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Modelling of riverine ecosystems by integrating models: conceptual approach, a case study and research agenda

Authors

  • Sonja C. Jähnig,

    Corresponding author
    1. Senckenberg Research Institute and Natural History Museum Frankfurt, Department of River Ecology and Conservation, Gelnhausen, Germany
    • Biodiversity and Climate Research Centre (BiK-F), Frankfurt/Main, Germany
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    • These authors contributed equally.
  • Mathias Kuemmerlen,

    1. Biodiversity and Climate Research Centre (BiK-F), Frankfurt/Main, Germany
    2. Senckenberg Research Institute and Natural History Museum Frankfurt, Department of River Ecology and Conservation, Gelnhausen, Germany
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    • These authors contributed equally.
  • Jens Kiesel,

    1. Department of Hydrology and Water Resources Management, Institute for Natural Resource Conservation, University of Kiel, Kiel, Germany
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    • These authors contributed equally.
  • Sami Domisch,

    1. Biodiversity and Climate Research Centre (BiK-F), Frankfurt/Main, Germany
    2. Senckenberg Research Institute and Natural History Museum Frankfurt, Department of River Ecology and Conservation, Gelnhausen, Germany
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  • Qinghua Cai,

    1. State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, China
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  • Britta Schmalz,

    1. Department of Hydrology and Water Resources Management, Institute for Natural Resource Conservation, University of Kiel, Kiel, Germany
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  • Nicola Fohrer

    1. Department of Hydrology and Water Resources Management, Institute for Natural Resource Conservation, University of Kiel, Kiel, Germany
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Correspondence: Sonja Jähnig, Senckenberg, Department of River Ecology and Conservation, Clamecystrasse 12, 63571 Gelnhausen, Germany.

E-mail: sonja.jaehnig@senckenberg.de

Abstract

Aim

Highly complex interactions between the hydrosphere and biosphere, as well as multifactorial relationships, characterize the interconnecting role of streams and rivers between different elements of a landscape. Applying species distribution models (SDMs) in these ecosystems requires special attention because rivers are linear systems and their abiotic and biotic conditions are structured in a linear fashion with significant influences from upstream/downstream or lateral influences from adjacent areas. Our aim was to develop a modelling framework for benthic invertebrates in riverine ecosystems and to test our approach in a data-rich study catchment.

Location

We present a case study of a 9-km section of the lowland Kielstau River located in northern Germany.

Methods

We linked hydrological, hydraulic and species distribution models to predict the habitat suitability for the bivalve Sphaerium corneum in a riverine system. The results generated by the hydrological model served as inputs into the hydraulic model, which was used to simulate the resulting water levels, velocities and sediment discharge within the stream channel.

Results

The ensemble model obtained good evaluation scores (area under the receiver operating characteristic curve 0.96; kappa 0.86; true skill statistic 0.95; sensitivity 86.14; specificity 85.75). Mean values for variables at the sampling sites were not significantly different from the values at the predicted distribution (Mann–Whitney U-test P > 0.05). High occurrence probabilities were predicted in the downstream half of the 9-km section of the Kielstau. The most important variable for the model was sediment discharge (contributing 40%), followed by water depth (30%), flow velocity (19%) and stream power (11%).

Main conclusions

The hydrological and hydraulic models are able to produce predictors, acting at different spatial scales, which are known to influence riverine organisms; which, in turn, are used by the SDMs as input. Our case study yielded good results, which corresponded well with ecological knowledge about our study organism. Although this method is feasible for making projections of habitat suitability on a local scale (here: a reach in a small catchment), we discuss remaining challenges for future modelling approaches and large-scale applications.

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