Abstract. Climate change is regarded as one of the most significant drivers of biodiversity loss and altered forest ecosystems. This study aimed to model the current species distribution of two dipterocarp species in Mount Makiling Forest Reserve as well as the future distribution under different climate emission scenarios and global climate models. A machine-learning algorithm based on the principle of maximum entropy (Maxent) was used to generate the potential distributions of two dipterocarp species – Shorea guiso and Parashorea malaanonan. The species occurrence records of these species and sets of bioclimatic and physical variables were used in Maxent to predict the current and future distribution of these dipterocarp species. The variables were initially reduced and selected using Principal Component Analysis (PCA). Moreover, two global climate models (GCMs) and climate emission scenarios (RCP4.5 and RCP8.5) projected to 2050 and 2070 were utilized in the study. The Maxent models predict that suitable areas for P. malaanonan will decline by 2050 and 2070 under RCP4.5 and RCP 8.5. On the other hand, S. guiso was found to benefit from future climate with increasing suitable areas. The findings of this study will provide initial understanding on how climate change affects the distribution of threatened species such as dipterocarps. It can also be used to aid decision-making process to better conserve the potential habitat of these species in current and future climate scenarios.
This study maps distribution and spatial congruence between Above-Ground Biomass (AGB) and species richness of IUCN listed conservation-dependent and endemic avian fauna in Palawan, Philippines. Grey Level Co-Occurrence Texture Matrices (GLCMs) extracted from Landsat and ALOS-PALSAR were used in conjunction with local field data to model and map local-scale field AGB using the Random Forest algorithm (r = 0.92 and RMSE = 31.33 Mg·ha-1). A support vector regression (SVR) model was used to identify the factors influencing variation in avian species richness at a 1km scale. AGB is one of the most important determinants of avian species richness for the study area. Topographic factors and anthropogenic factors such as distance from the roads were also found to strongly influence avian species richness. Hotspots of high AGB and high species richness concentration were mapped using hotspot analysis and the overlaps between areas of high AGB and avian species richness was calculated. Results show that the overlaps between areas of high AGB with high IUCN red listed avian species richness and endemic avian species richness were fairly limited at 13% and 8% at the 1-km scale. The overlap between 1) low AGB and low IUCN richness, and 2) low AGB and low endemic avian species richness was higher at 36% and 12% respectively. The enhanced capacity to spatially map the correlation between AGB and avian species richness distribution will further assist the conservation and protection of forest areas and threatened avian species.
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