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Johannes Kamp

Researcher at University of Göttingen

Publications -  73
Citations -  1788

Johannes Kamp is an academic researcher from University of Göttingen. The author has contributed to research in topics: Population & Biodiversity. The author has an hindex of 18, co-authored 63 publications receiving 1215 citations. Previous affiliations of Johannes Kamp include University of Münster & The Lodge.

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Harnessing the biodiversity value of Central and Eastern European farmland

TL;DR: In this paper, the authors argue that the continued underrepresentation of the low-intensity farmland in Central and Eastern Europe in the international literature and EU policy is impeding the development of sound, evidence-based conservation interventions.
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The Palaearctic steppe biome: a new synthesis

TL;DR: In this paper, a review of the literature on steppe ecology and conservation is provided, focusing on the role of steppe soils in the global carbon budget and the ecology and distribution of most animal groups except vertebrates.
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Unstructured citizen science data fail to detect long-term population declines of common birds in Denmark

TL;DR: In this article, the authors compared population trends for 103 bird species estimated over 28 years from a structured monitoring program and from unstructured citizen science data to assess whether trends estimated from the two data sources were correlated.
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Global population collapse in a superabundant migratory bird and illegal trapping in China.

TL;DR: The magnitude and speed of the decline of the Yellow-breasted Bunting is unprecedented among birds with a comparable range size, with the exception of the Passenger Pigeon, which went extinct in 1914 due to industrial-scale hunting.
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Mapping the timing of cropland abandonment and recultivation in northern Kazakhstan using annual Landsat time series

TL;DR: In this paper, a trajectory-based change detection approach was developed to identify cropland abandonment between 1988 and 2013 and recultivation between 1991 and 2013, with abandonment being detected more accurately (user's accuracy of 93%).