Transcript NBII - Center For Information Management, Integration and

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
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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
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Imagery
Vegetation maps
Habitat suitability for wildlife
Gaps in conservation coverage
Support for classification and metadata
standards
International
• U.S. lead for
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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
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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
Fish
University of Florida
Fish
University of Florida
detail
Fish
Tulane University
Fish
University of Michigan
Fish
“World Museum”
Principles (2)
Sharing requires shared
vocabularies
• Taxonomy -- ITIS
• Subject -- LOC, CERES
• Geolocation
• Methodology
Vocabularies are usercommunity specific
Natural extensions to XML,
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 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
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
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 (hoofand-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
Num be r of S pe cies
1 - 51
51 - 120
120 - 19 7
197 - 30 3
303 - 67 4
No Data
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
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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?
Online
Mapping
Alert
Classify
Experts
P
h
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Prediction
Model
Occurrences
Images
Extract
Abstract
Reward!
University
Museum
Interpretation
clients
NGO
Shared data
Outcomes
Border
Inspection
Providers
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 –
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It’s big
It’s green
It’s ugly
It’s…
Giant Cane (Arundo donax)
Needed: Digital fieldform technology
Objectives:
– Develop a standard
methodology for
collecting weed field
data
– train local projects in
its use
– Share data across
many watershed
groups
Team Arundo del Norte
Mapping and Digital Library Effort
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 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
INPUTS
OUTPUTS
County-level
data on vascular
plants (BONAP)
Distributed
National data on
birds, mammals, and
diseases (USGS)
Information
Management and
modeling (USGS,
NASA, CSU, UCD)
Predictive models
of habitats vulnerable
to invasion
Watershed-level
data on fishes
(USGS)
•Data gathering
• Species taxonomy
• Data formatting
• Synthesis
• Predictive modeling
• Analysis and
display tools
• Data accessibility
via the web
Predictive models
of the spread of
Invasive species
Point data on public
lands (USFWS,
NPS, USGS)
Vegetation and soils
plot data (USFS,
USGS, BLM)
CLEARING
HOUSE
Web-net
National-scale maps
of non-native
species distributions
National, regional,
and local priorities
for control efforts
Reports on the
status and trends
of non-native
species in the U.S.
Current Predictive Modeling Capabilities
1.
ArcGIS: Input satellite data,
veg., soils, topography, etc.
Field Data: Invasive species
data, veg., soils, topography, etc.
2.
S-Plus: Develop Multivariate Model,screen and normalize data, test for
tolerance/multi-colinearity, and run stepwise regression.
3. 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).
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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.
ArcView: produce maps of current distributions, potential distributions,
and vulnerable habitats, with known levels of uncertainty.
Future “Ecological Forecasting” Models:
Far more automated, instantaneous, and continuous!
1.
ArcView: Input satellite
data, via new sensors or
change detection models.
2.
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.
OR
Field Data: Early detection
or monitoring data, from
many sources.
3. Repeat Step 1 – always be looking for new data
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).