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2020/10/06 恭喜學程學生Mariana Reyes獲獎ICSD 2020優良論文獎



恭喜學程學生Mariana Reyes獲得 ICSD 2020優良論文獎



獲獎論文的摘要:
The coral reefs are important marine ecosystems but due to anthropogenic climate change and other harmful human activities, they face degradation. To slow down this decline, various coral restoration approaches exist such as remote sensing technology in detecting degraded coral reefs, encouraging the sexual propagation of corals, assisting their evolution such as selecting and breeding resistant genets, and/or stimulating gene flow by prioritizing coral species with desirable phenotypic traits various. However, these procedures are often expensive and are difficult to apply at a spatial scale that could be significant for the conservation of these ecosystems. Here, I propose a scalable restoration method which is based on a prior identification of the benthic communities and the selection of key coral morpho-functional groups that should be transplanted for encouraging population dynamics. A total of 55 transects among 5 geographical areas in Anilao, Mabini, Philippines at three depths (5m, 10m, 15) were selected for survey in this study. For each transect, the benthic composition using a morpho-function characterization will be documented after analyzing using photo-transects. The optimum k number of clusters will be identified among transects by using k -means clustering (unsupervised learning algorithm). In each benthic community, co-occurrence network analysis will be built to identify the key morpho-functional group with the highest betweenness centrality.
Among this group, coral species will be targeted and resilient colonies presenting no obvious sign of stress will be selected as donor colonies to implement a coral restoration protocol with local communities. Coral nubbins will be fixed in natural substrates using transplantation to the field and monitor the coral nubbins to see how impulse ecological succession. This project aims to advance toward a coral restoration strategy by the development of an inexpensive methodology bridging science and conservation effort by local communities.