Abstract
Long transients may be common in ecological dynamics, but the implications such dynamics have for ecological management have not been fully explored. Long transient periods can easily be mistaken for stable state dynamics, but may require dramatically different management policies. Here, I explore the optimal management of stochastic ecological systems that may contain either a tipping point or a ghost attractor: an important mechanism which can give rise to long transients. I consider three approaches of increasing sophistication: (1) dynamic management under a fixed model, (2) management that accounts for uncertainty over possible models, and (3) adaptive management that actively learns the correct model over the management process. This analysis confirms the prediction that long transients can create considerable uncertainty and give rise to very different optimal management policies, and also illustrates that dynamic management that can either plan for this uncertainty or actively learn to decrease the uncertainty can promote successful management of long transients.
Similar content being viewed by others
References
Boettiger C (2018) From noise to knowledge: how randomness generates novel phenomena and reveals information. Ecology Letters. https://doi.org/10.1111/ele.13085
Boettiger C, Ross N, Hastings A (2013) Early warning signals: the charted and uncharted territories. Theoretical Ecology. https://doi.org/10.1007/s12080-013-0192-6
Boettiger C, Memarzadeh M (2018) Mdplearning: Bayesian Learning Algorithms for Markov Decision Processes. (version 0.1.0). Zenodo. https://doi.org/10.5281/zenodo.1161519
Clark CW (1990) Mathematical Bioeconomics: The Optimal Management of Renewable Resources, 2nd Edn. Wiley-Interscience
Deco G, Jirsa VK (2012) Ongoing cortical activity at rest: criticality, multistability, and ghost attractors. The J. Neurosci.: The Official Journal of the Society for Neuroscience 32(10):3366–75. https://doi.org/10.1523/JNEUROSCI.2523-11.2012
Fischer J, Peterson GD, Gardner TA, Gordon LJ, Fazey I, Elmqvist T, Felton A, Folke C, Dovers S (2009) Integrating resilience thinking and optimisation for conservation. Trends in Ecology & Evolution 24(10):549–54. https://doi.org/10.1016/j.tree.2009.03.020
Folke C, Carpenter S, Walker B, Scheffer M, Elmqvist T, Gunderson L, Holling CS (2004) Regime shifts, resilience, and biodiversity in ecosystem management. Annu Rev Ecol Evol Syst 35(1):557–81. https://doi.org/10.1146/annurev.ecolsys.35.021103.105711
François-Lavet V, Henderson P, Islam R, Bellemare MG, Pineau J (2018) An introduction to deep reinforcement learning. Foundations and Trends® in Machine Learning 11(3-4):219–354. https://doi.org/10.1561/2200000071
Grossberg S (1980) Biological competition: decision rules, pattern formation, and oscillations. Proc Natl Acad Sci USA 77(4):2338–42. https://doi.org/10.1073/pnas.77.4.2338
Harvey BJ, Donato DC, Turner MG (2014) Recent mountain pine beetle outbreaks, wildfire severity, and postfire tree regeneration in the US Northern Rockies 111 (42). https://doi.org/10.1073/pnas.1411346111
Hastings A, Abbott KC, Cuddington K, Francis T, Gellner G, Lai Ying C, Morozov A, Petrovskii S, Scranton K, Zeeman ML (2018) Transient phenomena in ecology. Science, 361(6406). https://doi.org/10.1126/science.aat6412
Hastings A, Higgins K (1994) Persistence of transients in spatially structured ecological models. Science 263(5150):1133–6. https://doi.org/10.1126/science.263.5150.1133
Levins R (1966) The strategy of model building in population biology. American Scientist 54(4):421–31
Mangel M (1985) Decision and control in uncertain resource systems, 255. http://dl.acm.org/citation.cfm?id=537497
Marescot L, Chapron G, Chadès I, Fackler PL, Duchamp C, Marboutin E, Gimenez O (2013) Complex decisions made simple: a primer on stochastic dynamic programming. Methods in Ecology and Evolution 4(9):872–84. https://doi.org/10.1111/2041-210X.12082
May RM (1977) Thresholds and breakpoints in ecosystems with a multiplicity of stable states. Nature 269(5628):471–77. https://doi.org/10.1038/269471a0
Memarzadeh M, Boettiger C (2019) Resolving the measurement uncertainty paradox in ecological management. The American Naturalist 193(5):645–60. https://doi.org/10.1086/702704
Memarzadeh M, Britten GL, Worm B, Boettiger C (2019) Rebuilding global fisheries under uncertainty. Proc Nat Acad Sci 116(32):15985–90. https://doi.org/10.1073/pnas.1902657116
Polasky S, Carpenter SR, Folke C, Keeler B (2011) Decision-Making Under great uncertainty: environmental management in an era of global change. Trends in ecology & evolution, May 1–7. https://doi.org/10.1016/j.tree.2011.04.007
Raffa KF, Aukema BH, Bentz BJ, Carroll AL, Hicke JA, Turner MG, Romme WH (2008) Cross-scale drivers of natural disturbances prone to anthropogenic amplification: the dynamics of bark beetle eruptions. BioScience 58(6):501–17. https://doi.org/10.1641/B580607
Ratajczak Z, Carpenter SR, Ives AR, Kucharik CJ, Ramiadantsoa T, Allison Stegner M, Williams JW, Zhang J, Turner MG (2018) Abrupt change in ecological systems: inference and diagnosis. Trends Ecol Evol 33(7):513–26. https://doi.org/10.1016/j.tree.2018.04.013
R Core Team (2019) R: A Language and Environment for Statistical Computing (version 3.6.2). R Foundation for Statistical Computing, Vienna. https://www.R-project.org/
Scheffer M, Carpenter SR, Foley JA, Folke C, Walker BH (2001) Catastrophic shifts in ecosystems. Nature 413(6856):591–6. https://doi.org/10.1038/35098000
Smallwood RD, Sondik EJ (1973) The optimal control of partially observable markov processes over a finite horizon. Operations Res 21(5):1071–88. https://www.jstor.org/stable/168926
Sondik EJ (1978) The optimal control of partially observable markov processes over the infinite horizon : discounted costs. Operations Res 26(2):282–304
Walters CJ (1986) Adaptive Management of Renewable Resources. Biological Resource Management, Macmillan. http://www.amazon.com/Adaptive-Management-Renewable-Resources-Biological/dp/0029479703
Walters CJ, Hilborn R (1978) Ecological optimization and adaptive management. Annu Rev Ecol Syst 9(1):157–88. https://doi.org/10.1146/annurev.es.09.110178.001105
Acknowledgments
This work was inspired and influenced many discussions at the 2019 NIMBioS workshop on transient dynamics in ecology, and the 2019 organized oral session on ecological transients.
Funding
CB received computational resources from NSF’s XSEDE Jetstream (DEB160003) and Chameleon cloud platforms, as well as the support from UC Berkeley and the USDA Hatch project CA-B-INS-0162-H.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Boettiger, C. Ecological management of stochastic systems with long transients. Theor Ecol 14, 663–671 (2021). https://doi.org/10.1007/s12080-020-00477-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12080-020-00477-4