Reef complexity influences distribution and habitat choice of the corallivorous seastar Culcita schmideliana in the Maldives

Abstract

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.

Biogeochemistry and timing of methane-derived carbonate formation at Leirdjupet fault complex, SW Barents sea

“…Then, the images were processed in Agisoft Metashape ® , following a well-established photogrammetry workflow (Fallati et al, 2020;Montalbetti et al, 2022). As final outputs a 3D mesh, orthomosaics and Digital Terrain Models (DTMs) at high-resolution were obtained, allowing us to get detailed morphometric parameters of the carbonate outcrops at a cm-scale resolution.…”

The pressures and opportunities for coral reef preservation and restoration in the Maldives

“…The Maldivian reefs were severely affected: sea surface temperatures peaked from late April to mid-May 2016 with recorded temperatures over 32 • C. The overall percentage of bleached corals was recorded at around 75%, including corals sitting as far as 15 m depth (Ibrahim et al, 2017;Perry and Morgan, 2017). Other stressors also include an increased recurrence of different epizootic disease caused by fungi, protozoa and bacteria (Montano et al, 2012(Montano et al, , 2013(Montano et al, , 2014(Montano et al, , 2015cSeveso et al, 2012Seveso et al, , 2015, as well as by the anomalous outbreaks of corallivorous organisms that feed on corals, such as the crown-of-thorns seastar Acanthaster species and the cushion star Culcita species (Saponari et al, 2014(Saponari et al, , 2018Montalbetti et al, 2019Montalbetti et al, , 2022.…”

Characterizing Håkon Mosby Mud Volcano (Barents Sea) cold seep systems by combining ROV-based acoustic data and underwater photogrammetry

“…SfM is a computer vision technique that reconstructs 3D structures and camera motion from a sequence of overlapping 2D images or video frames (Westoby et al, 2012). This technique has gained popularity in marine imaging research, both in shallow and deep-sea environments (Leon et al, 2015;Robert et al, 2017;Price et al, 2019;Montalbetti et al, 2022). In this work, using a novel dataset, we combine data from ROV microbathymetry and ultra-high resolution SfM to enhance our understanding of the geomorphological and ecological characteristics of the Håkon Mosby Mud Volcano (HMMV).…”