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首页> 外文期刊>Canadian Journal of Forest Research >Predicting the severity of Cyclaneusma minus on Pinus radiata underTI Predicting the severity of Cyclaneusma minus on Pinus radiata under current climate in New Zealand
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Predicting the severity of Cyclaneusma minus on Pinus radiata underTI Predicting the severity of Cyclaneusma minus on Pinus radiata under current climate in New Zealand

机译:在TI下预测辐射松的气旋严重程度在新西兰当前气候下预测辐射松的气旋严重程度

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摘要

Despite being a damaging foliar disease of Pinus species, little research has characterized spatial variation in disease severity of Cyclaneusma needle cast at a macroscale. Using an extensive data set describing Cyclaneusma needle cast (S-sev) on plantation-grown Pinus radiata D. Don stands distributed widely across New Zealand, the objectives of this research were to (i) develop a regression model describing S-sev, (ii) use this model to identify key drivers of S-sev and their functional form and relative importance, and (iii) develop spatial predictions of S-sev for New Zealand P. radiata under current climate. Using an independent validation data set, the final model accounted for 73% of the variance in S-sev using four significant (P < 0.001) explanatory variables and an isotrophic exponential model to account for the spatial covariance in the data. S-sev was most sensitive to elevation followed by mean winter air temperature, mean relative humidity during July, and then stand age. S-sev increased to a maximum at mean winter air temperatures of between 7 and 9 degrees C before declining. Relationships between S-sev and all other variables were linear and positive. Spatial predictions of S-sev varied widely throughout New Zealand. Values of S-sev were highest in moderately warm, wet, and humid high-elevation environments located in the central North Island. In contrast, relatively low values of S-sev were predicted in drier eastern and southern regions of New Zealand.
机译:尽管是危害松树种的叶面疾病,但很少有研究以宏观尺度表征气旋针刺病害严重程度的空间变化。利用广泛的数据集描述了人工林辐射松D上的Cyclaneusma针铸树(S-sev)。Don在新西兰分布广泛,该研究的目标是(i)建立描述S-sev的回归模型,( ii)使用此模型来确定S-sev的关键驱动因素及其功能形式和相对重要性,并且(iii)在当前气候下针对新西兰辐射松开发S-sev的空间预测。使用一个独立的验证数据集,最终模型使用四个重要的(P <0.001)解释变量和一个等养指数模型来解释S-sev方差的73%,以解释数据中的空间协方差。 S-sev对海拔高度最敏感,其次是平均冬季气温,7月的平均相对湿度,然后是树龄。在冬季平均气温介于7到9摄氏度之间之前,S-sev最高,然后才下降。 S-sev与所有其他变量之间的关系为线性和正相关。在整个新西兰,S-sev的空间预测差异很大。 S-sev的值在北岛中部的中等温暖,潮湿和潮湿的高海拔环境中最高。相反,在较干燥的新西兰东部和南部地区,S-sev值较低。

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