Functional composition of the Amazonian tree flora and forests

Abstract

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.

Fig. 1. 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 pioneer; Max, maximum diameter. For description of the traits and units, see Supplementary box 1.

Fig. 2. 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 pioneer; Environmental variables: Annual, Annual precipitation (Bioclim12); CWD cumulative water deficit, CAPE Convective atmospheric potential energy, WTC Windthrow count; PZ podzol, white-sand forest, FL flooded (swamp forest; várzea; igapó); pH, soil acidity; SB, log(sum of bases); G.prob, geoglyph probability; DSpp, domesticated species. 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.

Fig. 3. 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.

Fig. 4. 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 limit. Base map source (country.shp, rivers.shp), ESRI (http://www.esri.com/data/basemaps, © Esri, DeLorme Publishing Company).

Fig. 5. 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 bases. Red lines indicate 95% confidence intervals. Biomass data from sources^,,. 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.