An improved methodology for quantifying causality in complex ecological systems.
This paper provides a statistical methodology for quantifying causality in complex dynamical systems, based on analysis of multidimensional time series data of the state variables. The methodology integrates Granger's causality analysis based on the log-likelihood function expansion (Partial pa...
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ftdoajarticles:oai:doaj.org/article:5320b6cb4f01475c8fcd664b30be78f9 2023-05-15T15:38:54+02:00 An improved methodology for quantifying causality in complex ecological systems. Hiroko Kato Solvang Sam Subbey 2019-01-01T00:00:00Z https://doi.org/10.1371/journal.pone.0208078 https://doaj.org/article/5320b6cb4f01475c8fcd664b30be78f9 EN eng Public Library of Science (PLoS) https://doi.org/10.1371/journal.pone.0208078 https://doaj.org/toc/1932-6203 1932-6203 doi:10.1371/journal.pone.0208078 https://doaj.org/article/5320b6cb4f01475c8fcd664b30be78f9 PLoS ONE, Vol 14, Iss 1, p e0208078 (2019) Medicine R Science Q article 2019 ftdoajarticles https://doi.org/10.1371/journal.pone.0208078 2022-12-31T13:17:44Z This paper provides a statistical methodology for quantifying causality in complex dynamical systems, based on analysis of multidimensional time series data of the state variables. The methodology integrates Granger's causality analysis based on the log-likelihood function expansion (Partial pair-wise causality), and Akaike's power contribution approach over the whole frequency domain (Total causality). The proposed methodology addresses a major drawback of existing methodologies namely, their inability to use time series observation of state variables to quantify causality in complex systems. We first perform a simulation study to verify the efficacy of the methodology using data generated by several multivariate autoregressive processes, and its sensitivity to data sample size. We demonstrate application of the methodology to real data by deriving inter-species relationships that define key food web drivers of the Barents Sea ecosystem. Our results show that the proposed methodology is a useful tool in early stage causality analysis of complex feedback systems. Article in Journal/Newspaper Barents Sea Directory of Open Access Journals: DOAJ Articles Barents Sea PLOS ONE 14 1 e0208078 |
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English |
topic |
Medicine R Science Q |
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Medicine R Science Q Hiroko Kato Solvang Sam Subbey An improved methodology for quantifying causality in complex ecological systems. |
topic_facet |
Medicine R Science Q |
description |
This paper provides a statistical methodology for quantifying causality in complex dynamical systems, based on analysis of multidimensional time series data of the state variables. The methodology integrates Granger's causality analysis based on the log-likelihood function expansion (Partial pair-wise causality), and Akaike's power contribution approach over the whole frequency domain (Total causality). The proposed methodology addresses a major drawback of existing methodologies namely, their inability to use time series observation of state variables to quantify causality in complex systems. We first perform a simulation study to verify the efficacy of the methodology using data generated by several multivariate autoregressive processes, and its sensitivity to data sample size. We demonstrate application of the methodology to real data by deriving inter-species relationships that define key food web drivers of the Barents Sea ecosystem. Our results show that the proposed methodology is a useful tool in early stage causality analysis of complex feedback systems. |
format |
Article in Journal/Newspaper |
author |
Hiroko Kato Solvang Sam Subbey |
author_facet |
Hiroko Kato Solvang Sam Subbey |
author_sort |
Hiroko Kato Solvang |
title |
An improved methodology for quantifying causality in complex ecological systems. |
title_short |
An improved methodology for quantifying causality in complex ecological systems. |
title_full |
An improved methodology for quantifying causality in complex ecological systems. |
title_fullStr |
An improved methodology for quantifying causality in complex ecological systems. |
title_full_unstemmed |
An improved methodology for quantifying causality in complex ecological systems. |
title_sort |
improved methodology for quantifying causality in complex ecological systems. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2019 |
url |
https://doi.org/10.1371/journal.pone.0208078 https://doaj.org/article/5320b6cb4f01475c8fcd664b30be78f9 |
geographic |
Barents Sea |
geographic_facet |
Barents Sea |
genre |
Barents Sea |
genre_facet |
Barents Sea |
op_source |
PLoS ONE, Vol 14, Iss 1, p e0208078 (2019) |
op_relation |
https://doi.org/10.1371/journal.pone.0208078 https://doaj.org/toc/1932-6203 1932-6203 doi:10.1371/journal.pone.0208078 https://doaj.org/article/5320b6cb4f01475c8fcd664b30be78f9 |
op_doi |
https://doi.org/10.1371/journal.pone.0208078 |
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PLOS ONE |
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14 |
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1 |
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e0208078 |
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1766370307073900544 |