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Effect of model formulation on the optimization of a genetic Takagi–Sugeno fuzzy system for fish habitat suitability evaluation

Author

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  • Fukuda, Shinji
  • De Baets, Bernard
  • Mouton, Ans M.
  • Waegeman, Willem
  • Nakajima, Jun
  • Mukai, Takahiko
  • Hiramatsu, Kazuaki
  • Onikura, Norio

Abstract

Species distribution models (SDMs), which evaluate species–environment relationships, are one of the key topics in ecology and biogeography. These models evaluate the current status of target ecosystems and potential impacts in both time and space. Although species distributions are often calculated based on the composite habitat suitability of several variables, there are no guidelines for calculating them. The present study assessed the effects of model formulation on habitat suitability evaluation and the accuracy of species distribution modelling. We employed a genetic algorithm (GA)-optimized fuzzy habitat preference model (FHPM) for evaluating habitat suitability of topmouth gudgeon (Pseudorasbora parva) in the Northwestern part of Kyushu Island in Japan. Four operations were used to calculate the composite habitat suitability from multiple habitat variables: arithmetic mean, geometric mean, product and minimum. To transform model outputs to presence/absence, four threshold criteria were compared based on model accuracy: prevalence, conventional 0.5, minimization of the sensitivity–specificity difference threshold (MDT), and maximization of the sensitivity–specificity sum threshold (MST). The models were first calibrated and validated based on the mean squared error (MSE) between composite habitat suitability and the observed presence–absence of the fish, and then evaluated using confusion matrix-derived measures such as the area under the receiver operating characteristics (ROC) curve (AUC), correctly classified instances (CCI), kappa and true skill statistic (TSS). The results clearly illustrated the effects of model formulation and threshold criteria on habitat suitability curves (HSCs) and accuracy in modelling species distributions. The use of the product model formulation led to the best accuracy in terms of MSE and AUC, and consistency in the shape of HSCs. The two threshold criteria of MST and MDT are also recommended for the consistently higher performance in terms of CCI, kappa and TSS. This case study of topmouth gudgeon illustrates the need for further studies on the model behaviour with regard to data characteristics (i.e., sample size and prevalence) and model structure (i.e., fuzzy sets and parameter settings of the GA).

Suggested Citation

  • 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.
  • Handle: RePEc:eee:ecomod:v:222:y:2011:i:8:p:1401-1413
    DOI: 10.1016/j.ecolmodel.2011.01.023
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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. 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.
    3. 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.
    4. 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.
    5. Stockwell, David R.B. & Noble, Ian R., 1992. "Induction of sets of rules from animal distribution data: A robust and informative method of data analysis," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 33(5), pages 385-390.
    6. Fukuda, Shinji & Hiramatsu, Kazuaki, 2008. "Prediction ability and sensitivity of artificial intelligence-based habitat preference models for predicting spatial distribution of Japanese medaka (Oryzias latipes)," Ecological Modelling, Elsevier, vol. 215(4), pages 301-313.
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    2. Muñoz-Mas, R. & Martínez-Capel, F. & Alcaraz-Hernández, J.D. & Mouton, A.M., 2015. "Can multilayer perceptron ensembles model the ecological niche of freshwater fish species?," Ecological Modelling, Elsevier, vol. 309, pages 72-81.
    3. 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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