Drivers of conservation status in Austrian wetlands

Abstract ID: 3.6
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Kapitany, E. (1)
Zechmeister, H. (1); Wrbka, T. (1); and Dullinger, S. (1)
(1) University of Vienna, Department of Botany and Biodiversity Research, Universitätsring 1, 1010 Wien, Wien, Austria
How to cite: Kapitany, E.; Zechmeister, H.; Wrbka, T.; and Dullinger, S.: Drivers of conservation status in Austrian wetlands-3.6
Categories: No categories defined
Keywords: Wetlands, Peatlands, Monitoring, Conservation status, Protected areas
Categories: No categories defined
Keywords: Wetlands, Peatlands, Monitoring, Conservation status, Protected areas
Abstract
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Wetlands are among the most threatened ecosystems in Austria, with drainage, agricultural intensification and climate change being the most important threats. Assessing their conservation status and understanding the underlying drivers is essential for evaluating conservation measures and prioritizing restoration efforts. To ensure effectiveness of measures, the differentiation between groundwater-fed fens, rainwater-fed bogs and other wetland types is important since their hydrology strongly influences their management needs and susceptibility to external pressures. Within this context, protected areas are a common conservation measure, yet their effectiveness in conserving wetlands remains understudied.

In this study, we identify the most important drivers of conservation status in Austrian wetlands, using the newly compiled Austrian peatland inventory published by the Environment Agency Austria. In particular, we analyze the effect of protection status and time since designation on the conservation status of Austrian wetlands. We additionally account for several abiotic (e.g., temperature, precipitation and topography) and anthropogenic drivers (e.g., drainage occurrence, atmospheric nitrogen deposition and agricultural management). We evaluate the effects that these variables have on the conservation status of sites, how these effects might interact and how they might differ between different wetland types.

Using regression models and machine learning approaches, this study quantifies the relative importance of different environmental and anthropogenic drivers of wetland conservation status to improve our understanding of wetland degradation in Austria over the past decades.

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