Skip to main content

Advertisement

SpringerLink
Log in

Modelling Occurrence of Invasive Water Hyacinth (Eichhornia crassipes) in Wetlands

  • General Wetland Science
  • Published:
Wetlands Aims and scope Submit manuscript

Abstract

Eichhornia crassipes is one of the most ubiquitous invasive aquatic species in the world that negatively impact on native fauna and flora. The information on the occurrence of such invasive plant is crucial for the river/wetland management. The aim of the present study is to apply classification tree model (testing with the highest level of pruning confidence factor) integrated with an optimizer technique (greedy stepwise search algorithm) to predict the occurrence of this exotic species based on water quality and physical-habitat parameters. The ten sites (in the Anzali wetland, located in northern Iran) were monthly measured where the exotic species was present in 50 % of sampling sites and it was absent in the remaining of the sites. In total, 120 samples of E. crassipes (60 presence and 60 absence instances) were monthly measured together with 12 environmental variables during 1-year study period (2017–2018). For the model, two-third of datasets (80 instances) was employed for the training and the remainder for the validation set (40 instances). Before model optimizing (with the pruning confidence factor, PCF = 0.01), six variables were predicted by the model in three folds confirming that the non-occurrence of the exotic species might be associated with increasing flow velocity, depth of ecosystem, water turbidity, bicarbonate and dissolved oxygen concentration, while the occurrence of the exotic species (in terms of the abundance) might show an increase with increasing the concentration of nutrients such as phosphate. After model optimizing (with PCF = 0.01), the model selected five variables in three folds (flow velocity, depth, phosphate, bicarbonate, and nitrate) so that except nitrate, other selected variables were in common before and after variable selection. CT integrated with GS model thus proved to have a high potential when applied for decision-making in the context of wetland management.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data Availability

If article and information are requested the corresponding author will be available to respond the request. Code availability. WEKA software is a free software which was used in the present study.

References

  • Albright TP, Moorhouse T, McNabb T (2004) The rise and fall of water hyacinth in Lake Victoria and the Kagera River Basin, 1989–2001. Journal of Aquatic Plant Management 42:73–84

    Google Scholar 

  • Ambelu A, Lock K, Goethals PLM (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 (1998) Standard methods for the examination of water and wastewater, 19th ed. Washington, DC, USA

  • Blum A, Langley P (1997) Selection of relevant features and examples in machine learning. Artificial Intelligence 97:245–271

    Article  Google Scholar 

  • Buckland ST, Elston DA (1993) Empirical-models for the spatial-distribution of wildlife. Journal of Applied Ecology 30:478–495

    Article  Google Scholar 

  • Butterworth R, Simovici DA, Santos GS, Ohno-Machado L (2004) A greedy algorithm for supervised discretization. Journal of Biomedical Informatics 37:285–292

    Article  CAS  PubMed  Google Scholar 

  • Clout MN, Williams PA (2009) Invasive species management: A handbook of principles and techniques. Oxford University Press, Oxford, 331 pp

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

    Article  Google Scholar 

  • Dakou E, D’heygere T, Dedecker AP, Goethals P, Lazaridou-Dimitriadou M, 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 

  • Darvish Bastami K, Neyestani MR, Molamohyedin N, Shafeian E, Haghparast S, Shirzadi IM, Baniamam M (2018) Bioavailability, mobility, and origination of metals in sediments from Anzali Wetland, Caspian Sea. Marine Pollution Bulletin 136:22–32

    Article  CAS  Google Scholar 

  • De’ath G, Fabricius KE (2000) Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology 81:3178–3192

    Article  Google Scholar 

  • 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

  • Dominguez-Granda L, Lock K, Goethals PLM (2011) Application of classification trees to determine biological and chemical indicators for river assessment: case study in the Chaguana watershed (Ecuador). Journal of Hydroinformatics 13:489

    Article  Google Scholar 

  • Epstein P (1998) Weeds bring disease to the east African waterways. Lancet 351:577

    Article  CAS  PubMed  Google Scholar 

  • Everaert G, Boets P, Lock K, Džeroski S, Goethals PLM (2011) Using classification trees to analyze the ecological impact of invasive species in polder lakes in Flanders, Belgium. Ecological Modelling 222:2202–2212

    Article  Google Scholar 

  • Faraway J, Chatfield C (1998) Time series forecasting with neural network: a comparative study using airline data. Journal of Applied Statistics 47:231–250

    Google Scholar 

  • Fernández-Zamudio R, García-Murilloa P, Cirujano S (2010) Germination characteristics and sporeling success of A. filiculoides Lam., an aquatic invasive fern, in a Mediterranean temporary wetland. Aquatic Botany 93:89–92

    Article  Google Scholar 

  • Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation 24(01):38–49

    Article  Google Scholar 

  • Gevrey M, Dimopoulos L, Lek S (2003) Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecological Modelling 160:249–264

    Article  Google Scholar 

  • Güereña D, Neufeldt H, Berazneva J, Dubyd S (2015) Water hyacinth control in Lake Victoria: Transforming an ecological catastrophe into economic, social, and environmental benefits. Sustainable Production Consumption 3:59–69

    Article  Google Scholar 

  • 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 766:103–119

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Heard TA, Winterton SL (2000) Interactions between nutrient status and weevil in the biological control of water hyacinth. Journal of Applied Ecology 37:117–127

    Article  Google Scholar 

  • Jeppesen E, Jensen J, Sandergaard M, Lauridsen T, Pedersen L, Jensen L (1997) Top-down control in freshwater lakes: the role of nutrient state, submerged macrophytes and water depth. Hydrobiologia 342:151–164

    Article  Google Scholar 

  • JICA (Japan International Cooperation Agency) (2005) The study on integrated management of the Anzali Wetland in the Islamic republic of Iran-final report, 222 pp

  • 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, 1137–1143

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

    Article  Google Scholar 

  • Lowe S, Browne M, Boudjelas S, De Poorter M (2000) 100 of the world’s worst invasive alien species. A selection from the global invasive species database. The Invasive Species Specialist Group (ISSG) a specialist group of the Species Survival Commission (SSC) of the World Conservation Union (IUCN)

  • Malik A (2007) Environmental challenge vis a vis opportunity: the case of water hyacinth. Environment International 33:122–138

    Article  CAS  PubMed  Google Scholar 

  • Manel S, Williams HC, Ormerod SJ (2001) Evaluating presence–absence models in ecology: the need to account for prevalence. Journal of Applied Ecology 38:921–931

    Article  Google Scholar 

  • Muramoto S, Aoyama I, Oki Y (1991) Effect of salinity on the concentration of some elements in water hyacinth (Eichhornia crassipes) at critical levels. Journal of Environmental Science Health 26:205–215

    Google Scholar 

  • MWBP/RSCP (2006) Invasive alien species in the lower mekong basin: Current state of play. Mekong Wetland Biodiversity Programme and Regional Species Conservation Programme, The World Conservation Union (IUCN), Asia, Sri Lanka, 22 pp

  • Nguyen T, Boets P, Lock K, Ambarita M (2015) Habitat suitability of the invasive water hyacinth and its relation to water quality and macroinvertebrate diversity in a tropical reservoir. Limnologica 52:67–74

    Article  CAS  Google Scholar 

  • Olivares E, Colonnello G (2000) Salinity gradient in the Mánamo River, a dammed distributary of the Orinoco Delta, and its influence on the presence of Eichhornia crassipes and Paspalum repens. Interciencia 25:242–248

    Google Scholar 

  • Opande GO, Onyango JC, Wagai SO (2004) Lake Victoria: The water hyacinth (Eichhornia crassipes [Mart.] Solms), its socio-economic effects, control measures and resurgence in the Winam gulf. Limnologica 34:105–109

    Article  Google Scholar 

  • Pimentel D (2005) Environmental and economic costs of the application of pesticides primarily in the United States. Environment, Development and Sustainability 7:229–252

    Article  Google Scholar 

  • Pushpa GS, Volin JC (2010) Does water hyacinth (Eichhornia crassipes) compensate for simulated defoliation? Implications for effective biocontrol. Biological Control 54:35–40

    Article  Google Scholar 

  • Quinlan JR (1986) Induction of decision trees. Machine Learning 1:81–106

    Article  Google Scholar 

  • Quinlan JR (1993) C4.5, Program for Machine Learning. Morgan Kaufmann Publishers, San Francisco, p 302

    Google Scholar 

  • Rodriguez LF (2006) Can invasive species facilitate native species? Evidence of how, when, and why these impacts occur. Biological Invasions 8:927–939

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Sadeghi R, Zarkami R, Sabetraftar K, Van Damme P (2012) 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, Sabetraftar K, Van Damme P (2013) A review of some ecological factors affecting the growth of Azolla spp. Caspian Journal of Environmental Science 11:65–76

    Google Scholar 

  • Sadeghi R, Zarkami R, Van Damme P (2017) Analyzing the occurrence of an invasive aquatic fern in wetland using data-driven and multivariate techniques. Wetlands Ecology and Management 25(4):485–500

    Article  Google Scholar 

  • Schultz R, Dibble E (2012) Effects of invasive macrophytes on freshwater fish and macroinvertebrate communities: the role of invasive plant traits. Hydrobiologia 684:1–14

    Article  Google Scholar 

  • Sharma A, Gupta MK, Singhal PK (1996) Toxic effects of leachate of water hyacinth decay on growth of Scenedesmus obliquus (Chlorophyta). Water Resources 30:2281–2286

    CAS  Google Scholar 

  • Shu X, Deng Q, Zhang Q, Wang W (2015) Comparative responses of two water hyacinth (Eichhornia crassipes) cultivars to different planting densities. Aquatic Botany 121:1–8

    Article  Google Scholar 

  • Stiers I, Crohain N, Josens G, Triest L (2011) Impact of three aquatic invasive species on native plants and macroinvertebrates in temperate ponds. Biological Invasions 13:2715–2726

    Article  Google Scholar 

  • Sun WH, Yu SW, Yang SY, Zhao PW, Yu ZW, Wu HM et al (1993) Allelochemicals from root exudates of water hyacinth. Acta Phytophysiol Sinica 19:92–96

    CAS  Google Scholar 

  • Téllez TR, López EM, Granado G, Pérez EA, López RM, Sanchez Guzmán JM (2008) The Water Hyacinth, Eichhornia crassipes: an invasive plant in the Guadiana River Basin (Spain). Aquatic Invasions 3:42–53

    Article  Google Scholar 

  • Theel HJ, Dibble ED, Madsen JD (2008) Differential influence of a monotypic and diverse native aquatic plant bed on a macroinvertebrate assemblage; an experimental implication of exotic plant induced habitat. Hydrobiologia 600(1):77–87

    Article  Google Scholar 

  • Thuiller W, Lafourcade B, Engler R, Araújo M (2009) BIOMOD - a platform for ensemble forecasting of species distributions. Ecography 32:369–373

    Article  Google Scholar 

  • UNEP, Global Environmental Alert Service (GEAS) (2013) Water hyacinth- Can its aggressive invasion be controlled? Environment and Development 7:139–154

    Article  Google Scholar 

  • Villamagna AM, Murphy BR (2010) Ecological and socio-economic impacts of invasive water hyacinth (Eichhornia crassipes): a review. Freshwater Biology 55:282–298

    Article  Google Scholar 

  • Wilson JR, Holst N, Rees M (2005) Determinants and patterns of population growth in water hyacinth. Aquatic Botany 81:51–67

    Article  Google Scholar 

  • Witten IH, Frank E, Hall MA (2011) Data mining, practical machine learning tools and techniques, 3rd edn. Morgan Kaufmann, San Francisco, 629 pp

    Google Scholar 

  • Zarkami R (2016) Invasive aquatic plants. Haghshanas Publishing Press. 184 pp (in Farsi)

  • 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 

  • Zarkami R, Hesami H, Sadeghi Pasvisheh R (2020a) Assessment, monitoring and modelling of the abundance of Dunaliella salina Teod in the Meighan wetland, Iran using decision tree model. Environmental Monitoring and Assessment 192:172. https://doi.org/10.1007/s10661-020-8148-y

    Article  CAS  PubMed  Google Scholar 

  • Zarkami R, Kia S, Sadeghi Pasvisheh R (2020b) Modelling the habitat preferences of the swan mussel (Anodonta cygnea) using data-driven model. Environmental Monitoring and Assessment 192:685. https://doi.org/10.1007/s10661-020-08651-1

    Article  CAS  PubMed  Google Scholar 

  • Zarkami R, Hesami H, Sadeghi Pasvisheh R (2020c) Prediction of the abundance of Artemia parthenogenetica in a hypersaline wetland using decision tree model. Wetlands. https://doi.org/10.1007/s13157-020-01332-2

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge Pourya Bahri for providing the map of the sampling sites. He is following his Master of Science study in the Department of Environmental Science, Faculty of Natural Resources, University of Guilan, Iran.

Author information

Authors and Affiliations

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

    Rahmat Zarkami & Javaneh Esfandi

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

    Roghayeh Sadeghi

Authors
  1. Rahmat Zarkami
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Javaneh Esfandi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Roghayeh Sadeghi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

RZ: sampling design, data collection and manuscript writing; JE: data collection; RS: sampling design, the proposed modelling techniques and manuscript writing.

Corresponding author

Correspondence to Rahmat Zarkami.

Ethics declarations

Conflicts of Interest/Competing Interests

The authors declare no conflicts of interest/competing interest. 

Ethics Approval

Approved by the faculty of natural resources, University of Guilan. 

Consent to Participate

All authors declared the consent to participate. 

Consent for Publication

All authors declared the consent for publication.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zarkami, R., Esfandi, J. & Sadeghi, R. Modelling Occurrence of Invasive Water Hyacinth (Eichhornia crassipes) in Wetlands. Wetlands 41, 8 (2021). https://doi.org/10.1007/s13157-021-01405-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13157-021-01405-w

Keywords

Search

Navigation