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Optimisation of a fuzzy physical habitat model for spawning European grayling (Thymallus thymallus L.) in the Aare river (Thun, Switzerland)

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  • Mouton, Ans M.
  • Schneider, Matthias
  • Peter, Armin
  • Holzer, Georg
  • Müller, Rudolf
  • Goethals, Peter L.M.
  • De Pauw, Niels

Abstract

Ecological expert knowledge is often based on qualitative rules consisting of linguistic terms such as ‘low’, ‘moderate’ or ‘high’. Since fuzzy systems transform these rules and terms into a mathematical framework, they allow implementing this expert knowledge in ecological models. However, the development of a reliable knowledge base is complex and time consuming. Recent research has shown that complementing fuzzy systems by data-driven techniques can solve this knowledge acquisition bottleneck. In this paper, a heuristic nearest ascent hill-climbing algorithm for rule base optimisation is applied to construct a fuzzy rule-based habitat suitability model for spawning European grayling (Thymallus thymallus L.) in the Aare river (Bern, Switzerland). Optimisation of the fuzzy rule-based model was based on two different training criteria, the weighted correctly classified instances (CCIw) and Cohen's Kappa. The ecological relevance of the results was assessed by comparing the optimised rule bases with a rule base derived from ecological expert knowledge. Optimisation based on Kappa appeared to generate acceptable results (CCI=0.70; Kappa=0.32) and was more practical than optimisation based on CCIw since the latter required fine tuning of a weight parameter, which accounted for the species prevalence. The optimal rules showed 74% similarity with the rules derived from expert knowledge, while 84% of all model errors was due to false positive predictions of the model. These errors might be due to the impact of variables, which were not included in this study on grayling presence and thus are not necessarily a model error. The habitat suitability model optimised in this paper is able to predict the effect of different impacts on the river system and to select the optimal restoration option. Hence, it could be a valuable decision support tool for river managers and ease the discussion between stakeholders.

Suggested Citation

  • Mouton, Ans M. & Schneider, Matthias & Peter, Armin & Holzer, Georg & Müller, Rudolf & Goethals, Peter L.M. & De Pauw, Niels, 2008. "Optimisation of a fuzzy physical habitat model for spawning European grayling (Thymallus thymallus L.) in the Aare river (Thun, Switzerland)," Ecological Modelling, Elsevier, vol. 215(1), pages 122-132.
    • Handle: RePEc:eee:ecomod:v:215:y:2008:i:1:p:122-132
    DOI: 10.1016/j.ecolmodel.2008.02.028
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    References listed on IDEAS

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    1. Bradley J. Cardinale & Margaret A. Palmer & Scott L. Collins, 2002. "Species diversity enhances ecosystem functioning through interspecific facilitation," Nature, Nature, vol. 415(6870), pages 426-429, January.
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    1. Mouton, A.M. & Dillen, A. & Van den Neucker, T. & Buysse, D. & Stevens, M. & Coeck, J., 2012. "Impact of sampling efficiency on the performance of data-driven fish habitat models," Ecological Modelling, Elsevier, vol. 245(C), pages 94-102.
    2. Muñoz-Mas, Rafael & Marcos-Garcia, Patricia & Lopez-Nicolas, Antonio & Martínez-García, Francisco J. & Pulido-Velazquez, Manuel & Martínez-Capel, Francisco, 2018. "Combining literature-based and data-driven fuzzy models to predict brown trout (Salmo trutta L.) spawning habitat degradation induced by climate change," Ecological Modelling, Elsevier, vol. 386(C), pages 98-114.
    3. Aude Zingraff-Hamed & Markus Noack & Sabine Greulich & Kordula Schwarzwälder & Karl Matthias Wantzen & Stephan Pauleit, 2018. "Model-Based Evaluation of Urban River Restoration: Conflicts between Sensitive Fish Species and Recreational Users," Sustainability, MDPI, vol. 10(6), pages 1-27, May.
    4. Fukuda, Shinji, 2009. "Consideration of fuzziness: Is it necessary in modelling fish habitat preference of Japanese medaka (Oryzias latipes)?," Ecological Modelling, Elsevier, vol. 220(21), pages 2877-2884.
    5. Mouton, Ans M. & De Baets, Bernard & Goethals, Peter L.M., 2010. "Ecological relevance of performance criteria for species distribution models," Ecological Modelling, Elsevier, vol. 221(16), pages 1995-2002.
    6. Mouton, Ans M. & De Baets, Bernard & Van Broekhoven, Ester & Goethals, Peter L.M., 2009. "Prevalence-adjusted optimisation of fuzzy models for species distribution," Ecological Modelling, Elsevier, vol. 220(15), pages 1776-1786.
    7. Yi, Yujun & Wang, Zhaoyin & Yang, Zhifeng, 2010. "Two-dimensional habitat modeling of Chinese sturgeon spawning sites," Ecological Modelling, Elsevier, vol. 221(5), pages 864-875.
    8. Yi, Yujun & Cheng, Xi & Yang, Zhifeng & Wieprecht, Silke & Zhang, Shanghong & Wu, Yingjie, 2017. "Evaluating the ecological influence of hydraulic projects: A review of aquatic habitat suitability models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 748-762.
    9. Dubos, Véronique & St-Hilaire, André & Bergeron, Normand E., 2023. "Fuzzy logic modelling of anadromous Arctic char spawning habitat from Nunavik Inuit knowledge," Ecological Modelling, Elsevier, vol. 477(C).
    10. Fukuda, Shinji & De Baets, Bernard & Mouton, Ans M. & Waegeman, Willem & Nakajima, Jun & Mukai, Takahiko & Hiramatsu, Kazuaki & Onikura, Norio, 2011. "Effect of model formulation on the optimization of a genetic Takagi–Sugeno fuzzy system for fish habitat suitability evaluation," Ecological Modelling, Elsevier, vol. 222(8), pages 1401-1413.
    11. Mocq, J. & St-Hilaire, A. & Cunjak, R.A., 2013. "Assessment of Atlantic salmon (Salmo salar) habitat quality and its uncertainty using a multiple-expert fuzzy model applied to the Romaine River (Canada)," Ecological Modelling, Elsevier, vol. 265(C), pages 14-25.

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