On 18th January 2018, 3pm CET BlueBRIDGE will hosted the webinar: "Managing tuna fisheries data at a global scale: the Tuna Atlas VRE" that presented how, through the Tuna Atlas Virtual Research Environment (VRE), users can easily produce their own datasets of fisheries at regional, multi-regional or global scale and how they can share these datasets in ways that allow other users to access, process and visualise them efficiently.

1. Managing tuna fisheries at global scale:
the Tuna Atlas VRE
Learn more at: i-marine.d4science.org/web/fao_tunaatlas/home
Speaker: Paul Taconet (IRD) (paul.taconet@ird.fr)
BlueBRIDGE webinar
2018-01-18

2. Plan
● => Rationale of the project
● => The Tuna Atlas VRE: data and tools available
● => How did we setup the Tuna Atlas VRE and how do we update it
Why ?
What ?
How ?

3. 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)
Why
?

4. Who does manage tuna fisheries ?
Areas of competences of the five Tuna Regional Fisheries Management Organizations (tuna RFMOs)
Why
?

5. How do tuna RFMOs manage the fisheries ?
….
Countries fish and
collect data... … that Tuna RFMO collate...
?
… and that scientists analyse
to provide advice ... … enabling
conservation of the
fish stocks
Why
?

6. Let’s summarise:
the DIKW pyramid applied to fisheries
Statistics and indicators to interpret trends
Sustainable tuna stocks and fisheries
Catch of tuna, fishing effort, fishes sizes,
biology, etc...
Stock status
The quality of each block depends on the quality on the previous one:
- The better the data, the better the information
- The better the information, the better the knowledge
- etc...
Why
?

7. How to improve the quality of the data ?
The quality of the data is improved when the data are ...
● OPEN -> 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 -> anyone can reproduce the processes that have been applied to generate the data
● INTER-OPERABLE -> it is easy to cross the data that come from various organization but means the
same thing
Why
?

8. Pros and cons of tuna RFMOs data
Tuna RFMOs data are:
● OPEN -> anyone can use them
● EASY TO LOCATE -> Many websites to seek
● WELL DESCRIBED -> The description is sparse, not standardized, not always easy to find
● EASY TO USE -> Code lists are not always available, geo-information is not easy to use, etc.
● TRANSPARENT -> Processes that have been applied to generate the datasets are not available
● REPRODUCIBLE -> Reproduction of the processes that have been applied to generate the
datasets is not possible
● INTER-OPERABLE -> many formats (csv, xlsx, mdb, etc.), many different code lists, around 20
different structures, etc.
Why
?

9. The tuna atlas project: main objectives
1) Build global datasets on tuna fisheries to be able to get global statistics on tuna fisheries,
compare fisheries between oceans, etc. (more here: Global tuna atlas: Achievable global research and fisheries management objectives)
1) Set-up online services to efficiently:
- Discover
- Access
- Process
- Visualize
the data & gather these services within 1 single website
Why
?

10. The tuna atlas project: main objectives
3) Make all the data:
● OPEN
● EASY TO LOCATE
● WELL DESCRIBED
● EASY TO USE
● TRANSPARENT
● REPRODUCIBLE
● INTER-OPERABLE
=> Information on tuna fisheries easily available to scientists, general public, NGOs, policy makers, etc.
=> Better management of tuna stocks
Why
?

11. Achievements (after 24 months of work):
the Tuna Atlas VRE
> Multiple datasets on tuna fisheries with associated metadata
> One database that stores all the data and metadata
> One metadata catalogue to discover and access the data
> One web viewer to discover, visualize and access the data
> A set of R codes to access and process the datasets
> A set of R codes to reproduce all the work
What ?

12. The datasets available
- [Collated] The public domain datasets from the 5 tuna RFMOs (IOTC, ICCAT, IATTC, WCPFC, CCSBT) as they deliver them (i.e.
without processings):
- Nominal catch (RFMO area of competence / 1 year) (e.g. IOTC‘s)
- Georeferenced catch (5° / 1 month) (e.g. ICCAT‘s)
- Georeferenced effort (5° / 1 month)
- [Created] Global datasets on tuna fisheries, built by merging the regional datasets and applying some scientific corrections to
get a more pertinent overview of tuna fisheries at global scale:
- Global nominal catch (e.g. here)
- Global georeferenced catch (e.g. here)
- [Collated] The code lists used by the 5 tuna RFMOs (for gears, species, fishing countries, etc.) (e.g. ICCAT’s gears) + global code
lists recommended by the CWP (e.g. ASFIS, ISSCFG)
- [Created] The mappings between tuna RFMOs code lists and global code lists, which are necessary to combine the datasets
expressed with sparse code lists (e.g. IOTC’s gears to ISSCFG)
+ Detailed metadata for each dataset
(title, abstract, contacts, genealogy (i.e. which source datasets were used to generate the dataset), lineage (i.e. how the data was generated))
More information on the data and the processes (scientific corrections) here
What ?

13. How to access the datasets and metadata
● Primarily stored on a PostgreSQL + PostGIS database
● Also stored as csv on a folder
A good start ! But …
● PostgreSQL : what if I do not know SQL ?
● Folder : a huge amount of datasets! Tricky to understand and to locate the one I need ...
Solutions :
=> The catalogue
=> The viewer
What ?

14. Discover and access the data with the online catalogue
What ?
Tuna Atlas VRE/Data catalogue/Geonetwork catalogue

15. Discover, visualize and access the data through the prototype viewer
developed by FAO
What ?
Tuna Atlas VRE/Visualise Data/Map viewer

16. Access and process the data with the R ‘rtunaatlas’ library
[demo script available here]
What ?

17. Create your own dataset of global catch
To come: share your own atlas to the community (with catalogue, viewer, etc.)
What ?
Tuna Atlas VRE/Data and processing services/Data miner/Execute an experiment
Click here to access the source R scripts

18. Summary of the tools available
Discover available data Access the data Process the data Visualize the data
● PostgreSQL / PostGIS database
database=tunaatlas, host=db-tuna.d4science.org,
login=tunaatlas_inv, pw=fle087
● Data and metadata catalogue
Tuna Atlas VRE/Data catalogue/Geonetwork
catalogue
● Viewer Tuna Atlas VRE/Visualise Data/Map viewer
● rtunaatlas R package package and demo code
● Create own tuna atlas
Tuna Atlas VRE/Data and processing services/Data
miner/Execute an experiment or source R scripts
What ?

19. The Tuna Atlas: How it was set up
1
Data Collation
& Harmonization
& Import
2
Web publication
IOTC
ICCAT
IATTC
CCSBT
WCPFC
Global datasets on
tuna fisheries
Catalogue
Viewer
How ?

20. The Tuna Atlas: How it was set up
Data and metadata collation & harmonization & import
Collation
[manual]
Tuna RFMOs
websites (public
domain datasets)
Open SQL
database storing
data + metadata
IOTC
ICCAT
IATTC
CCSBT
WCPFC
Global datasets on
tuna fisheries
4
Generate global
datasets and metadata
through R scripts
Harmonization of
the structures
through R scripts
Load data and
metadata within
the DB through R
scripts
1 single structure (csv) for data
1 single structure (csv) for metadata
321
- 1 folder with all the RFMOs
datasets (primary datasets, code
lists, mappings)
- 1 csv with metadata for each
dataset ( primary datasets, code
lists, mappings)
How ?

21. How to update the tuna atlas ?
(enough) Fishes in the sea => fisheries => data => NEED TO UPDATE THE TUNA ATLAS !
Collation
[manual]
Tuna RFMOs
websites (public
domain datasets)
Open SQL
database storing
data + metadata
IOTC
ICCAT
IATTC
CCSBT
WCPFC
Global datasets on
tuna fisheries
4
Generate global
datasets and metadata
through R scripts
Harmonization of
the structures
through R scripts
Load data and
metadata within
the DB through R
scripts
1 single structure (csv) for data
1 single structure (csv) for metadata
321
- 1 folder with the RFMOs
datasets (primary datasets, code
lists, mappings)
- 1 csv with metadata for each
dataset ( primary datasets, code
lists, mappings)
How ?

22. Register to the Tuna Atlas VRE to access all the data and services
Links

23. Thanks !