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. 2020 Jan 9;11:154. doi: 10.1038/s41467-019-14002-7

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — a The study area was divided into blocks defined by biogeographic region (BGR; names in map) and altitude ranges. Pie charts show the fractional cover of the land-cover types in the different BGRs. b Within each block, the largest possible set of land-cover types was chosen that allowed constructing gradients in landscape richness with all possible land-cover combinations realized (see (a) for color legend; crosses indicate land-cover types not used in the respective block). Landscape richness was thus orthogonal to fractional contribution of any land-cover type. Plots: the number of landscape plots; LCC: the number of unique land-cover compositions in the corresponding BGR and altitude ranges.