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1 December 2017 Constructing Standard Invasion Curves from Herbarium Data—Toward Increased Predictability of Plant Invasions
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Abstract
Prevention, early detection, rapid response, and prioritization are essential components of effective and cost-efficient invasive plant management. However, successfully implementing these strategies requires the ability to accurately predict the temporal and spatial dynamics of newly/recently detected nonnative species. Why some nonnative species become invasive and the source of variation in lag time between arrival and the onset of invasive expansion are poorly understood. One tool to fill these knowledge gaps is the “invasion curve,” which tracks nonnative species abundance (i.e., area invaded) over time after arrival in a new area. Since invasive species curves rely primarily on records from herbarium collections, we propose that these collections can be used as a springboard to develop a standardized approach to building invasion curves. This would allow researchers to compare the trajectories of nonnative species, improving risk assessment and our ability to recognize potential invasive species and factors contributing to both invasibility and invasiveness. While there have been admirable efforts to produce invasion curves, several barriers exist to their reliable production and standardization. In this paper, we explore the challenges related to the efficient production of these curves for plants using herbarium data and suggest ways in which progress could occur. It is our hope that this will better position herbaria and researchers to aid natural resource managers to prioritize needs, make effective management decisions, and develop targeted prevention and monitoring programs by taking advantage of lag times to implement timely responses.
© Weed Science Society of America, 2017
Pedro M. Antunes and Brandon Schamp "Constructing Standard Invasion Curves from Herbarium Data—Toward Increased Predictability of Plant Invasions," Invasive Plant Science and Management 10(4), (1 December 2017). https://doi.org/10.1017/inp.2017.38
Received: 18 August 2017; Accepted: 1 November 2017; Published: 1 December 2017
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