Potential Geographical Distribution of Lagerstroemia excelsa under Climate Change

Siwen Hao, Donglin Zhang and Yafeng Wen ()
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Siwen Hao: College of Landscape Architecture, Central South University of Forestry and Technology, Changsha 410004, China
Donglin Zhang: Department of Horticulture, University of Georgia, Athens, GA 30602, USA
Yafeng Wen: College of Landscape Architecture, Central South University of Forestry and Technology, Changsha 410004, China

Agriculture, 2024, vol. 14, issue 2, 1-14

Abstract: As a popular ornamental plant and an effective species for controlling rocky desertification, the identification and protection of potential habitats of Lagerstroemia excelsa habitats hold significant importance. To gain a comprehensive understanding of the natural resources and growing conditions for L. excelsa , predictive modeling was employed to estimate the potential geographical distribution of the species during the Mid-Holocene (MH), the present, and the years 2050 and 2070. The projection was based on current occurrences, and we selected the relevant environmental attributes through the Pearson analysis and the Maximum Entropy Model (MaxEnt). The analysis revealed that temperature and precipitation are the primary environmental factors influencing L. excelsa distribution, with the Wuling Mountains identified as a center distribution hub for this species. The anticipated suitable area for L. excelsa is expected to experience marginal expansion under future climate scenarios. These results are invaluable for guiding the protection and sustainable utilization of L. excelsa in the face of climate change. Additionally, the data generated can be leveraged for enhanced introduction, breeding, selection, and cultivation of L. excelsa , taking into account the challenges posed by global warming.

Keywords: crape myrtle; global warming; MaxEnt model; potential suitable area; sustainable utilization (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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