Using GIS-based multicriteria decision analysis to prioritize invasive plant treatment: A creative solution for a pernicious problem

Joshua G. Cohen, Helen D. Enander, Tyler J. Bassett, Clay M. Wilton and Ashley A. Cole-Wick

Ecological Modelling, 2024, vol. 495, issue C

Abstract: Invasive plant infestations are projected to rapidly increase with continued habitat fragmentation and climate change. The rate of spread often outpaces land managers’ ability to effectively detect and control infestations. Land stewards need tools that can match the rapid pace and scale of invasive infestations and help prioritize control efforts in the most ecologically important areas and optimize resource allocation. It is imperative that proposed control be informed by the best available ecological information. We present a spatially explicit modeling framework for prioritizing invasive plant treatment. In our case study from Michigan, we employ a multicriteria decision analysis model that synthesizes four critical factors for prioritizing invasive plant stewardship: integrity, biodiversity, resilience, and ecosystem services. Data for quantifying these factors were compiled from several state, federal, university, and non-governmental data sources. The model uses a geographic information system (GIS) to identify places that are impacted by invasive plant infestations that threaten to degrade ecosystem composition, structure, and function; are characterized by high ecosystem integrity; support high biodiversity; are resilient to disturbance; and provide ecosystem services. While specific to Michigan, our modeling framework is broadly applicable. This type of integrated analysis can help inform decisions about allocation of limited resources for numerous biodiversity stewardship and landscape-level conservation planning needs, regardless of geographic location.

Keywords: GIS-based multicriteria decision analysis; Invasive plant species; Biodiversity; Resilience; Integrity; Ecosystem services (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:495:y:2024:i:c:s0304380024001959

DOI: 10.1016/j.ecolmodel.2024.110807

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