Statistical analysis
Predictor variables were tested for collinearity. Distance to coast,
which was highly correlated to elevation (r = 0.75), was excluded
from further analyses (Appendix Table A1).
The native species richness and alien species richness per plot at grain
sizes of 9 m2 and of 1 m2 were
response variables in statistical models. We included ecologically
relevant interaction terms in our analyses. Underlying geology can
influence soil moisture (Huang et al., 2016, Kopec, 1995), and the
effect of elevation on species (Gerdol et al., 2017). Furthermore, we
expected that northness and elevation interact, since north-facing
slopes receive more sunlight in the southern hemisphere (Saremi et al.,
2014), which might particularly be beneficial to high elevation sites
which are generally cooler. Hillshade could affect soil moisture, with
wetter soils occurring in areas receiving less solar radiation
(Najafifar et al., 2019). In addition, elevation also affects the biotic
interaction effect of A. selago on co-existing species, as the
cushion plant facilitates plants at high elevations but competes with
them at low elevations (le Roux and McGeoch, 2010). Therefore, the
interaction terms geology*TWI, geology*elevation, northness*elevation,
hillshade*TWI, and presence/absence of A. selago *elevation were
included.
We also investigated the factors correlated with the differencein species richness between the large (9 m2) and small
(1 m2) spatial grains (henceforth
‘Δ9-1’) to assess
what may be shaping differences in patterns and drivers of richness at
different grain sizes. To calculate the difference in species richness
between grain sizes, the species richness of the nested central 1
m2 subplot was subtracted from that of the larger 9
m2 plot. We then tested which of the above predictor
variables and the interaction terms were related to
Δ9-1. By identifying the drivers of the difference in
richness between large and small grain, we could determine whether local
turnover occurs and what factors could be contributing to turnover.
We tested for spatial autocorrelation in the response variables using
Moran’s I . Moran’s I values were small, but significant,
suggesting some spatial autocorrelation (Table A2). Therefore, to
account for spatial autocorrelation, we opted for simultaneous
auto-regressive models (SAR) (Kissling and Carl, 2008), particularly the
SARerr model . We tested the effects of the explanatory
variables on species richness at each grain size, and on
Δ9-1, using SAR models. These statistical analyses were
run assuming a Poisson distribution. Best subset models with the best
set of predictors were created using the dredge function from the MuMIn
package, with model selection based on the lowest Akaike information
criteria (AIC) values (Barton and Barton, 2015).
Alien species, especially where they are abundant and/or have high
cover, may mask the effects of environmental drivers on native species
richness. This prompted us to (a) initially repeat the above analyses
only using a subset of the plots where
alien species were absent.
However, on Marion Island, alien species tend to be more prevalent in
coastal areas with biotic inputs, than in inland areas or areas far from
any bird or seal colonies (Haussmann et al., 2013, Figure 1). Most
coastal plots had some alien species present (Figure 1); therefore, this
analysis led to us excluding many of the coastal plots with the highest
native species richness. We thus present these results in the
supplementary materials but do not further discuss them. Instead, we
repeated these analyses using a subset of the data where (b) alien cover
was less than 10% in the plots, assuming that such low alien cover is
unlikely to significantly affect patterns of richness of native plants.
For alien species, analyses were conducted on the full dataset, and also
for a subset of plots where (c) at least one alien species was present
because we assumed that some alien species may not have reached niche
equilibrium with the environment (i.e., range occupancy of all suitable
sites) on Marion Island (le Roux et al., 2013b) and that if there is at
least one alien species in a plot, it is likely that more alien species
may have had time to colonise the plot.
All statistical analyses were run in R, version 4.2.0, using functions
from the spdep (Bivand et al., 2006), car (Fox et al., 2012), mass
(Ripley et al., 2013), nlme (Pinheiro et al., 2017), ape (Paradis and
Schliep, 2019), MuMIn (Barton and Barton, 2015) and ggplot2 (Wickham et
al., 2021) packages.