Mapping Impacts of Climate Change on the Distributions of Two Endemic Tree Species under Socioeconomic Pathway Scenarios (SSP)

Abstract

Pistacia eurycarpa Yalt and Pistacia khinjuk Stocks are two important endemic tree species inhabiting mountainous regions in Iraq. Their cultural, medical, and ecological benefits have captured the interest of this study. Numerous researchers have revealed how and to what extent global climate change alters species’ habitats and distribution. This approach aims to quantify the current and future distribution of these tree species in the region and to provide baseline data on how Pistacia respond to the changing environment. Three socioeconomic pathway scenarios (SSP 126, 245, and 585) in two general circulating models (GCMs), MIROC-ES2L and BCC-CSM2-MR, have been utilized to examine the probable future geographical shift of these species during different time periods (2041–2060, 2061–2080, and 2081–2100). This study used the MaxEnt model and geospatial techniques for: (i) anticipating the present level of distributions and assessing the impact of climate change on these species’ possible future distributions; (ii) estimating the areas of species overlap; and (iii) finding the most significant environmental variables shaping their distributions, according to 11 environmental variables and 161 known localities. The findings revealed that 30 out of 36 modeling results showed range expansion in both the MIROC-ES2L and BCC-CSM2-MR models with 16/18 for P. eurycarpa and 14/18 for P. khinjuk. The overall species range expansions and increase in habitat suitability (mainly in the north and northeast) were related to precipitation during the wettest months, topography, and soil type structure (i.e., Chromic Vertisols, Lithosols, and Calcic Xerosols). These recent discoveries provide priceless new information for forestry management efforts and the conservation plan in Iraq, particularly in the overlapping areas in the mountainous regions and highlands. Geospatial approaches and correlation-based modeling are effective tools for predicting the spatial pattern of tree species in the mountain environment.