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\begin{document}

\title{Community richness is the main factor determining the structure of
ant-plant mutualistic networks along environmental water gradients}



\author[1]{Bruno Melati}%
\author[2]{Paulo Guimarães Jr.}%
\author[3]{Laura Carolina Leal}%
\affil[1]{Federal University of the ABC}%
\affil[2]{Universidade de São Paulo}%
\affil[3]{Universidade Federal de São Paulo}%


\vspace{-1em}



  
  \date{May 28, 2024}


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\selectlanguage{english}
\begin{abstract}
Many plant species rely upon ants to protect against herbivores. In arid
environments, these plants often form stronger bonds with dominant ant
species that, in turn, provide a better anti-herbivory defense than low
aggressive subordinate ants. Dominant ants typically claim the plants
producing more nutritious nectar, commonly leaving less valuable plants
to be guarded by lower-quality bodyguards. As water scarcity increases,
the value of the extrafloral nectar also increases, which can increase
the control of the most valuable plants by dominant ants and of the
displacement of outcompeted ants to less valuable plants, reducing niche
overlap among ant species and, consequently, the generalization of
ant-plant interactions at the community level. To evaluate this
hypothesis, we crossed data from 63 empirical ant-plant networks with
the mean precipitation rate of the sites and period in which the
interactions were sampled. As the environment dries, ant-plant networks
decreased in species richness but maintained other network properties.
Surprisingly, the decline in the number of ant and plant species engaged
in the mutualism along the precipitation gradient increased the
interaction generalization in drier habitats. But this increased
generalization is possible due to the increase in probability of
interaction between all plant and ant species due to the lower richness.
Hence, water availability primarily influences ant-plant interactions
through its impact on ant and plant communities. This has significant
eco-evolutionary consequences, as possibly increasing the persistence of
this networks in drier environments, demonstrating a new pathway through
which environmental factors can impact ecological interactions.%
\end{abstract}%



\sloppy


\textbf{INTRODUCTION}

Mutualistic interactions often involve multiple, non-related species in
a community (Bascompte and Jordano 2007b), potentially affecting the
ecology and evolution of entire assemblages. However, mutualistic
interactions are not distributed homogeneously across the assemblage of
potentially interacting species (V\selectlanguage{ngerman}ázquez et al. 2005). Rather, some
species may interact with many potential partners available in the
community, while others interact with only a few (Bascompte et al. 2003,
Fagundes et al. 2017, Plowman et al. 2017, Guimarães 2020). This
heterogeneous distribution of interactions across species can be
influenced by the traits of the individuals and populations involved in
the mutualism and their response to the local biotic and abiotic
environment (e.g.Vázquez et al. 2005, Miller 2007, Albrecht et al. 2010,
Dáttilo et al. 2013b, Maia et al. 2019). Whenever habitat conditions
influence these mutualistic traits, it can indirectly affect the
patterns of interaction in mutualistic networks and, consequently, the
mechanisms shaping natural communities and determining species responses
to environmental conditions (see Tylianakis and Morris 2017). Therefore,
understand how interactions are distributed in response to environmental
conditions can help us to understand the consequences to species
interactions over global climatic changes.

Defensive mutualistic interactions between ants and plants with
extrafloral nectaries (EFNs) are an example of mutualistic interaction
in which ant and plant traits can be influenced by environmental factors
(Kersch \& Fonseca 2005, Heil 2008, Pringle et al. 2013). Ant
competitiveness determines their visitation patterns to plants with EFNs
and their quality as bodyguards (Leal and Peixoto 2017, Leal et al.
2022). Dominant ants display a series of aggressive behaviors that
ensures them a highly competitive ability (Stuble et al. 2017). This
aggressiveness also makes dominant ants better mutualistic partners for
plants with EFNs because they are more likely to behave aggressively
towards herbivores, unlike subordinate ant species who do not typically
exhibit aggression (Buckley and Gullan 1991, Xu and Chen 2010,
Flores-Flores et al. 2018, but see Melati \& Leal 2018). Like the ants,
plant species also vary in their attractiveness to ant bodyguards, with
those producing more concentrated and abundant nectar being more
appealing to their partners (Blüthgen and Fiedler 2004a, c,
Flores-Flores et al. 2018). Therefore, dominant ant species are likely
to monopolize the most attractive plant species in the community,
displacing subordinate species to less attractive plants (Blüthgen and
Fiedler 2004c). At a broad scale, this kind of assortative pairing can
predictably shape the patterns of ant-plant interaction along spatial
gradients (Dáttilo et al. 2013b), especially along those in which the
competitive pressure among ant species is also variable (Leal and
Peixoto 2017, da Silva et al. 2019, Lasmar et al. 2021). In this case,
ant-plant interactions should become less generalized and form groups of
interacting species as the stronger competitive pressure among ant
species, with dominant and subordinate ants interacting with a more
dissimilar group of plants in the community.

A meta-analysis found that, at a macroecological scale, plants with EFNs
benefit more from ant attendance as the environment dries out, due to an
increased probability of plant attendance by dominant ant species (Leal
and Peixoto 2017). This is because the extrafloral nectar, a water
source for ants (Heil 2011, 2015), becomes more valuable to ants when
water is scarce, making plants with EFNs more attractive to ants
(Ruffner and Clark 1986, Contreras et al. 2013, Leal and Peixoto 2017).
However, it neglects that, even in drier habitats, plant species would
not be equally valuable to ants. Then, dominant ant species might
increase the monopolization of the best plant partners available in
drier habitats, increasing the competitive exclusion of subordinate ants
from the more valuable plant species. Consequently, it is possible that
at a macroecological scale ant-plant interactions become more
specialized and grouped as scarcer the water availability -- something
that can have relevant implications for the eco-evolutionary dynamic of
this mutualism at a broad scale. For instance, increasing the
contribution of direct effects on trait matching over indirect effects
on interactions, accelerating the coevolutionary dynamics on interacting
species with the group of interactors.

Here, we investigated how emergent patterns of interactions between ants
and plants with EFNs (ant-plant interactions hereafter) at the community
level vary over environmental water gradients and evaluated the role of
ant competition for plant species secreting extrafloral nectar on the
emergence of such patterns. For that, we hypothesized that (i) ant-plant
interactions become more specialized and forming sub-groups of
interaction partners as drier the environment and that (ii) these
effects will be driven by a decline in the overlap of dominant and
subordinate ant species in the use of these plants over the
environmental water gradients. To evaluate our hypotheses, we used two
different approaches based on the ecological network theory (Bascompte
et al. 2003, Jordano et al. 2003, Bascompte and Jordano 2007b). In the
first approach, we evaluated how the structure of ant-plant ecological
networks and plant usage by all ant species varied along a
macroecological gradient of water availability (first hypothesis). In
the second approach, we evaluated the water availability effect on the
patterns of plant usage specifically by dominant and subordinate ant
species (second hypothesis).

2. \textbf{METHODS}

\emph{2.1. Networks studied}

To evaluate our hypotheses, we built a dataset compiling data from
empirical ant-plant networks available in published studies and
representing interactions between ants and plants with EFNs in different
communities worldwide (ant-plant networks hereafter). For that, we
searched published studies at Science Direct, Web of Science platforms
(Core library) and Google Scholar using the following key
terms:\emph{``ant-plant network''} (results without replicates: Science
Direct = 16; Web of Science = 50; Google Scholar = 121) and
``\emph{ant-extrafloral nectar network''} (results: Science Direct = 67;
Web of Science = 0; Google Scholar = 13). After this search, one of us
(BM) read the title and abstract of all papers, checking if the studies
reported ant-plant networks based on interactions observed in the field.
We did not consider papers reporting networks in which the ant-plant
interactions were experimentally manipulated (N = 1). After this first
screening, we gathered 32 potentially suitable papers describing
empirical ant-plant networks. Some of those papers investigated
different questions using the same ecological networks. In such cases,
we selected the oldest study from which we could extract the network's
information. Then, at the end of this first screening process, we
gathered 22 papers suitable to our study.

\emph{2.2. Precipitation data}

As a proxy for water availability in the habitat, we used the mean
precipitation rate at the time and site in which the ant-plant
interactions were sampled in each study (Konapala et al. 2020). This
metric allowed us to estimate the precipitation rate experienced by the
plants with EFNs and the ants when the interactions were observed,
leading to more refined results about the influence of the local
climatic conditions on the patterns of ant-plant interactions. We
obtained this information from TRMM\_3B43 dataset (TRMM from now on),
available at the NASA Giovanni web database, from NASA'S EOSDIS (NASA'S
Earth Observing System Data Information System) (TRMM, 2001). In this
database, the precipitation rate is measured as the amount of rainfall
per area per unit of time (TRMM, 2001). TRMM compiles global climatic
information for the period between 01/01/1998 to 31/12/2019, covering
the period when most of the empirical networks in our database were
collected (N = 21).

To retrieve the precipitation data, we extracted from each paper the
period and the geographical coordinates of the study site in which each
network was sampled. This information was available in most of the
papers included in our database. In two studies, the authors did not
report the period or site in which the interactions were observed. Then,
we excluded these studies from our dataset, which reduced our dataset to
19 studies. Among all networks described in these papers, only one
network reported interactions collected over a period not covered by
TRMM dataset (before 1998). In this case, we excluded this single
network from our dataset but included the other two networks described
in this study and collected after 1998 (Díaz-Castelazo et al. 2013).

In studies where the ant-plant interactions were sampled in an area
equal to or inferior to one minute latitudinal and longitudinal scale,
we used a one-minute scale as our standard spatial unit on TRMM (N = 43
networks, 68.25\%). Only two studies sampled the ant-plant networks in
an area larger than a one-minute scale (N = 20 networks, 31.75\%). In
these cases, we selected an area proportional to one minute at the
center of the sampling area reported in the original paper. After
setting the area, we informed the period of interest and extracted the
precipitation information as a ``time series of area-averaged values''.
By doing that, we could obtain the monthly averaged mean precipitation
rate in millimeters for the period in which each network included in our
dataset was sampled.

\emph{2.3. Hypothesis 1 -- Effect of water availability on the structure
of ant-plant networks}

To evaluate if ant-plant interactions become more specialized and
modular as drier the environment, we used binary bi-adjacency matrices
describing the networks reported in each study. In these matrices, the
ant-plant interactions were described as \(a_{\text{ij}}=1\), when the
ant species \emph{i} interacted with the plant species \emph{j} , and
as\(a_{\text{ij}}=0\) otherwise. Among the studies included in our
database, 69\% presented the frequency with which each ant and plant
species interacted with each other (weighted networks). Since this
quantitative information was not available in 31\% of our networks and
that weighted networks were not evenly distributed along our water
gradient, we transformed the weighted networks into binary ones (i.e.
presence/absence of interaction between each species pair), making all
networks comparable among each other.

For each network, we extracted the following metrics: species richness
(S), modularity (Q) (Newman 2006), connectance (C) and nestedness (NODF)
(Almeida-Neto et al. 2008). The network S is the number of ant and plant
species within the network and was calculated by summing the rows and
columns of each binary matrix. The modularity (Q) estimates how grouped
the interactions are within the networks. This modular pattern arises
from the formation of cohesive groups of interacting species (modules)
in which species within a given group are more densely connected to each
other than to species from other groups (Olesen et al. 2007). The
connectance (C) estimates the proportion of realized interactions from
the total possible interactions within the network (Blüthgen and Menzel
2006, Blüthgen et al. 2008, Pellissier et al. 2018). The higher the
connectance, the higher the proportion of realized interactions between
the ant-plant species pairs and, consequently, the more generalized the
interaction patterns (Blüthgen et al. 2008). Finally, the nestedness
(NODF) describes a pattern in which species with fewer interactions
often interact with a subset of the partners interacting with species
with a larger number of interactions. Nested networks often show (1)
interactions among generalists (species with multiple partners in the
network), (2) asymmetries in the number of interactions, in which
species with fewer interactions (specialists) often interact with
generalists, (3) interactions among specialists are rare or absent
(Bascompte et al. 2003, Guimaraes Jr et al. 2006, Bascompte and Jordano
2007a). In a perfect nested network, we expect a continuum in the degree
of species specialization with no species segregation into modules of
interacting species. Then, if the ant-plant interactions become more
specialized and modular as drier the environment, we expect that the
ant-plant networks exhibit lower connectivity and nestedness values and
higher modularity levels as lower the mean precipitation of the habitat.

When the S, Q, NODF, or C metrics were not directly reported in the
original studies, we calculated them using the raw data reported by the
authors in the supplementary material of each paper (n = 5) or kindly
sent to us by the authors of each study (n = 4; see the
acknowledgments). In the case of Q and NODF to obtain a normalized
metric (see below), we re-analyze all networks but two networks for the
nested metric in Luna et al. (2018). In one case, we could not retrieve
all these metrics, but the paper reported the matrix of interaction as a
figure (Dáttilo et al. 2014a). In this case, we recreated the matrix of
interaction and used it to calculate our metrics of interest. We could
not retrieve any focal metrics in six papers from our database. These
papers were discarded, and our final database included 13 papers and 63
networks (see the list of all papers used in our analysis in Supporting
information). When we retrieved some, but not all metrics of interest
from the studies, we excluded the study only from the dataset used to
analyze the specific absent metric. Because of it, we had datasets with
different sample sizes for different metrics. In one paper, the metrics
obtained corresponded to networks containing different types of
interaction (e.g. networks containing ant-EFN plants and
ant-aphides-plants -- Blüthgen and Fiedler 2004a). In this case, we
excluded the interactions not involving plants with EFNs and re-analyzed
the network formed only by the interactions between plant species with
EFNs and ant bodyguards.

When the C values were not directly reported in the studies, we
calculated them using the raw data following:

\(C=\frac{I}{\text{PA}}\),

in which \emph{I} is the number of realized interactions between
ant-plant species pairs, \emph{P} is the number of plant species,
and\emph{A} the number of ant species in the network. To calculate the
NODF and the Q values, we used the ANINHADO (Guimarães and Guimarães
2006) and MODULAR software (Marquitti et al. 2013), respectively. To
maximize the network`s modularity, we used the simulated-annealing
algorithm (Guimerà and Amaral 2005) available at MODULAR (Marquitti et
al. 2013).

Network's modularity and nestedness can be affected by variation in the
network S (Blüthgen et al. 2008). Because of it, direct comparisons of
these metrics between different networks may lead to a misleading
interpretation of the mechanisms driving the interactions between
species pairs (Blüthgen et al. 2008). Hence, after gathering all metrics
of interest, we estimated the Z-score of the Q and NODF values. Z-score
reduces such bias by standardizing mean Q and NODF values per unit of
the standard deviation of these metrics assuming a given theoretical
benchmark (\emph{z} Q and \emph{z} NODF from now on). To calculate the
Z-scores, we calculated the mean and the standard deviation of Q and
NODF values for 1000 simulated null networks based on each empirical
network in our dataset. To generate the null networks, we used the null
model type II (Bascompte et al. 2003), reported at ANINHADO as null
model CE, and at MODULAR as null model 2. The null model type II states
that the probability of interaction\(\ (P_{\text{ij}})\) between two
species (\emph{i} and \emph{j} ) is proportional to the degree (number
of interactions) of each species. Therefore, the type II null model of
an incidence matrix of size P \(X\) A (number of plant
species X number of ant species) could be depicted as:

\(P_{\text{ij}}=\frac{1}{2}\left(\frac{k_{i}}{A}+\frac{k_{j}}{P}\right)\),

in which \(k_{i}\) is the degree of each plant species and
\(k_{j}\) is the degree of each ant species. Therefore, the
Z-score of each metric of interest (NODF or Q) for each network was
calculated as:

\(z_{m}=\frac{m_{R}-{\overset{\overline{}}{m}}_{N}}{\sigma_{N}^{2}}\),

in which \emph{m} is the metric of interest (Q or NODF),
\(m_{R}\) is the metric of interest from the real
network,\({\overset{\overline{}}{m}}_{N}\) is the mean value of the metric of interest
generated by 1000 randomizations by type II null model
and\(\sigma_{N}^{2}\) is the standard deviation of the metric of
interest in the 1000 null matrices.

\emph{2.4. Hypothesis 2 -- Effect of water availability on the
interaction patterns of dominant and subordinate ants with the plants}

\emph{2.4.1. Ant dominance classification}

We obtained the identity of the ant species interacting with the plants
with EFNs in nine of the 13 papers, representing 38 of the 65 networks
in our dataset. We classified each ant species into categories of
hierarchical dominance based on the literature (Andersen and Patel 1994,
Andersen 1997, Cerdá et al. 2013, Stuble et al. 2017) (see Supporting
information to a list of ants and their dominance hierarchies). This
hierarchical classification tends to be relatively consistent across ant
genera in different biogeographical regions (Cerdá et al. 2013). Because
of it, this categorization has been successfully used to identify
emergent macroecological patterns involving ant communities worldwide
(Baccaro et al. 2011, Dáttilo et al. 2014b, Leal and Peixoto 2017, Leal
et al. 2022).

In the literature, ant species exhibiting mass recruitment, overly
aggressive behaviors, and a higher ability to monopolize resources and
territories are classified as \emph{dominants} (Cerdá et al. 2013). Ant
species exhibiting mass recruitment (but less expressive than dominant
species), a higher ability to monopolize food resources, but less
frequent aggressive behavior are classified as
\emph{sub-dominants}(Andersen 1995, Arnan et al. 2011). When dominant
ant species are absent in a community, sub-dominant ants could dominate,
being functionally similar to dominant species (e.g. Andersen 1995). In
such cases, sub-dominants have a similar ability to monopolize and
defend plants with EFNs than the truly dominant ant species (Horvitz and
Schemske 1984). Since we lack information regarding the composition of
the ant community occurring in the areas where each study was conducted
and that both dominant and sub-dominant ants can equally influence the
behavior of subordinate ant species (Parr and Gibb 2010), we pooled
dominant and sub-dominant ant species into the same group named as
dominants hereafter (similar approach used by Leal and Peixoto 2017,
Leal et al. 2022). Finally, we classified as \emph{subordinates} the ant
species that forage solitarily, not exhibiting mass recruitment and/or
aggressive behaviors (Cerdá et al. 2013).

\emph{2.4.2 Water effects on the patterns of dominant and subordinate
visitation to the plants}

To evaluate the role of water availability in the overlap of dominant
and subordinate ant species visiting the plants with EFNs, we used the
networks in which we had access to ant species identity (N = 37). In
three networks, no subordinate ant species interacted with the plants.
These networks were excluded from all the analyses focusing on the
subordinate species (N = 34 networks) but maintained in the analyses
focusing on the dominant ant species (N = 37 networks). For each
network, we calculated the proportion of dominant ant species.
Additionally, we calculated three metrics to explore the patterns of
plant use by dominant and subordinate ant species interaction: the
Connectance, the Resource range (RR), and the Jaccard Index. These
metrics are commonly used to describe the trophic specialization of
species in the network (Jordano 1987, Poisot et al. 2012), which allowed
us to evaluate the magnitude of dominant and subordinate trophic
specialization and overlapping in the networks along the water
gradients.

The connectance of each hierarchical group (\(C_{d}\)and
\(C_{s}\)) described the proportion of all interactions
reported in each network performed by dominant or subordinate ant
species to the total possible interactions within the networks. We
calculated \(C_{s}\ \)and \(C_{d}\)as a subset of the
general network connectance as follows:

\(C_{d}=\frac{\ I_{d}}{PA_{d}}\), \(C_{s}=\frac{\ I_{s}}{PA_{s}}\) ,

where \(C_{d}\) and \(C_{s}\ \)is the connectance of
dominant and subordinate ant species, respectively. \(I_{d}\)
\({(I}_{s})\) is the number of interactions performed by dominant
(subordinate) ant species,\emph{P} is the number of plant species, and
\(A_{d}\) \({(A}_{s})\) is the number of dominant
(subordinate) ant species in each network. If the decline in water
availability reduces dominant and subordinate overlap in using plants
with EFNs, \(C_{d}\) and \(C_{s}\ \)values will
decline when the mean precipitation rate also declines.

Resource Range (RR) is a binary metric resulting from the normalization
of the total number of interactions realized by the species within the
network (Poisot et al. 2012). In our case, it will describe the
proportion of plant species used by dominant and subordinate ant species
within each community. To calculate RR, we first counted the number of
interactions performed by each ant species within the networks. Then, we
estimated the mean RR of ants from each hierarchical group
(\(RR_{d}\)and \(RR_{s}\)) as follows:

\(\text{RR}_{d}=\frac{1}{A_{d}}\sum_{i=1}^{A_{d}}\frac{P-\ k_{i}}{P-1}\),\(\text{RR}_{s}=\frac{1}{A_{s}}\sum_{j=1}^{A_{d}}\frac{P-\ k_{j}}{P-1}\)

In which \(k_{i}\left(k_{j}\right)\) is the number of interactions the species
of dominant (subordinate) ant species \emph{i} (\emph{j} ). This metric
ranges from 0 to 1, with 0 indicating the use of all plant species
within the networks by ants from each group (i.e. complete
generalization) and 1, the use of only one plant species by all ants
from each group (i.e. complete specialization). Then, if decreasing in
water availability increase the monopolization of more valuable plants
by dominant ants and the competitive exclusion of the subordinate one to
less valuable plants, we expect that the \(RR_{d}\) and
\(RR_{s}\)values increase as the mean precipitation rate
decreases.

Finally, we used the Jaccard Index (\emph{J} ) as a proxy for the
overlapping degree of dominant and subordinate ant species interacting
with the plants in each network. To calculate it, we created P x 2
binary matrices describing the patterns of dominant and subordinate ant
interaction with the plants in the networks. In this case, we were not
interested in when each ant species from each group interacted with each
plant species. Instead, we focused on when each plant species interacted
with any dominant or subordinate ant in that specific community (1 when
any dominant and/or subordinate ant species visited the plant and 0
otherwise). Once we built the matrices for each network, we calculated
the Jaccard index, using the \emph{vegdist} function at
\emph{vegan}package in the R program. At \emph{vegdist} , we used the
method ``jaccard'' which estimates the dissimilarity between the
patterns of dominant and subordinate ants with the plants. To make it
more intuitive, we subtract one from the dissimilarity index (1 --
dissimilarity) to obtain the Jaccard similarity index. Therefore, the
lower the Jaccard similarity index for a network, the lower the overlap
among the plant species used by dominant and subordinate ants.
Therefore, if the decline in water availability improves the
monopolization of the more valuable plants in the community by dominant
ants and the displacement of subordinate ants to less valuable plants,
we expect that the Jaccard index will decrease as the mean precipitation
decreases.

2.5 \emph{Statistical analysis}

Initially, we created a series of linear mixed regression models (LMM)
to investigate all our predictions, assuming that the residuals of our
data were normally distributed and heteroscedastic. However, once we ran
the models, we observed that these assumptions were not true for almost
all LMM models. Because of that, we built different models according to
the nature of the dataset used for each analysis. For models that broke
the normality assumption (see Supporting information), we fitted a GLMM
(Generalized Linear Mixed Model), using the error distribution with the
best fit to the data. If the LMM model broke the assumption of variance
homogeneity (see Supporting information), we fitted a GLS (Generalized
Least Square) regression. We used the GLS because it allowed us to
adjust the residual variance while keeping the raw data, avoiding the
use of non-parametric tests or data transformation (Zuur et al. 2009).
In all models described below, we used the mean precipitation rate and
the paper identity as predictors and random factors, respectively.

To evaluate if ant-plant interactions become more specialized and
modular as drier the environment (hypothesis 1), we first evaluated the
effects of the mean precipitation on the S of the networks. For that, we
built a GLS model using the of 53 networks as our continuous response
variable. Then, to evaluate the effects of the mean precipitation rate
on the specialization and modularity of the networks, we built three
models, one for each of the following response variables: connectance
(C; N = 51 networks), zNODF (nestdness; N = 63), and zQ (modularity; N =
51). For the connectance model, we built a GLMM with a Gamma
distribution. Because the connectance can be influenced by the size of
the networks (Yodzis 1980, Blüthgen and Menzel 2006), we included the S
of each network as a weight in this model. For the zNODF and zQ, we
build GLS models using each metric as a response variable.

To investigate if the structure of the ant-plant networks along the
water gradient will be driven by changes in the patterns of dominant and
subordinate ant interaction with the plants with EFNs (hypothesis 2), we
first evaluated the effects of the mean precipitation rate on the
proportion of interactions performed by dominant and subordinate ants on
the networks. For that, we used a GLMM with a Gamma distribution,
including the proportion of dominant ant species in 37 networks as our
response variables. To investigate if water availability affected the
connectance of dominant and subordinate ant species in the networks, we
built two GLMMs with Gamma distribution. For each model, we used
the\(C_{d}\) (N = 37) and the \(C_{s}\) (N = 34)
values as our response variables. To investigate if the mean
precipitation rate affected the RR of dominant and subordinate ant
species, we built two GLMs, including the mean RR of dominant (N = 37)
and subordinate (N = 34) ants as our response variables in each model.
To investigate the overlapping in the plants used by dominant and
subordinate species, we used a GLS model including the Jaccard values of
each network as our response variable (N = 34). We built and analyzed
the GLMM and GLS models described above using the ``nlme'' (Bates and
Pinheiro 2000) and the ``lme4'' package (Bates et al. 2015) in R
software, respectively (R Core Team 2022).

\par\null

\textbf{3. RESULTS}

The networks in our dataset were sampled in sites where the
precipitation rate ranged from 15.53 to 433.82 mm. Our networks
presented an average S of 27.83 ± 18.5 species (mean ± SD), involving an
average 14.83 \(\pm\) 10.48 ant species and 13.41
\(\pm\) 10.00 plant species with EFNs. In the subset of
networks where plant or ant species were identified, there was a large
asymmetry in the number of plant and ant genera interacting in the
networks (plants: 191 genera, ants = 44 genera in total). Regarding the
ants, the most common genera interacting with the plants with EFNs were
\emph{Camponotus} (Formicinae) and\emph{Pseudomyrmex} (Pseudomyrmecinae)
(17\% and 12\%, respectively). Plants genera, in turn, was more variable
than ants in the interactions, with the most common genera being
\emph{Cylindropuntia} (Cactaceae) and\emph{Acacia} (Fabaceae) (5\% and
4\% respectively).

\emph{3.1 Effect of water availability on the structure of ant-plant
networks}

The networks were less rich as lower the mean precipitation rate
(GLS;\(F_{1,51}\)= 14.813, p \textless{} 0.001), losing almost one
species at every 100 millimeters decrease in the mean precipitation rate
(Coefficient value = 0.08; Fig. 1a.). The average connectance of the
networks was 0.32 ± 0.16. Conversely to the network species richness,
the network connectance was not affected by the variation in the mean
precipitation rate (mm) (GLMM; \(\chi^{2}\)= 0.5811, df = 1, p =
0.445; Fig. 1b). Similarly, the mean \emph{z} NODF were not affected by
variations in the mean precipitation rate (2.43 ± 2.57;
GLS;\(F_{1,51}\)= 1.72, p = 0.20; Fig. 1c). Regarding the
modularity, the networks are often less modular than expected by the
null model, presenting -0.16\(\pm\)1.65 \emph{z} Q and, like
zNODF, \emph{z} Q was not affected by the mean precipitation rate (GLS;
\(F_{1,49}\) = 0.379,\emph{p} = 0.540. Fig. 1d).

\emph{3.2 The role of ant competitive hierarchy on network structure}

Dominant ants realized, on average, 3.12 \(\pm\) 2.02 (mean
\(\pm\) SD) interaction and subordinate ants realized 2.47
\(\pm\ \)1.79 interactions with plants along the networks.
Dominant ants represented, on average, 71.62\% \(\pm\) 18.1\%
of all the ant species in the networks, and this proportion was not
affected by variation in the mean precipitation rate (GLMM;
\(\chi^{2}\) = 1.743, df = 1, p = 0.186). However, the connectance
of dominant (\(C_{d}\)) and subordinate (\(C_{s}\))
ant species varied along the precipitation gradient. Contrary to our
expectations, both increased as the precipitation rate decreased
(\(C_{d}\): 0.38 \(\pm\) 0.16; LMM;
\(\chi^{2}\) = 16.209. df = 1, p \textless{} 0.001; Estimate = 1;
Fig. 3a; \(C_{s}\): 0.32 \(\pm\)0.22; LMM;
\(\chi^{2}\) = 10.441, df = 1, p \textless{} 0.001; Estimate =
0.99; Fig. 3b).

The mean RR for dominant ant species was 0.72\(\ \pm\ \)0.16,
and it was not influenced by the variation in the mean precipitation
rate (GLM;\(\chi^{2}\) = 0.093, df = 1, p = 0.173; Fig. 2c). In
the same way, the mean RR of subordinate ants
(0.79\(\ \pm\ \)0.21) was similar to the RR of dominant ants and
it was also not affected by variation in the mean precipitation rate
(GLM; \(\chi^{2}\) = 0.149, df = 1, p = 0.149; Fig. 2d). We also
observed no variation in Jaccard index along the precipitation gradient
(0.40 \(\pm\) 0.31; GLS; \(F_{1,34}\) = 0.244, p =
0624), indicating that the dominant-subordinate overlap in the plant
usage is not influenced by water availability.

We observed a data gap at intermediary levels of precipitation rate
(254.11 to 433.82 mm -- see Fig.1). Because of this gap, data from the
wettest networks in our dataset (representing one paper with 12 networks
form the same location, all with mean precipitation rate of 433.82 mm)
could behave as outliers, compromising the fit of our models. To
evaluate this bias, we removed these networks from our dataset and
performed again all analyses described in the \emph{Statistical
analysis} section. In all cases, there was no qualitative change in any
results described above, indicating that our results were not biased by
the asymmetric distribution of the networks along the precipitation
gradient (see Support information).

\textbf{DISCUSSION}

Our results showed that as the habitats become drier, the number of ant
and plant species interacting with each other decreases, resulting in
smaller networks. However, the mean precipitation rate did not affect
any other metric describing the structure of the ant-plant networks.
Also different from our expectations, the connectance of dominant and
subordinate ants increased as the mean precipitation rate declined.
Considering that connectance is a metric that intuitively accounts for
the probability that any pair of species interact in the network (Landi
et al. 2018), it indicates that the decline in water availability at the
broader scales increases the generalization of ant-plant interactions.
In turn, it increases the chances of both dominant and subordinate ant's
species interact with species of EFN-bearing plants. Interestingly, the
mean precipitation rate did not affect the RR\textsubscript{d},
RR\textsubscript{s,} or Jaccard similarity index. This lack of effect
suggests that changes in the connectance of dominant and subordinate ant
species are not due to changes in competitive ant behavior towards the
plants but rather a consequence of processes related to the variation in
species richness of the network along the precipitation gradient.

Several studies have shown that ant and EFN-plant communities' richness
declines along precipitation gradients (Dunn et al. 2009, Luo et al.
2022, Queiroz et al. 2022). For this reason, it is not surprising that
the species richness of ant-plant networks declined with the decline of
the mean precipitation rate. However, we observed no effect of the mean
precipitation rate on any other descriptors of the ant-plant networks.
This is an unexpected result since species richness of networks network
is a crucial trait shaping the structure of ecological networks
(Boccaletti et al. 2006, Minoarivelo and Hui 2016, Mariani et al. 2019)
and, the influence of the mean precipitation rate on it would lead to
modifications in other aspects of the network. However, we controlled,
in our analysis, the effects of species richness on the metrics
describing the network structure (connectance, nestedness, and
modularity), either by using normalized metrics (as zQ and zNODF) or the
species richness as a weighted factor in our models. For this reason,
the lack of precipitation effect on the network structure indicates that
variation in water availability had no additional effect on the network
structure other than those deriving from the variation in the species
richness. This finding directly contrasts with the results from studies
evaluating the role of precipitation in ant-plant networks at the local
scale (Rico-gray et al. 1998, Rico-Gray et al. 2012, Câmara et al.
2018). At local scale, variation in climatic conditions across the
sampled habitats tends to be relatively smaller, resulting in the
observation of ant-plant interactions along a relatively narrower
precipitation gradient than ours. Therefore, it is possible that the
role of water availability in shaping the patterns of ant-plant
interactions at the community level is relatively stronger at the local
scale, with effects at a broader scale being likely an indirect
consequence of its effects on the diversity of ant and plant assemblages
across communities.

Similar to the metrics describing the structure of the ant-plant
networks, the progressive generalization of ant-plant interactions along
our macroecological water gradient is likely a consequence of the
negative effects of water scarcity on the number of ant and plant
species interacting with each other along the precipitation gradient.
Two mechanisms can drive this increased generalization. First, it is
possible that the decline of the richness of plant species available in
the drier environment increases the relative value of any plant partner
to ants, regardless of its quality. In this scenario, interacting with
the maximum of plant species available can be as or more advantageous to
dominant ants than monopolizing the few high-quality plant species
available in drier habitats. By reducing the monopolization strength of
high-quality plants, dominant ants allow the visitation of the
subordinate ones to more plant species explaining why both dominant and
subordinate ants increased their connectance with the decline in the
mean precipitation rate. Alternatively, it is possible that, although
less rich, plant species with EFNs are more abundant in drier habitats.
Since ants and plants are sessile organisms, their interaction depends
on how close the ant nests and plants are (Dáttilo et al. 2013c), and
this proximity should increase as more abundant ant and plant species
populations are. In this case, any increase in the abundance of plants
with EFNs in a community may increase the probability of all plant and
ant species interacting, leading to a more generalized pattern of
ant-plant interaction.

Our results related to the mean RR\textsubscript{d},
RR\textsubscript{s,} and the Jaccard index supports the mechanism
stating the increase in probability of interaction due to abundance and
spatial distribution of ants and plants. The resource range is a
normalization of how many links a given species makes, being not
influenced by the network connectance or species richness (Poisot et al.
2012). Then, if the generalization in the patterns of ant attendance to
EFN plants in drier environments would be driven by changes in the
competitive behavior of ants, we could expect an increase in the mean RR
for both dominant and subordinate ants. Additionally, if both dominant
and subordinate ants use more plant species available as drier the
habitat, we may expect an increase in dominant-subordinate overlap as
the mean precipitation rate decreases. However, we observe no effect of
the mean precipitation rate on the RR values or the degree of
dominant-subordinate overlapping along the gradient. It indicates that
ants and plants interact similarly along the water gradient, and the
generalization in the patterns of ant-plant interaction may be just a
consequence of an increased probability of ant-plant species interaction
in poorer drier communities.

The preponderant role of the species richness on the generalization of
ant-plant interactions may have two significant ecological implications
for the dynamic of these interactions across habitats. First,
theoretical models have shown that connectance tends to beget stability
and persistence of mutualistic networks in space and time (e.g. Thébault
and Fontaine 2010, Sauve et al. 2014). Therefore, more connected
ant-plant networks may be more stable (but see Allesina and Tang 2012)
and persistent than the ones from wetter habitats. In this case, by
increasing the overall connectance and the connectance of subordinate
and dominant ant species in the networks, the decline in water
availability may indirectly increase ant-plant interaction persistence,
including its persistence in the face of environmental disturbances. In
the face of the current biodiversity crisis, it suggests that, at
broader scale, the decline in water availability may be associated with
a decline in the susceptibility of ant-plant interactions to
environmental disturbance. Like the ones driven by human activities and
climate change.

Second, although water availability had no effect on the competitive
behavior of dominant ant species, it is likely that they are the main
ones driving the direct and indirect effects among ant and plant species
with EFNs and the network structure along water gradients. Due to their
numerical and behavioral dominance, dominant ant species are commonly
the most connected species in ant-plant networks worldwide, holding a
higher number of interactions with the plant species available (Dáttilo
et al. 2014b, Costa et al. 2016). Although both dominant and subordinate
ants became more connected to the plants as the precipitation declined,
water availability did not affect the degree of the network nestedness,
suggesting the maintenance of the role of dominant ant species in
regulating this mutualism, regardless of water availability. Finally, it
is important to highlight that the significant role of community
richness in shaping mutualistic networks along environmental
macroecological gradients has already been reported in other studies
using other mutualistic systems as a model, like pollination (e.g.
Devoto et al. 2005, Lance et al. 2017). It suggests that the role of
community diversity in shaping mutualistic networks at a broader scale
is not restricted to ant-plant interactions, being more general than
previously expected.

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\par\null

\textbf{FIGURES LEGENDS:}

\textbf{Figure 1} . Effect of the mean precipitation rate on the (a)
species richness, (b) connectance, (c) nestedness, and (d) modularity of
mutualistic networks representing interactions between ants and plants
bearing extrafloral nectaries. The species richness of networks
decreased with the decrease in the mean precipitation rate in the
habitat (mm), but no other predictor of the network structure varied
along the gradient. Each point represents one network, and the size of
each point indicates the species richness (S) of the respective network.

\textbf{Figure 2} . Effect of the mean precipitation rate (m) on the
connectance (C) and resource range (RR) of dominant (a and c,
respectively), and subordinate ant species (b and d,
respectively).\(C_{d}\) and \(C_{s}\) increased with
the decrease in the habitat' mean precipitation rate (mm), while
\(RR_{d}\) and \(RR_{s}\) did not vary along the water
gradient. Each point represents one network, and the size is the
richness (S) of each network.

\textbf{Figure 3.} Relationship between the mean precipitation rate (mm)
and the similarity in the patterns of dominant and subordinate ant
species interaction with plant species with extrafloral nectaries
(Jaccard index of similarity). Precipitation rate did not affect the
overlap between dominant and subordinate ant species visiting the plants
with EFNs. Each point represents one network, and the size is the
richness S of each network.

\textbf{Hosted file}

\verb`oik-10324-File003.pptx`  available at \url{https://authorea.com/users/640214/articles/655181-community-richness-is-the-main-factor-determining-the-structure-of-ant-plant-mutualistic-networks-along-environmental-water-gradients}

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