US EPA

4315023 

Journal Article 

Predicting the potential distribution of Lantana camara L. under RCP scenarios using ISI-MIP models 

Qin, Z; Zhang, JE; Ditommaso, A; Wang, RL; Liang, KM 

2016 

Yes 

Climatic Change
ISSN: 0165-0009
EISSN: 1573-1480 

134 

1-2 

193-208 

10.1007/s10584-015-1500-5 

WOS:000367198900014 

Projections of anthropogenically-induced global climate change and its impacts on potential distributions of invasive species are crucial for implementing effective conservation and management strategies. Lantana camara L., a popular ornamental plant native to tropical America, has become naturalized in some 50 countries and is considered one of the world's worst weeds. To increase our understanding of its potential extent of spread and examine the responses of global geographic distribution, predictive models incorporating global distribution data of L. camara were generated. These models were used to identify areas of environmental suitability and project the effects of future climate change based on an ensemble of the four global climate models (GCMs) within the Inter-Sectoral Impact Model Intercomparis on Project (ISI-MIP). Each model was run under the four emission scenarios (Representative Concentration Pathways, RCPs) using the Maximum entropy (Maxent) approach. Future model predictions through 2050 indicated an overall expansion of L. camara, despite future suitability varying considerably among continents. Under the four RCP scenarios, the range of L. camara expanded further inland in many regions (e.g. Africa, Australia), especially under the RCP85 emission scenario. The global distribution of L. camara, though restricted within geographical regions of similar latitude as at present (35A degrees N similar to 35A degrees S), was projected to expand equator-ward in response to future climate conditions. Considerable discrepancy in predicted environmental suitability for L. camara among GCMs highlights the complexities of the likely effects of climate change on its potential distribution and the need to improve the reliability of predictions in novel climates.