Bebber Daniel P, Castillo Ángela Delgado, Gurr Sarah J
Department of Biosciences, University of Exeter, Stocker Road, Exeter EX4 4QD, UK
Department of Biosciences, University of Exeter, Stocker Road, Exeter EX4 4QD, UK.
Philos Trans R Soc Lond B Biol Sci. 2016 Dec 5;371(1709). doi: 10.1098/rstb.2015.0458.
Many fungal plant diseases are strongly controlled by weather, and global climate change is thus likely to have affected fungal pathogen distributions and impacts. Modelling the response of plant diseases to climate change is hampered by the difficulty of estimating pathogen-relevant microclimatic variables from standard meteorological data. The availability of increasingly sophisticated high-resolution climate reanalyses may help overcome this challenge. We illustrate the use of climate reanalyses by testing the hypothesis that climate change increased the likelihood of the 2008-2011 outbreak of Coffee Leaf Rust (CLR, Hemileia vastatrix) in Colombia. We develop a model of germination and infection risk, and drive this model using estimates of leaf wetness duration and canopy temperature from the Japanese 55-Year Reanalysis (JRA-55). We model germination and infection as Weibull functions with different temperature optima, based upon existing experimental data. We find no evidence for an overall trend in disease risk in coffee-growing regions of Colombia from 1990 to 2015, therefore, we reject the climate change hypothesis. There was a significant elevation in predicted CLR infection risk from 2008 to 2011 compared with other years. JRA-55 data suggest a decrease in canopy surface water after 2008, which may have helped terminate the outbreak. The spatial resolution and accuracy of climate reanalyses are continually improving, increasing their utility for biological modelling. Confronting disease models with data requires not only accurate climate data, but also disease observations at high spatio-temporal resolution. Investment in monitoring, storage and accessibility of plant disease observation data are needed to match the quality of the climate data now available.This article is part of the themed issue 'Tackling emerging fungal threats to animal health, food security and ecosystem resilience'.
许多真菌性植物病害受天气的影响很大，因此全球气候变化可能已经影响了真菌病原体的分布及其影响。由于难以从标准气象数据中估算与病原体相关的微气候变量，对植物病害对气候变化的响应进行建模受到了阻碍。日益复杂的高分辨率气候再分析数据的可用性可能有助于克服这一挑战。我们通过检验气候变化增加了2008 - 2011年哥伦比亚咖啡叶锈病（CLR，咖啡驼孢锈菌）爆发可能性这一假设，来说明气候再分析数据的用途。我们开发了一个发芽和感染风险模型，并使用来自日本55年再分析（JRA - 55）的叶片湿润持续时间和冠层温度估计值来驱动该模型。基于现有的实验数据，我们将发芽和感染建模为具有不同最适温度的威布尔函数。我们没有发现1990年至2015年哥伦比亚咖啡种植区病害风险存在总体趋势的证据，因此，我们拒绝气候变化假设。与其他年份相比，2008年至2011年预测的CLR感染风险显著升高。JRA - 55数据表明2008年后冠层表面水分减少，这可能有助于终止疫情爆发。气候再分析数据的空间分辨率和准确性在不断提高，其在生物建模中的效用也在增加。用数据验证病害模型不仅需要准确的气候数据，还需要高时空分辨率的病害观测数据。需要对植物病害观测数据的监测、存储和可获取性进行投资，以匹配现有气候数据的质量。本文是主题为“应对真菌对动物健康、粮食安全和生态系统恢复力的新威胁”的特刊的一部分。
