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Linking Species and Communities to Ecosystem Management: A Perspective from the Experimental Lakes Experience

  • D. W. Schindler
Chapter

Summary

The Experimental Lakes Area (ELA) has been the focus of applied ecological studies since, 1968. Both communities and ecosystems have been studied in lakes that were deliberately perturbed with nutrients and strong acids to simulate contemporary lake management problems. Strengths and weaknesses of the approach are examined.

In addition to providing a scientific basis for management of eutrophication and acidification, ELA experiments have yielded several hundred articles in refereed pure science journals.

Following perturbation, it required several years for many features of communities and ecosystems to reach steady state, revealing the pitfalls of using short-term studies to guide applied ecology decisions. Results from the companion Northern Ontario Lake Size Survey (NOLSS) provide important information on spatial (size) scaling for the extrapolation of results from small ecosystems to large ones.

To determine accurately the effects of human perturbation on ecosystems, changes in the baseline state of ecosystems that are believed to be unperturbed must also be considered. Examples of changed “baseline” conditions in lakes of remote areas due to climatic warming, fisheries manipulations, and atmospheric transport of organochlorines are discussed.

Among ecosystem types in a region, lakes may be the best early indicators of human stress, due to the relative ease with which communities and ecosystems can be studied, and the paleoecological records of both aquatic and terrestrial events in lake sediments. The low functional redundancy in the communities of northern lakes allows ecosystem-wide changes to occur, even with slight human perturbation.

Keywords

Ecosystem Function Ecosystem Management Lake Trout Ecosystem Type Cyclopoid Copepod 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 1995

Authors and Affiliations

  • D. W. Schindler

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