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. 2019 Jul 19;10:3207. doi: 10.1038/s41467-019-11191-z

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© The Author(s) 2019

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. 2 — Structural equation models showing the connections between biodiversity and productivity at the plot level (N = 9640 plots). All retained arrows are significant (P < 0.05). Solid and dashed one-way arrows represent positive and negative effects, respectively. Solid and dashed two-way arrows represent positive and negative correlations, respectively. Standardized regression weights (along one-way arrows), correlations (along two-way arrows) and squared multiple correlations (beside Biodiversity and Productivity boxes) for the fitting model are shown. The exogenous unobserved variable err1 and err2 account for the unexplained errors in the estimation of biodiversity and productivity, respectively. Test statistic = 0.761, with 1 model degree of freedom and P = 0.383 (indicating close model-data fit). Source data are provided as a Source Data file