Tracking and forecasting ecosystem interactions in real time.

Content Provider	Semantic Scholar
Author	Deyle, Ethan Robert May, Robert M. Munch, Stephan Sugihara, George
Copyright Year	2016
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.
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://rspb.royalsocietypublishing.org/content/royprsb/283/1822/20152258.full.pdf
PubMed reference number	26763700v1
Alternate Webpage(s)	https://doi.org/10.1098/rspb.2015.2258
DOI	10.1098/rspb.2015.2258
Journal	Proceedings. Biological sciences
Volume Number	283
Issue Number	1822
Language	English
Access Restriction	Open
Subject Keyword	Kind of quantity - Equilibrium Projections and Predictions
Content Type	Text
Resource Type	Article