, Volume 742, Issue 1, pp 153–167 | Cite as

Predicting the constraint effect of environmental characteristics on macroinvertebrate density and diversity using quantile regression mixed model

  • Riccardo Fornaroli
  • R. Cabrini
  • L. Sartori
  • F. Marazzi
  • D. Vracevic
  • V. Mezzanotte
  • M. Annala
  • S. Canobbio
Primary Research Paper


Various factors, such as habitat availability, competition for space, predation, temperature, nutrient supplies, presence of waterfalls, flow variability and water quality, control the abundance, distribution and productivity of stream-dwelling organisms. Each of these factors can influence the response of the density of organisms to a specific environmental gradient, inflating variability and making difficult to understand the possible causal relationship. In our study, we used quantile regression mixed models and Akaike’s information criterion as an indicator of goodness to examine two different datasets, one belonging to Italy and one belonging to Finland, and to detect the limiting action of selected environmental variables. In the Italian dataset, we studied the relationships among five macroinvertebrate families and three physical habitat characteristics (water velocity, depth and substratum size); in the Finnish dataset the relationships between taxa richness and 16 environmental characteristics (chemical and physical). We found limiting relationships in both datasets and validated all of them on different datasets. These relationships are quantitative and can be used to predict the range of macroinvertebrate densities or taxa richness as a function of environmental characteristics. They can be a tool for management purposes, providing the basis for habitat-based models and for the development of ecological indices.


Habitat availability Limiting action Quantile regression Linear quantile mixed model Density–environment relationships pH 



We are grateful to Anna Brusadelli for the help in macroinvertebrate identification and to Brian S. Cade for his helpful comments on an earlier version of the manuscript, in particular for the important tips about the use of quantile regression and AICc. We thank Timo Muotka and Heikki Mykrä for lending us their data. We also thank two anonymous referees for their constructive comments on a previous version of the manuscript.

Supplementary material (35 kb)
Compressed file containing commented R codes and the data required to replicate the analysis (ZIP 36 kb)


  1. Allen, D. C. & C. C. Vaughn, 2010. Complex hydraulic and substrate variables limit freshwater mussel species richness and abundance. Journal of the North American Benthological Society 29: 383–394.CrossRefGoogle Scholar
  2. Annala, M., H. Mykrä, M. Tolkkinen, T. Kauppila, & T. Muotka, in press. Are biological communities in naturally unproductive streams resistant to additional anthropogenic stressors? Ecological applications [].
  3. AQEM Consortium, 2002. Manual for the application of the AQEM system, Version 1.0. “The Development and Testing of an Integrated Assessment System for the Ecological Quality of Streams and Rivers throughout Europe using Benthic Macroinvertebrates”.Google Scholar
  4. Arthur, J. W., J. A. Zischke & G. L. Ericksen, 1982. Effect of elevated water temperature on macroinvertebrate communities in outdoor experimental channels. Water Research 16: 1465–1477.CrossRefGoogle Scholar
  5. Åström, M., E. K. Aaltonen & J. Koivusaari, 2001. Effect of ditching operations on stream-water chemistry in a boreal forested catchment. The Science of the total environment 279: 117–129.PubMedCrossRefGoogle Scholar
  6. Austin, M., 2007. Species distribution models and ecological theory: a critical assessment and some possible new approaches. Ecological Modelling 200: 1–19.CrossRefGoogle Scholar
  7. Ayllón, D., A. Almodóvar, G. G. Nicola & B. Elvira, 2010. Modelling brown trout spatial requirements through physical habitat simulations. River Research and Applications 26: 1090–1102.CrossRefGoogle Scholar
  8. Bolker, B. M., M. E. Brooks, C. J. Clark, S. W. Geange, J. R. Poulsen, M. H. H. Stevens & J.-S. S. White, 2009. Generalized linear mixed models: a practical guide for ecology and evolution. Trends in ecology & evolution 24: 127–135.CrossRefGoogle Scholar
  9. Burnham, K. P. & D. Anderson, 2002. Model Selection and Multi-Model Inference. Springer, New York.Google Scholar
  10. Cabrini, R., S. Canobbio, L. Sartori, R. Fornaroli & V. Mezzanotte, 2013. Leaf packs in impaired streams: the influence of leaf type and environmental gradients on breakdown rate and invertebrate assemblage composition. Water, Air, & Soil Pollution 224: 1697.CrossRefGoogle Scholar
  11. Cade, B. S. & B. Noon, 2003. A gentle introduction to quantile regression for ecologists. Frontiers in Ecology and the Environment 1: 412–420.CrossRefGoogle Scholar
  12. Cade, B. S., J. W. Terrell & R. L. Schroeder, 1999. Estimating effects of limiting factors with regression quantiles. Ecology 80: 311–323.CrossRefGoogle Scholar
  13. Calizza, E., M. L. Costantini, D. Rossi, P. Carlino & L. Rossi, 2012. Effects of disturbance on an urban river food web. Freshwater Biology 57: 2613–2628.CrossRefGoogle Scholar
  14. Campbell, R. E. & A. R. McIntosh, 2013. Do isolation and local habitat jointly limit the structure of stream invertebrate assemblages? Freshwater Biology 58: 128–141.CrossRefGoogle Scholar
  15. Canobbio, S., V. Mezzanotte, F. Benvenuto & M. Siotto, 2010. Determination of Serio River (Lombardy, Italy) ecosystem dynamics using macroinvertebrate functional traits. Italian Journal of Zoology 77: 227–240.CrossRefGoogle Scholar
  16. Canobbio, S., A. Azzellino, R. Cabrini & V. Mezzanotte, 2013. A multivariate approach to assess habitat integrity in urban streams using benthic macroinvertebrate metrics. Water Science and Technology 67: 2832–2837.PubMedCrossRefGoogle Scholar
  17. 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
  18. Davies, J. & A. Boulton, 2009. Great house, poor food: effects of exotic leaf litter on shredder densities and caddisfly growth in 6 subtropical Australian streams. Journal of the North American Benthological Society 28: 491–503.CrossRefGoogle Scholar
  19. Doll, J. C., 2011. Predicting biological impairment from habitat assessments. Environmental monitoring and assessment 182: 259–277.PubMedCrossRefGoogle Scholar
  20. Downes, B., 2010. Back to the future: little-used tools and principles of scientific inference can help disentangle effects of multiple stressors on freshwater ecosystems. Freshwater Biology 55: 60–79.CrossRefGoogle Scholar
  21. Fanny, C., A. Virginie, F. Jean-François, B. Jonathan, R. Marie-Claude & D. Simon, 2013. Benthic indicators of sediment quality associated with run-of-river reservoirs. Hydrobiologia 703: 149–164.CrossRefGoogle Scholar
  22. Folk, R. L., 1974. Petrology of Sedimentary Rocks. Hemphill Publishing, Austin.Google Scholar
  23. Geraci, M., 2014. Linear quantile mixed models: the lqmm package for Laplace quantile regression. Journal of Statistical Software 57: 1–29.Google Scholar
  24. Geraci, M. & M. Bottai, 2007. Quantile regression for longitudinal data using the asymmetric Laplace distribution. Biostatistics 8: 140–154.PubMedCrossRefGoogle Scholar
  25. Geraci, M. & M. Bottai, 2014. Linear quantile mixed models. Statistics and Computing 24: 461–479.CrossRefGoogle Scholar
  26. Gordon, N. D., T. H. McMahon, B. L. Finlayson, C. J. Gippel & R. J. Nathan, 2004. Stream Hydrology. Wiley, Chichester.Google Scholar
  27. Gore, J. A., 1978. A technique for predicting in-stream flow requirements of benthic macroinvertebrates. Freshwater Biology 8: 141–151.CrossRefGoogle Scholar
  28. Gore, J. A., J. B. Layzer & J. Mead, 2001. Macroinvertebrate instream flow studies after 20 years: a role in stream management and restoration. Regulated Rivers: Research & Management 17: 527–542.CrossRefGoogle Scholar
  29. Gore, J., J. King & K. Hamman, 1991. Application of the instream flow incremental methodology to southern African rivers: protecting endemic fish of the Olifants River. Water SA 17: 225–236.Google Scholar
  30. Grueber, C. E., S. Nakagawa, R. J. Laws & I. G. Jamieson, 2011. Multimodel inference in ecology and evolution: challenges and solutions. Journal of evolutionary biology 24: 699–711.PubMedCrossRefGoogle Scholar
  31. Hansen, J. & D. Hayes, 2012. Long-term implications of dam removal for macroinvertebrate communities in Michigan and Wisconsin Rivers, United States. River Research and Applications 28: 1540–1550.CrossRefGoogle Scholar
  32. Hart, D. D. & C. M. Finelli, 1999. Physical-biological coupling in streams: the pervasive effects of flow on benthic organisms. Annual Review of Ecology and Systematics 30: 363–395.CrossRefGoogle Scholar
  33. Hawkins, C. P., Y. Cao & B. Roper, 2010. Method of predicting reference condition biota affects the performance and interpretation of ecological indices. Freshwater Biology 55: 1066–1085.CrossRefGoogle Scholar
  34. Heino, J. & H. Mykrä, 2006. Assessing physical surrogates for biodiversity: do tributary and stream type classifications reflect macroinvertebrate assemblage diversity in running waters? Biological Conservation 129: 418–426.CrossRefGoogle Scholar
  35. Heino, J., T. Muotka & R. Paavola, 2003. Determinants of macroinvertebrate diversity in headwater streams: regional and local influences. Journal of Animal Ecology 72: 425–434.CrossRefGoogle Scholar
  36. Henning, K., H. Estrup & H. Schröder, 2005. Rejecting the mean: estimating the response of fen plant species to environmental factors by non-linear quantile regression. Journal of Vegetation Science 16: 373–382.CrossRefGoogle Scholar
  37. Holden, J., P. J. Chapman & J. C. Labadz, 2004. Artificial drainage of peatlands: hydrological and hydrochemical process and wetland restoration. Progress in Physical Geography 28: 95–123.CrossRefGoogle Scholar
  38. Johnson, J. B. & K. S. Omland, 2004. Model selection in ecology and evolution. Trends in ecology & evolution 19: 101–108.CrossRefGoogle Scholar
  39. Jowett, I. G., 1997. Instream flow methods: a comparison of approaches. Regulated Rivers: Research & Management 13: 115–127.CrossRefGoogle Scholar
  40. Kail, J., J. Arle & S. Jähnig, 2012. Limiting factors and thresholds for macroinvertebrate assemblages in European rivers: empirical evidence from three datasets on water quality, catchment urbanization, and river restoration. Ecological Indicators 18: 63–72.CrossRefGoogle Scholar
  41. Koenker, R., 2013. quantreg: quantile regression. R package version 4: 98.Google Scholar
  42. Koenker, R. & G. Bassett, 1978. Regression quantiles. Econometrica 46: 33–50.CrossRefGoogle Scholar
  43. Lacan, I., V. Resh & J. R. McBride, 2010. Similar breakdown rates and benthic macroinvertebrate assemblages on native and Eucalyptus globulus leaf litter in Californian streams. Freshwater Biology 55: 739–752.CrossRefGoogle Scholar
  44. Lancaster, J. & L. Belyea, 2006. Defining the limits to local density: alternative views of abundance–environment relationships. Freshwater Biology 51: 783–796.CrossRefGoogle Scholar
  45. Lancaster, J. & B. J. Downes, 2010. Linking the hydraulic world of individual organisms to ecological processes: putting ecology into ecohydraulics. River Research and Applications 26: 385–403.CrossRefGoogle Scholar
  46. Lessard, J. & D. Hayes, 2003. Effects of elevated water temperature on fish and macroinvertebrate communities below small dams. River research and applications 19: 721–732.CrossRefGoogle Scholar
  47. Lytle, D. & N. Poff, 2004. Adaptation to natural flow regimes. Trends in Ecology & Evolution 19: 94–100.CrossRefGoogle Scholar
  48. Maddock, I., 1999. The importance of physical habitat assessment for evaluating river health. Freshwater biology 41: 373–391.CrossRefGoogle Scholar
  49. Mäki-Petäys, A., T. Muotka, A. Huusko, P. Tikkanen & P. Kreivi, 1997. Seasonal changes in habitat use and preference by juvenile brown trout, Salmo trutta, in a northern boreal river. Canadian Journal of Fisheries and Aquatic Sciences 54: 520–530.Google Scholar
  50. Morrissey, C. A., A. Boldt, A. Mapstone, J. Newton & S. J. Ormerod, 2013. Stable isotopes as indicators of wastewater effects on the macroinvertebrates of urban rivers. Hydrobiologia 700: 231–244.CrossRefGoogle Scholar
  51. Ostermiller, J. & C. Hawkins, 2004. Effects of sampling error on bioassessments of stream ecosystems: application to RIVPACS-type models. Journal of the North American Benthological Society 23: 363–382.CrossRefGoogle Scholar
  52. Petrin, Z., 2011. Species traits predict assembly of mayfly and stonefly communities along pH gradients. Oecologia 167: 513–524.PubMedCrossRefGoogle Scholar
  53. Petrin, Z., H. Laudon & B. Malmqvist, 2007a. Does freshwater macroinvertebrate diversity along a pH-gradient reflect adaptation to low pH? Freshwater Biology 52: 2172–2183.CrossRefGoogle Scholar
  54. Petrin, Z., B. McKie, I. Buffam, H. Laudon & B. Malmqvist, 2007b. Landscape-controlled chemistry variation affects communities and ecosystem function in headwater streams. Canadian Journal of Fisheries and Aquatic Sciences 64: 1563–1572.CrossRefGoogle Scholar
  55. Poff, N., J. Allan & M. Bain, 1997. The natural flow regime. BioScience 47: 769–784.CrossRefGoogle Scholar
  56. Power, M. E., R. J. Stout, C. E. Cushing, P. Harper, F. R. Hauer, W. J. Matthews, P. B. Moyle, B. Statzner & I. R. Wais De Bagden, 1988. Biotic and abiotic controls in river and stream communities. Journal of the North American Benthological Society 7: 456–479.CrossRefGoogle Scholar
  57. R Core Team, 2013. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna.Google Scholar
  58. Reid, D. J., J. M. Quinn & A. E. Wright-Stow, 2010. Responses of stream macroinvertebrate communities to progressive forest harvesting: influences of harvest intensity, stream size and riparian buffers. Forest Ecology and Management 260: 1804–1815.CrossRefGoogle Scholar
  59. Robinson, C. T., 2012. Long-term changes in community assembly, resistance, and resilience following experimental floods. Ecological Applications 22: 1949–1961.PubMedCrossRefGoogle Scholar
  60. Rosenfeld, J. & R. Ptolemy, 2012. Modelling available habitat versus available energy flux: do PHABSIM applications that neglect prey abundance underestimate optimal flows for juvenile salmonids? Canadian Journal of Fisheries and Aquatic Sciences 69: 1920–1934.CrossRefGoogle Scholar
  61. Sandin, L. & R. K. Johnson, 2004. Local, landscape and regional factors structuring benthic macroinvertebrate assemblages in Swedish streams. Landscape Ecology 19: 501–514.CrossRefGoogle Scholar
  62. Schmidt, T. S., W. H. Clements & B. S. Cade, 2012. Estimating risks to aquatic life using quantile regression. Freshwater Science 31: 709–723.CrossRefGoogle Scholar
  63. Schooley, R. & J. Wiens, 2005. Spatial ecology of cactus bugs: area constraints and patch connectivity. Ecology 86: 1627–1639.CrossRefGoogle Scholar
  64. Statzner, B. & B. Higler, 1986. Stream hydraulics as a major determinant of benthic invertebrate zonation patterns. Freshwater Biology 16: 127–139.CrossRefGoogle Scholar
  65. Statzner, B., K. Hoppenhaus, M.-F. Arens & P. Richoux, 1997. Reproductive traits, habitat use and templet theory: a synthesis of world-wide data on aquatic insects. Freshwater Biology 38: 109–135.CrossRefGoogle Scholar
  66. Tachet, H., P. Richoux, M. Bournaud & P. Usseglio-Polatera, 2000. Invertébrés d’eau douce. CNRS Editions, Paris.Google Scholar
  67. Townsend, C. R., S. Dolédec & M. R. Scarsbrook, 1997. Species traits in relation to temporal and spatial heterogeneity in streams: a test of habitat templet theory. Freshwater Biology 37: 367–387.CrossRefGoogle Scholar
  68. Wagenhoff, A., C. R. Townsend & C. D. Matthaei, 2012. Macroinvertebrate responses along broad stressor gradients of deposited fine sediment and dissolved nutrients: a stream mesocosm experiment. Journal of Applied Ecology 49: 892–902.CrossRefGoogle Scholar
  69. Wright, J., 1995. Development and use of a system for predicting the macroinvertebrate fauna in flowing waters. Australian Journal of Ecology 20: 181–197.CrossRefGoogle Scholar
  70. Wright, J. F., 1992. Spatial and temporal occurrence of invertebrates in a chalk stream, Berkshire, England. Hydrobiologia 248: 11–30.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Riccardo Fornaroli
    • 1
  • R. Cabrini
    • 1
  • L. Sartori
    • 1
  • F. Marazzi
    • 1
  • D. Vracevic
    • 1
  • V. Mezzanotte
    • 1
  • M. Annala
    • 2
  • S. Canobbio
    • 1
  1. 1.DISATUniversità degli Studi di Milano-BicoccaMilanItaly
  2. 2.Department of BiologyUniversity of OuluOuluFinland

Personalised recommendations