Anjar Dimara Sakti, Kurnia Putri Adillah, Cokro Santoso, Ismail Al Faruqi, Vempi Satriya Adi Hendrawan, Parwati Sofan, Rustam, Adam Irwansyah Fauzi, Yudi Setiawan, Inggita Utami, Alinda F.M. Zain, Muhammad Kamal
This study aimed to analyze the habitat suitability of the endangered Proboscis monkey (Nasalis larvatus) on Borneo using a multi-machine-learning approach. This study integrated physical, vegetational, meteorological, and human activity data to develop a comprehensive habitat suitability model. Four machine-learning algorithms, namely, maximum entropy (MaxEnt), random forest (RF), support vector machine (SVM), gradient tree boosting (GTB), and classification and regression trees (CART), were employed to model the habitat suitability index. A total of 1943 sample points were divided into training (70 %) and validation (30 %) sets for the analysis. This study included three main stages: geospatial database creation, spatial habitat modeling using multi-machine-learning algorithms, and habitat suitability evaluation. In addition, the pressure from human development on the habitat suitability index model was analyzed. This study identified a high level of suitability for Proboscis monkey habitats in nearshore areas. The maximum habitat suitability for Proboscis monkeys was observed to be 11.54 %, as evidenced by the consensus of the MaxEnt value and four machine-learning algorithms. Conversely, the minimum habitat suitability was recorded at 13.27 %, as indicated by disagreement among all algorithms. The AUC values for the machine-learning models ranged from 74 % to 90 %, indicating moderate to high predictive performance. This study provides valuable insights for the formulation of well-planned development programs for Proboscis monkeys. The results of this study will contribute to the accurate identification of potential Proboscis monkey habitats, thereby providing support for conservation efforts aimed at safeguarding this endangered species. © 2024 The Authors
Geographic Information Sciences and Technology Research Group, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Bandung, Indonesia; Center for Remote Sensing, Institut Teknologi Bandung, Bandung, Bandung, Indonesia; Department of Civil and Environmental Engineering, Universitas Gadjah Mada, Yogyakarta, Indonesia; National Research and Innovation Agency (BRIN), Jakarta, Indonesia; Faculty of Forestry, Mulawarman University, East Kalimantan, Samarinda, Indonesia; Faculty of Regional and Infrastructure Technology, Institut Teknologi Sumatera, Lampung, Indonesia; Department of Forest Resource Conservation and Ecotourism, Faculty of Forestry and Environment, IPB University, Bogor, Indonesia; Biology Department, Faculty of Applied Science and Technology, Ahmad Dahlan University, Yogyakarta, Indonesia; Department of Landscape Architecture, Faculty of Agriculture, IPB University, Bogor, Indonesia; Department of Geographic Information Science, Faculty of Geography, Universitas Gadjah Mada, Yogyakarta, Indonesia