MODELING SPECIES DISTRIBUTION of SHOREA GUISO (BLANCO) BLUME and PARASHOREA MALAANONAN (BLANCO) MERR in MOUNT MAKILING FOREST RESERVE USING MAXENT

Abstract

© 2020 Authors. 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.

Source or Periodical Title

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

ISSN

16821750

Page

441-448

Document Type

Article

Subject

climate change, climate emission scenarios, dipterocarp species, Global Climate Models, Maximum Entropy, Species distribution

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