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The use of the Mexican Hat and the Morlet wavelets for detection of ecological patterns.

机译:使用墨西哥帽和Morlet小波检测生态模式。

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In this paper, we compare the relationship between scale and period in ecological pattern analysis and wavelet analysis. We also adapt a commonly used wavelet, the Morlet, to ecological pattern analysis. Using Monte Carlo assessments, we apply methods of statistical significance test to wavelet analysis for pattern analysis. In order to understand the inherent strength and weakness of the Morlet and the Mexican Hat wavelets, we also investigate and compare the properties of two frequently used wavelets by testing with field data and four artificial transects of different typical patterns which is often encountered in ecological research. It is shown that the Mexican Hat provides better detection and localization of patch and gap events over the Morlet, whereas the Morlet offers improved detection and localization of scale over the Mexican Hat. There is always a trade-off between the detection and localization of scale versus patch and gap events. Therefore, the best composite analysis is the combination of their advantages. The properties of wavelet in dealing with ecological data may be affected by characteristics intrinsic to wavelet itself. The peaks of different scales in isograms of wavelet power spectrum from the Mexican Hat may overlap with each other. Alternatively, these peaks of different scales in isograms of wavelet power spectrum may combine with each other unless the size of the analysed scales is significantly different. These overlapping or combining lead to combining of peaks for different scales, or the masking of trough between peaks of different scales in the scalogram. Ecologists should combine all the information in scalogram and isograms of wavelet coefficient and wavelet power spectrum from different wavelets, which can provide us a broader view and precise pattern information..
机译:在本文中,我们比较了生态模式分析和小波分析中规模与周期之间的关系。我们还将常用的小波Morlet应用于生态模式分析。使用蒙特卡洛评估,我们将统计显着性检验的方法应用于小波分析以进行模式分析。为了了解Morlet小波和Mexican Hat小波的固有优势和劣势,我们还通过与生态学研究中经常遇到的野外数据和四个不同典型模式的人工样面进行测试,研究并比较了两个常用小波的特性。 。结果表明,墨西哥帽在Morlet上提供了更好的斑块和缺口事件检测和定位,而Morlet在墨西哥帽上提供了更好的鳞屑检测和定位。在水垢的检测和定位与斑块和缺口事件之间始终需要权衡取舍。因此,最好的综合分析是它们优势的结合。小波在处理生态数据时的特性可能会受到小波本身固有的特性的影响。来自墨西哥帽的小波功率谱等值图中不同尺度的峰可能会相互重叠。可选地,除非所分析的比例尺的大小显着不同,否则小波功率谱的等值图中不同比例尺的这些峰可以彼此组合。这些重叠或组合导致不同比例的峰的组合,或者在比例尺图中不同比例的峰之间的波谷被掩盖。生态学家应该将比例尺图和来自不同小波的小波系数和小波功率谱的等值线图的所有信息结合起来,以便为我们提供更广阔的视野和精确的模式信息。

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