, Volume 623, Issue 1, pp 153–171 | Cite as

The MEDiterranean Prediction And Classification System (MEDPACS): an implementation of the RIVPACS/AUSRIVAS predictive approach for assessing Mediterranean aquatic macroinvertebrate communities

  • José Manuel PoquetEmail author
  • Javier Alba-TercedorEmail author
  • Tura Puntí
  • Maria del Mar Sánchez-Montoya
  • Santiago Robles
  • Maruxa Álvarez
  • Carmen Zamora-Muñoz
  • Carmen Elisa Sáinz-Cantero
  • Maria Rosario Vidal-Abarca
  • Maria Luisa Suárez
  • Manuel Toro
  • Ana Maria Pujante
  • Maria Rieradevall
  • Narcís Prat
Primary research paper


In Spain, a national project known as GUADALMED, focusing on Mediterranean streams, has been carried out from 1998 to 2005 to implement the European water framework directive (WFD) requirements. One of the main objectives of the second phase of the project (2002–2005) was to develop a predictive system for the Spanish Mediterranean aquatic macroinvertebrate communities. A combined-season (spring, summer, and autumn) predictive model was developed by using the latest improvements on the selection of best predictor variables. Overall model performance measures were used to select the best discriminant function (DF) models, and also to evaluate their biases and precision. The final predictive model was based on the best five DF models. Each one of these models involved eight environmental variables. Final observed (O), expected (E), and O/E values for the number of macroinvertebrate families (NFAM) and two biotic indices (IBMWP and IASPT) were calculated by averaging their values, previously weighted by the quality of each DF model. Regression analyses among the final O and E values for the calibration dataset showed a high proximity to the ideal theoretical model, where the final E values explained 73–84% of the variation present in the macroinvertebrate communities of the Spanish Mediterranean watercourses. The ANOVA performed among the reference (calibration and validation) and test datasets showed clear differences for the O/E values. Finally, the assessments carried out by the predictive model were sensitive to anthropogenic pressure present in the study area and allowed the definition of five ecological status classes according to the WFD requirements.


Predictive modelling GUADALMED project Bioassessment Water framework directive Ecological status 



The authors would especially like to acknowledge the help and advice of M.T. Furse, R.T. Clarke, R.H. Norris, S. Nichols, S. Linke, and R.C. Bailey, including the warm welcomes extended to the first author during his stays in their laboratories. We thank N. Bonada for help and comments on previous drafts of this manuscript, as well as C.P. Hawkins and J. Van Sickle for supplying the ‘best-subsets’ scripts, and two anonymous reviewers for their comments that contributed to the improvement of this paper. This research was supported by the GUADALMED-2 project (REN2001-3438-C07), by the Eurolimpacs project (GOCE-CT-2003-505540), as well as by pre-doctoral grants to J.M. Poquet, T. Puntí, and M.M. Sánchez-Montoya from the Spanish Ministry of Science and Technology.


  1. Acuña, V., I. Muñoz, A. Giorgi, M. Omella, F. Sabater & S. Sabater, 2005. Drought and postdrought recovery cycles in an intermittent Mediterranean stream: structural and functional aspects. Journal of the North American Benthological Society 24: 919–933. CrossRefGoogle Scholar
  2. Alba-Tercedor, J., 1996. Macroinvertebrados acuáticos y calidad de las aguas de los ríos. IV Simposio del Agua en Andalucía (SIAGA) vol II. Instituto Tecnológico GeoMinero de España, Madrid: 203–213.Google Scholar
  3. Alba-Tercedor, J. & A. M. Pujante, 2000. Running-water biomonitoring in Spain: opportunities for a predictive approach. In Wright, J. F., D. W. Sutcliffe & M. T. Furse (eds), Assessing the Biological Quality of Fresh waters: RIVPACS and Other Techniques. Freshwater Biological Association, Ambleside, Cumbria: 207–216.Google Scholar
  4. Alba-Tercedor, J. & A. Sánchez-Ortega, 1988. Un método rápido y simple para evaluar la calidad biológica de las aguas corrientes basado en el de Hellawell. Limnetica 4: 51–56.Google Scholar
  5. Alba-Tercedor, J., P. Jáimez-Cuellar, M. Álvarez, J. Avilés, N. Bonada, J. Casas, A. Mellado, M. Ortega, I. Pardo, N. Prat, M. Rieradevall, S. Robles, C. E. Sáinz-Cantero, A. Sánchez-Ortega, M. L. Suárez, M. Toro, M. R. Vidal-Abarca, S. Vivas & C. Zamora-Muñoz, 2004. Caracterización del estado ecológico de ríos mediterráneos ibéricos mediante el índice IBMWP (antes BMWP’). Limnetica 21(2002): 175–185.Google Scholar
  6. Armengol, J., N. Prat & N. Bonada (eds), 2004. Resultados del proyecto GUADALMED. Limnetica 21 (2002). Publicacions i Edicions Universitat de Barcelona. Barcelona.Google Scholar
  7. Armitage, P. D., I. Pardo, M. T. Furse & J. F. Wright, 1990. Assessment and prediction of biological quality. A demonstration of a British macroinvertebrate based method in two Spanish rivers. Limnetica 6: 147–156.Google Scholar
  8. Bailey, R. C., R. H. Norris & T. B. Reynoldson, 2004. Bioassessment of Freshwater Ecosystems: Using the Reference Condition Approach. Kluwer Academic Publishers, Dordrecht.Google Scholar
  9. Belbin, L. & C. McDonald, 1993. Comparing three classification strategies for use in ecology. Journal of Vegetation Science 4: 341–348. CrossRefGoogle Scholar
  10. Bis, B., A. Zdanowicz & M. Zalewski, 2000. Effects of catchment properties on hydrochemistry, habitat complexity and invertebrate community structure in a lowland river. Hydrobiologia 422/423: 369–387.Google Scholar
  11. Bonada, N., N. Prat, A. Munné, M. Plans, C. Solà, M. Álvarez, I. Pardo, G. Moyá, G. Ramón, M. Toro, S. Robles, J. Avilés, M. L. Suárez, M. R. Vidal-Abarca, A. Mellado, J. L. Moreno, C. Guerrero, S. Vivas, M. Ortega, J. Casas, A. Sánchez-Ortega, P. Jáimez-Cuellar & J. Alba-Tercedor, 2004a. Intercalibración de la metodología GUADALMED Selección de un protocolo de muestreo para la determinación del estado ecológico de los ríos mediterráneos. Limnetica 21(2002): 13–33.Google Scholar
  12. Bonada, N., N. Prat, A. Munné, M. Rieradevall, J. Alba-Tercedor, M. Álvarez, J. Avilés, J. Casas, P. Jáimez-Cuellar, A. Mellado, G. Moyá, I. Pardo, S. Robles, G. Ramón, M. L. Suárez, M. Toro, M. R. Vidal-Abarca, S. Vivas & C. Zamora-Muñoz, 2004b. Criterios para la selección de condiciones de referencia en los ríos mediterráneos. Resultados del proyecto GUADALMED. Limnetica 21(2002): 99–114.Google Scholar
  13. Bonada, N., C. Zamora-Muñoz, M. Rieradevall & N. Prat, 2005. Ecological and historical filters constraining spatial caddisfly distribution in Mediterranean rivers. Freshwater Biology 50: 781–797. CrossRefGoogle Scholar
  14. Bonada, N., H. Dallas, M. Rieradevall & N. Prat, 2006a. A comparison of rapid bioassessment protocols used in 2 regions with Mediterranean climates, the Iberian Peninsula and South Africa. Journal of North American Benthological Society 25: 487–500.CrossRefGoogle Scholar
  15. Bonada, N., N. Prat, V. H. Resh & B. Statzner, 2006b. Developments in aquatic insect biomonitoring: a comparative analysis of recent approaches. Annual Review of Entomology 51: 495–523.PubMedCrossRefGoogle Scholar
  16. Bonada, N., M. Rieradevall, N. Prat & V. H. Resh, 2006c. Benthic macroinvertebrate assemblages and macrohabitat connectivity in Mediterranean-climate streams of northern California. Journal of North American Benthological Society 25: 32–43.CrossRefGoogle Scholar
  17. Cao, Y. & D. D. Williams, 1999. Rare species are important in bioassessment (Reply to the comment by Marchant). Limnology and Oceanography 44: 1841–1842.Google Scholar
  18. Cao, Y., D. D. Williams & N. E. Williams, 1998. How important are rare species in aquatic community ecology and bioassessment? Limnology and Oceanography 43: 1403–1409.CrossRefGoogle Scholar
  19. Cao, Y., D. P. Larsen & R. S. J. Thorne, 2001. Rare species in multivariate analysis for bioassessment: some considerations. Journal of North American Benthological Society 20: 144–153.CrossRefGoogle Scholar
  20. Caruso, B. S., 2002. Temporal and spatial patterns of extreme low flows and effects on stream ecosystems in Otago, New Zealand. Journal of Hydrology 257: 115–133. CrossRefGoogle Scholar
  21. Chessman, B. C., I. Growns, J. Curreys & N. Plunkett-Cole, 1999. Predicting diatom communities at the genus level for the rapid biological assessment of rivers. Freshwater Biology 41: 317–331. CrossRefGoogle Scholar
  22. Clarke, R. T., 2000. Uncertainty in estimates of biological quality based on RIVPACS. In Wright, J. F., D. W. Sutcliffe & M. T. Furse (eds), Assessing the Biological Quality of Fresh Waters: RIVPACS and Other Techniques. Freshwater Biological Association, Ambleside, Cumbria: 39–54.Google Scholar
  23. Clarke, R. T. & J. F. Murphy, 2006. Effects of locally rare taxa on the precision and sensitivity of RIVPACS bioassessment of freshwaters. Freshwater Biology 51: 1924–1940.CrossRefGoogle Scholar
  24. Clarke, R. T., M. T. Furse, J. F. Wright & D. Moss, 1996. Derivation of a biological quality index for river sites: comparison of the observed with the expected fauna. Journal of Applied Statistics 23: 311–332.CrossRefGoogle Scholar
  25. Clarke, R. T., J. F. Wright & M. T. Furse, 2003. RIVPACS models for predicting the expected macroinvertebrate fauna and assessing the ecological quality of rivers. Ecological Modelling 160: 219–233.CrossRefGoogle Scholar
  26. Davis, J., P. Horwitz, R. H. Norris, B. C. Chessman, M. McGuire & B. Sommer, 2006. Are river bioassessment methods using macroinvertebrates applicable to wetlands? Hydrobiologia 572: 115–128. CrossRefGoogle Scholar
  27. De Pauw, N., W. Gabriels & P. L. M. Goethals, 2006. River monitoring and assessment methods based on macroinvertebrates. In Ziglio, G., M. Siligardi & G. Flaim (eds), Biological Monitoring of Rivers: Applications and Perspectives. Wiley, Chichester: 113–134.Google Scholar
  28. Dewson, Z. S., A. B. W. James & R. G. Death, 2007. A review of the consequences of decreased flow for instream habitat and macroinvertebrates. Journal of the North American Benthological Society 26: 401–415. CrossRefGoogle Scholar
  29. European Commission, 2000. Directive 2000/60/EC of the European Parliament and the Council of 23rd October 2000 establishing a framework for community action in the field of water policy. Official Journal of the European Communities L327: 1–72.Google Scholar
  30. European Commission, 2003. Common implementation strategy for the water framework directive (2000/60/EC). Guidance Document No. 10, Rivers and lakes—typology, reference conditions and classification systems. Office for official publications of the European Communities, Luxembourg.Google Scholar
  31. Feio, M. J., S. F. P. Almeida, S. C. Craveiro & A. J. Calado, 2007a. Diatoms and macroinvertebrates provide consistent and complementary information on environmental quality. Fundamental and Applied Limnology 168: 247–258.CrossRefGoogle Scholar
  32. Feio, M. J., T. B. Reynoldson, V. Ferreira & M. A. S. Graça, 2007b. A predictive model for freshwater bioassessment (Mondego River, Portugal). Hydrobiologia 589: 55–68.CrossRefGoogle Scholar
  33. Ferréol, M., A. Dohet, H.-M. Cauchie & L. Hoffmann, 2008. An environmental typology of freshwater sites in Luxembourg as a tool for predicting macroinvertebrate fauna under non-polluted conditions. Ecological Modelling 212: 99–108.CrossRefGoogle Scholar
  34. Furse, M. T., D. Moss, J. F. Wright & P. D. Armitage, 1984. The influence of seasonal and taxonomic factors on the ordination and classification of running-water sites in Great Britain and on the prediction of their macro-invertebrate communities. Freshwater Biology 14: 257–280. CrossRefGoogle Scholar
  35. Gasith, A. & V. H. Resh, 1999. Streams in Mediterranean climate regions: abiotic influences and biotic responses to predictable seasonal events. Annual Review of Ecology and Systematics 30: 51–81.CrossRefGoogle Scholar
  36. Gómez, R., I. Hurtado, M. L. Suárez & M. R. Vidal-Abarca, 2005. Ramblas in south-east Spain: threatened and valuable ecosystems. Aquatic Conservation: Marine and Freshwater Ecosystems 15: 387–402. CrossRefGoogle Scholar
  37. Hargett, E. G., J. R. ZumBerge, C. P. Hawkins & J. R. Olson, 2007. Development of a RIVPACS-type predictive model for bioassessment of wadeable streams in Wyoming. Ecological Indicators 7: 807–826.CrossRefGoogle Scholar
  38. Hawkins, C. P., R. H. Norris, J. N. Hogue & J. W. Feminella, 2000. Development and evaluation of predictive models for measuring the biological integrity of streams. Ecological Applications 10: 1456–1477.CrossRefGoogle Scholar
  39. Hering, D., C. K. Feld, O. Moog & T. Ofenböck, 2006. Cook book for the development of a Multimetric Index for biological condition of aquatic ecosystems: experiences from the European AQEM and STAR projects and related initiatives. Hydrobiologia 566: 311–324. CrossRefGoogle Scholar
  40. Ihaka, R. & R. Gentleman, 1996. R: a language for data analysis and graphics. Journal of Computational and Graphical Statistics 5: 239–314. CrossRefGoogle Scholar
  41. Jáimez-Cuellar, P., S. Vivas, N. Bonada, S. Robles, A. Mellado, M. Álvarez, J. Avilés, J. Casas, M. Ortega, I. Pardo, N. Prat, M. Rieradevall, C. E. Sáinz-Cantero, A. Sánchez-Ortega, M. L. Suárez, M. Toro, M. R. Vidal-Abarca, C. Zamora-Muñoz & J. Alba-Tercedor, 2004. Protocolo GUADALMED (PRECE). Limnetica 21(2002): 187–204.Google Scholar
  42. Johnson, R. K. & L. Sandin, 2001. Development of a prediction and classification system for lake (Littoral, SWEPACLLI) and Stream (Riffle, SWEPACSRI) macroinvertebrate communities. Stencil, Department of Environmental Assessment, SLU, Uppsala.Google Scholar
  43. Joy, M. K. & R. G. Death, 2002. Predictive modelling of freshwater fish as a biomonitoring tool in New Zealand. Freshwater Biology 47: 2261–2275. CrossRefGoogle Scholar
  44. Kennard, M. J., B. J. Pusey, A. H. Arthington, B. D. Harch & S. J. Mackay, 2006. Development and application of a predictive model of freshwater fish assemblage composition to evaluate river health in eastern Australia. Hydrobiologia 572: 33–57.CrossRefGoogle Scholar
  45. Kokeš, J., S. Zahrádková, D. Nĕmejcová, J. Hodovský, J. Jarkowský & T. Soldán, 2006. The PERLA system in the Czech Republic: a multivariate approach for assessing the ecological status of running waters. Hydrobiologia 566: 343–354.CrossRefGoogle Scholar
  46. Köppen, W., 1923. De klimate der Erde. Bornträger, Berlin.Google Scholar
  47. Legendre, P. & L. Legendre, 1998. Numerical ecology, 2nd ed. Elsevier, Amsterdam.Google Scholar
  48. Linke, S., R. H. Norris, D. P. Faith & D. Stockwell, 2005. ANNA: a new prediction method for bioassessment programs. Freshwater Biology 50: 147–158.CrossRefGoogle Scholar
  49. Marchant, R., 1999. How important are rare species in aquatic community ecology and bioassessment? A comment on the conclusions of Cao et al. Limnology and Oceanography 44: 1840–1841.Google Scholar
  50. McElravy, E. P., G. A. Lamberti & V. H. Resh, 1989. Year-to-year variation in the aquatic macroinvertebrate fauna of northern California stream. Journal of North American Benthological Society 8: 51–63.CrossRefGoogle Scholar
  51. Moss, D., M. T. Furse, J. F. Wright & P. D. Armitage, 1987. The prediction of the macro-invertebrate fauna of unpolluted running-water sites in Great Britain using environmental data. Freshwater Biology 17: 41–52.CrossRefGoogle Scholar
  52. Moss, D., J. F. Wright, M. T. Furse & R. T. Clarke, 1999. A comparison of alternative techniques for prediction of the fauna of running-water sites in Great Britain. Freshwater Biology 41: 167–181.CrossRefGoogle Scholar
  53. Munné, A. & N. Prat, 2006. Comparing quantitative and qualitative metrics based on macroinvertebrates to measure biological quality and define reference conditions in mediterranean rivers types. In Libro de resumenes del XIII Congreso de la Asociación Española de Limnología—V Congreso Ibérico de Limnología. Asociación Española de Limnología. Barcelona: 61.Google Scholar
  54. Munné, A., N. Prat, C. Solà, N. Bonada & M. Rieradevall, 2003. A simple field method for assessing the ecological quality of riparian habitat in rivers and streams: QBR index. Aquatic Conservation: Marine and Freshwater Ecosystems 13: 147–163.CrossRefGoogle Scholar
  55. Niemi, G. J. & M. E. McDonald, 2004. Application of ecological indicators. Annual Review of Ecology, Evolution and Systematics 35: 89–111. CrossRefGoogle Scholar
  56. Parsons, M. & R. H. Norris, 1996. The effect of habitat specific sampling on biological assessment of water quality using a predictive model. Freshwater Biology 36: 419–434.CrossRefGoogle Scholar
  57. Quinn, G. P. & M. J. Keough, 2002. Experimental Design and Data Analysis for Biologists. Cambridge University Press, Cambridge.Google Scholar
  58. Resh, V. H., J. F. Jackson & E. P. McElravy, 1990. Disturbance, annual variability, and lotic benthos: examples from a California stream influenced by a Mediterranean climate. Memorie dell’Istituto Italiano di Idrobiologia 47: 309–329.Google Scholar
  59. Reynoldson, T. B., R. C. Bailey, K. E. Day & R. H. Norris, 1995. Biological guidelines for freshwater sediment based on Benthic Assessment of Sediment (the BEAST) using a multivariate approach for predicting biological state. Australian Journal of Ecology 20: 198–219. CrossRefGoogle Scholar
  60. Reynoldson, T. B., R. H. Norris, V. H. Resh, K. E. Day & D. M. Rosenberg, 1997. The reference condition: a comparison of multimetric and multivariate approaches to assess water-quality impairment using benthic macroinvertebrates. Journal of North American Benthological Society 16: 833–852. CrossRefGoogle Scholar
  61. Sánchez-Montoya, M. M., T. Puntí, M. L. Suárez, M. R. Vidal-Abarca, M. Rieradevall, J. M. Poquet, C. Zamora-Muñoz, S. Robles, M. Álvarez, J. Alba-Tercedor, M. Toro, A. M. Pujante, A. Munné & N. Prat, 2007. Concordance between ecotypes and macroinvertebrate assemblages in Mediterranean streams. Freshwater Biology 52: 2240–2255.CrossRefGoogle Scholar
  62. Schwarz, G., 1978. Estimating the dimension of a model. Annals of Statistics 6: 461–464.CrossRefGoogle Scholar
  63. Simpson, J. & R. H. Norris, 2000. Biological assessment of water quality: development of AUSRIVAS models and outputs. In Wright, J. F., D. W. Sutcliffe & M. T. Furse (eds), Assessing the Biological Quality of Fresh Waters: RIVPACS and Other Techniques. Freshwater Biological Association, Ambleside, Cumbria: 125–142.Google Scholar
  64. StatSoft, 2005. STATISTICA (data analysis software system), version 7.1.
  65. Statzner, B., S. Dolédec & B. Hugueny, 2004. Biological trait composition of European stream invertebrate communities: assessing the effects of various trait filter types. Ecography 27: 470–488.CrossRefGoogle Scholar
  66. Stoddard, J. L., D. P. Larsen, C. P. Hawkins, R. K. Johnson & R. H. Norris, 2006. Setting expectations for the ecological condition of streams: the concept of reference condition. Ecological Applications 16: 1267–1276.PubMedCrossRefGoogle Scholar
  67. Van Sickle, J., C. P. Hawkins, D. P. Larsen & A. H. Herlihy, 2005. A null model for the expected macroinvertebrate assemblage in streams. Journal of the North American Benthological Society 24: 178–191.CrossRefGoogle Scholar
  68. Van Sickle, J., D. D. Huff & C. P. Hawkins, 2006. Selecting discriminant function models for predicting the expected richness of aquatic macroinvertebrates. Freshwater Biology 51: 359–372.CrossRefGoogle Scholar
  69. Whittingham, M. J., P. A. Stephens, R. B. Bradbury & R. P. Freckleton, 2006. Why do we still use stepwise modelling in ecology and behaviour? Journal of Animal Ecology 75: 1182–1189.PubMedCrossRefGoogle Scholar
  70. Wright, J. F., P. D. Armitage, M. T. Furse & D. Moss, 1984. The classification of sites on British rivers using macroinvertebrates. Verhandlungen International Verein Limnology 22: 1939–1943.Google Scholar
  71. Wright, J. F., M. T. Furse & P. D. Armitage, 1993. RIVPACS—a technique for evaluating the biological quality of rivers in the UK. European Water Pollution Control 3: 15–25.Google Scholar
  72. Wright, J. F., D. W. Sutcliffe & M. T. Furse (eds), 2000. Assessing the Biological Quality of Fresh Waters: RIVPACS and Other Techniques. Freshwater Biological Association, Ambleside.Google Scholar
  73. Zamora-Muñoz, C. & J. Alba-Tercedor, 1996. Bioassessment of organically polluted Spanish rivers, using a biotic index and multivariate methods. Journal of the North American Benthological Society 15: 332–352.CrossRefGoogle Scholar
  74. Zamora-Muñoz, C., C. E. Sáinz-Cantero, A. Sánchez-Ortega & J. Alba-Tercedor, 1995. Are biological indices BMWP’ and ASPT’ and their significance regarding water quality seasonally dependent? Factors explaining their variations. Water Research 29: 285–290. CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • José Manuel Poquet
    • 1
    Email author
  • Javier Alba-Tercedor
    • 1
    Email author
  • Tura Puntí
    • 2
  • Maria del Mar Sánchez-Montoya
    • 3
  • Santiago Robles
    • 4
  • Maruxa Álvarez
    • 5
  • Carmen Zamora-Muñoz
    • 1
  • Carmen Elisa Sáinz-Cantero
    • 1
  • Maria Rosario Vidal-Abarca
    • 3
  • Maria Luisa Suárez
    • 3
  • Manuel Toro
    • 6
  • Ana Maria Pujante
    • 7
  • Maria Rieradevall
    • 2
  • Narcís Prat
    • 2
  1. 1.Departamento de Biología Animal, Facultad de CienciasUniversidad de GranadaGranadaSpain
  2. 2.Departamento de EcologíaUniversidad de BarcelonaBarcelonaSpain
  3. 3.Departamento de Ecología e HidrologíaUniversidad de MurciaMurciaSpain
  4. 4.Cimera Estudios Aplicados SL, Parque Científico de MadridMadridSpain
  5. 5.Área de Ecología Universidad de VigoVigoSpain
  6. 6.División de Ecología de los Sistemas Acuáticos Continentales CEDEXMadridSpain
  7. 7.Red-Control SL, Parque Tecnológico de ValenciaPaternaSpain

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