Building a richer understanding of diversity through causally consistent evenness measures

Abstract

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.