Lethal yellowing disease: insights from predicting potential distribution under different climate change scenarios

Coconut is recognized for its popularity in contributing to food and nutritional security. It generates income and helps to improve rural livelihood. However, these benefits are constrained by lethal yellowing disease (LYD). A clear understanding of climate suitable areas for disease invasion is essential for implementing quarantine measures. Therefore, we used a machine learning algorithm based on maximum entropy to model and map habitat suitability of LYD and coconut under current and future climate change scenarios using three Shared Socio-economic Pathways (SSPs) (1.26, 3.70 and 5.85) for three time periods (2041–2060, 2061–2080 and 2081–2100). Outside its current range, the model projected habitat suitability of LYD in Australia, Asia and South America. The distribution of coconut exceeded that of LYD. The area under the curve value of 0.98 was recorded for LYD, whereas 0.87 was obtained for the coconut model. The predictor variables that most influenced LYD projections were minimum temperature of the coldest month (88.4%) and precipitation of the warmest quarter (7.3%), whereas minimum temperature of the coldest month (85.9%) and temperature seasonality (8.7%) contributed most to the coconut model. Our study highlights potential climate suitable areas of LYD and coconut, and provides useful information for increasing quarantine measures and developing resistant or tolerant coconut varieties against the disease. Also, our study establishes an approach to model the climatic suitability for surveillance and monitoring of the disease, especially in areas that the disease has not been reported.