Modelling the habitat preferences of the swan mussel (Anodonta cygnea) using data-driven model.

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 (x = 26.53, p < 0.05), the habitat condition of A. cygnea is influenced by significant variations in the spatio-temporal patterns.