Marine Ecosystems as Complex Adaptive Systems: Emergent Patterns, Critical Transitions, and Public Goods
Complex adaptive systems provide a unified framework for explaining ecosystem phenomena. In the past 20 years, complex adaptive systems have been sharpened from an abstract concept into a series of tools that can be used to solve concrete problems. These advances have been led by the development of new techniques for coupling ecological and evolutionary dynamics, for integrating dynamics across multiple scales of organization, and for using data to infer the complex interactions among different components of ecological systems. Focusing on the development and usage of these new methods, we discuss how they have led to an improved understanding of three universal features of complex adaptive systems, emergent patterns; tipping points and critical phenomena; and cooperative behavior. We restrict our attention primarily to marine ecosystems, which provide numerous successful examples of the application of complex adaptive systems. Many of these are currently undergoing dramatic changes due to anthropogenic perturbations, and we take the opportunity to discuss how complex adaptive systems can be used to improve the management of public goods and to better preserve critical ecosystem services.
Keywordscomplex adaptive systems public goods emergent patterns critical transitions marine ecosystems evolution of cooperation theoretical ecology
Simon Levin acknowledges funding from the NSF Dimensions of Biodiversity Grant OCE-1046001, NSF Grant GEO-1211972, NSF Grant OCE-1426746, ARO Grant W911NF-11-1-0385, ARO Grant W911NF-14-1-0431 and by the Nordforsk-funded project Green Growth Based on Marine Resource: Ecological and Socio-Economic Constraints (GreenMAR). George Hagstrom acknowledges funding from NSF Dimensions of Biodiversity Grant OCE-1046001, ARO Grant W911NF-14-1-0431, and ARO Grant W911NF-11-1-0385.
Compliance with Ethical Standards
Conflict of interest
The authors declare that they have no conflicts of interest.
- Arthur WB. 1994. Increasing returns and path dependence in the economy. Ann Arbor (MI): University of Michigan Press. p 203.Google Scholar
- Axelrod RM. 2006. The evolution of cooperation. New York (NY): Basic books. p 247.Google Scholar
- Boettiger C, Hastings A. 2012a. Early warning signals and the prosecutor’s fallacy. Proc R Soc Lond B Biol Sci 279(1748):2085.Google Scholar
- Brush ER, Leonard NE, Levin SA. 2016. The content and availability of information affects the evolution of social-information gathering strategies. Theor Ecol 9:455–76.Google Scholar
- Bunin G. 2016. Interaction patterns and diversity in assembled ecological communities. arXiv preprint arXiv:1607.04734.
- Carpenter SR, Press MC, Huntly NJ, Levin SA. 2001. Alternate states of ecosystems: evidence and some implications. In: Ecology: achievement and challenge: the 41st symposium of the British Ecological Society sponsored by the Ecological Society of America held at Orlando, Florida, USA, 10–13 April 2000, pp. 357–83.Google Scholar
- Chapman S, Cowling TG. 1970. The mathematical theory of non-uniform gases: an account of the kinetic theory of viscosity, thermal conduction and diffusion in gases. Cambridge (UK): Cambridge University Press.Google Scholar
- Czirók A, Vicsek T. 2001. Collective motion. In: Vicsek T (ed) Fluctuations and scaling in biology. Oxford (UK): Oxford University Press. pp. 177–242.Google Scholar
- Darwin C. 1859. The origin of species. London (UK): Murray, pp. 495.Google Scholar
- Datta MS, Sliwerska E, Gore J, Polz MF, Cordero OX. 2016. Microbial interactions lead to rapid micro-scale successions on model marine particles. Nat Commun 7:11965. doi: 10.1038/ncomms11965.
- Diekmann O. 2004. A beginner’s guide to adaptive dynamics. Banach Center Publ 63:47–86.Google Scholar
- Gardiner CW. 1985. Handbook of stochastic methods, Vol. 3Berlin (DEU): Springer.Google Scholar
- Gattuso JP, Magnan A, Billé R, Cheung WWL, Howes EL, Joos F, Allemand D, Bopp L, Cooley SR, Eakin CM, Hoegh-Guldberg O, Kelly RP, Pörtner HO, Rogers AD, Baxter JM, Laffoley D, Osborn D, Rankovic A, Rochette J, Sumaila UR, Treyer S, Turley C. 2015. Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios. Science 349(6243):aac4722. doi: 10.1126/science.aac4722.
- Grimm V, Railsback SF. 2013. Individual-based modeling and ecology. Princeton (NJ): Princeton University Press.Google Scholar
- Grünbaum D, Okubo A. 1994. Modelling social animal aggregations. In Frontiers in mathematical biology. Berlin (DEU): Springer. pp. 296–325.Google Scholar
- Gunderson LH. 2001. Panarchy: understanding transformations in human and natural systems. Washington (DC): Island Press.Google Scholar
- Hagstrom GI, Levin SA, Martiny AC. 2016. Balance between resource supply and demand determines nutrient limitation of primary productivity in the ocean. doi: 10.1101/064543.
- Held H, Kleinen T. 2004. Detection of climate system bifurcations by degenerate fingerprinting. Geophys Res Lett 31(23). doi: 10.1029/2004GL020972.
- Holling CS. 1973. Resilience and stability of ecological systems. Annu Rev Ecol Syst 4:1–23.Google Scholar
- Holling CS. 1986. The resilience of terrestrial ecosystems: local surprise and global change. In: Clark WC, Seliger HH (eds) Sustainable development of the biosphere. Cambridge (UK): Cambridge University Press. pp. 292–317.Google Scholar
- Lenton TM, Footitt A, Dlugolecki A, Allianz Gruppe. 2009. Major tipping points in the earth’s climate system and consequences for the insurance sector. Technical report, World Wildlife Fund, Washington (DC).Google Scholar
- Litchman E, Klausmeier CA. 2008. Trait-based community ecology of phytoplankton. Annu Rev Ecol Evol Syst 39:615–639.Google Scholar
- Margalef R, Miyares ME, de Rubinat DBF. 1979. Functional morphology of organisms involved in red tides, as adapted to decaying turbulence. In: Toxic and dinoflagellate blooms. Amsterdam (NL): Elsevier.Google Scholar
- Murray JD. 2002. Mathematical biology I: an introduction. In: Antman SS, Marsden JE, Sirovich L, Wiggins S (eds) Interdisciplinary applied mathematics, Vol. 17. New York (NY): Springer.Google Scholar
- Pacala SW, Silander J. 1985. Neighborhood models of plant population dynamics. I. Single-species models of annuals. Am Nat 125(3):385–411.Google Scholar
- Redfield AC. 1958. The biological control of chemical factors in the environment. Am Sci 46(3):230A, 205–21.Google Scholar
- Rodriguez-Iturbe I, Rinaldo A. 1997. Fractal river networks: chance and self-organization. New York (NY): Cambridge University Press.Google Scholar
- Scheffer M. 2009. Critical transitions in nature and society. Princeton (NJ): Princeton University Press.Google Scholar
- Strogatz SH. 2014. Nonlinear dynamics and chaos: with applications to physics, biology, chemistry, and engineering. Boulder (CO): Westview Press.Google Scholar