Global Ecology and Biogeography, (Global Ecol. Biogeogr.) (2013) ••, ••–••
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R E S E A RC H
PAPERCommunity assembly processes shape analtitudinal gradient of forest biodiversity
Akira S Mori1*, Takayuki Shiono1, Dai Koide1, Ryo Kitagawa1, Aino T Ota1and Eri Mizumachi2

1

Graduate School of Environment andInformation Sciences, Yokohama National
University, Yokohama, Japan, 2Institutefor Integrated Cell-Material Sciences(WPI-iCeMS), Kyoto University, Kyoto, Japan

ABSTRACTAim Spatial patterns in biodiversity along environmental gradients are a central
theme in ecology. However, the ways in which local assembly processes control
changes in species turnover (b-diversity) along broader gradients have been lesswell documented. In this study, we aimed to elucidate factors and processes
governing the altitudinal gradients in the b-diversity of woody plants and grounddwelling oribatid mites.
Location Shiretoko National Park in Hokkaido, Japan.
Methods The diversity of plants and oribatids was investigated in seven plots(each containing 10 subplots) at different altitudes, and the b-diversity of the twoorganism groups was calculated for each altitude. The dependence of b-diversity on
the size of the species pool (g-diversity) is an issue of long-standing importance. We
therefore used null modelling, which randomly shuffles individuals among subplots while preserving the g-diversity, the relative abundance of each species perplot and the number of individuals per subplot. This approach enabled us toestimate how much the observed b-diversity deviates from the expected b-diversity
under stochastic assembly processes. Environmental data were collected to evaluate
the possible effects of habitat condition/heterogeneity on community processes.
Results In plants, deterministic processes dominated in the low-productivity,
high-altitude stands because of the finer-scale niche partitioning seen among small
individuals within less-stratified stands. In the structurally developed, low-altitude
stands, the community structure was more strongly affected by stochasticity, probably resulting from one-sided competition such that the canopy trees intercept the
majority of light, a primary resource for plants, and therefore the small understorey
individuals had limited access to light. Among the oribatids, the altitudinal gradientof b-diversity was less evident than among the studied plants. However, this nonlinearity does not support the notion that local assembly processes contribute little
to the spatial pattern of b-diversity. Indeed, local-scale environmental heterogeneity favoured a more deterministic assembly of oribatids at a given altitude.

*Correspondence: Akira S Mori, Graduate
School of Environment and Information
Sciences, Yokohama National University, 79-7
Tokiwadai, Hodogaya, Yokohama 240-8501,
Japan.
E-mail: akkym@kb3.so-net.ne.jp© 2013 Blackwell Publishing Ltd

Main conclusion The biogeographical patterns of b-diversity are not independent of community processes and, in reality, are shaped by local stochastic/
deterministic factors that change within a landscape.
Keywords
Beta diversity, community assembly, niche-based processes, oribatid mites,
stochastic processes, woody plants.

DOI: 10.1111/geb.12058
http://wileyonlinelibrary.com/journal/geb

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A. S. Mori et al.
I N T RO D U C T I O N
Understanding spatial patterns in biodiversity along environmental gradients is a central theme in ecology (Willig et al.,
2003; Ricklefs, 2004); it helps to address the looming threats tobiodiversity by contributing to regional-scale biological conservation activities, such as reserve design and habitat restoration.
The most widely recognized biogeographical patterns of diversity are the gradients that occur along altitude and latitude.
Knowledge of these patterns is also vital for predicting how
climate change will affect biodiversity, as most species are
expected to be forced to shift their distributions to higher
altitudes/latitudes in the warmer future (Sheldon et al., 2011).
Despite substantial efforts, however, the underlying drivers of
the spatial organization of diversity remain unknown (Mokany
et al., 2011). In particular, compared with local and regional
species richness (a- and g-diversity, respectively), the patterns
and processes related to how local assemblages of species aredifferentiated (i.e. b-diversity) across environments (including
altitudinal and latitudinal gradients) have been poorly explored.
Generally defined as the variation in the identities of species
among locations, b-diversity is at the heart of community
ecology (Anderson et al., 2010). Filling the knowledge gap
regarding b-diversity is thus critical in our quest to reveal how
biodiversity varies in space, both theoretically and practically(Mena & Vázquez-Domínguez, 2005; Baselga, 2010; Chase &Myers, 2011; Leprieur et al., 2011).
The factors driving variations in b-diversity are among themost important but also the most poorly understood influenceson global variations in biodiversity (Chase, 2010; Qian et al.,2012). A number of explanations have been proposed for thegeographical changes observed in b-diversity, including explanations based on dispersal limitation (Qian & Ricklefs, 2007;
Qian, 2009), habitat specialization (Jankowski et al., 2009) andenvironmental heterogeneity (Andrew et al., 2012). The available evidence implies that multiple processes operating at
various scales may control b-diversity (Carvalho et al., 2011).
Based on forest inventories, Kraft et al. (2011) claimed that thegeneral trend in b-diversity, which shows a linear decrease withincreasing altitude/latitude, is spurious and that this decrease isa simple function of a- and g-diversity. These authors concluded
that local-scale community assembly processes, such as niche
partitioning, contribute little to the geographical pattern ofb-diversity. However, this notion is still open to discussion(Qian et al., 2012; Tuomisto & Ruokolainen, 2012). In fact,observational (Andrew et al., 2012) and experimental (Chase,2010) studies have shown that the relative impact of stochastic/
deterministic processes on b-diversity varies with productivity.
Given the general correlation between altitude/latitude andproductivity in forest ecosystems (e.g. Nogues-Bravo et al.,2008), we hypothesize that the shifts between the stochastic anddeterministic processes underlying local assemblages acrossenvironmental variations drive the broad-scale spatial pattern inb-diversity in forest communities.
In forest ecosystems, resource distributions are spatially heterogeneous (Nicotra et al., 1999; Lindo & Winchester, 2009).
2

The high heterogeneity caused by the structural complexity offorests is known to strongly contribute to the maintenance of
local community structure for both plants (Kohyama, 1993;
Kohyama & Takada, 2009) and animals (Tews et al., 2004;Decaëns, 2010). Therefore, niche assembly probably accounts
for the processes shaping forest biodiversity at broader spatial
scales. In this study, we highlight different groups of organisms(woody plants and oribatid mites) because taxon-specific factors
may be important in structuring communities (e.g. dispersal
mode and habitat requirements; De Bie et al., 2012). Considering resource partitioning among woody plant species, we
focused on forest stratification, as this process determines thewithin-stand distribution of light, which is a primary resourcefor plants (Kohyama & Takada, 2009). In ground-dwellingoribatid mites, we considered the soil environment to be adeterminant of deterministic community-structuring processes(Lindo & Winchester, 2007; Decaëns, 2010). Vegetation, primarily through litter input, strongly influences the soil diversity,
including that of the oribatid mites, which are the most representative, species-rich group of the soil mesofauna (Hasegawa
et al., 2013). Incorporating these mites in addition to plants
when considering community processes may thus have an
important implication. That is, our multi-taxon approach will
be useful for determining whether universal processes drive the
distribution of b-diversity.
Based upon the above considerations, we aim to quantify therole of local community processes in creating the altitudinalpattern of b-diversity in the northern forests of the Shiretoko
Peninsula in Japan (Fig. 1), which are characterized by sharp
altitudinal changes in forest structure and productivity due tounique climatic conditions (see Materials and Methods). We
expect that our hypothesis can best be tested in a region with asharp environmental gradient, making our study site ideal forthis purpose.
MATERIALS AND METHODS
Study site
This study was conducted in Shiretoko National Park, which islocated in north-eastern Hokkaido, the northernmost island ofJapan (Fig. 1). Shiretoko Peninsula is registered as a UNESCO(United Nations Educational, Scientific and Cultural Organization) World Natural Heritage Site because of its high biodiversity. This site provides one of the richest northern temperate
ecosystems in the world and is an outstanding example of theinteraction between marine and terrestrial ecosystems and of
extraordinary ecosystem productivity (http://whc.unesco.org/
en/list/1193). This site has particular importance for a numberof species, some of which are endangered and endemic, suchas Blakiston’s fish owl (Ketupa blakistoni) and the Shiretokosumire plant (Viola kitamiana). Approximately 90% of the landis covered with pristine natural vegetation, which extends fromthe coast to above the tree line in belts graded by altitude.
On the peninsula, there is a remarkable difference in climate
between the east and west sides, which are separated by central

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Altitudinal changes in community assembly processes

Figure 1 Map of the Shiretoko Peninsula on Hokkaido Island, northern Japan. The vegetation and land-use classifications are indicatedwith different colours/shades. The biodiversity analysis was conducted along the indicated altitudinal transect (black dotted line) on thewestern slope of Mount Rausu. We established 10 0.01-ha subplots (a total of 70 subplots, 0.7 ha) in seven altitudinal zones.

mountains. The west coast is characterized by an annual rainfall
of approximately 900 mm, an average maximum temperature inAugust of approximately 22 °C, and average minimum temperature in February of approximately -16 °C, whereas the east coastpresents annual rainfall of approximately 1600 mm, an average
maximum temperature in August of approximately 19 °C andan average minimum temperature in February of approximately-11 °C. The region is dominated by the cold Northwest Asian
Monsoon, which prevails from November to March. Due to thelocation of the peninsula, its western side is particularly exposed
to strong cold winds. Because the vegetation on the western side
is greatly affected by winter winds, trees in the mid-to-highaltitudes generally creep leeward. We selected the forests on thisside of the peninsula as our study sites because of the sharpchange in forest structure observed in this area.
Sampling procedures
The diversity of woody plants and oribatid mites was investigated on the western slope of Mount Rausu (1661 m) in July
2010. Measurements were conducted at 50, 200, 400, 600, 800,
1000 and 1200 m above sea level (Fig. 1). All of the study plots
were located in areas of pristine vegetation. In each plot, weestablished 10 subplots of 10 m ¥ 10 m (0.01 ha). We randomly
selected mature stands and avoided stands where there had been

recent disturbances to minimize the effects of differences instand age on community composition. This scale may be smaller
than those used in other studies of tree diversity. However, thesize of our study subplots is the same as that used by Kraft et al.
(2011), who indicated that the method employed to calculate
the g-correction of b-diversity (described later) is not dependent
on scale. In particular, the complex fine-scale topography of theShiretoko Mountains (which is thought to contribute to theirhigh biodiversity) makes this scale useful, as small plots tend not
to include multiple topographical characteristics, such as different slopes. This scale has also been shown to be valid when
considering the distance-decay of tree b-diversity (Morlon et al.,2008). The maximum distances between the subplots in eachplot ranged between 311 and 435 m, with an average of 366 m.
The average intervals between adjacent subplots were not significantly different among plots (ANOVA, P > 0.10), suggestingthat our sampling scheme is designed to minimize the confounding effects of spatial factors (which can potentially lead todifferences in b-diversity among locations; see also Appendix S1
in Supporting Information for further explanation). A total of
70 subplots (0.7 ha) were established.
In each subplot, we recorded the number of individuals taller
than 0.5 m for all woody plant species. For all trees with a girthat breast height (GBH) > 10 cm (an approximate diameter of
3.18 cm), we measured the GBH and then estimated the total

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A. S. Mori et al.
basal area (BA) of the trees in each subplot. The height of thetallest tree in each subplot was recorded as the canopy height(CH) of the stand. In each subplot, we measured the diameterand length of all coarse woody debris (CWD; such as downed
logs and branches) with a diameter of greater than 5 cm toestimate the total volume for two categories: recent CWD (decayclasses 1–3) and old CWD (decay classes 4–5). For this purpose,
we followed the decay-class definitions of Sollins (1982). We alsomeasured understorey light (relative photosynthetic photon flux
density, rPPFD) with a quantum sensor (LI-190SZ; LI-COR
Inc., Lincoln, NB, USA) 2 m above the ground. Given the close
relationships between BA, CH and understorey light, we mainly
relied on BA to represent local stand structure (see Appendix
S2). Furthermore, we collected four soil samples from each
subplot using a soil corer (cylindrical, 20 cm2 bottom area and5 cm depth) and then transported them to our laboratory. Oribatid mites were extracted from the soil using Tullgren funnels
with 40 W bulbs over 72 h. The oribatid mites were removed
using forceps under a stereoscopic microscope (M3Z, Wild Inc.,
Heerbrugg, Switzerland). Their morphology was observed
under a Nomarski differential interference microscope (Eclipse
E-600, Nikon Corp., Ltd, Tokyo, Japan) after the specimens
had been mounted on slides with gum chloral. We counted thenumber of individuals of all oribatid mite species. The data fromthe four soil cores in each subplot were combined and taken as
the oribatid community data for the stand. In all of the cores, wemeasured the thickness and dry mass of the soil surface litter(A0 layer) remaining in the Tullgren funnel after the oribatid
mites were extracted. Again, the average values for litter massand thickness from the four cores were used as the data for each
subplot. Furthermore, we collected additional leaf litter (A0layer) from soil adjacent to the sampled locations over a surface
area of 10 cm ¥ 10 cm and measured the water content (WC),pH and carbon-to-nitrogen ratio (CN) in the laboratory. The
mass of the collected litter was measured shortly thereafter,
and the samples were air-dried for 1 week before the mass wasremeasured to estimate the WC. Next, the soil and roots were
removed from the samples using a sieve with a 2-mm mesh, andthe remaining portions of the samples were dried at 105 °C for
subsequent pH and CN measurements. CN was measured using
an NC analyser (Sumigraph NC-22, Sumika Chemical Analysis
Service, Ltd, Tokyo, Japan).
Data analyses
We calculated the changes in species diversity along the altitudinal gradient in terms of the local (a-diversity) and regional
species richness (g-diversity). The a-diversity is the species richness for each subplot and the g-diversity is the total numberof species at each altitude. We calculated b-diversity using the
traditional multiplicative metric of Whittaker (1960), definedas bw = 1 – a/g, where a is the mean a-diversity (mean species
richness for each subplot at each altitude), and g is theg-diversity. Because this index is the most widely applied index
of species turnover and has been described as a measure that iswell suited for ecological analyses, we relied mainly on this4

methodology. Furthermore, we calculated variance-based
b-diversity (bv) as proposed by Legendre et al. (2005), which issuitable for comparisons of the average community dissimilarity between sampling units and between different taxonomic
groups within the same area. We obtained data table Y by transforming the row data in table Xi,j, which contained the numberof individuals of species j in subplot i, based on the Hellinger
distance. Then, we calculated the total variance in the data tablewith n subplots as Var(Y) = SS(Y)/(n - 1), where SS(Y) is thesum over all species and all subplots of the squared deviations
from the species means presented in table Y. This variance corresponds to bv. The Hellinger transformation does not give highweights to rare species (Legendre & Gallagher, 2001). Given the
differences in abundance among plots (described later), theHellinger-standardized b-diversity estimate provides important
information for the interpretation of overall results.
In evaluating b-diversity, the dependence of b-diversity on thespecies pool size (g-diversity) is an issue of long-standing importance (Anderson et al., 2010; Chase & Myers, 2011). To overcome
this difficulty, we employed the null modelling approach of Kraft
et al. (2011), which corrects local b-diversity for g-dependence.
The null model randomly shuffles individuals among subplots
while preserving g-diversity, the relative abundance of eachspecies per plot (altitude) and the number of individuals per
subplot. We calculated b-deviation (bdev), defined as (bobs – bnull)/
bsd, where bobs is the observed b-diversity, bnull is the mean of thenull distribution of b-diversity and bsd is the standard deviationof the null distribution. The null distribution was calculated
based on 999 randomizations. Note that bdev is equivalent to a
standardized effect size, which indicates the magnitude of thedeviation from the expectation of a random (stochastic) assembly process, and positive and negative values indicate more andless b-diversity, respectively, than expected by chance.
Because we focused primarily on the effects of forest stratification on b-diversity for woody plants, we separated the plantcommunities into three layers: with layer I corresponding tocanopy trees > 10 cm in GBH, layer II to large trees > 1.3 m inheight, and layer III to small trees > 0.5 m in height. We calculated bw and bdev separately for individuals in layer I, for individuals in layers I and II, and for all individuals in the three layers toconsider the possible changes in niche partitioning among theplants along the vertical gradient in the stand structure.
After calculating the b-diversity and b-deviation of the plants
and oribatids for each altitude, we determined whether theseparameters show a significant relationship with altitude. Significance was tested using the jackknife regression test because thistest is suitable for a small number of observations, as no single
observation can have a disproportionate influence on the results(Tukey, 1958). Linearity was tested with both linear and loglinear models, and the results with high R2 values are shown.
Furthermore, to understand the possible contributions of
the extrinsic environmental variable(s) to the determinationof b-diversity, we tested whether there is a linear relationshipbetween bdev and any explanatory variable(s). Here, we preliminarily conducted a canonical redundancy analysis andselected meaningful environmental variables as determinants of

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Altitudinal changes in community assembly processes
community structure based on forward selection (999 permutations with an alpha criterion of 0.10) following Blanchet et al.
(2008). The Hellinger standardization was performed again inthis analysis. Altitude, soil properties (litter thickness, litter
mass, WC, pH and CN) and BA were taken as candidate determinants for woody species, and the same set of variables with
CWDs was included in the selection for oribatid mites. For
plants, altitude and BA were selected. BA is widely used as aproxy of stand structure and represents relative dominance,competitive interactions and resource and space occupancy
among plant species (Appendix S2; Kohyama, 1993; Kohyama &Takada, 2009). Additionally, this parameter clearly represents
the availability of light, which is the most critical resource forplants in the understorey (Appendix S2). For oribatids, litter
thickness and mass were selected in addition to altitude. Hence,
we relied on these soil variables as critical local environmental
factors underlying the structure of oribatid communities (Lindo& Winchester, 2007; Decaëns, 2010).
In testing the relationships between bdev and the explanatory
variable(s), we used the mean and coefficient of variation (CV)for each altitude to determine the possible effects of resource
availability and heterogeneity on the magnitude of bdev. The CV
is widely employed to evaluate environmental heterogeneity(e.g. Questad & Foster, 2008). Because the spatial structure ofcommunities differs between plants and oribatids, with thelatter being less spatially structured as a result of their potential
dispersibility (Appendix S1), the spatial distributions of theavailable resources in terms of their amount and heterogeneity are expected to have different effects on the deterministic
community-structuring processes of these two groups. The significance of the relationship between bdev and the explanatory
variable(s) was tested with the jackknife regression test. In addition, if an environmental variable is correlated with altitude, its
effects on bdev may be artefacts mediated by altitudinal change.
For this reason, we conducted a partial correlation test to control
for the effects of altitude. The partial correlation test was simultaneously used to test for a significant relationship between
bdev and altitude, with an environmental variable as a controlling
factor. Based on these partial correlation analyses, we determined which factor (environmental variable or altitude) determines the altitudinal changes in bdev.
All of the statistical analyses were performed with R Software,version 2.14.2 (R Core Team, 2012, http://www.R-project.org/).
For the null modelling we modified the code of Kraft et al.
(2011) with the help of the ‘vegan’ package for additional modelling of the pairwise b-diversity indices (see Appendices S4 &S5). We used the package ‘packfor’ for the forward selection. We
also relied on the ‘vegan’ package for a rarefaction approach (seethe discussion and Appendix S5).
RESULTS
Species richness
We examined the species diversity of woody plants and oribatid
mites along an altitudinal gradient on Mount Rausu (Fig. 1).

Figure 2 Altitudinal changes in species richness for woody plants
and oribatid mites. The g-diversity and mean a-diversity are
shown. The R2 values and significance were calculated using aquadratic regression. The significance levels are indicated withasterisks: ***P < 0.001, **P < 0.01, *P < 0.05.

Among woody plants with a height greater than 0.5 m, 59
species and 5359 individuals were recorded. Regarding oribatid
mites, 77 species and 3669 individuals were recorded. Both aand g-diversity showed a hump-shaped (unimodal) distribution
with a mid-altitude peak for both woody plants and oribatid
mites (Fig. 2).
Beta diversity
The b-diversity distributions of woody plants along the altitudinal gradient are illustrated in Fig. 3. The observed b-diversity
decreased with altitude, regardless of the forest-stratification
category, except in layer I (for both bw and bv). Likewise, theexpected bw based on a random assemblage decreased with altitude for all stratification categories. The observed bw and bv
values were always greater than would be expected for a random
assemblage. Among the investigated plants, the deviation ofb-diversity from a random assemblage (bdev) increased with altitude (for both b-diversity indices). In particular, increases in bdev
with altitude became more evident when lower-layer individuals
were included in the analysis. The relationship between standstructure and bdev is shown in Appendix S3. The bdev of plants
was significantly correlated with BA and CH (for bothb-diversity indices), whereas bdev was not correlated with the CV
values of either BA or CH. The deviations were again larger
when lower-layer individuals were included. As there was asignificant negative correlation between BA and altitude(Appendix S2), a partial correlation test was conducted to detect
any direct relationship between bdev and either BA or altitude(Table 1). The correlations between BA and bdev were still
significant after the effects of altitude were excluded (forboth b-diversity indices). In particular, the partial correlations
between BA and bdev were more evident in the analyses including
the lower-layer individuals. In contrast, bdev did not change withaltitude when BA was excluded from the correlation, except for
the analysis performed in layer I with bv, suggesting that BA, notaltitude, is the major determinant of higher bdev values at higher
elevations.

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A. S. Mori et al.
sion, R2jack = 0.438, P = 0.06), whereas bdev significantly increased
with CV values, regardless of the examined b-diversity index(Fig. 5). The CV values of these soil variables showed no linear
relationship with altitude (jackknife regression, R2 < 0.01 forboth litter variables), suggesting that the within-plot heterogeneity of the soil environmental variables is the determinant of
the nonlinear relationship of bdev with altitude.
DISCUSSION

Figure 3 Altitudinal changes in b-diversity for woody plants.
Results for bw and bv are shown. The circles and triangles indicate
the values of b-diversity and b-deviation, respectively. The open
and closed symbols indicate the observed values and those
expected from the null modelling, respectively. The red, orange
and blue symbols represent the results for layer I, layers I–II andlayers I–III, respectively. The R2 values based on the jackknife
regression (R2jack) are shown. The significance levels are indicatedwith asterisks: ***P < 0.001, **P < 0.01, *P < 0.05.

The b-diversity distributions of oribatid mites along altitude are illustrated in Fig. 4. Both the observed and expectedb-diversities decreased with altitude (for both bw and bv). In
contrast to what was observed for woody plants, the b-deviationof oribatids (bdev) showed no relationship with altitude (foreither b-diversity index). However, the observed b-diversity
values were always greater than would be expected from arandom community. The bdev of oribatids was not related to themean values of the soil variables (except for a weak positive
correlation of bdev based on bv with litter mass; jackknife regres6

We observed a positive departure of b-diversity from what
would be expected for a random (stochastic) community forboth woody plants and oribatid mites (Figs 3 & 4), suggesting
the importance of deterministic processes in structuring thecommunity assembly of forest biodiversity in this landscape.
Interestingly, the observed b-diversity was always greater than
expected at all locations for both groups of organisms (Figs 3 &4). Although this phenomenon has been reported previously inrecent works (Lindo & Winchester, 2008; Kraft et al., 2011; De
Cáceres et al., 2012), it remains unexplained.
For woody plants, the magnitude of b-deviation increased
with altitude (Fig. 3). This result contrasts with the previous
report by Kraft et al. (2011), who found no altitudinal/
latitudinal gradient in b-deviation for South American tree
communities. However, their study prompted a rebuttal (Qianet al., 2012; Tuomisto & Ruokolainen, 2012). Using the samedataset, Qian et al. (2012) detected a significant increase inb-deviation with latitude after correcting for the temperature
variations within each transect. Although this method of data
correction was subsequently criticized by the original authors(Kraft et al., 2012), our result regarding the altitudinal increasein b-deviation for woody plants in Shiretoko supports the suggestion of Qian et al. (2012). In the studied landscape, higherelevation (low-temperature) stands show greater b-deviation
among the investigated plants (Fig. 3), suggesting that, consistent with previous reports (Chase, 2010; Andrew et al., 2012), therole of deterministic assembly processes increases with decreasing productivity in plant communities (note that similar results
were observed for different b-diversity indices; Appendix S4).
As we hypothesized, the mechanisms underlying community
assembly (e.g. niche versus neutral) play an essential role increating biogeographic patterns of b-diversity (Chase & Myers,2011).
Our results suggest that niche-based processes in plant communities should dominate in high-altitude stands. Interestingly,
the altitudinal increase in b-deviation was more evident when
the plants growing in lower layers were included in the analysis(Fig. 3). A similar result was found for b-deviation along a
stand-structure gradient (Appendix S3). According to thepartial correlation analysis, basal area, not altitude, determines
the b-deviation gradient, especially when smaller understorey
plants are considered in the analysis (Table 1, Appendix S4).
These results imply that given the altitudinal changes in standstructure, the role of understorey plants in deterministic assembly becomes more dominant with altitude. In forests, light isthe primary resource for plants, but the availability of light is

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Altitudinal changes in community assembly processes
Table 1 Partial correlations (rp)between basal area/altitude and bdev forwoody plants.

b-diversity

Layer

Explanatory variable

Controlling factorrp

P-valuebw

I

Altitude
Basal area
Altitude
Basal area
Altitude
Basal area
Altitude
Basal area
Altitude
Basal area
Altitude
Basal area

Basal area
Altitude
Basal area
Altitude
Basal area
Altitude
Basal area
Altitude
Basal area
Altitude
Basal area
Altitude-0.617-0.6830.615-0.794-0.692

n.s.
n.s.
n.s.
< 0.10n.s.
< 0.05< 0.10n.s.
n.s.
< 0.01n.s.
< 0.05

I–II
I–III
bv

I
I–II
I–III

Analyses were conducted for different layers to evaluate the effects of stand stratification on bdev.
We employed two different b-diversity indices to evaluate bdev. Values of rp are shown with P-values(n.s. means non-significant at P > 0.10).

Figure 4 The altitudinal changes in b-diversity for oribatid
mites. Results for bw and bv are shown. The circles and triangles
indicate values of b-diversity and b-deviation, respectively. For bw,
the results for the observed and expected b-diversity showed a
log-linear relationship (upper panel), and the lines were fitted
on the log-transformed altitude for the analysis and thenback-transformed to the plots. The open and closed symbols
indicate the observed values and those expected from nullmodelling, respectively. The R2 values based on the jackknife
regression (R2jack) are shown. n.s. means that the relationship was
not significant (P > 0.10). The significance levels are indicated by+, corresponding to P < 0.10.

generally limited in the understorey (Canham et al., 1990;
Nicotra et al., 1999). In the studied forests, as low-elevation
stands are structurally well developed, light limitation is probably a stronger force in the understorey (Appendix S2). That is,
at low altitudes, asymmetric competition for light (one-sided

Figure 5 The relationships between two environmental variables(litter thickness and mass) and bdev for oribatid mites. Results
for bw and bv are shown. The CV values were used to test theirrelationships with bdev. Significance was tested with the jackknife
regression test. The significance level is indicated with asterisks:**P < 0.01, *P < 0.05.

competition; Kohyama & Takada, 2009) is more intense, so themajority of light resources are intercepted by canopy trees, while
only a small proportion of the light is available for the understorey plants, which constitute the majority of the stand assemblage. In contrast, understorey plants have a greater potential toobtain sufficient light in high-altitude stands (Appendix S2),
promoting more deterministic resource partitioning. It should
be noted that because plant density increased with elevation in

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lower layers (R2 > 0.68, P < 0.05), rare species were more likely to
be found at higher elevations. However, a rarefaction approach(Gotelli & Colwell, 2001) and our additional b-deviation calculations for each subplot (as opposed to the plot-level calculations) showed no significant evidence of this sampling effect(Appendix S5). Thus, we speculate that the greater abundanceat higher altitudes was the consequence of finer-scale niche
partitioning, which allows more individuals to exist together
in a given space, rather than the cause – generating artificial(abundance-driven) altitudinal differences in diversity patterns(see Appendix S5 for further explanation). In reality, in addition
to niche partitioning (limiting similarity), the local assembly ofa plant community may be structured by different (and sometimes opposing) processes, such as abiotic filtering and facilitation (Spasojevic & Suding, 2012). Nevertheless, we speculatethat niche partitioning is the key mechanism underlying theobserved spatial pattern of biodiversity.
Several lines of evidence suggest that both predictable andrandom processes operate simultaneously to shape species
assemblages, although the degree to which these processes contribute differs among sites (Ellwood et al., 2009; Chase, 2010;
Gotzenberger et al., 2012). This finding indicates that, in addition to niche-based mechanisms, it is essential to explore apotential factor generating stochasticity in local community
assemblies. Historical contingency resulting from a priority
effect can create multiple stable states (Chase, 2010) or an alternative transient state (Fukami & Nakajima, 2011) at different
sites, leading to high b-diversity. In this study, although wecannot discuss differences in the colonization history of theplant species among the localities, the higher stochasticity
observed in the low-altitude stands may result from stronger
one-sided competition for resources. In other words, because
the canopy trees in old forests generally colonized a site prior tomost understorey individuals, one-sided competition may playa role similar to that of priority effects in plant assemblages.
The predominance of a few canopy species, which negatively
influences understorey species by reducing the availability ofresources and space, is more prominent in low-altitude stands,
leading to the relative dominance of a quasi-random assembly.
Thus, b-diversity decreases with altitude, whereas b-deviation
increases with altitude. Although further studies are required,we speculate that the forest-architecture theory (Kohyama,
1993; Kohyama & Takada, 2009), which explains the importance of stand structure in creating niches allowing the successful coexistence of plant species, may explain the broad-scalepattern of b-diversity along a productivity gradient. Therefore,
the altitudinal change in stand stratification associated with thechange in the within-stand light distribution results in a
gradual change in complementary resource use among species.
Accordingly, the relative importance of stochastic/deterministic
assembly processes in forest-plant communities shifts along
altitude. Although our explanation for the shift in local processes is speculative, we believe that recent advances in related
theories, such as coexistence theory (HilleRisLambers et al.,2012), have the potential to address this unsolved issue in community ecology.
8

Among the oribatid mites, the altitudinal changes inb-diversity were not as evident as for woody plants, andb-deviation showed no significant altitudinal gradient (Fig. 4).
However, the lack of a geographical gradient is not convincing
evidence that local community assembly has no role in structuring the biogeographical gradient in biodiversity (Qian et al.,2012). We found that the observed b-diversity was always greaterthan would be expected from a random community (Fig. 4),
indicating that niche-based processes control the b-diversity oforibatids (see similar results for different b-diversity indices;
Appendix S4). In a Mediterranean beech forest and grasslands,
Caruso et al. (2012) recently found that the oribatid mite assemblages cannot be explained by a neutral model, suggesting dominance of a niche-based process. The diversity of plants often has
a major influence on the diversity of other groups of organisms
due to it determining the variety of resource and habitat elements that create niche spaces for different taxa (Hawkins &Porter, 2003), including soil fauna (Vanbergen et al., 2007). In
this study, we calculated the stand dissimilarity index, whichshowed a significant partial Mantel correlation with the compositional dissimilarity of the oribatids; this pattern most likely
resulted from vegetational control of the environmental conditions in the soil ecosystems (Appendix S6; St John et al., 2006;Decaëns, 2010). Thus, the absence of an altitudinal gradientin the b-deviation of oribatids may be a consequence of an
indirect response to the spatial gradient in plant diversity(St John et al., 2006). Compared with plants showing a clear
diversity gradient, more complex (and most likely multiple)
processes may operate to structure the oribatid assemblages
across the landscape gradient.
Ecological factors that play a deterministic role in diversity
patterns often show nonlinear relationships with altitude/
latitude (Decaëns, 2010; Qian et al., 2012). In this study, the two
selected environmental variables explained a significant amountof the variation in the b-deviation of the oribatid communities(Fig. 5, Appendix S4). In particular, instead of mean values, thevariation values determined the magnitude of deviation from astochastic assemblage at each altitude. This result advocates an
explanation for the relationship between heterogeneity and biodiversity (Tews et al., 2004; Decaëns, 2010; Andrew et al., 2012):
habitat heterogeneity generates niches across sites (Kerr &Packer, 1997; Vanbergen et al., 2007; Questad & Foster, 2008),
although some researchers have suggested that productivity may
be more important than environmental heterogeneity (Chase &Leibold, 2002; Chase, 2010). Although the dispersal of the oribatids is passive, they can be moved from site to site (e.g. by thewind; Maraun et al., 2007; Lehmitz et al., 2011). Given the highmicrohabitat specialization of oribatids (Anderson, 1978), it islikely that high environmental heterogeneity can create moreniche spaces, leading to more deterministic community assembly. In reality, the broad-scale gradient of b-diversity is theoutcome of various processes, including dispersal limitation,
speciation and climate differentiation among localities (Davidar
et al., 2007; Qian & Ricklefs, 2007; Qian, 2009). Specifically,
dispersal limitation is a critical factor influencing community
dissimilarity among localities (Lindo & Winchester, 2009; Qian,

Global Ecology and Biogeography, ••, ••–••, © 2013 Blackwell Publishing Ltd

Altitudinal changes in community assembly processes2009). The spatial dispersion of our sampling subplots at each
altitude was small, so dispersal limitation had no major influence on local (within-plot) community structures (Appendix
S1). In conclusion, our results imply that even when habitat
conditions show no clear trend along a primary environmental
gradient (i.e. altitude/productivity), the niche-based processes
determining the local community assembly should not be
undervalued.
To our knowledge, this study is the first to quantify the importance of local assembly processes in driving the altitudinalpattern of b-diversity across different taxa. In both the plants
and oribatids, we observed unimodal relationships of botha- and g-diversity with altitude (Fig. 2). The peaking of thiscongruent pattern of diversity at middle elevations has been
widely discussed (Cardelus et al., 2006; Nogues-Bravo et al.,2008; Jankowski et al., 2009; Li et al., 2009), although the causal
mechanisms underlying this pattern (e.g. climate, scale effects
and human impacts) are beyond the scope of this study. In thepresent study, despite this concordance, we found striking differences in the altitudinal patterns of b-deviation between thetwo groups of taxa, emphasizing that the deterministic factors
governing a biodiversity pattern along an environmental gradient differ among groups of organisms. This finding indicatesthat there is no universal rule shaping diversity distribution
across multiple taxa. However, our study significantly contributes to the unified theory of biodiversity by explicitly providing
this evidence of the importance of local assembly processes inthe biogeographical pattern of b-diversity, which is an issue thatis currently being actively debated.

ACKNOWLEDGEMENTS
We thank L. Gustafsson, K. Perhans, G. Thor, A. Frisch, T.
Nakamori, R. Maeshiro, ToeToeAung, T. Haraguchi, S. Matsuoka, A. Ikeda, T. Ogue, R. Hagiwara, H. Kondo, K. Koizumi, Y.
Miyake, K. Akiba, W. Azuma, T. Hishi, H. Kamauchi, S. Fujii, S.
Hobara, T. Osono and N. Mori for their contributions to thisstudy. We sincerely acknowledge Christy McCain, David Currie
and anonymous referees for insightful comments. This study
was supported by the Sumitomo Foundation and the Japan
Securities Scholarship Foundation. Logistical support for thefield study was provided by the Shiretoko Foundation.

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SUPPORTING INFORMATION
Additional supporting information may be found in the online
version of this article at the publisher’s web-site.
Appendix S1 Spatial factors affecting b-diversity.
Appendix S2 Stand structure and understorey light.
Appendix S3 Stand structure and b-diversity of woody plants.
Appendix S4 Additional analyses of b-diversity.
Appendix S5 Effects of abundance on diversity.
Appendix S6 Pairwise community dissimilarity and standdissimilarity.
BIOSKETCH
Akira Mori is an associate professor at Yokohama
National University. He is interested in interpretingthe spatial and temporal patterns of ecological andbiogeographical phenomena in forest ecosystems. In
particular, he is interested in understanding the roles ofnatural and human disturbances in determining thepatterns and processes in biodiversity.
Editor: Christy McCain

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