Presentation by Nicolas Bailly, FIN on how iMarine facilitated the production of distribution maps by reducing the calculation time.

1. iMarine Event; a platform for collaboration
29 September 2014, Brussels (Belgium)
Discovering the impact of climate change
on the marine species
iMarine Event 29 September 2014, Brussels Belgium
AquaMaps
Nicolas BAILLY
FishBase Information and Research Group
n.bailly@fin.ph
and WorldFish

2. Who we are
• FIN: FishBase Information and Research Group
• www.fin.ph, started in 2003 (NGO,
Philippines)
• Support the availability ooff ccoommpprreehheennssiivvee
information system(s) with key data on all
aquatic organisms of the world, easily
accessible and free-of-charge to the public
• FishBase, Catalogue of Life, AquaMaps,
SeaLifeBase

3. What our communities are
• Biodiversity Informatics
• Fisheries
• Aquatic Biodiversity Conservation
• Support the management of aquatic natural
resources balancing exploitation and
conservation for a sustainable use

4. Biodiversity data challenge
• Biodiversity ARE NOT ONLY point data
• Scattered in literature, researcher personal
files, archives on various medias, lost, not
digitized
• Not standardized (yet)
• Point data not properly curated by providers

5. Biodiversity data challenge
• Hooked to taxa through names, preferably
species (+ locality = populations/stocks)
• Taxa that are taxonomic group hypotheses
always falsifiable, including at species level
• Labeled with names that can change as
character strings
• With a too high rate of specimen
misidentification

6. Biodiversity data challenge
• IPR issues, in particular exploitation data
leading to aggregations, fuzzification, and
more … before dissemination
• Publishing and sharing data is only a recent
trend < 2 decades (TDWG since mid 80s)
• No common repositories iinn ggeenneerraall,, ssoommee
restricted domain exceptions, and when exist
no clear mandate to focus on data curation
• Competition between information systems
that countries want to control by themselves

7. iMarine helped us to address challenges
• Facilitating the connection between datasets
• Standardizing the access to datasets
• Providing hardware and software resources
that would not be affordable for small entities
like FIN iinn tteerrmmss ooff ffuunnddss ……
• … but even more in terms of trained staff
• Publishing, sharing and disseminating data

8. AquaMaps: From point data …
Gadus morhua, data and map from OBIS, ~860,000

9. … to distribution maps

10. AquaMaps – how does it work?
• Bathymetry
• Temperature
– Sea surface temperature (SST) for pelagic species (0-200 m)
– Bottom temperature for non-pelagic species (>200 m)
•• Salinity
– Sea surface salinity for pelagic species
– Bottom salinity for non-pelagic species
• Primary production
• Sea Ice Concentration
• Distance to land (for special cases)
Global raster: 0.5 degree lat x lon
= 180,000 cells

11. Probability
AquaMaps – how does it work?
Environmental envelopes
AquaMaps – Biodiversity Hotspots Climate Change
Observed
min
Observed
max
Optimal range
10th to 90th
percentiles
Environmental
layer
values
75th
percentile
+(IQR*1.5)
25th
percentile
-(IQR*1.5)
IQR = interquartile range

12. AquaMaps – how does it work?
AquaMaps – Biodiversity Hotspots Climate Change

13. AquaMaps – how does it work?
AquaMaps – Biodiversity Hotspots Climate Change
Solea Solea – Common Sole
Ready et al, accepted

14. … to climate change maps (year 2100)
2100
2014
iMarine Event 29 September 2014, Brussels Belgium

15. Standardized distribution maps for over 17,300 species
of fishes, marine mammals and invertebrates

16. Species
Richness by
Half degree
cell for 2010

17. Species
Richness by
Half degree
cell for 2050

18. 23 hotspots in
Coral Triangle
and adjacent
seas in 2010

19. 29 hotspots
in 2050
but smaller

20. Spratleys and
Visayan Sea
The areas
loosing
more species

21. But in
proportion
pelagic areas
are the
loosers

22. iMarine solution: previous situation
• Efforts of TDWG: intellectually outstanding,
but production of common tools slow
• General Biodiversity platforms (in 2008)
– only started to be planned (LifeWatch, BioFresh)
or being built (ALA),
– or too domain restricted (point/ooccccuurrrreennccee ddaattaa)),,
Taxon names (CoL, GNA/GNI), IUCN (threat
status),
– or no capacity of analysis (EoL)

23. iMarine solution: networking
• iMarine since D4Science (2008), with FAO in
the consortium, interesting from the fisheries
point of view: re-establishing collaboration
• OBIS (UNESCO) joined in 2012
• CoL aanndd WWooRRMMSS iinnvvoollvveedd
• GBIF data were made available

24. iMarine solution: shared resources
• High level support for developments (research
and technical) under new IT technologies
• Access to high level hardware resources and
computing capacities
• As well as software implementation (RR,, GGIISS))::
integration of any existing a priori possible
• Open source and “LAN-alone” usable

25. Outcome
• Management of large datasets
• Reduction of computing time of 3* 12,000
maps * 3 from 3 days down to 4-5h (live
demonstration by the end 2011 during the
D4Science-IIII ffiinnaall rreevviieeww))
• Possibility to compute GIS layers and make
them available for another e-infrastructure:
BioFresh
• Access to other environmental parameters

26. Outcome
• Potentiality to increase by one degree of
magnitude the frequency of AquaMaps
updates
• Progressive automation of all steps:
– Get ddaattaa ffrroomm GGBBIIFF//OOBBIISS
– Select good data: removing outliers
– BiOnym: matching names
– Environmental parameters: point data enrichment
– Portability of the algorithm to R: EU BON
– Map comparison: model vs specialist (FAO, IUCN)

27. HSPEN
HCAF
AquaMaps - Schematic Overview:
TaxonTable
(Name)?
Pointdata
GoodCells
Get point data
Determine presence cells
Generate envelopes
(except depth)
Provides BB/FAO
area (and depth
envelope) data
Provides Species ID
Functionalities partially
implemented in current
AquaMaps VRE
Alternative
modelling
algorithms
GBIF/
OBIS
Alternative
predictions
Envelope
Generation
HCAF
Species Data
Acquisition
Pre-Processing
AquaMaps project
functionalities tables etc
Functionalities implemented in
current AquaMaps VRE
Functionalities planned for
implementation during
D4Science II
Requires
synchronisation
after expert-review!
FishBase/
SLB
TableName?
Can be modified by direct or
indirect expert input
HSPEN
Generate probabilities of
species occurrence (PSO)
Interactive map
Displayed using
• Csquares mapper
• fully functional GIS tool
HCAF
Alternative temporal
spatial resolutions
Map probabilities Output for
further use
• image
• csv download
• GIS import/expert format
• Dynamic integration
with other VREs
Multi-species
analysis
• Biodiversity maps
• Lat / Lon transects
• MPA planning
•Conservation planning
tools
Environ-mental
input data
VRE: User-defined
generic
modification of
cell values
Validation
• expert-review
• extended expert-review
• comparison with FAO maps
•Statistical analysis
HSPEC
VRE: User-defined
weighting of
envelopes
Implemented in D4Science
AquaMaps VRE 10/2009
PSO
Species Generation
input data

28. Future: development
• A complete automated AquaMaps production
• Improve the dissemination of GIS layers
• Refine the scale (currently Half Degree Cell)
down to ¼ and 1/10 for ccooaassttaall aarreeaass ffoorr
national and local uses
• 3D
• Refine the validation tool by specialists

29. Future: production
Use of AquaMaps for :
• Seafood traceability (Frequenz)
• KBA definition: aquatic Key Biodiversity Areas
(GEF)
• Compute winners and losers wwiitthh rreessppeecctt ttoo
Climate Change (ACB, Philippine Seas)
Use iMarine for:
• triggering policy making

30. Data, Information, Knowledge,
(Understanding), Wisdom Understanding
Data A given specimen is 35 cm long and is in
a given maturity state
Information
A given species has a length at maturity
of 56 cm
Fishing a species below its length at
Knowledge
maturity collapses the fishery
Wisdom*
For a sustainable fishery, a decision is
taken that a given species must not be
fished smaller than the length at
*= management here maturity
•DIK(U)W hierarchy from Data to Wisdom
(Zeleny, 1987; Ackoff, 1989)
N. Bailly (WorldFish)

31. Only research?
Understanding
Data
Collect data following a protocol pre-established
Information
Synthesizing data into information with
analytical tools
Knowledge
Interpretation of information to
constitute a corpus of knowledge
Management
Transform the knowledge in political
and managerial decisions
N. Bailly (WorldFish)
TRIGGERS

32. Thank you