Quantifying the Adaptive Cycle

• Type of Material: Article
• Authors: David G. Angeler, Stockholm University; Craig R. Allen, University of Nebraska; Ahjond S. Garmestani, U.S. Environmental Protection Agency; Lance Gunderson, Emory University; Olle Hjerne, Stockholm University; Monika Winder, Stockholm University
• Language: English
• Date: 2015-12-30
• Publisher: Public Library of Science
• Publication Version: Final Published Version
• Copyright Statement: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose
• License: Creative Commons Creative Commons CC0 1.0 Universal
• Final Published Version (URL): http://dx.doi.org/10.1371/journal.pone.0146053
• Title of Journal or Parent Work: PLoS ONE
• ISSN: 1932-6203
• Volume: 10
• Issue: 12
• Start Page: e0146053
• End Page: e0146053
• Grant/Funding Information: Financial support from the Department of Ecology, Environment and Plant Sciences, Stockholm University, and by Stockholm University's Strategic Marine Environmental Research Programme on Baltic Ecosystem Adaptive Management (BEAM) and from the BONUS project BIO-C3, funded jointly by the EU and FORMAS is acknowledged.; Additional support was provided by grants from the Swedish Research Councils VR (2014-5828) and FORMAS (2014-1193). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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.