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Prediction of the Abundance of Artemia parthenogenetica in a Hypersaline Wetland Using Decision Tree Model

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Abstract

The hypersaline wetland of Meighan located in western Iran is an important habitat for Artemia parthenogenetica. The habitat condition of this native zooplankton is facing with various problems in the wetland, so its abundance has been reduced in the wetland in the recent years. The study aimed to optimize decision tree model with an optimizer (greedy stepwise) to predict the species abundance in 10 different sampling sites over one-year study period (2017–2018). The model output was the species abundance categorized into 4 classes (poor: 5–20; fair: 21–50; good: 51–100; very good:101–255 individuals) and measured with abiotic variables. The optimizer method improved the model performance leading to easy interpretation of the model. According to the model’s prediction, high abundance of species in the wetland is associated with high concentration of specific conductivity, dissolved oxygen and total dissolved solids. In contrast, increased concentration of chloride, total suspended solids, nitrate and precipitation might decrease the abundance of zooplankton. Chi-square test showed a significant difference between the species abundance and spatio-temporal patterns in the wetland (x2 = 160.2, p = 0.001) so that warm seasons (spring and summer) had more contribution to the zooplankton sampling than cold seasons (autumn and winter).

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References

  • Al Dhaheri S, Saji A (2013) Water quality and brine shrimp (Artemia sp.) population in Al Wathba Lake, Al Wathba wetland reserve, Abu Dhabi emirate, UAE. International Journal of Biodiversity and Conservation 5:281–288

    Google Scholar 

  • Ambelu A, Lock K, Goethals P (2010) Comparison of modelling techniques to predict macroinvertebrate community composition in rivers of Ethiopia. Ecological Informatics 5:147–152

    Article  Google Scholar 

  • APHA, AWWA, WEF (2012) Standard methods for the examination of water and wastewater, vol 9. American Public Health Association, Water Environment Federation, Washington

    Google Scholar 

  • Basil JA, Premkumar DRD, Lipton AP, Marian MP (1987) Artemia in the salt pans of Vedaranyam, southern India. In: Sorgeloos P, Bengtson DA, Decleir W, Jaspers E (eds) Artemia research and its application. Vol. 3, ecology, culturing, use in aquaculture. Universa Press, Wetteren, pp 141–143

    Google Scholar 

  • Bellinger EG (1992) A key to common algae: freshwater, estuarine and some coastal species, 4th edn. Institution of Water Engineers and Scientists, London, 95 p

    Google Scholar 

  • Ben Naceur H, Ben Rejeb Jenhani A, Romdhane MS (2012) Impacts of salinity, temperature, and pH on the morphology of Artemia salina (Branchiopoda: Anostraca) from Tunisia. Zoological Studies 51:453–462

    Google Scholar 

  • Bishop C (1995) Neural networks for pattern recognition. Clarendon Press, Oxford

    Google Scholar 

  • Borowitzka MA, Borowitzka LJ (1988) Limits to growth and carotenogenesis in laboratory and large-scale outdoor cultures of D. salina. In: Stadler T, Mollion J, Verdus MC, Karamanos Y, Morvan H, Christiaen D (eds)

  • Bowen ST, Buoncristiani MR, Carl JR (1988) Artemia habitats: ion concentrations tolerated by one superspecies. Hydrobiologia 158:201–214

    Article  CAS  Google Scholar 

  • Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and Regression Trees. Wadsworth & Brooks, Pacific Groove

    Google Scholar 

  • Browne RA, Wanigasekera G (2000) Combined effects of salinity and temperature on survival and reproduction of five species of Artemia. Journal of Experimental Marine Biology and Ecology 244:29–44

    Article  Google Scholar 

  • Clegg JS, Trotman CNA (2002) Physiological and biochemical aspects of Artemiaecology. In: Abatzopoulos T, Beardmore JA, Clegg JS, Sorgeloos P (eds) Artemia: basic and applied biology. Kluwer Academic Publishers, Dordrecht, pp 129–170

    Chapter  Google Scholar 

  • Cohen J (1960) A coefficient of agreement for nominal scale. Educational and Psychological Measurement 20:37–46

    Article  Google Scholar 

  • Dakou E, D'heygere T, Dedecker AP, Goethals PLM, Dimitriadou ML, De Pauw N (2007) Decision tree models for prediction of macroinvertebrate taxa in the river Axios (northern Greece). Aquatic Ecology 41:399–411

    Article  Google Scholar 

  • Dhont J, Levens P (1993) Tank production and use of on grown Artemia, laboratory of aquaculture and relevance Center university of Ghent, Belgium. P 164-194

  • Dom B, Niblack W, Sheinvald J (1989) Feature selection with stochastic complexity. In: Proceedings of IEEE on Computer Vision and Pattern Recognition, Rosemont 241–248

  • Eimanifar A, Van Stappen G, Marden B, Wink M (2014) Artemia biodiversity in Asia with the focus on the phylogeography of the introduced American species Artemia franciscana Kellogg, 1906. Molecular Phylogenetics and Evolution 79:392–403

    Article  Google Scholar 

  • Everaert G, Bennetsen E, Goethals P (2016) An applicability index for reliable and applicable decision trees in water quality modelling. Ecological Informatics 32:1–6

    Article  Google Scholar 

  • Ferreira CS, Nunes BA, Henriques-Almeida JM, Guilhermino L (2007) Acute toxicity of oxytetracycline and florfenicol to the microalgae Tetraselmis chuii and to the crustacean Artemia parthenogenetica. Ecotoxicol Environ Saf 67(3):452–458

  • Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation 24:38–49

    Article  Google Scholar 

  • Gerloff J, Cholnoky BJ (1970) Diatomaceae II. J. Cramer Verlag, Berlin

    Google Scholar 

  • Ghasemzadeh F, Matinfar A, Jamili Sh, Zare A (2005) Studying of Artemia sp. production in Gonabad salt-waters, Kavir-e-Namak basin, eastern Iran. Iranian Journal of Fisheries Sciences (in Farsi)

  • Goethals P (2005) Data driven development of predictive ecological models for benthic macroinvertebrates in rivers. PhD thesis, Ghent University, Gent, Belgium, 377 pp.

  • Haghi Vayghan A, Zarkami R, Sadeghi R, Fazli H (2015) Modelling habitat preferences of Caspian kutum, Rutilus frisii kutum (Kamensky, 1901) (Actinopterygii, Cypriniformes) in the Caspian Sea. Hydrobiologia. https://doi.org/10.1007/s10750-015-2446-3

  • Hall MA, Holmes G (2003) Benchmarking attribute selection techniques for discrete class data-mining. IEEE Transactions on Knowledge and Data Engineering 15:1437–1447

    Article  Google Scholar 

  • Hamedani H, Naqinezhad A, Fadaie F (2017) Ramsar international wetlands of Alagol, Almagol and Ajigol in eastern parts of the Caspian Sea: a floristic and habitat survey. Caspian Journal of Environmental Science 15:357–372

    Google Scholar 

  • Hesami H, Zarkami R, Agh N (2018) Habitat suitability of Artemia parthenogenetica in the Meighan wetland (Markazi province) using multivariate analysis. JOURNAL OF ANIMAL RESEARCHES 30:552–563 (in Farsi)

    Google Scholar 

  • Hoang TH, Lock K, Mouton A, Goethals P (2010) Application of classification trees and support vector machines to model the presence of macroinvertebrates in rivers in Vietnam. Ecological Informatics 5:140–146

    Article  Google Scholar 

  • Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Lavrac, M., Wrobel, S. (Eds.), Proceedings of the international joint conference on artificial intelligence, pp. 1137–1143

  • Libralato G, Prato E, Migliore L, Cicero AM, Manfra L (2016) A review of toxicity testing protocols and endpoints with Artemia spp. Ecological Indicators 69:35–49

    Article  CAS  Google Scholar 

  • Lock K, Goethals P (2012) Habitat suitability modelling for mayflies (Ephemeroptera) in Flanders (Belgium). Ecological Informatics 17:30–35

    Article  Google Scholar 

  • MacDonald GH (1980) The use of Artemia cysts as food by the flamingo (Phoenicopterus ruber roseus) and the shelduck (Tadorna tadorna). In: Persoone G, Sorgeloos P, Roels O, Jaspers E (eds) The brine shrimp Artemia. Ecology, culturing, use in aquaculture. Universa Press, Wetteren, Wetteren, pp 97, 428 p–104

    Google Scholar 

  • Padmaja TD (1972) Studies on coccoid blue-green algae, II. In: Desikachary, T.V. (ed.): Taxonomy and biology of blue-green algae, University of Madras Press pp, 75–127

  • Persoone G, Sorgeloos P (1980) General aspects of the ecology and biogeography of Artemia. In: Persoone, G. et al. (Ed.). The brine shrimp Artemia: proceedings of the international symposium on the brine shrimp Artemia salina, Corpus Christi, Texas, USA, august 20-23, 1979: 3. Ecology, culturing, use in aquaculture. Pp. 3-24

  • Quinlan JR (1993) C4.5, program for machine learning. Morgan Kaufmann publishers, San Francisco, 302 pp.

  • Sadeghi R, Zarkami R, Sabetraftar K, Van Damme P (2012a) 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 244:117–126

    Article  Google Scholar 

  • Sadeghi R, Zarkami R, Sabetraftar K, Van Damme P (2012b) Application of classification trees to model the distribution pattern of a new exotic species Azolla filiculoides (lam.) in Selkeh wildlife refuge, Anzali wetland, Iran. Ecological Modelling 243:8–17

    Article  Google Scholar 

  • Sadeghi R, Zarkami R, Sabetraftar K, Van Damme P (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, Iran. Ecological Modelling 251:44–53

    Article  Google Scholar 

  • Sadeghi R, Zarkami R, Van Damme P (2014) Modelling habitat preference of an alien aquatic fern, Azolla filiculoides (lam.), in Anzali wetland (Iran) using data-driven methods. Ecological Modelling 284:1–9

    Article  Google Scholar 

  • Sorgeloos P, Lavens P (2004) Manual on the production and use of live food for aquaculture (FAO) fisheries technical paper. No. 361. Rome, FAO. 295p. Laboratory of Aquaculture and Artemia Reference Center University of Ghent, Belgium

  • Sorgeloos P, Lavens P, Léger P, Tackaert W, Versichele D (1986) Manual for the culture and use of brine shrimp Artemia in aquaculture. State University of Ghent, Ghent, 319 pp

    Google Scholar 

  • Sorgeloos P, Dhert P, Candreva P (2001) Use of the brine shrimp, Artemia spp., in marine fish larviculture. Aquaculture 200:147–159

    Article  Google Scholar 

  • Tourenq C, Barcelo I, Drew C (2004) Annual report on water quality and Artemia monitoring at Al Wathba. TERC-ERWDA, Internal Report, p. 28

  • Walczak S, Cerpa N (1999) Heuristic principles for the design of artificial neural networks. Information and Software Technology 41:107–117

    Article  Google Scholar 

  • Wayne A, Wurtsbaugh WA, Gliwicz ZM (2001) Limnological control of brine shrimp population dynamics and cyst production in the great salt Lake, Utah. Hydrobiologia 466:119–132

    Article  Google Scholar 

  • Williams WD (2002) Environmental threats to salt lakes and the likely status of inland saline ecosystems in 2025. Environmental Conservation 29:154–167

    Article  Google Scholar 

  • Witten IH, Frank E, Mark A (2011) Data Mining: Practical machine learning tools and techniques, 3rd edn. Morgan Kaufmann, San Francisco, p 629

    Google Scholar 

  • Zarkami R, Goethals P, De Pauw N (2010) Use of classification tree methods to study the habitat requirements of tench (Tinca tinca) (L., 1758). Caspian Journal of Environmental Science 8:55–63

    Google Scholar 

  • Zarkami R, Sadeghi R, Goethals P (2012) Use of fish distribution modelling for river management. Ecological Modelling 230:44–49

    Article  Google Scholar 

  • Zarkami R, Sadeghi R, Goethals P (2014) Modelling occurrence of roach “Rutilus rutilus” in streams. Aquatic Ecology 48:161–177

    Article  Google Scholar 

  • Zarkami R, Moradi M, Sadeghi R, Bani A, Abbasi K (2018) Input variable selection with greedy stepwise search algorithm for analyzing the probability of fish occurrence: a case study for Alburnoides mossulensis in the Gamasiab River, Iran. Ecological Engineering 118:104–110

    Article  Google Scholar 

  • Zarkami R, Darizin Z, Sadeghi R, Bani A, Ghane A (2019) Use of data-driven model to analyse the occurrence patterns of an indicator fish species in river: a case study for Alburnoides eichwaldii (De Filippi, 1863) in Shafaroud River, north of Iran. Ecological Engineering 133:10–19

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank Pourya Bahri for building the satellite imagery for the sampling sites.

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Authors and Affiliations

  1. Department of Environmental Science, Faculty of Natural Resources, University of Guilan, P.O. Box 1144, Sowmeh Sara, Iran

    Rahmat Zarkami & Hedieh Hesami

  2. Department of Plants and Crops, Faculty of Bio-Science Engineering, Ghent University, Coupure Links, 653, 9000, Ghent, Belgium

    Roghayeh Sadeghi

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  1. Rahmat Zarkami
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  2. Hedieh Hesami
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Correspondence to Rahmat Zarkami.

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Zarkami, R., Hesami, H. & Sadeghi, R. Prediction of the Abundance of Artemia parthenogenetica in a Hypersaline Wetland Using Decision Tree Model. Wetlands 40, 1967–1979 (2020). https://doi.org/10.1007/s13157-020-01332-2

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