Coral reefs are distributed throughout the world's tropical oceans, directly occupying an estimated area of 250,000–600,000 km² [Figure 1; (Smith, 1978; Kleypas, 1997; Spalding and Grenfell, 1997)]. These values correspond to ~0.05–0.15% of the global ocean area, respectively, and about 5–15% of the shallow sea areas within 0–30 m depth. Coral reefs have ecological and economic importance that is disproportionately large relative to their areal extent. The global economic valuation of the direct and indirect use of coral reefs has been estimated near $10 trillion annually (Costanza et al., 2014). While the accuracy of this dollar amount is debatable, it is certain that coral reefs are important to the cultural and economic lives of hundreds of millions of people around the world, providing food for innumerable small subsistence economies, shoreline protection, superlative recreational resources, and biological storehouses for the biotechnology industry (Moberg and Folke, 1999).

Hundreds (if not thousands) of research papers have documented human impacts to reefs at the local scale. These are well summarized in several review papers and volumes (Smith and Buddemeier, 1992; Connell, 1997; Dollar and Grigg, 2004) and include destructive fishing practices, mining calcium carbonate, anchor damage, oil spills, and land-use practices leading to terrestrial sediment deposition, with subsequent resuspension that decreases water transparency vital for reef photosynthesis. The common result of all these impacts has been an ecological shift from coral- to algal-dominated reef benthic community structures. Accompanying this shift has been a similar decline in diversity of reef flora and fauna (Pandolfi et al., 2003). Compounding local scale impacts, reefs are thought to be among the first ecosystems to respond critically and dramatically to global climate change (Parry et al., 2007). The convolved influences of rising sea surface temperature and increasing ocean acidification are predicted to exacerbate and entrench a global phase-shift in reef benthic community structure away from coral-rich to coral-poor (Smith and Buddemeier, 1992; Hoegh-Guldberg et al., 2007, 2017; Pandolfi et al., 2011; Gattuso et al., 2015).

The concern for reef futures—and indeed documented ongoing coral loss at several reefs—has motivated assessment and monitoring efforts around the world. Local and regional surveys have disparate objectives and often utilize different methods, but they invariably share a common metric for reef status: benthic cover, or the relative abundances of corals, various algae, and sediment, which are fundamental reef benthic types (Done, 1992, 1995; Connell, 1997). It is coral cover, in particular, that occupies most focus among those assessing and predicting reef trends (e.g., Gatusso et al., 2014b).

The typical assessment approach is to make small-scale observations (10s of m) of benthic cover at multiple, well-separated (100s of m to km) sites using three common methods: (1) site-specific photo-quadrats or photo-mosaics that detail species composition at 1–10 m scales; (2) tape-measure or video transects that quantitatively describe community structure at 10–100 m scales; (3) manta-tows (pulling an observer behind a boat) that provide semiquantitative descriptions of the reef community at 100+ m scales. The different methods produce data with varying accuracies (Jokiel et al., 2015). The first two produce statistically rigorous results, with typical accuracies (95% confidence interval) of ±10% cover. The manta-tow is highly subjective, relying on individual diver estimates of coral cover into broad categories (e.g., coral = 0, 1–10, 11–30, 31–50, 51–75, 76–100%) at 2–5 min intervals (Miller and Müller, 1999). Boat and underwater logistics for these surveys limit total global surveyed reef area to 10–100s of km², or on the order of 0.01–0.1% of the world's reef area.

Our current understanding of integrated global reef condition derives from syntheses of these local studies (Connell, 1997; Pandolfi et al., 2003; Wilkinson, 2008; Moritz et al., 2018). These reports provide the best available estimates for current reef condition, as well as predictions for trajectories of future reef condition. Ultimately, because these reports are based on disparate and sparse data sources of variable quality, it is not possible to determine the accuracies of their assessments.

Importantly, to the best of our knowledge, there has been no attempt to investigate patterns of global reef condition in relation to local and global influences. While many environmental factors have been invoked to explain loss of coral for specific reefs, there are no generalizable models. In this study, we employ a variation of space-for-time substitution (Pickett, 1989; Blois et al., 2013; Damgaard, 2019) to explore the relationships between coral cover and the biogeophysical forcings that are generally accepted as impacting reefs. We assume that reefs globally have similar functions and similar responses to forcing factors (an assumption that is implicit in the aforementioned global syntheses). Thus, coral cover is an emergent property that varies from reef to reef due to differences in the forcing factors. Using only existing data sets, we investigate coral cover response to individual factors and to the combined suite of all factors.

Geospatial processing was performed using QGIS and MATLAB®. All statistical analyses were performed using MATLAB®.

Synthesis of readily and publicly available coral cover data revealed sparseness of surveys across regions and within reefs. Depending on the estimate used, these 3,826 reef cells represent 0.64% (of 600,000 km²) to 1.53% (of 250,000 km²) of the world's reef area. (Note the areas actually surveyed are far smaller.) The clear majority (86.8%) of binned 1 km × 1 km cells had fewer than 10 reported observations (Figure 2).

Observed trends in coral cover were as expected with respect to 10-year maximum BAA (Figure 3B, see number at top-right of panel for Spearman rank correlation coefficient), 10-year BAA trend (Figure 3C), number of SWH events (Figure 3E), number of coral species (Figure 3G), and local marine pollution threat (Figure 3J). However, the remaining individual correlations of coral cover with environmental factors known to impact reef distribution and condition did not exhibit expected trends (Figure 3). We found no significant trend with respect to Ω_arag (Figure 3D), and a decreasing trend in association with mean BAA (Figure 3A). We found a negative trend of coral cover in association with light availability (Figure 3F). With the exception of marine pollution, we found contradictory trends for local human threats (Figures 3H,I,K,L).

Correlations were weak between coral cover and each forcing parameter, regardless of direction or statistical significance (Figure 3). Of course, the various biogeophysical forcings do not act individually, but simultaneously. However, the optimized bagged ensemble of regression trees obtained an adjusted r² of 0.43 (Figure 4A), which means that there was more unexplained variance in coral cover than explained by the combined set of biogeophysical forcings.

Interestingly, despite showing no correlation with coral cover on an individual basis, the most important predictor in the multivariate model was Ω_arag (Figure 4B). This was followed by 10-year mean BAA, then the group of 10-year BAA trend, PAR, SWH events, and number of coral species. The remaining variables had relatively low importances. These importances were borne out in the partial dependence and individual conditional expectation plots (Figure 4C). For example, Ω_arag showed tight clustering of predicted responses, with variation across the range of Ω_arag. Conversely, the local threat variables showed a wide spread of predicted responses, with virtually no variation across threat levels.

At the very least, some of the results are unexpected. Both 10-year mean BAA and Ω_arag show individual trends (or lack thereof) counter to prevailing scientific thought, but both are important in the context of multiple forcings. All but one of local threats have trends counter to prevailing scientific thought, and none are important in the multivariate model. Perhaps most surprisingly, coral cover is poorly predicted by all of the biogeophysical forcings, both individually and collectively. Taken at face value, these results would necessitate a reevaluation of our understanding of the relationship between coral reefs and their environment.

Before challenging scientific wisdom built through innumerable peer-reviewed studies and across decades, it is prudent to ensure the results are wholly valid. It is possible that the underlying coral cover data are fundamentally flawed. To be sure, no error estimates are provided with any of the source data. However, those data are produced from multiple, independent research and monitoring programs, each comprising several individual observers and technicians. While errors are inherent to collated data sets of this size, it is not reasonable to expect that all, or even the bulk, of these programs generated bad data. It must be assumed that the data represent best available estimates of coral cover.

A similar issue holds for the biogeophysical forcings data. BAA and PAR are derived from satellite data products, SWH from regional wave model hindcasts, and local threats from statistical models of diverse data sets. Ω_arag and coral species richness are derived from observational data, extrapolated through spatial modeling. Thus, each of these parameters has its own associated errors, which can impact the analyses in this study. However, each parameter is supported by painstaking, peer-reviewed research, and each therefore represents a best available estimate.

A clear limitation of this study is the size of the data set. Countless other coral cover data sets are sure to exist, but they are not made readily available by their owners. Their inclusion would undoubtedly increase both the density and the geographic extent of the current data set, adding confidence to the analytical results. So, a strong recommendation can be made that, in the interest of fostering information exchange and scientific advancement, data sets should be made publicly available. The ideal situation would be an online active archive center to which researchers and managers can submit data, which can then be searched, collated, and compiled by users around the world. Data should have a consistent format, and error estimates should be required. All submissions should be accompanied by complete metadata.

Despite its limitations, the present data set comprises the best available information on collocated coral cover and biogeophysical forcings. This study's true limitation is that the data are not suited to the task at hand. Space-for-time substitution may be inappropriate across two decades and two oceans of reef survey data, because reefs in different regions and times may have different responses to environmental forcings. Certainly, there is the issue that the scales of virtually all existing surveys can easily miss ecosystem-scale variability (Edmunds and Bruno, 1996). A few 10s of m² of in-water reef survey simply cannot adequately represent 1 km² (= 1 × 10⁶ m²) of reef. Ultimately, these bivariate and multivariate results highlight the problem of using sparse, disparate data to synthesize a global model of reefs. Available data utilized in this study are not sufficient to quantitatively relate ecosystem-scale coral reef condition to the surrounding environment. Non-sparse data of uniform quality are required.

Owing to logistical and cost limitations inherent to field surveys, it is not possible to collect uniform, high-density, in-water data at the ecosystem scale across reef regions, much less globally. Remote sensing offers the only viable approach (Mumby et al., 1999, 2001; Hochberg, 2011; Hedley et al., 2016). However, current satellite sensors are largely inadequate for assessment of global coral reef status as they lack the necessary combined spectral and spatial capabilities to accurately quantify benthic cover (Hochberg and Atkinson, 2003; Dekker et al., 2018). Numerous case studies have demonstrated the ability for multispectral sensors with moderate or high spatial resolution (e.g., Landsat, WorldView, PlanetScope) to produce reasonably accurate maps of reef “habitats” or “biotopes,” which has led to global efforts such as the Millennium Coral Reef Mapping Project and the Allen Coral Atlas. In these efforts, habitat labels are created as qualitative descriptors comprising various combinations of substrate (e.g., sand, limestone, rubble), benthic functional type (e.g., coral, algae, seagrass), reef type (e.g., fringing, patch, barrier), and/or location within the reef system (e.g., slope, flat). Mapping of these non-standardized “habitats” has become common (reviewed in, Hedley et al., 2016). These efforts have generated good depictions of reef location and ecological zonation within reefs, but their underlying data lack the ability to spectrally discriminate between the fundamental reef benthic types—i.e., live coral from algae (Hochberg and Atkinson, 2003). Thus, their map products fail to answer one of the most fundamentally important questions to reef ecology: How much coral do the world's reefs possess?

Several satellite missions are under development that may meet the necessary requirements for measuring reef condition at the ecosystem scale (Dekker et al., 2018), such as the NASA Surface Biology and Geology (SBG) mission (National Academies of Sciences and, 2018; Cawse-Nicholson et al., 2021). SBG and others may provide useful data for reefs, at least to reasonable light penetration depths of 10–15 m (Hochberg et al., 2003). However, given that many reefs are already in decline, the information provided by these missions may come too late for application to policy and management. Airborne remote sensing offers an alternate solution. One example is the NASA Earth Venture Suborbital-2 (EVS-2) COral Reef Airborne Laboratory (CORAL) mission (https://coral.jpl.nasa.gov/, https://coral.bios.edu). During 2016–2017, CORAL used the NASA Jet Propulsion Laboratory Portal Remote Imaging SpectroMeter (PRISM, https://prism.jpl.nasa.gov/) to survey sections of the Great Barrier Reef, Main Hawaiian Islands, Mariana Islands, Palau, and Florida, ~5,000 km² reef area in all (~1% of the world's reef area). CORAL quantified benthic cover and modeled primary production and calcification at the reef scale, with the aim of improving our understanding of the relationship between reef ecosystems and their environments. Results are forthcoming after final data and analyses quality assurances.

Publicly available datasets were analyzed in this study. This data can be found here: http://data.unep-wcmc.org/datasets/1 https://www.coris.noaa.gov/ https://www.aims.gov.au/ https://coralreefwatch.noaa.gov/product/5km/index_5km_composite.php https://polar.ncep.noaa.gov/waves/hindcasts/ http://hdl.handle.net/102.100.100/137152?index=1 https://oceancolor.gsfc.nasa.gov/ http://www.iucnredlist.org https://www.ncei.noaa.gov/data/oceans/ncei/ocads/data/0139360/~https://www.wri.org/publication/reefs-risk-revisited/b.

EH conceived the project and led the data analysis. EH and MG collected data and wrote the manuscript. All authors contributed to the article and approved the submitted version.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.