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\begin{document}

\title{VEGETATION COVER AND REGENERATION AS PREDICTORS OF DESERTIFICATION
PROCESS IN DRY FOREST IN BRAZIL}



\author[1]{Stefanny Martins de Souza}%
\author[1]{Maiara Bezerra Ramos}%
\author[1]{Maria Gracielle Rodrigues Maciel}%
\author[1]{Sonaly Silva da Cunha}%
\author[2]{José João Lelis Leal de Souza}%
\author[1]{Sérgio de Faria Lopes}%
\affil[1]{Universidade Estadual da Paraiba}%
\affil[2]{Universidade Federal de Vicosa}%


\vspace{-1em}



  
  \date{May 25, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
The Brazilian Caatinga is one of the most diverse dry forests on the
planet. Half of its original coverage is degraded, and it is currently
identified as one of the main areas undergoing desertification. Natural
regeneration depends on climatic and edaphic conditions, as well as the
adult stratum present in an area. Despite its importance, this process
is little known in the Caatinga. Thus, we aimed to analyze how the
anthropic disturbance, vegetation cover, and soil properties influence
regenerating stratum under different Caatinga vegetation cover levels.
Our study was executed in the driest region of Brazil. We classified our
studied areas as Area I (less vegetation cover) and Area II (greater
coverage). Six plots of 50 x 20 m were delimited for sampling the adult
and regenerating stratum, and the soil in each area. Our results show
interactions between soil characteristics and adult and regenerating
stratum. Area II showed greater diversity and a greater number of
exclusive species; in contrast, a dominance of species more resistant to
limiting conditions was observed in Area I, such as \emph{Aspidosperma
pyrifolium} Mart. \& Zucc. The C and N contents in the soil indicated a
positive and significant correlation with the diversity of the
regenerating stratum. The data revealed that the area with less
vegetation cover, richness, and diversity presented indications of
desertification.%
\end{abstract}%



\sloppy


\textbf{VEGETATION COVER AND REGENERATION AS PREDICTORS OF
DESERTIFICATION PROCESS IN DRY FOREST IN BRAZIL}

Stefanny Martins de Souza\textsuperscript{a} (stefannym0396@gmail.com),
Maiara Bezerra Ramos\textsuperscript{a} (maiarabramos@hotmail.com),
Maria Gracielle Rodrigues
Maciel\textsuperscript{a*}(graci.rodrigues.maciel@gmail.com), Sonaly
Silva da Cunha\textsuperscript{a} (sonalysilva15@gmail.com), Jos\selectlanguage{ngerman}é João
Lelis Leal de Souza\textsuperscript{b} (jjlelis@ufv.br), Sérgio de Faria
Lopes\textsuperscript{a} (defarialopes@gmail.com)

\textsuperscript{a} Laboratório de Ecologia Neotropical. Universidade
Estadual da Paraíba, R. Baraúnas, 351 -- Bairro Universitário, Campina
Grande - PB, Brasil, 58429-500.

\textsuperscript{b} Departamento de Solos, Universidade Federal de
Viçosa. Avenida Prof. Peter Henry Rolfs, s/n, campus. Viçosa - MG,
Brasil, 36570-900

Corresponding author: Stefanny Martins de Souza
(stefannym0396@gmail.com)

This study investigates the regeneration rates of plant communities and
indicators of desertification in areas with different levels of
vegetation cover in the Caatinga, located in the driest region of
Brazil.

Short title: Regeneration in Dry Forest in Brazil

\textbf{Acknowledgments}

We are grateful to the entire team at the Neotropical Ecology Lab for
their support and help in the field, and all funding sources.

This work was supported by agreement CNPq {[}grant numbers 42 490 /
2018-6{]}; the PROPESQ / UEPB {[}2.05.03.00-8-375 / 2017-1{]}; and by
CAPES fellowship between 2018 and 2020 (SMS). There was no involvement
of funding sources in the development of the study.

\textbf{Abstract}

The Brazilian Caatinga is one of the most diverse dry forests on the
planet. Half of its original coverage is degraded, and it is currently
identified as one of the main areas undergoing desertification. Natural
regeneration depends on climatic and edaphic conditions, as well as the
adult stratum present in an area. Despite its importance, this process
is little known in the Caatinga. Thus, we aimed to analyze how the
anthropic disturbance, vegetation cover, and soil properties influence
regenerating stratum under different Caatinga vegetation cover levels.
Our study was executed in the driest region of Brazil. We classified our
studied areas as Area I (less vegetation cover) and Area II (greater
coverage). Six plots of 50 x 20 m were delimited for sampling the adult
and regenerating stratum, and the soil in each area. Our results show
interactions between soil characteristics and adult and regenerating
stratum. Area II showed greater diversity and a greater number of
exclusive species; in contrast, a dominance of species more resistant to
limiting conditions was observed in Area I, such as \emph{Aspidosperma
pyrifolium} Mart. \& Zucc. The C and N contents in the soil indicated a
positive and significant correlation with the diversity of the
regenerating stratum. The data revealed that the area with less
vegetation cover, richness, and diversity presented indications of
desertification.

\textbf{Keywords} : Semi-arid climate -- Plant Communities -- Vegetation
cover -- Chronic Anthropic Disturbance -- Desertification --
Regenerating Stratum.

Introduction

The increase in the human population and the demand for natural
resources are the main causes of global biodiversity loss and changes in
the structure of forests around the world (McKee, 2009). Habitat
fragmentation (Ribeiro et al., 2015) and changes in soil quality
(Ferreira et al., 2018) derived from anthropic pressure reduce tree
species richness and alter structural parameters such as stem diameter
and basal area (Sobrinho et al., 2016). Such changes over time can shape
the desertification process in less resilient areas (Alves et al.,
2009). Desertification is defined as a reduction or loss of the
biological productivity of drylands, their ecological complexity, and/or
their human values, manifested through a reduced provision of the sum of
dryland ecosystem services (Verstraete et al., 2009; Reid et al., 2005;
Scholes, 2009).

The natural regeneration process of disturbed forests varies according
to the abiotic and biotic characteristics of the environment. Seedling
establishment is related to climatic conditions of the environment, such
as precipitation, temperature and seasonality, richness and composition
of the adult plant community, and the history of land use (Ribeiro et
al. 2015; Sobrinho et al., 2016). Thus, arid and semi-arid ecosystems
are less tolerant to human disturbances, becoming more vulnerable to
desertification than humid tropical systems. These ecosystems
consequently have lower regeneration rates since seedling establishment,
which is the most vulnerable stage of life, can be compromised by
prolonged drought periods (Alves et al., 2010; Ferreira et al., 2018;
Marinho et al., 2016; Sobrinho et al., 2016). On the other hand, a
milder microclimate induced by adult stratum could favor ecosystem
regeneration (Derroire et al., 2016a; Lebrija-Trejos et al., 2011).

Seasonally Dry Tropical Forests (SDTF) are currently one of the most
threatened ecosystems in the world, with 97\% of their territory at risk
(Miles et al., 2006). These forests are characterized by high
seasonality, long periods of drought, and rainfall ranging from 240 mm
to 1,500 mm per year (Pennington et al., 2009). These environments are
home to high biodiversity and are main endemism centers, with species
marked by their morphophysiological adaptation mechanisms developed to
tolerate local climatic conditions (Murphy and Lugo, 1986).

The Brazilian Caatinga consists of one of the largest and most
diversified dry forests in the world (Costa et al., 2016; Prado et al.,
2015), with an extension of 912,529 km² (Silva et al., 2017).
Approximately 46\% of the native Caatinga vegetation has been cleared
due to agriculture and pastures developed since the
16\textsuperscript{th} century and currently due to industrial and
timber extraction (Lapola et al., 2014; Ribeiro et al., 2015; Sfair et
al., 2018). In addition, it is expected that the climatic changes which
have occurred will affect the maintenance of endemic species in the
Caatinga, reducing the vegetation cover and areas of climatic conditions
suitable for establishing communities (Silva et al., 2019).

Reduced vegetation cover makes the soils more susceptible to erosion and
compaction processes, reducing the water infiltration rates and the
Organic Carbon and Organic Nitrogen levels, in addition to making the
microclimate of the environment more stressful due to greater sun
exposure, compromising regeneration from the community (Derroire et al.,
2016a; Sousa et al., 2012). These factors induce the biotic
homogenization of degraded lands (Leal et al., 2005) by limiting the
occurrence of sensitive species and favoring the occurrence of stress
resistance (Lôbo et al. 2011; Ribeiro-Neto et al., 2016).

In this study, we hypothesized that regenerating stratum in areas with
less vegetation cover would show lower: i) richness, ii) diversity
indices and iii) individuals with lower height and average diameter than
in areas with denser vegetation cover. This hypothesis is due to higher
environmental stress caused by increased sun exposure (Derroire et al.,
2016b), limited soil nutrients (Sousa et al., 2012), and higher pressure
from herbivores on the vegetation as animal access is facilitated in
more open areas (Marinho et al., 2016; Skarpe et al., 2007).

Material and Methods

The study area encompasses the region known as Cariri Paraibano, in
Northeastern Brazil. The region is located in the Borborema Plateau,
between 400 and 1,100 m of altitude. This region extends over 11,192
km². According to IBGE (2010), the region has a population of 185,235
inhabitants.

Cariri Paraibano is one of the driest regions in Brazil. The climate is
hot semi-arid (BSh) according to the Koppen classification (Alvares et
al., 2013), with precipitation between 300 and 600 mm
year\textsuperscript{-1}, constituting the lowest rainfall levels in the
country. The rains are usually concentrated in consecutive months,
generally from January to July, resulting in long drought periods for
the rest of the year, with September being the driest month (Silva et
al., 2018). The average annual temperature is 27 °C, with a minimum of
23 °C and a maximum of 41 °C. Potential evapotranspiration is up to four
times greater than precipitation, and a marked water deficit
predominates in the region (Souza et al., 2015b).

The regional economy has been based on extensive agriculture and
livestock since the beginning of its occupation (Alves, 2009; Souza et
al., 2015b). Climatic conditions together with this anthropogenic
exploration regime have lead the region to become sensitie to the
desertification process (Souza et al., 2015a). The study by Oyama and
Nobre (2004) shows that this process generates changes in the
hydrological cycle, affecting precipitation, evapotranspiration,
atmospheric humidity, and decreased runoff, leading to the possibility
of significant local and large-scale climate change if the degradation
regime continues unchecked.

Mineral weathering is incipient due to the semi-arid climate. Luvisols
(40.9 \% of the total area), Leptosols (35.6 \%) and Regosols (3.9 \%)
dominates the Cariri (Araújo Filho et al., 2017). These soils are
dominantly loamy, shallow (\textless{}1 m deep), eutrophic, and have low
water retention capacity (Ferreira et al., 2018; Giongo et al., 2011;
Menezes et al., 2012; Rückamp et al., 2010).

\par\null

\emph{2.1 Site selection}

The sites were selected based on the normalized difference vegetation
index (NDVI), which estimates the biomass and productivity of the
vegetation by calculating: NDVI = (Infrared - Red) / (Infrared + Red),
using the ArcGIS 10.2. desktop software program. We used three images
captured by the Landsat-8 satellite between September to October 2017
(earthexplorer.usgs.gov) so that the entire length of the Cariri
Paraíbano was covered. This period was chosen because it is the drought
season, and the vegetation cover images may change in the rainy season
due to the herbaceous stratum established at this time. The calculations
were performed using composite images combining bands 4 (Red,
wave-length 0.64-0.67) and 5 (Near Infrared, wave-length 0.85-0.88) of
the satellite, which is satisfactory for describing vegetation cover.
NDVI values can range from -1 to +1, with values close to 0
corresponding to regions with little vegetation and higher values
corresponding to areas with denser vegetation cover. A total of 12 plots
were established, divided into two areas: a) six plots in sites of NDVI
values between 0.15 and 0.37, hereafter referred to as Area I, which
corresponds to the lowest vegetation cover, and; b) six plots in sites
of NDVI values between 0.44 to 0.66, hereafter referred to as Area II,
which comprises environments with higher vegetation cover levels (Figure
1). We first identified all of the sampling sites through the satellite
images, and we later visited the areas to confirm. All collections were
carried out from November 2018 to July 2019.

\emph{Note} : Here figure 1 will be inserted

\emph{2.2 Sampling of vegetation data}

First, 12 plots (50 x 20 m) were delimited for sampling the adult tree
stratum, totaling a sampling area of 1.2 ha. The adult stratum
represented the vegetation cover of our study and was composed of 2,519
individuals. A total of 1,053 individuals were sampled in Area I,
distributed in 24 species and ten families, while Area II presented
1,466 individuals distributed in 36 species and 16 families.

Each plot was subdivided into 10 subplots (10 x 10 m), with four
subplots being randomly chosen for sampling the regenerative stratum.
The height and diameter of all individuals were measured. The species of
all individuals were identified in the field. The main descriptive
characteristics (height, diameter, habit, floral attributes) for
unidentified individuals were recorded, and samples were collected for
later taxonomic determination in the laboratoryusing specialized
bibliography and consulting digitalized and herbal databases, and
classified according to the Angiosperm Phylogeny Group IV (APG IV,
2016). All living individuals with height \selectlanguage{english}[?]1 m and diameter at ground
level (DGL) \selectlanguage{english}[?]3 cm were identified as adult individuals (Rodal et al.,
2013). Living specimens with a height \textless{}1 m and a DGL between
0.5 and 2.9 were identified as seedlings (Farias et al., 2016).

Hill numbers of order 0, 1, and 2 were used to quantify diversity (Jost,
2006). The diversity of order 0 (D0) refers to species richness, which
is not sensitive to abundance; the diversity of order 1 (D1) considers
the abundance of each species and its weight in the community is given
accordingly. Thus, it is interpreted as the number of common species in
the community; diversity of order 2 (D2) is also sensitive to abundance
and favors the highest number of species, being interpreted as the
number of dominant species in the community (Jost, 2006). Since the
focus of our work is the data from the regenerating stratum, we will use
RegD0, RegD1, and RegD2 to refer to the diversity data of orders 0, 1,
and 2, respectively, of that stratum. The calculations were performed
through the website https://chao.shinyapps.io/iNEXTOnline/.
The\emph{iNext} is a species diversity interpolation and extrapolation
software program.

2.3 Anthropogenic disturbances

Next, three subplots were chosen in each vegetation plot by drawing lots
to measure pressure metrics by domestic herbivores. We used the fecal
pellets of these animals (goats, sheep, cows, and horses) as evidence of
their presence in the plot in question. All pellets in the selected
subplots were counted, and three of these were collected and weighed to
calculate average biomass of each pellet. Finally, the total fecal
biomass of each of these animals in the plot was calculated (Martorel
and Peters 2005).

The fecal biomass from goats, sheep, cattle, and horses were entered as
individual metrics in a Principal Component Analysis (PCA). The metrics
were correlated with the first two axes of the PCA, and then we
determined a single measure through the sum of their coordinates, which
we called the Herbivory Pressure Index (Martorel and Peters 2005).

\emph{2.4 Soil sampling and analysis}

A soil profile was opened, described, and classified in each vegetation
plot according to the World Reference Base for soil resources (FAO,
2015). We collected deformed composed samples in the superficial
horizons (A Horizon) in each profile, which varied from two to 10 cm in
depth. We only considered this horizon because it exerts a more
significant influence on the regenerating stratum since the roots of the
seedlings do not yet reach the deepest horizons (Taiz et al., 2017). The
collection method was based on the Field Soil Description and Collection
Manual (Santos et al., 2005). We used a properly sanitized garden
shovel, avoiding contamination risk of the samples, and collecting
approximately 2 kg of soil in plastic bags.

These samples were air-dried, ground, and sieved in a 2 mm sieve prior
to analysis. The coarse sand, fine sand, silt, and clay contents were
determined after slow agitation with 0.1 M NaOH by the sieve-pipette
method (Donagema et al., 2011). The soil pH was determined in deionized
water (1: 2.5). The Al\textsuperscript{3+}, Ca\textsuperscript{2+,} and
Mg\textsuperscript{2+} contents were determined by 1 M KCl extraction.
K\textsuperscript{+}, Na\textsuperscript{+} and P were determined after
Mehlich\textsuperscript{-1} extraction. Potential acidity (H+Al) was
determined by calcium acetate at pH 7.0. The soil organic carbon content
was determined by acid digestion, according to the Walkey-Black method
(Silva and Mendonca, 2007). The total nitrogen content was determined by
the Kjeldahl method. the sum of bases (SB), cation exchange capacity
(CEC), base saturation (V), and C/N ratio were subsequently calculated
from these procedures.

Undisturbed samples were collected using volumetric rings (Donagema et
al., 2011). The bulk density, particle density and the soil water
retention curve (0, -6, -10, -30, -100, -1500 kPa) were determined.
Total porosity, field capacity (FC), wilting point (WP), water subject
to drainage, available water, and unavailable water were calculated from
these data.

\emph{2.5 Statistical procedures}

Biomass of individuals was calculated based on the DGL using the
equation: Biomass = 0.173 DGLcm\textsuperscript{2.295} (Amorim et al.,
2005). We tested the normality of the data using the Shapiro Wilk test
and homogeneity using the Bartlett's test (Crawley, 2013). Plot values
for richness, abundance, height, diameter, biomass and each physical and
chemical soil property were averaged for each area. The areas were
compared using Student's T-tests and Pearson's chi-squared test for
parametric data and non-parametric data, respectively.

We performed Pearson's correlation analyses to elucidate the
relationships between vegetation and soil properties. Simple linear
regressions were performed between the variables with the highest
correlation, establishing the model which best described the results.
The adult and regenerative strata were also correlated, as the adult
vegetation acts as a vegetation cover and influences the regenerating
stratum. We subsequently generated graphs only for the significantly
correlated variables, using the plot function of the R statistical
software program (R Core Team, 2018). Only the soil properties in the
surface horizons were used in correlation analyses for the regenerating
stratum. As a result, all horizons below the surface horizon were
disregarded in the following analyses.

We performed a non-metric multidimensional scaling (NMDS) ranking to
observe possible ordering in the species composition of regenerating
stratum between the different areas using the metaMDS function of the
vegan software package. In addition, the similarity between environments
was analyzed using the ANOSIM test (Oksanen et al., 2018). All analyzes
were performed using the R Studio 3.5.0. statistical software program (R
Core Team, 2018).

Results

A total of 3,436 individuals were sampled, 2,519 of them in the adult
stratum, distributed in 41 species and 17 families (Appendix 1), which
represent the vegetation cover of our study. A total of 917 individuals
were sampled in the regenerating stratum, with 29 species identified
distributed in 15 families. The species \emph{Aspidosperma
pyrifolium}Mart (Apocynaceae) was the most frequent. The most frequent
family was Euphorbiaceae (six species). Area I presented 526 individuals
from the regenerating stratum, which were distributed in 11 species and
six families. Area II presented 391 individuals, 27 species and 15
families. Two species were exclusive to Area I, while 18 species and
nine families were exclusive to Area II.

The species were ecologically classified based on their distribution
among the areas, with four categories being established: Aridity
indicators, being dominant or exclusive species from the driest area
(Area I); Sensitive species, being those found only in the most humid
environments (Area II); Tolerant species, mainly distributed in the most
humid environments, however, some individuals were also found in the
drier area, confirming that the species tolerates such conditions; and
finally, Common species, being those that are uniformly distributed
between both areas (Appendix 1).

The NMDS ranking showed a low-stress value (stress = 0.06), and the
similarity analysis showed significant differences between the areas
regarding the composition and abundance of species present in the
regenerating stratum (ANOSIM, r = 0.20; P=0.03) (Figure 2).

\emph{Note} : Here figure 2 will be inserted

The mean comparison tests between Area I and II showed significant
differences in the seedling diameter and diversity. Area I presented the
largest average diameter of seedlings , and Area II had the highest
average diversity of orders 1 (RegD1) and 2 (RegD2). The Herbivory
Pressure Index in the plots ranged from 0.08 to 13.37, however there was
no significant difference between the areas (Table 1).

\emph{Note} : Here Table 1 will be inserted

Cambisol was the dominant soil group identified in the study area with
six soil profiles. We also found three Luvisols, one Gleysol, one
Planosol, and one Regosol. All soil properties have high standard
deviation values in Area I and Area II, indicating high soil variability
(Table 2). All A horizons were classified as ochric horizons. They
varied between 2 and 10 cm in thickness. The A horizon in Area I and
Area II is dominantly sandy clay loam and has base saturation above
80\%. The Ca\textsuperscript{2+}, Mg\textsuperscript{2+},
K\textsuperscript{+} and Na\textsuperscript{+}, and P contents were not
significantly different between Areas I and II. Only the average C and N
levels showed significant differences, with a higher average in Area II
(Table 2).

\emph{Note} : Here Table 2 will be inserted

The herbivory pressure index showed a weakly positive relationship with
the diameter at ground level of saplings (DGL) (Figure 3b) and a
negative relationship with the diversity of order 0 of the regenerating
stratum (RegD0) (Figure 3c). RegD0 is also showed a positive
relationship with biomass (Figure 3d), diversity (D0) (Figure 3e) and
abundance of the adult stratum (Figure 3f). The DGL of the regenerating
stratum showed a negative relationship with the abundance (Figure 3a)
and biomass of the adult stratum. The abundance of seedlings also showed
a negative relationship with the height of the adult stratum.

The diversity (RegD0) and biomass of the regenerating stratum also
showed positive relationships with soil carbon and nitrogen levels
(Figure 3g and Figure 3h). Despite showing relatively weak
relationships, these linear regressions are all significant (Figure 3).

\emph{Note} : Here figure 3 will be inserted

Discussion

Our results indicate significant differences in the floristic
composition of regenerating stratum, diversity, DGL, and C and N levels
in the soil between different vegetation cover areas. Species more
resistant to water and anthropic stress and common in degraded areas,
such as \emph{A. pyrifolium, C. pyramidales,} and \emph{T.
palmadora}(Meiado, 2012; Souza et al., 2015a, 2015b), are dominant in
Area I. On the other hand, Area II presents more than twice the number
of species in the first area, and also shows 18 exclusive species. The
Hill numbers D1 and D2 also showed higher mean richness in the area of
denser vegetable coverage (Area II), indicating a higher number of
common and dominant species in it, and consequently a more uniform
distribution in the abundances of each species.

The abundance of species and individuals of Euphorbiaceae family can be
attributed to their adaptive capacity, palatability, and potential for
use by humans (Ribeiro et al., 2015, Rito et al., 2017). The dominance
of \emph{A. pyrifolium} , especially in Area I, is attributed to its
high resistance to drought and because it is unpalatable to some
herbivores, mainly goats and sheep. Such properties are conferred by its
epicuticular wax, which in addition to ensuring greater efficiency in
the use of water, has a composition rich in toxic compounds, thus
presenting a competitive advantage over species more sensitive to
semi-arid climates (Medeiros et al., 2017). Moreover, the anthropic
pressure on this species is low since its wood has little utility
value.\emph{T. palmadora} , the second most abundant species in our
study, is morphophysiologically adapted and resistant to water stress,
unpalatable to animals due to its large amount of spines, and has a high
reproductive rate. It can spread asexually through its branches and
fruits even under limiting conditions (Meiado, 2012). \emph{C.
pyramidale} , the third most abundant specie in the study, is resistant
to water deficits, high salt concentrations, and high temperatures,
being widely found in degraded areas (Matias et al., 2018). Finally,
species of genus \emph{Jatropha} also have low wood density, making it
of little use for human use. It is considered unpalatable to animals,
and it has a high water reserve capacity, guaranteeing its survival and
reproductive success (Ribeiro et al., 2015; Silva et al., 2004).

The absence of statistical differences in the content of exchangeable
bases and texture between soils of Areas I and II can be attributed to:
a) affinity of the soils with the parent material, and; b) relative
homogeneity of sampled lithologies. The semi-arid climate in Northeast
Brazil favors low chemical weathering of minerals and incipient
pedogenesis (Araujo et al., 2017). These soils consequently express
characteristics associated with their parent material, especially
Cambisols and Neossols (FAO, 2015). Since gneiss is the main lithology
of Cariri Paraibano (Lages et al., 2018), it is expected that the soils
have similar characteristics. On the other hand, the statistical
difference in the C and N levels between Areas I and II indicate
environments with different inputs of plant residues to the soil and/or
different preservation levels.

The environmental conditions provided by the greater abundance, biomass
and diversity of adults observed in Area II favor the establishment of a
more diverse regenerating stratum by: a) guaranteeing a larger seed bank
in the community (Chazdon, 2014); b) attracting dispersing and
pollinating agents (Guevara et al., 1986); c) facilitating seedling
germination and establishment; and d) and providing greater protection
to seedlings. Adult stratum areas with higher biomass protect the
regenerating stratum against predation and trampling by herbivores
(Derroire et al., 2016b; Marinho et al., 2016). Large herbivores prefer
to forage in more open environments due to greater accessibility and to
the palatability of the established herbaceous layer (Skarpe et al.,
2007). Herbivore pressure negatively influenced species diversity, which
was lower in more open areas (Area I). We suggest that the pressure of
herbivores in Area I is favored by the ease of access and greater
abundance of herbaceous plants present in the area, where their pressure
can reduce species diversity.

In addition, microclimate conditions are milder under higher vegetation
levels. The shade provided in environments with a higher number of trees
reduces the transpiration rates of seedlings by decreasing their
exposure to high light and temperature levels, as well as retaining
greater moisture in the soil and providing a higher amount of organic
waste to the soil (Derroire et al., 2016a; Lebrija-Trejos et al., 2011).

The positive correlations between the diversity of regenerative stratum
(RegD0) with C and N levels in the soil indicate the importance of soil
organic matter content in the ecological succession of areas. The supply
of plant residues and their incorporation into the soil ensures nutrient
cycling (Menezes et al., 2012; Sousa et al., 2012). As semi-arid soils
are mostly eutrophic, the availability of nutrients derived from mineral
weathering, such as Ca, Mg, and K, is not limiting for plant growth. On
the other hand, C and N are primary macronutrients derived from the
decomposition of soil organic matter (Six et al., 2004), therefore they
are highly associated with soil organic matter content and quality
(Ostrowska and Por\selectlanguage{polish}ę\selectlanguage{english}bska, 2015). In addition, higher soil organic matter
levels favor aggregate formation, reducing soil density and increasing
porosity and water infiltration (Ferreira et al., 2018; Silva e
Mendon\selectlanguage{ngerman}ça, 2007).

The negative correlation between the abundance of seedlings and the
adult stratum height can be attributed to establishing a denser canopy
in forest conditions than open areas. Areas with taller trees guarantee
more favorable environmental conditions for plant growth (Lebrija-Trejos
et al., 2011), providing greater balance in the community than the
proliferation of more resistant species observed in more disturbed
environments such as Area I (Rito et al., 2017). The higher number of
exclusive species sensitive to degradation in Area II reinforces this
hypothesis (Barbosa et al., 2007, Souza et al., 2015a).

The higher mean DGL of the regenerating stratum in the area of lower
plant density (Area I) can be attributed to the decrease in competition
for light (Mclaren and Mcdonald, 2003). However, despite the greater
shading faced by seedlings in Area II, it is known that light is not one
of the most limiting resources in the Caatinga (Sampaio, 2003). Thus,
this factor alone does not fully explain our results. The DGL also
showed a positive correlation with pressure by herbivory, and we
speculate that these effects are indirect, and the greater secondary
growth in Area I is related to the plant's physiological adaptations to
water stress to which it is subjected.

The growth of some plant species in arid and semi-arid environments is
limited to the season of water availability, passing through growth
pulses during the rainy season and dormancy during the driest months
(García-Cervigón et al., 2017). Therefore, plants respond quickly to
precipitation events because of this limitation, investing in wood and
leaf production while water is available in soil. The irregular
distribution of rainfall observed in the Brazilian semi-arid region with
sporadic precipitation events in the dry months can stimulate several
growth pulses during the year (Aragão et al., 2019). Furthermore, the
distribution of photoassimilates by the plant varies according to the
availability of resources, and root growth and secondary growth become
the priority over primary growth when under limiting conditions (Mattos,
1999). Thus, the largest mean diameter observed in Area I can be
attributed to such factors. However, complementary physiological studies
are necessary to confirm this idea since these responses vary between
different species (Aragão et al., 2019).

As expected, RegD0 was negatively related to pressure by herbivory.
Herbivores generally cause adverse effects on plant communities, causing
habitat degradation, limiting the growth of individuals, and affecting
the taxonomic composition (Sfair et al., 2018; Souza et al., 2015a). The
effects on the regenerative stratum are even more robust, since the
animals have a preference for younger individuals due to their
palatability, and they are even more susceptible to trampling mortality,
compromising the diversity, structure, and maintenance of the plant
community in the future (Marinho et al., 2016).

Our results indicate a more advanced secondary succession stage in Area
II. In addition to the low diversity, Area I has many individuals
identified as \emph{A. pyrilifolium} , which suggests biotic
homogenization due to anthropic disturbance. Diversity in degraded
environments is reduced due to a decrease in species which are more
sensitive to environmental changes and an increase in the abundance of
the most resistant species (Ribeiro-Neto et al., 2016). The lower C and
N levels in the soils of Area I also seem to be directly related to the
anthropic disturbance. This scenario is the first step towards
establishing the desertification process. These results are a
consequence of the degradation regime imposed on the Caatinga since its
colonization, including agricultural activities (Travassos and Souza,
2014). Nowadays, deforestation and inappropriate land use in
agricultural areas compromise productivity, hampering natural
regeneration in these areas (Leal et al., 2005; Marinho et al., 2016;
Sousa et al., 2012; Sfair et al., 2018).

Considering the colonization and occupation processes of Cariri
Paraibano, the impacts could mainly be related to livestock activities,
especially goat farming, since it is a prevalent practice in the area,
in which animals are raised freely to feed on native vegetation (Leal et
al., 2005). This was evidenced in our study through the high biomass of
fecal pellets found. This fact is pointed out as one of the main reasons
for the vegetation cover loss in drylands and a strong driver of the
desertification process (Marinho et al., 2016).

These results highlight the importance of adjusting the grazing practice
of these areas to guarantee maintenance of plant communities and
biological diversity. One of the most efficient land management
practices is fallow, which isolates the land and allows it to recover
through its resilience, promoting recovery of soil fertility, water
storage capacity, and plant communities (Ferreira, et al., 2018). In the
case of the Brazilian semi-arid region, this practice is not yet
realistic for social and economic reasons. However, the development of
public policies to raise awareness and incentives for more sustainable
development is indicated. For example, better livestock management along
with the creation of trapped herders avoids continuous removal of native
vegetation andallows its restoration through natural regeneration (Leal
et al., 2005). Another measure of fundamental importance would be
creating Conservation Units of Integral Protection, which are still
scarce in the Caatinga (Antongiovanni et al., 2020; MMA, 2011).

Conclusions

Our results confirm the effects of vegetation cover on the natural
regeneration of plant communities in the Brazilian semi-arid region. The
higher biomass and diversity of the adult stratum added to a higher C
and N content in Area II soil led to the establishment of a higher
number of species, with even greater equity.

The results indicate overgrazing as one of the main threats and drivers
of the desertification process in the region, highlighting the
importance of implementing management techniques in the use of these
lands. Creating Conservation Units of Integral Protection and
encouraging more sustainable development through environmental education
are essential measures to be implemented.

\textbf{Conflict of Interest Statement}

The authors declare no conflicts of interest.

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\textbf{Titles and Legends}

Table 1. Mean (standard deviation) of regenerating community variables
in two areas with different vegetation cover levels in the Brazilian
semi-arid region. (Area I: Less coverage; Area II: Increased coverage)
*/= Bold values are statistically different.

Table 2. Mean (standard deviation) of soil properties in two areas with
different vegetation cover levels in the Brazilian semi-arid region.
(Area I: Less coverage; Area II: Increased coverage) */= Bold values are
statistically different.

Fig. 1 -- Dry forest biome in Brazil (a), study area location (b), and
sample distribution in the study area according to NDVI (c).

Fig. 2 -- Floristic groups of the regenerating stratum of the study
area. The vegetation plots are ordered for each of the two dimensions
produced by NMDS ranking performed using a Bray-Curtis similarity and
altitude matrix based on abundance and vegetation cover. The vegetation
plots of Area I and Area II are represented as black dots and gray dots,
respectively.

Fig. 3 -- Statistically significant linear regressions at p \textless{}
0.05 for relationships between regenerating stratum data with adult
vegetation data, herbivore pressure and soil properties e. Reg. =
Regenerating. 3.a) Reg DGL x Abundance of Adults; 3.b) Reg DGL x
herbivory pressure relationships; 3.c) Reg D0 x Herbivory pressure
relationships; 3.d) Reg D0 x Adult Biomass relationships; 3.e) Reg D0 x
D0 Adults relationships; 3.f) Reg D0 x Abundance of Adults; 3.g) Reg D0
x Organic Carbon relationships; 3.h) Reg D0 x Total Nitrogen
relationships.

Table 1\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
Community variables & Area I & Area II & p value* & t / w\tabularnewline
\midrule
\endhead
Families & 6 & 15 & - & -\tabularnewline
Observed richness & 11 & 27 & - & -\tabularnewline
Absolute abundance & 526 & 391 & - & -\tabularnewline
Abundance (Ind.) & 87.66 (45.72) & 65.16 (37.45) & 0.37 &
0.9324\tabularnewline
Diameter (cm) & 1.6 (0.65) & 1.0 (0.61) & \textbf{0.00} &
139770\tabularnewline
Height (m) & 0.49 (21.20) & 0.50 (26.24) & 0.91 & 103260\tabularnewline
Biomass (mg / m\selectlanguage{ngerman}²) & 50.08 (42.03) & 16.87 (19.60) & 0.17 &
27\tabularnewline
Order Div. 0 (RegD0) & 6.50 (1.72) & 11.47 (6.73) & 0.33 &
11.5\tabularnewline
Order Div 1 (RegD1) & 3.91 (0.96) & 5.94 (3.20) & \textbf{0.04} &
5\tabularnewline
Order Div 2 (RegD2) & 2.75 (0.84) & 4.84 (2.00) & \textbf{0.05} &
-2.3569\tabularnewline
Equability & 0.39 (0.10) & 0.51 (0.19) & 0.22 & -1.3175\tabularnewline
Herbivory Pressure Index & 0.43 (0.11) & 0.44 (6.66) & 0.44 &
10\tabularnewline
\bottomrule
\end{longtable}

Table 2\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\textbf{Soil propertie} & \textbf{Area I} & \textbf{Area II} & \textbf{p
value*} & \textbf{t / x}\tabularnewline
\midrule
\endhead
\textbf{Surface layer} & \textbf{Surface layer} & \textbf{Surface layer}
& \textbf{Surface layer} & \textbf{Surface layer}\tabularnewline
Organic carbon (\%) & 0.79 (0.50) & 1.93 (1.20) & \textbf{0.05} &
-2.2473\tabularnewline
Total nitrogen (\%) & 0.07 (0.02) & 0.16 (0.08) & \textbf{0.03} &
-2.5265\tabularnewline
C/N ratio & 10.38 (3.21) & 11.54 (1.43) & 0.48 & -0.7540\tabularnewline
Ca\textsuperscript{2+} (cmolc /dm\selectlanguage{ngerman}³) & 10.86 (4.33) & 10.30 (9.39) & 0.89
& 0.1399\tabularnewline
Mg\textsuperscript{2+} (cmolc /dm\selectlanguage{ngerman}³) & 1.60 (3.20) & 1.93 (1.52) & 0.87 &
19\tabularnewline
Na\textsuperscript{+} (mg /dm\selectlanguage{ngerman}³) & 24.93 (36.43) & 16.81 (59.99) & 0.51 &
22\tabularnewline
K\textsuperscript{+} (mg /dm\selectlanguage{ngerman}³) & 153.60 (80.63) & 237.71 (113.72) & 0.16
& -1.4991\tabularnewline
P (mg /L) & 23.2 (159.74) & 25.9 (51.13) & 0.75 & 15\tabularnewline
Base saturation (\%) & 88.44 (8.94) & 80.64 (11.94) & 0.22 &
1.2926\tabularnewline
Coarse sand (\%) & 34 (0.04) & 33 (0.10) & 0.94 &
0.071049\tabularnewline
Fine sand (\%) & 27 (0.14) & 28 (0.12) & 0.88 & -0.1491\tabularnewline
Silt (\%) & 18 (0.07) & 16 (0.05) & 0.59 & 0.55141\tabularnewline
Clay content (\%) & 21 (9) & 15 (14) & 0.20 & 26\tabularnewline
Available water (m\textsuperscript{3} /m\textsuperscript{3}) & 0.11
(0.37) & 0.10 (0.38) & &\tabularnewline
Unavailable water (m\textsuperscript{3} /m\textsuperscript{3}) & 0.07
(0.43) & 0.07 (0.40) & &\tabularnewline
Total porosity (m\textsuperscript{3} /m\textsuperscript{3}) & 0.42
(1.43) & 0.43 (1.69) & &\tabularnewline
Wilting point (m\textsuperscript{3} /m\textsuperscript{3}) & 0.07 (0.43)
& 0.07 (0.40) & &\tabularnewline
\bottomrule
\end{longtable}

Figure 1\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 2\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Figure
3

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