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\begin{document}

\title{Pore structure in detritusphere of soils under switchgrass and restored
prairie vegetation community}



\author[1]{Jin Ho Lee}%
\author[1]{Maik  Lucas }%
\author[1]{Andrey K. Guber}%
\author[1]{AN Kravchenko}%
\affil[1]{Michigan State University Department of Plant Soil and Microbial Sciences}%


\vspace{-1em}



  
  \date{May 30, 2024}


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\selectlanguage{english}
\begin{abstract}
Root detritusphere, i.e., the soil in vicinity of decomposing root
residues, plays an important role in soil microbial activity and C
sequestration. Pore structure (size distributions and connectivity of
soil pores) in the detritusphere serves as a major driver for these
processes and, in turn, is influenced by physical characteristics of
both soil and roots. This study compared pore structure characteristics
in root detritusphere of soils of contrasting texture and mineralogy
subjected to \textgreater{}6 years of contrasting vegetation:
monoculture switchgrass and polyculture prairie systems. Soil samples
were collected from five experimental sites in the US Midwest
representing three soil types. Soil texture and mineralogy were measured
using hydrometer and X-ray powder diffraction, respectively. The intact
cores were scanned with X-ray computed micro-tomography to identify
visible soil pores, biopores, and particulate organic matter (POM). We
specifically focused on pore structure within the detritusphere around
the POM of root origin. Results showed that detritusphere of
coarser-textured soils, characterized by high sand and quartz contents,
had lower porosity in the vicinity of POM compared to finer-textured
soils. POM vicinities in finer soils had high proportions of large
(\textgreater{}300 \selectlanguage{greek}μ\selectlanguage{english}m \selectlanguage{ngerman}Ø) pores, and their pores were better connected
than in coarser soils. Lower porosity in outer (\textgreater{}1 mm)
parts of detritusphere of switchgrass than of prairie suggested soil
compaction by roots, and the effect especially pronounced in coarser
soils. The results demonstrated that soil texture and mineralogy played
a major, while vegetation a more modest, role in defining the pore
structure in root detritusphere.%
\end{abstract}\selectlanguage{ngerman}%



\sloppy


\textbf{1 INTRODUCTION}

Pore structure, i.e., shapes, connectivity, size distributions of soil
pores, defines many functions and processes of the soil (Lucas, 2022;
Rabot, Wiesmeier, Schl\selectlanguage{ngerman}üter, \& Vogel, 2018). It regulates availability
of O\textsubscript{2}, water, and nutrients to soil microorganisms and
influences processing of soil organics (Bouckaert et al., 2013; Thomsen,
Schjønning, Jensen, Kristensen, \& Christensen, 1999). Pore connectivity
is especially important for providing a suitable habitat for
soil-dwelling organisms and enabling microorganisms to access soil
organic matter (SOM) (Negassa et al., 2015; Rabbi et al., 2016). Pores
of different size ranges have differential effects on the activity and
abundance of microorganisms. Specifically, micro-environments associated
with higher enzyme activities and greater microbial abundance are found
in pores ranging from tens to hundreds \selectlanguage{greek}μ\selectlanguage{english}m \selectlanguage{ngerman}Ø (Kravchenko et al., 2019;
Strong et al., 2004).

Plant roots are a major driver of soil pore formation and a source of
SOM (Bodner, Leitner, \& Kaul, 2014; Sokol, Kuebbing, Karlsen-Ayala, \&
Bradford, 2019). After the root senesces, its residues remain in the
soil as detritus, and a several millimeters thick region that surrounds
these decaying residues is called the detritusphere (Gaillard, Chenu,
Recous, \& Richard, 1999; Védère, Vieublé Gonod, Pouteau, Girardin, \&
Chenu, 2020). Soil pore structure within the detritusphere is distinct
from that of the bulk soil due to past activity of live roots as well as
due to biological and physical changes after roots' senescence. For
example, rearrangement of soil particles or micro-aggregates during root
growth (Mitchell \& Soga, 2005) may lead to an increase in porosity
adjacent to the root (Helliwell et al., 2017), while soil compaction can
occur near growing roots (Lucas, Schlüter, Vogel, \& Vetterlein, 2019a).
However, upon root senescence pore spaces can be partially or completely
refilled by soil particles during the decomposition of root residues
(Phalempin et al., 2022). Since the detritusphere is a main arena of
microbial activity and carbon (C) processing (Kuzyakov \& Blagodatskaya,
2015), characteristics of pore structure within detritusphere likely
play its special role for the whole soil volume.

Properties of the pore structure in detritusphere depend on a number of
factors, including but not limited to: (i) inherent characteristics of
soil particles that influence pore formation, such as soil texture and
mineralogy; (ii) inherent pore characteristics, i.e., the pore structure
within that specific location prior to the root growth within it; (iii)
composition of the soil microbial community; (iv) morphological,
chemical, and physical characteristics of the roots that generate the
detritus.

The structural stability of the detritusphere pores are affected by sand
content and abundance of quartz, both known to decrease stability of
soil aggregation (Almajmaie, Hardie, Doyle, Birch, \& Acuna, 2017;
Rivera \& Bonilla, 2020), likely due to the large size and low surface
area of sand grains as well as the absence of negative charges
(Bazzoffi, Mbagwu, \& Chukwu, 1995; Six, Elliott, \& Paustian, 2000).
Moreover, soils dominated by quartz tend to be more easily dispersed
than kaolinitic clays due to their lower binding capacity (Buhmann,
Rapp, \& Laker, 1996; Neaman, Singer, \& Stahr, 1999), thus such soils
are prone to be easily disaggregated under disruptive forces such as
rainfall (Wakindiki \& Ben-Hur, 2002).

Inherent soil characteristics affect root growth patterns and thus
formation of root-derived pores. Root systems have been shown to grow
more extensively in loose than in compact soil (Bengough et al., 2006;
Croser, Bengough, \& Pritchard, 1999), as well as in an undisturbed soil
than in that homogenized by sieving and packing (Phalempin, Lippold,
Vetterlein, \& Schlüter, 2021b). The roots preferably utilize existing
pore spaces, and indeed, the rhizosphere can be more porous than the
bulk soil when roots are able to grow into a highly connected pore
system (Lucas et al., 2019a). The established soil biopores that have
been frequently and continuously used by roots are more likely to be
stable due to root exudate and mucilage inputs (Traoré, Groleau-Renaud,
Plantureux, Tubeileh, \& Boeuf-Tremblay, 2000). Such pores can maintain
their structure in detritusphere upon the root senescence and root
residue decomposition.

Vegetation type directly affects pore structure via differences in root
types and characteristics. For example, presence of coarse root systems
increased the volume of \textgreater{} 70 \selectlanguage{greek}μ\selectlanguage{english}m \selectlanguage{ngerman}Ø pores by 30\%, whereas
plant species with dense fine root systems generated larger volume of
\textless{} 30 \selectlanguage{greek}μ\selectlanguage{english}m \selectlanguage{ngerman}Ø pores (Bodner et al., 2014). Total volumes of soil
biopores, i.e., the pores formed by the activity of living organisms
such as roots, in Ø \textless{}0.2 mm and 0.2--0.5 mm size classes
significantly differed among the plant species with different root
system characteristics (Lucas, Nguyen, Guber, \& Kravchenko, 2022). The
differences in pore structure generated by plants with contrasting root
systems are expected to be more pronounced in direct vicinity of the
roots (Helliwell, Sturrock, Miller, Whalley, \& Mooney, 2019), thus,
carried later into the properties of the detritusphere. After plant
dies, the root residues located in the biopores are decomposed, and the
difference in the magnitude of decomposition is likely to be affected by
the detritusphere's pore structure. Variations in residue decomposition
can result in variations in the size of the gap between the residues and
soil particles, potentially leading to further alterations of the pore
structure.

While the structure of pores within the rhizosphere under different soil
texture and contrasting vegetation has been actively explored (Helliwell
et al., 2017; Helliwell et al., 2019; Phalempin et al., 2022, 2021b),
very little information is available on pore structure of detritusphere.
For example, Helliwell et al. (2017) observed micro-scale structural
changes in pores surrounding growing root systems in uniformly packed
soils and found increases in porosity at the interface between roots and
soil as roots grow into loamy sand and clay loam soils. However, it is
still unclear what happens to the pores surrounding roots once the roots
die and decomposition begins. As the detritusphere is one of the most
important microbial hotspots (Kuzyakov \& Blagodatskaya, 2015), the lack
of such information in the pore structure limits progress in
understanding mechanisms of soil C cycling and sequestration.

The objective of this research was to characterize the pore structure in
root detritusphere of the soils of two contrasting vegetation systems:
monoculture switchgrass, where root detritus originated from switchgrass
roots, and polyculture restored prairie, where root residues originated
from a variety of herbaceous plant species. The two systems have been in
place for over 6 years, generating differences in soil C contents
(Sanford, 2014; Sprunger \& Robertson, 2018), pore structures (Juyal,
Guber, Oerther, Quigley, \& Kravchenko, 2021), and microbial
characteristics (Jesus et al., 2016; Li et al., 2022). We compared pore
connectivity and size distribution within the detritusphere of the two
systems at five experimental sites representing three soil types with
contrasting texture and mineralogy.

\textbf{2 MATERIALS AND METHODS}

2.1 Experimental design and soil sample collections

Two of the experimental sites used for this study were located in
Wisconsin (Oregon and Hancock) and three in Michigan (Lux Arbor, Lake
City, and Escanaba), USA. The soils of Oregon, Lux Arbor, and Escanaba
sites are Alfisols, and of Hancock and Lake City sites are Entisol and
Spodosol, respectively. At each site a randomized complete block design
experiment with 3 (Hancock) or 4 (the rest of the sites) replications
has been established in 2013. Details on the research sites and soil
descriptions have been reported by Kasmerchak and Schaetzl (2018) and
Lee et al. (2023).

The two studied vegetation systems were: (1) non-fertilized monoculture
switchgrass (\emph{Panicum virgatum} L.; Cave-in-Rock variety); and (2)
restored prairie, which consisted of 18 plant species of grasses
(including switchgrass), forbs, and legumes. Soil sampling was conducted
in 2020 (Oregon site) and 2019 (the other 4 sites). Two types of soil
samples were collected from each replicated plot. First, three intact
soil cores (5 cm in height and 5 cm Ø) were collected from 5 to 10 cm
depth for X-ray computed micro-tomography (\selectlanguage{greek}µ\selectlanguage{english}CT) scanning. Then, the
loose soil surrounding the cores was also collected for measurements of
soil texture and mineralogy. All samples were stored at 4 \selectlanguage{ngerman}°C until
scanning and measurements.

2.2 Measurements of soil porosity, texture, and mineralogy

Soil porosity was calculated from bulk and particle densities of the
collected samples (i.e., total porosity), and texture was determined
using the hydrometer method (Gee and Or, 2002) in all replicated plots
of all sites. Soil mineralogy composition was measured using X-ray
powder diffraction (XRD) carried out at the Illinois State Geological
Survey (Champaign, Illinois, USA). Because of the high costs of XRD
analyses only three replicated samples from each system in each site
were subjected to these measurements. Prior to XRD analyses, the samples
were cleaned, dried in a vacuum oven, and ground to \textless{} 44 \selectlanguage{greek}µ\selectlanguage{english}m.
Then, one subset of the prepared sample was powdered by the McCrone mill
(MBP) (McCrone Accessories \& Components, Westmont, IL, USA) for
quantification of non-clay minerals (quartz, clay, K-feldspar,
P-feldspar, calcite, dolomite, siderite, and pyrite/marcasite), and the
other was fractionized into \textless{} 2 \selectlanguage{greek}µ\selectlanguage{english}m powder for clay minerals
(smectite, mica, kaolinite, and chlorite). The prepared two types of
powders were then spread on a glass slide and analyzed using a
Siemens/Bruker D5000 X-ray Powder Diffraction instrument (Billerica, MA,
USA). The JADE software was used to identify percentages of
constituents in each powdered sample from XRD patterns.

2.3 Soil core scanning and image analysis

Pore structure assessments were performed via X-ray \selectlanguage{greek}µ\selectlanguage{english}CT. Soil cores were
drained at -28 kPa using a 5-bar pressure plate extractor (Soilmoisture
Equipment Corp., Goleta, CA) prior to \selectlanguage{greek}μ\selectlanguage{english}CT scanning to remove water from
pores \selectlanguage{ngerman}Ø \textgreater{}10 \selectlanguage{greek}μ\selectlanguage{english}m and increase the contrast between the solids
and air on X-ray \selectlanguage{greek}μ\selectlanguage{english}CT images. Then, the cores were scanned using X-ray
\selectlanguage{greek}μ\selectlanguage{english}CT machine (North Star Imaging, X3000, Rogers, MV, USA) at the
Department of Horticulture facility, Michigan State University. The
energy settings were 75 kV and 450 \selectlanguage{greek}μ\selectlanguage{english}A. The scanning resolution of 18 \selectlanguage{greek}μ\selectlanguage{english}m
was achieved using the Subpix-mode of the scanner, combining four
individual scans shifted half pixel in vertical and horizontal
directions. Scanned images of 3014 projections were reconstructed by efX
software (North Star, Rogers, MN, USA).

A schematic summary of the steps involved in the image processing for
this study is outlined in Figure 1. The image pre-processing was
conducted using ImageJ-Fiji software (Schindelin et al., 2012) to remove
artifacts and noises. First, to exclude sampling artifacts near the soil
core walls, images were centered and cropped into prisms (1500 \selectlanguage{ngerman}× 1500 ×
2240 pixels corresponded to 2.7 cm in length, 2.7 cm in width, and 4.1
cm height). Then, `Remove Background' tool in Xlib/Beat plugin was used
to remove shadowing effects from the images, followed by the removal of
ring artifacts on the image's polar domain using a stripe filter of the
Xlib/Beat plugin. After that, a 3D non-local mean filter (\selectlanguage{greek}σ\selectlanguage{english} \selectlanguage{english}= 0.1)
implemented in scikit-image (Walt et al., 2014) was used to reduce the
noise (Buades, Coll, \& Morel, 2011; Darbon, Cunha, Chan, Osher, \&
Jensen, 2008). The pre-processing steps dropped the resolution of images
from 18 to 36 \selectlanguage{greek}μ\selectlanguage{english}m.

Root residues, which we will refer to as particulate organic matter
(POM), were segmented from the filtered images with Ilastik software, a
machine learning-based tool (Berg et al., 2019). A random forest
classifier was used on a multi-dimensional feature space of the filtered
gray scale images. The classifier was trained using two cores from each
combination of different vegetation and sites (20 of total 114 cores)
and then applied on entire cores. The training dataset produced
out-of-bag error rate estimates less than 1.8\% in overall, and all
segmented POM images were visually inspected to ensure the accuracy and
integrity of the segmentation process. The outcome of POM segmentation
was denoised by removing objects smaller than 4 voxels in diameter from
the images.

Segmentation of the filtered grayscale images into pore and solid binary
images was performed to identify the pores visible at the image
resolution, referred further on as image-based pores. For each sample
the segmentation threshold was estimated as an ensemble of six
segmentation methods (i.e., Otsu, Triangle, Huang, IsoData, Li, and
Moments). The global thresholds for the stack of images in each
individual core, estimated using the six segmentation methods, were
averaged and applied to that stack to separate the solid and air-filled
voxels in the images using SimpleITK in Python (Beare, Lowekamp, \&
Yaniv, 2018; Lucas et al., 2022). Obtained images were used to compute
pore size distributions using `Local Thickness' tool, an approach based
on the maximum inscribed sphere method (Hildebrand \& R\selectlanguage{ngerman}üegsegger, 1997;
Vogel, Weller, \& Schlüter, 2010).

Biopores were identified as described by (Lucas et al., 2022; Lucas,
Schlüter, Vogel, \& Vetterlein, 2019b). Specifically, to employ
tubular-shaped features of biopores in differentiating them from other
irregularly shaped pores, we used the Tubeness plugin in ImageJ-Fiji for
shape detection. As rising \selectlanguage{greek}σ-\selectlanguage{english}values significantly increased the
computational time, binary images were scaled down to 50\% and 20\% for
Tubeness filtering with \selectlanguage{greek}σ-\selectlanguage{english}values ranging from 1-4 and 2-30, for each
scale respectively, with a step size of 1. Gaussian blurring was applied
to the entire binary image with varying \selectlanguage{greek}σ-\selectlanguage{english}values in order to efficiently
identify biopores of various diameters. The resulting tubular channels
were slightly smaller than the root channel itself due to the exclusion
of rough surface on biopore walls. Thus, to better capture the actual
width of biopores, 3D dilation steps were employed as a postprocessing
measure. After combining all elongated objects, misclassified objects
were removed (Phalempin, Lippold, Vetterlein, \& Schl\selectlanguage{ngerman}üter, 2021a).
Proportions of biopores in the entire pore system were calculated. After
that, proportions of biopores occupied by POM were computed by first
calculating the volume of POM located in biopores and then dividing this
volume by the entire volume of biopores.

Nine masks corresponding to interval regions nine distances away from
the segmented POM (0-0.25, 0.25-0.5, 0.5-1.0, 1.0-1.5, \ldots{},
3.0-3.5, and 3.5-4.0 mm) were created using 3D distance transform in
ImageJ-Fiji. Then, masks of interval regions were applied to the
pore-solid segmented image and to the pore size distribution image of
the entire sample to calculate the porosities and the size distributions
individually for each interval region. Contributions of pores of
different size classes to image-based porosity of the distance interval
regions were expressed as pore fractions (\%). We considered three pore
size classes, namely 36-150 \selectlanguage{greek}μ\selectlanguage{english}m, 150-300 \selectlanguage{greek}μ\selectlanguage{english}m, and \textgreater{} 300 \selectlanguage{greek}μ\selectlanguage{english}m \selectlanguage{ngerman}Ø.
The 36 \selectlanguage{greek}μ\selectlanguage{english}m \selectlanguage{ngerman}Ø corresponded to the smallest pore size that could be
reliably detected on the studied images. Pores \textless{} 150 \selectlanguage{greek}μ\selectlanguage{english}m \selectlanguage{ngerman}Ø are
known to have especially high microbial activity and strongly contribute
to the C processing (Kravchenko \& Guber, 2017; Kravchenko et al., 2019;
Strong et al., 2004), and pores \textless{} 300 \selectlanguage{greek}μ\selectlanguage{english}m \selectlanguage{ngerman}Ø function as the
secondary pathways for water and nutrient supplies to resident
microorganisms (Franklin et al., 2021).

A Connectivity tool of BoneJ plugin in the ImageJ-Fiji was used for the
pore connectivity calculations: first, Euler numbers (\selectlanguage{greek}χ\selectlanguage{english}) were computed,
and the numbers were divided by the total volume of corresponding
regions (\emph{V} ) (Odgaard \& Gundersen, 1993; Vogel \& Roth, 2001):

\(\chi_{V}=\ \frac{N-C+H}{V}\) (1)

Where \emph{N} is the number of isolated objects, \emph{C} is the number
of redundant connections or loops, and \emph{H} is the number of
completely enclosed cavities, which are typically negligible in soil
pore system (Lucas, Vetterlein, Vogel, \& Schl\selectlanguage{ngerman}üter, 2021; Vogel, 2002).
The minimum size of the object was 2 × 2 × 2 voxel. Higher, e.g.,
positive, \selectlanguage{greek}χ\selectlanguage{english}\emph{\textsubscript{V}} values calculated via Eq. (1)
correspond to lower connectivity, while lower, e.g., negative, to the
higher connectivity. To simplify the presentation of the connectivity
data we report the results as negative values of
\selectlanguage{greek}χ\selectlanguage{english}\emph{\textsubscript{V}} , that is, the high values of
-\selectlanguage{greek}χ\selectlanguage{english}\emph{\textsubscript{V}} correspond to high connectivity while the low
values to the low one.

Since pore connectivity can be affected by the volume of the soil in
which it is calculated, we did not assess it at the same distance
intervals as those that were used for the image-based porosity and pore
size distributions described above. Instead, we only calculated it in
immediate vicinity of the residue, i.e., the region of 0-0.25 mm away
from the POM, and for the entire soil volume. The resultant two
estimates of the connectivity were used for comparisons among the five
experimental sites and plant systems.

2.4 Statistical analysis

The data were analyzed using SAS 9.4 (SAS Institute Inc., NC, USA)
procedures of PROC MIXED and PROC GLIMMIX. Since we did not expect that
6-7 years of disparate vegetation influenced soil texture and
mineralogy, the statistical models for texture and mineralogy
characteristics included only the fixed effects of the experimental
sites. For the other soil properties, the statistical models included
the fixed effects of sites, plant systems, and their interaction.
Statistical models for analyses of image-based porosity data at
different distances from POM additionally included the same fixed
effects as the soil properties and individually tested by distance
intervals, as the interaction among the sites, plant systems, and
distances was significant. All models included the random effects of
experimental blocks nested within the sites and, when necessary, the
random effects of cores nested within the blocks, plant systems, and
sites. The latter were used as an error term for testing the plant
system effects. The assumptions of normality and variance homogeneity
were assessed using normal probability plots, plots of residuals vs.
predicted values, and Levene's tests for equal variances.

Additionally, we grouped the sites into two soil texture classes for
comparing pore size distributions and connectivity between
finer-textured soils and coarser-textured soils. The first group
included Oregon, Lux Arbor, and Escanaba sites, the three soils with
\textless{} 66 \% sand content, and the second group consisted of
Hancock and Lake City sites with \textgreater{} 82 \% sand content
(Table 1). Models for analysis of pore size distribution data within
each distance interval and of connectivity data within 0-0.25 mm
distance and entire image stack included the fixed effects of plant
systems, soil groups, and their interaction, and random effects of
experimental sites nested within soil groups, blocks nested within the
sites, and cores nested within the blocks, plant systems, sites, and
groups.

Linear relationships among soil texture variables, mineralogical and
clay mineralogy variables, proportions of biopores and POM in biopores,
and the distance-based porosities and connectivity were assessed using
Pearson's correlation coefficients.

\textbf{3 RESULTS}

3.1 Soil texture and mineralogy in the studied sites

Sand content varied greatly among the studied sites, with Oregon site
having the lowest content at 10\% and Lake City having the highest at
87\% (Table 1). The silt content was the highest in Oregon at 73\% and
the lowest in Lake City at 9\%, and the site with the highest clay
content was also Oregon at 17\%. In all five sites, quartz was the
dominant mineral (78-92\%) with 6-16\% of K-feldspars and P-feldspars,
and \textless{} 2\% contents of other minerals. Hancock and Lake City
had higher contents of quartz and lower contents of K-feldspars and
P-feldspars compared to Oregon, Lux Arbor, and Escanaba sites. Mica
dominated the clay fraction of all studied soils (55-67\%), while
smectite, kaolinite, and chlorite were present at \textless{} 20\%, and
did not differ among the sites.

Quartz was positively correlated with sand content, while silt and clay
contents were positively correlated with feldspars, dolomites, and
siderites across all studied sites, which (Table S1). Interestingly,
contents of smectite and kaolinite were positively correlated with sand
contents, while mica was negatively correlated with sand but positively
with silt and clay (Table S1).

3.2 Bioporosity and POM located within biopores

The soils of Oregon site had the lowest total and image-based porosity,
measuring at around 21\% and 10\% in both plant systems, respectively
(Fig. 2A). In soils of Lux Arbor and Escanaba, both types of porosities
were higher than those of Oregon. The soils of Hancock and Lake City had
the greatest total and image-based porosity, while lower bioporosity
than the other three sites (Fig. 2A). There were no significant
differences observed in both types of porosities and bioporosities
between the two plant systems, with the exception of the bioporosity of
Lux Arbor site (Fig. 2A). Proportions of the image-based porosity
occupied by biopores were also the greatest in soils of Oregon and Lux
Arbor compared to those of Hancock and Lake City soils under both plant
systems (Fig. 2C). The significant difference in the proportions
occupied by biopores between the two plant systems was found only in Lux
Arbor site, where biopores constituted 19\% and 24\% of the image-based
porosities of prairie and switchgrass systems, respectively (Fig. 2C).
The proportion of pore space occupied by biopores was negatively
correlated with sand content and quartz, while positively correlated to
silt, clay and P-feldspar (Table S2).

Neither the two plant systems nor the five studied sites showed
significant differences in POM fractions (Fig. 2B). However, proportions
of biopores occupied by POM varied among the studied soils (Fig. 2D). In
the soils of Hancock and Lake City POM occupied \textgreater{}30\% of
the bioporosity, while in Oregon and Lux Arbor it was \textless{}20\%
(Fig. 2D). The bioporosity occupied by POM was positively correlated
with sand and quartz contents and negatively correlated with silt, clay,
K- and P-feldspar contents (Table S2).

3.3 Pore structure changes with the distance from POM

Image-based porosity decreased with the distance from the POM surface in
all soil cores (Fig. 3). In both systems, at \textless{} 0.25 mm
distance from POM the image-based porosity tended to be higher in
finer-textured soils of Oregon, Lux Arbor, and Escanaba than in the
coarser-textured soils of Hancock and Lake City (Fig. 3). These pores
are shown as examples in Fig. 4. However, the differences in the
porosity among the sites at \textless{} 0.25 mm distance faded at
0.25-0.5 mm interval, and upon distance reaching the 0.5-1.0 mm the
image-based porosity of Hancock and Lake City exceeded that of the
Oregon, Lux Arbor, and Escanaba. The image-based porosities at
\textless{} 0.25 mm distance were negatively correlated with sand
content and quartz, while total image-based porosities were positively
correlated with them (Table S4). The total porosities were negatively
correlated with silt, clay, and P-feldspar.

The vegetation also affected the imaged-based porosity. From 0.1-1.5 mm
distance interval in Hancock and 2.5-3.0 mm in Lake City, the
image-based porosity was greater in prairie than that in switchgrass
system, while the porosity in switchgrass was numerically greater at
\textless{}0.25 distance (Fig. 3 and Table S3).

The contribution of the three pore-size classes into the visible
porosity changed with the distance from POM. In the region at
\textless{} 0.25 mm away from the POM, pore group within 36-150 \selectlanguage{greek}μ\selectlanguage{english}m \selectlanguage{ngerman}Ø
size range accounted for 4\% of the total porosity in finer-textured
soils and 11\% in coarser-textured soils (Fig. 5). Meanwhile, relatively
larger pores (\textgreater{} 300 \selectlanguage{greek}μ\selectlanguage{english}m \selectlanguage{ngerman}Ø) contributed to 75\% and 58\% of
the porosity in finer- and coarser-textured soil of the same distance
region, respectively. The contributions of 36-150 \selectlanguage{greek}μ\selectlanguage{english}m pores in both
textured groups of soils increased with the distance from POM, while
that of the larger pores decreased. Notably, the increases in the
fractions of smaller pores were more drastic in coarser-textured soils
compared to that in finer-textured soils (Fig. 5). Pores in the coarser
soils were mostly represented by finer size pores (36-300 \selectlanguage{greek}μ\selectlanguage{english}m \selectlanguage{ngerman}Ø) beyond
1.0 mm distance from POM, whereas the finer soils still had 35\% and 11
\% of the larger pores at 1.0 mm and at 4.0 mm distances from POM,
respectively. In coarser-textured soils, prairie system had greater
fraction of smaller size pores within interval regions from 0 to 3.0 mm
than that of switchgrass system (Fig. 5C and 5D), while the fractions of
such pores did not differ between two systems in finer-textured soils
(Fig. 5A and 5B).

In both plant systems, in the immediate vicinity of POM (\textless{}0.25
mm away from POM), pore connectivity was greater in finer-textured than
in coarser-textured soils. However, when examined across the entire soil
volumes, the connectivity was lower in finer-textured than in
coarser-textured soils (Fig. 6). The connectivity in the vicinity of POM
was negatively correlated with sand, quartz, and smectite contents,
while it was positively correlated with silt, clay, and P-feldspar
contents (Table S5). However, the total connectivity showed the opposite
trend, being positively correlated with sand, quartz, and smectite
contents.

\textbf{4 DISCUSSION}

The results demonstrated that soil pore structure in the root
detritusphere and in the whole soil volumes were affected by both soil
texture and plants. Coarser-textured soils had much higher image-based
porosity, yet fewer pores of biological origin than finer-textured
soils. The biopores of fine-textured soils were numerous and constituted
a significantly greater portion of the overall pore space, yet not
holding as much remaining POM as those in the coarse-textured soils.
Pore-size distributions in detritusphere as well as their spatial
distribution trends with distance from the decomposing roots also
markedly differed between the finer- and coarser-textured soils. Pores
in the immediate vicinity of POM were better connected in finer-textured
soils than in coarser-textured soils in both plant systems. While
switchgrass soil had more biopores than prairie, its detritusphere pores
consisted of relatively large size pores than those of the prairie,
especially in coarser-textured soils.

4.1 Influences of soil texture on detritusphere pores

The greatest porosities found at \textless{} 0.25 mm away from the POM
in soils of both plant systems indicated that the vicinity of the POM
was mostly air-filled (Fig. 3). This ``POM gap'' between soil particles
and root residues can be explained by incomplete filling of existing
pores by roots during their growth and decrease of roots' volume due to
shrinking upon drying and/or their decomposition (De Gryze et al.,
2006). Consistent with this explanation, roots of \emph{Agave
deserti}were found to shrunk by 34\% in 24 days of natural drought in a
greenhouse study (North \& Nobel, 1997), and transpiration shrank roots
of \emph{Lupinus albus} (Koebernick et al. 2018). Decreases in POM
volume due to decomposition were both visually observed and quantified
using X-ray \selectlanguage{greek}μ\selectlanguage{english}CT images of intact soil samples (Juyal et al., 2021; Kim,
Guber, Rivers, \& Kravchenko, 2020).

Our findings of inherent texture and mineralogy characteristics
influencing the contribution of biopores to the overall soil porosities
(Fig. 2C and Table 1) were consistent with expectations and previous
reports. In relatively sandy soils the biopores formed by roots were
partially or completely refilled by sand grains after root
decomposition, while in loamy soils the biopores that the roots left
behind still maintained their structure (Phalempin et al., 2022). Sand
grains have high volume-to-surface area ratios, and quartz on grain
surfaces often lacks negative charge (Bazzoffi, Mbagwu, \& Chukwu, 1995;
Schrader \& Zhang, 1997), resulting in low stability of particle
arrangements (Almajmaie et al., 2017). Thus, the subsidence and
displacement of the dispersed sand grains near decaying POM residues is
likely among the reasons for the lower contributions of biopores to
overall porosities in coarser-textured soils (Hancock and Lake City
sites) compared to that in finer-textured soils (Oregon, Lux Arbor, and
Escanaba sites) (Fig. 2C) and for the greater proportions of biopore
space occupied by POM (Fig. 2D). The lower pore connectivity near the
POM in coarser-textured than in finer-textured soils (Fig. 6) is another
outcome of low stability. Finer, i.e., lower sand and quartz contents,
soil particles are expected to facilitate maintenance of the structure
by pores around POM, as compared to that of pores in coarser-textured
soils.

The other two contributors to the observed differences in biopore
volumes and in POM presence within the biopores are the inherent
differences between coarser- and finer-textured soils in terms of (i)
root growth and (ii) root residue decomposition rates. The volume of
biopores and their occupation by roots might be overall lower in
coarser-textured soils due to poorer root growth conditions (Dodd \&
Lauenroth, 1997; Sainju, Allen, Lenssen, \& Ghimire, 2017). POM in soils
with high sand contents might decompose slower than that in the soils
with low sand contents due to lower microbial activity at organo-mineral
surfaces of sand grains (Haddix et al., 2020; Kaiser, Mueller,
Joergensen, Insam, \& Heinemeyer, 1992; K\selectlanguage{ngerman}ögel-Knabner et al., 2008).
Indeed, a negative correlation between sand contents and microbial
biomass C was found across our experimental sites in a parallel study
(Lee, Lucas, Guber, Li, \& Kravchenko, 2023). Thus, in coarser-textured
soils, the size of POM residues might not be decreasing as quickly as in
the finer-textured soils, and the region around the POM may not be
completely empty yet (Fig. 3). However, if the differences in plant
growth and decomposition rates had indeed played a significant role in
generating the observed differences in the biopore occupation by POM
(Fig. 2D), we would expect to also detect the differences in terms of
POM occupation between the two plant systems. Soils of restored prairie
have developed higher SOM (Sanford, 2014; Sprunger \& Robertson, 2018),
thus better plant growth conditions, and much more active and abundant
microbial communities (Lange et al., 2015), e.g., significantly higher
microbial biomass C (Lee et al., 2023), than those of the monoculture
switchgrass. Yet, there were no significant differences between the two
systems in terms of POM occupation of the biopores (Fig. 2D) as well as
the porosity in the detritusphere at least 1.0 mm away from POM (Table
S3), ruling out the importance of these contributors. Thus, we conclude
that the loss of structure and collapsing of biopores in
coarser-textured soils is the main reason of the observed effects and is
likely a wide-spread phenomenon.

Larger proportion of 36-150 \selectlanguage{greek}μ\selectlanguage{english}m \selectlanguage{ngerman}Ø pores in close proximity (\textless{}
0.25 mm distance) to POM in coarser-textured soils (Fig. 5) is
consistent with lower soil porosity at the same distance (Fig. 3). Sand
grains dominating coarser-textured soils can sporadically fill the POM
gaps (Phalempin et al., 2022; Schrader \& Zhang, 1997), and the filling
by the grains may fragment the space of the gaps into finer pores.
Indeed, porosities within the \textless{}0.25 mm distance to POM were
negatively correlated with sand contents (Table S5). However,
coarser-textured soils had larger contribution of such pores in
intervals of \textgreater{} 0.25 mm compared to finer-textured soils,
showing positive correlations between sand contents and porosities of
the entire volume (Table S5). Typically, in such regions beyond the
root-influenced zone -- areas where root-induced pores are negligible --
the porosity tends to increase with higher sand content (Ding, Zhao,
Feng, Peng, \& Si, 2016; Fan et al., 2021; Nimmo, 2013). Indeed, gaps
between sand particles are likely to primarily consist of pores that
range between 50-200 \selectlanguage{greek}μ\selectlanguage{english}m \selectlanguage{ngerman}Ø (Bantralexis, Markou, \& Zografos, 2023).
Therefore, the contrasting contributions of finer pores by distances are
an indication of a localized effect (\textasciitilde{} 0.25 mm) of roots
on the pore structure, beyond which the porosity was mostly controlled
by the soil texture.

4.2 Influences of vegetation on detritusphere pores

The overall influence of the studied plant systems, 5-6 years after
their establishment, on the pore characteristics of detritusphere was
much lower than that of the inherent soil characteristics, i.e., texture
and mineralogy. An important exception was the image-based porosity in
remote portions of detritusphere (\textgreater{} 1.0 mm): it tended to
be greater in the soils of restored prairie than in those of switchgrass
(Fig. 3 and Table S3). Switchgrass roots often reuse existing biopores
(Lucas, Santiago, Chen, Guber, \& Kravchenko, 2023), and their thick
roots were likely responsible for soil compaction and low porosity at
\textgreater{}1 mm distances (Aravena, Berli, Ghezzehei, \& Tyler, 2011;
Liu, Meng, Huang, Shi, \& Wu, 2022). On the contrary, finer and heavily
branching roots of many plant species of restored prairie likely
promoted formation of finer pore networks throughout the entire
detritusphere stabilizing them via root exudates and rhizodeposits
(Hairiah, Widianto, Suprayogo, \& Van Noordwijk, 2020; Smith,
Wynn-Thompson, Williams, \& Seiler, 2021). We surmise that these very
fine roots rapidly decomposed after soil sampling and thus could not be
detected as POM in the current study.

\textbf{5 CONCLUSIONS}

Soil pore structure both in the entire soil volume and in the root
detritusphere was significantly influenced by soil texture and
minerology. Coarse-textured quartz-rich soils had higher porosity but
lower bioporosity than fine-textured soils, as well as greater
proportions of biopore spaces occupied by POM. There were clear
differences between fine- and coarse-textured soils in spatial patterns
of pore size distributions as a function of distance from POM. In the
immediate vicinity of POM. Finer-textured soils had higher porosity in
close proximity of POM, that is greater POM-gap, consisting mainly of
large pores (\textgreater{}300 \selectlanguage{greek}μ\selectlanguage{english}m \selectlanguage{ngerman}Ø) as well as better pore connectivity
compared to those of the coarser-textured soils. Despite known
differences in the root characteristics of the studied plant systems,
i.e., monoculture switchgrass and restored prairie, their impact on
detritusphere pore structure was relatively minor. Lack of plant system
effect suggests that the observed differences in detritusphere pore
structure between finer- and coarser-textured soils are of primarily
physical/mineralogical origin, e.g., due to loss of structure and
collapsing of biopores in the latter, and the phenomenon present across
a wide range (Alfisols, Entisols, and Spodosols) of soil types. The
study provides an insight into the relationship among soil texture,
mineralogy, and detritusphere pore structure, which serves as an
important arena for microbial activity and soil C processing.

\textbf{ACKNOWLEDGEMENTS}

We would like to thank Michelle Quigley from Michigan State University
for conducting X-ray \selectlanguage{greek}μ\selectlanguage{english}CT scanning, Martin Pentrak from University of
Illinois-Champaign for conducting X-ray powder diffraction, Jenie Gil
Lugo and Maxwell Oerther for assistance with sample collection, and the
Kellogg Biological Station and Great Lakes Bioenergy Research Center
team for agronomic management of the field experiment. This research was
funded by the Great Lakes Bioenergy Research Center, U.S. Department of
Energy, Office of Science, Office of Biological and Environmental
Research under Award Number DE-SC0018409. The work was also supported by
the NSF LTER Program (DEB 1027253) at Kellogg Biological Station, by
Michigan State University's AgBioResearch, and by NC Regional Multistate
Project 1187.

\textbf{CONFLICT OF INTEREST STATEMENT}

The authors declare no competing financial interest.

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