Perveen Nighat, Muzaffar Sabir B, Jaradat Areej, Sparagano Olivier A, Willingham Arve L
Department of Biology, College of Science, United Arab Emirates University, Al-Ain, UAE.
Department of Veterinary Medicine, College of Agriculture and Veterinary Medicine, United Arab Emirates University, Al-Ain, UAE.
Parasitology. 2024 Aug;151(9):1024-1034. doi: 10.1017/S0031182024001161.
Ticks are important vectors and reservoirs of pathogens causing zoonotic diseases in camels and other livestock, rodents and other small mammals, birds and humans. is the most abundant tick species in Saudi Arabia and United Arab Emirates (UAE) affecting primarily camels, and to a lesser extent, other livestock. Species presence data, land use/landcover, elevation, slope and 19 bioclimatic variables were used to model current and future distribution of ticks using maximum entropy species distribution modelling (MaxEnt.). The model highlighted areas in the northern, eastern and southwestern parts of the study area as highly suitable for ticks. Several variables including land use/land cover (LULC) (53.1%), precipitation of coldest quarter (Bio19) (21.8%), elevation (20.6%), isothermality (Bio3) (1.9%), mean diurnal range [mean of monthly (max temp – min temp)] (Bio2) (1.8%), slope (0.5%), precipitation, seasonality (Bio15) (0.2%) influenced habitat suitability of ticks, predicting high tick density or abundance. Middle of the road scenario (ssp2-4.5) where CO levels remain similar to current levels, did not indicate a major change in the tick distributions. This tick distribution model could be used for targeting surveillance efforts and increasing the efficiency and accuracy of public health investigations and vector control strategies.
蜱是导致骆驼及其他家畜、啮齿动物及其他小型哺乳动物、鸟类和人类发生人畜共患病的病原体的重要传播媒介和宿主。[蜱的种类名称]是沙特阿拉伯和阿拉伯联合酋长国(阿联酋)最为常见的蜱种,主要影响骆驼,对其他家畜的影响较小。利用物种存在数据、土地利用/土地覆盖、海拔、坡度和19个生物气候变量,通过最大熵物种分布模型(MaxEnt.)对[蜱的种类名称]蜱的当前和未来分布进行建模。该模型突出显示研究区域北部、东部和西南部的区域非常适合[蜱的种类名称]蜱生存。包括土地利用/土地覆盖(LULC)(53.1%)、最冷月降水量(Bio19)(21.8%)、海拔(20.6%)、等温性(Bio3)(1.9%)、平均日较差[月平均(最高温度 - 最低温度)](Bio2)(1.8%)、坡度(0.5%)、降水量季节性变化(Bio15)(0.2%)在内的几个变量影响了[蜱的种类名称]蜱的栖息地适宜性,预测[蜱的种类名称]蜱密度或丰度较高。在CO水平保持与当前水平相似的中间道路情景(ssp2 - 4.5)下,[蜱的种类名称]蜱的分布未显示出重大变化。这种蜱分布模型可用于确定监测工作的目标,提高公共卫生调查和病媒控制策略的效率和准确性。