Shabani Farzin, Kumar Lalit
Ecosystem Management, School of Environmental and Rural Science, University of New England, Armidale, New South Wales, Australia.
PLoS One. 2013 Dec 10;8(12):e83404. doi: 10.1371/journal.pone.0083404. eCollection 2013.
Global climate model outputs involve uncertainties in prediction, which could be reduced by identifying agreements between the output results of different models, covering all assumptions included in each. Fusarium oxysporum f.sp. is an invasive pathogen that poses risk to date palm cultivation, among other crops. Therefore, in this study, the future distribution of invasive Fusarium oxysporum f.sp., confirmed by CSIRO-Mk3.0 (CS) and MIROC-H (MR) GCMs, was modeled and combined with the future distribution of date palm predicted by the same GCMs, to identify areas suitable for date palm cultivation with different risk levels of invasive Fusarium oxysporum f.sp., for 2030, 2050, 2070 and 2100. Results showed that 40%, 37%, 33% and 28% areas projected to become highly conducive to date palm are under high risk of its lethal fungus, compared with 37%, 39%, 43% and 42% under low risk, for the chosen years respectively. Our study also indicates that areas with marginal risk will be limited to 231, 212, 186 and 172 million hectares by 2030, 2050, 2070 and 2100. The study further demonstrates that CLIMEX outputs refined by a combination of different GCMs results of different species that have symbiosis or parasite relationship, ensure that the predictions become robust, rather than producing hypothetical findings, limited purely to publication.
全球气候模型输出结果在预测方面存在不确定性，通过识别不同模型输出结果之间的一致性（涵盖每个模型中的所有假设），这种不确定性可以降低。尖孢镰刀菌是一种入侵性病原菌，除其他作物外，对海枣种植构成风险。因此，在本研究中，对由CSIRO - Mk3.0（CS）和MIROC - H（MR）全球气候模型确认的入侵性尖孢镰刀菌的未来分布进行建模，并与相同全球气候模型预测的海枣未来分布相结合，以确定2030年、2050年、2070年和2100年适合海枣种植且具有不同入侵性尖孢镰刀菌风险水平的区域。结果表明，在所选年份中，预计对海枣高度适宜的区域分别有40%、37%、33%和28%处于其致死真菌的高风险之下，而处于低风险的分别为37%、39%、43%和42%。我们的研究还表明，到2030年、2050年、2070年和2100年，处于边缘风险的区域将分别限制在2.31亿公顷、2.12亿公顷、1.86亿公顷和1.72亿公顷。该研究进一步证明，通过结合具有共生或寄生关系的不同物种的不同全球气候模型结果来完善CLIMEX输出，可确保预测更加可靠，而不是产生纯粹为了发表的假设性结果。
