Open Access
Knowl. Manag. Aquat. Ecosyst.
Number 418, 2017
Article Number 20
Number of page(s) 8
Published online 05 May 2017
  • Armitage PD, Moss D, Wright JF, Furse MT. 1983. The performance of a new biological water quality score system based on macroinvertebrates over a wide range of unpolluted running-water sites. Water Res 17: 333–347. [CrossRef] [Google Scholar]
  • Bennett C, Owen R, Birk S, et al. 2011. Bringing European river quality into line: an exercise to intercalibrate macro-invertebrate classification methods. Hydrobiologia 667: 31–48. [CrossRef] [Google Scholar]
  • Bo T, Doretto A, Laini A, Bona F, Fenoglio S. 2016. Biomonitoring with macroinvertebrate communities in Italy: what happened to our past and what is the future? J Limnol. DOI: 10.4081/jlimnol.2016.1584. [Google Scholar]
  • Buffagni A, Erba S. 2007. Macroinvertebrati acquatici e Direttiva 2000/60/EC (W.F.D.) − Parte A. Metodo di campionamento per i fiumi guadabili. IRSA-CNR Notiziario dei Metodi Analitici 1: 1–27. [Google Scholar]
  • Buffagni A, Erba S, Cazzola M, Kemp JL. 2004. The AQEM multimetric system for the southern Italian Apennines: assessing the impact of water quality and habitat degradation on pool macroinvertebrates in Mediterranean rivers. Hydrobiologia 516: 313–329. [CrossRef] [Google Scholar]
  • Buffagni A, Erba S, Cazzola M, Murray-Bligh J, Soszka H, Genoni P. 2006. The STAR common metrics approach to the WFD intercalibration process: full application for small, lowland rivers in three European countries. Hydrobiologia 566: 379–399. [CrossRef] [Google Scholar]
  • Clarke RT, 2013. Estimating confidence of European WFD ecological status class and WISER Bioassessment Uncertainty Guidance Software (WISERBUGS). Hydrobiologia 704: 39–56. [CrossRef] [Google Scholar]
  • Clarke RT, Davy-Bowker J, Sandin L, Friberg N, Johnson RK, Bis B. 2006a. Estimates and comparisons of the effects of sampling variation using ‘national’ macroinvertebrate sampling protocols on the precision of metrics used to assess ecological status. Hydrobiologia 566: 477–503. [CrossRef] [Google Scholar]
  • Clarke RT, Lorenz A, Sandin L, et al. 2006b. Effects of sampling and sub-sampling variation using the STAR-AQEM sampling protocol on the precision of macroinvertebrate metrics. Hydrobiologia 566: 441–459. [CrossRef] [Google Scholar]
  • Dixon PM. 1993. The bootstrap and the jackknife: describing the precision of ecological indices. In: Scheiner S, Gurevitch J, eds. Design and analysis of ecological experiments. New York: Chapman and Hall, pp. 210–318. [Google Scholar]
  • Doberstein CP, Karr JR, Conquest LL. 2000. The effect of fixed-count subsampling on macroinvertebrate biomonitoring in small streams. Fresh Biol 44: 355–371. [CrossRef] [Google Scholar]
  • Dolph CL, Sheshukov AY, Chizinski CJ, Vondracek B, Wilson B. 2010. The Index of biological integrity and the bootstrap: can random sampling error affect stream impairment decisions? Ecol Indic 10: 527–537. [CrossRef] [Google Scholar]
  • European Union. 2000. Directive 2000/60/EC. Establishing a framework for community action in the field of water policy. Luxemburg: European Commission PE-CONS 3639/1/100 Rev 1. [Google Scholar]
  • Fattorini S. 2005. A simple method to fit geometric series and broken stick models in community ecology and island biogeography. Acta Oecol 28: 199–205. [CrossRef] [Google Scholar]
  • Friberg N. 2014. Impacts and indicators of change in lotic ecosystems. WIREs Water 1: 513–531. [CrossRef] [Google Scholar]
  • Friberg N, Bonada N, Bradley DC, et al. 2011. Biomonitoring of human impacts in freshwater ecosystems: the good, the bad and the ugly. Adv Ecol Res 44: 1–68. [CrossRef] [Google Scholar]
  • Ghetti PF. 1997. Manuale di applicazione. Indice Biotico Esteso (I.B.E.). I macroinvertebrati nel controllo della qualità degli ambienti di acque correnti. Trento: Provincia Autonoma di Trento, 222 p. [Google Scholar]
  • Gotelli NJ, Colwell RK. 2001. Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecol Lett 4: 379–391. [CrossRef] [Google Scholar]
  • Gotelli NJ, Graves GR. 1996. Null models in ecology. Washington, D.C.: Smithsonian Institution Press, 368 p. [Google Scholar]
  • Hering D, Buffagni A, Moog O, et al. 2003. The development of a system to assess the ecological quality of streams based on macroinvertebrates – design of the sampling programme within the AQEM project. Int Rev Hydrobiol 88: 345–361. [CrossRef] [Google Scholar]
  • ISPRA. 2014. Linee Guida per la valutazione della componente macrobentonica fluviale ai sensi del DM 260/2010. ISPRA Manuali e Linee Guida 107/2014. Available from: [Google Scholar]
  • Laini A, Vorti A, Bolpagni R, Viaroli P. 2014. Small-scale variability of benthic macroinvertebrates distribution and its effects on biological monitoring. Ann Limnol - Int J Lim 50: 211–216. [CrossRef] [EDP Sciences] [Google Scholar]
  • Li J, Herlihy A, Gerth W, et al. 2001. Variability in stream macroinvertebrates at multiple spatial scales. Fresh Biol 46: 87–97. [Google Scholar]
  • Lorenz A, Kirchner L, Hering D. 2004. ‘Electronic subsampling’ of macrobenthic samples: how many individuals are needed for a valid assessment result? Hydrobiologia 516: 299–312. [CrossRef] [Google Scholar]
  • Magurran AE. 2004. Measuring biological diversity. Oxford: Blackwell Publishing, 256 p. [Google Scholar]
  • Manly BF. 2006. Randomization, bootstrap and Monte Carlo methods in biology. Boca Raton: Chapman and Hall/CRC, 480 p. [Google Scholar]
  • Ofenböck T, Moog O, Gerritsen J, Barbour M. 2004. A stressor specific multimetric approach for monitoring running waters in Austria using benthic macroinvertebrates. Hydrobiologia 516: 251–268. [CrossRef] [Google Scholar]
  • Ostermiller JD, Hawkins CP. 2004. Effects of sampling error on bioassessments of stream ecosystems: application to RIVPACS-type models. J North Am Benthological Soc 23: 363–382. [CrossRef] [Google Scholar]
  • Pinto P, Rosado J, Morais M, Antunes I. 2004. Assessment methodology for southern siliceous basins in Portugal. Hydrobiologia 516: 191–214. [CrossRef] [Google Scholar]
  • R Development Core Team. 2016. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Available from: [Google Scholar]
  • Ramos-Merchante A, Prenda J. 2017. Macroinvertebrate taxa richness uncertainty and kick sampling in the establishment of Mediterranean rivers ecological status. Ecol Indic 72: 1–12. [CrossRef] [Google Scholar]
  • Shannon CE, Weaver W. 1949. The mathematical theory of communication. Urbana: University of Illinois Press, 144 p. [Google Scholar]
  • Taylor LR. 1984. Assessing and interpreting the spatial distributions of insect populations. Annu Rev Entomol 29: 321–357. [CrossRef] [Google Scholar]
  • Vlek HE. 2004. Comparison of (cost) effectiveness between various macroinvertebrate field and laboratory protocols. European Commission, STAR (Standardisation of river classifications), Deliverable N1, 78 p. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.