NBII Newark 10 02

The National Biological Information Infrastructure: 

The National Biological Information Infrastructure Access to Environmental Information

What is the National Biological Information Infrastructure(NBII)?: 

What is the National Biological Information Infrastructure (NBII)? Federal effort to establish standards, technologies, and partnerships to improve access to and exchange of biological information Result of the Summit of the Americas Conference on Sustainable Development in 1996 and a PCAST Panel on Biodiversity and Ecosystems report, 1998 WWW portal to environmental websites, databases, and experts Emphasis on latest political topics

NBII incorporates multiple federal environmental information resources: 

NBII incorporates multiple federal environmental information resources Wildlife data Audubon Christmas Counts Breeding Bird Survey Non-Indigenous Aquatic Species Wildlife Diseases Mapping National Vegetation Map All in relational databases or GIS

Taxonomic Services: 

Taxonomic Services Integrated Taxonomic Information System (ITIS) USDA Plants Species 2000, Global Biodiversity Information Facility (GBIF) Collaboration with museum community Species Analyst

State Partnerships: 

State Partnerships Gap Analysis Imagery Vegetation maps Habitat suitability for wildlife Gaps in conservation coverage Support for classification and metadata standards

International: 

International U.S. lead for Man and the Biosphere (UNESCO) IABIN (Summit of the Americas) NABIN (NAFTA) GBIF News flash – first World Data Centre for biological data All have embraced SW technologies as a basis for international exchange (at least in principle)

Services: 

Services Access to federal databases Topical news Search facilities (e.g. Biobot) Metadata standards Thesaurus services (CSA)

Network of “Nodes”: 

Network of “Nodes” Geographic Gulf Coast (Texas) Left Coast (California) Pacific Basin (Hawaii) Pacific Northwest Northern Rockies Southern Appalachians Southwest Thematic Avian Fisheries and watersheds Invasive Species Core Infrastructure Administration

Vision & Objectives : 

Vision & Objectives Principles for environmental informatics based on distributed nodes: Environmental information generally should be managed at its source Core data (“Darwin Core”) should be transparently shared, idiosyncratic data should be discoverable

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Fish University of Florida

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Fish University of Florida detail

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Fish Tulane University

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Fish University of Michigan

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Fish “World Museum”

Principles (2) : 

Principles (2) Sharing requires shared vocabularies Taxonomy -- ITIS Subject -- LOC, CERES Geolocation Methodology Vocabularies are user-community specific Natural extensions to XML, data mining technologies

Principles (3) : 

Principles (3) Incentives to share Tools Publication and professional recognition Peer review Danger: Garbage In, Gospel Out

The NBII California Information Node Project (CAIN): 

The NBII California Information Node Project (CAIN) Information Technology for Invasive Species Researchers and Managers

Friends and Colleagues: 

Friends and Colleagues Multinational: MAB IABIN NABIN GISP Mexico: CONABIO, UNAM Brazil: Base de Dados Tropical Venezuela: Universidad Central de Venezuela Russia Komarov Botanical Institute United States: USGS International Programs USGS Nonindigenous Aquatic Species Program Smithsonian Environmental Research Center Hawaiian Ecosystems at Risk Project NHM & Biodiversity Research Center, University of Kansas California: California Biodiversity Council California Exotic Plant Pest Council California Food & Agriculture California Department of Transportation

California Node : 

California Node Ongoing Funding Partnerships/Infrastructure USGS (BRD, FGDC) US EPA (Center for Ecological Health Research) NSF (PACI, STAR) NASA Center of Excellence CalFed Bay-Delta Program USDA (NRCS) California Biodiversity Council California Environmental Protection Agency California Department of Transportation

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Invasive Species: The Top Environmental Issue of the 21st Century Economic costs ($138 Billion/year). Environmental costs (40% of Threatened and Endangered Species, many native species declines). Human-health costs (West Nile Virus, Aids, malaria, others on the way). Increased unintentional spread, or threat of ecological terrorism (hoof-and-mouth, mad cow disease, crop pathogens). Notorious examples include Dutch elm disease, chestnut blight, and purple loosestrife in the northeast; kudzu, Brazilian peppertree, water hyacinth, nutria, and fire ants in the southeast; zebra mussels, leafy spurge, and Asian long-horn beetles in the Midwest; salt cedar, Russian olive, and Africanized bees in the southwest; yellow star thistle, European wild oats, oak wilt disease, Asian clams, and white pine blister rust in California; cheatgrass, various knapweeds and thistles in the Great Basin; whirling disease of salmonids in the northwest; hundreds of invasive species from microbes to mammals in Hawaii; and the brown tree snake in Guam. Hundreds new each year!

What invasives are:: 

What invasives are: Fire stimulators and cycle disruptors Water depleters Disease causers Crop decimators Forest destroyers Fisheries disruptors Impeders of navigation Clogger of water works Destroyer of homes and gardens Grazing land destroyers Noise polluters Species eliminators Modifiers of evolution GISP

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Data Synergies: inputs for early detection, risk assessment, and “ecological forecasting” models

California Invasive Species Information System (CRISIS):Client Products: 

California Invasive Species Information System (CRISIS): Client Products Interactive Mapping Alert Systems- new sightings of potentially invasive species e.g., GISP, FICMNW Prediction of invasive species spread Data Mining Oak Ridge Mercury Center

Important Information Types: 

Important Information Types Experts Organizations Species lists by organization Data resources Projects Fact sheets Occurrence data

Inquiries to support: 

Inquiries to support What is this? What kind of problem is it? Where else is it a problem? What are its vectors and pathways? Who knows something about it? Where might it go next? What are effective management methods?

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Museum NGO Border Inspection Providers University P h o t o Shared data Experts Occurrences Images Outcomes Interpretation clients Online Mapping Prediction Alert Abstract Extract Classify Model Reward!

So How is this Achieved?: 

So How is this Achieved?

Asian Longhorn Beetle (Anoplophora glabripennis): 

Asian Longhorn Beetle (Anoplophora glabripennis)

Asian Longhorn Beetle 1 - Native Distribution in Asia: 

Asian Longhorn Beetle 1 - Native Distribution in Asia

Slide30: 

Asian Longhorn Beetle 4 - Twenty Environmental Layers

Services needed: Identification aides: 

Services needed: Identification aides Polyclave keys – language appropriate – It’s big It’s green It’s ugly It’s…

Giant Cane (Arundo donax): 

Giant Cane (Arundo donax)

Team Arundo del Norte Mapping and Digital Library Effort: 

Team Arundo del Norte Mapping and Digital Library Effort Objectives: Develop a standard methodology for collecting weed field data train local projects in its use Share data across many watershed groups Needed: Digital fieldform technology

Needs: Assessing trust in citizen observations: 

Needs: Assessing trust in citizen observations Museum expert or Mrs. Smith’s 3rd grade class? (Ag commissioners, native plant societies…) Documentation (e.g. digital photos) Annotation methods (ex: CalFlora) Estimating reliability from subsequent use?

Web Services: 

Web Services Early warning systems Risk assessments Distributional mapping

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What do clients want? Pick and click on any point, land management unit, county, state, or region and determine The current invasion, and vulnerability to future invasion by many species. (help public and private land managers).

Weed mapping with aerial photos: 

Weed mapping with aerial photos

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Distributed Web-net

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Current Predictive Modeling Capabilities

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Field Data: Early detection or monitoring data, from many sources. Web-ware: Develop multivariate model, screen and normalize data, test for tolerance/multi-colinearity, and run combinatorial screening. Test residuals for auto-correlation and cross-correlation (Morans-I) and find the best models. If spatial autocorrelation exists, run kriging or co-kriging models. Develop map of models uncertainty (maps with standard errors). Produce maps of current distributions, potential distributions, and vulnerable habitats, with known levels of uncertainty. ArcView: Input satellite data, via new sensors or change detection models. 1. 2. 3. Future “Ecological Forecasting” Models: Far more automated, instantaneous, and continuous! OR Repeat Step 1 – always be looking for new data

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Or . . . Pick and click on any species or group of species, and get current distributions, potential distributions, potential rates of change, and levels of uncertainty. (We have much to learn here! HPCC example on West Nile Virus).