Prediction of the Abundance of Artemia parthenogenetica in a Hypersaline Wetland Using Decision Tree Model

Abstract

The hypersaline wetland of Meighan located in western Iran is an important habitat for Artemia parthenogenetica. The habitat condition of this native zooplankton is facing with various problems in the wetland, so its abundance has been reduced in the wetland in the recent years. The study aimed to optimize decision tree model with an optimizer (greedy stepwise) to predict the species abundance in 10 different sampling sites over one-year study period (2017–2018). The model output was the species abundance categorized into 4 classes (poor: 5–20; fair: 21–50; good: 51–100; very good:101–255 individuals) and measured with abiotic variables. The optimizer method improved the model performance leading to easy interpretation of the model. According to the model’s prediction, high abundance of species in the wetland is associated with high concentration of specific conductivity, dissolved oxygen and total dissolved solids. In contrast, increased concentration of chloride, total suspended solids, nitrate and precipitation might decrease the abundance of zooplankton. Chi-square test showed a significant difference between the species abundance and spatio-temporal patterns in the wetland (x2 = 160.2, p = 0.001) so that warm seasons (spring and summer) had more contribution to the zooplankton sampling than cold seasons (autumn and winter).