Browsing by Author "Artun, Ozan"

Now showing 1 - 2 of 2

Ecological Niche Modeling Of Acanthamoeba İn Türkiye
(Springer, 2025) Kavur, Hakan; Evyapan, Gulsah; Artun, Ozan
Acanthamoeba, is an opportunistic pathogenic organism with a global distribution and the potential to cause severe human infections. This study primarily aimed to identify the environmental factors influencing the distribution of Acanthamoeba by analyzing various bioclimatic and topographic variables, and to predict their potential current and future distribution under 2070 climate change scenarios using ecological niche modeling based on the MaxEnt algorithm. Niche modeling was performed on 20 water and 20 soil samples collected from hot springs, swimming pools, parks, and agricultural areas. The rates of positive water samples in Afyon and K & uuml;tahya were 70 and 50%, respectively. We detected 60 and 100% positive rates of soil samples collected in Afyon and K & uuml;tahya, respectively. Niche modeling incorporated 19 bioclimatic variables, with BIO3 (Isothermality), BIO4 (Temperature seasonality), BIO13 (Precipitation of the wettest month), and BIO15 (Precipitation seasonality) identified as the most influential predictors. The model showed high predictive performance, with AUC values of 0.991 and 0.977 for current and future projections, respectively. Results suggest a potential increase in Acanthamoeba distribution in future scenarios, especially in the southwestern region of Afyon and southern K & uuml;tahya. These findings highlight the importance of environmental monitoring and genotypic characterization of Acanthamoeba for public health risk assessment.
Estimation of Soil Losses Using Various Soil Erosion Models in a Sample Plot in Mediterranean Part of Turkey
(Parlar Scientific Publications (p S P), 2017) Artun, Ozan; Dinc, Akin Oguz; Satir, Onur
The aim of this study was to investigate the relationship between soil erosion and land use/cover change (LUCC) in an erosion hot point area using the RUSLE, modified Morgan-Morgan Finney (mMMF) and revised G2 erosion models in the Mediterranean Region of Turkey. Landsat satellite images that belong to the same months from different years (1990, 2010), were used to derive LUC maps. Maximum Likelihood Classifier (MLC) was applied to all images for classification, and overall accuracy for the determination of LUC maps was >90%. Meteorological and soil data, and information obtained from land observations were used to produce past and present erosion risk maps and potential soil losses. Results from the RUSLE, modified Morgan-Morgan Finney (mMMF) and revised G2 soil erosion methods were controlled by the field works. Results had shown that RUSLE and mMMF approaches produced more reliable/realistic results than rG2. Soil erosion had increased in the last 20 years according to all soil erosion prediction models.