Shorea leprosula belongs to the Dipterocarpaceae family, a dominant family in Indonesia's rainforest. The population of this species has been depleted due to extensive logging, high rates of deforestation and forest degradation in the past several decades. However, the current status of the species' range and distribution remains unknown. This research project aims to understand and predict the distribution of S. leprosula in Kalimantan using species distribution models (SDMs). This study used presence records and presence-absence records from field surveys and the Global Biodiversity Information Facility (GBIF) database. Two modelling methods, MaxEnt and generalized linear models (GLMs), were used to predict species distribution. Prediction maps varied with modelling methods and different datasets, producing different emphases on areas suitable for S. leprosula. Even though the descriptive and predictive capabilities of the models are considered modest, the models provide useful insights about environmental factors that affect the distribution pattern of S. leprosula. Given the limitations of species records used in this study, the model outputs also need to be cautiously interpreted to avoid prediction biases. This research project also highlights some issues that arise in using small sample sizes in developing the model. Despite its limitations, the prediction maps generated by the models can give some hints to identify the areas with high possibility of the presence of S. leprosula in Kalimantan. In addition, this research project also provides some important information on how to improve model predictions for future development to support species conservation in Indonesia's rainforests.
|Journal||IOP Conference Series: Earth and Environmental Science|
|Publication status||Published - 21 May 2019|
|Event||IUFRO International and Multi-disciplinary Scientific Conference on Forest-Related Policy and Governance: Analyses in the Environmental Social Sciences, IUFROBOGOR 2016 - Bogor, Indonesia|
Duration: 4 Oct 2016 → 7 Oct 2016