Rothamsted Research, West Common, Harpenden, AL5 2JQ, UK.
Institute of Science and the Environment, University of Worcester, Henwick Grove, Worcester, WR2 6AJ, UK.
Int J Biometeorol. 2018 Jun;62(6):979-990. doi: 10.1007/s00484-018-1500-z. Epub 2018 Jan 27.
The genus Leptosphaeria contains numerous fungi that cause the symptoms of asthma and also parasitize wild and crop plants. In search of a robust and universal forecast model, the ascospore concentration in air was measured and weather data recorded from 1 March to 31 October between 2006 and 2012. The experiment was conducted in three European countries of the temperate climate, i.e., Ukraine, Poland, and the UK. Out of over 150 forecast models produced using artificial neural networks (ANNs) and multivariate regression trees (MRTs), we selected the best model for each site, as well as for joint two-site combinations. The performance of all computed models was tested against records from 1 year which had not been used for model construction. The statistical analysis of the fungal spore data was supported by a comprehensive study of both climate and land cover within a 30-km radius from the air sampler location. High-performance forecasting models were obtained for individual sites, showing that the local micro-climate plays a decisive role in biology of the fungi. Based on the previous epidemiological studies, we hypothesized that dew point temperature (DPT) would be a critical factor in the models. The impact of DPT was confirmed only by one of the final best neural models, but the MRT analyses, similarly to the Spearman's rank test, indicated the importance of DPT in all but one of the studied cases and in half of them ranked it as a fundamental factor. This work applies artificial neural modeling to predict the Leptosphaeria airborne spore concentration in urban areas for the first time.
菜豆壳球腔菌属包含许多真菌，这些真菌引起哮喘症状，也寄生在野生和作物植物上。为了寻找一个强大而通用的预测模型，我们测量了 2006 年至 2012 年 3 月 1 日至 10 月 31 日期间空气中的分生孢子浓度，并记录了气象数据。该实验在三个温带气候的欧洲国家进行，即乌克兰、波兰和英国。在使用人工神经网络 (ANNs) 和多元回归树 (MRTs) 生成的 150 多个预测模型中，我们为每个站点选择了最佳模型，以及两个站点的联合组合。所有计算模型的性能都是针对未用于模型构建的 1 年记录进行测试的。对真菌孢子数据的统计分析得到了来自空气采样器位置 30 公里半径内气候和土地覆盖的综合研究的支持。为各个站点获得了高性能的预测模型，表明局部小气候在真菌生物学中起着决定性的作用。基于以前的流行病学研究，我们假设露点温度 (DPT) 将是模型中的一个关键因素。只有一个最终的最佳神经网络模型证实了 DPT 的影响，但 MRT 分析与 Spearman 秩检验一样，表明 DPT 在所有研究案例中都很重要，除了一个案例外，在其中一半案例中，它被列为一个基本因素。这项工作首次将人工神经网络建模应用于预测城市地区菜豆壳球腔菌气载孢子浓度。
