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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1174-1181 |
| Number of pages | 8 |
| Journal | Ecology |
| Volume | 96 |
| Issue number | 5 |
| DOIs | |
| State | Published - May 1 2015 |
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Keywords
- Causality
- Convergent cross mapping
- Dewdrop regression
- MultispatialCCM
- Spatial replication
- Time series
Cite this
Spatial convergent cross mapping to detect causal relationships from short time series. / Clark, Adam Thomas; Ye, Hao; Isbell, Forest; Deyle, Ethan R.; Cowles, Jane; Tilman, G. David; Sugihara, George; Inouye, B. D.
In: Ecology, Vol. 96, No. 5, 01.05.2015, p. 1174-1181.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Spatial convergent cross mapping to detect causal relationships from short time series
AU - Clark, Adam Thomas
AU - Ye, Hao
AU - Isbell, Forest
AU - Deyle, Ethan R.
AU - Cowles, Jane
AU - Tilman, G. David
AU - Sugihara, George
AU - Inouye, B. D.
PY - 2015/5/1
Y1 - 2015/5/1
N2 - 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.
AB - 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.
KW - Causality
KW - Convergent cross mapping
KW - Dewdrop regression
KW - MultispatialCCM
KW - Spatial replication
KW - Time series
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U2 - 10.1890/14-1479.1
DO - 10.1890/14-1479.1
M3 - Article
VL - 96
SP - 1174
EP - 1181
JO - Ecology
JF - Ecology
SN - 0012-9658
IS - 5
ER -