1. Functional trait differences between species are key drivers of community assembly and ecosystem functioning. Quantifying these differences routinely requires using approaches like the Gower distance to combine various types of traits into a multi‐trait dissimilarity.
2. Without special care, the Gower distance can however produce a multi‐trait dissimilarity with a disproportional contribution of certain traits, particularly categorical traits and bundle of correlated traits reflecting similar ecological functions. These effects persist even after applying multivariate analyses traditionally used to reduce trait dimensionality.
3. We propose the ‘gawdis’ R function, and corresponding package, to produce multi‐trait dissimilarity with more uniform contributions of different traits, including fuzzy coded ones. The approach is based on minimizing the differences in the correlation between the dissimilarity of each trait, or groups of traits, and the multi‐trait dissimilarity. This is done using either an analytical or a numerical solution, both available in the function.
4. Properly taking into account the contribution of multiple traits into multi‐trait dissimilarity is key for interpreting the ecological effects of complex species differences. The gawdis r package in CRAN can be further applied to improve equitability in distance‐based measures in other field of research, such as social sciences or marketing surveys, which routinely analyse mixed type data.

中文翻译：

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计算物种之间的功能差异时，要实现多个性状的更平衡组合

1. 物种之间的功能性状差异是社区组装和生态系统功能的主要驱动力。通常，要量化这些差异，需要使用高尔距离之类的方法，将各种类型的性状组合成一个多性状的相似性。
2. 然而，如果没有特别的照顾，高尔距离会产生多种性状的相似性，其中某些性状，尤其是分类性状和相关性状的组合反映了相似的生态功能，这会导致某些性状的不成比例的贡献。即使应用了传统上用于减少性状维度的多元分析，这些影响仍然存在。
3. 我们提出“ gawdis” R函数和相应的程序包，以产生具有模糊特征的不同特征，这些特征具有不同特征的更统一的贡献。该方法基于最小化每个特征或一组特征的不相似性与多特征不相似性之间的相关性差异。这可以通过解析或数值解决方案来完成，两者都可以在函数中使用。
4. 正确考虑多种性状对多种性状异质性的贡献是解释复杂物种差异的生态影响的关键。CRAN中的gawdis r软件包可以进一步应用于提高其他研究领域（例如社会科学或市场调查）中基于距离的度量的公平性，这些研究定期分析混合类型数据。