DOI: 10.5061/dryad.2bvq83bp8
title: Data from: Spatiotemporal dynamics of the ant community in a dry forest differ by vertical strata but not by successional stage

description: Ants are diverse and ecologically important organisms in tropical forests,
 where their spatiotemporal distribution can be highly complex. This
 complexity arises mainly from marked differences in microclimatic
 conditions and resource availability through space and time that is even
 more evident in highly seasonal environments, such as tropical dry
 forests. However, it is unclear how seasonality interacts with other
 factors that might shape temporal variation of ant composition
 (β-diversity), like vertical strata and habitat disturbance. Our goal was
 to examine the potential influence of vertical stratification and the
 successional stage on the spatiotemporal variation of a tropical dry
 forest's ant species composition. We assessed whether species
 turnover or nestedness was the main component determining the
 spatiotemporal β-diversity of ant communities across the canopy and litter
 strata. We sampled canopy and litter ants in ten plots, half in the early
 and half on the late-stage of secondary succession at four times, twice in
 wet and twice in dry season. A high species turnover defined the
 spatiotemporal β-diversity of canopy and litter ant communities across
 years and seasons in our focal dry forests. Importantly, the temporal ant
 species composition was much more stable in the canopy than in the litter.
 Moreover, we found that the ant community’s temporal dynamics was
 consistently high across successional stages, not differing in the
 temporal β-diversity between early and late succession. Our results
 provide valuable insights into the potential underlying causes of
 community assembly and spatiotemporal dynamics in seasonal habitats, like
 the highly-threatened and diverse tropical dry forests.
description: <b>Study area</b> We conducted
 this study in <i>Parque Estadual da Mata Seca</i> (PEMS;
 14°48′36″ – 14°56′12″ S and 43°55′12″ – 44°04′12″ W; 493 m a.s.l.), in SE
 Brazil. The PEMS is located in a transition zone between three major
 Brazilian biomes: <i>Cerrado</i> (Brazilian savanna),
 <i>Caatinga</i> (spiny-dry forest), and <i>Mata
 Atlântica</i> (Atlantic Forest). With a total area of 15,466 ha, the
 PEMS is inserted in a mosaic of primary and secondary (previously used for
 agriculture and livestock activities) forests (Madeira et al., 2009). The
 climate of the region is tropical semi-arid (Alvares et al., 2013),
 characterized by a severe dry season from May through September and a wet
 season from November through April. The average annual precipitation is
 818 ± 242 mm (mean ± SD), which is concentrated in the wet season months,
 and the average annual temperature is 25.1 °C (Pezzini et al.,
 2014). The whole study area was covered originally with
 tropical dry forests on fertile soils and mostly level terrain. The
 tropical dry forest of the area is comprised of deciduous vegetation that
 loses more than 90% of its leaves in the dry season (Pezzini et al.,
 2014). We chose areas belonging to two successional stages, hereafter
 early- and late-stages, with remarkable differences in their structure and
 land-use history (Madeira et al., 2009). The early-stage area was a
 pasture and has been in recovery for about 15 years. The early-stage
 contains mixed vegetation of trees less than 8 m in height, herbs, and
 grasses, without distinct vertical stratification. The late-stage area has
 had little to no anthropogenic disturbance for at least the last 60 years,
 with emerging trees that exceed 20 m in height. Moreover, the late-stage
 area has distinct canopy and understory strata, with direct sunlight
 reaching the soil level only in the dry season when the trees have lost
 their leaves. The late-stage areas also have low densities of new trees
 and lianas compared to other adjacent areas at different stages of
 regeneration (for a detailed description of the study areas see Madeira et
 al., 2009). <b>Ant sampling </b>
 We sampled ants in the canopy and litter strata in ten 20 m × 50
 m rectangular plots (Figure 1). These plots were spaced at least 200 m
 apart along a 7 km transect, with five in the early-stage and five in the
 late-stage of secondary succession. Plots from the same successional stage
 were located 0.2–1.0 km from each other (Antoniazzi et al., 2019; Madeira
 et al., 2009). All ten plots were subjected to similar topographic and
 climatic conditions, regardless of their successional stage (see Madeira
 et al. 2009). We acknowledge that the spatial distribution of our plots
 represents a pseudoreplicated design (i.e., ‘clumped segregation’;
 <i>sensu</i> Hurlbert 1984), but it is not considered a
 serious concern when monitoring natural experiments where site and
 disturbance/treatment effects may be confounded (see Davies &
 Gray, 2015, Oksanen, 2001). In each plot we chose a grouping of three
 trees at each of the four corners and in the center of each plot to sample
 ants, giving a total of 15 trees and litter samples per plot. The plots
 were sampled four times, twice in the dry season (September 2010 and
 September 2011) and twice in the wet season (February 2011 and February
 2012). The ant diversity in each stratum over the four periods was then
 used to calculate the beta-temporal diversity, as described
 below. We used specific methods for sampling the ants
 in each stratum. In the canopy, we used two complementary techniques:
 arboreal pitfall trapping and an entomological beating technique
 (Antoniazzi et al., 2019; Campos et al., 2006). Arboreal pitfall trapping
 consisted of placing 15 cm diameter plastic pots filled with water and
 soap and collecting after 48 hr. The entomological beating technique
 consisted of using an entomological umbrella made from an inverted cloth
 funnel, with a surface of 1 × 1 m, and a plastic bag attached to the
 bottom (for more details, see Antoniazzi et al., 2019). To sample the
 ground-dwelling ants, we used the Mini-Winkler method, a widely used
 technique to collect arthropods that live in the litter (Belshaw &
 Bolton, 1993; Castro et al., 2012; Silva & Brandão, 2010). At each
 sampling point below a tree, the litter from 1m² was collected and sieved
 through 1cm² mesh. The filtered material was then placed in the
 Mini-Winkler extractor that was suspended for 48 hours (see Fisher, 1999)
 so that the associated fauna fell into the collecting pot.
 We transported all the sampled material to the laboratory for
 sorting, and the ants were mounted and identified to the lowest possible
 taxonomic level. First, we identified all ants to genera using Baccaro et
 al. (2015). For species identification, we then used a combination of the
 keys of “Ants of Costa Rica” (Longino, 2007), and the information and
 images available on “AntWeb” (Fisher, 2002). For the current valid
 taxonomic name of each species, we consulted “AntCat” (Bolton, 2012). Our
 species determinations were then further verified by a reference
 collection from previous work in the same area (Antoniazzi et al., 2019).
 We deposited all material in the <i>Laboratório de Ecologia de
 Insetos</i> (LEI), <i>Universidade Federal de Minas
 Gerais</i> (UFMG), Brazil. <b>Statistical
 analysis </b> Firstly, we assessed the sampling
 completeness of the ant species for each successional stage separately.
 For this, we used the rarefaction and extrapolation (the number of samples
 doubled) of ant species richness (Chao et al., 2014), since the ant fauna
 was recorded using Winkler in the litter and arboreal pitfalls and beating
 at the canopy. The sample units (i.e., ten plots per time period and four
 time periods, totaling 40 samples) were used as the independent variable
 and ant species richness was the dependent variable. Thus, we were able to
 obtain information about the completeness of ant species in relation to
 each sampling unit added. We also assessed the alpha diversity in our
 smallest sampling unit for each successional stage apart, i.e., early and
 late. For this we used the hill number <sup>0</sup>D to show
 the observed species richness accumulated for our sampling units, with
 their confidence intervals. The overlapping of confidence intervals
 indicates that the two treatments present the same species richness. For
 the sampling coverage and the species richness, we used the package iNEXT
 (Hsieh et al., 2016) for R software. We assessed the
 spatial and temporal variation of the ant species composition
 (β-diversity) using the Sørensen dissimilarity in a multiple-space and
 time approach (see Baselga, 2010). For spatial analyses, the multiple
 samples from each stratum, i.e., litter and canopy, were pooled per plot
 for subsequent analyses. For temporal analyses, the multiple samples from
 a stratum within a plot were pooled for each time period, to give four
 plot-level data points for each stratum and each plot for subsequent
 analyses. We then calculated the spatial and temporal β-diversities
 (β<sub>SOR</sub>) using the presence/absence of species for
 each stratum and plot (10 plots in total) for spatial analyses, and over
 the four sampling periods for each stratum and plot (10 plots in total)
 for temporal analyses. The β-diversities (spatial and temporal) were
 further partitioned into the components derived from species turnover
 (β<sub>SIM</sub>) and the derived component of nestedness,
 i.e., species gain/loss (β<sub>NES</sub>), using the
 β-diversity partitioning method components. We used the
 "<i>beta.multi</i>" function of the R package
 betapart to the partition of spatial and temporal β-diversities (Baselga
 & Orme, 2012). Finally, we used the temporal
 β-diversity (β<sub>SOR</sub>) to test the predictions that the
 temporal β-diversity is higher in the litter and early-stage than in the
 canopy and late-stage. To compare the temporal β-diversity values we used
 a generalized linear mixed model (GLMM) with stratum, successional stage
 and the interaction between these variables as the fixed effect variables,
 and the identity of each successional plot as a random factor to account
 for spatial pseudoreplication (Bolker et al. 2009). We used the Gaussian
 error distribution, and we checked for the error distribution and
 over-dispersion of the data (Crawley 2013). We defined the minimal model
 after the removal of non-significant variables (<i>P</i>
 &gt; 0.05). We performed all analyses using the R software environment
 for statistical computing (R Development Team, 2019).