2.3.2 Distribution of ant assemblages along vertical and
horizontal gradients
To understand how ant richness and abundance changed vertically and
horizontally, we used linear regression models. The abundance
(ln( x+1) transformed) and richness of ants at each sampling point
were used as response variables with vertical height as a continuous
explanatory variable and horizontal position of vertical transects as a
categorical explanatory variable.
To explore vertical and horizontal differences in assemblage
composition, we used NMDS ordinations. These used both abundance-based
(ln (x+1) transformed) assemblage data and species
presence/absence data. 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. For all ant assemblage composition analyses analysis (NMDS and
MRMs, see below), as results from measures using abundance-based and
presence-absence based dissimilarity were similar, we only present
results using abundance-based dissimilarity in the main text (see
supplementary materials for results of presence-absence analyses). To
increase the sample size within groups for the vertical analyses, and
hence to increase statistical power, we assigned all sampling points
within ten-metre bins to the same groups (i.e. 0-10 m, 10-20 m, etc). We
tested for differences in assemblage composition between these groupings
using PERMANOVA analyses (adonis function in the vegan package,
999 permutations) (Oksanen et.al. 2013, R Core Team, 2013).
To explore relationships between beta diversity and spatial distance we
conducted multiple regressions on distance matrices (MRMs). We conducted
two different analyses: one looking at beta diversity horizontally by
summing assemblage data from entire transects across all heights, and a
second looking at beta diversity vertically by summing assemblage data
across all assemblages of the same height between different transects.
We then calculated the pairwise assemblage dissimilarity across these
summed data 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).
We then tested effects of horizontal/vertical distance on pairwise
assemblage dissimilarity between transects/strata by conducting multiple
regressions on these distance matrices (MRM function in R ecodist
package) (Golsee and Urban 2007). To test whether degree of turnover
differed horizontally and vertically, we conducted replicated MRM
analysis using data from individual transects (to assess vertical
turnover), and from individual heights (to assess horizontal turnover).
We then took the slopes and intercepts from the fitted MRMs, and used
linear models (function lm in R base package) to test for
differences in these parameters horizontally and vertically. Slopes
represent the strength of the distance-similarity decay relationship,
with a more positive slope indicating a more rapid increase in
dissimilarity. Intercepts represent the turnover at very small spatial
scales (technically when distance = 0 m).