Using e-Infrastructures for Biodiversity Conservation

BlueBRIDGE receives funding from the European Union’s Horizon 2020
research and innovation programme under grant agreement No. 675680 www.bluebridge-vres.eu
Using e-Infrastructures
for Biodiversity
Conservation
Gianpaolo Coro
National Research Council (CNR), Pisa, Italy
This work is licensed under the Creative
Commons CC-BY 4.0 licence

Aims of the lecture
1. Introduce concepts around research e-Infrastructures
2. Overview of approaches for biodiversity data
management and analysis
3. Explain how computer science can support the needs
of a “community of practice”
4. Show tools used by large international organizations,
e.g. FAO, Unesco, ICES, IOTC

Outline
• E-Infrastructures
• i-Marine
• Biodiversity data
• Geospatial data
• Data processing
• Examples

e-Infrastructures
e-Infrastructures enable researchers in different locations across the world
to collaborate in the context of their home institutions or in national or multinational scientific
initiatives. They can work together by having shared access to unique or distributed
scientific facilities (including data, instruments, computing and communications)*.”
Examples:
*Belief, http://www.beliefproject.org/
OpenAire, http://www.openaire.eu/
i-Marine, http://www.i-marine.eu/
EU-Brazil OpenBio,
http://www.eubrazilopenbio.eu/

e-Infrastructures
• Data e-Infrastructure: an e-Infrastructure promoting data sharing and
consumption. Addresses the needs of the research activity performed by a
certain community.
• Computational e-Infrastructure: an e-Infrastructures offering
computational resources distributed in a network environment. Uses Cloud
computing to execute calculations with a large number of connected
computers. Offers collaboration facilities for scientists to share experimental
results.

Virtual Research Environments
Virtual Research Environments: virtual organizations of communities of researchers for helping them
collaborating.
• Define sub-communities inside an e-Infrastructure;
• Allow temporary dedicated assignment of computational, storage, and data resources to a group of
people;
• Very important in fields where research is carried out in several teams which span institutions and
countries.
e-Infrastructure
VRE
VRE
VRE

Outline
i-Marine is both a Data and a Computational e-Infrastructure (Hybrid Data
Infrastructure)
• Used by several Projects: i-Marine, EUBrazil OpenBio, ENVRI,
BlueBRIDGE;
• Implements the notion of e-Infrastructure as-a-Service: it offers on demand
access to data management services and computational facilities;
• Hosts several VREs for Fisheries Managers, Biologists, Statisticians…and
Students.
DILIGENT
2004
BlueBRIDGE
Today

Social Network
A continuously updated list of events / news produced by users and
applications
Share News
Application-
shared News
User-shared
News

Workspace
A folder-based file system allowing to manage complex
information objects in a seamless way
Information objects can be
• files, dataset, workflows,
experiments, etc.
• organized
into folders and shared
• disseminated via URIs
• accessed via WebDAV

Services
Storage
Databases Cloud storage Geospatial data
Metadata generation
and management
Harmonisation Sharing
Processing
Data
management
Cloud computing Elastic resources
assignment
Multi-platform: R,
Java, Fortran

Architecture
Large Set of Biodiversity
and Taxonomic Datasets
connected
A Social
Network
to share
opinions and
useful news
Algorithms for Biology-
related experiments
Distributed Storage
System to store
datasets and
documents
A Network to
distribute and
access to
Geospatial Data

Online examples:
the i-Marine Web portal and basic functions
http://portal.i-marine.d4science.org

Biodiversity Data
• Taxonomies
• In biology, a taxon (plural taxa) is a group of one or more populations of an
organism or organisms seen by taxonomists to form a unit.
• Introduced by Linnaeus's system in Systema Naturae (10th edition, 1758).
• A taxon is usually known by a particular name and given a particular ranking,
especially if (and when) it is accepted or becomes established
• An accepted taxon is given a formal scientific name, according to nomenclature
codes, e.g. Gadus morhua (Linnaeus, 1758)*
• A "good" or "useful" taxon is one that reflects evolutionary relationships
* More on scientific names here: http://wiki.i-marine.eu/index.php/Taxa_Merging_Discussion

Taxa Representations
Biology
Computer
science
V
S

Biodiversity Data
Specimen, Human Observations (direct/indirect)
Records of species presence, usually provided by scientific surveys
Occurrence data

Biodiversity Data Providers
i-Marine hosts biodiversity datasets coming from several data providers:
• Some are remotely accessed and are maintained by the respective owners;
• Other ones are resident in the e-Infrastructure.
Currently, the accessible datasets are:
• Catalogue of Life (CoL),
• Global Biodiversity Information Facility (GBIF),
• Integrated Taxonomic Information System (ITIS),
• Interim Register of Marine and Nonmarine Genera (IRMNG),
• Ocean Biogeographic Information System (OBIS),
• World Register of Marine Species (WoRMS),
• World Register of Deep-Sea Species ( WoRDSS ).
Some data providers are collectors of other data providers, but the alignment is not guaranteed!
The datasets allow to retrieve:
• Occurrence points (presence points or specimen)
• Taxa names

Biodiversity Data Retrieval
Merge
OBIS
GBIF
Catalog of Life
Visualise and explore
Format 1
Format 2
Format 3
SameFormat:DarwinCore
i-Marine
SPD service

Remote
i-Marine Species Products Discovery
Species Products
Discovery allows to
retrieve detailed
information from
several data
providers
We can visualize the occurrence points
on a map and visually detect the errors
We can inspect the
points metadata

i-Marine Species View
Species View allows
to discover species
information from
FishBase
FishBase
Also images and GIS
maps may be
attached to the
species

Online example:
the i-Marine Species Products Discovery
https://i-marine.d4science.org/group/biodiversitylab/species-
data-discovery

Geospatial data
• Data that identify the geographic location of features and boundaries on Earth
• Usually stored as coordinates and topology
• Accessed and processed through Geographic Information Systems (GIS)

OGC Standards
Some standards:
Web Maps Service (WMS): XML-based protocol that allows to display the datasets on
an interactive map viewer
Web Coverage Service (WCS): XML-based representation of space-time varying
phenomena (especially used for raster maps)
Web Features Service (WFS): XML-based representation for discrete geospatial
features (especially used for polygonal maps)
The Open Geospatial Consortium (OGC) is an international organization involving
more than 400 organizations. Promotes the development and implementation of
standards to describe geospatial data content and processing.

i-Marine Geospatial data access and visualisation
GeoExplorer is a web application (Portlet) for
geo-spatial layers to:
• Discover
• Inspect
• Overlay
• Save
WMS, WCS, WFS
The map depicts the
native range (~actual
distribution) of Latimeria
chalumnae

GeoExplorer: Data Discovery and Visualization
30
Layers Stack
Functions
Visualization
Discovery Metadata

Example:
the i-Marine GeoExplorer
https://i-marine.d4science.org/group/biodiversitylab/geo-visualisation

Supporting information sharing and
collaborative research
Reusability, Reproducibility, Repeatability of
Science
Sharing methods, data and findings via social
networking
Supporting data intensive Science
Free access to scientific discoveries
Science 2.0: next generation scientific research
and technologies

The Statistical Manager is a set of web services that aim to:
• Help scientists in computational biology experiments
• Supply precooked state-of-the-art processes as-a-Service
• Perform calculations by using Cloud computing
• Share input, results, parameters and comments with colleagues by means of Virtual
Research Environment
Statistical Manager
Statistical
Manager
D4Science
Computational
Facilities
Sharing
Setup and execution

Data processing rationale
External
Computing
Facility
OGC
WPS
Interface
Data preparation
Data processing
WPS
1. Prepare data
2. Analyse
3. Recommend actions to decision
makers

Innovation through integration
Vision: integration, sharing, and remote hosting help
informing people and taking decisions

Users
2015
Avg Users per
month
~20 430
Number of
Algorithms
~100
Organizations
providing
algorithms
1. CNR
2. Geomar
3. FIN
4. FAO
5. T2
6. IRD
7. Agrocampus
8. Ifremer
9. ICES
10. Univ. of
Salerno
11. Univ. Fed.
de Mato
Grosso
FishBase (CA,US,PHL)
44%
Naturhistoriska Riksmuseet
23%
Academia Sinica (Taiwan)
14%
Universitaet Kiel
13%
Museum National D'histoire
Naturelle, Paris
5%
Beijing
1%
King Abdullah
University Of
Science And
Technology
0%
Consiglio
Nazionale
Delle
Ricerche
(PISA)
0%
Inra - Centre De
Recherches De Rennes
0%
Other (individuals)
0%
FishBase (CA,US,PHL) Naturhistoriska Riksmuseet
Academia Sinica (Taiwan) Universitaet Kiel
Museum National D'histoire Naturelle, Paris Beijing
King Abdullah University Of Science And Technology Consiglio Nazionale Delle Ricerche (PISA)
Inra - Centre De Recherches De Rennes Other (individuals)

Computational boost
Processes developed by scientist usually require long computational time and come under
several programming languages.
E.g. FAO stock assessment process has been imported on the D4Science e-Infrastructure with
several benefits.
Standard R environment
• Sequential execution
• For R experts only
• Requires 30 days
D4Science
• Cloud computation
• Web interface available
for non experts
• Requires 15h and 20
min
• Produces the same
output as the R process
• 97.8% processing time
reduction
Output snippet

Example:
The Statistical Manager
https://i-marine.d4science.org/group/biodiversitylab/processing-tools

Biodiversity
Fill knowledge gaps on marine species
Account for sampling biases
Define trends for common species
Plankton regime shift
Herring recovered after the fish ban
LME - MEOW

Stock assessment
Length-Weight Relations: estimates Length-
Weight relation parameters for marine species,
using Bayesian methods. Developed by R. Froese, T.
Thorson and R. B. Reyes
SGVM interpolation: interpolation of vessels
trajectories. Developed by the Study Group on VMS,
involving ICES
FAO MSY: stock assessment for FAO catch data.
Developed by the Resource Use and Conservation
Division of the FAO Fisheries and Aquaculture
Department (ref. Y. Ye)
ICCAT VPA: stock assessment method for
International Commission for the Conservation
of Atlantic Tunas (ICCAT) data. Developed by
Ifremer and IRD (ref. S. Bonhommeau, J. Bard)
CMSY:estimates Maximum Sustainable Yield
from catch statistics. Prime choice for ICES as
main stock assessment tool. Developed by R.
Froese, G. Coro, N. Demirel, K. Kleisner and H. Winker
Atlantic herring
i-Marine reduced time-to-market:
State-of-the-art models to estimate
Maximum Sustainable Yield
computational time reduced of 95%
in average

Ecology
Atlantic cod
Coelacanth
Giant squid
AquaMaps
Neural
Networks
Neural
Networks
and MaxEnt

Geospatial data processing
Maps
comparison
NetCDF
file
Data extraction
Signal processing Periodicity detection
Maps generation

The giant squid - Architeuthis
16th century 2012
The giant squid (Architeuthis) has been reported worldwide even before the
16th century, and has recently been observed live in its habitat for the first
time.

Why rare species?
• Biological and evolutionary investigations
• Fisheries management policies and conservation
• Vulnerable Marine Ecosystems
• Key role in affecting biodiversity richness
• Indicators of degradation for aquatic ecosystems

Detecting rare species
• How to build a reliable distribution from few
observations?
• How to account for absence
locations?
• Is there any approach for
rare species?

Data quality
For rare species, data quality is fundamental:
• Reliable presence data
• Reliable absence locations
• High quality environmental features
• Non-noisy environmental features

Tools
From i-Marine:
• Retrieve presence data
• Generate absence data
• Get environmental data
• Model, adjust data and
produce maps
• Share results

1. Presence data of A. dux from i-Marine
https://i-marine.d4science.org/group/biodiversitylab/species-data-discovery

2. Simulating A. dux absence locations from
AquaMaps
0<Prob. < 0.2AquaMaps Native

3. Environmental Features
https://i-
marine.d4science.org/group/biodiversitylab/ge
o-visualisation
https://i-
marine.d4science.org/group/biodiversitylab/pr
ocessing-tools
Most of these layers were
available in D4Science
Depth and Distance from land
were imported using the
Statistical Manager

4. MaxEnt model as filter
MaxEnt
Env. features most
correlated to the giant
squid
Presence data
Env. data

Filtered Environmental Features

5. Presence/absence modelling:
Artificial Neural Networks (ANN)
Model trained on positive
and negative examples
In terms of env. features
Trained model
Presence/absence data
Filtered env. features
1
(presence data)
0
(absence data)

6. Projection of the Neural Network

7. Comparison
MaxEnt
(presence-only)
22.01% 21.68%
Similarity calculated using Maps
Comparison,
by Coro, Ellenbroek, Pagano
DOI: 10.1080/15481603.2014.959391
Expert map,
Nesis, 2003
Aquamaps
Suitable
(expert system)
Neural Network
(presence/absence)
42.83%
https://i-
marine.d4science.org/group/bio
diversitylab/processing-tools

Conclusions
• Using data quality enhancement produces high performance
distribution
• A presence/absence ANN combines these data
• Biological, observation and expert evidence confirm the prediction
by the ANN

Summary: modelling rare species
distributions
1. Retrieve high quality presence locations by relying on the metadata of the records,
2. Use expert knowledge or an expert system to detect absence locations.
Select absence locations as widespread as possible,
3. Select a number of environmental characteristics correlated to the species presence,
4. Use MaxEnt to filter the environmental characteristics that are really important with
respect to the presence points,
5. Train an Artificial Neural Network on presence and absence locations and select the best
learning topology,
6. Project the ANN at global scale, using the a resolution equal to the maximum in the
environmental features,
7. Train a MaxEnt model as comparison system.

Coelacanth (Latimeria chalumnae,
Smith 1939)
Coelacanths were thought to have gone extinct in the Late
Cretaceous, but were rediscovered in 1938 off the coast of
South Africa.
Its current form is closely related to its form 400 million
years ago. It is related to lungfishes and tetrapods.

Coelacanth’s distribution
Coelacanth, Smith 1939
GARP
MaxEnt
AquaMaps
Neural Network
Coro, Gianpaolo, Pasquale Pagano, and Anton Ellenbroek.
"Combining simulated expert knowledge with Neural
Networks to produce Ecological Niche Models for Latimeria
chalumnae." Ecological Modelling 268 (2013): 55-63.

Using e-Infrastructures for Biodiversity Conservation

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