2.3.2 Distribution of ant assemblages along vertical and
horizontal gradients
In order to understand how ant assemblages changed vertically and
horizontally, we first applied linear regression models using the
abundance (ln( x+1) transformed) and richness of ants in each
sampling point as response variables, x , with vertical height as
continuous explanatory variable and horizontal position of vertical
transects as categorical explanatory variable. We then used both
abundance-based (ln (x+1) transformed) assemblage data and species
presence/absence data for the following assemblage composition analyses.
Two sampling points (comprising one ant individual each) from one
emergent layer were excluded from the analyses due to the lack of other
replicates at this height level, resulting in total of 61 ant
assemblages as our sample size. We generated non-metric
multi-dimensional scaling (NMDS) plots using Bray-Curtis distance index
for the abundance-based community and Jaccard distance index for the
presence/absence data of ant assemblages at each sampling point grouped
by vertical level and transect. To keep sample size same for analyses in
vertical and horizontal directions, we combined the 12 vertical levels
into six vertical groups for NMDS analyses. We tested for statistical
significance of these groupings by analyses of between-point assemblage
presence/absence variances using the adonis function in the vegan
package (999 permutations) (Oksanen et.al. 2013, R Core Team, 2013). We
then calculated the pairwise assemblage dissimilarity across sampling
points and further partitioned it into assemblage turnover (balanced
variation in abundance) and assemblage nestedness (abundance gradient)
between each pair of samples to understand which component drives beta
diversity patterns at different horizontal and vertical distances (Si et
al. 2017). This will allow us to distinguish the potential effects of
resource limitation, which is expected to lead simply to richness
gradients but no turnover pattern, as opposed to environmental
filtering, which is predicted to lead to species turnover as low canopy
species are replaced by high canopy ones. The analyses were calculated
using the beta.pair.abund function (for abundance-based
dissimilarity) and beta.pair function (For presence-absence data)
in the betapart package (Baselga and Orme 2012). For ant assemblage
composition analysis, as results from measures using abundance-based
pairwise Bray-Curtis dissimilarity and presence-absense based Jaccard
species dissimilarity were similar, we only present results using
abundance-based pairwise Bray-Curtis dissimilarity in the main text (see
online supplementary materials for presence-absence analyses).
We used four different methods to quantify the distance between sampling
points: 1) straight line distance between points, 2) maximum surface
travel distance, 3) horizontal distance between points, and 4) vertical
distance between points. To simplify distance calculations, we assumed
that all sampling points were on a straight line from above when
calculating the distance between points, although there were some small
deviations from this. Straight line distance modelled the case where
there was vegetation structure to directly connect the two sampling
points for ant movement (Fig. S1). Maximum surface travel distance was
calculated as the horizontal distance between the sampling points added
to the vertical height of each sampling point above the ground,
modelling the scenario where the ground provides the only horizontal
connectivity between two points (Fig. S1). We conducted
Multiple Regression on Distance
Matrices (MRM) using the MRM function in the ecodist package
(Goslee and Urban 2007) to test for relationships between different
distance measures and total pairwise dissimilarity, assemblage turnover
and nestedness. The tests of significance of MRM coefficients were
performed using 1000 permutations.
We then tested the effects of distance on assemblage dissimilarity
between and within vertical transects and vertical strata. We first
grouped the assemblages within the same vertical transect/vertical
stratum, and then tested effects of horizontal/vertical distance on
pairwise assemblage dissimilarity between transects/strata by MRM. To
measure within vertical transect/vertical stratum patterns, we conducted
post-hoc MRM tests on vertical/horizontal distance effects of pairwise
assemblage dissimilarity across sampling points within each transect and
stratum. We then conducted linear regression to compare the mean
assemblage dissimilarity value and linear effects of distance on
pairwise dissimilarity across two dimensions by comparing the intercept
and the slope from the MRM analyses of each transect/stratum. To test
for the different horizontal turnover patterns between vertical strata
expected if ant mosaics effect is present, we conducted linear
regression with horizontal pairwise dissimilarity within each stratum as
the response variable, horizontal distance and vertical height as well
as their interaction as explanatory variables.
Correlates of ant assemblage distribution patternsWe conducted constrained ordinations to further understand how
microclimate (air temperature relative humidity, and PPFD) and
microhabitat structure (total leaf area) affect ant assemblage
composition. We first conducted Detrended Correspondence Analysis
(DCA) using decorana function in vegan package and found
maximum axis length greater than 4 (range: 3.91-6.96) indicating that
Canonical Correspondence Analysis (CCA) constrained ordinations that
assume unimodal responses of species to environmental gradients were
appropriate. We then first checked the collinearity among all
explanatory variables using vif.cca function to reduce
redundancy in the model. Among all microclimate and microhabitat
factors, no strong collinearity (VIF<10) was been detected
and so all predictors were included in the Canonical Correspondence
Analysis (CCA). We then conducted CCA ordinations using ccafunction in the vegan package with all explanatory variables (air
temperature, relative humidity, and PPFD). We conducted backward model
selection using ordistep function in the vegan package to
identify the most significant variables in affecting the assemblage
composition based on permutation tests using 1000 permutations
(Blanchet et al. 2008). As the model selection process for CCA
analysis requires samples with all environmental factors available,
only 48 out of total 61 ant assemblages that had all environmental
information available were included for this analysis.
Results