Multivariate Conditional Granger Causality Analysis for Lagged Response of Soil Respiration in a Temperate Forest

Matteo Detto, Gil Bohrer, Jennifer Goedhart Nietz, Kyle D. Maurer, Chris S. Vogel, Chris M. Gough, Peter S. Curtis

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)4266-4284
Number of pages19
Issue number10
StatePublished - Oct 2013
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Mathematical Physics
  • Physics and Astronomy (miscellaneous)
  • General Physics and Astronomy
  • Electrical and Electronic Engineering


  • Entropy
  • Environmental studies
  • Multivariate Granger causality


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