Spatial Variation in Upper Limits of Coral Cover on the Great Barrier Reef

Aim

Identifying the maximum coral cover that a coral community can sustain (i.e., its ‘upper limit’) 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.

Location

Great Barrier Reef (GBR), Australia (over 1400 km).

Time Period

1990 to 2022.

Major Taxa Studied

Scleractinian corals.

Methods

We used 32 years of data on coral cover around reef perimeters. 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.

Results

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.

Main Conclusions

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.