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Quantifying the Adaptive Cycle.

Angeler DG, Allen CR, Garmestani AS, Gunderson LH, Hjerne O, Winder M - PLoS ONE (2015)

Bottom Line: The adaptive cycle was proposed as a conceptual model to portray patterns of change in complex systems.All predictions were supported by our analyses.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.

View Article: PubMed Central - PubMed

Affiliation: Stockholm University, Department of Ecology, Evolution and Plant Sciences, SE- 106 91, Stockholm, Sweden.

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.

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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.
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pone.0146053.g006: 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.

Mentions: Spearman rank correlation analysis revealed variability in the incidence of correlation of phytoplankton taxa with the NMDS dimensions over the study years (Fig 6). That is, positive correlations of species raw biovolume data with NMDS 1 and 2 dimensions indicate that a species dominates the phytoplankton community towards the right and upper part of the ordination, respectively. Negative correlations of species biovolumes with NMDS 1 and 2 highlight dominance towards the left and lower part of the ordination, respectively. For simplicity we only present species with high correlation coefficients (/0.8/—/1.0/), i.e. those dominating community dynamics described by the NMDS analysis. In practical terms we can describe several patterns that emerged from the analysis. First, during spring dynamics we observed broad shifts of communities from dominant diatoms (Bacillariophyceae), particularly Thalassiosira sp., Skeletonema sp. and Chaetoceros sp., to Dinophyta at both sites. Unidentified flagellates, Pyramimonas (Prasinophyceae), Eutreptiella (Euglenophyceae), and Mesodinium (Ciliophora) also contributed to these patterns, although to a different degree at both sites (Fig 6).


Quantifying the Adaptive Cycle.

Angeler DG, Allen CR, Garmestani AS, Gunderson LH, Hjerne O, Winder M - PLoS ONE (2015)

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.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4696843&req=5

pone.0146053.g006: 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.
Mentions: Spearman rank correlation analysis revealed variability in the incidence of correlation of phytoplankton taxa with the NMDS dimensions over the study years (Fig 6). That is, positive correlations of species raw biovolume data with NMDS 1 and 2 dimensions indicate that a species dominates the phytoplankton community towards the right and upper part of the ordination, respectively. Negative correlations of species biovolumes with NMDS 1 and 2 highlight dominance towards the left and lower part of the ordination, respectively. For simplicity we only present species with high correlation coefficients (/0.8/—/1.0/), i.e. those dominating community dynamics described by the NMDS analysis. In practical terms we can describe several patterns that emerged from the analysis. First, during spring dynamics we observed broad shifts of communities from dominant diatoms (Bacillariophyceae), particularly Thalassiosira sp., Skeletonema sp. and Chaetoceros sp., to Dinophyta at both sites. Unidentified flagellates, Pyramimonas (Prasinophyceae), Eutreptiella (Euglenophyceae), and Mesodinium (Ciliophora) also contributed to these patterns, although to a different degree at both sites (Fig 6).

Bottom Line: The adaptive cycle was proposed as a conceptual model to portray patterns of change in complex systems.All predictions were supported by our analyses.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.

View Article: PubMed Central - PubMed

Affiliation: Stockholm University, Department of Ecology, Evolution and Plant Sciences, SE- 106 91, Stockholm, Sweden.

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.

Show MeSH
Related in: MedlinePlus