Prioritizing Invasive Plant Management with Multi-Criteria Decision Analysis

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Abstract

Prioritizing management of invasive plants is important for large land management entities, such as federal and state public land stewards, because management resources are limited and multiple land uses and management objectives are differentially impacted. Management decisions also have important consequences for the likelihood of success and ultimate cost of control efforts. We applied multi-criteria decision analysis methods in a geographic information system using natural resource and land use data from Fort Bragg, North Carolina. Landscape-scale prioritization was based on a hierarchical model designed to increase invasive plant management efficiencies and reduce the risk of impacts to key installation management goals, such as training-land management and protected species conservation. We also applied spatial sensitivity analyses to evaluate the robustness of the prioritization to perturbations of the model weights, which were used to describe the relative importance of different elements of the hierarchical model. Based on stakeholders’ need for confidence in making management investments, we incorporated the results of the sensitivity analysis into the decision-making process. We identified high-priority sites that were minimally affected by the weight perturbations as being suitable for up-front management and evaluated how adopting this strategy affected management area, locations, and costs. We found that incorporating the results of the sensitivity analysis led to a reduced management area, different target locations, and lower costs for an equal area managed. Finally, we confirmed the distinctiveness of the approach by comparing this same subset of prioritized sites with locations representing species-centric strategies for three invasive plants and their aggregate distribution. By supplying pragmatic information about the localized effects of weighting uncertainty, spatial sensitivity analyses enhanced the invasive plant management decision-making process and increased stakeholder confidence.

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