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The invasive plant data landscape: a synthesis of spatial data and applications for research and management in the United States

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LANDSCAPE ECOLOGY
卷 -, 期 -, 页码 -

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SPRINGER
DOI: 10.1007/s10980-023-01623-z

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Invasive plants; Spatial data; Land management; Data use; Data accessibility; Data types

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This article summarizes the sources and applications of invasive plant distribution data, discusses the potential applications and limitations of existing data types in research and management, and proposes a path for improving the use of invasive plant data in future research and management.
ContextAn increase in the number and availability of datasets cataloging invasive plant distributions offers opportunities to expand our understanding, monitoring, and management of invasives across spatial scales. These datasets, created using on-the-ground observations and modeling techniques, are made both for and by researchers and managers.ObjectivesThe large number and variety of data types and associated datasets can be difficult to navigate, require high levels of data literacy, and can overwhelm the intended end-users. By providing a synthesis of available data types and datasets, this work may facilitate data understanding and use among researchers and managers.MethodsWe synthesize types of invasive plant distribution data sources, highlighting publicly available datasets and their potential applications and limitations for research and management.ResultsEight data types and their potential applications for research and management are described. We also describe gaps in current invasive species distribution data usability and outline a path forward for improving the use of invasive plant data in future research and management.ConclusionsAccessible and usable invasive plant spatial data are needed for developing landscape scale analysis and management plans. By synthesizing the invasive plant data available, with examples and limitations for application, this work will serve as a guide to facilitate appropriate and efficient data choices in current and future research and management.

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