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Quantitative Analysis of Land Mammal Zoogeographical Regions in China and Adjacent Regions

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Zuo-Fu Xiang, Xing-Cai Liang, Sheng Huo and Shi-Lai Ma (2004) Quantitative analysis of land mammal zoogeographical regions in China and adjacent regions. Zoological Studies 43(1): 142-160. In this paper, our aim was to determine, by means of quantitative analysis, the distribution patterns of the land mammals in China and, adjacent regions using physiographical regions as operative geographical units (OGUs). Based on the pre-sence or absence of 11 orders, 42 families, 197 genera, and 577 species of land mammals in their zoogeo-graphical regions, which were used as OGUs, we studied the biotic boundary between the Oriental Region (OR) and the Palaearctic Region (PR), as well as subregion boundaries. The boundary s statistical signifi-cance was tested by G-test as described by McCoy et al. A significantly strong biotic boundary was found to separate the PR from the OR, and there is a weak biotic boundary in the PR, which divides it into 2 subregions. We concluded that the biotic boundary which separates the PR and OR is a strong boundary. We suggest that the Qinghai-Xizang Plateau should be regarded as a subregion of the PR, which can embody its characteristics of high elevations and a frigid climatic, which is called the Qing-Zang subregion of the PR (QZSP).
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142
Quantitative Analysis of Land Mammal Zoogeographical Regions in
China and Adjacent Regions
Zuo-Fu Xiang1,2, Xing-Cai Liang1, Sheng Huo1,2 and Shi-Lai Ma1,*
1Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China
2Graduate School of Chinese Academy of Sciences, Beijing 100031, China
(Accepted July 14, 2003)
Zuo-Fu Xiang, Xing-Cai Liang, Sheng Huo and Shi-Lai Ma (2004) Quantitative analysis of land mammal
zoogeographical regions in China and adjacent regions. Zoological Studies 43(1): 142-160. In this paper, our
aim was to determine, by means of quantitative analysis, the distribution patterns of the land mammals in China
and, adjacent regions using physiographical regions as operative geographical units (OGUs). Based on the
pre-sence or absence of 11 orders, 42 families, 197 genera, and 577 species of land mammals in their zoogeo-
graphical regions, which were used as OGUs, we studied the biotic boundary between the Oriental Region
(OR) and the Palaearctic Region (PR), as well as subregion boundaries. The boundary,s statistical signifi-
cance was tested by G-test as described by McCoy et al. A significantly strong biotic boundary was found to
separate the PR from the OR, and there is a weak biotic boundary in the PR, which divides it into 2 subregions.
We concluded that the biotic boundary which separates the PR and OR is a strong boundary. We suggest that
the Qinghai-Xizang Plateau should be regarded as a subregion of the PR, which can embody its characteristics
of high elevations and a frigid climatic, which is called the Qing-Zang subregion of the PR (QZSP).
http://www.sinica.edu.tw/zool/zoolstud/43.1/142.pdf
Key words: Statistical test, Dendrogram, Cluster analysis, Qinghai-Xizang Plateau.
Zoological Studies 43(1): 142-160 (2004)
Zoogeographical regions can usually be dis-
tinguished by the distribution of animals in certain
physiographical regions. The research on zoo-
geographical regions can benefit to recognize the
history of fauna and the relations among different
taxa. In China, there are very intricate topogra-
phies, like the Qinghai-Xizang Plateau (QXP), the
plains of the lower reaches of the Yangtze River
and the Yellow River, etc., variable climates which
change from the frigid zone in northernmost to the
tropical zone southernmost areas, and very com-
plex geologic histories (i.e., uplift of the QXP). It is
these characteristics that make it very difficult to
identify the biogeographical patterns of China (Li
et al 1981, Committee of Physical Geography of
Chinese Academy of Sciences 1985). Integrating
the physiography of China and the distribution of
special taxa, Zhang (1997) divided Chinese zoo-
geographical region into 3 subregions, 2 belonging
to the Palaeartic Region (PR) and 1 belonging to
the Oriental Region (OR). They are the Northeast
Subregion of the PR which is divided into the
Northeast Division and the North China Division,
the Central Asian Subregion of the PR which is
divided into the Mengxin Division and the
Qingzang Division, and the Indochinese Subregion
of the OR which is divided into the Southwest
Division, Central China Division, and South China
Division. Recently, many Chinese scholars have
undertaken research on the biogeographical pat-
terns of China. For instance, Chen et al. (1996)
advocated that the QXP should be regarded as an
independent region, which equals the PR and OR,
and named it the Qinghai-Xizang Plateau Region
(QXPR), in view of the differentiation of the sub-
family Schizothroacinae and the genus Triplophysa
from the Tertiary to the present. Most mammal
species have been grouped by many authors,
results of which can be found in the literature, e.g.,
Zheng (1981), Ma et al. (1996), etc. Unfortunately,
142
*To whom correspondence and reprint requests should be addressed. Tel: 86-871-5198812. Fax: 86-871-5190441. E-mail:
masl@mail.kiz.ac.cn
Xiang et al. -- Quantitative Analysis of Mammal 143
all these studies are based on visual analyses.
Nevertheless, as reported by several authors, such
as Birks (1987), Real et al. (1992), and Saus-
Fuentes and Ventura (2000), the best method for
establishing groups of physiographical regions is
based on quantitative analyses in order to avoid
basic subjective errors and to produce consistent
results. Specifically, the main aim of the quantita-
tive analysis of the presence or absence of several
taxa in a number of areas is to detect repeatable
biogeographical patterns with the data in the form
of biotic regions (groups of areas with similar bio-
tas). Some quantitative methods of classification
used in defining boundaries between ordered loca-
tions can be found in the literature (see McCoy et
al. 1986 and references therein). The method out-
lined by McCoy et al. (1986) is an extension of the
probabilistic similarity technique (Crick 1980) used
to develop a matrix-analysis method of placing
boundaries between the biotas of ordered loca-
tions. The advantage of this method is that the G-
test can be applied to determine the contingency
of groups, which were set up by the unweighted
paired-group method using arithmetic averages
(UPGMA). This method, complemented by the
significance test of the resulting groups according
to the approach of Real et al. (1992), constitutes
an objective method for measuring the statistical
significance of groups obtained from a numerical
classification (see Saus-Fuentes and Ventura
2000). However, this method has not been applied
to the analysis of land mammal biogeographical
patterns of China. Since the foundation of the
People,s Republic of China (1950s), several
important field surveys on land mammals have
been carried out in certain regions (i.e., the QXP,
Xinjiang region, Transection Moutain region, etc.),
all of which have made it become possible for us
to apply a quantitative analytical method to the
land mammal biogeographical patterns of China
and adjacent regions.
The main aim of this paper was to determine
the distribution patterns of land mammals in China
and adjacent regions by appling quantitative analy-
sis using physiographical regions as operative
geographical units (OGUs; Crovello 1981).
Specifically, the goals of the study were: (a) to test
whether the zoogeographical regions drawn by
visual methods are reasonable; and (b) to test
whether the QXPR as established by Chen et al.
(1996) is rational. In order to achieve these goals,
the method of McCoy et al. (1986), complemented
by the modifications of Real et al. (1992), the clus-
ter analysis method (UPGMA), the Sokal and
Sneath probabilistic similarity index (Norusis
1997), and Baroni-Urbani and Buser,s similarity
index (Baroni-Urbani 1976) were used.
MATERIALS AND METHODS
Study area
The study area included China, Vietnam,
Laos, Thailand, Cambodia, Burma, Bhutan, and
part of Bengal and India. For analytical purposes,
China was divided into 10 physiographical regions
that are defined by the characteristic of geography,
climate, and vegetation (adapted from Chen et al.
1996, Zhang et al. 1997 1999), and the other adja-
cent regions were divided into 2 physiographical
regions (Fig. 1).
The Aerjin Mountain region (AJ) is an overlap-
ing portion of the QXPR and Mengxin Division.
Qing-Zang Plateau region (QZ) is what remains
when the AJ is subtracted from the QXPR. The
Mengxin region (MX) is what remains when the AJ
is subtracted from the Mengxin Division. The
Xinan Mountain region (XN) is the Southwest
Division minus the portion, which is already includ-
ed in the QZPR. Along the Yangtze River, the
Central China Division is divided into Bei-
Fig. 1. Operative geographical Units (OGUs) of China and
adjacent regions. (DB, Dongbei region; HB, Huabei region;
MX, Mengxin region; AJ, Aerjin Mountain region; QZ, Qing-
Zang Plateau region; BCH, Bei-Changjiang region; NCH, Nan-
Changjiang region; XN, Xinan Mountain region; HN, Huanan
region; DN, Diannan region; BB, Burma-Bengal region; VT,
Vietnam-Thailand region).
MX
MX
HN
HN
HN
DN
Kas-
hmir
MX
DB
HB
BCH
NCH
AJ
QZ
BB
BB
XN
VT
VT
VT
Mongolia
Russia
Nepal
Kored
40°
40°
30°
23.5°
20°
10°
70°80°
80°90°100°110 °120°130°
90°100°110 °120°130°140°
30°
23.5°
20°
10°
Zoological Studies 43(1): 142-160 (2004)
144
Changjiang region (BCH) and Nan-Changjiang
region (NCH). The South China Division is divided
into the Diannan region (DN) and Huanan region
(HN). The Dongbei region (DB) and the Huabei
region (HB) are equal to the Northeast Division
and North China Division, respectively. The adja-
cent regions of China are divided into the Vietnam-
Thailand region (VT) and the Burma-Bengal region
(BB).
Materials and characteristic of animal distribu-
tion in OGUs
According to the literature, we obtained a dis-
tribution matrix of 577 land mammals in 12 OGUs
(see Appendix, presence1and absence0;
information from Corbet 1978 1992, Hong et al.
1986, Wilson et al. 1992, Zhang et al. 1997 1999).
Of the land mammals in China, 2 species were not
considered for analysis because the common
muskrat musquash (Ondatra zibethica) is an
allochthonous not an acclimated species and
David,s deer (Elaphurus davidianus) is a reintro-
duced species that is still being raised in some
regions of China even after it was extirpated from
the field. We also did not consider the distribution
shrinkage of some great land mammals (i.e., tiger
Panthera tigris, panther Panthera pardus, etc.), so
the results more accurately reflected these natural
distribution patterns of the land mammals. The
characteristics of the distributions in each of the
OGUs are outlined as follows.
Dongbei region (DB, 6 orders, 19 families, 56
genera, 96 species)
Owing to its cold weather, the number of
species in this region is sparse. The main species
are of Insectivora, especially, Sorex in the
Soricidae, Cletrionomys in the Rodentia, and
Mustelidae species of the Carnivora. Repre-
sentative species are the least shrew (Sorex
mintutus), wood lemming (Myopus schisticolor)
and sable (Martes zibellina).
Huabei region (HB, 7 orders, 20 families, 55
genera, 83 species)
The number of land mammal species in this
region is fewer than that of the DB. Most are
southern extension of DB species. Only a few
kinds of Myospalax, i.e., Rothschild,s zokor (M.
rothschildi) etc., are endemic. But it should be
mentioned that some species that are usually dis-
tributed in tropical regions, i.e., the masked palm
civet (Paguma larvata), rhesus monkey (Macaca
mulatta), etc., appear in this region occasionally.
Mengxin region (MX, 7 orders, 22 families, 76
genera, 139 species)
This region is far removed from the sea, and
the weather is arid or semiarid. The majority of
species that adapted to this natural condition are
of the Rodentia, whose number is exceeds 1/2 of
the total. Most species of the Cricetinae and
Gerbillinae are endemic. Some representative
species, i.e., Przewalskii horse (Equus przewalskii)
and Mongolian wild ass (E. hemionus) of the
Perissodactyla, and Przewalski,s gazelle (Pro-
capra przewalskii) of the Artiodactyla, are very
remarkable.
Aerjin Mountain region (AJ, 7 orders, 17 fami-
lies, 48 genera, 73 species)
The natural weather of this region is extreme
desert or semidesert, and the faunal diversity is
very poor. The primary species are of the
Dipodidae and Gerbillinae. The most noticeable
species is the wild camel (Camelus bactrianus).
Qing-Zang Plateau region (QZ, 8 orders, 21
families, 79 genera, 148 species)
The topography of this region is primary
plateau, and the weather is high chilliness. All
endemic species are adapted to this extreme envi-
ronment, i.e., the wild yak (Bos grunniens), Tibet
gazelle (Procapra picticaudata), and mouping pika
(Ochotona spp.). Most species of the Ochotonidae
are distributed in this region. It should be noted
that some tropical species such as rhesus monkey
(Macaca mulatta), Assam macaque (M. assamen-
sis), pig-tailed macaque (M. nemestrina) and entel-
lus langur (Semnopithecus entellus) can be found
on the eastsouthern border of the plateau.
Bei-Chanjiang region (BCH, 7 orders, 23 fam-
ilies, 87 genera, 150 species) and Nan-Changjiang
region (NCH, 8 orders, 22 families, 80 genera, 149
species)
The number of species in these 2 regions is
greater than those of the DB and HB. There is no
significant difference between these 2 regions.
The most obvious difference concerns species of
the Order Pholidota, i.e., common pangolin (Manis
pentadactyla), found only in the NCH. The number
of species of the Order Chiroptera in the NCH is
higher than that in the BCH. Some land mammals,
such as Chinese river deer (Hydropotes intermis)
and Reeve,s muntjak (Muntiacus reevesii), are
very common in these regions. In addition, some
species that are mainly distributed in the DB, i.e.,
manchurian hedgehog (Erinaceus amurensis), and
common hedgehog (E. europaeus), are found only
in the BCH.
Xinan Mountain region (XN, 8 orders, 28 fami-
lies, 102 genera, 224 species)
Xiang et al. -- Quantitative Analysis of Mammal 145
Great mountains and deep gorges in the
Transection Mountain produce complex tempera-
ture gradients and many kinds of landscape, and
these form the basis of the rich diversity of this
region. There are many species of Chiroptera,
Carnivora, Insectivora, and Rodentia. Some
endemic species, such as the giant panda
(Ailuopoda melanolcuca) and golden monkey
(Rhinopithecus spp.), are very remarkable species
of the world. In addition, there are also many
Ochotonidae species, as in the QZ.
Huanan region (HN, 9 orders, 26 families, 90
genera, 183 species)
Most species are tropical, with few endemics
found in this region. Representative species are
Formosan rock monkey (Macaca cyclopis), Hainan
gibbon (Hylobates concolor), and Thamin,s deer
(Cervus eldi).
Diannan region (DN, 10 orders, 32 families,
89 genera, 190 species)
This region belongs to the tropical zone, and
species of Primates and Chiropteras are signifi-
cantly enriched. Asia elephant (Elephas maximus)
is found in this region.
Burma-Bengal region (BB, 11 orders, 30 fami-
lies, 121 genera, 254 species) and Vietnam-
Thailand region (VT, 11 orders, 32 families, 114
genera, 250 species)
Both regions have many species of
Chiropteras, Primates, and Rodentia. The differ-
ence at the family level is that the Craseony-
cteridae and Tragulidae are found in the VT, but
not in the BB. Some representative species of
these regions, such as the Indian rhinoceros
(Rhinoceros unicornis), Javan rhinoceros (R.
sondaicus), Asia two-horned rhinoceros
(Dicerorhinus sumatrensis), and Asia elephant
(Elephas maximus), are endemic.
OGU classification
Based on the information of the distribution of
577 land mammal species in China and adjacent
regions, a presence or absence matrix for species
in the 12 OGUs was constructed (see Appendix,
presence1, absence0). From the pres-
ence or absence matrix, the Sokal and Sneath
probabilistic similarity index (SSI; Norusis 1997)
matrix was obtained, and applied to classify
OGUs. The SSI is defined as follows: SSI = (A /
(A + B) + B / (A + C) + D / (B + D) + C / (C + D)) /
4; where A is the number of species which are pre-
sented in both OGUs; B is the number of species
which are only present in the 1st OGU but not in
the 2nd; C is the number of species which are only
present in the 2nd OGU but not in the 1st; and D is
the number of species which are present in neither
of the OGU. The UPGMA, which is also called
between-groups average linkagein some soft-
ware packages, was used as a cluster method for
classification, which displaying the results as a
dendrogram of the 12 physiographical regions (for
details see Marquez et al. 1997; in this study, the
software package for analysis is SPSS10.0 and
STATISTCS).
In order to determine the significance of simi-
larities in the matrix, the probability critical table of
the Baroni-Urbani and Busher similarity index (BBI,
Baroni-Urbani and Busher 1976) for binary data
was used. The BBI is defined as follows: BBI =
((A * D)0.5 + A) / ((A * D)0.5 + A + B + C); where A,
B, C, and D are the same meanings as in the SSI.
The similarity values were transformed into 3 cate-
gories, represented by“+”,“-”, and0nota-
tions, represting significantly higher or lower than
expected at random, and with no any significant
difference, respectively. The transformed values
were regarded as matrices of significant similari-
ties.
Following the method outlined by McCoy et
al. (1986) and the approach by Real et al. (1992),
for each node of the dendrogram, the presence of
strongly or weakly significant segregations
between physiographical regions separated by the
node was tested. At each node of the dendro-
gram, a submatrix, which only included the physio-
graphical region involved in the node, was estab-
lished from the matrix of significant similarities.
This submatrix was divided into 3 zones: zone A
and B, which corresponds to each group of regions
by the node; and zone A×B, which corresponds to
the intersection between both zones. From the
number of“+”and“-”values in each zone,
the parameters of DW(A×A), DW(B×B), DW, and
DS were calculated. DW is a measure of the effi-
ciency of a boundary to separate 2 groups of phy-
siographical regions, whose faunas are similar
within but not between each group. DW can be
separated into the parameters, DW (A×A) and
DW (B×B). These parameters measure the
extent to which the similarities higher than expect-
ed () tend to be in zones A and B, but not in A×
B, i.e., they measure the internal homogeneity in
zones A and B. DS is another measure of the effi-
ciency of a boundary, giving a measure of whether
the similarities lower than expected () tend to be
located in zones A×B, but not in zones A or B. In
Zoological Studies 43(1): 142-160 (2004)
146
a word, these D values (DW and DS) are mea-
sures of how well the putative boundary between
zones A and B similar locations (weak boundary)
or segregates dissimilar locations (strong bound-
ary). The statistical significance of each node of
the dendrogram was assessed by a G-test of inde-
pendence (following the application of Yates,cor-
rection) of the distribution of the values of in“+”,
“-”, and0in zones A and B and zone A×B
of the submatrix. By this test, we obtain parameter
GW, for a weak boundary, and GS, for strong seg-
regation (for details see McCoy et al. 1986).
RESULTS AND DISCUSSION
From the presence or absence matrix of 577
land mammals in the 12 OGUs (see Appendix,
presence1, absence0), using the method
described above, the Sokal and Sneath probabilis-
tic similarity index matrix was obtained (Table 1),
and its corresponding dendrogram was produced
(Fig. 2). Using Baroni-Urbani and Busher,s simi-
larity index, we obtained the matrix of significant
similarities for the 12 OGUs (Table 1). Applying
the method outlined by McCoy et al. (1986) and
Real et al. (1992), we tested the statistical signifi-
cance of groups which were determined by
UPGMA, and the results are presented in table 2
(Fig. 2).
The boundary between PR and OR in China is
an argumentative focus among many zoologists
(e.g., Udvardy 1975, Corbet et al. 1992, Zhang
1997 1999 2002, Hoffman 2001). Applying the site
sampling method and analyzing the rules of land
mammal faunas at particular sites, Hoffman (2001)
believed that a transition zone existed between the
PR and OR in China, and the central, southern and
northern edges were 30°
N, 28°
N and 33°
N,
respectively. The method outlined by McCoy et al.
(1986) and Real et al. (1992) revealed 2 operative
biogeographical units (OBUs): PR, including DB,
HB, MX, AJ, and QZ; and OR, including the
remaining BCH, NCH, XN, DN, HN, BB, and VT.
Both OBU groups were separated by a weak
boundary and a strong boundary (DW, DS > 0;
GW and GS were significant; see Table 2; Fig. 2).
Since the configuration and limits of biogeo-
graphical regions are strongly related to the type of
OGUs considered (see e.g., Real et al. 1996), our
results are only one-way of identifying biogeo-
graphical units. Thus when using biogeographical
units as OGUs, the Q-mode analysis revealed the
existence of a biogeographical pattern: in the view
of quantitative analysis of the land mammals
zoogeographical patterns, the OR and PR were
Table 1. Probabilistic similarity matrix of 12 operative geographical units in
China and adjacent regiona,b,c
DB HB MX AJ QZ BCH XN NCH HN DN BB VT
DB + + + + + 0 0 - - - -
HB 0.803 + + 0 + 0 0 - - - -
MX 0.794 0.724 + 0 - - - - - - -
AJ 0.642 0.616 0.786 + - - - - - - -
QZ 0.58 0.592 0.563 0.626 + + 0 0 0 - -
BCH 0.517 0.575 0.426 0.465 0.688 + + - 0 - -
XN 0.484 0.51 0.403 0.424 0.557 0.673 + + 0 + 0
NCH 0.485 0.538 0.409 0.429 0.574 0.647 0.769 - 0 0 +
HN 0.617 0.712 0.534 0.496 0.627 0.726 0.623 0.666 0 - -
DN 0.552 0.596 0.46 0.446 0.582 0.722 0.741 0.794 0.779 0 +
BB 0.39 0.42 0.32 0.365 0.497 0.582 0.711 0.65 0.507 0.6 +
VT 0.402 0.427 0.319 0.355 0.46 0.519 0.73 0.691 0.503 0.627 0.778
aThe right uppper part of the table is the Baroni-Urbani and Busher probability similarities index,
+, values significantly higher than expected at random, p< 0.05;“-”, values significantly
lower than expected at random, p< 0.05;0, values not different from those expected at ran-
dom.
bThe left lower part of the table is the Sokal and Sneath probability similarities index.
cDB, Dongbei region; HB, Huabei region; MX, Mengxin region; AJ, Aerjin mountain region; QZ,
Qing-Zang Plateau region; BCH, Bei-Changjiang region; NCH, Nan-Changjiang region; XN,
Xinan Mountain region; HN, Huanan region; DN, Diannan region; BB, Burma-Bengal region; VT,
Vietnam-Thailand region.
Xiang et al. -- Quantitative Analysis of Mammal 147
separated by a strong boundary in China, in spite
of typical OR species, i.e., masked palm civet
(Paguma larvata) and rhesus monkey (Macaca
mulatta), being distributed in the HB, and some
typical PR species, i.e., manchurian hedgehog
(Erinaceus amerensis), common hedgehog (E.
europaeus), and squirrel (Sciurus vulgaris) being
found in the BCH. This does not mean that our
result is contradictory to the viewpoint mentioned
above. In fact, McCoy et al. (1986) pointed out
that the implication of this dichotomous dendro-
gram is to place boundaries between locations
where resident species are independently distri-
buted, and this would necessarily result in arbitrary
groups. The GW value, which is calculated from a
series of a random matrix, may have a bimodal or
unimodal distribution pattern, and a range exists in
which the GW values are significant. Our results
might not conflict with the standpoint of Hoffman
(2001). Conversely, applying quantitative analysis,
we might conclude that the boundary between the
PR and OR adopted by Zhang et al. (1997) was
correct.
The Qinghai-Xizang Plateau (QXP) is a spe-
cial region, with high elevations that average over
4500 m, with many great mountains and a com-
plex geologic history (Li et al. 1979). Chen et al.
(1996) discussed this special region from a histori-
cally temporal and spatial view, and suggested it
should be considered an independent zoogeo-
graphical unit, which means that the QXP has an
equal position as a zoogeographical region to the
PR and OR, which was named the Qinghai-Xizang
Plateau Region (QXPR). In our study, we divided
the QXPR into the QZ and AJ. Our results reveal
that (1) the AJ clusters together with the MX, which
equals the DB and HB; (2) between the QZ and
AJ, DB, HB, and MX, a weak boundary exists (Fig.
2; Table 2; DW > 0, GW is significant), which
means that the AJ should not be included in the
QZPR, and that the QZPR can not be considered
a region independent of the PR and OR. Con-
sidering that the QZ is a special region with high
elevations and a very cold climate, we suppose
that the QZ should be regarded as a subregion of
the PR, which is named as Qing-Zang subregion
of the PR (QZSP), to embody the land mammal
pattern of the high chilliness characteristic fauna
as represented by some special species such as
the wild yak (Bos grunniens), Tibet gazelle
(Procapra picticaudata), and mouping pika
(Ochotona spp.). This opinion agrees with the
standpoint of Zhang (2002), who supposed that
the important geological event (uplifting of the
QXP) and different natural environments/natural
regions produced a very different fauna in the
QXP. Chen et al. (1996) pointed out that the QXP
should be regarded as an independent zoogeo-
graphic region; however, our results do not agree
with this standpoint. It may be the characteristics
of different research objects, which are responsible
for this discrepancy. Chen et al. (1996) used the
subfamily Schizothroacinae and the genus
Triplophysa, which lack dispersal ability and live in
water, as reseach objects, and geological historical
events imposed greater evolutionary pressures on
them than on the land mammals that we used in
our study. Based on differentiation of the subfami-
ly Schizothroacinae and the genus Triplophysa,
the QXP can be regarded as an independent zoo-
geographical region, but based on the differenria-
tion of land mammals, it cannot. In addition, the
AJ, MX, DB, and HB should be regarded as anoth-
er subregion of the PR, which is named the North
China subregion of the PR (NCSP). At the same
time, according to the results of the cluster analy-
sis (Fig. 2), where DB and HB, and MX and AJ are
closely clustered, we suggest taking the DB and
HB as a zoogeographical division, which is named
the East Division, and the MX and AJ as another
division, which is named the West Division.
Corbet et al. (1992) deemed that the XN,
Table 2. Significant segregations between the physiographical regions on the dendrogram,s forka
Groups set up by UPGMA Weak boundary Strong boundary
Group A Group B Coefficient DW (AXA) DW (BXB) DW GW pDS GS p
Choro-regions 1-5 6-12 0.957 0.515 -0.027 0.299 13.408 *** 0.201 17.841 ***
1-4 5 0.667 0.43 0.43 0.467 6.414 * 0 0 n.s
6-10 11-12 0.669 0.395 0.395 0.4 0.031 n.s 0.062 0.2977 n.s
aGW and GS indicate weak segregation and strong segregation between the groups, respectively. *p0.05; ***p0.001; n.s, no sig-
nificance. DW (AXA) and DW (BXB) quantify the internal homogeneity of each group analyzed. DW and DS quantify the value of
each boundary.
Zoological Studies 43(1): 142-160 (2004)
148
BCH, DN, HN, NCH, BB and VT should be regard-
ed as the Indochinese subregion of the OR, while
Zhang et al. (1997) also considered the XN, BCH,
DN, HN, and NCH to be the Indochinese subre-
gion of the OR. Our quantitative analysis also
reveals that there is no weak boundary between
the BB and VT, and the XN, BCH, DN, HN, and
NCH (Table 2; DW > 0, GS > 0; GW and GS are
not significant). Considering that our results agree
with these of Corbet, we suggest to naming it as
Indochinese subregion. On account of the cluster
analysis results (Fig. 2), we suggest calling the
HN, NCH, HN, XN, and DN as the South China
Division, and the BB and VT as the Central
Peninsula Division.
CONCLUSIONS
In conclusion, using the physiographical
regions in China and adjacent region as OGUs, we
applied a classification to them, and tested the sig-
nificance of the resulting groups. Although the
results obtained constitute the first quantitative
approach to the zoogeography of the land mam-
mals of China and adjacent regions, and just tests
whether the zoogeographical regions that already
recognized are reasonable, we suggest that the
QZ should be regarded as a subregion of the PR
and conclude that the boundary between the PR
and OR is strong. At the same time, we suggest
that one should use an objective method, such as
quantitative analysis, rather than a subjective
method, such as visual analysis, when analyzing
the zoogeography of a region in the future.
Acknowledgments: This work was supported by
the Project of Knowledge Innovation Program
(KSCX2-1-06A). We thank Prof. QK Zhao and Dr.
Earl D. McCoy for guiding the statistical work, Dr.
Maria A. Sans-Fuentes who offered some very
important literature, and 2 anonymous referees for
useful comments on the paper.
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Zoological Studies 43(1): 142-160 (2004)
150
Appendix: Presence (1) or absence (0) of 577 mammalian species in each OGUa.
Species
Operative geographical unit (OGU)
DB HB MX AJ QZ BCH XN DN NCH HN BB VT
Hylomys suillus 000000010011
Neohylomys hainanensis 000000000100
Neotetracus sinensis 000000100011
Erinaceus europaeus 110001001000
Erinaceus amurensis 110001000000
Hemiechinus dauuricus 111100000000
Hemiechinus hughi 000001100000
Hemiechinus auritus 001100000000
Sorex mintutus 101100000000
Sorex caecutiens 101000000000
Sorex araneus 111001101000
Sorex unguiculatus 101000000000
Sorex daphaenodon 101000000000
Sorex mirabilis 100000000000
Sorex cylindricauda 000001100000
Sorex bedfordiae 000001110010
Sorex buchariensis 000010000000
Blarinella quadraticauda 000001111010
Soriculus macrurus 000001111011
Soriculus lamula 000001100000
Soriculus parca 000000101011
Soriculus nigrescens 000010000010
Soriculus caudatus 000001101010
Soriculus leucops 000000110011
Soriculus hypsibius 000011100000
Soriculus parva (simithii) 000000111000
Soriculus salenskii 000001100000
Soriculus sodalis (fumidus) 000000000100
Neomys fodiens 101000000000
Suncus murinus 000000100111
Suncus etruscus 000000010011
Suncus stoliczkanus 000000010000
Crocidura horsfieldi 000000010111
Crocidura suaveolens 110110100000
Crocidura russula 000001111100
Crocidura leucodon 001100000000
Crocidura attenuata 000001111111
Crocidura dracula 000010111111
Crocidura lasiura 000000001000
Crocidura fuliginosa 000000000011
Anourosorex squamipes 000001111011
Chimarrogale himalayica 000011111111
Chimarrogale styani 000000100010
Nectogale elegans 000010100010
Uropsilus soricipes 000001100000
Uropsilus gracilis 000001101010
Uropsilus andersoni 000000100000
Scaptonyx fusicauda 000001101010
Scapanulus oweni 010001100000
Talpa longirostris 000001111001
Talpa grandis 000001100010
Talpa micrura 000000000011
Parascaptor leucura 000000110010
Xiang et al. -- Quantitative Analysis of Mammal 151
Appendix: (Cont.)
Scaptochirus moschalus 111001000000
Mogera robusta 100000000000
Mogera wogura 000001000100
Mogera insularis 000000001100
Tupiaia belangeri 000000010111
Denrogale murina 000000000001
Rousettus leschenaulti 000000110111
Pteropus dasymallus 000000000100
Pteropus giganteus 000000000010
Pteropus lylei 000000000001
Pteropus intermedius 000000000011
Pteropus vampyrus 000000000001
Sphaerias blanfordi 000010000011
Cynopterus horsfieldii 000000000001
Cynopterus sphinx 000010000111
Cynopterus brachyotis 000000000111
Eonycteris spelaca 000000010011
Macroglossus minimus 000000000001
Macroglossus sobrinus 000000000011
Megaerops ecaudatus 000000000001
Megaerops niphanae 000000000011
Taphozous melanopogon 000000010111
Taphozous longimanus 000000000011
Taphozous theobaldi 000000000001
Taphozous nudiventris 000000000010
Taphozous saccolaimus 000000000010
Megaderma lyra 000000111111
Megaderma spasma 000000000011
Rhinolophus ferrumequinum 110000111000
Rhinolophus affinis 000001111111
Rhinolophus rouxii 000011111111
Rhinolophus thomasi 000000111111
Rhinolophus cornutus 000011001100
Rhinolophus blythi 000010110111
Rhinolophus lepidus 000000111111
Rhinolophus monoceros 000000000100
Rhinolophus luctus 000000110111
Rhinolophus pearsoni 000011111111
Rhinolophus macrotis 000000011101
Rhinolophus rex 000000000100
Rhinolophus trifoliatus 000000000011
Rhinolophus paradoxolophus 000000000011
Rhinolophus marshalli 000000000011
Rhinolophus ceolophyllus 000000000011
Rhinolophus shameli 000000000011
Rhinolophus yunanensis 000000010011
Rhinolophus acuminatus 000000000001
Rhinolophus subbadius 000000000011
Rhinolophus megaphyllus 000000000001
Rhinolophus borneensis 000000000001
Rhinolophus malayanus 000000000001
Hipposideros larvatus 000000000111
Hipposideros pomona 000000011111
Species
Operative geographical unit (OGU)
DB HB MX AJ QZ BCH XN DN NCH HN BB VT
Zoological Studies 43(1): 142-160 (2004)
152
Appendix: (Cont.)
Hipposideros armiger 000000111111
Hipposideros pratti 000001111101
Hipposideros ater 000000000011
Hipposideros fulvus 000000000010
Hipposideros cineraceus 000000000011
Hipposideros halophyllus 000000000001
Hipposideros lylei 000000000011
Hipposideros turpis 000000000001
Hipposideros diadema 000000000001
Hipposideros lekaguli 000000000001
Paraceolops megalotis 000000000001
Aselliscus stoliczkanus 000000010111
Coelops frithii 000000111111
Tadarida plicata 000001001111
Tadarida teniotis 010001101100
Myotis mystacinus 111111111110
Myotis ikonnikovi 111000000000
Myotis altarium 000001101101
Myotis annectens 000000010011
Myotis siligorensis 000000011111
Myotis frater 100000101000
Myotis nattereri 100000000000
Myotis myotis 011001011100
Myotis blythi 001100000000
Myotis formosus 000001001110
Myotis daubentoni 100011111111
Myotis dasycneme 000001000000
Myotis pequinius 010001000000
Myotis capaccinii 000000011100
Myotis davidi 000001001100
Myotis adversus 000000000100
Myotis ricketti 010001011100
Myotis chinensis 000000111101
Myotis sicarius 000000000010
Myotis rosseti 000000000001
Myotis muricola 000000110111
Myotis montivagus 000000010111
Myotis horsfieldii 000000000111
Myotis hasseltii 000000000011
Vespertilio murinus 101101000000
Vespertilio superans 000001001000
Vespertilio orientalis 000001000100
Eptesicus nilssoni 101111010100
Eptesicus serotinus 111011111101
Eptesicus pachyotis 000000000011
Eudiscopus denticulus 000000000011
Nyctalus noctula 010101100111
Nyctalus velutinus 000001101100
Nyctalus lasiopterus 100001000000
Hesperoptenus tickelli 000000000011
Philetor brachypterus 000000000010
Pipistrellus cadornae 000000000011
Pipistrellus joffrei 000000000010
Species
Operative geographical unit (OGU)
DB HB MX AJ QZ BCH XN DN NCH HN BB VT
Xiang et al. -- Quantitative Analysis of Mammal 153
Appendix: (Cont.)
Pipistrellus circumdatus 000000010011
Pipstrellus javanicus 000100001111
Pipistrellus pipistrellus 000001101100
Pipistrellus abramus 110011111101
Pipistrellus coromandra 000000111111
Pipistrellus affinis 000010100110
Pipistrellus anthonyi 000000000010
Pipistrellus paterculus 000000110010
Pipistrellus kuhlii 000000010000
Pipistrellus mimus (tenuis) 000000011111
Pipistrellus ceylonicus 000000001111
Pipistrellus pulveratus 000001111101
Pipistrellus savii 111100000010
Glischropus tylopus 000000000011
Scotozous dormeri 000000000110
Ia io 000001011101
Ia longimana 000001101000
Tylonycteris pachypus 000000010111
Tylonycteris robustula 000000010011
Barbastella leucomelas 000000110010
Nycticeius emarginatus 000000001000
Scotomanes ornatus 000001111111
Scotophilus kuhlii 000000010111
Scotophilus heathi 000000010111
Plecotus austriacus 001110100000
Plecotus auritus 111000000000
Miniopterus schreibersi 010001111110
Miniopterus australis 000000000100
Miniopterus magnater 000000000011
Murina aurata 000010100110
Murina leucogaster 111011100011
Murina huttoni 000010001111
Murina cyclotis 000000000111
Murina rubex 000010000000
Murina tubinaris 000000000011
Murina puta 000000000100
Harpiocephalus harpia 000000000111
Harpiocephalus mordax 000000000011
Kerivoula picta 000000001111
Kerivoula hardwickii 000000011111
Kerivoula papillosa 000000000011
Rhinopoma hardwickii 000000000010
Craseonycteris thonglongyai 000000000001
Nycticebus intermedius 000000010001
Nycticebus coucang 000000110111
Nycticebus pygmaeus 000000110101
Macaca mulatta 010011111111
Macaca assamensis 000010110111
Macaca cyclopis 000000000100
Macaca nemestrina 000010110011
Macaca arctoides 000000110111
Macaca thibetana 000001101000
Macaca fascicularis 000000000011
Species
Operative geographical unit (OGU)
DB HB MX AJ QZ BCH XN DN NCH HN BB VT
Zoological Studies 43(1): 142-160 (2004)
154
Appendix: (Cont.)
Rhinopithecus roxellanae 000000100000
Rhinopithecus bieti 000000100000
Rhinopithecus avunculus 000000000001
Rhinopithecus brelichi 000000001000
Pygathrix nemaeus 000000000001
Pygathrix pileatus 000000100010
Semnopithecus entellus 000010000010
Semnopithecus phayrei 000000110011
Semnopithecus francoisi 000000001101
Semnopithecus cristatus 000000000001
Semnopithecus geei 000000000010
Hylobates pileatus 000000000001
Hylobates lar 000000010011
Hylobates hoolock 000000010010
Hylobates concolor 000000010101
Hylobates leucogenys 000000010001
Hylobates gabriellae 000000000001
Canis lupus 111111111100
Canis aureus 000000000011
Vulpes vulpes 111111111100
Vulpes corsac 100010100000
Vulpes ferrilata 000010100000
Nyctereutes procyonoides 110001011101
Cuon alpinus 101111111111
Ursus ursinus 000000000010
Ursus thibetanus 100011111111
Ursus arctos 101100000000
Ursus pruinosus 000010100000
Ursus malayanus 000000010000
Ailurus fulgens 000011110010
Ailuopoda melanolcuca 000001100000
Martes foina 011110100000
Martes zibellina 101000000000
Martes flavila 110011111111
Gulo gulo 101000000000
Mustela altaica 111111100000
Mustela erminea 111100000000
Mustela nivalis 101110100000
Mustela kathiah 000001111111
Mustela amurensis 101000000000
Mustela sibirica 111011111111
Mustela strigidorsa 000000010011
Mustela eversmanni 111111100000
Vormela peregusna 001100000000
Melogale moschata 000001111110
Melogale personata 000000000111
Meles meles 111111111100
Arctonyx collaris 011011111111
Lutra summatrana 000000000001
Lutra lutra 101011111111
Lutra perspicillata 000000010111
Aonyx cinerea 000010111111
Viverra zibetha 000011101111
Species
Operative geographical unit (OGU)
DB HB MX AJ QZ BCH XN DN NCH HN BB VT
Xiang et al. -- Quantitative Analysis of Mammal 155
Appendix: (Cont.)
Viverra megaspila 000000011111
Vivericula indica 000011111111
Prionodon pardicolor 000011111111
Paradoxurus hermaphroditus 000000111111
Paguma larvata 010011111111
Arctictis binturong 000000010111
Cynogale lowei 000000010001
Arctogalidia trivirgata 000000110011
Chrotogale owstoni 000000010101
Herpestes javanicus 000000011111
Herpestes urva 000001111111
Felis libyca 000110000000
Felis bieti 001110100000
Felis chaus 000000110111
Felis manul 101110110000
Felis marmorata 000000100011
Felis lynx 101110100000
Felis temmincki 000011111111
Felis bengalensis 110001111111
Felis viverrina 000000000011
Pardofelis nebulosa 000011111111
Panthera pardus 111011111111
Panthera tigris 111111111111
Panthera uncia 001110000000
Elephas maximus 000000010011
Rhinoceros unicornis 000000000010
Rhinoceros sondaicus 000000000011
Dicerorhinus sumatrensis 000000000011
Equus przewalskii 001000000000
Equus hemionus 001110000000
Sus scrofa 111111111111
Sus salvanius 000000000010
Camelus bactrianus 001100000000
Tragulus javanicus 000000010001
Tragulus napu 000000000001
Moschus moschiferus 111000000000
Moschus berezovskii 000011111101
Moschus sifanicus 000010101000
Moschus chrysogaster 000010000000
Moschus fuscus 000010000000
Hydropotes inermis 000001001100
Muntiacus rooseveltorum 000000000010
Muntiacus gongshanensis 000010100000
Muntiacus muntjack 000010111111
Muntiacus reevesii 000001111100
Muntiacus crinifrons 000000001000
Muntiacus feae 000000100011
Elaphodus cephalophus 000011111110
Axis porcinus 000000000011
Cervus duvaucelii 000000000010
Cervus unicolor 000010111111
Cervus eldii 000000000101
Cervus nippon 110001111101
Species
Operative geographical unit (OGU)
DB HB MX AJ QZ BCH XN DN NCH HN BB VT
Zoological Studies 43(1): 142-160 (2004)
156
Appendix: (Cont.)
Cervus albirostris 000010100000
Cervus elaphus 111110110000
Capreolus capreolus 111111000000
Alces alces 101000000000
Rangifer tarandus 001000000000
Bos gaurus 000000010011
Bos grunniens (mutus) 000010000000
Bos banleng (javanicus) 000000010011
Bos sauveli 000000000001
Bubalus arnee 000000000011
Procapra picticaudata 000011100000
Procapra przewalskii 001010000000
Procapra gutturosa 111000000000
Gazella subgutturosa 001110000000
Pantholops hodgsoni 000010100000
Saiga tatarica 001000000000
Budorcas taxicolor 000011100010
Naemorhedus cranbrooki 000010000010
Naemorhedus goral 111011111111
Naemorhedus sumatraensis 000011111111
Naemorhedus swinhoei 000000000100
Hemitragus jemlahicus 000010000000
Capra ibex 001110000000
Pseudois nayaur 000010100000
Pseudois schaeferi 000010000000
Ovis ammon 001110000000
Manis pentadactyla 000000111111
Manis crassicundata 000000100110
Manis javanica 000000000011
Trogopterus xanthipes 010011101100
Trogopterus pearsonii 000000010111
Petaurista petaurista 000011111111
Petaurista yunnanensis 000000010010
Petaurista hainana 000000000100
Petaurista alborufus 000001111100
Petaurista pectoralis 000000000100
Petaurista xanthotis 000011100000
Petaurista magmificus 000000100000
Petaurista philippensis 000000011111
Petaurista marica 000000010011
Petaurista elegans 000011111111
Petaurista sybilla 000000010010
Aeretes melanopterus 000001100000
Pteromys volans 111001101000
Petinomys setosus 000000000010
Petinomys electilis 000000000101
Hylopetes alboniger 000001111011
Eupetaurus cinereus 000000100000
Sciurus vulgaris 111100000000
Callosciurus erythraeus 000011111111
Callosciurus phayrei 000000010010
Callosciurus pygerythrus 000010010010
Callosciurus caniceps 000000000111
Species
Operative geographical unit (OGU)
DB HB MX AJ QZ BCH XN DN NCH HN BB VT
Xiang et al. -- Quantitative Analysis of Mammal 157
Appendix: (Cont.)
Callosciurus quinquestriatus 000000010010
Callosciurus finlaysonii 000000010011
Callosciurus inornatus 000000000001
Tamiops mcclellandii 000011110111
Tamiops swinhoei 001011111111
Tamiops rodolphii 000000000001
Dremomys lokriah 000010100010
Dremomys pernyi 000001111110
Dremomys rufogenis 000000010111
Dremomys pyrrhomerus 000000011100
Dremomys gularis 000000010001
Ratufa bicolor 000000110111
Menetes berdmorei 000000010011
Sciurotamias davidianus 010000100100
Sciurotamias forresti 000000110000
Eutamias sibiricus 111001100000
Citellus dauricus 111000000000
Citellus reliclus 001100000000
Citellus major 001000000000
Citellus erthrogenys 001000000000
Citellus undulatus 101000000000
Marmota baibacina 001100000000
Marmota bobak 101000000000
Marmota himalayana 000010100000
Marmota caudata 000100000000
Castor fiber 001000000000
Sicista concolor 101110000000
Sicista subtilis 001000000000
Eozapus setchuanus 010011100000
Cardiiocranius paradoxus 001000000000
Salpingotus kozlovi 001100000000
Salpingotus crassicauda 001000000000
Euchoreutes naso 001110000000
Allactaga sibirica 111110000000
Allactaga elater 001000000000
Allactaga bullata 001000000000
Alactagulus pumilio 001000000000
Dipus sagitta 101110000000
Stylodipus telum 001000000000
Atherurus macrourus 000000010111
Hystrix hodgsoni 000001111111
Hystrix yunnanensis 000000010000
Dryomys nitedula 001000000000
Chaetocauda sichuanensis 000000100000
Cricetulus migratorius 001110000000
Cricetulus barabensi 111001000000
Cricetulus longicaudatus 011111000000
Cricetulus kamensis 000010000000
Cricetulus eversmanni 001000000000
Cricetulus triton 111001001000
Cricetulus canus 000001100000
Phodopus sungorus 001100000000
Phodopus roborovskii 111110000000
Species
Operative geographical unit (OGU)
DB HB MX AJ QZ BCH XN DN NCH HN BB VT
Zoological Studies 43(1): 142-160 (2004)
158
Appendix: (Cont.)
Cricetus cricetus 001000000000
Myospalax fontanieri 111011000000
Myospalax psilurus 111001000000
Myospalax smithi 000001000000
Myospalax rothschildi 000001000000
Myospalax aspalax 111000000000
Typhlomys cinereus 000001011101
Rhizomys pruinosus 000000111111
Rhizomys sumatrensis 000000010011
Rhizomys sinensis 000001111111
Cannomys badius 000000010011
Myopus schisticolor 101000000000
Clethrionomys rutilus 111100000000
Clethrionomys frater 001100000000
Clethrionomys rufocanus 101000000000
Eothenomys shanseius 111001000000
Eothenomys melanogaster 000011111111
Eothenomys miletus 000000111110
Eothenomys eleusis 000000111011
Eothenomys olitor 000000101010
Eothenomys proditor 000000100000
Eothenomys chinensis 000000100000
Eothenomys wardi 000000100000
Eothenomys cachinus 000000100000
Eothenomys inez 010001000000
Eothenomys eva 010001100000
Alticola argentata 001100000000
Alticola strelzowi 001000000000
Alticola stracheyi 000110000000
Alticola stoliczkanus 000010000000
Lagurus lagurus 001100000000
Lagurus luteus 001110000000
Arvicola terrestris 001000000000
Pitymys leucurus 000010000000
Pitymys irene 000011100010
Pitymys sikimensis 000010000000
Pitymys judaschi 000110000000
Microtus socialis 001100000000
Microtus arvalis 101100000000
Microtus fortis 111001001000
Microtus clarkei 000000010011
Microtus kikuchii 000000000100
Microtus musseri 000000010000
Microtus ilaeus 001000000000
Microtus agrestis 001000000000
Microtus oeconomus 011110100000
Microtus millicens 000000100000
Microtus brandti 101000000000
Microtus mandarinus 011001000000
Microtus fuscus 000010000000
Microtus gregalis 111101000000
Microtus maximowiczii 111000000000
Proedromys bedfordi 000001100000
Species
Operative geographical unit (OGU)
DB HB MX AJ QZ BCH XN DN NCH HN BB VT
Xiang et al. -- Quantitative Analysis of Mammal 159
Appendix: (Cont.)
Meriones tamariscinus 001100000000
Meriones unguiculatus 111000000000
Meriones meridianus 011111000000
Meriones chengi 001100000000
Meriones erythrourus 001100000000
Brachiones przewalskii 001100000000
Rhombomys optinus 001000000000
Vernaya fulva 000001100010
Hapalomys delacouri 000000000111
Hapalomys longicaudatus 000000000011
Chiropodomys gliroides 000000010111
Chiropodomys jingdongnensis 000000010000
Vandeleuria oleracea 000000010011
Micromys minutus 111011111111
Apodemus peninsulae 111011101100
Apodemus latronum 000010100010
Apodemus sylvaticus 001100000000
Apodemus orestes 000000100000
Apodemus draco 000011111101
Apodemus agrarius 111001101100
Apodemus chevrieri 000001101000
Hadromys humei 000000010010
Dacnomys millardi 000000110011
Diomys crumpi 000000000010
Rattus rattus 110010111111
Rattus flavipectus 011011111111
Rattus remotus 000000000100
Rattus nitidus 000011111111
Rattus turkestanicus 000010100000
Rattus rattoides 000011111101
Rattus osgoodi 000001001001
Rattus argentiventer 000000000001
Rattus norvegicus 111011111111
Berylmys manipulus 000000100010
Berylmys berdmorei 000000010011
Berylmys bowersii 000010110111
Berylmys mackcenziei 000000000011
Niviventer niviventer 111011111111
Niviventer fulvescens 000011111111
Niviventer confucianus 000001111111
Niviventer tenaster 000000000011
Niviventer coninga 000011111100
Niviventer brahma 000000100010
Niviventer andersoni 000010110100
Niviventer langbianis 000000101011
Niviventer hinpoon 000000000001
Niviventer eha 000010100010
Chiromyscus chiropus 000000000011
Leopoldamys sabanus 000000000011
Leopoldamys edwardsi 000011111111
Leopoldamys neilli 000000000001
Maxomy surifer 000000000011
Maxomy moi 000000000001
Species
Operative geographical unit (OGU)
DB HB MX AJ QZ BCH XN DN NCH HN BB VT
Zoological Studies 43(1): 142-160 (2004)
160
Appendix: (Cont.)
Maxomy musschenbroekii 000000001001
Mus musculus 111111111111
Mus famulus 000000110011
Mus caroli 000000010101
Mus cervicolor 000000100011
Mus pahari 000000111111
Mus booduga 000000000010
Mus shortridgei 000000000011
Millardia kathleenae 000000000010
Bandicota savilei 000000000011
Bandicota bengalensis 000000000010
Bandicota indica 000000111111
Nesokia indica 001000000000
Lepus capensis 111101001000
Lepus peguensis 000000000111
Lepus hainanus 000000000100
Lepus timidus 101000000000
Lepus oiostolus 000010100000
Lepus sinensis 000000111100
Lepus mandschuricus 101000000000
Lepus yarkandensis 000100000000
Lepus comus 000000111000
Lepus melainus 100000000000
Caprolagus hispidus 000000000010
Ochotona thibetana 000011110000
Ochotona nubric 000010000000
Ochotona huangensis 010001100000
Ochotona cansus 100010100000
Ochotona thomasi 000010100000
Ochotona himalayana 000000100000
Ochotona roylei 000010100000
Ochotona macrotis 000110100000
Ochotona rutila 000100100000
Ochotona daurica 011000100000
Ochotona forresli 000010100000
Ochotona iliensis 001000000000
Ochotona curzoniae 000010100000
Ochotona koslowi 000010000000
Ochotona gaoligongensis 000000100000
Ochotona alpina 111000000000
Ochotona pallasi 001000000000
Ochotona shaanxiensis 010000000000
Ochotona kamensis 000010000000
Ochotona erythrotis 000010000000
Ochotona gloveri 000010100000
Ochotona ladacensis 000000100000
Ochotona muliensis 000000100000
aAbbreviations are described in Materials and Methods, and the legend of figure 1.
Species
Operative geographical unit (OGU)
DB HB MX AJ QZ BCH XN DN NCH HN BB VT
... Analyses of publications on regional theriofaunal zoning have demonstrated a notable diversity of methodologies (Kucheruk, 1959;Afanasyev, 1960;Matyushkin et al., 1972;Chernyavskiy, 1978;Tupikova, 1982;Neronov & Arsenyeva, 1980;Shvetsov et al., 1984;Skulkin & Puzachenko, 1986;Varshavskiy et al., 1997;Badgley & Fox, 2000;Lyamkin, 2002;Xiang et al., 2004;Heikinheimo et al., 2007;Escalante et al., 2010;Nobrega & Marco, 2011). Unlike studies on the zoning of all terrestrial areas or Palearctic, ecological approaches have been predominant in zoogeographical studies conducted on a local or regional scale (Bannikov, 1954a,b;Matyushkin, 1972;Yudin et al., 1979;Lyamkin, 2002). ...
... In practice, this problem is often solved in two ways. First, units of previous physiographic or zoogeographic zoning can be taken as primary units for analysis (Yudin et al., 1979;Shvetsov et al., 1984;Márquez et al., 2001;Xiang et al., 2004). Second, networks of regular squares have been successfully used in studies on regional scale zoning in the USA, Canada, Iran, Afghanistan, Mongolia, China, Europe and other regions (Hagmeier & Stults, 1964;Simpson, 1964;Kaiser et al., 1972;Wilson, 1974;Neronov, 1976;Neronov & Arsenyeva, 1980;Skulkin & Puzachenko, 1986;Márquez et al., 1997;Heikinheimo et al., 2007;Escalante et al., 2010;Barbosa et al., 2012). ...
... The commonly used UPGMA method of clustering (Hagmeier & Stults, 1964;Neronov, 1976;Neronov & Arsenyeva, 1980) yielded distances between clusters that were too small, and therefore, it was difficult to elucidate the cluster structure. We applied the Ward method, which is also commonly used (Simpson, 1964; Kaiser et al., 1972;Wilson, 1974;Skulkin & Puzachenko, 1986;Xiang et al., 2004;Heikinheimo et al., 2007). ...
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Zoogeographical studies of regional scale always deal with incompleteness of faunal information. Such information is usually available as a set of localities, covering the studied area as an irregular network. At the same time, full coverage of data is needed for any spatial analysis. In this study, we attempted to perform faunal zoning at a regional level, formalising the procedure to the greatest extent possible. We used 47 small mammal species distribution models (SDM) as initial data for faunal zoning. SDMs were previously constructed based on localities determined using museum labels and environmental data with the maximum entropy method. SDMs were converted to binary values using fixed threshold. We calculated 1-Jaccard similarity coefficients between unique sets of predicted species compositions in each raster cell. The resulting dissimilarity matrix was analysed using hierarchical cluster analysis with the Ward and unweighted average methods. We distinguished three large clusters with nine subclusters based the similarity of the fauna composition. Patterns of the spatial distribution of species numbers and species composition homogeneity were obtained. The relationships between the distribution of species richness and the spatial heterogeneity of the fauna with latitude, longitude, altitude and environmental factors were studied using regression and discriminant analysis. Finally, two faunas were found in South-Eastern Transbaikalia, and a large territory in this region is occupied by a zone of their interpenetration. Analysis of stacked SDMs proposed as important tool for investigation of regional zoogeographical heterogeneity. It is especially useful for extrapolation of faunal data to a larger unstudied territory.
... When trophic or locomotor data were not available to use for Paper I in NOW, complementary data were sourced from the Palaeobiology database (PBDB; http://www.fossilworks. org), and from literature Qi, 1979;Piller et al., 2000;Ye et al., 2000;Ginsburg, 2001;Moyà-Solà et al., 2001, 2009Harrison et al., 2002;Xiang et al., 2004;Alba et al., 2006Alba et al., , 2010Sun et al., 2007). The data from NOW included all mammal localities between 18 and 7 Ma range. ...
... fossilworks.org), and from the literature if trophic or locomotor data were not available in NOW Qi, 1979;Piller et al., 2000;Ye et al., 2000;Ginsburg, 2001; Moy a-Sol a et al., , 2009Harrison et al., 2002b;Xiang et al., 2004;Alba et al., 2006Alba et al., , 2010Sun et al., 2007). The data from NOW included all mammal localities from 18 to 7 Ma, with the exception of singletons, i.e., localities where only one specimen was recovered or cases where taxa were found at only a single locality (Alroy, 1996;. ...
... Most previous zoogeographical schemes were based on expert opinions (Cheng & Chang, 1956;Zhang & Zhao, 1978;Zhang, 1998). Recently, the number of quantitative zoogeographical studies of China has increased rapidly, but most of these have been lineage-specific (Xie et al., 2004;Chen & Bi, 2007;P€ ackert et al., 2012), used coarse grain sizes (Xiang et al., 2004;Heiser & Schmitt, 2013), or focussed on the local scale (Chen et al., 2008). Studies have been based on diverse area sizes and operational geographical units (e.g. ...
... First, we did not find support for a distinction between South China and Central China (Fig. 1a, Fig. 2), and the UPGMA only identified a single South China region (Fig. 2). This finding was consistent with other mammalian studies claiming that the boundary between Central China and South China is not significant and that Central China is best considered as part of a larger South China region (Xiang et al., 2004). Second, Taiwan was previously regarded as a subregion of South China ( Fig. 1a; Zhang, 1999). ...
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Aim Zoogeographical regionalizations have recently seen a revived interest, which has provided new insights into biogeographical patterns. However, few quantitative studies have focused on zoogeographical regions of China. Here, we analyse zoogeographical regions for terrestrial vertebrates in China and how these regions relate to environmental and geological drivers and evaluate levels of cross‐taxon congruence. Location China. Methods We applied hierarchical clustering and non‐metric multidimensional scaling ordination to β sim dissimilarity matrices to delineate zoogeographical regions of China, based on the species distribution of 2102 terrestrial vertebrates in 50 × 50 km grid cells. We used generalized linear models and deviance partitioning to investigate the roles of current climate, past climate change, vegetation and geological processes in shaping the zoogeographical regions. Finally, we used Mantel and Kruskal–Wallis tests to evaluate the levels of cross‐taxon congruence. Results Cluster analyses revealed 10 major zoogeographical regions: South China, the Yungui Plateau, Taiwan, North China, Northeast China, the Inner Mongolia Plateau, Northwest China, the Longzhong Plateau, the Tibetan Plateau and East Himalaya. In contrast to previous regionalizations, a major split was identified by clustering grid cell assemblages and dividing the eastern and western parts of China, followed by the northern part of China. Deviance partitioning showed that current climate and geological processes explained most of the deviance both jointly and independently. Congruence in species composition of endotherms and ectotherms was surprisingly low. Main conclusions We propose new zoogeographical regions for China based on our quantitative methods. In contrast to previous regionalizations, we consider Central China as a part of South China and identify the Longzhong Plateau and Taiwan as independent regions. While our results strongly support the notion of a broad biogeographical transition zone in East Asia, they also suggest a major south–north‐oriented Palaearctic‐Oriental boundary in China.
... Furthermore, our topology test finds strongest support for a clade comprising Lamproblattidae, Anaplectidae, Tryonicidae and Blattidae as the sister lineage to Xylophagodea. in Blattodea that were all younger than those in other studies (Bourguignon et al., 2018;Djernaes et al., 2015;Wang et al., 2017). Based on our divergence-time estimates, we suggest that the present-day distributions of some blattid species in Asia are largely the outcome of past geological events, especially the uplift of the Himalayas, which has been shown to be a biodiversity hotspot (e.g., Peng et al., 2006;Wu et al., 2016;Xiang et al., 2004;Xu et al., 2020;Yu et al., 2018). Our collected species of Blattinae and Archiblattinae mainly came from the provinces of Hainan and Yunnan in China and most of their lineages began to differentiate around or before 50 Ma (Figure 4). ...
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Cockroaches are an ecologically and economically important insect group, but some fundamental aspects of their evolutionary history remain unresolved. In particular, there are outstanding questions about some of the deeper relationships among cockroach families. As a group transferred from Blaberoidea Saussure to Blattoidea Latreille, the evolutionary history of the family Anaplectidae Walker requires re‐evaluation. In our study, we infer the phylogeny of Blattoidea based on the mitochondrial genomes of 28 outgroup taxa and 67 ingroup taxa, including 25 newly sequenced blattoid species mainly from the families Anaplectidae and Blattidae Latreille. Our results indicate that Blattoidea is the sister group of the remaining Blattodea Brunner von Wattenwyl and that Blattoidea can be divided into three main clades: Blattidae + Tryonicidae McKittrick & Mackerras, Lamproblattidae McKittrick + Anaplectidae and Termitoidae Latreille + Cryptocercidae Handlirsch. Our analyses provide robust support for previously uncertain hypotheses. The sister group of Termitoidae + Cryptocercidae (Xylophagodea Engel) is inferred to constitute the rest of Blattoidea, for the first time. Within Blattidae, Hebardina Bey‐Bienko is placed as the sister lineage to the rest of Blattidae. The subfamily Archiblattinae is polyphyletic, Blattinae is paraphyletic and Polyzosteriinae is monophyletic (Macrocercinae Roth not included); the genus Periplaneta Burmrister is polyphyletic. Based on the results of our phylogenetic analyses, we have revised these taxa. A new subfamily, Hebardininae subfam.nov., is proposed in Blattidae. Archiblattinae and Shelfordella Adelung are synonymized with Blattinae and Periplaneta, respectively: Archiblattinae Kirby syn.nov. and Shelfordella Adelung syn.nov. Our inferred divergence times indicate that Blattoidea emerged in the Late Triassic, with six families in Blattoidea diverging in the Middle and Late Jurassic. We suggest that the divergences among lineages of Asian Blattidae and Anaplectidae were driven by the uplift of the Himalayas and deglaciation during the Quaternary, leading to the present‐day distributions of these taxa. 1. Phylogeny reconstruction based on mitochondrial genomes provided a number of insights into the evolutionary history of Blattoidea. 2. A new subfamily and two synonyms were proposed in Blattidae: Hebardininae subfam. nov., Archiblattinae Kirby syn. nov. and Shelfordella Adelung syn. nov. 3. The divergence and modern distribution of Asian Blattidae and Anaplectidae species was driven by the uplift of the Himalayas and deglaciation during the Quaternary.
... The South China region is usually included in the Oriental realm in other studies (Zhang 1999, but our analysis indicates that for Cixiidae the South China region is closer to the Central, Southwest, and North China region (Sino-Japonese realm). This is consistent with the results of a quantitative analysis of terrestrial mammals in China and adjacent regions by Xiang et al. (2004), where clustering analysis showed the proximity of South China region to Central and Southwest China regions, and they suggested these regions as the South China Division. ...
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Does the distribution of the Hemiptera planthoppers Cixiidae, follow the patterns of biogeogaphical distribution already well established for their host plants or other taxa because they are all obligatory phytophagous taxa? Are their realms and boundaries recognized? What are their zoogeographical regions and usual connections? To investigate these issues, we provide here a referenced and comprehensive checklist of the 253 cixiid species currently reported from China, with their precise distribution at the regional level. Seventy seven of these species are new records for China. In the 8 Chinese main zoogeographical regions usually recognized and 2 adjacent areas, we analyzed further their diversity at the tribal, generic and specific levels using a non-metric multidimensional scaling and an unweighted pairwise group analysis using an arithmetic mean cluster analyses. The observed distribution patterns have shown that an intercalary Sino-Japanese realm is present between the Palaearctic and Oriental realms. At the regional level, the South China region cluster is more closely with the Southwest, Central and North China regions. Taiwan is clearly separated from the South China region and mainland China, but is more closely related to the Qinghai-Tibet region and Indochina countries. The Central and South China regions are close to each other, but the Qinghai-Tibet region is singularly different. An updated checklist of the 253 Cixiidae species currently known to occur in China that composes 10% of the Chinese planthopper fauna, is presented, based on original literature, collections in institutions, and museum records. More than 400 records distributed among the 28 provinces and regions in China are provided including 77 new records for China. More than 80% of the species (205 species, 81.03%) have been only reported from China, and most of them are Chinese endemic species, which reflects the great diversity of the Chinese regional and local biotypes and highlights the uniqueness of this fauna. These species are found in 8 zoogeographical regions in China: The Taiwan region is the most diversified with 161 species and the highest rate of endemic species (70.81%), followed by South China (76 species, 17.11%), Central China (62 species, 35.48%), Southwest China (42 species, 40.48%), North China (29 species, 34.48%), Qinghai-Tibet region (10 species, 20%), Northeast China (8 species, 12.5%), and 5 species found in the Inner Mongolia-Xinjiang region that are not endemic. Thirty eight main distribution patterns were identified, and 9 of them were bi-regionally and tri-regionally distributed. The South China-Taiwan pattern has the highest proportion of these major distribution patterns followed by the Central-South China-Taiwan pattern. Semonini and Pentrastirini tribes are widespread among the 8 Chinese zoological regions, representing, respectively, 20.55% and 17% of all species of Chinese cixiids. Cixiini are the most common species of planthopper composing of 45.85% of the total planthopper species found in China , and they occur in 7 Chinese regions but are absent from northeastern China. The next most common Tribes are: Andini with only 5.14% of these species distributed in the Sino-Japanese - Oriental Region; Eucarpini (6.32%) and Borysthenini (1.98%), which are mainly concentrated in the south of the Qingling Mountain-Huai River. The remaining four tribes, Bennini (0.40%), Brixini (0.79%), Oecleini (1.58%) and Stenophlepsini (0.04%) are relatively rare and restricted to Taiwan. A non-metric multidimensional scaling and an unweighted pairwise group method analysis using arithmetic mean clustering based on the Jaccard similarity coefficient matrix support a Palaearctic/Sino-Japanese boundary and a South China region closer to the Southwest, Central and North China regions. The Taiwan region appears clearly separated from the South China region and to mainland China, but more closely related to the Qinghai-Tibet region and Indochina countries. The Central and South China regions appear close to each other, but the Qinghai-Tibet region is singularly isolated.
... The Tibetan Plateau is known for its large number of endemic species; for instance, 3673 seed plant (Yu et al., 2018), 148 mammal (Xiang et al., 2004) and 167 bird (Wu et al., 2016) species are endemic in this area. As such, the Tibetan Plateau (including the Hengduan Mountains) is listed as one of the 25 worldwide hotspots of biodiversity and endemism (referred to as the 'Himalaya biodiversity hotspot' in the extended list of Mittermeier et al., 2005). ...
Article
With 34 described species or subspecies, Gnaptorina Reitter is the third-most species-rich genus in the darkling beetle subtribe Gnaptorinina (Tenebrionidae: Tenebrioninae). In this study, we reconstructed a phylogeny of the genus based on one nuclear and three mitochondrial genes and used this phylogeny to explore the historical biography and diversification of Gnaptorina species. We implemented multiple molecular species delimitation approaches to reassess the status of Gnaptorina species and taxonomic subdivisions of the genus. Dating and historical biogeography analyses suggest an early Eocene origin of the genus, with the southeastern regions of the Tibetan Plateau most likely as areas of origin. Based on these results, we propose a new classification for Gnaptorina with three major clades identified. Consequently, the monotypic subgenus Boreoptorina is newly synonymized with the more species-rich subgenus Hesperoptorina, and G. (G.) dongdashanensis Shi comb. n. is transferred from Hesperoptorina to the subgenus Gnaptorina. In addition, G. minxiana Medvedev, formerly treated as a subspecies of G. potanini Reitter, is elevated to species. Results of molecular species delimitation analyses are largely congruent and confirm the status of most morphological species.
... The mainland zoogeographic region in Zakaria-Ismail's system that contained Mekong, Chao Phraya and Mae Khlong drainages was adjacent to the South Region in the present system and these two areas must constitute a large region. This hypothesis had been supported previously by the results of Xiang et al. (2004): the mainland of South- east Asia and South China were grouped together by cluster analysis. ...
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The areal distribution of the snake family Colubridae in China was analyzed quantitatively with the aims of determining the zoogeographic regions, areas of endemism, priority areas for conservation and important environmental factors. A presence/absence data matrix of 141 Colubridae species was analyzed by the two-way indicator analysis (TWINSPAN) for regionalization, parsimony analysis of endemicity for areas of endemism, and linear programming for priority areas selection. Ecological niche modeling was integrated into priority areas selection to achieve the objective of protecting species potential suitable habitats. The Bioclim True/False model was used because of its conservatism property. Results indicated there are nine major zoogeographical regions based on Colubridae species, some of which had been documented by previous zoogeographical regionalizations of East Asia. Four endemic areas were identified by parsimony analysis of endemicity: One in Yunnan, one in Taiwan, and another two in Tibet Province. Optimal priority areas set identified thirty-five grid cells based on species' suitable habitat ranges.
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Background: Cixiidae are small strictly phytophagous hemipteran insects worldwide distributed. Ecology and systematics of Chinese fauna remains poorly investigated. For instance, does their distribution follows the patterns of biogeogaphical distribution established for their host plants or other related-taxa because they are all obligatory phytophagous taxa? Do they follow the usual distributional Chinese realms and boundaries already recognized? Which zoogeographical Chinese regions and connections between them do they depict. To investigate these issues, we provide here a referenced and comprehensive checklist of the 250 cixiid species currently reported from China (77 new records), with their precise distribution at the regional level. In the 8 Chinese main zoogeographical regions usually recognized and 2 adjacent areas, we analyzed further their diversity at the tribal, generic, and specific levels using a non-metric multidimensional scaling and an unweighted pairwise group analysis using an arithmetic mean cluster analyses. The observed distribution patterns shown that an intercalary Sino-Japanese realm is recognisable between the Palaearctic and Oriental realms. At the regional level, the South China region clusters more closely with the Southwest, Central and North China regions. Taiwan, clearly separated from the South China region and mainland China, is more closely related to the Qinghai-Tibet region and Indochina countries. Although Central and South China regions remain close to each other, the Qinghai-Tibet region appears singularly different. New information: An updated checklist of the 250 Cixiidae species, known to occur in China and counting for 10% of the Chinese planthopper fauna, is presented based on literature, recent collections, and museum records. More than 400 records distributed among the 28 provinces and 8 regions in China are extensively provided, including 77 new records. Of these, more than 80% of the species (205 species, 82%) have been only reported from China, and most of them are endemic species, which could reflects the great diversity degree of the Chinese regions and local biotypes highlights the uniqueness of this fauna. These species are found in 8 Chinese zoogeographical regions: The Taiwan region is the most diversified with 161 species and the highest rate of endemic species (69.57%), followed by South China (78 species, 17.95%), Central China (60 species, 33.33%), Southwest China (43 species, 39.53%), North China (29 species, 34.48%), Qinghai-Tibet region (10 species, 20%), Northeast China (8 species, 12.5%), and 5 species found in the Inner Mongolia-Xinjiang region that are not endemic ones. Endemism was analyzed for each region and repeated for species distribution patterns across them, 9 being bi-regionally and tri-regionally distributed. The South China-Taiwan pattern is the most richest one, followed by the Central-South China-Taiwan pattern. Semonini and Pentastirini tribes are widespread among all the zoological regions, representing respectively 21.20% and 17.20% of all the species, while Cixiini being is the most common tribe with 45.20%, remains absent from the North-Eastern China region. Andini with only 5.20% of the species is distributed in the Sino-Japanese - Oriental Region; Eucarpini (6.40%) and Borysthenini (2.00%) are mainly concentrated in the south of the Qingling Mountain-Huai River. The remaining four tribes, Bennini (0.40%), Briixini (0.80%), Oecleini (1.20%) and Stenophlepsiini (0.40%) are relatively rare and restricted to Taiwan. At the generic level, Kuvera (7.2%) is the most widely distributed genus in China while Cixius, Betacixius, Kuvera, Oecleopsis and Andes are the more diversified. One genus (Oliparisca) is distributed only in the Tibet region, while 10 genera are distributed only in the Taiwan region. In addition, nearly half of the genera (16 genera, 48.48%) are distributed south of the Palearctic/Oriental boundary. A non-metric multidimensional scaling and an unweighted pairwise group method analysis using arithmetic mean clustering based on the Jaccard similarity coefficient matrix support a Palaearctic/Sino-Japanese boundary and a South China region closer to the Southwest, Central and North China regions. The Taiwan region appears clearly separated from the South China region and to mainland China, and more closely related to the Qinghai-Tibet region and Indochina countries. The Central and South China regions appear close to each other, but the Qinghai-Tibet region is singularly isolated.
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Biogeography is currently in an exciting, challenging, revolutionary stage. Quantitative biogeography, both at the historical and ecological levels, will play an increasingly important role because rapidly increasing amounts of data will require quantitative methods and computer analysis. An effective way to comprehend quantitative methods and to determine when they can be effective is to consider any biogeographic study as a multistage decision process. Not only does this provide a framework for such activities, but it also emphasizes that one's conclusions are affected by decisions made at different stages of the study. The best perspective is that biogeography is an unending synthesis, both of many types of theories and of many types of data and analyses to test them. A corollary to this approach is that no single method or analysis can answer all questions of biogeographic interest. Finally, while computers and quantitative analyses can enhance the human mind, they can never replace it.