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The common patterns of nature

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Category
General Reference
author-supplied keywords
Ecological pattern
Maximum entropy
Neutral theories
Population genetics
keywords
authors
S. A. Frank
title
The common patterns of nature
type
generic
year
2009
source
Journal of Evolutionary Biology
pages
1563-1585
volume
22
issue
8
link
https://www.mendeley.com/catalogue/29046a0d-b3a5-3b6b-a451-ab1056aaf600/(0)

Abstract

We typically observe large-scale outcomes that arise from the interactions of many hidden, small-scale processes. Examples include age of disease onset, rates of amino acid substitutions, and composition of ecological communities. The macroscopic patterns in each problem often vary around a characteristic shape that can be generated by neutral processes. A neutral generative model assumes that each microscopic process follows unbiased stochastic fluctuations: random connections of network nodes; amino acid substitutions with no effect on fitness; species that arise or disappear from communities randomly. These neutral generative models often match common patterns of nature. In this paper, I present the theoretical background by which we can understand why these neutral generative models are so successful. I show how the classic patterns such as Poisson and Gaussian arise. Each classic pattern was often discovered by a simple neutral generative model. The neutral patterns share a special characteristic: they describe the patterns of nature that follow from simple constraints on information. For example, any aggregation of processes that preserves information only about the mean and variance attracts to the Gaussian pattern; any aggregation that preserves information only about the mean attracts to the exponential pattern; any aggregation that preserves information only about the geometric mean attracts to the power law pattern. I present an informational framework of the common patterns of nature based on the method of maximum entropy. This framework shows that each neutral generative model is a special case that helps to discover a particular set of informational constraints; those informational constraints define a much wider domain of non-neutral generative processes that attract to the same neutral pattern.

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Citation count From Scopus. Refreshed every 5 days.
117
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2

Identifiers

  • doi: 10.1111/j.1420-9101.2009.01775.x (Google search)
  • issn: 1010061X
  • sgr: 67650429936
  • isbn: 1420-9101
  • pmid: 19538344
  • arxiv: 0906.3507
  • scopus: 2-s2.0-67650429936
  • pui: 354915227

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