A review on the temporal pattern of deer-vehicle accidents: impact of seasonal, diurnal and lunar effects in cervids

Accid Anal Prev. 2014 May:66:168-81. doi: 10.1016/j.aap.2014.01.020. Epub 2014 Feb 2.

Abstract

The increasing number of deer-vehicle-accidents (DVAs) and the resulting economic costs have promoted numerous studies on behavioural and environmental factors which may contribute to the quantity, spatiotemporal distribution and characteristics of DVAs. Contrary to the spatial pattern of DVAs, data of their temporal pattern is scarce and difficult to obtain because of insufficient accuracy in available datasets, missing standardization in data aquisition, legal terms and low reporting rates to authorities. Literature of deer-traffic collisions on roads and railways is reviewed to examine current understanding of DVA temporal trends. Seasonal, diurnal and lunar peak accident periods are identified for deer, although seasonal pattern are not consistent among and within species or regions and data on effects of lunar cycles on DVAs is almost non-existent. Cluster analysis of seasonal DVA data shows nine distinct clusters of different seasonal DVA pattern for cervid species within the reviewed literature. Studies analyzing the relationship between time-related traffic predictors and DVAs yield mixed results. Despite the seasonal dissimilarity, diurnal DVA pattern are comparatively constant in deer, resulting in pronounced DVA peaks during the hours of dusk and dawn frequently described as bimodal crepuscular pattern. Behavioural aspects in activity seem to have the highest impact in DVAs temporal trends. Differences and variations are related to habitat-, climatic- and traffic characteristics as well as effects of predation, hunting and disturbance. Knowledge of detailed temporal DVA pattern is essential for prevention management as well as for the application and evaluation of mitigation measures.

Keywords: Cervidae; Collision data; DVA; Deer–vehicle-accident; Temporal activity; Traffic mortality.

Publication types

  • Review

MeSH terms

  • Accidents, Traffic / statistics & numerical data*
  • Animals
  • Cluster Analysis
  • Deer
  • Moon*
  • Seasons*
  • Spatio-Temporal Analysis*
  • Time Factors