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      Early Warning Signals of Ecological Transitions: Methods for Spatial Patterns

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          Abstract

          A number of ecosystems can exhibit abrupt shifts between alternative stable states. Because of their important ecological and economic consequences, recent research has focused on devising early warning signals for anticipating such abrupt ecological transitions. In particular, theoretical studies show that changes in spatial characteristics of the system could provide early warnings of approaching transitions. However, the empirical validation of these indicators lag behind their theoretical developments. Here, we summarize a range of currently available spatial early warning signals, suggest potential null models to interpret their trends, and apply them to three simulated spatial data sets of systems undergoing an abrupt transition. In addition to providing a step-by-step methodology for applying these signals to spatial data sets, we propose a statistical toolbox that may be used to help detect approaching transitions in a wide range of spatial data. We hope that our methodology together with the computer codes will stimulate the application and testing of spatial early warning signals on real spatial data.

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          Most cited references20

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          Early warnings of regime shifts: a whole-ecosystem experiment.

          Catastrophic ecological regime shifts may be announced in advance by statistical early warning signals such as slowing return rates from perturbation and rising variance. The theoretical background for these indicators is rich, but real-world tests are rare, especially for whole ecosystems. We tested the hypothesis that these statistics would be early warning signals for an experimentally induced regime shift in an aquatic food web. We gradually added top predators to a lake over 3 years to destabilize its food web. An adjacent lake was monitored simultaneously as a reference ecosystem. Warning signals of a regime shift were evident in the manipulated lake during reorganization of the food web more than a year before the food web transition was complete, corroborating theory for leading indicators of ecological regime shifts.
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            Methods for Detecting Early Warnings of Critical Transitions in Time Series Illustrated Using Simulated Ecological Data

            Many dynamical systems, including lakes, organisms, ocean circulation patterns, or financial markets, are now thought to have tipping points where critical transitions to a contrasting state can happen. Because critical transitions can occur unexpectedly and are difficult to manage, there is a need for methods that can be used to identify when a critical transition is approaching. Recent theory shows that we can identify the proximity of a system to a critical transition using a variety of so-called ‘early warning signals’, and successful empirical examples suggest a potential for practical applicability. However, while the range of proposed methods for predicting critical transitions is rapidly expanding, opinions on their practical use differ widely, and there is no comparative study that tests the limitations of the different methods to identify approaching critical transitions using time-series data. Here, we summarize a range of currently available early warning methods and apply them to two simulated time series that are typical of systems undergoing a critical transition. In addition to a methodological guide, our work offers a practical toolbox that may be used in a wide range of fields to help detect early warning signals of critical transitions in time series data.
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              Regular pattern formation in real ecosystems.

              Localized ecological interactions can generate striking large-scale spatial patterns in ecosystems through spatial self-organization. Possible mechanisms include oscillating consumer-resource interactions, localized disturbance-recovery processes and scale-dependent feedback. Despite abundant theoretical literature, studies revealing spatial self-organization in real ecosystems are limited. Recently, however, many examples of regular pattern formation have been discovered, supporting the importance of scale-dependent feedback. Here, we review these studies, showing regular pattern formation to be a general phenomenon rather than a peculiarity. We provide a conceptual framework explaining how scale-dependent feedback determines regular pattern formation in ecosystems. More empirical studies are needed to better understand regular pattern formation in ecosystems, and how this affects the response of ecosystems to global environmental change.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2014
                21 March 2014
                : 9
                : 3
                : e92097
                Affiliations
                [1 ]Institut des Sciences de l'Evolution, CNRS, Université de Montpellier II, Montpellier, France
                [2 ]Centre for Ecological Sciences, Indian Institute of Science, Bangalore, India
                [3 ]Department of Economics, University of Wisconsin, Madison, Wisconsin, United States of America
                [4 ]Department of Economics, University of Missouri, Columbia, Missouri, United States of America
                [5 ]Center for Limnology, University of Wisconsin, Madison, Wisconsin, United States of America
                [6 ]Harvard Forest, Harvard University, Petersham, Massachusetts, United States of America
                [7 ]National Physical Laboratory, Hampton Road, Teddington, United Kingdom
                [8 ]Department of Environmental Sciences, University of Virginia, Charlottesville, Virginia, United States of America
                [9 ]Department of Aquatic Ecology and Water Quality Management, Wageningen University, Wageningen, The Netherlands
                [10 ]Integrative Ecology Group, Estacion Biologica de Donana, Sevilla, Spain
                Universitat Pompeu Fabra, Spain
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: SK VG VD SRC DAS VNL. Performed the experiments: SK VG. Analyzed the data: SK VG. Contributed reagents/materials/analysis tools: SK VG VD. Wrote the paper: SK VG WAB SRC AME VNL DAS MS EHvN VD.

                Article
                PONE-D-13-33789
                10.1371/journal.pone.0092097
                3962379
                24658137
                9002f302-5b65-4871-bf6e-28e56ff92f94
                Copyright @ 2014

                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.

                History
                : 2 August 2013
                : 19 February 2014
                Page count
                Pages: 13
                Funding
                The authors are grateful to the Santa Fe Institute and the Arizona State University for the financial support. SK acknowledges support from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 283068 (CASCADE). VG acknowledges support from a Ramalingaswami Fellowship, Department of Biotechnology, Government of India, and the Ministry of Environment and Forests, Government of India. SRC's work is supported by NSF. AME is supported by NSF award 11-44056. VNL is supported by NERC (NE/F005474/1) postdoctoral fellowship of the AXA Research Fund and grant of the National Measurement Office (2013–2016). VD acknowledges a RUBICON (NWO funded) grant and an EU Marie Curie fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Computational Biology
                Ecosystem Modeling
                Ecology
                Ecosystems
                Global Change Ecology
                Spatial and Landscape Ecology
                Theoretical Ecology
                Computer and Information Sciences
                Geoinformatics
                Environmental Systems Modeling
                Earth Sciences
                Geography
                Ecology and Environmental Sciences
                Social Sciences

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                Uncategorized

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