Impact of community size fraction on plants and greenhouse gases dataset
Data files
Nov 11, 2024 version files 32.26 MB
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GHG_fluxes_raw_data_part_1.xlsx
15.70 MB
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GHG_fluxes_raw_data_part_2.xlsx
14.75 MB
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GHG_raw_data_simplified.xlsx
47.79 KB
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Microbial_Fractions_raw_data.xlsx
95.03 KB
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qPCR_fractions_raw_data.xlsx
11.28 KB
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README.md
7.59 KB
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Respirometer_raw_data.xlsx
1.59 MB
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Soil_Properties_Raw_Data_Microbial_Fractions.xlsx
31.30 KB
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Soil_Texture_Raw_Data_Microbial_Fractions.xlsx
21.41 KB
Abstract
Soil communities are essential to ecosystem functioning, yet the impact of reducing soil biota on root-associated communities, tree performance, and greenhouse gas (GHG) fluxes remains unclear. This study examines how different size fractions of soil biota from young and mature forests influence Alnus glutinosa performance, root-associated community composition, and GHG fluxes. We conducted a mesocosm experiment using soil community fractions (wet sieving through 250, 20, 11, and 3 µm) from young and mature forest developmental stages as inocula. The results indicate that the root-associated community composition was shaped by forest developmental stage but not by the size of the community fractions. Inoculation with the largest size fraction from mature forests negatively affected tree growth, likely due to increased competition between the plants and soil biota. In addition, GHG fluxes were not significantly impacted by either size fraction or forest developmental stage despite the different community composition supplied. Overall, our research indicates that A. glutinosa strongly selects the composition of the root-associated community, despite differences in the initial inoculum, and this composition varies depending on the stage of ecosystem development, impacting the performance of the trees but not GHG fluxes.
https://doi.org/10.5061/dryad.dfn2z35bg
Description of the data and file structure
The data was collected by visiting 12 forests (6 young and 6 mature) in the area of Drenthe in the Netherlands. Soils were brought back to the university in large bags and the processing of the samples started immediately.
The description of the materials and methods of the uploaded dataset has all the processes in very thorough detail.
Important details:
Most of the Metada can be found int he supplemented excel files. As a general rule, to understand the labels you need the following information:
-Number: A unique number given to each pot. This is not essential for use of the data it is only there to order the dataset and to match the rest of the variables between datasets.
-Forest: Forest identity. A unique name given to each forest that was visited to use as random factor
-Age: the age of each forest. Either young (10yo) or mature (100yo)
-Treatment: the experiment each pot belongs to. Buffer is the main experiment while the others (bactericide and fungicide) are supplementary experiments where the pots were additionally supplied with bactericide or fungicide. Compare each with its respective control.
Fraction: The community size fraction that each pot was inoculated with. Ranges between 250, 20, 11 and 3um. The smaller the fraction, the more simplified the community. Please refer to the dataset description for a very detailed explanation of what the fractionation process entailed.
Files and variables
File: GHG_raw_data_simplified.xlsx
Description: A simplified version of the Greenhouse gas datafile, organized for ready use. Only the final variables after processing and calculations appear in this file.
Variables
- Number: Unique number given to each pot so it can be matched between datasets
- Label: label of the pot. Abbreviation for forest, treatment and fraction
- Age: The age of the forest
- Treatment: the experiment the pot belongs to
- Fraction: The size of the community fraction that was inoculated to the pot
- Forest: A unique name given to each forest to be used as a random factor
- X flux per g: each flux expressed per gram soil per hour
- X flux per kg: each flux expressed per kg soil per hour
File: qPCR_fractions_raw_data.xlsx
Description: This file contains the raw data of the copy numbers for bacteria, fungi and soil animals obtained from a qPCR on the initial inoculum from each forest
Variables
- Number: Unique number given to each pot so it can be matched between datasets
- Age: The age of the forest
- Fraction: The size of the community fraction that was inoculated to the pot
- Forest: A unique name given to each forest to be used as a random factor
- number of COI copies: soil animal copy numbers
- number of ITS copies: fungi copy numbers
- number of 16S copies: bacteria copy numbers
File: GHG_fluxes_raw_data_part_1.xlsx
Description: This is the first part of the greenhouse gas raw data as it was measured from the instrument. The working data sheet shows how the calculations were done to reach the final variables.
Disclaimer: Blank cells in sheet 1 and working data in column Flux_no, between flux numbers are intentionally left blank so that the reader knows where each flux begins and ends (eg. Flux 1 begins at row 2 and ends at row 309 all highlighted in yellow with blank cells in between. Flux 2 then starts at 310 with another highlight color and blank cells between beginning and end).
Variables
- Flux number: refers to the number of the pot so it can be matched with the metadata
File: Respirometer_raw_data.xlsx
Description: Raw data from the respirometer experiment. The sheet “work data-simplified” has the data in ready to use format.
Variables
- Label no: Unique number given to each pot so it can be matched between datasets
- Label: label of the pot. Abbreviation for forest, treatment and fraction
- Age: The age of the forest
- Treatment: the experiment the pot belongs to
- Fraction: The size of the community fraction that was inoculated to the pot
- Forest: A unique name given to each forest to be used as a random factor
- Q10: the temperature coefficient normalized to 10C
File: GHG_fluxes_raw_data_part_2.xlsx
Description: This is the second part of the greenhouse gas raw data as it was measured from the instrument. The working data sheet shows how the calculations were done to reach the final variables.
Disclaimer: Blank cells in sheet 1 and working data in column Flux_no, between flux numbers are intentionally left blank so that the reader knows where each flux begins and ends (eg. Flux 1 begins at row 2 and ends at row 309 all highlighted in yellow with blank cells in between. Flux 2 then starts at 310 with another highlight color and blank cells between beginning and end).
Variables
- Flux number: refers to the number of the pot so it can be matched with the metadata
File: Microbial_Fractions_raw_data.xlsx
Description: The main dataset used for the experiments Contains data from the main and the supplementary experiments.
Variables
- Label no: Unique number given to each pot so it can be matched between datasets
- Label: label of the pot. Abbreviation for forest, treatment and fraction
- Age: The age of the forest
- Treatment: the experiment the pot belongs to
- Fraction: The size of the community fraction that was inoculated to the pot
- Forest: A unique name given to each forest to be used as a random factor
- Aboveground biomass: the dry weight of the stem and leaves after the harvest expressed in grams
- Belowground biomass: the dry weight of the root and scanned root subsample after the harvest expressed in grams
- nodule number: the number of Frankia alni nodules counted on the roots
- nodule density: the number of nodules per gram dry root
- The rest of the data is exactly what is written in the cell.
File: Soil_Properties_Raw_Data_Microbial_Fractions.xlsx
Description: Raw data of the soil properties measured from the 12 visited forests
Variables
- Forest: A unique name given to each forest to be used as a random factor
- number: replicate number from each forest
- the cup weight refers to the cups used to place the soil sample in
- %OM: percentage of organic matter calculated after burning the soil at 550C
- The rest of the data is exactly what is written in the cell.
File: Soil_Texture_Raw_Data_Microbial_Fractions.xlsx
Description: Raw data of the soil texture measured from the 12 visited forests
Variables
- Forest: A unique name given to each forest to be used as a random factor
- >2mm: the amount of soil that stayed on top of a 2mm sieve it is equal to the % gravel
- 2-1mm: the amount of soil that stayed between the 2 and 1mm sieves
- 1-0.25mm: the amount of soil that stayed between the 1 and 0.25mm sieves
- 0.25-0.063mm: the amount of soil that stayed between the 0.25 and 0.063mm sieves
- 0.063-0.045mm: the amount of soil that stayed between the 0.063-0.045mm sieves
- <0.045mm: the amount of soil that ended in the collection tray. It is equal to the % silt and clay
- %sand: the added value of the soil that ended up between the 2 and 0.045mm sieves
Code/software
All the data can be viewed in excel and the “work data” sheet from all datasets except the GHG flux raw data part 1 and 2 can be used directly in R.
To study the effect of a young and mature forest soil community, forests spanning two distinct developmental stages were selected. The forests were categorized based on their time of planting as either “young” (2010 – 2015) or “mature” (1880 – 1927). Within each developmental stage, six individual forests were sampled (6 young and 6 mature), each with their own soil community, characteristic of each developmental stage. Each individual forest acted as a replicate for its corresponding development stage. All sampled forests were located in the province of Drenthe in the Netherlands, had sandy soil and were initially planted with oak (Quercus robur and Quercus petraea). In each forest stand, soil was collected 15 cm deep from multiple points around the stand (February 2022). Each point was at least 10 m apart from the previous one and the soil from all points was placed in one bag to make one homogenate per forest stand. Additionally, three soil cores (150 cm3, 12 cm deep) were collected from each stand to measure soil properties. In total, we collected 12 bags of soil (one from each forest) and 36 soil cores. The soil from each bag was initially sieved through a 2 cm mesh to further homogenize the soil and remove large debris and stones.
The soil from the cores (150 cm3, 129.5 ± 0.76 g) was oven dried at 40 ℃ for 96 hours to measure soil moisture, pH (1:2 soil:water ratio) and the bulk density of the soil (Schofield et al., 1955). The loss on ignition (LOI) method was used to measure the soil organic matter of the soil by first drying the soil at 105 ℃ for 48 hours and then heating at 550 ℃ inside a muffle furnace for four hours (Heiri et al., 2001). The soil heated at 550 ℃ was then used to determine the sand/silt fractions using a HAVER EML 200 Premium automated shaker (Oelde, Germany). Soil texture was classified into gravel (>2 mm), Sand (2 - 0.045 mm) and Silt and Clay (<0.045 mm). Black alder (Alnus glutinosa, (L.) Gaertnr) was selected as the species to grow in the inoculated sterile soils of our experiments. A. glutinosa trees were present in all of the sampled sites despite a clear dominance of oak trees (Quercus robur and Quercus petraea). Seeds of native alder were acquired from the Nature Agency Staatsbosbeheer located in the Netherlands. The seeds were germinated in a nursery using commercial potting soil where they were grown for 3 weeks and subjected to a watering regime of three times a week.
In order to assess the impact of soil community size on A. glutinosa tree performance, we prepared four community size fractions (250, 20, 11 and 3 µm) from each of the forest soil samples, under sterile conditions, following methods from studies that have reliably proven that with reducing size fraction, the soil community simplifies (e.g. Wagg et al., 2014; Wang et al., 2019; Li et al., 2020). We prepared phosphate buffer (1 g KH₂PO₄ in 1 L DI H2O, pH 6.5) and mixed it with soil (1:2, w/w) in four batches, each followed by sieving through 1 mm and 250 µm meshes. One-quarter of the filtrate was stored at 4°C (the 250 µm fraction), and the rest was further processed. To prevent clogging of the filter papers, the filtrate was passed through 63 and 45 µm sieves to remove more soil particles, and then through 20, 11 and 3 µm Whatman filter papers to generate the remaining fractions. These steps were done using a Buchner funnel and a LABOPORT N816.3 KT.18 vacuum pump (KNF, Utrecht, the Netherlands). After each step, portions of the recovered filtrate (1/3rd at 20 µm, 1/2 at 11 µm and the rest at 3 µm) were stored at 4°C, representing the respective size fractions.
To prepare the inocula, each fraction stock was thawed and centrifuged to decant the supernatant (LB medium + glycerol). For each of the four fractions of each forest, the pellets were then resuspended in KH2PO4 buffer and transferred to a flask that was filled up to 200 ml with KH2PO4 buffer. The same procedure was followed for each of the four fractions of all twelve forests, resulting in a total of 48 liquid inocula with each individual forest acting as a replicate in its forest development stage (six replicates for young and six replicates for mature). Each of these inocula was randomly applied to 3.15 L (15x15x20cm) pots that were filled with gamma-sterilized grassland soil (Salonius et al., 1967). Finally, ten pots with gamma-sterilized grassland soil received just the KH2PO4 buffer without any community fraction inocula and another ten pots with gamma-sterilized grassland soil were left completely untreated and received neither buffer nor inocula bringing the total number of pots to 68.Two additional experiments were performed using the same setup but supplemented with either bactericide or fungicide with the aim of disentangling the effects of fungi and bacteria from each fraction.
After receiving the inocula, the pots were placed in a climate room with relative humidity 70%, light regime of 16h:8h (light:dark), air temperature of 20 ℃ (light) and 18 ℃ (dark). The pots were then left to settle for four days while the soil was kept moist.
On the fifth day after receiving the inocula, one four-week-old A. glutinosa seedling, germinated in autoclaved potting soil, of similar stem height (~ 2 cm) and number of leaves (at least two) was planted in each pot. The seeds were initially surface-sterilized with bleach solution (14%) to minimize surface contaminants (vertically shaking at 200 rpm for 10 minutes) before being thoroughly rinsed with autoclaved MQ H2O to remove residual bleach. Seedlings were watered three times per week. Each pot was fully saturated with water during each watering event. During the first week, four seedlings died and these were immediately replaced. After 18 weeks, stem height (cm) and diameter (mm) were measured. Hereafter, CO2, N2O and CH4 gas fluxes were measured for each tree, see below. When measuring GHG fluxes, the 11 µm size fraction was excluded from the measurements. Since access to the necessary equipment was limited, this size fraction was excluded as it was deemed to be the most redundant based on stem height measurements from the last week before the harvest.
Two days prior to the harvest of the pots, CO2, N2O and CH4 gas fluxes were measured for each of the trees (pot + tree system). The measurements were conducted using an UGGA GLA-915-0011 (Los Gatos Research, Inc., Mountain View, CA, USA) gas analyzer instrument for CO2 and CH4 fluxes and a LICOR LI-7820 (LI-COR Environmental., Lincoln, Nebraska USA) instrument for N2O fluxes. Each pot was placed in a 30 cm diameter PVC base on top of which we fitted a 100x30 cm (70 L) plexiglass chamber covered with double-layer black plastic bag to prevent photosynthesis and the fluxes were measured over a 5-minute enclosure time. A fan was placed at the top of the chamber, to ensure mixing during flux measurements. Between each flux measurement, the chamber was removed from the base and placed on its side so that it was flushed with atmospheric air. After each flux measurement soil moisture and temperature were measured in each pot using a Delta T probe HH2 moisture meter (Delta-T Devices Ltd, Cambridge, United Kingdom) and a HANNA checktemp1 (HANNA instruments, Nieuwegein, Netherlands) instrument, respectively. These two instruments were thoroughly washed with ethanol in between samples to avoid cross contamination.
Two days later, all plants were harvested. After removing the soil, roots were thoroughly rinsed with running tap water, and the root nodule density was recorded. Root subsamples were then collected for both morphological characterization (preserved in water at 4 ℃) and DNA analysis (preserved at -20 ℃). Root scanning was conducted using an Epson Perfection V850 Pro scanner at 1200 dpi, and the WinRHIZO software (Regent Instruments, Quebec, QC, Canada) was employed to determine root length, specific root length, and the percentage of fine roots (defined as those with a diameter < 0.3 mm). The subsample was then oven-dried for 96 hours (40 ℃). After removing the leaves, the stems were separated from the roots and all of them were oven-dried, similar to the root subsamples. The dry weight of the stems, leaves, roots and root subsamples was then recorded and this was used to calculate the above and belowground dry biomass of the trees.
After the plants were unpotted, the soil from each pot was thoroughly mixed to homogenize the top and bottom layer and subsamples of ~ 100 g of fresh soil were kept from each pot and stored at 4 ℃ for a week. The subsamples were sent to the university of Copenhagen to measure soil respiration. Roughly 50 g of each sample was placed in an incubator and all samples were normalized to 10% soil moisture. The temperature sensitivity of soil heterotrophic respiration (Q10) rate was measured for moisture-normalized samples (10% gravimetric water content) using a respirometer (Nordgren, 1988). Shortly, the soil sample was placed in a sealed cup and the respired CO2 produced from the soil dissolved in a 0.1 M KOH-solution placed inside the cup. The change in electric conductivity of the KOH-solution is directly proportional to the CO2 production. Data was recorded automatically on a computer. Soil CO2 respiration was measured at 9 temperature intervals from 5 ℃ to 25 ℃ with increments of +2.5 ℃.