http://knb.ecoinformatics.org http://seek.ecoinformatics.org

SEEK: Enabling Ecology and Biodiversity Science Through Cyberinfrastructure

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Time of publication

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Accident

Retirement or career change

Death

(Michener et al. 1997)

Data Entropy

What Users Really Want…

Contaminant TransportEcosystems, Biocomplexity

Marine Microorganisms Seismic Structure Response

SEEK Overview

Morpho

SEEK

Science Environment for Ecological Knowledge

EcoGrid Access to environmental data

Analysis and Modeling System Modeling scientific workflows

Semantic Mediation System “Smart” data discovery and integration

Knowledge Representation Classification and Nomenclature Biodiversity and Ecological Analysis and Modeling

Lotka-Volterra Predator Prey Model

SEEK EcoGrid

Integrate diverse data networks from ecology, biodiversity, and environmental sciences

Grid-standardized interfaces

Metadata-mediated data access (EML)

Computational access Pre-defined analytical services On-the-fly analytical services

EcoGrid client interactions

Modes of interaction Client-server Fully distributed Peer-to-peer

EcoGrid Registry Node discovery Service discovery

Aggregation services Centralized access Reliability Data preservation

Label data with semantic types Label inputs and outputs of analytical components with

semantic types

Use reasoning engines to generate transformation steps Beware analytical constraints

Use reasoning engine to discover relevant components

Semantic Mediation

Data Ontology Workflow Components

Ecological ontologies

What was measured (e.g., biomass) Type of measurement (e.g., energy) Context of measurement (e.g., Psychotria

limonensis) How it was measured (e.g., dry weight)

Growl Ontology Viewer Showing the Measurements Ontology

Geographic Space Ecological Space

occurrence points on native distribution

ecological niche modeling

Projection back onto geography

Native range prediction

Invaded range prediction

The SEEK Prototype: Ecological Niche Modeling

temperature

Model of niche in ecological dimensions

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Biodiversity information … e.g., data from

museum specimens

Geospatial and remotely sensed

data

Results taken to integration with

other data realms (e.g., human populations, public health,

etc.)

Species prediction map

PredictedDistribution:Amur snakehead(Channa argus)

Image from http://www.lifemapper.org

Future Scenarios

Future scenarios based on general circulation models (GCMs) present diverse visions of global climate futures. The implications of these different futures for biodiversity are only now being explored…

SEEK/Kepler Mammal-CC Project

Build pipelines for automated processing of CC/biodiversity analyses

Prototype application to mammals of Western Hemisphere

15-20 GCM scenarios,including all IPCC scenarios

2 major evolutionary-computingalgorithms (GA and NN)

2-3 dispersal scenarios

• 2000-3000 species• 2 algorithms• 100+ models/species/algorithm• 500,000 – 1,000,000 models• Test large-scale implementation of Kepler• Provide a hemisphere-wide view of mammal diversity• Provide a massive comparison of CC implications as function of

• region• clade• scenario

•Enable many other parallel apps

Networking

Biomedical Informatics Research NetworkCHESS

Education

SEEK New Faculty & Postdoc WorkshopJanuary 5-9, 2004

70 applicants16 slots

Citizen Science