Journal of Threatened Taxa |
www.threatenedtaxa.org | 26 April 2024 | 16(4): 25082–25088
ISSN 0974-7907
(Online) | ISSN 0974-7893 (Print)
https://doi.org/10.11609/jott.7690.16.4.25082-25088
#7690 | Received 02
October 2021 | Final received 16 September 2023 | Finally accepted 11 March
2024
Aquatic insects as bioindicators
of stream water quality - a seasonal analysis on Western Ghats river, Muthirapuzha, in central Kerala, India
M. Harinagaraj
1, Leenamma Joseph 2 & V.S. Josekumar 3
1–3 Department of Zoology, Mar Ivanios College (Autonomous), Nalanchira,
Thiruvananthapuram, Kerala 695015, India.
1 harinagura@gmail.com
(corresponding author), 2 leenamma.joseph@mic.ac.in, 3 vsjosekumar@gmail.com
Abstract: This study was conducted to
assess the water quality of Muthirapuzha River,
Idukki using aquatic insects as bioindicators. Insects were collected on a
seasonal basis from February 2014 to January 2015 from 12 sampling stations.
Insects were sampled using standard collection methods and were identified up
to family level. A total of 3,278 individuals belonging to seven orders and 37
families were collected during the study period. The greatest number of taxa
was represented by order Ephemetroptera during
monsoon (27%) and post-monsoon (25%), while Diptera
(22.7%) dominated the pre-monsoon season. Shannon-Weiner diversity index,
Simpson dominance index, and Margalef’s richness
index was highest at post-monsoon. The EPT score in Muthirapuzha
was for normal waters, however, pre-monsoon values were lowest, indicating
pollution load during this period. Hilsenhoff’s
family biotic index (HFBI) was used to estimate the status of organic pollution
along the river based on representative families of aquatic entomofauna; values
were highest at pre-monsoon season. The overall organic water quality level in
the Muthirapuzha was good to fair based on this
study.
Keywords: Biomonitoring, diversity indices,
EPT scores, Hilsenhoff’s family biotic index,
macro-invertebrates, Margalef’s richness index,
Munnar, Muthirapuzha River, Periyar
River, Shannon Weiner diversity index, Simpson dominance index.
Editor: R. Ramanibai,
University of Madras, Chennai, India. Date of publication: 26 April
2024 (online & print)
Citation: Harinagaraj, M., L. Joseph & V.S. Josekumar (2024). Aquatic insects as bioindicators of
stream water quality - a seasonal analysis on Western Ghats river, Muthirapuzha, in central Kerala, India. Journal of Threatened Taxa 16(4):
25082–25088. https://doi.org/10.11609/jott.7690.16.4.25082-25088
Copyright: © Harinagaraj et al. 2024. 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: The Higher Education Department, Government of Kerala, Thiruvananthapuram.
Competing interests: The authors declare no competing interests.
Author details: Dr. M. Harinagaraj was
a research scholar in Zoology of Mar Ivanios College, Thiruvananthapuram worked on macroinvertebrates
of
Western Ghats, pursuing studies on fresh water monitoring system. Dr Leenamma Joseph is a retired associate professor of Zoology, Mar Ivanios College (Autonomous). She studied on aquatic insect fauna as well as reproductive biology of insect pest. Dr V.S. Josekumar is retired associate professor of Zoology, Mar Ivanios College Thiruvananthapuram, formerly associate professor of Biology, ArbaMinch University Ethiopia. His research
interest is in fresh water aquatic macroinvertebrates and biodiversity conservation.
Author contributions: MH—data collection and manuscripts preparation. LJ—correction of manuscripts, language check. VSJ—conceptual support, Identification of macroinvertebrates, correction of final draft.
Acknowledgements: Financial assistance from Govt.
of Kerala to Harinagaraj M is gratefully
acknowledged. Authors are thankful to the Mar Ivanios
College administrations for the library and laboratory support.
INTRODUCTION
Rivers
provide fresh water for agricultural, industrial and domestic needs (Ridoutt et al. 2010; Sunil et al. 2010) that can create
enormous environmental pressures, including pollution leading to deteriorated
water quality adversely affecting aquatic life (Kamboj & Kamboj 2019; Sinha
et al. 2020). Biological communities provide a faithful reflection of
environmental conditions, since they are continually exposed to them (Rosenberg
& Resh 1993). Water quality changes are directly
reflected by aquatic fauna, which can be assessed to measure the health of
their ecosystems (Mulani et al. 2009; Saxena &
Singh 2020). This approach is widely exploited as a reliable technique for
assessing point and non-point sources of pollution of water bodies via
biomonitoring protocols. Benthic macroinvertebrates representing different
visible aquatic phyla exhibit a relatively wide range of response to chemical
and physical water quality stressors like pH, temperature, dissolved oxygen,
organic pollutants, heavy metals and sediments that can serve as a biological
indicator of water pollution (Marzelai et al. 2008). Latha & Thanga (2010)
identified macroinvertebrates as useful bioindicators in estuaries. Stream
insect communities were suggested for aquatic biomonitoring protocol by Morse
et al. (1994) and Subramanian & Sivaramkrishnan
(2005). Diversity of aquatic insects is relatively easy to measure for
assessing the health status of streams, and many biomonitoring studies are
reported from southern Indian rivers (Sheeba &
Ramanujan 2009; Priyanka & Prasad 2014). Stream entomofauna were targeted
in Killi Ar, an urban river
of Trivandrum corporation area, to assess the pollution status of the stream
(Dinesh et al. 2017).
Many tools
are employed in biological monitoring to assess the quality of water resources
(Buss et al. 2003). The effective use of these tools leads to a better
understanding of aquatic organisms that influence on biotic index results, and
occurrence of bioindicators (Czerniawska-Kusza 2005).
Distribution of bioindicator taxa is influenced by hydrological
characteristics, nutrient supply, substrate type, predation pressure and
natural or anthropogenic disturbances, in addition to variation in water
quality, that makes these biotic indices important tools for evaluate the
health of water ecosystems (Silveira et al. 2004). Comparative analyses of
biotic indices are now available to determine which index best reflects
ecosystem health (Gonçalves & Menezes 2011). William Hilsenhoff formulated family-level (Hilsenhoff
1988) versions of a biotic index, and tabulated interpretive criteria based on
known sensitivities of arthropod taxa to organic enrichment (i.e., sewage
pollution). This has been widely used in to characterize the health of
freshwater streams (Reynoldson & Metcalfe-Smith
1992; Hu et al. 2007).
The river Periyar, the longest river in Kerala
State (PWD 1974; CESS 1984) is considered to be the life line of central
Kerala. Muthirapuzha River, the major tributary of
the Periyar, forms the main drainage system south of Anamudi. This river
is the major water resource of five panchayath in Devikulam
Taluk of Idukki District. The Muthirapuzha watershed
includes Kannan Devan Tea plantations along with Eravikulam
National Park, and forms the highest watershed of the
Western Ghats. Munnar Township, one of major tourist destinations in Kerala,
extends along the banks of this stream. Thus this
river is experiencing active anthropogenic pressure chiefly due to tourism and
agricultural activities. In this study we undertook a rapid assessment of the
status of this river utilizing a biomonitoring protocol targeting aquatic
insects as bioindicators for stream water quality.
MATERIALS
AND METHODS
Study area
The Muthirapuzha is located at 10.172–9.951 0N &
77.077–76.983 0E (Figure 1). It originates from Umaya
Mala near Anamudi Peak and flows through Deikulam, Munnar, Pallivasal, Vellathooval and Konnathadi panchayths of Devikulam and Udumbanchola of Idukki District, and joins the Periyar River at Panamkutti,
covering a distance of 34 km.
Macroinvertebrate
analysis
Macroinvertebrates
were sampled once every four months from February 2014 to January 2015 at
twelve selected stations on the Muthirapuzha to capture
seasonal variations. A D-frame aquatic net (0.5 mm mesh) was used to collect
benthic organisms present in a 10 m2 area (Hellawell
1986). After each jab and sweep, the net was rinsed in a sieve bucket (250 μm mesh) to collect all the macroinvertebrates. Samples
were washed, separated through three sieves (2 mm, 1 mm, and 0.3 mm),
transferred to glass bottles after labeling and preserved in 5% formalin in the
field immediately after each collection. Each animal was then brush picked,
preserved in 4% formalin, sorted and identified in the laboratory according to
Edmondson (1992) and Pennak (1978). Aquatic insects
were counted and identified using a stereo microscope (Headz-HD600D) with the
help of standard keys (McCafferty 1983; Morse et al. 1994 & Yong
& Yule 2004) up to the family level. Taxonomic indices used for analyses of
aquatic insects include Shannon-Weiner diversity index, Simpson dominance
index, Margalef richness index (Shannon & Weiner
1963; Simpson 1949; Margalef 1958; Pielou 1966) and Hilsenhoff’s
Family Biotic Index (HFBI) (Hilsenhoff 1988) to
estimate the level of organic pollution. Biodiversity indices were calculated
using PAST ver. 1.34 software (Hammer et al. 2005).
RESULTS AND
DISCUSSION
The present
study identified 55 taxa represented by 37 families belonging to eight orders
among the 3,278 total aquatic insects collected during the study period in
pre-monsoon, monsoon, and post-monsoon seasons. Table 1 shows the overall
numbers of insects collected during the sampling period. The number of
individuals found in the pre-monsoon season was 1,313, 270 in monsoon, and
1,695 post monsoon. The greatest numbers of taxa were
represented by order Ephemeroptera in monsoon (27%) and post-monsoon (25%),
while Diptera (22.7%) dominated in pre-monsoon. The
overall analysis of aquatic insects indicated that the most abundant taxa were
Ephemeroptera (22%), followed by, Odonata (18.5%), Diptera
(18%), Trichoptera (11%), Hemiptera (10%), Coleoptera (9.7%), and Plecoptera
(7.9%) (Figure 2).
The
biological indices of aquatic insects computed for 12 sampling sites are
represented in Table 2, 3, & 4. Shannon-Weiner diversity index for pre monsoon season ranged between 3.807–3.211 and were found
to be maximum at station 2 and minimum at station 10. During monsoon it was
highest at station 1 (3.266) and lower index value was reported in station 10
(2.306). Shannon-Weiner diversity index was varying between 3.752 and 3.428;
these values are represented in stations 1 and 10, respectively. Simpson dominance
index also showed similar relation and varied from 0.974 to 0.943 in
pre-monsoon. Maximum dominance index was found in station 2 and minimum in
station 10. Index values were between 0.956 to 0.879 in monsoon and 0.972 to
0.948 in post- monsoon seasons. Margalef’s richness
index showed comparatively low value in monsoon season and the lowest value
(2.954) was identified from station 6, Chokkanadu
which is an urbanized site and higher (7.452) in station 1, Nayamakkadu
near the origin of stream. Richness index was higher in pre-monsoon and
post-monsoon seasons compared to monsoon. In pre-monsoon the maximum Margalef richness index was found in station 2 (10.98) and
minimum in station 11 (7.015). In post-monsoon season the richness index varied
from 10.08 to 7.856, respectively from station 2 and station 9. Highest
taxonomic indices were observed in post-monsoon season.
Among
aquatic insects, Ephemeroptera, Plecoptera, and Trichoptera (EPT) have a great role in low and medium order
stony cobble streams. The percentage of EPT in river Muthirapuzha
during the study period was represented in Table 5. These organisms are
sensitive to environmental perturbations and occur in clean and well oxygenated
waters. Therefore, EPT assemblages are frequently considered to be good
indicators of water quality (Rosenberg & Resh
1992; Priyanka & Prasad 2014), EPT is widely used for the measure of health
of fresh water ecosystem (Wallace & Jackson 1996).
In this
study the percentage of EPT was very high in sampling stations 1, 2, & 3 in
three sampling seasons. But it was
gradually decreased in the middle and lower streams of river Muthirapuzha. Especially the middle sampling sites
representing Munnar Township and nearby inhabited area exhibit a very low
percentage of EPT level. This clearly indicates that the water quality was
badly affected by pollution related activities at this stretch of river. The
percentage of EPT in lower stream varied from station to station which means
that each sampling stations were under different types of pollution stress
mainly due to anthropogenic and tourism related activities along the river, Muthirapuzha. The overall mean percentage of EPT score
indicated that the pre-monsoon season was polluted in nature compared to the
other two seasons (Figure 3)
Hilsenhoff family
biotic index (HFBI) is one of the most effective bio monitoring tool in stream ecology and is used to assess the level of
organic pollution in water bodies (Hilsenhoff 1988).
HFBI of river Muthirapuzha (Table 6) categorizes the
water quality based on the families identified from 12 stations along this
river. Water quality grade according to HFBI index is shown in table 7. HFBI
indicated that the water quality varies in each sampling station ranging from
excellent to fairly poor and the degree of organic pollution was comparatively
low in Muthirapuzha. Based on this study the water of
Muthirapuzha could be classified into four categories
using the HFBI, ‘excellent’, ‘very good’, ‘good’, and ‘fair’. The HFBI values
were higher in pre-monsoon and lower during monsoon seasons indicating the
organic loading during pre-monsoon.
When
classifying water quality during monsoon, the HFBI index gave scores of
‘excellent’ to ‘good’, however, station 11 was under some organic pollution
(Table 6) otherwise the overall water quality was very good during this period.
During post-monsoon season the HFBI ranged 3.78–5.34 which indicated the water
quality in between very well to fair (Table 6). Station 5, 6, 8, 11, & 12
came under ‘fairly substantial pollution likely’ (Table 7) during this season. Finally in pre-monsoon HBFI was comparatively higher with
the other two seasons; the water quality values come under the categories of
‘very good’ to ‘fairly poor’. Sampling stations 5 and 6 reported ‘substantial
pollution likely’ (Table 6, 7) during this period. It may be noted that these
sampling stations are representing the Munnar township segment of the stream.
‘Poor’ and ‘very poor’ water qualities were not reported at any sampling
stations during the course of sampling period.
According
to the HFBI, overall water quality was very good in monsoon, good in post
monsoon and fair in pre-monsoon seasons (Figure 3). Though the sampling points were located
within populated area except the first three, the HFBI did not reflect obvious
anthropogenic pressure on this river. Munnar Township and some small towns are
located in the middle and lower reaches of river Muthirapuzha,
which reported ‘fairly poor’ status of water at these stretches but the overall
water quality falls between very good to fair scale of HFBI. Present study
shows a temporal variation in bioassessment of Muthirapuzha
River that influence the judgment of the sites. Studies shows temporal
variations in bioassessment based on benthic macroinvertebrates (Linke et al. 2001; Nukeri et al.
2021). Substrate heterogeneity as well as land use changes are generally the
determinants of the macroinvertebrate distribution along streams (Semwal & Mishra 2019). Spatio-seasonal
flux of benthic macroinvertebrate assemblages as indicators of water quality in
a coastal basin of southern Chile was assessed by applying HFBI (Fierro et al.
2012). River Muthirapuzha seems sensitive to
anthropogenic activities due to tourism as indicated by the macroinvertebrate
community based biotic index.
CONCLUSION
River Muthirapuzha one of the major tributary of river Periyar, a mountain stream originated and flow
through the higher elevations of Western Ghats. There are 33 small and large
streams contribute water to river Muthirapuzha at
various stretches. The taxonomic indices of aquatic insects collected from this
river established a clear view of level of stream health. The season-wise
analysis of taxonomic Indies indicated that the water quality was good on
monsoon season and comparatively higher pollution in other two seasons. The EPT
scores indicated average water quality in the river, except at the middle
stream sampling sites, the anthropogenic pressure due to tourism activates
affects the water quality in this area. The study identified the water quality of
the river Muthirapuzha varied seasonally at every
sampling station, and the overall water quality was good based on HFBI
category, although pollution load was evident in pre-monsoon season.
Table 1. Aquatic insects
collected from river Muthirapuzha over different
seasons (2014–15).
|
ORDER |
FAMILY |
PRM* |
MON** |
POM*** |
|
Diptera |
Simuliidae |
33 |
2 |
39 |
|
Chironomidae |
155 |
10 |
129 |
|
|
Culicidae |
68 |
20 |
52 |
|
|
Tipulidae |
42 |
5 |
38 |
|
|
Hemiptera |
Nepidae |
20 |
3 |
16 |
|
Velliidae |
19 |
3 |
19 |
|
|
Hydrometridae |
9 |
7 |
26 |
|
|
Belostomatidae |
12 |
2 |
27 |
|
|
Gerridae |
85 |
13 |
66 |
|
|
Ephemeroptera |
Ephemeridae |
34 |
9 |
53 |
|
Heptageniidae |
24 |
11 |
68 |
|
|
Leptohyphidae |
64 |
14 |
108 |
|
|
Caenidae |
94 |
16 |
110 |
|
|
Ephemerellidae |
18 |
3 |
25 |
|
|
Baetidae |
27 |
20 |
63 |
|
|
Plecoptera |
Perlidae |
94 |
14 |
151 |
|
Odonata |
Coenagrionidae |
114 |
3 |
125 |
|
Chlorocyphidae |
24 |
3 |
31 |
|
|
Odonata |
Eupaeidae |
25 |
2 |
25 |
|
Calopterygidae |
17 |
0 |
17 |
|
|
Lestidae |
7 |
2 |
13 |
|
|
Platystictidae |
13 |
0 |
15 |
|
|
Cordullidae |
7 |
0 |
7 |
|
|
Gomphidae |
43 |
6 |
39 |
|
|
Aeshnidae |
22 |
5 |
43 |
|
|
Megaloptera |
Corydalidae |
17 |
4 |
30 |
|
Trichoptera |
Helicopsychidae |
27 |
8 |
47 |
|
Hydropsychidae |
11 |
15 |
46 |
|
|
Glossosomatidae |
18 |
1 |
26 |
|
|
Polycentropodidae |
6 |
4 |
21 |
|
|
Leptoceridae |
21 |
31 |
80 |
|
|
Coleoptera |
Haliplidae |
10 |
7 |
17 |
|
Hydrophildae |
68 |
11 |
55 |
|
|
Gyrinidae |
14 |
12 |
33 |
|
|
Dytiscidae |
51 |
4 |
35 |
PRM*—Pre-monsoon | MON**—Monsoon
| POM***—Post-monsoon
Table 2. Biodiversity indices of
aquatic insects in pre-monsoon season (2014–15).
|
Stations |
S1 |
S2 |
S3 |
S4 |
S5 |
S6 |
S7 |
S8 |
S9 |
S10 |
S11 |
S12 |
|
Taxa_S |
46 |
53 |
50 |
48 |
43 |
39 |
39 |
37 |
32 |
33 |
31 |
30 |
|
Individuals |
132 |
114 |
128 |
151 |
149 |
135 |
100 |
114 |
81 |
77 |
72 |
60 |
|
Simpson_1-D |
0.969 |
0.974 |
0.968 |
0.956 |
0.954 |
0.947 |
0.962 |
0.964 |
0.956 |
0.949 |
0.955 |
0.953 |
|
Shannon_H |
3.647 |
3.807 |
3.652 |
3.49 |
3.369 |
3.261 |
3.451 |
3.452 |
3.298 |
3.211 |
3.242 |
3.223 |
|
Margalef |
9.216 |
10.98 |
10.1 |
9.368 |
8.393 |
7.747 |
8.252 |
7.601 |
7.054 |
7.367 |
7.015 |
7.083 |
Table 3. Biodiversity indices of
aquatic insects in monsoon season (2014–15).
|
Stations |
S1 |
S2 |
S3 |
S4 |
S5 |
S6 |
S7 |
S8 |
S9 |
S10 |
S11 |
S12 |
|
Taxa_S |
30 |
23 |
23 |
15 |
12 |
9 |
14 |
14 |
12 |
12 |
12 |
12 |
|
Individuals |
49 |
33 |
30 |
22 |
19 |
15 |
18 |
22 |
17 |
21 |
20 |
18 |
|
Simpson_1-D |
0.956 |
0.949 |
0.951 |
0.922 |
0.903 |
0.871 |
0.92 |
0.905 |
0.899 |
0.879 |
0.89 |
0.901 |
|
Shannon_H |
3.266 |
3.061 |
3.078 |
2.626 |
2.406 |
2.119 |
2.582 |
2.5 |
2.395 |
2.306 |
2.346 |
2.399 |
|
Margalef |
7.452 |
6.292 |
6.468 |
4.529 |
3.736 |
2.954 |
4.498 |
4.206 |
3.883 |
3.613 |
3.672 |
3.806 |
Table 4. Biodiversity indices of
aquatic insects in post-monsoon season (2014–15).
|
Stations |
S1 |
S2 |
S3 |
S4 |
S5 |
S6 |
S7 |
S8 |
S9 |
S10 |
S11 |
S12 |
|
Taxa_S |
55 |
55 |
52 |
48 |
45 |
41 |
44 |
42 |
38 |
41 |
40 |
41 |
|
Individuals |
228 |
212 |
182 |
170 |
141 |
147 |
118 |
105 |
111 |
102 |
90 |
89 |
|
Simpson_1-D |
0.972 |
0.966 |
0.965 |
0.971 |
0.953 |
0.959 |
0.958 |
0.956 |
0.948 |
0.96 |
0.955 |
0.957 |
|
Shannon_H |
3.752 |
3.668 |
3.649 |
3.683 |
3.402 |
3.416 |
3.486 |
3.434 |
3.248 |
3.481 |
3.409 |
3.42 |
|
Margalef |
9.946 |
10.08 |
9.8 |
9.151 |
8.891 |
8.015 |
9.013 |
8.81 |
7.856 |
8.649 |
8.667 |
8.911 |
Table 5. Percentage of EPT in
river Muthirapuzha (2014–15).
|
Stations |
PRM* |
MON** |
POM*** |
|
1 - Nayamakkadu |
42.73 |
33.84 |
46.18 |
|
2 - Periyavarai |
45.74 |
47.61 |
48.56 |
|
3 - Mattupetty |
46.05 |
30.61 |
45.56 |
|
4 - Nallathanni |
22.7 |
15.21 |
28.78 |
|
5 - Munnar Town |
20.68 |
15.55 |
23.78 |
|
6 - Chokkanadu |
15.95 |
21.42 |
27.21 |
|
7 - Pallivasal |
27.78 |
54.57 |
36.87 |
|
8 - Kunjithanni |
31.13 |
44.82 |
39.59 |
|
9 - Panniyarkutti |
27.47 |
32.14 |
42.95 |
|
10 - Vellathooval |
24.09 |
55.17 |
36.71 |
|
11- Kallarkutti |
29.67 |
24.32 |
41.07 |
|
12 - Panamkutti |
25 |
56.1 |
46.28 |
|
Mean |
29.91 |
35.93 |
38.62 |
PRM*—Pre-monsoon | MON**—Monsoon
| POM***—Post-monsoon
Table 6. Hilsenhoff
family biotic index of river Muthirapuzha (2014–15).
|
Stations |
PRM |
MON |
POM |
|
S1 (Nayamakkadu) |
3.78 |
3.22 |
3.99 |
|
S2 (periyavarai) |
4.33 |
3.45 |
3.78 |
|
S3 (Mattupetty) |
4.51 |
3.37 |
4.12 |
|
S4 (Nallathanni) |
5.29 |
3.57 |
4.34 |
|
S5 (Munnar Town) |
5.75 |
3.8 |
5.19 |
|
S6 (Chokkanadu) |
5.8 |
3.78 |
5.34 |
|
S7 (Pallivasal) |
5.21 |
3.6 |
4.92 |
|
S8 (Kunjithanni) |
5.33 |
4.18 |
5.13 |
|
S9 (Panniyarkutti))
|
5.3 |
4.23 |
4.83 |
|
S10 (Vellathooval)
|
4.82 |
4.4 |
5.04 |
|
S11 (Kallarkutti) |
5.12 |
4.54 |
4.61 |
|
S12 (Panamkutti)
|
5.41 |
3.38 |
4.94 |
Table 7. Hilsenhoff
family biotic index for water quality grades.
|
HFBI |
Water quality |
Degree of organic pollution |
|
0.00–3.75 |
Excellent |
Organic Pollution Unlikely |
|
3.76–4.25 |
Very Good |
Possible Slight Organic
Pollution |
|
4.26–5.00 |
Good |
Some Organic Pollution Probable |
|
5.01–5.75 |
Fair |
Fairly Substantial Pollution
Likely |
|
5.76–6.50 |
Fairly Poor |
Substantial Pollution Likely |
|
6.51–7.25 |
Poor |
Very Substantial Pollution
Likely |
|
7.26–10.00 |
Very Poor |
Severe Organic Pollution Likely |
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REFERENCES
Buss, D.F., D.F. Baptista, M.P. Silveira, J.L. Nessimian
& L.F.M. Dorville
(2002). Influence of water chemistry and environmental quality on the
macroinvertebrate assemblages in a river basin in south-east Brazil. Hydrobiologia 481: 125–136.
https://doi.org/10.1007/s10750-005-1255-5
CESS (1984). Resource Atlas of Kerala. Centre
for Earth Science Studies.
Czerniawska-Kusza, I.
(2005). Comparing modified biological monitoring working party score system and
several biological indices based on macroinvertebrates for water-quality
assessment. Limnologica 35: 169–176. https://doi.org/10.1016/j.limno.2005.05.003
Dinesh, V., J. Leenamma & V.S. Josekumar (2017). Dependence
of upstream entomofauna to water quality in a semi-urbanized river (Killi Ar), south Kerala, India. Journal
of Aquatic Biology & Fisheries 5: 96–106.
Fierro, P.B., M. Carlos, Maritza Peña-Cortés, T. Fernando, H. Jaime
& V.C. Enrique (2012). Benthic macroinvertebrate assemblages as
indicators of water quality applying a modified biotic index in a spatio-seasonal context in a coastal basin of southern
Chile. Revista de Biología Marina y Oceanografía 47(1): 23–33. https://doi.org/10.4067/S0718-19572012000100003
Gonçalves, F.B. & M. S. de Menezes (2011). A
comparative analysis of biotic indices that use macro invertebrates to assess
water quality in a coastal river of Paraná state, southern Brazil. Biota
Neotropica 11(4): 27–36. https://doi.org/10.1590/S1676-06032011000400002
Hammer, Ø., D.A.T. Harper & P.D. Ryan (2005). PAST: palaeontological statistics, ver. 1.34. Paleontological
Museum, University of Oslo, Norway.
Hellawell, J.M.
(1986). Biological Indicators of Freshwater Pollution and Environmental
Management. Pollution Monitoring Series, 546 pp.
Hilsenhoff, W.L.
(1988). Rapid field assessment of organic pollution with a family-level biotic
index. Journal of the North American Benthological
Society 7(1): 65–68.
Hu, T.-J., H.-W. Wang & H.-Y. Lee (2007). Assessment
of environmental conditions of Nan-Shih stream in Taiwan. Ecological
Indicators 7: 430–441. https://doi.org/10.1016/j.ecolind.2006.04.003
Kamboj, N. & V. Kamboj (2019). Water
quality assessment using overall index of pollution in riverbed-mining area of
Ganga-River Haridwar, India. Water Science 33(1): 65–74. https://doi.org/10.1080/11104929.2019.1626631
Latha, C. &
V.S.G. Thanga (2010). Choice of
bioindicator species for estuaries of South Kerala: an approach based on
macroinvertebrate. The Ecoscan 4(4): 285–289.
Margalef, D.R.
(1958). Information Theory in Ecology. General Systems 3: 36–71.
Marziali, L., V. Lencioni, P. Parenti & B. Rossaro (2008). Benthic
macroinvertebrates as water quality indicators in Italian lakes. Boletin do Museu
Municipal do Funchal (Historia Natural) 13: 51–59.
McCafferty, W.P. (1983). Aquatic entomology: the
fishermen’s and ecologists illustrated guide to insects and their relatives.
Jones and Bartlett Learning, New York, 448 pp.
Morse, J.C., L. Yang
& L. Tian (1994). Aquatic
insects of China useful for monitoring water quality (13) Hohai
University Press, 570 pp.
Mulani, S.K., M.B.
Mule & S.U. Patil (2009). Studies on
water quality and zooplankton community of the Panchganga
river in Kolhapur city. Journal of Environmental Biology 30(3): 455–459.
Nukeri, S., A.
Addo-Bediako & M.B. Kekana (2021).
Macroinvertebrates assemblages in the Spekboom River
of the Olifants River System, South Africa. African
Journal of Ecology 59(1): 320–325.
Pennak, R.W.
(1978). Freshwater invertebrates of the United States, 2nd
ed. John Wiley & Sons, New York, 803 pp.
Pielou, E.C.
(1966). The measurement of diversity in different types of biological
collections. Journal of Theoretical Biology 13(2): 131–144.
Priyanka, G.L. & G. Prasad (2014). Diversity
of aquatic insects (Ephemeroptera, Plecoptera and Trichoptera) in Kallar Stream. Journal
of Aquatic Biology and Fisheries 2: 493–499.
PWD (1974). Water Resources of Kerala. Public Works
Department, Govt. of Kerala, Trivandrum, 130 pp.
Reynoldson, T.B.
& J.L. Metcalfe-Smith (1992). An overview of the assessment
of aquatic ecosystem health using benthic invertebrates. Journal of Aquatic
Ecosystem Stress and Recovery (Formerly Journal of Aquatic Ecosystem Health) 1(4):
295–308.
Ridoutt, B.G.
(2010). A revised approach to water foot printing to make transparent the
impacts of consumption and production on global freshwater scarcity. Global
Environmental Change 20: 113–120. https://doi.org/10.1016/j.gloenvcha.2009.08.003
Rosenberg, D.M. and V.H. Resh (1992). Freshwater
bio monitoring using individual organisms, populations, and species assemblages
of benthic macro-invertebrates. Chapman & Hall, New York, USA,
40-158.
Semwal, V.P.
& A.S. Mishra (2019). The distributional pattern of benthic macroinvertebrates
in a spring-fed foothill tributary of the Ganga River, western Himalaya, India.
Journal of Threatened Taxa 11(12): 14511–14517. https://doi.org/10.11609/jott.4648.11.12.14511-14517
Shannon, C.E. & W. Weaner (1963). The
mathematical communication of Communication. University of Illinos
Press, Urbana, 117.
Sheeba, S. &
N. Ramanujan (2009). Macroinvertebrate fauna of Ithikkara
River. Journal of Industrial Pollution Control 25(2): 151–154.
Silveira, M.P., J.F. Queiroz & R.C. Boeira (2004). Protocolo
de coleta e preparação de amostras
de macroinvertebrados bentônicos
em riachos. Embrapa, Jaguariúna. Comunicado Técnico 19. http://www.infoteca.cnptia.embrapa.br/infoteca/handle/doc/14553
Simpson, E.H. (1949). Measurement of diversity. Nature 163:
688.
Sinha, R., S. Das & T. Ghosh (2020). Pollution
and its consequences at Ganga Sagar mass bathing in
India. Environment, Development and Sustainability 22: 1413–1430. https://doi.org/10.1007/s10668-018-0255-3
Subramanian, K.A. & K.G. Sivaramakrishnan
(2005). Habitat and microhabitat distribution of stream insect communities of
the Western Ghats. Current Science 89(6): 976–987
Sunil, C.R., K. Somashekar & B.C. Nagaraja (2010). Riparian
vegetation assessment of Cauvery River Basin of South India. Environmental
Monitoring and Assessment 170: 545–553. https://doi.org/10.1007/s10661-009-1256-3
Yong, H.S.
& C.M. Yule (2004). Freshwater invertebrates of the Malaysian
region. Academy of Sciences Malaysia, 861 pp.