Factors Influencing Data Quality in Citizen Science Roadkill Projects

Abstract

Event Abstract Back to Event 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 Bager, A., and da Rosa, C. A. (2011). Influence of Sampling Effort on the Estimated Richness of Road-Killed Vertebrate Wildlife. Environ. Manage. 47, 851–858. doi:10.1007/s00267-011-9656-x. Beebee, T. J. C. (2013). Effects of Road Mortality and Mitigation Measures on Amphibian Populations. Conserv. Biol. doi:10.1111/cobi.12063. Chu, M., Leonard, P., and Stevenson, F. (2012). “Growing the Base for Citizen Science,” in Citizen Science: Public Participation in Environmental Research (New York: Cornell University Press), 69–81. Coffin, A. W. (2007). From roadkill to road ecology: A review of the ecological effects of roads. J. Transp. Geogr. 15, 396–406. doi:10.1016/j.jtrangeo.2006.11.006. Erritzoe, J., Mazgajski, T. D., and Rejt, Ł. (2003). Bird Casualties on European Roads — A Review. Acta Ornithol. 38, 77–93. doi:10.3161/068.038.0204. Fahrig, L., and Rytwinski, T. (2009). Effects of Roads on Animal Abundance: an Empirical Review and Synthesis. Ecol. Soc. 14. Available at: http://www.ecologyandsociety.org/vol14/iss1/art21/. Guinard, E., Prodon, R., and Barbraud, C. (2015). “Case Study: A Robust Method to Obtain Defendable Data on Wildlife Mortality,” in Handbook of Road Ecology (West Sussex, UK: John Wiley & Sons), 96–100. Heigl, F., and Zaller, J. G. (2014). Using a Citizen Science Approach in Higher Education: a Case Study reporting Roadkills in Austria. Hum. Comput. 1. Available at: http://hcjournal.org/ojs/index.php?journal=jhc&page=article&op=view&path%5B%5D=10 [Accessed January 21, 2015]. Helga Kromp-Kolb, and Thomas Gerersdorfer (2003). Auswirkungen von Klimaänderungen auf die Tierwelt – derzeitiger Wissensstand, fokussiert auf den Alpenraum und Österreich. Vienna: Institut für Meteorologie und Physik Universität für Bodenkultur Wien. Herry, M., Sedlacek, N., and Steinacher, I. (2012). Transport in Figures - Austria Edition 2011. Vienna: Federal Ministry for Transport, Innovation and Technology (bmvit). Jochimsen, D. M., Peterson, C., Andrews, K. M., and Whitfiled Gibbons, J. (2004). A Literature Review of the Effects of Roads on Amphibians and Reptiles and the Measures Used to Minimize Those Effects. Pocatello, Idaho: Idaho Fish and Game Department USDA Forest Service. Lewandowski, E., and Specht, H. (2015). Influence of volunteer and project characteristics on data quality of biological surveys. Conserv. Biol., n/a–n/a. doi:10.1111/cobi.12481. Olson, D. D., Bissonette, J. A., Cramer, P. C., Green, A. D., Davis, S. T., Jackson, P. J., et al. (2014). Monitoring Wildlife-Vehicle Collisions in the Information Age: How Smartphones Can Improve Data Collection. PLOS ONE 9. doi:10.1371/journal.pone.0098613. Parmesan, C. (2007). Influences of species, latitudes and methodologies on estimates of phenological response to global warming. Glob. Change Biol. 13, 1860–1872. doi:10.1111/j.1365-2486.2007.01404.x. Paul, K., Quinn, M. S., Huijser, M. P., Graham, J., and Broberg, L. (2014). An evaluation of a citizen science data collection program for recording wildlife observations along a highway. J. Environ. Manage. 139, 180 – 187. doi:http://dx.doi.org/10.1016/j.jenvman.2014.02.018. van der Ree, R., Smith, D. J., and Grilo, C. (2015). “The Ecological Effects of Linear Infrastructure and Traffic: Changes and Oppurtunities of Rapid Global Growth,” in Handbook of Road Ecology (West Sussex, UK: John Wiley & Sons), 1–9. Santos, S. M., Carvalho, F., and Mira, A. (2011). How Long Do the Dead Survive on the Road? Carcass Persistence Probability and Implications for Road-Kill Monitoring Surveys. PLoS ONE 6, e25383. doi:10.1371/journal.pone.0025383. Schmeller, D. S., Henry, P.-Y., Julliard, R., Gruber, B., Clobert, J., Dziock, F., et al. (2009). Advantages of Volunteer-Based Biodiversity Monitoring in Europe. Conserv. Biol. 23, 307–316. doi:10.1111/j.1523-1739.2008.01125.x. Shilling, F. M., Perkins, S. E., and Collinson, W. (2015). “Wildlife/Roadkill Observation and Reporting Systems,” in Handbook of Road Ecology (West Sussex, UK: John Wiley & Sons), 492–501. Shi, W., Shi, W., and Shi, S. (2002). Spatial Data Quality. London ; New York: Routledge Chapman Hall. Slater, F. M. (2002). An assessment of wildlife road casulties of wildlife road casualties – the potential discrepancy between numbers counted and numbers killed. Web Ecol. 3, 33–42. doi:10.5194/we-3-33-2002. Teixeira, F. Z., Pfeifer Coelho, A. V., Esperandio, I. B., and Kindel, A. (2013). Vertebrate road mortality estimates: Effects of sampling methods and carcass removal. Biol. Conserv. 157, 317–323. doi:10.1016/j.biocon.2012.09.006. Vercayie, D., and Herremans, M. (2015). Citizen science and smartphones take roadkill monitoring to the next level. Nat. Conserv. 11, 11, 29–40. doi:10.3897/natureconservation.11.4439. 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 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. 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 Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract Supplemental Data The Authors in Frontiers Florian Heigl Johann G Zaller Google Florian Heigl Johann G Zaller Google Scholar Florian Heigl Johann G Zaller PubMed Florian Heigl Johann G Zaller Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.