  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diversity for samples withdrawn from communities with different structures,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 the species diversity of a community. Our
methodology, comprised of two steps, naturally overcomes the previous mentioned
problems, and yields comparable and easy to interpret diversity values. We show
that our methodology retains all the functional properties of any diversity
index, and yet is far more flexible than entropy--based diversity indices. Our
methodology is easy to implement and is applicable to any community of
interest.
