Evaluating Alternative Weed Management Strategies for Three Montana Landscapes

by Leonardo Frid, David Hanna, Nathan Korb, Brad Bauer, Katherine Bryan, Brian Martin, and Brett Holzer 
Published in 2013, Invasive Plant Science and Management: January-March, Vol. 6:48-59. 


Determining the best strategy for allocating weed management resources across and between landscapes is challenging because of the uncertainties and large temporal and spatial scales involved. Ecological models of invasive plant spread and control provide a practical tool with which to evaluate alternative management strategies at landscape scales. We developed a spatially explicit model for the spread and control of spotted knapweed and leafy spurge across three Montana landscapes. The objective of the model was to determine the ecological and economic costs and benefits of alternative strategies across landscapes of varying size and stages of infestation. Our results indicate that (1) in the absence of management the area infested will continue to increase exponentially leading to a substantial cost in foregone grazing revenues; (2) even though the costs of management actions are substantial, there is a net economic benefit associated with a broad range of management strategies; (3) strategies a that prioritize targeting small new infestations consistently outperform strategies that target large established patches; and (4) inconsistent treatment and short-term delays can greatly reduce the economic and ecological benefits of management.

Nomenclature: Leafy spurge, Euphorbia esula L. EPHES; spotted knapweed,Centaurea stoebe L. CENMA.

Management Implications

Models of invasive plant spread and control provide a useful way to assess the performance of alternative management strategies and budget levels across broad temporal and spatial scales. To meet long-term goals for their landscape, managers should pursue strategies that are both ecologically effective and economically justified. Model results provide several insights for achieving that success.

Early detection and small-patch control strategies consistently outperformed large-patch strategies. Despite these results and previous recommendations for early detection and rapid response programs, managers are often mandated to focus on large infestations where weeds are well established and highly visible. Small infestations do not present an immediate loss of productivity and are often more remote and time-consuming to control. Consequently resources are directed toward locations where, based on our model results, treatment is less beneficial and long-term success is less likely. Our model results support the reallocation of resources to an effective early detection and treatment strategy.

Our results also indicate that managers should avoid delaying management or applying inconsistent treatment over time. In these cases, weed populations outpace management efforts and can reinvade previously treated areas, ultimately leading to a greater area invaded with greater economic costs. Preventative actions that reduce weed dispersal distances and spread rates will lower ultimate invasion levels and long-term management costs.

For landscapes with relatively few existing infestations of noxious weeds, managers should dedicate resources to detecting and controlling new infestations as early as possible to prevent the development of established populations. For invaded landscapes where large noxious weed infestations already exist, early detection and control remains a foundational strategy but managers should also maximize site-specific treatment success. At the broadest scale, resources should be allocated to landscapes with lower infestation levels and thus greater potential for long-term management success and return on investment, rather than highly invaded landscapes.


Article Citation

Leonardo Frid, David Hanna, Nathan Korb, Brad Bauer, Katherine Bryan, Brian Martin, and Brett Holzer (2013) Evaluating Alternative Weed Management Strategies for Three Montana Landscapes. Invasive Plant Science and Management: January-March, Vol. 6, No. 1, pp. 48-59.