Journal Article

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

Tilman, G. David, Sugihara, George, Ye, Hao, Cowles, Jane, Clark, Adam Thomas, Isbell, Forest, Deyle, Ethan R.

2015

Overview

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.

• Publisher: Ecological Society of America
• Subject: Causality / Causation / Complex systems / computer software / convergent cross mapping / Demography / dewdrop regression / Ecological modeling / Ecology / Ecosystem / Experiments / Human ecology / Libraries / Mapping / Marine ecology / multispatialCCM / Nitrates / Poaceae - physiology / Rain / Signal noise / spatial replication / Stochastic Processes / Systems analysis / Time Factors / Time series / time series analysis / Time series forecasting