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Updated: May 3, 2026

Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
Assessing anthropogenic impact on boreal lakes with historical fish species distribution data and hydrogeochemical
Salar Valinia1, Göran Englund, Filip Moldan
1Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences, Box 7050, Uppsala, SE-750 07, Sweden.
Abstract:
Quantifying the effects of human activity on the natural environment is dependent on credible estimates of reference conditions to define the state of the environment before the onset of adverse human impacts. In Europe, emission controls that aimed at restoring ecological status were based on hindcasts from process-based models or paleolimnological reconstructions. For instance, 1860 is used in Europe as the target for restoration from acidification concerning biological and chemical parameters. A more practical problem is that the historical states of ecosystems and their function cannot be observed directly. Therefore, we (i) compare estimates of acidification based on long-term observations of roach (Rutilus rutilus) populations with hindcast pH from the hydrogeochemical model MAGIC; (ii) discuss policy implications and possible scope for use of long-term archival data for assessing human impacts on the natural environment and (iii) present a novel conceptual model for interpreting the importance of physico-chemical and ecological deviations from reference conditions. Of the 85 lakes studied, 78 were coherently classified by both methods. In 1980, 28 lakes were classified as acidified with the MAGIC model, however, roach was present in 14 of these. In 2010, MAGIC predicted chemical recovery in 50% of the lakes, however roach only recolonized in five lakes after 1990, showing a lag between chemical and biological recovery. Our study is the first study of its kind to use long-term archival biological data in concert with hydrogeochemical modeling for regional assessments of anthropogenic acidification. Based on our results, we show how the conceptual model can be used to understand and prioritize management of physico-chemical and ecological effects of anthropogenic stressors on surface water quality.
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