Journal of Threatened Taxa |
www.threatenedtaxa.org | 26 June 2023 | 15(6): 23297–23306
ISSN
0974-7907 (Online) | ISSN 0974-7893 (Print)
https://doi.org/10.11609/jott.6612.15.6.23297-23306
#6612 |
Received 24 August 2020 | Final received 05 May 2023 | Finally accepted 01 June
2023
Diversity and abundance of aquatic birds in Koonthankulam
village pond, Tamil Nadu, India
Selvam Muralikrishnan 1, Esakkimuthu Shanmugam 2, Natarajan Arun Nagendran 3 &
Duraisamy Pandiaraja 4
1 National Centre of Excellence
(MHRD), Thiagarajar College, Madurai Kamaraj
University, Madurai, Tamil Nadu 625009, India.
2 Basic Engineering, Tamilnadu Government Polytechnic College, Madurai, Tamil
Nadu 625011, India.
3 Department of Zoology, Thiagarajar College, Madurai Kamaraj University, Madurai,
Tamil Nadu 625009, India.
4 Department of Mathematics, Thiagarajar College, Madurai Kamaraj University, Madurai,
Tamil Nadu 625009, India.
1 nilaasmurali@gmail.com
(corresponding author), 2 shamphdmat@gmail.com, 3 narunnagendran@gmail.com,
4 pandiaraja.d@gmail.com
Editor: P.A. Azeez, Coimbatore, Tamil
Nadu, India. Date of publication:
26 June 2023 (online & print)
Citation: Muralikrishnan,
S., E. Shanmugam, N.A. Nagendran & D. Pandiaraja (2023). Diversity and abundance of aquatic birds in Koonthankulam village pond, Tamil Nadu, India. Journal of Threatened Taxa 15(6): 23297–23306. https://doi.org/10.11609/jott.6612.15.6.23297-23306
Copyright: © Muralikrishnan et al.
2023.
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: This work was
supported by the National Centre of Excellence (Ministry of Human Resource
Development, New Delhi, India) [grant numbers F. No. 5-6/2013 -TS-VII dt. 28.09.2015].
Competing interests: The authors declare no competing interests.
Author details: S. Muralikrishnan is a research scholar in the National Centre of Excellence (MHRD), Thiagarajar College, Madurai. His research field is ecology with special reference to birds. He documents wetland birds of Tirunelveli, Madurai, Virudhunagar, Sivagangai and Ramanathapuram. E. Shanmugam is currently working as lecturer in Mathematics in Tamilnadu Government Polytechnic College, Madurai. He is pursuing his Doctoral Programme in the National Centre of
Excellence (MHRD), Thiagarajar College, Madurai as Part time research scholar. His field of research is graph theoretical modelling. N. Arun Nagendran is serving as associate professor of Zoology, and Joint Director of National Centre of
Excellence, Thiagarajar College, Madurai funded by MHRD, Government of India. He is interested in ecology and conservation. D. Pandiaraja is associate professor of Mathematics and serving as Principal, Thiagarajar College, Madurai. He is also the Director of National Centre of Excellence, Thiagarajar College, Madurai funded by MHRD, Government of India. He is specialized in Mathematical modelling and presently provides modelling for biological problems.
Author contributions: SM implemented the field surveys and collected the data; wrote the first draft. DP designed data analysis and done by ES. NAN supervised the research and provided multiple revisions in the early stages of writing. All authors read and approved the final manuscript.
Acknowledgements: This work was supported
by the National Centre of Excellence (Ministry of Human Resource Development,
New Delhi, India) [grant numbers F. No. 5-6/2013 -TS-VII dt.
28.09.2015].
Abstract: The diversity of
birds in Koonthankulam pond, located in Koonthankulam village (8.495N, 77.755E), Tirunelveli
district, southern Tamil Nadu, was studied. A total of 90 species belonging to
21 orders, 42 families, and 73 genera were recorded. The study recorded
seasonal migrants such as Black Ibis, Oriental White Ibis, Bar-headed Goose &
Spoonbill and indigenous species including the Pond Heron, Cattle Egret,
White-breasted Kingfisher, Red-wattled Lapwing,
Rose-ringed Parakeet, Purple-rumped Sunbird, Hoopoe,
and Indian Robin. The primary data were analyzed by principal component
analysis, cluster, and analysis of variance. Analysis of variance showed that
the Menhinick index is statistically significant P
<0.05. A structural equation model
was applied to analyze the physico-chemical
parameters of water samples collected from the sampling site. Analysis of
experimental data through the structural equation model indicates temperature
and dissolved oxygen may indirectly affect bird diversity.
Keywords: Avian fauna, migrants, principal
compound analysis, structural equation modeling.
Abbreviations: PCA—Principal
compound analysis | ANOVA—Analysis of variance | SEM—Structural equation model
| TDS—Total dissolved solids | DO—dissolved oxygen | CFI—Comparative Fit Index
| TLI—Tucker-Lewis Index | RMSEA—Root Mean Square Error of Approximation |
IUCN—International Union for Conservation of Nature | GFI—Goodness of Fit.
Introduction
The process of urbanization has fragmented and degraded different types
of habitats. One such habitat is ponds of varied sizes, especially in urban and
semi-urban areas. Under such conditions, the existing ponds provide little hope
for life and support for the survival of organisms. Wetlands are among the most
productive ecosystems in the world and play vital roles in flood control,
aquifer recharge, nutrient absorption, and erosion control. In addition,
wetlands provide home for a huge diversity of wildlife such as birds, mammals,
fish, frogs, insects, and plants (Buckton 2007). Among several organisms
surviving in and around water bodies, birds occupy a significant position, as
they are one of the critical ecosystem functionaries.
Birds play prominent roles in ecosystems, serving as pollinators (Stiles
1978), predators (Rudebeck 1950), scavengers (Roen 2005), prey (Rudebeck 1950),
and regulators of pest populations (Peterson 1980). Their interactions are wide
and varied with abiotic and biotic components of different ecosystems, i.e.,
they are not restricted to one particular system but also to adjacent systems
as they enjoy the power of flight. India hosts around 1,353 species of birds
(Praveen & Jaypal 2023). Analysis of avian
diversity portrays the status of their aquatic habitats and neighboring
ecosystems. As there is no detailed report on the diversity of Koonthankulam village pond, the present study was carried
out to analyze seasonal variation in bird diversity and their relationship with
water quality parameters.
Materials and Methods
The study was carried out in Koonthankulam
village pond (8.495N, 77.755E), Tirunelveli, southern Tamil Nadu, from January
2017 to November 2018. This pond is surrounded by agricultural fields, where
different crops are grown throughout the year. Macro-invertebrates of the
agricultural fields and grains scattered around after harvesting along with the
pond allure avifauna to this region. Bird watching and recording have been
carried out for six seasons (namely, spring, summer, early monsoon, late
monsoon, early winter, and late winter) by point count protocol as per Newson
et al. (2009). Observations were made using a binocular (Nikon 16x50
AculonA211), and photography was done with Canon 6D Mark II with zoom lenses.
The birds recorded were identified by referring to Ali & Ripley (1981).
Physical and chemical parameters such as temperature, pH, total
dissolved solids (TDS), conductivity, salinity, and dissolved oxygen (DO), were
measured on the spot using a water analyzer (Systronic
make 371). Other parameters (Hardness, magnesium, calcium, chloride,
alkalinity, and acidity) were determined following the standard procedure from
American Public Health Association (APHA) and Trivedy
& Goel (1984). The map has been generated using
the software QGIS 3.6.
Structural equation modeling (SEM) is a multivariate statistical tool
that can be used to describe linear relationships among variables (McCune &
Grace 2002; Grace 2006). SEM provides explicit regression estimations for all
parameters (Byrne 2001). Structural equation modeling of groundwater
physicochemical parameters data was used to characterize the groundwater
quality and to identify the controlling factors on bird diversity. IBM SPSS
AMOS 22.0 was used to analyze the structured model’s fit and estimate the
parameters of both observed and latent variables. Chi-square test, the root
mean square error of approximation (RMSEA), and the goodness fit index are used
as measures of model fit. A measure of minimum sample discrepancy is indicated
by the value chi-square divided by the degrees of freedom (CMIN/df) (Belkhiri & Narany 2015). This measure was used to analyze the fit of
the model. A value of less than 5 indicates the model’s fit is adequate
(Arbuckle 2012), less than 3 reflects that the model is acceptable (Kline
1998), whereas a value of 2 or less represents the model was fit as a good
model. Goodness-of-Fit statistics (GFI) was calculated as the variance
proportion accounted for by the estimated covariance (Tabachnick
& Fidell 2007). The RMSEA provides a way to
understand optimally chosen parameter estimates that would fit the covariance
matrix (Byrne 1998). When the proposed structural model has a (comparative fit
index) CFI>0.95 and an RMSEA <0.05, then the structural model is to be
considered a good model (Byrne 2010). The diversity indices were calculated
using PAST 3.14 software.
Result and Discussion
A total of 90 species of birds belonging to 21 orders and 42 families
were recorded. Of this, 87 species of birds are of ‘Least Concern’, and three
species are ‘Near Threatened’ (Table 1). Of these, 41 were waterbirds,
and 49 were terrestrial birds (Table 1). Waterbirds
constitute 12 species of waterfowl (swimmers), 25 species of waders, and three
species were divers. Out of 49 terrestrial birds, 35 species were passerine
birds, five were birds of prey, six were upland ground birds, two were night
birds, and two were tree-clinging birds. It is evident from the data that the
order Passeriformes is represented by the most families (Sturnidae,
Motacillidae, Corvidae, Dicruridae, Estrildidae, Muscicapidae, Alaudidae, Motacillidae, Nectariniidae, Passeridae, Pycnonotidae, Muscicapidae, Monarchidae, and Leiothrichidae). In contrast, the highest numbers of
species recorded were from Ardeidae, Anatidae, Cuculidae, Columbidae, Rallidae, and Threskiornithidae. The highest abundance index (9.2) was
seen for Anastomus oscitans.
Bubo bubo and Pandion haliaetus
had a low abundance index (0.02). Of the 90 species, 26 species have more than
1 abundance index.
The distribution of various species was analyzed using the principal
component analysis (PCA) method (Dauda et al. 2017). The distribution of the
occurrence of various species and their variance-covariance matrices were
analyzed through scatter diagrams generated from PCA, and the results were
further evaluated by 95% ellipses (Figure 1). The results revealed that the
species clustering differs with seasons.
In spring, Threskiornis melanocephalus (THME), Psittacula
krameri (PSKR), Passer domesticus
(PADO), and Corves splendens (COSP) were
found, while Ardeola grayii
(ARGR), Turdoides striata
(TUST), Acridotheres tristis (ACTR), E. garzetta
(EGGA) and T. melanocephalus (THME) were
recorded during summer. Similarly, A. tristis (ACTR),
E. garzetta (EGGA), C. splendens
(COSP), Bubulcus ibis (BUIB), and Pseudibis papillosa (PSPA)
were recorded in early monsoon, while A. oscitans (ANOS),
P. papillosa (PSPA), Himantopus
himantopus (HIHI), A. tristis
(ACTR), and C. splendens (COSP) were found
during late monsoon. In early winter, A. oscitans (ANOS),
Anser indicus (ANIN), Mycteria
leucocephala (MYLE), and C. splendens (COSP) were recorded, which distinguish themselves
from other species in abundance. More species were abundant in late winter than
in other seasons. From the results, it could be seen that the pond has been
dominated by C. splendens (COSP), E. garzetta (EGGA), A. tristis
(ACTR), etc.
The similarity in the species composition and abundance among the six
seasons analyzed by Bray-Curtis coefficient (Cluster analysis) clustered the
seasons into five, in the range of 55.25–100.00 (Figure 3). The five clusters
show that each season has a different composition of the bird populations. The
dendrogram showed that summer and early monsoon have a maximum similarity of
76.30. Two groups were identified among the six seasons. Early winter, late
winter, and spring formed a group and early monsoon, late monsoon, and summer
formed another group.
The number of individuals across seasons differed significantly (ANOVA,
F5,12 = 6.673, P <0.05; Table 2). A higher number of individuals
was present in late winter (3027.33 ± 498.27), whereas the lowest number was
recorded in late monsoon (1315.00 ± 158.24). The second-highest population of
birds appeared in spring (2980.33 ± 316.26). This implies that the number of
birds from December─March was high. The maximum Taxa_S was found in late winter (85.00 ± 1.73), whereas the
minimum was in late monsoon (80.66 ± 0.33), with the range of Taxa_S over all the seasons being 5. The results reveal
minimum deviation in species composition with high variation in population.
However, the highest Shannon_H diversity (3.70 ±
0.06) was in late monsoon and the lowest (3.36 ± 0.19) in spring, indicating a
more diverse and even species distribution in late monsoon. The Shannon_H diversity of birds among various seasons was not
significantly different (ANOVA, F5, 12 = 0.898, P >0.05).
The species evenness among the various seasons was measured by Buzas and Gibson’s index. Evenness was maximum in late
monsoon (0.50 ± 0.03) and minimum in spring (0.35 ± 0.06). However, the Buzas and Gibson’s evenness indices across various seasons
were not significantly different (ANOVA, F5, 12 = 1.24, P >0.05).
The richness was measured by the Menhinick species
richness index. The Menhinick richness index differed
significantly among the seasons (ANOVA, F5, 12 = 7.811, P <0.05).
Early (2.25 ± 0.12) and late monsoon (2.25 ± 0.13) have a high value of Menhinick richness index, and spring (1.56 ± 0.12) and late
winter (1.57 ± 0.09) have a low value. The Chao-1 estimator was used to analyze
singleton and doubleton species in the bird community. The maximum singleton and
doubleton species occurred in summer (101.27 ± 9.26) and the minimum in late
monsoon (90.89 ± 0.37). Among all the seasons, the Chao-1 estimator was not
significantly different (ANOVA, F5,12 = 0.518, P >0.05).
A correlation analysis has been carried out for all physico-chemical
parameters with swimmer, diver and wader species to understand the influence of
the water-quality parameters on the bird population (Table 3). The following
results were noticed in that analysis. The temperature was negatively correlated
with swimmer and wader populations at p = 0.01, and DO was positively
correlated with all the three water bird communities encountered in this study
at p = 0.05. In addition to this, a SEM model was designed and analyzed to
confirm that temperature and DO were the most important parameters that affect
bird counts. Thus, a structural equation model has been framed with the
following six parameters, temperature, DO, swimmer, diver, and wader
abundances. Bird’s abundance has been studied with reference to the effect of
temperature using structural equation modeling, as in Duclos et al. (2017).
Patra et al. (2010) used stepwise multiple regression analysis to study the physico-chemical parameters affecting the avifaunal
abundance; e1, e2, and e3 are added to the SEM to reduce the error value
between the variables. Temperature, DO, and errors are exogenous variables, and
swimmer, diver, and wader birds are endogenous variables.
Figure 3 expresses the conceptual framework for the model. The fitness
of a SEM is important to understand the reliability of the results. The measure
CMIN/df (0.518) <2, GFI (0.95) >0.90, and RMSEA
(0.00) <0.05 revealed that the model represented a realistic fit of the
data. The regression equations for the four endogenous variables with
standardized coefficients are
Birds = (-.13) Diver + (.65) Swimmer + (.46) Wader + (1) e4
Diver = (.36) DO + (1) e2
Swimmer = (.52) DO + (-.25) Temp + (1) e1
Wader = (.59) DO + (1) e3 + (-.29) Temp
Five path coefficients were significant at 0.05 (Table 4). From the
significance of these path coefficients, it is revealed that DO positively
influences swimmer and wader counts, while temperature negatively influences
wader counts. Duclos et al. (2017) reveal that temperature directly affects the
abundance of birds. Waders have a negative direct effect from temperature.
Dissolved oxygen positively influenced total avifaunal abundance (Patra et al.
2010). Both correlation analyses and SEM model confirmed that temperature and
DO are the main parameters that affect bird count in this study area.
Table 1. Occurrence, IUCN Red List, and abundance index of avian
communities in the Koonthankulam village pond,
Tirunelveli, Tamil Nadu, India.
|
|
Scientific Name |
Family |
Order |
IUCN Red List status |
Abundance index |
Behavior category |
|
1 |
Accipiter badius |
Accipitridae |
Accipitriformes |
LC |
0.15 |
Bird of prey |
|
2 |
Acridotheres tristis |
Sturnidae |
Passeriformes |
LC |
5.33 |
Percher |
|
3 |
Alcedo atthis |
Alcedinidae |
Coraciiformes |
LC |
0.24 |
Percher |
|
4 |
Amaurornis phoenicurus |
Rallidae |
Gruiformes |
LC |
0.41 |
Swimmer |
|
5 |
Anas acuta |
Anatidae |
Anseriformes |
LC |
0.31 |
Swimmer |
|
6 |
Anas arcuata |
Anatidae |
Anseriformes |
LC |
0.51 |
Swimmer |
|
7 |
Anas crecca |
Anatidae |
Anseriformes |
LC |
0.44 |
Swimmer |
|
8 |
Anas poecilorhyncha |
Anatidae |
Anseriformes |
LC |
0.79 |
Swimmer |
|
9 |
Anas querquedula |
Anatidae |
Anseriformes |
LC |
0.22 |
Swimmer |
|
10 |
Anastomus oscitans |
Ciconiidae |
Ciconiiformes |
LC |
9.2 |
Wader |
|
11 |
Anhinga melanogaster |
Anhingidae |
Suliformes |
NT |
0.46 |
Diver |
|
12 |
Anser indicus |
Anatidae |
Anseriformes |
LC |
2.7 |
Swimmer |
|
13 |
Anthus rufulus |
Motacillidae |
Passeriformes |
LC |
0.26 |
Percher |
|
14 |
Ardea cinerea |
Ardeidae |
Pelecaniformes |
LC |
0.52 |
Wader |
|
15 |
Ardea purpurea |
Ardeidae |
Pelecaniformes |
LC |
0.15 |
Wader |
|
16 |
Ardeola grayii |
Ardeidae |
Pelecaniformes |
LC |
1.32 |
Wader |
|
17 |
Artamus fuscus |
Artamidae |
Passeriformes |
LC |
2.7 |
Percher |
|
18 |
Athene brama |
Strigidae |
Strigiformes |
LC |
0.25 |
Night bird |
|
19 |
Bubo bubo |
Strigidae |
Strigiformes |
LC |
0.02 |
Night bird |
|
20 |
Bubulcus ibis |
Ardeidae |
Pelecaniformes |
LC |
3.61 |
Wader |
|
21 |
Butorides striatus |
Ardeidae |
Pelecaniformes |
LC |
0.53 |
Wader |
|
22 |
Calidris alpina |
Scolopacidae |
Charadriiformes |
LC |
0.15 |
Wader |
|
23 |
Casmerodius albus |
Ardeidae |
Pelecaniformes |
LC |
0.45 |
Wader |
|
24 |
Centropes sinensis |
Cuculidae |
Cuculiformes |
LC |
0.29 |
Percher |
|
25 |
Charadrius dubius |
Charadriidae |
Charadriiformes |
LC |
0.32 |
Wader |
|
26 |
Clamator jacobinus |
Cuculidae |
Cuculiformes |
LC |
0.29 |
Percher |
|
27 |
Columba livia |
Columbidae |
Columbiformes |
LC |
2.47 |
Upland ground |
|
28 |
Coracias benghalensis |
Coraciidae |
Coraciiformes |
LC |
0.75 |
Percher |
|
29 |
Corves macrorhynchos |
Corvidae |
Passeriformes |
LC |
1.28 |
Percher |
|
30 |
Corves splendens |
Corvidae |
Passeriformes |
LC |
8.11 |
Percher |
|
31 |
Cuculus poliocephalus |
Cuculidae |
Cuculiformes |
LC |
0.16 |
Percher |
|
32 |
Dendrocitta vagabunda |
Corvidae |
Passeriformes |
LC |
0.39 |
Percher |
|
33 |
Dicrurus leucophaeus |
Dicruridae |
Passeriformes |
LC |
0.44 |
Percher |
|
34 |
Dicrurus macrocercus |
Dicruridae |
Passeriformes |
LC |
1.45 |
Percher |
|
35 |
Dinopium benghalense |
Picidae |
Piciformes |
LC |
0.15 |
Tree clinging bird |
|
36 |
Dupetor flavicollis |
Ardeidae |
Pelecaniformes |
LC |
0.06 |
Wader |
|
37 |
Egretta garzetta |
Ardeidae |
Pelecaniformes |
LC |
4.22 |
Wader |
|
38 |
Egretta intermedia |
Ardeidae |
Pelecaniformes |
LC |
1.59 |
Wader |
|
39 |
Eudynamys scolopacea |
Cuculidae |
Cuculiformes |
LC |
0.34 |
Percher |
|
40 |
Euodice malabarica |
Estrildidae |
Passeriformes |
LC |
1.85 |
Percher |
|
41 |
Falco peregrinus |
Falconidea |
Falconiformes |
LC |
0.09 |
Bird of prey |
|
42 |
Francolinus pondicerianus |
Phasianidae |
Galliformes |
LC |
0.57 |
Upland ground |
|
43 |
Fulica atra |
Rallidae |
Gruiformes |
LC |
1.27 |
Swimmer |
|
44 |
Gallinula chloropus |
Rallidae |
Gruiformes |
LC |
0.61 |
Swimmer |
|
45 |
Halcyon smyrnensis |
Alcedinidae |
Coraciiformes |
LC |
0.57 |
Percher |
|
46 |
Haliastur indus |
Accipitridae |
Accipitriformes |
LC |
0.4 |
Bird of prey |
|
47 |
Himantopus himantopus |
Recurvirostridae |
Charadriiformes |
LC |
1.85 |
Wader |
|
48 |
Hydrophasianus chirurgus |
Jacanidea |
Charadriiformes |
LC |
0.78 |
Wader |
|
49 |
Lonchura punctulata |
Estrildidae |
Passeriformes |
LC |
0.16 |
Percher |
|
50 |
Luscinia brunnea |
Muscicapidae |
Passeriformes |
LC |
0.15 |
Percher |
|
51 |
Merops orientalis |
Meropidae |
Coraciiformes |
LC |
0.53 |
Percher |
|
52 |
Merops philippinus |
Meropidae |
Coraciiformes |
LC |
0.74 |
Percher |
|
53 |
Milvus migrans |
Accipitridae |
Accipitriformes |
LC |
0.13 |
Bird of prey |
|
54 |
Mirafra cantillans |
Alaudidae |
Passeriformes |
LC |
0.55 |
Percher |
|
55 |
Motacilla maderaspatensis |
Motacillidae |
Passeriformes |
LC |
1.07 |
Percher |
|
56 |
Mycteria leucocephala |
Ciconiidae |
Ciconiiformes |
NT |
3.18 |
Wader |
|
57 |
Nectarinia asiatica |
Nectariniidae |
Passeriformes |
LC |
0.74 |
Percher |
|
58 |
Nectarinia zeylonica |
Nectariniidae |
Passeriformes |
LC |
0.44 |
Percher |
|
59 |
Nycticorax nycticorax |
Ardeidae |
Pelecaniformes |
LC |
0.88 |
Wader |
|
60 |
Oriolus oriolus |
Oriolidae |
Passeriformes |
LC |
0.27 |
Percher |
|
61 |
Pandion haliaetus |
Pandionidea |
Accipitriformes |
LC |
0.02 |
Bird of prey |
|
62 |
Passer domesticus |
Passeridae |
Passeriformes |
LC |
2.15 |
Percher |
|
63 |
Pavo cristatus |
Phasianidae |
Galliformes |
LC |
1.45 |
Upland ground |
|
64 |
Pelecanus onocrotalus |
Pelecanidae |
Pelecaniformes |
LC |
0.4 |
Swimmer |
|
65 |
Pelecanus philippensis |
Pelecanidae |
Pelecaniformes |
LC |
1.28 |
Swimmer |
|
66 |
Phaenicophaeus viridirostris |
Cuculidae |
Cuculiformes |
LC |
0.28 |
Percher |
|
67 |
Phalacrocorax carbo |
Phalacrocoracidae |
Phalacrocoracidae |
LC |
0.13 |
Diver |
|
68 |
Phalacrocorax niger |
Phalacrocoracidae |
Phalacrocoracidae |
LC |
1.87 |
Diver |
|
69 |
Platalea leucorodia |
Threskiornithidae |
Pelecaniformes |
LC |
0.75 |
Wader |
|
70 |
Plegadis falcinellus |
Threskiornithidae |
Pelecaniformes |
LC |
0.81 |
Wader |
|
71 |
Porphyrio porphyrio |
Rallidae |
Gruiformes |
LC |
0.52 |
Swimmer |
|
72 |
Pseudibis papillosa |
Threskiornithidae |
Pelecaniformes |
LC |
1.88 |
Wader |
|
73 |
Psittacula krameri |
Psittacidae |
Psittaciformes |
LC |
3.32 |
Percher |
|
74 |
Pteroclesnamaqua |
Pteroclidea |
Pterocliformes |
LC |
0.09 |
Percher |
|
75 |
Pycnonotuscafer |
Pycnonotidae |
Passeriformes |
LC |
0.16 |
Percher |
|
76 |
Sarkidiornis sylvicola |
Anatidae |
Anseriformes |
LC |
0.87 |
Swimmer |
|
77 |
Saxicoloides fulicata |
Muscicapidae |
Passeriformes |
LC |
0.34 |
Percher |
|
78 |
Stactolaema olivacea |
Lybiidae |
Piciformes |
LC |
0.42 |
Tree clinging bird |
|
79 |
Streptopelia chinensis |
Columbidae |
Columbiformes |
LC |
0.18 |
Upland ground |
|
80 |
Streptopelia decaocto |
Columbidae |
Columbiformes |
LC |
0.3 |
Upland ground |
|
81 |
Streptopelia senegalensis |
Columbidae |
Columbiformes |
LC |
0.19 |
Upland ground |
|
82 |
Tachybaptus ruficollis |
Podicipedidae |
Podicipediformes |
LC |
0.72 |
Swimmer |
|
83 |
Tachymarptis melba |
Apodidae |
Apodiformes |
LC |
2.5 |
Percher |
|
84 |
Terpsiphone paradise |
Monarchidae |
Passeriformes |
LC |
0.19 |
Percher |
|
85 |
Threskiornis melanocephalus |
Threskiornithidae |
Pelecaniformes |
NT |
6.21 |
Wader |
|
86 |
Tringa nebularia |
Scolopacidae |
Charadriiformes |
LC |
0.36 |
Wader |
|
87 |
Turdoides striata |
Leiothrichidae |
Passeriformes |
LC |
1.33 |
Percher |
|
88 |
Upupa epops |
Upupidae |
Bucerotiformes |
LC |
0.32 |
Percher |
|
89 |
Vanellus indicus |
Charadriidae |
Charadriiformes |
LC |
0.57 |
Wader |
|
90 |
Vanellus malabaricus |
Charadriidae |
Charadriiformes |
LC |
0.48 |
Wader |
Table 2 Avifaunal diversity in different seasons of the Koonthankulam. *significant (P <0.05).
|
Biodiversity indices |
Spring |
Summer |
Early monsoon |
Late Monsoon |
Early winter |
Late winter |
Sum of squares |
F value |
|
Feb–Mar |
Apr–May |
Jun–Jul |
Aug–Sep |
Oct–Nov |
Dec–Jan |
|||
|
Taxa_S |
84.33 ± 3.18 |
82.00 ± 1.53 |
83.33 ± 2.40 |
80.66 ± 0.33 |
84.00 ± 2.08 |
85.00 ± 1.73 |
39.111 |
0.61 |
|
Individuals |
2980.33 ± 316.26 |
1902.66 ± 266.89 |
1408.33 ± 215.13 |
1315.00 ± 158.24 |
1741.66 ± 168.94 |
3027.33 ± 498.27 |
8665477.111 |
6.673* |
|
Shannon_H |
3.36 ± 0.19 |
3.53 ± 0.09 |
3.64 ± 0.09 |
3.70 ± 0.06 |
3.61 ± 0.11 |
3.55 ± 0.17 |
0.214 |
0.898 |
|
Buzas & Gibson's |
0.35 ± 0.06 |
0.42 ± 0.03 |
0.46 ± 0.03 |
0.50 ± 0.03 |
0.45 ± 0.04 |
0.42 ± 0.06 |
0.038 |
1.24 |
|
Menhinick |
1.56 ± 0.12 |
1.91 ± 0.11 |
2.25 ± 0.12 |
2.25 ± 0.13 |
2.03 ± 0.08 |
1.57 ± 0.09 |
1.435 |
7.811* |
|
Chao-1 |
94.77 ± 3.09 |
101.27 ± 9.26 |
94.48 ± 5.46 |
90.89 ± 0.37 |
93.73 ± 1.51 |
93.57 ± 3.34 |
179.678 |
0.518 |
Table 3. Correlation of physico-chemical
parameters, swimmer, diver, and wader. *significant at the level of 0.05 |
**significant at the level of 0.01
|
|
Swimmer |
Diver |
Wader |
Tempera-ture (˚C) |
pH |
DO |
TDS (ppm) |
Salinity (ppt) |
Conductivity (uS) |
Acidity (mg/l) |
Alkalinity (mg/l) |
Free Co2 (mg/l) |
Chloride (mg/l) |
Calcium (mg/l) |
Total hardness (mg/l ) |
Magnesium (mg/l) |
Nitrogen (mg/l) |
|
Swimmer |
1.000 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Diver |
0.553* |
1.000 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Wader |
0.874** |
0.721** |
1.000 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Temperature (˚C) |
-0.659** |
-0.454 |
-0.692* |
1.000 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
pH |
-0.168 |
-0.081 |
-0.014 |
0.463 |
1.000 |
|
|
|
|
|
|
|
|
|
|
|
|
|
DO (ppm) |
0.583* |
0.585* |
0.682* |
-0.352 |
0.226 |
1.000 |
|
|
|
|
|
|
|
|
|
|
|
|
TDS (ppm) |
0.160 |
-0.127 |
0.088 |
-0.398 |
0.066 |
0.227 |
1.000 |
|
|
|
|
|
|
|
|
|
|
|
Salinity (ppt) |
-0.083 |
-0.225 |
-0.197 |
-0.028 |
0.313 |
-0.044 |
0.730** |
1.000 |
|
|
|
|
|
|
|
|
|
|
Conductivity(uS) |
0.050 |
-0.012 |
0.008 |
-0.281 |
0.178 |
0.210 |
0.934** |
0.861** |
1.000 |
|
|
|
|
|
|
|
|
|
Acidity(mg/l) |
-0.259 |
-0.241 |
-0.341 |
0.248 |
0.670* |
0.052 |
-0.349 |
0.067 |
-0.223 |
1.000 |
|
|
|
|
|
|
|
|
Alkalinity (mg/l) |
-0.110 |
-0.263 |
-0.165 |
0.344 |
0.662* |
-0.019 |
0.055 |
0.344 |
0.105 |
0.647* |
1.000 |
|
|
|
|
|
|
|
Free Co2 (mg/l) |
-0.361 |
-0.098 |
-0.460 |
0.347 |
0.508 |
-0.177 |
-0.471 |
0.068 |
-0.246 |
0.819** |
0.461 |
1.000 |
|
|
|
|
|
|
Chloride (mg/l) |
0.212 |
-0.360 |
-0.025 |
0.298 |
0.329 |
-0.124 |
-0.147 |
0.089 |
-0.238 |
0.488 |
0.593* |
0.410 |
1.000 |
|
|
|
|
|
Calcium (mg/l) |
-0.110 |
-0.417 |
-0.319 |
-0.402 |
-0.535 |
-0.405 |
0.376 |
0.316 |
0.320 |
-0.405 |
-0.537 |
-0.309 |
-0.160 |
1.000 |
|
|
|
|
Total hardness (mg/l ) |
0.038 |
-0.432 |
-0.297 |
0.033 |
-0.085 |
-0.283 |
0.541 |
0.726** |
0.529 |
-0.030 |
0.327 |
0.041 |
0.457 |
0.421 |
1.000 |
|
|
|
Magnesium (mg/l) |
0.033 |
-0.369 |
-0.209 |
0.016 |
-0.069 |
-0.201 |
0.519 |
0.679* |
0.512 |
-0.151 |
0.316 |
-0.022 |
0.405 |
0.300 |
0.934** |
1.000 |
|
|
Nitrogen (mg/l) |
-0.204 |
-0.067 |
-0.061 |
0.501 |
0.505 |
0.135 |
-0.337 |
-0.172 |
-0.337 |
0.620* |
0.647* |
0.386 |
0.414 |
-.603* |
-0.239 |
-0.228 |
1.000 |
Table 4. Regression weights between parameters of the SEM.
|
|
|
|
Unstandardized Estimate |
Standardized Estimate |
S.E. |
C.R. |
P |
|
Swimmer |
<--- |
Temp |
-12.284 |
-0.253 |
10.072 |
-1.220 |
0.223 |
|
Swimmer |
<--- |
DO |
61.548 |
0.516 |
28.169 |
2.185 |
0.029 |
|
Diver |
<--- |
DO |
17.555 |
0.363 |
13.003 |
1.350 |
0.177 |
|
Wader |
<--- |
DO |
243.994 |
0.586 |
90.907 |
2.684 |
0.007 |
|
Wader |
<--- |
Temp |
-49.556 |
-0.292 |
21.093 |
-2.349 |
0.019 |
|
Birds |
<--- |
Swimmer |
3.645 |
0.650 |
0.625 |
5.828 |
0.000 |
|
Birds |
<--- |
Wader |
0.739 |
0.460 |
0.244 |
3.024 |
0.002 |
|
Birds |
<--- |
Diver |
-1.779 |
-0.129 |
1.436 |
-1.239 |
0.215 |
For
image & figures - - click here for full PDF
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