Conservation management of Saraca asoca (Roxb.) W. J. de Wilde (Fabaceae) using ecological niche modeling

Considering the medicinal and conservational significance of Saraca asoca, the present study employed three different geographical ranges for building ecological niche models. The vifstep procedure detected multicollinearity among 10 out of 19 predictor variables. The selected subset included mean diurnal range, isothermality, mean temperature of wettest quarter, mean temperature of driest quarter, annual precipitation, precipitation of driest month, precipitation seasonality, precipitation of warmest quarter, and precipitation of coldest quarter. The performances of machine learning and regression approaches were compared. Machine learning algorithm RF outweighed all other algorithms in performance. Following RF, model algorithms viz., Maxent, BRT, GLM, FDA, and Bioclim performed better in the declining order. Machine learning algorithms performed better than regression and profile-based approaches. The weighted average of True skill statistic was used to develop ensemble models. Potential habitats in native and introduced ranges in present and future conditions were identified. Introduction potential in unintroduced areas where herbal medicines were in greater use was also assessed. With rise in emissions, range of S. asoca may prefer an eastward expansion in native range and northward expansion in Andaman Nicobar Islands. If S. asoca is planted in recommended potential ranges in African and Latin American continents, eastward expansion in West Africa and westward expansion in Latin America may occur if temperature rises. The present study could develop a robust evidence-based hypothesis for ecologists, conservationists, herbal medicine manufactures, government agencies, and forest departments at national/international level to establish plantations for growing S. asoca.