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A hybrid empirical and parametric approach for managing ecosystem complexity: Water quality in Lake Geneva under nonstationary futures.
Deyle, Ethan R; Bouffard, Damien; Frossard, Victor; Schwefel, Robert; Melack, John; Sugihara, George.
Afiliação
  • Deyle ER; Department of Biology, Boston University, Boston, MA 02215.
  • Bouffard D; Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA 92093.
  • Frossard V; Department of Surface Waters-Research and Management, Eawag-Swiss Federal Institute of Aquatic Science and Technology, Kastanienbaum, 6047, Switzerland.
  • Schwefel R; Centre Alpin de Recherche sur les Réseaux Trophiques des Ecosystèmes Limniques, Univ. Savoie Mont Blanc, INRAE, Thonon-les-Bains, 73376, France.
  • Melack J; Department of Ecology, Evolution, and Marine Biology, University of California, Santa Barbara, CA 93106.
  • Sugihara G; Department of Ecohydrology and Biogeochemistry, Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Berlin, 12587, Germany.
Proc Natl Acad Sci U S A ; 119(26): e2102466119, 2022 06 28.
Article em En | MEDLINE | ID: mdl-35733249
Severe deterioration of water quality in lakes, characterized by overabundance of algae and declining dissolved oxygen in the deep lake (DOB), was one of the ecological crises of the 20th century. Even with large reductions in phosphorus loading, termed "reoligotrophication," DOB and chlorophyll (CHL) have often not returned to their expected pre-20th-century levels. Concurrently, management of lake health has been confounded by possible consequences of climate change, particularly since the effects of climate are not neatly separable from the effects of eutrophication. Here, using Lake Geneva as an iconic example, we demonstrate a complementary alternative to parametric models for understanding and managing lake systems. This involves establishing an empirically-driven baseline that uses supervised machine learning to capture the changing interdependencies among biogeochemical variables and then combining the empirical model with a more conventional equation-based model of lake physics to predict DOB over decadal time-scales. The hybrid model not only leads to substantially better forecasts, but also to a more actionable description of the emergent rates and processes (biogeochemical, ecological, etc.) that drive water quality. Notably, the hybrid model suggests that the impact of a moderate 3°C air temperature increase on water quality would be on the same order as the eutrophication of the previous century. The study provides a template and a practical path forward to cope with shifts in ecology to manage environmental systems for non-analogue futures.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Qualidade da Água / Lagos Tipo de estudo: Prognostic_studies País como assunto: Europa Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Qualidade da Água / Lagos Tipo de estudo: Prognostic_studies País como assunto: Europa Idioma: En Ano de publicação: 2022 Tipo de documento: Article