Cisneros, Felipe

Cordova Vela, Gonzalo

Díaz Granda, Catalina

Goethals, Peter

Iñiguez Vela, Xavier

Jerves Cobo, Rubén

Nopens, Ingmar

Van Echelpoel, Wout

English

Ghent University Academic Bibliography

13 February 2018• DEPARTMENT OF APPLIED ECOLOGY AND ENVIRONMENTAL BIOLOGYRESEARCH GROUP AQUATIC ECOLOGY (AECO)Model-based analysis of the potential of macroinvertebrates as indicators for microbial pathogens in riversRubén Jerves-Cobo, I. Nopens, P. GoethalsRuben.JervesCobo@UGent.BeBackground and objectiveMaterials and methodsResults ConclusionOUTLINENetherlands Annual Ecology Meeting 2018 (NAEM2018), Lunteren, The Netherlands, Date (13/02/2018)Department of Applied Ecology and Environmental Biology, Research Group Aquatic Ecology (AECO)Ruben.JervesCobo@UGent.be1-BACKGROUND AND OBJECTIVE (I)• The quality of the water must accomplish standards.• drinking water, recreational purpose, Irrigation• The indicators used to verify microbial contamination of water are:• total coliforms and fecal coliforms and/or Escherichia coliNetherlands Annual Ecology Meeting 2018 (NAEM2018), Lunteren, The Netherlands, Date (13/02/2018)Department of Applied Ecology and Environmental Biology, Research Group Aquatic Ecology (AECO)Ruben.JervesCobo@UGent.be1-BACKGROUND AND OBJECTIVE (II)• Checking the fulfillment requires expensive and highly trained personnel in laboratories• Biota works as a permanent monitor of water• biological samples can :• reflect an increase in pollution.• predict average values of chemical parameters Netherlands Annual Ecology Meeting 2018 (NAEM2018), Lunteren, The Netherlands, Date (13/02/2018)Department of Applied Ecology and Environmental Biology, Research Group Aquatic Ecology (AECO)Ruben.JervesCobo@UGent.beObjective- Introduce a quick way of checking the fulfillment of fecal coliforms standards using macrobenthos. Netherlands Annual Ecology Meeting 2018 (NAEM2018), Lunteren, The Netherlands, Date (13/02/2018)Department of Applied Ecology and Environmental Biology, Research Group Aquatic Ecology (AECO)Ruben.JervesCobo@UGent.beLOCATION2- MATERIALS AND METHODSLAND USEDATA COLLECTION:- Completed information on 33 locationsPhysicochemical, hydraulic, microbiological• Laboratory– BOD5, COD, Nitrate + Nitrite, Ammonia, Organic Nitrogen,  Phosphates, Total Phosphorus, Fecal and Total Coliforms, Real Color, Turbidity, Total Solids• Field: Flow Velocity, Ph, Conductivity, Temperature, Dissolved OxygenMacrobenthos39 families (taxa) foundVariables Variation- Ecuadorian Water Quality Regulation for Fecal ColiformsRegulations Water used toFecal ColiformsLimited ValueMPN/100 mlRecreational Recreational with primary contact ≤200Agriculture Agriculture and Livestock ≤1,000Raw waterraw water previous to non-conventional treatment*≤2,000* Conventional treatment refers to chemical addition, rapid mixing, flocculation and sedimentationNetherlands Annual Ecology Meeting 2018 (NAEM2018), Lunteren, The Netherlands, Date (13/02/2018)Department of Applied Ecology and Environmental Biology, Research Group Aquatic Ecology (AECO)Ruben.JervesCobo@UGent.beBIOLOGICAL WATER QUALITY• Biological Monitoring Working Party Index - Col• BMWP-Col = f(Sensitivity of Macrobenthos)• Sensitivity -> 1-10 (Low – High Sensitivity)  Class Quality BMWP ColorI Very Good > 100II Good 61 - 100III Moderate 36 - 60IV Deficient 16 - 35V Bad ≤ 15Netherlands Annual Ecology Meeting 2018 (NAEM2018), Lunteren, The Netherlands, Date (13/02/2018)Department of Applied Ecology and Environmental Biology, Research Group Aquatic Ecology (AECO)Ruben.JervesCobo@UGent.beMeasurement setTraining datasetEcosystem12Validation datasetModel development • Classification trees: searching for if-then rules (threshold values): 100% reliable and ‘safe’ modelsAbsencePresenceacbB<a B>=aO<=bO>bO<cO>=cOMethodologyPruned Multi-target Clustering Trees (PMCT)BModel Performance- Models must be evaluated based on statistical and ecological criteria.- Models must be as clear and simple as possible.Settings- Machine learning software: Waikato Environment for Knowledge Analysis (WEKA)- Three, five, ten-fold cross validation (k fcv) - Pruning process• Pruning confidence factors (PCF): 0.25, 0.10Netherlands Annual Ecology Meeting 2018 (NAEM2018), Lunteren, The Netherlands, Date (13/02/2018)Department of Applied Ecology and Environmental Biology, Research Group Aquatic Ecology (AECO)Ruben.JervesCobo@UGent.beModel Performance- Confusion matrix from Decision Tree Models:- Correctly Classified Instances (CCI):> 0.70- Cohen´s Kappa Statistic: > 0.40 - False positive (FP = 0)Predicted ClassYes NoObservedClassYes TP FNNo FP TNNetherlands Annual Ecology Meeting 2018 (NAEM2018), Lunteren, The Netherlands, Date (13/02/2018)Department of Applied Ecology and Environmental Biology, Research Group Aquatic Ecology (AECO)Ruben.JervesCobo@UGent.beModel Optimization:- Cost sensitive classifier (CSC): gives new weights in training instances FN, FP- Overall confusion entropy of a confusion matrix (CEN): evaluates the confusion level of the class distribution of misclassified samples:𝐶𝐸𝑁 = 𝑃1 + 𝑃2 𝐶𝐸𝑁𝑗𝑃1 =𝑇𝑃 + 𝐹𝑁2 𝑇𝑃 + 𝐹𝑁 + 𝐹𝑃 + 𝑇𝑁𝑎𝑛𝑑 𝑃2 =𝐹𝑃 + 𝑇𝑁2 𝑇𝑃 + 𝐹𝑁 + 𝐹𝑃 + 𝑇𝑁Netherlands Annual Ecology Meeting 2018 (NAEM2018), Lunteren, The Netherlands, Date (13/02/2018)Department of Applied Ecology and Environmental Biology, Research Group Aquatic Ecology (AECO)Ruben.JervesCobo@UGent.beModel Optimization:𝐶𝐸𝑁𝑗 = −𝑃𝐹𝑁𝑙𝑜𝑔2𝑃𝐹𝑁 − 𝑃𝐹𝑃𝑙𝑜𝑔2𝑃𝐹𝑃− 𝑃𝑗: confusion probability of class 𝑗− 𝐶𝐸𝑁𝑗: confusion entropy of class 𝑗.𝑃𝐹𝑃 =𝐹𝑃𝐹𝑁 + 𝐹𝑃 + 2𝑇𝑃𝑎𝑛𝑑 𝑃𝐹𝑁 =𝐹𝑁𝐹𝑁 + 𝐹𝑃 + 2𝑇𝑁- 𝑃𝐹𝑃 and 𝑃𝐹𝑁 are the misclassification probability of classifying the samples of class 𝑖 to class 𝑗 subject to class 𝑗- Higher accuracy corresponds to lower confusion entropyNetherlands Annual Ecology Meeting 2018 (NAEM2018), Lunteren, The Netherlands, Date (13/02/2018)Department of Applied Ecology and Environmental Biology, Research Group Aquatic Ecology (AECO)Ruben.JervesCobo@UGent.beBMWP-Col:- 9 good- 15 moderate- 6 poor- 3 badBIOLOGICAL WATER QUALITY3- RESULTSFulfillment of Fecal Coliforms limits in relationship to water use(a) Recreational with primary contact(b) Agricultural - Livestock use and (c) raw water Model Performance First Model: Recreational water use withPrimary Contact – Fecal Coliforms Regulation.CCI = 77%Kappa = 0.56FP = 0CEN = 0.64Model Optimization (1)Second Model: Recreational water use with Primary Contact – Fecal Coliforms Regulation.CCI = 79%Kappa = 0.58FP = 0CEN = 0.61Model Optimization (2)Third model: Irrigation use and raw waterprevious to non-conventional treatment use -Fecal Coliforms Regulation.CCI = 87%Kappa = 0.69FP = 0CEN = 0.53- Three models were selected.• Two Models: fecal coliforms threshold inrecreational with primary contact water use.• One Model: fecal coliforms limits in agricultural- livestock water use, or raw water for drinkingwater treated with non-conventional processes.- The cost-sensitive classifier (CSC) in the Weka can reduce false positives (FP) in the confusion matrix, improving the reliability of the resulting models.4- CONCLUSION Acknowledgement- VLIR-UOS IUC Programme - University of Cuenca - VLIR Ecuador Biodiversity Network Project - Council of the Machangara River BasinNetherlands Annual Ecology Meeting 2018 (NAEM2018), Lunteren, The Netherlands, Date (13/02/2018)Department of Applied Ecology and Environmental Biology, Research Group Aquatic Ecology (AECO)Ruben.JervesCobo@UGent.beThank you?Netherlands Annual Ecology Meeting 2018 (NAEM2018), Lunteren, The Netherlands, Date (13/02/2018)Department of Applied Ecology and Environmental Biology, Research Group Aquatic Ecology (AECO)Ruben.JervesCobo@UGent.be

Model-based analysis of the potential of macroinvertebrates as indicators for microbial pathogens in rivers

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Model-based analysis of the potential of macroinvertebrates as indicators for microbial pathogens in rivers

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