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Modelling habitat preference of an alien aquatic fern, Azolla filiculoides (Lam.), in Anzali wetland (Iran) using data-driven methods

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  • Sadeghi, Roghayeh
  • Zarkami, Rahmat
  • Van Damme, Patrick

Abstract

A crucial element in modelling habitat requirements of any alien plant species is selection of the most important predictive variables. A database consisting of measurements collected at 7 different sampling sites at Selkeh Wildlife Refuge (Anzali wetland, Iran) was applied to predict the habitat preferences of an exotic species, Azolla filiculoides (Lam.). The measured variables were a combination of physico-chemical, structural-habitat and cover percentage data of A. filiculoides collected during the 2007–2008 period. We used support vector machines (SVMs) combined with two search algorithms, i.e. genetic algorithm (GA) and greedy stepwise (GS) in order to select the most important explanatory variables for the target species. The models with the best performing exponent were run five times after randomization to check the models’ robustness and reproducibility. The results of paired Student's t-test showed that the two optimizers (GA and GS) were unable to improve the predictive performances of the SVMs. Yet, GA outperformed GS resulting in a better prediction. All applied methods showed that both structural-habitat and physico-chemical variables might play key roles for meeting the habitat preferences of the exotic fern in the wetland. However, structural-habitat parameters (particularly wetland depth and air temperature) were the most predictive ones. Among the water quality variables, orthophosphate and sulphate were also recognized as important predictors. The physico-chemical variables selected by the models revealed that reduction of industrial pollution loads and also decreasing nutrient and organic pollution inputs into the wetland could be effective way to reduce the growth of Azolla's population.

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  • 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.
    • Handle: RePEc:eee:ecomod:v:284:y:2014:i:c:p:1-9
    DOI: 10.1016/j.ecolmodel.2014.04.003
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    References listed on IDEAS

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    1. 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.
    2. S. T. Buckland & D. L. Borchers & A. Johnston & P. A. Henrys & T. A. Marques, 2007. "Line Transect Methods for Plant Surveys," Biometrics, The International Biometric Society, vol. 63(4), pages 989-998, December.
    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. Zarkami, Rahmat & Sadeghi, Roghayeh & Goethals, Peter, 2012. "Use of fish distribution modelling for river management," Ecological Modelling, Elsevier, vol. 230(C), pages 44-49.
    5. 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.
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    1. Muñoz-Mas, Rafael & Vezza, Paolo & Alcaraz-Hernández, Juan Diego & Martínez-Capel, Francisco, 2016. "Risk of invasion predicted with support vector machines: A case study on northern pike (Esox Lucius, L.) and bleak (Alburnus alburnus, L.)," Ecological Modelling, Elsevier, vol. 342(C), pages 123-134.
    2. Roghayeh Sadeghi Pasvisheh & Marie Anne Eurie Forio & Long Tuan Ho & Peter L. M. Goethals, 2021. "Evidence-Based Management of the Anzali Wetland System (Northern Iran) Based on Innovative Monitoring and Modeling Methods," Sustainability, MDPI, vol. 13(10), pages 1-16, May.
    3. Walker, Adam N. & Poos, Jan-Jaap & Groeneveld, Rolf A., 2015. "Invasive species control in a one-dimensional metapopulation network," Ecological Modelling, Elsevier, vol. 316(C), pages 176-184.
    4. 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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