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The common patterns of abundance: the log series and Zipf's law.

ABSTRACT: In a language corpus, the probability that a word occurs n times is often proportional to 1/ n 2. Assigning rank, s, to words according to their abundance, log s vs log n typically has a slope of minus one. That simple Zipf's law pattern also arises in the population sizes of cities, the sizes of corporations, and other patterns of abundance. By contrast, for the abundances of different biological species, the probability of a population of size n is typically proportional to 1/ n, declining exponentially for larger n, the log series pattern. This article shows that the differing patterns of Zipf's law and the log series arise as the opposing endpoints of a more general theory. The general theory follows from the generic form of all probability patterns as a consequence of conserved average values and the associated invariances of scale. To understand the common patterns of abundance, the generic form of probability distributions plus the conserved average abundance is sufficient. The general theory includes cases that are between the Zipf and log series endpoints, providing a broad framework for analyzing widely observed abundance patterns.

SUBMITTER: Frank SA

PROVIDER: S-EPMC6480937 | biostudies-other | 2019

REPOSITORIES: biostudies-other

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The common patterns of abundance: the log series and Zipf's law.

Frank Steven A SA

F1000Research 20190325

In a language corpus, the probability that a word occurs n times is often proportional to 1/ n2. Assigning rank, s, to words according to their abundance, log s vs log n typically has a slope of minus one. That simple Zipf's law pattern also arises in the population sizes of cities, the sizes of corporations, and other patterns of abundance. By contrast, for the abundances of different biological species, the probability of a population of size n< ...[more]

PMID: 31069071

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Project description:For species of conservation concern, an essential part of the recovery planning process is identifying discrete population units and their location with respect to one another. A common feature among geographically proximate populations is that the number of organisms tends to covary through time as a consequence of similar responses to exogenous influences. In turn, high covariation among populations can threaten the persistence of the larger metapopulation. Historically, explorations of the covariance in population size of species with many (>10) time series have been computationally difficult. Here, we illustrate how dynamic factor analysis (DFA) can be used to characterize diversity among time series of population abundances and the degree to which all populations can be represented by a few common signals. Our application focuses on anadromous Chinook salmon (Oncorhynchus tshawytscha), a species listed under the US Endangered Species Act, that is impacted by a variety of natural and anthropogenic factors. Specifically, we fit DFA models to 24 time series of population abundance and used model selection to identify the minimum number of latent variables that explained the most temporal variation after accounting for the effects of environmental covariates. We found support for grouping the time series according to 5 common latent variables. The top model included two covariates: the Pacific Decadal Oscillation in spring and summer. The assignment of populations to the latent variables matched the currently established population structure at a broad spatial scale. At a finer scale, there was more population grouping complexity. Some relatively distant populations were grouped together, and some relatively close populations - considered to be more aligned with each other - were more associated with populations further away. These coarse- and fine-grained examinations of spatial structure are important because they reveal different structural patterns not evident in other analyses.

Dataset Information

The common patterns of abundance: the log series and Zipf's law.

Publications

The common patterns of abundance: the log series and Zipf's law.

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