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Multi-decade patterns of gypsy moth fluctuations in the Carpathian Mountains and options for outbreak forecasting

机译:喀尔巴阡山脉吉普赛蛾波动的数十年格局和暴发预测的选择

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摘要

The gypsy moth is the most important defoliator of broadleaved forests in the Northern Hemisphere. We explored the patterns in the moth's long-term dynamics and produced outbreak forecasts for seven countries located in central to southeastern Europe and extending into the Carpathian Mountains. We investigated how the outbreak periods and trends in the size of outbreak areas differ among the countries, the extent to which pest dynamics are synchronized, and how the observed patterns can be used to forecast outbreaks. We used long-term time series on outbreaks from about 1947 to 2013. The outbreak period ranged from 13 years in the northwest to 8 years in the southeast of the region; the periods were statistically significant in six of the seven countries (alpha = 0.05). Two distinct patterns in outbreak size were observed, i.e. while outbreak areas in the northwest were increasing, exceptionally large outbreaks occasionally occurred in the southeastern part of the region. Outbreak forecasts based on combined use of the Fourier Transform and ARFIMA approaches showed that outbreak predictability differs among the countries. An increase in outbreak areas, the control of which would require increased resources, was forecasted mainly in the central part of the region. Although the forecasts can support the forest management, there are limits to their use because of the complex relationships between the pest and the environment, which were not captured by our empirical forecasting models.
机译:吉普赛蛾是北半球阔叶林最重要的落叶。我们探索了蛾的长期动态模式,并对位于欧洲中南部和东南部并延伸到喀尔巴阡山脉的七个国家的疫情做出了预报。我们调查了不同国家之间的暴发期和暴发区域的大小趋势如何不同,有害生物动态同步的程度,以及如何将观察到的模式用于预测暴发。我们使用了大约1947年至2013年暴发的长期时间序列。暴发期从西北地区的13年到该地区东南部的8年不等。在七个国家中的六个国家,这一时期具有统计学意义(alpha = 0.05)。观察到两种不同的暴发模式,即在西北暴发地区增加的同时,该地区东南部偶尔发生了特大暴发。基于傅立叶变换和ARFIMA方法结合使用的暴发预测表明,各国之间的暴发可预测性不同。预计暴发面积增加,要控制疫情需要增加资源,主要是在该区域中部。尽管这些预测可以支持森林管理,但由于有害生物与环境之间的复杂关系,因此其使用受到限制,而我们的经验预测模型并未对此加以说明。

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