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Management implications of long transients in ecological systems

Nature Ecology & Evolution volume 5, pages 285–294 (2021)Cite this article

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Abstract

The underlying biological processes that govern many ecological systems can create very long periods of transient dynamics. It is often difficult or impossible to distinguish this transient behaviour from similar dynamics that would persist indefinitely. In some cases, a shift from the transient to the long-term, stable dynamics may occur in the absence of any exogenous forces. Recognizing the possibility that the state of an ecosystem may be less stable than it appears is crucial to the long-term success of management strategies in systems with long transient periods. Here we demonstrate the importance of considering the potential of transient system behaviour for management actions across a range of ecosystem organizational scales and natural system types. Developing mechanistic models that capture essential system dynamics will be crucial for promoting system resilience and avoiding system collapses.

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Fig. 1: A modified adaptive management cycle that includes consideration of potential long transient system behaviour.
Fig. 2: Managing to stay on a transient, an example taken from invasion dynamics.
Fig. 3: Managing to escape a ghost attractor, an example taken from lake eutrophication.
Fig. 4: Potential outcomes of managing a ghost attractor present in a lake ecosystem.
Fig. 5: Schematic of a social–ecological system with slow and fast variables that produces long transients under fixed management schemes.
Fig. 6: Managing to avoid a slow–fast induced transient, based on a lake social–ecological system.

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Acknowledgements

This work was conducted as part of the Long Transients and Ecological Forecasting Working Group at the National Institute for Mathematical and Biological Synthesis, supported by the National Science Foundation through NSF Award no. DBI-1300426, with additional support from The University of Tennessee, Knoxville, and NSF Award no. CCS-1521672.

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Authors and Affiliations

  1. Puget Sound Institute, University of Washington, Tacoma, WA, USA

    Tessa B. Francis

  2. Department of Biology, Case Western Reserve University, Cleveland, OH, USA

    Karen C. Abbott

  3. Department of Biology, University of Waterloo, Waterloo, Ontario, Canada

    Kim Cuddington

  4. Department of Integrative Biology, University of Guelph, Guelph, Ontario, Canada

    Gabriel Gellner

  5. Department of Environmental Science and Policy, University of California, Davis, CA, USA

    Alan Hastings

  6. Santa Fe Institute, Santa Fe, NM, USA

    Alan Hastings

  7. School of Electrical Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA

    Ying-Cheng Lai

  8. School of Mathematics and Actuarial Science, University of Leicester, Leicester, UK

    Andrew Morozov & Sergei Petrovskii

  9. Institute of Ecology and Evolution, Russian Academy of Sciences, Moscow, Russia

    Andrew Morozov

  10. Department of Mathematics, Bowdoin College, Brunswick, ME, USA

    Mary Lou Zeeman

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Contributions

T.B.F. developed the concept; K.C.A., K.C., T.B.F., A.H., Y.-C.L. and M.L.Z. conceived of and wrote the case studies; K.C.A., K.C., T.B.F. and Y.-C.L. designed analytical tools and modelling experiments; K.C.A., K.C., T.B.F. and G.G. produced figures; K.C.A., K.C., T.B.F., G.G., A.H., Y.-C.L., A.M., S.P. and M.L.Z. wrote the paper. All authors discussed the results and implications and commented on the manuscript at all stages.

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Correspondence to Tessa B. Francis.

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Extended data

Extended Data Fig. 1 Illustrated Dynamical Systems Glossary.

Illustrations of terms and concepts (capitalized words) defined in Box 1. The left column represents a two-component system (e.g. a two-species community, or any system where the current state can be represented using two variables) as a landscape (blue surface). Dynamics are expected to proceed the way a ball would roll on these landscapes. Orange paths on these surfaces are examples of how a ball might roll from a particular starting point. The right column shows simulated dynamics, including stochasticity, for one species or variable on such a surface.

Extended Data Fig. 2 Invader density under alternative dynamical behavior assumptions and management.

Histograms of the time-averaged N2 density during 50 years of management, for 100 replicate simulations of the system in Fig. 1(d-f), for different models (column titles) and management actions (row titles). Note that the “Add N1” scenario when (K1,0) is a saddle (top middle and top right) had to be plotted using a different x-axis range than the others. Dashed vertical lines mark the mean of each distribution.

Extended Data Fig. 3 Management action frequency under alternative dynamical behavior assumptions.

Histograms of the number of management actions needed in the same replicate simulations as Fig. S1. Note that the “Add N1” scenario when (K1,0) is a saddle (top middle and top right) had to be plotted using a different x-axis range than the others. Dashed vertical lines mark the mean of each distribution.

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Francis, T.B., Abbott, K.C., Cuddington, K. et al. Management implications of long transients in ecological systems. Nat Ecol Evol 5, 285–294 (2021). https://doi.org/10.1038/s41559-020-01365-0

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