Seagrass is a valuable and important habitat, providing services such as coastal protection, supporting fisheries, and carbon sequestration. However, it is challenging to map accurately, as remote sensing has limits to how deep in the water column it can penetrate, and uncertainties such as distinguishing between algae and seagrass. Seagrass can exist at depths of theoretically 90 m deep in ultraoligotrophic waters, meaning that there is much of this habitat that cannot be mapped by remote sensing. Green turtles are an ideal candidate to help find seagrass blue carbon resources in the Red Sea. They go through an ontogenetic dietary shift to become almost completely herbivorous, and have a high fidelity to foraging sites. In this study we aim to assess the use of green turtles Chelonia mydas in discovering seagrass blue carbon. We use telemetry from 53 turtles tagged over 2018, 2019, and 2021 to map their foraging areas. 50 out of the 53 (94.34%) foraging sites had not been visited by previous seagrass studies in the Red Sea. We visited 18 locations in 14 of these foraging sites to ground truth them, and all 14 foraging sites (100%) had seagrass present. Comparatively, 18 out of 30 sites where seagrass was indicated by the remote sensing-based Allen Coral Atlas showed no seagrass. The turtles were seen to favour travelling shorter distances, thus it will be necessary to expand the area of tagging in order to achieve complete coverage of the Red Sea. Approximately 1/3 of the visited sites were deeper than 8 m, and so out of range of remote sensing, showing that considerable blue carbon resources may be discovered with the use of turtles. Samples were taken for carbon stock estimation from the ground truthed sites. A mean carbon stock of 4.89 ± 0.83 kg Corg m-2 was estimated for 1 m depth sediment. In the future it is important to develop methods for mapping the surface areas of the deep and inaccessible seagrass habitats that the turtles discover.
| Identifer | oai:union.ndltd.org:kaust.edu.sa/oai:repository.kaust.edu.sa:10754/686418 |
| Date | 27 November 2022 |
| Creators | Mann, Hugo F. |
| Contributors | Duarte, Carlos M., Biological and Environmental Science and Engineering (BESE) Division, Afifi, Abdulakader M., Johnson, Maggie D. |
| Source Sets | King Abdullah University of Science and Technology |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Rights | 2023-12-14, At the time of archiving, the student author of this thesis opted to temporarily restrict access to it. The full text of this thesis will become available to the public after the expiration of the embargo on 2023-12-14. |
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