Suitable habitat and landscape structure play a pivotal role in the success of forest restoration projects. This study aimed to model the habitat suitability of wild almond (Amygdalus scoparia Spach) using three individual species distribution models (SDMs), i.e., backpropagation artificial neural network (BP-ANN), maximum entropy (MaxEnt), generalized linear model (GLM), as well as the ensemble technique along with measuring the landscape metrics and analyzing the relationship between the distribution of the suitable habitat of the species in different landform classes in Fars Province, southern Iran. There was no clear difference in the prediction performance of the models. The BP-ANN had the highest accuracy (AUC = 0.935 and k= 0.757) in modeling habitat suitability of A. scoparia, followed by the ensemble technique, GLM, and MaxEnt models with the AUC values of 0.890, 0.887, and 0.777, respectively. The highest discrimination capacity was associated to the BP-ANN model, and the highest reliability was related to the ensemble technique. Moreover, evaluation of variable importance showed that the occurrence of A. scoparia was strongly dependent on climatic variables, particularly isothermality (Bio 3), temperature seasonality (Bio 4), and precipitation of driest quarter (Bio 17). Analysis of the distribution of species habitat in different landform classes revealed that the canyon, mountain top, upland drainage, and hills in valley classes had the highest suitability for the species establishment. Considering the importance of landform in the establishment of plant habitats, the combination of the outputs of the SDMs, landform, and the use of landscape metrics could provide both a clear view of habitat conditions and the possibility of analyzing habitat patches and their relationships that can be very useful in managing the remaining forests in semi-arid regions. The canyon, mountain top, and upland drainage classes were found to be the most important landforms to provide the highest suitable environmental conditions for the establishment of A. scoparia. Therefore, such landforms should be given priority in restoration projects of forest in the study area.

中文翻译：

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景观结构和合适的栖息地分析，以有效地进行半干旱山区森林的恢复规划

合适的栖息地和景观结构在森林恢复项目的成功中起着举足轻重的作用。这项研究旨在使用三个个体物种分布模型（SDM）对野生杏仁（Amygdalus scoparia Spach）的栖息地适宜性进行建模，即反向传播人工神经网络（BP-ANN），最大熵（MaxEnt），广义线性模型（GLM） ，以及集成技术以及景观指标的测量，并分析了伊朗南部Fars省不同地貌类别中该物种合适的栖息地的分布之间的关系。模型的预测性能没有明显差异。BP-ANN在模拟红景天的生境适应性方面具有最高的准确性（AUC = 0.935，k = 0.757），其次是集成技术GLM，和MaxEnt模型的AUC值分别为0.890、0.887和0.777。最高的判别能力与BP-ANN模型相关，最高的可靠性与集成技术相关。此外，变量重要性的评估表明，景天气荚菌的发生强烈依赖于气候变量，特别是等温性（Bio 3），温度季节性（Bio 4）和最干燥的四分之一的降水（Bio 17）。对不同地貌类别物种栖息地分布的分析表明，峡谷，山顶，山地排水系统和山谷类丘陵对物种建立的适应性最高。考虑到地貌在建立植物栖息地方面的重要性，将SDM的输出，地貌相结合，并且使用景观度量可以提供清晰的栖息地条件视图，并提供分析栖息地斑块及其关系的可能性，这对于管理半干旱地区的剩余森林非常有用。峡谷，山顶和山地排水系统被认为是最重要的地貌，可为建立A. scoparia提供最高的适宜环境条件。因此，在研究区的森林恢复项目中应优先考虑这些地貌。人们发现，高地排水类别是最重要的地貌，可为建立红景天提供最佳的环境条件。因此，在研究区的森林恢复项目中应优先考虑这些地貌。人们发现，高地排水类别是最重要的地貌，可为建立A. scoparia提供最高的适宜环境条件。因此，在研究区的森林恢复项目中应优先考虑这些地貌。