It is possible for pollution and overfishing to protect corals, but it is not enough
Coral Reef Growth in the Light of Recent Ocean Warming: A Parallel Regression Assumption and the Great Barrier Reef as a Case Study
The study found that by reducing land-based and sea-based impacts, the reefs might recover from marine heatwaves. But researchers warn that, in the absence of aggressive action to limit global warming, addressing impacts such as pollution and overfishing is insufficient to counter the growing threat. The results were published this week in Nature1 and come as record ocean temperatures scorch reefs off the coast of Florida and scientists fear the effects of El Nio on Australia’s Great Barrier Reef.
In the 12 years leading up to the heatwave, reefs with more fish — particularly herbivorous scrapers — and lower levels of wastewater pollution experienced 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. He says that they remove algae and clear space so that it’s easier to grow coral.
Gove says coral health can be affected by wastewater pollution from sewage disposal systems. “It’s not only that you have this soup of virulent bacteria and other things that can cause coral disease,” he says. People have everything from putting their sink down to flushing it down. You have household chemicals, you have pharmaceuticals, so you have lots of toxins.
Climate modelling shows that most coral reefs will see bleaching events every year by the year 50, under the business-as-usual emissions scenario, which Hughes gave the example of the Great Barrier Reef.
Here, the left hand side of the equation is used to determine the logit of the likelihood of a reef-builder category of j or lower. Reefs with low reef-builder cover contributed to the regression through calculation of the log odds. Each Bk is the MLE coefficient that is corresponding to the variable number of predictors used in a candidate model, and each Cj is the model intercept. A fundamental component of this model is the assumption of proportional odds, or parallel regression, which indicates that Bk values are independent of the logit level j. The validity of this parallel Regression assumption was determined using two tests.
There are two resource management scenarios presented in this picture. The rationale for selecting 4b was the same as the previous rationale. 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). Kealakekua Bay is also exposed to numerous land-based stressors, including high levels of wastewater pollution (258,000 l h−1 in 2019). Our value of 250 kilo Ha1 is an estimate of scrapers on a reef with strong fish stocks, but also with land-based stressors present. In addition, we compared our upper (250 kg ha−1) and lower (30 kg ha−1) scraper biomass values to the distribution of scraper biomass among all reefs (n = 80) in 2019, the most recent time point in which all reefs were surveyed within the same year (Supplementary Fig. 10). The upper and lower limits are defined as the 92nd and 36th percentiles. The grid cell values that fell along the 10 m isobath were used for wastewater pollution. 1c) 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 percentiles of the distribution were defined as the upper and lower values chosen for wastewater pollution.
The impact and environmental factors on the reefs were converted into the mean drop-one jackknife values for each impact. 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. outliers that fell outside a threshold of 2 standard deviations were removed before calculating the drop-one jackknife values. We formally tested for a difference in the local conditions of positive versus negative trajectory reefs using a multivariate permutational analysis of variance (PERMANOVA)89 based on a Euclidean distance similarity matrix, type III (partial) sums-of-squares and unrestricted permutations of the normalized data. The visualized results were obtained using a constrained analysis of principal coordinates and Cross- validation allocation success was calculated from the leave-one-out procedure.
Wave power (kW m−1) combines wave height and period and provides a more representative metric of wave exposure than wave height alone84. Waves are quantified over the reef environment at hourly intervals across our study region using nested grids and Simulating Waves Nearshore86. This study is updated with 87. 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).
We used two sources for the satellite derived chlorophyll-a and irradiance. The long-term mean in 8 days was obtained from ref. 80 The 888-666-1846 888-666-1846 is shown in fig. 2d and extended data The visible-infrared suite has high spatial ( 750 m) and temporal (daily) resolution data that began in the year 2014), provided by the Coral Reef Watch. Cloud cover and shallow waters were masked to make sure the data was quality controlled. TheSupplementary fig. 27 is called 83.
We created a categorical value for local fishing gear restrictions using regulation information and marine managed area boundary designations updated from ref. 80. The rules were evaluated for their prohibition of gear in relation to fishing for reef finfish species over time. There are six ranked fishing gear categories: full no- take, (2) no lay net, spear or aquarium, (3) no lay net or aquarium, fourth no lay net, and sixth open to all gear types.
The Integrated Valuation of Ecosystem Service and Tradeoffs model was used to derive long-term annual average total precipitation at the coast. 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 following the ref. 81. The ratings curve was assumed to scale with discharge. 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. 75,76. The data from each rain station was used to derive surfaces at annual time steps. The data was clipped to the coast and used to calculate the amount of rain in the area.
We calculated the total area of impervious surfaces within 10 km of the coastline at 100 m resolution for each year from 2000 to 2017. The data was from the years 2001 to 2010 and has previous data from 1992. The United States Geological Survey provided us with a single cloud-free Landsat 8 image of 2017; it was pan-sharpened. Linear interpolation was used over the years in between data availability.
We calculated wastewater effluent (l ha−1 yr−1) and nitrogen input (kg ha−1 yr−1) from onsite sewage disposal systems (for example, cesspools and septic tanks) and injection wells (collectively OSDS) in coastal waters at 100 m resolution. Only OSDS located within a modelled one-year groundwater 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 input were estimated on the basis of ref. 67 and discharge rates and nutrient loading according to ref. 68. dispersal was estimated at 2 km off the coast by using a Gaussian decay function. The same dispersal function was used to move water from one place to another.
We quantified human population density using NASA Gridded Population of the World v.4 (ref. 66). At 5-year intervals, the data is at 1 km resolution. When filling in the missing years, linear interpolation was used to create annual time steps of the human population within fifteen km of each 100 m grid cell.
Biomass biomass of the Hawaiian island of the Southern Hawaiian archipelago: Implications for species, habitats and reef ecosystems
We followed established methods for calculating fish biomass56. 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–weight fitting parameters were obtained from a comprehensive assessment of Hawai‘i specific parameters56 and FishBase65. 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.
Hawai‘i Island (19.55° N, 155.66° 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 main Hawaiian Islands contain a longest contiguous reef in the area, a large gradients in human population, local land–sea impacts and environmental factors that are comparable to reef ecosystems around the world. 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.
The coral reef in Kealakekua Bay, Australia experienced significant coral growth in the 12 years leading up to the Australian heatwave in 2019. reefs with more fish, particularly herbivorous scrapers, and lower levels of wastewater pollution experienced coral growth. The value of 250 kilograms of scraper biomass as a management target is an estimate of scrapers on a reef with strong fish stocks.