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MarineBase - Progress on European level -

MarineBase - Progress on European level -. Research leader Kari Nygaard, Department of Marine Ecology, Norwegian Institute for Water Research, NIVA, Norway. Data collection. Data has been collected both from marine conventions and from countries directly

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MarineBase - Progress on European level -

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  1. MarineBase - Progress on European level - Research leader Kari Nygaard,Department of Marine Ecology,Norwegian Institute for Water Research, NIVA,Norway

  2. Data collection • Data has been collected both from marine conventions and from countries directly • The following marine conventions has supplied data: • MEDPOL • HELCOM • OSPAR

  3. Harmful substances in biota - number of sampled stations per year - all species and components

  4. Data on HS - Sediments

  5. Data on nutrients, chlorophyll and oxygen

  6. Input data - HELCOM

  7. Input data - OSPAR • Input data from OSPAR is only available for aggregated geographic areas • Means no coordinates available • => Huge job to adapt it to GIS, unless this is already being done within the convention • => Necessary step forward

  8. Procedures for collection of data Conventions • Agreement with the conventions for data deliverance by station was agreed in January 2002 • Stations for nutrients, chlorophyll and oxygen were chosen either by countries or additionally by ICES based on frequency and multinational sampling • Convenient as dataformates are harmonised • Strong Quality Control of data

  9. Procedures for collection of data Country data • Direct deliverance from countries useful when it is not possible to canalise the data through the conventions • For country data, there is a need to harmonise reporting formats as this has turned out to be very variable and thereby extremely timeconsuming to manage • A web portal for reporting should therefore be established ASAP • Procedures for Quality Control

  10. Major challenges • Regularity of data reporting - time and countries • Good procedures for reporting data • Long term agreement with conventions • Web based reporting for countries (direct reporting) • Representative stations and identification of representative area for input data (also including geographic coordinates) • How to handle biology data - e.g. WFD

  11. Examples from assessment of data

  12. Qualitative information • Due to a lack of data and a relative small number of time series for the blue mussel in the data set for the North East Atlantic the results of trend detection as presented in Figure 1 are the best possible • The time series are rather short and often not complete • Due to the restricted geographical and time coverage, the conclusions drawn from these results on a European scale should be considered tentative

  13. Strength and weakness at data level (hazardous substances) • Atlantic/North Sea: OSPAR data is yearly updated • Baltic Sea: no ICES data in MARINEBASE on HS (only country data) • Mediterranean: MEDPOL data in MARINEBASE are old (newer data delivered by a few countries)

  14. Reliability, accuracy, robustness, uncertainty (at data level) • - Is highly dependent on completeness and length of time series • The concentrations of hazardous substances in organisms may be sensitive to seasonal variations, age and sex of the organism (AMAP, 2000) • Also the sampling and analysing procedure may affect the results • Due to relatively long half-life times of some hazardous substances in organisms, long time series may be needed to determine clear trends

  15. Further work required (for data level and indicator level) • Effort has to be put into getting comparable data on concentrations of hazardous substances in organisms from all European regional seas • Increased monitoring of PCB7 but also of other hazardous substances at country level will improve the geographical and time coverage of the data set • At the European level a monitoring strategy should be developed leading to standardised and harmonised sampling and analysing procedures and yearly reporting (on sampling conditions and the representativeness of the samples) (ICES, 2000).

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