Decision-Support Tool Utilizing Improved Dispersal Modeling to Develop Socio-Economically Efficient Invasive Species Management Programs
Grant
Overview
abstract
Traditionally, invasive species management strategies have focused on treatment in seriously impacted areas and more recently on early detection and treatment. Uncertainty exists for allocation of treatment resources at small to medium geographic scales which do not adequately account for the socio-economic costs and benefits of alternative invasive species management strategies. The uncertainty surrounding efficient resource allocation to minimize damage cost to agricultural and natural ecosystems is even greater at larger (e.g. regional) geographic scales, because managers have limited information about where and at what rate invasive species will move within a landscape. Knowing dispersal patterns in actual landscapes would help determine where defensible geographic boundaries are in order to minimize impacts of existing infestations and to
focus first to infestations with greater potential for expansion. Without some idea of where and at what rate a species may move within a landscape, we lack critical information to facilitate efficient deployment of resources to achieve landscape-level invasive species management objectives. This is because estimating the benefits of an invasive species management program requires estimation of invasive species dispersal and associated damage costs under no (or less effective) management scenarios. Thus, improving understanding of invasive species dispersal patterns at the landscape scale and developing decision tools that support the design, assessment and implementation of economically efficient management strategies are critical research areas for invasive species management. This project integrates predictive occurrence and dispersal models based on environmental conditions and plant
competition, with an economic decision-support tool so that socio-economically efficient invasive species management strategies can be developed. We plan to expand a decision support tool that is in development to incorporate dispersal models that use indicators of plant community susceptibility to invasion to predict how the invasive species would move across actual landscapes. Surveys will be used to augment economic damage cost estimates for use in refining the decision support tool. Workshops will be conducted to train land managers and extension faculty on use of the decision tool. An educational module will be developed for incorporation into formal instruction course work at Oregon State University, University of Idaho and University of Montana. Results from the research will be summarized and presented at conferences and written as manuscripts submitted for publication. The
decision tool will be available to land managers for use in strategic planning for invasive species management.
