写在前面
陆地生态系统中，生物多样性对初级生产力的影响是20多年来生态学研究的热点。在自然生态系统中，生物多样性-生产力的关系是高度可变的，尽管正向关系最为常见。理解不同关系产生的条件仍然是一个重大挑战。该研究通过分析“HerbDivNet”全球天然草地的调查数据表明，在全球范围内，生物多样性对稳定自然草地植物生产力的重要性。该研究表明，物种多样性对生产力的影响从低生产力群落的强正效应转变为高生产力群落的强负效应。因此，植物多样性使群落生产力保持在中等水平。多样性有助于稳定植物生产力，并对抗空间上的环境异质性。该研究在全球尺度上验证了生物多样性-生产力和生物多样性-空间稳定性的关系，为研究自然生态系统功能提供了一个新的研究视角。
原文信息
DOI | 10.1038/s41467-019-11191-z
摘要
The effect of biodiversity on primary productivity has been a hot topic in ecology for over 20 years. Biodiversity–productivity relationships in natural ecosystems are highly variable, although positive relationships are most common. Understanding the conditions under which different relationships emerge is still a major challenge. Here, by analyzing HerbDivNet data, a global survey of natural grasslands, we show that biodiversity stabilizes rather than increases plant productivity in natural grasslands at the global scale. Our results suggest that the effect of species richness on productivity shifts from strongly positive in lowproductivity communities to strongly negative in high-productivity communities. Thus, plant richness maintains community productivity at intermediate levels. As a result, it stabilizes plant productivity against environmental heterogeneity across space. Unifying biodiversity–productivity and biodiversity–spatial stability relationships at the global scale provides a new perspective on the functioning of natural ecosystems.
主要图表
Fig. 1 Hypothetical relationships between richness, productivity and stability in natural ecosystems. a The local effect of species richness on primary productivity is expected to vary along environmental gradients, leading to a convergence of richness–productivity relationships with increasing diversity. b As a result, the spatial stability of productivity between communities that experience different environments should increase as plant richness increases.
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.
Fig. 3 Global relationships between biodiversity and productivity in natural grasslands at the plot level. Marginal histograms show the frequency distribution of biodiversity and productivity across plots (N = 9640 plots). The 151 grids were divided into three equal groups depending on their mean productivity: low, medium, and high productivity (with 50–51 grids each), corresponding to different colors of the points (plots). Repeating the structural equation model analysis for the three groups separately, the partial effects ðmean ± SDÞ of biodiversity on productivity along the productivity gradient are shown in the inset.
Fig. 4 Relationships between species richness, productivity and spatial variability of productivity. a Relationships between richness and productivity varied among 151 grids with different average productivity or stress gradients in natural grasslands. b Standard deviation (SD) of productivity (i.e., inverse of spatial stability) decreased with species richness in global natural grasslands.
Fig. 5 Shift in the sign and strength of the biodiversity effects across productivity gradients in hierarchical Bayesian models. Each point represents the mean productivity (horizontal axis) and the median value of biodiversity effect on productivity (vertical axis) at each grid (N = 151 grids) or grid group (low, medium, and high productivity groups in the inset). Vertical lines represent the 95% of credible intervals (CI) of estimated biodiversity effects.
Fig. 6 Global relationship between biodiversity and spatial variability (N = 47 plot groups). a, c Simple regression models. b, d Hierarchical Bayesian models. Black points and vertical lines in b, d show the medians and 95% credible intervals (CI) of log-transformed productivity variation for each species richness level. Spatial variability in grassland productivity was measured as standard deviation (SD, top panel) or coefficient of variation (CV, bottom panel) across plots with the same richness. The black line represents the fitted relationship. The shaded areas in panels b and d represent the 95% CI of the fitted relationships. The texts in panels b and d show the median and 95% CI of the slope. Species richness was used to group plots containing one, two, three species, etc.
Fig. 7 Structural equation model showing the connections between biodiversity, productivity, and variability. The grouping of plots is consistent with Fig. 6 (N = 47 plot groups). Variability was measured either as the standard deviation (a), or as the coefficient of variation (b) of productivity across plots within each level of species richness. Solid and dashed one-way straight black arrows represent positive and negative effects, respectively. The one-way gray arrow represents an insignificant (P > 0.05) effect. Dashed two-way black arrows represent negative correlations. To avoid zero degree of freedom, the ‘productivity’ variable in b was not dropped from the model.
Data availability
The grassland data are from Fraser et al. 44 ; these data have been deposited by Fraser et al. in the Dryad repository (https://datadryad.org/resource/doi:10.5061/dryad.038q8; Title: raw plot data from globally distributed sites). Temperature and precipitation data are from WorldClim (http://worldclim.org/version2), and daylight hours data are from ‘NASA Surface Meteorology and Solar Energy: Global Data Sets’ (https://eosweb.larc. nasa.gov/cgi-bin/sse/global.cgi). The source data underlying Figs. 2, 3, 4a, b, 5, 6a–d, 7a, b, and Supplementary Figs. 1, 2a–f, 3a–f, 5a, b, and 6a, b are provided as a Source Data file.
Code availability
The code for the hierarchical Bayesian models and structural equation models (SEM) are available in Supplementary Notes 1 and 2, respectively.
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