Climate change impact on the distribution of Tossa jute using maximum entropy and educational global climate modelling

Abstract

Tossa (Corchorus olitorius L.) is a significant cash crop, cultivated commercially in the lower flood plain of Bangladesh. The climatic regimes in Bangladesh are changing as well as the world does. However, this species is threatened by climate change. Occurrences of data on threatened and endangered species are frequently sparse which makes it difficult to analyse the species suitable habitat distribution using various modelling approaches. The current paper used maximum entropy (Maxent) and educational global climate model (EdGCM) modelling to predict and conserve the suitable habitat distributions for Tossa species in Bangladesh to the year 2100. Nine environmental variables, 239 occurrence data and two Representative Concentration Pathway scenarios (RCP4.5 and RCP8.5) were used for the Maxent modelling to project the impact of climate change on the Tossa distributions. Furthermore, the EdGCM was used to study the climatic space suitability for the Tossa species in the context of Bangladesh. Both of the climatic scenarios were used for the prediction to the year 2100. The Maxent model performed better than random for the Tossa species with a high AUC value of 0.86. Under the RCP scenarios, the Maxent model predicted habitat reduction for RCP4.5 is 2%, RCP8.5 is 9% and EdGCM is 10.2% from the current localities. The predictive modelling approach presented here is promising and can be applied to other important species for conservation planning, monitoring and management, especially those under the threat of extinction due to climate change.

Predicting current and future climate suitability for arecanut (Areca catechu L.) in India using ensemble model

“…Species distribution models (SDMs), are valuable tools for determining the present and probable future geographic distributions [ 16 , 17 ]. Prediction of future climatic suitability of cash crops namely cocoa [ 18 ], jute [ 19 ], tea [ 20 ], and coconut [ 10 ] among others have opened up research on adaptive measures and sustainable cultivation of these crops. In order to predict species distributions based on environmental factors, a variety of SDM tools have been developed [ [21] , [22] , [23] ].…”

Comparison of machine learning and deep learning models for evaluating suitable areas for premium teas in Yunnan, China

“…Dai used the ME model to determine suitable habitat for tea tree cultivation under current climate scenarios [3]. In a prediction of the suitable areas of Tossa jute, ME and educational global climate models were used to study the effect of climate change on the distribution of Tossa [25]. Using a residual network, Yue et al simulated the distribution of Panax Notoginseng in its suitable areas in China [26].…”