DOI: 10.5281/zenodo.17053929
title: Data for "Plant drought tolerance is critical for biomass accumulation of an old-growth subtropical forest"

description: Plant functional traits 

Early each morning, healthy, leaf-bearing branches (5–9 mm in diameter) were collected from three to five individuals per species, with five to ten branches harvested per species. For hydraulic measurements, branch segments ranging from 60 to 100 cm in length were used (Zhu et al., 2019). The maximum vessel length of the species was assessed via an air infiltration technique (Brodribb & Feild 2000). Branch segments longer than the maximum vessel length were putted in a pressure sleeve (PMS, Corvallis, OR, USA). According to the method established by Sperry et al. (1988), the maximum hydraulic conductivity (Kh) was calculated using the equation Kh = FL/DP, where F represents the flow rate (kg s-1), L is the segment length (m), and DP denotes the pressure gradient (MPa) across the segment. The specific conductivity (KS, kg m-1 s-1 MPa-1) was determined by dividing Kh by the average sapwood cross-sectional area at both ends of the branch segment. The leaf area was quantified with a leaf area meter (Li-3000A, Li-Cor, USA). The ratio of leaf area to sapwood area (AL/AS, m2 cm–2) was then calculated (He et al., 2025).

Wood density (WD, g cm-3) was assessed using the same branch utilized for the measurements of hydraulic conductivity. Volume of fresh sapwood excluding bark and pith was measured using water displacement method (Poorter et al., 2010), and the dry mass was tested after oven-dried at 70 ºC for three days. WD was then calculated as dry mass/fresh volume. 

Turgor loss point (TLP, MPa) is an important trait for evaluating drought tolerance in various species and biomes (Zhu et al., 2018). For each species, foliar samples collected from 3-5 individual plants were fully hydrated until achieving a leaf water potential > -0.05 MPa. Initial fresh weight measurements were recorded prior to immediate placement in a pressure chamber for baseline water potential assessment. Sequential measurements of both leaf mass and water potential were conducted throughout the controlled dehydration process. The turgor loss point was quantified employing pressure-volume curve analysis following the methodology established by Schulte and Hinckley (1985). 

We selected five individuals of each species and harvested 5-6 sun-exposed leaves from each individual for the measurements of leaf photosynthetic traits. The maximum net CO2 assimilation rate (Amax, μmol m-2 s-1) and stomatal conductance (gs, mol m-2 s-1) were recorded between 9:00 and 11:00 a.m. using a portable photosynthesis system (Li-6400; Li-Cor, Lincoln, NE, USA). The photosynthetic photon flux density was set to 1500 µmol m-2 s-1, a level shown to achieve photosynthetic saturation for the species in previous studies (Zhu et al. 2013; He et al. 2019). Chamber CO2 concentration and leaf temperature were maintained at 400 ppm and 28 °C, respectively, for 15 minutes to stabilize the photosynthetic parameters. Leaf intrinsic water use efficiency (WUEi, μmol mol-1) was calculated as the ratio of Amax to gs.

Following the removal of leaf petioles and rachises, the leaf area was quantified with a leaf area meter (Li-3000A, Li-Cor, USA). The leaves were subsequently oven-dried at 70 °C for 72 hours to obtain their dry weight. From these data, the specific leaf area (SLA, cm² g⁻¹) was derived by dividing the leaf area by the dry mass. The dried leaf material was then further analyzed for nutrient content. Specifically, leaf nitrogen concentration (Nmass, mg g-1) was measured employing the Kjeldahl method, while leaf phosphorus concentration (Pmass, mg g-1) was assessed via atomic absorption spectrophotometry (Li et al., 2015). 

Aboveground biomass and productivity

 To determine aboveground living biomass (AGB; Mg ha-1), we used data from four censuses (2005, 2010, 2015, and 2000). All trees with DBH ≥ 1 cm in the 20 ha plot were considered. We calculated the AGB for each tree using the following equation from Chave et al. (2014): 

AGB = exp[ − 1.803 − 0.976 × (E) + 0.976 × ln(WD) + 2.673 × ln(DBH) − 0.0299 × (ln(DBH))2],

Where E represents the environmental stress at the site, which is influenced by water deficit and temperature seasonality, and has a value of 0.670237 at Dinghushan (extracted from http://chave.ups-tlse.fr/pantropical_allometry.htm using the retrieve_raster function in R). The biomass stock was determined by summing the biomass of all live individuals. AGB varied substantially (from 13 to 26-folds) across the 20 m × 20 m plots in the 20-ha permanent plot among the four community inventories (Figs. S3a-d; Table S1). In addition, AGB at four community inventories were strongly correlated with each other with R2 ranging from 0.62 to 0.86, and the correlations were weakened with the census interval lengthening (Figs. S4a-f). 

The aboveground biomass productivity (ABP; Mg ha-1 year-1) of the forest between two censuses was calculated as the sum of the growth of all woody species present in both censuses, along with the growth of newly recruited species (van der Sande et al., 2018; Da et al., 2023).