Coral reefs can be protected with pollution and overfishing, but not enough
Modeling coral loss and climate change in the six years leading up to a heatwave: A comparative study with a generalized additive mixed effects modelling framework
By addressing both land and sea human impacts simultaneously, the researchers think they can increase their chance of having higher coralcover in the four years after a heatwave by up to six times. But lower pollution levels and more scrapers — factors that drove increases in coral cover before the heatwave — offered little protection against temperature spikes. Reefs with the lowest levels of urban runoff lost slightly less cover, but the presence of more fish — particularly scrapers — did not have a substantial effect on coral loss or survival.
The 12 years leading up to the heatwave were when more fish and cleaner wastewater led to coral growth.
Scrapers “literally scrape the reef”, says Jamison Gove, an oceanographer at the Pacific Islands Fisheries Science Center at the US National Oceanic and Atmospheric Administration in Honolulu, Hawaii. “They not only remove fast-growing algae, but they clear space that allows for the settlement of crustose coralline algae, which is a precursor for coral growth,” he says.
On the flip side, wastewater pollution from sewage disposal systems and runoff from urban environments damage coral health, says Gove. “It’s not only that you have this soup of virulent bacteria and other things that can cause coral disease,” he says. You have every person in the house putting their sink down. You have household chemicals, you have pharmaceuticals, so you have all sorts of toxins.”
Hughes gives the example of back-to-back bleaching events that struck the Great Barrier Reef in 2016 and 2017, and says that climate modelling suggests that most coral reefs will experience bleaching events every year by 2050 under a business-as-usual emissions scenario.
We then tested for correlations between coral loss and our suite of predictor variables using a generalized additive mixed-effects modelling (GAMM) framework24 with the gamm4 (ref. 93) package for R (www.r-project.org) v.4.0.2. Before fitting a model, we considered any point that fell outside of a threshold of 2 standard deviations to be outliers in our predictor variables. 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. It is important to make sure that data points are included in the model-fitting process because of the cut-off applied for data inclusion. The following predictors have been turned to down-weight by the values at the extreme ends of their distributions. A transformation was done to the food.
The resource management scenarios presented in Fig. The rationale for selecting 4b is the same as that for the other ones. We chose 250 kg ha−1 as the management target for scraper biomass as this value approximates the long-term mean (2003–2019; n = 17) biomass of scrapers within Kealakekua Bay, a marine protected area where no fishing has been allowed since 1969 (Supplementary Fig. 10). There are also high levels of wastewater pollution in Kealakekua Bay. As such, our value of 250 kg ha−1 represents an estimate of scraper biomass on a reef with strong fisheries protection but with land-based stressors present. In the most recent time point in which all reef were surveyed within the same year, we compared the values of the upper (250 ha1) and the lower (30 ha1). The upper and lower limits represent the 92nd and 36th percentiles. We used the 100 m grid cell values that fell along the isobath for wastewater pollution. 1d) but constrained the latitudinal extent to be consistent with the northern- and southern-most locations of the 2019 reef surveys. This approach provided far greater replication and a more representative assessment of wastewater pollution along the coastline for which to assess our management scenarios. The 95th and 36th percentiles of the distribution were represented by the upper and lower values for wastewater pollution.
The difference in local human impacts and environmental factors between two diverging paths were then calculated as the difference in the jackknife values for each impact. There are upper and lower bars in the picture. The differences in jackknife values between positive and negative trajectory reefs are represented by 2d. We removed outliers that fell outside of the median threshold before calculating the drop-one jackknife values. A variety of permutations were used to test for a difference in the local conditions of positive and negative trajectory reefs. The cross-validation allocation success, a measure of group distinctness, is calculated from the leave-one-out procedure of the constrained analysis of principal coordinates analysis.
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 (Supplementary Fig. 26). The mean regional temperature is calculated from taking the 7 day running mean of daily values and then averaging them across all coastal areas of the region. Scientists across the world use a widely used metric to assess heat stress on coral reefs during the marine heatwave of 2015. All data were NOAA’s Coral Reef Watch v.3.1, available daily at 5 km resolution35.
We used satellite derived chlorophyll-a as well as irradiance from two sources. The long-term mean was obtained from ref. 80 and shown in Fig. The data is 2d and extended. The analysis was based on the visible-infrared data from the 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).
A categorical value for local fishing gear restrictions was created using information about the marine managed area boundary. There is a minimum of 80. All regulations were evaluated for their use in relation to fishing for reef finfish species over time. The rankings are as follows: full no-take, no lay net, spear or aquarium, no lay net or aquarium, no aquarium, and open to all gear types.
The long-term average annual total estimable was derived using the Integrated Valuation of Ecosystem Services and Tradeoffs model. We then modulated the long-term annual average sediment over time by watershed on the basis of discharge calculated from peak rainfall data (Rainfall section above). The discharge was calculated after ref. There is 81. Sediment load was assumed to scale with discharge according to a approximate ratings curve following ref. 82 (Supplementary Figs. 23 and 24).
We quantified annual rainfall (m3 ha−1 ) and peak rainfall (maximum 3-day rainfall total, m3 ha−1) at 100 m resolution. Daily rainfall data were generated following refs. 75, 78. Rainfall from each rain station was used to derive interpolated surfaces at annual time steps using Empirical Bayesian Kriging in ArcGIS. Subwatershed catchment data74 were clipped to 0–10 km from the coast and used to calculate rainfall per drainage area (Supplementary Figs. 21 and 22).
We quantified the impervious surface area within 10 km of the coastline at a 100 m resolution for each year from 2000 to 2017, in two graphs. Data were extracted from NOAA CCAP land-use land-cover data from 1992, 2001, 2005 and 2010. We also digitized 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 in between data availability were filled in by linear interpolation.
We calculated nutrient input (kg ha−1 yr−1) at 100 m resolution as the combination of total nitrogen from OSDS (Wastewater 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 Landsat cloud-free composite images created with Google Earth Engine. It took an annual nitrogen application rate of 585 kilo ha1 and a 32% Nitrogen Harvesting Rate to get the golf course area. We estimated the transport of nitrogen from golf courses to the coastline using data from the subwatershed and imposed a reduction in nitrogen that went to the ocean inland.
A study of invasive species on Hawai’i island using the NASA grid population of the world v.4 (ref. 66)
We quantified human population density using NASA Gridded Population of the World v.4 (ref. 66). The dataset is available in resolutions of 1 km. The time steps of human population were filled in by using linear interpolation which put them at 15 km across the 100 m grid cell.
The methods we followed to calculate fish biomass 56 were established. The biomass of individual fishes was estimated using the allometric length–weight conversion: W = aTLb, where parameters a and b are species-specific constants, TL is total length (cm) and W is weight (g). Length and weight fitting parameters were obtained from a comprehensive assessment of the state of Hawaii. 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.
The southeasternmost island of the Hawaiian archipelago is called Hawai’i Island, located in the northern central Pacific. The western section has roughly 200 km of coastline predominantly oriented north to south. The coastline of the main Hawaiian Islands has the longest contiguous reef in the world with a large gradient in human population, local land–sea impacts and environmental variables that are comparable to the global reef ecology. The region represents an ideal study location for resolving the land–sea human impacts driving reef ecosystem change and coral trajectories following acute climate-driven disturbance.
