IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v246y2012icp119-130.html
   My bibliography  Save this article

Use of sensitivity analysis to identify influential and non-influential parameters within an aquatic ecosystem model

Author

Listed:
  • Ciric, C.
  • Ciffroy, P.
  • Charles, S.

Abstract

Food-web models can be powerful tools to assess effects of chemicals on ecosystem structure and functioning. Indeed, they do not only account for direct ecotoxicological effects on aquatic species, but also consider the interactions between these species and the resulting indirect effects of chemicals. However, such models often contain a large number of parameters and are therefore difficult to calibrate on experimental data. Following this rationale, sensitivity analyses (SA) are essential tools that enable us to simplify the calibration process by identifying the non-influential input parameters which can be further fixed at a nominal value. In this study, the screening SA Morris method and the variance-based SA method EFAST were compared on two ecological food-web models (of 20 and 34 parameters). These sub-models represent two prey–predator chains, the first containing two outputs (periphyton and grazers) and the second one three outputs (green algae, rotifers and invertebrate predators). Both SA methods ranked the models’ parameters according to their influence on the outputs. Nevertheless, the Morris approach can a priori be sensitive to sampling design parameters that are (generally arbitrarily) chosen by the modeller. Therefore, we then tested different settings of this method, in particular the ‘level’ and 'sampling’ numbers of the design, as well as its aleatory component, and gave recommendations about the most accurate Morris sampling design. To test the accuracy of the Morris results, median ranks derived from this latter approach were compared to those obtained with EFAST. It was observed that Morris and EFAST approaches actually identified the same 8 and 15 non-influential parameters for the two sub-models. These parameters were mainly related to the mortality, respiration and excretion processes. None of the non-influential parameters were involved in the growth functions, expected to be the driving processes of the system.

Suggested Citation

  • Ciric, C. & Ciffroy, P. & Charles, S., 2012. "Use of sensitivity analysis to identify influential and non-influential parameters within an aquatic ecosystem model," Ecological Modelling, Elsevier, vol. 246(C), pages 119-130.
    • Handle: RePEc:eee:ecomod:v:246:y:2012:i:c:p:119-130
    DOI: 10.1016/j.ecolmodel.2012.06.024
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380012003031
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2012.06.024?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Confalonieri, R. & Bellocchi, G. & Bregaglio, S. & Donatelli, M. & Acutis, M., 2010. "Comparison of sensitivity analysis techniques: A case study with the rice model WARM," Ecological Modelling, Elsevier, vol. 221(16), pages 1897-1906.
    2. Saltelli, Andrea & Ratto, Marco & Tarantola, Stefano & Campolongo, Francesca, 2006. "Sensitivity analysis practices: Strategies for model-based inference," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1109-1125.
    3. Park, Richard A. & Clough, Jonathan S. & Wellman, Marjorie Coombs, 2008. "AQUATOX: Modeling environmental fate and ecological effects in aquatic ecosystems," Ecological Modelling, Elsevier, vol. 213(1), pages 1-15.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Benoit, David M. & Giacomini, Henrique C. & Chu, Cindy & Jackson, Donald A., 2021. "Identifying influential parameters of a multi-species fish size spectrum model for a northern temperate lake through sensitivity analyses," Ecological Modelling, Elsevier, vol. 460(C).
    2. Morris, David J. & Speirs, Douglas C. & Cameron, Angus I. & Heath, Michael R., 2014. "Global sensitivity analysis of an end-to-end marine ecosystem model of the North Sea: Factors affecting the biomass of fish and benthos," Ecological Modelling, Elsevier, vol. 273(C), pages 251-263.
    3. Ahmadi, Mehdi & Ascough, James C. & DeJonge, Kendall C. & Arabi, Mazdak, 2014. "Multisite-multivariable sensitivity analysis of distributed watershed models: Enhancing the perceptions from computationally frugal methods," Ecological Modelling, Elsevier, vol. 279(C), pages 54-67.
    4. Huber, Nica & Bugmann, Harald & Lafond, Valentine, 2018. "Global sensitivity analysis of a dynamic vegetation model: Model sensitivity depends on successional time, climate and competitive interactions," Ecological Modelling, Elsevier, vol. 368(C), pages 377-390.
    5. Courbaud, B. & Lafond, V. & Lagarrigues, G. & Vieilledent, G. & Cordonnier, T. & Jabot, F. & de Coligny, F., 2015. "Applying ecological model evaludation: Lessons learned with the forest dynamics model Samsara2," Ecological Modelling, Elsevier, vol. 314(C), pages 1-14.
    6. Yi, Xuan & Zou, Rui & Guo, Huaicheng, 2016. "Global sensitivity analysis of a three-dimensional nutrients-algae dynamic model for a large shallow lake," Ecological Modelling, Elsevier, vol. 327(C), pages 74-84.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Silva, R. & Pérez, M. & Berenguel, M. & Valenzuela, L. & Zarza, E., 2014. "Uncertainty and global sensitivity analysis in the design of parabolic-trough direct steam generation plants for process heat applications," Applied Energy, Elsevier, vol. 121(C), pages 233-244.
    2. Francisco A. Buendia-Hernandez & Maria J. Ortiz Bevia & Francisco J. Alvarez-Garcia & Antonio Ruizde Elvira, 2022. "Sensitivity of a Dynamic Model of Air Traffic Emissions to Technological and Environmental Factors," IJERPH, MDPI, vol. 19(22), pages 1-17, November.
    3. Imron, Muhammad Ali & Gergs, Andre & Berger, Uta, 2012. "Structure and sensitivity analysis of individual-based predator–prey models," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 71-81.
    4. Grechi, Laura & Franco, Antonio & Palmeri, Luca & Pivato, Alberto & Barausse, Alberto, 2016. "An ecosystem model of the lower Po river for use in ecological risk assessment of xenobiotics," Ecological Modelling, Elsevier, vol. 332(C), pages 42-58.
    5. Kanapaux, William & Kiker, Gregory A., 2013. "Development and testing of an object-oriented model for adaptively managing human disturbance of least tern (Sternula antillarum) nesting habitat," Ecological Modelling, Elsevier, vol. 268(C), pages 64-77.
    6. Cao, Jiaokun & Du, Farong & Ding, Shuiting, 2013. "Global sensitivity analysis for dynamic systems with stochastic input processes," Reliability Engineering and System Safety, Elsevier, vol. 118(C), pages 106-117.
    7. Doyeong Ku & Yeon-Ji Chae & Yerim Choi & Chang Woo Ji & Young-Seuk Park & Ihn-Sil Kwak & Yong-Jae Kim & Kwang-Hyeon Chang & Hye-Ji Oh, 2022. "Optimal Method for Biomass Estimation in a Cladoceran Species, Daphnia Magna (Straus, 1820): Evaluating Length–Weight Regression Equations and Deriving Estimation Equations Using Body Length, Width an," Sustainability, MDPI, vol. 14(15), pages 1-10, July.
    8. Drignei, Dorin, 2011. "A general statistical model for computer experiments with time series output," Reliability Engineering and System Safety, Elsevier, vol. 96(4), pages 460-467.
    9. Rui Zhang & Taotao Chen & Daocai Chi, 2020. "Global Sensitivity Analysis of the Standardized Precipitation Evapotranspiration Index at Different Time Scales in Jilin Province, China," Sustainability, MDPI, vol. 12(5), pages 1-19, February.
    10. Hu, Wen & Li, Chun-hua & Ye, Chun & Wang, Ji & Wei, Wei-wei & Deng, Yong, 2019. "Research progress on ecological models in the field of water eutrophication: CiteSpace analysis based on data from the ISI web of science database," Ecological Modelling, Elsevier, vol. 410(C), pages 1-1.
    11. DeJonge, Kendall C. & Ascough, James C. & Ahmadi, Mehdi & Andales, Allan A. & Arabi, Mazdak, 2012. "Global sensitivity and uncertainty analysis of a dynamic agroecosystem model under different irrigation treatments," Ecological Modelling, Elsevier, vol. 231(C), pages 113-125.
    12. Paleari, Livia & Confalonieri, Roberto, 2016. "Sensitivity analysis of a sensitivity analysis: We are likely overlooking the impact of distributional assumptions," Ecological Modelling, Elsevier, vol. 340(C), pages 57-63.
    13. López-Benito, Alfredo & Bolado-Lavín, Ricardo, 2017. "A case study on global sensitivity analysis with dependent inputs: The natural gas transmission model," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 11-21.
    14. Liu, Min & He, Honglin & Ren, Xiaoli & Sun, Xiaomin & Yu, Guirui & Han, Shijie & Wang, Huimin & Zhou, Guoyi, 2015. "The effects of constraining variables on parameter optimization in carbon and water flux modeling over different forest ecosystems," Ecological Modelling, Elsevier, vol. 303(C), pages 30-41.
    15. Reder, Klara & Alcamo, Joseph & Flörke, Martina, 2017. "A sensitivity and uncertainty analysis of a continental-scale water quality model of pathogen pollution in African rivers," Ecological Modelling, Elsevier, vol. 351(C), pages 129-139.
    16. Paleari, Livia & Movedi, Ermes & Zoli, Michele & Burato, Andrea & Cecconi, Irene & Errahouly, Jabir & Pecollo, Eleonora & Sorvillo, Carla & Confalonieri, Roberto, 2021. "Sensitivity analysis using Morris: Just screening or an effective ranking method?," Ecological Modelling, Elsevier, vol. 455(C).
    17. Masciantonio, Sergio, 2013. "Identifying, ranking and tracking systemically important financial institutions (SIFIs), from a global, EU and Eurozone perspective," MPRA Paper 46788, University Library of Munich, Germany.
    18. Niu, Zhiguang & Gou, Qianqian & Wang, Xiujun & Zhang, Ying, 2016. "Simulation of a water ecosystem in a landscape lake in Tianjin with AQUATOX: Sensitivity, calibration, validation and ecosystem prognosis," Ecological Modelling, Elsevier, vol. 335(C), pages 54-63.
    19. Su, Ziyi & Li, Xiaofeng, 2022. "Extraction of key parameters and simplification of sub-system energy models using sensitivity analysis in subway stations," Energy, Elsevier, vol. 261(PA).
    20. Frédéric Branger & Louis-Gaëtan Giraudet & Céline Guivarch & Philippe Quirion, 2014. "Sensitivity analysis of an energy-economy model of the residential building sector," CIRED Working Papers hal-01016399, HAL.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:246:y:2012:i:c:p:119-130. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.