1. Biological Monitoring Database in Greece Dr. Yorgos Chatzinikolaou Expert on ecological status assessment and monitoring, Athens, Greece yorgxatzinik@hotmail.com TAIEX Workshop on Establishment of a Biological Monitoring Database ANKARA 27 – 28 January 2016

2. Data Ministry of Environment & Energy, Special Secretariat for Water Hellenic Centre for Marine Research (Rivers, transitional & coastal waters) Greek Biotope /Wetland Centre (Lakes)

3. Biological monitoring in Greece BQEsRiversLakesTransitional watersCoastal waters PhytoplanktonXXX Phytobenthos Macrophytes Macroscopic algaeXX AngiospermsXX MacroinvertebratesXXX Fish(X)

4. Data in Greece Ministry: WBs’ status per QE & necessary info (data in reports) Hydromor phological data Biological data Chemical data

5. BQE data Rivers Transitional waters Coastal waters Lakes

6. Rivers (Wise: http://dd.eionet.europa.eu/GetSchema?id=DST2907) Station info(station info #2)Data aggregationClassification system CountryCode NationalStationIDWaterCategory NationalStationNameNationalStationIDDeterminandBiology WFDstationWaterbodyTypeNCSBQE WFD_EU_CDRepresentativeStationWaterbodyTypeICImpactBio RiverNameReferenceStationYearMetricName WaterCategoryImpactStationAggregationPeriodSamplingMethodBio WaterBodyIDLargestStationAggregationMonthsAnalysisMethodBio WaterBodyNameFluxStationAggregationLengthWaterbodyTypeNCS CatchmentNameGeologyDeterminandBiologyHMWB RBD-codeWaterColourAverageUnitBiologyArtificialWB RBDnameWaterColourLevelBQEReferenceCondition RegionAlkalinityAverageMetricScaleHG_Boundary SeaAreaNameAlkalinityLevelDeterminandStatusClassGM_Boundary SeaRegionNameHMWBNumberOfSamplesMP_Boundary SeaConventionAreaArtificialWBMinValuePB_Boundary LongitudePurposeMeanValueIntercalibratedDeterminandBiology LatitudeLengthFromSourceMaxValueIntercalibratedClassBoundaries CatchmentAreaRiverDischargeMedianValueIntercalibratedWaterbodyType AltitudeSubsiteLocationStDevValueRemarks

7. Lakes (Wise: http://dd.eionet.europa.eu/GetSchema?id=DST2907) Station info(station info #2)Data aggregationClassification system CountryCodeRepresentativeStationCountryCode NationalStationIDReferenceStationWaterCategory NationalStationNameImpactStationNationalStationIDDeterminandBiology WFDstationLargestStationWaterbodyTypeNCSBQE WFD_EU_CDGeologyWaterbodyTypeICImpactBio LakeNameWaterColourAverageYearMetricName WaterCategoryWaterColourLevelAggregationPeriodSamplingMethodBio WaterBodyIDAlkalinityAverageAggregationMonthsAnalysisMethodBio WaterBodyNameAlkalinityLevelAggregationLengthWaterbodyTypeNCS CatchmentNameHMWBDeterminandBiologyHMWB RBD-codeArtificialWBUnitBiologyArtificialWB RBDnamePurposeBQEReferenceCondition RegionLocationMetricScaleHG_Boundary LongitudeSurfaceAreaDeterminandStatusClassGM_Boundary LatitudeMeanDepthNumberOfSamplesMP_Boundary CatchmentAreaMaximumDepthMinValuePB_Boundary AltitudeResidenceTimeMeanValueIntercalibratedDeterminandBiology VolumeMaxValueIntercalibratedClassBoundaries ReservoirMedianValueIntercalibratedWaterbodyType SamplingDepthStDevValueRemarks TotalDepth NoOfSubsites MethodSubsitesSpatialAggreg Remarks

8. Transitional & Coastal waters (Wise: http://dd.eionet.europa.eu/GetSchema?id=DST3114) Station infoData aggregationClassification systemWaterbody aggregation CountryCode NationalStationIDWaterCategory NationalStationNameNationalStationIDDeterminandBiologyWaterBodyID WaterCategoryWaterbodyTypeNCS_TCwatersSubstrateLongitude WaterBodyIDWaterbodyTypeIC_TCwatersBQELatitude WaterBodyNameYearMetricNameYear WaterbodyTypeNCS_TCwatersSubstrateSamplingMethodBioUnitBiology WaterbodyTypeIC_TCwatersDeterminandBiologyWaterbodyTypeNCS_TCwatersDeterminandStatusClass RBD-codeUnitBiologyWaterbodyTypeIC_TCwatersNumberOfSamples RBDnameBQEReferenceConditionNoOfSubsites RegionDeterminandStatusClassHG_BoundaryEQR_Mean SubregionNumberOfSamplesGM_BoundaryStDevValue LongitudeMeanValueMP_BoundarySamplingTimes LatitudeStDevValuePB_BoundaryMethodAggregation RepresentativeStationSamplingTimesRemarksSurfaceAreaWB ReferenceStationMethodAggregationConfindenceClassification ImpactStationConfindenceClassificationRemarks SurfaceAreaWBRemarks SamplingDepth ConfindenceClassification Remarks

9. Rivers: Protocol Database Input Datasheet

10. Rivers: benthic macroinvertebrates Protocol

11. Rivers: benthic macroinvertebrates Database (MS Access)

12. Rivers: benthic macroinvertebrates Input Datasheet

13. Rivers: fish Protocol

14. Rivers: fish Database (Oracle, Java)

15. Rivers: fish Input Datasheet

16. Decentralized vs. centralized db 3 cases: 1.Many db in different institutions vs. many db in one institution/agency (e.g. MoFWA) 2.Many db in different institutions vs. one db in one institution 3.Many db vs. one db in one institution 4.One db in different institutions vs. one db in one institution

17. 3.Many db vs. one db in one institution/agency (e.g. MoFWA) Local orchestrated decentralized vs. one centralized db Performance improvements in terms of increased throughput and scalability and lower response time Easier to handle modifications over individual output per BQE (ref. conditions, index, classes) Higher specification over data (e.g. new species) Incorrect design of a decentralized system can lead to potential deadlock or non- optimal usage of system resources, higher risk. Data integrity is not guaranteed and data redundancy is a risk. Maintaining of data in a single db offers as accurate and as consistent as possible and enhances data reliability. In general centralized db offer bigger data security In case a single record or a group of records are lost recovery is more difficult

18. 1.Many db in different institutions vs. many db in one institution Various orchestrated decentralized vs. local decentralized db Data acquisition connected with data inputs and facilitation of sampling errors’ correction Easier to handle modifications by experts over individual output per BQE (ref. conditions, index, classes) Authority difficulties over data and outcomes Effort to maintain an increased level of communication between local db administrators and the various db in the final competent institution In case a geographical allocation occurs for the same BQE, problems of integrity and interpretation may occur, higher risk

19. 2.Various located db vs. one db in one institution/agency (e.g. MoFWA) Various orchestrated decentralized vs. one centralized db Centralised databases are highly dependent on network connectivity. The slower the internet connection is, the longer the database access time needed will be for the data acquisition personnel to entry Easier to handle modifications over individual output per BQE (ref. conditions, index, classes, status) Higher specification over data by experts (e.g. new species) Additional complexity to the system in terms of error recovery and fault handling Incorrect design of a decentralized system can lead to potential deadlock or non- optimal usage of system resources, higher risk Data integrity is not guaranteed and data redundancy is a risk. Maintaining of data in a single db offers as accurate and as consistent as possible and enhances data reliability. In case a single record or a group of records are lost recovery is more difficult

20. Decision drivers 1.Data and info authority 2.Data acquisition and stage of implemantation 3.Database outcome 4.Data specification and personnel placement 5.Overall data and db outcome quality control

21. Biological Monitoring Database in Greece Dr. Yorgos Chatzinikolaou Expert on ecological status assessment and monitoring, Athens, Greece yorgxatzinik@hotmail.com TAIEX Workshop on Establishment of a Biological Monitoring Database ANKARA 27 – 28 January 2016