Simulating Temperature in a Soil Incubation Experiment

Jianwei Li; Precious Areeveso; Xuehan Wang; Siyang Jian; Lahiru Gamage

doi:10.3791/64081

JoVE Journal > Environment

環境学

Simulating Temperature in a Soil Incubation Experiment

Published: October 28, 2022

doi:

10.3791/64081

Jianwei Li¹, Precious Areeveso¹, Xuehan Wang¹, Siyang Jian^1,2, Lahiru Gamage¹

¹Department of Agricultural and Environmental Sciences,Tennessee State University, ²Department of Plant Biology and Microbiology,University of Oklahoma, Norman

概要

Laboratory soil warming experiments usually employ two or more constant temperatures in multiple chambers. By presenting a sophisticated environmental chamber, we provide an accurate temperature control method to imitate the magnitude and amplitude of in situ soil temperature and improve the experimental design of soil incubation studies.

Abstract

The study of warming impact on soils requires a realistic and accurate representation of temperature. In laboratory incubation studies, a widely adopted method has been to render constant temperatures in multiple chambers, and via comparisons of soil responses between low- and high-temperature chambers, to derive the warming impact on soil changes. However, this commonly used method failed to imitate both the magnitude and amplitude of actual temperatures as observed in field conditions, thus potentially undermining the validity of such studies. With sophisticated environmental chambers becoming increasingly available, it is imperative to examine alternative methods of temperature control for soil incubation research. This protocol will introduce a state-of-the-art environmental chamber and demonstrate both conventional and new methods of temperature control to improve the experimental design of soil incubation. The protocol mainly comprises four steps: temperature monitoring and programming, soil collection, laboratory incubation, and warming effect comparison. One example will be presented to demonstrate different methods of temperature control and the resultant contrasting warming scenarios; that is, a constant temperature design referred to as stepwise warming (SW) and simulated in situ temperature design as gradual warming (GW), as well as their effects on soil respiration, microbial biomass, and extracellular enzyme activities. In addition, we present a strategy to diversify temperature change scenarios to meet specific climate change research needs (e.g., extreme heat). The temperature control protocol and the recommended well-tailored and diversified temperature change scenarios will assist researchers in establishing reliable and realistic soil incubation experiments in the laboratory.

Introduction

Global surface temperature is expected to increase this century by 1.8-6.4 °C¹^,². Global warming may increase CO₂ flux from soil to the atmosphere, resulting in positive feedback with warming³^,⁴^,⁵^,⁶. Because microbial communities play a critical role in regulating soil respiratory responses to warming⁷^,⁸, the changes in microbial respiration and the underlying microbial mechanisms with warming have been a research focus. Though soil warming experiments deployed in the field condition, via a heating cable⁹ and an open top chamber¹⁰, were advantageous in capturing natural soil features such as temperature¹¹, their high cost for installation and maintenance have limited their application. Alternatively, soil incubation experiments subject to different temperatures are a favorable choice. The primary advantage of soil incubation in a laboratory is that the well-controlled environmental conditions (e.g., temperature) are able to disentangle the one-factor effect from other confounding factors in a field experimental setting¹²^,¹³. Despite differences between growth chamber and field experiments (e.g., plant growth), translation from lab results to the field are readily available¹⁴. Incubating soil samples in a laboratory setting could help improve our mechanistic understanding of soil response to warming¹⁵.

Our literature review identified several temperature control methods and, consequently, distinct temperature change modes in past soil incubation studies (Table 1). First, instruments used to control temperature are mostly through an incubator, growth chamber, water bath, and in a rare case, heating cable. Given these instruments, three typical temperature change patterns have been generated (Figure 1). These include the most implemented mode, constant temperature (CT), linear change (LC) with a non-zero constant temperature change rate, and nonlinear change (NC) featured with a diurnal type of temperature. For a case of CT pattern, the temperature may vary in magnitude over time, though constant temperature remains for a certain time period during the incubation (Figure 1B). For LC, the rate of temperature change could vary in different studies at more than two orders of magnitude (e.g., 0.1 °C/day vs. 3.3 °C/h; Table 1); For NC cases, most relied upon the intrinsic capacity of instruments used, thus leading to various modes. Despite that a type of diurnal temperature change was claimed through a heating cable or incubator¹⁶^,¹⁷; however, the chamber temperatures in these experiments were not validated. Other major review results in Table 1 include the range of incubation temperature of 0-40 °C, with most between 5-25 °C; the duration of experiments ranged from a few hours (<1 day) to nearly 2 years (~725 days). Also, soils subjected to incubations were collected from forest, grassland, and cropland ecosystems, with dominant mineral horizon, organic horizon, and even contaminated soil, located mostly in the US, China, and Europe (Table 1).

Given the three major temperature change modes, several distinct warming scenarios achieved in the past studies were summarized in Table 2. They include stepwise warming (SW), SW with varying magnitude (SW_v), gradual warming linearly (GW_l), gradual warming nonlinearly (GW_n), and gradual warming diurnally (GW_d).

In summary, past soil incubations usually captured the average air or soil temperature in a site. In many cases, as shown in Table 1, incubators or chambers were manually programmed at a fixed temperature but incapable of automatically adjusting temperature as desired, lacking the ability to control the mode and rate of temperature change with time (Eq. 1), and thus leading to difficulty to imitate diurnal temperature of the local soil. On the other hand, though attempted in two experiments¹⁶^,¹⁷, we identified no studies that explicitly imitated gradual warming diurnally (GW_d) in their incubation experiments (Table 1). Based on the literature review, the major obstacle lies in poor experimental design, particularly lacking a sophisticated instrument that enables implementation and validation of diurnal or other gradual warming scenarios.

(Eq. 1)

Where ΔT is the quantity of temperature change, m is the mode of temperature change, r is the rate of temperature change, and t is the duration of change.

To improve the experimental rigor in soil incubation, an accurate and sophisticated temperature control method is presented in this study. Adopting a state-of-the-art environmental chamber, increasingly available and economically viable, the new design shall not only enable the accurate simulation of in situ soil temperature (e.g., diurnal pattern) but also, by accounting for possible temperature change extremes, provide a reliable way to minimize the artefacts of instrumental bias. The current soil incubation design should assist researchers to identify optimal strategies that meet their incubation and research needs. The overall goal of this method is to present soil biogeochemists with a highly operational approach to reform soil incubation design.

Protocol

1. Temperature monitoring and programming

Identify a sampling zone within a research plot. Install one or a few automatic temperature probes in soils at 10 cm depth. Connect the weather station to a computer via the data transmission cable and open the software on the computer.
Click on the Launch/ Properties toolbar button to configure the logger for the external sensors being used.
On the Properties screen, set the logger/station name (i.e., Soil incubation exp.) and the data collection interval (i.e., 60 min). Then, on the Properties screen, click Enabled on the external sensor ports being used and select the sensor/unit from the dropdown button for each sensor port (i.e., Port A; "Enabled": Temperature ˚C). Finally, click on OK to save the settings.
Monitor the probes' reading weekly to avoid malfunction and download the dataset once a month. Obtain a complete record for several months covering the growing season (i.e., April to September).
Conduct data analysis of the temperature records. Obtain the mean hourly temperature of the growing season by averaging all observations.
1. Obtain the mean temperature of each hour on a daily basis by averaging temperatures of the same hour across all days during the growing season.
In the sophisticated chamber, launch the software and click on the Profile button on the main menu screen to create a new file. In the file name input line, enter "SW low". By clicking on the Instant Change option, enter 15.9 °C as an initial temperature as obtained in step 1.5, and enter 2 on the Minutes row to maintain the temperature for 2 min and click on the Done button. Then, under the Ramp Time option, enter 15.9 °C as the target set point and on the Hours row enter 850 h to sustain the temperature. Fianlly, click on the Done button.
1. Repeat the above step in the second chamber by adding 5 °C to each temperature node and create a new file name "SW high".
2. Repeat step 1.4 in the third chamber by adding 23 additional steps corresponding to 23 observed hourly soil temperatures as obtained in step 1.5.1. At the last step, called JUMP, set 42 repeated loops (Jump Count 42). This leads to the scenario of gradual warming or GW low.
3. Repeat the above step in the fourth chamber with 5 °C added to each temperature node. This will allow a simulation of varying temperatures for 42 days at a higher temperature level (i.e., GW high).
Conduct a preliminary run for 24 h and output the temperatures recorded by the four chambers. Plot the temperatures recorded by the chambers against those as programmed (Figure 2A-D).
1. If the temperatures achieved in the chamber match the temperatures as programmed by a temperature difference <0.1 °C during the 24 h (Figure 2A,B,E,F), the chambers are suitable for the soil incubation experiment.
2. If the criteria were not satisfied in any of these chambers, repeat another 24 h test or seek a new chamber.

2. Soil collection and homogenizing

Near the temperature probe area, collect five soil samples at 0-20 cm depth and put them into one plastic bag after removing the surface litter layer.
Mix the sample thoroughly by twisting, pressing, and mingling the materials in the bag until no individual soil sample is visible.
Store the samples in a cooler filled with ice packs and transport the samples to the lab immediately.
Remove the roots in each core, sieve it through a soil sieve of 2 mm, and thoroughly mix and homogenize the sample prior to the following analysis.

3. Laboratory incubation

Prior to incubation, weigh 10.0 g of fresh soil, oven-dry it for 24 h at 105 °C, and weigh the dry soil. Derive the difference between fresh and dry soil samples and calculate the ratio of difference over dry soil weight to determine the soil moisture content in a spreadsheet.
Use the derived moisture content to calculate the soil microbial biomass carbon (MBC), extracellular enzyme activity (EEA), and soil heterotrophic respiration as described in the following steps. These data will help understand the treatment effects on soil respiration and the underlying microbial mechanisms.
Prior to incubation, weigh the field moist soil subsample (10 g) and quantify the soil MBC by chloroform fumigation-K₂SO₄ extraction and potassium persulfate digestion methods¹⁸.
Prior to incubation, weigh the subsample of field moist soil (1.0 g) and measure soil hydrolytic and oxidative EEA¹⁹.
Weigh 16 field moist soil subsamples (15.0 g equivalent of dry weight) in 16 polyvinyl chloride (PVC) cores (5 cm diameter, 7.5 cm tall) sealed with glass fiber paper on the bottom.
Place the PVC cores in Mason jars (~1 L) lined with a bed of glass beads to ensure that the cores do not absorb moisture.
Place four jars in each of the four chambers as described in step 1.4. Turn on the chambers and launch the program simultaneously in four chambers.
During the incubation, at 2 h, days 1, 2, 7, 14, 21, 28, 35, and 42, take all jars in each of four chambers and use a portable CO₂ gas analyzer to measure soil respiration rate (R_s) by putting the analyzer's collar to the top of each jar.
Destructively collect all jars at the end of incubation (i.e., day 42) and quantify soil MBC as described in step 3.3.
Destructively collect all jars at the end of incubation (i.e., day 42) and quantify soil enzyme activity as described in step 3.4.

4. Warming effect comparison

By assuming a constant respiration rate (Rs) between two consecutive collections, use the respiration rate times the duration to derive the cumulative respiration (R_c).
Conduct a three-way repeated measures analysis of variance (ANOVA) to test the main and interactive effects of time, temperature (warming), and temperature mode (warming scenario) on R_s and R_c. In addition, conduct a two-way ANOVA to test warming and warming scenario effects on MBC and EEA.

Representative Results

The selected state-of-the-art chambers replicated the target temperature with high precision (Figure 2A,B,E,F) and met the technical requirement of the incubation experiment. Given the easy use and operation, this signified the technique to improve the temperature simulation in soil warming studies and in other applications such as plant studies. The procedure has been employed in our recent case study based on a switchgrass cropland in Middle-Tennessee.

Research results showed that relative to control treatment, warming led to significantly greater respiratory losses (R_s and R_c) in both warming scenarios (SW and GW), and GW doubled the warming-induced respiratory loss (R_c) relative to SW, 81% vs. 40% (Figure 3). On day 42, MBC and EEA were also significantly different between SW and GW, such that MBC was higher in SW than in GW (69% vs. 38%; Figure 4) and glycosidases and peroxidase (e.g., AG, BG, BX, CBH, NAG, AP, LAP) were significantly higher in GW than in SW scenarios (Figure 5).

Figure 1: The illustration of temperature change mode in a soil warming experiment as conceptualized from Table 1. (A) Constant temperature (CT) adopted by most studies. (B) Constant temperature with varying magnitude (CT_v). (C,D) Linear change (LC) with positive and negative rates. (E,F) Nonlinear change (NC) with irregular pattern and diurnal pattern. Please click here to view a larger version of this figure.

Figure 2: Temperature targeted via programming and chamber temperature during a 24-h testing period. (A,B) Target temperature (grey line) and chamber temperature records (dashed line) under control and warming treatments of stepwise warming (SW); (C,D) Target temperature (grey line) and chamber temperature records (dashed line) under control and warming treatments of gradual warming (GW); (E, F) The temperature difference derived for records in panels C and D. Please click here to view a larger version of this figure.

Figure 3: Mean (± SE) cumulative soil respiration rate (R_c, µg CO₂-C·g_soil^-1) under control (hollow) and warming (dark) treatments in SW and GW in a 42-day soil incubation experiment. The insets show soil respiration rates (R_s, µg CO₂-C·h^-1·g_soil^-1) applied to estimate cumulative respiration, assuming R_s was constant until the following measurement. (A) Stepwise warming (SW) and (B) gradual warming (GW). N = 4 in each collection. Please click here to view a larger version of this figure.

Figure 4: Mean (± SE) MBC under control and warming treatments in SW and GW in a 42-day soil incubation experiment. MBC = microbial biomass carbon; N = 4 in each collection. S denotes significant effect of warming scenario (SW vs. GW), at p < 0.05, based on a three-way repeated measures ANOVA. Please click here to view a larger version of this figure.

Figure 5: Mean (± SE) glycosidases and peroxidase (µmol activity h^-1·gsoil^-1) under control and warming treatments in SW and GW in a 42-day incubation experiment. BX =β1,4-xylosidase; AP = Acid Phosphatase; LAP = Leucine Aminopeptidase; NAG =β-1,4-N-acetyl-glucosaminidase; OX = Oxidative enzymes; PHO = Phenol oxidase; PER = Peroxidase. N = 4 in each collection. S denotes significant effect of warming scenario (SW vs. GW), at p < 0.05, based on a three-way repeated measures ANOVA. Please click here to view a larger version of this figure.

Table 1: Literature review of temperature control methods and temperature change modes in soil incubation studies¹²^,¹³^,¹⁶^,¹⁷^,²⁰^,²¹^,²²^,²³^,²⁴^,²⁵^,²⁶^,²⁷^,²⁸^,²⁹^,³⁰^,³¹^,³²^,
³³^,³⁴^,³⁵^,³⁶^,³⁷^,³⁸^,³⁹^,⁴⁰^,⁴¹^,⁴²^,⁴³^,⁴⁴^,⁴⁵^,⁴⁶^,⁴⁷^,⁴⁸^,⁴⁹^,⁵⁰^,⁵¹^,
⁵²^,⁵³^,⁵⁴^,⁵⁵^,⁵⁶^,⁵⁷^,⁵⁸^,⁵⁹^,⁶⁰^,⁶¹^,⁶².
In total, 46 studies were included in the review. Please click here to download this Table.

Table 2: Major temperature change modes and the corresponding warming scenarios based on a literature review (Table 1). Five modes and scenarios were established to represent a wide range of possible temperature change and warming conditions. Please click here to download this Table.

Discussion

The constant temperature control method has been applied widely (Table 1). However, the magnitude and temporal pattern of temperature implemented in these procedures poorly simulate soil temperature observed in the field condition. Despite the emerging efforts imitating the diurnal pattern in the past, such studies were scarce and failed to clarify the equipment and procedure; neither did they validate the temperature simulation regarding accuracy and reliability¹⁶^,¹⁷. As the community strived to improve its understanding of soil warming responses, optimizing the soil incubation procedure with realistic temperature and feasible control is imperative. Nevertheless, such new methods have not been developed, and thus, a standard method for future incubation experiments is still out of reach. In the face of the increasing complexity of global temperature change in magnitude, amplitude, seasonality, duration, and extremality, a comprehensive procedure is in high demand.

Here, a method for manipulating a diurnal temperature change procedure was presented, relying upon the sophisticated chamber, to offer the capacity to establish constant, linear, and nonlinear temperature change and subsequently various warming scenarios for meeting future research needs. There are four critical steps within the protocol. The first is to determine soil temperature in the field condition. Because the soil type and depth of interest as well as the land use type can vary from one study to another, the number of temperature probes needed for the specific research site should be modified to best fit the actual conditions as much as possible. In general, soil depth for temperature probes shall meet the most research needs at 0-20 cm, and the number of probes to represent the soil temperature should be limited to one to three. The key is to achieve a long-term continuous and consecutive temperature record in at least one typical soil location.

The second critical step is to set up the program to achieve the targeted temperature magnitude and pattern in the chamber. Because of the high sensitivity and accuracy of chamber (Figure 4), it is feasible to program for an accurate representation of temperature as observed in the field condition. Although the current protocol only presented the observed hourly temperature as targeted in the chamber, a more frequent soil temperature monitoring, such as 30 min, 15 min, or even shorter, can be achieved through this procedure. Nevertheless, a test of the target and chamber temperatures must be conducted over 24 h, and prior to experiment, the test results must meet the criteria of less than 0.1 °C between the target and chamber temperatures at all time points. The more frequent the temperature observation is selected to simulate, the more steps are needed to set up the program in the chamber prior to the experiment.

The third critical step is to conduct the incubation itself. To reduce the influence of soil heterogeneities⁶³, homogenizing soil samples is key, and at least three replicates for each treatment are recommended. Prior to incubation, a pre-incubation treatment is required, and the current procedure can facilitate pre-treatment by programming the temperature and duration before the official start of the experiment. This is advantageous for one to reduce the experimental disturbance and orchestrate the entire incubation seamlessly. The last critical step is to include both constant temperature and varying temperature treatments so that a comparison can be made as to the soil warming responses.

This protocol can be easily modified to allow one to manipulate the magnitude, amplitude, and duration of temperature change. For example, extreme temperatures during a heat wave in summer and sudden frost in early spring due to climate change, can be represented using this procedure, in addition to its capacity to account for their varying duration and intensity. Simulating the regular and irregular temperatures in combination also allow one to simulate long-term complex temperature change effects as projected in the future. As summarized in Table 2, those warming scenarios that have been studied in many distinct studies can be accomplished collectively in one study. This protocol is expected to provide a sophisticated method to simulate temperature in soil incubation studies. With hope for a wide application, the adoption of this protocol will help identify or validate a more accurate method for future soil warming studies based on laboratory incubation.

An important limitation of the procedure is that the chamber used in the current protocol has a relatively small volume, thus is only able to accommodate nine incubation jars in each chamber. Though a smaller jar will increase the capacity of the chamber, a big volume of chamber is recommended. A new model (e.g., TestEquity 1007) will offer eight times more capacity and is thus recommended for large scale experiments. Despite the improvement of temperature control procedure in soil incubations, the potential complications with moisture and soil homogenization will not be relieved by adopting the current protocol.

We demonstrate significant advantages of the sophisticated temperature control procedure. It provides a reliable and affordable temperature control strategy to obtain accurate temperature simulation and offers a feasible way to improve soil incubation experiment required for a better understanding of soil warming responses. Although the constant temperature control is widely accepted and logistically easy to operate, the artifacts of long-term constant temperature on soil microbial communities may divert efforts to capture the genuine soil responses. The other reported laboratory warming methods are largely less controllable and replicable. The current protocol is superior due to its easy operation, high accuracy and replicability of temperature simulation, explicit programing, and capacity to combine various temperature change scenarios in a single experiment. The feasibility of temperature control with high accuracy will allow researchers to explore various temperature change scenarios.

開示

The authors have nothing to disclose.

Acknowledgements

Funding sources used to support the research include a US National Science Foundation (NSF) HBCU−EiR (No. 1900885), a US Department of Agriculture (USDA) Agricultural Research Service (ARS) 1890s Faculty Research Sabbatical Program (No. 58-3098-9-005), a USDA NIFA grant (No. 2021-67020-34933), and a USDA Evans−Allen Grant (No. 1017802). We thank assistance received from staff members at the TSU's Main Campus Agriculture Research and Extension Center (AREC) in Nashville, Tennessee.

Materials

10 mL-Syringe	Fisher Scientific	14-826-13	for soil respiration measurement
Composer Software	TestEquity	Model #107	for incubation temperature setup
Environmental chamber	TestEquity	Model #107	for soil incubation
Environmental gas analyzer	PP Systems	EGM5	for soil respiration measurement
Filter paper	Fisher Scientific	1005-125	for soil incubation
Mason jar	Ball	15381-3	for soil incubation
Oven	Fisher Scientific	15-103-0520	for soil moisture measurement
Plastic Zipper Seal Storage Bag	Fisher Scientific	09-800-16	for soil collection
Plate reader	Molecular devices	FilterMax F5	for soil extracellular enzyme analysis
R Software	The R Foundation	R version 4.1.3 (2022-03-10)	For statistical computing
Refrigerator/Freezer	Fisher Scientific	13-991-898	for soil storation
Screwdriver	Fisher Scientific	19-313-447	for soil collection
Sharpie	Fisher Scientific	50-111-3135	for soil collection
Sieve	Fisher Scientific	04-881G	for sieving soil sample
Silicone Septa	Duran Wheaton kimble	224100-070	for mason jars used for soil incubation
Soil auger	AMS	350.05	for soil collection
SpecWare Software	Spectrum Technologies	WatchDog E2700 (3340WD2)	for temperature collection interval setup
Temperature probe	Spectrum Technologies	WatchDog E2700 (3340WD2)	for soil temperature measurements
TOC/TN analyzer	Shimadzu	TOC-L series	for soil microbial biomass analysis

参考文献

Chatterjee, D., Saha, S., Bal, S., Mukherjee, J., Choudhury, B., Dhawan, A. Response of Soil Properties and Soil Microbial Communities to the Projected Climate Change. Advances in Crop Environment Interaction. , 87-136 (2018).
Feral, J., Pachauri, R. K., Meyer, L. A. . Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate. , 151 (2014).
Davidson, E. A. Carbon dioxide loss from tropical soils increases on warming. Nature. 584 (7820), 198-199 (2020).
Davidson, E. A., Janssens, I. A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature. 440 (7081), 165-173 (2006).
Van Gestel, N., et al. Predicting soil carbon loss with warming. Nature. 554 (7693), 4-5 (2018).
Tarnocai, C., et al. Soil organic carbon pools in the northern circumpolar permafrost region. Global Biogeochemical Cycles. 23 (2), 2023 (2009).
Allison, S. D., Treseder, K. K. Warming and drying suppress microbial activity and carbon cycling in boreal forest soils. Global Change Biology. 14 (12), 2898-2909 (2008).
Allison, S. D., Wallenstein, M. D., Bradford, M. A. Soil-carbon response to warming dependent on microbial physiology. Nature Geoscience. 3 (5), 336-340 (2010).
Melillo, J. M., et al. Soil warming, carbon-nitrogen interactions, and forest carbon budgets. Proceedings of the National Academy of Sciences. 108 (23), 9508-9512 (2011).
Pelini, S. L., et al. Heating up the forest: open-top chamber warming manipulation of arthropod communities at Harvard and Duke Forests. Methods in Ecology and Evolution. 2 (5), 534-540 (2011).
Hamdi, S., Moyano, F., Sall, S., Bernoux, M., Chevallier, T. Synthesis analysis of the temperature sensitivity of soil respiration from laboratory studies in relation to incubation methods and soil conditions. Soil Biology and Biochemistry. 58, 115-126 (2013).
Benton, T. G., Solan, M., Travis, J. M., Sait, S. M. Microcosm experiments can inform global ecological problems. Trends in Ecology & Evolution. 22 (10), 516-521 (2007).
Schädel, C., et al. Decomposability of soil organic matter over time: the Soil Incubation Database (SIDb, version 1.0) and guidance for incubation procedures. Earth System Science Data. 12 (3), 1511-1524 (2020).
Poorter, H., et al. Pampered inside, pestered outside? Differences and similarities between plants growing in controlled conditions and in the field. New Phytologist. 212 (4), 838-855 (2016).
Jian, S., et al. Multi-year incubation experiments boost confidence in model projections of long-term soil carbon dynamics. Nature Communications. 11 (1), 5864 (2020).
Zhu, B., Cheng, W. Constant and diurnally-varying temperature regimes lead to different temperature sensitivities of soil organic carbon decomposition. Soil Biology and Biochemistry. 43 (4), 866-869 (2011).
Whitby, T. G., Madritch, M. D. Native temperature regime influences soil response to simulated warming. Soil Biology and Biochemistry. 60, 202-209 (2013).
Brookes, P. C., Landman, A., Pruden, G., Jenkinson, D. S. Chloroform fumigation and the release of soil nitrogen: A rapid direct extraction method to measure microbial biomass nitrogen in soil. Soil Biology and Biochemistry. 17 (6), 837-842 (1985).
Saiya-Cork, K., Sinsabaugh, R., Zak, D. The effects of long term nitrogen deposition on extracellular enzyme activity in an Acer saccharum forest soil. Soil Biology and Biochemistry. 34 (9), 1309-1315 (2002).
Adekanmbi, A. A., Shu, X., Zhou, Y., Shaw, L. J., Sizmur, T. Legacy effect of constant and diurnally oscillating temperatures on soil respiration and microbial community structure. bioRxiv. , (2021).
Akbari, A., Ghoshal, S. Effects of diurnal temperature variation on microbial community and petroleum hydrocarbon biodegradation in contaminated soils from a sub-Arctic site. Environmental Microbiology. 17 (12), 4916-4928 (2015).
Bai, Z., et al. Shifts in microbial trophic strategy explain different temperature sensitivity of CO2 flux under constant and diurnally varying temperature regimes. FEMS Microbiology Ecology. 93 (5), (2017).
Bao, X., et al. Effects of soil temperature and moisture on soil respiration on the Tibetan plateau. PLoS One. 11 (10), 0165212 (2016).
Chang, X., et al. Temperature and moisture effects on soil respiration in alpine grasslands. Soil science. 177 (9), 554-560 (2012).
Chen, X., et al. Evaluating the impacts of incubation procedures on estimated Q10 values of soil respiration. Soil Biology and Biochemistry. 42 (12), 2282-2288 (2010).
Conant, R. T., Dalla-Betta, P., Klopatek, C. C., Klopatek, J. M. Controls on soil respiration in semiarid soils. Soil Biology and Biochemistry. 36 (6), 945-951 (2004).
Conant, R. T., et al. Sensitivity of organic matter decomposition to warming varies with its quality. Global Change Biology. 14 (4), 868-877 (2008).
Ding, J., et al. Linking temperature sensitivity of soil CO2 release to substrate, environmental, and microbial properties across alpine ecosystems. Global Biogeochemical Cycles. 30 (9), 1310-1323 (2016).
En, C., Al-Kaisi, M. M., Liange, W., Changhuan, D., Deti, X. Soil organic carbon mineralization as affected by cyclical temperature fluctuations in a karst region of southwestern China. Pedosphere. 25 (4), 512-523 (2015).
Fang, C., Moncrieff, J. The dependence of soil CO2 efflux on temperature. Soil Biology and Biochemistry. 33 (2), 155-165 (2001).
Fierer, N., Colman, B. P., Schimel, J. P., Jackson, R. B. Predicting the temperature dependence of microbial respiration in soil: A continental-scale analysis. Global Biogeochemical Cycles. 20 (3), 3026 (2006).
Guntinas, M., Gil-Sotres, F., Leiros, M., Trasar-Cepeda, C. Sensitivity of soil respiration to moisture and temperature. Journal of Soil Science and Plant Nutrition. 13 (2), 445-461 (2013).
Kittredge, H. A., Cannone, T., Funk, J., Chapman, S. K. Soil respiration and extracellular enzyme production respond differently across seasons to elevated temperatures. Plant and Soil. 425 (1), 351-361 (2018).
Knorr, W., Prentice, I. C., House, J., Holland, E. Long-term sensitivity of soil carbon turnover to warming. Nature. 433 (7023), 298-301 (2005).
Lefevre, R., et al. Higher temperature sensitivity for stable than for labile soil organic carbon-Evidence from incubations of long-term bare fallow soils. Global Change Biology. 20 (2), 633-640 (2014).
Li, J., et al. Asymmetric responses of soil heterotrophic respiration to rising and decreasing temperatures. Soil Biology and Biochemistry. 106, 18-27 (2017).
Li, J., et al. Biogeographic variation in temperature sensitivity of decomposition in forest soils. Global Change Biology. 26 (3), 1873-1885 (2020).
Li, J., et al. Rising temperature may trigger deep soil carbon loss across forest ecosystems. Advanced Science. 7 (19), 2001242 (2020).
Liang, J., et al. Methods for estimating temperature sensitivity of soil organic matter based on incubation data: A comparative evaluation. Soil Biology and Biochemistry. 80, 127-135 (2015).
Lin, J., Zhu, B., Cheng, W. Decadally cycling soil carbon is more sensitive to warming than faster-cycling soil carbon. Global Change Biology. 21 (12), 4602-4612 (2015).
Liu, H., et al. Differential response of soil respiration to nitrogen and phosphorus addition in a highly phosphorus-limited subtropical forest, China. Forest Ecology and Management. 448, 499-508 (2019).
Liu, H. S., et al. Respiratory substrate availability plays a crucial role in the response of soil respiration to environmental factors. Applied Soil Ecology. 32 (3), 284-292 (2006).
Liu, Y., et al. A new incubation and measurement approach to estimate the temperature response of soil organic matter decomposition. Soil Biology and Biochemistry. 138, 107596 (2019).
Meyer, N., Welp, G., Amelung, W. The temperature sensitivity (Q10) of soil respiration: Controlling factors and spatial prediction at regional scale based on environmental soil classes. Global Biogeochemical Cycles. 32 (2), 306-323 (2018).
Mikan, C. J., Schimel, J. P., Doyle, A. P. Temperature controls of microbial respiration in arctic tundra soils above and below freezing. Soil Biology and Biochemistry. 34 (11), 1785-1795 (2002).
Podrebarac, F. A., Laganière, J., Billings, S. A., Edwards, K. A., Ziegler, S. E. Soils isolated during incubation underestimate temperature sensitivity of respiration and its response to climate history. Soil Biology and Biochemistry. 93, 60-68 (2016).
Quan, Q., et al. type affects the coupled relationships of soil C and N mineralization in the temperate forests of northern China. Scientific Reports. 4 (1), 6584 (2014).
Robinson, J., et al. Rapid laboratory measurement of the temperature dependence of soil respiration and application to changes in three diverse soils through the year. Biogeochemistry. 133 (1), 101-112 (2017).
Sierra, C. A., Trumbore, S. E., Davidson, E. A., Vicca, S., Janssens, I. Sensitivity of decomposition rates of soil organic matter with respect to simultaneous changes in temperature and moisture. Journal of Advances in Modeling Earth Systems. 7 (1), 335-356 (2015).
Sihi, D., Inglett, P. W., Gerber, S., Inglett, K. S. Rate of warming affects temperature sensitivity of anaerobic peat decomposition and greenhouse gas production. Global Change Biology. 24 (1), 259-274 (2018).
Sihi, D., Inglett, P. W., Inglett, K. S. Warming rate drives microbial nutrient demand and enzyme expression during peat decomposition. Geoderma. 336, 12-21 (2019).
Subke, J. -. A., Bahn, M. On the ‘temperature sensitivity’of soil respiration: can we use the immeasurable to predict the unknown. Soil Biology and Biochemistry. 42 (9), 1653-1656 (2010).
Tucker, C. L., Bell, J., Pendall, E., Ogle, K. Does declining carbon-use efficiency explain thermal acclimation of soil respiration with warming. Global Change Biology. 19 (1), 252-263 (2013).
Wang, J., et al. Temperature sensitivity of soil carbon decomposition due to shifts in soil extracellular enzymes after afforestation. Geoderma. 374, 114426 (2020).
Wang, Q., et al. Important interaction of chemicals, microbial biomass and dissolved substrates in the diel hysteresis loop of soil heterotrophic respiration. Plant and Soil. 428 (1), 279-290 (2018).
Wang, Q., et al. Differences in SOM decomposition and temperature sensitivity among soil aggregate size classes in a temperate grasslands. PLoS One. 10 (2), 0117033 (2015).
Weedon, J. T., et al. Temperature sensitivity of peatland C and N cycling: does substrate supply play a role. Soil Biology and Biochemistry. 61, 109-120 (2013).
Wei, L., et al. Labile carbon matters more than temperature for enzyme activity in paddy soil. Soil Biology and Biochemistry. 135, 134-143 (2019).
Wetterstedt, J. M., Persson, T., Ågren, G. I. Temperature sensitivity and substrate quality in soil organic matter decomposition: results of an incubation study with three substrates. Global Change Biology. 16 (6), 1806-1819 (2010).
Winkler, J. P., Cherry, R. S., Schlesinger, W. H. The Q10 relationship of microbial respiration in a temperate forest soil. Soil Biology and Biochemistry. 28 (8), 1067-1072 (1996).
Yan, D., et al. The temperature sensitivity of soil organic carbon decomposition is greater in subsoil than in topsoil during laboratory incubation. Scientific Reports. 7, 5181 (2017).
Yang, K., et al. Temperature response of soil carbon decomposition depends strongly on forest management practice and soil layer on the eastern Tibetan Plateau. Scientific Reports. 7, 4777 (2017).
Li, J. W. Sampling soils in a heterogeneous research plot. Journal of Visualized Experiments. (143), e58519 (2019).

Play Video

PDF

DOI

DOWNLOAD MATERIALS LIST

記事を引用

Li, J., Areeveso, P., Wang, X., Jian, S., Gamage, L. Simulating Temperature in a Soil Incubation Experiment. J. Vis. Exp. (188), e64081, doi:10.3791/64081 (2022).

Simulating Temperature in a Soil Incubation Experiment