|Optimizing monitoring programs: a case study based on the OSPAR eutrophication assessment for UK waters|García-García, L.M.; Sivyer, D.; Devlin, M.; Painting, S.; Collingridge, K.; van der Molen, J. (2019). Optimizing monitoring programs: a case study based on the OSPAR eutrophication assessment for UK waters. Front. Mar. Sci. 5: 503. https://doi.org/10.3389/fmars.2018.00503
In: Frontiers in Marine Science. Frontiers Media: Lausanne. ISSN 2296-7745, meer
nutrients; chlorophyll; eutrophication; assessment; OSPAR; optimization; monitoring
|Auteurs|| || Top |
- García-García, L.M.
- Sivyer, D.
- Devlin, M.
- Painting, S.
- Collingridge, K., meer
- van der Molen, J., meer
The data and results of the UK second application of the OSPAR Common Procedure (COMP) for eutrophication were used as a case study to develop a generic system (i) to evaluate an observational network from a multi-variable point of view, (ii) to introduce additional datasets in the assessment, and (iii) to propose an optimized monitoring program to help reduce monitoring costs. The method consisted of tools to analyse, by means of simple statistical techniques, if any reduction of the available datasets could provide results comparable with the published assessments, and support a reduced monitoring program (and limited loss in confidence). The data reduction scenarios included the removal of an existing dataset or the inclusion of freely available third-party data (FerryBox, satellite observations) with existing datasets. Merging different datasets was problematic due to the heterogeneity of the techniques, sensors and scales, and a cross validation was carried out to assess possible biases between the different datasets. The results showed that there was little margin to remove any of the available datasets and that the use of extensive datasets, such as satellite data, has an important effect, often leading to a change in assessment results with respect to the thresholds, generally moving from threshold exceedance to non-exceedance. This suggested that the results of the original assessment might be biased toward sampling location and time and emphasized the importance of monitoring programmes providing better coverage over large spatial and temporal scales, and the opportunity to improve assessments by combining observations, satellite data, and model results.