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

Outline


• i-Marine


• 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/

http://www.beliefproject.org/

http://www.openaire.eu/

http://www.i-marine.eu/



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


• Geospatial data

• Data processing

• Examples

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

Outline


• i-Marine


• Geospatial data

• Data processing

• Examples

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

VS

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

http://www.catalogueoflife.org/

http://www.gbif.org/

http://www.itis.gov/

http://www.cmar.csiro.au/datacentre/irmng/

http://www.iobis.org/

http://www.marinespecies.org/

http://www.marinespecies.org/deepsea/

http://www.marinespecies.org/deepsea/

Biodiversity Data Retrieval

Merge

OBIS

GBIF

Catalog of Life

Visualise and explore

Format 1

Format 2

Format 3 Sam

e Form

at: Darw

in C

ore

i-Marine SPD service

Remote

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

Outline


• i-Marine


• Geospatial data

• Data processing

• Examples

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

Outline


• i-Marine


• Geospatial data

• Data processing

• Examples

Data Processing

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

Outline


• i-Marine


• Geospatial data

• Data processing

• Examples

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

Time series forecasting

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

One complete experiment

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. < 0.2 AquaMaps Native

3. Environmental Features

https://i-marine.d4science.org/group/biodiversitylab/geo-visualisation https://i-marine.d4science.org/group/biodiversitylab/processing-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%


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.

Thank you