|Buhler, Douglas - Doug|
Submitted to: Weed Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/17/1997
Publication Date: N/A
Interpretive Summary: The purpose of this paper is to present a framework in which the location of weed populations with similar seasonal growth cycles can be analyzed. The framework used digital representations of existing weed maps of the Midwest region in a geographical information system (GIS). Maps of individual weed species were converted to digital records showing where weeds were in one of three classes: rare, frequent, or common. The locations of various assemblages of weeds were defined by overlaying individual species maps. In this system, any combination of the individual species can be mapped using one or all of the map classes (rare, frequent, or common). Regional analysis of the location of weeds will increase knowledge about weed infestations, ecological adaptations, and expanded distribution of individual weeds and assemblages of weeds. The application of GIS technology to weed science should increase our understanding of the climatic and management factors regulating weed communities and provide important new information to resource managers, plant ecologists, the herbicide industry, and agricultural policy makers.
Technical Abstract: This paper presents a framework in which the spatial and temporal domain of weed populations can be analyzed using geographically referenced information. The framework for regional analysis is based on the premise that the domain of a weed species or an assemblage of species can be described in terms of the space and time in which they survive. Maps of the spatial distribution of individual weed species were converted to digital records defining their geographic domain. Digital records were imported into a geographically referenced data system. Assemblage maps were produced by digitally intersecting domains of individual species. The assemblage maps show areas of common occurrence and relative intensity of the species selected. The framework presented in this paper represents a tool to manage and manipulate weed distribution data and should have application to weed science by increasing the understanding of regional patterns of weed infestation and the factors that regulate them. This information will be useful to the herbicide industry, plant ecologists, resource managers, and agricultural policy makers.