Objectives

1. Synthesis workshop 2:Temporal synthesisregime shifts and early warning signalsreview of selected papers

2. Objectives • To review the empirical and theoretical support for regime shifts and early warning signals in large marine systems

3. Method • List relevant literature • Report on individual papers using a formatted template • Synthesize the main conclusions

4. The papers • 24 articles (17 reviewed) • Mixture of theoretical, methodological, empirical, case studies and review papers

5. Published science on regime shifts 2012 1991

6. Terminology • Catastrophic = critical = discontinuous, these are related to non linear dynamics • An abrupt transition can result from but does not necessarily imply non linearity

7. Are abrupt transition in marine systems non-linear? • Hsieh et al. 2005: Climate time series appear to be best modeled by random processes while biological series display properties of non-linear systems. -> true regime shifts are likely in biological systems but less so in climate systems

8. How to detect non-linear transitions? • Analysis of ecological response AND associated pressures (multistable?) or • Non-linear time series analysis of response only

9. Stable states • Theoretical support for multiple stable states in marine systems • Bistability is unlikely (only valid for simpler systems, e.g. lakes) • Many stable states lead to constant reorganisation but no return to previous conditions

10. Early warning signals • The theory behind EWS is related to non-linear systems approaching catastrophes • EWS do not make sense if the abrupt change does not result from a critical transition in a non-linear system

11. Performance of EWS • Optimistic: Advise to use several EWS because of the limited performance of individual ones (false positives and false negatives). • Pessimistic: detection performance far too low for use in management (sparse and noisy data)

12. What makes a good study of critical transitions • strong theoretical framework, • appropriate data (freq. precision), • process model, + obs. model • unambiguous conclusions All the example found are for very simple systems: not more than 2 species and one external control.

13. The Barents Sea case • There is no process model for the Barents Sea • The data series are short and noisy • Bi-stability is not likely, rather multi-stability • This does not look very promising!

14. What can we expect • Track temporal changes in diversity and other structural properties – OK • Pressure – State graph to investigate hysteresis – no inference, multistability? • Non-linear time series analysis – availability and applicability of the method doubtful, • EWS indicators – poor performance is expected

15. Andersen, T., Carstensen, J., Hernández-García, E., and Duarte, C. M. 2009. Ecological thresholds and regime shifts: approaches to identification. Trends in Ecology & Evolution, 24: 49-57. • Blenckner, T., and Niiranen, S. in press. 4.23 Biodiversity – Marine Food-Web Structure, Stability, and Regime Shifts. • Dakos, V., Carpenter, S. R., Brock, W. A., Ellison, A. M., Guttal, V., Ives, A. R., Kéfi, S., et al. 2012. Methods for Detecting Early Warnings of Critical Transitions in Time Series Illustrated Using Simulated Ecological Data. PLoS ONE, 7: e41010. • Hare, S. R., and Mantua, N. 2000. Empirical evidence for North Pacific regime shifts in 1977 and 1989. Progress in Oceanography, 47: 103-145. • Hsieh, C.-h., Glaser, S. M., Lucas, A. L., and Sugihara, G. 2005. Distinguishing random environmental fluctuations from ecological catastrophes for the North Pacific Ocean. Nature, 435: 336-340. • Ives, A. R. 1995. Measuring Resilience in Stochastic Systems. Ecological Monographs, 65: 217-233. • Ives, A. R., and Carpenter, S. R. 2007. Stability and diversity of ecosystems. Science, 317: 58-62. • Ives, A. R., Dennis, B., Cottingham, K. L., and Carpenter, S. R. 2003. ESTIMATING COMMUNITY STABILITY AND ECOLOGICAL INTERACTIONS FROM TIME-SERIES DATA. Ecological Monographs, 73: 301-330. • Lehman-Ziebarth, N., and Ives, A. R. 2006. The structure and stability of model ecosystems assembled in a variable environment. Oikos, 114: 451-464. • Lindegren, M., Dakos, V., Gröger, J. P., Gårdmark, A., Kornilovs, G., Otto, S. A., and Möllmann, C. 2012. Early Detection of Ecosystem Regime Shifts: A Multiple Method Evaluation for Management Application. PLoS ONE, 7: e38410. • Liu, H., Fogarty, M. J., Glaser, S. M., Altman, I., Hsieh, C., Kaufman, L. S., Rosenberg, A. A., et al. 2012. Nonlinear dynamic features and co-predictability of the Georges Bank fish community. Marine Ecology Progress Series, 464: 195-207. • Overland, J., Rodionov, S., Minobe, S., and Bond, N. 2008. North Pacific regime shifts: Definitions, issues and recent transitions. Progress in Oceanography, 77: 92-102. • Perretti, C. T., and Munch, S. B. 2012. Regime shift indicators fail under noise levels commonly observed in ecological systems. Ecological Applications, 22: 1772-1779. • Scheffer, M., Carpenter, S., Foley, J. A., Folke, C., and Walker, B. 2001. Catastrophic shifts in ecosystems. Nature, 413: 591-596. • Schooler, S. S., Salau, B., Julien, M. H., and Ives, A. R. 2011. Alternative stable states explain unpredictable biological control of Salvinia molesta in Kakadu. Nature, 470: 86-89. • Sugihara, G., and May, R. M. 1990. Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series. Nature, 344: 734-741. • Turchin, P., and Ellner, S. P. 2000. LIVING ON THE EDGE OF CHAOS: POPULATION DYNAMICS OF FENNOSCANDIAN VOLES. Ecology, 81: 3099-3116.