Journal
METHODS IN ECOLOGY AND EVOLUTION
Volume 10, Issue 12, Pages 2183-2188Publisher
WILEY
DOI: 10.1111/2041-210X.13299
Keywords
causal connections; correlative connections; hidden layers; layeranalyzer; processes; stochastic differential equations; time series
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Funding
- H2020 European Research Council [724324]
- Norges Forskningsrad [235073/F20]
- European Research Council (ERC) [724324] Funding Source: European Research Council (ERC)
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Distinguishing correlative and causal connections among time series is an important challenge in evolutionary biology, ecology, macroevolution and palaeobiology. Here, we present layeranalyzer, an r package that uses linear stochastic differential equations as a tool for parametrically describing evolutionary and ecological processes and for modelling temporal correlation and Granger causality between two or more time series. We describe the basic functions in layeranalyzer and briefly discuss modelling strategies by demonstrating our tool with three disparate case studies. First, we model a single time series of phenotypic evolution in a bird species; second, we extract cyclical connections in the well-known hare-lynx dataset; third, we infer the correlative and causal connections among the genus origination and extinction rates of brachiopods and bivalves. We summarize the advantages and limitations of using linear stochastic differential equations and layeranalyzer for studying correlative and causal connections.
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