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Use of fish distribution modelling for river management

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  • Zarkami, Rahmat
  • Sadeghi, Roghayeh
  • Goethals, Peter

Abstract

We developed decision trees (J48 algorithm) to predict the distribution of the pike (Esox lucius). Based on a historical data, 75 sampling sites were considered for the pike in 7 stream basins in Flanders (Belgium). In total, 108 instances were available in the given sites. The measured variables consisted of a combination of the structural-habitat, physical–chemical and biological variables (biomass, abundance and presence/absence of the pike). The predictive power of decision trees was assessed on the basis of the number of Correctly Classified Instances (CCI %) and Kappa statistic (k). In order to reduce the noise in the data and improve the predictive results with regard to complexity and accuracy of the predictions, different Pruning Confidence Factors (PCFs) were tested. The obtained results showed that the prediction of the pike (based on presence/absence data) was acceptable in terms of two model evaluations (CCI>70% and k>0.40). The habitat variables had more contribution to the prediction of distribution of pike relative to the water quality ones. The developed model presented a logical relationship between distribution of the pike and distance from the source, slope and followed by depth. These models can as such become essential tools to encourage river managers to make the necessary investments and/or activity changes as desired by society.

Suggested Citation

  • Zarkami, Rahmat & Sadeghi, Roghayeh & Goethals, Peter, 2012. "Use of fish distribution modelling for river management," Ecological Modelling, Elsevier, vol. 230(C), pages 44-49.
    • Handle: RePEc:eee:ecomod:v:230:y:2012:i:c:p:44-49
    DOI: 10.1016/j.ecolmodel.2012.01.011
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    References listed on IDEAS

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    1. Iebeling Kaastra & Milton S. Boyd, 1995. "Forecasting futures trading volume using neural networks," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 15(8), pages 953-970, December.
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    Cited by:

    1. Kadukothanahally Nagaraju Shivaprakash & Niraj Swami & Sagar Mysorekar & Roshni Arora & Aditya Gangadharan & Karishma Vohra & Madegowda Jadeyegowda & Joseph M. Kiesecker, 2022. "Potential for Artificial Intelligence (AI) and Machine Learning (ML) Applications in Biodiversity Conservation, Managing Forests, and Related Services in India," Sustainability, MDPI, vol. 14(12), pages 1-20, June.
    2. Sadeghi, Roghayeh & Zarkami, Rahmat & Sabetraftar, Karim & Van Damme, Patrick, 2012. "Application of classification trees to model the distribution pattern of a new exotic species Azolla filiculoides (Lam.) at Selkeh Wildlife Refuge, Anzali wetland, Iran," Ecological Modelling, Elsevier, vol. 243(C), pages 8-17.
    3. Sadeghi, Roghayeh & Zarkami, Rahmat & Sabetraftar, Karim & Van Damme, Patrick, 2012. "Use of support vector machines (SVMs) to predict distribution of an invasive water fern Azolla filiculoides (Lam.) in Anzali wetland, southern Caspian Sea, Iran," Ecological Modelling, Elsevier, vol. 244(C), pages 117-126.
    4. Sadeghi, Roghayeh & Zarkami, Rahmat & Sabetraftar, Karim & Van Damme, Patrick, 2013. "Application of genetic algorithm and greedy stepwise to select input variables in classification tree models for the prediction of habitat requirements of Azolla filiculoides (Lam.) in Anzali wetland,," Ecological Modelling, Elsevier, vol. 251(C), pages 44-53.
    5. Argaw Ambelu & Seblework Mekonen & Magaly Koch & Taffere Addis & Pieter Boets & Gert Everaert & Peter Goethals, 2014. "The Application of Predictive Modelling for Determining Bio-Environmental Factors Affecting the Distribution of Blackflies (Diptera: Simuliidae) in the Gilgel Gibe Watershed in Southwest Ethiopia," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-10, November.
    6. Sadeghi, Roghayeh & Zarkami, Rahmat & Van Damme, Patrick, 2014. "Modelling habitat preference of an alien aquatic fern, Azolla filiculoides (Lam.), in Anzali wetland (Iran) using data-driven methods," Ecological Modelling, Elsevier, vol. 284(C), pages 1-9.
    7. 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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