the imarine solutions in support to the ecosystem approach needs
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The iMarine solutions in support
to the Ecosystem
Approach needs
iMarine data platform for collaborations,
7th March 2014, FAO (Rome)
Marc Taconet (FAO)
Approach needs
iMarine data platform for collaborations , 7th March 2014, FAO (Rome)
PART 1 - Serving policy frameworks facing BIG challenges – example of EAF
Outline
iMarine data platform for collaborations , 7th March 2014, FAO (Rome)
PART 2 – iMarine solutions
The power of an e-Infrastructure
Synergies and efficiencies through Global collaborative communities
Serving policy framework facing BIG challenges – the EA
• FAO
• DG MARE
• Eurostat
• NEAFC
• MEDDE/DOF
• iMarine Board – the Community Governing body:
– representatives of influential community partners
– convey community requirements
� drivers of technology developments
– stimulus - three EA policy challenges (Business Cases)
Fis
he
rie
s
• IRD
• ICES
• IOC/OBIS
• FIN
• CRIA
• VLIZ
• T2/GENESI-
DEC
» BC1: Support to implementation of the EU Common Fishery Policy
» BC2: Support to FAO’s deep seas fisheries programme
» BC3: Support to regional tropical LME pelagic EAF community
• The three iMarine Business Cases:
Bio
div
ers
ity
En
vir
on
me
nt
iMarine data platform for collaborations , 7th March 2014, FAO (Rome)
Data needs in the EA policy framework
2. Fine grained catch & effort for resources
assessment, management of fishing quota, fishing
impact assessments
1. Species niche modeling to monitor health and trends
of the ecosystems
Serving policy framework facing BIG challenges – the EA
4. Comprehensive species knowledge base to
address impact of fishing on marine ecosystems
3. Mapping of sensitive or fragile habitats in need of
special management (EBSAs or VMEs)
iMarine data platform for collaborations , 7th March 2014, FAO (Rome)
PART 2 – iMarine solutions
The power of an e-Infrastructure
Outline
Synergies and efficiencies through Global collaborative communities
iMarine data platform for collaborations , 7th March 2014, FAO (Rome)
Monitoring ecosystem health and trends requires baselines
– Species predictive mapping
Synergies and efficiencies through Global collaboration & CoP
� Ecological niche modelling
Source: Kaschner et. al. (2006)
iMarine data platform for collaborations , 7th March 2014, FAO (Rome)
AQUAMAPS
� Biodiversity models
• Joint occurrence of n taxa
Maintaining ecosystem health requires
– Identification of Sensitive or fragile habitats
� Environmental
enrichment of species
occurrence data
Synergies and efficiencies through Global collaboration & CoP
Source: Coll et al. (2010)
iMarine data platform for collaborations , 7th March 2014, FAO (Rome)
Enriched species
occurrences
Synergies and efficiencies through Global collaboration & CoP
� Environmental enrichment of species occurrence data
Physical
variables
Environmental
variables
for each occurrence,
selection of the most
appropriate proxi data
according to:
-Time range
- Z range (depth)
iMarine data platform for collaborations , 7th March 2014, FAO (Rome)
Mapping of sensitive habitats is required for applying special
management measures:
– Aimed at reducing ecosystem impacts of Deep sea fishing on fragile ecosystems (VMEs
assessments)
Synergies and efficiencies through Global collaboration & CoP
iMarine data platform for collaborations , 7th March 2014, FAO (Rome)
���� Management of scientific data
collected on Board fishing vessels, e.g.
sharks as by-catch species identified by
Observers on Board tuna fishing vessels
Isurus oxyrinchus (Shortfin mako - shark)
Mapping of sensitive habitats is required for applying special
management measures:
– Aimed at reducing ecosystem impacts of Fishing on by-catch species
Synergies and efficiencies through Global collaboration & CoP
Source: IRD ECOSCOPE
Source: OBIS / IRD ECOSCOPE
Source: iMarine / AquaMaps
iMarine data platform for collaborations , 7th March 2014, FAO (Rome)
���� Modeling of species geographic
distribution, • Application of Predictive mapping models,
such as developed by Aquamaps
• Coarse resolution Atlantic herring catch statistics
…
Fisheries management and policy making requires
– Assessing high seas resources vs. resources inside EEZs (UNGA)
– Fish quotas allocation (RFMOs)
���� Finer grained catch and effort data can be obtained through spatial
reallocation process
Synergies and efficiencies through Global collaboration & CoP
SPREAD
iMarine data platform for collaborations , 7th March 2014, FAO (Rome)
… are re-allocated
by EEZ based on
the species
distribution
���� Near-real-time management of high resolution fisheries operational data
Synergies and efficiencies through Global collaboration & CoP
Fishery control and surveillance, and adaptive fishery management, require :
– Quota uptake monitoring, fishing impact assessment
iMarine data platform for collaborations , 7th March 2014, FAO (Rome)
���� Access to MCS data by science
VMS and e-LogBook data processing under confidentiality requirements
Synergies and efficiencies through Global collaboration & CoP
Fishery control and surveillance, and adaptive fishery management, require :
– Quota uptake monitoring, fishing impact assessment
iMarine data platform for collaborations , 7th March 2014, FAO (Rome)
VALIDATION
rules
More integrated and higher quality catch / effort / biological
occurrences data in coverage, timeliness, resolution and accuracy
���� promote simplified, efficient and quality workflow across domains through:
• common formats, harmonization support, validation rules
Synergies and efficiencies through Global collaboration & CoP
Flag
State 1e-Logbooks, VMS
(Fishing Activity
Reports) Metadata
Regional
DataBase
(FLUX)Reports)
iMarine data platform for collaborations , 7th March 2014, FAO (Rome)
HARMONISATION
support
Metadata
Standards
Flag
State 2
(FishFrame)
Scientific
observations
FLUX
FishFrame
SDMX
DWC
OGC
Analytical
processes
Synergies and efficiencies through Global collaboration & CoP
Synergies – a case study
• In fisheries monitoring, attention is
brought to:– By-catch species
– Species acting as indicators of fragile habitats (e.g.
VME encounters)
• Species identification guides are being developed
at regional / national level
– Local efforts would benefit from collated knowledge
Chondrichthyes (class), Scombridae (family), Lutjanus spp (genus)
Condrycthiens (vs. Chondrichthyes)
P. notialis (vs. Penaeus notialis)
Pomadasydés, Sparidae (Pageot), Penaeus sp, langouste, seiche
Farfantepenaeus notialis (deprecated) vs. Penaeus notialis (official)
• Issue: many species
names are reported,
with variations and
errors
iMarine data platform for collaborations , 7th March 2014, FAO (Rome)
Synergies – a case study:
���� Taxa name matching / mapping
– Names validation through cross-referencing
with global taxonomic databases
– Capturing mappings between local
expressions and authority lists
Cross-fertilization opportunities
between fisheries sciences and bio-ecological sciences
Synergies and efficiencies through Global collaboration & CoP
Plus …
���� Fisheries Linked Open Data (FLOD)
– Organizing concepts and terms in
comprehensive ontologies available as
Linked Open Data
���� Comprehensive knowledge bases on:
– Species vernacular / multilingual names
– broad range of species information
• contributing to fill the gap of knowledge
Results in …
Plus …
iMarine data platform for collaborations , 7th March 2014, FAO (Rome)
���� Species focused information
disseminated through mobile
Fisheries science will benefit from comprehensive knowledge bases
integrating fishery and biodiversity data sources:
Synergies and efficiencies through Global collaboration & CoP
APPLIFISH
disseminated through mobile
applications, for use by anyone at
consumer places
– Requires capacity to integrate multiple /
complementary sources
– Efficiency will rely on broad knowledge base
on species names, combined with GPS
information of Mobile user
iMarine data platform for collaborations , 7th March 2014, FAO (Rome)
• Seamless access to fisheries
information scattered among three
Synergies and efficiencies through Global collaboration & CoP
CHIMAERA: REGIONAL FISHERIES PORTAL FOR
SOUTH-WEST INDIAN OCEAN
Fisheries science will benefit from comprehensive knowledge bases
integrating fishery and biodiversity data sources:
information scattered among three
distinct information systems
– Requires capacity to integrate multiple /
complementary sources
iMarine data platform for collaborations , 7th March 2014, FAO (Rome)
Elements of synergies
– Various Business Cases complement each other:
– End-up building comprehensive toolkit required for entire flow from
raw data collection to policy indicators
– Data produced by A. are inputs to B. – all accessible from same
environment
– Tools are pooled and can be reused across different
communities having similar needs, and enriched
Synergies and efficiencies through Global collaboration & CoP
communities having similar needs, and enriched
– Data availability among disciplines enhance quality
– Bridge MCS data with science (confidentiality requirements)
Elements of efficiencies
– Partner systems tend to specialize on their strength and
delegate to others
iMarine data platform for collaborations , 7th March 2014, FAO (Rome)
CONCLUSION
What are the ingredients of success
Outline
What are the ingredients of success
iMarine data platform for collaborations , 7th March 2014, FAO (Rome)
Powerful technology
Community governance
Data access and sharing policies
Conclusion – ingredients of success
Data access and sharing policies
Business model for Exploitation of the data
infrastructure
– Public centered Partnership business model
iMarine data platform for collaborations , 7th March 2014, FAO (Rome)
Thanks for your attention
Outline
iMarine data platform for collaborations , 7th March 2014, FAO (Rome)
Matching the technology offer with community needs
A vision in support to EAF needs
Fishery resource
productivity
Biodiversity and
preservation
Biodiversity and
habitat
preservation
Social and
economic goals values
Objectives
Policy maker
Fishery manager
Serving policy framework facing BIG challenges – the EA
Indicators /
Ref points
Distribution
of fishery
activity
Socio-economic
communities
Socio-economic
structure of
fishing
communities
Abundance
levels fish
stocks
Distribution
habitats
Distribution
of non-target
species /
habitats
Fishery manager
Scientist
Data manager
models
Data
repositories
Statistician
iMarine data platform for collaborations , 7th March 2014, FAO (Rome)
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