Numerical phenetic and phylogenetic relationships in silico among brown seaweeds (Phaeophyceae) from Gunungkidul, Yogyakarta, Indonesia
Abstract. Ningrum AM, Chasani AR. 2021. Numerical phenetic and phylogenetic relationships in silico among brown seaweeds (Phaeophyceae) from Gunungkidul, Yogyakarta, Indonesia. Biodiversitas 22: 3057-3064. Human activities such as industrial and tourism development on coastal areas in Indonesia are...
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| Formato: | article |
| Lenguaje: | EN |
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MBI & UNS Solo
2021
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| Acceso en línea: | https://doaj.org/article/4b2ba378d84647eb9100811592baa19b |
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| Sumario: | Abstract. Ningrum AM, Chasani AR. 2021. Numerical phenetic and phylogenetic relationships in silico among brown seaweeds (Phaeophyceae) from Gunungkidul, Yogyakarta, Indonesia. Biodiversitas 22: 3057-3064. Human activities such as industrial and tourism development on coastal areas in Indonesia are dreaded to affect seaweed diversity, including brown seaweeds (Phaeophyceae). Though study on brown seaweeds diversity has been done quite a lot, there is no record of analysis of phenetic and phylogenetic relationships among brown seaweeds. Hence, this study aims to determine the phenetic and phylogenetic relationships in silico among brown seaweeds and define characters that play a role in the clustering of brown seaweeds from Gunungkidul, Yogyakarta, Indonesia. Exploration was done using purposive sampling method. Numerical phenetic analysis was generated using MVSP 3.1. Further clustering method was implemented to identify phenetic relationships The PCA method was used to reveal morphological, anatomical, and biochemical characters that determine the clustering pattern. The phylogenetic relationships in silico analysis were conducted using rbcL genes from NCBI GenBank database. All multiple sequences were aligned using ClustalW and phylogram reconstruction was performed using Neighbor-Joining (NJ) method in MEGA 7.0. Our study showed that both the analyses, i.e., numerical phenetic and phylogenetic relationships in silico resulted in two main clusters although the species composition of the clusters was slightly different. The PCA analysis indicated that the morphological characters i.e. blade shape, phylloid shape, thalli height, length of the main axis, and the water bladder shape play an important role in the clustering of brown seaweeds species. |
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