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Irene Hoppe

Title: Igel-Roadkill im Wiener Stadtgebiet - Analyse des Einflusses von Landnutzung mittels Citizen Science und anderer öffentlicher Daten.

Abstract:

This master thesis analyses the spatial and temporal distribution of hedgehog roadkills in Vienna. Furthermore, the data quality of the Austrian citizen science project Roadkill will be examined and its ecological significance will be evaluated. In the citizen science project, voluntary participants report dead vertebrate animals on or near roads. A four-stage scheme was developed for the evaluation of the data quality of the data. Criteria of this scheme were the presence of a photo, a description of the dead animal or the place where the dead animal was found as well as an exact taxonomy classification of the animal. About 34% of the data fall into the highest quality level. These data offer the highest information content by providing an exact specification of the animal species including a photo, as well as by describing the place of discovery and the dead animal itself. The high
quality of the data therefore makes it most likely to be validated. First, all roadkill project data from Austria will be examined. Subsequently, all reports, divided into the animal classes, are analysed. The reported data on hedgehog roadkills (Erinaceus europaeus and Erinaceus roumanicus) in Austria will then be filtered. For comparison purposes, further data on hedgehog roadkills in Vienna are used. In addition, a hedgehog roadkill monitoring was carried out in Vienna. All hedgehog roadkill data were combined with high-resolution geographical background data. The conditional probabilities in individual land use categories were then calculated and the results were compared with relevant scientific literature. The spatial analysis showed that traffic areas, built-up areas and green areas increase the
probability of a hedgehog roadkill. During the monitoring a total of three hedgehog roadkills could be mapped. So a professional roadkill monitoring is costly and time-consuming and Citizen Science projects make it possible to generate a lot of data over longer periods at a lower cost.