The cushion seastar Culcita schmideliana has gained major attention in the last few years because of its selective predation on juvenile corals, as well as its ability to generate large demographic assemblages, causing delays in coral recovery after large mortality events in the Republic of Maldives. However, a lack of data regarding the factors affecting its distribution and habitat selection still persists in this area. Here, we adopted a novel approach in the study of corallivorous seastar habitat selection that combined ecological and digital photogrammetry data. In this regard, we tested 3 different parameters as factors influencing seastar habitat choice in the South-East region of Faafu Atoll, Republic of Maldives, namely prey abundance, Linear Rugosity Index (LRI), and Average Slope (AS). The analysis of selectivity coefficient (Ei) of seastars for different habitat types showed a preference for reefs characterized by medium AS values (Ei = 0.268), a LRI included between 2 and 2.5 (Ei = 0.180), and a juvenile coral density ranging between 10 and 20 colonies m−2 (Ei = 0.154). A multiple linear regression analysis showed that different AS and LRI values explained the 43.1% (R2 = 0.431, P = 0.007) and the 48.1% (R2 = 0.481, P = 0.024) of variance in seastars abundance, respectively, while juvenile coral densities did not significantly affect this (R2 = 0.132, P = 0.202). These results provide new information on the distribution and behaviour of an important corallivore of Maldivian reefs, such as C. schmideliana.

Very shallow coral reefs (<5 m deep) are naturally exposed to strong sea surface temperature variations, UV radiation and other stressors exacerbated by climate change, raising great concern over their future. As such, accurate and ecologically informative coral reef maps are fundamental for their management and conservation. Since traditional mapping and monitoring methods fall short in very shallow habitats, shallow reefs are increasingly mapped with Unmanned Aerial Vehicles (UAVs). UAV imagery is commonly processed with Structure-from-Motion (SfM) to create orthomosaics and Digital Elevation Models (DEMs) spanning several hundred metres. Techniques to convert these SfM products into ecologically relevant habitat maps are still relatively underdeveloped. Here, we demonstrate that incorporating geomorphometric variables (derived from the DEM) in addition to spectral information (derived from the orthomosaic) can greatly enhance the accuracy of automatic habitat classification. Therefore, we mapped three very shallow reef areas off KAUST on the Saudi Arabian Red Sea coast with an RTK-ready UAV. Imagery was processed with SfM and classified through object-based image analysis (OBIA). Within our OBIA workflow, we observed overall accuracy increases of up to 11% when training a Random Forest classifier on both spectral and geomorphometric variables as opposed to traditional methods that only use spectral information. Our work highlights the potential of incorporating a UAV’s DEM in OBIA for benthic habitat mapping, a promising but still scarcely exploited asset.