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Factors Influencing Data Quality in Citizen Science Roadkill Projects

Florian Heigl1* and Johann G. Zaller1
  • 1 University of Natural Resources and Life Sciences Vienna, Institute of Zoology, Austria

Introduction
Roads are an essential part of Central European landscapes and therefore have a major impact on flora and fauna (Herry et al., 2012; van der Ree et al., 2015). The most direct negative effect of roads on animals is roadkill, i.e. the collision of animals with vehicles, leading to the decrease of populations of several animal groups (Beebee, 2013; Coffin, 2007; Erritzoe et al., 2003; Fahrig and Rytwinski, 2009). In several countries reporting systems for observations of road-killed animals have been established (Shilling et al., 2015). Some of these projects use a citizen science approach to get an overview of the type, number and distribution of road-killed animals. Citizen science is perhaps the only feasible method to cover a broad geographic range over a long time span (Vercayie and Herremans, 2015). Regardless which reporting method is used, collecting reliable data of road-killed animals over a wide area and over a long time span can be very challenging since many biases exist that could influence mortality estimates on roads (Bager and da Rosa, 2011; Guinard et al., 2015; Lewandowski and Specht, 2015; Paul et al., 2014; Santos et al., 2011; Teixeira et al., 2013). The aim of this study is to identify factors influencing data quality in citizen science based roadkill projects. Here data quality is defined as a measure of the difference between data and the reality they represent, whereas data quality is high when data fit their intended uses (Shi et al., 2002). Materials and Methods
We searched for publications publicized between 1900 and March 2016 in the scientific databases ISI Web of Knowledge and Scopus using the following search term combination: 'data quality' AND/OR 'citizen science' OR 'public participation' AND/OR 'roadkill' OR 'animal vehicle collision' OR 'road mortality' OR 'wildlife vehicle collision' (Suppl.Table 1). In a second step we ensured that the list of references contained no duplicates. For our presentation at the Austrian Citizen Science Conference 2016, we selected key publications and combined the information with our experiences from Project Roadkill (www.roadkill.at). Results and Discussion
The initial search yielded a total of 837 articles, books, book sections and conference proceedings published between 1960 and 2016 uniquely listed either in Web of Science or Scopus (Suppl.Table 1). Overall, using citizen science to monitor road-killed animals seems to be a relatively new approach. Information on factors influencing data quality is quite scarce and is scattered among remote research areas. Four articles concentrated on roadkill and data quality. Based on the literature found and our three-year experience in Project Roadkill (Heigl and Zaller, 2014), we built four category groups by which data quality in citizen science roadkill projects could be influenced: environmental conditions, collection method, material and participants (Suppl.Table 2). Environment Landscape and road characteristics influence the detectability of road-killed animals. Winding roads, densely vegetated roadside strips or even newly bituminized roads can make it difficult to see roadkills and therefore underestimate numbers of roadkills. Additionally, weather conditions influence migration behavior of many animal groups. Amphibian migration is timed by temperature (Kromp-Kolb and Gerersdorfer, 2003; Parmesan, 2007). Reptiles are using roads for thermoregulation and are therefore time dependently distributed on roads (Jochimsen et al., 2004). Fog, rain or bright sunshine can make it difficult to detect roadkills and weather is also one of the factors influencing the persistence of road-killed animals on streets. Most carcasses are gone within one day, depending not only on weather, but also on animal group, traffic volume and scavengers (Santos et al., 2011). From our Project Roadkill we know that a reporting bias exists in favor of eye-catching species. Additionally, working with different animal species in one project can be very challenging, since some species are hard to distinguish when road-killed, e.g. the group of true frogs (Pelophylax), rodents (Muroidea) or some passerine birds (Sylviidae). Collection Method Roadkill data are either collected via standardized monitoring or opportunistic data gathering (roving records). Standardized monitoring is more accepted in the scientific community, but it is more time consuming and more difficult to find participants (Vercayie and Herremans, 2015). Roving records contain 'presence only' data and often comprise big amounts of data collected over a wide geographic range. The only study in road ecology comparing these two approaches concludes that opportunistic data can indeed be robust and reliable as long as the search and report effort is documented (Paul et al., 2014). Material Dependent on the target audience, road-killed animals can be reported via Smartphone apps, pen/paper method, social media platforms, SMS message, Email or online forms (Olson et al., 2014; Shilling et al., 2015). Study design and communication tools determine data quality more than volunteer involvement per se (Schmeller et al., 2009). Focusing on communication and providing high quality and user targeted communication tools (e.g. education material and reporting platforms) raises the chance to get high quality data. Or as Chu et al., 2012 put it "Keeping users happy is important because of the value of long-term data from the same localities". Participants The main challenges in citizen science roadkill projects are correct species identification and spatial distribution” (Vercayie and Herremans, 2015). Species identification can be improved by high-quality educational training and a long-term commitment of participants. Spatial biases can be accounted for in statistical analyses or with specific sampling campaigns into areas where few data are reported. Moreover, based on personal experience and communication with participants, we found that participants are often distracted when driving on roads, resulting in overlooking small road-killed animals. Kind of travelling (car, bike, foot) and speed of travel also matters, searching on foot is much more effective than by car (Slater, 2002), but it is obvious, that there is a trade-off between accuracy and spatial coverage. Conclusion
Data quality in citizen science roadkill projects is influenced by many factors that need to be addressed in order to gather robust roadkill data. Taking these limitations into account, citizen science is an adequate method for covering wide geographic ranges and raising public awareness on accident risks and conservation.

Acknowledgements

Great thanks to our participants in Project Roadkill for investing time and reporting data. We are grateful to P. Hummer and R. Holzapfel who developed the new project platform, A. Bruckner who supported the development of the first online platform as head of the Institute of Zoology at the Univ. of Nat. Res. and Life Sciences Vienna, B. Dauth for his huge support, J. Freinschlag for the design of the Logo, H. Hellmeier for the illustrations and T. Posegga who developed the first website.

References

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Keywords: wildlife vehicle collision, Road mortality, wildlife observation systems, remote sensing, Public Engagement, digital reporting platforms, Public Participation

Conference: Austrian Citizen Science Conference 2016, Lunz am See, Austria, 18 Feb - 19 Feb, 2016.

Presentation Type: Oral Presentation

Topic: Citizen Science - Quo vadis?

Citation: Heigl F and Zaller JG (2016). Factors Influencing Data Quality in Citizen Science Roadkill Projects. Front. Environ. Sci. Conference Abstract: Austrian Citizen Science Conference 2016. doi: 10.3389/conf.FENVS.2016.01.00002

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Received: 29 Jul 2016; Published Online: 06 Sep 2016.

* Correspondence: Mr. Florian Heigl, University of Natural Resources and Life Sciences Vienna, Institute of Zoology, Vienna, Austria, florian.heigl@boku.ac.at