過去の観測を踏まえたイフガオ棚田の将米予測

京都大学新制・課程博士博士(地球環境学)甲第24954号地環博第245号京都大学大学院地球環境学舎環境マネジメント専攻(主査)教授 星野 敏, 准教授 鬼塚 健一郎, 教授 伊藤 孝行学位規則第4条第1項該当Doctor of Global Environmental StudiesKyoto UniversityDFA

A species-specific individual-based simulation model of mixed mangrove forest stands

A species-specific spatially explicit individual-based model has been developed to simulate the development of mixed mangrove forest stands featuring eight species. The model is a forest stand model that forecasts mangrove forest development in a 50 m x 50 m plot by simulating the recruitment, growth, and mortality of individual mangrove trees. Species-specific growth rates, shade responses, and salinity responses of each species were incorporated to observe differences in forest structure given different salinity conditions. The model used a modified Field of Neighborhood (FON) approach that considers species-specific responses to shading and a salinity response function that considers the species-specific salinity upper boundary value of optimum growth and maximum porewater salinity of a mangrove. Simulation results of 300 years given salinity conditions in a specific site in Katunggan It Ibajay (KII) showed matching dominant species in the site. Simulation results of 500 years given extreme low and high salinity values showed consistent forest dynamics where above-ground biomass and tree count approach certain limit values as the forest stand matures. Simulation results also of 300 years given salinity values ranging from 1 – 37 ppt showed the different dominant species for different salinity conditions

University Educator and Staff Well-being and Common Mental Health Symptoms during the COVID-19 Pandemic in the Philippines

Educators and staff adapted to work-from-home setup amidst the covid-19 pandemic.  The transition to full-online classes and services leads to poor mental health. The current study explored the association of educator and staff personal characteristics, well-being, and mental health.  326 university employees completed the demographic profile, mental health, and well-being scales. Various hierarchical regression was conducted to determine if personal characteristics and well-being predict common mental health symptoms (depression, anxiety, and stress). Series of multivariate analyses of variance (MANOVA) was conducted to determine the difference between the levels of mental health symptoms according to mental health category, and personal characteristics. The results support the hypothesis with psychological and emotional well-being inversely predicting depression, anxiety, and stress. However, social well-being failed to serve as a significant determinant of common mental health symptoms. MANOVA obtained a significant difference with common mental health symptoms and mental health category and personal characteristics

A statistical model of land use/cover change integrating logistic and linear models: An application to agricultural abandonment

Several land use/cover change (LUCC) models have been developed to simulate future LUCC. However, current models work with the assumption that the input non-spatial variables are significant to the LUCC in hand and there is still a lack of model that could identify which non-spatial variables are significant drivers of LUCC. This paper presents a statistical model of LUCC that integrates a logistic model based on spatial drivers and a linear model based on non-spatial drivers. The logistic model produces a probability map that represents local probabilities of LUCC while the linear model produces a global probability threshold that represents a global probability of LUCC, and by comparing the two variables, LUCC is mapped. The statistical model was utilized to model agricultural abandonment in the Ifugao rice terraces, Philippines. Statistical modeling identified the significant spatial and non-spatial drivers of agricultural abandonment in the terraces. Accuracy assessment showed that simulated maps achieved accuracies suitable for LUCC simulation, demonstrating that the statistical model can be a potential tool for prediction of future LUCC