Predicting tipping points in complex environmental systems

Ecologists have long recognized that ecosystems can exist and function in one state within predictable bounds for extended periods of time and then abruptly shift to an alternate state (1⇓⇓⇓–5). Desertification of grasslands, shrub expansion in the Arctic, the eutrophication of lakes, ocean acidification, the formation of marine dead zones, and the degradation of coral reefs represent real and potential ecological regime shifts marked by a tipping point or threshold in one or more external drivers or controlling variables within the system that when breached causes a major change in the system’s structure, function, or dynamics (6⇓⇓–9). Large or incremental alterations in climate, land use, biodiversity (invasive species or the overexploitation of species), and biogeochemical cycles represent external and internal drivers that when pushed too far cross thresholds that can could lead to regime shifts (Fig. 1). Seeing the tipping point after the fact and ascribing mechanisms to the change is one thing; predicting them using empirical data has been a challenge. The difficulty in predicting tipping points stems from the large number of species and interactions (high dimensionality) within ecological systems, the stochastic nature of the systems and their drivers, and the uncertainty and importance of initial conditions that the nonlinear nature of the systems introduce to outcomes. In PNAS, Jiang et al. (10) confront these issues using a dimension-reduction framework that uses empirical data from 59 complex multidimensional plant–pollinator mutualistic networks, some of which contain scores of species and interactions, to develop simpler 2D models for studying and predicting tipping points. Fig. 1. Tipping points and ecological regime shifts are difficult to predict. A and B represent hypothetical time series of the trajectories of the mean and variation about the mean of variables … [↵][1]1Email: John.Moore{at}colostate.edu. [1]: #xref-corresp-1-1