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Machine Learning Global Ecological Niche Modelling of Indigofera oblongifolia (Forssk.): A Palatable Desert Legume
  • Preet Mathur,
  • manish mathur
Preet Mathur
Jodhpur Institute of Engineering and Technology Universe

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manish mathur
Central Arid Zone Research Institute
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The goal of this study was to identify the global geographical distribution patterns of a lesser known indigenous legume species, Indigofera oblongifolia, using three bio-climatic timeframes (current, 2050, and 2070) and four greenhouse gas scenarios (RCPs 2.6, 4.5, 6.0, and 8.5), as well as non-climatic predictors like global livestock population, human modification of terrestrial ecosystem (GHMTE), global fertilizers application (nitrogen and phosphorus). In addition, we assess the degree of indigenousness using the area, habitat suitability categories, and number of polygons, and we identify the temporal effects of various bio-climatic variables on its fundamental and realized niche. The AUC for models built using current climate data and RCPs for the years 2050 and 2070 was 0.90. This research reveals that climatic predictors outperform non-climatic predictors in terms of improving model quality. Precipitation Seasonality is one of the most important factors influencing this species’ optimum habitat suitability up to 150 mm for the current, 2050-RCP 8.5, and 2070-RCPs 2.6, 4.5, and 8.5. The range of this parameter has altered from 79–176.9 to 85–196 as the climatic conditions and RCPs have improved. Our ellipsoid niche modelling extends the range of these bioclimatic variables to 637 mm and 26.5-31.80 degrees Celsius, respectively. India has a higher indigenous score in the optimal class than the African region. These findings indicate that this species inhabits more continuous areas in Africa, whereas it is fragmented into a number of smaller meta-populations in India (group of spatially separated population of the same species).