MOESM1 of Effect of root exudates of Eucalyptus urophylla and Acacia mearnsii on soil microbes under simulated warming climate conditions

Additional file 1: Table S1. Compounds of four kinds of root exudates from A. mearnsii. Table S2. Compounds of four kinds of root exudates from E.urophylla. Figure S1. Principal component analysis of 39 compounds for Acacia mearnsii. Figure S2. Principal component analysis of 35 compounds for E.urophylla. Figure S3. PLFAs of different microbial communities of Eucalyptus urophylla soil. Figure S4. PLFAs of different microbial communities of Acacia mearnsii. (DOCX 160 kb

DataSheet_3_Climate factors drive plant distributions at higher taxonomic scales and larger spatial scales.docx

IntroductionUnderstanding the environmental effects shaping plant distributions is crucial for predicting future ecosystems under climate change. The effects of different environmental factors may vary in their importance in determining plant distributions at different spatial and taxonomic scales, which affects our understanding of plant–environment relationships. However, this has not yet been systematically explored.MethodsHere we combined global distribution data of 205 widely distributed plant families and environmental data from multiple global databases. We then used the random forest algorithm to quantify the relative importance of environmental factors (including climate, soil, and topography) on the distribution of plants at three taxonomic levels (family, genus, and species) and multiple spatial scales (10 spatial extents from 1° × 1° to 10° × 10° randomly located across the globe). Mixed-effect models were used to assess the significance of spatial and taxonomic scales on relative environmental effects across the globe.ResultsWe found that climate factors had increasing importance on plant distributions at higher taxonomic scales and larger spatial scales (yet stochastic effects at spatial extents finer than 4° × 4°). Edaphic factors congruously decreased their importance on plant distributions as spatial and taxonomic scales increased. Topographic factors had a relatively larger influence at higher taxonomic levels (i.e., family>genus>species), but with a relatively slow rise with the increase in spatial scale.DiscussionsOur findings are generally aligned with current knowledge but have also indicated the potential complexity underlying the scale-dependence of relative environmental effects on plant distributions. Overall, we highlight a multi-scale insight into ecological patterns and underlying mechanistic processes.</p

DataSheet_1_Climate factors drive plant distributions at higher taxonomic scales and larger spatial scales.docx

IntroductionUnderstanding the environmental effects shaping plant distributions is crucial for predicting future ecosystems under climate change. The effects of different environmental factors may vary in their importance in determining plant distributions at different spatial and taxonomic scales, which affects our understanding of plant–environment relationships. However, this has not yet been systematically explored.MethodsHere we combined global distribution data of 205 widely distributed plant families and environmental data from multiple global databases. We then used the random forest algorithm to quantify the relative importance of environmental factors (including climate, soil, and topography) on the distribution of plants at three taxonomic levels (family, genus, and species) and multiple spatial scales (10 spatial extents from 1° × 1° to 10° × 10° randomly located across the globe). Mixed-effect models were used to assess the significance of spatial and taxonomic scales on relative environmental effects across the globe.ResultsWe found that climate factors had increasing importance on plant distributions at higher taxonomic scales and larger spatial scales (yet stochastic effects at spatial extents finer than 4° × 4°). Edaphic factors congruously decreased their importance on plant distributions as spatial and taxonomic scales increased. Topographic factors had a relatively larger influence at higher taxonomic levels (i.e., family>genus>species), but with a relatively slow rise with the increase in spatial scale.DiscussionsOur findings are generally aligned with current knowledge but have also indicated the potential complexity underlying the scale-dependence of relative environmental effects on plant distributions. Overall, we highlight a multi-scale insight into ecological patterns and underlying mechanistic processes.</p

Interspecific Neighbor Interactions Promote the Positive Diversity-Productivity Relationship in Experimental Grassland Communities

<div><p>Because the frequency of heterospecific interactions inevitably increases with species richness in a community, biodiversity effects must be expressed by such interactions. However, little is understood how heterospecific interactions affect ecosystem productivity because rarely are biodiversity ecosystem functioning experiments spatially explicitly manipulated. To test the effect of heterospecific interactions on productivity, direct evidence of heterospecific neighborhood interaction is needed. In this study we conducted experiments with a detailed spatial design to investigate whether and how heterospecific neighborhood interactions promote primary productivity in a grassland community. The results showed that increasing the heterospecific: conspecific contact ratio significantly increased productivity. We found there was a significant difference in the variation in plant height between monoculture and mixture communities, suggesting that height-asymmetric competition for light plays a central role in promoting productivity. Heterospecific interactions make tall plants grow taller and short plants become smaller in mixtures compared to monocultures, thereby increasing the efficiency of light interception and utilization. Overyielding in the mixture communities arises from the fact that the loss in the growth of short plants is compensated by the increased growth of tall plants. The positive correlation between species richness and primary production was strengthened by increasing the frequency of heterospecific interactions. We conclude that species richness significantly promotes primary ecosystem production through heterospecific neighborhood interactions.</p></div

DataSheet_2_Climate factors drive plant distributions at higher taxonomic scales and larger spatial scales.docx

IntroductionUnderstanding the environmental effects shaping plant distributions is crucial for predicting future ecosystems under climate change. The effects of different environmental factors may vary in their importance in determining plant distributions at different spatial and taxonomic scales, which affects our understanding of plant–environment relationships. However, this has not yet been systematically explored.MethodsHere we combined global distribution data of 205 widely distributed plant families and environmental data from multiple global databases. We then used the random forest algorithm to quantify the relative importance of environmental factors (including climate, soil, and topography) on the distribution of plants at three taxonomic levels (family, genus, and species) and multiple spatial scales (10 spatial extents from 1° × 1° to 10° × 10° randomly located across the globe). Mixed-effect models were used to assess the significance of spatial and taxonomic scales on relative environmental effects across the globe.ResultsWe found that climate factors had increasing importance on plant distributions at higher taxonomic scales and larger spatial scales (yet stochastic effects at spatial extents finer than 4° × 4°). Edaphic factors congruously decreased their importance on plant distributions as spatial and taxonomic scales increased. Topographic factors had a relatively larger influence at higher taxonomic levels (i.e., family>genus>species), but with a relatively slow rise with the increase in spatial scale.DiscussionsOur findings are generally aligned with current knowledge but have also indicated the potential complexity underlying the scale-dependence of relative environmental effects on plant distributions. Overall, we highlight a multi-scale insight into ecological patterns and underlying mechanistic processes.</p

Experimental esign of the two experiments.

<p>(<i>a</i>) The first experiment comprised 9 blocks involving 8 species. Each block consisted of 36 plots (1×1 m in size). Illustrated here is one block but only shows 9 plots. The other 27 plots are not shown, including 21 monoculture plots for other 7 species (each being sown at low, medium and high density) and six mixcultures (i.e., the two spatial configurations for the 3 density levels, the same as the second and third rows). The aggregated and dispersed mixcultures both had the same 8 species but different spatial configurations. (<i>b</i>) The second experiment comprised 5 blocks involving 8 species. Each block consisted of 32 1×1 m plots. Illustrated here is one block but only shows 8 plots. The other 24 plots are not shown, including 6 monoculture plots for the other 6 species and 3 replicates for each of the six mixcultures (i.e., the two spatial configurations for the 3 diversity level). In both experiments, blocks were separated by 2 m walkways, and plots were separated 1 m apart.</p

Effects of the number of species and heterospecific interactions on above- and belowbiomass.

<p>Mean ± SE for the Box-Cox transformed above-ground biomass (<i>a</i> and <i>b</i>) and below-ground biomass (<i>c</i> and <i>d</i>) of experiment I and for the Box-Cox transformed above-ground biomass for experiment II (<i>e</i> and <i>f</i>). The <i>x</i>-axis label on the left column is the number of species. Experiment I had 2 richness levels (1 and 8 species). Experiment II had four richness levels (1, 2, 4 and 8 species).</p

Overyielding and the magnitude of complementary effects.

<p>(<i>a</i>) Linear relationships between aboveground biomass and species richness for aggregated (dashed lines and open circles) and dispersed mixtures (solid lines and filled circles) of experiment II. (<i>b</i>) Difference in aboveground biomass between the observed mixture plots and the mean monoculture biomass of all species across diversity gradients for experiment II. This difference measures overyielding and the degree of difference indicates the degree of complementarity effects. Dashed lines and open circles refer to plots with aggregated mixtures, and solid lines and filled circles refer to dispersed plots.</p

Appendix A. A table of tree abundance and basal area of the five species used in manipulative experiments in the 6 ha plots, and figures showing a sketch map for experimental design in the laboratory, a sketch map for experimental design in the field, density effect in the control treatment and the sterilization treatment as a function of basal area of adult trees in the 6 ha plots, and density effects in the control treatment and the fungicide treatment as a function of basal area of adult...

A table of tree abundance and basal area of the five species used in manipulative experiments in the 6 ha plots, and figures showing a sketch map for experimental design in the laboratory, a sketch map for experimental design in the field, density effect in the control treatment and the sterilization treatment as a function of basal area of adult trees in the 6 ha plots, and density effects in the control treatment and the fungicide treatment as a function of basal area of adult trees in the 6 ha plots

Biomass allocation and plant height variation for experiment I.

<p>(<i>a</i>) The mean root/shoot ratio varied with heterospecific interactions from monocultures, aggregated to dispersed mixture plots. (<i>b</i>) The within-plot mean (left bars) and standard deviation (right bars) in plant height (in cm, calculated over all grids in each plot) versus heterospecific interactions. (<i>c</i>) The average height for each of the eight species varied from monocultures, aggregated mixtures to dispersed mixtures. Data bars for the root/shoot ratio in (<i>a</i>) and the mean height in (<i>b</i>) are mean+1 standard error. Bars with different letters above are significantly different at <i>P</i> = 0.05. The values for the standard deviation for height are shown in (<i>b</i>).</p