Meshgi Behnam, Majidi-Rad Morteza, Hanafi-Bojd Ahmad Ali, Fathi Saeid
Department of Parasitology, Faculty of Veterinary Medicine, University of Tehran, Tehran-Iran (Center of Excellent of Ecosystem and Ultrastructural changes of Helminthes), Iran.
Department of Parasitology, Faculty of Veterinary Medicine, University of Tehran, Tehran-Iran (Center of Excellent of Ecosystem and Ultrastructural changes of Helminthes), Iran.
Acta Trop. 2019 Oct;198:105079. doi: 10.1016/j.actatropica.2019.105079. Epub 2019 Jul 9.
The present study aimed to determine the number of cases of animal fascioliasis per district in the southern littoral of Caspian Sea and to model suitable ecological niches for Fasciola infection in the region. Stool samples (n = 2688) were collected from cattle and sheep in Guilan, Mazandaran and Golestan provinces. The samples were tested using flotation method, and the number of parasite eggs per gram (EPG) of feces was recorded for each sample. Occurrence-only data of Fasciola were collected from the field. A total of 96 points/locations were used to model the ecological niche of Fasciola in maximum entropy (MaxEnt) and geographical information system (GIS). The spatial layers were compiled from 23 bioclimatic and biophysical variables for modeling analysis. Jackknife analysis was used to determine the relative importance of all variables in the model. In the present study, the proportion of fascioliasis in both hosts was highest in Guilan province (sheep: 12.34%, cattle: 15.16%), followed by Mazandaran (sheep: 7.3%, cattle: 6.25%) and Golestan (sheep: 0%, cattle: 0.94%) provinces. The Area Under Curve (AUC) value of the model was 0.909, indicating a good predictive power of the model. Our modeling results indicate that four variables, which were markedly incorporated into the model, are the major predictors of the presence probability of Fasciola spp. in the region: Bio17 (Precipitation of driest quarter; 45.5%), Bio14 (Precipitation of driest month; 24.8%), aspect (9%), and altitude (7.2%). The data presented herein show expansion of the potential high-risk areas of fascioliasis in the northern part of Iran, located at the southern littoral of Caspian Sea, especially in Guilan province. However, the extent of the predicted risk zones varied between the different areas of the region and within provinces, such that at the present, many parts of Golestan province are less environmentally suitable for Fasciola distribution than other areas in the region.
本研究旨在确定里海南岸每个地区动物片形吸虫病的病例数，并为该地区片形吸虫感染建立合适的生态位模型。从吉兰省、马赞德兰省和戈勒斯坦省的牛和羊中采集了粪便样本（n = 2688）。使用浮选法对样本进行检测，并记录每个样本每克粪便中的寄生虫卵数（EPG）。从实地收集了仅关于片形吸虫的出现数据。总共96个点/位置用于在最大熵（MaxEnt）和地理信息系统（GIS）中对片形吸虫的生态位进行建模。空间图层由23个生物气候和生物物理变量编制而成，用于建模分析。使用刀切法分析来确定模型中所有变量的相对重要性。在本研究中，两个宿主中片形吸虫病的比例在吉兰省最高（绵羊：12.34%，牛：15.16%），其次是马赞德兰省（绵羊：7.3%，牛：6.25%）和戈勒斯坦省（绵羊：0%，牛：0.94%）。模型的曲线下面积（AUC）值为0.909，表明模型具有良好的预测能力。我们的建模结果表明，显著纳入模型的四个变量是该地区片形吸虫属存在概率的主要预测因子：Bio17（最干燥季度降水量；45.5%）、Bio14（最干燥月份降水量；24.8%）、坡向（9%）和海拔（7.2%）。本文提供的数据显示，位于里海南岸的伊朗北部，特别是吉兰省，片形吸虫病潜在高风险区域有所扩大。然而，预测的风险区域范围在该地区的不同区域和省份内部有所不同，以至于目前戈勒斯坦省的许多地区在环境上比该地区的其他地区更不适合片形吸虫分布。
