Exploring Tree-Habitat Associations in a Chinese
Subtropical Forest Plot Using a Molecular Phylogeny
Generated from DNA Barcode Loci
Nancai Pei1,2., Ju-Yu Lian1., David L. Erickson3, Nathan G. Swenson4, W. John Kress3, Wan-Hui Ye1*,
Xue-Jun Ge1*
1 Key Laboratory of Plant Resource Conservation and Sustainable Utilization, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, People?s Republic
of China, 2 The Graduate University of the Chinese Academy of Sciences, Beijing, People?s Republic of China, 3Department of Botany, National Museum of Natural History
Smithsonian Institution, Washington, D.C., United States of America, 4Department of Plant Biology, Michigan State University, East Lansing, Michigan, United States of
America
Abstract
Elucidating the ecological mechanisms underlying community assembly in subtropical forests remains a central challenge
for ecologists. The assembly of species into communities can be due to interspecific differences in habitat associations, and
there is increasing evidence that these associations may have an underlying phylogenetic structure in contemporary
terrestrial communities. In other words, by examining the degree to which closely related species prefer similar habitats and
the degree to which they co-occur, ecologists are able to infer the mechanisms underlying community assembly. Here we
implement this approach in a diverse subtropical tree community in China using a long-term forest dynamics plot and a
molecular phylogeny generated from three DNA barcode loci. We find that there is phylogenetic signal in plant-habitat
associations (i.e. closely related species tend to prefer similar habitats) and that patterns of co-occurrence within habitats are
typically non-random with respect to phylogeny. In particular, we found phylogenetic clustering in valley and low-slope
habitats in this forest, indicating a filtering of lineages plays a dominant role in structuring communities in these habitats
and we found evidence of phylogenetic overdispersion in high-slope, ridge-top and high-gully habitats, indicating that
distantly related species tended to co-occur in these high elevation habitats and that lineage filtering is less important in
structuring these communities. Thus we infer that non-neutral niche-based processes acting upon evolutionarily conserved
habitat preferences explain the assembly of local scale communities in the forest studied.
Citation: Pei N, Lian J-Y, Erickson DL, Swenson NG, Kress WJ, et al. (2011) Exploring Tree-Habitat Associations in a Chinese Subtropical Forest Plot Using a
Molecular Phylogeny Generated from DNA Barcode Loci. PLoS ONE 6(6): e21273. doi:10.1371/journal.pone.0021273
Editor: Simon Joly, Montreal Botanical Garden, Canada
Received December 9, 2010; Accepted May 27, 2011; Published June 20, 2011
Copyright:  2011 Pei et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study was financially supported by National Basic Research Program of China (973 Program) (2007CB411600), China National Program for R & D
Infrastructure and Facility Development (2008BAC39B02), the Research Fund for the Large-Scale Scientific Facilities of the Chinese Academy of Sciences (2009-LSF-
GBOWS-01), Major Innovation Program of CAS (KSCX2-YW-N-0807) and Key Innovation Project of CAS (KZCX2-YW-430). The funders had no role in study design,
data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: xjge@scbg.ac.cn (X-JG); why@scbg.ac.cn (W-HY)
. These authors contributed equally to this work.
Introduction
Determining the ecological and evolutionary processes under-
lying community assembly remains a central goal in community
ecology. Perhaps nowhere has the debate regarding the assembly
of communities been more vigorous than in tropical tree
community ecology. Proposed assembly mechanisms invoke the
relative importance of niche- [1,2,3] and neutral-based [4,5,6]
processes. Tests of these niche- and neutral-based mechanisms
have occasionally focused on the degree to which species are
associated with the underlying environment where strong
associations are indicative of niche-based mechanisms dominating
the assembly process [7,8]. Recent work has demonstrated that
both species and entire clades have strong associations with soil
habitats [9]. In other words there may often be substantial
phylogenetic signal in plant-soil habitat associations where closely
related species tend to be found on similar soils. This suggests that
the evolutionary history of species may help explain their present
day distribution and co-occurrence patterns along habitat
gradients and that niche-based process can be detected using
phylogenetic information.
The use of phylogenetic information in plant community
ecology has dramatically increased since the pioneering work of
Webb [10]. A conceptual framework has emerged from this
literature that is designed to identify the relative influence of niche-
based versus neutral processes during the assembly of communi-
ties. Specifically this conceptual framework integrates the
phylogenetic signal in species traits or niches (i.e. the degree of
trait or niche similarity between closely related species) with
patterns of community phylogenetic structure (i.e. phylogenetic
clustering or overdispersion) in order to infer the relative influence
of habitat filtering, limiting similarity or neutrality during
community assembly (Table 1).
In tropical tree community ecology phylogenetic analyses of
communities have generally used one of three approaches: (i) they
have examined only the phylogenetic structure of communities
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[10,11,12,13]; (ii) they have examined only the phylogenetic signal
in plant-soil associations [9] or (iii) they have integrated patterns of
phylogenetic signal in functional traits with patterns of phyloge-
netic community structure [14,15]. While many of these studies
have inferred the relative influence of niche-based or neutral
processes, none has successfully implemented the conceptual
framework presented in Table 1 where phylogenetic signal of
habitat associations is integrated with patterns of phylogenetic
community structure. This is surprising because a popular
explanatory niche-based mechanism for the maintenance of
tropical forest tree species diversity and community assembly is
habitat partitioning [16,17,18]. This mechanism is expected to
lead to non-random patterns of species-habitat associations. If
these associations have strong phylogenetic signal or ?anti-signal?
(i.e. closely related species have non-randomly diverged in their
soil habitat associations), then niche-based processes should result
in non-random phylogenetic community structure (Table 1).
Although there has been no study that has explicitly linked
levels of phylogenetic signal in tropical tree-habitat associations
with patterns of phylogenetic community structure, there have
been two studies that have examined the phylogenetic commu-
nity structure of tropical trees in different habitats. Both of these
studies have been conducted in the 50-ha Barro Colorado Island
(BCI) forest dynamics plot in Panama. The first study was
conducted by Kembel and Hubbell [11] who found that species
in ?dry plateau? and ?young? forest habitats tended to be more
phylogenetically related than expected by chance (i.e. phyloge-
netically clustered) and species in the ?slope? and ?swamp? habitats
tended to be more distantly related than expected by chance (i.e.
phylogenetic overdispersion). Kembel and Hubbell [11] inferred
that in the former case environmental filtering acting on
evolutionarily conserved traits was the community assembly
mechanism and biotic interactions acting on evolutionarily
conserved traits was the community assembly mechanism in the
latter case. This work was important because a previous study
from this forest had found little evidence for species-specific
habitat associations [8]. Thus the discrepancies between the two
studies suggested that analyses that include phylogenetic data
may refine our understanding of the role of niche-based processes
during tropical tree community assembly.
The study by Kembel and Hubbell [11] utilized a phylogenetic
hypothesis estimated by an informatics tool called Phylomatic
[19]. This phylogeny contained many unresolved relationships (i.e.
soft polytomies) particularly within families. It was unclear at the
time of the Kembel and Hubbell [11] study how much this lack of
phylogenetic resolution influenced their results and inferences.
Recently Kress et al. [13] revisited the analyses of Kembel and
Hubbell [11] using a highly resolved molecular phylogeny
constructed from three DNA barcode loci. They concluded that
many of the results in the original Kembel and Hubbell [11] study
were not supported when using the resolved molecular phylogeny.
Further the results from Kress et al. [13] showed that the work by
Kembel and Hubbell [11], which relied upon the poorly resolved
Phylomatic phylogeny, generally underestimated the degree of
phylogenetic structuring of the tree communities in the seven BCI
habitats. The stronger patterns of structuring found was taken as
evidence that terminal phylogenetic resolution provided by a
molecular phylogeny generated using DNA barcode loci is
critical for identifying the underlying phylogenetic structure of
communities and that a lack of resolution may lead to type II
statistical errors as previously suggested by Swenson [20]. Thus
the implementation of phylogenetic information that allows for
species-level resolution should improve the quality of community
phylogenetic analyses.
The majority of the phylogenetic analyses of tree communities
that have been performed to date are from North and South
America or in Southeast Asia. Ideally a greater breadth of forests
that have distinctive biogeographic histories should be studied in
order to elucidate whether or not any general trends or emergent
properties exist. For example, local species richness is known to be
highly correlated with regional scale species richness [21,22]. Thus
regional scale differences in species richness likely plays a
predominant role in determining differences in local scale richness
from region to region. That said, it is still of interest how local scale
processes ?scale-up? to produce differences in regional species
richness and/or whether the strength of niche-based or neutral
processes varies between local communities occurring in different
regions [23]. In other words, are local scale niche-based processes
such as habitat filtering uniformly important in two regions with
vastly different levels of biodiversity? In order to answer such a
question, researchers must continue to sample, analyze and
compare the phylogenetic structure of tree communities from as
many regions as possible.
Here we utilize a molecular phylogeny constructed from three
DNA barcode loci (rbcL, matK, and trnH-psbA) representing 183
woody plant species in the 20-ha Dinghushan forest dynamics plot
in China. The phylogeny, observed spatial distribution patterns for
the 188 species in different habitats and a conceptual framework
that integrates the degree of phylogenetic signal in plant-habitat
associations with the phylogenetic structure of communities are
used to ask the central question of whether niche-based (i.e. habitat
partitioning or limiting similarity) or neutral processes determine
the assembly of species in this subtropical seasonal forest? In
answering this central question we also compare and contrast
results of the analyses when utilizing a molecular phylogeny versus
a phylogeny derived from Phylomatic.
Table 1. A conceptual framework integrating the degree of phylogenetic signal in plant-soil habitat associations and the
phylogenetic structure of the assemblage.
Phylogenetically Clustered
Assemblage
Phylogenetically Random
Assemblage
Phylogenetically
Overdispersed Assemblage
Phylogenetic Signal in Plant-
Soil Habitat Associations
Habitat Filtering Neutrality Limiting Similarity
Phylogenetic ?Anti-Signal? in
Plant-Soil Habitat Associations
Limiting Similarity Neutrality Habitat Filtering
Niche-based processes (i.e. habitat filtering of limiting similarity) are indicated by a non-random phylogenetic structure of the assemblage, but which processes can only
be inferred by considering the degree of phylogenetic signal in plant-soil habitat associations. A Neutral model is supported when the assemblage is random with
respect to phylogeny regardless of the degree of phylogenetic signal in plant-soil habitat associations. (Adapted from Kraft NJB, Cornwell WK, Webb CO, Ackerly DD
(2007) Trait evolution, community assembly, and the phylogenetic structure of ecological communities. Am Nat 170: 271?283).
doi:10.1371/journal.pone.0021273.t001
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Materials and Methods
Research site and DNA sequencing
The present study took place in the Dinghushan forest dynamics
plot (DHS FDP) located within the Dinghushan National Nature
Reserve (23u099210?23u119300N, 112u309390?112u339410E) in
Guangdong province, south China. The DHS FDP is a key node
in the Chinese Forest Biodiversity Monitoring Network and a part
of the Center for Tropical Forest Science (CTFS) global network
of forest dynamics plots, The DHS FDP is a subtropical forest with
a mean annual rainfall of 1,985 mm. A total number of 71,336
woody stems greater than or equal to 1 cm diameter at breast
height have been mapped and identified to species in the
400 m6500 m plot. There are 183 species (188 taxa) of trees
and shrubs in the DHS FDP. These 183 species represent 24
orders, 51 families, and 110 genera (38 genera containing $ two
species; 10 genera containing $ four species; and two genera
containing $ eight species).
A molecular community phylogeny was generated for the 183
species in the DHS FDP by sequencing three DNA barcoding loci
(rbcL, trnH-psbA, and matK). DNA sequences were generated for 1?
2 tagged individuals located within the plot. Genomic DNA was
extracted from leaf and/or bark tissue using a standard CTAB
protocol [24]. Standard barcode primers (rbcL, matK, and trnH-
psbA) suggested by the Consortium for the Barcode of Life (http://
barcoding.si.edu/) were used in the study. The PCR cycling
conditions utilized in this study were as follows: rbcL and trnH-psbA
used 95uC for 3 min, (95uC 30 sec, 53uC 45 sec, 72uC 1 min)634
cycles, 72uC 7 min, while matK required lower annealing
temperatures, longer extension time and more cycles (95uC
3 min (95uC 30 sec, 51uC 45 sec, 72uC 1.5 min)638 cycles,
72uC 7 min) [25], adding a final concentration of 5% for DMSO.
Sequences of rbcL (,650 bp for the sequence length) which can be
sequenced via one reaction, had 1-fold coverage, but the matK
(,900 bp) and trnH-psbA (280?870 bp) had 2-fold coverage. All
DNA sequence data were submitted to GenBank and their
accession numbers are provided in Table S1.
Phylogenetic reconstruction
We reconstructed two types of community phylogenies
representing the 183 plant species found in the DHS FDP. The
first phylogeny we generated was a molecular phylogeny using the
three sequenced barcode loci described in the previous section. A
DNA supermatrix was generated that contained all three markers
(rbcL + matK + trnH-psbA) ([13]; see also Text S1 for more detail on
alignment and matrix construction). The DNA supermatrix was
then analyzed using RA6ML [26] via the CIPRES supercom-
puter cluster [27] to infer a maximum likelihood (ML) community
phylogeny. Node support was estimated using bootstrap values
with nodes with less than 50% support being collapsed into soft
polytomies. The familial topology in this molecular phylogeny was
concordant with that observed in the APG III classification. In
Fig. 1 we show a comparison of the family-level relationships
within the Asterids clade between our ML analysis of the barcode
sequence data and that derived by the APG III.
A second phylogenetic tree was generated for this work using
the informatics tool Phylomatic [19]. This tree ?grafts? taxonomic
relationship to a stored phylogenetic ?backbone? is generally
resolved to the family level. Thus relationships between species
within a genus and genera within a family are generally level
unresolved. Community phylogenies derived from Phylomatic are
the typical approach in community phylogenetics investigations,
because molecular phylogenies of most tropical taxa are not
available. As such the Phylomatic tree in this study was generated
in order to compare whether any information would be lost if only
a Phylomatic tree, exhibiting lower rates of resolution, was utilized.
Habitat types and spatial scales classification
Five habitat types in the DHS FDP were classified using the
topographic variables slope, elevation and convexity [28,29]. In
particular, habitats were classified using a quantitative method
where the observed slope and elevation was compared to the plot
median value. The specific classification scheme is given in
Table 2. The quantitative classification of habitat types allows for
them to be ordered by similarity, as valley (V), low-slope (LS),
high-gully (HG), ridge-top (RT) and high-slope (HS). A habitat
type was assigned to each given 20620 m quadrat in the DHS
FDP. Topographical variations in the DHS FDP are larger than
that of the BCI forest plot in Panama (Table 2, and Fig. 2). Thus it
is difficult to compare the habitat types of the two plots. The
majority of the analyses were conducted by dividing the 20-ha plot
into 500 20 m620 m quadrats. Two additional spatial scales were
used, specifically 40 m640 m and 100 m6100 m. In sum, five
Figure 1. A comparison of the family-level relationships within
the Asterid clade. The topology on the left-hand side represents the
phylogenetic relationships of families obtained from the APG III
consensus phylogeny, while the topology on the right-hand side
represents the DHS phylogeny generated with the ML analysis of the
barcode sequence data.
doi:10.1371/journal.pone.0021273.g001
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habitat types and three spatial scales were used to quantify the
community phylogenetic structure in the DHS FDP.
Habitat association ? randomization tests and
phylogenetic signal
In order to quantify the degree to which individual species in the
DHS FDP are associated with specific habitat types we used the
habitat randomization procedure described in Harms et al. [10].
Specifically a torus translation was utilized where the habitat map
was ?rotated? or iterated. During each iteration, a ?null? species-
habitat association was calculated for each of the 99 most common
tree species. These 99 species were selected because they were
common enough (n.20) to provide a robust estimate of their
habitat association. This generated a null distribution to which we
could compare the observed association. Each simulated map
included 173 valley, 77 high-gully, 115 low-slope, 73 high-slope,
and 62 ridge-top quadrats. This randomization procedure
maintains the observed spatial autocorrelation of both the habitat
data and the species distributions.
We also quantified the phylogenetic signal in plant-habitat
associations in order to implement the conceptual framework
presented in Table 1. Phylogenetic signal was measured on the
median habitat in which individuals of each species are found
using the five habitat categories as ordered variables as described
in the above section. The descriptive statistic K presented in
Blomberg et al. [30] was used to measure the phylogenetic signal
in habitat associations. The significance of the observed K value
was determined using a permutation test. Specifically, the names
of taxa were randomized across the tips of the phylogeny 999
times. During each iteration, a null K value was calculated and
recorded. This generated a distribution of 999 null K values to
which the observed could be compared.
Community phylogenetic structure analyses
One of the 19 equally likely phylogenetic trees of the three-locus
ML analysis of 183 species was randomly selected to use in the
present community phylogenetic analysis. Non-parametric rate
smoothing in the R package ?ape? [31,32] was used to generate an
ultrametric phylogeny. This ultrametric barcode phylogeny was
used for all subsequent community phylogenetic analyses.
Using both the molecular and Phylomatic phylogenies, we
quantified the Net Relatedness Index (NRI) and the Nearest
Taxon Index (NTI) [33,34] for each 400 m2 quadrats (n = 500).
The NRI and NTI are calculated as follows:
NRI~{1| (MPD{ rndMPD)=sdrndMPD
NTI~{1| (MMPD{ rndMMPD)=sdrndMMPD
Where MPD represents the mean pairwise phylogenetic distance
between all taxa within a local assemblage and MMPD represents
the mean phylogenetic distance for each taxa to its nearest relative
within a local assemblage. The rndMPD and rndMMPD represent
the mean MPD and mean MMPD from 999 randomly generated
assemblages. An independent swap null model was used to
generate these 999 random assemblages [35]. This is the same null
model as the ?constrained? null model in Kembel and Hubbell
[11]. The NRI is generally considered to be a ?basal? metric while
the NTI is generally considered to be a ?terminal? metric. Negative
Table 2. Criterions of habitat classification, areas of each habitat, total numbers of species and stems $1-cm d.b.h. in 2005 census,
and total stem densities by habitat for the 20-ha Forest Dynamics Plot of Dinghushan, China.
Habitat Valley High-gully Low-slope High-slope Ridge-top
Area (ha) 6.92 3.08 4.60 2.92 2.48
Slope (degrees) ,33 $33 $33 $33 ,33
Elevation (m) ,326.3 $326.3 ,326.3 $326.3 $326.3
Convexity (degrees) all ,0 all . .0
Mean 6 s.e. (species diversity) 25.5860.46 28.2760.88 27.5760.48 34.7360.84 27.5360.85
Total number of species 149 133 135 135 105
Total number of stems [density (no.ha21)] 19,501 (2828.06) 11,052 (3588.31) 17,215 (3742.39) 14,174 (4854.11) 9,394 (3787.90)
Notes: Valley (slope , median (slope), elevation , median (elevation)); Low-slope (slope $ median(slope), elevation , median(elevation)); High-slope (slope $ median
(slope), elevation $ median(elevation), convexity .0); High-gully (slope $ median(slope), elevation $ median(elevation), c$onvexity ,0); Ridge-top (slope , median
(slope), elevation $ median(elevation), convexity .0).
Median slope = 33 degrees; Median elevation = 326.3 m.
doi:10.1371/journal.pone.0021273.t002
Figure 2. The spatial distribution of the five habitat types in
the 20-ha Dinghushan plot. Colors represent different habitat types
at the spatial scale of 20 m620 m.
doi:10.1371/journal.pone.0021273.g002
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values of both metrics indicate phylogenetic overdispersion. In
other words species in local assemblages are more phylogenetically
diverse than expected by chance. Positive values of both metrics
indicate phylogenetic clustering. In other words species in local
assemblages are more closely related than expected by chance.
Because the NRI and NTI values in the 500 quadrats were
spatially autocorrelated, we estimated the mean NRI and NTI
values within habitats using simultaneous spatial autoregression
analyses. We used generalized least-squares models with a first-
order spatial neighbor SAR covariance structures in S+Spatial-
Stats [36] to perform these analyses. Next, following Kembel and
Hubbell [11], we defined habitats for each 400 m2 quadrat and
tested whether each habitat type tended to contain quadrats that
were on average phylogenetically clustered, overdispersed or
random using t-tests.
Then, following the methods of Kress et al. [13], we asked
whether the results for each individual habitat type generated from
the molecular phylogeny and the Phylomatic phylogeny were
significantly different. For all of the 500 quadrats combined, we
compared the NRI and NTI values quantified from the molecular
phylogeny to those calculated from the Phylomatic phylogeny
using a paired t-test.
Lastly, we performed all analyses using the species lists in the
five habitats as individual communities and the forest plot species
list as the species pool. This analysis was designed to address
whether or not the entire species assemblage in a habitat was
phylogenetically non-random.
Results
Habitat-species association and community assembly
The habitat association tests recovered 52 significant positive
and negative plant-habitat associations out of a potential 495
species-habitat combinations. Thus 10.5% of the tests were
positive, which is greater than the expected false discovery rate
of 5%. There were 29 significant positive or negative associations
in the habitats that were phylogenetically overdispersed (23 for the
high-slope habitat and 5 for the high-gully habitat) or phyloge-
netically clustered (one for the low-slope habitat, but no significant
positive or negative associations in the valley habitat). Another 23
significant positive or negative associations were found in the
ridge-top habitat which contained phylogenetically random
assemblages. A total of 52 of the 99 most common species had a
significantly positive or negative association with at least one
habitat type (Table 3). The detailed results regarding which species
were associated with individual habitat types are provided as
Supplemental Material (Text S2).
The 19 most abundant species accounted for 74.77% of all stems
in the DHS FDP. Of these species 12 had at least one positive or
negative habitat association, while 7 were not significantly
associated with a habitat (Table 3). We detected significant
difference (F= 26.414, P,0.001) in species richness between the
forest communities that were phylogenetically clustered (30.286
0.54, mean 6 se) and those that were phylogenetically over-
dispersed (26.3860.34) using ANOVA. We further found that the
high-slope habitat had the highest species richness (34.7360.84),
followed by habitats of the high-gully (28.2760.88), the low-slope
(27.5760.48), the ridge-top (27.5360.85) and the valley
(25.5860.46) (Table 2).
Phylogenetic signal in habitat associations
We utilized the descriptive statistic K to quantify the phylogenetic
signal in habitat associations using the median habitat type for all
individuals of a species and treating habitat type as an ordered
variable. The observed K value was 0.80 and this value was
compared to a distribution of 999 null K values generated with a
permutation test. The observed K value was significantly higher
than that expected (P=0.019) using a two-tailed test. A higher than
expected K value indicates there is phylogenetic signal in species-
habitat associations. In other words closely related species tended to
be more similar in their habitat associations than expected.
Community phylogenetic structures in different habitat
types
A total of five habitat types were classified in the DHS FDP and
they are mapped using different colors in Fig. 2. The Net
Relatedness Index (NRI) value and the Nearest Taxon Index
(NTI) value in each quadrat is marked in the 20-ha plot in Fig. 3.
The results from the Phylomatic phylogeny found phylogenetic
clustering in the valley habitat using both the NRI and the NTI
metrics, and in the low-slope habitat using the NRI metric.
Phylogenetic overdispersion was found in the high-gully, the high-
slope, and the ridge-top habitats using both the NRI and NTI
metrics (Table 4 and Fig. 3). When using the molecular phylogeny,
we found significant phylogenetic structuring in six of the 10 tests
(two metrics per habitat type). Specifically we found phylogenetic
clustering in the valley and the low-slope habitats, phylogenetic
overdispersion in the high-slope and the ridge-top habitats, and a
phylogenetically random pattern in the high-gully habitat using
both the NRI and the NTI metrics.
When comparing our molecular phylogeny to the less well-
resolved Phylomatic tree, five of ten inferences were similar.
Analyses based on the molecular phylogeny identified significant
Table 3. Randomized-habitat tests for habitat associations on the 20-ha Forest Dynamics Plot of Dinghushan, China.
Habitat association 99 species 19 species Habitat association 99 species 19 species
Valley + 0 0 Valley - 0 0
High-gully + 4 1 High-gully - 1 0
Low-slope + 1 1 Low-slope - 0 0
High-slope + 22 2 High-slope - 1 0
Ridge-top + 11 4 Ridge-top - 12 4
Total + 38 8 Total - 14 4
The first column contains results for the 99 common species for which there were $20 stems in the plot in the 2005 census. The second column contains results for the
19 most abundant species, all of which had $1000 stems in the plot in the 2005 census. For each habitat, ??+?? indicates significant positive association and ??2?? indicates
significant negative association (two-tailed test, a= 0.05).
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phylogenetic structuring in the low-slope habitat using the NTI
metric for which the Phylomatic phylogeny did not. In the
remaining four cases, the Phylomatic phylogeny demonstrated
significant phylogenetic structuring but the molecular phylogeny
did not. It is important to note that although the molecular
phylogeny generally provided higher NRI and NTI values, this did
Figure 3. The spatial patterns of NRI and NTI values in the forest plot. Values of NRI and NTI for each 400 m2 quadrat in the 20-ha forest
dynamics plot in Dinghushan, south China, are calculated using the molecular phylogeny and the Phylomatic phylogeny. Negative NRI and NTI values
indicate phylogenetic overdispersion and positive values indicate phylogenetic clustering. The color scales across all NRI and NTI maps are made
equivalent to allow for direct visual comparisons between the four maps. a. Barcode NRI; b. Phylomatic NRI; c. Barcode NTI; d. Phylomatic NTI
doi:10.1371/journal.pone.0021273.g003
Table 4. The estimated mean and standard error of the NRI and the NTI values in the DHS habitat types estimated using first order
simultaneous spatial autoregression for the molecular phylogeny (columns labeled ??Molecular NRI/NTI??) or for the Phylomatic
phylogeny (columns labeled ??Phylomatic NRI/NTI??).
Habitat type/Spatial scale Molecular NRI Phylomatic NRI NRI difference Molecular NTI Phylomatic NTI NTI difference
Valley 0.6160.10*** 0.5360.09*** 0.0860.09 0.02?0.09 0.40?0.08*** 20.38?0.13**
High-gully 20.07?0.13 20.45?0.13** 0.38?0.13** 20.01?0.12 20.41?0.12** 0.40?0.18*
Low-slope 0.3260.09** 0.2860.10** 0.0460.09 0.19?0.08* 0.18?0.10 0.0160.14
High-slope 20.4560.12*** 20.4860.12*** 0.0360.11 20.2860.11* 20.4760.09*** 0.1960.14
Ridge-top 20.5760.09*** 20.6560.11*** 0.0860.13 20.11?0.12 20.41?0.09*** 0.30?0.17*
20 m620 m 0.14160.053** 0.02260.052 0.11960.048* 20.00860.047 20.00660.046 20.00260.069
40 m640 m 0.10460.103 0.01260.097 0.09260.108 0.00960.088 20.02360.090 0.03260.140
100 m6100 m 0.07160.247 20.00360.137 0.07460.261 0.15760.025 0.17460.145 20.01760.255
Notes: The P values in the ??Molecular NRI/NTI?? and ??Phylomatic NRI/NTI?? columns were calculated using a two-tailed t-test to assess whether the mean NRI and NTI
values in the habitat types and spatial scales were higher or lower than expected. Negative values indicate that the observed average NRI or NTI was phylogenetically
overdispersed. Positive values indicate that the observed average NRI or NTI score was phylogenetically clustered. The columns labeled ??NRI or NTI difference?? provided
the mean of the difference between the molecular phylogeny and Phylomatic NRI and NTI values in each habitat type or spatial scale and were calculated using a two-
tailed paired t-test to assess whether the NRI and NTI values in a habitat type or spatial scale calculated from the barcode phylogeny were significantly different than
those calculated using the Phylomatic phylogeny. We found all differences among habitats in NRI and NTI were statistically significant according to the spatial GLS tests
(P,0.01). The asterisk ***, **or * indicate the significance at the level of P,0.001, 0.01 or 0.05 respectively.
doi:10.1371/journal.pone.0021273.t004
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not necessarily mean these values were more often non-random.
For example, an insignificant mean NTI value of 20.01 in the HG
habitat was recorded using the molecular phylogeny, but a lower
and significant mean NTI value of 20.41 was recorded in this
habitat using the Phylomatic phylogeny (Table 4). When directly
comparing the NRI and NTI values from the 500 individual
quadrats within the five habitat types using paired t-tests, four out
of the ten comparisons were significantly different when
comparing the results from the molecular and Phylomatic
phylogenies (Table 4). We also quantified the NRI and NTI for
each habitat type using all species found in a habitat as the
assemblage. We found that NRI values from the molecular
phylogeny were positive in seven cases and negative in two others
cases while NRI values from the Phylomatic phylogeny were
positive in two occasions and negative in two cases. The NTI
values from the molecular phylogeny were negative in nine cases
while the NTI values from the Phylomatic phylogeny were positive
in four cases and negative in six cases (Fig. 4).
Discussion
The niche versus neutral debate is particularly important in
tropical forest community ecology given the elevated levels of
species richness and often low population sizes of many species in
these systems. Previous analyses of tropical tree communities have
suggested that lineages non-randomly sort into different habitat
types [11] thereby indicating the potential importance of niche-
based processes during tropical tree community assembly. Beyond
simply finding support for niche-based assembly, this phylogenet-
ically-based analysis was important in that it detected non-random
habitat associations that traditional species-based analyses could
not detect [8]. This work has been important for our understand-
ing of tropical tree community assembly and for its depiction of the
additional information that may be gleaned when using phyloge-
netic trees. That said, this work comes from a single forest plot in
Panama and similar studies from other tropical regions could help
determine the generality of these findings. Further, the non-
random sorting of lineages into different habitat types in Panama
was not integrated with information regarding the degree to which
closely related species share similar habitat preferences. In other
words whether or not there is phylogenetic signal in species-habitat
associations. The present study aimed to quantify whether the
phylogenetic structure of a sub-tropical Chinese tree community
was non-random across habitats as what has been done in
Panama. Next, we quantified whether there was phylogenetic
signal in plant-habitat associations ? information critical for
inferring which ecological process has influenced community
assembly the most (Table 1). Finally, previous work has shown that
molecular phylogenies generated from three DNA barcode loci
may provide substantially different results than those generated
Figure 4. The total distributions of NRI or NTI values in different habitats generated from the molecular and Phylomatic
phylogenies. The solid black line across the color box represents the median value. A hollow circle indicates an outlier value of NRI or NTI. HS, High-
slope; RT, Ridge-top; HG, High-gully; LS, Low-slope; V, Valley.
doi:10.1371/journal.pone.0021273.g004
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using a less-well resolved Phylomatic phylogeny [13]. In this study
we compare and contrast the results from a molecular phylogeny
to those to a Phylomatic phylogeny to determine whether the
results of previous work [13] are generally applicable. In the
following we discuss the results of our study with respect to
community assembly and the use of molecular versus Phylomatic
phylogenies.
Community Assembly, habitat specialization and species
diversity
Phylogenetic investigations of plant communities have been used
to determine whether patterns of species co-occurrence have
phylogenetic structure [34,37]. It is recognized that non-random
phylogenetic structure (phylogenetic overdispersion or clustering)
indeed exists in animal, plant andmicrobial communities [38,39,40]
where approximately sixty percent of previous studies have found
evidence for phylogenetic clustering in contemporary terrestrial and
plant communities [41]. Two general types of niche-based processes
can produce these patterns of non-random phylogenetic community
structure ? environmental filtering and strong negative or positive
biotic interactions. Environmental filtering during community
assembly dictates that only a small subset of species share similar
ecological strategies or niches can co-occur in a given environment.
If closely related species have similar strategies or niches, then
environmental filtering should produce a pattern of phylogenetic
clustering. Conversely, if closely related species have very divergent
strategies or niches, then environmental filtering should result in
phylogenetic overdispersion. Non-random biotic interactions (i.e.
competition, facilitation, etc) dictating community assembly should
generate a pattern of phylogenetic overdispersion if closely related
species are similar or phylogenetic clustering if closely related
species are very dissimilar. Thus while non-random patterns of
phylogenetic community structure are indicative of niche-based
processes, it is not possible to identify which process without
information pertaining to the similarity of closely related species (i.e.
phylogenetic signal) (Table 1).
The present study found that many species (.10%) have a
significant positive or negative association with a habitat type in the
DHS FDP. Subsequent analyses of the phylogenetic signal in
species-habitat associations found that there was significant
phylogenetic signal. Thus while only some species were significantly
associated with a particular habitat, closely related species did on
average tend to be associated with similar habitats. Thus any
observed patterns of phylogenetic clustering in this system should be
indicative of habitat filtering while patterns of phylogenetic
overdispersion should be indicative of biotic interactions.
The phylogenetic structuring analyses showed that mean NRI of
all 0.04-ha quadrats (i.e. local assemblages) was significantly
different from the null expectation of zero (P=0.008). This
deviation indicates niche-based community assembly in this forest,
but the inferred process varies with the habitat considered. In
particular, both the valley and the low-slope habitats had assem-
blages that were phylogenetically clustered. We therefore infer that
habitat filtering drives the assembly of the communities and co-
occurrence of species in these two habitats. In the high-slope and
ridge-top habitats communities were generally phylogenetically
overdispersed indicating a large role for biotic interactions driving
the assembly and co-occurrence of species in these habitats.
Interestingly these habitats are apt to suffer drought and they likely
have low soil nutrients concentrations. Thus it is possible that
facilitation influences co-occurrence and therefore generates a
pattern of dissimilar co-occurring species. Lastly, the high-gully
species assemblages were no different from those expected by
chance, which suggests one of two possibilities. First neutrality may
dominate the assembly process in these habitats. Second the
strength of habitat filtering is ?balanced? by the strength of biotic
interactions resulting in a random pattern from non-random
processes acting in opposing directions [15].
The results showed that the phylogenetic structure of the species
in an entire habitat type often mirrored those found in individual
quadrats within that habitat type (Table S2). This finding suggests
that the filtering of lineages at the ?habitat-scale? largely explains
the local-scale phylogenetic pattern. We do note, however, that in
some instances this was not the case where the habitat-scale
pattern was not found locally. This suggests that non-random
ecological interactions within habitats may play a large role in
determining local phylogenetic structure.
Comparative analyses of molecular and Phylomatic
phylogenies
Previous work has suggested that the lack of terminal resolution
in phylogenies generated by the informatics program Phylomatic
may bias investigations of community phylogenetic structure
[13,20]. One of these studies was conducted in Panama [13] and
the other was simulation based [20]. Therefore it is unclear how
generalizable the findings are to other systems. Thus additional
studies that compare the results generated from the resolved
molecular phylogenies to those from a Phylomatic phylogeny are
needed. The present study has performed such a comparison.
The results from the molecular phylogeny generated from three
DNA barcode loci had higher values of both NRI and NTI metrics
than those generated using the Phylomatic tree in nine out of ten
comparisons (Table 4). In other words the results from the resolved
molecular phylogeny tended to show more phylogenetic clustering
than that found using the Phylomatic phylogeny. This result is
similar to that found in Panama [12] and in previous simulation
work [20] that suggests that increased resolution provided by
molecular phylogenies should allow for the detection of non-
random phylogenetic community structure that a Phylomatic
phylogeny cannot detect. In other words, the lack of resolution in a
Phylomatic phylogeny likely leads to Type II statistical errors.
Conclusions
The present study sought to determine whether niche-based or
neutral processes dominate the assembly of tree communities in a
sub-tropical Chinese forest. The work quantified the phylogenetic
structure of tree communities in five habitat types and the
phylogenetic signal in plant-habitat associations. Using a concep-
tual framework that integrates the level of phylogenetic signal in
plant-habitat associations with the phylogenetic dispersion of
species in a community (Table 1) we infer that niche-based
processes (habitat filtering and facilitation) drive the assembly of
communities in this forest. These results are consistent with
findings from a similar study in Panama [13] suggesting that local-
scale niche-based processes are important in both of these regions
despite their very different biogeographic histories and regional
species pool compositions. The work also provides further
evidence that less well-resolved Phylomatic phylogenies tend to
generate Type II statistical errors and that utilizing resolved
molecular phylogenies is therefore advised when feasible. It is
suggested that the feasibility of generating such molecular
community phylogenies is enhanced through the utilization of
sequence data from three commonly used DNA barcoding loci
(rbcL, trnH-psbA, and matK). We expect that as barcoding becomes
more widespread, community phylogenetics researchers will
benefit from ?tapping into? the vast resource that is a DNA
barcode library.
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Supporting Information
Table S1 A list of taxa, GenBank accession numbers,
and tree tag numbers.
(DOC)
Table S2 The estimated mean and standard error (S.E.)
of the NRI and NTI values in the DHS habitat types
estimated using first order simultaneous spatial auto-
regression for the barcode phylogeny (columns labeled
??Barcode NRI/NTI??) or for the phylomatic phylogeny
(columns labeled ??Phylomatic NRI/NTI??), using habi-
tat sample files.
(DOC)
Text S1 Sequence editing and alignment of three
barcode loci.
(DOC)
Text S2 A description of which individual species were
associated with particular habitat types.
(DOC)
Acknowledgments
We thank Campbell O. Webb for discussing running the software
Phylocom, and Jin-Long Zhang for instructing R-package usage. We also
thank Dr. Simon Joly and two anonymous reviewers for their helpful
comments and criticisms on earlier versions of this manuscript.
Author Contributions
Conceived and designed the experiments: X-JG W-HY. Performed the
experiments: NP. Analyzed the data: NP J-YL DLE NGS. Contributed
reagents/materials/analysis tools: J-YL W-HY. Wrote the paper: NP NGS
DLE WJK X-JG W-HY.
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