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Species distribution model to predict the occurrence of Malayan partridge

Leong, Darren Chien Hsiung (2025) Species distribution model to predict the occurrence of Malayan partridge. Final Year Project, UTAR.

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    Abstract

    Climate change has caused several problems in Malaysia such as increase of temperature and change in precipitation patterns. Malayan Partridge (Arborophila campbelli) is a bird species found in Peninsular Malaysia that is facing the threat of habitat loss due to climate changes. Currently, this species is understudied and that leads to less information about the future occurrence of this species. Therefore, this study aims to produce a prediction of current and future occurrence of Malayan Partridge in Peninsular Malaysia with different models Species Distribution Model (SDM) that are Maximum Entropy Model (MaxEnt), Random Forest (RF), Support Vector Machine (SVM), Generalized Linear Model (GLM) and Bioclim. Different pseudoabsence data settings will be implemented to identify the best setting to predict the occurrence of the species. Species occurrence data were collected from public biodiversity databases 19 bioclimatic variables were sourced from WorldClim to predict the current occurrence of the species. A variable selection process will be used to identify the important bioclimatic variables. These variables will be used for the models of SDM. To predict the potential future occurrence of the species, Shared Socioeconomic Pathway (SSP) will be implemented. The performance of the model will be evaluated through Area Under Curve (AUC) and cross-validation techniques. Habitat suitability maps will be produced because of the model to provide visualization.

    Item Type: Final Year Project / Dissertation / Thesis (Final Year Project)
    Subjects: T Technology > T Technology (General)
    Divisions: Faculty of Information and Communication Technology > Bachelor of Computer Science (Honours)
    Depositing User: ML Main Library
    Date Deposited: 28 Dec 2025 23:32
    Last Modified: 28 Dec 2025 23:32
    URI: http://eprints.utar.edu.my/id/eprint/7095

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