The comparison between spatial or temporal patterns is often needed for model evaluation and change detection in ecological studies. The statistics developed for image quality assessment, such as the structural similarity index (SSIM) and the composite similarity measure based on means, standard deviations, and correlation coefficient (CMSC), have been introduced for comparing ecological patterns. However, these measures can be applied only when a positive relationship is expected between patterns having the same scale. We propose a new index, generic composite similarity measure (GCSM), to meet a wide range of potential applications. A set of numerical experiments was performed to illustrate the properties of GCSM in comparison with SSIM and CMSC. Two case studies were conducted examining the (dis)agreement between two products of gross primary production (GPP), and the relative (dis)similarity between GPP and precipitation, respectively. GCSM has advantages over both SSIM and CMSC, including higher sensitivity and the ability to quantify the dissimilarity, which cannot be properly revealed with the latter two indices. The normalization preprocessing constructs universal criteria for assessing the relative (dis)similarity between patterns having unequal scales. The GCSM, overcoming the limitations of preexisting composite measures in quantifying the similarity or dissimilarity between patterns, would aid assessment of heterogeneous relationship between ecological factors over space or time.

在生态学研究中，模型评估和变化检测经常需要空间或时间模式之间的比较。为了比较生态模式，引入了为图像质量评估而开发的统计数据，例如结构相似性指数（SSIM）和基于均值，标准差和相关系数（CMSC）的复合相似性度量。但是，仅当在具有相同比例的图案之间期望正相关时才可以应用这些措施。我们提出了一种新的索引，通用复合相似性度量（GCSM），以满足广泛的潜在应用。进行了一组数值实验，以说明与SSIM和CMSC相比GCSM的特性。进行了两个案例研究，分别考察了两种主要生产总值（GPP）的（不一致）一致性，以及GPP与降水之间的相对（不一致）相似性。GCSM具有优于SSIM和CMSC的优点，包括更高的灵敏度和量化差异的能力，而后两项指标无法正确揭示这些差异。归一化预处理构建了通用标准，用于评估尺度不等的图案之间的相对（不相似）相似性。GCSM克服了现有的综合措施在量化模式之间的相似性或不相似性方面的局限性，将有助于评估空间或时间上生态因子之间的异质关系。GCSM具有优于SSIM和CMSC的优点，包括更高的灵敏度和量化差异的能力，而后两项指标无法正确揭示这些差异。归一化预处理构建了通用标准，用于评估尺度不等的图案之间的相对（不相似）相似性。GCSM克服了现有的综合措施在量化模式之间的相似性或不相似性方面的局限性，将有助于评估空间或时间上生态因子之间的异质关系。GCSM具有优于SSIM和CMSC的优点，包括更高的灵敏度和量化差异的能力，而后两项指标无法正确揭示这些差异。归一化预处理构建了通用标准，用于评估尺度不等的图案之间的相对（不相似）相似性。GCSM克服了现有的综合措施在量化模式之间的相似性或不相似性方面的局限性，将有助于评估空间或时间上生态因子之间的异质关系。归一化预处理构建了通用标准，用于评估尺度不等的图案之间的相对（不相似）相似性。GCSM克服了现有的综合措施在量化模式之间的相似性或不相似性方面的局限性，将有助于评估空间或时间上生态因子之间的异质关系。归一化预处理构建了通用标准，用于评估尺度不等的图案之间的相对（不相似）相似性。GCSM克服了现有的综合措施在量化模式之间的相似性或不相似性方面的局限性，将有助于评估空间或时间上生态因子之间的异质关系。