Modeling potential invasion range of alien invasive species,Hyptis suaveolens (L.) Poit. in India: Comparison of MaxEnt and GARP |
| |
| Institution: | 1. School of Environment and Natural Resources, Doon University, Dehradun, Uttarakhand, 248001, India;2. Indian Institute of Remote Sensing, Dehradun, Uttarakhand, 248001, India;3. Forest Research Institute, Dehradun, Uttarakhand, 248001, India;1. Department of Ecology & Environmental Science, Assam University, Silchar, Assam, India;2. Centre for Biodiversity and Natural Resource Conservation, Assam University, Silchar, Assam, India;3. Department of Forestry, North Eastern Regional Institute of Science and Technology, Nirjuli, Arunachal Pradesh, India;1. Centre for Climate Change and Adaptation Research, Anna University, Chennai 600 025, India;2. Environmental Informatics and Spatial Modeling Lab (EISML), Department of Ecology and Environmental Sciences, School of Life sciences, Pondicherry University, Puducherry 605 014, India;1. Department of Forest Resources Management, Faculty of Forestry, University of British Columbia, Canada;2. Forestry and Ecology Department, Indian Institute of Remote Sensing, ISRO, Dehradun 248001, India |
| |
| Abstract: | Bushmint (Hyptis suaveolens (L.) Poit.) is one among the world's most noxious weeds. Bushmint is rapidly invading tropical ecosystems across the world, including India, and is major threat to native biodiversity, ecosystems and livelihoods. Knowledge about the likely areas under bushmint invasion has immense importance for taking rapid response and mitigation measures. In the present study, we model the potential invasion range of bushmint in India and investigate prediction capabilities of two popular species distribution models (SDM) viz., MaxEnt (Maximum Entropy) and GARP (Genetic Algorithm for Rule-Set Production). We compiled spatial layers on 22 climatic and non-climatic (soil type and land use land cover) environmental variables at India level and selected least correlated 14 predictor variables. 530 locations of bushmint along with 14 predictor variables were used to predict bushmint distribution using MaxEnt and GARP. We demonstrate the relative contribution of predictor variables and species-environmental linkages in modeling bushmint distribution. A receiver operating characteristic (ROC) curve was used to assess each model's performance and robustness. GARP had a relatively lower area under curve (AUC) score (AUC: 0.75), suggesting its lower ability in discriminating the suitable/unsuitable sites. Relative to GARP, MaxEnt performed better with an AUC value of 0.86. Overall the outputs of MaxEnt and GARP matched in terms of geographic regions predicted as suitable/unsuitable for bushmint in India, however, predictions were closer in the spatial extent in Central India and Western Himalayan foothills compared to North-East India, Chottanagpur and Vidhayans and Deccan Plateau in India. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
