Building a richer understanding of diversity through causally consistent evenness measures,Ecology and Evolution

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Building a richer understanding of diversity through causally consistent evenness measures
Ecology and Evolution ( IF 2.3 ) Pub Date : 2020-05-26 , DOI: 10.1002/ece3.6353
Kawika Pierson ₁

Affiliation

Causally consistent evenness measures can only be changed when the populations they refer to change. This novel property is deeply important for making causal inferences, and yet every prominent evenness measure is not causally consistent. This paper proposes a family of causally consistent evenness measures, and while any evenness measure can be made to be causally consistent, the family I introduce has the added benefit of a straightforward interpretation as a percentage evenness. I go on to illustrate the performance of these measures, and demonstrate the importance of causal consistency not only for causal inference but also for correctly reflecting the evenness of ecological communities. I also present several alternative transformations of my preferred measures, which work to address potential critiques in advance, communicate evenness to nontechnical audiences, and connect my work to more familiar ecological indicators.

中文翻译：

通过因果一致的均匀性度量建立对多样性的更丰富的理解

因果一致的均匀度测量只有当它们所涉及的群体发生变化时才能改变。这种新颖的特性对于做出因果推论非常重要，但每个突出的均匀性度量都不是因果一致的。本文提出了一系列因果一致的均匀性度量，虽然任何均匀性度量都可以实现因果一致，但我介绍的系列具有直接解释为百分比均匀性的额外好处。我继续说明这些措施的表现，并论证因果一致性不仅对于因果推理而且对于正确反映生态群落的均匀性的重要性。我还提出了我喜欢的衡量标准的几种替代转变，这些转变致力于提前解决潜在的批评，向非技术受众传达公平性，并将我的工作与更熟悉的生态指标联系起来。

更新日期：2020-05-26

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