title: Detecting causality in complex ecosystems.
abstract: Identifying causal networks is important for effective policy and management recommendations on climate, epidemiology, financial regulation, and much else. We introduce a method, based on nonlinear state space reconstruction, that can distinguish causality from correlation. It extends to nonseparable weakly connected dynamic systems (cases not covered by the current Granger causality paradigm). The approach is illustrated both by simple models (where, in contrast to the real world, we know the underlying equations/relations and so can check the validity of our method) and by application to real ecological systems, including the controversial sardine-anchovy-temperature problem.
authors.affiliation: Scripps Institution of Oceanography, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA. gsugihara@ucsd.edu
authors.forename: George
authors.initials: G
authors.lastname: Sugihara
authors.forename: Robert
authors.initials: R
authors.lastname: May
authors.forename: Hao
authors.initials: H
authors.lastname: Ye
authors.forename: Chih-hao
authors.initials: CH
authors.lastname: Hsieh
authors.forename: Ethan
authors.initials: E
authors.lastname: Deyle
authors.forename: Michael
authors.initials: M
authors.lastname: Fogarty
authors.forename: Stephan
authors.initials: S
authors.lastname: Munch
mesh_terms: D015984:Causality; D016798:Ciliophora; D017753:Ecosystem; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D010247:Paramecium