Prioritizing Invasive Forest Plant Management Using Multi-Criteria Decision Analysis in Minnesota, USA

Abstract

Invasive plants are a concern in many forest ecosystems because they can impact tree regeneration and recruitment, alter hydrology, and degrade wildlife habitats. Management efforts are generally planned locally, based on the severity of the infestation, species involved, and characteristics of the forest stand. A broad, landscape-level context can provide additional information and help with planning efforts but is often lacking. In this study, we estimated landscape-level priorities for the management of five invasive forest plants in Minnesota. We used a multi-criteria decision analysis approach to integrate plant distribution models and data with geographic information about areas of conservation concern, recreational areas, and the economic benefits of treatment. The results varied across Ecological Classification System provinces and Minnesota native plant community classes. Four of the five invasive plants considered demonstrated an abundance of Medium- and High-priority areas for management in the Eastern Broadleaf Forest province of Minnesota. The average priority was generally lower in the Prairie Parklands and Tallgrass Aspen Parklands provinces, with Rhamnus cathartica as the only species demonstrating Medium or higher priorities in the latter. The mean priorities were Medium or higher for R. cathartica and Frangula alnus in mesic hardwood community types across the state, in addition to several fire-dependent systems. The priority distribution was most limited for Rosa multiflora, where the only Medium or higher priority results were found in a mesic hardwood system in the southeastern corner of the state. The results presented here highlight broad-scale patterns that can provide a synoptic overview of invasive plant priorities at the landscape scale.

A Participatory and Spatial Multicriteria Decision Approach to Prioritize the Allocation of Ecosystem Services to Management Units

“…For example, Caglayan et al [2] combined a participatory MCDA, mixed integer programming, and a Geographic Information System (GIS) approach to assign ESs priority to MUs based on ESs sustainable development goals. Reinhardt et al [20] created landscape-level prioritization for the management of five invasive forest plants, using a spatial MCDA methodology, whereas Povak et al [21] developed a combined approach of MCDA and logic models, using EMDS, to prioritize landscape treatment units for invasive species removal and native forest protection from non-native species invasions. Bottero et al [19] applied an MCDA and GIS to identify suitable areas for biodiversity conservation to be included in spatial planning decision support processes.…”