Many ecological systems can exist in two or more states that differ in abundance or composition of species, rates of ecological processes, and ecosystem services provided by them (Beisner et al. 2003, Suding et al. 2004). Smooth, gradual transitions between ecosystem states are unremarkable, occurring during succession or as ecosystems track gradually changing environmental conditions. In contrast, abrupt transitions between ecosystem states are typically unexpected and can have wide-ranging, negative impacts. Abrupt transitions happen either when the gradually changing environment passes a critical point or when discrete perturbations cause sudden changes in underlying environmental drivers. Abrupt and irreversible transitions are forecast to increase as climatic changes and depletion of natural resources both accelerate (Millennium Ecosystem Assessment 2005, Fagre et al. 2009). Such forecasting, however, is difficult because there are many different causes of state changes (Hastings and Wysham 2010) and because existing approaches demand far more data than are normally available (Carpenter et al. 2011).
Managing state changes is as difficult as forecasting them. When environmental changes are not severe, or when organisms with short lifespans and generation times rapidly track environmental drivers, some state changes can be reversed in relatively short periods of time (?50 years) if drivers are returned to pre-change conditions or perturbations are eliminated (Jones and Schmitz 2009). In other cases, environmental change can result in state changes that persist long after environmental drivers have returned to earlier conditions. The persistence of these socalled ''ecological thresholds'', ''regime shifts'', ''phase shifts'', or ''catastrophes'' (Hughes 1994, Scheffer et al. 2001, Groffman et al. 2006) is caused by time-lags in the responses of biological systems to environmental change (hysteresis), differences in the relationships between state variables and environmental drivers before and after the state change, or the appearance of novel feedbacks among state variables and drivers that reinforce the new state (Scheffer et al. 2001, Lindig-Cisneros et al. 2003, Briske et al. 2006, Suding and Hobbs 2009).
The development of management strategies to mitigate abrupt transitions requires strong linkages among theory, data, and case studies, but there is little guidance available for using historical or ongoing studies to detect or respond to abrupt transitions. There is confusion and disagreement about what changes constitute transitions (Rudnick and Davis 2003, Schroder et al. 2005) and a limited understanding of ecological mechanisms causing them (Brown and Archer 1999, Collie
et al. 2004). Empiricists disagree about how to best gather and interpret relevant data (Petraitis and Latham 1999, Bertness et al. 2002, Schroder et al. 2005), while theoreticians develop leading indicators of abrupt transitions that demand large amounts of data (Carpenter and Brock 2006, Biggs et al. 2009, Contamin and Ellison 2009). There is little clarity regarding the use of existing data and the design of future studies to detect and mitigate undesired state changes (Bestelmeyer 2006, Groffman et al. 2006).
A common, systematic approach to analyzing state changes could allow ecologists to marshal a large body of useful data and detailed knowledge to help society better understand and, ultimately, manage abrupt transitions. Here, we illustrate a general, data-based, and mechanism-centered analysis of abrupt transitions using four datasets from the US Long-Term Ecological Research (LTER) program on pelagic ocean, coastal benthic, polar marine, and semi-arid terrestrial ecosystems. These LTER data include some of the longest time-series available for both causal environmental drivers and biological response variables, and interpretations of associations between the drivers and the response variables are enhanced by experimental and mechanistic studies conducted at the same sites.
We first lay out a synthetic framework for describing abrupt transitions and state changes that can be used to compare and contrast among case studies. We then propose a standard analytical approach that provides strong tests for detecting abrupt transitions between states. This approach revealed unexpected results for the pelagic ocean system for which a ''regime shift'' had been described previously, provided stronger evidence for hypothesized state changes in the coastal benthic ecosystem, and yielded new evidence for state changes in the polar marine and semi-arid terrestrial ecosystems. Our analyses illustrate how to identify and interpret causes of abrupt transitions, and also illustrate limitations common to many datasets used to study abrupt transitions and state changes. We conclude with recommendations for improving ongoing and nascent long-term research programs aimed at detecting and forecasting state changes.
A common framework for describing state change
Studies across a wide range of ecosystems reveal five common data elements used in the recognition and analyses of state change: environmental drivers; triggers; biological responses; response mechanisms; and contextual information (Fig. 1). We introduce these element categories based on earlier syntheses (Scheffer et al. 2001, Andersen et al. 2009, Suding and Hobbs 2009) and consideration of the datasets presented herein.