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Spatial convergent cross mapping to detect causal relationships from short time series

TitleSpatial convergent cross mapping to detect causal relationships from short time series
Publication TypeJournal Article
Year of Publication2015
AuthorsClark A.T, Ye H, Isbell F., Deyle ER, Cowles J., Tilman G.D, Sugihara G
Date Published2015/05
Type of ArticleArticle
ISBN Number0012-9658
Accession NumberWOS:000354119300003
Keywordscausality; chaos; convergent cross mapping; dewdrop regression; ecological-systems; multispatialCCM; spatial replication; Time series

Recent developments in complex systems analysis have led to new techniques for detecting causal relationships using relatively short time series, on the order of 30 sequential observations. Although many ecological observation series are even shorter, perhaps fewer than ten sequential observations, these shorter time series are often highly replicated in space (i.e., plot replication). Here, we combine the existing techniques of convergent cross mapping (CCM) and dewdrop regression to build a novel test of causal relations that leverages spatial replication, which we call multispatial CCM. Using examples from simulated and real-world ecological data, we test the ability of multispatial CCM to detect causal relationships between processes. We find that multispatial CCM successfully detects causal relationships with as few as five sequential observations, even in the presence of process noise and observation error. Our results suggest that this technique may constitute a useful test for causality in systems where experiments are difficult to perform and long time series are not available. This new technique is available in the multispatialCCM package for the R programming language.

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