Forest pest risk analysis in dynamic landscapes

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 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. We will apply efficient, flexible algorithms for population growth and dispersal in complex landscapes that allow processes at different scales in time and space to contribute to pest activity. This technology will make available models of forest pest growth and spread that will be of immediate use to individuals and agencies involved in forest health. The software will allow estimates of future impact from native pests using standardized monitoring data. It will also facilitate estimates of the rate of spread of invasive species. The option of grid-based and metapopulation modeling will encourage model comparison and validation. At present, the greatest limitation in landscape modeling is the availability of accurate and detailed information on landscape structure. However, the number and quality of GIS databases is increasingly rapidly. FHTET, for example, has produced a series of highly detailed risk maps summarizing the habitat suitability of the entire nation?s forests for the pests of greatest concern. As data availability increases, techniques such as we propose to develop that apply population dynamic models in data-rich landscape contexts will give managers unprecedented capability to predict and manage forest pest risks.