Comprehensive soil greenhouse gas flux dataset from a temperate old-growth forest: Effects of decade-long nitrogen addition and biological factor manipulation

Abstract

This dataset presents greenhouse gas (CO₂, CH₄, N₂O) flux measurements and associated environmental variables from a temperate old-growth forest (approximately 300 years old) under different nitrogen addition regimes (control, short-term 1-year, and long-term 10-year treatments) combined with biological factor manipulations (root exclusion, fungal suppression, and litter removal). Data were collected throughout the 2018 growing season (May to September) at 10-day intervals. The dataset includes measurements of three greenhouse gas fluxes, δ¹³C isotopic composition, soil temperature and moisture, pH, ammonium and nitrate nitrogen, dissolved organic carbon, microbial biomass carbon and nitrogen, and total soil carbon and nitrogen. Gas fluxes were measured using the static chamber-gas chromatography method, while soil parameters were determined using standard methods including chloroform fumigation and KCl extraction techniques. The experimental design employed a systematic factorial approach with comprehensive biological factor manipulations. These data can be utilized to analyze the long-term effects of nitrogen deposition on greenhouse gas emissions from forest soils and their biological regulatory mechanisms, providing support for research on global climate change and terrestrial carbon-nitrogen cycling.

Specifications Table

Subject	Earth & Environmental Sciences
Specific subject area	Forest ecosystem biogeochemistry and soil greenhouse gas dynamics under long-term nitrogen deposition.
Type of data	Table; Raw
Data collection	Greenhouse gas flux (CO₂, CH₄, N₂O) were collected via static chamber-gas chromatography (Agilent 7890B GC). δ¹³C were determined by isotope ratio mass spectrometry (Thermo Fisher DELTA plus XP). Soil pH was measured with a pH meter (Ohaus ST3100). NH₄⁺/NO₃⁻ were quantified using a continuous flow analyzer (SAN++). Microbial biomass and dissolved organic carbon were determined with a TOC/TN analyzer(Shimadzu TOC-L CPH). Total soil carbon and nitrogen were measured using an elemental analyzer (Elementar vario EL III). Samples were collected every 10 days from May to September 2018. with 10 % duplicates, blanks, CRMs, and screening applied for QC.
Data source location	School of life sciences, Qufu normal university, 57 Jingxuan West Road, Qufu, Shandong, 273,165
Data accessibility	Repository name: https://zenodo.org/ Data identification number: 10.5281/zenodo.15178734 Direct URL to data: https://zenodo.org/records/15,178,734
Related research article	None

1. Value of the Data

• •This dataset provides unique greenhouse gas emission measurements from 300-year-old forests under controlled nitrogen addition experiments, addressing critical knowledge gaps in how mature, equilibrium-state forest ecosystems respond to long-term nitrogen deposition.
• •The systematic factorial experimental design incorporates root exclusion, fungicide application, and litter removal treatments to quantify how different biological pathways—plant roots, fungi, and litter decomposition—contribute to and interact in nitrogen-mediated greenhouse gas fluxes. This factorial approach empirically distinguishes between above and belowground regulatory mechanisms.
• •Comprehensive environmental parameters (soil temperature, moisture, pH, nitrogen speciation, microbial biomass) support predictive modeling of greenhouse gas emissions and carbon-nitrogen cycling dynamics. Researchers can reuse this dataset to develop and validate biogeochemical models, test hypotheses on soil carbon flux drivers, or create machine learning algorithms for emission predictions. The inclusion of δ¹³C isotope data enables researchers to differentiate CO₂ emission sources, allowing for detailed source partitioning studies and linking specific microbial activities with observed carbon flux patterns.
• •This dataset enables cross-regional comparisons across forest types and provides scientific foundations for forest management strategies. Researchers can reuse these data to compare mature forest nitrogen responses with younger stands, conduct meta-analyses on nitrogen saturation thresholds, develop greenhouse gas emission inventories for old-growth forests, or inform forest carbon offset programs. The systematic experimental design makes the dataset particularly valuable for modeling studies seeking to parameterize the relative contributions of biological pathways to carbon and nitrogen cycling in forest ecosystems.

2. Background

Nitrogen deposition represents a substantial anthropogenic driver influencing forest ecosystem functions globally [1,2]. This dataset addresses critical knowledge gaps concerning nitrogen deposition effects on greenhouse gas emissions in mature forest ecosystems. Despite documentation of increasing global nitrogen deposition [3] and its ecosystem impacts [4], comprehensive data regarding long-term nitrogen deposition effects in old-growth forests remain limited [5]. Existing methodologies have primarily employed short-term experiments or focused on young forest stands [6], resulting in significant uncertainty regarding chronic nitrogen impacts on established ecosystems [7]. Moreover, the proportional contributions of various biological components—roots, soil microorganisms, and litter—to greenhouse gas emissions under nitrogen deposition conditions remain inadequately characterized [8,9].

To address these research gaps, we established short-term (1 year) and long-term (10 years) nitrogen addition experiments in a temperate old-growth forest (approximately 300 years old), incorporating root exclusion, fungicide application, and litter removal treatments to systematically quantify fluxes of three greenhouse gases (CO₂, CH₄, N₂O) and their driving factors.

3. Data Description

The dataset “GHG_Soil.xlsx” contains measurements of soil greenhouse gas emissions and related soil properties collected from experimental plots under different nitrogen treatments and biological manipulations. Each data sheet includes treatment codes that identify specific experimental conditions: TRT_N indicates nitrogen addition treatments (CK: control with no nitrogen addition, ST: short-term 1-year nitrogen addition, LT: long-term 10-year nitrogen addition), while TRT_B indicates biological factor manipulations (A: root exclusion + fungicide + litter removal, B: root exclusion only, C: fungicide application only, D: litter removal only, E: control with no manipulations). The Excel file consists of 13 sheets, each containing different parameters:

Meta_info: Provides metadata about all parameters measured, including their descriptions and units. This sheet serves as a reference guide for interpreting the data in all other sheets.

CO₂: Contains measurements of soil CO₂ emissions (mg C m⁻² h⁻¹; Analytical uncertainty: ±2.5 %) with sampling dates, nitrogen treatments (TRT_N), biological factor manipulations (TRT_B), mean values, and standard errors.

CH₄: Contains measurements of soil CH₄ fluxes (µg C m⁻² h⁻¹; Analytical uncertainty: ±3.1 %) with sampling dates, treatments, mean values, and standard errors.

N₂O: Contains measurements of soil N₂O fluxes (µg N m⁻² h⁻¹; Analytical uncertainty: ±4.2 %) with sampling dates, treatments, mean values, and standard errors.

DOC: Contains measurements of dissolved organic carbon (g kg⁻¹; Analytical uncertainty: ±2.0 %) with sampling dates, treatments, mean values, and standard errors.

pH: Contains soil pH measurements (Analytical uncertainty: ±0.01 units) with sampling dates, treatments, mean values, and standard errors.

NO₃_NH4: Contains measurements of soil nitrate (NO₃⁻) and ammonium (NH₄⁺) concentrations (Detection limit: 0.01 mg kg⁻¹) with sampling dates and treatments.

Soil_temperature: Contains soil temperature measurements at 5 cm and 10 cm depths (°C; Measurement precision: ±0.1 °C) with sampling dates and treatments.

Soil_moisture: Contains soil moisture measurements at 10 cm depth ( %; Measurement precision: ±2 %) with sampling dates and treatments.

Isotope_Data: Contains δ¹³C measurements (Analytical precision: ±0.1 ‰) with sampling dates, treatments, mean values, and standard errors.

SOC_SON: Contains measurements of soil organic carbon content ( %; Analytical precision: ±0.1 %), soil nitrogen content ( %; Analytical precision: ±0.1 %), and C/N ratio with sampling year, month, treatments, mean values, and standard errors.

Soil_bulk_density: Contains soil bulk density measurements (g cm⁻³; Measurement precision: ±0.02 g cm⁻³) with sampling year, month, treatments, mean values, and standard errors.

MBC: Contains microbial biomass carbon measurements (g kg⁻¹; Analytical uncertainty: ±2.0 %) with sampling year, month, treatments, mean values, and standard errors.

The dataset was collected in 2018 at Changbai mountain, Jilin province, China, from experimental plots subjected to different nitrogen addition treatments (TRT_N) and biological manipulations (TRT_B). These data can be visualized using boxplot to assess variability in response to nitrogen addition and in-situ biological manipulations (Fig. 1) and also be combined with other variables (Tables 1, 2).

Fig. 1.

Soil greenhouse gas fluxes in response to nitrogen addition and in-situ biological manipulations in a temperate old-growth forest. Boxplots show fluxes of carbon dioxide (CO₂, top), methane (CH₄, middle), and nitrous oxide (N₂O, bottom) across three nitrogen (N) addition regimes: Control (CK), Short-Term (ST, 1 year), and Long-Term (LT, 10 years). Within each N regime, fluxes were measured in five sub-treatments (x-axis) designed to isolate biological drivers: (A) Complete biological exclusion (root exclusion + fungicide + litter removal), (B) Root exclusion only, © Fungicide application only, (D) Litter removal only, and (E) In-plot control (no manipulation). Each boxplot displays the median (center line), interquartile range (box), and whiskers (1.5 × IQR).

Table 1.

Average of SOC, SON, MBC, BD and pH.

TRT_N	TRT_B	SOC ( %)	SON ( %)	MBC (g kg⁻¹)	BD (g cm⁻³)	pH
CK	A	5.8 (1.2)	0.5 (0.1)	0.78 (0.21)	2.01 (0.08)	5.27 (0.21)
	B	6.6 (2.0)	0.6 (0.1)	1.11 (0.24)	1.81 (0.06)	5.47 (0.20)
	C	6.7 (1.6)	0.6 (0.1)	1.29 (0.30)	1.70 (0.04)	5.44 (0.15)
	D	10.2 (4.1)	0.8 (0.3)	1.83 (0.52)	1.81 (0.07)	5.70 (0.19)
	E	9.7 (2.9)	0.8 (0.2)	1.49 (0.32)	1.80 (0.08)	5.60 (0.20)
ST	A	5.7 (0.8)	0.6 (0.1)	0.63 (0.23)	1.98 (0.09)	5.35 (0.15)
	B	6.8 (2.0)	0.7 (0.2)	1.25 (0.31)	1.87 (0.03)	5.54 (0.16)
	C	5.8 (1.7)	0.6 (0.2)	0.96 (0.42)	1.88 (0.03)	5.45 (0.19)
	D	5.3 (0.5)	0.5 (0.1)	1.01 (0.34)	1.98 (0.08)	5.42 (0.15)
	E	9.2 (3.7)	0.8 (0.3)	1.43 (0.28)	1.68 (0.01)	5.57 (0.17)
LT	A	8.7 (2.7)	0.8 (0.2)	1.36 (0.49)	1.80 (0.04)	4.94 (0.23)
	B	6.9 (2.0)	0.7 (0.2)	1.10 (0.22)	1.95 (0.03)	4.88 (0.20)
	C	7.2 (2.1)	0.7 (0.2)	1.39 (0.29)	1.82 (0.06)	5.03 (0.20)
	D	7.6 (2.9)	0.7 (0.2)	1.43 (0.41)	1.88 (0.03)	4.72 (0.25)
	E	10.6 (4.9)	0.9 (0.3)	1.80 (0.54)	1.73 (0.08)	4.99 (0.22)

Table 2.

Average of DOC, Ts, SM, NH₄⁺ and NO₃⁻.

TRT_N	TRT_B	DOC (g/kg)	TS ( °C)	SM ( %)	NH₄⁺ (mg/kg)	NO₃⁻ (mg/kg)
CK	A	0.21 (0.025)	14.7 (1.1)	36.5 (1.7)	0.22 (0.04)	9.28 (1.96)
	B	0.23 (0.027)	14.6 (1.0)	31.2 (1.6)	0.20 (0.05)	6.35 (0.84)
	C	0.23 (0.030)	14.7 (1.1)	29.2 (2.1)	0.25 (0.06)	6.12 (0.93)
	D	0.23 (0.025)	14.7 (1.1)	36.1 (1.9)	0.20 (0.05)	4.10 (0.74)
	E	0.20 (0.018)	14.8 (1.1)	31.2 (1.9)	0.19 (0.04)	3.94 (0.62)
ST	A	0.22 (0.024)	14.7 (1.0)	39.1 (1.3)	0.24 (0.05)	8.93 (1.27)
	B	0.20 (0.023)	14.8 (1.0)	37.2 (1.1)	0.20 (0.05)	7.99 (1.22)
	C	0.20 (0.027)	14.6 (1.0)	35.3 (1.9)	0.24 (0.05)	6.92 (1.10)
	D	0.22 (0.023)	14.6 (1.0)	37.4 (1.9)	0.24 (0.06)	5.75 (1.00)
	E	0.22 (0.022)	14.6 (1.0)	32.3 (2.0)	0.26 (0.06)	10.16 (5.18)
LT	A	0.30 (0.027)	15.0 (1.0)	39.2 (1.7)	0.61 (0.13)	31.48 (9.69)
	B	0.22 (0.024)	15.0 (1.0)	38.6 (1.6)	0.30 (0.07)	10.78 (2.51)
	C	0.27 (0.034)	15.0 (1.0)	38.4 (1.7)	0.35 (0.09)	10.42 (1.73)
	D	0.22 (0.023)	15.1 (1.0)	38.0 (1.5)	0.47 (0.12)	14.99 (6.43)
	E	0.22 (0.022)	15.1 (1.0)	35.6 (2.2)	0.26 (0.07)	8.42 (1.65)

Note: Numbers are means followed by standard errors. SOC: soil organic carbon content; SON: soil nitrogen content; MBC: soil microbial biomass carbon; BD: bulk density; DOC: dissolved organic carbon; T_S: soil temperature at 10 cm depth; SM: soil moisture at 10 cm depth; NO₃⁻: nitrate; NH₄⁺: ammonium.

4. Experimental Design, Materials and Methods

4.1. Study site and plot establishment

The study was conducted in a temperate old-growth forest (approximately 300 years old) located at Changbai Mountain, Jilin Province, China (42°24′N, 128°28′E, elevation 750 m). The forest is dominated by mature Pinus koraiensis, Tilia amurensis, and Quercus mongolica with a well-developed understory. The region experiences a temperate continental monsoon climate with mean annual temperature of 3.6 °C and precipitation of 713 mm.

Three experimental sites were established representing different nitrogen addition regimes: Control site (CK): No nitrogen addition, Short-term nitrogen addition site (ST): 1 year of nitrogen application and Long-term nitrogen addition site (LT): 10 years of continuous nitrogen application. At each site, nitrogen was applied at a rate of 50 kg N ha⁻¹ yr⁻¹ in the form of NH₄NO₃ solution (dissolved in distilled water at 1:10 ratio), applied uniformly using backpack sprayers once per month from May to September. Control plots received equivalent volumes of distilled water.

4.2. Experimental design

A three-factor, two-level factorial design was implemented to examine the effects of different biological components on greenhouse gas emissions:

Factor A: Root exclusion treatment (A1: yes, A2: no)

Factor B: Fungicide application (B1: yes, B2: no)

Factor C: Litter removal (C1: yes, C2: no)

This factorial design results in 8 possible treatment combinations (2³), but due to logistical constraints, 5 representative combinations were selected based on their scientific relevance (TRT_B, Table 3):

• 1.Treatment A (A1B1C1): Root exclusion + Fungicide + Litter removal - Complete biological exclusion to quantify abiotic soil processes
• 2.Treatment B (A1B2C2): Root exclusion only - Isolates belowground plant contributions
• 3.Treatment C (A2B1C2): Fungicide application only - Reduces fungal activity while maintaining plant and litter inputs
• 4.Treatment D (A2B2C1): Litter removal only - Eliminates surface litter decomposition
• 5.Treatment E (A2B2C2): Control - Natural conditions with all biological components intact

Table 3.

Experimental treatment combinations for the three-factor biological manipulation design in forest soil greenhouse gas flux measurements.

Treatment Code	Root Exclusion	Fungicide	Lltter Removal	Description
A	Yes	Yes	Yes	Complete biological exclusion
B	Yes	No	No	Root exclusion only
C	No	Yes	No	Fungicide application only
D	No	No	Yes	Litter removal only
E	No	No	No	Control (no manipulation)

Each treatment combination was replicated four times at each nitrogen addition site, resulting in 60 total plots (3 sites × 5 treatments × 4 replicates). Each plot measured 2 m × 2 m with 1 m buffer zones between plots to prevent edge effects.

Treatment Implementation Protocols:

Root exclusion: Mesh screens with 1 mm pore size were installed vertically around designated plots to a depth of 30 cm, completely enclosing the plot perimeter. The mesh material was made of polyethylene to prevent degradation. Installation was completed 6 months before data collection began to allow soil disturbance effects to stabilize. Existing roots within the plots were severed during installation, and mesh screens prevented new root intrusion from surrounding vegetation.

Litter removal: All surface organic matter including fresh leaf litter (OL horizon) and partially decomposed litter (OF horizon) was manually removed from designated plots at the beginning of the experiment. Subsequently, newly fallen litter was collected weekly throughout the growing season using fine mesh nets and removed from plots within 24 h. The removed material was weighed and recorded to quantify litter input rates.

Fungicide application: Chlorothalonil fungicide was applied as a 0.15 g m⁻² solution (diluted 1:750 with distilled water) every 7 days throughout the growing season. The fungicide was applied using fine-mist sprayers during early morning hours to minimize evaporation losses. Application was suspended for 48 h following rainfall events to prevent immediate washoff. Fungicide effectiveness was monitored through ergosterol analysis of soil samples collected monthly.

4.3. Sample collection protocols

Gas samples were collected every 10 days from May 15 to September 30, 2018 (growing season) using the static chamber-gas chromatography method. Sampling was conducted using static chambers (50 cm diameter, 30 cm height) constructed from PVC with removable lids sealed with rubber gaskets. Chambers were installed 24 h before first sampling to minimize soil disturbance effects.

Chambers were deployed between 09:00–11:00 h to represent daily average conditions. After chamber closure, samples (60 ml each) were collected at 0, 15, 30, and 45 min for CO₂, CH₄, and N₂O flux measurements, with a final 100 ml sample collected for δ¹³C isotope analysis. All samples were immediately transferred to pre-evacuated glass vials sealed with butyl rubber septa. Samples were analyzed within 48 h of collection. Internal chamber temperature,soil temperature and moisture at 5 cm and 10 cm depths were monitored continuously using digital thermometers. Sampling was postponed for 48 h following rainfall events exceeding 5 mm to avoid artifacts from soil moisture changes.

For each sampling event, soil samples (0–10 cm depth) were collected from three randomly selected points within the destructive sampling zone of each plot. After removing plant residues and large particles, samples were stored at 0–4 °C. Ammonium nitrogen, nitrate nitrogen, pH, and dissolved organic carbon were determined every 10 days, concurrent with gas sampling. Soil bulk density, pH, microbial biomass carbon, total carbon, and nitrogen were measured in May, July, and September (early, middle, and late growing season). All samples were collected on clear days, with sampling delayed by 2 days following rainfall events.

5. Analytical Methods

5.1. Gas analysis

Concentrations of CO₂, CH₄, and N₂O were analyzed using gas chromatography (Agilent 7890B GC, Agilent Technologies, USA). Standard gas mixtures were analyzed every 10 samples for calibration. Analytical precision based on replicate standards was ±2.5 % for CO₂, ±3.1 % for CH₄, and ±4.2 % for N₂O. δ¹³C isotope analysis was performed using isotope ratio mass spectrometry (Thermo Fisher DELTA plus XP, Thermo Fisher Scientific, USA). Samples were analyzed against Vienna PDB standard with analytical precision of ±0.1 ‰ based on replicate reference materials.

5.2. Gas flux calculations

Gas fluxes were calculated based on the rate of concentration change over time using Eq. (1).

$$\mathrm{F}=\rho \times \mathrm{V}/\mathrm{A}\times \text{dc}/\text{dt}\times 273/\left(273+\mathrm{T}\right)$$ (1)

Where F is the gas flux (mg C m⁻² h⁻¹ for CO₂; µg C m⁻² h⁻¹ for CH₄; µg N m⁻² h⁻¹ for N₂O), ρ is the gas density at STP (mg L⁻¹ or µg L⁻¹), V is the chamber volume (L), A is the chamber base area (m²), dc/dt is the rate of concentration change over time (ppm h⁻¹), and T is the average temperature inside the chamber during sampling ( °C).

Linear regression was applied only when R² ≥ 0.90. Non-linear flux patterns were fit using exponential decay models when appropriate.

5.3. Soil chemical analysis

Soil pH was measured in a 1:2.5 (w/v) soil suspension using a pH meter (ST3100, Ohaus Corporation, USA) with glass electrode (precision ±0.01 pH units). Extractable NH₄⁺ and NO₃⁻ were determined by extracting 10 g fresh soil with 50 mL of 0.5 mol L⁻¹ KCl for 1 hour on a horizontal shaker (200 rpm). Extracts were filtered (0.45 µm) and analyzed using a continuous flow analyzer (SAN++, SKALAR) with detection limits of 0.01 mg kg⁻¹. Air-dried soil samples were ground to <0.15 mm and treated with 1 mol L⁻¹ HCl to remove carbonates. Soil organic carbon (SOC) and nitrogen (TN) were determined using an elemental analyzer (Elementar vario EL III; Elementar, Germany) with precision of ±0.1 % based on acetanilide standards. Soil microbial biomass carbon (C) was assessed using the chloroform fumigation-extraction method. Duplicate soil samples (equivalent to 10 g dry weight) were either fumigated with CHCl₃ for 24 h or left unfumigated, then extracted with 0.5 mol L⁻¹ K₂SO₄. Microbial biomass C was calculated as the difference in K2SO4-extractable C between fumigated and unfumigated soils, divided by coefficients of 0.45 [10]. The dissolved organic carbon (DOC) was quantified as the concentration of C extracted from unfumigated soil with 50 mL of 0.5 mol L⁻¹ K₂SO₄ solution and measured using a TOC/TN analyzer (TOC-L CPH, Shimadzu, Japan) with precision of ±2.0 %. Soil bulk density was determined using the core method with samples collected using a stainless steel cylinder (100cm³). Three samples per plot were collected, oven-dried at 105 °C for 24 h, and weighed. Measurement precision: ±0.02 g cm⁻³.

5.4. Quality control and data validation

Instrument Calibration: All analytical instruments were calibrated daily using certified reference standards.

Replicate Analysis: 10 % of all samples were analyzed in duplicate to assess analytical precision.

Blank Analysis: Method blanks were analyzed with each batch to assess contamination.

Reference Materials: Certified soil reference materials were analyzed monthly to verify accuracy.

Data Screening: All data points exceeding 3 standard deviations from treatment means were flagged and re-analyzed.

Limitations

While this dataset provides valuable information on soil greenhouse gas emissions and related parameters under different nitrogen treatments and biological manipulations, it is subject to certain limitations:

• •The dataset covers only one growing season (May to September 2018), capturing cumulative effects of a decade-long experiment but limiting its ability to analyze inter-seasonal, inter-annual variability and more recent environmental changes.
• •The fungicide treatment specifically targeted fungal communities without addressing other microbial groups such as bacteria, potentially underestimating the contribution of the entire microbial community to soil processes.
• •The soil type and climatic conditions specific to the study area represent particular ecological conditions, which necessitates caution when extrapolating results to other ecosystems.

These limitations should be considered when using this dataset for modeling purposes or when making broader ecological inferences beyond the specific conditions of this study.

Ethics Statement

The authors of this study have read and adhered to the ethical requirements of Data in Brief. This research does not involve human subjects, animal experiments, or data collection from social media platforms.

CRediT Author Statement

Junhui Zhang: Conceptualization, Methodology, Investigation, Data Curation, Writing, Resources, Project Administration, Funding Acquisition. Yue Gu: Validation, Investigation, Writing-review & editing.

Data Availability

• zenodoComprehensive Soil Greenhouse Gas Flux Dataset from a Temperate Old-Growth Forest: Effects of Decade-Long Nitrogen Addition and Biological Factor Manipulation (Original data)