Estrada-Peña A, Estrada-Sánchez A, Estrada-Sánchez D
Department of Parasitology, University of Zaragoza, Zaragoza, Spain.
Department of Geography, University of Zaragoza, Zaragoza, Spain.
Vet Parasitol. 2015 Feb 28;208(1-2):14-25. doi: 10.1016/j.vetpar.2014.12.016. Epub 2014 Dec 23.
Interest is increasing in inferring the climate niche of health-threatening arthropods and projecting such inferences onto a territory. This approach is intended to predict the range of tick distribution and to elucidate tick responses to climate scenarios, using so-called correlative models. However, some methodological gaps might prevent achieving an adequate background for hypothesis testing. We explore, describe, and illustrate these procedural inaccuracies with examples focused on the tick Ixodes ricinus and examine how these factors might affect modelling outcomes. Our aim was to develop a backdrop of rules for developing reliable models for these parasites. The use of partial sets of tick occurrences might produce unreliable associations with climate because the algorithms cannot capture the complete niche with which the tick is associated. Reliability measures of the model cannot detect these inaccuracies, and undesirable estimations of the niche will prevail in the chain of further calculations. The use of inadequate environmental variables (covariates) may lead to inflation of the results of the model through two statistical processes, autocorrelation and colinearity. We demonstrate the high colinearity existing in climate products derived from interpolation of climate recording stations. Our explicit advice is to focus on the training of climate models with satellite-derived information of climate, from which colinearity of the time series has been removed through a harmonic regression. We also emphasize the high uncertainty if inference about the climate niche is expanded into different time slices, like projected climate scenarios.
推断对健康构成威胁的节肢动物的气候生态位并将此类推断应用于某一区域的研究兴趣正在增加。这种方法旨在利用所谓的相关模型预测蜱虫的分布范围，并阐明蜱虫对气候情景的反应。然而，一些方法上的差距可能会妨碍为假设检验提供充分的背景。我们以蓖麻硬蜱为例，探索、描述并说明这些程序上的不准确之处，并研究这些因素如何影响建模结果。我们的目标是为开发针对这些寄生虫的可靠模型制定一套规则背景。使用部分蜱虫出现数据集可能会产生与气候的不可靠关联，因为算法无法捕捉蜱虫所关联的完整生态位。模型的可靠性度量无法检测到这些不准确之处，在进一步的计算链中，对生态位的不良估计将占主导。使用不充分的环境变量（协变量）可能会通过自相关和共线性这两个统计过程导致模型结果膨胀。我们展示了从气候记录站插值得到的气候产品中存在的高共线性。我们明确建议专注于利用卫星衍生的气候信息训练气候模型，通过谐波回归已消除了时间序列的共线性。我们还强调，如果将对气候生态位的推断扩展到不同的时间切片，如预测的气候情景，会存在高度的不确定性。
