Causality and Persistence in Ecological Systems: A Nonparametric Spectral Granger Causality Approach

• Published in: The American naturalist 2012-04, Vol.179 (4), p.524-535
• Main Authors: Detto, Matteo, Molini, Annalisa, Katul, Gabriel, Stoy, Paul, Palmroth, Sari, Baldocchi, Dennis
• Format: Article
• Language: English
• Subjects: Animal and plant ecology; Animal behavior; Animal, plant and microbial ecology; Biological and medical sciences; Carbon dioxide; Causality; Correlation analysis; Correlations; Ecosystem; Ecosystems; Fundamental and applied biological sciences. Psychology; General aspects; Modeling; Models, Theoretical; Photosynthesis; Respiration; Soil; Soil respiration; Soil temperature; Statistics, Nonparametric; Time series; Trees
• ISSN: 0003-0147; 1537-5323
• DOI: 10.1086/664628

Directionality in coupling, defined as the linkage relating causes to their effects at a later time, can be used to explain the core dynamics of ecological systems by untangling direct and feedback relationships between the different components of the systems. Inferring causality from measured ecological variables sampled through time remains a formidable challenge further made difficult by the action of periodic drivers overlapping the natural dynamics of the system. Periodicity in the drivers can often mask the self-sustained oscillations originating from the autonomous dynamics. While linear and direct causal relationships are commonly addressed in the time domain, using the well-established machinery of Granger causality (G-causality), the presence of periodic forcing requires frequency-based statistics (e.g., the Fourier transform), able to distinguish coupling induced by oscillations in external drivers from genuine endogenous interactions. Recent nonparametric spectral extensions of G-causality to the frequency domain pave the way for the scale-by-scale decomposition of causality, which can improve our ability to link oscillatory behaviors of ecological networks to causal mechanisms. The performance of both spectral G-causality and its conditional extension for multivariate systems is explored in quantifying causal interactions within ecological networks. Through two case studies involving synthetic and actual time series, it is demonstrated that conditional G-causality outperforms standard G-causality in identifying causal links and their concomitant timescales.