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Characteristics of Beaver Activity in Bulgaria and Testing of a UAV-Based Method for Its Detection

Maria Kachamakova,Polina K. Nikova,Vladimir Todorov,Blagovesta Zheleva,Yordan S. Koshev

Published 2025 in Conservation

ABSTRACT

After a series of successful reintroductions, the Eurasian beaver (Castor fiber) is expanding its range throughout Europe. Timely monitoring of beaver activity contributes to early detection of environmental impacts and aids in mitigating human–wildlife conflicts and other threats. However, the signs of beaver presence are difficult to detect in some environments, e.g., densely vegetated river banks or in areas with considerable water level variability. In these cases, new technologies can offer opportunities for easier and faster monitoring. In the current study, we provide a characterisation of the wood-gnawing activity of a newly established beaver population in Northern Bulgaria, using a traditional transect method. In addition, we test the application of unmanned aerial vehicles (UAVs) to detect and map the signs of beaver activity. The overall gnawing-activity characteristics of newly established Castor fiber populations in Bulgaria follow the pattern documented in earlier studies: the affected trees were mainly willow and poplar, located at less than 10 m from the riverbank, with a diameter mostly under 30 cm. However, there were considerable differences in the tree size and distance from the water between the two studied habitats—the Danube River and its tributaries. No dams were recorded, probably due to the rivers’ sizes. We found no significant difference in the detection rates of the UAV with and without canopy cover. Overall, the UAV-based transects were reliable for the detection of the species’ presence, but not for quantification of its activity patterns, due to the low detection rates, in comparison with ground-level transects. We believe that the method is promising because it is cost- and time-saving but could be improved using cameras with better resolution and by involving machine learning algorithms. The drone detection method could help identify the areas with the densest populations of the species, where Natura 2000 protected zones could then be established.

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