An e-Infrastructure is a distributed network of service nodes, residing on multiple sites and managed by one or more organizations. e-Infrastructures allow scientists residing at distant places to collaborate. They offer a multiplicity of facilities as-a-service, supporting data sharing and usage at different levels of abstraction, e.g. data transfer, data harmonization, data processing workflows etc. e-Infrastructures are gaining an important place in the field of biodiversity conservation. Their computational capabilities help scientists to reuse models, obtain results in shorter time and share these results with other colleagues. They are also used to access several and heterogeneous biodiversity catalogues. In this course, the D4Science e-Infrastructure will be used to conduct experiments in the field of biodiversity conservation. D4Science hosts models and contributions by several international organizations involved in the biodiversity conservation field. The course will give students an overview of the models, the practices and the methods that large international organizations like FAO and UNESCO apply by means of D4Science. At the same time, the course will introduce students to the basic concepts under e-Infrastructures, Virtual Research Environments, data sharing and experiments reproducibility.

1. Using e-Infrastructures for
Biodiversity Conservation
Gianpaolo Coro
ISTI-CNR, Pisa, Italy

2. Module 5 - Outline
1. Aims and methods of the i-Marine e-Infrastructure
2. Community oriented applications
3. Data analytics
4. Tabular Data Manager

3. 1. Aims and methods of the i-Marine e-Infrastructure
2. Community oriented applications
3. Data analytics
4. Tabular Data Manager

4. Supply a scientific network of
• Models
• Approaches
• Computational facilities
To scientists that have limited access to hardware and
software for data analytics

5. “It worked very well at the WKLIFE IV workshop and is likely to be used by ICES.”,
Rainer Froese, senior scientist at the Helmholtz Center for Ocean Research, Kiel
Germany.
“When looking at the performance maps […], CMSY was the most frequent best
performer.”,
FAO Report on Stock Assessment and Global Productivity in Fisheries, 2014
CMSY is available in iMarine on the Statistical Manager service
Given the catch statistics and
(optionally) the biomass or
CPUE history of a stock,
CMSY estimates:
• Maximum Sustainable
Yield
• Exploitation Rate
• Current Biomass
• Resilience
• Productivity
Bayesian Catch-Maximum Sustainable Yield (CMSY), Froese & Coro 2014
herring
estimated
resilience r
and
productivity k
Usages:
• Harvesting Control Rules (HCR)
• Total Allowable Catch (TAC)
• Health status monitoring
• Stock Assessment

6. Training with iMarine
• Collecting and curating data for the
Giant squid
• Producing maps using different
Bayesian approaches and
environmental features from
iMarine
• Importing maps from reference
literature approaches
• Comparing the results

7. 1. Aims and methods of the i-Marine e-Infrastructure
2. Community oriented applications
3. Data analytics
4. Tabular Data Manager

8. Statistical Manager
• Processes data using statistical
models
• Integrates models from
communities
• Hides algorithms complexity
• Automatically generates the
interface
• Uses Cloud computing
Statistical
Manager
D4Science
Computational
Facilities
Sharing
Setup and execution

9. Fisheries
CMSY: estimates Maximum Sustainable Yield
from catch statistics LWR: transforms fish length into weight
CatchSeriesAnalysis: estimates the
effect of piracy on catch trends
VTI: classifies vessels activities and
estimates effort per 0.5 deg areas

10. Biodiversity
Plankton regime
shift
Herring recovered after the fish
ban
AquaMaps and ANN: calculates the potential
and actual niche of a species
Clustering: detects common and rare species
in marine areas
Trendylyzer: detects ecosystem changes and
their effect on species trends
BiOnym: a flexible and powerful search
engine in large taxonomic trees

11. Environment
MaxEntropy: finds the correlation between one
phenomenon and N environmental variables
OccurrenceEnrichment: enriches presence
points with environmental data
Rasterization: transforms vectorial datasets
into raster datasets
XYZT - DataExtraction: extracts
environmental data with uniform geographic
projection
X

12. Open Platform Approach
External
Computing
Facility
OGC
WPS
Interface
People can contribute with:
• R scripts
• Java programs
• Linux programs
• OGC-WPS services

13. Numbers
FishBase (US, CA,
TW)
Geomar
Naturhistoriska
riksmuseet:
Startsida
Agrocampus
Anonymous
Individuals
INRA
King Abdullah
University of Science
and Technology
ISTI
Users
2013 2014
Avg Users
per month
200 20100
Number of
Algorithms
50 100
Number of
contributing
Organization
s providing
algorithms
2
CNR,
Geomar
7
CNR,
Geomar,
FIN,
FAO,
T2,
IRD,
Agrocampus
Publications 8 13
Sum Impact
Factor
2.66 12.17

14. Statistical Manager related Wiki links:
Descriptions:
http://gcube.wiki.gcube-system.org/gcube/index.php/Statistical_Manager
http://gcube.wiki.gcube-system.org/gcube/index.php/Statistical_Manager_Algorithms
Tutorials:
http://gcube.wiki.gcube-system.org/gcube/index.php/Statistical_Manager_Tutorial
http://gcube.wiki.gcube-
system.org/gcube/index.php/How_to_Interact_with_the_Statistical_Manager_by_client
http://gcube.wiki.gcube-system.org/gcube/index.php/How-
to_Implement_Algorithms_for_the_Statistical_Manager
Experiments:
http://wiki.i-marine.eu/index.php/MaxEnt
http://wiki.i-marine.eu/index.php/IOTC_Area_Predictive_analysis
http://wiki.i-marine.eu/index.php/ICES_SGVMS

15. 1. Aims and methods of the i-Marine e-Infrastructure
2. Community oriented applications
3. Data analytics
4. Tabular Data Manager

16. Data Analytics
1. Prepare data 2. Analyse data
3. Recommend actions to decision makers

17. From several tools to One Online Platform

18. 1. Aims and methods of the i-Marine e-Infrastructure
2. Community oriented applications
3. Data analytics
4. Tabular Data Manager

19. Tabular Data Manager
• Manipulates Big Tabular
Datasets
• Prepares data for
Analyses
• Makes data compliant
with external Code Lists
• Visualizes, represents
and inspects data

20. Advanced Processing
1- TabMan imports datasets with tuna catch statistics
Yellowfin Skipjack

21. Advanced Processing
2- Allows modifying columns

22. Advanced Processing
3- Allows curating columns
b. Column splitting
using regular
expressions
c. Changing
columns types
and Codelists
compliancy
e. Denormalization:
one column per row
value
a. Duplicates
deletion
d. Produce a new
codelist

23. Advanced Processing
4- Adding Geometries

24. Advanced Processing
5- …and Csquare codes and FAO Ocean Areas codes

25. Advanced Processing
6- Datasets can become interactive GIS maps published under standard formats
(WMS, WFS, WCS)

26. Advanced Processing
7- Catch can be forecasted and analysed using an online R development environment

27. Advanced Processing
8- Charts and analytics help discovering indicators

28. Online experiment:
Tabular Data Manager
https://i-marine.d4science.org/group/tabulardatalab