Journal of Threatened Taxa |
www.threatenedtaxa.org | 26 November 2021 | 13(13): 19976–19984
ISSN 0974-7907 (Online) | ISSN 0974-7893
(Print)
https://doi.org/10.11609/jott.7414.13.13.19976-19984
#7414 | Received 09 May 2021 | Final received
12 August 2021 | Finally accepted 29 October 2021
Phenotypic plasticity in Barilius vagra (Hamilton,
1822) (Teleostei: Danionidae)
from two geographically distinct river basins of Indian Himalaya
Sumit Kumar 1, Sharali Sharma 2 & Deepak Singh 3
1–3 Freshwater Biodiversity
Laboratory, Department of Zoology, H.N.B. Garhwal
University, Srinagar (Garhwal), Uttarakhand 246174,
India.
1 sumitsharmaraina2109@gmail.com, 2
sharmashanali@gmail.com, 3 bhandaridrdeepak5@gmail.com
(corresponding author)
Editor: Mandar Paingankar,
Government Science College Gadchiroli, Maharashtra,
India. Date of publication: 26 November 2021
(online & print)
Citation: Kumar, S., S. Sharma & D.
Singh (2021). Phenotypic plasticity in Barilius vagra (Hamilton,
1822) (Teleostei: Danionidae)
from two geographically distinct river basins of Indian Himalaya. Journal of Threatened Taxa 13(13): 19976–19984. https://doi.org/10.11609/jott.7414.13.13.19976-19984
Copyright: © Kumar et al. 2021. Creative Commons Attribution
4.0 International License. JoTT allows unrestricted use, reproduction, and
distribution of this article in any medium by providing adequate credit to the
author(s) and the source of publication.
Funding: No funding
received by the authors. However,
Sumit Kumar got UGC non-NET fellowship through
HNB Garhwal University during
his PhD work.
Competing interests: The authors
declare no competing interests.
Author details: Dr. Sumit Kumar—presently working as wildlife biologist (fish) in a research project at
Wildlife Institute of India, Dehradun. Dr.
Sharali Sharma—specialization in fishery
science and presently working as Assistant Professor in Zoology at the
Chandigarh University, Chandigarh, Punjab. Dr.
Deepak Singh—working as an Associate Professor at the HNB Garhwal University, Srinagar (Garhwal).
Author contributions: SK--—collected and analysed the data for the present study. SS—helped during sampling of fishes and
contributed in writing the manuscript. DS—supervised during the whole study, checked the
manuscript and helped in data analysis and interpretation.
Acknowledgements: The library and laboratory
facilities during the present investigation provided by the Department of Zoology,
H.N.B. Garhwal University, Srinagar (Garhwal) are hereby thankfully acknowledged.
Abstract: Truss-based morphometric analysis
was used to examine phenotypic plasticity of Barilius
vagra (Hamilton, 1822) inhabiting the tributaries
of the Alaknanda (Ganga River basin) and Chenab
(Indus River basin), two geographically distinct river basins in the Indian
Himalaya. Fourteen landmarks were connected to generate a truss network of 90
parameters on the body of fish. Eighty morphometric traits out of ninety
morphometric measurements explained statistically significant difference among
six sampling locations of Barilius vagra from streams in the Alaknanda
and Chenab basins. Discriminant function analysis revealed 82% of Barilius vagra
specimens originally classified into their own groups. 95% of the variance was
explained by 13 principal components. Morphometric characters (1–6, 1–13, 2–5,
2–6, 2–14, 3–6, 4–6, 4–14, 6–12, 7–8, 7–9, 10–11, and 13–14) contributed
greatly in differentiation of B. vagra
populations from different river basins. The Alaknanda
basin reflected some mixing within populations, which may be due to common
environmental conditions and fish migration in these streams. This study will
be helpful in framing site-specific conservation and management strategies,
such as net mesh size selection, avoiding overexploitation, stock augmentation
and food availability for different fish populations.
Keywords: Danionidae, DFA, differentiation,
morphometry, truss network.
INTRODUCTION
Phenotypic
plasticity is the ability of an organism to change especially in response to
varying environmental conditions (Sahoo et al. 2020). Long term geographic
isolation and limited migration causes phenotypic plasticity among the
population within a species (Cadrin 2005). The Alaknanda and Chenab rivers drained from the Indian
Himalaya are geographically isolated and rich in fish fauna.
Fishes show
higher degree of variation within and between populations than other
vertebrates, and they are more susceptible to environmentally induced
morphological variation (Wimberger 1992). It has been
suggested that the morphological characters of fish are determined by
environment, genetic and interaction between them (Poulet et al. 2004). During
the early development stages the individual’s phenotype is more amenable to
environment influence (Pinheiro et al. 2005). The phenotypic variability may
not necessarily reflect population differentiation at genetic level (Ihssen et al. 1981). A sufficient degree of isolation may
result in notable phenotypic and genetic differentiation among fish populations
within a species, as a basis for separation and management of distinct
populations (Turanet al. 2004).
Among the
various tools used for stock assessment and phenotypic plasticity, morphometry
is one of the frequently used and cost-effective tools. Traditional
multivariate morphometrics, accounting for variation in size and shape have
successfully discriminated between many stocks (Turan
1999). As the traditional morphometric measurements have biased coverage and
metric selection over the body structure of fishes under experimentation, this
method might not be useful for discriminate species when there is morphological
plasticity (Takács et al. 2016). However, with the time this traditional
method has been enhanced by image processing technique which is more effective
in description of shape and stock identification (Mir et al. 2013).
Advance
tool kits such as truss network system and geometric morphometrics is the best
alternative used to study phenotypic plasticity within and between species (Turan 1999). Truss morphometric approach is an effective
method for capturing information about the shape of an organism (Cavalcanti et
al. 1999). It has been used to identify stocks of many fish species from marine
and fresh waters (Sajina et al. 2011; Garcia-Roudriguez et al. 2010; Sen et al. 2011; Khan et al. 2012; Miyan et al. 2015, Dwivedi et al. 2019). Different stocks
identified on the basis of environmentally induced morphometric variations play
a significant role in the fisheries management (Begg
et al. 1999). Insufficient knowledge on the population structure hinders the
rate of production and reduces yields (Cadrin 2005).
Good knowledge and right information of fish stocks will help us in the proper
management and conservation of endangered species and stock enhancement of
cultivable species.
Bariline fishes belonging to family Danionidae
are characterized by a compressed body, blue-black bars or spots on the body
and dorsal fin inserted behind the middle of the body (Rahman, 1989).
Thirty-two bariline species are reported globally out
of which 23 species so far reported from India (Singh et al. 2016). The species
of genus Barilius including Barilius vagra
(Hamilton, 1822) are commonly called hill trouts.
These minnows inhabit both shallow lentic and lotic waters of Himalayan region
(Sahoo et al. 2009). The hill stream fishes are important part of food as well
as source of income to the fishermen of the Himalayan region (Kumar & Singh
2019). There are a few studies available on the population structure of Barilius bendelisis (Mir
et al. 2015; Saxsena et al. 2015; Kumar & Singh
2019). However, there is paucity of published information on the population
structure of Barilius vagra
from Indian waters. Therefore, the present study was carried out with the
objective to examine the phenotypic plasticity among the different populations
of B. vagra from two distinct river basins of
Indian Himalaya.
MATERIALS AND METHODS
Sampling
and Measurements
Total 257 Barilius vagra
specimens were sampled from Alaknanda River basin
(132 specimens) and Chenab River basin (125 specimens) of Indian Himalaya using
different fishing gears (cast nets and gill nets) from March 2015 to April
2017. The GPS coordinates; altitude and number of samples from each site of two
river basins are presented in Table 1. The specimens of Barilius
vagra were collected before the breeding season
and after the spawning period (April to June) to avoid a bias towards size
difference. The fish specimens were identified by using identification keys of
Mirza (1991), Talwar & Jhingran (1991), and Kullander et al. (1999). After image capture, each fish was
dissected for sex determination by macroscopic examination of the gonads. The
gender was used as the class variable in ANOVA to test for significance
difference in morphometric characters, if any, between male and female of B.
vagra.
The truss
network system described by Strauss & Bookstein
(1982) was used to extract the 90 morphometric measurements of fish. Fish
specimens were placed on water resistant graph paper as background and a
digital camera of (Nikon D3400) was used to take the photographs (Figure 1)
from same height and angle. Some specimens were submitted to the animal museum
of the Department of Zoology of H.N.B. Garhwal
University, Uttarakhand and others were fixed in 10% formalin solution for
preservation.
The truss
protocol used for the hill trout in the present study was based on 14 landmarks
and the truss network constructed by interconnecting them to form a total of 90
truss measurements (Figure 1). The extraction of truss distances from the
digital images of specimens was conducted using linear combination of three softwares, tpsUtil, tpsDig2 v2.1
(Rohlf 2006) and Paleontological Statistics (PAST)
(Hammer et al. 2001).
Data
analysis
Size
dependent variations in truss measurements were removed, using the equation
given by Elliott et al. (1995) as “Madj =
M (Ls/L0)b” Here Madj,
is size adjusted measurement, M is original measurement of length, L0
is standard length of fish, Ls the overall mean standard length, and
b slope of the regression of log M on log L0 which is
estimated for each character from the observed.
Univariate
analysis of variance (ANOVA) was applied to 90 morphometric characters to
evaluate the significance of difference among the mean values of the individual
morphological character among different six populations of B. vagra. The characters expressing significant
differences were subjected to the discriminant function analysis (DFA) and
principal component analysis (PCA). The principal component analysis helps in morphometeric data reduction (Veasey et al. 2001), in
decreasing redundance among the variables (Samaee et al. 2006) and in extracting a number of
independent variables for population differentiation (Samaee
et al. 2009). The standardized coefficients are used to compare variables
measured on different scales. Coefficients with large absolute values
correspond to variables with greater discriminating ability.
The DFA was
used to calculate the percentage of correctly classified (PCC) fish. The Wilks’
lambda test of DFA was used to compare the differences between six populations,
each three of which were collected from two geographically distinct river
basins of Indian Himalaya. Statistical analysis for morphometric data were
performed using the SPSS (ver. 16.1) and Microsoft Excel 2007.
List of extracted 90 truss
generated morphometric measurements of Barilius
vagra.
|
|
Landmark No. |
Particulars of Truss distance |
|
1 |
1–2 |
Tip of snout to the anterior
border of eye |
|
2 |
1–3 |
Tip of the snout to the
posterior border of eye |
|
3 |
1–4 |
Tip of snout to the posterior
border of operculum |
|
4 |
1–5 |
Tip of snout to end of frontal
bone |
|
5 |
1–6 |
Tip of snout to pectoral fin
origin |
|
6 |
1–7 |
Tip of snout to dorsal fin
origin |
|
7 |
1–8 |
Tip of snout to pelvic fin
origin |
|
8 |
1–9 |
Tip of snout to dorsal fin
termination |
|
9 |
1–10 |
Tip of snout to origin of anal
fin |
|
10 |
1–11 |
Tip of snout to termination of
anal fin |
|
11 |
1–12 |
Tip of snout to dorsal side of
caudal peduncle |
|
12 |
1–13 |
Tip of snout to ventral side of
caudal peduncle |
|
13 |
1–14 |
Tip of snout to termination of
lateral line |
|
14 |
2–3 |
Anterior border of eye to
posterior border of eye |
|
15 |
2–4 |
Anterior border of eye to
posterior border of operculum |
|
16 |
2–5 |
Anterior border of eye to end
of frontal bone |
|
17 |
2–6 |
Anterior border of eye to
pectoral fin origin |
|
18 |
2–7 |
Anterior border of eye to
dorsal fin origin |
|
19 |
2–8 |
Anterior border of eye to
pelvic fin origin |
|
20 |
2–9 |
Anterior border of eye to
dorsal fin termination. |
|
21 |
2–10 |
Anterior border of eye to
origin of anal fin |
|
22 |
2–11 |
Anterior border of eye to
termination of anal fin |
|
23 |
2–12 |
Anterior border of eye to
dorsal side of caudal peduncle |
|
24 |
2–13 |
Anterior border of eye to
ventral side of caudal peduncle |
|
25 |
2–14 |
Anterior border of eye to
termination of lateral line |
|
26 |
3–4 |
Posterior border of eye to
posterior border of operculum |
|
27 |
3–5 |
Posterior border of eye to end
of frontal bone |
|
28 |
3–6 |
Posterior border of eye to
pectoral fin origin |
|
29 |
3–7 |
Posterior border of eye to
dorsal fin origin |
|
30 |
3–8 |
Posterior border of eye to
pelvic fin origin |
|
31 |
3–9 |
Posterior border of eye to
dorsal fin termination |
|
32 |
3–10 |
Posterior border of eye to
origin of anal fin |
|
33 |
3–11 |
Posterior border of eye to
termination of anal fin |
|
34 |
3–12 |
Posterior border of eye to
dorsal side of caudal peduncle |
|
35 |
3–13 |
Posterior border of eye to
ventral side of caudal peduncle |
|
36 |
3–14 |
Posterior border of eye to
termination of lateral line |
|
37 |
4–5 |
Posterior border of operculum
to end of frontal bone |
|
38 |
4–6 |
Posterior border of operculum
to pectoral fin origin |
|
39 |
4–7 |
Posterior border of operculum
to dorsal fin origin |
|
40 |
4–8 |
Posterior border of operculum
to pelvic fin origin |
|
41 |
4–9 |
Posterior border of operculum
to dorsal fin termination |
|
42 |
4–10 |
Posterior border of operculum
to origin of anal fin |
|
43 |
4–11 |
Posterior border of operculum
to termination of anal fin |
|
44 |
4–12 |
Posterior border of operculum
to dorsal side of caudal peduncle. |
|
45 |
4–13 |
Posterior border of operculum
to ventral side of caudal peduncle |
|
46 |
4–14 |
Posterior border of operculum
to termination of lateral line |
|
47 |
5–6 |
End of frontal bone to pectoral
fin origin |
|
48 |
5–7 |
End of frontal bone to dorsal
fin origin |
|
49 |
5–8 |
End of frontal bone to pelvic
fin origin |
|
50 |
5–9 |
End of frontal bone to dorsal
fin termination |
|
51 |
5–10 |
End of frontal bone to origin
of anal fin |
|
52 |
5–11 |
End of frontal bone to
termination of anal fin |
|
53 |
5–12 |
End of frontal bone to dorsal
side of caudal peduncle |
|
54 |
5–13 |
End of frontal bone to ventral
side of caudal peduncle |
|
55 |
5–14 |
End of frontal bone to
termination of lateral line |
|
56 |
6–7 |
Pectoral fin origin to dorsal
fin origin |
|
57 |
6–8 |
Pectoral fin origin to pelvic
fin origin |
|
58 |
6–9 |
Pectoral fin origin to dorsal
fin termination |
|
59 |
6–10 |
Pectoral fin origin to origin
of anal fin |
|
60 |
6–11 |
Pectoral fin origin to
termination of anal fin |
|
61 |
6–12 |
Pectoral fin origin to dorsal
side of caudal peduncle |
|
62 |
6–13 |
Pectoral fin origin to ventral
side of caudal peduncle |
|
63 |
6–14 |
Pectoral fin origin to
termination of lateral line |
|
64 |
7–8 |
Dorsal fin origin to pelvic fin
origin |
|
65 |
7–9 |
Dorsal fin origin to dorsal fin
termination |
|
66 |
7–10 |
Dorsal fin origin to origin of
anal fin |
|
67 |
7–11 |
Dorsal fin origin to
termination of anal fin |
|
68 |
7–12 |
Dorsal fin origin to dorsal
side of caudal peduncle |
|
69 |
7–13 |
Dorsal fin origin to ventral
side of caudal peduncle |
|
70 |
7–14 |
Dorsal fin origin to
termination of lateral line |
|
71 |
8–9 |
Pelvic fin origin to dorsal fin
termination |
|
72 |
8–10 |
Pelvic fin origin to origin of
anal fin |
|
73 |
8–11 |
Pelvic fin origin to
termination of anal fin |
|
74 |
8–12 |
Pelvic fin origin to dorsal
side of caudal peduncle |
|
75 |
8–13 |
Pelvic fin origin to ventral
side of caudal peduncle |
|
76 |
8–14 |
Pelvic fin origin to origin of
anal fin |
|
77 |
9–10 |
Dorsal fin termination to
origin of anal fin |
|
78 |
9–11 |
Dorsal fin termination to
termination of anal fin |
|
79 |
9–12 |
Dorsal fin termination to
dorsal side of caudal peduncle |
|
80 |
9–13 |
Dorsal fin termination to
ventral side of caudal peduncle |
|
81 |
9–14 |
Dorsal fin termination to
termination of lateral line |
|
82 |
10–11 |
Origin of anal fin to
termination of anal fin |
|
83 |
10–12 |
Origin of anal fin to dorsal
side of caudal peduncle |
|
84 |
10–13 |
Origin of anal fin to ventral
side of caudal peduncle |
|
85 |
10–14 |
Origin of anal fin to
termination of lateral line |
|
86 |
11–12 |
Termination of anal fin to
dorsal side of caudal peduncle |
|
87 |
11–13 |
Termination of anal fin to
ventral side of caudal peduncle |
|
88 |
11–14 |
Termination of anal fin to
termination of lateral line |
|
89 |
12–13 |
Dorsal side of caudal peduncle
to ventral side of caudal peduncle |
|
90 |
13–14 |
Ventral side of caudal peduncle
to termination of lateral line |
RESULTS
The morphometric characters
between two sexes of B. vagra did not differ
significantly (p >0.05), hence the data for both sexes were pooled for all
subsequent analysis. Univariate analysis
of variance (ANOVA) extracted eighty morphometyric
measurements having significant differences (p <0.05) and 10 measurements
(1–7, 2–4, 3–4, 3–7, 4–5, 5–7, 7–12, 7–13, 8–9, and 9–11) did not show
significant differences among six populations of B. vagra.
Principal component analysis (PCA) of these significant measurements extracted
13 principal components having eigenvalues greater than one (Figure 2)
explaining cumulative variance of 94.79%. The first principal component (PC1)
accounted for 21.55% of the variation followed by 18.62%, 13.86%, 8.01%, and
6.52% variance, respectively by second, third, fourth, and fifth principal
component (Table 2). Forward stepwise discriminant analysis of the significant
variables produced five discriminant functions (DFs). The first, second, third,
fourth and fifth discriminant functions explained 68.4%, 18.4%, 6.8%, 5.1%, and
1.3% of variance, respectively (Table 3). Plotting DF1 and DF2 showed clear
specimen differentiation of stocks from different tributaries, Dudhar, Jhajjar, and Jhuni streams of Chenab River basin. However; slight
intermingling in the population of Barilius
vagra from three different tributaries, Dugadda, Khandah, and Khankhra of Alaknanda river basin
was also noticed (Figure 3).
Thirteen truss morphometric
measurements 1–6, 1–13, 2–5, 2–6, 2–14, 3–6, 4–6, 4–14, 6–12, 7–8, 7–9, 10–11,
and 13–14 contributed largely in the discriminant function analysis of B. vagra (Table 4). A total of 81.7% of specimens of Barilius vagra were
classified into their original groups. Maximum 87.0% and minimum 76.2% of the
specimens were found in their own groups of Khankhra
and Dugadda streams, respectively from the Alaknanda river basin (Table 5). Some mixing in the
populations of Alaknanda river basin was also found.
Wilks’ Lambda test reflected highly significant variations among the six
populations of B. vagra from different
tributaries of Alaknanda and Chenab River basins
(Table 6).
DISCUSSION
Morphological differentiation can
enable individuals to survive with existing environmental variability (Senay et al. 2015). Hossain et al. (2010) reported that
phenotypic plasticity is very high in fishes. A sufficient degree of isolation
may result in phenotypic and genetic differentiation among fish populations
within a species (Turan et al. 2004). Franssen et al.
(2013) also suggested that the selective pressure of the environmental
conditions leading to genetic-environmental interactions influence the pattern
of phenotypic variation at intraspecific level. The results of the present
study showed significant phenotypic heterogeneity among the populations of B.
vagra from two geographically distinct river
basins. High level of morphometric differentiation was reported within the
Chenab River basin as compared to the Alaknanda river
basin as shown by the DFA plot. Chenab River is largely fragmented as compared
to the Alaknanda river basin, might be one of the
reasons for the cause.
Discriminant
function analysis (DFA) could be a useful method to distinguish different
stocks of the same species (Karakousis et al. 1991).
In the present study, 81.7% of specimens were classified into their original
groups by DFA, showing high variation in the stocks of Alaknanda
and Chenab River basins. Eighty truss measurements in the whole body from head
to tail were found to have significant differences (p <0.05) among the six
populations of both the river basins. 13 morphometric measurements (1–6, 1–13,
2–5, 2–6, 2–14, 3–6, 4–6, 4–14, 6–12, 7–8, 7–9, 10–11, and 13–14) extracted
from DFA largely contributed in the discrimination of six populations. These
all variations in the morphometric measurements of fishes were attributed to
the environmental conditions of those particular streams and the fishes adapted
to the existing environmental conditions by altering their morphology. It was
interesting to note that most of these parameters were linked to the head, eye
diameter and fin (Dorsal and anal) of the fish body. Rajput et al. (2013) while
studying the eco-morphology of Schizothorax
richardsonii reported strong correlation between
the environmental variables and morphometric parameters like the fin morphology
and body shape. Sajina et al. (2011) studied the
stock structure of Megalepis cordyla from the east (Bay of Bengal) and west coast
(Arabian Sea) of the Indian peninsula using truss morphometric analysis and
found significant heterogeneity among the stocks, attributed it to the uncommon
hydrological conditions of habitats. Mir et al. (2013) investigated phenotypic
variation in Schizothorax richardsonii from four rivers Jhelum, Lidder, Alaknanda, and Mandakini by using DFA and PCA and reported morphological
discrimination among the stocks due to environmental factors.
Intermingling
was noticed in three populations of Ganga River basin, which may be due to some
common environmental conditions, migration and similar genetic origin at
earlier period. Dwivedi et al. (2019) observed low level of morphometric
differentiation among wild populations of Cirrhinus
mrigala from ten different tributaries of Ganges
and attributed it to the migration of individuals within the basin and common
ancestry in the prehistoric period. In the present investigation Wilks λ test
of discriminant function analysis indicated significant differences in
morphometric characters of six populations of B. vagra
from two river basins, similar findings were reported by (Mir et al. 2013) in
case of Schizothorax richardsonii.
Truss
system can be successfully used to investigate stock separation within a
species, as reported for other species in freshwater and marine environments.
Among the 13 measurements which contributed to the five discriminant functions,
four measurements (2–6, 3–6, 4–6, and 7–8) dominantly contributed to fifth
discriminant function explaining variance in six populations of B. vagra. Mahfuj et al. (2019)
while studying the meristic and morphometrics variations of Macrognathus
pancalus using truss network system from the
freshwaters of Bangladesh explained that out of fifteen truss measurements,
five measurements contributed to the 1st DF, six measurements
contributed to the 2nd DF and remaining four measurements to the 3rd
DF. Kenthao and Jearranaiprepame
(2018) also conducted similar kind of study in Yclocheilichthys
apogon from three different rivers Pong, Chi, and
Mun of northeastern
Thailand. The first three principal components explained 49.29% of variance and
first three discriminant functions explained 72% of variation among the
samples. However, in the present study, PCA explained 94.79% of variance by
using 13 principal components.
In this
study, truss system revealed clear separation of B. vagra
populations from two distinct river basins which will help in site-specific
conservation and management strategies such as implementation of appropriate
mesh sizes for fish harvesting, avoiding over-exploitation, augmentation of
fish stock by culture, and making available sufficient food to fishes for their
proper growth in different drainages of the Alaknanda
and Chenab rivers. This will be instrumental in sustaining this resource for
future use.
CONCLUSION
Truss
protocol revealed phenotypic plasticity among six different populations of Alakanda and Chenab River drainages of Indian Himalaya. A
clear separation of B. vagra populations
between two geographically distinct river basins of Indian Himalaya was also
found suggesting a need for separate conservation and management strategies to
sustain the stock for future use.
Table 1. GPS coordinates of sites
from Alaknanda and Chenab River basins.
|
Sampling site |
Sample size |
Latitude (°N) |
Longitude (°E) |
Altitude (m) |
|
Dugadda |
42 |
30.26 |
78.72 |
740 |
|
Khankhara |
46 |
30.23 |
78.93 |
668 |
|
Khandah |
44 |
30.19 |
78.78 |
718 |
|
Dudhar |
40 |
32.92 |
75.03 |
486 |
|
Jhajar |
46 |
32.87 |
74.99 |
555 |
|
Jhuni |
39 |
32.89 |
75.95 |
754 |
Table 2. Eigenvalues, percentage
of variance and percentage of cumulative variance for the 13 PCs in case of
morphometric measurements for Barilius vagra.
|
Component |
Eigenvalues |
||
|
Total |
% of Variance |
Cumulative % |
|
|
PC 1 |
17.244 |
21.555 |
21.555 |
|
PC 2 |
14.895 |
18.618 |
40.173 |
|
PC 3 |
11.090 |
13.862 |
54.035 |
|
PC 4 |
6.407 |
8.009 |
62.045 |
|
PC 5 |
5.213 |
6.516 |
68.561 |
|
PC 6 |
5.106 |
6.383 |
74.944 |
|
PC 7 |
4.011 |
5.014 |
79.958 |
|
PC 8 |
3.127 |
3.909 |
83.867 |
|
PC 9 |
2.523 |
3.154 |
87.021 |
|
PC 10 |
2.125 |
2.656 |
89.677 |
|
PC11 |
1.765 |
2.206 |
91.884 |
|
PC 12 |
1.268 |
1.585 |
93.469 |
|
PC 13 |
1.056 |
1.320 |
94.789 |
Table 3. Eigenvalues and total variance explained by
five discriminant functions.
|
Eigenvalues |
||||
|
Function |
Eigenvalue |
% of Variance |
Cumulative % |
Canonical Correlation |
|
DF 1 |
5.878a |
68.4 |
68.4 |
0.924 |
|
DF 2 |
1.582a |
18.4 |
86.8 |
0.783 |
|
DF 3 |
0.584a |
6.8 |
93.6 |
0.607 |
|
DF 4 |
0.438a |
5.1 |
98.7 |
0.552 |
|
DF 5 |
0.109a |
1.3 |
100.0 |
0.313 |
|
a First 5 canonical
discriminant functions were used in the analysis. |
||||
Table 4. Discriminant function
coefficients expressed by different morphometric measurements of Barilius vagra
collected from tributaries of Alaknanda and Chenab
rivers. (Bold digits indicates largest absolute correlation between each
variable and any discriminant function)
|
Standardized canonical
discriminant function coefficients |
|||||
|
Variables |
Function |
||||
|
DF 1 |
DF 2 |
DF 3 |
DF 4 |
DF 5 |
|
|
VAR 1-6 |
0.550 |
1.044 |
-0.003 |
0.556 |
-0.896 |
|
VAR 1-13 |
-0.310 |
-0.046 |
0.652 |
-0.447 |
0.112 |
|
VAR 2-5 |
0.033 |
-0.026 |
0.197 |
0.702 |
0.418 |
|
VAR 2-6 |
1.895 |
-0.705 |
0.779 |
-1.366 |
2.319 |
|
VAR 2-14 |
0.040 |
1.232 |
-0.664 |
-1.299 |
-0.139 |
|
VAR 3-6 |
-1.515 |
-0.409 |
-0.730 |
1.021 |
-1.842 |
|
VAR 4-6 |
0.183 |
0.594 |
-0.098 |
0.578 |
0.606 |
|
VAR 4-14 |
1.195 |
-0.482 |
1.176 |
1.388 |
0.386 |
|
VAR 6-12 |
-0.798 |
-0.342 |
-0.640 |
-0.080 |
0.299 |
|
VAR 7-8 |
0.237 |
-0.063 |
-0.438 |
-0.151 |
-0.457 |
|
VAR 7-9 |
-0.201 |
0.453 |
0.316 |
0.177 |
-0.152 |
|
VAR 10-11 |
-0.148 |
0.035 |
0.374 |
-0.337 |
-0.084 |
|
VAR 13-14 |
-0.649 |
0.141 |
0.304 |
-0.526 |
0.048 |
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