Study sites
Nine forest fragments (hereafter, sites) were selected (Fig. 1; Fig. S1; Table S2), in the northern region of Parana state, in southern Brazil, some of which belong to the Long-Term Ecological Research Program "Mata Atlântica do Norte do Paraná" (PELD-MANP). Forest fragments were selected to maximize size variation (in hectares), and level of isolation from other fragments. The nine fragments are surrounded by an agricultural matrix (Garcia 2022).
The climate in the region is Cfa according to the Köppen classification, a subtropical climate. The mean annual temperature ranges from 21ºC to 23ºC. The average temperature in the coldest month is below 17ºC, and in the hottest month above 26ºC. Mean annual precipitation ranges from 1200 to 1800 mm (Nitsche et al. 2019). Rainfall is concentrated in the summer months, but there is no defined dry season (Nitsche et al. 2019). Frosts are infrequent.
The soils in the region are classified as eutrophic dark red oxisol and eutroferric nitosol (Bhering et al. 2007), of basaltic origin, and are highly fertile. The vegetation belongs to the Atlantic Forest domain (IBGE 2012) covering an inland region with less influence from the Atlantic Ocean; the Semideciduous Seasonal Forest is one of the most threatened Atlantic Forest types (Carlucci et al. 2021). The studied landscape is dominated by soybean and maize plantations. The forest cover ranges from 8 to 12% of the landscape, including remnants of mature and regenerating forests.
Figure 1
The occurrence of A. sexdens nests was determined by surveying belt transects that were 250 m long and 20 m wide (0.5 hectare per sampling unit) each. All transects were positioned perpendicular to the forest edge and were also used for placing the vegetation plots (see below). One transect was established for every 100 hectares of forest fragment area, with a minimum of one and a maximum of five transects per fragment. A minimum distance of 500 m was adopted between the transects established in the same forest fragment. The arrangement of transects perpendicular to the edges is part of another study (Garcia 2022) which evaluated the variation in nest density in relation to the distance from the edge. Ribeiro et al. (2009) and Tabarelli et al. (2010) suggest that the extent of edge effects on the Atlantic Forest vegetation is around 100 m. Therefore, we decided to extrapolate this value and use 250 m long transects, which were measured from the forest border with agriculture (0 m).
The transect method for ant nest sampling was adapted from that described by Jaffe and Vilela (1989) and Wirth et al. (2003). On each transect, nests were located visually or through evidence of foraging activity (e.g. foraging trails or ant activity) and georeferenced with GNSS receivers. The nests were classified as active or inactive; in the case of active nests, they were also classified as new or juvenile/adult. Colonies with a mound of earth up to 2 m² that did not have a defense caste (soldiers) were considered new; the others were considered juvenile/adult colonies (Autuori 1941). New nests are vulnerable to several biotic (predation by anteaters and armadillos, infection of the fungus garden by invasive microorganisms) and abiotic (heavy rainfall) factors that cause high colony mortality (Meyer et al. 2009; Vieira-Neto and Vasconcelos 2010). Therefore, only established (juvenile/adult) colonies were included in the analysis, as commonly adopted when studying Atta colonies (Meyer et al. 2009; Silva et al. 2009; Wirth et al. 2003).
Ten workers, preferably soldiers, were collected from each nest sampled in the transects for species-level identification. All collected material was placed in vials with 70% alcohol. All ant samples were identified to species level with the help of an ant taxonomist.
In a previous work (Garcia 2022) it was found that from the edge to 50 m there was a total of seven active nests, from 50 to 100 m three, and from 100 to 150 m two. No active nests were observed in any forest fragment in the distances from 150 to 250 m. Furthermore, two concurrent nests were never recorded in the same 50 m transect segment; the minimum distance between two nests exceeded 50 m. Thus, in the current study, we chose to consider the presence or absence of A. sexdens according to the vegetation sampling plots (hereafter, plots; see below). When the nest, foraging trails, or scouts of A. sexdens occurred inside the vegetation plot or within the 10 m field of view of the vegetation sampling plot, it was considered as the presence of Atta, and the reverse as the absence. Thus, to compare the floristic composition and functional traits between plots with or without A. sexdens, only data from the three plots per transect, at distances from 0 to 150 m were considered. As there was a discrepancy in the number of plots with the presence (21 plots) and absence of nests (59 plots), 21 plots without ant nests were randomly excluded, in order to balance the sample.
Regenerating woody plant sampling
We determined the species richness and abundance of woody plants within each transect, in six 5 x 5 m permanent plots, allocated at a distance of approximately 50 m from each other. In each plot, all woody individuals ≥ 1 m in height and diameter at breast height (1.30 m from the soil surface) ≤ 2 cm were counted and measured. This criterion of inclusion was adopted in order to avoid both seedlings (known to be a transitory, highly dynamic group), and adult individuals (that were probably established in the study sites before the establishment of the LCA nests).
Species-level identification of the sampled plant individuals was carried out in the field whenever possible, but samples were taken and confirmed or identified in the Herbarium of the State University of Londrina (UEL) – FUEL, where vouchers of all species collected were also deposited.
A total of 122 species belonging to 82 genera and 37 families, and two undetermined species were sampled (Garcia 2022). Those containing 11 or more individuals, comprising a list of 28 species belonging to 20 genera and 16 families of plants, representing 78.5% of all individuals sampled in the plots were selected for the functional trait analyses.
Functional traits
Ten leaves of at least 5 different individuals were collected from the 28 most abundant species, alternating between young and old leaves, and between leaves exposed to the sun or in the shade. The collection was made without excluding the petioles and giving priority to leaves without any type of damage (herbivory, fungal infection, water stress, etc.). Transport and storage of the leaves were carried out under refrigerated and humid conditions (Pérez-Harguindeguy et al. 2013).
For all species, ten physical, ecological, nutritional, and chemical functional traits commonly associated with herbivore attraction or inhibition were measured (Table S3). The factors considered, hereafter called functional traits were; specific leaf area (SLA), leaf thickness (LTh), presence of leaf trichomes (LTr), gap dependence (GAP), deciduousness (DEC), leaf nitrogen (N) and carbon (C) mass percentage, leaf C/N ratio, presence of latex (LX), and foliar concentration of condensed tannins (CT).
The LTh and SLA traits were measured based on Pérez-Harguindeguy et al. (2013) recommendations. Information about CT, LTr, LX, GAP, and DEC were retrieved from scientific publications and databases (see information in Table S4). N and C leaf content were measured at the EMBRAPA facilities (the Brazilian Agricultural Research Corporation) using a TOC device analyzer Elementar, model Vario TOC Cube (Fontana and Bianchi 2017). The concentration of condensed tannins (CT) was determined using the protein precipitation method of Hagerman and Butler (1978).
Data analysis
To assess whether the occurrence of A. sexdens influenced the diversity of plants present in forest fragments (see the list of species in Table S7), linear mixed models (LMMs) were performed (Zuur et al. 2009) with data on the richness and abundance of woody regenerating plants and the presence or absence of A. sexdens (explanatory variable), using plots as sampling units; study site identity and plot number were included as random variables. The study site identity was included to consider the natural variability of environmental characteristics at the local scale, and the plots because of the spatial dependence (neighborhood) among them. For the LMMs, the lmer function of the ‘lmer4’ package (Bates et al. 2014) was used because the residuals presented normal distribution and homoscedasticity. The maximum likelihood method was used to select the best model.
Subsequently, to analyze the influence of the occurrence of A. sexdens on the floristic composition, a Non-Metric Multidimensional Scaling (NMDS; Legendre and Legendre 1998) was performed, using the plot-level abundances of woody regenerating plants, and labeling plots with or without A. sexdens. Bray-Curtis dissimilarity was used and abundance data transformation was performed using log (x + 1) (Zar 2010). To confirm the graphic results of the NMDS, a similarity analysis (ANOSIM) was used to compare the floristic composition between the plot groups with or without A. sexdens (Clarke and Warwick 2000). Complementarily, the metaMDS function of the ‘vegan’ package (Oksanen et al. 2018) and the Bray-Curtis coefficient were used in the rankings produced to adjust the variable presence or absence of A. sexdens, through the envfit function of the ‘vegan’ package (Oksanen et al. 2018). This procedure evaluates the relationship between the occurrence of A. sexdens and the composition of the plant communities studied.
In order to compare the plant functional traits between plots with or without A. sexdens, data from the 28 most abundant species were used. For the quantitative traits, the mean of the attributes was used, weighted by the abundance of the species for each plot, whereas for categorical attributes (LTr, LX, DEC, and GAP) the proportion of occurrence was used (number of individuals with the functional attribute per plot/number of individuals occurring in the plot). For the attribute GAP, the proportion of gap-specialist individuals was used in the analysis, and for DEC, the proportions of deciduous and semi-deciduous individuals were used. We first built a Spearman correlation coefficient matrix (Zar 2010) with the ten functional traits to find those that were strongly correlated with each other (r ≥ 0.70; Table S5; Petchey and Gaston 2002). Based on the results, the following functional traits were excluded from the analysis: C/N ratio; SLA and gap dependence.
The remaining traits were used to calculate the functional diversity index using the “FD” package (Laliberte and Legendre 2010). The analysis used data on the abundance of each species in the plots with and without A. sexdens, and data on the functional attributes of each species. Distance matrices were then produced using the gowdis function from the “FD” package. The values of functional richness (FRic), functional diversity (FDiv), and functional equity (FEve) were analyzed from the “dbFD” function between sites with and without ant nests. The Wilcoxon test (Zar 2010) was used to compare the results of functional diversity indices obtained in sites with and without ant nests.
Still using the “FD” package, the CWM (Community-level Weighted Means) was calculated (Lavorel et al. 2008), then the data were transformed into a matrix by the vegdist function of the “vegan” package. To evalaute possible differences between the functional attributes of sites with or without A. sexdens, an ANOSIM-type similarity analysis was performed using the “vegan” package. Presence or absence data, such as trichomes and deciduousness, were transformed before analysis using the functcom function. The CWM values of each trait were also compared between sites with and without A. sexdens using the Wilcoxon test.
All analyses were performed with the functional traits as a whole, individually, and also organized in groups: ecological (DEC), nutritional (C and N), and defense (LTh, LTr, LX and CT) traits. In the case of the ecological group, it was not possible to calculate the results of FRic, FDiv, and FEve of the “FD” package, because a single trait (DEC) does not generate a distance matrix. Results were considered significant when α < 0.05, and all statistical analyses were performed in R 4.1.0 (R Core Team 2020).