Modeling the distribution of the West Nile and Rift Valley Fever vector Culex pipiens in arid and semi-arid regions of the Middle East and North Africa.
Modeling the distribution of the West Nile and Rift Valley Fever vector Culex pipiens in arid and semi-arid regions of the Middle East and North Africa.
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DOI：
10.1186/1756-3305-7-289
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发表时间：
2014-06-24
影响因子：
3.2
通讯作者：
Beier JC
中科院分区：
文献类型：
--
作者：
Conley AK;Fuller DO;Haddad N;Hassan AN;Gad AM;Beier JC
中东北非地区不断受到西尼罗河病毒和裂谷热病毒重新出现的威胁，这两种病原体是由库蚊传播的。预测疾病传播的高风险地区需要一个准确的病媒分布模型，然而，大多数Cx。尖音鸟的分布模型一直局限于温带森林栖息地。在一个异质景观结构的物种分布建模需要一个灵活的建模方法来捕捉蚊子的变化响应预测以及发生的数据点采取足够的栖息地类型的范围。我们使用来自埃及和黎巴嫩的仅存在数据来模拟Cx的人口分布。在中东和北非的一部分，也包括约旦，叙利亚和以色列。模型创建了一套环境预测，包括生物气候数据，人口密度，水文数据和植被指数，并建立了最大熵（Maxent）和提升回归树（BRT）的方法。建立的模型包括人口密度和不包括人口密度。Maxent模型和BRT模型的预测结果在发生概率高的生境中相关性较强（Pearson's r = 0.774，r = 0.734），在超出训练数据范围的生境中相关性较弱（r = 0.666，r = 0.558）。所有的模型都一致预测，在主要城市地区，沿着尼罗河两岸，以色列，黎巴嫩和约旦的山谷，以及沙特阿拉伯西南部的占用概率很高。Cx最强有力的预测因子。影响尖音鸟栖息地的主要因素是人口密度（60.6%Maxent模型，34.9%BRT模型）和增强植被指数（EVI）的季节性（44.7%Maxent模型，16.3%BRT模型）。Maxent模型往往由一个单一的预测因素主导。概率高的地区与独立调查或以前疾病暴发的地点相对应。库蚊pipiens的发生与人口密度高和植被覆盖一致的地区呈正相关，但温度和降雨量并不显著，这表明人类引起的生境变化，如灌溉和城市基础设施，对该地区病媒分布的影响大于温带地区。
The Middle East North Africa (MENA) region is under continuous threat of the re-emergence of West Nile virus (WNV) and Rift Valley Fever virus (RVF), two pathogens transmitted by the vector species Culex pipiens. Predicting areas at high risk for disease transmission requires an accurate model of vector distribution, however, most Cx. pipiens distribution modeling has been confined to temperate, forested habitats. Modeling species distributions across a heterogeneous landscape structure requires a flexible modeling method to capture variation in mosquito response to predictors as well as occurrence data points taken from a sufficient range of habitat types. We used presence-only data from Egypt and Lebanon to model the population distribution of Cx. pipiens across a portion of the MENA that also encompasses Jordan, Syria, and Israel. Models were created with a set of environmental predictors including bioclimatic data, human population density, hydrological data, and vegetation indices, and built using maximum entropy (Maxent) and boosted regression tree (BRT) methods. Models were created with and without the inclusion of human population density. Predictions of Maxent and BRT models were strongly correlated in habitats with high probability of occurrence (Pearson’s r = 0.774, r = 0.734), and more moderately correlated when predicting into regions that exceeded the range of the training data (r = 0.666,r = 0.558). All models agreed in predicting high probability of occupancy around major urban areas, along the banks of the Nile, the valleys of Israel, Lebanon, and Jordan, and southwestern Saudi Arabia. The most powerful predictors of Cx. pipiens habitat were human population density (60.6% Maxent models, 34.9% BRT models) and the seasonality of the enhanced vegetation index (EVI) (44.7% Maxent, 16.3% BRT). Maxent models tended to be dominated by a single predictor. Areas of high probability corresponded with sites of independent surveys or previous disease outbreaks. Cx. pipiens occurrence was positively associated with areas of high human population density and consistent vegetation cover, but was not significantly driven by temperature and rainfall, suggesting human-induced habitat change such as irrigation and urban infrastructure has a greater influence on vector distribution in this region than in temperate zones.
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影响因子：
3
作者：
Cohen JM;Ernst KC;Lindblade KA;Vulule JM;John CC;Wilson ML
通讯作者：
Wilson ML
影响因子：
2.1
作者：
Diuk-Wasser, Maria A.;Brown, Heidi E.;Fish, Durland
通讯作者：
Fish, Durland
影响因子：
2.7
作者：
Burkett-Cadena, Nathan D.;McClure, Christopher J. W.;Eubanks, Micky D.
通讯作者：
Eubanks, Micky D.
DOI：
10.20506/rst.25.1.1648
发表时间：
2006-04-01
影响因子：
0.6
作者：
Davies, F. G.
通讯作者：
Davies, F. G.
影响因子：
4.8
作者：
Elith, J.;Leathwick, J. R.;Hastie, T.
通讯作者：
Hastie, T.