Habitat modeling and potential distribution of the Malayan sun bear (Helarctos Malayanus raffles) using geospatial information technolog

While tropical rain forests are known as biological hotspot, few studies have been conducted to determine the potential distribution of species. Distribution modeling in tropical areas with high rate of deforestation and loosing connectivity is critically important for species management programs. I...

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Main Author:	Nazeri, Mona
Format:	Thesis
Language:	English English
Published:	2011
Subjects:	Sun bear - Habitat Habitat (Ecology) Sun bear - Geographical distribution
Online Access:	http://psasir.upm.edu.my/id/eprint/27229/1/FH%202011%2015R.pdf
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id	my-upm-ir.27229
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spelling	my-upm-ir.272292014-01-20T01:32:14Z Habitat modeling and potential distribution of the Malayan sun bear (Helarctos Malayanus raffles) using geospatial information technolog 2011-06 Nazeri, Mona While tropical rain forests are known as biological hotspot, few studies have been conducted to determine the potential distribution of species. Distribution modeling in tropical areas with high rate of deforestation and loosing connectivity is critically important for species management programs. Identifying the ecological requirements of the species and delineating the distribution of species throughout the entire habitat are fundamental in nature conservation. Simulations of spatially – explicit habitat enables conservation planners to identify key areas to protect, detection of wildlife corridors and preserve landscapes. This study assessed the application of Species Distribution Modeling (SDM) to Malayan Sun Bear habitat using the Maximum Entropy (MaxEnt) and Ecological Niche Factor Analysis (ENFA) with special emphasis on remote sensing and geographic information system (GIS) data. In order to test the models, two different spatial scales of Krau Wildlife Reserve (KWR) and Peninsular Malaysia were selected to model suitable habitat of Malayan Sun Bear. Results showed that both modeling outputs were acceptable in two different scales even though, MaxEnt could discriminate marginal and high suitable habitats better when applied on larger scale. On the other hand, ENFA had better results when applying in smaller scale. These contrasts in suitability maps were admitted by Area Under the receiver operator characteristic Curve (AUC) plots as well. AUC values for models ranged from 0.87 in small scale of KWR and 0.97 for large scale of Peninsular Malaysia, suggesting strong and accurate predictable species-Ecogeographical matching. The environmental variables applied in methodology such as land cover, vegetation indexes and climatic variables had higher correlation with suitability map creation. Comparing the output of suitability maps of the models showed that in Peninsular Malaysia, MaxEnt separated high suitable area by covering 5% of the total 131598 km2 and 13% of the total area to the marginal habitat. On the other hand, ENFA suitable habitat was doubled to 10% and for marginal habitat it was covering 24% of Peninsular Malaysia. Results of the best model revealed that the protected areas covered only 21.9% of the total marginal and suitable habitat. Extending the boundaries of protected areas and establishing new areas has the highest priority for any conservation action plans. This study plays an important role in increasing the limited knowledge of habitat preferences of the Malayan Sun Bear. Sun bear - Habitat Habitat (Ecology) Sun bear - Geographical distribution 2011-06 Thesis http://psasir.upm.edu.my/id/eprint/27229/ http://psasir.upm.edu.my/id/eprint/27229/1/FH%202011%2015R.pdf application/pdf en public phd doctoral Universiti Putra Malaysia Sun bear - Habitat Habitat (Ecology) Sun bear - Geographical distribution Faculty of Forestry English
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English English
topic	Sun bear - Habitat Habitat (Ecology) Sun bear - Geographical distribution
spellingShingle	Sun bear - Habitat Habitat (Ecology) Sun bear - Geographical distribution Nazeri, Mona Habitat modeling and potential distribution of the Malayan sun bear (Helarctos Malayanus raffles) using geospatial information technolog
description	While tropical rain forests are known as biological hotspot, few studies have been conducted to determine the potential distribution of species. Distribution modeling in tropical areas with high rate of deforestation and loosing connectivity is critically important for species management programs. Identifying the ecological requirements of the species and delineating the distribution of species throughout the entire habitat are fundamental in nature conservation. Simulations of spatially – explicit habitat enables conservation planners to identify key areas to protect, detection of wildlife corridors and preserve landscapes. This study assessed the application of Species Distribution Modeling (SDM) to Malayan Sun Bear habitat using the Maximum Entropy (MaxEnt) and Ecological Niche Factor Analysis (ENFA) with special emphasis on remote sensing and geographic information system (GIS) data. In order to test the models, two different spatial scales of Krau Wildlife Reserve (KWR) and Peninsular Malaysia were selected to model suitable habitat of Malayan Sun Bear. Results showed that both modeling outputs were acceptable in two different scales even though, MaxEnt could discriminate marginal and high suitable habitats better when applied on larger scale. On the other hand, ENFA had better results when applying in smaller scale. These contrasts in suitability maps were admitted by Area Under the receiver operator characteristic Curve (AUC) plots as well. AUC values for models ranged from 0.87 in small scale of KWR and 0.97 for large scale of Peninsular Malaysia, suggesting strong and accurate predictable species-Ecogeographical matching. The environmental variables applied in methodology such as land cover, vegetation indexes and climatic variables had higher correlation with suitability map creation. Comparing the output of suitability maps of the models showed that in Peninsular Malaysia, MaxEnt separated high suitable area by covering 5% of the total 131598 km2 and 13% of the total area to the marginal habitat. On the other hand, ENFA suitable habitat was doubled to 10% and for marginal habitat it was covering 24% of Peninsular Malaysia. Results of the best model revealed that the protected areas covered only 21.9% of the total marginal and suitable habitat. Extending the boundaries of protected areas and establishing new areas has the highest priority for any conservation action plans. This study plays an important role in increasing the limited knowledge of habitat preferences of the Malayan Sun Bear.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Nazeri, Mona
author_facet	Nazeri, Mona
author_sort	Nazeri, Mona
title	Habitat modeling and potential distribution of the Malayan sun bear (Helarctos Malayanus raffles) using geospatial information technolog
title_short	Habitat modeling and potential distribution of the Malayan sun bear (Helarctos Malayanus raffles) using geospatial information technolog
title_full	Habitat modeling and potential distribution of the Malayan sun bear (Helarctos Malayanus raffles) using geospatial information technolog
title_fullStr	Habitat modeling and potential distribution of the Malayan sun bear (Helarctos Malayanus raffles) using geospatial information technolog
title_full_unstemmed	Habitat modeling and potential distribution of the Malayan sun bear (Helarctos Malayanus raffles) using geospatial information technolog
title_sort	habitat modeling and potential distribution of the malayan sun bear (helarctos malayanus raffles) using geospatial information technolog
granting_institution	Universiti Putra Malaysia
granting_department	Faculty of Forestry
publishDate	2011
url	http://psasir.upm.edu.my/id/eprint/27229/1/FH%202011%2015R.pdf
_version_	1747811578953072640

Habitat modeling and potential distribution of the Malayan sun bear (Helarctos Malayanus raffles) using geospatial information technolog

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