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**Acknowledgements** D.L.M.C. was supported by the London Natural Environmental Research Council Doctoral Training Partnership grant (grant no. NE/LO02485/1). This paper developed from analysing data from the African Tropical Rainforest Observatory Network (AfriTRON), curated at ForestPlots.net. AfriTRON has been supported by numerous people and grants since its inception. We sincerely thank the people of the many villages and local communities who welcomed our field teams and without whose support this work would not have been possible. Grants that have funded the AfriTRON network, including data in this paper, are a European Research Council Advanced Grant (T-FORCES; 291585; Tropical Forests in the Changing Earth System), a NERC standard grant (NER/A/S/2000/01002), a Royal Society University Research Fellowship to S.L.L., a NERC New Investigators Grant to S.L.L., a Philip Leverhulme Award to S.L.L., a European Union FP7 grant (GEOCARBON; 283080), Leverhulme Program grant (Valuing the Arc); a NERC Consortium Grant (TROBIT; NE/D005590)/, NERC Large Grant (CongoPeat; NE/R016860/1) the Gordon and Betty Moore Foundation the David and Lucile Packard Foundation, the Centre for International Forestry Research (CIFOR), and Gabon's National Parks Agency (ANPN). This paper was supported by ForestPlots.net approved Research Project 81, 'Comparative Ecology of African Tropical Forests'. The development of ForestPlots.net and data curation has been funded by several grants, including NE/B503384/1, NE/N012542/1, ERC Advanced Grant 291585—T-FORCES', NE/F005806/1, NERC New Investigators Awards, the Gordon and Betty Moore Foundation, a Royal Society University Research Fellowship and a Leverhulme Trust Research Fellowship. Fieldwork in the Democratic Republic of the Congo (Yangambi and Yoko sites) was funded by the Belgian Science Policy Office BELSPO (SD/AR/01A/COBIMFO, BR/132/A1/AFRIFORD, BR/143/A3/HERBAXYLAREDD, FED-TWIN2019-prf-075/CongoFORCE, EF/211/TREE4FLUX); by the Flemish Interuniversity Council VLIR-UOS (CD2018TEA459A103, FORMONCO II); by L'Académie de recherche et d'enseignement supérieur ARES (AFORCO project) and by the European Union through the

FORETS project (Formation, Recherche, Environnement dans la TShopo) supported by the XIth European Development Fund. EMV was supported by fellowship from the CNPq (Grant 308543/2021-1). RAPELD plots in Brazil were supported by the Program for Biodiversity Research (PPBio) and the National Institute for Amazonian Biodiversity (INCT-CENBAM). BGL post-doc grant no. 2019/03379-4, São Paulo Research Foundation (FAPESP). D.A.C. was supported by the CCI Collaborative fund. Plots in Mato Grosso, Brazil, were supported by the National Council for Scientific and Technological Development (CNPq), PELD-TRAN 441244/2016-5 and 441572/2020-0, and Mato Grosso State Research Support Foundation (FAPEMAT)—0346321/2021. We thank E. Chezeaux, R. Condit, W. J. Eggeling, R. M. Ewers, O. J. Hardy, P. Jeanmart, K. L. Khoon, J. L. Lloyd, A. Marjokorpi, W. Marthy, H. Ntahobavuka, D. Paget, J. T. A. Proctor, R. P. Salomão, P. Saner, S. Tan, C. O. Webb, H. Woell and N. Zweifel for contributing forest inventory data. We thank numerous field assistants for their invaluable contributions to the collection of forest inventory data, including A. Nkwasibwe, ITFC field assistant.

**Author contributions** D.L.M.C. and S.L.L. conceived and developed the study. D.L.M.C. performed the analysis with M.J.P.S. and P.I.P. and input from S.L.L. D.L.M.C., P.I.P., G.C.P., A.L. and M.J.P.S. developed tools to support the analysis. D.L.M.C. and S.L.L. wrote the manuscript with significant input from M.J.P.S., R.G.P. and M.I.D. S.L.L., B.S. and C.E.N.E. curated the AfriTRON forest plot data. N.B., P.P. and G.D. curated the Central African Plot Network forest plot data. H.T.S. curated the ATDN forest plot data. F.S. curated the Slik et al.⁴,²⁵ Southeast Asia forest plot data. S.L.L. and O.L.P. curated the T-FORCES Southeast Asia carbon monitoring network. O.L.P., S.L.L., T.R.B., B.S, B.S.M., C.E., E.H., L.Q., A.L. and G.P. provided ForestPlots.net pan-tropical data management. All co-authors contributed data, reviewed, approved and had the opportunity to comment on the manuscript.

**Competing interests** The authors declare no competing interests.

**Additional information**

**Supplementary information** The online version contains supplementary material available at
https://doi.org/10.1038/s41586-023-06820-z.

**Correspondence and requests for materials** should be addressed to Declan L. M. Cooper or
Simon L. Lewis.

**Peer review information** Nature thanks Miguel Martinez-Ramos, Thorsten Wiegand and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer review reports are available.

**Reprints and permissions information** is available at http://www.nature.com/reprints.
<figure><img src="image_10.png" /></figure>

**Extended Data Fig. 1** | Impact of plot size on rarefaction curves of total species (a) and number of hyperdominants (b) in the Asia data. Red points represent the full Southeast Asia data (mean values across iterations of

<figure><img src="image_11.png" /></figure>

subsamples), including all plot sizes (mean plot size: 0.877 ha, median plot size: 0.5 ha); Purple points represent the Southeast Asia data restricted to plots ≥0.9 ha (mean plot size: 1.59 ha, median plot size: 1 ha).
<figure><img src="image_11.png" /></figure>

**Extended Data Fig. 1** | Impact of plot size on rarefaction curves of total species (a) and number of hyperdominants (b) in the Asia data. Red points represent the full Southeast Asia data (mean values across iterations of

<figure><img src="image_12.png" /></figure>

subsamples), including all plot sizes (mean plot size: 0.877 ha, median plot size: 0.5 ha); Purple points represent the Southeast Asia data restricted to plots ≥0.9 ha (mean plot size: 1.59 ha, median plot size: 1 ha).
<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

**Extended Data Fig. 2** | Impact of spatial clustering of plots on rarefaction curves of hyperdominant percentage (first row) and Fisher's Alpha (second row) in the Amazonia data. Purple points and confidence intervals represent the full data; black points and confidence intervals represent a subset of the data in which one plot is sampled from each spatial cluster of plots; other coloured points represent subsets of the data in which 2,3,4,...,10 plots (or the

total number of plots in the cluster) are sampled from each spatial cluster of plots. Points give the mean values across iterations of subsamples. Confidence intervals are derived via the standard deviation across iterations of subsamples taken with replacement at each sampling point. Note that although resampling for rarefaction was done by subsampling tree inventory plots, the curves are re-plotted with an x-axis of number of stems.
<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>

**Extended Data Fig. 2** | Impact of spatial clustering of plots on rarefaction curves of hyperdominant percentage (first row) and Fisher's Alpha (second row) in the Amazonia data. Purple points and confidence intervals represent the full data; black points and confidence intervals represent a subset of the data in which one plot is sampled from each spatial cluster of plots; other coloured points represent subsets of the data in which 2,3,4,...,10 plots (or the

total number of plots in the cluster) are sampled from each spatial cluster of plots. Points give the mean values across iterations of subsamples. Confidence intervals are derived via the standard deviation across iterations of subsamples taken with replacement at each sampling point. Note that although resampling for rarefaction was done by subsampling tree inventory plots, the curves are re-plotted with an x-axis of number of stems.
<figure><img src="image_16.png" /><figcaption>c</figcaption></figure>

<figure><img src="image_17.png" /></figure>

**Extended Data Fig. 3** | Complete rarefaction curves showing the effect of increasing sampling on the number of hyperdominants (a), total species (b), hyperdominant percentage (c), and fitted values of Fisher's α (d). In tropical Africa (magenta), Amazonia (cyan), Southeast Asia (blue). Markers represent rarefied points (mean values across iterations of subsamples); shaded areas

<figure><img src="image_18.png" /><figcaption>d</figcaption></figure>

<figure><img src="image_19.png" /></figure>

represent confidence intervals (CIs). Confidence intervals are derived via the standard deviation across iterations of subsamples taken with replacement at each sampling point. Note that although resampling for rarefaction was done by subsampling tree inventory plots, the curves are re-plotted with an x-axis of number of stems.
a

<figure><img src="image_17.png" /></figure>

c

<figure><img src="image_18.png" /></figure>

**Extended Data Fig. 3** | Complete rarefaction curves showing the effect of increasing sampling on the number of hyperdominants (a), total species (b), hyperdominant percentage (c), and fitted values of Fisher's α (d). In tropical Africa (magenta), Amazonia (cyan), Southeast Asia (blue). Markers represent rarefied points (mean values across iterations of subsamples); shaded areas

b

<figure><img src="image_19.png" /></figure>

d

<figure><img src="image_20.png" /></figure>

represent confidence intervals (CIs). Confidence intervals are derived via the standard deviation across iterations of subsamples taken with replacement at each sampling point. Note that although resampling for rarefaction was done by subsampling tree inventory plots, the curves are re-plotted with an x-axis of number of stems.
<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /></figure>

<figure><img src="image_24.png" /></figure>

<figure><img src="image_25.png" /></figure>

**Extended Data Fig. 4** | Preston plots (top row) and rank abundance distributions (bottom row) showing the empirical species abundance distributions for Africa (left) Amazonia (middle) and Southeast Asia (right) with log series fits overlaid. Histogram bars display the empirical species abundance distributions as Preston plots (top row); black markers show the

empirical species abundance distributions as rank abundance distributions (bottom row); overlaid points and lines show log series fits to empirical species abundance distributions in Africa (magenta), Amazonia (cyan), and Southeast Asia (blue).
<figure><img src="image_21.png" /></figure>

<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /></figure>

<figure><img src="image_24.png" /></figure>

<figure><img src="image_25.png" /></figure>

<figure><img src="image_26.png" /></figure>

**Extended Data Fig. 4** | Preston plots (top row) and rank abundance distributions (bottom row) showing the empirical species abundance distributions for Africa (left) Amazonia (middle) and Southeast Asia (right) with log series fits overlaid. Histogram bars display the empirical species abundance distributions as Preston plots (top row); black markers show the

empirical species abundance distributions as rank abundance distributions (bottom row); overlaid points and lines show log series fits to empirical species abundance distributions in Africa (magenta), Amazonia (cyan), and Southeast Asia (blue).
<figure><img src="image_26.png" /></figure>

**Extended Data Fig. 5** | Bias correction of estimates of species richness (first column), number of hyperdominants (second column), percentage hyperdominance (third column) for the Amazonia (first row), Africa (second row) and Southeast Asia (third row) datasets. X-axes show estimated values derived from samples of the simulated communities taken with conspecific aggregation, Y-axes show true values of the simulated communities. Points show estimated true values for each of the 250 simulated communities.

1:1 equivalence shown by straight line in each plot. For number of hyperdominants and total species plots, simulated communities containing 100 to 25,000 species in Amazonia and Southeast Asia, 100 to 10,000 species in Africa are shown. For percentage hyperdominance, simulated communities containing 10,000 to 25,000 species in Amazonia and Southeast Asia, 2,000 to 10,000 species in Africa are shown.
<figure><img src="image_27.png" /></figure>

**Extended Data Fig. 5** | Bias correction of estimates of species richness (first column), number of hyperdominants (second column), percentage hyperdominance (third column) for the Amazonia (first row), Africa (second row) and Southeast Asia (third row) datasets. X-axes show estimated values derived from samples of the simulated communities taken with conspecific aggregation, Y-axes show true values of the simulated communities. Points show estimated true values for each of the 250 simulated communities.

1:1 equivalence shown by straight line in each plot. For number of hyperdominants and total species plots, simulated communities containing 100 to 25,000 species in Amazonia and Southeast Asia, 100 to 10,000 species in Africa are shown. For percentage hyperdominance, simulated communities containing 10,000 to 25,000 species in Amazonia and Southeast Asia, 2,000 to 10,000 species in Africa are shown.
<table><caption>Extended Data Table 1 | Empirical summary statistics and hyperdominance results for tree species data in Africa, Amazonia, and Southeast Asia</caption><thead><tr><th></th><th>Plots</th><th>Stems</th><th>%ID</th><th>H#</th><th>TS</th><th>H%</th><th>α</th></tr></thead><tbody><tr><td>Africa</td><td>368</td><td>189,948</td><td>92.7</td><td>82</td><td>1,416</td><td>5.79</td><td>210</td></tr><tr><td>Amazonia</td><td>1,097</td><td>736,270</td><td>93.7</td><td>224</td><td>4,492</td><td>4.99</td><td>644</td></tr><tr><td>Southeast Asia</td><td>103</td><td>77,587</td><td>91.6</td><td>172</td><td>2,585</td><td>6.65</td><td>526</td></tr></tbody></table>

#H = Number of hyperdominants, TS = Total Species, H% = Percentage hyperdominance, α = Fisher's α, Stems = Total number of stems, Plots = Total number of plots, %ID = Percentage of stems identified to the species level.
<table><caption>Extended Data Table 1 | Empirical summary statistics and hyperdominance results for tree species data in Africa, Amazonia, and Southeast Asia</caption><thead><tr><th></th><th>Plots</th><th>Stems</th><th>%ID</th><th>H#</th><th>TS</th><th>H%</th><th>α</th></tr></thead><tbody><tr><td>Africa</td><td>368</td><td>189,948</td><td>92.7</td><td>82</td><td>1,416</td><td>5.79</td><td>210</td></tr><tr><td>Amazonia</td><td>1,097</td><td>736,270</td><td>93.7</td><td>224</td><td>4,492</td><td>4.99</td><td>644</td></tr><tr><td>Southeast Asia</td><td>103</td><td>77,587</td><td>91.6</td><td>172</td><td>2,585</td><td>6.65</td><td>526</td></tr></tbody></table>

#H = Number of hyperdominants, TS = Total Species, H% = Percentage hyperdominance, α = Fisher's α, Stems = Total number of stems, Plots = Total number of plots, %ID = Percentage of stems identified to the species level.
<table><caption>Extended Data Table 2 | Rarefied minimum number of species required to account for 10%, 20%, ..., 90% of trees in the Africa, Amazonia, and Southeast Asia data, resampled to the common sample size of the Asia dataset (77,587 stems)</caption><thead><tr><th></th><th>10%</th><th>20%</th><th>30%</th><th>40%</th><th>50%</th><th>60%</th><th>70%</th><th>80%</th><th>90%</th></tr></thead><tbody><tr><td>Africa</td><td>4<br>[2,7]</td><td>13<br>[8,17]</td><td>26<br>[19,33]</td><td>46<br>[36,57]</td><td>77<br>[62,92]</td><td>121<br>[102,139]</td><td>186<br>[163,209]</td><td>296<br>[264,328]</td><td>592<br>[494,689]</td></tr><tr><td>Amazonia</td><td>8<br>[4,13]</td><td>28<br>[17,38]</td><td>60<br>[42,77]</td><td>106<br>[79,133]</td><td>174<br>[134,215]</td><td>276<br>[214,337]</td><td>434<br>[339,528]</td><td>709<br>[553,864]</td><td>1413<br>[1029,1797]</td></tr><tr><td>Southeast Asia</td><td>9<br>[5,13]</td><td>26<br>[17,35]</td><td>54<br>[36,72]</td><td>98<br>[66,130]</td><td>172<br>[125,219]</td><td>285<br>[220,350]</td><td>468<br>[380,556]</td><td>790<br>[670,910]</td><td>1778<br>[1427,2129]</td></tr></tbody></table>

Percentage headings represent the different dominance thresholds. Confidence intervals are derived from the standard deviation across iterations of subsamples taken with replacement at the sample size of the Asia dataset.
<table><caption>Extended Data Table 2 | Rarefied minimum number of species required to account for 10%, 20%, ..., 90% of trees in the Africa, Amazonia, and Southeast Asia data, resampled to the common sample size of the Asia dataset (77,587 stems)</caption><thead><tr><th></th><th>10%</th><th>20%</th><th>30%</th><th>40%</th><th>50%</th><th>60%</th><th>70%</th><th>80%</th><th>90%</th></tr></thead><tbody><tr><td>Africa</td><td>4<br>[2,7]</td><td>13<br>[8,17]</td><td>26<br>[19,33]</td><td>46<br>[36,57]</td><td>77<br>[62,92]</td><td>121<br>[102,139]</td><td>186<br>[163,209]</td><td>296<br>[264,328]</td><td>592<br>[494,689]</td></tr><tr><td>Amazonia</td><td>8<br>[4,13]</td><td>28<br>[17,38]</td><td>60<br>[42,77]</td><td>106<br>[79,133]</td><td>174<br>[134,215]</td><td>276<br>[214,337]</td><td>434<br>[339,528]</td><td>709<br>[553,864]</td><td>1413<br>[1029,1797]</td></tr><tr><td>Southeast Asia</td><td>9<br>[5,13]</td><td>26<br>[17,35]</td><td>54<br>[36,72]</td><td>98<br>[66,130]</td><td>172<br>[125,219]</td><td>285<br>[220,350]</td><td>468<br>[380,556]</td><td>790<br>[670,910]</td><td>1778<br>[1427,2129]</td></tr></tbody></table>

Percentage headings represent the different dominance thresholds. Confidence intervals are derived from the standard deviation across iterations of subsamples taken with replacement at the sample size of the Asia dataset.
<table><caption>Extended Data Table 3 | Rarefied proportion of total species required to account for 10%, 20%, ..., 90% of trees in the Africa, Amazonia, and Southeast Asia data, resampled to the common sample size of the Asia dataset (77,587 stems)</caption><thead><tr><th></th><th>10%</th><th>20%</th><th>30%</th><th>40%</th><th>50%</th><th>60%</th><th>70%</th><th>80%</th><th>90%</th></tr></thead><tbody><tr><td>Africa</td><td>0.36<br>[0.12,0.61]</td><td>1.11<br>[0.71,1.51]</td><td>2.28<br>[1.64,2.93]</td><td>4.09<br>[3.1,5.07]</td><td>6.79<br>[5.39,8.20]</td><td>10.67<br>[8.89,12.44]</td><td>16.47<br>[14.28,18.65]</td><td>26.18<br>[23.22,29.15]</td><td>52.28<br>[43.00,61.56]</td></tr><tr><td>Amazonia</td><td>0.33<br>[0.15,0.51]</td><td>1.09<br>[0.67,1.50]</td><td>2.32<br>[1.63,3.01]</td><td>4.14<br>[3.09,5.19]</td><td>6.80<br>[5.24,8.36]</td><td>10.74<br>[8.40,13.09]</td><td>16.90<br>[13.30,20.50]</td><td>27.63<br>[21.68,33.58]</td><td>55.06<br>[40.37,69.76]</td></tr><tr><td>Southeast Asia</td><td>0.35<br>[0.19,0.51]</td><td>1.01<br>[0.61,1.41]</td><td>2.09<br>[1.27,2.91]</td><td>3.79<br>[2.38,5.20]</td><td>6.65<br>[4.59,8.71]</td><td>11.03<br>[8.24,13.82]</td><td>18.10<br>[14.47,21.73]</td><td>30.56<br>[25.78,35.34]</td><td>68.78<br>[52.50,84.97]</td></tr></tbody></table>

Percentage headings represent the different dominance thresholds. Confidence intervals are derived from the standard deviation across iterations of subsamples taken with replacement at the sample size of the Asia dataset.
<table><caption>Extended Data Table 3 | Rarefied proportion of total species required to account for 10%, 20%, ..., 90% of trees in the Africa, Amazonia, and Southeast Asia data, resampled to the common sample size of the Asia dataset (77,587 stems)</caption><thead><tr><th></th><th>10%</th><th>20%</th><th>30%</th><th>40%</th><th>50%</th><th>60%</th><th>70%</th><th>80%</th><th>90%</th></tr></thead><tbody><tr><td>Africa</td><td>0.36<br>[0.12,0.61]</td><td>1.11<br>[0.71,1.51]</td><td>2.28<br>[1.64,2.93]</td><td>4.09<br>[3.1,5.07]</td><td>6.79<br>[5.39,8.20]</td><td>10.67<br>[8.89,12.44]</td><td>16.47<br>[14.28,18.65]</td><td>26.18<br>[23.22,29.15]</td><td>52.28<br>[43.00,61.56]</td></tr><tr><td>Amazonia</td><td>0.33<br>[0.15,0.51]</td><td>1.09<br>[0.67,1.50]</td><td>2.32<br>[1.63,3.01]</td><td>4.14<br>[3.09,5.19]</td><td>6.80<br>[5.24,8.36]</td><td>10.74<br>[8.40,13.09]</td><td>16.90<br>[13.30,20.50]</td><td>27.63<br>[21.68,33.58]</td><td>55.06<br>[40.37,69.76]</td></tr><tr><td>Southeast Asia</td><td>0.35<br>[0.19,0.51]</td><td>1.01<br>[0.61,1.41]</td><td>2.09<br>[1.27,2.91]</td><td>3.79<br>[2.38,5.20]</td><td>6.65<br>[4.59,8.71]</td><td>11.03<br>[8.24,13.82]</td><td>18.10<br>[14.47,21.73]</td><td>30.56<br>[25.78,35.34]</td><td>68.78<br>[52.50,84.97]</td></tr></tbody></table>

Percentage headings represent the different dominance thresholds. Confidence intervals are derived from the standard deviation across iterations of subsamples taken with replacement at the sample size of the Asia dataset.
Extended Data Table 4 | Extrapolated minimum number of species required to account for 10%, 20%, ..., 90% of trees in Africa, Amazonia, Southeast Asia, and the cross-regional total at the regional scale

<table><thead><tr><th></th><th>10%</th><th>20%</th><th>30%</th><th>40%</th><th>50%</th><th>60%</th><th>70%</th><th>80%</th><th>90%</th></tr></thead><tbody><tr><td>Africa</td><td>7<br>[4,10]</td><td>22<br>[19,25]</td><td>41<br>[38,44]</td><td>69<br>[66,71]</td><td>104<br>[101,107]</td><td>154<br>[151,157]</td><td>228<br>[225,231]</td><td>354<br>[351,357]</td><td>713<br>[709,717]</td></tr><tr><td>Amazonia</td><td>20<br>[16,25]</td><td>62<br>[58,66]</td><td>120<br>[116,124]</td><td>196<br>[192,201]</td><td>299<br>[295,304]</td><td>443<br>[438,447]</td><td>651<br>[647,656]</td><td>1000<br>[995,1005]</td><td>1892<br>[1886,1899]</td></tr><tr><td>Southeast Asia</td><td>12<br>[1,23]</td><td>51<br>[40,62]</td><td>106<br>[95,116]</td><td>179<br>[168,189]</td><td>278<br>[268,289]</td><td>417<br>[406,427]</td><td>625<br>[614,636]</td><td>988<br>[977,1000]</td><td>2243<br>[2225,2262]</td></tr><tr><td>Total</td><td>39<br>[21,58]</td><td>135<br>[117,153]</td><td>267<br>[249,284]</td><td>444<br>[426,461]</td><td>681<br>[664,700]</td><td>1014<br>[995,1031]</td><td>1504<br>[1486,1523]</td><td>2342<br>[2323,2362]</td><td>4848<br>[4820,4878]</td></tr></tbody></table>

Percentage headings represent the different dominance thresholds. Prediction intervals combine uncertainty from the standard error of predicted means and the residual standard deviation of the regression of the bias correction fit. 'Total' minimum number of species required to account for 10%-90% of trees across all of the regions are calculated as the sum of the number of hyperdominants across the three major tropical forest regions.
Extended Data Table 4 | Extrapolated minimum number of species required to account for 10%, 20%, ..., 90% of trees in Africa, Amazonia, Southeast Asia, and the cross-regional total at the regional scale

<table><thead><tr><th></th><th>10%</th><th>20%</th><th>30%</th><th>40%</th><th>50%</th><th>60%</th><th>70%</th><th>80%</th><th>90%</th></tr></thead><tbody><tr><td>Africa</td><td>7<br>[4,10]</td><td>22<br>[19,25]</td><td>41<br>[38,44]</td><td>69<br>[66,71]</td><td>104<br>[101,107]</td><td>154<br>[151,157]</td><td>228<br>[225,231]</td><td>354<br>[351,357]</td><td>713<br>[709,717]</td></tr><tr><td>Amazonia</td><td>20<br>[16,25]</td><td>62<br>[58,66]</td><td>120<br>[116,124]</td><td>196<br>[192,201]</td><td>299<br>[295,304]</td><td>443<br>[438,447]</td><td>651<br>[647,656]</td><td>1000<br>[995,1005]</td><td>1892<br>[1886,1899]</td></tr><tr><td>Southeast Asia</td><td>12<br>[1,23]</td><td>51<br>[40,62]</td><td>106<br>[95,116]</td><td>179<br>[168,189]</td><td>278<br>[268,289]</td><td>417<br>[406,427]</td><td>625<br>[614,636]</td><td>988<br>[977,1000]</td><td>2243<br>[2225,2262]</td></tr><tr><td>Total</td><td>39<br>[21,58]</td><td>135<br>[117,153]</td><td>267<br>[249,284]</td><td>444<br>[426,461]</td><td>681<br>[664,700]</td><td>1014<br>[995,1031]</td><td>1504<br>[1486,1523]</td><td>2342<br>[2323,2362]</td><td>4848<br>[4820,4878]</td></tr></tbody></table>

Percentage headings represent the different dominance thresholds. Prediction intervals combine uncertainty from the standard error of predicted means and the residual standard deviation of the regression of the bias correction fit. 'Total' minimum number of species required to account for 10%-90% of trees across all of the regions are calculated as the sum of the number of hyperdominants across the three major tropical forest regions.
<table><caption>Extended Data Table 5 | Extrapolated proportion of total species required to account for 10%, 20%, 30%, ..., 90% of trees in Africa, Amazonia, Southeast Asia, and the cross-regional total at the regional scale</caption><thead><tr><th></th><th>10%</th><th>20%</th><th>30%</th><th>40%</th><th>50%</th><th>60%</th><th>70%</th><th>80%</th><th>90%</th></tr></thead><tbody><tr><th>Africa</th><td>0.16</td><td>0.48</td><td>0.90</td><td>1.48</td><td>2.23</td><td>3.31</td><td>4.90</td><td>7.60</td><td>15.29</td></tr><tr><th>Amazonia</th><td>0.14</td><td>0.44</td><td>0.86</td><td>1.41</td><td>2.16</td><td>3.21</td><td>4.72</td><td>7.25</td><td>13.73</td></tr><tr><th>Southeast Asia</th><td>0.14</td><td>0.46</td><td>0.91</td><td>1.5</td><td>2.32</td><td>3.45</td><td>5.14</td><td>8.10</td><td>18.34</td></tr><tr><th>Total</th><td>0.13</td><td>0.44</td><td>0.88</td><td>1.46</td><td>2.24</td><td>3.33</td><td>4.94</td><td>7.70</td><td>15.93</td></tr></tbody></table>

Percentage headings represent the different dominance thresholds. 'Total' minimum proportion of total species required to account for 10%-90% of trees across all of the regions are calculated as the proportion between the sum of the number of hyperdominants and the sum of total species across the three major tropical forest regions.
<table><caption>Extended Data Table 5 | Extrapolated proportion of total species required to account for 10%, 20%, 30%, ..., 90% of trees in Africa, Amazonia, Southeast Asia, and the cross-regional total at the regional scale</caption><thead><tr><th></th><th>10%</th><th>20%</th><th>30%</th><th>40%</th><th>50%</th><th>60%</th><th>70%</th><th>80%</th><th>90%</th></tr></thead><tbody><tr><th>Africa</th><td>0.16</td><td>0.48</td><td>0.90</td><td>1.48</td><td>2.23</td><td>3.31</td><td>4.90</td><td>7.60</td><td>15.29</td></tr><tr><th>Amazonia</th><td>0.14</td><td>0.44</td><td>0.86</td><td>1.41</td><td>2.16</td><td>3.21</td><td>4.72</td><td>7.25</td><td>13.73</td></tr><tr><th>Southeast Asia</th><td>0.14</td><td>0.46</td><td>0.91</td><td>1.5</td><td>2.32</td><td>3.45</td><td>5.14</td><td>8.10</td><td>18.34</td></tr><tr><th>Total</th><td>0.13</td><td>0.44</td><td>0.88</td><td>1.46</td><td>2.24</td><td>3.33</td><td>4.94</td><td>7.70</td><td>15.93</td></tr></tbody></table>

Percentage headings represent the different dominance thresholds. 'Total' minimum proportion of total species required to account for 10%-90% of trees across all of the regions are calculated as the proportion between the sum of the number of hyperdominants and the sum of total species across the three major tropical forest regions.
<figure><img src="image_27.png" /></figure>

# Reporting Summary

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## Statistics

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*Our web collection on statistics for biologists contains articles on many of the points above.*

## Software and code

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Data collection No software was used for data collection.

Data analysis The custom R code (version 4.3.1) used to run the analyses and produce the figures is available on GitHub with the identifier https://github.com/declancooper/CommonSpecies2022.git

For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Portfolio *guidelines for submitting code & software* for further information.

## Data

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The species abundance data that support the findings of this study are available from https://doi.org/10.6084/m9.fishshare.21670883. WorldClim bioclimatic data are available from https://www.worldclim.org/data/bioclim.html.
# Reporting Summary

Nature Portfolio wishes to improve the reproducibility of the work that we publish. This form provides structure for consistency and transparency in reporting. For further information on Nature Portfolio policies, see our *Editorial Policies* and the *Editorial Policy Checklist*.

## Statistics

For all statistical analyses, confirm that the following items are present in the figure legend, table legend, main text, or Methods section.

n/a Confirmed

☐ ☑ The exact sample size (n) for each experimental group/condition, given as a discrete number and unit of measurement

☐ ☑ A statement on whether measurements were taken from distinct samples or whether the same sample was measured repeatedly

☑ ☐ The statistical test(s) used AND whether they are one- or two-sided
*Only common tests should be described solely by name; describe more complex techniques in the Methods section.*

☐ ☑ A description of all covariates tested

☐ ☑ A description of any assumptions or corrections, such as tests of normality and adjustment for multiple comparisons

☐ ☑ A full description of the statistical parameters including central tendency (e.g. means) or other basic estimates (e.g. regression coefficient) AND variation (e.g. standard deviation) or associated estimates of uncertainty (e.g. confidence intervals)

☑ ☐ For null hypothesis testing, the test statistic (e.g. *F*, *t*, *r*) with confidence intervals, effect sizes, degrees of freedom and *P* value noted
*Give *P* values as exact values whenever suitable.*

☑ ☐ For Bayesian analysis, information on the choice of priors and Markov chain Monte Carlo settings

☑ ☐ For hierarchical and complex designs, identification of the appropriate level for tests and full reporting of outcomes

☑ ☐ Estimates of effect sizes (e.g. Cohen's *d*, Pearson's *r*), indicating how they were calculated

*Our web collection on statistics for biologists contains articles on many of the points above.*

## Software and code

Policy information about *availability of computer code*

Data collection No software was used for data collection.

Data analysis The custom R code (version 4.3.1) used to run the analyses and produce the figures is available on GitHub with the identifier https://github.com/declancooper/CommonSpecies2022.git

For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Portfolio *guidelines for submitting code & software* for further information.

## Data

Policy information about *availability of data*

All manuscripts must include a *data availability statement*. This statement should provide the following information, where applicable:

- Accession codes, unique identifiers, or web links for publicly available datasets

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- For clinical datasets or third party data, please ensure that the statement adheres to our *policy*

The species abundance data that support the findings of this study are available from https://doi.org/10.6084/m9 figshare.21670883. WorldClim bioclimatic data are available from https://www.worldclim.org/data/bioclim.html.
## Human research participants

Policy information about studies involving human research participants and Sex and Gender in Research.

Reporting on sex and gender NA

Population characteristics NA

Recruitment NA

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Note that full information on the approval of the study protocol must also be provided in the manuscript.

## Field-specific reporting

Please select the one below that is the best fit for your research. If you are not sure, read the appropriate sections before making your selection.

<figure><img src="image_28.png" /></figure>

Life sciences

<figure><img src="image_29.png" /></figure>

Behavioural & social sciences

<figure><img src="image_30.png" /></figure>

Ecological, evolutionary & environmental sciences

For a reference copy of the document with all sections, see [nature.com/documents/nr-reporting-summary-flat.pdf](https://nature.com/documents/nr-reporting-summary-flat.pdf)

## Life sciences study design

All studies must disclose on these points even when the disclosure is negative.

Sample size

No sample size calculation was performed. We selected all available plots meeting the following criteria: forest inventory plots ≥0.2 ha in size, situated in structurally intact (no logging or fire), closed canopy (not dry forest or savanna) tropical forest, with enumeration of all stems ≥ 10 cm diameter, in which ≥ 80% of stems are identified to the species level. These criteria allow direct comparisons to be made with results from previous studies investigating common species abundances in Amazonia. The major tropical forest regions of Amazonia, Africa, and Southeast Asia are adequately represented. This is the largest dataset of repeatedly measured plots ever used to calculate long-term trends in African forest carbon dynamics.

Data exclusions

Plots not meeting the following criteria were excluded from analysis: forest inventory plots ≥0.2 ha in size, situated in structurally intact (no logging or fire), closed canopy (not dry forest or savanna) tropical forest, with enumeration of all stems ≥ 10 cm diameter, in which ≥ 80% of stems are identified to the species level. These conditions were pre-established in line with previous studies.

Replication

Replication of repeated random subsampling 200 times ensured that derived results were stable and reproducible.

Randomization

Forest inventory data were partitioned into the Amazonia, Africa, and Southeast Asia study regions by location. In all of the analyses, sampling and sub-sampling by plots and by trees within plots was done completely randomly within regions.

Blinding

Blinding is not relevant to this study.

## Reporting for specific materials, systems and methods

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### Materials & experimental systems

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<figure><img src="image_31.png" /></figure>

<figure><img src="image_32.png" /></figure>

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<figure><img src="image_33.png" /></figure>

<figure><img src="image_34.png" /></figure>

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<figure><img src="image_35.png" /></figure>

<figure><img src="image_36.png" /></figure>

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<figure><img src="image_42.png" /></figure>

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### Methods

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<figure><img src="image_43.png" /></figure>

<figure><img src="image_44.png" /></figure>

ChIP-seq

<figure><img src="image_45.png" /></figure>

<figure><img src="image_46.png" /></figure>

Flow cytometry

<figure><img src="image_47.png" /></figure>

<figure><img src="image_48.png" /></figure>

MRI-based neuroimaging
## Human research participants

Policy information about studies involving human research participants and Sex and Gender in Research.

Reporting on sex and gender NA

Population characteristics NA

Recruitment NA

Ethics oversight NA

Note that full information on the approval of the study protocol must also be provided in the manuscript.

## Field-specific reporting

Please select the one below that is the best fit for your research. If you are not sure, read the appropriate sections before making your selection.

<figure><img src="image_28.png" /></figure>

Life sciences

<figure><img src="image_29.png" /></figure>

Behavioural & social sciences

<figure><img src="image_30.png" /></figure>

Ecological, evolutionary & environmental sciences

For a reference copy of the document with all sections, see [nature.com/documents/nr-reporting-summary-flat.pdf](https://nature.com/documents/nr-reporting-summary-flat.pdf)

## Life sciences study design

All studies must disclose on these points even when the disclosure is negative.

Sample size

No sample size calculation was performed. We selected all available plots meeting the following criteria: forest inventory plots ≥0.2 ha in size, situated in structurally intact (no logging or fire), closed canopy (not dry forest or savanna) tropical forest, with enumeration of all stems ≥ 10 cm diameter, in which ≥ 80% of stems are identified to the species level. These criteria allow direct comparisons to be made with results from previous studies investigating common species abundances in Amazonia. The major tropical forest regions of Amazonia, Africa, and Southeast Asia are adequately represented. This is the largest dataset of repeatedly measured plots ever used to calculate long-term trends in African forest carbon dynamics.

Data exclusions

Plots not meeting the following criteria were excluded from analysis: forest inventory plots ≥0.2 ha in size, situated in structurally intact (no logging or fire), closed canopy (not dry forest or savanna) tropical forest, with enumeration of all stems ≥ 10 cm diameter, in which ≥ 80% of stems are identified to the species level. These conditions were pre-established in line with previous studies.

Replication

Replication of repeated random subsampling 200 times ensured that derived results were stable and reproducible.

Randomization

Forest inventory data were partitioned into the Amazonia, Africa, and Southeast Asia study regions by location. In all of the analyses, sampling and sub-sampling by plots and by trees within plots was done completely randomly within regions.

Blinding

Blinding is not relevant to this study.

## Reporting for specific materials, systems and methods

We require information from authors about some types of materials, experimental systems and methods used in many studies. Here, indicate whether each material, system or method listed is relevant to your study. If you are not sure if a list item applies to your research, read the appropriate section before selecting a response.

### Materials & experimental systems

n/a Involved in the study

<figure><img src="image_31.png" /></figure>

<figure><img src="image_32.png" /></figure>

Antibodies

<figure><img src="image_33.png" /></figure>

<figure><img src="image_34.png" /></figure>

Eukaryotic cell lines

<figure><img src="image_35.png" /></figure>

<figure><img src="image_36.png" /></figure>

Palaeontology and archaeology

<figure><img src="image_37.png" /></figure>

<figure><img src="image_38.png" /></figure>

Animals and other organisms

<figure><img src="image_39.png" /></figure>

<figure><img src="image_40.png" /></figure>

Clinical data

<figure><img src="image_41.png" /></figure>

<figure><img src="image_42.png" /></figure>

Dual use research of concern

### Methods

n/a Involved in the study

<figure><img src="image_43.png" /></figure>

<figure><img src="image_44.png" /></figure>

ChIP-seq

<figure><img src="image_45.png" /></figure>

<figure><img src="image_46.png" /></figure>

Flow cytometry

<figure><img src="image_47.png" /></figure>

<figure><img src="image_48.png" /></figure>

MRI-based neuroimaging