The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-4/W19
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W19, 449–454, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W19-449-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W19, 449–454, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W19-449-2019

  23 Dec 2019

23 Dec 2019

SPECIES DISTRIBUTION MODELLING OF TWO SPECIES ENDEMIC TO THE PHILIPPINES TO SHOW THE APPLICABILITY OF MAXENT

M. Z. G. Untalan, D. F. M. Burgos, and K. P. Martinez M. Z. G. Untalan et al.
  • Department of Geodetic Engineering, University of the Philippines Diliman, Philippines

Keywords: Maxent, Species Distribution Modelling, Endemic Species, Likelihood

Abstract. Maxent is a machine learning model used for species distribution modelling (SDM) that is rising in popularity. As with any species distribution model, it needs to be validated for certain species before being used to generate insights and trusted predictions. Using Maxent, SDM of two endemic species in the Philippines, Varanus palawanensis (Palawan monitor lizard) and Caprimulgus manillensis (Philippine nightjar), were created using presence-only data, with 14 V. palawanensis and 771 C. manillensis occurrences, and 19 bioclimatic variables from BIOCLIM. This study shows the consistency to historical facts of Maxent on two endemic species of the Philippines of varying nature. The applicability of Maxent on the two very different species show that Maxent has high likelihood to give good results for other species. Showing that Maxent is applicable to the species of the Philippines gives additional tools for ecologists and national administrators to lead the development of the Philippines in the direction that conserves the biodiversity of the Philippines and that increases the productivity and quality of life in the Philippines.