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).