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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. 5 — Matrices show overall (across block) and block-adjusted (within block) Pearson correlation coefficients (r) for 250 × 250 m landscapes (a) and 500 × 500 m landscapes (b). Adjusting for block reduces correlations of landscape richness and environmental variables (all n.s.). log(LR): log-transformed landscape richness; Temp: mean annual temperature; Precip: annual precipitation; Irrad: mean irradiation; Alt: altitude; Slope: slope; N-aspect: north-south aspect; $\bar{EVI}$ and EVI_GS: mean and integrated growing-season productivity; GSL: growing-season length; α_NIR: near-infrared albedo; NE: net diversity effect for these variables; CV⁻¹: temporal stability of these variables; n = 237 and 77 for 250 × 250 m and 500 × 500 m land-cover composition × block combinations, respectively. Source data are provided as a Source Data file.