BlueBRIDGE workshop: "Supporting Blue Growth with innovative applications based on EU e-infrastructures" - Brussels February 2018

1. BlueBRIDGE receives funding from the European Union’s Horizon 2020
research and innovation programme under grant agreement No. 675680 www.bluebridge-vres.eu
IRD: Paul Taconet, Julien Barde
AZTI: Josean Fernandes, Ibon Galparsoro
Fostering global data management
with public tuna fisheries data
Main partners of the project:
IRD, FAO, CNR, the tuna RFMOs

2. Tuna fisheries:
a very “hot” and up-to-date topic
Evolution of the global catches of tuna from 1950 to 2013 *
Mean annual distribution of tuna catches from 2005 to 2015 *
*source: global tuna atlas database using data collated from IOTC, ICCAT, IATTC, WCPFC, CCSBT
Tuna purse seiner in Victoria (Seychelles)
Greenpeace website
8% of the worldwide fisheries
85 countries fishing
(FAO, 2013)

3. The data: the fuel for sustainable fisheries...
….
Countries fish and
collect data... … that Regional Fisheries
Management Organization
collate...
?
… and that scientists analyse
to provide advice ... … enabling
conservation of the
fish stocks
Data Information Knowledge Wisdom
Good Data Good Management
Better Data Better Management

4. … but somehow under-exploited
The “best” data are...
Public Anyone can use them, data are improved
Easy to locate They are easy to find
Well described The data comes with information (metadata):
description, contacts, rights, etc...
Easy to use It is available in various formats, accessible
through programmatic protocols, etc.
Transparent
Anyone can understand the processes that
have been applied to generate the data
Reproducible
Interoperable
It is easy to cross the data that come from
various organization but means the same thing
Anyone can reproduce the processes that
have been applied to generate the data
Tuna fisheries data
are...

5. Main objectives:
Foster data management of public tuna fisheries data by:
2) Setting up web services on top of data to improve :
- Data discovery
- Data access
- Data processing
- Data visualization
1) Standardizing data structures and formats of main data types :
- catch
- fishing effort
- fish size frequencies
- stock assessment models outputs
- other types: tagging, biological samples….
as a requisite for...

6. Before...
To improve data discovery & access:
online catalogues

7. Tuna Atlas VRE/Data catalogue/Geonetwork catalogue
To improve data discovery & access:
online catalogues

8. Tuna Atlas VRE/Data catalogue/Geonetwork catalogue
To improve data discovery & access:
online catalogues

9. To improve data visualization:
Statistical interfaces (interactive dashboards)
SS 3 VRE /
ICCAT BFT VRE /

10. To improve data visualization:
Mapping interfaces
Tuna Atlas VRE/Visualise Data/Map viewer

11. To improve data visualization:
Mapping interfaces
Tuna Atlas VRE/Visualise Data/Map viewer

12. To improve data processing:
Create own datasets with own assumptions
Tuna Atlas VRE/Data and processing services/Data miner/Execute an experiment

13. Global fishery datasets: some practical utilities
Jose A. Fernandes
jfernandes@azti.es
Senior Scientist in Big Data for Marine Research and Innovation (AZTI)
Haritz Arrizabalaga
Hilario Murua
Ibon Galparsoro
Josu Santiago
Other contributors: Igor Granado, Xabier Irigoien

14. Decision making and communication made easy (shiny):
Useful for managers, scientists, stakeholders and general public

15. Global distribution of main commercial tuna species
based on CPUE reconstruction

16. Ecoregions identification for ecosystem-based management
• Spatial patterns in catches/CPUE show how species co-
occur and clear preference of certain species for
certain areas

17. Global CPUE based in catches reconstruction and AIS data

18. Marine Spatial planning

19. Development and implementation of tools supporting Maritime Spatial Planning and
Blue Growth
Gimpel, A., V. Stelzenmüller, S. Töpsch, I. Galparsoro, D. Miller, A. Murillas, K. Pınarbaşı, G. R. Carceller, Accepted. A GIS-based tool for an integrate
Pınarbaşı, K., I. Galparsoro, Á. Borja, V. Stelzenmüller, C. N. Ehler, A. Gimpel, 2017. Decision support tools in marine spatial planning: Present applica
Buhl-Mortensen, L., I. Galparsoro, T. Vega Fernández, K. Johnson, G. D'Anna, F. Badalamenti, G. Garofalo, J. Carlström, J. Piwowarczyk, M. Rabaut, J
1. Aquaculture:
Integrated assessment of spatial planning trade-offs with
aquaculture: The AquaSpace tool
2. Windfarms
Integrated Feasibility Analysis for Offshore Wind Energy
Platforms
Systematic Conservation Planning
How much of fishing
effort is affected by
declaring different sites?

20. Global and regional model projections
Tittensor et al. (2018) A protocol for the intercomparison of marine fishery and ecosystem models: Fish-MIP v1.0. Geoscientific Model Development Discussions.
Queiros, Fernades et al., (2018) Climate change alters fish community size-structure, requiring adaptive policy targets. Fish and Fisheries. Accepted.
Fernandes et al., (2018) Changes of potential catches for North East Atlantic pelagic fisheries under climate change scenarios. Fish and Fisheries Submitted
Lotze et al. (2018) Ensemble projections reveal consistent declines of global fish biomass with climate change. Nature Climate Change. Submitted.
https://www.surveymonkey.com/r/C3S_MCF_SIS

21. Índice
Txatxarramendi ugarte z/g
48395 Sukarrieta, Bizkaia
Herrera Kaia. Portualdea z/g
20110 Pasaia, Gipuzkoa
Astondo Bidea, Edificio 609
Parque Tecnológico de Bizkaia
48160 Derio, Bizkaia
Please, remember to visit and fill he survey:
C3S Marine, Coastal and Fisheries Sectoral Information System (Copernicus)
https://www.surveymonkey.com/r/C3S_MCF_SIS
Jose A. Fernandes
jfernandes@azti.es
Senior Scientist in Big Data for Marine Research and Innovation (AZTI)
PML fellow (PML)

22. Other data types & Fisheries
Reuse the methodology and the infrastructure
for similar projects:
• (Meta-)data catalogs and servers:
• catch/effort/size class,
• stock assessment
• Data processing servers...
• ….for other kinds of data (eg Biological
Sampling, tagging data) in collaboration with
other RFMOs (SWIOFC, CCAMLR..)

23. Metadata catalogs

24. Metadata catalogs

25. Data servers (eg NetCDF)

26. Mapping Interface

27. Map viewer (eg SPC)

28. Statistical viewer (eg Shiny)

29. Infrastructures to archive

30. Infrastructures to
disseminate

31. Conclusion
Foster a generic & collaborative approach :
• (Locally) Fisheries related community with:
• standards for (meta-)data formats and access
protocols and processes:
• metadata catalogs => Data Discovery
• data servers => Data Access
• processing servers => Data Processing
• VREs => applications to implement the standards
• (Globally) BlueBridge VREs & Standards =>
interoperability with other infrastructures (eg
INSPIRE, OpenAire..).

32. SDI workshop (INSPIRE)