作为多样性指数的多样性度量
Divergence Measures as Diversity Indices
摘要：
概率分布的熵测度在生态学、生物学、遗传学等领域中被广泛应用，用来量化群落的物种多样性〈动〉<动>。不幸的是，基于熵的多样性指数，或简称多样性指数，遭受三个问题。首先，当计算从不同结构的社区中提取的样本的多样性时，多样性指数很容易产生不可比和难以解释的结果。第二，多样性指数施加权重方案的物种分布，不必要地强调低丰富的稀有物种，或错误地确定的。第三，多样性指数不允许相互比较分布，而当一个社区有一个众所周知的物种分布时，这是必要的。在本文中，我们提出了一个新的通用方法，基于信息论的原则来量化的物种多样性〈动〉性<动>的一个社区。我们的方法，包括两个步骤，自然克服了前面提到的问题，并产生可比的和易于解释的多样性值。我们表明，我们的方法保留了任何多样性指数的所有功能特性，但远比基于熵的多样性指数更灵活。我们的方法易于实施，适用于任何感兴趣的社区。
英文摘要：
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 First, when computing the diversity for samples withdrawn from communities with different structures, diversity indices can easily yield non-comparable and hard to interpret results.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.
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