Introduction: Recent decades have seen a major international effort to inventory tree communities in the Amazon Basin and Guiana Shield (Amazonia), but the vast extent and record diversity of these forests have hampered an understanding of basinwide patterns. To overcome this obstacle, we compiled and standardized species-level data on more than half a million trees in 1170 plots sampling all major lowland forest types to explore patterns of commonness, rarity, and richness.Methods: The ~6-million-km²Amazonian lowlands were divided into 1° cells, and mean tree density was estimated for each cell by using a loess regression model that included no environmental data but had its basis exclusively in the geographic location of tree plots. A similar model, allied with a bootstrapping exercise to quantify sampling error, was used to generate estimated Amazon-wide abundances of the 4962 valid species in the data set. We estimated the total number of tree species in the Amazon by fitting the mean rank-abundance data to Fisher’s log-series distribution.Results: Our analyses suggest that lowland Amazonia harbors 3.9 × 10¹¹trees and ~16,000 tree species. We found 227 “hyperdominant” species (1.4% of the total) to be so common that together they account for half of all trees in Amazonia, whereas the rarest 11,000 species account for just 0.12% of trees. Most hyperdominants are habitat specialists that have large geographic ranges but are only dominant in one or two regions of the basin, and a median of 41% of trees in individual plots belong to hyperdominants. A disproportionate number of hyperdominants are palms, Myristicaceae, and Lecythidaceae.Discussion: The finding that Amazonia is dominated by just 227 tree species implies that most biogeochemical cycling in the world’s largest tropical forest is performed by a tiny sliver of its diversity. The causes underlying hyperdominance in these species remain unknown. Both competitive superiority and widespread pre-1492 cultivation by humans are compelling hypotheses that deserve testing. Although the data suggest that spatial models can effectively forecast tree community composition and structure of unstudied sites in Amazonia, incorporating environmental data may yield substantial improvements. An appreciation of how thoroughly common species dominate the basin has the potential to simplify research in Amazonian biogeochemistry, ecology, and vegetation mapping. Such advances are urgently needed in light of the >10,000 rare, poorly known, and potentially threatened tree species in the Amazon.

Forest structure and dynamics vary across the Amazon Basin in an east-west gradient coincident with variations in soil fertility and geology. This has resulted in the hypothesis that soil fertility may play an important role in explaining Basin-wide variations in forest biomass, growth and stem turnover rates. Soil samples were collected in a total of 59 different forest plots across the Amazon Basin and analysed for exchangeable cations, carbon, nitrogen and pH, with several phosphorus fractions of likely different plant availability also quantified. Physical properties were additionally examined and an index of soil physical quality developed. Bivariate relationships of soil and climatic properties with above-ground wood productivity, stand-level tree turnover rates, above-ground wood biomass and wood density were first examined with multivariate regression models then applied. Both forms of analysis were undertaken with and without considerations regarding the underlying spatial structure of the dataset. Despite the presence of autocorrelated spatial structures complicating many analyses, forest structure and dynamics were found to be strongly and quantitatively related to edaphic as well as climatic conditions. Basin-wide differences in stand-level turnover rates are mostly influenced by soil physical properties with variations in rates of coarse wood production mostly related to soil phosphorus status. Total soil P was a better predictor of wood production rates than any of the fractionated organic- or inorganic-P pools. This suggests that it is not only the immediately available P forms, but probably the entire soil phosphorus pool that is interacting with forest growth on longer timescales. A role for soil potassium in modulating Amazon forest dynamics through its effects on stand-level wood density was also detected. Taking this into account, otherwise enigmatic variations in stand-level biomass across the Basin were then accounted for through the interacting effects of soil physical and chemical properties with climate. A hypothesis of self-maintaining forest dynamic feedback mechanisms initiated by edaphic conditions is proposed. It is further suggested that this is a major factor determining endogenous disturbance levels, species composition, and forest productivity across the Amazon Basin.

Past human influences on Amazonian forest: The marks of prehistoric human societies on tropical forests can still be detected today. Levis et al. performed a basin-wide comparison of plant distributions, archaeological sites, and environmental data. Plants domesticated by pre-Columbian peoples are much more likely to be dominant in Amazonian forests than other species. Furthermore, forests close to archaeological sites often have a higher abundance and richness of domesticated species. Thus, modern-day Amazonian tree communities across the basin remain largely structured by historical human use. Science , this issue p. 925

Aboveground tropical tree biomass and carbon storage estimates commonly ignore tree height (H). We estimate the effect of incorporating H on tropics-wide forest biomass estimates in 327 plots across four continents using 42 656 H and diameter measurements and harvested trees from 20 sites to answer the following questions: 1. What is the best H-model form and geographic unit to include in biomass models to minimise site-level uncertainty in estimates of destructive biomass? 2. To what extent does including H estimates derived in (1) reduce uncertainty in biomass estimates across all 327 plots? 3. What effect does accounting for H have on plot- and continental-scale forest biomass estimates? The mean relative error in biomass estimates of destructively harvested trees when including H (mean 0.06), was half that when excluding H (mean 0.13). Power- and Weibull-H models provided the greatest reduction in uncertainty, with regional Weibull-H models preferred because they reduce uncertainty in smaller-diameter classes (≤40 cm D) that store about one-third of biomass per hectare in most forests. Propagating the relationships from destructively harvested tree biomass to each of the 327 plots from across the tropics shows that including H reduces errors from 41.8 Mg ha⁻¹ (range 6.6 to 112.4) to 8.0 Mg ha⁻¹ (−2.5 to 23.0). For all plots, aboveground live biomass was −52.2 Mg ha⁻¹ (−82.0 to −20.3 bootstrapped 95% CI), or 13%, lower when including H estimates, with the greatest relative reductions in estimated biomass in forests of the Brazilian Shield, east Africa, and Australia, and relatively little change in the Guiana Shield, central Africa and southeast Asia. Appreciably different stand structure was observed among regions across the tropical continents, with some storing significantly more biomass in small diameter stems, which affects selection of the best height models to reduce uncertainty and biomass reductions due to H. After accounting for variation in H, total biomass per hectare is greatest in Australia, the Guiana Shield, Asia, central and east Africa, and lowest in east-central Amazonia, W. Africa, W. Amazonia, and the Brazilian Shield (descending order). Thus, if tropical forests span 1668 million km² and store 285 Pg C (estimate including H), then applying our regional relationships implies that carbon storage is overestimated by 35 Pg C (31–39 bootstrapped 95% CI) if H is ignored, assuming that the sampled plots are an unbiased statistical representation of all tropical forest in terms of biomass and height factors. Our results show that tree H is an important allometric factor that needs to be included in future forest biomass estimates to reduce error in estimates of tropical carbon stocks and emissions due to deforestation.