Data variability and uncertainty limits the capacity to identify and predict critical changes in coastal systems - A review of key concepts

Lars Håkanson*, Carlos M. Duarte

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


How do inherent variations and uncertainties in empirical data constrain approaches to predictions and possibilities to identify critical thresholds and points of no return? This work addresses this question in discussing and reviewing key concepts and methods for coastal ecology and management. The main focus is not on the mechanisms regulating the concentration of a given variable but on patterns in variations in concentrations for many standard variables in entire lagoons, bays, estuaries or Fjords (i.e., on variations at the ecosystem scale). We address and review problems related to(1)The balance between the changes in predictive power and the accumulated uncertainty as models grow in size and include an increasing number of x-variables.(2)An approach to reduce uncertainties in empirical data.(3)Methods to maximize the predictive power of regression models by transformations of model variables and by creating time and area compatible model variables.(4)Patterns in variations within and among coastal systems of standard water variables.(5)Based on the results of the review, we also discuss the concept "Optimal Model Scale" (OMS) and an algorithm to calculate OMS, which accounts for key factors related to the predictive power at different time scales (daily to yearly prediction) and to uncertainties in predictions in relation to access to empirical data and the work (sampling effort) needed to achieve predictive power at different time scales.

Original languageEnglish (US)
Pages (from-to)671-688
Number of pages18
JournalOcean and Coastal Management
Issue number10
StatePublished - 2008
Externally publishedYes

Bibliographical note

Funding Information:
This work has been carried out within the framework of the Thresholds-project, and integrated EU project (no. 003933-2), and we would like to acknowledge the financial support from EU and the constructive cooperation within the project. Many thanks to Jens Hansen for support concerning data and knowledge regarding Ringkobing Fjord.

ASJC Scopus subject areas

  • Oceanography
  • Aquatic Science
  • Management, Monitoring, Policy and Law


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