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The choice of temporal resolution has high importance in ecological modeling, which can greatly affect the identification of the main drivers of an organism's distribution, considering the spatiotemporal dynamism of environmental predictors as well as organisms’ abundance. The present study aimed to identify the spatiotemporal distribution patterns of Caspian Kutum, Rutilus frisii, along the southern coast of the Caspian Sea, north of Iran, evaluating multiple temporal resolutions of data. The boosted regression trees (BRT) method was used to model fish catch distribution using a set of environmental predictors. Three temporal scales of data, including seasonal, sub‐seasonal, and monthly time frames over the catch season (October–April), were considered in our modeling analyses. The monthly models, utilizing more detailed data scales, exhibited the highest potential in identifying the overall distribution patterns of the fish, compared to temporally‐coarse BRT models. The best models were the BRTs fitted using data from March and April, which represented the final months of the catch season with the highest catch levels. In the monthly models, the main determinants of the Kutum's aggregation points were found to be dynamic variables including sea surface temperature, particulate organic and inorganic carbon, as opposed to static topographic parameters such as distance to river inlets. Seasonal and sub‐seasonal models identified particulate inorganic matter and distance to river inlets as the predictors with the highest influence on fish distribution. The geographical distributions of fish biomass hotspots revealed the presence of a stable number of fish aggregation hotspot points along the eastern coast, while some cold‐spot points were identified along the central and western coasts of the Caspian Sea. Our findings indicate that utilizing fine time scales in modeling analyses can result in a more reliable explanation and prediction of fish distribution dynamics. The investigated approach allows for the identification of intra‐seasonal fluctuations in environmental conditions, particularly dynamic parameters, and their relationship with fish aggregation. In our study, we attempted to investigate the key role of the temporal resolution of data in distribution modeling for an important fish species of the Caspian Sea, with high conservation and commercial importance. Our findings indicate that utilizing fine time scales in modeling analyses can result in a more reliable explanation and prediction of fish distribution dynamics. The investigated approach allows for the identification of intra‐seasonal fluctuations in environmental conditions, particularly dynamic parameters, and their relationship with fish aggregation.

The original version of this article unfortunately contained a typo in university name in second affiliation. The correct affiliation is Department of Aquatic Production and Exploitation, Faculty of Fisheries and Environmental Sciences, Gorgan University of Agricultural Sciences and Natural Resource, Gorgan, Iran

This study was conducted to investigate bioaccumulation of copper in two internal organs (mantle and foot) of swan mussel, Anodonta cygnea (Linnaeus, 1758) in exposure to copper oxide nanoparticles (CuO NPs). Basal concentration of Cu in the mantle (3.15 ± 1.09 µg g−1 DW) was significantly (p < 0.05) lower than the foot (5.43 ± 1.54 µg g−1 DW). At the end of the exposure period, the highest concentration of Cu in both organs belonged to the highest exposure concentration. Calculated bioconcentration factor (BCF) values showed significant (p < 0.05) higher values for the mantle in each day and each exposure concentration (except the lowest exposure concentration) than the foot. For both organs, the highest and lowest BCFs occurred at the lowest and highest exposure concentrations, respectively. Cu concentration in both organs was significantly (p < 0.05) decreased after day 4. Based on the results, it was obvious that exposure to sub-lethal concentrations of CuO NPs would lead to the significant accumulation of copper in mantle and foot that may have adverse effects on this organism.

The present study aimed to investigate the effect of spatial resolution of data on distribution modelling performance for the Caspian Kutum, Rutilus frisii. A set of spatial resolutions (4, 8, 16, 32, and 64 km) were considered in the modelling analyses, using sea surface temperature, chlorophyll-a concentration, particulate organic carbon content, bottom slope, and depth as environmental predictors of fish catch-per-unit-of-effort (CPUE). The boosted regression trees (BRT) method was applied as the modelling technique. The results showed considerable reductions in data variability (coefficient of variation (%) and variance) with decreasing spatial resolution for most environmental variables and CPUEs. The model performance (adj-R2) was improved with decreasing resolutions, but the best prediction ability of the models was obtained with the BRTs fitted on the lowest resolutions (i.e. 4 and 8 km). While sea surface temperature was the main influencing predictor in the fitted BRTs at all resolutions, resolution-dependence differences were observed in the significance and response curves of other predictors of the models across the spatial resolutions. Overall, our findings indicated that using different levels of spatial resolution highly affects the modelling process, with more relevant explanations and higher prediction power using finer resolutions.

A biometric study was conducted on the populations of swan mussel, Anodonta cygnea, belonged to water bodies with different water current velocity (high current: HC; low current: LC). The shell length, width, height, weight and age of the collected mussels were measured. The two groups had different age-classes distributions. The HC mussels had larger mean and maximal values of the biometric parameters. A high and medium correlation coefficient (width-length, height-length, and weight-length) were found in the HC and LC mussels, respectively. The weight-length relationships showed negative allometric pattern (HC: weight=3.6121x0.8561; LC: weight= 3.1362x0.8508). The 1-2 years old mussels had the highest rates of increase in length, width and height in both groups. Based on the results, water current velocity influences biometric features and population structure of A. cygnea.