The National Biological Information Infrastructure Access to Environmental Information

The National Biological Information Infrastructure

Access to Environmental Information

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

• 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

• Integrated Taxonomic Information System (ITIS)– USDA Plants– Species 2000, Global Biodiversity Information

Facility (GBIF)

• Collaboration with museum community– Species Analyst

State Partnerships

• Gap Analysis– Imagery– Vegetation maps– Habitat suitability for wildlife– Gaps in conservation coverage– Support for classification and metadata

standards

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

• Access to federal databases

• Topical news

• Search facilities (e.g. Biobot)

• Metadata standards

• Thesaurus services (CSA)

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

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

FishUniversity of Florida

FishUniversity of Florida

detail

FishTulane University

FishUniversity of Michigan

Fish“World Museum”

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)

Incentives to share• Tools• Publication and professional recognition• Peer review

Danger: Garbage In, Gospel Out

The NBII California Information Node Project

(CAIN)

Information Technology for Invasive Species Researchers and Managers

Friends and ColleaguesMultinational:

MAB

IABIN

NABIN

GISP

Mexico:CONABIO, UNAM

Brazil: Base de Dados Tropical

Venezuela:Universidad Central de Venezuela

RussiaKomarov 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 Ongoing Funding Partnerships/Infrastructure

USGS (BRD, FGDC)US EPA (Center for Ecological Health Research)NSF (PACI, STAR)NASA Center of ExcellenceCalFed Bay-Delta Program USDA (NRCS)California Biodiversity CouncilCalifornia Environmental Protection AgencyCalifornia Department of Transportation

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:

• 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

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

No Data

Number of Species1 - 5151 - 120120 - 197197 - 303303 - 674

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

• Experts• Organizations• Species lists by

organization• Data resources• Projects• Fact sheets• Occurrence data

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?

Museum NGOBorder

InspectionProviders

University

Photo

Shared data

Experts Occurrences Images Outcomes

Interpretation clients

OnlineMapping PredictionAlert

AbstractExtract

Classify Model

Reward!

So How is this Achieved?

Asian Longhorn Beetle(Anoplophora glabripennis)

Asian Longhorn Beetle 1 - Native Distribution in Asia

Asian Longhorn Beetle 4 - Twenty Environmental Layers

Services needed: Identification aides

• Polyclave keys – language appropriate –– It’s big– It’s green– It’s ugly– It’s…

Giant Cane (Arundo donax)

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

• 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

• Early warning systems

• Risk assessments

• Distributional mapping

What do clients want?• Pick and click on any point, land managementunit, county, state, orregion and determineThe current invasion,and vulnerability tofuture invasion by manyspecies.

(help public and privateland managers).

Weed mapping with aerial photos

•Data gathering• Species taxonomy• Data formatting• Synthesis• Predictive modeling• Analysis anddisplay tools• Data accessibilityvia the web

InformationManagement andmodeling (USGS,

NASA, CSU, UCD)

Reports on the status and trends

of non-native species in the U.S.

National-scale mapsof non-native

species distributions

Predictive modelsof habitats vulnerable

to invasion

Predictive modelsof the spread ofInvasive species

National, regional,and local prioritiesfor control efforts

CLEARINGHOUSE

OUTPUTSINPUTS

Vegetation and soilsplot data (USFS,

USGS, BLM)

County-leveldata on vascularplants (BONAP)

National data onbirds, mammals, and

diseases (USGS)

Watershed-leveldata on fishes

(USGS)

Point data on publiclands (USFWS,

NPS, USGS)

•Data gathering• Species taxonomy• Data formatting• Synthesis• Predictive modeling• Analysis anddisplay tools• Data accessibilityvia the web

InformationManagement andmodeling (USGS,

NASA, CSU, UCD)

Reports on the status and trends

of non-native species in the U.S.

National-scale mapsof non-native

species distributions

Predictive modelsof habitats vulnerable

to invasion

Predictive modelsof the spread ofInvasive species

National, regional,and local prioritiesfor control efforts

CLEARINGHOUSE

OUTPUTSINPUTS

Vegetation and soilsplot data (USFS,

USGS, BLM)

County-leveldata on vascularplants (BONAP)

National data onbirds, mammals, and

diseases (USGS)

Watershed-leveldata on fishes

(USGS)

Point data on publiclands (USFWS,

NPS, USGS)

Distributed

Web-net

S-Plus: test residuals for auto-correlation and cross-correlation (Morans-I) and find the best model (ordinary least squares, gausian, etc. using AICC criteria).

ArcView: produce maps of current distributions, potential distributions, and vulnerable habitats, with known levels of uncertainty.

Field Data: Invasive species data, veg., soils, topography, etc.

S-Plus: Develop Multivariate Model,screen and normalize data, test for tolerance/multi-colinearity, and run stepwise regression.

ArcGIS: Input satellite data, veg., soils, topography, etc.

S-Plus/Fortran: If spatially autocorrelated, run kriging or co-kriging models.

ArcInfo GIS: develop map of model uncertainty from S-Plus output, Monte-Carlo simulations, observed-expected values.

6.

1.

2.

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Current Predictive Modeling Capabilities

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.

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2.

3.

Future “Ecological Forecasting” Models:Far more automated, instantaneous, and continuous!

OR

Repeat Step 1 – always be looking for new data

Or . . .Pick and click on anyspecies or group ofspecies, and get currentdistributions, potentialdistributions, potentialrates of change,and levels of uncertainty.

(We have much to learnhere! HPCC exampleon West Nile Virus).