Acheson Emily Sohanna, Kerr Jeremy Thomas
Department of Biology, University of Ottawa , Ottawa, Ontario, Canada .
Vector Borne Zoonotic Dis. 2015 Mar;15(3):173-83. doi: 10.1089/vbz.2014.1742.
Arthropod disease vectors, most notably mosquitoes, ticks, tsetse flies, and sandflies, are strongly influenced by environmental conditions and responsible for the vast majority of global vector-borne human diseases. The most widely used statistical models to predict future vector distributions model species niches and project the models forward under future climate scenarios. Although these methods address variations in vector distributions through space, their capacity to predict changing distributions through time is far less certain. Here, we review modeling methods used to validate and forecast future distributions of arthropod vectors under the effects of climate change and outline the uses or limitations of these techniques. We then suggest a validation approach specific to temporal extrapolation models that is gaining momentum in macroecological modeling and has great potential for epidemiological modeling of disease vectors. We performed systematic searches in the Web of Science, ScienceDirect, and Google Scholar to identify peer-reviewed English journal articles that model arthropod disease vector distributions under future environment scenarios. We included studies published up to and including June, 2014. We identified 29 relevant articles for our review. The majority of these studies predicted current species niches and projected the models forward under future climate scenarios without temporal validation. Historically calibrated forecast models improve predictions of changing vector distributions by tracking known shifts through recently observed time periods. With accelerating climate change, accurate predictions of shifts in disease vectors are crucial to target vector control interventions where needs are greatest.
节肢动物疾病传播媒介，最显著的是蚊子、蜱虫、采采蝇和白蛉，受到环境条件的强烈影响，并且是全球绝大多数人类媒介传播疾病的罪魁祸首。预测未来媒介分布最广泛使用的统计模型是对物种生态位进行建模，并在未来气候情景下向前推算这些模型。尽管这些方法解决了媒介分布在空间上的变化问题，但它们预测随时间变化的分布的能力却远不那么确定。在这里，我们回顾了用于验证和预测气候变化影响下节肢动物媒介未来分布的建模方法，并概述了这些技术的用途或局限性。然后，我们提出了一种特定于时间外推模型的验证方法，这种方法在宏观生态建模中越来越受到关注，并且在疾病媒介的流行病学建模中具有巨大潜力。我们在科学网、科学Direct和谷歌学术上进行了系统搜索，以识别在未来环境情景下对节肢动物疾病媒介分布进行建模的同行评审英文期刊文章。我们纳入了截至2014年6月（包括该月）发表的研究。我们确定了29篇相关文章用于我们的综述。这些研究中的大多数预测了当前物种的生态位，并在未来气候情景下向前推算模型，而没有进行时间验证。历史校准的预测模型通过追踪最近观测时间段内已知的变化来改进对媒介分布变化的预测。随着气候变化加速，准确预测疾病媒介的变化对于在需求最大的地方开展媒介控制干预至关重要。
