Journal of Biogeography
SUPPORTING INFORMATION
Functional redundancy of multiple forest taxa along an elevational gradient:
predicting the consequences of non -random species loss
Akira S. Mori, Takayuki Shiono, Takashi F. Haraguchi, Aino T. Ota, Dai Koide,
Takayuki Ohgue, R yo Kitagawa, R yo Maeshiro, ToeToe Aung, Taizo Nakamori,
Yusuke Hagiwara, Shunsuke Matsuoka, Anzu Ikeda, Takuo Hishi, Satoru Hobara,
Eri Mizumachi, Andreas Frisch , Göran Thor, Takashi Osono, Saori Fujii and Lena
Gustafsson
Appendix S1 Details of the functional effect traits.
For the selection of functional effect traits of vascular plants and mosses, we followed the
recommendations of Cornelissen et al. (2003) and Cornelissen et al. (2007), respectively. We
collected trait data for plants from a variety of available data sources. For woody plants, we relied
on growth form, maximum attainable height, leaf phenology, leaf width, specific leaf area and wood
density. For herbaceous plants, we used the same effect traits apart from wood density. For ferns,
we used growth form, maximum attainable height, leaf phenology, stem diameter, petiole length,
leaf width, ramentum length and pinna length as functional effect traits. For mosses, we focused on
habitat types, shade preference, maximum attainable height, branch length, leaf length and leaf cell
width. Some argue that habitat type is not a trait (e.g. Violle et al., 2007) while others regard
preferred habitat as an important behavioural/morphological characteristic (e.g. Schleuter et al.,
2010). Because of the possible significance of the presence of a particular bryophyte species in a
given habitat based on biogeochemical cycles (Cornelissen et al., 2007), we used this trait. For
oribatid mites we used body mass, length-to-width ratio of body size, head-to-body size ratio,
number of claws, sensillus types, seta types, surface structures, dietary types and the MPG
classification (Macropylina, Gymnonota or Poronota) (Aoki, 1983; Mori et al., 2015). Traits for
oribatids were determined by measurement in the laboratory in addition to a review of the literature;
for the measurements, we used a minimum of five individuals for each species and used mean
values to describe species-specific traits. Similarly, we measured the functional effect traits of
ground-dwelling spiders, including body length, number of claws, visual acuity, ability to walk and
feeding guild (Cardoso et al., 2011). We omitted several traits that were correlated with the above
traits from our datasets. Table S1 summarizes the descriptions of trait data.
There are potential issues that need to be specified because metrics of functional diversity
are sensitive to available trait data. For example, if the range variations are different between traits
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(e.g. the values of some traits represent the global range while those of others reflect the local
range), the results will not be the same as those calibrated using data that all span the global range.
Although we confirmed with preliminary analyses that the variation of some traits reflected the
global range well, we could not analyse fully whether this was true across all traits. Another caveat
that should be stressed is a possible lack of key traits for disentangling the main issues, which is
often the case for calculations of functional diversity. Further empirical investigations are thus
needed to understand the conditions under which functional redundancy emerges along a
biogeographical gradient.
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Table S1 Functional effect traits used in the analysis. Nominal data were based on several categories and
continuous data could take any value within a certain range.
Taxonomic group
Functional effect trait
Type
Growth form
Nominal
Woody plants
Height
Continuous
Leaf phenology
Nominal
Leaf width
Continuous
Wood density
Continuous
Specific leaf area
Continuous
Growth form
Nominal
Herbaceous plants
Height
Continuous
Leaf phenology
Nominal
Leaf width
Continuous
Wood density
Continuous
Specific leaf area
Continuous
Growth form
Nominal
Ferns
Height
Continuous
Leaf phenology
Nominal
Leaf width
Continuous
Branch length
Continuous
Petiole length
Continuous
Stem diameter
Continuous
Ramentum length
Continuous
Pinna length
Continuous
Preferred habitat (ground/log/soil/mound/trunk/rock)
Nominal
Mosses
Shade preference
Nominal
Branch length
Continuous
Stem height
Continuous
Leaf length
Continuous
Leaf cell width
Continuous
Body mass
Continuous
Oribatid mites
Body ratio (length to width)
Continuous
Head fraction (ratio head length to body length)
Continuous
Number of claws
Ordinal
Sensillus type
Nominal
Seta type
Nominal
Surface structure
Nominal
Dietary type
Nominal
Macropylina/Gymnonota/Poronota category
Nominal
Body length
Continuous
Ground-dwelling spiders
Number of claws
Ordinal
Visual acuity (ratio ocellus diameter to body length)
Continuous
Ability to walk (ratio femur diameter to body length)
Continuous
Feeding guild
Nominal
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Appendix S2 Analysis of variation partitioning methodology.
Because we are primarily interested in taxon-dependent turnover of species (see also Qian, 2009;
Chytrý et al., 2012; Qian & Ricklefs, 2012; Wang et al., 2012), we evaluated our data to see
whether the values of taxonomic turnover reflected the degree of dispersal limitation that each
taxonomic group experiences in the mountainous landscape of Shiretoko National Park,
Hokkaido, Japan. Because of this objective, we separated vascular plants into five different
groups based on dispersal vector: woody plants dispersed by autochory, woody plants dispersed
by allochory, herbaceous plants dispersed by autochory, herbaceous plants dispersed by
allochory, and ferns.
For each group of organisms (eight groups in total), we estimated the relative weight of the
spatial fraction as well as environmental and unknown fractions that determine -diversity
(Sørensen’s index) following the methods of Peres-Neto et al. (2006). In this analysis, we used
subplot-based data to create an adequate number of samples. Prior to the analysis, Hellinger
standardization was performed to reduce the effects of regionally rare species (Legendre &
Gallagher, 2001). We then conducted a canonical redundancy analysis and selected meaningful
environmental variables as determinants of community structure based on forward selection (999
permutations with an alpha criterion = 0.10) following Blanchet et al. (2008). Elevation, light,
soil properties [litter thickness, litter mass, water content (WC), pH and carbon to nitrogen ratio
(C:N)], basal area (BA) and coarse woody debris (CWD) were taken as candidate determinants.
Elevation, light and BA were selected for woody plants. Elevation and light were selected for
herbaceous plants and ferns. Elevation, pH, CWD and light were selected for mosses. Elevation
and litter thickness were selected for oribatids. Elevation, pH and litter mass were selected for
spiders. We relied on these sets of variables as critical local environmental factors underlying the
structure of communities for each group. We constructed spatial models using spatial variables
extracted based on Moran’s eigenvector maps (MEMs; Borcard et al., 2004) to assess the
importance of spatial structuring in the communities. The MEM analysis produced a set of
orthogonal spatial variables derived from the geographical coordinates of the sampling locations.
We used the MEM that best accounted for autocorrelation and then conducted forward selection
(1000 permutations with an alpha criterion = 0.10) to select meaningful spatial factors that
influenced community dissimilarities. Based on these environmental and spatial models, we
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performed variation partitioning by calculating adjusted R2-values for each fraction (Peres-Neto
et al., 2006).
The fractions obtained from each group were then tested using their slope values (the slope of the
linear regression of the -diversity index against pairwise absolute elevational differences) and
the Spearman’s rank correlation; we expected the fraction reflecting the spatial structure of
meta-communities to be negatively correlated with the rate of species turnover (i.e.
dispersal-limited groups structured more by space to have faster taxonomic turnover). Our study
compared the spatial community structure across eight organism groups. To date, only the study
of De Bie et al. (2012) is comparable with our multi-taxon approach designed to test a
relationship between spatial structure and dispersal vector. According to De Bie et al. (2012),
propagule size as a dispersal vector is the major determinant of the degree of dispersal limitation
for passive dispersers in aquatic communities. As our focal plant groups are also dispersed
passively, mean seed/spore mass was tested with each fraction of variation partitioning. As data
for the mass of seeds and spores were only available for vascular plants, we limited this analysis
to the vascular plant groups. For this test, we again used the Spearman’s rank correlation to make
our findings comparable with those of De Bie et al. (2012).
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Appendix S3 Changes in species richness of the eight taxonomic groups along the elevational
gradient.
Species richness tended to peak at mid-elevations (Fig. S1). Although the hump-shaped
(unimodal) distribution of species richness along the elevational gradient was not clear for some
groups, such as ferns and mosses, the trend was, to some extent, consistent across the focal
taxonomic groups. The lack of a discrepancy in species richness distribution among the groups
needs to be confirmed; otherwise, a contrasted richness pattern may potentially affect the level of
taxonomic turnover.
Figure S1 Results for changes in species richness (α-diversity) of the focal taxonomic groups
along the elevational gradient. The numbers of species recorded (γ-diversity) were 25, 52, 56, 44,
22, 86, 74 and 43 for autochorous (AU) woody plants, allochorous (AL) woody plants, AU
herbaceous plants, AL herbaceous plants, ferns, mosses, oribatid mites and ground-dwelling
spiders, respectively.
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