INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue VI, June 2026  
`Seasonal Variation in Primary Productivity in Relation to Physico-  
Chemical Parameters of Lower Tiger Hills Reservoirs, Nilgiris District,  
Tamil Nadu, India  
M. Abareethan, K. Kavitha  
Department of Zoology,Government Arts College (Autonomous) Salem, Tamil Nadu  
DOI: https://doi.org/10.51583/IJLTEMAS.2026.150500223  
Received: 25 May 2026; Accepted: 30 May 2026; Published: 18 June 2026  
ABSTRACT  
The present study investigated the variation in primary productivity in the Lower Tiger Hills Reservoirs located  
in Udhagamandalam. The relationship between primary productivity and selected physico-chemical parameters,  
including air temperature, water temperature, conductivity, and dissolved oxygen, was also evaluated. Water  
samples were collected from November 2025 to April 2026. Primary productivity was estimated using the dark  
bottle method. The results revealed a significant relationship between primary productivity and the measured  
physico-chemical parameters. lower reservoirs exhibited comparatively high productivity, supporting the  
development and sustenance of plankton communities. The findings indicate that physico-chemical  
characteristics play an important role in regulating primary productivity and maintaining ecological balance in  
these freshwater ecosystems.  
Keywords: Primary productivity, Lower Tiger Hills Reservoir, Dissolved oxygen, Conductivity, Temperature  
INTRODUCTION  
Primary productivity is a fundamental ecological process in aquatic ecosystems. It determines the amount of  
energy that flows through the ecosystem and the quantity of organic matter available to higher trophic levels.  
Primary productivity is defined as the rate at which autotrophic organisms convert inorganic substances into  
organic matter through photosynthesis.  
Water quality significantly influences primary productivity in aquatic environments. The productivity of an  
ecosystem reflects its capacity to support biological communities and is closely associated with various  
physicochemical factors, including temperature, dissolved oxygen, conductivity, and nutrient availability.  
Among these factors, temperature plays a crucial role in regulating chemical reactions, metabolic activities, and  
other biological processes within aquatic systems.  
Freshwater ecosystems are increasingly threatened by anthropogenic activities, resulting in habitat degradation  
and altered water quality. Therefore, monitoring physicochemical parameters and primary productivity is  
essential for understanding ecosystem health and developing effective management strategies. High  
phytoplankton productivity contributes to the maintenance of aquatic food webs and supports biodiversity by  
providing a primary source of energy for higher trophic levels.  
The Nilgiris region of Tamil Nadu is characterized by a cool and humid climate and forms part of the Nilgiri  
Biosphere Reserve, a recognized biodiversity hotspot. Despite the ecological significance of this region,  
information on the productivity dynamics of many freshwater reservoirs remains limited. Therefore, the present  
study aims to evaluate the temporal variation in primary productivity and examine its relationship with selected  
physicochemical parameters in the Lower Tiger Hills Reservoirs of the Nilgiris, Tamil Nadu, India.  
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MATERIALS AND METHODS  
Study Area  
The Lower Tiger Hills Reservoirs are located near Doddabetta Peak in Udhagamandalam, Tamil Nadu, India,  
and are surrounded by shola forests.  
Lower Tiger Hills Reservoir: 11°23′49.09″ N, 76°43′41.13″ E  
Lower Tiger hills reservoirs are lentic freshwater ecosystems situated at high altitudes in the Nilgiri Hills.  
Sample Collection and Analysis  
Water samples were collected over a six-month period from November 2025 to April 2026. Sampling was  
conducted twice monthly from four selected area in the reservoir. The collected samples were analyzed using  
standard methods for the assessment of physico-chemical parameters and primary productivity.  
Figure 1. Satellite view of the Lower Tiger Hills Reservoirs.  
The following parameters were analyzed:  
Air temperature  
Water temperature  
Electrical conductivity  
Dissolved oxygen (DO)  
Gross Primary Productivity (GPP)  
Net Primary Productivity (NPP)  
Community Respiration (CR)  
RESULTS AND DISCUSSION  
The air temperature varied between 16°C and 13°C during the study period. The minimum value 13°C was  
recorded in October and December 2025 in Lower Tiger Hills Reservoir. The maximum air temperature 16°C  
was recorded. This may be due to the changes in the weather factor and the difference in the time of observation.  
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During summer solar radiation and clear sky enhanced the atmospheric temperature. The surface water  
temperature reflects to the dynamics of the living organisms such as metabolic and physiological behavior of  
aquatic ecosystem. (ArchanaGupte and NisarShaikh, 2013).  
The water temperature varied between 21°C to 16°C during the study period of Lower Tiger Hills Reservoir.  
The maximum water temperature 21°C was recorded in the month of December’25 April’26 and and minimum  
water temperature 16°C was observed in the month of Feb ‘26 and Lower Tiger Hills Reservoir was observed  
the maximum water temperature 21°C in the month of Dec 2025 and April 2026 and minimum water  
temperature observed 16°C in the month of Feb 2026. Similar results were reported by ArchanaGupte and  
NisarShaike (2013) in Shelar Lake, Thane and Karthick et al., 2016 in Kadamba Tank, Thoothukudi.  
The GPP was found to have significant relationship with Air Temperature (r=0.71205843), Water  
Temperature(r=0.62830300), Conductivity (r=0.64011300) and dissolved oxygen ((r=0.67505606)  
The Quadratic fit model (figure – 4) is the best fit to establish relationship of Air Temperature with GPP can be  
expressed as  
GPP = 2.904529 A T + 4.129744 A T + 1.5022016 A T 2  
Where AT = Air Temperature  
Figure: 2 Quadratic Fit showing the mathematical relationship between GPP and Air Temperature  
S = 61.72678033  
r = 0.71205843  
4
4
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9
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3
7
1
8
3
0
11.4  
12.6  
13.8  
15.0  
16.2  
17.4  
18.6  
Air Temperature  
The productivity of the any aquatic ecosystem depends on light energy. Transparency measures the light  
penetrating through the water body. The transparency appears to depend primarily of organic and inorganic  
materials particularly received during the rainy season. During the present study, the transparency was high in  
lower tiger hills reservoir. The minimum value of Conductivity (23.1 µS) was recorded during the month of  
March 2026 in both Stations. The maximum value (116` µS) was recorded during the month of November2025  
in Lower Tiger Hills Reservoir. It is due to the influence of South West monsoon during the month of June.  
According to (Verma et al., 2010), a high level of conductivity reflects the pollution status as well as tropic level  
of the aquatic body. According to (Tamot et al., 2008), dissolved oxygen concentration in water is primarily  
dependent upon temperature, dissolved salts, wind velocity, pollution load, photosynthetic activity, and  
respiration rate.  
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The Polynomial fit model (figure – 6) in the best to establish relationship of conductivity with GPP. It can be  
expressed as  
GPP= 3.633169 C + 2.596173 C + 5.86644922 C + 5.2580203 C  
Where C=Conductivity  
(Pazhanisamy, 2005) also reported Polynomial fit relationship while studying the ecology of reservoir.  
Figure: 3 Linear Fit model showing the mathematical relationship between GPP and Water Temperature  
S = 66.95493352  
r = 0.62830300  
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4
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7
9
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8
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12.6  
15.4  
18.2  
21.0  
23.8  
26.6  
29.4  
Water Temperature  
Figure: 4 Polynomial Fit showing the mathematical relationship between GPP and Conductivity  
S = 72.86266709  
r = 0.61060751  
4
4
5
6
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9
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7
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3
7
1
8
3
0
11.3  
34.9  
58.4  
82.0  
105.6  
129.1  
152.7  
Conductivity  
Figure: 5 Linear Fit model showing the mathematical relationship between GPP and dissolved oxygen  
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S = 72.46268507  
r = 0.53953233  
4
4
5
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7
9
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8
8
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3
7
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3
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2.6  
3.5  
4.5  
5.5  
6.4  
7.4  
8.4  
Dissloved oxygen  
The Linear fit model (figure – 5) in the best to establish relationship of dissolved oxygen with GPP can  
be expressed as  
GPP=1.740837 + 2.067642 DO  
Where DO= Dissolved Oxygen  
Observation on Primary Production in fresh water body was carried out GPP and Dissolved Oxygen has  
significantly relationship in Linear fit model. Meenakshi (2014) reported that dissolved oxygen does not  
influence of the productivity.  
The NPP has the best fit and significant relationship with Air Temperature (r=0.51095020), Water  
Temperature(r=0.58452605), Conductivity(r =0.63446130) and Dissolved Oxygen (r =0.67505606)  
The Polynomial fit model (figure – 6) is the best to establish relationship of Air Temperature with NPP and it  
can be expressed as  
NPP = 1.877659 A T + 4.916947 AT + 4.7330782 AT + 1.9857343 AT  
Where AT= Air Temperature  
The Polynomial fit model (figure – 7) in the best to establish relationship of Water Temperature with NPP. It  
can be expressed as  
NPP = 6.819317 WT+ 7.274521 WT  
Where WT= Water Temperature  
The study preliminarily conducted in peninsular India reveals the influence of Water Temperature on NPP  
(Sheeja,2004), also reported that the Water Temperature does not have influence on the productivity. In this  
present study also water Temperature shows significant influence by the Linear fit model. This may be due to  
the closed nature of Reservoir low level of Temperature, high altitude and the dynamics of water temperature.  
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The Quadratic fit model (figure – 8) in the best to establish relationship of conductivity with NPP. It can be  
expressed as  
NPP= 1.310838 C +2.231183 C +6.1571232 C  
Where C = Conductivity  
According to (Pazanisamy, 2005), and (Meenakshi, 2014) also NPP and Conductivity does not agree  
significantly with Quadratic fit model. In this present study, NPP and Conductivity shows significant with  
Quadratic fit model.  
The Quardratic fit model (figure – 9) in the best to establish relationship of dissolved oxygen with NPP and it  
can be expressed as  
NPP=1.740837 DO+ 2.067642 DO  
Where DO= Dissolved Oxygen  
The Mathematical modeling studies preliminary conducted in peninsular India reveals the influence of Air  
temperature, Water Temperature and Community Respiration. (Meenakshi, 2014) showed that the Air  
Temperature and Water Temperature  
does not have influence the productivity. However, the result of the  
present study reveals that the Air Temperature and Water Temperature have a significant role in productivity  
and it can be expressed by linear fit model. Direct influence on CR may be due to the closed nature of Reservoir  
and the dynamics of water temperature.  
The CR has the best fit and significant relationship with Air Temperature (r= 0.6222), Water Temperature  
(r=0.5817) and Conductivity (r=0.5024).  
The Linear fit model (figure – 10) in the best to establish relationship of air temperature with CR. It can be  
expressed as  
CR= 2.496822 AT+1.959645 AT  
Where AT= Air Temperature  
The Linear fit model (figure – 11) is the best to establish relationship of Water Temperature with CR. It can be  
expressed as  
CR= 1.145818 W T+ 7.798103 W T  
Where WT= Water Temperature  
The Polynomical fit model (figure – 12) in the best to establish relationship of conductivity with CR can be  
expressed as  
CR = 6.880187 C + 1.069766 C + 4.869653 C2+9.079610 C3  
Where C = Conductivity  
Our results on CR and Conductivity has significant Correlation with Polynomial fit model .It is also related to  
(Palanisamy, 2005), and (Meenakshi, 2014).  
Altitude or terrain elevation plays an important role in water quality. As the altitude increases the temperature  
decreases and it impact changes in water quality. Water temperature, stream velocity, altitude, and level of  
organic matter content affect the amount of dissolved oxygen that the water can hold. Cold water contains more  
dissolved oxygen than warm water. The physico-chemical parameters of the water body are good in nature and  
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are suitable for domestic uses and drinking. The results of the present study also reveal that polynomial fit can  
be an alternate model to linear model for expressing the relationship of productivity with other environmental  
parameters.  
Figure: 6 Polynomial Fit showing the mathematical relationship between NPP and Air Temperature  
S = 41.00342551  
r = 0.51095020  
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11.4  
12.6  
13.8  
15.0  
16.2  
17.4  
18.6  
Air Temperature  
Figure: 7 Polynomial Fit model showing the mathematical relationship between NPP and Water  
Temperature  
S = 36.20296508  
r = 0.58452605  
1
5
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12.6  
15.4  
18.2  
21.0  
23.8  
26.6  
29.4  
Water Temperature  
Figure: 8 Quadratic Fit modelshowing the mathematical relationship between NPP and Conductivity  
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S = 35.23045507  
r = 0.63446130  
1
5
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11.3  
34.9  
58.4  
82.0  
105.6  
129.1  
152.7  
Conductivity  
Figure: 9 Quadratic Fit model showing the mathematical relationship between NPP and Dissolved  
Oxygen  
S = 0.95089999  
r = 0.79644650  
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17.3  
43.1  
68.9  
94.7  
120.5  
146.3  
172.1  
Dissolved Oxygen  
Figure: 10 Linear Fit model showing the mathematical relationship between CR and Air Temperature  
S = 37.62296868  
r = 0.62221593  
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11.4  
12.6  
13.8  
15.0  
16.2  
17.4  
18.6  
Air Temperature  
Figure: 11 Linear Fit model showing the mathematical relationship between CR and Water  
Temperature  
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S = 3.16642089  
r = 0.69073539  
0
0
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1.4  
37.8  
74.2  
110.5  
146.9  
183.3  
219.7  
Water Temperature  
Figure: 12 Polynomial Fit model showing the mathematical relationship betwee CR and Conductivity  
S = 44.42157654  
r = 0.50244074  
6
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11.3  
34.9  
58.4  
82.0  
105.6  
129.1  
152.7  
Conductivity  
Table: 1  
Physical and chemical factors in Lower Tiger Hills Reservoir  
S.NO  
PARAMETERS  
METHOD  
1
2
3
4
5
6
7
Air Temperature (°C)  
Celsius Thermometer  
Water Temperature (°C)  
Conductivity (µS)  
Celsius Thermometer  
Conductivity meter (Elico CM 180)  
Winkler’s Iodomeric method  
Dissolved Oxygen(mg/l)  
Gross Primary Productivity (GPP)  
Net Primary Productivity(NPP)  
Community Respiration(CR)  
Light and dark bottle method of Gaarder and Gran  
Light and dark bottle method of Gaarder and Gran  
Light and dark bottle method of Gaarder and Gran  
Table: 2 Physico-chemical factors in Lower Tiger Hills Reservoir  
Parameters  
/Months  
Oct  
Nov  
Dec  
Jan  
Fe  
Mar  
Apr  
’25  
’25  
’25  
’26  
‘26  
‘26  
‘26  
Air Temperature(°C)  
Water Temperature(°C)  
Coductivity (µS)  
13  
14  
13  
14  
15  
14  
16  
19  
20  
21  
17  
16  
19  
21  
98.1  
116  
69.4  
90.3  
74.7  
23.1  
76.2  
Dissolved Oxygen(mg/l)  
GPP  
4.08  
4.16  
4.82  
5.13  
4.08  
3.09  
4.08  
52.12  
71.61  
78.91  
90.16  
140.11  
155.12  
201.19  
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mgC/m3/hr  
NPP mgC/m3/hr  
41.12  
11  
61.37  
10.24  
53.69  
25.22  
68.12  
22.04  
81.16  
58.95  
120.82  
34.3  
151.26  
49.93  
CR mgC/m3/hr  
CONCLUSION  
The present study demonstrated that primary productivity in the Lower Tiger Hills Reservoirs is strongly  
influenced by physico-chemical parameters. Seasonal variations in temperature, conductivity, and dissolved  
oxygen significantly affected Gross Primary Productivity (GPP), Net Primary Productivity (NPP), and  
Community Respiration (CR).  
The reservoirs exhibited favourable environmental conditions that support plankton growth and overall  
ecosystem productivity. Regression analysis indicated that polynomial and quadratic models effectively  
described the relationships between productivity parameters and environmental variables.  
Overall, the water quality of both reservoirs remained within acceptable limits and appears suitable for domestic  
and drinking purposes following standard treatment procedures. Continuous monitoring is recommended to  
ensure the sustainable management and conservation of these freshwater resources.  
REFERENCE  
1. Gupte, A., & Nisar Shaikh, N. (2013). Seasonal variations in physico-chemical parameters and primary  
productivity of Shelar Lake, Bhiwandi, Thane, Maharashtra. Universal Journal of Environmental  
Research and Technology, 3(4), 523–530.  
2. Karthick, S., Aanand, S., Srinivasan, A., &Jawahar, P. (2016). Assessment of seasonal variations in water  
quality parameters of Kadamba Tank in Thoothukudi District, Tamil Nadu, India. International Journal  
of Applied Research, 2(11), 329–334.  
3. Meenakshi, N. (2014). Ecosystem dynamics of Katteri Reservoir, Nilgiri District, Tamil Nadu (Doctoral  
dissertation, Bharathiar University).  
4. Palanisamy, S. (2005). Reservoir ecology of Lower Anaicut, Thanjavur District, Tamil Nadu (Doctoral  
dissertation, Bharathidasan University).  
5. Sheeja, B. D. (2004). Seasonal variation in the limnological characteristics of selected aquatic ecosystems  
of the Kaveri Delta (Doctoral dissertation, Bharathidasan University).  
6. Verma, A. K., &Saksena, D. N. (2010). Assessment of water quality and pollution status of Kalpi (Morar)  
River, Gwalior, Madhya Pradesh, with special reference to conservation and management plans. Asian  
Journal of Experimental Biological Sciences, 2(3), 419–429.  
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