William Balee’s research while affiliated with Tulane University and other places

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Publications (12)


Trait space of 353 Amazonian tree genera on 2253 plots with genus level identification
Only genera with complete trait data were used (353 genera, of the 812 in our plots). PC1 has an Eigenvalue of 2.98 and represents the leaf economic spectrum (SLA, N, P, C:N). PC2 (Eigenvalue 1.62) represents the stature-recruitment trade-off (WD, SM) and is strongly linked to short lived pioneers (SLP, negatively) and old-growth species and maximum diameter (OGS, Max, positively). Legend: Colours indicate the probability of trait combinations in the trait space defined by the PCA (red = high probability; yellow = low probability). Contour lines indicate 0.99, 0.50, and 0.25 quantiles of the probability distribution. N leaf nitrogen concentration, C:N ratio of leaf carbon to leaf nitrogen, SLA specific leaf area, SM seed mass, P leaf phosphorus concentration, AA aluminium accumulation, Nfix atmospheric N-fixation, WD wood density (overlapping with OGS), C leaf carbon content, FF fleshy fruit, EM ectomycorrhiza, Her hermaphroditic, Nectar nectar producing. Life histories (dark green): OGS old-growth species, LLP long-lived pioneer, SLP short-lived pioneer⁶⁴; Max, maximum diameter¹⁶⁵. For description of the traits and units, see Supplementary box 1.
Trait space of 2054 tree communities with traits at genus and species level
PC1 has an Eigenvalue of 4.39 (explained variance 33.3%), and appears to be related to the ‘leaf economic spectrum’ (SLA, N, P, C:N) but also WD, SMC, and hermaphroditism contribute to this axis. Life-history forms SLP and LLP are also positively correlated with PC1. Environmental factors sum of bases and pH are strongly positively correlated to this axis. PC2 is linked to nodulation of Fabaceae and fleshy fruits and poorly correlated to the climatic factors used. Legend: Colours indicate the probability of trait combinations in the trait space defined by the PCA (red = high probability; yellow = low probability). Contour lines indicate 0.99, 0.50, and 0.25 quantiles of the probability distribution. N leaf nitrogen concentration, C:N ratio of leaf carbon to leaf nitrogen, SLA specific leaf area, SM seed mass, P leaf phosphorus concentration, AA aluminium accumulation, Nfix atmospheric N-fixation, WD wood density, C leaf carbon content, FF fleshy fruit, EM ectomycorrhiza, Her hermaphroditic, Nectar nectar producing. Life histories (dark green): OGS old-growth species, LLP long-lived pioneer, SLP short-lived pioneer⁶⁴; Environmental variables: Annual, Annual precipitation (Bioclim12)¹⁶⁶; CWD cumulative water deficit, CAPE Convective atmospheric potential energy⁶⁵, WTC Windthrow count⁶⁵; PZ podzol, white-sand forest, FL flooded (swamp forest; várzea; igapó); pH, soil acidity; SB, log(sum of bases)¹⁵⁴; G.prob, geoglyph probability⁵⁸; DSpp, domesticated species⁵⁷. Note that SLA, N and P are overlapping, as are DSpp, G.prob, pH and SB. For description of the traits and units, see Supplementary box 1.
PC1 plot scores of community trait values related to forest types and Amazon regions. a ‘The fast-slow forest spectrum’ as determined by forest type
‘The fast-slow forest spectrum’ is associated mostly with the economic spectra, and the order of forest types appears determined by general soil fertility (see Supplementary Fig. 29a). Note the very high value of the poorest soils in Amazonia (lowest sum of bases (Supplementary Fig. 29a), white sand podzol (PZ). b‘The fast-slow forest spectrum’ as determined by Amazonian region. The order of regions also appears follow general soil fertility (Supplementary Fig 29b). From rich to poor: TFPB terra firme Pebas Formation, VA várzea, SW swamp forest, TFBS terra firme Brazilian Shield, IG igapó, TFGS terra firme Guiana Shield, PZ white sand forest, SWA south west Amazonia, NWA northwest Amazonia, SA southern Amazonia, EA eastern Amazonia, CA central Amazonia, GS Guiana Shield. Colours follow the major forest type (SWA, NWA: TFPB; SA: TFBS; CA, GS: TFGS; EA: mix of TFBS, TFGS). Red dotted line: mean of all data.
Functional characterisation of Amazonian forests
Forest with positive score on the ‘fast-slow forest spectrum’ (yellow, beige) are forests at the “slow”, tough side of economic spectra (high CN ratio, low SLA, N and P), high wood density, low numbers of fleshy fruit, high levels of hermaphroditism, high in nectar producing individuals, occurring mainly on low to very low nutrient soils. Forests with negative score on the ‘fast-slow forest spectrum’ (blue, purple) are the opposite in terms of trait values and occur mainly on nutrient rich soils. The isolines divide Amazonia into three regions, tough-slow (PC1 > 0.65, yellow-beige), soft-fast (PC1 < -1.2 blue-purple) and intermediate (green). Colouring the plots based on their PC1 scores shows that their colour mostly matches the area colour, except if they are white sand plots (PZ) in a green area, and várzea plots (blue dots) in green and yellow areas. Note that the legend has been truncated at 2 standard deviations. Red polygon: Amazonian Biome limit¹⁶⁷. Base map source (country.shp, rivers.shp), ESRI (http://www.esri.com/data/basemaps, © Esri, DeLorme Publishing Company).
The ‘fast-slow forest spectrum’ and soil fertility as potential drivers of aboveground biomass and biomass productivity
a ‘Slow’ forests (positive value) have much higher above ground woody biomass (AWB) than ‘fast’ forests (negative values) b Absolute above ground woody productivity (AGWP) does not vary significantly with the ‘fast-slow forest spectrum’. c Biomass produced per biomass standing (= Relative AGWP [100*AGWP/AGB]) is highest in ‘fast’ forests (negative values for slow-fast forest spectrum). d Relative AGWP is positively correlated with predicted sum of bases¹⁶. Red lines indicate 95% confidence intervals. Biomass data from sources55,83,168. Colours: Red, terra firme Pebas formation; brown, terra firme Brazilian Shield; orange, terra firme Guiana Shield; yellow, white sand forest; purple, swamp forest; light blue, várzea.
Functional composition of the Amazonian tree flora and forests
  • Article
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March 2025

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890 Reads

Communications Biology

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Georgia Pickavance

Plants cope with the environment by displaying large phenotypic variation. Two spectra of global plant form and function have been identified: a size spectrum from small to tall species with increasing stem tissue density, leaf size, and seed mass; a leaf economics spectrum reflecting slow to fast returns on investments in leaf nutrients and carbon. When species assemble to communities it is assumed that these spectra are filtered by the environment to produce community level functional composition. It is unknown what are the main drivers for community functional composition in a large area such as Amazonia. We use 13 functional traits, including wood density, seed mass, leaf characteristics, breeding system, nectar production, fruit type, and root characteristics of 812 tree genera (5211 species), and find that they describe two main axes found at the global scale. At community level, the first axis captures not only the ‘fast-slow spectrum’, but also most size-related traits. Climate and disturbance explain a minor part of this variance compared to soil fertility. Forests on poor soils differ largely in terms of trait values from those on rich soils. Trait composition and soil fertility exert a strong influence on forest functioning: biomass and relative biomass production.

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Functional, demographic, and integrative axis of variation. First principal components using leaf (specific leaf area; nitrogen, phosphorous, and carbon content, N, P, and C) wood (wood density, and maximum diameter, Diametermax) and demographic characteristics (maximum growth rate, Growth R.max; mortality rate, Mortality R. and seed mass). Only genera with complete data for all variables are represented (197 genera). Variable contributions are shown as arrows, coloured in light blue (leaf and wood functional traits) and red (demographic characteristics). Principal‐axis interpretation is shown in bold letters. Pictures of seeds (fruit when no seed images are available), leaves and whole trees for four species representing the four extreme strategies are shown. Phylogenetic signal and amount of variance explained by each axis in percentage are shown for each axis.
Variance–covariance networks. Trait correlation networks among leaf (specific leaf area; nitrogen, phosphorous and carbon content, N, P, and C), wood (wood density, and maximum diameter, Diametermax) and demographic characteristics (maximum growth rate, Growth R.max; mortality rate, Mortality R. and seed mass). Leaf and wood functional traits are represented as nodes (circles) in light blue and demographic functional traits are shown in light red. Edges (lines connecting nodes) represent (a) total correlation, (b) phylogenetically conserved portion of the correlation and (c) non‐phylogenetically conserved portion of the total correlation. Solid green lines represent statistically significant positive correlation coefficients and dashed red lines represent significant negative correlation coefficients. Line width is proportional to the absolute value of the correlation coefficient. Pie charts in b and c represent trait variance related to the phylogeny (i.e., phylogenetic signal) and trait variance not related to the phylogeny, respectively. Node size is proportional to the number of connections per node (i.e., degree). Three network metrics are shown in each case.
Functional and demographic axis correlations. Total, phylogenetically conserved and non‐phylogenetically conserved correlation portions between functional and demographic principal components. Phylogenetic signal is also shown and represented as pie charts for each principal component. Values for genera with complete observations are plotted on the genus‐level phylogeny. Bars are coloured by taxonomic order and the most important taxonomic order names are shown. Signif. codes: “***”: p < 0.001; “ns”: p > 0.1.
Phylogenetic conservatism in the relationship between functional and demographic characteristics in Amazon tree taxa

November 2024

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951 Reads

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2 Citations

Leaf and wood functional traits of trees are related to growth, reproduction, and survival, but the degree of phylogenetic conservatism in these relationships is largely unknown. In this study, we describe the variability of strategies involving leaf, wood and demographic characteristics for tree genera distributed across the Amazon Region, and quantify phylogenetic signal for the characteristics and their relationships. Leaf and wood traits are aligned with demographic variables along two main axes of variation. The first axis represents the coordination of leaf traits describing resource uptake and use, wood density, seed mass, and survival. The second axis represents the coordination between size and growth. Both axes show strong phylogenetic signal, suggesting a constrained evolution influenced by ancestral values, yet the second axis also has an additional, substantial portion of its variation that is driven by functional correlations unrelated to phylogeny, suggesting simultaneously higher evolutionary lability and coordination. Synthesis. Our results suggest that life history strategies of tropical trees are generally phylogenetically conserved, but that tree lineages may have some capability of responding to environmental changes by modulating their growth and size. Overall, we provide the largest‐scale synopsis of functional characteristics of Amazonian trees, showing substantial nuance in the evolutionary patterns of individual characteristics and their relationships. Read the free Plain Language Summary for this article on the Journal blog.


Variation in composition and relative abundance of 5188 tree species in 2023 forest-inventory plots (1 ha) across Amazonian forests
Ordination biplots showing the two first principal components with inventory plots coloured by (a) ecological forest categories based on hydrology and soil characteristics and (b) geographic regions. a Ecological categories: VA, Várzea forests; SW, swamp forests; IG, igapó forests; PZ, white-sand (podzol) forests; TFGS, terra-firme on the Guiana Shield; TFBS terra-firme on the Brazilian Shield, TFPB terra-firme on the Pebas sedimentary basin. b Geographical regions: CA Central Amazonia, EA Eastern Amazonia, SA Southern Amazonia, GS Guiana Shield, NWA Northwestern Amazonia, SWA Southwestern Amazonia. Arrows indicate vectors constructed with envfit()⁸¹ for 14 environmental predictors: Flooded flooding vs. non-flooding terrains, WTD water table depth, Temp_avg average annual temperature, MCWD maximum climatological water deficit), Annal_ppt Annual Rainfall, Podzol White Sand vs. Clay-Silt terrains, ALOS_MTPI Multiscale Topographic Position Index, TopoDiver Topographic Diversity Index, Ppt_sea precipitation seasonality, ALOS_3D elevation, Temp_range temperature range, Temp_seas temperature seasonality, pH soil pH, SB soil sum of bases.
Variation in interpolated composition and relative abundance of 5,188 tree species in 47,441 grid cells (0.1-degree squares) across Amazonian forests
Ordination biplots showing the two first DCA axes with grid cells coloured by geographic region: CA Central Amazonia, EA Eastern Amazonia, GS Guiana Shield, NWA Northwestern Amazonia, SWA Southwestern Amazonia, SA Southern Amazonia. Black marks show the average position for the abundance distribution of the 20 tree species with the highest interpolated total abundance. The distributions of these species in geographical and ordination space are shown in Supplementary Figs. 5–24.
Maps of the broad-scale spatial variation of tree species composition across Amazonia
Scores of (a) DCA Axis 1, (b) DCA Axis2 (both from Fig. 2). In both maps, grey lines are the isolines linking equal levels of DCA scores, with the spatial distance between consecutive isolines being inversely related to the rate of compositional change across space and used to mark sharp compositional turnover zones (if closer together) or smoother compositional turnover (consecutive isolines further apart). In (a), the blue isoline corresponds to DCA score of 1.0 and the red isoline to soil pH = 5 (west of that line having a soil pH >5). In (b), the red isoline corresponds to maximum climatological water deficit (MCWD) = − 275 mm (south of that line having MCWD < −275), and the blue isoline to MCWD = −100 (west that line having MCWD > −100). The dark green line delimits the Amazonian tropical forests⁹⁵, with white areas within these limits corresponding to montane areas (above 500 m elevation) and non-forested habitats such as savannas. Major river courses are shown in blue. Base map source for countries: https://www.naturalearthdata.com/; rivers⁶¹. Maps created with custom R⁸⁸ script.
Niche positions and niche breadths of 5188 tree species along environmental and compositional gradients in Amazonia as calculated with data from 2023 1-ha forest-inventory plots
Gradients along the x axis: (a) Annual rainfall (mm); (b) maximum climatological water deficit (mm); (c) log(soil sum of bases (Ca+Mg+K)); (d) soil acidity (pH); (e) DCA1 scores from Fig. 2; and (f) DCA2 scores from Fig. 2. The black dots mark the mean niche position or optimum (weighted average value) for each species and the grey lines depict the niche breadths or tolerance (±standard deviation for the variable in sites where the species was observed). The red lines show the mean niche breadth (determined by loess regression). Coloured lines correspond to the lines also visible in Fig. 3 (DCA1, DCA2, pH, MCWD). Species are shown from bottom to top in the order of increasing niche position. (See supplementary data 1 for the niche breadth and position values of all tree species).
The associations of species niche positions on compositional and environmental gradients
In the first row the species niche positions on the DCA1 scores gradient in relation to edaphic niche position gradients: (a) Soil sum of bases, (b) Soil pH. The second row shows the species niche positions along the DCA2 scores gradient in relation to climatic gradients: (c) Annual Rainfall, (d) Maximum climatological water deficit. Plot colours correspond to colours in Fig. 3. Coloured lines correspond to the lines (DCA1, pH, MCWD) also visible in Fig. 3.
The biogeography of the Amazonian tree flora

October 2024

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1,925 Reads

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2 Citations

Communications Biology

We describe the geographical variation in tree species composition across Amazonian forests and show how environmental conditions are associated with species turnover. Our analyses are based on 2023 forest inventory plots (1 ha) that provide abundance data for a total of 5188 tree species. Within-plot species composition reflected both local environmental conditions (especially soil nutrients and hydrology) and geographical regions. A broader-scale view of species turnover was obtained by interpolating the relative tree species abundances over Amazonia into 47,441 0.1-degree grid cells. Two main dimensions of spatial change in tree species composition were identified. The first was a gradient between western Amazonia at the Andean forelands (with young geology and relatively nutrient-rich soils) and central–eastern Amazonia associated with the Guiana and Brazilian Shields (with more ancient geology and poor soils). The second gradient was between the wet forests of the northwest and the drier forests in southern Amazonia. Isolines linking cells of similar composition crossed major Amazonian rivers, suggesting that tree species distributions are not limited by rivers. Even though some areas of relatively sharp species turnover were identified, mostly the tree species composition changed gradually over large extents, which does not support delimiting clear discrete biogeographic regions within Amazonia.




Distribution of inventory data used to create habitat-specific compositional grids
Sampled sites include 1,705 mostly 1-ha tree inventory plots with full information on species composition and abundances. Plots were classified as terra firme (n = 1,250, 73%), várzea (n = 271, 16%), or igapó (n = 184, 11%), following habitat designations of ATDN contributors.
Schematic of the methods used to compare floodplain and terra firme tree compositions, illustrated for two grid cells
(a) Forest plot inventories (colored dots) were separated into várzea, igapó and terra firme categories, and species abundance information for separate várzea, igapó and terra firme grids was calculated at each 1-degree cell (only two shown), using distance-weighted interpolations of inventory plot data from an approximately 300 km circular window (red lines). (b) Floodplain and terra firme grids were overlaid and species turnover computed at analogous (vertically overlapping) cells. (c) Spatially-continuous grids of species turnover for várzea-terra firme and igapó-terra firme comparisons. The number of cells where species turnover is calculated depends on the spatial distribution of floodplain inventories and how it overlaps with terra firme inventories. This included 301 cells and 347 cells for várzea-terra firme and igapó-terra firme comparisons, respectively. In an alternative procedure of calculating species turnover, the interpolation step was excluded and cell compositional data was pooled only from plots located inside cells. For this second approach, the resulting number of cells where species turnover was calculated was 25 and 22 for várzea-terra firme and igapó-terra firme comparisons, respectively.
Comparison of flooding relationships with species turnover using two alternative procedures for populating cell compositional data
While interpolating species abundances maximizes the number of cells where species turnover can be calculated, it introduces spatial autocorrelation. On the other hand, pooling inventories within grid cells reduces the number of cells where species turnover can be calculated, but it maintains spatial independence among cells. We compared both methods to assess the robustness of our results to spatial dependencies. For the approach based on pooling, species cell abundance information was pooled only from inventories located inside individual grid cells, rather than interpolated from inventories from a larger 300 km circular window, in order to avoid residual spatial autocorrelation. Quantile regression slopes (at tau = 0.1) and their 95% confidences intervals are shown for várzea- and igapó-terra firme. The lower bounds of várzea-terra firme species turnover with flooding are statistically equivalent between pooled compositional data (slope ± 95% CI = 1.29 × 10⁻² ± 1.21 × 10⁻², t = 2.20, n = 25, p = 0.038) and interpolated data (slope ± 95% CI = 1.21 × 10⁻² ± 2.48 × 10⁻³, t = 9.57, n = 301, p < 0.001). The lower bounds for igapó-terra firme are likewise similar between pooled (slope ± 95% CI = 1.54 × 10⁻² ± 1.19 × 10⁻², t = 2.66, n = 22, p = 0.015) and interpolated methods (slope ± 95% CI = 1.05 × 10⁻² ± 2.60 × 10⁻³, t = 7.86, n = 347, p < 0.001). Slopes from all comparisons were significant (p < 0.05) and had overlapping 95% confidence intervals.
Broad-scale geographic and environmental patterning of species turnover across floodplain and adjacent terra firme forest habitats, for várzea–terra firme and igapó–terra firme comparisons
a, Spatial patterns of species turnover for várzea and igapó, showing a concentration of high species turnover located centrally within the fluvial network. Grey rivers are masked out because they either correspond to a different floodplain habitat or did not meet minimum sampling criteria for analysis. b, Regional differences in seasonal flooding are described as an annual flood wave that originates in Andean headwaters, peaks in central Amazonia and dissipates near the Amazon mouth. Floodplains positioned at the peak of this flood wave are seasonally inundated by the highest-amplitude and longest-lasting floods. LWT, land water thickness. c, Patterning of species turnover of várzea and igapó with surrounding terra firme along the flood wave. The black dashed line shows the lower bound of species turnover with flooding, assessed with quantile regression at τ = 0.1. d, Mapped residuals from quantile regression modelling for várzea and igapó. Throughout much of western Amazonia, species turnover is relatively higher than expected given the lower flooding implied by its headwater position on the flood wave.
Relationships between species turnover and the relative abundance and richness of floodplain specialists, habitat generalists and spillover from terra firme (terra firme specialists) in várzea and igapó
With increasing levels of species turnover, floodplain specialists become more dominant, while spillover from terra firme species decreases. The proportions are derived from interpolated compositional grids of várzea and igapó after cross-referencing with the names of the 1,666 species tested for habitat association. The relationships with species turnover are derived from simple least squares models. The coloured boxes indicate the proportion of explained variance (r²) and P values. The trend lines (black) are bounded by coloured bands showing the 95% CIs. Density plots for the relative abundance and richness of each species group are shown in the right margins.
One sixth of Amazonian tree diversity is dependent on river floodplains

March 2024

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1,234 Reads

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15 Citations

Nature Ecology & Evolution

Amazonia’s floodplain system is the largest and most biodiverse on Earth. Although forests are crucial to the ecological integrity of floodplains, our understanding of their species composition and how this may differ from surrounding forest types is still far too limited, particularly as changing inundation regimes begin to reshape floodplain tree communities and the critical ecosystem functions they underpin. Here we address this gap by taking a spatially explicit look at Amazonia-wide patterns of tree-species turnover and ecological specialization of the region’s floodplain forests. We show that the majority of Amazonian tree species can inhabit floodplains, and about a sixth of Amazonian tree diversity is ecologically specialized on floodplains. The degree of specialization in floodplain communities is driven by regional flood patterns, with the most compositionally differentiated floodplain forests located centrally within the fluvial network and contingent on the most extraordinary flood magnitudes regionally. Our results provide a spatially explicit view of ecological specialization of floodplain forest communities and expose the need for whole-basin hydrological integrity to protect the Amazon’s tree diversity and its function.


Geography and ecology shape the phylogenetic composition of Amazonian tree communities

February 2024

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1,166 Reads

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8 Citations

Aim: Amazonia hosts more tree species from numerous evolutionary lineages, both young and ancient, than any other biogeographic region. Previous studies have shown that tree lineages colonized multiple edaphic environments and dispersed widely across Amazonia, leading to a hypothesis, which we test, that lineages should not be strongly associated with either geographic regions or edaphic forest types. Location: Amazonia. Taxon: Angiosperms (Magnoliids; Monocots; Eudicots). Methods: Data for the abundance of 5082 tree species in 1989 plots were combined with a mega-phylogeny. We applied evolutionary ordination to assess how phylogenetic composition varies across Amazonia. We used variation partitioning and Moran's eigenvector maps (MEM) to test and quantify the separate and joint contributions of spatial and environmental variables to explain the phylogenetic composition of plots. We tested the indicator value of lineages for geographic regions and edaphic forest types and mapped associations onto the phylogeny. Results: In the terra firme and várzea forest types, the phylogenetic composition varies by geographic region, but the igapó and white-sand forest types retain a unique evolutionary signature regardless of region. Overall, we find that soil chemistry, climate and topography explain 24% of the variation in phylogenetic composition, with 79% of that variation being spatially structured (R2 = 19% overall for combined spatial/environmental effects). The phylogenetic composition also shows substantial spatial patterns not related to the environmental variables we quantified (R2 = 28%). A greater number of lineages were significant indicators of geographic regions than forest types. Main Conclusion: Numerous tree lineages, including some ancient ones (>66 Ma), show strong associations with geographic regions and edaphic forest types of Amazonia. This shows that specialization in specific edaphic environments has played a long-standing role in the evolutionary assembly of Amazonian forests. Furthermore, many lineages, even those that have dispersed across Amazonia, dominate within a specific region, likely because of phylogenetically conserved niches for environmental conditions that are prevalent within regions.


Tree alpha-diversity (Fisher’s alpha) in Amazonia
A Histogram of tree alpha-diversity in 2046 ATDN plots. Red lines, mean and mean ± 2 sd. B Tree alpha-diversity by major forest type. C Map of tree alpha-diversity across Amazonia. Legend truncated at 0 and mean + 2 standard deviation of the mean. Amazonian Biome limit - red⁷⁹. D Observed values of tree diversity vs modelled values of tree diversity on the 2046 plots used for mapping. The significance or Moran’s I was tested with the function Moran.I() of ape⁶¹. Marker colours: Red: Terra Firme Pebas Formation; Brown: Terra Firme Brazilian Shield; Orange: Terra Firme Guyana Shield; Yellow: White sand forest; Light blue: Varzea; Dark blue: Igapo; Purple: Swamp. Map created with custom R⁸⁰ script. Base map source (country.shp, rivers.shp): ESRI (http://www.esri.com/data/basemaps, © Esri, DeLorme Publishing Company).
Tree species-richness (species/ha) in Amazonia
A Histogram of tree species-richness in 2046 ATDN plots. B Tree species-richness by major forest type. C Map of tree species-richness across Amazonia. Legend truncated at mean ± 2 standard deviations of the mean. Amazonian Biome limit - red⁷⁹. D Observed values of tree species-richness vs modelled values of tree species-richness on the 2046 plots used for mapping. The significance or Moran’s I was tested with the function Moran.I() of ape⁶¹. Marker colours: Red: Terra Firme Pebas Formation; Brown: Terra Firme Brazilian Shield; Orange: Terra Firme Guyana Shield; Yellow: White sand forest; Light blue: Varzea; Dark blue: Igapo; Purple: Swamp. Map created with custom R⁸⁰ script. Base map source (country.shp, rivers.shp): ESRI (http://www.esri.com/data/basemaps, © Esri, DeLorme Publishing Company).
Tree alpha-diversity and tree species-richness of terra-firme forest in Amazonia
A Map of interpolated tree alpha-diversity (Fisher’s alpha), based on 1441 terra firme plots. B Map of tree species-richness (number of species/ha by plot), based on 1441 terra firme plots. Red polygon: Amazonian Biome limit⁷⁹. Map created with custom R⁸⁰ script. Base map source (country.shp, rivers.shp): ESRI (http://www.esri.com/data/basemaps, © Esri, DeLorme Publishing Company).
The effect of cumulative water deficit (mm), tree density, and temperature seasonality on tree species-richness
A Tree species-richness observed. B Tree species-richness as predicted by cumulative water deficit, regional tree density, and temperature seasonality. C Model performance, showing predicted and observed tree species-richness. D Residuals of tree species-richness predicted by cumulative water deficit, regional tree density, and temperature seasonality (A, B). All figures based on 1441 terra firme plots. Amazonian Biome limit - red⁷⁹. Map created with custom R⁸⁰ script. Base map source (country.shp, rivers.shp): ESRI (http://www.esri.com/data/basemaps, © Esri, DeLorme Publishing Company).
Mapping density, diversity and species-richness of the Amazon tree flora

November 2023

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2,234 Reads

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19 Citations

Communications Biology

ARTICLE Mapping density, diversity and species-richness of the Amazon tree flora Using 2.046 botanically-inventoried tree plots across the largest tropical forest on Earth, we mapped tree species-diversity and tree species-richness at 0.1-degree resolution, and investigated drivers for diversity and richness. Using only location, stratified by forest type, as predictor, our spatial model, to the best of our knowledge, provides the most accurate map of tree diversity in Amazonia to date, explaining approximately 70% of the tree diversity and species-richness. Large soil-forest combinations determine a significant percentage of the variation in tree species-richness and tree alpha-diversity in Amazonian forest-plots. We suggest that the size and fragmentation of these systems drive their large-scale diversity patterns and hence local diversity. A model not using location but cumulative water deficit, tree density, and temperature seasonality explains 47% of the tree species-richness in the terra-firme forest in Amazonia. Over large areas across Amazonia, residuals of this relationship are small and poorly spatially structured, suggesting that much of the residual variation may be local. The Guyana Shield area has consistently negative residuals, showing that this area has lower tree species-richness than expected by our models. We provide extensive plot meta-data, including tree density, tree alpha-diversity and tree species-richness results and gridded maps at 0.1-degree resolution.




Citations (6)


... Plants increase their productivity and tend to die younger because they have invested all their energy in growth rather than defense and longevity. For example, plants of the same species in fertile soils tend to grow faster and have lower wood density, so they are more likely to break in stronger winds (23,56,71,72). ...

Reference:

Mapping giant-tree density in the Amazon
Phylogenetic conservatism in the relationship between functional and demographic characteristics in Amazon tree taxa

... Additionally, the considerable diversity of species in certain plant families like Melostomataceae, which produce small fruits important for many frugivore species, may also play a key role in sustaining high values of β-diversity (Messeder et al. 2021). The elevated β-diversity observed may reflect the elevated functionality of the systems (Mori et al. 2018), highlighting the immense complexity of megadiverse regions (Valiente-Banuet et al. 2015, Solar et al. 2015, Bruno et al. 2024. ...

The biogeography of the Amazonian tree flora

Communications Biology

... 1. Expand protected areas ○ Establish and enforce new protected zones focused on freshwater biodiversity, particularly in vulnerable floodplain regions (Correa et al. 2022). ○ Prioritize areas critical for the reproduction and survival of migratory fish and aquatic species (Householder et al. 2024). ...

One sixth of Amazonian tree diversity is dependent on river floodplains

Nature Ecology & Evolution

... The evoPCA not only allows the representation of continuous gradients (as opposed to hard boundaries defined by hierarchical clustering methods), it also allows identifying specific phylogenetic lineages associated with assemblage positioning in the ordination space and is a powerful tool for analysing phylogenetic patterns along spatial and environmental gradients (Pavoine 2016). Thus, it can serve to test hypotheses about dispersal limitations and environmental filtering (Luize et al. 2024). We fit divergent colour gradients of a red-greenblue spectrum to the points in the ordinations using the first three axes of the ordination, to visualise the compositional phylogenetic distance between assemblages. ...

Geography and ecology shape the phylogenetic composition of Amazonian tree communities

... Amazonia is often cited as having the most diverse flora on the planet [1][2][3], which includes mountain areas with a small surface area but with a fascinating endemism [4]. However, these regions are seriously threatened by extensive land use and land cover changes that have occurred over the past few decades [5,6]. ...

Mapping density, diversity and species-richness of the Amazon tree flora

Communications Biology

... Human occupation dates back to 10,000 years ago in the area 59 , which constitutes one of the world's biodiversity hotspots at global level and together with other factors mentioned, offered an ideal setting to study differences in conservation effectiveness across different territories and the social-ecological contexts shaping Indigenous forest stewardship. ...

The geoglyph sites of Acre, Brazil: 10 000-year-old land-use practices and climate change in Amazonia

Antiquity