OOS 50-2
Detecting causality in complex ecosystems

Friday, August 15, 2014: 8:20 AM
304/305, Sacramento Convention Center
George Sugihara, UC San Diego
Irit Altman, Zoology, University of New Hampshire, Durham, NH
Les Kaufman, Boston University
Andrew Rosenberg, University of New Hampshire and Conservation International
Emily Klein, Ecology & Evolutionary Biology, Princeton University
Robert M. May, Oxford University
Michael J. Fogarty, Ecosystem Assessment Program, NOAA NMFS Northeast Fisheries Science Center, Woods Hole, MA
Stephan B. Munch, Southwest Fisheries Science Center, National Oceanographic and Atmospheric Administration, Santa Cruz, CA
Ethan Deyle, UC San Diego
Chih-hao Hsieh, National Taiwan University
Sarah Glaser, University of Denver
Charles Perretti, UC San Diego
Hao Ye, UC San Diego
Background/Question/Methods

While everyone knows Berkeley’s 1710 dictum “correlation does not imply causation” few realize that the converse “causation does not imply correlation” is also true. This conundrum runs counter to deeply ingrained heuristic thinking that is at the basis of modern science.  Ecosystems are particularly perverse on this issue by exhibiting mirage correlations that can continually cause us to rethink relationships we thought we understood.

Identifying causal networks is important for effective policy and management recommendations on climate, epidemiology, financial regulation, and much else. Here we introduce a method that can distinguish causality from correlation. It is a radically different empirical approach for leveraging time series information from complex systems of interacting parts.

Results/Conclusions

(see above)