1. Jennifer A. Dunne Santa Fe Institute Pacific Ecoinformatics & Computational Ecology Lab Rich William, Neo Martinez, et al. www.foodwebs.org Challenges & Opportunities for Ecological Informatics

2. 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 observation Value of observation Challenge #1: Basic Data

3. 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 observation Value of observation

4. Ecoinformatics: technologies and practices for gath- ering, analyzing, visualizing, storing, retrieving and other- wise managing ecological knowledge and information Time since observation Value of observation Ecoinformatic fantasy

5. 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

6. 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)www.arkive.com From Data Bases to Knowledge Bases Challenges: usability, quality control, security, accessibility, storage, maintenance

7. Linking KBs through Semantic Webs Tools “ The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and 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

8. 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

10. 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 language RDF: resource description framework SWRL*: semantic web rules language SPARQL: SPARQL protocol and RDF query language

11. Projects funded by the U.S. National Science Foundation: Webs on the Web: Internet Database, Analysis & Visualization of Ecological Networks Science on the Semantic Web: Science on the Semantic Web: Prototypes in Bioinformatics Science Environment for Ecological Knowledge (NCEAS)