using e-infrastructures for biodiversity conservation - module 3

Using e-Infrastructures for Biodiversity Conservation

Gianpaolo Coro ISTI-CNR, Pisa, Italy

• Biodiversity and geospatial data• Trends in biodiversity observations• Combining species observations• Combining biodiversity and geospatial data

Module 3 - Outline

D4ScienceD4Science is both a Data and a Computational e-Infrastructure

• Used by several Projects: i-Marine, EUBrazil OpenBio, ENVRI;

• 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.

D4Science - ResourcesLarge Set of Biodiversity and Taxonomic Datasets connected

A Network to distribute and access to Geospatial Data

Distributed Storage System to store datasets and documents

A Social Networkto share opinions and useful news

Algorithms for Biology-related experiments

• Biodiversity and geospatial data• Trends in biodiversity observations• Combining species observations• Combining biodiversity and geospatial data

Module 3 - Outline

Biodiversity and Geospatial 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

Online Examples:http://www.catalogueoflife.org/

http://www.gbif.org/http://www.iobis.org/

Geospatial Data Providers

Bio-ORACLE

NetCDF NetCDFASCIIArcGIS

ASCII Raw formatsWorld Ocean Atlas

Online Examples:http://www.myocean.eu

https://www.nodc.noaa.gov/OC5/woa13/http://www.oracle.ugent.be/

ToolsUI ftp://ftp.unidata.ucar.edu/pub/netcdf-java/v4.5/toolsUI-4.5.jar

• Biodiversity and geospatial data• Trends in biodiversity observations• Combining species observations• Combining biodiversity and geospatial data

TrendylyzerTrendylyzer allows to discover species observation trends.It is based on the OBIS collector

OBIS

This trend tells the story of the Coelacanth discovery

Online Example: the i-Marine Trendylyzer

https://i-marine.d4science.org/group/biodiversitylab/trends-production

• Biodiversity and geospatial data• Trends in biodiversity observations• Combining species observations• Combining biodiversity and geospatial data

Cleaning

Union – Difference - Intersection

Occurrences Points Operations

A

x,y

Event Date

Modif Date

Author

Species Scientific Name

d(x,y) < Distance Thr

=

LD(Author) * LD(SciName) > Lexical Thr

<Take the most recent>

B

x,y

Event Date

Modif Date

Author

Species Scientific Name

Evaluate

Experiment

Solea solea57 085 Records2 324 Records

1 871 Records10 542 Records

Duplicates Deletionwith Exact Match(DThr=0; LThr=1)

Subtraction

DThr=0.01; LThr=0 DThr=0.01; LThr=1DThr=0.0001;

LThr=0.8

183 Records 0 Records 0 Records

Main remarks:

• The “recordedBy” fields contain differences in names formats

• The Scientific Names fields are different (names vs names and codes)

• D4Science helps in collecting a larger number of Solea solea unique occurrence records

• Even if GBIF collects data from OBIS, the coverage is not updated

Occurrences Points Operations

Occurrences Duplicates Deleter:An algorithm for deleting similar occurrences in a sets of occurrence points coming from the Species Discovery Facility of D4Science.

A

Occurrences Points Operations

Occurrences Intersection: Between two Ocurrence Sets A and B, keeps the elements of the B that are similar to elements in A.

A B

Occurrences Points Operations

Occurrences Subtraction:Between two Ocurrence Sets A and B, keeps the elements of the A that are not similar to any element in B

A B

Occurrences Points Operations

Occurrences Merger:Between two Ocurrence Sets A and B, enriches A with the elements of B that are not in the A. Updates the elements of the A with more recent elements in B. If one element in A corresponds to several recent elements in B, these are substituted to the element of A.

A

B

Online experiments: the i-Marine

Occurrence Management systemhttps://i-marine.d4science.org/group/biodiversitylab/processing-tools

• Biodiversity and geospatial data• Trends in biodiversity observations• Combining species observations• Combining biodiversity and geospatial data

Module 3 - Outline

Combining Biodiversity and Geospatial data

Environmental layers

Species occurrence dataset

Enriched dataset

Online Experiments:https://i-marine.d4science.org/group/biodiversitylab/processing-tools

One practical application

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 forrare species?

Data qualityFor rare species, data quality is fundamental:

• Reliable presence data • Reliable absence locations• High quality environmental features• Non-noisy environmental features

Tools – i-marine.d4science.orgD4Science e-Infrastructure:

• Retrieve presence data• Generate absence data• Get environmental data• Model, adjust data and

produce maps• Share results

1. Presence data of A. dux from D4S

https://i-marine.d4science.org/group/biodiversitylab/species-data-discovery

2. Simulating A. dux absence locations from AquaMaps

https://i-marine.d4science.org/group/biodiversitylab/processing-tools

0<Prob. < 0.2AquaMaps 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 landwere imported using the Statistical Manager

4. MaxEnt model as filter

https://i-marine.d4science.org/group/biodiversitylab/processing-tools

MaxEntEnv. 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 examplesIn terms of env. features

Binary file

https://i-marine.d4science.org/group/biodiversitylab/processing-tools

Presence/absence data

Filtered env. features

6. Projection of the Neural Network

https://i-marine.d4science.org/group/biodiversitylab/processing-tools

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/biodiversitylab/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.

Just another exampleCoelacanth, 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.