title: Temporal patterns of deer-vehicle collisions consistent with deer activity pattern and density increase but not general accident risk.
abstract: The increasing number of deer-vehicle collisions (DVCs) across Europe during recent decades poses a serious threat to human health and animal welfare and increasing costs for society. DVCs are triggered by both a human-related and a deer-related component. Mitigation requires an understanding of the processes driving temporal and spatial collision patterns. Separating human-related from deer-related processes is important for identifying potentially effective countermeasures, but this has rarely been done. We analysed two time series of 341,655 DVCs involving roe deer and 854,659 non-deer-related accidents (non-DVCs) documented between 2002 and 2011. Nonparametric smoothing and temporal parametric modelling were used to estimate annual, seasonal, weekly and diurnal patterns in DVCs, non-DVCs and adjusted DVCs. As we had access to data on both DVCs and non-DVCs, we were able to disentangle the relative role of human-related and deer-related processes contributing to the overall temporal DVC pattern. We found clear evidence that variation in DVCs was mostly driven by deer-related and not human-related activity on annual, seasonal, weekly and diurnal scales. A very clear crepuscular activity pattern with high activity after sunset and around sunrise throughout the year was identified. Early spring and the mating season between mid-July and mid-August are typically periods of high roe deer activity, and as expected we found a high number of DVC during these periods, although these patterns differed tremendously during different phases of a day. The role of human activity was mainly reflected in fewer DVCs on weekends than on weekdays. Over the ten-year study period, we estimated that DVCs increased by 25%, whereas the number of non-DVCs decreased by 10%. Increasing deer densities are the most likely driver behind this rise in DVCs. Precise estimates of DVC patterns and their relationship to deer and human activity patterns allow implementation of specific mitigation measures, such as tailored driver warning systems or temporary speed limits. To prevent a further increase in DVCs, state-wide measures to decrease roe deer density are required.
authors.affiliation: Institut für Epidemiologie, Biostatistik und Prävention, Universität Zürich, Hirschengraben 84, CH-8001 Zürich, Switzerland. Electronic address: Torsten.Hothorn@uzh.ch.
authors.forename: Torsten
authors.initials: T
authors.lastname: Hothorn
authors.affiliation: Nationalpark Bayerischer Wald, Freyunger Straße 2, DE-94481 Grafenau, Germany; Lehrstuhl für Terrestrische Ökologie, Technische Universität München, Hans-Carl-von-Carlowitz-Platz 2, DE-85354 Freising, Germany.
authors.forename: Jörg
authors.initials: J
authors.lastname: Müller
authors.affiliation: Institut für Epidemiologie, Biostatistik und Prävention, Universität Zürich, Hirschengraben 84, CH-8001 Zürich, Switzerland.
authors.forename: Leonhard
authors.initials: L
authors.lastname: Held
authors.affiliation: Institut für Statistik, Ludwig-Maximilians-Universität München, Ludwigstraße 33, DE-80539 München, Germany.
authors.forename: Lisa
authors.initials: L
authors.lastname: Möst
authors.affiliation: Centre for Ecological and Evolutionary Synthesis, University of Oslo, P.O. Box 1066 Blindern, NO-0316 Oslo, Norway.
authors.forename: Atle
authors.initials: A
authors.lastname: Mysterud
mesh_terms: D000063:Accidents, Traffic / Q000517:prevention & control* / Q000706:statistics & numerical data*; D000818:Animals; D001334:Automobile Driving / Q000706:statistics & numerical data*; D001522:Behavior, Animal; D002940:Circadian Rhythm; D003670:Deer; D005858:Germany; D006801:Humans; D011158:Population Growth; D012306:Risk; D012307:Risk Factors; D012621:Seasons
keywords: Deer density; Deer–vehicle accident; Seasonality; Traffic mortality; Ungulate-vehiclecollision