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\begin{document}

\title{A Pugnacious ant (Anoplolepis custodiens) confounds ant assemblage
responses to bush encroachment}



\author[1]{Thinandavha Munyai}%
\author[2]{Stefan Foord}%
\author[1]{Rob Slotow}%
\author[1]{Nomathamsanqa Mkhize}%
\affil[1]{University of KwaZulu-Natal College of Agriculture Engineering and Science}%
\affil[2]{University of Venda}%


\vspace{-1em}



  
  \date{June 13, 2024}


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\selectlanguage{english}
\begin{abstract}
Habitat structure is a key determinant of variation in biodiversity. The
effects of increased vertical and horizontal vegetation structure can
result in marked shifts in animal communities. This is particularly true
for ants in response to woody thickening, with predicted negative
impacts on ant diversity. We used pitfall traps to study the response of
epigeic ants in two co-occurring dominant habitats (closed and open) of
an African savanna biome experiencing extensive woody thickening.
Although species richness was higher in open habitats, evenness was
significantly lower. Thickening explained significant amounts of
variation in ant composition, but site-specific characteristics and
seasonality were more important. These site-specific characteristics
were largely linked to Anoplolepis custodiens, a species that were
locally abundant in open habitats with clayey soils, where they often
accounted for more than 90\% of all ant activity. As A. custodiens also
responds positively to disturbance, indiscriminate bush clearing could
lead to knock on effects associated with the numerical and behavioural
dominance of this species.%
\end{abstract}%



\sloppy


\textbf{Introduction}

The savanna biome dominates southern Africa, and is characterised by
continuous grassy understory, a discontinuous woody overstory (Archer et
al., 2017; Bond, 2019), and, therefore, considered a largely open
ecosystem (Pausas \& Bond, 2020). However, various anthropogenic
activities (Osborne et al., 2018) pose a threat to this biome. One
pervasive threat is that of woody plant encroachment into savannas and
grasslands, linked to climate change in particular (Criado, Myers-Smith,
Bjorkman, Lehmann, \& Stevens, 2020). Over the past two centuries
(Archer et al., 2017) encroachment rates have varied between -0.131 to
1.275\% per year in South Africa (O'Connor, Puttick, \& Hoffman, 2014),
and 0.25\% per year across the rest of Africa (Stevens, Lehmann, Murphy,
\& Durigan, 2017).

Broad-scale studies predict that woody plant encroachment threatens
biodiversity (Parr, Gray, \& Bond, 2012), reducing the quality and
quantity of the habitat, and jeopardizing both ecological functioning
and services. In communal grazing lands and protected areas, bush
encroachment also reduce production forage and increase ectoparasites
(Archer et al., 2017). Overall, biodiversity is strongly affected by
woody plant encroachment, e.g. in Grampians National Park, southern
Australia, species richness of herbaceous growth forms was significantly
reduced (Price \& Morgan, 2008) while bird communities in the Kingdom of
Eswatini showed significant shifts in structure (Sirami \& Monadjem,
2012). This has real implications for conservation managers that aim to
maintain diversity.

Wingless arthropods and those with low dispersal ability will be the
worst affected by local habitat change (Blaum, Seymour, Rossmanith,
Schwager, \& Jeltsch, 2009). However, gradients of shrub cover vary in
their impact, with grasshopper richness peaking at intermediate shrub
cover, but not affecting butterfly richness (Koch, Edwards,
Blanckenhorn, Walter, \& Hofer, 2015). Hering et al. (2019) and Blaum et
al. (2009) reported similar mixed effects on beetles, with some families
increasing in abundance and richness at higher encroachment levels.
Different metrics might also respond differently, e.g. shrub species in
Australian woodlands were important drivers of arthropod community
composition, but did not affect their abundance (Kwok \& Eldridge,
2016). However, general consensus is that woody plant encroachment will
have adverse effects on the ecological and ecosystem services (Eldridge
et al., 2011) provided by arthropods, for example pollination
(Kettenbach, Miller-Struttmann, Moffett, \& Galen, 2017; Lara-Romero,
Garcia, Morente-Lopez, \& Iriondo, 2016), and dung decomposition (Blaum
et al., 2009).

Ants (Hymenoptera: Formicidae), are among the most abundant terrestrial
arthropods (H\selectlanguage{ngerman}ölldobler \& Wilson, 1990), and perform a myriad of
ecological functions (Wills \& Landis, 2018) and ecosystem services (Del
Toro, Ribbons, \& Pelini, 2012). Their response to encroachment has
rarely been investigated and varies geographically from increased
abundance in the southern Kalahari (Blaum et al., 2009) and increased
richness in the south western USA (Bestelmeyer, 2005). However, Parr et
al. (2012) found no real differences between ant richness and activity
in open and closed habitats in Hluhluwe-Imfolozi Park, but observed that
more species were restricted to closed habitats and that these
restricted taxa were largely predatory (80\% in the forest compared to
the 7\% in open habitats). Their results mainly focus on these rare
taxa. Although they analyzed compositional differences, the
distance-based approach used leads to confounding location and
dispersion effects and has been shown to not reflect community
composition (Warton, Wright, \& Wang, 2012). Here, we use generalized
linear models and their multivariate extensions to model ant species
abundance (Warton et al., 2012). Ant functional roles is largely the
result of dominant taxa in ant assemblages. Here, we complement their
study by focusing on the community as a whole, and ask (1) how
seasonality, soil and structural habitat complexity interact to affect
ant diversity in an African savanna biome experiencing extensive woody
thickening, (2) and identify species associated with these differences.

\textbf{Methods}

\emph{Study area}

The study was conducted in the Hluhluwe section of Hluhluwe-iMfolozi
Park Park (900 km\textsuperscript{2}), situated in Zululand, northern
KwaZulu-Natal, South Africa (28.2198° S, 31.9519° E). The park receives
rainfall ranging from 700 mm to 985 mm, usually in summer between
October-March (Boundja \& Midgley, 2010). Most of the park is covered
with woodland savanna interspersed with shrub thicket. The majority of
habitats vary from semi-deciduous forest on the northern side (Hluhluwe
Game Reserve) to open savanna woodland (iMfolozi Game Reserve). The park
is managed by Ezemvelo KwaZulu-Natal (KZN) Wildlife, and is a wildlife
conservation area (Gray \& Bond, 2013).

\emph{Site selection}

Six-paired sampling sites were selected for this study: Gontshi (Gon),
Isivivane (Isi), Magangeni (Mag), Mansinya (Man), Memorial gate (Mem),
and Zincakeni (Zin). Replicates within each site were at least
\textgreater{}300m apart while the actual sites were at least 2 km
apart. Each site had four paired plots (one in the closed and open
respectively), for a total of eight plots per site. The closed habitat
plots were in the middle of this habitat, while the open replicates were
30 m away from the edge. In total there were 48 grids. Canopy cover was
used to categorize the habitat type. As described in Parr et al. (2012),
habitat-type with a \emph{ca.} mean canopy cover of \selectlanguage{english}[?] 3\% were treated
as open savanna, and habitat-type with a \emph{ca.}mean canopy cover \selectlanguage{english}[?]
55\% were treated as closed habitat.

\emph{Ant sampling}

Ground dwelling worker ants were sampled following a standardized
pitfall trapping method as described in Munyai and Foord (2015).
Sampling at each site was conducted in January 2017 (wet-hot season) and
early October 2017 (dry-hot season). Pitfall traps (62mm) contained 50\%
solution of propylene glycol as a preservative that neither attracts nor
repels ants (Munyai \& Foord, 2012). There were ten pitfall traps in
each plot. The pitfall traps were laid out in a 5 X 2 sampling grid and
the pitfall traps were at least 10 m apart, and were left open for five
days.

After collecting data, samples were washed in the laboratory, and the
specimens counted, separated, and stored in 70\% ethanol. They were then
identified to genus level using Fisher and Bolton (2016), and identified
to species level where possible. Unidentified ants were given
morphospecies number codes, i.e. genus name UKZN\_01, 02, etc. (Munyai
\& Foord, 2015). Voucher specimens of each species are held in the
School of Life Science, University of KwaZulu-Natal, and will later be
accessioned into the Kwazulu-Natal Museum.

\emph{Environmental variables}

Vertical and horizontal vegetation cover were quantified during each
sampling period. The horizontal vegetation cover was quantified by
visually estimating percentage area covered by bare ground, leaf litter,
rocks, and vegetation within a 1 m2 grid over each pitfall trap (Botes
et al., 2006; Munyai \& Foord, 2012). To quantify the vertical
distribution of vegetation, number of hits (i.e., the number of contacts
with vegetation) from the rod were recorded at 25 cm intervals using a
1.5 m rod. The 1.5 m rod was placed four times at 90deg angles from a
pitfall trap, which was used as a central point. This method was used as
a measure vertical vegetation complexity.

Soil samples were collected in October 2017 at each replicate, 10
subsoil samples were collected using a soil auger and then pooled for
that replicate. A total of 48 soil samples were sent to the Research and
Technology Development: Analytical service labs, in Cedara,
Pietermaritzburg, for composition analysis (\% organic carbon, \%
nitrogen and \% clay), chemical content (phosphorus, potassium, calcium,
manganese, zinc, magnesium and copper), and texture (\% clay, \% sand,
\% silt and \% moisture).

\emph{Statistical Analysis}

Collinearity between predictor variables (soil and habitat structure)
was accounted for by doing a principle component analysis of the two
sets of variables respectively. The first two axes of the PCA for soils
explained 66.55 \% of the variation, the first axis 51.98 \%, and the
second 14.57 \%. PC1 was positively associated with more sandy soil and
negatively associated with clay soils (Figure S1). The first two axes of
the PCA for habitat structure explained 69.28 \% of the variation, first
axis, 41.92, and the second 27.36 \%. The first axis was positively
related to increased canopy cover, leaf litter, and more complex
structure.

These principle components were used to model gradients in ant diversity
in response to habitat structure and soil respectively. Two further
categorical variables, habitat (open vs closed) and season (hot-wet and
hot-dry) were also included. Species richness and effective number of
species for Shannon diversity and Simpson's diversity (Jost, 2006) were
modelled using Generalized Linear Mixed Models (GLMM), with site as the
random factor and Poisson error distributions, a loglink function for
richness and Gaussian error distributions, and an identity link function
for Shannon and Simpson's effective number of species. The best model
was identified using a Alkaike Information Criterion, the lowest being
the best. Models that were \textless{} \selectlanguage{greek}Δ\selectlanguage{english}2AIC were also included. The
relative contribution of marginal (fixed,
R\textsuperscript{2}\textsubscript{m}) and conditional (fixed and
random, R\textsuperscript{2}\textsubscript{c}) in the variation
explained were calculated for each model (Nakagawa \& Schielzeth, 2013).

Compositional variation was modelled using a model-based multivariate
approach where we fitted multivariate generalized linear models (GLMs)
to ant species abundance data in the R package ``mvabund'' (Wang,
Naumann, Wright, \& Warton, 2012) . Predictors included were similar to
those included into the univariate GLMMs. This model-based approach
deals with confounding mean--variance relationships typical of count
data that are zero inflated (Warton, Thibaut, \& Wang, 2017). Likelihood
ratio statistics for each taxon were summed, that results in a
community-level measure for each predictor. Correlation across species
was accounted for by using the PIT-residual bootstrap method to derive
p-values by resampling 999 rows of the data set (Warton et al., 2017).
Predictors were included in the model individually to explore the
marginal (variation explained by the predictor on its own) deviance
explained.

Model fit for both univariate and multivariate models were evaluated by
visually inspecting residual plots for deviations from normality,
heteroscedasticity, systematics patterns, and autocorrelation.
Ordination of ant assemblages was done using Bayesian ordination and
regression (Hui, 2016). To aid visualization, samples for the two
seasons were pooled.

Responses of ant assemblages to gradients in soil and habitat structure
were modelled using Threshold Indicator Taxa Analysis (TITAN) from the
``TITAN2'' package (Baker \& King, 2010). This method uses
standardized\emph{z} -scores obtained from indicator species analysis
(Indicator Value) to detect the species-specific change points, and the
direction of response along a gradient (Baker \& King, 2010; Costas,
Pardo, Mendez-Fernandez, Martinez-Madrid, \& Rodriguez, 2018).
Increasing responses (z+) are distinguished from those decreasing
(\emph{z-} ) at a specific change point (Baker \& King, 2010). TITAN
also estimates indicator reliability and the proportion of times that a
taxon is given the same classification through bootstrapping, as well as
uncertainty around the location of individual taxa and community change
points (Baker \& King, 2010).

\textbf{Results}

A total of 61 008 ant specimens were collected, comprising 74 species in
25 genera, and five subfamilies. Myrmicinae (47 species and eight
genera) was the most diverse and abundant subfamily, followed by
Ponerinae (13 species and eight genera), and Formicinae (15 species and
six genera). The most specious genera were \emph{Tetramorium} (22
species), \emph{Monomorium} and \emph{Pheidole} each had six species,
while \emph{Camponotus} , \emph{Lepisiota} , and \emph{Pheidole} had
five species each. Three quarters of all the ant individuals caught
belonged to one species,\emph{Anoplolepis custodiens} . This species,
and those in the genus\emph{Pheidole} , accounted for 90\% of all the
ants.

The best model for species richness included season and habitat
(closed/open), with richness being significantly higher in the open
habitat and during the wet season (Table 1). The second-best model
included soil PC1, with very weak evidence for an increase in richness
in more sandy soils (estimate = 0.014 \selectlanguage{ngerman}± 0.015). However, this trend was
reversed when considering Hill no.1, with strong evidence that there was
significantly less Shannon diversity in the open habitats. Similar to
richness, Soil-pc1 was included in the second model, with weaker
evidence for a positive response to sandy soil (estimate = 0.026 ±
0.05). The negative impact of open habitats became even more pronounced
when Simpsons dominance was modelled. Soil-pc1 was also included in the
second-best model. All three models explained around 22\% of the
variation observed, while at least half of this variation was linked to
the random factor, i.e. site-specific characteristics (Table 1).

Site-specific characteristics not measured in this study explained the
largest amount of variation in ant assemblage structure (Table 2, Figure
2). This impact of sites on assemblages was exemplified by the distinct
ant assemblages comprised of \emph{Odontomachus troglodytes}
,\emph{Tetramorium} UKZN\_14 (\emph{simillimum} gp.),
and\emph{Lepisiota} UKZN\_02 (\emph{capensis} gp.), associated with the
sandy soils of Mansinya (Figure 2 \& Figure S1).

Habitat followed seasons in explaining the most variation in species
composition, particularly when considering conditional variation
explained (Figure 2). None of the structural and soil variables
explained significant amounts of variation beyond that explained by the
previous three variables, although the first soil principle component
(increasing sandy soil, see Figure S2), explained the third largest
amount of marginal variation (Table 2). Similarly, no taxa were
associated with any of the gradients in habitat structure, while eight
indicator taxa were associated with the sand-clay gradient in the study
site (Figure 3). \emph{A. custodiens} notably preferred the clay soils
across open and closed habitats. The activities of \emph{Odontomachus
troglodytes} increased in more sandy soil in both open and closed
habitats, while \emph{Anoplolepis custodiens} and
\emph{Tetramorium}UKZN\_22 (\emph{gabonense} gp.) had their highest
activities in clay soil. \emph{Myrmicaria natalensis} ,
\emph{Bothroponera soror} and\emph{Monomorium junodi} activities peaked
in loamy soils.

Five species were distributed across all sites, and mostly sampled in
both the closed and open habitat types. Three of these,
\emph{Pheidole}UKZN\_01 (\emph{megacephala} gp.), \emph{Mesoponera
caffraria} and\emph{Monomorium} UKZN\_03 were associated with open
habitat types, while two species, \emph{Pheidole crassinoda ruspolii}
and\emph{Tetramorium} UKZN\_18 (\emph{squaminode} gp.) were associated
with closed habitat (Figure S3).

\textbf{Discussion}

Although site-specific characteristics and seasonality dominated ant
assemblage structure, habitat type (open vs closed) had important
impacts on ant richness and composition. Open habitat had significantly
more species, but were less even than closed habitats; and, although
ants responded to gradients in soil characteristics, responses to
habitat structure changes were not gradual (Figure S4). This is
evidenced by the relatively larger role of categorial classification
(open versus closed) in explaining ant diversity. Responses to
thickening are, therefore, predicted to be abrupt. This threshold
response can partly be explained by the presence of the numerical
dominant \emph{A. custodiens} in open habitats of some sites that were
mainly on clayey soils and \emph{Odontomachus traglodytes} which is a
specialised as well as aggressive predator in sandy soils.

Andersen (2019) reasoned that the amount of vegetation cover is an
important driver of both flora and fauna communities. However in
contrast to our study, Nooten, Schultheiss, Rowe, Facey, and Cook (2019)
reported fewer ant species in open habitat. Notably their study aite was
in highly altered urban gardens of Greater Sydney Region of south-east
Australia. Pacheco and Vasconcelos (2012) also reported fewer species in
the least structurally complex habitats of the Brazilian Cerrado. In
contrast, Lassau and Hochuli (2004), Yusah and Foster (2016) and
Ahuatzin et al. (2019) reported a negative response of ant species
richness and diversity with habitat complexity. Similarly, Hethcoat et
al. (2019) reported more open and warmer pasturelands supporting more
ants than the compared primary or secondary forest.

Overall, habitat explained the third largest amount of conditional
variation in the ant assemblages of this study. Similarly, across the
western Soutpansberg mountains, vegetation types explained a significant
amount of variation in ant assemblages (Munyai \& Foord, 2012) . This is
not surprising, given that habitat structure had been thought to
determine the movements of ants as they are usually impeded by grass
stems or woody twigs (Nooten et al., 2019). In the Amazon basin,
Guilherme et al. (2019) reported the effects of habitat complexity on
ant species foraging strategies and behaviour.

Not surprisingly, ant assemblages in the two habitats were significantly
different. It is, however, expected that natural characteristics in a
site, for example the amount of sunlight intensity, plant cover and
overall variation in habitat structure must have a clear effect on the
ant assemblage richness and composition (Campos, Soares, Martins, \&
Ribeiro, 2006). This is largely because of the effect of ground surface
rugosity, which affects foraging activities, for example ant running
speed (Greve, Blaha, Teuber, Rothmaier, \& Feldhaar, 2019). In this
study, closed habitat is largely structural more complex. In total, 24
\% of the species were only recorded in the open habitat, compared to
the 8 \% restricted to closed habitat. This, once again, emphasises the
important role of habitat openness, as detailed in Andersen (2019), and
references there in, as a crucial driver of ant assemblage composition.
However, this larger number of species in the open habitat could also be
explained by the species-area effect. Currently, open Savannas dominate
the Hluhluwe--iMfolozi Park is largely dominated by open savanna
vegetation type (Charles-Dominique, Staver, Midgley, \& Bond, 2015),
covering \textgreater{}75\% of the landscape. This in itself would
explain the larger number of species observed in the open habitat.

One site, Mansinya, had a distinct ant assemblage and therefore
highlighting on the importance of site-specific characteristics,
particularly that related to soil characteristics. The Mansinya site is
on sandy soil with specific species associated with this site (Figure
S2). Similarly, sites on their own were the most influential predictors
of ant assemblage structure, as suggested by both conditional and
marginal effects (Table 1). Whether these differences are related to
historical factors, spatial autocorrelation or variables that we did not
measure is difficult to say and beyond the scope of this study. In
general, Radnan, Gibb, and Eldridge (2018) noted that ants tend to be
more responsive to small-scale alteration on the soil surface than
overall changes in vegetation community composition. The influence of
site-specific characteristics, namely, vegetational complexity, litter,
and soil chemical properties were reported in Parui, Chatterjee, and
Basu (2015) as the driver of variation in ant assemblage composition in
the forested habitats of Eastern India.

The activities of the hyperabundant (72 \% of the total
abundance)\emph{Anoplolepis custodiens} , increased in site with clay
soils (Figure 3). In contrast, activities of \emph{Odontomachus
troglodytes} peaked in sandy soils. Three other species
(\emph{Myrmicaria natalensis} ,\emph{Bothroponera soror} and
\emph{Monomorium junodi} ), were more active in loamy soils. Along an
alluvial fan of the Tehuacán Valley of central Mexico, Rios-Casanova,
Valiente-Banuet, and Rico-Gray (2006) reported high ant abundance in
sandy soils. The latter study suggested that sandy soils and habitat
complexity could have favoured the abundance of the dominant ants, which
in turn could have also affected overall ant diversity patterns in their
study. Bownes, Moore, and Villet (2014) also observed very large number
of \emph{Anoplolepis custodiens}in in citrus orchards of the Eastern
Cape, South Africa while \emph{A. custodiens} has been observed to nest
in open and well insulated soils of the Karoo (Dean, 1992).

The high abundance, dominant behaviour, and impact of \emph{A.
custodiens} on ant diversity have been documented in several southern
African studies (Mauda, Joseph, Seymour, Munyai, \& Foord, 2018; Parr,
2008; Samways, 1983). Although this species is native to the region, it
is well known to prey on a variety of other invertebrates (Keiser,
Wright, \& Pruitt, 2015), and also small livestock (Prins, Robertson, \&
Prins, 1990), it is also a major pest in agriculture landscapes (Addison
\& Samways, 2006; Addison \& Samways, 2000). \emph{A. custodiens} is
also well known for tending honeydew producing pest insect species, and
being nectar collectors, hence, regarded as one of the serious pests in
the agricultural systems (Dean, 1992).

\emph{A. custodiens} respond positively to disturbance (Addison \&
Samways, 2000; Mauda et al., 2018). Coupled with the formation of
supercolonies in open shallow soils in particular (TC Munyai Personal
observation), suggests that manual removal of trees and shrubs should
not be done indiscriminately. The fact that this species can completely
dominate open habitat (Addison \& Samways, 2006), clearing of sites
could facilitate invasion with negative impacts not only on ant
diversity, but also ecosystem services. Particularly since \emph{A.
custodiens} is a polygynous (Samways, 1990), generalist predator of
other invertebrates (Dean, 1992), and small vertebrates (Mauda et al.,
2018; Prins et al., 1990), displacing other ant species (Mitchell,
2000), and control ant assemblage structure in the South African savanna
( Parr, 2008).

\emph{A. custodiens} is particularly active in clay soils, and clearing
of woody species in the park need to be context specific. Exploring the
response of \emph{A. custodiens} to various woody species control
measures, be it manual removal of trees and shrubs, using fire or
browsing mammals as a management tool, should provide a better
understanding of possible impacts. Generally though, we consider this a
cautionary note around predicting local impacts based on broad-scale
global patterns.

\textbf{Conflict of interest}

The authors declare on conflicts of interest regarding the publication
of this paper. \textbf{Acknowledgments}

We thank the National Research Funding for funding to the first author
(Grant No. 114416) and the Ezemvelo KZN wildlife for granting us a
permit to conduct this research at Hluhluwe-iMfolozi Park. Field
assistants from Nokubonga Thabethe, Sbongiseni Xolo, Caroline Kunene,
Joshua Mutavhatsindi, Phethele Xaba, Gundo Makhuvha and the
Hluhluwe-iMfolozi game guards, Falakhe Dlamini and Eric Khumalo.

\textbf{Author contributions}

TCM and SHF designed the study, analysed the data and wrote the paper.
TCM and NNM collected the data, TCM identified the ants. TCM, NNM, RHS
and SHF revised the article..

\textbf{Conflict of Interest Statement}

No conflict of interest among the authors.

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\textbf{Table 1:} Best models and the marginal and conditional variation
explained based on Generalized Linear Mixed Effects Models (GLMM) of
species richness, Shannon and Simpsons diversity. Shannon and Simpson
indices are represented by there effective numebers\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
\begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Model\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Best model\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
AIC\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
\selectlanguage{greek}Δ\selectlanguage{english}AIC (second best)\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
R\textsuperscript{2}\textsubscript{m}\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
R\textsuperscript{2}\textsubscript{c}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Species Richness\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textasciitilde{}Habitat* + Season***\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
549.84\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1.3\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.11\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.23\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Best model equation: e\textsuperscript{0.15Open + 0.21Wet}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Shannon (Effective number of species)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textasciitilde{}Habitat** + Season\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
417.2\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1.7\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.12\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.22\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Best model equation: e\textsuperscript{-0.3Open - 0.15Wet}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Simpsons (Effective number of species)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\textasciitilde{}Habitat** + Season\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
364.9\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
1.1\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
0.08\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
0.27\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Best model equation: e\textsuperscript{-0.37pen - 0.19Wet}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2} : Conditional (Likelihood ratio and its significance)
and marginal effects (Deviance) of predictors for ant assembalge in
Hluhluwe\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
\begin{minipage}[b]{0.32\columnwidth}\raggedright\strut
\textbf{Predictor}\strut
\end{minipage} & \begin{minipage}[b]{0.32\columnwidth}\raggedright\strut
\textbf{Conditional effects} \textbf{Likelihood ratio (LR)}\strut
\end{minipage} & \begin{minipage}[b]{0.32\columnwidth}\raggedright\strut
\textbf{Marginal effects} \textbf{Deviance}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
Site\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
26.2**\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
705.1\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
Season\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
16.01**\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
1486.6\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
Habitat\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
15.38**\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
1503.8\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
Soil\_pc1\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
9.99\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
1491.0\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
Str\_pc1\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
10.07\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
1579.9\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-1-Boxplot-of-the-Hill-numbers---ant-species-richness/Figure-1-Boxplot-of-the-Hill-numbers---ant-species-richness}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-2-Ordination-of-ant-assemblages-at-each-of-the-sites-across-to-seasons/Figure-2-Ordination-of-ant-assemblages-at-each-of-the-sites-across-to-seasons}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-3-Significant-ant-indicator-taxa-identified-in-threshold-indicator-taxa-analysis/Figure-3-Significant-ant-indicator-taxa-identified-in-threshold-indicator-taxa-analysis}

\end{center}
\end{figure}

\selectlanguage{english}
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\end{document}

