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      Temporal patterns of roe deer traffic accidents: Effects of season, daytime and lunar phase

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          Abstract

          Wildlife-related accidents, especially deer-vehicle accidents, pose a serious problem for road safety and animal protection in many countries. Knowledge of spatial and temporal patterns of deer-vehicle accidents is inevitable for accident analysis and mitigation efforts with temporal deer-vehicle accident data being much more difficult to obtain in sufficient data quality. We described the temporal patterns of roe deer ( Capreolus capreolus) roadkills occurring in the period 2002–2006 in southeastern Austria. Using a comprehensive dataset, consisting of 11.771 data points, we examined the influence of different time units (i.e. season, month, day of week, day of year), illumination categories (coarse and fine temporal resolution) and lunar phases on deer-vehicle accidents by performing linear and generalized additive models. Thereby, we identified peak accident periods within the analyzed time units. Highest frequencies of deer-vehicle accidents occurred in November, May and October, on Fridays, and during nights. Relationships between lunar phases and roe deer-vehicle accidents were analysed, providing evidence for high frequencies of deer-vehicle accidents during full moon phases. We suggest that deer-vehicle accidents are dependent both on human activity in traffic and wildlife activity, which is in turn affected by phenology, intra- and interspecific competition, climatic and astronomical events. Our results highlight, that short-term mitigation measures (e.g. traffic controls and speed limits) can be highly effective to reduce deer-vehicle accidents, but should be flexibly adapted to specific temporal periods.

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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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            Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models

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              Human Domination of Earth's Ecosystems

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: Writing – original draft
                Role: ValidationRole: VisualizationRole: Writing – review & editing
                Role: Formal analysisRole: SoftwareRole: Visualization
                Role: ConceptualizationRole: Supervision
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                30 March 2021
                2021
                : 16
                : 3
                : e0249082
                Affiliations
                [1 ] Institute of Wildlife Biology and Game Management, University of Natural Resources and Life Sciences, Vienna (BOKU), Austria
                [2 ] Institute of Statistics, University of Natural Resources and Life Sciences, Vienna (BOKU), Austria
                Tsinghua University, CHINA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0003-2494-4594
                https://orcid.org/0000-0003-3096-5885
                Article
                PONE-D-20-38850
                10.1371/journal.pone.0249082
                8009364
                33784325
                98d7b767-e2ac-4a92-b4a7-559d2ce15d72
                © 2021 Steiner et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 10 December 2020
                : 10 March 2021
                Page count
                Figures: 4, Tables: 2, Pages: 18
                Funding
                Funded by: Open Access Publication Fund of the University of Natural Resources and Life Sciences, Vienna
                Award ID: None
                The publication was supported by the Open Access Publication Fund of the University of Natural Resources and Life Sciences, Vienna. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Vertebrates
                Amniotes
                Mammals
                Ruminants
                Deer
                Biology and Life Sciences
                Zoology
                Animals
                Vertebrates
                Amniotes
                Mammals
                Ruminants
                Deer
                Social Sciences
                Law and Legal Sciences
                Criminal Justice System
                Law Enforcement
                Police
                People and Places
                Population Groupings
                Professions
                Police
                Earth Sciences
                Seasons
                Physical Sciences
                Astronomical Sciences
                Celestial Objects
                Moons
                Physical Sciences
                Astronomical Sciences
                Planetary Sciences
                Moons
                Biology and Life Sciences
                Chronobiology
                Daylight
                Ecology and Environmental Sciences
                Daylight
                Engineering and Technology
                Civil Engineering
                Transportation Infrastructure
                Roads
                Engineering and Technology
                Transportation
                Transportation Infrastructure
                Roads
                Biology and Life Sciences
                Chronobiology
                Daylight
                Sunrise
                Ecology and Environmental Sciences
                Daylight
                Sunrise
                Biology and Life Sciences
                Chronobiology
                Daylight
                Twilight
                Ecology and Environmental Sciences
                Daylight
                Twilight
                Custom metadata
                All relevant data are within the manuscript and its Supporting Information files.

                Uncategorized
                Uncategorized

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