# Presentation: 792813

## Invasive Species: 

**An Important Stressor in the Mid-Atlantic Area**

## James K. AndreasenDaniel A. KluzaU.S. EPAOffice of Research and DevelopmentNational Center for Environmental AssessmentWashington, DC

## Mid-Atlantic Invasive Species

- Ecosystem structure and function
- Broad array of taxa
- Plants, insects, pathogens
- Regional habitat diversity
- Forest
*Alliaria petiolata*
*Discula destructiva*
*Adelges tsugae*
*Lymantria dispar*

## Mid-Atlantic Invasive Species

- Regional habitat diversity
- Streams, rivers
*Dreissena polymorpha*
*Hydrilla verticillata*
- Wetlands
*Lythrum salicaria*
*Microstegium vimineum*
- Chesapeake Bay
*Cygnus olor*
*Myocastor coypus*

## Mid-Atlantic Invasive Species

- Ecosystem goods and services, socioeconomic costs
*Dreissena polymorpha*
*Hydrilla verticillata*
- Public health
*Aedes albopictus*
*Heracleum mantegazzianum*

- Chestnut blight
*Cryphonectria parasitica*

## Significant, but often overlooked problem
Existing strategies are reactive
Action taken only after detection of invasion/collateral damage
Due to time-lag, species may already be established
Such an approach is environmental catch-up

- Existing strategies are reactive
- Action taken only after detection of invasion/collateral damage
- Due to time-lag, species may already be established
- Such an approach is environmental catch-up

- Mid-Atlantic Invasive Species

## Accumulate occurrence data on native range

Build ecological niche model

Project niche model to areas of actual or potential invasion

- Predictive Modeling

- Accumulate occurrence data on native range
- Build ecological niche model
- Project niche model to areas of actual or potential invasion

## Predictive Modeling: Genetic Algorithm for Rule-set Prediction (GARP)

- Uses multiple rule types (BIOCLIM, logistic regression, etc.)
- Different decision rules may apply to different sectors of species’ distributions
- Extensive testing indicates excellent predictive ability

- Predictive Modeling: Genetic Algorithm for Rule-set Prediction (GARP)

## Tests of GARP Model Predictivity

- Anderson, R. P., M. Gomez, and A. T. Peterson. 2002. Geographical distributions of spiny pocket mice in South America: Insights from predictive models. Global Ecology and Biogeography 11:131-141.
- Anderson, R. P., M. Laverde, and A. T. Peterson. In press. Niche-based models of species' potential macrodistributions suggest competition and competitive release in spiny pocket mice. Oikos.
- Anderson, R. P., D. Lew, and A. T. Peterson. 2003. Using error distributions to select best subsets of predictive models of species' distributions. Ecological Modelling.
- Feria, T. P., and A. T. Peterson. 2002. Using point occurrence data and inferential algorithms to predict local communities of birds. Diversity and Distributions 8:49-56.
- Peterson, A. T. 2001. Predicting species' geographic distributions based on ecological niche modeling. Condor 103:599-605.
- Peterson, A. T., L. G. Ball, and K. C. Cohoon. 2002a. Predicting distributions of tropical birds. Ibis 144:e27-e32.
- Peterson, A. T., and K. C. Cohoon. 1999. Sensitivity of distributional prediction algorithms to geographic data completeness. Ecological Modelling 117:159-164.
- Peterson, A. T., S. L. Egbert, V. Sanchez-Cordero, and K. P. Price. 2000. Geographic analysis of conservation priorities using distributional modelling and complementarity: Endemic birds and mammals in Veracruz, Mexico. Biological Conservation 93:85-94.
- Peterson, A. T., M. A. Ortega-Huerta, J. Bartley, V. Sanchez-Cordero, J. Soberon, R. H. Buddemeier, and D. R. B. Stockwell. 2002b. Future projections for Mexican faunas under global climate change scenarios. NATURE 416:626-629.
- Peterson, A. T., V. Sanchez-Cordero, J. Soberon, J. Bartley, R. H. Buddemeier, and A. G. Navarro-Siguenza. 2001. Effects of global climate change on geographic distributions of Mexican Cracidae. Ecological Modelling 144:21-30.
- Peterson, A. T., R. Scachetti-Pereira, and W. W. Hargrove. Submitted. Potential distribution of Asian longhorned beetles (*Anoplophora glabripennis*) in North America. Journal of Economic Entomology.
- Peterson, A. T., J. Soberon, and V. Sanchez-Cordero. 1999. Conservatism of ecological niches in evolutionary time. Science 285:1265-1267.
- Peterson, A. T., D. R. B. Stockwell, and D. A. Kluza. 2002c. Distributional prediction based on ecological niche modeling of primary occurrence data. Pages 617-623 *in* Predicting Species Occurrences: Issues of Scale and Accuracy. '('J. M. Scott, P. J. Heglund, and M. L. Morrison, Ed.)'.Island Press, Washington, D.C.
- Peterson, A. T., and D. A. Vieglais. 2001. Predicting species invasions using ecological niche modeling. BioScience 51:363-371.
- Stockwell, D. R. B., and A. T. Peterson. 2002a. Controlling bias in biodiversity data. Pages 537-546 *in* Predicting Species Occurrences: Issues of Scale and Accuracy. '('J. M. Scott, P. J. Heglund, and M. L. Morrison, Ed.)'.Island Press, Washington, D.C.
- Stockwell, D. R. B., and A. T. Peterson. 2002b. Effects of sample size on accuracy of species distribution models. Ecological Modelling 148:1-13.

## Hydrilla verticillata

## Ecological Niche Modeling

- <date/time>

**Ecological Niche Modeling**

- Native range locality data

- Specimen records

- Temperature
- Precipitation
- Solar radiation
- Snow cover
- Frost-free days

## Rule set for prediction

- 0    r    0.50  0.99  28.55 0.35  0.504
- 	IF       - Elev*0.26 r + Precip*0.19 r - Temp*0.10 r
- 	THEN    Taxon=BACKGROUND
- 4    r    0.53  0.86  23.58 0.51  0.314
- 	IF       + Elev*0.32 r - Precip*0.19 r - Temp*0.10 r
- 	THEN    Taxon=PRESENT
- 1    r    0.49  0.91  26.32 0.39  0.122
- 	IF       - Elev*0.02 r + Precip*0.28 r - Temp*0.30 r
- 	THEN    Taxon=BACKGROUND
- 3    m    0.49  0.85  23.73 0.44  0.028
- 	IF      Elev=[1482,3360]r AND Precip=[ 1, 4]r AND Temp=[ 2, 4]r
- 	THEN    Taxon=PRESENT
- 6    d    0.49  0.86  20.90 0.33  0.019
- 	IF      Elev=[1937,3241]r
- 	THEN    Taxon=PRESENT
- 2    d    0.49  0.85  23.78 0.44  0.013
- 	IF      Elev=[ 0,2727]r AND Precip=[ 4, 9]r
- 	THEN    Taxon=BACKGROUND
- 5    d    0.48  0.83  22.11 0.41  0.000
- 	IF      Elev=[1640,2866]r AND Precip=[ 1, 4]r AND Temp=[ 2, 5]r
- 	THEN    Taxon=PRESENT

## Ecological Niche Modeling

- <date/time>

**Ecological Niche Modeling**

## Hydrilla verticillata

## Hydrilla verticillataPotential Distribution as Percent of Watershed Area

*Hydrilla verticillata*Potential Distribution as Percent of Watershed Area

## Sudden Oak Death(Phytophthora ramorum)

## Sudden Oak Death(Phytophthora ramorum)

- Sudden Oak Death(*Phytophthora ramorum*)

## Section  LobataeSpecies Richness 

- Section  LobataeSpecies Richness

## Sudden Oak DeathNative Range?

## Ecological Niche Modeling

- <date/time>

**Ecological Niche Modeling**

## Ecological Niche Modeling

- <date/time>

**Ecological Niche Modeling**

## Ecological Niche Modeling

- <date/time>

**Ecological Niche Modeling**

## Hydrilla verticillataPotential Distribution

*Hydrilla verticillata*Potential Distribution

## Hydrilla verticillataPotential Distribution, 2020

**Hadley CM2 HSDX20 Model**

## Hydrilla verticillataPotential Distribution as Percent of Watershed Area

*Hydrilla verticillata*Potential Distribution as Percent of Watershed Area

## Hydrilla verticillataPotential Distribution as Percent of Watershed Area, 2020

**Hadley CM2 HSDX20 Model**

## Invasive Species Risk Assessment

- <date/time>

- Native range locality data

- Ecological
- Niche
- Modeling
- (GARP)

- Assess Risks
- Species by
- Species

- Policy!

## Other Applications

- Model/predict spread of emerging diseases
- Predict and avoid environmental impacts
- Design agricultural systems to avoid pests
- Develop optimal conservation strategies
- Design species reintroduction programs

## James K. Andreasenandreasen.james@epa.gov

- Daniel A. Kluzakluza.daniel@epa.gov

- http://www.lifemapper.org/desktopgarp

- http://www.lifemapper.org