Population
variations in the Fungoid Frog Hylarana malabarica (Anura:
Ranidae) from northern Western Ghats of India
Anand Padhye 1,
Anushree Jadhav 2, Manawa Diwekar 3 & Neelesh
Dahanukar 4
1,2 Department of Zoology, Abasaheb
Garware College, Karve Road, Pune, Maharashtra 411004 India
3,4 Indian Institute of Science
Education and Research, Sai Trinity, Sus Road, Pashan, Pune, Maharashtra
411021, India
Email: 1 adpadhye@gmail.com
(corresponding author), 2 anushreejadhav@gmail.com, 3 dmanawa@iiserpune.ac.in, 4 n.dahanukar@iiserpune.ac.in
Date
of publication (online): 26 February 2012
Date
of publication (print): 26 February 2012
ISSN
0974-7907 (online) | 0974-7893 (print)
Editor: Annemarie
Ohler
Manuscript details:
Ms
# o2863
Received
06 July 2011
Final
received 06 December 2011
Finally
accepted 16 January 2012
Citation:Padhye, A., A. Jadhav, M. Diwekar & N. Dahanukar (2012). Population
variations in the Fungoid Frog Hylarana
malabarica (Anura: Ranidae) from northern Western Ghats of India. Journal of
Threatened Taxa 4(2): 2343–2352.
Copyright: © Anand Padhye,
Anushree Jadhav, Manawa Diwekar & Neelesh Dahanukar 2012. Creative Commons Attribution 3.0 Unported License. JoTT
allows unrestricted use of this article in any medium for non-profit purposes,
reproduction and distribution by providing adequate credit to the authors and
the source of publication.
Author
Details: Anand Padhye is Associate Professor of Zoology in M.E.S. Abasaheb
Garware College, Pune. He is a member of the Amphibian Specialist Group of the
IUCN. He has published several scientific papers on biodiversity of the
northern Western Ghats.
Anushree Jadhav has
completed her Masters in Biodiversity at Abasaheb Garware College, Department
of Biodiversity.
Manawa Diwekar is a
molecular biologist with special interests in understanding molecular
evolution.
Neelesh Dahanukar works
in ecology and evolutionary biology with an emphasis on statistical and
mathematical analysis.
Author Contribution:AP designed the study. AP and AJ
collected specimens and data for morphometry. AJ and MD performed RAPD
analysis. ND performed statistical analysis. AP and ND wrote the paper.
Acknowledgement:Authors are thankful to Board of
College and University Development (BCUD), Pune University, for funding this
project. We thank authorities of
IISER for providing the facility for the molecular work. We also thank Principal, Abasaheb
Garware College as well as head of the Zoology Department for the
infrastructural facilities. Sheetal Shelke, Rohan Pandit, Amod Zambre, Sandesh Apte, Ankur Padhye,
Hemant Ogale, Ajit More, Sanjay Khatawkar, Ravindra Gavari, Vitthal Bhoye and
Driver Anil Pujari helped us in the field surveys. We thank Dr. H.V. Ghate and Prof. Milind Watve for their
valuable suggestions during the work.
Abstract: Widely
distributed species often show interpopulation variation. Studying such variations can be helpful
in understanding contributing factors and distinguishing widespread species and
species complexes. We studied six
populations of Hylarana
malabarica distributed along the northern
Western Ghats of India using morphometric and genetic analysis. Of 24 size-adjusted morphometric
characters, 14 were significantly different among populations. Hierarchical clustering and
discriminant analysis of morphometric characters suggested that the six
populations form at least four distinct clusters. Analysis of morphometric data was supported by genetic
polymorphism data obtained by the Randomly Amplified Polymorphic DNA (RAPD)
method. Since the similarity and
variation observed among populations was independent of their spatial
distribution, it is possible that this widely-distributedspecies may be a species complex.
Keywords: Genetic
variation, Hylarana malabarica, morphological variation, Western Ghats.
For
figures, images, tables -- click here
INTRODUCTION
The high level of endemism
among vertebrate and plant species has led the Western Ghats of India and Sri
Lanka to be considered a hotspot of global biological diversity (Myers et al.
2000). The Western Ghats are rich in amphibian fauna, and while the first species was
discovered in the early 1800s the discovery trend for Western Ghats amphibians
has yet to reach a plateau (Aravind et al. 2007). A recent record of a new frog family from the Western Ghats
(Biju & Bossuyt 2003) reflects the limitation of our knowledge of the
amphibian diversity of this important biogeographic region (Hedges 2003), as do
recent descriptions of new amphibian species (Gururaja et al. 2007; Kuramoto et
al. 2007; Biju & Bossuyt 2009; Zachariah et al. 2011).
Recent investigations of
Western Ghats amphibians have shown that several populations contain cryptic
species revealed by in-depth study (Kuramoto et al. 2007). The broad application of molecular
techniques to phylogenetic reconstruction has been used effectively to unveil
haplotypes or morphologically cryptic species (Kuramoto et al. 2007; Biju et
al. 2009). Discovering these
cryptic species is important for our understanding of species richness and
essential for the design and implementation of conservation action plans
(Bickford et al. 2007; McLeod 2010). This is especially true for species which show
wide distribution, which may turn out to be species complexes (Inger et al.
2009).
The Fungoid Frog Hylarana malabarica (Tschudi, 1838) is widely
distributed in peninsular India (Daniel 2000; Padhye & Ghate 2002), Assam
and Meghalaya (Dutta 1997). Because of its wide distribution the species is categorized under Least
Concern in the IUCN Red List of Threatened Species (Biju et al. 2004). Despite its widespread distribution, H. malabarica shows patchy distribution
in the northern Western Ghats. In
our initial studies we observed variation among the individuals from different
populations of H. malabarica from the northern Western Ghats, and in the current work we have
studied morphological and genetic variation among six populations of H. malabarica collected from six
isolated locations. Our analysis,
based on morphological and genetic studies, indicates that the six populations
of H. malabarica form at least four separate clusters, raising the possibility
that H. malabarica could be a species complex.
METHODS
Sampling
Six study sites were
identified (Table 1, Image 1). Field visits were conducted during the breeding season (July to
September) in 2009 and 2010. While
a number of individuals were collected for morphometry, only two to three
individuals were brought back to the laboratory; remaining individuals were
released back in the same habitat. Individuals brought back to the laboratory were etherized and fixed in
100% ethanol. The tissues (liver
and muscles) from these specimens were used for DNA extraction. Amboli and Dhamapur specimens were
collected from the same congregation, while Velneshwar, Tamhini, Kolvan and
Ghatghar specimens were collected from different places in the same area. Fifteen specimens collected during the
survey are deposited in the collection of Department of Zoology, Abasaheb
Garware College, Pune, under the accession numbers AGCZRL Amphibia 19, 20, 21,
23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 35 and 36.
Morphometric analysis
Morphometry was done in the
field with the help of digital Vernier calipers (Areospace®, least count
0.01mm). Morphometric measurements
were taken for snout-vent length and 24 different characters (Table 2). Since females were poorly represented
in our samples we considered only males for morphometric analysis to avoid any
effect of sexual dimorphism in the morphological analysis.
Since different individuals
differed in their snout to vent length and all other morphometric measurements
were strongly correlated with the snout-vent length, we first adjusted the
morphological measurements of all the individuals for mean snout to vent length
so as to remove the size and shape effect. We used allometric adjustment (Lleonart et al. 2000) given
by the formula, Madj = Mobs (SL0/SL)b, where, Madj is the size adjusted length
of a morphological character, M obs is the observed length of
the character, SL0 is the mean snout to vent length of all the
individuals, SL is the snout to vent length of the given individual, and, b is
the allometric exponent of power function relation between the character and
the standard length of all individuals (in other words it is the slope of the
line between logMobs versus logSL). Efficiency of size adjustment was assessed by testing the significance
of correlation between transformed variables and standard length, which was not
significantly different from zero. This corrected morphometric data of 24 characters was used for further
statistical analysis. Since allometric adjustment nullifies both the size and
shape bias the resultant data was independent of the ontogenic variations among
the individuals.
We performed ANOVA (Analysis
of Variance)to understand which standardized
morphometric characters differed among the populations. Since multiple tests were performed on
the same data we applied sequential Bonferroni correction to the
a values wherever applicable. Variables which where significant after sequential Bonferroni correction
were further analyzed using unpaired t test to find out differences between
populations. We performed
hierarchical cluster analysis based on mean values of significantly different
standardized characters for a given population to understand the general
pattern in similarity among the populations. Euclidian distances between populations were calculated and
Ward’s method was used for clustering.
Discriminant analysis (DA)
was performed on the significantly different characters to check whether the
populations form distinct clusters as well as to identify the discriminating
characters (Legendre & Legendre 1998). DA supposes priory groups, which in the current case
populations situated in different geographical locations. DA explicitly attempts to model the
difference between the classes of data by extracting factors that maximize
inter class variation and minimize intra class variations. DA is therefore, more appropriate
choice than principle component analysis (PCA), which gives equal weight to all
the available variables because of which it cannot reveal the differences among
closely related clusters in less number of dimensions. However, since DA considers prior
groups, to test whether our analysis is biased by this
grouping we tested for intra-group homogeneity by two methods. First, the null hypothesis, which
states that the mean vectors of the six populations are equal, was tested using
Pillai’s trace (Harris 2001). Second, we calculated Mahalanobis distances (Harris
2001) among
the individuals and computed Fisher’s distances between six populations as the
distance between the centroids of the clusters, divided by the sum of their
standard deviations to check if the clusters formed by six populations are
significantly different. Statistical analysis was performed in Microsoft EXCEL® and
Systat 12®.
DNA Extraction and
Randomly Amplified Polymorphic DNA (RAPD) analysis
The tissue was digested at
50°C for two hours using the extraction buffer (0.1M NaCl, 0.05 M Tris-HCl,
0.01M EDTA, 1%SDS) with 15µl Proteinase K (20mg/ml). DNA was then extracted
using the conventional phenol-chloroform method (Sambrook et al. 1989). Polymerase chain reaction was performed
to randomly amplify the polymorphic DNA. Primers used for the study were based on Wei et al. (2001). Primers that gave consistent results
under repeated experiments are given in the Table 3. The PCR amplifications were conducted in 20µl reaction
volume containing 2µl of template DNA, 2µl of 10X reaction buffer (100 mM Tris
pH9.0, 500 mM KCl, 15 mM MgCl2, 0.1% Gelatin), 1µl of 25mM MgCl2, 1µl
of 10mM dNTPs, 1µl of primer, 0.8µl Taq polymerase and
11.2µl sterile distilled water. The cycling profile used was 5 min at 950C,
and 35 cycles of 1 min at 950C, 1 min at 300C and 2 min
at 700C, followed by 10 min at 720C. Amplified DNA
fragments were checked using 1% agarose gel electrophoresis and further
analyzed.
Presence
and absence of a given fragment amplified in RAPD was represented by ‘1’ and ‘0’
characters respectively. Only
clear and reproducible bands were recorded as ‘1’. No or non-reproducible bands were recorded as ‘0’. We used Nei and Li (NL) coefficient for
comparison between the RAPD patterns between diffident individuals (Lamboy 1994). The NL coefficient, which denotes a
value of the similarity between two samples (Nei & Li 1979), is given by
the formula, NL = 2a/(b+c), where a is the number of similar bands from two
samples, and b and c are the total numbers of bands from each sample. Based on the NL similarity coefficient,
which ranged between 0 and 1, we performed cluster analysis using five methods,
viz., single linkage, complete linkage, flexible linkage, unweight pair-group
average and weight pair-group average (Sneath & Sokal 1973) and the most
consistent tree topology was chosen for plotting.
RESULTS
Morphometric analysis
Out of total 24
size-adjusted morphological characters, 14 were significantly different for six
populations, while another three were different but could not qualify as
significant after Bonferroni correction (Table 2). Differences in these 14 characters between six populations
are given in Table 4. Tamhini
Population differed from all the other populations in three characters namely,
toe 5 length, hind limb length and inter narial
distance. Even though, there was
no particular character in which the Dhamapur population differed from all
others, this population could be distinguished by a combination of characters:
inter-narial distance, width of upper eyelid, tympanum diameter vertical,
tympanum diameter horizontal, tympanum to eye distance, forelimb length,
hindlimb length, tibia length, toe 4 length and toe 5 length. A maximum number of characters were different
between the Tamhini and Dhamapur populations, followed by Tamhini and
Velneshwar. Least differences were
seen between Kolvan and Ghatghar and Velneshwar and Amboli. Image 2 shows individuls from different
populations used in the analysis.
A dendrogram based on the
mean values of the standardized characters for a population is shown in Fig. 1.
Tamhini and Dhamapur populations were distinctly different from other
populations, while Kolvan and Ghatghar, and Amboli and Velneshwar shared more
similarity with each other.
Discriminant Analysis extracted
five factors, out of which first three factors explained around 86.19% of the
total variation in the data. The
means vectors of the six populations were significantly different (Pillai’s
trace = 3.439, F70,220 = 6.923, P <
0.0001), indicating that the six populations formed six significantly different
clusters (Fig. 2). Higher values
of variables such as inter-narial distance followed by toe 3 length and eye
diameter separated Tamhini from other populations, while higher values of
variables like width of upper eyelid, tympanum diameter horizontal and forelimb
length separated Dhamapur population from all the rest of the populations on
the first two axes (Table 5). Among the remaining four populations, Kolvan and Ghatghar had negative
factor loading on the third canonical axis, while Amboli and Velneshwar had
positive factor loading (Fig. 2a). Fisher’s distances between centroids of all six populations were
significant indicating that these six populations formed different clusters
(Fig. 2b). However, Fisher’s
distance between centroids of Amboli and Velneswar was the lowest (3.205) while
Fisher’s distances between Tamhini and all other populations were very high
(Fig. 2b).
Standardized factor
coefficient (Table 5) suggests that on the third axis head width, tympanum
diameter vertical, tympanum diameter horizontal had high positive factor
loading, while hindlimb length, forelimb length, inter-orbital distance and
nostril to snout distance had negative factor loading with high magnitude. Thus
these characters separate Amboli and Velneshwar from, Kolvan and Ghatghar
populations.
RAPD analysis
Out of the five methods of
cladistic analysis used: single linkage, complete linkage, flexible linkage,
unweight pair-group average and weight pair-group average, the last three
methods gave consistent tree topology while the first two gave two different tree
topologies. A consensus tree based
on NL coefficient and flexible linkage, unweight pair-group average and weight pair-group average methods is shown in Image 3.
RAPD analysis revealed four
different clusters. Tamhini
population had the least genetic similarity with all other populations, while
Dhamapur formed a separate cluster. Among the remaining four populations, Kolvan and Ghatghar formed one
cluster while Amboli and Velneshwar formed another cluster (Image 3). This pattern is similar to the pattern
depicted in the morphometric data (Fig. 1) and hence supports the results of
morphometric analyses.
DISCUSSION
Both morphological and
genetic analysis revealed that the six populations in the current study lie in
at least four different clusters: (1) Tamhini, (2) Dhamapur, (3) Kolvan and
Ghatghar, and (4) Amboli and Velneshwar. The morphological differences between the populations are likely to be
independent of sexual dimorphism, as we have considered only males in the
population. Further, we nullified
the effect of ontogenetic allometry as we applied allometric adjustments to the
data to nullify any size and shape bias as suggested by Lleonart et al.
(2000). Further, morphological as
well as genetic similarity and differences among the six isolated populations
were not dependent on their geographical distances (Table 1). Kolvan and Ghatghar populations shared
more similarity (Fig. 1, Image 3) though they are separated by 80km. Where as Kolvan and Tamhini do not show
any similarity (Fig. 1, Image 3) yet are 20km apart. Similarly, Amboli population shared
more similarity with Velneshwar population than with Dhamapur population, even
though Amboli and Dhamapur are just 44km apart while Amboli and Velneshwar are
182km apart. Further, Amboli and Velneshwar
have a large difference in altitude (Table 1), as Velneshwar is on the
coastline while Amboli is on the crest line of the Western Ghats (which form a
geographical barrier between these two populations). There is also a difference of 20 in the latitudinal
distribution of these two populations (Table1). Such kind of pattern suggests possibility of more than one
species that are together considered as Hylarana malabarica.
Recent trends in the
discoveries of new species of anurans (Gururaja et al. 2007; Kuramoto et al.
2007; Biju & Bossuyt 2009; Biju et al. 2009; Zachariah et al. 2011)
suggests that there are likely to be many more species of anurans still waiting
to be described from the Western Ghats of India. The continual increase in the discovery trend of Western
Ghats’ amphibians (Aravind et al. 2007), further bolsters this fact. It is possible that several of these
species could be cryptic with no apparent easily distinguishable morphological
differences (Biju et al. 2009). Such species will require detailed morphological and molecular
phylogenetic studies for establishing their taxonomic status. For example,
Kuromoto et al. (2007) described four cryptic species of anuran genus Fejervarya from central Western
Ghats. These four species of Fejervarya are not easily
distinguishable morphologically but show their distinctness in both detailed
morphometric analysis and molecular analysis based on DNA sequencing (Kuromoto
et al. 2007; Meenakshi et al. 2010). Our finding of morphological and genetic variations in different
populations of Hylarana malabarica suggests the possibility of recent events in
speciation and presence of cryptic species. However, since genetic studies using RAPD suffers from low
reproducibility of results, to bolster our arguments it is essential to study
the molecular markers in different populations of H. malabarica. Molecular phylogeny of these different populations will be
able to help us in separating the population level variations from the species
level variations and help us in studying the monophyly of H. malabarica. Méndez et al. (2004) also
suggested similar strategy to reveal the presence of recent speciation in Bufo spinulosus, who conducted similar
study on this species. Additionally, ecological studies on these populations using niche
modeling method (Raxworthy et al. 2007) will also be interesting and may
probably help in resolving the phylogeny of H. malabarica. Rissler & Apodaca
(2007) have shown the application of ecological niche modeling in defining cryptic
species in Black Salamander Aneides flavipunctatus.
Recent trends in the
amphibian taxonomy have revealed several lineages of cryptic species (Stuart et
al. 2006; Elmer et al. 2007) especially in the wide spread species (Inger et
al. 2009; McLead 2010). Understanding this cryptic diversity is essential for
species management and conservation (Bickford et al. 2007; McLead 2010). Hylarana malabarica is assessed as Least
Concern (Biju et al. 2004) owing to its widespread distribution in India with
no major widespread threats. However, if our assertion that the Hylarana malabarica is a species complex with
several cryptic species is true, then it is possible that some of the cryptic
species might have more restricted distribution and may require immediate conservation
attention.
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