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In this study, the effect of diets containing different levels of Pontogammarus maeoticus extract as natural nutrient attractant was investigated on growth indices, chemical composition and mucosal Immunity of Rutilus kutum. For this purpose, fish with an average weight of 0.35±0.067 were stored in 100L fiberglass tanks containing 30 liters water with a density of 20 fish per tank for eight weeks. To perform this study, Gammaridae extracts were mixed with distilled water in three concentrations: 1:25 (25%), 1:50 (50%), 1:75 (75%) and two percent of each dilution was added to manual meals before each feeding. This experiment was performed as four triplicated treatments. Larvae were fed four times a day. At the end of the culture, growth indices such as: mean body weight gain, specific growth factor, food conversion ratio, survival, chemical analysis of carcasses and some mucosal immune parameters including; lysozyme, immunoglobulin, alkaline phosphatase and protease were studied. The results showed that although growth factor and carcass analysis were improved in all treatments (25%), this increase was not significant compared to the control (p>0.05). All Mucus immune factors significantly increased in all treatments compare to control (p<0.05). The result of this study showed that the Gammaridae extract had no significant effect on the improvement of growth performance, but increased the immunity of Rutilus frisii in culture conditions.

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Investigating the quantitative and qualitative characteristics of resources is one of the key pillars of sustainable development and sound management practices in various fields of environment, fisheries and agriculture. The impact of pollutants on organisms varies according to their type and volume of input. These effects at the highest levels cause the destruction of the fauna and flora of the area and in small amounts eliminate the resistant species. In recent years large-scale invertebrates inhabit sediments reflecting the effects of environmental contamination as a change in their diversity or density, which is why more attention has been paid to biological monitoring studies. In each half-line, sediment samples were taken from the surface layer at 3 different depths of 5, 10, 15 m. Sediment samples were analyzed by ICP-OES (Varian Vista -MPX model) according to ASTM D5258-92, 2013. Shannon Wiener Index, Species Richness Index and Margalef Index were used to assess the Diversity, Dominance and Richness of Macrobentic in the study area. On average, the lowest Shannon Wiener Index of Fereidonkenar Port in spring (0.63), in Summer (0.25), in Autumn (0.54), in Winter (0.37). The lowest Margalev Index of Fereidonkenar Port in Spring (0.45), in Summer (0.25), in Autumn (0.46), in Winter (0.28) was achieved. In the present study, were significantly  higher (p<0.05) compared to Shannon and Margalph Index. Also, the result  shown were different temporal and spatial variations in the structure of Benthic Communities and  stress and contamination were moderate.
 

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Introduction
Today, the phenomenon of climate change has become one of the biggest challenges and serious threats to the biodiversity of aquatic ecosystems (Mostafavi et al., 2017). Based on studies, climate change has already affected the distribution of aquatic life (Lam et al., 2020; Alegria et al., 2023). Due to the role of human factors, this phenomenon is occurring with a greater speed and intensity, therefore species and ecosystems may not have enough time to adapt and harmonize with environmental changes (Lovejoy and Hannah, 2006) and Finally, it is possible with changes in species communities (Zurell et al., 2020), spatial and temporal changes in the level of species interactions (Kelly et al., 2012; Forrester, 2014), displacement of ecological niches and distribution basins (Bellard et al., 2012) as well as extinction or adaptation of species (Román-Palacios et al., 2020). The results of studies show that during the next few decades, climate change can be one of the biggest threats to biodiversity in the world (Mostfavi et al., 2014, 2015; Makki et al., 2023). In addition, changes in the distribution of species, population and structure of communities will bring many threats in the future (Moss et al., 2009). During the last century, the most vulnerable habitats to climate change were freshwater ecosystems (Bouska et al., 2015). The decrease in river flow, the decrease in rainfall, and the increase in temperature in these ecosystems have all had negative consequences on freshwater fish (Harrod, 2015).
Methodology
The studied area is the rivers of the southern basin of the Caspian Sea, which has native species with economic and fishery value. In this study, attendance method was used for modeling. Environmental and climatic data used for modeling including habitat and climatic data were extracted from reliable foreign sites such as www.worldclim.org and internal (from domestic organizations such as natural resources and environment). In this connection, 9 variables include the maximum width of the river, Elevation, slope, Flow Accumulation, temperature range, average The average temperature, the average minimum temperature, the average maximum temperature and (Ave-Precipitation) were considered as primary variables, which after performing the Spearman correlation test If two variables have a correlation above 75%, one of them is selected according to the expert opinion and according to the ecological needs of the species (Mostafavi et al., 2014; Makki et al., 2023) (Fig. 1).

Figure 1: Iranian rivers in the southern basin of the Caspian Sea (Arc GIS ver. 10.8)
Then species distribution modeling using MaxEnt model (Phillips et al., 2017) in R v3.2.3 software environment (R Core Team, 2020) and dismo v1.1-4 software package (Hijmans et al., 2017) was done. In order to evaluate the accuracy of the model performance and modeling results, the area under the curve (AUC) (Table 1) of the system performance characteristic (ROC) was calculated (Lobo et al., 2008). According to the range of AUC between 0 and 1, values ​​less than 0.5 indicate random prediction performance and 1 values ​​with perfect prediction. In fact, values ​​less than 0.5 indicate inappropriate models (Elith et al., 2009). Also, using the jackknife test, the variable that had the greatest effect in determining the distribution of the studied species was determined. Finally, the distribution map of big fish sauce in the Caspian watershed was produced under climate scenarios in 2050 and 2080 (Tables 1 and 2).
Table 1: A quantitative and qualitative classification of model performance based on the AUC index