The Anzali wetland (located in northern Iran) and many parts of its catchment are considered important habitats for the swan mussel (Anodonta cygnea). The habitat of this native bioindicator mussel is being threatened in many locations of the catchment due to various anthropogenic activities. The present study aimed to apply a classification tree model (J48 algorithm) to predict the habitat preferences of A. cygnea in 12 sampling sites based on various water quality and physical-habitat variables. The species was present in 50% of sampling sites, while it was absent in the remaining of the sampling sites. In total, 144 samples of A. cygnea (72 presence and 72 absence instances) were monthly measured together with the abiotic variables during 1-year study period (2017–2018). For the CT model, two-thirds of datasets (96 instances) served as a training and the remainder was employed for the validation set (48 instances). Among 25 environmental variables introduced to the model (with pruning confidence factor = 0.10, threefold cross-validation and 5 times randomization effort), the validity of 6 variables was confirmed by the model in all three subsets. Water salinity, flow velocity, water depth and water turbidity were jointly predicted by the model in three subsets. The model predicted that the absence of A. cygnea might be associated with increasing flow velocity, total phosphate and water turbidity. In contrast, the presence of A. cygnea might be related to decreased water depth and increased calcium concentration. The model also confirmed that all predicted variables for the species might be completely dependent on the water salinity. According to the chi-square test (x² = 26.53, p < 0.05), the habitat condition of A. cygnea is influenced by significant variations in the spatio-temporal patterns.

Anzali湿地（位于伊朗北部）及其流域的许多地方被认为是天鹅贻贝（Anodonta cygnea）的重要栖息地。由于各种人为活动，该本地生物指示贻贝的栖息地在流域的许多地方受到威胁。本研究旨在应用分类树模型（J48算法），基于各种水质和自然栖息地变量来预测12个采样点的猕猴的生境偏好。该物种存在于50％的采样点中，而其余的采样点中则不存在。在总共144个样本A. cygnea在为期一年的研究期间（2017-2018年），每月测量72例存在和72例不存在的病例以及非生物变量。对于CT模型，三分之二的数据集（96个实例）用作训练，其余部分用于验证集（48个实例）。在引入模型的25个环境变量中（修剪置信度= 0.10，三重交叉验证和5倍随机化努力），模型在所有三个子集中确认了6个变量的有效性。该模型通过三个子集共同预测水盐度，流速，水深和水浊度。该模型预测，无猕猴桃可能与流速增加，总磷酸盐和水混浊有关。相反，存在猕猴桃可能与减少水深和增加钙浓度有关。该模型还证实，该物种的所有预测变量可能完全取决于水盐度。根据卡方检验（x ²  = 26.53，p  <0.05），猕猴桃的生境条件受时空模式的显着变化影响。