Topoclimate as a driver of within-habitat structure
Analyses performed at within-habitat scale revealed the importance of
both environmental and spatial variables on community composition in
most cases (Table 2; Table S6), which is in line with previous research
on soil microarthropod communities (Arribas et al., 2021b; Bahram et
al., 2016; Ingimarsdóttir et al., 2012; Lindo & Winchester, 2009).
Disentangling the relative contribution of environmental vs.
spatial processes has proven to be challenging as environmental
variation is often spatially autocorrelated, which can lead to a
spurious inflation of the inferred environmental contribution (Clappe,
Dray, & Peres-Neto, 2018; Vellend et al., 2014). In our study, spatial
predictors generally explained less variance than topoclimatic factors
in mvGLMs (Table S6), and their effect became non-significant after
applying a forward selection approach in dbRDA (Table 2; Table S5).
These results along with the relatively low degree of collinearity
between spatial and topoclimatic axes (VIF <7; Vittinghoff,
Glidden, Shiboski, & McCulloch, 2012), emphasize the role of
environmental filtering as a key driver of metacommunity structure
(Brown et al., 2017). Our results would complement several
morphology-based studies suggesting that community composition of soil
microarthropods is driven by environmental filtering, primarily in
response to gradients of edaphic parameters (Caruso et al., 2019; Gao et
al., 2016; Gan, Zak, & Hunter, 2019; Grear & Schmitz, 2005). However,
they appear to contrast with the recent wocDNA metabarcoding study of
Arribas et al. (2021b), where dispersal limitation was identified as the
main driver of community assembly at within-habitat scale. This
discrepancy cannot be attributed to taxonomic resolution, as both
studies used very similar protocols to retrieve ASVs and OTUs, but it
could be partly explained by differences in sampling scale, as the
generally broader sampling extent of our study could enhance the role of
environmental filtering as a consequence of encompassing higher
environmental heterogeneity (Chase, 2014). Yet we also found
environmental filtering to prevail in our narrowly distributed habitats
(e.g., Cb, Pn) with observational scales slightly
smaller (<7 km) than those of Arribas et al. (2021b).
Additionally, the overall stronger effect of environmental filtering in
our study system may reflect context-dependency (Soininen, 2014), with
environmental processes playing a more important role in systems
characterized by high topoclimatic heterogeneity. While in Arribas et
al. (2021b) there were only moderate altitudinal gradients
(~200-670 m elevation difference), our sampling spanned
a steep elevational (1470 m elevation difference) and environmental
gradient, with topoclimatic conditions varying greatly even across short
distances, both within and across habitats (Figure S1). This phenomenon
may be common in topographically complex regions, where dispersal
limitation may actually be imposed by environmental heterogeneity rather
than by geography per se (Liu et al., 2018), and points out the
relevance of detailed topoclimatic characterization for understanding
metacommunity structure within mountainous landscapes. However, it is
noteworthy that the total variance explained by some models was
relatively low (R 2ADJ<5-10%, Table 2). This was not unexpected, as it is a common
finding among metacommunity studies (Cottenie, 2005), and has been
traditionally attributed to other ecological processes that are not
frequently measured (Vellend, 2010). Particularly, stochastic
demographic processes including ecological drift in the absence of
dispersal limitation (Bahram et al., 2016; Zinger et al., 2019) or
priority effects via niche preempting (Fukami, 2015) have been
hypothesized as relevant forces potentially interfering with community
assembly in the soil environment. Additionally, we have not considered
explicitly the effect of edaphic variables (e.g., organic matter,
nutrient content or pH; Gao et al., 2016; Gan et al., 2019), although
some of their variation is likely captured by forest habitat type and by
certain topoclimatic variables, which are thought to influence specific
soil attributes (horizon depth, moisture; Florinsky, 2012; Hillel,
2008).