Epidemiology is one of many fields that use ecological niche modeling to assess potential distributions or potential range expansions of species. When such models are transferred in space and time, it is important to understand sources and location of uncertainty in their predictions. Here, we used the tick species Rhipicephalus sanguineus sensu lato (distributed in different areas around the world) as an example; for the first time, we characterized its global geographic distribution using ecological niche modeling, and explore the uncertainty involved in transferring models in space and time. We assessed uncertainties based on risks of strict extrapolation and amounts and patterns of variation in our predictions. We integrated occurrence records and climate data to calibrate models for 5 world regions, and to project them to 11 general circulation models (GCMs) and two representative concentration pathway emissions scenarios (RCPs) for 2050. Models created in different calibration areas showed high agreement of suitable areas among model predictions from the eastern United States, southern Mexico, South America, Europe, North Africa, sub-Saharan countries, Asia, and Australia. The global potential distributions of R. sanguineus sensulato were very similar between the two RCPs, but GCMs, model replicates, and model parametrizations contributed importantly to the overall variation detected. Patterns of uncertainty (strict extrapolation areas and variation) in our model predictions depended on the calibration area, and underlined the important implications of not considering variability and extrapolation risk in interpretations of ecological niche model projections.

流行病学是使用生态位生态模型来评估物种的潜在分布或潜在范围扩展的众多领域之一。当此类模型在时空上传递时，重要的是要了解其预测中不确定性的来源和位置。在这里，我们以壁虱物种Rhipicephalus sanguineus sensu lato（分布在世界各地）为例。我们首次使用生态位模型表征了其全球地理分布，并探讨了在时空模型转换中所涉及的不确定性。我们根据严格推断的风险以及预测的数量和变化模式评估了不确定性。我们整合了发生记录和气候数据，以校准5个世界区域的模型，并将其投影到2050年的11种通用循环模型（GCM）和两个代表性的浓度路径排放情景（RCP）。在不同校准区域创建的模型显示，美国东部，墨西哥南部，南部的模型预测中合适的区域高度吻合美国，欧洲，北非，撒哈拉以南国家，亚洲和澳大利亚。在两个RCP之间，R。sanguineus sensulato的全球潜在分布非常相似，但是GCM，模型复制和模型参数化对检测到的总体变化起重要作用。在我们的模型预测中，不确定性的模式（严格的外推区域和变化）取决于校准区域，