Divergence Measures as Diversity Indices
Karim T. Abou-Moustafa

TL;DR
This paper introduces a new information-theoretic methodology for measuring species diversity that overcomes limitations of traditional entropy-based indices, providing more comparable, interpretable, and flexible diversity measures.
Contribution
The paper proposes a novel, two-step methodology for diversity measurement that addresses comparability, weighting biases, and distribution comparison issues of existing indices.
Findings
Method is easy to implement and applicable to various communities.
Retains functional properties of traditional diversity indices.
Provides more comparable and interpretable diversity values.
Abstract
Entropy measures of probability distributions are widely used measures in ecology, biology, genetics, and in other fields, to quantify species diversity of a community. Unfortunately, entropy-based diversity indices, or diversity indices for short, suffer from three problems. First, when computing the diversity for samples withdrawn from communities with different structures, diversity indices can easily yield non-comparable and hard to interpret results. Second, diversity indices impose weighting schemes on the species distributions that unnecessarily emphasize low abundant rare species, or erroneously identified ones. Third, diversity indices do not allow for comparing distributions against each other, which is necessary when a community has a well-known species' distribution. In this paper we propose a new general methodology based on information theoretic principles to quantify…
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Taxonomy
TopicsPlant and animal studies · Ecology and Vegetation Dynamics Studies · Species Distribution and Climate Change
