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Title: Novel covariance-based neutrality test of time-series data reveals asymmetries in ecological and economic systems

Abstract

Systems as diverse as the interacting species in a community, alleles at a genetic locus, and companies in a market are characterized by competition (over resources, space, capital, etc) and adaptation. Neutral theory, built around the hypothesis that individual performance is independent of group membership, has found utility across the disciplines of ecology, population genetics, and economics, both because of the success of the neutral hypothesis in predicting system properties and because deviations from these predictions provide information about the underlying dynamics. However, most tests of neutrality are weak, based on static system properties such as species-abundance distributions or the number of singletons in a sample. Time-series data provide a window onto a system’s dynamics, and should furnish tests of the neutral hypothesis that are more powerful to detect deviations from neutrality and more informative about to the type of competitive asymmetry that drives the deviation. Here, we present a neutrality test for time-series data. We apply this test to several microbial time-series and financial time-series and find that most of these systems are not neutral. Our test isolates the covariance structure of neutral competition, thus facilitating further exploration of the nature of asymmetry in the covariance structure of competitivemore » systems. Much like neutrality tests from population genetics that use relative abundance distributions have enabled researchers to scan entire genomes for genes under selection, we anticipate our time-series test will be useful for quick significance tests of neutrality across a range of ecological, economic, and sociological systems for which time-series data are available. Here, future work can use our test to categorize and compare the dynamic fingerprints of particular competitive asymmetries (frequency dependence, volatility smiles, etc) to improve forecasting and management of complex adaptive systems.« less

Authors:
ORCiD logo [1];  [2];  [2];  [3]
  1. Duke Univ., Durham, NC (United States)
  2. Princeton Univ., Princeton, NJ (United States)
  3. Univ. of Chicago, Chicago, IL (United States)
Publication Date:
Research Org.:
Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1335699
Grant/Contract Number:  
AC02-09CH11466
Resource Type:
Accepted Manuscript
Journal Name:
PLoS Computational Biology (Online)
Additional Journal Information:
Journal Name: PLoS Computational Biology (Online); Journal Volume: 12; Journal Issue: 9; Journal ID: ISSN 1553-7358
Publisher:
Public Library of Science
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; covariance; tongue; community ecology; neutral theory; ecological economics; finance; invasive species; population ecology

Citation Formats

Washburne, Alex D., Burby, Joshua W., Lacker, Daniel, and Allesina, Stefano. Novel covariance-based neutrality test of time-series data reveals asymmetries in ecological and economic systems. United States: N. p., 2016. Web. doi:10.1371/journal.pcbi.1005124.
Washburne, Alex D., Burby, Joshua W., Lacker, Daniel, & Allesina, Stefano. Novel covariance-based neutrality test of time-series data reveals asymmetries in ecological and economic systems. United States. doi:10.1371/journal.pcbi.1005124.
Washburne, Alex D., Burby, Joshua W., Lacker, Daniel, and Allesina, Stefano. Fri . "Novel covariance-based neutrality test of time-series data reveals asymmetries in ecological and economic systems". United States. doi:10.1371/journal.pcbi.1005124. https://www.osti.gov/servlets/purl/1335699.
@article{osti_1335699,
title = {Novel covariance-based neutrality test of time-series data reveals asymmetries in ecological and economic systems},
author = {Washburne, Alex D. and Burby, Joshua W. and Lacker, Daniel and Allesina, Stefano},
abstractNote = {Systems as diverse as the interacting species in a community, alleles at a genetic locus, and companies in a market are characterized by competition (over resources, space, capital, etc) and adaptation. Neutral theory, built around the hypothesis that individual performance is independent of group membership, has found utility across the disciplines of ecology, population genetics, and economics, both because of the success of the neutral hypothesis in predicting system properties and because deviations from these predictions provide information about the underlying dynamics. However, most tests of neutrality are weak, based on static system properties such as species-abundance distributions or the number of singletons in a sample. Time-series data provide a window onto a system’s dynamics, and should furnish tests of the neutral hypothesis that are more powerful to detect deviations from neutrality and more informative about to the type of competitive asymmetry that drives the deviation. Here, we present a neutrality test for time-series data. We apply this test to several microbial time-series and financial time-series and find that most of these systems are not neutral. Our test isolates the covariance structure of neutral competition, thus facilitating further exploration of the nature of asymmetry in the covariance structure of competitive systems. Much like neutrality tests from population genetics that use relative abundance distributions have enabled researchers to scan entire genomes for genes under selection, we anticipate our time-series test will be useful for quick significance tests of neutrality across a range of ecological, economic, and sociological systems for which time-series data are available. Here, future work can use our test to categorize and compare the dynamic fingerprints of particular competitive asymmetries (frequency dependence, volatility smiles, etc) to improve forecasting and management of complex adaptive systems.},
doi = {10.1371/journal.pcbi.1005124},
journal = {PLoS Computational Biology (Online)},
number = 9,
volume = 12,
place = {United States},
year = {2016},
month = {9}
}

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