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When all types in the dataset of interest are equally common, all p i values equal 1 / R, and the Shannon index hence takes the value ln(R). The more unequal the abundances of the types, the larger the weighted geometric mean of the p i values, and the smaller the corresponding Shannon entropy. If practically all abundance is concentrated to ...
There are many ways to measure biodiversity within a given ecosystem. However, the two most popular are Shannon-Weaver diversity index, [4] commonly referred to as Shannon diversity index, and the other is Simpsons diversity index. [5] Although many scientists prefer to use Shannon's diversity index simply because it takes into account species ...
The Shannon index is the most commonly used way to quantitatively determine species diversity, H, as modeled by the following equation: = = The Shannon index factors in both species evenness and species richness, as represented by the variables p i and s, respectively. The lowest possible value of H is zero, and the higher a community’s H ...
The source for this is a 1989 work by Charles J. Krebs. In a newer work, however (CJ Krebs. Ecology: the experimental analysis of distribution and abundance, 5th edition. p617-618), the same author calls the index the Shannon-Wiener index. Is there other information that could be used to find the "correct" name for the index, if one exists?
The concept of information entropy was introduced by Claude Shannon in his 1948 paper "A Mathematical Theory of Communication", [2] [3] and is also referred to as Shannon entropy. Shannon's theory defines a data communication system composed of three elements: a source of data, a communication channel, and a receiver. The "fundamental problem ...
The Wiener index is named after Harry Wiener, who introduced it in 1947; at the time, Wiener called it the "path number". [2] It is the oldest topological index related to molecular branching. [ 3 ] Based on its success, many other topological indexes of chemical graphs, based on information in the distance matrix of the graph, have been ...
In particular, the value of these standardized indices does not depend on the number of categories or number of samples. For any index, the closer to uniform the distribution, the larger the variance, and the larger the differences in frequencies across categories, the smaller the variance.
The Shannon information is closely related to entropy, which is the expected value of the self-information of a random variable, quantifying how surprising the random variable is "on average". This is the average amount of self-information an observer would expect to gain about a random variable when measuring it.