title: Quantifying the Adaptive Cycle.
abstract: The adaptive cycle was proposed as a conceptual model to portray patterns of change in complex systems. Despite the model having potential for elucidating change across systems, it has been used mainly as a metaphor, describing system dynamics qualitatively. We use a quantitative approach for testing premises (reorganisation, conservatism, adaptation) in the adaptive cycle, using Baltic Sea phytoplankton communities as an example of such complex system dynamics. Phytoplankton organizes in recurring spring and summer blooms, a well-established paradigm in planktology and succession theory, with characteristic temporal trajectories during blooms that may be consistent with adaptive cycle phases. We used long-term (1994-2011) data and multivariate analysis of community structure to assess key components of the adaptive cycle. Specifically, we tested predictions about: reorganisation: spring and summer blooms comprise distinct community states; conservatism: community trajectories during individual adaptive cycles are conservative; and adaptation: phytoplankton species during blooms change in the long term. All predictions were supported by our analyses. Results suggest that traditional ecological paradigms such as phytoplankton successional models have potential for moving the adaptive cycle from a metaphor to a framework that can improve our understanding how complex systems organize and reorganize following collapse. Quantifying reorganization, conservatism and adaptation provides opportunities to cope with the intricacies and uncertainties associated with fast ecological change, driven by shifting system controls. Ultimately, combining traditional ecological paradigms with heuristics of complex system dynamics using quantitative approaches may help refine ecological theory and improve our understanding of the resilience of ecosystems.
authors.affiliation: Stockholm University, Department of Ecology, Evolution and Plant Sciences, SE- 106 91, Stockholm, Sweden.|Swedish University of Agricultural Sciences, Department of Aquatic Sciences and Assessment, Box 7050, SE- 750 07, Uppsala, Sweden.
authors.forename: David G
authors.initials: DG
authors.lastname: Angeler
authors.affiliation: U.S. Geological Survey-Nebraska Cooperative Fish & Wildlife Research Unit, University of Nebraska, Lincoln, NE, 68583, United States of America.
authors.forename: Craig R
authors.initials: CR
authors.lastname: Allen
authors.affiliation: U.S. Environmental Protection Agency, National Risk Management Research Laboratory, Cincinnati, OH, 45268, United States of America.
authors.forename: Ahjond S
authors.initials: AS
authors.lastname: Garmestani
authors.affiliation: Department of Environmental Sciences, Emory University, Atlanta, Georgia, 30322, United States of America.
authors.forename: Lance H
authors.initials: LH
authors.lastname: Gunderson
authors.affiliation: Stockholm University, Department of Ecology, Evolution and Plant Sciences, SE- 106 91, Stockholm, Sweden.
authors.forename: Olle
authors.initials: O
authors.lastname: Hjerne
authors.affiliation: Stockholm University, Department of Ecology, Evolution and Plant Sciences, SE- 106 91, Stockholm, Sweden.
authors.forename: Monika
authors.initials: M
authors.lastname: Winder
mesh_terms: D000222:Adaptation, Physiological; D018533:Biomass; D005068:Eutrophication; D008962:Models, Theoretical; D010839:Phytoplankton / Q000254:growth & development*; D011157:Population Dynamics; D012621:Seasons