Monitoring Wildlife-Vehicle Collisions in the Information Age: How Smartphones Can Improve Data Collection

Abstract

BackgroundCurrently there is a critical need for accurate and standardized wildlife-vehicle collision data, because it is the underpinning of mitigation projects that protect both drivers and wildlife. Gathering data can be challenging because wildlife-vehicle collisions occur over broad areas, during all seasons of the year, and in large numbers. Collecting data of this magnitude requires an efficient data collection system. Presently there is no widely adopted system that is both efficient and accurate.Methodology/Principal FindingsOur objective was to develop and test an integrated smartphone-based system for reporting wildlife-vehicle collision data. The WVC Reporter system we developed consisted of a mobile web application for data collection, a database for centralized storage of data, and a desktop web application for viewing data. The smartphones that we tested for use with the application produced accurate locations (median error = 4.6–5.2 m), and reduced location error 99% versus reporting only the highway/marker. Additionally, mean times for data entry using the mobile web application (22.0–26.5 s) were substantially shorter than using the pen/paper method (52 s). We also found the pen/paper method had a data entry error rate of 10% and those errors were virtually eliminated using the mobile web application. During the first year of use, 6,822 animal carcasses were reported using WVC Reporter. The desktop web application improved access to WVC data and allowed users to easily visualize wildlife-vehicle collision patterns at multiple scales.Conclusions/SignificanceThe WVC Reporter integrated several modern technologies into a seamless method for collecting, managing, and using WVC data. As a result, the system increased efficiency in reporting, improved accuracy, and enhanced visualization of data. The development costs for the system were minor relative to the potential benefits of having spatially accurate and temporally current wildlife-vehicle collision data.

Wildlife-vehicle collision hotspots at US highway extents: scale and data source effects

“…Many observation systems have appeared over the last five years and they vary in their specific purpose, taxonomic breadth, and use of social networks for collecting data and outreach. A few use smartphone-based applications to facilitate data entry from the field (Olson et al 2014) and some use social media and communication tools to receive observations (e.g., Project Splatter in the UK, http://projectsplatter.co.uk/). One purpose of this study was to find out whether it is possible to use the data from web-based informatics systems containing volunteer wildlife observations, to plan for WVC mitigation at the scale of US states.…”

Underreporting of wildlife-vehicle collisions does not hinder predictive models for large ungulates

“…Collision reported data are afflicted with underreporting because some WVCs include insufficient property damage to warrant reporting; motorist decide not to report; or police, natural resource, and transportation agency conclude that the accident does not merit reporting (Huijser et al, 2008). Carcass removal data are afflicted with underreporting because of long time intervals between carcass collection activities, injured animals move away from roads following collisions (e.g., Snow et al, 2012), carcasses are scavenged or decomposed, carcasses are out of sight and not detected, or the carcass is not a species of concern (e.g., Knapp et al, 2005;Olson et al, 2014). Reports of WVCs are usually greater in number for carcass removal data (e.g., Donaldson and Lafon, 2010), but the spatial coverage of carcass removal datasets often vary based on program funding and prioritized roads for carcass removal (e.g., Knapp et al, 2005).…”

Wildlife/Roadkill Observation and Reporting Systems

“…FURTHER READING Bissonette et al (2008): Estimated the cost to the public and drivers from deer-vehicle collisions on state highways, which in combination with WVC occurrence data has been very useful in proposing driver safety projects to reduce WVC. Olson et al (2014): Outlined methods for collecting WVC data using smartphone technology. Code supplied as an appendix.…”

“…The metadata that can automatically accompany every roadkill observation in a web database means that many tools can be used to enter or retrieve the data (Olson et al 2014). For example, it is theoretically possible to use the metadata attached to an image file sent from a smartphone to automatically create a roadkill record associated with a known user, geolocation and time stamp and potentially other infor mation (such as observation method).…”