DOI: 10.5061/dryad.c59zw3r76
title: Data for: Resolving whole-plant economics from leaf, stem and root traits of 1467 Amazonian tree species

description: It remains unclear how evolutionary and ecological processes have shaped
 the wide variety of plant life strategies, especially in highly diverse
 ecosystems like tropical forests. Some evidence suggests that species have
 diversified across a gradient of ecological strategies, with different
 plant tissues converging to optimize resource use across environmental
 gradients. Alternative hypotheses propose that species have diversified
 following independent selection on different tissues, resulting in a
 decoupling of trait syndromes across organs. To shed light on the subject,
 we assembled an unprecedented dataset combining 19 leaf, stem and root
 traits for 1467 tropical tree species inventoried across 71 0.1-ha plots
 spanning broad environmental gradients in French Guiana. Nearly 50% of the
 overall functional heterogeneity was expressed along four orthogonal
 dimensions, after accounting for phylogenetic dependences among species.
 The first dimension related to fine root functioning, while the second and
 third dimensions depicted two decoupled leaf economics spectra, and the
 fourth dimension encompassed a wood economics spectrum. Traits involved in
 orthogonal plant functional strategies, five leaf traits in particular but
 also trunk bark thickness, were consistently associated with a same
 gradient of soil texture and nutrient availability. Root traits did not
 show any significant association with edaphic variation, possibly because
 of the prevailing influence of other factors (mycorrhizal symbiosis,
 phylogenetic constraints). Our study emphasises the existence of multiple
 functional dimensions that allow tropical tree species to optimize their
 performance in a given environment, bringing new insights into the debate
 around the presence of a whole plant economic spectrum in tropical forest
 tree communities. It also emphasizes the key role that soil heterogeneity
 plays in shaping tree species assembly. The extent to which different
 organs are decoupled and respond to environmental gradients may help to
 improve our predictions of species distribution changes in responses to
 habitat modification and environmental changes.
description: <b><i>Study
 area</i></b> Tree species inventories were
 carried out in ten lowland and lower mountain sites covered by mature
 tropical moist forests across French Guiana (Fig. 1a), at the eastern edge
 of the Guiana shield. Mean annual rainfall (calculated over the 2010-2018
 period) across inventory sites ranges between 2150 and 3700 mm and is
 distributed seasonally throughout the year (Gourlet-Fleury et al. 2004;
 Table 1), with a dry season occurring between August and November (monthly
 rainfall ≤ 100 mm) and usually more pronounced toward the interior of the
 continent. Mean annual temperature (2010-2018) oscillates around 25°C,
 with low seasonal variation, and averages at 22°C in a relatively higher
 site (&gt; 500 m a.s.l.) located at Mont Itoupé in the centre of
 French Guiana. Each of the ten inventory sites comprised two to 12 0.1-ha
 plots (71 plots in total), separated by at least 500 m, and located on
 contrasted habitats: (i) terra firme, (ii) white-sand and (iii) seasonally
 flooded forests. Additional details regarding the description of the study
 area are available in Appendix S1. <b><i>Tree
 inventories</i></b> We used a modified version of the
 Gentry plots proposed by Phillips et al. (2003) and described in Baraloto
 et al. (2013), consisting of ten parallel transects of 2 × 50 m departing
 perpendicularly every 20 m from a 200 m central transect, successively
 oriented in alternate directions, and delimiting an area of 1.9 ha. Within
 each transect, all stems with a circumference at 1.3 m above soil level
 &gt; 8 cm (corresponding to a DBH of ca. 2.5 cm) were inventoried.
 Voucher specimen were collected at least once for each putative distinct
 species per plot in the field, plus additional vouchers for individuals
 that were not completely identified in the field. Duplicate vouchers are
 currently stored in reference collections of UMR EcoFoG (Kourou, French
 Guiana), UMR AMAP (Montpellier, France), and/or ICTB (Miami, USA). A list
 of species with their voucher reference is available in Appendix S2. Our
 inventories resulted in a dataset comprising 13,736 trees belonging to
 1,467 species (excluding 25 palm and fern species for which we could not
 sample whole plant functional traits), 348 genera and 81 families, with an
 average of 79 species (± SD = 28) per plot (additional details regarding
 tree diversity and climatic characteristics in each study site are
 provided in Table 1). <b><i>Functional
 traits </i></b> We assembled a dataset of 19
 functional traits for the 1467 tree species (excluding ferns and palms)
 inventoried in our 71 plots, including 11 leaf traits, two stem traits,
 one coarse root trait and five fine root traits (Table 2). Details on
 trait measurement protocol are provided in Appendix S1. Trait values were
 obtained from a dataset comprising measurements from tissue samples
 collected on 8345 individual trees belonging to 1625 species distributed
 in 371 genera, 78 families and 26 orders in the Rosidae, Asteridae and
 early eudicots (Appendix S3). 5735 of these individuals (68.7%)
 corresponded to trees inventoried in our 71 plots and represented 783 out
 of the 1467 inventoried species (53.4%). Additional measurements were made
 on tissue samples collected, respectively, on 1746 and 858 individuals
 from the Brazilian Amazon (near Manaus) and Peru (representing,
 respectively, 248 and 541 species, among which 119 and 60 were also
 observed in French Guiana). We assumed that trait values for the Brazilian
 and Peruvian samples were representative of the species present in French
 Guiana (Ackerly 2003, Crisp et al. 2012). We deem this assumption
 reasonable, based on positive and highly significant correlations
 (<i>t</i>-test of Pearson’s product moment) of species mean
 trait values between French Guiana and the two outside regions (Brazil and
 Peru), for 12 out of 18 traits that could be compared (two traits could
 not be tested due to sampling limitations; see details in Appendix
 S1). The
 imputations were performed using the matrix of 8345 individuals (belonging
 to 1625 species) x 19 traits (i.e., the <i>NBA</i> matrix).
 779 out of the 1467 species of our French Guiana inventories were
 represented in this matrix. We therefore added 688 lines (1467-779) of
 empty trait values, corresponding to the species that were not represented
 in the <i>NBA</i> matrix, therefore producing a matrix of 9033
 individuals and 2313 species on which we performed the BHPMF imputations.
 The percentage of missing values in this trait matrix ranged between
 34.96% for the leaf area up to 87.90% for the coarse root wood-specific
 gravity (see Appendix S1 for details). Although the latter percentage was
 high, fine root traits were measured on species covering a wide
 phylogenetic range (31 families and 9 orders in the Asteridae, Rosidae and
 Magnoliidae), while previous studies have suggested that phylogenetic
 trait conservatism represents a major determinant of root functional
 traits (Valverde-Barrantes <i>et al</i>. 2017). Similarly,
 woody traits, which showed proportions of missing values that exceeded
 65%, have shown strong conservatism (Chave et al. 2006). To fill the missing trait values, we
 used the <i>Bayesian Hierarchical Matrix Factorization</i>
 method (BHMF, Fazayeli <i>et al</i>. 2014), an imputation
 procedure based on both (i) covariations among traits and (ii) trait
 information at higher taxonomic levels (species, genus, family, order,
 class), in a hierarchical way. The BHPMF method has been proved efficient
 in assessing missing trait values even with a percentage of missing values
 higher than 90%, providing that there is a good phylogenetic coverage
 and/or a sufficient number of traits measured among species (Schrodt et
 al. 2015). This was the case with our traits as they were measured on
 species covering a wide phylogenetic range, while there were on average
 11.3 ± 4.0 (standard deviation) traits measured per species. Prior to the
 imputations, outliers in the distribution of each trait were eliminated
 following Zuur <i>et al</i>. (2010) as the BHMF method is
 sensitive to extreme values, then traits were normalised (Box-Cox
 transformation) and standardised (z-score transformation). We then
 detrended each trait with the height of individuals to remove any
 ontogenetic variation effect, which is rarely taken into account in
 ecological studies, by regressing each trait on height and using the
 residuals of each regression as our trait values. Imputations were then
 calculated using the <i>GapFilling</i> function in the R
 <i>BHPMF</i> package (Fazayeli <i>et al</i>.
 2014), using observed information available at the genus, family, order,
 subclass and class level to estimate missing trait values. We then
 extracted the imputed values at the individual level and calculated a
 matrix of mean trait values at the species level for the 1467 species of
 our French Guiana inventories. <i>Post Hoc</i> analyses were
 carried out to evaluate the reliability of our imputations. These analyses
 showed that the whole correlation structure among traits was well
 preserved after imputation (Appendix S1). <b><i>Environmental
 data</i></b> Soil sampling and analyses were
 carried out using the protocol described in Baraloto et al. (2011). We
 collected ten bulked soil cores using 5 cm-diameter auger at 0-10, 10-20
 and 20-30 cm depth in each plot, at each intersection between the ten
 parallel transects and the main central line. For each plot, the ten cores
 were then combined into a composite 500 g sample which was air-dried then
 sieved across 2 mm mesh and shipped all together for physicochemical
 characterisation at CIRAD soil lab (France) using standard soil analysis
 protocols (Pansu & Gautheyrou 2006). We retrieved nine soil
 variables, including two physical variables corresponding to soil texture
 (percentages of sand and clay), and seven chemical variables: soil organic
 carbon content, C:N ratio, available phosphorus (P), total soil Nitrogen
 content (TN), and the availability of three base cations (Ca, Mg and K).
 The other variables used in our analyses comprised the elevation a.s.l. of
 each plot and three climatic variables calculated using data extracted
 from www.worldclim.org,
 via the R package <i>raster</i> (Hijmans 2019): the mean
 annual rainfall (mm), the standard deviation of the mean monthly rainfall
 calculated over 12 months (to quantifies the unevenness of precipitation
 throughout the year). A Dry Season Index (DSI) was also calculated for
 each site as the sum (over 12 months) of the ratios between mean monthly
 temperature and mean monthly rainfall to estimate of the potential water
 stress accumulated during the dry seasons.