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Background: Climate change significantly influences the geographical distribution of plant species worldwide, especially endemics. Endemic species are plants that live in limited distribution ranges of unique ecology and, thus, are the most vulnerable species to climate change. Therefore, understanding the impacts of climate change on the distribution of these species can assist in developing appropriate plans for their conservation. In this study, we aimed to apply various species distribution models (SDMs) to predict the current potential distributions of two endangered plant species, Silene leucophylla (S. leucophylla, endemic) and Silene schimperiana (S. schimperiana, near-endemic), in Saint Catherine protected area (St. Catherine PA), Egypt. Then, using the best-fit model to project their future distribution under the maximum climate emission scenario (Representative Concentration Pathway 8.5 (RCP8.5)). Six different SDMs were constructed using different geospatial raster imagery sets of environmental factors. For each model, five machine learning (ML) algorithms were used. The results of these ML algorithms were then ensembled by calculating the weighted average of their predictions. Results: Based on the analysis of digital geospatial imageries produced by the best-fitting model, the predicted suitable areas of S. leucophylla and S. schimperiana were 23.1 km2 and 125 km2, respectively. These sites are located mainly in the high-elevation middle northern part of the study area. Annual precipitation, mean temperature of the driest quarter, altitude, and precipitation seasonality were the essential predictors of the distributions of both species. Future predictions of both species indicated opposing results between the studied species. Predictions in the 2050 and 2070 future conditions revealed significant range contraction for the distribution of S. leucophylla. For S. schimperiana, a range shift is predicted, with both range contraction and range expansion of its current suitable habitats, for the same future projections. Unfortunately, in 2080 predictions, both species could be projected to a complete loss from the entire area. Conclusion: This study highlights the importance of including diverse types of environmental variables in SDMs to produce more accurate predictions, rather than relying only on one variable type. It also revealed the potential negative impacts of future climate change on the distributions of two endangered plant species, S. leucophylla and S. schimperiana, inhabiting St. Catherine PA. Consequently, we urgently recommend the initiation of different plans and strategies seeking their conservation. [ABSTRACT FROM AUTHOR]

Simple Summary: Trachycarpus nanus is a national, second-class, rare and endangered plant in China, and a plant species endemic to Yunnan. This species has a high medicinal, ecological, and scientific value, but it is currently on the verge of extinction. Therefore, it is urgent to learn its extinction risk under global warming. We predict its multi-temporal distribution pattern based on the optimized MaxEnt model. The results show that precipitation is the most important factor. In the future, drastic climatic changes and human disturbances may lead to the extinction of this species, so it is necessary to strengthen the protection of this species using specific strategies. Climate change affects the geographical distribution of plant species. Rare Trachycarpus nanus with a narrow distribution range, high medicinal value and extremely small population is facing increasing extinction risks under global climate change. In this study, 96 recorded occurrences and 23 environmental factors are used to predict the potential suitable area of T. nanus based on the optimized MaxEnt (3.4.4) model and ArcGIS (10.7) software. The results show that when the parameters are FC = LQ and RM = 1, the MaxEnt model is optimal and AUC = 0.946. The distribution patterns were predicted in the past, present, and four future phases, i.e., 2021–2040 (2030), 2041–2060 (2050), 2061–2080 (2070), and 2081–2100 (2090). The main factors are the annual precipitation (bio12), mean temperature of the coldest quarter (bio11), temperature seasonality (bio4), precipitation of the wettest quarter (bio16), and isothermality (bio3). The potential distribution of T. nanus is primarily concentrated in central Chuxiong, encompassing a total potential suitable area of 5.65 × 104 km2. In historical periods, the total habitat area is smaller than that in the present. In the future, the potential suitable area is generally increased. The centroid analysis shows that T. nanus will move to a high-altitude area and to the southeast. But its dispersal capacity may not keep up with the climate change rate. Therefore, additional protection sites for this species should be appropriately established and the habitat connectivity should be enhanced. [ABSTRACT FROM AUTHOR]