MATERIALS AND METHODS
Study
area
The study area is located in northeastern China, in a temperate monsoon
climate which is characterized by four distinct seasonal conditions. The
annual mean temperature of the area is approximately
2.54oC. The average coldest monthly temperature is
-18.42oC (January), and the average hottest monthly
temperature is 20.54oC (July). The mean annual
precipitation is 622 mm, the rainy season is from June to September.
Forest plot
network
During the summer months of 2017 and 2018, an extensive network of
forest plots was established. The network includes 397 circular field
plots each covering an area of 0.1 ha (radius: 17.85 m; Figure 2). All
the natural temperate forest types in the region are included. The plots
are located between 40°47.134′ to 53°22.053′ N, and 120°3.546′ to
134°1.008′ E. The elevations of the plots range from 79 to 1,255 m. The
distances between the individual plots range from 24 to 60 km.
Each circular plot was divided into four subplots in north-south and
east-west directions. All trees with DBH ≥ 5 cm were assessed. The x/y
coordinate, species, DBH, height, and crown width of each tree was
recorded. 30,539 individual trees were recorded, belonging to 62
species, 33 genera, and 16 families.
Spatial
scales
On the basis of the published vegetation regionalization map of China
(Zhang 2007), the region in this study was divided into 3 zones, 9
areas, and 17 districts (Fig. 3). The species compositions and
environmental conditions are similar within each habitat, but dissimilar
among different habitats. Five different spatial scales were used: zones
within the region (region-zone), areas within the zones (area-zone),
districts within areas (district-area), plots within the districts
(plot-district), and the within-plot scales. These divisions are
effective in detecting the signatures of particular ecological processes
at each spatial scale.
Beta Diversity
Calculation
Following Legendre & De Cáceres (2013), the β-diversity was calculated
as the total variance of the Hellinger-transformed community matrix,
i.e. YHel, for each
forest plot as follows:
BDTotal = Var (YHel) = SS Total/ (n −1)(1)
Where SSTotal indicates the sum of the
squared deviations from the column means of the entire
YHel matrix; and n is
the number of subplots. Since the Hellinger distance was bounded between
0 and \(\sqrt{}2\), the BDTotal had varied between 0 and 1, which
indicated that all of the subplots had identical composition and each
subplot contained a unique set of species, respectively.
Null Models and Partitioning of
Ecological
Effects
Individual-based randomizations were used to partition the effects of
the environmental filtering and spatial aggregation processes on
community compositions. At the region-zone scale, all plots were pooled
and the individuals were randomly shuffled 1,000 times. This process
removed the effects of both the environmental filtering among the zones
and the spatial aggregation within each zone. For each shuffling
process, the number of individuals in each plot and the number of
species, as well as the abundance of each species, were preserved. Then,
the β-diversity of each plot was calculated after each shuffle. The mean
value of the 1,000 iterations was used to represent the expected
β-diversity at the regional level and referred to as\(\overset{\overline{}}{\beta}\)exp_region. In
addition, in order to remove the effects of the spatial aggregation, the
plots within each zone were pooled and the individuals were randomly
shuffled for 1,000 times. For each shuffling process, the number of
individuals within each plot and the number of species, as well as the
abundance of each species within each zone, were preserved. The mean
value of the β-diversity values calculated after 1,000 iterations was
used to represent the expected β-diversity at the zone level, and
referred to as\(\overset{\overline{}}{\beta}\)exp_zone. Subsequently,
the effects of environmental filtering on each plot were calculated as\(\overset{\overline{}}{\beta}\)exp_region -\(\overset{\overline{}}{\beta}\)exp_zone. The mean
value of \(\overset{\overline{}}{\beta}\)exp_region -\(\overset{\overline{}}{\beta}\)exp_zone for all the
plots was used to represent the effects of the environmental filtering
processes at the region-zone scale. Similarly, at the zone-area,
area-district, and district-plot scales, the plots were pooled within 9
areas and 17 districts, respectively. Individuals were randomly shuffled
1,000 times within each area, district, and plot, respectively, on an
independent basis. For each shuffling process, the number of individuals
within each plot and the number of species, as well as the abundance of
each species within each area, district, and plot, were preserved,
respectively. The expected β-diversities at the area, district, and plot
levels (i.e. \(\overset{\overline{}}{\beta}\)exp_area,\(\overset{\overline{}}{\beta}\)exp_district, and\(\overset{\overline{}}{\beta}\)exp_plot) were
calculated as the mean values of the 1,000 iterations. The effects of
the environmental filtering on each plot at the zone-area,
area-district, and district-plot scales were calculated as\(\overset{\overline{}}{\beta}\)exp_zone -\(\overset{\overline{}}{\beta}\)exp_area,\(\overset{\overline{}}{\beta}\)exp_area -\(\overset{\overline{}}{\beta}\)exp_districtand\(\ \overset{\overline{}}{\beta}\)exp_district -\(\overset{\overline{}}{\beta}\)exp_plot. The mean
values of \(\overset{\overline{}}{\beta}\)exp_zone -\(\overset{\overline{}}{\beta}\)exp_area,\(\overset{\overline{}}{\beta}\)exp_area -\(\overset{\overline{}}{\beta}\)exp_district,
and\(\ \overset{\overline{}}{\beta}\)exp_district -\(\overset{\overline{}}{\beta}\)exp_plot of all the
plots was used to represent the effects of the environmental filtering
effects at the zone-area, area-district, and district-plot scales. We
calculated the observed β-diversity of each forest plot. Then,\(\overset{\overline{}}{\beta}\)obs -\(\overset{\overline{}}{\beta}\)exp_plot was used to
measure the effects of the spatial aggregation processes on each forest
plot. The mean value of\(\overset{\overline{}}{\beta}\)obs -\(\overset{\overline{}}{\beta}\)exp_plot of all the
examined plots was calculated to represent the effects of the spatial
aggregation processes at the within-plot scale. A summary of the models
used to measure the effects of environmental filtering and spatial
aggregation are presented in Table 1.
Key environmental factors
identification
A multiple stepwise regression method was used to identify the key
factors of the environmental filtering processes. The candidate
environmental factors included 21 climate variables and 5 plot
attributes. The climate data were collected from WorldClim Version
2 (Fick & Hijmans 2017). The 5 plot attributes were obtained from
field observations, including elevation, slope, aspect, soil depth, and
litter thickness. The statistical information of these environmental
variables is shown in Table S1.1 of Appendix S1. To mitigate the
inherent collinearity among the climate variables, the 11 temperature
variables and the 8 precipitation variables, respectively, were
subjected to principle component analysis (PCA) based on their
correlation matrixes. The first two axes were determined to account for
over 92% and 95%, respectively, of the variances in the temperature
and precipitation variables, as detailed in Tables S1.2 and S1.3 of
Appendix S1.
In this study, all of the calculations and statistical analyses were
conducted using the statistical program R 3.5.1.