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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
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)
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
… 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...
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...
Before...
To improve data discovery & access:
online catalogues
Tuna Atlas VRE/Data catalogue/Geonetwork catalogue
To improve data discovery & access:
online catalogues
Tuna Atlas VRE/Data catalogue/Geonetwork catalogue
To improve data discovery & access:
online catalogues
To improve data visualization:
Statistical interfaces (interactive dashboards)
SS 3 VRE /
ICCAT BFT VRE /
To improve data visualization:
Mapping interfaces
Tuna Atlas VRE/Visualise Data/Map viewer
To improve data visualization:
Mapping interfaces
Tuna Atlas VRE/Visualise Data/Map viewer
To improve data processing:
Create own datasets with own assumptions
Tuna Atlas VRE/Data and processing services/Data miner/Execute an experiment
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
Decision making and communication made easy (shiny):
Useful for managers, scientists, stakeholders and general public
Global distribution of main commercial tuna species
based on CPUE reconstruction
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
Global CPUE based in catches reconstruction and AIS data
Marine Spatial planning
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?
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
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)
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..)
Metadata catalogs
Metadata catalogs
Data servers (eg NetCDF)
Mapping Interface
Map viewer (eg SPC)
Statistical viewer (eg Shiny)
Infrastructures to archive
Infrastructures to
disseminate
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..).
SDI workshop (INSPIRE)

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Fostering global data management with public tuna fisheries data

  • 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
  • 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..)
  • 25. Data servers (eg NetCDF)
  • 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..).