Using e-Infrastructures for Biodiversity Conservation

Gianpaolo Coro ISTI-CNR, Pisa, Italy

Module 5 - Outline

1. Aims and methods of the i-Marine e-Infrastructure

2. Community oriented applications

3. Data analytics

4. Tabular Data Manager

1. Aims and methods of the i-Marine e-Infrastructure

2. Community oriented applications

3. Data analytics

4. Tabular Data Manager

Supply a scientific network of • Models• Approaches• Computational facilities

To scientists that have limited access to hardware and software for data analytics

“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

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

1. Aims and methods of the i-Marine e-Infrastructure

2. Community oriented applications

3. Data analytics

4. Tabular Data Manager

Statistical Manager• Processes data using statistical

models• Integrates models from

communities• Hides algorithms complexity• Automatically generates the

interface• Uses Cloud computing

StatisticalManager

D4ScienceComputational

FacilitiesSharing

Setup and execution

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

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

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

Open Platform Approach

External Computing

Facility

OGC WPS

Interface

People can contribute with:

• R scripts• Java programs• Linux programs• OGC-WPS services

Numbers

FishBase (US, CA, TW)GeomarNaturhistoriska riksmuseet: StartsidaAgrocampusAnonymous Individ-ualsINRAKing Abdullah Uni-versity of Science and TechnologyISTI

Users

2013 2014Avg Users per month 200 20100

Number of Algorithms 50 100

Number of contributing Organizations providing algorithms

2 CNR,

Geomar

7CNR,

Geomar,FIN,FAO,T2,IRD,

AgrocampusPublications 8 13Sum Impact

Factor 2.66 12.17

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

1. Aims and methods of the i-Marine e-Infrastructure

2. Community oriented applications

3. Data analytics

4. Tabular Data Manager

Data Analytics

1. Prepare data 2. Analyse data

3. Recommend actions to decision makers

From several tools to One Online Platform

1. Aims and methods of the i-Marine e-Infrastructure

2. Community oriented applications

3. Data analytics

4. Tabular Data Manager

Tabular Data Manager

• Manipulates Big Tabular Datasets

• Prepares data for Analyses

• Makes data compliant with external Code Lists

• Visualizes, represents and inspects data

Advanced Processing1- TabMan imports datasets with tuna catch statistics

Yellowfin Skipjack

Advanced Processing2- Allows modifying columns

Advanced Processing3- Allows curating columns

b. Column splitting using regular expressions

c. Changing columns typesand Codelists compliancy

e. Denormalization:one column per row value

a. Duplicates deletion

d. Produce a new codelist

Advanced Processing4- Adding Geometries

Advanced Processing5- …and Csquare codes and FAO Ocean Areas codes

Advanced Processing6- Datasets can become interactive GIS maps published under standard formats

(WMS, WFS, WCS)

Advanced Processing7- Catch can be forecasted and analysed using an online R development environment

Advanced Processing8- Charts and analytics help discovering indicators

Online experiment: Tabular Data Manager

https://i-marine.d4science.org/group/tabulardatalab