Implementation of mycorrhizal mechanisms into soil carbon model improves the prediction of long-term processes of plant litter decomposition

10 Ecosystems dominated by plants featuring ectomycorrhizae (EM) and arbuscular mycorrhizae (AM) promote distinct soil carbon dynamics. AM and EM soil environments can thus have different impacts on litter decomposition. However, current soil carbon models treat mycorrhizal impacts on the processes of soil carbon transformation as a black box. We re-formulated the soil carbon model Yasso15, and incorporated impacts of mycorrhizal vegetation on soil carbon pools of different recalcitrance. We examined alternative conceptualizations of mycorrhizal impacts on transformations of labile and 15 stable carbon, and quantitatively assessed the performance of the selected optimal model in terms of the long-term fate of plant litter. We found that mycorrhizal impacts on pools of labile carbon in the litter are distinct from that on recalcitrant pools. Plant litter of the same chemical composition decomposes slower when exposed to EM-dominated ecosystems compared to AMdominated ones, and across time, EM-dominated ecosystems accumulate more recalcitrant residues of non-decomposed litter. 20 Overall, adding our mycorrhizal module into the Yasso model improved the accuracy of the temporal dynamics of carbon sequestration predictions. Our results suggest that mycorrhizal impacts on litter decomposition are underpinned by distinct decomposition pathways in AMand EM-dominated ecosystems. Ignoring mycorrhiza-induced mechanisms will thus lead to an overestimation of climate impacts on decomposition dynamics. Our new model provides a benchmark for mechanistic and quantitative modelling of 25 microbial impact on soil carbon. It helps to determine the relative importance of mycorrhizal associations and climate on organic matter decomposition rate and reduces the uncertainties in estimating soil carbon sequestration.


Introduction
Long-term soil carbon sequestration is to a large extent determined by complex soil-plant rhizosphere and microbial interactions (Dijkstra and Cheng, 2007;Fernandez and Kennedy, 2016;Fontaine et al., 2007;Ostle et al., 2009). These 30 interactions contribute to atmospheric CO2 balance (Ostle et al., 2009;Todd-Brown et al., 2012) and are increasingly recognized as processes that counteract climate change (Terrer et al., 2016). Plant associations with fungi, so-called mycorrhizas, is the most widespread symbiosis on Earth, featured by the majority of vascular plants including trees, shrubs and herbs (Brundrett and Tedersoo, 2018). Mycorrhizae are hypothesized to play especially important roles in soil carbon sequestration, yet the actual mechanisms of mycorrhizal impacts on soil carbon dynamics are poorly understood.

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Mycorrhizal fungi themselves are not capable of meaningfully obtaining carbon from decomposing plant litter (Bödeker et al., 2016;Lindahl and Tunlid, 2015). Instead, they receive carbon from their symbiotic host plants. However, the relation between https://doi.org/10.5194/bg-2021-275 Preprint. Discussion started: 22 October 2021 c Author(s) 2021. CC BY 4.0 License. mycorrhizal fungi and soil C dynamics is enabled through three potential pathways that likely complement each other (Frey, 2019): (i) provisioning of substrate for decomposition (Leake et al., 2004;Soudzilovskaia et al., 2015), (ii) mediating plant litter quality and amounts (Averill et al., 2019;Cornelissen et al., 2001;Phillips et al., 2013), and (iii) controlling the 40 environment of plant litter decomposition, including mediation of the microbial community (Fernandez and Kennedy, 2016;Frey, 2019). The temporal dynamics of plant litter decomposition is underpinned by the dynamics of decomposition of distinct types of carbon-containing molecules (Berg and McClaugherty, 2008;Cornelissen et al., 2007), which could be generally grouped as labile and recalcitrant SOC components. The fate of carbon originating from components of various chemical recalcitrance will ultimately determine the decomposition dynamics (Aponte et al., 2012;Cusack et al., 2009;Kalbitz et al., 45 2003;McClaugherty et al., 1985). Among the three major pathways of mycorrhizal impacts on soil C dynamic, the pathway of mycorrhizal fungal control on decomposition environment is arguably understood the least.
To understand mycorrhizal fungal impacts on soil carbon dynamics, we need to distinguish between arbuscular mycorrhiza (AM) and ectomycorrhiza (EM) types of symbiosis. Together, these types are possessed by over 80% of plant species compromising the majority of terrestrial plant biomass (Brundrett and Tedersoo, 2018;. While 50 they are present in almost all Earth ecosystems, it has been proposed that distinct mycorrhizal types are associated with specific ecosystems and soil attributes (Craig et al., 2018;Read and Perez-Moreno, 2003;Steidinger et al., 2019). Moreover, distinct mycorrhizal guilds differ in the pathways of nutrient acquisition from decomposing plant litter. AM fungi (AMF) have limited or no ability to depolymerize organic macromolecules. They do not possess enzymes enabling nitrogen extraction and uptake from soil organic matter (Orwin et al., 2011;Treseder et al., 2016;Treseder and Allen, 2002), but primarily acquire inorganic 55 nutrients mobilized by saprotrophic fungi and bacteria. Accordingly, plant litter subjected to AM fungi-dominated decomposition environment is likely to undergo a more balanced decomposition process with both labile and recalcitrant components being degraded by saprotrophic decomposers. On the other hand, compared to AM fungi, most EM fungi (EMF) can produce enzymes involved in decomposing organic compounds of plant litter (Fernandez and Kennedy, 2015;Lindahl and Tunlid, 2015;Zak et al., 2019), and therefore have easier access to organic nutrients, especially so to nitrogen. It has been 60 proposed that EMF increase recalcitrance of decomposing litter, as their ability of nitrogen uptake while withholding carbon compounds from breaking down increases carbon-to-nitrogen ratios in the decomposing plant litter (Nicolá s et al., 2019;Read and Perez-Moreno, 2003). This process of gradually increasing recalcitrance of plant litter subjected to EM-dominated decomposition environment is further magnified by the suppression of saprotrophic decomposer activities, the effect known as the Gadgil effect (Fernandez and Kennedy, 2015;Gadgil and Gadgil, 1971;Smith and Wan, 2019). Yet the magnitude of 65 the impacts induced by the differential roles of mycorrhizal types on the recalcitrance and dynamics of decomposing plant litter is understood very poorly, especially so in quantitative terms.
It is crucial to improve the current understanding of the role of mycorrhizas in SOM dynamics (Brzostek et al., 2014;Liang et al., 2017), especially since it is expected to affect modelling of vegetation change impacts on soil-atmospheric carbon exchange Steidinger et al., 2019;Terrer et al., 2019). However, traditional field experiments are typically 70 too short to assess the full complexity of the mechanisms underpinning the potential difference of AM and EM impacts on the plant litter decomposition processes over time. Besides, traditional field experiments have limitations in explicitly distinguishing the individual mechanisms of the mycorrhizal impacts of the decomposition process, and are hardly able to assess the impacts of mycorrhizas on the decomposition of distinct chemical fractions of litter. An alternative tool to progress in our understanding of mycorrhizal impacts on plant litter decomposition, is testing different formulations of mycorrhizal 75 impacts in process-based models of litter decomposition, and examining how well the models fit the observations. Current deterministic models of soil C decomposition (e.g. CENTURY, DAYCENT, DAISY, DNDC, NCSOIL, RothC and Struc-C etc.) do not explicitly account for mycorrhizas as a driver of plant litter decomposition processes. Instead, climate and litter quality, the well-acknowledged regulators of SOC and litter decomposition (Cornwell et al., 2008;Coûteaux et al., 1998;https://doi.org/10.5194/bg-2021-275 Preprint. Discussion started: 22 October 2021 c Author(s) 2021. CC BY 4.0 License. Cusack et al., 2009;Parton et al., 2007;Zhang et al., 2008) are being modelled as primary drivers of all aspects of SOC 80 dynamics. A body of recent studies have questioned the recognition of climate and litter quality as the only dominant regulators in SOC and litter decomposition (Bradford et al., 2016;Garcí a-Palacios et al., 2013;Wall et al., 2008), and plead for explicit inclusion of microbial and especially so mycorrhizal impacts (Johnson et al., 2006;Shi et al., 2016) on soil C dynamics into biogeochemical models (Clemmensen et al., 2013;Craig et al., 2018;Todd-Brown et al., 2012;Wieder et al., 2013). However, so far, models assessing the role of mycorrhizas in SOM dynamics (e.g. Liang et al., 2017;Orwin et al., 2011;Shi et al., 2016) 85 do not compare the relative impacts of mycorrhiza vs. climate on SOM decomposition processes.
In this study, we aim to develop a framework allowing incorporation of mycorrhizal impacts on the decomposition of plant litter into a generic soil C model, specifically addressing one of the most poorly understood mechanisms of mycorrhizal impact on plant litter decompositionthe impact through controlling decomposition environment, separately from climate and other factors. Hereto we focus on answering the following four questions:

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-What is the best conceptualization, and accordingly the best representation in a soil C dynamics model, of mycorrhizal impacts on decomposition of plant litter labile and recalcitrant carbon compounds? -To what extent does modelling mycorrhiza-associated mechanisms in mediating litter decomposition process improve model performance, in terms of model errors and temporal dynamics?
-What is the sensitivity of model prediction to the uncertainty of parameters and input describing the mechanics of 95 decomposition as affected by mycorrhiza vs climate and other factors? -How are plant litter decomposition patterns affected both in terms of total C loss and loss of C from compounds of distinct recalcitrance by AMF-and EMF-dominated decomposition environments?

Methods
Among available models of plant litter decomposition, the Yasso model (Tuomi et al., 2011a) provides an ideal framework for 100 a mechanistic integration of mycorrhizal impacts into the modelling of plant litter decomposition processes. Yasso is among the models that underpin IPCC predictions of impacts of environmental change scenarios on global C cycles (IPCC, 2006;IPCC, 2019), which has been widely applied in global process estimations (Steidinger et al., 2019;Tuomi et al., 2009). In the Yasso model, the plant litter decomposition process is presented as a classification of the organic matter into five compartments, characterized on the measurable basis of a chemical solubility of organic matter : compounds soluble in 105 water (W), carbon compounds hydrolysable in acid (denoted with A), components soluble in a non-polar solvent, e.g. ethanol or dichloromethane (E), compounds neither soluble nor hydrolysable (N), and humus (H) (Berg and Agren, 1984;. The W, A and E pools together form the group of labile C fractions of soil organic matter, N a recalcitrant but yet not a mineral bound C fraction, and the H pool represents a fraction of very stable soil C that remains in the soil for decades or centuries. 110 Figure 1 presents the schematic representation of the Yasso model, with carbon flows quantified from results of the original Yasso model formulation (Tuomi et al., 2011a;Viskari et al., 2020). H pool-related flows are not specified in this figure, because humus can only be produced in deeper soil accessible to mineral compounds, thus is not considered in this study of 10-years litter decomposition simulations. This model presents the litter decomposition process as a system of linear differential equations, and the total amount of carbon released from each pool is the result of flux transferred between pools The conceptualization of litter decomposition, as the process of C conversion into compartments representing measurable C fractions, makes Yasso a particularly suitable model for the mechanistic modelling of plant litter decomposition process, allowing new (in our case, mycorrhizal) pathways to be incorporated in a truly mechanistic way. 120 Fig.1 Conceptual diagram of decomposition and mass flows between five carbon pools in Yasso. Conceptual diagram of carbon pools and fluxes in original Yasso model (Tuomi et al., 2011). The fate of organic matter entering soil as plant litter material is represented as a series of carbon fluxes between carbon pools characterized by distinct decomposability (i.e chemical solubility) levels. Values in arrows show the amount of C transformed between pools and leaving the pools according to the original Yasso formulation and parameterizations (Tuomi, 125 et al., 2011;Viskari et al., 2020).

Implementation of mycorrhizal impacts on decomposition in Yasso: general principles and data
We modified the Yasso model by adding mycorrhiza as a factor controlling plant litter decomposition processes. Our model focuses on explaining the fate of aboveground plant litter that is decomposed at the topsoil layer before entering into deeper mineral soil or subsoil. During this stage, decomposers pre-process plant litter, liberating carbon compounds whichin a later 130 stage-contribute to the accumulation of mineral associated organic matter MAOM and particulate organic matter (POM) through different pathways in deeper soils (Bradford et al., 2016;Cotrufo et al., 2013Cotrufo et al., , 2015Cotrufo et al., , 2019Sokol et al., 2019).
We conceptualized the process of soil organic matter decomposition as being controlled by the factors currently accounted by Yasso (decomposition environment of temperature and moisture, and litter chemical composition), and additionally as being dependent on the mycorrhizal environment. We modelled impacts of the mycorrhizal environment on plant litter 135 decomposition, as the sum of impacts caused by the predominance of AM and EM fungal types. As there is no data currently available about the global distribution of mycorrhizal fungal biomass, we approximated the AM and EM fungal biomass to be proportional to the amount of C obtained from each type of mycorrhizal vegetation through photosynthesis. Thus AM and EM fungal biomass were estimated as products of proportions of AM and EM plant biomass in vegetation, and vegetation Gross Primary Production (GPP, using MODIS product-MOD17 data) (Running et al., 2004;Zhao et al., 2005). Further, each of the 140 AM and EM fungal impacts depends on the fungal-type-specific ability to affect the litter decomposition process.
We parameterized our new model using litter decomposition databases (Appendix B) used in Yasso modelling (Tuomi et al., 2009(Tuomi et al., , 2011b(Tuomi et al., , 2011a: CIDET with the measurements from Canada (Trofymow, 1998), LIDET with data from the USA and Central America (Gholz et al., 2000) and Eurodeco (ED) with data gathered from several European research projects (Berg et al., 1991). For each site in these datasets, climate and chemical composition data were supplemented with information on the 145 fractions of AM and EM vegetation within total plant biomass, which was extracted from the global mycorrhizal distribution

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We accounted for mycorrhizal impacts in the original Yasso litter decomposition model. Figure The Mi term is described by Eq. (2): where miAM and miEM are the impacts of AM and EM mycorrhizas on C loss from pool i; λAM and λEM are the fractions of AM and EM vegetation within the total vegetation biomass; Gpp is the gross primary production of mycorrhizal vegetation. Myco-Yasso.v4a model where mycorrhiza affects only carbon loss from the recalcitrant soil C pool (N) (Fig.3d). We used a Bayesian framework and a Differential Evolution Markov Chain with snooker updater (DEzs, Braak and Vrugt, 2008) algorithm-Markov Chain Monte Carlo (MCMC) (Haario et al., 2001) for calibrating all the relevant parameters 185 following the original Yasso framework (Tuomi et al., 2011a;Viskari et al., 2020). Essential parameters from the original Yasso and newly derived mycorrhizal dependencies with corresponding symbols and units are explained in Table 1. We allowed miAM and miEM to vary from negative to positive values. The only control on priors of miAM and miEM is limiting Mi > -1 in Eq.
(1) to make the algorithm meaningful. The other parameter priors were adopted according to previous Yasso research (Tuomi et al., 2009). We performed cross-validation for each model, using 80% of data randomly drawn from the dataset for 190 calibration and the remaining 20% of the data used for validation. After parameterization, all model versions were examined for Pearson's r and RMSE values of the correlation between the predicted and observed data with both the validation dataset and the full dataset. To account for the fact that the data in the different datasets varied in measurement uncertainty and the number of observations, we opted to compare the performance of models separately for each dataset. We use root mean square error (RMSE), Akaike information criterion (AIC) and Bayesian information criterion (BIC) as the criteria for comparing 195 relative quality of models and thereby selecting the optimal model. The conceptualization with the lowest RMSE, AIC and BIC was selected as the optimal model with best performance.

Model residuals and uncertainty analysis
We examined the residuals (differences between measurements and model predicted litter decomposition) as a function of AM and EM fractions in the biomass of mycorrhizal vegetation. Then, the uncertainty of the selected Myco-Yasso model was 205 assessed in two aspects: (a) Variability in estimating total C mass loss through litter decomposition. The variability in the percentage of C mass remaining after 10 years of litter decomposition, as revealed by the original Yasso model and the selected Myco-Yasso model was examined by conducting Monte Carlo simulations for a hypothetic site. In line with previous sensitivity tests of Yasso , we chose the following input data to represent the conditions of decomposition: mean annual temperature 210 5.2°C, annual precipitation 840mm. For the Myco-Yasso model, the mycorrhizal impact in Eq.
(2) was quantified by assuming an AM mycorrhizal plant biomass proportion of 38%, EM mycorrhizal plant biomass proportion of 36% and a GPP of 1516g· m -2 · a -1 . We used the following values for the chemical composition of the litter: W fraction-20.6%, A fraction-43.0%, E fraction-8.7% and N fraction-27.7%. We ran 1000 simulations using parameter values randomly selected from even distributions of the input parameters within their uncertainty ranges. Additionally, we conducted the same simulations to test model consistency using different initial inputs with chemical fractions 230 of typical root and leaf litter (Appendix D).   Table C1.

Model performance across the range of mycorrhizal plant biomass fractions in vegetation
The standardized residuals for the litter decomposition measurements (% of C decomposed from initial plant litter) as a function of AM and EM fractions in the biomass of mycorrhizal vegetation are shown in Fig.4. Within the 95% probability

Variability in litter decomposition estimations
The 1000

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The sensitivity of the Myco-Yasso model to the individual litter decomposition parameters is shown in Fig.6. Overall, the model sensitivity to each subset of parameters decreased (Fig.C3). Model sensitivity to input values is shown in Fig.7. The magnitude of sensitivity of plant litter decomposition to mycorrhizal impact is comparable to the sensitivity of climate (Fig.6,   Fig.7 and Fig.C3).
The Myco-Yasso model showed the highest sensitivity to the impact of arbuscular mycorrhizal vegetation of the N pool 275 (mN_AM) out of the four added mycorrhizal impact parameters (Fig.6). This implies that AM environment has a much stronger stimulating effect on the decomposition of the recalcitrant pool compared to EM environment. In contrast, the decomposition from the labile pools was only a bit more stimulated by EM environment than by AM environment. Concerning the decomposition rate parameters, the overall carbon loss in the Myco-Yasso model has a considerably lower sensitivity to the total decomposition rate of the N pool (αN), and a slightly increased sensitivity to the decomposition rate of the A pool (αA) 280 compared to Yasso15. However, the total impact of all α terms together to the sensitivity of carbon loss prediction is generally similar in Myco-Yasso compared to Yasso15 (Fig.C3). shows a slight decrease in sensitivity to climate variables compared to Yasso15, confirming our supposition that potential mycorrhizal impacts were partly accounted for by climate variables in the original Yasso15.

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Assessments of the dynamics of total litter mass decomposition under the dominance of AM and EM vegetation with Myco-Yasso ( Fig.9a) revealed that, at the 10 th year of decomposition, plant litter (with equal initial chemical composition) will have ca.15% less carbon remaining if decomposed in an AM-dominated environment compared to an EM-dominated environment.
During the 1 st decomposition year, litter subjected to AM or EM decomposition environments decomposes with a similar rate, while at the later stages (after 1 year), litter subjected to an AM environment decomposes faster. The difference in the total 320 mass remaining in an AM vs EM dominant environment increases during the decomposition period from 2-10 years.
Examining the dynamics of carbon loss from distinct individual decomposition compartments (Fig.9b-e) shows that labile carbon components of plant litter (WAE) decompose with a similar rate in AM and EM environments. Recalcitrant carbon compounds of litter (N compounds) tend to accumulate during the first two years. After that, C loss starts to take place in an EM-dominant environment promoting the accumulation of N components compare to an AM-dominant environment.

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Comparison among distinct litter types reveals that this pattern is not affected by initial litter quality ( Fig.D1 and Fig.D2).

Discussion
Mycorrhizal vegetation types are widely recognized to have a strong impact on plant litter decomposition processes and soil carbon pools dynamics. Yet, the mechanisms of mycorrhizal impacts on the soil C cycle are not well-understood, and available data of the relationship between soil C pools and dominance of distinct mycorrhizal types of vegetation are often 335 contrasting each other both at the local (Craig et al., 2018;Phillips et al., 2013) and global scale (Sousssdzilovskaia et al., 2019;Steidinger et al., 2019). The matter is additionally complicated by the fact that mycorrhizas affect C cycles via three mechanistically distinct pathways of (1) provisioning dead mycelium as the substrate for decomposition, (2) mediating plant litter quality and amounts, and (3) controlling the environment of plant litter decomposition. Earlier works did not explicitly differentiate between these pathways (Johnson et al., 2006) or focused mainly on the second pathway (Brzostek et al., 2014).

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Our study is the first attempt to mechanistically incorporate the impacts of different types of mycorrhizal environment, i.e. the third pathway, into a plant litter decomposition model. Herewith, we explicitly focus on impacts of the mycorrhizal environment on the plant litter decomposition process in topsoil profiles, where plant litter is transformed into soil organic matter and carbon compounds are pre-processed for further potential incorporation into particulate organic matter or minerally-associated (i.e. stable) organic matter. We assessed a full range of concepts representing mycorrhizal impacts on 345 labile and stable components of decomposing litter across a wide range of eco-environmental conditions varying in plant species, litter types and climate variables (Table B1)

Improved representation of temporal dynamics of litter C
The temporal dynamics of organic matter decomposition is among the worst understood aspects of soil C cycling. SOM decomposition encompasses changes in both the composition of soil C components as well as in their breakdown (Garcí a- Palacios et al., 2016). This duality, in combination with the long term nature of the processes involved, makes experimental 355 assessments of temporal dynamics of SOM formation to be extremely difficult, and pleas for using modelling approaches.
Incorporation of mycorrhizal impacts into Yasso improved the overall model predictions of soil C in the long-term, indicating that mycorrhizal impact is a vital factor to be accounted for in analyses of long-term litter decomposition processes, at least in the topsoil layer. The mycorrhizal impacts are likely less visible in the short-term (< 3 years), and detectable effects of the mycorrhizal environment of litter should be assessed over a longer period. This is in agreement with earlier studies (e.g. 360 Paterson et al., 2008) that have shown in short-term 13 C-labelling experiments that labile and recalcitrant plant litter fractions are utilized by distinct microbial communities, but in the short-term, these communities are not shaped by the presence and activity of mycorrhizal fungi.

Explicit separation of climate vs mycorrhizal impacts
Our model allows explicit quantification of mycorrhizal impacts on decomposition and separates them from climatic factors.

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In the original Yasso model, soil C pools are controlled by litter quality and climate, with the 'climate' factor implicitly accounting for all global variation of environmental conditions. That original model had high predictive power, especially so for short-term decomposition processes, to which our re-formulation could provide only an incremental improvement.
However, the oversimplification of the role of climate without considering microbial factors hinders the ability of the models to examine future impacts of alterations in the climate on soil C dynamics (Pongratz et al., 2018). Such a lack of mechanistic 370 and quantitative representation of belowground processes is recognized to be a principal source of uncertainty in our quantifications of global terrestrial biogeochemical cycles (Nyawira et al., 2017;Pongratz et al., 2018;Todd-Brown et al., 2013;Trumbore, 2006). Hereto our study constitutes a step forward providing a benchmark in the incorporation of microbial impacts into the modelling of SOC dynamics.
Compared to the original Yasso15 model, the Myco-Yasso model has a lower sensitivity to the total variation in temperature.

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The decrease of decomposition sensitivity to temperature suggests that the impact of temperature on decomposition could have been overestimated in previous global modelling attempts that did not consider mycorrhizae as a driving factor. Undoubtedly, the temperature regime controls soil and litter respiration (Hobbie, 1996), making the sensitivity to temperature in a soil C cycle model to be an essential issue for better estimating future soil C stocks change and its feedback to climate. While modelling approaches allow distinguishing these mechanisms, separation of these two factors from global field observations 380 is extremely difficult, because of a tight correlation of mycorrhizal distributions to gradients of temperature (Barceló et al., 2019;Soudzilovskaia et al., 2015).

Mycorrhizal impact on labile litter pools is distinct from that on recalcitrant litter pools
We tested four principally distinct concepts on the impact of the mycorrhizal environment on plant litter decomposition. The selected model imposes effects of distinct magnitude and direction on both labile and recalcitrant carbon pools. This finding 385 supports the theory that the turnover of OM depends largely on its composition and recalcitrance of biopolymers (Baldrian, 2017;Berg and McClaugherty, 2008;Cornelissen et al., 2007;Gui et al., 2017) decomposition, the theory that AMF can exert an indirect influence on this process through regulating free-living groups of decomposers in the soil is well supported. AM fungi alter the physicochemical environment for the microbial community, and modify the soil bacterial communities (Gui et al., 2017;Nuccio et al., 2013;Offre et al., 2007). AMF stimulate the activity of particular bacteria (Franco-Correa et al., 2010), which are known to be capable of catalyzing the efficient degradation of labile and recalcitrant plant litter (Bayer et al., 1998;Kersters et al., 2006). Furthermore, AMF has been shown to prime the 395 decomposition of organic matter by supplying plant-derived labile C to saprotrophic fungi and bacteria (de Vries & Caruso, 2016), which result in higher microbial turnover and respiration, priming decomposition of SOC, and decreasing the soil C pool.
In contrast, efficient nutrient uptake by EM fungi promotes immobilization of soil nitrogen in complex organic molecules of high recalcitrance, and therewith promotes the formation of microbial communities, mostly saprotrophic fungi, able to 400 decompose such recalcitrant organic substrates (Fernandez and Kennedy, 2016;Langley and Hungate, 2003). While multiple studies examining the genetic potential of ectomycorrhizal fungi have shown that EM fungi are capable of producing enzymes degrading complex C and humus (Nicolá s et al., 2019), the abundance of such genes is generally low compared to saprotrophic fungal guilds.
Yet, the question in which direction EM impacts on soil C prevails in the long term has remained unanswered. Similarly, the 405 long term impacts of AM fungi on saprotrophic communities have to our knowledge been never evaluated quantitatively. Our modelling exercise provides mechanistic and quantitatively examination on the long-term consequences of the differential mechanisms of AM and EM impacts on soil C, and suggest that more C is conserved in an EM-dominant environment than an AM environment particularly due to the accumulation of recalcitrant carbon compounds (independent of the associated litter quality). More intriguingly, we show that the long-term impacts of both types of mycorrhizas on labile carbon components are 410 similar.

Future improvements of mycorrhizal impacts of SOC modelling
Our model greatly improves the accuracy of SOM dynamics over time even though we assessed the litter decomposition process in topsoil profiles across 10-year only. Formation of the most recalcitrant compartment of soil, defined by Yasso model as "humus" (Tuomi et al., 2011a) is not examined in our study, because we assumed that a 10-year period of litter 415 decomposition for which we had detailed data for model calibration, was not long enough for forming humus. Future work should aim at including mycorrhizal impacts on the humus formation process, linking short-and medium-term decomposition processes to the ultra-long SOM dynamics.
Furthermore, our current work examines the dynamics of SOM in terms of labile and stable compounds, yet not addressing the fate of stable, minerally-associated soil C, the ultimate pool of soil-sequestered C. During the last decade, the question of 420 whether the minerally-associated soil C originates from labile C components, possibly undergoing microbial transformation (Cotrufo et al., 2015(Cotrufo et al., , 2019Mambelli et al., 2011) or occurs through direct sorption of poorly decomposed plant compounds, was intensively debated (Bradford et al., 2016;Sokol et al., 2019). Recent research (Sokol et al., 2019)  the pool of minerally-associated C needs to be further evaluated. Such evaluation should additionally consider the processes omitted in this study such as fluxes of labile C from the root and fungal exudates and C fluxes originating from the decomposition of dead mycelium of mycorrhizal fungi (Baskaran et al., 2017;See et al., 2021). has its specific decomposition rate (independent from litter type and the initial amount of the composition) 450 Tuomi et al., 2011a). It presents the litter decomposition process as a system of linear differential equations (A1):

Conclusions
where, x(t) is a vector describing the mass of individual carbon pools as a function of time (t); x(0) = x0 represent the initial amount of each carbon fraction; b(t) is the litter input; A(C) is a matrix describing the total decomposition as a function of climatic conditions (C), where the diagonal values represent the fraction of C being removed from the pool and the non-455 diagonal terms specify the amount of C transferred to other pools (Viskari et al., 2020).
The total amount of carbon released from individual WAENH pools is the result of two fluxes: (1) carbon transformation flow from and to other pools, (2) the carbon that is not transferred to other pools but is released to the atmosphere as carbon dioxide.
The mass fluxes between the different pools and outside the system are accordingly determined by two parameter sets: pij represent the mass transportation between pools; αi represent the total decomposition rate of each pool, i.e. the C mass leaving 460 the pool (the sum of C transfer to other pools and C released into the atmosphere). The total mass flux between two pools is thus a product of these two parameters, e.g. the mass flux from pool A to pool W is αA*pAW.
The total decomposition represented by matrix A(C) within the whole system can be represented as a mathematical equation In the matrix K(C), each element ( ) describing decomposition of WAENH is a function of temperature(T), and the annual precipitation(P) modelled in (A3): where, βi1 and βi2 are parameters describing the dependency of heterotrophic respiration on temperature, assessed through a 470 Gaussian model (Tuomi et al., 2009); γi is a parameter describing the dependency of heterotrophic respiration of precipitation, assessed through an exponential function (Tuomi et al., 2009). Systematic error in the litter decomposition resulting from litter leaching out of the litter bags was corrected by leaching parameters.

Appendix B: Methodological details of calibration and databases of litter decomposition data used
There are three main litter decomposition databases used in original Yasso modelling (Tuomi et al., 2011) and our new model 475 parameterization: CIDET dataset with the measurements from Canada (Trofymow, 1998), LIDET dataset with data from the USA and Central America (Gholz et al., 2000) and Eurodeco (ED) dataset with data gathered from several European research projects (Berg et al., 1991). The distributions of these experimental sites are shown in Fig.B1. Details of these datasets used to parametrize our new model are shown in Table B1.

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The original Yasso model also uses a dataset with information of SOC accumulation along thousands of years at sites in Finland  and a large global soil C stock measurements dataset (Zinke et al., 1986) to infer the dynamics of the most stable carbon -Humus pool in soil (see Fig.1

Code availability
The initial Yasso15 model is available from the developers repository at https://github.com/YASSOmodel/YASSO15, the code used for calibrating Yasso are available at https://doi.org/10.5194/gmd-2021-273 (Viskari et al., 2021). The extended code for calibrating the models and produce the results, input data and scripts used in this paper is archived on Zenodo at 535 10.5281/zenodo.5579682 (Huang et al., 2021).

Data availability
Original litter decomposition data used for this work were provided by the data owners of the different long-term experiments. Please contact them to get access to the data (see Table B1).