Department of Parasitology, Faculty of Veterinary Medicine, University of Zaragoza, Miguel Servet 177, 50013 Zaragoza, Spain.
Int J Health Geogr. 2013 Sep 26;12:43. doi: 10.1186/1476-072X-12-43.
Modelling the environmental niche and spatial distribution of pathogen-transmitting arthropods involves various quality and methodological concerns related to using climate data to capture the environmental niche. This study tested the potential of MODIS remotely sensed and interpolated gridded covariates to estimate the climate niche of the medically important ticks Ixodes ricinus and Hyalomma marginatum. We also assessed model inflation resulting from spatial autocorrelation (SA) and collinearity (CO) of covariates used as time series of data (monthly values of variables), principal components analysis (PCA), and a discrete Fourier transformation. Performance of the models was measured using area under the curve (AUC), autocorrelation by Moran's I, and collinearity by the variance inflation factor (VIF).
The covariate spatial resolution slightly affected the final AUC. Consistently, models for H. marginatum performed better than models for I. ricinus, likely because of a species-derived rather than covariate effect because the former occupies a more limited niche. Monthly series of interpolated climate always better captured the climate niche of the ticks, but the SA was around 2 times higher and the maximum VIF between covariates around 30 times higher in interpolated than in MODIS-derived covariates. Interpolated or remotely sensed monthly series of covariates always had higher SA and CO than their transformations by PCA or Fourier. Regarding the effects of background point selection on AUC, we found that selection based on a set of rules for the distance to the core distribution and the heterogeneity of the landscape influenced model outcomes. The best selection relied on a random selection of points as close as possible to the target organism area of distribution, but effects are variable according to the species modelled.
Testing for effects of SA and CO is necessary before incorporating these covariates into algorithms building a climate envelope. Results support a higher SA and CO in an interpolated climate dataset than in remotely sensed covariates. Satellite-derived information has fewer drawbacks compared to interpolated climate for modelling tick relationships with environmental niche. Removal of SA and CO by a harmonic regression seems most promising because it retains both biological and statistical meaning.
模拟病原体传播节肢动物的环境生态位和空间分布涉及到使用气候数据来捕捉环境生态位的各种质量和方法学问题。本研究测试了 MODIS 遥感和插值网格化协变量来估计医学上重要的蜱虫硬蜱和边缘璃眼蜱的气候生态位的潜力。我们还评估了由于协变量的空间自相关（SA）和共线性（CO）而导致的模型膨胀，这些协变量被用作时间序列数据（每月变量值）、主成分分析（PCA）和离散傅立叶变换。通过曲线下面积（AUC）、Moran's I 自相关和方差膨胀因子（VIF）来衡量模型的性能。
协变量的空间分辨率对最终 AUC 有轻微影响。一致的是，边缘璃眼蜱的模型表现优于硬蜱的模型，这可能是由于物种的影响而不是协变量的影响，因为前者占据的生态位更有限。插值气候的每月序列总是更好地捕捉蜱虫的气候生态位，但 SA 约高出 2 倍，在插值协变量中，协变量之间的最大 VIF 高出 30 倍，而在 MODIS 衍生协变量中。插值或遥感的每月协变量序列的 SA 和 CO 总是高于 PCA 或傅立叶变换的协变量。关于背景点选择对 AUC 的影响，我们发现，基于核心分布距离和景观异质性的一套规则选择点会影响模型结果。最好的选择是基于尽可能接近目标生物分布区域的随机点选择，但根据所建模的物种，效果是可变的。
在将这些协变量纳入构建气候包络的算法之前，有必要测试 SA 和 CO 的影响。结果支持在插值气候数据集中的 SA 和 CO 高于遥感协变量。与插值气候相比，卫星衍生信息对于建模蜱虫与环境生态位的关系具有较少的缺点。通过谐波回归消除 SA 和 CO 似乎最有希望，因为它保留了生物学和统计学的意义。
