, Volume 552, Issue 1, pp 147–158

Setting Goals and Measuring Success: Linking Patterns and Processes in Stream Restoration


DOI: 10.1007/s10750-005-1512-7

Cite this article as:
Ryder, D.S. & Miller, W. Hydrobiologia (2005) 552: 147. doi:10.1007/s10750-005-1512-7


Successful stream restoration requires the setting of appropriate goals and an ability to measure restoration success using quantitative ecological indicators. At present, a dichotomy exists between the setting of restoration goals to enhance ecosystem ‘processes’ or ‘functions’ such as sustainability, and measuring the success of these goals using ‘patterns’ or ‘structural’ ecosystem attributes. The presence of a structural facade may be no indication of a viable ecosystem as this requires evaluation of whether key ecosystem processes have been restored and whether the system is ecologically sustainable. We briefly discuss the benefits and drawbacks associated with setting restoration goals and measuring their success based on ecosystem patterns and processes. Two case studies are provided based on measurements of biofilm chlorophyll a and Dissolved Organic Carbon (DOC) to debunk the myth that these structural variables can be used as surrogates for ecosystem processes of productivity and respiration in rivers. We suggest that the discipline of restoration ecology will benefit and grow from a greater appreciation of the functional role of biological communities within stream ecosystems, and from targeting some restoration towards the re-establishment of structurally significant species and functionally significant processes. This approach to stream restoration with a well-founded conceptual base and defined scientific and management goals should expand our knowledge of stream function and contribute to the effective restoration of stream systems.


restoration ecosystem structure ecosystem function river biofilm DOC 

Copyright information

© Springer 2005

Authors and Affiliations

  1. 1.Ecosystem ManagementUniversity of New EnglandArmidaleAustralia

Personalised recommendations