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Biodiversity and Conservation
ISSN 0960-3115
Volume 22
Number 1
Biodivers Conserv (2013) 22:141-152
DOI 10.1007/s10531-012-0408-x
Species-level phenological responses
to ‘global warming’ as evidenced by
herbarium collections in the Tibetan
Autonomous Region
Zhongrong Li, Ning Wu, Xinfen Gao,
Yan Wu & Krishna P.Oli
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ORIGINAL PAPER
Species-level phenological responses to ‘global warming’
as evidenced by herbarium collections in the Tibetan
Autonomous Region
Zhongrong Li •Ning Wu •Xinfen Gao •
Yan Wu •Krishna P. Oli
Received: 13 March 2012 / Accepted: 22 November 2012 / Published online: 7 December 2012
ÓSpringer Science+Business Media Dordrecht 2012
Abstract In recent years attention has been given to assess the impacts of warming on
the plant flowering phenology. There is a growing realization that herbarium-based col-
lections could offer a reliable and relatively time-saving baseline data source to identify
these effects. This article examines the magnitude and trends of warming effects on the
average flowering timing (AFT) of plants in Tibet Autonomous Region using analysis of
herbarium specimens collected for 4 decades. Mixed model with randomized blocks was
used to analyze a set of 41 species (total 909 specimens) which were collected during the
period of 1961–2000. Results showed that an earlier AFT emerged within 40 years period
in comparison to the recorded data of the year of 2000 (0.5 days per year), and that
7.5 days early flowering was contributed by mean summer (i.e., June–August) temperature.
It is proposed that temporary shifts in flowering phenology responding to continuing
temperature rise could quantify the extent to which climate affects plant species. Analysis
of well recorded herbarium specimens could provide a reasonable indication on the
impacts of rising temperature on plant phenology. The result of this study could also
facilitate a bridge between the scientific knowledge and indigenous knowledge of Tibetan
communities.
Keywords Climate warming Flowering phenology Herbarium specimens
Mixed model Tibet autonomous region
Electronic supplementary material The online version of this article (doi:10.1007/s10531-012-0408-x)
contains supplementary material, which is available to authorized users.
Z. Li N. Wu X. Gao (&)Y. Wu
The ECORES Lab, Chengdu Institute of Biology, Chinese Academy of Sciences,
Chengdu 610041, China
e-mail: xfgao@cib.ac.cn
N. Wu K. P. Oli
International Centre for Integrated Mountain Development (ICIMOD), Kathmandu, Nepal
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Biodivers Conserv (2013) 22:141–152
DOI 10.1007/s10531-012-0408-x
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Introduction
Global warming is affecting natural processes around the world. Of the biological
responses to warming detected to date, changes in phenological events are among the most
sensitive (Parmesan and Yohe 2003). Most studies that have documented the impact of
climate change on phenological events have relied on long-term written records (Molau
et al. 2005). Although many such records have been found and analyzed in Europe and
North America, such studies are still rare in other parts of the world to adequately address
the questions of how phenological events are changing due to climate change. In this
context, over the last few decades, the Tibetan Plateau (TP) have received considerable
attention due to its unique physical environment, and the presence of endemic and
threatened biodiversity resources. Floristically, the Tibet Autonomous Region (TAR) is
among the richest regions in China (Lo
´pez-Pujol et al. 2006), belongs to an ecologically
important global hotspot (Myers et al. 2000) and is very sensitive to climate change due to
its fragility and vulnerability (Cruz et al. 2007). As warming continues, it is predicted that
some irreparable consequences including threats to species, their distribution, shift of
habitat, and even species extinction will occur (McCarty 2001; Klein et al. 2004). Although
a wide variety of ecosystems in TAR support specialized biodiversity resources with many
globally threatened, endemic and migratory species, the biodiversity has not been fully
documented due to inaccessibility. The ongoing natural biological phenomena in the region
are not clearly understood. Therefore there is an urgent need to explore the area applying
new approaches and methodologies to learn the responses of plant species to global
warming and the related processes before they disappear.
In recent years plant phenology has gained wide acceptance as a timely reminder to
trace the footprint of global warming (Cleland et al. 2007). This is because evidence
suggests that temperature largely controls the flowering time of many taxa in different
regions, even across climatic zones (Sparks and Menzel 2002; Gallagher et al. 2009;
Hudson et al. 2010). Generally, there is an advancing trend in the time of plant flowering
with exposure to warming. For instance, from analyzing the average first flowering time of
385 British plant species with a comparative method between the periods 1991–2000 and
1954–1990, Fitter and Fitter (2002) reported that there is an early flowering with an
advance in mean 4.5 days per 1 °C increase since the 1990s, especially for spring-flow-
ering species.
It has been suggested that studies on the impacts of climate warming necessitate the
application of long-term monitoring phenological datasets (Sparks and Menzel 2002).
Unfortunately few datasets are available for researchers from remote mountainous regions,
especially in the TAR. To complement this gap specimen-based data recording have
increasingly been used as an alternative data source in demystifying phenological patterns
of species and to observe their long-term change (e.g., Primack et al. 2004; Gallagher et al.
2009; Gaira et al. 2011). Some of these methods show strong evidences for the consistency
of the results from herbarium data with independent field datasets, thus enhancing the
reliability and validity of herbarium collections (Borchert 1996; Bolmgren and Lo
¨nnberg
2005; Robbirt et al. 2011).
Based on remote sensing data for steppe and meadow vegetations in the TP, Yu et al.
(2010) reported that increasing winter temperature from the mid-1990s may delay spring
(May and June) vegetation phenology, despite warm spring inducing an earlier onset of the
growing season. Yet the mechanisms of individual plant/species responding to warming
temperatures still remain unclear. In order to correlate plants dynamics and physiology for
scientific substantiation, both meteorological and phenological data are insufficient.
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However, evidence on indigenous knowledge regarding climate change impacts on plant
phenology by the local communities have exhibited perspicacious judgments such as early
bud burst or flowering (Salick and Byg 2007; Chaudhary and Bawa 2011).
In this context this study attempts to use the long-term dataset derived from specimen
records, collected from TAR across a 40 year period (1961–2000), to examine the
responses of plant flowering phenology to rising temperature. The study has focused more
on how the magnitude and trends of average flowering time in a species group has changed
with an increase in annual or seasonal mean temperature. It is anticipated that such kind of
this knowledge could enhance our understanding about the impact of climate change on
plant phenology and inform local communities and policy makers on climate change
adaptation.
Methods
Herbarium database and candidate species
The majority of herbarium specimen records collected from TAR were retrieved from
the online databases (http://www.cvh.org.cn;http://pe.ibcas.ac.cn) of specimens stored
in three leading herbaria: Chinese National Herbarium of Institute of Botany (PE),
Herbarium of Kunming Institute of Botany (KUN) and Herbarium of Northwest
Institute of Plateau Biology (HNWP), Chinese Academy of Sciences (CAS). These
specimens and their records could reflect the realistic status of herbarium-based species
in TAR.
There were more than 100,000 specimens with clear labels describing the place of
origin in TAR along with their collection dates. Specimens without precise data and no
clear identification were discarded, and further duplicates in collecting number, species
name and collecting year were removed. Due to unavailability of temperature data before
1960 specimen records before 1960 were not used. This resulted in a dataset with 28,985
specimens being used for later analysis.
To analyze the phenological responses to warming, the following criteria were
adopted: (1) species collected after 1960 (as mentioned above) with records spanning at
least three of the four decades were considered (Table 1). Most of the chosen species,
with a flowering period of 3–4 months, normally initiate flowering in May and June. It
was reported that the duration of flowering period is independent to temperature change
(Primack et al. 2004), i.e., all chosen species from high altitudes could be used to study
their response to warming effect regardless of the length of flowering period. (2) For a
given species with records from the same site and same altitude, only the first record was
kept. For example, Aconitum gymnandrum was collected on 15 and 30 August 1974 at
Songduo Transportation Station of Gongbujiangda county with the same altitude of
4,200 m a.s.l., so the latter was discarded. (3) For those species collected in the same
year but from the different site with the same altitude, only the earliest record was used.
(4) For those species collected at the same time and same site but with the different
altitudes, only the record with the highest elevation was kept. The species lists including
total 41 species and related details were shown in Table 1. After this, the remained 909
flowering specimens were used to examine the relationship between flowering phenology
and climate warming.
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Table 1 Species lists analyzed in this study
Species No. of
records (n =)
Time span
(years)
Altitude
ranges (m)
Life forms Flowering
months
Balasaminaceae
Impatiens arguta 21 36 280–3000 Herb perennial Jul–Sep
Impatiens infirma 8 36 2300–3500 Herb annual Jul–Sep
Boraginaceae
Microula hispidissima 2 35 3600–3940 Herb biennial Jun
Onosma waddellii 13 40 2900–4350 Herb annual Aug–Sep
Onosma hookeri 6 39 3020–4100 Herb perennial Jun–Jul
Compositae
Aster albescens var. pilosus 11 36 2900–3700 Shrub Jun–Sep
Aster asteroides 45 40 3710–5400 Herb perennial Jun–Aug
Aster flaccidus 42 40 3600–5500 Herb perennial Jun–Sep
Aster fulgidulus 5 36 2200–2950 Shrub Jun–Jul
Aster souliei 27 40 3000–5900 Herb perennial May–Jul
Astragalus strictus 57 40 800–4800 Herb perennial Jul–Aug
Erigeron multiradiatus 47 40 2700–4680 Herb perennial Jul–Sep
Heteropappus crenatifolius 20 36 1750–4450 Herb annual May–Sep
Heteropappus semiprostratus 35 36 3300–5100 Herb perennial Jul–Sep
Caryophyllaceae
Arenaria densissima 9 35 4200–5600 Herb perennial Jun–Aug
Fabaceae
Astragalus monbeigii 4 38 2000–4500 Herb perennial Jun–Jul
Medicago lupulina 20 40 1950–3980 Herb perennial Apr–Sep
Oxytropis kansuensis 13 40 2950–4200 Herb perennial Jun–Aug
Tibetia himalaica 17 36 2900–4200 Herb perennial May–Jun
Geraniaceae
Geranium pratense 3 36 3350–4300 Herb perennial Jun–Jul
Geranium robertianum 12 36 2000–3150 Herb annual Apr–Jun
Lamiaceae
Dracocephalum heterophyllum 39 40 3500–5100 Herb perennial Jun–Aug
Dracocephalum tanguticum 29 36 3200–5100 Herb perennial Jun–Sep
Liliaceae
Allium prattii 25 40 3600–4700 Herb perennial Jun–Sep
Ranunculaceae
Aconitum gymnandrum 27 40 3200–4800 Herb annual Jun–Aug
Anemone imbricata 14 40 3800–5300 Herb perennial May–Jul
Anemone obtusiloba ssp. ovalifolia 53 40 2080–5100 Herb perennial May–Jul
Callianthemum pimpinelloides 11 40 3750–5800 Herb perennial Apr–Jun
Clematis connata 12 36 2200–3400 Vines woody Aug–Oct
Clematis pseudopogonandra 7 36 2250–4145 Vines woody Jun–Jul
Clematis tibetana var. vernayi 45 40 2700–4800 Vines woody Jul–Oct
Delphinium kamaonense
var. glabrescens
36 36 2550–4800 Herb perennial Jun–Aug
Halerpestes tricuspis var. variifolia 16 40 2950–4460 Herb perennial May–Jul
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Temperature data
In terms of its availability and accessibility, published datasets were used to analyze
temperature change in TAR during the period of 1961–2000 (Du 2001). This was con-
sidered baseline information because it included 36 major meteorological stations across
most of the territory of TAR, within an altitude gradient of 2300–4900 m a.s.l. (See further
details in Table S1; Available Online). All annual or seasonal temperature data points were
extracted with the software DigitiZeIt 1.5.8 (Share-it!, 2006).
Statistical analysis
For the analysis of data, changes in flowering time across four decades in response to
climate variable (temperature) were considered, rather than other parameters such as
flowering time and altitudinal gradients.
Thus, in view of the specimens collected across different altitudes or sites, a mixed
model with fixed treatment and random block effects was applied to analyze the dataset.
All of the specimens collected from different years, different altitudes and different
locations were considered as a randomized-block experimental design (Ha
¨kkinen et al.
1995). All species collected from the same altitude were grouped together, i.e., every
altitude was treated randomly as a block, but the year and temperature were considered as
fixed treatment. The particular collecting dates (i.e., flowering time) expressed as the
number of days in a year, were considered as the only response variable. Thus a mixed
model with randomized blocks was expressed as: y =intercept ?vector of fixed
effects ?vector of random effects ?vector of random error.
Given the non-normality of the original data, a reference baseline point, the sampling
data in the year of 2000 was used, and then variables considered here were treated as the
difference between the original and this baseline point. The dataset was grouped by life
forms in order to take account of heterogeneity among them. Thus, the PROC MIXED
procedure in the software SAS 9.2 (SAS Institute 2008) was used for mixed model analysis
in which there is no assumption of independence with constant variance. Residuals were
Table 1 continued
Species No. of
records (n =)
Time span
(years)
Altitude
ranges (m)
Life forms Flowering
months
Ranunculus nephelogenes 72 40 2950–5400 Herb perennial Jun–Aug
Ranunculus tanguticus 45 40 3040–5100 Herb perennial Jun–Oct
Urticaceae
Parietaria micrantha 9 36 3000–4000 Herbs annual Jun–Jul
Pilea racemosa 19 36 2000–5400 Herb perennial May–Jun
Urtica ardens 4 36 1500–2550 Herb perennial Jul–Aug
Urtica hyperborea 11 40 3900–5200 Herb perennial Jun–Jul
Urtica laetevirens 5 36 3000–3500 Herb perennial Jun–Aug
Verbenaceae
Caryopteris trichosphaera 12 36 3100–3900 Shrub Aug–Sep
The information about year and altitude ranges consults those of specimen records with clear labels in the current
herbarium database. The details about life forms or flowering month in each species are derived from Flora
Reipublicae Popularis Sinicae (FRPS) or Flora of China (FOC) (http:www.efloras.org)
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examined to meet the assumption of homoscedasticity and normality. Parameters were
estimated by a method of maximum likelihood. In addition, due to a relatively large sample
size (n=909) in this analysis, it was believed that it could further increase the robustness
of results.
Results
The temperature change in TAR, from Du’s publication (Du 2001), was re-summarized in
Fig. 1, which illustrates a rising trend in temperature during the period of 1961–2000 with
an average increase of 0.26 °C per decade (P\0.0001), and more rise during 1984–2000
(Fig. 1b). It is also found that there is a seasonal warming in both summer (from June to
August) and winter (from December to next February), the warming trend in winter is more
pronounced than that in summer (0.29 °C in winter [0.16 °C in summer per decade, paired
ttest, P\0.0001; Fig. 1a). Within the latitude ranges of TAR, the general warming has
been obviously recorded across the past forty years (Fig. 2). At latitudes of both less than
29°Mand more than 32°M, the average increases per decade are 0.21 °C(P\0.05). In
contrast, at latitudes of 29–30°Mand 30–31°M, the temperatures rise slightly less but more
significantly by 0.19 °C and 0.14 °C per decade, respectively (P\0.01), whereas there is
an increase of 0.22 °C per decade at latitudes of 31–32°M(P\0.05).
All collected specimens were grouped in life-forms, and a smoothed line for all of data
points were drawn (Fig. 3). The plot showed little evidence of earlier flowering time with
increasing mean annual temperature (AMT) and winter temperature (MWT) (Fig. 3a, d),
Fig. 1 Long-term trend of an
increase in temperature in TAR
for the period 1961–2000:
aseasonal or bannual mean
temperature plotted against the
time, respectively, as indicated
by an ordinary linear regression
(OLS): summer (square); winter
(triangle); annual (dot). The
dashed line shows the average
value over that 40-years’ period.
Data extracted from Du (2001)
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Fig. 2 Changes in annual mean
temperature of different latitudes
in TAR during the period
1961–2000, where an asterisk
shows significance at the level
of P\0.05, two asterisks at the
level of P\0.01. Data from
Du (2001)
Fig. 3 Adjusted flowering time (Adj_flowering time, days of year) for each life-form group and for all of the
data points plotted against aadjusted annual mean temperature (Adj_AMT, °C), byear, cadjusted mean
summer temperature (Adj_MST, °C), dadjusted mean winter temperature (Adj_MWT, °C), in terms of a
baseline year 2000. Annuals as circle, perennials as triangle, woods as square, with a smoothed line for all data
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but somewhat implied that flowering time might occur early over time (Fig. 3b) or as the
mean summer temperature (MST) increases (Fig. 3c).
The further mixed-model analysis (Table 2) illustrated that there is a significant advance
in the average flowering time (AFT) for the study group with 41 species. Moreover, the
contribution of different temperature variables was different to the commencement of
flowering time, for example, the rising AMT nonsignificantly resulted in a 2 days’ earlier
flowering (F=0.22, P=0.640) while higher MST led to a significant advance of
7.5 days (F=10.53, P=0.001). Across the 40-year period, there was only a small
advance in the timing of flowering, of 0.5 days. However, this was highly significant
(F=10.67, P=0.001). The MWT did not result in any significant change in pheno-
logical response (F=0.67, P=0.414), despite there was a 2 days’ advance of flowering
time.
Discussion
The contribution of herbarium collections to understanding local and regional scale
impacts of climate change on ecological processes has recently been realized, just as the
case of flowering phenology (Lavoie and Lachance 2006; Gallagher et al. 2009; Robbirt
et al. 2011). Most recently, Johnson et al. (2011) highlight the often overlooked potential
of herbarium sources. The present data generally supported this perspective, and revealed
a negative trend of 0.5 days per year, equivalent to an advance of 20 days in the AFT over
a 40-year period. Similar findings have been reported by other studies (e. g., Primack et al.
2004; Menzel et al. 2006; Gaira et al. 2011). In our case study, for example, Erigeron
multiradiatus, a perennial herb as folk medicine in local communities, was found to flower
early by about 2 days per year (Fig. 4). In the Himalaya adjacent to the TAR, another
perennial alpine herb Aconitum heterophyllum has been thought to show a 0.2 days per
year earlier flowering (Gaira et al. 2011). These phenological shifts have largely resulted
from changes in ambient temperature, which match the global warming pattern, and in fact,
there is a negative trend of AFT against AMT despite lack of significance.
Based on specimen collected data at individual level, it seems that MST contributed
much more to the phenological advancement than MWT, which is contrary to the finding by
Yu et al. (2010). At high elevations such as TAR, warmer summer occurred more frequently
during period of 1980–2000 in comparison to 1960–1979, while winter became milder. It
has been argued that warmer summer is tightly coupled with both the earlier melting of
Table 2 A mixed model analysis of mean deviations in flowering dates of a set of 41 species affected by
long-term climate variables with fixed treatments and random block effects
Predictive variables Parameter estimates SE FP
Year -0.48 0.14 10.98 0.001
Annual mean temperature (AMT) -2.2 4.68 0.22 0.640
Mean summer temperature (MST) -7.5 2.29 10.53 0.001
Mean winter temperature (MWT) -1.7 2.13 0.66 0.417
In order to conform to assumption of normality, the collecting data from the year 2000 was used as a
reference point. Year and adjusted temperature were thus considered as the levels of fixed effects and
adjusted altitude as random blocking effects. All species were grouped by life forms. Model parameters were
estimated by a maximum likelihood method. Parameter estimates of fixed effects and SE, F value (F), and
Pvalue (P) were presented (n=909)
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snow cover and the renewed vigor of plant growth (Jolly et al. 2005). Such association
might be especially significant for those regions like TAR where a warmer growing season
is beneficial to the accomplishment of plant’s life-cycle (Ko
¨rner 2003). In TAR, the growing
season often starts from late May or early June. Given that there is a remarkable feature of
flower bud preformation for almost all high-mountain plants (Billings 1974), it could be that
a small increase in temperature substantially contributes to early flowering and rapid
development. As there is ample soil moisture supplied by snowmelt and frozen soils thaws,
warmer soils directly contribute to the dormancy break and also to floral initiation in some
alpine plants (Spomer and Salisbury 1968; Billings 1974). Warming experiments in the
growth chamber have shown that increasing temperatures directly favor the floral dormancy
break, inducing early flowering (Suzuki and Kudo 1997), regardless of the short or long
photoperiods (Rochow 1970), which appear be tightly associated with protection from frost
damage and acceleration of bud expansion. Additionally, the large amounts of carbohydrate
stored in the underground organs of alpine plants have been found to be in close relation to
their phenological changes (Mooney and Billings 1960). In the following season restoration
from dormancy and re-growth initiation benefit from such usage of carbon sink once the
surrounding physical temperature is favorable (Wyka 1999). Another possible advantage of
early flowering is that plants can effectively absorb available soil nutrients (especially N)
during the growing season and hence adapt efficiently to the climate change in alpine belt.
Taken together, it is reasonable to demonstrate the phenological advance triggered by
warmer summer, because such synchronization between phenology and temperature is
particularly important in the alpine regions for assuring plant survival such as successful
pollination and seed ripening against the rigors. Indeed, Yu et al. (2010) also reported that
an increase in temperatures during the period from May to August significantly induces an
earlier occurrence of the phenological phases. However, the question of whether winter
warming delays spring phenology or not remains still open. Yu et al. (2010) attribute that
delay to insufficient chilling requirements which, however, seem not necessary but could be
partially compensated by a higher temperature or longer photoperiod (e.g., Rochow 1970).
Shen (2011) pointed out that such delay occurs on condition that both spring temperature
decreases and winter temperature increases. In this study the phenological response to
Fig. 4 A smoothed line for
Erigeron multiradiatus,a
perennial alpine herb as folk
medicine in local communities,
where it tends to flower early
during the period 1961–2000.
Terms as in Fig. 3
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winter warming at species level presented an advancing trend although non-significant. The
different temporary scales and different methodologies might also have caused such dis-
crepancy in results, as in the case of some European regions (Cleland et al. 2007).
In order to address the challenges posed by a warming of climate, it seems necessary to
integrate scientific proof and local knowledge. This is particularly important in a multi-
cultural or multiracial interwoven context as well as with highly vulnerable ecosystem
having its unique biological diversity, such as the Tibetan Himalayas. Visual observations
by local people have conveyed strong impression of the effects of climate change such as
early flowering, less snow cover, and other critical processes related to local livelihoods or
food security (Chaudhary and Bawa 2011). The current analysis as evidenced by herbarium
specimen provided a solid foundation for the extension of such indigenous knowledge. The
combination of scientific knowledge with local practice and experience might act as
important functions in bridging gaps in climate change adaptation (Mackinson 2001).
Based on an analysis of long-term herbarium collection data in TAR, there was a clear
indication in the occurrence of early flowering due to warming of climates, despite many
gaps for specimen collection (Fig. 3; see also Primack et al. 2004). Specimen records
through a careful calibration could be a significant source for the quantitative assessment of
climate change, especially for the inaccessible mountains such as Himalayan region where
establishing long-term monitoring systems are difficult. Lastly, whereas the present anal-
ysis add evidence to the significant effect by warmer summer, it is desirable to further
corroborate our results by incorporating other key processes such as freezing-thawing
cycles (Chen et al. 2011) and ecological degradation under human’s intervention.
Acknowledgments We thank all member herbaria of Chinese Virtual Herbarium for their specimen-
digitalized contributions and the herbarium (PE), Institute of Botany, Chinese Academy of Sciences
(IBCAS). We also take this opportunity to thank Drs. H. Sun, Z. K. Zhou, W. Y. Chen in Kunming Institute
of Botany, CAS, Drs. H. N. Qin, L. Q. Li, B. J. Bao, Q. Lin in IBCAS, and Dr. G. Pan in Tibet Agricultural
and Animal Husbandry College for their great support over the years. Special thanks are due to those
invaluable efforts from many field botanists to herbarium collections in TAR. Their thoughtful suggestions
from the anonymous referees and editors led to great improvements of the manuscript. This study was
funded by the Ministry of Science and Technology (2007FY110100) and Tibet Biodiversity Assessment
project by the Ministry of Environmental Protection of China to X. F. G., the National Natural Science
Foundation of China (31150110471) and Chinese Academy of Sciences (XDA05050407) to Y. W.
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