Long-term environmental monitoring for assessment of change: measurement inconsistencies over time and potential solutions

  • Kari E. EllingsenEmail author
  • Nigel G. Yoccoz
  • Torkild Tveraa
  • Judi E. Hewitt
  • Simon F. Thrush


The importance of long-term environmental monitoring and research for detecting and understanding changes in ecosystems and human impacts on natural systems is widely acknowledged. Over the last decades, a number of critical components for successful long-term monitoring have been identified. One basic component is quality assurance/quality control protocols to ensure consistency and comparability of data. In Norway, the authorities require environmental monitoring of the impacts of the offshore petroleum industry on the Norwegian continental shelf, and in 1996, a large-scale regional environmental monitoring program was established. As a case study, we used a sub-set of data from this monitoring to explore concepts regarding best practices for long-term environmental monitoring. Specifically, we examined data from physical and chemical sediment samples and benthic macroinvertebrate assemblages from 11 stations from six sampling occasions during the period 1996–2011. Despite the established quality assessment and quality control protocols for this monitoring program, we identified several data challenges, such as missing values and outliers, discrepancies in variable and station names, changes in procedures without calibration, and different taxonomic resolution. Furthermore, we show that the use of different laboratories over time makes it difficult to draw conclusions with regard to some of the observed changes. We offer recommendations to facilitate comparison of data over time. We also present a new procedure to handle different taxonomic resolution, so valuable historical data is not discarded. These topics have a broader relevance and application than for our case study.


Data comparability Long-term monitoring Macrobenthos Oil and gas industry Taxonomic resolution 



KEE was supported by the Norwegian Oil and Gas Association (project no. 20-2013), the Norwegian Environment Agency (project no. 1204110 and 4013045), the Norwegian Research Council (project no. 212135), and Norwegian Institute for Nature Research (NINA). NGY and TT were supported by NINA. We thank one anonymous referee for useful comments on this article.

Supplementary material

10661_2017_6317_MOESM1_ESM.docx (17 kb)
ESM 1 Online Resource 1. Table S1. Consulting companies responsible for fieldwork, identification of taxa, and laboratory analyses. (DOCX 16 kb)
10661_2017_6317_MOESM2_ESM.docx (213 kb)
ESM 2 Online Resource 2. Fig. S1. Non-metric multidimensional scaling (NMDS) using faunal data based on the splitting procedure. Fig. S2. Canonical Correspondence Analysis (CCA) using faunal data based on the splitting procedure. (DOCX 213 kb)


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Kari E. Ellingsen
    • 1
    Email author
  • Nigel G. Yoccoz
    • 1
    • 2
  • Torkild Tveraa
    • 1
  • Judi E. Hewitt
    • 3
  • Simon F. Thrush
    • 4
  1. 1.Fram CentreNorwegian Institute for Nature Research (NINA)TromsøNorway
  2. 2.Department of Arctic and Marine BiologyUiT The Arctic University of NorwayTromsøNorway
  3. 3.National Institute of Water and Atmospheric ResearchHamiltonNew Zealand
  4. 4.Institute of Marine SciencesUniversity of AucklandAucklandNew Zealand

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