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      Influence of the COVID ‐19 pandemic on amphibian road mortality

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          Abstract

          <p id="d2254908e266">The COVID‐19 pandemic and its related human activity shutdowns provide unique opportunities for biodiversity monitoring through what has been termed the “anthropause” or the “great human confinement experiment.” The pandemic caused immense disruption to human activity in the northeastern United States in the spring of 2020, with notable reductions in traffic levels. These shutdowns coincided with the seasonal migration of adult amphibians, which are typically subject to intense vehicle‐impact mortality. Using data collected as part of an annual community science monitoring program in Maine from 2018 to 2021, we examined how amphibian mortality probabilities responded to reductions in traffic during the pandemic. While we detected a 50% decline for all amphibians, this was driven entirely by reductions in frog mortality. Wildlife collision data from the Maine Department of Transportation on other wildlife species support our finding of drastic declines in wildlife road mortality in spring 2020 when compared with immediately previous and subsequent years. Additionally, we find that frogs suffer significantly higher road mortality than salamanders, particularly when conditions are warmer and wetter. </p>

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          Fitting Linear Mixed-Effects Models Usinglme4

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            Simultaneous inference in general parametric models.

            Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the pre-specified significance level. Simultaneous inference procedures have to be used which adjust for multiplicity and thus control the overall type I error rate. In this paper we describe simultaneous inference procedures in general parametric models, where the experimental questions are specified through a linear combination of elemental model parameters. The framework described here is quite general and extends the canonical theory of multiple comparison procedures in ANOVA models to linear regression problems, generalized linear models, linear mixed effects models, the Cox model, robust linear models, etc. Several examples using a variety of different statistical models illustrate the breadth of the results. For the analyses we use the R add-on package multcomp, which provides a convenient interface to the general approach adopted here. Copyright 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
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              COVID-19 lockdown allows researchers to quantify the effects of human activity on wildlife

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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Conservation Science and Practice
                Conservat Sci and Prac
                Wiley
                2578-4854
                2578-4854
                September 29 2021
                Affiliations
                [1 ]School of Biology and Ecology University of Maine System Orono Maine USA
                [2 ]Department of Biology University of New England Biddeford Maine USA
                [3 ]Center for Wildlife Studies North Yarmouth Maine USA
                Article
                10.1111/csp2.535
                af13bfa5-9299-4543-8105-613a5ef905f8
                © 2021

                http://creativecommons.org/licenses/by/4.0/

                http://doi.wiley.com/10.1002/tdm_license_1.1

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