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.
3.
4.
5.
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.
1.
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).