The potential threats and habitat preferences of noxious plants in tropical countries are poorly known. Species distribution modeling (SDM) is a robust tool that can be used in conservation planning for early detection of invasion risks. However, the use of SDM for the strategic management of increasing risks of such plant invasions in Sri Lanka has not been undertaken due to several underlying reasons including the long-lasting data gap, technical, technological, and financial issues. In addition, the literature relevant to SDM applications in the country is substantially poor, scattered, and unpublished. Therefore, in this article, we explore SDM applications relevant to invasive plants in Sri Lanka with implications similar to other countries in the tropics. We examine the challenges and potentials for utilization of SDM technology in conservation planning in Sri Lanka and discuss data gap as a major obstacle. We also identify the potential SDM interventions relevant to invasive plants control and management in Sri Lanka and recommend conservation planners to prioritize them and apply for the strategic management of invasive plants in Sri Lanka. Finally, we suggest some recommendations to make an enabling environment in relevant institutions for the utilization of SDM technology in ecosystem management planning in Sri Lanka.

Neglected and underutilized fruit species (NUFS) can make an important contribution to the economy, food security and nutrition requirement for Sri Lanka. Identifying suitable areas for cultivation of NUFS is of paramount importance to deal with impending climate change issues. Nevertheless, limited studies have been carried out to assess the impact of climate change on the potential distribution of NUFS. Therefore, we examined the potential range changes of NUFS in a tropical climate using a case study from Sri Lanka. We prioritized and modeled the potentially suitable areas for four NUFS, namely Aegle marmelos, Annona muricata, Limonia acidissima and Tamarindus indica under current and projected climates (RCP 4.5 and RCP 8.5) for 2050 and 2070 using the maximum entropy (Maxent) species distribution modeling (SDM) approach. Potentially suitable areas for NUFS are predicted to decrease in the future under both scenarios. Out of the four NUFS, T. indica appears to be at the highest risk due to reduction in potential areas that are suitable for its growth under both emissions scenarios. The predicted suitable area reductions of this species for 2050 and 2070 are estimated as >75% compared to the current climate. A region of potentially higher climatic suitability was found around mid-county for multiple NUFS, which is also predicted to decrease under projected climate change. Further, the study identified high-potential agro-ecological regions (AERs) located in the mid-country’s wet and intermediate zones as the most suitable areas for promoting the cultivation of NUFS. The findings show the potential for incorporating predictive modeling into the management of NUFS under projected climate change. This study highlights the requirements of climate change adaptation strategies and focused research that can increase the resilience of NUFS to future changes in climate.

Plant invasion has been widely recognized as an agent of global change that has the potential to have severe impacts under climate change. The challenges posed by invasive alien plant species (IAPS) on biodiversity and ecosystem stability is growing and not adequately studied, especially in developing countries. Defining climate suitability for multiple invasive plants establishment is important for early and strategic interventions to control and manage plant invasions. We modeled priority IAPS in Sri Lanka to identify the areas of greatest climatic suitability for their establishment and observed how these areas could be altered under projected climate change. We used Maximum Entropy method to model 14 nationally significant IAPS under representative concentration pathways 4.5 and 8.5 for 2050 and 2070. The combined climate suitability map produced by summing up climatic suitability of 14 IAPS was further classified into five classes in ArcMap as very high, high, moderate, low, and very low. South and west parts of Sri Lanka are projected to have potentially higher climatic suitability for a larger number of IAPS. We observed suitable area changes (gains and losses) in all five classes of which two were significant enough to make an overall negative impact i.e., (i) contraction of the very low class and (ii) expansion of the moderate class. Both these changes trigger the potential risk from IAPS in Sri Lanka in the future.