3 years ago

Use of long-term data to evaluate loss and endangerment status of Natura 2000 habitats and effects of protected areas

Zsolt Molnár, János Bölöni, Marianna Biró
Habitat loss is a key driver of biodiversity loss. However, hardly any long-term time series analyses of habitat loss are available above the local scale for finer-level habitat categories. In this paper we analyse, from a long-term perspective, the habitat specificity of habitat area loss, the change in trends since the fall of communism and the impact of protected areas on habitat loss. We studied twenty semi-natural habitat types in 5000 randomly selected localities over seven time periods between 1783 and 2013, using historical maps, archival and recent aerial/satellite imagery, botanical descriptions and field data. We developed a method for estimating habitat types using information transfer between historical sources. Habitat loss trends over time were habitat specific. We found seven types regarding functional form of loss over time: linear, exponential, linear/exponential, delayed, minimum, maximum, and disappearance. Most habitats experienced monotonic area loss; one disappeared completely. After the fall of the communism, average annual habitat loss rates increased, but the trend reversed after 2002. Compared to areas never enjoying protected status, those that were later designated as protected experienced lower habitat loss until 1942, although during the communist era, grassland loss increased severely. Nature conservation measures impacted significantly on habitat loss, halting net losses completely, albeit only inside protected areas. When calculating the degree of endangerment using short-term data (52y), we classified only one habitat as Critically Endangered, but this increased to seven when using long-term data (230y). Hungary will probably reach the global CBD target (to decelerate loss), but will probably not achieve the EU target of halting habitat loss by 2020. We found long-term trend data to be highly useful when interpreting recent habitat loss data in a wider context. Our method could be applied effectively in other countries to augment shorter-term datasets of habitat area trends. This article is protected by copyright. All rights reserved

Publisher URL: http://onlinelibrary.wiley.com/resolve/doi

DOI: 10.1111/cobi.13038

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