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Machine learning based Prediction of Habitat Suitability of Prosopis juliflora under present and future climatic scenarios in arid coastal region of Pakistan

Authors :

Saima Shaikh¹ Sana Arshad² Jamil Hasan Kazmi³

1- University of Bahawalpur, Bahawalpur 2- University of Karachi 3- FatimyiahHigher Education System (FHES)

Keywords :

Species Distribution Modelling،invasive plants،Google Earth Engine،Gradient Boosting Regression

Abstract :

The invasion of alien plant species poses a significant global threat to biodiversity, exacerbated by climate change. Pakistan is particularly vulnerable to the rapid spread of Prosopis juliflora, introduced in 1857 during the unpartitioned Indian subcontinent. This species thrives in harsh environmental conditions without ecological competition, making Pakistan’s coastal regions, especially Karachi, highly susceptible to its invasion. Climate change has further accelerated this process. Despite its ecological and environmental impact, limited studies have assessed its invasion or predicted its future spread under changing climatic conditions.

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