Alcala-Canto Yazmin, Figueroa-Castillo Juan Antonio, Ibarra-Velarde Froylán, Vera-Montenegro Yolanda, Cervantes-Valencia María Eugenia, Salem Abdelfattah Z M, Cuéllar-Ordaz Jorge Alfredo
Parasitology Department, Faculty of Veterinary Medicine, National Autonomous University of Mexico, Mexico City.
Geospat Health. 2018 May 7;13(1):624. doi: 10.4081/gh.2018.624.
The tick genus Ripicephalus (Boophilus), particularly R. microplus, is one of the most important ectoparasites that affects livestock health and considered an epidemiological risk because it causes significant economic losses due, mainly, to restrictions in the export of infested animals to several countries. Its spatial distribution has been tied to environmental factors, mainly warm temperatures and high relative humidity. In this work, we integrated a dataset consisting of 5843 records of Rhipicephalus spp., in Mexico covering close to 50 years to know which environmental variables mostly influence this ticks' distribution. Occurrences were georeferenced using the software DIVA-GIS and the potential current distribution was modelled using the maximum entropy method (Maxent). The algorithm generated a map of high predictive capability (Area under the curve = 0.942), providing the various contribution and permutation importance of the tested variables. Precipitation seasonality, particularly in March, and isothermality were found to be the most significant climate variables in determining the probability of spatial distribution of Rhipicephalus spp. in Mexico (15.7%, 36.0% and 11.1%, respectively). Our findings demonstrate that Rhipicephalus has colonized Mexico widely, including areas characterized by different types of climate. We conclude that the Maxent distribution model using Rhipicephalus records and a set of environmental variables can predict the extent of the tick range in this country, information that should support the development of integrated control strategies.
璃眼蜱属(牛蜱属),尤其是微小牛蜱,是影响家畜健康的最重要的体外寄生虫之一,并且被视为一种流行病学风险因素,因为它主要通过限制受感染动物出口到多个国家而造成重大经济损失。其空间分布与环境因素有关,主要是温暖的温度和高相对湿度。在这项研究中,我们整合了一个数据集,该数据集包含墨西哥近50年里璃眼蜱属的5843条记录,以了解哪些环境变量对这种蜱虫的分布影响最大。利用DIVA-GIS软件对出现地点进行地理定位,并使用最大熵方法(Maxent)对当前潜在分布进行建模。该算法生成了一张具有高预测能力的地图(曲线下面积 = 0.942),给出了测试变量的各种贡献和置换重要性。发现降水季节性,尤其是3月的降水季节性以及等温性是决定墨西哥璃眼蜱属空间分布概率的最重要气候变量(分别为15.7%、36.0%和11.1%)。我们的研究结果表明,璃眼蜱已在墨西哥广泛定殖,包括具有不同气候类型的地区。我们得出结论,使用璃眼蜱记录和一组环境变量的Maxent分布模型可以预测该国蜱虫分布范围,这些信息应有助于制定综合控制策略。