Publication Date

2017

Journal or Book Title

Ecosphere

Abstract

Measuring and predicting invasive plant abundance is critical for understanding impacts on ecosystems and economies. Although spatial abundance datasets remain rare, occurrence datasets are increasingly available across broad regional scales. We asked whether the frequency of these point occurrences can be used as a proxy for abundance of invasive plants. We compiled both occurrence and abundance data for 13 regionally important invasive plants in the northeast United States from herbarium records and several contributed distribution datasets. We integrated all available abundance information based on infested area, stem count, percent cover, or qualitative descriptions into abundance rankings ranging from 0 (absent) to 4 (highly abundant). Within equal-area grid cells of 800 m, we counted numbers of occurrence points and used ordinal regression to test whether higher densities of occurrence points increased the odds of a higher abundance ranking. We compiled a total of 86,854 occurrence points in 34,596 grid cells, of which 26,114 points (30%) within 11,976 cells (35%) had some form of abundance information. Eleven of the 13 species had a slight but significantly positive odds ratio; that is, more occurrence points of a species increased the odds that the species was abundant within the grid cell. However, the predictive ability of the models was poor (κ < 0.2) for the majority of species. Additionally, most grid cells contained only one or two occurrence points, making it impossible to infer abundance in all but a few locations. These results suggest that currently available occurrence datasets do not effectively represent abundance, which could explain why many distribution models based on occurrence data are poor predictors of abundance. Increased efforts to consistently collect and report invasive species abundance, ideally estimating both infested area and average cover, are strongly needed for regional-scale assessments of potential abundance and associated impact.

DOI

10.1002/ecs2.1800

Volume

8

Issue

5

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

License

UMass Amherst Open Access Policy

Funder

This research was supported by the National Science Foundation Award BCS 1560925 and the National Institute of Food and Agriculture, U.S. Department of Agriculture, the Massachusetts Agricultural Experiment Station, and the Department of Environmental Conservation under Project Numbers MAS00016 and MAS00410.

Share

COinS