title: Tracking and forecasting ecosystem interactions in real time.
abstract: Evidence shows that species interactions are not constant but change as the ecosystem shifts to new states. Although controlled experiments and model investigations demonstrate how nonlinear interactions can arise in principle, empirical tools to track and predict them in nature are lacking. Here we present a practical method, using available time-series data, to measure and forecast changing interactions in real systems, and identify the underlying mechanisms. The method is illustrated with model data from a marine mesocosm experiment and limnologic field data from Sparkling Lake, WI, USA. From simple to complex, these examples demonstrate the feasibility of quantifying, predicting and understanding state-dependent, nonlinear interactions as they occur in situ and in real time--a requirement for managing resources in a nonlinear, non-equilibrium world.
authors.affiliation: Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA.
authors.forename: Ethan R
authors.initials: ER
authors.lastname: Deyle
authors.affiliation: Department of Zoology, University of Oxford, Oxford OX1 3PS, UK.
authors.forename: Robert M
authors.initials: RM
authors.lastname: May
authors.affiliation: National Marine Fisheries Service, Southwest Fisheries Science Center, Santa Cruz, CA, USA.
authors.forename: Stephan B
authors.initials: SB
authors.lastname: Munch
authors.affiliation: Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA gsugihara@ucsd.edu.
authors.forename: George
authors.initials: G
authors.lastname: Sugihara
mesh_terms: D000818:Animals; D059001:Aquatic Organisms / Q000502:physiology; D017753:Ecosystem; D008962:Models, Theoretical; D017711:Nonlinear Dynamics; D011157:Population Dynamics; D013997:Time Factors; D015048:Zooplankton / Q000502:physiology
keywords: S-map; changing interaction strength; community matrix; empirical dynamics; nonlinear; state space reconstruction