Habitat suitability models can be generated using methods requiring information on presence of species or presence and absence of species. Rhinopoma muscatellum is one of the six mouse-tailed bats (Rhinopomatidae) and is known as an extremely frequent bat in Iran. In this study, 76 presence points were identified and recorded in distribution range of species. Presence-only (Domain, Bioclim, and one-class SVM) and presence/pseudo-absence (P/PA) data-based methods were used to model the distribution of R. muscatellum in Iran. In this study, in order to establish the pseudo-absence points, the output of presence-only map with the highest validity on AUC statistics was used. Using the output of the Domain method map (AUC = 0.8), 720 pseudo-absence points of the species were designed and entered into the P/PA models, including generalized linear model (GLM), maximum entropy (MaxEnt), maximum likelihood (MaxLike), classification and regression trees (CART), rough set, back-propagation artificial neural networks (BP-ANN), and two-class support vector machine (two-class SVM). The models were validated by the kappa coefficient of agreement as a threshold-based index. The coefficient of agreement was measured above 0.8 for all running models. Then, all binary maps were entered into the ensemble method, and the distribution map was presented as the output map with the result of ten implemented models. In order to evaluate the effect of each habitat variable on species distribution, sensitivity was measured by logistic regression method. The results of the modeling showed that the southeastern, southern, and southwestern regions of the country have a high suitability for this species, which was also confirmed by the ensemble modeling method. Based on the sensitivity results, the maximum temperature of the warmest months of the year, mean daily temperature, and distance from the mines had the highest effect on species distribution. The results of this study showed that R. muscatellum distribution models can be useful in identifying the species habitat suitability and the distribution maps obtained from these models can be considered as a suitable tool for population studies.

可以使用需要关于物种存在或物种存在与否的信息的方法来生成栖息地适应性模型。穆斯氏鼻蝠是六只鼠尾蝙蝠（Rhinopomatidae）之一，在伊朗被称为极频繁的蝙蝠。在这项研究中，确定了76个存在点并记录在物种分布范围内。仅存在（域，Bioclim和一类SVM）和基于存在/伪缺席（P / PA）数据的方法被用于建模muscatellum的分布在伊朗。在这项研究中，为了建立伪缺席点，使用了在AUC统计上具有最高有效性的仅存在地图的输出。使用域方法图（AUC = 0.8）的输出，设计了该物种的720个伪缺失点，并将其输入P / PA模型，包括广义线性模型（GLM），最大熵（MaxEnt），最大似然（ MaxLike），分类和回归树（CART），粗糙集，反向传播人工神经网络（BP-ANN）和两类支持向量机（两类SVM）。通过协议的卡伯系数作为基于阈值的指标来验证模型。所有运行模型的一致性系数均在0.8以上。然后，所有二进制映射都输入到集成方法中，并以10个实施模型的结果将分布图表示为输出图。为了评估每个生境变量对物种分布的影响，通过逻辑回归方法测量了敏感性。建模结果表明，该国的东南，南部和西南部地区对该物种具有很高的适应性，这也得到了整体建模方法的证实。根据敏感度结果，一年中最温暖月份的最高温度，平均日温度以及与矿井的距离对物种分布的影响最大。这项研究的结果表明 建模结果表明，该国的东南，南部和西南部地区对该物种具有很高的适应性，这也得到了整体建模方法的证实。根据敏感度结果，一年中最温暖月份的最高温度，平均日温度以及与矿井的距离对物种分布的影响最大。这项研究的结果表明 建模结果表明，该国的东南，南部和西南部地区对该物种具有很高的适应性，这也得到了整体建模方法的证实。根据敏感性结果，一年中最温暖月份的最高温度，平均日温度以及与矿井的距离对物种分布的影响最大。这项研究的结果表明muscatellum分布模型可用于确定物种栖息地的适宜性，从这些模型中获得的分布图可被认为是进行种群研究的合适工具。