maintenance of constant functional diversity during secondary succession of a subtropical forest in...

Journal of Vegetation Science && (2013)

Maintenance of constant functional diversity duringsecondary succession of a subtropical forest in China

Martin B€ohnke, Wenzel Kr€ober, Erik Welk, ChristianWirth & Helge Bruelheide

Keywords

BEF-China; Chronosequence; Community

assembly; Functional evenness; Gutianshan

National Nature Reserve; Partitioning of

functional diversity; Random assembly;

Randomization techniques; Secondary forest

succession; Trait dissimilarity

Nomenclature

Flora of China (http://flora.huh.harvard.edu/

china)

Received 31May 2013

Accepted 12 July 2013

Co-ordinating Editor: Ingolf K€uhn

Bruelheide, H. (corresponding author,

[email protected]),

B€ohnke, M. ([email protected]),

Kr€ober, W. (wenzel.kroeber@botanik.

uni-halle.de) &Welk, E. (erik.welk@botanik.

uni-halle.de): Institute of Biology/Geobotany

and Botanical Garden, Martin Luther University

Halle Wittenberg, Am Kirchtor 1, D-06108,

Halle (Saale), Germany

Wirth, C. ([email protected]): Institute of

Biology I, University of Leipzig, Johannisallee

21-23, D-04103, Leipzig, Germany

Wirth, C. & Bruelheide, H. : German Centre

for Integrative Biodiversity Research (iDiv),

Deutscher Platz 5e, D-04103, Leipzig, Germany

Abstract

Questions: Does the importance of biotic interactions between tree species

increase during secondary forest succession? Do functional trait values become

increasingly divergent from early towards late successional stages and how is

functional diversity affected by trait identity, species identity and species rich-

ness effects?

Location: Gutianshan National Nature Reserve, Zhejiang Province, southeast

China.

Methods: Based on 26 leaf and wood traits for 120 woody species, we calcu-

lated functional diversity, using Rao’s formula for quadratic entropy, trait dis-

similarity, defined as half the mean trait-based distance of all species in the

community, and functional evenness, defined as the degree to which functional

diversity is maximized. We employed randomization techniques to disentangle

the effects of trait identity, species identity and species richness on these three

components of functional diversity.

Results: Against expectations, functional diversity did not show any succes-

sional trend because the communities compensated for a loss in trait dissimilar-

ity by distributing the trait values more evenly among the resident species, thus

increasing functional evenness. Randomization tests showed that functional

diversity was not affected by trait identity, by species identity or by species rich-

ness, which indicates that functional diversity was neither determined by partic-

ular single traits or by single species with outstanding trait values.

Conclusions: The constant functional diversity suggests constant functionality

in this subtropical forest, which might temporally maintain stable immigration

conditions during the course of succession, and thus provides an explanation

why these subtropical forests becomemore species-rich with time.

Introduction

Forest succession series have been found to be ideal model

systems in which to study the processes that shape com-

munity assembly (Lebrija-Trejos et al. 2010). Succession

studies are also relevant for research on key ecosystem pro-

cesses in forests, as primary production and litter decompo-

sition often show clear trends over successional time

(Gower et al. 1996). It has been demonstrated that these

processes are reflected in community mean values of func-

tional traits (Raevel et al. 2012), i.e. abundance-weighted

mean trait values of the species’ morphological, physiologi-

cal and phenological characteristics that affect growth,

reproduction and survival (Violle et al. 2007). In principle,

there are two processes that shape ecological communities.

On the one hand, the resident species have to cope with

the abiotic environmental setting, resulting in abiotic envi-

ronmental filtering of certain trait values (e.g. Ordo~nez

et al. 2009). On the other hand, the species in the commu-

nity have to be sufficiently different in their niches, and

thus also in the trait values that reflect the niches to avoid

competitive exclusion (MacArthur & Levins 1967; Pacala

& Tilman 1994). These two processes are not mutually

exclusive, and species abundances have been successfully

predicted by first accounting for environmental filtering

and then maximizing the information entropy of propor-

tional abundances (Shipley et al. 2006). Strong environ-

mental filtering has been shown for a 200-yr forest

chronosequence on the coast of South Island in New Zea-

land (Mason et al. 2011). Aggregate trait values of specific

Journal of Vegetation ScienceDoi: 10.1111/jvs.12114© 2013 International Association for Vegetation Science 1

leaf area and leaf nitrogen content increased with on-going

succession, indicating a shift towards increased leaf palat-

ability and decomposability. A contrasting pattern was

found for a subtropical secondary forest chronosequence

of stand ages between <20 yrs and >80 yrs in southeast

China (Kr€ober et al. 2012). Here, shifts in trait values fol-

lowed the leaf economics spectrum, with decreasing spe-

cific leaf area and leaf nutrient content over successional

time. The importance of environmental filtering was also

confirmed in Kraft et al. (2008) for the tropical forest

community of the Yasun�ı Forest Dynamics Plot in Ecua-

dor. For the six functional traits analysed, Kraft & Ackerly

(2010) described the high importance of filtering processes

at spatial scales of 25 m2 to 10 000 m2. However, while

environmental filtering seems to be ubiquitous in forest

succession, the importance of maximizing differences in

trait values to avoid competitive exclusion is much less

clear. For example, Mason et al. (2011) found increasing

functional richness with successional time; interpreted as

a shift from stress-dominated to competition-dominated

communities. An increasing role of biotic interactions in

community assembly during succession has also been con-

cluded from decreasing phylogenetic relatedness of species

in tropical forest successions in Mexico, Costa Rica and

Brazil (Letcher et al. 2012). The validity of this conclusion

strongly depends on the degree to which functional traits

that determine ecosystem processes are phylogenetically

conserved. Thus, trait-based studies are required along for-

est succession series to determine whether competitive

exclusion becomes increasingly important from early

towards late successional stages, which would be evident

in increasingly divergent functional trait values. Here, we

carried out such a trait-based study, using a subtropical

forest succession series from southeast China (Bruelheide

et al. 2011), where a large number of different leaf and

wood traits of woody species has been determined

(B€ohnke et al. 2011; Kr€ober et al. 2012). Morpho-ana-

tomical and chemical leaf traits in particular have been

proved to affect important functions in a forest ecosystem,

as they describe longevity, growth and nitrogen status

(e.g. Wright et al. 2004). Especially for tropical forests,

wood traits have been shown to reflect niche differentia-

tion among species (Poorter et al. 2006), and to be orthog-

onal to the spectrum of leaf traits (Baraloto et al. 2010).

Divergence in trait values can be mathematically

expressed through plant functional diversity (FD) mea-

sures (Walker et al. 1999; Petchey & Gaston 2002; Botta-

Duk�at 2005). It has been recognized that FD has different

dimensions, and attempts been made to describe these

dimensions through different measures (Walker et al.

1999; Mouillot et al. 2005). Vill�eger et al. (2008) pointed

out that at least three different indices are needed to

capture different dimensions of FD: (i) regularity of the

distribution of trait abundances, corresponding to func-

tional divergence (Vill�eger et al. 2008), functional disper-

sion (Lalibert�e & Legendre 2010) and Rao’s (1982)

quadratic entropy (FDQ); (ii) range of values of functional

traits in the community, termed trait dissimilarity (Hille-

brand & Matthiessen 2009) or functional richness (Mason

et al. 2005; Vill�eger et al. 2008); and (iii) regularity of the

distribution of functional trait values in the community,

called functional evenness (Mason et al. 2005; Vill�eger

et al. 2008). It is far from clear how these three FD compo-

nents are related to successional time. For example, Mason

et al. (2011) only encountered a significant increase in

functional richness (i.e. in trait dissimilarity) but not in the

other FDmeasures.

Functional diversity and the different FD dimensions

mentioned above have their cause in the distribution of

trait values among species, as well as in the abundance dis-

tribution and richness of species. (i) Species with outstand-

ing performancemight be characterized by particularly low

or large values for a specific trait, resulting in unique func-

tions (‘trait identity’ effects). Trait identity effects are con-

nected to the question of which traits are considered

relevant in a study on functional biodiversity (Lep�s et al.

2006; Bernhardt-R€omermann et al. 2008). (ii) Species

might abundantly contribute to functioning because some

species that have particularly divergent traits are particu-

larly abundant (‘species identity’ effects sensu strictu).

Finally, (iii) increasing performance might be brought

about by a mere species richness effect without involving

(i) and (ii). Functional diversity after Rao’s Q (FDQ) tends to

increasewith increasing species richness (Petchey&Gaston

2006) because FDQ (in contrast to TDQ) depends on species

abundances. Drawing species randomly from a community

while retaining their abundances results in more uneven

relative abundances among species in species-poor as com-

pared to species-rich communities. Thus, in real communi-

ties, species abundances tend to become more even with

increasing species richness, and FDQ is on average lower in

species-poor than in species-rich communities.

To our knowledge, no approach has yet been published

for trait identity effects (i). With respect to species identity

effects, the formulae provided in Schmera et al. (2009) to

calculate functional values separately by species offer

options to identify those species with a particularly high

impact on FD, and thus would offer an option to address

species identity effects (ii). However, when asking whether

species identity effects play a role at all, i.e. across the

whole community, there is no need to calculate single spe-

cies contributions. Walker et al. (2008) provided formulae

and algorithms to account for (iii) and showed that apply-

ing them to FAD results in obtaining FDQ. Similarly,

Ricotta et al. (2010, 2012) presented sample-based rarefac-

tion formulae for comparing functional richness for

Journal of Vegetation Science2 Doi: 10.1111/jvs.12114© 2013 International Association for Vegetation Science

Functional diversity during secondary forest succession M. B€ohnke et al.

different plot numbers, based on presence/absence or

abundances. However, these methods aimed at standard-

izing FD across plots, not across species in one plot. Here,

we explore the capacity of randomization techniques to (i)

randomly select a fixed number of k traits out of the total

number of available traits K in the study (holding constant

the link of trait values to species and the species number

in plots); (ii) randomly assign sets of trait values to species

(holding constant the trait number and species number);

and (iii) randomly select a fixed number of species (hold-

ing constant the trait number and the set of values among

species). First (i), a random selection of a fixed number of

traits out of the total number that were measured in a

study should remove the effect of single traits with a very

large influence on FD as these traits would only contribute

to FDQ in a minority of runs. Thus, we would expect that

trait identity effects become more important the more the

FDQ based on random trait selection differs from the

observed FDQ. Second (ii), potential species identity effects

should be removed from FD if species are assembled ran-

domly. In order not to change species number per site,

species should be reshuffled (i.e. rows in the species x site

matrix should be interchanged; Gotelli 2000), which can

be done in several different ways. (1) Reshuffling trait val-

ues among species within the whole regional pool hypoth-

esizes that there is neither environmental filtering nor

limiting similarity. All combinations of trait values would

be able to occur in any plot, irrespective of its successional

age. Observed FD would be lower or higher than this ran-

dom pattern if environmental filtering or competitive

exclusion due to limiting similarity among co-existing spe-

cies prevailed. (2) Randomizing trait values within the

same successional stage hypothesizes that only succes-

sional age operates as a filter, and there is no considerable

environmental heterogeneity within successional stages.

(3) When trait values of species are only reshuffled within

one particular plot, which is identical to only reshuffling

the species abundances within a plot, this creates a depar-

ture from randomness only by limiting similarity. Third

(3), basing the calculation of FD on a fixed species number

and comparing the resulting FDQ values with the observed

values should reveal any effect of species richness on FD.

Along the subtropical forest succession series from

southeast China (Bruelheide et al. 2011), we tested for

trait identity, species identity and species richness effects.

In particular, we tested the hypothesis:

1. Functional diversity expressed as FDQ increases in the

course of this succession series, as do trait dissimilarity and

functional evenness. We expect that testing these FD

components separately for temporal relationships will pro-

vide insights into the underlying drivers of community

assembly in this subtropical forest.

Under the assumption that hypothesis 1 is confirmed,

we further tested the following hypotheses:

2. The dependence of functional diversity FDQ on succes-

sional age becomes weaker if a subset of traits is randomly

selected out of the total number of analysed traits, thus

revealing the existence of trait identity effects.

3. Similarly, successional age relationships of functional

diversity FDQ become weaker if species in the community

are randomly assigned sets of trait values from the species

pool, thus revealing the existence of species identity

effects.

4. Finally, FDQ–age relationships become weaker if a cer-

tain number of species in the community is randomly cho-

sen from all species in the community, thus revealing the

existence of species richness effects.

Note that hypotheses 2, 3 and 4 are not mutually exclu-

sive. For example, according to hypothesis 2 the relation-

ship of FD with successional time depends only on one

particular trait, e.g. maximum tree height. Such an effect

would disappear after randomly selecting a fixed number

of traits out of all available traits. At the same time, the

FD–age relationship (albeit mainly brought about by one

trait, e.g. tree height) might disappear when trait values

are reshuffled among species within the whole regional

pool.

Methods

Study site

The study was carried out in the Gutianshan National

Nature Reserve (GNNR) in the western part of

Zhejiang Province, China (29°8′18″–29°17′29″N, 118°2′14″–118°11′12″E), a large densely wooded mountain range

without human settlements. A set of 27 plots was estab-

lished in the study area during summer 2008. The

30 m 9 30 m plots were placed randomly, orientated

north and stratified by successional age. The plots were

scattered over an area of about 9 km 9 8 km across the

whole nature reserve. The presence of stumps in younger

plots and relics of agricultural terraces in almost all plots,

as well as the occurrence of charcoal in almost all soil

profiles gave clear evidence that succession began after

logging events. However, because of very steep slopes,

some exceeding 30°, the Gutianshan area was only mar-

ginally used for agricultural activities, and thus excep-

tionally intact forest cover has been preserved. The plots

were assigned to five successional stages, determined by

estimating the age of the oldest tree individuals and on

local knowledge of the most recent logging event, and

randomly selected within the following age classes: 1:

<20 yrs, 2: <40 yrs, 3: <60 yrs, 4: <80 yrs, 5: >80 yrs.

The classification was later confirmed by measurements


M. B€ohnke et al. Functional diversity during secondary forest succession

of diameter at breast height (DBH) of all trees with

>10 cm DBH in the plot and by coring 64 different tree

species across all plots. Tree age of the oldest trees in the

plots corresponded to the age classes given above when

disregarding the few remnant trees that in some plots

were not cut in the logging event. Thus, we used the age

of the fifth largest tree in the plot as a measure of succes-

sional age. For details on the vegetation, environmental

setting and age class determination, see Bruelheide et al.

(2011). Species abundances were obtained from a com-

plete inventory of all tree and shrub individuals (>1 m in

height) and used to calculate Shannon diversity and

evenness (Magurran 2004).

Sampling and trait analyses

Completely developed, intact leaves were sampled from

sun-exposed branches using an expandable pruner. Seven

leaves were collected per individual, and the majority of

species were sampled in five to seven plots. Every species

was sampled only from one individual per plot. Rare spe-

cies that occurred with less than five individuals in all plots

had a correspondingly lower number of replicates. Leaf

traits were measured following the protocols provided in

Cornelissen et al. (2003). For details of trait analysis see

Table 1.

Out of the total of 148 woody species encountered in

the 27 plots (Bruelheide et al. 2011), a full set of traits

could be obtained for 120 species. These 120 species repre-

sented 15 336 (95%) of 16 120 individuals encountered

in the total area of 2.32 ha of all plots. Among the 784 indi-

viduals not represented by complete trait sets, 74% were

conifers (mainly Pinus massoniana Lamb. and Cunninghamia

lanceolata (Lamb.) Hook.), which were not included in the

trait analysis. A further 15% could not be sampled because

the leaves only occurred > 20 m above the ground and

therefore were not accessible (e.g. Ilex micrococca Maxim.),

and 11% (86 individuals) could not be identified. In the

following, all calculations refer to the 120 species for which

a full set of traits was obtained.

Data analysis

A trait matrix was constructed using trait averages across

all plots per species. All mean trait values were standard-

ized to a mean of 0 and SD of 1.

The requirements for FD measures have been discussed

in Mason et al. (2003), Botta-Duk�at (2005), Ricotta

(2005), Pavoine & Bonsall (2010) and Lalibert�e & Legendre

(2010), such as reflecting the contribution of each species

in proportion to its abundance and being unaffected by the

number of species. There are several measures fulfilling

the criteria, among them Rao’s (1982) index, termed

quadratic entropy (Q):

FDQ ¼XN�1

i¼1

XNj¼iþ1

dijpipj ¼ 1

2

XNi¼1

XNj¼1

dijpipj ð1Þ

where N is the number of species in a plot, dij is the differ-

ence in trait values between the ith and jth species; pi and

pj are the proportions of the ith and jth species, calculated

as number of individuals per species relative to the total

number of individuals in the community. Please note that

Rao’s (1982) initial formulation integrates over both

halves of the distance matrix and thus gives twice the

value of Eq. 1. However, as suggested by Champely &

Chessel (2002) and used in current software packages,

such as the R package FD (R Foundation for Statistical

Computing, Vienna, AT; Lalibert�e & Legendre 2010) and

ade4 (Dray & Dufour 2007), in Eq. 1 the distances are

summed only once.

Trait dissimilarity and functional evenness, as the two

other aspects of FD, can be described by partitioning FDQ

into different components, in analogy to partitioning FDQ

into spatial components (Pavoine&Dol�edec 2005; DeBello

et al. 2009). While FDQ in Eq. 1 reflects functional diver-

gence, functional evenness FEQ (sensu Vill�eger et al. 2008)

can be expressed as the ratio of the observed value of FDQ to

a value of FD that is obtained by assuming equal distances

in trait values among species. This is achieved using d as

mean distance of all trait values in the community:

d ¼

PN�1

i¼1

PNj¼iþ1

dij

N�ðN�1Þ2

¼

PNi¼1

PNj¼1

dij

N � N � N

N � 1

ð2Þ

To define trait dissimilarity TDQ in analogy to FDQ it is

convenient to include the zeros in the diagonal to divide

by two, using only one half of the quadratic distance

matrix:

TDQ ¼ 1

2

PNi¼1

PNj¼1

dij

N � Nð3Þ

It should be noted that TDQ is linearly related to func-

tional attribute diversity (FAD) according to Walker et al.

(1999), to functional diversity (FD) according to Petchey &

Gaston (2002) and to modified functional attribute diver-

sity (MFAD) according to Schmera et al. (2009).

TDQ relates do the mean trait difference d as follows:

TDQ ¼ d � N�1N

2ð4Þ

Please note that the mean trait distance across the trian-

gular matrix excludes the number of values in the diagonal



and differs from the mean across the quadratic matrix by

N/(N�1).

TDQ is obtained from Rao’s FDQ in Eq. 1 by keeping the

original distances between trait values dij and assuming

equal abundances of all species p (Eq. 5):

FDQ ¼XN�1

i¼1

XNj¼iþ1

dijpp ¼ 1

2

XNi¼1

XNj¼1

dijpp

¼ 1

2

XNi¼1

XNj¼1

dij � 1N

� 1N

¼ TDQ

ð5Þ

p is defined as the mean proportional abundance of all

species in the community:

p ¼PNi¼1

pi

N¼ 1

Nð6Þ

FDQ is also related to TDQ when using d as constant

mean distance between all trait values and keeping the

original abundance distributions pi and pj of species

(Eq. 7):

Table 1. List of 26 traits measured on a total of 120 tree and shrub species in the plots in the Gutianshan National Nature Reserve.

Trait Abbreviation Analytical technique Data type Unit/Categories

Leaf dry weight1 DW Balance Numerical mg

Leaf dry matter content2 LDMC Balance Numerical mg�g�1

Leaf area3 LA Scanner Numerical mm²

Specific leaf area3 SLA Scanner, Balance Numerical mm²�mg�1

Leaf carbon:nitrogen ratio4 CN CN Analyser Numerical g�g�1

Leaf carbon content4 C CN Analyser Numerical g�g�1

Leaf nitrogen content4 N CN Analyser Numerical g�g�1

Leaf calcium content5 Ca AAS Numerical μg�g�1

Leaf magnesium content5 Mg AAS Numerical μg�g�1

Leaf potassium content5 K AAS Numerical μg�g�1

Leaf aluminium content6 Al ICP Numerical μg�g�1

Leaf copper content6 Cu ICP Numerical μg�g�1

Leaf iron content6 Fe ICP Numerical μg�g�1

Leaf manganese content6 Mn ICP Numerical μg�g�1

Leaf phosphorus content6 P ICP Numerical μg�g�1

Leaf strontium content6 Sr ICP Numerical μg�g�1

Leaf sulphur content6 S ICP Numerical μg�g�1

Fraction of stomata-coveredarea 7 StoA Microscopy Numerical %

Stomatal density7 StoD Microscopy Numerical mm�2

Stomata length8 StoL Microscopy Numerical μm

Stomata width8 StoW Microscopy Numerical μm

Leaf pinnation LP Observation Binary entire/pinnate

Leaf margin LM Observation Binary entire/dentate

Leaf habit LH Literature Binary evergreen/deciduous

Wood density9 WD Pycnometry Numerical g�cm�3

Height10 Hei Literature Numerical m

ICP, inductively coupled plasma mass spectrometry; AAS, atom absorption spectrometry; CN Analyser, carbon:nitrogen analyser. All analyses were carried

out on the full set of replicates (n = 5–7 for most species), except for ICP analysis, where only one replicate per species was analysed.1Leaf dry mass was weighed after drying leaves for 48 h at 80 °C.2The collected leaf samples were stored in damp PVC bags before determining fresh weight, leaf dry matter content and leaf area at the end of the day.3Leaf area was obtained by scanning fresh leaves and analysing the data digitally with Winfolia Pro S (Regent Instruments Inc., Quebec, Canada).4Measured with CN Analyser elementar Vario EL.5Measured with atom absorption spectrometer (AAS) Vario 6 (Analytik, Jena, DE).6Determinedwith an inductively coupled plasmaoptical emission spectrometer (ICP-EOS) Ciros CCD (Spectro Analytical Instruments GmbH, Kleve, Germany).7Stomatal density was assessed on leaves stored in 70% ethanol using microscope observations on both leaf surfaces, thus two enumerations per replicate.

Minimum leaf area for stomata counts was 50 000 lm².8Length and width of three stomata per replicate were measured with a light-optical microscope (Zeiss Axioskop 2 plus, G€ottingen, Germany), and image

analysis was carried out with the Axio Vision (v. 3.0) software (Carl Zeiss Microscopy GmbH, G€ottingen, Germany).9Wood cores were taken at breast height in a north–south direction with an increment borer (Suunto 400, Vantaa, FI). In addition, for shrub species and tree

individuals with insufficient girth, branches were sampled. Wood density was measured with pycnometry. Using the tight regression of core density on

branch density (core density = 0.712 * branch density + 0.227), all values were transformed into core wood densities.10Taken from the online version of the Flora of China (http://flora.huh.harvard.edu/china).



FDQ ¼XN�1

i¼1

XNj¼iþ1

dpipj ¼ d

PNi¼1

PNj¼1

pipj �PNi¼1

p2i

2

¼ d

21�

XNi¼1

p2i

!¼ TDQ

N

N � 11�

XNi¼1

p2i

!ð7Þ

It has already been pointed out by Shimatani (2001)

that Rao’s FDQ results in the Simpson’s index SI (Simpson

1949) – the term in brackets on the right-hand side of

Eq. 7 – when all the species are equally different. FDQ cal-

culated with d approaches TDQ with increasing evenness

and is equal to TDQ when evenness is maximal, which is

the case when all species have the same proportion pi = 1/

N (Eq. 8).

SI ¼ 1�XNi¼1

p2i ¼ 1�XNi¼1

1

N2¼ 1� N

N2¼ N � 1

Nð8Þ

Thus, FDQ approaches TDQ either when distances in trait

values among species become more similar or when abun-

dances of species becomemore even.

Functional evenness FEQ is then obtained as the ratio of

FDQ and TDQ:

FEQ ¼ FDQ

TDQ

ð9Þ

In analogy to the definition of Pielou evenness as the

degree to which Shannon diversity is maximized (Magur-

ran 2004), functional evenness can also mathematically

be equivalently expressed as the ratio of the observed

value of FDQ to a value of FD that is obtained by assum-

ing equal abundances of all species. There is also an anal-

ogy to the suggestion of Champely & Chessel (2002) to

scale FDQ by its maximal value over all species abun-

dance distributions, which is implemented with the

‘scale’ option in the ‘divc’ function in R (Chessel et al.

2004). However, the scale function runs the risk that the

outliers in trait distances determine the maximum value

of FDQ, thus scaling by TDQ, which corresponds to the

‘uniform distribution’ mentioned in Champely & Chessel

(2002), presents a more stable and ecologically more

meaningful solution. Equation 9 also reconciles the criti-

cized ambiguity of functional evenness. On the one hand,

FEQ denotes the regularity of the species’ trait values

within species abundance space; on the other hand, this

equation points out the evenness of abundances within

trait space (Pavoine & Bonsall 2010). Trait over-disper-

sion occurs when species with more than mean distances

in trait space show larger than average abundance values

(for an example see Appendix S1, and for R code for this

example see Appendix S2).

Functional diversity FDQ, trait dissimilarity TDQ and

functional evenness FEQ were calculated according to

Eqs. 1, 2 and 3, using all 26 traits. Calculation of FDQ was

based on Euclidean distances dij in the trait values between

the species i and j (i.e. on mean differences in trait values),

in contrast to using squared distances dij2 as suggested in

Champely & Chessel (2002) and employed in the ade4

(Chessel et al. 2004) and FD package (Lalibert�e & Legendre

2010) in R. Shannon diversity, evenness, FDQ, TDQ and

FEQ were related to successional age via linear regression.

All statistics were calculated with R 2.8.1.

Hypotheses 2, 3 and 4 were tested using randomization

techniques. First, including a large range of different traits

creates the risk of either missing important functional

aspects because potentially crucial traits might be con-

cealed by predominance of trivial traits, or of overrating

certain traits because of a high degree of co-variation

among traits (Petchey & Gaston 2002; Vill�eger et al.

2008). Thus, we followed the recommendation of Lep�s

et al. (2006) to reduce the trait space to a set of uncorre-

lated traits by submitting the total of 26 traits from

Table 1 to a principal components analysis (PCA; see

Appendix S3), using the ‘prcomp’ command of the stats

package in R. Appendix S4 shows the eigenvalues of the

first ten PCA axes. The traits with the highest absolute

loadings on each of the first ten axes (as shown in Appen-

dix S5) were then used to calculate FDQ, TDQ and FEQ:

CN, Al, DW, StoA, Hei, LM, Cu, Mn (see Table 1). We

confined this particular analysis to eight traits because the

best correlating traits of PCA axes 2 and 6 were identical

to those of PCA axes 4 and 10, respectively (Appendix

S5). The first and second PCA axes reflect the leaf eco-

nomics spectrum (SLA vs CN) and the degree of sclero-

phylly (Al and S vs StoD), respectively. Other axes reflect

transpiration control (StoA) and competitive ability (Hei).

These selected eight traits were much less inter-correlated

that the full set of all 26 traits (Appendix S6). The mean

correlation across all 26 traits (averaging absolute values

and disregarding the diagonal in Appendix S6) was 0.194,

as compared to 0.108 when using only the eight selected

traits listed above. To achieve full independence of differ-

ent trait dimensions, FDQ, TDQ and FEQ were also calcu-

lated directly from the PCA scores of the first eight axes,

which explained 74% of the total variance in all trait val-

ues. For assessing a potential trait identity effect, we then

compared observed FDQ, TDQ and FEQ as well as FDQ, TDQ

and FEQ based on the eight maximally uncorrelated traits

with FDQ, TDQ and FEQ of eight randomly selected traits

in 1000 repetitions. Observed values were considered sig-

nificantly different from randomly chosen traits if the ran-

dom runs had larger or smaller values than those

observed in more than 975 runs, indicating the effect on

FDQ for that particular trait selection of environmental fil-

tering or competitive exclusion, respectively (one-tailed at

a = 0.025). Linear regressions were applied to relate the



values of FDQ, TDQ and FEQ of each random run as well as

their mean values to successional age. The number of ran-

dom runs, thresholds and linear regressions was calcu-

lated in the same way as all other randomizations

described below.

Second, for assessing a potential species identity effect,

the sets of trait values among species in the community

were randomly reshuffled. This was done by random

assignment of a trait value set of all 26 traits from Table 1

to a species. Selection of trait sets was performed without

replacement, i.e. each trait value set was only assigned

once. Assignment of trait value sets was done from three

different pools of trait sets, (1) from the total pool of 120

trait value sets (i.e. species) in all 27 plots, (2) from the

pool of all trait value sets in the successional stage to

which the plot belonged, and (3) within each plot. The

last corresponds to a random reshuffling of trait value sets

among species (i.e. interchanging trait value sets among

species), while in (1) and (2) the total suite of trait value

sets in each plot was different in each run. All three

reshuffling randomizations kept constant the total abun-

dances per plot and per species, as recommended in De

Bello et al. (2009). FDQ, TDQ and FEQ were calculated

using the observed species abundances in the plots, thus

keeping the observed species number in each plot. The

values of FDQ of each random run were regressed against

successional age. The observed coefficients of correlation

(r) were considered significantly different from the ran-

domly obtained correlations if values were smaller or lar-

ger in more than 975 random runs (one-tailed at

a = 0.025). Similarly, the observed FDQ, TDQ and FEQ

(based on the whole set of 26 traits) were plotted against

the randomly obtained values. Observed FDQ, TDQ and

FEQ values were considered significantly different from

those randomly obtained if the observed values were

smaller or larger in more than 975 random runs (one-

tailed at a = 0.025).

Third, to assess the effect of species richness on FD, a

fixed number of species was randomly chosen from all

species in a plot, using the whole set of all 26 traits from

Table 1. In a deliberate selection process, the 20 species

with the highest abundances in each plot were chosen and

FDQ, TDQ and FEQ were calculated using the observed spe-

cies abundances in the plots.We chose 20 because the low-

est species richness in a plot was 23 (in plot 5, 23 was the

lowest number of species for which a full trait set was

available, original species richness was 25). Calculation of

FDQ, TDQ and FEQ used the observed abundances of the

species in the plots, but recalculated relative abundances

for each random run from the selected species. As above,

the values of FDQ of each random run were regressed

against successional age and correlations coefficients (r) as

well as the values of FDQ, TDQ and FEQ based on 20

randomly chosen species from all the species of this plot

were then compared to observed values.

All randomizations were calculated with Visual Basic 8

(Microsoft).

Results

Functional diversity as a function of successional age

Both, Shannon diversity and evenness increased with

stand age of the plots (Fig. 1a,b). Against expectations, FD

calculated with Rao’s FDQ was not related to successional

age (Fig. 2a), but was maintained at a mean value of

0.347 � 0.031 (�SD). Trait dissimilarity TDQ showed a

marginally significant negative relationship (Fig. 2b), with

a mean value of 0.441 � 0.029, while functional evenness

FEQ was significantly positively related to stand age

(Fig. 2c), with a mean value of 0.788 � 0.074.

Any observed significances in the age relationships dis-

appeared when FDQ, TDQ and FEQ were calculated with a

trait matrix comprising only the eight most uncorrelated

traits (r = 0.153, P = 0.446, r = �0.309, P = 0.116 and

r = 0.354, P = 0.070, respectively). Consistently, FDQ, TDQ

and FEQ did not show any significance with respect to suc-

cessional age when based on the scores of the first eight

PCA axes (r = 0.072, P = 0.722, r = �0.338, P = 0.085

and r = 0.280, P = 0.157, respectively).

Effect of trait identity on functional diversity

Correlation coefficients between FDQ based on random

selection of eight traits and the age of the fifth oldest tree

did not differ from the observed correlation coefficient

(mean r = 0.121 and observed r = 0.176, respectively,

P = 0.429). Similarly, the regression of mean FDQ of all

1000 random runs on successional age produced the same

results as in Fig. 2a (Appendix S7: Fig. a). The observed

FDQ, TDQ and FEQ mean values across all plots were not

different from those obtained from randomization

(P = 0.481, 0.522 and 0.485, respectively; Appendix S8).

Similarly, FDQ based on the eight most uncorrelated traits

was not significantly different from mean FDQ obtained

from random trait selection (Appendix S8). Accordingly,

the regression of FDQ on successional age obtained by ran-

domization also did not differ from observed values. Only

540 out of the total of 1000 random runs based on ran-

domly selected eight traits showed a higher F-value than

the observed F-value. In accordance to the observed FEQ

based on the whole set of 26 traits, mean FEQ obtained

from the 1000 runs of randomly selected eight traits

increased with successional age (Appendix S7: Fig. c).

Similarly, mean TDQ in the random trait selection showed

a marginally significant negative relationship to stand age

(Appendix S7: Fig. b).



Effect of species identity on functional diversity

Disconnecting the traits from the species by randomly inter-

changing the sets of trait values of a species with the trait

values of any other species from the total trait pool of 120

species resulted in a FDQ that was always higher than the

observed FDQ (Fig. 3a), which was significant for 20 out of

the total of 27 plots. FDQ values obtained from randomiza-

tion across all plots tended to be larger than observed FDQ

(P = 0.065; Appendix S8). The overall mean of FDQ across

all runs and all plots was 0.430 � 0.031. The number of

runs that transgressed the observed FDQ increased with

stand age (r = 0.545, P = 0.003), as did the difference

between FDQ observed and FDQ obtained by randomization

(r = 0.350, P = 0.041). Similarly, TDQ values obtained by

randomization were higher than those observed in all but

four plots, with a mean of 0.474 � 0.004 (Fig. 3b). Twelve

of these TDQ values were significantly higher than those

observed. In accordance with FDQ, the difference between

TDQ observed and TDQ obtained by randomization

increased with stand age (r = 0.347, P = 0.043). In conse-

quence, FEQ values obtained by randomization were also

higher than those observed, with a mean of 0.907 � 0.061

(Fig. 3c). However, the difference between FEQ observed

and FEQ obtained by randomization was unrelated to suc-

cessional age (r = 0.092, P = 0.647).

In contrast to observed FDQ (Fig. 2a), mean FDQ values

obtained by randomizing the sets of trait values across the

whole species pool showed a significant increase with

stand age (Fig. 4a). The correlation coefficients between

FDQ based on randomized values across the whole species

pool and successional age (mean r = 0.341) were larger

than the observed correlations, which however was not

significant (P = 0.242). The pattern was essentially the

same for confining the randomization of trait sets within

successional stages (Fig. 4b), with overall FDQ means of

0.419 � 0.028, and 19 plots with a significantly higher

FDQ than the observed values. FDQ values obtained from

randomization of trait sets within successional stages also

tended to be larger than observed FDQ (P = 0.084; Appen-

dix S8). In contrast, the relationship to the age of the fifth

largest tree disappeared when based on randomization

within each plot (Fig. 4c), however with similarly high

overall FDQ means of 0.400 � 0.031. Only 11 plots

showed a significantly higher FDQ than the observed

value. FDQ values obtained from randomization of trait sets

within plots were also not different from those observed

(P = 0.149; Appendix S8). Consistently, correlation coeffi-

cients between FDQ based on randomized values within

each plot and successional age did not differ from the

observed correlation coefficient (mean r = 0.127 and

observed r = 0.176, P = 0.419).

In contrast to the observed TDQ (Fig. 2b), mean TDQ

obtained by randomizing trait sets across the whole trait

set pool was not correlated with successional age

(r = 0.163, P = 0.416). As a consequence of increasing

FDQ and constant TDQ, FEQ showed the same pattern to

that observed (Fig. 2c), with significantly increasing values

with stand age (r = 0.585, P = 0.001).

Effect of species richness on functional diversity

Functional diversity FDQ depended on the number of

species that were randomly selected from every plot to

0.55

0.60

0.65

0.70

0.75

0.80

0.85

Tree age of the 5th largest individual [years]

Sha

nnon

eve

nnes

s

(b)

20 40 60 80 10020 40 60 80 100

2.0

2.5

3.0


Sha

nnon

div

ersi

ty(a)

Succ. stage 1Succ. stage 2Succ. stage 3Succ. stage 4Succ. stage 5

Fig. 1. (a) Shannon diversity (r = 0.533, P = 0.004) and (b) Shannon evenness (r = 0.364, P < 0.001) of the 27 plots in the Gutianshan National Nature

Reserve as a function of plot age, assessed as the age of the fifth largest individual in the plot. The different colours show the different successional stages.



calculate FDQ. FDQ increased from 0.139 for two species to

0.328 for 22 species, calculated as mean across 1000 runs

and all 27 plots (Appendix S9). Thus, FDQ gradually

approached the observed mean value of 0.347 (compare

Fig. 2a). Thus, fixing the species number to 20 gave the

same results as in the observed FD variables. The observed

FDQ, TDQ and FEQ mean values across all plots were not

different from those obtained from a random selection of

20 species (P = 0.349, 0.738 and 0.171, respectively;

Appendix S8). Similarly, correlation coefficients between

FDQ based on 20 randomly selected species and succes-

sional age did not differ from the observed correlation coef-

ficient (mean r = 0.155, P = 0.242). Calculating FDQ, TDQ

and FEQ based on the 20 most abundant species in each

plot showed similar relationships to stand age as the

observed values (r = 0.052, P = 0.797; r = �0.544,

P = 0.003 and r = 0.557, P = 0.003). Completely in accor-

dance, selecting 20 species randomly from the species in

each plot resulted in no significant relationship between

FDQ and the age of the fifth largest tree (Appendix 10:

Fig. a), in a significantly negative relationship for TDQ

(Appendix S10: Fig. b) and in a significantly positive rela-

tionship for FEQ (Appendix S10: Fig. c).

Discussion

Patterns of FD during the course of the succession

Although we could not fully confirm our first hypothesis,

as we failed to detect any trend in FD as described by Rao’s

(1982) quadratic entropy FDQ, we encountered a clear

trend in TDQ and FEQ, which allowed us to test hypotheses

2, 3 and 4. We were able to unravel the reasons for the

absence of a trend in FDQ. FDQ was maintained at a con-

stant value, although trait dissimilarity TDQ decreased

because the trait values were distributed more evenly in

the community (i.e. FEQ increased). This clearly demon-

strates that partitioning of FD makes sense, as we detected

patterns in FE and TD, which were not seen in FDQ

because TDQ and FEQ cancelled out each other. Compared

to similar procedures published recently (e.g. Mason et al.

2005; Vill�eger et al. 2008), our approach relies on parsi-

mony, in using only a single, but powerful index. The trait

values in the community convergedwith successional time

and the species became more similar to each other. These

findings conform to results of decreasing trait dissimilarity

20 40 60 80 100

0.30

0.32

0.34

0.36

0.38

0.40

0.42


FDQ

(a) Succ. stage 1Succ. stage 2Succ. stage 3Succ. stage 4Succ. stage 5


TDQ

(b)

20 40 60 80 100

0.35

0.40

0.45

0.50

20 40 60 80 100

0.65

0.70

0.75

0.80

0.85

0.90

0.95


FEQ

(c)

Fig. 2. Observed functional diversity: FD variables calculated from the full

set of the measured 26 traits and observed species abundances in the 27

plots in the Gutianshan National Nature Reserve as a function of plot age,

assessed as the age of the fifth largest individual in the plot. The different

colours show the different successional stages. (a) Functional diversity FDQ

(r = 0.176, P = 0.381), (b) trait dissimilarity TDQ (r = �0.370, P = 0.057)

and (c) functional evenness FEQ (r = 0.417, P = 0.030).



from old-field successions (Fukami et al. 2005; Fu et al.

2009), but contradict results from forest successions

(Mason et al. 2011) and biodiversity–ecosystem function-

ing (BEF) experiments (Tilman et al. 2001; Cardinale et al.

2007), where complementarity in trait space increased

with time. The discrepancy to the increasing trait dissimi-

larity in the succession series from shrubland to forest in

New Zealand might also be explained by TDQ and FEQ can-

celling out each other, however in different directions as in

our study, because Mason et al. (2011) also found no sig-

nificant trend in functional divergence (corresponding to

FDQ). The incongruence to BEF experiments might be

explained by the invariant rank-dominance structure of

species which the experimenter does not allow to change

much over time (Cardinale et al. 2007). In contrast, in nat-

ural communities there is immigration and extinction,

resulting in a change in species composition, as was also

observed in our succession series (Bruelheide et al. 2011).

This compositional change explains why the observed age–

TDQ relationship disappeared when species were reshuffled

across the whole species pool.

Trait identity effects

We have to reject the second hypothesis of certain traits

being responsible for the observed patterns in FD as we did

not encounter trait identity effects. Choosing eight traits

randomly out of the 26 measured traits gave principally

the same results for FDQ, TDQ and FEQ as for the full trait

set. Two possibilities could explain this result. One expla-

nationwould be that the majority of traits were highly cor-

related, thus making the choice of traits unimportant.

However, the PCA showed that only a limited number of

traits were correlated with each other. Thus, more proba-

bly, the differences in trait values were generally similar

among different traits, thus rendering it unimportant

which traits were chosen. Obviously, this similarity in trait

value differences was lost when calculating the FD mea-

sures with the eight most uncorrelated traits because then

trait convergence was not detected, and all relationships

between FD and successional age disappeared. Focussing

on uncorrelated traits might have produced an artefact by

selecting traits without relevance, sacrificing relationships

FD Q (random within pool)

FDQ (o

bser

ved)

(a)


TD Q (random within pool)

TDQ (o

bser

ved)

(b)

0.30 0.35 0.40 0.45

0.30

0.35

0.40

0.45

0.36 0.38 0.40 0.42 0.44 0.46 0.48

0.36

0.38

0.40

0.42

0.44

0.46

0.48

0.65 0.70 0.75 0.80 0.85 0.90 0.95

0.65

0.70

0.75

0.80

0.85

0.90

0.95

FE Q (random within pool)

FEQ (o

bser

ved)

(c)

Fig. 3. Observed FD variables vs FD variables obtained through

randomization. Random values represent the mean of 1000 runs with

randomly selected trait value sets obtained by randomizing trait value sets

across the trait pool of 120 species, keeping the observed species

abundances and total species richness in the 27 plots in the Gutianshan

National Nature Reserve. The different colours show the different

successional stages. Values below the 1:1 line are lower than expected

from the randomization. (a) Functional diversity FDQ, (b) trait dissimilarity

TDQ and (c) functional evenness FEQ.



for the sake of traits being uncorrelated with others, thus

precluding the detection of trait convergence, which is

brought about by increased similarity of a multitude of

traits. As Vill�eger et al. (2008) pointed out, the competi-

tive, stress-tolerant and ruderal (CSR) strategies according

to Grime (1979) are characterized by highly correlated

traits. For these reasons, we consider recommendations of

Lep�s et al. (2006), Mouillot et al. (2005) and Vill�eger et al.

(2008) to reduce the trait set to a limited number of uncor-

related traits, to be problematic. The number of traits

included in a study (i.e. the dimensionality of trait space)

does affect the relationship between FD and species rich-

ness. The restriction to fewer dimensions increases the

importance of community composition and functional

redundancy (Petchey & Gaston 2002), while using more

dimensions increases the importance of species richness. In

consequence, we recommend performing an a priori selec-

tion of traits based on ordination techniques. Another

application of selecting traits randomly is to make different

studies comparable that used a different number of traits.

Species identity effects

We failed to confirm the third hypothesis, as we did not

find species identity effects for the patterns observed.

Assigning random sets of trait values to the species within

the same plot, holding constant trait number and species

number, resulted in the same relationships between FDQ

or FEQ to successional age as for observed values (i.e. the

absence of a correlation for FDQ and a negative correlation

for FEQ). From this, we have to conclude that the trait

value sets between species are equivalent in contributing

to FD. More precisely, all trait value sets were similarly dif-

ferent, thus making it irrelevant which were assigned to

the species in the community. However, it should be

pointed out that functional equivalence was not fully

achieved, as this would not have resulted in the observed

pattern of trait convergence (i.e. decreasing trait dissimilar-

ity) with successional time. Another conclusion from

within-community randomization is that particularly

diverging trait values in a plot did not belong to species

with particularly high cover values.


FDQ (r

ando

m w

ithin

poo

l)

(a)



FDQ (r

ando

m w

ithin

suc

cess

iona

l sta

ge)

(b)

20 40 60 80 100

0.34

0.36

0.38

0.40

0.42

0.44

0.46

20 40 60 80 1000.34

0.36

0.38

0.40

0.42

0.44

20 40 60 80 100

0.34

0.36

0.38

0.40

0.42

0.44


FDQ (r

ando

m w

ithin

plo

t)

(c)

Fig. 4. Random species selection: FD variables calculated as the mean of

1000 runs with randomly selected trait value sets, keeping the observed

species abundances and total species richness in the 27 plots in the

Gutianshan National Nature Reserve, as a function of plot age, assessed as

the age of the fifth largest individual in the plot. The different colours show

the different successional stages. (a) Functional diversity FDQ obtained by

randomizing trait value sets across the trait pool of 120 species (r = 0.570,

P = 0.002), (b) FDQ obtained by randomizing trait value sets within the

successional stage to which the plot belonged (r = 0.504, P = 0.007), and

(c) FDQ obtained by randomizing trait value sets within each plot, i.e. by

reshuffling the trait value sets among species (r = 0.221, P = 0.268).



Interestingly, the assignment of random trait value sets

across the whole trait pool resulted in higher mean FDQ

than the observed FDQ values. This finding shows that the

communities along the successional series are not assem-

bled randomly from the regional species pool but are fil-

tered by the environment. This was particularly true for

later successional stages, as the number of runs that trans-

gressed the observed FDQ values increased with succes-

sional age, also pointing to increasing environmental

filtering in later successional stages. Young successional

stages showed a tendency to be randomly assembled from

the total trait pool, while later stages were composed from

a more confined trait pool. This is consistent with the

observation that randomizing trait values within the same

successional stage showed the same tendency (of larger

FDQ values obtained from randomization than those

observed) as randomizing across the whole trait pool. This

allows us to conclude that successional age operates as the

main filter and that there is no considerable environmental

heterogeneity within successional stages. Further support

for this conclusion is the decrease in observed trait dissimi-

larity TDQ in the course of succession.When trait value sets

were randomized across the whole pool of trait value sets,

FD approached TD and FE approached unity because the

randomization gave each value set an equal chance to be

connected to every abundance value, thus rendering it

unimportant which values these abundances took. As this

type of random sampling complies with predictions from

neutral models (Hubbell 2001, 2005), we can conclude

that species in this subtropical forest community are not

assembled randomly. It is also worth pointing out that we

did not encounter trait over-dispersion (Pavoine & Bonsall

2010), i.e. FE values > 1, showing that communities were

by far less complementary in trait space than they could be

in theory.

Species richness effects

The finding that the observed decrease in trait dissimilar-

ity TDQ with time did not lead to a decreasing functional

diversity FDQ can either be explained by an increasing

species richness (as described for the study sites in Brue-

lheide et al. 2011) or a more even distribution of species

abundances (as seen in the increasing Shannon evenness;

Fig. 1b). The decision of which of these two factors com-

pensates for trait convergence can be unequivocally

answered with the third and last randomization approach

of keeping a fixed species number. As expected from Pet-

chey & Gaston (2006), FDQ increased with species rich-

ness. The underlying reason is that when drawing species

randomly from a community and keeping their abun-

dances results in more uneven relative abundances

among species in species-poor as compared to species-rich

communities. Using a fixed species number of 20 did not

have an effect on any of the observed relationships of

FDQ, TDQ and FEQ to successional age. This was the case

for deliberately choosing the 20 most abundant species in

the plots or selecting them randomly. Thus, the fourth

hypothesis must also be rejected.

Interestingly, species richness effects have not been

considered much in FD studies, although a positive rela-

tionship between species richness and variation in trait

values is an underlying, but mostly unexpressed, assump-

tion of all BEF experiments with random extinction sce-

narios (e.g. Roscher et al. 2004; Bell et al. 2009). In such

a design, the species-within-diversity treatments are

assembled randomly from the species pool in the experi-

ment. Thus, a larger number of randomly selected species

with random trait values will result in a higher variation

in trait values. While in experiments the results can be

sequentially assigned to species richness and species

identity effects (Bell et al. 2009), such a procedure is not

possible in natural communities because usually mono-

cultures are not available and too many confounding

factors have to be considered. Thus, particularly in obser-

vational studies, randomizations with a fixed species

number provide an appropriate tool to remove pure spe-

cies richness effects from FD calculations.

Having eliminated species richness as a potential cause

for maintaining FD in the course of succession, the increas-

ing evenness in species abundances remains the only fac-

tor that explains the observed patterns. We conclude that a

loss in trait dissimilarity TDQ in the course of succession

was compensated for by distributing the trait values more

evenly among the resident species. In consequence, a con-

stant functional diversity is maintained in this subtropical

forest during succession.

Conclusions

Here we have shown that neither complementarity nor

identity effects played a role in the secondary succession

in the studied Chinese subtropical forest, as the patterns

in FD remained unchanged in spite of randomly reshuf-

fling trait value sets or species. This finding of mainte-

nance of a constant FD in this forest community has

important ecological consequences, as it suggests a con-

stant degree of competitive exclusion along the succession

series. The logical next step would now be to link FD to

measured functions in our ecosystem, such as productiv-

ity, nutrient cycling, herbivory resistance and invasibility.

Thus, we hypothesize that these ecosystem functioning

responses remain unchanged along the succession series.

A constant FD might also be the reason for the encoun-

tered increase in species richness with time. Discussing

the possible reasons for this observation, Bruelheide et al.



(2011) suggested constant immigration of new species

during succession. Immigration of non-resident species,

albeit belonging to the regional species pool, can be con-

sidered a type of invasion into the community. Current

theories predict a decreasing invasibility of communities

with increasing diversity (e.g. Fargione & Tilman 2005),

explained as a decreasing level of unused resources. It has

been clearly demonstrated that the mechanism behind

this is not caused by diversity per se but by FD (Pokorny

et al. 2005; Hooper & Dukes 2010). A constant FD

throughout succession means that the communities

remain equally open for colonization by species new to

the community, and that the accumulation of species

described in Bruelheide et al. (2011) does not slow with

on-going succession. Thus, constant FD might be one of

the prerequisites that allows this forest community to

become richer with time.

Acknowledgements

We are grateful to the Gutianshan NNR administration for

the access permit to the forest reserve. In particular, we are

indebted to Teng Fang for species identification. Themanu-

script was very much improved by the constructive criti-

cism of Francesco de Bello and further anonymous

reviewers. The funding from the German Science Founda-

tion (DFG FOR 891/1 and BR 1698/9-1), as well as various

travel grants to prepare the project financed by DFG, NSFC

and the Sino-German Centre for Research Promotion in

Beijing (GZ 524, 592, 698 and699) is highly acknowledged.

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Supporting Information

Additional supporting information may be found in the

online version of this article:

Appendix S1. Example of calculating functional

diversity FDQ, trait dissimilarity TDQ and functional even-

ness FEQ.

Appendix S2. R code for the calculations in Appen-

dix S1.

Appendix S3. Principal components analysis (PCA)

of all 26 traits.

Appendix S4. Scree plot of eigenvalues of the first

ten PCA axes.

Appendix S5. Loadings of all 26 traits on the first ten

axes in the PCA.

Appendix S6. Inter-relationships of all 26 traits.

Appendix S7. FD variables based on random trait

selection.

Appendix S8. Results of the randomization appro-

aches.

Appendix S9. Functional diversity FDQ as a function

of number of randomly drawn species.

Appendix S10. FD variables based on random selec-

tion of 20 species.



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