Data for: Effect of introduced Casuarina trees on the vulnerability of sea turtle nesting beaches to erosion
The aim of this study is to estimate the erosion vulnerability of important sea turtle nesting beaches in the Indian Ocean and South East Asia (IOSEA) region to determine whether Casuarina trees enhance erosion vulnerability over and above other factors, such as urban development. The specific objectives are 1) to quantify the distribution of non-native Casuarina trees on sea turtle nesting beaches of the IOSEA region and 2) to create an erosion vulnerability score (based on risk and threat) for the nesting sites based on global datasets of erosion indicators. Then, 3) to apply the vulnerability index to 50 sea turtle nesting beaches, and 4) to assess whether the presence of Casuarina trees could be used as an indicator of enhanced beach erosion. We hypothesise that the characteristics of Casuarina trees are not conducive to them being an effective coastal protection tool and predict that beaches backed by these trees will have higher erosion vulnerability scores than those beaches without these trees. To calculate beach erosion vulnerability as a measure of risk and threat, the Coastal Vulnerability Index (CVI) developed by Gornitz et al. (1994) for site-specific studies was adapted for use at a regional scale. Risk indicators, as suggested by existing literature, evaluate physical beach features, including: backshore width; beach exposure; modal beach energy, as measured by wave height and tide range; and state of the dune system. Threat indicators focus on erosion threats to beaches, such as: development; sea-level rise; and storm frequency and intensity. The indicators included in this study included those above, modified by data availability at an ocean-basin scale. Scores were normalized for each indicator to yield a value between zero and one (i.e. allocated score was divided by the maximum score of that category) and beach vulnerability was visualised by plotting the sum of normalized scores for risk indicators (x-axis) against the sum of normalized scores for threat indicators (y-axis). Recognising that it might be impossible for a beach to attain the highest score in every category (i.e., have no backshore, experience rapid sea-level rise, and very frequent extreme storms) and that scoring the lowest value for every category gave a total that was not zero, we calculated the theoretical minimum and maximum values attainable, and visualised relative vulnerability based on those ranges. The theoretical minimum score was calculated as the lowest possible score attainable, and the theoretical maximum was calculated as the sum of the maximum value attained for each metric. The midpoint of each range (risk and threat) was used to divide the data into four quadrants: 1) High Risk-High Threat; 2) High Risk-Low Threat; 3) Low Risk-High Threat; 4) Low Risk-Low Threat, which correspond to four categories of erosion vulnerability.