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Schwefel R, Nkwalale LGT, Jordan S, Rinke K, Hupfer M. Temperatures and hypolimnetic oxygen in German lakes: Observations, future trends and adaptation potential. AMBIO 2024:10.1007/s13280-024-02046-z. [PMID: 38967897 DOI: 10.1007/s13280-024-02046-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 05/23/2024] [Accepted: 06/13/2024] [Indexed: 07/06/2024]
Abstract
We investigated trends in temperature, stratification, and hypolimnetic oxygen concentration of German lakes under climate change using observational data and hydrodynamic modelling. Observations from 46 lakes revealed that annually averaged surface temperatures increased by + 0.5 °C between 1990 and 2020 while bottom temperatures remained almost constant. Modelling of 12 lakes predicted further increases in surface temperatures by 0.3 °C/decade until the year 2099 in the most pessimistic emission scenario RCP 8.5 (RCP 4.5: + 0.18 °C/decade; RCP 2.6: + 0.04 °C/decade). Again, bottom temperatures increased much less while summer stratification extended by up to 38 days. Using a simplified oxygen model, we showed that hypolimnetic oxygen concentrations decreased by 0.7-1.9 mg L-1 in response to the extended stratification period. However, model runs assuming lower productivity (e. g. through nutrient reduction) resulted in increased oxygen concentrations even in the most pessimistic emission scenario. Our results suggest that the negative effects of climate change on the oxygen budget of lakes can be efficiently mitigated by nutrient control.
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Affiliation(s)
- Robert Schwefel
- Department of Ecohydrology and Biogeochemistry, Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 301, 12587, Berlin, Germany.
| | - Lipa G T Nkwalale
- Department of Lake Research, Helmholtz Centre for Environmental Research - UFZ, Brückstr. 3a, 39114, Magdeburg, Germany
| | - Sylvia Jordan
- Department of Ecohydrology and Biogeochemistry, Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 301, 12587, Berlin, Germany
| | - Karsten Rinke
- Department of Lake Research, Helmholtz Centre for Environmental Research - UFZ, Brückstr. 3a, 39114, Magdeburg, Germany
| | - Michael Hupfer
- Department of Ecohydrology and Biogeochemistry, Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 301, 12587, Berlin, Germany
- Department of Aquatic Ecology, Brandenburg University of Technology Cottbus-Senftenberg, Seestraße 45, 15526, Bad Saarow, Germany
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Irani Rahaghi A, Odermatt D, Anneville O, Sepúlveda Steiner O, Reiss RS, Amadori M, Toffolon M, Jacquet S, Harmel T, Werther M, Soulignac F, Dambrine E, Jézéquel D, Hatté C, Tran-Khac V, Rasconi S, Rimet F, Bouffard D. Combined Earth observations reveal the sequence of conditions leading to a large algal bloom in Lake Geneva. COMMUNICATIONS EARTH & ENVIRONMENT 2024; 5:229. [PMID: 38706883 PMCID: PMC11062928 DOI: 10.1038/s43247-024-01351-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 03/26/2024] [Indexed: 05/07/2024]
Abstract
Freshwater algae exhibit complex dynamics, particularly in meso-oligotrophic lakes with sudden and dramatic increases in algal biomass following long periods of low background concentration. While the fundamental prerequisites for algal blooms, namely light and nutrient availability, are well-known, their specific causation involves an intricate chain of conditions. Here we examine a recent massive Uroglena bloom in Lake Geneva (Switzerland/France). We show that a certain sequence of meteorological conditions triggered this specific algal bloom event: heavy rainfall promoting excessive organic matter and nutrients loading, followed by wind-induced coastal upwelling, and a prolonged period of warm, calm weather. The combination of satellite remote sensing, in-situ measurements, ad-hoc biogeochemical analyses, and three-dimensional modeling proved invaluable in unraveling the complex dynamics of algal blooms highlighting the substantial role of littoral-pelagic connectivities in large low-nutrient lakes. These findings underscore the advantages of state-of-the-art multidisciplinary approaches for an improved understanding of dynamic systems as a whole.
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Affiliation(s)
- Abolfazl Irani Rahaghi
- Eawag, Swiss Federal Institute of Aquatic Science & Technology, Surface Waters – Research and Management, 8600 Duebendorf, Switzerland
- Department of Geography, University of Zurich, 8057 Zurich, Switzerland
| | - Daniel Odermatt
- Eawag, Swiss Federal Institute of Aquatic Science & Technology, Surface Waters – Research and Management, 8600 Duebendorf, Switzerland
- Department of Geography, University of Zurich, 8057 Zurich, Switzerland
| | - Orlane Anneville
- Université Savoie Mont Blanc, INRAE, UMR CARRTEL, 74200 Thonon-les-Bains, France
| | - Oscar Sepúlveda Steiner
- Eawag, Swiss Federal Institute of Aquatic Science & Technology, Surface Waters – Research and Management, 6047 Kastanienbaum, Switzerland
- Department of Civil & Environmental Engineering, University of California, Davis, Davis, CA USA
| | - Rafael Sebastian Reiss
- Ecological Engineering Laboratory (ECOL), Institute of Environmental Engineering (IIE), Faculty of Architecture, Civil and Environmental Engineering (ENAC), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Marina Amadori
- Institute for Electromagnetic Sensing of the Environment (IREA), National Research Council of Italy (CNR), 20133 Milan, Italy
| | - Marco Toffolon
- Department of Civil, Environmental and Mechanical Engineering, University of Trento, 38122 Trento, Italy
| | - Stéphan Jacquet
- Université Savoie Mont Blanc, INRAE, UMR CARRTEL, 74200 Thonon-les-Bains, France
| | | | - Mortimer Werther
- Eawag, Swiss Federal Institute of Aquatic Science & Technology, Surface Waters – Research and Management, 8600 Duebendorf, Switzerland
| | - Frédéric Soulignac
- Commission Internationale pour la Protection des Eaux du Léman (CIPEL), Nyon, Switzerland
| | - Etienne Dambrine
- Université Savoie Mont Blanc, INRAE, UMR CARRTEL, 74200 Thonon-les-Bains, France
| | - Didier Jézéquel
- Université Savoie Mont Blanc, INRAE, UMR CARRTEL, 74200 Thonon-les-Bains, France
- Université Paris Cité, Institut de Physique du Globe de Paris, CNRS, 75005 Paris, France
| | - Christine Hatté
- Laboratoire des Sciences du Climat et de l’Environnement, CEA, CNRS, UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
- Institute of Physics, Silesian University of Technology, 44-100 Gliwce, Poland
| | - Viet Tran-Khac
- Université Savoie Mont Blanc, INRAE, UMR CARRTEL, 74200 Thonon-les-Bains, France
| | - Serena Rasconi
- Université Savoie Mont Blanc, INRAE, UMR CARRTEL, 74200 Thonon-les-Bains, France
| | - Frédéric Rimet
- Université Savoie Mont Blanc, INRAE, UMR CARRTEL, 74200 Thonon-les-Bains, France
| | - Damien Bouffard
- Eawag, Swiss Federal Institute of Aquatic Science & Technology, Surface Waters – Research and Management, 6047 Kastanienbaum, Switzerland
- Faculty of Geosciences and Environment, Institute of Earth Surface Dynamics, University of Lausanne, Geopolis, Mouline, 1015 Lausanne, Switzerland
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Edwards AM, Rogers LA, Holt CA. Explaining empirical dynamic modelling using verbal, graphical and mathematical approaches. Ecol Evol 2024; 14:e10903. [PMID: 38751824 PMCID: PMC11094587 DOI: 10.1002/ece3.10903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 01/14/2024] [Indexed: 05/18/2024] Open
Abstract
Empirical dynamic modelling (EDM) is becoming an increasingly popular method for understanding the dynamics of ecosystems. It has been applied to laboratory, terrestrial, freshwater and marine systems, used to forecast natural populations and has addressed fundamental ecological questions. Despite its increasing use, we have not found full explanations of EDM in the ecological literature, limiting understanding and reproducibility. Here we expand upon existing work by providing a detailed introduction to EDM. We use three progressively more complex approaches. A short verbal explanation of EDM is then explicitly demonstrated by graphically working through a simple example. We then introduce a full mathematical description of the steps involved. Conceptually, EDM translates a time series of data into a path through a multi-dimensional space, whose axes are lagged values of the time series. A time step is chosen from which to make a prediction. The state of the system at that time step corresponds to a 'focal point' in the multi-dimensional space. The set (called the library) of candidate nearest neighbours to the focal point is constructed, to determine the nearest neighbours that are then used to make the prediction. Our mathematical explanation explicitly documents which points in the multi-dimensional space should not be considered as focal points. We suggest a new option for excluding points from the library that may be useful for short-term time series that are often found in ecology. We focus on the core simplex and S-map algorithms of EDM. Our new R package, pbsEDM, enhances understanding (by outputting intermediate calculations), reproduces our results and can be applied to new data. Our work improves the clarity of the inner workings of EDM, a prerequisite for EDM to reach its full potential in ecology and have wide uptake in the provision of advice to managers of natural resources.
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Affiliation(s)
- Andrew M. Edwards
- Pacific Biological StationFisheries and Oceans CanadaNanaimoBritish ColumbiaCanada
- Department of BiologyUniversity of VictoriaVictoriaBritish ColumbiaCanada
| | - Luke A. Rogers
- Pacific Biological StationFisheries and Oceans CanadaNanaimoBritish ColumbiaCanada
| | - Carrie A. Holt
- Pacific Biological StationFisheries and Oceans CanadaNanaimoBritish ColumbiaCanada
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Munch SB, Rogers TL, Symons CC, Anderson D, Pennekamp F. Constraining nonlinear time series modeling with the metabolic theory of ecology. Proc Natl Acad Sci U S A 2023; 120:e2211758120. [PMID: 36930600 PMCID: PMC10041132 DOI: 10.1073/pnas.2211758120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 02/08/2023] [Indexed: 03/18/2023] Open
Abstract
Forecasting the response of ecological systems to environmental change is a critical challenge for sustainable management. The metabolic theory of ecology (MTE) posits scaling of biological rates with temperature, but it has had limited application to population dynamic forecasting. Here we use the temperature dependence of the MTE to constrain empirical dynamic modeling (EDM), an equation-free nonlinear machine learning approach for forecasting. By rescaling time with temperature and modeling dynamics on a "metabolic time step," our method (MTE-EDM) improved forecast accuracy in 18 of 19 empirical ectotherm time series (by 19% on average), with the largest gains in more seasonal environments. MTE-EDM assumes that temperature affects only the rate, rather than the form, of population dynamics, and that interacting species have approximately similar temperature dependence. A review of laboratory studies suggests these assumptions are reasonable, at least approximately, though not for all ecological systems. Our approach highlights how to combine modern data-driven forecasting techniques with ecological theory and mechanistic understanding to predict the response of complex ecosystems to temperature variability and trends.
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Affiliation(s)
- Stephan B. Munch
- Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Santa Cruz, CA95060
- Department of Applied Mathematics, University of California, Santa Cruz, CA95060
| | - Tanya L. Rogers
- Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Santa Cruz, CA95060
| | - Celia C. Symons
- Department of Ecology and Evolutionary Biology, University of California, Irvine, CA92697
| | - David Anderson
- Department of Zoology, University of British Columbia, Vancouver, BCV6T 1Z4, Canada
| | - Frank Pennekamp
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich8057, Switzerland
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