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\begin{document}

\title{Continental-scale patterns of extracellular enzyme activity in the
subsoil: an overlooked reservoir of microbial activity}



\author[1]{Nicholas Dove}%
\author[2]{Keshav Arogyaswamy}%
\author[3]{Sharon Billings}%
\author[2]{Jon Bothoff}%
\author[4]{Chelsea Carey}%
\author[5]{Caitlin Cisco}%
\author[6]{Jared DeForest}%
\author[7]{Dawson Fairbanks}%
\author[8]{Noah Fierer}%
\author[7]{Rachel Gallery}%
\author[9]{Jason Kaye}%
\author[10]{Kathleen Lohse}%
\author[2]{Mia Maltz}%
\author[11]{Emilio Mayorga}%
\author[12]{Jennifer Pett-Ridge}%
\author[5]{Wendy Yang}%
\author[13]{Stephen Hart}%
\author[2]{Emma Aronson}%
\affil[1]{Oak Ridge National Laboratory}%
\affil[2]{University of California Riverside}%
\affil[3]{University of Kansas}%
\affil[4]{Point Blue Conservation Science}%
\affil[5]{University of Illinois at Urbana-Champaign}%
\affil[6]{Ohio University}%
\affil[7]{University of Arizona}%
\affil[8]{University of Colorado Boulder}%
\affil[9]{The Pennsylvania State University}%
\affil[10]{Idaho State University}%
\affil[11]{University of Washington}%
\affil[12]{Lawrence Livermore National Laboratory}%
\affil[13]{University of California Merced}%


\vspace{-1em}



  
  \date{June 15, 2024}


\begingroup
\let\center\flushleft
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\selectlanguage{english}
\begin{abstract}
Stabilization of microbial-derived products such as extracellular
enzymes (EE) has gained attention as a possibly important mechanism
leading to the persistence of soil organic carbon (SOC). While the
controls on EE activities and their stabilization in the surface soil
are reasonably well-understood, how these activities change with soil
depth and possibly diverge from those at the soil surface due to
distinct physical, chemical, and biotic conditions remains unclear. We
assessed EE activity to a depth of 1 m (10 cm increments) in 19 soil
profiles across the Critical Zone Observatory Network, which represents
a wide range of climates, soil orders, and vegetation types. Activities
of four carbon (C)-acquiring enzymes (\selectlanguage{greek}α-\selectlanguage{english}glucosidase, \selectlanguage{greek}β-\selectlanguage{english}glucosidase,
\selectlanguage{greek}β-\selectlanguage{english}xylosidase, and cellobiohydrolase), two nitrogen (N)-acquiring enzymes
(N-acetylglucosaminidase and leucine aminopeptidase), and one phosphorus
(P)-acquiring enzyme (acid phosphatase) were measured fluorometrically
along with SOC, total N, Olsen P, pH, clay concentration, and
phospholipid fatty acids, which we used to characterize the microbial
community composition and biomass (MB). For all EEs, activities per gram
soil correlated positively with MB and SOC; all of which decreased
logarithmically with depth (p \textless{} 0.05). Across all sites, over
half of the potential soil EE activities per gram soil consistently
occurred below 20 cm for all measured EEs. Activities per unit MB or SOC
were substantially higher at depth (soils below 20 cm accounted for 80\%
of whole-profile EE activity), suggesting an accumulation of stabilized
(i.e., mineral sorbed) EEs in subsoil horizons. The pronounced enzyme
stabilization in subsurface horizons was corroborated by mixed-effects
models that showed a significant, positive relationship between clay
concentration and MB-normalized EE activities in the subsoil.
Furthermore, the negative relationships between soil C, N, and P and C-,
N-, and P-acquiring EEs found in the surface soil decoupled at 20 cm,
which could have also been caused by EE stabilization. This suggesting
that EEs do not reflect soil nutrient availabilities at depth. Taken
together, our results suggest that deeper soil horizons hold a
significant reservoir of EEs, and that the controls of subsoil EEs
differ from their surface soil counterparts.%
\end{abstract}%



\sloppy


\par\null

INTRODUCTION

Globally, soils store approximately 1,500 Pg of soil organic carbon
(SOC) in the upper meter of the soil profile, with 50-67\% of SOC
occurring below 20 cm (Jobb\selectlanguage{ngerman}ágy \& Jackson 2000). The persistence of this
C pool is in part controlled by extracellular enzymes (EEs) primarily
released by soil microorganisms that decompose soil organic matter
(Burns \emph{et al.} 2013). However, even though the majority of SOC
occurs in the subsoil, most studies of soil microorganisms and the EEs
they secrete focus on the upper soil layers. While the age (and thus
persistence) of SOC increases with depth (Trumbore \emph{et al.} 1996;
Paul \emph{et al.} 1997; Rumpel \emph{et al.} 2002), recent studies have
shown that subsoil (\textgreater{}20 cm depth) C is still vulnerable to
decomposition. Indeed, subsurface microbial communities have resource
demands that rival those of surface soils when normalized to a microbial
biomass (MB) basis (Jones \emph{et al.} 2018). Understanding subsurface
processes is critical in an age of global change because vulnerability
of SOC to EE attack could be enhanced by increased temperatures or
wetting/drying cycles (Schimel \emph{et al.} 2011; Hicks Pries \emph{et
al.} 2017). This means that if subsoils are disturbed (either physically
or through altered environmental conditions), portions of the soil
organic matter pool at depth could become accessible to EEs, resulting
in the mineralization of significant quantities of C and nutrients.
Therefore, increased understanding of EE patterns at depth could help
elucidate the mechanisms of subsoil organic matter decomposition and aid
in predicting how pools of SOC and nutrients will be affected by ongoing
global change factors.

Because EEs both respond to and influence soil properties, the study of
EEs has led to greater insights into soil C persistence (Billings \&
Ballantyne 2013; Birge \emph{et al.} 2015; Dove \emph{et al.} 2019),
nitrogen (N) and phosphorus (P) mineralization (Weintraub \& Schimel
2003; Waring \emph{et al.} 2014; Chen \emph{et al.} 2018), ecosystem
development (Olander \& Vitousek 2000; Selmants \& Hart 2010;
Turner\emph{et al.} 2014), and microbial metabolism (Sinsabaugh \& Shah
2011, 2012; Sinsabaugh \emph{et al.} 2013). Given that the methods for
measuring EE activity in soils are relatively high-throughput,
inexpensive, and reproducible across laboratories (Dick \emph{et
al.}2018), it is one of the most common soil biogeochemical measurements
(`Soil extracellular enzyme activity' resulted in 2,013 records using
Clarivate Analytics Web of Science as of Jan. 28, 2020). However,
despite the widespread measurement of soil EEs, most studies have
focused on EE activities in surface horizons, with few studies exploring
EE activity patterns in soil horizons below 20 cm (but see
Taylor\emph{et al.} 2002; Kramer \emph{et al.} 2013; Stone \emph{et al.}
2014; Ta\selectlanguage{polish}ş \selectlanguage{english}\emph{et al.} 2014; Schnecker \emph{et al.} 2015; Loeppmann
\emph{et al.} 2016; Jing \emph{et al.} 2017).

Numerous soil physical and biogeochemical properties change with depth.
As organic matter (both SOC and organically bound nutrients) moves into
the subsoil, it becomes increasingly more microbially processed and
sorbed onto charged mineral surfaces (Rumpel \& K\selectlanguage{ngerman}ögel-Knabner 2010),
which concomitantly increase with depth. Soil pH may also increase with
depth in instances where the parent material is enriched in base cations
(Brubaker \emph{et al.} 1993). These gradients in soil properties result
in subsoil microbial communities that are vastly different than their
surface soil counterparts (Eilers \emph{et al.} 2012; Brewer \emph{et
al.} 2019). Soil pH (Sinsabaugh \emph{et al.} 2008; Kivlin \& Treseder
2014), substrate availability and demand (Olander \& Vitousek 2000;
Dove\emph{et al.} 2019), and microbial community composition
(Schnecker\emph{et al.} 2015) influence EE activities in surface soils.
Because these factors change along soil profiles, EE activities should
also change with soil depth. Two main generalizations have emerged from
the few studies that have investigated EE activities in subsoils: 1) EE
activities decline with depth in association with decreases in soil
organic matter concentrations and decreases in microbial biomass
(Taylor\emph{et al.} 2002; Stone \emph{et al.} 2014; Loeppmann \emph{et
al.}2016), and 2) EE activities at depth are less responsive to surface
conditions, manipulations, and management practices (Kramer \emph{et
al.} 2013; Jing \emph{et al.} 2017; Yao \emph{et al.} 2019). However,
our ability to quantify the total EE pool and elucidate the controls on
EEs in subsoils has been hindered by unstandardized ancillary
measurements, assay parameters, and depths of sampling across studies
(Nannipieri \emph{et al.} 2018).

Systematic, continental- and global-scale assessments and meta-analyses
of EEs in surface soils have begun to clarify controls and correlates of
EE activity (Sinsabaugh \emph{et al.} 2008, 2009; Xiao \emph{et
al.}2018). For instance, EE stoichiometry (the ratio of C-, N-, and
P-acquiring enzymes), which can represent the relative C, N, and P
demand (Sinsabaugh \& Shah 2012), scales at 1:1:1 (C:N:P) globally
across soil, freshwater, and saltwater ecosystems, suggesting that the
plasticity of microbial resource demand is somewhat constrained
(Sinsabaugh \emph{et al.} 2008, 2009). These large-scale assessments
also confirm that pH, substrate availability, and microbial demand
influence EE activity in surface soils (Sinsabaugh \emph{et al.} 2008,
2009; Xiao \emph{et al.} 2018). However, it is currently unknown if
these controls in surface soils extend into the subsoil. We posit that
EE activities at depth may follow different patterns than in the surface
horizons given that EEs at depth are less responsive to environmental
perturbations (Jing \emph{et al.} 2017), subsoils have greater
heterogeneity of organic substrates than at the surface (Salomé \emph{et
al.} 2010), and the microbial communities at depth are dominated by
oligotrophic microorganisms (Brewer \emph{et al.} 2019).

To quantify EE activities and elucidate their controls throughout the
soil profile, we sampled the upper meter of mineral soil at 10 cm
increments in 19 soil pits across the 10 United States National Science
Foundation-supported Critical Zone Observatories (CZOs) in the United
States of America (USA). We hypothesized that EE activities per gram (g)
soil would decline with depth due to decreased SOC and MB
concentrations; however, a significant proportion of EE activity in the
top meter of soil would occur below 20 cm depth. We also hypothesized
that the fundamental controls on EE activities would differ between
surface and subsoil horizons due to shifting biological, chemical, and
physical conditions throughout the soil profile. Specifically, as
organically bound microbial resources decrease with depth, mineral
sorption of both substrates and EEs will become a more dominant control
of potential EE activity. Our overall goal was to quantify potential EE
activity in the subsoil over a diverse set of soils, ecosystems, and
climates to elucidate how EE activity mediates subsoil C and limiting
nutrient availabilities in order to improve predictive understanding.

METHODS

\emph{Site selection and sampling}

Samples were collected from the network of 10 Critical Zone
Observatories (CZOs, http://criticalzone.org) across the USA, which
represents a wide range of hydrogeological provinces, soil orders, and
vegetation types as described in Brewer et al. (2018). Soils were
collected at peak greenness (as estimated from NASA's MODIS:
MODerate-resolution Imaging Spectroradiometer) between April 2016 and
November 2016, with the exception of the Eel River CZO samples, which
were collected in May 2017 (also at peak-greenness). At each CZO, we
excavated two separate soil profiles (``sites'') selected to represent
distinct soil types and landscape positions (Table 1). Any organic
horizon was first removed, and then mineral soils were sampled in 10-cm
increments with a sterile hand trowel dug into the face of each soil pit
to a depth of at least 100 cm or to refusal.

All soil samples were shipped overnight at 4 °C to the University of
California, Riverside for processing. A portion of each field sample was
sieved (\textless{} 2 mm), homogenized, divided into subsamples for
further analyses, and frozen (\selectlanguage{english}-20 degC). For some soils (particularly some
wet, finely textured depth intervals), sieving was impractical. These
samples were homogenized and larger root and rock fragments were removed
by hand. In addition, as samples from SHAL (70-100 cm depth; see Table 1
for site abbreviations) consisted almost entirely of medium-sized
weathered bedrock (Cr material), soil was collected by manually crushing
weathered bedrock with a ceramic mortar and pestle with this material
then passed through a 2-mm sieve.

\emph{Soil physiochemical measurements}

Soil pH, gravimetric water content, and clay concentration were measured
using modified Long-Term Ecological Research (LTER) protocols
(Robertson\emph{et al.} 1999). Briefly, soil pH was determined in a 1:1
(weight to volume) solution using 15 g of field-moist soil and 15 ml of
Milli-Q water (Millipore Sigma, Burlington, MA, USA). The solution was
measured on a Hannah Instruments (HI; Woonsocket, RI, USA) 3220 pH meter
equipped with a HI 1053B combination glass pH electrode, designed for
use with solid suspensions. For determining gravimetric water content,
approximately 7 g field-moist soil was dried at 105 oC for a minimum of
24 h. Soil texture was measured on oven-dried and sieved soil using the
hydrometer method following Gee \& Bauder (2018).

Prior to soil total organic C and N analysis, soils were freeze-dried
using a Savant Novalyphe-NL500 freezer dryer (Savant, Farmingdale, NY,
USA) and ground to a fine powder using a roller mill. If effervescence
occurred when a drop of 1 M HCl was added to a subsample of each soil
sample, then inorganic C was removed from 2 g of the soil sample by
twice-washing with 30 mL 0.1 N HCl (allowing the soil slurry to stand
for 1 h during each wash), twice-washing with 30 mL DI, and then
freeze-dried. The soil samples were analyzed for total organic C and
total N by continuous-flow, direct combustion using a Vario Micro Cube
elemental analyzer (Elementar, Hanau, Germany).

Microbially available orthophosphate, referred hereafter as Olsen P, was
estimated by extracting 1 g of soil with 200 ml of 0.5 M
NaHCO\textsubscript{3} at pH 8.5 (Olsen \emph{et al.} 1954). Briefly,
slurries were shaken for 30 min and filtered through Whatman No. 42
filters. Orthophosphate was measured colormetrically using a Lachat AE
Flow Injection Auto Analyzer (Method 12-115-01-1-Q, Lachat Instruments,
Inc., Milwaukee, WI, USA).

\emph{Phospholipid Fatty Acid Analysis}

We used phospholipid fatty acids (PLFAs) to determine differences in the
microbial biomass (MB) and the ratios of fungal to bacterial biomass.
Briefly, total lipids were extracted using 10 ml of methanol, 5 ml
chloroform, and 4 ml of a 50 mM phosphate buffer (pH = 7.4) from 5 g of
lyophilized soil (White \emph{et al.} 1979; DeForest \emph{et al.}2004).
To determine analytical recovery, phospholipid 19:0
(1,2-dinonadecanoyl-\emph{sn} -glycero-3-phosphocholine) and 21:0
(1,2-diheneicosanoyl-\emph{sn} -glycero-3-phosphocholine) standards
(Avanti Polar Lipids, Inc., Alabaster, AL, USA) were added during the
extraction phase (DeForest \emph{et al.} 2012). Polar lipids were
separated from other lipids using silicic acid solid-phase
chromatography columns (500 mg 6 ml\textsuperscript{-1}; Thermo
Scientific, Waltham, MA, USA), and the separated polar lipids were
converted to fatty acid methyl esters (FAME) through methanolysis
(Guckert \emph{et al.} 1985). The resulting FAMEs were separated using a
HP GC-FID (HP6890 series, Agilent Technologies, Inc. Santa Clara, CA,
USA) gas chromatograph, and peaks/biomarkers were identified using the
Sherlock System (v. 6.1, MIDI, Inc., Newark, DE, USA). External FAME
standards (K104 FAME mix, Grace, Deerfield, IL, USA) were used to
determine concentrations. The sum of all detected 14--19 C-length PLFAs
was used to calculate MB because longer PLFAs can be indicators of
mosses and higher plants (Zelles 1999). Ratios of fungal to bacterial
biomass (fungi:bacteria) were calculated by dividing the amount (mol) of
the fungal biomarker 18:2\selectlanguage{greek}ω\selectlanguage{english}6c by the sum of all other microbial
biomarkers (i.e., mol 18:2\selectlanguage{greek}ω\selectlanguage{english}6c /(mol MB -- mol 18:2\selectlanguage{greek}ω\selectlanguage{english}6c)).

\emph{Extracellular enzyme activity}

We measured potential extracellular enzyme activity of \selectlanguage{greek}α-\selectlanguage{english}glucosidase
(AG), \selectlanguage{greek}β-\selectlanguage{english}glucosidase (BG), cellobiohydrolase (CB), \selectlanguage{greek}β-\selectlanguage{english}xylosidase (BX),
N-acetylglucosaminidase (NAG), leucine aminopeptidase (LAP), and acid
phosphatase (AP) fluorometrically following Bell \emph{et al.} (2013).
Briefly, an 800 \selectlanguage{greek}µ\selectlanguage{english}l soil slurry consisting of 2.75 g of field-moist soil
in 91 ml of 50 mM sodium acetate buffer (pH = 5.5) was incubated with
200 \selectlanguage{greek}µ\selectlanguage{english}l of each of the 100 \selectlanguage{greek}µ\selectlanguage{english}M 4-methylumbelliferone (MUB)-linked or
7-amido-4-methylcoumarin (AMC)-linked substrates (only LAP was
AMC-linked) in 96-deep well plates. After a 3-h incubation at 20 \selectlanguage{ngerman}°C,
plates were centrifuged, and the supernatant was transferred to black,
flat-bottom 96-well plates. Fluorescence was measured on a Tecan M200
Pro (Tecan Group Ltd., Männedorf, Switzerland) using an excitation
wavelength of 365 nm and an emission wavelength of 450 nm.

The enzymes, AG, BG, BX, and CB are involved in the degradation of
organic C, and total C-acquiring enzyme activity (C\textsubscript{sum})
was operationally defined as the sum of these four enzyme activities.
The enzyme, NAG is involved in releasing N-acetylglucosamine from
aminopolysaccharides such as chitin and peptidoglycan, and LAP catalyzes
the hydrolysis of leucine residues at the N-terminus of peptides and
proteins. Both NAG and LAP are considered N-acquiring enzymes and were
similarly summed to define the variable N\textsubscript{sum}, which we
use as a proxy for N acquisition by decomposition. Acid phosphatase is
involved in releasing phosphate from monoester bonds, representing a
P-mineralizing enzyme (Burns \emph{et al.} 2013).

Extracellular enzyme activities were expressed per soil mass (mmol EE
activity kg\textsuperscript{-1} soil h\textsuperscript{-1}), SOC (mmol
EE activity kg\textsuperscript{-1} SOC h\textsuperscript{-1}), and MB
(mmol EE activity kg\textsuperscript{-1} MB h\textsuperscript{-1}).
These latter two variables are called SOC-normalized and MB-normalized,
respectively in this paper. We also measured the ratio of C-, N-, and
P-acquiring enzymes. Because EEs mediate nutrient acquisition for soil
microorganisms, they can be used to determine relative nutrient demand
(Olander \& Vitousek 2000; Sinsabaugh \& Shah 2012). Hence, we used
C\textsubscript{sum}:N\textsubscript{sum}, C\textsubscript{sum}:AP, and
N\textsubscript{sum}:AP as proxies for C:N, C:P, and N:P relative demand
ratios, respectively.

\emph{Statistical analysis}

All statistical tests and visualizations were conducted in R (R
Development Core Team 2008) using the lme4 (Bates \emph{et al.} 2015)
and MuMin (Barton 2018) packages. We used mixed-effects models with site
as a random effect to examine the relationship between depth, SOC, MB,
clay, and fungi:bacteria and EE activity (expressed on soil mass, SOC,
and MB bases). We similarly used mixed-effects models with site as a
random effect to examine the effect of soil stoichiometry (using ratios
of SOC, total N, and available P) on enzyme stoichiometry. These models
were conducted on the complete dataset, the surface soil dataset (depth
\textless{} 20 cm), and the subsoil dataset (depth \textgreater{} 20 cm)
to determine differences in the controls of EE activities between the
surface and subsoils. Because we did not characterize the horizonation
of the sampling pits, we \emph{a priori} chose 20 cm to represent the
subsoil because most EE studies do not sample below this depth. However,
we also conducted our analysis using a 30 cm threshold, and statistical
significance and overall interpretation remained unchanged. Therefore,
for clarity, we report results using only the 20 cm threshold for the
subsoil. To report the variance explained by the model, we report the
marginal R\textsuperscript{2} value, which expresses the increase in
explained variance by including the fixed effect(s) (Nakagawa \&
Schielzeth 2013). We also used analysis of variance (ANOVA) to determine
if the fraction of EE activity below 20 cm differed by soil order. We
assessed significance at the \selectlanguage{greek}α\selectlanguage{english}~=~0.05 level and marginal significance at
\selectlanguage{greek}α\selectlanguage{english}~=~0.10. If significant differences were detected, we used Tukey's Test
of Honest Significant Differences to determine which soil orders were
significantly different.

We used QQ-plots and scale-location plots to inspect normality and
homoscedasticity, respectively. Because many of the mixed-effects models
failed to meet parametric assumptions, all dependent and independent
continuous variables were natural log-transformed and re-analyzed. The
resulting models, along with the ANOVAs, met the assumptions of
parametric tests. For visualization purposes, data are left
untransformed unless otherwise stated.

RESULTS

\emph{Whole profile soil properties among sites}

Soil organic C, total N, available P, and fungi:bacteria decreased while
clay percentage increased with depth across the CZO network (all: p
\textless{} 0.001, Fig. S1A-E). Across all sites, soil pH was relatively
constant throughout the depth profile (p = 0.236, Fig. S1F). However,
some individual sites showed decreases (e.g., SCST: p = 0.036) or
increases (e.g., HARD: p = 0.040, PROV: p = 0.003) in pH with depth.

\emph{Distribution of extracellular enzyme activity is related to
microbial biomass and organic carbon throughout the top meter of soil}

For all assayed EEs, EE activity per mass of soil declined
logarithmically with depth (p \textless{} 0.001, Fig. 1 and S2), but
about 50\% of the total-profile EE activity kg\textsuperscript{-1} soil
in the top meter occurred below 20 cm (Fig. 2A). However, the proportion
of the EE activity below 20 cm differed by the soil order for many of
the assayed EEs (Table S1). Mollisols had about a 1.5 times greater
percentage of the sum of C- and N-acquiring EE activity
kg\textsuperscript{-1} soil below 20 cm than Inceptisols or Ultisols (p
\textless{} 0.050 for all comparisons, Fig. 2B). For AP, the 39\% higher
proportion in the subsoil for Mollisols compared to Inceptisols was only
marginally significant (p = 0.063).

There were also differences in the percentage of MB and SOC in the
subsoil among soil orders (MB: p \textless{} 0.001, SOC: p = 0.013),
with Mollisols having an almost two times greater proportion of MB and
SOC below 20 cm than Inceptisols (MB: p = 0.006, SOC: p = 0.013; Fig.
2B). While the proportion of MB below 20 cm was significantly higher in
Mollisols compared to Ultisols (about 1.5 times greater, p = 0.001), the
difference in the proportion of SOC below 20 cm between Mollisols and
Ultisols was only marginally significant (p = 0.057).

Microbial biomass-normalized EE activity increased with depth for all
enzymes (Fig. S3; all: p \textless{} 0.05). The strongest increases were
for LAP and AP, which increased six- and seven-fold, respectively, while
NAG and BG increased by 85\% and 103\%, respectively. Throughout the top
meter, over 80\% of MB-normalized EE activity occurred below 20 cm (Fig.
S4A). However, because the proportion of MB below 20 cm also varied
among soil orders, the proportion of MB-normalized EE activity below 20
cm was consistent among soil orders for most assayed EEs (AG: p = 0.333,
BG: p = 0.175, CB: p = 0.278, BX: p = 0.211, NAG: p = 0.027, LAP: p =
0.537, AP: p = 0.048; Fig. S4A). Nevertheless, the proportion of
MB-normalized NAG activity below 20 cm was 15\% greater in Ultisols
compared to Inceptisols (p = 0.025), and the proportion of MB-normalized
AP activity below 20 cm was 17\% greater in Ultisols compared to
Mollisols (p = 0.042).

There were inconsistent patterns of EE activity normalized by SOC with
depth. N-acetylglucosaminidase normalized by SOC decreased with depth (p
= 0.004); AG, LAP, and AP increased with depth (AG: p = 0.016, LAP: p =
0.002, AP: p \textless{} 0.001), and BG, CB, and BX did not change with
depth (BG: p = 0.322, CB: p = 0.344, BX: p = 0.198; Fig. S5). Similar to
the proportion of MB-normalized EE activity below 20 cm, the proportion
of EE normalized by SOC below 20 cm averaged about 80\% and did not
differ among soil orders (all: p \textgreater{} 0.1; Fig. S6).

\emph{Controls on extracellular enzyme activity throughout the top meter
of soil}

Consistently, MB, SOC, and fungi:bacteria were better predictors of EE
activities per mass of soil than pH or clay concentrations (Table S2).
This was generally consistent among surface soil- and subsoil-only
datasets except for fungi:bacteria, which was only a strong predictor in
the surface soil (Table S3).

Normalized by SOC or MB, soil pH was generally not a significant
predictor of the assayed EE activities. The exception was LAP
kg\textsuperscript{-1} SOC, which correlated positively with pH (p =
0.037; all other EEs: p \textgreater{} 0.05; Table S4), a pattern that
was consistent among surface soil- and subsoil-only datasets (Table 2).
In contrast, when EE activities were normalized per unit MB, clay
concentrations and fungi:bacteria were generally correlated positively
with EE activities (Table S4). When surface and subsoil EE data were
analyzed separately, the effect of clay concentrations and
fungi:bacteria on MB-normalized EE activities was more often significant
in the subsoil (Table 2).

\emph{Relating soil and extracellular enzyme stoichiometries throughout
the top meter of soil}

When considering soils from all depth increments, only
soil\textsubscript{C:N} and EE\textsubscript{C:N} were correlated (C:N:
p = 0.013, C:P: p = 0.292, N:P: p = 0.276), but this negative
correlation between soil\textsubscript{C:N} and EE\textsubscript{C:N}was
relatively weak (marginal R\textsuperscript{2} = 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
R\textsuperscript{2} = 0.268; C:P: p = 0.002, marginal
R\textsuperscript{2} = 0.193; N:P: p = 0.004, marginal
R\textsuperscript{2} = 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\textsuperscript{-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 \emph{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
(Boerner\emph{et 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 \emph{et al.} 1989, Burns \emph{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 (Burns\emph{et 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 \emph{et al.} 1996). Secondly, the relative
abundance of fungi, which produce more EEs per unit MB than bacteria
(Roman\selectlanguage{ngerman}í \emph{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 \emph{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 \emph{et al.} 2013; Jing\emph{et
al.} 2017; Yao \emph{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 \emph{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 \emph{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 \emph{et al.} 2013; Jing \emph{et
al.} 2017; Yao \emph{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 \emph{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 \emph{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 \emph{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 \emph{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 \emph{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 \emph{in situ} rates of EE activity
(Burns \emph{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\textsuperscript{-1} SOC decreased with soil pH, while LAP
kg\textsuperscript{-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 \emph{et al.} 2014). Thus, the
decrease in BG, CB, and NAG activity kg\textsuperscript{-1} C with
greater soil pH in Sinsabaugh \emph{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 \emph{in
situ}conditions (Burns \emph{et al.} 2013), we conclude that across
ecosystems, \emph{in situ} rates of BG, CB, and NAG activity are likely
lower in alkaline soils because of discrepancies between EE pH optima
and \emph{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 \emph{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 \emph{et al.} 2016; Abramoff \emph{et al.} 2017; Sulman \emph{et
al.} 2018). For instance, Schimel \emph{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
(Blume\emph{et al.} 2002; Ta\selectlanguage{polish}ş \selectlanguage{english}\emph{et al.} 2014; Hicks Pries \emph{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

\textbf{Table 1} : Characteristics of the 19 study sites across ten
Critical Zone Observatories (CZOs).\selectlanguage{english}
\begin{longtable}[]{@{}lllllllllllll@{}}
\toprule
Site & CZO & Latitude & Longitude &
pH\textsubscript{1:1(water)}\textsuperscript{1} & Elevation (m) &
Elevation (m) & Elevation (m) & MAP (mm) & MAT (\selectlanguage{ngerman}°C) & Parent Material &
Soil Order & Vegetation\tabularnewline
\midrule
\endhead
AGRI & Christina & 39.8622 & -75.7834 & 4.57 - 7.03 & 4.57 - 7.03 & 105
& 105 & 1145 & 12 & fluvium & Inceptisol & Forest\tabularnewline
BSLT & Reynolds Creek & 43.1171 & -116.7258 & 7.69 - 9.40 & 7.69 - 9.40
& 1917 & 1917 & 479 & 6.3 & basalt & Mollisol & Shrubland\tabularnewline
CTNA & Catalina-Jemez & 32.4293 & -110.7610 & 4.62 - 5.87 & 4.62 - 5.87
& 2100 & 2100 & 840 & 12 & granite & Entisol & Forest\tabularnewline
FLUD & Christina & 39.8625 & -75.7830 & 5.86 - 6.33 & 5.86 - 6.33 & 113
& 113 & 1145 & 12 & quartzite & Ultisol & Forest\tabularnewline
GARN & Shale Hills & 40.6949 & -77.9199 & 3.34 - 4.99 & 3.34 - 4.99 &
554 & 554 & 1050 & 9.5 & sandstone & Inceptisol & Forest\tabularnewline
GOOS & IML & 40.4374 & -88.5552 & 7.79 - 8.34 & 7.79 - 8.34 & 250 & 250
& 1000 & 11 & fluvium & Mollisol & Cropland\tabularnewline
GRNT & Reynolds Creek & 43.1927 & -116.8105 & 8.91 - 9.29 & 8.91 - 9.29
& 1565 & 1565 & 616 & 7 & granite & Mollisol & Shrubland\tabularnewline
HARD & Calhoun & 34.6064 & -81.7234 & 7.96 - 8.79 & 7.96 - 8.79 & 183 &
183 & 1250 & 16 & gneiss & Ultisol & Forest\tabularnewline
ICAC & Luquillo & 18.2814 & -65.7909 & 4.21 - 4.67 & 4.21 - 4.67 & 690 &
690 & 5000 & 19 & quartzite & Inceptisol & Forest\tabularnewline
LVRD & Luquillo & 18.3237 & -65.8185 & 4.23 - 5.64 & 4.23 - 5.64 & 343 &
343 & 3456 & 23 & volcaniclastic & Oxisol & Forest\tabularnewline
MEAD & Boulder Creek & 40.0210 & -105.4796 & 6.56 - 7.75 & 6.56 - 7.75 &
2642 & 2642 & 519 & 5.1 & gneiss & Mollisol & Grassland\tabularnewline
NSLP & Boulder Creek & 40.0125 & -105.4690 & 6.36 - 9.81 & 6.36 - 9.81 &
2521 & 2521 & 519 & 5.1 & gneiss & Inceptisol & Forest\tabularnewline
PINE & Calhoun & 34.6074 & -81.7228 & -- & -- & 184 & 184 & 1250 & 16 &
gneiss & Ultisol & Forest\tabularnewline
PRAR & IML & 40.4275 & -88.6032 & 7.19 - 7.67 & 7.19 - 7.67 & 250 & 250
& 1000 & 11 & loess & Mollisol & Cropland\tabularnewline
PROV & Southern Sierra & 37.0675 & -119.1950 & 6.46 - 8.09 & 6.46 - 8.09
& 2016 & 2016 & 1200 & 8 & granite & Inceptisol &
Shrubland\tabularnewline
SCST & Catalina-Jemez & 32.4263 & -110.7612 & 6.11 - 6.83 & 6.11 - 6.83
& 2100 & 2100 & 840 & 12 & schist & Entisol & Forest\tabularnewline
SHAL & Shale Hills & 40.6640 & -77.9064 & 4.24 - 4.99 & 4.24 - 4.99 &
282 & 282 & 1050 & 9.5 & shale & Ultisol & Forest\tabularnewline
SJER & Southern Sierra & 37.1088 & -119.7314 & 5.90 - 6.25 & 5.90 - 6.25
& 405 & 405 & 513 & 16.4 & granite & Alfisol & Grassland\tabularnewline
MDRN & Eel River & 39.7294 & -123.6419 & -- & -- & -- & 487 & 1500 & 12
& sandstone & Alfisol & Forest\tabularnewline
\bottomrule
\end{longtable}

\textsuperscript{1}pH was not measured on PINE and MDRN soils because of
limited soil collected.

\textbf{Table 2} : Marginal R\textsuperscript{2} values for
mixed-effects models with soil clay concentration, pH, or fungi:bacteria
as the sole fixed effect, and soil pit as a random effect on EE activity
normalized by microbial biomass (MB) or soil organic carbon (SOC)
concentration in top- (\textless{} 20 cm) and sub-soils (\textgreater{}
20 cm). Key: \selectlanguage{greek}α-\selectlanguage{english}glucosidase (AG), \selectlanguage{greek}β-\selectlanguage{english}glucosidase (BG), cellobiohydrolase
(CB), \selectlanguage{greek}β-\selectlanguage{english}xylosidase (BX), N-acetylglucosamine (NAG), leucine
aminopeptidase (LAP), and acid phosphatase (AP) activity. Bolded values
represent a significant (\selectlanguage{greek}α \selectlanguage{english}= 0.05) effect and +/- signifies the
direction of the effect (surface soil: n = 29, subsoil: n = 114).\selectlanguage{english}
\begin{longtable}[]{@{}lllllllllllll@{}}
\toprule
& & & \emph{MB-normalized\textsuperscript{1}} &
\emph{MB-normalized\textsuperscript{1}} &
\emph{MB-normalized\textsuperscript{1}} &
\emph{MB-normalized\textsuperscript{1}} & & &
\emph{SOC-normalized\textsuperscript{2}} &
\emph{SOC-normalized\textsuperscript{2}} &
\emph{SOC-normalized\textsuperscript{2}} &\tabularnewline
\midrule
\endhead
& Enzyme & Enzyme & Clay & Clay & pH & fungi:bacteria & fungi:bacteria &
Clay & Clay & pH & fungi:bacteria & fungi:bacteria\tabularnewline
\emph{Surface soil} & \emph{Surface soil} & & & & & & & & & &
&\tabularnewline
& AG & AG & \textless{} 0.001 & \textless{} 0.001 & 0.020 & 0.016 &
0.016 & 0.002 & 0.002 & 0.003 & 0.016 & 0.016\tabularnewline
& BG & BG & \textless{} 0.001 & \textless{} 0.001 & 0.068 & 0.013 &
0.013 & 0.001 & 0.001 & 0.095 & 0.008 & 0.008\tabularnewline
& CB & CB & 0.003 & 0.003 & 0.067 & 0.052 & 0.052 & 0.002 & 0.002 &
0.129 & 0.002 & 0.002\tabularnewline
& BX & BX & 0.004 & 0.004 & 0.024 & 0.011 & 0.011 & \textless{} 0.001 &
\textless{} 0.001 & 0.017 & \textbf{-0.095} &
\textbf{-0.095}\tabularnewline
& NAG & NAG & 0.067 & 0.067 & 0.009 & \textbf{+0.298} & \textbf{+0.298}
& 0.011 & 0.011 & 0.067 & \textbf{+0.146} &
\textbf{+0.146}\tabularnewline
& LAP & LAP & 0.004 & 0.004 & 0.002 & 0.007 & 0.007 & \textbf{+0.020} &
\textbf{+0.020} & 0.002 & 0.039 & 0.039\tabularnewline
& AP & AP & 0.004 & 0.004 & 0.025 & \textless{} 0.001 & \textless{}
0.001 & \textbf{+0.102} & \textbf{+0.102} & 0.001 & \textbf{-0.226} &
\textbf{-0.226}\tabularnewline
\emph{Subsoil} & \emph{Subsoil} & & & & & & & & & & &\tabularnewline
& AG & AG & \textbf{+0.097} & \textbf{+0.097} & 0.006 & 0.019 & 0.019 &
0.019 & 0.019 & 0.006 & 0.005 & 0.005\tabularnewline
& BG & BG & 0.063 & 0.063 & 0.025 & \textbf{+0.171} & \textbf{+0.171} &
0.001 & 0.001 & \textbf{+0.075} & 0.014 & 0.014\tabularnewline
& CB & CB & 0.043 & 0.043 & 0.034 & \textbf{+0.037} & \textbf{+0.037} &
\textless{} 0.001 & \textless{} 0.001 & 0.010 & 0.001 &
0.001\tabularnewline
& BX & BX & \textbf{+0.094} & \textbf{+0.094} & 0.002 & \textbf{+0.146}
& \textbf{+0.146} & 0.002 & 0.002 & 0.034 & 0.001 & 0.001\tabularnewline
& NAG & NAG & 0.020 & 0.020 & 0.022 & \textbf{+0.080} & \textbf{+0.080}
& 0.032 & 0.032 & 0.015 & 0.006 & 0.006\tabularnewline
& LAP & LAP & 0.001 & 0.001 & \textless{} 0.001 & 0.001 & 0.001 & 0.009
& 0.009 & 0.017 & \textless{} 0.001 & \textless{} 0.001\tabularnewline
& AP & AP & \textbf{+0.142} & \textbf{+0.142} & 0.025 & 0.002 & 0.002 &
0.210 & 0.210 & 0.005 & 0.002 & 0.002\tabularnewline
\bottomrule
\end{longtable}

\textsuperscript{1} Enzyme activity per unit microbial biomass

\textsuperscript{2} Enzyme activity per unit soil organic carbon

FIGURES

\textbf{Figure 1} : Distribution of activity of \selectlanguage{greek}α-\selectlanguage{english}glucosidase (AG),
\selectlanguage{greek}β-\selectlanguage{english}glucosidase (BG), \selectlanguage{greek}β-\selectlanguage{english}xylosidase (BX), cellobiohydrolase (CB),
N-acetylglucosaminidase (NAG), leucine aminopeptidase (LAP), and acid
phosphatase (AP) per soil mass as a function of depth throughout the top
meter of soil across sites. See Table 1 for site abbreviations.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 2} : Percentage of \selectlanguage{greek}α-\selectlanguage{english}glucosidase (AG), \selectlanguage{greek}β-\selectlanguage{english}glucosidase
(BG), cellobiohydrolase (CB), \selectlanguage{greek}β-\selectlanguage{english}xylosidase (BX), N-acetylglucosamine
(NAG), leucine aminopeptidase (LAP), and acid phosphatase (AP) activity
below 20 cm in the top meter (A); and proportion of soil organic carbon
(SOC), microbial biomass (MB), sum of C-degrading enzymes
(C\textsubscript{sum}= AG + BG + CB + BX), sum of nitrogen-mineralizing
enzymes (N\textsubscript{sum} = NAG + LAP), and acid phosphatase (AP)
below 20 cm in the top meter of soil among soil orders (B). Error bars
show \selectlanguage{ngerman}± one standard error of the mean (Figure panel A: n = 19; Figure
panel B: Inceptisol: n = 4, Mollisol: n = 5, Ultisol: n = 3).

\par\null

\textbf{Figure 3} : Correlations between soil and extracellular enzyme
(EE) stoichiometry of carbon (C), nitrogen (N), and phosphorus (P) in
surface (\textless{} 20 cm depth) and subsoils (\textgreater{} 20 cm
depth). Blue lines show significant (\selectlanguage{greek}α \selectlanguage{english}= 0.05) mixed-effects models of
the relationship between soil and EE stoichiometry (site was used as a
random effect; lines were not drawn where correlations were not
significant). Gray ribbons show the standard error of the model. Data
points represent individual soil samples (depths within each pit). Note
the scales of the axes differ among plots.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Hosted file}

\verb`image4.emf`  available at \url{https://authorea.com/users/305676/articles/436472-continental-scale-patterns-of-extracellular-enzyme-activity-in-the-subsoil-an-overlooked-reservoir-of-microbial-activity}

\textbf{Figure 4} : Conceptual model of changing controls on
extracellular enzyme activity (EEA) between surface soil and subsoil.
Solid lines represent fluxes and dashed lines represent moderating
controls. Boxes represent pools or concentrations, and other shapes
represent moderating variables. Blue parameters represent microbial
parameters, and green boxes represent edaphic variables such as
substrate (including carbon {[}C{]} and nutrients) and clay
concentrations. The differences in the size of boxes between the surface
and subsoil represent the relative size of the pool, and differences in
the thickness of arrows between the surface and subsoil represent the
hypothesized relative magnitude of the flux or control. A portion of the
substrate pool is available to microbial biomass (MB) and is moderated
by clay concentration and active EEA. Substrate availability moderates
substrate demand. Bacterial biomass, fungal biomass, and substrate
demand influence active EEA. Additionally, our conceptual model
incorporates stabilized EEA (i.e., EEs sorbed onto clay particles),
which is primarily influenced by clay concentrations. At depth, the
impact of clay on substrate availability and stabilized EEA increases,
while the absolute impact of substrates and microbial properties (i.e.,
microbial biomass and substrate demand) decreases.

SUPPLEMENTAL TABLES

\textbf{Table S1} : Proportion (and standard error, n = 19) of
extracellular enzyme activity (kg\textsuperscript{-1} soil) below 20 cm
across the three main soil orders represented in the study. Different
superscript letters represent significant differences among soil orders
for each enzyme (\selectlanguage{greek}α \selectlanguage{english}= 0.05). Key: AG = \selectlanguage{greek}α-\selectlanguage{english}glucosidase, BG = \selectlanguage{greek}β-\selectlanguage{english}glucosidase,
CB = cellobiohydrolase, XYL = \selectlanguage{greek}β-\selectlanguage{english}xylosidase, LAP = leucine
aminopeptidase, NAG = N-acetylglucosamine, AP = acid phosphatase.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
Enzyme & Enzyme & Inceptisols & Mollisols & Ultisols\tabularnewline
\midrule
\endhead
AG & 33.6\% (9.6) & 33.6\% (9.6) & 59.5\% (8.4) & 62.3\%
(8.3)\tabularnewline
BG & 40.9\% (4.8)\textsuperscript{a} & 40.9\% (4.8)\textsuperscript{a} &
65.1\% (5.8)\textsuperscript{b} & 46.6\%
(1.2)\textsuperscript{ab}\tabularnewline
CB & 41.0\% (9.7) & 41.0\% (9.7) & 58.8\% (7.6) & 36.0\%
(4.7)\tabularnewline
BX & 32.2\% (4.7)\textsuperscript{a} & 32.2\% (4.7)\textsuperscript{a} &
63.7\% (3.1)\textsuperscript{b} & 51.8\%
(6.6)\textsuperscript{b}\tabularnewline
NAG & 31.8\% (8.1)\textsuperscript{b} & 31.8\% (8.1)\textsuperscript{b}
& 65.3\% (4.9)\textsuperscript{a} & 39.6\%
(2.8)\textsuperscript{b}\tabularnewline
LAP & 53.2\% (3.1)\textsuperscript{ab} & 53.2\%
(3.1)\textsuperscript{ab} & 71.9\% (6.8)\textsuperscript{b} & 49.0\%
(5.2)\textsuperscript{a}\tabularnewline
AP & 47.0\% (6.4) & 47.0\% (6.4) & 65.4\% (3.7) & 56.6\%
(5.1)\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table S2} : Marginal R\textsuperscript{2} values for
mixed-effects models with soil microbial biomass (MB), soil organic
carbon (SOC) concentration, clay concentration, pH, or fungi:bacteria as
the sole fixed effect, and soil pit as a random effect on EE activities
(kg\textsuperscript{-1} soil). Key: \selectlanguage{greek}α-\selectlanguage{english}glucosidase (AG), \selectlanguage{greek}β-\selectlanguage{english}glucosidase
(BG), cellobiohydrolase (CB), \selectlanguage{greek}β-\selectlanguage{english}xylosidase (BX), N-acetylglucosamine
(NAG), leucine aminopeptidase (LAP), acid phosphatase (AP), sum of
C-degrading enzymes (C\textsubscript{sum} = AG + BG + CB + BX), and sum
of nitrogen-mineralizing enzymes (N\textsubscript{sum} = NAG + LAP).
Bolded values represent a significant (\selectlanguage{greek}α \selectlanguage{english}= 0.05) effect and +/-
signifies the direction of the effect (MB, SOC, fungi:bacteria: n = 178,
clay and pH: n = 143).\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
Enzyme & MB (mol PLFA) & SOC (\%) & Clay (\%) & pH (1:1 w/v
H\textsubscript{2}O) & fungi:bacteria\tabularnewline
\midrule
\endhead
C\textsubscript{sum} & \textbf{+0.383} & \textbf{+0.341} &
\textbf{-0.253} & 0.002 & \textbf{+0.046}\tabularnewline
AG & \textbf{+0.182} & \textbf{+0.281} & \textbf{-0.120} & 0.003 &
\textbf{+0.067}\tabularnewline
BG & \textbf{+0.433} & \textbf{+0.340} & \textbf{-0.224} & 0.006 &
\textbf{+0.051}\tabularnewline
CB & \textbf{+0.271} & \textbf{+0.293} & \textbf{-0.150} & 0.006 &
\textbf{+0.043}\tabularnewline
BX & \textbf{+0.458} & \textbf{+0.424} & \textbf{-0.231} & 0.009 &
\textbf{+0.032}\tabularnewline
N\textsubscript{sum} & \textbf{+0.537} & \textbf{+0.531} &
\textbf{-0.280} & 0.040 & \textbf{+0.106}\tabularnewline
NAG & \textbf{+0.472} & \textbf{+0.448} & \textbf{-0.239} & 0.012 &
\textbf{+0.105}\tabularnewline
LAP & \textbf{+0.241} & \textbf{+0.283} & \textbf{-0.246} & 0.002 &
\textbf{+0.029}\tabularnewline
AP & \textbf{+0.497} & \textbf{+0.517} & \textbf{-0.294} & 0.008 &
\textbf{+0.062}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table S3} : Marginal r\textsuperscript{2} values for
mixed-effects models with soil microbial biomass (MB), soil organic
carbon (SOC) concentration, clay concentration, pH, or fungi:bacteria as
the sole fixed effect, and soil pit as a random effect on EE activities
(kg\textsuperscript{-1} soil) in surface (\textless{} 20 cm) and
subsoils (\textgreater{} 20 cm). Key: \selectlanguage{greek}α-\selectlanguage{english}glucosidase (AG), \selectlanguage{greek}β-\selectlanguage{english}glucosidase
(BG), cellobiohydrolase (CB), \selectlanguage{greek}β-\selectlanguage{english}xylosidase (BX), N-acetylglucosamine
(NAG), leucine aminopeptidase (LAP), acid phosphatase (AP), sum of
C-degrading enzymes (C\textsubscript{sum} = AG + BG + CB + BX), and sum
of nitrogen-mineralizing enzymes (N\textsubscript{sum} = NAG + LAP).
Bolded values represent a significant (\selectlanguage{greek}α \selectlanguage{english}= 0.05) effect and +/-
signifies the direction of the effect (surface soil MB, SOC,
fungi:bacteria: n = 38, subsoil MB, SOC, fungi:bacteria: n = 140,
surface soil clay \& pH: n = 29, subsoil clay and pH: n = 114).\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
& Enzyme & MB (mol PLFA) & MB (mol PLFA) & MB (mol PLFA) & SOC (\%) &
Clay (\%) & pH (1:1 w/v H\textsubscript{2}O) &
fungi:bacteria\tabularnewline
\midrule
\endhead
\emph{Surface soil} & \emph{Surface soil} & \emph{Surface soil} & & & &
& &\tabularnewline
& C\textsubscript{sum} & \textbf{+0.196} & \textbf{+0.196} &
\textbf{+0.196} & \textbf{+0.301} & \textbf{-0.200} & 0.026 &
\textbf{+0.251}\tabularnewline
& AG & \textbf{+0.186} & \textbf{+0.186} & \textbf{+0.186} &
\textbf{+0.320} & 0.073 & 0.002 & \textbf{+0.293}\tabularnewline
& BG & \textbf{+0.158} & \textbf{+0.158} & \textbf{+0.158} &
\textbf{+0.280} & \textbf{-0.218} & 0.036 &
\textbf{+0.254}\tabularnewline
& CB & \textbf{+0.231} & \textbf{+0.231} & \textbf{+0.231} &
\textbf{+0.277} & 0.102 & 0.015 & \textbf{+0.219}\tabularnewline
& BX & \textbf{+0.197} & \textbf{+0.197} & \textbf{+0.197} &
\textbf{+0.240} & \textbf{-0.158} & \textless{} 0.001 &
\textbf{+0.117}\tabularnewline
& N\textsubscript{sum} & \textbf{+0.356} & \textbf{+0.356} &
\textbf{+0.356} & \textbf{+0.557} & \textbf{-0.205} & 0.004 &
\textbf{+0.536}\tabularnewline
& NAG & \textbf{+0.367} & \textbf{+0.367} & \textbf{+0.367} &
\textbf{+0.555} & \textbf{-0.167} & \textless{} 0.001 &
\textbf{+0.531}\tabularnewline
& LAP & \textbf{+0.108} & \textbf{+0.108} & \textbf{+0.108} &
\textbf{+0.139} & \textbf{-0.196} & \textless{} 0.001 &
\textbf{+0.163}\tabularnewline
& AP & \textbf{+0.523} & \textbf{+0.523} & \textbf{+0.523} &
\textbf{+0.584} & 0.126 & 0.032 & \textbf{+0.311}\tabularnewline
\emph{Subsoil} & \emph{Subsoil} & \emph{Subsoil} & & & & &
&\tabularnewline
& C\textsubscript{sum} & \textbf{+0.213} & \textbf{+0.213} &
\textbf{+0.213} & \textbf{+0.175} & \textbf{-0.090} & 0.040 &
\textless{} 0.001\tabularnewline
& AG & \textbf{+0.041} & \textbf{+0.041} & \textbf{+0.041} &
\textbf{+0.069} & 0.001 & \textless{} 0.001 & 0.002\tabularnewline
& BG & \textbf{+0.285} & \textbf{+0.285} & \textbf{+0.285} &
\textbf{+0.185} & \textbf{-0.070} & 0.042 & 0.001\tabularnewline
& CB & \textbf{+0.075} & \textbf{+0.075} & \textbf{+0.075} &
\textbf{+0.077} & 0.002 & 0.022 & \textless{} 0.001\tabularnewline
& BX & \textbf{+0.299} & \textbf{+0.299} & \textbf{+0.299} &
\textbf{+0.244} & 0.054 & 0.580 & 0.002\tabularnewline
& N\textsubscript{sum} & \textbf{+0.350} & \textbf{+0.350} &
\textbf{+0.350} & \textbf{+0.291} & \textbf{-0.089} & \textbf{+0.142} &
\textless{} 0.001\tabularnewline
& NAG & \textbf{+0.277} & \textbf{+0.277} & \textbf{+0.277} &
\textbf{+0.189} & 0.052 & 0.067 & 0.001\tabularnewline
& LAP & \textbf{+0.083} & \textbf{+0.083} & \textbf{+0.083} &
\textbf{+0.222} & \textbf{-0.078} & 0.071 & \textless{}
0.001\tabularnewline
& AP & \textbf{+0.337} & \textbf{+0.337} & \textbf{+0.337} &
\textbf{+0.268} & \textbf{-0.101} & 0.009 & 0.001\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table S4} : Marginal r\textsuperscript{2} values for
mixed-effects models with clay concentration, pH, or fungi:bacteria as
the sole fixed effect and soil pit as a random effect on EE activities
normalized by microbial biomass (MB) or soil organic carbon (SOC)
concentration. Key: \selectlanguage{greek}α-\selectlanguage{english}glucosidase (AG), \selectlanguage{greek}β-\selectlanguage{english}glucosidase (BG),
cellobiohydrolase (CB), \selectlanguage{greek}β-\selectlanguage{english}xylosidase (BX), N-acetylglucosamine (NAG),
leucine aminopeptidase (LAP), and phosphatase (AP). Bolded values
represent a significant (\selectlanguage{greek}α \selectlanguage{english}= 0.05) effect and +/- signifies the
direction of the effect (Clay: n = 143, pH: n = 143, fungi:bacteria: n =
178).\selectlanguage{english}
\begin{longtable}[]{@{}llllllllll@{}}
\toprule
& & \emph{MB-normalized\textsuperscript{1}} &
\emph{MB-normalized\textsuperscript{1}} &
\emph{MB-normalized\textsuperscript{1}} &
\emph{MB-normalized\textsuperscript{1}} &
\emph{MB-normalized\textsuperscript{1}} &
\emph{SOC-normalized\textsuperscript{2}} &
\emph{SOC-normalized\textsuperscript{2}} &
\emph{SOC-normalized\textsuperscript{2}}\tabularnewline
\midrule
\endhead
Enzyme & Enzyme & Enzyme & Clay & Clay & pH & fungi:bacteria & Clay & pH
& fungi:bacteria\tabularnewline
AG & \textbf{+0.144} & \textbf{+0.144} & \textbf{+0.144} & 0.002 & 0.002
& 0.007 & 0.020 & 0.002 & 0.001\tabularnewline
BG & \textbf{+0.107} & \textbf{+0.107} & \textbf{+0.107} & 0.051 & 0.051
& \textbf{+0.100} & \textless{} 0.001 & 0.074 & 0.007\tabularnewline
CB & \textbf{+0.067} & \textbf{+0.067} & \textbf{+0.067} & 0.049 & 0.049
& 0.023 & 0.001 & 0.022 & \textless{} 0.001\tabularnewline
BX & \textbf{+0.144} & \textbf{+0.144} & \textbf{+0.144} & 0.017 & 0.017
& \textbf{+0.068} & 0.001 & 0.049 & \textless{} 0.001\tabularnewline
NAG & \textbf{+0.048} & \textbf{+0.048} & \textbf{+0.048} & 0.032 &
0.032 & \textbf{+0.099} & 0.019 & 0.013 & \textbf{+0.027}\tabularnewline
LAP & 0.019 & 0.019 & 0.019 & \textless{} 0.001 & \textless{} 0.001 &
0.003 & \textbf{+0.034} & \textbf{+0.035} & 0.005\tabularnewline
AP & \textbf{+0.199} & \textbf{+0.199} & \textbf{+0.199} & \textless{}
0.001 & \textless{} 0.001 & \textless{} 0.001 & \textbf{+0.083} & 0.029
& 0.004\tabularnewline
\bottomrule
\end{longtable}

\textsuperscript{1} Enzyme activity g\textsuperscript{-1} microbial
biomass

\textsuperscript{2} Enzyme activity g\textsuperscript{-1} soil organic
carbon

SUPPLEMENTAL FIGURES

\textbf{Figure S1} : Boxplots showing soil organic carbon (SOC, A),
total nitrogen (N, B), Olsen phosphorus (P, C), clay (D), the ratio of
fungal to bacterial biomass (E), and pH (F) at each depth interval
throughout the top meter of soil across sites. For the boxplots, the
solid black line represents the median, and the box represents the
interquartile region (IQR). Lines stemming from the boxplots represent
1.5x the IQR and points show data outside of 1.5x the IQR. The blue line
shows the best-fit loess regression with standard error highlighted by
the gray ribbon.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Figure S2} : Potential activity of \selectlanguage{greek}α-\selectlanguage{english}glucosidase (AG),
\selectlanguage{greek}β-\selectlanguage{english}glucosidase (BG), \selectlanguage{greek}β-\selectlanguage{english}xylosidase (BX), cellobiohydrolase (CB),
N-acetylglucosaminidase (NAG), leucine aminopeptidase (LAP), and acid
phosphatase (AP) as a function of depth throughout the top meter of soil
across sites. Boxplots show the distribution of extracellular enzyme
activities at each depth and the blue line shows the best-fit loess
regression with standard error highlighted by the gray ribbon. Note:
scale of x-axis differs among extracellular enzymes.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

\textbf{Figure S3} : Potential activity of \selectlanguage{greek}α-\selectlanguage{english}glucosidase (AG),
\selectlanguage{greek}β-\selectlanguage{english}glucosidase (BG), \selectlanguage{greek}β-\selectlanguage{english}xylosidase (BX), cellobiohydrolase (CB),
N-acetylglucosaminidase (NAG), leucine aminopeptidase (LAP), and acid
phosphatase (AP) normalized by soil microbial biomass (mol phospholipid
fatty acid {[}PLFA{]}) as a function of depth throughout the top meter
of soil across sites. Boxplots show the distribution of extracellular
enzyme activities at each depth and the blue line shows the best-fit
loess regression with standard error highlighted by the gray ribbon.
Note: scale of x-axis differs among enzymes.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

\textbf{Figure S4} : Proportion of soil microbial biomass-normalized
extracellular enzyme activity below 20 cm in the top meter for
\selectlanguage{greek}α-\selectlanguage{english}glucosidase (AG), \selectlanguage{greek}β-\selectlanguage{english}glucosidase (BG), cellobiohydrolase (CB),
\selectlanguage{greek}β-\selectlanguage{english}xylosidase (BX), N-acetylglucosamine (NAG), leucine aminopeptidase
(LAP), and acid phosphatase (AP) across (A) and among (B) soil orders.
Error bars show standard error of the mean (Figure panel A: n = 19;
Figure panel B: Inceptisol: n = 4, Mollisol: n = 5, Ultisol: n = 3).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

\textbf{Figure S5} : Potential activity of \selectlanguage{greek}α-\selectlanguage{english}glucosidase (AG),
\selectlanguage{greek}β-\selectlanguage{english}glucosidase (BG), \selectlanguage{greek}β-\selectlanguage{english}xylosidase (BX), cellobiohydrolase (CB),
N-acetylglucosaminidase (NAG), leucine aminopeptidase (LAP), and acid
phosphatase (AP) normalized by kg\textsuperscript{-1} soil organic
carbon (C) as a function of depth throughout the top meter of soil
across sites. Boxplots show the distribution of extracellular enzyme
activities at each depth and the blue line shows the best-fit loess
regression with standard error highlighted by the gray ribbon. Note:
scale of x-axis differs among enzymes.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image9/image9}

\end{center}
\end{figure}

\textbf{Figure S6} : Proportion of extracellular enzyme activity
(kg\textsuperscript{-1} soil organic carbon {[}C{]}) below 20 cm in the
top meter for \selectlanguage{greek}α-\selectlanguage{english}glucosidase (AG), \selectlanguage{greek}β-\selectlanguage{english}glucosidase (BG), cellobiohydrolase
(CB), \selectlanguage{greek}β-\selectlanguage{english}xylosidase (BX), N-acetylglucosamine (NAG), leucine
aminopeptidase (LAP), and acid phosphatase (AP) across (A) and among (B)
soil orders. Error bars show standard error of the mean (Figure panel A:
n = 19; Figure panel B: Inceptisol: n = 4, Mollisol: n = 5, Ultisol: n =
3).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image10/image10}

\end{center}
\end{figure}

\textbf{Figure S7} : Lack of relationships between soil and
extracellular enzyme (EE) stoichiometry across the whole soil profile.
Data points represent individual soil samples (depths within each pit).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image11/image11}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

