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The aim of the current study is to determine the fish biodiversity in the shallow waters (0 - 20 m) of Gokceada in the North Aegean Sea. The samples were collected monthly between June 2013 - June 2014 using by a beach seine (0 - 2 m) and beam trawl (5 - 20 m). Field sampling was carried out in two stages. A preliminary sampling survey was carried out in the first phase (March-April 2013). During this period, sampling was conducted in each station around the entire island where sampling gear was feasible to use, and the most productive sampling stations were determined. The second stage was the biodiversity monitoring work. During the study period, 13-monthly sampling surveys were carried out at six seine and dredge stations around the island. During the whole period, a total of 30509 individuals belonging to 80 species were sampled. From the seine samplings (0-2 m), 18702 individuals from 40 species were obtained. The beam trawl samplings (5-10 and 10-20 m) identified 11807 individuals from 66 species. Biodiversity indices were calculated, and species diversities and abundances were determined for the seasons. The most copious seasons in terms of number of species abundance was identified in fall and summer. Fish species diversity and abundance maps were generated for each station. As a result, it was determined that the coastal ecosystems of Gokceada, northern Aegean Sea, incorporated many important habitats that is rich in biodiversity. In addition, many commercial species that had been over-exploited in the region was determined to use these coastal habitats for feeding, breeding, sheltering, and growing.

Water quality is one of the main characteristics of a river system and prediction of water quality is the key factor in water resource management. Different physical, biological and chemical parameters including heavy metals can be used to assess river water quality. Evaluation of the water quality in the rivers is quite difficult and requires more time and effort because of the fact that many factors affect water quality. Traditional data processing methods are insufficient to solve this problem. Therefore, in this study, an adaptive neuro-fuzzy inference system (ANFIS) model was developed to predict the concentrations of cadmium (Cd) in the Filyos River, Turkey. For this purpose, water samples collected at 7 sampling locations in the river during December 2014-2015 were used to develop ANFIS model. The available data set was apportioned into two separate sections for training and testing the ANFIS model. Developed models aimed to use the least parameters to estimate Cd concentration. As a result, a relatively higher correlation (R-2=0.91) was found between observed and modelled Cd concentrations. The results indicated that the ANFIS model gave reasonable estimates for the concentrations of Cd with a high degree accuracy and robustness. In conclusion, this paper suggests that ANFIS methodology produce very successful findings and has the ability to predict Cd concentration in water resources. The outcomes of this research provide more information, simulation, and prediction about heavy metal concentration in natural aquatic ecosystems. Therefore, ANFIS can be used in further researches on water quality monitoring.

Length-weight relationships (LWRs) of 49 fish species belonging to 18 families from the shallow waters (0-20 m) of Gokceada Island were reported. Fish specimens were sampled monthly with a beach seine (0-2 m) and beam trawl (5-20 m) between June 2013 and June 2014. The b values estimates varied between 2.66 and 3.59. Five of the 49 species (Arnoglossus kessleri, Scorpaena maderensis, Symphodus ocellatus, Symphodus rostratus, and Gobius geniporus) presented maximum length values that exceeded their maximum lengths listed in FishBase.