“One if by land, two if by sea. . .”Documenting, Mapping, and Predicting the Invasionof Non-native Plants, Animals, and Diseases in the United States.

Mike Ielmini (USFWS), John Schnase and Jim Smith (NASA), Pam Fuller, Josh Dein, John Sauer,

Carl Korcshgen, Linda Leake, Doug Posson, Anne Frondorf, Tom Muir, Bill Gregg, Sue Haseltine, Tom Owen, (USGS), John Kartesz (UNC), Jim Quinn

(UCD), Ken Stolte (USFS) and many more.

Tom Stohlgren, USGSMidcontinent Ecological Science Center, Fort Collins, CO

and Research TeamGeneva Chong and Catherine Crosier (USGS)

Mohammed Kalkhan, Robin Reich,Dave Barnett, Sara Simonson,

and Rick Shory (CSU)

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!

Why Us, Why Now?

• There are many data collectors, but few scientists who specialize in data synthesis and predictive modeling at multiple scales.

• The USGS, with the cooperation of many partners, is uniquely qualified to lead invasive species research that integrates species traits, vulnerability of populations and habitats to invasion, early detection, risk analysis, and predictive models for “ecological forecasting.”

• There is extreme urgency at local, regional, and national scales – the invasion is not only underway, it is accelerating and we’re unprepared.

Why Us, Why Right Now?

• Our expanded research team was tired of writing “white papers, budget initiatives, and progress reports on “case studies.” We want to accomplish much more!

• Clients (USFWS, BLM, USFS, NPS, states) demanded that we synthesis data, design new surveys and monitoring methods, and rapidly develop predictive models for better early detection and control of many invasive species.

• We approached several colleagues to begin a “data cooperative” of sorts – the first nation-wide collection of data on non-native plants, animals, and diseases integrated with new capabilities for the predictive modeling of species, populations, and habitats at multiple scales.

• The response has been incredible! We must seize the moment!

On the Policy Front:• The U.S. is beginning the

development of the “Implementation Plan for the National Invasive Species Management Plan.”

• APHIS is suggesting strong policy changes regarding the import of plants and animals.

• There is increasing awareness of the effects of rapid biological invasions.

“Needed: A National CenterFor Biological InvasionsBy Don Schmitz and DanSimberloff” Issues in ScienceAnd Technology Summer 2001

   

 

DEPARTMENT OF AGRICULTURE Animal and Plant Health InspectionService 7 CFR Part 330[Docket No. 95-095-2]RIN 05789-AA80

Plant Pest Regulations; Update of Current Provisions AGENCY: Animal and Plant HealthInspection Service, USDA.ACTION: Proposed Rule.

On the Science Front:• Better survey and

monitoring techniques have been developed. (multi-phase, multi-scale, nested-intensity designs).

• Better modeling techniques have been developed.

• More access and uses of high performance computing capabilities (Beowulf clusters, supercomputers, leased power).

• Fewer barriers exist to sharing data.

0 10 20 30 400

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XCOORD

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40

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XCOORD

0

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0 10 20 30 40 500

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% Soil Clay

Native Species

Exotic Species

% Soil N

YC

OO

RD

Current information capabilities: analysis and outreach, primarily with USGS data

Birds +Mammals

Plants

WildlifeDiseases

Fish +Aquaticplants

Hawaii

SW

Midwest

East/South

Texas

California

Geographic Approach(slightly better funded, but locally scaled)

Taxonomic Approach(grossly under-funded,but national scale)

Improving capabilities for synthesis, research, and outreach, with data from all sources

Birds +Mammals

Plants

HighPerformance

ComputerModels

High Resolution

HabitatMaps

WildlifeDiseases

Fish +Aquaticplants

WebTools forResearch

+ Outreach

Hawaii

SW

Midwest

East/South

Texas

California

Reston/MESC

Geographic Synergies

Thematic Approach

Taxonomic Synergies

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

No Data

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

Data Synergies: Weeds in Colorado

•County Quarter-Quad

•National Parks, National Refuges, and Military Lands, LTER sites.

•Forest Health Monitoring Plots, other forest data.

•County Level Data

•Natural Heritage Network

•Modified Whittaker Plots

(USGS and others)

•Nature Conservancy

•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

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).

Plants P= Animals P= Diseases P=Cheatgrass 1.0 Norway rat 1.0 Blister rust .5

Musk thistle

.99 Fire ants .01 plague .4

Leafy spurge

.65 Brown trout

.01

Water hyacinth

.02

Cover %

Musk thistle Cardus iforgotus

Refuge:LaCreek Wildlife RefugeSouth DakotaUpdated:10/02/02Regional zoom?Metadata?Plot data?Control Info?

Yes

Yes

Map Uncertainty?

Yes

Yes

Plant ID Help? Yes

Zoom?

Yes

Yes

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).

SolutionsObstacles

5. Dedication combined withenthusiasm and perseverance.

1. Lack of data sharing.

2. Uncoordinated budgetprocess (DOI, USDA, Commerce).

5. Urgency combined withinadequate funding.

1. Incentives, support,rewards for sharing.

2. Joint budget committee,share “line items” ideas.

3. Computing power, leaving the “PC stage.”

3. Beowulf clusters,supercomputer use.

4. Modeling spatial and temporalvariation simultaneously.

4. “Frontiers of science”challenge.

A growing list of partners:U.S. Geological Survey: T. Stohlgren, G. Chong, and C. Crosier (Midcontinent Ecological Science Center, plants, data management), J. Sauer (Patuxent Wildlife Research Center, birds, mammals), P. Fuller (Florida Caribbean Science Center, fish), J. Dein (National Wildlife Health Center, diseases), C. Korschgen (Columbia Environmental Research Center, web-tools), L. Leake ( Upper Midwest Environmental Science Center, data management), T. Owen (Center for Biological Informatics, information mapping), A. Frondorf (Reston Office, high-performance computing), M. Ruggiero (Integrated Taxonomic Information System, taxonomy, synonyms), W. Gregg (Invasive Species Coordinator, Reston Office). T. Muir, R. Westbrooks (early detection), S. Haseltine (HQ).U.S. Fish and Wildlife Service: M. Ielmini (Washington Office, Wildlife Refuges), W. King (Region 6 Wildlife Refuges).Biota of North America Program, University of North Carolina: J. Kartesz and M. Nishiko (plants).National Park Service: G. Williams (Inventory and Monitoring), C. Axtell (Biological Resource Management Division); M. Wotawa (Biological Inventories and NPSpecies).U.S. Forest Service: K. Stolte (Forest Health Monitoring Program).National Aeronautics and Space Administration: J. Schnase and J. Smith and several others (ecological forecasting, high-performance computing, remote sensing and modeling).Colorado State University/Natural Resources Ecology Laboratory: M. Kalkhan and R. Reich (Spatial modeling), D. Barnett, S. Simonson, R. Shory (data management, outreach).University of California, Davis: J. Quinn (information management, modeling).Long Term Ecological Research (LTER): J. GoszColorado Natural Heritage Program: B. Strom (director), A. Black (GIS specialist)Center for Environmental Management of Military Lands: B. ShawThe Nature Conservancy: A. Bartuska, J. RandallState of Colorado: E. Lane (State Weed Coordinator), B. Cheatum (GIS)Agriculture Experiment Station: L. Sommers

Leveraging Funds, Data, and Expertise

Funds: USFWS ($166K, $266K),NASA ($250K, $250K, $250K),BRD ($50K), USGS Venture Capital ($35K), MESC ($5K), State of Colorado Agriculture Experiment Station ($28K, $25K, $25K).

Expertise:USGS (6 centers), NASA, EDC,CSU, USFWS, BONAP, NPS,TNC, BLM, NPS, CEMML,UCD, UWY, State of Colorado,LTER, ITIS, APHIS, CNHP,Students and post-docs.

Data:USGS (6 centers), USFWS,BONAP, NPS, TNC, BLM,NPS, CEMML, UCD, UWY,USFS (FHM), State of Colorado,LTER, APHIS, and CNHP.

No new USGS FTEs

data =$multiple millions

Few USGS funds

“One if by land, two if by sea. . .”Documenting, Mapping, and Predicting the Invasionof Non-native Plants, Animals, and Diseases in the United States.

The Future: It’s What We Make It!

• Many more partnerships (DoD, CDC, APHIS, many universities, more states, and more agency offices).• Joint budget initiatives and proposals(coordinating at higher levels in each agency).• More shared expertise (data base design, web tools, metadata, parallel processing and programming, HPCC staff, additional modeling approaches).• More staff (discussing Ph.D. and post-doc options with several students, EDC, NBII, and APHIS).• “Status and Trends of the Nation’s Invasive Species”• A much larger “Invasive Species” program in the USGS – one of our 8 future science activities(We can’t do it alone, but we can do it together!)