©Ferdi Rizkiyanto©Ferdi Rizkiyanto©Ferdi Rizkiyanto
Marine Litter Baselines in Europe
Anna M Addamo, Georg Hanke, MSFD-Technical Group of Marine Litter*European Commission Joint Research Centre (EC-JRC, Italy)
6th International Marine Debris ConferenceSan Diego (USA), 12-16 March 2018
*MSFD-TGML
Data science is the core of policy support
Quantitative baselines of litter are needed in order to provide comparable assessments and to monitor the progress of litter-reduction measures.
The “first-ever” pan-European dataset on ML
The 1st ever pan-European dataset on marine beach litter that allows comparison and data analysis in support of policy decisions and prioritization across Europe
• Member States
• Regional Sea Conventions
• Research
• NGOs
• EU Policymakers
Baseline Concept
DEFINITION*
A starting point that provides a first large-scale comprehensivecharacterization of marine litter in a specific year and location.
*MSFD-TGML
It is against which to monitor, measure and assess progress and effectiveness during and after the implementation of measures or plan once is completed.
Baseline Concept
SETTINGS*
ML Baseline requires data with sufficient:
• spatial coverage and resolution
• temporal coverage (in duration and frequency)
• appropriate quality (“fit-for-purpose”)
*MSFD-TGML
Baseline Concept
COLLABORATIVE APPROACH*
1. Creation of ML data availability overview
*MSFD-TGML
2. Identification of priority test scenario(s)
3. Identification of options for baseline calculation
4. Testing scenarios with different time coverage/spatial aggregation
5. Discussion and identification of common principles for the baseline setting
All-inclusive data sources
• Member States (23 MS out of 28)- National monitoring programmes
• Research projects (e.g. DeFishGear)
• NGOs (e.g. Legambiente)
• Citizen Science (e.g. MarineLitterWatch)
• Regional Sea Conventions (OSPAR, Barcelona, HELCOM, Bucharest)- Regional monitoring programmes
Spatial - Temporal Scale
• Timing of baseline setting (at/from-to)
• Spatial aggregation of data (distribution/measures)
At which level should we work?
The scale for baseline depends on the compliance with reduction goals and efficiency of measure
Scenario Testing – Proposal*
Beach, macro litter, all items (individual/groups), 2012-2016, regional, sub-regional, national
*MSFD-TGML
• Countries: 21 MS out of 23
• Beaches: 369 locations
• Surveys: 3831 transects
• 5-Years (4 seasons): 2012-2016
Data analysis: metadata
• Marine Areas: 4 regions
• Sampling effort: + beaches
• Total ML: > 1.200.000 items
• Abundance: ~ 300 items/100m
Data analysis: preliminary outcomes
No. Surveys
No. Beaches
Data compilation: hindrances
• Sampling method heterogeneity: e.g. variable transect lengths (50m – 3km)
• Data template heterogeneity: 19 different formats
Harmonization Transect 100m
Harmonization Data Format
approach
In collaboration with
Data compilation: hindrances
• Spatio-temporal heterogeneity: e.g. variable No. of surveys by season/country
approach
No lower spatio-temporal scale
In collaboration with
Data compilation: hindrances
• Code Mapping: 3 code lists (TGML, OSPAR, UNEMAP) –Not always 1:1 correspondance
approach
Harmonization code - categories/subcategories
In collaboration with
CASE: Top Marine Beach Litter Items in EU
• Countries: 17 MS out of 23
• Beaches: 276 locations
• Surveys: 679 transects
• 1-Year (4 seasons): 2016
• Items: 355.671 objects
• Marine Areas: 4 Regions
CASE: Top Marine Beach Litter Items in EU
• Temporal scale: Seasonality
• Spatial scale: Europe – Regional*
• Support: Plastic Strategy
• Goal: Reduce most widespread items
• Action: Single Use Plastic Items
Outlook Baseline set basis for:
• Setting Thresholds
• Identifying Scale of Measures
• Revising of Guidance for Monitoring
• Revising of Master List Categories
• Implementing Efficient Policy
Thank YouAny questions? Any suggestions?You can find me [email protected]
Thanks to Giorgetti A., Vinci M., Molina Jack M.E., Brosich A. and Montero Chaves M. for their contribution and fruitful collaboration.