishing for reef fintish species overtime: line fishing, lay nets, spear fishing and aquarium collection. Ranked fishing gear categories areas follows: (1) full no -take, (2) no laynet, spearoraquarium,(3) no laynetoraquarium, (4) no lay net, (5) no aquarium and (6) open to all gear types (Supple- mentaryTable land Supplementary Fig.25). Sea surface temperature and heat stress. The mean and variability (that is, standard deviation) in summertime sea surface temperature (SST) was calculated over a 90-day window centred on the maximum value of a 7-day moving window average for each SST pixel (Supple- mentary Fig.26). Mean regional temperature (Fig.3a) wascalculated by taki ngthe 7-day running mea n of daily values and then averaging across all coastal pixels within our study region. Heat stress on reefs during the 2015 marine heatwave was assessed using DH W39, a widely used metric by coral reef scientists across the world. All data were NOAA's Coral Reef Watch v.3.1, available daily at5 km resolutioe. Phytoplankton biomass and irradlance. We used satellite derived chlorophyll-a(mg m'; a proxy for phytoplankton biomass) and irradi- ance (E m 2 d-i) from two sources. The long-term mean (2002-2013) in 8-day, 4 km data were obtained from ref. 80 and shown in Fig. 2d and Extended Data Fig.3. All subsequentanalysis used the visible -infrared Imaging/radiometer suite, which has high spatial (750 m) and tempo- ral (daily) resolution data starting in 2014 (provided by NOAKs Coral Reef Watch). All data were quality controlled and masked to account for cloud cover (Supplementary Information) and optically shallow waters following ref.83 (Supplementary Fig.27). Wave exposure. Wave power (kW m') combines wave height and period and provides a more representative metric of wave exposure than wave heightalone84. A series of nested grids (fromglobal to 50 m) using WAVE WATCH III' and Simulating Waves Nearshore" were used to quantify wave transformation over the reef environment at 50 m, at hourly intervals across our study region from ref. 87 and updated for this study. Annual data were then generated for each 50 m.grid cell by taking the mean of the top 97.5%in daily maximum wave power (Supplementary Fig. 28). Depth. Depth of the reef floor (m) was measured using diver depth gauges during the in -water reef surveys. Statistical analyses Coral reef trajectories predisturbance. We quantified the change 1n coral cover at 23 reefs from 2003 to 2014. A reef was considered to have a positive trajectory or negative trajectory if coral cover from the2003 survey to the 2014 survey increased or decreased by greater than 3%, respectively (Fig. 2b).This cut-off was based on the range in mean coral cover among all 23 reefs across the 12-year period (range 2.8%; minimum 34.1%; maximum of 36.9%). We then quantified local human impacts and environmental factors at each reef as follows: fish biomass metrics were from the mean of all annual surveys for each year from 2003 to 2014; human population, wastewater pol- lution, nutrient loading, urban runoff, annual rainfall, peak rainfall, SST mean and SST variability from the mean of all data from 2000 to 2014. Phytoplankton biomass and irradiance were from the maximum monthlyclimatology from2002 to 2Ol3. Sedimentand waveexposure came from the mean of the top five events from each year spanning 2000-2014. Fishing gear restrictions were from marine managed area designation at theonset of reef surveys and the depth came from In -water diver -assessed values. The difference in local human impacts and environmental factors between positive and negative trajectory reefswere then calculated as the difference in the mean drop-onejackknife values for each impact or factor. Upper and lower bars in Fig.2d represent the respective maximum and minimum differences in drop -one jackknife values between positive and negative trajectory reefs. Before calculating the drop -one Jackknife values, we identified and removed outliers that fell outside a threshold of ±2 standard deviations of the median. We formally tested for a difference in the local conditions of positive versus negative trajectory reefs using a multivariate permutational analysis of variance (PERMANOVA)' based on a Euclidean distance similarity matrix, type III (partial) sums -of -squares and unrestricted permutationsof the normalized data. Wevisualized the results in Fig.2c using a constrained analysis of principal coordinates90 and calculated the cross -validation allocation success (a measure of group distinct- ness) from the leave -one -out procedure of the constrained analysis of principal coordinates analysis. Coral response to the 2015 marine heatwave. Our goal was to as- sess the local land -sea human impacts and environmental factors that best explained changes in coral cover as a consequence ofthe 2015 marine heatwave. Any potential to observe change, however, could be influenced by variations in starting condition. Reefs with higher initial cover (such as those on positive coral cover trajectories predisturbance, Fig. 2b) had greater scope for loss and vice versa" (Extended Data Fig.S). To account for this and ensure comparability across reefs (Supplementary Fig.4) we calculated coral cover change following ref. 92 as: %differenceA = [(A,,j-Ab,j)/Ab,j I x 100 whereAb andA, are the mean coral cover values at each reef in 2014 or 2015, and 2016, respectively. We then calculated the following predictors based on current litera- ture and our hypotheses of the principal factors that drive changes in coral cover owing to severe heat stress (Extended Data Table 1). Fish biomass metrics included the mean of fish data that were coupled with benthic surveys: 2014 in =40) or 2015 (n = 40) and 2016 (n = 80); human population, wastewater pollution, nutrient loading, urban run- off, annual rainfall, peak rainfall and wave exposure were taken from the mean of all data from 2012 to 2016, sediment was measured from 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 was the maximum value for 2015; phytoplankton biomass and irra- dia nce was the mean from June to November 2015, representing the time inclusive of the marine heatwave; fishing gear restrictions was the marine managed area designation before the marine heatwave (2014 or 2015, depending on the reef surveyed) and depth came from In -water diver -assessed values. We then tested for correlations between coral loss and our suite of predictor variables using a generalized additive mixed -effects mod- elling (GAMM) framework34 with the gamm4 (ref. 93) package for R (www.r-project.org) v.4.0.2. Before 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 25%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 cut-off for data inclusion. The following predictors were square -root transformed to down-weightthe influenceofvalues atthe extreme ends of their distributions: all fish biomass metrics, wastewater Article pollution, urban runoff, nutrient loading, phytoplankton biomass and peak rainfall. A fourth -root transformation was applied to sediment. To reduce model overrating, 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 strive for model parsimony, we a priori excluded human population density from the model -fitting process as it was a poor indicator of human -driven land -to -sea impacts on local scales (Figs. lc and 2d and Extended Data Fig.3). We also excluded browser biomass as they rep- resented less than 10%on average oftotal herbivore biomassacross all reefsbefore, duringand postdisturbance. This resulted lathe following predictors included in the models (correlated predictors in parentheses were removed): total fish biomass, biomass of scrapers, biomass of grazers (total herbivore biomass), DHW (SST mean and variability), wastewater pollution, nutrient input, urban runoff, sediment input and peak rainfall (annual rainfall correlated with both), wave power, phy- toplankton biomass (irradiance), fishing gear restrictions and depth. The decision of which correlated predictors to retain was based on a hypothesis -driven approach,1n part whether the given predictor had the potential to directly (for example, sediment input) rather than Indirectly (for example, annual rainfall d riving sedi men t input) affect heat -driven coral loss. We incorporated a random spatial factor to account for the possible influence of a change 1n an u nderlying varia hie along thecoastl1ne not quantified in this study. This was done by breaking the coastline up into discrete 10 km sections running north to south. Section size was determined using hierarchical clustering based on pairwise Euclid- ean distances between reefs and identifying an inflection point 1n the Intragroup variance'4 (Supplementary Fig. 7). We fitted GAM Ms for all possible candidate models (unique combinations of the predictor variables) using the UGamm wrapper function, in combination with the dredge function in the MuMIn package94. Nonlinear smoothness in the models was determined using penalized cubic regression splines, with the number of knots (limited to fourto reduce overfitting) spread evenly throughout each covariate. All possible candidate models were computed (unique combinations of the predictor variables) but limit- ing the total number of predictors in anygiven ca ndidate model to rive to reduce overritting. We used Akaike's information criterion with a bias correction for small sample sizes95(Al Cc) for model comparison and all models within 4AlCc 5 2 of thetop model (AAICc = 0) are presented in Extended Data Table 2. To visualize the effect of predictor terms on coral cover change, we averaged the coefficients from the top models (that is, AAICc 5 2) to generate a predicted dataset and set all other predictor terms to their median value. Finally, we calculated a meas- ure of predictor variable relative importance within each candidate model bycalculatingthe sum ofAICc model weights foreach predictor (that is, the sum of model weights across all models containing each predictor; Fig. 3). Coral reefs four years postdisturbance. Our goal was to assess the local land -sea human impacts and environmental factors that best explained variations in thecoverof reef-buildingorganisms four years Following the marine heatwave. The cover of reef -building organisms for reefs surveyed in 2019 in =55) were parsed into three categories on the basis of the following percentiles: low, less than or equal to the 25th; moderate, morethan 25th and less than 75th; and high, morethan or equal to the 75th. We then performed ordinal logistic regression" to determine the probability of a given reef having high, moderate or low cover of reef-buildingorganisms on the basis of the prevailing local human impacts and environmental factors(that is, predictor variables; Extended Data Table 1). Logit models are multivariate extensions of generalized linearregression models that provide parameterestimates by means of maximum likelihood estimation (MLE) to model the rela- tive log odds of observing a reef -builder cover category or less versus observing the remaining higher categories: P(Y 5l) In P(Y j)-Ci+B¢rt+... +Bxzre r' Here, i indexes each of N observations, with categoriesy, and the left-hand side of the equation is the logit of the probability of a reef-buildercategory of/or lower, forj=l (high) or 2 (moderate). Reefs with low reef -builder covercontributed to the regression through cal- culation of the log odds. Each Cisan MLE-computed model intercept, and each Bk is the MLE coefficientcorresponding to the standardized independent variable za, for k =1 through n, where n is the variable number of predictors used in agiven candidate model, hence the ellipsis( ... ). A fundamental component of this model is the assumption of proportional odds, or parallel regression, which indicates that Bk values are independent orthe logic level j. The validity of this parallel regression assumption was ascertained using Brant's Wald test'r, as well as a likelihood ratio test (a= 0.05). We then calculated the following predictors based on current litera- ture and our hypotheses of the principal factors that drive changes in reef -builder cover across space and time following a major thermal disturbance: fish biomass metrics, wastewater pollution, nutrient loading, urban runoff, annual rainfall, peak rainfall, wave exposure, phytoplankton biomass and irradiance: the mean of all data from 2016 to 2019; sediment was measured as the mean of top three events over the 2006-2019 time period; SST mean and SST variability: mean of all data from 2000 to 2018. Note that2O19 was excluded in SST mean and SST variabi lity owing to the marine heatwave that affected Hawai'i', but occurred after our2019 fish and benthic surveys; fishing gear restric- tions involved the marine managed area designation in 2016 and depth was assessed by in -water diver -assessed values. We used the same process as in the GAMM analysis to remove outli ers in our predictor variables (above). We then square -root transformed the following predictors to down -weight the influence of values atthe extreme ends of their distributions: total fish biomass, wastewater pollution, sediment input and nutrient loading. Pearson's correlation coefficients were calculated among all predictors (Supplementary Fig. 8), removing highly correlated (r> 0.7) predictors. For the rea- sons outlined in our GAMM analysis and for continuity, we a priori excluded human population density and the biomass of browsers from the model -fitting process. This resulted in the following predictors included in the models (correlated predictors in parentheses were removed): total fish biomass, biomass of scrapers, biomass of grazers (total herbivore biomass), wastewater pollution, nutrient input, sedi- ment input, urban runoff (phytoplankton biomass), wave exposure, Fishing gear restrictions and depth. The decision of which correlated predictors to retain followed the samelogic asour GAMM analysis. The mean and variability in SST were excluded given the negligible range of values among reefs (0.1 and 0.025 °C, respectively). All possible candidate models were computed while limiting the total number of predictors in anygiven candidate model to four (to reduce overfitting and to account for the lower response variable replication compared to our GAMM analysis). Models were computed using the multino- mial logistic regression function mnrfit in MATLAB. We again used AICc for model comparison and all modelswithin AAICc 5 2 of the top model (AAICc= 0) arepresented in Extended Data Table3. McFadden's pseudo-R' was computed for the highest ranked models and ranged from 0.21 to 0.22. Unlike traditional R'values, McFadden's pseudo-R' of more than 0.2 represents an excellent rit". Modelswithin AAICc <2 of model l in Extended Data Table3 demonstrated comparable levels of goodness of fitand parsimony"'00. Many ofthe parametercoefficients within these modelswere sensitive to the underlyingvariability in the data and their estimates did notdiffer significantly from zero (P<0.05). The top model contained parameters with covariate estimates signifi- cantly different from zero, namely scraper biomass and wastewater pollution. Using model 1, we examined changes in the probability of a From: Leila Morrison Sent: Sunday, August 17, 2025 4:15 PM To: Council Testimony Cc: Kimball, Heather; Kagiwada, Jennifer; Onishi, Dennis; Kierkiewia, Ashley; Kanealii- Kleinfelder, Matt; Hustace, James; Galimba, Michelle M.; Villegas, Rebecca�UPabad Holeka a _ n � Subject: Testimony in Strong Support of Resolution 234-25 c�a --iz co C C �C Aloha Chair Villegas and members of the Policy Committee on Environmental and Natural Resource' zs Management, g N Mahalo for the opportunity to testify in strong support of Resolution 234-25, which urges the state to ensffre the health and safety of Hawai'i Island residents by requesting that the military cease all bombineand' desecration activities at P6hakuloa Training Area (PTA), reject land swaps and lease renewals without strict stipulations, and conduct a full Ka Pa'akai Analysis of the area. In May 2025, the state's Board of Land and Natural Resources (BLNR) did not accept PTA's Final Environmental Impact Statement (FEIS), citing incomplete archaeology inventory, lack of endangered species data and analyses and an inadequate Cultural Impact Assessment, which is used to address Ka Pa'akai factors. Sustained bombing and training activities have damaged pristine lands, the Saddle Region aquifer system, and cultural sites for over 60 years. The resolution aligns with long-standing community, concerns that military activity continues to pose real threats to public health, cultural integrity, and environmental stability. This resolution is not about questioning the Army's contributions in regard to disaster response, emergency services or providing a training venue for the county's first responders. Rather, it is about establishing limits, demanding accountability, and ensuring oversight and respect by urging the cessation of unwarranted bombing and live -fire training and the cleanup of PTA as required by their lease. Approving Resolution 234-25 sends a powerful message that Hawaii County stands with its people — families, communities, cultural practitioners, and environmental stewards who have long raised concerns about PTA's short- and long-term impacts. It signals that Hawai'i County will no longeraccept unilateral military decisions that compromise our land, culture, and health without meaningful engagement, cleanup commitments, and legal compliance with public trust obligations. Mahalo for your time, Leila Morrison