Sürdürülebilir Kalkınma Amaçları
Yazarlar
- Ozcan, E.I.(2)
- Bulut, H.(1)
- Kilmaç, C.(1)
Yayınlar
- MakaleAçık Erişim2024Scopus SJRQ3VARIATION OF ZOOPLANKTON IN THE HALBORI SPRINGS (TUNCELI, TURKEY)
Bulut, H.; Ozcan, E.I.; Kilmaç, C.
This study examined certain water quality parameters and zooplankton fauna at two stations in Halbori Springs (Tunceli, Turkey), over a 12-month period from June 2022 to May 2023. The highest water temperature of 15.4°C was recorded at Station 2 in August, while the lowest water temperature of 5.0°C was recorded at Station 2 in January. The pH value of Halbori Springs ranged from 6.4 to 8.5. Dissolved oxygen varied between 8.1 and 11.9 mg/L during the study period. A total of 26 zooplankton species were identified in Halbori Springs. Nineteen of these species belong to Rotifera, five Cladocera and two Copepoda. According to the index analysis results of Station 1, the month with the highest species richness index, as measured by the Shannon Wiener H′ index, was October with a value of 1.97, while the lowest value was 0.45 in January. The highest value for the Margalef species diversity D index was 0.78 in October, and the lowest was 0.12 in January. According to the index analysis results of the station 2, the month with the highest species richness index, Shannon Wiener H’, was September with a value of 1.68, while the lowest value was observed in January with 0.67. As for the Margalef species diversity index D, the highest value was recorded in September with 0.64, and the lowest value was observed in January with 0.12. In Halbori Springs, 1 species of Brachionus and 2 species of Trichocerca were found. The QB/T ratio was calculated to be 0.5, suggesting that the study area has oligotrophic characteristics.
AtıfScopus1WoS1
İncele - MakaleAçık Erişim2019Scopus SJRQ3Artificial Neural Networks (a new statistical approach) method in length-weight relationships of Alburnus Mossulensis in Murat River (Palu-Elaziğ) Turkey
Ozcan, E.I.
Artificial Neural Networks (ANNs) is a reliable and alternative method used specially in the growth characteristics of some fish species. The purpose of this work was to compare with length-weight relationships and Artificial Neural Networks, the length-weight relation of 415 (232 female and 183 male) Alburnus mossulensis captured from Murat River between January 2018 and December 2018. The total length and the weight of the fish changed between 7.2-19.9 cm (12.81±0.095) and 3.24-40.6 g (13.51±0.30) respectively. The length-weight relationships were determined for females, males and all individuals as W=0.0097L2.812 (R2=0.95), W=0.0168L2.599 (R2=0.95) and W=0.0130L2.701 (R2=0.95), respectively. The growth type of A. mossulensis was negative allometric growth for female, male and all individuals. Regression analysis showed that length has high significantly correlation with weight (R = 0.97, R2 = 0.95, F1,413 = 79550.3, P <0.001) and 95% remain in weight was due to length remain. t-test results was used for the importance condition of the regression coefficients (t=89.193, P<0.01). Length-length relations were highly significant with R2 values greater than 0.97. The condition factor values were ranged between 0.430 and 0.975 for all individuals. According to the comparison of the results obtained with MAPE (%), ANNs provided better results than the LWRs. With this study, it can be seen that ANNs can be used as an alternative useful method for population parameters prediction.
AtıfScopus7WoS9OpenAlex6İncele - DergiApplied Ecology and Environmental Research