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\begin{document}

\title{LIVESTOCK GRAZING REDUCES SOIL QUALITY AND THREATENS RECOVERY OF A
DEGRADED ANDEAN ARAUCARIA FOREST.}



\author[1]{Oscar  Crovo}%
\author[2]{Clara  da Costa-Reidel}%
\author[2]{Rolando Rodríguez }%
\author[2]{Felipe Aburto}%
\affil[1]{Universidad de Concepción}%
\affil[2]{Universidad de Concepcion}%


\vspace{-1em}



  
  \date{June 18, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Araucaria araucana is an iconic long-lived endangered tree species
exclusively distributed in Southern Chile and Argentina. Araucaria
forest ecosystems provide a myriad of ecosystem benefits to local
aboriginal Mapuche-Pehuenche communities. Among the main current threats
for Araucaria forests are the increasing frequency and severity of
wildfires and overgrazing. This study evaluates the effect of
uncontrolled livestock grazing on soil quality indicators linked to
critical functions relevant to forest regeneration and ecosystem service
provision. We also aim to determine a set of soil quality indicators
that are sensitive enough to grazing pressure, so they are useful as
early indicators of degradation or the effectiveness of restoration
practices. This study evaluated twenty soil quality indicators in two
contiguous degraded forest areas with contrasting grazing pressure. We
observed a substantial shift in forest structure, a reduction in tree
coverage on the overgrazed sites. Overgrazing has produced significant
deterioration of most soil physical, chemical, and biological quality
indicators making soil conditions less suitable for seed germination and
sapling establishment. We also observed an alteration in C, N, and P
biogeochemical pools. Besides, soil physical indicators alterations
suggest changes in these soils' hydrological behavior, potentially
reducing water storage, availability, and increasing runoff. We show
that uncontrolled grazing in native protected areas degrades soils and
forest health, restricting forest regeneration and potentially
accelerating erosive processes. Our results emphasized the need for an
improved conservation plan for these forests that systematically
evaluates and monitors livestock grazing and all its direct and indirect
effects, including soil quality.%
\end{abstract}%



\sloppy


LIVESTOCK GRAZING REDUCES SOIL QUALITY AND THREATENS RECOVERY OF A
DEGRADED ANDEAN ARAUCARIA FOREST.

Oscar Crovo\textsuperscript{1}, Clara da
Costa-Reidel\textsuperscript{1}, Francisco Montecino\textsuperscript{1},
Rolando Rodr\selectlanguage{ngerman}íguez\textsuperscript{2}, Felipe Aburto\textsuperscript{1*}

\textsuperscript{1} Laboratorio de Investigación en Suelos, Aguas y
Bosques (LISAB). Departamento de Silvicultura, Universidad de
Concepción, Facultad de Ciencias Forestales.

\textsuperscript{2} Departamento de Manejo de Bosques y Medioambiente,
Universidad de Concepción, Facultad de Ciencias Forestales, Concepción,
Chile.

* feaburto@udec.cl, Victoria 631, Facultad de Ciencias Forestales,
Barrio Universitario, Concepción, Chile

ABSTRACT

\emph{Araucaria araucana} is an iconic long-lived endangered tree
species exclusively distributed in Southern Chile and Argentina.
Araucaria forests provide a myriad of ecosystem benefits to local
aboriginal Mapuche-Pehuenche communities. Among the main current threats
for Araucaria forests are the increasing frequency and severity of
wildfires and overgrazing. This study evaluates the effect of
uncontrolled livestock grazing on soil quality indicators linked to
critical functions relevant to forest regeneration and ecosystem service
provision. We also aim to determine a set of sensitive soil quality
indicators to grazing pressure, so they are useful as early indicators
of degradation or restoration practices' effectiveness. This study
evaluated twenty soil quality indicators in two contiguous degraded
forest areas with contrasting grazing pressure. We observed a
substantial shift in forest structure, a reduction in tree coverage on
the overgrazed sites. Overgrazing has significantly affected most soil
physical, chemical, and biological quality indicators making soil
conditions less suitable for seed germination and sapling establishment.
We also observed an alteration in C, N, and P biogeochemical pools.
Besides, soil physical indicators alterations suggest changes in these
soils' hydrological behavior, potentially reducing water storage,
availability, and increasing runoff. We show that uncontrolled grazing
in native protected areas degrades soils and forest health, restricting
forest regeneration and potentially accelerating erosive processes. Our
results emphasized the need for an improved conservation plan for these
forests that systematically evaluates and monitors livestock grazing and
its direct and indirect effects, including soil quality.

\textbf{Keywords} : soil health, overgrazing, tree regeneration,
mountainous forest, volcanic soils

INTRODUCTION

Mountain forest ecosystems are a complex socioecological system where
competitive land-uses, management practices, and biodiversity
conservation are in a continuous quarrel (Concostrina\selectlanguage{english}-Zubiri et al.,
2017; Renison et al., 2010). Most of the ecosystem services that forests
provide, such as clean water provision, carbon sequestration, nutrient
cycling, and biodiversity conservation, depend on maintaining adequate
soil functioning and quality (Dominati et al., 2010; Wu, 2013). However,
large areas of these natural forest ecosystems have been pushed to a
limit where soil conservation and functioning are at risk. The main
drivers for forest ecosystem degradation processes are timber extraction
and overgrazing, which affect both soil quality and natural forest
regeneration and successional processes.

In many South American mountainous areas, where grazing is a relevant
activity, there have been reports of land and forest degradation (e.g.,
Renison et al. (2010)). In the past decades, researchers have shown
strong evidence of the negative impacts of overgrazing on plant
communities and a few other on soil degradation (Concostrina\selectlanguage{english}-Zubiri et
al., 2017; Greenwood \& McKenzie, 2001; Krummelbein et al., 2009;
Manzano \& Navar, 2000; Suet al.,2004). The reduction of canopy cover,
the destruction of topsoil structure and soil compaction followed by a
decrease in soil infiltration and chemical properties (Krummelbein et
al., 2009; Steffens., 2008) induced by grazing has led to accelerated
erosion and land degradation in many regions around the world (Steffens
et al., 2008).

Zamorano-Elgueta et al. (2012) studied cattle activity
effects\emph{Araucaria araucana} (Molina) K. Koch forests in the Chilean
coastal mountain range, finding that overgrazing produced soil
compaction and diminished tree recruitment. On the Argentinian side of
the Cordillera de los Andes, Renison et al. (2010) found that domestic
livestock is indeed the primary driver of forest and soil degradation.
Currently, there is no governmental regulation concerning the use of
forests for pasturing animals in Chile (Dube et al., 2016). Thus, forest
and soil degradation could become worse in the future decades. The
control of these potentially irreversible soil degradation processes is
critical for Araucaria forests as this is an endangered species (Premoli
et al., 2013) with innumerable cultural and environmental value.
Moreover, Araucaria is an internationally protected species in both
Chile and Argentina through its listing in I appendix of the Convention
on International Trade in Endangered Species of Wild Fauna and Flora
(CITES).

Soil quality indicators for soil derived from volcanic materials have
been relatively less studied than for other soil types, with a few
exceptions (e.g., Alfaro et al. 2018, Valle and Carrasco 2018 Zuniga et
al., 2015). Valle and Carrasco (2018) tested a complete set of soil
quality indicators in three volcanic soils identifying a group of them
sensitive to contrasting land-use changes (native forest to prairies and
cropland). The main parameters highlighted for these authors were bulk
density, water retention-derived properties, SOC, P-Olsen, extractable
Al, and base saturation.

In this study, we assessed the effect of exotic livestock overgrazing
in\emph{Araucaria} forests' soil quality, using the relatively recently
created Ralco National Reserve (Biobio Region, Chile) as a study site.
This National Reserve was created for environmental conservation
considering the Mapuche-Pehuenche local indigenous communities'
historical use for livestock grazing and other relevant cultural
services like collecting araucaria tree seed (pinon).

Here we aim to evaluate the effect of grazing in critical soil
properties related to ecosystem functions that sustain forest
regeneration. We also seek to identify which properties are more
sensitive to this impact. We hypothesized that livestock grazing had
hampered soil quality in these weakly developed volcanic soils,
affecting critical functions to plant recruitment and forest health. We
expect that the soil properties identified here will help build a robust
and sensitive set of soil quality indicators that could alert land
managers of ongoing degradation in similar ecosystems across the Andean
Region.

2. MATERIALS AND METHODS

\emph{2.1 Study site.}

The study was carried out at the Ralco National Preserve (37deg52'50.47''
S - 51deg20'50, 90'' O), specifically in the Quillaicahue River Valley
with an average altitude of 1,350 m above sea level. The climate has a
distinct snow season with a medium winter temperature below 0degC (Donoso
et al., 2014), with precipitations that can hold up to 4,000 mm in the
form of snow during winter (Veblen, 1982). The Ralco national reserve is
located in a zone with the strong influence of two large volcanoes, the
Callaqui (37\selectlanguage{english}@56'S, 71@ \selectlanguage{english}26'W, 3100 m.a.s.l.) and the Copahue (37@\selectlanguage{english}51'S
71@\selectlanguage{english}10'W, 2979 m.a.s.l.). The geomorphological processes that formed this
high-altitude valley consist of series of volcanic, glacial, and fluvial
processes (Zaio \& Mardones, 1991). Soils have developed on interlayered
pyroclastic materials and volcanic-derived fluvial deposits overlying an
old basaltic-andesitic lava flow. Like most national protected areas in
Chile, no comprehensive soil survey has been conducted (CONAF 1996).
Therefore, before establishing the sampling plots, we carried out a
general soil survey within May 2015 and May 2016, defining the prevalent
soils of the Quillaicahue valley, which were classified as
either\emph{Vitric hapludands} and \emph{Vitrandic udortents} (Soil
Survey Staff, 2014).

The site presents a temperate Andean resinous forest of Araucaria and
Dombeyi's beech vegetation zone (Luebert \& Pliscoff, 2006). The main
tree species is \emph{Araucaria araucana\textbf{,}} accompanied
by\emph{Nothofagus} \emph{dombeyi, Nothofagus obliqua,}
and\emph{Nothofagus antarctica} . The understory comprises evergreen
shrubs such as \emph{Colletia spinosa, Berberis sp,} and sparse grasses
such as\emph{Festuca sp.,} which is the primary palatable grazing
resource\emph{.} Before creating this protected area, the historical
land use was associated with the productive activities present in this
region, mainly forestry and livestock grazing (Moreira-Mu\selectlanguage{ngerman}ñoz \& Borsdorf
2014). The Ralco national reserve was established in 1987 to protect the
most septentrional population of \emph{Araucaria araucana} woods
alongside to its natural resources such as soil and water and allowing
the rational use of these resources by the indigenous communities (CONAF
1996). Despite the conservation efforts, there has been considerable
overgrazing in the communal fields during the snow-free seasons. Thus,
18 years ago, a fence was installed to separate the high grazing areas
from the low grazing areas. The differences between the high and low
grazing areas is mainly due to fencing and the proximity to the communal
grazing fields located directly north of our study areas.

\emph{2.2. Soil Sampling and Laboratory Analysis.}

The experimental site was divided into two areas with different grazing
pressure treatments (Figure 1). The open grazing pressure area (OG) is a
communal grazing field contiguous to pastoral communities. In contrast,
the controlled grazing area (CG) corresponds to a fenced field
restricting the livestock's free movement, especially cows.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1.} General map of the study site, the experimental
plots, and sampling points. In the upper left and lower right corners of
the map, there are zoom layers displaying the four 100 x 100 m plots for
each condition (OG and CG fenced area).

In each zone (CG and OG), four plots of 100 x 100 m were randomly
established using the Geographic Data Analysis R package (Hijmans \& Van
Etten, 2016). Then, at each plot, eight random sampling points were
selected using the same R package. Once the sampling points were
established in the field, in each one of the random plots, we stratified
four of these points in a bare soil condition and four under Araucaria
canopy to consider the heterogeneity derived from the high clustering of
tree coverage in the study area (Figure 1).

In each point, and to consider the spatial variation of the soils
(Kirwan et al., 2005), four auger samples per sampling point were
composited at two established depths (0 -- 15 cm and 15 -- 30 cm)
(n=128). The samples were collected in sterile and hermetic bags and
separated into two aliquots, one for keeping it at four \selectlanguage{ngerman}°C for
NO\textsubscript{3}\textsuperscript{-} and
NH\textsubscript{4}\textsuperscript{+}. These were measured
colorimetrically within 48h after sampling (Forster, 1995; Miranda et
al., 2001). The second larger aliquot was air-dried (25°C) and sieved
(\textless{}2mm) for later analysis. Total C and N were quantified by
Dumbas combustion using a SERCON© EA total CHNS analyzer. Besides, we
determined N following the Kjeldahl distillation method (Sadzawka et
al., 2006). Total, organic and inorganic phosphorus were determined
following the protocol described by Bowman \& Moir (1993), using EDTA as
the extraction solution. Available phosphorus was measured by the Olsen
extraction method adapted by Sadzawka et al. (2006). Permanganate
oxidizable carbon (POXC) was determined following the procedure
described in Weil et al. (2003) as an index for easily decomposable
carbon (Culman et al., 2012). Soil pH was measured with combined
electrodes in both 1:1 soil/water and 1:50 NaF suspension. Cation
exchange capacity (CEC) was determined following the protocol described
in Sadzawka et al. (2006) buffered (pH=7) ammonium acetate as the
extractant solution. All these analyses were performed at the Soil,
Water, and Forest Research Laboratory of the University of Concepción
(http://www.lisab.udec.cl).

For physical parameters, in each sampling point, infiltration tests were
conducted using a Mini-Disk infiltrometer (Decagon ©). Also and
following the protocol described in Mohanty et al. (1994), a 750cc
undisturbed soil sample was taken from the soil surface up to 10 cm
depth and carefully transported into the laboratory for soil saturated
conductivity measurements (K\textsubscript{sat}) using the
''Constant-Head'' method (Black et al.,1965). Following the
K\textsubscript{sat} measurements, the samples were analyzed for soil
bulk density, particle density (Blake \& Hartge, 1986), texture (Day,
1965), and water holding capacity as described in Burt (2004). Also,
undisturbed soil samples were taken at each sampling point from the top
5 soil cm and used to estimate aggregates stability using a wet sieving
apparatus (Eijkelkamp ®) following the methodology described by Kemper
\& Rosenau (1986) and the adaptation described in Rivera \& Bonilla
(2020) for the Eijkelkamp wet sieving apparatus.

\emph{2.3 Forest evaluation and land cover classification}

For the forest evaluation, we used a simple random sampling design using
the same four 1 ha random plots per condition as sampling units,
measuring every tree above 1.3 m height and breast height diameter (BHD)
in the plot (DBH \textgreater{}1cm). Additionally, we used partial data
from a paired \emph{A.araucana} regeneration study project with the
Chilean national forest corporation described in (Montecino et al., In
Prep), which consisted of four more 1 ha random plots, which allowed us
to reduce error and to represent forest stand conditions better

Forest structure was evaluated by analyzing the observed stand diameter
distribution and fitting the most common probability density functions
such as lognormal, negative exponential, Weibull, and gamma functions
(Newton, 2007). Following goodness-of-fit indices such as the Akaike
Information Criterion (AIC) and Kolmogorov- Smirnov (K-S) test to
determine the best-fit diameter distribution models.

A high-resolution multispectral satellite image was used to generate an
unsupervised land cover classification of the area. The image was
obtained from the World View II satellite with a 0.46m resolution from
February 2015. These images supply great detail and geospatial accuracy,
allowing to conduct an unsupervised classification in ENVI 4.7 software,
using the ISODATA clustering algorithm.

\emph{2.4 Statistical analysis.}

For each chemical and physical parameter, comparisons between treatments
were carried out using a two-way ANOVA following the general linear
model of the ''CAR'' R package (Fox et al., 2012), followed by posthoc
multiple comparisons (LSD test). All the data was checked to meet the
assumptions of normality of residuals and homoscedasticity and
transformed when needed. For P Olsen, we used a Negative Binomial Hurdle
model approximation because of the amounts of zeros (concentrations
below the detection limit). Soil data were subjected to a principal
component analysis (PCA) for data ordination and reduction and
distinguished if conditions were different, and determined what groups
of soil indicators were more effective on differentiating grazing
intensity. The data was auto-scaled previous to the PCA because of the
different natural scales between variables and to attribute an equal
weight to each variable (Sena et al.,2002). All statistical analysis was
conducted in R (R Core Team, 2013).

3. RESULTS

\emph{3.1 Forest and vegetation cover}

The dominant tree species among the study sites were \emph{Araucaria
araucana} and \emph{Nothofagus obliqua.} Even though both conditions
best fitted a lognormal distribution closely followed by a gamma
distribution (S. Material 1), the dominance of small-diameter trees in
both stands suggests that both stands suffered the same relatively
recent disturbance event (logging and clearing for grazing) (Newton,
2007). However, the number of young individuals (\textless{}10 cm BHD)
is considerably higher in the controlled grazing condition, which fits a
negative exponential distribution better than OG, which highlights an
adequate regeneration and more stable growth in the controlled grazing
sites (Donoso et al., 2014b) (Figure 2).

The differences between tree coverage between both conditions can be
visualized in the study area's land cover classification (Figure 3). The
OG condition tree coverage was 28\%, while the CG was 45.5 (a 17.5\%
higher). Though these sites did not show a relevant difference in the
percentage of bare soil, they displayed differences on the shrubs and
herbs cover, where the OG site almost doubled the CG site (44.2 versus
24\%).

\emph{3.2 Soil physical quality indexes}

Soil particle distribution varies across the study site, but they were
dominantly coarse-textured ranging from sandy loam and loamy sand for
both grazing conditions (S. Material 2). Sand contents vary from 65 to
90\%, with very little clay content in most cases (\textless{}5 \%).

None of the soil physical properties showed an interaction among
factors; therefore, each factor was analyzed independently. The
infiltration rate (K) and saturated hydraulic conductivity
(K\textsubscript{sat}) were significantly different between the CG
condition and the OG condition (Table I).

Most soil physical variables showed a significant difference between the
bare soil and the under canopy conditions, except for porosity and water
holding capacity (WHC). Bulk density was significantly lower in the
under-canopy condition and both saturated and unsaturated hydraulic
conductivity. Additionally, the under-canopy condition showed much more
stables aggregates than in the bare-soil condition\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 2.} Observed and fitted distributions of the number of
individuals ha \textsuperscript{-1} for each site (A for controlled
grazing and B for open grazing) according to the different diameter
classes.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 3} . Unsupervised land cover classification of the study
area. The top right (OG) and left bottom (CG) layers are zoom-ins of the
established plots allowing a better view of the real site conditions.

\emph{3.3 Soil chemical quality indicators}

Total Kjeldahl Nitrogen (g/kg) showed a statistically significant
difference between sites, where the nitrogen content was higher in the
CG sites than in the OG sites. Both Total Carbon and Total Nitrogen (\%)
were higher in the CG sites, but no significant difference was found
(Table I). Available ammonium showed relevant differences among sites,
where the CG sites showed lower values than in the OG area. On the other
hand, nitrate was not significantly different. Total phosphorus (mg/kg)
was significantly different between sites, showing higher CG values than
the OG condition, mostly because of differences in the inorganic P
content. Organic P (mg/kg) was higher in the CG sites, but no
statistically significant difference was found. However, the organic P
fraction was significantly larger under canopy across grazing
conditions. Available Olsen P (mg/kg) was consistently lower in the CG
site than in the OG site.

On the other hand, mineralizable N was higher in the CG sites than in
the OG sites, but no statistically significant. Similarly, mean POXC/C
showed no statistically significant difference between conditions, but
it was significantly greater under the tree canopy condition. Soil pH
(H\textsubscript{2}O) showed a significant statistical difference
between conditions, where the pH values in the CG were less acidic than
in the OG site. The overall mean soil CEC was significantly different,
where the CG sites' values were consistently higher than in the OG
sites. All exchangeable bases were markedly higher in the CG condition,
except for sodium that was higher under the OG condition.

\emph{3.4 Selecting soil quality indicators}

To evaluate the sensitivity of different soil chemical and physical
quality indicators, we performed a principal component analysis. The
first three principal components were selected because they accounted
for approximately 50\% of the total variation. Though the contribution
of the three PC to the explanation of the total variation is relatively
small, it still displays loadings above 0.4 with at least three soil
properties (S. Material 3). CEC, total carbon, total Phosphorus, and
Inorganic Phosphorus contributed more strongly to the variability of
PC1. PC2 is positive related to available ammonium, available Olsen-P
and Porosity, and negative related to nitrate. PC3, which explains the
11\% of the total variation, have large negative loadings for the
infiltration rate and K-RUSLE and positive with porosity.

The k-means clustering algorithm display that the CG site tends to
differ from the OG sites even though both PC's only accounts for
approximately 40\% of the total variation (Figure 4). The chemical
properties allow a fair clustering of the sites where loadings of total
and inorganic Phosphorus, CEC, and Kjeldahl Nitrogen contribute the
most. All the differences described above and shown in Figure 5.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 4} . Biplot for PC1 x PC2(A), PC1 x PC3 (B), and PC2 x
PC3 (C) with all the soil variables with their respective eigenvectors.
Differences in the loadings in PC1 allow to cluster by controlled
grazing sites (CG) and the open grazing sites (OG).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Figure 5.} Radar chart includes all the measured variables
scaled by the min-max method to visualize better the principal
differences among the controlled grazing sites (CG) and the open grazing
sites (OG). ``*'' [?] 0.05, ``**'' [?] 0.01, ``***''[?]0.001 according to the
two-way ANOVA results (Table 1).

4. DISCUSSION

Opposite to what has been reported elsewhere, most of the soil's
physical properties did not respond very strongly to augmented grazing
pressure. For example, soil bulk density has been used widely as an
indicator of soil compressing effect caused by overgrazing, especially
in the topsoil; however, the evaluated sites did not display this
behavior (Donkor et al., 2002; Greenwood \& McKenzie, 2001; Manzano \&
N\selectlanguage{ngerman}ávar, 2000; Willatt \& Pullar, 1984). The lack of differences in soil
bulk density (Table I) may be explained by the coarse nature of the soil
texture in these sites (Figure 2), which was dominated by sandy textures
(Sandy loam, Loamy sand). Valle and Carrasco (2018) proposed bulk
density as one of the best indicators for volcanic soils of Chile;
however, they worked with more developed andosols with relatively finer
textures. The low clay content alongside the relatively low bulk density
makes the soils in our study relatively less compressive (Etana et al.,
1997). Besides, grazing in this site is restricted mostly for the summer
season (i.e., ''\emph{veranadas''} ), so at the moment of grazing
perturbations, the soil water content is generally low, so compression
potential is reduced (Sands et al.,1979).

Porosity should also respond to the higher mechanical stress under
greater grazing pressure, negatively affecting gas flux and water flow
(Dörner et al., 2012). In this study, no apparent difference in porosity
was found between grazing conditions. Beck\selectlanguage{english}-Broichsitter et al. (2016)
reported similar soil pore space values for soils derived from volcanic
tephra in the southern-Andean region of Chile, showing that this soil
has low-compression (0,017 PSI m\textsuperscript{-}3). This may explain
why cattle compaction is not perceptible between sites (Figure 5).

Water holding capacity is a relevant soil hydraulic property (Klute \&
Dirksen, 1986) and has been proved as a useful quality indicator of
grazing-induced ecological functioning changes (Greenwood \& McKenzie,
2001). However, like soil porosity, no significant differences were
found in the water holding capacity between grazing conditions.

The results for saturated hydraulic conductivity (K\textsubscript{sat})
are typical values for sandy loam and loamy sand textures. Higher
K\textsubscript{sat} values in the OG suggest that controlling grazing
significantly diminished saturated conductivity (Table I). Experimental
evidence indicates that K\textsubscript{sat} is determined by the large
pores space rather than the micropores (Alaoui et al.,2011), strongly
linked to soil structure (Dorner et al., 2012). Thus, changes in
saturated hydraulic conditions may help us understand possible changes
in pore space's continuity and distribution (Cuevas et al., 2013). The
effect of cattle trampling causes a predisposition for finer soil
particle losses by wind and water erosion (Huang et al., 2007;
Krummelbein et al., 2009; Su et al., 2004), causing a redistribution of
pore continuity in the topsoil thus impairing water flow and
conductivity. This effect is more likely to occur in the open grazing
site as textures tend to be coarser; however, K\textsubscript{sat}values
were higher in this condition. Despite this apparent contradiction,
K\textsubscript{sat} was significantly higher under tree cover, a more
dominant cover in the CG condition. The latter implies that even though
OG had a considerably higher K\textsubscript{sat} at the sampling
points, this condition may not be spatially dominant across the whole
ecosystem.

Like K\textsubscript{sat}, infiltration rates were significantly faster
in the OG condition than in the CG condition. The infiltration rates (K)
were slightly slower than typical values for coarse texture soils. This
could be likely the result of a strong water repellence (hydrophobicity)
we observed in the field (S. Material 4). These soil types under
Araucaria could show a higher water repellence caused by the
accumulation of hydrophobic organic compounds as it is common for other
conifer forests (Doerr et al., 2009). This effect is notably higher in
soils with low clay percentages like the one in this study (Cuevas
Becerra, 2006; Doerr et al., 2009; Robichaud \& Hungerford, 2000). The
fact that the OG site exhibits a significantly faster infiltration rate
(Table I) may be due to a lower accumulation of hydrophobic substances
because of a relatively lower amount of Araucaria trees (Figure 3) and
the lower canopy coverage (Figure 4). Even though a higher
K\textsubscript{sat} and infiltrability are desirable functional
properties in most farmlands with fine texture soils, in this case, it
could translate into excessive percolation during snowmelt and
consequent excessive leaching of C and nutrients. This is coherent with
our chemical indicators that showed lower N and P availability and lower
carbon accretion in the OG and clearings conditions.

A consistent number of investigations have reported a reduction in
nitrogen content induced by grazing (Steffens et al., 2008; Su et al.,
2004). In our case, continuous grazing has reduced not only the total
Kjeldahl nitrogen (Table 1) but also the total phosphorus concentrations
(Figure 5). Also, our data suggest accelerated acidification in the open
grazing condition. We also found significantly lower CEC and base
saturation in both the OG and the clearing conditions. Therefore, it is
clear from this data that uncontrolled grazing has reduced soil
fertility, affecting not only commonly limiting elements like N and P
but also exchangeable bases (Ca, Mg, K and Na). This has affected
productivity, reducing C content and forest regeneration in the OG
condition (Figure 3).

The differences in tree density and ground cover between the controlled
grazing conditions and the open grazing site could explain the
differences we found on available phosphorus (Olsen P) between
conditions. The higher organic phosphorus fraction pool in the CG
condition could be considered evidence of the higher proportion of P
absorbed, cycle, and stored by vegetation. Similarly, the significantly
lower concentration of the main soil nitrogen available form in this
forest (i.e., ammonium) is also the result of higher plant uptake and
storage in vegetation, litter layers, and soil organic matter in the CG
condition. Therefore, more densely vegetated CG sites are likely more
conservative forest ecosystems with higher nutrient internal recycling
than the OG conditions.

Indicators of biologic activity such as mineralizable nitrogen and
POXC/C were not sensitive enough to found differences between sites.
Still, if we look at the differences between cover conditions, most of
our indicators (including biological) become significantly different
(Table I). The increase in shrubs and herbs under open grazing areas is
most likely due to combined historic logging activity, forest clearing
for communal grazing, and impaired tree regeneration due to intensive
grazing activity. Our analysis of coverage analysis was not able to
differentiate between grasses and shrubs. However, we observed a clear
difference in the relative abundance of grasses compared to shrub
between conditions (S. Material 2), being the contribution of grasses
much lower in the open grazing condition. Considering the higher removal
of grass in more intensive grazing conditions (Greenwood \& McKenzie,
2001), we could explain the higher contribution of small unpalatable
thorny shrubs like \emph{Colletia spinosa} Lam. and bare soil
conditions. The latter is direct evidence of deteriorating physical,
chemical, and biological soil quality, thus accelerating soil
degradation and reducing ecosystem services such as water, nutrient
cycling, and biodiversity maintenance.

Even though aggregate stability is the main soil physical property that
explains the OG site's clustering away from the CG site across PC 1
(Figure 5), no significant difference was found between conditions for
this property (Table I, Figure 5). Aggregate Stability greatly depends
on soil texture and the organic matter content (Bissonnais, 1996;
Staricka \& Benoit, 1995), and as we mentioned, the soil textures for
both conditions were very similar (S. Material 2) as well as soil total
carbon content (Table 1). The lack of a clear deterioration aggregate
stability could mislead us to conclude that livestock has no negative
impact. However, like other physical properties, the aggregate stability
in soils under the canopy was significantly higher than in the
clearings.

Chemical indicators allow us to explain the effect of grazing (Figure 4)
more clearly. Among all variables, total phosphorus and CEC (PC1), along
with available ammonium and available Olsen-P (PC2), are the best
indicators. These indicators are easily and routinely measured in most
soil labs, and they can be useful and specific soil quality indexes for
these coarse-textured volcanic soils. Moreover, the use of sensitive
soil quality indicators could be a valuable assessment tool for forest
managers and local communities to better plan their grazing activities
in this endangered and particularly valuable forest ecosystem.

The fact that most physical and chemical indicators were significantly
better under tree cover highlights the effects of more continuous and
intense trapping and herbivory during livestock grazing in the clearing
areas than more sporadic browsing of tree foliage and seeds under tree
patches. The latter could also be the effect of higher tree litter
inputs (i.e., exudates, root decay, litterfall, etc.) and consequential
enhanced soil carbon accretion and nutrient cycling under the
tree-covered patches than in the forest clearings. Enhancing nutrient
and water availability and the consequent effect on productivity in tree
and shrub patches has also been observed in similar semiarid mountainous
ecosystems (Escudero et al., 2004; Kerns, et al., 2003). These tree
patches present greater soil functionality (i.e., water and nutrient
cycling regulation), acting as critical refuges for tree regeneration,
particularly relevant for conserving endangered tree species
like\emph{A. araucana} .

5. CONCLUSIONS

The study area holds the most septentrional population of\emph{Araucaria
araucana} , making it particularly sensitive to anthropogenic
disturbances and climate change. Our results indicate that uncontrolled
livestock grazing, together with other anthropogenic disturbances, has
reduced soil quality and forest coverage, affecting tree regeneration.

Most soil quality indicators respond to this anthropogenic forcing.
Overall, livestock overgrazing has reduced hydraulic functionality,
depleted nutrient reservoirs, acidified the soils, and increased erosion
susceptibility, suggesting that overgrazed sites may become increasingly
limiting for regeneration in coming years. Conversely, limiting grazing
(18 years) has significantly favored tree regeneration, and it has
helped maintain a more functional and healthy soil-forest system.

For these particularly coarse volcanic soils, soil physical indicators
behave differently than expected. Thus, soil quality indexes must
consider the specific expected response that soil properties and related
functions will have to anthropic disturbances in contrasting soils and
the consequential effect on the ecosystem services derived from them.

It is urgent to implement a combined forest restoration program in this
endangered forest considering a communal livestock integrative
management plan to secure the indigenous community's livelihood and soil
health, and biodiversity conservation goals.

6. ACKNOWLEDGEMENTS

This project was funded by CONAF -Universidad de Concepcion
Collaboration Project: ''Evaluacion del efecto de perturbaciones
antropicas en la regeneracion de bosques cordilleranos de araucaria en
la region del Biobio.'' The authors would like to sincerely thank the
administration of the Reserva Nacional Ralco for all their support and
help during sampling campaigns and the local Pehuenche communities'
Quepuca-Ralco and Ralco-Lepoy for allowing us to work on their ancestral
lands. We would also like to thank CONAF Biobio provincial office for
their support. The authors thank all students and technical personnel
that provided help during the field campaign and laboratory analysis

7. CONFLICT OF INTEREST STATEMENT

The authors have declared no conflict of interest.

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8. TABLES

\textbf{Table 1.} Measured soil quality indicators in relation to the
site grazing treatment (open or controlled) and soil coverage condition
(mean +- SE) followed by the respective P-value. Means followed by
different letter (s) across rows are significantly different (p
\textless{} 0.05) for site and condition.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\textbf{Soil Quality Indicators} & \textbf{Site treatment} &
\textbf{Site treatment} & \textbf{Site treatment} & \textbf{Soil
Coverage Condition} & \textbf{Soil Coverage Condition} & \textbf{Soil
Coverage Condition}\tabularnewline
\midrule
\endhead
& Open-grazing & Controlled grazing & P-value & Under canopy & Clearings
& P-value\tabularnewline
Bulk density (g/cm3) & 0.93 \selectlanguage{ngerman}± 0.15 & 0.92 \selectlanguage{ngerman}± 0.12 & 0.86 & 0.88 \selectlanguage{ngerman}± 0.14
\textbf{a} & 0.97 \selectlanguage{ngerman}± 0.13 \textbf{b} & \textbf{0.02}\tabularnewline
Ksat (cm/s) & 0.004 \selectlanguage{ngerman}± 0.002 \textbf{a} & 0.003 \selectlanguage{ngerman}± 0.002 \textbf{b} &
\textbf{0.03} & 0.0043 \selectlanguage{ngerman}± 0.0025 \textbf{a} & 0.0041 \selectlanguage{ngerman}± 0.014 \textbf{b} &
\textbf{0.03}\tabularnewline
Infiltration (cm/s) & 0.0032 \selectlanguage{ngerman}± 0.005 \textbf{a} & 0.0014 \selectlanguage{ngerman}± 0.001
\textbf{b} & \textbf{0.02} & 0.002 \selectlanguage{ngerman}± 0.005 \textbf{a} & 0.003 \selectlanguage{ngerman}± 0.003
\textbf{b} & \textbf{0.01}\tabularnewline
WHC (\%) & 12.26 \selectlanguage{ngerman}± 6.2 & 12.65 \selectlanguage{ngerman}± 6.1 & 0.89 & 12.87 \selectlanguage{ngerman}± 5.7 & 12.03 \selectlanguage{ngerman}± 6.6
& 0.46\tabularnewline
Aggregate Stability (\%) & 54.92 \selectlanguage{ngerman}± 16.4 & 55.72 \selectlanguage{ngerman}± 12.76 & 0.81 & 61.57 \selectlanguage{ngerman}±
12.5 \textbf{a} & 48.28 \selectlanguage{ngerman}±14.0 \textbf{b} &
\textbf{\textless{}.001}\tabularnewline
Porosity (\%) & 57.1 \selectlanguage{ngerman}± 10.9 & 52.25 \selectlanguage{ngerman}± 12.44 & 0.11 & 55.68 \selectlanguage{ngerman}± 14.8 &
53.65 \selectlanguage{ngerman}± 8.2 & 0.5\tabularnewline
Total carbon (\%) & 4.36 \selectlanguage{ngerman}± 1.17 & 4.71 \selectlanguage{ngerman}± 1.31 & 0.23 & 4.95 \selectlanguage{ngerman}± 1.1
\textbf{a} & 4.09 \selectlanguage{ngerman}± 1.3 \textbf{b} & \textbf{0.005}\tabularnewline
Kjeldahl (mg/kg) & 2.42 \selectlanguage{ngerman}± 0.48 \textbf{a} & 2.76 \selectlanguage{ngerman}± 0.67 \textbf{b} &
\textbf{0.01} & 2.74 \selectlanguage{ngerman}± 0.6 \textbf{a} & 2.42 \selectlanguage{ngerman}± 0.58 \textbf{b} &
\textbf{0.03}\tabularnewline
Ammonium (mg/kg) & 9.57 \selectlanguage{ngerman}± 5.76 \textbf{a} & 1.28 \selectlanguage{ngerman}± 1.01 \textbf{b} &
\textbf{\textless{}.001} & 6.26 \selectlanguage{ngerman}± 5.56 \textbf{a} & 4.46 \selectlanguage{ngerman}± 4.84
\textbf{b} & \textbf{0.05}\tabularnewline
Nitrate (mg/kg) & 4.06 \selectlanguage{ngerman}± 3.12 & 5.32 \selectlanguage{ngerman}± 4.04 & 0.103 & 4.15 \selectlanguage{ngerman}± 4.04 & 5.26
\selectlanguage{ngerman}± 3.1 & 0.11\tabularnewline
Total Phosphorus (mg/kg) & 360.55 \selectlanguage{ngerman}± 55.94 \textbf{a} & 519.46 \selectlanguage{ngerman}± 124.17
\textbf{b} & \textbf{\textless{}.001} & 453.81 \selectlanguage{ngerman}± 125.26 & 424.96 \selectlanguage{ngerman}±
124.96 & 0.19\tabularnewline
Inorganic Phosphorus (mg/kg) & 200.52 \selectlanguage{ngerman}± 44.69 \textbf{a} & 338.67 \selectlanguage{ngerman}± 90.9
\textbf{b} & \textbf{\textless{}.001} & 268.76 \selectlanguage{ngerman}± 93.6 & 270.41 \selectlanguage{ngerman}± 106.23
& 0.92\tabularnewline
Organic Phosphorus (mg/kg) & 160.03 \selectlanguage{ngerman}± 39.23 & 180.79 \selectlanguage{ngerman}± 68.92 & 0.11 &
185.05 \selectlanguage{ngerman}± 64.07 \textbf{a} & 154.54 \selectlanguage{ngerman}± 44.18 \textbf{b} &
\textbf{0.03}\tabularnewline
Olsen Phosphorus (mg/kg) & 4.8 \selectlanguage{ngerman}± 5.06\textbf{a} & 0.38 \selectlanguage{ngerman}± 0.61\textbf{b}
& \textbf{\textless{}.001} & 2.86 \selectlanguage{ngerman}± 4.67 & 2.39 \selectlanguage{ngerman}± 3.72 &
0.5\tabularnewline
pH & 5.42 \selectlanguage{ngerman}± 0.31 \textbf{a} & 6.10 \selectlanguage{ngerman}± 0.31 \textbf{b} &
\textbf{\textless{}.001} & 5.91 \selectlanguage{ngerman}± 0.51 \textbf{a} & 5.61 \selectlanguage{ngerman}± 0.36
\textbf{b} & \textbf{\textless{}.001}\tabularnewline
POXC/C & 0.13 \selectlanguage{ngerman}± 0.03 & 0.13 \selectlanguage{ngerman}± 0.04 & 0.43 & 0.14 \selectlanguage{ngerman}± 0.03 \textbf{a} &
0.12 \selectlanguage{ngerman}± 0.04 \textbf{b} & \textbf{0.05}\tabularnewline
Mineralizable Nitrogen (mg/kg) & 14.71 \selectlanguage{ngerman}± 5.23 & 15.62 \selectlanguage{ngerman}± 5.9 & 0.47 &
17.24 \selectlanguage{ngerman}± 4.55 \textbf{a} & 13.08 \selectlanguage{ngerman}± 5.67 \textbf{b} &
\textbf{0.002}\tabularnewline
Total Nitrogen (\%) & 0.24 \selectlanguage{ngerman}± 0.06 & 0.27 \selectlanguage{ngerman}± 0.12 & 0.43 & 0.25 \selectlanguage{ngerman}± 0.06 &
0.25 \selectlanguage{ngerman}± 0.12 & 0.14\tabularnewline
CEC (cmolc/kg) & 1.4 \selectlanguage{ngerman}± 1.47 \textbf{a} & 3.99 \selectlanguage{ngerman}± 2.75 \textbf{b} &
\textbf{\textless{}.001} & 3.96 \selectlanguage{ngerman}± 2.78 \textbf{a} & 1.25 \selectlanguage{ngerman}± 1.08
\textbf{b} & \textbf{\textless{}.001}\tabularnewline
K Rusle Factor & 0.06 \selectlanguage{ngerman}± 0.02 & 0.08 \selectlanguage{ngerman}± 0.05 & 0.09 & 0.09 \selectlanguage{ngerman}± 0.04
\textbf{a} & 0.05 \selectlanguage{ngerman}± 0.03 \textbf{b} & \textbf{0.02}\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-1/Figure-1}

\end{center}
\end{figure}\selectlanguage{english}
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