Predicting the current and future suitable habitat distribution of Myristica dactyloides Gaertn. using MaxEnt model in the Eastern Ghats, India

• Published in: Ecological engineering Vol. 82; pp. 184 - 188
• Main Authors: Remya, K., Ramachandran, A., Jayakumar, S.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2015
• Subjects: Annual precipitation; Assessments; Biodiversity; Climate change; Habitat suitability; Habitats; India; Mathematical models; MaxEnt; Myristica; Myristica dactyloides Gaertn; Wildlife conservation; India; Climate change; Habitat suitability; Myristica dactyloides Gaertn; MaxEnt
• ISSN: 0925-8574; 1872-6992
• DOI: 10.1016/j.ecoleng.2015.04.053

Global biodiversity has already been altered by the climatic changes in various means like species migration, changes in habitat distribution, seasonality changes in phenology etc. In order to implement sustainable conservation or adaptation strategy, it is necessary to understand the impacts of climate change on both ecosystem and species level. Here we present an assessment on current and future habitat suitability distribution of Myristica dactyloides Gaertn. (MD), a medicinally and ecologically important tree species by using a maximum entropy (MaxEnt) species distribution model. The future predictions were done for the year 2050 and 2070 using the bioclimatic variables having 1km spatial resolution from two different models of fifth phase of the coupled model intercomparison project (CMIP5). This study was carried out in the Kolli hill, Eastern Ghats of India. The AUC values confirmed the accuracy of model prediction based on 22 occurrence points. Environmental variables’ contributions were evaluated using jackknife test. The more influencing variables will be annual temperature, annual precipitation and precipitation of wettest month. Finally, this study found that there will be a significant reduction in the habitat suitability distribution of Myristica dactyloides in the year 2050 and 2070 in the study area. Hence with the performance of the model, this study found that MaxEnt could be an effective tool for species rehabilitation and biodiversity conservation planning in the light of climate change.