Jennifer A. Dunne

Santa Fe InstitutePacific Ecoinformatics & Computational

Ecology Lab

Rich William, Neo Martinez, et al.

www.foodwebs.org

Challenges & Opportunitiesfor Ecological Informatics

• Ecologists make observations and save them as spreadsheet or text files

• Raw data rarely published or made available online• Summaries provide minimal ad-hoc descriptions of data context• Context (why, when, where, how) lost as time proceeds

Time since observationTime since observation

Valu

e o

f ob

serv

ati

on

Valu

e o

f ob

serv

ati

on

Challenge #1: Basic Data

Challenge #2: Finding & Integrating Data

• Data relevant for ecological questions is DIVERSE & DISPERSED

• Available databases often primitive (little or no metadata)• Data gathering, integration, and synthesis are done by

hand

Time since observationTime since observation

Valu

e o

f ob

serv

ati

on

Valu

e o

f ob

serv

ati

on

Ecoinformatics: technologies and practices for gath-ering, analyzing, visualizing, storing, retrieving and other-wise managing ecological knowledge and information

Time since observationTime since observation

Valu

e o

f ob

serv

ati

on

Valu

e o

f ob

serv

ati

on

Ecoinformatic fantasy

Early food-web researchers (primarily Joel Cohen) introduced sharable catalogs of ecological datasets:

1978: First published “catalog” included 30 food webs

1986: Catalog expanded to 113 food webs

1989: EcOWEB, the first “machine-readable data base of food webs” (now >200 webs)

Interest in comparative studies led to a culture of data sharing and an early “first-generation” data base.

BUT, current access to food web data is still primitive: Requesting EcOWEB floppy disc from Cohen/Rockefeller Hand-mining individual datasets from literature Contacting researchers individually Emailing me for second generation data

From Data to Data Bases

In Final Development: Webs on the Web (WoW)

1) Knowledge Base -100s to 1000s of food webs and other ecological networks, flexible data format-10Ks to 100Ks instances of feeding interactions-Species info (taxonomy, phylogeny, biomass, body size, metabolic rates, etc.)-Quantitative link info (frequency, flow, preference, etc.)-Additional info (geographic, provenance, versions, citations, geographic, etc.)-Downloading, uploading, annotation capabilities

2) Analysis Tools-Calculation of dozens of network structure properties-Modeling (network structure, nonlinear bioenergetic dynamics)-In silico experiments (biodiversity loss, invasions, etc.)-Link to other software (Pajek, Mage, EcoPath/EcoSim, etc.)-Trophic inference (phylogenetic, morphological)-Pipeline architecture allows users to plug in their own algorithms

3) Visualization Tools-Highly interactive and customizable 3D visualizations of ecological networks-Animations of dynamics, and graphical output of simulations-Images & movies of species & interactions (www.arkive.com - Images of Life on

Earth)

From Data Bases to Knowledge Bases

Challenges: usability, quality control, security, accessibility, storage, maintenance

Linking KBs through Semantic Webs Tools

““The Semantic WebThe Semantic Web is an extension of the is an extension of the current web in which information is given well-current web in which information is given well-defined meaning, better enabling computers and defined meaning, better enabling computers and people to work in cooperation.”people to work in cooperation.”

-Tim Berners-Lee et al. (2001) The Semantic Web. Scientific American

Semantic Web technologies are being “designed to improve communications between people using differing terminologies to extend the interoperability of databases.”

-Jim Hendler (2003) Science and the Semantic Web. Science

How to integrate dispersed, heterogeneous ecological information

resources on the WWW?

SPiRE: Semantic Prototypes in Research Ecoinformatics

SWISST: Semantic Web Informatics for Species in Space & Time

Creating a first generation of user-friendly and highly extensible open-source software that stores, retrieves, analyzes, visualizes, and integrates distributed information relating to variation within and among species.

Ontologies: mapping between current terminologies and cleaned-up, structured categories Integrative web services: queries & information mashups Integrative reasoning tools Easy-to-use user interfaces

Three Central Tasks for SPiRE & SWISST:

1) Implement new information architectures to increase functionality of ecoinformatic software

2) Implement new client tools with user-friendly GUIs (for concept refining; data entry, discovery, analysis; data visualization)

3) DBs into KBs: extend ontologies & increase metadata

New Semantic Web Standards (W3C):

OWL: ontology web languageRDF: resource description frameworkSWRL*: semantic web rules languageSPARQL: SPARQL protocol and RDF query language

Projects funded by the U.S. National Science Foundation:Projects funded by the U.S. National Science Foundation:

Webs on the Web: Webs on the Web: Internet Database, Analysis & Visualization of Ecological NetworksInternet Database, Analysis & Visualization of Ecological Networks

Science on the Semantic Web: Science on the Semantic Web: Prototypes in BioinformaticsPrototypes in Bioinformatics

Science Environment for Ecological Knowledge (NCEAS)Science Environment for Ecological Knowledge (NCEAS)