Functional trait dataset of roots from Linyang Nanmu and Gadaqi red pine plantations

The dataset is compatible with mainstream scientific software such as Origin, SPSS, Python, and WinRHIZO for reading and analysis.I. Full Dataset Generation Process, Processing Steps, Instruments and Tools(I) Field Sampling StageSampling Time and Spatial Layout:Field sampling was uniformly conducted in August 2025, covering three typical study regions: the natural Korean pine distribution area in Dailing, Lesser Khingan Range (DL, 47°08′05″ N, 128°47′03″ E), the Nanmu introduction area in the Greater Khingan Range (NM, 48°10′38″ N, 122°54′25″ E), and the Jiagedaqi introduction area in the Greater Khingan Range (JGDQ, 50°18′39″ N, 124°06′56″ E).

Three standard plots of 20 m × 30 m were established at each site, and five standard trees were randomly selected within each plot for root and soil sample collection. Meteorological data cover complete monthly observations across the full introduction cycle of Korean pine, with a spatial resolution of 1 km grid data around the sampling points.1. Root Sample Collection and Processing: Complete 1st–3rd order root segments were excavated from the 0–20 cm soil layer within the horizontal root zone 1–3 m from the trunk of standard trees.

After rinsing with deionized water to remove attached soil, samples were divided into two portions: one portion was preserved in FAA fixative under low-temperature refrigeration for anatomical sectioning, and the other was frozen at –20 °C for morphological and chemical indicator measurements. Simultaneously, undisturbed soil samples from the 0–15 cm layer were collected using cutting rings and sealed in aluminum boxes for determination of soil moisture content and bulk density, with additional surface air-dried soil collected for nutrient indicator testing.2.

Field Tools Used: Cutting rings, aluminum boxes, low-temperature冷藏箱, FAA fixative, shovels.(II) Indoor Indicator Measurement Procedures and Supporting Equipment1. Root Anatomical Trait Measurement: According to the root order classification standard of Pregitzer et al. [7], 30 roots of each of the 1st–3rd orders were randomly selected from each experimental plot for structural analysis. These samples were subjected to ethanol gradient dehydration, paraffin embedding, and safranin-fast green double staining, and then sectioned into 8-μm-thick cross-sections of roots.

Microscopic images of different order roots were captured using a biological microscope (Olympus Electronics Inc., Tsukuba, Japan) equipped with a Motic 3000 CCD camera (Motic Corporation, Xiamen, China). Root cortex thickness (CT) and stele diameter (SD) were measured using Motic Images Advanced 3.2 software, and the cortex/stele radius ratio (CSR) was subsequently calculated.2. Root Morphological Trait Measurement: Frozen root samples were thawed and sorted by order.

Root images were scanned using an Epson digital scanner (Expression 10000XL 1.0) and analyzed with WinRHIZO (2014b) root image analysis software to determine total root length, root surface area, root volume, and average diameter. Samples were then dried at 65 °C for 48 h to constant weight and weighed to calculate specific root length, specific root area, and root tissue density.3. Root and Soil Chemical Indicator Measurement: Ground and sieved root and air-dried soil samples were analyzed for total carbon and total nitrogen using an elemental analyzer (Vario MACRO, Elementar, Germany).

After digestion with sulfuric acid-perchloric acid, total phosphorus was determined by the molybdenum-antimony colorimetric method, and total potassium by flame photometry. Soil pH was measured using a pH meter; soil organic matter was determined by the potassium dichromate oxidation titration method; and soil moisture content and bulk density were calculated by the oven-drying weighing method.4. Meteorological Data Acquisition Tool: Obtained from the China Meteorological Data Network (data.cma.cn/).(III) Data Statistical Analysis Processing Steps and Software Tools1.

One-way ANOVA and Duncan's multiple range test (significance threshold α = 0.05) were performed using SPSS 22.0 to test differences in root and soil indicators among different regions.2. Principal component analysis (PCA) of root functional traits, permutation tests, random forest assessment of environmental factor importance, and linear regression fitting were completed based on Python 2024.3. All bar charts, box plots, scatter regression plots, and factor importance ranking plots were generated using Origin 2021 and Python 2024.

All numerical values are uniformly presented as mean ± standard error (mean ± SE), and categorical variables are annotated with lowercase letters to indicate significant differences among groups.II. Spatiotemporal Information and Resolution Description of the Dataset1. Temporal Dimension: The instantaneous time of field sampling is August 2025 during the growing season.

Meteorological data cover the full introduction period of Korean pine, with a data recording granularity of monthly meteorological indicators. No finer temporal stratification resolution is available.2. Spatial Dimension: The three study regions are located in the natural distribution area of the Lesser Khingan Range and two introduction areas in the Greater Khingan Range, with precise geolocation of plots.

The spatial resolution of meteorological data is a 1 km buffer around the sampling points. Soil and root samples were all taken from the 0–20 cm root zone and the 0–15 cm surface soil layer at each plot, with a vertical stratification resolution of 15–20 cm soil depth gradient.III. Data Table Structure, Number of Records, Labels, and Units of MeasurementThe dataset includes 2 core basic data tables (Table 1.

Basic stand information; Table 2. Soil physicochemical properties), accompanied by raw numerical datasets for all charts (root morphological, chemical, and anatomical traits, PCA, and environmental factor regression raw data):1. Table 1.

Basic stand information: 3 rows of records (DL, NM, JGDQ plots). Column labels are plot name, stand density, tree height, diameter at breast height (DBH), individual tree volume, and stand volume. Units of measurement: stand density in trees·hm⁻², tree height in m, DBH in cm, individual tree volume in m³, stand volume in m³·hm⁻².

Row labels: DL = Dailing, NM = Nanmu, JGDQ = Jiagedaqi; lowercase letters a/b indicate significant differences among groups.2. Table 2. Soil physicochemical properties: 3 rows of records (3 plots).

Column labels include pH, organic matter, soil moisture content, total carbon, total nitrogen, C/N ratio, total phosphorus, and soil bulk density. Units of measurement: organic matter, total carbon, total nitrogen, and total phosphorus in g·kg⁻¹; moisture content in %; bulk density in g·cm⁻³; pH is unitless. Row labels have the same meanings as in Table 1.3.

Root trait supporting data tables: Organized by 1st–3rd order roots, divided into three sub-datasets: morphological, chemical, and anatomical traits. Each indicator includes replicated observations from the 3 plots. Units of morphological indicators: root diameter in mm, specific root area in cm²·g⁻¹, specific root length in cm·g⁻¹, root tissue density in g·cm⁻³.

Units of chemical indicators: nitrogen/carbon in mg·g⁻¹, phosphorus in mg·g⁻¹, potassium in g·kg⁻¹, C/N ratio. Units of anatomical indicators: cortex thickness and stele diameter in μm, cortex/stele radius ratio.4. Environmental and statistical data tables: Random forest factor importance values, PCA PC1/PC2 coordinates, and linear regression fitting data.IV.

Data Missing and Error Source Description1. Data Missing Status: All indicators in this dataset, covering 3 plots and 3 root order gradients from field sampling, indoor testing, and statistical analysis, are fully covered with no missing or null observed values. No data imputation or interpolation has been performed.2.

Data Error Sources and Ranges(1) Field sampling error: Random sampling errors arising from forest floor microtopography and soil spatial heterogeneity were reduced by repeated sampling with 3 plots per site and 5 standard trees per plot.(2) Instrumental detection systematic error: Minor systematic deviations in elemental analyzers, microscopes, and root scanners were controlled by regular instrument calibration and by taking the mean of three repeated measurements, with error magnitude below 0.05.(3) Manual measurement error: Minor human errors in manual annotation of root sections and image measurement were minimized by standardized measurement protocols.

All errors were controlled through repeated sampling, parallel testing, and mean correction, without altering the significance patterns of intergroup differences.V. Dataset File Contents, Formats, and Compatible SoftwareThe submitted dataset includes 4 types of data files, all in common scientific formats with no proprietary or niche formats:1. Excel (.xlsx) basic data tables: Including Table 1, Table 2, raw measurement data for root morphological/chemical/anatomical traits, environmental factor observation data, and PCA and random forest statistical results.

Compatible with Microsoft Excel 2016 and above, and WPS Spreadsheets.VI. The dataset fully supports all analytical results of the paper "Root Adaptive Strategies and Their Environmental Drivers of Korean Pine Following Introduction to the Greater Khingan Range" and enables reproduction of all figures, ANOVA, PCA, random forest, and regression analyses presented in the paper. It is suitable for reuse and secondary mining in research related to fine-root functional traits of temperate conifers, species introduction adaptation, belowground ecological trade-off strategies, and soil–vegetation environmental interactions.