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.

Fig 1. Schematic description of the adaptive cycle.

It shows transitioning between four phases (α, r, K, Ω) within a specific regime (full white arrows) and the potential to change to a new regime in the reorganisation phase (dotted white arrow). Modified from Gunderson and Holling (2002).

Fig 2. Phytoplankton biomass.

Box plots showing phytoplankton biomass (microgram C L^-1) between March and August for the period 1994–2011 at two sites (coastal, offshore) in the Baltic Sea. Two periods with peak biomass in April and July occur at both sites, and characterize distinct spring and summer phytoplankton blooms in the Baltic Sea.

Fig 3. Temporal patterns of abiotic and biotic variables.

Temporal patterns of phytoplankton biomass (Chlorophyll a) and abiotic variables at two stations (coastal, offshore) and seasons (spring, summer). Shown are seasonal averages ± SDs between 1994 and 2011.

Fig 4. Multivariate ordination.

Nonmetric multidimensional scaling (NMDS) ordinations showing seasonal averages of water quality trajectories during spring and summer in the coastal and offshore site between 1994 and 2011. The length of each arrow reflects change from one sampling year to the next.

Fig 5. Multivariate ordination.

Nonmetric multidimensional scaling (NMDS) ordinations showing community trajectories during spring and summer sampling occasions in the coastal and offshore site. Different colors represent averaged time periods. The length of each arrow reflects community change from one sampling event to the next.

Fig 6. Species correlations with multivariate patterns.

Score plots showing contributions of phytoplankton species to temporal trajectories of phytoplankton community dynamics identified by NMDS during spring and summer at the coastal and offshore site, based on Spearman rank correlation analyses. Only taxa with significant correlations with NMDS dimensions (P < 0.05) and high correlation coefficient (Spearman´s rho > 0.8) are shown. For better visibility taxa are aggregated to genus level.

Fig 7. Conceptual figure.

Summary of results of this study showing the complementarity of the traditional phytoplankton successional model (lower diagram) with adaptive cycles theory. Shown are spring and summer blooms and how they change interannually from an adaptive cycle perspective. Different shapes indicate that community changes within blooms are dynamic but these changes must not necessarily reflect adaptive cycle phases. These shapes of community change are often recurrent highlighting conservative patterns. Despite this, species contributions to these dynamics may change over time (different shades of gray), affecting spring and summer blooms distinctly, highlighting potential adaptive responses to environmental change.