The number of tree species on Earth
Contributed by Peter B. Reich; received September 19, 2021; accepted December 7, 2021; reviewed by Robert Colwell and Fangliang He
Correction
March 23, 2022
PNAS Updates
December 19, 2022
Significance
Tree diversity is fundamental for forest ecosystem stability and services. However, because of limited available data, estimates of tree diversity at large geographic domains still rely heavily on published lists of species descriptions that are geographically uneven in coverage. These limitations have precluded efforts to generate a global perspective. Here, based on a ground-sourced global database, we estimate the number of tree species at biome, continental, and global scales. We estimated a global tree richness (≈73,300) that is ≈14% higher than numbers known today, with most undiscovered species being rare, continentally endemic, and tropical or subtropical. These results highlight the vulnerability of global tree species diversity to anthropogenic changes.
Abstract
One of the most fundamental questions in ecology is how many species inhabit the Earth. However, due to massive logistical and financial challenges and taxonomic difficulties connected to the species concept definition, the global numbers of species, including those of important and well-studied life forms such as trees, still remain largely unknown. Here, based on global ground-sourced data, we estimate the total tree species richness at global, continental, and biome levels. Our results indicate that there are ∼73,000 tree species globally, among which ∼9,000 tree species are yet to be discovered. Roughly 40% of undiscovered tree species are in South America. Moreover, almost one-third of all tree species to be discovered may be rare, with very low populations and limited spatial distribution (likely in remote tropical lowlands and mountains). These findings highlight the vulnerability of global forest biodiversity to anthropogenic changes in land use and climate, which disproportionately threaten rare species and thus, global tree richness.
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In 1994, Robert May (1) provided the optimistic observation that, by 2044, we would roughly know the current number of species on Earth. Half of that time period has already lapsed, and we are still far from that goal. Even for trees, which are among the largest and most widespread organisms on the planet (2–6), provide a wealth of ecosystem services for humans (7–9), and support much of terrestrial biodiversity (10), we still lack a fundamental understanding of how many species exist on our planet (3, 4, 11–13).
A growing body of evidence highlights details and mechanisms regarding the biogeographic patterns in tree species diversity, such as the number of species increasing consistently toward equatorial regions (14–16). With a manageable number of taxa, tree species in the higher latitudes are relatively well characterized. However, if hyperdominance of a small fraction of species in the tropics (17) is a general phenomenon, it would mean that these regions generally harbor a very large number of rare species, many of which are endemic. The contribution of rare species to ecosystem services may be relevant and is a topic of active research (18, 19), but it is challenging as most remain poorly documented (20–26). Therefore, estimating the number of tree species is essential to inform, optimize, and prioritize forest conservation efforts across the globe. Knowing diversity’s extents will be useful in several ways. First, it can help us to infer the evolutionary mechanisms that have generated diversity, so that we can predict how those same mechanisms may play out in the future. Second, it may assist in assessment of which systems may be most resilient to global change. Third, if undetected species are mostly rare and rare species are more vulnerable to extinction risk, having a better grasp of those numbers is essential to managing for biodiversity preservation. Finally, with an understanding of total species pools, it is possible to quantify the impacts of regional conservation efforts while also improving the ability to predict extinctions, manage diversity hotspots, or collect germplasm (22, 23).
Because of the limited extent of data available, estimates of tree species diversity in large geographic domains still rely heavily on expert opinions and compiled published lists of species descriptions that are geographically uneven in coverage (24, 25). Although local specialists have been increasingly joining efforts to consolidate species lists in many domains, these limitations have precluded efforts to scale this information to generate a global perspective. Here, based on a ground-sourced global database numbering ∼64,100 species [a value similar to a prior enumeration of the total of known tree species of ∼60,000 (17)], we developed estimates of the number of tree species at biome, continental, and global scales. Specifically, by comparing species accumulation curves (SACs) of tree species across different spatial scales, we estimated the number of species that have not been recorded in the global data compilation used herein.
Results and Discussion
Global-, Continental-, and Biome-Level Patterns.
We compiled a comprehensive global occurrence dataset with 9,353 (100- × 100-km) grid cell samples (called “samples” or “sampling units” hereafter of ∼1°) (Materials and Methods) by combining an abundance-based tree species dataset (the Global Forest Biodiversity Initiative [GFBI]) (Fig. 1), based on forest plots worldwide and comprising ∼38 million trees for 28,192 species, with a large high-quality occurrence-based dataset (TREECHANGE) that includes forest plots and botanical vouchers (26) (Materials and Methods). It is important to note that despite the large number of grid cells, extensive data, and high mean global sample coverage (96.4%) (Table 1), sampling within grid cells in many regions of the world remains very sparse.
Fig. 1.
Table 1.
| Continent | No. of sampling units (∼1° grid cells) | Species (observed) | Sample coverage, % | Chao2 (asymptotic) | 95% CI lower Chao2 (asymptotic) | 95% CI upper Chao2 (asymptotic) | Chao2adj | 95% CI lower Chao2adj | 95% CI upper Chao2adj | To be discovered (∼Chao2adj − Sobs) | Hotspot biomes |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Global | 9,353 | 64,088 | 96.4 | 89,147 | 89,141 | 89,152 | 73,274 | 73,271 | 73,276 | 9,186 | |
| Africa | 1,575 | 10,441 | 96.0 | 14,031 | 14,028 | 14,033 | 11,875 | 11,874 | 11,877 | 1,434 | Tropical /subtropical moist and dry forests, mainly in the Congo River basin |
| Eurasia | 2,896 | 14,071 | 94.3 | 18,311 | 18,305 | 18,316 | 16,264 | 16,262 | 16,265 | 2,193 | Tropical/subtropical moist and dry forests, mainly in Southeast Asia |
| North America | 2,418 | 8,646 | 98.6 | 10,295 | 10,290 | 10,299 | 11,131 | 11,129 | 11,134 | 2,485 | Tropical subtropical moist and dry forests, mainly in Central America |
| South America | 1,461 | 27,186 | 95.0 | 46,738 | 46,729 | 46,747 | 31,112 | 31,110 | 31,115 | 3,926 | Tropical/subtropical forests, grasslands, savannas, and shrublands, mainly in the Amazon River basin and Andean high mountains |
| Oceania | 1,003 | 6,680 | 97.4 | 9,273 | 9,267 | 9,277 | 8,235 | 8,232 | 8,237 | 1,555 | Tropical/subtropical moist forests, mainly in northeast Australia and the Pacific Islands |
We also list some biomes that are hotspots of undiscovered species in each continent (SI Appendix, Table S2 ). Bold indicates the number of species to be discovered globally and continentally.
From this dataset, with a nonparametric estimator [Chao2 (27)], we calculated occurrence-based values of potential global and continental tree species richness (Materials and Methods, Fig. 2A, and Table 1). This estimator is sensitive to accurate quantification of the numbers of uniques and duplicates (below and Materials and Methods), and it is known that there are problems with false uniques in forest species richness datasets (24). These Chao2 values may thus represent an overestimate to the degree that tree species recorded in only one sampling unit have been mistakenly identified as unique. Therefore, we estimated the true number (Chao2adj) of unique species (28) (Materials and Methods) by accounting for the relationships of uniques, duplicates, triplets, and quadruplets to constrain the estimated numbers of unique species and from this adjusted number, computed a more conservative estimate of global tree richness, which is ∼73,300 species (Chao2adj). Based on the good performance of this estimator and its adjusted version reported in previous studies (9, 29–33), we considered the adjusted value (Chao2adj) our most reasonable approximation to global tree species richness. We then derived SACs at global (Fig. 2A) and continental (Fig. 2B) scales. Moreover, for each continent, from the observed number of tree species, we also estimated the asymptotic richness at the within-continent biome-level extent (Fig. 3 and SI Appendix, Table S2 ).
Fig. 2.
Fig. 3.
At the global scale, we infer that there likely are ∼9,200 tree species yet to be discovered (Table 1), given the ∼64,000 species already encountered (3, 4, 34–37). Our estimates at continental scales (Fig. 2B and Table 1) show that roughly 43% of all Earth’s tree species occur in South America, followed by Eurasia (22%), Africa (16%), North America (15%), and Oceania (11%). However, a lack of saturation (driven by the existence of high numbers of species uncommon in the landscape, incomplete sampling, or both), particularly in the South American accumulation curve (Fig. 2B), suggests that our estimates may still be incomplete accounts of continental and global tree species richness. More undiscovered species likely occur in South America than any other continent. Our findings are in general agreement with recent studies of Amazonian plant diversity, which suggested that there are many undiscovered species; moreover, different approaches to the problem arrived at different estimates of total numbers of known and unknown species, suggesting that, as a scientific community, we still have much more work to do to arrive at accurate estimates regionally, continentally, or globally (12, 24, 34–37). Additionally, a considerable number of species have likely not yet been encountered in each of the other four continents as well (Table 1), most likely in the species-rich and more poorly studied tropical regions within each (see below).
Our biome-level estimates of tree richness (Fig. 3 and SI Appendix, Table S2 ) provide a more detailed description of the distribution of species richness within continents and shed more light on South America’s extremely high total tree diversity. As expected, the highest estimates of tree species in all continents are for the tropical and subtropical moist forest biome; for example, roughly half to two-thirds of all already known species occur in these forests on all five continents (SI Appendix, Table S2 ). Moreover, the hotspots of undiscovered species (Table 1 and SI Appendix, Table S2 ) may largely occur in these same species-rich and undersampled (37) regions. However, high numbers of known and unknown species also occur in other biomes, including tropical and subtropical dry forests, temperate forests, mangrove forests, and areas classified as nonforested biomes (e.g., lowland and montane grasslands, savanna, shrublands, deserts) but that include considerable areas of tree-rich, and often speciose, vegetation. The high total tree diversity in South America is dominated by the lowland wet tropics and subtropics, yet roughly one-third of all tree species on that continent are found only outside of that biome.
Rarity in Forests Worldwide.
We also calculated indices of tree species rarity (percentages of singletons and doubletons) at continental and global scales (SI Appendix, Table S3 ) to help illuminate possible within-sample and among-sample abundance patterns. In fact, since the sample coverage deficit (1 – coverage = slope of the SAC at its right-hand end) is a statistically rigorous way of assessing the incompleteness of sampling (38), the proportion of singletons/uniques is, thus, strongly driven not only by long tails on the underlying species abundance/occurrence distribution but also, by sampling intensity/completeness.
Our most reliable abundance-based asymptotic richness estimates depend on the total number of observed species and the number of species with only one (singletons) or two (doubletons) individuals in each sample (which may represent measures of abundance-based rarity). Similarly, occurrence-based estimates depend on the number of species present in only one (unique) or two (duplicate) samples of each continent (which may represent measures of occurrence-based rarity). Rarity data within samples (α; i.e., from the abundance-based dataset) provide an indication of the relative proportion of species that are rare at the landscape to small regional scale represented by individual grid cells (100 × 100 km). The global rarity value is 33%, with Africa (38%) and South America (37%) having the highest percentage of species rare within samples and North America (17%) and Eurasia (24%) having the lowest (SI Appendix, Table S3 ). It is important to note that our data do not mean that one-third of all species occur only once or twice in nature; instead, their rarity in our dataset suggests their rarity in nature but with unknown distributions of real occurrences. The ratio of singletons to doubletons within grid cells is higher in Africa and Oceania followed by South America and is quite low in North America and Eurasia.
From the rarity data among samples (occurrence-based rarity), we estimated that South America accounts for the highest total number of rare species (∼8,200) followed by Eurasia (∼6,100) and Africa (∼3,900). In Eurasia and North America, the percentage of species rare among grid cells was ∼43%, and it was <40% in the other continents, with the lowest value in South America (∼30%) (SI Appendix, Table S3 ). The ratio of singletons to doubletons among grid cells in North America (1.83) is the highest among continents; for all other continents, it is lower than 1.5. At a global scale, percentage abundance-based rarity is higher than occurrence-based rarity, while the ratio of singletons to doubletons shows the opposite trend. Since we were aware that the numbers (and proportions) of singletons/uniques and doubletons/duplicates (and their relative magnitudes) are very much a function not only of true rarity but also of sampling effort, in relation to true richness, we estimated all indices adjusting them for “true singletons/uniques” (Materials and Methods). However, our findings still confirm that most forests are likely to be dominated by just a few tree species (17) and include a long tail of rare species, which represents a consistent 30 to 40% of the overall tree richness in all continents. Although more species-rich regions (such as South America and Africa) have higher abundance-based rarity, North America and Eurasia (which contain more of a mix of biomes) showed higher occurrence-based rarity, and this finding could provide insights to better understand the biogeography of tree species on Earth.
Overall, almost a third of global tree richness on Earth is made up of rare species, which appear only once or twice in our samples. Thus, if the global forest system is dominated by a relatively modest number of abundant tree species, the global number of tree species strongly depends on those rarely detected (∼35%) (SI Appendix, Table S3 ) and undetected species (some large fraction of the ∼9,200 unobserved over the ∼73,300 estimated) (Table 1) (34). These results highlight the vulnerability of global forest biodiversity to anthropogenic changes, particularly land use and climate, because the survival of rare taxa is disproportionately threatened by these pressures (16–19). The higher threats for rare species are an important concern if we consider that their functions in ecosystems, the services they provide, and the ecoevolutionary patterns of these hyperrare tree species are still poorly known (16–20, 25).
Comparisons across Continents.
To better understand the biogeography of richness patterns across land masses, we also estimated species turnover among continents (Fig. 4). Specifically, we combined the data of the five continents to obtain the values of estimated tree species richness in all 31 possible intersections (Materials and Methods). The two continents that share the highest estimated numbers of tree species are North and South America (Fig. 4), which is not surprising since these continents are interconnected by land (since about 3 Mya) in a region where nearby species-rich tropical forests occur on both continents. Consistent with this pattern, the second-highest number of shared species is between Eurasia and Oceania (Fig. 4), which had a geological continuity through the Southeast Asian archipelago that is another hotspot of tree diversity. Overall, other than the highest number of rare species, South America also shows the highest estimated percentage (49%) of continental endemic species (Fig. 4), while Eurasia and Africa account together for almost another 32% of unique tree species in the world. The percentage of shared species estimated among all five continents is lower than 0.1 (Fig. 4).
Fig. 4.
To summarize our main findings, we estimated that the absolute number of tree species on Earth is considerably higher than previously reported, with 14.3% more species than currently known to science (3). By establishing a quantitative benchmark, this information could contribute to tree and forest conservation efforts and the future discovery of new trees and associated species in certain parts of the world. For instance, considering that we estimated that about 31,100 tree species are expected in South America (Chao2adj estimator) and those known to science are about 27,200 (Table 1), there might be about 3,900 tree species yet to be discovered in this continent, and most of them could be endemic (Fig. 4) and located in diversity hotspots of the Amazon basin and the Andes–Amazon interface. This makes forest conservation of paramount priority in South America, especially considering the current tropical forest crisis from anthropogenic impacts such as deforestation, fires, and climate change. Similar arguments can be made about the prioritization of conservation of tropical and subtropical forests on other continents given the considerable numbers of likely undiscovered species on each and their likely rarity. For example, there are likely high numbers of undiscovered species in Central America and in Southeast Asia.
This study accelerates our science by estimating global tree richness with a more extensive dataset and more advanced statistical methods than previous attempts. However, both the underlying data and Chao richness estimators and adjustments are imperfect. We recognize several methodological issues that might have potentially biased our estimates and/or contributed to uncertainty. The first involves the uneven and unrepresentative distribution of the sampling areas in the globe and within continents, which is an issue despite the high–sample coverage metrics that we used. The second involves the possibility that some species might have been misclassified due to misidentification, failure to update taxonomic name changes, and misspellings, which could reduce accuracy in estimates of species numbers (24, 33, 34). There is compelling evidence of errors in most biodiversity datasets due to the inclusion of false uniques (24). For example, if two botanists in different parts of the same forest region encounter the same species of rare and unfamiliar tree, they may identify it differently or use different synonyms to identify it, biasing the count of uniques and the estimators. Therefore, because of the likely discrepancy between the actual proportion of uniques in a sample and the observed unique count included in our datasets, we estimated the true number of unique species (28) and from this adjusted number, computed and focus on a more conservative estimate of global tree richness, which is ∼73,300 species (Chao2adj). There is also uncertainty about the accuracy of nonparametric estimators. Previous studies report that nonparametric estimators give lower values of tree species richness than parametric ones for the Amazon basin (34–36). However, our nonparametric estimate of tree species diversity in South American tropical forest biomes was higher than both parametric estimation and previous estimates in the Amazon (36). This might have resulted from previous studies being mainly based on Amazon lowlands, ignoring highlands. Thus, we examined sample completeness comparing continents but limiting their latitude to 23°N and S (tropical regions) (SI Appendix, Table S4 ). Results generally showed similar sample coverage at the grid-scale size used.
Future estimates of tree species richness in tropical, subtropical, and montane areas on all continents will be more accurate if an increased sample size is obtained (37), especially from areas poorly investigated. This begs the question on why South America alone could harbor >40% of all tree species. Compared with forest ecosystems on other continents, South America could have offered a larger continuous tropical forest area, a higher rate of speciation, a more robust mechanism of biodiversity maintenance, and reduced extinction rates [for instance, mild climates and the shortest period of human disturbance (39, 40)]. We also noticed that the SAC of South America continued to rise along the samples, whereas those of other continents start to level off, supporting the idea that undiscovered species numbers are likely high there, including in the Andean forests between 1,000- and 3,500-m altitude. A key challenge now is to install more plots in the Amazon-Andean transition zones, and to identify and monitor the trees within these plots.
Overall, our study points toward an estimated global tree richness (∼73,300) that is roughly 14% higher than numbers known today (3, 4), with many unknown species belonging to the tail of rare ones and often endemic to certain regions all across the globe. These results highlight the vulnerability of global tree species diversity to anthropogenic land use changes and to future climate (16–18). Losing regions of forest that contain these rare species will have direct and potentially long-lasting impacts on the global species diversity and their provisioning of ecosystem services (18–20). These results demonstrate both the lack of knowledge we still have about the tree species within our global forest systems and the value of approaches to help fill those gaps, which will be useful in providing fundamental insights about the diversity of life on our planet and its needed conservation.
Materials and Methods
Dataset and Sample Coverage.
We used the tree definition agreed on by IUCN's (International Union for Conservation of Nature) Global Tree Specialist Group (GTSG): “a woody plant with usually a single stem growing to a height of at least two meters, or if multi-stemmed, then at least one vertical stem five centimeters in diameter at breast height.” A tree inventory abundance dataset from 105,749 forest plots, ∼38 million stems of 28,192 species, distributed across all five continents was compiled from the GFBI (https://gfbinitiative.net/) database. For the Tonga and Niue data in the GFBI dataset, the original source was the New Zealand National Vegetation Survey Databank. For the estimation of the total number of tree species worldwide, we further compiled an independent occurrence dataset that we combined with the GFBI data. The occurrence-based dataset (hereafter, TREECHANGE) consists of taxonomy and location of >6 million tree individuals. Being a major data infrastructure itself, this dataset represents species occurrence information and encompasses a huge variety of data—from ground-sourced forest plot data (similar to the GFBI). Supported by a large body of collaborating institutions all over the world, this dataset features extensive global coverage and has been used across many large-scale studies (26). A limitation of the TREECHANGE dataset is that its underlying datasets do not have a coherent and consistent design and sampling scheme, but as described below, it complements the calculation of the estimated total number of tree species worldwide based on GFBI data. We extracted taxonomic data and associated geographic coordinates from five main data aggregators of species occurrences: the Global Biodiversity Information Facility [accessed through rgbif R package (41)], the public domain of the Botanical Information and Ecological Network v.3 [accessed through the BIEN R package (42)], the Latin American Seasonally Dry Tropical Forest Floristic Network [DRYFLOR (43)], the RAINBIO database (44), and the Atlas of Living Australia [ALA; accessed through the ALA4 R package (45)]. The species list was initially extracted from a world tree species checklist [GlobalTreeSearch (46)]. We checked for taxonomic correctness using the Taxonomic Name Resolution online tool (47), following a quality assessment and control of the data using the workflow outlined in ref. 26. This workflow minimized common errors associated with occurrence data (43). GlobalTreeSearch uses the tree definition agreed on by IUCN's GTSG above mentioned.
For abundance-based analyses, we used the GFBI tree species dataset (at its original plot size), whose samples cover a total area of more than 73,000 ha (SI Appendix, Table S1 ). Then, to perform occurrence-based estimations, we compiled a larger and more comprehensive global dataset with 100- × 100-km sampling units (∼1° grid cells) by combining the abundance-based data in the GFBI tree species dataset, which were converted in presence/absence occurrence data and pooled with the high-quality large occurrence-based TREECHANGE dataset. Globally, this yielded a dataset of 9,353 sampling units, with 696,063 occurrences. At the continental level, the combination of the two datasets to obtain a large occurrence-based dataset also yielded a number of sampling units somewhat comparable, in the sense of being a similar order of magnitude (Africa: 1,575; Eurasia: 2,896; North America: 2,418; South America: 1,461; Oceania: 1,003).
To ensure that our estimations of species richness were not biased by differences in sample coverage (e.g., an estimate of the total probability of occurrence of the species observed in the sample, taking into account species present but not detected) among continents, we estimated the inventory completeness (as defined by ref. 48) for the complete database and for each continent separately using the Chao–Shen sample coverage estimator (38, 48), which is a bias-reduced estimator of sample completeness:where f1 and f2 are the numbers of singletons and doubletons (for abundance-based data) or the species occurred in only one (uniques) and in two (duplicates) 100- × 100-km (∼1°) samples (for occurrence-based data), respectively; n is the total number of individuals (for abundance-based data) or occurrences (for occurrence-based data) in the sample; and Cn is the proportion of the total number of individuals (for abundance-based data) or occurrences (for occurrence-based data) in an assemblage (observed and not observed) that belong to the species represented in the sample (49, 50).
Because estimates of species richness can be strongly dependent on differences in inventory completeness, we checked whether sample coverage was similar in all five continents. Since all continents showed a similar proportion of sample coverage (all >94%), both from occurrence- (Table 1) and abundance-based data (SI Appendix, Table S1 ), we confirmed that our global estimate—based on global sample coverage of 96.44% (occurrence data) and 99.97% (abundance data)—was not disproportionately influenced by any specific continent. However, the slightly lower occurrence-based sample coverage of South America and Eurasia, with 95 and 94.26%, respectively, and the clustered distribution of some plots could explain the nonsaturating trend of their accumulation curves compared with the other continents (Fig. 2B). We also note that sample completeness at finer scales would be lower in all continents.
We selected the continental scale for our estimates, together with the common study frames of biomes (51), because nonparametric species richness estimators perform better when samples are collected in a continuous incremental area without relevant landmass separation such as oceans (31–33).
For instance, working at a global biome-level only would ensure that the current climatic conditions are similar, but this approach to estimate species richness, taken alone, would reduce the information implied in the estimates because they would be affected by several factors. 1) Within each across-ocean biome, there are still important ecological and evolutionary differences that would affect the estimates at the global biome level [in fact, conventional levels of ecological hierarchical organization are not scale dependent (52), whereas species richness estimates are]. 2) With nonparametric estimates based on SACs, it is better to ensure a continuity of sampled areas (e.g., continuous terrestrial lands) (53, 54). 3) The ecological conditions that have shaped the evolutionary patterns (phylogeny and diversity) of tree species on Earth were much different when continents were conglomerate in Pangea (55) and then slowly shifted away (i.e., during this long geological time, biomes were much different to current ones) (56–58).
Therefore, other than estimating global tree species richness at a global biome level (Fig. 3 and SI Appendix, Table S2 ), we analyzed continental richness to also account for evolutionary changes in response to the biome main variables (latitude, climate, solar radiation, etc.), which shaped current tree diversity. Adding the figures at a continental (and a continental biome) level, we ensured that our estimates are based on the 135-My biogeographical and temporal continuity of the five main vegetated landmasses, which is an implied assumption of the estimators. This approach also allows a better discussion of the results for species turnover among continents (Fig. 4), which might be a result of their connections in Laurasia and Gondwana and the following continental drift.
Species Richness Estimators.
We initially computed a parametric estimate of species richness on the abundance-based data for 28,192 species from the GFBI dataset (SI Appendix, Table S1 ). In particular, we considered the Fisher’s α for abundance data (calculated from http://groundvegetationdb-web.com/ground_veg/home/diversity_index).
We found that the abundance-based Fisher’s α underestimated the absolute species richness because our global (SI Appendix, Table S1 ) Fisher estimate was close but lower than the observed number of species in our occurrence-based dataset (64,100 from GFBI + TREECHANGE). Because this parametric estimator assumes a log-series distribution of abundances (59), we performed a goodness-of-fit test and evaluated it with a Kolmogorov–Smirnov test of whether our global and abundance data fit a log-series distribution. Since all datasets (global: D = 0.1, P = 1; Africa: D = 0.1, P = 1; Eurasia: D = 0.5, P = 0.17; North America: D = 0.2, P = 0.99; Oceania: D = 0.2, P = 0.99; South America: D = 0.2, P = 0.99) follow a log-series distribution, we calculated the α-values. At a global level, we obtained a Fisher’s α-value of 3,040 (SI Appendix, Table S1 ).
We used this value and the most recent estimates on the global number of trees by Crowther et al. (60) to estimate the global number of species from Fisher’s classical equation (61):where N is the total number of trees and α is the Fisher’s α-parameter. This yielded an estimate of 62,624 to 62,915 species (lower–upper bootstrap 95% CI) from the 3.04 ± 0.19 × 1012 (±95% CI) global tree stems calculated by Crowther et al. (60). Although Fisher’s parametric approach stands on the very strict assumption of infinite log-series species abundance distributions, giving rise to overestimation of hyperrarity (62, 63), it estimated slightly less than the observed number of species in our occurrence-based dataset (using the α-value derived from our abundance-based dataset). We thus did not further employ this estimate. Instead, with the larger occurrence dataset composed of GFBI (converted to presence/absence) and TREECHANGE data, we then calculated the Chao2 index, which is a lower-bound estimator and considered one of the most reliable and less affected by bias among all nonparametric indices (27, 64–66). The values of the estimators from the samples to plot the curves shown in Fig. 2 were randomized, interpolated, and extrapolated with the package iNext in R (67).
The Chao2 estimator (bias corrected) is calculated by the following formula:where Sobs are the actual numbers of species observed in the samples (m) and Q1 and Q2 are the species that appear in only one (unique) and two (duplicate) sampling units, respectively (27, 29). The 95% CI (CI bias corrected) of this index can be calculated by the formula
This estimation yielded a global value of 89,147 ± 1,101.5 species (Chao2 ± 95% CI) (Table 1). We are well aware that some studies provide different preferred estimators (68–70). However, many analyses, including simulation-based experiments, encourage the use of Chao2 to minimize bias (a summary is in ref. 71). This is the reason we considered the Chao2 index (based on occurrence data) our more useful estimator. Nonetheless, this estimator is sensitive to accurate quantification of the numbers of uniques and duplicates, and it is known that there are problems with false uniques in forest species richness datasets (24). Our Chao2 values may, thus, represent an overestimate to the degree that tree species recorded in only one sampling unit have been mistakenly identified as unique. Therefore, to check the reliability of our nonparametric estimates, we calculated the true number of uniques (28) in each continent and at a global scale to understand whether our values were influenced by the number of “falsely unique species.” This estimation of the true number of uniques is calculated with the formula adapted from ref. 28 for incidence-based data:where is the estimated true number of uniques; T is the number of sampling units (map cells); and Q2, Q3, and Q4 are observed duplicates, triplicates, and quadruplicates.
At the global level, the estimate of the true number of uniques is 13,162 compared with the observed 24,768. At the continental level, the number of estimated uniques was much lower than the observed one in South America (4,888 vs. 13,110) and somewhat lower in Eurasia (3,424 vs. 5,806), Africa (2,192 vs. 3,466), and Oceania (1,444 vs. 2,208), but it was slightly higher in North America (2,460 vs. 2,360). We then used the adjusted number of uniques in the Chao2 equation (see above) to calculate the Chao2adj estimates, (27–29).
We also calculated tree species rarity at continental and global scales for abundance (abundance-based rarity; i.e., based on the number of adjusted singletons [S1] and doubletons [S2]) and occurrence (occurrence-based rarity; i.e., based on the adjusted number of unique species and the number of duplicate ones). We defined the number of rare species as the sum of adjusted singletons and doubletons. We also computed an index of rarity importance using our occurrence-based dataset as the proportion of rare species over total richness and an S1adjusted/S2 ratio, which is the proportion of singletons over doubletons.
Continental Biodiversity Partitioning.
We estimated the number of species shared among continents and unique to each continent using the Chao2 estimator (Fig. 4) from the occurrence-based data, and we represented them in a Venn diagram. We combined the observations of species richness for the five continents (n = 5) in all possible 25 − 1 = 31 combinations.
First, we calculated asymptotic species richness (Chao2) from occurrences observed in each continent; then, we intersected (creating a unique presence/absence binary entry for each species) the observed occurrences per each pair, triplet, quadruplet, and all five of continents (obtaining the occurrences of all the observed species in each combination of continents) and calculated the asymptotic species richness (Chao2) per each pair, triplet, quadruplet, and quintuplet continents. Therefore, a total of 31 estimates were obtained by the Chao2 index and plotted in a Venn diagram with the R package VennDiagram (72).
Additional cross-checks of the data pooling approach and q1/q2 relationship are in SI Appendix, SI Methods .
Data Availability
Anonymized numeric data have been deposited in GFBI, https://gfbinitiative.net/data/.
Acknowledgments
We thank the following agencies, initiatives, teams, and individuals for data collection and other technical support: the GFBI for establishing the data standards and collaborative framework; the US Department of Agriculture, Forest Service, Forest Inventory and Analysis Program; the University of Alaska Fairbanks; the SODEFOR, Ivory Coast; University Félix Houphouët-Boigny (Ivory Coast); the Queensland Herbarium; and past Queensland Government Forestry and Natural Resource Management Departments and staff for data collection for over seven decades. We thank Javier Eduardo Silva Espejo, Rahman Laskar, Salam Dilip, Bijit, Bironjoy, and Samar; Badru Mugerwa and Emmanuel Akampurira together with a team of field assistants (Valentine and Lawrence); all persons who made the Third Spanish Forest Inventory possible, especially the main coordinator J. A. Villanueva (IFN3); the French National Forest Inventory (NFI; NFI campaigns [raw data 2005 and following annual surveys] were downloaded by the GFBI at https://inventaire-forestier.ign.fr/dataIFN/, site accessed on 1 January 2015); the Italian Forest Inventory (NFI campaigns raw data 2005 and following surveys were downloaded by the GFBI at https://www.inventarioforestale.org/, site accessed on 27 April 2019); Swiss National Forest Inventory, Swiss Federal Research Institute WSL, and Federal Office for the Environment FOEN, Switzerland; Coordination for the Improvement of Higher Education Personnel of Brazil (CAPES) Grant 88881.064976/2014-01; Rafael Ávila and Sharon van Tuylen, Instituto Nacional de Bosques, Guatemala for facilitating Guatemalan data; the National Focal Center for Forest condition monitoring of Serbia, Institute of Forestry, Belgrade, Serbia; the Thünen Institute of Forest Ecosystems (Germany) for providing NFI data; the Food and Agriculture Organization of the United Nations and the United Nations High Commissioner for Refugees undertaking the Safe Access to Fuel and Energy project; Russian Science Foundation Project 21-46-07002 for the plot data collected in the Krasnoyarsk region; and the Amazon Forest Inventory Network (RAINFOR), the African Tropical Rainforest Observation Network, and the ForestPlots.net initiative for their contributions from Amazonian and African forests. The Natural Forest plot data were collected between January 2009 and March 2014 by the LUCAS Programme for the New Zealand Ministry for the Environment, as provided by the New Zealand National Vegetation Survey Databank. All Tropical Ecology Assessment and Monitoring (TEAM) data are provided by the TEAM Network, a collaboration between Conservation International, the Missouri Botanical Garden, the Smithsonian Institution, and the Wildlife Conservation Society, and partially funded by these institutions, the Gordon and Betty Moore Foundation, and other donors, with thanks to all current and previous TEAM site managers and other collaborators who helped with collecting data; the people of the Redidoti, Pierrekondre, and Cassipora villages who were instrumental in assisting with the collection of data and sharing local knowledge of their forest; and the dedicated members of the field crew of the Kabo 2012 census. This research was supported in part through computational resources provided by Information Technology at Purdue, West Lafayette, Indiana. We also thank Robert K. Colwell and Fangliang He for insightful comments and recommendations during the revisions of this manuscript. This work is supported in part by US Department of Agriculture National Institute of Food and Agriculture McIntire Stennis Projects 1017711 and 1016676; a faculty start-up grant from the Department of Forestry and Natural Resources, Purdue University; US NSF Biology Integration Institutes Program NSF-DBI-2021898; Key Project of National Key Research and Development Plan, China Grant 2017YFC0504005; São Paulo Research Foundation Grant 2014/14503-7; Proyecto FONACIT Grant 1998003436 and UNELLEZ Grant 23198105; EU, Sumforest–REFORM, Risk Resilient Forest Management, FKZ Grant 2816ERA02S; German Research Foundation, KROOF Tree and stand-level growth reactions on drought in mixed versus pure forests of Norway spruce and European beech Grant PR 292/12-1; Bavarian State Ministry for Food, Agriculture and Forestry, W07 long-term yield experiments, Grant 7831-26625-2017 and Project E33; the Deutsche Forschungsgemeinschaft Priority Program 1374 Biodiversity Exploratories; the International Tropical Timber Organization Project PD 53/00 Rev.3 (F); the State Forest Management Centre, Estonia, and the Environmental Investment Centre, Estonia; Natural Sciences and Engineering Research Council of Canada Discover Grant Project Grants RGPIN-2014-04181 and STPGP428641; European Structural Funds by FEDER 2014 to 2020 Grant GY0006894; European Investment Funds by FEDER/COMPETE/POCI-Operacional Competitiveness and Internacionalization Programme under Project POCI-01-0145-FEDER-006958 and National Funds by FCT–Portuguese Foundation for Science and Technology Project UIDB/04033/2020. ICNF-Instituto de Conservação da Natureza e Florestas. 6º Inventário Florestal Nacional; Chilean research Grants Fondecyt No. 1191816 and FONDEF No. ID19 10421; Vietnam National Foundation for Science and Technology Development Grant NAFOSTED-106-NN.06-2016.10; German Research Foundation Grant FOR 1246; the Project LIFE+ ForBioSensing PL “Comprehensive monitoring of stand dynamics in Białowieża Forest supported with remote sensing techniques” cofunded by Life Plus Contract LIFE13 ENV/PL/000048 and National Fund for Environmental Protection and Water Management in Poland Contract 485/2014/WN10/OP-NM-LF/D; National Natural Scientific Foundation of China Grants 31660055 and 31660074; the Polish State Forests National Forest Holding (2016); National Science Center (Poland) Grant 2011/02/A/NZ9/00108; the Dutch Ministry of Economic Affairs for funding the Dutch National Forest Inventory; US NASA Grant 11-TE11-0100; the TEAM/Conservation International project for funding the data collection and the Instituto Nacional de Pesquisas da Amazônia; the Ministère des Forêts, de la Faune et des Parcs du Québec (Canada); the exploratory plots of FunDivEUROPE received funding from European Union Seventh Framework Programme FP7/2007-2013 under Grant 265171; DBT, Government of India through the project “Mapping and quantitative assessment of geographic distribution and population status of plant resources of Eastern Himalayan region” (Sanction Order BT/PR7928/NDB/52/9/2006 dated 29 September 2006); financial support from the Natural Sciences and Engineering Research Council of Canada (to S.D.); Czech Science Foundation Standard Grant 19-14620S and European Research Council Advanced Grant 669609; RFBR Grant 16-05-00496; the project implementation demonstration object on the transformation of declining spruce forests into ecologically more stable multifunctional ecosystems, ITMS Grant 26220220026, supported by the Research & Development Operational Program funded by the ERDF; the Swedish NFI, Department of Forest Resource Management, Swedish University of Agricultural Sciences SLU; National Research Foundation of South Africa Grants 89967 and 109244 and the South African Research Chair Initiative; the University Research Committee of the University of the South Pacific and New Colombo Plan funding through the Department of Foreign Affairs and Trade of the Australian government; the TEAM project in Uganda supported by the Moore Foundation and the Buffett Foundation through Conservation International and Wildlife Conservation Society; the COBIMFO project funded by Belgian Science Policy Office Contract SD/AR/01A; the German Federal Ministry of Education and Research Grant FKZ 01LL0908AD for the project Land Use and Climate Change Interactions in the Vu Gia Thu Bon River basin, Central Vietnam; Programme Tropenbos Côte d'Ivoire Project 04/97-1111a du Complément d’Inventaire de la Flore dans le Parc National de Taï; Danish Council for Independent Research | Natural Sciences TREECHANGE Grant 6108-00078B (to J.-C.S.)and VILLUM FONDEN Grant 16549 (to J.-C.S.); ERC Advanced Grant 291585 (“T-FORCES”) and a Royal Society Wolfson Research Merit Award (to O.L.P.); RAINFOR plots supported by the Gordon and Betty Moore Foundation and the UK Natural Environment Research Council (NERC), notably NERC Consortium Grants AMAZONICA (NE/F005806/1), TROBIT (NE/D005590/1), and BIO- RED (NE/N012542/1); Fundação de Amparo à Pesquisa e Inovação de Santa Catarina, FAPESC Grant 2016TR2524, Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq Grant 312075/2013-8; “Investissement d’Avenir” grant managed by Agence Nationale de la Recherche (CEBA, reference ANR-10-LABX-25-01); CIFOR's Global Comparative Study on REDD+ funded by the Norwegian Agency for Development Cooperation (Norad), the Australian Department of Foreign Affairs and Trade, the European Union, the International Climate Initiative of the German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety, and the CGIAR Research Program on Forests, Trees and Agroforestry, and donors to the CGIAR Fund; the Nature and Biodiversity Conservation Union under the project entitled “Biodiversity under Climate Change: Community Based Conservation, Management and Development Concepts for the Wild Coffee Forests” funded by the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety through the International Climate Initiative; the Conselho Nacional de Desenvolvimento Científico e Tecnológico; the institutional project “EXTEMIT - K” CZ.02.1.01/0.0/0.0/15_003/0000433 financed by OP RDE; EC DG VIII Grants BZ-5041 (ECOSYN), NWO-WOTRO (W84-204), and GTZ; AfriTRON network plots funded by the local communities and NERC, ERC, the European Union, the Royal Society, and Leverhume Trust; BOLFOR (Proyecto de Manejo Forestal Sostenible Bolivia); the Global Environment Research Fund Grants F-071 and D-1006, JSPS KAKENHI Grant JP17K15289; the National Institute of Biology (now the Research Center for Biology), Indonesian Institute of Sciences, Indonesia IFBN Project Contract 4000114425/15/NL/FF/gp funded by ESA; NSF Grants DBI-1565046, DEB-0424767, DEB-0639393, and DEB-1147429; NASA Terrestrial Ecology Program; Swiss National Science Foundation Grants 130720 and 147092; Projects D/9170/07, D/018222/08, D/023225/09, and D/032548/10 funded by the Spanish Agency for International Development Cooperation (Agencia Española de Cooperación Internacional para el Desarrollo) and Fundación Biodiversidad in cooperation with the Universidad Mayor de San Simón, the Manejo Forestal en las Tierras Tropicales de Bolivia project, and Compañía Industrial Maderera Ltda.; the Agency for Economic and Environmental Development of the north province of New Caledonia (Projects Ecofor & Cogefor, 2011 to 2016); Russian Science Foundation Grant 16-17-10284 The accumulation of carbon in forest soils and forest succession status; the Norwegian Ministry of Food and Agriculture; a grant from the Royal Society and the Natural Environment Research Council (United Kingdom; to S.L.L.); the Spanish Agency for International Development Cooperation (Agencia Española de Cooperación Internacional para el Desarrollo) and Fundación Biodiversidad, in cooperation with the governments of Syria and Lebanon; COBIMFO Project, Federal Science Policy, Belgium; Consejo Nacional de Ciencia y Tecnología, Mexico; Comisión Nacional Forestal, Mexico; BEF-China Project FOR 891 funded by the German Research Foundation; WWF Russell Train Fellowship Grant ST54 to PMU; Wildlife Conservation Society DRC Program under CARPE Funding; the Seoul National University Big Data Institute through the Data Science Research Project 2016, R&D Program for Forest Science Technology Projects 2013069C10-1719-AA03 and S111215L020110 funded by Korea Forest Service (the Korea Forestry Promotion Institute); Department of Biotechnology, Government of India Grant BT/PR12899/NDB/39/506/2015 (dated 20 June 2017) and Science and Engineering Research Board, Government of India Grant YSS/2015/000479 (dated 12 January 2016); Tropenbos International-Suriname; the Institute for World Forestry, University of Hamburg; REMBIOFOR Project “Remote sensing based assessment of woody biomass and carbon storage in forests” funded by the National Centre for Research and Development, Warsaw, Poland, under BIOSTRATEG Program Agreement BIOSTRATEG1/267755/4/NCBR/2015; Project “Environmental and genetic factors affecting productivity of forest ecosystems on forest and post-industrial habitats” (2011 to 2015) Grant OR/2717/3/11 funded by the General Directorate of State Forests, Warsaw, Poland; Project “Carbon balance of the major forest-forming tree species in Poland” (2007 to 2011) Grant 1/07 funded by the General Directorate of State Forests, Warsaw, Poland; the research professorship for “Ecosystem-based sustainable development” funded by Eberswalde University for Sustainable Development; Pontificia Universidad Católica del Ecuador supported the fieldwork census in Yasuni National Park; the National Forest Programme of the National Institute of Agricultural Research; São Paulo Research Foundation Grants #2014/14503-7, 2017/05662-2, and 03/12595-7; MAUA group supported by FAPEAM-PRONEX Grant 1600/2006; CNPq/FAPEAM-PELD Grant 403792/2012-6; ATTO Project Grants MCTI-FINEP 1759/10 and BMBF 01LB1001A; Czech Science Foundation Standard Grants 17-07378S and 17-19376S; the Long-Term Research Development Project RVO 67985939 of Institute of Botany of the Czech Academy of Sciences; Slovak Research and Development Agency Project APVV-20-0168; the Strategic Science Investment Fund of the New Zealand Ministry for Business, Innovation and Employment; FAPESP Grants 2014/14503-7 and 2017/05662-2; and CNPq Universal Grant 479599/2008-4. The Digital Environment for Enabling Data-Driven Science Project is funded by NSF Grant CIF21 DIBBs: EI: 1724728; Natural Forest plot data collected between January 2002 and March 2007 by the LUCAS programme for the Ministry for the Environment; and Human Modified Tropical Forests Programme of NERC Grant NE/K016377/1. P.B.R. acknowledges funding support from US NSF Long-Term Ecological Research Grant DEB-1831944 and Biological Integration Institutes Grant NSF-DBI-2021898.
Supporting Information
Materials/Methods, Supplementary Text, Tables, Figures, and/or References
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Published in
Cover image: Pictured are white-tailed deer. Suresh V. Kuchipudi et al. tested dozens of lymph node samples from free-living white-tailed deer and deer living in managed environments in Iowa for SARS-CoV-2 RNA. The samples were collected following peak deer hunting season and high human case counts in the winter of 2020, and 82.5% contained SARS-CoV-2 RNA. Geographic distribution of infection clusters suggested multiple human-to-deer transmission events followed by deer-to-deer spread. The findings underscore the need to explore the persistence and evolution of SARS-CoV-2 in deer, including the potential for spillover into other animals and back into humans. See the article by Kuchipudi et al., e2121644119. Image credit: Peter J. Hudson.
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Copyright
Copyright © 2022 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).
Data Availability
Anonymized numeric data have been deposited in GFBI, https://gfbinitiative.net/data/.
Submission history
Received: September 19, 2021
Accepted: December 7, 2021
Published online: January 31, 2022
Published in issue: February 8, 2022
Change history
March 23, 2022: Figure 1 has been updated; please see accompanying Correction for details. Co-author Laura Dee's affiliation has been updated. Previous version (January 31, 2022)
Keywords
Acknowledgments
We thank the following agencies, initiatives, teams, and individuals for data collection and other technical support: the GFBI for establishing the data standards and collaborative framework; the US Department of Agriculture, Forest Service, Forest Inventory and Analysis Program; the University of Alaska Fairbanks; the SODEFOR, Ivory Coast; University Félix Houphouët-Boigny (Ivory Coast); the Queensland Herbarium; and past Queensland Government Forestry and Natural Resource Management Departments and staff for data collection for over seven decades. We thank Javier Eduardo Silva Espejo, Rahman Laskar, Salam Dilip, Bijit, Bironjoy, and Samar; Badru Mugerwa and Emmanuel Akampurira together with a team of field assistants (Valentine and Lawrence); all persons who made the Third Spanish Forest Inventory possible, especially the main coordinator J. A. Villanueva (IFN3); the French National Forest Inventory (NFI; NFI campaigns [raw data 2005 and following annual surveys] were downloaded by the GFBI at https://inventaire-forestier.ign.fr/dataIFN/, site accessed on 1 January 2015); the Italian Forest Inventory (NFI campaigns raw data 2005 and following surveys were downloaded by the GFBI at https://www.inventarioforestale.org/, site accessed on 27 April 2019); Swiss National Forest Inventory, Swiss Federal Research Institute WSL, and Federal Office for the Environment FOEN, Switzerland; Coordination for the Improvement of Higher Education Personnel of Brazil (CAPES) Grant 88881.064976/2014-01; Rafael Ávila and Sharon van Tuylen, Instituto Nacional de Bosques, Guatemala for facilitating Guatemalan data; the National Focal Center for Forest condition monitoring of Serbia, Institute of Forestry, Belgrade, Serbia; the Thünen Institute of Forest Ecosystems (Germany) for providing NFI data; the Food and Agriculture Organization of the United Nations and the United Nations High Commissioner for Refugees undertaking the Safe Access to Fuel and Energy project; Russian Science Foundation Project 21-46-07002 for the plot data collected in the Krasnoyarsk region; and the Amazon Forest Inventory Network (RAINFOR), the African Tropical Rainforest Observation Network, and the ForestPlots.net initiative for their contributions from Amazonian and African forests. The Natural Forest plot data were collected between January 2009 and March 2014 by the LUCAS Programme for the New Zealand Ministry for the Environment, as provided by the New Zealand National Vegetation Survey Databank. All Tropical Ecology Assessment and Monitoring (TEAM) data are provided by the TEAM Network, a collaboration between Conservation International, the Missouri Botanical Garden, the Smithsonian Institution, and the Wildlife Conservation Society, and partially funded by these institutions, the Gordon and Betty Moore Foundation, and other donors, with thanks to all current and previous TEAM site managers and other collaborators who helped with collecting data; the people of the Redidoti, Pierrekondre, and Cassipora villages who were instrumental in assisting with the collection of data and sharing local knowledge of their forest; and the dedicated members of the field crew of the Kabo 2012 census. This research was supported in part through computational resources provided by Information Technology at Purdue, West Lafayette, Indiana. We also thank Robert K. Colwell and Fangliang He for insightful comments and recommendations during the revisions of this manuscript. This work is supported in part by US Department of Agriculture National Institute of Food and Agriculture McIntire Stennis Projects 1017711 and 1016676; a faculty start-up grant from the Department of Forestry and Natural Resources, Purdue University; US NSF Biology Integration Institutes Program NSF-DBI-2021898; Key Project of National Key Research and Development Plan, China Grant 2017YFC0504005; São Paulo Research Foundation Grant 2014/14503-7; Proyecto FONACIT Grant 1998003436 and UNELLEZ Grant 23198105; EU, Sumforest–REFORM, Risk Resilient Forest Management, FKZ Grant 2816ERA02S; German Research Foundation, KROOF Tree and stand-level growth reactions on drought in mixed versus pure forests of Norway spruce and European beech Grant PR 292/12-1; Bavarian State Ministry for Food, Agriculture and Forestry, W07 long-term yield experiments, Grant 7831-26625-2017 and Project E33; the Deutsche Forschungsgemeinschaft Priority Program 1374 Biodiversity Exploratories; the International Tropical Timber Organization Project PD 53/00 Rev.3 (F); the State Forest Management Centre, Estonia, and the Environmental Investment Centre, Estonia; Natural Sciences and Engineering Research Council of Canada Discover Grant Project Grants RGPIN-2014-04181 and STPGP428641; European Structural Funds by FEDER 2014 to 2020 Grant GY0006894; European Investment Funds by FEDER/COMPETE/POCI-Operacional Competitiveness and Internacionalization Programme under Project POCI-01-0145-FEDER-006958 and National Funds by FCT–Portuguese Foundation for Science and Technology Project UIDB/04033/2020. ICNF-Instituto de Conservação da Natureza e Florestas. 6º Inventário Florestal Nacional; Chilean research Grants Fondecyt No. 1191816 and FONDEF No. ID19 10421; Vietnam National Foundation for Science and Technology Development Grant NAFOSTED-106-NN.06-2016.10; German Research Foundation Grant FOR 1246; the Project LIFE+ ForBioSensing PL “Comprehensive monitoring of stand dynamics in Białowieża Forest supported with remote sensing techniques” cofunded by Life Plus Contract LIFE13 ENV/PL/000048 and National Fund for Environmental Protection and Water Management in Poland Contract 485/2014/WN10/OP-NM-LF/D; National Natural Scientific Foundation of China Grants 31660055 and 31660074; the Polish State Forests National Forest Holding (2016); National Science Center (Poland) Grant 2011/02/A/NZ9/00108; the Dutch Ministry of Economic Affairs for funding the Dutch National Forest Inventory; US NASA Grant 11-TE11-0100; the TEAM/Conservation International project for funding the data collection and the Instituto Nacional de Pesquisas da Amazônia; the Ministère des Forêts, de la Faune et des Parcs du Québec (Canada); the exploratory plots of FunDivEUROPE received funding from European Union Seventh Framework Programme FP7/2007-2013 under Grant 265171; DBT, Government of India through the project “Mapping and quantitative assessment of geographic distribution and population status of plant resources of Eastern Himalayan region” (Sanction Order BT/PR7928/NDB/52/9/2006 dated 29 September 2006); financial support from the Natural Sciences and Engineering Research Council of Canada (to S.D.); Czech Science Foundation Standard Grant 19-14620S and European Research Council Advanced Grant 669609; RFBR Grant 16-05-00496; the project implementation demonstration object on the transformation of declining spruce forests into ecologically more stable multifunctional ecosystems, ITMS Grant 26220220026, supported by the Research & Development Operational Program funded by the ERDF; the Swedish NFI, Department of Forest Resource Management, Swedish University of Agricultural Sciences SLU; National Research Foundation of South Africa Grants 89967 and 109244 and the South African Research Chair Initiative; the University Research Committee of the University of the South Pacific and New Colombo Plan funding through the Department of Foreign Affairs and Trade of the Australian government; the TEAM project in Uganda supported by the Moore Foundation and the Buffett Foundation through Conservation International and Wildlife Conservation Society; the COBIMFO project funded by Belgian Science Policy Office Contract SD/AR/01A; the German Federal Ministry of Education and Research Grant FKZ 01LL0908AD for the project Land Use and Climate Change Interactions in the Vu Gia Thu Bon River basin, Central Vietnam; Programme Tropenbos Côte d'Ivoire Project 04/97-1111a du Complément d’Inventaire de la Flore dans le Parc National de Taï; Danish Council for Independent Research | Natural Sciences TREECHANGE Grant 6108-00078B (to J.-C.S.)and VILLUM FONDEN Grant 16549 (to J.-C.S.); ERC Advanced Grant 291585 (“T-FORCES”) and a Royal Society Wolfson Research Merit Award (to O.L.P.); RAINFOR plots supported by the Gordon and Betty Moore Foundation and the UK Natural Environment Research Council (NERC), notably NERC Consortium Grants AMAZONICA (NE/F005806/1), TROBIT (NE/D005590/1), and BIO- RED (NE/N012542/1); Fundação de Amparo à Pesquisa e Inovação de Santa Catarina, FAPESC Grant 2016TR2524, Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq Grant 312075/2013-8; “Investissement d’Avenir” grant managed by Agence Nationale de la Recherche (CEBA, reference ANR-10-LABX-25-01); CIFOR's Global Comparative Study on REDD+ funded by the Norwegian Agency for Development Cooperation (Norad), the Australian Department of Foreign Affairs and Trade, the European Union, the International Climate Initiative of the German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety, and the CGIAR Research Program on Forests, Trees and Agroforestry, and donors to the CGIAR Fund; the Nature and Biodiversity Conservation Union under the project entitled “Biodiversity under Climate Change: Community Based Conservation, Management and Development Concepts for the Wild Coffee Forests” funded by the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety through the International Climate Initiative; the Conselho Nacional de Desenvolvimento Científico e Tecnológico; the institutional project “EXTEMIT - K” CZ.02.1.01/0.0/0.0/15_003/0000433 financed by OP RDE; EC DG VIII Grants BZ-5041 (ECOSYN), NWO-WOTRO (W84-204), and GTZ; AfriTRON network plots funded by the local communities and NERC, ERC, the European Union, the Royal Society, and Leverhume Trust; BOLFOR (Proyecto de Manejo Forestal Sostenible Bolivia); the Global Environment Research Fund Grants F-071 and D-1006, JSPS KAKENHI Grant JP17K15289; the National Institute of Biology (now the Research Center for Biology), Indonesian Institute of Sciences, Indonesia IFBN Project Contract 4000114425/15/NL/FF/gp funded by ESA; NSF Grants DBI-1565046, DEB-0424767, DEB-0639393, and DEB-1147429; NASA Terrestrial Ecology Program; Swiss National Science Foundation Grants 130720 and 147092; Projects D/9170/07, D/018222/08, D/023225/09, and D/032548/10 funded by the Spanish Agency for International Development Cooperation (Agencia Española de Cooperación Internacional para el Desarrollo) and Fundación Biodiversidad in cooperation with the Universidad Mayor de San Simón, the Manejo Forestal en las Tierras Tropicales de Bolivia project, and Compañía Industrial Maderera Ltda.; the Agency for Economic and Environmental Development of the north province of New Caledonia (Projects Ecofor & Cogefor, 2011 to 2016); Russian Science Foundation Grant 16-17-10284 The accumulation of carbon in forest soils and forest succession status; the Norwegian Ministry of Food and Agriculture; a grant from the Royal Society and the Natural Environment Research Council (United Kingdom; to S.L.L.); the Spanish Agency for International Development Cooperation (Agencia Española de Cooperación Internacional para el Desarrollo) and Fundación Biodiversidad, in cooperation with the governments of Syria and Lebanon; COBIMFO Project, Federal Science Policy, Belgium; Consejo Nacional de Ciencia y Tecnología, Mexico; Comisión Nacional Forestal, Mexico; BEF-China Project FOR 891 funded by the German Research Foundation; WWF Russell Train Fellowship Grant ST54 to PMU; Wildlife Conservation Society DRC Program under CARPE Funding; the Seoul National University Big Data Institute through the Data Science Research Project 2016, R&D Program for Forest Science Technology Projects 2013069C10-1719-AA03 and S111215L020110 funded by Korea Forest Service (the Korea Forestry Promotion Institute); Department of Biotechnology, Government of India Grant BT/PR12899/NDB/39/506/2015 (dated 20 June 2017) and Science and Engineering Research Board, Government of India Grant YSS/2015/000479 (dated 12 January 2016); Tropenbos International-Suriname; the Institute for World Forestry, University of Hamburg; REMBIOFOR Project “Remote sensing based assessment of woody biomass and carbon storage in forests” funded by the National Centre for Research and Development, Warsaw, Poland, under BIOSTRATEG Program Agreement BIOSTRATEG1/267755/4/NCBR/2015; Project “Environmental and genetic factors affecting productivity of forest ecosystems on forest and post-industrial habitats” (2011 to 2015) Grant OR/2717/3/11 funded by the General Directorate of State Forests, Warsaw, Poland; Project “Carbon balance of the major forest-forming tree species in Poland” (2007 to 2011) Grant 1/07 funded by the General Directorate of State Forests, Warsaw, Poland; the research professorship for “Ecosystem-based sustainable development” funded by Eberswalde University for Sustainable Development; Pontificia Universidad Católica del Ecuador supported the fieldwork census in Yasuni National Park; the National Forest Programme of the National Institute of Agricultural Research; São Paulo Research Foundation Grants #2014/14503-7, 2017/05662-2, and 03/12595-7; MAUA group supported by FAPEAM-PRONEX Grant 1600/2006; CNPq/FAPEAM-PELD Grant 403792/2012-6; ATTO Project Grants MCTI-FINEP 1759/10 and BMBF 01LB1001A; Czech Science Foundation Standard Grants 17-07378S and 17-19376S; the Long-Term Research Development Project RVO 67985939 of Institute of Botany of the Czech Academy of Sciences; Slovak Research and Development Agency Project APVV-20-0168; the Strategic Science Investment Fund of the New Zealand Ministry for Business, Innovation and Employment; FAPESP Grants 2014/14503-7 and 2017/05662-2; and CNPq Universal Grant 479599/2008-4. The Digital Environment for Enabling Data-Driven Science Project is funded by NSF Grant CIF21 DIBBs: EI: 1724728; Natural Forest plot data collected between January 2002 and March 2007 by the LUCAS programme for the Ministry for the Environment; and Human Modified Tropical Forests Programme of NERC Grant NE/K016377/1. P.B.R. acknowledges funding support from US NSF Long-Term Ecological Research Grant DEB-1831944 and Biological Integration Institutes Grant NSF-DBI-2021898.
Notes
Reviewers: R.C., University of Connecticut; and F.H., University of Alberta.
Authors
Competing Interests
Competing interest statement: M.L.B. is an employee of PNAS.
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