The habitat suitability of Salvadora oleoides and Tamarix aphylla can be one of the most significant steps towards conserving these tree species. Habitat loss presents a critical threat to the existence of S. oleoides and T. aphylla. Protecting their suitable habitats and implementing conservation approaches is crucial to address this challenge. By ensuring the preservation of their habitats and adopting effective conservation strategies, we can mitigate the threat of habitat loss and promote the survival of these species. The potential distribution of S. oleoides and T. aphylla was predicted using a MaxEnt model. This study also presents the conservation status of S. oleoides and T. aphylla in the tropical thorn forests of the Bahawalpur subdivision. Data were gathered from the field survey based on bioclimatic variables. Overall, 20 sample plots were taken, and the coordinates were recorded for each sample plot. MaxEnt software and the environmental variables were used to study each tree species separately (19 bioclimatic variables were used). The Jackknife test was conducted to find the total general tree cover and mean temperature. The MaxEnt model showed high accuracy for each tree species, with the receiver operating characteristics (ROC) area under the curve (AUC) training mean testing values for S. oleoides being 0.976 and T. aphylla 0.987. The study showed that both species were distributed irregularly in the tropical thorn forest of the Bahawalpur subdivision. The results highlight that it is essential to implement proven long-term management and conservation techniques to ensure the well-being and sustainability of forest trees in the Bahawalpur sub-division. In conclusion, concerted efforts to map, understand habitat suitability, and raise awareness of endangered species in the tropical thorn forest are crucial for effective conservation planning and resource allocation in the face of climate change.
Copyright: © 2024 Hussain et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.