Volume 12, Issue 2 (Summer 2022)                   J. Aqua. Eco 2022, 12(2): 11-26 | Back to browse issues page

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قانع ا. The assessment of the habitat preferences of the River prawn (Macrobrachium nipponens) in wetland using decision tree and generalized linear model. J. Aqua. Eco 2022; 12 (2) :11-26
URL: http://jae.hormozgan.ac.ir/article-1-922-en.html
Abstract:   (1012 Views)
Four sampling sites were selected in different parts of the Anzali wetland watershed to predict the habitat preferences of the river prawn (Macrobrachium nipponens). A set of abiotic variables together with the abundance of the species were monthly measured at each sampling location during the 1- year study period (2017-2018). The results of Mann-Whitney test (given the non-normal data) showed a significant difference between the presence and absence of shrimp and the most studied variables (p< 0.01). In fact, the results of the test were based on the abiotic variables in which presence/absence of the species was observed. The prediction of the decision tree model showed that the increase of dynamic and structural variables of ecosystem such as water depth and flow velocity and the increase of water quality parameters such as salinity or the decrease of pH might lead to the absence of the species. According to the results of the generalized linear model, there was a significant difference (p< 0.01) between the presence and the absence of shrimp related to the variables (some of the variables with high collinearity were excluded) so that the probability of shrimp presence decreases if flow velocity, depth of sampling sites, electrical conductivity, chloride, salinity, sulphate and total hardness increase. On the contrary, the increase of dissolved oxygen concentration, water turbidity and acidity might increase the probability of shrimp presence in the wetland. Given the factors predicted by the applied models, it is necessary to pay more attention to these variables in future monitoring for the habitat preferences of shrimp
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Type of Study: Research | Subject: Special
Published: 2023/01/30

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