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      Is the cure worse than the disease? Comparing the ecological effects of an invasive aquatic plant and the herbicide treatments used to control it

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          Abstract

          Invasive species are known to have negative ecological effects. However, few studies have evaluated the impacts of invasive species relative to the effects of invasive species control, thereby limiting our ability to make informed decisions considering the benefits and drawbacks of a given management approach. To address this gap, we compared the ecological effects of the invasive aquatic plant Eurasian watermilfoil ( Myriophyllum spicatum L.) with the effects of lake-wide herbicide treatments used for M. spicatum control using aquatic plant data collected from 173 lakes in Wisconsin, USA. First, a pre–post analysis of aquatic plant communities found significant declines in native plant species in response to lake-wide herbicide treatment. Second, multi-level modeling using a large data set revealed a negative association between lake-wide herbicide treatments and native aquatic plants, but no significant negative effect of invasive M. spicatum. Taken together, our results indicate that lake-wide herbicide treatments aimed at controlling M. spicatum had larger effects on native aquatic plants than did the target of control—invasive M. spicatum. Our comparison reveals an important management tradeoff and encourages careful consideration of how we balance the real and perceived impacts of invasive species and the methods used for their control.

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          Fitting Linear Mixed-Effects Models Usinglme4

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            Random effects structure for confirmatory hypothesis testing: Keep it maximal.

            Linear mixed-effects models (LMEMs) have become increasingly prominent in psycholinguistics and related areas. However, many researchers do not seem to appreciate how random effects structures affect the generalizability of an analysis. Here, we argue that researchers using LMEMs for confirmatory hypothesis testing should minimally adhere to the standards that have been in place for many decades. Through theoretical arguments and Monte Carlo simulation, we show that LMEMs generalize best when they include the maximal random effects structure justified by the design. The generalization performance of LMEMs including data-driven random effects structures strongly depends upon modeling criteria and sample size, yielding reasonable results on moderately-sized samples when conservative criteria are used, but with little or no power advantage over maximal models. Finally, random-intercepts-only LMEMs used on within-subjects and/or within-items data from populations where subjects and/or items vary in their sensitivity to experimental manipulations always generalize worse than separate F 1 and F 2 tests, and in many cases, even worse than F 1 alone. Maximal LMEMs should be the 'gold standard' for confirmatory hypothesis testing in psycholinguistics and beyond.
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              An Ordination of the Upland Forest Communities of Southern Wisconsin

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                Author and article information

                Journal
                FACETS
                FACETS
                Canadian Science Publishing
                2371-1671
                January 01 2020
                January 01 2020
                : 5
                : 1
                : 353-366
                Affiliations
                [1 ]Bureau of Water Quality, Division of Environmental Management, Wisconsin Department of Natural Resources, 101 S Webster Street, Madison, WI 53703, USA
                [2 ]Bureau of Science Services, Wisconsin Department of Natural Resources, 2801 Progress Road, Madison, WI 53716, USA
                [3 ]Bureau of Water Quality, Division of Environmental Management, Wisconsin Department of Natural Resources, 107 Sutliff Avenue, Rhinelander, WI 54501, USA
                [4 ]Williston, VT 05495, USA
                [5 ]Aquatic Sciences Center, University of Wisconsin-Madison, 1975 Willow Drive, Madison, WI 53706, USA
                [6 ]Center for Limnology, University of Wisconsin-Madison, 680 N Park Street, Madison, WI 53706, USA
                Article
                10.1139/facets-2020-0002
                018f3c36-4888-4fa5-9be5-bed456a0e2cc
                © 2020
                History

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