The Effective Ecological Factors and Vegetation at Koh Chang Island, Trat Province, Thailand

This study aims to characterize the tropical rain forest present in the Chang Island, Trat Province, Thailand, and to analyze the environmental factors to determine its composition and structure. Thirty one plots were sampled, plant cover was measured in 20 × 40 m2 plots, and the importance value index was calculated. A total of 78 species belonging to 32 families were identified.Twenty soil samples were analyzed, and cluster analysis was employed to classify the vegetation communities. Floristic and environmental data were evaluated and ordered using canonical correspondence analysis. The results showed that the vegetation communities could be divided into 4 types and were significantly (p Calophyllum thorelii Pierrecommunity (Type 2). The Dipterocarpus (Hopea pierrei Heim) community (Type 3) was more likely to occur in regions with moderate to high levels of TWI, but the result from cluster analysis showed that some of the plot samples from the Dipterocarpus community were separated by characteristic importance value index (IVI) values. There was also evidence that the area was impacted by an old disturbance created by a rubber plantation. This impact was referred to as a secondary succession community (Type 4).

Environmental factors control and climate change impact on forest type: Dong PraYa Yen-KhaoYai world heritage in Thailand

Climate is a major determinant of global vegetation patterns and has a significant influence on the distribution and structure of forest ecosystems. Dong PraYa Yen-KhaoYai Forest Complex has been a UNESCO natural world heritage site since 2007, but little is known about its plant community. Our study aims to identify each plant community within the world heritage area and calculate its potential for carbon content. We determine both the relationship between forest type and both physio-chemical soil properties and climate change impact. We employed allometric equations to calculate aboveground biomass and both cluster analysis and canonical correspondence analysis (CCA) to examine the relationship between forest type and physiochemical soil properties. An equation for each physical parameter was used to predict the forest model. The climate scenario under A2 and B2 was applied to calculate future predominant forest types. Our results reveal that the forest ecosystems at Tab Lan (TL) have the highest species count (332 species) followed by Pang Srida (PD), KhaoYai (KY), Dong Yai (DY), and Tapraya (TY), with 293, 271, 169, and 99 species, respectively. We found KY to have the highest recorded carbon storage value at 2507.6 tC/ha followed by TL, PD, TY, and DY (1613.8, 1269.1, 844 and 810.7 tC/ha, respectively). Cluster analysis results indicated that the dominant species in each forest type is different. Moreover, CCA revealed that soil organic matter (SOM) and soil acid-base indicators are the best parameters to establish correlation for each forest type. Based on our results, future climate predictions show a negative impact on evergreen forests, but a positive one on deciduous ones.