S Pati Ño

J Lloyd

R Paiva

T R Baker

C A Quesada

L M Mercado

J Schmerler

M Schwarz

A J B Santos

A Aguilar

C I Czimczik

J Gallo

V Horna

E J Hoyos

E M Jimenez

W Palomino

J Peacock

A Pe Ña-Cruz

C Sarmiento

A Sota

J D Turriago

B Villanueva

P Vitzthum

E Alvarez

L Arroyo

C Baraloto

D Bonal

J Chave

A C L Costa

R Herrera

N Higuchi

T Killeen

E Leal

F Luizão

P Meir

A Monteagudo

D Neil

P N Ú Ñez-Vargas

M C Pe Ñuela

N Pitman

N Priante Filho

A Prieto

S N Panfil

A Rudas

R Salomão

N Silva

M Silveira

S Soares Dealmeida

A Torres-Lezama

R Vásquez-Martínez

I Vieira

Y Malhi

O L Phillips

Branch xylem density variations across the Amazon Basin

Xylem density is a physical property of wood that varies between individuals, species and environments. It reflects the physiological strategies of trees that lead to growth, survival and reproduction. Measurements of branch xylem density, ρ x , were made for 1653 trees representing 598 species, sampled from 87 sites across the Amazon basin. Measured values ranged from 218 kg m -3 for a Cordia sagotii (Boraginaceae) from Mountagne de Tortue, French Guiana to 1130 kg m -3 for an Aiouea sp. (Lauraceae) from Caxiuana, Central Pará, Brazil. Analysis of variance showed significant differences in average ρ x across regions and sampled plots as well as significant differences between families, genera and species. A partitioning of the total variance in the dataset showed that species identity (family, genera and species) accounted for 33% with environment (geographic location and plot) accounting for an additional 26%; the remaining "residual" variance accounted for 41% of the total variance. Variations in plot means, were, however, not only accountable by differences in species composition because xylem density of the most widely distributed species in our dataset varied systematically from plot to plot. Thus, as well as having a genetic component, branch xylem density is a plastic trait that, for any given species, varies according to where the tree is growing in a predictable manner. Within the analysed taxa, exceptions to this general rule seem to be pioneer species belonging for example to the Urticaceae whose branch xylem density is more constrained than most species sampled in this study. These patterns of variation of branch xylem density across Amazonia suggest a large functional diversity amongst Amazonian trees which is not well understood.

Introduction

Xylem tissue (wood) is a complex organic material composed of a matrix of hemicelluloses and lignin in which cellulose fibrils are embedded [START_REF] Harada | Ultrastructure of Angiosperm Vessels and Ray Parenchyma[END_REF][START_REF] Hamad | Cellulosic Material: Fibers, Networks, and Composites[END_REF][START_REF] Pallardy | The Woody Plant Body[END_REF]. It has a variety of functions in trees, such as structural support, actuation of the tree itself and of different organs [START_REF] Niklas | Plant Biomechanics[END_REF][START_REF] Fratzl | Cellulose fibrils direct plant organ movements[END_REF], long distance transport of water, inorganic ions, organic compounds and proteins from roots to leaves, and storage of water, carbohydrates and fat [START_REF] Gartner | Physiological Ecology Series: Plant Stems: Physiology and Functional Morphology[END_REF][START_REF] Smith | Conservation of element coneenbation in xylem sap of red spruce[END_REF][START_REF] Kehr | Analysis of xylem sap proteins from Brassica napus[END_REF]. Wood also contains the majority of the carbon stored in a tree [START_REF] Gartner | Physiological Ecology Series: Plant Stems: Physiology and Functional Morphology[END_REF]. As the structure of xylem tissue changes as a result of environmental requirements and phylogenetic constrains, so does xylem function [START_REF] Carlquist | Ecological Strategies of Xylem Evolution[END_REF][START_REF] Tyree | The hydraulic architecture of trees and other woody plants[END_REF][START_REF] Niklas | Plant Biomechanics[END_REF][START_REF] Gartner | Physiological Ecology Series: Plant Stems: Physiology and Functional Morphology[END_REF][START_REF] Tyree | Xylem Structure and the Ascent Sap[END_REF][START_REF] Bass | Evolution of Xylem Physiology[END_REF] and the quantity of stored carbon within this tissue Correspondence to: S. Patiño (sanpatiga@gmail.com) too [START_REF] Elias | Assessing inter-and intra-specific variation in trunk carbon concentration for 32 neotropical tree species[END_REF]. Density, ρ, (the ratio between oven-dry mass and fresh volume of xylem tissue) is one of the physical properties of wood [START_REF] Kollmann | Principles of Wood Science and Technology[END_REF] and provides an index of the balance between solid material (i.e. cell wall, parenchyma) and void (i.e. lumen of fibres, tracheids and conductive elements) of the xylem tissue. Therefore, changes in wood density are directly associated with structural variations at the molecular, cellular and organ levels. These structural differences are strongly correlated with the tree's mechanical properties [START_REF] Givnish | Economics of Support[END_REF][START_REF] Niklas | Plant Biomechanics[END_REF][START_REF] Gartner | Physiological Ecology Series: Plant Stems: Physiology and Functional Morphology[END_REF], water transport efficiency and safety [START_REF] Hacke | Functional and ecological xylem anatomy[END_REF][START_REF] Tyree | Xylem Structure and the Ascent Sap[END_REF][START_REF] Jacobsen | Do Xylem Fibers Affect Vessel Cavitation Resistance?[END_REF][START_REF] Holbrook | Vascular Transport in Plants[END_REF][START_REF] Pittermann | Mechanical reinforcement of tracheids compromises the hydraulic efficiency of conifer xylem[END_REF], rates of carbon exchange [START_REF] Tyree | Hydraulic limits on tree performance: transpiration, carbon gain and growth of trees[END_REF][START_REF] Jacobsen | Do Xylem Fibers Affect Vessel Cavitation Resistance?[END_REF][START_REF] Ishida | Coordination between leaf and stem traits related to leaf carbon gain and hydraulics across 32 drought-tolerant angiosperms[END_REF] and perhaps resistance to pathogens and herbivores [START_REF] Rowe | Plant growth forms: an ecological and evolutionary perspective[END_REF]. Different species from different taxonomic, phylogenetic and architectural groups show convergence of these functional characteristics in response to the environment [START_REF] Meinzer | Functional convergence in plant responses to the environment[END_REF].

In this work we make the distinction between the density of the wood from the main trunk (here defined as wood density, ρ w ) normally measured at 1.3 m from the ground (possibly including both sapwood and heartwood that may have been air or oven-dried) and that of the sapwood or functional xylem of small (ca. 1.5 cm diameter) terminal branches of trees (here defined as xylem density, ρ x ). Xylem density is considered as a potential proxy for tree hydraulic architecture (water transport) [START_REF] Stratton | Stem water storage capacity and efficiency of water transport: their functional significance in a Hawaiian dry forest[END_REF][START_REF] Gartner | Structure-Function Relationships in Sapwood Water Transport and Storage[END_REF][START_REF] Meinzer | Constraints on physiological function associated with branch architecture and wood density in tropical forest trees[END_REF]. There is evidence supporting the idea that hydraulic architecture may limit tree performance in terms of transpiration, carbon exchange and growth, [START_REF] Tyree | Hydraulic limits on tree performance: transpiration, carbon gain and growth of trees[END_REF][START_REF] Meinzer | Constraints on physiological function associated with branch architecture and wood density in tropical forest trees[END_REF]. For example, there have been reports showing how ρ x scales negatively with leaf gas exchange and water balance for neotropical forest trees with contrasting phenologies subjected to contrasting rainfall regimes [START_REF] Santiago | Leaf photosynthetic traits scale with hydraulic conductivity and wood density in Panamanian forest canopy trees[END_REF][START_REF] Meinzer | Constraints on physiological function associated with branch architecture and wood density in tropical forest trees[END_REF], for neotropical savannah trees [START_REF] Bucci | Functional convergency in hydraulic architecture and water relations of tropical savanna trees: from leaf to whole plant[END_REF][START_REF] Scholz | Biophysical properties and functional significance of stem water storage tissues in Neotropical savanna trees[END_REF], Hawaiian dry forests trees [START_REF] Stratton | Stem water storage capacity and efficiency of water transport: their functional significance in a Hawaiian dry forest[END_REF] and Californian chaparral species [START_REF] Pratt | Relationships among xylem transport, biomechanics and storage in stems and roots of nine Rhamnaceae species of the California chaparral[END_REF]. For different environments (California chaparral, South African Mediterranean-type climate, Sonoran desert, Great Basin of central Utah) and for both gymnosperm and angiosperm trees and shrubs with distinct xylem structure (ring porous and diffuse porous), ρ x scales positively with xylem resistance to cavitation and mechanical strength [START_REF] Hacke | Functional and ecological xylem anatomy[END_REF][START_REF] Pratt | Relationships among xylem transport, biomechanics and storage in stems and roots of nine Rhamnaceae species of the California chaparral[END_REF][START_REF] Scholz | Biophysical properties and functional significance of stem water storage tissues in Neotropical savanna trees[END_REF]Jacobsen et al., 2007a, b;Dalla-Salda et al., 2008). It also, has been proposed that high density wood is necessary to avoid xylem implosion due to negative water tension inside xylem conduits [START_REF] Hacke | Functional and ecological xylem anatomy[END_REF]. These findings strongly suggest that xylem density could be used as a "trait" to predict the different physiological strategies of trees in tropical forests.

For any given species ρ x and ρ w should be related (Swenson and [START_REF] Swenson | The relationship between stem and branch wood specific gravity and the ability of each measure to predict leaf area[END_REF][START_REF] Sarmiento | Variation in trunk and branch xylem density in French Guiana[END_REF] as both reflect an individual species' water transport strategy and mechanical requirements [START_REF] Asner | Correlating Stem Biomechanical Properties of Hawaiian Canopy Trees with Hurricane Wind Damagein[END_REF][START_REF] Wagner | Tradeoffs between hydraulic efficiency and mechanical strength in the stems of four co-occurring species of chaparral shrubs[END_REF][START_REF] Taneda | The criteria for biomass partitioning of the current shoot: water transport versus mechanical support[END_REF]. Nonetheless, there are many important structural and functional differences between branch and trunk wood as a result of different loading, hydraulic, architectural, and genetic constrains (Zobel and van Buijtenen, 1989;[START_REF] Gartner | Physiological Ecology Series: Plant Stems: Physiology and Functional Morphology[END_REF][START_REF] Domec | Age-and position-related changes in hydraulic versus mechanical dysfunction of xylem: inferring the design criteria for Douglas-fir wood structure[END_REF][START_REF] Cochard | Hydraulic architecture correlates with bud organogenesis and primary shoot growth in beech (Fagus sylvatica)[END_REF]Dalla-Salda et al., 2008). Trunk wood density may also be affected by factors in addition to those modulating ρ x . For example, it may reflect differences in the storage of resins or variation in the storage of secondary compounds within bole heartwood over time, or intrinsic species-specific differences on wood density gradients within the main trunk (Wiemann andWilliamson, 1988, 1989;[START_REF] Parolin | Radial gradients in wood specific gravity in trees of central Amazonian floodplains[END_REF][START_REF] Knapic | Within-tree and between-tree variation of wood density components in cork oak trees in two sites in Portugal[END_REF]. In branches these additional effects may not occur, or at least not to the same extent.

It has long being known that ρ w is a genetically conserved trait, and this characteristic has been used extensively in tree breeding (Zobel and van Buijtenen, 1989;Zobel and Jett, 1995;Yang et al., 2001). However, in plantations it is well known that for a given tree species, marked variations may also occur due to differences in genotype, climate, soil factors and management [START_REF] Cown | Radiata pine wood properties survey[END_REF][START_REF] Beets | Wood density of radiata pine: effect of nitrogen supply[END_REF][START_REF] Roque | Effect of management treatment and growing regions on wood properties of Gmelina arborea in Costa Rica[END_REF][START_REF] Thomas | Why does phosphorus limitation increase wood density in Eucalyptus grandis seedlings?[END_REF]. In neotropical forests, particularly in the Amazon basin, site-specific differences have been noticed when comparing the same species growing in different forests and/or site conditions (Wiemann and Williamson, 1989;[START_REF] Gonzalez | Variation in selected wood Properties of Vochysia guatemalensis from four sites in Costa Rica[END_REF][START_REF] Woodcock | Wood characteristics of Amazon forest types[END_REF][START_REF] Muller-Landau | Interspecific and inter-site variation in wood specific gravity of tropical trees[END_REF][START_REF] Roque | Effect of management treatment and growing regions on wood properties of Gmelina arborea in Costa Rica[END_REF][START_REF] Nogueira | Wood density in dense forest in central Amazonia, Brazil[END_REF][START_REF] Nogueira | Wood density in forests of Brazil's "arc of deforestation": Implications for biomass and flux of carbon from land-use change in Amazonia[END_REF][START_REF] Schöngart | Wood growth patterns of Macrolobium acaciifolium (Benth.) Benth. (Fabaceae) in Amazonian black-water and whitewater floodplain forests[END_REF][START_REF] Wittmann | Wood specific gravity of trees in Amazonian white-water forest in relation to flooding[END_REF]. A special case of complex systems seems to be the Amazonian floodplains. When comparing the same species growing on nutrient-rich white water floodplains (várzea) and nutrient-poor black water floodplains (igapó), [START_REF] Parolin | Radial gradients in wood specific gravity in trees of central Amazonian floodplains[END_REF] and [START_REF] Parolin | Are there differences in specific wood gravities between trees in varzea and igapo (Central Amazonia)?[END_REF] found higher ρ w values in igapó forest. Such differences might have been due to the combined effect of forest successional stages (young successional stages in the várzea and old-growth forest in the igapó) and differences in soil nutrient availability. For Macrolobium acciifolium studied in both habitats at the same successesional stage (old-growth forest) and at the same elevation showed that in várzea ρ w was higher than in igapó [START_REF] Schöngart | Wood growth patterns of Macrolobium acaciifolium (Benth.) Benth. (Fabaceae) in Amazonian black-water and whitewater floodplain forests[END_REF]. At the community level, low ρ w is often associated with one or a combination of high soil fertility, high rates of forest disturbance, early and secondary successional vegetation and/or high rates of tree growth and mortality, [START_REF] Saldarriaga | Recovery Following Shifting Cultivation: A Century of Succession in the Upper Rio Negro, In: Amazonian rain forests. Ecosystem disturbance and recovery[END_REF]Wiemann and Williamson, 1989;[START_REF] Enquist | Allometric scaling of production and life-history variation in vascular plants[END_REF][START_REF] Woodcock | Wood characteristics of Amazon forest types[END_REF][START_REF] Roderick | Linking wood density with tree growth and environment: a theoretical analysis based on the motion of water[END_REF][START_REF] Ter Steege | Character convergence, diversity, and disturbance in tropical rain forest in Guyana[END_REF][START_REF] Muller-Landau | Interspecific and inter-site variation in wood specific gravity of tropical trees[END_REF][START_REF] Baker | Variation in wood density determines spatial patterns in Amazonian biomass[END_REF][START_REF] Nogueira | Wood density in dense forest in central Amazonia, Brazil[END_REF][START_REF] King | Tree growth is related to light interception and wood density in two mixed dipterocarp forest of Malaysia[END_REF][START_REF] King | The role of wood density and stem support costs in the growth and mortality of tropical trees[END_REF][START_REF] Erskine | Growth performance and management of a mixed rainforest tree plantation[END_REF][START_REF] Wittmann | Wood specific gravity of trees in Amazonian white-water forest in relation to flooding[END_REF][START_REF] Chao | Growth and wood density predict tree mortality in Amazon forests[END_REF][START_REF] Slik | Wood Density as a Conservation Tool: Quantification of Disturbance and Identification of Conservation-Priority Areas in Tropical Forests[END_REF].

The Amazon Basin is the most diverse and largest contiguous tropical forest on the planet [START_REF] Malhi | Tropical forests and atmospheric carbon dioxide[END_REF][START_REF] Laurance | Pervasive alteration of tree communities in undisturbed Amazonian forest[END_REF]. Different ecological systems and vegetation formations with contrasting species compositions and life history traits [START_REF] Ter Steege | An analysis of the floristic composition and diversity of Amazonian forests including those of the Guiana Shield[END_REF][START_REF] Ter Steege | Continental-scale patterns of canopy tree composition and function across Amazonia[END_REF], geological origins [START_REF] Fittkau | Productivity, Biomass, and Population Dynamics in Amazonian Water Bodies[END_REF]Quesada et al., 2009a), climates [START_REF] Sombroek | Spatial and Temporal Patterns of Amazon Rainfall[END_REF][START_REF] Baker | Variation in wood density determines spatial patterns in Amazonian biomass[END_REF], and an enormous diversity of soils [START_REF] Sombroek | Amazonian landforms and soils in relations to biological diversity[END_REF]Quesada et al., 2009b) exist within its boundary creating a mosaic of forests and vegetation types with such a floristic complexity the basis of which is still not well understood [START_REF] Phillips | Habitat association among Amazonian tree species: a landscape-scale approach[END_REF]. How and why species are distributed [START_REF] Leigh | Why Do Some Tropical Forests Have So Many Species of Trees?[END_REF][START_REF] Pitman | Tree Community Change across 700 km of Lowland Amazonian Forest from the Andean Foothills to Brazil[END_REF], what explains differential productivity [START_REF] Malhi | Spatial patterns and recent trends in the climate of tropical rainforest regions[END_REF] and dynamic patterns across Amazonian regions [START_REF] Phillips | Increasing turnover through time in tropical forests[END_REF][START_REF] Phillips | Increasing dominance of large lianas in Amazonian forests[END_REF][START_REF] Phillips | Pattern and process in Amazon tree turnover 1976-2001[END_REF][START_REF] Lewis | Concerted changes in tropical forest structure and dynamics: evidence from 50 South American long-term plots[END_REF]Baker et al., 2004a), how much carbon is being absorbed and released to the atmosphere [START_REF] Grace | Carbon-dioxide uptake by an undisturbed tropical rain-forest in southwest Amazonia, 1992 to[END_REF][START_REF] Phillips | Changes in the carbon balance of tropical forests: Evidence from long-term plots[END_REF][START_REF] Malhi | Tropical forests and atmospheric carbon dioxide[END_REF][START_REF] Malhi | Spatial patterns and recent trends in the climate of tropical rainforest regions[END_REF][START_REF] Malhi | The regional variation of aboveground live biomass in old-growth Amazonian forests[END_REF][START_REF] Clark | Are tropical forest an important carbon sink? Reanalysis of the long-term plot data[END_REF], how Amazonian forests are responding to global change [START_REF] Phillips | The changing ecology of tropical forests[END_REF][START_REF] Cox | Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model[END_REF][START_REF] Laurance | Pervasive alteration of tree communities in undisturbed Amazonian forest[END_REF][START_REF] Wright | Tropical forests in a changing environment[END_REF][START_REF] Phillips | Drought Sensitivity of the Amazon Rainforest[END_REF] are some of the questions that have motivated this research [START_REF] Lloyd | Introducing the "Biogeochemistry and Function of Amazon Forest" project[END_REF]. By studying ρ x across Amazonia we hoped to gain insights into the understanding of the functioning of Amazonian forests and, for the first time, rigorously examine the importance of both environmental and genetic controls on a plant trait over large scales for the tropical forest biome. By analysing the geographic and taxonomic patterns of branch xylem density from different trees and forests across Amazonia, we address the following three questions:

1. Are there detectable patterns of ρ x across Amazonia? If so, are those patterns related to taxonomic differences and/or to overall site conditions?

2. Are there differences in average values between forests and between different taxonomic groups?

3. Does the xylem density of particular species change across the basin according to the observed regional patterns?

Based on what it is known for ρ w and ρ x we hypothesised that ρ x is a "plastic" trait that reflects both phylogenetic constrains and environmental gradients. We predict that individuals of species that are widespread across Amazonia will show an increase in ρ x as the average-plot ρ x increases and that the intra-specific variation of these common species is larger than the variations of different species growing in the same forest. There is evidence that substantial functional convergence exits between species from different phylogenetic and taxonomic groups in the same environment [START_REF] Meinzer | Functional convergence in plant responses to the environment[END_REF] and long term acclimatisation in response to the environment has been observed for xylem tissue [START_REF] Hietz | Long-term trends in cellulose 13 C and water-use efficiency of tropical Cedrela and Swietenia from Brazil[END_REF][START_REF] Holste | Long-term acclimatization of hydraulic properties, xylem conduit size, wall strength and cavitation resistance in Phaseolus vulgaris in response to different environmental effects[END_REF].

Methods

Study sites

Eighty-seven forest plots from across the Amazon basin were sampled, typically at the end of the rainy season, between January 2001 and December 2005. Two plots were sampled in Paracou, French Guiana in September 2007 and seven additional plots were sampled between May 2007 and September 2008 (see details below). The first 82 plots form part of the RAINFOR project (www.rainfor.org; [START_REF] Malhi | An international network to monitor the structure, composition and dynamics of Amazonian forests (RAINFOR)[END_REF] and span local, regional and Basin-wide environmental gradients. Many of the plots have been described in detail elsewhere [START_REF] Vinceti | Spatial and temporal analysis of Floristic composition and Dynamics in some lowland Amazonian forests[END_REF][START_REF] Malhi | Spatial patterns and recent trends in the climate of tropical rainforest regions[END_REF][START_REF] Phillips | Pattern and process in Amazon tree turnover 1976-2001[END_REF]Baker et al., 2004a, b). The additional seven plots form part of the BRIDGE, ANR project (http://www.ecofog.gf/ Bridge/). Appendix A lists all the plots visited, including those not previously described, and in some cases with updated information.

Sampling of plant material

The RAINFOR protocol

Normally, around 20 trees greater than 10 cm dbh (diameter at breast height i.e. at 1.3 m from the base of the tree) were chosen in each plot for wood density sampling. On some occasions, such as when plots were unusually heterogeneous, as a consequence of topographic variations and/or shape (i.e. 1000×10 m) more trees were sampled (e.g. BOG-plots). For two of the Caxiuana plots (Central Pará, Brazil) we sampled in two consecutive years (2002 and 2003) and since there was no statistical difference in ρ x for the two years, we combined all this data for the following analyses. When a plot was clearly composed of different defined landscapes, and each landscape was considered as an individual plot, on average 10 trees were sampled for each landscape (e.g. Jacaranda Plots, Km 34 Manaus, Brazil).

Trees were not chosen completely at random, sampling within each plot accounted for two factors. First, there was a selection of three to six contrasting areas (e.g. slopes, valleys, gaps, creeks, swamps) where these were present. Secondly, a professional tree climber then chose a "climbable tree" within the identified areas. Naturally, this "climbable tree" varied from climber to climber according to the technique employed and overall climbing skills. Nevertheless a general consideration was that from the "climbable tree", upper branches (exposed to light) of at least three neighbouring trees were reachable, either by moving himself from tree to tree or by using a clipper pole. In each plot we also sampled branches from low, middle and upper crown from a subsample (three to 5 trees) of the total number of trees sampled. These trees were selected on the basis of having three types of branches: upper canopy = exposed to light, middle = midlight and lower = shaded.

The sampling strategy for the Guyaflux plots

For the Guyaflux plots, mostly lower branches from subcanopy trees were sampled using a chain saw manipulated from the ground. To determine if data from lower branches introduces a bias in the data, we compared ρ x of upper and lower branches from our sub-sample of 272 trees. We found no statistical differences between the density of branches from the two positions on the trees (ANOVA, DF =1, F =0.18, P =0.674, mean upper branches=619 kg m -3 , mean lower branches=615 kg m -3 ). The ρ x values of trees sampled at Paracou followed a normal distribution, and included the range of densities measured for ρ w of 309 trees of a neighbouring plot. The composition of the trees sampled was also similar to the abundance distribution of the main families present in the Guyaflux plots (Jacques Beauchêne, BRIDGE unpublished data).

The BRIDGE protocol

For the BRIDGE protocol, the sampling strategy was basically the same: professional climbers selected "climbable trees" and from there moved across the canopy collecting upper branches from 70 to 100% of the trees present in the plots. From these branches 40 to 90% were used for xylem density determinations.

In order to assess the representativeness of the sampling strategy generally utilised across the Basin (usually only 20 trees per one hectare plot), we took advantage of the more comprehensive BRIDGE measurements to assess how representative this sampling strategy really was. Thus, for each comprehensively sampled BRIDGE plot we chose a sub-set of four clusters of five trees selected randomly across the sampled area (this also taking into account any topographic variability), comparing the estimated "plot level" values as calculated from these twenty trees only with the true plot mean.

Species identification

Details of the species identification from the permanent plots are described elsewhere [START_REF] Baker | Variation in wood density determines spatial patterns in Amazonian biomass[END_REF]

Xylem density determinations

A pair of consecutive segments of 0.05 to 0.1 m long and 0.01 to 0.02 m diameter were cut from each branch after harvesting and immediately placed in plastic bags to avoid desiccation and returned to the laboratory or field station. Normally within 12 h of sampling (but sometimes as long as 36 h later) the outer bark and phloem were removed from one of the two sample stems (the second sample was dried with the leaves and stored for possible further analysis) and its fresh volume calculated from its length and the average diameter of the two perpendicular diameters at each end. When the pith was wider than 2 mm diameter the stem was cut into a small segment (0.02 to 0.05 m long) and the pith removed with a small screw driver or scalpel. When the pith was thinner than 2 mm it was not removed from the stem (as it was thus assumed to be of negligible mass) but its volume subtracted from the volume of the stem without bark. Pith volume was calculated by measuring the average diameter (two measurements of diameter at each end of the stem) and stem length. All stems were then dried at 70-90 • C for three to four days (to constant mass) and weighed. Xylem density, ρ x , was then determined as the dry mass divided by the green volume of the sample.

Statistical analysis

Basic statistics shown in Figs. 1, 3 and 4 were performed with Minitab 15 (Minitab Inc.).

In order to apportion the variance within the dataset [START_REF] Searle | Variance Components[END_REF] into geographical and taxonomic components, we fitted a model according to

ρ x = µ + r/p + f/g/s + ε (1)
where µ represents the overall mean of the dataset (619 kg m -3 ); effects of location are incorporated in the term r/p, which denotes that within each region (r) are nested more than one plot (p); genetic effects are represented by the term f /g/s, which denotes that within each family (f ) are nested various genera (g), within which are nested several species (s); and (ε) represents the residual variance. All effects were taken as random variables, as we had sampled only a limited subset of plots within distinct but not comprehensive regions; we also sampled a more or less random (and incomplete) selection of Amazon families, genera and species. Variance partitioning for Fig. 5 was accomplished by applying Residual Error Maximum Likelihood (REML) analysis [START_REF] Gilmour | Average information REML, an efficient algorithm for variance parameterisation in linear mixed models[END_REF] employing GENSTAT Discovery Edition. Xylem density, kg m All Standard Major Axis line-fittings for Fig. 6a, b, c were undertaken using SMATR package [START_REF] Warton | Bivariate line-fitting methods for allometry[END_REF]. Mixed-effect modelling (Fig. 7) was carried out using "lmne" [START_REF] Bates | Linear Mixed-Effects Models Using S4 Classes, R package version 0[END_REF] and rank-based linear regression (Fig. 8) accomplished as in [START_REF] Terpstra | Rank Based analysis of linear models using R[END_REF], both using the "R" statistical computing package (R Development Core Team, 2007). For the latter analysis, we applied the "high-breakpoint" option to account for the possibility of "contaminated" data having been included in any of the ρ w values assimilated from a wide range of sources into the RAINFOR "wood density" database.

In order to determine the extent to which ρ x changed 1) in a given species within the same plot and between plots and 2) to estimate the variation within a given plot we calculated IPP (index of phenotypic plasticity) and IV (index of variation) respectively [START_REF] Valladares | Plastic phenotypic response to light of 16 congeneric shrubs from a Panamanian rainforest[END_REF]. IPP and IV were computed as the, absolute difference between the maximum value and the minimum value divided by the maximum value.

Results

We measured ρ x of 1653 trees (see supplementary material:

http://www.biogeosciences.net/6/545/2009/ bg-6-545-2009-supplement.zip) from 87 plots (Appendix A) across the Amazon basin. Data for ρ x followed normal distribution with mean and median values of 619 kg m -3 and 612 kg m -3 , respectively; normality test (StDev=0.124, N=1653, AD=1.202 P <0.005).

Of all the trees sampled, 95% (1568) had been identified to the family level, 89% ( 1475) to the genus level, and 72% ( 1199) to the species level. The trees sampled accounted for 60 families, representing 41% of the total number of families present in the neotropics [START_REF] Mass | Neotropical Plant Families[END_REF] with 283 genera, and 598 species being sampled. The most common families sampled in our data set in order of abundance were Fabaceae, Sapotaceae, Lecythidaceae, Moraceae, Burseraceae, Myristicaceae, Lauraceae, Annonaceae, Euphorbiaceae, Chrysobalanaceae, with the most common genera being Eschweilera, Pouteria, Protium, Inga, Licania, Virola, Pseudolmedia, Pourouma, Lecythis, Miconia. The most common species were Eschweilera coriacea, Pseudolmedia laevis, Rinorea guianensis, Tetragastris altissima, Minquartia guianensis, Pourouma guianensis, Pseudolmedia macrophylla, Lecythis persistens, Miconia poeppigii, and Pourouma minor. At the family level we had 86 (5%) undetermined individuals. At the genus level we had 21 undetermined Protium sp., 18 Pouteria sp., 14 Inga sp., 11 Ocotea sp., 11 Eschweilera sp., 10 Virola sp. among others. The distribution of families and genera in our dataset represents well previous descriptions of floristic composition across Amazonia [START_REF] Terborgh | The composition of Amazonian forests: patterns at local and regional scales[END_REF][START_REF] Ter Steege | An analysis of the floristic composition and diversity of Amazonian forests including those of the Guiana Shield[END_REF][START_REF] Ter Steege | Continental-scale patterns of canopy tree composition and function across Amazonia[END_REF].

Geographic variation

Arithmetic means of ρ x for the 87 plots are shown in Fig. 1, which also shows our separation into 13 discrete geographical regions mainly determined by the proximity of plots. These regions are used for subsequent analysis.

From Fig. 1 a gradient of increasing ρ x from North and South towards the Amazon River is apparent with high ρ x being concentrated along the river itself. Plots located close to the Andes, North and South from the Amazon River tended to have the lowest ρ x . For example, all plots in Ecuador, some in N-Perú, all in S-Perú and AC-Brazil and Bolivia had relatively low ρ x compared to some plots in Colombia and N-Perú which were all at lower altitudes and closer to the Amazon River. Northern (NE-Venezuela, SW-Venezuela and Guiana) and Southern (Bolivia and MT-Brazil) regions tended to have lower ρ x compared to regions paralleling Amazon River (some plots in N-Peru and Colombia, AM-Brazil, WP-Brazil, CP-Brazil, EP-Brazil). To explore this trend, plot coordinates (latitude and longitude), altitude, mean annual temperature, and mean annual rainfall (see Appendix A) were plotted against xylem density (Fig. 2). High density sites were located between 0 • and -5 • while low density sites occurred in all the latitudinal range covered by this study: ≈10 • to -15 • (Fig. 2a). Xylem density also tended to increase from West to East (Fig. 2b) as has been reported for ρ w [START_REF] Baker | Variation in wood density determines spatial patterns in Amazonian biomass[END_REF][START_REF] Chave | Regional and phylogenetic variation of wood density across 2,456 neotropical tree species[END_REF][START_REF] Ter Steege | Continental-scale patterns of canopy tree composition and function across Amazonia[END_REF]. The western margin is marked by the low ρ x of the Ecuadorian plots (Fig. 2b). These were the plots closest to the Andes with higher altitudes (Fig. 2c), lower annual mean temperatures (Fig. 2d), higher annual rainfall (Fig. 2e), and more fertile soils [START_REF] Malhi | Spatial patterns and recent trends in the climate of tropical rainforest regions[END_REF]Quesada et al., 2008). The Eastern most plots, located in EP-Brazil include a mangrove forest which had higher ρ x than rest of forest plots in the same region (Region 12, Fig. 1). An inverse relationship between altitude and ρ x (Fig. 2c) and a positive relationship with average air temperature (Fig. 2d) points to an effect of environmental conditions upon ρ x . Low density sites are found in the two extremes of the rainfall range (Fig. 2e). In the low rainfall range (Bolivia and NE-Venezuela for example) there are the Bolivian seasonally flooded (LSL-01 and LSL-02), liana (CHO-01) and gallery forests plots (HCC-21 and HCC-22) where soils may retain enough soil moisture thus high soil water potential during the dry season. In the rest of the Bolivian and Venezuelan forests, trees may have distinct mechanisms common in seasonal forests such as low density wood, high stem water storage capacity and/or deciduous leaves (Choat et al., 2005) to cope with prolonged drought. In the high rainfall range there are the low density Ecuadorian sites and the intermediate density Guiana plots.

Taking the Basin as a whole (no division into regions), statistically significant differences existed between plot means (P <0.001) ranging from 800±50 kg m -3 (± standard deviation) at the dry experimental plot at Caxiuana (Projecto Secaflor), CAX-04, with the nearby control plot CAX-03 being the second highest at 780±120 kg m -3 . These are both terra firme forests on acrisol soils with 80% sand in its upper layer (Quesada et al., 2009b). The lowest plot means were for TAM-03, a swamp forests in Tambopata, and JAS-05 a forest growing on recently deposited river sediments (fluvisol) in Jatun Sacha in Ecuador. Both these plots had a mean ρ x of 470 kg m -3 . Data for all 87 plots are summarised in Appendix B.

Figure 3 gives means (± standard deviations) for all plots, grouped according to region, with regions being presented sequentially from top to bottom according to the overall mean ρ x for the trees sampled within them. This shows that, although considerable plot-to-plot variation existed within regions (e.g. N-Perú and Colombia) large statistical differences between regions also existed (P <0.001). Of these, the highest overall value was for CP-Brazil (754±126 kg m -3 , N=143) which had significantly higher ρ x (Tukey Test) than the rest of the regions while Ecuador had the lowest overall values (535±89 kg m -3 ). Nevertheless, Ecuador did not differ significantly from Bolivia, S-Perú and AC-Brazil, MT-Brazil, and Colombia. Within some regions: PC-Brazil, PE-Brazil, N-Perú, PW-Brazil, Colombia, S-Perú, MT-Brazil and Ecuador, mean ρ x of plots varied considerably (Appendix B), while in some regions Bolivia, AC-Brazil, NE-Venezuela, SW-Venezuela, plots were not significantly different from each other. The most variable plots were TAP-04, CAX-02, M17-11 (IV=0.76, 0.76 and 0.73, respectively) with the least variable being BNT-04, YAN-02, ALP-12 and TAP-03 (IV=0.24, 0.27, 0.27, and 0.27, respectively). IV values for all the plots can be seen in Appendix B.

Taxonomic variation

In a similar manner to the Region/Plot analysis above, variation in ρ x at the family and genera level is summarised in Fig. 4. Overall there were significant differences between the families sampled (F =8.08 DF=57 P <0.001). Families with ρ x higher than the basin mean were Olacaceae, Celastraceae, Chrysobalanaceae, Humiriaceae, Ochnaceae, Linaceae, Scrophulariaceae, Myrtaceae, and Lecythidaceae. Families with lower ρ x were Boraginaceae, Bixaceae, Sabiaceae, Lepidobotryaceae, Lacistemataceae, Rhamnaceae, Malvaceae, Annonaceae, Myristicaceae, Urticaceae, Vochysiaceae, Araliaceae, Dichapetalaceae, Bignoniaceae, and Euphorbiaceae. The remaining families all contained genera characterised by both high and low ρ x and include some of the most abundant families across the basin: Fabaceae, Rubiaceae, Lauraceae, Sapotaceae, Apocynaceae (Fig. 4). There were also significant differences between genera (F =3.78 DF=249 P <0.0001) with the highest density genera being Aiouea, Callichlamys, Pithecellobium, Vatairea, Stachyarrhena, Dipteryx, and Machaerium. The genera with the lowest densities were Annona, Matisia, Tetrorchidium, Collophora, Onychopetalum, Hyeronima, and Luehea.

Partialling out geographical and taxonomic differences

Results of this analysis are shown in Fig. 5. Taxonomic variance in ρ x was inferred as 33% of the total variance in the dataset, with species and family accounting for 19% and genus per se accounting only for 9%. Taken together, the geographical parameters (region and plot) accounted for 26% of the total variation with 20% of this being attributable to between region variation (this represents the average variation between plots in any one region). Overall, the proportion of the variance in the dataset that remained unexplained was 41%. This is the "residual variance" reflecting tree-totree variation within individual species, perhaps as a result of architectural changes due to space constraints [START_REF] Cochard | Hydraulic architecture correlates with bud organogenesis and primary shoot growth in beech (Fagus sylvatica)[END_REF], but also incorporating any measurement error.

The analysis here differs from others [START_REF] Baker | Variation in wood density determines spatial patterns in Amazonian biomass[END_REF][START_REF] Ter Steege | Continental-scale patterns of canopy tree composition and function across Amazonia[END_REF][START_REF] Chave | Regional and phylogenetic variation of wood density across 2,456 neotropical tree species[END_REF]) in that we have not taken overall means for each species, but rather included intra-specific variation and the possibility of systematic plotto-plot variations in our interpretation. Figure 5 thus suggests that geographic location is as important as taxonomic identity in determining the value of ρ x observed for any given tree but with considerable variation accountable for by neither. The first point is demonstrated further in Fig. 6, where we have taken the more widely abundant families (Fig. 6a) genera (Fig. 6b) and species (Fig. 6c) in our data set and plotted the average values observed in each of the plots were they were sampled as a function of the average density of all other trees sampled in the same plot. Our hypothesis had been that ρ x of the most abundant families, genera and species across the basin would scale isometrically with the average of all other trees in the plot where they were found and thus we rationalised that ρ x of individuals of the same species growing in different forests will reflect the mean values of the other trees in the same plot. Thus, we tested for a common slope amongst all the groups containing more than four observations at each taxonomic level. We found significant statistical indications that at the three levels there where common slopes, but in all cases the slopes fitted were significantly greater than 1.0: for families the common slope was 1.45, for genera 1.40 and for species 1.28 (Appendix C). We further tested for differences in elevation and shift across the SMA common slope and at the three levels there were significant shifts in elevation and along the common slopes. Notable exceptions did however exist for the Urticaceae (Fig. 6a, For each fitted line a plot mean was calculated excluding the family, genera or species for which the analysis was done and plotted against the average of that family, genus or species. Families used in the analysis were collected in at least 6 plots; genera in at least 8 plots and species in at least 6 plots. Regression lines in blue where not highly significant although follow the general trend. No regression lines in panels (A) M and U; (B) G and I; and (C) F, K, and M indicate that there were not significant relationships. All analysis were performed using SMATR.

panel T), that consist mostly of pioneer species [START_REF] Whitmore | Canopy Gaps and the Two Major Groups of Forest Trees[END_REF]. Non-significant relationships might be related to the "pioneer" character of the examined species i.e. Pourouma minor, Pourouma guianensis and also related to the reduced number of observations. Detailed outputs of all the analyses are given in Appendix C.

Geographical and taxonomic contributions to stand level differences

In order to evaluate the extent to which overall plot-to-plot variation might be caused by geographical or taxonomic www.biogeosciences.net/6/545/2009/ Biogeosciences, 6, 545-568, 2009 effects, we utilised estimates of the individual plot and species effects from Eq. ( 1) and compared them to direct stand-level calculations. This was achieved by first estimating the average value for each species within each plot and then obtaining a weighted average value for ρ x for that plot according to the observed abundance of each species within it, denoted here as ρ x . A similar calculation was done for the REML "species effects" which are plotted along with REML fixed plot effects (the r/p term from Eq. 1) as a function of ρ x in Fig. 7. This analysis shows that by far the most of the variation in ρ x was accountable in terms of plot-to-plot differences, with the plot effects increasing almost linearly with ρ x with a slope close to 1.0. By contrast the species (i.e. f/g/s) effects were more or less constant (and close to zero) for ρ x > ca. 550 kg m -3 , although declining slightly thereafter. We treated our plot term as a fixed effect for the analysis in Fig. 7 (as opposed to a random effect in Fig. 5), as this permitted us to allow for different plots to have different intrinsic variances consistent with differences in topography and soils heterogeneity between the various plots. This also removed a slight bias in the residuals which was present when treating the r/p term as random.

Phenotypic plasticity and index of variation

In order to determine the intra-specific variation we compared the IPP of the same species collected several times within one plot and over several plots. The IPP of individual species collected in more than two plots (mean=0.29±0.13, N=19) was significantly higher (mean=0.15±0.07, N =86) than the variation of the same species collected more than twice within one plot (DF=1, F =16.24, P <0001). IPP values are given in Appendix D.

Discussion

Our results show that there are significant variations of branch xylem density across Amazonia with regional and local patterns and with considerable plasticity observed for many species growing in different forests. This suggests that branch xylem density may not be a simple genetically inherited trait that is predictable on the basis of the knowledge of plant taxonomy alone, and that across the Basin patterns of branch xylem density may not be only explained by patterns of species composition and abundance as has previously been considered to be the case for ρ w [START_REF] Baker | Variation in wood density determines spatial patterns in Amazonian biomass[END_REF][START_REF] Chave | Regional and phylogenetic variation of wood density across 2,456 neotropical tree species[END_REF]ter Steege, 2006).

Regional variations of ρ x could be explained by spatial patterns of climatic factors [START_REF] Sombroek | Spatial and Temporal Patterns of Amazon Rainfall[END_REF][START_REF] Malhi | Spatial patterns and recent trends in the climate of tropical rainforest regions[END_REF], geomorphologic and geochemical conditions [START_REF] Fittkau | Productivity, Biomass, and Population Dynamics in Amazonian Water Bodies[END_REF] as well as by the sorting of species across landscapes [START_REF] Ter Steege | Continental-scale patterns of canopy tree composition and function across Amazonia[END_REF][START_REF] Chave | Regional and phylogenetic variation of wood density across 2,456 neotropical tree species[END_REF] 1) and estimated genotypic effects (the f /g/s term of Eq. ( 1) to stand level variations in wood density.

2006). Local differences may be associated to topographic and physiognomic variations along with soil chemical and physical properties. In plantations it is well known that the addition of nutrients (e.g. N and/or P ) reduces wood density [START_REF] Beets | Wood density of radiata pine: effect of nitrogen supply[END_REF][START_REF] Wang | Variation in xylem anatomy of selected populations of lodgepole pine[END_REF][START_REF] Thomas | Why does phosphorus limitation increase wood density in Eucalyptus grandis seedlings?[END_REF] and plots paralleling the Andes generally have higher soil P availability than those paralleling the Amazon River (Quesada et al., 2009b). Patterns of ρ x across Amazonia support the idea that species are not randomly distributed across landscapes but follow the "habitat tracking" hypothesis [START_REF] Ackerly | Community assembly, niche conservatism and adaptive evolution in changing environments[END_REF]. For example, irrespective of the taxonomic level examined (Fig. 6), ρ x observed varied by as much as 400 kg m -3 across sites. Moreover, this variation was systematic with different trees sampled within a given family/genus/species tending to have higher values of ρ x along with other trees in the same plot (and vice versa). At each taxonomic level there was a common slope which was steeper than one, indicating that, at least for the widespread species examined in this study there exists considerable phenotypic plasticity in ρ x ; i.e. there is long term acclimation and thus adaptation of the xylem tissue to any given environment. That the slopes in Fig. 6 fitted through SMA are consistently greater than 1.0, suggests a continual replacement of species towards the maximum end of their ρ x range by species with a characteristically lower ρ x under the same growth conditions. Additional evidence for widespread plasticity comes from the REML variance partitioning of Fig. 5 in which the combined effects of Region/Plot are shown to have contributed to about the same proportion of the overall variation in the data set as did Family/Genus/Species. Since wood density is an important parameter in estimating forest carbon stocks (Baker, 2004a) one question to answer was: can ρ w be predicted by knowledge of ρ x ? We examined our species level means for ρ x as a function of species mean ρ w using an expanded database (RAIN-FOR wood density data base) from that presented in [START_REF] Baker | Variation in wood density determines spatial patterns in Amazonian biomass[END_REF]. We found a reasonably good relationship (Fig. 8a). Similar results have been shown for Puerto Rican [START_REF] Swenson | The relationship between stem and branch wood specific gravity and the ability of each measure to predict leaf area[END_REF], Colombian (Juliana Agudelo and Pablo Stevenson, unpublished data) and Guiana forest species (Sarmiento et al., 2008). It is worth noting that the average ρ x for this study i.e. for the Amazon basin, (619 kg m -3 ) is very similar to previous values reported for ρ w for Amazonia. For example, [START_REF] Brown | Biomass of tropical forests: a new estimate based on forest volumes[END_REF] estimated 620 kg m -3 as the average wood density of tropical America, [START_REF] Chave | Regional and phylogenetic variation of wood density across 2,456 neotropical tree species[END_REF] reported 650 kg m -3 for Central and South America together and [START_REF] Baker | Variation in wood density determines spatial patterns in Amazonian biomass[END_REF] estimated 620 kg m -3 as the overall species-level mean for Amazonia.

As reflected in Fig. 8b, our results do, however, differ from [START_REF] Baker | Variation in wood density determines spatial patterns in Amazonian biomass[END_REF] and [START_REF] Chave | Regional and phylogenetic variation of wood density across 2,456 neotropical tree species[END_REF] in that whilst all of their variation in ρ w was attributed to genotype, in our case, variations in ρ x are also attributable to both site www.biogeosciences.net/6/545/2009/ Biogeosciences, 6, 545-568, 2009 and genetic variations. There is a strong tendency of many species, genera and even families to be geographically confined to certain areas of the Basin (ter Steege et al., 2006) and thus, if there is some equivalence between ρ x and ρ w , what has previously been interpreted as a solely genetic effect for the latter, may in fact be partly a geographic (site and regional) effect: this being attribute to variations in climate, and soils. In that respect it is only by studying replicated species growing across a wide range of environments that we have been able to show the strong environmental influence on ρ x (and by implication ρ w ). For example we show that altitude is negatively correlated with ρ x (Fig. 2c) and this effect has been suggested for ρ w (Wiemann and Williamson, 1989;[START_REF] Chave | Regional and phylogenetic variation of wood density across 2,456 neotropical tree species[END_REF]. Temperature was also positively correlated with ρ x as shown in Fig. 2d. The physical basis of this effect of temperature on wood density (water viscosity decreases as temperature increases) has been proposed by [START_REF] Roderick | Linking wood density with tree growth and environment: a theoretical analysis based on the motion of water[END_REF] and has been experimentally supported by [START_REF] Thomas | Changes in wood density of Eucalyptus camaldulensis due to temperature-the physiological link between water viscosity and wood anatomy[END_REF]. There is also evidence that physical and chemical properties of soils may have an influence ρ w [START_REF] Hacke | Influence of soil porosity on water use in Pinus taeda[END_REF][START_REF] Parolin | Are there differences in specific wood gravities between trees in varzea and igapo (Central Amazonia)?[END_REF][START_REF] Parolin | Are there differences in specific wood gravities between trees in varzea and igapo (Central Amazonia)?[END_REF]. In essence the REML species effect in Fig. 8b for ρ x represents the inferred value that each species would have were it to be found on some sort of "overall average site". It is also worth noting that, in contrast to the general trend, long-lived pioneer species [START_REF] Whitmore | Canopy Gaps and the Two Major Groups of Forest Trees[END_REF] within the Urticaceae often associated to gap colonisation, secondary vegetation and/or late stages of forest succession, showed little tendency to exhibit variation in ρ x across the sites where they were found (Fig. 6a, panel T, respectively). This brings the question of whether species showing little phenotypic plasticity and intermediate ρ x values are present in sites where the majority of trees have relative low xylem density. These species when found in terra firme old grow forests (this study) may be more restricted to specific edaphic and microclimatic conditions that sustain colonisation and fast growth (i.e. gaps with enough water supply from the soil, nutrients, optimal temperature, not too much wind, sufficient light). This is because if they where in a high density site (stressful conditions) they could not cope with the environment. Also, species such as Pourouma minor and P. guianensis, which are generally considered low-density, fast-growing species did not have the lowest branch densities in our study; xylem density varied from 410 to 690 kg m -3 ; comparable to some of the slow-growth climax species observed.

Further evidence of the influence of site conditions on ρ x of trees comes from our own data. In a Mangrove forest in East Pará, Brazil (EP-Brazil, BRA-01, Appendix A) only two species were sampled (10 individuals per species) Avicennia germinans and Rhizophora mangle which mean ρ x were 722±87 kg m -3 and 723±99 kg m -3 , respectively. The two species are not phylogenetically related since they belong to two different families (Scrophulariaceae and Rhizophoraceae) and two different orders (Lamiales and Malpighiales). Nevertheless they converged to an almost identical high ρ x . Mangroves are well known for having special water dynamics, fluctuating salinity, low oxygen concentration in the soil, and particular soil chemical and physical characteristics (Lovelock et al., 2006a). These environmental factors constrain tree water relations, gas exchange and growth (Lovelock et al., 2006b).

As suggested from experimental studies done on different species from different environments, high ρ x is an adaptive response to severe environmental conditions such as drought (Dalla-Salda et al., 2008), high temperature [START_REF] Thomas | Changes in wood density of Eucalyptus camaldulensis due to temperature-the physiological link between water viscosity and wood anatomy[END_REF], porous soils [START_REF] Hacke | Influence of soil porosity on water use in Pinus taeda[END_REF], poor nutrient conditions and long periods of high floods [START_REF] Beets | Wood density of radiata pine: effect of nitrogen supply[END_REF][START_REF] Wang | Variation in xylem anatomy of selected populations of lodgepole pine[END_REF][START_REF] Thomas | Why does phosphorus limitation increase wood density in Eucalyptus grandis seedlings?[END_REF][START_REF] Wittmann | Wood specific gravity of trees in Amazonian white-water forest in relation to flooding[END_REF]. For Amazonian species it is difficult to imagine that high ρ x , of the order we have found (for example from 750 kg m -3 to 1130 kg m -3 ) are directly associated with extreme drought conditions -those regions of the Amazon where severe water stress is most likely to occur are those regions with a long dry season i.e. Bolivia, parts of Venezuela, Guiana, and East Brazil. These regions were characterised by low and intermediate ρ x . High xylem density most likely is related to variation in resource availability and/or different site dependent soil physical characteristics and hydrological constraints. ρ x is a trait that reflects environmental constrains (Cochard et al., 2008), and so aggregation of species according to ρ x (ter Steege et al., 2006, for example) should also reflect environmental constraints imposed upon "species" of trees. We conclude that variations of ρ x across basin reflect an enormous functional diversity among trees and Amazonian forests. Any change in ρ x may reflect changes at various levels of organisation. For example, as ρ x increases, microfibril angle, cell wall thickness, modulus of elasticity and resistance to cavitation also increase, but hydraulic efficiency and rates of gas exchange decrease. Additional studies on these subjects, particularly how variations in ρ x relates to other plant physiological characteristics (e.g. [START_REF] Fyllas | Basin-wide variations in foliar properties of Amazon forest trees: Phylogeny, soils and climate[END_REF] are needed to better understand the functional diversity of Amazonian trees.

Appendix A

Description of forests plots from which ρ x data was obtained.

More precise coordinates will be available (Andersoson et al., 2009) Abbreviations in regions are: AC=Acre, AM= Amazonas, MT=Mato Grosso, CP=Central Pará, EP=East Pará, WP=West Pará, N=North, S=South, NE=North East, SW=South West. * * not a permanent plot, samples were taken from trees around the Eddy covariance tower, -data not available. Additional information of plots can be found in: [START_REF] Malhi | An international network to monitor the structure, composition and dynamics of Amazonian forests (RAINFOR)[END_REF](Malhi et al., , 2003;;[START_REF] Baker | Increasing biomass in Amazonian forest plots, Philos[END_REF][START_REF] Vinceti | Spatial and temporal analysis of Floristic composition and Dynamics in some lowland Amazonian forests[END_REF].

  52

Fig. 1 .

 1 Figure 1

Fig. 2 .

 2 Figure 2

Figure 3

 3 Figure3

Fig. 3 .

 3 Fig. 3. Variation of ρ x between and within regions. Regions and plots are indicated in the left and right axes, respectively. Horizontal lines represent the standard deviation. Vertical straight lines represent confidence limits defined using a Tukey test. Complementary information is given in Appendix B. Grey and white shadows separate the regions. Vertical dashed-blue line represents the mean ρ x of the basin.

  Figure 4

Fig. 4 .

 4 Fig. 4. Variation of xylem wood density, ρ x (kg m -3 ), between and within families. Each dot represents the average ρ x of each genus. Left vertical axes represent genera, right vertical axes represent families and X-axis is the ρ x . Grey and white shadows separate the families. Vertical dashed-blue line represents the mean ρ x of the basin. Horizontal lines represent the Standard Deviation. Families in the Figure are sorted from high to low ρ x from top-right (A) to left-bottom (C). The three panels (A, B, and C) represent one continuous Figure, divided only for purpose of presentation.Abbreviation of the families are listed below ρ x : IXO=Ixonanthaceae, OLA=Olacaceae, CHR=Chrysobalanaceae, CEL=Celastraceae, HUM=Humiriaceae, OCH=Ochnaceae, LIN=Linaceae, SCR=Scrophulariaceae, RHI=Rhizophoraceae, CLU=Clusiaceae, MYRT=Myrtaceae, APO=Apocynaceae, LEC=Lecythidaceae, SAPI=Sapindaceae, MYRS=Myrsinaceae, SAPO=Sapotaceae, AQUI=Aquifoliaceae, STYR=Styracaceae, FAB=Fabaceae, ICA=Icacinaceae, COM=Combretaceae, ELA=Elaeocarpaceae, CAR=Caryocaraceae, MELA=Melastomataceae, VIO=Violaceae, RUB=Rubiaceae, VER=Verbenaceae, RUT=Rutaceae, NYC=Nyctaginaceae, SAL=Salicaceae, LAU=Lauraceae, SIM=Simaroubaceae, MELI=Meliaceae, ROS=Rosaceae, ULM=Ulmaceae, ANA=Anacardiaceae, PRO=Proteaceae, BUR=Burseraceae, OLEA=Oleaceae, MALP=Malpighiaceae, MON=Monimiaceae, POL=Polygonaceae, EUP=Euphorbiaceae, DIC=Dichapetalaceae, MOR=Moraceae, BIG=Bignoniaceae, ANN=Annonaceae, ARAL=Araliaceae, URT=Urticaceae, VOC=Vochysiaceae, MYRI=Myristicaceae, STA=Staphyleaceae, RHA=Rhamnaceae, MAL=Malvaceae, LAC=Lacistemataceae, LEP=Lepidobotryaceae, SAB=Sabiaceae, BIX=Bixaceae, and BOR=Boraginaceae.

Fig. 5 .

 5 Figure5

Fig. 6a .

 6a Figure 6A

  Figure 6B

  Figure 6C

  60

Figure 7 Fig. 7 .

 77 Figure 7

Fig. 8 .

 8 Figure 8

Table A1 .

 A1 Plot Name and Description Region CodeRegion Plot Code latitude longitude Altitude (m) Mean T ( • C)

								Forest Type	Principal Investigator
	Sinop	MT-Brazil	SIN-01	-11.41	-55.33	325	25.4	Terra firme	M. Silveira
	Alta Foresta	MT-Brazil	ALF-01	-9.60	-55.94	255	25.6	Terra firme	M. Silveira
	Los Fierros Bosque I	Bolivia	LFB-01	-14.56	-60.93	230	25.1	Terra firme	T. Killeen
	Los Fierros Bosque II	Bolivia	LFB-02	-14.58	-60.83	225	25.1	Terra firme	T. Killeen
	Huanchaca Dos, plot1	Bolivia	HCC-21	-14.56	-60.75	720	25.1	Gallery	L. Arroyo
	Huanchaca Dos, plot2	Bolivia	HCC-22	-14.57	-60.75	735	25.1	Gallery	L. Arroyo
	Las Londras, plot 1	Bolivia	LSL-01	-14.41	-61.14	170	25.9	Seasonally flooded	L. Arroyo
	Las Londras, plot 2	Bolivia	LSL-02	-14.41	-61.14	170	25.9	Seasonally flooded	L. Arroyo
	Chore 1	Bolivia	CHO-01	-14.39	-61.15	170	25.9	Liana forest	T. Killeen
	Tambopata plot zero	S-Peru	TAM-01	-12.84	-69.29	205	25.1	Terra firme	O. Phillips and R. Vasquez
	Tambopata plot one	S-Peru	TAM-02	-12.84	-69.29	210	25.1	Terra firme	O. Phillips and R. Vasquez
	Tambopata plot
	two swamp	S-Peru	TAM-03	-12.84	-69.28	205	25.1	Swamp	O. Phillips and R. Vasquez
	Tambopata plot two
	swamp edge clay	S-Peru	TAM-04	-12.84	-69.28	205	25.1	Terra firme	O. Phillips and R. Vasquez
	Tambopata plot
	three	S-Peru	TAM-05	-12.83	-69.27	220	25.1	Terra firme	O. Phillips and R. Vasquez
	Tambopata plot
	four (cerca rio)	S-Peru	TAM-06	-12.84	-69.30	200	25.1	Terra firme	O. Phillips and R. Vasquez
	Tambopata plot six	S-Peru	TAM-07	-12.83	-69.26	225	25.1	Terra firme	O. Phillips and R. Vasquez
	Cuzco Amazónico,
	CUZAM2E	S-Peru	CUZ-03	-12.50	-68.96	195	25.1	Terra firme	O. Phillips and R. Vasquez
	Jurua, PAA-05	AC-Brazil	PAA-05	-8.88	-72.79	245	26.2	Terra firme	M. Silveira
	RESEX Alto Juruá:
	Seringal Restaurac ¸ão	AC-Brazil	RES-02	-9.04	-72.27	275	25.9	Terra firme	M. Silveira
	RESEX Chico Mendes:
	Seringal Porongaba 1	AC-Brazil	RES-03	-10.82	-68.78	275	25.8	Terra firme	M. Silveira
	RESEX Chico Mendes:
	Seringal Porongaba 2	AC-Brazil	RES-04	-10.80	-68.77	270	25.8	Terra firme	M. Silveira
	RESEX Chico Mendes:
	Seringal Dois Irmãos 1	AC-Brazil	RES-05	-10.57	-68.31	200	26.0	Terra firme	M. Silveira
	RESEX Chico Mendes:
	Seringal Dois Irmãos 2	AC-Brazil	RES-06	-10.56	-68.30	210	26.0	Bamboo forest	M. Silveira
	Allpahuayo A,
	poorly drained	N-Peru	ALP-11	-3.95	-73.43	125	26.5	Terra firme	O. Phillips and R. Vasquez
	Allpahuayo A,
	well drained	N-Peru	ALP-12	-3.95	-73.43	125	26.5	Terra firme	O. Phillips and R. Vasquez
	Allpahuayo B, sandy	N-Peru	ALP-21	-3.95	-73.43	125	26.5	Terra firme	O. Phillips and R. Vasquez
	Allpahuayo B, clayey	N-Peru	ALP-22	-3.95	-73.43	115	26.4	Terra firme	O. Phillips and R. Vasquez
	Alpahuayo C	N-Peru	ALP-30	-3.95	-73.43	125	26.4	Tall caatinga?	O. Phillips and R. Vasquez
	Sucusari A	N-Peru	SUC-01	-3.23	-72.90	110	26.4	Terra firme	O. Phillips and R. Vasquez
	Sucusari B	N-Peru	SUC-02	-3.23	-72.90	110	26.4	Terra firme	O. Phillips and R. Vasquez
	Sucusari C	N-Peru	SUC-03	-3.25	-72.93	110	26.4	Seasonally flooded	O. Phillips, A. Monteagudo
	Sucusari D	N-Peru	SUC-04	-3.25	-72.89	160	26.4	Terra firme	O. Phillips, A. Monteagudo, T. Baker
	Yanamono A	N-Peru	YAN-01	-3.43	-72.85	105	26.4	Terra firme	O. Phillips and R. Vasquez
	Yanamono B	N-Peru	YAN-02	-3.43	-72.84	105	26.4	Terra firme	O. Phillips and R. Vasquez
	Jenaro Herrera A-
	Clay rich high terrace	N-Peru	JEN-11	-4.88	73.63	130	26.8	Terra firme	T.R. Baker and O. Phillips
	Jenaro Herrera
	B-sandy	N-Peru	JEN-12	-4.90	-73.63	130	26.8	Terra firme	T.R. Baker and O. Phillips
	Sumaco	Ecuador	SUM-01	-1.75	-77.63	1200	-	Premontane forest	D. Neill
	Jatun Sacha 2	Ecuador	JAS-02	-1.07	-77.60	435	23.3	Terra firme	D. Neill
	Jatun Sacha 3	Ecuador	JAS-03	-1.07	-77.67	410	23.3	Terra firme	D. Neill
	Jatun Sacha 4	Ecuador	JAS-04	-1.07	-77.67	430	23.3	Terra firme	D. Neill
	Jatun Sacha 5	Ecuador	JAS-05	-1.07	-77.67	395	23.3	Terra firme	D. Neill
	Bogi 1	Ecuador	BOG-01	-0.70	-76.48	270	26.0	Terra firme	N. Pitman, T. DiFiore
	Bogi 2	Ecuador	BOG-02	-0.70	-76.47	270	26.0	Terra firme	N. Pitman, T. DiFiore
	Tiputini 3	Ecuador	TIP-03	-0.64	-76.16	250	26.0	Seasonally flooded	N. Pitman
	Tiputini 5	Ecuador	TIP-05	-0.64	-76.14	245	26.0	Terra firme	N. Pitman
	Amacayacu: Lorena E	Colombia	LOR-01	-3.06	-69.99	95	25.9	Terra firme	A. Rudas and A. Prieto
	Amacayacu:
	Lorena U	Colombia	LOR-02	-3.06	-69.99	95	25.9	Terra firme	A. Rudas and A. Prieto
	Amacayacu:
	Agua Pudre E	Colombia	AGP-01	-3.72	-70.31	105	25.8	Terra firme	A. Rudas and A. Prieto
	Amacayacu:
	Agua Pudre U	Colombia	AGP-02	-3.72	-70.30	110	25.8	Terra firme	A. Rudas and A. Prieto
	www.biogeosciences.net/6/545/2009/						Biogeosciences, 6, 545-568, 2009

Table A1 .

 A1 Continued.

	Plot Name and Description	Region Code	Region	Plot Code latitude longitude Altitude (m) Mean T ( • C)	Forest Type	Principal Investigator
	EL Zafire:
	Varillal	6	Colombia	ZAR-01	-4.01	-69.91	130	25.6	Caatinga	M. C. Penuela and E. Alvarez
	EL Zafire:
	Rebalse	6	Colombia	ZAR-02	-4.00	-69.90	120	25.6	Seasonally flooded	M. C. Penuela and E. Alvarez
	EL Zafire:
	Terra Firme	6	Colombia	ZAR-03	-3.99	-69.90	135	25.6	Terra firme	M. C. Penuela and E. Alvarez
	Altura	6	Colombia	ZAR-04	-4.01	-69.90	120	25.6	Terra firme	M. C. Penuela and E. Alvarez
	San Carlos
	Oxisol	7	SW-Venezuela	SCR-01	1.93	-67.02	120	26.0	Terra firme	R. Herrera
	San Carlos
	Tall Caatinga	7	SW-Venezuela	SCR-04	1.93	-67.04	120	26.0	Tall caatinga	R. Herrera
	San Carlos Yevaro	7	SW-Venezuela	SCR-05	1.93	-67.04	120	26.0	Terra firme	R. Herrera
	Rio Grande, plots DA1
	(RIO-01) and DA2 (RIO-02)	8	NE-Venezuela	RIO-12	8.11	-61.69	270	24.9	Terra firme	A. Torres-Lezama
	El Dorado, km 91, plots G1
	(ELD-01) and G2 (ELD-02)	8	NE-Venezuela	ELD-12	6.10	-61.40	200	24.9	Terra firme	A. Torres-Lezama
	El Dorado, km 98,
	plots G3 (ELD-03)
	and G4 (ELD-04)	8	NE-Venezuela	ELD-34	6.08	-61.41	360	24.9	Terra firme	A. Torres-Lezama
	Manaus K34, plato	9	AM-Brazil	MAN-01	-2.61	-60.21	65	27.3	Terra firme	N. Higuchi
	Manaus K34, vertiente	9	AM-Brazil	MAN-02	-2.61	-60.21	50	27.3	Terra firme	N. Higuchi
	Manaus K34, campinarana	9	AM-Brazil	MAN-03	-2.60	-60.22	65	27.3	Tall caatinga	N. Higuchi
	Manaus K34, baxio	9	AM-Brazil	MAN-04	-2.61	-60.22	45	27.3	Caatinga/swampy valley	N. Higuchi
	Bionte 4:
	Manaus K 23	9	AM-Brazil	BNT-04	-2.63	-60.15	105	27.3	Terra firme	N. Higuchi
	Manaus K14. Tower * *	9	AM-Brazil	MAN-05	-2.59	-60.,12	108	27.3		??
	Tapajos,
	RP014, 1-4		WP-Brazil	TAP-01	-3.31	-54.94	187	26.5	Terra firme	N. Silva
	Tapajos, RP014, 5-8		WP-Brazil	TAP-02	-3.31	-54.95	187	26.5	Terra firme	N. Silva
	Tapajos, RP014, 9-12		WP-Brazil	TAP-03	-3.31	-54.94	187	26.5	Terra firme	N. Silva
	Tapajos, LBA Tower,
	Transects 1, 2, 3 and 4		WP-Brazil	TAP-04	-2.85	-54.96	73	26.5	Terra firme	S. Saleska, Hammond-Pyle,
	Hutyra, Wofsy,
	de Camargo, Vieira
	Caxiuaná 1		CP-Brazil	CAX-01	-1.74	-51.46	40	25.6	Terra firme	S. Almeida
	Caxiuaná 2		CP-Brazil	CAX-02	-1.74	-51.46	40	25.6	Terra firme	S. Almeida
	Caxiuana 3:
	A (Control drought
	experiment). Secaflor		CP-Brazil	CAX-03	-1.73	-51.46	15	25.6	Terra firme	S. Almeida, A. L. da Costa, L de Sa, J. Grace,
										da Costa, L. de Sa, J. Grace,
										P. Meir and Y. Malhi
	Caxiuana 4: B (Drought
	experiment). Secaflor		CP-Brazil	CAX-04	-1.73	-51.46	15	25.6	Terra firme	S. Almeida, A. L.
										da Costa, L. de Sa, J. Grace,
										P. Meir and Y. Malhi
	Caxiuana 5:
	Eddy tower		CP-Brazil	CAX-05	-1.72	-51.46	15	25.6	Terra firme	S. Almeida,
										L. de Sa, J. Grace, P.
										Meir and Y. Malhi
	Jari 1		CP-Brazil	JRI-01	-0.89	-52.19	127	26.5	Terra firme	N. Silva
	Braganca		EP-Brazil	BRA-01	-0.83	-46.64	10	25.8		A. L. da Costa
										and Y. Malhi
	Mocambo 1		EP-Brazil	MBO-01	-1.45	-48.45	24	26.8	Terra firme	R. Salomao
	Capitao Poc ¸o		EP-Brazil	CPP-01	-2.19	-47.33	66	25.9	Terra firme	I. Viera and E. Leal
	Acarouany, A11		Guiana	ACA-11	4.08	52.69	30	-	Terra firme	C. Baraloto, J. Chave
	BAFOG, B4		Guiana	BAF-04	5.55	53.88	22	-	Terra firme	C. Baraloto, J. Chave
	Guyaflux 7		Guiana	GFX-07	-	-	-	-	Flooded	D. Bonal
	Guyaflux 9		Guiana	GFX-09	-	-	-	-	Terra firme	D. Bonal
	Montagne Tortue, M1711		Guiana	M17-11	4.94	52.54	240	-	Terra firme	C. Baraloto, J. Chave
	Nouragues-20H, NH20		Guiana	NGH-20	5.07	53.00	76	-	Terra firme	C. Baraloto, J. Chave
	Nouragues-11L, NL11		Guiana	NGL-11	5.07	53.00	22	-	Terra firme	C. Baraloto, J. Chave
	Saut Lavilette, LV1		Guiana	SLV-01	4.22	52.41	54	-	Terra firme	C. Baraloto, J. Chave
	Tresor, T1		Guiana	TRE-01	3.24	52.28	87	-	Terra firme	C. Baraloto, J. Chave

Table B1 .

 B1

	Region/	DF	F	P	Plot	Plot size	N	Mean	SE	St	IV
	Country				Code	(ha)		(kg m -3 ) Mean	Dev
	CP-Brazil
	754 (126)	5	4.32	0.001	CAX-02 *	1	15	669		203	0.757
					CAX-05	0.25	19	733		84	0.341
					CAX-01	1	20	740		166	0.505
					JRI-01	1	20	757		116	0.483
					CAX-03 *	1	38	788		112	0.489
					CAX-04 *	1	32	797		105	0.489
	AM-Brazil
	702 (082)	5	2.11	0.74	MAN-02 *	1	6	639		106	0.327
					MAN-04	1	10	675		72	0.277
					MAN-01	1	13	688		67	0.275
					MAN-05 * *		20	694		95	0.401
					MAN-03	1	9	729		97	0.295
					BNT-04 *	1	21	736		51	0.240
	EP-Brazil
	668 (109)	3	4.58	0.014	MBO-01	1	18	627		126	0.605
					CPP-01	1	20	649		92	0.490
					BRA-01 *	1	20	723		91	0.333
	12	4.89 < 0.001 YAN-02 *	1	8	521	23		0.268
					YAN-01 *	1	17	570		101	0.459
					SUC-01 *	1	19	629		120	0.411
					ALP-12	0.4	9	644		64	0.272
					SUC-04	1	20	657		77	0.308
					JEN-11	1	19	659		138	0.549
					SUC-02	1	16	659		90	0.346
					ALP-11	0.44	10	672		114	0.372
					ALP-22	0.44	12	678		75	0.312
					SUC-03	1	18	694		125	0.512
					ALP-21	0.48	6	720		135	0.373
					JEN-12 *	1	20	746		115	0.414
					ALP-30 *	1	12	765		58	0.281
	WP-Brazil
	663 (114)	3	3.07	0.032	TAP-04 *	4	33	627		136	0.758
					TAP-01	1	16	659		113	0.541
					TAP-03	1	20	673		60	0.272
					TAP-02 *	1	19	722		96	0.405
	SW-Venezuela
	610 (106)	2	2.35	0.102	SCR-04	1	26	594		98	0.487
					SCR-05	1	34	596		101	0.561
					SCR-01	1	21	653		117	0.471
	Guiana
	620 (123)	8	2.42	0.017	BAF-04	1	20	574		116	0.535
					M17-11	1	21	578		154	0.725
					NGL-11	1	20	579		120	0.5
					GFX-07	0.5	20	601		145	0.691
					ACA-11	1	20	602		100	0.514
					TRE-01	1	20	641		90	0.391
					NGH-20	1	20	645		113	0.499
					GFX-09	0.42	28	661		119	0.542
					SLV-01 *	1	20	684		107	0.427

www.biogeosciences.net/6/545/2009/ Biogeosciences, 6, 545-568, 2009

Table B1 .

 B1 Continued.

	Table C1.
		Family		n	R 2	sig	Slope Slope 95% CI	Interc	Interc 95% CI
		Fabaceae		73 0.439 * * * * 1.155 (0.968 1.377) -0.078	(-0.205 0.050)
	Region/	Sapotaceae	DF	F 61 0.253 * * * * 1.176 (0.941 1.470) -0.086 P Plot Plot size N Mean	SE (-0.252 0.080) St	IV
	Country	Moraceae		52 0.160	* * *	Code 1.477 (1.142 1.911) -0.322 (-0.557 -0.087) (ha) (kg m -3 ) Mean Dev
	Lecythidaceae S-Peru and AC-Brazil 589 (100) Myristicaceae Burseraceae Lauraceae Annonaceae Euphorbiaceae Malvaceae Chrysobalanaceae 31 0.364 * * * * 1.556 (1.154 2.097) -0.232 48 0.342 * * * * 1.239 (0.977 1.573) -0.095 13 2.90 0.001 TAM-03 * 0.58 6 468 48 0.144 * * * 1.645 (1.254 2.158) -0.473 (-0.754 -0.191) (-0.282 0.092) 98 CUZ-03 1 23 573 70 46 0.169 * * * 1.165 (0.886 1.532) -0.140 (-0.344 0.064) TAM-01 1 22 578 99 41 0.252 * * * 1.729 (1.311 2.279) -0.485 (-0.791 -0.178) TAM-04 0.42 15 588 95 39 0.410 * * * * 2.112 (1.640 2.721) -0.760 (-1.093 -0.426) TAM-06 0.96 21 588 81 35 0.359 * * * * 1.271 (0.961 1.682) -0.199 (-0.421 0.023) TAM-02 1 19 625 33 0.318 * * * * 2.160 (1.603 2.910) -0.767 (-1.160 -0.373) 114 0.545 0.395 0.399 0.511 0.379 0.387 TAM-07 1 20 637 (-0.529 0.065) 100 0.479 TAM-05 1 20 642 90 Meliaceae 30 0.231 * * * 1.504 (1.078 2.099) -0.272 (-0.573 0.028) 0.375 POR-01 1 19 545 Urticaceae 22 0.009 ns 0.951 (0.607 1.490) -0.018 (-0.281 0.245) 100 0.571 DOI-02 1 18 551 93 Rubiaceae 22 0.229 * * 1.618 (1.086 2.410) -0.313 (-0.702 0.075) 0.534 POR-02 1 20 557 Olacaceae 19 0.474 * * * 1.393 (0.969 2.002) -0.149 (-0.476 0.178) 105 0.568 RST-01 1 20 583 Apocynaceae 17 0.277 * * 1.930 (1.228 3.033) -0.572 (-1.150 0.006) 107 0.591 DOI-01 1 18 613 75 Violaceae 17 0.277 * * 1.266 (0.805 1.990) -0.208 (-0.593 0.176) 0.386 JUR-01 1 13 634 Melastomataceae 15 0.388 * * 1.656 (1.053 2.606) -0.342 (-0.812 0.128) 109 0.559 Clusiaceae 15 0.406 * * 1.714 (1.096 2.678) -0.409 (-0.908 0.090) MT-Brazil 575 (093) 1 9.55 0.004 ALF-01 * 1 26 543 68 Sapindaceae 13 0.058 ns 1.101 (0.601 2.019) -0.015 (-0.450 0.420) 0.475 SIN-01 * 1 17 625 Vochysiaceae 10 0.035 ns 1.049 (0.504 2.183) -0.068 (-0.551 0.416) 105 0.478 Elaeocarpaceae 10 0.404 * * 1.371 (0.757 2.482) -0.197 (-0.718 0.325) NE-Venezuela 568 (125) 2 1.29 0.284 ELD-34 0.5 16 528 Bignoniaceae 10 0.529 * * 1.242 (0.728 2.118) -0.193 (-0.619 0.233) 121 0.625 RIO-12 0.5 19 582 102 0.527 Icacinaceae 8 0.403 ns 1.239 (0.609 2.521) -0.149 (-0.755 0.456)
							ELD-12	0.5	16	593	149 0.615
	Bolivia
	561 (106)	6	0.74	0.62		LSL-02	1	16	530	85	0.525
							CHO-01	1	18	549	107 0.540
							HCC-22	1	21	550	114 0.481
							LFB-01	1	18	560	98	0.495
							LSL-01	1	14	569	140 0.602
							HCC-21	1	20	574	121 0.615
							LFB-02	1	16	601	68	0.355
	Colombia
	593 (105)	7	8.00 < 0.001	ZAR-02	1	20	572	84	0.441
							ZAR-03	1	18	612	78	0.346
							ZAR-04	1	20	616	82	0.380
							ZAR-01 *	1	20	712	88	0.372
							LOR-02	1	16	513	130 0.355
							AGP-02	1	20	545	87	0.5
							AGP-01	1	20	574	97	0.565
							LOR-01	1	17	582	81	0.602
	Ecuador
	535 (089)	8	2.32	0.021	JAS-05 *	1	20	472	74	0.424
							SUM-01	1	18	510	69	0.432
							JAS-03	1	19	526	73	0.388
							JAS-02	1	21	531	86	0.464
							BOG-02	1	33	536	100 0.572
							TIP-03	1	20	550	54	0.385
							BOG-01	1	44	554	98	0.593
							JAS-04	0.92	22	559	84	0.472
							TIP-05	1	11	568	115 0.490

Table C2 .

 C2 ) (g cm -3 ) (within) (between)

	Table D1.
		Species						Plot	N	Max	Min	IPP	IPP
		(g cm -3 Aldina kunhardtiana SCR-04 2 0.594	0.491	0.174	0.118
								SCR-01		2	0.579	0.543
		Brosimum lactescens			SUC-03		2	0.655	0.628	0.212	0.099
								TAM-01		3	0.613	0.516
		Eperua falcata					GFX-09		4	0.733	0.387	0.473	0.300
								GFX-07		4	0.589	0.513
		Erisma uncinatum				CHO-01		2	0.533	0.489	0.107	0.074
								LFB-01		2	0.509	0.476
		Eschweilera coriacea			CAX-04		2	0.823	0.679	0.304	0.108
							MBO-01 2	0.750	0.631
								CAX-02		2	0.744	0.670
								CPP-01		3	0.722	0.709
								LOR-01		2	0.683	0.638
								AGP-01		2	0.659	0.573
		Inga laurina					LSL-01		3	0.726	0.669	0.225	0.057
								CUZ-03		2	0.584	0.563
		Lecythis idatimon				CAX-01		2	0.803	0.706	0.194	0.118
								CPP-01		2	0.731	0.647
		Lecythis persistens				TRE-01		3	0.784	0.686	0.657	0.287
								NGL-11		2	0.782	0.690
								GFX-07		2	0.705	0.269
		Manilkara bidentata			CAX-03		2	0.858	0.796	0.182	0.117
								JRI-01		2	0.837	0.702
		Miconia poeppigii				LSL-01		3	0.685	0.493	0.354	0.290
								LSL-02		7	0.632	0.443
		Minquartia guianensis			CAX-04		4	1.079	0.844	0.271	0.156
	Genera	n Otoba glycycarpa	R 2	sig	Slope JRI-01 JAS-04	4 2	Slope 95% CI 0.869 0.516	0.787 0.496	Interc 0.166	Interc 95% CI 0.078
	Eschweilera (Lec)	40 0.252	* * *	1.1907 YAN-01	2	(0.8995 1.5761) 0.487 0.430	-0.063767	-0.277379 0.149845
	Pouteria (Sap) Oxandra asbeckii 37 0.141		*	1.0595 SLV-01	2	(0.7748 1.4489) 0.851 0.759	0.020563 0.186	(-0.190905 0.232031) 0.144
	Ocotea (Lau)	34 0.211	* *	1.2725 GFX-09	7	(0.9289 1.743) 0.845 0.693	-0.208803	(-0.468646 0.05104)
	Virola (Myri) Pourouma guianensis 33 0.111		*	1.7147 HCC-22	4	(1.2218 2.4065) 0.611 0.406	-0.528158 0.301	0.158
	Inga (Fab)		32 0.409 * * * *	1.3688 LFB-01	2	(1.0315 1.8165) 0.595 0.498	-0.20076	(-0.435863 0.034343)
	Licania (Chry)	26 0.351	* * *	1.3635 CHO-01	2	(0.9772 1.9024) 0.505 0.495	-0.103841	(-0.399524 0.191843)
	Pseudolmedia (Mor) 19 0.114		ns	1.1711 HCC-21	2	(0.7358 1.8638) 0.484 0.427	-0.109588	(-0.441129 0.221952)
	Tachigali (Fab) Pseudolmedia laevis 17 0.446	* *	1.4143 LFB-02	2	(0.949 2.1075) 0.670 0.507	-0.227317 0.419	(-0.58844 0.133806) 0.179
	Micropholis (Sap)	16 0.385	* *	1.3497 CUZ-03	4	(0.8732 2.0863) 0.652 0.560	-0.255261	(-0.637631 0.12711)
	Lecythis (Lec)	16 0.267		*	1.5616 BOG-02	2	(0.9731 2.5062) 0.607 0.544	-0.345006	(-0.843226 0.153214)
	Ocotea (Lau)	16	0.22		*	1.0082 LFB-01	3	(0.6196 1.6406) 0.503 0.389	-0.055156	(-0.386934 0.276623)
	Pourouma (Urt) Pseudolmedia macrophylla 16 0	ns	-0.8993 (-1.552 -0.5211) LFB-01 2 0.647 0.468	1.071904 0.277	(0.763112 1.380696) 0.141
	Brosimum (Mor)	15 0.012		ns	1.0845 TAM-02	2	(0.6164 1.9082) 0.633 0.574	-0.086202	(-0.485705 0.313302)
	Swartzia (Fab)	13 0.683 * * * *	1.0007 TAM-04	3	(0.6943 1.44240 0.599 0.492	0.036976	(-0.210473 0.284425)
	Guarea (Meli)	12 0.678	* * *	1.3031 TAM-01	2	(0.8822 1.9248) 0.545 0.537	-0.148985	(-0.447886 0.149916)
	Miconia (Mel) Qualea paraensis 12 0.119		ns	1.5847 LFB-02	2	(0.8517c2.9483) 0.638 0.497	-0.307446 0.221	(-0.925763 0.310871) 0.136
	Rinorea (Vio)	11 0.192		ns	1.5168 LSL-02	2	(0.8044c2.8599) 0.607 0.575	-0.392198	(-1.081057 0.29666)
	Minquartia (Ola) Rinorea guianensis 11 0.588	* *	1.5207 CAX-03	2	(0.9535c2.4253) 0.773 0.742	-0.243791 0.419	(-0.72481 0.237228) 0.120
	Tetragastris		11 0.274		*	1.5604 TAP-01	4	(0.8521c2.8575) 0.723 0.632	-0.410824	(-1.056808 0.23516)
	Manilkara (Sap)	10 0.401		*	1.9426 TAP-02	2	(1.0712c3.5229) 0.713 0.686	-0.635196	(-1.464367 0.193974)
	Aspidosperma (Apo) 10 0.368		*	1.3236 TAP-03	5	(0.7193c2.4354) 0.712 0.646	-0.081181	(-0.627278 0.464915)
	Sloanea (Ela)	9	0.46		*	1.3939 CAX-01	2	90.7523c2.583) 0.645 0.449	-0.219332	(-0.776561 0.337897)
	Unonopsis (Ann) Tetragastris altissima 7 0.598		*	1.8996 DOI-01	4	(0.9658c3.7363) 0.649 0.461	-0.586441 0.350	(-1.386101 0.213218) 0.163
	Duguetia (Ann)	6	0.699		*	1.4467 POR-01	2	(0.717c2.9192) 0.629 0.560	-0.246887	(-0.96251 0.468736)
	Drypetes (Eup)	6	0.888			POR-02		6	0.530	0.421
								RIO-12		2	0.512	0.489

www.biogeosciences.net/6/545/2009/ Biogeosciences, 6, 545-568, 2009

www.biogeosciences.net/6/545/2009/ Biogeosciences, 6, 545-568, 2009

Biogeosciences, 6, 545-568, 2009 www.biogeosciences.net/6/545/2009/

* * 2.7519 (1.7565c4.3114) -0.976167 (-1.713112 -0.239221) www.biogeosciences.net/6/545/2009/ Biogeosciences, 6, 545-568, 2009

Acknowledgements. This paper is a product of the RAINFOR network. RAINFOR is currently supported by the Gordon and Betty Moore Foundation. We wish to thank Markus Wagner for his help organising our data set and for his useful comments on the manuscript, and Nikolaos Fyllas and Tim Paine for advice on statistical analysis. We also thanks the students from the FTH module in UMR-ECOFOG 2007: Elodie Alberny, Sophie Dutrey, Jerôme Fournier, Hélène Richard for help in sampling the French Guiana plots and the VPN Corporation for making available Genstat Discovery Edition for SP when she was working in Colombia. We thank Juliana Stropp for help with the elaboration of the map shown in Fig. 1 and Nick Rowe for comments on the manuscript. We also thank Ian Wright and an anonymous referee for criticisms and suggestions that improved the quality of this manuscript. Sandra Patiño was involved in all aspects of manuscript preparation: field and laboratory work, data analysis and writing; and Jon Lloyd was involved in analysing data and manuscript writing, also occasionally providing assistance in the field. Listed after this (alphabetically) are the core "RAINFOR Biogeochemsitry" team who helped obtain all data, followed by those who further contributed with fieldwork at selected sites and/or by the contribution of primary data. A third alphabetic list that includes all the people who contributed with plot tree-by-tree data. Yadviunder Malhi and Oliver Phillips led the initiation of the RAINFOR project.

Edited by: J. Kesselmeier

Appendix B

Analysis of variance for each region. In the first column, the number below the name of the region is the mean followed by the standard deviation in parenthesis of that region. DF=degrees of freedom; F=statistical values, P=probability, N=number of samples, SE=standard error of mean, StDev=Standard deviation. IV=index of variation and plots size are also given; * after plot code means "significantly different" (Tukey test) and * * "not a permanent plot".

Appendix C

Pairwise relationships between average plot ρ x and average ρ x of family (Table C1), genera (Table C2) and species (Table C3). Slope of the SMA, Pearson's R correlation coefficient, sig the significance of the correlation, and n the number of cases used, sig: * * * * <0.001, * * * <0.01, * * <0.05, * <0.1, ns=not significant.

Appendix D

Index of phenotypic plasticity within and between plots. N is the number of individual in each plot, Max and Min are the maximum and minimum ρ x of each species within one plot. IPP within is the average IPP within plots, IPP between takes the max and min of each species occurring in different plots.