Spatial landscape model to characterize biological diversity using R statistical computing environment. (15th January 2018) - Singh, Hariom; Garg, R.D.; Karnatak, Harish C.; Roy, Arijit
Abstract: Due to urbanization and population growth, the degradation of natural forests and associated biodiversity are now widely recognized as a global environmental concern. Hence, there is an urgent need for rapid assessment and monitoring of biodiversity on priority using state-of-art tools and technologies. The main purpose of this research article is to develop and implement a new methodological approach to characterize biological diversity using spatial model developed during the study viz . Spatial Biodiversity Model (SBM). The developed model is scale, resolution and location independent solution for spatial biodiversity richness modelling. The platform-independent computation model is based on parallel computation. The biodiversity model based on open-source software has been implemented on R statistical computing platform. It provides information on high disturbance and high biological richness areas through different landscape indices and site specific information (e.g. forest fragmentation (FR), disturbance index (DI) etc.). The model has been developed based on the case study of Indian landscape; however it can be implemented in any part of the world. As a case study, SBM has been tested for Uttarakhand state in India. Inputs for landscape ecology are derived through multi-criteria decision making (MCDM) techniques in an interactive command line environment. MCDM with sensitivity analysis in spatial domain has been carried out to illustrate the model stability and robustness. Furthermore, spatial regression analysis has been made for the validation of the output. Graphical abstract: Image 1 Highlights: Spatial biodiversity model developed using R statistical computing environment. Landscape indices in parallel computing environment for large spatial datasets. Predicts spatial biological richness for Indian landscape and other indices. Integrated multi-criteria decision analysis for assigning weights. Sensitivity analysis for uncertainty assessment and robust decision making.