# Consistent patterns of common species across tropical tree communities

Declan L M Cooper, Simon L Lewis, Martin J P Sullivan, Paulo I Prado, Hans ter Steege, Nicolas Barbier, Ferry Slik, Bonaventure Sonké, Corneille E N Ewango, Stephen Adu-Bredu, et al.

► To cite this version:

Declan L M Cooper, Simon L Lewis, Martin J P Sullivan, Paulo I Prado, Hans ter Steege, et al. Consistent patterns of common species across tropical tree communities. Nature, 2024, 625, pp.728-734. 10.1038/s41586-023-06820-z. hal-04414779

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Distributed under a Creative Commons Attribution 4.0 International License
# Consistent patterns of common species across tropical tree communities

Declan L M Cooper, Simon L Lewis, Martin J P Sullivan, Paulo I Prado, Hans ter Steege, Nicolas Barbier, Ferry Slik, Bonaventure Sonké, Corneille E N Ewango, Stephen Adu-Bredu, et al.

<figure><img src="image_1.png" /></figure>

To cite this version:

Declan L M Cooper, Simon L Lewis, Martin J P Sullivan, Paulo I Prado, Hans ter Steege, et al. Consistent patterns of common species across tropical tree communities. Nature, 2024, 625, pp.728-734. 10.1038/s41586-023-06820-z. hal-04414779

HAL Id: hal-04414779

https://hal.inrae.fr/hal-04414779v1

Submitted on 24 Jan 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Consistent patterns of common species across tropical tree communities

https://doi.org/10.1038/s41586-023-06820-z

Received: 3 December 2022

Accepted: 1 November 2023

Published online: 10 January 2024

Open access

<figure><img src="image_2.png" /></figure>

Check for updates

Trees structure the Earth's most biodiverse ecosystem, tropical forests. The vast number of tree species presents a formidable challenge to understanding these forests, including their response to environmental change, as very little is known about most tropical tree species. A focus on the common species may circumvent this challenge. Here we investigate abundance patterns of common tree species using inventory data on 1,003,805 trees with trunk diameters of at least 10 cm across 1,568 locations¹⁻⁶ in closed-canopy, structurally intact old-growth tropical forests in Africa, Amazonia and Southeast Asia. We estimate that 2.2%, 2.2% and 2.3% of species comprise 50% of the tropical trees in these regions, respectively. Extrapolating across all closed-canopy tropical forests, we estimate that just 1,053 species comprise half of Earth's 800 billion tropical trees with trunk diameters of at least 10 cm. Despite differing biogeographic, climatic and anthropogenic histories⁷, we find notably consistent patterns of common species and species abundance distributions across the continents. This suggests that fundamental mechanisms of tree community assembly may apply to all tropical forests. Resampling analyses show that the most common species are likely to belong to a manageable list of known species, enabling targeted efforts to understand their ecology. Although they do not detract from the importance of rare species, our results open new opportunities to understand the world's most diverse forests, including modelling their response to environmental change, by focusing on the common species that constitute the majority of their trees.

Tropical forests are a crucial component of the Earth system; they cover around 10% of the Earth's land surface⁸ but contribute approximately 33% of terrestrial net primary productivity⁹. They account for around 40% of the carbon stored in live vegetation¹⁰ and are globally important carbon sinks¹¹. Tropical forests are also extraordinarily biodiverse, harbouring two-thirds of all known species¹² and the majority of the world's biodiversity hotspots¹³. Of note, as many tree species can be found in a single hectare of tropical forest as in the entire native Western European tree flora¹⁴. Recent estimates suggest that there are approximately 37,900 named tropical tree species in the scientific literature¹⁵, with potentially thousands more yet to be identified by scientists¹⁶. This extraordinary diversity means that little is known about the biology of the vast majority of tropical tree species. Our understanding of tropical forest ecology, productivity and carbon storage and how they may respond to environmental change is hindered by this lack of knowledge. This limited understanding also curtails scientific input into land use, biodiversity, climate and other forest-related policy and management.

Our understanding of tropical forests may improve through a focus on the most common tree species. This is a promising avenue, given that species abundance distributions (SADs) showing a modest number of common species and much larger numbers of rare species have been documented across taxa globally¹⁷⁻¹⁹. Indeed, analyses of tropical forest inventory data from Amazonia have shown that a relatively small number of common species comprise a majority of trees in the region⁶,²⁰⁻²⁴. However, whether such patterns hold in other tropical forests is unknown, as there have been no comparable analyses for African or Southeast Asian tropical forests. Perhaps, given the substantial

differences in total tree species richness²⁵, forest structure¹, contemporary climate²⁶ and biogeographic and human-occupancy histories⁷ among continents, important contrasts in patterns of common species would be expected. Alternatively, if the same processes or mechanisms apply to all tropical forests²⁷, highly consistent patterns may be expected. Crucially, if a tractably modest number of common species do comprise the majority of tropical trees on Earth, this could open new ways of understanding tropical forests by investigating the ecology of the common species.

Cross-continental comparisons of common species patterns are complicated by unresolved differences in the results from published Amazon forest studies⁶,²⁰,²². Estimates of hyperdominance—describing the minimum number of species required to account for 50% of all trees in a sample—range from 1.4% to 8.2% of the total number of species found in each of the Amazon forest datasets analysed (corresponding to 224 and 1,312 hyperdominant species respectively, assuming 16,000 Amazon tree species). Therefore, here we: (1) investigate sample-related biases and standardize our sampling to enable meaningful comparisons among datasets; (2) test whether patterns of hyperdominance differ across Amazonia, Africa and Southeast Asia; (3) extrapolate our results to assess how many species comprise half of all Earth's tropical trees; (4) assess species abundance patterns, with differing classifications of 'common species' beyond hyperdominance; and (5) use resampling techniques to assess which sampled species are likely to be hyperdominant.

We analyse species abundance data from networks of inventory plots across three continents. We limit our analysis to closed canopy
# Consistent patterns of common species across tropical tree communities

https://doi.org/10.1038/s41586-023-06820-z

Received: 3 December 2022

Accepted: 1 November 2023

Published online: 10 January 2024

Open access

<figure><img src="image_3.png" /></figure>

Check for updates

Trees structure the Earth's most biodiverse ecosystem, tropical forests. The vast number of tree species presents a formidable challenge to understanding these forests, including their response to environmental change, as very little is known about most tropical tree species. A focus on the common species may circumvent this challenge. Here we investigate abundance patterns of common tree species using inventory data on 1,003,805 trees with trunk diameters of at least 10 cm across 1,568 locations¹⁻⁶ in closed-canopy, structurally intact old-growth tropical forests in Africa, Amazonia and Southeast Asia. We estimate that 2.2%, 2.2% and 2.3% of species comprise 50% of the tropical trees in these regions, respectively. Extrapolating across all closed-canopy tropical forests, we estimate that just 1,053 species comprise half of Earth's 800 billion tropical trees with trunk diameters of at least 10 cm. Despite differing biogeographic, climatic and anthropogenic histories⁷, we find notably consistent patterns of common species and species abundance distributions across the continents. This suggests that fundamental mechanisms of tree community assembly may apply to all tropical forests. Resampling analyses show that the most common species are likely to belong to a manageable list of known species, enabling targeted efforts to understand their ecology. Although they do not detract from the importance of rare species, our results open new opportunities to understand the world's most diverse forests, including modelling their response to environmental change, by focusing on the common species that constitute the majority of their trees.

Tropical forests are a crucial component of the Earth system; they cover around 10% of the Earth's land surface⁸ but contribute approximately 33% of terrestrial net primary productivity⁹. They account for around 40% of the carbon stored in live vegetation¹⁰ and are globally important carbon sinks¹¹. Tropical forests are also extraordinarily biodiverse, harbouring two-thirds of all known species¹² and the majority of the world's biodiversity hotspots¹³. Of note, as many tree species can be found in a single hectare of tropical forest as in the entire native Western European tree flora¹⁴. Recent estimates suggest that there are approximately 37,900 named tropical tree species in the scientific literature¹⁵, with potentially thousands more yet to be identified by scientists¹⁶. This extraordinary diversity means that little is known about the biology of the vast majority of tropical tree species. Our understanding of tropical forest ecology, productivity and carbon storage and how they may respond to environmental change is hindered by this lack of knowledge. This limited understanding also curtails scientific input into land use, biodiversity, climate and other forest-related policy and management.

Our understanding of tropical forests may improve through a focus on the most common tree species. This is a promising avenue, given that species abundance distributions (SADs) showing a modest number of common species and much larger numbers of rare species have been documented across taxa globally¹⁷⁻¹⁹. Indeed, analyses of tropical forest inventory data from Amazonia have shown that a relatively small number of common species comprise a majority of trees in the region⁶,²⁰⁻²⁴. However, whether such patterns hold in other tropical forests is unknown, as there have been no comparable analyses for African or Southeast Asian tropical forests. Perhaps, given the substantial

differences in total tree species richness²⁵, forest structure¹, contemporary climate²⁶ and biogeographic and human-occupancy histories⁷ among continents, important contrasts in patterns of common species would be expected. Alternatively, if the same processes or mechanisms apply to all tropical forests²⁷, highly consistent patterns may be expected. Crucially, if a tractably modest number of common species do comprise the majority of tropical trees on Earth, this could open new ways of understanding tropical forests by investigating the ecology of the common species.

Cross-continental comparisons of common species patterns are complicated by unresolved differences in the results from published Amazon forest studies⁶,²⁰,²². Estimates of hyperdominance—describing the minimum number of species required to account for 50% of all trees in a sample—range from 1.4% to 8.2% of the total number of species found in each of the Amazon forest datasets analysed (corresponding to 224 and 1,312 hyperdominant species respectively, assuming 16,000 Amazon tree species). Therefore, here we: (1) investigate sample-related biases and standardize our sampling to enable meaningful comparisons among datasets; (2) test whether patterns of hyperdominance differ across Amazonia, Africa and Southeast Asia; (3) extrapolate our results to assess how many species comprise half of all Earth's tropical trees; (4) assess species abundance patterns, with differing classifications of 'common species' beyond hyperdominance; and (5) use resampling techniques to assess which sampled species are likely to be hyperdominant.

We analyse species abundance data from networks of inventory plots across three continents. We limit our analysis to closed canopy
<figure><img src="image_3.png" /><figcaption>**Fig. 1** | Location of the 1,568 plots, tropical forest regions, and tropical forest biome extent used in the study. Dots show the location of the plots analysed, coloured by continental region. Dark green shows the Amazonia,</figcaption></figure>

Africa and Southeast Asia regions that we extrapolate to. Light green shows 'tropical and subtropical moist broadleaf forests'⁶⁰, which we extrapolate to as the closed canopy tropical forest biome.

structurally intact old-growth tropical forests. For Amazonia, defined as the lowland Amazon Basin and Guiana Shield, we use the Amazon Tree Diversity Network and RAINFOR datasets (n = 1,097 plots). For Africa, encompassing West, central and East Africa, we use the African Tropical Rainforest Observatory Network (AfriTRON)¹, Central African Plot Network, and two smaller networks²,³ (n = 368 plots). For Southeast Asia, defined as extending from Myanmar in the West to Sulawesi in the East, we use a tree diversity⁴ and a carbon monitoring⁵ network (n = 103 plots). We limit our analysis to trees with trunk diameter of at least 10 cm at breast height (1.3 m along the stem or above any buttresses or deformities), the widely used minimum size for inventorying tropical trees. The combined dataset includes 1,003,805 trees, of which 93.3% are identified to species (Fig. 1 and Extended Data Table 1).

## Consistent patterns of commonness

The Africa, Amazonia and Southeast Asia datasets differ in the number and size of plots sampled and the number of trees sampled (Extended Data Table 1). We therefore excluded small plots (below 0.9 ha; Extended Data Fig. 1 and Methods) and used rarefaction—that is, repeated random subsampling of plots to comparable numbers of trees—to standardize sampling across the three datasets (Fig. 2).

Rarefying to a common sample size of 77,587 stems, the size of the Asia dataset (equivalent to 150, 116 and 103 plots in Africa, Amazonia and Southeast Asia respectively), we find that 77 species (95% confidence interval: 62–92) in Africa comprise 50% of individual trees, compared with 174 species (95% confidence interval: 134–215) in Amazonia and 172 species (95% confidence interval: 125–217) in Southeast Asia (Table 1 and Fig. 2). However, the substantially lower number of hyperdominant species in Africa compared with Amazonia and Southeast Asia scales with the substantially lower number of total species. We find just 1,132 species in our standardized 77,587 tree sample in Africa, compared with 2,565 and 2,585 species in Amazonia and Southeast Asia, respectively for the same sample size. Consequently, percentage hyperdominance is statistically indistinguishable among the continents at 6.79% (95% confidence interval: 5.39%–8.20%), 6.80% (95% confidence interval: 5.24%–8.36%) and 6.65% (95% confidence interval: 4.59%–8.71%) in Africa, Amazonia and Southeast Asia, respectively (Table 1). This consistency is not affected by the aggregated spatial distribution of plots within each region (Extended Data Fig. 2) and holds true for analyses based

solely on 1-ha plots (Methods). Thus, once sampling is standardized, there is marked pan-tropical consistency in the proportion of the total number of tree species accounted for by the most common species.

The consistency of commonness is not limited to defining common species as those that account for 50% of all individual trees in a dataset. The proportions of the total number of species required to account for thresholds between 10% and 90% of individual trees are also highly consistent across the rarefied data for the three continents (Fig. 3 and Extended Data Table 3). Thus, the data from the three continents appear to result from the same underlying statistical distribution.

Our rarefaction analysis shows that the number of hyperdominants, the total number of species and the percentage hyperdominance are dependent on sample size. This is because as plots—and therefore trees—are added to the sample, increasing numbers of rare species start to appear. Meanwhile, most common species have, by definition, already appeared, but their abundances increase. Thus, with increasing sample size, the number of hyperdominants increases, but at an ever-decreasing rate that tends towards saturation (Fig. 2 and Extended Data Fig. 3). The total number of species increases at a decreasing rate with increasing sample size, without apparent saturation. Therefore, as sample sizes increase, the percentage hyperdominance decreases gradually, but does not appear to saturate (Fig. 2 and Extended Data Fig. 3). This sample size dependence is likely to explain the published differences in percentage hyperdominance in Amazonian forests, which follow expectations given the sample size in each study⁶,²⁰,²².

Amazonia and Southeast Asia show remarkably similar patterns of commonness and diversity. The rarefaction curves of the number of species accounting for 50% of all trees (Fig. 2a), total number of species (Fig. 2b), percentage hyperdominance (Fig. 2c) and Fisher's α—the parameter of the log series distribution shown to best describe tropical tree species abundance distributions²¹ (Fig. 2d)—are almost identical between the two datasets. Furthermore, the numbers of species required to account for any threshold between 10% and 90% of trees in the respective rarefied samples of 77,587 trees are statistically indistinguishable (Table 1 and Extended Data Tables 2 and 3). This equivalence in overall tropical forest diversity patterns between these similarly species-rich regions is particularly striking given their very different biogeographic, climatic and anthropogenic histories, and the fact that Amazonia is one large contiguous region, whereas Southeast Asia is a series of islands and island-like regions.
<figure><img src="image_4.png" /><figcaption>**Fig. 1 | Location of the 1,568 plots, tropical forest regions, and tropical forest biome extent used in the study.** Dots show the location of the plots analysed, coloured by continental region. Dark green shows the Amazonia,</figcaption></figure>

Africa and Southeast Asia regions that we extrapolate to. Light green shows 'tropical and subtropical moist broadleaf forests'⁶⁰, which we extrapolate to as the closed canopy tropical forest biome.

structurally intact old-growth tropical forests. For Amazonia, defined as the lowland Amazon Basin and Guiana Shield, we use the Amazon Tree Diversity Network and RAINFOR datasets (n = 1,097 plots). For Africa, encompassing West, central and East Africa, we use the African Tropical Rainforest Observatory Network (AfriTRON)¹, Central African Plot Network, and two smaller networks²,³ (n = 368 plots). For Southeast Asia, defined as extending from Myanmar in the West to Sulawesi in the East, we use a tree diversity⁴ and a carbon monitoring⁵ network (n = 103 plots). We limit our analysis to trees with trunk diameter of at least 10 cm at breast height (1.3 m along the stem or above any buttresses or deformities), the widely used minimum size for inventorying tropical trees. The combined dataset includes 1,003,805 trees, of which 93.3% are identified to species (Fig. 1 and Extended Data Table 1).

## Consistent patterns of commonness

The Africa, Amazonia and Southeast Asia datasets differ in the number and size of plots sampled and the number of trees sampled (Extended Data Table 1). We therefore excluded small plots (below 0.9 ha; Extended Data Fig. 1 and Methods) and used rarefaction—that is, repeated random subsampling of plots to comparable numbers of trees—to standardize sampling across the three datasets (Fig. 2).

Rarefying to a common sample size of 77,587 stems, the size of the Asia dataset (equivalent to 150, 116 and 103 plots in Africa, Amazonia and Southeast Asia respectively), we find that 77 species (95% confidence interval: 62–92) in Africa comprise 50% of individual trees, compared with 174 species (95% confidence interval: 134–215) in Amazonia and 172 species (95% confidence interval: 125–217) in Southeast Asia (Table 1 and Fig. 2). However, the substantially lower number of hyperdominant species in Africa compared with Amazonia and Southeast Asia scales with the substantially lower number of total species. We find just 1,132 species in our standardized 77,587 tree sample in Africa, compared with 2,565 and 2,585 species in Amazonia and Southeast Asia, respectively for the same sample size. Consequently, percentage hyperdominance is statistically indistinguishable among the continents at 6.79% (95% confidence interval: 5.39%–8.20%), 6.80% (95% confidence interval: 5.24%–8.36%) and 6.65% (95% confidence interval: 4.59%–8.71%) in Africa, Amazonia and Southeast Asia, respectively (Table 1). This consistency is not affected by the aggregated spatial distribution of plots within each region (Extended Data Fig. 2) and holds true for analyses based

solely on 1-ha plots (Methods). Thus, once sampling is standardized, there is marked pan-tropical consistency in the proportion of the total number of tree species accounted for by the most common species.

The consistency of commonness is not limited to defining common species as those that account for 50% of all individual trees in a dataset. The proportions of the total number of species required to account for thresholds between 10% and 90% of individual trees are also highly consistent across the rarefied data for the three continents (Fig. 3 and Extended Data Table 3). Thus, the data from the three continents appear to result from the same underlying statistical distribution.

Our rarefaction analysis shows that the number of hyperdominants, the total number of species and the percentage hyperdominance are dependent on sample size. This is because as plots—and therefore trees—are added to the sample, increasing numbers of rare species start to appear. Meanwhile, most common species have, by definition, already appeared, but their abundances increase. Thus, with increasing sample size, the number of hyperdominants increases, but at an ever-decreasing rate that tends towards saturation (Fig. 2 and Extended Data Fig. 3). The total number of species increases at a decreasing rate with increasing sample size, without apparent saturation. Therefore, as sample sizes increase, the percentage hyperdominance decreases gradually, but does not appear to saturate (Fig. 2 and Extended Data Fig. 3). This sample size dependence is likely to explain the published differences in percentage hyperdominance in Amazonian forests, which follow expectations given the sample size in each study⁶,²⁰,²².

Amazonia and Southeast Asia show remarkably similar patterns of commonness and diversity. The rarefaction curves of the number of species accounting for 50% of all trees (Fig. 2a), total number of species (Fig. 2b), percentage hyperdominance (Fig. 2c) and Fisher's α—the parameter of the log series distribution shown to best describe tropical tree species abundance distributions²¹ (Fig. 2d)—are almost identical between the two datasets. Furthermore, the numbers of species required to account for any threshold between 10% and 90% of trees in the respective rarefied samples of 77,587 trees are statistically indistinguishable (Table 1 and Extended Data Tables 2 and 3). This equivalence in overall tropical forest diversity patterns between these similarly species-rich regions is particularly striking given their very different biogeographic, climatic and anthropogenic histories, and the fact that Amazonia is one large contiguous region, whereas Southeast Asia is a series of islands and island-like regions.
<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /><figcaption>**Fig. 2 | Rarefaction curves showing the effect of increasing sample size on the number of hyperdominants, total species, hyperdominant percentage and fitted values of Fisher's α in tropical tree communities. a–d.** The effect of increasing sample size 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). Rarefied data (mean</figcaption></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

values across iterations of subsamples) are shown as points joined by lines for clarity, shaded areas represent 95% confidence intervals (derived via the s.d. across iterations of subsamples taken with replacement at each sampling point). Note that resampling for rarefaction was by subsampling of plots, but curves are re-plotted on an x axis of number of stems.

In contrast to the similarity between Amazonia and Southeast Asia, our results provide sample size-corrected validation of the 'odd-one-out' observation²⁸,²⁹ of much lower tree species richness in Africa compared with Amazonia and Southeast Asia. Here we add a similar odd-one-out observation of a much lower number of common species in Africa than in Amazonia and Southeast Asia. However, in combination these two results lead to an almost identical percentage hyperdominance in the African, Amazonian and Southeast Asian rarefied data. This consistency extends to the proportion of species required to account for all thresholds between 10% and 90% of trees in the rarefied data (Fig. 3 and Extended Data Table 3). This pan-tropical invariance recasts the tropical forests of Africa from 'odd' in terms of

<table><caption>**Table 1 | Tree species hyperdominance results for African, Amazonian and Southeast Asian tropical forests, resampled to the common sample size of 77,587 trees**</caption><thead><tr><th></th><th>Number of hyperdominants</th><th>Total species</th><th>Hyperdominant percentage</th><th>Fisher's α</th></tr></thead><tbody><tr><td>Africa</td><td>77 [62, 92]</td><td>1,132 [1,069, 1,194]</td><td>6.79 [5.39, 8.20]</td><td>191 [161, 220]</td></tr><tr><td>Amazonia</td><td>174 [134, 215]</td><td>2,565 [2,419, 2,711]</td><td>6.80 [5.24, 8.36]</td><td>525 [475, 575]</td></tr><tr><td>Southeast Asia</td><td>172 [125, 219]</td><td>2,585 [2,440, 2,730]</td><td>6.65 [4.59, 8.71]</td><td>526 [476, 577]</td></tr></tbody></table>

Numbers in brackets are confidence intervals derived from the s.d. across iterations of subsamples taken with replacement at the sample size of the Asia dataset. Resampling done by plot; 77,587 is the size of the Southeast Asia dataset.

species richness to statistically indistinguishable from those in Amazonia and Southeast Asia in terms of proportional patterns of abundance. Overall, using standardization by rarefaction, we find consistent patterns of species abundance across Africa, Amazonia and Southeast Asia.

## Scaling to the study region

Next, we estimate commonness patterns in each of our three study regions: Africa, Amazonia and Southeast Asia. We extrapolate log series fits to the empirical Africa, Amazonia and Southeast Asia datasets (Extended Data Fig. 4), including a correction to account for the clumped spatial occurrence of species, to the total number of trees with trunk diameter of at least 10 cm in each study region. We estimate that just 104 species (95% confidence interval: 101–107) account for 50% of the 113 billion trees in Africa's closed canopy tropical forests (Table 2). We also estimate that just 299 species (95% confidence interval: 295–304) account for 50% of the 344 billion trees in Amazonia's closed canopy tropical forest, and 278 (95% confidence interval: 268–289) account for 50% of the 129 billion trees in Southeast Asia's closed canopy tropical forests (Table 2). Our results from Amazonia match those derived using a different extrapolation approach³⁰.

Our extrapolations again outline consistent percentage hyperdominance: just 2.2% of African, 2.2% Amazonian and 2.3% of Southeast Asian species account for 50% of all trees with trunk diameters of at least 10 cm in each region (Table 2). The dominant proportions of total species required to account for 10% to 90% of trees are also very similar across continents (Fig. 3 and Extended Data Table 5). The lower percentage dominance values from the extrapolated data compared
<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /><figcaption>**Fig. 2 | Rarefaction curves showing the effect of increasing sample size on the number of hyperdominants, total species, hyperdominant percentage and fitted values of Fisher's α in tropical tree communities. a–d.** The effect of increasing sample size 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). Rarefied data (mean</figcaption></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

values across iterations of subsamples) are shown as points joined by lines for clarity, shaded areas represent 95% confidence intervals (derived via the s.d. across iterations of subsamples taken with replacement at each sampling point). Note that resampling for rarefaction was by subsampling of plots, but curves are re-plotted on an x-axis of number of stems.

In contrast to the similarity between Amazonia and Southeast Asia, our results provide sample size-corrected validation of the 'odd-one-out' observation²⁸,²⁹ of much lower tree species richness in Africa compared with Amazonia and Southeast Asia. Here we add a similar odd-one-out observation of a much lower number of common species in Africa than in Amazonia and Southeast Asia. However, in combination these two results lead to an almost identical percentage hyperdominance in the African, Amazonian and Southeast Asian rarefied data. This consistency extends to the proportion of species required to account for all thresholds between 10% and 90% of trees in the rarefied data (Fig. 3 and Extended Data Table 3). This pan-tropical invariance recasts the tropical forests of Africa from 'odd' in terms of

<table><caption>**Table 1 | Tree species hyperdominance results for African, Amazonian and Southeast Asian tropical forests, resampled to the common sample size of 77,587 trees**</caption><thead><tr><th></th><th>Number of hyperdominants</th><th>Total species</th><th>Hyperdominant percentage</th><th>Fisher's α</th></tr></thead><tbody><tr><td>Africa</td><td>77 [62, 92]</td><td>1,132 [1,069, 1,194]</td><td>6.79 [5.39, 8.20]</td><td>191 [161, 220]</td></tr><tr><td>Amazonia</td><td>174 [134, 215]</td><td>2,565 [2,419, 2,711]</td><td>6.80 [5.24, 8.36]</td><td>525 [475, 575]</td></tr><tr><td>Southeast Asia</td><td>172 [125, 219]</td><td>2,585 [2,440, 2,730]</td><td>6.65 [4.59, 8.71]</td><td>526 [476, 577]</td></tr></tbody></table>

Numbers in brackets are confidence intervals derived from the s.d. across iterations of subsamples taken with replacement at the sample size of the Asia dataset. Resampling done by plot; 77,587 is the size of the Southeast Asia dataset.

species richness to statistically indistinguishable from those in Amazonia and Southeast Asia in terms of proportional patterns of abundance. Overall, using standardization by rarefaction, we find consistent patterns of species abundance across Africa, Amazonia and Southeast Asia.

## Scaling to the study region

Next, we estimate commonness patterns in each of our three study regions: Africa, Amazonia and Southeast Asia. We extrapolate log series fits to the empirical Africa, Amazonia and Southeast Asia datasets (Extended Data Fig. 4), including a correction to account for the clumped spatial occurrence of species, to the total number of trees with trunk diameter of at least 10 cm in each study region. We estimate that just 104 species (95% confidence interval: 101–107) account for 50% of the 113 billion trees in Africa's closed canopy tropical forests (Table 2). We also estimate that just 299 species (95% confidence interval: 295–304) account for 50% of the 344 billion trees in Amazonia's closed canopy tropical forest, and 278 (95% confidence interval: 268–289) account for 50% of the 129 billion trees in Southeast Asia's closed canopy tropical forests (Table 2). Our results from Amazonia match those derived using a different extrapolation approach³⁰.

Our extrapolations again outline consistent percentage hyperdominance: just 2.2% of African, 2.2% Amazonian and 2.3% of Southeast Asian species account for 50% of all trees with trunk diameters of at least 10 cm in each region (Table 2). The dominant proportions of total species required to account for 10% to 90% of trees are also very similar across continents (Fig. 3 and Extended Data Table 5). The lower percentage dominance values from the extrapolated data compared
<figure><img src="image_8.png" /><figcaption>**Fig. 3** | The minimum percentage of total species required to account for given dominance thresholds of the total number of stems when this varies from 10% to 90%. Circles show results as rarefied to the size of the Southeast Asia dataset (mean values across iterations of subsamples with 77,587 stems). Diamonds show the extrapolated results at the scale of the regions. Estimated rarefaction confidence intervals are derived from the s.d. across iterations of subsamples taken with replacement at 77,587 stems.</figcaption></figure>

with those from the rarefied data are consistent with the pattern, described above, of many more rare species being added as the number of trees increases while many fewer common species are added (Fig. 2). Overall, the extrapolated results show that there are a tractable number of common species in tropical forests in Africa, Amazonia and Southeast Asia.

## Scaling to the tropics

We next estimate the number of common tropical tree species on Earth by multiplying the pan-tropical proportion of common species by the total number of tropical tree species on Earth. Our results suggest a pan-tropical hyperdominant percentage of 2.24% (Table 2). However, our extrapolations cannot provide an estimate of the total number of tropical tree species because we do not—for this study—have data from all tropical regions, including a lack of data from Central America, New Guinea and Micronesia. Furthermore, there is no consensus estimate of the total number of tropical tree species on Earth.

A compilation of lists of species known to science suggests a total of 60,065 tree species globally¹⁵. Tropical forest biomes likely comprise 63% of this list (E. Beech, personal communication, 2021), implying that there are around 37,900 known tropical tree species. This minimum estimate does not account for species that are yet to be identified and described by scientists. An alternative extrapolation method estimated that there are 46,900 species for the closed canopy tropical forest biome²⁵ (range 40,500–53,300 species), implying that there are 9,000 yet-to-be-identified species. This is in agreement with a recent global study suggesting that there are around 9,200 tree species remaining yet to be formally named, almost all in the tropics¹⁶. Thus, together, these studies suggest there are likely to be approximately 47,000 tropical tree species in the world's closed canopy tropical forests.

Our best estimate is that 1,053 tree species (2.24% of 47,000 species) account for half of Earth's 800 billion trees with trunk diameters of at least 10 cm found in the closed-canopy tropical forest biome. Although the true number may be lower or higher, the conclusion that a tractable

<table><caption>**Table 2** | Extrapolated tree species hyperdominance results for African, Amazonian, Southeast Asian tropical forests at the regional scale</caption><thead><tr><th></th><th>Number of hyperdominants</th><th>Total species</th><th>Hyperdominant percentage</th></tr></thead><tbody><tr><td>Africa</td><td>104 [101, 107]</td><td>4,638 [4,511, 4,764]</td><td>2.23</td></tr><tr><td>Amazonia</td><td>299 [295, 304]</td><td>13,826 [13,615, 14,036]</td><td>2.16</td></tr><tr><td>Southeast Asia</td><td>278 [268, 289]</td><td>11,963 [11,451, 12,475]</td><td>2.32</td></tr><tr><td>Total<sup>a</sup></td><td>681 [664, 700]</td><td>30,427 [29,577, 31,275]</td><td>2.24</td></tr></tbody></table>

number of species dominate tropical forests is clear. Some of these species are likely to be extraordinarily common: our best estimate is that just 61 species account for 80 billion individual trees (0.13% of 47,000 species). At the other end of the spectrum, we estimate that the rarest approximately 39,500 species account for just 80 billion trees, or 10% of individuals. Meanwhile, the other 90% of all trees are estimated to belong to just 7,487 species (15.93% of 47,000 species). Thus, these results open the possibility of focusing efforts on understanding the biology of a tractable number of species in tropical forests to approximate the whole stand.

## Identifying the most common species

Our analyses showing that 104, 299 and 278 common species account for 50% of the trees in our African, Amazonian and Southeast Asian study regions, respectively, do not yield a list of named species. To assess which named species are likely to be hyperdominant, we use a subsampling procedure similar to the rarefaction methodology above. We randomly subsample from approximately 10,000 trees per subsample (drawn by plot) and increase the size of the subsample in 10,000-tree increments until the size of each regional dataset is reached, and repeat this process 100 times. For each sampled increment of 10,000 trees we then calculate the proportion of random subsamples in which each species qualifies as hyperdominant (Supplementary Table 1). We then assign the species to one of four groups:

(1) Both hyperdominant in the full data and hyperdominant in the majority of subsamples even at very small sample sizes. These 50, 95 and 105 species in our Africa, Amazonia and Southeast Asia datasets, respectively, represent 3.5%, 2.1% and 4.1% of sampled species in each dataset. These species are likely to be geographically widespread and abundant.

(2) Both hyperdominant in the full data and hyperdominant in the majority of subsamples, but at the smallest sample sizes only occasionally hyperdominant. These 32, 129 and 67 species in our Africa, Amazonia and Southeast Asia datasets, respectively, represent 2.3%, 2.9% and 2.6% of sampled species in each dataset. These species are likely to be geographically widespread but not always abundant.

(3) Not quite hyperdominant in the full data, but hyperdominant in a substantial proportion of subsamples. These 102, 339 and 200 species in our Africa, Amazonia and Southeast Asia datasets, respectively, represent 7.2%, 7.5% and 7.7% of sampled species in each dataset. These species are probably locally abundant but not necessarily geographically widespread.

(4) Not hyperdominant in the full data and almost never hyperdominant in the subsamples. These 1,232, 3,929 and 2,213 species in our Africa, Amazonia and Southeast Asia datasets, respectively, represent 87%, 87.5% and 85.6% of sampled species in each dataset. These species are probably neither geographically widespread nor abundant.

<footnote>ªCalculated as the sum of the number of hyperdominants and total species across the three major tropical forest regions with hyperdominance percentage derived therefrom. Prediction intervals (in brackets) combine uncertainty from the standard error of predicted means and the residual s.d. of the regression of the bias correction fit.</footnote>
<figure><img src="image_9.png" /><figcaption>**Fig. 3** | The minimum percentage of total species required to account for given dominance thresholds of the total number of stems when this varies from 10% to 90%. Circles show results as rarefied to the size of the Southeast Asia dataset (mean values across iterations of subsamples with 77,587 stems). Diamonds show the extrapolated results at the scale of the regions. Estimated rarefaction confidence intervals are derived from the s.d. across iterations of subsamples taken with replacement at 77,587 stems.</figcaption></figure>

with those from the rarefied data are consistent with the pattern, described above, of many more rare species being added as the number of trees increases while many fewer common species are added (Fig. 2). Overall, the extrapolated results show that there are a tractable number of common species in tropical forests in Africa, Amazonia and Southeast Asia.

## Scaling to the tropics

We next estimate the number of common tropical tree species on Earth by multiplying the pan-tropical proportion of common species by the total number of tropical tree species on Earth. Our results suggest a pan-tropical hyperdominant percentage of 2.24% (Table 2). However, our extrapolations cannot provide an estimate of the total number of tropical tree species because we do not—for this study—have data from all tropical regions, including a lack of data from Central America, New Guinea and Micronesia. Furthermore, there is no consensus estimate of the total number of tropical tree species on Earth.

A compilation of lists of species known to science suggests a total of 60,065 tree species globally¹⁵. Tropical forest biomes likely comprise 63% of this list (E. Beech, personal communication, 2021), implying that there are around 37,900 known tropical tree species. This minimum estimate does not account for species that are yet to be identified and described by scientists. An alternative extrapolation method estimated that there are 46,900 species for the closed canopy tropical forest biome²⁵ (range 40,500–53,300 species), implying that there are 9,000 yet-to-be-identified species. This is in agreement with a recent global study suggesting that there are around 9,200 tree species remaining yet to be formally named, almost all in the tropics¹⁶. Thus, together, these studies suggest there are likely to be approximately 47,000 tropical tree species in the world's closed canopy tropical forests.

Our best estimate is that 1,053 tree species (2.24% of 47,000 species) account for half of Earth's 800 billion trees with trunk diameters of at least 10 cm found in the closed-canopy tropical forest biome. Although the true number may be lower or higher, the conclusion that a tractable

<table><caption>**Table 2** | Extrapolated tree species hyperdominance results for African, Amazonian, Southeast Asian tropical forests at the regional scale</caption><thead><tr><th></th><th>Number of hyperdominants</th><th>Total species</th><th>Hyperdominant percentage</th></tr></thead><tbody><tr><td><strong>Africa</strong></td><td><strong>104</strong> [101, 107]</td><td><strong>4,638</strong> [4,511, 4,764]</td><td><strong>2.23</strong></td></tr><tr><td><strong>Amazonia</strong></td><td><strong>299</strong> [295, 304]</td><td><strong>13,826</strong> [13,615, 14,036]</td><td><strong>2.16</strong></td></tr><tr><td><strong>Southeast Asia</strong></td><td><strong>278</strong> [268, 289]</td><td><strong>11,963</strong> [11,451, 12,475]</td><td><strong>2.32</strong></td></tr><tr><td><strong>Total<sup>a</sup></strong></td><td><strong>681</strong> [664, 700]</td><td><strong>30,427</strong> [29,577, 31,275]</td><td><strong>2.24</strong></td></tr></tbody></table>

number of species dominate tropical forests is clear. Some of these species are likely to be extraordinarily common: our best estimate is that just 61 species account for 80 billion individual trees (0.13% of 47,000 species). At the other end of the spectrum, we estimate that the rarest approximately 39,500 species account for just 80 billion trees, or 10% of individuals. Meanwhile, the other 90% of all trees are estimated to belong to just 7,487 species (15.93% of 47,000 species). Thus, these results open the possibility of focusing efforts on understanding the biology of a tractable number of species in tropical forests to approximate the whole stand.

## Identifying the most common species

Our analyses showing that 104, 299 and 278 common species account for 50% of the trees in our African, Amazonian and Southeast Asian study regions, respectively, do not yield a list of named species. To assess which named species are likely to be hyperdominant, we use a subsampling procedure similar to the rarefaction methodology above. We randomly subsample from approximately 10,000 trees per subsample (drawn by plot) and increase the size of the subsample in 10,000-tree increments until the size of each regional dataset is reached, and repeat this process 100 times. For each sampled increment of 10,000 trees we then calculate the proportion of random subsamples in which each species qualifies as hyperdominant (Supplementary Table 1). We then assign the species to one of four groups:

(1) Both hyperdominant in the full data and hyperdominant in the majority of subsamples even at very small sample sizes. These 50, 95 and 105 species in our Africa, Amazonia and Southeast Asia datasets, respectively, represent 3.5%, 2.1% and 4.1% of sampled species in each dataset. These species are likely to be geographically widespread and abundant.

(2) Both hyperdominant in the full data and hyperdominant in the majority of subsamples, but at the smallest sample sizes only occasionally hyperdominant. These 32, 129 and 67 species in our Africa, Amazonia and Southeast Asia datasets, respectively, represent 2.3%, 2.9% and 2.6% of sampled species in each dataset. These species are likely to be geographically widespread but not always abundant.

(3) Not quite hyperdominant in the full data, but hyperdominant in a substantial proportion of subsamples. These 102, 339 and 200 species in our Africa, Amazonia and Southeast Asia datasets, respectively, represent 7.2%, 7.5% and 7.7% of sampled species in each dataset. These species are probably locally abundant but not necessarily geographically widespread.

(4) Not hyperdominant in the full data and almost never hyperdominant in the subsamples. These 1,232, 3,929 and 2,213 species in our Africa, Amazonia and Southeast Asia datasets, respectively, represent 87%, 87.5% and 85.6% of sampled species in each dataset. These species are probably neither geographically widespread nor abundant.

<footnote>ªCalculated as the sum of the number of hyperdominants and total species across the three major tropical forest regions with hyperdominance percentage derived therefrom. Prediction intervals (in brackets) combine uncertainty from the standard error of predicted means and the residual s.d. of the regression of the bias correction fit.</footnote>
We suggest that if all trees in a region were sampled, the hyperdominant species would be drawn from the first three groups, which are listed in Supplementary Table 2. This candidate list of 1,119 hyperdominant species contains 184 species in Africa, 563 species in Amazonia and 372 species in Southeast Asia, with no species appearing on more than one region's list. Thus, the list of species that are likely candidates for hyperdominance is manageably small.

There is uncertainty in our candidate hyperdominant list owing to the limitations of the underlying samples of plots across the landscape. Specifically, some species that always have low local abundance but are geographically widespread and lack habitat restrictions may require larger sample sizes for their hyperdominance to become clear. Similarly, species that combine low local abundance and habitat specificity pose challenges. If the distribution and extent of specialist habitat is great enough to result in hyperdominance of specialists but is not sufficiently captured in our sampling, such species might not appear in our candidate list. By contrast, some species in our candidate hyperdominant list will not be true hyperdominants. Of particular note, some apparently common species may actually comprise a group of cryptic species, with none of these cryptic species being hyperdominant by itself³¹⁻³³. However, the striking similarly in species abundance patterns across the Africa, Amazonia and Southeast Asia datasets, despite differing sampling intensity on each continent, suggests that these potential limitations do not substantially affect the overall patterns found. We therefore expect a high overlap between our list of candidate hyperdominant species and eventual elucidation of the actual hyperdominants of these three regions and the pan-tropics.

Our list of 1,119 candidate hyperdominant species represents a tractable number of species on which to prioritize autecological research. Indeed, given their commonness, ecological data already exists for many of these species: 95% have some autecological data recorded in a large global database³⁴; 83% have at least 10 different types of measurement, typically including their growth form, maximum height, wood density and aspects of leaf chemistry. This indicates that these species are already relatively well known. Therefore, only limited additional data may be required to open new approaches to better understanding tropical forests through their most common tree species, including how they may react to today's era of rapid global environmental change.

## Discussion

Charles Darwin wrote in *The Origin of Species* that “rarity is the attribute of a vast number of species of all classes and in all countries”²³⁵. If this is the case, then common species are themselves rare. Our results concur: despite their formidable diversity, the trees in tropical forests fit the ‘rare is common, common is rare’ pattern³⁶ which has been documented in many other taxa¹⁷⁻¹⁹,³⁶,³⁷. Beyond this, our analyses reveal highly consistent patterns of commonness across three major tropical forest regions. Notably, despite substantial inter-continental variation in biogeographic history, contemporary environment, forest structure and species composition, we have found an emergent property of the tropical forest system. For the trees that structure tropical forests, a consistent -2.2% of the total species pool accounts for 50% of all individual trees in Africa, Amazonia and Southeast Asia. This consistency is all the more notable given relatively lower tree species richness of African tropical forests compared with Amazonian and Southeast Asian forests, probably owing to higher extinction rates in African forests, with evidence of major losses of African species at the Oligocene–Miocene boundary³⁸, and contractions of rainforest area due to drier conditions during repeated glacial–interglacial cycles over the past 2.6 million years³⁹.

We find common diversity patterns despite the very different histories of human occupancy in Amazonian, African and Southeast Asian tropical forests⁴⁰. The relatively recent arrival of humans in Amazonia approximately 20,000 years ago has been linked to greater Pleistocene extinctions, in contrast to much longer human occupancy in the

tropical forests of Africa and Southeast Asia⁴¹. Some have also suggested that Amazonian forest composition was altered by humans through the incipient domestication of tree species, increasing the abundance of a small number of favoured species⁴². Others have reported large areas of deforestation associated with the African Iron Age⁴³. How can such different human histories result in near-identical patterns of tree species dominance? The most parsimonious explanation is that the system tends to return to a state with a similar species abundance pattern.

Nevertheless, consistent patterns of commonness do not necessarily imply the same causal mechanisms. The ubiquity of the broad ‘rare is common, common is rare’ pattern in ecology, which is also found in non-biological complex systems⁴⁴, means inferences as to the cause of this broad pattern are challenging²⁷,⁴⁵. Although combinatoric methods⁴⁵ and models that maximize the entropy of information⁴⁶,⁴⁷ both produce the ubiquitous ‘reverse lazy-J’ pattern, empirical observations show fewer common species and more rare species than expected by statistical controls alone⁴⁵. Similarly, neutral models produce the same broad pattern, but produce too few individuals of the most common Amazonian tree species⁴⁸. This suggests that biological mechanisms influence tree community assembly to produce a consistent proportion of common species across continents.

Recent analyses have revealed that the same few families contribute most of the species richness in Africa and Amazonia⁴⁹, which when combined with analyses showing that more diverse families have more common species⁵⁰, may indicate a role for deep evolutionary mechanisms driving the patterns we find. Yet, considering the substantially smaller regional species pool in Africa compared with Amazonia and Southeast Asia, one might expect differing continental patterns of species dominance if evolutionary drivers were the primary mechanism, not the highly consistent patterns that we find. Similarly, if environmental filtering were a key mechanism, the different contemporary environments, with Africa much drier on average than the other two continents²⁶, and Southeast Asia consisting of scattered island-like areas of forest compared with the contiguous forested region of Amazonia, would also imply differing continental patterns of species dominance, not the near-identical patterns that we find. These constraints limit the potential mechanisms that could apply across our three-continent context.

One potential cross-continental mechanism is dispersal limitation, where the dispersal capabilities of species result in some suitable habitat patches remaining unoccupied. Another mechanism is density- or distance-dependent mortality, which appears widespread across tropical forests⁵¹. Here, specialist species-specific natural enemies such as pathogens and herbivores reduce seed or juvenile conspecific survival rates near conspecific adults or in areas of high juvenile conspecific density, thereby reducing competitive exclusion and contributing to the maintenance of high tree species richness in tropical forests⁵¹. It is possible that common species have largely evaded density- and/or distance-dependent mortality. Analyses showing that species abundance can be either high or low within given genera⁵² support this hypothesis. Further progress on putative mechanisms can be made, for example, by exploring whether ecological or functional traits differ between common and rare species, and assessing the consistency of any differences among tropical continents⁵³. Although deducing mechanisms is complex, the identification of a tractable number of common species in tropical forests will facilitate progress in understanding of tropical forests beyond species abundance distributions.

Refining our results, particularly the naming of common species, requires improved sampling of tropical forests, both in terms of geographic scope and taxonomic identification of trees within plots. Expanding sampling to include Central America, New Guinea, Indonesia and other regions would improve the generality of our results. Better identifying trees in existing plots would increase the utility of available samples: in our Southeast Asia region we excluded 142 plots
We suggest that if all trees in a region were sampled, the hyperdominant species would be drawn from the first three groups, which are listed in Supplementary Table 2. This candidate list of 1,119 hyperdominant species contains 184 species in Africa, 563 species in Amazonia and 372 species in Southeast Asia, with no species appearing on more than one region's list. Thus, the list of species that are likely candidates for hyperdominance is manageably small.

There is uncertainty in our candidate hyperdominant list owing to the limitations of the underlying samples of plots across the landscape. Specifically, some species that always have low local abundance but are geographically widespread and lack habitat restrictions may require larger sample sizes for their hyperdominance to become clear. Similarly, species that combine low local abundance and habitat specificity pose challenges. If the distribution and extent of specialist habitat is great enough to result in hyperdominance of specialists but is not sufficiently captured in our sampling, such species might not appear in our candidate list. By contrast, some species in our candidate hyperdominant list will not be true hyperdominants. Of particular note, some apparently common species may actually comprise a group of cryptic species, with none of these cryptic species being hyperdominant by itself³¹⁻³³. However, the striking similarly in species abundance patterns across the Africa, Amazonia and Southeast Asia datasets, despite differing sampling intensity on each continent, suggests that these potential limitations do not substantially affect the overall patterns found. We therefore expect a high overlap between our list of candidate hyperdominant species and eventual elucidation of the actual hyperdominants of these three regions and the pan-tropics.

Our list of 1,119 candidate hyperdominant species represents a tractable number of species on which to prioritize autecological research. Indeed, given their commonness, ecological data already exists for many of these species: 95% have some autecological data recorded in a large global database³⁴; 83% have at least 10 different types of measurement, typically including their growth form, maximum height, wood density and aspects of leaf chemistry. This indicates that these species are already relatively well known. Therefore, only limited additional data may be required to open new approaches to better understanding tropical forests through their most common tree species, including how they may react to today's era of rapid global environmental change.

## Discussion

Charles Darwin wrote in *The Origin of Species* that “rarity is the attribute of a vast number of species of all classes and in all countries”²³. If this is the case, then common species are themselves rare. Our results concur: despite their formidable diversity, the trees in tropical forests fit the ‘rare is common, common is rare’ pattern³⁶ which has been documented in many other taxa¹⁷⁻¹⁹,³⁶,³⁷. Beyond this, our analyses reveal highly consistent patterns of commonness across three major tropical forest regions. Notably, despite substantial inter-continental variation in biogeographic history, contemporary environment, forest structure and species composition, we have found an emergent property of the tropical forest system. For the trees that structure tropical forests, a consistent -2.2% of the total species pool accounts for 50% of all individual trees in Africa, Amazonia and Southeast Asia. This consistency is all the more notable given relatively lower tree species richness of African tropical forests compared with Amazonian and Southeast Asian forests, probably owing to higher extinction rates in African forests, with evidence of major losses of African species at the Oligocene–Miocene boundary³⁸, and contractions of rainforest area due to drier conditions during repeated glacial–interglacial cycles over the past 2.6 million years³⁹.

We find common diversity patterns despite the very different histories of human occupancy in Amazonian, African and Southeast Asian tropical forests⁴⁰. The relatively recent arrival of humans in Amazonia approximately 20,000 years ago has been linked to greater Pleistocene extinctions, in contrast to much longer human occupancy in the

tropical forests of Africa and Southeast Asia⁴¹. Some have also suggested that Amazonian forest composition was altered by humans through the incipient domestication of tree species, increasing the abundance of a small number of favoured species⁴². Others have reported large areas of deforestation associated with the African Iron Age⁴³. How can such different human histories result in near-identical patterns of tree species dominance? The most parsimonious explanation is that the system tends to return to a state with a similar species abundance pattern.

Nevertheless, consistent patterns of commonness do not necessarily imply the same causal mechanisms. The ubiquity of the broad ‘rare is common, common is rare’ pattern in ecology, which is also found in non-biological complex systems⁴⁴, means inferences as to the cause of this broad pattern are challenging²⁷,⁴⁵. Although combinatoric methods⁴⁵ and models that maximize the entropy of information⁴⁶,⁴⁷ both produce the ubiquitous ‘reverse lazy-J’ pattern, empirical observations show fewer common species and more rare species than expected by statistical controls alone⁴⁵. Similarly, neutral models produce the same broad pattern, but produce too few individuals of the most common Amazonian tree species⁴⁸. This suggests that biological mechanisms influence tree community assembly to produce a consistent proportion of common species across continents.

Recent analyses have revealed that the same few families contribute most of the species richness in Africa and Amazonia⁴⁹, which when combined with analyses showing that more diverse families have more common species⁵⁰, may indicate a role for deep evolutionary mechanisms driving the patterns we find. Yet, considering the substantially smaller regional species pool in Africa compared with Amazonia and Southeast Asia, one might expect differing continental patterns of species dominance if evolutionary drivers were the primary mechanism, not the highly consistent patterns that we find. Similarly, if environmental filtering were a key mechanism, the different contemporary environments, with Africa much drier on average than the other two continents²⁶, and Southeast Asia consisting of scattered island-like areas of forest compared with the contiguous forested region of Amazonia, would also imply differing continental patterns of species dominance, not the near-identical patterns that we find. These constraints limit the potential mechanisms that could apply across our three-continent context.

One potential cross-continental mechanism is dispersal limitation, where the dispersal capabilities of species result in some suitable habitat patches remaining unoccupied. Another mechanism is density- or distance-dependent mortality, which appears widespread across tropical forests⁵¹. Here, specialist species-specific natural enemies such as pathogens and herbivores reduce seed or juvenile conspecific survival rates near conspecific adults or in areas of high juvenile conspecific density, thereby reducing competitive exclusion and contributing to the maintenance of high tree species richness in tropical forests⁵¹. It is possible that common species have largely evaded density- and/or distance-dependent mortality. Analyses showing that species abundance can be either high or low within given genera⁵² support this hypothesis. Further progress on putative mechanisms can be made, for example, by exploring whether ecological or functional traits differ between common and rare species, and assessing the consistency of any differences among tropical continents⁵³. Although deducing mechanisms is complex, the identification of a tractable number of common species in tropical forests will facilitate progress in understanding of tropical forests beyond species abundance distributions.

Refining our results, particularly the naming of common species, requires improved sampling of tropical forests, both in terms of geographic scope and taxonomic identification of trees within plots. Expanding sampling to include Central America, New Guinea, Indonesia and other regions would improve the generality of our results. Better identifying trees in existing plots would increase the utility of available samples: in our Southeast Asia region we excluded 142 plots
(approximately 120,000 stems) because they did not have more than 80% of trees identified to species. Furthermore, additional taxonomic research on even the most common species is needed given that some of the most common Amazonian³³ and African⁵⁴,⁵⁵ tree species have been found to be complexes of several distinct species that are difficult to distinguish in the field. However, the similarity of our results across the three continental regions suggests that the occurrence of such species complexes may also be similar across the continental regions, again implying the operation of fundamental processes in differing forests. Overall, our work underscores the need for investment in taxonomy, particularly given the thousands of rare species we and others¹⁸ document, but also when considering the most common species.

Our best estimate, using extrapolation, that for the tropics as a whole just 1,053 species account for half of Earth's 800 billion tropical trees has potentially profound implications. Rather than attempting to understand tens of thousands of species of tropical trees, a focus on just a few hundred of the most common species can provide a simplified characterization of these otherwise complex forests. Our analyses indicate that the most common of these species are reliably named and relatively well known. Our list of candidate hyperdominants can therefore readily serve new research, including in facilitating targeted autecological data collection to understand their role in providing ecological functions and services. Practically, this species-specific information could enhance tropical forest modelling by focusing on common species instead of relying on functional types or traits, thereby potentially improving predictions of future forest change.

In the future, analyses should be extended to investigate forest carbon stocks and hyperdominant species and their role in the provision of ecosystem services. In Amazonia, even fewer tree species were found to account for 50% of aboveground carbon stocks than the minimum number required to account for 50% of trees²². More generally, the set of common species is likely to include foundation species that define broader community assemblages, the environmental sensitivity of which will probably drive tropical forest responses to environmental change³⁶. Of course, striving to understand and protect rare and non-hyperdominant species remains crucial, particularly as they face greater extinction risk and probably also contribute to the functioning of ecosystems, particularly when more functions⁵⁷, longer timescales⁵⁸ and imposed environmental changes⁵⁹ are considered, and given that the hyperdominants of the future may be rarer today. Nonetheless, with a complementary grasp of the most common species, mapping, understanding and modelling of the world's tropical forests will be a much more tractable proposition.

## Online content

Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi.org/10.1038/s41586-023-06820-z.

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25. Slik, J. W. et al. An estimate of the number of tropical tree species. *Proc. Natl Acad. Sci. USA* **112**, 7472–7477 (2015).

26. Parmentier, I. et al. The odd man out? Might climate explain the lower tree α-diversity of African rain forests relative to Amazonian rain forests? *J. Ecol.* **95**, 1058–1071 (2007).

27. McGill, B. J. & Nekola, J. C. Mechanisms in macroecology: AWOL or purloined letter? Towards a pragmatic view of mechanism. *Oikos* **119**, 591–603 (2010).

28. Richards, P. W. in *Tropical Forest Ecosystems of Africa and South America: A Comparative Review* (eds Meggers, B. J., Ayensu, E. S. & Duckworth, W. D.) 21–26 (Smithsonian Institution Press, 1973).

29. Couvreur, T. L. Odd man out: why are there fewer plant species in African rain forests? *Plant Syst. Evol.* **301**, 1299–1313 (2015).

30. Tovo, A. et al. Upscaling species richness and abundances in tropical forests. *Sci. Adv.* **3**, e1701438 (2017).

31. Cardoso, D. et al. Amazon plant diversity revealed by a taxonomically verified species list. *Proc. Natl Acad. Sci. USA* **114**, 10695–10700 (2017).

32. Ter Steege, H. et al. Towards a dynamic list of Amazonian tree species. *Sci. Rep.* **9**, 3501 (2019).

33. Damasco, G. et al. Revisiting the hyperdominance of Neotropical tree species under a taxonomic, functional and evolutionary perspective. *Sci. Rep.* **11**, 9585 (2021).

34. Kattge, J. et al. TRY plant trait database—enhanced coverage and open access. *Glob. Change Biol.* **26**, 119–188 (2020).

35. Darwin, C. On *The Origin of Species* by Means of Natural Selection: *Or, the Preservation of Favored Races in the Struggle for Life* (J. Murray, 1859).

36. McGill, B. J. in *Biological Diversity: Frontiers In Measurement and Assessment* (ed. Magurran, A. E. & McGill, B. J.) 105–122 (2011).

37. Henderson, P. A. & Magurran, A. E. Linking species abundance distributions in numerical abundance and biomass through simple assumptions about community structure. *Proc. R. Soc. B* **277**, 1561–1570 (2010).

38. Curran, E., Jacobs, B. & Pan, A. Is Africa really an "odd man out"? Evidence for diversity decline across the Oligocene–Miocene boundary. *Int. J. Plant Sci.* **182**, 551–563 (2021).

39. Morley, R. J. *Origin and Evolution of Tropical Rain Forests* (John Wiley & Sons, 2000).

40. Siceri, E. M. L., Roberts, P., Maezumi, S. Y. & Mahi, Y. Tropical forests in the deep human past. *Phil. Trans. R. Soc. B* **377**, 20200500 (2022).

41. Sandom, C., Faurby, S., Sandel, B. & Svenning, J.-C. Global late Quaternary megafauna extinctions linked to humans, not climate change. *Proc. R. Soc. B* **281**, 20133254 (2014).

42. Levis, C. et al. Persistent effects of pre-Columbian plant domestication on Amazonian forest composition. *Science* **355**, 925–931 (2017).

43. Garcin, Y. et al. Early anthropogenic impact on Western Central African rainforests 2,600 y ago. *Proc. Natl Acad. Sci. USA* **115**, 3261–3266 (2018).

44. Nekola, J. C. & Brown, J. H. The wealth of species: ecological communities, complex systems and the legacy of Frank Preston. *Ecol. Lett.* **10**, 188–196 (2007).

45. Diaz, R. M., Ye, H. & Ernest, S. K. M. Empirical abundance distributions are more uneven than expected given their statistical baseline. *Ecol. Lett.* **24**, 1739–2039 (2021).

46. Harte, J. & Newman, E. A. Maximum information entropy: a foundation for ecological theory. *Trends Ecol. Evol.* **29**, 384–389 (2014).

47. Harte, J., Brush, M., Newman, E. A. & Umemura, K. An equation of state unifies diversity, productivity, abundance and biomass. *Commun. Biol.* **5**, 874 (2022).

48. Pos, E. et al. Scaling issues of neutral theory reveal violations of ecological equivalence for dominant Amazonian tree species. *Ecol. Lett.* **22**, 1072–1082 (2019).

49. Silva de Miranda, P. L. et al. Dissecting the difference in tree species richness between Africa and South America. *Proc. Natl Acad. Sci. USA* **119**, e2112336119 (2022).

50. Webb, C. O. & Pitman, N. C. Phylogenetic balance and ecological evenness. *Syst. Biol.* **51**, 898–907 (2002).
(approximately 120,000 stems) because they did not have more than 80% of trees identified to species. Furthermore, additional taxonomic research on even the most common species is needed given that some of the most common Amazonian³³ and African⁵⁴,⁵⁵ tree species have been found to be complexes of several distinct species that are difficult to distinguish in the field. However, the similarity of our results across the three continental regions suggests that the occurrence of such species complexes may also be similar across the continental regions, again implying the operation of fundamental processes in differing forests. Overall, our work underscores the need for investment in taxonomy, particularly given the thousands of rare species we and others¹⁸ document, but also when considering the most common species.

Our best estimate, using extrapolation, that for the tropics as a whole just 1,053 species account for half of Earth's 800 billion tropical trees has potentially profound implications. Rather than attempting to understand tens of thousands of species of tropical trees, a focus on just a few hundred of the most common species can provide a simplified characterization of these otherwise complex forests. Our analyses indicate that the most common of these species are reliably named and relatively well known. Our list of candidate hyperdominants can therefore readily serve new research, including in facilitating targeted autecological data collection to understand their role in providing ecological functions and services. Practically, this species-specific information could enhance tropical forest modelling by focusing on common species instead of relying on functional types or traits, thereby potentially improving predictions of future forest change.

In the future, analyses should be extended to investigate forest carbon stocks and hyperdominant species and their role in the provision of ecosystem services. In Amazonia, even fewer tree species were found to account for 50% of aboveground carbon stocks than the minimum number required to account for 50% of trees²². More generally, the set of common species is likely to include foundation species that define broader community assemblages, the environmental sensitivity of which will probably drive tropical forest responses to environmental change³⁶. Of course, striving to understand and protect rare and non-hyperdominant species remains crucial, particularly as they face greater extinction risk and probably also contribute to the functioning of ecosystems, particularly when more functions⁵⁷, longer timescales⁵⁸ and imposed environmental changes⁵⁹ are considered, and given that the hyperdominants of the future may be rarer today. Nonetheless, with a complementary grasp of the most common species, mapping, understanding and modelling of the world's tropical forests will be a much more tractable proposition.

## Online content

Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi.org/10.1038/s41586-023-06820-z.

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**Publisher's note** Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

<figure><img src="image_9.png" /></figure>

**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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

© The Author(s) 2024
51. Comita, L. S. et al. Testing predictions of the Janzen–Connell hypothesis: a meta-analysis of experimental evidence for distance-and density-dependent seed and seedling survival. *J. Ecol.* **102**, 845–856 (2014).

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58. Isbell, F. et al. Quantifying effects of biodiversity on ecosystem functioning across times and places. *Ecol. Lett.* **21**, 763–778 (2018).

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60. Olson, D. M. et al. Terrestrial Ecoregions of the World: a new map of life on Earth: a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. *Bioscience* **51**, 933–938 (2001).

**Publisher's note** Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

<figure><img src="image_10.png" /></figure>

**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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

© The Author(s) 2024
Declan L. M. Cooper¹,²,³, Simon L. Lewis¹,³,⁴, Martin J. P. Sullivan 3,4, Paulo I. Prado 5,6, Hans ter Steege 7, Nicolas Barbier 8,9, Ferry Slik 10, Bonaventure Sonke 11, 12, Corneille E. N. Ewango 12, Stephen Adu-Bredi 13, Kofi Affium-Baffoe 14, Daniel P. P. de Aguiar 15,16, Manuel Augusto Ahuite Reategui 17, Shin-Ichoi Aiba 18, Maria Weiss Albuquerque 19, Francisca Dionizia de Almeida Matos 20, Alfonso Alonso 21, Christian A. Amani 22,23, Dário Dantas do Amaral 24, lêda Leão do Amaral 20, Anandra 25, Irres Paula de Andrade Miranda 26, Ilondea B. Angoboy 27, Alejandro Araujo-Murakami 27, Nicolás Castaño Arboleda 28, Luzmila Arroyo 27, Peter Ashton 29, Gerardo A. Aymard C30,: Philip R. Baker 3, Michael Bessie Balinga 33, Henrik Batslev 34, Lindsay F. Banin 35, Olaf S. Bánki 5, Chris Baraloto 36, Edelcilo Bionema 28, Roberta Barbosa 29, Flávia Rodrigues Barbosa 37, Jos Barlow 38, Jean-Francois Bastin 39, Hans Beekman 40, SERGE BEGNE 3,11,12, Natacha Nsii Bengone 13, Erika Berenguer 38,42, Nicholas Berry 43, Robert Bitariho 44, Pascal Boeckx 45, Jan Bogaert 46, Bernard Bonyoma 47, Patrick Boundja 22,48, Nils Bourland 40,49,50,51, Faustin Boyemba Bosela 52, Fabian Brambach 53, Roel Briën 54, David F. R. P. Burslem 54, José Luis Camargo 25, Wegliane Campelo 55, Angela Cano 56,57, Sasha Cárdenas 56, Dairon Cárdenas López 28, Rainiellen de Sá Carpanedo 57, Yra Andreina Carrero Márquez 26, Fernanda Antunes Carvalho 59,60, Luisa Fernanda Casas 56, Hernán Castellanos 51, Carolina V. Castilho 62, Carlos Cerón 63, Colin A. Chapman 64,56,66, Jerome Chave 67, Pourhin Chhang 68, Wanlop Chutipong 69, George B. Chuyong 70, Bruno Barçante Ladvocat Craña 71, Connie J. Clark 72, Fernanda Coelho de Souza 59,73,74, James A. Comiskey 75,76, David A. Coomes 77, Fernando Cornejo Valverde 78, Diego F. Correa 56,76, Flávia R. C. Costa 59, Janaina Barbaosa Pedrosa Costa 80, Pierre Couteron 8,9, Heike Culmsee 81, Aida Cuni-Sanchez 82,83, Francisco Dallmeier 21, Gabriel Damasco 84, Gilles Dauby 8, Nállaret Dévila 45, Hilda Pauleta Dévila Doña 85, Jose Don T. De Alban 87,88, Rafael L. de Assis 89, Charles de Canniere 90, Thales de Haulleville 40, Marcelo de Jesus Veiga Carim 91, Layon O. Demeritz 91, Kyle G. Dexter 92, Anthony Di Fiore 94,95, Hazimah Haji Mohammad Din 96, Mathias I. Disney 91, Brice Yannick Djofack 40,97,98, Marie-Noël K. Djikouko 97, Tran Van Do 99, Jean-Louis Doucet 100, Freddie C. Draper 101, Vincent Drossaix 8,9, Joost F. Duivenvoorden 102, Julien Engel 8,10,3, Vittoria Estienne 48, William Farfan-Rios 104,105, Sophie Fauset 106, Kenneth J. Feeley 107,108, Yuri Oliveira Feitosa 109, Ted R. Feldpausch 73,110, Cid Ferreira 20,93, Joice Ferreira 111, Leandro Villa Ferreira 24, Christine D. Fletcher 112, Bernardo Monteiro Flores 113, Alusine Fofanah 114, Ernest G. Folí 113, Émile Fonty 115,16, Gabriella M. Fredriksson 117, Alfredo Fuentes 105,118, David Galbraith 3, George Pepe Gallardo Gonzales 86, Karina Garcia-Cabreta 119, Roosevelt Agro-Villacorta 120,121, Vitor H. F. Gomes 122,123, Ricardo Zárate Gómez 124, Therany Gonzales 125, Rogerio Gribel 20, Marcelino Carneiro Guedes 80, Juan Ernesto Guevara 126,127, Khalid Rahman Hakeem 128, Jefferson S. Hall 129, Keith C. Hamer 130, Alan C. Hamilton 131, David J. Harris 93, Rhett D. Harrison 132, Terese B. Hart 133,134, Andy Hector 135, Terry W. Henkel 136, John Herhohn 137, Mireille B. N. Hockemba 48, Bruce Hoffman 138, Milena Holmgren 139, Euridice N. Honorio Coronado 140,141, Isau Huamantupa-Chuquimaco 142, Wannes Hubau 3,40,143, Nobuo Imai 144, Mariana Victoria Urme 20, Patrick A. Jansen 145,146, Richard J. Jeffery 147, Eliana M. Jimenez 148, Tommaso Jucker 149, André Braga Junqueira 150, Michelle Kalamandeen 151, Narcisa G. Kamdem 311, Kuswata Kartawinata 152, Emmanuel Kasongi Yakusu 40,98,99, Manos M. Katembo 52, Elizabeth Karsley 54, David Kenfack 129, Michael Kessler 155, Thiri Toe Khing 156,157, Timothy J. Killeen 158, Kanehiro Kitayama 159, Bente Klittgaard 160, Nicolas Labrièvre 67, Yves Laumonier 161, Susan G. W. Launce 162, William F. Launce 162, Félix Laurent 40,97,98, Tinh Cong Le 163, Trai Trong Le 163, Miguel E. Leal 164, Evlyn Márcia Leão de Moraes Novo 165, Aurora Levesley 9, Moses B. Libalah 9,11,16, Juan C. Licona 167, Diógenes de Andrade Lima Filho 20, Jeremy A. Lindsell 168,169, Aline Lopes 170, Maria Aparecida Lopes 171, Jon C. Lovett 3,172, Richard Lowe 173, José Rafael Lozada 74, Xinghui Lu 75, Nestor K. Luamba 40,97,97,162, Bruno Garcia Luiz 85, Paul Maas 6, José Leonardo Lima Magalhães 178,78,97, William E. Magnusson 59, Ni Putu Diana Mahayani 180, Jean-Remy Makana 181, Yadvinder Mahi 22, Lorena Maniguaje Rincón 20, Asyraf Mansor 182,183, Angelo Gilberto Manzatto 184, Beatriz S. Marimon 185, Ben Hur Marimon-Junior 185, Andrew R Marshall 82,186,187, Maria Pires Martes 20, Faustin M. Mbayu 188, Marcelo Brilhante de Medeiros 189, Italo Mesones 190, Faizah Metali 191, Vianet Mihindou 192,193, Jerome Millet 194, William Milliken 195, Hugo F. Mogollón 196, Jean-François Molino 8,9, Mohd. Nizam Mohd. Said 197, Abel Monteaudekroza 185,189, Juan Carlos Montele 16,17, Walter Palacios Cuenca 207, Susamar Pansini 208, Marcelo Petratti Pansonato 20,32, Marcos Rios Paredes 46, Ekanaka Paulde 209, Daniela Pauleto 210, Richard G.षेपण 20, José Luis Marcelo Pena 211, R. Toby Pennington 93,110, Carlos A. Peres 212, Andrea Permana 213, Pascal Petronelli 214, Maria Cristina Peñuela Mora 215, Juan Fernando Phillips 216, Oliver L. Phillips 27, Georgia Pickavance 28, Maria Teresa Fernandez Piedade 189, Nigel C. A. Pritman 217, Pierre Ploton 8,9, Andreas Popelier 40,98,153, John R. Poulsen 72,218, Adriana Prieto 219, Richard B. Primack 220, Hari Priyadi 221, Lan Que 3,222, Adriano Costa Quaresma 19,22,33, Helder Lima de Queiroz 224, Hirma Ramirez-Angulo 225, José Ferreira Ramos 20, Neidiane Farias Costa Reis 208, Jan Reitsma 228, Juan David Cardenas Revilla 20, Terhi Riutta 42,227, Gonzalo Rivas-Torres 95,220, Iyan Robiansyah 229,230, Maira Rocha 19, Domingos de Jesus Rodrigues 37, M. Elizabeth Rodriguez-Ronderos 67,23,11 Francesco Rovero 232,233, Andes H. Rozak 234, Agustín Rudas 219, Ervan Rutishausier 235, Daniel Sabatier 8,9, Le Bienfaiteur Sagano 9,11,236, Adeilva Felipe Sampaio 208, Ismaya Samsoedin 237, Manichanh Satichdanh 209, Juliana Schietti 20, Jochen Schönwart 19, Veridiana Vizoni Scudeller 238, Naret Seutaturien 239, Douglas Shell 240, Rodrigo Sierra 241, Miles R. Silman 119, Thiago Sanna Freire Silva 242, José Renan da Silva Guimarães 243, Murielle Simo-Drossi 241, Marcelo Fragomeni Simon 189, Pinlo Sist 244, Thaiane R. Sousa 245, Manuelle de Sousa Farias 246,247, Luiz de Souza Coelho 20, Dominick V. Spracklen 248, Suzanne M. Stas 248, Robert Steinmetz 239, Pablo R. Stevenson 56, Juliana Stropp 249, Rahayu S. Sukri 196, Terry C. H. Sunderland 22,250, Eizi Suzuki 261, Michael D. Swaine 252, Jianwei Tang 253, James Taplin 254, David M. Taylor 231, J. Sebastian Tello 255, John Terborgh 256,257, Nicolas Texier 258, Ida Theilirade 259, Duncan W. Thomas 260, Raquel Thomas 261, Sean C. Thomas 262, Milton Tapado 241, Benjamin Toirambe 20,46,263, José Julio de Toledo 55,

Kyle W. Tomlinson 156,264, Armando Torres-Lezama 225, Hieu Dang Tran 163, John Tshibamba Mukendi 40,153,265, Roven D. Tumaneg 88,266, Maria Natalia Umaña 267, Peter M. Umunay 268,269, Ligia Estela Urrego Giraldo 270, Elvis H. Valdermann Sandoval 271,272, Luis Valenzuela Gamarra 188, Tinde R. Van Anel 6,273, Martin van de Bull 274, Jaqueline van de Pol 275, Geertje van der Heijden 276, Rodolfo Vasquez 188, César I. A. Vela 277, Eduardo Martins Venticinque 278, Hans Verbeek 279, Rizza Karen A. Veridiano 88,280, Alberto Vicentini 59, Ima Célia Guimarães Vieira 24, Emilio Vilanova Torre 225,268, Daniel Villarroel 27,281, Boris Eduardo Villa Zegarra 285, Jason Vlemminck 28,283, Patricio von Hildebrandt 284, Vincent Antoine Vos 285, Corine Vriesendorf 217, Edward L. Webb 286,287, Lee J. T. White 41,47,288, Serge Wich 289, Florian Wittmann 16,223, Roderick Zagt 290, Runguo Zang 291, Charles Eugene Zartman 20, Lise Zemagho 9,11, Eglée L. Zent 292 & Stanford Kent 292

¹Department of Geography, University College London, London, UK. ²Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, UK. ³School of Geography, University of Leeds, Leeds, UK.

⁴Department of Natural Sciences, Manchester Metropolitan University, Manchester, UK.

⁵Instituto de Biociências, Departamento de Ecologia, Universidade de Sao Paulo (USP), São Paulo, Brazil. ⁶Naturalis Biodiversity Center, Leiden, The Netherlands. ⁷Quantitative Biodiversity Dynamics, Department of Biology, Utrecht University, Utrecht, The Netherlands.

⁸AMAP, Université de Montpellier, IRD, Cirad, CNRS, INRAE, Montpellier, France. ⁹International Joint Laboratory DYCOFAC, IRD-UIY-IRGM, Yaoundé, Cameroon. ¹⁰Environmental and Life Sciences, Faculty of Science, Universiti Brunei Darussalam, Gadong, Brunei Darussalam.

¹¹Plant Systematics and Ecology Laboratory, Higher Teachers' Training College, University of Yaoundé I, Yaoundé, Cameroon. ¹²Faculty of Renewable Natural Resources Management and Faculty of Sciences, University of Kisangani, Kisangani, Democratic Republic of the Congo.

¹³Forestry Research Institute of Ghana (FORIG), Kumasi, Ghana. ¹⁴Mensuration Unit, Forestry Commission of Ghana, Kumasi, Ghana. ¹⁵Procuradoria-Geral de Justiça, Ministério Público do Estado do Amazonas, Manaus, Brazil. ¹⁶Instituto Nacional de Pesquisas da Amazônia (INPA), Manaus, Brazil. ¹⁷Medio Ambiente, PLUSRETOL, Iquitos, Peru. ¹⁸Faculty of Environmental Earth Science, Hokkaido University, Sapporo, Japan. ¹⁹Ecology, Monitoring and Sustainable Use of Wetlands (MAUA), Instituto Nacional de Pesquisas da Amazônia (INPA), Manaus, Brazil.

²⁰Coordenação de Biodiversidade, Instituto Nacional de Pesquisas da Amazônia (INPA), Manaus, Brazil. ²¹Center for Conservation and Sustainability, Smithsonian Conservation Biology Institute, Washington, DC, USA. ²²Center for International Forestry Research (CIFOR), Bogor, Indonesia. ²³Université Officielle de Bukavu, Bukavu, Democratic Republic of the Congo. ²⁴Coordenação de Botânica, Museu Paraense Emilio Goeldi, Belém, Brazil. ²⁵Projeto Dinâmica Biológica de Fragmentos Florestais, Instituto Nacional de Pesquisas da Amazônia (INPA), Manaus, Brazil. ²⁶Institut National pour l'Etude et la Recherche Agronomiques, Bukavu, Democratic Republic of the Congo. ²⁷Museo de Historia Natural Noel Kempff Mercado, Universidad Autónoma Gabriel Rene Moreno, Santa Cruz, Santa Cruz, Bolivia. ²⁸Herbario Amazonico Colombiano, Instituto SINCHI, Bogotá, Colombia. ²⁹Bullard Emeritus Professor of Forestry, Harvard University, Cambridge, MA, USA. ³⁰Programa de Ciencias del Agro y el Mar, Herbario Universitario (PORT), UNELLEZ-Guanar, Guanare, Venezuela. ³¹The Mauritian Herbarium, Agricultural Services, Ministry of Agro-Industry and Food Security, Reduit, Mauritius. ³²Instituto de Biociências, Departamento de Ecologia, Universidade de São Paulo (USP), São Paulo, Brazil. ³³Tetra Tech ARD, Accra, Ghana. ³⁴Department of Biology, Aarhus University, Aarhus C, Aarhus, Denmark. ³⁵UK Centre for Ecology and Hydrology, Penicuik, UK. ³⁶International Center for Tropical Botany, Department of Biological Sciences, Florida International University, Miami, FL, USA. ³⁷CNHCS, Federal University of Mato Grosso, Sinop, Brazil. ³⁸Lancaster Environment Centre, Lancaster University, Lancaster, UK. ³⁹TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liege, Gembloux, Belgium.

⁴⁰Service of Wood Biology, Royal Museum for Central Africa, Tervuren, Belgium. ⁴¹Ministry of Forests, Seas, Environment and Climate, Libreville, Gabon. ⁴²Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, UK.

⁴³The Landscapes and Livelihoods Group, Edinburgh, UK. ⁴⁴Institute of Tropical Forest Conservation, Mbarara University of Science and Technology (MUST), Mbarara, Uganda.

⁴⁵Isotope Bioscience Laboratory (ISOFYS), Ghent University, Ghent, Belgium. ⁴⁶Biodiversity and Landscape Unit, Gembloux Agro-Bio Tech, Université de Liège, Liège, Belgium. ⁴⁷Section de la Foresterie, Institut National pour l'Etude et la Recherche Agronomique Yangambi, Yangambi, Democratic Republic of the Congo. ⁴⁸Congo Programme, Wildlife Conservation Society, Brazzaville, Republic of Congo. ⁴⁹CIFOR, Bogor, Indonesia. ⁵⁰Forest Resources Management, Gembloux Agro-Bio Tech, University of Liège, Liège, Belgium. ⁵¹Resources and Synergies Development, Singapore, Singapore. ⁵²Laboratory of Ecology and Forest Management, Faculty of Sciences, University of Kisangani, Kisangani, Democratic Republic of the Congo. ⁵³Biodiversity, Macroecology and Biogeography, University of Göttingen, Göttingen, Germany. ⁵⁴School of Biological Sciences, University of Aberdeen, Aberdeen, UK.

⁵⁵Universidade Federal do Amapá, Ciências Ambientais, Macapá, Brazil. ⁵⁶Laboratorio de Ecología de Bosques Tropicales y Primatología, Universidad de los Andes, Bogotá, Colombia.

⁵⁷Cambridge University Botanic Garden, Cambridge, UK. ⁵⁸Programa de Maestría de Manejo de Bosques, Universidad de los Andes, Mérida, Venezuela. ⁵⁹Coordenação de Pesquisas em Ecologia, Instituto Nacional de Pesquisas da Amazônia (INPA), Manaus, Brazil.

⁶⁰Departamento de Genética, Ecologia e Evolução, Instituto de Ciências Biológicas, Belo Horizonte, Brazil. ⁶¹Centro de Investigaciones Ecológicas de Guaya, Universidad Nacional Experimental de Guayana, Puerto Ordaz, Venezuela. ⁶²Centro de Pesquisa Agroforestal de Roraima, Embrapa Roraima, Boa Vista, Brazil. ⁶³Escuela de Biología Herbario Alfredo Paredes, Universidad Central, Quito, Ecuador. ⁶⁴Biology Department, Vancouver Island Paredes, Nanaimo, British Columbia, Canada. ⁶⁵Shaanxi Key Laboratory for Animal Conservation,
Declan L. M. Cooper¹,²,³, Simon L. Lewis¹,³,⁴, Martin J. P. Sullivan 3,4, Paulo I. Prado 5,6, Hans ter Steege 7, Nicolas Barbier 8,9, Ferry Slik 10, Bonaventure Sonke 11, 12, Corneille E. N. Ewango 12, Stephen Adu-Bredi 13, Kofi Affium-Baffoe 14, Daniel P. P. de Aguiar 15,16, Manuel Augusto Ahuite Reategui 17, Shin-Ichoi Aiba 18, Maria Weiss Albuquerque 19, Francisca Dionizia de Almeida Matos 20, Alfonso Alonso 21, Christian A. Amani 22,23, Dário Dantas do Amaral 24, lêda Leão do Amaral 20, Anandra 25, Irres Paula de Andrade Miranda 26, Ilondea B. Angoboy 27, Alejandro Araujo-Murakami 27, Nicolás Castaño Arboleda 28, Luzmila Arroyo 27, Peter Ashton 29, Gerardo A. Aymard C30,: Philip R. Baker 3, Michael Bessie Balinga 33, Henrik Batslev 34, Lindsay F. Banin 35, Olaf S. Bánki 5, Chris Baraloto 36, Edelcilo Bionema Barbosa 20, Flávia Rodrigues Barbosa 37, Jos Barlow 38, Jean-Francois Bastin 39, Hans Beekman 40, Rogerene 8,311, Natacha Nssi Bengone 3, Erika Berenguer 38,42, Nicholas Berry 43, Robert Bitariho 44, Pascal Boeckx 45, Jan Bogaert 46, Bernard Bonyoma 47, Patrick Boundja 22,48, Nils Bourland 40,49,50,51, Faustin Boyemba Bosela 52, Fabian Brambach 53, Roel Briën 54, David F. R. P. Burslem 54, José Luis Camargo 25, Wegliane Campelo 55, Angela Cano 56,57, Sasha Cárdenas 56, Dairon Cárdenas López 28, Rainiellen de Sá Carpanedo 57, Yra Andreina Carrero Márquez 26, Fernanda Antunes Carvalho 59,60, Luisa Fernanda Casas 56, Hernán Castellanos 51, Carolina V. Castilho 62, Carlos Cerón 63, Colin A. Chapman 64,56,66, Jerome Chave 57, Pourhin Chhang 58, Wanlop Chutipong 59, George B. Chuyong 70, Bruno Barçante Ladvocat Craña 71, Connie J. Clark 72, Fernanda Coelho de Souza 59,73,74, James A. Comiskey 75,76, David A. Coomes 77, Fernando Cornejo Valverde 78, Diego F. Correa 56,77, Flávia R. C. Costa 59, Janaina Barbosa Pedrosa Costa 80, Pierre Couteron 8,9, Heike Culmsee 81, Aida Cuni-Sanchez 82,83, Francisco Dallmeier 21, Gabriel Damasco 84, Gilles Dauby 8,9, Nállaret Dévila 45, Hilda Pauleta Dévila Doña 85, Jose Don T. De Alban 67,88, Rafael L. de Assis 89, Charles de Canniere 90, Thales de Haulleville 40, Marcelo de Jesus Veiga Carim 91, Layon O. Demeritz 91, Kyle G. Dexter 92, Anthony Di Fiore 49,95, Hazimah Haji Mohammad Din 94, Mathias I. Disney 91, Brice Yannick Djojafack 40,97,98, Marie-Noël K. Djikouko 97, Tran Van Do 99, Jean-Louis Doucet 100, Freddie C. Draper 101, Vincent Drossaix 8,9, Joost F. Duivenvoorden 102, Julien Engel 8,103, Vittoria Estienne 48, William Farfan-Rios 104,105, Sophie Fauset 106, Kenneth J. Feeley 107,108, Yuri Oliveira Feitosa 109, Ted R. Feldpausch 73,110, Cid Ferreira 20,93, Jooce Ferreira 11, Leandro Villa Ferreira 24, Christine D. Fletcher 117, Bernardo Monteiro Flores 113, Alusine Fofanah 114, Ernest G. Folí 131, Émile Fonty 115,16, Gabriella M. Fredriksson 117, Alfredo Fuentes 105,118, David Galbraith 3, George Pepe Gallardo Gonzales 86, Karina Garcia-Cabreta 119, Roosevelt Agro-Villacorta 120,121, Vitor H. F. Gomes 122,123, Ricardo Zárate Gómez 124, Therany Gonzales 125, Rogerio Gribel 20, Marcelino Carneiro Guedes 80, Juan Ernesto Guevara 126,127, Khalid Rahman Hakeem 128, Jefferson S. Hall 129, Keith C. Hamer 130, Alan C. Hamilton 131, David J. Harris 93, Rhett D. Harrison 132, Terese B. Hart 133,134, Andy Hector 135, Terry W. Henkel 136, John Herborn 137, Mireille B. N. Hockemba 48, Bruce Hoffman 138, Milena Holmgren 139, Euridice N. Honorio Coronado 140,141, Isau Huamantupa-Chuquimaco 142, Wannes Hubau 3,40,143, Nobuo Imai 144, Mariana Victoria Urume 20, Patrick A. Jansen 145,146, Richard J. Jeffery 147, Eliana M. Jimenez 148, Tommaso Jucker 149, André Braga Junqueira 150, Michelle Kalamandeetan 151, Narcisa G. Kamdem 311, Kuswata Kartawinata 152, Emmanuel Kasongi Yakusu 40,98,99, Manos M. Katembo 52, Elizabeth Karsley 54, David Kenfack 129, Michael Kessler 155, Thiri Toe Khing 56,157, Timothy J. Killeen 158, Kanehiro Kitayama 159, Bente Klittgaard 160, Nicolas Labrièrre 67, Yves Laumonier 161, Susan G. W. Laurnance 162, William F. Laursence 162, Félix Laurent 40,97,98, Tinh Cong Le 163, Trai Trong Le 163, Miguel E. Leal 164, Evlyn Márcia Leão de Moraes Novo 165, Aurora Levesley 9, Moses B. Libalah 9,11,16, Juan C. Licona 167, Diógenes de Andrade Lima Filho 20, Jeremy A. Lindsell 168,169, Aline Lopes 170, Maria Aparecida Lopes 171, Jon C. Lovett 3,177, Richard Lowe 173, José Rafael Lozada 74, Xinghui Lu 75, Nestor K. Luamba 40,97,97,162, Bruno Garcia Luiz 85, Paul Maas 6, José Leonardo Lima Magalhães 178,78,97, William E. Magnusson 59, Ni Putu Diana Mahayani 180, Jean-Remy Makana 181, Yadvinder Mahi 221, Lorena Maniguaje Rincón 20, Asyraf Mansor 182,183, Angelo Gilberto Manzatto 184, Beatriz S. Marimon 185, Ben Hur Marimon-Junior 185, Andrew R Marshall 82,186,187, Maria Pires Martes 20, Faustin M. Mbayu 188, Marcelo Brilhante de Medeiros 189, Italo Mesones 190, Faizah Metali 191, Vianet Mihindou 192,193, Jerome Millet 194, William Milliken 195, Hugo F. Mogollón 196, Jean-François Molino 8,9, Mohd. Nizam Mohd. Said 197, Abel Monteagudo de Queiroz 188,190, Juan Carlos Montele 16,17, Walter Palacios Cuenca 207, Susamar Pansini 208, Marcelo Petratti Pansonato 20,32, Marcos Rios Paredes 46, Ekanaka Paulde 209, Daniela Pauleto 210, Richard G. Pearson 2, José Luis Marcelo Pena 211, R. Toby Pennington 93,110, Carlos A. Peres 212, Andrea Permana 213, Pascal Petronelli 214, Maria Cristina Peñuela Mora 215, Juan Fernando Phillips 216, Oliver L. Phillips 2, Georgia Pickavance 2, Maria Teresa Fernandez Piedade 19, Nigel C. A. Pritman 217, Pierre Ploton 8,9, Andreas Popelier 40,98,153, John R. Poulsen 72,218, Adriana Prieto 219, Richard B. Primack 220, Hari Priyadi 221, Lan Que 3,222, Adriano Costa Quaresma 19,22,33, Helder Lima de Queiroz 224, Hirma Ramirez-Angulo 225, José Ferreira Ramos 20, Neidiane Farias Costa Reis 208, Jan Reitsma 228, Juan David Cardenas Revilla 20, Terhi Riutta 42,227, Gonzalo Rivas-Torres 95,220, Iyan Robiansyah 229,230, Maira Rocha 19, Domingos de Jesus Rodrigues 37, M. Elizabeth Rodriguez-Ronderos 67,231, Francesco Rovero 232,233, Andes H. Rozak 234, Agustín Rudas 219, Ervan Rutishausier 235, Daniel Sabatier 8,9, Le Bienfaiteur Sagano 9,11,236, Adeilva Felipe Sampaio 208, Ismaya Samsoedin 237, Manichanh Satichdanh 209, Juliana Schietti 20, Jochen Schönig 19, Veridiana Vizoni Scudeller 238, Naret Seutaturien 239, Douglas Shell 240, Rodrigo Sierra 241, Miles R. Silman 119, Thiago Sanna Freire Silva 242, José Renan da Silva Guimarães 243, Murielle Simo-Droissart 311, Marcelo Fragomeni Simon 189, Pinlo Sist 244, Thaiane R. Sousa 245, Manuelle de Sousa Farias 246,247, Luiz de Souza Coelho 20, Dominick V. Spracklen 248, Suzanne M. Stas 248, Robert Steinmetz 239, Pablo R. Stevenson 56, Juliana Stropp 249, Rahayu S. Sukri 196, Terry C. H. Sunderland 22,250, Eizi Suzuki 261, Michael D. Swaine 252, Jianwei Tang 253, James Taplin 254, David M. Taylor 231, J. Sebastian Tello 255, John Terborgh 256,257, Nicolas Texier 258, Ida Theilirade 259, Duncan W. Thomas 260, Raquel Thomas 261, Sean C. Thomas 262, Milton Toirado 241, Benjamin Toirambe 20,263, José Julio de Toledo 55,

Kyle W. Tomlinson 156,264, Armando Torres-Lezama 225, Hieu Dang Tran 163, John Tshibamba Mukendi 40,153,265, Roven D. Tumaneg 88,266, Maria Natalia Umaña 267, Peter M. Umunay 268,269, Ligia Estela Urrego Giraldo 270, Elvis H. Valdermann Sandoval 271,272, Peter Valenzuela Gamarra 188, Tinde R. Van Anel 6,273, Martin van de Bull 274, Jaqueline van de Pol 275, Geertje van der Heijden 276, Rodolfo Vasquez 188, César I. A. Vela 277, Eduardo Martins Venticinque 278, Hans Verbeek 279, Rizza Karen A. Veridiano 88,280, Alberto Vicentini 59, Ima Célia Guimarães Vieira 24, Emilio Vilanova Torre 225,268, Daniel Villarroel 27,281, Boris Eduardo Villa Zegarra 285, Jason Vlemminckx 28,283, Patricio von Hildebrandt 284, Vincent Antoine Vos 285, Corine Vriesendorf 217, Edward L. Webb 286,287, Lee J. T. White 41,47,288, Serge Wich 289, Florian Wittmann 16,223, Roderick Zagt 290, Rungo Zang 291, Charles Eugene Zartman 20, Lise Zemagho 9,11, Eglée L. Zent 292 & Stanford Kent 292

¹Department of Geography, University College London, London, UK. ²Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, UK. ³School of Geography, University of Leeds, Leeds, UK.

⁴Department of Natural Sciences, Manchester Metropolitan University, Manchester, UK.

⁵Instituto de Biociências, Departamento de Ecologia, Universidade de Sao Paulo (USP), São Paulo, Brazil. ⁶Naturalis Biodiversity Center, Leiden, The Netherlands. ⁷Quantitative Biodiversity Dynamics, Department of Biology, Utrecht University, Utrecht, The Netherlands.

⁸AMAP, Université de Montpellier, IRD, Cirad, CNRS, INRAE, Montpellier, France. ⁹International Joint Laboratory DYCOFAC, IRD-UYI-IRGM, Yaoundé, Cameroon. ¹⁰Environmental and Life Sciences, Faculty of Science, Universiti Brunei Darussalam, Gadong, Brunei Darussalam.

¹¹Plant Systematics and Ecology Laboratory, Higher Teachers' Training College, University of Yaoundé I, Yaoundé, Cameroon. ¹²Faculty of Renewable Natural Resources Management and Faculty of Sciences, University of Kisangani, Kisangani, Democratic Republic of the Congo.

¹³Forestry Research Institute of Ghana (FORIG), Kumasi, Ghana. ¹⁴Mensuration Unit, Forestry Commission of Ghana, Kumasi, Ghana. ¹⁵Procuradoria-Geral de Justiça, Ministério Público do Estado do Amazonas, Manaus, Brazil. ¹⁶Instituto Nacional de Pesquisas da Amazônia (INPA), Manaus, Brazil. ¹⁷Medio Ambiente, PLUSRETOL, Iquitos, Peru. ¹⁸Faculty of Environmental Earth Science, Hokkaido University, Sapporo, Japan. ¹⁹Ecology, Monitoring and Sustainable Use of Wetlands (MAUA), Instituto Nacional de Pesquisas da Amazônia (INPA), Manaus, Brazil.

²⁰Coordenação de Biodiversidade, Instituto Nacional de Pesquisas da Amazônia (INPA), Manaus, Brazil. ²¹Center for Conservation and Sustainability, Smithsonian Conservation Biology Institute, Washington, DC, USA. ²²Center for International Forestry Research (CIFOR), Bogor, Indonesia. ²³Université Officielle de Bukavu, Bukavu, Democratic Republic of the Congo. ²⁴Coordenação de Botânica, Museu Paraense Emilio Goeldi, Belém, Brazil. ²⁵Projeto Dinâmica Biológica de Fragmentos Florestais, Instituto Nacional de Pesquisas da Amazônia (INPA), Manaus, Brazil. ²⁶Institut National pour l'Etude et la Recherche Agronomiques, Bukavu, Democratic Republic of the Congo. ²⁷Museo de Historia Natural Noel Kempff Mercado, Universidad Autónoma Gabriel Rene Moreno, Santa Cruz, Bolivia. ²⁸Herbario Amazonico Colombiano, Instituto SINCHI, Bogotá, Colombia. ²⁹Bullard Emeritus Professor of Forestry, Harvard University, Cambridge, MA, USA. ³⁰Programa de Ciencias del Agro y el Mar, Herbario Universitario (PORT), UNELLEZ-Guanar, Guanare, Venezuela. ³¹The Mauritian Herbarium, Agricultural Services, Ministry of Agro-Industry and Food Security, Reduit, Mauritius. ³²Instituto de Biociências, Departamento de Ecologia, Universidade de São Paulo (USP), São Paulo, Brazil. ³³Tetra Tech ARD, Accra, Ghana. ³⁴Department of Biology, Aarhus University, Aarhus C, Aarhus, Denmark. ³⁵UK Centre for Ecology and Hydrology, Penicuik, UK. ³⁶International Center for Tropical Botany, Department of Biological Sciences, Florida International University, Miami, FL, USA. ³⁷CNHCS, Federal University of Mato Grosso, Sinop, Brazil. ³⁸Lancaster Environment Centre, Lancaster University, Lancaster, UK. ³⁹TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liege, Gembloux, Belgium.

⁴⁰Service of Wood Biology, Royal Museum for Central Africa, Tervuren, Belgium. ⁴¹Ministry of Forests, Seas, Environment and Climate, Libreville, Gabon. ⁴²Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, UK.

⁴³The Landscapes and Livelihoods Group, Edinburgh, UK. ⁴⁴Institute of Tropical Forest Conservation, Mbarara University of Science and Technology (MUST), Mbarara, Uganda.

⁴⁵Isotope Bioscience Laboratory (ISOFYS), Ghent University, Ghent, Belgium. ⁴⁶Biodiversity and Landscape Unit, Gembloux Agro-Bio Tech, Université de Liège, Liège, Belgium. ⁴⁷Section de la Foresterie, Institut National pour l'Etude et la Recherche Agronomique Yangambi, Yangambi, Democratic Republic of the Congo. ⁴⁸Congo Programme, Wildlife Conservation Society, Brazzaville, Republic of Congo. ⁴⁹CIFOR, Bogor, Indonesia. ⁵⁰Forest Resources Management, Gembloux Agro-Bio Tech, University of Liège, Liège, Belgium. ⁵¹Resources and Synergies Development, Singapore, Singapore. ⁵²Laboratory of Ecology and Forest Management, Faculty of Sciences, University of Kisangani, Kisangani, Democratic Republic of the Congo. ⁵³Biodiversity, Macroecology and Biogeography, University of Göttingen, Göttingen, Germany. ⁵⁴School of Biological Sciences, University of Aberdeen, Aberdeen, UK.

⁵⁵Universidade Federal do Amapá, Ciências Ambientais, Macapá, Brazil. ⁵⁶Laboratorio de Ecología de Bosques Tropicales y Primatología, Universidad de los Andes, Bogotá, Colombia.

⁵⁷Cambridge University Botanic Garden, Cambridge, UK. ⁵⁸Programa de Maestría de Manejo de Bosques, Universidad de los Andes, Mérida, Venezuela. ⁵⁹Coordenação de Pesquisas em Ecologia, Instituto Nacional de Pesquisas da Amazônia (INPA), Manaus, Brazil.

⁶⁰Departamento de Genética, Ecologia e Evolução, Instituto de Ciências Biológicas, Belo Horizonte, Brazil. ⁶¹Centro de Investigaciones Ecológicas de Guaya, Universidad Nacional Experimental de Guayana, Puerto Ordaz, Venezuela. ⁶²Centro de Pesquisa Agroforestal de Roraima, Embrapa Roraima, Boa Vista, Brazil. ⁶³Escuela de Biodiía Herbario Alfredo Paredes, Universidad Central, Quito, Ecuador. ⁶⁴Biology Department, Vancouver Island Paredes, Nanaimo, British Columbia, Canada. ⁶⁵Shaanxi Key Laboratory for Animal Conservation,
Northwest University, Xi'an, China.⁶⁶School of Life Sciences, University of KwaZulu-Natal, Scottsville, South Africa.⁶⁷Laboratoire Évolution et Diversité Biologique, CNRS and Université Paul Sabatier, Toulouse, France.⁶⁸Institute of Forest and Wildlife Research and Development (IRD), Phnom Penh, Cambodia.⁶⁹Conservation Ecology Program, King Mongkut's University of Technology Thonburi, Bangkok, Thailand.⁷⁰Faculty of Science, Department of Plant Science, University of Buea, Buea, Cameroon.⁷¹Instituto de Biociências, Departamento Botânica, Universidade de São Paulo (USP), São Paulo, Brazil.⁷²Nicholas School of the Environment, Duke University, Durham, NC, USA.⁷³University of Leeds, Leeds, UK.⁷⁴BeZero, London, UK.⁷⁵Inventory and Monitoring Program, National Park Service, Fredericksburg, VA, USA.⁷⁶Smithsonian Conservation Biology Institute, Washington, DC, USA.⁷⁷Department of Plant Sciences and Conservation Research Institute, University of Cambridge, Cambridge, UK.⁷⁸Andes to Amazon Biodiversity Program, Madre de Dios, Madre de Dios, Peru.⁷⁹The University of Queensland, Brisbane, Queensland, Australia.⁸⁰Empresa Brasileira de Pesquisa Agropecuária, Embrapa Amapá, Macapá, Brazil.⁸¹State Agency for Environment, Nature Conservation and Geology, Güstrow, Germany.⁸²Department of Environment and Geography, University of York, York, UK.⁸³Department of International Environmental and Development Studies (NORAGRIC), Norwegian University of Life Sciences, Ås, Norway.⁸⁴Gothenburg Global Biodiversity Centre, University of Gothenburg, Gothenburg, Sweden.⁸⁵Departamento de Biologia Vegetal, Instituto de Biologia, Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil.⁸⁶Servicios de Biodiversidad EIRL, Iquitos, Peru.⁸⁷Centre for Nature-Based Climate Solutions, Department of Biological Sciences, National University of Singapore, Singapore, Singapore.⁸⁸Phillipines Programme, Fauna and Flora International, Cambridge, UK.⁸⁹Biodiversity and Ecosystem Services, Instituto Tecnológico Vale, Belém, Brazil.⁹⁰Landscape Ecology and Vegetal Production Systems Unit, Universite Libre de Bruxelles, Brussels, Belgium.⁹¹Departamento de Botânica, Instituto de Pesquisas Científicas e Tecnológicas do Amapá (IEPA), Macapá, Brazil.⁹²School of Geosciences, University of Edinburgh, Edinburgh, UK.⁹³Royal Botanic Garden Edinburgh, Edinburgh, UK.⁹⁴Department of Anthropology, University of Texas at Austin, Austin, TX, USA.⁹⁵Estación de Biodiversidad Tiputini, Colegio de Ciencias Biodiversas y Ambientales, Universidad San Francisco de Quito (USFQ), Quito, Ecuador.⁹⁶Institute for Biodiversity and Environmental Research, Universiti Brunei Darussalam, Bandar Seri Begawan, Brunei Darussalam.⁹⁷Institut National pour l'Etude et la Recherche Agronomiques (NERA), Wood Laboratory of Yangambi, Yangambi, Democratic Republic of the Congo.⁹⁸UGent-Woodlab, Laboratory of Wood Technology, Department of Environment, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium.⁹⁹Silviculture Research Institute, Vietnamese Academy of Forest Sciences, Hanoi, Vietnam.¹⁰⁰Forest Is Life, TERRA, Gembloux Agro-Bio Tech, Liège University, Liège, Belgium.¹⁰¹Department of Geography and Planning, University of Liverpool, Liverpool, UK.¹⁰²Institute of Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, The Netherlands.¹⁰³Florida International University, Miami, FL, USA.¹⁰⁴Living Earth Collaborative, Washington University in Saint Louis, St Louis, MO, USA.¹⁰⁵Missouri Botanical Garden, St Louis, MO, USA.¹⁰⁶School of Geography, Earth and Environmental Sciences, University of Plymouth, Plymouth, UK.¹⁰⁷Department of Biology, University of Miami, Coral Gables, FL, USA.¹⁰⁸Fairchild Tropical Botanic Garden, Coral Gables, FL, USA.¹⁰⁹Programa de Pós-Graduação em Biologia (Botânica), Instituto Nacional de Pesquisas da Amazônia (INPA), Manaus, Brazil.¹¹⁰Department of Geography, College of Life and Environmental Sciences, University of Exeter, Exeter, UK.¹¹¹Empresa Brasileira de Pesquisa Agropecuária, Embrapa Amazônia Oriental, Belém, Brazil.¹¹²Forest Research Institute Malaysia, Kepong, Malaysia.¹¹³Postgraduate Program in Ecology, Federal University of Santa Catarina, Florianópolis, Brazil.¹¹⁴The Gola Rainforest National Park, Kenema, Sierra Leone.¹¹⁵Direction Régionale de la Guyane, Office National des Forés, Cayenne, French Guiana.¹¹⁶Université de Montpellier, Montpellier, France.¹¹⁷Pro Natura Foundation, Balikpapan, Indonesia.¹¹⁸Herbario Nacional de Bolivia, Instituto de Ecología, Carrera de Biología, Universidad Mayor de San Andrés, La Paz, Bolivia.¹¹⁹Biology Department and Center for Energy, Environment and Sustainability, Wake Forest University, Winston Salem, NC, USA.¹²⁰Programa Restauración de Ecosistemas (PRE), Centro de Innovación Científica Amazónica (CINCIA), Tambopata, Peru.¹²¹Peruvian Center for Biodiversity and Conservation (PCBC), Iquitos, Peru.¹²²Escola de Negócios Tecnológicos e Inovação, Centro Universitário do Pará, Belém, Brazil.¹²³Universidade Federal do Pará, Belém, Brazil.¹²⁴PROTERRA, Instituto de Investigaciones de la Amazonía Peruana (IIAP), Iquitos, Peru.¹²⁵ACEER Foundation, Puerto Maldonado, Peru.¹²⁶Grupo de Investigación en Biodiversidad, Medio Ambiente y Salud-BIOMAS, Universidad de las Américas, Quito, Ecuador.¹²⁷The Field Museum, Chicago, IL, USA.¹²⁸Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia.¹²⁹Forest Global Earth Observatory (ForestGEO), Smithsonian Tropical Research Institute, Washington, DC, USA.¹³⁰School of Biology, University of Leeds, Leeds, UK.¹³¹Honorary Professor, Kunming Institute of Botany, Chinese Academy of Science, Kunming, China.¹³²World Agroforestry, Lusaka, Zambia.¹³³Lukuru Wildlife Research Foundation, Kinshasa, Democratic Republic of the Congo.¹³⁴Division of Vertebrate Zoology, Yale Peabody Museum of Natural History, New Haven, CT, USA.¹³⁵Department of Plant Sciences, University of Oxford, Oxford, UK.¹³⁶Department of Biological Sciences, California State Polytechnic University, Humboldt, Arcata, CA, USA.¹³⁷Tropical Forests and People Research Centre, University of the Sunshine Coast, Maroochydore DC, Queensland, Australia.¹³⁸Amazon Conservation Team, Arlington, USA.¹³⁹Resource Ecology Group, Wageningen University and Research, Wageningen, The Netherlands.¹⁴⁰Instituto de Investigaciones de la Amazonía Peruana (IIAP), Iquitos, Peru.¹⁴¹University of St Andrews, St Andrews, UK.¹⁴²Herbario HAG, Universidad Nacional Amazónica de Madre de Dios (UNAMAD), Puerto Maldonado, Peru.¹⁴³Department of Environment, Laboratory of Wood Technology (Woodlab), Ghent University, Ghent, Belgium.¹⁴⁴Department of Forest Science, Tokyo University of Agriculture, Tokyo, Japan.¹⁴⁵Smithsonian Tropical Research Institute, Ancon, Panama.¹⁴⁶Department of

Environmental Sciences, Wageningen University and Research, Wageningen, The Netherlands.¹⁴⁷Department of Biological and Environmental Sciences, University of Stirling, Stirling, UK.¹⁴⁸Grupo de Ecología y Conservación de Fauna y Flora Silvestre, Instituto Amazónico de Investigaciones Imani, Universidad Nacional de Colombia sede Amazonía, Leticia, Colombia.¹⁴⁹School of Biological Sciences, University of Bristol, Bristol, UK.¹⁵⁰Instituto de Ciencia i Tecnologia Ambientals, Universitat Autònoma de Barcelona, Barcelona, Spain.¹⁵¹School of Earth, Environment and Society, McMaster University, Hamilton, Ontario, Canada.¹⁵²Integrative Research Center, The Field Museum of Natural History, Chicago, IL, USA.¹⁵³Faculté de Gestion de Ressources Naturelles Renouvelables, Université de Kisangani, Kisangani, Democratic Republic of the Congo.¹⁵⁴Computational and Applied Vegetation Ecology (CAVelab), Department of Environment, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium.¹⁵⁵Department of Systematic and Evolutionary Botany, University of Zurich, Zurich, Switzerland.¹⁵⁶Center for Integrative Conservation, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Menglun, Mengla, China.¹⁵⁷University of the Chinese Academy of Sciences, Beijing, China.¹⁵⁸Agteka—Amazonica, Santa Cruz, Bolivia.¹⁵⁹Graduate School of Agriculture, Kyoto University, Kyoto, Japan.¹⁶⁰Department for Accelerated Taxonomy, Royal Botanic Gardens, Richmond, UK.¹⁶¹Forest and Environment Program, Center for International Forestry Research (CIFOR), Bogor, Indonesia.¹⁶²Centre for Tropical Environmental and Sustainability Science and College of Science and Engineering, James Cook University, Cairns, Queensland, Australia.¹⁶³Viet Nature Conservation Centre, Hanoi, Viet Nam.¹⁶⁴Uganda Programme, Wildlife Conservation Society, Kampala, Uganda.¹⁶⁵Divisa dos Campos, Brazil.¹⁶⁶Department of Plant Biology, Faculty of Science, University of Yaoundé I, Yaoundé, Cameroon.¹⁶⁷Instituto Boliviano de Investigación Forestal, Santa Cruz, Santa Cruz, Bolivia.¹⁶⁸The RSPB, Sandy, UK.¹⁶⁹A Rocha International, Cambridge, UK.¹⁷⁰Department of Ecology, Institute of Biological Sciences, University of Brasília, Brasília, Brazil.¹⁷¹Instituto de Ciências Biológicas, Universidade Federal do Pará, Belém, Brazil.¹⁷²Herbarium, Royal Botanic Gardens Kew, Richmond, UK.¹⁷³Botany Department, University of Ibadan, Ibadan, Nigeria.¹⁷⁴Facultad de Ciencias Forestales e Ambientales, Instituto de Investigaciones para el Desarrollo Forestal, Universidad de los Andes, Mérida, Mérida, Venezuela.¹⁷⁵Institute of Forest Ecology, Environment and Protection, Chinese Academy of Forestry, Beijing, China.¹⁷⁶Faculty of Renewable Natural Resources Management, University of Kisangani, Kisangani, Democratic Republic of the Congo.¹⁷⁷Faculté des sciences Agronomiques, Université Officière de Mbujimayi, Mbujimayi, Democratic Republic of the Congo.¹⁷⁸Programa de Pós-Graduação em Ecologia, Universidade Federal do Pará, Belém, Brazil.¹⁷⁹Embrapa Amazônia Oriental, Belém, Brazil.¹⁸⁰Faculty of Forestry, Universitas Gadjah Mada, Yogyakarta, Indonesia.¹⁸¹Faculté des Sciences, Laboratoire d'Écologie et Aménagement Forestier, Université de Kisangani, Kisangani, Democratic Republic of the Congo.¹⁸²School of Biological Sciences, Universiti Sains Malaysia, George Town, Malaysia.¹⁸³Centre for Marine and Coastal Studies, Universiti Sains Malaysia, George Town, Malaysia.¹⁸⁴Departamento de Biologia, Universidade Federal de Rondônia, Unir, Porto Velho, Brazil.¹⁸⁵Programa de Pós-Graduação em Ecologia e Conservação, Universidade do Estado de Mato Grosso, Nova Xavantina, Brazil.¹⁸⁶Flamingo Land, Kirby Misperton, UK.¹⁸⁷Forest Research Institute, University of the Sunshine Coast, Sippy Downs, Queensland, Australia.¹⁸⁸Jardín Botánico de Missouri, Oxapampa, Peru.¹⁸⁹Embrapa Recursos Genéticos e Biotecnologia, Brasília, Brazil.¹⁹⁰Department of Integrative Biology, University of California, Berkeley, CA, USA.¹⁹¹Environmental and Life Sciences Programme, Faculty of Science, Universities Brunei Darussalam, Bandar Seri Begawan, Brunei Darussalam.¹⁹²Agence Nationale des Parcs Nationaux, Libreville, Gabon.¹⁹³Ministère de la Forêt, de la Mer, de l'Environnement, Chargé du Plan Climat, Libreville, Gabon.¹⁹⁴Office français de la biodiversité, Vincennes, France.¹⁹⁵Department for Ecosystem Stewardship, Royal Botanic Gardens, Richmond, UK.¹⁹⁶Endangered Species Coalition, Silver Spring, MD, USA.¹⁹⁷Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi, Malaysia.¹⁹⁸Herbario Vargas, Universidad Nacional de San Antonio Abad del Cusco, Cuzco, Peru.¹⁹⁹Facultad de Ciencias Agrícolas, Universidad Autónoma Gabriel René Moreno, Santa Cruz, Santa Cruz, Bolivia.²⁰⁰Department of Environment and Development Studies, United International University, Dhaka, Bangladesh.²⁰¹Department of Ecosystems and Conservation, Sokone University of Agriculture, Morogoro, Tanzania.²⁰²The Kyoto University Museum, Kyoto University, Kyoto, Japan.²⁰³Laboratório de Ciências Ambientais, Universidade Estadual do Norte Fluminense, Campos dos Goyatacazes, Brazil.²⁰⁴Universidad Estatal Amazónica, Puyo, Ecuador.²⁰⁵Forest Research Centre, Sandakan, Malaysia.²⁰⁶University of Abeokuta, Abeokuta, Nigeria.²⁰⁷Herbario Nacional del Ecuador, Universidad Técnica del Norte, Quito, Ecuador.²⁰⁸Programa de Pós-Graduação em Biodiversidade e Biotecnologia PPG-Bionorte, Universidade Federal de Rondônia, Porto Velho, Brazil.²⁰⁹Centre for Mountain Ecosystem Studies, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, China.²¹⁰Instituto de Biodiversidade e Florestas, Universidade Federal do Oeste do Pará, Santarém, Brazil.²¹¹Universidad Nacional de Jaén, Cajamarca, Peru.²¹²School of Environmental Sciences, University of East Anglia, Norwich, UK.²¹³University of Warwick, Warwick, UK.²¹⁴Cirad UMR Ecofog, AgrosParisTech, CNRS, INRAE, Université Guyane, Kourou Cedex, France.²¹⁵Universidad Regional Amazónica IKIAM, Tena, Ecuador.²¹⁶Fundación Puerto Rastrojo, Bogotá, Colombia.²¹⁷Science and Education, The Field Museum, Chicago, IL, USA.²¹⁸The Nature Conservancy, Boulder, CO, USA.²¹⁹Instituto de Ciencias Naturales, Universidad Nacional de Colombia, Bogotá, Colombia.²²⁰Biology Department, Boston University, Boston, MA, USA.²²¹Department of Resource and Environmental Economics (ESL), IPB University, Bogor, Indonesia.²²²School of Life Sciences, University of Lincoln, Lincoln, UK.²²³Wetland Department, Institute of Geography and Geocology, Karlruhe Institute of Technology (KIT), Rastatt, Germany.²²⁴Dirección Técnico-Científica, Instituto de Desenvolvimento Sustentável Mamirauá, Tefé, Brazil.²²⁵Instituto de Investigaciones para el Desarrollo Forestal (INDEFOR), Universidad de los Andes, Mérida, Mérida, Venezuela.
Northwest University, Xi'an, China.⁶⁶School of Life Sciences, University of KwaZulu-Natal, Scottsville, South Africa.⁶⁷Laboratoire Évolution et Diversité Biologique, CNRS and Université Paul Sabatier, Toulouse, France.⁶⁸Institute of Forest and Wildlife Research and Development (IRD), Phnom Penh, Cambodia.⁶⁹Conservation Ecology Program, King Mongkut's University of Technology Thonburi, Bangkok, Thailand.⁷⁰Faculty of Science, Department of Plant Science, University of Buea, Buea, Cameroon.⁷¹Instituto de Biociências, Departamento Botânica, Universidade de São Paulo (USP), São Paulo, Brazil.⁷²Nicholas School of the Environment, Duke University, Durham, NC, USA.⁷³University of Leeds, Leeds, UK.⁷⁴BeZero, London, UK.⁷⁵Inventory and Monitoring Program, National Park Service, Fredericksburg, VA, USA.⁷⁶Smithsonian Conservation Biology Institute, Washington, DC, USA.⁷⁷Department of Plant Sciences and Conservation Research Institute, University of Cambridge, Cambridge, UK.⁷⁸Andes to Amazon Biodiversity Program, Madre de Dios, Madre de Dios, Peru.⁷⁹The University of Queensland, Brisbane, Queensland, Australia.⁸⁰Empresa Brasileira de Pesquisa Agropecuária, Embrapa Amapá, Macapá, Brazil.⁸¹State Agency for Environment, Nature Conservation and Geology, Güstrow, Germany.⁸²Department of Environment and Geography, University of York, York, UK.⁸³Department of International Environmental and Development Studies (NORAGRIC), Norwegian University of Life Sciences, Ås, Norway.⁸⁴Gothenburg Global Biodiversity Centre, University of Gothenburg, Gothenburg, Sweden.⁸⁵Departamento de Biologia Vegetal, Instituto de Biologia, Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil.⁸⁶Servicios de Biodiversidad EIRL, Iquitos, Peru.⁸⁷Centre for Nature-Based Climate Solutions, Department of Biological Sciences, National University of Singapore, Singapore, Singapore.⁸⁸Phillipines Programme, Fauna and Flora International, Cambridge, UK.⁸⁹Biodiversity and Ecosystem Services, Instituto Tecnológico Vale, Belém, Brazil.⁹⁰Landscape Ecology and Vegetal Production Systems Unit, Universite Libre de Bruxelles, Brussels, Belgium.⁹¹Departamento de Botânica, Instituto de Pesquisas Científicas e Tecnológicas do Amapá (IEPA), Macapá, Brazil.⁹²School of Geosciences, University of Edinburgh, Edinburgh, UK.⁹³Royal Botanic Garden Edinburgh, Edinburgh, UK.⁹⁴Department of Anthropology, University of Texas at Austin, Austin, TX, USA.⁹⁵Estación de Biodiversidad Tiputini, Colegio de Ciencias Biodiversas y Ambientales, Universidad San Francisco de Quito (USFQ), Quito, Ecuador.⁹⁶Institute for Biodiversity and Environmental Research, Universiti Brunei Darussalam, Bandar Seri Begawan, Brunei Darussalam.⁹⁷Institut National pour l'Etude et la Recherche Agronomiques (INAERA), Wood Laboratory of Yangambi, Yangambi, Democratic Republic of the Congo.⁹⁸UGent-Woodlab, Laboratory of Wood Technology, Department of Environment, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium.⁹⁹Silviculture Research Institute, Vietnamese Academy of Forest Sciences, Hanoi, Vietnam.¹⁰⁰Forest Is Life, TERRA, Gembloux Agro-Bio Tech, Liège University, Liège, Belgium.¹⁰¹Department of Geography and Planning, University of Liverpool, Liverpool, UK.¹⁰²Institute of Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, The Netherlands.¹⁰³Florida International University, Miami, FL, USA.¹⁰⁴Living Earth Collaborative, Washington University in Saint Louis, St Louis, MO, USA.¹⁰⁵Missouri Botanical Garden, St Louis, MO, USA.¹⁰⁶School of Geography, Earth and Environmental Sciences, University of Plymouth, Plymouth, UK.¹⁰⁷Department of Biology, University of Miami, Coral Gables, FL, USA.¹⁰⁸Fairchild Tropical Botanic Garden, Coral Gables, FL, USA.¹⁰⁹Programa de Pós-Graduação em Biologia (Botânica), Instituto Nacional de Pesquisas da Amazônia (INPA), Manaus, Brazil.¹¹⁰Department of Geography, College of Life and Environmental Sciences, University of Exeter, Exeter, UK.¹¹¹Empresa Brasileira de Pesquisa Agropecuária, Embrapa Amazônia Oriental, Belém, Brazil.¹¹²Forest Research Institute Malaysia, Kepong, Malaysia.¹¹³Postgraduate Program in Ecology, Federal University of Santa Catarina, Florianópolis, Brazil.¹¹⁴The Gola Rainforest National Park, Kenema, Sierra Leone.¹¹⁵Direction Régionale de la Guyane, Office National des Forés, Cayenne, French Guiana.¹¹⁶Université de Montpellier, Montpellier, France.¹¹⁷Pro Natura Foundation, Balikpapan, Indonesia.¹¹⁸Herbario Nacional de Bolivia, Instituto de Ecología, Carrera de Biología, Universidad Mayor de San Andrés, La Paz, Bolivia.¹¹⁹Biology Department and Center for Energy, Environment and Sustainability, Wake Forest University, Winston Salem, NC, USA.¹²⁰Programa Restauración de Ecosistemas (PRE), Centro de Innovación Científica Amazónica (CINCIA), Tambopata, Peru.¹²¹Peruvian Center for Biodiversity and Conservation (PCBC), Iquitos, Peru.¹²²Escola de Negócios Tecnológicos e Inovação, Centro Universitário do Pará, Belém, Brazil.¹²³Universidade Federal do Pará, Belém, Brazil.¹²⁴PROTERRA, Instituto de Investigaciones de la Amazonía Peruana (IIAP), Iquitos, Peru.¹²⁵ACEER Foundation, Puerto Maldonado, Peru.¹²⁶Grupo de Investigación en Biodiversidad, Medio Ambiente y Salud-BIOMAS, Universidad de las Américas, Quito, Ecuador.¹²⁷The Field Museum, Chicago, IL, USA.¹²⁸Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia.¹²⁹Forest Global Earth Observatory (ForestGEO), Smithsonian Tropical Research Institute, Washington, DC, USA.¹³⁰School of Biology, University of Leeds, Leeds, UK.¹³¹Honorary Professor, Kunming Institute of Botany, Chinese Academy of Science, Kunming, China.¹³³World Agroforestry, Lusaka, Zambia.¹³³Lukuru Wildlife Research Foundation, Kinshasa, Democratic Republic of the Congo.¹³⁴Division of Vertebrate Zoology, Yale Peabody Museum of Natural History, New Haven, CT, USA.¹³⁵Department of Plant Sciences, University of Oxford, Oxford, UK.¹³⁶Department of Biological Sciences, California State Polytechnic University, Humboldt, Arcata, CA, USA.¹³⁷Tropical Forests and People Research Centre, University of the Sunshine Coast, Maroochydore DC, Queensland, Australia.¹³⁸Amazon Conservation Team, Arlington, USA.¹³⁹Resource Ecology Group, Wageningen University and Research, Wageningen, The Netherlands.¹⁴⁰Instituto de Investigaciones de la Amazonía Peruana (IIAP), Iquitos, Peru.¹⁴¹University of St Andrews, St Andrews, UK.¹⁴²Herbario HAG, Universidad Nacional Amazónica de Madre de Dios (UNAMAD), Puerto Maldonado, Peru.¹⁴³Department of Environment, Laboratory of Wood Technology (Woodlab), Ghent University, Ghent, Belgium.¹⁴⁴Department of Forest Science, Tokyo University of Agriculture, Tokyo, Japan.¹⁴⁵Smithsonian Tropical Research Institute, Ancon, Panama.¹⁴⁶Department of

Environmental Sciences, Wageningen University and Research, Wageningen, The Netherlands.¹⁴⁷Department of Biological and Environmental Sciences, University of Stirling, Stirling, UK.¹⁴⁸Grupo de Ecología y Conservación de Fauna y Flora Silvestre, Instituto Amazónico de Investigaciones Imani, Universidad Nacional de Colombia sede Amazonía, Leticia, Colombia.¹⁴⁹School of Biological Sciences, University of Bristol, Bristol, UK.¹⁵⁰Instituto de Ciencia i Tecnologia Ambientals, Universitat Autònoma de Barcelona, Barcelona, Spain.¹⁵¹School of Earth, Environment and Society, McMaster University, Hamilton, Ontario, Canada.¹⁵²Integrative Research Center, The Field Museum of Natural History, Chicago, IL, USA.¹⁵³Faculté de Gestion de Ressources Naturelles Renouvelables, Université de Kisangani, Kisangani, Democratic Republic of the Congo.¹⁵⁴Computational and Applied Vegetation Ecology (CAVelab), Department of Environment, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium.¹⁵⁵Department of Systematic and Evolutionary Botany, University of Zurich, Zurich, Switzerland.¹⁵⁶Center for Integrative Conservation, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Menglun, Mengla, China.¹⁵⁷University of the Chinese Academy of Sciences, Beijing, China.¹⁵⁸Agteka—Amazonica, Santa Cruz, Bolivia.¹⁵⁹Graduate School of Agriculture, Kyoto University, Kyoto, Japan.¹⁶⁰Department for Accelerated Taxonomy, Royal Botanic Gardens, Richmond, UK.¹⁶¹Forest and Environment Program, Center for International Forestry Research (CIFOR), Bogor, Indonesia.¹⁶²Centre for Tropical Environmental and Sustainability Science and College of Science and Engineering, James Cook University, Cairns, Queensland, Australia.¹⁶³Viet Nature Conservation Centre, Hanoi, Viet Nam.¹⁶⁴Uganda Programme, Wildlife Conservation Society, Kampala, Uganda.¹⁶⁵Divisa dos Campos, Brazil.¹⁶⁶Department of Plant Biology, Faculty of Science, University of Yaoundé I, Yaoundé, Cameroon.¹⁶⁷Instituto Boliviano de Investigación Forestal, Santa Cruz, Santa Cruz, Bolivia.¹⁶⁸The RSPB, Sandy, UK.¹⁶⁹A Rocha International, Cambridge, UK.¹⁷⁰Department of Ecology, Institute of Biological Sciences, University of Brasília, Brasília, Brazil.¹⁷¹Instituto de Ciências Biológicas, Universidade Federal do Pará, Belém, Brazil.¹⁷²Herbarium, Royal Botanic Gardens Kew, Richmond, UK.¹⁷³Botany Department, University of Ibadan, Ibadan, Nigeria.¹⁷⁴Facultad de Ciencias Forestales e Ambientales, Instituto de Investigaciones para el Desarrollo Forestal, Universidad de los Andes, Mérida, Mérida, Venezuela.¹⁷⁵Institute of Forest Ecology, Environment and Protection, Chinese Academy of Forestry, Beijing, China.¹⁷⁶Faculty of Renewable Natural Resources Management, University of Kisangani, Kisangani, Democratic Republic of the Congo.¹⁷⁷Faculté des sciences Agronomiques, Université Officière de Mbujimayi, Mbujimayi, Democratic Republic of the Congo.¹⁷⁸Programa de Pós-Graduação em Ecologia, Universidade Federal do Pará, Belém, Brazil.¹⁷⁹Embrapa Amazônia Oriental, Belém, Brazil.¹⁸⁰Faculty of Forestry, Universitas Gadjah Mada, Yogyakarta, Indonesia.¹⁸¹Faculté des Sciences, Laboratoire d'Écologie et Aménagement Forestier, Université de Kisangani, Kisangani, Democratic Republic of the Congo.¹⁸²School of Biological Sciences, Universiti Sains Malaysia, George Town, Malaysia.¹⁸³Centre for Marine and Coastal Studies, Universiti Sains Malaysia, George Town, Malaysia.¹⁸⁴Departamento de Biologia, Universidade Federal de Rondônia, Unir, Porto Velho, Brazil.¹⁸⁵Programa de Pós-Graduação em Ecologia e Conservação, Universidade do Estado de Mato Grosso, Nova Xavantina, Brazil.¹⁸⁶Flamingo Land, Kirby Misperton, UK.¹⁸⁷Forest Research Institute, University of the Sunshine Coast, Sippy Downs, Queensland, Australia.¹⁸⁸Jardín Botánico de Missouri, Oxapampa, Peru.¹⁸⁹Embrapa Recursos Genéticos e Biotecnologia, Brasília, Brazil.¹⁹⁰Department of Integrative Biology, University of California, Berkeley, CA, USA.¹⁹¹Environmental and Life Sciences Programme, Faculty of Science, Universiti Brunei Darussalam, Bandar Seri Begawan, Brunei Darussalam.¹⁹²Agence Nationale des Parcs Nationaux, Libreville, Gabon.¹⁹³Ministère de la Forêt, de la Mer, de l'Environnement, Chargé du Plan Climat, Libreville, Gabon.¹⁹⁴Office français de la biodiversité, Vincennes, France.¹⁹⁵Department for Ecosystem Stewardship, Royal Botanic Gardens, Richmond, UK.¹⁹⁶Endangered Species Coalition, Silver Spring, MD, USA.¹⁹⁷Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi, Malaysia.¹⁹⁸Herbario Vargas, Universidad Nacional de San Antonio Abad del Cusco, Cuzco, Peru.¹⁹⁹Facultad de Ciencias Agrícolas, Universidad Autónoma Gabriel René Moreno, Santa Cruz, Santa Cruz, Bolivia.²⁰⁰Department of Environment and Development Studies, United International University, Dhaka, Bangladesh.²⁰¹Department of Ecosystems and Conservation, Sokone University of Agriculture, Morogoro, Tanzania.²⁰²The Kyoto University Museum, Kyoto University, Kyoto, Japan.²⁰³Laboratório de Ciências Ambientais, Universidade Estadual do Norte Fluminense, Campos dos Goyatacazes, Brazil.²⁰⁴Universidad Estatal Amazónica, Puyo, Ecuador.²⁰⁵Forest Research Centre, Sandakan, Malaysia.²⁰⁶University of Abeokuta, Abeokuta, Nigeria.²⁰⁷Herbario Nacional del Ecuador, Universidad Técnica del Norte, Quito, Ecuador.²⁰⁸Programa de Pós-Graduação em Biodiversidade e Biotecnologia PPG-Bionorte, Universidade Federal de Rondônia, Porto Velho, Brazil.²⁰⁹Centre for Mountain Ecosystem Studies, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, China.²¹⁰Instituto de Biodiversidade e Florestas, Universidade Federal do Oeste do Pará, Santarém, Brazil.²¹¹Universidad Nacional de Jaén, Cajamarca, Peru.²¹²School of Environmental Sciences, University of East Anglia, Norwich, UK.²¹³University of Warwick, Warwick, UK.²¹⁴Cirad UMR Ecofog, AgrosParisTech, CNRS, INRAE, Université Guyane, Kourou Cedex, France.²¹⁵Universidad Regional Amazónica IKIAM, Tena, Ecuador.²¹⁶Fundación Puerto Rastrojo, Bogotá, Colombia.²¹⁷Science and Education, The Field Museum, Chicago, IL, USA.²¹⁸The Nature Conservancy, Boulder, CO, USA.²¹⁹Instituto de Ciencias Naturales, Universidad Nacional de Colombia, Bogotá, Colombia.²²⁰Biology Department, Boston University, Boston, MA, USA.²²¹Department of Resource and Environmental Economics (ESL), IPB University, Bogor, Indonesia.²²²School of Life Sciences, University of Lincoln, Lincoln, UK.²²³Wetland Department, Institute of Geography and Geocology, Karlruhe Institute of Technology (KIT), Rastatt, Germany.²²⁴Dirección Técnico-Científica, Instituto de Desenvolvimento Sustentável Mamirauá, Tefé, Brazil.²²⁵Instituto de Investigaciones para el Desarrollo Forestal (INDEFOR), Universidad de los Andes, Mérida, Mérida, Venezuela.
²²⁶Waardenburg Ecology, Culemborg, The Netherlands. ²²⁷College of Life Sciences, University of Exeter, Exeter, UK. ²²⁸University of Florida, Gainesville, FL, USA. ²²⁹Department of Biological Sciences, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia. ²³⁰Center for Plant Conservation Bogor Botanic Gardens, Indonesian Institute of Science, Bogor, Indonesia.

²³¹Department of Geography, National University of Singapore, Singapore, Singapore.

²³²Department of Biology, University of Florence, Sesto Fiorentino, Italy. ²³³Tropical Biodiversity Section, Museo delle Scienze (MUSE), Trento, Italy. ²³⁴Research Center for Plant Conservation, Botanic Gardens and Forestry, National Research and Innovation Agency (BRIN), Bogor, Indonesia. ²³⁵InfoFlora, Botanical Garden of Geneva, Geneva, Switzerland. ²³⁶Institute of the Environment and Sustainability, University of California, Los Angeles, CA, USA. ²³⁷Forest Research and Development Center, Research, Development and Innovation Agency, Ministry of Environment and Forestry, Bogor, Indonesia. ²³⁸Departamento de Biologia, Universidade Federal do Amazonas (UFAM)—Instituto de Ciências Biológicas (ICB1), Manaus, Brazil. ²³⁹World Wildlife Fund Thailand, Bangkok, Thailand. ²⁴⁰Forest Ecology and Forest Management Group, Wageningen University and Research, Wageningen, The Netherlands. ²⁴¹GeoIS, Quito, Ecuador. ²⁴²Biological and Environmental Sciences, University of Stirling, Stirling, UK.

²⁴³Amcel Amapá Florestal e Celulose SA, Santana, Brazil. ²⁴⁴Cirad-ES, Campus International de Baillarguet, TA C-105/D, Montpellier, France. ²⁴⁵Programa de Pós-Graduação em Ecologia, Instituto Nacional de Pesquisas da Amazônia (INPA), Manaus, Brazil. ²⁴⁶Laboratório de Ecologia de Doenças Transmissíveis da Amazônia (EDTA), Instituto Leônicas e Maria Deane, Fiocruz, Manaus, Brazil. ²⁴⁷Instituto Oswaldo Cruz (IOC/FIOCRUZ), Rio de Janeiro, Brazil.

²⁴⁸School of Earth and Environment, University of Leeds, Leeds, UK. ²⁴⁹Biogeography Department, Trier University, Trier, Germany. ²⁵⁰Faculty of Forestry, University of British Columbia, Vancouver, British Columbia, Canada. ²⁵¹Research Center for the Pacific Islands, Kagoshima University, Kagoshima, Japan. ²⁵²Department of Plant and Soil Science, School of Biological Sciences, University of Aberdeen, Aberdeen, UK. ²⁵³Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, China. ²⁵⁴UK Research and Innovation, Innovate UK, London, UK. ²⁵⁵Center for Conservation and Sustainable Development, Missouri Botanical Garden, St Louis, MO, USA. ²⁵⁶Department of Biology and Florida Museum of Natural History, University of Florida, Gainesville, FL, USA. ²⁵⁷James Cook University, Cairns, Queensland, Australia. ²⁵⁸Université Libre de Bruxelles, Brussels, Belgium. ²⁵⁹Department of Food and Resource Economics, University of Copenhagen, Copenhagen, Denmark. ²⁶⁰School of Biological Sciences, Washington State University, Vancouver, WA, USA. ²⁶¹Iwokrama

International Centre for Rain Forest Conservation and Development, Georgetown, Guyana.

²⁶²Institute of Forestry and Conservation, University of Toronto, Toronto, Ontario, Canada.

²⁶³Ministère de l'Environnement et Développement Durable, Kinshasa, Democratic Republic of the Congo. ²⁶⁴Center of Conservation Biology, Core Botanical Gardens, Chinese Academy of Sciences, Menglun, China. ²⁶⁵Faculté des Sciences Appliquées, Université de Mbujimayi, Mbujimayi, Democratic Republic of the Congo. ²⁶⁶Emerging Technology Development Division, Department of Science and Technology Philippine Council for Industry, Energy and Emerging Technology Research and Development (DOST-PCIEERD), Taguig City, Philippines.

²⁶⁷Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA. ²⁶⁸Wildlife Conservation Society, New York, NY, USA. ²⁶⁹Yale School of Forestry and Environmental Studies, Yale University, New Haven, CT, USA. ²⁷⁰Departamento de Ciencias Forestales, Universidad Nacional de Colombia, Medellín, Colombia. ²⁷¹Department of Biology, University of Missouri, St Louis, MO, USA. ²⁷²Universidad Nacional de la Amazonía Peruana, Iquitos, Peru. ²⁷³Wageningen University, Wageningen, The Netherlands. ²⁷⁴Doi Tung Development Project, Social Development Department, Chiang Rai, Thailand. ²⁷⁵Compagnie des Bois du Gabon, Port Gentil, Gabon. ²⁷⁶University of Nottingham, Nottingham, UK. ²⁷⁷Escuela Profesional de Ingeniería Forestal, Universidad Nacional de San Antonio Abad del Cusco, Puerto Maldonado, Peru. ²⁷⁸Centro de Biociências, Departamento de Ecologia, Universidade Federal do Rio Grande do Norte, Natal, Brazil. ²⁷⁹CAVELab—Computational and Applied Vegetation Ecology, Department of Environment, Ghent University, Ghent, Belgium.

²⁸⁰FORLIANCE, Bonn, Germany. ²⁸¹Fundación Amigos de la Naturaleza (FAN), Santa Cruz, Bolivia. ²⁸²Dirección de Evaluación Forestal y de Fauna Silvestre, Magdalena del Mar, Peru.

²⁸³Faculté des Sciences, Service d'Évolution Biologique et Écologie, Université Libre de Bruxelles, Brussels, Belgium. ²⁸⁴Fundación Estación de Biología, Bogotá, Colombia.

²⁸⁵Instituto de Investigaciones Forestales de la Amazonía, Universidad Autónoma del Beni José Ballivián, Riberalta, Beni, Bolivia. ²⁸⁶Viikki Tropical Resources Institute, Department of Forest Sciences, University of Helsinki, Helsinki, Finland. ²⁸⁷Helsinki Institute of Sustainability Science (HELSUS), Helsinki, Finland. ²⁸⁸Institut de Recherche en Écologie Tropicale, Libreville, Gabon. ²⁸⁹School of Biological and Environmental Sciences, Liverpool John Moores University, Liverpool, UK. ²⁹⁰Tropenbos International, Ede, The Netherlands. ²⁹¹Key Laboratory of Forest Ecology and Environment of State Forestry Administration, Institute of Forest Ecology, Environment and Protection, Chinese Academy of Forestry, Beijing, China. ²⁹²Laboratory of Human Ecology, Instituto Venezolano de Investigaciones Científicas (IVIC), Caracas, Venezuela. ²⁹³Deceased: Cid Ferreira.
²²⁶Waardenburg Ecology, Culemborg, The Netherlands. ²²⁷College of Life Sciences, University of Exeter, Exeter, UK. ²²⁸University of Florida, Gainesville, FL, USA. ²²⁹Department of Biological Sciences, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia. ²³⁰Center for Plant Conservation Bogor Botanic Gardens, Indonesian Institute of Science, Bogor, Indonesia.

²³¹Department of Geography, National University of Singapore, Singapore, Singapore.

²³²Department of Biology, University of Florence, Sesto Fiorentino, Italy. ²³³Tropical Biodiversity Section, Museo delle Scienze (MUSE), Trento, Italy. ²³⁴Research Center for Plant Conservation, Botanic Gardens and Forestry, National Research and Innovation Agency (BRIN), Bogor, Indonesia. ²³⁵InfoFlora, Botanical Garden of Geneva, Geneva, Switzerland. ²³⁶Institute of the Environment and Sustainability, University of California, Los Angeles, CA, USA. ²³⁷Forest Research and Development Center, Research, Development and Innovation Agency, Ministry of Environment and Forestry, Bogor, Indonesia. ²³⁸Departamento de Biologia, Universidade Federal do Amazonas (UFAM)—Instituto de Ciências Biológicas (ICB1), Manaus, Brazil. ²³⁹World Wildlife Fund Thailand, Bangkok, Thailand. ²⁴⁰Forest Ecology and Forest Management Group, Wageningen University and Research, Wageningen, The Netherlands. ²⁴¹GeoIS, Quito, Ecuador. ²⁴²Biological and Environmental Sciences, University of Stirling, Stirling, UK.

²⁴³Amcel Amapá Florestal e Celulose SA, Santana, Brazil. ²⁴⁴Cirad-ES, Campus International de Baillarguet, TA C-105/D, Montpellier, France. ²⁴⁵Programa de Pós-Graduação em Ecologia, Instituto Nacional de Pesquisas da Amazônia (INPA), Manaus, Brazil. ²⁴⁶Laboratório de Ecologia de Doenças Transmissíveis da Amazônia (EDTA), Instituto Leônicas e Maria Deane, Fiocruz, Manaus, Brazil. ²⁴⁷Instituto Oswaldo Cruz (IOC/FIOCRUZ), Rio de Janeiro, Brazil.

²⁴⁸School of Earth and Environment, University of Leeds, Leeds, UK. ²⁴⁹Biogeography Department, Trier University, Trier, Germany. ²⁵⁰Faculty of Forestry, University of British Columbia, Vancouver, British Columbia, Canada. ²⁵¹Research Center for the Pacific Islands, Kagoshima University, Kagoshima, Japan. ²⁵²Department of Plant and Soil Science, School of Biological Sciences, University of Aberdeen, Aberdeen, UK. ²⁵³Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, China. ²⁵⁴UK Research and Innovation, Innovate UK, London, UK. ²⁵⁵Center for Conservation and Sustainable Development, Missouri Botanical Garden, St Louis, MO, USA. ²⁵⁶Department of Biology and Florida Museum of Natural History, University of Florida, Gainesville, FL, USA. ²⁵⁷James Cook University, Cairns, Queensland, Australia. ²⁵⁸Université Libre de Bruxelles, Brussels, Belgium. ²⁵⁹Department of Food and Resource Economics, University of Copenhagen, Copenhagen, Denmark. ²⁶⁰School of Biological Sciences, Washington State University, Vancouver, WA, USA. ²⁶¹Iwokrama

International Centre for Rain Forest Conservation and Development, Georgetown, Guyana.

²⁶²Institute of Forestry and Conservation, University of Toronto, Toronto, Ontario, Canada.

²⁶³Ministère de l'Environnement et Développement Durable, Kinshasa, Democratic Republic of the Congo. ²⁶⁴Center of Conservation Biology, Core Botanical Gardens, Chinese Academy of Sciences, Menglun, China. ²⁶⁵Faculté des Sciences Appliquées, Université de Mbujimayi, Mbujimayi, Democratic Republic of the Congo. ²⁶⁶Emerging Technology Development Division, Department of Science and Technology Philippine Council for Industry, Energy and Emerging Technology Research and Development (DOST-PCIEERD), Taguig City, Philippines.

²⁶⁷Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA. ²⁶⁸Wildlife Conservation Society, New York, NY, USA. ²⁶⁹Yale School of Forestry and Environmental Studies, Yale University, New Haven, CT, USA. ²⁷⁰Departamento de Ciencias Forestales, Universidad Nacional de Colombia, Medellín, Colombia. ²⁷¹Department of Biology, University of Missouri, St Louis, MO, USA. ²⁷²Universidad Nacional de la Amazonía Peruana, Iquitos, Peru. ²⁷³Wageningen University, Wageningen, The Netherlands. ²⁷⁴Doi Tung Development Project, Social Development Department, Chiang Rai, Thailand. ²⁷⁵Compagnie des Bois du Gabon, Port Gentil, Gabon. ²⁷⁶University of Nottingham, Nottingham, UK. ²⁷⁷Escuela Profesional de Ingeniería Forestal, Universidad Nacional de San Antonio Abad del Cusco, Puerto Maldonado, Peru. ²⁷⁸Centro de Biociências, Departamento de Ecologia, Universidade Federal do Rio Grande do Norte, Natal, Brazil. ²⁷⁹CAVELab—Computational and Applied Vegetation Ecology, Department of Environment, Ghent University, Ghent, Belgium.

²⁸⁰FORLIANCE, Bonn, Germany. ²⁸¹Fundación Amigos de la Naturaleza (FAN), Santa Cruz, Bolivia. ²⁸²Dirección de Evaluación Forestal y de Fauna Silvestre, Magdalena del Mar, Peru.

²⁸³Faculté des Sciences, Service d'Évolution Biologique et Écologie, Université Libre de Bruxelles, Brussels, Belgium. ²⁸⁴Fundación Estación de Biología, Bogotá, Colombia.

²⁸⁵Instituto de Investigaciones Forestales de la Amazonía, Universidad Autónoma del Beni José Ballivián, Riberalta, Beni, Bolivia. ²⁸⁶Viikki Tropical Resources Institute, Department of Forest Sciences, University of Helsinki, Helsinki, Finland. ²⁸⁷Helsinki Institute of Sustainability Science (HELSUS), Helsinki, Finland. ²⁸⁸Institut de Recherche en Écologie Tropicale, Libreville, Gabon. ²⁸⁹School of Biological and Environmental Sciences, Liverpool John Moores University, Liverpool, UK. ²⁹⁰Tropenbos International, Ede, The Netherlands. ²⁹¹Key Laboratory of Forest Ecology and Environment of State Forestry Administration, Institute of Forest Ecology, Environment and Protection, Chinese Academy of Forestry, Beijing, China. ²⁹²Laboratory of Human Ecology, Instituto Venezolano de Investigaciones Científicas (IVIC), Caracas, Venezuela. ²⁹³Deceased: Cid Ferreira.
# Methods

## Data compilation and pre-processing

We collated data from forest inventory plots ≥0.2 ha in size, situated in structurally intact (no detectable past 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. Following Sullivan et al.⁶¹, small (≤0.5 ha) plots within 1 km of each other were grouped for analysis to minimize the effect of stochastic tree fall events in smaller areas⁶². These criteria allow direct comparisons to be made with hyperdominance results from Amazonia⁶,²¹. The data from each continent comprise the following:

Africa: 483 plots, covering a total of 504 ha (mean plot area 1.04 ha, median 1 ha, range 0.2–10 ha). These data are from four sources: 299 plots from the African Tropical Rainforest Observatory Network¹,⁶³ (AfriTRON: www.afritron.org, accessed 1 March 2020), curated at http://www.ForestPlots.net⁶⁴; 127 plots from the Central African Plot Network (https://central-african-plot-network.netlift.app); 52 plots from the TEAM network²; and 5 × 1 ha plots from 5 different soil types, extracted from one 50-ha plot in Korup, Cameroon from the SIGEO/CTFS network³.

Amazonia: 1,417 plots, covering a total of 1,591 ha (mean plot area 1.12 ha, median 1 ha, range 0.1–78.8 ha) from the Amazon Tree Diversity Network (ATDN: http://atdn.myspecies.info/, includes plots from the RAINFOR network), accessed 8 January 2020.

Southeast Asia: 230 plots, covering a total of 202 ha (mean plot area 0.88 ha, median 0.49 ha, range 0.21–4.5 ha). These data are from two sources: 143 plots from Slik et al.⁴,²⁵—a decrease from the published Indo-Pacific dataset in Slik et al.⁴,²⁵ due to our ≥80% species identification criterion and our Southeast Asia study region excluding Australia, India, and Papua New Guinea; and 87 plots from the T-Forces network⁶⁴ curated at http://www.ForestPlots.net, accessed 03/02/2021.

Species names were checked for orthography and standardized (synonyms identified from the reference databases corrected to their accepted names) using the African Flowering Plants Database (https://www.ville-ge.ch/musinfo/bd/cjb/africa), Taxonomic Name Resolution Service⁶⁵, and Asian Plant Synonym Lookup (F. Slik, personal communication), for Africa, Amazonia and Southeast Asia, respectively. Trees not identified to species level (7.3%, 6.3% and 8.4% of stems in the Africa, Southeast Asia datasets respectively) were classed as 'indeterminate' (Indet). Indet stems contributed to plot-level and dataset-wide stem abundance totals but are necessarily absent from species totals.

For the purposes of our study we delimited tropical forests according to the 'tropical and subtropical moist broadleaf forests' biome delineation from the World Wildlife Fund ecoregion map⁶⁰. The total number of tropical trees ≥10 cm trunk diameter in each of our regions was then estimated by summing tree abundances in countries in which we have at least one sampled plot from the 'map of Global Tree Density'⁶⁶ (derived from 429,775 ground-based estimates of tree density) and masking according to the 'tropical and subtropical moist broadleaf forests' borders using ArcGIS v3.10.1⁶⁷. Thus, we estimate that there are ~92 billion, ~331 billion trees, and ~217 billion trees in our Africa, Amazonia, and Southeast Asia regions, respectively, totalling 640 billion trees. Including abundance from countries in the 'tropical and subtropical moist broadleaf forests' biome in which we have no sampled plots, we estimate ~799 billion total trees across all of Earth's moist tropical forests.

## Data format, commonness and diversity parameters

The species abundance distribution (SAD), defined as a vector of abundances (number of individuals observed) of all species encountered in a community¹⁷, formed the basis for our analyses of the three tropical forest datasets. For each dataset, we tallied the number of trees of each species in each plot to give plot-level SADs and combined these SADs across all plots to get regional-level abundance matrices with rows representing plots, columns representing species, and entries

representing the abundance of each species in each plot. To capture patterns of commonness and species composition we calculated the number of hyperdominants (H#), defined as the minimum number of species required to account for 50% of the population of an assemblage⁶, hyperdominant species identities, total number of species (TS), hyperdominant percentage of total species (H% = H#/TS) and Fisher's α (ref. 68). To investigate the sensitivity of results to the 'hyperdominant' definition of the most common species, we looked beyond the 50% threshold used for hyperdominance, at the minimum number of species required to account for 10%, 20%, 30%, ..., 90% of the population, here termed 'dominants'.

## Sampling standardization, subsampling and comparison of continental data

We identified variations in the number of plots, stems, and species, and the size and spatial clustering of plots as potential confounding factors liable to skew dominance and diversity results from our regional datasets and impede rigorous comparisons between them. We used sample-based rarefaction to quantify and account for the effect of differences in sample size (number of plots and stems) on our diversity measures of interest; namely species richness, number, ranking and identity of hyperdominants, hyperdominant percentage of total species, and Fisher's α. To quantify the effect of plot size, which is smaller in Southeast Asia data (mean 0.88 ha, median 0.49 ha) than in Amazonia and Africa data (both mean -1 ha, median 1 ha) we compared results from the full data to those from plots >0.9 ha. We found that small plots (≤1 ha) inflate per-plot species totals relative to larger plots (because the rate of encountering new species is higher the smaller the plot size; Extended Data Fig. 1), so we limited our analyses to plots >0.9 ha to enable like-for-like comparison.

For Africa, we retained 368 plots covering 450 ha (mean plot area 1.22 ha, median 1 ha, range 0.92–10 ha; 2% of plots 0.9–0.99 ha, 88% of plots 1 ha, 8% of plots 1.01–5 ha, 1% of plots >5 ha) with mean temperature of 24.3 °C (range 16.2–27.6 °C), mean annual precipitation 1,802 mm yr⁻¹, (range 1,066–2,747 mm yr⁻¹), and mean elevation of 511 m above sea level (range 41–2,070 m) per WorldClim⁶⁹. For Amazonia we retained 1,097 plots covering 1,434 ha (mean plot area 1.31 ha, median 1 ha, range 0.9–78.8 ha; 2% of plots 0.9–0.99 ha, 90% of plots 1 ha, 7% of plots 1.01–5 ha, 1% of plots >5 ha) with mean temperature of 26.0 °C (range 20.9–27.6 °C), mean annual precipitation 2,397 mm yr⁻¹ (range 1,119–4,284 mm yr⁻¹), and mean elevation of 154 m (range 0–1,142 m). For Southeast Asia we retained 103 plots covering 164 ha (mean plot area 1.59 ha, median 1 ha, range 0.96–4.5 ha; 1% of plots 0.9–0.99 ha, 48% of plots 1 ha, 52% of plots 1.01–5 ha, 0% of plots >5 ha) with mean temperature of 25.7 °C (range 20.1–27.5 °C), mean precipitation 2,680 mm yr⁻¹ (range 1,466–3,941 mm yr⁻¹), and mean elevation of 288 m (range 10–934 m). We assessed if the remaining differences in plot size affected the results, using only the 1 ha plots from Africa (n = 323) and Amazonia (n = 988), rarefied to the size of the Asia dataset, again finding near-identical per cent hyperdominance on the two continents (Africa: 7.30%, 95% confidence interval: 6.56–8.04; Amazonia: 7.35%, 95% confidence interval: 6.61–8.10).

To quantify the effect of the spatial clustering of plots, we compared results from the full Amazonia data, as the largest dataset, to those from subsets of the Amazonia data in which 1,2,3,...,10 plots were sampled from each spatial cluster. We found that spatial clustering had a negligible and not statistically significant effect on hyperdominant percentage and fitted values of Fisher's α (Extended Data Fig. 2). Therefore, we retain all plots for our analyses to maximize sample sizes. Computation of percentage hyperdominance and dominance accounts for the effects of variations in species richness on the number of hyperdominants and dominants.

For sample-based rarefaction, 200 subsamples of 1, 2, ..., Nₚ plots were drawn, without replacement, from the Nₚ total number of plots in the pth dataset, the stems contained in each subsample were pooled,
# Methods

## Data compilation and pre-processing

We collated data from forest inventory plots ≥0.2 ha in size, situated in structurally intact (no detectable past 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. Following Sullivan et al.⁶¹, small (≤0.5 ha) plots within 1 km of each other were grouped for analysis to minimize the effect of stochastic tree fall events in smaller areas⁶². These criteria allow direct comparisons to be made with hyperdominance results from Amazonia⁶,²¹. The data from each continent comprise the following:

Africa: 483 plots, covering a total of 504 ha (mean plot area 1.04 ha, median 1 ha, range 0.2–10 ha). These data are from four sources: 299 plots from the African Tropical Rainforest Observatory Network¹,⁶³ (AfriTRON: www.afritron.org, accessed 1 March 2020), curated at http://www.ForestPlots.net⁶⁴; 127 plots from the Central African Plot Network (https://central-african-plot-network.netlift.app); 52 plots from the TEAM network²; and 5 × 1 ha plots from 5 different soil types, extracted from one 50-ha plot in Korup, Cameroon from the SIGEO/CTFS network³.

Amazonia: 1,417 plots, covering a total of 1,591 ha (mean plot area 1.12 ha, median 1 ha, range 0.1–78.8 ha) from the Amazon Tree Diversity Network (ATDN: http://atdn.myspecies.info/, includes plots from the RAINFOR network), accessed 8 January 2020.

Southeast Asia: 230 plots, covering a total of 202 ha (mean plot area 0.88 ha, median 0.49 ha, range 0.21–4.5 ha). These data are from two sources: 143 plots from Slik et al.⁴,²⁵—a decrease from the published Indo-Pacific dataset in Slik et al.⁴,²⁵ due to our ≥80% species identification criterion and our Southeast Asia study region excluding Australia, India, and Papua New Guinea; and 87 plots from the T-Forces network⁶⁴ curated at http://www.ForestPlots.net, accessed 03/02/2021.

Species names were checked for orthography and standardized (synonyms identified from the reference databases corrected to their accepted names) using the African Flowering Plants Database (https://www.ville-ge.ch/musinfo/bd/cjb/africa), Taxonomic Name Resolution Service⁶⁵, and Asian Plant Synonym Lookup (F. Slik, personal communication), for Africa, Amazonia and Southeast Asia, respectively. Trees not identified to species level (7.3%, 6.3% and 8.4% of stems in the Africa, Southeast Asia datasets respectively) were classed as ‘indeterminate’ (Indet). Indet stems contributed to plot-level and dataset-wide stem abundance totals but are necessarily absent from species totals.

For the purposes of our study we delimited tropical forests according to the 'tropical and subtropical moist broadleaf forests' biome delineation from the World Wildlife Fund ecoregion map⁶⁰. The total number of tropical trees ≥10 cm trunk diameter in each of our regions was then estimated by summing tree abundances in countries in which we have at least one sampled plot from the 'map of Global Tree Density'⁶⁶ (derived from 429,775 ground-based estimates of tree density) and masking according to the 'tropical and subtropical moist broadleaf forests' borders using ArcGIS v3.10.1⁶⁷. Thus, we estimate that there are ~92 billion, ~331 billion trees, and ~217 billion trees in our Africa, Amazonia, and Southeast Asia regions, respectively, totalling 640 billion trees. Including abundance from countries in the 'tropical and subtropical moist broadleaf forests' biome in which we have no sampled plots, we estimate ~799 billion total trees across all of Earth's moist tropical forests.

## Data format, commonness and diversity parameters

The species abundance distribution (SAD), defined as a vector of abundances (number of individuals observed) of all species encountered in a community¹⁷, formed the basis for our analyses of the three tropical forest datasets. For each dataset, we tallied the number of trees of each species in each plot to give plot-level SADs and combined these SADs across all plots to get regional-level abundance matrices with rows representing plots, columns representing species, and entries

representing the abundance of each species in each plot. To capture patterns of commonness and species composition we calculated the number of hyperdominants (H#), defined as the minimum number of species required to account for 50% of the population of an assemblage⁶, hyperdominant species identities, total number of species (TS), hyperdominant percentage of total species (H% = H#/TS) and Fisher's α (ref. 68). To investigate the sensitivity of results to the 'hyperdominant' definition of the most common species, we looked beyond the 50% threshold used for hyperdominance, at the minimum number of species required to account for 10%, 20%, 30%, ..., 90% of the population, here termed 'dominants'.

## Sampling standardization, subsampling and comparison of continental data

We identified variations in the number of plots, stems, and species, and the size and spatial clustering of plots as potential confounding factors liable to skew dominance and diversity results from our regional datasets and impede rigorous comparisons between them. We used sample-based rarefaction to quantify and account for the effect of differences in sample size (number of plots and stems) on our diversity measures of interest; namely species richness, number, ranking and identity of hyperdominants, hyperdominant percentage of total species, and Fisher's α. To quantify the effect of plot size, which is smaller in Southeast Asia data (mean 0.88 ha, median 0.49 ha) than in Amazonia and Africa data (both mean -1 ha, median 1 ha) we compared results from the full data to those from plots >0.9 ha. We found that small plots (≤1 ha) inflate per-plot species totals relative to larger plots (because the rate of encountering new species is higher the smaller the plot size; Extended Data Fig. 1), so we limited our analyses to plots >0.9 ha to enable like-for-like comparison.

For Africa, we retained 368 plots covering 450 ha (mean plot area 1.22 ha, median 1 ha, range 0.92–10 ha; 2% of plots 0.9–0.99 ha, 88% of plots 1 ha, 8% of plots 1.01–5 ha, 1% of plots >5 ha) with mean temperature of 24.3 °C (range 16.2–27.6 °C), mean annual precipitation 1,802 mm yr⁻¹, (range 1,066–2,747 mm yr⁻¹), and mean elevation of 511 m above sea level (range 41–2,070 m) per WorldClim⁶⁹. For Amazonia we retained 1,097 plots covering 1,434 ha (mean plot area 1.31 ha, median 1 ha, range 0.9–78.8 ha; 2% of plots 0.9–0.99 ha, 90% of plots 1 ha, 7% of plots 1.01–5 ha, 1% of plots >5 ha) with mean temperature of 26.0 °C (range 20.9–27.6 °C), mean annual precipitation 2,397 mm yr⁻¹ (range 1,119–4,284 mm yr⁻¹), and mean elevation of 154 m (range 0–1,142 m). For Southeast Asia we retained 103 plots covering 164 ha (mean plot area 1.59 ha, median 1 ha, range 0.96–4.5 ha; 1% of plots 0.9–0.99 ha, 48% of plots 1 ha, 52% of plots 1.01–5 ha, 0% of plots >5 ha) with mean temperature of 25.7 °C (range 20.1–27.5 °C), mean precipitation 2,680 mm yr⁻¹ (range 1,466–3,941 mm yr⁻¹), and mean elevation of 288 m (range 10–934 m). We assessed if the remaining differences in plot size affected the results, using only the 1 ha plots from Africa (n = 323) and Amazonia (n = 988), rarefied to the size of the Asia dataset, again finding near-identical per cent hyperdominance on the two continents (Africa: 7.30%, 95% confidence interval: 6.56–8.04; Amazonia: 7.35%, 95% confidence interval: 6.61–8.10).

To quantify the effect of the spatial clustering of plots, we compared results from the full Amazonia data, as the largest dataset, to those from subsets of the Amazonia data in which 1,2,3,...,10 plots were sampled from each spatial cluster. We found that spatial clustering had a negligible and not statistically significant effect on hyperdominant percentage and fitted values of Fisher's α (Extended Data Fig. 2). Therefore, we retain all plots for our analyses to maximize sample sizes. Computation of percentage hyperdominance and dominance accounts for the effects of variations in species richness on the number of hyperdominants and dominants.

For sample-based rarefaction, 200 subsamples of 1, 2, ..., Nₚ plots were drawn, without replacement, from the Nₚ total number of plots in the pth dataset, the stems contained in each subsample were pooled,
and the mean total species, number of hyperdominants, hyperdomi-
nance percentage, and Fisher's α were calculated across the subsam-
ples. Similarly, we tallied the number of subsamples in which each
species in the dataset qualified as hyperdominant at each level of sub-
sampling and compared results between datasets at subsample sizes
equating to a mean 10,000, 20,000, ..., *I*<sub>*p*</sub> individual trees, where *I*<sub>*p*</sub> is
the total number of trees in the *p*th dataset. Confidence intervals were
calculated as confidence interval = μ ± 1.96 × σ, where μ values are the
means of the diversity metrics calculated across the 200 iterations of
subsamples taken without replacement, and σ values are the s.d. of
the mean of diversity metrics calculated across the 200 iterations of
subsamples taken with replacement (to reduce the degree to which
confidence intervals were conditional on the sample). For point esti-
mates, all datasets were compared at the common sample size of the
Southeast Asia dataset (77,587 stems equivalent to 150, 116 and 103
plots in Africa, Amazonia and Southeast Asia, respectively).

Extrapolation and bias correction of log series fits to the
empirical data

We extrapolated our empirical SADs to SADs at the scale of the entire
Amazonian, African, and Southeast Asian regional level via analytical
expansion and bias correction of Fisher's log series fits following the
methodology of ter Steege et al.²¹ developed using the ATDN data that
comprise our Amazonia dataset.

Ter Steege²¹ et al. found that simulations of sampling of plots with conspecific aggregation from log series-modelled SADs provide extremely good approximations of the processes that generate tropical forest inventory data—that is, non-random sampling of plots containing species with limited dispersal and/or ecological preferences. They further found that estimates of species richness derived from samples taken with conspecific aggregation from the simulated SADs substantially underestimated the true species richness of the simulated SADs, but that a linear relationship with low variance existed between the true and sample-derived values. Thus, although conspecific aggregation in the empirical data introduces bias in the log series-modelled SADs extrapolated therefrom, quantification and correction of the effects of this bias on regional estimates of species richness is possible. Therefore, to estimate species richness at the regional level, they fitted Fisher's log series to empirical species abundance data, quantified the effect of conspecific aggregation on these estimates via simulation, and applied quantified corrections to give more accurate estimates of regional species richness taking into conspecific aggregation. Thus, this approach corrects for species-specific aggregation at the plot scale depending on species density.

To estimate regional numbers and proportions of dominants and hyperdominants as well as species richness, we extended the methodology of ter Steege et al.²¹ to log series-derived estimates of regional numbers and proportions of dominants and hyperdominants. Initially, values of Fisher's α were fitted to the empirical species abundance vectors from each region using maximum likelihood and numerical optimization in the 'sads' R package⁷⁰ and fits visualized with Preston plots⁷¹ and rank abundance distributions (RAD)³⁶ (Extended Data Fig. 4). Regional species totals S, not accounting for bias introduced by conspecific aggregation, were then estimated⁶⁸ via $S = \alpha \times \ln(1 + \frac{N}{\alpha})$ with total number of trees ≥10 cm trunk diameter at the continental level (N) from the Global Tree Density map of Crowther et al.⁶⁶ with each tropical region delineated within the 'tropical and subtropical moist broadleaf forests' biome of Olson et al.⁶⁰. An inverse quantile function from the sads R package⁷⁰ was then applied to generate (uncorrected) continental-scale SADs for each region using the above fitted α, estimated S and N.

For the quantification of bias and computation of corrections, we
first simulated 250 log series SADs with known values of total species,
$S_k$, randomly drawn from the range of plausible regional species totals
(10,000–25,000 in Amazonia and Southeast Asia; 2,000–10,000 in

Africa) and N, the number of trees in each region ≥10 cm trunk diam-
eter from Crowther et al.⁶⁶. We calculated known values of numbers of
hyperdominants, *H*<sub>*k*</sub>, and percentage hyperdominance, *P*<sub>*k*</sub>, from each
of these simulated distributions. Using a negative binomial distribution
to simulate conspecific aggregation per ter Steege et al.²¹, we then
simulated *j* random samples of 1-ha plots from each of the 250 simulated
SADs, with *j* equal to the number of plots in the empirical data, and the
expected abundance of each species in each plot equal to its mean
regional density (total abundance/regional area). We then estimated
(uncorrected) species richness, *S*<sub>*u*</sub>, from each of the samples by fitting
Fisher's α to the sampled data and applying the formula *S*<sub>*u*</sub> = α × ln(1 + *N*/*α*).
From each of the samples we also derived continental-scale uncor-
rected SADs (see above), from which the number of hyperdominants,
*H*<sub>*u*</sub>, and percentage hyperdominance, *P*<sub>*u*</sub>, could be directly calculated,
via analytical expansion of the log series using the fitted values of α
and corresponding values of *S*<sub>*u*</sub>. We then regressed the known values
of *S*<sub>*k*</sub>, *H*<sub>*k*</sub> and *P*<sub>*k*</sub> from the simulated SADs against the estimated (uncor-
rected) values *S*<sub>*u*</sub>, *H*<sub>*u*</sub> and *P*<sub>*u*</sub> from the samples drawn with conspecific
aggregation across all 250 simulations—that is, fit linear models of the
form *A*<sub>*k*</sub> = *m* × *A*<sub>*u*</sub> + *c* for *A* = *S*, *H*, *P*. This same procedure was also applied
to the number and proportion of dominants.

Across all three regional datasets, the above procedure outlined a
linear relationship with low variance between known values of species
richness, number of dominants and hyperdominants, and percentage
hyperdominance and dominance, and values thereof estimated from
sampling with conspecific aggregation (Extended Data Fig. 5). Thus,
constant terms with low variance were readily applicable to correct
for bias in the point estimates of species richness, number of domi-
nants/hyperdominants, and percentage hyperdominance/dominance,
derived from the empirical Africa, Amazonia, and Southeast Asia data.
To capture uncertainty around each bias-corrected point estimate,
prediction intervals (PI) were derived as PI = μ + 1.96 × σ<sub>PI</sub>, where μ is
the predicted mean value of the point estimate according to the linear
regression, and σ<sub>PI</sub> is the PI standard error, calculated as σ<sub>PI</sub> = √(σ<sup>2</sup> + σ<sub>R</sub><sup>2</sup>),
where σ is the standard error of predicted means and σ<sub>R</sub> is the residual
s.d. (and 1.96 is the 0.05 quantile of a *t*-distribution).

**Reporting summary**

Further information on research design is available in the Nature Port-
folio Reporting Summary linked to this article.

**Data availability**

The species abundance data that support the findings of this study are
available from https://doi.org/10.6084/m9.fishshare.21670883 (format-
ting notes: a column for each species, rows for each plot, entries are
the number of trees ≥10 cm diameter of each species in each plot).
WorldClim⁶⁹ bioclimatic data are available from https://www.worldclim.
org/data/bioclim.html.

**Code availability**

R code (version 4.3.1) to run the analyses and produce the figures and
tables is available from https://github.com/declancooper/Common-
Species2022.git.

<footnote n="61">61. Sullivan, M. J. P. et al. Long-term thermal sensitivity of Earth's tropical forests. *Science* **368**, 869–874 (2020).</footnote>

<footnote n="62">62. Clark, D. B. & Clark, D. A. Landscape-scale variation in forest structure and biomass in a tropical rain forest. *For. Ecol. Manag.* **137**, 185–198 (2000).</footnote>

<footnote n="63">63. ForestPlots.net. Taking the pulse of Earth's tropical forests using networks of highly distributed plots. *Biol. Conserv.* **260**, 1088–49 (2021).</footnote>

<footnote n="64">64. Lopez-Gonzalez, G., Lewis, S. L., Burkitt, M. & Phillips, O. L. ForestPlots.net: a web application and research tool to manage and analyse tropical forest plot data. *J. Veg. Sci.* **22**, 610–613 (2011).</footnote>

<footnote n="65">65. Boyle, B. et al. The taxonomic name resolution service: an online tool for automated standardization of plant names. *BMC Bioinformatics* **14**, 16 (2013).</footnote>
and the mean total species, number of hyperdominants, hyperdomi-
nance percentage, and Fisher's α were calculated across the subsam-
ples. Similarly, we tallied the number of subsamples in which each
species in the dataset qualified as hyperdominant at each level of sub-
sampling and compared results between datasets at subsample sizes
equating to a mean 10,000, 20,000, ..., *I*<sub>p</sub> individual trees, where *I*<sub>p</sub> is
the total number of trees in the *p*th dataset. Confidence intervals were
calculated as confidence interval = μ ± 1.96 × σ, where μ values are the
means of the diversity metrics calculated across the 200 iterations of
subsamples taken without replacement, and σ values are the s.d. of
the mean of diversity metrics calculated across the 200 iterations of
subsamples taken with replacement (to reduce the degree to which
confidence intervals were conditional on the sample). For point esti-
mates, all datasets were compared at the common sample size of the
Southeast Asia dataset (77,587 stems equivalent to 150, 116 and 103
plots in Africa, Amazonia and Southeast Asia, respectively).

Extrapolation and bias correction of log series fits to the
empirical data

We extrapolated our empirical SADs to SADs at the scale of the entire
Amazonian, African, and Southeast Asian regional level via analytical
expansion and bias correction of Fisher's log series fits following the
methodology of ter Steege et al.²¹ developed using the ATDN data that
comprise our Amazonia dataset.

Ter Steege²¹ et al. found that simulations of sampling of plots with conspecific aggregation from log series-modelled SADs provide extremely good approximations of the processes that generate tropical forest inventory data—that is, non-random sampling of plots containing species with limited dispersal and/or ecological preferences. They further found that estimates of species richness derived from samples taken with conspecific aggregation from the simulated SADs substantially underestimated the true species richness of the simulated SADs, but that a linear relationship with low variance existed between the true and sample-derived values. Thus, although conspecific aggregation in the empirical data introduces bias in the log series-modelled SADs extrapolated therefrom, quantification and correction of the effects of this bias on regional estimates of species richness is possible. Therefore, to estimate species richness at the regional level, they fitted Fisher's log series to empirical species abundance data, quantified the effect of conspecific aggregation on these estimates via simulation, and applied quantified corrections to give more accurate estimates of regional species richness taking into conspecific aggregation. Thus, this approach corrects for species-specific aggregation at the plot scale depending on species density.

To estimate regional numbers and proportions of dominants and hyperdominants as well as species richness, we extended the methodology of ter Steege et al.²¹ to log series-derived estimates of regional numbers and proportions of dominants and hyperdominants. Initially, values of Fisher's α were fitted to the empirical species abundance vectors from each region using maximum likelihood and numerical optimization in the 'sads' R package⁷⁰ and fits visualized with Preston plots⁷¹ and rank abundance distributions (RAD)³⁶ (Extended Data Fig. 4). Regional species totals S, not accounting for bias introduced by conspecific aggregation, were then estimated⁶⁸ via $S = \alpha \times \ln(1 + \frac{N}{\alpha})$ with total number of trees ≥10 cm trunk diameter at the continental level (N) from the Global Tree Density map of Crowther et al.⁶⁶ with each tropical region delineated within the 'tropical and subtropical moist broadleaf forests' biome of Olson et al.⁶⁰. An inverse quantile function from the sads R package⁷⁰ was then applied to generate (uncorrected) continental-scale SADs for each region using the above fitted α, estimated S and N.

For the quantification of bias and computation of corrections, we
first simulated 250 log series SADs with known values of total species,
$S_k$, randomly drawn from the range of plausible regional species totals
(10,000–25,000 in Amazonia and Southeast Asia; 2,000–10,000 in

Africa) and N, the number of trees in each region ≥10 cm trunk diam-
eter from Crowther et al.⁶⁶. We calculated known values of numbers of
hyperdominants, *H*<sub>k</sub>, and percentage hyperdominance, *P*<sub>k</sub>, from each
of these simulated distributions. Using a negative binomial distribution
to simulate conspecific aggregation per ter Steege et al.²¹, we then
simulated *j* random samples of 1-ha plots from each of the 250 simulated
SADs, with *j* equal to the number of plots in the empirical data, and the
expected abundance of each species in each plot equal to its mean
regional density (total abundance/regional area). We then estimated
(uncorrected) species richness, *S*<sub>u</sub>, from each of the samples by fitting
Fisher's α to the sampled data and applying the formula *S*<sub>u</sub> = α × ln(1 + *N*/*α*).
From each of the samples we also derived continental-scale uncor-
rected SADs (see above), from which the number of hyperdominants,
*H*<sub>u</sub>, and percentage hyperdominance, *P*<sub>u</sub>, could be directly calculated,
via analytical expansion of the log series using the fitted values of α
and corresponding values of *S*<sub>u</sub>. We then regressed the known values
of *S*<sub>k</sub>, *H*<sub>k</sub> and *P*<sub>k</sub> from the simulated SADs against the estimated (uncor-
rected) values *S*<sub>u</sub>, *H*<sub>u</sub> and *P*<sub>u</sub> from the samples drawn with conspecific
aggregation across all 250 simulations—that is, fit linear models of the
form *A*<sub>k</sub> = *m* × *A*<sub>u</sub> + *c* for *A* = *S*, *H*, *P*. This same procedure was also applied
to the number and proportion of dominants.

Across all three regional datasets, the above procedure outlined a
linear relationship with low variance between known values of species
richness, number of dominants and hyperdominants, and percentage
hyperdominance and dominance, and values thereof estimated from
sampling with conspecific aggregation (Extended Data Fig. 5). Thus,
constant terms with low variance were readily applicable to correct
for bias in the point estimates of species richness, number of domi-
nants/hyperdominants, and percentage hyperdominance/dominance,
derived from the empirical Africa, Amazonia, and Southeast Asia data.
To capture uncertainty around each bias-corrected point estimate,
prediction intervals (PI) were derived as PI = μ + 1.96 × σ<sub>PI</sub>, where μ is
the predicted mean value of the point estimate according to the linear
regression, and σ<sub>PI</sub> is the PI standard error, calculated as σ<sub>PI</sub> = √(σ<sup>2</sup> + σ<sub>R</sub><sup>2</sup>),
where σ is the standard error of predicted means and σ<sub>R</sub> is the residual
s.d. (and 1.96 is the 0.05 quantile of a *t*-distribution).

Reporting summary

Further information on research design is available in the Nature Port-
folio Reporting Summary linked to this article.

Data availability

The species abundance data that support the findings of this study are
available from https://doi.org/10.6084/m9.fishshare.21670883 (format-
ting notes: a column for each species, rows for each plot, entries are
the number of trees ≥10 cm diameter of each species in each plot).
WorldClim⁶⁹ bioclimatic data are available from https://www.worldclim.
org/data/bioclim.html.

Code availability

R code (version 4.3.1) to run the analyses and produce the figures and
tables is available from https://github.com/declancooper/Common-
Species2022.git.

<footnote n="61">61. Sullivan, M. J. P. et al. Long-term thermal sensitivity of Earth's tropical forests. *Science* **368**, 869–874 (2020).</footnote>

<footnote n="62">62. Clark, D. B. & Clark, D. A. Landscape-scale variation in forest structure and biomass in a tropical rain forest. *For. Ecol. Manag.* **137**, 185–198 (2000).</footnote>

<footnote n="63">63. ForestPlots.net. Taking the pulse of Earth's tropical forests using networks of highly distributed plots. *Biol. Conserv.* **260**, 1088–49 (2021).</footnote>

<footnote n="64">64. Lopez-Gonzalez, G., Lewis, S. L., Burkitt, M. & Phillips, O. L. ForestPlots.net: a web application and research tool to manage and analyse tropical forest plot data. *J. Veg. Sci.* **22**, 610–613 (2011).</footnote>

<footnote n="65">65. Boyle, B. et al. The taxonomic name resolution service: an online tool for automated standardization of plant names. *BMC Bioinformatics* **14**, 16 (2013).</footnote>
66. Crowther, T. W. et al. Mapping tree density at a global scale. *Nature* **525**, 201–205 (2015).

67. Esri, A. D. ArcGIS Release 10. Documentation Manual (Environmental Systems Research Institute, 2011).

68. Fisher, R. A., Corbet, A. S. & Williams, C. B. The relation between the number of species and the number of individuals in a random sample of an animal population. *J. Anim. Ecol.* **12**, 42–58 (1943).

69. Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. *Int. J. Climatol.* **37**, 4302–4315 (2017).

70. Prado, P. I., Miranda, M. D., Chalom, A., Prado, M. P. I. & Imports, M. sads: maximum likelihood models for species abundance distributions. R package version 0.4.2 (2018).

71. Preston, F. W. The commonness, and rarity, of species. *Ecology* **29**, 254–283 (1948).

**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.