DOI: 10.5061/dryad.1g1jwsv9b
title: Data from: Biomass production of tropical trees across space and time: The shifting roles of diameter growth and wood density

description: Woody biomass in tropical trees contributes significantly to global carbon
 stocks; however, these stocks are increasingly affected by climate and
 land-use changes. Understanding the growth mechanisms driving woody
 biomass production is essential for assessing the short- and long-term
 contributions to carbon stocks and dynamics in tropical forests. Trees
 accumulate biomass by increasing their size (wood volume) and/or tissue
 density (wood density). However, estimates of tree biomass production are
 often based solely on size increment through measurements of stem diameter
 growth, overlooking the potential spatial and temporal variation in wood
 density within trees. Tree-ring analysis can be applied to reconstruct
 past tree volume growth and wood density variations, allowing the
 quantification of their relative contributions when reconstructing past
 woody biomass production. Here, we studied trees of the widespread
 Neotropical genus Cedrela (C. fissilis, C. odorata, and C. montana) along
 an environmental (climate and soil) gradient to address two key questions:
 1) How does temporal variation in tree diameter growth and wood density
 affect biomass production? 2) To what extent do these relationships vary
 along the environmental gradient? We examined long-term (ontogenetic,
 variation from pit to bark) and short-term (annual, interannual variation)
 variations in diameter growth and wood density, covering eighteen sites in
 the Amazon rainforest, Atlantic Forest, Cerrado savanna, and Caatinga dry
 forest. We found that diameter growth and wood density drive short- and
 long-term biomass production dynamics. Interestingly, diameter growth
 patterns predominantly explained short-term variability in biomass
 production at all sites, whereas wood density explained ontogenetic
 biomass patterns mainly at humid sites. These results highlight the
 importance of accounting for both short- and long-term variation,
 including climatic and ontogenetic drivers, to increase the accuracy of
 biomass estimations in tropical trees, particularly in humid forest
 ecosystems such as the Amazon.  Diameter growth is an important
 and good indicator of forest carbon production. However, size-related
 changes in wood density, which are usually neglected, are critical for
 accurate short- and long-term carbon assessments, especially in tropical
 humid sites.
description: <em>Sample
 collection</em> Samples were collected
 from eighteen forest sites across Brazil and Peru, covering a range of
 forest types and environmental characteristics. The study sites included
 national, state, and municipal parks and private forests located in
 tropical and subtropical regions. These sites represent ecosystems from
 the Amazon Rainforest, Atlantic Forest, Brazilian
 <em>Cerrado</em> savanna, and <em>Caatinga
 </em>dry forest. Samples from sites in the Amazon were collected in
 sustainable-use conservation units (National and State Forests), where
 legal forest management operations are conducted. In these areas, samples
 were obtained by collecting cross-sectional wedges from felled trees as
 part of authorized logging activities. In the remaining areas, samples
 were collected from state, municipal, and private forests where forest
 management is not legally authorized. In these cases, non-destructive
 coring methods were used to extract wood samples. Each population was
 sampled in different years for various dendrochronological applications.
 We included only samples that spanned from the pith to the bark, contained
 the pith, or were close enough to estimate the distance to the pith. At
 each location, the number of trees we used ranged from eight to eighteen,
 due to the availability of samples with complete pith and sufficient
 material for wood density analysis.
 <em>Sample preparation and wood density
 analysis</em> We selected one radius
 from each tree to perform the analysis. All the cores were previously
 polished and scanned at high resolution (1,200-2,400 dpi), and tree rings
 were marked following the marginal parenchyma bands associated with
 semi-porous or porous xylem. To measure the growth rate and wood density,
 cores were sectioned transversely into radial sections of 0.5-2 cm wide
 and 1.5 mm thick, while wedges were cut into sections of 2 cm wide and 1.5
 mm thick. Wood density was determined following the protocol established
 by Quintilhan
 et al. (2021) and Tomazello et al. (2009). Radial
 sections were conditioned in a climatic chamber at 20°C and 60% relative
 humidity until they reached a stable moisture content of 12%. All samples
 were scanned in an X-ray densitometry chamber (Faxitron X-Ray, Illinois,
 USA) equipped with a measurement unit scale and a cellulose acetate
 calibration wedge for wood density calibration. Digital X-ray images of
 the radial section samples (96 dpi, .tiff) were analyzed with WinDendro
 Density 2017a® software (Regent Instruments Inc., Canada) to obtain the
 tree-ring width (TRW) and tree-ring wood density at 12% moisture content
 (WD<sub>U12%</sub>) per year. Sample preparation, X-ray
 imaging, and wood density measurements were performed at the Tree-Ring and
 Wood Anatomy Laboratory of the University of São Paulo (Piracicaba, São
 Paulo, Brazil). Data processing and data analysis were conducted at the
 Dendroecology and Wood Biology Laboratory at the State University of São
 Paulo (Campinas, São Paulo, Brazil).
 <em>Diameter reconstruction and biomass estimation
 </em> For each tree, the cumulative
 diameter (D) and cumulative basal area (BA) during the year of ring
 formation were reconstructed by summing the previous tree-ring widths,
 assuming a circular growth pattern. For tree-ring samples without a
 visible pith, we estimated the distance based on the concentric circle
 method, using CDendro and CooRecorder® software (Cybis
 Electronic, 2013). To calculate the
 aboveground biomass production per year, we first reconstructed the total
 historical aboveground biomass per year (B) using the pantropical equation
 described by Chave et
 al. (2014) (equation 1). Not all sites had tree height data
 available; therefore, we used the pantropical equation that included a
 diameter–height allometric model. This model has a stress factor (E, Table
 S2) that represents the environmental conditions of the site where the
 trees grow. Chave et
 al. (2014) demonstrated that factor E is strongly dependent on
 the diameter-height allometry relationship and is particularly useful for
 improving model accuracy in the absence of tree height data.
 For each tree and year, biomass was estimated based on D and a
 tree-level weighted average wood density (WD<sub>WA</sub>).
 The WD<sub>WA</sub> value was obtained from the weighted
 average of the WD<sub>U12%</sub> values by the basal area
 increment of each year (Equation 2). This calculation was designed to
 prevent the influence of the wood proportion near the pith (Williamson and
 Wiemann, 2010). Before weighing, WD<sub>U12%</sub> was
 transformed into basic wood density (WD) by multiplying
 WD<sub>U12%</sub> by 0.838 (Vieilledent et al., 2018). The
 absolute ABP was obtained by subtracting the total biomass from the
 present year minus the total biomass of the previous year (ABP =
 B<sub>t</sub>-B<sub>t-1</sub>). We also calculated
 the relative change in biomass per year by dividing the ABP from the
 present year by the total biomass from the previous year
 (ABP<sub>P</sub> =
 ABP<sub>t</sub>/B<sub>t-1</sub>) to reduce the
 size-ABP relationship bias. Equation 1:
 <em>B <sub> </sub>= exp[-18.03 – 0.976E + 0.976
 ln(WD<sub>WA</sub>) + 2.673 ln(D) – 0.0299
 [ln(D)<sup>2</sup>]</em> Where B is
 the historical aboveground biomass (kg); <em>D</em> is the
 total diameter of each tree per year reconstructed by the accumulated
 tree-ring width increment (cm);
 <em>WD<sub>WA</sub></em><sub>
 </sub>is<em> </em>the average basic<em>
 </em>wood density-weighted by basal area increment (g/cm³);
 <em>E</em> is the environmental stress factor unitless for
 each site obtained from the raster file available in Chave et al.
 (2014).  Equation
 2:                                                                                        WD<sub>WA </sub> = (BAIt * WDt)/BAt Where WD<sub>WA</sub><em>  </em>is the weighted average wood density  (g/cm³); <em>BAIt</em> is the basal area increment in year <em>t </em>(m²); <em>WD<sub> </sub></em><sub> </sub>is the basic wood density (g/cm³), obtained from <em>WD<sub>U12%</sub></em> * 0.838 in year t; <em>BAt</em> is the total basal area in year <em>t </em>(m²).
description: # Data from: Biomass production of tropical trees across space and time:
 The shifting roles of diameter growth and wood density Dataset DOI:
 [10.5061/dryad.1g1jwsv9b](10.5061/dryad.1g1jwsv9b) ## Description of the
 data and file structure This dataset was collected as part of a study on
 trees of the widespread Neotropical genus *Cedrela* (*C. fissilis*, *C.
 odorata*, and *C. montana*), sampled across eighteen sites spanning
 diverse biomes, including the Amazon rainforest, Atlantic Forest, Cerrado
 savanna, and Caatinga dry forest. The research aimed to address two main
 questions: 1\) How does temporal variation in tree diameter growth and
 wood density affect biomass production? 2\) To what extent do these
 relationships vary along the environmental gradient? To answer these
 questions, we measured both long-term (ontogenetic, from pith to bark) and
 short-term (annual to interannual) variations in diameter growth and wood
 density. Biomass estimates were derived using the pantropical allometric
 equation from Chave et al. (2014), enabling the assessment of productivity
 patterns about environmental heterogeneity. ### Files and variables ####
 File: metadata.txt **Description:**  ##### Variables * ID_area: Identifier
 for the study area or site. - local_descr: Description of the location
 (e.g., private area, concession) * lat: Latitude of the site , Decimal
 degrees -  long: Longitude of the site , Decimal degrees * biome: Biome
 classification (e.g., Amazon, Cerrado, Atlantic Forest) - veget:
 Vegetation type (e.g., dry forest) * specie: Scientific name of the
 studied tree species (CF = Cedrela fissilis, CO = Cedrela odorata, CM =
 Cedrela montana) - position: Position on the tree where the sample was
 taken (e.g., DBH, base) #### File: climate_soil_dataset.txt
 **Description:**  ##### Variables * ID_area: Identifier for the study area
 or site. - CWD: Climatic Water Deficit in mm fromChavee et a,l 2014 * E:
 Environmental stress factor affecting tree allometry, as defined in Chave
 et al. (2014) , Unitless - PETt: Mean annual Potential Evapotranspiration
 in mm from TerraClimate (1990–2000 mean) * MAPt: Mean Annual Precipitation
 in mm from TerraClimate (1990–2000 mean) - SRADt: Mean annual Solar
 Radiation in MJ/m²/day or W/m² from TerraClimate (1990–2000 mean) * TMAXt
 Mean annual Maximum Temperature °C from TerraClimate (1990–2000 mean) -
 TMINt Mean annual Minimum Temperature °C from TerraClimate (1990–2000
 mean) * VPDt: Mean annual Vapor Pressure Deficit in kPa from TerraClimate
 (1990–2000 mean) - MAPqmax: Precipitation in the wettest quarter in mm
 from TerraClimate (1990–2000 mean) * MAPqmin: Precipitation in the driest
 quarter in mm from TerraClimate (1990–2000 mean) - phh2o: Soil pH measured
 in water solution, unitless from SoilGrids * soc_gkg: Soil Organic Carbon
 content in g/kg from SoilGrids - clay_g100g: Clay content of the soil in
 g/100g from SoilGrids * cec_cmolc_kg: Soil Cation Exchange Capacity,
 reflecting soil fertility potential in cmol(+)/kg from SoilGrids -
 lat_site: Latitude of the site , Decimal degrees * long_site: Longitude of
 the site , Decimal degrees #### File: cedrela_biomass_dataset.txt
 **Description:**  ##### Variables * ID_area: Identifier for the study area
 or site. - ID_tree: Unique identifier for each sampled tree. * ID_radii:
 Identifier for the specific tree core or radius sampled. - ID_full:
 Composite identifier combining multiple IDs (e.g., area, tree, and core)
 for full traceability. * age_pith_to_bark: Estimated age (years) from the
 pith (center) to the bark (outer edge) of the tree core. - rw_mm: Ring
 width in millimeters; annual radial growth. * rd_gcm3: Apparent wood
 density, g/cm³. - rd_filter: Apparent wood density excluding rd above 1.2
 and below 0.2, we need to use it to remove from the final analysis *
 rd_pred_bas_gcm3: basic wood density calculated considering rd -
 rd_pond_gcm3: rd_pred_bas_gcm3 weighted by basal area, used only to
 calculate biomass * diam_cum_cm: Cumulative diameter (in centimeters) of
 the tree, measured from pith to bark. - diam_inc_cm: Annual diameter
 increment (in centimeters), representing the increase in stem diameter
 over one year. * ba_m2: Cumulative basal area (in square meters),
 calculated from the diameter and representing the cross-sectional area of
 the tree trunk at breast height. - bai_m2: Basal area increment (in square
 meters), showing the annual increase in basal area. * bio_cum_kg:
 Cumulative aboveground biomass (in kilograms), representing the total
 biomass accumulated by the tree up to a given year. - bio_incr_kg: Annual
 biomass increment (in kilograms), indicating the amount of biomass
 produced in a single year. * bio_incr_div_kg: Relative change in biomass
 per year by dividing the ABP from the present year by the total biomass
 from the previous year (ABPP = ABPt/Bt-1) to reduce the size-ABP
 relationship bias. ## Code/software This dataset is provided in a
 tab-delimited text format (`.txt`and can be accessed using either R or
 Python. Below is a description of the software environment and packages
 used for the analysis: **Software environment:** R version 4.3.2
 (2023-10-31), RStudio version 2024.04.2+761 **Packages:** dplyr to make
 tabular data manipulation easier, and tidyverse to enable swift conversion
 between different data formats. Example of how to install packages in
 RStudio: ``` install.packages(c("tidyverse", "dplyr"))
 ``` ## Access information Other publicly accessible locations of the data:
 * The full dataset is also available upon request by contacting the first
 author, Bruna Hornink (email:
 [bhornink@gmail.com](mailto:bhornink@gmail.com)). Data was derived from
 the following sources: * The environmental data in
 `"climate_soil_dataset.txt"` Were derived from publicly
 available global datasets: * Climate data: Extracted from TerraClimate
 (Abatzoglou et al., 2018), using long-term means from 1991 to 2020. * Soil
 data: Obtained from SoilGrids (Poggio et al., 2021), which provides
 gridded predictions of global soil properties at 250 m resolution.