Multivariate Conditional Granger Causality Analysis for Lagged Response of Soil Respiration in a Temperate Forest
Ecological multivariate systems offer a suitable data set on which to apply recent advances in information theory and causality detection. These systems are driven by the interplay of various environmental factors: meteorological and hydrological forcing, which are often correlated with each other a...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2013-10-01
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| Series: | Entropy |
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| Online Access: | http://www.mdpi.com/1099-4300/15/10/4266 |
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| author | Peter S. Curtis Chris S. Vogel Chris M. Gough Jennifer Goedhart Nietz Kyle D. Maurer Matteo Detto Gil Bohrer |
| author_facet | Peter S. Curtis Chris S. Vogel Chris M. Gough Jennifer Goedhart Nietz Kyle D. Maurer Matteo Detto Gil Bohrer |
| author_sort | Peter S. Curtis |
| collection | DOAJ |
| description | Ecological multivariate systems offer a suitable data set on which to apply recent advances in information theory and causality detection. These systems are driven by the interplay of various environmental factors: meteorological and hydrological forcing, which are often correlated with each other at different time lags; and biological factors, primary producers and decomposers with both autonomous and coupled dynamics. Here, using conditional spectral Granger causality, we quantify directional causalities in a complex atmosphere-plant-soil system involving the carbon cycle. Granger causality is a statistical approach, originating in econometrics, used to identify the presence of linear causal interactions between time series of data, based on prediction theory. We first test to see if there was a significant difference in the causal structure among two treatments where carbon allocation to roots was interrupted by girdling. We then expanded the analysis, introducing radiation and soil moisture. The results showed a complex pattern of multilevel interactions, with some of these interactions depending upon the number of variables in the system. However, no significant differences emerged in the causal structure of above and below ground carbon cycle among the two treatments. |
| first_indexed | 2024-04-11T13:16:27Z |
| format | Article |
| id | doaj.art-e3ec3d5c729949f6b3fe1be75cd29923 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T13:16:27Z |
| publishDate | 2013-10-01 |
| publisher | MDPI AG |
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| series | Entropy |
| spelling | doaj.art-e3ec3d5c729949f6b3fe1be75cd299232022-12-22T04:22:24ZengMDPI AGEntropy1099-43002013-10-0115104266428410.3390/e15104266Multivariate Conditional Granger Causality Analysis for Lagged Response of Soil Respiration in a Temperate ForestPeter S. CurtisChris S. VogelChris M. GoughJennifer Goedhart NietzKyle D. MaurerMatteo DettoGil BohrerEcological multivariate systems offer a suitable data set on which to apply recent advances in information theory and causality detection. These systems are driven by the interplay of various environmental factors: meteorological and hydrological forcing, which are often correlated with each other at different time lags; and biological factors, primary producers and decomposers with both autonomous and coupled dynamics. Here, using conditional spectral Granger causality, we quantify directional causalities in a complex atmosphere-plant-soil system involving the carbon cycle. Granger causality is a statistical approach, originating in econometrics, used to identify the presence of linear causal interactions between time series of data, based on prediction theory. We first test to see if there was a significant difference in the causal structure among two treatments where carbon allocation to roots was interrupted by girdling. We then expanded the analysis, introducing radiation and soil moisture. The results showed a complex pattern of multilevel interactions, with some of these interactions depending upon the number of variables in the system. However, no significant differences emerged in the causal structure of above and below ground carbon cycle among the two treatments.http://www.mdpi.com/1099-4300/15/10/4266multivariate Granger causalityentropyenvironmental studies |
| spellingShingle | Peter S. Curtis Chris S. Vogel Chris M. Gough Jennifer Goedhart Nietz Kyle D. Maurer Matteo Detto Gil Bohrer Multivariate Conditional Granger Causality Analysis for Lagged Response of Soil Respiration in a Temperate Forest Entropy multivariate Granger causality entropy environmental studies |
| title | Multivariate Conditional Granger Causality Analysis for Lagged Response of Soil Respiration in a Temperate Forest |
| title_full | Multivariate Conditional Granger Causality Analysis for Lagged Response of Soil Respiration in a Temperate Forest |
| title_fullStr | Multivariate Conditional Granger Causality Analysis for Lagged Response of Soil Respiration in a Temperate Forest |
| title_full_unstemmed | Multivariate Conditional Granger Causality Analysis for Lagged Response of Soil Respiration in a Temperate Forest |
| title_short | Multivariate Conditional Granger Causality Analysis for Lagged Response of Soil Respiration in a Temperate Forest |
| title_sort | multivariate conditional granger causality analysis for lagged response of soil respiration in a temperate forest |
| topic | multivariate Granger causality entropy environmental studies |
| url | http://www.mdpi.com/1099-4300/15/10/4266 |
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