Forest pest risk analysis in dynamic landscapes

Award Information
Agency: Department of Agriculture
Branch: N/A
Contract: N/A
Agency Tracking Number: 2009-01116
Amount: $79,244.00
Phase: Phase I
Program: SBIR
Awards Year: 2008
Solicitation Year: N/A
Solicitation Topic Code: N/A
Solicitation Number: N/A
Small Business Information
APPLIED BIOMATHEMATICS INC
100 N COUNTRY RD, East Setauket, NY, 11733
DUNS: 178047015
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Nicholas Friedenberg
 Research Scientist
 (631) 751-4350
 nick@ramas.com
Business Contact
 Nicholas Friedenberg
Title: Research Scientist
Phone: (631) 751-4350
Email: nick@ramas.com
Research Institution
N/A
Abstract
Forest insect pests cause significant economic and ecological damage every year. Dramatically increased pest activity in recent years suggests that changing climate conditions will inflate the uncertainty associated with pest risk assessments. Advances in forest pest risk analysis methodology are needed to allow managers to better explore the consequences and value of alternative management scenarios and to facilitate improvements in the prioritization of threats to natural resources. Although tools are currently available for mapping the risk of pest activity according to landscape features, host distributions, and climate, most equate habitat suitability with risk. A necessary but underdeveloped way to improve these risk assessments would incorporate information about population dynamics. We propose to develop the first software tool that will accept a wide range of GIS-based factors, including habitat suitability maps, and predict risk based on spatially explicit simulations of forest pest population dynamics. A key aspect of this tool will be feedback between the pest and its hosts, allowing forest structure (and therefore habitat suitability) to evolve dynamically over time. The software tool will generate maps representing the area and intensity of impact on hosts and graphs describing the uncertainty associated with model output.

* information listed above is at the time of submission.

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