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Prevalence-adjusted optimisation of fuzzy models for species distribution

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  • Mouton, Ans M.
  • De Baets, Bernard
  • Van Broekhoven, Ester
  • Goethals, Peter L.M.

Abstract

Most performance criteria which have been applied to train ecological models focus on the accuracy of the model predictions. However, these criteria depend on the prevalence of the training set and often do not take into account ecological issues such as the distinction between omission and commission errors. Moreover, a previous study indicated that model training based on different performance criteria results in different optimised models. Therefore, model developers should train models based on different performance criteria and select the most appropriate model depending on the modelling objective. This paper presents a new approach to train fuzzy models based on an adjustable performance criterion, called the adjusted average deviation (aAD). This criterion was applied to develop a species distribution model for spawning grayling in the Aare River near Thun, Switzerland. To analyse the strengths and weaknesses of this approach, it was compared to model training based on other performance criteria. The results suggest that model training based on accuracy-based performance criteria may produce unrealistic models at extreme prevalences of the training set, whereas the aAD allows for the identification of more accurate and more reliable models. Moreover, the adjustable parameter in this criterion enables modellers to situate the optimised models in the search space and thus provides an indication of the ecological model relevance. Consequently, it may support modellers and river managers in the decision making process by improving model reliability and insight into the modelling process. Due to the universality and the flexibility of the approach, it could be applied to any other ecosystem or species, and may therefore be valuable to ecological modelling and ecosystem management in general.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:ecomod:v:220:y:2009:i:15:p:1776-1786
    DOI: 10.1016/j.ecolmodel.2009.04.020
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    References listed on IDEAS

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    1. 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.
    2. Austin, Mike, 2007. "Species distribution models and ecological theory: A critical assessment and some possible new approaches," Ecological Modelling, Elsevier, vol. 200(1), pages 1-19.
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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. Everaert, Gert & Boets, Pieter & Lock, Koen & Džeroski, Sašo & Goethals, Peter L.M., 2011. "Using classification trees to analyze the impact of exotic species on the ecological assessment of polder lakes in Flanders, Belgium," Ecological Modelling, Elsevier, vol. 222(14), pages 2202-2212.
    3. 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.
    4. Gutiérrez-Estrada, Juan C. & Pulido-Calvo, Inmaculada & Bilton, David T., 2013. "Consistency of fuzzy rules in an ecological context," Ecological Modelling, Elsevier, vol. 251(C), pages 187-198.
    5. 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.
    6. Verwaeren, Jan & Waegeman, Willem & De Baets, Bernard, 2012. "Learning partial ordinal class memberships with kernel-based proportional odds models," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 928-942.
    7. Holguin-Gonzalez, Javier E. & Boets, Pieter & Alvarado, Andres & Cisneros, Felipe & Carrasco, María C. & Wyseure, Guido & Nopens, Ingmar & Goethals, Peter L.M., 2013. "Integrating hydraulic, physicochemical and ecological models to assess the effectiveness of water quality management strategies for the River Cuenca in Ecuador," Ecological Modelling, Elsevier, vol. 254(C), pages 1-14.
    8. 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.
    9. Choi, Jong-Kuk & Oh, Hyun-Joo & Koo, Bon Joo & Ryu, Joo-Hyung & Lee, Saro, 2011. "Crustacean habitat potential mapping in a tidal flat using remote sensing and GIS," Ecological Modelling, Elsevier, vol. 222(8), pages 1522-1533.
    10. Gobeyn, Sacha & Mouton, Ans M. & Cord, Anna F. & Kaim, Andrea & Volk, Martin & Goethals, Peter L.M., 2019. "Evolutionary algorithms for species distribution modelling: A review in the context of machine learning," Ecological Modelling, Elsevier, vol. 392(C), pages 179-195.

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