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PREDICTING HABITAT DISTRIBUTION OF ENDEMIC AND CRITICALLY ENDANGERED DIPTEROCARPUS LITTORALIS IN NUSAKAMBANGAN, INDONESIA

Iyan Robiansyah
Reinwardtia (Sinta 2)Vol. 0 No. 031 Desember 2017

Abstrak

ROBIANSYAH, I. 2017. Predicting habitat distribution of endemic and critically endangered Dipterocarpus littoralis in Nusakambangan, Indonesia. Reinwardtia 16(1): 11 - 18. - The tree species Dipterocarpus littoralis (Bl.) Kurz. is endemic to Nusakambangan and categorized as critically endangered. In the present study, the habitat suitability of the species in Nusakambangan was predicted using logistic regression analysis and Maxent model. Three topographic variables (elevation, slope, and aspect), distance from river and coastline, and one vegetation index (Normalized Difference Vegetation Index (NDVI)) as well as two water content indexes (Normalized Difference Water Index (NDWI) and Normalized Difference Moisture Index (NDMI)) were used as predictors of the models. Employing initial number of 82 presence and 250 absence data of D. littoralis, both models were able to predict the suitable areas for the species with fairly high success rate. The AUC and Kappa value for logistic regression were 0.77 ± 0.027 and 0.34 ± 0.058, respectively, while the respected values for Maxent were 0.91 ± 0.062 and 0.37 ± 0.025. Logistic regression analysis identified a total area of 26.13 km2 to be suitable for D. littoralis, while a smaller suitable area (7.85 km2) was predicted by Maxent model. Coastal areas in the west part of the island were predicted by both models as areas with high suitability for D. littoralis. Furthermore, distance from coastline and river, elevation, NDVI, NDWI and NDMI were suggested to be very important for the species ecology and distribution. The results of this study may serve as a basis for population reinforcement and reintroduction programs of D. littoralis and guide for ecosystem management of Nusakambangan Island as a whole.

Kata Kunci

Critically endangeredDipterocarpus littoralisendemic specieslogistic regressionMaxentNusakambangan

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