Mycorrhizal fungi respiration dynamics in relation to gross primary production in a Hungarian dry grassland

doi:10.21203/rs.3.rs-3218373/v1

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Mycorrhizal fungi respiration dynamics in relation to gross primary production in a Hungarian dry grassland

https://doi.org/10.21203/rs.3.rs-3218373/v1

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published 24 Feb, 2024

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Aims: Soil respiration (R_s) is a highly complex process including a wide range of soil biota and different pathways of carbon cycling, all being under the control of various drivers. The most important biotic driver is the photosynthetic activity of the vegetation providing supply mainly for the autotrophic component of R_s: roots and their symbiotic partners. The objective of this study was to describe the time-lagged relationship between gross primary production (GPP) and the mycorrhizal R_s component in order to determine the amount of carbon derived from GPP appearing as mycorrhizal respiration (R_myc).

Methods: Measurements of R_s were conducted in three treatments – i) undisturbed, root and AMF-included (R_s), ii) root-excluded (R_het+myc) and iii) root- and AMF-excluded (R_het) plots – for three consecutive years in the Central-Hungarian dry sandy grassland study site. GPP data were derived from eddy-covariance measurements, while an automated system was used for continuous measurements of R_s. We analysed the relationship between R_myc and GPP by using cross-correlation and by fitting sine wave models on the diel datasets.

Results: GPP was found to be the main driver of R_myc, responding with an average time lag of 18 hours. The greatest lags were detected during periods characterized by minimal photosynthetic activity, while lags tended to be the smallest during active periods.

Conclusion: Based on the seasonal changes in the delay, we concluded that GPP and soil temperature had simultaneous effects on the diel pattern of CO₂ emission of the different autotrophic components depending on the vegetation activity and environmental conditions.

arbuscular mycorrhizal fungi

grasslands

gross primary production

soil respiration

time lag

Soil respiration (R_s) is the main component of ecosystem’s carbon source activity, hence it has a significant impact on the carbon balance, which can be defined as the sum of the source and sink activities (Baldocchi et al. 2018; Phillips et al. 2017). The complex process of soil respiration is governed by environmental and biotic (autotrophic and heterotrophic biota) drivers and includes various pathways of the carbon cycle such as carbon allocation and decomposition (Balogh et al. 2011; Moyano et al. 2013; Vargas et al. 2011), on which fungal colonization has a major impact (Frey 2019). Even though these processes have received increasingly more attention in the last decade – mainly due to the need for mitigating the effects of greenhouse gas emissions (Rustad et al. 2000) – there is still a knowledge gap in our understanding of the links between them. In order to improve carbon cycle models (Hopkins et al. 2013) and to decrease the uncertainties related to the estimations of ecosystem respiration (Barba et al. 2018) a comprehensive clarification of the underlying processes is needed.

Approximately 8-52% of carbon fixed by terrestrial gross primary production (GPP) is respired back into the atmosphere via the rhizospheric activity after passing through the soil (Lambers and Oliveira 2019). Also, 23-83% of GPP built in plant tissues in the form of C (DeLucia et al. 2007) is returned to the atmosphere by decomposer organisms. Ecosystem-to-atmosphere fluxes, such as soil respiration (R_s) thus balance GPP (Jian et al. 2022). R_s determines net carbon sequestration in terrestrial ecosystems and can be divided into several components although there is a lack of knowledge as to what extent GPP is shared between them. Generally, root and rhizomicrobial (mycorrhizal fungi and rhizospheric microbes) respiration is referred to as the autotrophic component, while the amount of CO₂ resulting from the microbial decomposition of soil organic matter (SOM) represents the heterotrophic respiration (Baggs 2006; Moyano et al. 2009). Some studies indicated that R_s components showed no differences in their response to changes in the main environmental factors (Graham et al. 2014; Irvine et al. 2005; Sulzman et al. 2005), while other studies reported significant dissimilarities (Matteucci et al. 2015; Moyano et al. 2007; Wang et al. 2014), which shows that R_s,in general, is highly influenced by several biotic, abiotic and management factors (Balogh et al. 2011). Concerning the biotic factors, land cover, more specifically the vegetation composition and structure, as well as phenology all determine soil CO₂efflux, since carbon uptake and allocation vary in the different ecosystems (Balogh et al. 2019; Gavrichkova et al. 2010; Han et al. 2014; Pumpanen et al. 2015). Furthermore, management factors like biomass amount and land-use practices (Gavrichkova et al. 2010; Lieth 1973) can also influence soil respiration. As for the abiotic drivers, soil CO₂ efflux was proven to be significantly dependent on soil temperature (Fang and Moncrieff 2001) and soil moisture (Saiz et al. 2007; Wan et al. 2007).

Separating the components of soil respiration is a key factor when estimating their contribution to soil carbon processes given that increased autotrophic respiration may indicate a higher C input to the soil by belowground carbon allocation (Högberg et al. 2001) and larger root biomass (Gregory 2006). Intense heterotrophic respiration, on the other hand, may result in carbon loss from the soil and decreasing soil C storage (Grace 2004). The importance of mycorrhizal symbioses became acknowledged mostly due to its key role in nutrient exchange and increased plant resistance and tolerance against biotic and abiotic stressors (Selim et al. 2021; Wang et al. 2022). In addition, mycorrhizal fungi, as an important component of soil microbial community, also make a significant contribution to soil respiration (from 6% to 25% of total R_s) (Delavaux et al. 2017; Heinemeyer et al. 2007; Moyano et al. 2007). The carbon demand of arbuscular mycorrhizal fungi (AMF) is entirely satiated by the host plant. In the course of time carbon transferred to the fungal partner is accumulated in the soil in the form of glomalin-related soil protein (GRSP) or fungal chitin (Prescott 2010). By acting as a source of CO₂ and a pathway of carbon to the SOM, their role in carbon balance is unquestionable (Godbold et al. 2006), not to mention that mycorrhizal C allocation could determine long-term C storage potential of ecosystems’ (Zhu and Miller 2003). Arbuscular mycorrhizas are formed in a huge variety of terrestrial plants and can be found in the majority of habitats (Smith and Read 2008).

Drought-prone areas are extremely significant in the aspects of the global CO₂ sink, since semi-arid ecosystems were found to be the largest fraction of net carbon flux (39% of global net biome production (NBP)) (Ahlström et al. 2015). Furthermore, the autotrophic respiration significantly contributes to the R_s of grasslands (ranging from one-third to more than one-half) (Balogh et al. 2016), and thus it plays a prominent role in ecosystem-level carbon cycling (Hasibeder et al. 2015). In these vegetation types root-associated AMF represent a considerable segment of the autotrophic component (Moyano et al. 2007), since – as multiple studies have shown – AM fungi are present in the ecosystem as a major pathway of carbon (Bago et al. 2000; Godbold et al. 2006; Wilson et al. 2009). This was also verified by Johnson et al. (Johnson et al. 2002) by measuring a significant and fast flux of C from plant to the atmosphere through AMF in grasslands. Using the pulse labelling method it has already been stated that the respiratory release of C from AMF hyphae peaks from 12 to 16 hours after its fixation and the rate of this process is governed by the balance between the rates of respiration and the rates of C incorporation into the AM hyphae (Leake et al. 2004). In our previous studies we aimed to estimate the contribution of rhizospheric, mycorrhizal and heterotrophic components to the total soil respiration (Balogh et al. 2016; Papp et al. 2018). However, the importance and the presence of the large number of AMF in dry grasslands motivated us to focus on this direct relationship between plants and fungi, which also functions as a main pathway in the belowground carbon allocation and hence in the global carbon cycle. In order to better understand their role in the carbon balance we decided to examine the relationship between GPP and arbuscular mycorrhizas. Therefore, the aim of this present study is to reveal the dynamics and characteristics of the relationship between the mycorrhizal soil respiration component (R_myc) and the GPP. Accordingly, we set out to test the following hypotheses:

Changes of mycorrhizal fungi respiration in dry grasslands are driven primarily by the belowground carbon allocation resulting from GPP rather than by soil temperature.
There is a rapid time-lagged relationship between GPP and mycorrhizal fungi respiration.
Climatic and environmental conditions can alter the impact of belowground carbon allocation on autotrophic soil respiration components with a seasonal pattern.

There are multiple options and various approaches for the quantification of R_s components: root exclusion, component integration and isotopic measurements (CHIN et al. 2023; Hanson et al. 2000). Root exclusion via the membrane mesh-collar technique (Heinemeyer et al. 2007, 2012b; Moyano et al. 2007) in association with automated soil respiration measurements (Suleau et al. 2011) could provide insights into the alteration of soil CO₂ efflux in situ. However, changes in the environmental factors (e.g. changing soil water content) caused by the experimental techniques (Kuzyakov 2006; Moyano et al. 2007; Subke et al. 2006) also need to be taken into account when calculating R_s.

The determination of time lags between GPP and R_myc was done by quantifying the C allocated from plants to AMF partners applying an in situ soil mesh-collar experimental system in a dry sandy grassland ecosystem, combined with a high-resolution automated chamber-based measurement.

2.1 Site description

The study was carried out from August 2010 to May 2014 on a semi-arid sandy grassland dominated by Festuca pseudovina, Carex stenophylla and Cynodon dactylon located at the Kiskunság National Park in Hungary, at Bugac site (location 46.69 N, 19.6 E, 114 m a. s. l.). The mean annual precipitation (2004-2013) was 575 mm, and the mean annual air temperature was +10.4 °C. The grassland has been kept under extensive management (grazed by cattle) for a minimum of 20 years (Koncz et al. 2014). According to the FAO classification (Driessen et al. 2001) the soil is classified as Chernozem with a soil texture of sandy loam with a sand: silt:clay ratio of 81:11:8 % in the top soil layer and with high total organic carbon (TOC) and total nitrogen (TN) contents (Balogh et al. 2015).

2.2 Soil and mycorrhizal sampling and analyses

At the beginning of the experiment soil samples were taken from the root-excluded and root- and mycorrhiza excluded soil cores in 2 replicates from each at a depth of 0-10 cm, 10-30 cm, 30-50 cm and 50-80 cm.

To avoid soil disturbance soil samples for microbial analyses were taken at the end of the experimental period (May 2014) in two replicates from each soil core (from both the verge and the middle of the tubes) from five depths (0–10; 10–20; 20–30; 30–40; 40–50 cm). Fluorescein diacetate (FDA) hydrolysis assay was used to estimate the total microbial activity in soil samples expressed in microgram fluorescein released g^-1 dry soil (Adam and Duncan 2001).

The length of arbuscular mycorrhizal (AM) fungal hyphae separated by wet-sieving and centrifugation (Bååth and Soderstrom 1979) were measured after being stained with agar solution (0.75%) containing trypan blue (0.05%) and were then dried at 70 °C for 24 h. Intersection method (Tennant 1975) was used to determine the hyphal length under a binocular microscope.

2.3 Experimental design

In September 2010, prior to the beginning of the experiment, ten soil cores (with the parameters of 800 mm in depth, 160 mm in diameter, except for one with an inner diameter of 500 mm) were placed into the soil after the removal of roots by sieving. Four soil layers were separated: 0-10 cm, 10-30 cm, 30-50 cm and 50-80 cm and the root-free soil was then re-packed layer by layer into vertically placed PVC tubes. The walls of the five deep soil collars were partially removed and the resulting windows were covered with inox mesh membranes (40 µm pore size) to exclude root in-growth, while allowing the penetration of mycorrhizal hyphae (R_het+myc). Five other tubes were left intact with no window cuts, hence the penetration of both roots and mycorrhizal hyphae was excluded in these repetitions (R_het). Soil tubes were placed at a distance of 50 cm from one another and 6 m away to the south from the eddy covariance tower . Additional control plots (R_s) with undisturbed soil and vegetation were also selected in the vicinity of the tubes (within a distance of 2 m).

Since regular regrowth removal was applied (seedlings were removed) in the tubes, grass litter in these plots originated from the surrounding vegetation, whereas grass litter in the control plots were produced in situ.

2.4 Gas exchange measurements

In the present study gas exchange was monitored by applying two different systems: eddy covariance system (EC) and automated soil respiration measuring system (ASRS) and the extent of the measured area differed by several orders of magnitude (the size of the EC flux footprint area was larger). The average vegetation composition and cover and soil characteristics in the EC footprint area (Koncz et al. 2014) served as a basis when selecting the area for the establishment of the experiment with the partitioning setup. The NEE and GPP estimates can be considered to be representative also of the small-scale ASRS measurements.

2.4.1 Eddy covariance setup and measurements

The EC system at the Bugac site was established in 2002 and since then it has been continuously measuring the CO₂ and heat fluxes (sensible and latent). The system consists of a CSAT3 sonic anemometer (Campbell Scientific, USA) and a Li-7500 (Licor Inc, USA) open-path infra-red gas analyzer (IRGA) placed at a height of 4 m (anemometer direction: N), both connected to a CR5000 data logger (Campbell Scientific, USA) via an SDM (synchronous device for measurement) interface. Additional measurements were made in our study: precipitation (ARG 100 rain gauge, Campbell, UK), air temperature and relative humidity (HMP35AC, Vaisala, Finland), global radiation (dual pyranometer, Schenk, Austria from 2002 and CMP3, Kipp&Zonen, The Netherlands from 2013), incoming and reflected photosynthetically active radiation (SKP215, Campbell, UK), volumetric soil moisture content (CS616, Campbell, UK) and soil temperature (105T, Campbell, UK). The listed measurements were performed in the method described in Refs. (Nagy et al. 2007) and (Pintér et al. 2010).

2.4.2 Automated soil respiration system (ASRS)

R_s was measured using an open dynamic automated soil respiration system consisting of an SBA-4 infrared gas analyser (PPSystems, UK), pumps, flow meters (D6F-01A1-110, Omron Co., Japan), electro-magnetic valves and ten PVC/metal soil chambers. Soil CO₂ was continuously monitored from July 2011, several months after the installation of the tubes in order to allow the mycorrhizal hyphae to penetrate the meshed tubes and to avoid artefacts from soil disturbance. The small size (10.4 cm high with a diameter of 5 cm, covering a soil surface area of 19.6 cm²) of the chambers allowed us to apply the ASRS without the need to cut the shoots or leaves of plants, thus avoiding the interruption of vegetal transport processes. A white metal cylinder with 2 mm airspace in between was applied to enclose the PVS chambers, with which application chambers could be stabilized and warming caused by direct radiation could be prevented. Four vent holes with a total area of 0.95 cm², also serving as an ingress for precipitation into the covered area, were drilled on the top of the chambers. This ASRS - suitable for continuous, long-term and unattended measurements of R_s – was used in previous experiments as well (Balogh et al. 2011, 2015).

Respiration rates of autotrophic and mycorrhizal components could not be calculated directly. Differences between the average respiration rates of specific plots were used to determine the trend in the components’ contribution to the R_s. We calculated the respiration components as follows:

Measurements using the ASRS chambers were carried out as follows: (i) 6 chambers measured R_s in the control plots (R_s, randomized positions within the area), (ii) R_{het + myc} was measured by 2 chambers in the windowed PVC tubes with inox mesh (iii) and the remaining 2 chambers measured R_het in the windowless, intact PVC tubes. To obtain sequential spatial replications for every treatment type, the positions of chambers were changed within the corresponding treatments every two weeks during the study period. Following each hour of operation, the system was kept at rest for one hour, thus data were collected in every 2 hours from each treatment. It should, however, be noted that data recorded in the first cycle (first 30 minutes of every 2 hours) was excluded from the analysis since the optimalisation of the system takes approximately 30 minutes, during which time the build-up of CO₂ concentration can be observed within the chambers (despite the vent holes). One chamber was measured for 3 minutes with the reference/analysis air being switched in every 7 seconds, which resulted in 12 measurements (36 min) / day for each chamber.

In addition, soil temperature and moisture of an R_s plot (T_Rs) and an R_het plot (T_Rhet) were measured at a depth of 5-9 cm by two sensors (5TM, Decagon Devices, USA) attached to the system.

2.5 Data processing and modelling

Data processing and statistical analysis were done in R (Team 2022). Gaussian error propagation was used to calculate propagated uncertainties for the averages and model parameters.

Diel patterns of respiration for R_s, R_het+myc, R_het, R_rhizmyc, R_myc and R_rhiz were modelled using a sine wave function (Savage et al. 2013):

where y0 represents the mean respiration rate over the time period modelled (µmol CO₂ m^-2 s^-1), a is diel amplitude (µmol CO₂ m^-2 s^-1), c corresponds to the shift of minimum and maximum diel peaks (radians), and TOD is time of day in hundreds. Using parameter c we calculated the peak timing of respiration (PTR). Differences between PTR values and peak time of GPP and T_s (obtained by the fitting of eq. 4) were used to calculate time lags between the respiration activities, GPP and T_s.

As another approach to estimate the time lags between these processes we also used GPP data and the calculated components of soil respiration to calculate Pearson correlation coefficients at different time lags using ccf function (cross-correlation) of R.

Time lag calculations based on sine wave model fitting (eq. 4) and cross-correlation were applied on the whole dataset and in a moving window approach as well. Model fitting and ccf function were used at every data point ±5 days. Only significant model fits and correlations (P<0.05 for model parameters) were used for further analysis and interpretation.

3.1 Mycorrhizal abundance

Fluorescein diacetate (FDA) analyses indicated a significantly lower amount of fluorescein released in the R_het treatment in every soil depth compared to R_het+myc and R_s as well. Overall hyphal length was found to be the greatest in the R_het+myc treatment (3.764 m g^-1 dry soil in average) and the lowest in R_s treatment (2.34 m g^-1 dry soil in average). Values of R_s and R_het+myc only differed significantly in the 10-20 cm soil depth (4.7 and 7.23 m g^-1 dry soil). R_s contained the most AMF spores (9.17 number g^-1 dry soil) on average in 0-50 cm soil depth.

Table 1: Effect of soil depth on the abundance of AMF in each treatment.

	FDA (mg kg-1 dry soil)			Total number of spores (Ts)			Hyphal length (m g-1 dry soil)
Soil depth	Rs	Rbasal+myc	Rbasal	Rs	Rbasal+myc	Rbasal	Rs	Rbasal+myc	Rbasal
0-10 cm	1.12	0.84	0.38	7.67	5.88	15.1	4.15	3.24	2.53
10-12 cm	0.89	0.82	0.34	11.33	5.13	13.5	4.698	7.23	2.65
20-30 cm	0.87	0.79	0.22	13.33	4.75	6.8	3.99	3.81	2.58
30-40 cm	0.88	0.71	0.18	6	4.75	6.2	2.52	2.52	1.78
40-50 cm	0.73	0.67	0.18	7.5	3.63	1.5	1.9	2.02	2.16
Average	0.898	0.766	0.26	9.166	4.828	8.62	3.4516	3.764	2.34

3.2 Environmental conditions, GPP, total soil respiration and component respiration during the study period

Besides the respiration activities in the different treatments, environmental conditions measured by the EC tower or the sensors attached to the ASRS during the experimental period are shown in Fig. 1. Soil temperature measured by the soil temperature sensors attached to the ASRS varied from -4 to 39.9 in the R_s (Figure 1a) and from -1.81 to 39.93 °C in the R_het+myc treatment (data not shown). SWC measured by the soil moisture sensors connected to the ASRS ranged from 3 to 49.99 % (v/v) in the R_s treatment (Figure 1a) and from 3.0 to 52.2 % (v/v) in the R_het+myc treatment (data not shown). Maximum and minimum T_s values were observed in July 2012 (39.97 °C) and February 2012 (-4 °C), while SWC was the highest in November 2013 (52.2 %) and the lowest in July 2012 (3.0 %).

During the years under investigation spring periods were characterized by a sharp increase in GPP (Figure 1b) followed by a peak in June and then a strong decline due to the lack of precipitation and the resultant lower SWC values. GPP was found to be the highest in August 2014 (38.9 µmol CO₂m^-2 s^-1). NDVI with values (Figure 1b) varying between 0.4003 and 0.7779 with a seasonal pattern similar to GPP.

Changes in soil respiration (Figure 1c) showed similarities with the trend of GPP. Soil CO₂ emission from R_s treatment was overall the largest with an average value of 4.49 ± 2.74 μmol CO₂ m⁻² s⁻¹, while average CO₂ efflux values of R_het+mycand R_het during the whole study period were 3.55 ± 2.28 μmol CO₂ m⁻² s⁻¹ and 2.84 ± 1.76 μmol CO₂ m⁻² s⁻¹, with significant differences (p < 0.001) between R_s, R_het+myc and R_het. Rainfall events after drought usually caused sudden peaks in the CO₂ efflux of R_het+mycand R_het treatments resulting in higher values compared to the measured total R_s values. Soil CO₂ emission of R_s treatment was the highest in May 2013 (17.82 μmol CO₂ m⁻² s⁻¹) and the lowest in March 2014 (0.15 μmol CO₂ m⁻² s⁻¹), while R_het+myc reached its highest value in May 2013 (21.94 μmol CO₂ m⁻² s⁻¹) and the lowest one in February 2012 (0.166 μmol CO₂ m⁻² s⁻¹). R_het peaked in July 2012 (14.72 μmol CO₂ m⁻² s⁻¹) and was at its lowest in February 2012 as well (0.17 μmol CO₂ m⁻² s⁻¹).

Annual sums of precipitation differed significantly from the ten-year average (575 mm) in 2011, 2012 and 2014 (436, 431 and 806 mm year^-1, respectively), while in 2013 (590 mm) there was no notable deviation from it. Even though in 2011 the sum of precipitation was low, no drought period could be distinguished due to the large amount of precipitation in 2010 (961 mm annual sum). This resulted in good water availability at the beginning of the vegetation period of 2011 and there was no sudden decrease in sink activities during the summer (GPP and NDVI, Fig. 1b). There was an overall sink activity (net ecosystem exchange of CO₂) in 2011 (-135 g C m^-2 year^-1), source activity in 2012 (38 g C m^-2 year^-1) and weak sink activity in 2013 and 2014 (-64 and -35 g C m^-2 year^-1, respectively).

3.3 Diurnal patterns of environmental factors and component of soil CO₂ emissions

Soil CO₂ effluxes were measured twelve times a day by the ASRS, therefore we were able to monitor the diel changes in soil CO₂ emissions alongside the diurnal pattern of environmental factors by fitting sine wave model (eq. 1). The peak of T_s (Figure 2a) occurred at about 13:55, while SWC showed very small diel changes with the highest values measured in the morning (8:30, Figure 2a). The peak of GPP (Figure 2a) was observed at 11:45, being roughly coincident with the peak of R_het and R_het+myc (Figure 2b), which occurred at 11:15 and 11:45, respectively.

Having reached their highest values at 21:00, R_rhizmyc and R_rhiz showed a significant time lag with GPP, while the peak of R_myc occurred at 6:45 in the morning (Figure 2c).

3.4 Changes of diel patterns of autotrophic and mycorrhizal respiration components during the study period

GPP and T_s are drivers of soil respiration with significant diurnal changes that could possibly influence the diel trend of soil respiration. Therefore, we calculated the differences between the peak time of these drivers and the peak times of the respiration components. Moreover, we analysed the effect of these drivers on the diel changes of the mycorrhizal fungi respiration component (R_myc) as the correlation between these drivers and the amplitude of the fitted sine wave model (a in eq. 4).

The results of the sine wave fitting showed a time lag of 1-10 hours between the peaks of T_s with R_rhiz and R_rhizmyc (with an average of 5.8 and 6.1 hours, respectively), while R_myc peaked 10-24 hours (with an average of 18.2 hours) after soil temperature reaching its highest values. Peaks in R_rhiz and R_rhizmyc appeared 1-15 hours (with an average of 8.7 and 8.8 hours, respectively) after the peak of GPP, while R_myc showed an even longer delay (~8-36 with an average of 18.2 hours) (Figure 3). The longest time lags (~7-16 hours in R_rhiz, ~7-17 hours in R_rhizmyc and ~20-36 hours in R_myc) could be observed during summer periods, when drought often occurs, and in the dormant period (during winter) at the end of 2011 and the beginning of 2012 (~7-15 hours in R_rhiz and R_rhizmyc). Fall periods were characterized by decreasing time lags in 2011 and 2012 reaching their lowest values (~1-5 hours in R_rhiz, ~0.5-5 hours in R_rhizmyc and ~8-20 hours in R_myc) around November. However, during the fall period of 2013 no significant reduction could be detected in the time lag between T_s or GPP and soil respiration (Figure 3).

35.42 %, 16.27 % and 11.45 % of R_rhiz, R_rhizmyc and R_myc values, respectively, fell in the 0-5 hour time lag segment. This means, that time lags longer than 24 hours could appear in the 0-5-hour segment when plotted in a daily timescale. This is relevant in the case of R_myc, where time lags of 24< hours could be detected. Since assimilated carbon needs to be allocated to the roots and then to the fungal partner before returning to the atmosphere through mycorrhizal respiration, R_myc cannot appear before R_rhiz and R_rhizmyc, hence time lags of both R_rhiz and R_rhizmyc must be less than that of R_myc. Considering this fact, we shifted R_myc values by 24 hours if R_myc occurred before R_rhizmyc. This resulted in no GPP – R_myc time lags falling in the 0-5 hours segment.

Time lags between GPP and R_myc soil respiration component were also calculated by a cross-correlation analysis using a ten-day moving window (Figure 4). The results acquired with this technique corresponded with the ones obtained with the sine wave model fitting, as the time lag between GPP and mycorrhizal respiration (lag at maximum correlation) varied between 5-36 hours.

Figure 5 shows the linear regression between the diel amplitude of R_myc obtained from the fitted sine wave model (eq. 4) and the 24-hour sum of GPP prior to the measurement. Significant correlations were found between the amplitude of the fitted model and T_s and SWC, but the correlation coefficients were much lower (R= 0.3, R= 0.16, respectively) than that with GPP sum (R= 0.6). Similar correlations were found between the mean R_myc (mean respiration term, y0 in eq. 4) and GPP, T_s and SWC values (R= 0.61, R= 0.074, R= 0.23, respectively). The ratio of the diel amplitude of R_myc and mean R_myc (a/y0, eq. 1) varied between 0.09 and 3.8 with an average of 0.4.

4.1 Consequences of the root exclusion technique

By choosing the root exclusion technique to separate the components of soil CO₂ efflux we needed to cope with the massive soil disturbances generated by the installation procedure. For this reason, measurements only started 10 months after the insertion of the tubes. Despite the delayed initiation, differences were found in certain parameters due to the inhibited water regime, altered temperature balance and the lack of roots in the R_het+myc and R_het treatments. R_het+myc and R_het plots were characterized by higher soil moisture and R_het treatment by higher soil temperature at a depth of 5 cm than R_s plots. These inequalities can be attributed to the exclusion of roots, since their absence in R_het+myc and R_het treatments was verified just like the higher hyphal density in R_het+myc and R_s (Papp et al. 2018). These differences could result in the overestimation of R_myc values. Furthermore, the decomposition of plant residues and SOM depended also on the presence of living roots in the soil and the priming effect of the rhizosphere (Cheng and Kuzyakov 2005; Kuzyakov 2002). Therefore, R_het was probably underestimated, especially when considering the limitation of fresh SOM supply (dead roots and litter) in these plots. With these inhibitions a constantly increasing difference could be detected in the intensity of soil respiration between the three treatments causing the separation of R_s, R_het+myc and R_het during the three years of the study period (Fig. 1). Therefore, the direct estimation of the heterotrophic and mycorrhizal respiration using R_het+myc and R_het respiration rates was not possible due to these dissimilarities between the treatments. Instead, R_rhizmyc, R_rhiz and R_myc rates were calculated and then used to describe their diel changes and the time lags with T_s and GPP.

4.2 Soil respiration and component flux responses to environmental and biotic factors

R_s values obtained from plots with undisturbed soil ranged between 0.15 and 17.81 μmol CO₂ m⁻² s⁻¹ corresponding to previously reported fluxes (Bahn et al. 2008; Heinemeyer et al. 2012a; Wang et al. 2005). The highest values in all treatments were observed during late spring and early summer (Figure 1c), when higher rates of plant activities – demonstrated by the NDVI and GPP values (Figure 1b) – generated increased soil respiration. A secondary peak could be observed in the CO₂ efflux of all three treatments during the fall period of 2012 and 2013 and these variations also showed similarity with the alteration of GPP and NDVI (Figure 1b). These seasonal peaks in other studies appeared in August in temperate grasslands (Heinemeyer et al. 2012a) from May to June in prairie grasslands (Gomez-Casanovas et al. 2012) and in June in arid perennial grasslands (Carbone et al. 2008). However, since R_het did not have any connections with living plants, inter-annual variability of CO₂ efflux of the heterotrophic component could be assumed to be driven mainly by other factors, such as microbial biomass changes, temperature and water availability (Yao et al. 2021). Nonetheless, under normal conditions components were co-existing, thus the mechanisms of rhizosphere priming effects had a significant influence on basal soil respiration as well (Cheng and Kuzyakov 2005).

It was previously found that in grasslands precipitation, and therefore soil moisture, significantly affected the C allocation to roots (Ford et al. 2012) and hence to the AMF partner. A significant decrease in total respiration was shown during periods characterized by lack of precipitation compared to the heterotrophic respiration where the decrease was smaller. The relative mycorrhizal contribution to soil respiration was most significant during periods when soil water content was the highest, yet, compared to the changes in R_rhizmyc, only a small decrease could be detected in R_myc during drought (Balogh et al. 2016). This observation allowed us to come to the conclusion that mycorrhizal CO₂ efflux was not as strongly dependent on soil moisture as root-derived respiration. The heterotrophic component was found to be the least sensitive to soil moisture in accordance with the results of Gomez-Casanovas et al. (Gomez-Casanovas et al. 2012), which brought us to the conclusion that heterotrophic soil respiration varied mainly with the soil temperature. Based on these results, we believe that the autotrophic, mycorrhizal and heterotrophic components of soil respiration might show differences in sensitivities to biotic and abiotic factors controlling soil CO₂ efflux, a conclusion which was reached in previous studies (Gomez-Casanovas et al. 2012; Taneva and Gonzalez-Meler 2011) as well.

4.3 Effects of soil temperature, soil moisture and GPP on the diurnal patterns of soil respiration components

Soil temperature and gross primary production showed significant diel changes, while the changes of soil moisture were not significant on a diurnal scale (Figure 2a). For this reason, we did not handle it as a factor responsible for the diel pattern of soil respiration. The peak of GPP occurred 2 hours before T_s reaching its highest values. Multiple studies were conducted in order to examine the time lag between soil respiration and its drivers at different time scales (Balogh et al. 2019; Jia et al. 2018; Vargas et al. 2010) and there was no consensus on which factor had greater effect on the diel changes of R_s. We found that T_s peaked at the same time as R_s (Figure 2b) but regarding the components of soil respiration, peaks in R_rhiz and R_myc occurred 7 and 17 hours later, respectively (Figure 2c). In contrast, GPP reached its highest values 2 hours before R_s and showed lags of 9 and 19 hours when compared to peaks of R_rhiz and R_myc, respectively. In a previous study time lags of < 1 h were measured between soil respiration and soil temperature at herbaceous vegetations (Vargas et al. 2010). In a recent study 1-10-hour lags were recorded but the results indicated that PAR, therefore the photosynthetic activity of the vegetation, regulated the diurnal hysteresis pattern between T_s and R_s (Chu et al. 2023), hence the larger time lags. We observed that R_het generally reached its maximum values not long before T_s did. This could be due to the decreasing amount of available water in the topsoil layer and to the litter layer outweighing the effect of increasing temperature (Moyano et al. 2013). This observation was also verified by the results of the cross-correlation analysis regarding the complete dataset.

4.4 The relationship between R_myc and GPP, T_s

We decided to compare two different data processing techniques – sine wave model and cross-correlation - in order to arrive at a valid conclusion on the time lag between the two drivers and R_myc. Since the fluctuation of soil temperature is a physical process, its impact on soil respiration was expected to appear instantaneously or at least within a very short period of time (Savage et al. 2013; Vargas et al. 2010). However, regarding the time lag between soil temperature and autotrophic soil respiration components, the effect of T_s on CO₂ emission appeared usually with a big time difference, with time lags as long as several hours between soil temperature and CO₂ efflux changes, thus T_s did not seem to be the main direct driving factor of R_rhiz and R_myc (Kuzyakov et al. 1999, 2001; Kuzyakov and Gavrichkova 2010). On the other hand, it took hours for the assimilated carbon to return to the atmosphere through the respiration flux after its biosynthesis, depending on whether it was allocated to the roots (few hours) or to the symbiotic mycorrhizal fungal partner (~ 6-36 hours), which meant that the time lag between GPP and soil respiration corresponded to the transport time. Based on this observation, we can confirm our first hypothesis that diel changes of mycorrhizal fungi respiration in dry grasslands are driven primarily by the belowground carbon allocation resulting from GPP rather than by soil temperature. We detected an average delay of 18.2 hours between the carbon sequestration and mycorrhizal respiration, hence our second hypothesis that there is a rapid time-lagged relationship between GPP and mycorrhizal fungi respiration can also be confirmed. We found that GPP regulated soil respiration not only on diurnal, but also on seasonal timescales (Gavrichkova and Kuzyakov 2016; Wingate et al. 2010) and the length of time lags varied as well depending on multiple factors (Bahn et al. 2009; Kuzyakov and Gavrichkova 2010; Rowland et al. 2015). Time lags tended to be larger during summer periods (Figure 3), when the lack of precipitation caused insufficient soil moisture and altered photosynthetic, root and microbial activities (Arredondo et al. 2018; Esch et al. 2017). Time lags became smaller during the fall period, when the activity of the vegetation increased due to precipitation. This decrease in time lags did not appear in 2013, since the fall period in this year was extremely dry, resulting in low soil moisture values. R_myc showed the largest time lags with GPP, sometimes reaching a delay of >32 hours, while time lag of R_rhiz did not surpass 17 hours. Overall, the smallest time lags were detected in R_rhiz, followed by R_rhizmyc with a slight delay and changes in CO_s efflux of R_myc appearing at the latest. This difference originates from the pathway of C appearing in the mycorrhizal component of soil respiration, since carbon imported to living roots needs to be transferred to their fungal partner before leaving the soil (Farrar and Williams 1990). In addition, extremely small time lags were detected at significantly lower temperatures, which brought us to the conclusion that during periods when the effect of soil temperature on the biological activity of plants was remarkable, T_s could have a major impact on soil CO₂ emission (Hursh et al. 2017). Based on the results, we can also confirm our third hypothesis that climatic and environmental conditions can alter the impact of belowground carbon allocation to autotrophic soil respiration components with a seasonal pattern.

The amplitude of the fitted sine wave model could reflect the size of the driver’s effect on respiration since it corresponds to the average change between the average respiration and peak respiration (Savage et al. 2013). We found higher correlations between the amplitude of R_myc and the 24-hour GPP sum prior to the respiration measurements than with T_s or SWC. Therefore, we concluded that GPP had the largest effect on R_myc determining the range of the daily respiration by the amount of the photosynthetic supply given to the mycorrhizal fungi filaments (Kuzyakov and Gavrichkova 2010). R_myc amplitude was significant compared to the mean respiration rate and the average magnitude of the diel change (2 x amplitude) was similar to the mean respiration rate.

Considering our results, we found that the main driver of mycorrhizal fungi respiration in our dry grassland study site was the belowground carbon allocation originating from gross primary production. However, depending on the environmental and climatic conditions, the impact of the different drivers (GPP, T_s, SWC) might vary over a seasonal time scale.

The results of our study suggest that the components of soil respiration are driven by different factors, depending on multiple conditions. The autotrophic respiration component depended mostly on the photosynthetic activity of plants, thus the gross primary production. Based on the time lags observed between the autotrophic respiration components and GPP, we concluded that it took ~9 hours on average for the carbon sequestrated by plants to reappear in the atmosphere through rhizospheric respiration, while 18 hours on average (ranging from 10-36 hours) as mycorrhizal fungi CO₂ efflux, with the time lags showing a seasonal pattern. When roots were included (R_rhizmyc) the average time lag with GPP was 8.7 hours (varying between 0-15 hours). Even though GPP seems to be the main driver of mycorrhizal fungi respiration, soil temperature and respiration can alter the CO₂ emission as well, depending on the climatic and environmental conditions.

AM: arbuscular mycorrhizal
AMF: arbuscular mycorrhizal fungi
ASRS: automated soil respiration system
ccf: cross-correlation function
EC: eddy-covariance
FDA: fluorescein diacetate
GPP: gross primary production
GRSP: glomalin-related soil protein
IRGA: infa-red gas analyzer
NBP: net biome production
NDVI: normalized difference vegetation index
NEE: net ecosystem exchange
PTR: peak timing of respiration
R_het: root- and AMF-excluded treatment, heterotrophic soil respiration component
R_het+myc: root-excluded treatment
R_myc: mycorrhizal mycelial respiration, mycorrhizal soil respiration component
R_rhiz: rhizobial respiration
R_rhizmyc: rhizomicorrhizal respiration
R_s: soil respiration, undisturbed treatment with all soil respiration components
SOM: soil organic matter
SWC: soil water content
TN: total nitrogen
TOC: total organic carbon
TOD: time of day
T_Rbasal: soil temperature of an R_basal plot
T_Rs: soil temperature of an R_s plot
T_s: soil temperature

Acknowledgment: TÉT (2021-1.2.4-TÉT-2021-00057)

Funding: This work was supported by 2021-1.2.4-TÉT-2021-00057. Author Giulia De Luca has received research support from the National Research, Development and Innovation Office of the Ministry of Culture and Innovation.

Competing Interests: The authors have no relevant financial or non-financial interests to disclose.

Author contributions: All authors contributed to the study conception and design. Material preparation, data collection was performed by Marianna Papp, analysis were performed by János Balogh and Giulia De Luca. The first draft of the manuscript was written by Giulia De Luca and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Data availability: The datasets generated during and/or analysed during the current study are available from the corresponding authors on reasonable request.

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published 24 Feb, 2024

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You are reading this latest preprint version

Mycorrhizal fungi respiration dynamics in relation to gross primary production in a Hungarian dry grassland

Status:

Journal Publication

Version 1

Abstract

Figures

1. Introduction

2. Materials and methods

2.1 Site description

2.2 Soil and mycorrhizal sampling and analyses

2.3 Experimental design

2.4 Gas exchange measurements

2.4.1 Eddy covariance setup and measurements

2.4.2 Automated soil respiration system (ASRS)

2.5 Data processing and modelling

3. Results

3.1 Mycorrhizal abundance

3.3 Diurnal patterns of environmental factors and component of soil CO₂ emissions

4. Discussion

4.1 Consequences of the root exclusion technique

4.2 Soil respiration and component flux responses to environmental and biotic factors

4.3 Effects of soil temperature, soil moisture and GPP on the diurnal patterns of soil respiration components

4.4 The relationship between R_myc and GPP, T_s

Conclusions

Abbreviations

Declarations

References

Status:

Journal Publication

Version 1

Mycorrhizal fungi respiration dynamics in relation to gross primary production in a Hungarian dry grassland

Status:

Journal Publication

Version 1

Abstract

Figures

1. Introduction

2. Materials and methods

2.1 Site description

2.2 Soil and mycorrhizal sampling and analyses

2.3 Experimental design

2.4 Gas exchange measurements

2.4.1 Eddy covariance setup and measurements

2.4.2 Automated soil respiration system (ASRS)

2.5 Data processing and modelling

3. Results

3.1 Mycorrhizal abundance

3.3 Diurnal patterns of environmental factors and component of soil CO2 emissions

4. Discussion

4.1 Consequences of the root exclusion technique

4.2 Soil respiration and component flux responses to environmental and biotic factors

4.3 Effects of soil temperature, soil moisture and GPP on the diurnal patterns of soil respiration components

4.4 The relationship between Rmyc and GPP, Ts

Conclusions

Abbreviations

Declarations

References

Status:

Journal Publication

Version 1

3.3 Diurnal patterns of environmental factors and component of soil CO₂ emissions

4.4 The relationship between R_myc and GPP, T_s