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Figure from:Diversity and carbon storage across the tropical forest biome

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Table 3. Correlations (Kendall’s T) between carbon and tree diversity in South America (n= 158 plots), Africa (n= 162) and Asia (n= 40). Power analysis was used to estimate the minimum effect size (presented as both t and Pearson's r) detectable with 80% power. Correlations with taxon richness per 300 stems are shown in parentheses. Correlations with other diversity metrics shown in Supplementary Table 4. — Table 3 Correlations (Kendall’s T) between carbon and tree diversity in South America (n= 158 plots), Africa (n= 162) and Asia (n= 40). Power analysis was used to estimate the minimum effect size (presented as both t and Pearson's r) detectable with 80% power. Correlations with taxon richness per 300 stems are shown in parentheses. Correlations with other diversity metrics shown in Supplementary Table 4.

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Table 1. Pan-tropical and continental studies assessing the diversity-carbon relationship. Sampling locations are groups of plots in close proximity to each other (individual large plots in ref. 22, TEAM study sites in ref. 24, “forest sites” in ref. 23, groups of plots within 5 km of each other in this study). The number of sampling locations in the largest blocs of forest in each continent are given, these are the Amazon basin and surrounding contiguous forest, the Congo basin and surrounding contiguous forest, and Borneo. + indicates a positive diversity-carbon relationship, NA indicates the relationship was not studied at the given scale. In this study, ref. 22 and ref. 24 all stems >10cm d.b.h. were measured, in ref. 23 the minimum stem diameter measured varied among plots (either 5cm or 10cm). “Sample size not stated, so maximum possible number of 1 ha and 0.04ha subplots given. >Stem density was included as a covariate in analysis. “Relationship analysed among 1 ha plots within sampling locations, not among sampling locations. “0.1 ha not 0.04ha. ‘Relationship among sampling locations.

Figure 1. No relationship across the tropical forest biome between carbon stocks per unit area and tree species richness. Green circles = plots in South America (n = 158), orange squares = Africa (n= 162) and purple triangles = Asia (n = 40). Boxplots show variation in species richness and biomass carbon stocks in each continent. Both carbon and species richness differed significantly between continents (Table 2), but no significant correlation exists between carbon and species richness, neither within each continent (1 < 0.132, P>0.12), nor across all three (linear regression weighted by sampling density in each continent, 8 < —0.001, t= 0.843, P=0.4, weights = 1.2 for South America, 0.6 for Africa and 1.8 for Asia). Results for other diversity metrics are similar (Supplementary Fig. $13).

Table 2. Mean carbon stocks per unit area and tree diversity in forest inventory plots in South America (n= 158), Africa (n= 162) and Asia (n = 40). 95% confidence limits derived from 10,000 bootstrap resamples of the data (sampling with replacement) are shown in parentheses. Different letters indicate significant differences between continents (ANOVA and subsequent Tukey’s all-pair comparison, P< 0.05). Data for other diversity metrics shown in Supplementary Table 2. Figure 2. Decay in similarity (Sorensen index) of tree communities with distance in South America (green), Africa (orange) and Asia (purple). Solid lines show fitted relationships of the form In(similarity) = a+ x distance +¢. Estimated « and B parameters for each continent are given in Supplementary Fig. $12, ¢ denotes binomial errors. Differences in the « parameter indicate differences in the similarity of neighbouring stands, while differences in the 8 parameter indicate differences in the distance deca of tree community similarity. Filled polygons show 95% confidence intervals derived from 10000 bootstrap resamples. Data underlying these relationships are shown in insets, with contours (0.05 and 0.25 quantiles) overlain to show the density of points following kernel smoothing.

Figure 3. Stand-level effect of diversity on carbon stocks per unit area. (A) Location of clusters of forest inventory plots in South America (n= 158 plots), Africa (n= 162 plots) and Asia (n= 40 plots) (some cluster centroids are not visible due to over plotting). (B & C) Diversity metric coefficients in multiple regressions relating carbon to diversity, climate and soil. Results have been presented for (B) non-spatial (OLS) and (C) simultaneous autoregressive error (SAR) models. Bars show model-averaged parameter estimates, with error bars showing standard errors. Asterisks denote variables that were significant in the average model (P< 0.05), with the summed AIC, weights of models in which a variable appears shown beneath bars (where >0.75). Taxa/ stem denotes richness estimates per 300 stems. SAR models indicate that increasing species richness by 1 SD (from 86 to 151 species.ha~') increased carbon by 1.5 Mg.ha~! in South America, 0.2 Mg.ha~! in Africa and 15.8 Mg.ha“! in Asia (note only the relationship in Asia was statistically significant). Green shading in (A) shows the extent of broadleaved evergreen and fresh water regularly flooded forest classes from**. Model coefficients are given in Supplementary Table 5. Maps were created in R version 3.02 (http://www.R-project.org/)°* using base maps from maps package version 2.3-9 (http://CRAN.R-project.org/package = maps)**.

Figure 4. Variation in the coefficient (8) of the relationship between species richness and carbon among 0.04ha subplots within 266 1 ha plots. Coefficients come from multiple regression models also containing the number of stems as a second-order polynomial term to allow for a saturating relationship. Coefficients from plots in South America are shown in green, Africa in orange and Asia in purple. Mean values of coefficients are shown in the inset, with error bars showing 95% confidence intervals derived from 10000 bootstrap resamples (with replacement) of the dataset, with asterisks denoting significant differences from zero (one- sample Wilcoxon test, **P < 0.01, *P< 0.05). Across all plots, doubling species richness in 0.04 ha subplots increased carbon by 6.9%. The horizontal line in the inset and bold vertical line in the main figure show where coefficients = 0. 8 is in units of In(Mg.ha~! carbon) per In(tree species).

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