Spatial variation in upper limits of coral cover on the Great Barrier Reef (EcoRRAP)

Spatial variation in upper limits of coral cover on the Great Barrier Reef

Alvarez-Noriega, Mariana; Ortiz, Juan; Ceccarelli, Daniela; Emslie, Michael; Fabricius, Katharina; Jonker, Michelle; Puotinen, Marji; Robson, Barbara; Roelfsema, Chris; Sinclair-Taylor, Tane; Ferrari, Renata

Abstract

Identifying the maximum coral cover that a coral community can sustain (i.e., its &lsquo;upper limit&rsquo;) is important for predicting community dynamics and improving management strategies. Here, we quantify the relationship between estimated upper limits and key environmental factors on coral reefs: hard substrate availability, temperature, and water clarity. We used 32 years (1990-2022) of data on coral cover around reef perimeters in the Great Barrier Reef, Australia. Each reef was divided into four wave exposure habitats depending on prevailing wind conditions. For each site, we determined if hard coral cover had reached a plateau or upper limit. Next, we extracted existing estimates of hard substrate availability, modelled water temperature, and Secchi depth. Then, we quantified the relationship between these environmental variables and the upper limits. We found varying upper limits across the GBR, with a median of 33% coral cover and only 17% of the estimated upper limits exceeded 50% coral cover. Upper limits increased towards the southern reefs. Our results show that upper limits increased with increasing hard substrate availability and decreased with temperature and, to a lesser extent, with water clarity. The upper limits estimated in this study are much lower than what is commonly assumed when modelling ecological dynamics, most likely resulting in predicted recovery rates being inappropriately high. Although hard substrate ultimately restricted upper limits, there are mechanisms constraining the proportion of hard substrate that is covered by hard corals. The negative relationship between temperature and upper limits cannot be explained by changes in macroalgal abundance but may be related to changes in species composition. The quantitative relationships between the upper limits of coral cover and environmental variables will provide critical information to prioritise sites for management interventions.

Methods

Coral cover data

We used hard coral cover data from manta tow surveys conducted by the Australian Institute of Marine Science&rsquo;s (AIMS) Long-term Monitoring Program (LTMP) from 1990 to 2022 (Emslie et al., 2020). Manta tow surveys quantified broadscale patterns in percent coral cover on a ten-meter swath of the reef slope around the entire perimeters of 173 reefs on the Great Barrier Reef, Australia (from 12&deg;S to 24&deg;S and 143&deg;E to 152&deg;E). Reefs were visited a maximum of once a year.

Each reef was divided into four reef zones or sites based on prevailing wind exposure: one back (leeward) site that was the most protected, one front (windward) site being the most exposed, and two flank sites with intermediate exposure. Sites were classified based on reef geomorphology and local knowledge of prevailing wind conditions. Each tow was assigned a reef zone, and from 2014 onwards, the tow paths were georeferenced. Depending on weather conditions and variable reef structure, following the exact path was not always possible. Since community composition changes with wave exposure, we looked for upper limits in each of the four sites within reef separately.

Observers were towed by a boat at constant speed, and for each two-minute tow, visually estimated the proportion of the reef slope covered by live hard corals using a categorical coral cover score (0, &gt;0-5%, &gt;5-10%, &gt;10-20%, &gt;20-30%, &gt;30-40%, &gt;40-50%, &gt;50-62.5%, &gt;62.5-75%, &gt;75-87.5%, &gt;87.5-100%) (Miller et al., 2018). During the two-minute tow, the observer is towed parallel to the reef crest, aiming to visually scan a ten-meter band of the reef slope, just below the reef crest. The length of the transect varied depending on the distance covered during the two-minute duration of the tow, with a mean tow length of 200 m (with 95% of the tows between 134 &ndash; 269 m). Only one categorical hard coral cover score was recorded for each two-minute tow.

For analyses, coral cover for each two-minute tow was converted to the midpoint of the cover category recorded. For each year surveyed, all tows from each site within reef were summarised by calculating the median to minimise the effect of outliers. A small subset of reefs had inconsistent wave exposure zone assignations that could not be corrected using the georeferenced tows and therefore we excluded sites within reefs that were problematic (three entire reefs were excluded, and 13 additional reefs had at least one site excluded).

To estimate the upper limit of coral cover for each site within each reef, we first calculated the five-year running mean of the median coral cover. From the five-year running mean, we identified instances when coral cover reached a plateau. We called coral cover at the plateau the &lsquo;upper limit&rsquo;, and we defined it as the maximum running mean cover over a period of five undisturbed years. The upper limit had to: 1) be within 80% of the maximum running mean for that site across all surveyed years, 2) the two previous and the two following years also had a running mean within 80% of the maximum value, and 3) the running mean estimate was calculated with at least three data points (i.e., only two years out the five could have an interpolated value). We chose these criteria to ensure that we only captured instances of stable high coral cover within each site, but with some flexibility (e.g., in terms of interpolation) so that we had enough upper limit estimates to capture their variation across space. Since the specific criteria are somewhat arbitrary, we repeated the process of estimating upper limits with different running mean windows (5, 7, 9, and 11 years) and different thresholds for the difference between the running mean and the maximum coral cover (80, 85, and 90%). In total, we had 12 sets of upper limit estimates, each estimated using different criteria. For any definition of upper limits, each reef could have a maximum of four upper limit values, one for each site (one exposed, one sheltered, and two intermediate reef sites). 

Environmental variables

We extracted long-term medians of the environmental values for hard substrate availability, temperature, and water clarity, assuming it to be representative of the general environmental conditions across sites. 

We extracted estimates of the proportion of reef surfaces constituting hard substrate, derived from freely available benthic maps known as &ldquo;Great Barrier Reef 10m Grid GBRMP Benthic&rdquo; (Lyons et al., 2020; Great Barrier Reef Marine Park Authority, 2021). The maps are generated using high spatial resolution satellite imagery, bathymetry, wave height estimates, and field data for training and validation (Roelfsema et al., 2021). In the maps, each 10m-by-10m of planar area pixel down to 10m mean sea level depth is assigned one of four types of substrate: coral or macroalgae, rock, rubble, or sand. Coral cannot be distinguished from macroalgae in the satellite imagery, and any pixels labelled as coral/macroalgae or rock (i.e., hard substrate) were considered suitable habitat for corals to establish and grow, unlike rubble and sand. 

Each reef site had multiple pixels with a benthic composition label. To ensure we captured the benthic composition of the same locations that were surveyed with the manta tows, we divided each reef into 100m x 100m squared grid cells, and we assigned each cell with a georeferenced tow to a reef site. For each reef site, we extracted benthic composition estimates from all assigned cells and determined the proportion of 10m x 10m pixels labelled as hard substrate divided by the total number of pixels.

To investigate the relationship between upper limits and key environmental variables, we summarised long-term medians in temperature and Secchi depth (a measure of water clarity, with values above 10 - 15 m representing high water clarity) from hydrodynamic and biogeochemical models. Daily sea water temperatures at a depth of 7.5m below mean sea level and Secchi depth were extracted from the eReefs marine models (GBR1 v2.0 and GBR4, respectively) (Steven et al., 2019) using the R-package &ldquo;ereefs&rdquo; (Robson, 2023). eReefs is an information system that predicts biogeochemical and hydrodynamic variables across the Great Barrier Reef. Daily estimates were available at a spatial resolution coarser than the reef site (~1 x 1 km and ~4 x 4 km) during a period of six and eight years for temperature and Secchi depth, respectively (temperature: 1 January 2015 to 1 January 2021, Secchi depth: 1 December 2010 to 1 December 2018). For these variables, we used the georeferenced tows to find the latitude and longitude at the centre of the reef site and computed the median of the daily estimates for the grid cell upon which the centre of each reef site fell. The eReefs hydrodynamic models consist of a nominal 1 km resolution hydrodynamic model covering the Great Barrier Reef World Heritage Area nested within a nominal 4 km resolution regional model that extends across the Coral Sea, which is in turn nested within a global circulation model. Among its outputs, the hydrodynamic model generates predictions for water temperature using the three-dimensional, baroclinic, finite difference hydrodynamic model Sparse Hydrodynamic Ocean Code (SHOC; Herzfeld, 2006).  Although reefs differed in their depth distribution and geomorphology, we expect a depth of 7.5m to overlap with all manta tows swaths. Estimates of Secchi depth were obtained from the eReefs biogeochemical model that has a 4 km resolution. The optical model used to generate Secchi depth predictions can reproduce remote-sensing reflectance patterns that arise from specific biogeochemical states (Baird et al., 2016). Secchi depth was included as a proxy for water clarity and net productivity.

Statistical analyses

All analyses were performed in R version 4.3.1 (R Core Team, 2023). We fitted a series of linear mixed effects models in a Bayesian framework using the R package &ldquo;brms&rdquo; version 2.20.4 (B&uuml;rkner, 2017) to quantify how sites&rsquo; upper limit estimates related to environmental conditions. Because reefs could have up to four data points each (in different wave exposure sites), we included a random effect of reef in the models. Because our response variable - upper limit &ndash; was a proportion (converted to percentage in the figures), we fitted the models assuming a beta distribution.

For each of the 12 sets of upper limit estimates (i.e., one for each running mean window and percentage threshold combination), we fitted seven models to test whether the combination of fixed effects affected the magnitude and direction of the environmental variable coefficients. There was one model that included all three environmental variables (the proportion of hard substrate, temperature, and Secchi depth) as fixed effects, three models that included two out of the three environmental variables only, and three models that included only one of the environmental variables. This gave a total of 84 fitted models.

In each model, the environmental variables were standardised as Z-scores ( , where  is the value of the environmental variable for that site, and  and  are the mean and standard deviation across all observations, respectively). For simplicity and since one third of the sets of upper limit estimates had less than 50 estimates, we avoided fitting interactions between environmental variables.

Additionally, to test whether the relationship between coral cover and the three environmental variables differed among high, medium, and low coral cover at a site, we fitted three quantile regressions. Each quantile regression represented high (95th quantile), medium (50th quantile), and low (10th quantile) coral cover. The response variable was the yearly coral cover at each site (n = 7787). Temperature, hard substrate availability, and Secchi depth (as Z-scores) were included as the fixed explanatory variables and, since there were multiple data points per reef site, site nested within reef was included as a random effect.

For all models, the priors were specified by using the function brms &lsquo;get_prior&rsquo;, which gave a flat prior for the fixed effects and intercept and weakly informative priors for the hierarchical parameters (a Student&rsquo;s t-distribution with a normality parameter of 3, with a mean of 0, and a standard deviation of 2.5 for the models with a beta distribution and a standard deviation of 14.8 for the quantile regressions). Each model had three chains of 20,000 iterations (half of which were discarded as warm up) and a thinning of 5, resulting in 6,000 posterior distribution draws. To investigate how consistent was the relationship between each environmental variable and coral cover upper limits, we recorded the coefficient estimate for each variable included in each model and whether its 95% credible interval overlapped with zero.

Please refer to the published manuscript for more detailed information on data and processing.

References

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Organization: Australian Ocean Data Network