DISCUSSION
In this study, we compared and analyzed the variations in species
composition in the observed and randomized communities at five different
spatial scales. Our study is the first to quantitatively partition the
hierarchical effects of environmental filtering and spatial aggregation
processes on the variations in species compositions of forest
communities. The results revealed that the community structure is a
result of both the ecological processes of environmental filtering and
spatial aggregation at varying strengths depending on the spatial scale.
Specifically, three hypotheses regarding the variations in the
ecological effects across spatial scales and along latitudinal
gradients, as well as the key factors of environmental filtering were
examined and discussed in the present study to reveal the mechanisms
that determine the origin and maintenance of the studied local community
assemblages.
Our study agrees with a growing number of studies which have assumed or
verified the hierarchical nature of ecological effects (Whittaker et al.
2001; McGill 2010; De Bello et al. 2013; Chalmandrier et al. 2013;
Scherrer et al. 2019). In related investigations, De Bello et al. (2013)
tested the relative influences of multiple environmental filters on
plant functional trait structures. The results confirmed that the
large-scale environmental filters tended to select a pool of species
adapted to a specific site, and then the finer-scale filters determined
the species abundance and local species coexistence. Scherrer et al.
(2019) quantified the relative importance of habitat filtering and
limitations of similarity in species traits in grassland environments.
It was found that habitat filtering was the dominant assembly process at
the plot level, with diminished effects at the subplot level, whereas
limiting similarity prevailed at the subplot level, with weaker average
effects at the plot level. To be more concrete, our study partitioned
the ecological effects layer by layer across the five examined spatial
scales, ranging from the region level which covered the entire natural
forest areas of northeastern China, to the plot level (0.1 ha) which was
on a smaller scale but sufficient for tree community sampling (Kraft et
al. 2011). The results indicated that the species compositions of the
local communities were controlled by both the up-scale environmental
filtering and the down-scale spatial aggregation. The effects of the
environmental filtering and spatial aggregation processes on β-diversity
at the five examined spatial scales were all significant. However, it
was determined that the environmental filtering at the region-zone and
district-plot scales, as well as spatial aggregation at the within-plot
scale, were the most dominant effects.
Inconsistent with H1 , the effects of the
environmental filtering did not vary monotonically with the spatial
scales. The results suggest that the degree to which the environmental
conditions filtered the species from a large pool to a small pool may
varies with the spatial scale. The species compositions of each plot
were mainly affected by the environmental filtering at the smaller
district-plot scale, less than by that at the broader region-zone scale.
These results are not in agreement with those obtained by Cornell &
Hughes (2007), who demonstrated that the environmental filtering in
coral community assemblages did not change appreciably with spatial
scales due to the habitat differences being the same regardless of the
scale. We speculate that the variations across the spatial scales in
this study are due to the fact that the effects of the environmental
filtering at the region-zone scale produced marked effects mainly at the
higher latitudes, while the effects at the district-plot scale are
conspicuous throughout the study area (Figure 4 and 5). The results
highlight the fact that the environmental factors which filtered the
species from the regional pool to the zone level as well as from the
district to the plot level are crucial for the species compositions in
northeastern China.
Consistent with H2 , it was found the
environmental filtering at the broad scales had positive correlations
with latitude, but negative correlations at the finer scales. The
positive correlations at the broad scales indicates that fewer species
had adapted to the environments at the higher latitudes (Qiao et al.
2015), which was linked to the theory of the conservation of cold
tolerance across the species (Algar et al. 2009). This was found to be
in line with the results presented by Freestone & Osman (2011), who
found that the proportion of species from the regional species pool that
were present at the local scale had increased from the tropics to the
temperate zone, thereby demonstrating that higher-latitude communities
may experience greater influencing effects of species filtering from the
regional pool than communities at lower latitudes. At the fine scales,
the negative relationships between the environmental filtering effects
and the latitudes reveal that more pronounced environmental filtering
had resulted from greater habitat heterogeneity (Pianka 1966), which
subsequently contributed to higher β-diversity values at the lower
latitudes (Xing & He 2019). Myers et al. (2013) found that among forest
plots spanning local scales, environmental filtering explained a larger
proportion of the variations in β-diversity (after correcting for
sampling effects) in temperate forests when compared with those of
tropical forests. Our results emphasized the fact that the β-diversity
values along the latitudinal gradients had responded differently to
environmental filtering at the different spatial scales. Therefore,
hierarchical analyses are necessary in view of possible confounding
effects across multiple scales.
Our study confirmed that environmental filtering effects are related to
different variables at each spatial scale. Part of the results are
consistent with the priori criteria of the vegetation regionalization
map of China, in which temperature and precipitation factors are always
significant variables that may have resulted from different regulators.
For example, at the broad scale (e.g. the region-zone scale) the effects
are mainly mediated by latitude and elevation regulating factors (Table
4). However, at the fine scale (e.g. the district-plot scale), the local
topographical environmental conditions, such as slope and aspect, are
the dominant regulators (Table 4). In addition, some further clarifying
details were obtained, including that wind speed and solar radiation
were important filtering factors at the broad scales. They may have
influenced the abundance and distributions of species by altering
species dispersal potentials (Tackenberg et al. 2003) and creating
heterogeneous light environments (Parks & Mulligan 2010). We also found
that soil depth was a significant variable at the region-zone scale, and
litter thickness was a significant variable at both the zone-area and
district-plot scales. These findings reveal that soil attributes also
contributed to the environmental filtering processes. Overall, the
identified variables were sufficient to explain the environmental
filtering effects at the region-zone scale (approximately 70% of the
variation). Additional variations could probably be explained if more
soil variables would have been available (John et al. 2007; Jones et al.
2008), particularly at the zone-area, area-district, and district-plot
scales where the explained variances (approximately 20 to 30%) were
much lower than those at the region-zone scale.
Spatial aggregation can reflect interactions between individuals and
their close neighbors, which emphasizes the fundamental importance of
the local distributions of trees within communities. We found that the
effects of spatial aggregation in this study were significant and
remarkable, which provided support for the theory that spatial
aggregation is one of the dominant factors underlying the variations in
species compositions (Cornell et al. 2007; Flinn et al. 2010).
Consistent with H3 , our study showed that the
effects of spatial aggregation decreases with increasing latitude,
indicating that the spatial aggregation was more dominant in the
high-diversity communities. These results are causing the negative
gradients in the observed β-diversity values along latitudes (Figure 4
and 5). In previous studies, the index of beta-deviation was considered
to be closely related to spatial aggregation (Kraft et al. 2011; Qian et
al. 2013; Xu et al. 2015; Xing & He 2019). Kraft et al. (2011) showed
no global latitudinal gradient of beta deviation in global extent; Qian
et al. (2013) showed a negative gradient in New World North and China,
but no relation in New World South; Xu et al. (2015) observed a positive
gradient in New World South, but no relation in a global extent and New
World North. Xing & He (2019) found a strong unimodal latitudinal
gradient in North America. The reasons for the various relationships may
have been due to the different spatial extents among the studies
(Patrick & Yuan 2019). Therefore, considering that spatial aggregation
effects along geographical gradients could be various (positive,
negative, unimodal, or non-relationship), specific spatial extents
should be noted in future studies.
In conclusion, the stepwise partitioning of the environmental filtering
and spatial aggregation processes used in this study highlighted the
scale dependencies of the ecological effects on community structures.
Accordingly, we recommend that greater emphasis should be placed on the
scale effects of ecological processes (McGill 2010). Our study also
emphasized that the divisions of spatial scales should be optimized to
match the environmental patches which maximize environmental
heterogeneity (Viana & Chase 2019). This will be particularly important
due to the fact that the specific patterns and definitions of spatial
scales will affect the variations in the data, and thereby the
conclusions (Jelinski & Wu 1996).