Coral composition and bottom-wave metrics improve understanding of the patchiness of cyclone damage on reefs.

Coral reefs are likely to be exposed to more intense cyclones under climate change. Cyclone impacts are spatially highly variable given complex hydrodynamics, and coral-specific sensitivity to wave impacts. Predicting reef vulnerability to cyclones is critical to management but requires high resolution environmental data that are difficult to obtain over broad spatial scales. Using 30m-resolution wave modelling, we tested cyclonic and non-cyclonic wave metrics as predictors of coral damage on 22 reefs after severe cyclone Ita impacted the northern Great Barrier Reef, Australia in 2014. Analyses of coral cover change accounting for the type of coral along a gradient of vulnerability to wave damage (e.g., massive, branching, Acroporids) excluded cyclone-generated surface wave metrics (derived from wave height) as important predictors. Increased bottom stress wave environment (near-bed wave orbital velocity) due to Ita (Ita-U) explained spatial patterns of 17% to 46% total coral cover loss only when the initial abundance of Acroporids was accounted for, and only when exceeding 35% cover. Greater coral losses occurred closer to the cyclone path irrespective of coral type. Massive and encrusting corals, however, had losses exacerbated in higher non-cyclonic bottom-wave energy environments (nc-U). The effect of community composition on structural vulnerability to wave damage was more important predicting damage that the magnitude of the cyclone-generated waves, especially when reefs are surveyed well beyond where damaging waves are expected to occur. Exposure to Ita-U was greater in typically high nc-U environments with relatively low cover of the most fragile morphologies explaining why these were the least affected overall. We reveal that the common surface-wave metrics of cyclone intensity may not always be able to predict spatial impacts and conclude that reef vulnerability assessments need to account for chronic wave patterns and differences in community composition in order to provide predictive tools for future conservation and restoration.