Early Detection of Ecosystem Regime Shifts: A Multiple Method Evaluation for Management Application Martin Lindegren1,2*, Vasilis Dakos3, Joachim P. Gro¨ ger4,5, Anna G a˚rdmark6, Georgs Kornilovs7, Saskia A. Otto8, Christian Mo¨ llmann8 1 Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, United States of America, 2 National Institute of Aquatic Resources, Technical University of Denmark, Charlottenlund, Denmark, 3 Department of Aquatic Ecology & Water Quality Management, Wageningen University, Wageningen, The Netherlands, 4 Institute for Sea Fisheries, von-Thu¨ nen Institute, Federal Research Institute for Rural Areas, Forestry and Fisheries, Hamburg, Germany, 5 Institute for Bio-Sciences, University of Rostock, Rostock, Germany, 6 Department of Aquatic Resources, Swedish University of Agricultural Sciences, Institute of Coastal Research, O¨ regrund, Sweden, 7 Department of Fish Resources Research, Institute of Food Safety, Animal Health and Environment, Riga, Latvia, 8 Institute for Hydrobiology and Fisheries Science, University of Hamburg, Hamburg, Germany Abstract Critical transitions between alternative stable states have been shown to occur across an array of complex systems. While our ability to identify abrupt regime shifts in natural ecosystems has improved, detection of potential early-warning signals previous to such shifts is still very limited. Using real monitoring data of a key ecosystem component, we here apply multiple early-warning indicators in order to assess their ability to forewarn a major ecosystem regime shift in the Central Baltic Sea. We show that some indicators and methods can result in clear early-warning signals, while other methods may have limited utility in ecosystem-based management as they show no or weak potential for early-warning. We therefore propose a multiple method approach for early detection of ecosystem regime shifts in monitoring data that may be useful in informing timely management actions in the face of ecosystem change. Citation: Lindegren M, Dakos V, Gr o¨ger JP, Ga˚rdmark A, Kornilovs G, et al. (2012) Early Detection of Ecosystem Regime Shifts: A Multiple Method Evaluation for Management Application. PLoS ONE 7(7): e38410. doi:10.1371/journal.pone.0038410 Editor: Simon Thrush, National Institute of Water & Atmospheric Research, New Zealand Received February 8, 2012; Accepted May 6, 2012; Published July 10, 2012 Copyright: 2012 Lindegren et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: VD was supported by an European Research Council (ERC) grant awarded to M. Scheffer and SAO by BONUS-AMBER. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: mlindegren@ucsd.edu Introduction Transitions between alternative states, i.e., regime shifts, have been shown to occur across an array of complex systems [1,2], including ecosystems [3]. Our ability to identify abrupt shifts in real ecosystems has improved through advances in theory and statistical methods [4]. However, these methods are primarily designed to detect regime shifts once having occurred. Recent theoretical studies suggest that several indicators may be used as early-warnings of an approaching transition [3]. Although needed for short-term management efforts to maintain key ecosystem goods and services, empirical applications of early detection of abrupt shifts in real ecosystems have so far mainly been limited to experimental studies [5,6] or paleo-climatic reconstructions over vast temporal scales [7,8]. Several early-warning indicators have been proposed to describe the temporal dynamics of complex systems close to a critical transition [3]. The basic rationale behind these indicators lies in the fact that the recovery of a system to equilibrium after a perturbation becomes slower close to a transition [9]. This phenomenon is known as ‘critical slowing down’ [10] and causes the variance and autocorrelation in the fluctuations of a system to increase prior to a regime shift [3,11,12]. In addition, the spatial dynamics of complex systems may also change close to a transition, where alterations in the spatial patterns of variance and correlation of key ecological features may serve as a complimentary set of early-warning indicators [13–15]. Although the merit of these indicators is that they can be detected across an array of ecosystems and types of transitions [16], their disadvan- tage is that they require long time series of high resolution for their estimation. Moreover, the potential for early-detection in practice is based on the assumption that the time series accurately represent the response of the ecosystem around its present equilibrium state [3]. Since ecological monitoring records are typically of limited length, lack detailed information on spatial distribution patterns of key organisms, and often include substantial measurement error, the practical use of any of the proposed early-warning indicators for ecosystem management may prove problematic. Given that these limitations can lead to both false positive and false negative signals [3], the use of multiple spatial and temporal indicators should ideally be considered [16] and alternative methods should be tested [12]. Yet, studies of early-warning signals in real ecosystems have so-far been restricted to only a narrow range of possible temporal [8] or spatial indicators [17]. Large-scale patterns of ecosystem change have been observed in marine ecosystems across the Northern hemisphere [18,19], including the Baltic Sea [20]. A key question for marine management is whether these regime shifts could have been detected by early-warning indicators. Using real monitoring data of the copepods Pseudocalanus acuspes and Acartia spp., two key indicator species significantly contributing to the reorganization of the Baltic Sea ecosystem (Figure 1) [20], we here apply a set of PLoS ONE | www.plosone.org 1 July 2012 | Volume 7 | Issue 7 | e38410