Relating soil and extracellular enzyme stoichiometries throughout the top meter of soil
When considering soils from all depth increments, only soilC:N and EEC:N were correlated (C:N: p = 0.013, C:P: p = 0.292, N:P: p = 0.276), but this negative correlation between soilC:N and EEC:Nwas relatively weak (marginal R2 = 0.038; Fig. S7). However, using the surface soil-only dataset, all soil and EE stoichiometries were negatively correlated (C:N: p = 0.003, marginal R2 = 0.268; C:P: p = 0.002, marginal R2 = 0.193; N:P: p = 0.004, marginal R2 = 0.260; Figure 3). In the subsoil, these correlations decoupled such that none of the stoichiometries were significantly correlated (C:N: p = 0.288, C:P: p = 0.358, N:P: p = 0.282; Fig. 3).
DISCUSSION
Our continental-scale sampling efforts show that microbial activity at depth is non-negligible, and the relative proportion of EE activity (g-1 soil) at depth depends predominately on soil development (i.e., soil order; Fig. 2B). This is likely due to changes in the vertical distribution of substrate (organic C) and MB among these soil orders (Batjes 1996; Fig. 2B), which strongly correlate with EE activity (Sinsabaugh et al. 2008; Table S2). Hence, we show that SOC and MB are the strongest controls of EE activities throughout the soil profile.
Increases in the MB-normalized EE activities at depth suggest an accumulation of stabilized EEs. While MB-normalized EE activity is often related to the relative activity of the microbial community or differences in metabolic strategies among microbial taxa (Boerneret al. 2005), we alternatively hypothesize that the increase in MB-normalized EE activity is due to EE stabilization, namely the sorption of the EEs onto clay or organic matter particles that impedes EE degradation (Sarkar et al. 1989, Burns et al. 2013). Because EE activities are often measured in a salt-buffered soil slurry that disrupts the stabilization of EEs (as is the case in our study), EE activity assays generally measure both active and stabilized EEs (Burnset al. 2013). We hypothesize that higher subsoil MB-normalized EE activities with depth is primarily a product of EE stabilization instead of differences in the metabolic qualities of the microbial community for three reasons. First of all, MB-normalized respiration (i.e., microbial metabolic quotient), which is another measure of the relative activity of the microbial community, generally does not increase with depth (Dominy & Haynes 2002; Fang & Moncrieff 2005; but see Lavahun et al. 1996). Secondly, the relative abundance of fungi, which produce more EEs per unit MB than bacteria (Romaní et al. 2006), decreased with depth. Finally, the decoupling of soil stoichiometry and EE stoichiometry at depth suggests that EE activities are not responsive to altered nutrient availabilities. Taken together, these results suggest that the physiochemical process of EE stabilization, a largely abiotic process, is the major control of EE activity in the subsoil.
Extracellular enzyme stabilization as a major mechanism in the subsoil is corroborated by our finding that the influence of clay concentration on MB-normalized EE activity is higher in the subsoil than the surface soil (Table 2). Furthermore, we may have underestimated EE activity in high clay soils because clay can increase the pH optima of EEs 1-2 pH units (McLaren & Estermann 1957; Ramírez-Martínez & McLaren 1966). Whereas many EEs have native pH optima between 4-6.5 (Parham & Deng 2000; Niemi & Vepsäläinen 2005; Turner 2010; Min et al. 2014), an increase of two pH units would be significantly higher than the pH of our assay buffer (pH = 5.5). Therefore, we conclude that EE stabilization is a major process when microbial activity is relatively low and clay concentrations are relatively high, which is often the case in subsurface soil layers.
Extracellular enzyme stabilization could be partially responsible for the muted treatment effects on subsoil EE activity commonly found throughout the literature (e.g., Kramer et al. 2013; Jinget al. 2017; Yao et al. 2019). When the stabilized EE pool is significantly greater than the active EE pool, the ability to detect changes in the active pool is decreased. For example, if we assume that there is negligible EE stabilization in the surface soil and that the actualized MB-normalized EE activity in situ is constant throughout the soil profile, our results show that across our study sites at least 29-71% of the assayed MB-normalized EE activity at depth can be attributed to stabilized EEs, depending on the EE (Equation 1).
Z = ((Y – X)/Y) *100
X = Average MB-normalized EE activity in surface soil
Y = Average MB-normalized EE activity in subsoil
Z = Percent MB-normalized EE activity in subsoil attributed to stabilized EEs
This calculation likely represents the lower bound of the estimated stabilized MB-normalized EE activity because any stabilization in the surface soil (X), would increase Z, and the relative proportion of fungal biomass, which release comparatively more EEs than bacteria per unit MB (Romaní et al. 2006), decreased with depth. Nevertheless, this implies that if the stabilized EE pool is resistant to treatment effects in experiments (e.g., Kramer et al. 2013; Jing et al. 2017; Yao et al. 2019), the ability to detect significant changes in microbial activity at depth using EE assays at depth is also reduced by at least 29-71%. In instances where the magnitude of the treatment effect is modest, it is unlikely that a significant change in subsoil EE activity will be detected. However, this should not necessarily be interpreted as a lack of microbial response, and caution should be exercised in interpreting the effect of a surface manipulation or treatment on subsoil EE activity.
The discrepancy between soil and EE stoichiometry at depth may also be caused by the increased discontinuity of substrates in the subsoil and the reduced ability of the microbial community to respond to changes in resource availability (Allison et al. 2007). This would prevent subsoil microorganisms altering their EE stoichiometry to different nutrient conditions. Resource availability is typically higher in surface soils than in subsoils (Salomé et al. 2010). Recent work in soil enzymography show that C-degrading EE activities are enriched only 0.5-2 mm from C-rich rhizodeposits (Ma et al. 2018). The EE assays that we and most others employed disrupt the spatial arrangement of EEs and substrates such that our results express bulk EE activities and bulk resource concentrations, which may not be representative of smaller, more localized heterogeneity in resources.
In contrast to earlier work (Sinsabaugh et al. 2008), we generally did not find pH to be well-correlated with EE activity (on a soil mass-, MB- , or SOC-basis) at any depth (Table 2, S2-S4). We attribute this discrepancy to differences in methodologies that measure different aspects of the EE pool. Fluorometric measurements in the commonly used microplate EE activity assay are impacted by slurry pH (Burns et al. 2013). Therefore, slurries are generally buffered either by a consistent pH (as in our case) or by a pH characteristic of the native soil (as in Sinsabaugh et al. 2008). When buffered by a constant pH (near pH optima), EE activities better reflect the size of the EE pool, whereas EE activity assays buffered by a pH corresponding to the native soil better reflect in situ rates of EE activity (Burns et al. 2013). Sinsabaugh et al. (2008) buffered soil slurries from alkaline soils at pH 8 and found that BG, CB, NAG, and AP activity kg-1 SOC decreased with soil pH, while LAP kg-1 C increased. However, across multiple biomes, BG, CB, and NAG have acidic pH optima (4-6.5; Parham & Deng 2000; Niemi & Vepsäläinen 2005; Turner 2010; Min et al. 2014). Thus, the decrease in BG, CB, and NAG activity kg-1 C with greater soil pH in Sinsabaugh et al. (2008) could be caused by a buffer pH for alkaline soils that is higher than the pH optima of the EEs. Because this buffer pH was chosen to reflect in situconditions (Burns et al. 2013), we conclude that across ecosystems, in situ rates of BG, CB, and NAG activity are likely lower in alkaline soils because of discrepancies between EE pH optima and in situ soil pH. Soil pH can affect EE concentrations through its impact on the microbial communities (e.g., Acosta-Martínez & Tabatabai 2000; Ekenler & Tabatabai 2003; Stark et al. 2014); however, we find little evidence for an effect of soil pH on the size of the EE pool across the wide range of soil types studied here.
Taken together, our results suggest that the relative importance of the different controls on EE activities change with depth. We summarize this in a conceptual model, where the active EE pool is controlled by microbial EE production (proximately influenced by MB and resource demand), and the stabilized EE pool is primarily influenced by EE stabilization onto clay particles (Figure 4). Because MB and resource demand decrease with depth as C becomes more limiting and clay concentrations increase, the subsoil total EE pool is maintained because of the relatively large proportion of stabilized (sorbed on soil colloids) EEs that decay slower than unstabilized (present in the bulk soil solution) EEs. Understanding how soil texture affects EE stabilization and decay dynamics is a critical knowledge gap in enzyme-explicit microbial models (e.g., Schimel & Weintraub 2003; Manzoni et al. 2016; Abramoff et al. 2017; Sulman et al. 2018). For instance, Schimel et al. (2017) estimated EE decay dynamics in multiple soils by measuring EE activities for weeks after sterilization. While these soils varied in texture, there did not appear to be a consistent pattern between soil texture and EE decay, possibly because of changes in other edaphic factors (i.e., moisture, substrate, etc.). Future work should systematically study EE decay and its relation to multiple edaphic factors including clay concentration to test our proposed conceptual model.
Overall, our results imply that the vast majority of EE studies are missing a large portion of the total EE activity in soils, and that the unmeasured subsoil EE activity varies in its response to environmental conditions. Therefore, one cannot simply extrapolate surface soil EE values into the subsoil. As numerous other experiments have shown (Blumeet al. 2002; Taş et al. 2014; Hicks Pries et al.2017), ignoring subsoils and exclusively focusing on surface soils can limit our ability to understand whole-profile EE-dynamics and soil C storage.
ACKNOWLEDGEMENTS
We thank N. Blair, A. Bissett, T. Brewer, A.N. Campbell, G. King, M. Firestone, M. Leon, G. Logan, N. Lu, F. Meyer, S.M. Owens, A. Packman, A.F. Plante, D.D. Richter, W.L. Silver and E. Starr for their contributions to this research effort and B. Boudinot for assistance with PLFA analysis. This research was supported by the NSF EarthCube program (ICER-1541047), the Critical Zone Observatory Network (EAR-1331939), and a University of California Merced Graduate Fellowship Award derived from a match provided by the Southern Sierra Critical Zone Observatory (to NCD). Work conducted by researchers at Lawrence Livermore National Laboratory was performed under the auspices of the U.S. Department of Energy by under contract DE-AC52-07NA27344, and supported by a US Department of Energy Early Career Research Program Award to J. Pett-Ridge (SCW1478). Support for this research was also provided by National Science Foundation for RC CZO Cooperative agreement, EAR-1331872.
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TABLES
Table 1 : Characteristics of the 19 study sites across ten Critical Zone Observatories (CZOs).