Welcome to International Network for Natural Sciences | INNSpub

Maxent modeling of the habitat distribution of the critically endangered Pterocarpus indicus Willd. forma indicus Inmindanao, Philippines

Research Paper | March 1, 2017

| Download 11

Joseph C. Paquit, Nelson M. Pampolina, Cristino L.Tiburan Jr., Mutya Ma. Q. Manalo

Key Words:

J. Bio. Env. Sci.10( 3), 112-122, March 2017


JBES 2017 [Generate Certificate]


A current and projected suitable habitat distribution models for Pterocarpus indicus Willd. forma indicus were generated using Maximum Entropy Modeling algorithm (MaxEnt). The Receiver Operating Characteristic (ROC) – Area Under Curve (AUC) of the training and test data were 0.854 and 0.920 respectively. It was highly above the random prediction AUC of 0.5; therefore the model performance was good, reasonable and valid. The predicted suitable habitat distribution of Smooth Narra was heavily influenced by climatic variables. The variable with largest contribution was Mean Temperature of Warmest Quarter (MTWQ) with 31.2%. It was followed by Soil with 20.3%. Annual Precipitation (AP) and Precipitation of Driest Quarter (PDQ) belonged to 3rd and 5th in contribution rank with 12.8% and 8.8% respectively. This study also found out that the spatial pattern of distribution of suitable habitats is clustered. The study also predicted changes with suitable area coverage in terms of land class, protected areas and administrative boundaries would likely occur as climatic conditions change.


Copyright © 2017
By Authors and International Network for
Natural Sciences (INNSPUB)
This article is published under the terms of the Creative
Commons Attribution Liscense 4.0

Maxent modeling of the habitat distribution of the critically endangered Pterocarpus indicus Willd. forma indicus Inmindanao, Philippines

Araujo MB, Guisan A. 2006. Five challenges for species distribution modelling. Journal of Biogeography 33, 1677e1688.

Balmford A, Bond W. 2005. Trends in the state of nature and their implications for human well-being. Ecology Letters, 8(11), 1218–34. http://dx.doi.org/10.1111/j.1461-0248.2005.00814.x

Elith J, Leathwick JR. 2006. Species Distribution Models: Ecological Explanation and Prediction across Space and Time.  Evol. Syst.

Elith J, Phillips SJ, Hastie T, Dudík M, Chee, YE, Yates CJ. 2006. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17(1), 43–57. http://dx.doi.org/10.1111/j.1472-4642.2010.00725.x

Garcia K, Lasco R, Ines A, Lyon B, Pulhin F. 2013. Predicting geographic distribution and habitat suitability due to climate change of selected threatened forest tree species in the Philippines. Applied Geography 44, 12–22. http://dx.doi.org/10.1016/j.apgeog.2013.07.005

Guisan A, Thuiller W. 2005. Predicting species distribution: offering more than simple habitat models. Ecology Letters 8(9), 993e1009.

Hernandez PA, Graham CH, Master LL,   Albert DL. 2006. The effect of sample size and species characteristics on performance of different species distribution modelling methods. Ecography, 29, 773e785.

Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A. 2005. Very high resolution interpolated climate surfaces for global land areas. Inter. J. Climatology.

HOFAR, JANSSON R, Nilsson C. 2012. The usefulness of elevation as a predictor variable in species distribution modelling. Ecological Modelling, 246, 86–90. http://dx.doi.org/10.1016/j.ecolmodel.2012.07.028   www.iucnredlist.org/apps/redlist/search/link/4eaa46f4-087a0212

Intergovernmental Panel on Climate Change (IPCC). 2007. Summary for policymakers. In S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, (Eds.), Climate change 2007: The physical science basis. Contribution ofWorking Group 1 to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 1e18). Cambridge: Cambridge University Press, UK Available at www.ipcc.ch/publications_and_data/publications

Khanum R, Mumtaz AS, Kumar S. 2013. Predicting impacts of climate change on medicinal asclepiads of Pakistan using MaxEnt modeling. Acta Oecologica, 49, 23–31. http://dx.doi.org/10.1016/j.actao.2013.02.007

Leathwick JR, Elith J, Hastie T. 2006. Comparative performance of generalized additive models and multivariate adaptive regression splines for statistical modelling of species distributions. Ecological Modelling, 199(2), 188–196. http://dx.doi.org/10.1016/j.ecolmodel.2006.05.022

Lemieux CJ, Scott DJ. 2011. Changing climate, challenging choices: Identifying and evaluating climate change adaptation options for protected areas management in Ontario, Canada. Environmental Management; http://dx.doi.org/10.1007/s00267-011-9700-x.

PAGASA. 2011. Climate change scenarios in the Philippines, (February).

Phillips SJ, Dudık M, Schapire RE. 2004. A maximum entropy approach to species distribution modeling. In: Proceedings of the 21st International Conference on Machine Learning. ACM Press, New York.

Soares-Filho B, Moutinho P, Nepstad D, Rodrigues A,  Garcia H, Dietzsch R, MErry L, Bowman F, Letícia H, Rafaella S,   Cláudio M. 2009. Role of Brazilian Amazon Protected Areas in Climate Change Mitigation.

Tererai F, Wood A. 2014. On the present and potential distribution of Ageratina Adenophora (Asteraceae)  in South Africa. South African Journal of Botany. 152-158 http://dx.doi.org/10.1016/j.sajb.2014.09.001.

Thomson LA. 2006. Pterocarpus indicus (NARRA). Traditional Trees of Pacific Islands: Their Culture, Environment, and Use. Permanent Agriculture Resources, Holualoa, Hawaii.

Trisurat Y, Shrestha R,  Kjelgren R. 2011. Plant species vulnerability to climate change in Peninsular Thailand. Applied Geography, 31,1106e1114.

Wisz MS, Hijmans RJ, Li J, Peterson AT. Graham CH, Guisan A. 2008. Predicting Species Distributions Working Group, Effects of sample size on the performance of species distribution models.  Divers. Dist.