Adjusting the effect of seasonal variability in the bioassessment of streams

  • Sónia R. Q. Serra
  • Ana Raquel Calapez
  • Amaia Pérez-Bilbao
  • Maria João Feio


Bioassessment tools should distinguish between the effects of anthropogenic degradation in communities and natural temporal changes. The present study tests the influence of natural seasonal variability on macroinvertebrate stream communities assessed by a predictive model (PORTRIV) and a multimetric index (IPtI) calibrated for spring. The scores of PORTRIV decreased significantly between spring and autumn, and between spring and winter (ca. 37 to 53 %, respectively), while those of IPtI did not change significantly between seasons. For non-reference samples, the results of the predictive model also indicate no significant differences. A correction factor (CF) was calculated to adjust the existing differences in the model assessments between seasons, based on the percentage of variation of reference site scores from spring to autumn and winter. After the application of the CF to the OE50 scores of spring reference samples, the differences were no longer significant. Independent reference validation sites confirmed this tendency. This method has the advantage of avoiding large efforts required for the construction of databases from other seasons and the development of new models to allow the assessment of streams in seasons other than spring. Further tests with models developed in regions with more marked seasonal changes should be done to confirm its wider applicability.


Streams Rivers Invertebrates Predictive model Multimetric index Seasonality 



This study was possible by funding from the Fundação para a Ciência e Tecnologia to the AQUAWEB project (PTDC/AACAMB/105297/2008). We acknowledge the Institute of Water (INAG IP) and all teams involved in the collection of data for the use of the national database and the software AMIIB@ (2011 Beta version. Application for calculating benthic invertebrate metrics and indices), and Dr. John Van Sickle to make the “best subsets” R scripts available that were adapted here for model building and under his additional clarifications.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Sónia R. Q. Serra
    • 1
  • Ana Raquel Calapez
    • 1
  • Amaia Pérez-Bilbao
    • 1
  • Maria João Feio
    • 1
  1. 1.IMAR–CMA, Marine and Environmental Research Centre, A/c Department of Life Sciences, Faculty of Science and TechnologyUniversity of CoimbraCoimbraPortugal

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