DOI: 10.5061/dryad.fn2z34v2d
title: Data from: Contrasting drivers of aboveground woody biomass and aboveground woody productivity in lowland forests of Colombia

description: The relative importance of abiotic and biotic factors in shaping forest
 biomass stocks and fluxes remains a controversial issue. Here, using data
 gathered from 39 1-ha plots located in flooded and terra firme mature
 tropical lowland forests of the Amazon and Orinoquia regions of Colombia,
 we evaluated the importance of climate, soil fertility, flooding as well
 as tree taxonomic/phylogenetic diversity and forest structural properties,
 in determining the aboveground biomass stocks (AGB; Mg ha-1) and
 aboveground woody productivity (AWP; Mg ha-1 y-1). Using
 information-theoretic multimodel inference and variance partitioning we
 found that forest structural features, such as the number of trees with
 diameter at breast height ≥ 70 cm and wood density, are the main drivers
 of variation in AGB. However, taxonomic diversity also contributes to AGB
 because it is associated with more large trees in these forests. In
 contrast, the key drivers of AWP in these forests were soil P and Mg
 concentration, with no significant effects of diversity indices. These
 findings emphasize the need to include other major soil cations than N and
 P (e.g., Mg) in experimental studies to improve our understanding about
 the extent to which soil fertility can modulate increases in forest AWP
 due to climate change. Terra firme forests had higher AGB stocks than
 flooded forests, but both had similar AWP; and we found similar results
 for the drivers of AGB and AWP between flooded and terra firme forests.
 Our results provide limited evidence for strong effects of plant diversity
 on AGB or AWP. Therefore, we call for caution on generalizations of
 nature-based initiatives aiming to preserve diversity based on maximizing
 carbon stocks and productivity due to the complex nature of the processes
 controlling carbon accumulation and carbon fluxes in tropical forests.
description: This study was conducted using tree census data collected at 39
 1-ha forest inventory plots situated across a latitudinal range from -3.8°
 to 4.8°, a longitudinal range from -77.1° to -67.8°, and an elevation
 range from 79 m asl to 963 m asl, in Colombia.  The mean annual
 temperature (MAT) of plots ranged from 21.5 to 25.5 °C (mean = 24.7 ± 0.6
 °C; mean ± SD) and mean annual precipitation (MAP) of the plots ranged
 from 1797 to 3716 mm y<sup>-1</sup> (3076.4 ± 299.9 mm
 y<sup>-1</sup>) (External Databases S1). There were 8 plots
 located in the Orinoquia region (González-Abella et al. 2021) and 31 in
 the Amazonia region of Colombia (Fig. S1). Plots were censused between
 2005 and 2021. In the locality of Amacayacu, the data employed come from a
 continuous 25-ha plot located on Tierra firme soils derived from the Pebas
 formation (Zuleta et al. 2017).  Therefore, to avoid pseudo replication
 and over representation of this site, we only used the minimum, maximum,
 and mean value of the AGB, AWP, and AWRT at the 1-ha scale. Including
 these three values allow us to have a landscape representation of the
 variation of carbon stocks and fluxes in this geological formation
 characterized for having the relatively more fertile soils in the Amazon
 Tierra firme forests (Hoorn 1994). In total, 11 out of the 39 plots were
 located on floodplains, 8 of which were in Igapo and 3 in
 Varzea. In each plot, all stems of trees and palms
 (hereafter trees) with tree diameter at breast height (DBH; tree diameter
 at 1.3 m height) ≥ 10 cm were measured. We corrected all DBH measured over
 1.4 m height using the trunk taper equation proposed by Cushman et al.
 (2021). Some trees with an annual negative growth in DBH &lt; 1 cm,
 were corrected to a negative annual growth of DBH equals to - 1.0
 cm.  The aboveground biomass (AGB) of each tree was
 estimated using the allometric equation proposed by Chave et al. (2014),
 defined as: ; where AGB (kg) is the estimated aboveground biomass, DBH
 (cm) as defined above, H (m) is the estimated total height, and WD (g
 cm<sup>-3</sup>) is the stem wood density (see Supplementary
 Information for more details). The AGB of each palm was estimated using
 the allometric equation proposed by Goodman et al. (2013) defined as:
  where <em>dmf</em> is the dry mass fraction calculated as the
 ratio between dry mass and fresh mass. AGB per plot was estimated as the
 sum of AGB of all individuals in each plot. All plots
 were censused at least twice (elapsed time ranged between 2 and 10 years),
 and the aboveground woody productivity (AWP in Mg
 ha<sup>-1</sup> y<sup>-1</sup>), and aboveground
 woody residence time (AWRT in y) of each plot were estimated. The AWP was
 calculated as the sum of the annualized AGB recruitment and AGB growth.
 The annualized recruitment AGB (rAGB; Mg ha<sup>-1</sup>
 y<sup>-1</sup>) is the sum of the AGB of individuals recruited
 into DBH ≥ 10 cm between censuses divided by the time between
 measurements. However, for each tree recruited (DBH ≥ 10 cm), we
 subtracted the corresponding AGB associated with a tree of 9.99 cm (i.e.,
 just below the detection limit) to avoid overestimations of the overall
 increase in AGB due to recruitment (Talbot et al. 2014). The annualized
 AGB growth (gAGB; Mg ha<sup>-1</sup>
 y<sup>-1</sup>) was estimated as the sum of the increase in
 AGB of all individuals with DBH ≥ 10 cm that survived between censuses
 divided by the time between censuses. The AWRT was calculated as the
 quotient between the annualized values of AGB and AWP in each plot
 (Muller-Landau et al. 2021). We also calculated the net change in AGB as
 the annualized difference in AGB between the final and first census of
 each plot divided by the elapsed time between measurements (Duque et al.
 2021).  The aboveground biomass (AGB) of each tree was
 estimated using the allometric equation proposed by Chave et al. (2014),
 defined as: ; where AGB (kg) is the estimated aboveground biomass, DBH
 (cm) as defined above, H (m) is the estimated total height, and WD (g
 cm<sup>-3</sup>) is the stem wood density (see Supplementary
 Information for more details). The AGB of each palm was estimated using
 the allometric equation proposed by Goodman et al. (2013) defined as:
  where <em>dmf</em> is the dry mass fraction calculated as the
 ratio between dry mass and fresh mass. AGB per plot was estimated as the
 sum of AGB of all individuals in each plot. All plots
 were censused at least twice (elapsed time ranged between 2 and 10 years),
 and the aboveground woody productivity (AWP in Mg
 ha<sup>-1</sup> y<sup>-1</sup>), and aboveground
 woody residence time (AWRT in y) of each plot were estimated. The AWP was
 calculated as the sum of the annualized AGB recruitment and AGB growth.
 The annualized recruitment AGB (rAGB; Mg ha<sup>-1</sup>
 y<sup>-1</sup>) is the sum of the AGB of individuals recruited
 into DBH ≥ 10 cm between censuses divided by the time between
 measurements. However, for each tree recruited (DBH ≥ 10 cm), we
 subtracted the corresponding AGB associated with a tree of 9.99 cm (i.e.,
 just below the detection limit) to avoid overestimations of the overall
 increase in AGB due to recruitment (Talbot et al. 2014). The annualized
 AGB growth (gAGB; Mg ha<sup>-1</sup>
 y<sup>-1</sup>) was estimated as the sum of the increase in
 AGB of all individuals with DBH ≥ 10 cm that survived between censuses
 divided by the time between censuses. The AWRT was calculated as the
 quotient between the annualized values of AGB and AWP in each plot
 (Muller-Landau et al. 2021). We also calculated the net change in AGB as
 the annualized difference in AGB between the final and first census of
 each plot divided by the elapsed time between measurements (Duque et al.
 2021) Climate variables at each plot location were
 extracted from the CHELSA (Karger et al. 2017) bioclimatic rasters at a
 resolution of 30-arcsec (approximately 1 km<sup>2</sup> at the
 equator). We first extracted the 11 climatic variables associated with
 temperature and the 8 climatic variables associated with precipitation
 available in the CHELSA repository (see Supplementary Information). Then,
 we selected mean annual Temperature (MAT; °C), total annual precipitation
 (TAP; mm y<sup>-1</sup>), and precipitation seasonality (PS;
 kg m<sup>-2</sup>), defined as the coefficient of variation of
 the monthly precipitation estimates expressed as a percentage of the mean
 of those estimates (i.e. the annual mean). Once these three variables were
 selected, the remaining 16 variables included showed a correlation ≥ 0.7
 with MAT, TAP, or PS and were excluded. To estimate
 soil fertility, samples of soil A horizon (i.e., the mineral soil after
 removing the organic layer) were collected from a minimum of five points
 in each plot at a depth of 10–30 cm. The five samples from each plot were
 then combined, and a 500g composite sample was taken and air-dried after
 removing macroscopic organic matter. Sodium (Na; mg
 kg<sup>-1</sup>), aluminum (Al), Cation exchange capacity
 (CEC), total phosphorus (P; mg kg<sup>-1</sup>), calcium (Ca;
 mg kg<sup>-1</sup>), magnesium (Mg; mg
 kg<sup>-1</sup>), potassium (K; mg
 kg<sup>-1</sup>), and contents of organic matter (OM %) were
 analyzed for each plot (see Supplementary Information for details about
 lab analysis). Before assessing phylogenetic and
 taxonomic diversity, all species and genus names were checked and
 standardized using the Taxonomic Name Resolution Service (Boyle et al.
 2013). In the dataset, 90.4% of stems were identified to species level,
 5.3% to genus, 1.0% to family, and 3.3% remained unidentified. Based on
 the individuals fully identified to the species level, we assessed the
 number of species in each plot as a metric of taxonomic richness
 (<em>see</em> Duque et al. 2017), and the inverse of the
 Simpson index as a metric of taxonomic diversity (<em>see</em>
 Coelho de Souza et al. 2019). These metrics were estimated using vegan R
 package (Oksanen et al. 2022). We calculated three
 metrics of phylogenetic diversity: phylogenetic diversity <em>sensu
 stricto</em> (PD), Net Relatedness Index (NRI) and the Nearest Taxon
 Index (NTI). NRI and NTI were weighted by abundance. To calculate these
 indexes, we generated a phylogenetic tree from all fully identified
 species using the backbone phylogeny proposed by Smith and Brown (2018);
 this analysis was done employing V.PhyloMaker (Jin and Qian 2019). The PD
 of each plot was calculated as the total sum of the phylogenetic branch
 lengths connecting the co-occurring species in each plot along the minimum
 spanning path to the root of the tree. The NRI and NTI are based on the
 mean pairwise distance and the mean nearest pairwise distance,
 respectively (Webb 2000). These observed distances were standardized with
 respect to random distribution obtained from a null model. We used an
 independent swap null model that allow us to fit species frequency and
 abundance in each randomization. The standardized values are multiplied by
 -1. NRI and NTI positive values represent phylogenetic clustering and
 negative values represent phylogenetic overdispersion. These metrics were
 estimated using the picante package in R (Kembel et al. 2010).
 Forest structural properties were represented by forest stand
 descriptors, such as number of individuals (N), average wood density per
 plot (WD), number of trees with DBH ≥ 70 cm (D70), and maximum DBH (Dmax),
 which have shown to play an important role in shaping AGB, AWP, and AWRT
 in tropical forests (Slik et al. 2013, Fauset et al. 2015, Duque et al.
 2021). Other biological processes such as biological associations (e.g.,
 micorrhizas) and species competition are not considered here.
 We used an Information Theory (IT) natural model-averaging
 technique (Burnham and Anderson 2002) to identify the dominant ecological
 drivers of AGB, AWP, and AWRT across the Amazonia and Orinoquia regions of
 Colombia. The explanatory variables associated with our hypotheses were:
 climate and soil fertility as abiotic factors, and phylogenetic/taxonomic
 diversity and forest structure as biotic factors. The natural
 model-averaging technique allows us to infer and predict AGB, AWP, and
 AWRT based on a set of alternative models. The natural model-averaging
 technique calculates the average of each variable´s parameter estimates
 over the models where the variable was selected (Symonds and Moussalli
 2011, Galipaud et al. 2017). The explanatory variables were previously
 standardized to have mean = 0 and standard deviation = 1, and then each
 parameter standardized by their partial standard deviations (Cade 2015).
 The use of the partial standard deviations (Cade 2015) aims to correct for
 the likely effect of multiple correlation among variables. The partial
 standard deviation (s*ₓᵢ) is calculated as follows.
description: # Contrasting drivers of aboveground woody biomass and aboveground woody
 productivity in lowland forests of Colombia
 Tree census data collected
 at 39 1-ha forest inventory plots situated in the Orinoquia region and
 Amazonia region of Colombia. Plots were censused between 2005 and 2021. We
 aim to assess the importance of abiotic and biotic factors in controlling
 the variation in aboveground biomass stocks and fluxes. In each plot, all
 stems of trees and palms (hereafter trees) with tree diameter at breast
 height (DBH; tree diameter at 1.3 m height) ≥ 10 cm were measured. The
 aboveground biomass (AGB) of each tree was estimated using the allometric
 equation proposed by Chave et al. (2014). All plots were censused at least
 twice (elapsed time ranged between 2 and 10 years), and the aboveground
 woody productivity (AWP in Mg ha-1 y-1), and aboveground woody residence
 time (AWRT in y) of each plot were estimated. To estimate soil fertility,
 samples of soil A horizon (i.e., the mineral soil after removing the
 organic layer) were collected from a minimum of five points in each plot
 at a depth of 10–30 cm. The five samples from each plot were then combined
 and analyzed. We calculated three metrics of phylogenetic diversity:
 phylogenetic diversity *sensu stricto* (PD), Net Relatedness Index (NRI)
 and the Nearest Taxon Index (NTI). NRI and NTI were weighted by abundance.
 The PD of each plot was calculated as the total sum of the phylogenetic
 branch lengths connecting the co-occurring species in each plot along the
 minimum spanning path to the root of the tree. The NRI and NTI are based
 on the mean pairwise distance and the mean nearest pairwise distance,
 respectively. We found there were significant differences between flooded
 and Tierra firme forests in Aboveground biomass and Aboveground Woody
 Residence Time. These forests are gaining carbon as shown by a positive
 Aboveground biomass net change. The difference in Aboveground biomass net
 change between flooded and Tierra firme forests was marginally
 significant, being negative and with higher variability in flooded than in
 Tierra firme forests. Diversity, forest structure, climate, and soils were
 independently correlated with the spatial variation of the Aboveground
 biomass. when we sequentially removed the variables representing each
 independent hypothesis, forest structure, here represented by the number
 of trees with DBH ≥ 70 cm (D70) and mean wood density, had a pure total
 explained variation of 40 % and the strongest effect in determining the
 Aboveground biomass All independent variables selected were correlated
 with the spatial variation of the Aboveground biomass in Tierra firme. The
 full models for all plots and Tierra firme employed to assess the drivers
 of Aboveground productivity included soils and forest structure as the
 most important factors. In both cases, P, Mg, and the number of big trees
 (D70) were selected as the key drivers of Aboveground productivity. File
 data set structure: ID plot number, Plot name, Longitude (◦), Latitude
 (◦), Flooded/Terra firme, Mean annual temperature (◦C)(MAT), Total annual
 precipitation (mm y-1)(TAP), Precipitation seasonality (PS), Number of
 individuals (ha-1)(Density), Mean wood density (gr cm-3)( WD_mean),
 Aboveground biomass (Mg ha-1)(AGB), Aboveground productivity (Mg ha-1
 y-1)(AWP), Net carbon change (Mg ha-1 y-1)(Net_change), Elapsed time
 between censuses (y)( Time (y-1)), Number of genus per plot (Genus),
 Number of species per plot (Sp), Inverse of Simpson index (Simpson_inv),
 Net Relatedness Index (NRI), Nearest Taxon Index (NTI), Phylogenetic
 Diversity (PD), soil pH (pH), Calcium (mg kg-1)(Ca), Potasium  (mg
 kg-1)(K), Magnesium  (mg kg-1)(Mg), Sodium  (mg kg-1)(Na), Aluminium (mg
 kg-1)(Al), Cation Excahnge Capacity (CEC), Phosporous (mg kg-1)(P),
 Organic carbon (%)(CO), Number of trees with DBH ≥ 70 cm ha-1 (D70),
 Maximum DBH (cm)(Dmax) Note: Plot number in red are the two plots selected
 from the 25-ha Amacayacu plot with aboveground biomass maximum and minimum
 values. In blue those for aboveground woody productivity