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

TLDR

Recent advances in complex systems analysis enable detection of causal relationships from short time series, even when ecological observations are fewer than ten points but spatially replicated. Here, we combine convergent cross mapping and dewdrop regression into multispatial CCM, a novel test that leverages spatial replication. We evaluate multispatial CCM on simulated and real ecological datasets to assess its causal detection performance. Multispatial CCM detects causal relationships with as few as five observations, even amid process noise and observation error, and offers a useful test for causality when experiments and long time series are unavailable; the method is implemented in the multispatialCCM R package.

Abstract

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.